From 4ffca750653acc19e31254a924b09defe3e49b1c Mon Sep 17 00:00:00 2001
From: Roman Grundkiewicz <rgrundki@exseed.ed.ac.uk>
Date: Sat, 10 Nov 2018 17:51:18 +0000
Subject: [PATCH 001/838] Fix #343

---
 src/3rd_party/CLI/App.hpp | 11 +++++++++++
 1 file changed, 11 insertions(+)

diff --git a/src/3rd_party/CLI/App.hpp b/src/3rd_party/CLI/App.hpp
index b943ef1a4..555f480c1 100644
--- a/src/3rd_party/CLI/App.hpp
+++ b/src/3rd_party/CLI/App.hpp
@@ -1591,6 +1591,7 @@ class App {
 
         // Unlimited vector parser
         if(num < 0) {
+            bool emptyVectorArgs = true;
             while(!args.empty() && _recognize(args.back()) == detail::Classifer::NONE) {
                 if(collected >= -num) {
                     // We could break here for allow extras, but we don't
@@ -1603,12 +1604,22 @@ class App {
                 parse_order_.push_back(op.get());
                 args.pop_back();
                 collected++;
+                emptyVectorArgs = false;
             }
 
             // Allow -- to end an unlimited list and "eat" it
             if(!args.empty() && _recognize(args.back()) == detail::Classifer::POSITIONAL_MARK)
                 args.pop_back();
 
+            if(emptyVectorArgs) {
+                if(op->get_implicit()) {
+                    op->add_result(op->get_implicitval());
+                    parse_order_.push_back(op.get());
+                } else if (op->get_expected() < 0) {
+                    parse_order_.push_back(op.get());
+                    throw ArgumentMismatch(op->get_name(), op->get_expected(), 0);
+                }
+            }
         } else {
             while(num > 0 && !args.empty()) {
                 num--;

From af0ce265d3ddacaf619a0c42263c14dd99c7987d Mon Sep 17 00:00:00 2001
From: Roman Grundkiewicz <rgrundki@exseed.ed.ac.uk>
Date: Mon, 10 Dec 2018 13:47:25 +0000
Subject: [PATCH 002/838] Log invalid options from config files

---
 src/common/cli_wrapper.cpp   | 9 +++++----
 src/common/cli_wrapper.h     | 7 +++++--
 src/common/config_parser.cpp | 7 +++----
 3 files changed, 13 insertions(+), 10 deletions(-)

diff --git a/src/common/cli_wrapper.cpp b/src/common/cli_wrapper.cpp
index 28826bb28..702aa8940 100755
--- a/src/common/cli_wrapper.cpp
+++ b/src/common/cli_wrapper.cpp
@@ -3,6 +3,7 @@
 #include "common/logging.h"
 #include "common/options.h"
 #include "common/timer.h"
+#include "common/utils.h"
 #include "common/version.h"
 
 namespace marian {
@@ -126,8 +127,8 @@ std::string CLIWrapper::failureMessage(const CLI::App *app, const CLI::Error &e)
   return header;
 }
 
-bool CLIWrapper::updateConfig(const YAML::Node &config) {
-  bool success = true;
+void CLIWrapper::updateConfig(const YAML::Node &config, const std::string& errorMsg) {
+  std::vector<std::string> invalidKeys;
   auto cmdOptions = getParsedOptionNames();
   for(auto it : config) {
     auto key = it.first.as<std::string>();
@@ -138,10 +139,10 @@ bool CLIWrapper::updateConfig(const YAML::Node &config) {
       config_[key] = YAML::Clone(it.second);
       options_[key].modified = true;
     } else {
-      success = false;
+      invalidKeys.push_back(key);
     }
   }
-  return success;
+  ABORT_IF(!invalidKeys.empty(), errorMsg + std::string(": ") + utils::join(invalidKeys, ", "));
 }
 
 std::string CLIWrapper::dumpConfig(bool skipDefault /*= false*/) const {
diff --git a/src/common/cli_wrapper.h b/src/common/cli_wrapper.h
index cf47a3106..4e8701cc2 100755
--- a/src/common/cli_wrapper.h
+++ b/src/common/cli_wrapper.h
@@ -9,6 +9,7 @@
 #include <map>
 #include <string>
 #include <unordered_set>
+#include <vector>
 
 namespace marian {
 
@@ -232,9 +233,11 @@ class CLIWrapper {
    * This should be a preferred way of updating config options as the class keeps track of options,
    * which values have changed.
    *
-   * @param node YAML config with new default values for options
+   * @param config YAML config with new default values for options
+   * @param errorMsg error message printed if config contains undefined keys. The message is
+   *   appended with ": * <comma-separated list of invalid options>"
    */
-  bool updateConfig(const YAML::Node &config);
+  void updateConfig(const YAML::Node &config, const std::string &errorMsg);
 
   // Get textual YAML representation of the config
   std::string dumpConfig(bool skipDefault = false) const;
diff --git a/src/common/config_parser.cpp b/src/common/config_parser.cpp
index d39f3b1ad..a55d5d50c 100755
--- a/src/common/config_parser.cpp
+++ b/src/common/config_parser.cpp
@@ -666,8 +666,8 @@ void ConfigParser::expandAliases(cli::CLIWrapper& cli) {
   }
 
   if(config) {
-    auto success = cli.updateConfig(config);
-    ABORT_IF(!success, "Unknown option(s) in aliases, check if aliases consist of correct options");
+    cli.updateConfig(config,
+                     "Unknown option(s) in aliases, check if aliases consist of correct options");
   }
 }
 
@@ -703,8 +703,7 @@ void ConfigParser::parseOptions(int argc, char** argv, bool doValidate) {
   auto configPaths = findConfigPaths();
   if(!configPaths.empty()) {
     auto config = loadConfigFiles(configPaths);
-    auto success = cli.updateConfig(config);
-    ABORT_IF(!success, "There are option(s) in a config file that are not expected");
+    cli.updateConfig(config, "There are option(s) in a config file that are not expected");
   }
 
   if(get<bool>("interpolate-env-vars")) {

From 3a88bd6d759836de269b19a8869aa36bf3f1084e Mon Sep 17 00:00:00 2001
From: Roman Grundkiewicz <rgrundki@exseed.ed.ac.uk>
Date: Mon, 10 Dec 2018 14:06:53 +0000
Subject: [PATCH 003/838] Handle 'version' in config options

---
 src/common/cli_wrapper.cpp |  6 +++++-
 src/common/config.cpp      | 11 +++++------
 2 files changed, 10 insertions(+), 7 deletions(-)

diff --git a/src/common/cli_wrapper.cpp b/src/common/cli_wrapper.cpp
index 702aa8940..ce24f211a 100755
--- a/src/common/cli_wrapper.cpp
+++ b/src/common/cli_wrapper.cpp
@@ -135,6 +135,10 @@ void CLIWrapper::updateConfig(const YAML::Node &config, const std::string& error
     // skip options specified via command-line to allow overwriting them
     if(cmdOptions.count(key))
       continue;
+    // skip special option "version" that possibly can be loaded from model.npz:special.yml
+    if(key == "version")
+      continue;
+
     if(options_.count(key)) {
       config_[key] = YAML::Clone(it.second);
       options_[key].modified = true;
@@ -142,7 +146,7 @@ void CLIWrapper::updateConfig(const YAML::Node &config, const std::string& error
       invalidKeys.push_back(key);
     }
   }
-  ABORT_IF(!invalidKeys.empty(), errorMsg + std::string(": ") + utils::join(invalidKeys, ", "));
+  ABORT_IF(!invalidKeys.empty(), errorMsg + ": " + utils::join(invalidKeys, ", "));
 }
 
 std::string CLIWrapper::dumpConfig(bool skipDefault /*= false*/) const {
diff --git a/src/common/config.cpp b/src/common/config.cpp
index e5208b0d5..83c214307 100755
--- a/src/common/config.cpp
+++ b/src/common/config.cpp
@@ -95,15 +95,14 @@ void Config::initialize(int argc, char** argv, cli::mode mode, bool validate) {
           version,
           buildVersion());
     else
-      LOG(info,
-          "[config] Loaded model has been created with Marian {}",
-          version);
+      LOG(info, "[config] Loaded model has been created with Marian {}", version);
+
+    // Remove "version" from config to make it consistent among different start-up scenarios
+    config_.remove("version");
   }
   // If this is a newly started training
   else if(mode == cli::mode::training) {
-    LOG(info,
-        "[config] Model is being created with Marian {}",
-        buildVersion());
+    LOG(info, "[config] Model is being created with Marian {}", buildVersion());
   }
 }
 

From a1c1cb9154bf5ae524ab8b233c539b21377d2212 Mon Sep 17 00:00:00 2001
From: Roman Grundkiewicz <rgrundki@exseed.ed.ac.uk>
Date: Mon, 10 Dec 2018 15:33:01 +0000
Subject: [PATCH 004/838] Use shorter error message for aliases

---
 src/common/config_parser.cpp | 3 +--
 1 file changed, 1 insertion(+), 2 deletions(-)

diff --git a/src/common/config_parser.cpp b/src/common/config_parser.cpp
index a55d5d50c..74b73dc67 100755
--- a/src/common/config_parser.cpp
+++ b/src/common/config_parser.cpp
@@ -666,8 +666,7 @@ void ConfigParser::expandAliases(cli::CLIWrapper& cli) {
   }
 
   if(config) {
-    cli.updateConfig(config,
-                     "Unknown option(s) in aliases, check if aliases consist of correct options");
+    cli.updateConfig(config, "Unknown option(s) in aliases");
   }
 }
 

From 2000d1a1eba50849878d6a8065eda0492451aa26 Mon Sep 17 00:00:00 2001
From: Roman Grundkiewicz <rgrundki@exseed.ed.ac.uk>
Date: Mon, 10 Dec 2018 15:38:16 +0000
Subject: [PATCH 005/838] Remove unnecessary from_config()

---
 src/models/model_factory.h | 2 --
 1 file changed, 2 deletions(-)

diff --git a/src/models/model_factory.h b/src/models/model_factory.h
index 2ec7fe752..c97303d1a 100644
--- a/src/models/model_factory.h
+++ b/src/models/model_factory.h
@@ -53,7 +53,5 @@ typedef Accumulator<EncoderDecoderFactory> encoder_decoder;
 Ptr<ModelBase> by_type(std::string type, usage, Ptr<Options> options);
 
 Ptr<ModelBase> from_options(Ptr<Options> options, usage);
-
-Ptr<ModelBase> from_config(Ptr<Config> config, usage);
 }  // namespace models
 }  // namespace marian

From ad1bc691eae5464e60fe3aeaa5115ff7984ac09a Mon Sep 17 00:00:00 2001
From: Roman Grundkiewicz <rgrundki@exseed.ed.ac.uk>
Date: Mon, 10 Dec 2018 15:40:30 +0000
Subject: [PATCH 006/838] Remove unnecessary init()

---
 src/models/model_task.h     | 1 -
 src/translator/translator.h | 4 +---
 2 files changed, 1 insertion(+), 4 deletions(-)

diff --git a/src/models/model_task.h b/src/models/model_task.h
index 932fba620..1fcf6eca5 100644
--- a/src/models/model_task.h
+++ b/src/models/model_task.h
@@ -9,7 +9,6 @@ struct ModelTask {
 };
 
 struct ModelServiceTask {
-  virtual void init() = 0;
   virtual std::string run(const std::string&) = 0;
 };
 }  // namespace marian
diff --git a/src/translator/translator.h b/src/translator/translator.h
index 9f973113f..5fbfe8aac 100755
--- a/src/translator/translator.h
+++ b/src/translator/translator.h
@@ -145,9 +145,7 @@ class TranslateService : public ModelServiceTask {
 public:
   virtual ~TranslateService() {}
 
-  TranslateService(Ptr<Options> options) : options_(options) { init(); }
-
-  void init() override {
+  TranslateService(Ptr<Options> options) : options_(options) {
     // initialize vocabs
     options_->set("inference", true);
 

From 2829e2b1d5453644b97b627b29735d0853edf1d8 Mon Sep 17 00:00:00 2001
From: Roman Grundkiewicz <rgrundki@exseed.ed.ac.uk>
Date: Mon, 10 Dec 2018 15:54:41 +0000
Subject: [PATCH 007/838] Add regression-tests as a submodule

---
 .gitmodules      | 3 +++
 regression-tests | 1 +
 2 files changed, 4 insertions(+)
 create mode 160000 regression-tests

diff --git a/.gitmodules b/.gitmodules
index 5c3c00f1e..efe6bc5ec 100644
--- a/.gitmodules
+++ b/.gitmodules
@@ -1,6 +1,9 @@
 [submodule "examples"]
 	path = examples
 	url = https://github.com/marian-nmt/marian-examples
+[submodule "regression-tests"]
+	path = regression-tests
+	url = https://github.com/marian-nmt/marian-regression-tests
 [submodule "src/3rd_party/sentencepiece"]
 	path = src/3rd_party/sentencepiece
 	url = https://github.com/marian-nmt/sentencepiece
diff --git a/regression-tests b/regression-tests
new file mode 160000
index 000000000..edc6a6dde
--- /dev/null
+++ b/regression-tests
@@ -0,0 +1 @@
+Subproject commit edc6a6ddec3deb693fba5909539ac78000c5b8a2

From b6c265a6f197790f23041cec5e91779f029e3b28 Mon Sep 17 00:00:00 2001
From: Roman Grundkiewicz <rgrundki@exseed.ed.ac.uk>
Date: Mon, 10 Dec 2018 16:11:47 +0000
Subject: [PATCH 008/838] Update submodule with regression tests

---
 regression-tests | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/regression-tests b/regression-tests
index edc6a6dde..febdc3f56 160000
--- a/regression-tests
+++ b/regression-tests
@@ -1 +1 @@
-Subproject commit edc6a6ddec3deb693fba5909539ac78000c5b8a2
+Subproject commit febdc3f56f75929b1f7b5b38a4b9b96ea8f648e7

From 77a97911a239195f1e267d1af6917c1553c1ecb3 Mon Sep 17 00:00:00 2001
From: Roman Grundkiewicz <rgrundki@exseed.ed.ac.uk>
Date: Mon, 10 Dec 2018 16:51:28 +0000
Subject: [PATCH 009/838] Merge split() and splitAny()

---
 src/common/utils.cpp | 46 +++++++++++++++-----------------------------
 src/common/utils.h   |  9 +++++----
 2 files changed, 21 insertions(+), 34 deletions(-)

diff --git a/src/common/utils.cpp b/src/common/utils.cpp
index b47cd1f4e..7860ee3a6 100755
--- a/src/common/utils.cpp
+++ b/src/common/utils.cpp
@@ -26,21 +26,25 @@ void trimLeft(std::string& s) {
   CLI::detail::ltrim(s, " \t\n");
 }
 
-// @TODO: use more functions from CLI instead of own implementations
 void split(const std::string& line,
            std::vector<std::string>& pieces,
            const std::string del /*= " "*/,
-           bool keepEmpty) {
+           bool keepEmpty /*= false*/,
+           bool anyOf /*= false*/) {
   size_t begin = 0;
   size_t pos = 0;
   std::string token;
-  while((pos = line.find(del, begin)) != std::string::npos) {
+  size_t delSize = anyOf ? 1 : del.size();
+  while(true) {
+    pos = anyOf ? line.find_first_of(del, begin) : line.find(del, begin);
+    if(pos == std::string::npos)
+      break;
     if(pos >= begin) {
       token = line.substr(begin, pos - begin);
       if(token.size() > 0 || keepEmpty)
         pieces.push_back(token);
     }
-    begin = pos + del.size();
+    begin = pos + delSize;
   }
   if(pos >= begin) {
     token = line.substr(begin, pos - begin);
@@ -51,45 +55,27 @@ void split(const std::string& line,
 
 std::vector<std::string> split(const std::string& line,
                                const std::string del /*= " "*/,
-                               bool keepEmpty) {
+                               bool keepEmpty /*= false*/,
+                               bool anyOf /*= false*/) {
   std::vector<std::string> pieces;
-  split(line, pieces, del, keepEmpty);
+  split(line, pieces, del, keepEmpty, anyOf);
   return pieces;
 }
 
-// @TODO: splitAny() shares all but 2 expressions with split(). Merge them.
 void splitAny(const std::string& line,
               std::vector<std::string>& pieces,
               const std::string del /*= " "*/,
-              bool keepEmpty) {
-  size_t begin = 0;
-  size_t pos = 0;
-  std::string token;
-  while((pos = line.find_first_of(del, begin)) != std::string::npos) {
-    if(pos >= begin) {
-      token = line.substr(begin, pos - begin);
-      if(token.size() > 0 || keepEmpty)
-        pieces.push_back(token);
-    }
-    begin = pos + 1;
-  }
-  if(pos >= begin) {
-    token = line.substr(begin, pos - begin);
-    if(token.size() > 0 || keepEmpty)
-      pieces.push_back(token);
-  }
+              bool keepEmpty /*= false*/) {
+  split(line, pieces, del, keepEmpty, /*anyOf =*/true);
 }
 
 std::vector<std::string> splitAny(const std::string& line,
                                   const std::string del /*= " "*/,
-                                  bool keepEmpty) {
-  std::vector<std::string> pieces;
-  splitAny(line, pieces, del, keepEmpty);
-  return pieces;
+                                  bool keepEmpty /*= false*/) {
+  return split(line, del, keepEmpty, /*anyOf =*/true);
 }
 
-std::string join(const std::vector<std::string>& words,
-                 const std::string& del /*= " "*/) {
+std::string join(const std::vector<std::string>& words, const std::string& del /*= " "*/) {
   std::stringstream ss;
   if(words.empty()) {
     return "";
diff --git a/src/common/utils.h b/src/common/utils.h
index cd4fc6de3..a782abe1c 100755
--- a/src/common/utils.h
+++ b/src/common/utils.h
@@ -13,11 +13,13 @@ void trimRight(std::string& s);
 void split(const std::string& line,
            std::vector<std::string>& pieces,
            const std::string del = " ",
-           bool keepEmpty = false);
+           bool keepEmpty = false,
+           bool anyOf = false);
 
 std::vector<std::string> split(const std::string& line,
                                const std::string del = " ",
-                               bool keepEmpty = false);
+                               bool keepEmpty = false,
+                               bool anyOf = false);
 
 void splitAny(const std::string& line,
               std::vector<std::string>& pieces,
@@ -28,8 +30,7 @@ std::vector<std::string> splitAny(const std::string& line,
                                   const std::string del = " ",
                                   bool keepEmpty = false);
 
-std::string join(const std::vector<std::string>& words,
-                 const std::string& del = " ");
+std::string join(const std::vector<std::string>& words, const std::string& del = " ");
 
 std::string exec(const std::string& cmd);
 

From 8c23cbd57a32e2d2dec911b4c536cb92692bf407 Mon Sep 17 00:00:00 2001
From: Roman Grundkiewicz <rgrundki@exseed.ed.ac.uk>
Date: Mon, 10 Dec 2018 17:07:28 +0000
Subject: [PATCH 010/838] Simplify split() calls

---
 src/common/config.cpp            |  7 +++----
 src/data/corpus_nbest.cpp        |  6 ++----
 src/data/default_vocab.cpp       | 10 ++++------
 src/rescorer/score_collector.cpp |  3 +--
 4 files changed, 10 insertions(+), 16 deletions(-)

diff --git a/src/common/config.cpp b/src/common/config.cpp
index 83c214307..043a757f5 100755
--- a/src/common/config.cpp
+++ b/src/common/config.cpp
@@ -155,10 +155,9 @@ void Config::log() {
   std::string configString = out.c_str();
 
   // print YAML prepending each line with [config]
-  std::vector<std::string> results;
-  utils::split(configString, results, "\n");
-  for(auto& r : results)
-    LOG(info, "[config] {}", r);
+  auto lines = utils::split(configString, "\n");
+  for(auto& line : lines)
+    LOG(info, "[config] {}", line);
 }
 
 // Parse the device-spec parameters (--num-devices, --devices, --cpu-threads) into an array of
diff --git a/src/data/corpus_nbest.cpp b/src/data/corpus_nbest.cpp
index 328c3c0d7..c5a85a231 100644
--- a/src/data/corpus_nbest.cpp
+++ b/src/data/corpus_nbest.cpp
@@ -15,8 +15,7 @@ CorpusNBest::CorpusNBest(std::vector<std::string> paths,
     : CorpusBase(paths, vocabs, options) {}
 
 int numFromNbest(const std::string& line) {
-  std::vector<std::string> fields;
-  utils::split(line, fields, " ||| ", true);
+  auto fields = utils::split(line, " ||| ", true);
   ABORT_IF(fields.size() < 4,
            "Too few fields ({}) in line \"{}\", is this a correct n-best list?",
            fields.size(),
@@ -25,8 +24,7 @@ int numFromNbest(const std::string& line) {
 }
 
 std::string lineFromNbest(const std::string& line) {
-  std::vector<std::string> fields;
-  utils::split(line, fields, " ||| ", true);
+  auto fields = utils::split(line, " ||| ", true);
   ABORT_IF(fields.size() < 4,
            "Too few fields ({}) in line \"{}\", is this a correct n-best list?",
            fields.size(),
diff --git a/src/data/default_vocab.cpp b/src/data/default_vocab.cpp
index 1ce055db8..4cd8a929e 100755
--- a/src/data/default_vocab.cpp
+++ b/src/data/default_vocab.cpp
@@ -36,7 +36,7 @@ class DefaultVocab : public VocabBase {
     VocabFreqOrderer(const std::unordered_map<std::string, size_t>& counter)
             : counter_(counter) {}
 
-    // order first by decreasing frequency, 
+    // order first by decreasing frequency,
     // if frequencies are the same order lexicographically by vocabulary string
     bool operator()(const std::string& a, const std::string& b) const {
       return counter_.at(a) > counter_.at(b) || (counter_.at(a) == counter_.at(b) && a < b);
@@ -56,8 +56,7 @@ class DefaultVocab : public VocabBase {
   }
 
   Words encode(const std::string& line, bool addEOS, bool /*inference*/) const override {
-    std::vector<std::string> lineTokens;
-    utils::split(line, lineTokens, " ");
+    auto lineTokens = utils::split(line, " ");
     return (*this)(lineTokens, addEOS);
   }
 
@@ -205,7 +204,7 @@ class DefaultVocab : public VocabBase {
               "Vocabulary file '{}' exists. Not overwriting",
               path.string());
     }
-    
+
     std::unordered_map<std::string, size_t> counter;
     for(const auto& trainPath : trainPaths)
       addCounts(counter, trainPath);
@@ -223,8 +222,7 @@ class DefaultVocab : public VocabBase {
 
     std::string line;
     while(getline(*trainStrm, line)) {
-      std::vector<std::string> toks;
-      utils::split(line, toks, " ");
+      auto toks = utils::split(line, " ");
 
       for(const std::string& tok : toks) {
         auto iter = counter.find(tok);
diff --git a/src/rescorer/score_collector.cpp b/src/rescorer/score_collector.cpp
index ac118a6a4..28d582609 100644
--- a/src/rescorer/score_collector.cpp
+++ b/src/rescorer/score_collector.cpp
@@ -113,8 +113,7 @@ std::string ScoreCollectorNBest::addToNBest(const std::string nbest,
                                             const std::string feature,
                                             float score,
                                             const data::SoftAlignment& align) {
-  std::vector<std::string> fields;
-  utils::split(nbest, fields, "|||");
+  auto fields = utils::split(nbest, "|||");
   std::stringstream ss;
   if(!alignment_.empty() && !align.empty())
     ss << " " << getAlignment(align) << " |||";

From 8b33487c8899b2137d2713b84fbebfde8a3977ac Mon Sep 17 00:00:00 2001
From: Roman Grundkiewicz <rgrundki@exseed.ed.ac.uk>
Date: Mon, 10 Dec 2018 18:09:20 +0000
Subject: [PATCH 011/838] Separate unit tests and apps

---
 src/tests/CMakeLists.txt                      | 60 ++++++-------------
 src/tests/{cli_test.cpp => cli.cpp}           |  0
 src/tests/{conv_test.cu => conv.cu}           |  0
 src/tests/{conv_char_test.cu => conv_char.cu} |  0
 src/tests/{dropout_test.cpp => dropout.cpp}   |  0
 src/tests/{logger_test.cpp => logger.cpp}     |  0
 src/tests/{pooling_test.cpp => pooling.cpp}   |  0
 src/tests/{sqlite_test.cpp => sqlite.cpp}     |  0
 src/tests/{tensor_test.cu => tensor.cu}       |  0
 src/tests/units/CMakeLists.txt                | 19 ++++++
 src/tests/{ => units}/attention_tests.cpp     |  0
 src/tests/{ => units}/graph_tests.cpp         |  0
 src/tests/{ => units}/operator_tests.cpp      |  0
 src/tests/{ => units}/rnn_tests.cpp           |  0
 src/tests/{ => units}/run_tests.cpp           |  0
 15 files changed, 37 insertions(+), 42 deletions(-)
 rename src/tests/{cli_test.cpp => cli.cpp} (100%)
 rename src/tests/{conv_test.cu => conv.cu} (100%)
 rename src/tests/{conv_char_test.cu => conv_char.cu} (100%)
 rename src/tests/{dropout_test.cpp => dropout.cpp} (100%)
 rename src/tests/{logger_test.cpp => logger.cpp} (100%)
 rename src/tests/{pooling_test.cpp => pooling.cpp} (100%)
 rename src/tests/{sqlite_test.cpp => sqlite.cpp} (100%)
 rename src/tests/{tensor_test.cu => tensor.cu} (100%)
 create mode 100644 src/tests/units/CMakeLists.txt
 rename src/tests/{ => units}/attention_tests.cpp (100%)
 rename src/tests/{ => units}/graph_tests.cpp (100%)
 rename src/tests/{ => units}/operator_tests.cpp (100%)
 rename src/tests/{ => units}/rnn_tests.cpp (100%)
 rename src/tests/{ => units}/run_tests.cpp (100%)

diff --git a/src/tests/CMakeLists.txt b/src/tests/CMakeLists.txt
index 347a18b78..2df971407 100644
--- a/src/tests/CMakeLists.txt
+++ b/src/tests/CMakeLists.txt
@@ -1,49 +1,25 @@
 # Unit tests
-set(UNIT_TESTS
-    graph_tests
-    operator_tests
-    rnn_tests
-    attention_tests
-)
-
-foreach(test ${UNIT_TESTS})
-    add_executable("run_${test}" run_tests.cpp "${test}.cpp")
-    target_link_libraries("run_${test}" marian ${EXT_LIBS} Catch)
-
-    if(CUDA_FOUND)
-      target_link_libraries("run_${test}" marian marian_cuda ${EXT_LIBS} Catch)
-    endif(CUDA_FOUND)
-
-    add_test(NAME ${test} COMMAND "run_${test}")
-endforeach(test)
+add_subdirectory(units)
 
 
 # Testing apps
-add_executable(logger_test logger_test.cpp)
-add_executable(dropout_test dropout_test.cpp)
-add_executable(prod_test prod.cpp)
-add_executable(cli_test cli_test.cpp)
-
-if(CUDA_FOUND)
-add_executable(pooling_test pooling_test.cpp)
-target_link_libraries(pooling_test marian ${EXT_LIBS} Catch)
-target_link_libraries(pooling_test marian marian_cuda ${EXT_LIBS} Catch)
-endif(CUDA_FOUND)
-
-add_executable(sqlite_test sqlite_test.cpp)
+set(APP_TESTS
+    logger
+    dropout
+    sqlite
+    prod
+    cli
+    pooling
+)
 
-foreach(exec
-        logger_test
-        dropout_test
-        sqlite_test
-        prod_test
-        cli_test
-    )
-    target_link_libraries(${exec} marian ${EXT_LIBS} Catch)
-    if(CUDA_FOUND)
-      target_link_libraries(${exec} marian marian_cuda ${EXT_LIBS} Catch)
-    endif(CUDA_FOUND)
+foreach(test ${APP_TESTS})
+  add_executable("test_${test}" "${test}.cpp")
 
-    set_target_properties(${exec} PROPERTIES RUNTIME_OUTPUT_DIRECTORY "${CMAKE_BINARY_DIR}")
-endforeach(exec)
+  if(CUDA_FOUND)
+    target_link_libraries("test_${test}" marian marian_cuda ${EXT_LIBS})
+  else(CUDA_FOUND)
+    target_link_libraries("test_${test}" marian ${EXT_LIBS})
+  endif(CUDA_FOUND)
 
+  set_target_properties("test_${test}" PROPERTIES RUNTIME_OUTPUT_DIRECTORY "${CMAKE_BINARY_DIR}")
+endforeach(test)
diff --git a/src/tests/cli_test.cpp b/src/tests/cli.cpp
similarity index 100%
rename from src/tests/cli_test.cpp
rename to src/tests/cli.cpp
diff --git a/src/tests/conv_test.cu b/src/tests/conv.cu
similarity index 100%
rename from src/tests/conv_test.cu
rename to src/tests/conv.cu
diff --git a/src/tests/conv_char_test.cu b/src/tests/conv_char.cu
similarity index 100%
rename from src/tests/conv_char_test.cu
rename to src/tests/conv_char.cu
diff --git a/src/tests/dropout_test.cpp b/src/tests/dropout.cpp
similarity index 100%
rename from src/tests/dropout_test.cpp
rename to src/tests/dropout.cpp
diff --git a/src/tests/logger_test.cpp b/src/tests/logger.cpp
similarity index 100%
rename from src/tests/logger_test.cpp
rename to src/tests/logger.cpp
diff --git a/src/tests/pooling_test.cpp b/src/tests/pooling.cpp
similarity index 100%
rename from src/tests/pooling_test.cpp
rename to src/tests/pooling.cpp
diff --git a/src/tests/sqlite_test.cpp b/src/tests/sqlite.cpp
similarity index 100%
rename from src/tests/sqlite_test.cpp
rename to src/tests/sqlite.cpp
diff --git a/src/tests/tensor_test.cu b/src/tests/tensor.cu
similarity index 100%
rename from src/tests/tensor_test.cu
rename to src/tests/tensor.cu
diff --git a/src/tests/units/CMakeLists.txt b/src/tests/units/CMakeLists.txt
new file mode 100644
index 000000000..7d842c427
--- /dev/null
+++ b/src/tests/units/CMakeLists.txt
@@ -0,0 +1,19 @@
+# Unit tests
+set(UNIT_TESTS
+    graph_tests
+    operator_tests
+    rnn_tests
+    attention_tests
+)
+
+foreach(test ${UNIT_TESTS})
+  add_executable("run_${test}" run_tests.cpp "${test}.cpp")
+
+  if(CUDA_FOUND)
+    target_link_libraries("run_${test}" marian marian_cuda ${EXT_LIBS} Catch)
+  else(CUDA_FOUND)
+    target_link_libraries("run_${test}" marian ${EXT_LIBS} Catch)
+  endif(CUDA_FOUND)
+
+  add_test(NAME ${test} COMMAND "run_${test}")
+endforeach(test)
diff --git a/src/tests/attention_tests.cpp b/src/tests/units/attention_tests.cpp
similarity index 100%
rename from src/tests/attention_tests.cpp
rename to src/tests/units/attention_tests.cpp
diff --git a/src/tests/graph_tests.cpp b/src/tests/units/graph_tests.cpp
similarity index 100%
rename from src/tests/graph_tests.cpp
rename to src/tests/units/graph_tests.cpp
diff --git a/src/tests/operator_tests.cpp b/src/tests/units/operator_tests.cpp
similarity index 100%
rename from src/tests/operator_tests.cpp
rename to src/tests/units/operator_tests.cpp
diff --git a/src/tests/rnn_tests.cpp b/src/tests/units/rnn_tests.cpp
similarity index 100%
rename from src/tests/rnn_tests.cpp
rename to src/tests/units/rnn_tests.cpp
diff --git a/src/tests/run_tests.cpp b/src/tests/units/run_tests.cpp
similarity index 100%
rename from src/tests/run_tests.cpp
rename to src/tests/units/run_tests.cpp

From 96fbb77a1c8ea4338f4f1e9a0346fb9adefa132f Mon Sep 17 00:00:00 2001
From: Roman Grundkiewicz <rgrundki@exseed.ed.ac.uk>
Date: Mon, 10 Dec 2018 21:34:40 +0000
Subject: [PATCH 012/838] Clean TextInput

---
 src/data/corpus.cpp      |  2 +-
 src/data/corpus_base.cpp |  4 +--
 src/data/dataset.h       |  1 +
 src/data/text_input.cpp  | 53 ++++++++++++++++++----------------------
 src/data/text_input.h    |  4 +--
 5 files changed, 29 insertions(+), 35 deletions(-)

diff --git a/src/data/corpus.cpp b/src/data/corpus.cpp
index 7a7a846e1..38b503e06 100755
--- a/src/data/corpus.cpp
+++ b/src/data/corpus.cpp
@@ -18,7 +18,7 @@ Corpus::Corpus(std::vector<std::string> paths,
     : CorpusBase(paths, vocabs, options), shuffleInRAM_(options_->get<bool>("shuffle-in-ram")) {}
 
 SentenceTuple Corpus::next() {
-  for (;;) { // (this is a retry loop for skipping invalid sentences)
+  for(;;) { // (this is a retry loop for skipping invalid sentences)
     // get index of the current sentence
     size_t curId = pos_; // note: at end, pos_  == total size
     // if corpus has been shuffled, ids_ contains sentence indexes
diff --git a/src/data/corpus_base.cpp b/src/data/corpus_base.cpp
index c97043135..2a088537a 100755
--- a/src/data/corpus_base.cpp
+++ b/src/data/corpus_base.cpp
@@ -101,7 +101,7 @@ CorpusBase::CorpusBase(Ptr<Options> options, bool translate)
         std::set<std::string> paths; // contains all paths that are used for training the vocabulary
         size_t size;                 // contains the maximum vocabulary size
       };
-      
+
       // Group training files based on vocabulary path. If the same
       // vocab path corresponds to different training files, this means
       // that a single vocab should combine tokens from all files.
@@ -120,7 +120,7 @@ CorpusBase::CorpusBase(Ptr<Options> options, bool translate)
         auto pathsAndSize = groupVocab[vocabPaths[i]];
         std::vector<std::string> groupedPaths(pathsAndSize.paths.begin(), pathsAndSize.paths.end());
         int vocSize = vocab->loadOrCreate(vocabPaths[i], groupedPaths, pathsAndSize.size);
-        
+
         // TODO: this is not nice as it modifies the option object and needs to expose the changes
         // outside the corpus as models need to know about the vocabulary size; extract the vocab
         // creation functionality from the class.
diff --git a/src/data/dataset.h b/src/data/dataset.h
index 343789544..830fcb3bc 100755
--- a/src/data/dataset.h
+++ b/src/data/dataset.h
@@ -15,6 +15,7 @@ class DatasetBase {
 protected:
   std::vector<std::string> paths_;
   Ptr<Options> options_;
+
   // Data processing may differ in training/inference settings
   bool inference_{false};
 
diff --git a/src/data/text_input.cpp b/src/data/text_input.cpp
index 78fbc7af0..85ae3a770 100755
--- a/src/data/text_input.cpp
+++ b/src/data/text_input.cpp
@@ -5,9 +5,7 @@ namespace marian {
 namespace data {
 
 TextIterator::TextIterator() : pos_(-1), tup_(0) {}
-
-TextIterator::TextIterator(TextInput& corpus)
-    : corpus_(&corpus), pos_(0), tup_(corpus_->next()) {}
+TextIterator::TextIterator(TextInput& corpus) : corpus_(&corpus), pos_(0), tup_(corpus_->next()) {}
 
 void TextIterator::increment() {
   tup_ = corpus_->next();
@@ -25,40 +23,37 @@ const SentenceTuple& TextIterator::dereference() const {
 TextInput::TextInput(std::vector<std::string> inputs,
                      std::vector<Ptr<Vocab>> vocabs,
                      Ptr<Options> options)
-    // TODO: fix this: input text is stored in an inherited variable named
-    // paths_ that is very confusing
-    : DatasetBase(inputs, options),
-      vocabs_(vocabs) {
+    : DatasetBase(inputs, options), vocabs_(vocabs) {
+  // note: inputs are automatically stored in the inherited variable named paths_, but these are
+  // texts not paths!
   for(const auto& text : paths_)
     files_.emplace_back(new std::istringstream(text));
 }
 
+// TextInput is mainly used for inference in the server mode, not for training, so skipping too long
+// or ill-formed inputs is not necessary here
 SentenceTuple TextInput::next() {
-  // @TODO: This code mixes two patterns (while and early exit). Fix that.
-  bool cont = true;
-  while(cont) {
-    // get index of the current sentence
-    size_t curId = pos_++;
-
-    // fill up the sentence tuple with sentences from all input files
-    SentenceTuple tup(curId);
-    for(size_t i = 0; i < files_.size(); ++i) {
-      std::string line;
-      io::InputFileStream dummyStream(*files_[i]);
-      if(io::getline(dummyStream, line)) {
-        Words words = vocabs_[i]->encode(line, /*addEOS =*/ true, /*inference =*/ inference_);
-        if(words.empty())
-          words.push_back(0);
-        tup.push_back(words);
-      }
+  // get index of the current sentence
+  size_t curId = pos_++;
+
+  // fill up the sentence tuple with source and/or target sentences
+  SentenceTuple tup(curId);
+  for(size_t i = 0; i < files_.size(); ++i) {
+    io::InputFileStream dummyStream(*files_[i]);
+    std::string line;
+    if(io::getline(dummyStream, line)) {
+      Words words = vocabs_[i]->encode(line, /*addEOS =*/ true, /*inference =*/ inference_);
+      if(words.empty())
+        words.push_back(DEFAULT_EOS_ID);
+      tup.push_back(words);
     }
-
-    // continue only if each input file has provided an example
-    cont = tup.size() == files_.size();
-    if(cont)
-      return tup;
   }
+
+  // check if each input file provided an example
+  if(tup.size() == files_.size())
+    return tup;
   return SentenceTuple(0);
 }
+
 }  // namespace data
 }  // namespace marian
diff --git a/src/data/text_input.h b/src/data/text_input.h
index bf1bc1ebc..bdbcf64e9 100755
--- a/src/data/text_input.h
+++ b/src/data/text_input.h
@@ -36,9 +36,7 @@ class TextInput : public DatasetBase<SentenceTuple, TextIterator, CorpusBatch> {
 public:
   typedef SentenceTuple Sample;
 
-  TextInput(std::vector<std::string> inputs,
-            std::vector<Ptr<Vocab>> vocabs,
-            Ptr<Options> options);
+  TextInput(std::vector<std::string> inputs, std::vector<Ptr<Vocab>> vocabs, Ptr<Options> options);
 
   Sample next() override;
 

From 741406e1e53cd615b339e1a951566d3cd4d3a64a Mon Sep 17 00:00:00 2001
From: Roman Grundkiewicz <rgrundki@exseed.ed.ac.uk>
Date: Mon, 10 Dec 2018 21:46:56 +0000
Subject: [PATCH 013/838] Make --learn-rate a float

---
 src/common/config_parser.cpp  | 4 ++--
 src/optimizers/optimizers.cpp | 2 +-
 2 files changed, 3 insertions(+), 3 deletions(-)

diff --git a/src/common/config_parser.cpp b/src/common/config_parser.cpp
index 74b73dc67..df986b53c 100755
--- a/src/common/config_parser.cpp
+++ b/src/common/config_parser.cpp
@@ -314,9 +314,9 @@ void ConfigParser::addOptionsTraining(cli::CLIWrapper& cli) {
      "Use synchronous SGD instead of asynchronous for multi-gpu training");
 
   // learning rate options
-  cli.add<double>("--learn-rate,-l",
+  cli.add<float>("--learn-rate,-l",
      "Learning rate",
-     0.0001);
+     0.0001f);
   cli.add<bool>("--lr-report",
      "Report learning rate for each update");
 
diff --git a/src/optimizers/optimizers.cpp b/src/optimizers/optimizers.cpp
index d7a58be31..be077c0f5 100755
--- a/src/optimizers/optimizers.cpp
+++ b/src/optimizers/optimizers.cpp
@@ -270,7 +270,7 @@ void Adam::resetStats() {
 }
 
 Ptr<OptimizerBase> Optimizer(Ptr<Options> options) {
-  float lrate = (float)options->get<double>("learn-rate"); // @TODO: should this be <float>?
+  float lrate = options->get<float>("learn-rate");
   auto params = options->has("optimizer-params")
                     ? options->get<std::vector<float>>("optimizer-params")
                     : std::vector<float>({});

From d7dcaa599f2904a01efcec34b861a51c81e75b73 Mon Sep 17 00:00:00 2001
From: Roman Grundkiewicz <rgrundki@exseed.ed.ac.uk>
Date: Mon, 10 Dec 2018 21:48:50 +0000
Subject: [PATCH 014/838] Make --clip-norm a float

---
 src/common/config_parser.cpp  | 2 +-
 src/optimizers/optimizers.cpp | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/src/common/config_parser.cpp b/src/common/config_parser.cpp
index df986b53c..0745cbb5e 100755
--- a/src/common/config_parser.cpp
+++ b/src/common/config_parser.cpp
@@ -351,7 +351,7 @@ void ConfigParser::addOptionsTraining(cli::CLIWrapper& cli) {
 
   cli.add<double>("--label-smoothing",
      "Epsilon for label smoothing (0 to disable)");
-  cli.add<double>("--clip-norm",
+  cli.add<float>("--clip-norm",
      "Clip gradient norm to  argcli.add<int>(0 to disable)",
      1.f);
   cli.add<float>("--exponential-smoothing",
diff --git a/src/optimizers/optimizers.cpp b/src/optimizers/optimizers.cpp
index be077c0f5..0fad790a1 100755
--- a/src/optimizers/optimizers.cpp
+++ b/src/optimizers/optimizers.cpp
@@ -276,7 +276,7 @@ Ptr<OptimizerBase> Optimizer(Ptr<Options> options) {
                     : std::vector<float>({});
 
   Ptr<ClipperBase> clipper = nullptr;
-  float clipNorm = (float)options->get<double>("clip-norm"); // @TODO: should this be <float>?
+  float clipNorm = options->get<float>("clip-norm");
   if(clipNorm > 0)
     clipper = Clipper<Norm>(clipNorm);
 

From 2d1030159f955c4644966b2950d5e4f08d76aeaf Mon Sep 17 00:00:00 2001
From: Roman Grundkiewicz <rgrundki@exseed.ed.ac.uk>
Date: Mon, 10 Dec 2018 21:49:30 +0000
Subject: [PATCH 015/838] Fix description of --clip-norm

---
 src/common/config_parser.cpp | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/common/config_parser.cpp b/src/common/config_parser.cpp
index 0745cbb5e..ce09f6806 100755
--- a/src/common/config_parser.cpp
+++ b/src/common/config_parser.cpp
@@ -352,7 +352,7 @@ void ConfigParser::addOptionsTraining(cli::CLIWrapper& cli) {
   cli.add<double>("--label-smoothing",
      "Epsilon for label smoothing (0 to disable)");
   cli.add<float>("--clip-norm",
-     "Clip gradient norm to  argcli.add<int>(0 to disable)",
+     "Clip gradient norm to  arg  (0 to disable)",
      1.f);
   cli.add<float>("--exponential-smoothing",
      "Maintain smoothed version of parameters for validation and saving with smoothing factor. 0 to disable",

From 07d7f32254241eb741f663ccc0f095586d63a491 Mon Sep 17 00:00:00 2001
From: Roman Grundkiewicz <rgrundki@exseed.ed.ac.uk>
Date: Tue, 11 Dec 2018 11:12:26 +0000
Subject: [PATCH 016/838] A few cosmetic changes in training state

---
 src/training/scheduler.h      | 30 ++++++++++--------
 src/training/training_state.h | 57 ++++++++++++++++++++---------------
 2 files changed, 49 insertions(+), 38 deletions(-)

diff --git a/src/training/scheduler.h b/src/training/scheduler.h
index dee62496d..acf8b5243 100755
--- a/src/training/scheduler.h
+++ b/src/training/scheduler.h
@@ -16,7 +16,8 @@ class Scheduler : public TrainingObserver {
 
   Ptr<TrainingState> state_;
 
-  timer::Timer timer_, heartBeatTimer_;
+  timer::Timer timer_;
+  timer::Timer heartBeatTimer_;
 
   float getLearningRate(TrainingState& state) {
     float baselr = options_->get<float>("learn-rate");
@@ -24,7 +25,8 @@ class Scheduler : public TrainingObserver {
     float mult1 = 1.f;
     auto warmup = SchedulingParameter::parse(options_->get<std::string>("lr-warmup"));
     if(warmup) {
-      ABORT_IF(state.warmupStart && state.warmupStart.unit != warmup.unit, "lr-warmup and warmup-start must have the same unit");
+      ABORT_IF(state.warmupStart && state.warmupStart.unit != warmup.unit,
+               "lr-warmup and warmup-start must have the same unit");
       auto bno = state.getProgressIn(warmup.unit) - state.warmupStart.n;
       mult1 = std::min(1.f, (float)bno / (float)warmup.n);
     }
@@ -32,7 +34,9 @@ class Scheduler : public TrainingObserver {
     float mult2 = 1.f;
     auto decayGoogle = SchedulingParameter::parse(options_->get<std::string>("lr-decay-inv-sqrt"));
     if(decayGoogle) {
-      mult2 = std::min(1.f, (float)(std::sqrt(decayGoogle.n) / std::sqrt(state.getProgressIn(decayGoogle.unit))));
+      mult2 = std::min(
+          1.f,
+          (float)(std::sqrt(decayGoogle.n) / std::sqrt(state.getProgressIn(decayGoogle.unit))));
     }
 
     baselr = baselr * mult1 * mult2;
@@ -45,8 +49,7 @@ class Scheduler : public TrainingObserver {
   }
 
 public:
-  Scheduler(Ptr<Options> options, Ptr<TrainingState> state)
-      : options_(options), state_(state) {
+  Scheduler(Ptr<Options> options, Ptr<TrainingState> state) : options_(options), state_(state) {
     state_->eta = getLearningRate(*state);
   }
 
@@ -163,14 +166,15 @@ class Scheduler : public TrainingObserver {
   }
 
   void update(float cost, const std::vector<Ptr<data::Batch>>& batches, Ptr<IMPIWrapper> mpi = nullptr) {
-    state_->rememberPreviousProgress(); // note: epoch increases happen at the wrong place, hence -freq parameters do not support epoch units
+    state_->rememberPreviousProgress();  // note: epoch increases happen at the wrong place, hence
+                                         // -freq parameters do not support epoch units
     state_->validated = false;
 
     size_t batchSize = 0;    // number of sentences in batch
     size_t batchLabels = 0;  // number of target words in batch
 
     for(const auto& batch : batches) {
-      if (batch) { // (nullptr is allowed as result of split)
+      if(batch) { // (nullptr is allowed as result of split)
         batchSize += batch->size();
         batchLabels += batch->words(-1);
       }
@@ -178,8 +182,9 @@ class Scheduler : public TrainingObserver {
 
     // extrapolate cost across MPI processes, so that we have numbers in the right range
     // When doing the actual log, we then aggregate across MPI processes to get the accurate number.
-    if (mpi)
-      cost *= mpi->numMPIProcesses(); // @BUGBUG: this is presently correct for ce-sum, but possibly not the av-based losses
+    if(mpi)
+      cost *= mpi->numMPIProcesses();  // @BUGBUG: this is presently correct for ce-sum, but
+                                       // possibly not the av-based losses
 
     // reconstruct sum cost, for displaying epoch-level averages instead of minibatch-level
     auto costType = options_->get<std::string>("cost-type");
@@ -208,13 +213,13 @@ class Scheduler : public TrainingObserver {
     if(state_->enteredNewPeriodOf(options_->get<std::string>("disp-freq")) ||
        state_->batches <= options_->get<size_t>("disp-first")) {
       // if MPI then aggregate precise cost across workers
-      if (mpi) {
+      if(mpi) {
         //LOG(info, "all-reducing cost from {}", state_->costSum);
         state_->costSum /= mpi->numMPIProcesses(); // undo the extra scaling
         mpi->allReduce(&state_->costSum, &state_->costSum, 1, MPI_FLOAT, MPI_SUM);
         //LOG(info, "all-reduced cost to {}", state_->costSum);
       }
-      if (mpi && mpi->myMPIRank() != 0)
+      if(mpi && mpi->myMPIRank() != 0)
         ; // skip the report on alternate worker processes
       else if(dispLabelCounts) {
         if(options_->get<bool>("lr-report")) {  // if true then show the learning rate
@@ -419,8 +424,7 @@ class Scheduler : public TrainingObserver {
 
     if(factor > 0.0) {
       if(options_->get<std::string>("lr-decay-strategy") == "stalled") {
-        size_t startStalled
-            = options_->get<std::vector<size_t>>("lr-decay-start").front();
+        size_t startStalled = options_->get<std::vector<size_t>>("lr-decay-start").front();
         if(startStalled && state.stalled && state.stalled % startStalled == 0) {
           state.factor *= factor;
           state.eta = baselr * state.factor;
diff --git a/src/training/training_state.h b/src/training/training_state.h
index 2deefe471..c7c2a7771 100755
--- a/src/training/training_state.h
+++ b/src/training/training_state.h
@@ -25,20 +25,22 @@ enum class SchedulingUnit {
   updates,   // "u": number of updates so far (batches)
   epochs     // "e": number of epochs begun so far (very first epoch is 1)
 };
+
 struct SchedulingParameter {
   size_t n{0};                                  // number of steps measured in 'unit'
   SchedulingUnit unit{SchedulingUnit::updates}; // unit of value
 
   // parses scheduling parameters of the form NU where N=unsigned int and U=unit
-  // Examples of valid inputs: "16000u" (16000 updates), "32000000t" (32 million target labels), "100e" (100 epochs).
+  // Examples of valid inputs: "16000u" (16000 updates), "32000000t" (32 million target labels),
+  // "100e" (100 epochs).
   static SchedulingParameter parse(std::string param) {
     SchedulingParameter res;
-    if (!param.empty() && param.back() >= 'a') {
-      switch (param.back()) {
-      case 't': res.unit = SchedulingUnit::trgLabels; break;
-      case 'u': res.unit = SchedulingUnit::updates;   break;
-      case 'e': res.unit = SchedulingUnit::epochs;    break;
-      default: ABORT("invalid unit '{}' in {}", param.back(), param);
+    if(!param.empty() && param.back() >= 'a') {
+      switch(param.back()) {
+        case 't': res.unit = SchedulingUnit::trgLabels; break;
+        case 'u': res.unit = SchedulingUnit::updates;   break;
+        case 'e': res.unit = SchedulingUnit::epochs;    break;
+        default: ABORT("invalid unit '{}' in {}", param.back(), param);
       }
       param.pop_back();
     }
@@ -49,11 +51,11 @@ struct SchedulingParameter {
   operator bool() const { return n > 0; } // check whether it is specified
 
   operator std::string() const { // convert back for storing in config
-    switch (unit) {
-    case SchedulingUnit::trgLabels: return std::to_string(n) + "t";
-    case SchedulingUnit::updates  : return std::to_string(n) + "u";
-    case SchedulingUnit::epochs   : return std::to_string(n) + "e";
-    default: ABORT("corrupt enum value");
+    switch(unit) {
+      case SchedulingUnit::trgLabels: return std::to_string(n) + "t";
+      case SchedulingUnit::updates  : return std::to_string(n) + "u";
+      case SchedulingUnit::epochs   : return std::to_string(n) + "e";
+      default: ABORT("corrupt enum value for scheduling unit");
     }
   }
 };
@@ -120,11 +122,11 @@ class TrainingState {
 
   // return the totals count that corresponds to the given unit (batches, labels, or epochs)
   size_t getProgressIn(SchedulingUnit u) const {
-    switch (u) {
-    case SchedulingUnit::trgLabels: return labelsTotal;
-    case SchedulingUnit::updates  : return batches;
-    case SchedulingUnit::epochs   : return epochs;
-    default: ABORT("corrupt enum value");
+    switch(u) {
+      case SchedulingUnit::trgLabels: return labelsTotal;
+      case SchedulingUnit::updates  : return batches;
+      case SchedulingUnit::epochs   : return epochs;
+      default: ABORT("corrupt enum value");
     }
   }
 
@@ -137,11 +139,11 @@ class TrainingState {
   }
 
   size_t getPreviousProgressIn(SchedulingUnit u) const {
-    switch (u) {
-    case SchedulingUnit::trgLabels: return prevLabelsTotal;
-    case SchedulingUnit::updates  : return prevBatches;
-    case SchedulingUnit::epochs   : return prevEpochs;
-    default: ABORT("corrupt enum value");
+    switch(u) {
+      case SchedulingUnit::trgLabels: return prevLabelsTotal;
+      case SchedulingUnit::updates  : return prevBatches;
+      case SchedulingUnit::epochs   : return prevEpochs;
+      default: ABORT("corrupt enum value");
     }
   }
 
@@ -149,6 +151,7 @@ class TrainingState {
   // unit in which that parameter is given. There are a few edge cases:
   //  - this function will be called many times within the same epoch
   //  - labelsTotal does not increment by 1, so simple modulus does not work
+  //
   // So instead of modulus==0, this function compares the previous progress/period
   // to the current, and triggers if they differ (i.e. the border between two
   // periods was crossed). This requires that rememberPreviousProgress() is called
@@ -158,7 +161,8 @@ class TrainingState {
   bool enteredNewPeriodOf(std::string schedulingParam) const {
     auto period = SchedulingParameter::parse(schedulingParam);
     ABORT_IF(period.unit == SchedulingUnit::epochs,
-        "Unit {} is not supported for frequency parameters (the one(s) with value {})", schedulingParam);
+             "Unit {} is not supported for frequency parameters (the one(s) with value {})",
+             schedulingParam);
     auto previousProgress = getPreviousProgressIn(period.unit);
     auto progress = getProgressIn(period.unit);
     return period && progress / period.n != previousProgress / period.n;
@@ -204,13 +208,16 @@ class TrainingState {
     epochs = config["epochs"].as<size_t>();
     batches = config["batches"].as<size_t>();
     batchesEpoch = config["batches-epoch"].as<size_t>();
-    // (different serialization name for back compat)
+    // different serialization name for backward compatibility
     samplesEpoch = config["samples"].as<size_t>();
-    // (optional for back compat)
+
+    // clang-format off
+    // optional for backward compatibility
     labelsTotal     = config["labels-total"]      ? config["labels-total"].as<size_t>()      : 0;
     prevLabelsTotal = config["prev-labels-total"] ? config["prev-labels-total"].as<size_t>() : 0;
     prevBatches     = config["prev-batches"]      ? config["prev-batches"].as<size_t>()      : 0;
     prevEpochs      = config["prev-epochs"]       ? config["prev-epochs"].as<size_t>()       : 0;
+    // clang-format on
 
     stalled = config["stalled"].as<size_t>();
     maxStalled = config["stalled-max"].as<size_t>();

From d7919a898be79b150d2fe9183c614fb6322f9827 Mon Sep 17 00:00:00 2001
From: Roman Grundkiewicz <rgrundki@exseed.ed.ac.uk>
Date: Tue, 11 Dec 2018 11:19:43 +0000
Subject: [PATCH 017/838] Make --lr-decay a float

---
 src/common/config_parser.cpp    |  2 +-
 src/common/config_validator.cpp |  2 +-
 src/training/scheduler.h        | 11 ++++-------
 3 files changed, 6 insertions(+), 9 deletions(-)

diff --git a/src/common/config_parser.cpp b/src/common/config_parser.cpp
index ce09f6806..ceb744bb7 100755
--- a/src/common/config_parser.cpp
+++ b/src/common/config_parser.cpp
@@ -320,7 +320,7 @@ void ConfigParser::addOptionsTraining(cli::CLIWrapper& cli) {
   cli.add<bool>("--lr-report",
      "Report learning rate for each update");
 
-  cli.add<double>("--lr-decay",
+  cli.add<float>("--lr-decay",
      "Per-update decay factor for learning rate: lr <- lr * arg (0 to disable)");
   cli.add<std::string>("--lr-decay-strategy",
      "Strategy for learning rate decaying: epoch, batches, stalled, epoch+batches, epoch+stalled",
diff --git a/src/common/config_validator.cpp b/src/common/config_validator.cpp
index 5086c7263..5f2eae09c 100755
--- a/src/common/config_validator.cpp
+++ b/src/common/config_validator.cpp
@@ -89,7 +89,7 @@ void ConfigValidator::validateOptionsTraining() const {
       "There should be as many validation sets as training sets");
 
   // validations for learning rate decaying
-  ABORT_IF(get<double>("lr-decay") > 1.0, "Learning rate decay factor greater than 1.0 is unusual");
+  ABORT_IF(get<float>("lr-decay") > 1.f, "Learning rate decay factor greater than 1.0 is unusual");
 
   auto strategy = get<std::string>("lr-decay-strategy");
 
diff --git a/src/training/scheduler.h b/src/training/scheduler.h
index acf8b5243..efc597410 100755
--- a/src/training/scheduler.h
+++ b/src/training/scheduler.h
@@ -318,7 +318,7 @@ class Scheduler : public TrainingObserver {
   }
 
   void actAfterEpoch(TrainingState& state) override {
-    float factor = (float)options_->get<double>("lr-decay"); // @TODO: <float>?
+    float factor = options_->get<float>("lr-decay");
 
     float baselr = getLearningRate(state);
     state.eta = baselr * state.factor;
@@ -352,10 +352,7 @@ class Scheduler : public TrainingObserver {
       if(decay) {
         state.factor *= factor;
         state.eta = baselr * state.factor;
-        LOG(info,
-            "Decaying learning rate to {} in epoch {}",
-            state.eta,
-            state.epochs);
+        LOG(info, "Decaying learning rate to {} in epoch {}", state.eta, state.epochs);
 
         state.reset = options_->get<bool>("lr-decay-reset-optimizer");
         if(state.reset)
@@ -370,7 +367,7 @@ class Scheduler : public TrainingObserver {
   }
 
   void actAfterBatches(TrainingState& state) override {
-    float factor = (float)options_->get<double>("lr-decay"); // @TODO: <float>?
+    float factor = options_->get<float>("lr-decay");
     state.reset = false;
 
     float baselr = getLearningRate(state);
@@ -416,7 +413,7 @@ class Scheduler : public TrainingObserver {
   }
 
   void actAfterStalled(TrainingState& state) override {
-    float factor = (float)options_->get<double>("lr-decay"); // @TODO: <float>?
+    float factor = options_->get<float>("lr-decay");
     state.reset = false;
 
     float baselr = getLearningRate(state);

From 980233bb04140e310b7d348ae402a957f9824869 Mon Sep 17 00:00:00 2001
From: Roman Grundkiewicz <rgrundki@exseed.ed.ac.uk>
Date: Tue, 11 Dec 2018 11:37:50 +0000
Subject: [PATCH 018/838] Add TODOs in scheduler

---
 src/training/scheduler.h | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/src/training/scheduler.h b/src/training/scheduler.h
index efc597410..b7506e8f2 100755
--- a/src/training/scheduler.h
+++ b/src/training/scheduler.h
@@ -41,6 +41,7 @@ class Scheduler : public TrainingObserver {
 
     baselr = baselr * mult1 * mult2;
 
+    // TODO: why lr-warmup-start-rate is extracted from options_ instead of using state.warmupStart?
     float lrStart = options_->get<float>("lr-warmup-start-rate");
     if(lrStart > 0)
       baselr = baselr - lrStart * mult1 * mult2 + lrStart * mult2;
@@ -393,6 +394,7 @@ class Scheduler : public TrainingObserver {
 
           if(options_->get<bool>("lr-decay-repeat-warmup")) {
             LOG(info, "Restarting learning rate warmup");
+            // TODO: avoid repeating this many times and minimize calls to options_->get
             state.warmupStart.n = state.getProgressIn(SchedulingParameter::parse(options_->get<std::string>("lr-warmup")).unit);
           }
         }

From 84af8c0e50f135f674aefc9718942990415449bf Mon Sep 17 00:00:00 2001
From: Roman Grundkiewicz <rgrundki@exseed.ed.ac.uk>
Date: Wed, 12 Dec 2018 12:26:14 +0000
Subject: [PATCH 019/838] Fix providing default value for cpu-threads

---
 src/common/config_parser.cpp | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/src/common/config_parser.cpp b/src/common/config_parser.cpp
index ceb744bb7..d0c9310a5 100755
--- a/src/common/config_parser.cpp
+++ b/src/common/config_parser.cpp
@@ -560,12 +560,12 @@ void ConfigParser::addSuboptionsDevices(cli::CLIWrapper& cli) {
 #endif
 #ifdef CUDA_FOUND
   cli.add<size_t>("--cpu-threads",
-      "Use CPU-based computation with this many independent threads, 0 means GPU-based computation")
-      ->default_val("0")->implicit_val("1");
+      "Use CPU-based computation with this many independent threads, 0 means GPU-based computation",
+      0)->implicit_val("1");
 #else
   cli.add<size_t>("--cpu-threads",
-      "Use CPU-based computation with this many independent threads, 0 means GPU-based computation")
-      ->default_val("1");
+      "Use CPU-based computation with this many independent threads, 0 means GPU-based computation",
+      1);
 #endif
   // clang-format on
 }

From cf6cfc370e604f1241a1166b32c68f603bc59836 Mon Sep 17 00:00:00 2001
From: Roman Grundkiewicz <rgrundki@exseed.ed.ac.uk>
Date: Wed, 12 Dec 2018 12:46:03 +0000
Subject: [PATCH 020/838] Replace add_nondefault() with add() for --log and
 --log-time-zone

---
 src/common/config_parser.cpp |  4 +--
 src/common/logging.cpp       | 55 ++++++++++++++----------------------
 2 files changed, 23 insertions(+), 36 deletions(-)

diff --git a/src/common/config_parser.cpp b/src/common/config_parser.cpp
index d0c9310a5..637200471 100755
--- a/src/common/config_parser.cpp
+++ b/src/common/config_parser.cpp
@@ -58,12 +58,12 @@ void ConfigParser::addOptionsGeneral(cli::CLIWrapper& cli) {
   cli.add<size_t>("--workspace,-w",
       "Preallocate  arg  MB of work space",
       defaultWorkspace);
-  cli.add_nondefault<std::string>("--log",
+  cli.add<std::string>("--log",
      "Log training process information to file given by  arg");
   cli.add<std::string>("--log-level",
      "Set verbosity level of logging: trace, debug, info, warn, err(or), critical, off",
      "info");
-  cli.add_nondefault<std::string>("--log-time-zone",
+  cli.add<std::string>("--log-time-zone",
      "Set time zone for the date shown on logging");
   cli.add<bool>("--quiet",
      "Suppress all logging to stderr. Logging to files still works");
diff --git a/src/common/logging.cpp b/src/common/logging.cpp
index 0170d633c..0f8eefcb1 100755
--- a/src/common/logging.cpp
+++ b/src/common/logging.cpp
@@ -14,26 +14,22 @@
 #define noinline __attribute__((noinline))
 #endif
 
-std::shared_ptr<spdlog::logger> createStderrLogger(
-    const std::string& name,
-    const std::string& pattern,
-    const std::vector<std::string>& files,
-    bool quiet) {
+std::shared_ptr<spdlog::logger> createStderrLogger(const std::string& name,
+                                                   const std::string& pattern,
+                                                   const std::vector<std::string>& files,
+                                                   bool quiet) {
   std::vector<spdlog::sink_ptr> sinks;
 
   auto stderr_sink = spdlog::sinks::stderr_sink_mt::instance();
-
   if(!quiet)
     sinks.push_back(stderr_sink);
 
   for(auto&& file : files) {
-    auto file_sink
-        = std::make_shared<spdlog::sinks::simple_file_sink_st>(file, true);
+    auto file_sink = std::make_shared<spdlog::sinks::simple_file_sink_st>(file, true);
     sinks.push_back(file_sink);
   }
 
-  auto logger
-      = std::make_shared<spdlog::logger>(name, begin(sinks), end(sinks));
+  auto logger = std::make_shared<spdlog::logger>(name, begin(sinks), end(sinks));
 
   spdlog::register_logger(logger);
   logger->set_pattern(pattern);
@@ -56,9 +52,7 @@ bool setLoggingLevel(spdlog::logger& logger, std::string const level) {
   else if(level == "off")
     logger.set_level(spdlog::level::off);
   else {
-    logger.warn("Unknown log level '{}' for logger '{}'",
-                level.c_str(),
-                logger.name().c_str());
+    logger.warn("Unknown log level '{}' for logger '{}'", level.c_str(), logger.name().c_str());
     return false;
   }
   return true;
@@ -69,7 +63,7 @@ void createLoggers(const marian::Config* options) {
   std::vector<std::string> generalLogs;
   std::vector<std::string> validLogs;
 
-  if(options && options->has("log")) {
+  if(options && !options->get<std::string>("log").empty()) {
     generalLogs.push_back(options->get<std::string>("log"));
 #ifndef _WIN32
     // can't open the same file twice in Windows for some reason
@@ -77,16 +71,13 @@ void createLoggers(const marian::Config* options) {
 #endif
   }
 
-  if(options && options->has("valid-log")
-     && !options->get<std::string>("valid-log").empty()) {
+  if(options && options->has("valid-log") && !options->get<std::string>("valid-log").empty()) {
     validLogs.push_back(options->get<std::string>("valid-log"));
   }
 
   bool quiet = options && options->get<bool>("quiet");
-  Logger general{
-      createStderrLogger("general", "[%Y-%m-%d %T] %v", generalLogs, quiet)};
-  Logger valid{
-      createStderrLogger("valid", "[%Y-%m-%d %T] [valid] %v", validLogs, quiet)};
+  Logger general{createStderrLogger("general", "[%Y-%m-%d %T] %v", generalLogs, quiet)};
+  Logger valid{createStderrLogger("valid", "[%Y-%m-%d %T] [valid] %v", validLogs, quiet)};
 
   if(options && options->has("log-level")) {
     std::string loglevel = options->get<std::string>("log-level");
@@ -95,34 +86,30 @@ void createLoggers(const marian::Config* options) {
     setLoggingLevel(*valid, loglevel);
   }
 
-  if (options && options->has("log-time-zone")) {
+  if(options && !options->get<std::string>("log-time-zone").empty()) {
     std::string timezone = options->get<std::string>("log-time-zone");
-    if (timezone != "") {
 #ifdef _WIN32
 #define setenv(var, val, over) SetEnvironmentVariableA(var, val) // ignoring over flag
 #endif
-      setenv("TZ", timezone.c_str(), true);
-      tzset();
-    }
+    setenv("TZ", timezone.c_str(), true);
+    tzset();
   }
 
   setErrorHandlers();
 }
 
 static void unhandledException() {
-  if (std::current_exception()) {
+  if(std::current_exception()) {
     try {
-      throw; // rethrow so that we can get access to what()
-    }
-    catch (const std::exception& e) {
+      throw;  // rethrow so that we can get access to what()
+    } catch(const std::exception& e) {
       ABORT("Unhandled {}: {}", typeid(e).name(), e.what());
-    }
-    catch (...) {
+    } catch(...) {
       ABORT("Unhandled exception");
     }
-  }
-  else
+  } else {
     std::abort();
+  }
 }
 
 static void setErrorHandlers() {
@@ -144,7 +131,7 @@ static void setErrorHandlers() {
 // This is called upon initializing MPI. It is needed to associated error messages to ranks.
 void switchtoMultinodeLogging(std::string nodeIdStr) {
   Logger log = spdlog::get("general");
-  if (log)
+  if(log)
     log->set_pattern("[%Y-%m-%d %T " + nodeIdStr + "] %v");
 }
 

From 3abba2873a13bee3e555eaf667c78aea5ba69c85 Mon Sep 17 00:00:00 2001
From: Roman Grundkiewicz <rgrundki@exseed.ed.ac.uk>
Date: Wed, 12 Dec 2018 12:52:31 +0000
Subject: [PATCH 021/838] Replace add_nondefault() with add() for --dump-config

---
 src/common/config_parser.cpp | 7 ++++---
 1 file changed, 4 insertions(+), 3 deletions(-)

diff --git a/src/common/config_parser.cpp b/src/common/config_parser.cpp
index 637200471..166858c49 100755
--- a/src/common/config_parser.cpp
+++ b/src/common/config_parser.cpp
@@ -77,8 +77,9 @@ void ConfigParser::addOptionsGeneral(cli::CLIWrapper& cli) {
      "allow the use of environment variables in paths, of the form ${VAR_NAME}");
   cli.add<bool>("--relative-paths",
      "All paths are relative to the config file location");
-  cli.add_nondefault<std::string>("--dump-config",
-     "Dump current (modified) configuration to stdout and exit. Possible values: full, minimal")
+  cli.add<std::string>("--dump-config",
+     "Dump current (modified) configuration to stdout and exit. Possible values: full, minimal",
+     "false")
     ->implicit_val("full");
   // clang-format on
 }
@@ -717,7 +718,7 @@ void ConfigParser::parseOptions(int argc, char** argv, bool doValidate) {
   // remove extra config files from the config to avoid redundancy
   config_.remove("config");
 
-  if(has("dump-config") && get<std::string>("dump-config") != "false") {
+  if(get<std::string>("dump-config") != "false") {
     bool skipDefault = get<std::string>("dump-config") == "minimal";
     config_.remove("dump-config");
     std::cout << cli.dumpConfig(skipDefault) << std::endl;

From a0794f46bd088e4c924b033cd6e1d2f6e071ab19 Mon Sep 17 00:00:00 2001
From: Roman Grundkiewicz <rgrundki@exseed.ed.ac.uk>
Date: Wed, 12 Dec 2018 13:53:02 +0000
Subject: [PATCH 022/838] Replace add_nondefault() with add() for --valid-log

---
 src/common/config_parser.cpp | 2 +-
 src/common/logging.cpp       | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/src/common/config_parser.cpp b/src/common/config_parser.cpp
index 166858c49..a6f38467d 100755
--- a/src/common/config_parser.cpp
+++ b/src/common/config_parser.cpp
@@ -446,7 +446,7 @@ void ConfigParser::addOptionsValidation(cli::CLIWrapper& cli) {
 
   cli.add<bool>("--keep-best",
       "Keep best model for each validation metric");
-  cli.add_nondefault<std::string>("--valid-log",
+  cli.add<std::string>("--valid-log",
      "Log validation scores to file given by  arg");
   // clang-format on
 }
diff --git a/src/common/logging.cpp b/src/common/logging.cpp
index 0f8eefcb1..586336025 100755
--- a/src/common/logging.cpp
+++ b/src/common/logging.cpp
@@ -71,7 +71,7 @@ void createLoggers(const marian::Config* options) {
 #endif
   }
 
-  if(options && options->has("valid-log") && !options->get<std::string>("valid-log").empty()) {
+  if(options && !options->get<std::string>("valid-log").empty()) {
     validLogs.push_back(options->get<std::string>("valid-log"));
   }
 

From 0a24ca422cae59919445b038763a098afca81d4a Mon Sep 17 00:00:00 2001
From: Roman Grundkiewicz <rgrundki@exseed.ed.ac.uk>
Date: Wed, 12 Dec 2018 13:53:36 +0000
Subject: [PATCH 023/838] Rename options to config for clarity

---
 src/common/logging.cpp | 22 +++++++++++-----------
 1 file changed, 11 insertions(+), 11 deletions(-)

diff --git a/src/common/logging.cpp b/src/common/logging.cpp
index 586336025..430000228 100755
--- a/src/common/logging.cpp
+++ b/src/common/logging.cpp
@@ -59,35 +59,35 @@ bool setLoggingLevel(spdlog::logger& logger, std::string const level) {
 }
 
 static void setErrorHandlers();
-void createLoggers(const marian::Config* options) {
+void createLoggers(const marian::Config* config) {
   std::vector<std::string> generalLogs;
   std::vector<std::string> validLogs;
 
-  if(options && !options->get<std::string>("log").empty()) {
-    generalLogs.push_back(options->get<std::string>("log"));
+  if(config && !config->get<std::string>("log").empty()) {
+    generalLogs.push_back(config->get<std::string>("log"));
 #ifndef _WIN32
     // can't open the same file twice in Windows for some reason
-    validLogs.push_back(options->get<std::string>("log"));
+    validLogs.push_back(config->get<std::string>("log"));
 #endif
   }
 
-  if(options && !options->get<std::string>("valid-log").empty()) {
-    validLogs.push_back(options->get<std::string>("valid-log"));
+  if(config && !config->get<std::string>("valid-log").empty()) {
+    validLogs.push_back(config->get<std::string>("valid-log"));
   }
 
-  bool quiet = options && options->get<bool>("quiet");
+  bool quiet = config && config->get<bool>("quiet");
   Logger general{createStderrLogger("general", "[%Y-%m-%d %T] %v", generalLogs, quiet)};
   Logger valid{createStderrLogger("valid", "[%Y-%m-%d %T] [valid] %v", validLogs, quiet)};
 
-  if(options && options->has("log-level")) {
-    std::string loglevel = options->get<std::string>("log-level");
+  if(config && config->has("log-level")) {
+    std::string loglevel = config->get<std::string>("log-level");
     if(!setLoggingLevel(*general, loglevel))
       return;
     setLoggingLevel(*valid, loglevel);
   }
 
-  if(options && !options->get<std::string>("log-time-zone").empty()) {
-    std::string timezone = options->get<std::string>("log-time-zone");
+  if(config && !config->get<std::string>("log-time-zone").empty()) {
+    std::string timezone = config->get<std::string>("log-time-zone");
 #ifdef _WIN32
 #define setenv(var, val, over) SetEnvironmentVariableA(var, val) // ignoring over flag
 #endif

From fefd438402a7a73c4c57e79f5b52f1b380472722 Mon Sep 17 00:00:00 2001
From: Roman Grundkiewicz <rgrundki@exseed.ed.ac.uk>
Date: Wed, 12 Dec 2018 14:01:13 +0000
Subject: [PATCH 024/838] Fix --valid-log

---
 src/common/logging.cpp | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/src/common/logging.cpp b/src/common/logging.cpp
index 430000228..5e431cdda 100755
--- a/src/common/logging.cpp
+++ b/src/common/logging.cpp
@@ -71,7 +71,8 @@ void createLoggers(const marian::Config* config) {
 #endif
   }
 
-  if(config && !config->get<std::string>("valid-log").empty()) {
+  // valid-log is available only for training
+  if(config && config->has("valid-log") && !config->get<std::string>("valid-log").empty()) {
     validLogs.push_back(config->get<std::string>("valid-log"));
   }
 

From c2b6d10c658cfd47563b00828b925fac1235d282 Mon Sep 17 00:00:00 2001
From: Roman Grundkiewicz <rgrundki@exseed.ed.ac.uk>
Date: Wed, 12 Dec 2018 14:52:12 +0000
Subject: [PATCH 025/838] Add Options::nonempty()

---
 src/common/options.h | 25 +++++++++++++++++++++++++
 src/tests/cli.cpp    |  7 ++++++-
 2 files changed, 31 insertions(+), 1 deletion(-)

diff --git a/src/common/options.h b/src/common/options.h
index 9f1bb8dba..b413f7db3 100755
--- a/src/common/options.h
+++ b/src/common/options.h
@@ -92,6 +92,31 @@ class Options {
       return defaultValue;
   }
 
+  /**
+   * @brief Check if a sequence or string option is defined and nonempty
+   *
+   * Aborts if the option does not store a sequence or string value. Returns false if an option with
+   * the given key does not exist.
+   *
+   * @param key option name
+   *
+   * @return true if the option is defined and is a nonempty sequence or string
+   */
+  bool nonempty(const std::string& key) const {
+    if(!has(key)) {
+      return false;
+    }
+    if(options_[key].IsSequence()) {
+      return options_[key].size() != 0;
+    }
+    try {
+      return !options_[key].as<std::string>().empty();
+    } catch(const YAML::BadConversion& e) {
+      ABORT("Option '{}' is neither a sequence nor a text");
+    }
+    return false;
+  }
+
   bool has(const std::string& key) const { return options_[key]; }
 };
 
diff --git a/src/tests/cli.cpp b/src/tests/cli.cpp
index 2f40bb133..c3210514b 100644
--- a/src/tests/cli.cpp
+++ b/src/tests/cli.cpp
@@ -41,7 +41,7 @@ int main(int argc, char** argv) {
   {
     auto w = New<CLIWrapper>(options);
     w->add<int>("-i,--int", "help message")->implicit_val("555")->default_val("123");
-    w->add<std::string>("-s,--str", "help message")->default_val("foo");
+    w->add<std::string>("-s,--str", "help message");
     w->add<std::vector<float>>("-v,--vec", "help message")->expected(-2);
     w->switchGroup("My group");
     w->add<std::vector<std::string>>("--defvec,-d", "help message")->default_val("foo");
@@ -67,5 +67,10 @@ int main(int argc, char** argv) {
   YAML::Emitter emit;
   OutputYaml(options->getYaml(), emit);
   std::cout << emit.c_str() << std::endl;
+
+  std::cout << "===" << std::endl;
+  std::cout << "vec/str.nonempty? " << options->nonempty("vec") << " " << options->nonempty("str") << std::endl;
+  std::cout << "vec/str.has?      " << options->has("vec") << " " << options->has("str") << std::endl;
+
   return 0;
 }

From ea51591671f2e444f0d15098ee9602511cbf5539 Mon Sep 17 00:00:00 2001
From: Roman Grundkiewicz <rgrundki@exseed.ed.ac.uk>
Date: Wed, 12 Dec 2018 14:55:16 +0000
Subject: [PATCH 026/838] Replace add_nondefault() with add() for
 --data-weighting

---
 src/common/config_parser.cpp |  2 +-
 src/data/corpus.h            |  2 +-
 src/data/corpus_base.cpp     |  2 +-
 src/data/corpus_base.h       |  2 +-
 src/data/corpus_sqlite.cpp   |  3 +--
 src/data/corpus_sqlite.h     |  2 +-
 src/layers/weight.cpp        |  2 +-
 src/models/costs.h           | 10 +++++-----
 8 files changed, 12 insertions(+), 13 deletions(-)

diff --git a/src/common/config_parser.cpp b/src/common/config_parser.cpp
index a6f38467d..ab4b2b7aa 100755
--- a/src/common/config_parser.cpp
+++ b/src/common/config_parser.cpp
@@ -367,7 +367,7 @@ void ConfigParser::addOptionsTraining(cli::CLIWrapper& cli) {
   cli.add<double>("--guided-alignment-weight",
      "Weight for guided alignment cost",
      0.1);
-  cli.add_nondefault<std::string>("--data-weighting",
+  cli.add<std::string>("--data-weighting",
      "Path to a file with sentence or word weights");
   cli.add<std::string>("--data-weighting-type",
      "Processing level for data weighting: sentence, word",
diff --git a/src/data/corpus.h b/src/data/corpus.h
index 119f3aab2..e916403d9 100755
--- a/src/data/corpus.h
+++ b/src/data/corpus.h
@@ -98,7 +98,7 @@ class Corpus : public CorpusBase {
 
     if(options_->get("guided-alignment", std::string("none")) != "none" && alignFileIdx_)
       addAlignmentsToBatch(batch, batchVector);
-    if(options_->has("data-weighting") && weightFileIdx_)
+    if(options_->nonempty("data-weighting") && weightFileIdx_)
       addWeightsToBatch(batch, batchVector);
 
     return batch;
diff --git a/src/data/corpus_base.cpp b/src/data/corpus_base.cpp
index 2a088537a..11304030a 100755
--- a/src/data/corpus_base.cpp
+++ b/src/data/corpus_base.cpp
@@ -174,7 +174,7 @@ CorpusBase::CorpusBase(Ptr<Options> options, bool translate)
     ABORT_IF(files_.back()->empty(), "File with alignments '{}' is empty", path);
   }
 
-  if(training && options_->has("data-weighting")) {
+  if(training && options_->nonempty("data-weighting")) {
     auto path = options_->get<std::string>("data-weighting");
 
     ABORT_IF(!filesystem::exists(path), "Weight file does not exist");
diff --git a/src/data/corpus_base.h b/src/data/corpus_base.h
index 8ecdf2337..9eb5a2984 100755
--- a/src/data/corpus_base.h
+++ b/src/data/corpus_base.h
@@ -337,7 +337,7 @@ class CorpusBatch : public Batch {
       batch->setGuidedAlignment(std::move(alignment));
     }
 
-    if(options->has("data-weighting")) {
+    if(options->nonempty("data-weighting")) {
       auto weightsSize = batchSize;
       if(options->get<std::string>("data-weighting-type") != "sentence")
         weightsSize *= lengths.back();
diff --git a/src/data/corpus_sqlite.cpp b/src/data/corpus_sqlite.cpp
index cbab750eb..714a70f01 100644
--- a/src/data/corpus_sqlite.cpp
+++ b/src/data/corpus_sqlite.cpp
@@ -25,8 +25,7 @@ void CorpusSQLite::fillSQLite() {
   if(options_->get<std::string>("sqlite") == "temporary") {
     LOG(info, "[sqlite] Creating temporary database in {}", tempDir);
 
-    db_.reset(
-        new SQLite::Database("", SQLite::OPEN_READWRITE | SQLite::OPEN_CREATE));
+    db_.reset(new SQLite::Database("", SQLite::OPEN_READWRITE | SQLite::OPEN_CREATE));
     db_->exec("PRAGMA temp_store_directory = '" + tempDir + "';");
 
     fill = true;
diff --git a/src/data/corpus_sqlite.h b/src/data/corpus_sqlite.h
index 641da2a8e..ba40ecb49 100755
--- a/src/data/corpus_sqlite.h
+++ b/src/data/corpus_sqlite.h
@@ -104,7 +104,7 @@ class CorpusSQLite : public CorpusBase {
 
     if(options_->has("guided-alignment") && alignFileIdx_)
       addAlignmentsToBatch(batch, batchVector);
-    if(options_->has("data-weighting") && weightFileIdx_)
+    if(options_->nonempty("data-weighting") && weightFileIdx_)
       addWeightsToBatch(batch, batchVector);
 
     return batch;
diff --git a/src/layers/weight.cpp b/src/layers/weight.cpp
index ab7fe0729..98e97e4e9 100755
--- a/src/layers/weight.cpp
+++ b/src/layers/weight.cpp
@@ -3,7 +3,7 @@
 namespace marian {
 
 Ptr<WeightingBase> WeightingFactory(Ptr<Options> options) {
-  ABORT_IF(!options->has("data-weighting"),
+  ABORT_IF(!options->nonempty("data-weighting"),
            "No data-weighting specified in options");
   return New<DataWeighting>(options->get<std::string>("data-weighting-type"));
 }
diff --git a/src/models/costs.h b/src/models/costs.h
index d38a445e8..dd8459b7d 100755
--- a/src/models/costs.h
+++ b/src/models/costs.h
@@ -33,9 +33,9 @@ class EncoderDecoderCE : public CostBase {
     loss_ = LossFactory(options_, inference_);
 
     toBeWeighted_
-        = (options_->has("data-weighting") && !inference_)
-          || (options_->has("dynamic-weighting")
-              && options_->get<bool>("dynamic-weighting") && !inference_);
+        = (options_->nonempty("data-weighting") && !inference_)
+          || (options_->has("dynamic-weighting") && options_->get<bool>("dynamic-weighting")
+              && !inference_);
     if(toBeWeighted_)
       weighter_ = WeightingFactory(options_);
   }
@@ -116,7 +116,7 @@ class LogSoftmaxStep : public CostStep {
   virtual Ptr<DecoderState> apply(Ptr<DecoderState> state) override {
     // decoder needs normalized probabilities (note: skipped if beam 1 and --skip-cost)
     auto logits = state->getLogProbs();
-    
+
     auto logprobs = logsoftmax(logits);
 
     state->setLogProbs(logprobs);
@@ -131,7 +131,7 @@ class GumbelSoftmaxStep : public CostStep {
 public:
   virtual Ptr<DecoderState> apply(Ptr<DecoderState> state) override {
     auto logits = state->getLogProbs();
-    
+
     auto logprobs = logsoftmax(logits + constant_like(logits, inits::gumbel));
 
     state->setLogProbs(logprobs);

From 69b795e238e67541375193135154f21d9302c6e6 Mon Sep 17 00:00:00 2001
From: Roman Grundkiewicz <rgrundki@exseed.ed.ac.uk>
Date: Wed, 12 Dec 2018 16:47:13 +0000
Subject: [PATCH 027/838] Replace some add_nondefault() with add()

---
 src/common/config_parser.cpp              | 6 +++---
 src/common/config_validator.cpp           | 3 ++-
 src/models/decoder.h                      | 2 +-
 src/models/s2s.h                          | 2 +-
 src/models/transformer.h                  | 6 +++---
 src/optimizers/optimizers.cpp             | 4 +---
 src/training/graph_group_async.cpp        | 2 +-
 src/training/graph_group_multinode.h      | 2 +-
 src/training/graph_group_multinode_sync.h | 2 +-
 src/training/graph_group_singleton.h      | 2 +-
 src/training/graph_group_sync.cpp         | 2 +-
 11 files changed, 16 insertions(+), 17 deletions(-)

diff --git a/src/common/config_parser.cpp b/src/common/config_parser.cpp
index ab4b2b7aa..37b75ac45 100755
--- a/src/common/config_parser.cpp
+++ b/src/common/config_parser.cpp
@@ -97,7 +97,7 @@ void ConfigParser::addOptionsModel(cli::CLIWrapper& cli) {
       "model.npz");
 
     if(mode_ == cli::mode::training) {
-      cli.add_nondefault<std::string>("--pretrained-model",
+      cli.add<std::string>("--pretrained-model",
         "Path prefix for pre-trained model to initialize model weights");
     }
   }
@@ -305,7 +305,7 @@ void ConfigParser::addOptionsTraining(cli::CLIWrapper& cli) {
   cli.add<std::string>("--optimizer,-o",
      "Optimization algorithm: sgd, adagrad, adam",
      "adam");
-  cli.add_nondefault<std::vector<float>>("--optimizer-params",
+  cli.add<std::vector<float>>("--optimizer-params",
      "Parameters for optimization algorithm, e.g. betas for adam");
   cli.add<size_t>("--optimizer-delay",
      "SGD update delay, 1 = no delay",
@@ -552,7 +552,7 @@ void ConfigParser::addSuboptionsDevices(cli::CLIWrapper& cli) {
   cli.add<std::vector<std::string>>("--devices,-d",
       "Specifies GPU ID(s) to use for training. Defaults to 0..num-devices-1",
       std::vector<std::string>({"0"}));
-  cli.add_nondefault<size_t>("--num-devices",
+  cli.add<size_t>("--num-devices",
       "Number of GPUs to use for this process. Defaults to length(devices) or 1");
 #ifdef USE_NCCL
   if(mode_ == cli::mode::training)
diff --git a/src/common/config_validator.cpp b/src/common/config_validator.cpp
index 5f2eae09c..8a6845633 100755
--- a/src/common/config_validator.cpp
+++ b/src/common/config_validator.cpp
@@ -72,7 +72,8 @@ void ConfigValidator::validateOptionsTraining() const {
   auto trainSets = get<std::vector<std::string>>("train-sets");
 
   ABORT_IF(has("embedding-vectors")
-               && get<std::vector<std::string>>("embedding-vectors").size() != trainSets.size(),
+               && get<std::vector<std::string>>("embedding-vectors").size() != trainSets.size()
+               && !get<std::vector<std::string>>("embedding-vectors").empty(),
            "There should be as many embedding vector files as training sets");
 
   filesystem::Path modelPath(get<std::string>("model"));
diff --git a/src/models/decoder.h b/src/models/decoder.h
index 39e56e1f3..c76ea763c 100755
--- a/src/models/decoder.h
+++ b/src/models/decoder.h
@@ -50,7 +50,7 @@ class DecoderBase {
     if(options_->has("embedding-fix-trg"))
       yEmbFactory("fixed", opt<bool>("embedding-fix-trg"));
 
-    if(options_->has("embedding-vectors")) {
+    if(options_->nonempty("embedding-vectors")) {
       auto embFiles = opt<std::vector<std::string>>("embedding-vectors");
       yEmbFactory("embFile", embFiles[batchIndex_])  //
           ("normalization", opt<bool>("embedding-normalization"));
diff --git a/src/models/s2s.h b/src/models/s2s.h
index 92ba9a7d1..a1087a824 100755
--- a/src/models/s2s.h
+++ b/src/models/s2s.h
@@ -136,7 +136,7 @@ class EncoderS2S : public EncoderBase {
     if(options_->has("embedding-fix-src"))
       embFactory("fixed", opt<bool>("embedding-fix-src"));
 
-    if(options_->has("embedding-vectors")) {
+    if(options_->nonempty("embedding-vectors")) {
       auto embFiles = opt<std::vector<std::string>>("embedding-vectors");
       embFactory                              //
           ("embFile", embFiles[batchIndex_])  //
diff --git a/src/models/transformer.h b/src/models/transformer.h
index c30d06c8e..3cd7140a8 100755
--- a/src/models/transformer.h
+++ b/src/models/transformer.h
@@ -500,13 +500,13 @@ class EncoderTransformer : public Transformer<EncoderBase> {
     int dimVoc = opt<std::vector<int>>("dim-vocabs")[subBatchIndex];
     int dimEmb = opt<int>("dim-emb");
     auto embFactory = embedding(graph_)("dimVocab", dimVoc)("dimEmb", dimEmb);
-    if (opt<bool>("tied-embeddings-src") || opt<bool>("tied-embeddings-all"))
+    if(opt<bool>("tied-embeddings-src") || opt<bool>("tied-embeddings-all"))
       embFactory("prefix", "Wemb");
     else
       embFactory("prefix", prefix_ + "_Wemb");
-    if (options_->has("embedding-fix-src"))
+    if(options_->has("embedding-fix-src"))
       embFactory("fixed", opt<bool>("embedding-fix-src"));
-    if (options_->has("embedding-vectors")) {
+    if(options_->nonempty("embedding-vectors")) {
       auto embFiles = opt<std::vector<std::string>>("embedding-vectors");
       embFactory("embFile", embFiles[subBatchIndex])
                 ("normalization", opt<bool>("embedding-normalization"));
diff --git a/src/optimizers/optimizers.cpp b/src/optimizers/optimizers.cpp
index 0fad790a1..b037963e7 100755
--- a/src/optimizers/optimizers.cpp
+++ b/src/optimizers/optimizers.cpp
@@ -271,9 +271,7 @@ void Adam::resetStats() {
 
 Ptr<OptimizerBase> Optimizer(Ptr<Options> options) {
   float lrate = options->get<float>("learn-rate");
-  auto params = options->has("optimizer-params")
-                    ? options->get<std::vector<float>>("optimizer-params")
-                    : std::vector<float>({});
+  auto params = options->get<std::vector<float>>("optimizer-params", std::vector<float>({}));
 
   Ptr<ClipperBase> clipper = nullptr;
   float clipNorm = options->get<float>("clip-norm");
diff --git a/src/training/graph_group_async.cpp b/src/training/graph_group_async.cpp
index 1d0042019..ec722b0ad 100755
--- a/src/training/graph_group_async.cpp
+++ b/src/training/graph_group_async.cpp
@@ -325,7 +325,7 @@ void AsyncGraphGroup::load() {
             setFn(i, data.begin() + begin, data.begin() + end);
           }
         });
-    } else if(options_->has("pretrained-model")) {
+    } else if(options_->nonempty("pretrained-model")) {
       std::string nameInit = options_->get<std::string>("pretrained-model");
       LOG(info,
           "Initialize model weights with the pre-trained model {}",
diff --git a/src/training/graph_group_multinode.h b/src/training/graph_group_multinode.h
index c86225ebc..d4158bd07 100755
--- a/src/training/graph_group_multinode.h
+++ b/src/training/graph_group_multinode.h
@@ -397,7 +397,7 @@ class MultiNodeGraphGroup : public MultiNodeGraphGroupBase {
         size_t i = 0;
         for(auto graph : clientGraphs_)
           clientBuilders_[i++]->load(graph, name);
-      } else if(options_->has("pretrained-model")) {
+      } else if(options_->nonempty("pretrained-model")) {
         std::string init = options_->get<std::string>("pretrained-model");
         LOG(info,
             "Initialize model weights with the pre-trained model {}",
diff --git a/src/training/graph_group_multinode_sync.h b/src/training/graph_group_multinode_sync.h
index bfef2050c..21613e2c0 100755
--- a/src/training/graph_group_multinode_sync.h
+++ b/src/training/graph_group_multinode_sync.h
@@ -163,7 +163,7 @@ class MultiNodeGraphGroupSync : public MultiNodeGraphGroupBase {
         size_t i = 0;
         for(auto graph : clientGraphs_)
           clientBuilders_[i++]->load(graph, name);
-      } else if(options_->has("pretrained-model")) {
+      } else if(options_->nonempty("pretrained-model")) {
         std::string init = options_->get<std::string>("pretrained-model");
         LOG(info,
             "Initialize model weights with the pre-trained model {}",
diff --git a/src/training/graph_group_singleton.h b/src/training/graph_group_singleton.h
index 1eb589d91..eb0a3f4ae 100755
--- a/src/training/graph_group_singleton.h
+++ b/src/training/graph_group_singleton.h
@@ -69,7 +69,7 @@ class SingletonGraph : public GraphGroup, public ExponentialSmoothing {
           /*scatterStateFn=*/[&](const std::vector<float>& data, const OptimizerBase::ScatterStateSetFunc& setFn) {
             setFn(/*localDeviceIndex=*/0, data.begin(), data.end());
           });
-      } else if(options_->has("pretrained-model")) {
+      } else if(options_->nonempty("pretrained-model")) {
         std::string init = options_->get<std::string>("pretrained-model");
         LOG(info,
             "Initialize model weights with the pre-trained model {}",
diff --git a/src/training/graph_group_sync.cpp b/src/training/graph_group_sync.cpp
index 6699484c1..fca75fb4b 100755
--- a/src/training/graph_group_sync.cpp
+++ b/src/training/graph_group_sync.cpp
@@ -253,7 +253,7 @@ void SyncGraphGroup::load() /*override*/ {
         [&](const std::vector<float>& optimizerStateVector, const OptimizerBase::ScatterStateSetFunc& setShardFn) {
           comm_->scatterState(optimizerStateVector, setShardFn);
         });
-    } else if(options_->has("pretrained-model")) {
+    } else if(options_->nonempty("pretrained-model")) {
       std::string nameInit = options_->get<std::string>("pretrained-model");
       LOG(info,
           "Initialize model weights with the pre-trained model {}",

From 4cf97df51e462d2d80e2a296828596b493f058b6 Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Wed, 12 Dec 2018 18:40:46 -0800
Subject: [PATCH 028/838] minibatch-size warmup (manually merged over from
 fseide/covbias); minibatches are now fed in GPU-sized chunks rather than a
 massive joint batch for all GPUs in the update; Adam hyper-parameter
 adjustment limited to learning rate, as momentum adjustment is
 counterproductive for MB scaling; log output now includes the last batch
 size; log output now shows current best for stalled validation metrics; bug
 fix: Adam optimizer should persist denominators; bug fix: Adam and Adagrad
 should use correct element size when persisting; min and max renamed to
 minimum and maximum, for consistency with other toolkits; pathie now compiles
 in manual VS Project

---
 src/3rd_party/ExceptionWithCallStack.h        |   2 +
 .../pathie-cpp/src/entry_iterator.cpp         |   2 +-
 src/3rd_party/pathie-cpp/src/path.cpp         |  12 +-
 src/3rd_party/sentencepiece                   |   2 +-
 src/command/marian_train.cpp                  |   2 +
 src/common/config_parser.cpp                  |  21 +-
 src/common/definitions.h                      |  10 +-
 src/common/file_stream.h                      |   9 +-
 src/common/filesystem.h                       |   2 +-
 src/common/io.cpp                             |   4 +-
 src/common/logging.cpp                        |   6 +-
 src/common/logging.h                          |  10 +
 src/common/utils.cpp                          |  35 ++
 src/common/utils.h                            |   4 +
 src/data/batch.h                              |   2 +-
 src/data/batch_generator.h                    |  34 +-
 src/data/batch_stats.h                        |  13 +
 src/data/corpus.cpp                           |   2 +-
 src/data/corpus.h                             |   4 +-
 src/data/corpus_base.h                        |  61 +--
 src/examples/mnist/dataset.h                  |   2 +-
 src/functional/tmp.h                          |  21 +
 src/graph/expression_graph.h                  |   4 +-
 src/graph/expression_operators.h              |   4 +-
 src/graph/node_operators.h                    |   2 +-
 src/graph/node_operators_binary.h             |   6 +-
 src/graph/node_operators_unary.h              |   0
 src/optimizers/optimizers.cpp                 |  96 +++--
 src/optimizers/optimizers.h                   |  54 ++-
 src/tensors/gpu/add.cu                        |   0
 src/tensors/gpu/element.inc                   |   6 +
 src/tensors/tensor_allocator.h                |   2 +-
 src/training/communicator.cpp                 |   4 +-
 src/training/communicator.h                   |   1 -
 src/training/communicator_nccl.h              |  67 ++--
 src/training/exponential_smoothing.h          |  30 +-
 src/training/graph_group.h                    |   9 +-
 src/training/graph_group_async.cpp            |   2 +-
 src/training/graph_group_async.h              |   2 +-
 src/training/graph_group_multinode.h          |   2 +-
 src/training/graph_group_multinode_sync.h     |   2 +-
 src/training/graph_group_singleton.h          |   4 +-
 src/training/graph_group_sync.cpp             | 225 ++++++++---
 src/training/graph_group_sync.h               |  10 +-
 src/training/scheduler.h                      | 112 +++++-
 src/training/training.h                       |   8 +-
 src/training/training_state.h                 |  13 +-
 src/training/validator.h                      |   3 +-
 vs/Marian.sln                                 | 376 +-----------------
 vs/Marian.vcxproj                             | 203 +++++++++-
 vs/Marian.vcxproj.filters                     | 278 ++++++++++++-
 51 files changed, 1132 insertions(+), 653 deletions(-)
 mode change 100644 => 100755 src/3rd_party/ExceptionWithCallStack.h
 mode change 100644 => 100755 src/3rd_party/pathie-cpp/src/entry_iterator.cpp
 mode change 100644 => 100755 src/3rd_party/pathie-cpp/src/path.cpp
 mode change 100644 => 100755 src/functional/tmp.h
 mode change 100644 => 100755 src/graph/expression_operators.h
 mode change 100644 => 100755 src/graph/node_operators.h
 mode change 100644 => 100755 src/graph/node_operators_unary.h
 mode change 100644 => 100755 src/tensors/gpu/add.cu
 mode change 100644 => 100755 src/tensors/gpu/element.inc

diff --git a/src/3rd_party/ExceptionWithCallStack.h b/src/3rd_party/ExceptionWithCallStack.h
old mode 100644
new mode 100755
index 5b961bd9b..488b1277f
--- a/src/3rd_party/ExceptionWithCallStack.h
+++ b/src/3rd_party/ExceptionWithCallStack.h
@@ -5,6 +5,8 @@
 // ExceptionWithCallStack.h - debug util functions
 //
 
+#pragma once
+
 #include <string>
 
 namespace Microsoft { namespace MSR { namespace CNTK {
diff --git a/src/3rd_party/pathie-cpp/src/entry_iterator.cpp b/src/3rd_party/pathie-cpp/src/entry_iterator.cpp
old mode 100644
new mode 100755
index e2ecb2fe1..24e5b1101
--- a/src/3rd_party/pathie-cpp/src/entry_iterator.cpp
+++ b/src/3rd_party/pathie-cpp/src/entry_iterator.cpp
@@ -178,7 +178,7 @@ entry_iterator& entry_iterator::operator++(int)
 /// Same as the other operator++().
 entry_iterator& entry_iterator::operator++()
 {
-  return (operator++());
+  return (operator++(0));
 }
 
 /**
diff --git a/src/3rd_party/pathie-cpp/src/path.cpp b/src/3rd_party/pathie-cpp/src/path.cpp
old mode 100644
new mode 100755
index 3dc1e14bb..12aa02703
--- a/src/3rd_party/pathie-cpp/src/path.cpp
+++ b/src/3rd_party/pathie-cpp/src/path.cpp
@@ -51,7 +51,7 @@
 #include <shlwapi.h>
 //#include <ntifs.h> // Currently not in msys2
 
-// @TODO: This is a hack to make it compile under Windows, check if this is save.
+// @TODO: This is a hack to make it compile under Windows, check if this is safe.
 #define F_OK    0
 
 #elif defined(_PATHIE_UNIX)
@@ -1546,7 +1546,7 @@ bool Path::is_directory() const
       throw(Pathie::ErrnoError(errsav));
   }
 
-  return s.st_mode & S_IFDIR;
+  return (s.st_mode & S_IFDIR) != 0;
 #else
 #error Unsupported system.
 #endif
@@ -1590,7 +1590,7 @@ bool Path::is_file() const
       throw(Pathie::ErrnoError(errno));
   }
 
-  return s.st_mode & S_IFREG;
+  return (s.st_mode & S_IFREG) != 0;
 #else
 #error Unsupported system.
 #endif
@@ -1710,9 +1710,9 @@ void Path::remove() const
    * function uses the apropriate native Win32API function
    * calls accordingly therefore. */
   if (is_directory())
-    result = RemoveDirectoryW(utf16.c_str());
+    result = RemoveDirectoryW(utf16.c_str()) != 0;
   else
-    result = DeleteFileW(utf16.c_str());
+    result = DeleteFileW(utf16.c_str()) != 0;
 
   if (!result) {
     DWORD err = GetLastError();
@@ -3282,7 +3282,7 @@ bool Path::fnmatch(const std::string& pattern, int flags /* = 0 */) const
 #elif defined(_WIN32)
   std::wstring utf16path = utf8_to_utf16(m_path);
   std::wstring utf16pattern = utf8_to_utf16(pattern);
-  return PathMatchSpecW(utf16path.c_str(), utf16pattern.c_str());
+  return PathMatchSpecW(utf16path.c_str(), utf16pattern.c_str()) != 0;
 #else
 #error Unsupported system.
 #endif
diff --git a/src/3rd_party/sentencepiece b/src/3rd_party/sentencepiece
index 1a38d26a1..21309542e 160000
--- a/src/3rd_party/sentencepiece
+++ b/src/3rd_party/sentencepiece
@@ -1 +1 @@
-Subproject commit 1a38d26a13cc67b1aae641d4983b624bef6d5305
+Subproject commit 21309542e69e1821ff8e905fa60d8852ac12a73f
diff --git a/src/command/marian_train.cpp b/src/command/marian_train.cpp
index 889806244..9f255e5c3 100755
--- a/src/command/marian_train.cpp
+++ b/src/command/marian_train.cpp
@@ -11,6 +11,8 @@
 #include "training/graph_group_multinode.h"
 #endif
 
+#include "3rd_party/ExceptionWithCallStack.h"
+
 int main(int argc, char** argv) {
   using namespace marian;
 
diff --git a/src/common/config_parser.cpp b/src/common/config_parser.cpp
index d39f3b1ad..06eabd320 100755
--- a/src/common/config_parser.cpp
+++ b/src/common/config_parser.cpp
@@ -335,9 +335,10 @@ void ConfigParser::addOptionsTraining(cli::CLIWrapper& cli) {
       "Reset running statistics of optimizer whenever learning rate decays");
   cli.add<bool>("--lr-decay-repeat-warmup",
      "Repeat learning rate warmup when learning rate is decayed");
-  cli.add<std::string/*SchedulerPeriod*/>("--lr-decay-inv-sqrt",
-     "Decrease learning rate at arg / sqrt(no. batches) starting at arg  (append 't' or 'e' for sqrt(target labels or epochs))",
-      "0");
+  cli.add<std::vector<std::string/*SchedulerPeriod*/>>("--lr-decay-inv-sqrt",
+     "Decrease learning rate at arg / sqrt(no. batches) starting at arg  (append 't' or 'e' for sqrt(target labels or epochs)). "
+      "Add second argument to define the starting point",
+      {"0"});
 
   cli.add<std::string/*SchedulerPeriod*/>("--lr-warmup",
      "Increase learning rate linearly for  arg  first batches (append 't' for  arg  first target labels)",
@@ -354,9 +355,10 @@ void ConfigParser::addOptionsTraining(cli::CLIWrapper& cli) {
   cli.add<double>("--clip-norm",
      "Clip gradient norm to  argcli.add<int>(0 to disable)",
      1.f);
-  cli.add<float>("--exponential-smoothing",
-     "Maintain smoothed version of parameters for validation and saving with smoothing factor. 0 to disable",
-     0)->implicit_val("1e-4");
+  cli.add<std::vector<float>>("--exponential-smoothing",
+     "Maintain smoothed version of parameters for validation and saving with smoothing factor. 0 to disable. "
+     "Add a second number to specify a reference batch size (in target words).",
+     { 0.f })->implicit_val("1e-4");
   cli.add<std::string>("--guided-alignment",
      "Path to a file with word alignments. Use guided alignment to guide attention or 'none'",
      "none");
@@ -604,6 +606,13 @@ void ConfigParser::addSuboptionsBatching(cli::CLIWrapper& cli) {
 
   cli.add<bool>("--shuffle-in-ram",
       "Keep shuffled corpus in RAM, do not write to temp file");
+
+  cli.add<std::vector<std::string/*SchedulerPeriod*/>>("--mini-batch-warmup",
+      "linear ramp-up of MB size, up to this #updates (append 't' for up to this #target labels);"
+      "optional second number is reference batch size at which to stop scaling up (instead of full batch size)",
+      {"0"});
+  cli.add<bool>("--mini-batch-track-lr",
+      "Dynamically track mini-batch size inverse to actual learning rate (not considering lr-warmup)");
   // clang-format on
 }
 
diff --git a/src/common/definitions.h b/src/common/definitions.h
index 3b4a6eddb..101200fff 100755
--- a/src/common/definitions.h
+++ b/src/common/definitions.h
@@ -51,8 +51,16 @@ struct DeviceId {
   DeviceId() : no{0}, type{DeviceType::gpu} {}
   DeviceId(size_t no_, DeviceType type_) : no(no_), type(type_) {}
 
+  std::string typeAsString() const {
+    return (type == DeviceType::gpu ? "gpu" : "cpu");
+  }
+
+  operator std::string() const {
+    return typeAsString() + std::to_string(no);
+  }
+
   friend std::ostream& operator<<(std::ostream& out, DeviceId deviceId) {
-    out << (deviceId.type == DeviceType::gpu ? "gpu" : "cpu") << deviceId.no;
+    out << std::string(deviceId);
     return out;
   }
 
diff --git a/src/common/file_stream.h b/src/common/file_stream.h
index 9abe7e238..52fa8e9c6 100755
--- a/src/common/file_stream.h
+++ b/src/common/file_stream.h
@@ -178,11 +178,9 @@ class InputFileStream {
   bool empty() { return istream_->peek() == std::ifstream::traits_type::eof(); }
 
   void setbufsize(size_t size) const {
-#ifdef 0 // this is buggy, do nothing
     istream_->rdbuf()->pubsetbuf(0, 0);
-    readBuf_.reset(new char[size]);
-    istream_->rdbuf()->pubsetbuf(readBuf_.get(), 0);
-#endif
+    readBuf_.resize(size);
+    istream_->rdbuf()->pubsetbuf(readBuf_.data(), readBuf_.size());
   }
 
   template <typename T>
@@ -206,9 +204,8 @@ class InputFileStream {
   std::unique_ptr<std::istream> istream_;
 
   boost::iostreams::file_descriptor_source fds_;
+  mutable std::vector<char> readBuf_; // for setbuf()
   std::unique_ptr<boost::iostreams::stream_buffer<boost::iostreams::file_descriptor_source>> fdsBuffer_;
-
-  mutable UPtr<char[]> readBuf_; // for setbuf()
 };
 
 // wrapper around std::getline() that handles Windows input files with extra CR
diff --git a/src/common/filesystem.h b/src/common/filesystem.h
index f9c061046..08d5995f1 100755
--- a/src/common/filesystem.h
+++ b/src/common/filesystem.h
@@ -12,7 +12,7 @@
 #pragma GCC diagnostic ignored "-Wsuggest-override"
 #endif
 
-#include "3rd_party/pathie-cpp/include/path.hpp"
+#include "3rd_party/pathie-cpp/include/path.hpp"  // @TODO: update to latest Pathie
 #include "3rd_party/pathie-cpp/include/errors.hpp"
 
 #ifdef __GNUC__
diff --git a/src/common/io.cpp b/src/common/io.cpp
index ae768e183..ee19105f4 100755
--- a/src/common/io.cpp
+++ b/src/common/io.cpp
@@ -128,10 +128,12 @@ void saveItemsNpz(const std::string& fileName, const std::vector<Item>& items) {
   std::vector<cnpy::NpzItem> npzItems;
   for(auto& item : items) {
     std::vector<unsigned int> shape(item.shape.begin(), item.shape.end());
-    char type = 'f';
+    char type;
 
     if(item.type == Type::float32)
       type = cnpy::map_type(typeid(float));
+    else if(item.type == Type::float64)
+      type = cnpy::map_type(typeid(double));
     else if(item.type == Type::int8)
       type = cnpy::map_type(typeid(char));
     else
diff --git a/src/common/logging.cpp b/src/common/logging.cpp
index 0170d633c..da583878d 100755
--- a/src/common/logging.cpp
+++ b/src/common/logging.cpp
@@ -84,7 +84,7 @@ void createLoggers(const marian::Config* options) {
 
   bool quiet = options && options->get<bool>("quiet");
   Logger general{
-      createStderrLogger("general", "[%Y-%m-%d %T] %v", generalLogs, quiet)};
+      createStderrLogger("general", "[%Y-%m-%d %T %t] %v", generalLogs, quiet)};
   Logger valid{
       createStderrLogger("valid", "[%Y-%m-%d %T] [valid] %v", validLogs, quiet)};
 
@@ -115,7 +115,7 @@ static void unhandledException() {
       throw; // rethrow so that we can get access to what()
     }
     catch (const std::exception& e) {
-      ABORT("Unhandled {}: {}", typeid(e).name(), e.what());
+      ABORT("Unhandled exception of type '{}': {}", typeid(e).name(), e.what());
     }
     catch (...) {
       ABORT("Unhandled exception");
@@ -145,7 +145,7 @@ static void setErrorHandlers() {
 void switchtoMultinodeLogging(std::string nodeIdStr) {
   Logger log = spdlog::get("general");
   if (log)
-    log->set_pattern("[%Y-%m-%d %T " + nodeIdStr + "] %v");
+    log->set_pattern("[%Y-%m-%d %T " + nodeIdStr + ":%t] %v");
 }
 
 
diff --git a/src/common/logging.h b/src/common/logging.h
index cdaa806c8..2889d6c7f 100755
--- a/src/common/logging.h
+++ b/src/common/logging.h
@@ -21,6 +21,16 @@ namespace marian {
  */
 #define LOG(level, ...) checkedLog("general", #level, __VA_ARGS__)
 
+// variant that prints the log message only upon the first time the call site is executed
+#define LOG_ONCE(level, ...) do { \
+  static bool logged = false;     \
+  if (!logged)                    \
+  {                               \
+    logged = true;                \
+    LOG(level, __VA_ARGS__);      \
+  }                               \
+} while(0)
+
 /**
  * Prints logging message regarding validation into stderr and a file specified
  * with `--valid-log` option.
diff --git a/src/common/utils.cpp b/src/common/utils.cpp
index b47cd1f4e..992c74ac8 100755
--- a/src/common/utils.cpp
+++ b/src/common/utils.cpp
@@ -149,5 +149,40 @@ bool endsWith(const std::string& text, const std::string& suffix) {
          && !text.compare(text.size() - suffix.size(), suffix.size(), suffix);
 }
 
+std::string toUpper(const std::string& s) {
+  std::locale loc;
+  std::string res; res.reserve(s.capacity());
+  for (auto c : s) // @BUGBUG: This won't work with UTF-8 characters.
+    res.push_back((char)std::toupper(c, loc));
+  return res;
+}
+
+double parseDouble(std::string s) {
+  double res;
+  char c; // dummy char--if we succeed to parse this, then there were extraneous characters after the number
+  auto rc = sscanf(s.c_str(), "%lf%c", &res, &c);
+  ABORT_IF(rc != 1, "Mal-formed number: {}", s);
+  return res;
+}
+
+// parses a user-friendly number that can have commas and (some) units
+double parseNumber(std::string param) {
+  // get unit prefix
+  double factor = 1.;
+  if (!param.empty() && param.back() >= 'A') {
+    switch (param.back()) {
+    case 'k': factor = 1.e3;  break;
+    case 'M': factor = 1.e6;  break;
+    case 'G': factor = 1.e9;  break;
+    case 'T': factor = 1.e12; break;
+    default: ABORT("Invalid or unsupported unit prefix '{}' in {}", param.back(), param);
+    }
+    param.pop_back();
+  }
+  // we allow users to place commas in numbers (note: we are not actually verifying that they are in the right place)
+  std::remove_if(param.begin(), param.end(), [](char c) { return c == ','; });
+  return factor * parseDouble(param);
+}
+
 }  // namespace utils
 }  // namespace marian
diff --git a/src/common/utils.h b/src/common/utils.h
index cd4fc6de3..7c4d56432 100755
--- a/src/common/utils.h
+++ b/src/common/utils.h
@@ -38,5 +38,9 @@ std::pair<std::string, int> hostnameAndProcessId();
 std::string withCommas(size_t n);
 bool endsWith(const std::string& text, const std::string& suffix);
 
+std::string toUpper(const std::string& s);
+double parseDouble(std::string s);
+double parseNumber(std::string s);
+
 }  // namespace utils
 }  // namespace marian
diff --git a/src/data/batch.h b/src/data/batch.h
index a7832585f..f4b870177 100755
--- a/src/data/batch.h
+++ b/src/data/batch.h
@@ -19,7 +19,7 @@ class Batch {
 
   virtual void debug(){};
 
-  virtual std::vector<Ptr<Batch>> split(size_t n) = 0;
+  virtual std::vector<Ptr<Batch>> split(size_t n, size_t sizeLimit = SIZE_MAX) = 0;
 
   const std::vector<size_t>& getSentenceIds() const { return sentenceIds_; }
   void setSentenceIds(const std::vector<size_t>& ids) { sentenceIds_ = ids; }
diff --git a/src/data/batch_generator.h b/src/data/batch_generator.h
index 4578c3bb7..488d3e55b 100755
--- a/src/data/batch_generator.h
+++ b/src/data/batch_generator.h
@@ -56,7 +56,7 @@ class BatchGenerator : public RNGEngine {
   typedef typename DataSet::batch_ptr BatchPtr;
 
   typedef typename DataSet::Sample Sample;
-  typedef std::vector<Sample> Samples;     // @TODO: type names should be capitalized
+  typedef std::vector<Sample> Samples;
 
   typedef BatchIterator<BatchGenerator> iterator;
   friend iterator;
@@ -83,7 +83,6 @@ class BatchGenerator : public RNGEngine {
 
   // this runs on a bg thread; sequencing is handled by caller, but locking is done in here
   std::deque<BatchPtr> fetchBatches() {
-    //LOG(info, "fillBatches entered");
     typedef typename Sample::value_type Item;
     auto itemCmp = [](const Item& sa, const Item& sb) { return sa.size() < sb.size(); }; // sort by element length, not content
 
@@ -118,8 +117,6 @@ class BatchGenerator : public RNGEngine {
     size_t maxBatchSize = options_->get<int>("mini-batch");
     size_t maxSize = maxBatchSize * options_->get<int>("maxi-batch");
 
-    // LOG(info, "Preloading batches");
-
     // consume data from corpus into maxi-batch (single sentences)
     // sorted into specified order (due to queue)
     if(newlyPrepared_) {
@@ -141,8 +138,6 @@ class BatchGenerator : public RNGEngine {
     }
     size_t numSentencesRead = maxiBatch->size();
 
-    // LOG(info, "Turning samples into batches");
-
     // construct the actual batches and place them in the queue
     Samples batchVector;
     size_t currentWords = 0;
@@ -152,7 +147,6 @@ class BatchGenerator : public RNGEngine {
 
     // process all loaded sentences in order of increasing length
     // @TODO: we could just use a vector and do a sort() here; would make the cost more explicit
-    //LOG(info, "begin form batches, #lines = {}", maxiBatch->size());
     const size_t mbWords = options_->get<size_t>("mini-batch-words", 0);
     const bool useDynamicBatching = options_->has("mini-batch-fit");
     BatchStats::const_iterator cachedStatsIter;
@@ -205,15 +199,25 @@ class BatchGenerator : public RNGEngine {
     }
 
     // turn rest into batch
+    // @BUGBUG: This can create a very small batch, which with ce-mean-words can artificially
+    // inflate the contribution of the sames in the batch, causing instability.
+    // I think a good alternative would be to carry over the left-over sentences into the next round.
     if(!batchVector.empty())
       tempBatches.push_back(data_->toBatch(batchVector));
-    //LOG(info, "end form batches, #tempBatches = {}", tempBatches.size());
 
     // Shuffle the batches
     if(shuffle_) {
       std::shuffle(tempBatches.begin(), tempBatches.end(), eng_);
     }
-    LOG(debug, "[data] fetched {} batches with {} sentences.", tempBatches.size(), numSentencesRead);
+    double totalSent{}, totalLabels{};
+    for (auto& b : tempBatches) {
+        totalSent += (double)b->size();
+        totalLabels += (double)b->words(-1);
+    }
+    auto totalDenom = tempBatches.empty() ? 1 : tempBatches.size(); // (make 0/0 = 0)
+    LOG(info, "[data] fetched {} batches with {} sentences. Per batch: {} sentences, {} labels.",
+        tempBatches.size(), numSentencesRead,
+        (double)totalSent / (double)totalDenom, (double)totalLabels / (double)totalDenom);
     return tempBatches;
   }
 
@@ -300,6 +304,18 @@ class BatchGenerator : public RNGEngine {
 
     return true;
   }
+
+  // this is needed for dynamic MB scaling. Returns 0 if size is not known in words.
+  size_t estimateTypicalTrgBatchWords() const {
+    const size_t mbWords = options_->get<size_t>("mini-batch-words", 0);
+    const bool useDynamicBatching = options_->has("mini-batch-fit");
+    if (useDynamicBatching && stats_)
+      return stats_->estimateTypicalTrgWords();
+    else if (mbWords)
+      return mbWords;
+    else
+      return 0;
+  }
 };
 
 class CorpusBatchGenerator : public BatchGenerator<CorpusBase>,
diff --git a/src/data/batch_stats.h b/src/data/batch_stats.h
index 50791ed0b..581f83df2 100755
--- a/src/data/batch_stats.h
+++ b/src/data/batch_stats.h
@@ -49,6 +49,19 @@ class BatchStats {
       map_[lengths] = batchSize;
   }
 
+  // return a rough minibatch size in labels
+  // We average over all (batch sizes * max trg length).
+  size_t estimateTypicalTrgWords() const {
+    size_t sum = 0;
+    for (const auto& entry : map_) {
+      auto maxTrgLength = entry.first.back();
+      auto numSentences = entry.second;
+      auto numLabels = numSentences * maxTrgLength;
+      sum += numLabels;
+    }
+    return sum / map_.size();
+  }
+
   // helpers for multi-node  --note: presently unused, but keeping them around for later use
   // serialize into a flat vector, for MPI data exchange
   std::vector<size_t> flatten() const {
diff --git a/src/data/corpus.cpp b/src/data/corpus.cpp
index 7a7a846e1..7f51b12ba 100755
--- a/src/data/corpus.cpp
+++ b/src/data/corpus.cpp
@@ -102,7 +102,7 @@ void Corpus::restore(Ptr<TrainingState> ts) {
 }
 
 void Corpus::shuffleData(const std::vector<std::string>& paths) {
-  LOG(info, "[data] Shuffling files");
+  LOG(info, "[data] Shuffling data");
 
   size_t numStreams = paths.size();
 
diff --git a/src/data/corpus.h b/src/data/corpus.h
index 119f3aab2..59d82ef55 100755
--- a/src/data/corpus.h
+++ b/src/data/corpus.h
@@ -63,8 +63,8 @@ class Corpus : public CorpusBase {
 
     std::vector<size_t> sentenceIds;
 
-    std::vector<int> maxDims;
-    for(auto& ex : batchVector) {
+    std::vector<int> maxDims;       // @TODO: What's this? widths? maxLengths?
+    for(auto& ex : batchVector) {   // @TODO: rename 'ex' to 'sample' or 'sentenceTuple'
       if(maxDims.size() < ex.size())
         maxDims.resize(ex.size(), 0);
       for(size_t i = 0; i < ex.size(); ++i) {
diff --git a/src/data/corpus_base.h b/src/data/corpus_base.h
index 8ecdf2337..4d9fd4cc9 100755
--- a/src/data/corpus_base.h
+++ b/src/data/corpus_base.h
@@ -164,48 +164,51 @@ class SubBatch {
    */
   size_t batchWidth() { return width_; };
   /**
-   * @brief The total number of words in the batch, considering the mask.
+   * @brief The total number of words in the batch (not counting masked-out words).
    */
   size_t batchWords() { return words_; }
 
   /**
-   * @brief Splits the subbatch into subbatches of equal size.
+   * @brief Splits the stream into sub-batches of equal size (except for last).
    *
-   * @param n Number of splits
+   * @param n number of sub-batches to split into
    *
-   * @return Vector of pointers to new subbatches.
+   * @param sizeLimit Pretend the batch only has this many sentences. Used for MB-size ramp-up.
+   *
+   * @return Vector of pointers to new sub-batches (or nullptrs where run out of sub-batches)
    *
    * @see marian::data::Batch::split(size_t n)
    */
-  std::vector<Ptr<SubBatch>> split(size_t n) {
-    ABORT_IF(size_ == 0, "Encoutered sub-batch size of 0");
+  std::vector<Ptr<SubBatch>> split(size_t n, size_t sizeLimit /*or SIZE_MAX*/) {
+    ABORT_IF(size_ == 0, "Encountered sub-batch size of 0");
 
-    size_t subSize = (size_t)(std::ceil(size_ / (float)n));
+    auto size = std::min(size_, sizeLimit); // if limit is given then pretend the batch only has that many sentences
+    size_t targetSubSize = (size_t)(std::ceil(size / (float)n)); // aim at forming sub-batches of this #sentences
 
     std::vector<Ptr<SubBatch>> splits;
-    for(size_t pos = 0; pos < size_; pos += subSize) {
-      size_t size = std::min(subSize, size_ - pos);
+    for(size_t pos = 0; pos < size; pos += targetSubSize) { // loop over ranges of size targetSubSize to form sub-batches of this size
+      size_t subSize = std::min(targetSubSize, size - pos); // actual number of sentences can be smaller at the end
 
-      // determine actual width
+      // determine actual width (=max length) of this sub-batch, which may be smaller than the overall max length
       size_t subWidth = 0;
       for(size_t j = 0; j < width_; ++j) {
-        for(size_t i = 0; i < size; ++i) {
+        for(size_t i = 0; i < subSize; ++i) {
           if(mask_[j * size_ + (pos + i)] != 0)
             if (subWidth < j + 1)
               subWidth = j + 1;
         }
       }
       //if (subWidth < width_)
-      //  LOG(info, "[data] sub-batch {} of {} wide batch has effective width of {}", pos / subSize, width_, subWidth);
+      //  LOG(info, "[data] sub-batch {} of {} wide batch has effective width of {}", pos / targetSize, width_, subWidth);
 
       // create sub-batch
-      auto sb = New<SubBatch>(size, subWidth, vocab_);
+      auto sb = New<SubBatch>(subSize, subWidth, vocab_);
 
       size_t words = 0;
       for(size_t j = 0; j < subWidth; ++j) {
-        for(size_t i = 0; i < size; ++i) {
-          sb->data()[j * size + i] = indices_[j * size_ + (pos + i)];
-          sb->mask()[j * size + i] =    mask_[j * size_ + (pos + i)];
+        for(size_t i = 0; i < subSize; ++i) {
+          sb->data()[j * subSize + i] = indices_[j * size_ + (pos + i)];
+          sb->mask()[j * subSize + i] =    mask_[j * size_ + (pos + i)];
 
           if(mask_[j * size_ + (pos + i)] != 0)
             words++;
@@ -263,8 +266,8 @@ class CorpusBatch : public Batch {
   size_t size() const override { return subBatches_[0]->batchSize(); }
 
   /**
-   * @brief The total number of words for the longest sentence in the batch plus
-   * one. Pass which=0 for source and -1 for target.
+   * @brief The total number of words in the batch (not counting masked-out words).
+   * Pass which=0 for source words and -1 for target words.
    */
   size_t words(int which = 0) const override {
     return subBatches_[which >= 0 ? which
@@ -349,25 +352,27 @@ class CorpusBatch : public Batch {
   }
 
   /**
-   * @brief Splits the batch into batches of equal size.
+   * @brief Splits the batch into batches of equal size (except for last).
+   *
+   * @param n number of sub-batches to split into
    *
-   * @param n number of splits
+   * @param sizeLimit Clip batch content to the first sizeLimit sentences in the batch
    *
-   * @return Vector of pointers to new batches.
+   * @return Vector of pointers to new sub-batches (or nullptrs where run out of sub-batches)
    *
    * @see marian::data::SubBatch::split(size_t n)
    */
-  std::vector<Ptr<Batch>> split(size_t n) override {
+  std::vector<Ptr<Batch>> split(size_t n, size_t sizeLimit /*=SIZE_MAX*/) override {
     ABORT_IF(size() == 0, "Encoutered batch size of 0");
 
-    std::vector<std::vector<Ptr<SubBatch>>> subs;
-    // split each subbatch separately
-    for(auto subBatch : subBatches_) {
-      size_t i = 0;
-      for(auto splitSubBatch : subBatch->split(n)) {
+    std::vector<std::vector<Ptr<SubBatch>>> subs; // [subBatchIndex][streamIndex]
+    // split each stream separately
+    for(auto batchStream : subBatches_) {
+      size_t i = 0; // index into split batch
+      for(auto splitSubBatch : batchStream->split(n, sizeLimit)) {
         if(subs.size() <= i)
           subs.resize(i + 1);
-        subs[i++].push_back(splitSubBatch);
+        subs[i++].push_back(splitSubBatch); // this forms tuples across streams
       }
     }
 
diff --git a/src/examples/mnist/dataset.h b/src/examples/mnist/dataset.h
index 2152f850d..4a4f5ae45 100755
--- a/src/examples/mnist/dataset.h
+++ b/src/examples/mnist/dataset.h
@@ -63,7 +63,7 @@ class DataBatch : public Batch {
 
   void push_back(Input input) { inputs_.push_back(input); }
 
-  virtual std::vector<Ptr<Batch>> split(size_t /*n*/) override { ABORT("Not implemented"); }
+  virtual std::vector<Ptr<Batch>> split(size_t /*n*/, size_t /*sizeLimit*/) override { ABORT("Not implemented"); }
 
   Data& features() { return inputs_[0].data(); }
 
diff --git a/src/functional/tmp.h b/src/functional/tmp.h
old mode 100644
new mode 100755
index 720901bce..083836603
--- a/src/functional/tmp.h
+++ b/src/functional/tmp.h
@@ -81,6 +81,27 @@ struct FApply<4, Functor> {
   }
 };
 
+template <class Functor>
+struct FApply<5, Functor> {
+  __HDI__ static float apply(
+      Functor functor,
+      functional::Array<functional::Tensor<float>, 5>& in,
+      const functional::Array<int, 5>& indices) {
+    return functor(in[0][indices[0]],
+                   in[1][indices[1]],
+                   in[2][indices[2]],
+                   in[3][indices[3]],
+                   in[4][indices[4]]);
+  }
+
+  __HDI__ static float apply(
+      Functor functor,
+      functional::Array<functional::Tensor<float>, 5>& in,
+      int index) {
+    return functor(in[0][index], in[1][index], in[2][index], in[3][index], in[4][index]);
+  }
+};
+
 template <size_t K, class Functor>
 __HDI__ float apply(Functor functor,
                     functional::Array<functional::Tensor<float>, K>& in,
diff --git a/src/graph/expression_graph.h b/src/graph/expression_graph.h
index 815d90804..bf4f256d3 100755
--- a/src/graph/expression_graph.h
+++ b/src/graph/expression_graph.h
@@ -63,7 +63,7 @@ class Tensors {
       tensors_->allocate(node->grad(), node->shape(), node->value_type());
   }
 
-  void free(Tensor& tensor) { tensors_->free(tensor); }
+  void free(const Tensor& tensor) { tensors_->free(tensor); }
 
   // @TODO: get rid of this, not really used or can be done better
   Ptr<Allocator> allocator() { return tensors_->allocator(); }
@@ -437,7 +437,7 @@ class ExpressionGraph : public std::enable_shared_from_this<ExpressionGraph> {
       tensors_->allocateBackward(node);
   }
 
-  void free(Tensor& tensor) {
+  void free(const Tensor& tensor) {
     if(tensors_)
       tensors_->free(tensor);
   }
diff --git a/src/graph/expression_operators.h b/src/graph/expression_operators.h
old mode 100644
new mode 100755
index 6c0a11b39..6a887aa0a
--- a/src/graph/expression_operators.h
+++ b/src/graph/expression_operators.h
@@ -66,9 +66,9 @@ Expr operator/(Expr a, float b);
 
 Expr logaddexp(Expr a, Expr b);
 
-Expr max(Expr a, Expr b);  // TODO: haggle over the name (max vs. elementMax)
+Expr maximum(Expr a, Expr b);
 
-Expr min(Expr a, Expr b);  // TODO: haggle over the name
+Expr minimum(Expr a, Expr b);
 
 Expr dot(Expr a,
          Expr b,
diff --git a/src/graph/node_operators.h b/src/graph/node_operators.h
old mode 100644
new mode 100755
index 018d05331..01c7f5904
--- a/src/graph/node_operators.h
+++ b/src/graph/node_operators.h
@@ -50,7 +50,7 @@ struct ParamNode : public Node {
   ~ParamNode() {}
 
   virtual size_t allocate() override {
-    ABORT_IF(!val_, "Parameters should be allocated by their graph");
+    ABORT_IF(!val_, "Parameters should be allocated by their graph. Parameter {} was not", name_);
     return 0;
   }
 
diff --git a/src/graph/node_operators_binary.h b/src/graph/node_operators_binary.h
index 7da85443b..0f972bc79 100755
--- a/src/graph/node_operators_binary.h
+++ b/src/graph/node_operators_binary.h
@@ -42,7 +42,7 @@ class DotNodeOp : public NaryNodeOp {
     Shape outShape = shapeA;
     outShape.set(outShape.size() - 1, shapeB[shapeB.size() - 1]);
     ABORT_IF(shapeA[shapeA.size() - 1] != shapeB[shapeB.size() - 2],
-             "matrix product requires dimensions to match");
+             "Matrix product requires dimensions to match");
     return outShape;
   }
 
@@ -165,7 +165,7 @@ class AffineNodeOp : public NaryNodeOp {
     Shape outShape = shapeA;
     outShape.set(outShape.size() - 1, shapeB[shapeB.size() - 1]);
     ABORT_IF(shapeA[shapeA.size() - 1] != shapeB[shapeB.size() - 2],
-             "matrix product requires dimensions to match");
+             "Matrix product requires dimensions to match");
     return outShape;
   }
 
@@ -309,7 +309,7 @@ class DotBatchedNodeOp : public NaryNodeOp {
     Shape outShape = shapeA;
     outShape.set(-1, shapeB[-1]);
     ABORT_IF(shapeA[-1] != shapeB[-2],
-             "matrix product requires dimensions to match");
+             "Batched matrix product requires dimensions to match");
     return outShape;
   }
 
diff --git a/src/graph/node_operators_unary.h b/src/graph/node_operators_unary.h
old mode 100644
new mode 100755
diff --git a/src/optimizers/optimizers.cpp b/src/optimizers/optimizers.cpp
index d7a58be31..807135949 100755
--- a/src/optimizers/optimizers.cpp
+++ b/src/optimizers/optimizers.cpp
@@ -2,10 +2,12 @@
 
 #include "common/io.h"
 #include "tensors/tensor_operators.h"
+#include <array>
 
 namespace marian {
 
-void Sgd::updateImpl(Tensor params, Tensor grads) {
+void Sgd::updateImpl(Tensor params, Tensor grads, size_t actualMBSize, size_t refMBSize) {
+  actualMBSize, refMBSize; // (no correction for base update needed beyond using ce-sum)
   using namespace functional;
   Element(_1 -= eta_ * _2,
           params,
@@ -14,9 +16,10 @@ void Sgd::updateImpl(Tensor params, Tensor grads) {
   params->getBackend()->synchronize();
 }
 
-// Aagrad
+// Adagrad
 
-void Adagrad::updateImpl(Tensor params, Tensor grads) {
+void Adagrad::updateImpl(Tensor params, Tensor grads, size_t actualMBSize, size_t refMBSize) {
+  ABORT_IF(actualMBSize != refMBSize, "Adagrad does not support rational hyper-parameter adjustment");
   if(!alloc_)
     alloc_ = New<TensorAllocator>(params->getBackend());
 
@@ -62,7 +65,7 @@ void Adagrad::load(const std::string& name,
     if(item.name == "adagrad_gt") {
       vGt.resize(totalSize);
       std::copy(
-          (float*)item.data(), (float*)item.data() + totalSize, vGt.begin());
+          (float*)item.data(), ((float*)item.data()) + totalSize, vGt.begin());
     }
   }
   if(vGt.empty()) {
@@ -109,7 +112,7 @@ void Adagrad::save(const std::string& name,
   item.type = Type::float32;
   item.bytes.resize(vGt.size() * sizeOf(item.type));
   std::copy(
-      (char*)vGt.data(), (char*)vGt.data() + vGt.size(), item.bytes.begin());
+      (char*)vGt.data(), (char*)(vGt.data() + vGt.size()), item.bytes.begin());
 
   io::saveItems(name, {item});
 }
@@ -121,7 +124,8 @@ void Adagrad::resetStats() {
 
 // Adam
 
-void Adam::updateImpl(Tensor params, Tensor grads) {
+void Adam::updateImpl(Tensor params, Tensor grads, size_t actualMBSize, size_t refMBSize) {
+  // lazy allocation
   if(!alloc_)
     alloc_ = New<TensorAllocator>(params->getBackend());
 
@@ -130,29 +134,42 @@ void Adam::updateImpl(Tensor params, Tensor grads) {
     alloc_->reserveExact(2 * params->memory()->size());
     alloc_->allocate(mt_, {1, elements});
     mt_->set(0.f);
-
     alloc_->allocate(vt_, {1, elements});
     vt_->set(0.f);
   }
 
-  t_++;
-  float denom1 = 1 - (float)std::pow(beta1_, t_);
-  float denom2 = 1 - (float)std::pow(beta2_, t_);
+  double Tref = (double)refMBSize;
+  double T    = (double)actualMBSize;
 
-  using namespace functional;
+  // adjust for minibatch-size changes if Adam parameters are given a reference size (else do nothing)
+  double eta   = eta_ * (T/Tref);
+  double beta1 = beta1_;
+  double beta2 = beta2_;
+  double decay = w_    ;
 
-  Element(_1 = (beta1_ * _1) + ((1 - beta1_) * _2), mt_, grads);
-  Element(_1 = (beta2_ * _1) + ((1 - beta2_) * (_2 * _2)), vt_, grads);
+  // denominators. At steady state: =1. This recursion does the same as the Adam beta correction term.
+  denom1_ = (beta1 * denom1_) + (1 - beta1); // momentum smoothing
+  denom2_ = (beta2 * denom2_) + (1 - beta2); // RMS normalization
 
-  Element(_1 -= eta_                         // learning-rate: x_t = x_{t-1} - \eta * (...)
-                * ((_2 / denom1)             // 1st moment: m_{t-1}
-                / (sqrt(_3 / denom2) + eps_) // 2nd moment: \sqrt(v_{t-1})
-                + w_ * _1),                  // weight-decay: w * x_{t-1}
-          params,
-          mt_,
-          vt_);
+  LOG_ONCE(info, "[adam] First update: Tref = {}, T = {}, eta = {} -> {}, beta = {}, {}", Tref, T, eta_, eta, beta1, beta2);
 
-  params->getBackend()->synchronize();
+  // numerators. Divide by T to convert ce-sum gradient to avg gradient.
+  using namespace functional;
+  Element(_1 = ((float)beta1 * _1) + float((1 - beta1) / T    ) *  _2,       mt_, grads); // momentum smoothing. At steady state: =smoothed avg gradient
+  Element(_1 = ((float)beta2 * _1) + float((1 - beta2) / T / T) * (_2 * _2), vt_, grads); // RMS normalization.  At steady state: =mean square of the avg gradients
+
+  // apply Adam normalization
+  float etaf = (float)eta, denom1f = (float)denom1_, denom2f = (float)denom2_, decayf = (float)decay; // (get casts out of Element expression for readability)
+  Element(_1 -= etaf                               // learning-rate: x_t = x_{t-1} - \eta * (...)
+                * ((  (     _2 / denom1f)          // momentum-smoothed per-sample gradient: m_{t-1}
+                    / (sqrt(_3 / denom2f) + eps_)) // normalize by RMS: \sqrt(v_{t-1})
+                   + decayf * _1),                 // weight-decay: w * x_{t-1}
+          params, // =_1
+          mt_,    // =_2
+          vt_     // =_3
+          );
+
+  params->getBackend()->synchronize(); // @TODO: This should not be in here. Maybe in the wrapper. Why is it needed at all?
 }
 
 void Adam::load(const std::string& name,
@@ -168,6 +185,7 @@ void Adam::load(const std::string& name,
 
   std::vector<float> vMt;
   std::vector<float> vVt;
+  std::array<double, 2> vDenoms;
 
   auto items = io::loadItems(name);
   for(auto item : items) {
@@ -178,12 +196,18 @@ void Adam::load(const std::string& name,
     if(item.name == "adam_mt") {
       vMt.resize(totalSize);
       std::copy(
-          (float*)item.data(), (float*)item.data() + totalSize, vMt.begin());
+          (float*)item.data(), ((float*)item.data()) + totalSize, vMt.begin());
     }
-    if(item.name == "adam_vt") {
+    else if(item.name == "adam_vt") {
       vVt.resize(totalSize);
       std::copy(
-          (float*)item.data(), (float*)item.data() + totalSize, vVt.begin());
+          (float*)item.data(), ((float*)item.data()) + totalSize, vVt.begin());
+    }
+    else if(item.name == "adam_denoms") {
+      ABORT_IF(totalSize != 2, "adam_denoms should have 2 entries");
+      std::copy(
+          (double*)item.data(), ((double*)item.data()) + totalSize, vDenoms.begin());
+      // Back compat note: Old files lacked "adam_denoms". For those, vDenoms will remain 0, which reproduces the old behavior.
     }
   }
   if(vMt.empty() || vVt.empty()) {
@@ -212,6 +236,9 @@ void Adam::load(const std::string& name,
     auto opt = std::dynamic_pointer_cast<Adam>(opts[id]);
     opt->vt_->set(std::vector<float>(begin, end));
   });
+
+  denom1_ = vDenoms[0];
+  denom2_ = vDenoms[1];
   //LOG(info, "done loading Adam params");
 }
 
@@ -248,7 +275,7 @@ void Adam::save(const std::string& name,
   itemMt.type = Type::float32;
   itemMt.bytes.resize(vMt.size() * sizeOf(itemMt.type));
   std::copy(
-      (char*)vMt.data(), (char*)vMt.data() + vMt.size(), itemMt.bytes.begin());
+      (char*)vMt.data(), (char*)(vMt.data() + vMt.size()), itemMt.bytes.begin());
 
   io::Item itemVt;
   itemVt.name = "adam_vt";
@@ -256,9 +283,19 @@ void Adam::save(const std::string& name,
   itemVt.type = Type::float32;
   itemVt.bytes.resize(vVt.size() * sizeOf(itemVt.type));
   std::copy(
-      (char*)vVt.data(), (char*)vVt.data() + vVt.size(), itemVt.bytes.begin());
+      (char*)vVt.data(), (char*)(vVt.data() + vVt.size()), itemVt.bytes.begin());
+
+  // @TODO: this pattern is duplicated several times; refactor it
+  std::array<double, 2> vDenoms{denom1_, denom2_};
+  io::Item itemDenoms;
+  itemDenoms.name = "adam_denoms";
+  itemDenoms.shape = Shape({1, (int)vDenoms.size()});
+  itemDenoms.type = Type::float64;
+  itemDenoms.bytes.resize(vDenoms.size() * sizeOf(itemDenoms.type));
+  std::copy(
+      (char*)vDenoms.data(), (char*)(vDenoms.data() + vDenoms.size()), itemDenoms.bytes.begin());
 
-  io::saveItems(name, {itemMt, itemVt});
+  io::saveItems(name, {itemMt, itemVt, itemDenoms});
 }
 
 void Adam::resetStats() {
@@ -267,6 +304,9 @@ void Adam::resetStats() {
 
   if(vt_)
     vt_->set(0.f);
+
+  denom1_ = 0; // @BUGBUG: or 1 or refMBSize if so specified. Fix once we have proper parameterization for that.
+  denom2_ = 0;
 }
 
 Ptr<OptimizerBase> Optimizer(Ptr<Options> options) {
@@ -287,7 +327,7 @@ Ptr<OptimizerBase> Optimizer(Ptr<Options> options) {
   } else if(opt == "adagrad") {
     return Optimizer<Adagrad>(lrate, clipper, params);
   } else if(opt == "adam") {
-    return Optimizer<Adam>(lrate, clipper, params);
+    return Optimizer<Adam>(lrate, clipper, params); // @TODO: parse the parameters here, or just pass the options object
   } else {
     ABORT("Unknown optimizer: {}", opt);
   }
diff --git a/src/optimizers/optimizers.h b/src/optimizers/optimizers.h
index 9b83afb6d..76e2780eb 100755
--- a/src/optimizers/optimizers.h
+++ b/src/optimizers/optimizers.h
@@ -21,19 +21,29 @@ class OptimizerBase : public TrainingObserver {
   OptimizerBase(float eta, Ptr<ClipperBase> clipper = nullptr)
       : eta_(eta), clipper_(clipper) {}
 
-  void update(Ptr<ExpressionGraph> graph) {
+  static constexpr size_t mbSizeNotProvided = SIZE_MAX;
+
+  void update(Ptr<ExpressionGraph> graph, size_t mbSize = mbSizeNotProvided) {
     Tensor p = graph->params()->vals();
     Tensor g = graph->params()->grads();
 
-    update(p, g);
+    update(p, g, mbSize);
   }
 
-  void update(Tensor params, Tensor grads) {
+  void update(Tensor params, Tensor grads, size_t mbSize = mbSizeNotProvided) {
     if(clipper_)
       clipper_->clip(grads);
 
-    // In case we want to add a multiply factor to our learning rate
-    updateImpl(params, grads);
+    size_t refMBSize = refMBSize_;
+    if (refMBSize == 0) { // optimizer not configured to use hyper-parameter auto-adjustment
+      refMBSize = mbSize = 1; // neutral settings that keep the standard behavior
+    }
+    else { // optimizer is configured to auto-adjust hyper-parameters
+      ABORT_IF(mbSize == mbSizeNotProvided, "Using rational optimizer auto-adjustment with trainer that does not provide MB size");
+      // note: this behavior is only meaningful if using the ce-sum criterion
+    }
+
+    updateImpl(params, grads, mbSize, refMBSize);
   }
 
   virtual void init(TrainingState& state) override {
@@ -78,7 +88,7 @@ class OptimizerBase : public TrainingObserver {
                     bool /*isMainProcess*/ = true) {}
 
 protected:
-  virtual void updateImpl(Tensor params, Tensor grads) = 0;
+  virtual void updateImpl(Tensor params, Tensor grads, size_t actualMBSize, size_t refMBSize) = 0;
   virtual void parseParams(const std::vector<float>& params) = 0;
   virtual void resetStats() = 0;
 
@@ -86,6 +96,8 @@ class OptimizerBase : public TrainingObserver {
   float eta_;
   // Clip gradient norm
   Ptr<ClipperBase> clipper_;
+  // Reference MB size. This enables automatic adjustment of optimizer hyper-parameters to MB size.
+  size_t refMBSize_{0}; // 0 means no adjustment
 };
 
 /**
@@ -97,7 +109,7 @@ class Sgd : public OptimizerBase {
       : OptimizerBase(eta, clipper) {}
 
 private:
-  void updateImpl(Tensor params, Tensor grads) override;
+  void updateImpl(Tensor params, Tensor grads, size_t actualMBSize, size_t refMBSize) override;
 
   virtual void parseParams(const std::vector<float>& /*params*/) override {}
   virtual void resetStats() override {}
@@ -123,7 +135,7 @@ class Adagrad : public OptimizerBase {
             bool /*isMainProcess*/ = true) override;
 
 private:
-  void updateImpl(Tensor params, Tensor grads) override;
+  void updateImpl(Tensor params, Tensor grads, size_t actualMBSize, size_t refMBSize) override;
   void resetStats() override;
 
   void parseParams(const std::vector<float>& params) override {
@@ -140,11 +152,13 @@ class Adagrad : public OptimizerBase {
  * @brief Adam optimizer
  *
  * https://arxiv.org/pdf/1412.6980v8.pdf
+ *
+ * with Frank's modifications for automatic hyper-parameter adjustment.
  */
 class Adam : public OptimizerBase {
 public:
   Adam(float eta, Ptr<ClipperBase> clipper = nullptr)
-      : OptimizerBase(eta, clipper), t_(0) {}
+      : OptimizerBase(eta, clipper) {}
 
   void load(const std::string& name,
             const std::vector<Ptr<OptimizerBase>>& opts,
@@ -156,9 +170,11 @@ class Adam : public OptimizerBase {
             bool isMainProcess = true) override;
 
 private:
-  void updateImpl(Tensor params, Tensor grads) override;
+  void updateImpl(Tensor params, Tensor grads, size_t actualMBSize, size_t refMBSize) override;
   void resetStats() override;
 
+  // Adam parameters:
+  // [beta1, beta2, eps, w, refMBSize]
   virtual void parseParams(const std::vector<float>& params) override {
     if(params.size() > 0)
       beta1_ = params[0];
@@ -169,15 +185,29 @@ class Adam : public OptimizerBase {
 
     // weighted decay for AdamW, to be explored, disabled by default
     if(params.size() > 3)
-      w_ = params[3];
+      w_ = params[3]; // default (disabled): 0
+
+    // automatic learning-rate adjustment
+    // If users provide, in addition to the hyper-parameters, a reference minibatch size,
+    // that these hyper-parameters were originally tuned for, then the learning-rate gets
+    // adjusted accordingly. Note: Requires user to also use ce-sum criterion.
+    if(params.size() > 4) {
+      refMBSize_ = (size_t)params[4]; // default (disabled): 0
+      LOG(info, "Note: Modified Adam optimizer: automatically adjusting learning rate as if minibatch size was {}", refMBSize_);
+    }
   }
 
+  // hyper-parameters
   float beta1_ = 0.9f;
   float beta2_ = 0.999f;
   float eps_ = 1e-8f;
   float w_ = 0.0f;
-  size_t t_;
 
+  // CPU-side running accumulators
+  double denom1_ = 0;
+  double denom2_ = 0;
+
+  // GPU-side running accumulators
   Ptr<TensorAllocator> alloc_;
   Tensor mt_;
   Tensor vt_;
diff --git a/src/tensors/gpu/add.cu b/src/tensors/gpu/add.cu
old mode 100644
new mode 100755
diff --git a/src/tensors/gpu/element.inc b/src/tensors/gpu/element.inc
old mode 100644
new mode 100755
index 50ae97936..96debcc2f
--- a/src/tensors/gpu/element.inc
+++ b/src/tensors/gpu/element.inc
@@ -48,3 +48,9 @@ template void Element<Assign<Var<1>, BinaryFunctor<elem::Div, BinaryFunctor<elem
 template void Element<Assign<Var<1>, UnaryFunctor<elem::Neg, UnaryFunctor<elem::Log, UnaryFunctor<elem::Neg, UnaryFunctor<elem::Log, Assignee<1>>>>>>>(Assign<Var<1>, UnaryFunctor<elem::Neg, UnaryFunctor<elem::Log, UnaryFunctor<elem::Neg, UnaryFunctor<elem::Log, Assignee<1>>>>>>, marian::Tensor);
 template void Element<Assign<Var<1>, UnaryFunctor<elem::Neg, UnaryFunctor<elem::Log, BinaryFunctor<elem::Plus, UnaryFunctor<elem::Neg, UnaryFunctor<elem::Log, BinaryFunctor<elem::Plus, Assignee<1>, Capture>>>, Capture>>>>>(Assign<Var<1>, UnaryFunctor<elem::Neg, UnaryFunctor<elem::Log, BinaryFunctor<elem::Plus, UnaryFunctor<elem::Neg, UnaryFunctor<elem::Log, BinaryFunctor<elem::Plus, Assignee<1>, Capture>>>, Capture>>>>, marian::Tensor);
 template void Element<Assign<Var<1>, BinaryFunctor<elem::Mult, BinaryFunctor<elem::Lt, Assignee<1>, Capture>, Capture>>>(Assign<Var<1>, BinaryFunctor<elem::Mult, BinaryFunctor<elem::Lt, Assignee<1>, Capture>, Capture> >, marian::Tensor);
+template void Element<Assign<Var<1>, BinaryFunctor<elem::Minus, Assignee<1>, BinaryFunctor<elem::Mult, Capture, BinaryFunctor<elem::Plus, BinaryFunctor<elem::Div, BinaryFunctor<elem::Div, Assignee<2>, Capture>, BinaryFunctor<elem::Plus, UnaryFunctor<elem::Sqrt, BinaryFunctor<elem::Plus, BinaryFunctor<elem::Div, UnaryFunctor<elem::sReLU, BinaryFunctor<elem::Minus, Assignee<3>, BinaryFunctor<elem::Mult, BinaryFunctor<elem::Mult, Capture, Assignee<4> >, Assignee<4> > > >, Capture>, BinaryFunctor<elem::Mult, Assignee<4>, Assignee<4> > > >, Capture> >, BinaryFunctor<elem::Mult, Capture, Assignee<1> > > > > >, std::shared_ptr<marian::TensorBase>, std::shared_ptr<marian::TensorBase>, std::shared_ptr<marian::TensorBase> >(Assign<Var<1>, BinaryFunctor<elem::Minus, Assignee<1>, BinaryFunctor<elem::Mult, Capture, BinaryFunctor<elem::Plus, BinaryFunctor<elem::Div, BinaryFunctor<elem::Div, Assignee<2>, Capture>, BinaryFunctor<elem::Plus, UnaryFunctor<elem::Sqrt, BinaryFunctor<elem::Plus, BinaryFunctor<elem::Div, UnaryFunctor<elem::sReLU, BinaryFunctor<elem::Minus, Assignee<3>, BinaryFunctor<elem::Mult, BinaryFunctor<elem::Mult, Capture, Assignee<4> >, Assignee<4> > > >, Capture>, BinaryFunctor<elem::Mult, Assignee<4>, Assignee<4> > > >, Capture> >, BinaryFunctor<elem::Mult, Capture, Assignee<1> > > > > >, std::shared_ptr<marian::TensorBase>, std::shared_ptr<marian::TensorBase>, std::shared_ptr<marian::TensorBase>, std::shared_ptr<marian::TensorBase>);
+template void Element<Assign<Var<1>, BinaryFunctor<elem::Minus, Assignee<1>, BinaryFunctor<elem::Mult, Capture, BinaryFunctor<elem::Plus, BinaryFunctor<elem::Div, BinaryFunctor<elem::Div, Assignee<2>, Capture>, BinaryFunctor<elem::Plus, UnaryFunctor<elem::Sqrt, BinaryFunctor<elem::Plus, BinaryFunctor<elem::Div, UnaryFunctor<elem::sReLU, BinaryFunctor<elem::Minus, Assignee<3>, BinaryFunctor<elem::Div, BinaryFunctor<elem::Mult, BinaryFunctor<elem::Mult, Capture, Assignee<4> >, Assignee<4> >, Capture> > >, Capture>, BinaryFunctor<elem::Div, BinaryFunctor<elem::Mult, Assignee<4>, Assignee<4> >, Capture> > >, Capture> >, BinaryFunctor<elem::Mult, Capture, Assignee<1> > > > > >, std::shared_ptr<marian::TensorBase>, std::shared_ptr<marian::TensorBase>, std::shared_ptr<marian::TensorBase> >(Assign<Var<1>, BinaryFunctor<elem::Minus, Assignee<1>, BinaryFunctor<elem::Mult, Capture, BinaryFunctor<elem::Plus, BinaryFunctor<elem::Div, BinaryFunctor<elem::Div, Assignee<2>, Capture>, BinaryFunctor<elem::Plus, UnaryFunctor<elem::Sqrt, BinaryFunctor<elem::Plus, BinaryFunctor<elem::Div, UnaryFunctor<elem::sReLU, BinaryFunctor<elem::Minus, Assignee<3>, BinaryFunctor<elem::Div, BinaryFunctor<elem::Mult, BinaryFunctor<elem::Mult, Capture, Assignee<4> >, Assignee<4> >, Capture> > >, Capture>, BinaryFunctor<elem::Div, BinaryFunctor<elem::Mult, Assignee<4>, Assignee<4> >, Capture> > >, Capture> >, BinaryFunctor<elem::Mult, Capture, Assignee<1> > > > > >, std::shared_ptr<marian::TensorBase>, std::shared_ptr<marian::TensorBase>, std::shared_ptr<marian::TensorBase>, std::shared_ptr<marian::TensorBase>);
+template void Element<Assign<Var<1>, BinaryFunctor<elem::Minus, Assignee<1>, BinaryFunctor<elem::Mult, Capture, BinaryFunctor<elem::Plus, BinaryFunctor<elem::Div, BinaryFunctor<elem::Div, Assignee<2>, Capture>, BinaryFunctor<elem::Plus, UnaryFunctor<elem::Sqrt, BinaryFunctor<elem::Div, BinaryFunctor<elem::Mult, Assignee<4>, Assignee<4> >, Capture> >, Capture> >, BinaryFunctor<elem::Mult, Capture, Assignee<1> > > > > >, std::shared_ptr<marian::TensorBase>, std::shared_ptr<marian::TensorBase>, std::shared_ptr<marian::TensorBase> >(Assign<Var<1>, BinaryFunctor<elem::Minus, Assignee<1>, BinaryFunctor<elem::Mult, Capture, BinaryFunctor<elem::Plus, BinaryFunctor<elem::Div, BinaryFunctor<elem::Div, Assignee<2>, Capture>, BinaryFunctor<elem::Plus, UnaryFunctor<elem::Sqrt, BinaryFunctor<elem::Div, BinaryFunctor<elem::Mult, Assignee<4>, Assignee<4> >, Capture> >, Capture> >, BinaryFunctor<elem::Mult, Capture, Assignee<1> > > > > >, std::shared_ptr<marian::TensorBase>, std::shared_ptr<marian::TensorBase>, std::shared_ptr<marian::TensorBase>, std::shared_ptr<marian::TensorBase>);
+template void Element<Assign<Var<1>, BinaryFunctor<elem::Minus, Assignee<1>, BinaryFunctor<elem::Mult, Capture, BinaryFunctor<elem::Plus, BinaryFunctor<elem::Div, BinaryFunctor<elem::Div, Assignee<2>, Capture>, BinaryFunctor<elem::Plus, UnaryFunctor<elem::Sqrt, BinaryFunctor<elem::Div, Assignee<3>, Capture> >, Capture> >, BinaryFunctor<elem::Mult, Capture, Assignee<1> > > > > >, std::shared_ptr<marian::TensorBase>, std::shared_ptr<marian::TensorBase>, std::shared_ptr<marian::TensorBase> >(Assign<Var<1>, BinaryFunctor<elem::Minus, Assignee<1>, BinaryFunctor<elem::Mult, Capture, BinaryFunctor<elem::Plus, BinaryFunctor<elem::Div, BinaryFunctor<elem::Div, Assignee<2>, Capture>, BinaryFunctor<elem::Plus, UnaryFunctor<elem::Sqrt, BinaryFunctor<elem::Div, Assignee<3>, Capture> >, Capture> >, BinaryFunctor<elem::Mult, Capture, Assignee<1> > > > > >, std::shared_ptr<marian::TensorBase>, std::shared_ptr<marian::TensorBase>, std::shared_ptr<marian::TensorBase>, std::shared_ptr<marian::TensorBase>);
+template void Element<Assign<Var<1>, BinaryFunctor<elem::Minus, Assignee<1>, BinaryFunctor<elem::Mult, Capture, BinaryFunctor<elem::Plus, BinaryFunctor<elem::Div, BinaryFunctor<elem::Div, Assignee<2>, Capture>, BinaryFunctor<elem::Plus, UnaryFunctor<elem::Sqrt, BinaryFunctor<elem::Plus, BinaryFunctor<elem::Div, UnaryFunctor<elem::sReLU, BinaryFunctor<elem::Plus, BinaryFunctor<elem::Minus, Assignee<3>, BinaryFunctor<elem::Div, BinaryFunctor<elem::Mult, BinaryFunctor<elem::Mult, Capture, Assignee<5> >, Assignee<4> >, Capture> >, BinaryFunctor<elem::Div, BinaryFunctor<elem::Mult, BinaryFunctor<elem::Mult, Capture, Assignee<4> >, Assignee<4> >, Capture> > >, Capture>, BinaryFunctor<elem::Div, BinaryFunctor<elem::Mult, Assignee<4>, Assignee<4> >, Capture> > >, Capture> >, BinaryFunctor<elem::Mult, Capture, Assignee<1> > > > > >, std::shared_ptr<marian::TensorBase>, std::shared_ptr<marian::TensorBase>, std::shared_ptr<marian::TensorBase>, std::shared_ptr<marian::TensorBase> >(Assign<Var<1>, BinaryFunctor<elem::Minus, Assignee<1>, BinaryFunctor<elem::Mult, Capture, BinaryFunctor<elem::Plus, BinaryFunctor<elem::Div, BinaryFunctor<elem::Div, Assignee<2>, Capture>, BinaryFunctor<elem::Plus, UnaryFunctor<elem::Sqrt, BinaryFunctor<elem::Plus, BinaryFunctor<elem::Div, UnaryFunctor<elem::sReLU, BinaryFunctor<elem::Plus, BinaryFunctor<elem::Minus, Assignee<3>, BinaryFunctor<elem::Div, BinaryFunctor<elem::Mult, BinaryFunctor<elem::Mult, Capture, Assignee<5> >, Assignee<4> >, Capture> >, BinaryFunctor<elem::Div, BinaryFunctor<elem::Mult, BinaryFunctor<elem::Mult, Capture, Assignee<4> >, Assignee<4> >, Capture> > >, Capture>, BinaryFunctor<elem::Div, BinaryFunctor<elem::Mult, Assignee<4>, Assignee<4> >, Capture> > >, Capture> >, BinaryFunctor<elem::Mult, Capture, Assignee<1> > > > > >, std::shared_ptr<marian::TensorBase>, std::shared_ptr<marian::TensorBase>, std::shared_ptr<marian::TensorBase>, std::shared_ptr<marian::TensorBase>, std::shared_ptr<marian::TensorBase>);
+template void Element<Assign<Var<1>, BinaryFunctor<elem::Minus, Assignee<1>, BinaryFunctor<elem::Mult, Capture, BinaryFunctor<elem::Plus, BinaryFunctor<elem::Div, BinaryFunctor<elem::Div, Assignee<2>, Capture>, BinaryFunctor<elem::Plus, UnaryFunctor<elem::Sqrt, BinaryFunctor<elem::Div, Assignee<3>, Capture> >, Capture> >, BinaryFunctor<elem::Mult, Capture, Assignee<1> > > > > >, std::shared_ptr<marian::TensorBase>, std::shared_ptr<marian::TensorBase>, std::shared_ptr<marian::TensorBase>, std::shared_ptr<marian::TensorBase> >(Assign<Var<1>, BinaryFunctor<elem::Minus, Assignee<1>, BinaryFunctor<elem::Mult, Capture, BinaryFunctor<elem::Plus, BinaryFunctor<elem::Div, BinaryFunctor<elem::Div, Assignee<2>, Capture>, BinaryFunctor<elem::Plus, UnaryFunctor<elem::Sqrt, BinaryFunctor<elem::Div, Assignee<3>, Capture> >, Capture> >, BinaryFunctor<elem::Mult, Capture, Assignee<1> > > > > >, std::shared_ptr<marian::TensorBase>, std::shared_ptr<marian::TensorBase>, std::shared_ptr<marian::TensorBase>, std::shared_ptr<marian::TensorBase>, std::shared_ptr<marian::TensorBase>);
diff --git a/src/tensors/tensor_allocator.h b/src/tensors/tensor_allocator.h
index 3a2d99e60..d65b50e7c 100755
--- a/src/tensors/tensor_allocator.h
+++ b/src/tensors/tensor_allocator.h
@@ -74,7 +74,7 @@ class TensorAllocator {
     }
   }
 
-  void free(Tensor& t) { allocator_->free(t->memory()); }
+  void free(const Tensor& t) { allocator_->free(t->memory()); }
 
   Tensor asTensor() {
     auto mem = allocator_->memory();
diff --git a/src/training/communicator.cpp b/src/training/communicator.cpp
index e158fcb28..35835a0ed 100755
--- a/src/training/communicator.cpp
+++ b/src/training/communicator.cpp
@@ -72,7 +72,7 @@ class MPIWrapper : public IMPIWrapper
 
 public:
   MPIWrapper(bool multiThreaded) {
-    int requiredThreadingMode = multiThreaded ? MPI_THREAD_MULTIPLE : MPI_THREAD_SINGLE;
+    int requiredThreadingMode = multiThreaded ? MPI_THREAD_MULTIPLE : MPI_THREAD_FUNNELED; // FUNNELED means only one thread ever calls MPI
 
     int argc = 1; char* argv[] = { const_cast<char*>("this.exe") }; char** argvp = argv; // dummy argc/argv since MPI_Init needs something here
     int providedThreadingMode;
@@ -124,6 +124,8 @@ class MPIWrapper : public IMPIWrapper
     HANDLE_MPI_ERROR(MPI_Recv(buf, (int)count, datatype, (int)sourceRank, tag, comm, status));
   }
   virtual void allReduce(const void* sendbuf, void* recvbuf, size_t count, MPI_Datatype datatype, MPI_Op op, MPI_Comm comm) const override {
+    if (sendbuf == recvbuf)
+      sendbuf = MPI_IN_PLACE; // MSMPI requires this
     HANDLE_MPI_ERROR(MPI_Allreduce(sendbuf, recvbuf, (int)count, datatype, op, comm));
   }
   virtual void finalize() override {
diff --git a/src/training/communicator.h b/src/training/communicator.h
index eef136f2d..7e705cfac 100755
--- a/src/training/communicator.h
+++ b/src/training/communicator.h
@@ -203,7 +203,6 @@ class DefaultCommunicator : public ICommunicator {
 
   void swapParams(const std::vector<Tensor>& paramShards) const override {
     // Update all graphs with parameter shard
-    
     auto gather = [this, paramShards](size_t idx, size_t begin, size_t end) {
       ABORT_IF(end - begin != paramShards[idx]->size(), "inconsistent shard size (swapParams, [{}], {} vs {})??", idx, end-begin, paramShards[idx]->size());
       // Copy parameter shard to each graph, apart from last graph
diff --git a/src/training/communicator_nccl.h b/src/training/communicator_nccl.h
index bf1f07dfc..09b70f3e6 100755
--- a/src/training/communicator_nccl.h
+++ b/src/training/communicator_nccl.h
@@ -4,10 +4,11 @@
 #include "3rd_party/threadpool.h"
 #include "tensors/gpu/cuda_helpers.h"
 
+#include "common/timer.h"
+
 // Generated by NCCL make files in build/nccl/include;
 // include dir has been set in CMake files. NCCL add version number etc.
 #include "nccl.h"
-
 #include <cuda_runtime.h>
 
 #if (NCCL_MAJOR<3 || NCCL_MINOR<2)
@@ -44,6 +45,14 @@ class NCCLCommunicator : public ICommunicator {
     }
   }
 
+  void synchronizeAllOnNullStream() const {
+    for (int i = 0; i < graphs_.size(); ++i) {
+      auto backend = graphs_[i]->params()->vals()->getBackend();
+      backend->setDevice();
+      backend->synchronize(); // note: synchronize() does not set the device by itself
+    }
+  }
+
   std::string mpiIdStr() const { // (for logging)
     return mpi_ ? mpi_->idStr() : "";
   }
@@ -150,6 +159,16 @@ class NCCLCommunicator : public ICommunicator {
       CUDA_CHECK(cudaStreamCreate(&streams_[i]));
     }
 
+    // Note: due to a bug in NCCL 2.3.5, NCCL's allocation of shared memory intermittently fails with
+    //          Failed, NCCL error 2 'unhandled system error' - ncclGroupEnd()
+    //          include/shm.h:26 NCCL WARN Unable to allocate shared memory (4263936 bytes) : Interrupted system call
+    // This is caused by SIGPROF signals being raised, causing EINTR, which NCCL does not handle.
+    // Reported as Issue #137 on the NCCL Github, and supposedly fixed for 2.3.7 (to be verified).
+    // To work around, we disable the SIGPROF signal during NCCL initialization.
+#define SIG_BAD 27 // SIGPROF
+    BlockSignal blockThread(SIG_BAD, pthread_sigmask); // Note: I don't know yet which of these two makes the difference.
+    BlockSignal blockProc(SIG_BAD, sigprocmask);       // So for now just block both.
+
     // set up NCCL
     // Since we want to use MPI, we cannot use NCCL's handy convenience function. Instead, we must go the laborious route.
     // cf. https://docs.nvidia.com/deeplearning/sdk/nccl-developer-guide/index.html#multidevprothrd
@@ -160,35 +179,19 @@ class NCCLCommunicator : public ICommunicator {
       NCCL_CHECK(ncclGetUniqueId(&uniqueId));
 
     if (mpi_) {
-      //LOG(info, "[{}] before bcast", mpiIdStr());
       static_assert(sizeof(uniqueId) == NCCL_UNIQUE_ID_BYTES, "wrong NCCL_UNIQUE_ID_BYTES??"); // (this value is used in NVidia examples)
       mpi_->bCast(&uniqueId, sizeof(uniqueId), MPI_BYTE, 0);
-      //LOG(info, "[{}] after bcast", mpiIdStr());
     }
 
-    //mpiBarrier(); // should not be needed since bCast is a barrier
-
-    // Note: due to a bug in NCCL 2.3.5, NCCL's allocation of shared memory intermittently fails with
-    //          Failed, NCCL error 2 'unhandled system error' - ncclGroupEnd()
-    //          include/shm.h:26 NCCL WARN Unable to allocate shared memory (4263936 bytes) : Interrupted system call
-    // This is caused by SIGPROF signals being raised, causing EINTR, which NCCL does not handle.
-    // Reported as Issue #137 on the NCCL Github.
-    // To work around, we disable the SIGPROF signal during NCCL initialization.
-#define SIG_BAD 27 // SIGPROF
-    BlockSignal blockThread(SIG_BAD, pthread_sigmask); // Note: I don't know yet which of these two makes the difference.
-    BlockSignal blockProc(SIG_BAD, sigprocmask);       // So for now just block both.
-
     groupStart();
     for (int localDeviceIndex = 0; localDeviceIndex < devices_.size(); localDeviceIndex++) {
       CUDA_CHECK(cudaSetDevice(devices_[localDeviceIndex]));
-      //LOG(info, "[{}] ncclCommInitRank {} out of {}: GPU[{}]", mpiIdStr(), myNcclRank(localDeviceIndex), numNcclRanks(), localDeviceIndex);
       NCCL_CHECK(ncclCommInitRank(&comms_[localDeviceIndex], numNcclRanks(), uniqueId, myNcclRank(localDeviceIndex)));
-      //LOG(info, "[{}] done ncclCommInitRank {} out of {}, GPU[{}]", mpiIdStr(), myNcclRank(localDeviceIndex), numNcclRanks(), localDeviceIndex);
     }
     groupEnd();
 
     mpiBarrier(); // (synchronize the log messages)
-    LOG(debug, "NCCLCommunicator constructed successfully for {}", mpiIdStr());
+    LOG(info, "NCCLCommunicator constructed successfully.");
     mpiBarrier(); // (synchronize the log messages)
   }
 
@@ -206,61 +209,46 @@ class NCCLCommunicator : public ICommunicator {
     for(size_t i = 0; i < graphs_.size(); ++i) {
       size_t begin, end; std::tie
       (begin, end) = localShardRange(i);
-      //std::cerr << "[" << mpiIdStr() << "] foreach " << begin << " " << end << std::endl;
-try{
       if (parallel)
         threadResults_[i] = threadPool_.enqueue(func, i, begin, end);
-        //group.emplace_back(func, i, begin, end);
-        //threadPool_.enqueue([&](size_t i){
-        //  func(i, begin, end);
-        //}, i);
       else
         func(i, begin, end);
-}
-catch (const std::exception& e) // something leaks thread handles
-{
-  // keeping this around, in case the error still happens  --@TODO: remove once this has not been observed anymore
-  LOG(info, "caught exception in foreach {}", i);
-  system("ps -T -A");
-  throw;
-}
     }
     if (parallel)
       for(size_t i = 0; i < graphs_.size(); ++i)
         threadResults_[i].wait();
-    //for(auto& t : group) // (note: group is empty is not parallel)
-    //  t.join();
   }
 
   void scatterReduce() const override {
+    synchronizeAllOnNullStream();
+
     groupStart();
     for(int i = 0; i < graphs_.size(); ++i) {
       size_t begin, end; std::tie
       (begin, end) = localShardRange(i);
-      //std::cerr << "[" << mpiIdStr() << "] scatterReduce " << begin << " " << end << std::endl;
 
       auto grads = graphs_[i]->params()->grads();
       const auto* sendbuf = grads->data();
       auto*       recvbuf = grads->subtensor(begin, end-begin)->data();
       size_t      bufsize = shardSize();
+      ABORT_IF(grads->subtensor(begin, end-begin)->size() != bufsize, "unexpected subtensor size??");
 
       NCCL_CHECK(ncclReduceScatter(sendbuf, recvbuf, bufsize, ncclFloat, ncclSum, comms_[i], streams_[i]));
     }
     groupEnd();
-    //std::cerr << "scatterReduce submitted" << std::endl;
     synchronizeAll();
-    //std::cerr << "scatterReduce completed" << std::endl;
   }
 
   // This distributes all 64 model shards to all 64 GPUs.
   // @TODO: For unknown reasons, this takes longer than any other operation incl. scatterReduce().
   //        But both should have the same number of data transfers of the same size.
   void allGather() const override {
+    synchronizeAllOnNullStream();
+
     groupStart();
     for(int i = 0; i < graphs_.size(); ++i) {
       size_t begin, end; std::tie
       (begin, end) = localShardRange(i);
-      //std::cerr << "[" << mpiIdStr() << "] allGather " << begin << " " << end << std::endl;
 
       auto vals = graphs_[i]->params()->vals();
       const auto* sendbuf = vals->subtensor(begin, end-begin)->data();
@@ -281,14 +269,12 @@ catch (const std::exception& e) // something leaks thread handles
     auto distributedParams = gatherState([&](size_t localDeviceIndex) {
       std::vector<float> tmp;
       distributedParamShards[localDeviceIndex]->get(tmp);
-      //LOG(info, "[{}] swapParams.getFn({}) -> size {}, ({}, {}, {}, ...)", mpiIdStr(), localDeviceIndex, tmp.size(), tmp[0], tmp[1], tmp[2]);
       return tmp;
     });
     // Now all MPI processes hold an identical copy of a concatenation of all distributedParamShards[] across local and remote devices.
     std::vector<float> localParams;
     graphs_[0]->params()->vals()->get(localParams);
     // Now all MPI processes hold an identical copy of params() (remember, we assumed all devices hold the same params()).
-    //LOG(info, "[{}] swapParams: distributedParams.size = {}, localParams.size = {}", mpiIdStr(), distributedParams.size(), localParams.size());
     ABORT_IF(distributedParams.size() != localParams.size(), "distributed sharded and local params have different size??");
 
     // swap
@@ -331,7 +317,6 @@ catch (const std::exception& e) // something leaks thread handles
       tmp = getFn(localDeviceIndex);
       localData.insert(localData.end(), tmp.begin(), tmp.end());
     }
-    //LOG(info, "[{}] gatherState: localData.size = {}", mpiIdStr(), localData.size());
     // second, concatenate across MPI processes
     // Note that all local devices occupy consecutive ncclRanks in order.
     std::vector<float> data;
diff --git a/src/training/exponential_smoothing.h b/src/training/exponential_smoothing.h
index bc2f2761d..fb1d3b4ac 100755
--- a/src/training/exponential_smoothing.h
+++ b/src/training/exponential_smoothing.h
@@ -3,6 +3,7 @@
 #include "common/definitions.h"
 #include "functional/functional.h"
 #include "tensors/tensor_operators.h"
+#include "optimizers/optimizers.h"
 
 namespace marian {
 
@@ -12,18 +13,33 @@ namespace marian {
  */
 class ExponentialSmoothing {
 public:
-  ExponentialSmoothing(float decay = 0.0f)
-      : mvAvg_{decay > 0}, mvDecay_{decay} {}
+    ExponentialSmoothing(Ptr<Options> options) {
+      auto args = options->get<std::vector<float>>("exponential-smoothing");
+      ABORT_IF(args.size() < 1 || args.size() > 2, "exponential-smoothing parameter must be one or two numbers");
+      mvDecayBy_ = args[0];
+      if (args.size() > 1)
+        refBatchTrgWords_ = (size_t)args[1];
+      mvAvg_ = (mvDecayBy_ > 0);
+    }
 
 protected:
-  void updateAvgParams(Tensor paramsAvg, Tensor params, size_t batches) {
+  void updateAvgParams(Tensor paramsAvg, Tensor params, size_t batches, size_t actualBatchTrgWords = OptimizerBase::mbSizeNotProvided) {
+    double beta = 1. - mvDecayBy_;
+    // correction term if batch size is different from what mvDecayBy_ was specified for
+    if (refBatchTrgWords_) {
+      ABORT_IF(actualBatchTrgWords == OptimizerBase::mbSizeNotProvided,
+               "This graph-group type does not support reference batch size specification for exponential-smoothing");
+      beta = pow(beta, (double)actualBatchTrgWords / (double)refBatchTrgWords_);
+    }
+    // reduce effect of decay parameter in early training stages
+    float decayBy = std::max(1.f - (float)beta,
+                             1.f - (float)(batches + 1) / (float)(batches + 10));
     using namespace functional;
-    float decay = std::max(mvDecay_,
-                           1.f - (float)(batches + 1) / (float)(batches + 10));
-    Element(_1 = ((1.f - decay) * _1) + (decay * _2), paramsAvg, params);
+    Element(_1 = ((1.f - decayBy) * _1) + (decayBy * _2), paramsAvg, params);
   }
 
   bool mvAvg_{false};
-  float mvDecay_{1e-4f};
+  float mvDecayBy_{1e-4f};     // decay prior model by this factor
+  size_t refBatchTrgWords_{0}; // mvDecayBy_ is specified for this batch size (in target words)
 };
 }  // namespace marian
diff --git a/src/training/graph_group.h b/src/training/graph_group.h
index fc372adce..46a317c32 100755
--- a/src/training/graph_group.h
+++ b/src/training/graph_group.h
@@ -33,6 +33,10 @@ class GraphGroup {
 
   virtual void save(bool isFinal = false) = 0;
 
+  void validate() {
+    ABORT_IF(finalized_, "Training has already finished.");
+  }
+
   virtual void finalize() {
     finalized_ = true;
   }
@@ -48,6 +52,7 @@ class GraphGroup {
    * The actual allowed size is then determined by multiplying it with the
    * number of devices, which is passed in as the 'multiplier'.
    */
+  // @TODO: Can this be made const? It seems wrong to have a stateful method that still returns a result.
   virtual Ptr<data::BatchStats> collectStats(Ptr<ExpressionGraph> graph,
                                              Ptr<models::ModelBase> model,
                                              size_t multiplier = 1) {
@@ -194,10 +199,8 @@ class MultiNodeGraphGroupBase : public GraphGroup {
   }
 
   virtual void finalize() override {
-    if (mpi_) {
+    if (mpi_)
       finalizeMPI(std::move(mpi_));
-      ABORT_IF(mpi_, "MPI not finalized??");
-    }
     Base::finalize();
   }
 };
diff --git a/src/training/graph_group_async.cpp b/src/training/graph_group_async.cpp
index 1d0042019..b272a4b7f 100755
--- a/src/training/graph_group_async.cpp
+++ b/src/training/graph_group_async.cpp
@@ -7,7 +7,7 @@ namespace marian {
 
 AsyncGraphGroup::AsyncGraphGroup(Ptr<Options> config)
     : GraphGroup(config),
-      ExponentialSmoothing{options_->get<float>("exponential-smoothing")},
+      ExponentialSmoothing(options_),
       devices_{Config::getDevices(options_)},
       shardSync_(devices_.size()),
       optimizerDelay_{options_->get<size_t>("optimizer-delay")} {
diff --git a/src/training/graph_group_async.h b/src/training/graph_group_async.h
index d3af0f22b..713e30064 100755
--- a/src/training/graph_group_async.h
+++ b/src/training/graph_group_async.h
@@ -55,7 +55,7 @@ class AsyncGraphGroup : public GraphGroup, public ExponentialSmoothing {
   AsyncGraphGroup(Ptr<Options> config);
 
   void update(Ptr<data::Batch> batch) override {
-    ABORT_IF(finalized_, "Training has already finished");
+    validate();
     execute(batch);
   }
 
diff --git a/src/training/graph_group_multinode.h b/src/training/graph_group_multinode.h
index c86225ebc..39a20e062 100755
--- a/src/training/graph_group_multinode.h
+++ b/src/training/graph_group_multinode.h
@@ -376,7 +376,7 @@ class MultiNodeGraphGroup : public MultiNodeGraphGroupBase {
    * Update any client model with given batch if batch is assigned to this node.
    */
   void update(Ptr<data::Batch> batch) override {
-    ABORT_IF(finalized_, "Training has already finished");
+    validate();
     // Only take batch assigned to this node
     if(batchIter_ % mpi_->numMPIProcesses() == (size_t)mpi_->myMPIRank()) {
       execute(batch);
diff --git a/src/training/graph_group_multinode_sync.h b/src/training/graph_group_multinode_sync.h
index bfef2050c..7685b6789 100755
--- a/src/training/graph_group_multinode_sync.h
+++ b/src/training/graph_group_multinode_sync.h
@@ -143,7 +143,7 @@ class MultiNodeGraphGroupSync : public MultiNodeGraphGroupBase {
    * Update any client model with given batch if batch is assigned to this node.
    */
   void update(Ptr<data::Batch> batch) override {
-    ABORT_IF(finalized_, "Training has already finished");
+    validate();
     if(batchIter_ % mpi_->numMPIProcesses() == mpi_->myMPIRank()) {  // Only take batch assigned to this node
       execute(batch);
     }
diff --git a/src/training/graph_group_singleton.h b/src/training/graph_group_singleton.h
index 1eb589d91..9e417b7d6 100755
--- a/src/training/graph_group_singleton.h
+++ b/src/training/graph_group_singleton.h
@@ -25,7 +25,7 @@ class SingletonGraph : public GraphGroup, public ExponentialSmoothing {
 public:
   SingletonGraph(Ptr<Options> config)
       : GraphGroup(config),
-        ExponentialSmoothing(options_->get<float>("exponential-smoothing")) {
+        ExponentialSmoothing(config) {
     // Get device ID
     auto devices = Config::getDevices(options_);
     ABORT_IF(devices.size() != 1, "Only one device ID should be provided for singleton training");
@@ -40,7 +40,7 @@ class SingletonGraph : public GraphGroup, public ExponentialSmoothing {
   }
 
   void update(Ptr<data::Batch> batch) override {
-    ABORT_IF(finalized_, "Training has already finished");
+    validate();
     execute(batch);
   }
 
diff --git a/src/training/graph_group_sync.cpp b/src/training/graph_group_sync.cpp
index 6699484c1..cedf5c54e 100755
--- a/src/training/graph_group_sync.cpp
+++ b/src/training/graph_group_sync.cpp
@@ -4,7 +4,7 @@ namespace marian {
 
 SyncGraphGroup::SyncGraphGroup(Ptr<Options> config)
     : GraphGroup(config),
-      ExponentialSmoothing{options_->get<float>("exponential-smoothing")},
+      ExponentialSmoothing(config),
       delay_{options_->get<size_t>("optimizer-delay")} { // @TODO: rename to something else; delay means delayed updated, not accumulation
 
   mpi_ = initMPI(/*multiThreaded=*/false); // when not running under MPI, this will be a fake object that represents a one-MPI-process setup
@@ -25,9 +25,16 @@ SyncGraphGroup::SyncGraphGroup(Ptr<Options> config)
   // This part of the code will not special-case any of this here.
   // Rather, it is assumed that the communicator knows to reduce unnecessary transfers to no-ops.
   comm_ = createCommunicator(graphs_, /*noNccl=*/options_->get<bool>("no-nccl", false), /*mpi=*/mpi_);
+
+  auto type = utils::toUpper(devices_.front().typeAsString()) + "s";
+  if (mpi_->numMPIProcesses() > 1)
+    LOG(info, "[training] Using {} {}, distributed over {} MPI processes", mpi_->numMPIProcesses() * devices_.size(), type, mpi_->numMPIProcesses());
+  else
+    LOG(info, "[training] Using {} {}", devices_.size(), type);
 }
 
 void SyncGraphGroup::setScheduler(Ptr<Scheduler> scheduler) /*override*/ {
+  validate();
   scheduler_ = scheduler;
   // optimizer has to be registered last to see changes of learning rate
   // @TODO: ^^Fix this comment. Either it refers to the scheduler, or it should be moved. Which one?
@@ -101,31 +108,144 @@ void SyncGraphGroup::initializeAvg() {
 }
 
 Ptr<data::BatchStats> SyncGraphGroup::collectStats() {
-  // @TODO: This should only run on MPI process 0. Also we can share vv this vv expression with update().
-  size_t multiplier = devices_.size() * mpi_->numMPIProcesses() * delay_;
-  return GraphGroup::collectStats(graphs_[0], builders_[0], multiplier);
+  // @TODO: This is an incompatible change. Decide how to handle that.
+  //size_t multiplier = devices_.size() * mpi_->numMPIProcesses() * delay_;
+  return GraphGroup::collectStats(graphs_[0], builders_[0]/*, multiplier*/);
+}
+
+// helper for MB scaling: quantize the ratio with a given error margin
+static double roundUpRatio(double ratio) {
+  if (ratio == 0)
+    return ratio;
+  // find largest power of two that fits into ratio
+  double p = 1;
+  while (p*2 < ratio)
+    p *= 2;
+  // round up to nearest multiple of a largest power of 2 where relative error is within margin
+  // 25% error margin seems acceptable:
+  //  - using a 25% larger MB size should not break convergence
+  //  - @TODO: not using the first 25% of the next block is OK since those are dominated by data exchange
+  double maxError = 0.25;
+  while (p >= 1) {
+    double proposedRatio = ceil(ratio / p) * p;
+    double error = (proposedRatio - ratio) / ratio;
+    if (fabs(error) <= maxError)
+      return proposedRatio;
+    p /= 2;
+  }
+  return ratio;
+}
+
+// helper routine that handles accumulation and load-balancing of sub-batches to fill all devices
+// It adds 'newBatch' to 'pendingBatches_', and if sufficient batches have been queued, then
+// returns 'pendingBatches_' in 'subBatches' and resets it. If not, it returns false.
+bool SyncGraphGroup::tryGetSubBatches(Ptr<data::Batch> newBatch, std::vector<Ptr<data::Batch>>& subBatches) {
+  pendingBatches_.push_back(newBatch);
+  size_t warpSize = devices_.size() * mpi_->numMPIProcesses(); // warp := set of batches processed concurrently across GPus and workers
+
+  size_t pendingTrgWords = 0; // diagnosics only: compute how many target labels are pending so far
+  for (const auto& batch : pendingBatches_)
+    pendingTrgWords += batch->wordsTrg();
+
+  // MB-size warm-up and dynamic scaling
+  double ratio;
+  bool isDynamic = scheduler_->tryGetDynamicMBSizeMultiplier(ratio);
+  if (isDynamic)
+    ratio = roundUpRatio(ratio); // round up to full batches if within a certain error margin  --@BUGBUG: Not invariant w.r.t. GPU size, as ratio is relative to what fits into 1 GPU
+  else   // if dynamic scaling not enabled, then fill each GPU with a batch
+    ratio = (double)(delay_ * warpSize);
+  if (pendingBatches_.size() < ratio)
+    return false; // not enough data yet
+
+  // now we have enough to fill at least 'ratio' batches
+  if (pendingBatches_.size() == ratio)
+    return true; // nothing to do, e.g. warm-up not enabled
+
+  // warm-up is happening
+  LOG_ONCE(info, "[training] Mini-batch-warmup enabled");
+
+  // shorten all batches a little to accurately reflect ratio
+  // e.g. ratio = 3.3 for 4 batches: Reduce each by 3.3/4
+  // Alternatively, we could just shorten the last 'warp', but that would not be invariant to warp size.
+  size_t before = 0, after = 0;
+  for (auto& batch : pendingBatches_) {
+    auto reducedBatchSize = (size_t)ceil((double)batch->size() * ratio / (double)pendingBatches_.size());
+    size_t minSize = 1;
+    if (pendingBatches_.size() == 1) { // enforce a minimum (only needed/correct if still in first batch)
+      size_t minTrgWords = 256;    // don't go below this number of target words, as it seems excessive  --@TODO: parameterize?
+      minSize = 1 + (minTrgWords * batch->size() - 1) / batch->wordsTrg(); // approximately convert minTrgWords into a #sentences
+    }
+    reducedBatchSize = std::max(reducedBatchSize, minSize);
+    before += batch->wordsTrg();
+    if (reducedBatchSize < batch->size())
+      batch = batch->split(/*numSubBatches=*/1, reducedBatchSize).front();
+    after += batch->wordsTrg();
+  }
+
+  // load-balance: distribute the last numWarps-group's batches over GPUs
+  // This is tricky since batches do not have the same length, therefore we can only split, but not merge.
+  auto numWarps = (pendingBatches_.size() - 1) / warpSize + 1; // = ceil(#buffers / (#GPUs * #workers))
+  auto availableBatches = numWarps * warpSize; // we got this many GPUs anyways, so we better make use of them
+  if (pendingBatches_.size() < availableBatches) {
+    // we are not using all available GPUs -> try to load-balance a bit better
+    auto fullBatches = (numWarps - 1) * warpSize;
+    auto expandLast = pendingBatches_.size() - fullBatches;
+    auto toLast = availableBatches - fullBatches;
+    LOG(info, "attempt to redistribute {} last batches over {}", expandLast, toLast);
+    auto splitInto = toLast / expandLast; // unfortunately we can only split in integer ratios
+    // @TODO: We can do better since the last batch is typically smaller.
+    if (splitInto > 1) {
+      // split each of last numWarps's batches into 'splitInto' batches
+      // pop them first
+      std::vector<Ptr<data::Batch>> batchesToSplit;
+      while (pendingBatches_.size() > fullBatches) {
+        batchesToSplit.push_back(pendingBatches_.back());
+        pendingBatches_.pop_back();
+      }
+      // now split them
+      for (auto& batchToSplit : batchesToSplit) {
+        LOG(info, "{}-way splitting batchToSplit with size {}", splitInto, batchToSplit->size());
+        auto splitBatches = batchToSplit->split(splitInto);
+        for (auto& splitBatch : splitBatches) {
+          LOG(info, " -> getting batchToSplit with size {}", splitBatch->size());
+          pendingBatches_.push_back(splitBatch);
+        }
+      }
+    }
+    ABORT_IF(pendingBatches_.size() > availableBatches, "somehow split into too many batches??");
+  }
+  subBatches = std::move(pendingBatches_);
+
+  // @TODO: sort by width, so that in case of delay > 1, each GPU gets about the same size
+  return true;
 }
 
-void SyncGraphGroup::update(Ptr<data::Batch> batch) /*override*/ {
-  ABORT_IF(finalized_, "Training has already finished");
+void SyncGraphGroup::update(Ptr<data::Batch> newBatch) /*override*/ {
+  validate();
 
-  // distribute the batch over (delay, local device, MPI rank)
-  size_t numSubBatches = delay_ * devices_.size() * mpi_->numMPIProcesses();
-  auto subBatches = batch->split(numSubBatches);
-  subBatches.resize(numSubBatches); // pad with nullptrs if out of data
+  std::vector<Ptr<data::Batch>> subBatches;
+  bool gotSubBatches = tryGetSubBatches(newBatch, subBatches);
+
+  // not enough data yet: return right away
+  if (!gotSubBatches)
+    return;
 
   // Helper to access the subBatches array
-  auto getSubBatch = [&](size_t t, size_t localDeviceIndex, size_t rank) {
+  auto getSubBatch = [&](size_t t, size_t localDeviceIndex, size_t rank) -> Ptr<data::Batch> {
     // 't' (the delay) should be slowest changing dimension. If subBatches are sorted by
     // length, then grouping sentences of similar length into the same delay step can
     // reduce unnecessary time spent in padding.
-    return subBatches[(t * mpi_->numMPIProcesses() + rank) * devices_.size() + localDeviceIndex];
+    auto index = (t * mpi_->numMPIProcesses() + rank) * devices_.size() + localDeviceIndex;
+    if (index < subBatches.size())
+      return subBatches[index];
+    else
+      return nullptr;
   };
 
   // Upon very first execution, reset everything
   if(first_) {
-    LOG(debug, "[{}] Processing first minibatch. Batches are processed as {} processes x {} GPUs/process x {} delay steps",
-         mpi_->idStr(), mpi_->numMPIProcesses(), devices_.size(), delay_);
+    LOG(info, "[training] Processing first minibatch. Batches are processed as {} processes x {} GPUs/process",
+        mpi_->numMPIProcesses(), devices_.size());
     initialize(subBatches.front());
     if(mvAvg_ && paramsAvg_.empty())
       initializeAvg();
@@ -133,33 +253,34 @@ void SyncGraphGroup::update(Ptr<data::Batch> batch) /*override*/ {
   }
 
   // Compute gradients
-  // This happens in multiple steps in case of delay_ > 1.
+  // This happens in multiple steps in case of delay > 1.
   std::vector<float> localDeviceCosts(devices_.size(), 0.f); // [local device index] aggregate cost for each local device
-  for (size_t t = 0; t < delay_; t++) {
+  for (size_t t = 0; getSubBatch(t, 0, 0); t++) { // @TODO: rename 't' to 'delay'
     // Execute single forward/backward step
     auto forwardBackward = [&](size_t localDeviceIndex, size_t /*begin*/, size_t /*end*/) {
       auto graph = graphs_[localDeviceIndex];
       auto subBatch = getSubBatch(t, localDeviceIndex, mpi_->myMPIRank());
 
       if(subBatch) {
-        timer::Timer timer;
         auto costNode = builders_[localDeviceIndex]->build(graph, subBatch);
-        //LOG(info, timer.format(2, "after build: %ws"));
         graph->forward();
-        //LOG(info, timer.format(2, "after forward (no sync): %ws"));
         localDeviceCosts[localDeviceIndex] += costNode->scalar();
         graph->backward(/*zero=*/t == 0); // only reset gradients to 0 if t = 0
-        //LOG(info, timer.format(2, "after backward (no sync): %ws"));
-        //localDeviceCosts[localDeviceIndex] += costNode->scalar(); // moved here for time measurements; @TODO: move this back
-        //LOG(info, timer.format(2, "after scalar() (that's a sync): %ws"));
       }
       else { // empty batch: execute do-nothing fw-bw step for proper inits and resets
+#if 1   // @TODO: double-check whether the #else branch is the same; and if so, use it instead
+        graph->params()->allocateBackward();
+        if (t == 0) // these have already been sized
+          graph->params()->set_zero_adjoint();
+#else
+        graph->clear(); // instead of build()
         graph->forward();
         graph->backward(/*zero=*/t == 0);
+#endif
       }
     };
 
-    comm_->foreach(forwardBackward); // compute gradients in parallel on each device. Aggregate if delay_ > 1.
+    comm_->foreach(forwardBackward); // compute gradients in parallel on each device. Aggregate if delay > 1.
   }
   // At this point, each device on each MPI process has a gradient aggregated over a subset of the sub-batches.
 
@@ -177,23 +298,25 @@ void SyncGraphGroup::update(Ptr<data::Batch> batch) /*override*/ {
       Element(_1 = _1 / (float)div, curGrad);
     }
 
+    // determine num words for dynamic hyper-parameter adjustment
+    size_t mbWords = OptimizerBase::mbSizeNotProvided;
+    if (options_->get<std::string>("cost-type") == "ce-sum") { // presently only supported for ce-sum
+      mbWords = 0;
+      for (const auto& batch : subBatches)
+        mbWords += batch->words(-1);  // @TODO: use wordsTrg (it's the same)
+    }
+
     // actual model update
-    shardOpt_[idx]->update(curParam, curGrad);
+    shardOpt_[idx]->update(curParam, curGrad, mbWords);
 
     if(mvAvg_)
       updateAvgParams(
-          paramsAvg_[idx], curParam, scheduler_->numberOfBatches());
+          paramsAvg_[idx], curParam, scheduler_->numberOfBatches(), mbWords);
   };
 
-  timer::Timer timer;
   comm_->scatterReduce(); // reduce gradients across all devices (globally) into shards
-  //LOG(info, timer.format(2, "after scatterReduce (has sync): %ws"));
   comm_->foreach(update); // per-shard model-update
-  //LOG(info, timer.format(2, "after model update (no sync): %ws"));
-  //graphs_.front()->getBackend()->synchronize(); // @TODO: This is strictly for time measurement. Make sure it doesn't accidentally stay in here!!
-  //LOG(info, timer.format(2, "after model update sync (which is unnecessary except for time measurements): %ws"));
   comm_->allGather();     // distribute param value shards back
-  //LOG(info, timer.format(2, "after allGather (has sync): %ws"));
 
   // cost across all local devices (scheduler will aggregate cross-process)
   float localCost = 0;
@@ -202,7 +325,7 @@ void SyncGraphGroup::update(Ptr<data::Batch> batch) /*override*/ {
 
   // if localCost is average-based, we need to turn the sum over devices into an average as well
   if(options_->get<std::string>("cost-type") != "ce-sum")
-    localCost /= numSubBatches;
+    localCost /= subBatches.size();
 
   if(scheduler_) {
     // track and log localCost
@@ -224,6 +347,7 @@ void SyncGraphGroup::update(Ptr<data::Batch> batch) /*override*/ {
 }
 
 void SyncGraphGroup::load() /*override*/ {
+  validate();
 
   // This function loads the main parameters in the graphs.
   // In case of exponential smoothing, we also need to restore paramsAvg_.
@@ -253,10 +377,11 @@ void SyncGraphGroup::load() /*override*/ {
         [&](const std::vector<float>& optimizerStateVector, const OptimizerBase::ScatterStateSetFunc& setShardFn) {
           comm_->scatterState(optimizerStateVector, setShardFn);
         });
+      LOG(info, "[training] Model reloaded from {}", name);
     } else if(options_->has("pretrained-model")) {
       std::string nameInit = options_->get<std::string>("pretrained-model");
       LOG(info,
-          "Initialize model weights with the pre-trained model {}",
+          "[training] Initializing model weights with the pre-trained model {}",
           nameInit);
 
       size_t i = 0;
@@ -267,44 +392,34 @@ void SyncGraphGroup::load() /*override*/ {
 }
 
 void SyncGraphGroup::save(bool final) /*override*/ {
+  validate();
   barrier(); // (for better grouping of log messages)
-  //LOG(info, "[{}] save() line {}!", this->mpi_->idStr(), __LINE__);
   // do final validation
   if(final && scheduler_) {
     // bring the smoothed model in
     // Note that it is sharded. For multi-node, it is sharded over multiple machines, so this is a network access.
     // Also note that the swap must run on all MPI processes concurrently, although only one actually validates.
-    //LOG(info, "[{}] save() line {}", this->mpi_->idStr(), __LINE__);
     swapParamsAvg();
-    //LOG(info, "[{}] save() line {}", this->mpi_->idStr(), __LINE__);
     if (isMainProcess()) // in multi-node, only first MPI process saves the model (they are all identical)
       scheduler_->validate(graphs_, true);
-    //LOG(info, "[{}] save() line {}", this->mpi_->idStr(), __LINE__);
     swapParamsAvg();
-    //LOG(info, "[{}] save() line {}", this->mpi_->idStr(), __LINE__);
   }
 
   std::string name = options_->get<std::string>("model");
 
-  //LOG(info, "[{}] save() line {}", this->mpi_->idStr(), __LINE__);
   barrier(); // (for better grouping of log messages)
-  //LOG(info, "[{}] save() line {}", this->mpi_->idStr(), __LINE__);
   // if smoothing then save original (unsmoothed) parameters as well
-  // @TODO: Check whether we are reloading the correct file (the unsmoothed one).
   if(mvAvg_ && paramsAvg_.size() > 0 && isMainProcess()) // only save from one MPI process
     // Save the original parameters in model.npz.orig.npz
     builders_[0]->save(graphs_[0], name + ".orig.npz", true);
 
   // Temporarily switch to the averaged parameters
   // Note: the smoothed model is sharded across GPUs, and across MPI processes if applicable. This brings it into MPI process[*].device[*]
-  //LOG(info, "[{}] save() line {}", this->mpi_->idStr(), __LINE__);
   swapParamsAvg();
-  //LOG(info, "[{}] save() line {}", this->mpi_->idStr(), __LINE__);
 
   // save main model file
   if (isMainProcess()) { // only save from one MPI process
     // if not overwrite then save a copy with number of updates in the model pathname
-    //LOG(info, "[{}] save() line {}", this->mpi_->idStr(), __LINE__);
     if(!options_->get<bool>("overwrite") && !final) {
       std::string numberOfBatches
           = scheduler_ ? std::to_string(scheduler_->numberOfBatches())
@@ -313,40 +428,34 @@ void SyncGraphGroup::save(bool final) /*override*/ {
       nameOverwrite.replace(name.size() - 4, 4, ".iter" + numberOfBatches + ".npz"); // @TODO: use insert?
       builders_[0]->save(graphs_[0], nameOverwrite);
     }
-    //LOG(info, "[{}] save() line {}", this->mpi_->idStr(), __LINE__);
     // save main model file
     builders_[0]->save(graphs_[0], name, true);
-    //LOG(info, "[{}] save() line {}", this->mpi_->idStr(), __LINE__);
     // save scheduler-related state
     if (scheduler_)
       scheduler_->save(name);
-    //LOG(info, "[{}] save() line {}", this->mpi_->idStr(), __LINE__);
   }
 
   // Switch back to the original parameters
-  //LOG(info, "[{}] save() line {}", this->mpi_->idStr(), __LINE__);
   swapParamsAvg();
-  //LOG(info, "[{}] save() line {}", this->mpi_->idStr(), __LINE__);
 
-#if 0 // temporary, for testing of saving distributed models; must be identical to .orig.npz
-  if(mvAvg_ && paramsAvg_.size() > 0 && isMainProcess())
-    builders_[0]->save(graphs_[0], name + ".orig_after_swapping.npz", true);
-#endif
-  //LOG(info, "[{}] save() line {}", this->mpi_->idStr(), __LINE__);
   barrier(); // (for better grouping of log messages)
-  //LOG(info, "[{}] save() line {}", this->mpi_->idStr(), __LINE__);
 
   // persist optimizer state
-  //LOG(info, "[{}] save() line {}", this->mpi_->idStr(), __LINE__);
+  LOG(info, "[{}] save() line {}", this->mpi_->idStr(), __LINE__);
   shardOpt_[0]->save(name + ".optimizer.npz", shardOpt_,
     [&](const OptimizerBase::GatherStateGetFunc& getShardFn) {
       return comm_->gatherState(getShardFn);
     },
     isMainProcess());
-  //LOG(info, "[{}] save() line {}", this->mpi_->idStr(), __LINE__);
+  LOG(info, "[{}] save() line {}", this->mpi_->idStr(), __LINE__);
 
   barrier(); // (for better grouping of log messages)
-  //LOG(info, "[{}] save() line {}", this->mpi_->idStr(), __LINE__);
+}
+
+void SyncGraphGroup::finalize() /*override*/ {
+  validate();
+  finalizeMPI(std::move(mpi_));
+  Base::finalize();
 }
 
 }  // namespace marian
diff --git a/src/training/graph_group_sync.h b/src/training/graph_group_sync.h
index 478166996..5d4b11912 100755
--- a/src/training/graph_group_sync.h
+++ b/src/training/graph_group_sync.h
@@ -7,6 +7,7 @@
 namespace marian {
 
 class SyncGraphGroup : public GraphGroup, public ExponentialSmoothing {
+  using Base = GraphGroup;
   const size_t delay_{ 1 }; // optimizer-delay parameter
 
   Ptr<ICommunicator> comm_; // [not null] communicator, e.g. NCCLCommunicator
@@ -23,7 +24,10 @@ class SyncGraphGroup : public GraphGroup, public ExponentialSmoothing {
   std::vector<Ptr<TensorAllocator>> paramsAllocs_; // [deviceIndex] we must hold a reference to the memory until this class dies
   // @TODO: move this nto ExponentialSmoothing, together with paramsAvg_?
 
-  bool first_{ true }; // gets interpreted and cleared by update()
+  // state for update()
+  bool first_{ true };                           // gets interpreted and cleared by update()
+  std::vector<Ptr<data::Batch>> pendingBatches_; // in case of delay, multi-worker, and/or multi-GPU, we buffer up batches
+  size_t typicalTrgWords_{};                     // typical batch size in words (labels); remembered from collectStats()
 
   void initialize(const Ptr<data::Batch>& exampleBatch);
   void initializeAvg();
@@ -32,6 +36,8 @@ class SyncGraphGroup : public GraphGroup, public ExponentialSmoothing {
   void barrier() const { mpi_->barrier(); } // (we need this several times)
   void swapParamsAvg() { if (mvAvg_ && paramsAvg_.size() > 0) comm_->swapParams(paramsAvg_); } // note: must call this on all MPI ranks in parallel
 
+  bool tryGetSubBatches(Ptr<data::Batch> newBatch, std::vector<Ptr<data::Batch>>& subBatches);
+
 public:
   SyncGraphGroup(Ptr<Options> config);
 
@@ -42,6 +48,8 @@ class SyncGraphGroup : public GraphGroup, public ExponentialSmoothing {
   void load() override;
   void save(bool final = false) override;
 
+  void finalize() override;
+
   Ptr<data::BatchStats> collectStats();
   // @TODO: consider to make this a virtual as well? Currently it is a template dispatch
 };
diff --git a/src/training/scheduler.h b/src/training/scheduler.h
index dee62496d..783845c8c 100755
--- a/src/training/scheduler.h
+++ b/src/training/scheduler.h
@@ -13,12 +13,34 @@ class Scheduler : public TrainingObserver {
   std::vector<Ptr<ValidatorBase>> validators_;
 
   bool first_{true};
+  size_t typicalTrgBatchWords_{0}; // for dynamic batch sizing
 
   Ptr<TrainingState> state_;
 
   timer::Timer timer_, heartBeatTimer_;
 
-  float getLearningRate(TrainingState& state) {
+  // determine LR decay factor from --lr-decay-inv-sqrt option
+  float getLearningRateDecayFactor(const TrainingState& state) const {
+    auto args = options_->get<std::vector<std::string>>("lr-decay-inv-sqrt");
+    ABORT_IF(args.empty() || args.size() > 2, "--lr-decay-inv-sqrt argument must be one or two numbers with units");
+    auto decayGoogle = SchedulingParameter::parse(args[0]);
+    size_t progress = state.getProgressIn(decayGoogle.unit);
+    size_t start = decayGoogle.n;
+    if (args.size() > 1) {
+      auto decayStart = SchedulingParameter::parse(args[1]);
+      ABORT_IF(decayStart && decayStart.unit != decayGoogle.unit, "both --lr-decay-inv-sqrt arguments must have the same unit");
+      start = decayStart.n;
+    }
+    if (decayGoogle && progress > start) {
+      progress = progress - start + decayGoogle.n; // shift so that we get 1 at progress==start
+      return (float)(std::sqrt((double)decayGoogle.n / (double)progress));
+    }
+    else
+      return 1.f;
+  }
+
+  // determine the dynamically adjusted learning rate, incl. warm-up and decay
+  float getLearningRate(const TrainingState& state) const {
     float baselr = options_->get<float>("learn-rate");
 
     float mult1 = 1.f;
@@ -29,11 +51,7 @@ class Scheduler : public TrainingObserver {
       mult1 = std::min(1.f, (float)bno / (float)warmup.n);
     }
 
-    float mult2 = 1.f;
-    auto decayGoogle = SchedulingParameter::parse(options_->get<std::string>("lr-decay-inv-sqrt"));
-    if(decayGoogle) {
-      mult2 = std::min(1.f, (float)(std::sqrt(decayGoogle.n) / std::sqrt(state.getProgressIn(decayGoogle.unit))));
-    }
+    float mult2 = getLearningRateDecayFactor(state);
 
     baselr = baselr * mult1 * mult2;
 
@@ -45,6 +63,54 @@ class Scheduler : public TrainingObserver {
   }
 
 public:
+  void setTypicalTrgBatchWords(size_t typicalTrgBatchWords) { // needed for tryGetDynamicMBSizeMultiplier()
+    typicalTrgBatchWords_ = typicalTrgBatchWords;
+    LOG(info, "batch size estimate is {} target words", typicalTrgBatchWords_);
+  }
+
+  // determine dynamic MB size, if respective parameters are given (return false if not)
+  bool tryGetDynamicMBSizeMultiplier(double /*out*/ &ratio) const {
+    auto mbWarmupOpts = options_->get<std::vector<std::string>>("mini-batch-warmup");
+    ABORT_IF(mbWarmupOpts.empty() || mbWarmupOpts.size() > 2, "--mini-batch-warmup argument must be one or two numbers with units");
+    auto mbWarmup = SchedulingParameter::parse(mbWarmupOpts[0]);
+    if (!mbWarmup)
+      return false;
+
+    ratio = 1.0;
+    // mini-batch-warmup
+    LOG_ONCE(info, "[scheduler] Mini-batch size warmup {}", std::string(mbWarmup));
+
+    // This scales MB size up from the start.
+    // now scale batch size relative to progress within warm-up period
+    size_t progress = state_->getProgressIn(mbWarmup.unit); // number of updates/labels processed
+    auto progressRatio = (double)progress / (double)mbWarmup.n; // where are we relatively within target warm-up period
+    if (mbWarmup.unit == SchedulingUnit::trgLabels)
+      progressRatio = std::sqrt(progressRatio);
+    // apply ratio to actual batch size
+    ratio *= progressRatio;
+
+    // adjust for reference batch size if given
+    // At progress == mbWarmup.n (ratio=1), we would like to have refBatchLabels instead of whichever
+    // the actual batch size is. We approximate the latter as typicalTrgBatchWords_, and scale ratio accordingly.
+    if (mbWarmupOpts.size() > 1) {
+      ABORT_IF(typicalTrgBatchWords_ == 0, "dynamic scaling with words target requires MB size to be known in words"); // happens if MB size is specified in sentences
+      auto refBatchLabels = (size_t)std::stoull(mbWarmupOpts[1]);
+      LOG_ONCE(info, "[scheduler] Scaling to {} reference labels. Typical actual batch words is {}", refBatchLabels, typicalTrgBatchWords_);
+      ratio *= (double)refBatchLabels / (double)typicalTrgBatchWords_;
+    }
+
+    // dynamic MB-size tracking with learning rate
+    // As LR goes down, MB gets ramped up by the same ratio, which has been found to be safe.
+    auto mbTracking = options_->get<bool>("mini-batch-track-lr");
+    if (mbTracking) {
+      auto lrFactor = getLearningRateDecayFactor(*state_);
+      if (lrFactor != 1)
+        LOG_ONCE(info, "[scheduler] Dynamic mini-batch size adjustment enabled and kicking in");
+      ratio /= lrFactor;
+    }
+    return true;
+  }
+
   Scheduler(Ptr<Options> options, Ptr<TrainingState> state)
       : options_(options), state_(state) {
     state_->eta = getLearningRate(*state);
@@ -120,12 +186,13 @@ class Scheduler : public TrainingObserver {
       float value = validator->validate(graphs);
       if(validator->stalled() > 0) {
         LOG_VALID(info,
-                  "Ep. {} : Up. {} : {} : {} : stalled {} times",
+                  "Ep. {} : Up. {} : {} : {} : stalled {} times (last best: {})",
                   state_->epochs,
                   state_->batches,
                   validator->type(),
                   value,
-                  validator->stalled());
+                  validator->stalled(),
+                  validator->lastBest());
       } else {
         LOG_VALID(info,
                   "Ep. {} : Up. {} : {} : {} : new best",
@@ -170,13 +237,12 @@ class Scheduler : public TrainingObserver {
     size_t batchLabels = 0;  // number of target words in batch
 
     for(const auto& batch : batches) {
-      if (batch) { // (nullptr is allowed as result of split)
-        batchSize += batch->size();
-        batchLabels += batch->words(-1);
-      }
+      batchSize += batch->size();
+      batchLabels += batch->words(-1);
     }
 
-    // extrapolate cost across MPI processes, so that we have numbers in the right range
+    // Since batchLabels is counted across all MPI processes, we also should temporarily
+    // extrapolate cost across MPI processes, to have numbers in the right range.
     // When doing the actual log, we then aggregate across MPI processes to get the accurate number.
     if (mpi)
       cost *= mpi->numMPIProcesses(); // @BUGBUG: this is presently correct for ce-sum, but possibly not the av-based losses
@@ -203,42 +269,42 @@ class Scheduler : public TrainingObserver {
     state_->samplesEpoch += batchSize;   // sentences processed in this epoch
     state_->labelsTotal  += batchLabels; // total labels processed
 
-    state_->newBatch();
+    state_->newUpdate(batches.size());
 
     if(state_->enteredNewPeriodOf(options_->get<std::string>("disp-freq")) ||
        state_->batches <= options_->get<size_t>("disp-first")) {
       // if MPI then aggregate precise cost across workers
       if (mpi) {
-        //LOG(info, "all-reducing cost from {}", state_->costSum);
         state_->costSum /= mpi->numMPIProcesses(); // undo the extra scaling
         mpi->allReduce(&state_->costSum, &state_->costSum, 1, MPI_FLOAT, MPI_SUM);
-        //LOG(info, "all-reduced cost to {}", state_->costSum);
       }
       if (mpi && mpi->myMPIRank() != 0)
         ; // skip the report on alternate worker processes
       else if(dispLabelCounts) {
         if(options_->get<bool>("lr-report")) {  // if true then show the learning rate
           LOG(info,
-              "Ep. {} : Up. {} : Sen. {} : Cost {:.8f} * {} after {} : Time {:.2f}s : {:.2f} "
+              "Ep. {} : Up. {} : Sen. {} : Cost {:.8f} * {} @ {} after {} : Time {:.2f}s : {:.2f} "
               "words/s : L.r. {:.4e}",
               state_->epochs,
               state_->batches,
               utils::withCommas(state_->samplesEpoch),
               state_->costSum / state_->costCount,
               utils::withCommas(state_->costCount),  // show cost as "av * count"
+              batchLabels,
               utils::withCommas(state_->labelsTotal),
               timer_.elapsed(),
               state_->wordsDisp / timer_.elapsed(),
               state_->eta);
         } else {
           LOG(info,
-              "Ep. {} : Up. {} : Sen. {} : Cost {:.8f} * {} after {} : Time {:.2f}s : {:.2f} "
+              "Ep. {} : Up. {} : Sen. {} : Cost {:.8f} * {} @ {} after {} : Time {:.2f}s : {:.2f} "
               "words/s",
               state_->epochs,
               state_->batches,
               utils::withCommas(state_->samplesEpoch),
               state_->costSum / state_->costCount,
               utils::withCommas(state_->costCount),
+              batchLabels,
               utils::withCommas(state_->labelsTotal),
               timer_.elapsed(),
               state_->wordsDisp / timer_.elapsed());
@@ -272,12 +338,14 @@ class Scheduler : public TrainingObserver {
     }
     // progress heartbeat for MS-internal Philly compute cluster
     // This environment variable exists when running on the cluster.
+    using namespace std::chrono;
     if((!mpi || mpi->myMPIRank() == 0) && getenv("PHILLY_JOB_ID")
        && heartBeatTimer_.elapsed<std::chrono::minutes>() >= 10) {
-      printf("PROGRESS: %.2f%%\nEVALERR: %.7f\n", (double)state_->epochs, state_->costSum / state_->costCount), fflush(stdout);
-#if 0
-      LOG(info, "heart beat after {} updates", state_->batches);
-#endif
+      printf("PROGRESS: %.2f%%\nEVALERR: %.7f%%\n",
+          (double)state_->epochs,
+          state_->costSum / state_->costCount / (mpi ? mpi->numMPIProcesses() : 1));
+      fflush(stdout);
+      std::cout << "MBSIZE: " << batchLabels << " after " << state_->batches << " updates = " << state_->labelsTotal << " labels" << std::endl << std::flush;
       heartBeatTimer_.start();
     }
   }
diff --git a/src/training/training.h b/src/training/training.h
index 2209b96e2..174768542 100755
--- a/src/training/training.h
+++ b/src/training/training.h
@@ -56,7 +56,9 @@ class Train : public ModelTask {
     }
 
     auto batchGenerator = New<CorpusBatchGenerator>(dataset, options_, stats);
+
     scheduler->registerTrainingObserver(batchGenerator);
+    scheduler->setTypicalTrgBatchWords(batchGenerator->estimateTypicalTrgBatchWords()); // needed for dynamic MB scaling
 
     auto model = New<ModelWrapper>(options_);
     model->setScheduler(scheduler);
@@ -85,12 +87,14 @@ class Train : public ModelTask {
     }
     scheduler->finished();
 
-    model->finalize();
-
     // Avoid saving the model twice if it has been loaded and training did not
     // progress
     if(!trainState->loaded)
       model->save(true);
+
+    // finalize, including communicating successful completion to MPI
+    // @BUGBUG: This is wrong for async, but needed for sync. How to solve it?
+    model->finalize();
   }
 };
 }  // namespace marian
diff --git a/src/training/training_state.h b/src/training/training_state.h
index 2deefe471..65cefcbce 100755
--- a/src/training/training_state.h
+++ b/src/training/training_state.h
@@ -2,6 +2,7 @@
 
 #include "common/definitions.h"
 #include "common/filesystem.h"
+#include "common/utils.h"
 
 #include <fstream>
 #include <vector>
@@ -42,7 +43,9 @@ struct SchedulingParameter {
       }
       param.pop_back();
     }
-    res.n = (size_t)std::stoull(param);
+    double number = utils::parseNumber(param);
+    res.n = (size_t)number;
+    ABORT_IF(number != (double)res.n, "Scheduling parameters must be whole numbers");
     return res;
   }
 
@@ -62,9 +65,9 @@ class TrainingState {
 public:
   // Current epoch
   size_t epochs{1};
-  // The total number of batches (=updates) processed since beginning of training   --@TODO: rename to 'updates'
+  // The total number of updates since beginning of training   --@TODO: rename to 'updates'
   size_t batches{0};
-  // The number of batches seen in this epoch  --@TODO: rename to 'updatesEpoch' or 'updatesInCurrentEpoch'
+  // The number of batches seen in this epoch  --note: not updates; an update can consist of multiple batches
   size_t batchesEpoch{0};
   // The number of sentences seen in this epoch  --@TODO: rename to 'sentencesEpoch'
   size_t samplesEpoch{0};
@@ -172,9 +175,9 @@ class TrainingState {
     batchesEpoch = 0;
   }
 
-  void newBatch() {
+  void newUpdate(size_t batchesInUpdate) {
     ++batches;
-    ++batchesEpoch;
+    batchesEpoch += batchesInUpdate;
     loaded = false;
     validated = false;
     for(auto observer : observers_)
diff --git a/src/training/validator.h b/src/training/validator.h
index efed50cb0..682495410 100755
--- a/src/training/validator.h
+++ b/src/training/validator.h
@@ -137,7 +137,7 @@ class Validator : public ValidatorBase {
       lastBest_ = val;
       if(options_->get<bool>("keep-best"))
         keepBest(graphs);
-    } else {
+    } else if (lastBest_ != val) { // (special case 0 at start)  @TODO: needed? Seems stall count gets reset each time it does improve. If not needed, remove "if(...)" again.
       stalled_++;
     }
   }
@@ -166,7 +166,6 @@ class CrossEntropyValidator : public Validator<data::Corpus> {
 
 protected:
   virtual float validateBG(const std::vector<Ptr<ExpressionGraph>>& graphs) override {
-
     auto ctype = options_->get<std::string>("cost-type");
     options_->set("cost-type", "ce-sum");
 
diff --git a/vs/Marian.sln b/vs/Marian.sln
index c2eb49c99..9cbbcefb7 100755
--- a/vs/Marian.sln
+++ b/vs/Marian.sln
@@ -1,377 +1,25 @@
+
 Microsoft Visual Studio Solution File, Format Version 12.00
 # Visual Studio 15
-Project("{8BC9CEB8-8B4A-11D0-8D11-00A0C91BC942}") = "ALL_BUILD", "ALL_BUILD.vcxproj", "{5216F769-E887-369E-AD1E-D6A1F69E834E}"
-	ProjectSection(ProjectDependencies) = postProject
-		{17E8F84B-76CD-326B-B50A-C4F3C3A8CE33} = {17E8F84B-76CD-326B-B50A-C4F3C3A8CE33}
-		{806A44E1-15D4-3368-B0B9-2A6CC352D505} = {806A44E1-15D4-3368-B0B9-2A6CC352D505}
-		{5AF43E07-5917-3D8F-9BF0-B41F698242EA} = {5AF43E07-5917-3D8F-9BF0-B41F698242EA}
-		{885D3D2B-7278-30EF-BB1B-50E83D1635C4} = {885D3D2B-7278-30EF-BB1B-50E83D1635C4}
-		{3CD61EAE-244E-33AB-8C7D-F5182481E033} = {3CD61EAE-244E-33AB-8C7D-F5182481E033}
-		{97131187-E592-3981-886F-222EE20FB669} = {97131187-E592-3981-886F-222EE20FB669}
-		{25A05D30-AFC2-3F0E-B475-0B2B81530151} = {25A05D30-AFC2-3F0E-B475-0B2B81530151}
-		{8A6B1F60-8E2D-3171-828B-07E732C8E7D7} = {8A6B1F60-8E2D-3171-828B-07E732C8E7D7}
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-		{EA3973A2-F92E-3124-9817-81B2458EC8DC} = {EA3973A2-F92E-3124-9817-81B2458EC8DC}
-		{36953645-6D01-37E4-ACF7-D3F9BFFCA49D} = {36953645-6D01-37E4-ACF7-D3F9BFFCA49D}
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-		{5857EF98-C87F-3197-A399-F0F9A20913FC} = {5857EF98-C87F-3197-A399-F0F9A20913FC}
-		{F6E7B14E-D9E6-343C-B58D-CA0381A3BB8F} = {F6E7B14E-D9E6-343C-B58D-CA0381A3BB8F}
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-	ProjectSection(ProjectDependencies) = postProject
-		{5216F769-E887-369E-AD1E-D6A1F69E834E} = {5216F769-E887-369E-AD1E-D6A1F69E834E}
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-	ProjectSection(ProjectDependencies) = postProject
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-	ProjectSection(ProjectDependencies) = postProject
-	EndProjectSection
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-	ProjectSection(ProjectDependencies) = postProject
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-	ProjectSection(ProjectDependencies) = postProject
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-	ProjectSection(ProjectDependencies) = postProject
-		{806A44E1-15D4-3368-B0B9-2A6CC352D505} = {806A44E1-15D4-3368-B0B9-2A6CC352D505}
-		{885D3D2B-7278-30EF-BB1B-50E83D1635C4} = {885D3D2B-7278-30EF-BB1B-50E83D1635C4}
-		{97131187-E592-3981-886F-222EE20FB669} = {97131187-E592-3981-886F-222EE20FB669}
-		{D9D20410-4011-370C-8E15-A6F5C311F337} = {D9D20410-4011-370C-8E15-A6F5C311F337}
-		{4A20AD5F-7334-31D3-B31D-9AAF53CC6678} = {4A20AD5F-7334-31D3-B31D-9AAF53CC6678}
-	EndProjectSection
-EndProject
-Project("{8BC9CEB8-8B4A-11D0-8D11-00A0C91BC942}") = "marian_cuda", "src\marian_cuda.vcxproj", "{97131187-E592-3981-886F-222EE20FB669}"
-	ProjectSection(ProjectDependencies) = postProject
-		{806A44E1-15D4-3368-B0B9-2A6CC352D505} = {806A44E1-15D4-3368-B0B9-2A6CC352D505}
-	EndProjectSection
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-Project("{8BC9CEB8-8B4A-11D0-8D11-00A0C91BC942}") = "marian_decoder", "src\marian_decoder.vcxproj", "{25A05D30-AFC2-3F0E-B475-0B2B81530151}"
-	ProjectSection(ProjectDependencies) = postProject
-		{806A44E1-15D4-3368-B0B9-2A6CC352D505} = {806A44E1-15D4-3368-B0B9-2A6CC352D505}
-		{885D3D2B-7278-30EF-BB1B-50E83D1635C4} = {885D3D2B-7278-30EF-BB1B-50E83D1635C4}
-		{97131187-E592-3981-886F-222EE20FB669} = {97131187-E592-3981-886F-222EE20FB669}
-		{D9D20410-4011-370C-8E15-A6F5C311F337} = {D9D20410-4011-370C-8E15-A6F5C311F337}
-		{4A20AD5F-7334-31D3-B31D-9AAF53CC6678} = {4A20AD5F-7334-31D3-B31D-9AAF53CC6678}
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diff --git a/vs/Marian.vcxproj b/vs/Marian.vcxproj
index bebb987bf..15486147a 100755
--- a/vs/Marian.vcxproj
+++ b/vs/Marian.vcxproj
@@ -76,7 +76,7 @@
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       <SubSystem>Console</SubSystem>
       <GenerateDebugInformation>true</GenerateDebugInformation>
-      <AdditionalDependencies>zlib.lib; msmpi.lib; mkl_intel_ilp64.lib; mkl_sequential.lib; mkl_core.lib; kernel32.lib; user32.lib; gdi32.lib; winspool.lib; comdlg32.lib; advapi32.lib; shell32.lib; ole32.lib; oleaut32.lib; uuid.lib; odbc32.lib; odbccp32.lib; %(AdditionalDependencies)</AdditionalDependencies>
+      <AdditionalDependencies>zlib.lib;msmpi.lib;mkl_intel_ilp64.lib;mkl_sequential.lib;mkl_core.lib;kernel32.lib;user32.lib;gdi32.lib;winspool.lib;comdlg32.lib;advapi32.lib;shell32.lib;ole32.lib;oleaut32.lib;uuid.lib;odbc32.lib;odbccp32.lib;shlwapi.lib;%(AdditionalDependencies)</AdditionalDependencies>
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       <TreatLinkerWarningAsErrors>true</TreatLinkerWarningAsErrors>
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@@ -106,13 +106,20 @@
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       <OptimizeReferences>true</OptimizeReferences>
-      <AdditionalDependencies>zlib.lib; msmpi.lib; mkl_intel_ilp64.lib; mkl_sequential.lib; mkl_core.lib; kernel32.lib; user32.lib; gdi32.lib; winspool.lib; comdlg32.lib; advapi32.lib; shell32.lib; ole32.lib; oleaut32.lib; uuid.lib; odbc32.lib; odbccp32.lib; %(AdditionalDependencies)</AdditionalDependencies>
+      <AdditionalDependencies>zlib.lib;msmpi.lib;mkl_intel_ilp64.lib;mkl_sequential.lib;mkl_core.lib;kernel32.lib;user32.lib;gdi32.lib;winspool.lib;comdlg32.lib;advapi32.lib;shell32.lib;ole32.lib;oleaut32.lib;uuid.lib;odbc32.lib;odbccp32.lib;shlwapi.lib;%(AdditionalDependencies)</AdditionalDependencies>
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       <TreatLinkerWarningAsErrors>true</TreatLinkerWarningAsErrors>
     </Link>
   </ItemDefinitionGroup>
   <ItemGroup>
     <ClCompile Include="..\src\3rd_party\ExceptionWithCallStack.cpp" />
+    <ClCompile Include="..\src\3rd_party\pathie-cpp\src\entry_iterator.cpp" />
+    <ClCompile Include="..\src\3rd_party\pathie-cpp\src\errors.cpp" />
+    <ClCompile Include="..\src\3rd_party\pathie-cpp\src\path.cpp" />
+    <ClCompile Include="..\src\3rd_party\pathie-cpp\src\pathie.cpp" />
+    <ClCompile Include="..\src\3rd_party\pathie-cpp\src\pathie_ifstream.cpp" />
+    <ClCompile Include="..\src\3rd_party\pathie-cpp\src\pathie_ofstream.cpp" />
+    <ClCompile Include="..\src\3rd_party\pathie-cpp\src\temp.cpp" />
     <ClCompile Include="..\src\3rd_party\sentencepiece\src\bpe_model.cc">
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@@ -316,6 +323,103 @@
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     <ClCompile Include="..\src\3rd_party\yaml-cpp\yaml-node.cpp" />
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+    </ClInclude>
+    <ClInclude Include="..\src\3rd_party\nccl\src\collectives\device\all_gather.h">
+      <ExcludedFromBuild Condition="'$(Configuration)|$(Platform)'=='Release|x64'">true</ExcludedFromBuild>
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+    <ClInclude Include="..\src\3rd_party\nccl\src\collectives\device\all_reduce.h">
+      <ExcludedFromBuild Condition="'$(Configuration)|$(Platform)'=='Release|x64'">true</ExcludedFromBuild>
+    </ClInclude>
+    <ClInclude Include="..\src\3rd_party\nccl\src\collectives\device\broadcast.h">
+      <ExcludedFromBuild Condition="'$(Configuration)|$(Platform)'=='Release|x64'">true</ExcludedFromBuild>
+    </ClInclude>
+    <ClInclude Include="..\src\3rd_party\nccl\src\collectives\device\common.h">
+      <ExcludedFromBuild Condition="'$(Configuration)|$(Platform)'=='Release|x64'">true</ExcludedFromBuild>
+    </ClInclude>
+    <ClInclude Include="..\src\3rd_party\nccl\src\collectives\device\common_kernel.h">
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+    </ClInclude>
+    <ClInclude Include="..\src\3rd_party\nccl\src\collectives\device\ll_kernel.h">
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+    <ClInclude Include="..\src\3rd_party\nccl\src\collectives\device\primitives.h">
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+    <ClInclude Include="..\src\3rd_party\pathie-cpp\include\pathie.hpp" />
+    <ClInclude Include="..\src\3rd_party\pathie-cpp\include\pathie_ifstream.hpp" />
+    <ClInclude Include="..\src\3rd_party\pathie-cpp\include\pathie_ofstream.hpp" />
+    <ClInclude Include="..\src\3rd_party\pathie-cpp\include\temp.hpp" />
     <ClInclude Include="..\src\3rd_party\sentencepiece\src\bpe_model.h">
       <ExcludedFromBuild Condition="'$(Configuration)|$(Platform)'=='Debug|x64'">true</ExcludedFromBuild>
       <ExcludedFromBuild Condition="'$(Configuration)|$(Platform)'=='Release|x64'">true</ExcludedFromBuild>
@@ -459,7 +563,6 @@
     <ClCompile Include="..\src\data\corpus_nbest.cpp" />
     <ClCompile Include="..\src\data\text_input.cpp" />
     <ClCompile Include="..\src\3rd_party\cnpy\cnpy.cpp" />
-    <ClCompile Include="..\src\3rd_party\svd\svd.cpp" />
     <ClCompile Include="..\src\layers\loss.cpp" />
     <ClCompile Include="..\src\layers\weight.cpp" />
     <ClCompile Include="..\src\microsoft\quicksand.cpp">
@@ -620,8 +723,6 @@
     <ClInclude Include="..\src\3rd_party\spdlog\tests\catch.hpp" />
     <ClInclude Include="..\src\3rd_party\spdlog\tests\includes.h" />
     <ClInclude Include="..\src\3rd_party\spdlog\tests\utils.h" />
-    <ClInclude Include="..\src\3rd_party\svd\defs_and_types.h" />
-    <ClInclude Include="..\src\3rd_party\svd\svd.h" />
     <ClInclude Include="..\src\3rd_party\yaml-cpp\anchor.h" />
     <ClInclude Include="..\src\3rd_party\yaml-cpp\binary.h" />
     <ClInclude Include="..\src\3rd_party\yaml-cpp\collectionstack.h" />
@@ -738,7 +839,6 @@
     <ClInclude Include="..\src\models\decoder.h" />
     <ClInclude Include="..\src\models\encoder.h" />
     <ClInclude Include="..\src\models\encoder_decoder.h" />
-    <ClInclude Include="..\src\models\hardatt.h" />
     <ClInclude Include="..\src\models\model_base.h" />
     <ClInclude Include="..\src\models\model_factory.h" />
     <ClInclude Include="..\src\models\model_task.h" />
@@ -808,6 +908,96 @@
     <ClInclude Include="..\src\translator\translator.h" />
   </ItemGroup>
   <ItemGroup>
+    <None Include="..\src\3rd_party\nccl\src\bootstrap.cu">
+      <ExcludedFromBuild Condition="'$(Configuration)|$(Platform)'=='Release|x64'">true</ExcludedFromBuild>
+    </None>
+    <None Include="..\src\3rd_party\nccl\src\collectives\all_gather.cu">
+      <ExcludedFromBuild Condition="'$(Configuration)|$(Platform)'=='Release|x64'">true</ExcludedFromBuild>
+    </None>
+    <None Include="..\src\3rd_party\nccl\src\collectives\all_reduce.cu">
+      <ExcludedFromBuild Condition="'$(Configuration)|$(Platform)'=='Release|x64'">true</ExcludedFromBuild>
+    </None>
+    <None Include="..\src\3rd_party\nccl\src\collectives\broadcast.cu">
+      <ExcludedFromBuild Condition="'$(Configuration)|$(Platform)'=='Release|x64'">true</ExcludedFromBuild>
+    </None>
+    <None Include="..\src\3rd_party\nccl\src\collectives\device\all_gather.cu">
+      <ExcludedFromBuild Condition="'$(Configuration)|$(Platform)'=='Release|x64'">true</ExcludedFromBuild>
+    </None>
+    <None Include="..\src\3rd_party\nccl\src\collectives\device\all_reduce.cu">
+      <ExcludedFromBuild Condition="'$(Configuration)|$(Platform)'=='Release|x64'">true</ExcludedFromBuild>
+    </None>
+    <None Include="..\src\3rd_party\nccl\src\collectives\device\broadcast.cu">
+      <ExcludedFromBuild Condition="'$(Configuration)|$(Platform)'=='Release|x64'">true</ExcludedFromBuild>
+    </None>
+    <None Include="..\src\3rd_party\nccl\src\collectives\device\functions.cu">
+      <ExcludedFromBuild Condition="'$(Configuration)|$(Platform)'=='Release|x64'">true</ExcludedFromBuild>
+    </None>
+    <None Include="..\src\3rd_party\nccl\src\collectives\device\Makefile">
+      <ExcludedFromBuild Condition="'$(Configuration)|$(Platform)'=='Release|x64'">true</ExcludedFromBuild>
+    </None>
+    <None Include="..\src\3rd_party\nccl\src\collectives\device\reduce.cu">
+      <ExcludedFromBuild Condition="'$(Configuration)|$(Platform)'=='Release|x64'">true</ExcludedFromBuild>
+    </None>
+    <None Include="..\src\3rd_party\nccl\src\collectives\device\reduce_scatter.cu">
+      <ExcludedFromBuild Condition="'$(Configuration)|$(Platform)'=='Release|x64'">true</ExcludedFromBuild>
+    </None>
+    <None Include="..\src\3rd_party\nccl\src\collectives\reduce.cu">
+      <ExcludedFromBuild Condition="'$(Configuration)|$(Platform)'=='Release|x64'">true</ExcludedFromBuild>
+    </None>
+    <None Include="..\src\3rd_party\nccl\src\collectives\reduce_scatter.cu">
+      <ExcludedFromBuild Condition="'$(Configuration)|$(Platform)'=='Release|x64'">true</ExcludedFromBuild>
+    </None>
+    <None Include="..\src\3rd_party\nccl\src\init.cu">
+      <ExcludedFromBuild Condition="'$(Configuration)|$(Platform)'=='Release|x64'">true</ExcludedFromBuild>
+    </None>
+    <None Include="..\src\3rd_party\nccl\src\Makefile">
+      <ExcludedFromBuild Condition="'$(Configuration)|$(Platform)'=='Release|x64'">true</ExcludedFromBuild>
+    </None>
+    <None Include="..\src\3rd_party\nccl\src\misc\enqueue.cu">
+      <ExcludedFromBuild Condition="'$(Configuration)|$(Platform)'=='Release|x64'">true</ExcludedFromBuild>
+    </None>
+    <None Include="..\src\3rd_party\nccl\src\misc\group.cu">
+      <ExcludedFromBuild Condition="'$(Configuration)|$(Platform)'=='Release|x64'">true</ExcludedFromBuild>
+    </None>
+    <None Include="..\src\3rd_party\nccl\src\misc\ibvwrap.cu">
+      <ExcludedFromBuild Condition="'$(Configuration)|$(Platform)'=='Release|x64'">true</ExcludedFromBuild>
+    </None>
+    <None Include="..\src\3rd_party\nccl\src\misc\nvmlwrap.cu">
+      <ExcludedFromBuild Condition="'$(Configuration)|$(Platform)'=='Release|x64'">true</ExcludedFromBuild>
+    </None>
+    <None Include="..\src\3rd_party\nccl\src\misc\rings.cu">
+      <ExcludedFromBuild Condition="'$(Configuration)|$(Platform)'=='Release|x64'">true</ExcludedFromBuild>
+    </None>
+    <None Include="..\src\3rd_party\nccl\src\misc\utils.cu">
+      <ExcludedFromBuild Condition="'$(Configuration)|$(Platform)'=='Release|x64'">true</ExcludedFromBuild>
+    </None>
+    <None Include="..\src\3rd_party\nccl\src\nccl.h.in">
+      <ExcludedFromBuild Condition="'$(Configuration)|$(Platform)'=='Release|x64'">true</ExcludedFromBuild>
+    </None>
+    <None Include="..\src\3rd_party\nccl\src\ring.cu">
+      <ExcludedFromBuild Condition="'$(Configuration)|$(Platform)'=='Release|x64'">true</ExcludedFromBuild>
+    </None>
+    <None Include="..\src\3rd_party\nccl\src\transport.cu">
+      <ExcludedFromBuild Condition="'$(Configuration)|$(Platform)'=='Release|x64'">true</ExcludedFromBuild>
+    </None>
+    <None Include="..\src\3rd_party\nccl\src\transport\net.cu">
+      <ExcludedFromBuild Condition="'$(Configuration)|$(Platform)'=='Release|x64'">true</ExcludedFromBuild>
+    </None>
+    <None Include="..\src\3rd_party\nccl\src\transport\net_ib.cu">
+      <ExcludedFromBuild Condition="'$(Configuration)|$(Platform)'=='Release|x64'">true</ExcludedFromBuild>
+    </None>
+    <None Include="..\src\3rd_party\nccl\src\transport\net_socket.cu">
+      <ExcludedFromBuild Condition="'$(Configuration)|$(Platform)'=='Release|x64'">true</ExcludedFromBuild>
+    </None>
+    <None Include="..\src\3rd_party\nccl\src\transport\p2p.cu">
+      <ExcludedFromBuild Condition="'$(Configuration)|$(Platform)'=='Release|x64'">true</ExcludedFromBuild>
+    </None>
+    <None Include="..\src\3rd_party\nccl\src\transport\shm.cu">
+      <ExcludedFromBuild Condition="'$(Configuration)|$(Platform)'=='Release|x64'">true</ExcludedFromBuild>
+    </None>
+    <None Include="..\src\3rd_party\pathie-cpp\CHANGELOG" />
+    <None Include="..\src\3rd_party\pathie-cpp\LICENSE" />
+    <None Include="..\src\3rd_party\pathie-cpp\README.md" />
     <None Include="..\src\3rd_party\sentencepiece\src\sentencepiece.proto">
       <ExcludedFromBuild Condition="'$(Configuration)|$(Platform)'=='Debug|x64'">true</ExcludedFromBuild>
       <ExcludedFromBuild Condition="'$(Configuration)|$(Platform)'=='Release|x64'">true</ExcludedFromBuild>
@@ -846,6 +1036,7 @@
     <None Include=".editorConfig" />
   </ItemGroup>
   <ItemGroup>
+    <Text Include="..\src\3rd_party\pathie-cpp\CMakeLists.txt" />
     <Text Include="..\src\3rd_party\sentencepiece\src\CMakeLists.txt">
       <ExcludedFromBuild Condition="'$(Configuration)|$(Platform)'=='Debug|x64'">true</ExcludedFromBuild>
       <ExcludedFromBuild Condition="'$(Configuration)|$(Platform)'=='Release|x64'">true</ExcludedFromBuild>
diff --git a/vs/Marian.vcxproj.filters b/vs/Marian.vcxproj.filters
index 12aa5b4e4..ce6d8441b 100755
--- a/vs/Marian.vcxproj.filters
+++ b/vs/Marian.vcxproj.filters
@@ -31,9 +31,6 @@
     <ClCompile Include="..\src\3rd_party\cnpy\cnpy.cpp">
       <Filter>3rd_party\cnpy</Filter>
     </ClCompile>
-    <ClCompile Include="..\src\3rd_party\svd\svd.cpp">
-      <Filter>3rd_party\svd</Filter>
-    </ClCompile>
     <ClCompile Include="..\src\tensors\backend.cpp">
       <Filter>tensors</Filter>
     </ClCompile>
@@ -421,8 +418,26 @@
     <ClCompile Include="..\src\rescorer\score_collector.cpp">
       <Filter>rescorer</Filter>
     </ClCompile>
-    <ClCompile Include="..\src\command\marian_train.cpp">
-      <Filter>command</Filter>
+    <ClCompile Include="..\src\3rd_party\pathie-cpp\src\entry_iterator.cpp">
+      <Filter>3rd_party\pathie-cpp\src</Filter>
+    </ClCompile>
+    <ClCompile Include="..\src\3rd_party\pathie-cpp\src\errors.cpp">
+      <Filter>3rd_party\pathie-cpp\src</Filter>
+    </ClCompile>
+    <ClCompile Include="..\src\3rd_party\pathie-cpp\src\path.cpp">
+      <Filter>3rd_party\pathie-cpp\src</Filter>
+    </ClCompile>
+    <ClCompile Include="..\src\3rd_party\pathie-cpp\src\pathie.cpp">
+      <Filter>3rd_party\pathie-cpp\src</Filter>
+    </ClCompile>
+    <ClCompile Include="..\src\3rd_party\pathie-cpp\src\pathie_ifstream.cpp">
+      <Filter>3rd_party\pathie-cpp\src</Filter>
+    </ClCompile>
+    <ClCompile Include="..\src\3rd_party\pathie-cpp\src\pathie_ofstream.cpp">
+      <Filter>3rd_party\pathie-cpp\src</Filter>
+    </ClCompile>
+    <ClCompile Include="..\src\3rd_party\pathie-cpp\src\temp.cpp">
+      <Filter>3rd_party\pathie-cpp\src</Filter>
     </ClCompile>
   </ItemGroup>
   <ItemGroup>
@@ -640,12 +655,6 @@
     <ClInclude Include="..\src\3rd_party\spdlog\tests\utils.h">
       <Filter>3rd_party\spdlog\tests</Filter>
     </ClInclude>
-    <ClInclude Include="..\src\3rd_party\svd\defs_and_types.h">
-      <Filter>3rd_party\svd</Filter>
-    </ClInclude>
-    <ClInclude Include="..\src\3rd_party\svd\svd.h">
-      <Filter>3rd_party\svd</Filter>
-    </ClInclude>
     <ClInclude Include="..\src\3rd_party\yaml-cpp\anchor.h">
       <Filter>3rd_party\yaml-cpp</Filter>
     </ClInclude>
@@ -988,9 +997,6 @@
     <ClInclude Include="..\src\models\encoder_decoder.h">
       <Filter>models</Filter>
     </ClInclude>
-    <ClInclude Include="..\src\models\hardatt.h">
-      <Filter>models</Filter>
-    </ClInclude>
     <ClInclude Include="..\src\models\model_base.h">
       <Filter>models</Filter>
     </ClInclude>
@@ -1345,6 +1351,118 @@
     <ClInclude Include="..\src\3rd_party\sentencepiece\src\word_model_trainer.h">
       <Filter>3rd_party\sentencepiece\src</Filter>
     </ClInclude>
+    <ClInclude Include="..\src\command\marian_train.cpp" />
+    <ClInclude Include="..\src\3rd_party\nccl\src\collectives\collectives.h">
+      <Filter>3rd_party\nccl\src\collectives</Filter>
+    </ClInclude>
+    <ClInclude Include="..\src\3rd_party\nccl\src\collectives\device\all_gather.h">
+      <Filter>3rd_party\nccl\src\collectives\device</Filter>
+    </ClInclude>
+    <ClInclude Include="..\src\3rd_party\nccl\src\collectives\device\all_reduce.h">
+      <Filter>3rd_party\nccl\src\collectives\device</Filter>
+    </ClInclude>
+    <ClInclude Include="..\src\3rd_party\nccl\src\collectives\device\broadcast.h">
+      <Filter>3rd_party\nccl\src\collectives\device</Filter>
+    </ClInclude>
+    <ClInclude Include="..\src\3rd_party\nccl\src\collectives\device\common.h">
+      <Filter>3rd_party\nccl\src\collectives\device</Filter>
+    </ClInclude>
+    <ClInclude Include="..\src\3rd_party\nccl\src\collectives\device\common_kernel.h">
+      <Filter>3rd_party\nccl\src\collectives\device</Filter>
+    </ClInclude>
+    <ClInclude Include="..\src\3rd_party\nccl\src\collectives\device\ll_kernel.h">
+      <Filter>3rd_party\nccl\src\collectives\device</Filter>
+    </ClInclude>
+    <ClInclude Include="..\src\3rd_party\nccl\src\collectives\device\primitives.h">
+      <Filter>3rd_party\nccl\src\collectives\device</Filter>
+    </ClInclude>
+    <ClInclude Include="..\src\3rd_party\nccl\src\collectives\device\reduce.h">
+      <Filter>3rd_party\nccl\src\collectives\device</Filter>
+    </ClInclude>
+    <ClInclude Include="..\src\3rd_party\nccl\src\collectives\device\reduce_kernel.h">
+      <Filter>3rd_party\nccl\src\collectives\device</Filter>
+    </ClInclude>
+    <ClInclude Include="..\src\3rd_party\nccl\src\collectives\device\reduce_scatter.h">
+      <Filter>3rd_party\nccl\src\collectives\device</Filter>
+    </ClInclude>
+    <ClInclude Include="..\src\3rd_party\nccl\src\include\bootstrap.h">
+      <Filter>3rd_party\nccl\src\include</Filter>
+    </ClInclude>
+    <ClInclude Include="..\src\3rd_party\nccl\src\include\common_coll.h">
+      <Filter>3rd_party\nccl\src\include</Filter>
+    </ClInclude>
+    <ClInclude Include="..\src\3rd_party\nccl\src\include\core.h">
+      <Filter>3rd_party\nccl\src\include</Filter>
+    </ClInclude>
+    <ClInclude Include="..\src\3rd_party\nccl\src\include\debug.h">
+      <Filter>3rd_party\nccl\src\include</Filter>
+    </ClInclude>
+    <ClInclude Include="..\src\3rd_party\nccl\src\include\enqueue.h">
+      <Filter>3rd_party\nccl\src\include</Filter>
+    </ClInclude>
+    <ClInclude Include="..\src\3rd_party\nccl\src\include\group.h">
+      <Filter>3rd_party\nccl\src\include</Filter>
+    </ClInclude>
+    <ClInclude Include="..\src\3rd_party\nccl\src\include\ibvwrap.h">
+      <Filter>3rd_party\nccl\src\include</Filter>
+    </ClInclude>
+    <ClInclude Include="..\src\3rd_party\nccl\src\include\nccl_net.h">
+      <Filter>3rd_party\nccl\src\include</Filter>
+    </ClInclude>
+    <ClInclude Include="..\src\3rd_party\nccl\src\include\net.h">
+      <Filter>3rd_party\nccl\src\include</Filter>
+    </ClInclude>
+    <ClInclude Include="..\src\3rd_party\nccl\src\include\nvlink.h">
+      <Filter>3rd_party\nccl\src\include</Filter>
+    </ClInclude>
+    <ClInclude Include="..\src\3rd_party\nccl\src\include\nvmlwrap.h">
+      <Filter>3rd_party\nccl\src\include</Filter>
+    </ClInclude>
+    <ClInclude Include="..\src\3rd_party\nccl\src\include\param.h">
+      <Filter>3rd_party\nccl\src\include</Filter>
+    </ClInclude>
+    <ClInclude Include="..\src\3rd_party\nccl\src\include\ring.h">
+      <Filter>3rd_party\nccl\src\include</Filter>
+    </ClInclude>
+    <ClInclude Include="..\src\3rd_party\nccl\src\include\rings.h">
+      <Filter>3rd_party\nccl\src\include</Filter>
+    </ClInclude>
+    <ClInclude Include="..\src\3rd_party\nccl\src\include\shm.h">
+      <Filter>3rd_party\nccl\src\include</Filter>
+    </ClInclude>
+    <ClInclude Include="..\src\3rd_party\nccl\src\include\socket.h">
+      <Filter>3rd_party\nccl\src\include</Filter>
+    </ClInclude>
+    <ClInclude Include="..\src\3rd_party\nccl\src\include\topo.h">
+      <Filter>3rd_party\nccl\src\include</Filter>
+    </ClInclude>
+    <ClInclude Include="..\src\3rd_party\nccl\src\include\transport.h">
+      <Filter>3rd_party\nccl\src\include</Filter>
+    </ClInclude>
+    <ClInclude Include="..\src\3rd_party\nccl\src\include\utils.h">
+      <Filter>3rd_party\nccl\src\include</Filter>
+    </ClInclude>
+    <ClInclude Include="..\src\3rd_party\pathie-cpp\include\entry_iterator.hpp">
+      <Filter>3rd_party\pathie-cpp\include</Filter>
+    </ClInclude>
+    <ClInclude Include="..\src\3rd_party\pathie-cpp\include\errors.hpp">
+      <Filter>3rd_party\pathie-cpp\include</Filter>
+    </ClInclude>
+    <ClInclude Include="..\src\3rd_party\pathie-cpp\include\path.hpp">
+      <Filter>3rd_party\pathie-cpp\include</Filter>
+    </ClInclude>
+    <ClInclude Include="..\src\3rd_party\pathie-cpp\include\pathie.hpp">
+      <Filter>3rd_party\pathie-cpp\include</Filter>
+    </ClInclude>
+    <ClInclude Include="..\src\3rd_party\pathie-cpp\include\pathie_ifstream.hpp">
+      <Filter>3rd_party\pathie-cpp\include</Filter>
+    </ClInclude>
+    <ClInclude Include="..\src\3rd_party\pathie-cpp\include\pathie_ofstream.hpp">
+      <Filter>3rd_party\pathie-cpp\include</Filter>
+    </ClInclude>
+    <ClInclude Include="..\src\3rd_party\pathie-cpp\include\temp.hpp">
+      <Filter>3rd_party\pathie-cpp\include</Filter>
+    </ClInclude>
   </ItemGroup>
   <ItemGroup>
     <Filter Include="3rd_party">
@@ -1386,9 +1504,6 @@
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     <Filter Include="3rd_party\sentencepiece\src">
       <UniqueIdentifier>{638bf0e1-4f83-4b37-9077-2be549d75909}</UniqueIdentifier>
     </Filter>
+    <Filter Include="3rd_party\nccl">
+      <UniqueIdentifier>{0ba105eb-79fb-4e2a-8940-f1ecebbcd4fe}</UniqueIdentifier>
+    </Filter>
+    <Filter Include="3rd_party\nccl\src">
+      <UniqueIdentifier>{fbc17f5e-3f10-44a9-b3ad-66ce12573174}</UniqueIdentifier>
+    </Filter>
+    <Filter Include="3rd_party\nccl\src\collectives">
+      <UniqueIdentifier>{c6036c35-5848-4fd5-b1a0-59e2042cbb69}</UniqueIdentifier>
+    </Filter>
+    <Filter Include="3rd_party\nccl\src\misc">
+      <UniqueIdentifier>{7b9a131d-9e0a-4c28-8a51-08232ff2e35e}</UniqueIdentifier>
+    </Filter>
+    <Filter Include="3rd_party\nccl\src\transport">
+      <UniqueIdentifier>{0bd9cca8-660b-46f6-aac6-691fb50245f0}</UniqueIdentifier>
+    </Filter>
+    <Filter Include="3rd_party\nccl\src\include">
+      <UniqueIdentifier>{2beba56f-5dda-4994-bef0-16170b6552b4}</UniqueIdentifier>
+    </Filter>
+    <Filter Include="3rd_party\nccl\src\collectives\device">
+      <UniqueIdentifier>{ac585624-4e66-42cd-8e4e-62cb90029610}</UniqueIdentifier>
+    </Filter>
+    <Filter Include="3rd_party\pathie-cpp">
+      <UniqueIdentifier>{825beb7c-2997-408b-af81-34ab5f14593a}</UniqueIdentifier>
+    </Filter>
+    <Filter Include="3rd_party\pathie-cpp\include">
+      <UniqueIdentifier>{db1dd5a2-f331-495d-9e3b-6dc1c01528ab}</UniqueIdentifier>
+    </Filter>
+    <Filter Include="3rd_party\pathie-cpp\src">
+      <UniqueIdentifier>{5d5ee615-192f-4b7f-bdfd-fb8316ceabc8}</UniqueIdentifier>
+    </Filter>
   </ItemGroup>
   <ItemGroup>
     <None Include=".editorConfig" />
@@ -1524,10 +1669,109 @@
     <None Include="..\src\3rd_party\sentencepiece\src\sentencepiece_model.proto">
       <Filter>3rd_party\sentencepiece\src</Filter>
     </None>
+    <None Include="..\src\3rd_party\nccl\src\bootstrap.cu">
+      <Filter>3rd_party\nccl\src</Filter>
+    </None>
+    <None Include="..\src\3rd_party\nccl\src\init.cu">
+      <Filter>3rd_party\nccl\src</Filter>
+    </None>
+    <None Include="..\src\3rd_party\nccl\src\Makefile">
+      <Filter>3rd_party\nccl\src</Filter>
+    </None>
+    <None Include="..\src\3rd_party\nccl\src\nccl.h.in">
+      <Filter>3rd_party\nccl\src</Filter>
+    </None>
+    <None Include="..\src\3rd_party\nccl\src\ring.cu">
+      <Filter>3rd_party\nccl\src</Filter>
+    </None>
+    <None Include="..\src\3rd_party\nccl\src\transport.cu">
+      <Filter>3rd_party\nccl\src</Filter>
+    </None>
+    <None Include="..\src\3rd_party\nccl\src\collectives\all_gather.cu">
+      <Filter>3rd_party\nccl\src\collectives</Filter>
+    </None>
+    <None Include="..\src\3rd_party\nccl\src\collectives\all_reduce.cu">
+      <Filter>3rd_party\nccl\src\collectives</Filter>
+    </None>
+    <None Include="..\src\3rd_party\nccl\src\collectives\broadcast.cu">
+      <Filter>3rd_party\nccl\src\collectives</Filter>
+    </None>
+    <None Include="..\src\3rd_party\nccl\src\collectives\reduce.cu">
+      <Filter>3rd_party\nccl\src\collectives</Filter>
+    </None>
+    <None Include="..\src\3rd_party\nccl\src\collectives\reduce_scatter.cu">
+      <Filter>3rd_party\nccl\src\collectives</Filter>
+    </None>
+    <None Include="..\src\3rd_party\nccl\src\collectives\device\all_gather.cu">
+      <Filter>3rd_party\nccl\src\collectives\device</Filter>
+    </None>
+    <None Include="..\src\3rd_party\nccl\src\collectives\device\all_reduce.cu">
+      <Filter>3rd_party\nccl\src\collectives\device</Filter>
+    </None>
+    <None Include="..\src\3rd_party\nccl\src\collectives\device\broadcast.cu">
+      <Filter>3rd_party\nccl\src\collectives\device</Filter>
+    </None>
+    <None Include="..\src\3rd_party\nccl\src\collectives\device\functions.cu">
+      <Filter>3rd_party\nccl\src\collectives\device</Filter>
+    </None>
+    <None Include="..\src\3rd_party\nccl\src\collectives\device\Makefile">
+      <Filter>3rd_party\nccl\src\collectives\device</Filter>
+    </None>
+    <None Include="..\src\3rd_party\nccl\src\collectives\device\reduce.cu">
+      <Filter>3rd_party\nccl\src\collectives\device</Filter>
+    </None>
+    <None Include="..\src\3rd_party\nccl\src\collectives\device\reduce_scatter.cu">
+      <Filter>3rd_party\nccl\src\collectives\device</Filter>
+    </None>
+    <None Include="..\src\3rd_party\nccl\src\misc\enqueue.cu">
+      <Filter>3rd_party\nccl\src\misc</Filter>
+    </None>
+    <None Include="..\src\3rd_party\nccl\src\misc\group.cu">
+      <Filter>3rd_party\nccl\src\misc</Filter>
+    </None>
+    <None Include="..\src\3rd_party\nccl\src\misc\ibvwrap.cu">
+      <Filter>3rd_party\nccl\src\misc</Filter>
+    </None>
+    <None Include="..\src\3rd_party\nccl\src\misc\nvmlwrap.cu">
+      <Filter>3rd_party\nccl\src\misc</Filter>
+    </None>
+    <None Include="..\src\3rd_party\nccl\src\misc\rings.cu">
+      <Filter>3rd_party\nccl\src\misc</Filter>
+    </None>
+    <None Include="..\src\3rd_party\nccl\src\misc\utils.cu">
+      <Filter>3rd_party\nccl\src\misc</Filter>
+    </None>
+    <None Include="..\src\3rd_party\nccl\src\transport\net.cu">
+      <Filter>3rd_party\nccl\src\transport</Filter>
+    </None>
+    <None Include="..\src\3rd_party\nccl\src\transport\net_ib.cu">
+      <Filter>3rd_party\nccl\src\transport</Filter>
+    </None>
+    <None Include="..\src\3rd_party\nccl\src\transport\net_socket.cu">
+      <Filter>3rd_party\nccl\src\transport</Filter>
+    </None>
+    <None Include="..\src\3rd_party\nccl\src\transport\p2p.cu">
+      <Filter>3rd_party\nccl\src\transport</Filter>
+    </None>
+    <None Include="..\src\3rd_party\nccl\src\transport\shm.cu">
+      <Filter>3rd_party\nccl\src\transport</Filter>
+    </None>
+    <None Include="..\src\3rd_party\pathie-cpp\CHANGELOG">
+      <Filter>3rd_party\pathie-cpp</Filter>
+    </None>
+    <None Include="..\src\3rd_party\pathie-cpp\LICENSE">
+      <Filter>3rd_party\pathie-cpp</Filter>
+    </None>
+    <None Include="..\src\3rd_party\pathie-cpp\README.md">
+      <Filter>3rd_party\pathie-cpp</Filter>
+    </None>
   </ItemGroup>
   <ItemGroup>
     <Text Include="..\src\3rd_party\sentencepiece\src\CMakeLists.txt">
       <Filter>3rd_party\sentencepiece\src</Filter>
     </Text>
+    <Text Include="..\src\3rd_party\pathie-cpp\CMakeLists.txt">
+      <Filter>3rd_party\pathie-cpp</Filter>
+    </Text>
   </ItemGroup>
 </Project>
\ No newline at end of file

From 9d3028599ebae1ba51e2323602be3eb7e89a0165 Mon Sep 17 00:00:00 2001
From: Roman Grundkiewicz <rgrundki@exseed.ed.ac.uk>
Date: Thu, 13 Dec 2018 09:48:58 +0000
Subject: [PATCH 029/838] Replace more add_nondefault() with add()

---
 src/common/config_parser.cpp      | 12 ++++++------
 src/common/config_validator.cpp   |  7 ++++---
 src/data/shortlist.h              |  3 +--
 src/training/training.h           |  2 +-
 src/training/validator.h          | 18 +++++++++---------
 src/translator/output_collector.h |  4 +---
 src/translator/scorers.cpp        |  4 ++--
 src/translator/translator.h       |  2 +-
 8 files changed, 25 insertions(+), 27 deletions(-)

diff --git a/src/common/config_parser.cpp b/src/common/config_parser.cpp
index 37b75ac45..4641d31a1 100755
--- a/src/common/config_parser.cpp
+++ b/src/common/config_parser.cpp
@@ -374,7 +374,7 @@ void ConfigParser::addOptionsTraining(cli::CLIWrapper& cli) {
      "sentence");
 
   // embedding options
-  cli.add_nondefault<std::vector<std::string>>("--embedding-vectors",
+  cli.add<std::vector<std::string>>("--embedding-vectors",
      "Paths to files with custom source and target embedding vectors");
   cli.add<bool>("--embedding-normalization",
      "Normalize values from custom embedding vectors to [-1, 1]");
@@ -397,7 +397,7 @@ void ConfigParser::addOptionsValidation(cli::CLIWrapper& cli) {
   cli.switchGroup("Validation set options");
 
   // clang-format off
-  cli.add_nondefault<std::vector<std::string>>("--valid-sets",
+  cli.add<std::vector<std::string>>("--valid-sets",
       "Paths to validation corpora: source target");
   cli.add<std::string/*SchedulerPeriod*/>("--valid-freq",
       "Validate model every  arg  updates (append 't' for every  arg  target labels)",
@@ -436,12 +436,12 @@ void ConfigParser::addOptionsValidation(cli::CLIWrapper& cli) {
       1000);
 
   // options for validation script
-  cli.add_nondefault<std::string>("--valid-script-path",
+  cli.add<std::string>("--valid-script-path",
      "Path to external validation script."
      " It should print a single score to stdout."
      " If the option is used with validating translation, the output"
      " translation file will be passed as a first argument");
-  cli.add_nondefault<std::string>("--valid-translation-output",
+  cli.add<std::string>("--valid-translation-output",
      "Path to store the translation");
 
   cli.add<bool>("--keep-best",
@@ -492,9 +492,9 @@ void ConfigParser::addOptionsTranslation(cli::CLIWrapper& cli) {
   cli.add<bool>("--skip-cost",
       "Ignore model cost during translation, not recommended for beam-size > 1");
 
-  cli.add_nondefault<std::vector<std::string>>("--shortlist",
+  cli.add<std::vector<std::string>>("--shortlist",
      "Use softmax shortlist: path first best prune");
-  cli.add_nondefault<std::vector<float>>("--weights",
+  cli.add<std::vector<float>>("--weights",
       "Scorer weights");
   cli.add<bool>("--output-sampling",
       "Noise output layer with gumbel noise",
diff --git a/src/common/config_validator.cpp b/src/common/config_validator.cpp
index 8a6845633..4b3bd4315 100755
--- a/src/common/config_validator.cpp
+++ b/src/common/config_validator.cpp
@@ -85,9 +85,10 @@ void ConfigValidator::validateOptionsTraining() const {
   ABORT_IF(!modelDir.empty() && !filesystem::isDirectory(modelDir),
            "Model directory does not exist");
 
-  ABORT_IF(
-      has("valid-sets") && get<std::vector<std::string>>("valid-sets").size() != trainSets.size(),
-      "There should be as many validation sets as training sets");
+  ABORT_IF(has("valid-sets")
+               && get<std::vector<std::string>>("valid-sets").size() != trainSets.size()
+               && !get<std::vector<std::string>>("valid-sets").empty(),
+           "There should be as many validation sets as training sets");
 
   // validations for learning rate decaying
   ABORT_IF(get<float>("lr-decay") > 1.f, "Learning rate decay factor greater than 1.0 is unusual");
diff --git a/src/data/shortlist.h b/src/data/shortlist.h
index 55fe4f5bc..4763d6589 100755
--- a/src/data/shortlist.h
+++ b/src/data/shortlist.h
@@ -182,8 +182,7 @@ class LexicalShortlistGenerator : public ShortlistGenerator {
         srcIdx_(srcIdx),
         trgIdx_(trgIdx),
         shared_(shared) {
-    std::vector<std::string> vals
-        = options_->get<std::vector<std::string>>("shortlist");
+    std::vector<std::string> vals = options_->get<std::vector<std::string>>("shortlist");
 
     ABORT_IF(vals.empty(), "No path to filter path given");
     std::string fname = vals[0];
diff --git a/src/training/training.h b/src/training/training.h
index 2209b96e2..fd077da6a 100755
--- a/src/training/training.h
+++ b/src/training/training.h
@@ -49,7 +49,7 @@ class Train : public ModelTask {
     auto trainState = New<TrainingState>(options_->get<float>("learn-rate"));
     auto scheduler = New<Scheduler>(options_, trainState);
 
-    if((options_->has("valid-sets") || options_->has("valid-script-path"))
+    if((options_->nonempty("valid-sets") || options_->nonempty("valid-script-path"))
        && SchedulingParameter::parse(options_->get<std::string>("valid-freq"))) {
       for(auto validator : Validators(dataset->getVocabs(), options_))
         scheduler->addValidator(validator);
diff --git a/src/training/validator.h b/src/training/validator.h
index efed50cb0..351bf15d2 100755
--- a/src/training/validator.h
+++ b/src/training/validator.h
@@ -104,7 +104,6 @@ class Validator : public ValidatorBase {
 public:
 
   virtual float validate(const std::vector<Ptr<ExpressionGraph>>& graphs) override {
-
     for(auto graph : graphs)
       graph->setInference(true);
 
@@ -224,7 +223,7 @@ class ScriptValidator : public Validator<data::Corpus> {
       : Validator(vocabs, options, false) {
     builder_ = models::from_options(options_, models::usage::raw);
 
-    ABORT_IF(!options_->has("valid-script-path"), "valid-script metric but no script given");
+    ABORT_IF(!options_->nonempty("valid-script-path"), "valid-script metric but no script given");
   }
 
   virtual float validate(const std::vector<Ptr<ExpressionGraph>>& graphs) override {
@@ -255,9 +254,8 @@ class TranslationValidator : public Validator<data::Corpus> {
         quiet_(options_->get<bool>("quiet-translation")) {
     builder_ = models::from_options(options_, models::usage::translation);
 
-    if(!options_->has("valid-script-path"))
-      LOG_VALID(warn,
-                "No post-processing script given for validating translator");
+    if(!options_->nonempty("valid-script-path"))
+      LOG_VALID(warn, "No post-processing script given for validating translator");
 
     createBatchGenerator(/*isTranslating=*/true);
   }
@@ -287,7 +285,7 @@ class TranslationValidator : public Validator<data::Corpus> {
     std::string fileName;
     Ptr<io::TemporaryFile> tempFile;
 
-    if(options_->has("valid-translation-output")) {
+    if(options_->nonempty("valid-translation-output")) {
       fileName = options_->get<std::string>("valid-translation-output");
     } else {
       tempFile.reset(new io::TemporaryFile(options_->get<std::string>("tempdir"), false));
@@ -303,7 +301,9 @@ class TranslationValidator : public Validator<data::Corpus> {
     timer::Timer timer;
     {
       auto printer = New<OutputPrinter>(options_, vocabs_.back());
-      auto collector = options_->has("valid-translation-output")
+      // @TODO: This can be simplified. If there is no "valid-translation-output", fileName already
+      // contains the name of temporary file that should be used?
+      auto collector = options_->nonempty("valid-translation-output")
                            ? New<OutputCollector>(fileName)
                            : New<OutputCollector>(*tempFile);
 
@@ -358,7 +358,7 @@ class TranslationValidator : public Validator<data::Corpus> {
     float val = 0.0f;
 
     // Run post-processing script if given
-    if(options_->has("valid-script-path")) {
+    if(options_->nonempty("valid-script-path")) {
       auto command = options_->get<std::string>("valid-script-path") + " " + fileName;
       auto valStr = utils::exec(command);
       val = (float)std::atof(valStr.c_str());
@@ -442,7 +442,7 @@ class BleuValidator : public Validator<data::Corpus> {
       auto printer = New<OutputPrinter>(options_, vocabs_.back());
 
       Ptr<OutputCollector> collector;
-      if(options_->has("valid-translation-output")) {
+      if(options_->nonempty("valid-translation-output")) {
         auto fileName = options_->get<std::string>("valid-translation-output");
         collector = New<OutputCollector>(fileName); // for debugging
       }
diff --git a/src/translator/output_collector.h b/src/translator/output_collector.h
index 51b47159c..c616f03ff 100755
--- a/src/translator/output_collector.h
+++ b/src/translator/output_collector.h
@@ -49,9 +49,7 @@ class OutputCollector {
   OutputCollector(std::string outFile);
 
   template <class T>
-  OutputCollector(T&& arg) 
-    : nextId_(0), 
-      outStrm_(new io::OutputFileStream(arg)) {}
+  OutputCollector(T&& arg) : nextId_(0), outStrm_(new io::OutputFileStream(arg)) {}
 
   OutputCollector(const OutputCollector&) = delete;
 
diff --git a/src/translator/scorers.cpp b/src/translator/scorers.cpp
index 9afa1d7b8..6c0031bff 100755
--- a/src/translator/scorers.cpp
+++ b/src/translator/scorers.cpp
@@ -53,7 +53,7 @@ std::vector<Ptr<Scorer>> createScorers(Ptr<Options> options) {
   auto models = options->get<std::vector<std::string>>("models");
 
   std::vector<float> weights(models.size(), 1.f);
-  if(options->has("weights"))
+  if(options->nonempty("weights"))
     weights = options->get<std::vector<float>>("weights");
 
   size_t i = 0;
@@ -83,7 +83,7 @@ std::vector<Ptr<Scorer>> createScorers(Ptr<Options> options, const std::vector<c
   std::vector<Ptr<Scorer>> scorers;
 
   std::vector<float> weights(ptrs.size(), 1.f);
-  if(options->has("weights"))
+  if(options->nonempty("weights"))
     weights = options->get<std::vector<float>>("weights");
 
   size_t i = 0;
diff --git a/src/translator/translator.h b/src/translator/translator.h
index 5fbfe8aac..877312b81 100755
--- a/src/translator/translator.h
+++ b/src/translator/translator.h
@@ -41,7 +41,7 @@ class Translate : public ModelTask {
     trgVocab_->load(vocabs.back());
     auto srcVocab = corpus_->getVocabs()[0];
 
-    if(options_->has("shortlist"))
+    if(options_->nonempty("shortlist"))
       shortlistGenerator_ = New<data::LexicalShortlistGenerator>(
           options_, srcVocab, trgVocab_, 0, 1, vocabs.front() == vocabs.back());
 

From 49d1cf85fa402a11442a6fc9940a93a0e4672c67 Mon Sep 17 00:00:00 2001
From: Roman Grundkiewicz <rgrundki@exseed.ed.ac.uk>
Date: Thu, 13 Dec 2018 13:09:53 +0000
Subject: [PATCH 030/838] Replace add_nondefault() with add() for --alignment
 and --summary

---
 src/common/config_parser.cpp |  6 +++---
 src/microsoft/quicksand.cpp  |  2 +-
 src/models/transformer.h     |  2 +-
 src/rescorer/rescorer.h      | 13 ++++++-------
 src/translator/beam_search.h |  2 +-
 5 files changed, 12 insertions(+), 13 deletions(-)

diff --git a/src/common/config_parser.cpp b/src/common/config_parser.cpp
index 4641d31a1..2e73ad5d7 100755
--- a/src/common/config_parser.cpp
+++ b/src/common/config_parser.cpp
@@ -479,7 +479,7 @@ void ConfigParser::addOptionsTranslation(cli::CLIWrapper& cli) {
       "Allow unknown words to appear in output");
   cli.add<bool>("--n-best",
       "Generate n-best list");
-  cli.add_nondefault<std::string>("--alignment",
+  cli.add<std::string>("--alignment",
      "Return word alignment. Possible values: 0.0-1.0, hard, soft")
     ->implicit_val("1");
 
@@ -531,10 +531,10 @@ void ConfigParser::addOptionsScoring(cli::CLIWrapper& cli) {
       "Feature name to be inserted into n-best list", "Score");
   cli.add<bool>("--normalize,-n",
       "Divide translation score by translation length");
-  cli.add_nondefault<std::string>("--summary",
+  cli.add<std::string>("--summary",
       "Only print total cost, possible values: cross-entropy (ce-mean), ce-mean-words, ce-sum, perplexity")
       ->implicit_val("cross-entropy");
-  cli.add_nondefault<std::string>("--alignment",
+  cli.add<std::string>("--alignment",
      "Return word alignments. Possible values: 0.0-1.0, hard, soft")
      ->implicit_val("1"),
 
diff --git a/src/microsoft/quicksand.cpp b/src/microsoft/quicksand.cpp
index 962bda182..2a82263f7 100755
--- a/src/microsoft/quicksand.cpp
+++ b/src/microsoft/quicksand.cpp
@@ -136,7 +136,7 @@ class BeamSearchDecoder : public IBeamSearchDecoder {
         auto score = std::get<2>(result);
         // determine alignment if present
         AlignmentSets alignmentSets;
-        if (options_->has("alignment"))
+        if (options_->nonempty("alignment"))
         {
           float alignmentThreshold;
           auto alignment = options_->get<std::string>("alignment"); // @TODO: this logic now exists three times in Marian
diff --git a/src/models/transformer.h b/src/models/transformer.h
index 3cd7140a8..ee5020e26 100755
--- a/src/models/transformer.h
+++ b/src/models/transformer.h
@@ -789,7 +789,7 @@ class DecoderTransformer : public Transformer<DecoderBase> {
           // decoding or scoring return the attention weights of one head of the last layer.
           // @TODO: maybe allow to return average or max over all heads?
           bool saveAttentionWeights = false;
-          if(j == 0 && (options_->get("guided-alignment", std::string("none")) != "none" || options_->has("alignment"))) {
+          if(j == 0 && (options_->get("guided-alignment", std::string("none")) != "none" || options_->nonempty("alignment"))) {
             size_t attLayer = decDepth - 1;
             std::string gaStr = options_->get<std::string>("transformer-guided-alignment-layer", "last");
             if(gaStr != "last")
diff --git a/src/rescorer/rescorer.h b/src/rescorer/rescorer.h
index fa456856a..a837d15dc 100644
--- a/src/rescorer/rescorer.h
+++ b/src/rescorer/rescorer.h
@@ -49,10 +49,10 @@ class Rescore : public ModelTask {
 
 public:
   Rescore(Ptr<Options> options) : options_(options) {
-    ABORT_IF(options_->has("summary") && options_->has("alignment"),
+    ABORT_IF(options_->nonempty("summary") && options_->nonempty("alignment"),
              "Alignments can not be produced with summarized score");
 
-    ABORT_IF(options_->has("summary") && options_->get<bool>("normalize"),
+    ABORT_IF(options_->nonempty("summary") && options_->get<bool>("normalize"),
              "Normalization by length cannot be used with summary scores");
 
     options_->set("inference", true);
@@ -99,12 +99,11 @@ class Rescore : public ModelTask {
                                            New<ScoreCollectorNBest>(options_))
                                      : New<ScoreCollector>(options_);
 
-    std::string alignment = options_->get<std::string>("alignment", "");
-    bool summarize = options_->has("summary");
+    auto alignment = options_->get<std::string>("alignment", "");
+    auto summary = options_->get<std::string>("summary", "");
+    bool summarize = !summary.empty();
     bool normalize = options_->get<bool>("normalize");
 
-    std::string summary = summarize ? options_->get<std::string>("summary") : "cross-entropy";
-
     float sumCost = 0;
     size_t sumWords = 0;
     size_t sumSamples = 0;
@@ -126,7 +125,7 @@ class Rescore : public ModelTask {
 
           // @TODO: normalize by length as in normalize
           // Once we have Frank's concept of ce-sum with sample size by words we will return a pair
-          // here which will make it trivial to report all variants. 
+          // here which will make it trivial to report all variants.
           auto costNode = builder->build(graph, batch);
 
           graph->forward();
diff --git a/src/translator/beam_search.h b/src/translator/beam_search.h
index 798312fed..50bf5ccbc 100755
--- a/src/translator/beam_search.h
+++ b/src/translator/beam_search.h
@@ -42,7 +42,7 @@ class BeamSearch {
     Beams newBeams(beams.size());
 
     std::vector<float> align;
-    if(options_->has("alignment"))
+    if(options_->nonempty("alignment"))
       // Use alignments from the first scorer, even if ensemble
       align = scorers_[0]->getAlignment();
 

From f078e1717dc9bd374ebb8210397bd2d317a1c1d9 Mon Sep 17 00:00:00 2001
From: Roman Grundkiewicz <rgrundki@exseed.ed.ac.uk>
Date: Thu, 13 Dec 2018 14:10:48 +0000
Subject: [PATCH 031/838] Remove --special-vocab

---
 src/common/cli_wrapper.cpp     | 12 ++++++++++--
 src/common/config.cpp          |  6 +++---
 src/common/config_parser.cpp   |  5 ++---
 src/models/encoder_decoder.cpp |  1 -
 4 files changed, 15 insertions(+), 9 deletions(-)

diff --git a/src/common/cli_wrapper.cpp b/src/common/cli_wrapper.cpp
index ce24f211a..df647bfe4 100755
--- a/src/common/cli_wrapper.cpp
+++ b/src/common/cli_wrapper.cpp
@@ -9,6 +9,14 @@
 namespace marian {
 namespace cli {
 
+// clang-format off
+const std::unordered_set<std::string> DEPRECIATED_OPTIONS = {
+  "version",
+  "special-vocab"
+};
+// clang-format on
+
+
 /*
 static uint16_t guess_terminal_width(uint16_t max_width, uint16_t default_width) {
   uint16_t cols = 0;
@@ -135,8 +143,8 @@ void CLIWrapper::updateConfig(const YAML::Node &config, const std::string& error
     // skip options specified via command-line to allow overwriting them
     if(cmdOptions.count(key))
       continue;
-    // skip special option "version" that possibly can be loaded from model.npz:special.yml
-    if(key == "version")
+    // skip options that might exist in config files generated by older versions of Marian
+    if(DEPRECIATED_OPTIONS.count(key))
       continue;
 
     if(options_.count(key)) {
diff --git a/src/common/config.cpp b/src/common/config.cpp
index 043a757f5..c1a99cad9 100755
--- a/src/common/config.cpp
+++ b/src/common/config.cpp
@@ -33,13 +33,13 @@ void Config::initialize(int argc, char** argv, cli::mode mode, bool validate) {
   // echo version and command line
   LOG(info, "[marian] Marian {}", buildVersion());
   std::string cmdLine;
-  for (int i = 0; i < argc; i++) {
+  for(int i = 0; i < argc; i++) {
     std::string arg = argv[i];
     std::string quote; // attempt to quote special chars
-    if (arg.empty() || arg.find_first_of(" #`\"'\\${}|&^?*!()%><") != std::string::npos)
+    if(arg.empty() || arg.find_first_of(" #`\"'\\${}|&^?*!()%><") != std::string::npos)
       quote = "'";
     arg = regex::regex_replace(arg, regex::regex("'"), "'\\''");
-    if (!cmdLine.empty())
+    if(!cmdLine.empty())
       cmdLine.push_back(' ');
     cmdLine += quote + arg + quote;
   }
diff --git a/src/common/config_parser.cpp b/src/common/config_parser.cpp
index 2e73ad5d7..bce007d5a 100755
--- a/src/common/config_parser.cpp
+++ b/src/common/config_parser.cpp
@@ -22,7 +22,8 @@
 
 namespace marian {
 
-// TODO: move to CLIWrapper
+// TODO: Move this to CLIWrapper and allow to mark options as paths in the same place they are
+// defined
 // clang-format off
 const std::set<std::string> PATHS = {
   "model",
@@ -146,8 +147,6 @@ void ConfigParser::addOptionsModel(cli::CLIWrapper& cli) {
       "Train right-to-left model");
   cli.add<bool>("--best-deep",
       "Use Edinburgh deep RNN configuration (s2s)");
-  cli.add_nondefault<std::vector<size_t>>("--special-vocab",
-      "Model-specific special vocabulary ids");
   cli.add<bool>("--tied-embeddings",
       "Tie target embeddings and output embeddings in output layer");
   cli.add<bool>("--tied-embeddings-src",
diff --git a/src/models/encoder_decoder.cpp b/src/models/encoder_decoder.cpp
index 3edb7b335..49593a15c 100755
--- a/src/models/encoder_decoder.cpp
+++ b/src/models/encoder_decoder.cpp
@@ -23,7 +23,6 @@ EncoderDecoder::EncoderDecoder(Ptr<Options> options)
                     "skip",
                     "layer-normalization",
                     "right-left",
-                    "special-vocab",
                     "tied-embeddings",
                     "tied-embeddings-src",
                     "tied-embeddings-all"};

From 219ea012e4de76a4dc735b9420f46c171be6c07c Mon Sep 17 00:00:00 2001
From: Roman Grundkiewicz <rgrundki@exseed.ed.ac.uk>
Date: Thu, 13 Dec 2018 14:47:02 +0000
Subject: [PATCH 032/838] Make --port available only for marian-server

---
 src/command/marian_server.cpp   |  2 +-
 src/command/marian_train.cpp    |  2 +-
 src/common/config.cpp           | 26 +++++++++++++-------------
 src/common/config.h             |  2 +-
 src/common/config_parser.cpp    | 19 +++++++++++++++----
 src/common/config_parser.h      |  7 +++++--
 src/common/config_validator.cpp |  3 +++
 7 files changed, 39 insertions(+), 22 deletions(-)

diff --git a/src/command/marian_server.cpp b/src/command/marian_server.cpp
index a9a1b0be1..5df944d71 100644
--- a/src/command/marian_server.cpp
+++ b/src/command/marian_server.cpp
@@ -12,7 +12,7 @@ int main(int argc, char **argv) {
   using namespace marian;
 
   // Initialize translation task
-  auto options = parseOptions(argc, argv, cli::mode::translation, true);
+  auto options = parseOptions(argc, argv, cli::mode::server, true);
   auto task = New<TranslateService<BeamSearch>>(options);
 
   // Initialize web server
diff --git a/src/command/marian_train.cpp b/src/command/marian_train.cpp
index 889806244..6be2abbba 100755
--- a/src/command/marian_train.cpp
+++ b/src/command/marian_train.cpp
@@ -14,7 +14,7 @@
 int main(int argc, char** argv) {
   using namespace marian;
 
-  auto options = parseOptions(argc, argv);
+  auto options = parseOptions(argc, argv, cli::mode::training);
 
   // selects MultiNodeGraphGroup family
   //
diff --git a/src/common/config.cpp b/src/common/config.cpp
index c1a99cad9..ddfb5199e 100755
--- a/src/common/config.cpp
+++ b/src/common/config.cpp
@@ -16,7 +16,7 @@ size_t Config::seed = (size_t)time(0);
 
 Config::Config(int argc,
                char** argv,
-               cli::mode mode /*= cli::mode::training*/,
+               cli::mode mode,
                bool validate /*= true*/) {
   initialize(argc, argv, mode, validate);
 }
@@ -56,7 +56,17 @@ void Config::initialize(int argc, char** argv, cli::mode mode, bool validate) {
   }
 
   // load model parameters
-  if(mode != cli::mode::translation) {
+  if(mode == cli::mode::translation || mode == cli::mode::server) {
+    auto model = get<std::vector<std::string>>("models")[0];
+    try {
+      if(!get<bool>("ignore-model-config"))
+        loadModelParameters(model);
+    } catch(std::runtime_error& ) {
+      LOG(info, "[config] No model configuration found in model file");
+    }
+  }
+  // if cli::mode::training or cli::mode::scoring
+  else {
     auto model = get<std::string>("model");
     if(filesystem::exists(model) && !get<bool>("no-reload")) {
       try {
@@ -67,16 +77,6 @@ void Config::initialize(int argc, char** argv, cli::mode mode, bool validate) {
       }
     }
   }
-  // if cli::mode::translation
-  else {
-    auto model = get<std::vector<std::string>>("models")[0];
-    try {
-      if(!get<bool>("ignore-model-config"))
-        loadModelParameters(model);
-    } catch(std::runtime_error& ) {
-      LOG(info, "[config] No model configuration found in model file");
-    }
-  }
 
   // echo full configuration
   log();
@@ -264,7 +264,7 @@ std::vector<DeviceId> Config::getDevices(Ptr<Options> options,
 
 Ptr<Options> parseOptions(int argc,
                           char** argv,
-                          cli::mode mode /*= cli::mode::training*/,
+                          cli::mode mode,
                           bool validate /*= true*/) {
   auto config = New<Config>(argc, argv, mode, validate);
   auto options = New<Options>();
diff --git a/src/common/config.h b/src/common/config.h
index ec462a532..326dbcf1d 100755
--- a/src/common/config.h
+++ b/src/common/config.h
@@ -115,7 +115,7 @@ class Config {
  */
 Ptr<Options> parseOptions(int argc,
                           char** argv,
-                          cli::mode mode = cli::mode::training,
+                          cli::mode mode,
                           bool validate = true);
 
 }  // namespace marian
diff --git a/src/common/config_parser.cpp b/src/common/config_parser.cpp
index bce007d5a..cea33ac47 100755
--- a/src/common/config_parser.cpp
+++ b/src/common/config_parser.cpp
@@ -85,6 +85,14 @@ void ConfigParser::addOptionsGeneral(cli::CLIWrapper& cli) {
   // clang-format on
 }
 
+void ConfigParser::addOptionsServer(cli::CLIWrapper& cli) {
+  // clang-format off
+  cli.add<size_t>("--port,-p",
+      "Port number for web socket server",
+      8080);
+  // clang-format on
+}
+
 void ConfigParser::addOptionsModel(cli::CLIWrapper& cli) {
   cli.switchGroup("Model options");
 
@@ -499,9 +507,6 @@ void ConfigParser::addOptionsTranslation(cli::CLIWrapper& cli) {
       "Noise output layer with gumbel noise",
        false);
 
-  // TODO: the options should be available only in server
-  cli.add_nondefault<size_t>("--port,-p",
-      "Port number for web socket server");
   // add ULR settings
   addSuboptionsULR(cli);
 
@@ -561,7 +566,8 @@ void ConfigParser::addSuboptionsDevices(cli::CLIWrapper& cli) {
 #ifdef CUDA_FOUND
   cli.add<size_t>("--cpu-threads",
       "Use CPU-based computation with this many independent threads, 0 means GPU-based computation",
-      0)->implicit_val("1");
+      0)
+    ->implicit_val("1");
 #else
   cli.add<size_t>("--cpu-threads",
       "Use CPU-based computation with this many independent threads, 0 means GPU-based computation",
@@ -678,6 +684,8 @@ void ConfigParser::parseOptions(int argc, char** argv, bool doValidate) {
                       40);
 
   addOptionsGeneral(cli);
+  if(modeServer_)
+    addOptionsServer(cli);
   addOptionsModel(cli);
 
   // clang-format off
@@ -692,6 +700,9 @@ void ConfigParser::parseOptions(int argc, char** argv, bool doValidate) {
     case cli::mode::scoring:
       addOptionsScoring(cli);
       break;
+    default:
+      ABORT("wrong CLI mode");
+      break;
   }
   // clang-format on
 
diff --git a/src/common/config_parser.h b/src/common/config_parser.h
index de1cb70e1..ff305f738 100755
--- a/src/common/config_parser.h
+++ b/src/common/config_parser.h
@@ -14,7 +14,7 @@
 namespace marian {
 
 namespace cli {
-enum struct mode { training, translation, scoring };
+enum struct mode { training, translation, scoring, server };
 }  // namespace cli
 
 /**
@@ -25,7 +25,8 @@ enum struct mode { training, translation, scoring };
 class ConfigParser {
 public:
   ConfigParser(int argc, char** argv, cli::mode mode, bool validate = false)
-      : mode_(mode) {
+      : modeServer_(mode == cli::mode::server),
+        mode_(mode == cli::mode::server ? cli::mode::translation : mode) {
     parseOptions(argc, argv, validate);
   }
 
@@ -51,6 +52,7 @@ class ConfigParser {
   YAML::Node getConfig() const;
 
 private:
+  bool modeServer_;
   cli::mode mode_;
   YAML::Node config_;
 
@@ -68,6 +70,7 @@ class ConfigParser {
   }
 
   void addOptionsGeneral(cli::CLIWrapper&);
+  void addOptionsServer(cli::CLIWrapper&);
   void addOptionsModel(cli::CLIWrapper&);
   void addOptionsTraining(cli::CLIWrapper&);
   void addOptionsValidation(cli::CLIWrapper&);
diff --git a/src/common/config_validator.cpp b/src/common/config_validator.cpp
index 4b3bd4315..9345b0a56 100755
--- a/src/common/config_validator.cpp
+++ b/src/common/config_validator.cpp
@@ -28,6 +28,9 @@ void ConfigValidator::validateOptions(cli::mode mode) const {
       validateOptionsParallelData();
       validateOptionsTraining();
       break;
+    default:
+      ABORT("wrong CLI mode");
+      break;
   }
   // clang-format on
 

From eaa0350091f31cdd90deb52db0c8f5adee2670e3 Mon Sep 17 00:00:00 2001
From: Roman Grundkiewicz <rgrundki@exseed.ed.ac.uk>
Date: Thu, 13 Dec 2018 15:13:29 +0000
Subject: [PATCH 033/838] Remove CLIWrapper::add_nondefault()

---
 src/common/cli_wrapper.h | 55 ++++++++--------------------------------
 1 file changed, 11 insertions(+), 44 deletions(-)

diff --git a/src/common/cli_wrapper.h b/src/common/cli_wrapper.h
index 4e8701cc2..263d62120 100755
--- a/src/common/cli_wrapper.h
+++ b/src/common/cli_wrapper.h
@@ -159,8 +159,7 @@ class CLIWrapper {
                          args,
                          help,
                          val,
-                         /*defaulted =*/true,
-                         /*addToConfig =*/true);
+                         /*defaulted =*/true);
   }
 
   /**
@@ -186,33 +185,7 @@ class CLIWrapper {
                          args,
                          help,
                          T(),
-                         /*defaulted =*/false,
-                         /*addToConfig =*/true);
-  }
-
-  /**
-   * @brief Define a non-defaulted option
-   *
-   * The option will not be present in the config file unless given as a
-   * command-line argument.
-   *
-   * @param args Comma-separated list of short and long option names
-   * @param help Help message
-   *
-   * @return Option object
-   *
-   * @TODO: consider removing this method during final refactorization of
-   * command-line/config parsers in the future as all options should either
-   * have a default value or be non-defaulted
-   */
-  template <typename T>
-  CLI::Option *add_nondefault(const std::string &args, const std::string &help) {
-    return add_option<T>(keyName(args),
-                         args,
-                         help,
-                         T(),
-                         /*defaulted =*/false,
-                         /*addToConfig =*/false);
+                         /*defaulted =*/false);
   }
 
   /**
@@ -256,11 +229,9 @@ class CLIWrapper {
                           const std::string &args,
                           const std::string &help,
                           T val,
-                          bool defaulted,
-                          bool addToConfig) {
-    // define YAML entry if requested
-    if(addToConfig)
-      config_[key] = val;
+                          bool defaulted) {
+    // add key to YAML
+    config_[key] = val;
 
     // create option tuple
     CLIOptionTuple option;
@@ -305,11 +276,9 @@ class CLIWrapper {
                           const std::string &args,
                           const std::string &help,
                           T val,
-                          bool defaulted,
-                          bool addToConfig) {
-    // define YAML entry if requested
-    if(addToConfig)
-      config_[key] = val;
+                          bool defaulted) {
+    // add key to YAML
+    config_[key] = val;
 
     // create option tuple
     CLIOptionTuple option;
@@ -364,11 +333,9 @@ class CLIWrapper {
                           const std::string &args,
                           const std::string &help,
                           T val,
-                          bool defaulted,
-                          bool addToConfig) {
-    // define YAML entry if requested
-    if(addToConfig)
-      config_[key] = val;
+                          bool defaulted) {
+    // add key to YAML
+    config_[key] = val;
 
     // create option tuple
     CLIOptionTuple option;

From 6407e1b5372fe0f723ab05bf8f81c43528bab19a Mon Sep 17 00:00:00 2001
From: Roman Grundkiewicz <rgrundki@exseed.ed.ac.uk>
Date: Thu, 13 Dec 2018 15:19:27 +0000
Subject: [PATCH 034/838] Fix misspelling

---
 src/common/config_parser.cpp | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/common/config_parser.cpp b/src/common/config_parser.cpp
index cea33ac47..466693332 100755
--- a/src/common/config_parser.cpp
+++ b/src/common/config_parser.cpp
@@ -281,7 +281,7 @@ void ConfigParser::addOptionsTraining(cli::CLIWrapper& cli) {
       "Display information every  arg  updates (append 't' for every  arg  target labels)",
       "1000u");
   cli.add<size_t>("--disp-first",
-      "Display nformation for the first  arg  updates");
+      "Display information for the first  arg  updates");
   cli.add<bool>("--disp-label-counts",
       "Display label counts when logging loss progress");
   cli.add<std::string/*SchedulerPeriod*/>("--save-freq",

From c733eb1e352a6883aa89a813d7e50d3e1c61cd5b Mon Sep 17 00:00:00 2001
From: Roman Grundkiewicz <rgrundki@exseed.ed.ac.uk>
Date: Thu, 13 Dec 2018 16:09:10 +0000
Subject: [PATCH 035/838] Rename add_option to addOption

---
 src/common/cli_wrapper.h     | 54 +++++++++++++++++++-----------------
 src/common/config_parser.cpp | 11 +++-----
 2 files changed, 33 insertions(+), 32 deletions(-)

diff --git a/src/common/cli_wrapper.h b/src/common/cli_wrapper.h
index 263d62120..1590441a0 100755
--- a/src/common/cli_wrapper.h
+++ b/src/common/cli_wrapper.h
@@ -147,6 +147,8 @@ class CLIWrapper {
   /**
    * @brief Define an option with a default value
    *
+   * Explicit default values will appear in help messages.
+   *
    * @param args Comma-separated list of short and long option names
    * @param help Help message
    * @param val Default value
@@ -155,11 +157,11 @@ class CLIWrapper {
    */
   template <typename T>
   CLI::Option *add(const std::string &args, const std::string &help, T val) {
-    return add_option<T>(keyName(args),
-                         args,
-                         help,
-                         val,
-                         /*defaulted =*/true);
+    return addOption<T>(keyName(args),
+                        args,
+                        help,
+                        val,
+                        /*defaulted =*/true);
   }
 
   /**
@@ -171,6 +173,8 @@ class CLIWrapper {
    * option is 0, for a string is an empty string, and for a vector is an empty
    * vector.
    *
+   * Implicit default values will *NOT* appear in help messages.
+   *
    * @param args Comma-separated list of short and long option names
    * @param help Help message
    *
@@ -181,11 +185,11 @@ class CLIWrapper {
    */
   template <typename T>
   CLI::Option *add(const std::string &args, const std::string &help) {
-    return add_option<T>(keyName(args),
-                         args,
-                         help,
-                         T(),
-                         /*defaulted =*/false);
+    return addOption<T>(keyName(args),
+                        args,
+                        help,
+                        T(),
+                        /*defaulted =*/false);
   }
 
   /**
@@ -225,11 +229,11 @@ class CLIWrapper {
             // options with numeric and string-like values
             CLI::enable_if_t<!CLI::is_bool<T>::value && !CLI::is_vector<T>::value,
                              CLI::detail::enabler> = CLI::detail::dummy>
-  CLI::Option *add_option(const std::string &key,
-                          const std::string &args,
-                          const std::string &help,
-                          T val,
-                          bool defaulted) {
+  CLI::Option *addOption(const std::string &key,
+                         const std::string &args,
+                         const std::string &help,
+                         T val,
+                         bool defaulted) {
     // add key to YAML
     config_[key] = val;
 
@@ -272,11 +276,11 @@ class CLIWrapper {
   template <typename T,
             // options with vector values
             CLI::enable_if_t<CLI::is_vector<T>::value, CLI::detail::enabler> = CLI::detail::dummy>
-  CLI::Option *add_option(const std::string &key,
-                          const std::string &args,
-                          const std::string &help,
-                          T val,
-                          bool defaulted) {
+  CLI::Option *addOption(const std::string &key,
+                         const std::string &args,
+                         const std::string &help,
+                         T val,
+                         bool defaulted) {
     // add key to YAML
     config_[key] = val;
 
@@ -329,11 +333,11 @@ class CLIWrapper {
   template <typename T,
             // options with boolean values, called flags in CLI11
             CLI::enable_if_t<CLI::is_bool<T>::value, CLI::detail::enabler> = CLI::detail::dummy>
-  CLI::Option *add_option(const std::string &key,
-                          const std::string &args,
-                          const std::string &help,
-                          T val,
-                          bool defaulted) {
+  CLI::Option *addOption(const std::string &key,
+                         const std::string &args,
+                         const std::string &help,
+                         T val,
+                         bool defaulted) {
     // add key to YAML
     config_[key] = val;
 
diff --git a/src/common/config_parser.cpp b/src/common/config_parser.cpp
index 466693332..a4699e023 100755
--- a/src/common/config_parser.cpp
+++ b/src/common/config_parser.cpp
@@ -79,8 +79,7 @@ void ConfigParser::addOptionsGeneral(cli::CLIWrapper& cli) {
   cli.add<bool>("--relative-paths",
      "All paths are relative to the config file location");
   cli.add<std::string>("--dump-config",
-     "Dump current (modified) configuration to stdout and exit. Possible values: full, minimal",
-     "false")
+     "Dump current (modified) configuration to stdout and exit. Possible values: full, minimal")
     ->implicit_val("full");
   // clang-format on
 }
@@ -628,8 +627,7 @@ void ConfigParser::addSuboptionsULR(cli::CLIWrapper& cli) {
   // clang-format off
   // support for universal encoder ULR https://arxiv.org/pdf/1802.05368.pdf
   cli.add<bool>("--ulr",
-      "Enable ULR (Universal Language Representation)",
-      false);
+      "Enable ULR (Universal Language Representation)");
   // reading pre-trained universal embeddings for multi-sources.
   // Note that source and target here is relative to ULR not the translation langs
   // queries: EQ in Fig2 : is the unified embeddings projected to one space.
@@ -642,8 +640,7 @@ void ConfigParser::addSuboptionsULR(cli::CLIWrapper& cli) {
       "Path to file with universal sources embeddings of traget keys from projection into universal space",
       "");
   cli.add<bool>("--ulr-trainable-transformation",
-      "Make Query Transformation Matrix A trainable",
-      false);
+      "Make Query Transformation Matrix A trainable");
   cli.add<int>("--ulr-dim-emb",
       "ULR monolingual embeddings dimension");
   cli.add<float>("--ulr-dropout",
@@ -728,7 +725,7 @@ void ConfigParser::parseOptions(int argc, char** argv, bool doValidate) {
   // remove extra config files from the config to avoid redundancy
   config_.remove("config");
 
-  if(get<std::string>("dump-config") != "false") {
+  if(!get<std::string>("dump-config").empty() && get<std::string>("dump-config") != "false") {
     bool skipDefault = get<std::string>("dump-config") == "minimal";
     config_.remove("dump-config");
     std::cout << cli.dumpConfig(skipDefault) << std::endl;

From 58c493a37d1455f7ea2d8637c216ba301eb86318 Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Thu, 13 Dec 2018 13:55:53 -0800
Subject: [PATCH 036/838] removed reference-MB-size arguments throughout, and
 replaced it with a single argument --mini-batch-words-ref honored at multiple
 places

---
 src/common/config_parser.cpp         | 61 ++++++++++++-----------
 src/optimizers/optimizers.cpp        | 23 ++++-----
 src/optimizers/optimizers.h          | 72 ++++++++++++++--------------
 src/training/exponential_smoothing.h |  7 +--
 src/training/scheduler.h             | 11 ++---
 5 files changed, 87 insertions(+), 87 deletions(-)

diff --git a/src/common/config_parser.cpp b/src/common/config_parser.cpp
index 06eabd320..377e8d2e6 100755
--- a/src/common/config_parser.cpp
+++ b/src/common/config_parser.cpp
@@ -108,7 +108,7 @@ void ConfigParser::addOptionsModel(cli::CLIWrapper& cli) {
       "amun");
   cli.add<std::vector<int>>("--dim-vocabs",
       "Maximum items in vocabulary ordered by rank, 0 uses all items in the provided/created vocabulary file",
-      std::vector<int>({0, 0}));
+      {0, 0});
   cli.add<int>("--dim-emb",
       "Size of embedding vector",
       512);
@@ -205,11 +205,11 @@ void ConfigParser::addOptionsModel(cli::CLIWrapper& cli) {
       "Number of highway network layers after max-pooling in char-s2s model",
       4);
   cli.add<std::vector<int>>("--char-conv-filters-num",
-      "Numbers of convolution filters of correspoding width in char-s2s model",
-      std::vector<int>({200, 200, 250, 250, 300, 300, 300, 300}));
+      "Numbers of convolution filters of corresponding width in char-s2s model",
+      {200, 200, 250, 250, 300, 300, 300, 300});
   cli.add<std::vector<int>>("--char-conv-filters-widths",
       "Convolution window widths in char-s2s model",
-      std::vector<int>({1, 2, 3, 4, 5, 6, 7, 8}));
+      {1, 2, 3, 4, 5, 6, 7, 8});
 #endif
 
   if(mode_ == cli::mode::training) {
@@ -305,17 +305,19 @@ void ConfigParser::addOptionsTraining(cli::CLIWrapper& cli) {
      "Optimization algorithm: sgd, adagrad, adam",
      "adam");
   cli.add_nondefault<std::vector<float>>("--optimizer-params",
-     "Parameters for optimization algorithm, e.g. betas for adam");
-  cli.add<size_t>("--optimizer-delay",
-     "SGD update delay, 1 = no delay",
-     1);
+     "Parameters for optimization algorithm, e.g. betas for Adam. "
+      "Auto-adjusted to --mini-batch-words-ref if given");
+  cli.add<double>("--optimizer-delay",
+     "SGD update delay, 1 = no delay. Can be fractional, e.g. 0.1 to use only 10% of each batch",
+     1.);
 
   cli.add<bool>("--sync-sgd",
      "Use synchronous SGD instead of asynchronous for multi-gpu training");
 
   // learning rate options
   cli.add<double>("--learn-rate,-l",
-     "Learning rate",
+     "Learning rate. "
+      "Auto-adjusted to --mini-batch-words-ref if given",
      0.0001);
   cli.add<bool>("--lr-report",
      "Report learning rate for each update");
@@ -327,7 +329,7 @@ void ConfigParser::addOptionsTraining(cli::CLIWrapper& cli) {
      "epoch+stalled");
   cli.add<std::vector<size_t>>("--lr-decay-start",
      "The first number of (epoch, batches, stalled) validations to start learning rate decaying (tuple)",
-     std::vector<size_t>({10,1}));
+     {10,1});
   cli.add<size_t>("--lr-decay-freq",
      "Learning rate decaying frequency for batches, requires --lr-decay-strategy to be batches",
      50000);
@@ -337,8 +339,8 @@ void ConfigParser::addOptionsTraining(cli::CLIWrapper& cli) {
      "Repeat learning rate warmup when learning rate is decayed");
   cli.add<std::vector<std::string/*SchedulerPeriod*/>>("--lr-decay-inv-sqrt",
      "Decrease learning rate at arg / sqrt(no. batches) starting at arg  (append 't' or 'e' for sqrt(target labels or epochs)). "
-      "Add second argument to define the starting point",
-      {"0"});
+     "Add second argument to define the starting point (default: same as first value)",
+     {"0"});
 
   cli.add<std::string/*SchedulerPeriod*/>("--lr-warmup",
      "Increase learning rate linearly for  arg  first batches (append 't' for  arg  first target labels)",
@@ -355,10 +357,10 @@ void ConfigParser::addOptionsTraining(cli::CLIWrapper& cli) {
   cli.add<double>("--clip-norm",
      "Clip gradient norm to  argcli.add<int>(0 to disable)",
      1.f);
-  cli.add<std::vector<float>>("--exponential-smoothing",
+  cli.add<float>("--exponential-smoothing",
      "Maintain smoothed version of parameters for validation and saving with smoothing factor. 0 to disable. "
-     "Add a second number to specify a reference batch size (in target words).",
-     { 0.f })->implicit_val("1e-4");
+      "Auto-adjusted to --mini-batch-words-ref if given.",
+     0.f)->implicit_val("1e-4");
   cli.add<std::string>("--guided-alignment",
      "Path to a file with word alignments. Use guided alignment to guide attention or 'none'",
      "none");
@@ -405,8 +407,8 @@ void ConfigParser::addOptionsValidation(cli::CLIWrapper& cli) {
       "10000u");
   cli.add<std::vector<std::string>>("--valid-metrics",
       "Metric to use during validation: cross-entropy, ce-mean-words, perplexity, valid-script, "
-      " translation, bleu, bleu-detok. Multiple metrics can be specified",
-      std::vector<std::string>({"cross-entropy"}));
+      "translation, bleu, bleu-detok. Multiple metrics can be specified",
+      {"cross-entropy"});
   cli.add<size_t>("--early-stopping",
      "Stop if the first validation metric does not improve for  arg  consecutive validation steps",
      10);
@@ -458,7 +460,7 @@ void ConfigParser::addOptionsTranslation(cli::CLIWrapper& cli) {
   // clang-format off
   cli.add<std::vector<std::string>>("--input,-i",
       "Paths to input file(s), stdin by default",
-      std::vector<std::string>({"stdin"}));
+      {"stdin"});
   cli.add<std::string>("--output,-o",
       "Path to output file, stdout by default",
       "stdout");
@@ -496,10 +498,10 @@ void ConfigParser::addOptionsTranslation(cli::CLIWrapper& cli) {
   cli.add_nondefault<std::vector<std::string>>("--shortlist",
      "Use softmax shortlist: path first best prune");
   cli.add_nondefault<std::vector<float>>("--weights",
-      "Scorer weights");
+     "Scorer weights");
   cli.add<bool>("--output-sampling",
-      "Noise output layer with gumbel noise",
-       false);
+     "Noise output layer with gumbel noise",
+      false);
 
   // TODO: the options should be available only in server
   cli.add_nondefault<size_t>("--port,-p",
@@ -523,9 +525,9 @@ void ConfigParser::addOptionsScoring(cli::CLIWrapper& cli) {
       "Path to output file, stdout by default",
       "stdout");
   cli.add<std::vector<std::string>>("--vocabs,-v",
-      "Paths to vocabulary files have to correspond to --train-sets."
-      " If this parameter is not supplied we look for vocabulary files source.{yml,json} and target.{yml,json}."
-      " If these files do not exists they are created");
+      "Paths to vocabulary files have to correspond to --train-sets. "
+      "If this parameter is not supplied we look for vocabulary files source.{yml,json} and target.{yml,json}. "
+      "If these files do not exists they are created");
   cli.add<bool>("--n-best",
       "Score n-best list instead of plain text corpus");
   cli.add<std::string>("--n-best-feature",
@@ -552,7 +554,7 @@ void ConfigParser::addSuboptionsDevices(cli::CLIWrapper& cli) {
   // clang-format off
   cli.add<std::vector<std::string>>("--devices,-d",
       "Specifies GPU ID(s) to use for training. Defaults to 0..num-devices-1",
-      std::vector<std::string>({"0"}));
+      {"0"});
   cli.add_nondefault<size_t>("--num-devices",
       "Number of GPUs to use for this process. Defaults to length(devices) or 1");
 #ifdef USE_NCCL
@@ -607,9 +609,12 @@ void ConfigParser::addSuboptionsBatching(cli::CLIWrapper& cli) {
   cli.add<bool>("--shuffle-in-ram",
       "Keep shuffled corpus in RAM, do not write to temp file");
 
-  cli.add<std::vector<std::string/*SchedulerPeriod*/>>("--mini-batch-warmup",
-      "linear ramp-up of MB size, up to this #updates (append 't' for up to this #target labels);"
-      "optional second number is reference batch size at which to stop scaling up (instead of full batch size)",
+  cli.add<int>("--mini-batch-words-ref",
+      "If given, the following hyper parameters are adjusted as-if we had this mini-batch size: "
+      "--learn-rate, --optimizer-params, --exponential-smoothing, --mini-batch-warmup");
+  cli.add<std::string/*SchedulerPeriod*/>("--mini-batch-warmup",
+      "Linear ramp-up of MB size, up to this #updates (append 't' for up to this #target labels). "
+      "Auto-adjusted to --mini-batch-words-ref if given",
       {"0"});
   cli.add<bool>("--mini-batch-track-lr",
       "Dynamically track mini-batch size inverse to actual learning rate (not considering lr-warmup)");
diff --git a/src/optimizers/optimizers.cpp b/src/optimizers/optimizers.cpp
index 807135949..75501a370 100755
--- a/src/optimizers/optimizers.cpp
+++ b/src/optimizers/optimizers.cpp
@@ -6,8 +6,8 @@
 
 namespace marian {
 
-void Sgd::updateImpl(Tensor params, Tensor grads, size_t actualMBSize, size_t refMBSize) {
-  actualMBSize, refMBSize; // (no correction for base update needed beyond using ce-sum)
+void Sgd::updateImpl(Tensor params, Tensor grads, size_t actualMBSize, size_t refMBWords) {
+  actualMBSize, refMBWords; // (no correction for base update needed beyond using ce-sum)
   using namespace functional;
   Element(_1 -= eta_ * _2,
           params,
@@ -18,8 +18,8 @@ void Sgd::updateImpl(Tensor params, Tensor grads, size_t actualMBSize, size_t re
 
 // Adagrad
 
-void Adagrad::updateImpl(Tensor params, Tensor grads, size_t actualMBSize, size_t refMBSize) {
-  ABORT_IF(actualMBSize != refMBSize, "Adagrad does not support rational hyper-parameter adjustment");
+void Adagrad::updateImpl(Tensor params, Tensor grads, size_t actualMBSize, size_t refMBWords) {
+  ABORT_IF(actualMBSize != refMBWords, "Adagrad does not support rational hyper-parameter adjustment");
   if(!alloc_)
     alloc_ = New<TensorAllocator>(params->getBackend());
 
@@ -124,7 +124,7 @@ void Adagrad::resetStats() {
 
 // Adam
 
-void Adam::updateImpl(Tensor params, Tensor grads, size_t actualMBSize, size_t refMBSize) {
+void Adam::updateImpl(Tensor params, Tensor grads, size_t actualMBSize, size_t refMBWords) {
   // lazy allocation
   if(!alloc_)
     alloc_ = New<TensorAllocator>(params->getBackend());
@@ -138,7 +138,7 @@ void Adam::updateImpl(Tensor params, Tensor grads, size_t actualMBSize, size_t r
     vt_->set(0.f);
   }
 
-  double Tref = (double)refMBSize;
+  double Tref = (double)refMBWords;
   double T    = (double)actualMBSize;
 
   // adjust for minibatch-size changes if Adam parameters are given a reference size (else do nothing)
@@ -305,7 +305,7 @@ void Adam::resetStats() {
   if(vt_)
     vt_->set(0.f);
 
-  denom1_ = 0; // @BUGBUG: or 1 or refMBSize if so specified. Fix once we have proper parameterization for that.
+  denom1_ = 0; // @BUGBUG: or 1 or refMBWords if so specified. Fix once we have proper parameterization for that.
   denom2_ = 0;
 }
 
@@ -314,6 +314,7 @@ Ptr<OptimizerBase> Optimizer(Ptr<Options> options) {
   auto params = options->has("optimizer-params")
                     ? options->get<std::vector<float>>("optimizer-params")
                     : std::vector<float>({});
+  size_t refMBWordsParam = options->get<size_t>("mini-batch-words-ref"); // adjust hyper-parameters as if our MB size (in target labels) was this value
 
   Ptr<ClipperBase> clipper = nullptr;
   float clipNorm = (float)options->get<double>("clip-norm"); // @TODO: should this be <float>?
@@ -323,13 +324,13 @@ Ptr<OptimizerBase> Optimizer(Ptr<Options> options) {
   auto opt = options->get<std::string>("optimizer");
 
   if(opt == "sgd") {
-    return Optimizer<Sgd>(lrate, clipper, params);
+    return Optimizer<Sgd>(lrate, refMBWordsParam, clipper, params);
   } else if(opt == "adagrad") {
-    return Optimizer<Adagrad>(lrate, clipper, params);
+    return Optimizer<Adagrad>(lrate, refMBWordsParam, clipper, params);
   } else if(opt == "adam") {
-    return Optimizer<Adam>(lrate, clipper, params); // @TODO: parse the parameters here, or just pass the options object
+    return Optimizer<Adam>(lrate, refMBWordsParam, clipper, params);
   } else {
-    ABORT("Unknown optimizer: {}", opt);
+    ABORT("Unknown optimizer kind: {}", opt);
   }
 }
 }  // namespace marian
diff --git a/src/optimizers/optimizers.h b/src/optimizers/optimizers.h
index 76e2780eb..cc8bbae57 100755
--- a/src/optimizers/optimizers.h
+++ b/src/optimizers/optimizers.h
@@ -18,8 +18,16 @@ namespace marian {
  */
 class OptimizerBase : public TrainingObserver {
 public:
-  OptimizerBase(float eta, Ptr<ClipperBase> clipper = nullptr)
-      : eta_(eta), clipper_(clipper) {}
+  OptimizerBase(float eta, size_t refMBWordsParam, Ptr<ClipperBase> clipper)
+      : eta_(eta), clipper_(clipper), refMBWordsParam_(refMBWordsParam) {
+
+    // automatic learning-rate adjustment
+    // If users provide, in addition to the hyper-parameters, a reference minibatch size,
+    // that these hyper-parameters were originally tuned for, then the learning-rate gets
+    // adjusted accordingly. Note: Requires user to also use ce-sum criterion.
+    if (refMBWordsParam_ != 0)
+      LOG(info, "Note: Learning rate gets automatically adjusted as if minibatch size was {}", refMBWordsParam_);
+  }
 
   static constexpr size_t mbSizeNotProvided = SIZE_MAX;
 
@@ -34,16 +42,16 @@ class OptimizerBase : public TrainingObserver {
     if(clipper_)
       clipper_->clip(grads);
 
-    size_t refMBSize = refMBSize_;
-    if (refMBSize == 0) { // optimizer not configured to use hyper-parameter auto-adjustment
-      refMBSize = mbSize = 1; // neutral settings that keep the standard behavior
+    size_t refMBWords = refMBWordsParam_;
+    if (refMBWords == 0) { // optimizer not configured to use hyper-parameter auto-adjustment
+      refMBWords = mbSize = 1; // neutral settings that keep the standard behavior
     }
     else { // optimizer is configured to auto-adjust hyper-parameters
       ABORT_IF(mbSize == mbSizeNotProvided, "Using rational optimizer auto-adjustment with trainer that does not provide MB size");
       // note: this behavior is only meaningful if using the ce-sum criterion
     }
 
-    updateImpl(params, grads, mbSize, refMBSize);
+    updateImpl(params, grads, mbSize, refMBWords);
   }
 
   virtual void init(TrainingState& state) override {
@@ -68,7 +76,7 @@ class OptimizerBase : public TrainingObserver {
       resetStats();
   }
 
-  void setParams(const std::vector<float>& params) { parseParams(params); }
+  virtual void setParams(const std::vector<float>& params) = 0;
 
   typedef std::function<void(size_t /*localDeviceIndex*/,
                              std::vector<float>::const_iterator /*begin*/,
@@ -88,8 +96,7 @@ class OptimizerBase : public TrainingObserver {
                     bool /*isMainProcess*/ = true) {}
 
 protected:
-  virtual void updateImpl(Tensor params, Tensor grads, size_t actualMBSize, size_t refMBSize) = 0;
-  virtual void parseParams(const std::vector<float>& params) = 0;
+  virtual void updateImpl(Tensor params, Tensor grads, size_t actualMBSize, size_t refMBWords) = 0;
   virtual void resetStats() = 0;
 
   // Learning rate
@@ -97,7 +104,7 @@ class OptimizerBase : public TrainingObserver {
   // Clip gradient norm
   Ptr<ClipperBase> clipper_;
   // Reference MB size. This enables automatic adjustment of optimizer hyper-parameters to MB size.
-  size_t refMBSize_{0}; // 0 means no adjustment
+  size_t refMBWordsParam_{0}; // 0 means no adjustment
 };
 
 /**
@@ -105,13 +112,13 @@ class OptimizerBase : public TrainingObserver {
  */
 class Sgd : public OptimizerBase {
 public:
-  Sgd(float eta, Ptr<ClipperBase> clipper = nullptr)
-      : OptimizerBase(eta, clipper) {}
+  Sgd(float eta, size_t refMBWordsParam = 0, Ptr<ClipperBase> clipper = nullptr)
+      : OptimizerBase(eta, refMBWordsParam, clipper) {}
 
+  virtual void setParams(const std::vector<float>& /*params*/) override {}
 private:
-  void updateImpl(Tensor params, Tensor grads, size_t actualMBSize, size_t refMBSize) override;
+  void updateImpl(Tensor params, Tensor grads, size_t actualMBSize, size_t refMBWords) override;
 
-  virtual void parseParams(const std::vector<float>& /*params*/) override {}
   virtual void resetStats() override {}
 };
 
@@ -122,8 +129,8 @@ class Sgd : public OptimizerBase {
  */
 class Adagrad : public OptimizerBase {
 public:
-  Adagrad(float eta, Ptr<ClipperBase> clipper = nullptr)
-      : OptimizerBase(eta, clipper) {}
+  Adagrad(float eta, size_t refMBWordsParam = 0, Ptr<ClipperBase> clipper = nullptr)
+      : OptimizerBase(eta, refMBWordsParam, clipper) {}
 
   void load(const std::string& name,
             const std::vector<Ptr<OptimizerBase>>& opts,
@@ -134,15 +141,15 @@ class Adagrad : public OptimizerBase {
             const GatherStateFunc& gatherFn,
             bool /*isMainProcess*/ = true) override;
 
-private:
-  void updateImpl(Tensor params, Tensor grads, size_t actualMBSize, size_t refMBSize) override;
-  void resetStats() override;
-
-  void parseParams(const std::vector<float>& params) override {
+  void setParams(const std::vector<float>& params) override {
     if(params.size() > 0)
       eps_ = params[0];
   }
 
+private:
+  void updateImpl(Tensor params, Tensor grads, size_t actualMBSize, size_t refMBWords) override;
+  void resetStats() override;
+
   float eps_ = 1e-8f;
   Ptr<TensorAllocator> alloc_;
   Tensor gt_;
@@ -157,8 +164,8 @@ class Adagrad : public OptimizerBase {
  */
 class Adam : public OptimizerBase {
 public:
-  Adam(float eta, Ptr<ClipperBase> clipper = nullptr)
-      : OptimizerBase(eta, clipper) {}
+  Adam(float eta, size_t refMBWordsParam = 0, Ptr<ClipperBase> clipper = nullptr)
+      : OptimizerBase(eta, refMBWordsParam, clipper) {}
 
   void load(const std::string& name,
             const std::vector<Ptr<OptimizerBase>>& opts,
@@ -170,12 +177,12 @@ class Adam : public OptimizerBase {
             bool isMainProcess = true) override;
 
 private:
-  void updateImpl(Tensor params, Tensor grads, size_t actualMBSize, size_t refMBSize) override;
+  void updateImpl(Tensor params, Tensor grads, size_t actualMBSize, size_t refMBWords) override;
   void resetStats() override;
 
   // Adam parameters:
-  // [beta1, beta2, eps, w, refMBSize]
-  virtual void parseParams(const std::vector<float>& params) override {
+  // [beta1, beta2, eps, w, refMBWords]
+  virtual void setParams(const std::vector<float>& params) override {
     if(params.size() > 0)
       beta1_ = params[0];
     if(params.size() > 1)
@@ -186,15 +193,6 @@ class Adam : public OptimizerBase {
     // weighted decay for AdamW, to be explored, disabled by default
     if(params.size() > 3)
       w_ = params[3]; // default (disabled): 0
-
-    // automatic learning-rate adjustment
-    // If users provide, in addition to the hyper-parameters, a reference minibatch size,
-    // that these hyper-parameters were originally tuned for, then the learning-rate gets
-    // adjusted accordingly. Note: Requires user to also use ce-sum criterion.
-    if(params.size() > 4) {
-      refMBSize_ = (size_t)params[4]; // default (disabled): 0
-      LOG(info, "Note: Modified Adam optimizer: automatically adjusting learning rate as if minibatch size was {}", refMBSize_);
-    }
   }
 
   // hyper-parameters
@@ -214,10 +212,10 @@ class Adam : public OptimizerBase {
 };
 
 template <class Algorithm>
-Ptr<OptimizerBase> Optimizer(float eta,
+Ptr<OptimizerBase> Optimizer(float eta, size_t refMBWordsParam = 0,
                              Ptr<ClipperBase> clipper = nullptr,
                              std::vector<float> params = {}) {
-  auto opt = Ptr<OptimizerBase>(new Algorithm(eta, clipper));
+  auto opt = Ptr<OptimizerBase>(new Algorithm(eta, refMBWordsParam, clipper));
   opt->setParams(params);
   return opt;
 }
diff --git a/src/training/exponential_smoothing.h b/src/training/exponential_smoothing.h
index fb1d3b4ac..b018fe9ea 100755
--- a/src/training/exponential_smoothing.h
+++ b/src/training/exponential_smoothing.h
@@ -14,11 +14,8 @@ namespace marian {
 class ExponentialSmoothing {
 public:
     ExponentialSmoothing(Ptr<Options> options) {
-      auto args = options->get<std::vector<float>>("exponential-smoothing");
-      ABORT_IF(args.size() < 1 || args.size() > 2, "exponential-smoothing parameter must be one or two numbers");
-      mvDecayBy_ = args[0];
-      if (args.size() > 1)
-        refBatchTrgWords_ = (size_t)args[1];
+      mvDecayBy_ = options->get<float>("exponential-smoothing");
+      refBatchTrgWords_ = options->get<size_t>("mini-batch-words-ref"); // adjust as if our MB size (in target labels) was this value
       mvAvg_ = (mvDecayBy_ > 0);
     }
 
diff --git a/src/training/scheduler.h b/src/training/scheduler.h
index 783845c8c..2176e85d7 100755
--- a/src/training/scheduler.h
+++ b/src/training/scheduler.h
@@ -70,9 +70,7 @@ class Scheduler : public TrainingObserver {
 
   // determine dynamic MB size, if respective parameters are given (return false if not)
   bool tryGetDynamicMBSizeMultiplier(double /*out*/ &ratio) const {
-    auto mbWarmupOpts = options_->get<std::vector<std::string>>("mini-batch-warmup");
-    ABORT_IF(mbWarmupOpts.empty() || mbWarmupOpts.size() > 2, "--mini-batch-warmup argument must be one or two numbers with units");
-    auto mbWarmup = SchedulingParameter::parse(mbWarmupOpts[0]);
+    auto mbWarmup = SchedulingParameter::parse(options_->get<std::string>("mini-batch-warmup"));
     if (!mbWarmup)
       return false;
 
@@ -89,13 +87,14 @@ class Scheduler : public TrainingObserver {
     // apply ratio to actual batch size
     ratio *= progressRatio;
 
+    // @TODO: move this out
     // adjust for reference batch size if given
     // At progress == mbWarmup.n (ratio=1), we would like to have refBatchLabels instead of whichever
     // the actual batch size is. We approximate the latter as typicalTrgBatchWords_, and scale ratio accordingly.
-    if (mbWarmupOpts.size() > 1) {
-      ABORT_IF(typicalTrgBatchWords_ == 0, "dynamic scaling with words target requires MB size to be known in words"); // happens if MB size is specified in sentences
-      auto refBatchLabels = (size_t)std::stoull(mbWarmupOpts[1]);
+    auto refBatchLabels = options_->get<size_t>("mini-batch-words-ref");
+    if (refBatchLabels != 0) {
       LOG_ONCE(info, "[scheduler] Scaling to {} reference labels. Typical actual batch words is {}", refBatchLabels, typicalTrgBatchWords_);
+      ABORT_IF(typicalTrgBatchWords_ == 0, "dynamic scaling with words target requires MB size to be known in words"); // happens if MB size is specified in sentences
       ratio *= (double)refBatchLabels / (double)typicalTrgBatchWords_;
     }
 

From 12fe1a654919174a4d753f0bc84cf77a34413312 Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Thu, 13 Dec 2018 14:10:28 -0800
Subject: [PATCH 037/838] bug fix: optimizer-delay is now a double, and should
 be read out as such

---
 src/optimizers/optimizers.h               | 6 +++---
 src/training/exponential_smoothing.h      | 2 +-
 src/training/graph_group_async.cpp        | 3 ++-
 src/training/graph_group_multinode_sync.h | 2 +-
 src/training/graph_group_sync.cpp         | 5 +++--
 src/training/graph_group_sync.h           | 2 +-
 src/training/scheduler.h                  | 2 +-
 7 files changed, 12 insertions(+), 10 deletions(-)

diff --git a/src/optimizers/optimizers.h b/src/optimizers/optimizers.h
index cc8bbae57..51e43f871 100755
--- a/src/optimizers/optimizers.h
+++ b/src/optimizers/optimizers.h
@@ -19,7 +19,7 @@ namespace marian {
 class OptimizerBase : public TrainingObserver {
 public:
   OptimizerBase(float eta, size_t refMBWordsParam, Ptr<ClipperBase> clipper)
-      : eta_(eta), clipper_(clipper), refMBWordsParam_(refMBWordsParam) {
+      : eta_(eta), refMBWordsParam_(refMBWordsParam), clipper_(clipper) {
 
     // automatic learning-rate adjustment
     // If users provide, in addition to the hyper-parameters, a reference minibatch size,
@@ -101,10 +101,10 @@ class OptimizerBase : public TrainingObserver {
 
   // Learning rate
   float eta_;
-  // Clip gradient norm
-  Ptr<ClipperBase> clipper_;
   // Reference MB size. This enables automatic adjustment of optimizer hyper-parameters to MB size.
   size_t refMBWordsParam_{0}; // 0 means no adjustment
+  // Clip gradient norm
+  Ptr<ClipperBase> clipper_;
 };
 
 /**
diff --git a/src/training/exponential_smoothing.h b/src/training/exponential_smoothing.h
index b018fe9ea..e24c1e74b 100755
--- a/src/training/exponential_smoothing.h
+++ b/src/training/exponential_smoothing.h
@@ -37,6 +37,6 @@ class ExponentialSmoothing {
 
   bool mvAvg_{false};
   float mvDecayBy_{1e-4f};     // decay prior model by this factor
-  size_t refBatchTrgWords_{0}; // mvDecayBy_ is specified for this batch size (in target words)
+  size_t refBatchTrgWords_{0}; // mvDecayBy_ is specified for this batch size (in target words) (0 means not specified)
 };
 }  // namespace marian
diff --git a/src/training/graph_group_async.cpp b/src/training/graph_group_async.cpp
index b272a4b7f..e5547cb40 100755
--- a/src/training/graph_group_async.cpp
+++ b/src/training/graph_group_async.cpp
@@ -10,7 +10,8 @@ AsyncGraphGroup::AsyncGraphGroup(Ptr<Options> config)
       ExponentialSmoothing(options_),
       devices_{Config::getDevices(options_)},
       shardSync_(devices_.size()),
-      optimizerDelay_{options_->get<size_t>("optimizer-delay")} {
+      optimizerDelay_((size_t)options_->get<double>("optimizer-delay")) {
+  ABORT_IF((double)optimizerDelay_ != options_->get<double>("optimizer-delay"), "AsyncGraphGroup does not support fractional values for --optimizer-delay");
   pool_.reset(new ThreadPool(devices_.size(), devices_.size()));
 
   for(auto device : devices_) {
diff --git a/src/training/graph_group_multinode_sync.h b/src/training/graph_group_multinode_sync.h
index 7685b6789..c4b3683d2 100755
--- a/src/training/graph_group_multinode_sync.h
+++ b/src/training/graph_group_multinode_sync.h
@@ -133,7 +133,7 @@ class MultiNodeGraphGroupSync : public MultiNodeGraphGroupBase {
    */
   MultiNodeGraphGroupSync(Ptr<Options> options)
       : Base(options),
-        tau_{options_->get<size_t>("optimizer-delay")},
+        tau_{(size_t)options_->get<double>("optimizer-delay")},
         syncOptimizer_{Optimizer(options_)},
         movingAvg_{options_->get<float>("exponential-smoothing") > 0},
         mvDecay_{options_->get<float>("exponential-smoothing")} {
diff --git a/src/training/graph_group_sync.cpp b/src/training/graph_group_sync.cpp
index cedf5c54e..e96e85607 100755
--- a/src/training/graph_group_sync.cpp
+++ b/src/training/graph_group_sync.cpp
@@ -5,7 +5,7 @@ namespace marian {
 SyncGraphGroup::SyncGraphGroup(Ptr<Options> config)
     : GraphGroup(config),
       ExponentialSmoothing(config),
-      delay_{options_->get<size_t>("optimizer-delay")} { // @TODO: rename to something else; delay means delayed updated, not accumulation
+      delay_{options_->get<double>("optimizer-delay")} { // @TODO: rename to something else; delay means delayed updated, not accumulation
 
   mpi_ = initMPI(/*multiThreaded=*/false); // when not running under MPI, this will be a fake object that represents a one-MPI-process setup
 
@@ -153,7 +153,8 @@ bool SyncGraphGroup::tryGetSubBatches(Ptr<data::Batch> newBatch, std::vector<Ptr
   if (isDynamic)
     ratio = roundUpRatio(ratio); // round up to full batches if within a certain error margin  --@BUGBUG: Not invariant w.r.t. GPU size, as ratio is relative to what fits into 1 GPU
   else   // if dynamic scaling not enabled, then fill each GPU with a batch
-    ratio = (double)(delay_ * warpSize);
+    ratio = delay_ * (double)warpSize; // note: delay_ may be fractional
+  // @TODO: adjust ratio here for reference MB size; i.e. also for optimizer-delay
   if (pendingBatches_.size() < ratio)
     return false; // not enough data yet
 
diff --git a/src/training/graph_group_sync.h b/src/training/graph_group_sync.h
index 5d4b11912..45f90112e 100755
--- a/src/training/graph_group_sync.h
+++ b/src/training/graph_group_sync.h
@@ -8,7 +8,7 @@ namespace marian {
 
 class SyncGraphGroup : public GraphGroup, public ExponentialSmoothing {
   using Base = GraphGroup;
-  const size_t delay_{ 1 }; // optimizer-delay parameter
+  const double delay_{1.}; // optimizer-delay parameter. Fractional means to use a fraction of whatever the MB size is
 
   Ptr<ICommunicator> comm_; // [not null] communicator, e.g. NCCLCommunicator
   Ptr<IMPIWrapper> mpi_;    // [not null] all MPI-like communication goes through this (this is a dummy implementation if no MPI run)
diff --git a/src/training/scheduler.h b/src/training/scheduler.h
index 2176e85d7..873086dda 100755
--- a/src/training/scheduler.h
+++ b/src/training/scheduler.h
@@ -87,7 +87,7 @@ class Scheduler : public TrainingObserver {
     // apply ratio to actual batch size
     ratio *= progressRatio;
 
-    // @TODO: move this out
+    // @TODO: move this out, and just return ratio instead
     // adjust for reference batch size if given
     // At progress == mbWarmup.n (ratio=1), we would like to have refBatchLabels instead of whichever
     // the actual batch size is. We approximate the latter as typicalTrgBatchWords_, and scale ratio accordingly.

From 0cf43717f476be89d642506b837009e4a52c562d Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Thu, 13 Dec 2018 14:16:29 -0800
Subject: [PATCH 038/838] bug fix: optimizer-delay is now a double, and should
 be read out as such

---
 src/training/graph_group_async.cpp   |  2 +-
 src/training/graph_group_multinode.h |  2 +-
 src/training/graph_group_sync.cpp    | 13 ++++++++++++-
 src/training/scheduler.h             | 15 ++++-----------
 4 files changed, 18 insertions(+), 14 deletions(-)

diff --git a/src/training/graph_group_async.cpp b/src/training/graph_group_async.cpp
index e5547cb40..4c85e168a 100755
--- a/src/training/graph_group_async.cpp
+++ b/src/training/graph_group_async.cpp
@@ -11,7 +11,7 @@ AsyncGraphGroup::AsyncGraphGroup(Ptr<Options> config)
       devices_{Config::getDevices(options_)},
       shardSync_(devices_.size()),
       optimizerDelay_((size_t)options_->get<double>("optimizer-delay")) {
-  ABORT_IF((double)optimizerDelay_ != options_->get<double>("optimizer-delay"), "AsyncGraphGroup does not support fractional values for --optimizer-delay");
+  ABORT_IF((double)optimizerDelay_ != options_->get<double>("optimizer-delay"), "AsyncGraphGroup presently does not implement fractional values for --optimizer-delay");
   pool_.reset(new ThreadPool(devices_.size(), devices_.size()));
 
   for(auto device : devices_) {
diff --git a/src/training/graph_group_multinode.h b/src/training/graph_group_multinode.h
index 39a20e062..873155df7 100755
--- a/src/training/graph_group_multinode.h
+++ b/src/training/graph_group_multinode.h
@@ -354,7 +354,7 @@ class MultiNodeGraphGroup : public MultiNodeGraphGroupBase {
   MultiNodeGraphGroup(Ptr<Options> options)
       : Base(options),
         clientCommOverlap{options_->get<bool>("multi-node-overlap")},
-        tau_{options_->get<size_t>("optimizer-delay")} { }
+        tau_{(size_t)options_->get<double>("optimizer-delay")} { }
 
   /**
    * (Destructor) Shut down server shard thread and (if comm. overlap enabled)
diff --git a/src/training/graph_group_sync.cpp b/src/training/graph_group_sync.cpp
index e96e85607..44db971d5 100755
--- a/src/training/graph_group_sync.cpp
+++ b/src/training/graph_group_sync.cpp
@@ -154,7 +154,18 @@ bool SyncGraphGroup::tryGetSubBatches(Ptr<data::Batch> newBatch, std::vector<Ptr
     ratio = roundUpRatio(ratio); // round up to full batches if within a certain error margin  --@BUGBUG: Not invariant w.r.t. GPU size, as ratio is relative to what fits into 1 GPU
   else   // if dynamic scaling not enabled, then fill each GPU with a batch
     ratio = delay_ * (double)warpSize; // note: delay_ may be fractional
-  // @TODO: adjust ratio here for reference MB size; i.e. also for optimizer-delay
+
+  // adjust for reference batch size if given
+  // At progress == mbWarmup.n (ratio=1), we would like to have refBatchLabels instead of whichever
+  // the actual batch size is. We approximate the latter as typicalTrgBatchWords, and scale ratio accordingly.
+  auto refBatchLabels = options_->get<size_t>("mini-batch-words-ref");
+  if (refBatchLabels != 0) {
+    auto typicalTrgBatchWords = scheduler_->getTypicalTrgBatchWords();
+    LOG_ONCE(info, "[scheduler] Scaling to {} reference labels. Typical actual batch words is {}", refBatchLabels, typicalTrgBatchWords);
+    ABORT_IF(typicalTrgBatchWords == 0, "dynamic scaling with words target requires MB size to be known in words"); // happens if MB size is specified in sentences
+    ratio *= (double)refBatchLabels / (double)typicalTrgBatchWords;
+  }
+
   if (pendingBatches_.size() < ratio)
     return false; // not enough data yet
 
diff --git a/src/training/scheduler.h b/src/training/scheduler.h
index 873086dda..08cd753a1 100755
--- a/src/training/scheduler.h
+++ b/src/training/scheduler.h
@@ -63,10 +63,14 @@ class Scheduler : public TrainingObserver {
   }
 
 public:
+  // @TODO: move this out from here
   void setTypicalTrgBatchWords(size_t typicalTrgBatchWords) { // needed for tryGetDynamicMBSizeMultiplier()
     typicalTrgBatchWords_ = typicalTrgBatchWords;
     LOG(info, "batch size estimate is {} target words", typicalTrgBatchWords_);
   }
+  size_t getTypicalTrgBatchWords(size_t typicalTrgBatchWords) { // needed for tryGetDynamicMBSizeMultiplier()
+    return typicalTrgBatchWords;
+  }
 
   // determine dynamic MB size, if respective parameters are given (return false if not)
   bool tryGetDynamicMBSizeMultiplier(double /*out*/ &ratio) const {
@@ -87,17 +91,6 @@ class Scheduler : public TrainingObserver {
     // apply ratio to actual batch size
     ratio *= progressRatio;
 
-    // @TODO: move this out, and just return ratio instead
-    // adjust for reference batch size if given
-    // At progress == mbWarmup.n (ratio=1), we would like to have refBatchLabels instead of whichever
-    // the actual batch size is. We approximate the latter as typicalTrgBatchWords_, and scale ratio accordingly.
-    auto refBatchLabels = options_->get<size_t>("mini-batch-words-ref");
-    if (refBatchLabels != 0) {
-      LOG_ONCE(info, "[scheduler] Scaling to {} reference labels. Typical actual batch words is {}", refBatchLabels, typicalTrgBatchWords_);
-      ABORT_IF(typicalTrgBatchWords_ == 0, "dynamic scaling with words target requires MB size to be known in words"); // happens if MB size is specified in sentences
-      ratio *= (double)refBatchLabels / (double)typicalTrgBatchWords_;
-    }
-
     // dynamic MB-size tracking with learning rate
     // As LR goes down, MB gets ramped up by the same ratio, which has been found to be safe.
     auto mbTracking = options_->get<bool>("mini-batch-track-lr");

From 10438e87c436fc9e3244dff18732fb3c2bbefebe Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Thu, 13 Dec 2018 14:35:45 -0800
Subject: [PATCH 039/838] typicalTrgBatchWords_ moved from Scheduler to
 SyncGraphGroup

---
 src/training/graph_group.h        |  5 +++++
 src/training/graph_group_sync.cpp | 31 +++++++++++++------------------
 src/training/scheduler.h          | 10 ----------
 src/training/training.h           | 11 +++++++----
 4 files changed, 25 insertions(+), 32 deletions(-)

diff --git a/src/training/graph_group.h b/src/training/graph_group.h
index 46a317c32..94fad7a86 100755
--- a/src/training/graph_group.h
+++ b/src/training/graph_group.h
@@ -21,6 +21,7 @@ class GraphGroup {
   Ptr<OptimizerBase> opt_;   // the optimizer
   Ptr<Scheduler> scheduler_; // scheduler that keeps track of how much has been processed
   bool finalized_{false};    // 'true' if training has completed (further updates are no longer allowed)
+  size_t typicalTrgBatchWords_{ 0 }; // for dynamic batch sizing
 
 public:
   GraphGroup(Ptr<Options> options) : options_(options), opt_(Optimizer(options)) {}
@@ -110,6 +111,10 @@ class GraphGroup {
     }
     return stats;
   }
+
+  void setTypicalTrgBatchWords(size_t typicalTrgBatchWords) { // needed for dynamic MB scaling
+    typicalTrgBatchWords_ = typicalTrgBatchWords;
+  }
 };
 
 /**
diff --git a/src/training/graph_group_sync.cpp b/src/training/graph_group_sync.cpp
index 44db971d5..5d2327e37 100755
--- a/src/training/graph_group_sync.cpp
+++ b/src/training/graph_group_sync.cpp
@@ -57,10 +57,10 @@ void SyncGraphGroup::initialize(const Ptr<data::Batch>& exampleBatch) {
   ThreadPool pool(graphs_.size() - 1, graphs_.size() - 1);
   for(size_t i = 1; i < graphs_.size(); ++i) {
     auto init = [&](size_t i) {
-      // initialize t-th graph and weights
+      // initialize i-th graph and weights
       builders_[i]->build(graphs_[i], exampleBatch);
       graphs_[i]->forward();
-      // overwrite weights of t-th graph with weights from 0th graph
+      // overwrite weights of i-th graph with weights from 0-th graph
       graphs_[i]->params()->vals()->copyFrom(graphs_[0]->params()->vals());
     };
     pool.enqueue(init, i);
@@ -143,10 +143,6 @@ bool SyncGraphGroup::tryGetSubBatches(Ptr<data::Batch> newBatch, std::vector<Ptr
   pendingBatches_.push_back(newBatch);
   size_t warpSize = devices_.size() * mpi_->numMPIProcesses(); // warp := set of batches processed concurrently across GPus and workers
 
-  size_t pendingTrgWords = 0; // diagnosics only: compute how many target labels are pending so far
-  for (const auto& batch : pendingBatches_)
-    pendingTrgWords += batch->wordsTrg();
-
   // MB-size warm-up and dynamic scaling
   double ratio;
   bool isDynamic = scheduler_->tryGetDynamicMBSizeMultiplier(ratio);
@@ -160,10 +156,9 @@ bool SyncGraphGroup::tryGetSubBatches(Ptr<data::Batch> newBatch, std::vector<Ptr
   // the actual batch size is. We approximate the latter as typicalTrgBatchWords, and scale ratio accordingly.
   auto refBatchLabels = options_->get<size_t>("mini-batch-words-ref");
   if (refBatchLabels != 0) {
-    auto typicalTrgBatchWords = scheduler_->getTypicalTrgBatchWords();
-    LOG_ONCE(info, "[scheduler] Scaling to {} reference labels. Typical actual batch words is {}", refBatchLabels, typicalTrgBatchWords);
-    ABORT_IF(typicalTrgBatchWords == 0, "dynamic scaling with words target requires MB size to be known in words"); // happens if MB size is specified in sentences
-    ratio *= (double)refBatchLabels / (double)typicalTrgBatchWords;
+    LOG_ONCE(info, "[scheduler] Scaling to {} reference labels. Typical actual batch words is {}", refBatchLabels, typicalTrgBatchWords_);
+    ABORT_IF(typicalTrgBatchWords_ == 0, "dynamic scaling with words target requires MB size to be known in words"); // happens if MB size is specified in sentences
+    ratio *= (double)refBatchLabels / (double)typicalTrgBatchWords_;
   }
 
   if (pendingBatches_.size() < ratio)
@@ -243,11 +238,11 @@ void SyncGraphGroup::update(Ptr<data::Batch> newBatch) /*override*/ {
     return;
 
   // Helper to access the subBatches array
-  auto getSubBatch = [&](size_t t, size_t localDeviceIndex, size_t rank) -> Ptr<data::Batch> {
-    // 't' (the delay) should be slowest changing dimension. If subBatches are sorted by
+  auto getSubBatch = [&](size_t delay, size_t localDeviceIndex, size_t rank) -> Ptr<data::Batch> {
+    // 'delay' should be slowest changing dimension. If subBatches are sorted by
     // length, then grouping sentences of similar length into the same delay step can
     // reduce unnecessary time spent in padding.
-    auto index = (t * mpi_->numMPIProcesses() + rank) * devices_.size() + localDeviceIndex;
+    auto index = (delay * mpi_->numMPIProcesses() + rank) * devices_.size() + localDeviceIndex;
     if (index < subBatches.size())
       return subBatches[index];
     else
@@ -267,27 +262,27 @@ void SyncGraphGroup::update(Ptr<data::Batch> newBatch) /*override*/ {
   // Compute gradients
   // This happens in multiple steps in case of delay > 1.
   std::vector<float> localDeviceCosts(devices_.size(), 0.f); // [local device index] aggregate cost for each local device
-  for (size_t t = 0; getSubBatch(t, 0, 0); t++) { // @TODO: rename 't' to 'delay'
+  for (size_t delay = 0; getSubBatch(delay, 0, 0); delay++) {
     // Execute single forward/backward step
     auto forwardBackward = [&](size_t localDeviceIndex, size_t /*begin*/, size_t /*end*/) {
       auto graph = graphs_[localDeviceIndex];
-      auto subBatch = getSubBatch(t, localDeviceIndex, mpi_->myMPIRank());
+      auto subBatch = getSubBatch(delay, localDeviceIndex, mpi_->myMPIRank());
 
       if(subBatch) {
         auto costNode = builders_[localDeviceIndex]->build(graph, subBatch);
         graph->forward();
         localDeviceCosts[localDeviceIndex] += costNode->scalar();
-        graph->backward(/*zero=*/t == 0); // only reset gradients to 0 if t = 0
+        graph->backward(/*zero=*/delay == 0); // only reset gradients to 0 if delay = 0
       }
       else { // empty batch: execute do-nothing fw-bw step for proper inits and resets
 #if 1   // @TODO: double-check whether the #else branch is the same; and if so, use it instead
         graph->params()->allocateBackward();
-        if (t == 0) // these have already been sized
+        if (delay == 0) // these have already been sized
           graph->params()->set_zero_adjoint();
 #else
         graph->clear(); // instead of build()
         graph->forward();
-        graph->backward(/*zero=*/t == 0);
+        graph->backward(/*zero=*/delay == 0);
 #endif
       }
     };
diff --git a/src/training/scheduler.h b/src/training/scheduler.h
index 08cd753a1..f1b65bd0e 100755
--- a/src/training/scheduler.h
+++ b/src/training/scheduler.h
@@ -13,7 +13,6 @@ class Scheduler : public TrainingObserver {
   std::vector<Ptr<ValidatorBase>> validators_;
 
   bool first_{true};
-  size_t typicalTrgBatchWords_{0}; // for dynamic batch sizing
 
   Ptr<TrainingState> state_;
 
@@ -63,15 +62,6 @@ class Scheduler : public TrainingObserver {
   }
 
 public:
-  // @TODO: move this out from here
-  void setTypicalTrgBatchWords(size_t typicalTrgBatchWords) { // needed for tryGetDynamicMBSizeMultiplier()
-    typicalTrgBatchWords_ = typicalTrgBatchWords;
-    LOG(info, "batch size estimate is {} target words", typicalTrgBatchWords_);
-  }
-  size_t getTypicalTrgBatchWords(size_t typicalTrgBatchWords) { // needed for tryGetDynamicMBSizeMultiplier()
-    return typicalTrgBatchWords;
-  }
-
   // determine dynamic MB size, if respective parameters are given (return false if not)
   bool tryGetDynamicMBSizeMultiplier(double /*out*/ &ratio) const {
     auto mbWarmup = SchedulingParameter::parse(options_->get<std::string>("mini-batch-warmup"));
diff --git a/src/training/training.h b/src/training/training.h
index 174768542..17512efd4 100755
--- a/src/training/training.h
+++ b/src/training/training.h
@@ -58,10 +58,10 @@ class Train : public ModelTask {
     auto batchGenerator = New<CorpusBatchGenerator>(dataset, options_, stats);
 
     scheduler->registerTrainingObserver(batchGenerator);
-    scheduler->setTypicalTrgBatchWords(batchGenerator->estimateTypicalTrgBatchWords()); // needed for dynamic MB scaling
 
     auto model = New<ModelWrapper>(options_);
     model->setScheduler(scheduler);
+    model->setTypicalTrgBatchWords(batchGenerator->estimateTypicalTrgBatchWords()); // needed for dynamic MB scaling
     model->load();
 
     // @TODO: shuffle_ as a private attribute in BG
@@ -69,16 +69,19 @@ class Train : public ModelTask {
     bool restored = !options_->get<bool>("no-restore-corpus")
                     && batchGenerator->restore(trainState, shuffle);
 
+    // -- main training loop
     scheduler->started();
     while(scheduler->keepGoing()) {
       if(!restored)
         batchGenerator->prepare(shuffle);
       restored = false;
 
-      // @TODO: try to use for(auto ...)
-      for(auto batchIt = std::begin(*batchGenerator);
-          batchIt != std::end(*batchGenerator) && scheduler->keepGoing();
+      // main training loop for one epoch
+      for(auto batchIt = std::begin(*batchGenerator); // @TODO: try to use for(auto ...)
+          batchIt != std::end(*batchGenerator);
           batchIt++) {
+        if (!scheduler->keepGoing())
+          break;
         model->update(*batchIt);
       }
 

From 2cd890809871581f5bdd1993f3439f1a2404aadd Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Thu, 13 Dec 2018 17:49:01 -0800
Subject: [PATCH 040/838] streamlined all cases for dynamic scaling and read
 sizes

---
 src/common/config_parser.cpp              |   2 +-
 src/data/batch_stats.h                    |   4 +-
 src/training/graph_group.h                |   4 +-
 src/training/graph_group_multinode_sync.h |   2 +-
 src/training/graph_group_sync.cpp         | 133 +++++++++++++---------
 src/training/graph_group_sync.h           |   5 +-
 src/training/scheduler.h                  |  38 ++++---
 src/training/training.h                   |   9 +-
 8 files changed, 118 insertions(+), 79 deletions(-)

diff --git a/src/common/config_parser.cpp b/src/common/config_parser.cpp
index 377e8d2e6..b5ea081e8 100755
--- a/src/common/config_parser.cpp
+++ b/src/common/config_parser.cpp
@@ -308,7 +308,7 @@ void ConfigParser::addOptionsTraining(cli::CLIWrapper& cli) {
      "Parameters for optimization algorithm, e.g. betas for Adam. "
       "Auto-adjusted to --mini-batch-words-ref if given");
   cli.add<double>("--optimizer-delay",
-     "SGD update delay, 1 = no delay. Can be fractional, e.g. 0.1 to use only 10% of each batch",
+     "SGD update delay (#batches between updates). 1 = no delay. Can be fractional, e.g. 0.1 to use only 10% of each batch",
      1.);
 
   cli.add<bool>("--sync-sgd",
diff --git a/src/data/batch_stats.h b/src/data/batch_stats.h
index 581f83df2..2fc642938 100755
--- a/src/data/batch_stats.h
+++ b/src/data/batch_stats.h
@@ -39,11 +39,11 @@ class BatchStats {
     return it->second;
   }
 
-  void add(Ptr<data::CorpusBatch> batch, size_t multiplier = 1) {
+  void add(Ptr<data::CorpusBatch> batch, double multiplier = 1.) {
     std::vector<size_t> lengths;
     for(size_t i = 0; i < batch->sets(); ++i)
       lengths.push_back((*batch)[i]->batchWidth());
-    size_t batchSize = batch->size() * multiplier;
+    size_t batchSize = (size_t)ceil((double)batch->size() * multiplier);
 
     if(map_[lengths] < batchSize)
       map_[lengths] = batchSize;
diff --git a/src/training/graph_group.h b/src/training/graph_group.h
index 94fad7a86..c1d6c818a 100755
--- a/src/training/graph_group.h
+++ b/src/training/graph_group.h
@@ -21,7 +21,7 @@ class GraphGroup {
   Ptr<OptimizerBase> opt_;   // the optimizer
   Ptr<Scheduler> scheduler_; // scheduler that keeps track of how much has been processed
   bool finalized_{false};    // 'true' if training has completed (further updates are no longer allowed)
-  size_t typicalTrgBatchWords_{ 0 }; // for dynamic batch sizing
+  size_t typicalTrgBatchWords_{ 0 }; // for dynamic batch sizing: typical batch size in words
 
 public:
   GraphGroup(Ptr<Options> options) : options_(options), opt_(Optimizer(options)) {}
@@ -56,7 +56,7 @@ class GraphGroup {
   // @TODO: Can this be made const? It seems wrong to have a stateful method that still returns a result.
   virtual Ptr<data::BatchStats> collectStats(Ptr<ExpressionGraph> graph,
                                              Ptr<models::ModelBase> model,
-                                             size_t multiplier = 1) {
+                                             double multiplier = 1.) {
     auto stats = New<data::BatchStats>();
 
     size_t numFiles
diff --git a/src/training/graph_group_multinode_sync.h b/src/training/graph_group_multinode_sync.h
index c4b3683d2..7b93b2a84 100755
--- a/src/training/graph_group_multinode_sync.h
+++ b/src/training/graph_group_multinode_sync.h
@@ -222,7 +222,7 @@ class MultiNodeGraphGroupSync : public MultiNodeGraphGroupBase {
    */
   Ptr<data::BatchStats> collectStats() {
     return GraphGroup::collectStats(
-        clientGraphs_[0], clientBuilders_[0], devices_.size());
+        clientGraphs_[0], clientBuilders_[0], (double)devices_.size());
   }
 };
 }  // namespace marian
diff --git a/src/training/graph_group_sync.cpp b/src/training/graph_group_sync.cpp
index 5d2327e37..f25e321c3 100755
--- a/src/training/graph_group_sync.cpp
+++ b/src/training/graph_group_sync.cpp
@@ -108,9 +108,17 @@ void SyncGraphGroup::initializeAvg() {
 }
 
 Ptr<data::BatchStats> SyncGraphGroup::collectStats() {
-  // @TODO: This is an incompatible change. Decide how to handle that.
-  //size_t multiplier = devices_.size() * mpi_->numMPIProcesses() * delay_;
-  return GraphGroup::collectStats(graphs_[0], builders_[0]/*, multiplier*/);
+  // This function determines the granularity in which the reader provides data.
+  // If no mini-batch-fit, then user provides a constant number. It reads that much. We won't get into this function.
+  // If mini-batch-fit, then we get here and set miniBatchFitMultiplier_. Then...
+  // If dynamic MB scaling, then we want fine-grained minibatches of the size of one GPU.
+  // If not, we prefer a single large batch that can be split into equal-size parts over GPUs,
+  // so that we have perfect load balancing and read precisely as much as we need (no waste).
+  double multiplier = devices_.size() * mpi_->numMPIProcesses() * delay_;
+  bool isDynamic = scheduler_->isDynamicMBSizeScaling();
+  double readerMultiplier = isDynamic ? 1. : multiplier; // multiplier applied already by reader
+  updateMultiplier_ = isDynamic ? multiplier : 1.;       // multiplier applied later in update()
+  return GraphGroup::collectStats(graphs_[0], builders_[0], readerMultiplier);
 }
 
 // helper for MB scaling: quantize the ratio with a given error margin
@@ -140,76 +148,99 @@ static double roundUpRatio(double ratio) {
 // It adds 'newBatch' to 'pendingBatches_', and if sufficient batches have been queued, then
 // returns 'pendingBatches_' in 'subBatches' and resets it. If not, it returns false.
 bool SyncGraphGroup::tryGetSubBatches(Ptr<data::Batch> newBatch, std::vector<Ptr<data::Batch>>& subBatches) {
-  pendingBatches_.push_back(newBatch);
+  // The reader delivers in chunks of these sizes, according to case:
+  //  - no dynamic MB-size scaling:
+  //     - reader batch size = update batch size, with...
+  //     - mini-batch-fit:
+  //        - update batch size = what fits into all GPUs, times decay_ to allow experimenting with fractional sizes
+  //     - no mini-batch-fit:
+  //        - update batch size = user-specified size (user guarantees that it fits if distributed over delay_ GPUs)
+  //  - dynamic MB-size scaling:
+  //     - update batch size = aggregate reader batch size * (dynamic progress-based ratio * reference adjustment), with...
+  //     - mini-batch-fit:
+  //        - aggregate reader batch size = equal to what fits into one GPU * warpSize * delay_
+  //     - no mini-batch-fit:
+  //        - aggregate reader batch size = user-specified size (user guarantees that it fits if distributed over delay_ GPUs)
+  //     - reference adjustment =
+  //        - reference batch size specified: (reference batch size / typical aggregate reader batch size)
+  //        - no ref size specified: 1
+
   size_t warpSize = devices_.size() * mpi_->numMPIProcesses(); // warp := set of batches processed concurrently across GPus and workers
 
-  // MB-size warm-up and dynamic scaling
-  double ratio;
-  bool isDynamic = scheduler_->tryGetDynamicMBSizeMultiplier(ratio);
-  if (isDynamic)
-    ratio = roundUpRatio(ratio); // round up to full batches if within a certain error margin  --@BUGBUG: Not invariant w.r.t. GPU size, as ratio is relative to what fits into 1 GPU
-  else   // if dynamic scaling not enabled, then fill each GPU with a batch
-    ratio = delay_ * (double)warpSize; // note: delay_ may be fractional
-
-  // adjust for reference batch size if given
-  // At progress == mbWarmup.n (ratio=1), we would like to have refBatchLabels instead of whichever
-  // the actual batch size is. We approximate the latter as typicalTrgBatchWords, and scale ratio accordingly.
+  // if not dynamic then return the big batch, but first split it over GPUs as it may be too large
+  if (!scheduler_->isDynamicMBSizeScaling()) {
+    // If mini-batch-fit, then the read batch is (devices_.size() * mpi_->numMPIProcesses() * delay_)
+    // times what fits one GPU. If not mini-batch-fit, it is whatever the user has specified, which
+    // is the user's responsibility to guarantee that it fits into 'delay_' warps.
+    // Distribute evenly over all GPUs we have, using multiple warps if needed.
+    size_t numWarps = (size_t)ceil(delay_);
+    subBatches = newBatch->split(numWarps * warpSize);
+    return true;
+  }
+  LOG_ONCE(info, "[training] Dynamic mini-batch scaling enabled");
+
+  // if dynamic and mini-batch-fit, then we get batches in the size of what fits into one GPU
+  pendingBatches_.push_back(newBatch);
+
+  // what ratio do we want, based on current training progress schedule?
+  double ratio = scheduler_->getDynamicMBSizeMultiplier();
+
+  // relative to what base? (what does ratio == 1 mean)
+  ratio *= updateMultiplier_; // if mini-batch-fit, this is = warpSize * delay_, otherwise 1
+
+  // If a reference is given, then at progress == mbWarmup.n (ratio=1), we would like to have refBatchLabels instead of whichever
+  // the actual batch size is. Since we cannot know the future actual batch sizes that will be delivered
+  // by the reader, we approximate them with (typicalTrgBatchWords * updateMultiplier), and scale ratio accordingly.
   auto refBatchLabels = options_->get<size_t>("mini-batch-words-ref");
   if (refBatchLabels != 0) {
-    LOG_ONCE(info, "[scheduler] Scaling to {} reference labels. Typical actual batch words is {}", refBatchLabels, typicalTrgBatchWords_);
-    ABORT_IF(typicalTrgBatchWords_ == 0, "dynamic scaling with words target requires MB size to be known in words"); // happens if MB size is specified in sentences
-    ratio *= (double)refBatchLabels / (double)typicalTrgBatchWords_;
+    LOG_ONCE(info, "[scheduler] Scaling to {} reference labels, using actual-batch-word estimate of {}", refBatchLabels, typicalTrgBatchWords_);
+    ABORT_IF(typicalTrgBatchWords_ == 0, "Dynamic scaling with words target requires MB size to be known in words"); // happens if MB size is specified in sentences
+    ratio *= (double)refBatchLabels / (double)(typicalTrgBatchWords_ * updateMultiplier_);
   }
 
+  // round up to full batches if within a certain error margin  --@BUGBUG: Not invariant w.r.t. GPU size, as ratio is relative to what fits into 1 GPU
+  ratio = roundUpRatio(ratio);
+
   if (pendingBatches_.size() < ratio)
     return false; // not enough data yet
 
   // now we have enough to fill at least 'ratio' batches
-  if (pendingBatches_.size() == ratio)
-    return true; // nothing to do, e.g. warm-up not enabled
-
-  // warm-up is happening
-  LOG_ONCE(info, "[training] Mini-batch-warmup enabled");
+  // @BUGBUG: We do not handle the case that fixed MB size * ratio exceeds GPU memory (we'd need to split that).
 
-  // shorten all batches a little to accurately reflect ratio
-  // e.g. ratio = 3.3 for 4 batches: Reduce each by 3.3/4
+  // in fact, we got too much, so make up for it by shortening all batches to accurately reflect desried ratio
+  // e.g. ratio = 3.3 for 4 batches -> Reduce each by 3.3/4
   // Alternatively, we could just shorten the last 'warp', but that would not be invariant to warp size.
-  size_t before = 0, after = 0;
   for (auto& batch : pendingBatches_) {
     auto reducedBatchSize = (size_t)ceil((double)batch->size() * ratio / (double)pendingBatches_.size());
     size_t minSize = 1;
     if (pendingBatches_.size() == 1) { // enforce a minimum (only needed/correct if still in first batch)
-      size_t minTrgWords = 256;    // don't go below this number of target words, as it seems excessive  --@TODO: parameterize?
+      size_t minTrgWords = 256;        // don't go below this number of target words, as it seems excessive  --@TODO: parameterize?
       minSize = 1 + (minTrgWords * batch->size() - 1) / batch->wordsTrg(); // approximately convert minTrgWords into a #sentences
     }
     reducedBatchSize = std::max(reducedBatchSize, minSize);
-    before += batch->wordsTrg();
     if (reducedBatchSize < batch->size())
       batch = batch->split(/*numSubBatches=*/1, reducedBatchSize).front();
-    after += batch->wordsTrg();
   }
 
   // load-balance: distribute the last numWarps-group's batches over GPUs
   // This is tricky since batches do not have the same length, therefore we can only split, but not merge.
   auto numWarps = (pendingBatches_.size() - 1) / warpSize + 1; // = ceil(#buffers / (#GPUs * #workers))
-  auto availableBatches = numWarps * warpSize; // we got this many GPUs anyways, so we better make use of them
-  if (pendingBatches_.size() < availableBatches) {
-    // we are not using all available GPUs -> try to load-balance a bit better
-    auto fullBatches = (numWarps - 1) * warpSize;
-    auto expandLast = pendingBatches_.size() - fullBatches;
-    auto toLast = availableBatches - fullBatches;
-    LOG(info, "attempt to redistribute {} last batches over {}", expandLast, toLast);
-    auto splitInto = toLast / expandLast; // unfortunately we can only split in integer ratios
-    // @TODO: We can do better since the last batch is typically smaller.
-    if (splitInto > 1) {
+  auto availableGPUSlots = numWarps * warpSize; // we will run this many GPUs: better use them all
+  if (pendingBatches_.size() < availableGPUSlots) {
+    // last warp does not use all available GPUs: try to re-balance
+    auto fullWarpsBatches = (numWarps - 1) * warpSize; // number of batches in all but the last warp. Those warps that are fully used.
+    auto lastWarpSize = pendingBatches_.size() - fullWarpsBatches; // the last warp is possibly not fully used
+    LOG(info, "attempt to redistribute {} last batches over {}", lastWarpSize, warpSize);
+    auto splitInto = warpSize / lastWarpSize;
+    if (splitInto > 1) { // unfortunately we can only split in integer ratios
       // split each of last numWarps's batches into 'splitInto' batches
       // pop them first
       std::vector<Ptr<data::Batch>> batchesToSplit;
-      while (pendingBatches_.size() > fullBatches) {
+      while (pendingBatches_.size() > fullWarpsBatches) {
         batchesToSplit.push_back(pendingBatches_.back());
         pendingBatches_.pop_back();
       }
-      // now split them
+      // now split them and push them back
       for (auto& batchToSplit : batchesToSplit) {
         LOG(info, "{}-way splitting batchToSplit with size {}", splitInto, batchToSplit->size());
         auto splitBatches = batchToSplit->split(splitInto);
@@ -219,7 +250,7 @@ bool SyncGraphGroup::tryGetSubBatches(Ptr<data::Batch> newBatch, std::vector<Ptr
         }
       }
     }
-    ABORT_IF(pendingBatches_.size() > availableBatches, "somehow split into too many batches??");
+    ABORT_IF(pendingBatches_.size() > availableGPUSlots, "somehow split into too many batches??");
   }
   subBatches = std::move(pendingBatches_);
 
@@ -238,15 +269,15 @@ void SyncGraphGroup::update(Ptr<data::Batch> newBatch) /*override*/ {
     return;
 
   // Helper to access the subBatches array
-  auto getSubBatch = [&](size_t delay, size_t localDeviceIndex, size_t rank) -> Ptr<data::Batch> {
-    // 'delay' should be slowest changing dimension. If subBatches are sorted by
+  auto getSubBatch = [&](size_t warp, size_t localDeviceIndex, size_t rank) -> Ptr<data::Batch> {
+    // Warp should be slowest changing dimension. If subBatches are sorted by
     // length, then grouping sentences of similar length into the same delay step can
     // reduce unnecessary time spent in padding.
-    auto index = (delay * mpi_->numMPIProcesses() + rank) * devices_.size() + localDeviceIndex;
+    auto index = (warp * mpi_->numMPIProcesses() + rank) * devices_.size() + localDeviceIndex;
     if (index < subBatches.size())
       return subBatches[index];
     else
-      return nullptr;
+      return nullptr; // null if we reached beyond the end
   };
 
   // Upon very first execution, reset everything
@@ -262,27 +293,27 @@ void SyncGraphGroup::update(Ptr<data::Batch> newBatch) /*override*/ {
   // Compute gradients
   // This happens in multiple steps in case of delay > 1.
   std::vector<float> localDeviceCosts(devices_.size(), 0.f); // [local device index] aggregate cost for each local device
-  for (size_t delay = 0; getSubBatch(delay, 0, 0); delay++) {
+  for (size_t warp = 0; getSubBatch(warp, 0, 0); warp++) {
     // Execute single forward/backward step
     auto forwardBackward = [&](size_t localDeviceIndex, size_t /*begin*/, size_t /*end*/) {
       auto graph = graphs_[localDeviceIndex];
-      auto subBatch = getSubBatch(delay, localDeviceIndex, mpi_->myMPIRank());
+      auto subBatch = getSubBatch(warp, localDeviceIndex, mpi_->myMPIRank());
 
       if(subBatch) {
         auto costNode = builders_[localDeviceIndex]->build(graph, subBatch);
         graph->forward();
         localDeviceCosts[localDeviceIndex] += costNode->scalar();
-        graph->backward(/*zero=*/delay == 0); // only reset gradients to 0 if delay = 0
+        graph->backward(/*zero=*/warp == 0); // only reset gradients to 0 if warp = 0
       }
       else { // empty batch: execute do-nothing fw-bw step for proper inits and resets
 #if 1   // @TODO: double-check whether the #else branch is the same; and if so, use it instead
         graph->params()->allocateBackward();
-        if (delay == 0) // these have already been sized
+        if (warp == 0) // these have already been sized
           graph->params()->set_zero_adjoint();
 #else
         graph->clear(); // instead of build()
         graph->forward();
-        graph->backward(/*zero=*/delay == 0);
+        graph->backward(/*zero=*/warp == 0);
 #endif
       }
     };
diff --git a/src/training/graph_group_sync.h b/src/training/graph_group_sync.h
index 45f90112e..6bb3a4861 100755
--- a/src/training/graph_group_sync.h
+++ b/src/training/graph_group_sync.h
@@ -26,8 +26,9 @@ class SyncGraphGroup : public GraphGroup, public ExponentialSmoothing {
 
   // state for update()
   bool first_{ true };                           // gets interpreted and cleared by update()
-  std::vector<Ptr<data::Batch>> pendingBatches_; // in case of delay, multi-worker, and/or multi-GPU, we buffer up batches
-  size_t typicalTrgWords_{};                     // typical batch size in words (labels); remembered from collectStats()
+  std::vector<Ptr<data::Batch>> pendingBatches_; // in case of dynamic MB-size scaling, we temporarly buffer up batches across update() calls until enough
+  size_t typicalTrgWords_{};                     // typical batch size in words (labels), 0 if unknown (e.g. specified in sentences)
+  double updateMultiplier_{1};                  // multiplier not applied in collectStats() (no multiplier if not mini-batch-fit)
 
   void initialize(const Ptr<data::Batch>& exampleBatch);
   void initializeAvg();
diff --git a/src/training/scheduler.h b/src/training/scheduler.h
index f1b65bd0e..802282354 100755
--- a/src/training/scheduler.h
+++ b/src/training/scheduler.h
@@ -62,24 +62,30 @@ class Scheduler : public TrainingObserver {
   }
 
 public:
-  // determine dynamic MB size, if respective parameters are given (return false if not)
-  bool tryGetDynamicMBSizeMultiplier(double /*out*/ &ratio) const {
+  // test if any parameters specify dynamic MB scaling
+  bool isDynamicMBSizeScaling() const {
     auto mbWarmup = SchedulingParameter::parse(options_->get<std::string>("mini-batch-warmup"));
-    if (!mbWarmup)
-      return false;
+    auto mbTracking = options_->get<bool>("mini-batch-track-lr");
+    return mbWarmup || mbTracking;
+  }
 
-    ratio = 1.0;
-    // mini-batch-warmup
-    LOG_ONCE(info, "[scheduler] Mini-batch size warmup {}", std::string(mbWarmup));
+  // determine dynamic MB scaling factor
+  double getDynamicMBSizeMultiplier() const {
+    double ratio = 1.0;
 
-    // This scales MB size up from the start.
-    // now scale batch size relative to progress within warm-up period
-    size_t progress = state_->getProgressIn(mbWarmup.unit); // number of updates/labels processed
-    auto progressRatio = (double)progress / (double)mbWarmup.n; // where are we relatively within target warm-up period
-    if (mbWarmup.unit == SchedulingUnit::trgLabels)
-      progressRatio = std::sqrt(progressRatio);
-    // apply ratio to actual batch size
-    ratio *= progressRatio;
+    auto mbWarmup = SchedulingParameter::parse(options_->get<std::string>("mini-batch-warmup"));
+    if (mbWarmup) {
+      // mini-batch-warmup
+      LOG_ONCE(info, "[scheduler] Mini-batch size warmup {}", std::string(mbWarmup));
+      // This scales MB size up from the start, relative to progress within warm-up period.
+      size_t progress = state_->getProgressIn(mbWarmup.unit); // number of updates/labels processed
+      auto progressRatio = (double)progress / (double)mbWarmup.n; // where are we relatively within target warm-up period
+      // if unit is labels, then account for the fact that our increment itself is not constant
+      if (mbWarmup.unit == SchedulingUnit::trgLabels)
+        progressRatio = std::sqrt(progressRatio);
+      // apply ratio to actual batch size
+      ratio *= progressRatio;
+    }
 
     // dynamic MB-size tracking with learning rate
     // As LR goes down, MB gets ramped up by the same ratio, which has been found to be safe.
@@ -90,7 +96,7 @@ class Scheduler : public TrainingObserver {
         LOG_ONCE(info, "[scheduler] Dynamic mini-batch size adjustment enabled and kicking in");
       ratio /= lrFactor;
     }
-    return true;
+    return ratio;
   }
 
   Scheduler(Ptr<Options> options, Ptr<TrainingState> state)
diff --git a/src/training/training.h b/src/training/training.h
index 17512efd4..401f6bdf0 100755
--- a/src/training/training.h
+++ b/src/training/training.h
@@ -34,6 +34,9 @@ class Train : public ModelTask {
 
     dataset->prepare();
 
+    auto trainState = New<TrainingState>(options_->get<float>("learn-rate"));
+    auto scheduler = New<Scheduler>(options_, trainState);
+
     Ptr<BatchStats> stats;
     if(options_->get<bool>("mini-batch-fit")) {
       LOG(info,
@@ -42,13 +45,11 @@ class Train : public ModelTask {
           options_->get<size_t>("mini-batch-fit-step"));
       // @TODO, better fake batch with vocabulary
       auto model = New<ModelWrapper>(options_);
-      THREAD_GUARD(stats = model->collectStats());
+      model->setScheduler(scheduler); // collectStats() needs to know about dynamic MB scaling
+      stats = model->collectStats();
       LOG(info, "[batching] Done");
     }
 
-    auto trainState = New<TrainingState>(options_->get<float>("learn-rate"));
-    auto scheduler = New<Scheduler>(options_, trainState);
-
     if((options_->has("valid-sets") || options_->has("valid-script-path"))
        && SchedulingParameter::parse(options_->get<std::string>("valid-freq"))) {
       for(auto validator : Validators(dataset->getVocabs(), options_))

From fbf6aa4bebc3dee78b09a88799bc295330b204ba Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Thu, 13 Dec 2018 17:57:26 -0800
Subject: [PATCH 041/838] (minor)

---
 src/data/batch_stats.h            | 3 +--
 src/training/graph_group_sync.cpp | 2 +-
 2 files changed, 2 insertions(+), 3 deletions(-)

diff --git a/src/data/batch_stats.h b/src/data/batch_stats.h
index 2fc642938..bba055d5b 100755
--- a/src/data/batch_stats.h
+++ b/src/data/batch_stats.h
@@ -56,8 +56,7 @@ class BatchStats {
     for (const auto& entry : map_) {
       auto maxTrgLength = entry.first.back();
       auto numSentences = entry.second;
-      auto numLabels = numSentences * maxTrgLength;
-      sum += numLabels;
+      sum += numSentences * maxTrgLength;
     }
     return sum / map_.size();
   }
diff --git a/src/training/graph_group_sync.cpp b/src/training/graph_group_sync.cpp
index f25e321c3..107c9a4bf 100755
--- a/src/training/graph_group_sync.cpp
+++ b/src/training/graph_group_sync.cpp
@@ -182,7 +182,7 @@ bool SyncGraphGroup::tryGetSubBatches(Ptr<data::Batch> newBatch, std::vector<Ptr
   // if dynamic and mini-batch-fit, then we get batches in the size of what fits into one GPU
   pendingBatches_.push_back(newBatch);
 
-  // what ratio do we want, based on current training progress schedule?
+  // what ratio (how many batches in reader's batch size) do we want, based on current training progress schedule?
   double ratio = scheduler_->getDynamicMBSizeMultiplier();
 
   // relative to what base? (what does ratio == 1 mean)

From b31c9eecd9d217c831777c1db581e59165840356 Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Thu, 13 Dec 2018 18:35:52 -0800
Subject: [PATCH 042/838] moved gradient resetting into scatterReduce function,
 in prep for residuals

---
 src/graph/parameters.h            |  1 +
 src/training/communicator.h       |  8 ++++----
 src/training/communicator_nccl.h  | 14 +++++++++++---
 src/training/graph_group_sync.cpp | 12 ++++++------
 4 files changed, 22 insertions(+), 13 deletions(-)

diff --git a/src/graph/parameters.h b/src/graph/parameters.h
index 32f88a1e4..71c538fcd 100755
--- a/src/graph/parameters.h
+++ b/src/graph/parameters.h
@@ -73,6 +73,7 @@ class Parameters {
       for(auto p : params_)
         if(!p->grad())
           grads_->allocate(p->grad(), p->shape());
+      grads()->set(0.f);
     }
   }
 
diff --git a/src/training/communicator.h b/src/training/communicator.h
index 7e705cfac..4190401a8 100755
--- a/src/training/communicator.h
+++ b/src/training/communicator.h
@@ -37,8 +37,8 @@ class ICommunicator {
   virtual void foreach(const ForeachFunc& func, bool parallel = true) const = 0;
   // @TODO: We probably can still share foreach() between the two implementations. Just need to move some helper functions from the .cu file.
 
-  virtual void scatterReduce() const = 0; // reduce param gradients and scatter into gradient shards
-  virtual void allGather() const = 0;     // redistribute value shards into param values
+  virtual void scatterReduceAndResetGrads() const = 0; // reduce param gradients and scatter into gradient shards
+  virtual void allGatherParams() const = 0;     // redistribute value shards into param values
 
   virtual void swapParams(const std::vector<Tensor>& paramShards) const = 0;
 
@@ -153,7 +153,7 @@ class DefaultCommunicator : public ICommunicator {
       t.join();
   }
 
-  void scatterReduce() const override {
+  void scatterReduceAndResetGrads() const override {
     const_cast<DefaultCommunicator*>(this)->lazyInit();
 
     int totalSize = (int)graphs_[0]->params()->vals()->size();
@@ -178,7 +178,7 @@ class DefaultCommunicator : public ICommunicator {
     foreach(scatter);
   }
 
-  void allGather() const override {
+  void allGatherParams() const override {
     int totalSize = (int)graphs_[0]->params()->vals()->size();
     int shardSize = (int)ceil(totalSize / (float)graphs_.size());
 
diff --git a/src/training/communicator_nccl.h b/src/training/communicator_nccl.h
index 09b70f3e6..1b9512102 100755
--- a/src/training/communicator_nccl.h
+++ b/src/training/communicator_nccl.h
@@ -219,7 +219,7 @@ class NCCLCommunicator : public ICommunicator {
         threadResults_[i].wait();
   }
 
-  void scatterReduce() const override {
+  void scatterReduceAndResetGrads() const override {
     synchronizeAllOnNullStream();
 
     groupStart();
@@ -237,12 +237,20 @@ class NCCLCommunicator : public ICommunicator {
     }
     groupEnd();
     synchronizeAll();
+
+    // reset gradients
+    // In the future, we can keep quantization residuals here straight in the grads themselves.
+    auto resetGrads = [&](size_t localDeviceIndex, size_t /*begin*/, size_t /*end*/) {
+      auto graph = graphs_[localDeviceIndex];
+      graph->params()->set_zero_adjoint();
+    };
+    foreach(resetGrads);
   }
 
   // This distributes all 64 model shards to all 64 GPUs.
-  // @TODO: For unknown reasons, this takes longer than any other operation incl. scatterReduce().
+  // @TODO: For unknown reasons, this takes longer than any other operation incl. scatterReduceAndResetGrads().
   //        But both should have the same number of data transfers of the same size.
-  void allGather() const override {
+  void allGatherParams() const override {
     synchronizeAllOnNullStream();
 
     groupStart();
diff --git a/src/training/graph_group_sync.cpp b/src/training/graph_group_sync.cpp
index 107c9a4bf..9aa5d288e 100755
--- a/src/training/graph_group_sync.cpp
+++ b/src/training/graph_group_sync.cpp
@@ -303,17 +303,17 @@ void SyncGraphGroup::update(Ptr<data::Batch> newBatch) /*override*/ {
         auto costNode = builders_[localDeviceIndex]->build(graph, subBatch);
         graph->forward();
         localDeviceCosts[localDeviceIndex] += costNode->scalar();
-        graph->backward(/*zero=*/warp == 0); // only reset gradients to 0 if warp = 0
+        graph->backward(/*zero=*/false); // gradients are reset by the scatterReduce op
       }
       else { // empty batch: execute do-nothing fw-bw step for proper inits and resets
 #if 1   // @TODO: double-check whether the #else branch is the same; and if so, use it instead
         graph->params()->allocateBackward();
-        if (warp == 0) // these have already been sized
-          graph->params()->set_zero_adjoint();
+        //if (warp == 0) // these have already been sized
+        //  graph->params()->set_zero_adjoint();
 #else
         graph->clear(); // instead of build()
         graph->forward();
-        graph->backward(/*zero=*/warp == 0);
+        graph->backward(/*zero=*/false);
 #endif
       }
     };
@@ -352,9 +352,9 @@ void SyncGraphGroup::update(Ptr<data::Batch> newBatch) /*override*/ {
           paramsAvg_[idx], curParam, scheduler_->numberOfBatches(), mbWords);
   };
 
-  comm_->scatterReduce(); // reduce gradients across all devices (globally) into shards
+  comm_->scatterReduceAndResetGrads(); // reduce gradients across all devices (globally) into shards
   comm_->foreach(update); // per-shard model-update
-  comm_->allGather();     // distribute param value shards back
+  comm_->allGatherParams();     // distribute param value shards back
 
   // cost across all local devices (scheduler will aggregate cross-process)
   float localCost = 0;

From e1f81ee97f6ca44ef1c2a33f195659fa68f5d11e Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Thu, 13 Dec 2018 19:21:17 -0800
Subject: [PATCH 043/838] bug fix: reduceScatter should not reset its on result

---
 src/training/communicator_nccl.h | 11 ++++++++---
 1 file changed, 8 insertions(+), 3 deletions(-)

diff --git a/src/training/communicator_nccl.h b/src/training/communicator_nccl.h
index 1b9512102..dbfe2d877 100755
--- a/src/training/communicator_nccl.h
+++ b/src/training/communicator_nccl.h
@@ -240,9 +240,14 @@ class NCCLCommunicator : public ICommunicator {
 
     // reset gradients
     // In the future, we can keep quantization residuals here straight in the grads themselves.
-    auto resetGrads = [&](size_t localDeviceIndex, size_t /*begin*/, size_t /*end*/) {
-      auto graph = graphs_[localDeviceIndex];
-      graph->params()->set_zero_adjoint();
+    auto resetGrads = [&](size_t i, size_t begin, size_t end) {
+      auto grads = graphs_[i]->params()->grads();
+      auto size = grads->size();
+      // reset everything outside the shard that we reduce in
+      if (begin > 0)
+        grads->subtensor(0, begin)->set(0.f);
+      if (end < size)
+        grads->subtensor(end, size - end)->set(0.f);
     };
     foreach(resetGrads);
   }

From d216d1e5b562612590202f9fee831e167203e662 Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Thu, 13 Dec 2018 20:05:42 -0800
Subject: [PATCH 044/838] bug fix: need to reset gradientttttttt shard as well

---
 src/training/graph_group_sync.cpp | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/src/training/graph_group_sync.cpp b/src/training/graph_group_sync.cpp
index 9aa5d288e..d948612f2 100755
--- a/src/training/graph_group_sync.cpp
+++ b/src/training/graph_group_sync.cpp
@@ -303,6 +303,7 @@ void SyncGraphGroup::update(Ptr<data::Batch> newBatch) /*override*/ {
         auto costNode = builders_[localDeviceIndex]->build(graph, subBatch);
         graph->forward();
         localDeviceCosts[localDeviceIndex] += costNode->scalar();
+        //graph->backward(/*zero=*/warp == 0); // gradients are reset by the scatterReduce op
         graph->backward(/*zero=*/false); // gradients are reset by the scatterReduce op
       }
       else { // empty batch: execute do-nothing fw-bw step for proper inits and resets
@@ -346,6 +347,7 @@ void SyncGraphGroup::update(Ptr<data::Batch> newBatch) /*override*/ {
 
     // actual model update
     shardOpt_[idx]->update(curParam, curGrad, mbWords);
+    curGrad->set(0.f);
 
     if(mvAvg_)
       updateAvgParams(

From 8dcebdbe30acf5a24d401a19988fd2827006d0d5 Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Thu, 13 Dec 2018 21:17:08 -0800
Subject: [PATCH 045/838] initialize() now also allocates and resets the
 gradients, and uses foreach()

---
 src/graph/parameters.h            |  1 -
 src/training/graph_group_sync.cpp | 48 +++++++++++++++++--------------
 2 files changed, 27 insertions(+), 22 deletions(-)

diff --git a/src/graph/parameters.h b/src/graph/parameters.h
index 71c538fcd..32f88a1e4 100755
--- a/src/graph/parameters.h
+++ b/src/graph/parameters.h
@@ -73,7 +73,6 @@ class Parameters {
       for(auto p : params_)
         if(!p->grad())
           grads_->allocate(p->grad(), p->shape());
-      grads()->set(0.f);
     }
   }
 
diff --git a/src/training/graph_group_sync.cpp b/src/training/graph_group_sync.cpp
index d948612f2..339d46b1f 100755
--- a/src/training/graph_group_sync.cpp
+++ b/src/training/graph_group_sync.cpp
@@ -45,28 +45,34 @@ void SyncGraphGroup::setScheduler(Ptr<Scheduler> scheduler) /*override*/ {
 }
 
 void SyncGraphGroup::initialize(const Ptr<data::Batch>& exampleBatch) {
-  // Initialize 0th graph with random weights in one forward step
-  // @TODO: Why do we need the THREAD_GUARD here? Why not run this on the main thread?
-  THREAD_GUARD({
-    builders_[0]->build(graphs_[0], exampleBatch);
-    graphs_[0]->forward();
+  // Initialize graphs with random weights in one forward step
+  // Also allocate and clear the gradients
+  comm_->foreach([&](size_t i, size_t /*begin*/, size_t /*end*/) {
+    builders_[i]->build(graphs_[i], exampleBatch);
+    graphs_[i]->forward();
+    graphs_[i]->params()->allocateBackward();
+    graphs_[i]->params()->set_zero_adjoint();
   });
 
-  // Copy weights from 0th graph to all other graphs
+  // Copy weights from 0-th graph to all other graphs
   // to have equal weights across devices
-  ThreadPool pool(graphs_.size() - 1, graphs_.size() - 1);
-  for(size_t i = 1; i < graphs_.size(); ++i) {
-    auto init = [&](size_t i) {
-      // initialize i-th graph and weights
-      builders_[i]->build(graphs_[i], exampleBatch);
-      graphs_[i]->forward();
-      // overwrite weights of i-th graph with weights from 0-th graph
+  comm_->foreach([&](size_t i, size_t /*begin*/, size_t /*end*/) {
+    if (i > 0)
       graphs_[i]->params()->vals()->copyFrom(graphs_[0]->params()->vals());
-    };
-    pool.enqueue(init, i);
-  }
-  // ThreadPool destructor waits until completion of all tasks.
-  // @TODO: can we use comm_->foreach()?
+  });
+  //ThreadPool pool(graphs_.size() - 1, graphs_.size() - 1);
+  //for(size_t i = 1; i < graphs_.size(); ++i) {
+  //  auto init = [&](size_t i) {
+  //    // initialize i-th graph and weights
+  //    builders_[i]->build(graphs_[i], exampleBatch);
+  //    graphs_[i]->forward();
+  //    // overwrite weights of i-th graph with weights from 0-th graph
+  //    graphs_[i]->params()->vals()->copyFrom(graphs_[0]->params()->vals());
+  //  };
+  //  pool.enqueue(init, i);
+  //}
+  //// ThreadPool destructor waits until completion of all tasks.
+  //// @TODO: can we use comm_->foreach()?
 }
 
 void SyncGraphGroup::initializeAvg() {
@@ -308,7 +314,7 @@ void SyncGraphGroup::update(Ptr<data::Batch> newBatch) /*override*/ {
       }
       else { // empty batch: execute do-nothing fw-bw step for proper inits and resets
 #if 1   // @TODO: double-check whether the #else branch is the same; and if so, use it instead
-        graph->params()->allocateBackward();
+        //graph->params()->allocateBackward();
         //if (warp == 0) // these have already been sized
         //  graph->params()->set_zero_adjoint();
 #else
@@ -355,8 +361,8 @@ void SyncGraphGroup::update(Ptr<data::Batch> newBatch) /*override*/ {
   };
 
   comm_->scatterReduceAndResetGrads(); // reduce gradients across all devices (globally) into shards
-  comm_->foreach(update); // per-shard model-update
-  comm_->allGatherParams();     // distribute param value shards back
+  comm_->foreach(update);              // per-shard model-update
+  comm_->allGatherParams();            // distribute param value shards back
 
   // cost across all local devices (scheduler will aggregate cross-process)
   float localCost = 0;

From a5f0d2eebd375284aabbd08b5d4ff1f01519e1d3 Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Fri, 14 Dec 2018 01:33:31 -0800
Subject: [PATCH 046/838] (minor cleanup)

---
 src/data/batch_generator.h        |  4 ++--
 src/training/graph_group_sync.cpp | 12 ++++++------
 2 files changed, 8 insertions(+), 8 deletions(-)

diff --git a/src/data/batch_generator.h b/src/data/batch_generator.h
index 488d3e55b..7d02b8303 100755
--- a/src/data/batch_generator.h
+++ b/src/data/batch_generator.h
@@ -211,8 +211,8 @@ class BatchGenerator : public RNGEngine {
     }
     double totalSent{}, totalLabels{};
     for (auto& b : tempBatches) {
-        totalSent += (double)b->size();
-        totalLabels += (double)b->words(-1);
+      totalSent += (double)b->size();
+      totalLabels += (double)b->words(-1);
     }
     auto totalDenom = tempBatches.empty() ? 1 : tempBatches.size(); // (make 0/0 = 0)
     LOG(info, "[data] fetched {} batches with {} sentences. Per batch: {} sentences, {} labels.",
diff --git a/src/training/graph_group_sync.cpp b/src/training/graph_group_sync.cpp
index 339d46b1f..d51840b71 100755
--- a/src/training/graph_group_sync.cpp
+++ b/src/training/graph_group_sync.cpp
@@ -330,9 +330,9 @@ void SyncGraphGroup::update(Ptr<data::Batch> newBatch) /*override*/ {
   // At this point, each device on each MPI process has a gradient aggregated over a subset of the sub-batches.
 
   // Update parameter shard with gradient shard
-  auto update = [&](size_t idx, size_t begin, size_t end) {
-    auto curGrad = graphs_[idx]->params()->grads()->subtensor(begin, end-begin);
-    auto curParam = graphs_[idx]->params()->vals()->subtensor(begin, end-begin);
+  auto update = [&](size_t i, size_t begin, size_t end) {
+    auto curGrad = graphs_[i]->params()->grads()->subtensor(begin, end-begin);
+    auto curParam = graphs_[i]->params()->vals()->subtensor(begin, end-begin);
 
     // if individual gradients were averages, then need to average again over all subBatches
     auto div = subBatches.size();
@@ -348,16 +348,16 @@ void SyncGraphGroup::update(Ptr<data::Batch> newBatch) /*override*/ {
     if (options_->get<std::string>("cost-type") == "ce-sum") { // presently only supported for ce-sum
       mbWords = 0;
       for (const auto& batch : subBatches)
-        mbWords += batch->words(-1);  // @TODO: use wordsTrg (it's the same)
+        mbWords += batch->wordsTrg();
     }
 
     // actual model update
-    shardOpt_[idx]->update(curParam, curGrad, mbWords);
+    shardOpt_[i]->update(curParam, curGrad, mbWords);
     curGrad->set(0.f);
 
     if(mvAvg_)
       updateAvgParams(
-          paramsAvg_[idx], curParam, scheduler_->numberOfBatches(), mbWords);
+          paramsAvg_[i], curParam, scheduler_->numberOfBatches(), mbWords);
   };
 
   comm_->scatterReduceAndResetGrads(); // reduce gradients across all devices (globally) into shards

From 9cc5b176db0f7bd3c23d7e49a6b46d9826441f12 Mon Sep 17 00:00:00 2001
From: Marcin Junczys-Dowmunt <marcinjd@microsoft.com>
Date: Fri, 14 Dec 2018 15:11:34 -0800
Subject: [PATCH 047/838] set nvcc host compiler to match general compiler

---
 CMakeLists.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/CMakeLists.txt b/CMakeLists.txt
index 28e648aa9..b54766ae9 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -224,7 +224,7 @@ else(CMAKE_BUILD_TYPE STREQUAL "Debug")
 endif(CMAKE_BUILD_TYPE STREQUAL "Debug")
 if(NOT MSVC)
   # @TODO: add warnings here too
-  list(APPEND CUDA_NVCC_FLAGS -std=c++11; -Xcompiler\ -fPIC; -Xcompiler\ -Wno-unused-result; -Xcompiler\ -Wno-deprecated; -Xcompiler\ -Wno-pragmas; -Xcompiler\ -Wno-unused-value; -Xcompiler\ -Werror;)
+  list(APPEND CUDA_NVCC_FLAGS -ccbin ${CMAKE_C_COMPILER}; -std=c++11; -Xcompiler\ -fPIC; -Xcompiler\ -Wno-unused-result; -Xcompiler\ -Wno-deprecated; -Xcompiler\ -Wno-pragmas; -Xcompiler\ -Wno-unused-value; -Xcompiler\ -Werror;)
 else()
   list(APPEND CUDA_NVCC_FLAGS -Xcompiler\ /FS; )
 endif()

From 48597eeb9f5340e7ca367634c82b8743e53cb7ef Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Mon, 17 Dec 2018 11:46:27 -0800
Subject: [PATCH 048/838] temporarily added override to enable/disable
 TensorCores; some cleanup

---
 src/tensors/gpu/prod.cu              | 32 ++++++++++++++++++++++++++--
 src/training/exponential_smoothing.h |  1 +
 src/training/graph_group_sync.cpp    |  8 +++----
 src/training/training.h              |  2 +-
 4 files changed, 36 insertions(+), 7 deletions(-)
 mode change 100644 => 100755 src/tensors/gpu/prod.cu

diff --git a/src/tensors/gpu/prod.cu b/src/tensors/gpu/prod.cu
old mode 100644
new mode 100755
index 9558a67f2..5b2889328
--- a/src/tensors/gpu/prod.cu
+++ b/src/tensors/gpu/prod.cu
@@ -11,6 +11,32 @@ namespace marian {
 
 namespace gpu {
 
+static void setTensorMode(cublasHandle_t cublasHandle) {
+  static int mode = 0;  // 1: use TC; -1: do not use TC; 0: not set yet
+  if (mode == 0) { // multi-thread note: this is sort-of thread-safe, since multiple threads would determine the same value
+    const char* var = getenv("ENABLE_CUBLAS_TENSOR_OP_MATH_FP32");
+    if (!var)
+      var = "0";
+    switch(var[0]) {
+    case '0': mode = -1; break;
+    case '1': mode =  1; break;
+    default: ABORT("Invalid ENABLE_CUBLAS_TENSOR_OP_MATH_FP32={}", var);
+    }
+    if (mode > 0) { // try whether it can be set   --@TODO: check whether this actually works
+      cublasSetMathMode(cublasHandle, CUBLAS_TENSOR_OP_MATH);
+      cublasMath_t actual = CUBLAS_DEFAULT_MATH;
+      cublasGetMathMode(cublasHandle, &actual);
+      if (actual != CUBLAS_TENSOR_OP_MATH) {
+        LOG(info, "WARNING: TensorCores requested but not available");
+        mode = -1;
+      }
+    }
+    if (mode > 0)
+      LOG(info, "TensorCores enabled");
+  }
+  cublasSetMathMode(cublasHandle, mode > 0 ? CUBLAS_TENSOR_OP_MATH : CUBLAS_DEFAULT_MATH);
+}
+
 void Prod(marian::Tensor C,
           const marian::Tensor& A,
           const marian::Tensor& B,
@@ -45,7 +71,8 @@ void Prod(marian::Tensor C,
                           ->getCublasHandle();
 
 #if CUDA_VERSION >= 9000
-  cublasSetMathMode(cublasHandle, CUBLAS_TENSOR_OP_MATH);
+  setTensorMode(cublasHandle);
+  //cublasSetMathMode(cublasHandle, CUBLAS_TENSOR_OP_MATH);
 #endif
 
   cublasSgemm(cublasHandle,
@@ -170,7 +197,8 @@ void ProdBatched(marian::Tensor C,
   CudaCopy(cptr.data(), cptr.data() + cptr.size(), mp_cptr->data<float*>());
 
 #if CUDA_VERSION >= 9000
-  cublasSetMathMode(cublasHandle, CUBLAS_TENSOR_OP_MATH);
+  setTensorMode(cublasHandle);
+  //cublasSetMathMode(cublasHandle, CUBLAS_TENSOR_OP_MATH);
 #endif
   cublasSgemmBatched(cublasHandle,
                      opB,
diff --git a/src/training/exponential_smoothing.h b/src/training/exponential_smoothing.h
index e24c1e74b..f8786fca1 100755
--- a/src/training/exponential_smoothing.h
+++ b/src/training/exponential_smoothing.h
@@ -24,6 +24,7 @@ class ExponentialSmoothing {
     double beta = 1. - mvDecayBy_;
     // correction term if batch size is different from what mvDecayBy_ was specified for
     if (refBatchTrgWords_) {
+      LOG_ONCE(info, "Exponential smoothing gets automatically adjusted as if update size was {} target words", refBatchTrgWords_);
       ABORT_IF(actualBatchTrgWords == OptimizerBase::mbSizeNotProvided,
                "This graph-group type does not support reference batch size specification for exponential-smoothing");
       beta = pow(beta, (double)actualBatchTrgWords / (double)refBatchTrgWords_);
diff --git a/src/training/graph_group_sync.cpp b/src/training/graph_group_sync.cpp
index d51840b71..de2e34b17 100755
--- a/src/training/graph_group_sync.cpp
+++ b/src/training/graph_group_sync.cpp
@@ -231,12 +231,12 @@ bool SyncGraphGroup::tryGetSubBatches(Ptr<data::Batch> newBatch, std::vector<Ptr
   // load-balance: distribute the last numWarps-group's batches over GPUs
   // This is tricky since batches do not have the same length, therefore we can only split, but not merge.
   auto numWarps = (pendingBatches_.size() - 1) / warpSize + 1; // = ceil(#buffers / (#GPUs * #workers))
-  auto availableGPUSlots = numWarps * warpSize; // we will run this many GPUs: better use them all
-  if (pendingBatches_.size() < availableGPUSlots) {
+  auto availableDevices = numWarps * warpSize; // we will run this many GPUs: better use them all
+  if (pendingBatches_.size() < availableDevices) {
     // last warp does not use all available GPUs: try to re-balance
     auto fullWarpsBatches = (numWarps - 1) * warpSize; // number of batches in all but the last warp. Those warps that are fully used.
     auto lastWarpSize = pendingBatches_.size() - fullWarpsBatches; // the last warp is possibly not fully used
-    LOG(info, "attempt to redistribute {} last batches over {}", lastWarpSize, warpSize);
+    LOG(info, "attempt to redistribute last {} batches over {} devices", lastWarpSize, warpSize);
     auto splitInto = warpSize / lastWarpSize;
     if (splitInto > 1) { // unfortunately we can only split in integer ratios
       // split each of last numWarps's batches into 'splitInto' batches
@@ -256,7 +256,7 @@ bool SyncGraphGroup::tryGetSubBatches(Ptr<data::Batch> newBatch, std::vector<Ptr
         }
       }
     }
-    ABORT_IF(pendingBatches_.size() > availableGPUSlots, "somehow split into too many batches??");
+    ABORT_IF(pendingBatches_.size() > availableDevices, "somehow split into too many batches??");
   }
   subBatches = std::move(pendingBatches_);
 
diff --git a/src/training/training.h b/src/training/training.h
index 401f6bdf0..ce9c5d4cf 100755
--- a/src/training/training.h
+++ b/src/training/training.h
@@ -47,7 +47,7 @@ class Train : public ModelTask {
       auto model = New<ModelWrapper>(options_);
       model->setScheduler(scheduler); // collectStats() needs to know about dynamic MB scaling
       stats = model->collectStats();
-      LOG(info, "[batching] Done");
+      LOG(info, "[batching] Done. Typical MB size is {} target words", stats->estimateTypicalTrgWords());
     }
 
     if((options_->has("valid-sets") || options_->has("valid-script-path"))

From fe35160259cb0b103e67e0e8791d90957c3b799b Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Tue, 18 Dec 2018 09:30:38 -0800
Subject: [PATCH 049/838] temporarily allows to enable/disable TensorCores

---
 src/tensors/gpu/prod.cu           | 4 ++--
 src/training/graph_group_sync.cpp | 8 ++++----
 2 files changed, 6 insertions(+), 6 deletions(-)

diff --git a/src/tensors/gpu/prod.cu b/src/tensors/gpu/prod.cu
index 5b2889328..9da32e65a 100755
--- a/src/tensors/gpu/prod.cu
+++ b/src/tensors/gpu/prod.cu
@@ -16,7 +16,7 @@ static void setTensorMode(cublasHandle_t cublasHandle) {
   if (mode == 0) { // multi-thread note: this is sort-of thread-safe, since multiple threads would determine the same value
     const char* var = getenv("ENABLE_CUBLAS_TENSOR_OP_MATH_FP32");
     if (!var)
-      var = "0";
+      var = "1";
     switch(var[0]) {
     case '0': mode = -1; break;
     case '1': mode =  1; break;
@@ -32,7 +32,7 @@ static void setTensorMode(cublasHandle_t cublasHandle) {
       }
     }
     if (mode > 0)
-      LOG(info, "TensorCores enabled");
+      LOG(info, "16-bit TensorCores enabled for float32 matrix operations");
   }
   cublasSetMathMode(cublasHandle, mode > 0 ? CUBLAS_TENSOR_OP_MATH : CUBLAS_DEFAULT_MATH);
 }
diff --git a/src/training/graph_group_sync.cpp b/src/training/graph_group_sync.cpp
index de2e34b17..638642786 100755
--- a/src/training/graph_group_sync.cpp
+++ b/src/training/graph_group_sync.cpp
@@ -236,7 +236,7 @@ bool SyncGraphGroup::tryGetSubBatches(Ptr<data::Batch> newBatch, std::vector<Ptr
     // last warp does not use all available GPUs: try to re-balance
     auto fullWarpsBatches = (numWarps - 1) * warpSize; // number of batches in all but the last warp. Those warps that are fully used.
     auto lastWarpSize = pendingBatches_.size() - fullWarpsBatches; // the last warp is possibly not fully used
-    LOG(info, "attempt to redistribute last {} batches over {} devices", lastWarpSize, warpSize);
+    //LOG(info, "attempting to redistribute last {} batches over {} devices", lastWarpSize, warpSize);
     auto splitInto = warpSize / lastWarpSize;
     if (splitInto > 1) { // unfortunately we can only split in integer ratios
       // split each of last numWarps's batches into 'splitInto' batches
@@ -248,10 +248,10 @@ bool SyncGraphGroup::tryGetSubBatches(Ptr<data::Batch> newBatch, std::vector<Ptr
       }
       // now split them and push them back
       for (auto& batchToSplit : batchesToSplit) {
-        LOG(info, "{}-way splitting batchToSplit with size {}", splitInto, batchToSplit->size());
+        //LOG(info, "{}-way splitting batchToSplit with size {}", splitInto, batchToSplit->size());
         auto splitBatches = batchToSplit->split(splitInto);
         for (auto& splitBatch : splitBatches) {
-          LOG(info, " -> getting batchToSplit with size {}", splitBatch->size());
+          //LOG(info, " -> getting batchToSplit with size {}", splitBatch->size());
           pendingBatches_.push_back(splitBatch);
         }
       }
@@ -288,7 +288,7 @@ void SyncGraphGroup::update(Ptr<data::Batch> newBatch) /*override*/ {
 
   // Upon very first execution, reset everything
   if(first_) {
-    LOG(info, "[training] Processing first minibatch. Batches are processed as {} processes x {} GPUs/process",
+    LOG(info, "[training] Batches are processed as {} process(es) x {} devices/process",
         mpi_->numMPIProcesses(), devices_.size());
     initialize(subBatches.front());
     if(mvAvg_ && paramsAvg_.empty())

From e627684b48497102935af685e5ef79bcc16ce6c5 Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Tue, 18 Dec 2018 14:44:07 -0800
Subject: [PATCH 050/838] new experimental option --mini-batch-overstuff

---
 src/common/config_parser.cpp      |  3 ++
 src/optimizers/optimizers.cpp     |  2 +-
 src/training/graph_group_sync.cpp | 65 ++++++++++++++++++++-----------
 src/training/graph_group_sync.h   |  2 +-
 src/training/scheduler.h          | 19 ++++-----
 5 files changed, 55 insertions(+), 36 deletions(-)

diff --git a/src/common/config_parser.cpp b/src/common/config_parser.cpp
index b5ea081e8..77421d691 100755
--- a/src/common/config_parser.cpp
+++ b/src/common/config_parser.cpp
@@ -618,6 +618,9 @@ void ConfigParser::addSuboptionsBatching(cli::CLIWrapper& cli) {
       {"0"});
   cli.add<bool>("--mini-batch-track-lr",
       "Dynamically track mini-batch size inverse to actual learning rate (not considering lr-warmup)");
+  cli.add<double>("--mini-batch-overstuff",
+      "Stuff this much more data into a minibatch, but scale down the LR and progress counter",
+      1.0);
   // clang-format on
 }
 
diff --git a/src/optimizers/optimizers.cpp b/src/optimizers/optimizers.cpp
index 75501a370..685c917f9 100755
--- a/src/optimizers/optimizers.cpp
+++ b/src/optimizers/optimizers.cpp
@@ -138,8 +138,8 @@ void Adam::updateImpl(Tensor params, Tensor grads, size_t actualMBSize, size_t r
     vt_->set(0.f);
   }
 
-  double Tref = (double)refMBWords;
   double T    = (double)actualMBSize;
+  double Tref = (double)refMBWords;
 
   // adjust for minibatch-size changes if Adam parameters are given a reference size (else do nothing)
   double eta   = eta_ * (T/Tref);
diff --git a/src/training/graph_group_sync.cpp b/src/training/graph_group_sync.cpp
index 638642786..a2e530ec6 100755
--- a/src/training/graph_group_sync.cpp
+++ b/src/training/graph_group_sync.cpp
@@ -153,7 +153,8 @@ static double roundUpRatio(double ratio) {
 // helper routine that handles accumulation and load-balancing of sub-batches to fill all devices
 // It adds 'newBatch' to 'pendingBatches_', and if sufficient batches have been queued, then
 // returns 'pendingBatches_' in 'subBatches' and resets it. If not, it returns false.
-bool SyncGraphGroup::tryGetSubBatches(Ptr<data::Batch> newBatch, std::vector<Ptr<data::Batch>>& subBatches) {
+bool SyncGraphGroup::tryGetSubBatches(Ptr<data::Batch> newBatch, double overstuff,
+    std::vector<Ptr<data::Batch>>& subBatches, size_t& numReadBatches) {
   // The reader delivers in chunks of these sizes, according to case:
   //  - no dynamic MB-size scaling:
   //     - reader batch size = update batch size, with...
@@ -181,6 +182,7 @@ bool SyncGraphGroup::tryGetSubBatches(Ptr<data::Batch> newBatch, std::vector<Ptr
     // Distribute evenly over all GPUs we have, using multiple warps if needed.
     size_t numWarps = (size_t)ceil(delay_);
     subBatches = newBatch->split(numWarps * warpSize);
+    numReadBatches = 1;
     return true;
   }
   LOG_ONCE(info, "[training] Dynamic mini-batch scaling enabled");
@@ -204,6 +206,9 @@ bool SyncGraphGroup::tryGetSubBatches(Ptr<data::Batch> newBatch, std::vector<Ptr
     ratio *= (double)refBatchLabels / (double)(typicalTrgBatchWords_ * updateMultiplier_);
   }
 
+  // overstuff: blow up ratio by a factor, which we later factor into the learning rate
+  ratio *= overstuff;
+
   // round up to full batches if within a certain error margin  --@BUGBUG: Not invariant w.r.t. GPU size, as ratio is relative to what fits into 1 GPU
   ratio = roundUpRatio(ratio);
 
@@ -213,7 +218,9 @@ bool SyncGraphGroup::tryGetSubBatches(Ptr<data::Batch> newBatch, std::vector<Ptr
   // now we have enough to fill at least 'ratio' batches
   // @BUGBUG: We do not handle the case that fixed MB size * ratio exceeds GPU memory (we'd need to split that).
 
-  // in fact, we got too much, so make up for it by shortening all batches to accurately reflect desried ratio
+  numReadBatches = pendingBatches_.size(); // remember original batch-counter increment from reader (which is not always the same as subBatches.size() in the end)
+
+  // in fact, we got too much, so make up for it by shortening all batches to accurately reflect desired ratio
   // e.g. ratio = 3.3 for 4 batches -> Reduce each by 3.3/4
   // Alternatively, we could just shorten the last 'warp', but that would not be invariant to warp size.
   for (auto& batch : pendingBatches_) {
@@ -267,13 +274,29 @@ bool SyncGraphGroup::tryGetSubBatches(Ptr<data::Batch> newBatch, std::vector<Ptr
 void SyncGraphGroup::update(Ptr<data::Batch> newBatch) /*override*/ {
   validate();
 
+  double overstuff = options_->get<double>("mini-batch-overstuff");
+  if (overstuff != 1)
+    LOG_ONCE(info, "Overstuffing minibatches by a factor of {}", overstuff);
   std::vector<Ptr<data::Batch>> subBatches;
-  bool gotSubBatches = tryGetSubBatches(newBatch, subBatches);
+  size_t numReadBatches; // actual #batches delivered by reader, for restoring from checkpoint   --@TODO: reader should checkpoint itself; should not go via the scheduler
+  bool gotSubBatches = tryGetSubBatches(newBatch, overstuff, subBatches, numReadBatches);
 
   // not enough data yet: return right away
   if (!gotSubBatches)
     return;
 
+  // determine num words for dynamic hyper-parameter adjustment
+  // @TODO: We can return these directly from tryGetSubBatches()
+  size_t batchSize = 0;
+  size_t batchTrgWords = 0;
+  for (const auto& batch : subBatches) {
+    batchSize     += batch->size();
+    batchTrgWords += batch->wordsTrg();
+  }
+  // effective batch size: batch should be weighted like this
+  size_t effectiveBatchTrgWords = (size_t)ceil(batchTrgWords / overstuff);
+  size_t effectiveBatchSize     = (size_t)ceil(batchSize     / overstuff);
+
   // Helper to access the subBatches array
   auto getSubBatch = [&](size_t warp, size_t localDeviceIndex, size_t rank) -> Ptr<data::Batch> {
     // Warp should be slowest changing dimension. If subBatches are sorted by
@@ -301,7 +324,7 @@ void SyncGraphGroup::update(Ptr<data::Batch> newBatch) /*override*/ {
   std::vector<float> localDeviceCosts(devices_.size(), 0.f); // [local device index] aggregate cost for each local device
   for (size_t warp = 0; getSubBatch(warp, 0, 0); warp++) {
     // Execute single forward/backward step
-    auto forwardBackward = [&](size_t localDeviceIndex, size_t /*begin*/, size_t /*end*/) {
+    comm_->foreach([&](size_t localDeviceIndex, size_t /*begin*/, size_t /*end*/) { // parallel across devices. Aggregate for warp > 1.
       auto graph = graphs_[localDeviceIndex];
       auto subBatch = getSubBatch(warp, localDeviceIndex, mpi_->myMPIRank());
 
@@ -323,41 +346,37 @@ void SyncGraphGroup::update(Ptr<data::Batch> newBatch) /*override*/ {
         graph->backward(/*zero=*/false);
 #endif
       }
-    };
-
-    comm_->foreach(forwardBackward); // compute gradients in parallel on each device. Aggregate if delay > 1.
+    });
   }
   // At this point, each device on each MPI process has a gradient aggregated over a subset of the sub-batches.
 
+  // If individual gradients were averages, then need to average again over all subBatches
+  auto div = subBatches.size();
+  if (options_->get<std::string>("cost-type") == "ce-sum")
+    div = 1;
+
   // Update parameter shard with gradient shard
   auto update = [&](size_t i, size_t begin, size_t end) {
     auto curGrad = graphs_[i]->params()->grads()->subtensor(begin, end-begin);
     auto curParam = graphs_[i]->params()->vals()->subtensor(begin, end-begin);
 
-    // if individual gradients were averages, then need to average again over all subBatches
-    auto div = subBatches.size();
-    if (options_->get<std::string>("cost-type") == "ce-sum")
-      div = 1;
     if(div != 1) {
       using namespace functional;
-      Element(_1 = _1 / (float)div, curGrad);
-    }
-
-    // determine num words for dynamic hyper-parameter adjustment
-    size_t mbWords = OptimizerBase::mbSizeNotProvided;
-    if (options_->get<std::string>("cost-type") == "ce-sum") { // presently only supported for ce-sum
-      mbWords = 0;
-      for (const auto& batch : subBatches)
-        mbWords += batch->wordsTrg();
+      Element(_1 = _1 / (float)div, curGrad);   // average once again in case of ce-mean*
     }
 
     // actual model update
-    shardOpt_[i]->update(curParam, curGrad, mbWords);
+    auto updateTrgWords =
+        /*if*/(options_->get<std::string>("cost-type") == "ce-sum") ?
+          effectiveBatchTrgWords // if overstuffing then scale the LR down by that factor  --@TODO: how about momentum?
+        /*else*/:
+          OptimizerBase::mbSizeNotProvided;
+    shardOpt_[i]->update(curParam, curGrad, updateTrgWords);
     curGrad->set(0.f);
 
     if(mvAvg_)
       updateAvgParams(
-          paramsAvg_[i], curParam, scheduler_->numberOfBatches(), mbWords);
+          paramsAvg_[i], curParam, scheduler_->numberOfBatches(), updateTrgWords);
   };
 
   comm_->scatterReduceAndResetGrads(); // reduce gradients across all devices (globally) into shards
@@ -375,7 +394,7 @@ void SyncGraphGroup::update(Ptr<data::Batch> newBatch) /*override*/ {
 
   if(scheduler_) {
     // track and log localCost
-    scheduler_->update(localCost, subBatches, mpi_);
+    scheduler_->update(localCost, numReadBatches, effectiveBatchSize, effectiveBatchTrgWords, mpi_);
 
     // save intermediate model (and optimizer state) to file
     if(scheduler_->saving())
diff --git a/src/training/graph_group_sync.h b/src/training/graph_group_sync.h
index 6bb3a4861..22e5c6f28 100755
--- a/src/training/graph_group_sync.h
+++ b/src/training/graph_group_sync.h
@@ -37,7 +37,7 @@ class SyncGraphGroup : public GraphGroup, public ExponentialSmoothing {
   void barrier() const { mpi_->barrier(); } // (we need this several times)
   void swapParamsAvg() { if (mvAvg_ && paramsAvg_.size() > 0) comm_->swapParams(paramsAvg_); } // note: must call this on all MPI ranks in parallel
 
-  bool tryGetSubBatches(Ptr<data::Batch> newBatch, std::vector<Ptr<data::Batch>>& subBatches);
+  bool tryGetSubBatches(Ptr<data::Batch> newBatch, double overstuff, std::vector<Ptr<data::Batch>>& subBatches, size_t& numReadBatches);
 
 public:
   SyncGraphGroup(Ptr<Options> config);
diff --git a/src/training/scheduler.h b/src/training/scheduler.h
index 802282354..5d8189758 100755
--- a/src/training/scheduler.h
+++ b/src/training/scheduler.h
@@ -214,21 +214,18 @@ class Scheduler : public TrainingObserver {
   }
 
   void update(float cost, Ptr<data::Batch> batch) {
-    update(cost, std::vector<Ptr<data::Batch>>({batch}));
+    update(cost,
+        /*numReadBatches=*/1, /*batchSize=*/batch->size(), /*batchLabels=*/batch->wordsTrg());
   }
 
-  void update(float cost, const std::vector<Ptr<data::Batch>>& batches, Ptr<IMPIWrapper> mpi = nullptr) {
+  void update(float cost,
+              size_t numReadBatches, // number of batches read by the reader (for seeking in case of restart)
+              size_t batchSize,      // total number of sentences in batch
+              size_t batchLabels,    // total number of target words in batch
+              Ptr<IMPIWrapper> mpi = nullptr) {
     state_->rememberPreviousProgress(); // note: epoch increases happen at the wrong place, hence -freq parameters do not support epoch units
     state_->validated = false;
 
-    size_t batchSize = 0;    // number of sentences in batch
-    size_t batchLabels = 0;  // number of target words in batch
-
-    for(const auto& batch : batches) {
-      batchSize += batch->size();
-      batchLabels += batch->words(-1);
-    }
-
     // Since batchLabels is counted across all MPI processes, we also should temporarily
     // extrapolate cost across MPI processes, to have numbers in the right range.
     // When doing the actual log, we then aggregate across MPI processes to get the accurate number.
@@ -257,7 +254,7 @@ class Scheduler : public TrainingObserver {
     state_->samplesEpoch += batchSize;   // sentences processed in this epoch
     state_->labelsTotal  += batchLabels; // total labels processed
 
-    state_->newUpdate(batches.size());
+    state_->newUpdate(numReadBatches);
 
     if(state_->enteredNewPeriodOf(options_->get<std::string>("disp-freq")) ||
        state_->batches <= options_->get<size_t>("disp-first")) {

From 9fb672b6de176180ab74a184597b05e55f223ac8 Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Tue, 18 Dec 2018 14:55:52 -0800
Subject: [PATCH 051/838] (minor fix in overstuffing)

---
 src/training/graph_group_sync.cpp | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/training/graph_group_sync.cpp b/src/training/graph_group_sync.cpp
index a2e530ec6..6e0b4d482 100755
--- a/src/training/graph_group_sync.cpp
+++ b/src/training/graph_group_sync.cpp
@@ -331,7 +331,7 @@ void SyncGraphGroup::update(Ptr<data::Batch> newBatch) /*override*/ {
       if(subBatch) {
         auto costNode = builders_[localDeviceIndex]->build(graph, subBatch);
         graph->forward();
-        localDeviceCosts[localDeviceIndex] += costNode->scalar();
+        localDeviceCosts[localDeviceIndex] += costNode->scalar() / overstuff;
         //graph->backward(/*zero=*/warp == 0); // gradients are reset by the scatterReduce op
         graph->backward(/*zero=*/false); // gradients are reset by the scatterReduce op
       }

From c0b4dff958d122dcc09dd405a1227f1a038333f8 Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Tue, 18 Dec 2018 16:13:18 -0800
Subject: [PATCH 052/838] bug fix: overstuff must scale down both gradient and
 denominator

---
 src/training/graph_group_sync.cpp | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/src/training/graph_group_sync.cpp b/src/training/graph_group_sync.cpp
index 6e0b4d482..d2fc13f1c 100755
--- a/src/training/graph_group_sync.cpp
+++ b/src/training/graph_group_sync.cpp
@@ -351,9 +351,9 @@ void SyncGraphGroup::update(Ptr<data::Batch> newBatch) /*override*/ {
   // At this point, each device on each MPI process has a gradient aggregated over a subset of the sub-batches.
 
   // If individual gradients were averages, then need to average again over all subBatches
-  auto div = subBatches.size();
+  float div = (float)subBatches.size();
   if (options_->get<std::string>("cost-type") == "ce-sum")
-    div = 1;
+    div = (float)overstuff;
 
   // Update parameter shard with gradient shard
   auto update = [&](size_t i, size_t begin, size_t end) {
@@ -362,7 +362,7 @@ void SyncGraphGroup::update(Ptr<data::Batch> newBatch) /*override*/ {
 
     if(div != 1) {
       using namespace functional;
-      Element(_1 = _1 / (float)div, curGrad);   // average once again in case of ce-mean*
+      Element(_1 = _1 / div, curGrad);   // average once again in case of ce-mean*
     }
 
     // actual model update

From da57654bc4dfe9988665ab0a44fd3c7ac2633e54 Mon Sep 17 00:00:00 2001
From: Roman Grundkiewicz <rgrundki@exseed.ed.ac.uk>
Date: Wed, 19 Dec 2018 15:18:46 +0000
Subject: [PATCH 053/838] Add aliases and --problem

---
 src/common/cli_wrapper.h     | 44 ++++++++++++++++++++++++---
 src/common/config_parser.cpp | 59 ++++++++++++++++++++++++++++++------
 src/common/config_parser.h   |  3 +-
 3 files changed, 91 insertions(+), 15 deletions(-)

diff --git a/src/common/cli_wrapper.h b/src/common/cli_wrapper.h
index 1590441a0..d06bdd718 100755
--- a/src/common/cli_wrapper.h
+++ b/src/common/cli_wrapper.h
@@ -51,9 +51,7 @@ class CLIFormatter : public CLI::Formatter {
 // @TODO: in this file review the use of naked pointers. We use Ptr<Type> anywhere else,
 // what's up with that?
 
-/**
- * The helper structure storing an option object, the associated variable and creation index.
- */
+// The helper structure storing an option object, the associated variable and creation index
 struct CLIOptionTuple {
   CLI::Option *opt;
   Ptr<any_type> var;
@@ -61,6 +59,13 @@ struct CLIOptionTuple {
   bool modified{false};
 };
 
+// Helper structure used for aliases and storing an option key, value, and YAML node
+struct CLIAliasTuple {
+  std::string key;
+  std::string value;
+  YAML::Node config;
+};
+
 /**
  * @brief The class used to define and parse command-line arguments.
  *
@@ -82,9 +87,12 @@ class CLIWrapper {
   std::unordered_map<std::string, CLIOptionTuple> options_;
   // Counter for created options
   size_t counter_{0};
+  // List of alias tuples
+  std::vector<CLIAliasTuple> aliases_;
   // Command-line argument parser
   Ptr<CLI::App> app_;
 
+
   // Name of the default option group
   std::string defaultGroup_{""};
   // Name of the current option group
@@ -192,6 +200,25 @@ class CLIWrapper {
                         /*defaulted =*/false);
   }
 
+  /**
+   * @brief Define an alias that is a shortcut for a set of options
+   *
+   * Option values are compared as std::string.
+   *
+   * @param key Option name
+   * @param value Option value that trigger the alias
+   * @param fun Function initializing options
+   */
+  void alias(const std::string &key,
+             const std::string &value,
+             const std::function<void(YAML::Node &config)> &fun) {
+    ABORT_IF(!options_.count(key), "Option '{}' is not defined so alias can not be created", key);
+    aliases_.resize(aliases_.size() + 1);
+    aliases_.back().key = key;
+    aliases_.back().value = value;
+    fun(aliases_.back().config);
+  }
+
   /**
    * Switch to different option group or to the default group if argument is empty.
    *
@@ -202,6 +229,15 @@ class CLIWrapper {
   // Parse command-line arguments. Handles --help and --version options
   void parse(int argc, char **argv);
 
+  void parseAliases(const YAML::Node &config) {
+    for(const auto& alias : aliases_) {
+      if(config[alias.key] && config[alias.key].as<std::string>() == alias.value) {
+          updateConfig(alias.config,
+                       "Unknown option(s) in alias '" + alias.key + ": " + alias.value + "'");
+      }
+    }
+  }
+
   /*
    * @brief Overwrite values for unparsed options
    *
@@ -212,7 +248,7 @@ class CLIWrapper {
    *
    * @param config YAML config with new default values for options
    * @param errorMsg error message printed if config contains undefined keys. The message is
-   *   appended with ": * <comma-separated list of invalid options>"
+   *   appended with ": <comma-separated list of invalid options>"
    */
   void updateConfig(const YAML::Node &config, const std::string &errorMsg);
 
diff --git a/src/common/config_parser.cpp b/src/common/config_parser.cpp
index a4699e023..f1f9a71de 100755
--- a/src/common/config_parser.cpp
+++ b/src/common/config_parser.cpp
@@ -394,8 +394,12 @@ void ConfigParser::addOptionsTraining(cli::CLIWrapper& cli) {
   cli.add<bool>("--multi-node-overlap",
      "Overlap model computations with MPI communication",
      true);
+
   // add ULR settings
   addSuboptionsULR(cli);
+
+  cli.add<std::string>("--problem",
+     "Use predefined set of options. Possible values: transformer");
   // clang-format on
 }
 
@@ -652,11 +656,10 @@ void ConfigParser::addSuboptionsULR(cli::CLIWrapper& cli) {
   // clang-format on
 }
 
-void ConfigParser::expandAliases(cli::CLIWrapper& cli) {
-  YAML::Node config;
+void ConfigParser::addAliases(cli::CLIWrapper& cli) {
   // The order of aliases does matter as later options overwrite earlier
 
-  if(config_["best-deep"].as<bool>()) {
+  cli.alias("best-deep", "true", [](YAML::Node& config) {
     config["layer-normalization"] = true;
     config["tied-embeddings"] = true;
     config["enc-type"] = "alternating";
@@ -666,11 +669,42 @@ void ConfigParser::expandAliases(cli::CLIWrapper& cli) {
     config["dec-cell-high-depth"] = 2;
     config["dec-depth"] = 4;
     config["skip"] = true;
-  }
-
-  if(config) {
-    cli.updateConfig(config, "Unknown option(s) in aliases");
-  }
+  });
+
+  cli.alias("problem", "transformer", [](YAML::Node& config) {
+    config["type"] = "transformer";
+    config["enc-depth"] = 6;
+    config["dec-depth"] = 6;
+    config["transformer-heads"] = 8;
+    config["learn-rate"] = 0.0003;
+    config["cost-type"] = "ce-mean-words";
+    config["lr-warmup"] = 16000;
+    config["lr-decay-inv-sqrt"] = 16000;
+    config["transformer-dropout"] = 0.1;
+    config["label-smoothing"] = 0.1;
+    config["clip-norm"] = 5;
+  });
+
+  cli.alias("problem", "transformer-big", [](YAML::Node& config) {
+    config["type"] = "transformer";
+    config["enc-depth"] = 6;
+    config["dec-depth"] = 6;
+    config["dim-emb"] = 1024;
+    config["transformer-dim-ffn"] = 4096;
+    config["transformer-heads"] = 16;
+    config["transformer-postprocess"] = "dan";
+    config["transformer-preprocess"] = "d";
+    config["transformer-ffn-activation"] = "relu";
+    config["learn-rate"] = 0.0002;
+    config["cost-type"] = "ce-mean-words";
+    config["lr-warmup"] = 8000;
+    config["lr-decay-inv-sqrt"] = 8000;
+    config["transformer-dropout"] = 0.1;
+    config["transformer-attention-dropout"] = 0.1;
+    config["transformer-ffn-dropout"] = 0.1;
+    config["label-smoothing"] = 0.1;
+    config["clip-norm"] = 5;
+  });
 }
 
 void ConfigParser::parseOptions(int argc, char** argv, bool doValidate) {
@@ -690,6 +724,7 @@ void ConfigParser::parseOptions(int argc, char** argv, bool doValidate) {
     case cli::mode::training:
       addOptionsTraining(cli);
       addOptionsValidation(cli);
+      addAliases(cli);
       break;
     case cli::mode::translation:
       addOptionsTranslation(cli);
@@ -718,6 +753,7 @@ void ConfigParser::parseOptions(int argc, char** argv, bool doValidate) {
   }
 
   if(doValidate) {
+    // TODO: Do not check some constraints if --dump-config, e.g. -t
     // this aborts the program on first validation error
     ConfigValidator(config_).validateOptions(mode_);
   }
@@ -725,14 +761,17 @@ void ConfigParser::parseOptions(int argc, char** argv, bool doValidate) {
   // remove extra config files from the config to avoid redundancy
   config_.remove("config");
 
+  // TODO: Consider expanding aliases after dumping config
+  cli.parseAliases(config_);
+  config_.remove("best-deep");
+  config_.remove("problem");
+
   if(!get<std::string>("dump-config").empty() && get<std::string>("dump-config") != "false") {
     bool skipDefault = get<std::string>("dump-config") == "minimal";
     config_.remove("dump-config");
     std::cout << cli.dumpConfig(skipDefault) << std::endl;
     exit(0);
   }
-
-  expandAliases(cli);
 }
 
 std::vector<std::string> ConfigParser::findConfigPaths() {
diff --git a/src/common/config_parser.h b/src/common/config_parser.h
index ff305f738..d6822cc1a 100755
--- a/src/common/config_parser.h
+++ b/src/common/config_parser.h
@@ -77,11 +77,12 @@ class ConfigParser {
   void addOptionsTranslation(cli::CLIWrapper&);
   void addOptionsScoring(cli::CLIWrapper&);
 
+  void addAliases(cli::CLIWrapper&);
+
   void addSuboptionsDevices(cli::CLIWrapper&);
   void addSuboptionsBatching(cli::CLIWrapper&);
   void addSuboptionsInputLength(cli::CLIWrapper&);
   void addSuboptionsULR(cli::CLIWrapper&);
-  void expandAliases(cli::CLIWrapper&);
 
   // Extract paths to all config files found in the config object.
   // Look at --config option and model.npz.yml files.

From 680857507873c8590eaaf6f1f1aa08cce0fcdffd Mon Sep 17 00:00:00 2001
From: Roman Grundkiewicz <rgrundki@exseed.ed.ac.uk>
Date: Wed, 19 Dec 2018 19:26:40 +0000
Subject: [PATCH 054/838] Add --dump-config explain

---
 src/common/config_parser.cpp | 24 ++++++++++++++++--------
 1 file changed, 16 insertions(+), 8 deletions(-)

diff --git a/src/common/config_parser.cpp b/src/common/config_parser.cpp
index f1f9a71de..f4d86c567 100755
--- a/src/common/config_parser.cpp
+++ b/src/common/config_parser.cpp
@@ -79,7 +79,8 @@ void ConfigParser::addOptionsGeneral(cli::CLIWrapper& cli) {
   cli.add<bool>("--relative-paths",
      "All paths are relative to the config file location");
   cli.add<std::string>("--dump-config",
-     "Dump current (modified) configuration to stdout and exit. Possible values: full, minimal")
+     "Dump current (modified) configuration to stdout and exit. "
+     "Possible values: full, minimal, explain")
     ->implicit_val("full");
   // clang-format on
 }
@@ -761,17 +762,24 @@ void ConfigParser::parseOptions(int argc, char** argv, bool doValidate) {
   // remove extra config files from the config to avoid redundancy
   config_.remove("config");
 
-  // TODO: Consider expanding aliases after dumping config
-  cli.parseAliases(config_);
-  config_.remove("best-deep");
-  config_.remove("problem");
-
   if(!get<std::string>("dump-config").empty() && get<std::string>("dump-config") != "false") {
-    bool skipDefault = get<std::string>("dump-config") == "minimal";
+    auto type = get<std::string>("dump-config");
     config_.remove("dump-config");
-    std::cout << cli.dumpConfig(skipDefault) << std::endl;
+
+    if(type == "explain") {
+      cli.parseAliases(config_);
+      config_.remove("best-deep");
+      config_.remove("problem");
+    }
+
+    bool minimal = (type == "minimal" || type == "explain");
+    std::cout << cli.dumpConfig(minimal) << std::endl;
     exit(0);
   }
+
+  cli.parseAliases(config_);
+  config_.remove("best-deep");
+  config_.remove("problem");
 }
 
 std::vector<std::string> ConfigParser::findConfigPaths() {

From 81b2c7acbe5e64c24ef42693ab324ced51a67af8 Mon Sep 17 00:00:00 2001
From: Roman Grundkiewicz <rgrundki@exseed.ed.ac.uk>
Date: Wed, 19 Dec 2018 19:39:55 +0000
Subject: [PATCH 055/838] Do not require --train-sets if --dump-config is given

---
 src/common/config_validator.cpp | 9 ++++++++-
 src/common/config_validator.h   | 3 +--
 2 files changed, 9 insertions(+), 3 deletions(-)

diff --git a/src/common/config_validator.cpp b/src/common/config_validator.cpp
index 9345b0a56..1566e1cfc 100755
--- a/src/common/config_validator.cpp
+++ b/src/common/config_validator.cpp
@@ -10,7 +10,10 @@ bool ConfigValidator::has(const std::string& key) const {
   return config_[key];
 }
 
-ConfigValidator::ConfigValidator(const YAML::Node& config) : config_(config) {}
+ConfigValidator::ConfigValidator(const YAML::Node& config)
+    : config_(config),
+      dump_(config["dump-config"] && !config["dump-config"].as<std::string>().empty()
+            && config["dump-config"].as<std::string>() != "false") {}
 
 ConfigValidator::~ConfigValidator() {}
 
@@ -55,6 +58,10 @@ void ConfigValidator::validateOptionsTranslation() const {
 }
 
 void ConfigValidator::validateOptionsParallelData() const {
+  // Do not check these constraints if only goal is to dump config
+  if(dump_)
+    return;
+
   auto trainSets = get<std::vector<std::string>>("train-sets");
   ABORT_IF(trainSets.empty(), "No train sets given in config file or on command line");
 
diff --git a/src/common/config_validator.h b/src/common/config_validator.h
index 59cd11864..fb40ea6c6 100644
--- a/src/common/config_validator.h
+++ b/src/common/config_validator.h
@@ -5,11 +5,10 @@
 
 namespace marian {
 
-// TODO: Finally refactorize Config, Options, ConfigParser and ConfigValidator
-// classes.
 class ConfigValidator {
 private:
   const YAML::Node& config_;
+  bool dump_{false};
 
   bool has(const std::string& key) const;
 

From b2be66cf11eab7bbd2acecc65108b30d7636c6d1 Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Thu, 20 Dec 2018 16:58:22 -0800
Subject: [PATCH 056/838] new experimental parameter --understuff; cleaned up
 MPI uninitialization

---
 src/common/config_parser.cpp      |  9 ++++--
 src/training/graph_group_sync.cpp | 50 ++++++++++++++++++++++++-------
 src/training/graph_group_sync.h   |  9 ++++--
 src/training/training.h           |  6 ++--
 4 files changed, 53 insertions(+), 21 deletions(-)

diff --git a/src/common/config_parser.cpp b/src/common/config_parser.cpp
index 77421d691..70a30a5fd 100755
--- a/src/common/config_parser.cpp
+++ b/src/common/config_parser.cpp
@@ -618,9 +618,12 @@ void ConfigParser::addSuboptionsBatching(cli::CLIWrapper& cli) {
       {"0"});
   cli.add<bool>("--mini-batch-track-lr",
       "Dynamically track mini-batch size inverse to actual learning rate (not considering lr-warmup)");
-  cli.add<double>("--mini-batch-overstuff",
-      "Stuff this much more data into a minibatch, but scale down the LR and progress counter",
-      1.0);
+  cli.add<size_t>("--mini-batch-overstuff",
+      "[experimental] Stuff this much more data into a minibatch, but scale down the LR and progress counter",
+      1);
+  cli.add<size_t>("--mini-batch-understuff",
+      "[experimental] Break each batch into this many updates",
+      1);
   // clang-format on
 }
 
diff --git a/src/training/graph_group_sync.cpp b/src/training/graph_group_sync.cpp
index d2fc13f1c..08503ae89 100755
--- a/src/training/graph_group_sync.cpp
+++ b/src/training/graph_group_sync.cpp
@@ -153,7 +153,7 @@ static double roundUpRatio(double ratio) {
 // helper routine that handles accumulation and load-balancing of sub-batches to fill all devices
 // It adds 'newBatch' to 'pendingBatches_', and if sufficient batches have been queued, then
 // returns 'pendingBatches_' in 'subBatches' and resets it. If not, it returns false.
-bool SyncGraphGroup::tryGetSubBatches(Ptr<data::Batch> newBatch, double overstuff,
+bool SyncGraphGroup::tryGetSubBatches(Ptr<data::Batch> newBatch, size_t overstuff,
     std::vector<Ptr<data::Batch>>& subBatches, size_t& numReadBatches) {
   // The reader delivers in chunks of these sizes, according to case:
   //  - no dynamic MB-size scaling:
@@ -207,7 +207,7 @@ bool SyncGraphGroup::tryGetSubBatches(Ptr<data::Batch> newBatch, double overstuf
   }
 
   // overstuff: blow up ratio by a factor, which we later factor into the learning rate
-  ratio *= overstuff;
+  ratio *= (double)overstuff;
 
   // round up to full batches if within a certain error margin  --@BUGBUG: Not invariant w.r.t. GPU size, as ratio is relative to what fits into 1 GPU
   ratio = roundUpRatio(ratio);
@@ -274,7 +274,7 @@ bool SyncGraphGroup::tryGetSubBatches(Ptr<data::Batch> newBatch, double overstuf
 void SyncGraphGroup::update(Ptr<data::Batch> newBatch) /*override*/ {
   validate();
 
-  double overstuff = options_->get<double>("mini-batch-overstuff");
+  size_t overstuff = options_->get<size_t>("mini-batch-overstuff");
   if (overstuff != 1)
     LOG_ONCE(info, "Overstuffing minibatches by a factor of {}", overstuff);
   std::vector<Ptr<data::Batch>> subBatches;
@@ -285,6 +285,30 @@ void SyncGraphGroup::update(Ptr<data::Batch> newBatch) /*override*/ {
   if (!gotSubBatches)
     return;
 
+  // for testing the hypothesis that one can always go smaller. This is independent of overstuff.
+  size_t understuff = options_->get<size_t>("mini-batch-understuff");
+  if (understuff != 1)
+    LOG_ONCE(info, "Understuffing minibatches by a factor of {}", understuff);
+  if (understuff == 1)
+    update(subBatches, numReadBatches);
+  else {
+    std::vector<Ptr<data::Batch>> subBatches1;
+    for (auto& b : subBatches) {
+      auto bbs = b->split(understuff);
+      for (auto& bb : bbs)
+        subBatches1.push_back(bb);
+    }
+    for (size_t i = 0; i < understuff; i++) {
+      std::vector<Ptr<data::Batch>> subBatchRange(subBatches1.begin() + i * subBatches1.size() / understuff, subBatches1.begin() + (i+1) * subBatches1.size() / understuff);
+      if (!subBatchRange.empty())
+        update(subBatchRange, numReadBatches * (i+1) / understuff - numReadBatches * i / understuff);
+    }
+  }
+}
+
+void SyncGraphGroup::update(std::vector<Ptr<data::Batch>> subBatches, size_t numReadBatches) {
+  size_t overstuff  = options_->get<size_t>("mini-batch-overstuff");
+  //size_t understuff = options_->get<size_t>("mini-batch-understuff");
   // determine num words for dynamic hyper-parameter adjustment
   // @TODO: We can return these directly from tryGetSubBatches()
   size_t batchSize = 0;
@@ -293,9 +317,9 @@ void SyncGraphGroup::update(Ptr<data::Batch> newBatch) /*override*/ {
     batchSize     += batch->size();
     batchTrgWords += batch->wordsTrg();
   }
-  // effective batch size: batch should be weighted like this
-  size_t effectiveBatchTrgWords = (size_t)ceil(batchTrgWords / overstuff);
-  size_t effectiveBatchSize     = (size_t)ceil(batchSize     / overstuff);
+  // effective batch size: batch should be weighted like this. This will weight down the learning rate.
+  size_t effectiveBatchTrgWords = (size_t)ceil(batchTrgWords / (double)overstuff);
+  size_t effectiveBatchSize     = (size_t)ceil(batchSize     / (double)overstuff);
 
   // Helper to access the subBatches array
   auto getSubBatch = [&](size_t warp, size_t localDeviceIndex, size_t rank) -> Ptr<data::Batch> {
@@ -331,7 +355,7 @@ void SyncGraphGroup::update(Ptr<data::Batch> newBatch) /*override*/ {
       if(subBatch) {
         auto costNode = builders_[localDeviceIndex]->build(graph, subBatch);
         graph->forward();
-        localDeviceCosts[localDeviceIndex] += costNode->scalar() / overstuff;
+        localDeviceCosts[localDeviceIndex] += costNode->scalar() / (float)overstuff;
         //graph->backward(/*zero=*/warp == 0); // gradients are reset by the scatterReduce op
         graph->backward(/*zero=*/false); // gradients are reset by the scatterReduce op
       }
@@ -353,7 +377,7 @@ void SyncGraphGroup::update(Ptr<data::Batch> newBatch) /*override*/ {
   // If individual gradients were averages, then need to average again over all subBatches
   float div = (float)subBatches.size();
   if (options_->get<std::string>("cost-type") == "ce-sum")
-    div = (float)overstuff;
+    div = (float)overstuff; // (note: with Adam, a constant here makes no difference)
 
   // Update parameter shard with gradient shard
   auto update = [&](size_t i, size_t begin, size_t end) {
@@ -362,13 +386,13 @@ void SyncGraphGroup::update(Ptr<data::Batch> newBatch) /*override*/ {
 
     if(div != 1) {
       using namespace functional;
-      Element(_1 = _1 / div, curGrad);   // average once again in case of ce-mean*
+      Element(_1 = _1 / div, curGrad);   // average once again for ce-mean*, or scale down if overstuffed for ce-sum
     }
 
     // actual model update
     auto updateTrgWords =
         /*if*/(options_->get<std::string>("cost-type") == "ce-sum") ?
-          effectiveBatchTrgWords // if overstuffing then scale the LR down by that factor  --@TODO: how about momentum?
+          effectiveBatchTrgWords // if overstuffing then bring the count back to the original value
         /*else*/:
           OptimizerBase::mbSizeNotProvided;
     shardOpt_[i]->update(curParam, curGrad, updateTrgWords);
@@ -519,8 +543,12 @@ void SyncGraphGroup::save(bool final) /*override*/ {
 
 void SyncGraphGroup::finalize() /*override*/ {
   validate();
-  finalizeMPI(std::move(mpi_));
   Base::finalize();
 }
 
+SyncGraphGroup::~SyncGraphGroup() /*override*/ {
+  comm_ = nullptr;
+  finalizeMPI(std::move(mpi_));
+}
+
 }  // namespace marian
diff --git a/src/training/graph_group_sync.h b/src/training/graph_group_sync.h
index 22e5c6f28..d5f818c54 100755
--- a/src/training/graph_group_sync.h
+++ b/src/training/graph_group_sync.h
@@ -37,11 +37,15 @@ class SyncGraphGroup : public GraphGroup, public ExponentialSmoothing {
   void barrier() const { mpi_->barrier(); } // (we need this several times)
   void swapParamsAvg() { if (mvAvg_ && paramsAvg_.size() > 0) comm_->swapParams(paramsAvg_); } // note: must call this on all MPI ranks in parallel
 
-  bool tryGetSubBatches(Ptr<data::Batch> newBatch, double overstuff, std::vector<Ptr<data::Batch>>& subBatches, size_t& numReadBatches);
+  bool tryGetSubBatches(Ptr<data::Batch> newBatch, size_t overstuff, std::vector<Ptr<data::Batch>>& subBatches, size_t& numReadBatches);
+  void update(std::vector<Ptr<data::Batch>> subBatches, size_t numReadBatches);
 
 public:
   SyncGraphGroup(Ptr<Options> config);
 
+  Ptr<data::BatchStats> collectStats();
+  // @TODO: consider to make this a virtual as well? Currently it is a template dispatch
+
   void setScheduler(Ptr<Scheduler> scheduler) override;
 
   void update(Ptr<data::Batch> batch) override;
@@ -51,7 +55,6 @@ class SyncGraphGroup : public GraphGroup, public ExponentialSmoothing {
 
   void finalize() override;
 
-  Ptr<data::BatchStats> collectStats();
-  // @TODO: consider to make this a virtual as well? Currently it is a template dispatch
+  ~SyncGraphGroup() override;
 };
 }  // namespace marian
diff --git a/src/training/training.h b/src/training/training.h
index ce9c5d4cf..b2290db75 100755
--- a/src/training/training.h
+++ b/src/training/training.h
@@ -91,14 +91,12 @@ class Train : public ModelTask {
     }
     scheduler->finished();
 
+    model->finalize();
+
     // Avoid saving the model twice if it has been loaded and training did not
     // progress
     if(!trainState->loaded)
       model->save(true);
-
-    // finalize, including communicating successful completion to MPI
-    // @BUGBUG: This is wrong for async, but needed for sync. How to solve it?
-    model->finalize();
   }
 };
 }  // namespace marian

From 7843c0c135b0d1fbe0e30c3102c52be17883dc9d Mon Sep 17 00:00:00 2001
From: Marcin Junczys-Dowmunt <Marcin.Junczys-Dowmunt@microsoft.com>
Date: Thu, 20 Dec 2018 17:01:20 -0800
Subject: [PATCH 057/838] small clean-ups to get through regression tests, part
 1

---
 src/common/logging.cpp            |  2 +-
 src/data/batch_generator.h        |  2 +-
 src/training/graph_group_sync.cpp | 12 +++++++-----
 src/training/graph_group_sync.h   |  2 ++
 src/training/scheduler.h          |  5 ++---
 src/training/training.h           |  6 ++----
 6 files changed, 15 insertions(+), 14 deletions(-)

diff --git a/src/common/logging.cpp b/src/common/logging.cpp
index da583878d..203acfe79 100755
--- a/src/common/logging.cpp
+++ b/src/common/logging.cpp
@@ -84,7 +84,7 @@ void createLoggers(const marian::Config* options) {
 
   bool quiet = options && options->get<bool>("quiet");
   Logger general{
-      createStderrLogger("general", "[%Y-%m-%d %T %t] %v", generalLogs, quiet)};
+      createStderrLogger("general", "[%Y-%m-%d %T] %v", generalLogs, quiet)};
   Logger valid{
       createStderrLogger("valid", "[%Y-%m-%d %T] [valid] %v", validLogs, quiet)};
 
diff --git a/src/data/batch_generator.h b/src/data/batch_generator.h
index 7d02b8303..ce673cd2a 100755
--- a/src/data/batch_generator.h
+++ b/src/data/batch_generator.h
@@ -215,7 +215,7 @@ class BatchGenerator : public RNGEngine {
       totalLabels += (double)b->words(-1);
     }
     auto totalDenom = tempBatches.empty() ? 1 : tempBatches.size(); // (make 0/0 = 0)
-    LOG(info, "[data] fetched {} batches with {} sentences. Per batch: {} sentences, {} labels.",
+    LOG(debug, "[data] fetched {} batches with {} sentences. Per batch: {} sentences, {} labels.",
         tempBatches.size(), numSentencesRead,
         (double)totalSent / (double)totalDenom, (double)totalLabels / (double)totalDenom);
     return tempBatches;
diff --git a/src/training/graph_group_sync.cpp b/src/training/graph_group_sync.cpp
index 638642786..30d163661 100755
--- a/src/training/graph_group_sync.cpp
+++ b/src/training/graph_group_sync.cpp
@@ -438,7 +438,7 @@ void SyncGraphGroup::load() /*override*/ {
 }
 
 void SyncGraphGroup::save(bool final) /*override*/ {
-  validate();
+  // validate(); @TODO: get rid of this everywhere (SyncGraphGroup)
   barrier(); // (for better grouping of log messages)
   // do final validation
   if(final && scheduler_) {
@@ -487,21 +487,23 @@ void SyncGraphGroup::save(bool final) /*override*/ {
   barrier(); // (for better grouping of log messages)
 
   // persist optimizer state
-  LOG(info, "[{}] save() line {}", this->mpi_->idStr(), __LINE__);
   shardOpt_[0]->save(name + ".optimizer.npz", shardOpt_,
     [&](const OptimizerBase::GatherStateGetFunc& getShardFn) {
       return comm_->gatherState(getShardFn);
     },
     isMainProcess());
-  LOG(info, "[{}] save() line {}", this->mpi_->idStr(), __LINE__);
-
+  
   barrier(); // (for better grouping of log messages)
 }
 
 void SyncGraphGroup::finalize() /*override*/ {
   validate();
-  finalizeMPI(std::move(mpi_));
   Base::finalize();
 }
+ 
+SyncGraphGroup::~SyncGraphGroup() /*override*/ {
+  comm_ = nullptr;
+  finalizeMPI(std::move(mpi_));
+}
 
 }  // namespace marian
diff --git a/src/training/graph_group_sync.h b/src/training/graph_group_sync.h
index 6bb3a4861..2171cbf0f 100755
--- a/src/training/graph_group_sync.h
+++ b/src/training/graph_group_sync.h
@@ -53,5 +53,7 @@ class SyncGraphGroup : public GraphGroup, public ExponentialSmoothing {
 
   Ptr<data::BatchStats> collectStats();
   // @TODO: consider to make this a virtual as well? Currently it is a template dispatch
+
+  ~SyncGraphGroup() override;
 };
 }  // namespace marian
diff --git a/src/training/scheduler.h b/src/training/scheduler.h
index 802282354..3d46de096 100755
--- a/src/training/scheduler.h
+++ b/src/training/scheduler.h
@@ -174,13 +174,12 @@ class Scheduler : public TrainingObserver {
       float value = validator->validate(graphs);
       if(validator->stalled() > 0) {
         LOG_VALID(info,
-                  "Ep. {} : Up. {} : {} : {} : stalled {} times (last best: {})",
+                  "Ep. {} : Up. {} : {} : {} : stalled {} times",  /*"(last best: {})",*/ // @TODO (LOGGING CHANGE)
                   state_->epochs,
                   state_->batches,
                   validator->type(),
                   value,
-                  validator->stalled(),
-                  validator->lastBest());
+                  validator->stalled() /*, validator->lastBest()*/); // @TODO (LOGGING CHANGE)
       } else {
         LOG_VALID(info,
                   "Ep. {} : Up. {} : {} : {} : new best",
diff --git a/src/training/training.h b/src/training/training.h
index ce9c5d4cf..b2290db75 100755
--- a/src/training/training.h
+++ b/src/training/training.h
@@ -91,14 +91,12 @@ class Train : public ModelTask {
     }
     scheduler->finished();
 
+    model->finalize();
+
     // Avoid saving the model twice if it has been loaded and training did not
     // progress
     if(!trainState->loaded)
       model->save(true);
-
-    // finalize, including communicating successful completion to MPI
-    // @BUGBUG: This is wrong for async, but needed for sync. How to solve it?
-    model->finalize();
   }
 };
 }  // namespace marian

From 496ed37d56f6e1af92b8fbff1db9fb0e04f226b5 Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Thu, 20 Dec 2018 17:10:24 -0800
Subject: [PATCH 058/838] bug fix (perf): loop over warps should not
 synchronize worker threads after each warp

---
 src/training/graph_group_sync.cpp | 40 +++++++++++++------------------
 1 file changed, 16 insertions(+), 24 deletions(-)

diff --git a/src/training/graph_group_sync.cpp b/src/training/graph_group_sync.cpp
index 08503ae89..c6ef37486 100755
--- a/src/training/graph_group_sync.cpp
+++ b/src/training/graph_group_sync.cpp
@@ -346,32 +346,24 @@ void SyncGraphGroup::update(std::vector<Ptr<data::Batch>> subBatches, size_t num
   // Compute gradients
   // This happens in multiple steps in case of delay > 1.
   std::vector<float> localDeviceCosts(devices_.size(), 0.f); // [local device index] aggregate cost for each local device
-  for (size_t warp = 0; getSubBatch(warp, 0, 0); warp++) {
-    // Execute single forward/backward step
-    comm_->foreach([&](size_t localDeviceIndex, size_t /*begin*/, size_t /*end*/) { // parallel across devices. Aggregate for warp > 1.
-      auto graph = graphs_[localDeviceIndex];
+  comm_->foreach([&](size_t localDeviceIndex, size_t /*begin*/, size_t /*end*/) { // parallel across devices. Aggregate for warp > 1.
+    auto graph = graphs_[localDeviceIndex];
+    // reset gradient  --presently done outside
+    //graph->params()->allocateBackward();
+    //if (warp == 0) // these have already been sized
+    //  graph->params()->set_zero_adjoint();
+    for (size_t warp = 0; ; warp++) {
+      // Execute single forward/backward step
       auto subBatch = getSubBatch(warp, localDeviceIndex, mpi_->myMPIRank());
+      if (!subBatch)
+        break;
 
-      if(subBatch) {
-        auto costNode = builders_[localDeviceIndex]->build(graph, subBatch);
-        graph->forward();
-        localDeviceCosts[localDeviceIndex] += costNode->scalar() / (float)overstuff;
-        //graph->backward(/*zero=*/warp == 0); // gradients are reset by the scatterReduce op
-        graph->backward(/*zero=*/false); // gradients are reset by the scatterReduce op
-      }
-      else { // empty batch: execute do-nothing fw-bw step for proper inits and resets
-#if 1   // @TODO: double-check whether the #else branch is the same; and if so, use it instead
-        //graph->params()->allocateBackward();
-        //if (warp == 0) // these have already been sized
-        //  graph->params()->set_zero_adjoint();
-#else
-        graph->clear(); // instead of build()
-        graph->forward();
-        graph->backward(/*zero=*/false);
-#endif
-      }
-    });
-  }
+      auto costNode = builders_[localDeviceIndex]->build(graph, subBatch);
+      graph->forward();
+      localDeviceCosts[localDeviceIndex] += costNode->scalar() / (float)overstuff;
+      graph->backward(/*zero=*/false); // (gradients are reset before we get here)
+    }
+  });
   // At this point, each device on each MPI process has a gradient aggregated over a subset of the sub-batches.
 
   // If individual gradients were averages, then need to average again over all subBatches

From 0af017e2e2aaae0897be8e19e93ea1a57ff00df4 Mon Sep 17 00:00:00 2001
From: Marcin Junczys-Dowmunt <marcinjd@microsoft.com>
Date: Thu, 20 Dec 2018 17:14:06 -0800
Subject: [PATCH 059/838] move pointer to right sentencepiece version

---
 src/3rd_party/sentencepiece | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/3rd_party/sentencepiece b/src/3rd_party/sentencepiece
index 21309542e..61ae30fa8 160000
--- a/src/3rd_party/sentencepiece
+++ b/src/3rd_party/sentencepiece
@@ -1 +1 @@
-Subproject commit 21309542e69e1821ff8e905fa60d8852ac12a73f
+Subproject commit 61ae30fa8313c72ea36c41f1ea0402c7ed8c2fe0

From 3293dddb4f5e56ddb8540727ed05201309256d1c Mon Sep 17 00:00:00 2001
From: Marcin Junczys-Dowmunt <Marcin.Junczys-Dowmunt@microsoft.com>
Date: Thu, 20 Dec 2018 18:23:16 -0800
Subject: [PATCH 060/838] restore old validation behaviour

---
 src/training/validator.h | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/training/validator.h b/src/training/validator.h
index 682495410..2b76d6578 100755
--- a/src/training/validator.h
+++ b/src/training/validator.h
@@ -137,7 +137,7 @@ class Validator : public ValidatorBase {
       lastBest_ = val;
       if(options_->get<bool>("keep-best"))
         keepBest(graphs);
-    } else if (lastBest_ != val) { // (special case 0 at start)  @TODO: needed? Seems stall count gets reset each time it does improve. If not needed, remove "if(...)" again.
+    } else /* if (lastBest_ != val) */ { // (special case 0 at start)  @TODO: needed? Seems stall count gets reset each time it does improve. If not needed, remove "if(...)" again.
       stalled_++;
     }
   }

From c5c25974109c2af7175c4792ebd2b0fb382cc3fd Mon Sep 17 00:00:00 2001
From: Marcin Junczys-Dowmunt <Marcin.Junczys-Dowmunt@microsoft.com>
Date: Fri, 21 Dec 2018 11:52:52 -0800
Subject: [PATCH 061/838] upcast single element to single element sequence in
 config if vector is expected

---
 src/common/cli_wrapper.cpp | 24 ++++++++++++++++++++++--
 1 file changed, 22 insertions(+), 2 deletions(-)

diff --git a/src/common/cli_wrapper.cpp b/src/common/cli_wrapper.cpp
index 28826bb28..6b7262c79 100755
--- a/src/common/cli_wrapper.cpp
+++ b/src/common/cli_wrapper.cpp
@@ -135,8 +135,28 @@ bool CLIWrapper::updateConfig(const YAML::Node &config) {
     if(cmdOptions.count(key))
       continue;
     if(options_.count(key)) {
-      config_[key] = YAML::Clone(it.second);
-      options_[key].modified = true;
+      // this is a default value, so it has a node type
+      if(config_[key]) { // types don't match, handle this
+        if(config_[key].Type() != it.second.Type()) {
+          // default value is a sequence and incoming node is a scalar, hence we can upcast to single element sequence
+          if(config_[key].Type() == YAML::NodeType::Sequence && it.second.Type() == YAML::NodeType::Scalar) {
+            YAML::Node sequence;
+            sequence.push_back(YAML::Clone(it.second));
+            
+            // overwrite so default values are replaced too
+            config_[key] = sequence;
+            options_[key].modified = true;
+          } else { // Cannot convert other non-matching types, e.g. scalar <- list should fail
+            success = false;
+          }
+        } else { // types match, go ahead
+          config_[key] = YAML::Clone(it.second);
+          options_[key].modified = true;
+        }
+      } else {
+        config_[key] = YAML::Clone(it.second);
+        options_[key].modified = true;
+      }
     } else {
       success = false;
     }

From f8a89dcceacd882cc69dcb91b21499bfeaa78d31 Mon Sep 17 00:00:00 2001
From: Marcin Junczys-Dowmunt <Marcin.Junczys-Dowmunt@microsoft.com>
Date: Fri, 21 Dec 2018 13:01:25 -0800
Subject: [PATCH 062/838] adjust comments

---
 src/common/cli_wrapper.cpp | 10 ++++------
 1 file changed, 4 insertions(+), 6 deletions(-)

diff --git a/src/common/cli_wrapper.cpp b/src/common/cli_wrapper.cpp
index 6b7262c79..1c7ad16bb 100755
--- a/src/common/cli_wrapper.cpp
+++ b/src/common/cli_wrapper.cpp
@@ -135,16 +135,14 @@ bool CLIWrapper::updateConfig(const YAML::Node &config) {
     if(cmdOptions.count(key))
       continue;
     if(options_.count(key)) {
-      // this is a default value, so it has a node type
-      if(config_[key]) { // types don't match, handle this
-        if(config_[key].Type() != it.second.Type()) {
+      if(config_[key]) { // it exists, so this is a default value, hence it has a node type
+        if(config_[key].Type() != it.second.Type()) { // types don't match, handle this
           // default value is a sequence and incoming node is a scalar, hence we can upcast to single element sequence
           if(config_[key].Type() == YAML::NodeType::Sequence && it.second.Type() == YAML::NodeType::Scalar) {
+            // create single element sequence
             YAML::Node sequence;
             sequence.push_back(YAML::Clone(it.second));
-            
-            // overwrite so default values are replaced too
-            config_[key] = sequence;
+            config_[key] = sequence; // overwrite to replace default values
             options_[key].modified = true;
           } else { // Cannot convert other non-matching types, e.g. scalar <- list should fail
             success = false;

From c10f2c91bc07c0f6b1bc6f9f76b7e8345e28a166 Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Fri, 21 Dec 2018 14:29:18 -0800
Subject: [PATCH 063/838] [experiental] exponent for warmup; compensation term
 for ramp-up of exponential smoothing

---
 src/common/config_parser.cpp         |  3 +++
 src/training/exponential_smoothing.h |  4 ++++
 src/training/graph_group_sync.cpp    | 12 +++++++-----
 src/training/scheduler.h             |  5 +++++
 4 files changed, 19 insertions(+), 5 deletions(-)

diff --git a/src/common/config_parser.cpp b/src/common/config_parser.cpp
index 70a30a5fd..14c266c8b 100755
--- a/src/common/config_parser.cpp
+++ b/src/common/config_parser.cpp
@@ -618,6 +618,9 @@ void ConfigParser::addSuboptionsBatching(cli::CLIWrapper& cli) {
       {"0"});
   cli.add<bool>("--mini-batch-track-lr",
       "Dynamically track mini-batch size inverse to actual learning rate (not considering lr-warmup)");
+  cli.add<double>("--mini-batch-warmup-exp",
+      "[experimental] Soften initial ramp-up curve with this exponent (until ramp-up reached 100%)",
+      1.);
   cli.add<size_t>("--mini-batch-overstuff",
       "[experimental] Stuff this much more data into a minibatch, but scale down the LR and progress counter",
       1);
diff --git a/src/training/exponential_smoothing.h b/src/training/exponential_smoothing.h
index f8786fca1..30e64430b 100755
--- a/src/training/exponential_smoothing.h
+++ b/src/training/exponential_smoothing.h
@@ -28,6 +28,10 @@ class ExponentialSmoothing {
       ABORT_IF(actualBatchTrgWords == OptimizerBase::mbSizeNotProvided,
                "This graph-group type does not support reference batch size specification for exponential-smoothing");
       beta = pow(beta, (double)actualBatchTrgWords / (double)refBatchTrgWords_);
+      // If actual size differs from reference, then try to estimate the equivalent number of batches.
+      // E.g. if MB size is growing over time, then this is an overestimate, which would diminish the
+      // effect overly quickly, but in a range where that should be OK.
+      batches = std::max(batches, batches * actualBatchTrgWords / refBatchTrgWords_); // @BUGBUG: Does not consider that batch size is changing
     }
     // reduce effect of decay parameter in early training stages
     float decayBy = std::max(1.f - (float)beta,
diff --git a/src/training/graph_group_sync.cpp b/src/training/graph_group_sync.cpp
index a11d48c54..b89efde87 100755
--- a/src/training/graph_group_sync.cpp
+++ b/src/training/graph_group_sync.cpp
@@ -344,14 +344,13 @@ void SyncGraphGroup::update(std::vector<Ptr<data::Batch>> subBatches, size_t num
   }
 
   // Compute gradients
-  // This happens in multiple steps in case of delay > 1.
   std::vector<float> localDeviceCosts(devices_.size(), 0.f); // [local device index] aggregate cost for each local device
   comm_->foreach([&](size_t localDeviceIndex, size_t /*begin*/, size_t /*end*/) { // parallel across devices. Aggregate for warp > 1.
     auto graph = graphs_[localDeviceIndex];
     // reset gradient  --presently done outside
     //graph->params()->allocateBackward();
-    //if (warp == 0) // these have already been sized
-    //  graph->params()->set_zero_adjoint();
+    //graph->params()->set_zero_adjoint();
+    // This happens in multiple steps if there are more subbatches than devices.
     for (size_t warp = 0; ; warp++) {
       // Execute single forward/backward step
       auto subBatch = getSubBatch(warp, localDeviceIndex, mpi_->myMPIRank());
@@ -535,10 +534,13 @@ void SyncGraphGroup::finalize() /*override*/ {
   validate();
   Base::finalize();
 }
- 
+
 SyncGraphGroup::~SyncGraphGroup() /*override*/ {
   comm_ = nullptr;
-  finalizeMPI(std::move(mpi_));
+  static int c = 0;
+  c++;
+  if (c == 2) // HACKHACK: MPI can only be closed once
+    finalizeMPI(std::move(mpi_));
 }
 
 }  // namespace marian
diff --git a/src/training/scheduler.h b/src/training/scheduler.h
index 2f2fc5a21..14f2bb07f 100755
--- a/src/training/scheduler.h
+++ b/src/training/scheduler.h
@@ -83,6 +83,11 @@ class Scheduler : public TrainingObserver {
       // if unit is labels, then account for the fact that our increment itself is not constant
       if (mbWarmup.unit == SchedulingUnit::trgLabels)
         progressRatio = std::sqrt(progressRatio);
+      // soften the very initial ramp-up if requested
+      if (progressRatio < 1) {
+        double exp = options_->get<double>("mini-batch-warmup-exp");
+        progressRatio = pow(progressRatio, exp); // e.g. 1.5 => linear ramp-up -> sublinear ramp-up
+      }
       // apply ratio to actual batch size
       ratio *= progressRatio;
     }

From 776aa2a7d38af110ee80d9e4339495e0c04e7057 Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Fri, 21 Dec 2018 15:10:17 -0800
Subject: [PATCH 064/838] bug fix: MPI must only be finalized once

---
 src/training/graph_group.h                | 14 ++------------
 src/training/graph_group_async.cpp        |  3 ++-
 src/training/graph_group_async.h          |  2 +-
 src/training/graph_group_async_drop.h     |  4 ++--
 src/training/graph_group_multinode.h      |  4 ++--
 src/training/graph_group_multinode_sync.h |  4 ++--
 src/training/graph_group_singleton.h      |  3 ++-
 src/training/graph_group_sync.cpp         | 17 +++--------------
 src/training/graph_group_sync.h           |  4 +---
 src/training/training.h                   | 17 ++++++++++-------
 10 files changed, 27 insertions(+), 45 deletions(-)

diff --git a/src/training/graph_group.h b/src/training/graph_group.h
index c1d6c818a..9a888bda8 100755
--- a/src/training/graph_group.h
+++ b/src/training/graph_group.h
@@ -136,11 +136,8 @@ class MultiNodeGraphGroupBase : public GraphGroup {
   std::vector<Ptr<ExpressionGraph>> clientGraphs_; // [num local GPUs]
 
 public:
-  MultiNodeGraphGroupBase(Ptr<Options> options)
-    : Base(options) {
-
-    // Setup MPI
-    setupMPI();
+  MultiNodeGraphGroupBase(Ptr<Options> options, Ptr<IMPIWrapper> mpi)
+    : Base(options), mpi_(mpi) {
 
     // Set up devices for this node
     std::vector<size_t> devices; // set of GPU device ids for this MPI process
@@ -157,13 +154,6 @@ class MultiNodeGraphGroupBase : public GraphGroup {
     }
   }
 
-  /**
-   * Setup MPI world size and rank of this node.
-   */
-  void setupMPI() {
-    mpi_ = initMPI(/*multiThreaded=*/!options_->get<bool>("sync-sgd"));
-  }
-
   /**
    * Load the GPU configuration of this node (i.e. which GPUs to use) and the
    * number of GPUs on the other nodes.
diff --git a/src/training/graph_group_async.cpp b/src/training/graph_group_async.cpp
index 4c85e168a..ca7f7b487 100755
--- a/src/training/graph_group_async.cpp
+++ b/src/training/graph_group_async.cpp
@@ -5,12 +5,13 @@
 
 namespace marian {
 
-AsyncGraphGroup::AsyncGraphGroup(Ptr<Options> config)
+AsyncGraphGroup::AsyncGraphGroup(Ptr<Options> config, Ptr<IMPIWrapper> mpi)
     : GraphGroup(config),
       ExponentialSmoothing(options_),
       devices_{Config::getDevices(options_)},
       shardSync_(devices_.size()),
       optimizerDelay_((size_t)options_->get<double>("optimizer-delay")) {
+  ABORT_IF(mpi->numMPIProcesses() != 1, "AsyncGraphGroup presently does not support multiple MPI processes");
   ABORT_IF((double)optimizerDelay_ != options_->get<double>("optimizer-delay"), "AsyncGraphGroup presently does not implement fractional values for --optimizer-delay");
   pool_.reset(new ThreadPool(devices_.size(), devices_.size()));
 
diff --git a/src/training/graph_group_async.h b/src/training/graph_group_async.h
index 713e30064..d77fff7f3 100755
--- a/src/training/graph_group_async.h
+++ b/src/training/graph_group_async.h
@@ -52,7 +52,7 @@ class AsyncGraphGroup : public GraphGroup, public ExponentialSmoothing {
   void execute(Ptr<data::Batch> batch);
 
 public:
-  AsyncGraphGroup(Ptr<Options> config);
+  AsyncGraphGroup(Ptr<Options> config, Ptr<IMPIWrapper> mpi);
 
   void update(Ptr<data::Batch> batch) override {
     validate();
diff --git a/src/training/graph_group_async_drop.h b/src/training/graph_group_async_drop.h
index 7d22208b3..3e313440f 100755
--- a/src/training/graph_group_async_drop.h
+++ b/src/training/graph_group_async_drop.h
@@ -30,8 +30,8 @@ class AsyncGraphGroupDrop : public AsyncGraphGroup {
                    int device_id) override;
 
 public:
-  AsyncGraphGroupDrop(Ptr<Options> options)
-      : AsyncGraphGroup(options),
+  AsyncGraphGroupDrop(Ptr<Options> options, Ptr<IMPIWrapper> mpi)
+      : AsyncGraphGroup(options, mpi),
         dropping_warmup{options->get<size_t>("grad-dropping-warmup")},
         droping_rate{options->get<float>("grad-dropping-rate")},
         dropping_momentum{options->get<float>("grad-dropping-momentum")} {}
diff --git a/src/training/graph_group_multinode.h b/src/training/graph_group_multinode.h
index 873155df7..a05787e56 100755
--- a/src/training/graph_group_multinode.h
+++ b/src/training/graph_group_multinode.h
@@ -351,8 +351,8 @@ class MultiNodeGraphGroup : public MultiNodeGraphGroupBase {
   /**
    * (Constructor) Call super class and initialize client graphs and builders.
    */
-  MultiNodeGraphGroup(Ptr<Options> options)
-      : Base(options),
+  MultiNodeGraphGroup(Ptr<Options> options, Ptr<IMPIWrapper> mpi)
+      : Base(options, mpi),
         clientCommOverlap{options_->get<bool>("multi-node-overlap")},
         tau_{(size_t)options_->get<double>("optimizer-delay")} { }
 
diff --git a/src/training/graph_group_multinode_sync.h b/src/training/graph_group_multinode_sync.h
index 7b93b2a84..cdb95ae6c 100755
--- a/src/training/graph_group_multinode_sync.h
+++ b/src/training/graph_group_multinode_sync.h
@@ -131,8 +131,8 @@ class MultiNodeGraphGroupSync : public MultiNodeGraphGroupBase {
   /**
    * (Constructor) Call super class and initialize client graphs and builders.
    */
-  MultiNodeGraphGroupSync(Ptr<Options> options)
-      : Base(options),
+  MultiNodeGraphGroupSync(Ptr<Options> options, Ptr<IMPIWrapper> mpi)
+      : Base(options, mpi),
         tau_{(size_t)options_->get<double>("optimizer-delay")},
         syncOptimizer_{Optimizer(options_)},
         movingAvg_{options_->get<float>("exponential-smoothing") > 0},
diff --git a/src/training/graph_group_singleton.h b/src/training/graph_group_singleton.h
index 9e417b7d6..f9e67fb90 100755
--- a/src/training/graph_group_singleton.h
+++ b/src/training/graph_group_singleton.h
@@ -23,9 +23,10 @@ class SingletonGraph : public GraphGroup, public ExponentialSmoothing {
   void execute(Ptr<data::Batch> batch);
 
 public:
-  SingletonGraph(Ptr<Options> config)
+  SingletonGraph(Ptr<Options> config, Ptr<IMPIWrapper> mpi)
       : GraphGroup(config),
         ExponentialSmoothing(config) {
+    ABORT_IF(mpi->numMPIProcesses() != 1, "SingletonGraph does not support multiple MPI processes");
     // Get device ID
     auto devices = Config::getDevices(options_);
     ABORT_IF(devices.size() != 1, "Only one device ID should be provided for singleton training");
diff --git a/src/training/graph_group_sync.cpp b/src/training/graph_group_sync.cpp
index b89efde87..c00a4fc2f 100755
--- a/src/training/graph_group_sync.cpp
+++ b/src/training/graph_group_sync.cpp
@@ -2,12 +2,9 @@
 
 namespace marian {
 
-SyncGraphGroup::SyncGraphGroup(Ptr<Options> config)
-    : GraphGroup(config),
-      ExponentialSmoothing(config),
-      delay_{options_->get<double>("optimizer-delay")} { // @TODO: rename to something else; delay means delayed updated, not accumulation
-
-  mpi_ = initMPI(/*multiThreaded=*/false); // when not running under MPI, this will be a fake object that represents a one-MPI-process setup
+SyncGraphGroup::SyncGraphGroup(Ptr<Options> config, Ptr<IMPIWrapper> mpi)
+    : GraphGroup(config), ExponentialSmoothing(config),
+      delay_{options_->get<double>("optimizer-delay")}, mpi_(mpi) { // @TODO: rename delay_ to something else; delay means delayed updated, not accumulation
 
   devices_ = Config::getDevices(options_, mpi_->myMPIRank(), mpi_->numMPIProcesses());
   for(auto device : devices_) {
@@ -535,12 +532,4 @@ void SyncGraphGroup::finalize() /*override*/ {
   Base::finalize();
 }
 
-SyncGraphGroup::~SyncGraphGroup() /*override*/ {
-  comm_ = nullptr;
-  static int c = 0;
-  c++;
-  if (c == 2) // HACKHACK: MPI can only be closed once
-    finalizeMPI(std::move(mpi_));
-}
-
 }  // namespace marian
diff --git a/src/training/graph_group_sync.h b/src/training/graph_group_sync.h
index d7ecec2ff..03ee8c4d2 100755
--- a/src/training/graph_group_sync.h
+++ b/src/training/graph_group_sync.h
@@ -41,7 +41,7 @@ class SyncGraphGroup : public GraphGroup, public ExponentialSmoothing {
   void update(std::vector<Ptr<data::Batch>> subBatches, size_t numReadBatches);
 
 public:
-  SyncGraphGroup(Ptr<Options> config);
+  SyncGraphGroup(Ptr<Options> config, Ptr<IMPIWrapper> mpi);
 
   void setScheduler(Ptr<Scheduler> scheduler) override;
 
@@ -54,7 +54,5 @@ class SyncGraphGroup : public GraphGroup, public ExponentialSmoothing {
 
   Ptr<data::BatchStats> collectStats();
   // @TODO: consider to make this a virtual as well? Currently it is a template dispatch
-
-  ~SyncGraphGroup() override;
 };
 }  // namespace marian
diff --git a/src/training/training.h b/src/training/training.h
index b2290db75..c388d6195 100755
--- a/src/training/training.h
+++ b/src/training/training.h
@@ -36,15 +36,15 @@ class Train : public ModelTask {
 
     auto trainState = New<TrainingState>(options_->get<float>("learn-rate"));
     auto scheduler = New<Scheduler>(options_, trainState);
+    auto mpi = initMPI(/*multiThreaded=*/!options_->get<bool>("sync-sgd")); // @TODO: do we need the multiThreaded distinction at all?
 
     Ptr<BatchStats> stats;
     if(options_->get<bool>("mini-batch-fit")) {
       LOG(info,
-          "[batching] Collecting statistics for batch fitting with step size "
-          "{}",
+          "[batching] Collecting statistics for batch fitting with step size {}",
           options_->get<size_t>("mini-batch-fit-step"));
       // @TODO, better fake batch with vocabulary
-      auto model = New<ModelWrapper>(options_);
+      auto model = New<ModelWrapper>(options_, mpi);
       model->setScheduler(scheduler); // collectStats() needs to know about dynamic MB scaling
       stats = model->collectStats();
       LOG(info, "[batching] Done. Typical MB size is {} target words", stats->estimateTypicalTrgWords());
@@ -60,7 +60,7 @@ class Train : public ModelTask {
 
     scheduler->registerTrainingObserver(batchGenerator);
 
-    auto model = New<ModelWrapper>(options_);
+    auto model = New<ModelWrapper>(options_, mpi);
     model->setScheduler(scheduler);
     model->setTypicalTrgBatchWords(batchGenerator->estimateTypicalTrgBatchWords()); // needed for dynamic MB scaling
     model->load();
@@ -91,12 +91,15 @@ class Train : public ModelTask {
     }
     scheduler->finished();
 
-    model->finalize();
+    model->finalize(); // allow async to sync before final save   --@TODO: rename, or move into save()
 
-    // Avoid saving the model twice if it has been loaded and training did not
-    // progress
+    // Avoid saving the model twice if it has been loaded and training did not progress
     if(!trainState->loaded)
       model->save(true);
+
+    // Signal success to a potential MPI runner
+    model = nullptr; // release any reference to MPI that model may hold
+    finalizeMPI(std::move(mpi));
   }
 };
 }  // namespace marian

From fdd1e4c9cd8dc0ede09228061d9824909d7e901a Mon Sep 17 00:00:00 2001
From: Roman Grundkiewicz <rgrundki@exseed.ed.ac.uk>
Date: Mon, 24 Dec 2018 12:09:37 +0000
Subject: [PATCH 065/838] Fix implicit values for vectors

---
 src/3rd_party/CLI/App.hpp | 6 ++++--
 1 file changed, 4 insertions(+), 2 deletions(-)

diff --git a/src/3rd_party/CLI/App.hpp b/src/3rd_party/CLI/App.hpp
index 555f480c1..fe430c16c 100644
--- a/src/3rd_party/CLI/App.hpp
+++ b/src/3rd_party/CLI/App.hpp
@@ -1613,8 +1613,10 @@ class App {
 
             if(emptyVectorArgs) {
                 if(op->get_implicit()) {
-                    op->add_result(op->get_implicitval());
-                    parse_order_.push_back(op.get());
+                    for(const auto& ival : detail::split_up(op->get_implicitval())) {
+                        op->add_result(ival);
+                        parse_order_.push_back(op.get());
+                    }
                 } else if (op->get_expected() < 0) {
                     parse_order_.push_back(op.get());
                     throw ArgumentMismatch(op->get_name(), op->get_expected(), 0);

From 0d0fe5906a5f9e16494894ca5018071a2800a88d Mon Sep 17 00:00:00 2001
From: Roman Grundkiewicz <rgrundki@exseed.ed.ac.uk>
Date: Mon, 24 Dec 2018 13:10:57 +0000
Subject: [PATCH 066/838] Add comment for parseAliases

---
 src/common/cli_wrapper.h | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/src/common/cli_wrapper.h b/src/common/cli_wrapper.h
index d06bdd718..57400f6ca 100755
--- a/src/common/cli_wrapper.h
+++ b/src/common/cli_wrapper.h
@@ -229,8 +229,10 @@ class CLIWrapper {
   // Parse command-line arguments. Handles --help and --version options
   void parse(int argc, char **argv);
 
+  // Expand aliases based on arguments parsed with parse(int, char**) method
   void parseAliases(const YAML::Node &config) {
     for(const auto& alias : aliases_) {
+      // note: options values are always compared as strings
       if(config[alias.key] && config[alias.key].as<std::string>() == alias.value) {
           updateConfig(alias.config,
                        "Unknown option(s) in alias '" + alias.key + ": " + alias.value + "'");

From 9b2d58ab5ab309011a28281399ef2285019f9502 Mon Sep 17 00:00:00 2001
From: Roman Grundkiewicz <rgrundki@exseed.ed.ac.uk>
Date: Mon, 24 Dec 2018 13:26:55 +0000
Subject: [PATCH 067/838] Remove parsed aliases automatically

---
 src/common/cli_wrapper.cpp   | 21 +++++++++++++++++++++
 src/common/cli_wrapper.h     | 18 ++++++++----------
 src/common/config_parser.cpp |  8 ++------
 3 files changed, 31 insertions(+), 16 deletions(-)

diff --git a/src/common/cli_wrapper.cpp b/src/common/cli_wrapper.cpp
index df647bfe4..65f34aa84 100755
--- a/src/common/cli_wrapper.cpp
+++ b/src/common/cli_wrapper.cpp
@@ -128,6 +128,27 @@ void CLIWrapper::parse(int argc, char **argv) {
   }
 }
 
+void CLIWrapper::parseAliases() {
+  if(aliases_.empty())
+    return;
+
+  std::set<std::string> aliasKeys;
+  for(const auto& alias : aliases_) {
+    // Note: options values are always compared as strings
+    if(config_[alias.key] && config_[alias.key].as<std::string>() == alias.value) {
+        updateConfig(alias.config,
+                     "Unknown option(s) in alias '" + alias.key + ": " + alias.value + "'");
+    }
+    aliasKeys.insert(alias.key);
+  }
+
+  // Remove aliases from the config
+  for(const auto& key : aliasKeys) {
+    config_.remove(key);
+  }
+}
+
+
 std::string CLIWrapper::failureMessage(const CLI::App *app, const CLI::Error &e) {
   std::string header = "Error: " + std::string(e.what()) + "\n";
   if(app->get_help_ptr() != nullptr)
diff --git a/src/common/cli_wrapper.h b/src/common/cli_wrapper.h
index 57400f6ca..a748c560e 100755
--- a/src/common/cli_wrapper.h
+++ b/src/common/cli_wrapper.h
@@ -229,16 +229,14 @@ class CLIWrapper {
   // Parse command-line arguments. Handles --help and --version options
   void parse(int argc, char **argv);
 
-  // Expand aliases based on arguments parsed with parse(int, char**) method
-  void parseAliases(const YAML::Node &config) {
-    for(const auto& alias : aliases_) {
-      // note: options values are always compared as strings
-      if(config[alias.key] && config[alias.key].as<std::string>() == alias.value) {
-          updateConfig(alias.config,
-                       "Unknown option(s) in alias '" + alias.key + ": " + alias.value + "'");
-      }
-    }
-  }
+  /**
+   * @brief Expand aliases based on arguments parsed with parse(int, char**)
+   *
+   * Should be called after parse(int, char**) to take an effect.
+   *
+   * All options defined as aliases are removed from the config object.
+   */
+  void parseAliases();
 
   /*
    * @brief Overwrite values for unparsed options
diff --git a/src/common/config_parser.cpp b/src/common/config_parser.cpp
index f4d86c567..355641cb2 100755
--- a/src/common/config_parser.cpp
+++ b/src/common/config_parser.cpp
@@ -767,9 +767,7 @@ void ConfigParser::parseOptions(int argc, char** argv, bool doValidate) {
     config_.remove("dump-config");
 
     if(type == "explain") {
-      cli.parseAliases(config_);
-      config_.remove("best-deep");
-      config_.remove("problem");
+      cli.parseAliases();
     }
 
     bool minimal = (type == "minimal" || type == "explain");
@@ -777,9 +775,7 @@ void ConfigParser::parseOptions(int argc, char** argv, bool doValidate) {
     exit(0);
   }
 
-  cli.parseAliases(config_);
-  config_.remove("best-deep");
-  config_.remove("problem");
+  cli.parseAliases();
 }
 
 std::vector<std::string> ConfigParser::findConfigPaths() {

From a9a0f65380622f1c90944d0570f3cc94d02a3b29 Mon Sep 17 00:00:00 2001
From: Roman Grundkiewicz <rgrundki@exseed.ed.ac.uk>
Date: Mon, 24 Dec 2018 13:47:51 +0000
Subject: [PATCH 068/838] Fix options in transformer-big

---
 src/common/config_parser.cpp | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/src/common/config_parser.cpp b/src/common/config_parser.cpp
index 355641cb2..3a8f054af 100755
--- a/src/common/config_parser.cpp
+++ b/src/common/config_parser.cpp
@@ -701,8 +701,8 @@ void ConfigParser::addAliases(cli::CLIWrapper& cli) {
     config["lr-warmup"] = 8000;
     config["lr-decay-inv-sqrt"] = 8000;
     config["transformer-dropout"] = 0.1;
-    config["transformer-attention-dropout"] = 0.1;
-    config["transformer-ffn-dropout"] = 0.1;
+    config["transformer-dropout-attention"] = 0.1;
+    config["transformer-dropout-ffn"] = 0.1;
     config["label-smoothing"] = 0.1;
     config["clip-norm"] = 5;
   });

From b013bab0d3a18ade9d3abc362f7368ed93e6014a Mon Sep 17 00:00:00 2001
From: Roman Grundkiewicz <rgrundki@exseed.ed.ac.uk>
Date: Mon, 24 Dec 2018 14:00:20 +0000
Subject: [PATCH 069/838] Make --problem a vector

---
 src/common/cli_wrapper.cpp   | 17 ++++++++++++++---
 src/common/config_parser.cpp |  4 ++--
 2 files changed, 16 insertions(+), 5 deletions(-)

diff --git a/src/common/cli_wrapper.cpp b/src/common/cli_wrapper.cpp
index 65f34aa84..27c8031a8 100755
--- a/src/common/cli_wrapper.cpp
+++ b/src/common/cli_wrapper.cpp
@@ -134,12 +134,23 @@ void CLIWrapper::parseAliases() {
 
   std::set<std::string> aliasKeys;
   for(const auto& alias : aliases_) {
-    // Note: options values are always compared as strings
-    if(config_[alias.key] && config_[alias.key].as<std::string>() == alias.value) {
+    if(config_[alias.key]) {
+      bool expand = false;
+      if(config_[alias.key].IsSequence()) {
+        // Note: options values are always extracted as vectors and compared as strings
+        auto aliasOpts = config_[alias.key].as<std::vector<std::string>>();
+        expand = std::find(aliasOpts.begin(), aliasOpts.end(), alias.value) != aliasOpts.end();
+      } else {
+        // Note: options values are always compared as strings
+        expand = config_[alias.key].as<std::string>() == alias.value;
+      }
+
+      if(expand) {
         updateConfig(alias.config,
                      "Unknown option(s) in alias '" + alias.key + ": " + alias.value + "'");
+      }
+      aliasKeys.insert(alias.key);
     }
-    aliasKeys.insert(alias.key);
   }
 
   // Remove aliases from the config
diff --git a/src/common/config_parser.cpp b/src/common/config_parser.cpp
index 3a8f054af..1e75e7590 100755
--- a/src/common/config_parser.cpp
+++ b/src/common/config_parser.cpp
@@ -399,8 +399,8 @@ void ConfigParser::addOptionsTraining(cli::CLIWrapper& cli) {
   // add ULR settings
   addSuboptionsULR(cli);
 
-  cli.add<std::string>("--problem",
-     "Use predefined set of options. Possible values: transformer");
+  cli.add<std::vector<std::string>>("--problem",
+     "Use predefined set of options. Possible values: transformer, transformer-big");
   // clang-format on
 }
 

From 71cf7009d612912c83b44f36dbfaee305a136544 Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Wed, 26 Dec 2018 11:04:44 -0800
Subject: [PATCH 070/838] changed back to log last best in validation

---
 src/training/scheduler.h | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/src/training/scheduler.h b/src/training/scheduler.h
index 14f2bb07f..5db0cfdea 100755
--- a/src/training/scheduler.h
+++ b/src/training/scheduler.h
@@ -179,12 +179,12 @@ class Scheduler : public TrainingObserver {
       float value = validator->validate(graphs);
       if(validator->stalled() > 0) {
         LOG_VALID(info,
-                  "Ep. {} : Up. {} : {} : {} : stalled {} times",  /*"(last best: {})",*/ // @TODO (LOGGING CHANGE)
+                  "Ep. {} : Up. {} : {} : {} : stalled {} times (last best: {})", // @TODO (LOGGING CHANGE)
                   state_->epochs,
                   state_->batches,
                   validator->type(),
                   value,
-                  validator->stalled() /*, validator->lastBest()*/); // @TODO (LOGGING CHANGE)
+                  validator->stalled(), validator->lastBest()); // @TODO (LOGGING CHANGE)
       } else {
         LOG_VALID(info,
                   "Ep. {} : Up. {} : {} : {} : new best",

From 8fec73b7712e8083b0427724fc658976c2e18a36 Mon Sep 17 00:00:00 2001
From: Marcin Junczys-Dowmunt <Marcin.Junczys-Dowmunt@microsoft.com>
Date: Wed, 26 Dec 2018 11:50:24 -0800
Subject: [PATCH 071/838] enable last-best reporting

---
 src/training/scheduler.h | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/src/training/scheduler.h b/src/training/scheduler.h
index 2f2fc5a21..10d171874 100755
--- a/src/training/scheduler.h
+++ b/src/training/scheduler.h
@@ -174,12 +174,12 @@ class Scheduler : public TrainingObserver {
       float value = validator->validate(graphs);
       if(validator->stalled() > 0) {
         LOG_VALID(info,
-                  "Ep. {} : Up. {} : {} : {} : stalled {} times",  /*"(last best: {})",*/ // @TODO (LOGGING CHANGE)
+                  "Ep. {} : Up. {} : {} : {} : stalled {} times (last best: {})",
                   state_->epochs,
                   state_->batches,
                   validator->type(),
                   value,
-                  validator->stalled() /*, validator->lastBest()*/); // @TODO (LOGGING CHANGE)
+                  validator->stalled(), validator->lastBest());
       } else {
         LOG_VALID(info,
                   "Ep. {} : Up. {} : {} : {} : new best",

From 1e143f61fb9c46c0b33d834510c7379e36fe22a6 Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Wed, 26 Dec 2018 14:20:16 -0800
Subject: [PATCH 072/838] added elementwise comparison operators (lt(), eq(),
 ); bug fix: Maximum and Minimum got it backwards; streamlined the operator
 tests a little

---
 src/functional/predicates.h        | 15 +++----
 src/graph/expression_operators.cpp | 21 ++++++++++
 src/graph/expression_operators.h   | 25 +++++++++++-
 src/graph/node_operators_binary.h  | 29 ++++++++++++++
 src/tensors/gpu/element.cu         |  0
 src/tensors/gpu/element.inc        | 11 ++++-
 src/tests/operator_tests.cpp       | 64 +++++++++++++++++-------------
 src/tests/prod.cpp                 |  1 -
 vs/Marian.vcxproj                  | 58 +++++++++++++++++++++++++++
 vs/Marian.vcxproj.filters          | 51 ++++++++++++++++++++++++
 10 files changed, 235 insertions(+), 40 deletions(-)
 mode change 100644 => 100755 src/tensors/gpu/element.cu
 mode change 100644 => 100755 src/tests/operator_tests.cpp
 mode change 100644 => 100755 src/tests/prod.cpp

diff --git a/src/functional/predicates.h b/src/functional/predicates.h
index fce5da473..4d92a0224 100755
--- a/src/functional/predicates.h
+++ b/src/functional/predicates.h
@@ -98,15 +98,12 @@ BINARY(Div, operator/, x / y);
 
 BINARY(LogAddExp,
        logaddexp,
-       (/*if*/ (x < y)
-            ?  // Note: This may not be ideal for CUDA; cf. CNTK implementation
-            (y + log1pf(expf(x - y)))
-            /*else*/
-            : (x + log1pf(expf(y - x)))));
-BINARY(Maximum,
-       max,
-       (x > y) ? y : x);  // note: std::max not available on CUDA it seems
-BINARY(Minimum, min, (x < y) ? y : x);
+       (/*if*/ (x < y) ?  // Note: This may not be ideal for CUDA; cf. CNTK implementation
+          (y + log1pf(expf(x - y)))
+        /*else*/ :
+          (x + log1pf(expf(y - x)))));
+BINARY(Maximum, max, (x > y) ? x : y);  // Note: std::max not available on CUDA it seems
+BINARY(Minimum, min, (x < y) ? x : y);
 
 UNARY(Negate, operator!, !x);
 BINARY(Eq, operator==, x == y);
diff --git a/src/graph/expression_operators.cpp b/src/graph/expression_operators.cpp
index 2f4f5ecf9..4870a6664 100755
--- a/src/graph/expression_operators.cpp
+++ b/src/graph/expression_operators.cpp
@@ -107,6 +107,27 @@ Expr minimum(Expr a, Expr b) {
   return Expression<MinimumNodeOp>(a, b);
 }
 
+Expr lt(Expr a, Expr b) { return Expression<CmpNodeOp>(a, b, -1, false); }
+Expr eq(Expr a, Expr b) { return Expression<CmpNodeOp>(a, b,  0, false); }
+Expr gt(Expr a, Expr b) { return Expression<CmpNodeOp>(a, b,  1, false); }
+Expr ge(Expr a, Expr b) { return Expression<CmpNodeOp>(a, b, -1,  true); }
+Expr ne(Expr a, Expr b) { return Expression<CmpNodeOp>(a, b,  0,  true); }
+Expr le(Expr a, Expr b) { return Expression<CmpNodeOp>(a, b,  1,  true); }
+
+Expr lt(float a, Expr b) { return Expression<CmpNodeOp>(b->graph()->constant({}, inits::from_value(a), b->value_type()), b, -1, false); }
+Expr eq(float a, Expr b) { return Expression<CmpNodeOp>(b->graph()->constant({}, inits::from_value(a), b->value_type()), b,  0, false); }
+Expr gt(float a, Expr b) { return Expression<CmpNodeOp>(b->graph()->constant({}, inits::from_value(a), b->value_type()), b,  1, false); }
+Expr ge(float a, Expr b) { return Expression<CmpNodeOp>(b->graph()->constant({}, inits::from_value(a), b->value_type()), b, -1,  true); }
+Expr ne(float a, Expr b) { return Expression<CmpNodeOp>(b->graph()->constant({}, inits::from_value(a), b->value_type()), b,  0,  true); }
+Expr le(float a, Expr b) { return Expression<CmpNodeOp>(b->graph()->constant({}, inits::from_value(a), b->value_type()), b,  1,  true); }
+
+Expr lt(Expr a, float b) { return Expression<CmpNodeOp>(a, a->graph()->constant({}, inits::from_value(b), a->value_type()), -1, false); }
+Expr eq(Expr a, float b) { return Expression<CmpNodeOp>(a, a->graph()->constant({}, inits::from_value(b), a->value_type()),  0, false); }
+Expr gt(Expr a, float b) { return Expression<CmpNodeOp>(a, a->graph()->constant({}, inits::from_value(b), a->value_type()),  1, false); }
+Expr ge(Expr a, float b) { return Expression<CmpNodeOp>(a, a->graph()->constant({}, inits::from_value(b), a->value_type()), -1,  true); }
+Expr ne(Expr a, float b) { return Expression<CmpNodeOp>(a, a->graph()->constant({}, inits::from_value(b), a->value_type()),  0,  true); }
+Expr le(Expr a, float b) { return Expression<CmpNodeOp>(a, a->graph()->constant({}, inits::from_value(b), a->value_type()),  1,  true); }
+
 /*********************************************************/
 
 Expr operator+(Expr a, float b) {
diff --git a/src/graph/expression_operators.h b/src/graph/expression_operators.h
index 6a887aa0a..ab1b364b8 100755
--- a/src/graph/expression_operators.h
+++ b/src/graph/expression_operators.h
@@ -66,10 +66,33 @@ Expr operator/(Expr a, float b);
 
 Expr logaddexp(Expr a, Expr b);
 
+// Note: Following numpy, minimum() is element-wise, while min() is along an axis in both Numpy and PyTorch.
 Expr maximum(Expr a, Expr b);
-
 Expr minimum(Expr a, Expr b);
 
+// Note: We cannot overload the relational operators, as they also mean something for Expr itself.
+// Note: These names follow PyTorch convention.
+Expr lt(Expr a, Expr b);
+Expr eq(Expr a, Expr b);
+Expr gt(Expr a, Expr b);
+Expr ge(Expr a, Expr b);
+Expr ne(Expr a, Expr b);
+Expr le(Expr a, Expr b);
+
+Expr lt(float a, Expr b);
+Expr eq(float a, Expr b);
+Expr gt(float a, Expr b);
+Expr ge(float a, Expr b);
+Expr ne(float a, Expr b);
+Expr le(float a, Expr b);
+
+Expr lt(Expr a, float b);
+Expr eq(Expr a, float b);
+Expr gt(Expr a, float b);
+Expr ge(Expr a, float b);
+Expr ne(Expr a, float b);
+Expr le(Expr a, float b);
+
 Expr dot(Expr a,
          Expr b,
          bool transA = false,
diff --git a/src/graph/node_operators_binary.h b/src/graph/node_operators_binary.h
index 0f972bc79..868ee4ebd 100755
--- a/src/graph/node_operators_binary.h
+++ b/src/graph/node_operators_binary.h
@@ -814,6 +814,35 @@ struct MinimumNodeOp : public ElementBinaryNodeOp {
   const std::string type() override { return "min"; }
 };
 
+struct CmpNodeOp : public ElementBinaryNodeOp {
+  CmpNodeOp(Expr a, Expr b, int cmp_, bool not_) : ElementBinaryNodeOp(a, b), cmp_(cmp_), not_(not_) {
+    setTrainable(false); // has no gradient
+  }
+
+  NodeOps forwardOps() override {
+    using namespace functional;
+
+    return {
+      NodeOp(Element(_1 = ((((_2 > _3) - (_2 < _3)) == (float)cmp_) != not_),
+             val_, child(0)->val(), child(1)->val()))};
+  }
+
+  NodeOps backwardOps() override { return {}; }
+
+  const std::string type() override {
+    switch (cmp_) {
+    case -1: return not_ ? "ge" : "lt";
+    case  0: return not_ ? "ne" : "eq";
+    case  1: return not_ ? "le" : "gt";
+    }
+    ABORT("Should not get here??");
+  }
+
+private:
+  int cmp_;  // -1: less; 0: equal; 1: greater
+  bool not_; // invert result if true
+};
+
 // In each j-th row, take the corresponding j-th label index i from indices and compute:
 // For each vocabulary item v, the only non-zero element in a row in the sum is the item
 // that matches the label indexed by i (the picked element).
diff --git a/src/tensors/gpu/element.cu b/src/tensors/gpu/element.cu
old mode 100644
new mode 100755
diff --git a/src/tensors/gpu/element.inc b/src/tensors/gpu/element.inc
index 96debcc2f..66f763018 100755
--- a/src/tensors/gpu/element.inc
+++ b/src/tensors/gpu/element.inc
@@ -52,5 +52,12 @@ template void Element<Assign<Var<1>, BinaryFunctor<elem::Minus, Assignee<1>, Bin
 template void Element<Assign<Var<1>, BinaryFunctor<elem::Minus, Assignee<1>, BinaryFunctor<elem::Mult, Capture, BinaryFunctor<elem::Plus, BinaryFunctor<elem::Div, BinaryFunctor<elem::Div, Assignee<2>, Capture>, BinaryFunctor<elem::Plus, UnaryFunctor<elem::Sqrt, BinaryFunctor<elem::Plus, BinaryFunctor<elem::Div, UnaryFunctor<elem::sReLU, BinaryFunctor<elem::Minus, Assignee<3>, BinaryFunctor<elem::Div, BinaryFunctor<elem::Mult, BinaryFunctor<elem::Mult, Capture, Assignee<4> >, Assignee<4> >, Capture> > >, Capture>, BinaryFunctor<elem::Div, BinaryFunctor<elem::Mult, Assignee<4>, Assignee<4> >, Capture> > >, Capture> >, BinaryFunctor<elem::Mult, Capture, Assignee<1> > > > > >, std::shared_ptr<marian::TensorBase>, std::shared_ptr<marian::TensorBase>, std::shared_ptr<marian::TensorBase> >(Assign<Var<1>, BinaryFunctor<elem::Minus, Assignee<1>, BinaryFunctor<elem::Mult, Capture, BinaryFunctor<elem::Plus, BinaryFunctor<elem::Div, BinaryFunctor<elem::Div, Assignee<2>, Capture>, BinaryFunctor<elem::Plus, UnaryFunctor<elem::Sqrt, BinaryFunctor<elem::Plus, BinaryFunctor<elem::Div, UnaryFunctor<elem::sReLU, BinaryFunctor<elem::Minus, Assignee<3>, BinaryFunctor<elem::Div, BinaryFunctor<elem::Mult, BinaryFunctor<elem::Mult, Capture, Assignee<4> >, Assignee<4> >, Capture> > >, Capture>, BinaryFunctor<elem::Div, BinaryFunctor<elem::Mult, Assignee<4>, Assignee<4> >, Capture> > >, Capture> >, BinaryFunctor<elem::Mult, Capture, Assignee<1> > > > > >, std::shared_ptr<marian::TensorBase>, std::shared_ptr<marian::TensorBase>, std::shared_ptr<marian::TensorBase>, std::shared_ptr<marian::TensorBase>);
 template void Element<Assign<Var<1>, BinaryFunctor<elem::Minus, Assignee<1>, BinaryFunctor<elem::Mult, Capture, BinaryFunctor<elem::Plus, BinaryFunctor<elem::Div, BinaryFunctor<elem::Div, Assignee<2>, Capture>, BinaryFunctor<elem::Plus, UnaryFunctor<elem::Sqrt, BinaryFunctor<elem::Div, BinaryFunctor<elem::Mult, Assignee<4>, Assignee<4> >, Capture> >, Capture> >, BinaryFunctor<elem::Mult, Capture, Assignee<1> > > > > >, std::shared_ptr<marian::TensorBase>, std::shared_ptr<marian::TensorBase>, std::shared_ptr<marian::TensorBase> >(Assign<Var<1>, BinaryFunctor<elem::Minus, Assignee<1>, BinaryFunctor<elem::Mult, Capture, BinaryFunctor<elem::Plus, BinaryFunctor<elem::Div, BinaryFunctor<elem::Div, Assignee<2>, Capture>, BinaryFunctor<elem::Plus, UnaryFunctor<elem::Sqrt, BinaryFunctor<elem::Div, BinaryFunctor<elem::Mult, Assignee<4>, Assignee<4> >, Capture> >, Capture> >, BinaryFunctor<elem::Mult, Capture, Assignee<1> > > > > >, std::shared_ptr<marian::TensorBase>, std::shared_ptr<marian::TensorBase>, std::shared_ptr<marian::TensorBase>, std::shared_ptr<marian::TensorBase>);
 template void Element<Assign<Var<1>, BinaryFunctor<elem::Minus, Assignee<1>, BinaryFunctor<elem::Mult, Capture, BinaryFunctor<elem::Plus, BinaryFunctor<elem::Div, BinaryFunctor<elem::Div, Assignee<2>, Capture>, BinaryFunctor<elem::Plus, UnaryFunctor<elem::Sqrt, BinaryFunctor<elem::Div, Assignee<3>, Capture> >, Capture> >, BinaryFunctor<elem::Mult, Capture, Assignee<1> > > > > >, std::shared_ptr<marian::TensorBase>, std::shared_ptr<marian::TensorBase>, std::shared_ptr<marian::TensorBase> >(Assign<Var<1>, BinaryFunctor<elem::Minus, Assignee<1>, BinaryFunctor<elem::Mult, Capture, BinaryFunctor<elem::Plus, BinaryFunctor<elem::Div, BinaryFunctor<elem::Div, Assignee<2>, Capture>, BinaryFunctor<elem::Plus, UnaryFunctor<elem::Sqrt, BinaryFunctor<elem::Div, Assignee<3>, Capture> >, Capture> >, BinaryFunctor<elem::Mult, Capture, Assignee<1> > > > > >, std::shared_ptr<marian::TensorBase>, std::shared_ptr<marian::TensorBase>, std::shared_ptr<marian::TensorBase>, std::shared_ptr<marian::TensorBase>);
-template void Element<Assign<Var<1>, BinaryFunctor<elem::Minus, Assignee<1>, BinaryFunctor<elem::Mult, Capture, BinaryFunctor<elem::Plus, BinaryFunctor<elem::Div, BinaryFunctor<elem::Div, Assignee<2>, Capture>, BinaryFunctor<elem::Plus, UnaryFunctor<elem::Sqrt, BinaryFunctor<elem::Plus, BinaryFunctor<elem::Div, UnaryFunctor<elem::sReLU, BinaryFunctor<elem::Plus, BinaryFunctor<elem::Minus, Assignee<3>, BinaryFunctor<elem::Div, BinaryFunctor<elem::Mult, BinaryFunctor<elem::Mult, Capture, Assignee<5> >, Assignee<4> >, Capture> >, BinaryFunctor<elem::Div, BinaryFunctor<elem::Mult, BinaryFunctor<elem::Mult, Capture, Assignee<4> >, Assignee<4> >, Capture> > >, Capture>, BinaryFunctor<elem::Div, BinaryFunctor<elem::Mult, Assignee<4>, Assignee<4> >, Capture> > >, Capture> >, BinaryFunctor<elem::Mult, Capture, Assignee<1> > > > > >, std::shared_ptr<marian::TensorBase>, std::shared_ptr<marian::TensorBase>, std::shared_ptr<marian::TensorBase>, std::shared_ptr<marian::TensorBase> >(Assign<Var<1>, BinaryFunctor<elem::Minus, Assignee<1>, BinaryFunctor<elem::Mult, Capture, BinaryFunctor<elem::Plus, BinaryFunctor<elem::Div, BinaryFunctor<elem::Div, Assignee<2>, Capture>, BinaryFunctor<elem::Plus, UnaryFunctor<elem::Sqrt, BinaryFunctor<elem::Plus, BinaryFunctor<elem::Div, UnaryFunctor<elem::sReLU, BinaryFunctor<elem::Plus, BinaryFunctor<elem::Minus, Assignee<3>, BinaryFunctor<elem::Div, BinaryFunctor<elem::Mult, BinaryFunctor<elem::Mult, Capture, Assignee<5> >, Assignee<4> >, Capture> >, BinaryFunctor<elem::Div, BinaryFunctor<elem::Mult, BinaryFunctor<elem::Mult, Capture, Assignee<4> >, Assignee<4> >, Capture> > >, Capture>, BinaryFunctor<elem::Div, BinaryFunctor<elem::Mult, Assignee<4>, Assignee<4> >, Capture> > >, Capture> >, BinaryFunctor<elem::Mult, Capture, Assignee<1> > > > > >, std::shared_ptr<marian::TensorBase>, std::shared_ptr<marian::TensorBase>, std::shared_ptr<marian::TensorBase>, std::shared_ptr<marian::TensorBase>, std::shared_ptr<marian::TensorBase>);
-template void Element<Assign<Var<1>, BinaryFunctor<elem::Minus, Assignee<1>, BinaryFunctor<elem::Mult, Capture, BinaryFunctor<elem::Plus, BinaryFunctor<elem::Div, BinaryFunctor<elem::Div, Assignee<2>, Capture>, BinaryFunctor<elem::Plus, UnaryFunctor<elem::Sqrt, BinaryFunctor<elem::Div, Assignee<3>, Capture> >, Capture> >, BinaryFunctor<elem::Mult, Capture, Assignee<1> > > > > >, std::shared_ptr<marian::TensorBase>, std::shared_ptr<marian::TensorBase>, std::shared_ptr<marian::TensorBase>, std::shared_ptr<marian::TensorBase> >(Assign<Var<1>, BinaryFunctor<elem::Minus, Assignee<1>, BinaryFunctor<elem::Mult, Capture, BinaryFunctor<elem::Plus, BinaryFunctor<elem::Div, BinaryFunctor<elem::Div, Assignee<2>, Capture>, BinaryFunctor<elem::Plus, UnaryFunctor<elem::Sqrt, BinaryFunctor<elem::Div, Assignee<3>, Capture> >, Capture> >, BinaryFunctor<elem::Mult, Capture, Assignee<1> > > > > >, std::shared_ptr<marian::TensorBase>, std::shared_ptr<marian::TensorBase>, std::shared_ptr<marian::TensorBase>, std::shared_ptr<marian::TensorBase>, std::shared_ptr<marian::TensorBase>);
+template void Element<Assign<Var<1>, BinaryFunctor<elem::Minus, Assignee<1>, BinaryFunctor<elem::Mult, Capture, BinaryFunctor<elem::Plus, BinaryFunctor<elem::Div, BinaryFunctor<elem::Div, Assignee<2>, Capture>, BinaryFunctor<elem::Plus, UnaryFunctor<elem::Sqrt, BinaryFunctor<elem::Plus, BinaryFunctor<elem::Div, UnaryFunctor<elem::sReLU, BinaryFunctor<elem::Plus, BinaryFunctor<elem::Minus, Assignee<3>, BinaryFunctor<elem::Div, BinaryFunctor<elem::Mult, BinaryFunctor<elem::Mult, Capture, Assignee<5> >, Assignee<4> >, Capture> >, BinaryFunctor<elem::Div, BinaryFunctor<elem::Mult, BinaryFunctor<elem::Mult, Capture, Assignee<4> >, Assignee<4> >, Capture> > >, Capture>, BinaryFunctor<elem::Div, BinaryFunctor<elem::Mult, Assignee<4>, Assignee<4> >, Capture> > >, Capture> >, BinaryFunctor<elem::Mult, Capture, Assignee<1> > > > > >, std::shared_ptr<marian::TensorBase>, std::shared_ptr<marian::TensorBase>, std::shared_ptr<marian::TensorBase>, std::shared_ptr<marian::TensorBase> >(Assign<Var<1>, BinaryFunctor<elem::Minus, Assignee<1>, BinaryFunctor<elem::Mult, Capture, BinaryFunctor<elem::Plus, BinaryFunctor<elem::Div, BinaryFunctor<elem::Div, Assignee<2>, Capture>, BinaryFunctor<elem::Plus, UnaryFunctor<elem::Sqrt, BinaryFunctor<elem::Plus, BinaryFunctor<elem::Div, UnaryFunctor<elem::sReLU, BinaryFunctor<elem::Plus, BinaryFunctor<elem::Minus, Assignee<3>, BinaryFunctor<elem::Div, BinaryFunctor<elem::Mult, BinaryFunctor<elem::Mult, Capture, Assignee<5> >, Assignee<4> >, Capture> >, BinaryFunctor<elem::Div, BinaryFunctor<elem::Mult, BinaryFunctor<elem::Mult, Capture, Assignee<4> >, Assignee<4> >, Capture> > >, Capture>, BinaryFunctor<elem::Div, BinaryFunctor<elem::Mult, Assignee<4>, Assignee<4> >, Capture> > >, Capture> >, BinaryFunctor<elem::Mult, Capture, Assignee<1> > > > > >, std::shared_ptr<marian::TensorBase>, std::shared_ptr<marian::TensorBase>, std::shared_ptr<marian::TensorBase>, std::shared_ptr<marian::TensorBase>, std::shared_ptr<marian::TensorBase>);
+template void Element<Assign<Var<1>, BinaryFunctor<elem::Minus, Assignee<1>, BinaryFunctor<elem::Mult, Capture, BinaryFunctor<elem::Plus, BinaryFunctor<elem::Div, BinaryFunctor<elem::Div, Assignee<2>, Capture>, BinaryFunctor<elem::Plus, UnaryFunctor<elem::Sqrt, BinaryFunctor<elem::Div, Assignee<3>, Capture> >, Capture> >, BinaryFunctor<elem::Mult, Capture, Assignee<1> > > > > >, std::shared_ptr<marian::TensorBase>, std::shared_ptr<marian::TensorBase>, std::shared_ptr<marian::TensorBase>, std::shared_ptr<marian::TensorBase> >(Assign<Var<1>, BinaryFunctor<elem::Minus, Assignee<1>, BinaryFunctor<elem::Mult, Capture, BinaryFunctor<elem::Plus, BinaryFunctor<elem::Div, BinaryFunctor<elem::Div, Assignee<2>, Capture>, BinaryFunctor<elem::Plus, UnaryFunctor<elem::Sqrt, BinaryFunctor<elem::Div, Assignee<3>, Capture> >, Capture> >, BinaryFunctor<elem::Mult, Capture, Assignee<1> > > > > >, std::shared_ptr<marian::TensorBase>, std::shared_ptr<marian::TensorBase>, std::shared_ptr<marian::TensorBase>, std::shared_ptr<marian::TensorBase>, std::shared_ptr<marian::TensorBase>);
+template void marian::gpu::Element<marian::functional::Assign<marian::functional::Var<1>, marian::functional::BinaryFunctor<marian::functional::elem::NEq, marian::functional::BinaryFunctor<marian::functional::elem::Eq, marian::functional::BinaryFunctor<marian::functional::elem::Minus, marian::functional::BinaryFunctor<marian::functional::elem::Gt, marian::functional::Assignee<2>, marian::functional::Assignee<3> >, marian::functional::BinaryFunctor<marian::functional::elem::Lt, marian::functional::Assignee<2>, marian::functional::Assignee<3> > >, marian::functional::Capture>, marian::functional::Capture> >, std::shared_ptr<marian::TensorBase>, std::shared_ptr<marian::TensorBase> >(marian::functional::Assign<marian::functional::Var<1>, marian::functional::BinaryFunctor<marian::functional::elem::NEq, marian::functional::BinaryFunctor<marian::functional::elem::Eq, marian::functional::BinaryFunctor<marian::functional::elem::Minus, marian::functional::BinaryFunctor<marian::functional::elem::Gt, marian::functional::Assignee<2>, marian::functional::Assignee<3> >, marian::functional::BinaryFunctor<marian::functional::elem::Lt, marian::functional::Assignee<2>, marian::functional::Assignee<3> > >, marian::functional::Capture>, marian::functional::Capture> >, std::shared_ptr<marian::TensorBase>, std::shared_ptr<marian::TensorBase>, std::shared_ptr<marian::TensorBase>);
+// How to add new specializations:
+// When you use a new specialization, it will cause a link error of this form (example):
+//   .../src/tensors/tensor_operators.h:41: undefined reference to `void marian::gpu::Element<marian::functional::Assign< ... > ( ... )'
+// To fix this, copy the line with the error message in here and:
+//   - replace up to including "undefined reference to `" by "template"
+//   - replace final ' by a semicolon
diff --git a/src/tests/operator_tests.cpp b/src/tests/operator_tests.cpp
old mode 100644
new mode 100755
index 7c17ec8fd..30f4dfb70
--- a/src/tests/operator_tests.cpp
+++ b/src/tests/operator_tests.cpp
@@ -1,11 +1,13 @@
 #include "catch.hpp"
 #include "graph/expression_graph.h"
 #include "graph/expression_operators.h"
+#include <cmath>
 
 using namespace marian;
 
 void tests(DeviceType device) {
-  auto floatApprox = [](float x, float y) { return x == Approx(y); };
+  auto floatApprox = [](float x, float y) -> bool { return x == Approx(y); };
+  auto floatEqual  = [](float x, float y) -> bool { return x == y; };
 
   Config::seed = 1234;
 
@@ -38,38 +40,46 @@ void tests(DeviceType device) {
     std::vector<float> vA({1, -2, 3, -4});
     std::vector<float> vB({0.5, 1.5});
 
-    std::vector<float> vAdd({1.5, -0.5, 3.5, -2.5});
-    std::vector<float> vMinus({-0.5, 3.5, -2.5, 5.5});
-    std::vector<float> vMult({0.5, -3.0, 1.5, -6.0});
-    std::vector<float> vDiv({2.0f, -1.33333f, 6.0f, -2.66667f});
-
     auto a = graph->constant({2, 2, 1}, inits::from_vector(vA));
     auto b = graph->constant({2, 1}, inits::from_vector(vB));
 
-    auto add = a + b;
-    auto minus = b - a;
-    auto mult = a * b;
-    auto div = a / b;
+    auto compare = [&](Expr res, std::function<float(float,float)> f, bool exactMatch) -> bool {
+      if (res->shape() != Shape({ 2, 2, 1 }))
+          return false;
+      res->val()->get(values);
+      std::vector<float> ref{f(vA[0], vB[0]), f(vA[1], vB[1]), f(vA[2], vB[0]), f(vA[3], vB[1])};
+      return std::equal(values.begin(), values.end(), ref.begin(), exactMatch ? floatEqual : floatApprox);
+    };
+
+    auto rplus  = a + b;
+    auto rminus = a - b;
+    auto rmult  = a * b;
+    auto rdiv   = a / b;
+    auto rlae   = logaddexp(a, b);
+    auto rmax   = maximum(a, b);
+    auto rmin   = minimum(a, b);
+    auto rlt    = lt(a, b);
+    auto req    = eq(a, b);
+    auto rgt    = gt(a, b);
+    auto rge    = ge(a, b);
+    auto rne    = ne(a, b);
+    auto rle    = le(a, b);
 
     graph->forward();
 
-    CHECK(add->shape() == Shape({2, 2, 1}));
-    CHECK(minus->shape() == Shape({2, 2, 1}));
-    CHECK(mult->shape() == Shape({2, 2, 1}));
-    CHECK(div->shape() == Shape({2, 2, 1}));
-
-    add->val()->get(values);
-    CHECK( values == vAdd );
-
-    minus->val()->get(values);
-    CHECK( values == vMinus );
-
-    mult->val()->get(values);
-    CHECK( values == vMult );
-
-    div->val()->get(values);
-    CHECK( std::equal(values.begin(), values.end(),
-                      vDiv.begin(), floatApprox) );
+    CHECK(compare(rplus,  [](float a, float b) {return a + b;}, true));
+    CHECK(compare(rminus, [](float a, float b) {return a - b;}, true));
+    CHECK(compare(rmult,  [](float a, float b) {return a * b;}, true));
+    CHECK(compare(rdiv,   [](float a, float b) {return a / b;}, /*exactMatch=*/false));
+    CHECK(compare(rlae,   [](float a, float b) {return logf(expf(a) + expf(b));}, /*exactMatch=*/false));
+    CHECK(compare(rmax,   [](float a, float b) {return std::max(a, b);}, true));
+    CHECK(compare(rmin,   [](float a, float b) {return std::min(a, b);}, true));
+    CHECK(compare(rlt,    [](float a, float b) {return a <  b;}, true));
+    CHECK(compare(req,    [](float a, float b) {return a == b;}, true));
+    CHECK(compare(rgt,    [](float a, float b) {return a >  b;}, true));
+    CHECK(compare(rge,    [](float a, float b) {return a >= b;}, true));
+    CHECK(compare(rne,    [](float a, float b) {return a != b;}, true));
+    CHECK(compare(rle,    [](float a, float b) {return a <= b;}, true));
   }
 
   SECTION("transposing and reshaping") {
diff --git a/src/tests/prod.cpp b/src/tests/prod.cpp
old mode 100644
new mode 100755
index 5ade67168..c329d403e
--- a/src/tests/prod.cpp
+++ b/src/tests/prod.cpp
@@ -68,6 +68,5 @@ int main(int argc, char** argv) {
         }
     }
 
-
     return 0;
 }
diff --git a/vs/Marian.vcxproj b/vs/Marian.vcxproj
index 15486147a..300e4c91b 100755
--- a/vs/Marian.vcxproj
+++ b/vs/Marian.vcxproj
@@ -588,6 +588,50 @@
     <ClCompile Include="..\src\models\model_factory.cpp" />
     <ClCompile Include="..\src\models\encoder_decoder.cpp" />
     <ClCompile Include="..\src\tensors\rand.cpp" />
+    <ClCompile Include="..\src\tests\attention_tests.cpp">
+      <ExcludedFromBuild Condition="'$(Configuration)|$(Platform)'=='Debug|x64'">true</ExcludedFromBuild>
+      <ExcludedFromBuild Condition="'$(Configuration)|$(Platform)'=='Release|x64'">true</ExcludedFromBuild>
+    </ClCompile>
+    <ClCompile Include="..\src\tests\cli_test.cpp">
+      <ExcludedFromBuild Condition="'$(Configuration)|$(Platform)'=='Debug|x64'">true</ExcludedFromBuild>
+      <ExcludedFromBuild Condition="'$(Configuration)|$(Platform)'=='Release|x64'">true</ExcludedFromBuild>
+    </ClCompile>
+    <ClCompile Include="..\src\tests\dropout_test.cpp">
+      <ExcludedFromBuild Condition="'$(Configuration)|$(Platform)'=='Debug|x64'">true</ExcludedFromBuild>
+      <ExcludedFromBuild Condition="'$(Configuration)|$(Platform)'=='Release|x64'">true</ExcludedFromBuild>
+    </ClCompile>
+    <ClCompile Include="..\src\tests\graph_tests.cpp">
+      <ExcludedFromBuild Condition="'$(Configuration)|$(Platform)'=='Debug|x64'">true</ExcludedFromBuild>
+      <ExcludedFromBuild Condition="'$(Configuration)|$(Platform)'=='Release|x64'">true</ExcludedFromBuild>
+    </ClCompile>
+    <ClCompile Include="..\src\tests\logger_test.cpp">
+      <ExcludedFromBuild Condition="'$(Configuration)|$(Platform)'=='Debug|x64'">true</ExcludedFromBuild>
+      <ExcludedFromBuild Condition="'$(Configuration)|$(Platform)'=='Release|x64'">true</ExcludedFromBuild>
+    </ClCompile>
+    <ClCompile Include="..\src\tests\operator_tests.cpp">
+      <ExcludedFromBuild Condition="'$(Configuration)|$(Platform)'=='Debug|x64'">true</ExcludedFromBuild>
+      <ExcludedFromBuild Condition="'$(Configuration)|$(Platform)'=='Release|x64'">true</ExcludedFromBuild>
+    </ClCompile>
+    <ClCompile Include="..\src\tests\pooling_test.cpp">
+      <ExcludedFromBuild Condition="'$(Configuration)|$(Platform)'=='Debug|x64'">true</ExcludedFromBuild>
+      <ExcludedFromBuild Condition="'$(Configuration)|$(Platform)'=='Release|x64'">true</ExcludedFromBuild>
+    </ClCompile>
+    <ClCompile Include="..\src\tests\prod.cpp">
+      <ExcludedFromBuild Condition="'$(Configuration)|$(Platform)'=='Debug|x64'">true</ExcludedFromBuild>
+      <ExcludedFromBuild Condition="'$(Configuration)|$(Platform)'=='Release|x64'">true</ExcludedFromBuild>
+    </ClCompile>
+    <ClCompile Include="..\src\tests\rnn_tests.cpp">
+      <ExcludedFromBuild Condition="'$(Configuration)|$(Platform)'=='Debug|x64'">true</ExcludedFromBuild>
+      <ExcludedFromBuild Condition="'$(Configuration)|$(Platform)'=='Release|x64'">true</ExcludedFromBuild>
+    </ClCompile>
+    <ClCompile Include="..\src\tests\run_tests.cpp">
+      <ExcludedFromBuild Condition="'$(Configuration)|$(Platform)'=='Debug|x64'">true</ExcludedFromBuild>
+      <ExcludedFromBuild Condition="'$(Configuration)|$(Platform)'=='Release|x64'">true</ExcludedFromBuild>
+    </ClCompile>
+    <ClCompile Include="..\src\tests\sqlite_test.cpp">
+      <ExcludedFromBuild Condition="'$(Configuration)|$(Platform)'=='Debug|x64'">true</ExcludedFromBuild>
+      <ExcludedFromBuild Condition="'$(Configuration)|$(Platform)'=='Release|x64'">true</ExcludedFromBuild>
+    </ClCompile>
     <ClCompile Include="..\src\training\communicator.cpp" />
     <ClCompile Include="..\src\training\graph_group_multinode_sync.cpp" />
     <ClCompile Include="..\src\translator\history.cpp" />
@@ -1032,6 +1076,19 @@
     <None Include="..\src\tensors\gpu\tensor_operators.cu">
       <ExcludedFromBuild Condition="'$(Configuration)|$(Platform)'=='Debug|x64'">true</ExcludedFromBuild>
     </None>
+    <None Include="..\src\tests\conv_char_test.cu">
+      <ExcludedFromBuild Condition="'$(Configuration)|$(Platform)'=='Debug|x64'">true</ExcludedFromBuild>
+      <ExcludedFromBuild Condition="'$(Configuration)|$(Platform)'=='Release|x64'">true</ExcludedFromBuild>
+    </None>
+    <None Include="..\src\tests\conv_test.cu">
+      <ExcludedFromBuild Condition="'$(Configuration)|$(Platform)'=='Debug|x64'">true</ExcludedFromBuild>
+      <ExcludedFromBuild Condition="'$(Configuration)|$(Platform)'=='Release|x64'">true</ExcludedFromBuild>
+    </None>
+    <None Include="..\src\tests\README.md" />
+    <None Include="..\src\tests\tensor_test.cu">
+      <ExcludedFromBuild Condition="'$(Configuration)|$(Platform)'=='Debug|x64'">true</ExcludedFromBuild>
+      <ExcludedFromBuild Condition="'$(Configuration)|$(Platform)'=='Release|x64'">true</ExcludedFromBuild>
+    </None>
     <None Include="..\src\training\communicator_nccl.h" />
     <None Include=".editorConfig" />
   </ItemGroup>
@@ -1041,6 +1098,7 @@
       <ExcludedFromBuild Condition="'$(Configuration)|$(Platform)'=='Debug|x64'">true</ExcludedFromBuild>
       <ExcludedFromBuild Condition="'$(Configuration)|$(Platform)'=='Release|x64'">true</ExcludedFromBuild>
     </Text>
+    <Text Include="..\src\tests\CMakeLists.txt" />
   </ItemGroup>
   <Import Project="$(VCTargetsPath)\Microsoft.Cpp.targets" />
   <ImportGroup Label="ExtensionTargets">
diff --git a/vs/Marian.vcxproj.filters b/vs/Marian.vcxproj.filters
index ce6d8441b..e488f7a06 100755
--- a/vs/Marian.vcxproj.filters
+++ b/vs/Marian.vcxproj.filters
@@ -439,6 +439,39 @@
     <ClCompile Include="..\src\3rd_party\pathie-cpp\src\temp.cpp">
       <Filter>3rd_party\pathie-cpp\src</Filter>
     </ClCompile>
+    <ClCompile Include="..\src\tests\attention_tests.cpp">
+      <Filter>tests</Filter>
+    </ClCompile>
+    <ClCompile Include="..\src\tests\cli_test.cpp">
+      <Filter>tests</Filter>
+    </ClCompile>
+    <ClCompile Include="..\src\tests\dropout_test.cpp">
+      <Filter>tests</Filter>
+    </ClCompile>
+    <ClCompile Include="..\src\tests\graph_tests.cpp">
+      <Filter>tests</Filter>
+    </ClCompile>
+    <ClCompile Include="..\src\tests\logger_test.cpp">
+      <Filter>tests</Filter>
+    </ClCompile>
+    <ClCompile Include="..\src\tests\operator_tests.cpp">
+      <Filter>tests</Filter>
+    </ClCompile>
+    <ClCompile Include="..\src\tests\pooling_test.cpp">
+      <Filter>tests</Filter>
+    </ClCompile>
+    <ClCompile Include="..\src\tests\prod.cpp">
+      <Filter>tests</Filter>
+    </ClCompile>
+    <ClCompile Include="..\src\tests\rnn_tests.cpp">
+      <Filter>tests</Filter>
+    </ClCompile>
+    <ClCompile Include="..\src\tests\run_tests.cpp">
+      <Filter>tests</Filter>
+    </ClCompile>
+    <ClCompile Include="..\src\tests\sqlite_test.cpp">
+      <Filter>tests</Filter>
+    </ClCompile>
   </ItemGroup>
   <ItemGroup>
     <ClInclude Include="..\src\marian.h" />
@@ -1627,6 +1660,9 @@
     <Filter Include="3rd_party\pathie-cpp\src">
       <UniqueIdentifier>{5d5ee615-192f-4b7f-bdfd-fb8316ceabc8}</UniqueIdentifier>
     </Filter>
+    <Filter Include="tests">
+      <UniqueIdentifier>{a86d650a-2268-43d9-9d74-cb17cd6b534b}</UniqueIdentifier>
+    </Filter>
   </ItemGroup>
   <ItemGroup>
     <None Include=".editorConfig" />
@@ -1765,6 +1801,18 @@
     <None Include="..\src\3rd_party\pathie-cpp\README.md">
       <Filter>3rd_party\pathie-cpp</Filter>
     </None>
+    <None Include="..\src\tests\conv_char_test.cu">
+      <Filter>tests</Filter>
+    </None>
+    <None Include="..\src\tests\conv_test.cu">
+      <Filter>tests</Filter>
+    </None>
+    <None Include="..\src\tests\README.md">
+      <Filter>tests</Filter>
+    </None>
+    <None Include="..\src\tests\tensor_test.cu">
+      <Filter>tests</Filter>
+    </None>
   </ItemGroup>
   <ItemGroup>
     <Text Include="..\src\3rd_party\sentencepiece\src\CMakeLists.txt">
@@ -1773,5 +1821,8 @@
     <Text Include="..\src\3rd_party\pathie-cpp\CMakeLists.txt">
       <Filter>3rd_party\pathie-cpp</Filter>
     </Text>
+    <Text Include="..\src\tests\CMakeLists.txt">
+      <Filter>tests</Filter>
+    </Text>
   </ItemGroup>
 </Project>
\ No newline at end of file

From efc20adacc9e88785fc8f42c98b002ebf6a47f3a Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Wed, 26 Dec 2018 14:20:16 -0800
Subject: [PATCH 073/838] added elementwise comparison operators (lt(), eq(),
 ); bug fix: Maximum and Minimum got it backwards; streamlined the operator
 tests a little

---
 src/functional/predicates.h        | 15 +++----
 src/graph/expression_operators.cpp | 21 ++++++++++
 src/graph/expression_operators.h   | 25 +++++++++++-
 src/graph/node_operators_binary.h  | 29 ++++++++++++++
 src/tensors/gpu/element.cu         |  0
 src/tensors/gpu/element.inc        | 11 ++++-
 src/tests/operator_tests.cpp       | 64 +++++++++++++++++-------------
 src/tests/prod.cpp                 |  1 -
 vs/Marian.vcxproj                  | 58 +++++++++++++++++++++++++++
 vs/Marian.vcxproj.filters          | 51 ++++++++++++++++++++++++
 10 files changed, 235 insertions(+), 40 deletions(-)
 mode change 100644 => 100755 src/tensors/gpu/element.cu
 mode change 100644 => 100755 src/tests/operator_tests.cpp
 mode change 100644 => 100755 src/tests/prod.cpp

diff --git a/src/functional/predicates.h b/src/functional/predicates.h
index fce5da473..4d92a0224 100755
--- a/src/functional/predicates.h
+++ b/src/functional/predicates.h
@@ -98,15 +98,12 @@ BINARY(Div, operator/, x / y);
 
 BINARY(LogAddExp,
        logaddexp,
-       (/*if*/ (x < y)
-            ?  // Note: This may not be ideal for CUDA; cf. CNTK implementation
-            (y + log1pf(expf(x - y)))
-            /*else*/
-            : (x + log1pf(expf(y - x)))));
-BINARY(Maximum,
-       max,
-       (x > y) ? y : x);  // note: std::max not available on CUDA it seems
-BINARY(Minimum, min, (x < y) ? y : x);
+       (/*if*/ (x < y) ?  // Note: This may not be ideal for CUDA; cf. CNTK implementation
+          (y + log1pf(expf(x - y)))
+        /*else*/ :
+          (x + log1pf(expf(y - x)))));
+BINARY(Maximum, max, (x > y) ? x : y);  // Note: std::max not available on CUDA it seems
+BINARY(Minimum, min, (x < y) ? x : y);
 
 UNARY(Negate, operator!, !x);
 BINARY(Eq, operator==, x == y);
diff --git a/src/graph/expression_operators.cpp b/src/graph/expression_operators.cpp
index 2f4f5ecf9..4870a6664 100755
--- a/src/graph/expression_operators.cpp
+++ b/src/graph/expression_operators.cpp
@@ -107,6 +107,27 @@ Expr minimum(Expr a, Expr b) {
   return Expression<MinimumNodeOp>(a, b);
 }
 
+Expr lt(Expr a, Expr b) { return Expression<CmpNodeOp>(a, b, -1, false); }
+Expr eq(Expr a, Expr b) { return Expression<CmpNodeOp>(a, b,  0, false); }
+Expr gt(Expr a, Expr b) { return Expression<CmpNodeOp>(a, b,  1, false); }
+Expr ge(Expr a, Expr b) { return Expression<CmpNodeOp>(a, b, -1,  true); }
+Expr ne(Expr a, Expr b) { return Expression<CmpNodeOp>(a, b,  0,  true); }
+Expr le(Expr a, Expr b) { return Expression<CmpNodeOp>(a, b,  1,  true); }
+
+Expr lt(float a, Expr b) { return Expression<CmpNodeOp>(b->graph()->constant({}, inits::from_value(a), b->value_type()), b, -1, false); }
+Expr eq(float a, Expr b) { return Expression<CmpNodeOp>(b->graph()->constant({}, inits::from_value(a), b->value_type()), b,  0, false); }
+Expr gt(float a, Expr b) { return Expression<CmpNodeOp>(b->graph()->constant({}, inits::from_value(a), b->value_type()), b,  1, false); }
+Expr ge(float a, Expr b) { return Expression<CmpNodeOp>(b->graph()->constant({}, inits::from_value(a), b->value_type()), b, -1,  true); }
+Expr ne(float a, Expr b) { return Expression<CmpNodeOp>(b->graph()->constant({}, inits::from_value(a), b->value_type()), b,  0,  true); }
+Expr le(float a, Expr b) { return Expression<CmpNodeOp>(b->graph()->constant({}, inits::from_value(a), b->value_type()), b,  1,  true); }
+
+Expr lt(Expr a, float b) { return Expression<CmpNodeOp>(a, a->graph()->constant({}, inits::from_value(b), a->value_type()), -1, false); }
+Expr eq(Expr a, float b) { return Expression<CmpNodeOp>(a, a->graph()->constant({}, inits::from_value(b), a->value_type()),  0, false); }
+Expr gt(Expr a, float b) { return Expression<CmpNodeOp>(a, a->graph()->constant({}, inits::from_value(b), a->value_type()),  1, false); }
+Expr ge(Expr a, float b) { return Expression<CmpNodeOp>(a, a->graph()->constant({}, inits::from_value(b), a->value_type()), -1,  true); }
+Expr ne(Expr a, float b) { return Expression<CmpNodeOp>(a, a->graph()->constant({}, inits::from_value(b), a->value_type()),  0,  true); }
+Expr le(Expr a, float b) { return Expression<CmpNodeOp>(a, a->graph()->constant({}, inits::from_value(b), a->value_type()),  1,  true); }
+
 /*********************************************************/
 
 Expr operator+(Expr a, float b) {
diff --git a/src/graph/expression_operators.h b/src/graph/expression_operators.h
index 6a887aa0a..ab1b364b8 100755
--- a/src/graph/expression_operators.h
+++ b/src/graph/expression_operators.h
@@ -66,10 +66,33 @@ Expr operator/(Expr a, float b);
 
 Expr logaddexp(Expr a, Expr b);
 
+// Note: Following numpy, minimum() is element-wise, while min() is along an axis in both Numpy and PyTorch.
 Expr maximum(Expr a, Expr b);
-
 Expr minimum(Expr a, Expr b);
 
+// Note: We cannot overload the relational operators, as they also mean something for Expr itself.
+// Note: These names follow PyTorch convention.
+Expr lt(Expr a, Expr b);
+Expr eq(Expr a, Expr b);
+Expr gt(Expr a, Expr b);
+Expr ge(Expr a, Expr b);
+Expr ne(Expr a, Expr b);
+Expr le(Expr a, Expr b);
+
+Expr lt(float a, Expr b);
+Expr eq(float a, Expr b);
+Expr gt(float a, Expr b);
+Expr ge(float a, Expr b);
+Expr ne(float a, Expr b);
+Expr le(float a, Expr b);
+
+Expr lt(Expr a, float b);
+Expr eq(Expr a, float b);
+Expr gt(Expr a, float b);
+Expr ge(Expr a, float b);
+Expr ne(Expr a, float b);
+Expr le(Expr a, float b);
+
 Expr dot(Expr a,
          Expr b,
          bool transA = false,
diff --git a/src/graph/node_operators_binary.h b/src/graph/node_operators_binary.h
index 0f972bc79..868ee4ebd 100755
--- a/src/graph/node_operators_binary.h
+++ b/src/graph/node_operators_binary.h
@@ -814,6 +814,35 @@ struct MinimumNodeOp : public ElementBinaryNodeOp {
   const std::string type() override { return "min"; }
 };
 
+struct CmpNodeOp : public ElementBinaryNodeOp {
+  CmpNodeOp(Expr a, Expr b, int cmp_, bool not_) : ElementBinaryNodeOp(a, b), cmp_(cmp_), not_(not_) {
+    setTrainable(false); // has no gradient
+  }
+
+  NodeOps forwardOps() override {
+    using namespace functional;
+
+    return {
+      NodeOp(Element(_1 = ((((_2 > _3) - (_2 < _3)) == (float)cmp_) != not_),
+             val_, child(0)->val(), child(1)->val()))};
+  }
+
+  NodeOps backwardOps() override { return {}; }
+
+  const std::string type() override {
+    switch (cmp_) {
+    case -1: return not_ ? "ge" : "lt";
+    case  0: return not_ ? "ne" : "eq";
+    case  1: return not_ ? "le" : "gt";
+    }
+    ABORT("Should not get here??");
+  }
+
+private:
+  int cmp_;  // -1: less; 0: equal; 1: greater
+  bool not_; // invert result if true
+};
+
 // In each j-th row, take the corresponding j-th label index i from indices and compute:
 // For each vocabulary item v, the only non-zero element in a row in the sum is the item
 // that matches the label indexed by i (the picked element).
diff --git a/src/tensors/gpu/element.cu b/src/tensors/gpu/element.cu
old mode 100644
new mode 100755
diff --git a/src/tensors/gpu/element.inc b/src/tensors/gpu/element.inc
index 96debcc2f..66f763018 100755
--- a/src/tensors/gpu/element.inc
+++ b/src/tensors/gpu/element.inc
@@ -52,5 +52,12 @@ template void Element<Assign<Var<1>, BinaryFunctor<elem::Minus, Assignee<1>, Bin
 template void Element<Assign<Var<1>, BinaryFunctor<elem::Minus, Assignee<1>, BinaryFunctor<elem::Mult, Capture, BinaryFunctor<elem::Plus, BinaryFunctor<elem::Div, BinaryFunctor<elem::Div, Assignee<2>, Capture>, BinaryFunctor<elem::Plus, UnaryFunctor<elem::Sqrt, BinaryFunctor<elem::Plus, BinaryFunctor<elem::Div, UnaryFunctor<elem::sReLU, BinaryFunctor<elem::Minus, Assignee<3>, BinaryFunctor<elem::Div, BinaryFunctor<elem::Mult, BinaryFunctor<elem::Mult, Capture, Assignee<4> >, Assignee<4> >, Capture> > >, Capture>, BinaryFunctor<elem::Div, BinaryFunctor<elem::Mult, Assignee<4>, Assignee<4> >, Capture> > >, Capture> >, BinaryFunctor<elem::Mult, Capture, Assignee<1> > > > > >, std::shared_ptr<marian::TensorBase>, std::shared_ptr<marian::TensorBase>, std::shared_ptr<marian::TensorBase> >(Assign<Var<1>, BinaryFunctor<elem::Minus, Assignee<1>, BinaryFunctor<elem::Mult, Capture, BinaryFunctor<elem::Plus, BinaryFunctor<elem::Div, BinaryFunctor<elem::Div, Assignee<2>, Capture>, BinaryFunctor<elem::Plus, UnaryFunctor<elem::Sqrt, BinaryFunctor<elem::Plus, BinaryFunctor<elem::Div, UnaryFunctor<elem::sReLU, BinaryFunctor<elem::Minus, Assignee<3>, BinaryFunctor<elem::Div, BinaryFunctor<elem::Mult, BinaryFunctor<elem::Mult, Capture, Assignee<4> >, Assignee<4> >, Capture> > >, Capture>, BinaryFunctor<elem::Div, BinaryFunctor<elem::Mult, Assignee<4>, Assignee<4> >, Capture> > >, Capture> >, BinaryFunctor<elem::Mult, Capture, Assignee<1> > > > > >, std::shared_ptr<marian::TensorBase>, std::shared_ptr<marian::TensorBase>, std::shared_ptr<marian::TensorBase>, std::shared_ptr<marian::TensorBase>);
 template void Element<Assign<Var<1>, BinaryFunctor<elem::Minus, Assignee<1>, BinaryFunctor<elem::Mult, Capture, BinaryFunctor<elem::Plus, BinaryFunctor<elem::Div, BinaryFunctor<elem::Div, Assignee<2>, Capture>, BinaryFunctor<elem::Plus, UnaryFunctor<elem::Sqrt, BinaryFunctor<elem::Div, BinaryFunctor<elem::Mult, Assignee<4>, Assignee<4> >, Capture> >, Capture> >, BinaryFunctor<elem::Mult, Capture, Assignee<1> > > > > >, std::shared_ptr<marian::TensorBase>, std::shared_ptr<marian::TensorBase>, std::shared_ptr<marian::TensorBase> >(Assign<Var<1>, BinaryFunctor<elem::Minus, Assignee<1>, BinaryFunctor<elem::Mult, Capture, BinaryFunctor<elem::Plus, BinaryFunctor<elem::Div, BinaryFunctor<elem::Div, Assignee<2>, Capture>, BinaryFunctor<elem::Plus, UnaryFunctor<elem::Sqrt, BinaryFunctor<elem::Div, BinaryFunctor<elem::Mult, Assignee<4>, Assignee<4> >, Capture> >, Capture> >, BinaryFunctor<elem::Mult, Capture, Assignee<1> > > > > >, std::shared_ptr<marian::TensorBase>, std::shared_ptr<marian::TensorBase>, std::shared_ptr<marian::TensorBase>, std::shared_ptr<marian::TensorBase>);
 template void Element<Assign<Var<1>, BinaryFunctor<elem::Minus, Assignee<1>, BinaryFunctor<elem::Mult, Capture, BinaryFunctor<elem::Plus, BinaryFunctor<elem::Div, BinaryFunctor<elem::Div, Assignee<2>, Capture>, BinaryFunctor<elem::Plus, UnaryFunctor<elem::Sqrt, BinaryFunctor<elem::Div, Assignee<3>, Capture> >, Capture> >, BinaryFunctor<elem::Mult, Capture, Assignee<1> > > > > >, std::shared_ptr<marian::TensorBase>, std::shared_ptr<marian::TensorBase>, std::shared_ptr<marian::TensorBase> >(Assign<Var<1>, BinaryFunctor<elem::Minus, Assignee<1>, BinaryFunctor<elem::Mult, Capture, BinaryFunctor<elem::Plus, BinaryFunctor<elem::Div, BinaryFunctor<elem::Div, Assignee<2>, Capture>, BinaryFunctor<elem::Plus, UnaryFunctor<elem::Sqrt, BinaryFunctor<elem::Div, Assignee<3>, Capture> >, Capture> >, BinaryFunctor<elem::Mult, Capture, Assignee<1> > > > > >, std::shared_ptr<marian::TensorBase>, std::shared_ptr<marian::TensorBase>, std::shared_ptr<marian::TensorBase>, std::shared_ptr<marian::TensorBase>);
-template void Element<Assign<Var<1>, BinaryFunctor<elem::Minus, Assignee<1>, BinaryFunctor<elem::Mult, Capture, BinaryFunctor<elem::Plus, BinaryFunctor<elem::Div, BinaryFunctor<elem::Div, Assignee<2>, Capture>, BinaryFunctor<elem::Plus, UnaryFunctor<elem::Sqrt, BinaryFunctor<elem::Plus, BinaryFunctor<elem::Div, UnaryFunctor<elem::sReLU, BinaryFunctor<elem::Plus, BinaryFunctor<elem::Minus, Assignee<3>, BinaryFunctor<elem::Div, BinaryFunctor<elem::Mult, BinaryFunctor<elem::Mult, Capture, Assignee<5> >, Assignee<4> >, Capture> >, BinaryFunctor<elem::Div, BinaryFunctor<elem::Mult, BinaryFunctor<elem::Mult, Capture, Assignee<4> >, Assignee<4> >, Capture> > >, Capture>, BinaryFunctor<elem::Div, BinaryFunctor<elem::Mult, Assignee<4>, Assignee<4> >, Capture> > >, Capture> >, BinaryFunctor<elem::Mult, Capture, Assignee<1> > > > > >, std::shared_ptr<marian::TensorBase>, std::shared_ptr<marian::TensorBase>, std::shared_ptr<marian::TensorBase>, std::shared_ptr<marian::TensorBase> >(Assign<Var<1>, BinaryFunctor<elem::Minus, Assignee<1>, BinaryFunctor<elem::Mult, Capture, BinaryFunctor<elem::Plus, BinaryFunctor<elem::Div, BinaryFunctor<elem::Div, Assignee<2>, Capture>, BinaryFunctor<elem::Plus, UnaryFunctor<elem::Sqrt, BinaryFunctor<elem::Plus, BinaryFunctor<elem::Div, UnaryFunctor<elem::sReLU, BinaryFunctor<elem::Plus, BinaryFunctor<elem::Minus, Assignee<3>, BinaryFunctor<elem::Div, BinaryFunctor<elem::Mult, BinaryFunctor<elem::Mult, Capture, Assignee<5> >, Assignee<4> >, Capture> >, BinaryFunctor<elem::Div, BinaryFunctor<elem::Mult, BinaryFunctor<elem::Mult, Capture, Assignee<4> >, Assignee<4> >, Capture> > >, Capture>, BinaryFunctor<elem::Div, BinaryFunctor<elem::Mult, Assignee<4>, Assignee<4> >, Capture> > >, Capture> >, BinaryFunctor<elem::Mult, Capture, Assignee<1> > > > > >, std::shared_ptr<marian::TensorBase>, std::shared_ptr<marian::TensorBase>, std::shared_ptr<marian::TensorBase>, std::shared_ptr<marian::TensorBase>, std::shared_ptr<marian::TensorBase>);
-template void Element<Assign<Var<1>, BinaryFunctor<elem::Minus, Assignee<1>, BinaryFunctor<elem::Mult, Capture, BinaryFunctor<elem::Plus, BinaryFunctor<elem::Div, BinaryFunctor<elem::Div, Assignee<2>, Capture>, BinaryFunctor<elem::Plus, UnaryFunctor<elem::Sqrt, BinaryFunctor<elem::Div, Assignee<3>, Capture> >, Capture> >, BinaryFunctor<elem::Mult, Capture, Assignee<1> > > > > >, std::shared_ptr<marian::TensorBase>, std::shared_ptr<marian::TensorBase>, std::shared_ptr<marian::TensorBase>, std::shared_ptr<marian::TensorBase> >(Assign<Var<1>, BinaryFunctor<elem::Minus, Assignee<1>, BinaryFunctor<elem::Mult, Capture, BinaryFunctor<elem::Plus, BinaryFunctor<elem::Div, BinaryFunctor<elem::Div, Assignee<2>, Capture>, BinaryFunctor<elem::Plus, UnaryFunctor<elem::Sqrt, BinaryFunctor<elem::Div, Assignee<3>, Capture> >, Capture> >, BinaryFunctor<elem::Mult, Capture, Assignee<1> > > > > >, std::shared_ptr<marian::TensorBase>, std::shared_ptr<marian::TensorBase>, std::shared_ptr<marian::TensorBase>, std::shared_ptr<marian::TensorBase>, std::shared_ptr<marian::TensorBase>);
+template void Element<Assign<Var<1>, BinaryFunctor<elem::Minus, Assignee<1>, BinaryFunctor<elem::Mult, Capture, BinaryFunctor<elem::Plus, BinaryFunctor<elem::Div, BinaryFunctor<elem::Div, Assignee<2>, Capture>, BinaryFunctor<elem::Plus, UnaryFunctor<elem::Sqrt, BinaryFunctor<elem::Plus, BinaryFunctor<elem::Div, UnaryFunctor<elem::sReLU, BinaryFunctor<elem::Plus, BinaryFunctor<elem::Minus, Assignee<3>, BinaryFunctor<elem::Div, BinaryFunctor<elem::Mult, BinaryFunctor<elem::Mult, Capture, Assignee<5> >, Assignee<4> >, Capture> >, BinaryFunctor<elem::Div, BinaryFunctor<elem::Mult, BinaryFunctor<elem::Mult, Capture, Assignee<4> >, Assignee<4> >, Capture> > >, Capture>, BinaryFunctor<elem::Div, BinaryFunctor<elem::Mult, Assignee<4>, Assignee<4> >, Capture> > >, Capture> >, BinaryFunctor<elem::Mult, Capture, Assignee<1> > > > > >, std::shared_ptr<marian::TensorBase>, std::shared_ptr<marian::TensorBase>, std::shared_ptr<marian::TensorBase>, std::shared_ptr<marian::TensorBase> >(Assign<Var<1>, BinaryFunctor<elem::Minus, Assignee<1>, BinaryFunctor<elem::Mult, Capture, BinaryFunctor<elem::Plus, BinaryFunctor<elem::Div, BinaryFunctor<elem::Div, Assignee<2>, Capture>, BinaryFunctor<elem::Plus, UnaryFunctor<elem::Sqrt, BinaryFunctor<elem::Plus, BinaryFunctor<elem::Div, UnaryFunctor<elem::sReLU, BinaryFunctor<elem::Plus, BinaryFunctor<elem::Minus, Assignee<3>, BinaryFunctor<elem::Div, BinaryFunctor<elem::Mult, BinaryFunctor<elem::Mult, Capture, Assignee<5> >, Assignee<4> >, Capture> >, BinaryFunctor<elem::Div, BinaryFunctor<elem::Mult, BinaryFunctor<elem::Mult, Capture, Assignee<4> >, Assignee<4> >, Capture> > >, Capture>, BinaryFunctor<elem::Div, BinaryFunctor<elem::Mult, Assignee<4>, Assignee<4> >, Capture> > >, Capture> >, BinaryFunctor<elem::Mult, Capture, Assignee<1> > > > > >, std::shared_ptr<marian::TensorBase>, std::shared_ptr<marian::TensorBase>, std::shared_ptr<marian::TensorBase>, std::shared_ptr<marian::TensorBase>, std::shared_ptr<marian::TensorBase>);
+template void Element<Assign<Var<1>, BinaryFunctor<elem::Minus, Assignee<1>, BinaryFunctor<elem::Mult, Capture, BinaryFunctor<elem::Plus, BinaryFunctor<elem::Div, BinaryFunctor<elem::Div, Assignee<2>, Capture>, BinaryFunctor<elem::Plus, UnaryFunctor<elem::Sqrt, BinaryFunctor<elem::Div, Assignee<3>, Capture> >, Capture> >, BinaryFunctor<elem::Mult, Capture, Assignee<1> > > > > >, std::shared_ptr<marian::TensorBase>, std::shared_ptr<marian::TensorBase>, std::shared_ptr<marian::TensorBase>, std::shared_ptr<marian::TensorBase> >(Assign<Var<1>, BinaryFunctor<elem::Minus, Assignee<1>, BinaryFunctor<elem::Mult, Capture, BinaryFunctor<elem::Plus, BinaryFunctor<elem::Div, BinaryFunctor<elem::Div, Assignee<2>, Capture>, BinaryFunctor<elem::Plus, UnaryFunctor<elem::Sqrt, BinaryFunctor<elem::Div, Assignee<3>, Capture> >, Capture> >, BinaryFunctor<elem::Mult, Capture, Assignee<1> > > > > >, std::shared_ptr<marian::TensorBase>, std::shared_ptr<marian::TensorBase>, std::shared_ptr<marian::TensorBase>, std::shared_ptr<marian::TensorBase>, std::shared_ptr<marian::TensorBase>);
+template void marian::gpu::Element<marian::functional::Assign<marian::functional::Var<1>, marian::functional::BinaryFunctor<marian::functional::elem::NEq, marian::functional::BinaryFunctor<marian::functional::elem::Eq, marian::functional::BinaryFunctor<marian::functional::elem::Minus, marian::functional::BinaryFunctor<marian::functional::elem::Gt, marian::functional::Assignee<2>, marian::functional::Assignee<3> >, marian::functional::BinaryFunctor<marian::functional::elem::Lt, marian::functional::Assignee<2>, marian::functional::Assignee<3> > >, marian::functional::Capture>, marian::functional::Capture> >, std::shared_ptr<marian::TensorBase>, std::shared_ptr<marian::TensorBase> >(marian::functional::Assign<marian::functional::Var<1>, marian::functional::BinaryFunctor<marian::functional::elem::NEq, marian::functional::BinaryFunctor<marian::functional::elem::Eq, marian::functional::BinaryFunctor<marian::functional::elem::Minus, marian::functional::BinaryFunctor<marian::functional::elem::Gt, marian::functional::Assignee<2>, marian::functional::Assignee<3> >, marian::functional::BinaryFunctor<marian::functional::elem::Lt, marian::functional::Assignee<2>, marian::functional::Assignee<3> > >, marian::functional::Capture>, marian::functional::Capture> >, std::shared_ptr<marian::TensorBase>, std::shared_ptr<marian::TensorBase>, std::shared_ptr<marian::TensorBase>);
+// How to add new specializations:
+// When you use a new specialization, it will cause a link error of this form (example):
+//   .../src/tensors/tensor_operators.h:41: undefined reference to `void marian::gpu::Element<marian::functional::Assign< ... > ( ... )'
+// To fix this, copy the line with the error message in here and:
+//   - replace up to including "undefined reference to `" by "template"
+//   - replace final ' by a semicolon
diff --git a/src/tests/operator_tests.cpp b/src/tests/operator_tests.cpp
old mode 100644
new mode 100755
index 7c17ec8fd..30f4dfb70
--- a/src/tests/operator_tests.cpp
+++ b/src/tests/operator_tests.cpp
@@ -1,11 +1,13 @@
 #include "catch.hpp"
 #include "graph/expression_graph.h"
 #include "graph/expression_operators.h"
+#include <cmath>
 
 using namespace marian;
 
 void tests(DeviceType device) {
-  auto floatApprox = [](float x, float y) { return x == Approx(y); };
+  auto floatApprox = [](float x, float y) -> bool { return x == Approx(y); };
+  auto floatEqual  = [](float x, float y) -> bool { return x == y; };
 
   Config::seed = 1234;
 
@@ -38,38 +40,46 @@ void tests(DeviceType device) {
     std::vector<float> vA({1, -2, 3, -4});
     std::vector<float> vB({0.5, 1.5});
 
-    std::vector<float> vAdd({1.5, -0.5, 3.5, -2.5});
-    std::vector<float> vMinus({-0.5, 3.5, -2.5, 5.5});
-    std::vector<float> vMult({0.5, -3.0, 1.5, -6.0});
-    std::vector<float> vDiv({2.0f, -1.33333f, 6.0f, -2.66667f});
-
     auto a = graph->constant({2, 2, 1}, inits::from_vector(vA));
     auto b = graph->constant({2, 1}, inits::from_vector(vB));
 
-    auto add = a + b;
-    auto minus = b - a;
-    auto mult = a * b;
-    auto div = a / b;
+    auto compare = [&](Expr res, std::function<float(float,float)> f, bool exactMatch) -> bool {
+      if (res->shape() != Shape({ 2, 2, 1 }))
+          return false;
+      res->val()->get(values);
+      std::vector<float> ref{f(vA[0], vB[0]), f(vA[1], vB[1]), f(vA[2], vB[0]), f(vA[3], vB[1])};
+      return std::equal(values.begin(), values.end(), ref.begin(), exactMatch ? floatEqual : floatApprox);
+    };
+
+    auto rplus  = a + b;
+    auto rminus = a - b;
+    auto rmult  = a * b;
+    auto rdiv   = a / b;
+    auto rlae   = logaddexp(a, b);
+    auto rmax   = maximum(a, b);
+    auto rmin   = minimum(a, b);
+    auto rlt    = lt(a, b);
+    auto req    = eq(a, b);
+    auto rgt    = gt(a, b);
+    auto rge    = ge(a, b);
+    auto rne    = ne(a, b);
+    auto rle    = le(a, b);
 
     graph->forward();
 
-    CHECK(add->shape() == Shape({2, 2, 1}));
-    CHECK(minus->shape() == Shape({2, 2, 1}));
-    CHECK(mult->shape() == Shape({2, 2, 1}));
-    CHECK(div->shape() == Shape({2, 2, 1}));
-
-    add->val()->get(values);
-    CHECK( values == vAdd );
-
-    minus->val()->get(values);
-    CHECK( values == vMinus );
-
-    mult->val()->get(values);
-    CHECK( values == vMult );
-
-    div->val()->get(values);
-    CHECK( std::equal(values.begin(), values.end(),
-                      vDiv.begin(), floatApprox) );
+    CHECK(compare(rplus,  [](float a, float b) {return a + b;}, true));
+    CHECK(compare(rminus, [](float a, float b) {return a - b;}, true));
+    CHECK(compare(rmult,  [](float a, float b) {return a * b;}, true));
+    CHECK(compare(rdiv,   [](float a, float b) {return a / b;}, /*exactMatch=*/false));
+    CHECK(compare(rlae,   [](float a, float b) {return logf(expf(a) + expf(b));}, /*exactMatch=*/false));
+    CHECK(compare(rmax,   [](float a, float b) {return std::max(a, b);}, true));
+    CHECK(compare(rmin,   [](float a, float b) {return std::min(a, b);}, true));
+    CHECK(compare(rlt,    [](float a, float b) {return a <  b;}, true));
+    CHECK(compare(req,    [](float a, float b) {return a == b;}, true));
+    CHECK(compare(rgt,    [](float a, float b) {return a >  b;}, true));
+    CHECK(compare(rge,    [](float a, float b) {return a >= b;}, true));
+    CHECK(compare(rne,    [](float a, float b) {return a != b;}, true));
+    CHECK(compare(rle,    [](float a, float b) {return a <= b;}, true));
   }
 
   SECTION("transposing and reshaping") {
diff --git a/src/tests/prod.cpp b/src/tests/prod.cpp
old mode 100644
new mode 100755
index 5ade67168..c329d403e
--- a/src/tests/prod.cpp
+++ b/src/tests/prod.cpp
@@ -68,6 +68,5 @@ int main(int argc, char** argv) {
         }
     }
 
-
     return 0;
 }
diff --git a/vs/Marian.vcxproj b/vs/Marian.vcxproj
index 15486147a..300e4c91b 100755
--- a/vs/Marian.vcxproj
+++ b/vs/Marian.vcxproj
@@ -588,6 +588,50 @@
     <ClCompile Include="..\src\models\model_factory.cpp" />
     <ClCompile Include="..\src\models\encoder_decoder.cpp" />
     <ClCompile Include="..\src\tensors\rand.cpp" />
+    <ClCompile Include="..\src\tests\attention_tests.cpp">
+      <ExcludedFromBuild Condition="'$(Configuration)|$(Platform)'=='Debug|x64'">true</ExcludedFromBuild>
+      <ExcludedFromBuild Condition="'$(Configuration)|$(Platform)'=='Release|x64'">true</ExcludedFromBuild>
+    </ClCompile>
+    <ClCompile Include="..\src\tests\cli_test.cpp">
+      <ExcludedFromBuild Condition="'$(Configuration)|$(Platform)'=='Debug|x64'">true</ExcludedFromBuild>
+      <ExcludedFromBuild Condition="'$(Configuration)|$(Platform)'=='Release|x64'">true</ExcludedFromBuild>
+    </ClCompile>
+    <ClCompile Include="..\src\tests\dropout_test.cpp">
+      <ExcludedFromBuild Condition="'$(Configuration)|$(Platform)'=='Debug|x64'">true</ExcludedFromBuild>
+      <ExcludedFromBuild Condition="'$(Configuration)|$(Platform)'=='Release|x64'">true</ExcludedFromBuild>
+    </ClCompile>
+    <ClCompile Include="..\src\tests\graph_tests.cpp">
+      <ExcludedFromBuild Condition="'$(Configuration)|$(Platform)'=='Debug|x64'">true</ExcludedFromBuild>
+      <ExcludedFromBuild Condition="'$(Configuration)|$(Platform)'=='Release|x64'">true</ExcludedFromBuild>
+    </ClCompile>
+    <ClCompile Include="..\src\tests\logger_test.cpp">
+      <ExcludedFromBuild Condition="'$(Configuration)|$(Platform)'=='Debug|x64'">true</ExcludedFromBuild>
+      <ExcludedFromBuild Condition="'$(Configuration)|$(Platform)'=='Release|x64'">true</ExcludedFromBuild>
+    </ClCompile>
+    <ClCompile Include="..\src\tests\operator_tests.cpp">
+      <ExcludedFromBuild Condition="'$(Configuration)|$(Platform)'=='Debug|x64'">true</ExcludedFromBuild>
+      <ExcludedFromBuild Condition="'$(Configuration)|$(Platform)'=='Release|x64'">true</ExcludedFromBuild>
+    </ClCompile>
+    <ClCompile Include="..\src\tests\pooling_test.cpp">
+      <ExcludedFromBuild Condition="'$(Configuration)|$(Platform)'=='Debug|x64'">true</ExcludedFromBuild>
+      <ExcludedFromBuild Condition="'$(Configuration)|$(Platform)'=='Release|x64'">true</ExcludedFromBuild>
+    </ClCompile>
+    <ClCompile Include="..\src\tests\prod.cpp">
+      <ExcludedFromBuild Condition="'$(Configuration)|$(Platform)'=='Debug|x64'">true</ExcludedFromBuild>
+      <ExcludedFromBuild Condition="'$(Configuration)|$(Platform)'=='Release|x64'">true</ExcludedFromBuild>
+    </ClCompile>
+    <ClCompile Include="..\src\tests\rnn_tests.cpp">
+      <ExcludedFromBuild Condition="'$(Configuration)|$(Platform)'=='Debug|x64'">true</ExcludedFromBuild>
+      <ExcludedFromBuild Condition="'$(Configuration)|$(Platform)'=='Release|x64'">true</ExcludedFromBuild>
+    </ClCompile>
+    <ClCompile Include="..\src\tests\run_tests.cpp">
+      <ExcludedFromBuild Condition="'$(Configuration)|$(Platform)'=='Debug|x64'">true</ExcludedFromBuild>
+      <ExcludedFromBuild Condition="'$(Configuration)|$(Platform)'=='Release|x64'">true</ExcludedFromBuild>
+    </ClCompile>
+    <ClCompile Include="..\src\tests\sqlite_test.cpp">
+      <ExcludedFromBuild Condition="'$(Configuration)|$(Platform)'=='Debug|x64'">true</ExcludedFromBuild>
+      <ExcludedFromBuild Condition="'$(Configuration)|$(Platform)'=='Release|x64'">true</ExcludedFromBuild>
+    </ClCompile>
     <ClCompile Include="..\src\training\communicator.cpp" />
     <ClCompile Include="..\src\training\graph_group_multinode_sync.cpp" />
     <ClCompile Include="..\src\translator\history.cpp" />
@@ -1032,6 +1076,19 @@
     <None Include="..\src\tensors\gpu\tensor_operators.cu">
       <ExcludedFromBuild Condition="'$(Configuration)|$(Platform)'=='Debug|x64'">true</ExcludedFromBuild>
     </None>
+    <None Include="..\src\tests\conv_char_test.cu">
+      <ExcludedFromBuild Condition="'$(Configuration)|$(Platform)'=='Debug|x64'">true</ExcludedFromBuild>
+      <ExcludedFromBuild Condition="'$(Configuration)|$(Platform)'=='Release|x64'">true</ExcludedFromBuild>
+    </None>
+    <None Include="..\src\tests\conv_test.cu">
+      <ExcludedFromBuild Condition="'$(Configuration)|$(Platform)'=='Debug|x64'">true</ExcludedFromBuild>
+      <ExcludedFromBuild Condition="'$(Configuration)|$(Platform)'=='Release|x64'">true</ExcludedFromBuild>
+    </None>
+    <None Include="..\src\tests\README.md" />
+    <None Include="..\src\tests\tensor_test.cu">
+      <ExcludedFromBuild Condition="'$(Configuration)|$(Platform)'=='Debug|x64'">true</ExcludedFromBuild>
+      <ExcludedFromBuild Condition="'$(Configuration)|$(Platform)'=='Release|x64'">true</ExcludedFromBuild>
+    </None>
     <None Include="..\src\training\communicator_nccl.h" />
     <None Include=".editorConfig" />
   </ItemGroup>
@@ -1041,6 +1098,7 @@
       <ExcludedFromBuild Condition="'$(Configuration)|$(Platform)'=='Debug|x64'">true</ExcludedFromBuild>
       <ExcludedFromBuild Condition="'$(Configuration)|$(Platform)'=='Release|x64'">true</ExcludedFromBuild>
     </Text>
+    <Text Include="..\src\tests\CMakeLists.txt" />
   </ItemGroup>
   <Import Project="$(VCTargetsPath)\Microsoft.Cpp.targets" />
   <ImportGroup Label="ExtensionTargets">
diff --git a/vs/Marian.vcxproj.filters b/vs/Marian.vcxproj.filters
index ce6d8441b..e488f7a06 100755
--- a/vs/Marian.vcxproj.filters
+++ b/vs/Marian.vcxproj.filters
@@ -439,6 +439,39 @@
     <ClCompile Include="..\src\3rd_party\pathie-cpp\src\temp.cpp">
       <Filter>3rd_party\pathie-cpp\src</Filter>
     </ClCompile>
+    <ClCompile Include="..\src\tests\attention_tests.cpp">
+      <Filter>tests</Filter>
+    </ClCompile>
+    <ClCompile Include="..\src\tests\cli_test.cpp">
+      <Filter>tests</Filter>
+    </ClCompile>
+    <ClCompile Include="..\src\tests\dropout_test.cpp">
+      <Filter>tests</Filter>
+    </ClCompile>
+    <ClCompile Include="..\src\tests\graph_tests.cpp">
+      <Filter>tests</Filter>
+    </ClCompile>
+    <ClCompile Include="..\src\tests\logger_test.cpp">
+      <Filter>tests</Filter>
+    </ClCompile>
+    <ClCompile Include="..\src\tests\operator_tests.cpp">
+      <Filter>tests</Filter>
+    </ClCompile>
+    <ClCompile Include="..\src\tests\pooling_test.cpp">
+      <Filter>tests</Filter>
+    </ClCompile>
+    <ClCompile Include="..\src\tests\prod.cpp">
+      <Filter>tests</Filter>
+    </ClCompile>
+    <ClCompile Include="..\src\tests\rnn_tests.cpp">
+      <Filter>tests</Filter>
+    </ClCompile>
+    <ClCompile Include="..\src\tests\run_tests.cpp">
+      <Filter>tests</Filter>
+    </ClCompile>
+    <ClCompile Include="..\src\tests\sqlite_test.cpp">
+      <Filter>tests</Filter>
+    </ClCompile>
   </ItemGroup>
   <ItemGroup>
     <ClInclude Include="..\src\marian.h" />
@@ -1627,6 +1660,9 @@
     <Filter Include="3rd_party\pathie-cpp\src">
       <UniqueIdentifier>{5d5ee615-192f-4b7f-bdfd-fb8316ceabc8}</UniqueIdentifier>
     </Filter>
+    <Filter Include="tests">
+      <UniqueIdentifier>{a86d650a-2268-43d9-9d74-cb17cd6b534b}</UniqueIdentifier>
+    </Filter>
   </ItemGroup>
   <ItemGroup>
     <None Include=".editorConfig" />
@@ -1765,6 +1801,18 @@
     <None Include="..\src\3rd_party\pathie-cpp\README.md">
       <Filter>3rd_party\pathie-cpp</Filter>
     </None>
+    <None Include="..\src\tests\conv_char_test.cu">
+      <Filter>tests</Filter>
+    </None>
+    <None Include="..\src\tests\conv_test.cu">
+      <Filter>tests</Filter>
+    </None>
+    <None Include="..\src\tests\README.md">
+      <Filter>tests</Filter>
+    </None>
+    <None Include="..\src\tests\tensor_test.cu">
+      <Filter>tests</Filter>
+    </None>
   </ItemGroup>
   <ItemGroup>
     <Text Include="..\src\3rd_party\sentencepiece\src\CMakeLists.txt">
@@ -1773,5 +1821,8 @@
     <Text Include="..\src\3rd_party\pathie-cpp\CMakeLists.txt">
       <Filter>3rd_party\pathie-cpp</Filter>
     </Text>
+    <Text Include="..\src\tests\CMakeLists.txt">
+      <Filter>tests</Filter>
+    </Text>
   </ItemGroup>
 </Project>
\ No newline at end of file

From f6de599980cd3d645dc57b6162270281ed33f454 Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Wed, 26 Dec 2018 16:35:56 -0800
Subject: [PATCH 074/838] generalized step() to narrow() and sliceView(), new
 class Slice; bug fix: SliceViewNodeOp should use correct size for memory
 piece; new operation stopGradient()

---
 src/common/shape.h                 | 27 ++++++++++++
 src/graph/expression_operators.cpp | 11 ++++-
 src/graph/expression_operators.h   | 10 ++++-
 src/graph/node_operators_unary.h   | 71 ++++++++++++++++--------------
 src/tests/operator_tests.cpp       | 31 ++++++++++++-
 vs/Marian.vcxproj                  |  1 -
 vs/Marian.vcxproj.filters          |  3 --
 7 files changed, 113 insertions(+), 41 deletions(-)

diff --git a/src/common/shape.h b/src/common/shape.h
index 89a120fe6..20fb26070 100755
--- a/src/common/shape.h
+++ b/src/common/shape.h
@@ -12,6 +12,22 @@
 
 namespace marian {
 
+struct Slice // Python-like slice/index descriptor
+{
+  Slice(int b, int e, int s) : begin(b), end(e), stride(s) {}
+  Slice(int b, int e) : Slice(b, e, 1) {}
+  Slice() : Slice(0, END) {}
+  Slice(int i) : Slice(i, i + 1) {}
+  Slice(const Slice& other) : Slice(other.begin, other.end, other.stride) {}
+  const Slice& operator=(const Slice& other) { begin = other.begin; end = other.end; stride = other.stride; return *this; }
+  const Slice& operator=(int i) { begin = i; end = i + 1; stride = 1; return *this; }
+  bool operator==(const Slice& other) const { return begin == other.begin && end == other.end && stride == other.stride; }
+  bool operator!=(const Slice& other) const { return !(*this == other); }
+  /*const*/ int begin, end, stride;
+  static const int END = INT_MAX;
+};
+typedef std::vector<Slice> Slices;
+
 struct Shape {
 private:
   std::vector<int> shape_;
@@ -147,6 +163,17 @@ struct Shape {
       return ax;
   }
 
+  Slice slice(Slice slice, int ax) const { // interpret negative and special values in Slice
+    int n = dim(ax);
+    if (slice.begin < 0)
+      slice.begin += n;
+    if (slice.end < 0)
+      slice.end += n;
+    else if (slice.end == Slice::END)
+      slice.end = n;
+    return slice;
+  }
+
   static Shape broadcast(const std::vector<Shape>& shapes) {
     size_t maxDims = 0;
     for(auto& s : shapes)
diff --git a/src/graph/expression_operators.cpp b/src/graph/expression_operators.cpp
index 4870a6664..c1b298f8d 100755
--- a/src/graph/expression_operators.cpp
+++ b/src/graph/expression_operators.cpp
@@ -232,6 +232,13 @@ Expr flatten_2d(Expr a) {
   return Expression<ReshapeNodeOp>(a, shape);
 }
 
+Expr stopGradient(Expr a) {
+  // implemented as a dummy reshape that is not trainable
+  auto res = reshape(a, a->shape());
+  res->setTrainable(false);
+  return res;
+}
+
 Expr constant_like(Expr a, const NodeInitializer& init) {
   const auto& shape = a->shape();
   auto graph = a->graph();
@@ -442,8 +449,8 @@ Expr swapAxes(Expr x, int axis1, int axis2)
   return transpose(x, axes);
 }
 
-Expr step(Expr a, int step, int axis) {
-  return Expression<StepNodeOp>(a, step, axis);
+Expr sliceView(Expr a, const Slice& slice, int axis) { // numpy __getitem__ semantics
+  return Expression<SliceViewNodeOp>(a, slice, axis);
 }
 
 Expr cross_entropy(Expr a, Expr indices) {
diff --git a/src/graph/expression_operators.h b/src/graph/expression_operators.h
index ab1b364b8..49a48f6f3 100755
--- a/src/graph/expression_operators.h
+++ b/src/graph/expression_operators.h
@@ -134,6 +134,8 @@ Expr constant_like(Expr a, const NodeInitializer& init);
 Expr flatten(Expr a);
 Expr flatten_2d(Expr a);
 
+Expr stopGradient(Expr a);
+
 Expr rows(Expr a, Expr indices);
 Expr rows(Expr a, const std::vector<IndexType>& indices);
 
@@ -163,7 +165,13 @@ Expr scalar_product(Expr a, Expr b, int ax = 0);
 
 Expr weighted_average(Expr in, Expr weights, int ax = 0);
 
-Expr step(Expr a, int step, int axis);
+Expr sliceView(Expr a, const Slice& slice, int axis);
+static inline Expr narrow(Expr a, int start, int length, int axis) { // PyTorch name
+  return sliceView(a, Slice(start, start + length), axis);
+}
+static inline Expr step(Expr a, int step, int axis) {
+  return sliceView(a, Slice(step), axis);
+}
 
 Expr sqrt(Expr a, float eps = 0.f);
 Expr square(Expr a);
diff --git a/src/graph/node_operators_unary.h b/src/graph/node_operators_unary.h
index b8b192082..462d81cf7 100755
--- a/src/graph/node_operators_unary.h
+++ b/src/graph/node_operators_unary.h
@@ -737,29 +737,36 @@ class ReshapeNodeOp : public UnaryNodeOp {
   }
 };
 
-class StepNodeOp : public UnaryNodeOp {
+// narrow an axis to [begin, end)
+// The resulting object must be consecutive in memory.
+class SliceViewNodeOp : public UnaryNodeOp {
 private:
-  Expr stepNode_;
-  int step_;
-  int axis_;
+  Expr viewedNode_; // viewed underlying node
+  Slice slice_;     // index range
+  int axis_;        // and axis along which it is viewed
+  size_t byteOffset_, byteSize_; // viewed segment in bytes (memory-consecutive)
 
 public:
-  StepNodeOp(Expr a, int step, int axis)
-      : UnaryNodeOp(a, newShape(a, axis)), stepNode_(a), step_(step) {
+  SliceViewNodeOp(Expr a, Slice slice, int axis)
+      : UnaryNodeOp(a, newShape(a, slice, axis)), viewedNode_(a), slice_(slice), axis_(axis) {
     Node::destroy_ = false;
-  }
-
-  Shape newShape(Expr a, int axis) {
-    Shape outShape = a->shape();
-
-    axis_ = outShape.axis(axis);
-#if 0  // this check currently fails in translation; I think should not fail for
-       // step==0
-    for(int i = 0; i < axis_; ++i)
-      ABORT_IF(outShape[i] != 1, "non-consecutive slices are presently not supported by step()");
-#endif
-    outShape.set(axis_, 1);
-
+    auto byteStride = a->shape().stride(axis) * sizeOf(value_type());
+    byteOffset_ = slice.begin * byteStride;
+    byteSize_ = shape()[axis] * byteStride;
+  }
+
+  static Shape newShape(Expr a, Slice& slice, int& axis) { // note: normalizes slice and axis in-place
+    const auto& shape = a->shape();
+    axis  = shape.axis(axis);         // normalize negative axis
+    slice = shape.slice(slice, axis); // normalize negative slice values
+    // enforce consecutive memory
+    if (slice.begin != 0 || slice.end != shape[axis] || slice.stride != 1) { // unless it's a no-op
+      ABORT_IF(slice.stride != 1, "Strides other than 1 are presently not supported by sliceView()");
+      for(int i = 0; i < axis; ++i)
+        ABORT_IF(shape[i] != 1, "Non-consecutive slices are presently not supported by sliceView()");
+    }
+    Shape outShape = shape;
+    outShape.set(axis, slice.end - slice.begin);
     return outShape;
   }
 
@@ -769,36 +776,34 @@ class StepNodeOp : public UnaryNodeOp {
   void forward() override {}
   void backward() override {}
 
-  void init_dependent() override { stepNode_->init_dependent(); }
+  void init_dependent() override { viewedNode_->init_dependent(); }
 
-  void set_zero_adjoint() override { stepNode_->set_zero_adjoint(); }
+  void set_zero_adjoint() override { viewedNode_->set_zero_adjoint(); } // lazily allocate and zero out gradient (only runs once)
 
   Tensor& val() override {
-    auto childVal = stepNode_->val();
-    size_t offset = step_ * shape().elements() * sizeof(float);
-    auto mem = New<MemoryPiece>(childVal->memory()->data() + offset,
-                                childVal->memory()->size());
+    auto childVal = viewedNode_->val();
+    auto mem = New<MemoryPiece>(childVal->memory()->data() + byteOffset_, byteSize_);
     val_.reset(new TensorBase(mem, shape(), childVal->getBackend()));
     return val_;
   };
 
   Tensor& grad() override {
-    auto childGrad = stepNode_->grad();
-    size_t offset = step_ * shape().elements() * sizeof(float);
-    auto mem = New<MemoryPiece>(childGrad->memory()->data() + offset,
-                                childGrad->memory()->size());
+    auto childGrad = viewedNode_->grad();
+    auto mem = New<MemoryPiece>(childGrad->memory()->data() + byteOffset_, byteSize_);
     adj_.reset(new TensorBase(mem, shape(), childGrad->getBackend()));
     return adj_;
   };
 
-  const std::string type() override { return "step"; }
+  const std::string type() override { return "sliceView"; }
 
   const std::string color() override { return "grey"; }
 
   virtual size_t hash() override {
     if(!hash_) {
       hash_ = NaryNodeOp::hash();
-      util::hash_combine(hash_, step_);
+      util::hash_combine(hash_, slice_.begin);
+      util::hash_combine(hash_, slice_.end);
+      util::hash_combine(hash_, slice_.stride);
       util::hash_combine(hash_, axis_);
     }
     return hash_;
@@ -807,10 +812,10 @@ class StepNodeOp : public UnaryNodeOp {
   virtual bool equal(Expr node) override {
     if(!NaryNodeOp::equal(node))
       return false;
-    Ptr<StepNodeOp> cnode = std::dynamic_pointer_cast<StepNodeOp>(node);
+    Ptr<SliceViewNodeOp> cnode = std::dynamic_pointer_cast<SliceViewNodeOp>(node);
     if(!cnode)
       return false;
-    if(step_ != cnode->step_)
+    if(slice_ != cnode->slice_)
       return false;
     if(axis_ != cnode->axis_)
       return false;
diff --git a/src/tests/operator_tests.cpp b/src/tests/operator_tests.cpp
index 30f4dfb70..40867d851 100755
--- a/src/tests/operator_tests.cpp
+++ b/src/tests/operator_tests.cpp
@@ -102,6 +102,8 @@ void tests(DeviceType device) {
     auto t4 = transpose(reshape(a, {2, 1, 2, 2}), {1, 3, 2, 0});
     auto t5 = transpose(reshape(a, {2, 1, 2, 2}), {2, 0, 1, 3});
 
+    auto t6 = stopGradient(a);
+
     graph->forward();
 
     CHECK(t1->shape() == Shape({4, 2}));
@@ -109,6 +111,7 @@ void tests(DeviceType device) {
     CHECK(t3->shape() == Shape({2, 2, 2}));
     CHECK(t4->shape() == Shape({1, 2, 2, 2}));
     CHECK(t5->shape() == Shape({2, 2, 1, 2}));
+    CHECK(t6->shape() == a->shape());
 
     t1->val()->get(values);
     CHECK( values == vT1 );
@@ -124,6 +127,10 @@ void tests(DeviceType device) {
 
     t5->val()->get(values);
     CHECK( values == vT5 );
+
+    t6->val()->get(values);
+    CHECK(values == vA);
+    CHECK(!t6->trainable());
   }
 
   SECTION("softmax and logsoftmax") {
@@ -531,7 +538,7 @@ void tests(DeviceType device) {
     CHECK( values == values2 );
   }
 
-  SECTION("select operator") {
+  SECTION("select, step, sliceView operators") {
     using Indices = std::vector<IndexType>;
 
     graph->clear();
@@ -545,6 +552,9 @@ void tests(DeviceType device) {
     std::vector<float> vD1(vB4);
     std::vector<float> vD2({5, -6, 11, -12});
     std::vector<float> vD3({1, -2, 5, -6, 7, -8, 11, -12});
+    std::vector<float> vS1({7, -8, 9});
+    std::vector<float> vS2({-4, 5, -6, 7, -8, 9});
+    std::vector<float> vS3({7, -8, 9, -10, 11, -12});
 
     auto A = graph->param("4x3", {4,3}, inits::from_vector(in));
     auto B1 = select(A, Indices({0}), 0);
@@ -556,6 +566,11 @@ void tests(DeviceType device) {
     auto D1 = select(C, Indices({0}), 0);
     auto D2 = select(C, Indices({2}), -2);
     auto D3 = select(C, Indices({0,2}), 1);
+
+    auto S1 = step(A, 2, 0);
+    auto S2 = narrow(A, 1, -1, 0);
+    auto S3 = sliceView(A, Slice(-2, Slice::END), 0);
+
     graph->forward();
 
     CHECK(B1->shape() == Shape({1, 3}));
@@ -587,6 +602,20 @@ void tests(DeviceType device) {
     CHECK(D3->shape() == Shape({2, 2, 2}));
     D3->val()->get(values);
     CHECK( values == vD3 );
+
+    values.clear();
+
+    CHECK(S1->shape() == Shape({1,3}));
+    S1->val()->get(values);
+    CHECK(values == vS1);
+
+    CHECK(S2->shape() == Shape({2,3}));
+    S2->val()->get(values);
+    CHECK(values == vS2);
+
+    CHECK(S3->shape() == Shape({3,3})); // -> 2,3
+    S3->val()->get(values);
+    CHECK(values == vS3);
   }
 
   SECTION("rows/cols as select operations") {
diff --git a/vs/Marian.vcxproj b/vs/Marian.vcxproj
index 300e4c91b..c13d7841d 100755
--- a/vs/Marian.vcxproj
+++ b/vs/Marian.vcxproj
@@ -691,7 +691,6 @@
     <ClInclude Include="..\src\layers\weight.h" />
     <ClInclude Include="..\src\marian.h" />
     <ClInclude Include="..\src\3rd_party\catch.hpp" />
-    <ClInclude Include="..\src\3rd_party\exception.h" />
     <ClInclude Include="..\src\3rd_party\reduce_all.h" />
     <ClInclude Include="..\src\3rd_party\threadpool.h" />
     <ClInclude Include="..\src\3rd_party\cnpy\cnpy.h" />
diff --git a/vs/Marian.vcxproj.filters b/vs/Marian.vcxproj.filters
index e488f7a06..745e130da 100755
--- a/vs/Marian.vcxproj.filters
+++ b/vs/Marian.vcxproj.filters
@@ -478,9 +478,6 @@
     <ClInclude Include="..\src\3rd_party\catch.hpp">
       <Filter>3rd_party</Filter>
     </ClInclude>
-    <ClInclude Include="..\src\3rd_party\exception.h">
-      <Filter>3rd_party</Filter>
-    </ClInclude>
     <ClInclude Include="..\src\3rd_party\reduce_all.h">
       <Filter>3rd_party</Filter>
     </ClInclude>

From 1718a88419a1e1f5841d4f4a715eca409f7eab9e Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Wed, 26 Dec 2018 17:12:23 -0800
Subject: [PATCH 075/838] bug fix in op tests: narrow() takes length, not end,
 and its args should be unsigned

---
 src/graph/expression_operators.h | 4 ++--
 src/tests/operator_tests.cpp     | 4 ++--
 2 files changed, 4 insertions(+), 4 deletions(-)

diff --git a/src/graph/expression_operators.h b/src/graph/expression_operators.h
index 49a48f6f3..7c3797069 100755
--- a/src/graph/expression_operators.h
+++ b/src/graph/expression_operators.h
@@ -166,8 +166,8 @@ Expr scalar_product(Expr a, Expr b, int ax = 0);
 Expr weighted_average(Expr in, Expr weights, int ax = 0);
 
 Expr sliceView(Expr a, const Slice& slice, int axis);
-static inline Expr narrow(Expr a, int start, int length, int axis) { // PyTorch name
-  return sliceView(a, Slice(start, start + length), axis);
+static inline Expr narrow(Expr a, size_t start, size_t length, int axis) { // PyTorch name
+  return sliceView(a, Slice((int)start, (int)(start + length)), axis);
 }
 static inline Expr step(Expr a, int step, int axis) {
   return sliceView(a, Slice(step), axis);
diff --git a/src/tests/operator_tests.cpp b/src/tests/operator_tests.cpp
index 40867d851..0ece15a23 100755
--- a/src/tests/operator_tests.cpp
+++ b/src/tests/operator_tests.cpp
@@ -568,7 +568,7 @@ void tests(DeviceType device) {
     auto D3 = select(C, Indices({0,2}), 1);
 
     auto S1 = step(A, 2, 0);
-    auto S2 = narrow(A, 1, -1, 0);
+    auto S2 = narrow(A, 1, 2, 0);
     auto S3 = sliceView(A, Slice(-2, Slice::END), 0);
 
     graph->forward();
@@ -613,7 +613,7 @@ void tests(DeviceType device) {
     S2->val()->get(values);
     CHECK(values == vS2);
 
-    CHECK(S3->shape() == Shape({3,3})); // -> 2,3
+    CHECK(S3->shape() == Shape({2,3}));
     S3->val()->get(values);
     CHECK(values == vS3);
   }

From 7c206c45275eedc8c31328b9a1b685e9d9867254 Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Mon, 31 Dec 2018 15:33:35 -0800
Subject: [PATCH 076/838] MB-size warmup now stops once it reached 1.0

---
 src/optimizers/optimizers.cpp | 22 ++++++++++++++++++++++
 src/training/scheduler.h      |  6 +++---
 2 files changed, 25 insertions(+), 3 deletions(-)

diff --git a/src/optimizers/optimizers.cpp b/src/optimizers/optimizers.cpp
index 685c917f9..c4e9951f0 100755
--- a/src/optimizers/optimizers.cpp
+++ b/src/optimizers/optimizers.cpp
@@ -169,6 +169,28 @@ void Adam::updateImpl(Tensor params, Tensor grads, size_t actualMBSize, size_t r
           vt_     // =_3
           );
 
+  // log the magnitude (with good ole CPU code)
+  static int logCount = 0;
+  logCount++;
+  if (logCount <= 10 || logCount % 100 == 0) {
+    std::vector<float> g, mt, vt;
+    grads->get(g); mt_->get(mt); vt_->get(vt);
+    size_t n = g.size(); ABORT_IF(mt.size() != g.size() || vt.size() != g.size(), "mismatching sizes??");
+    double gs = 0, ms = 0; // square sum
+    for (size_t i = 0; i < n; i++) {
+      auto gi = g[i] / T;        // raw average gradient
+      auto mi = mt[i] / denom1f; // momentum-smoothed average gradient
+      auto di = sqrtf(vt[i] / denom2f) + eps_;
+      gi /= di; // RMS-normalize both
+      mi /= di;
+      gs += gi * gi;
+      ms += mi * mi;
+    }
+    auto grms = std::sqrt(gs / n);  // hmm... expecting 1.0 here, no?
+    auto mrms = std::sqrt(ms / n);
+    LOG(info, "Adam[{}]: grms = {}, mrms = {}", logCount, grms, mrms);
+  }
+
   params->getBackend()->synchronize(); // @TODO: This should not be in here. Maybe in the wrapper. Why is it needed at all?
 }
 
diff --git a/src/training/scheduler.h b/src/training/scheduler.h
index 51320af03..cad01db20 100755
--- a/src/training/scheduler.h
+++ b/src/training/scheduler.h
@@ -83,13 +83,13 @@ class Scheduler : public TrainingObserver {
       // if unit is labels, then account for the fact that our increment itself is not constant
       if (mbWarmup.unit == SchedulingUnit::trgLabels)
         progressRatio = std::sqrt(progressRatio);
-      // soften the very initial ramp-up if requested
       if (progressRatio < 1) {
+        // soften the very initial ramp-up if requested
         double exp = options_->get<double>("mini-batch-warmup-exp");
         progressRatio = pow(progressRatio, exp); // e.g. 1.5 => linear ramp-up -> sublinear ramp-up
+        // apply ratio to actual batch size
+        ratio *= progressRatio;
       }
-      // apply ratio to actual batch size
-      ratio *= progressRatio;
     }
 
     // dynamic MB-size tracking with learning rate

From 35aa00f8b6b6385b469c51717a41aa6d75d860d2 Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Mon, 31 Dec 2018 16:15:59 -0800
Subject: [PATCH 077/838] refactored state.eta update; bug fix: dynamic MB
 scaling should consider state-based LR decay

---
 src/training/scheduler.h      | 60 +++++++++++++++++++----------------
 src/training/training_state.h | 11 +++++--
 2 files changed, 40 insertions(+), 31 deletions(-)

diff --git a/src/training/scheduler.h b/src/training/scheduler.h
index 10d171874..d59644aae 100755
--- a/src/training/scheduler.h
+++ b/src/training/scheduler.h
@@ -18,8 +18,8 @@ class Scheduler : public TrainingObserver {
 
   timer::Timer timer_, heartBeatTimer_;
 
-  // determine LR decay factor from --lr-decay-inv-sqrt option
-  float getLearningRateDecayFactor(const TrainingState& state) const {
+  // determine scheduled LR decay factor (--lr-decay-inv-sqrt option)
+  float getScheduledLRDecayFactor(const TrainingState& state) const {
     auto args = options_->get<std::vector<std::string>>("lr-decay-inv-sqrt");
     ABORT_IF(args.empty() || args.size() > 2, "--lr-decay-inv-sqrt argument must be one or two numbers with units");
     auto decayGoogle = SchedulingParameter::parse(args[0]);
@@ -38,27 +38,33 @@ class Scheduler : public TrainingObserver {
       return 1.f;
   }
 
-  // determine the dynamically adjusted learning rate, incl. warm-up and decay
-  float getLearningRate(const TrainingState& state) const {
+  // update current learning rate in state.eta
+  // This considers
+  //  - base LR (--learn-rate)
+  //  - LR warm-up (--lr-warmup, --lr=warmup-start-rate)
+  //  - scheduled LR decay (--lr-decay-inv-sqrt)
+  //  - state-based LR decay (--lr-decay, --lr-decay-strategy)
+  void updateLearningRate(TrainingState& state) const {
     float baselr = options_->get<float>("learn-rate");
 
-    float mult1 = 1.f;
-    auto warmup = SchedulingParameter::parse(options_->get<std::string>("lr-warmup"));
-    if(warmup) {
-      ABORT_IF(state.warmupStart && state.warmupStart.unit != warmup.unit, "lr-warmup and warmup-start must have the same unit");
-      auto bno = state.getProgressIn(warmup.unit) - state.warmupStart.n;
-      mult1 = std::min(1.f, (float)bno / (float)warmup.n);
+    // warm-up factor
+    float warmupFactor = 1.f;
+    auto warmupParam = SchedulingParameter::parse(options_->get<std::string>("lr-warmup"));
+    if(warmupParam) {
+      ABORT_IF(state.warmupStart && state.warmupStart.unit != warmupParam.unit, "lr-warmup and warmup-start must have the same unit");
+      auto bno = state.getProgressIn(warmupParam.unit) - state.warmupStart.n;
+      warmupFactor = std::min(1.f, (float)bno / (float)warmupParam.n);
     }
 
-    float mult2 = getLearningRateDecayFactor(state);
-
-    baselr = baselr * mult1 * mult2;
-
     float lrStart = options_->get<float>("lr-warmup-start-rate");
-    if(lrStart > 0)
-      baselr = baselr - lrStart * mult1 * mult2 + lrStart * mult2;
+    baselr = lrStart + (baselr - lrStart) * warmupFactor; // linear interpolation between lr-warmup-start-rate to learn-rate
+
+    // schedule-based decay factor (--lr-decay-inv-sqrt)
+    float scheduledDecayFactor = getScheduledLRDecayFactor(state);
+    baselr = baselr * scheduledDecayFactor;
 
-    return baselr;
+    // factor in state-based decay and set final LR as state.eta
+    state.updateEta(baselr);
   }
 
 public:
@@ -91,7 +97,7 @@ class Scheduler : public TrainingObserver {
     // As LR goes down, MB gets ramped up by the same ratio, which has been found to be safe.
     auto mbTracking = options_->get<bool>("mini-batch-track-lr");
     if (mbTracking) {
-      auto lrFactor = getLearningRateDecayFactor(*state_);
+      auto lrFactor = getScheduledLRDecayFactor(*state_) * state_->factor; // (don't include lr-warmup)
       if (lrFactor != 1)
         LOG_ONCE(info, "[scheduler] Dynamic mini-batch size adjustment enabled and kicking in");
       ratio /= lrFactor;
@@ -101,7 +107,8 @@ class Scheduler : public TrainingObserver {
 
   Scheduler(Ptr<Options> options, Ptr<TrainingState> state)
       : options_(options), state_(state) {
-    state_->eta = getLearningRate(*state);
+    ABORT_IF(state_->factor != 1, "state.factor unexpectedly not 1 at this point??");
+    updateLearningRate(*state);
   }
 
   bool keepGoing() {
@@ -367,8 +374,7 @@ class Scheduler : public TrainingObserver {
   void actAfterEpoch(TrainingState& state) override {
     float factor = (float)options_->get<double>("lr-decay"); // @TODO: <float>?
 
-    float baselr = getLearningRate(state);
-    state.eta = baselr * state.factor;
+    updateLearningRate(state);
 
     if(factor > 0.0) {
       bool decay = false;
@@ -398,7 +404,7 @@ class Scheduler : public TrainingObserver {
 
       if(decay) {
         state.factor *= factor;
-        state.eta = baselr * state.factor;
+        updateLearningRate(state);
         LOG(info,
             "Decaying learning rate to {} in epoch {}",
             state.eta,
@@ -420,8 +426,7 @@ class Scheduler : public TrainingObserver {
     float factor = (float)options_->get<double>("lr-decay"); // @TODO: <float>?
     state.reset = false;
 
-    float baselr = getLearningRate(state);
-    state.eta = baselr * state.factor;
+    updateLearningRate(state);
 
     if(factor > 0.0) {
       if(options_->get<std::string>("lr-decay-strategy") == "batches") {
@@ -431,7 +436,7 @@ class Scheduler : public TrainingObserver {
         if(start > 0 && freq > 0 && state.batches >= start
            && ((state.batches - start) % freq == 0)) {
           state.factor *= factor;
-          state.eta = baselr * state.factor;
+          updateLearningRate(state);
           LOG(info,
               "Decaying learning rate to {} after {} batches",
               state.eta,
@@ -466,8 +471,7 @@ class Scheduler : public TrainingObserver {
     float factor = (float)options_->get<double>("lr-decay"); // @TODO: <float>?
     state.reset = false;
 
-    float baselr = getLearningRate(state);
-    state.eta = baselr * state.factor;
+    updateLearningRate(state);
 
     if(factor > 0.0) {
       if(options_->get<std::string>("lr-decay-strategy") == "stalled") {
@@ -475,7 +479,7 @@ class Scheduler : public TrainingObserver {
             = options_->get<std::vector<size_t>>("lr-decay-start").front();
         if(startStalled && state.stalled && state.stalled % startStalled == 0) {
           state.factor *= factor;
-          state.eta = baselr * state.factor;
+          updateLearningRate(state);
           LOG(info,
               "Decaying learning rate to {} after stalled {} time(s)",
               state.eta,
diff --git a/src/training/training_state.h b/src/training/training_state.h
index 65cefcbce..33cf92f84 100755
--- a/src/training/training_state.h
+++ b/src/training/training_state.h
@@ -89,9 +89,12 @@ class TrainingState {
   // Reset optimizer parameters
   bool reset{false};
 
-  // Learning rate
+  // Current learning rate, representing all adjustment processes and factors
   float eta;
-  // Multiplication factor for learning rate
+  void updateEta(float dynamicBaseLR) { // note: no other function may write to 'eta' (besides load())
+    eta = dynamicBaseLR * factor;
+  }
+  // State-based multiplication factor for learning rate
   float factor{1.f};
   SchedulingParameter warmupStart; // has same unit as lr-warmup
 
@@ -114,7 +117,9 @@ class TrainingState {
   // Set flag if the model was validated in the current batch
   bool validated{false};
 
-  TrainingState(float learnRate) : eta(learnRate) {}
+  TrainingState(float learnRate) {
+    updateEta(learnRate);
+  }
 
   void registerObserver(Ptr<TrainingObserver> observer) {
     observers_.push_back(observer);

From 734097b3dfbac37cceb8fda3c186122f2a2c4183 Mon Sep 17 00:00:00 2001
From: Marcin Junczys-Dowmunt <Marcin.Junczys-Dowmunt@microsoft.com>
Date: Wed, 2 Jan 2019 16:55:13 -0800
Subject: [PATCH 078/838] first attempts at BERT

---
 src/models/classifier.h         | 137 +++++++++++++++++++++
 src/models/costs.h              |  47 ++++++++
 src/models/decoder.h            |   2 +
 src/models/encoder.h            |   2 +
 src/models/encoder_classifier.h | 208 ++++++++++++++++++++++++++++++++
 src/models/encoder_decoder.cpp  |   2 +-
 src/models/encoder_decoder.h    |   8 +-
 src/models/model_factory.cpp    |  30 ++++-
 src/models/model_factory.h      |  34 ++++++
 src/models/states.h             |  23 ++++
 src/models/transformer.h        |  90 +++++++++++---
 11 files changed, 560 insertions(+), 23 deletions(-)
 create mode 100644 src/models/classifier.h
 create mode 100644 src/models/encoder_classifier.h

diff --git a/src/models/classifier.h b/src/models/classifier.h
new file mode 100644
index 000000000..21fe8b6f9
--- /dev/null
+++ b/src/models/classifier.h
@@ -0,0 +1,137 @@
+#pragma once
+
+#include "marian.h"
+#include "models/states.h"
+#include "layers/constructors.h"
+#include "layers/factory.h"
+
+namespace marian {
+
+class ClassifierBase {
+protected:
+  Ptr<Options> options_;
+  std::string prefix_{"classifier"};
+  bool inference_{false};
+  size_t batchIndex_{0};
+
+public:
+  ClassifierBase(Ptr<Options> options)
+      : options_(options),
+        prefix_(options->get<std::string>("prefix", "classifier")),
+        inference_(options->get<bool>("inference", false)),
+        batchIndex_(options->get<size_t>("index", 1)) {} // assume that training input has batch index 0 and labels has 1
+
+  virtual ~ClassifierBase() {}
+
+  virtual Ptr<ClassifierState> apply(Ptr<ExpressionGraph>, Ptr<data::CorpusBatch>, const std::vector<Ptr<EncoderState>>&) = 0;
+
+  template <typename T>
+  T opt(const std::string& key) const {
+    return options_->get<T>(key);
+  }
+
+  virtual void clear() = 0;
+};
+
+// This is a model that pretrains BERT for classification
+class BertMaskedLM : public ClassifierBase {
+public:
+  BertMaskedLM(Ptr<Options> options) : ClassifierBase(options) {}
+
+  Ptr<ClassifierState> apply(Ptr<ExpressionGraph> graph, Ptr<data::CorpusBatch> bertBatch, const std::vector<Ptr<EncoderState>>& encoderStates) override {
+    ABORT_IF(encoderStates.size() != 1, "Currently we only support a single encoder BERT model");
+
+    auto context = encoderStates[0]->getContext();
+    auto classEmbeddings = step(context, /*i=*/0, /*axis=*/-2); // [CLS] symbol is first symbol in each sequence
+
+    // Since this is a classifier we are not masking anything on the target. We can (mis)use the mask to hold 
+    // indices of words in the encoder that have been masked out for BERT's masked LM training. 
+    // Masks are floats, hence the conversion to IndexType.
+    const auto& maskedRowsFloats = (*bertBatch)[batchIndex_]->mask(); 
+    std::vector<IndexType> maskedRows(maskedRowsFloats.size());
+    std::copy(maskedRowsFloats.begin(), maskedRowsFloats.end(), maskedRows.begin());
+
+    classEmbeddings = rows(classEmbeddings, graph->indices(maskedRows)); // subselect stuff that has actually been masked out;
+    
+    int dimModel = classEmbeddings->shape()[-1];
+
+    int dimTrgVoc = opt<std::vector<int>>("dim-vocabs")[0]; // set to first one, @TODO: make this modular.
+
+    auto layerTanh = mlp::dense(graph)   //
+        ("dim", dimModel)                //
+        ("activation", mlp::act::tanh); //
+
+    auto layerOut = mlp::output(graph)  //
+        ("dim", dimTrgVoc);
+
+    if(opt<bool>("tied-embeddings") || opt<bool>("tied-embeddings-all")) {
+      std::string tiedPrefix = prefix_ + "_Wemb";
+      if(opt<bool>("tied-embeddings-all") || opt<bool>("tied-embeddings-src"))
+        tiedPrefix = "Wemb";
+      layerOut.tie_transposed("W", tiedPrefix);
+    }
+
+    // [-4: beam depth=1, -3: max length, -2: batch size, -1: vocab dim]
+    // assemble layers into MLP and apply to embeddings, decoder context and
+    // aligned source context
+    auto output = mlp::mlp(graph)                 //
+        ("prefix", prefix_ + "_ff_logit_bert_out") //
+        .push_back(layerTanh)                      //
+        .push_back(layerOut)                       //
+        .construct();
+    
+    auto logits = output->apply(classEmbeddings);
+    
+    auto state = New<ClassifierState>();
+    state->setLogProbs(logits);
+
+    // filled automatically during masking, these are the vocab indices of masked words
+    const auto& classLabels = (*bertBatch)[batchIndex_]->data();
+    state->setTargetIndices(graph->indices(classLabels));
+
+    return state;
+  }
+
+  virtual void clear() override {}
+};
+
+// This is a model that uses a pre-trained BERT model to build a classifier on top of the encoder
+class BertClassifier : public ClassifierBase {
+public:
+  BertClassifier(Ptr<Options> options) : ClassifierBase(options) {}
+
+  // The batch has been filled with external classifier labels, @TODO: figure out how to do that
+  Ptr<ClassifierState> apply(Ptr<ExpressionGraph> graph, Ptr<data::CorpusBatch> bertBatch, const std::vector<Ptr<EncoderState>>& encoderStates) override {
+    ABORT_IF(encoderStates.size() != 1, "Currently we only support a single encoder BERT model");
+
+    auto context = encoderStates[0]->getContext();
+    auto classEmbeddings = step(context, /*i=*/0, /*axis=*/-2); // [CLS] symbol is first symbol in each sequence
+    
+    int dimModel = classEmbeddings->shape()[-1];
+    int dimTrgCls = opt<int>("bert-classes");
+
+    auto output = mlp::mlp(graph)                                 //
+                    ("prefix", prefix_ + "_ff_logit")             //
+                    .push_back(mlp::dense(graph)                  //
+                                 ("dim", dimModel)                //
+                                 ("activation", mlp::act::tanh))  //
+                    .push_back(mlp::dense(graph)                  //
+                                 ("dim", dimTrgCls))              //
+                    .construct();
+    
+    auto logits = output->apply(classEmbeddings); // class logits for each batch entry
+    
+    auto state = New<ClassifierState>();
+    state->setLogProbs(logits);
+
+    // filled externally
+    const auto& classLabels = (*bertBatch)[batchIndex_]->data();
+    state->setTargetIndices(graph->indices(classLabels));
+
+    return state;
+  }
+
+  virtual void clear() override {}
+};
+
+}
\ No newline at end of file
diff --git a/src/models/costs.h b/src/models/costs.h
index d38a445e8..4d9845768 100755
--- a/src/models/costs.h
+++ b/src/models/costs.h
@@ -5,6 +5,7 @@
 #include "layers/loss.h"
 #include "layers/weight.h"
 #include "models/encoder_decoder.h"
+#include "models/encoder_classifier.h"
 
 namespace marian {
 namespace models {
@@ -72,6 +73,37 @@ class EncoderDecoderCE : public CostBase {
   }
 };
 
+class EncoderClassifierCE : public CostBase {
+protected:
+  Ptr<Options> options_;
+  bool inference_{false};
+  Ptr<LossBase> loss_;
+
+public:
+  EncoderClassifierCE(Ptr<Options> options)
+      : options_(options), inference_(options->get<bool>("inference", false)) {
+    loss_ = LossFactory(options_, inference_);
+  }
+
+  Expr apply(Ptr<ModelBase> model,
+             Ptr<ExpressionGraph> graph,
+             Ptr<data::Batch> batch,
+             bool clearGraph = true) override {
+
+    auto enccls = std::static_pointer_cast<EncoderClassifier>(model);
+    auto corpusBatch = std::static_pointer_cast<data::CorpusBatch>(batch);
+
+    auto state = enccls->apply(graph, corpusBatch, clearGraph);
+
+    Expr cost = loss_->getCost(state->getLogProbs(),
+                               state->getTargetIndices(),
+                               /*mask=*/nullptr,
+                               /*weights=*/nullptr);
+
+    return cost;
+  }
+};
+
 class Trainer : public ModelBase {
 protected:
   Ptr<ModelBase> model_;
@@ -231,5 +263,20 @@ inline Ptr<ModelBase> add_cost(Ptr<EncoderDecoder> encdec,
     default: return encdec;
   }
 }
+
+inline Ptr<ModelBase> add_cost(Ptr<EncoderClassifier> enccls,
+                               Ptr<Options> options) {
+  switch(options->get<usage>("usage", usage::raw)) {
+    case usage::training:
+      return New<Trainer>(enccls, New<EncoderClassifierCE>(options));
+    case usage::scoring:
+      return New<Scorer>(enccls, New<EncoderClassifierCE>(options));
+    case usage::translation:
+      ABORT("Classifier cannot be used for translation");
+    case usage::raw:
+    default: return enccls;
+  }
+}
+
 }  // namespace models
 }  // namespace marian
diff --git a/src/models/decoder.h b/src/models/decoder.h
index 39e56e1f3..ddedb4836 100755
--- a/src/models/decoder.h
+++ b/src/models/decoder.h
@@ -24,6 +24,8 @@ class DecoderBase {
         inference_(options->get<bool>("inference", false)),
         batchIndex_(options->get<size_t>("index", 1)) {}
 
+  virtual ~DecoderBase() {}
+
   virtual Ptr<DecoderState> startState(Ptr<ExpressionGraph>,
                                        Ptr<data::CorpusBatch> batch,
                                        std::vector<Ptr<EncoderState>>&)
diff --git a/src/models/encoder.h b/src/models/encoder.h
index 6d1ee852e..79424ef28 100755
--- a/src/models/encoder.h
+++ b/src/models/encoder.h
@@ -80,6 +80,8 @@ class EncoderBase {
         inference_(options->get<bool>("inference", false)),
         batchIndex_(options->get<size_t>("index", 0)) {}
 
+  virtual ~EncoderBase() {}
+
   virtual Ptr<EncoderState> build(Ptr<ExpressionGraph>, Ptr<data::CorpusBatch>)
       = 0;
 
diff --git a/src/models/encoder_classifier.h b/src/models/encoder_classifier.h
new file mode 100644
index 000000000..aae6fe235
--- /dev/null
+++ b/src/models/encoder_classifier.h
@@ -0,0 +1,208 @@
+#pragma once
+
+#include "marian.h"
+
+#include "models/encoder.h"
+#include "models/classifier.h"
+#include "models/model_base.h"
+#include "models/states.h"
+
+namespace marian {
+
+class EncoderClassifierBase : public models::ModelBase {
+public:
+  virtual ~EncoderClassifierBase() {}
+
+  virtual void load(Ptr<ExpressionGraph> graph,
+                    const std::string& name,
+                    bool markedReloaded = true) override
+      = 0;
+
+  virtual void mmap(Ptr<ExpressionGraph> graph,
+                    const void* ptr,
+                    bool markedReloaded = true)
+      = 0;
+
+  virtual void save(Ptr<ExpressionGraph> graph,
+                    const std::string& name,
+                    bool saveTranslatorConfig = false) override
+      = 0;
+
+  virtual void clear(Ptr<ExpressionGraph> graph) override = 0;
+
+  virtual Ptr<ClassifierState> apply(Ptr<ExpressionGraph>, Ptr<data::CorpusBatch>, bool) = 0;
+
+
+  virtual Expr build(Ptr<ExpressionGraph> graph,
+                     Ptr<data::Batch> batch,
+                     bool clearGraph = true) override
+      = 0;
+
+  virtual Expr build(Ptr<ExpressionGraph> graph,
+                     Ptr<data::CorpusBatch> batch,
+                     bool clearGraph = true)
+      = 0;
+
+  virtual Ptr<Options> getOptions() = 0;
+};
+
+class EncoderClassifier : public EncoderClassifierBase {
+protected:
+  Ptr<Options> options_;
+
+  std::string prefix_;
+
+  std::vector<Ptr<EncoderBase>> encoders_;
+  std::vector<Ptr<ClassifierBase>> classifiers_;
+
+  bool inference_{false};
+
+  std::set<std::string> modelFeatures_;
+
+  Config::YamlNode getModelParameters() {
+    Config::YamlNode modelParams;
+    for(auto& key : modelFeatures_)
+        modelParams[key] = options_->getYaml()[key];
+
+    if(options_->has("original-type"))
+        modelParams["type"] = options_->getYaml()["original-type"];
+
+    modelParams["version"] = buildVersion();
+    return modelParams;
+  }
+
+  std::string getModelParametersAsString() {
+    auto yaml = getModelParameters();
+    YAML::Emitter out;
+    cli::OutputYaml(yaml, out);
+    return std::string(out.c_str());
+  }
+
+//   virtual void createClassifierConfig(const std::string& name) {}
+
+public:
+  typedef data::Corpus dataset_type;
+
+  // @TODO: lots of code-duplication with EncoderDecoder
+  EncoderClassifier(Ptr<Options> options)
+    : options_(options),
+      prefix_(options->get<std::string>("prefix", "")),
+      inference_(options->get<bool>("inference", false)) {
+    modelFeatures_ = {"type",
+                    "dim-vocabs",
+                    "dim-emb",
+                    "dim-rnn",
+                    "enc-cell",
+                    "enc-type",
+                    "enc-cell-depth",
+                    "enc-depth",
+                    "dec-depth",
+                    "dec-cell",
+                    "dec-cell-base-depth",
+                    "dec-cell-high-depth",
+                    "skip",
+                    "layer-normalization",
+                    "right-left",
+                    "special-vocab",
+                    "tied-embeddings",
+                    "tied-embeddings-src",
+                    "tied-embeddings-all"};
+
+    modelFeatures_.insert("transformer-heads");
+    modelFeatures_.insert("transformer-no-projection");
+    modelFeatures_.insert("transformer-dim-ffn");
+    modelFeatures_.insert("transformer-ffn-depth");
+    modelFeatures_.insert("transformer-ffn-activation");
+    modelFeatures_.insert("transformer-dim-aan");
+    modelFeatures_.insert("transformer-aan-depth");
+    modelFeatures_.insert("transformer-aan-activation");
+    modelFeatures_.insert("transformer-aan-nogate");
+    modelFeatures_.insert("transformer-preprocess");
+    modelFeatures_.insert("transformer-postprocess");
+    modelFeatures_.insert("transformer-postprocess-emb");
+    modelFeatures_.insert("transformer-decoder-autoreg");
+    modelFeatures_.insert("transformer-tied-layers");
+    modelFeatures_.insert("transformer-guided-alignment-layer");
+  }
+
+  virtual Ptr<Options> getOptions() override { return options_; }
+
+  std::vector<Ptr<EncoderBase>>& getEncoders() { return encoders_; }
+  std::vector<Ptr<ClassifierBase>>& getClassifiers() { return classifiers_; }
+
+  void push_back(Ptr<EncoderBase> encoder) { encoders_.push_back(encoder); }
+  void push_back(Ptr<ClassifierBase> classifier) { classifiers_.push_back(classifier); }
+
+  void load(Ptr<ExpressionGraph> graph,
+            const std::string& name,
+            bool markedReloaded) override {
+    graph->load(name, markedReloaded && !opt<bool>("ignore-model-config", false));
+  }
+
+  void mmap(Ptr<ExpressionGraph> graph,
+            const void* ptr,
+            bool markedReloaded) override {
+    graph->mmap(ptr, markedReloaded && !opt<bool>("ignore-model-config", false));
+  }
+
+  void save(Ptr<ExpressionGraph> graph,
+            const std::string& name,
+            bool saveModelConfig) override {
+    LOG(info, "Saving model weights and runtime parameters to {}", name);
+    graph->save(name , getModelParametersAsString());
+  }
+
+  void clear(Ptr<ExpressionGraph> graph) override {
+    graph->clear();
+
+    for(auto& enc : encoders_)
+      enc->clear();
+    for(auto& cls : classifiers_)
+      cls->clear();
+  }
+
+  template <typename T>
+  T opt(const std::string& key) {
+    return options_->get<T>(key);
+  }
+
+  template <typename T>
+  T opt(const std::string& key, const T& def) {
+    return options_->get<T>(key, def);
+  }
+
+  template <typename T>
+  void set(std::string key, T value) {
+    options_->set(key, value);
+  }
+
+  /*********************************************************************/
+
+  Ptr<ClassifierState> apply(Ptr<ExpressionGraph> graph, Ptr<data::CorpusBatch> batch, bool clearGraph) override {
+    if(clearGraph)
+      clear(graph);
+
+    std::vector<Ptr<EncoderState>> encoderStates;
+    for(auto& encoder : encoders_)
+        encoderStates.push_back(encoder->build(graph, batch));
+
+    return classifiers_[0]->apply(graph, batch, encoderStates);
+  }
+
+  virtual Expr build(Ptr<ExpressionGraph> graph,
+                     Ptr<data::CorpusBatch> batch,
+                     bool clearGraph = true) override {
+    auto state = apply(graph, batch, clearGraph);
+    // returns raw logits
+    return state->getLogProbs();                  
+  }
+
+  virtual Expr build(Ptr<ExpressionGraph> graph,
+                     Ptr<data::Batch> batch,
+                     bool clearGraph = true) override {
+    auto corpusBatch = std::static_pointer_cast<data::CorpusBatch>(batch);
+    return build(graph, corpusBatch, clearGraph);
+  }
+};
+
+}  // namespace marian
diff --git a/src/models/encoder_decoder.cpp b/src/models/encoder_decoder.cpp
index 3edb7b335..e8f93bc1c 100755
--- a/src/models/encoder_decoder.cpp
+++ b/src/models/encoder_decoder.cpp
@@ -1,4 +1,4 @@
-#include "encoder_decoder.h"
+#include "models/encoder_decoder.h"
 #include "common/cli_helper.h"
 #include "common/version.h"
 
diff --git a/src/models/encoder_decoder.h b/src/models/encoder_decoder.h
index 5818c2370..0ea89b4f6 100644
--- a/src/models/encoder_decoder.h
+++ b/src/models/encoder_decoder.h
@@ -2,10 +2,10 @@
 
 #include "marian.h"
 
-#include "decoder.h"
-#include "encoder.h"
-#include "model_base.h"
-#include "states.h"
+#include "models/decoder.h"
+#include "models/encoder.h"
+#include "models/model_base.h"
+#include "models/states.h"
 
 namespace marian {
 
diff --git a/src/models/model_factory.cpp b/src/models/model_factory.cpp
index d42f07c8b..28178e768 100644
--- a/src/models/model_factory.cpp
+++ b/src/models/model_factory.cpp
@@ -1,7 +1,8 @@
 #include "marian.h"
 
-#include "models/encoder_decoder.h"
 #include "models/model_factory.h"
+#include "models/encoder_decoder.h"
+#include "models/encoder_classifier.h"
 
 #include "models/costs.h"
 
@@ -49,6 +50,13 @@ Ptr<DecoderBase> DecoderFactory::construct() {
   ABORT("Unknown decoder type");
 }
 
+Ptr<ClassifierBase> ClassifierFactory::construct() {
+  if(options_->get<std::string>("type") == "bert")
+    return New<BertMaskedLM>(options_);
+  ABORT("Unknown classifier type");
+}
+
+
 Ptr<ModelBase> EncoderDecoderFactory::construct() {
   Ptr<EncoderDecoder> encdec;
 
@@ -69,6 +77,18 @@ Ptr<ModelBase> EncoderDecoderFactory::construct() {
   return add_cost(encdec, options_);
 }
 
+Ptr<ModelBase> EncoderClassifierFactory::construct() {
+  auto enccls = New<EncoderClassifier>(options_);
+
+  for(auto& ef : encoders_)
+    enccls->push_back(ef(options_).construct());
+
+  for(auto& cf : classifiers_)
+    enccls->push_back(cf(options_).construct());
+
+  return add_cost(enccls, options_);
+}
+
 Ptr<ModelBase> by_type(std::string type, usage use, Ptr<Options> options) {
   // clang-format off
   if(type == "s2s" || type == "amun" || type == "nematus") {
@@ -197,6 +217,14 @@ Ptr<ModelBase> by_type(std::string type, usage use, Ptr<Options> options) {
             .construct();
   }
 
+  if(type == "bert") {
+    return models::encoder_classifier()(options)
+        ("usage", use)
+        .push_back(models::encoder()("type", "transformer")("original-type", type))
+        .push_back(models::classifier()("index", 2)) // indices 0 and 1 used by encoder
+        .construct();
+  }
+
 #ifdef COMPILE_EXAMPLES
   // @TODO: examples should be compiled optionally
   if(type == "mnist-ffnn") {
diff --git a/src/models/model_factory.h b/src/models/model_factory.h
index 2ec7fe752..72c3a4a61 100644
--- a/src/models/model_factory.h
+++ b/src/models/model_factory.h
@@ -4,6 +4,7 @@
 
 #include "layers/factory.h"
 #include "models/encoder_decoder.h"
+#include "models/encoder_classifier.h"
 
 namespace marian {
 namespace models {
@@ -26,6 +27,15 @@ class DecoderFactory : public Factory {
 
 typedef Accumulator<DecoderFactory> decoder;
 
+class ClassifierFactory : public Factory {
+public:
+  ClassifierFactory(Ptr<ExpressionGraph> graph = nullptr) : Factory(graph) {}
+
+  virtual Ptr<ClassifierBase> construct();
+};
+
+typedef Accumulator<ClassifierFactory> classifier;
+
 class EncoderDecoderFactory : public Factory {
 private:
   std::vector<encoder> encoders_;
@@ -50,6 +60,30 @@ class EncoderDecoderFactory : public Factory {
 
 typedef Accumulator<EncoderDecoderFactory> encoder_decoder;
 
+class EncoderClassifierFactory : public Factory {
+private:
+  std::vector<encoder> encoders_;
+  std::vector<classifier> classifiers_;
+
+public:
+  EncoderClassifierFactory(Ptr<ExpressionGraph> graph = nullptr)
+      : Factory(graph) {}
+
+  Accumulator<EncoderClassifierFactory> push_back(encoder enc) {
+    encoders_.push_back(enc);
+    return Accumulator<EncoderClassifierFactory>(*this);
+  }
+
+  Accumulator<EncoderClassifierFactory> push_back(classifier cls) {
+    classifiers_.push_back(cls);
+    return Accumulator<EncoderClassifierFactory>(*this);
+  }
+
+  virtual Ptr<ModelBase> construct();
+};
+
+typedef Accumulator<EncoderClassifierFactory> encoder_classifier;
+
 Ptr<ModelBase> by_type(std::string type, usage, Ptr<Options> options);
 
 Ptr<ModelBase> from_options(Ptr<Options> options, usage);
diff --git a/src/models/states.h b/src/models/states.h
index 964617340..cb94e4bb7 100755
--- a/src/models/states.h
+++ b/src/models/states.h
@@ -99,4 +99,27 @@ class DecoderState {
 
   virtual void blacklist(Expr /*totalCosts*/, Ptr<data::CorpusBatch> /*batch*/) {}
 };
+
+class ClassifierState {
+private:
+  Expr logProbs_;
+  std::vector<Ptr<EncoderState>> encStates_;
+  Ptr<data::CorpusBatch> batch_;
+
+  Expr targetMask_;
+  Expr targetIndices_;
+
+public:
+  virtual Expr getLogProbs() const { return logProbs_; }
+  virtual void setLogProbs(Expr logProbs) { logProbs_ = logProbs; }
+
+  virtual Expr getTargetIndices() const { return targetIndices_; };
+
+  virtual void setTargetIndices(Expr targetIndices) { targetIndices_ = targetIndices; }
+
+  virtual Expr getTargetMask() const { return targetMask_; };
+
+  virtual void setTargetMask(Expr targetMask) { targetMask_ = targetMask; }
+};
+
 }  // namespace marian
diff --git a/src/models/transformer.h b/src/models/transformer.h
index c30d06c8e..baf7d261b 100755
--- a/src/models/transformer.h
+++ b/src/models/transformer.h
@@ -47,26 +47,81 @@ class Transformer : public EncoderOrDecoderBase {
 
   static Expr transposeTimeBatch(Expr input) { return transpose(input, {0, 2, 1, 3}); }
 
-  Expr addPositionalEmbeddings(Expr input, int start = 0) const {
+  Expr addPositionalEmbeddings(Expr input, int start = 0, bool learnedPosEmbeddings = false) const {
     int dimEmb   = input->shape()[-1];
     int dimWords = input->shape()[-3];
 
-    float num_timescales = (float)dimEmb / 2;
-    float log_timescale_increment = std::log(10000.f) / (num_timescales - 1.f);
+    Expr embeddings = input;
+    if(learnedPosEmbeddings) {
+      int maxLength = opt<int>("max-length");
 
-    std::vector<float> vPos(dimEmb * dimWords, 0);
-    for(int p = start; p < dimWords + start; ++p) {
-      for(int i = 0; i < num_timescales; ++i) {
-        float v = p * std::exp(i * -log_timescale_increment);
-        vPos[(p - start) * dimEmb + i] = std::sin(v);
-        vPos[(p - start) * dimEmb + (int)num_timescales + i] = std::cos(v); // @TODO: is int vs. float correct for num_timescales?
+      auto posEmbFactory = embedding(graph_)
+                            ("prefix", "Wpos") // share positional embeddings across all encoders/decorders
+                            ("dimVocab", maxLength)
+                            ("dimEmb", dimEmb)
+                            .construct();
+
+      std::vector<IndexType> positions(dimWords);
+      std::iota(positions.begin(), positions.end(), 0); // fill with increasing numbers until current length
+
+      auto signal = rows(posEmbFactory, graph_->indices(positions));
+      embeddings = embeddings + signal;
+    } else {
+      float num_timescales = (float)dimEmb / 2;
+      float log_timescale_increment = std::log(10000.f) / (num_timescales - 1.f);
+
+      std::vector<float> vPos(dimEmb * dimWords, 0);
+      for(int p = start; p < dimWords + start; ++p) {
+        for(int i = 0; i < num_timescales; ++i) {
+          float v = p * std::exp(i * -log_timescale_increment);
+          vPos[(p - start) * dimEmb + i] = std::sin(v);
+          vPos[(p - start) * dimEmb + (int)num_timescales + i] = std::cos(v); // @TODO: is int vs. float correct for num_timescales?
+        }
       }
+
+      // shared across batch entries
+      auto signal = graph_->constant({dimWords, 1, dimEmb}, inits::from_vector(vPos));
+      embeddings = embeddings + signal;
+    }
+
+    return embeddings;
+  }
+
+  Expr addSentenceEmbeddings(Expr input, int start, Ptr<data::CorpusBatch> batch) const {
+    Expr embeddings = input;
+
+    // do nothing if we are not training with sentence pairs
+    if(opt<bool>("bert-sentencepair", false)) {
+      int dimEmb = input->shape()[-1];
+
+      auto sentEmbFactory = embedding(graph_)
+                            ("prefix", "Wsent")
+                            ("dimVocab", 2) // sentence A or sentence B
+                            ("dimEmb", dimEmb)
+                            .construct();
+
+      const auto& sentenceIndices = (*batch)[1]->data(); // @TODO: do not use constant index
+      
+      // @TODO: note this is going to be really slow due to atomicAdd in backward step
+      // with only two classes;
+      // instead two masked reduce operations, maybe in parallel streams?
+      auto signal = rows(sentEmbFactory, graph_->indices(sentenceIndices)); 
+
+      embeddings = embeddings + signal;
     }
+    return embeddings;
+  }
+
+  Expr addSpecialEmbeddings(Expr input, int start = 0, Ptr<data::CorpusBatch> batch = nullptr) const {
+    bool iAmBert = opt<std::string>("original-type", "undefined") == "bert";
+    bool learnedPosEmbeddings = opt<bool>("transformer-learned-positions", false) || iAmBert;
 
-    // shared across batch entries
-    auto signal
-        = graph_->constant({dimWords, 1, dimEmb}, inits::from_vector(vPos));
-    return input + signal;
+    input = addPositionalEmbeddings(input, start, learnedPosEmbeddings);
+
+    if(iAmBert && batch)
+      input = addSentenceEmbeddings(input, start, batch);
+
+    return input;
   }
 
   Expr triangleMask(int length) const {
@@ -532,8 +587,7 @@ class EncoderTransformer : public Transformer<EncoderBase> {
       std::tie(batchEmbeddings, batchMask)
         = EncoderBase::ulrLookup(graph_, embeddings, batch);
     }
-    else
-    {
+    else {
       auto embeddings = wordEmbeddings(batchIndex_);
       std::tie(batchEmbeddings, batchMask)
         = EncoderBase::lookup(graph_, embeddings, batch);
@@ -546,7 +600,9 @@ class EncoderTransformer : public Transformer<EncoderBase> {
     }
     // according to paper embeddings are scaled up by \sqrt(d_m)
     auto scaledEmbeddings = std::sqrt((float)dimEmb) * batchEmbeddings;
-    scaledEmbeddings = addPositionalEmbeddings(scaledEmbeddings);
+    
+    scaledEmbeddings = addSpecialEmbeddings(scaledEmbeddings, /*start=*/0, batch);
+
     // reorganize batch and timestep
     scaledEmbeddings = atleast_nd(scaledEmbeddings, 4);
     batchMask = atleast_nd(batchMask, 4);
@@ -698,7 +754,7 @@ class DecoderTransformer : public Transformer<DecoderBase> {
     int startPos = (int)state->getPosition();
 
     scaledEmbeddings
-      = addPositionalEmbeddings(scaledEmbeddings, startPos);
+      = addSpecialEmbeddings(scaledEmbeddings, startPos);
 
     scaledEmbeddings = atleast_nd(scaledEmbeddings, 4);
 

From 963305b1c62b96a7bd43053289311830ebc7f4ca Mon Sep 17 00:00:00 2001
From: Marcin Junczys-Dowmunt <Marcin.Junczys-Dowmunt@microsoft.com>
Date: Thu, 3 Jan 2019 13:31:39 -0800
Subject: [PATCH 079/838] multi-objective learning for classifiers

---
 src/data/corpus_base.h          | 16 ++++++++----
 src/models/classifier.h         | 23 +++++++----------
 src/models/costs.h              | 14 ++++++++---
 src/models/encoder_classifier.h | 15 +++++++----
 src/models/model_factory.cpp    | 21 ++++++++++++----
 src/models/transformer.h        | 44 +++++++++++++++------------------
 6 files changed, 77 insertions(+), 56 deletions(-)

diff --git a/src/data/corpus_base.h b/src/data/corpus_base.h
index 8ecdf2337..765d39c78 100755
--- a/src/data/corpus_base.h
+++ b/src/data/corpus_base.h
@@ -110,7 +110,7 @@ class SentenceTuple {
  */
 class SubBatch {
 private:
-  std::vector<Word> indices_;
+  std::vector<Words> indices_;
   std::vector<float> mask_;
 
   size_t size_;
@@ -128,7 +128,7 @@ class SubBatch {
    * @param width Number of words in the longest sentence
    */
   SubBatch(size_t size, size_t width, const Ptr<Vocab>& vocab)
-      : indices_(size * width, 0),
+      : indices_(1, Words(size * width, 0)),
         mask_(size * width, 0),
         size_(size),
         width_(width),
@@ -142,7 +142,12 @@ class SubBatch {
    * idx_{w,0},idx_{w,1},\dots,idx_{w,s}\f$, where \f$w\f$ is the number of
    * words (width) and \f$s\f$ is the number of sentences (size).
    */
-  std::vector<Word>& data() { return indices_; }
+  std::vector<Word>& data() { return indices_[0]; }
+  std::vector<Word>& data(int index) { return indices_[index]; }
+
+  void setNumFactors(size_t num) { indices_.resize(num, Words(size_ * width_, 0)); } // @TODO: for now factors have no vocabulary, they are generated from the other inputs
+  size_t numFactors() { return indices_.size(); }
+
   /**
    * @brief Flat masking vector; 0 is used for masked words.
    *
@@ -204,8 +209,9 @@ class SubBatch {
       size_t words = 0;
       for(size_t j = 0; j < subWidth; ++j) {
         for(size_t i = 0; i < size; ++i) {
-          sb->data()[j * size + i] = indices_[j * size_ + (pos + i)];
-          sb->mask()[j * size + i] =    mask_[j * size_ + (pos + i)];
+          for(size_t k = 0; k < sb->numFactors(); ++k)
+            sb->data(k)[j * size + i] = indices_[k][j * size_ + (pos + i)];
+          sb->mask()[j * size + i] = mask_[j * size_ + (pos + i)];
 
           if(mask_[j * size_ + (pos + i)] != 0)
             words++;
diff --git a/src/models/classifier.h b/src/models/classifier.h
index 21fe8b6f9..ea92cf669 100644
--- a/src/models/classifier.h
+++ b/src/models/classifier.h
@@ -42,8 +42,7 @@ class BertMaskedLM : public ClassifierBase {
     ABORT_IF(encoderStates.size() != 1, "Currently we only support a single encoder BERT model");
 
     auto context = encoderStates[0]->getContext();
-    auto classEmbeddings = step(context, /*i=*/0, /*axis=*/-2); // [CLS] symbol is first symbol in each sequence
-
+    
     // Since this is a classifier we are not masking anything on the target. We can (mis)use the mask to hold 
     // indices of words in the encoder that have been masked out for BERT's masked LM training. 
     // Masks are floats, hence the conversion to IndexType.
@@ -51,32 +50,27 @@ class BertMaskedLM : public ClassifierBase {
     std::vector<IndexType> maskedRows(maskedRowsFloats.size());
     std::copy(maskedRowsFloats.begin(), maskedRowsFloats.end(), maskedRows.begin());
 
-    classEmbeddings = rows(classEmbeddings, graph->indices(maskedRows)); // subselect stuff that has actually been masked out;
+    auto classEmbeddings = rows(context, graph->indices(maskedRows)); // subselect stuff that has actually been masked out;
     
     int dimModel = classEmbeddings->shape()[-1];
 
-    int dimTrgVoc = opt<std::vector<int>>("dim-vocabs")[0]; // set to first one, @TODO: make this modular.
+    int dimVoc = opt<std::vector<int>>("dim-vocabs")[batchIndex_ - 1]; // unsafe
 
     auto layerTanh = mlp::dense(graph)   //
         ("dim", dimModel)                //
         ("activation", mlp::act::tanh); //
 
     auto layerOut = mlp::output(graph)  //
-        ("dim", dimTrgVoc);
+        ("dim", dimVoc);
 
-    if(opt<bool>("tied-embeddings") || opt<bool>("tied-embeddings-all")) {
-      std::string tiedPrefix = prefix_ + "_Wemb";
-      if(opt<bool>("tied-embeddings-all") || opt<bool>("tied-embeddings-src"))
-        tiedPrefix = "Wemb";
-      layerOut.tie_transposed("W", tiedPrefix);
-    }
+    layerOut.tie_transposed("W", "Wemb"); // We are a BERT model, hence tie with input
 
     // [-4: beam depth=1, -3: max length, -2: batch size, -1: vocab dim]
     // assemble layers into MLP and apply to embeddings, decoder context and
     // aligned source context
     auto output = mlp::mlp(graph)                 //
         ("prefix", prefix_ + "_ff_logit_bert_out") //
-        .push_back(layerTanh)                      //
+        .push_back(layerTanh)                      // @TODO: do we actually need this?
         .push_back(layerOut)                       //
         .construct();
     
@@ -96,6 +90,7 @@ class BertMaskedLM : public ClassifierBase {
 };
 
 // This is a model that uses a pre-trained BERT model to build a classifier on top of the encoder
+// Can be used for next sentence prediction task
 class BertClassifier : public ClassifierBase {
 public:
   BertClassifier(Ptr<Options> options) : ClassifierBase(options) {}
@@ -114,7 +109,7 @@ class BertClassifier : public ClassifierBase {
                     ("prefix", prefix_ + "_ff_logit")             //
                     .push_back(mlp::dense(graph)                  //
                                  ("dim", dimModel)                //
-                                 ("activation", mlp::act::tanh))  //
+                                 ("activation", mlp::act::tanh))  // @TODO: do we actually need this?
                     .push_back(mlp::dense(graph)                  //
                                  ("dim", dimTrgCls))              //
                     .construct();
@@ -124,7 +119,7 @@ class BertClassifier : public ClassifierBase {
     auto state = New<ClassifierState>();
     state->setLogProbs(logits);
 
-    // filled externally
+    // filled externally, for BERT these are NextSentence prediction labels
     const auto& classLabels = (*bertBatch)[batchIndex_]->data();
     state->setTargetIndices(graph->indices(classLabels));
 
diff --git a/src/models/costs.h b/src/models/costs.h
index 4d9845768..da190e9ca 100755
--- a/src/models/costs.h
+++ b/src/models/costs.h
@@ -93,13 +93,21 @@ class EncoderClassifierCE : public CostBase {
     auto enccls = std::static_pointer_cast<EncoderClassifier>(model);
     auto corpusBatch = std::static_pointer_cast<data::CorpusBatch>(batch);
 
-    auto state = enccls->apply(graph, corpusBatch, clearGraph);
+    auto states = enccls->apply(graph, corpusBatch, clearGraph);
 
-    Expr cost = loss_->getCost(state->getLogProbs(),
-                               state->getTargetIndices(),
+    Expr cost = loss_->getCost(states[0]->getLogProbs(),
+                               states[0]->getTargetIndices(),
                                /*mask=*/nullptr,
                                /*weights=*/nullptr);
 
+    // multi-objective training
+    for(int i = 1; i < states.size(); ++i) {
+      cost = cost + loss_->getCost(states[i]->getLogProbs(),
+                                   states[i]->getTargetIndices(),
+                                   /*mask=*/nullptr,
+                                   /*weights=*/nullptr);
+    }
+
     return cost;
   }
 };
diff --git a/src/models/encoder_classifier.h b/src/models/encoder_classifier.h
index aae6fe235..48f5d6c0e 100644
--- a/src/models/encoder_classifier.h
+++ b/src/models/encoder_classifier.h
@@ -30,7 +30,7 @@ class EncoderClassifierBase : public models::ModelBase {
 
   virtual void clear(Ptr<ExpressionGraph> graph) override = 0;
 
-  virtual Ptr<ClassifierState> apply(Ptr<ExpressionGraph>, Ptr<data::CorpusBatch>, bool) = 0;
+  virtual std::vector<Ptr<ClassifierState>> apply(Ptr<ExpressionGraph>, Ptr<data::CorpusBatch>, bool) = 0;
 
 
   virtual Expr build(Ptr<ExpressionGraph> graph,
@@ -178,7 +178,7 @@ class EncoderClassifier : public EncoderClassifierBase {
 
   /*********************************************************************/
 
-  Ptr<ClassifierState> apply(Ptr<ExpressionGraph> graph, Ptr<data::CorpusBatch> batch, bool clearGraph) override {
+  std::vector<Ptr<ClassifierState>> apply(Ptr<ExpressionGraph> graph, Ptr<data::CorpusBatch> batch, bool clearGraph) override {
     if(clearGraph)
       clear(graph);
 
@@ -186,15 +186,20 @@ class EncoderClassifier : public EncoderClassifierBase {
     for(auto& encoder : encoders_)
         encoderStates.push_back(encoder->build(graph, batch));
 
-    return classifiers_[0]->apply(graph, batch, encoderStates);
+    std::vector<Ptr<ClassifierState>> classifierStates;
+    for(auto& classifier : classifiers_)
+      classifierStates.push_back(classifier->apply(graph, batch, encoderStates));
+    
+    return classifierStates;
   }
 
   virtual Expr build(Ptr<ExpressionGraph> graph,
                      Ptr<data::CorpusBatch> batch,
                      bool clearGraph = true) override {
-    auto state = apply(graph, batch, clearGraph);
+    ABORT("Don't use this");
+    auto states = apply(graph, batch, clearGraph);
     // returns raw logits
-    return state->getLogProbs();                  
+    return states[0]->getLogProbs();
   }
 
   virtual Expr build(Ptr<ExpressionGraph> graph,
diff --git a/src/models/model_factory.cpp b/src/models/model_factory.cpp
index 28178e768..4aaf9cd52 100644
--- a/src/models/model_factory.cpp
+++ b/src/models/model_factory.cpp
@@ -51,8 +51,10 @@ Ptr<DecoderBase> DecoderFactory::construct() {
 }
 
 Ptr<ClassifierBase> ClassifierFactory::construct() {
-  if(options_->get<std::string>("type") == "bert")
+  if(options_->get<std::string>("type") == "bert-masked-lm")
     return New<BertMaskedLM>(options_);
+  if(options_->get<std::string>("type") == "bert-classifier")
+    return New<BertClassifier>(options_);
   ABORT("Unknown classifier type");
 }
 
@@ -218,10 +220,19 @@ Ptr<ModelBase> by_type(std::string type, usage use, Ptr<Options> options) {
   }
 
   if(type == "bert") {
-    return models::encoder_classifier()(options)
-        ("usage", use)
-        .push_back(models::encoder()("type", "transformer")("original-type", type))
-        .push_back(models::classifier()("index", 2)) // indices 0 and 1 used by encoder
+    return models::encoder_classifier()(options) //
+        ("usage", use)                           //
+        .push_back(models::encoder()             //
+                    ("type", "transformer")      //
+                    ("original-type", type)      //
+                    ("index", 0))                // close to original transformer encoder
+        .push_back(models::classifier()          //
+                    ("type", "bert-masked-lm")   //
+                    ("index", 1))                // multi-task learning with MaskedLM
+        .push_back(models::classifier()          //
+                    ("type", "bert-classifier")  //
+                    ("bert-classes", 2)          //
+                    ("index", 2))                // and next sentence prediction 
         .construct();
   }
 
diff --git a/src/models/transformer.h b/src/models/transformer.h
index baf7d261b..2c37034a6 100755
--- a/src/models/transformer.h
+++ b/src/models/transformer.h
@@ -25,7 +25,7 @@ class Transformer : public EncoderOrDecoderBase {
   typedef EncoderOrDecoderBase Base;
 
 protected:
-  using Base::options_; using Base::inference_;
+  using Base::options_; using Base::inference_; using Base::batchIndex_;
   std::unordered_map<std::string, Expr> cache_;
 
   // attention weights produced by step()
@@ -87,29 +87,22 @@ class Transformer : public EncoderOrDecoderBase {
     return embeddings;
   }
 
-  Expr addSentenceEmbeddings(Expr input, int start, Ptr<data::CorpusBatch> batch) const {
-    Expr embeddings = input;
-
-    // do nothing if we are not training with sentence pairs
-    if(opt<bool>("bert-sentencepair", false)) {
-      int dimEmb = input->shape()[-1];
-
-      auto sentEmbFactory = embedding(graph_)
-                            ("prefix", "Wsent")
-                            ("dimVocab", 2) // sentence A or sentence B
-                            ("dimEmb", dimEmb)
-                            .construct();
+  Expr addSentenceEmbeddings(Expr embeddings, int start, Ptr<data::CorpusBatch> batch) const {
+    int dimEmb = embeddings->shape()[-1];
 
-      const auto& sentenceIndices = (*batch)[1]->data(); // @TODO: do not use constant index
-      
-      // @TODO: note this is going to be really slow due to atomicAdd in backward step
-      // with only two classes;
-      // instead two masked reduce operations, maybe in parallel streams?
-      auto signal = rows(sentEmbFactory, graph_->indices(sentenceIndices)); 
+    auto sentenceEmbeddings = embedding(graph_)
+                                ("prefix", "Wsent")
+                                ("dimVocab", 2) // sentence A or sentence B
+                                ("dimEmb", dimEmb)
+                                .construct();
 
-      embeddings = embeddings + signal;
-    }
-    return embeddings;
+    const auto& sentenceIndices = (*batch)[batchIndex_]->data(1);
+    
+    // @TODO: note this is going to be really slow due to atomicAdd in backward step
+    // with only two classes;
+    // instead two masked reduce operations, maybe in parallel streams?
+    auto signal = rows(sentenceEmbeddings, graph_->indices(sentenceIndices)); 
+    return embeddings + signal;
   }
 
   Expr addSpecialEmbeddings(Expr input, int start = 0, Ptr<data::CorpusBatch> batch = nullptr) const {
@@ -118,7 +111,7 @@ class Transformer : public EncoderOrDecoderBase {
 
     input = addPositionalEmbeddings(input, start, learnedPosEmbeddings);
 
-    if(iAmBert && batch)
+    if(iAmBert)
       input = addSentenceEmbeddings(input, start, batch);
 
     return input;
@@ -555,7 +548,10 @@ class EncoderTransformer : public Transformer<EncoderBase> {
     int dimVoc = opt<std::vector<int>>("dim-vocabs")[subBatchIndex];
     int dimEmb = opt<int>("dim-emb");
     auto embFactory = embedding(graph_)("dimVocab", dimVoc)("dimEmb", dimEmb);
-    if (opt<bool>("tied-embeddings-src") || opt<bool>("tied-embeddings-all"))
+
+    bool iAmBert = opt<std::string>("original-type") == "bert";
+
+    if (opt<bool>("tied-embeddings-src") || opt<bool>("tied-embeddings-all") || iAmBert)
       embFactory("prefix", "Wemb");
     else
       embFactory("prefix", prefix_ + "_Wemb");

From a99c8b89ae560e8546ace7535acc311c2eab9b48 Mon Sep 17 00:00:00 2001
From: Marcin Junczys-Dowmunt <Marcin.Junczys-Dowmunt@microsoft.com>
Date: Thu, 3 Jan 2019 15:29:12 -0800
Subject: [PATCH 080/838] move bert-specifig code to bert.h

---
 src/data/corpus_base.h          |  16 ++--
 src/models/bert.h               | 131 ++++++++++++++++++++++++++++++++
 src/models/classifier.h         |  96 -----------------------
 src/models/encoder_classifier.h |   2 +-
 src/models/model_factory.cpp    |  18 +++--
 src/models/transformer.h        |  10 ++-
 6 files changed, 156 insertions(+), 117 deletions(-)
 create mode 100644 src/models/bert.h

diff --git a/src/data/corpus_base.h b/src/data/corpus_base.h
index 765d39c78..8ecdf2337 100755
--- a/src/data/corpus_base.h
+++ b/src/data/corpus_base.h
@@ -110,7 +110,7 @@ class SentenceTuple {
  */
 class SubBatch {
 private:
-  std::vector<Words> indices_;
+  std::vector<Word> indices_;
   std::vector<float> mask_;
 
   size_t size_;
@@ -128,7 +128,7 @@ class SubBatch {
    * @param width Number of words in the longest sentence
    */
   SubBatch(size_t size, size_t width, const Ptr<Vocab>& vocab)
-      : indices_(1, Words(size * width, 0)),
+      : indices_(size * width, 0),
         mask_(size * width, 0),
         size_(size),
         width_(width),
@@ -142,12 +142,7 @@ class SubBatch {
    * idx_{w,0},idx_{w,1},\dots,idx_{w,s}\f$, where \f$w\f$ is the number of
    * words (width) and \f$s\f$ is the number of sentences (size).
    */
-  std::vector<Word>& data() { return indices_[0]; }
-  std::vector<Word>& data(int index) { return indices_[index]; }
-
-  void setNumFactors(size_t num) { indices_.resize(num, Words(size_ * width_, 0)); } // @TODO: for now factors have no vocabulary, they are generated from the other inputs
-  size_t numFactors() { return indices_.size(); }
-
+  std::vector<Word>& data() { return indices_; }
   /**
    * @brief Flat masking vector; 0 is used for masked words.
    *
@@ -209,9 +204,8 @@ class SubBatch {
       size_t words = 0;
       for(size_t j = 0; j < subWidth; ++j) {
         for(size_t i = 0; i < size; ++i) {
-          for(size_t k = 0; k < sb->numFactors(); ++k)
-            sb->data(k)[j * size + i] = indices_[k][j * size_ + (pos + i)];
-          sb->mask()[j * size + i] = mask_[j * size_ + (pos + i)];
+          sb->data()[j * size + i] = indices_[j * size_ + (pos + i)];
+          sb->mask()[j * size + i] =    mask_[j * size_ + (pos + i)];
 
           if(mask_[j * size_ + (pos + i)] != 0)
             words++;
diff --git a/src/models/bert.h b/src/models/bert.h
new file mode 100644
index 000000000..9ce80635f
--- /dev/null
+++ b/src/models/bert.h
@@ -0,0 +1,131 @@
+#pragma once
+
+#include "data/corpus_base.h"
+#include "models/encoder_classifier.h"
+
+namespace marian {
+namespace data {
+
+class BertBatch : public CorpusBatch {
+private:
+  std::vector<IndexType> maskedPositions_;
+  std::vector<IndexType> maskedIndices_;
+  std::vector<IndexType> sentenceIndices_;
+
+  void init() {
+    ABORT("Not implemented");
+  }
+
+public:
+  BertBatch(Ptr<CorpusBatch> batch) : CorpusBatch(*batch) {
+    std::cerr << "Creating BERT batch" << std::endl;
+    init();
+  }
+
+  const std::vector<IndexType>& bertMaskedPositions() { return maskedPositions_; }
+  const std::vector<IndexType>& bertMaskedIndices()   { return maskedIndices_; }
+  const std::vector<IndexType>& bertSentenceIndices() { return sentenceIndices_; }
+};
+
+}
+
+class BertEncoderClassifier : public EncoderClassifier {
+public:
+  BertEncoderClassifier(Ptr<Options> options) : EncoderClassifier(options) {}
+
+  std::vector<Ptr<ClassifierState>> apply(Ptr<ExpressionGraph> graph, Ptr<data::CorpusBatch> batch, bool clearGraph) override {
+    Ptr<data::BertBatch> bertBatch = New<data::BertBatch>(batch); // intercept batch and anotate with BERT-specific concepts
+    return EncoderClassifier::apply(graph, bertBatch, clearGraph);
+  }
+};
+
+// Can be used for next sentence prediction task
+class BertClassifier : public ClassifierBase {
+public:
+  BertClassifier(Ptr<Options> options) : ClassifierBase(options) {}
+
+  // The batch has been filled with external classifier labels, @TODO: figure out how to do that
+  Ptr<ClassifierState> apply(Ptr<ExpressionGraph> graph, Ptr<data::CorpusBatch> batch, const std::vector<Ptr<EncoderState>>& encoderStates) override {
+    ABORT_IF(encoderStates.size() != 1, "Currently we only support a single encoder BERT model");
+
+    auto context = encoderStates[0]->getContext();
+    auto classEmbeddings = step(context, /*i=*/0, /*axis=*/-2); // [CLS] symbol is first symbol in each sequence
+    
+    int dimModel = classEmbeddings->shape()[-1];
+    int dimTrgCls = opt<int>("classifier-classes");
+
+    auto output = mlp::mlp(graph)                                 //
+                    ("prefix", prefix_ + "_ff_logit")             //
+                    .push_back(mlp::dense(graph)                  //
+                                 ("dim", dimModel)                //
+                                 ("activation", mlp::act::tanh))  // @TODO: do we actually need this?
+                    .push_back(mlp::dense(graph)                  //
+                                 ("dim", dimTrgCls))              //
+                    .construct();
+    
+    auto logits = output->apply(classEmbeddings); // class logits for each batch entry
+    
+    auto state = New<ClassifierState>();
+    state->setLogProbs(logits);
+
+    // filled externally, for BERT these are NextSentence prediction labels
+    const auto& classLabels = (*batch)[batchIndex_]->data();
+    state->setTargetIndices(graph->indices(classLabels));
+
+    return state;
+  }
+
+  virtual void clear() override {}
+};
+
+// This is a model that pretrains BERT for classification
+class BertMaskedLM : public ClassifierBase {
+public:
+  BertMaskedLM(Ptr<Options> options) : ClassifierBase(options) {}
+
+  Ptr<ClassifierState> apply(Ptr<ExpressionGraph> graph, Ptr<data::CorpusBatch> batch, const std::vector<Ptr<EncoderState>>& encoderStates) override {
+    Ptr<data::BertBatch> bertBatch = std::dynamic_pointer_cast<data::BertBatch>(batch);
+
+    ABORT_IF(!bertBatch, "Batch could not be converted to batch for BERT training");
+    ABORT_IF(encoderStates.size() != 1, "Currently we only support a single encoder BERT model");
+
+    auto context = encoderStates[0]->getContext();
+    
+    auto bertMaskedPositions = graph->indices(bertBatch->bertMaskedPositions()); // positions in batch of masked entries
+    auto bertMaskedIndices   = graph->indices(bertBatch->bertMaskedIndices());   // vocab ids of entries that have been masked
+    
+    auto classEmbeddings = rows(context, bertMaskedPositions); // subselect stuff that has actually been masked out;
+    
+    int dimModel = classEmbeddings->shape()[-1];
+
+    int dimVoc = opt<std::vector<int>>("dim-vocabs")[batchIndex_]; 
+
+    auto layerTanh = mlp::dense(graph)    //
+        ("dim", dimModel)                 //
+        ("activation", mlp::act::tanh);   //
+    auto layerOut = mlp::output(graph)    //
+        ("dim", dimVoc);                  //
+    layerOut.tie_transposed("W", "Wemb"); // We are a BERT model, hence tie with input
+
+    // [-4: beam depth=1, -3: max length, -2: batch size, -1: vocab dim]
+    // assemble layers into MLP and apply to embeddings, decoder context and
+    // aligned source context
+    auto output = mlp::mlp(graph)                 //
+        ("prefix", prefix_ + "_ff_logit_maskedlm_out") //
+        .push_back(layerTanh)                      // @TODO: do we actually need this?
+        .push_back(layerOut)                       //
+        .construct();
+    
+    auto logits = output->apply(classEmbeddings);
+    
+    auto state = New<ClassifierState>();
+    state->setLogProbs(logits);
+    state->setTargetIndices(bertMaskedIndices);
+
+    return state;
+  }
+
+  virtual void clear() override {}
+};
+
+}
\ No newline at end of file
diff --git a/src/models/classifier.h b/src/models/classifier.h
index ea92cf669..61c4d64e3 100644
--- a/src/models/classifier.h
+++ b/src/models/classifier.h
@@ -33,100 +33,4 @@ class ClassifierBase {
   virtual void clear() = 0;
 };
 
-// This is a model that pretrains BERT for classification
-class BertMaskedLM : public ClassifierBase {
-public:
-  BertMaskedLM(Ptr<Options> options) : ClassifierBase(options) {}
-
-  Ptr<ClassifierState> apply(Ptr<ExpressionGraph> graph, Ptr<data::CorpusBatch> bertBatch, const std::vector<Ptr<EncoderState>>& encoderStates) override {
-    ABORT_IF(encoderStates.size() != 1, "Currently we only support a single encoder BERT model");
-
-    auto context = encoderStates[0]->getContext();
-    
-    // Since this is a classifier we are not masking anything on the target. We can (mis)use the mask to hold 
-    // indices of words in the encoder that have been masked out for BERT's masked LM training. 
-    // Masks are floats, hence the conversion to IndexType.
-    const auto& maskedRowsFloats = (*bertBatch)[batchIndex_]->mask(); 
-    std::vector<IndexType> maskedRows(maskedRowsFloats.size());
-    std::copy(maskedRowsFloats.begin(), maskedRowsFloats.end(), maskedRows.begin());
-
-    auto classEmbeddings = rows(context, graph->indices(maskedRows)); // subselect stuff that has actually been masked out;
-    
-    int dimModel = classEmbeddings->shape()[-1];
-
-    int dimVoc = opt<std::vector<int>>("dim-vocabs")[batchIndex_ - 1]; // unsafe
-
-    auto layerTanh = mlp::dense(graph)   //
-        ("dim", dimModel)                //
-        ("activation", mlp::act::tanh); //
-
-    auto layerOut = mlp::output(graph)  //
-        ("dim", dimVoc);
-
-    layerOut.tie_transposed("W", "Wemb"); // We are a BERT model, hence tie with input
-
-    // [-4: beam depth=1, -3: max length, -2: batch size, -1: vocab dim]
-    // assemble layers into MLP and apply to embeddings, decoder context and
-    // aligned source context
-    auto output = mlp::mlp(graph)                 //
-        ("prefix", prefix_ + "_ff_logit_bert_out") //
-        .push_back(layerTanh)                      // @TODO: do we actually need this?
-        .push_back(layerOut)                       //
-        .construct();
-    
-    auto logits = output->apply(classEmbeddings);
-    
-    auto state = New<ClassifierState>();
-    state->setLogProbs(logits);
-
-    // filled automatically during masking, these are the vocab indices of masked words
-    const auto& classLabels = (*bertBatch)[batchIndex_]->data();
-    state->setTargetIndices(graph->indices(classLabels));
-
-    return state;
-  }
-
-  virtual void clear() override {}
-};
-
-// This is a model that uses a pre-trained BERT model to build a classifier on top of the encoder
-// Can be used for next sentence prediction task
-class BertClassifier : public ClassifierBase {
-public:
-  BertClassifier(Ptr<Options> options) : ClassifierBase(options) {}
-
-  // The batch has been filled with external classifier labels, @TODO: figure out how to do that
-  Ptr<ClassifierState> apply(Ptr<ExpressionGraph> graph, Ptr<data::CorpusBatch> bertBatch, const std::vector<Ptr<EncoderState>>& encoderStates) override {
-    ABORT_IF(encoderStates.size() != 1, "Currently we only support a single encoder BERT model");
-
-    auto context = encoderStates[0]->getContext();
-    auto classEmbeddings = step(context, /*i=*/0, /*axis=*/-2); // [CLS] symbol is first symbol in each sequence
-    
-    int dimModel = classEmbeddings->shape()[-1];
-    int dimTrgCls = opt<int>("bert-classes");
-
-    auto output = mlp::mlp(graph)                                 //
-                    ("prefix", prefix_ + "_ff_logit")             //
-                    .push_back(mlp::dense(graph)                  //
-                                 ("dim", dimModel)                //
-                                 ("activation", mlp::act::tanh))  // @TODO: do we actually need this?
-                    .push_back(mlp::dense(graph)                  //
-                                 ("dim", dimTrgCls))              //
-                    .construct();
-    
-    auto logits = output->apply(classEmbeddings); // class logits for each batch entry
-    
-    auto state = New<ClassifierState>();
-    state->setLogProbs(logits);
-
-    // filled externally, for BERT these are NextSentence prediction labels
-    const auto& classLabels = (*bertBatch)[batchIndex_]->data();
-    state->setTargetIndices(graph->indices(classLabels));
-
-    return state;
-  }
-
-  virtual void clear() override {}
-};
-
 }
\ No newline at end of file
diff --git a/src/models/encoder_classifier.h b/src/models/encoder_classifier.h
index 48f5d6c0e..7d7570213 100644
--- a/src/models/encoder_classifier.h
+++ b/src/models/encoder_classifier.h
@@ -178,7 +178,7 @@ class EncoderClassifier : public EncoderClassifierBase {
 
   /*********************************************************************/
 
-  std::vector<Ptr<ClassifierState>> apply(Ptr<ExpressionGraph> graph, Ptr<data::CorpusBatch> batch, bool clearGraph) override {
+  virtual std::vector<Ptr<ClassifierState>> apply(Ptr<ExpressionGraph> graph, Ptr<data::CorpusBatch> batch, bool clearGraph) override {
     if(clearGraph)
       clear(graph);
 
diff --git a/src/models/model_factory.cpp b/src/models/model_factory.cpp
index 4aaf9cd52..49a489ff6 100644
--- a/src/models/model_factory.cpp
+++ b/src/models/model_factory.cpp
@@ -3,6 +3,7 @@
 #include "models/model_factory.h"
 #include "models/encoder_decoder.h"
 #include "models/encoder_classifier.h"
+#include "models/bert.h"
 
 #include "models/costs.h"
 
@@ -80,7 +81,12 @@ Ptr<ModelBase> EncoderDecoderFactory::construct() {
 }
 
 Ptr<ModelBase> EncoderClassifierFactory::construct() {
-  auto enccls = New<EncoderClassifier>(options_);
+  Ptr<EncoderClassifier> enccls;
+  if(options_->get<std::string>("type") == "bert") {
+    enccls = New<BertEncoderClassifier>(options_);
+  } else {
+    enccls = New<EncoderClassifier>(options_);
+  } 
 
   for(auto& ef : encoders_)
     enccls->push_back(ef(options_).construct());
@@ -226,13 +232,13 @@ Ptr<ModelBase> by_type(std::string type, usage use, Ptr<Options> options) {
                     ("type", "transformer")      //
                     ("original-type", type)      //
                     ("index", 0))                // close to original transformer encoder
-        .push_back(models::classifier()          //
-                    ("type", "bert-masked-lm")   //
-                    ("index", 1))                // multi-task learning with MaskedLM
         .push_back(models::classifier()          //
                     ("type", "bert-classifier")  //
-                    ("bert-classes", 2)          //
-                    ("index", 2))                // and next sentence prediction 
+                    ("classifier-classes", 2)    //
+                    ("index", 1))                // next sentence prediction 
+        .push_back(models::classifier()          //
+                    ("type", "bert-masked-lm")   //
+                    ("index", 0))                // multi-task learning with MaskedLM
         .construct();
   }
 
diff --git a/src/models/transformer.h b/src/models/transformer.h
index 2c37034a6..d702dbad1 100755
--- a/src/models/transformer.h
+++ b/src/models/transformer.h
@@ -11,6 +11,7 @@
 #include "models/encoder.h"
 #include "models/states.h"
 #include "models/transformer_factory.h"
+#include "models/bert.h"
 #include "rnn/constructors.h"
 
 namespace marian {
@@ -88,6 +89,10 @@ class Transformer : public EncoderOrDecoderBase {
   }
 
   Expr addSentenceEmbeddings(Expr embeddings, int start, Ptr<data::CorpusBatch> batch) const {
+    Ptr<data::BertBatch> bertBatch = std::dynamic_pointer_cast<data::BertBatch>(batch);
+
+    ABORT_IF(!bertBatch, "Batch could not be converted for BERT training");
+
     int dimEmb = embeddings->shape()[-1];
 
     auto sentenceEmbeddings = embedding(graph_)
@@ -96,12 +101,11 @@ class Transformer : public EncoderOrDecoderBase {
                                 ("dimEmb", dimEmb)
                                 .construct();
 
-    const auto& sentenceIndices = (*batch)[batchIndex_]->data(1);
-    
     // @TODO: note this is going to be really slow due to atomicAdd in backward step
     // with only two classes;
     // instead two masked reduce operations, maybe in parallel streams?
-    auto signal = rows(sentenceEmbeddings, graph_->indices(sentenceIndices)); 
+    auto sentenceIndices = graph_->indices(bertBatch->bertSentenceIndices());
+    auto signal = rows(sentenceEmbeddings, sentenceIndices); 
     return embeddings + signal;
   }
 

From 9b84dbf51bdcdeb427b782807e4172a4ff00a054 Mon Sep 17 00:00:00 2001
From: Marcin Junczys-Dowmunt <Marcin.Junczys-Dowmunt@microsoft.com>
Date: Thu, 3 Jan 2019 16:21:55 -0800
Subject: [PATCH 081/838] move more bert stuff to bert.h

---
 src/models/bert.h            | 33 ++++++++++++++++++++++++++
 src/models/model_factory.cpp |  7 ++++--
 src/models/transformer.h     | 46 +++++++-----------------------------
 3 files changed, 47 insertions(+), 39 deletions(-)

diff --git a/src/models/bert.h b/src/models/bert.h
index 9ce80635f..37005875a 100644
--- a/src/models/bert.h
+++ b/src/models/bert.h
@@ -2,6 +2,7 @@
 
 #include "data/corpus_base.h"
 #include "models/encoder_classifier.h"
+#include "models/transformer.h"
 
 namespace marian {
 namespace data {
@@ -39,6 +40,38 @@ class BertEncoderClassifier : public EncoderClassifier {
   }
 };
 
+class BertEncoder : public EncoderTransformer {
+public:
+  BertEncoder(Ptr<Options> options) : EncoderTransformer(options) {}
+
+  Expr addSentenceEmbeddings(Expr embeddings, int start, Ptr<data::CorpusBatch> batch) const {
+    Ptr<data::BertBatch> bertBatch = std::dynamic_pointer_cast<data::BertBatch>(batch);
+
+    ABORT_IF(!bertBatch, "Batch could not be converted for BERT training");
+
+    int dimEmb = embeddings->shape()[-1];
+
+    auto sentenceEmbeddings = embedding(graph_)
+                                ("prefix", "Wsent")
+                                ("dimVocab", 2) // sentence A or sentence B
+                                ("dimEmb", dimEmb)
+                                .construct();
+
+    // @TODO: note this is going to be really slow due to atomicAdd in backward step
+    // with only two classes;
+    // instead two masked reduce operations, maybe in parallel streams?
+    auto sentenceIndices = graph_->indices(bertBatch->bertSentenceIndices());
+    auto signal = rows(sentenceEmbeddings, sentenceIndices); 
+    return embeddings + signal;
+  }
+
+  virtual Expr addSpecialEmbeddings(Expr input, int start = 0, Ptr<data::CorpusBatch> batch = nullptr) const override {
+    input = addPositionalEmbeddings(input, start, true); // true for BERT
+    input = addSentenceEmbeddings(input, start, batch);
+    return input;
+  }
+};
+
 // Can be used for next sentence prediction task
 class BertClassifier : public ClassifierBase {
 public:
diff --git a/src/models/model_factory.cpp b/src/models/model_factory.cpp
index 49a489ff6..1146c8523 100644
--- a/src/models/model_factory.cpp
+++ b/src/models/model_factory.cpp
@@ -39,6 +39,10 @@ Ptr<EncoderBase> EncoderFactory::construct() {
     // return New<EncoderTransformer>(options_);
     return NewEncoderTransformer(options_);
 
+  if(options_->get<std::string>("type") == "bert-encoder")
+    // return New<EncoderTransformer>(options_);
+    return New<BertEncoder>(options_);
+
   ABORT("Unknown encoder type");
 }
 
@@ -229,8 +233,7 @@ Ptr<ModelBase> by_type(std::string type, usage use, Ptr<Options> options) {
     return models::encoder_classifier()(options) //
         ("usage", use)                           //
         .push_back(models::encoder()             //
-                    ("type", "transformer")      //
-                    ("original-type", type)      //
+                    ("type", "bert-encoder")     //
                     ("index", 0))                // close to original transformer encoder
         .push_back(models::classifier()          //
                     ("type", "bert-classifier")  //
diff --git a/src/models/transformer.h b/src/models/transformer.h
index d702dbad1..6d7f7a894 100755
--- a/src/models/transformer.h
+++ b/src/models/transformer.h
@@ -11,7 +11,6 @@
 #include "models/encoder.h"
 #include "models/states.h"
 #include "models/transformer_factory.h"
-#include "models/bert.h"
 #include "rnn/constructors.h"
 
 namespace marian {
@@ -88,36 +87,10 @@ class Transformer : public EncoderOrDecoderBase {
     return embeddings;
   }
 
-  Expr addSentenceEmbeddings(Expr embeddings, int start, Ptr<data::CorpusBatch> batch) const {
-    Ptr<data::BertBatch> bertBatch = std::dynamic_pointer_cast<data::BertBatch>(batch);
-
-    ABORT_IF(!bertBatch, "Batch could not be converted for BERT training");
-
-    int dimEmb = embeddings->shape()[-1];
-
-    auto sentenceEmbeddings = embedding(graph_)
-                                ("prefix", "Wsent")
-                                ("dimVocab", 2) // sentence A or sentence B
-                                ("dimEmb", dimEmb)
-                                .construct();
-
-    // @TODO: note this is going to be really slow due to atomicAdd in backward step
-    // with only two classes;
-    // instead two masked reduce operations, maybe in parallel streams?
-    auto sentenceIndices = graph_->indices(bertBatch->bertSentenceIndices());
-    auto signal = rows(sentenceEmbeddings, sentenceIndices); 
-    return embeddings + signal;
-  }
-
-  Expr addSpecialEmbeddings(Expr input, int start = 0, Ptr<data::CorpusBatch> batch = nullptr) const {
-    bool iAmBert = opt<std::string>("original-type", "undefined") == "bert";
-    bool learnedPosEmbeddings = opt<bool>("transformer-learned-positions", false) || iAmBert;
-
+  virtual Expr addSpecialEmbeddings(Expr input, int start = 0, Ptr<data::CorpusBatch> batch = nullptr) const {
+    batch;
+    bool learnedPosEmbeddings = opt<bool>("transformer-learned-positions", false);
     input = addPositionalEmbeddings(input, start, learnedPosEmbeddings);
-
-    if(iAmBert)
-      input = addSentenceEmbeddings(input, start, batch);
-
     return input;
   }
 
@@ -529,6 +502,7 @@ class Transformer : public EncoderOrDecoderBase {
 class EncoderTransformer : public Transformer<EncoderBase> {
 public:
   EncoderTransformer(Ptr<Options> options) : Transformer(options) {}
+  virtual ~EncoderTransformer() {}
 
   // returns the embedding matrix based on options
   // and based on batchIndex_.
@@ -569,13 +543,13 @@ class EncoderTransformer : public Transformer<EncoderBase> {
     return embFactory.construct();
   }
 
-  Ptr<EncoderState> build(Ptr<ExpressionGraph> graph,
-                          Ptr<data::CorpusBatch> batch) override {
+  virtual Ptr<EncoderState> build(Ptr<ExpressionGraph> graph,
+                                  Ptr<data::CorpusBatch> batch) override {
     graph_ = graph;
     return apply(batch);
   }
 
-  Ptr<EncoderState> apply(Ptr<data::CorpusBatch> batch) {
+  virtual Ptr<EncoderState> apply(Ptr<data::CorpusBatch> batch) {
     int dimEmb = opt<int>("dim-emb");
     int dimBatch = (int)batch->size();
     int dimSrcWords = (int)(*batch)[batchIndex_]->batchWidth();
@@ -637,7 +611,7 @@ class EncoderTransformer : public Transformer<EncoderBase> {
     return New<EncoderState>(context, batchMask, batch);
   }
 
-  void clear() override {}
+  virtual void clear() override {}
 };
 
 class TransformerState : public DecoderState {
@@ -753,9 +727,7 @@ class DecoderTransformer : public Transformer<DecoderBase> {
     // Used for position embeddings and creating new decoder states.
     int startPos = (int)state->getPosition();
 
-    scaledEmbeddings
-      = addSpecialEmbeddings(scaledEmbeddings, startPos);
-
+    scaledEmbeddings = addSpecialEmbeddings(scaledEmbeddings, startPos);
     scaledEmbeddings = atleast_nd(scaledEmbeddings, 4);
 
     // reorganize batch and timestep

From 13e8640d0fa894b725cc009afd1b5390fd79f137 Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Mon, 7 Jan 2019 09:33:49 -0800
Subject: [PATCH 082/838] (spelling error)

---
 src/training/scheduler.h | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/training/scheduler.h b/src/training/scheduler.h
index bcf7175e9..d6c527900 100755
--- a/src/training/scheduler.h
+++ b/src/training/scheduler.h
@@ -486,7 +486,7 @@ class Scheduler : public TrainingObserver {
           state.factor *= factor;
           updateLearningRate(state);
           LOG(info,
-              "Decaying learning rate to {} after stalled {} time(s)",
+              "Decaying learning rate to {} after having stalled {} time(s)",
               state.eta,
               state.stalled);
 

From d765d06f50e3de741a3f9b2c47fb4d987f3b5aa3 Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Mon, 7 Jan 2019 09:42:26 -0800
Subject: [PATCH 083/838] removed some experimental code

---
 src/common/config_parser.cpp  |  3 ---
 src/optimizers/optimizers.cpp | 22 ----------------------
 src/training/scheduler.h      |  9 ++-------
 3 files changed, 2 insertions(+), 32 deletions(-)

diff --git a/src/common/config_parser.cpp b/src/common/config_parser.cpp
index 14c266c8b..70a30a5fd 100755
--- a/src/common/config_parser.cpp
+++ b/src/common/config_parser.cpp
@@ -618,9 +618,6 @@ void ConfigParser::addSuboptionsBatching(cli::CLIWrapper& cli) {
       {"0"});
   cli.add<bool>("--mini-batch-track-lr",
       "Dynamically track mini-batch size inverse to actual learning rate (not considering lr-warmup)");
-  cli.add<double>("--mini-batch-warmup-exp",
-      "[experimental] Soften initial ramp-up curve with this exponent (until ramp-up reached 100%)",
-      1.);
   cli.add<size_t>("--mini-batch-overstuff",
       "[experimental] Stuff this much more data into a minibatch, but scale down the LR and progress counter",
       1);
diff --git a/src/optimizers/optimizers.cpp b/src/optimizers/optimizers.cpp
index c4e9951f0..685c917f9 100755
--- a/src/optimizers/optimizers.cpp
+++ b/src/optimizers/optimizers.cpp
@@ -169,28 +169,6 @@ void Adam::updateImpl(Tensor params, Tensor grads, size_t actualMBSize, size_t r
           vt_     // =_3
           );
 
-  // log the magnitude (with good ole CPU code)
-  static int logCount = 0;
-  logCount++;
-  if (logCount <= 10 || logCount % 100 == 0) {
-    std::vector<float> g, mt, vt;
-    grads->get(g); mt_->get(mt); vt_->get(vt);
-    size_t n = g.size(); ABORT_IF(mt.size() != g.size() || vt.size() != g.size(), "mismatching sizes??");
-    double gs = 0, ms = 0; // square sum
-    for (size_t i = 0; i < n; i++) {
-      auto gi = g[i] / T;        // raw average gradient
-      auto mi = mt[i] / denom1f; // momentum-smoothed average gradient
-      auto di = sqrtf(vt[i] / denom2f) + eps_;
-      gi /= di; // RMS-normalize both
-      mi /= di;
-      gs += gi * gi;
-      ms += mi * mi;
-    }
-    auto grms = std::sqrt(gs / n);  // hmm... expecting 1.0 here, no?
-    auto mrms = std::sqrt(ms / n);
-    LOG(info, "Adam[{}]: grms = {}, mrms = {}", logCount, grms, mrms);
-  }
-
   params->getBackend()->synchronize(); // @TODO: This should not be in here. Maybe in the wrapper. Why is it needed at all?
 }
 
diff --git a/src/training/scheduler.h b/src/training/scheduler.h
index d6c527900..1fadad0be 100755
--- a/src/training/scheduler.h
+++ b/src/training/scheduler.h
@@ -83,19 +83,14 @@ class Scheduler : public TrainingObserver {
     if (mbWarmup) {
       // mini-batch-warmup
       LOG_ONCE(info, "[scheduler] Mini-batch size warmup {}", std::string(mbWarmup));
-      // This scales MB size up from the start, relative to progress within warm-up period.
+      // This ramps up MB size at start, relative to progress within warm-up period.
       size_t progress = state_->getProgressIn(mbWarmup.unit); // number of updates/labels processed
       auto progressRatio = (double)progress / (double)mbWarmup.n; // where are we relatively within target warm-up period
       // if unit is labels, then account for the fact that our increment itself is not constant
       if (mbWarmup.unit == SchedulingUnit::trgLabels)
         progressRatio = std::sqrt(progressRatio);
-      if (progressRatio < 1) {
-        // soften the very initial ramp-up if requested
-        double exp = options_->get<double>("mini-batch-warmup-exp");
-        progressRatio = pow(progressRatio, exp); // e.g. 1.5 => linear ramp-up -> sublinear ramp-up
-        // apply ratio to actual batch size
+      if (progressRatio < 1)
         ratio *= progressRatio;
-      }
     }
 
     // dynamic MB-size tracking with learning rate

From 3800c03d096fed196e46111ba6a411c042679825 Mon Sep 17 00:00:00 2001
From: Marcin Junczys-Dowmunt <Marcin.Junczys-Dowmunt@microsoft.com>
Date: Mon, 7 Jan 2019 14:06:28 -0800
Subject: [PATCH 084/838] classifier input

---
 src/common/config_parser.cpp    |  4 ++++
 src/data/corpus.cpp             |  2 +-
 src/data/corpus_base.cpp        | 22 ++++++++++++++++++++--
 src/data/corpus_base.h          |  9 ++++++++-
 src/data/corpus_nbest.cpp       |  4 ++--
 src/data/corpus_sqlite.cpp      |  2 +-
 src/models/bert.h               |  8 ++++----
 src/models/encoder_classifier.h |  1 +
 src/models/encoder_decoder.cpp  |  1 +
 src/models/model_factory.cpp    | 13 ++++++++++++-
 10 files changed, 54 insertions(+), 12 deletions(-)

diff --git a/src/common/config_parser.cpp b/src/common/config_parser.cpp
index d39f3b1ad..2d77661bf 100755
--- a/src/common/config_parser.cpp
+++ b/src/common/config_parser.cpp
@@ -143,6 +143,10 @@ void ConfigParser::addOptionsModel(cli::CLIWrapper& cli) {
       "Enable layer normalization");
   cli.add<bool>("--right-left",
       "Train right-to-left model");
+  cli.add<std::vector<std::string>>("--input-types",
+      "Provide type of input data if different than 'sequence'. "
+      "Possible values: sequence, labels. You need to provide one type per input.",
+      {});
   cli.add<bool>("--best-deep",
       "Use Edinburgh deep RNN configuration (s2s)");
   cli.add_nondefault<std::vector<size_t>>("--special-vocab",
diff --git a/src/data/corpus.cpp b/src/data/corpus.cpp
index 7a7a846e1..ffa0a24c6 100755
--- a/src/data/corpus.cpp
+++ b/src/data/corpus.cpp
@@ -55,7 +55,7 @@ SentenceTuple Corpus::next() {
       } else if(i > 0 && i == weightFileIdx_) {
         addWeightsToSentenceTuple(line, tup);
       } else {
-        addWordsToSentenceTuple(line, i, tup);
+        addWordsToSentenceTuple(line, i, tup, addEOS_[i]);
       }
     }
 
diff --git a/src/data/corpus_base.cpp b/src/data/corpus_base.cpp
index c9df1c42d..a8297c638 100755
--- a/src/data/corpus_base.cpp
+++ b/src/data/corpus_base.cpp
@@ -57,6 +57,23 @@ CorpusBase::CorpusBase(Ptr<Options> options, bool translate)
   else
     paths_ = options_->get<std::vector<std::string>>("input");
 
+  addEOS_.resize(paths_.size(), true);
+
+  // @TODO: think if this should be checked and processed here or in a validation step in config?
+  auto inputTypes = options_->get<std::vector<std::string>>("input-types", {}); // empty list by default
+  ABORT_IF(inputTypes.size() > 0 && inputTypes.size() != paths_.size(), 
+           "Input types are specified ({}) you need to specify one per input ({})", 
+           inputTypes.size(), 
+           paths_.size());
+  // Currently input types affects only EOS symbol
+  for(int i = 0; i < inputTypes.size(); ++i)
+    if(inputTypes[i] == "labels")
+      addEOS_[i] = false;
+    else if(inputTypes[i] == "sequence")
+      addEOS_[i] = true;
+    else
+      ABORT("Unknown input type {}: {}", i, inputTypes[i]);
+
   std::vector<std::string> vocabPaths;
   if(!options_->get<std::vector<std::string>>("vocabs").empty())
     vocabPaths = options_->get<std::vector<std::string>>("vocabs");
@@ -189,12 +206,13 @@ CorpusBase::CorpusBase(Ptr<Options> options, bool translate)
 
 void CorpusBase::addWordsToSentenceTuple(const std::string& line,
                                          size_t i,
-                                         SentenceTuple& tup) const {
+                                         SentenceTuple& tup,
+                                         bool addEOS) const {
 
   // This turns a string in to a sequence of numerical word ids. Depending
   // on the vocabulary type, this can be non-trivial, e.g. when SentencePiece
   // is used.
-  Words words = vocabs_[i]->encode(line, /*addEOS =*/ true, inference_);
+  Words words = vocabs_[i]->encode(line, /*addEOS =*/ addEOS, inference_);
 
   if(words.empty())
     words.push_back(0);
diff --git a/src/data/corpus_base.h b/src/data/corpus_base.h
index 8ecdf2337..be33036bf 100755
--- a/src/data/corpus_base.h
+++ b/src/data/corpus_base.h
@@ -513,6 +513,12 @@ class CorpusBase
   std::vector<UPtr<io::InputFileStream>> files_;
   std::vector<Ptr<Vocab>> vocabs_;
 
+  /**
+   * brief Determines if a EOS symbol should be added. By default this is true for any sequence,
+   * but should be false for instance for classifier labels. This is set per input stream, hence a vector.
+   */
+  std::vector<bool> addEOS_; 
+
   size_t pos_{0};
 
   size_t maxLength_{0};
@@ -537,7 +543,8 @@ class CorpusBase
    */
   void addWordsToSentenceTuple(const std::string& line,
                                size_t i,
-                               SentenceTuple& tup) const;
+                               SentenceTuple& tup,
+                               bool addEOS) const;
   /**
    * @brief Helper function parsing a line with word alignments and adding them
    * to the sentence tuple.
diff --git a/src/data/corpus_nbest.cpp b/src/data/corpus_nbest.cpp
index 328c3c0d7..4fd560992 100644
--- a/src/data/corpus_nbest.cpp
+++ b/src/data/corpus_nbest.cpp
@@ -61,9 +61,9 @@ SentenceTuple CorpusNBest::next() {
                    "Too few lines in input {}",
                    i);
         }
-        addWordsToSentenceTuple(lastLines_[i], i, tup);
+        addWordsToSentenceTuple(lastLines_[i], i, tup, addEOS_[i]);
       }
-      addWordsToSentenceTuple(curr_text, last, tup);
+      addWordsToSentenceTuple(curr_text, last, tup, addEOS_[last]);
       lastNum_ = curr_num;
     }
 
diff --git a/src/data/corpus_sqlite.cpp b/src/data/corpus_sqlite.cpp
index cbab750eb..7127aafda 100644
--- a/src/data/corpus_sqlite.cpp
+++ b/src/data/corpus_sqlite.cpp
@@ -120,7 +120,7 @@ SentenceTuple CorpusSQLite::next() {
       } else if(i > 0 && i == weightFileIdx_) {
         addWeightsToSentenceTuple(line, tup);
       } else {
-        addWordsToSentenceTuple(line, i, tup);
+        addWordsToSentenceTuple(line, i, tup, addEOS_[i]);
       }
     }
 
diff --git a/src/models/bert.h b/src/models/bert.h
index 37005875a..a1c7d4588 100644
--- a/src/models/bert.h
+++ b/src/models/bert.h
@@ -85,7 +85,7 @@ class BertClassifier : public ClassifierBase {
     auto classEmbeddings = step(context, /*i=*/0, /*axis=*/-2); // [CLS] symbol is first symbol in each sequence
     
     int dimModel = classEmbeddings->shape()[-1];
-    int dimTrgCls = opt<int>("classifier-classes");
+    int dimTrgCls = opt<std::vector<int>>("dim-vocabs")[batchIndex_]; // Target vocab is used as class labels
 
     auto output = mlp::mlp(graph)                                 //
                     ("prefix", prefix_ + "_ff_logit")             //
@@ -143,10 +143,10 @@ class BertMaskedLM : public ClassifierBase {
     // [-4: beam depth=1, -3: max length, -2: batch size, -1: vocab dim]
     // assemble layers into MLP and apply to embeddings, decoder context and
     // aligned source context
-    auto output = mlp::mlp(graph)                 //
+    auto output = mlp::mlp(graph)                      //
         ("prefix", prefix_ + "_ff_logit_maskedlm_out") //
-        .push_back(layerTanh)                      // @TODO: do we actually need this?
-        .push_back(layerOut)                       //
+        .push_back(layerTanh)                          // @TODO: do we actually need this?
+        .push_back(layerOut)                           //
         .construct();
     
     auto logits = output->apply(classEmbeddings);
diff --git a/src/models/encoder_classifier.h b/src/models/encoder_classifier.h
index 7d7570213..9a650311a 100644
--- a/src/models/encoder_classifier.h
+++ b/src/models/encoder_classifier.h
@@ -103,6 +103,7 @@ class EncoderClassifier : public EncoderClassifierBase {
                     "skip",
                     "layer-normalization",
                     "right-left",
+                    "input-types",
                     "special-vocab",
                     "tied-embeddings",
                     "tied-embeddings-src",
diff --git a/src/models/encoder_decoder.cpp b/src/models/encoder_decoder.cpp
index e8f93bc1c..047d960d1 100755
--- a/src/models/encoder_decoder.cpp
+++ b/src/models/encoder_decoder.cpp
@@ -23,6 +23,7 @@ EncoderDecoder::EncoderDecoder(Ptr<Options> options)
                     "skip",
                     "layer-normalization",
                     "right-left",
+                    "input-types",
                     "special-vocab",
                     "tied-embeddings",
                     "tied-embeddings-src",
diff --git a/src/models/model_factory.cpp b/src/models/model_factory.cpp
index 1146c8523..d800308ee 100644
--- a/src/models/model_factory.cpp
+++ b/src/models/model_factory.cpp
@@ -237,7 +237,6 @@ Ptr<ModelBase> by_type(std::string type, usage use, Ptr<Options> options) {
                     ("index", 0))                // close to original transformer encoder
         .push_back(models::classifier()          //
                     ("type", "bert-classifier")  //
-                    ("classifier-classes", 2)    //
                     ("index", 1))                // next sentence prediction 
         .push_back(models::classifier()          //
                     ("type", "bert-masked-lm")   //
@@ -245,6 +244,18 @@ Ptr<ModelBase> by_type(std::string type, usage use, Ptr<Options> options) {
         .construct();
   }
 
+  if(type == "bert-classifier") {
+    return models::encoder_classifier()(options) //
+        ("usage", use)                           //
+        .push_back(models::encoder()             //
+                    ("type", "bert-encoder")     //
+                    ("index", 0))                // close to original transformer encoder
+        .push_back(models::classifier()          //
+                    ("type", "bert-classifier")  //
+                    ("index", 1))                // next sentence prediction 
+        .construct();
+  }
+
 #ifdef COMPILE_EXAMPLES
   // @TODO: examples should be compiled optionally
   if(type == "mnist-ffnn") {

From bf41d04947d4eb1f308de248ec6220421874a50b Mon Sep 17 00:00:00 2001
From: Marcin Junczys-Dowmunt <Marcin.Junczys-Dowmunt@microsoft.com>
Date: Mon, 7 Jan 2019 16:09:50 -0800
Subject: [PATCH 085/838] fix errors for first classifier training

---
 src/data/corpus_base.cpp     | 20 ++++++++++++++++++--
 src/models/bert.h            | 19 ++++++++++++-------
 src/models/model_factory.cpp |  4 ++--
 src/models/transformer.h     |  5 ++---
 4 files changed, 34 insertions(+), 14 deletions(-)

diff --git a/src/data/corpus_base.cpp b/src/data/corpus_base.cpp
index a8297c638..319d5f2ac 100755
--- a/src/data/corpus_base.cpp
+++ b/src/data/corpus_base.cpp
@@ -43,6 +43,23 @@ CorpusBase::CorpusBase(const std::vector<std::string>& paths,
     files_.emplace_back(new io::InputFileStream(path));
     ABORT_IF(files_.back()->empty(), "File '{}' is empty", path);
   }
+
+  addEOS_.resize(paths_.size(), true);
+  // @TODo: think if this should be checked and processed here or in a validation step in config?
+  auto inputTypes = options_->get<std::vector<std::string>>("input-types", {}); // empty list by default
+  ABORT_IF(inputTypes.size() > 0 && inputTypes.size() != paths_.size(), 
+           "Input types are specified ({}) you need to specify one per input ({})", 
+           inputTypes.size(), 
+           paths_.size());
+  // Currently input types affects only EOS symbol
+  for(int i = 0; i < inputTypes.size(); ++i)
+    if(inputTypes[i] == "labels")
+      addEOS_[i] = false;
+    else if(inputTypes[i] == "sequence")
+      addEOS_[i] = true;
+    else
+      ABORT("Unknown input type {}: {}", i, inputTypes[i]);
+
 }
 
 CorpusBase::CorpusBase(Ptr<Options> options, bool translate)
@@ -58,8 +75,7 @@ CorpusBase::CorpusBase(Ptr<Options> options, bool translate)
     paths_ = options_->get<std::vector<std::string>>("input");
 
   addEOS_.resize(paths_.size(), true);
-
-  // @TODO: think if this should be checked and processed here or in a validation step in config?
+  // @TODo: think if this should be checked and processed here or in a validation step in config?
   auto inputTypes = options_->get<std::vector<std::string>>("input-types", {}); // empty list by default
   ABORT_IF(inputTypes.size() > 0 && inputTypes.size() != paths_.size(), 
            "Input types are specified ({}) you need to specify one per input ({})", 
diff --git a/src/models/bert.h b/src/models/bert.h
index a1c7d4588..b25f608be 100644
--- a/src/models/bert.h
+++ b/src/models/bert.h
@@ -40,6 +40,7 @@ class BertEncoderClassifier : public EncoderClassifier {
   }
 };
 
+// @TODO: this should be in transformer.h
 class BertEncoder : public EncoderTransformer {
 public:
   BertEncoder(Ptr<Options> options) : EncoderTransformer(options) {}
@@ -50,6 +51,8 @@ class BertEncoder : public EncoderTransformer {
     ABORT_IF(!bertBatch, "Batch could not be converted for BERT training");
 
     int dimEmb = embeddings->shape()[-1];
+    int dimBatch = embeddings->shape()[-2];
+    int dimWords = embeddings->shape()[-3];
 
     auto sentenceEmbeddings = embedding(graph_)
                                 ("prefix", "Wsent")
@@ -62,6 +65,7 @@ class BertEncoder : public EncoderTransformer {
     // instead two masked reduce operations, maybe in parallel streams?
     auto sentenceIndices = graph_->indices(bertBatch->bertSentenceIndices());
     auto signal = rows(sentenceEmbeddings, sentenceIndices); 
+    signal = reshape(signal, {dimWords, dimBatch, dimEmb});
     return embeddings + signal;
   }
 
@@ -87,13 +91,14 @@ class BertClassifier : public ClassifierBase {
     int dimModel = classEmbeddings->shape()[-1];
     int dimTrgCls = opt<std::vector<int>>("dim-vocabs")[batchIndex_]; // Target vocab is used as class labels
 
-    auto output = mlp::mlp(graph)                                 //
-                    ("prefix", prefix_ + "_ff_logit")             //
-                    .push_back(mlp::dense(graph)                  //
-                                 ("dim", dimModel)                //
-                                 ("activation", mlp::act::tanh))  // @TODO: do we actually need this?
-                    .push_back(mlp::dense(graph)                  //
-                                 ("dim", dimTrgCls))              //
+    auto output = mlp::mlp(graph)                                     //
+                    .push_back(mlp::dense(graph)                      //
+                                 ("prefix", prefix_ + "_ff_logit_l1") //
+                                 ("dim", dimModel)                    //
+                                 ("activation", mlp::act::tanh))      // @TODO: do we actually need this?
+                    .push_back(mlp::output(graph)                      //
+                                 ("dim", dimTrgCls))                  //
+                                 ("prefix", prefix_ + "_ff_logit_l2") //
                     .construct();
     
     auto logits = output->apply(classEmbeddings); // class logits for each batch entry
diff --git a/src/models/model_factory.cpp b/src/models/model_factory.cpp
index d800308ee..2b19b4990 100644
--- a/src/models/model_factory.cpp
+++ b/src/models/model_factory.cpp
@@ -233,7 +233,7 @@ Ptr<ModelBase> by_type(std::string type, usage use, Ptr<Options> options) {
     return models::encoder_classifier()(options) //
         ("usage", use)                           //
         .push_back(models::encoder()             //
-                    ("type", "bert-encoder")     //
+                    ("type", "bert-encoder")     // transformer encoder for now
                     ("index", 0))                // close to original transformer encoder
         .push_back(models::classifier()          //
                     ("type", "bert-classifier")  //
@@ -248,7 +248,7 @@ Ptr<ModelBase> by_type(std::string type, usage use, Ptr<Options> options) {
     return models::encoder_classifier()(options) //
         ("usage", use)                           //
         .push_back(models::encoder()             //
-                    ("type", "bert-encoder")     //
+                    ("type", "transformer")     //
                     ("index", 0))                // close to original transformer encoder
         .push_back(models::classifier()          //
                     ("type", "bert-classifier")  //
diff --git a/src/models/transformer.h b/src/models/transformer.h
index 6d7f7a894..188080a99 100755
--- a/src/models/transformer.h
+++ b/src/models/transformer.h
@@ -65,6 +65,7 @@ class Transformer : public EncoderOrDecoderBase {
       std::iota(positions.begin(), positions.end(), 0); // fill with increasing numbers until current length
 
       auto signal = rows(posEmbFactory, graph_->indices(positions));
+      signal = reshape(signal, {dimWords, 1, dimEmb});
       embeddings = embeddings + signal;
     } else {
       float num_timescales = (float)dimEmb / 2;
@@ -527,9 +528,7 @@ class EncoderTransformer : public Transformer<EncoderBase> {
     int dimEmb = opt<int>("dim-emb");
     auto embFactory = embedding(graph_)("dimVocab", dimVoc)("dimEmb", dimEmb);
 
-    bool iAmBert = opt<std::string>("original-type") == "bert";
-
-    if (opt<bool>("tied-embeddings-src") || opt<bool>("tied-embeddings-all") || iAmBert)
+    if (opt<bool>("tied-embeddings-src") || opt<bool>("tied-embeddings-all"))
       embFactory("prefix", "Wemb");
     else
       embFactory("prefix", prefix_ + "_Wemb");

From 9023bf8b90c0d5ca68890dc2683dce50e7ab637f Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Tue, 8 Jan 2019 17:15:39 -0800
Subject: [PATCH 086/838] fixed build break due to MPI change in examples

---
 src/examples/mnist/mnist_ffnn.cpp |  0
 src/examples/mnist/training.h     |  7 +++++-
 vs/Marian.vcxproj                 | 20 +++++++++++++++
 vs/Marian.vcxproj.filters         | 42 +++++++++++++++++++++++++++++++
 4 files changed, 68 insertions(+), 1 deletion(-)
 mode change 100644 => 100755 src/examples/mnist/mnist_ffnn.cpp
 mode change 100644 => 100755 src/examples/mnist/training.h

diff --git a/src/examples/mnist/mnist_ffnn.cpp b/src/examples/mnist/mnist_ffnn.cpp
old mode 100644
new mode 100755
diff --git a/src/examples/mnist/training.h b/src/examples/mnist/training.h
old mode 100644
new mode 100755
index 8cdacb5e0..64ccfcbd0
--- a/src/examples/mnist/training.h
+++ b/src/examples/mnist/training.h
@@ -30,8 +30,11 @@ class TrainMNIST : public ModelTask {
     auto scheduler = New<Scheduler>(options_, trainState);
     scheduler->addValidator(New<AccuracyValidator>(options_));
 
+    // Multi-node training
+    auto mpi = initMPI(/*multiThreaded=*/false);
+
     // Prepare model
-    auto model = New<ModelWrapper>(options_);
+    auto model = New<ModelWrapper>(options_, mpi);
     model->setScheduler(scheduler);
     model->load();
 
@@ -47,6 +50,8 @@ class TrainMNIST : public ModelTask {
         scheduler->increaseEpoch();
     }
     scheduler->finished();
+    model = nullptr;
+    finalizeMPI(std::move(mpi));
   }
 };
 }  // namespace marian
diff --git a/vs/Marian.vcxproj b/vs/Marian.vcxproj
index c13d7841d..c77cbd006 100755
--- a/vs/Marian.vcxproj
+++ b/vs/Marian.vcxproj
@@ -563,6 +563,15 @@
     <ClCompile Include="..\src\data\corpus_nbest.cpp" />
     <ClCompile Include="..\src\data\text_input.cpp" />
     <ClCompile Include="..\src\3rd_party\cnpy\cnpy.cpp" />
+    <ClCompile Include="..\src\examples\iris\helper.cpp">
+      <ExcludedFromBuild Condition="'$(Configuration)|$(Platform)'=='Debug|x64'">true</ExcludedFromBuild>
+      <ExcludedFromBuild Condition="'$(Configuration)|$(Platform)'=='Release|x64'">true</ExcludedFromBuild>
+    </ClCompile>
+    <ClCompile Include="..\src\examples\iris\iris.cpp" />
+    <ClCompile Include="..\src\examples\mnist\mnist_ffnn.cpp">
+      <ExcludedFromBuild Condition="'$(Configuration)|$(Platform)'=='Debug|x64'">true</ExcludedFromBuild>
+      <ExcludedFromBuild Condition="'$(Configuration)|$(Platform)'=='Release|x64'">true</ExcludedFromBuild>
+    </ClCompile>
     <ClCompile Include="..\src\layers\loss.cpp" />
     <ClCompile Include="..\src\layers\weight.cpp" />
     <ClCompile Include="..\src\microsoft\quicksand.cpp">
@@ -687,6 +696,11 @@
     <ClInclude Include="..\src\common\timer.h" />
     <ClInclude Include="..\src\common\types.h" />
     <ClInclude Include="..\src\common\version.h" />
+    <ClInclude Include="..\src\examples\mnist\dataset.h" />
+    <ClInclude Include="..\src\examples\mnist\model.h" />
+    <ClInclude Include="..\src\examples\mnist\model_lenet.h" />
+    <ClInclude Include="..\src\examples\mnist\training.h" />
+    <ClInclude Include="..\src\examples\mnist\validator.h" />
     <ClInclude Include="..\src\layers\loss.h" />
     <ClInclude Include="..\src\layers\weight.h" />
     <ClInclude Include="..\src\marian.h" />
@@ -1049,6 +1063,11 @@
       <ExcludedFromBuild Condition="'$(Configuration)|$(Platform)'=='Debug|x64'">true</ExcludedFromBuild>
       <ExcludedFromBuild Condition="'$(Configuration)|$(Platform)'=='Release|x64'">true</ExcludedFromBuild>
     </None>
+    <None Include="..\src\examples\cmake_install.cmake" />
+    <None Include="..\src\examples\iris\iris.data" />
+    <None Include="..\src\examples\Makefile" />
+    <None Include="..\src\examples\mnist\download.sh" />
+    <None Include="..\src\examples\README.md" />
     <None Include="..\src\tensors\gpu\add.cu">
       <ExcludedFromBuild Condition="'$(Configuration)|$(Platform)'=='Debug|x64'">true</ExcludedFromBuild>
     </None>
@@ -1097,6 +1116,7 @@
       <ExcludedFromBuild Condition="'$(Configuration)|$(Platform)'=='Debug|x64'">true</ExcludedFromBuild>
       <ExcludedFromBuild Condition="'$(Configuration)|$(Platform)'=='Release|x64'">true</ExcludedFromBuild>
     </Text>
+    <Text Include="..\src\examples\CMakeLists.txt" />
     <Text Include="..\src\tests\CMakeLists.txt" />
   </ItemGroup>
   <Import Project="$(VCTargetsPath)\Microsoft.Cpp.targets" />
diff --git a/vs/Marian.vcxproj.filters b/vs/Marian.vcxproj.filters
index 745e130da..9e5cbf5f4 100755
--- a/vs/Marian.vcxproj.filters
+++ b/vs/Marian.vcxproj.filters
@@ -472,6 +472,15 @@
     <ClCompile Include="..\src\tests\sqlite_test.cpp">
       <Filter>tests</Filter>
     </ClCompile>
+    <ClCompile Include="..\src\examples\mnist\mnist_ffnn.cpp">
+      <Filter>examples\mnist</Filter>
+    </ClCompile>
+    <ClCompile Include="..\src\examples\iris\helper.cpp">
+      <Filter>examples\iris</Filter>
+    </ClCompile>
+    <ClCompile Include="..\src\examples\iris\iris.cpp">
+      <Filter>examples\iris</Filter>
+    </ClCompile>
   </ItemGroup>
   <ItemGroup>
     <ClInclude Include="..\src\marian.h" />
@@ -1493,6 +1502,21 @@
     <ClInclude Include="..\src\3rd_party\pathie-cpp\include\temp.hpp">
       <Filter>3rd_party\pathie-cpp\include</Filter>
     </ClInclude>
+    <ClInclude Include="..\src\examples\mnist\dataset.h">
+      <Filter>examples\mnist</Filter>
+    </ClInclude>
+    <ClInclude Include="..\src\examples\mnist\model.h">
+      <Filter>examples\mnist</Filter>
+    </ClInclude>
+    <ClInclude Include="..\src\examples\mnist\model_lenet.h">
+      <Filter>examples\mnist</Filter>
+    </ClInclude>
+    <ClInclude Include="..\src\examples\mnist\training.h">
+      <Filter>examples\mnist</Filter>
+    </ClInclude>
+    <ClInclude Include="..\src\examples\mnist\validator.h">
+      <Filter>examples\mnist</Filter>
+    </ClInclude>
   </ItemGroup>
   <ItemGroup>
     <Filter Include="3rd_party">
@@ -1810,6 +1834,21 @@
     <None Include="..\src\tests\tensor_test.cu">
       <Filter>tests</Filter>
     </None>
+    <None Include="..\src\examples\mnist\download.sh">
+      <Filter>examples\mnist</Filter>
+    </None>
+    <None Include="..\src\examples\iris\iris.data">
+      <Filter>examples\iris</Filter>
+    </None>
+    <None Include="..\src\examples\cmake_install.cmake">
+      <Filter>examples</Filter>
+    </None>
+    <None Include="..\src\examples\Makefile">
+      <Filter>examples</Filter>
+    </None>
+    <None Include="..\src\examples\README.md">
+      <Filter>examples</Filter>
+    </None>
   </ItemGroup>
   <ItemGroup>
     <Text Include="..\src\3rd_party\sentencepiece\src\CMakeLists.txt">
@@ -1821,5 +1860,8 @@
     <Text Include="..\src\tests\CMakeLists.txt">
       <Filter>tests</Filter>
     </Text>
+    <Text Include="..\src\examples\CMakeLists.txt">
+      <Filter>examples</Filter>
+    </Text>
   </ItemGroup>
 </Project>
\ No newline at end of file

From 664e29ff6ec4a83d34f42c3398de52ac9181a17d Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Tue, 8 Jan 2019 17:59:05 -0800
Subject: [PATCH 087/838] removed operator-> for Factories

---
 src/layers/constructors.h     | 2 --
 src/models/s2s.h              | 8 ++++----
 src/rnn/constructors.h        | 2 --
 src/tests/attention_tests.cpp | 2 +-
 src/tests/rnn_tests.cpp       | 8 ++++----
 vs/Marian.vcxproj             | 5 ++++-
 6 files changed, 13 insertions(+), 14 deletions(-)
 mode change 100644 => 100755 src/layers/constructors.h
 mode change 100644 => 100755 src/tests/attention_tests.cpp
 mode change 100644 => 100755 src/tests/rnn_tests.cpp

diff --git a/src/layers/constructors.h b/src/layers/constructors.h
old mode 100644
new mode 100755
index c063f44c8..16765341f
--- a/src/layers/constructors.h
+++ b/src/layers/constructors.h
@@ -146,8 +146,6 @@ class MLPFactory : public Factory {
     return mlp;
   }
 
-  Ptr<MLP> operator->() { return construct(); }
-
   template <class LF>
   Accumulator<MLPFactory> push_back(const LF& lf) {
     layers_.push_back(New<LF>(lf));
diff --git a/src/models/s2s.h b/src/models/s2s.h
index 92ba9a7d1..b4600892f 100755
--- a/src/models/s2s.h
+++ b/src/models/s2s.h
@@ -86,8 +86,8 @@ class EncoderS2S : public EncoderBase {
       rnnBw.push_back(stacked);
     }
 
-    auto context = concatenate({rnnFw->transduce(embeddings, mask),
-                                rnnBw->transduce(embeddings, mask)},
+    auto context = concatenate({rnnFw.construct()->transduce(embeddings, mask),
+                                rnnBw.construct()->transduce(embeddings, mask)},
                                /*axis =*/ -1);
 
     if(second > 0) {
@@ -114,7 +114,7 @@ class EncoderS2S : public EncoderBase {
       }
 
       // transduce context to new context
-      context = rnnUni->transduce(context);
+      context = rnnUni.construct()->transduce(context);
     }
     return context;
   }
@@ -269,7 +269,7 @@ class DecoderS2S : public DecoderBase {
                && opt<std::string>("original-type") == "nematus")  //
       );
 
-      start = mlp->apply(meanContexts);
+      start = mlp.construct()->apply(meanContexts);
     } else {
       int dimBatch = (int)batch->size();
       int dimRnn = opt<int>("dim-rnn");
diff --git a/src/rnn/constructors.h b/src/rnn/constructors.h
index ec0e1bd85..657d8ad16 100755
--- a/src/rnn/constructors.h
+++ b/src/rnn/constructors.h
@@ -187,8 +187,6 @@ class RNNFactory : public Factory {
     return rnn;
   }
 
-  Ptr<RNN> operator->() { return construct(); }
-
   template <class F>
   Accumulator<RNNFactory> push_back(const F& f) {
     layerFactories_.push_back(New<F>(f));
diff --git a/src/tests/attention_tests.cpp b/src/tests/attention_tests.cpp
old mode 100644
new mode 100755
index eff555729..1f4ae5b6d
--- a/src/tests/attention_tests.cpp
+++ b/src/tests/attention_tests.cpp
@@ -61,7 +61,7 @@ void tests(DeviceType type) {
           .push_back(rnn::cell(graph)) //
           .construct();
 
-    auto context = rnn->transduce(input, mask);
+    auto context = rnn.construct()->transduce(input, mask);
 
     auto encState = New<EncoderState>(context, mask, nullptr);
 
diff --git a/src/tests/rnn_tests.cpp b/src/tests/rnn_tests.cpp
old mode 100644
new mode 100755
index 87639c860..145828788
--- a/src/tests/rnn_tests.cpp
+++ b/src/tests/rnn_tests.cpp
@@ -51,7 +51,7 @@ void tests(DeviceType type) {
           .push_back(rnn::cell(graph)) //
           .construct();
 
-    auto output = rnn->transduce(input);
+    auto output = rnn.construct()->transduce(input);
 
     graph->forward();
 
@@ -161,8 +161,8 @@ void tests(DeviceType type) {
         rnnBw.push_back(stacked);
       }
 
-      auto context = concatenate({rnnFw->transduce(input, mask),
-                                  rnnBw->transduce(input, mask)},
+      auto context = concatenate({rnnFw.construct()->transduce(input, mask),
+                                  rnnBw.construct()->transduce(input, mask)},
                                   /*axis =*/ input->shape().size() - 1);
 
       if(second > 0) {
@@ -188,7 +188,7 @@ void tests(DeviceType type) {
         }
 
         // transduce context to new context
-        context = rnnUni->transduce(context);
+        context = rnnUni.construct()->transduce(context);
       }
       return context;
     };
diff --git a/vs/Marian.vcxproj b/vs/Marian.vcxproj
index c77cbd006..64faded63 100755
--- a/vs/Marian.vcxproj
+++ b/vs/Marian.vcxproj
@@ -567,7 +567,10 @@
       <ExcludedFromBuild Condition="'$(Configuration)|$(Platform)'=='Debug|x64'">true</ExcludedFromBuild>
       <ExcludedFromBuild Condition="'$(Configuration)|$(Platform)'=='Release|x64'">true</ExcludedFromBuild>
     </ClCompile>
-    <ClCompile Include="..\src\examples\iris\iris.cpp" />
+    <ClCompile Include="..\src\examples\iris\iris.cpp">
+      <ExcludedFromBuild Condition="'$(Configuration)|$(Platform)'=='Debug|x64'">true</ExcludedFromBuild>
+      <ExcludedFromBuild Condition="'$(Configuration)|$(Platform)'=='Release|x64'">true</ExcludedFromBuild>
+    </ClCompile>
     <ClCompile Include="..\src\examples\mnist\mnist_ffnn.cpp">
       <ExcludedFromBuild Condition="'$(Configuration)|$(Platform)'=='Debug|x64'">true</ExcludedFromBuild>
       <ExcludedFromBuild Condition="'$(Configuration)|$(Platform)'=='Release|x64'">true</ExcludedFromBuild>

From 209d38724ca5e2b38baa8ff81e98a06a1924fad7 Mon Sep 17 00:00:00 2001
From: Marcin Junczys-Dowmunt <Marcin.Junczys-Dowmunt@microsoft.com>
Date: Wed, 9 Jan 2019 09:21:36 -0800
Subject: [PATCH 088/838] fix batch-size-related error, choose correct axis for
 classifier

---
 src/common/config_parser.cpp         |  2 +
 src/data/corpus_base.cpp             | 25 ++++----
 src/data/corpus_base.h               |  4 +-
 src/data/default_vocab.cpp           | 96 +++++++++++++---------------
 src/data/vocab.cpp                   | 15 ++++-
 src/graph/node_operators_binary.h    |  3 +
 src/layers/loss.cpp                  | 37 +++++------
 src/models/bert.h                    |  2 +-
 src/tensors/cpu/tensor_operators.cpp | 20 +++---
 src/training/graph_group.h           | 24 +++++--
 src/training/scheduler.h             | 14 ++--
 11 files changed, 131 insertions(+), 111 deletions(-)

diff --git a/src/common/config_parser.cpp b/src/common/config_parser.cpp
index 2d77661bf..1910c85de 100755
--- a/src/common/config_parser.cpp
+++ b/src/common/config_parser.cpp
@@ -280,6 +280,8 @@ void ConfigParser::addOptionsTraining(cli::CLIWrapper& cli) {
       "Display nformation for the first  arg  updates");
   cli.add<bool>("--disp-label-counts",
       "Display label counts when logging loss progress");
+  cli.add<int>("--disp-wps-index",
+      "Display words-per-second ratio based on i-th sub-batch (-1 is last)", -1);
   cli.add<std::string/*SchedulerPeriod*/>("--save-freq",
       "Save model file every  arg  updates (append 't' for every  arg  target labels)",
       "10000u");
diff --git a/src/data/corpus_base.cpp b/src/data/corpus_base.cpp
index 319d5f2ac..b35bb032b 100755
--- a/src/data/corpus_base.cpp
+++ b/src/data/corpus_base.cpp
@@ -77,18 +77,6 @@ CorpusBase::CorpusBase(Ptr<Options> options, bool translate)
   addEOS_.resize(paths_.size(), true);
   // @TODo: think if this should be checked and processed here or in a validation step in config?
   auto inputTypes = options_->get<std::vector<std::string>>("input-types", {}); // empty list by default
-  ABORT_IF(inputTypes.size() > 0 && inputTypes.size() != paths_.size(), 
-           "Input types are specified ({}) you need to specify one per input ({})", 
-           inputTypes.size(), 
-           paths_.size());
-  // Currently input types affects only EOS symbol
-  for(int i = 0; i < inputTypes.size(); ++i)
-    if(inputTypes[i] == "labels")
-      addEOS_[i] = false;
-    else if(inputTypes[i] == "sequence")
-      addEOS_[i] = true;
-    else
-      ABORT("Unknown input type {}: {}", i, inputTypes[i]);
 
   std::vector<std::string> vocabPaths;
   if(!options_->get<std::vector<std::string>>("vocabs").empty())
@@ -162,6 +150,19 @@ CorpusBase::CorpusBase(Ptr<Options> options, bool translate)
         vocabs_.emplace_back(vocab);
       }
     }
+
+    ABORT_IF(inputTypes.size() > 0 && inputTypes.size() != paths_.size(), 
+           "Input types are specified ({}) you need to specify one per input ({})", 
+            inputTypes.size(), 
+            paths_.size());
+    // Currently input types affects only EOS symbol
+    for(int i = 0; i < inputTypes.size(); ++i)
+      if(inputTypes[i] == "labels")
+        addEOS_[i] = false;
+      else if(inputTypes[i] == "sequence")
+        addEOS_[i] = true;
+      else
+        ABORT("Unknown input type {}: {}", i, inputTypes[i]);
   }
 
   if(translate) {
diff --git a/src/data/corpus_base.h b/src/data/corpus_base.h
index be33036bf..b8bc556cc 100755
--- a/src/data/corpus_base.h
+++ b/src/data/corpus_base.h
@@ -47,7 +47,7 @@ class SentenceTuple {
   /**
    * @brief Adds a new sentence at the end of the tuple.
    *
-   * @param words A vector of word indexes.
+   * @param words A vector of word indices.
    */
   void push_back(const Words& words) { tuple_.push_back(words); }
 
@@ -460,7 +460,7 @@ class CorpusBatch : public Batch {
     std::cerr << "batches: " << sets() << std::endl;
 
     if(!sentenceIds_.empty()) {
-      std::cerr << "indexes: ";
+      std::cerr << "indices: ";
       for(auto id : sentenceIds_)
         std::cerr << id << " ";
       std::cerr << std::endl;
diff --git a/src/data/default_vocab.cpp b/src/data/default_vocab.cpp
index e344eadf9..bac5c72ec 100755
--- a/src/data/default_vocab.cpp
+++ b/src/data/default_vocab.cpp
@@ -112,8 +112,6 @@ class DefaultVocab : public VocabBase {
       ABORT_IF(in.bad(), "DefaultVocabulary file {} could not be read", vocabPath);
     }
 
-    std::unordered_set<Word> seenSpecial;
-
     id2str_.reserve(vocab.size());
     for(auto&& pair : vocab) {
       auto str = pair.first;
@@ -126,59 +124,13 @@ class DefaultVocab : public VocabBase {
     }
     ABORT_IF(id2str_.empty(), "Empty vocabulary: ", vocabPath);
 
-    // look up ids for </s> and <unk>, which are required
-    // The name backCompatStr is alternatively accepted for Yaml vocabs if id
-    // equals backCompatId.
-    auto getRequiredWordId = [&](const std::string& str,
-                                const std::string& backCompatStr,
-                                Word backCompatId) {
-      // back compat with Nematus Yaml dicts
-      if(isJson) {
-        // if word id 0 or 1 is either empty or has the Nematus-convention string,
-        // then use it
-        if(backCompatId < id2str_.size()
-          && (id2str_[backCompatId].empty()
-              || id2str_[backCompatId] == backCompatStr)) {
-          LOG(info,
-              "[data] Using unused word id {} for {}",
-              backCompatStr,
-              backCompatId,
-              str);
-          return backCompatId;
-        }
-      }
-      auto iter = str2id_.find(str);
-      ABORT_IF(iter == str2id_.end(),
-              "DefaultVocabulary file {} is expected to contain an entry for {}",
-              vocabPath,
-              str);
-      return iter->second;
-    };
-    eosId_ = getRequiredWordId(DEFAULT_EOS_STR, NEMATUS_EOS_STR, DEFAULT_EOS_ID);
-    unkId_ = getRequiredWordId(DEFAULT_UNK_STR, NEMATUS_UNK_STR, DEFAULT_UNK_ID);
-
-    // some special symbols for hard attention
-    if(!seenSpecial.empty()) {
-      auto requireWord = [&](Word id, const std::string& str) {
-        auto iter = str2id_.find(str);
-        // word already in vocab: must be at right index, else fail
-        if(iter != str2id_.end())
-          ABORT_IF(iter->second != id,
-                  "special vocabulary entry '{}' is expected to have id {}",
-                  str,
-                  id);
-        else
-          insertWord(id, str);
-      };
-      // @TODO: the hard-att code has not yet been updated to accept EOS at any id
-      requireWord(DEFAULT_EOS_ID, DEFAULT_EOS_STR);
-    }
+    addRequiredVocabulary(vocabPath, isJson);
 
     return std::max(id2str_.size(), maxSize);
   }
 
   // for fakeBatch()
-  void createFake() override {
+  virtual void createFake() override {
     eosId_ = insertWord(DEFAULT_EOS_ID, DEFAULT_EOS_STR);
     unkId_ = insertWord(DEFAULT_UNK_ID, DEFAULT_UNK_STR);
   }
@@ -214,6 +166,39 @@ class DefaultVocab : public VocabBase {
 
 private:
 
+  virtual void addRequiredVocabulary(const std::string& vocabPath, bool isJson) {
+    // look up ids for </s> and <unk>, which are required
+    // The name backCompatStr is alternatively accepted for Yaml vocabs if id
+    // equals backCompatId.
+    auto getRequiredWordId = [&](const std::string& str,
+                                const std::string& backCompatStr,
+                                Word backCompatId) {
+      // back compat with Nematus Yaml dicts
+      if(isJson) {
+        // if word id 0 or 1 is either empty or has the Nematus-convention string,
+        // then use it
+        if(backCompatId < id2str_.size()
+          && (id2str_[backCompatId].empty()
+              || id2str_[backCompatId] == backCompatStr)) {
+          LOG(info,
+              "[data] Using unused word id {} for {}",
+              backCompatStr,
+              backCompatId,
+              str);
+          return backCompatId;
+        }
+      }
+      auto iter = str2id_.find(str);
+      ABORT_IF(iter == str2id_.end(),
+              "DefaultVocabulary file {} is expected to contain an entry for {}",
+              vocabPath,
+              str);
+      return iter->second;
+    };
+    eosId_ = getRequiredWordId(DEFAULT_EOS_STR, NEMATUS_EOS_STR, DEFAULT_EOS_ID);
+    unkId_ = getRequiredWordId(DEFAULT_UNK_STR, NEMATUS_UNK_STR, DEFAULT_UNK_ID);
+  }
+
   void addCounts(std::unordered_map<std::string, size_t>& counter,
                  const std::string& trainPath) {
     std::unique_ptr<io::InputFileStream> trainStrm(
@@ -298,8 +283,19 @@ class DefaultVocab : public VocabBase {
   };
 };
 
+// This is a vocabulary class that does not enforce </s> or <unk>. 
+// This is used for class lists in a classifier. 
+class LabelsVocab : public DefaultVocab {
+private:
+  virtual void addRequiredVocabulary(const std::string& vocabPath, bool isJson) override {} // Do nothing.
+};
+
 Ptr<VocabBase> createDefaultVocab() {
   return New<DefaultVocab>();
 }
 
+Ptr<VocabBase> createLabelsVocab() {
+  return New<LabelsVocab>();
+}
+
 }
diff --git a/src/data/vocab.cpp b/src/data/vocab.cpp
index 62b021a3f..1e6a341b2 100755
--- a/src/data/vocab.cpp
+++ b/src/data/vocab.cpp
@@ -4,17 +4,26 @@
 namespace marian {
 
 Ptr<VocabBase> createDefaultVocab();
+Ptr<VocabBase> createLabelsVocab();
 Ptr<VocabBase> createSentencePieceVocab(const std::string& /*vocabPath*/, Ptr<Options>, size_t /*batchIndex*/);
 
 // @TODO: make each vocab peek on type
 Ptr<VocabBase> createVocab(const std::string& vocabPath, Ptr<Options> options, size_t batchIndex) {
   auto vocab = createSentencePieceVocab(vocabPath, options, batchIndex);
-  return vocab ? vocab : createDefaultVocab();
+  if(vocab) {
+    return vocab; // this is defined which means that a sentencepiece vocabulary could be created, so return it
+  } else {
+    auto inputType = options->get<std::vector<std::string>>("input-types")[batchIndex];
+    if(inputType == "labels")
+      return createLabelsVocab();
+    else
+      return createDefaultVocab();
+  }
 }
 
 size_t Vocab::loadOrCreate(const std::string& vocabPath,
-                        const std::vector<std::string>& trainPaths,
-                        size_t maxSize) {
+                           const std::vector<std::string>& trainPaths,
+                           size_t maxSize) {
   size_t size = 0;
   if(vocabPath.empty()) {
     // No vocabulary path was given, attempt to first find a vocabulary
diff --git a/src/graph/node_operators_binary.h b/src/graph/node_operators_binary.h
index 7da85443b..8bd518bec 100755
--- a/src/graph/node_operators_binary.h
+++ b/src/graph/node_operators_binary.h
@@ -821,6 +821,9 @@ struct MinimumNodeOp : public ElementBinaryNodeOp {
 struct CrossEntropyNodeOp : public NaryNodeOp {
   CrossEntropyNodeOp(Expr a, Expr indices) : NaryNodeOp({a, indices}, newShape(a)) {
     matchOrAbort<IndexType>(indices->value_type());
+    int rows   = a->shape().elements() / a->shape()[-1];
+    int labels = indices->shape().elements();
+    ABORT_IF(rows != labels, "Number of examples and labels does not match: {} != {}", rows, labels);
   }
 
   Shape newShape(Expr a) {
diff --git a/src/layers/loss.cpp b/src/layers/loss.cpp
index 03b796823..4dbbb99ee 100755
--- a/src/layers/loss.cpp
+++ b/src/layers/loss.cpp
@@ -64,14 +64,14 @@ Expr CrossEntropyMeanWordsLoss::getCost(Expr logits,
                                         Expr mask,
                                         Expr weights) {
   auto ce = getCrossEntropy(logits, indices, mask, weights);
-  // if(weights) {
-  //   return (sum(sum(ce, /*axis =*/ -3), /*axis =*/ -2)
-  //          / sum(sum(mask * weights, /*axis =*/ -3), /*axis =*/ -2));
-  // }
-  // else {
+  if(mask) {
     return sum(sum(ce, /*axis =*/ -3), /*axis =*/ -2) // sum CE over all words in the batch
            / sum(sum(mask, /*axis =*/ -3), /*axis =*/ -2); // divide by number of words (sum over mask)
-  // }
+  } else {
+    return sum(sum(ce, /*axis =*/ -3), /*axis =*/ -2) // sum CE over all words in the batch
+           / ce->shape().elements(); // no mask, hence divide by all number of all elements
+  }
+
 }
 
 Expr CrossEntropySumLoss::getCost(Expr logits,
@@ -79,13 +79,7 @@ Expr CrossEntropySumLoss::getCost(Expr logits,
                                   Expr mask,
                                   Expr weights) {
   auto ce = getCrossEntropy(logits, indices, mask, weights);
-  // if(weights) {
-  //   return sum(sum(ce, /*axis =*/ -3), /*axis =*/ -2)
-  //          / mean(mean(mask * weights, /*axis =*/ -3), /*axis =*/ -2);
-  // }
-  // else {
-    return sum(sum(ce, /*axis =*/ -3), /*axis =*/ -2);
-  // }
+  return sum(sum(ce, /*axis =*/ -3), /*axis =*/ -2);
 }
 
 Expr PerplexityLoss::getCost(Expr logits,
@@ -93,14 +87,13 @@ Expr PerplexityLoss::getCost(Expr logits,
                              Expr mask,
                              Expr weights) {
   auto ce = getCrossEntropy(logits, indices, mask, weights);
-  // if(weights) {
-  //   return exp(sum(sum(ce, /*axis =*/ -3), /*axis =*/ -2)
-  //              / sum(sum(mask * weights, /*axis =*/ -3), /*axis =*/ -2));
-  // }
-  // else {
+  if(mask) {
     return exp(sum(sum(ce, /*axis =*/ -3), /*axis =*/ -2) // sum CE over all words in the batch
                / sum(sum(mask, /*axis =*/ -3), /*axis =*/ -2)); // divide by number of words (sum over mask)
-  // }
+  } else {
+    return exp(sum(sum(ce, /*axis =*/ -3), /*axis =*/ -2) // sum CE over all words in the batch
+               / ce->shape().elements()); // divide by number of words (sum over mask)
+  }
 }
 
 Expr CrossEntropyRescoreLoss::getCost(Expr logits,
@@ -117,7 +110,11 @@ Expr CrossEntropyRescoreMeanLoss::getCost(Expr logits,
                                           Expr weights) {
   auto ce = getCrossEntropy(logits, indices, mask, weights);
   // divide by number of words in sentence
-  return -sum(ce, /*axis =*/ -3) / sum(mask, /*axis =*/ -3);
+  if(mask) {
+    return -sum(ce, /*axis =*/ -3) / sum(mask, /*axis =*/ -3);
+  } else {
+    return -sum(ce, /*axis =*/ -3) / ce->shape()[-3];
+  }
 }
 
 }  // namespace marian
diff --git a/src/models/bert.h b/src/models/bert.h
index b25f608be..7dfc83c90 100644
--- a/src/models/bert.h
+++ b/src/models/bert.h
@@ -86,7 +86,7 @@ class BertClassifier : public ClassifierBase {
     ABORT_IF(encoderStates.size() != 1, "Currently we only support a single encoder BERT model");
 
     auto context = encoderStates[0]->getContext();
-    auto classEmbeddings = step(context, /*i=*/0, /*axis=*/-2); // [CLS] symbol is first symbol in each sequence
+    auto classEmbeddings = step(context, /*i=*/0, /*axis=*/-3); // [CLS] symbol is first symbol in each sequence
     
     int dimModel = classEmbeddings->shape()[-1];
     int dimTrgCls = opt<std::vector<int>>("dim-vocabs")[batchIndex_]; // Target vocab is used as class labels
diff --git a/src/tensors/cpu/tensor_operators.cpp b/src/tensors/cpu/tensor_operators.cpp
index a3b6bf9a6..4cc5daf0d 100755
--- a/src/tensors/cpu/tensor_operators.cpp
+++ b/src/tensors/cpu/tensor_operators.cpp
@@ -661,36 +661,34 @@ void GRUFastBackward(std::vector<Tensor> outputs,
   }
 }
 
-void CrossEntropyPick(Tensor out_, Tensor in_, Tensor pick_) {
-  matchOrAbort<IndexType>(pick_->type());
+void CrossEntropyPick(Tensor out, Tensor in, Tensor pick) {
+  matchOrAbort<IndexType>(pick->type());
 
-  float* out = out_->data();
   // Shape& outShape = out_->shape();
-  const float* in = in_->data();
-  Shape& inShape = in_->shape();
+  Shape& inShape = in->shape();
 
   int rows = inShape.elements() / inShape.back();
   int cols = inShape.back();
 
-#pragma omp parallel for
+  #pragma omp parallel for
   for(int j = 0; j < rows; ++j) {
-    const float* sp = in + j * cols;
+    const float* sp = in->data() + j * cols;
     float max = sp[0];
-#pragma omp simd reduction(max : max)
+    #pragma omp simd reduction(max : max)
     for(int i = 1; i < cols; ++i) {
       max = std::max(max, sp[i]);
     }
 
     float sum = 0.f;
-#pragma omp simd reduction(+ : sum)
+    #pragma omp simd reduction(+ : sum)
     for(int i = 0; i < cols; ++i) {
       sum += std::exp(sp[i] - max);
     }
 
     // cross-entropy
-    int i = (int)pick_->data<IndexType>()[j];
+    IndexType i = pick->data<IndexType>()[j];
     // This appears to be safe i.e. that i >= 0 && i < cols is known
-    out[j] = std::log(sum) - sp[i] + max;
+    out->data()[j] = std::log(sum) - sp[i] + max;
   }
 }
 
diff --git a/src/training/graph_group.h b/src/training/graph_group.h
index fc372adce..2bd98d40b 100755
--- a/src/training/graph_group.h
+++ b/src/training/graph_group.h
@@ -53,8 +53,7 @@ class GraphGroup {
                                              size_t multiplier = 1) {
     auto stats = New<data::BatchStats>();
 
-    size_t numFiles
-        = options_->get<std::vector<std::string>>("train-sets").size();
+    size_t numFiles = options_->get<std::vector<std::string>>("train-sets").size();
 
     // Initialize first batch to step size
     size_t first = options_->get<size_t>("mini-batch-fit-step");
@@ -65,31 +64,42 @@ class GraphGroup {
     size_t maxLength = options_->get<size_t>("max-length");
     maxLength = (size_t)(std::ceil(maxLength / (float)step) * step);
 
-    // @TODO: ugly
-    auto toptions = New<Options>();
-    toptions->merge(options_);
+    // restrict maximum length for labels to 1
+    std::vector<size_t> localMaxes(numFiles, maxLength);
+    auto inputTypes = options_->get<std::vector<std::string>>("input-types", {});
+    for(int i = 0; i < inputTypes.size(); ++i)
+      if(inputTypes[i] == "labels")
+        localMaxes[i] = 1;
+    
 
     size_t maxBatch = 512;
     bool fits = true;
     while(fits) {
       std::vector<size_t> lengths(numFiles, first);
-      auto batch = data::CorpusBatch::fakeBatch(lengths, maxBatch, toptions);
+      for(int j = 0; j < lengths.size(); ++j) // apply length restrictions
+        lengths[j] = std::min(lengths[j], localMaxes[j]);
+
+      auto batch = data::CorpusBatch::fakeBatch(lengths, maxBatch, options_);
       auto cost = model->build(graph, batch);
       fits = graph->fits();
       if(fits)
         maxBatch *= 2;
     }
 
+    // Do a binary search for maxmimum batch size that fits into given workspace memory 
+    // for a tested sentence length. 
     for(size_t i = step; i <= maxLength; i += step) {
       size_t start = 1;
       size_t end = maxBatch;
 
       std::vector<size_t> lengths(numFiles, i);
+      for(int j = 0; j < lengths.size(); ++j)  // apply length restrictions
+        lengths[j] = std::min(lengths[j], localMaxes[j]);
       fits = true;
 
       do {
         size_t current = (start + end) / 2;
-        auto batch = data::CorpusBatch::fakeBatch(lengths, current, toptions);
+        auto batch = data::CorpusBatch::fakeBatch(lengths, current, options_);
         auto cost = model->build(graph, batch);
         fits = graph->fits();
 
diff --git a/src/training/scheduler.h b/src/training/scheduler.h
index dee62496d..1585fd8a4 100755
--- a/src/training/scheduler.h
+++ b/src/training/scheduler.h
@@ -10,11 +10,11 @@ namespace marian {
 class Scheduler : public TrainingObserver {
 private:
   Ptr<Options> options_;
+  Ptr<TrainingState> state_;
   std::vector<Ptr<ValidatorBase>> validators_;
 
   bool first_{true};
-
-  Ptr<TrainingState> state_;
+  int dispIndex_{-1};
 
   timer::Timer timer_, heartBeatTimer_;
 
@@ -46,7 +46,8 @@ class Scheduler : public TrainingObserver {
 
 public:
   Scheduler(Ptr<Options> options, Ptr<TrainingState> state)
-      : options_(options), state_(state) {
+      : options_(options), state_(state),
+        dispIndex_{options_->get<int>("disp-wps-index", -1)} {
     state_->eta = getLearningRate(*state);
   }
 
@@ -168,11 +169,13 @@ class Scheduler : public TrainingObserver {
 
     size_t batchSize = 0;    // number of sentences in batch
     size_t batchLabels = 0;  // number of target words in batch
+    size_t batchDisp = 0;    // number of words in chosen sub-batch, last by default unless set differently in dispIndex_. Used for displaying speed.
 
     for(const auto& batch : batches) {
       if (batch) { // (nullptr is allowed as result of split)
-        batchSize += batch->size();
+        batchSize   += batch->size();
         batchLabels += batch->words(-1);
+        batchDisp   += batch->words(dispIndex_);
       }
     }
 
@@ -199,7 +202,8 @@ class Scheduler : public TrainingObserver {
       state_->costSum   += cost * batchSize;
       state_->costCount += batchSize;
     }
-    state_->wordsDisp    += batchLabels; // target words processed since last display, for speed display
+
+    state_->wordsDisp    += batchDisp;   // words at given input processed since last display, for speed display
     state_->samplesEpoch += batchSize;   // sentences processed in this epoch
     state_->labelsTotal  += batchLabels; // total labels processed
 

From b978ca51b3d6d085ada9bbe482a6a4f9a79cc5e3 Mon Sep 17 00:00:00 2001
From: Marcin Junczys-Dowmunt <Marcin.Junczys-Dowmunt@microsoft.com>
Date: Wed, 9 Jan 2019 10:52:11 -0800
Subject: [PATCH 089/838] accuracy validator

---
 src/models/bert.h               |  2 +-
 src/models/encoder_classifier.h |  1 -
 src/training/validator.cpp      |  3 ++
 src/training/validator.h        | 88 +++++++++++++++++++++++++++++++++
 4 files changed, 92 insertions(+), 2 deletions(-)

diff --git a/src/models/bert.h b/src/models/bert.h
index 7dfc83c90..f0a1f8eec 100644
--- a/src/models/bert.h
+++ b/src/models/bert.h
@@ -96,7 +96,7 @@ class BertClassifier : public ClassifierBase {
                                  ("prefix", prefix_ + "_ff_logit_l1") //
                                  ("dim", dimModel)                    //
                                  ("activation", mlp::act::tanh))      // @TODO: do we actually need this?
-                    .push_back(mlp::output(graph)                      //
+                    .push_back(mlp::output(graph)                     //
                                  ("dim", dimTrgCls))                  //
                                  ("prefix", prefix_ + "_ff_logit_l2") //
                     .construct();
diff --git a/src/models/encoder_classifier.h b/src/models/encoder_classifier.h
index 9a650311a..0bf048b93 100644
--- a/src/models/encoder_classifier.h
+++ b/src/models/encoder_classifier.h
@@ -197,7 +197,6 @@ class EncoderClassifier : public EncoderClassifierBase {
   virtual Expr build(Ptr<ExpressionGraph> graph,
                      Ptr<data::CorpusBatch> batch,
                      bool clearGraph = true) override {
-    ABORT("Don't use this");
     auto states = apply(graph, batch, clearGraph);
     // returns raw logits
     return states[0]->getLogProbs();
diff --git a/src/training/validator.cpp b/src/training/validator.cpp
index a3f55c71b..f63c9d0b5 100644
--- a/src/training/validator.cpp
+++ b/src/training/validator.cpp
@@ -31,6 +31,9 @@ std::vector<Ptr<Validator<data::Corpus>>> Validators(
     } else if(metric == "bleu-detok") {
       auto validator = New<BleuValidator>(vocabs, config, true);
       validators.push_back(validator);
+    } else if(metric == "accuracy") {
+      auto validator = New<AccuracyValidator>(vocabs, config);
+      validators.push_back(validator);
     } else {
       LOG_VALID(warn, "Unrecognized validation metric: {}", metric);
     }
diff --git a/src/training/validator.h b/src/training/validator.h
index efed50cb0..1ff6d59ee 100755
--- a/src/training/validator.h
+++ b/src/training/validator.h
@@ -218,6 +218,94 @@ class CrossEntropyValidator : public Validator<data::Corpus> {
   }
 };
 
+class AccuracyValidator : public Validator<data::Corpus> {
+public:
+  AccuracyValidator(std::vector<Ptr<Vocab>> vocabs, Ptr<Options> options)
+      : Validator(vocabs, options, /*lowerIsBetter=*/false) {
+    createBatchGenerator(/*isTranslating=*/false);
+
+    // @TODO: check if this is required.
+    Ptr<Options> opts = New<Options>();
+    opts->merge(options);
+    opts->set("inference", true);
+    builder_ = models::from_options(opts, models::usage::raw);
+  }
+
+  std::string type() override { return "accuracy"; }
+
+protected:
+  virtual float validateBG(const std::vector<Ptr<ExpressionGraph>>& graphs) override {
+
+    size_t correct     = 0;
+    size_t totalLabels = 0;
+    size_t batchId     = 0;
+
+    {
+      threadPool_.reserve(graphs.size());
+
+      TaskBarrier taskBarrier;
+      for(auto batch : *batchGenerator_) {
+        auto task = [=, &correct, &totalLabels](size_t id) {
+          thread_local Ptr<ExpressionGraph> graph;
+          thread_local auto builder = models::from_options(options_, models::usage::raw);
+
+          if(!graph) {
+            graph = graphs[id % graphs.size()];
+          }
+
+          // Future: requires argmax implementation and integer arithmetics
+          // builder->clear(graph);
+          // auto predicted = argmax(builder->build(graph, batch), /*axis*/-1);
+          // auto labels    = graph->indices(batch->back()->data());
+          // auto correct   = sum(flatten(predicted) == labels);
+          // graph->forward();
+
+          // std::unique_lock<std::mutex> lock(mutex_);
+          // totalLabels += labels->shape().elements(); 
+          // correct     += correct->scalar<int>();          
+          
+          builder->clear(graph);
+          auto logits = builder->build(graph, batch);
+          graph->forward();
+
+          std::vector<float> vLogits;
+          logits->val()->get(vLogits);
+          const auto& labels = batch->back()->data();
+
+          IndexType cols = logits->shape()[-1];
+
+          size_t thisCorrect = 0;
+          size_t thisLabels  = labels.size();
+
+          for(int i = 0; i < thisLabels; ++i) {
+            // CPU-side Argmax
+            IndexType bestIndex = 0;
+            float bestValue = std::numeric_limits<float>::lowest();
+            for(IndexType j = 0; j < cols; ++j) {
+              float currValue = vLogits[i * cols + j];
+              if(currValue > bestValue) {
+                bestValue = currValue;
+                bestIndex = j;
+              }
+            }
+            thisCorrect += (size_t)(bestIndex == labels[i]);
+          }
+
+          std::unique_lock<std::mutex> lock(mutex_);
+          totalLabels += thisLabels; 
+          correct     += thisCorrect;
+        };
+
+        taskBarrier.push_back(threadPool_.enqueue(task, batchId));
+        batchId++;
+      }
+      // ~TaskBarrier waits until all are done
+    }
+
+    return (float)correct / (float)totalLabels;
+  }
+};
+
 class ScriptValidator : public Validator<data::Corpus> {
 public:
   ScriptValidator(std::vector<Ptr<Vocab>> vocabs, Ptr<Options> options)

From c077d2cf163298064567515b6fb5fed94895a187 Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Wed, 9 Jan 2019 13:47:40 -0800
Subject: [PATCH 090/838] towards turning embedding into a more regular layer

---
 src/layers/factory.h     |  0
 src/layers/generic.h     | 66 ++++++++++++++++++++++++++++++++--------
 src/models/decoder.h     | 38 +++++++++--------------
 src/models/encoder.h     | 22 ++------------
 src/models/s2s.h         |  7 ++---
 src/models/transformer.h |  9 +++---
 6 files changed, 77 insertions(+), 65 deletions(-)
 mode change 100644 => 100755 src/layers/factory.h

diff --git a/src/layers/factory.h b/src/layers/factory.h
old mode 100644
new mode 100755
diff --git a/src/layers/generic.h b/src/layers/generic.h
index b5c53b462..bc3930aad 100755
--- a/src/layers/generic.h
+++ b/src/layers/generic.h
@@ -17,15 +17,14 @@ enum struct act : int { linear, tanh, sigmoid, ReLU, LeakyReLU, PReLU, swish };
 YAML_REGISTER_TYPE(marian::mlp::act, int)
 
 namespace marian {
-namespace mlp {
 
-class Layer {
+class BaseLayer {   // @TODO: better name. Ideally change uses of Layer to IUnaryLayer; then explicit derive from IUnaryLayer in each layer
 protected:
   Ptr<ExpressionGraph> graph_;
   Ptr<Options> options_;
 
 public:
-  Layer(Ptr<ExpressionGraph> graph, Ptr<Options> options)
+  BaseLayer(Ptr<ExpressionGraph> graph, Ptr<Options> options)
       : graph_(graph), options_(options) {}
 
   template <typename T>
@@ -37,11 +36,19 @@ class Layer {
   T opt(const std::string key, T defaultValue) {
     return options_->get<T>(key, defaultValue);
   }
+};
 
+namespace mlp {
+struct IUnaryLayer {
   virtual Expr apply(const std::vector<Expr>&) = 0;
   virtual Expr apply(Expr) = 0;
 };
 
+class Layer : public BaseLayer, public IUnaryLayer {
+public:
+  Layer(Ptr<ExpressionGraph> graph, Ptr<Options> options) : BaseLayer(graph, options) { }
+};
+
 class Dense : public Layer {
 public:
   Dense(Ptr<ExpressionGraph> graph, Ptr<Options> options)
@@ -162,10 +169,16 @@ class Output : public Layer {
 
 }  // namespace mlp
 
-struct EmbeddingFactory : public Factory {
-  EmbeddingFactory(Ptr<ExpressionGraph> graph) : Factory(graph) {}
+struct IEmbedding {
+  virtual std::tuple<Expr/*embeddings*/, Expr/*mask*/> apply(Ptr<data::SubBatch> subBatch) const = 0;
+  // alternative version from index vector, and with batch dim
+  virtual Expr apply(const std::vector<IndexType>& embIdx, int dimBatch, int dimBeam) const = 0;
+};
 
-  Expr construct() {
+class Embedding : public BaseLayer, public IEmbedding {
+  Expr E_;
+public:
+  Embedding(Ptr<ExpressionGraph> graph, Ptr<Options> options) : BaseLayer(graph, options) {
     std::string name = opt<std::string>("prefix");
     int dimVoc = opt<int>("dimVocab");
     int dimEmb = opt<int>("dimEmb");
@@ -173,18 +186,45 @@ struct EmbeddingFactory : public Factory {
     bool fixed = opt<bool>("fixed", false);
 
     NodeInitializer initFunc = inits::glorot_uniform;
-  if (options_->has("embFile")) {
-    std::string file = opt<std::string>("embFile");
-    if (!file.empty()) {
-      bool norm = opt<bool>("normalization", false);
-      initFunc = inits::from_word2vec(file, dimVoc, dimEmb, norm);
+    if (options_->has("embFile")) {
+      std::string file = opt<std::string>("embFile");
+      if (!file.empty()) {
+        bool norm = opt<bool>("normalization", false);
+        initFunc = inits::from_word2vec(file, dimVoc, dimEmb, norm);
+      }
     }
+
+    E_ = graph_->param(name, {dimVoc, dimEmb}, initFunc, fixed);
   }
-  
-    return graph_->param(name, {dimVoc, dimEmb}, initFunc, fixed);
+
+  std::tuple<Expr/*embeddings*/, Expr/*mask*/> apply(Ptr<data::SubBatch> subBatch) const override final {
+    auto graph = E_->graph();
+    int dimBatch = (int)subBatch->batchSize();
+    int dimEmb = E_->shape()[-1];
+    int dimWords = (int)subBatch->batchWidth();
+    // @TODO: merge this with below. Currently can't only due to the extra beam dimension
+    auto chosenEmbeddings = rows(E_, subBatch->data());
+    auto batchEmbeddings = reshape(chosenEmbeddings, { dimWords, dimBatch, dimEmb });
+    auto batchMask = graph->constant({ dimWords, dimBatch, 1 },
+                                     inits::from_vector(subBatch->mask()));
+    return std::make_tuple(batchEmbeddings, batchMask);
+  }
+
+  // special version used in decoding
+  Expr apply(const std::vector<IndexType>& embIdx, int dimBatch, int dimBeam) const override final {
+    int dimEmb = E_->shape()[-1];
+    auto selectedEmbs = rows(E_, embIdx);
+    return reshape(selectedEmbs, { dimBeam, 1, dimBatch, dimEmb });
   }
 };
 
+struct EmbeddingFactory : public Factory {
+  EmbeddingFactory(Ptr<ExpressionGraph> graph) : Factory(graph) {}
+
+  Ptr<IEmbedding> construct() {
+    return New<Embedding>(graph_, options_);
+  }
+};
 
 struct ULREmbeddingFactory : public Factory {
 ULREmbeddingFactory(Ptr<ExpressionGraph> graph) : Factory(graph) {}
diff --git a/src/models/decoder.h b/src/models/decoder.h
index 39e56e1f3..67ab8a214 100755
--- a/src/models/decoder.h
+++ b/src/models/decoder.h
@@ -59,16 +59,9 @@ class DecoderBase {
     auto yEmb = yEmbFactory.construct();
 
     auto subBatch = (*batch)[batchIndex_];
-    int dimBatch = (int)subBatch->batchSize();
-    int dimWords = (int)subBatch->batchWidth();
 
-    auto chosenEmbeddings = rows(yEmb, subBatch->data());
-
-    auto y
-        = reshape(chosenEmbeddings, {dimWords, dimBatch, opt<int>("dim-emb")});
-
-    auto yMask = graph->constant({dimWords, dimBatch, 1},
-                                 inits::from_vector(subBatch->mask()));
+    Expr y, yMask; std::tie
+    (y, yMask) = yEmb->apply(subBatch);
 
     Expr yData;
     if(shortlist_) {
@@ -92,24 +85,23 @@ class DecoderBase {
     int dimTrgEmb = opt<int>("dim-emb");
     int dimTrgVoc = opt<std::vector<int>>("dim-vocabs")[batchIndex_];
 
-    // embeddings are loaded from model during translation, no fixing required
-    auto yEmbFactory = embedding(graph)  //
-        ("dimVocab", dimTrgVoc)          //
-        ("dimEmb", dimTrgEmb);
-
-    if(opt<bool>("tied-embeddings-src") || opt<bool>("tied-embeddings-all"))
-      yEmbFactory("prefix", "Wemb");
-    else
-      yEmbFactory("prefix", prefix_ + "_Wemb");
-
-    auto yEmb = yEmbFactory.construct();
-
     Expr selectedEmbs;
     if(embIdx.empty()) {
       selectedEmbs = graph->constant({1, 1, dimBatch, dimTrgEmb}, inits::zeros);
     } else {
-      selectedEmbs = rows(yEmb, embIdx);
-      selectedEmbs = reshape(selectedEmbs, {dimBeam, 1, dimBatch, dimTrgEmb});
+      // embeddings are loaded from model during translation, no fixing required
+      auto yEmbFactory = embedding(graph)  //
+          ("dimVocab", dimTrgVoc)          //
+          ("dimEmb", dimTrgEmb);
+  
+      if(opt<bool>("tied-embeddings-src") || opt<bool>("tied-embeddings-all"))
+        yEmbFactory("prefix", "Wemb");
+      else
+        yEmbFactory("prefix", prefix_ + "_Wemb");
+  
+      auto yEmb = yEmbFactory.construct();
+
+      selectedEmbs = yEmb->apply(embIdx, dimBatch, dimBeam);
     }
     state->setTargetEmbeddings(selectedEmbs);
   }
diff --git a/src/models/encoder.h b/src/models/encoder.h
index 6d1ee852e..f8f16ed11 100755
--- a/src/models/encoder.h
+++ b/src/models/encoder.h
@@ -12,26 +12,8 @@ class EncoderBase {
   bool inference_{false};
   size_t batchIndex_{0};
 
-  // @TODO: This used to be virtual, but is never overridden.
-  // virtual
-  std::tuple<Expr, Expr> lookup(Ptr<ExpressionGraph> graph,
-    Expr srcEmbeddings,
-    Ptr<data::CorpusBatch> batch) const {
-    auto subBatch = (*batch)[batchIndex_];
-    int dimBatch = (int)subBatch->batchSize();
-    int dimEmb = srcEmbeddings->shape()[-1];
-    int dimWords = (int)subBatch->batchWidth();
-    auto chosenEmbeddings = rows(srcEmbeddings, subBatch->data());
-    auto batchEmbeddings = reshape(chosenEmbeddings, { dimWords, dimBatch, dimEmb });
-    auto batchMask = graph->constant({ dimWords, dimBatch, 1 },
-                                     inits::from_vector(subBatch->mask()));
-
-    return std::make_tuple(batchEmbeddings, batchMask);
-  }
-
-  std::tuple<Expr, Expr> ulrLookup(Ptr<ExpressionGraph> graph,
-      std::vector<Expr> urlEmbeddings,
-    Ptr<data::CorpusBatch> batch) const {
+  std::tuple<Expr, Expr> ulrLookup(std::vector<Expr> urlEmbeddings, Ptr<data::CorpusBatch> batch) const {
+    auto graph = urlEmbeddings.front()->graph();
     auto subBatch = (*batch)[batchIndex_];
     // is their a better way to do this?
     assert(urlEmbeddings.size() == 6);
diff --git a/src/models/s2s.h b/src/models/s2s.h
index b4600892f..de3754057 100755
--- a/src/models/s2s.h
+++ b/src/models/s2s.h
@@ -119,7 +119,7 @@ class EncoderS2S : public EncoderBase {
     return context;
   }
 
-  Expr buildSourceEmbeddings(Ptr<ExpressionGraph> graph) {
+  Ptr<IEmbedding> buildSourceEmbeddings(Ptr<ExpressionGraph> graph) {
     // create source embeddings
     int dimVoc = opt<std::vector<int>>("dim-vocabs")[batchIndex_];
     int dimEmb = opt<int>("dim-emb");
@@ -153,9 +153,8 @@ class EncoderS2S : public EncoderBase {
     auto embeddings = buildSourceEmbeddings(graph);
 
     // select embeddings that occur in the batch
-    Expr batchEmbeddings, batchMask;
-    std::tie(batchEmbeddings, batchMask)
-        = EncoderBase::lookup(graph, embeddings, batch);
+    Expr batchEmbeddings, batchMask; std::tie
+    (batchEmbeddings, batchMask) = embeddings->apply((*batch)[batchIndex_]);
 
     // apply dropout over source words
     float dropProb = inference_ ? 0 : opt<float>("dropout-src");
diff --git a/src/models/transformer.h b/src/models/transformer.h
index c30d06c8e..523add21a 100755
--- a/src/models/transformer.h
+++ b/src/models/transformer.h
@@ -495,7 +495,7 @@ class EncoderTransformer : public Transformer<EncoderBase> {
     return embFactory.construct();
   }
 
-  Expr wordEmbeddings(size_t subBatchIndex) const {
+  Ptr<IEmbedding> createWordEmbeddingLayer(size_t subBatchIndex) const {
     // standard encoder word embeddings
     int dimVoc = opt<std::vector<int>>("dim-vocabs")[subBatchIndex];
     int dimEmb = opt<int>("dim-emb");
@@ -530,13 +530,12 @@ class EncoderTransformer : public Transformer<EncoderBase> {
     if (options_->has("ulr") && options_->get<bool>("ulr") == true) {
       auto embeddings = ULREmbeddings(); // embedding uses ULR
       std::tie(batchEmbeddings, batchMask)
-        = EncoderBase::ulrLookup(graph_, embeddings, batch);
+        = EncoderBase::ulrLookup(embeddings, batch);
     }
     else
     {
-      auto embeddings = wordEmbeddings(batchIndex_);
-      std::tie(batchEmbeddings, batchMask)
-        = EncoderBase::lookup(graph_, embeddings, batch);
+      auto embedding = createWordEmbeddingLayer(batchIndex_);
+      std::tie(batchEmbeddings, batchMask) = embedding->apply((*batch)[batchIndex_]);
     }
     // apply dropout over source words
     float dropoutSrc = inference_ ? 0 : opt<float>("dropout-src");

From b2e3427924cc58665cbd01261f720a0155a8aef5 Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Wed, 9 Jan 2019 13:54:18 -0800
Subject: [PATCH 091/838] Ptr<Layer> changed to interface Ptr<IUnaryLayer>

---
 src/layers/constructors.h | 10 +++++-----
 src/layers/generic.h      | 24 +++++++++++-------------
 2 files changed, 16 insertions(+), 18 deletions(-)

diff --git a/src/layers/constructors.h b/src/layers/constructors.h
index 16765341f..cf51ce55f 100755
--- a/src/layers/constructors.h
+++ b/src/layers/constructors.h
@@ -26,7 +26,7 @@ struct LayerFactory : public Factory {
     return as<Cast>() != nullptr;
   }
 
-  virtual Ptr<Layer> construct() = 0;
+  virtual Ptr<IUnaryLayer> construct() = 0;
 };
 
 /**
@@ -36,7 +36,7 @@ class DenseFactory : public LayerFactory {
 public:
   DenseFactory(Ptr<ExpressionGraph> graph) : LayerFactory(graph) {}
 
-  Ptr<Layer> construct() override {
+  Ptr<IUnaryLayer> construct() override {
     auto dense = New<Dense>(graph_, options_);
     return dense;
   }
@@ -73,7 +73,7 @@ class OutputFactory : public LayerFactory {
     return Accumulator<OutputFactory>(*this);
   }
 
-  Ptr<Layer> construct() override {
+  Ptr<IUnaryLayer> construct() override {
     auto output = New<Output>(graph_, options_);
     for(auto& p : tiedParamsTransposed_)
       output->tie_transposed(p.first, p.second);
@@ -101,7 +101,7 @@ class MLP {
   Ptr<ExpressionGraph> graph_;
   Ptr<Options> options_;
 
-  std::vector<Ptr<Layer>> layers_;
+  std::vector<Ptr<IUnaryLayer>> layers_;
 
 public:
   MLP(Ptr<ExpressionGraph> graph, Ptr<Options> options)
@@ -123,7 +123,7 @@ class MLP {
     return output;
   }
 
-  void push_back(Ptr<Layer> layer) { layers_.push_back(layer); }
+  void push_back(Ptr<IUnaryLayer> layer) { layers_.push_back(layer); }
 };
 
 /**
diff --git a/src/layers/generic.h b/src/layers/generic.h
index bc3930aad..c4e2858eb 100755
--- a/src/layers/generic.h
+++ b/src/layers/generic.h
@@ -18,13 +18,15 @@ YAML_REGISTER_TYPE(marian::mlp::act, int)
 
 namespace marian {
 
-class BaseLayer {   // @TODO: better name. Ideally change uses of Layer to IUnaryLayer; then explicit derive from IUnaryLayer in each layer
+// Each layer consists of Layer and one or more apply() functions according to
+// a layer-type specific interface (different layers may require different signatures).
+class Layer {
 protected:
   Ptr<ExpressionGraph> graph_;
   Ptr<Options> options_;
 
 public:
-  BaseLayer(Ptr<ExpressionGraph> graph, Ptr<Options> options)
+  Layer(Ptr<ExpressionGraph> graph, Ptr<Options> options)
       : graph_(graph), options_(options) {}
 
   template <typename T>
@@ -38,18 +40,14 @@ class BaseLayer {   // @TODO: better name. Ideally change uses of Layer to IUnar
   }
 };
 
-namespace mlp {
+// Simplest layer interface: Unary function.
 struct IUnaryLayer {
-  virtual Expr apply(const std::vector<Expr>&) = 0;
   virtual Expr apply(Expr) = 0;
+  virtual Expr apply(const std::vector<Expr>&) = 0;
 };
 
-class Layer : public BaseLayer, public IUnaryLayer {
-public:
-  Layer(Ptr<ExpressionGraph> graph, Ptr<Options> options) : BaseLayer(graph, options) { }
-};
-
-class Dense : public Layer {
+namespace mlp {
+class Dense : public Layer, public IUnaryLayer {
 public:
   Dense(Ptr<ExpressionGraph> graph, Ptr<Options> options)
       : Layer(graph, options) {}
@@ -116,7 +114,7 @@ class Dense : public Layer {
   Expr apply(Expr input) override { return apply(std::vector<Expr>({input})); }
 };
 
-class Output : public Layer {
+class Output : public Layer, public IUnaryLayer {
 private:
   std::map<std::string, Expr> tiedParams_;
   Ptr<data::Shortlist> shortlist_;
@@ -175,10 +173,10 @@ struct IEmbedding {
   virtual Expr apply(const std::vector<IndexType>& embIdx, int dimBatch, int dimBeam) const = 0;
 };
 
-class Embedding : public BaseLayer, public IEmbedding {
+class Embedding : public Layer, public IEmbedding {
   Expr E_;
 public:
-  Embedding(Ptr<ExpressionGraph> graph, Ptr<Options> options) : BaseLayer(graph, options) {
+  Embedding(Ptr<ExpressionGraph> graph, Ptr<Options> options) : Layer(graph, options) {
     std::string name = opt<std::string>("prefix");
     int dimVoc = opt<int>("dimVocab");
     int dimEmb = opt<int>("dimEmb");

From ce1e3093e8161babc82a9792758cb7fc03057683 Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Wed, 9 Jan 2019 14:24:15 -0800
Subject: [PATCH 092/838] disentangled embedding layers and make them look like
 other layers

---
 src/layers/generic.h     | 130 +++++++++++++++++++++++++++------------
 src/models/encoder.h     |  46 +-------------
 src/models/s2s.h         |   2 +-
 src/models/transformer.h |  18 +++---
 4 files changed, 100 insertions(+), 96 deletions(-)

diff --git a/src/layers/generic.h b/src/layers/generic.h
index c4e2858eb..32ea291cc 100755
--- a/src/layers/generic.h
+++ b/src/layers/generic.h
@@ -18,15 +18,16 @@ YAML_REGISTER_TYPE(marian::mlp::act, int)
 
 namespace marian {
 
-// Each layer consists of Layer and one or more apply() functions according to
-// a layer-type specific interface (different layers may require different signatures).
-class Layer {
+// Each layer consists of LayerBase and IXXXLayer which defines one or more apply()
+// functions for the respective layer type (different layers may require different signatures).
+// This base class contains configuration info for creating parameters and executing apply().
+class LayerBase {
 protected:
   Ptr<ExpressionGraph> graph_;
   Ptr<Options> options_;
 
 public:
-  Layer(Ptr<ExpressionGraph> graph, Ptr<Options> options)
+  LayerBase(Ptr<ExpressionGraph> graph, Ptr<Options> options)
       : graph_(graph), options_(options) {}
 
   template <typename T>
@@ -40,17 +41,24 @@ class Layer {
   }
 };
 
-// Simplest layer interface: Unary function.
+// Simplest layer interface: Unary function
 struct IUnaryLayer {
   virtual Expr apply(Expr) = 0;
   virtual Expr apply(const std::vector<Expr>&) = 0;
 };
 
+// Embedding from corpus sub-batch to (emb, mask)
+struct IEmbeddingLayer {
+  virtual std::tuple<Expr/*embeddings*/, Expr/*mask*/> apply(Ptr<data::SubBatch> subBatch) const = 0;
+  // alternative version from index vector, and with batch dim
+  virtual Expr apply(const std::vector<IndexType>& embIdx, int dimBatch, int dimBeam) const = 0;
+};
+
 namespace mlp {
-class Dense : public Layer, public IUnaryLayer {
+class Dense : public LayerBase, public IUnaryLayer {
 public:
   Dense(Ptr<ExpressionGraph> graph, Ptr<Options> options)
-      : Layer(graph, options) {}
+      : LayerBase(graph, options) {}
 
   Expr apply(const std::vector<Expr>& inputs) override {
     ABORT_IF(inputs.empty(), "No inputs");
@@ -114,7 +122,7 @@ class Dense : public Layer, public IUnaryLayer {
   Expr apply(Expr input) override { return apply(std::vector<Expr>({input})); }
 };
 
-class Output : public Layer, public IUnaryLayer {
+class Output : public LayerBase, public IUnaryLayer {
 private:
   std::map<std::string, Expr> tiedParams_;
   Ptr<data::Shortlist> shortlist_;
@@ -125,7 +133,7 @@ class Output : public Layer, public IUnaryLayer {
 
 public:
   Output(Ptr<ExpressionGraph> graph, Ptr<Options> options)
-      : Layer(graph, options) {}
+      : LayerBase(graph, options) {}
 
   void tie_transposed(const std::string& param, const std::string& tied) {
     tiedParams_[param] = graph_->get(tied);
@@ -167,16 +175,10 @@ class Output : public Layer, public IUnaryLayer {
 
 }  // namespace mlp
 
-struct IEmbedding {
-  virtual std::tuple<Expr/*embeddings*/, Expr/*mask*/> apply(Ptr<data::SubBatch> subBatch) const = 0;
-  // alternative version from index vector, and with batch dim
-  virtual Expr apply(const std::vector<IndexType>& embIdx, int dimBatch, int dimBeam) const = 0;
-};
-
-class Embedding : public Layer, public IEmbedding {
+class Embedding : public LayerBase, public IEmbeddingLayer {
   Expr E_;
 public:
-  Embedding(Ptr<ExpressionGraph> graph, Ptr<Options> options) : Layer(graph, options) {
+  Embedding(Ptr<ExpressionGraph> graph, Ptr<Options> options) : LayerBase(graph, options) {
     std::string name = opt<std::string>("prefix");
     int dimVoc = opt<int>("dimVocab");
     int dimEmb = opt<int>("dimEmb");
@@ -216,25 +218,16 @@ class Embedding : public Layer, public IEmbedding {
   }
 };
 
-struct EmbeddingFactory : public Factory {
-  EmbeddingFactory(Ptr<ExpressionGraph> graph) : Factory(graph) {}
-
-  Ptr<IEmbedding> construct() {
-    return New<Embedding>(graph_, options_);
-  }
-};
-
-struct ULREmbeddingFactory : public Factory {
-ULREmbeddingFactory(Ptr<ExpressionGraph> graph) : Factory(graph) {}
-
-  std::vector<Expr> construct() {
+class ULREmbedding : public LayerBase, public IEmbeddingLayer {
+  std::vector<Expr> ulrEmbeddings_; // @TODO: These can now better be written as 6 named class members
+public:
+  ULREmbedding(Ptr<ExpressionGraph> graph, Ptr<Options> options) : LayerBase(graph, options) {
     std::string name = "url_embed"; //opt<std::string>("prefix");
     int dimKeys = opt<int>("dimTgtVoc");
     int dimQueries = opt<int>("dimSrcVoc");
     int dimEmb = opt<int>("dimEmb");
     int dimUlrEmb =  opt<int>("dimUlrEmb"); // ULR mono embed size
     bool fixed = opt<bool>("fixed", false);
-    std::vector<Expr> ulrEmbeds;
     NodeInitializer initFunc = inits::glorot_uniform;
     std::string queryFile = opt<std::string>("ulrQueryFile");
     std::string keyFile = opt<std::string>("ulrKeysFile");
@@ -244,23 +237,23 @@ ULREmbeddingFactory(Ptr<ExpressionGraph> graph) : Factory(graph) {}
       name = "ulr_query";
       fixed = true;
       auto query_embed = graph_->param(name, { dimQueries, dimUlrEmb }, initFunc, fixed);
-      ulrEmbeds.push_back(query_embed);
+      ulrEmbeddings_.push_back(query_embed);
       // keys embeds
       initFunc = inits::from_word2vec(keyFile, dimKeys, dimUlrEmb, false);
       name = "ulr_keys";
       fixed = true;
       auto key_embed = graph_->param(name, { dimKeys, dimUlrEmb }, initFunc, fixed);
-      ulrEmbeds.push_back(key_embed);
+      ulrEmbeddings_.push_back(key_embed);
       // actual  trainable embedding
       initFunc = inits::glorot_uniform;
       name = "ulr_embed";
       fixed = false;
       auto ulr_embed = graph_->param(name, {dimKeys , dimEmb }, initFunc, fixed);  // note the reverse dim
-      ulrEmbeds.push_back(ulr_embed);
+      ulrEmbeddings_.push_back(ulr_embed);
       // init  trainable src embedding
       name = "ulr_src_embed";
       auto ulr_src_embed = graph_->param(name, { dimQueries, dimEmb }, initFunc, fixed);
-      ulrEmbeds.push_back(ulr_src_embed);
+      ulrEmbeddings_.push_back(ulr_src_embed);
       // ulr transformation matrix
       //initFunc = inits::eye(1.f); // identity matrix  - is it ok to init wiht identity or shall we make this to the fixed case only
       if (trainTrans) {
@@ -273,18 +266,77 @@ ULREmbeddingFactory(Ptr<ExpressionGraph> graph) : Factory(graph) {}
         fixed = true;
       }
       name = "ulr_transform";
-      auto ulr_transform = graph_->param(name, { dimUlrEmb, dimUlrEmb }, initFunc, fixed);
-      ulrEmbeds.push_back(ulr_transform);
+      auto ulrTransform = graph_->param(name, { dimUlrEmb, dimUlrEmb }, initFunc, fixed);
+      ulrEmbeddings_.push_back(ulrTransform);
 
       initFunc = inits::from_value(1.f);  // TBD: we should read sharable flags here - 1 means all sharable - 0 means no universal embeddings - should be zero for top freq only
       fixed = true;
       name = "ulr_shared";
       auto share_embed = graph_->param(name, { dimQueries, 1 }, initFunc, fixed);
-      ulrEmbeds.push_back(share_embed);
-
+      ulrEmbeddings_.push_back(share_embed);
     }
+  }
+
+  std::tuple<Expr/*embeddings*/, Expr/*mask*/> apply(Ptr<data::SubBatch> subBatch) const override final {
+    auto queryEmbed   = ulrEmbeddings_[0]; // Q : dimQueries*dimUlrEmb
+    auto keyEmbed     = ulrEmbeddings_[1]; // K : dimKeys*dimUlrEmb
+    auto uniEmbed     = ulrEmbeddings_[2]; // E : dimQueries*dimEmb
+    auto srcEmbed     = ulrEmbeddings_[3]; // I : dimQueries*dimEmb
+    auto ulrTransform = ulrEmbeddings_[4]; // A : dimUlrEmb *dimUlrEmb
+    auto ulrSharable  = ulrEmbeddings_[5]; // alpha : dimQueries*1
+    int dimBatch = (int)subBatch->batchSize();
+    int dimEmb = uniEmbed->shape()[-1];
+    int dimWords = (int)subBatch->batchWidth();
+    // D = K.A.QT
+    // dimm(K) = univ_tok_vocab*uni_embed_size
+    // dim A = uni_embed_size*uni_embed_size
+    // dim Q: uni_embed_size * total_merged_vocab_size
+    // dim D = univ_tok_vocab * total_merged_vocab_size
+    // note all above can be precombuted and serialized if A is not trainiable and during decoding (TBD)
+    // here we need to handle the mini-batch
+    // extract raws corresponding to Xs in this minibatch from Q
+    auto queryEmbeddings = rows(queryEmbed, subBatch->data());
+    auto srcEmbeddings = rows(srcEmbed, subBatch->data());   // extract trainable src embeddings
+    auto alpha = rows(ulrSharable, subBatch->data());  // extract sharable flags
+    auto qt = dot(queryEmbeddings, ulrTransform, false, false);  //A: transform embeddings based on similarity A :  dimUlrEmb*dimUlrEmb
+    auto sqrtDim=std::sqrt((float)queryEmbeddings->shape()[-1]);
+    qt = qt/sqrtDim;  // normalize accordin to embed size to avoid dot prodcut growing large in magnitude with larger embeds sizes
+    auto z = dot(qt, keyEmbed, false, true);      // query-key similarity 
+    float dropProb = this->options_->get<float>("ulr-dropout", 0.0f);  // default no dropout
+    z = dropout(z, dropProb);
+    float tau = this->options_->get<float>("ulr-softmax-temperature", 1.0f);  // default no temperature
+    // temperature in softmax is to control randomness of predictions
+    // high temperature Softmax outputs are more close to each other
+    // low temperatures the softmax become more similar to  "hardmax" 
+    auto weights = softmax(z / tau);  // assume default  is dim=-1, what about temprature? - scaler ??
+    auto chosenEmbeddings = dot(weights, uniEmbed);  // AVERAGE 
+    auto chosenEmbeddings_mix = srcEmbeddings + alpha * chosenEmbeddings;  // this should be elementwise  broadcast
+    auto batchEmbeddings = reshape(chosenEmbeddings_mix, { dimWords, dimBatch, dimEmb });
+    auto graph = ulrEmbeddings_.front()->graph();
+    auto batchMask = graph->constant({ dimWords, dimBatch, 1 },
+                                     inits::from_vector(subBatch->mask()));
+    return std::make_tuple(batchEmbeddings, batchMask);
+  }
+
+  Expr apply(const std::vector<IndexType>& embIdx, int dimBatch, int dimBeam) const override final {
+    embIdx; dimBatch; dimBeam;
+    ABORT("not implemented"); // ULR cannot be used for decoding
+  }
+};
+
+struct EmbeddingFactory : public Factory {
+  EmbeddingFactory(Ptr<ExpressionGraph> graph) : Factory(graph) {}
+
+  Ptr<IEmbeddingLayer> construct() {
+    return New<Embedding>(graph_, options_);
+  }
+};
+
+struct ULREmbeddingFactory : public Factory {
+  ULREmbeddingFactory(Ptr<ExpressionGraph> graph) : Factory(graph) {}
 
-    return ulrEmbeds;
+  Ptr<IEmbeddingLayer> construct() {
+    return New<ULREmbedding>(graph_, options_);
   }
 };
 
diff --git a/src/models/encoder.h b/src/models/encoder.h
index f8f16ed11..8eab61fb4 100755
--- a/src/models/encoder.h
+++ b/src/models/encoder.h
@@ -11,50 +11,6 @@ class EncoderBase {
   std::string prefix_{"encoder"};
   bool inference_{false};
   size_t batchIndex_{0};
-
-  std::tuple<Expr, Expr> ulrLookup(std::vector<Expr> urlEmbeddings, Ptr<data::CorpusBatch> batch) const {
-    auto graph = urlEmbeddings.front()->graph();
-    auto subBatch = (*batch)[batchIndex_];
-    // is their a better way to do this?
-    assert(urlEmbeddings.size() == 6);
-    auto queryEmbed = urlEmbeddings[0]; //Q : dimQueries*dimUlrEmb
-    auto keyEmbed = urlEmbeddings[1]; // K  : dimKeys*dimUlrEmb
-    auto uniEmbed = urlEmbeddings[2]; // E  : dimQueries*dimEmb
-    auto srcEmbed = urlEmbeddings[3]; // I  : dimQueries*dimEmb
-    auto ulrTransform = urlEmbeddings[4]; //A   : dimUlrEmb *dimUlrEmb
-    auto ulrSharable = urlEmbeddings[5]; //alpha  : dimQueries*1
-    int dimBatch = (int)subBatch->batchSize();
-    int dimEmb = uniEmbed->shape()[-1];
-    int dimWords = (int)subBatch->batchWidth();
-    // D = K.A.QT
-    // dimm(K) = univ_tok_vocab*uni_embed_size
-    // dim A = uni_embed_size*uni_embed_size
-    // dim Q: uni_embed_size * total_merged_vocab_size
-    // dim D = univ_tok_vocab * total_merged_vocab_size
-    // note all above can be precombuted and serialized if A is not trainiabale and during decoding (TBD)
-    // here we need to handle the mini-batch
-    // extract raws corresponding to Xs in this mini batch from Q
-    auto queryEmbeddings = rows(queryEmbed, subBatch->data());
-    auto srcEmbeddings = rows(srcEmbed, subBatch->data());   // extract trainable src embeddings
-    auto alpha = rows(ulrSharable, subBatch->data());  // extract sharable flags
-    auto qt = dot(queryEmbeddings, ulrTransform, false, false);  //A: transform embeddings based on similarity A :  dimUlrEmb *dimUlrEmb
-    auto sqrtDim=std::sqrt((float)queryEmbeddings->shape()[-1]);
-    qt = qt/sqrtDim;  // normalize accordin to embed size to avoid dot prodcut growing large in magintude with larger embeds sizes
-    auto z = dot(qt, keyEmbed, false, true);      // query-key similarity 
-    float dropProb = this->options_->get<float>("ulr-dropout", 0.0f);  // default no dropout
-    z = dropout(z, dropProb);
-    float tau = this->options_->get<float>("ulr-softmax-temperature", 1.0f);  // default no temperature
-    // temperature in softmax is to control randomness of predictions
-    // high temperature Softmax outputs are more close to each other
-    // low temperatures the softmax become more similar to  "hardmax" 
-    auto weights = softmax(z / tau);  // assume default  is dim=-1, what about temprature? - scaler ??
-    auto chosenEmbeddings = dot(weights, uniEmbed);  // AVERAGE 
-    auto chosenEmbeddings_mix = srcEmbeddings + alpha * chosenEmbeddings;  // this should be elementwise  broadcast
-    auto batchEmbeddings = reshape(chosenEmbeddings_mix, { dimWords, dimBatch, dimEmb });
-    auto batchMask = graph->constant({ dimWords, dimBatch, 1 },
-                                     inits::from_vector(subBatch->mask()));
-    return std::make_tuple(batchEmbeddings, batchMask);
-  }
 public: 
   EncoderBase(Ptr<Options> options)
       : options_(options),
@@ -62,7 +18,7 @@ class EncoderBase {
         inference_(options->get<bool>("inference", false)),
         batchIndex_(options->get<size_t>("index", 0)) {}
 
-  virtual Ptr<EncoderState> build(Ptr<ExpressionGraph>, Ptr<data::CorpusBatch>)
+  virtual Ptr<EncoderState> build(Ptr<ExpressionGraph>, Ptr<data::CorpusBatch>) // @TODO: rename to apply()?
       = 0;
 
   template <typename T>
diff --git a/src/models/s2s.h b/src/models/s2s.h
index de3754057..fe746877d 100755
--- a/src/models/s2s.h
+++ b/src/models/s2s.h
@@ -119,7 +119,7 @@ class EncoderS2S : public EncoderBase {
     return context;
   }
 
-  Ptr<IEmbedding> buildSourceEmbeddings(Ptr<ExpressionGraph> graph) {
+  Ptr<IEmbeddingLayer> buildSourceEmbeddings(Ptr<ExpressionGraph> graph) {
     // create source embeddings
     int dimVoc = opt<std::vector<int>>("dim-vocabs")[batchIndex_];
     int dimEmb = opt<int>("dim-emb");
diff --git a/src/models/transformer.h b/src/models/transformer.h
index 523add21a..3292cabfe 100755
--- a/src/models/transformer.h
+++ b/src/models/transformer.h
@@ -481,7 +481,7 @@ class EncoderTransformer : public Transformer<EncoderBase> {
   // returns the embedding matrix based on options
   // and based on batchIndex_.
 
-  std::vector<Expr> ULREmbeddings() const {
+  Ptr<IEmbeddingLayer> createULREmbeddingLayer() const {
     // standard encoder word embeddings
     int dimSrcVoc = opt<std::vector<int>>("dim-vocabs")[0];  //ULR multi-lingual src
     int dimTgtVoc = opt<std::vector<int>>("dim-vocabs")[1];  //ULR monon tgt
@@ -495,7 +495,7 @@ class EncoderTransformer : public Transformer<EncoderBase> {
     return embFactory.construct();
   }
 
-  Ptr<IEmbedding> createWordEmbeddingLayer(size_t subBatchIndex) const {
+  Ptr<IEmbeddingLayer> createWordEmbeddingLayer(size_t subBatchIndex) const {
     // standard encoder word embeddings
     int dimVoc = opt<std::vector<int>>("dim-vocabs")[subBatchIndex];
     int dimEmb = opt<int>("dim-emb");
@@ -527,16 +527,12 @@ class EncoderTransformer : public Transformer<EncoderBase> {
     // create the embedding matrix, considering tying and some other options
     // embed the source words in the batch
     Expr batchEmbeddings, batchMask;
-    if (options_->has("ulr") && options_->get<bool>("ulr") == true) {
-      auto embeddings = ULREmbeddings(); // embedding uses ULR
-      std::tie(batchEmbeddings, batchMask)
-        = EncoderBase::ulrLookup(embeddings, batch);
-    }
+    Ptr<IEmbeddingLayer> embedding;
+    if (options_->has("ulr") && options_->get<bool>("ulr") == true)
+      embedding = createULREmbeddingLayer(); // embedding uses ULR
     else
-    {
-      auto embedding = createWordEmbeddingLayer(batchIndex_);
-      std::tie(batchEmbeddings, batchMask) = embedding->apply((*batch)[batchIndex_]);
-    }
+      embedding = createWordEmbeddingLayer(batchIndex_);
+    std::tie(batchEmbeddings, batchMask) = embedding->apply((*batch)[batchIndex_]);
     // apply dropout over source words
     float dropoutSrc = inference_ ? 0 : opt<float>("dropout-src");
     if(dropoutSrc) {

From 2e86b2250ba45d78df14c185a42014c465a34c4f Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Wed, 9 Jan 2019 14:27:14 -0800
Subject: [PATCH 093/838] updated CharS2S

---
 src/models/char_s2s.h | 7 +++----
 src/models/s2s.h      | 4 ++--
 2 files changed, 5 insertions(+), 6 deletions(-)
 mode change 100644 => 100755 src/models/char_s2s.h

diff --git a/src/models/char_s2s.h b/src/models/char_s2s.h
old mode 100644
new mode 100755
index 6d5d1db16..928c6a18d
--- a/src/models/char_s2s.h
+++ b/src/models/char_s2s.h
@@ -13,12 +13,11 @@ class CharS2SEncoder : public EncoderS2S {
 
   virtual Ptr<EncoderState> build(Ptr<ExpressionGraph> graph,
                                   Ptr<data::CorpusBatch> batch) override {
-    auto embeddings = buildSourceEmbeddings(graph);
+    auto embedding = createSourceEmbedding(graph);
 
     // select embeddings that occur in the batch
-    Expr batchEmbeddings, batchMask;
-    std::tie(batchEmbeddings, batchMask)
-        = EncoderBase::lookup(graph, embeddings, batch);
+    Expr batchEmbeddings, batchMask; std::tie
+    (batchEmbeddings, batchMask) = embedding->apply(batch->front());
 
     // apply dropout over source words
     float dropProb = inference_ ? 0 : opt<float>("dropout-src");
diff --git a/src/models/s2s.h b/src/models/s2s.h
index fe746877d..0522176fd 100755
--- a/src/models/s2s.h
+++ b/src/models/s2s.h
@@ -150,11 +150,11 @@ class EncoderS2S : public EncoderBase {
 
   virtual Ptr<EncoderState> build(Ptr<ExpressionGraph> graph,
                                   Ptr<data::CorpusBatch> batch) override {
-    auto embeddings = buildSourceEmbeddings(graph);
+    auto embedding = buildSourceEmbeddings(graph);
 
     // select embeddings that occur in the batch
     Expr batchEmbeddings, batchMask; std::tie
-    (batchEmbeddings, batchMask) = embeddings->apply((*batch)[batchIndex_]);
+    (batchEmbeddings, batchMask) = embedding->apply((*batch)[batchIndex_]);
 
     // apply dropout over source words
     float dropProb = inference_ ? 0 : opt<float>("dropout-src");

From e2ec8a68040326117a45c611580759c33e32629b Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Wed, 9 Jan 2019 14:28:11 -0800
Subject: [PATCH 094/838] updated CharS2S

---
 src/models/s2s.h | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/src/models/s2s.h b/src/models/s2s.h
index 0522176fd..2ee090d6f 100755
--- a/src/models/s2s.h
+++ b/src/models/s2s.h
@@ -119,7 +119,7 @@ class EncoderS2S : public EncoderBase {
     return context;
   }
 
-  Ptr<IEmbeddingLayer> buildSourceEmbeddings(Ptr<ExpressionGraph> graph) {
+  Ptr<IEmbeddingLayer> createSourceEmbedding(Ptr<ExpressionGraph> graph) {
     // create source embeddings
     int dimVoc = opt<std::vector<int>>("dim-vocabs")[batchIndex_];
     int dimEmb = opt<int>("dim-emb");
@@ -150,7 +150,7 @@ class EncoderS2S : public EncoderBase {
 
   virtual Ptr<EncoderState> build(Ptr<ExpressionGraph> graph,
                                   Ptr<data::CorpusBatch> batch) override {
-    auto embedding = buildSourceEmbeddings(graph);
+    auto embedding = createSourceEmbedding(graph);
 
     // select embeddings that occur in the batch
     Expr batchEmbeddings, batchMask; std::tie

From 777045e6081b1c4adba908e5fa980cb7b76bdfd4 Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Wed, 9 Jan 2019 15:09:18 -0800
Subject: [PATCH 095/838] removed graph_ from Factory

---
 src/graph/expression_operators.cpp |  7 +++---
 src/layers/constructors.h          | 20 +++++++--------
 src/layers/factory.h               |  4 +--
 src/layers/generic.h               |  8 +++---
 src/models/decoder.h               |  4 +--
 src/models/model_factory.cpp       | 29 +++++++++++-----------
 src/models/model_factory.h         |  6 ++---
 src/models/s2s.h                   | 17 +++++++------
 src/models/transformer.h           |  8 +++---
 src/rnn/attention_constructors.h   |  4 +--
 src/rnn/constructors.h             | 40 +++++++++++++++---------------
 11 files changed, 75 insertions(+), 72 deletions(-)
 mode change 100644 => 100755 src/models/model_factory.cpp
 mode change 100644 => 100755 src/models/model_factory.h
 mode change 100644 => 100755 src/rnn/attention_constructors.h

diff --git a/src/graph/expression_operators.cpp b/src/graph/expression_operators.cpp
index c1b298f8d..cbfd8054b 100755
--- a/src/graph/expression_operators.cpp
+++ b/src/graph/expression_operators.cpp
@@ -511,16 +511,17 @@ Expr highway(Expr y, Expr x, Expr t) {
 Expr highway(const std::string prefix, Expr x) {
   // clang-format off
   size_t outDim = x->shape()[-1];
-  auto g = mlp::dense(x->graph())
+  auto graph = x->graph();
+  auto g = mlp::dense(graph)
       ("prefix", prefix + "_highway_d1")
       ("dim", outDim)
       ("activation", mlp::act::sigmoid)
-      .construct()->apply(x);
+      .construct(graph)->apply(x);
   auto relued = mlp::dense(x->graph())
       ("prefix", prefix + "_highway_d2")
       ("dim", outDim)
       ("activation", mlp::act::ReLU)
-      .construct()->apply(x);
+      .construct(graph)->apply(x);
   return (g * relued) + ((1 - g) * x);
   // clang-format on
 }
diff --git a/src/layers/constructors.h b/src/layers/constructors.h
index cf51ce55f..f5d6aef6b 100755
--- a/src/layers/constructors.h
+++ b/src/layers/constructors.h
@@ -26,7 +26,7 @@ struct LayerFactory : public Factory {
     return as<Cast>() != nullptr;
   }
 
-  virtual Ptr<IUnaryLayer> construct() = 0;
+  virtual Ptr<IUnaryLayer> construct(Ptr<ExpressionGraph> graph) = 0;
 };
 
 /**
@@ -36,13 +36,13 @@ class DenseFactory : public LayerFactory {
 public:
   DenseFactory(Ptr<ExpressionGraph> graph) : LayerFactory(graph) {}
 
-  Ptr<IUnaryLayer> construct() override {
-    auto dense = New<Dense>(graph_, options_);
+  Ptr<IUnaryLayer> construct(Ptr<ExpressionGraph> graph) override {
+    auto dense = New<Dense>(graph, options_);
     return dense;
   }
 
   DenseFactory clone() {
-    DenseFactory aClone(graph_);
+    DenseFactory aClone(nullptr);
     aClone.options_->merge(options_);
     return aClone;
   }
@@ -73,8 +73,8 @@ class OutputFactory : public LayerFactory {
     return Accumulator<OutputFactory>(*this);
   }
 
-  Ptr<IUnaryLayer> construct() override {
-    auto output = New<Output>(graph_, options_);
+  Ptr<IUnaryLayer> construct(Ptr<ExpressionGraph> graph) override {
+    auto output = New<Output>(graph, options_);
     for(auto& p : tiedParamsTransposed_)
       output->tie_transposed(p.first, p.second);
     output->set_shortlist(shortlist_);
@@ -82,7 +82,7 @@ class OutputFactory : public LayerFactory {
   }
 
   OutputFactory clone() {
-    OutputFactory aClone(graph_);
+    OutputFactory aClone(nullptr);
     aClone.options_->merge(options_);
     aClone.tiedParamsTransposed_ = tiedParamsTransposed_;
     aClone.shortlist_ = shortlist_;
@@ -137,11 +137,11 @@ class MLPFactory : public Factory {
 public:
   MLPFactory(Ptr<ExpressionGraph> graph) : Factory(graph) {}
 
-  Ptr<MLP> construct() {
-    auto mlp = New<MLP>(graph_, options_);
+  Ptr<MLP> construct(Ptr<ExpressionGraph> graph) {
+    auto mlp = New<MLP>(graph, options_);
     for(auto layer : layers_) {
       layer->getOptions()->merge(options_);
-      mlp->push_back(layer->construct());
+      mlp->push_back(layer->construct(graph));
     }
     return mlp;
   }
diff --git a/src/layers/factory.h b/src/layers/factory.h
index 0e84fd168..6ebc63fe3 100755
--- a/src/layers/factory.h
+++ b/src/layers/factory.h
@@ -7,11 +7,11 @@ namespace marian {
 class Factory : public std::enable_shared_from_this<Factory> {
 protected:
   Ptr<Options> options_;
-  Ptr<ExpressionGraph> graph_;
+  //Ptr<ExpressionGraph> graph_;
 
 public:
   Factory(Ptr<ExpressionGraph> graph)
-      : options_(New<Options>()), graph_(graph) {}
+      : options_(New<Options>())/*, graph_(graph)*/ {}
 
   virtual ~Factory() {}
 
diff --git a/src/layers/generic.h b/src/layers/generic.h
index 32ea291cc..c7a5c3bed 100755
--- a/src/layers/generic.h
+++ b/src/layers/generic.h
@@ -327,16 +327,16 @@ class ULREmbedding : public LayerBase, public IEmbeddingLayer {
 struct EmbeddingFactory : public Factory {
   EmbeddingFactory(Ptr<ExpressionGraph> graph) : Factory(graph) {}
 
-  Ptr<IEmbeddingLayer> construct() {
-    return New<Embedding>(graph_, options_);
+  Ptr<IEmbeddingLayer> construct(Ptr<ExpressionGraph> graph) {
+    return New<Embedding>(graph, options_);
   }
 };
 
 struct ULREmbeddingFactory : public Factory {
   ULREmbeddingFactory(Ptr<ExpressionGraph> graph) : Factory(graph) {}
 
-  Ptr<IEmbeddingLayer> construct() {
-    return New<ULREmbedding>(graph_, options_);
+  Ptr<IEmbeddingLayer> construct(Ptr<ExpressionGraph> graph) {
+    return New<ULREmbedding>(graph, options_);
   }
 };
 
diff --git a/src/models/decoder.h b/src/models/decoder.h
index 67ab8a214..b982b84a3 100755
--- a/src/models/decoder.h
+++ b/src/models/decoder.h
@@ -56,7 +56,7 @@ class DecoderBase {
           ("normalization", opt<bool>("embedding-normalization"));
     }
 
-    auto yEmb = yEmbFactory.construct();
+    auto yEmb = yEmbFactory.construct(graph);
 
     auto subBatch = (*batch)[batchIndex_];
 
@@ -99,7 +99,7 @@ class DecoderBase {
       else
         yEmbFactory("prefix", prefix_ + "_Wemb");
   
-      auto yEmb = yEmbFactory.construct();
+      auto yEmb = yEmbFactory.construct(graph);
 
       selectedEmbs = yEmb->apply(embIdx, dimBatch, dimBeam);
     }
diff --git a/src/models/model_factory.cpp b/src/models/model_factory.cpp
old mode 100644
new mode 100755
index d42f07c8b..8198f46c0
--- a/src/models/model_factory.cpp
+++ b/src/models/model_factory.cpp
@@ -24,7 +24,7 @@
 namespace marian {
 namespace models {
 
-Ptr<EncoderBase> EncoderFactory::construct() {
+Ptr<EncoderBase> EncoderFactory::construct(Ptr<ExpressionGraph> graph) {
   if(options_->get<std::string>("type") == "s2s")
     return New<EncoderS2S>(options_);
 
@@ -40,7 +40,7 @@ Ptr<EncoderBase> EncoderFactory::construct() {
   ABORT("Unknown encoder type");
 }
 
-Ptr<DecoderBase> DecoderFactory::construct() {
+Ptr<DecoderBase> DecoderFactory::construct(Ptr<ExpressionGraph> graph) {
   if(options_->get<std::string>("type") == "s2s")
     return New<DecoderS2S>(options_);
   if(options_->get<std::string>("type") == "transformer")
@@ -49,7 +49,7 @@ Ptr<DecoderBase> DecoderFactory::construct() {
   ABORT("Unknown decoder type");
 }
 
-Ptr<ModelBase> EncoderDecoderFactory::construct() {
+Ptr<ModelBase> EncoderDecoderFactory::construct(Ptr<ExpressionGraph> graph) {
   Ptr<EncoderDecoder> encdec;
 
   if(options_->get<std::string>("type") == "amun")
@@ -61,15 +61,16 @@ Ptr<ModelBase> EncoderDecoderFactory::construct() {
     encdec = New<EncoderDecoder>(options_);
 
   for(auto& ef : encoders_)
-    encdec->push_back(ef(options_).construct());
+    encdec->push_back(ef(options_).construct(graph));
 
   for(auto& df : decoders_)
-    encdec->push_back(df(options_).construct());
+    encdec->push_back(df(options_).construct(graph));
 
   return add_cost(encdec, options_);
 }
 
 Ptr<ModelBase> by_type(std::string type, usage use, Ptr<Options> options) {
+  Ptr<ExpressionGraph> graph = nullptr; // graph unknown at this stage
   // clang-format off
   if(type == "s2s" || type == "amun" || type == "nematus") {
     return models::encoder_decoder()(options)
@@ -77,7 +78,7 @@ Ptr<ModelBase> by_type(std::string type, usage use, Ptr<Options> options) {
         ("original-type", type)
             .push_back(models::encoder()("type", "s2s"))
             .push_back(models::decoder()("type", "s2s"))
-            .construct();
+            .construct(graph);
   }
 
   if(type == "transformer") {
@@ -85,7 +86,7 @@ Ptr<ModelBase> by_type(std::string type, usage use, Ptr<Options> options) {
         ("usage", use)
         .push_back(models::encoder()("type", "transformer"))
         .push_back(models::decoder()("type", "transformer"))
-        .construct();
+        .construct(graph);
   }
 
   if(type == "transformer_s2s") {
@@ -94,7 +95,7 @@ Ptr<ModelBase> by_type(std::string type, usage use, Ptr<Options> options) {
         ("original-type", type)
             .push_back(models::encoder()("type", "transformer"))
             .push_back(models::decoder()("type", "s2s"))
-            .construct();
+            .construct(graph);
   }
 
   if(type == "lm") {
@@ -111,7 +112,7 @@ Ptr<ModelBase> by_type(std::string type, usage use, Ptr<Options> options) {
             .push_back(models::decoder()
                        ("index", idx)
                        ("dim-vocabs", dimVocabs))
-            .construct();
+            .construct(graph);
   }
 
   if(type == "multi-s2s") {
@@ -128,7 +129,7 @@ Ptr<ModelBase> by_type(std::string type, usage use, Ptr<Options> options) {
 
     ms2sFactory.push_back(models::decoder()("index", numEncoders));
 
-    return ms2sFactory.construct();
+    return ms2sFactory.construct(graph);
   }
 
   if(type == "shared-multi-s2s") {
@@ -145,7 +146,7 @@ Ptr<ModelBase> by_type(std::string type, usage use, Ptr<Options> options) {
 
     ms2sFactory.push_back(models::decoder()("index", numEncoders));
 
-    return ms2sFactory.construct();
+    return ms2sFactory.construct(graph);
   }
 
   if(type == "multi-transformer") {
@@ -161,7 +162,7 @@ Ptr<ModelBase> by_type(std::string type, usage use, Ptr<Options> options) {
     }
     mtransFactory.push_back(models::decoder()("index", numEncoders));
 
-    return mtransFactory.construct();
+    return mtransFactory.construct(graph);
   }
 
   if(type == "shared-multi-transformer") {
@@ -177,7 +178,7 @@ Ptr<ModelBase> by_type(std::string type, usage use, Ptr<Options> options) {
     }
     mtransFactory.push_back(models::decoder()("index", numEncoders));
 
-    return mtransFactory.construct();
+    return mtransFactory.construct(graph);
   }
 
   if(type == "lm-transformer") {
@@ -194,7 +195,7 @@ Ptr<ModelBase> by_type(std::string type, usage use, Ptr<Options> options) {
             .push_back(models::decoder()
                        ("index", idx)
                        ("dim-vocabs", dimVocabs))
-            .construct();
+            .construct(graph);
   }
 
 #ifdef COMPILE_EXAMPLES
diff --git a/src/models/model_factory.h b/src/models/model_factory.h
old mode 100644
new mode 100755
index 2ec7fe752..724561cc7
--- a/src/models/model_factory.h
+++ b/src/models/model_factory.h
@@ -12,7 +12,7 @@ class EncoderFactory : public Factory {
 public:
   EncoderFactory(Ptr<ExpressionGraph> graph = nullptr) : Factory(graph) {}
 
-  virtual Ptr<EncoderBase> construct();
+  virtual Ptr<EncoderBase> construct(Ptr<ExpressionGraph> graph);
 };
 
 typedef Accumulator<EncoderFactory> encoder;
@@ -21,7 +21,7 @@ class DecoderFactory : public Factory {
 public:
   DecoderFactory(Ptr<ExpressionGraph> graph = nullptr) : Factory(graph) {}
 
-  virtual Ptr<DecoderBase> construct();
+  virtual Ptr<DecoderBase> construct(Ptr<ExpressionGraph> graph);
 };
 
 typedef Accumulator<DecoderFactory> decoder;
@@ -45,7 +45,7 @@ class EncoderDecoderFactory : public Factory {
     return Accumulator<EncoderDecoderFactory>(*this);
   }
 
-  virtual Ptr<ModelBase> construct();
+  virtual Ptr<ModelBase> construct(Ptr<ExpressionGraph> graph);
 };
 
 typedef Accumulator<EncoderDecoderFactory> encoder_decoder;
diff --git a/src/models/s2s.h b/src/models/s2s.h
index 2ee090d6f..4dfe32d97 100755
--- a/src/models/s2s.h
+++ b/src/models/s2s.h
@@ -86,8 +86,8 @@ class EncoderS2S : public EncoderBase {
       rnnBw.push_back(stacked);
     }
 
-    auto context = concatenate({rnnFw.construct()->transduce(embeddings, mask),
-                                rnnBw.construct()->transduce(embeddings, mask)},
+    auto context = concatenate({rnnFw.construct(graph)->transduce(embeddings, mask),
+                                rnnBw.construct(graph)->transduce(embeddings, mask)},
                                /*axis =*/ -1);
 
     if(second > 0) {
@@ -114,7 +114,7 @@ class EncoderS2S : public EncoderBase {
       }
 
       // transduce context to new context
-      context = rnnUni.construct()->transduce(context);
+      context = rnnUni.construct(graph)->transduce(context);
     }
     return context;
   }
@@ -143,7 +143,7 @@ class EncoderS2S : public EncoderBase {
           ("normalization", opt<bool>("embedding-normalization"));
     }
 
-    return embFactory.construct();
+    return embFactory.construct(graph);
   }
 
   EncoderS2S(Ptr<Options> options) : EncoderBase(options) {}
@@ -235,7 +235,7 @@ class DecoderS2S : public DecoderBase {
       rnn.push_back(highCell);
     }
 
-    return rnn.construct();
+    return rnn.construct(graph);
   }
 
 public:
@@ -266,9 +266,10 @@ class DecoderS2S : public DecoderBase {
           ("nematus-normalization",
            options_->has("original-type")
                && opt<std::string>("original-type") == "nematus")  //
-      );
+      )
+      .construct(graph);
 
-      start = mlp.construct()->apply(meanContexts);
+      start = mlp->apply(meanContexts);
     } else {
       int dimBatch = (int)batch->size();
       int dimRnn = opt<int>("dim-rnn");
@@ -351,7 +352,7 @@ class DecoderS2S : public DecoderBase {
       output_ = mlp::mlp(graph)         //
                     .push_back(hidden)  //
                     .push_back(last)
-                    .construct();
+                    .construct(graph);
     }
 
     Expr logits;
diff --git a/src/models/transformer.h b/src/models/transformer.h
index 3292cabfe..ac925a793 100755
--- a/src/models/transformer.h
+++ b/src/models/transformer.h
@@ -456,7 +456,7 @@ class Transformer : public EncoderOrDecoderBase {
         ("dropout", dropoutRnn)                                    //
         ("layer-normalization", opt<bool>("layer-normalization"))  //
         .push_back(rnn::cell(graph_))                              //
-        .construct();
+        .construct(graph_);
 
     float dropProb = inference_ ? 0 : opt<float>("transformer-dropout");
     auto opsPre = opt<std::string>("transformer-preprocess");
@@ -492,7 +492,7 @@ class EncoderTransformer : public Transformer<EncoderBase> {
                                            ("ulrTrainTransform", opt<bool>("ulr-trainable-transformation"))
                                            ("ulrQueryFile", opt<std::string>("ulr-query-vectors"))
                                            ("ulrKeysFile", opt<std::string>("ulr-keys-vectors"));
-    return embFactory.construct();
+    return embFactory.construct(graph_);
   }
 
   Ptr<IEmbeddingLayer> createWordEmbeddingLayer(size_t subBatchIndex) const {
@@ -511,7 +511,7 @@ class EncoderTransformer : public Transformer<EncoderBase> {
       embFactory("embFile", embFiles[subBatchIndex])
                 ("normalization", opt<bool>("embedding-normalization"));
     }
-    return embFactory.construct();
+    return embFactory.construct(graph_);
   }
 
   Ptr<EncoderState> build(Ptr<ExpressionGraph> graph,
@@ -630,7 +630,7 @@ class DecoderTransformer : public Transformer<DecoderBase> {
     // aligned source context
     output_ = mlp::mlp(graph_)      //
                   .push_back(layerOut)  //
-                  .construct();
+                  .construct(graph_);
   }
 
 public:
diff --git a/src/rnn/attention_constructors.h b/src/rnn/attention_constructors.h
old mode 100644
new mode 100755
index 9fd1e966b..e377090a3
--- a/src/rnn/attention_constructors.h
+++ b/src/rnn/attention_constructors.h
@@ -17,9 +17,9 @@ class AttentionFactory : public InputFactory {
 public:
   AttentionFactory(Ptr<ExpressionGraph> graph) : InputFactory(graph) {}
 
-  Ptr<CellInput> construct() override {
+  Ptr<CellInput> construct(Ptr<ExpressionGraph> graph) override {
     ABORT_IF(!state_, "EncoderState not set");
-    return New<Attention>(graph_, options_, state_);
+    return New<Attention>(graph, options_, state_);
   }
 
   Accumulator<AttentionFactory> set_state(Ptr<EncoderState> state) {
diff --git a/src/rnn/constructors.h b/src/rnn/constructors.h
index 657d8ad16..406c0c480 100755
--- a/src/rnn/constructors.h
+++ b/src/rnn/constructors.h
@@ -27,7 +27,7 @@ struct StackableFactory : public Factory {
 
 struct InputFactory : public StackableFactory {
   InputFactory(Ptr<ExpressionGraph> graph) : StackableFactory(graph) {}
-  virtual Ptr<CellInput> construct() = 0;
+  virtual Ptr<CellInput> construct(Ptr<ExpressionGraph> graph) = 0;
 };
 
 class CellFactory : public StackableFactory {
@@ -37,42 +37,42 @@ class CellFactory : public StackableFactory {
 public:
   CellFactory(Ptr<ExpressionGraph> graph) : StackableFactory(graph) {}
 
-  virtual Ptr<Cell> construct() {
+  virtual Ptr<Cell> construct(Ptr<ExpressionGraph> graph) {
     std::string type = options_->get<std::string>("type");
     if(type == "gru") {
-      auto cell = New<GRU>(graph_, options_);
+      auto cell = New<GRU>(graph, options_);
       cell->setLazyInputs(inputs_);
       return cell;
     } else if(type == "gru-nematus") {
-      auto cell = New<GRUNematus>(graph_, options_);
+      auto cell = New<GRUNematus>(graph, options_);
       cell->setLazyInputs(inputs_);
       return cell;
     } else if(type == "lstm") {
-      auto cell = New<LSTM>(graph_, options_);
+      auto cell = New<LSTM>(graph, options_);
       cell->setLazyInputs(inputs_);
       return cell;
     } else if(type == "mlstm") {
-      auto cell = New<MLSTM>(graph_, options_);
+      auto cell = New<MLSTM>(graph, options_);
       cell->setLazyInputs(inputs_);
       return cell;
     } else if(type == "mgru") {
-      auto cell = New<MGRU>(graph_, options_);
+      auto cell = New<MGRU>(graph, options_);
       cell->setLazyInputs(inputs_);
       return cell;
     } else if(type == "tanh") {
-      auto cell = New<Tanh>(graph_, options_);
+      auto cell = New<Tanh>(graph, options_);
       cell->setLazyInputs(inputs_);
       return cell;
     } else if(type == "relu") {
-      auto cell = New<ReLU>(graph_, options_);
+      auto cell = New<ReLU>(graph, options_);
       cell->setLazyInputs(inputs_);
       return cell;
     } else if(type == "sru") {
-      auto cell = New<SRU>(graph_, options_);
+      auto cell = New<SRU>(graph, options_);
       cell->setLazyInputs(inputs_);
       return cell;
     } else if(type == "ssru") {
-      auto cell = New<SSRU>(graph_, options_);
+      auto cell = New<SSRU>(graph, options_);
       cell->setLazyInputs(inputs_);
       return cell;
     } else {
@@ -81,7 +81,7 @@ class CellFactory : public StackableFactory {
   }
 
   CellFactory clone() {
-    CellFactory aClone(graph_);
+    CellFactory aClone(nullptr);
     aClone.options_->merge(options_);
     aClone.inputs_ = inputs_;
     return aClone;
@@ -105,8 +105,8 @@ class StackedCellFactory : public CellFactory {
 public:
   StackedCellFactory(Ptr<ExpressionGraph> graph) : CellFactory(graph) {}
 
-  Ptr<Cell> construct() override {
-    auto stacked = New<StackedCell>(graph_, options_);
+  Ptr<Cell> construct(Ptr<ExpressionGraph> graph) override {
+    auto stacked = New<StackedCell>(graph, options_);
 
     int lastDimInput = options_->get<int>("dimInput");
 
@@ -124,11 +124,11 @@ class StackedCellFactory : public CellFactory {
           for(auto f : inputs_)
             cellFactory->add_input(f);
 
-        stacked->push_back(cellFactory->construct());
+        stacked->push_back(cellFactory->construct(graph));
       } else {
         auto inputFactory = sf->as<InputFactory>();
         inputFactory->getOptions()->merge(options_);
-        auto input = inputFactory->construct();
+        auto input = inputFactory->construct(graph);
         stacked->push_back(input);
         lastDimInput += input->dimOutput();
       }
@@ -152,8 +152,8 @@ class RNNFactory : public Factory {
 public:
   RNNFactory(Ptr<ExpressionGraph> graph) : Factory(graph) {}
 
-  Ptr<RNN> construct() {
-    auto rnn = New<RNN>(graph_, options_);
+  Ptr<RNN> construct(Ptr<ExpressionGraph> graph) {
+    auto rnn = New<RNN>(graph, options_);
     for(size_t i = 0; i < layerFactories_.size(); ++i) {
       auto lf = layerFactories_[i];
 
@@ -182,7 +182,7 @@ class RNNFactory : public Factory {
           lf->getOptions()->set("direction", (int)rnn::dir::backward);
       }
 
-      rnn->push_back(lf->construct());
+      rnn->push_back(lf->construct(graph));
     }
     return rnn;
   }
@@ -194,7 +194,7 @@ class RNNFactory : public Factory {
   }
 
   RNNFactory clone() {
-    RNNFactory aClone(graph_);
+    RNNFactory aClone(nullptr);
     aClone.options_->merge(options_);
     for(auto lf : layerFactories_)
       aClone.push_back(lf->clone());

From 5d65214ee56e8e29b578d0670a06b503f0860a79 Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Wed, 9 Jan 2019 15:53:05 -0800
Subject: [PATCH 096/838] removed graph from Factory constructor (and derived
 classes')

---
 src/graph/expression_operators.cpp |  4 +--
 src/layers/constructors.h          | 15 +++-------
 src/layers/factory.h               | 10 +++----
 src/layers/generic.h               |  4 ---
 src/models/decoder.h               |  8 ++---
 src/models/model_factory.h         |  6 ++--
 src/models/s2s.h                   | 47 +++++++++++++++---------------
 src/models/transformer.h           | 20 ++++++-------
 src/rnn/attention_constructors.h   |  2 +-
 src/rnn/constructors.h             | 13 ++-------
 10 files changed, 55 insertions(+), 74 deletions(-)

diff --git a/src/graph/expression_operators.cpp b/src/graph/expression_operators.cpp
index cbfd8054b..cdfeff271 100755
--- a/src/graph/expression_operators.cpp
+++ b/src/graph/expression_operators.cpp
@@ -512,12 +512,12 @@ Expr highway(const std::string prefix, Expr x) {
   // clang-format off
   size_t outDim = x->shape()[-1];
   auto graph = x->graph();
-  auto g = mlp::dense(graph)
+  auto g = mlp::dense()
       ("prefix", prefix + "_highway_d1")
       ("dim", outDim)
       ("activation", mlp::act::sigmoid)
       .construct(graph)->apply(x);
-  auto relued = mlp::dense(x->graph())
+  auto relued = mlp::dense()
       ("prefix", prefix + "_highway_d2")
       ("dim", outDim)
       ("activation", mlp::act::ReLU)
diff --git a/src/layers/constructors.h b/src/layers/constructors.h
index f5d6aef6b..650c814b0 100755
--- a/src/layers/constructors.h
+++ b/src/layers/constructors.h
@@ -10,7 +10,7 @@ namespace mlp {
  * Base class for layer factories, can be used in a multi-layer network factory.
  */
 struct LayerFactory : public Factory {
-  LayerFactory(Ptr<ExpressionGraph> graph) : Factory(graph) {}
+  LayerFactory() : Factory() {}
   LayerFactory(const LayerFactory&) = default;
   LayerFactory(LayerFactory&&) = default;
 
@@ -34,15 +34,12 @@ struct LayerFactory : public Factory {
  */
 class DenseFactory : public LayerFactory {
 public:
-  DenseFactory(Ptr<ExpressionGraph> graph) : LayerFactory(graph) {}
-
   Ptr<IUnaryLayer> construct(Ptr<ExpressionGraph> graph) override {
-    auto dense = New<Dense>(graph, options_);
-    return dense;
+    return New<Dense>(graph, options_);
   }
 
   DenseFactory clone() {
-    DenseFactory aClone(nullptr);
+    DenseFactory aClone;
     aClone.options_->merge(options_);
     return aClone;
   }
@@ -60,8 +57,6 @@ class OutputFactory : public LayerFactory {
   Ptr<data::Shortlist> shortlist_;
 
 public:
-  OutputFactory(Ptr<ExpressionGraph> graph) : LayerFactory(graph) {}
-
   Accumulator<OutputFactory> tie_transposed(const std::string& param,
                                             const std::string& tied) {
     tiedParamsTransposed_.push_back({param, tied});
@@ -82,7 +77,7 @@ class OutputFactory : public LayerFactory {
   }
 
   OutputFactory clone() {
-    OutputFactory aClone(nullptr);
+    OutputFactory aClone;
     aClone.options_->merge(options_);
     aClone.tiedParamsTransposed_ = tiedParamsTransposed_;
     aClone.shortlist_ = shortlist_;
@@ -135,8 +130,6 @@ class MLPFactory : public Factory {
   std::vector<Ptr<LayerFactory>> layers_;
 
 public:
-  MLPFactory(Ptr<ExpressionGraph> graph) : Factory(graph) {}
-
   Ptr<MLP> construct(Ptr<ExpressionGraph> graph) {
     auto mlp = New<MLP>(graph, options_);
     for(auto layer : layers_) {
diff --git a/src/layers/factory.h b/src/layers/factory.h
index 6ebc63fe3..4db11d605 100755
--- a/src/layers/factory.h
+++ b/src/layers/factory.h
@@ -7,11 +7,12 @@ namespace marian {
 class Factory : public std::enable_shared_from_this<Factory> {
 protected:
   Ptr<Options> options_;
-  //Ptr<ExpressionGraph> graph_;
 
 public:
-  Factory(Ptr<ExpressionGraph> graph)
-      : options_(New<Options>())/*, graph_(graph)*/ {}
+  Factory() : options_(New<Options>()) {}
+  Factory(Ptr<Options> options) : Factory() {
+    options_->merge(options);
+  }
 
   virtual ~Factory() {}
 
@@ -35,8 +36,7 @@ class Accumulator : public BaseFactory {
   typedef BaseFactory Factory;
 
 public:
-  Accumulator() : Factory(nullptr) {}
-  Accumulator(Ptr<ExpressionGraph> graph) : Factory(graph) {}
+  Accumulator() : Factory() {}
   Accumulator(const Factory& factory) : Factory(factory) {}
   Accumulator(const Accumulator&) = default;
   Accumulator(Accumulator&&) = default;
diff --git a/src/layers/generic.h b/src/layers/generic.h
index c7a5c3bed..80ec4ec0f 100755
--- a/src/layers/generic.h
+++ b/src/layers/generic.h
@@ -325,16 +325,12 @@ class ULREmbedding : public LayerBase, public IEmbeddingLayer {
 };
 
 struct EmbeddingFactory : public Factory {
-  EmbeddingFactory(Ptr<ExpressionGraph> graph) : Factory(graph) {}
-
   Ptr<IEmbeddingLayer> construct(Ptr<ExpressionGraph> graph) {
     return New<Embedding>(graph, options_);
   }
 };
 
 struct ULREmbeddingFactory : public Factory {
-  ULREmbeddingFactory(Ptr<ExpressionGraph> graph) : Factory(graph) {}
-
   Ptr<IEmbeddingLayer> construct(Ptr<ExpressionGraph> graph) {
     return New<ULREmbedding>(graph, options_);
   }
diff --git a/src/models/decoder.h b/src/models/decoder.h
index b982b84a3..99fbd4ad6 100755
--- a/src/models/decoder.h
+++ b/src/models/decoder.h
@@ -38,8 +38,8 @@ class DecoderBase {
     int dimVoc = opt<std::vector<int>>("dim-vocabs")[batchIndex_];
     int dimEmb = opt<int>("dim-emb");
 
-    auto yEmbFactory = embedding(graph)  //
-        ("dimVocab", dimVoc)             //
+    auto yEmbFactory = embedding()  //
+        ("dimVocab", dimVoc)        //
         ("dimEmb", dimEmb);
 
     if(opt<bool>("tied-embeddings-src") || opt<bool>("tied-embeddings-all"))
@@ -90,8 +90,8 @@ class DecoderBase {
       selectedEmbs = graph->constant({1, 1, dimBatch, dimTrgEmb}, inits::zeros);
     } else {
       // embeddings are loaded from model during translation, no fixing required
-      auto yEmbFactory = embedding(graph)  //
-          ("dimVocab", dimTrgVoc)          //
+      auto yEmbFactory = embedding()  //
+          ("dimVocab", dimTrgVoc)     //
           ("dimEmb", dimTrgEmb);
   
       if(opt<bool>("tied-embeddings-src") || opt<bool>("tied-embeddings-all"))
diff --git a/src/models/model_factory.h b/src/models/model_factory.h
index 724561cc7..1f099a603 100755
--- a/src/models/model_factory.h
+++ b/src/models/model_factory.h
@@ -10,7 +10,7 @@ namespace models {
 
 class EncoderFactory : public Factory {
 public:
-  EncoderFactory(Ptr<ExpressionGraph> graph = nullptr) : Factory(graph) {}
+  EncoderFactory(Ptr<ExpressionGraph> graph = nullptr) : Factory() {}
 
   virtual Ptr<EncoderBase> construct(Ptr<ExpressionGraph> graph);
 };
@@ -19,7 +19,7 @@ typedef Accumulator<EncoderFactory> encoder;
 
 class DecoderFactory : public Factory {
 public:
-  DecoderFactory(Ptr<ExpressionGraph> graph = nullptr) : Factory(graph) {}
+  DecoderFactory(Ptr<ExpressionGraph> graph = nullptr) : Factory() {}
 
   virtual Ptr<DecoderBase> construct(Ptr<ExpressionGraph> graph);
 };
@@ -33,7 +33,7 @@ class EncoderDecoderFactory : public Factory {
 
 public:
   EncoderDecoderFactory(Ptr<ExpressionGraph> graph = nullptr)
-      : Factory(graph) {}
+      : Factory() {}
 
   Accumulator<EncoderDecoderFactory> push_back(encoder enc) {
     encoders_.push_back(enc);
diff --git a/src/models/s2s.h b/src/models/s2s.h
index 4dfe32d97..e6300e228 100755
--- a/src/models/s2s.h
+++ b/src/models/s2s.h
@@ -34,7 +34,7 @@ class EncoderS2S : public EncoderBase {
 
     float dropoutRnn = inference_ ? 0 : opt<float>("dropout-rnn");
 
-    auto rnnFw = rnn::rnn(graph)                                   //
+    auto rnnFw = rnn::rnn()                                        //
         ("type", opt<std::string>("enc-cell"))                     //
         ("direction", (int)forward)                                //
         ("dimInput", embeddings->shape()[-1])                      //
@@ -44,7 +44,7 @@ class EncoderS2S : public EncoderBase {
         ("skip", opt<bool>("skip"));
 
     for(int i = 1; i <= first; ++i) {
-      auto stacked = rnn::stacked_cell(graph);
+      auto stacked = rnn::stacked_cell();
       for(int j = 1; j <= opt<int>("enc-cell-depth"); ++j) {
         std::string paramPrefix = prefix_ + "_bi";
         if(i > 1)
@@ -53,14 +53,14 @@ class EncoderS2S : public EncoderBase {
           paramPrefix += "_cell" + std::to_string(j);
         bool transition = (j > 1);
 
-        stacked.push_back(rnn::cell(graph)         //
+        stacked.push_back(rnn::cell()              //
                           ("prefix", paramPrefix)  //
                           ("transition", transition));
       }
       rnnFw.push_back(stacked);
     }
 
-    auto rnnBw = rnn::rnn(graph)                                   //
+    auto rnnBw = rnn::rnn()                                        //
         ("type", opt<std::string>("enc-cell"))                     //
         ("direction", (int)backward)                               //
         ("dimInput", embeddings->shape()[-1])                      //
@@ -70,7 +70,7 @@ class EncoderS2S : public EncoderBase {
         ("skip", opt<bool>("skip"));
 
     for(int i = 1; i <= first; ++i) {
-      auto stacked = rnn::stacked_cell(graph);
+      auto stacked = rnn::stacked_cell();
       for(int j = 1; j <= opt<int>("enc-cell-depth"); ++j) {
         std::string paramPrefix = prefix_ + "_bi_r";
         if(i > 1)
@@ -79,7 +79,7 @@ class EncoderS2S : public EncoderBase {
           paramPrefix += "_cell" + std::to_string(j);
         bool transition = (j > 1);
 
-        stacked.push_back(rnn::cell(graph)         //
+        stacked.push_back(rnn::cell()              //
                           ("prefix", paramPrefix)  //
                           ("transition", transition));
       }
@@ -95,7 +95,7 @@ class EncoderS2S : public EncoderBase {
       // previous bidirectional RNN through multiple layers
 
       // construct RNN first
-      auto rnnUni = rnn::rnn(graph)                                  //
+      auto rnnUni = rnn::rnn()                                       //
           ("type", opt<std::string>("enc-cell"))                     //
           ("dimInput", 2 * opt<int>("dim-rnn"))                      //
           ("dimState", opt<int>("dim-rnn"))                          //
@@ -104,11 +104,11 @@ class EncoderS2S : public EncoderBase {
           ("skip", opt<bool>("skip"));
 
       for(int i = first + 1; i <= second + first; ++i) {
-        auto stacked = rnn::stacked_cell(graph);
+        auto stacked = rnn::stacked_cell();
         for(int j = 1; j <= opt<int>("enc-cell-depth"); ++j) {
           std::string paramPrefix = prefix_ + "_l" + std::to_string(i) + "_cell"
                                     + std::to_string(j);
-          stacked.push_back(rnn::cell(graph)("prefix", paramPrefix));
+          stacked.push_back(rnn::cell()("prefix", paramPrefix));
         }
         rnnUni.push_back(stacked);
       }
@@ -124,8 +124,8 @@ class EncoderS2S : public EncoderBase {
     int dimVoc = opt<std::vector<int>>("dim-vocabs")[batchIndex_];
     int dimEmb = opt<int>("dim-emb");
 
-    auto embFactory = embedding(graph)  //
-        ("dimVocab", dimVoc)            //
+    auto embFactory = embedding()  //
+        ("dimVocab", dimVoc)       //
         ("dimEmb", dimEmb);
 
     if(opt<bool>("tied-embeddings-src") || opt<bool>("tied-embeddings-all"))
@@ -180,7 +180,7 @@ class DecoderS2S : public DecoderBase {
   Ptr<rnn::RNN> constructDecoderRNN(Ptr<ExpressionGraph> graph,
                                     Ptr<DecoderState> state) {
     float dropoutRnn = inference_ ? 0 : opt<float>("dropout-rnn");
-    auto rnn = rnn::rnn(graph)                                     //
+    auto rnn = rnn::rnn()                                          //
         ("type", opt<std::string>("dec-cell"))                     //
         ("dimInput", opt<int>("dim-emb"))                          //
         ("dimState", opt<int>("dim-rnn"))                          //
@@ -196,11 +196,11 @@ class DecoderS2S : public DecoderBase {
     size_t decoderHighDepth = opt<size_t>("dec-cell-high-depth");
 
     // setting up conditional (transitional) cell
-    auto baseCell = rnn::stacked_cell(graph);
+    auto baseCell = rnn::stacked_cell();
     for(size_t i = 1; i <= decoderBaseDepth; ++i) {
       bool transition = (i > 2);
       auto paramPrefix = prefix_ + "_cell" + std::to_string(i);
-      baseCell.push_back(rnn::cell(graph)         //
+      baseCell.push_back(rnn::cell()              //
                          ("prefix", paramPrefix)  //
                          ("final", i > 1)         //
                          ("transition", transition));
@@ -212,8 +212,7 @@ class DecoderS2S : public DecoderBase {
 
           auto encState = state->getEncoderStates()[k];
 
-          baseCell.push_back(
-              rnn::attention(graph)("prefix", attPrefix).set_state(encState));
+          baseCell.push_back(rnn::attention()("prefix", attPrefix).set_state(encState));
         }
       }
     }
@@ -223,12 +222,12 @@ class DecoderS2S : public DecoderBase {
     // Add more cells to RNN (stacked RNN)
     for(size_t i = 2; i <= decoderLayers; ++i) {
       // deep transition
-      auto highCell = rnn::stacked_cell(graph);
+      auto highCell = rnn::stacked_cell();
 
       for(size_t j = 1; j <= decoderHighDepth; j++) {
         auto paramPrefix
             = prefix_ + "_l" + std::to_string(i) + "_cell" + std::to_string(j);
-        highCell.push_back(rnn::cell(graph)("prefix", paramPrefix));
+        highCell.push_back(rnn::cell()("prefix", paramPrefix));
       }
 
       // Add cell to RNN (more layers)
@@ -257,14 +256,14 @@ class DecoderS2S : public DecoderBase {
     Expr start;
     if(!meanContexts.empty()) {
       // apply single layer network to mean to map into decoder space
-      auto mlp = mlp::mlp(graph).push_back(
-          mlp::dense(graph)                                          //
+      auto mlp = mlp::mlp().push_back(
+          mlp::dense()                                               //
           ("prefix", prefix_ + "_ff_state")                          //
           ("dim", opt<int>("dim-rnn"))                               //
           ("activation", (int)mlp::act::tanh)                        //
           ("layer-normalization", opt<bool>("layer-normalization"))  //
           ("nematus-normalization",
-           options_->has("original-type")
+          options_->has("original-type")
                && opt<std::string>("original-type") == "nematus")  //
       )
       .construct(graph);
@@ -322,7 +321,7 @@ class DecoderS2S : public DecoderBase {
 
     if(!output_) {
       // construct deep output multi-layer network layer-wise
-      auto hidden = mlp::dense(graph)                                //
+      auto hidden = mlp::dense()                                     //
           ("prefix", prefix_ + "_ff_logit_l1")                       //
           ("dim", opt<int>("dim-emb"))                               //
           ("activation", mlp::act::tanh)                             //
@@ -333,7 +332,7 @@ class DecoderS2S : public DecoderBase {
 
       int dimTrgVoc = opt<std::vector<int>>("dim-vocabs")[batchIndex_];
 
-      auto last = mlp::output(graph)           //
+      auto last = mlp::output()                 //
           ("prefix", prefix_ + "_ff_logit_l2")  //
           ("dim", dimTrgVoc);
 
@@ -349,7 +348,7 @@ class DecoderS2S : public DecoderBase {
 
       // assemble layers into MLP and apply to embeddings, decoder context and
       // aligned source context
-      output_ = mlp::mlp(graph)         //
+      output_ = mlp::mlp()              //
                     .push_back(hidden)  //
                     .push_back(last)
                     .construct(graph);
diff --git a/src/models/transformer.h b/src/models/transformer.h
index ac925a793..f96ce257c 100755
--- a/src/models/transformer.h
+++ b/src/models/transformer.h
@@ -448,14 +448,14 @@ class Transformer : public EncoderOrDecoderBase {
                        int /*startPos*/) const {
     float dropoutRnn = inference_ ? 0.f : opt<float>("dropout-rnn");
 
-    auto rnn = rnn::rnn(graph_)                                    //
+    auto rnn = rnn::rnn()                                          //
         ("type", opt<std::string>("dec-cell"))                     //
         ("prefix", prefix)                                         //
         ("dimInput", opt<int>("dim-emb"))                          //
         ("dimState", opt<int>("dim-emb"))                          //
         ("dropout", dropoutRnn)                                    //
         ("layer-normalization", opt<bool>("layer-normalization"))  //
-        .push_back(rnn::cell(graph_))                              //
+        .push_back(rnn::cell())                                    //
         .construct(graph_);
 
     float dropProb = inference_ ? 0 : opt<float>("transformer-dropout");
@@ -487,11 +487,11 @@ class EncoderTransformer : public Transformer<EncoderBase> {
     int dimTgtVoc = opt<std::vector<int>>("dim-vocabs")[1];  //ULR monon tgt
     int dimEmb = opt<int>("dim-emb");
     int dimUlrEmb = opt<int>("ulr-dim-emb");
-    auto embFactory = ulr_embedding(graph_)("dimSrcVoc", dimSrcVoc)("dimTgtVoc", dimTgtVoc)
-                                           ("dimUlrEmb", dimUlrEmb)("dimEmb", dimEmb)
-                                           ("ulrTrainTransform", opt<bool>("ulr-trainable-transformation"))
-                                           ("ulrQueryFile", opt<std::string>("ulr-query-vectors"))
-                                           ("ulrKeysFile", opt<std::string>("ulr-keys-vectors"));
+    auto embFactory = ulr_embedding()("dimSrcVoc", dimSrcVoc)("dimTgtVoc", dimTgtVoc)
+                                     ("dimUlrEmb", dimUlrEmb)("dimEmb", dimEmb)
+                                     ("ulrTrainTransform", opt<bool>("ulr-trainable-transformation"))
+                                     ("ulrQueryFile", opt<std::string>("ulr-query-vectors"))
+                                     ("ulrKeysFile", opt<std::string>("ulr-keys-vectors"));
     return embFactory.construct(graph_);
   }
 
@@ -499,7 +499,7 @@ class EncoderTransformer : public Transformer<EncoderBase> {
     // standard encoder word embeddings
     int dimVoc = opt<std::vector<int>>("dim-vocabs")[subBatchIndex];
     int dimEmb = opt<int>("dim-emb");
-    auto embFactory = embedding(graph_)("dimVocab", dimVoc)("dimEmb", dimEmb);
+    auto embFactory = embedding()("dimVocab", dimVoc)("dimEmb", dimEmb);
     if (opt<bool>("tied-embeddings-src") || opt<bool>("tied-embeddings-all"))
       embFactory("prefix", "Wemb");
     else
@@ -611,7 +611,7 @@ class DecoderTransformer : public Transformer<DecoderBase> {
 
     int dimTrgVoc = opt<std::vector<int>>("dim-vocabs")[batchIndex_];
 
-    auto layerOut = mlp::output(graph_)        //
+    auto layerOut = mlp::output()              //
         ("prefix", prefix_ + "_ff_logit_out")  //
         ("dim", dimTrgVoc);
 
@@ -628,7 +628,7 @@ class DecoderTransformer : public Transformer<DecoderBase> {
     // [-4: beam depth=1, -3: max length, -2: batch size, -1: vocab dim]
     // assemble layers into MLP and apply to embeddings, decoder context and
     // aligned source context
-    output_ = mlp::mlp(graph_)      //
+    output_ = mlp::mlp()                //
                   .push_back(layerOut)  //
                   .construct(graph_);
   }
diff --git a/src/rnn/attention_constructors.h b/src/rnn/attention_constructors.h
index e377090a3..a878f57f6 100755
--- a/src/rnn/attention_constructors.h
+++ b/src/rnn/attention_constructors.h
@@ -15,7 +15,7 @@ class AttentionFactory : public InputFactory {
   Ptr<EncoderState> state_;
 
 public:
-  AttentionFactory(Ptr<ExpressionGraph> graph) : InputFactory(graph) {}
+//  AttentionFactory(Ptr<ExpressionGraph> graph) : InputFactory(graph) {}
 
   Ptr<CellInput> construct(Ptr<ExpressionGraph> graph) override {
     ABORT_IF(!state_, "EncoderState not set");
diff --git a/src/rnn/constructors.h b/src/rnn/constructors.h
index 406c0c480..3a5826d64 100755
--- a/src/rnn/constructors.h
+++ b/src/rnn/constructors.h
@@ -8,7 +8,7 @@ namespace marian {
 namespace rnn {
 
 struct StackableFactory : public Factory {
-  StackableFactory(Ptr<ExpressionGraph> graph) : Factory(graph) {}
+  StackableFactory() : Factory() {}
   StackableFactory(const StackableFactory&) = default;
   StackableFactory(StackableFactory&&) = default;
 
@@ -26,7 +26,6 @@ struct StackableFactory : public Factory {
 };
 
 struct InputFactory : public StackableFactory {
-  InputFactory(Ptr<ExpressionGraph> graph) : StackableFactory(graph) {}
   virtual Ptr<CellInput> construct(Ptr<ExpressionGraph> graph) = 0;
 };
 
@@ -35,8 +34,6 @@ class CellFactory : public StackableFactory {
   std::vector<std::function<Expr(Ptr<rnn::RNN>)>> inputs_;
 
 public:
-  CellFactory(Ptr<ExpressionGraph> graph) : StackableFactory(graph) {}
-
   virtual Ptr<Cell> construct(Ptr<ExpressionGraph> graph) {
     std::string type = options_->get<std::string>("type");
     if(type == "gru") {
@@ -81,7 +78,7 @@ class CellFactory : public StackableFactory {
   }
 
   CellFactory clone() {
-    CellFactory aClone(nullptr);
+    CellFactory aClone;
     aClone.options_->merge(options_);
     aClone.inputs_ = inputs_;
     return aClone;
@@ -103,8 +100,6 @@ class StackedCellFactory : public CellFactory {
   std::vector<Ptr<StackableFactory>> stackableFactories_;
 
 public:
-  StackedCellFactory(Ptr<ExpressionGraph> graph) : CellFactory(graph) {}
-
   Ptr<Cell> construct(Ptr<ExpressionGraph> graph) override {
     auto stacked = New<StackedCell>(graph, options_);
 
@@ -150,8 +145,6 @@ class RNNFactory : public Factory {
   std::vector<Ptr<CellFactory>> layerFactories_;
 
 public:
-  RNNFactory(Ptr<ExpressionGraph> graph) : Factory(graph) {}
-
   Ptr<RNN> construct(Ptr<ExpressionGraph> graph) {
     auto rnn = New<RNN>(graph, options_);
     for(size_t i = 0; i < layerFactories_.size(); ++i) {
@@ -194,7 +187,7 @@ class RNNFactory : public Factory {
   }
 
   RNNFactory clone() {
-    RNNFactory aClone(nullptr);
+    RNNFactory aClone;
     aClone.options_->merge(options_);
     for(auto lf : layerFactories_)
       aClone.push_back(lf->clone());

From 1211c275581779e769174c3937cee7a993576864 Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Wed, 9 Jan 2019 16:33:55 -0800
Subject: [PATCH 097/838] minor further factoring for super-simple factories

---
 src/layers/factory.h |  8 ++++++++
 src/layers/generic.h | 17 ++++-------------
 2 files changed, 12 insertions(+), 13 deletions(-)

diff --git a/src/layers/factory.h b/src/layers/factory.h
index 4db11d605..e3b742442 100755
--- a/src/layers/factory.h
+++ b/src/layers/factory.h
@@ -31,6 +31,14 @@ class Factory : public std::enable_shared_from_this<Factory> {
   }
 };
 
+// simplest form of Factory that just passes on options to the constructor of a layer type
+template<class Class>
+struct ConstructingFactory : public Factory {
+  Ptr<Class> construct(Ptr<ExpressionGraph> graph) {
+    return New<Class>(graph, options_);
+  }
+};
+
 template <class BaseFactory>
 class Accumulator : public BaseFactory {
   typedef BaseFactory Factory;
diff --git a/src/layers/generic.h b/src/layers/generic.h
index 80ec4ec0f..054589570 100755
--- a/src/layers/generic.h
+++ b/src/layers/generic.h
@@ -55,6 +55,7 @@ struct IEmbeddingLayer {
 };
 
 namespace mlp {
+
 class Dense : public LayerBase, public IUnaryLayer {
 public:
   Dense(Ptr<ExpressionGraph> graph, Ptr<Options> options)
@@ -98,7 +99,6 @@ class Dense : public LayerBase, public IUnaryLayer {
 
           outputs.push_back(layerNorm(dot(in, W), gamma, b));
         }
-
       } else {
         outputs.push_back(affine(in, W, b));
       }
@@ -219,7 +219,7 @@ class Embedding : public LayerBase, public IEmbeddingLayer {
 };
 
 class ULREmbedding : public LayerBase, public IEmbeddingLayer {
-  std::vector<Expr> ulrEmbeddings_; // @TODO: These can now better be written as 6 named class members
+  std::vector<Expr> ulrEmbeddings_; // @TODO: These could now better be written as 6 named class members
 public:
   ULREmbedding(Ptr<ExpressionGraph> graph, Ptr<Options> options) : LayerBase(graph, options) {
     std::string name = "url_embed"; //opt<std::string>("prefix");
@@ -324,17 +324,8 @@ class ULREmbedding : public LayerBase, public IEmbeddingLayer {
   }
 };
 
-struct EmbeddingFactory : public Factory {
-  Ptr<IEmbeddingLayer> construct(Ptr<ExpressionGraph> graph) {
-    return New<Embedding>(graph, options_);
-  }
-};
-
-struct ULREmbeddingFactory : public Factory {
-  Ptr<IEmbeddingLayer> construct(Ptr<ExpressionGraph> graph) {
-    return New<ULREmbedding>(graph, options_);
-  }
-};
+typedef ConstructingFactory<Embedding> EmbeddingFactory;
+typedef ConstructingFactory<ULREmbedding> ULREmbeddingFactory;
 
 typedef Accumulator<EmbeddingFactory> embedding;
 typedef Accumulator<ULREmbeddingFactory> ulr_embedding;

From 93c11dbd03b1d604472b6a0c5e08195b515469be Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Wed, 9 Jan 2019 16:55:27 -0800
Subject: [PATCH 098/838] tied_transposed() no longer takes the name parameter
 which is always "W"

---
 src/layers/constructors.h | 7 +++----
 src/layers/generic.h      | 4 ++--
 src/models/s2s.h          | 2 +-
 src/models/transformer.h  | 2 +-
 4 files changed, 7 insertions(+), 8 deletions(-)

diff --git a/src/layers/constructors.h b/src/layers/constructors.h
index 650c814b0..8c7345d4b 100755
--- a/src/layers/constructors.h
+++ b/src/layers/constructors.h
@@ -57,9 +57,8 @@ class OutputFactory : public LayerFactory {
   Ptr<data::Shortlist> shortlist_;
 
 public:
-  Accumulator<OutputFactory> tie_transposed(const std::string& param,
-                                            const std::string& tied) {
-    tiedParamsTransposed_.push_back({param, tied});
+  Accumulator<OutputFactory> tie_transposed(const std::string& tied) {
+    tiedParamsTransposed_.push_back({"W", tied});
     return Accumulator<OutputFactory>(*this);
   }
 
@@ -71,7 +70,7 @@ class OutputFactory : public LayerFactory {
   Ptr<IUnaryLayer> construct(Ptr<ExpressionGraph> graph) override {
     auto output = New<Output>(graph, options_);
     for(auto& p : tiedParamsTransposed_)
-      output->tie_transposed(p.first, p.second);
+      output->tie_transposed(p.second);
     output->set_shortlist(shortlist_);
     return output;
   }
diff --git a/src/layers/generic.h b/src/layers/generic.h
index 054589570..15a1aec5e 100755
--- a/src/layers/generic.h
+++ b/src/layers/generic.h
@@ -135,8 +135,8 @@ class Output : public LayerBase, public IUnaryLayer {
   Output(Ptr<ExpressionGraph> graph, Ptr<Options> options)
       : LayerBase(graph, options) {}
 
-  void tie_transposed(const std::string& param, const std::string& tied) {
-    tiedParams_[param] = graph_->get(tied);
+  void tie_transposed(const std::string& tied) {
+    tiedParams_["W"] = graph_->get(tied);
   }
 
   void set_shortlist(Ptr<data::Shortlist> shortlist) { shortlist_ = shortlist; }
diff --git a/src/models/s2s.h b/src/models/s2s.h
index e6300e228..58d1377cc 100755
--- a/src/models/s2s.h
+++ b/src/models/s2s.h
@@ -340,7 +340,7 @@ class DecoderS2S : public DecoderBase {
         std::string tiedPrefix = prefix_ + "_Wemb";
         if(opt<bool>("tied-embeddings-all") || opt<bool>("tied-embeddings-src"))
           tiedPrefix = "Wemb";
-        last.tie_transposed("W", tiedPrefix);
+        last.tie_transposed(tiedPrefix);
       }
 
       if(shortlist_)
diff --git a/src/models/transformer.h b/src/models/transformer.h
index f96ce257c..e979a9261 100755
--- a/src/models/transformer.h
+++ b/src/models/transformer.h
@@ -619,7 +619,7 @@ class DecoderTransformer : public Transformer<DecoderBase> {
       std::string tiedPrefix = prefix_ + "_Wemb";
       if(opt<bool>("tied-embeddings-all") || opt<bool>("tied-embeddings-src"))
         tiedPrefix = "Wemb";
-      layerOut.tie_transposed("W", tiedPrefix);
+      layerOut.tie_transposed(tiedPrefix);
     }
 
     if(shortlist_)

From 3ed01fd3d03b7f2fac82898f6502e69d5bdddb4f Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Wed, 9 Jan 2019 17:11:37 -0800
Subject: [PATCH 099/838] removed the no-longer-used map for
 Output::tiedParams, replaced with a single value

---
 src/layers/constructors.h | 15 +++++++--------
 src/layers/generic.h      | 15 +++++++--------
 src/models/s2s.h          |  4 ++--
 src/models/transformer.h  |  4 ++--
 4 files changed, 18 insertions(+), 20 deletions(-)

diff --git a/src/layers/constructors.h b/src/layers/constructors.h
index 8c7345d4b..d0ac34873 100755
--- a/src/layers/constructors.h
+++ b/src/layers/constructors.h
@@ -53,32 +53,31 @@ typedef Accumulator<DenseFactory> dense;
  */
 class OutputFactory : public LayerFactory {
 protected:
-  std::vector<std::pair<std::string, std::string>> tiedParamsTransposed_;
+  std::string tiedTransposedName_;
   Ptr<data::Shortlist> shortlist_;
 
 public:
-  Accumulator<OutputFactory> tie_transposed(const std::string& tied) {
-    tiedParamsTransposed_.push_back({"W", tied});
+  Accumulator<OutputFactory> tieTransposed(const std::string& tied) {
+    tiedTransposedName_ = tied;
     return Accumulator<OutputFactory>(*this);
   }
 
-  Accumulator<OutputFactory> set_shortlist(Ptr<data::Shortlist> shortlist) {
+  Accumulator<OutputFactory> setShortlist(Ptr<data::Shortlist> shortlist) {
     shortlist_ = shortlist;
     return Accumulator<OutputFactory>(*this);
   }
 
   Ptr<IUnaryLayer> construct(Ptr<ExpressionGraph> graph) override {
     auto output = New<Output>(graph, options_);
-    for(auto& p : tiedParamsTransposed_)
-      output->tie_transposed(p.second);
-    output->set_shortlist(shortlist_);
+    output->tieTransposed(graph->get(tiedTransposedName_));
+    output->setShortlist(shortlist_);
     return output;
   }
 
   OutputFactory clone() {
     OutputFactory aClone;
     aClone.options_->merge(options_);
-    aClone.tiedParamsTransposed_ = tiedParamsTransposed_;
+    aClone.tiedTransposedName_ = tiedTransposedName_;
     aClone.shortlist_ = shortlist_;
     return aClone;
   }
diff --git a/src/layers/generic.h b/src/layers/generic.h
index 15a1aec5e..a9e2be016 100755
--- a/src/layers/generic.h
+++ b/src/layers/generic.h
@@ -124,7 +124,7 @@ class Dense : public LayerBase, public IUnaryLayer {
 
 class Output : public LayerBase, public IUnaryLayer {
 private:
-  std::map<std::string, Expr> tiedParams_;
+  Expr tiedParam_;
   Ptr<data::Shortlist> shortlist_;
 
   Expr W_;
@@ -135,25 +135,24 @@ class Output : public LayerBase, public IUnaryLayer {
   Output(Ptr<ExpressionGraph> graph, Ptr<Options> options)
       : LayerBase(graph, options) {}
 
-  void tie_transposed(const std::string& tied) {
-    tiedParams_["W"] = graph_->get(tied);
+  void tieTransposed(Expr tied) {
+    tiedParam_ = tied;
   }
 
-  void set_shortlist(Ptr<data::Shortlist> shortlist) { shortlist_ = shortlist; }
+  void setShortlist(Ptr<data::Shortlist> shortlist) { shortlist_ = shortlist; }
 
   Expr apply(Expr input) override {
     if(!W_) {
       auto name = options_->get<std::string>("prefix");
       auto dim = options_->get<int>("dim");
-      std::string nameW = "W";
 
-      if(tiedParams_.count(nameW)) {
+      if(tiedParam_) {
         transposeW_ = true;
-        W_ = tiedParams_[nameW];
+        W_ = tiedParam_;
         if(shortlist_)
           W_ = rows(W_, shortlist_->indices());
       } else {
-        W_ = graph_->param(name + "_" + nameW,
+        W_ = graph_->param(name + "_W",
                            {input->shape()[-1], dim},
                            inits::glorot_uniform);
         if(shortlist_)
diff --git a/src/models/s2s.h b/src/models/s2s.h
index 58d1377cc..edda79bc8 100755
--- a/src/models/s2s.h
+++ b/src/models/s2s.h
@@ -340,11 +340,11 @@ class DecoderS2S : public DecoderBase {
         std::string tiedPrefix = prefix_ + "_Wemb";
         if(opt<bool>("tied-embeddings-all") || opt<bool>("tied-embeddings-src"))
           tiedPrefix = "Wemb";
-        last.tie_transposed(tiedPrefix);
+        last.tieTransposed(tiedPrefix);
       }
 
       if(shortlist_)
-        last.set_shortlist(shortlist_);
+        last.setShortlist(shortlist_);
 
       // assemble layers into MLP and apply to embeddings, decoder context and
       // aligned source context
diff --git a/src/models/transformer.h b/src/models/transformer.h
index e979a9261..d91dfb7f8 100755
--- a/src/models/transformer.h
+++ b/src/models/transformer.h
@@ -619,11 +619,11 @@ class DecoderTransformer : public Transformer<DecoderBase> {
       std::string tiedPrefix = prefix_ + "_Wemb";
       if(opt<bool>("tied-embeddings-all") || opt<bool>("tied-embeddings-src"))
         tiedPrefix = "Wemb";
-      layerOut.tie_transposed(tiedPrefix);
+      layerOut.tieTransposed(tiedPrefix);
     }
 
     if(shortlist_)
-      layerOut.set_shortlist(shortlist_);
+      layerOut.setShortlist(shortlist_);
 
     // [-4: beam depth=1, -3: max length, -2: batch size, -1: vocab dim]
     // assemble layers into MLP and apply to embeddings, decoder context and

From dd104c34f2d152ba8b09910cf7de9803f228081c Mon Sep 17 00:00:00 2001
From: Marcin Junczys-Dowmunt <Marcin.Junczys-Dowmunt@microsoft.com>
Date: Thu, 10 Jan 2019 16:10:10 -0800
Subject: [PATCH 100/838] fix masking

---
 src/common/config_parser.cpp                |   8 +-
 src/data/corpus_base.h                      |   9 +-
 src/data/default_vocab.cpp                  |   2 +-
 src/models/bert.h                           | 415 ++++++++++++--------
 src/models/model_factory.cpp                |  14 +-
 src/training/graph_group.h                  |   5 +-
 src/training/graph_group_async.cpp          |   4 +-
 src/training/graph_group_async.h            |   5 +-
 src/training/graph_group_multinode.cpp      |   4 +-
 src/training/graph_group_multinode.h        |   4 +-
 src/training/graph_group_multinode_sync.cpp |   3 +-
 src/training/graph_group_multinode_sync.h   |   4 +-
 src/training/graph_group_singleton.h        |   4 +-
 src/training/graph_group_sync.cpp           |   4 +-
 src/training/graph_group_sync.h             |   2 +-
 src/training/scheduler.h                    |   8 +-
 src/training/training.h                     |   5 +-
 17 files changed, 291 insertions(+), 209 deletions(-)

diff --git a/src/common/config_parser.cpp b/src/common/config_parser.cpp
index 1910c85de..da6f7d6a9 100755
--- a/src/common/config_parser.cpp
+++ b/src/common/config_parser.cpp
@@ -201,6 +201,10 @@ void ConfigParser::addOptionsModel(cli::CLIWrapper& cli) {
       "Operation after each transformer layer: d = dropout, a = add, n = normalize",
       "dan");
 
+  cli.add<std::string>("--bert-mask-symbol", "Masking symbol for BERT masked-LM training", "[MASK]");
+  cli.add<std::string>("--bert-sep-symbol", "Sentence separator symbol for BERT next sentence prediction training", "[SEP]");
+  cli.add<std::string>("--bert-class-symbol", "Class symbol BERT classifier training", "[CLS]");
+  cli.add<float>("--bert-masking-fraction", "Fraction of masked out tokens during training", 0.15);
 #ifdef CUDNN
   cli.add<int>("--char-stride",
       "Width of max-pooling layer after convolution layer in char-s2s model",
@@ -280,8 +284,8 @@ void ConfigParser::addOptionsTraining(cli::CLIWrapper& cli) {
       "Display nformation for the first  arg  updates");
   cli.add<bool>("--disp-label-counts",
       "Display label counts when logging loss progress");
-  cli.add<int>("--disp-wps-index",
-      "Display words-per-second ratio based on i-th sub-batch (-1 is last)", -1);
+  cli.add<int>("--disp-label-index",
+      "Display label counts based on i-th sub-batch (-1 is last)", -1);
   cli.add<std::string/*SchedulerPeriod*/>("--save-freq",
       "Save model file every  arg  updates (append 't' for every  arg  target labels)",
       "10000u");
diff --git a/src/data/corpus_base.h b/src/data/corpus_base.h
index b8bc556cc..e25d7d451 100755
--- a/src/data/corpus_base.h
+++ b/src/data/corpus_base.h
@@ -226,7 +226,7 @@ class SubBatch {
  * such as guided alignments and sentence or word-leve weighting.
  */
 class CorpusBatch : public Batch {
-private:
+protected:
   std::vector<Ptr<SubBatch>> subBatches_;
   std::vector<float> guidedAlignment_;
   std::vector<float> dataWeights_;
@@ -307,17 +307,16 @@ class CorpusBatch : public Batch {
    *
    * @return Fake batch of the same size as the real batch.
    */
-  static Ptr<CorpusBatch> fakeBatch(std::vector<size_t>& lengths,
+  static Ptr<CorpusBatch> fakeBatch(const std::vector<size_t>& lengths,
+                                    const std::vector<Ptr<Vocab>>& vocabs, 
                                     size_t batchSize,
                                     Ptr<Options> options) {
     std::vector<Ptr<SubBatch>> batches;
 
     size_t idx = 0;
     for(auto len : lengths) {
-      auto vocab = New<Vocab>(options, 0);
-      vocab->createFake();
       // data: gets initialized to 0. No EOS symbol is distinguished.
-      auto sb = New<SubBatch>(batchSize, len, vocab);
+      auto sb = New<SubBatch>(batchSize, len, vocabs[idx]);
       // set word indices to different values to avoid same hashes
       std::fill(sb->data().begin(), sb->data().end(), (unsigned int)idx++);
       // mask: no items ask being masked out
diff --git a/src/data/default_vocab.cpp b/src/data/default_vocab.cpp
index bac5c72ec..96ae61e64 100755
--- a/src/data/default_vocab.cpp
+++ b/src/data/default_vocab.cpp
@@ -74,7 +74,7 @@ class DefaultVocab : public VocabBase {
 
 
   const std::string& operator[](Word id) const override {
-    ABORT_IF(id >= id2str_.size(), "Unknown word id: ", id);
+    ABORT_IF(id >= id2str_.size(), "Unknown word id: {}", id);
     return id2str_[id];
   }
 
diff --git a/src/models/bert.h b/src/models/bert.h
index f0a1f8eec..232f7e88f 100644
--- a/src/models/bert.h
+++ b/src/models/bert.h
@@ -1,169 +1,248 @@
-#pragma once
-
-#include "data/corpus_base.h"
-#include "models/encoder_classifier.h"
-#include "models/transformer.h"
-
-namespace marian {
-namespace data {
-
-class BertBatch : public CorpusBatch {
-private:
-  std::vector<IndexType> maskedPositions_;
-  std::vector<IndexType> maskedIndices_;
-  std::vector<IndexType> sentenceIndices_;
-
-  void init() {
-    ABORT("Not implemented");
-  }
-
-public:
-  BertBatch(Ptr<CorpusBatch> batch) : CorpusBatch(*batch) {
-    std::cerr << "Creating BERT batch" << std::endl;
-    init();
-  }
-
-  const std::vector<IndexType>& bertMaskedPositions() { return maskedPositions_; }
-  const std::vector<IndexType>& bertMaskedIndices()   { return maskedIndices_; }
-  const std::vector<IndexType>& bertSentenceIndices() { return sentenceIndices_; }
-};
-
-}
-
-class BertEncoderClassifier : public EncoderClassifier {
-public:
-  BertEncoderClassifier(Ptr<Options> options) : EncoderClassifier(options) {}
-
-  std::vector<Ptr<ClassifierState>> apply(Ptr<ExpressionGraph> graph, Ptr<data::CorpusBatch> batch, bool clearGraph) override {
-    Ptr<data::BertBatch> bertBatch = New<data::BertBatch>(batch); // intercept batch and anotate with BERT-specific concepts
-    return EncoderClassifier::apply(graph, bertBatch, clearGraph);
-  }
-};
-
-// @TODO: this should be in transformer.h
-class BertEncoder : public EncoderTransformer {
-public:
-  BertEncoder(Ptr<Options> options) : EncoderTransformer(options) {}
-
-  Expr addSentenceEmbeddings(Expr embeddings, int start, Ptr<data::CorpusBatch> batch) const {
-    Ptr<data::BertBatch> bertBatch = std::dynamic_pointer_cast<data::BertBatch>(batch);
-
-    ABORT_IF(!bertBatch, "Batch could not be converted for BERT training");
-
-    int dimEmb = embeddings->shape()[-1];
-    int dimBatch = embeddings->shape()[-2];
-    int dimWords = embeddings->shape()[-3];
-
-    auto sentenceEmbeddings = embedding(graph_)
-                                ("prefix", "Wsent")
-                                ("dimVocab", 2) // sentence A or sentence B
-                                ("dimEmb", dimEmb)
-                                .construct();
-
-    // @TODO: note this is going to be really slow due to atomicAdd in backward step
-    // with only two classes;
-    // instead two masked reduce operations, maybe in parallel streams?
-    auto sentenceIndices = graph_->indices(bertBatch->bertSentenceIndices());
-    auto signal = rows(sentenceEmbeddings, sentenceIndices); 
-    signal = reshape(signal, {dimWords, dimBatch, dimEmb});
-    return embeddings + signal;
-  }
-
-  virtual Expr addSpecialEmbeddings(Expr input, int start = 0, Ptr<data::CorpusBatch> batch = nullptr) const override {
-    input = addPositionalEmbeddings(input, start, true); // true for BERT
-    input = addSentenceEmbeddings(input, start, batch);
-    return input;
-  }
-};
-
-// Can be used for next sentence prediction task
-class BertClassifier : public ClassifierBase {
-public:
-  BertClassifier(Ptr<Options> options) : ClassifierBase(options) {}
-
-  // The batch has been filled with external classifier labels, @TODO: figure out how to do that
-  Ptr<ClassifierState> apply(Ptr<ExpressionGraph> graph, Ptr<data::CorpusBatch> batch, const std::vector<Ptr<EncoderState>>& encoderStates) override {
-    ABORT_IF(encoderStates.size() != 1, "Currently we only support a single encoder BERT model");
-
-    auto context = encoderStates[0]->getContext();
-    auto classEmbeddings = step(context, /*i=*/0, /*axis=*/-3); // [CLS] symbol is first symbol in each sequence
-    
-    int dimModel = classEmbeddings->shape()[-1];
-    int dimTrgCls = opt<std::vector<int>>("dim-vocabs")[batchIndex_]; // Target vocab is used as class labels
-
-    auto output = mlp::mlp(graph)                                     //
-                    .push_back(mlp::dense(graph)                      //
-                                 ("prefix", prefix_ + "_ff_logit_l1") //
-                                 ("dim", dimModel)                    //
-                                 ("activation", mlp::act::tanh))      // @TODO: do we actually need this?
-                    .push_back(mlp::output(graph)                     //
-                                 ("dim", dimTrgCls))                  //
-                                 ("prefix", prefix_ + "_ff_logit_l2") //
-                    .construct();
-    
-    auto logits = output->apply(classEmbeddings); // class logits for each batch entry
-    
-    auto state = New<ClassifierState>();
-    state->setLogProbs(logits);
-
-    // filled externally, for BERT these are NextSentence prediction labels
-    const auto& classLabels = (*batch)[batchIndex_]->data();
-    state->setTargetIndices(graph->indices(classLabels));
-
-    return state;
-  }
-
-  virtual void clear() override {}
-};
-
-// This is a model that pretrains BERT for classification
-class BertMaskedLM : public ClassifierBase {
-public:
-  BertMaskedLM(Ptr<Options> options) : ClassifierBase(options) {}
-
-  Ptr<ClassifierState> apply(Ptr<ExpressionGraph> graph, Ptr<data::CorpusBatch> batch, const std::vector<Ptr<EncoderState>>& encoderStates) override {
-    Ptr<data::BertBatch> bertBatch = std::dynamic_pointer_cast<data::BertBatch>(batch);
-
-    ABORT_IF(!bertBatch, "Batch could not be converted to batch for BERT training");
-    ABORT_IF(encoderStates.size() != 1, "Currently we only support a single encoder BERT model");
-
-    auto context = encoderStates[0]->getContext();
-    
-    auto bertMaskedPositions = graph->indices(bertBatch->bertMaskedPositions()); // positions in batch of masked entries
-    auto bertMaskedIndices   = graph->indices(bertBatch->bertMaskedIndices());   // vocab ids of entries that have been masked
-    
-    auto classEmbeddings = rows(context, bertMaskedPositions); // subselect stuff that has actually been masked out;
-    
-    int dimModel = classEmbeddings->shape()[-1];
-
-    int dimVoc = opt<std::vector<int>>("dim-vocabs")[batchIndex_]; 
-
-    auto layerTanh = mlp::dense(graph)    //
-        ("dim", dimModel)                 //
-        ("activation", mlp::act::tanh);   //
-    auto layerOut = mlp::output(graph)    //
-        ("dim", dimVoc);                  //
-    layerOut.tie_transposed("W", "Wemb"); // We are a BERT model, hence tie with input
-
-    // [-4: beam depth=1, -3: max length, -2: batch size, -1: vocab dim]
-    // assemble layers into MLP and apply to embeddings, decoder context and
-    // aligned source context
-    auto output = mlp::mlp(graph)                      //
-        ("prefix", prefix_ + "_ff_logit_maskedlm_out") //
-        .push_back(layerTanh)                          // @TODO: do we actually need this?
-        .push_back(layerOut)                           //
-        .construct();
-    
-    auto logits = output->apply(classEmbeddings);
-    
-    auto state = New<ClassifierState>();
-    state->setLogProbs(logits);
-    state->setTargetIndices(bertMaskedIndices);
-
-    return state;
-  }
-
-  virtual void clear() override {}
-};
-
+#pragma once
+
+#include "data/corpus_base.h"
+#include "models/encoder_classifier.h"
+#include "models/transformer.h"
+#include "data/rng_engine.h"
+
+namespace marian {
+namespace data {
+
+class BertBatch : public CorpusBatch {
+private:
+  std::mt19937& eng_;
+  
+  std::vector<IndexType> maskedPositions_;
+  std::vector<IndexType> maskedIndices_;
+  std::vector<IndexType> sentenceIndices_;
+
+  std::string maskSymbol_;
+  std::string sepSymbol_;
+  std::string clsSymbol_;
+
+  std::unique_ptr<std::uniform_int_distribution<Word>> randomWord_;
+  std::unique_ptr<std::uniform_int_distribution<int>> randomPercent_;
+
+  std::unordered_set<Word> dontMask_;
+
+  Word maskOut(Word word, Word mask) {
+    auto subBatch = subBatches_.front();
+
+    int r = (*randomPercent_)(eng_);
+    if (r < 10) { // for 10% of cases return same word
+      return word;
+    } else if (r < 20) { // for 10% return random word
+      Word randWord = (*randomWord_)(eng_);
+      if(dontMask_.count(randWord) > 0) // the random word is a forbidden word
+        return mask;                    // hence return mask symbol
+      else 
+        return randWord;                // else return the random word
+    } else { // for 80% of words apply mask symbol
+      return mask;
+    }
+  }
+
+public:
+  BertBatch(Ptr<CorpusBatch> batch, 
+            std::mt19937& engine,
+            float maskFraction,
+            const std::string& maskSymbol, 
+            const std::string& sepSymbol, 
+            const std::string& clsSymbol)
+    : CorpusBatch(*batch), eng_(engine),
+      maskSymbol_(maskSymbol), sepSymbol_(sepSymbol), clsSymbol_(clsSymbol) {
+
+    auto subBatch = subBatches_.front();
+
+    randomWord_.reset(new std::uniform_int_distribution<Word>(0, subBatch->vocab()->size()));
+    randomPercent_.reset(new std::uniform_int_distribution<int>(0, 100));
+
+    auto& words = subBatch->data();
+
+    Word maskId  = (*subBatch->vocab())[maskSymbol_];
+    Word clsId   = (*subBatch->vocab())[clsSymbol_];
+    Word sepId   = (*subBatch->vocab())[sepSymbol_];
+
+    ABORT_IF(maskId == subBatch->vocab()->getUnkId(),
+             "BERT masking symbol {} not found in vocabulary", maskSymbol_);
+
+    ABORT_IF(sepId == subBatch->vocab()->getUnkId(),
+             "BERT separator symbol {} not found in vocabulary", sepSymbol_);
+    
+    ABORT_IF(clsId == subBatch->vocab()->getUnkId(),
+             "BERT class symbol {} not found in vocabulary", clsSymbol_);
+
+    dontMask_.insert(clsId); // don't mask class token
+    dontMask_.insert(sepId); // don't mask separator token
+    dontMask_.insert(subBatch->vocab()->getEosId()); // don't mask </s>
+    // it's ok to mask <unk>
+
+    std::vector<int> selected;
+    selected.reserve(words.size());
+    for(int i = 0; i < words.size(); ++i) // collect words among which we will mask
+      if(dontMask_.count(words[i]) == 0)  // do not add indices of special words
+        selected.push_back(i);
+    std::shuffle(selected.begin(), selected.end(), eng_);
+    selected.resize(std::ceil(selected.size() * maskFraction)); // select first x percent from shuffled indices
+    
+    for(int i : selected) {
+      maskedPositions_.push_back(i);        // where is the original word?
+      maskedIndices_.push_back(words[i]);   // what is the original word?
+      words[i] = maskOut(words[i], maskId); // mask that position
+    }
+  }
+
+  const std::vector<IndexType>& bertMaskedPositions() { return maskedPositions_; }
+  const std::vector<IndexType>& bertMaskedIndices()   { return maskedIndices_; }
+  const std::vector<IndexType>& bertSentenceIndices() { return sentenceIndices_; }
+};
+
+}
+
+class BertEncoderClassifier : public EncoderClassifier, public data::RNGEngine {
+public:
+  BertEncoderClassifier(Ptr<Options> options) 
+  : EncoderClassifier(options) {}
+
+  std::vector<Ptr<ClassifierState>> apply(Ptr<ExpressionGraph> graph, Ptr<data::CorpusBatch> batch, bool clearGraph) override {
+    // intercept batch and anotate with BERT-specific concepts
+    auto bertBatch = New<data::BertBatch>(batch,
+                                          eng_,
+                                          opt<float>("bert-masking-fraction"),
+                                          opt<std::string>("bert-mask-symbol"),
+                                          opt<std::string>("bert-sep-symbol"),
+                                          opt<std::string>("bert-class-symbol"));
+    return EncoderClassifier::apply(graph, bertBatch, clearGraph);
+  }
+};
+
+// @TODO: this should be in transformer.h
+class BertEncoder : public EncoderTransformer {
+public:
+  BertEncoder(Ptr<Options> options) : EncoderTransformer(options) {}
+
+  Expr addSentenceEmbeddings(Expr embeddings, int start, Ptr<data::CorpusBatch> batch) const {
+    Ptr<data::BertBatch> bertBatch = std::dynamic_pointer_cast<data::BertBatch>(batch);
+
+    ABORT_IF(!bertBatch, "Batch could not be converted for BERT training");
+
+    int dimEmb = embeddings->shape()[-1];
+    int dimBatch = embeddings->shape()[-2];
+    int dimWords = embeddings->shape()[-3];
+
+    auto sentenceEmbeddings = embedding(graph_)
+                                ("prefix", "Wsent")
+                                ("dimVocab", 2) // sentence A or sentence B
+                                ("dimEmb", dimEmb)
+                                .construct();
+
+    // @TODO: note this is going to be really slow due to atomicAdd in backward step
+    // with only two classes;
+    // instead two masked reduce operations, maybe in parallel streams?
+    auto sentenceIndices = graph_->indices(bertBatch->bertSentenceIndices());
+    auto signal = rows(sentenceEmbeddings, sentenceIndices);
+    signal = reshape(signal, {dimWords, dimBatch, dimEmb});
+    return embeddings + signal;
+  }
+
+  virtual Expr addSpecialEmbeddings(Expr input, int start = 0, Ptr<data::CorpusBatch> batch = nullptr) const override {
+    input = addPositionalEmbeddings(input, start, true); // true for BERT
+    input = addSentenceEmbeddings(input, start, batch);
+    return input;
+  }
+};
+
+// Can be used for next sentence prediction task
+class BertClassifier : public ClassifierBase {
+public:
+  BertClassifier(Ptr<Options> options) : ClassifierBase(options) {}
+
+  // The batch has been filled with external classifier labels, @TODO: figure out how to do that
+  Ptr<ClassifierState> apply(Ptr<ExpressionGraph> graph, Ptr<data::CorpusBatch> batch, const std::vector<Ptr<EncoderState>>& encoderStates) override {
+    ABORT_IF(encoderStates.size() != 1, "Currently we only support a single encoder BERT model");
+
+    auto context = encoderStates[0]->getContext();
+    auto classEmbeddings = step(context, /*i=*/0, /*axis=*/-3); // [CLS] symbol is first symbol in each sequence
+
+    int dimModel = classEmbeddings->shape()[-1];
+    int dimTrgCls = opt<std::vector<int>>("dim-vocabs")[batchIndex_]; // Target vocab is used as class labels
+
+    auto output = mlp::mlp(graph)                                     //
+                    .push_back(mlp::dense(graph)                      //
+                                 ("prefix", prefix_ + "_ff_logit_l1") //
+                                 ("dim", dimModel)                    //
+                                 ("activation", mlp::act::tanh))      // @TODO: do we actually need this?
+                    .push_back(mlp::output(graph)                     //
+                                 ("dim", dimTrgCls))                  //
+                                 ("prefix", prefix_ + "_ff_logit_l2") //
+                    .construct();
+
+    auto logits = output->apply(classEmbeddings); // class logits for each batch entry
+
+    auto state = New<ClassifierState>();
+    state->setLogProbs(logits);
+
+    // filled externally, for BERT these are NextSentence prediction labels
+    const auto& classLabels = (*batch)[batchIndex_]->data();
+    state->setTargetIndices(graph->indices(classLabels));
+
+    return state;
+  }
+
+  virtual void clear() override {}
+};
+
+// This is a model that pretrains BERT for classification
+class BertMaskedLM : public ClassifierBase {
+public:
+  BertMaskedLM(Ptr<Options> options) : ClassifierBase(options) {}
+
+  Ptr<ClassifierState> apply(Ptr<ExpressionGraph> graph, Ptr<data::CorpusBatch> batch, const std::vector<Ptr<EncoderState>>& encoderStates) override {
+    Ptr<data::BertBatch> bertBatch = std::dynamic_pointer_cast<data::BertBatch>(batch);
+
+    ABORT_IF(!bertBatch, "Batch could not be converted to batch for BERT training");
+    ABORT_IF(encoderStates.size() != 1, "Currently we only support a single encoder BERT model");
+
+    auto context = encoderStates[0]->getContext();
+
+    auto bertMaskedPositions = graph->indices(bertBatch->bertMaskedPositions()); // positions in batch of masked entries
+    auto bertMaskedIndices   = graph->indices(bertBatch->bertMaskedIndices());   // vocab ids of entries that have been masked
+
+    int dimModel = context->shape()[-1];
+    int dimBatch = context->shape()[-2];
+    int dimTime  = context->shape()[-3];
+
+    auto maskedEmbeddings = rows(reshape(context, {dimBatch * dimTime, dimModel}), bertMaskedPositions); // subselect stuff that has actually been masked out;
+
+    int dimVoc = opt<std::vector<int>>("dim-vocabs")[batchIndex_];
+
+    auto layerTanh = mlp::dense(graph)    //
+        ("prefix", prefix_ + "_ff_logit_maskedlm_out_l1") //
+        ("dim", dimModel)                 //
+        ("activation", mlp::act::tanh);   //
+    auto layerOut = mlp::output(graph)    //
+        ("prefix", prefix_ + "_ff_logit_maskedlm_out_l2") //
+        ("dim", dimVoc);                  //
+    layerOut.tie_transposed("W", "Wemb"); // We are a BERT model, hence tie with input
+
+    // [-4: beam depth=1, -3: max length, -2: batch size, -1: vocab dim]
+    // assemble layers into MLP and apply to embeddings, decoder context and
+    // aligned source context
+    auto output = mlp::mlp(graph)                      //
+        .push_back(layerTanh)                          // @TODO: do we actually need this?
+        .push_back(layerOut)                           //
+        .construct();
+
+    auto logits = output->apply(maskedEmbeddings);
+
+    auto state = New<ClassifierState>();
+    state->setLogProbs(logits);
+    state->setTargetIndices(bertMaskedIndices);
+
+    return state;
+  }
+
+  virtual void clear() override {}
+};
+
 }
\ No newline at end of file
diff --git a/src/models/model_factory.cpp b/src/models/model_factory.cpp
index 2b19b4990..0d66b95d8 100644
--- a/src/models/model_factory.cpp
+++ b/src/models/model_factory.cpp
@@ -90,7 +90,7 @@ Ptr<ModelBase> EncoderClassifierFactory::construct() {
     enccls = New<BertEncoderClassifier>(options_);
   } else {
     enccls = New<EncoderClassifier>(options_);
-  } 
+  }
 
   for(auto& ef : encoders_)
     enccls->push_back(ef(options_).construct());
@@ -233,14 +233,14 @@ Ptr<ModelBase> by_type(std::string type, usage use, Ptr<Options> options) {
     return models::encoder_classifier()(options) //
         ("usage", use)                           //
         .push_back(models::encoder()             //
-                    ("type", "bert-encoder")     // transformer encoder for now
+                    ("type", "transformer")      // @TODO: replace with 'bert-encoder'
                     ("index", 0))                // close to original transformer encoder
-        .push_back(models::classifier()          //
-                    ("type", "bert-classifier")  //
-                    ("index", 1))                // next sentence prediction 
         .push_back(models::classifier()          //
                     ("type", "bert-masked-lm")   //
                     ("index", 0))                // multi-task learning with MaskedLM
+        .push_back(models::classifier()          //
+                    ("type", "bert-classifier")  //
+                    ("index", 1))                // next sentence prediction
         .construct();
   }
 
@@ -248,11 +248,11 @@ Ptr<ModelBase> by_type(std::string type, usage use, Ptr<Options> options) {
     return models::encoder_classifier()(options) //
         ("usage", use)                           //
         .push_back(models::encoder()             //
-                    ("type", "transformer")     //
+                    ("type", "transformer")      //
                     ("index", 0))                // close to original transformer encoder
         .push_back(models::classifier()          //
                     ("type", "bert-classifier")  //
-                    ("index", 1))                // next sentence prediction 
+                    ("index", 1))                // next sentence prediction
         .construct();
   }
 
diff --git a/src/training/graph_group.h b/src/training/graph_group.h
index 2bd98d40b..ea1705698 100755
--- a/src/training/graph_group.h
+++ b/src/training/graph_group.h
@@ -50,6 +50,7 @@ class GraphGroup {
    */
   virtual Ptr<data::BatchStats> collectStats(Ptr<ExpressionGraph> graph,
                                              Ptr<models::ModelBase> model,
+                                             const std::vector<Ptr<Vocab>>& vocabs,
                                              size_t multiplier = 1) {
     auto stats = New<data::BatchStats>();
 
@@ -79,7 +80,7 @@ class GraphGroup {
       for(int j = 0; j < lengths.size(); ++j) // apply length restrictions
         lengths[j] = std::min(lengths[j], localMaxes[j]);
 
-      auto batch = data::CorpusBatch::fakeBatch(lengths, maxBatch, options_);
+      auto batch = data::CorpusBatch::fakeBatch(lengths, vocabs, maxBatch, options_);
       auto cost = model->build(graph, batch);
       fits = graph->fits();
       if(fits)
@@ -99,7 +100,7 @@ class GraphGroup {
 
       do {
         size_t current = (start + end) / 2;
-        auto batch = data::CorpusBatch::fakeBatch(lengths, current, options_);
+        auto batch = data::CorpusBatch::fakeBatch(lengths, vocabs, current, options_);
         auto cost = model->build(graph, batch);
         fits = graph->fits();
 
diff --git a/src/training/graph_group_async.cpp b/src/training/graph_group_async.cpp
index 1d0042019..3195c5624 100755
--- a/src/training/graph_group_async.cpp
+++ b/src/training/graph_group_async.cpp
@@ -254,8 +254,8 @@ void AsyncGraphGroup::execute(Ptr<data::Batch> batch) {
 
       if(optimizerDelay_ > 1) {
         std::vector<size_t> fakeLength = {1, 1};
-        auto fb = data::CorpusBatch::fakeBatch(
-            fakeLength, num_seen_sentences, NULL);
+        std::vector<Ptr<Vocab>> vocabs;
+        auto fb = data::CorpusBatch::fakeBatch(fakeLength, vocabs, num_seen_sentences, NULL);
         fb->front()->setWords(num_seen_words);
         scheduler_->update(cost, fb);
 
diff --git a/src/training/graph_group_async.h b/src/training/graph_group_async.h
index d3af0f22b..e8c794edd 100755
--- a/src/training/graph_group_async.h
+++ b/src/training/graph_group_async.h
@@ -63,8 +63,9 @@ class AsyncGraphGroup : public GraphGroup, public ExponentialSmoothing {
   void save(bool final = false) override;
   void save(Ptr<ExpressionGraph>, bool final = false);
 
-  Ptr<data::BatchStats> collectStats() {
-    return GraphGroup::collectStats(graphs_[0], builders_[0]);
+  // @TODO: give it a fake batch generator which own vocabs instead of passing vocabs
+  Ptr<data::BatchStats> collectStats(const std::vector<Ptr<Vocab>>& vocabs) {
+    return GraphGroup::collectStats(graphs_[0], builders_[0], vocabs);
   }
 
   virtual void finalize() override;
diff --git a/src/training/graph_group_multinode.cpp b/src/training/graph_group_multinode.cpp
index f5cbafd36..bac76b945 100755
--- a/src/training/graph_group_multinode.cpp
+++ b/src/training/graph_group_multinode.cpp
@@ -597,8 +597,8 @@ void MultiNodeGraphGroup::execute(Ptr<data::Batch> batch) {
 
       if(tau_ > 1) {
         std::vector<size_t> fakeLength = {1, 1};
-        auto fb = data::CorpusBatch::fakeBatch(
-            fakeLength, num_seen_sentences, NULL);
+        std::vector<Ptr<Vocab>> vocabs;
+        auto fb = data::CorpusBatch::fakeBatch(fakeLength, vocabs, num_seen_sentences, NULL);
         fb->front()->setWords(num_seen_words);
         scheduler_->update(cost, fb);
       } else {
diff --git a/src/training/graph_group_multinode.h b/src/training/graph_group_multinode.h
index c86225ebc..ae4f1400f 100755
--- a/src/training/graph_group_multinode.h
+++ b/src/training/graph_group_multinode.h
@@ -454,8 +454,8 @@ class MultiNodeGraphGroup : public MultiNodeGraphGroupBase {
   /**
    * Collect statistics from first client's graph.
    */
-  Ptr<data::BatchStats> collectStats() {
-    return GraphGroup::collectStats(clientGraphs_[0], clientBuilders_[0]);
+  Ptr<data::BatchStats> collectStats(const std::vector<Ptr<Vocab>>& vocabs) {
+    return GraphGroup::collectStats(clientGraphs_[0], clientBuilders_[0], vocabs);
   }
 };
 }  // namespace marian
diff --git a/src/training/graph_group_multinode_sync.cpp b/src/training/graph_group_multinode_sync.cpp
index 328657057..05abd6d5f 100755
--- a/src/training/graph_group_multinode_sync.cpp
+++ b/src/training/graph_group_multinode_sync.cpp
@@ -224,8 +224,7 @@ void MultiNodeGraphGroupSync::execute(Ptr<data::Batch> fullBatch) {
 
     if(tau_ > 1) {
       std::vector<size_t> fakeLength = {1, 1};
-      auto fb
-          = data::CorpusBatch::fakeBatch(fakeLength, num_seen_sentences, NULL);
+      auto fb = data::CorpusBatch::fakeBatch(fakeLength, std::vector<Ptr<Vocab>>(), num_seen_sentences, NULL);
       fb->front()->setWords(num_seen_words);
       scheduler_->update(cost, fb);
     } else {
diff --git a/src/training/graph_group_multinode_sync.h b/src/training/graph_group_multinode_sync.h
index bfef2050c..55474b3ed 100755
--- a/src/training/graph_group_multinode_sync.h
+++ b/src/training/graph_group_multinode_sync.h
@@ -220,9 +220,9 @@ class MultiNodeGraphGroupSync : public MultiNodeGraphGroupBase {
   /**
    * Collect statistics from first client's graph.
    */
-  Ptr<data::BatchStats> collectStats() {
+  Ptr<data::BatchStats> collectStats(const std::vector<Ptr<Vocab>>& vocabs) {
     return GraphGroup::collectStats(
-        clientGraphs_[0], clientBuilders_[0], devices_.size());
+        clientGraphs_[0], clientBuilders_[0], vocabs, devices_.size());
   }
 };
 }  // namespace marian
diff --git a/src/training/graph_group_singleton.h b/src/training/graph_group_singleton.h
index 1eb589d91..93a6c0699 100755
--- a/src/training/graph_group_singleton.h
+++ b/src/training/graph_group_singleton.h
@@ -125,8 +125,8 @@ class SingletonGraph : public GraphGroup, public ExponentialSmoothing {
       });
   }
 
-  Ptr<data::BatchStats> collectStats() {
-    return GraphGroup::collectStats(graph_, builder_);
+  Ptr<data::BatchStats> collectStats(const std::vector<Ptr<Vocab>>& vocabs) {
+    return GraphGroup::collectStats(graph_, builder_, vocabs);
   }
 
   virtual void finalize() override { finalized_ = true; }
diff --git a/src/training/graph_group_sync.cpp b/src/training/graph_group_sync.cpp
index 6699484c1..c3b025e31 100755
--- a/src/training/graph_group_sync.cpp
+++ b/src/training/graph_group_sync.cpp
@@ -100,10 +100,10 @@ void SyncGraphGroup::initializeAvg() {
   comm_->foreach(init, /*parallel=*/false); // @TODO: is sequential operation necessary here? (is the allocation stuff sufficiently reentrant or thread-separated?)
 }
 
-Ptr<data::BatchStats> SyncGraphGroup::collectStats() {
+Ptr<data::BatchStats> SyncGraphGroup::collectStats(const std::vector<Ptr<Vocab>>& vocabs) {
   // @TODO: This should only run on MPI process 0. Also we can share vv this vv expression with update().
   size_t multiplier = devices_.size() * mpi_->numMPIProcesses() * delay_;
-  return GraphGroup::collectStats(graphs_[0], builders_[0], multiplier);
+  return GraphGroup::collectStats(graphs_[0], builders_[0], vocabs, multiplier);
 }
 
 void SyncGraphGroup::update(Ptr<data::Batch> batch) /*override*/ {
diff --git a/src/training/graph_group_sync.h b/src/training/graph_group_sync.h
index 478166996..39008fda2 100755
--- a/src/training/graph_group_sync.h
+++ b/src/training/graph_group_sync.h
@@ -42,7 +42,7 @@ class SyncGraphGroup : public GraphGroup, public ExponentialSmoothing {
   void load() override;
   void save(bool final = false) override;
 
-  Ptr<data::BatchStats> collectStats();
+  Ptr<data::BatchStats> collectStats(const std::vector<Ptr<Vocab>>&);
   // @TODO: consider to make this a virtual as well? Currently it is a template dispatch
 };
 }  // namespace marian
diff --git a/src/training/scheduler.h b/src/training/scheduler.h
index 1585fd8a4..1e9491cc8 100755
--- a/src/training/scheduler.h
+++ b/src/training/scheduler.h
@@ -47,7 +47,7 @@ class Scheduler : public TrainingObserver {
 public:
   Scheduler(Ptr<Options> options, Ptr<TrainingState> state)
       : options_(options), state_(state),
-        dispIndex_{options_->get<int>("disp-wps-index", -1)} {
+        dispIndex_{options_->get<int>("disp-label-index", -1)} {
     state_->eta = getLearningRate(*state);
   }
 
@@ -169,13 +169,11 @@ class Scheduler : public TrainingObserver {
 
     size_t batchSize = 0;    // number of sentences in batch
     size_t batchLabels = 0;  // number of target words in batch
-    size_t batchDisp = 0;    // number of words in chosen sub-batch, last by default unless set differently in dispIndex_. Used for displaying speed.
 
     for(const auto& batch : batches) {
       if (batch) { // (nullptr is allowed as result of split)
         batchSize   += batch->size();
-        batchLabels += batch->words(-1);
-        batchDisp   += batch->words(dispIndex_);
+        batchLabels += batch->words(dispIndex_);
       }
     }
 
@@ -203,7 +201,7 @@ class Scheduler : public TrainingObserver {
       state_->costCount += batchSize;
     }
 
-    state_->wordsDisp    += batchDisp;   // words at given input processed since last display, for speed display
+    state_->wordsDisp    += batchLabels; // words at given input processed since last display, for speed display
     state_->samplesEpoch += batchSize;   // sentences processed in this epoch
     state_->labelsTotal  += batchLabels; // total labels processed
 
diff --git a/src/training/training.h b/src/training/training.h
index 2209b96e2..94d8c8900 100755
--- a/src/training/training.h
+++ b/src/training/training.h
@@ -40,9 +40,10 @@ class Train : public ModelTask {
           "[batching] Collecting statistics for batch fitting with step size "
           "{}",
           options_->get<size_t>("mini-batch-fit-step"));
-      // @TODO, better fake batch with vocabulary
+      // @TODO this should receive a function object that can generate a fake batch
+      // that way vocabs would not be exposed. 
       auto model = New<ModelWrapper>(options_);
-      THREAD_GUARD(stats = model->collectStats());
+      THREAD_GUARD(stats = model->collectStats(dataset->getVocabs()));
       LOG(info, "[batching] Done");
     }
 

From 5b0885670b11a73a5122b086a2b7f9f37ccf8b92 Mon Sep 17 00:00:00 2001
From: Marcin Junczys-Dowmunt <Marcin.Junczys-Dowmunt@microsoft.com>
Date: Thu, 10 Jan 2019 16:15:23 -0800
Subject: [PATCH 101/838] renamed function name

---
 src/models/bert.h | 10 +++++-----
 1 file changed, 5 insertions(+), 5 deletions(-)

diff --git a/src/models/bert.h b/src/models/bert.h
index 232f7e88f..1a7fb58e1 100644
--- a/src/models/bert.h
+++ b/src/models/bert.h
@@ -13,7 +13,7 @@ class BertBatch : public CorpusBatch {
   std::mt19937& eng_;
   
   std::vector<IndexType> maskedPositions_;
-  std::vector<IndexType> maskedIndices_;
+  std::vector<IndexType> maskedWords_;
   std::vector<IndexType> sentenceIndices_;
 
   std::string maskSymbol_;
@@ -87,13 +87,13 @@ class BertBatch : public CorpusBatch {
     
     for(int i : selected) {
       maskedPositions_.push_back(i);        // where is the original word?
-      maskedIndices_.push_back(words[i]);   // what is the original word?
+      maskedWords_.push_back(words[i]);   // what is the original word?
       words[i] = maskOut(words[i], maskId); // mask that position
     }
   }
 
   const std::vector<IndexType>& bertMaskedPositions() { return maskedPositions_; }
-  const std::vector<IndexType>& bertMaskedIndices()   { return maskedIndices_; }
+  const std::vector<IndexType>& bertMaskedWords()   { return maskedWords_; }
   const std::vector<IndexType>& bertSentenceIndices() { return sentenceIndices_; }
 };
 
@@ -206,7 +206,7 @@ class BertMaskedLM : public ClassifierBase {
     auto context = encoderStates[0]->getContext();
 
     auto bertMaskedPositions = graph->indices(bertBatch->bertMaskedPositions()); // positions in batch of masked entries
-    auto bertMaskedIndices   = graph->indices(bertBatch->bertMaskedIndices());   // vocab ids of entries that have been masked
+    auto bertMaskedWords     = graph->indices(bertBatch->bertMaskedWords());   // vocab ids of entries that have been masked
 
     int dimModel = context->shape()[-1];
     int dimBatch = context->shape()[-2];
@@ -237,7 +237,7 @@ class BertMaskedLM : public ClassifierBase {
 
     auto state = New<ClassifierState>();
     state->setLogProbs(logits);
-    state->setTargetIndices(bertMaskedIndices);
+    state->setTargetIndices(bertMaskedWords);
 
     return state;
   }

From bc9851ccea0f3d55d4ca1f315c3e891de7ccf84e Mon Sep 17 00:00:00 2001
From: Marcin Junczys-Dowmunt <Marcin.Junczys-Dowmunt@microsoft.com>
Date: Thu, 10 Jan 2019 16:35:14 -0800
Subject: [PATCH 102/838] randomize fake batch content

---
 src/data/corpus_base.h | 6 ++++--
 src/models/bert.h      | 4 ++--
 2 files changed, 6 insertions(+), 4 deletions(-)

diff --git a/src/data/corpus_base.h b/src/data/corpus_base.h
index e25d7d451..a1c70e63d 100755
--- a/src/data/corpus_base.h
+++ b/src/data/corpus_base.h
@@ -315,12 +315,14 @@ class CorpusBatch : public Batch {
 
     size_t idx = 0;
     for(auto len : lengths) {
-      // data: gets initialized to 0. No EOS symbol is distinguished.
       auto sb = New<SubBatch>(batchSize, len, vocabs[idx]);
       // set word indices to different values to avoid same hashes
-      std::fill(sb->data().begin(), sb->data().end(), (unsigned int)idx++);
+      // rand() is OK, this does not affect state in any way
+      std::transform(sb->data().begin(), sb->data().end(), sb->data().begin(),
+                     [&](Word) { return rand() % vocabs[idx]->size(); }); 
       // mask: no items ask being masked out
       std::fill(sb->mask().begin(), sb->mask().end(), 1.f);
+      idx++;
 
       batches.push_back(sb);
     }
diff --git a/src/models/bert.h b/src/models/bert.h
index 1a7fb58e1..69b826993 100644
--- a/src/models/bert.h
+++ b/src/models/bert.h
@@ -87,13 +87,13 @@ class BertBatch : public CorpusBatch {
     
     for(int i : selected) {
       maskedPositions_.push_back(i);        // where is the original word?
-      maskedWords_.push_back(words[i]);   // what is the original word?
+      maskedWords_.push_back(words[i]);     // what is the original word?
       words[i] = maskOut(words[i], maskId); // mask that position
     }
   }
 
   const std::vector<IndexType>& bertMaskedPositions() { return maskedPositions_; }
-  const std::vector<IndexType>& bertMaskedWords()   { return maskedWords_; }
+  const std::vector<IndexType>& bertMaskedWords()     { return maskedWords_; }
   const std::vector<IndexType>& bertSentenceIndices() { return sentenceIndices_; }
 };
 

From c5839f07e0805cd645c86884fe758fac4c9a1c2d Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Thu, 10 Jan 2019 21:14:55 -0800
Subject: [PATCH 103/838] towards factored embeddings; towards csr_dot()
 operation

---
 src/CMakeLists.txt                 |  1 +
 src/common/config_parser.cpp       |  3 ++
 src/graph/expression_operators.cpp |  7 ++++
 src/graph/expression_operators.h   |  2 ++
 src/graph/node_operators_binary.h  | 54 ++++++++++++++++++++++++++++++
 src/layers/generic.h               | 33 ++----------------
 src/models/transformer.h           |  4 +++
 src/tensors/cpu/prod.cpp           |  9 +++++
 src/tensors/gpu/backend.h          | 17 ++++------
 src/tensors/gpu/prod.cu            | 37 ++++++++++++++++++++
 src/tensors/gpu/prod.h             |  5 +++
 src/tensors/tensor_operators.h     |  1 +
 vs/Marian.vcxproj                  |  1 +
 vs/Marian.vcxproj.filters          |  3 ++
 14 files changed, 136 insertions(+), 41 deletions(-)
 mode change 100644 => 100755 src/tensors/gpu/prod.h

diff --git a/src/CMakeLists.txt b/src/CMakeLists.txt
index 03524117b..a3b2724ea 100644
--- a/src/CMakeLists.txt
+++ b/src/CMakeLists.txt
@@ -47,6 +47,7 @@ add_library(marian STATIC
   graph/node_initializers.cpp
 
   layers/convolution.cpp
+  layers/generic.cpp
   layers/loss.cpp
   layers/weight.cpp
 
diff --git a/src/common/config_parser.cpp b/src/common/config_parser.cpp
index 70a30a5fd..e301fb30b 100755
--- a/src/common/config_parser.cpp
+++ b/src/common/config_parser.cpp
@@ -30,6 +30,7 @@ const std::set<std::string> PATHS = {
   "train-sets",
   "vocabs",
   "embedding-vectors",
+  "embedding-factors",
   "valid-sets",
   "valid-script-path",
   "valid-log",
@@ -385,6 +386,8 @@ void ConfigParser::addOptionsTraining(cli::CLIWrapper& cli) {
      "Fix source embeddings. Affects all encoders");
   cli.add<bool>("--embedding-fix-trg",
      "Fix target embeddings. Affects all decoders");
+  cli.add_nondefault<std::vector<std::string>>("--embedding-factors",
+     "Paths to (factor map, factor list) file for factored embeddings");
 
   cli.add<bool>("--multi-node",
      "Enable asynchronous multi-node training through MPI (and legacy sync if combined with --sync-sgd)");
diff --git a/src/graph/expression_operators.cpp b/src/graph/expression_operators.cpp
index cdfeff271..12f5464a6 100755
--- a/src/graph/expression_operators.cpp
+++ b/src/graph/expression_operators.cpp
@@ -417,6 +417,13 @@ Expr affine(Expr a, Expr b, Expr bias, bool transA, bool transB, float scale) {
   }
 }
 
+// multiply a CSR matrix A with a matrix B
+// A[i,j] is at A_values[A_offsets[i]+k], where k is position of j in A_indices[A_offsets[i]:A_offsets[i+1]]
+// Result shape is (Aoffsets.size() - 1, B->shape(-1))
+Expr csr_dot(Expr A_values, Expr A_indices, Expr A_offsets, Expr B) {
+  return Expression<CSRDotNodeOp>(A_values, A_indices, A_offsets, B);
+}
+
 // swap the last two axes
 // @TODO: change to swapAxes(a, -1, -2)
 Expr transpose(Expr a) {
diff --git a/src/graph/expression_operators.h b/src/graph/expression_operators.h
index 7c3797069..080fbf2d5 100755
--- a/src/graph/expression_operators.h
+++ b/src/graph/expression_operators.h
@@ -112,6 +112,8 @@ Expr affine(Expr a,
             bool transB = false,
             float scalar = 1.f);
 
+Expr csr_dot(Expr Avalues, Expr Aindices, Expr Aoffsets, Expr B);
+
 Expr transpose(Expr a);
 Expr transpose(Expr a, const std::vector<int>& axes);
 
diff --git a/src/graph/node_operators_binary.h b/src/graph/node_operators_binary.h
index 868ee4ebd..e27aa6f74 100755
--- a/src/graph/node_operators_binary.h
+++ b/src/graph/node_operators_binary.h
@@ -409,6 +409,60 @@ class DotBatchedNodeOp : public NaryNodeOp {
   const std::string color() override { return "orange"; }
 };
 
+class CSRDotNodeOp : public NaryNodeOp {
+public:
+  CSRDotNodeOp(Expr A_values, Expr A_indices, Expr A_offsets, Expr B)
+      : NaryNodeOp({ A_values, A_indices, A_offsets, B }, newShape(A_values, A_indices, A_offsets, B)) {
+    matchOrAbort<IndexType>(A_indices->value_type());
+    matchOrAbort<IndexType>(A_offsets->value_type());
+  }
+
+  Shape newShape(Expr A_values, Expr A_indices, Expr A_offsets, Expr B) {
+    ABORT_IF(A_values->shape().size() != 1 || A_indices->shape().size() != 1 || A_offsets->shape().size() != 1,
+        "Sparse matrix components must all be vectors.");
+    auto outShape = B->shape();
+    outShape.set(0, A_offsets->shape()[0] - 1); // A_offsets = A.numRows + 1
+    return outShape;
+  }
+
+  NodeOps forwardOps() override {
+    // C = dot(A, B)
+    return {NodeOp(CSRProd(val_,
+                           child(0)->val(),
+                           child(1)->val(),
+                           child(2)->val(),
+                           child(3)->val()))};
+  }
+
+  NodeOps backwardOps() override {
+#if 1
+    return {}; // this is coming next
+#else
+    return {NodeOp(Prod(child(0)->grad(),
+                        adj_,
+                        child(1)->val(),
+                        false,
+                        true,
+                        1.0,
+                        scalar_)),
+            NodeOp(Prod(child(1)->grad(),
+                        child(0)->val(),
+                        adj_,
+                        true,
+                        false,
+                        1.0,
+                        scalar_))};
+#endif
+  }
+
+  const std::string type() override { return "•"; }
+
+  const std::string color() override { return "orange"; }
+};
+
+
+
+
 struct ScalarProductNodeOp : public NaryNodeOp {
   ScalarProductNodeOp(Expr a, Expr b, int axis)
       : NaryNodeOp({a, b}, newShape(a, b, axis)) {}
diff --git a/src/layers/generic.h b/src/layers/generic.h
index a9e2be016..0289e57b7 100755
--- a/src/layers/generic.h
+++ b/src/layers/generic.h
@@ -176,38 +176,11 @@ class Output : public LayerBase, public IUnaryLayer {
 
 class Embedding : public LayerBase, public IEmbeddingLayer {
   Expr E_;
+  Ptr<class EmbeddingFactorMapping> embeddingFactorMapping_;
 public:
-  Embedding(Ptr<ExpressionGraph> graph, Ptr<Options> options) : LayerBase(graph, options) {
-    std::string name = opt<std::string>("prefix");
-    int dimVoc = opt<int>("dimVocab");
-    int dimEmb = opt<int>("dimEmb");
-
-    bool fixed = opt<bool>("fixed", false);
-
-    NodeInitializer initFunc = inits::glorot_uniform;
-    if (options_->has("embFile")) {
-      std::string file = opt<std::string>("embFile");
-      if (!file.empty()) {
-        bool norm = opt<bool>("normalization", false);
-        initFunc = inits::from_word2vec(file, dimVoc, dimEmb, norm);
-      }
-    }
-
-    E_ = graph_->param(name, {dimVoc, dimEmb}, initFunc, fixed);
-  }
+  Embedding(Ptr<ExpressionGraph> graph, Ptr<Options> options);
 
-  std::tuple<Expr/*embeddings*/, Expr/*mask*/> apply(Ptr<data::SubBatch> subBatch) const override final {
-    auto graph = E_->graph();
-    int dimBatch = (int)subBatch->batchSize();
-    int dimEmb = E_->shape()[-1];
-    int dimWords = (int)subBatch->batchWidth();
-    // @TODO: merge this with below. Currently can't only due to the extra beam dimension
-    auto chosenEmbeddings = rows(E_, subBatch->data());
-    auto batchEmbeddings = reshape(chosenEmbeddings, { dimWords, dimBatch, dimEmb });
-    auto batchMask = graph->constant({ dimWords, dimBatch, 1 },
-                                     inits::from_vector(subBatch->mask()));
-    return std::make_tuple(batchEmbeddings, batchMask);
-  }
+  std::tuple<Expr/*embeddings*/, Expr/*mask*/> apply(Ptr<data::SubBatch> subBatch) const override final;
 
   // special version used in decoding
   Expr apply(const std::vector<IndexType>& embIdx, int dimBatch, int dimBeam) const override final {
diff --git a/src/models/transformer.h b/src/models/transformer.h
index d91dfb7f8..c142d0dd7 100755
--- a/src/models/transformer.h
+++ b/src/models/transformer.h
@@ -511,6 +511,10 @@ class EncoderTransformer : public Transformer<EncoderBase> {
       embFactory("embFile", embFiles[subBatchIndex])
                 ("normalization", opt<bool>("embedding-normalization"));
     }
+    if (options_->has("embedding-factors")) {
+      embFactory("embedding-factors", opt<std::string>("embedding-factors"));
+      embFactory("vocab", opt<std::vector<std::string>>("vocabs")[subBatchIndex]);
+    }
     return embFactory.construct(graph_);
   }
 
diff --git a/src/tensors/cpu/prod.cpp b/src/tensors/cpu/prod.cpp
index 69923f872..f3dcb4fee 100755
--- a/src/tensors/cpu/prod.cpp
+++ b/src/tensors/cpu/prod.cpp
@@ -167,5 +167,14 @@ void ProdWithBias(marian::Tensor C,
   cpu::int16::AddBias(C, bias);
 }
 
+void CSRProd(marian::Tensor C,
+             const marian::Tensor& A_values,
+             const marian::Tensor& A_indices,
+             const marian::Tensor& A_offsets,
+             const marian::Tensor& B) {
+  C, A_values, A_indices, A_offsets, B;
+  ABORT("CSRProd is not yet implemented for CPU");
+}
+
 }  // namespace cpu
 }  // namespace marian
diff --git a/src/tensors/gpu/backend.h b/src/tensors/gpu/backend.h
index ab1968c1f..87f6407c5 100755
--- a/src/tensors/gpu/backend.h
+++ b/src/tensors/gpu/backend.h
@@ -6,6 +6,7 @@
 
 #include <cuda.h>
 #include <cublas_v2.h>
+#include <cusparse.h>
 #include <curand.h>
 
 namespace marian {
@@ -15,11 +16,13 @@ class Backend : public marian::Backend {
 public:
   Backend(DeviceId deviceId, size_t seed) : marian::Backend(deviceId, seed) {
     setDevice();
-    setHandles();
+    cublasCreate(&cublasHandle_);
+    cusparseCreate(&cusparseHandle_);
   }
 
   ~Backend() {
     setDevice();
+    cusparseDestroy(cusparseHandle_);
     cublasDestroy(cublasHandle_);
   }
 
@@ -28,19 +31,11 @@ class Backend : public marian::Backend {
   void synchronize() override { cudaStreamSynchronize(0); }
 
   cublasHandle_t getCublasHandle() { return cublasHandle_; }
+  cusparseHandle_t getCusparseHandle() { return cusparseHandle_; }
 
 private:
   cublasHandle_t cublasHandle_;
-
-  void setHandles() {
-    cublasHandle_ = create_handle();
-  }
-
-  cublasHandle_t create_handle() {
-    cublasHandle_t cublasHandle;
-    cublasCreate(&cublasHandle);
-    return cublasHandle;
-  }
+  cusparseHandle_t cusparseHandle_;
 };
 }  // namespace gpu
 }  // namespace marian
diff --git a/src/tensors/gpu/prod.cu b/src/tensors/gpu/prod.cu
index 9da32e65a..d2ac84a2a 100755
--- a/src/tensors/gpu/prod.cu
+++ b/src/tensors/gpu/prod.cu
@@ -1,5 +1,6 @@
 
 #include <cublas_v2.h>
+#include <cusparse.h>
 
 // clang-format off
 #include "tensors/gpu/prod.h"
@@ -224,5 +225,41 @@ void ProdBatched(marian::Tensor C,
   allocator->free(mp_cptr);
 }
 
+void CSRProd(marian::Tensor C,
+             const marian::Tensor& A_values,
+             const marian::Tensor& A_indices,
+             const marian::Tensor& A_offsets,
+             const marian::Tensor& B) {
+  cudaSetDevice(C->getDeviceId().no);
+  auto cusparseHandle = std::static_pointer_cast<gpu::Backend>(C->getBackend())
+                              ->getCusparseHandle();
+  const auto& shapeB = B->shape();
+  const auto& shapeC = C->shape();
+  int k = (int)shapeB[0];                         // number of columns of sparse matrix A = #rows of B
+  int n = (int)shapeB.elements() / k;             // number of columns of dense matrices B and C
+  int m = (int)A_offsets->shape().elements() - 1; // number of rows of sparse matrix A
+  ABORT_IF(m != shapeC[0], "CSR matrix has wrong number of rows");
+  ABORT_IF(A_values->shape() != A_indices->shape(), "CSR constituents has inconsistent dimensions");
+  int nnz = (int)A_values->shape().elements();
+  float alpha = 1;
+  float beta = 0;
+  cusparseMatDescr_t descrA;
+  cusparseCreateMatDescr(&descrA);
+  cusparseSetMatType     (descrA, CUSPARSE_MATRIX_TYPE_GENERAL);
+  cusparseSetMatIndexBase(descrA, CUSPARSE_INDEX_BASE_ZERO);
+  auto rc = cusparseScsrmm(cusparseHandle, 
+      /*transA=*/ CUSPARSE_OPERATION_NON_TRANSPOSE,
+      m, n, k, nnz, &alpha, descrA,
+      /*csrValA=*/          A_values->data<float>(),
+      /*csrRowPtrA=*/ (int*)A_indices->data<IndexType>(),
+      /*csrColIndA=*/ (int*)A_offsets->data<IndexType>(),
+      B->data(),
+      /*ldb=*/ k,
+      &beta,
+      C->data(),
+      /*ldc=*/ m);
+  cusparseDestroyMatDescr(descrA);
+}
+
 }  // namespace gpu
 }  // namespace marian
diff --git a/src/tensors/gpu/prod.h b/src/tensors/gpu/prod.h
old mode 100644
new mode 100755
index ce1f6cdca..f1c59bd9c
--- a/src/tensors/gpu/prod.h
+++ b/src/tensors/gpu/prod.h
@@ -33,5 +33,10 @@ void ProdBatched(marian::Tensor C,
                  bool transB,
                  float beta = 0,
                  float scalar = 1);
+void CSRProd(marian::Tensor C,
+             const marian::Tensor& A_values,
+             const marian::Tensor& A_indices,
+             const marian::Tensor& A_offsets,
+             const marian::Tensor& B);
 }  // namespace gpu
 }  // namespace marian
diff --git a/src/tensors/tensor_operators.h b/src/tensors/tensor_operators.h
index 2cac284ab..692d3a2e0 100755
--- a/src/tensors/tensor_operators.h
+++ b/src/tensors/tensor_operators.h
@@ -77,6 +77,7 @@ void Reduce(Functor functor, marian::Tensor out, Tensors... tensors) {
 // clang-format off
 DISPATCH7(Prod, marian::Tensor, const marian::Tensor&, const marian::Tensor&, bool, bool, float, float)
 DISPATCH8(ProdBatched, marian::Tensor, Ptr<Allocator>, const marian::Tensor, const marian::Tensor, bool, bool, float, float)
+DISPATCH5(CSRProd, marian::Tensor, const marian::Tensor&, const marian::Tensor&, const marian::Tensor&, const marian::Tensor&)
 
 DISPATCH2(Softmax, marian::Tensor, marian::Tensor)
 DISPATCH3(SoftmaxGrad, marian::Tensor, marian::Tensor, marian::Tensor)
diff --git a/vs/Marian.vcxproj b/vs/Marian.vcxproj
index 64faded63..c29fd372c 100755
--- a/vs/Marian.vcxproj
+++ b/vs/Marian.vcxproj
@@ -575,6 +575,7 @@
       <ExcludedFromBuild Condition="'$(Configuration)|$(Platform)'=='Debug|x64'">true</ExcludedFromBuild>
       <ExcludedFromBuild Condition="'$(Configuration)|$(Platform)'=='Release|x64'">true</ExcludedFromBuild>
     </ClCompile>
+    <ClCompile Include="..\src\layers\generic.cpp" />
     <ClCompile Include="..\src\layers\loss.cpp" />
     <ClCompile Include="..\src\layers\weight.cpp" />
     <ClCompile Include="..\src\microsoft\quicksand.cpp">
diff --git a/vs/Marian.vcxproj.filters b/vs/Marian.vcxproj.filters
index 9e5cbf5f4..c70527a1e 100755
--- a/vs/Marian.vcxproj.filters
+++ b/vs/Marian.vcxproj.filters
@@ -481,6 +481,9 @@
     <ClCompile Include="..\src\examples\iris\iris.cpp">
       <Filter>examples\iris</Filter>
     </ClCompile>
+    <ClCompile Include="..\src\layers\generic.cpp">
+      <Filter>layers</Filter>
+    </ClCompile>
   </ItemGroup>
   <ItemGroup>
     <ClInclude Include="..\src\marian.h" />

From 6305f225dc2c8f7f3f39875054a7f17a58ccf4c9 Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Thu, 10 Jan 2019 21:57:28 -0800
Subject: [PATCH 104/838] added gradient of csr_dot; added transA flag to
 CSRPrd

---
 src/graph/node_operators_binary.h | 31 ++++++------------------------
 src/tensors/cpu/prod.cpp          |  5 +++--
 src/tensors/gpu/prod.cu           | 32 +++++++++++++++++++------------
 src/tensors/tensor_operators.h    |  2 +-
 4 files changed, 30 insertions(+), 40 deletions(-)

diff --git a/src/graph/node_operators_binary.h b/src/graph/node_operators_binary.h
index e27aa6f74..411d89b15 100755
--- a/src/graph/node_operators_binary.h
+++ b/src/graph/node_operators_binary.h
@@ -428,31 +428,15 @@ class CSRDotNodeOp : public NaryNodeOp {
   NodeOps forwardOps() override {
     // C = dot(A, B)
     return {NodeOp(CSRProd(val_,
-                           child(0)->val(),
-                           child(1)->val(),
-                           child(2)->val(),
-                           child(3)->val()))};
+                           child(0)->val(), child(1)->val(), child(2)->val(), child(3)->val(),
+                           /*transA=*/false, /*beta=*/0))};
   }
 
   NodeOps backwardOps() override {
-#if 1
-    return {}; // this is coming next
-#else
-    return {NodeOp(Prod(child(0)->grad(),
-                        adj_,
-                        child(1)->val(),
-                        false,
-                        true,
-                        1.0,
-                        scalar_)),
-            NodeOp(Prod(child(1)->grad(),
-                        child(0)->val(),
-                        adj_,
-                        true,
-                        false,
-                        1.0,
-                        scalar_))};
-#endif
+    return {nullptr, // can't backprop into the sparse matrix, as it would be dense
+            NodeOp(CSRProd(child(1)->grad(),
+                           child(0)->val(), child(1)->val(), child(2)->val(), adj_,
+                           /*transA=*/true, /*beta=*/1))};
   }
 
   const std::string type() override { return "•"; }
@@ -460,9 +444,6 @@ class CSRDotNodeOp : public NaryNodeOp {
   const std::string color() override { return "orange"; }
 };
 
-
-
-
 struct ScalarProductNodeOp : public NaryNodeOp {
   ScalarProductNodeOp(Expr a, Expr b, int axis)
       : NaryNodeOp({a, b}, newShape(a, b, axis)) {}
diff --git a/src/tensors/cpu/prod.cpp b/src/tensors/cpu/prod.cpp
index f3dcb4fee..478fe9901 100755
--- a/src/tensors/cpu/prod.cpp
+++ b/src/tensors/cpu/prod.cpp
@@ -171,8 +171,9 @@ void CSRProd(marian::Tensor C,
              const marian::Tensor& A_values,
              const marian::Tensor& A_indices,
              const marian::Tensor& A_offsets,
-             const marian::Tensor& B) {
-  C, A_values, A_indices, A_offsets, B;
+             const marian::Tensor& B,
+             bool transA, float beta) {
+  C, A_values, A_indices, A_offsets, B, transA, beta;
   ABORT("CSRProd is not yet implemented for CPU");
 }
 
diff --git a/src/tensors/gpu/prod.cu b/src/tensors/gpu/prod.cu
index d2ac84a2a..80e774864 100755
--- a/src/tensors/gpu/prod.cu
+++ b/src/tensors/gpu/prod.cu
@@ -229,35 +229,43 @@ void CSRProd(marian::Tensor C,
              const marian::Tensor& A_values,
              const marian::Tensor& A_indices,
              const marian::Tensor& A_offsets,
-             const marian::Tensor& B) {
+             const marian::Tensor& B,
+             bool transA, float beta) {
   cudaSetDevice(C->getDeviceId().no);
   auto cusparseHandle = std::static_pointer_cast<gpu::Backend>(C->getBackend())
                               ->getCusparseHandle();
-  const auto& shapeB = B->shape();
   const auto& shapeC = C->shape();
-  int k = (int)shapeB[0];                         // number of columns of sparse matrix A = #rows of B
-  int n = (int)shapeB.elements() / k;             // number of columns of dense matrices B and C
-  int m = (int)A_offsets->shape().elements() - 1; // number of rows of sparse matrix A
-  ABORT_IF(m != shapeC[0], "CSR matrix has wrong number of rows");
+  const auto& shapeB = B->shape();
+  auto numValues  = A_values->shape().elements();
+  auto numOffsets = A_offsets->shape().elements() - 1; // -1 since last value is length
+  auto rowsC = shapeC[0];
+  auto colsC = shapeC.elements() / rowsC;
+  auto rowsB = shapeB[0];
+  auto colsB = shapeB.elements() / rowsB;
+  auto rowsA = transA ? rowsB : numOffsets; // we don't know the dimension of the sparse axis from A directly
+  auto colsA = transA ? numOffsets : rowsB;
+  ABORT_IF((transA ? colsA : rowsA) != rowsC || (transA ? rowsA : colsA) != rowsB || colsB != colsC, "Inconsistent dimensions in CSR product");
   ABORT_IF(A_values->shape() != A_indices->shape(), "CSR constituents has inconsistent dimensions");
-  int nnz = (int)A_values->shape().elements();
   float alpha = 1;
-  float beta = 0;
   cusparseMatDescr_t descrA;
   cusparseCreateMatDescr(&descrA);
   cusparseSetMatType     (descrA, CUSPARSE_MATRIX_TYPE_GENERAL);
   cusparseSetMatIndexBase(descrA, CUSPARSE_INDEX_BASE_ZERO);
   auto rc = cusparseScsrmm(cusparseHandle, 
-      /*transA=*/ CUSPARSE_OPERATION_NON_TRANSPOSE,
-      m, n, k, nnz, &alpha, descrA,
+      transA ? CUSPARSE_OPERATION_TRANSPOSE : CUSPARSE_OPERATION_NON_TRANSPOSE,
+      /*m=*/ rowsA, // #rows of sparse A
+      /*n=*/ colsB, // #cols of dense B and C
+      /*k=*/ colsA, // #cols of sparse A
+      /*nnz=*/ (int)numValues,
+      &alpha, descrA,
       /*csrValA=*/          A_values->data<float>(),
       /*csrRowPtrA=*/ (int*)A_indices->data<IndexType>(),
       /*csrColIndA=*/ (int*)A_offsets->data<IndexType>(),
       B->data(),
-      /*ldb=*/ k,
+      /*ldb=*/ rowsB,
       &beta,
       C->data(),
-      /*ldc=*/ m);
+      /*ldc=*/ rowsC);
   cusparseDestroyMatDescr(descrA);
 }
 
diff --git a/src/tensors/tensor_operators.h b/src/tensors/tensor_operators.h
index 692d3a2e0..d84ca7974 100755
--- a/src/tensors/tensor_operators.h
+++ b/src/tensors/tensor_operators.h
@@ -77,7 +77,7 @@ void Reduce(Functor functor, marian::Tensor out, Tensors... tensors) {
 // clang-format off
 DISPATCH7(Prod, marian::Tensor, const marian::Tensor&, const marian::Tensor&, bool, bool, float, float)
 DISPATCH8(ProdBatched, marian::Tensor, Ptr<Allocator>, const marian::Tensor, const marian::Tensor, bool, bool, float, float)
-DISPATCH5(CSRProd, marian::Tensor, const marian::Tensor&, const marian::Tensor&, const marian::Tensor&, const marian::Tensor&)
+DISPATCH7(CSRProd, marian::Tensor, const marian::Tensor&, const marian::Tensor&, const marian::Tensor&, const marian::Tensor&, bool, float)
 
 DISPATCH2(Softmax, marian::Tensor, marian::Tensor)
 DISPATCH3(SoftmaxGrad, marian::Tensor, marian::Tensor, marian::Tensor)

From fab4fdf1f69256e579dc872bf6223e21f758b448 Mon Sep 17 00:00:00 2001
From: Marcin Junczys-Dowmunt <Marcin.Junczys-Dowmunt@microsoft.com>
Date: Fri, 11 Jan 2019 08:31:51 -0800
Subject: [PATCH 105/838] learnable positional embeddings

---
 src/common/config_parser.cpp     |   2 +
 src/graph/node_initializers.cpp  |  21 ++++++
 src/graph/node_initializers.h    |   2 +
 src/models/bert.h                |  64 ++++++++++++-----
 src/models/model_factory.cpp     |   2 +-
 src/models/transformer.h         |  18 +----
 src/models/transformer_factory.h |   4 +-
 src/training/validator.cpp       |   6 ++
 src/training/validator.h         | 118 +++++++++++++++++++++++++++++--
 9 files changed, 196 insertions(+), 41 deletions(-)

diff --git a/src/common/config_parser.cpp b/src/common/config_parser.cpp
index da6f7d6a9..e745e99d5 100755
--- a/src/common/config_parser.cpp
+++ b/src/common/config_parser.cpp
@@ -200,6 +200,8 @@ void ConfigParser::addOptionsModel(cli::CLIWrapper& cli) {
   cli.add<std::string>("--transformer-postprocess",
       "Operation after each transformer layer: d = dropout, a = add, n = normalize",
       "dan");
+  cli.add<bool>("--transformer-learned-positions",
+      "Use learned positional embeddings instead of trigonometric embeddings");
 
   cli.add<std::string>("--bert-mask-symbol", "Masking symbol for BERT masked-LM training", "[MASK]");
   cli.add<std::string>("--bert-sep-symbol", "Sentence separator symbol for BERT next sentence prediction training", "[SEP]");
diff --git a/src/graph/node_initializers.cpp b/src/graph/node_initializers.cpp
index dc71562d8..84f60044f 100755
--- a/src/graph/node_initializers.cpp
+++ b/src/graph/node_initializers.cpp
@@ -157,6 +157,27 @@ NodeInitializer from_item(const io::Item& item) {
   }
 }
 
+NodeInitializer positions(int start) {
+  return [start](Tensor t) {
+    int dimEmb   = t->shape()[-1];
+    int dimWords = t->size() / dimEmb;
+
+    float numTimescales = (float)dimEmb / 2;
+    float logTimescaleIncrement = std::log(10000.f) / (numTimescales - 1.f);
+
+    std::vector<float> vPos(dimEmb * dimWords, 0);
+    for(int p = start; p < dimWords + start; ++p) {
+      for(int i = 0; i < numTimescales; ++i) {
+        float v = p * std::exp(i * -logTimescaleIncrement);
+        vPos[(p - start) * dimEmb + i                     ] = std::sin(v);
+        vPos[(p - start) * dimEmb + (int)numTimescales + i] = std::cos(v); // @TODO: is int vs. float correct for num_timescales?
+      }
+    }
+
+    from_vector(vPos)(t);
+  };
+}
+
 }  // namespace inits
 
 }  // namespace marian
diff --git a/src/graph/node_initializers.h b/src/graph/node_initializers.h
index 58690e4fb..185ab1191 100755
--- a/src/graph/node_initializers.h
+++ b/src/graph/node_initializers.h
@@ -42,6 +42,8 @@ NodeInitializer from_vector(const std::vector<IndexType>& v);
 
 NodeInitializer from_item(const io::Item& item);
 
+NodeInitializer positions(int start);
+
 NodeInitializer from_sparse_vector(
     std::pair<std::vector<size_t>, std::vector<float>>& v);
 
diff --git a/src/models/bert.h b/src/models/bert.h
index 69b826993..9994f8668 100644
--- a/src/models/bert.h
+++ b/src/models/bert.h
@@ -11,7 +11,7 @@ namespace data {
 class BertBatch : public CorpusBatch {
 private:
   std::mt19937& eng_;
-  
+
   std::vector<IndexType> maskedPositions_;
   std::vector<IndexType> maskedWords_;
   std::vector<IndexType> sentenceIndices_;
@@ -35,7 +35,7 @@ class BertBatch : public CorpusBatch {
       Word randWord = (*randomWord_)(eng_);
       if(dontMask_.count(randWord) > 0) // the random word is a forbidden word
         return mask;                    // hence return mask symbol
-      else 
+      else
         return randWord;                // else return the random word
     } else { // for 80% of words apply mask symbol
       return mask;
@@ -43,11 +43,11 @@ class BertBatch : public CorpusBatch {
   }
 
 public:
-  BertBatch(Ptr<CorpusBatch> batch, 
+  BertBatch(Ptr<CorpusBatch> batch,
             std::mt19937& engine,
             float maskFraction,
-            const std::string& maskSymbol, 
-            const std::string& sepSymbol, 
+            const std::string& maskSymbol,
+            const std::string& sepSymbol,
             const std::string& clsSymbol)
     : CorpusBatch(*batch), eng_(engine),
       maskSymbol_(maskSymbol), sepSymbol_(sepSymbol), clsSymbol_(clsSymbol) {
@@ -68,7 +68,7 @@ class BertBatch : public CorpusBatch {
 
     ABORT_IF(sepId == subBatch->vocab()->getUnkId(),
              "BERT separator symbol {} not found in vocabulary", sepSymbol_);
-    
+
     ABORT_IF(clsId == subBatch->vocab()->getUnkId(),
              "BERT class symbol {} not found in vocabulary", clsSymbol_);
 
@@ -84,12 +84,26 @@ class BertBatch : public CorpusBatch {
         selected.push_back(i);
     std::shuffle(selected.begin(), selected.end(), eng_);
     selected.resize(std::ceil(selected.size() * maskFraction)); // select first x percent from shuffled indices
-    
+
     for(int i : selected) {
       maskedPositions_.push_back(i);        // where is the original word?
       maskedWords_.push_back(words[i]);     // what is the original word?
       words[i] = maskOut(words[i], maskId); // mask that position
     }
+
+    int dimBatch = subBatch->batchSize();
+    int dimWords = subBatch->batchWidth();
+
+    sentenceIndices_.resize(words.size());
+    std::vector<IndexType> sentPos(dimBatch, 0);
+    for(int i = 0; i < dimWords; ++i) {
+      for(int j = 0; j < dimBatch; ++j) {
+        int k = i * dimBatch + j;
+        sentenceIndices_[k] = sentPos[j];
+        if(words[k] == sepId)
+          sentPos[j]++;
+      }
+    }
   }
 
   const std::vector<IndexType>& bertMaskedPositions() { return maskedPositions_; }
@@ -101,7 +115,7 @@ class BertBatch : public CorpusBatch {
 
 class BertEncoderClassifier : public EncoderClassifier, public data::RNGEngine {
 public:
-  BertEncoderClassifier(Ptr<Options> options) 
+  BertEncoderClassifier(Ptr<Options> options)
   : EncoderClassifier(options) {}
 
   std::vector<Ptr<ClassifierState>> apply(Ptr<ExpressionGraph> graph, Ptr<data::CorpusBatch> batch, bool clearGraph) override {
@@ -114,6 +128,11 @@ class BertEncoderClassifier : public EncoderClassifier, public data::RNGEngine {
                                           opt<std::string>("bert-class-symbol"));
     return EncoderClassifier::apply(graph, bertBatch, clearGraph);
   }
+
+  // for externally created BertBatch for instance in BertValidator
+  std::vector<Ptr<ClassifierState>> apply(Ptr<ExpressionGraph> graph, Ptr<data::BertBatch> bertBatch, bool clearGraph) {
+    return EncoderClassifier::apply(graph, bertBatch, clearGraph);
+  }
 };
 
 // @TODO: this should be in transformer.h
@@ -121,7 +140,9 @@ class BertEncoder : public EncoderTransformer {
 public:
   BertEncoder(Ptr<Options> options) : EncoderTransformer(options) {}
 
-  Expr addSentenceEmbeddings(Expr embeddings, int start, Ptr<data::CorpusBatch> batch) const {
+  Expr addSentenceEmbeddings(Expr embeddings,
+                             Ptr<data::CorpusBatch> batch,
+                             bool learnedPosEmbeddings) const {
     Ptr<data::BertBatch> bertBatch = std::dynamic_pointer_cast<data::BertBatch>(batch);
 
     ABORT_IF(!bertBatch, "Batch could not be converted for BERT training");
@@ -130,24 +151,29 @@ class BertEncoder : public EncoderTransformer {
     int dimBatch = embeddings->shape()[-2];
     int dimWords = embeddings->shape()[-3];
 
-    auto sentenceEmbeddings = embedding(graph_)
-                                ("prefix", "Wsent")
-                                ("dimVocab", 2) // sentence A or sentence B
-                                ("dimEmb", dimEmb)
-                                .construct();
+    Expr sentenceEmbeddings;
+    if(learnedPosEmbeddings) {
+      sentenceEmbeddings = embedding(graph_)
+                               ("prefix", "Wsent")
+                               ("dimVocab", 2) // sentence A or sentence B
+                               ("dimEmb", dimEmb)
+                               .construct();
+    } else {
+      // trigonometric positions, no backprob
+      sentenceEmbeddings = graph_->constant({2, dimEmb}, inits::positions(0));
+    }
 
-    // @TODO: note this is going to be really slow due to atomicAdd in backward step
-    // with only two classes;
-    // instead two masked reduce operations, maybe in parallel streams?
     auto sentenceIndices = graph_->indices(bertBatch->bertSentenceIndices());
+
     auto signal = rows(sentenceEmbeddings, sentenceIndices);
     signal = reshape(signal, {dimWords, dimBatch, dimEmb});
     return embeddings + signal;
   }
 
   virtual Expr addSpecialEmbeddings(Expr input, int start = 0, Ptr<data::CorpusBatch> batch = nullptr) const override {
-    input = addPositionalEmbeddings(input, start, true); // true for BERT
-    input = addSentenceEmbeddings(input, start, batch);
+    bool learnedPosEmbeddings = opt<bool>("transformer-learned-positions", true);
+    input = addPositionalEmbeddings(input, start, learnedPosEmbeddings);
+    input = addSentenceEmbeddings(input, batch, learnedPosEmbeddings);
     return input;
   }
 };
diff --git a/src/models/model_factory.cpp b/src/models/model_factory.cpp
index 0d66b95d8..bf0de22d6 100644
--- a/src/models/model_factory.cpp
+++ b/src/models/model_factory.cpp
@@ -233,7 +233,7 @@ Ptr<ModelBase> by_type(std::string type, usage use, Ptr<Options> options) {
     return models::encoder_classifier()(options) //
         ("usage", use)                           //
         .push_back(models::encoder()             //
-                    ("type", "transformer")      // @TODO: replace with 'bert-encoder'
+                    ("type", "bert-encoder")     // @TODO: replace with 'bert-encoder'
                     ("index", 0))                // close to original transformer encoder
         .push_back(models::classifier()          //
                     ("type", "bert-masked-lm")   //
diff --git a/src/models/transformer.h b/src/models/transformer.h
index 188080a99..d57f23b95 100755
--- a/src/models/transformer.h
+++ b/src/models/transformer.h
@@ -68,20 +68,8 @@ class Transformer : public EncoderOrDecoderBase {
       signal = reshape(signal, {dimWords, 1, dimEmb});
       embeddings = embeddings + signal;
     } else {
-      float num_timescales = (float)dimEmb / 2;
-      float log_timescale_increment = std::log(10000.f) / (num_timescales - 1.f);
-
-      std::vector<float> vPos(dimEmb * dimWords, 0);
-      for(int p = start; p < dimWords + start; ++p) {
-        for(int i = 0; i < num_timescales; ++i) {
-          float v = p * std::exp(i * -log_timescale_increment);
-          vPos[(p - start) * dimEmb + i] = std::sin(v);
-          vPos[(p - start) * dimEmb + (int)num_timescales + i] = std::cos(v); // @TODO: is int vs. float correct for num_timescales?
-        }
-      }
-
-      // shared across batch entries
-      auto signal = graph_->constant({dimWords, 1, dimEmb}, inits::from_vector(vPos));
+      auto signal = graph_->constant({dimWords, 1, dimEmb},
+                                     inits::positions(start));
       embeddings = embeddings + signal;
     }
 
@@ -573,7 +561,7 @@ class EncoderTransformer : public Transformer<EncoderBase> {
     }
     // according to paper embeddings are scaled up by \sqrt(d_m)
     auto scaledEmbeddings = std::sqrt((float)dimEmb) * batchEmbeddings;
-    
+
     scaledEmbeddings = addSpecialEmbeddings(scaledEmbeddings, /*start=*/0, batch);
 
     // reorganize batch and timestep
diff --git a/src/models/transformer_factory.h b/src/models/transformer_factory.h
index aa31e4d15..16ce91bdc 100755
--- a/src/models/transformer_factory.h
+++ b/src/models/transformer_factory.h
@@ -9,6 +9,6 @@
 //#include "layers/factory.h"
 
 namespace marian {
-Ptr<EncoderBase> NewEncoderTransformer(Ptr<Options> options);
-Ptr<DecoderBase> NewDecoderTransformer(Ptr<Options> options);
+static Ptr<EncoderBase> NewEncoderTransformer(Ptr<Options> options);
+static Ptr<DecoderBase> NewDecoderTransformer(Ptr<Options> options);
 }  // namespace marian
diff --git a/src/training/validator.cpp b/src/training/validator.cpp
index f63c9d0b5..14b854ff3 100644
--- a/src/training/validator.cpp
+++ b/src/training/validator.cpp
@@ -34,6 +34,12 @@ std::vector<Ptr<Validator<data::Corpus>>> Validators(
     } else if(metric == "accuracy") {
       auto validator = New<AccuracyValidator>(vocabs, config);
       validators.push_back(validator);
+    } else if(metric == "bert-lm-accuracy") {
+      auto validator = New<BertAccuracyValidator>(vocabs, config, true);
+      validators.push_back(validator);
+    } else if(metric == "bert-sentence-accuracy") {
+      auto validator = New<BertAccuracyValidator>(vocabs, config, false);
+      validators.push_back(validator);
     } else {
       LOG_VALID(warn, "Unrecognized validation metric: {}", metric);
     }
diff --git a/src/training/validator.h b/src/training/validator.h
index 1ff6d59ee..f22dd56e0 100755
--- a/src/training/validator.h
+++ b/src/training/validator.h
@@ -15,6 +15,7 @@
 #include "translator/output_collector.h"
 #include "translator/output_printer.h"
 #include "translator/scorers.h"
+#include "models/bert.h"
 
 #include <cstdio>
 #include <cstdlib>
@@ -261,9 +262,9 @@ class AccuracyValidator : public Validator<data::Corpus> {
           // graph->forward();
 
           // std::unique_lock<std::mutex> lock(mutex_);
-          // totalLabels += labels->shape().elements(); 
-          // correct     += correct->scalar<int>();          
-          
+          // totalLabels += labels->shape().elements();
+          // correct     += correct->scalar<int>();
+
           builder->clear(graph);
           auto logits = builder->build(graph, batch);
           graph->forward();
@@ -292,7 +293,7 @@ class AccuracyValidator : public Validator<data::Corpus> {
           }
 
           std::unique_lock<std::mutex> lock(mutex_);
-          totalLabels += thisLabels; 
+          totalLabels += thisLabels;
           correct     += thisCorrect;
         };
 
@@ -306,6 +307,115 @@ class AccuracyValidator : public Validator<data::Corpus> {
   }
 };
 
+class BertAccuracyValidator : public Validator<data::Corpus> {
+private:
+  bool evalMaskedLM_{true};
+
+public:
+  BertAccuracyValidator(std::vector<Ptr<Vocab>> vocabs, Ptr<Options> options, bool evalMaskedLM)
+      : Validator(vocabs, options, /*lowerIsBetter=*/false),
+        evalMaskedLM_(evalMaskedLM) {
+    createBatchGenerator(/*isTranslating=*/false);
+
+    // @TODO: check if this is required.
+    Ptr<Options> opts = New<Options>();
+    opts->merge(options);
+    opts->set("inference", true);
+    builder_ = models::from_options(opts, models::usage::raw);
+  }
+
+  std::string type() override {
+    if(evalMaskedLM_)
+      return "bert-lm-accuracy";
+    else
+      return "bert-sentence-accuracy";
+  }
+
+protected:
+  virtual float validateBG(const std::vector<Ptr<ExpressionGraph>>& graphs) override {
+
+    size_t correct     = 0;
+    size_t totalLabels = 0;
+    size_t batchId     = 0;
+
+    {
+      threadPool_.reserve(graphs.size());
+
+      TaskBarrier taskBarrier;
+      for(auto batch : *batchGenerator_) {
+        auto task = [=, &correct, &totalLabels](size_t id) {
+          thread_local Ptr<ExpressionGraph> graph;
+          thread_local auto builder = models::from_options(options_, models::usage::raw);
+          thread_local std::unique_ptr<std::mt19937> engine;
+
+          if(!graph) {
+            graph = graphs[id % graphs.size()];
+          }
+
+          if(!engine)
+            engine.reset(new std::mt19937(Config::seed + id));
+
+          auto bertBatch = New<data::BertBatch>(batch,
+                                                *engine,
+                                                options_->get<float>("bert-masking-fraction"),
+                                                options_->get<std::string>("bert-mask-symbol"),
+                                                options_->get<std::string>("bert-sep-symbol"),
+                                                options_->get<std::string>("bert-class-symbol"));
+
+          builder->clear(graph);
+          auto classifierStates = std::dynamic_pointer_cast<BertEncoderClassifier>(builder)->apply(graph, bertBatch, true);
+          graph->forward();
+
+          auto maskedLMLogits = classifierStates[0]->getLogProbs();
+          const auto& maskedLMLabels = bertBatch->bertMaskedWords();
+
+          auto sentenceLogits = classifierStates[1]->getLogProbs();
+          const auto& sentenceLabels = bertBatch->back()->data();
+
+          auto count = [=, &correct, &totalLabels](Expr logits, const std::vector<IndexType>& labels) {
+            IndexType cols = logits->shape()[-1];
+            size_t thisCorrect = 0;
+            size_t thisLabels  = labels.size();
+
+            std::vector<float> vLogits;
+            logits->val()->get(vLogits);
+
+            for(int i = 0; i < thisLabels; ++i) {
+              // CPU-side Argmax
+              IndexType bestIndex = 0;
+              float bestValue = std::numeric_limits<float>::lowest();
+              for(IndexType j = 0; j < cols; ++j) {
+                float currValue = vLogits[i * cols + j];
+                if(currValue > bestValue) {
+                  bestValue = currValue;
+                  bestIndex = j;
+                }
+              }
+              thisCorrect += (size_t)(bestIndex == labels[i]);
+            }
+
+            std::unique_lock<std::mutex> lock(mutex_);
+            totalLabels += thisLabels;
+            correct     += thisCorrect;
+          };
+
+          if(evalMaskedLM_)
+            count(maskedLMLogits, maskedLMLabels);
+          else
+            count(sentenceLogits, sentenceLabels);
+        };
+
+        taskBarrier.push_back(threadPool_.enqueue(task, batchId));
+        batchId++;
+      }
+      // ~TaskBarrier waits until all are done
+    }
+
+    return (float)correct / (float)totalLabels;
+  }
+};
+
+
 class ScriptValidator : public Validator<data::Corpus> {
 public:
   ScriptValidator(std::vector<Ptr<Vocab>> vocabs, Ptr<Options> options)

From 060c959172afd112815d9d80e303186e5b9c2c74 Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Fri, 11 Jan 2019 13:36:01 -0800
Subject: [PATCH 106/838] minor fixes

---
 src/common/utils.cpp              |  8 +++---
 src/common/utils.h                |  8 +++---
 src/graph/node_operators_binary.h |  8 +++++-
 src/layers/constructors.h         |  6 ++++
 src/layers/generic.h              |  6 ----
 src/models/decoder.h              | 42 +++++++++++++++-------------
 src/models/s2s.h                  |  1 +
 src/models/transformer.h          | 21 ++++++++------
 src/models/transformer_factory.h  |  1 +
 src/optimizers/optimizers.h       |  2 +-
 src/tensors/cpu/prod.cpp          |  3 +-
 src/tensors/gpu/cuda_helpers.h    |  6 ++++
 src/tensors/gpu/prod.cu           | 31 +++++++++++----------
 src/tensors/gpu/prod.h            |  4 ++-
 src/tests/operator_tests.cpp      | 46 +++++++++++++++++++++++++++++--
 src/training/communicator_nccl.h  |  2 +-
 16 files changed, 131 insertions(+), 64 deletions(-)

diff --git a/src/common/utils.cpp b/src/common/utils.cpp
index 992c74ac8..bde788359 100755
--- a/src/common/utils.cpp
+++ b/src/common/utils.cpp
@@ -29,7 +29,7 @@ void trimLeft(std::string& s) {
 // @TODO: use more functions from CLI instead of own implementations
 void split(const std::string& line,
            std::vector<std::string>& pieces,
-           const std::string del /*= " "*/,
+           const std::string& del /*= " "*/,
            bool keepEmpty) {
   size_t begin = 0;
   size_t pos = 0;
@@ -50,7 +50,7 @@ void split(const std::string& line,
 }
 
 std::vector<std::string> split(const std::string& line,
-                               const std::string del /*= " "*/,
+                               const std::string& del /*= " "*/,
                                bool keepEmpty) {
   std::vector<std::string> pieces;
   split(line, pieces, del, keepEmpty);
@@ -60,7 +60,7 @@ std::vector<std::string> split(const std::string& line,
 // @TODO: splitAny() shares all but 2 expressions with split(). Merge them.
 void splitAny(const std::string& line,
               std::vector<std::string>& pieces,
-              const std::string del /*= " "*/,
+              const std::string& del /*= " "*/,
               bool keepEmpty) {
   size_t begin = 0;
   size_t pos = 0;
@@ -81,7 +81,7 @@ void splitAny(const std::string& line,
 }
 
 std::vector<std::string> splitAny(const std::string& line,
-                                  const std::string del /*= " "*/,
+                                  const std::string& del /*= " "*/,
                                   bool keepEmpty) {
   std::vector<std::string> pieces;
   splitAny(line, pieces, del, keepEmpty);
diff --git a/src/common/utils.h b/src/common/utils.h
index 7c4d56432..94113a0ec 100755
--- a/src/common/utils.h
+++ b/src/common/utils.h
@@ -12,20 +12,20 @@ void trimRight(std::string& s);
 
 void split(const std::string& line,
            std::vector<std::string>& pieces,
-           const std::string del = " ",
+           const std::string& del = " ",
            bool keepEmpty = false);
 
 std::vector<std::string> split(const std::string& line,
-                               const std::string del = " ",
+                               const std::string& del = " ",
                                bool keepEmpty = false);
 
 void splitAny(const std::string& line,
               std::vector<std::string>& pieces,
-              const std::string del = " ",
+              const std::string& del = " ",
               bool keepEmpty = false);
 
 std::vector<std::string> splitAny(const std::string& line,
-                                  const std::string del = " ",
+                                  const std::string& del = " ",
                                   bool keepEmpty = false);
 
 std::string join(const std::vector<std::string>& words,
diff --git a/src/graph/node_operators_binary.h b/src/graph/node_operators_binary.h
index 411d89b15..61eb9ed46 100755
--- a/src/graph/node_operators_binary.h
+++ b/src/graph/node_operators_binary.h
@@ -419,7 +419,9 @@ class CSRDotNodeOp : public NaryNodeOp {
 
   Shape newShape(Expr A_values, Expr A_indices, Expr A_offsets, Expr B) {
     ABORT_IF(A_values->shape().size() != 1 || A_indices->shape().size() != 1 || A_offsets->shape().size() != 1,
-        "Sparse matrix components must all be vectors.");
+        "Sparse matrix components must all be vectors");
+    ABORT_IF(A_values->shape() != A_indices->shape(),
+        "Sparse matrix values and indices must have the same shape");
     auto outShape = B->shape();
     outShape.set(0, A_offsets->shape()[0] - 1); // A_offsets = A.numRows + 1
     return outShape;
@@ -434,9 +436,13 @@ class CSRDotNodeOp : public NaryNodeOp {
 
   NodeOps backwardOps() override {
     return {nullptr, // can't backprop into the sparse matrix, as it would be dense
+#if 0
+        nullptr};
+#else
             NodeOp(CSRProd(child(1)->grad(),
                            child(0)->val(), child(1)->val(), child(2)->val(), adj_,
                            /*transA=*/true, /*beta=*/1))};
+#endif
   }
 
   const std::string type() override { return "•"; }
diff --git a/src/layers/constructors.h b/src/layers/constructors.h
index d0ac34873..5ed7f3f53 100755
--- a/src/layers/constructors.h
+++ b/src/layers/constructors.h
@@ -147,4 +147,10 @@ class MLPFactory : public Factory {
 // @TODO: change naming convention.
 typedef Accumulator<MLPFactory> mlp;
 }  // namespace mlp
+
+typedef ConstructingFactory<Embedding> EmbeddingFactory;
+typedef ConstructingFactory<ULREmbedding> ULREmbeddingFactory;
+
+typedef Accumulator<EmbeddingFactory> embedding;
+typedef Accumulator<ULREmbeddingFactory> ulr_embedding;
 }  // namespace marian
diff --git a/src/layers/generic.h b/src/layers/generic.h
index 0289e57b7..25b51dc13 100755
--- a/src/layers/generic.h
+++ b/src/layers/generic.h
@@ -295,10 +295,4 @@ class ULREmbedding : public LayerBase, public IEmbeddingLayer {
     ABORT("not implemented"); // ULR cannot be used for decoding
   }
 };
-
-typedef ConstructingFactory<Embedding> EmbeddingFactory;
-typedef ConstructingFactory<ULREmbedding> ULREmbeddingFactory;
-
-typedef Accumulator<EmbeddingFactory> embedding;
-typedef Accumulator<ULREmbeddingFactory> ulr_embedding;
 }  // namespace marian
diff --git a/src/models/decoder.h b/src/models/decoder.h
index 99fbd4ad6..abf6bbd1e 100755
--- a/src/models/decoder.h
+++ b/src/models/decoder.h
@@ -4,6 +4,7 @@
 #include "states.h"
 
 #include "data/shortlist.h"
+#include "layers/constructors.h"
 #include "layers/generic.h"
 
 namespace marian {
@@ -31,6 +32,7 @@ class DecoderBase {
 
   virtual Ptr<DecoderState> step(Ptr<ExpressionGraph>, Ptr<DecoderState>) = 0;
 
+  std::vector<Ptr<IEmbeddingLayer>> embedding_; // @TODO: move away, also rename
   virtual void embeddingsFromBatch(Ptr<ExpressionGraph> graph,
                                    Ptr<DecoderState> state,
                                    Ptr<data::CorpusBatch> batch) {
@@ -38,30 +40,32 @@ class DecoderBase {
     int dimVoc = opt<std::vector<int>>("dim-vocabs")[batchIndex_];
     int dimEmb = opt<int>("dim-emb");
 
-    auto yEmbFactory = embedding()  //
-        ("dimVocab", dimVoc)        //
-        ("dimEmb", dimEmb);
-
-    if(opt<bool>("tied-embeddings-src") || opt<bool>("tied-embeddings-all"))
-      yEmbFactory("prefix", "Wemb");
-    else
-      yEmbFactory("prefix", prefix_ + "_Wemb");
-
-    if(options_->has("embedding-fix-trg"))
-      yEmbFactory("fixed", opt<bool>("embedding-fix-trg"));
-
-    if(options_->has("embedding-vectors")) {
-      auto embFiles = opt<std::vector<std::string>>("embedding-vectors");
-      yEmbFactory("embFile", embFiles[batchIndex_])  //
-          ("normalization", opt<bool>("embedding-normalization"));
+    // @TODO: code dup with EncoderTransformer
+    if (embedding_.empty() || !embedding_[batchIndex_]) { // lazy
+      embedding_.resize(batch->sets());
+      auto embFactory = embedding()("dimVocab", dimVoc)("dimEmb", dimEmb);
+      if(opt<bool>("tied-embeddings-src") || opt<bool>("tied-embeddings-all"))
+        embFactory("prefix", "Wemb");
+      else
+        embFactory("prefix", prefix_ + "_Wemb");
+      if(options_->has("embedding-fix-trg"))
+        embFactory("fixed", opt<bool>("embedding-fix-trg"));
+      if(options_->has("embedding-vectors")) {
+        auto embFiles = opt<std::vector<std::string>>("embedding-vectors");
+        embFactory("embFile", embFiles[batchIndex_])  //
+            ("normalization", opt<bool>("embedding-normalization"));
+      }
+      if (options_->has("embedding-factors")) {
+        embFactory("embedding-factors", opt<std::vector<std::string>>("embedding-factors"));
+        embFactory("vocab", opt<std::vector<std::string>>("vocabs")[batchIndex_]);
+      }
+      embedding_[batchIndex_] = embFactory.construct(graph);
     }
 
-    auto yEmb = yEmbFactory.construct(graph);
-
     auto subBatch = (*batch)[batchIndex_];
 
     Expr y, yMask; std::tie
-    (y, yMask) = yEmb->apply(subBatch);
+    (y, yMask) = embedding_[batchIndex_]->apply(subBatch);
 
     Expr yData;
     if(shortlist_) {
diff --git a/src/models/s2s.h b/src/models/s2s.h
index edda79bc8..2f4a45797 100755
--- a/src/models/s2s.h
+++ b/src/models/s2s.h
@@ -124,6 +124,7 @@ class EncoderS2S : public EncoderBase {
     int dimVoc = opt<std::vector<int>>("dim-vocabs")[batchIndex_];
     int dimEmb = opt<int>("dim-emb");
 
+    // @TODO: code dup with Decider and EncoderTransformer; actually diverged by now. Unify this.
     auto embFactory = embedding()  //
         ("dimVocab", dimVoc)       //
         ("dimEmb", dimEmb);
diff --git a/src/models/transformer.h b/src/models/transformer.h
index c142d0dd7..48335d1d0 100755
--- a/src/models/transformer.h
+++ b/src/models/transformer.h
@@ -6,7 +6,6 @@
 #include "marian.h"
 
 #include "layers/constructors.h"
-#include "layers/factory.h"
 #include "models/decoder.h"
 #include "models/encoder.h"
 #include "models/states.h"
@@ -495,7 +494,7 @@ class EncoderTransformer : public Transformer<EncoderBase> {
     return embFactory.construct(graph_);
   }
 
-  Ptr<IEmbeddingLayer> createWordEmbeddingLayer(size_t subBatchIndex) const {
+  Ptr<IEmbeddingLayer> createSourceEmbeddingLayer(size_t subBatchIndex) const {
     // standard encoder word embeddings
     int dimVoc = opt<std::vector<int>>("dim-vocabs")[subBatchIndex];
     int dimEmb = opt<int>("dim-emb");
@@ -512,7 +511,7 @@ class EncoderTransformer : public Transformer<EncoderBase> {
                 ("normalization", opt<bool>("embedding-normalization"));
     }
     if (options_->has("embedding-factors")) {
-      embFactory("embedding-factors", opt<std::string>("embedding-factors"));
+      embFactory("embedding-factors", opt<std::vector<std::string>>("embedding-factors"));
       embFactory("vocab", opt<std::vector<std::string>>("vocabs")[subBatchIndex]);
     }
     return embFactory.construct(graph_);
@@ -524,6 +523,7 @@ class EncoderTransformer : public Transformer<EncoderBase> {
     return apply(batch);
   }
 
+  std::vector<Ptr<IEmbeddingLayer>> embedding_; // @TODO: move away, also rename
   Ptr<EncoderState> apply(Ptr<data::CorpusBatch> batch) {
     int dimEmb = opt<int>("dim-emb");
     int dimBatch = (int)batch->size();
@@ -531,12 +531,15 @@ class EncoderTransformer : public Transformer<EncoderBase> {
     // create the embedding matrix, considering tying and some other options
     // embed the source words in the batch
     Expr batchEmbeddings, batchMask;
-    Ptr<IEmbeddingLayer> embedding;
-    if (options_->has("ulr") && options_->get<bool>("ulr") == true)
-      embedding = createULREmbeddingLayer(); // embedding uses ULR
-    else
-      embedding = createWordEmbeddingLayer(batchIndex_);
-    std::tie(batchEmbeddings, batchMask) = embedding->apply((*batch)[batchIndex_]);
+
+    if (embedding_.empty() || !embedding_[batchIndex_]) { // lazy
+      embedding_.resize(batch->sets());
+      if (options_->has("ulr") && options_->get<bool>("ulr") == true)
+        embedding_[batchIndex_] = createULREmbeddingLayer(); // embedding uses ULR
+      else
+        embedding_[batchIndex_] = createSourceEmbeddingLayer(batchIndex_);
+    }
+    std::tie(batchEmbeddings, batchMask) = embedding_[batchIndex_]->apply((*batch)[batchIndex_]);
     // apply dropout over source words
     float dropoutSrc = inference_ ? 0 : opt<float>("dropout-src");
     if(dropoutSrc) {
diff --git a/src/models/transformer_factory.h b/src/models/transformer_factory.h
index aa31e4d15..825c32b93 100755
--- a/src/models/transformer_factory.h
+++ b/src/models/transformer_factory.h
@@ -1,3 +1,4 @@
+// @TODO: rename to transformer.h eventually. This is not a Factory as in factory.h.
 #pragma once
 
 #include "marian.h"
diff --git a/src/optimizers/optimizers.h b/src/optimizers/optimizers.h
index 51e43f871..44d603120 100755
--- a/src/optimizers/optimizers.h
+++ b/src/optimizers/optimizers.h
@@ -26,7 +26,7 @@ class OptimizerBase : public TrainingObserver {
     // that these hyper-parameters were originally tuned for, then the learning-rate gets
     // adjusted accordingly. Note: Requires user to also use ce-sum criterion.
     if (refMBWordsParam_ != 0)
-      LOG(info, "Note: Learning rate gets automatically adjusted as if minibatch size was {}", refMBWordsParam_);
+      LOG(info, "[optimizers] Learning rate gets automatically adjusted as if minibatch size was {}", refMBWordsParam_);
   }
 
   static constexpr size_t mbSizeNotProvided = SIZE_MAX;
diff --git a/src/tensors/cpu/prod.cpp b/src/tensors/cpu/prod.cpp
index 478fe9901..43f99faef 100755
--- a/src/tensors/cpu/prod.cpp
+++ b/src/tensors/cpu/prod.cpp
@@ -172,7 +172,8 @@ void CSRProd(marian::Tensor C,
              const marian::Tensor& A_indices,
              const marian::Tensor& A_offsets,
              const marian::Tensor& B,
-             bool transA, float beta) {
+             bool transA,
+             float beta) {
   C, A_values, A_indices, A_offsets, B, transA, beta;
   ABORT("CSRProd is not yet implemented for CPU");
 }
diff --git a/src/tensors/gpu/cuda_helpers.h b/src/tensors/gpu/cuda_helpers.h
index ba890490e..920cec8c4 100755
--- a/src/tensors/gpu/cuda_helpers.h
+++ b/src/tensors/gpu/cuda_helpers.h
@@ -13,6 +13,12 @@ const int MAX_BLOCKS = 65535;
         "CUDA error {} '{}' - {}:{}: {}", rc, cudaGetErrorString(rc),  __FILE__, __LINE__, #expr); \
 } while(0)
 
+#define CUBLAS_CHECK(expr) do {                                              \
+  cublasStatus_t rc = (expr);                                                \
+  ABORT_IF(rc != CUBLAS_STATUS_SUCCESS,                                      \
+           "Cublas Error: {} - {}:{}: {}", rc, __FILE__, __LINE__, #expr);   \
+} while(0)
+
 #define CUSPARSE_CHECK(expr) do {                                              \
   cusparseStatus_t rc = (expr);                                                \
   ABORT_IF(rc != CUSPARSE_STATUS_SUCCESS,                                      \
diff --git a/src/tensors/gpu/prod.cu b/src/tensors/gpu/prod.cu
index 80e774864..4acb01669 100755
--- a/src/tensors/gpu/prod.cu
+++ b/src/tensors/gpu/prod.cu
@@ -24,18 +24,18 @@ static void setTensorMode(cublasHandle_t cublasHandle) {
     default: ABORT("Invalid ENABLE_CUBLAS_TENSOR_OP_MATH_FP32={}", var);
     }
     if (mode > 0) { // try whether it can be set   --@TODO: check whether this actually works
-      cublasSetMathMode(cublasHandle, CUBLAS_TENSOR_OP_MATH);
+      CUBLAS_CHECK(cublasSetMathMode(cublasHandle, CUBLAS_TENSOR_OP_MATH));
       cublasMath_t actual = CUBLAS_DEFAULT_MATH;
       cublasGetMathMode(cublasHandle, &actual);
       if (actual != CUBLAS_TENSOR_OP_MATH) {
-        LOG(info, "WARNING: TensorCores requested but not available");
+        LOG(warn, "[gpu] TensorCores requested but not available");
         mode = -1;
       }
     }
     if (mode > 0)
-      LOG(info, "16-bit TensorCores enabled for float32 matrix operations");
+      LOG(info, "[gpu] 16-bit TensorCores enabled for float32 matrix operations");
   }
-  cublasSetMathMode(cublasHandle, mode > 0 ? CUBLAS_TENSOR_OP_MATH : CUBLAS_DEFAULT_MATH);
+  CUBLAS_CHECK(cublasSetMathMode(cublasHandle, mode > 0 ? CUBLAS_TENSOR_OP_MATH : CUBLAS_DEFAULT_MATH));
 }
 
 void Prod(marian::Tensor C,
@@ -76,7 +76,7 @@ void Prod(marian::Tensor C,
   //cublasSetMathMode(cublasHandle, CUBLAS_TENSOR_OP_MATH);
 #endif
 
-  cublasSgemm(cublasHandle,
+  CUBLAS_CHECK(cublasSgemm(cublasHandle,
               opB,
               opA,
               n,
@@ -89,7 +89,7 @@ void Prod(marian::Tensor C,
               lda,
               &beta,
               C->data(),
-              ldc);
+              ldc));
 #if CUDA_VERSION >= 9000
   cublasSetMathMode(cublasHandle, CUBLAS_DEFAULT_MATH);
 #endif
@@ -108,6 +108,7 @@ __global__ void gAddBias(float* out,
   }
 }
 
+#if 0 // @TODO: remove, then rename from .cu to .cpp
 void AddBias(marian::Tensor C, const marian::Tensor bias) {
   cudaSetDevice(C->getDeviceId().no);
 
@@ -117,9 +118,9 @@ void AddBias(marian::Tensor C, const marian::Tensor bias) {
   int threads = std::min(MAX_THREADS, length);
   int blocks = std::min(MAX_BLOCKS, length / threads + (length % threads != 0));
 
-  gAddBias<<<blocks, threads>>>(C->data(), bias->data(), length, cols);
+  gAddBias<<<blocks, threads>>>(C->data(), bias->data(), length, cols); // @TODO: CUDA_CHECK
 
-  cudaStreamSynchronize(0);
+  CUDA_CHECK(cudaStreamSynchronize(0)); // @BUGBUG: Should not be here. Prod() also does not have this.
 }
 
 void ProdWithBias(marian::Tensor C,
@@ -133,6 +134,7 @@ void ProdWithBias(marian::Tensor C,
   marian::gpu::Prod(C, A, B, transA, transB, beta, scalar);
   marian::gpu::AddBias(C, bias);
 }
+#endif
 
 void ProdBatched(marian::Tensor C,
                  Ptr<Allocator> allocator,
@@ -201,7 +203,7 @@ void ProdBatched(marian::Tensor C,
   setTensorMode(cublasHandle);
   //cublasSetMathMode(cublasHandle, CUBLAS_TENSOR_OP_MATH);
 #endif
-  cublasSgemmBatched(cublasHandle,
+  CUBLAS_CHECK(cublasSgemmBatched(cublasHandle,
                      opB,
                      opA,
                      n,
@@ -215,7 +217,7 @@ void ProdBatched(marian::Tensor C,
                      &beta,
                      mp_cptr->data<float*>(),
                      ldc,
-                     batchC);
+                     batchC));
 #if CUDA_VERSION >= 9000
   cublasSetMathMode(cublasHandle, CUBLAS_DEFAULT_MATH);
 #endif
@@ -230,7 +232,8 @@ void CSRProd(marian::Tensor C,
              const marian::Tensor& A_indices,
              const marian::Tensor& A_offsets,
              const marian::Tensor& B,
-             bool transA, float beta) {
+             bool transA,
+             float beta) {
   cudaSetDevice(C->getDeviceId().no);
   auto cusparseHandle = std::static_pointer_cast<gpu::Backend>(C->getBackend())
                               ->getCusparseHandle();
@@ -248,10 +251,10 @@ void CSRProd(marian::Tensor C,
   ABORT_IF(A_values->shape() != A_indices->shape(), "CSR constituents has inconsistent dimensions");
   float alpha = 1;
   cusparseMatDescr_t descrA;
-  cusparseCreateMatDescr(&descrA);
+  CUSPARSE_CHECK(cusparseCreateMatDescr(&descrA));
   cusparseSetMatType     (descrA, CUSPARSE_MATRIX_TYPE_GENERAL);
   cusparseSetMatIndexBase(descrA, CUSPARSE_INDEX_BASE_ZERO);
-  auto rc = cusparseScsrmm(cusparseHandle, 
+  CUSPARSE_CHECK(cusparseScsrmm(cusparseHandle, 
       transA ? CUSPARSE_OPERATION_TRANSPOSE : CUSPARSE_OPERATION_NON_TRANSPOSE,
       /*m=*/ rowsA, // #rows of sparse A
       /*n=*/ colsB, // #cols of dense B and C
@@ -265,7 +268,7 @@ void CSRProd(marian::Tensor C,
       /*ldb=*/ rowsB,
       &beta,
       C->data(),
-      /*ldc=*/ rowsC);
+      /*ldc=*/ rowsC));
   cusparseDestroyMatDescr(descrA);
 }
 
diff --git a/src/tensors/gpu/prod.h b/src/tensors/gpu/prod.h
index f1c59bd9c..1443e53a8 100755
--- a/src/tensors/gpu/prod.h
+++ b/src/tensors/gpu/prod.h
@@ -37,6 +37,8 @@ void CSRProd(marian::Tensor C,
              const marian::Tensor& A_values,
              const marian::Tensor& A_indices,
              const marian::Tensor& A_offsets,
-             const marian::Tensor& B);
+             const marian::Tensor& B,
+             bool transA,
+             float beta = 0);
 }  // namespace gpu
 }  // namespace marian
diff --git a/src/tests/operator_tests.cpp b/src/tests/operator_tests.cpp
index 0ece15a23..5002a0ff7 100755
--- a/src/tests/operator_tests.cpp
+++ b/src/tests/operator_tests.cpp
@@ -302,9 +302,17 @@ void tests(DeviceType device) {
     graph->clear();
     values.clear();
 
-    std::vector<float> vA({1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12});
-    std::vector<float> vB({1, 2, 3, 4, 5, 6});
-    std::vector<float> vC({22, 28, 49, 64, 76, 100, 103, 136});
+    std::vector<float> vA({1, 2, 3,
+                           4, 5, 6,
+                           7, 8, 9,
+                           10, 11, 12});
+    std::vector<float> vB({1, 2,
+                           3, 4,
+                           5, 6});
+    std::vector<float> vC({22, 28,
+                           49, 64,
+                           76, 100,
+                           103, 136});
 
     auto A = graph->param("A", {2, 2, 3}, inits::from_vector(vA));
     auto B = graph->param("B", {3, 2}, inits::from_vector(vB));
@@ -314,6 +322,38 @@ void tests(DeviceType device) {
     CHECK(C->shape() == Shape({2, 2, 2}));
     C->val()->get(values);
     CHECK(values == vC);
+
+    // CSR dot product
+    std::vector<float> vS({1, 0, 0, 1,
+                           0, 0, 1, 2});
+    std::vector<float> vR({1, 2, 3,
+                           4, 5, 6,
+                           7, 8, 9,
+                           10, 11, 12});
+    std::vector<float> vSxR({11, 13, 15,
+                             27, 30, 33.1});
+    std::vector<float> SV;
+    std::vector<IndexType> SI, SO;
+    SO.push_back((IndexType)SI.size());
+    for (IndexType i = 0; i < 2; i++) {    // convert to CSR
+      for (IndexType j = 0; j < 4; j++) {
+        auto k = 4 * i + j;
+        if (vS[k] != 0) {
+          SV.push_back(vS[k]);
+          SI.push_back(j);
+        }
+      }
+      SO.push_back((IndexType)SI.size());
+    }
+    auto R = graph->param("A", {4, 3}, inits::from_vector(vR));
+    auto SxR = csr_dot(
+          graph->constant({ (int)SV.size() }, inits::from_vector(SV), Type::float32),
+          graph->constant({ (int)SI.size() }, inits::from_vector(SI), Type::uint32),
+          graph->constant({ (int)SO.size() }, inits::from_vector(SO), Type::uint32),
+          A);
+    CHECK(SxR->shape() == Shape({2, 3}));
+    SxR->val()->get(values);
+    CHECK(values == vSxR);
   }
 
   SECTION("affine transformation") {
diff --git a/src/training/communicator_nccl.h b/src/training/communicator_nccl.h
index dbfe2d877..55b485287 100755
--- a/src/training/communicator_nccl.h
+++ b/src/training/communicator_nccl.h
@@ -191,7 +191,7 @@ class NCCLCommunicator : public ICommunicator {
     groupEnd();
 
     mpiBarrier(); // (synchronize the log messages)
-    LOG(info, "NCCLCommunicator constructed successfully.");
+    LOG(info, "[comm] NCCLCommunicator constructed successfully.");
     mpiBarrier(); // (synchronize the log messages)
   }
 

From e626f0a16727f934e39a7dda57f66bc6f09d235f Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Fri, 11 Jan 2019 14:02:31 -0800
Subject: [PATCH 107/838] updated tests w.r.t. earlier refactoring; towards
 test for csr_dot()

---
 src/tests/attention_tests.cpp |  8 ++---
 src/tests/operator_tests.cpp  | 18 ++++++-----
 src/tests/rnn_tests.cpp       | 58 +++++++++++++++++------------------
 3 files changed, 43 insertions(+), 41 deletions(-)

diff --git a/src/tests/attention_tests.cpp b/src/tests/attention_tests.cpp
index 1f4ae5b6d..9b1b61859 100755
--- a/src/tests/attention_tests.cpp
+++ b/src/tests/attention_tests.cpp
@@ -53,15 +53,15 @@ void tests(DeviceType type) {
     auto mask = graph->constant({dimTime, dimBatch, 1},
                                 inits::from_vector(vMask));
 
-    auto rnn = rnn::rnn(graph)         //
+    auto rnn = rnn::rnn()         //
           ("prefix", "rnntest")        //
           ("type", "gru")              //
           ("dimInput", 16)             //
           ("dimState", 8)              //
-          .push_back(rnn::cell(graph)) //
-          .construct();
+          .push_back(rnn::cell()) //
+          .construct(graph);
 
-    auto context = rnn.construct()->transduce(input, mask);
+    auto context = rnn->transduce(input, mask);
 
     auto encState = New<EncoderState>(context, mask, nullptr);
 
diff --git a/src/tests/operator_tests.cpp b/src/tests/operator_tests.cpp
index 5002a0ff7..814046159 100755
--- a/src/tests/operator_tests.cpp
+++ b/src/tests/operator_tests.cpp
@@ -317,11 +317,6 @@ void tests(DeviceType device) {
     auto A = graph->param("A", {2, 2, 3}, inits::from_vector(vA));
     auto B = graph->param("B", {3, 2}, inits::from_vector(vB));
     auto C = dot(A, B);
-    graph->forward();
-
-    CHECK(C->shape() == Shape({2, 2, 2}));
-    C->val()->get(values);
-    CHECK(values == vC);
 
     // CSR dot product
     std::vector<float> vS({1, 0, 0, 1,
@@ -335,7 +330,7 @@ void tests(DeviceType device) {
     std::vector<float> SV;
     std::vector<IndexType> SI, SO;
     SO.push_back((IndexType)SI.size());
-    for (IndexType i = 0; i < 2; i++) {    // convert to CSR
+    for (IndexType i = 0; i < 2; i++) {    // convert to CSR!
       for (IndexType j = 0; j < 4; j++) {
         auto k = 4 * i + j;
         if (vS[k] != 0) {
@@ -345,12 +340,19 @@ void tests(DeviceType device) {
       }
       SO.push_back((IndexType)SI.size());
     }
-    auto R = graph->param("A", {4, 3}, inits::from_vector(vR));
+    auto R = graph->param("R", {4, 3}, inits::from_vector(vR));
     auto SxR = csr_dot(
           graph->constant({ (int)SV.size() }, inits::from_vector(SV), Type::float32),
           graph->constant({ (int)SI.size() }, inits::from_vector(SI), Type::uint32),
           graph->constant({ (int)SO.size() }, inits::from_vector(SO), Type::uint32),
-          A);
+          R);
+
+    graph->forward();
+
+    CHECK(C->shape() == Shape({ 2, 2, 2 }));
+    C->val()->get(values);
+    CHECK(values == vC);
+
     CHECK(SxR->shape() == Shape({2, 3}));
     SxR->val()->get(values);
     CHECK(values == vSxR);
diff --git a/src/tests/rnn_tests.cpp b/src/tests/rnn_tests.cpp
index 145828788..8310f2332 100755
--- a/src/tests/rnn_tests.cpp
+++ b/src/tests/rnn_tests.cpp
@@ -43,15 +43,15 @@ void tests(DeviceType type) {
     auto input = graph->constant({4, 1, 4},
                                  inits::glorot_uniform);
 
-    auto rnn = rnn::rnn(graph)         //
-          ("prefix", "rnntest")        //
-          ("type", "tanh")             //
-          ("dimInput", 4)              //
-          ("dimState", 4)              //
-          .push_back(rnn::cell(graph)) //
-          .construct();
+    auto rnn = rnn::rnn()         //
+          ("prefix", "rnntest")   //
+          ("type", "tanh")        //
+          ("dimInput", 4)         //
+          ("dimState", 4)         //
+          .push_back(rnn::cell()) //
+          .construct(graph);
 
-    auto output = rnn.construct()->transduce(input);
+    auto output = rnn->transduce(input);
 
     graph->forward();
 
@@ -117,7 +117,7 @@ void tests(DeviceType type) {
       auto backward = type == "alternating" ? rnn::dir::alternating_backward
                                             : rnn::dir::backward;
 
-      auto rnnFw = rnn::rnn(graph)           //
+      auto rnnFw = rnn::rnn()                //
           ("type", cellType)                 //
           ("direction", forward)             //
           ("dimInput", dimEmb)               //
@@ -126,7 +126,7 @@ void tests(DeviceType type) {
           ("skip", skip);
 
       for(int i = 1; i <= first; ++i) {
-        auto stacked = rnn::stacked_cell(graph);
+        auto stacked = rnn::stacked_cell();
         for(int j = 1; j <= cellDepth; ++j) {
           std::string paramPrefix = prefix + "_bi";
           if(i > 1)
@@ -134,21 +134,21 @@ void tests(DeviceType type) {
           if(i > 1 || j > 1)
             paramPrefix += "_cell" + std::to_string(j);
 
-          stacked.push_back(rnn::cell(graph)("prefix", paramPrefix));
+          stacked.push_back(rnn::cell()("prefix", paramPrefix));
         }
         rnnFw.push_back(stacked);
       }
 
-      auto rnnBw = rnn::rnn(graph)            //
-          ("type", cellType)                  //
-          ("direction", backward)             //
-          ("dimInput", dimEmb)                //
-          ("dimState", dimRnn)                //
-          ("layer-normalization", layerNorm)  //
+      auto rnnBw = rnn::rnn()                //
+          ("type", cellType)                 //
+          ("direction", backward)            //
+          ("dimInput", dimEmb)               //
+          ("dimState", dimRnn)               //
+          ("layer-normalization", layerNorm) //
           ("skip", skip);
 
       for(int i = 1; i <= first; ++i) {
-        auto stacked = rnn::stacked_cell(graph);
+        auto stacked = rnn::stacked_cell();
         for(int j = 1; j <= cellDepth; ++j) {
           std::string paramPrefix = prefix + "_bi_r";
           if(i > 1)
@@ -156,13 +156,13 @@ void tests(DeviceType type) {
           if(i > 1 || j > 1)
             paramPrefix += "_cell" + std::to_string(j);
 
-          stacked.push_back(rnn::cell(graph)("prefix", paramPrefix));
+          stacked.push_back(rnn::cell()("prefix", paramPrefix));
         }
         rnnBw.push_back(stacked);
       }
 
-      auto context = concatenate({rnnFw.construct()->transduce(input, mask),
-                                  rnnBw.construct()->transduce(input, mask)},
+      auto context = concatenate({rnnFw.construct(graph)->transduce(input, mask),
+                                  rnnBw.construct(graph)->transduce(input, mask)},
                                   /*axis =*/ input->shape().size() - 1);
 
       if(second > 0) {
@@ -170,25 +170,25 @@ void tests(DeviceType type) {
         // previous bidirectional RNN through multiple layers
 
         // construct RNN first
-        auto rnnUni = rnn::rnn(graph)           //
-            ("type", cellType)                  //
-            ("dimInput", 2 * dimRnn)            //
-            ("dimState", dimRnn)                //
-            ("layer-normalization", layerNorm)  //
+        auto rnnUni = rnn::rnn()               //
+            ("type", cellType)                 //
+            ("dimInput", 2 * dimRnn)           //
+            ("dimState", dimRnn)               //
+            ("layer-normalization", layerNorm) //
             ("skip", skip);
 
         for(int i = first + 1; i <= second + first; ++i) {
-          auto stacked = rnn::stacked_cell(graph);
+          auto stacked = rnn::stacked_cell();
           for(int j = 1; j <= cellDepth; ++j) {
             std::string paramPrefix = prefix + "_l" + std::to_string(i) + "_cell"
                                       + std::to_string(j);
-            stacked.push_back(rnn::cell(graph)("prefix", paramPrefix));
+            stacked.push_back(rnn::cell()("prefix", paramPrefix));
           }
           rnnUni.push_back(stacked);
         }
 
         // transduce context to new context
-        context = rnnUni.construct()->transduce(context);
+        context = rnnUni.construct(graph)->transduce(context);
       }
       return context;
     };

From 2c05491f7aa4fe172fba3f12ba8a69edcfe75ccb Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Fri, 11 Jan 2019 14:23:19 -0800
Subject: [PATCH 108/838] bug fix: sparse int arrays should be passed in
 correct order

---
 src/tensors/gpu/prod.cu      |  4 ++--
 src/tests/operator_tests.cpp | 20 +++++++++++++-------
 2 files changed, 15 insertions(+), 9 deletions(-)

diff --git a/src/tensors/gpu/prod.cu b/src/tensors/gpu/prod.cu
index 4acb01669..776079577 100755
--- a/src/tensors/gpu/prod.cu
+++ b/src/tensors/gpu/prod.cu
@@ -262,8 +262,8 @@ void CSRProd(marian::Tensor C,
       /*nnz=*/ (int)numValues,
       &alpha, descrA,
       /*csrValA=*/          A_values->data<float>(),
-      /*csrRowPtrA=*/ (int*)A_indices->data<IndexType>(),
-      /*csrColIndA=*/ (int*)A_offsets->data<IndexType>(),
+      /*csrRowPtrA=*/ (int*)A_offsets->data<IndexType>(),
+      /*csrColIndA=*/ (int*)A_indices->data<IndexType>(),
       B->data(),
       /*ldb=*/ rowsB,
       &beta,
diff --git a/src/tests/operator_tests.cpp b/src/tests/operator_tests.cpp
index 814046159..7ade98967 100755
--- a/src/tests/operator_tests.cpp
+++ b/src/tests/operator_tests.cpp
@@ -340,21 +340,27 @@ void tests(DeviceType device) {
       }
       SO.push_back((IndexType)SI.size());
     }
+    auto S = graph->param("S", {2, 4}, inits::from_vector(vS));
     auto R = graph->param("R", {4, 3}, inits::from_vector(vR));
-    auto SxR = csr_dot(
-          graph->constant({ (int)SV.size() }, inits::from_vector(SV), Type::float32),
-          graph->constant({ (int)SI.size() }, inits::from_vector(SI), Type::uint32),
-          graph->constant({ (int)SO.size() }, inits::from_vector(SO), Type::uint32),
+    auto SxRs = csr_dot(
+          graph->constant({(int)SV.size()}, inits::from_vector(SV), Type::float32),
+          graph->constant({(int)SI.size()}, inits::from_vector(SI), Type::uint32),
+          graph->constant({(int)SO.size()}, inits::from_vector(SO), Type::uint32),
           R);
+    auto SxRd = dot(S, R);
 
     graph->forward();
 
-    CHECK(C->shape() == Shape({ 2, 2, 2 }));
+    CHECK(C->shape() == Shape({2, 2, 2}));
     C->val()->get(values);
     CHECK(values == vC);
 
-    CHECK(SxR->shape() == Shape({2, 3}));
-    SxR->val()->get(values);
+    CHECK(SxRd->shape() == Shape({2, 3}));
+    SxRd->val()->get(values);
+    CHECK(values == vSxR);
+
+    CHECK(SxRs->shape() == Shape({2, 3}));
+    SxRs->val()->get(values);
     CHECK(values == vSxR);
   }
 

From 08298bc4c9ad53e81debf04e51e0f517450e6c91 Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Fri, 11 Jan 2019 15:02:49 -0800
Subject: [PATCH 109/838] bug fix: dense matrices passed to cusparse are
 col-major

---
 src/tensors/gpu/prod.cu      | 24 +++++++++++++++++++++++-
 src/tests/operator_tests.cpp | 28 ++++++++++++++--------------
 2 files changed, 37 insertions(+), 15 deletions(-)

diff --git a/src/tensors/gpu/prod.cu b/src/tensors/gpu/prod.cu
index 776079577..5a98a04a6 100755
--- a/src/tensors/gpu/prod.cu
+++ b/src/tensors/gpu/prod.cu
@@ -250,11 +250,32 @@ void CSRProd(marian::Tensor C,
   ABORT_IF((transA ? colsA : rowsA) != rowsC || (transA ? rowsA : colsA) != rowsB || colsB != colsC, "Inconsistent dimensions in CSR product");
   ABORT_IF(A_values->shape() != A_indices->shape(), "CSR constituents has inconsistent dimensions");
   float alpha = 1;
+#if 1
+  // Marian uses row-major storage, but CUSPARSE/CUBLAS assume column-major.
+  // Hence, we compute C = spA * B as C' = B' * spA'. where B' and C' are
+  // column-major views on the data of B and C, and likewise, spA' is
+  // the CSR matrix reinterpreted as a CSC matrix.
+  CUSPARSE_CHECK(cusparseSgemmi(cusparseHandle,
+      /*m=*/ colsB, // #rows of A = #cols of row-major B
+      /*n=*/ rowsC, // #cols of B and C = #rows of row-major C
+      /*k=*/ rowsB, // #cols of A = #rows of row-major B
+      /*nnz=*/ (int)numValues,
+      &alpha,
+      /*A=*/ B->data(),
+      /*lda=*/ colsB, // stride
+      /*cscValB=*/          A_values->data<float>(),  // second arg
+      /*cscRowPtrB=*/ (int*)A_offsets->data<IndexType>(),
+      /*cscColIndB=*/ (int*)A_indices->data<IndexType>(),
+      &beta,
+      C->data(),
+      /*ldc=*/ colsC)); // stride
+#else
+  // Incorrect code that assumes col-major matrices. Reuse that later for dense x sparse.
   cusparseMatDescr_t descrA;
   CUSPARSE_CHECK(cusparseCreateMatDescr(&descrA));
   cusparseSetMatType     (descrA, CUSPARSE_MATRIX_TYPE_GENERAL);
   cusparseSetMatIndexBase(descrA, CUSPARSE_INDEX_BASE_ZERO);
-  CUSPARSE_CHECK(cusparseScsrmm(cusparseHandle, 
+  CUSPARSE_CHECK(cusparseScsrmm(cusparseHandle,
       transA ? CUSPARSE_OPERATION_TRANSPOSE : CUSPARSE_OPERATION_NON_TRANSPOSE,
       /*m=*/ rowsA, // #rows of sparse A
       /*n=*/ colsB, // #cols of dense B and C
@@ -270,6 +291,7 @@ void CSRProd(marian::Tensor C,
       C->data(),
       /*ldc=*/ rowsC));
   cusparseDestroyMatDescr(descrA);
+#endif
 }
 
 }  // namespace gpu
diff --git a/src/tests/operator_tests.cpp b/src/tests/operator_tests.cpp
index 7ade98967..89cf0fb0b 100755
--- a/src/tests/operator_tests.cpp
+++ b/src/tests/operator_tests.cpp
@@ -320,18 +320,20 @@ void tests(DeviceType device) {
 
     // CSR dot product
     std::vector<float> vS({1, 0, 0, 1,
-                           0, 0, 1, 2});
-    std::vector<float> vR({1, 2, 3,
-                           4, 5, 6,
-                           7, 8, 9,
-                           10, 11, 12});
-    std::vector<float> vSxR({11, 13, 15,
-                             27, 30, 33.1});
-    std::vector<float> SV;
+                           0, 0, 1, 1.5});
+    std::vector<float> vR({1, 2, 3, 1.2, 5.6,
+                           4, 5, 6, 2.3, 6.7,
+                           7, 8, 9, 3.4, 7.8,
+                           1, 1, 2, 4.5, 8.9});
+    std::vector<float> vSxR({2.0, 3.0,  5.0,  5.7 , 14.5 ,
+                             8.5, 9.5, 12.0, 10.15, 21.15});
+    auto S = graph->param("S", { 2, 4 }, inits::from_vector(vS));
+    auto R = graph->param("R", { 4, 5 }, inits::from_vector(vR));
+    std::vector<float> SV;    // create CSR version of S
     std::vector<IndexType> SI, SO;
     SO.push_back((IndexType)SI.size());
-    for (IndexType i = 0; i < 2; i++) {    // convert to CSR!
-      for (IndexType j = 0; j < 4; j++) {
+    for (IndexType i = 0; i < S->shape()[0]; i++) {
+      for (IndexType j = 0; j < S->shape()[1]; j++) {
         auto k = 4 * i + j;
         if (vS[k] != 0) {
           SV.push_back(vS[k]);
@@ -340,8 +342,6 @@ void tests(DeviceType device) {
       }
       SO.push_back((IndexType)SI.size());
     }
-    auto S = graph->param("S", {2, 4}, inits::from_vector(vS));
-    auto R = graph->param("R", {4, 3}, inits::from_vector(vR));
     auto SxRs = csr_dot(
           graph->constant({(int)SV.size()}, inits::from_vector(SV), Type::float32),
           graph->constant({(int)SI.size()}, inits::from_vector(SI), Type::uint32),
@@ -355,11 +355,11 @@ void tests(DeviceType device) {
     C->val()->get(values);
     CHECK(values == vC);
 
-    CHECK(SxRd->shape() == Shape({2, 3}));
+    CHECK(SxRd->shape() == Shape({2, 5}));
     SxRd->val()->get(values);
     CHECK(values == vSxR);
 
-    CHECK(SxRs->shape() == Shape({2, 3}));
+    CHECK(SxRs->shape() == Shape({2, 5}));
     SxRs->val()->get(values);
     CHECK(values == vSxR);
   }

From 8fa02dbfac9a4c51d4337a8cf29c82b28c666036 Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Fri, 11 Jan 2019 16:08:07 -0800
Subject: [PATCH 110/838] scaffolding for csr_dot(transA)

---
 src/graph/expression_operators.cpp |  4 +-
 src/graph/expression_operators.h   |  2 +-
 src/graph/node_operators_binary.h  | 29 +++++++------
 src/tensors/cpu/prod.cpp           |  1 +
 src/tensors/gpu/prod.cu            | 67 ++++++++++++++++++++----------
 src/tensors/gpu/prod.h             |  1 +
 src/tensors/tensor_operators.h     |  2 +-
 src/tests/operator_tests.cpp       | 29 ++++++++-----
 8 files changed, 87 insertions(+), 48 deletions(-)

diff --git a/src/graph/expression_operators.cpp b/src/graph/expression_operators.cpp
index 12f5464a6..ce009f290 100755
--- a/src/graph/expression_operators.cpp
+++ b/src/graph/expression_operators.cpp
@@ -420,8 +420,8 @@ Expr affine(Expr a, Expr b, Expr bias, bool transA, bool transB, float scale) {
 // multiply a CSR matrix A with a matrix B
 // A[i,j] is at A_values[A_offsets[i]+k], where k is position of j in A_indices[A_offsets[i]:A_offsets[i+1]]
 // Result shape is (Aoffsets.size() - 1, B->shape(-1))
-Expr csr_dot(Expr A_values, Expr A_indices, Expr A_offsets, Expr B) {
-  return Expression<CSRDotNodeOp>(A_values, A_indices, A_offsets, B);
+Expr csr_dot(const Shape& A_shape, Expr A_values, Expr A_indices, Expr A_offsets, Expr B, bool transA /*= false*/) {
+  return Expression<CSRDotNodeOp>(A_shape, A_values, A_indices, A_offsets, B, transA);
 }
 
 // swap the last two axes
diff --git a/src/graph/expression_operators.h b/src/graph/expression_operators.h
index 080fbf2d5..c9a66daec 100755
--- a/src/graph/expression_operators.h
+++ b/src/graph/expression_operators.h
@@ -112,7 +112,7 @@ Expr affine(Expr a,
             bool transB = false,
             float scalar = 1.f);
 
-Expr csr_dot(Expr Avalues, Expr Aindices, Expr Aoffsets, Expr B);
+Expr csr_dot(const Shape& A_shape, Expr Avalues, Expr Aindices, Expr Aoffsets, Expr B, bool transA = false);
 
 Expr transpose(Expr a);
 Expr transpose(Expr a, const std::vector<int>& axes);
diff --git a/src/graph/node_operators_binary.h b/src/graph/node_operators_binary.h
index 61eb9ed46..994b5004a 100755
--- a/src/graph/node_operators_binary.h
+++ b/src/graph/node_operators_binary.h
@@ -128,7 +128,7 @@ class DotNodeOp : public NaryNodeOp {
                         scalar_))};
   }
 
-  const std::string type() override { return "•"; }
+  const std::string type() override { return "dot"; }
 
   const std::string color() override { return "orange"; }
 };
@@ -404,48 +404,51 @@ class DotBatchedNodeOp : public NaryNodeOp {
                                scalar_))};
   }
 
-  const std::string type() override { return "•"; }
+  const std::string type() override { return "bdot"; }
 
   const std::string color() override { return "orange"; }
 };
 
 class CSRDotNodeOp : public NaryNodeOp {
+  bool transA_;
 public:
-  CSRDotNodeOp(Expr A_values, Expr A_indices, Expr A_offsets, Expr B)
-      : NaryNodeOp({ A_values, A_indices, A_offsets, B }, newShape(A_values, A_indices, A_offsets, B)) {
+  CSRDotNodeOp(const Shape& A_shape, Expr A_values, Expr A_indices, Expr A_offsets, Expr B, bool transA)
+      : NaryNodeOp({ A_values, A_indices, A_offsets, B }, newShape(A_shape, A_values, A_indices, A_offsets, B, transA)), transA_(transA) {
     matchOrAbort<IndexType>(A_indices->value_type());
     matchOrAbort<IndexType>(A_offsets->value_type());
   }
 
-  Shape newShape(Expr A_values, Expr A_indices, Expr A_offsets, Expr B) {
+  Shape newShape(const Shape& A_shape, Expr A_values, Expr A_indices, Expr A_offsets, Expr B, bool transA) {
     ABORT_IF(A_values->shape().size() != 1 || A_indices->shape().size() != 1 || A_offsets->shape().size() != 1,
         "Sparse matrix components must all be vectors");
     ABORT_IF(A_values->shape() != A_indices->shape(),
         "Sparse matrix values and indices must have the same shape");
+    ABORT_IF(A_shape.size() != 2,
+        "Sparse matrix must have rank 2");
+    ABORT_IF(A_offsets->shape()[0] - 1 != A_shape[0],
+        "Sparse matrix offset vector has incorrect size");
     auto outShape = B->shape();
-    outShape.set(0, A_offsets->shape()[0] - 1); // A_offsets = A.numRows + 1
+    outShape.set(0, transA ? A_shape[1] : A_shape[0]);
     return outShape;
   }
 
   NodeOps forwardOps() override {
     // C = dot(A, B)
     return {NodeOp(CSRProd(val_,
+                           graph()->allocator(),
                            child(0)->val(), child(1)->val(), child(2)->val(), child(3)->val(),
-                           /*transA=*/false, /*beta=*/0))};
+                           /*transA=*/transA_, /*beta=*/0))};
   }
 
   NodeOps backwardOps() override {
     return {nullptr, // can't backprop into the sparse matrix, as it would be dense
-#if 0
-        nullptr};
-#else
             NodeOp(CSRProd(child(1)->grad(),
+                           graph()->allocator(),
                            child(0)->val(), child(1)->val(), child(2)->val(), adj_,
-                           /*transA=*/true, /*beta=*/1))};
-#endif
+                           /*transA=*/!transA_, /*beta=*/1))};
   }
 
-  const std::string type() override { return "•"; }
+  const std::string type() override { return "csr_dot"; }
 
   const std::string color() override { return "orange"; }
 };
diff --git a/src/tensors/cpu/prod.cpp b/src/tensors/cpu/prod.cpp
index 43f99faef..f17d23f84 100755
--- a/src/tensors/cpu/prod.cpp
+++ b/src/tensors/cpu/prod.cpp
@@ -168,6 +168,7 @@ void ProdWithBias(marian::Tensor C,
 }
 
 void CSRProd(marian::Tensor C,
+             Ptr<Allocator> /*allocator*/,
              const marian::Tensor& A_values,
              const marian::Tensor& A_indices,
              const marian::Tensor& A_offsets,
diff --git a/src/tensors/gpu/prod.cu b/src/tensors/gpu/prod.cu
index 5a98a04a6..9e986ef01 100755
--- a/src/tensors/gpu/prod.cu
+++ b/src/tensors/gpu/prod.cu
@@ -228,6 +228,7 @@ void ProdBatched(marian::Tensor C,
 }
 
 void CSRProd(marian::Tensor C,
+             Ptr<Allocator> allocator,
              const marian::Tensor& A_values,
              const marian::Tensor& A_indices,
              const marian::Tensor& A_offsets,
@@ -237,39 +238,63 @@ void CSRProd(marian::Tensor C,
   cudaSetDevice(C->getDeviceId().no);
   auto cusparseHandle = std::static_pointer_cast<gpu::Backend>(C->getBackend())
                               ->getCusparseHandle();
+  // dimensions
   const auto& shapeC = C->shape();
   const auto& shapeB = B->shape();
-  auto numValues  = A_values->shape().elements();
-  auto numOffsets = A_offsets->shape().elements() - 1; // -1 since last value is length
   auto rowsC = shapeC[0];
   auto colsC = shapeC.elements() / rowsC;
   auto rowsB = shapeB[0];
   auto colsB = shapeB.elements() / rowsB;
-  auto rowsA = transA ? rowsB : numOffsets; // we don't know the dimension of the sparse axis from A directly
-  auto colsA = transA ? numOffsets : rowsB;
+  auto rowsA = transA ? rowsB : rowsC;
+  auto colsA = transA ? rowsC : rowsB;
   ABORT_IF((transA ? colsA : rowsA) != rowsC || (transA ? rowsA : colsA) != rowsB || colsB != colsC, "Inconsistent dimensions in CSR product");
-  ABORT_IF(A_values->shape() != A_indices->shape(), "CSR constituents has inconsistent dimensions");
+  // sparse arrays
+  auto numValues  = A_values->shape().elements();
+  auto numOffsets = A_offsets->shape().elements() - 1; // -1 since last value is length
+  LOG(info, "n={}, transA={}, rowsB={}, rowsC={}", numOffsets,transA, rowsB, rowsC);
+  ABORT_IF(numOffsets != (transA ? rowsB : rowsC), "CSR offset array dimension mismatch: n={}, transA={}, rowsB={}, rowsC={}", numOffsets,transA, rowsB, rowsC);
+  ABORT_IF(numOffsets != (transA ? rowsB : rowsC), "CSR offset array dimension mismatch");
+  ABORT_IF(A_values->shape() != A_indices->shape(), "CSR values and indices must have the same size");
   float alpha = 1;
-#if 1
   // Marian uses row-major storage, but CUSPARSE/CUBLAS assume column-major.
   // Hence, we compute C = spA * B as C' = B' * spA'. where B' and C' are
   // column-major views on the data of B and C, and likewise, spA' is
   // the CSR matrix reinterpreted as a CSC matrix.
-  CUSPARSE_CHECK(cusparseSgemmi(cusparseHandle,
-      /*m=*/ colsB, // #rows of A = #cols of row-major B
-      /*n=*/ rowsC, // #cols of B and C = #rows of row-major C
-      /*k=*/ rowsB, // #cols of A = #rows of row-major B
-      /*nnz=*/ (int)numValues,
-      &alpha,
-      /*A=*/ B->data(),
-      /*lda=*/ colsB, // stride
-      /*cscValB=*/          A_values->data<float>(),  // second arg
-      /*cscRowPtrB=*/ (int*)A_offsets->data<IndexType>(),
-      /*cscColIndB=*/ (int*)A_indices->data<IndexType>(),
-      &beta,
-      C->data(),
-      /*ldc=*/ colsC)); // stride
-#else
+  if (transA) {
+   //// transpose the second argument
+   //ABORT("not implemented");
+   //CUSPARSE_CHECK(cusparseSgemmi(cusparseHandle,
+   //    /*m=*/ colsB, // #rows of A = #cols of row-major B
+   //    /*n=*/ rowsC, // #cols of B and C = #rows of row-major C
+   //    /*k=*/ rowsB, // #cols of A = #rows of row-major B
+   //    /*nnz=*/ (int)numValues,
+   //    &alpha,
+   //    /*A=*/ B->data(),
+   //    /*lda=*/ colsB, // stride
+   //    /*cscValB=*/          A_values->data<float>(),  // second arg   --these get replaced
+   //    /*cscRowPtrB=*/ (int*)A_offsets->data<IndexType>(),
+   //    /*cscColIndB=*/ (int*)A_indices->data<IndexType>(),
+   //    &beta,
+   //    C->data(),
+   //    /*ldc=*/ colsC)); // stride
+  }
+  else {
+    CUSPARSE_CHECK(cusparseSgemmi(cusparseHandle,
+        /*m=*/ colsB, // #rows of A = #cols of row-major B
+        /*n=*/ rowsC, // #cols of B and C = #rows of row-major C
+        /*k=*/ rowsB, // #cols of A = #rows of row-major B
+        /*nnz=*/ (int)numValues,
+        &alpha,
+        /*A=*/ B->data(),
+        /*lda=*/ colsB, // stride
+        /*cscValB=*/          A_values->data<float>(),  // second arg
+        /*cscRowPtrB=*/ (int*)A_offsets->data<IndexType>(),
+        /*cscColIndB=*/ (int*)A_indices->data<IndexType>(),
+        &beta,
+        C->data(),
+        /*ldc=*/ colsC)); // stride
+  }
+#if 0
   // Incorrect code that assumes col-major matrices. Reuse that later for dense x sparse.
   cusparseMatDescr_t descrA;
   CUSPARSE_CHECK(cusparseCreateMatDescr(&descrA));
diff --git a/src/tensors/gpu/prod.h b/src/tensors/gpu/prod.h
index 1443e53a8..35bde4f79 100755
--- a/src/tensors/gpu/prod.h
+++ b/src/tensors/gpu/prod.h
@@ -34,6 +34,7 @@ void ProdBatched(marian::Tensor C,
                  float beta = 0,
                  float scalar = 1);
 void CSRProd(marian::Tensor C,
+             Ptr<Allocator> allocator,
              const marian::Tensor& A_values,
              const marian::Tensor& A_indices,
              const marian::Tensor& A_offsets,
diff --git a/src/tensors/tensor_operators.h b/src/tensors/tensor_operators.h
index d84ca7974..a9bd58480 100755
--- a/src/tensors/tensor_operators.h
+++ b/src/tensors/tensor_operators.h
@@ -77,7 +77,7 @@ void Reduce(Functor functor, marian::Tensor out, Tensors... tensors) {
 // clang-format off
 DISPATCH7(Prod, marian::Tensor, const marian::Tensor&, const marian::Tensor&, bool, bool, float, float)
 DISPATCH8(ProdBatched, marian::Tensor, Ptr<Allocator>, const marian::Tensor, const marian::Tensor, bool, bool, float, float)
-DISPATCH7(CSRProd, marian::Tensor, const marian::Tensor&, const marian::Tensor&, const marian::Tensor&, const marian::Tensor&, bool, float)
+DISPATCH8(CSRProd, marian::Tensor, Ptr<Allocator>, const marian::Tensor&, const marian::Tensor&, const marian::Tensor&, const marian::Tensor&, bool, float)
 
 DISPATCH2(Softmax, marian::Tensor, marian::Tensor)
 DISPATCH3(SoftmaxGrad, marian::Tensor, marian::Tensor, marian::Tensor)
diff --git a/src/tests/operator_tests.cpp b/src/tests/operator_tests.cpp
index 89cf0fb0b..954f4adce 100755
--- a/src/tests/operator_tests.cpp
+++ b/src/tests/operator_tests.cpp
@@ -15,7 +15,7 @@ void tests(DeviceType device) {
   graph->setDevice({0, device});
   graph->reserveWorkspaceMB(16);
 
-  std::vector<float> values;
+  std::vector<float> values, values2;
 
   SECTION("scalar multiplication") {
     graph->clear();
@@ -325,8 +325,6 @@ void tests(DeviceType device) {
                            4, 5, 6, 2.3, 6.7,
                            7, 8, 9, 3.4, 7.8,
                            1, 1, 2, 4.5, 8.9});
-    std::vector<float> vSxR({2.0, 3.0,  5.0,  5.7 , 14.5 ,
-                             8.5, 9.5, 12.0, 10.15, 21.15});
     auto S = graph->param("S", { 2, 4 }, inits::from_vector(vS));
     auto R = graph->param("R", { 4, 5 }, inits::from_vector(vR));
     std::vector<float> SV;    // create CSR version of S
@@ -343,25 +341,36 @@ void tests(DeviceType device) {
       SO.push_back((IndexType)SI.size());
     }
     auto SxRs = csr_dot(
+          S->shape(),
           graph->constant({(int)SV.size()}, inits::from_vector(SV), Type::float32),
           graph->constant({(int)SI.size()}, inits::from_vector(SI), Type::uint32),
           graph->constant({(int)SO.size()}, inits::from_vector(SO), Type::uint32),
           R);
     auto SxRd = dot(S, R);
+    auto STxRs = csr_dot(   // and transpose; use result of previous since dimensions match
+          S->shape(),
+          graph->constant({(int)SV.size()}, inits::from_vector(SV), Type::float32),
+          graph->constant({(int)SI.size()}, inits::from_vector(SI), Type::uint32),
+          graph->constant({(int)SO.size()}, inits::from_vector(SO), Type::uint32),
+          SxRd, /*transA=*/true);
+    auto STxRd = dot(S, SxRd, /*transA=*/true);
+
+    CHECK(C->shape() == Shape({2, 2, 2}));
+    CHECK(SxRs->shape() == SxRd->shape());
+    CHECK(STxRs->shape() == STxRd->shape());
 
     graph->forward();
 
-    CHECK(C->shape() == Shape({2, 2, 2}));
     C->val()->get(values);
     CHECK(values == vC);
 
-    CHECK(SxRd->shape() == Shape({2, 5}));
-    SxRd->val()->get(values);
-    CHECK(values == vSxR);
+    SxRd->val()->get(values2); // dense
+    SxRs->val()->get(values);  // sparse
+    CHECK(values == values2);  // must be the same
 
-    CHECK(SxRs->shape() == Shape({2, 5}));
-    SxRs->val()->get(values);
-    CHECK(values == vSxR);
+    STxRd->val()->get(values2);
+    STxRs->val()->get(values);
+    CHECK(values == values2);
   }
 
   SECTION("affine transformation") {

From 7e9eed4da6a8a0aa16ab8c40cec4bf17d6e2f0ca Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Fri, 11 Jan 2019 16:25:01 -0800
Subject: [PATCH 111/838] csr_dot(transA) complete, with test passing

---
 src/graph/expression_operators.cpp |  2 +-
 src/tensors/gpu/prod.cu            | 51 ++++++++++++++++++++----------
 2 files changed, 35 insertions(+), 18 deletions(-)

diff --git a/src/graph/expression_operators.cpp b/src/graph/expression_operators.cpp
index ce009f290..6828bf103 100755
--- a/src/graph/expression_operators.cpp
+++ b/src/graph/expression_operators.cpp
@@ -419,7 +419,7 @@ Expr affine(Expr a, Expr b, Expr bias, bool transA, bool transB, float scale) {
 
 // multiply a CSR matrix A with a matrix B
 // A[i,j] is at A_values[A_offsets[i]+k], where k is position of j in A_indices[A_offsets[i]:A_offsets[i+1]]
-// Result shape is (Aoffsets.size() - 1, B->shape(-1))
+// @TODO: Define a proper sparse tensor type.
 Expr csr_dot(const Shape& A_shape, Expr A_values, Expr A_indices, Expr A_offsets, Expr B, bool transA /*= false*/) {
   return Expression<CSRDotNodeOp>(A_shape, A_values, A_indices, A_offsets, B, transA);
 }
diff --git a/src/tensors/gpu/prod.cu b/src/tensors/gpu/prod.cu
index 9e986ef01..e816ecf10 100755
--- a/src/tensors/gpu/prod.cu
+++ b/src/tensors/gpu/prod.cu
@@ -251,7 +251,6 @@ void CSRProd(marian::Tensor C,
   // sparse arrays
   auto numValues  = A_values->shape().elements();
   auto numOffsets = A_offsets->shape().elements() - 1; // -1 since last value is length
-  LOG(info, "n={}, transA={}, rowsB={}, rowsC={}", numOffsets,transA, rowsB, rowsC);
   ABORT_IF(numOffsets != (transA ? rowsB : rowsC), "CSR offset array dimension mismatch: n={}, transA={}, rowsB={}, rowsC={}", numOffsets,transA, rowsB, rowsC);
   ABORT_IF(numOffsets != (transA ? rowsB : rowsC), "CSR offset array dimension mismatch");
   ABORT_IF(A_values->shape() != A_indices->shape(), "CSR values and indices must have the same size");
@@ -261,22 +260,40 @@ void CSRProd(marian::Tensor C,
   // column-major views on the data of B and C, and likewise, spA' is
   // the CSR matrix reinterpreted as a CSC matrix.
   if (transA) {
-   //// transpose the second argument
-   //ABORT("not implemented");
-   //CUSPARSE_CHECK(cusparseSgemmi(cusparseHandle,
-   //    /*m=*/ colsB, // #rows of A = #cols of row-major B
-   //    /*n=*/ rowsC, // #cols of B and C = #rows of row-major C
-   //    /*k=*/ rowsB, // #cols of A = #rows of row-major B
-   //    /*nnz=*/ (int)numValues,
-   //    &alpha,
-   //    /*A=*/ B->data(),
-   //    /*lda=*/ colsB, // stride
-   //    /*cscValB=*/          A_values->data<float>(),  // second arg   --these get replaced
-   //    /*cscRowPtrB=*/ (int*)A_offsets->data<IndexType>(),
-   //    /*cscColIndB=*/ (int*)A_indices->data<IndexType>(),
-   //    &beta,
-   //    C->data(),
-   //    /*ldc=*/ colsC)); // stride
+    // cusparse does not support this specific version of transpose; do it explicitly
+    auto At_values  = allocator->alloc<float>(numValues);
+    auto At_indices = allocator->alloc<int>(numValues);
+    auto At_offsets = allocator->alloc<int>(colsA + 1);
+    // transpose the second argument
+    CUSPARSE_CHECK(cusparseScsr2csc(cusparseHandle,
+        /*m=*/ rowsA, // number of rows of matrix
+        /*n=*/ colsA, // number of columns of matrix
+        /*nnz=*/ (int)numValues,
+        /*csrcVal=*/          A_values->data<float>(),  // second arg
+        /*csrcRowPtr=*/ (int*)A_offsets->data<IndexType>(),
+        /*csrcColInd=*/ (int*)A_indices->data<IndexType>(),
+        /*cscVal=*/    At_values->data<float>(),  // transposed version goes here
+        /*cscRowInd=*/ At_indices->data<int>(),
+        /*cscColPtr=*/ At_offsets->data<int>(),
+        /*copyValues=*/ CUSPARSE_ACTION_NUMERIC,
+        /*idxBase=*/ CUSPARSE_INDEX_BASE_ZERO));
+    CUSPARSE_CHECK(cusparseSgemmi(cusparseHandle,
+        /*m=*/ colsB, // #rows of A = #cols of row-major B
+        /*n=*/ rowsC, // #cols of B and C = #rows of row-major C
+        /*k=*/ rowsB, // #cols of A = #rows of row-major B
+        /*nnz=*/ (int)numValues,
+        &alpha,
+        /*A=*/ B->data(),
+        /*lda=*/ colsB, // stride
+        /*cscValB=*/    At_values->data<float>(),  // second arg, transposed
+        /*cscRowPtrB=*/ At_offsets->data<int>(),
+        /*cscColIndB=*/ At_indices->data<int>(),
+        &beta,
+        C->data(),
+        /*ldc=*/ colsC)); // stride
+    allocator->free(At_values);
+    allocator->free(At_indices);
+    allocator->free(At_offsets);
   }
   else {
     CUSPARSE_CHECK(cusparseSgemmi(cusparseHandle,

From 9c7f977b632e418569a7b5cb49f00d9efa28b4cf Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Fri, 11 Jan 2019 16:34:56 -0800
Subject: [PATCH 112/838] (comments)

---
 src/graph/node_operators_binary.h | 10 ++++++----
 1 file changed, 6 insertions(+), 4 deletions(-)

diff --git a/src/graph/node_operators_binary.h b/src/graph/node_operators_binary.h
index 994b5004a..357c24b69 100755
--- a/src/graph/node_operators_binary.h
+++ b/src/graph/node_operators_binary.h
@@ -436,15 +436,17 @@ class CSRDotNodeOp : public NaryNodeOp {
     // C = dot(A, B)
     return {NodeOp(CSRProd(val_,
                            graph()->allocator(),
-                           child(0)->val(), child(1)->val(), child(2)->val(), child(3)->val(),
+                           child(0)->val(), child(1)->val(), child(2)->val(),
+                           child(3)->val(),
                            /*transA=*/transA_, /*beta=*/0))};
   }
 
   NodeOps backwardOps() override {
-    return {nullptr, // can't backprop into the sparse matrix, as it would be dense
-            NodeOp(CSRProd(child(1)->grad(),
+    return {nullptr, // can't backprop into the sparse matrix (the gradient is dense)
+            NodeOp(CSRProd(child(3)->grad(), // child(3) = B
                            graph()->allocator(),
-                           child(0)->val(), child(1)->val(), child(2)->val(), adj_,
+                           child(0)->val(), child(1)->val(), child(2)->val(), // children(0..2) = A
+                           adj_,
                            /*transA=*/!transA_, /*beta=*/1))};
   }
 

From eb2c5bccc77d4b271d33cccfbb156ca12b02dc06 Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Fri, 11 Jan 2019 16:48:29 -0800
Subject: [PATCH 113/838] csr_dot operation

---
 src/common/utils.cpp               |   8 +-
 src/common/utils.h                 |   8 +-
 src/graph/expression_operators.cpp |   7 ++
 src/graph/expression_operators.h   |   2 +
 src/graph/node_operators_binary.h  |  50 ++++++++++-
 src/optimizers/optimizers.h        |   2 +-
 src/tensors/cpu/prod.cpp           |  12 +++
 src/tensors/gpu/backend.h          |  17 ++--
 src/tensors/gpu/cuda_helpers.h     |   6 ++
 src/tensors/gpu/prod.cu            | 132 ++++++++++++++++++++++++++---
 src/tensors/gpu/prod.h             |   8 ++
 src/tensors/tensor_operators.h     |   1 +
 src/tests/attention_tests.cpp      |   8 +-
 src/tests/operator_tests.cpp       |  67 +++++++++++++--
 src/tests/rnn_tests.cpp            |  58 ++++++-------
 src/training/communicator_nccl.h   |   2 +-
 16 files changed, 317 insertions(+), 71 deletions(-)
 mode change 100644 => 100755 src/tensors/gpu/prod.h

diff --git a/src/common/utils.cpp b/src/common/utils.cpp
index 992c74ac8..bde788359 100755
--- a/src/common/utils.cpp
+++ b/src/common/utils.cpp
@@ -29,7 +29,7 @@ void trimLeft(std::string& s) {
 // @TODO: use more functions from CLI instead of own implementations
 void split(const std::string& line,
            std::vector<std::string>& pieces,
-           const std::string del /*= " "*/,
+           const std::string& del /*= " "*/,
            bool keepEmpty) {
   size_t begin = 0;
   size_t pos = 0;
@@ -50,7 +50,7 @@ void split(const std::string& line,
 }
 
 std::vector<std::string> split(const std::string& line,
-                               const std::string del /*= " "*/,
+                               const std::string& del /*= " "*/,
                                bool keepEmpty) {
   std::vector<std::string> pieces;
   split(line, pieces, del, keepEmpty);
@@ -60,7 +60,7 @@ std::vector<std::string> split(const std::string& line,
 // @TODO: splitAny() shares all but 2 expressions with split(). Merge them.
 void splitAny(const std::string& line,
               std::vector<std::string>& pieces,
-              const std::string del /*= " "*/,
+              const std::string& del /*= " "*/,
               bool keepEmpty) {
   size_t begin = 0;
   size_t pos = 0;
@@ -81,7 +81,7 @@ void splitAny(const std::string& line,
 }
 
 std::vector<std::string> splitAny(const std::string& line,
-                                  const std::string del /*= " "*/,
+                                  const std::string& del /*= " "*/,
                                   bool keepEmpty) {
   std::vector<std::string> pieces;
   splitAny(line, pieces, del, keepEmpty);
diff --git a/src/common/utils.h b/src/common/utils.h
index 7c4d56432..94113a0ec 100755
--- a/src/common/utils.h
+++ b/src/common/utils.h
@@ -12,20 +12,20 @@ void trimRight(std::string& s);
 
 void split(const std::string& line,
            std::vector<std::string>& pieces,
-           const std::string del = " ",
+           const std::string& del = " ",
            bool keepEmpty = false);
 
 std::vector<std::string> split(const std::string& line,
-                               const std::string del = " ",
+                               const std::string& del = " ",
                                bool keepEmpty = false);
 
 void splitAny(const std::string& line,
               std::vector<std::string>& pieces,
-              const std::string del = " ",
+              const std::string& del = " ",
               bool keepEmpty = false);
 
 std::vector<std::string> splitAny(const std::string& line,
-                                  const std::string del = " ",
+                                  const std::string& del = " ",
                                   bool keepEmpty = false);
 
 std::string join(const std::vector<std::string>& words,
diff --git a/src/graph/expression_operators.cpp b/src/graph/expression_operators.cpp
index cdfeff271..6828bf103 100755
--- a/src/graph/expression_operators.cpp
+++ b/src/graph/expression_operators.cpp
@@ -417,6 +417,13 @@ Expr affine(Expr a, Expr b, Expr bias, bool transA, bool transB, float scale) {
   }
 }
 
+// multiply a CSR matrix A with a matrix B
+// A[i,j] is at A_values[A_offsets[i]+k], where k is position of j in A_indices[A_offsets[i]:A_offsets[i+1]]
+// @TODO: Define a proper sparse tensor type.
+Expr csr_dot(const Shape& A_shape, Expr A_values, Expr A_indices, Expr A_offsets, Expr B, bool transA /*= false*/) {
+  return Expression<CSRDotNodeOp>(A_shape, A_values, A_indices, A_offsets, B, transA);
+}
+
 // swap the last two axes
 // @TODO: change to swapAxes(a, -1, -2)
 Expr transpose(Expr a) {
diff --git a/src/graph/expression_operators.h b/src/graph/expression_operators.h
index 7c3797069..c9a66daec 100755
--- a/src/graph/expression_operators.h
+++ b/src/graph/expression_operators.h
@@ -112,6 +112,8 @@ Expr affine(Expr a,
             bool transB = false,
             float scalar = 1.f);
 
+Expr csr_dot(const Shape& A_shape, Expr Avalues, Expr Aindices, Expr Aoffsets, Expr B, bool transA = false);
+
 Expr transpose(Expr a);
 Expr transpose(Expr a, const std::vector<int>& axes);
 
diff --git a/src/graph/node_operators_binary.h b/src/graph/node_operators_binary.h
index 868ee4ebd..357c24b69 100755
--- a/src/graph/node_operators_binary.h
+++ b/src/graph/node_operators_binary.h
@@ -128,7 +128,7 @@ class DotNodeOp : public NaryNodeOp {
                         scalar_))};
   }
 
-  const std::string type() override { return "•"; }
+  const std::string type() override { return "dot"; }
 
   const std::string color() override { return "orange"; }
 };
@@ -404,7 +404,53 @@ class DotBatchedNodeOp : public NaryNodeOp {
                                scalar_))};
   }
 
-  const std::string type() override { return "•"; }
+  const std::string type() override { return "bdot"; }
+
+  const std::string color() override { return "orange"; }
+};
+
+class CSRDotNodeOp : public NaryNodeOp {
+  bool transA_;
+public:
+  CSRDotNodeOp(const Shape& A_shape, Expr A_values, Expr A_indices, Expr A_offsets, Expr B, bool transA)
+      : NaryNodeOp({ A_values, A_indices, A_offsets, B }, newShape(A_shape, A_values, A_indices, A_offsets, B, transA)), transA_(transA) {
+    matchOrAbort<IndexType>(A_indices->value_type());
+    matchOrAbort<IndexType>(A_offsets->value_type());
+  }
+
+  Shape newShape(const Shape& A_shape, Expr A_values, Expr A_indices, Expr A_offsets, Expr B, bool transA) {
+    ABORT_IF(A_values->shape().size() != 1 || A_indices->shape().size() != 1 || A_offsets->shape().size() != 1,
+        "Sparse matrix components must all be vectors");
+    ABORT_IF(A_values->shape() != A_indices->shape(),
+        "Sparse matrix values and indices must have the same shape");
+    ABORT_IF(A_shape.size() != 2,
+        "Sparse matrix must have rank 2");
+    ABORT_IF(A_offsets->shape()[0] - 1 != A_shape[0],
+        "Sparse matrix offset vector has incorrect size");
+    auto outShape = B->shape();
+    outShape.set(0, transA ? A_shape[1] : A_shape[0]);
+    return outShape;
+  }
+
+  NodeOps forwardOps() override {
+    // C = dot(A, B)
+    return {NodeOp(CSRProd(val_,
+                           graph()->allocator(),
+                           child(0)->val(), child(1)->val(), child(2)->val(),
+                           child(3)->val(),
+                           /*transA=*/transA_, /*beta=*/0))};
+  }
+
+  NodeOps backwardOps() override {
+    return {nullptr, // can't backprop into the sparse matrix (the gradient is dense)
+            NodeOp(CSRProd(child(3)->grad(), // child(3) = B
+                           graph()->allocator(),
+                           child(0)->val(), child(1)->val(), child(2)->val(), // children(0..2) = A
+                           adj_,
+                           /*transA=*/!transA_, /*beta=*/1))};
+  }
+
+  const std::string type() override { return "csr_dot"; }
 
   const std::string color() override { return "orange"; }
 };
diff --git a/src/optimizers/optimizers.h b/src/optimizers/optimizers.h
index 51e43f871..44d603120 100755
--- a/src/optimizers/optimizers.h
+++ b/src/optimizers/optimizers.h
@@ -26,7 +26,7 @@ class OptimizerBase : public TrainingObserver {
     // that these hyper-parameters were originally tuned for, then the learning-rate gets
     // adjusted accordingly. Note: Requires user to also use ce-sum criterion.
     if (refMBWordsParam_ != 0)
-      LOG(info, "Note: Learning rate gets automatically adjusted as if minibatch size was {}", refMBWordsParam_);
+      LOG(info, "[optimizers] Learning rate gets automatically adjusted as if minibatch size was {}", refMBWordsParam_);
   }
 
   static constexpr size_t mbSizeNotProvided = SIZE_MAX;
diff --git a/src/tensors/cpu/prod.cpp b/src/tensors/cpu/prod.cpp
index 69923f872..f17d23f84 100755
--- a/src/tensors/cpu/prod.cpp
+++ b/src/tensors/cpu/prod.cpp
@@ -167,5 +167,17 @@ void ProdWithBias(marian::Tensor C,
   cpu::int16::AddBias(C, bias);
 }
 
+void CSRProd(marian::Tensor C,
+             Ptr<Allocator> /*allocator*/,
+             const marian::Tensor& A_values,
+             const marian::Tensor& A_indices,
+             const marian::Tensor& A_offsets,
+             const marian::Tensor& B,
+             bool transA,
+             float beta) {
+  C, A_values, A_indices, A_offsets, B, transA, beta;
+  ABORT("CSRProd is not yet implemented for CPU");
+}
+
 }  // namespace cpu
 }  // namespace marian
diff --git a/src/tensors/gpu/backend.h b/src/tensors/gpu/backend.h
index ab1968c1f..87f6407c5 100755
--- a/src/tensors/gpu/backend.h
+++ b/src/tensors/gpu/backend.h
@@ -6,6 +6,7 @@
 
 #include <cuda.h>
 #include <cublas_v2.h>
+#include <cusparse.h>
 #include <curand.h>
 
 namespace marian {
@@ -15,11 +16,13 @@ class Backend : public marian::Backend {
 public:
   Backend(DeviceId deviceId, size_t seed) : marian::Backend(deviceId, seed) {
     setDevice();
-    setHandles();
+    cublasCreate(&cublasHandle_);
+    cusparseCreate(&cusparseHandle_);
   }
 
   ~Backend() {
     setDevice();
+    cusparseDestroy(cusparseHandle_);
     cublasDestroy(cublasHandle_);
   }
 
@@ -28,19 +31,11 @@ class Backend : public marian::Backend {
   void synchronize() override { cudaStreamSynchronize(0); }
 
   cublasHandle_t getCublasHandle() { return cublasHandle_; }
+  cusparseHandle_t getCusparseHandle() { return cusparseHandle_; }
 
 private:
   cublasHandle_t cublasHandle_;
-
-  void setHandles() {
-    cublasHandle_ = create_handle();
-  }
-
-  cublasHandle_t create_handle() {
-    cublasHandle_t cublasHandle;
-    cublasCreate(&cublasHandle);
-    return cublasHandle;
-  }
+  cusparseHandle_t cusparseHandle_;
 };
 }  // namespace gpu
 }  // namespace marian
diff --git a/src/tensors/gpu/cuda_helpers.h b/src/tensors/gpu/cuda_helpers.h
index ba890490e..920cec8c4 100755
--- a/src/tensors/gpu/cuda_helpers.h
+++ b/src/tensors/gpu/cuda_helpers.h
@@ -13,6 +13,12 @@ const int MAX_BLOCKS = 65535;
         "CUDA error {} '{}' - {}:{}: {}", rc, cudaGetErrorString(rc),  __FILE__, __LINE__, #expr); \
 } while(0)
 
+#define CUBLAS_CHECK(expr) do {                                              \
+  cublasStatus_t rc = (expr);                                                \
+  ABORT_IF(rc != CUBLAS_STATUS_SUCCESS,                                      \
+           "Cublas Error: {} - {}:{}: {}", rc, __FILE__, __LINE__, #expr);   \
+} while(0)
+
 #define CUSPARSE_CHECK(expr) do {                                              \
   cusparseStatus_t rc = (expr);                                                \
   ABORT_IF(rc != CUSPARSE_STATUS_SUCCESS,                                      \
diff --git a/src/tensors/gpu/prod.cu b/src/tensors/gpu/prod.cu
index 9da32e65a..e816ecf10 100755
--- a/src/tensors/gpu/prod.cu
+++ b/src/tensors/gpu/prod.cu
@@ -1,5 +1,6 @@
 
 #include <cublas_v2.h>
+#include <cusparse.h>
 
 // clang-format off
 #include "tensors/gpu/prod.h"
@@ -23,18 +24,18 @@ static void setTensorMode(cublasHandle_t cublasHandle) {
     default: ABORT("Invalid ENABLE_CUBLAS_TENSOR_OP_MATH_FP32={}", var);
     }
     if (mode > 0) { // try whether it can be set   --@TODO: check whether this actually works
-      cublasSetMathMode(cublasHandle, CUBLAS_TENSOR_OP_MATH);
+      CUBLAS_CHECK(cublasSetMathMode(cublasHandle, CUBLAS_TENSOR_OP_MATH));
       cublasMath_t actual = CUBLAS_DEFAULT_MATH;
       cublasGetMathMode(cublasHandle, &actual);
       if (actual != CUBLAS_TENSOR_OP_MATH) {
-        LOG(info, "WARNING: TensorCores requested but not available");
+        LOG(warn, "[gpu] TensorCores requested but not available");
         mode = -1;
       }
     }
     if (mode > 0)
-      LOG(info, "16-bit TensorCores enabled for float32 matrix operations");
+      LOG(info, "[gpu] 16-bit TensorCores enabled for float32 matrix operations");
   }
-  cublasSetMathMode(cublasHandle, mode > 0 ? CUBLAS_TENSOR_OP_MATH : CUBLAS_DEFAULT_MATH);
+  CUBLAS_CHECK(cublasSetMathMode(cublasHandle, mode > 0 ? CUBLAS_TENSOR_OP_MATH : CUBLAS_DEFAULT_MATH));
 }
 
 void Prod(marian::Tensor C,
@@ -75,7 +76,7 @@ void Prod(marian::Tensor C,
   //cublasSetMathMode(cublasHandle, CUBLAS_TENSOR_OP_MATH);
 #endif
 
-  cublasSgemm(cublasHandle,
+  CUBLAS_CHECK(cublasSgemm(cublasHandle,
               opB,
               opA,
               n,
@@ -88,7 +89,7 @@ void Prod(marian::Tensor C,
               lda,
               &beta,
               C->data(),
-              ldc);
+              ldc));
 #if CUDA_VERSION >= 9000
   cublasSetMathMode(cublasHandle, CUBLAS_DEFAULT_MATH);
 #endif
@@ -107,6 +108,7 @@ __global__ void gAddBias(float* out,
   }
 }
 
+#if 0 // @TODO: remove, then rename from .cu to .cpp
 void AddBias(marian::Tensor C, const marian::Tensor bias) {
   cudaSetDevice(C->getDeviceId().no);
 
@@ -116,9 +118,9 @@ void AddBias(marian::Tensor C, const marian::Tensor bias) {
   int threads = std::min(MAX_THREADS, length);
   int blocks = std::min(MAX_BLOCKS, length / threads + (length % threads != 0));
 
-  gAddBias<<<blocks, threads>>>(C->data(), bias->data(), length, cols);
+  gAddBias<<<blocks, threads>>>(C->data(), bias->data(), length, cols); // @TODO: CUDA_CHECK
 
-  cudaStreamSynchronize(0);
+  CUDA_CHECK(cudaStreamSynchronize(0)); // @BUGBUG: Should not be here. Prod() also does not have this.
 }
 
 void ProdWithBias(marian::Tensor C,
@@ -132,6 +134,7 @@ void ProdWithBias(marian::Tensor C,
   marian::gpu::Prod(C, A, B, transA, transB, beta, scalar);
   marian::gpu::AddBias(C, bias);
 }
+#endif
 
 void ProdBatched(marian::Tensor C,
                  Ptr<Allocator> allocator,
@@ -200,7 +203,7 @@ void ProdBatched(marian::Tensor C,
   setTensorMode(cublasHandle);
   //cublasSetMathMode(cublasHandle, CUBLAS_TENSOR_OP_MATH);
 #endif
-  cublasSgemmBatched(cublasHandle,
+  CUBLAS_CHECK(cublasSgemmBatched(cublasHandle,
                      opB,
                      opA,
                      n,
@@ -214,7 +217,7 @@ void ProdBatched(marian::Tensor C,
                      &beta,
                      mp_cptr->data<float*>(),
                      ldc,
-                     batchC);
+                     batchC));
 #if CUDA_VERSION >= 9000
   cublasSetMathMode(cublasHandle, CUBLAS_DEFAULT_MATH);
 #endif
@@ -224,5 +227,114 @@ void ProdBatched(marian::Tensor C,
   allocator->free(mp_cptr);
 }
 
+void CSRProd(marian::Tensor C,
+             Ptr<Allocator> allocator,
+             const marian::Tensor& A_values,
+             const marian::Tensor& A_indices,
+             const marian::Tensor& A_offsets,
+             const marian::Tensor& B,
+             bool transA,
+             float beta) {
+  cudaSetDevice(C->getDeviceId().no);
+  auto cusparseHandle = std::static_pointer_cast<gpu::Backend>(C->getBackend())
+                              ->getCusparseHandle();
+  // dimensions
+  const auto& shapeC = C->shape();
+  const auto& shapeB = B->shape();
+  auto rowsC = shapeC[0];
+  auto colsC = shapeC.elements() / rowsC;
+  auto rowsB = shapeB[0];
+  auto colsB = shapeB.elements() / rowsB;
+  auto rowsA = transA ? rowsB : rowsC;
+  auto colsA = transA ? rowsC : rowsB;
+  ABORT_IF((transA ? colsA : rowsA) != rowsC || (transA ? rowsA : colsA) != rowsB || colsB != colsC, "Inconsistent dimensions in CSR product");
+  // sparse arrays
+  auto numValues  = A_values->shape().elements();
+  auto numOffsets = A_offsets->shape().elements() - 1; // -1 since last value is length
+  ABORT_IF(numOffsets != (transA ? rowsB : rowsC), "CSR offset array dimension mismatch: n={}, transA={}, rowsB={}, rowsC={}", numOffsets,transA, rowsB, rowsC);
+  ABORT_IF(numOffsets != (transA ? rowsB : rowsC), "CSR offset array dimension mismatch");
+  ABORT_IF(A_values->shape() != A_indices->shape(), "CSR values and indices must have the same size");
+  float alpha = 1;
+  // Marian uses row-major storage, but CUSPARSE/CUBLAS assume column-major.
+  // Hence, we compute C = spA * B as C' = B' * spA'. where B' and C' are
+  // column-major views on the data of B and C, and likewise, spA' is
+  // the CSR matrix reinterpreted as a CSC matrix.
+  if (transA) {
+    // cusparse does not support this specific version of transpose; do it explicitly
+    auto At_values  = allocator->alloc<float>(numValues);
+    auto At_indices = allocator->alloc<int>(numValues);
+    auto At_offsets = allocator->alloc<int>(colsA + 1);
+    // transpose the second argument
+    CUSPARSE_CHECK(cusparseScsr2csc(cusparseHandle,
+        /*m=*/ rowsA, // number of rows of matrix
+        /*n=*/ colsA, // number of columns of matrix
+        /*nnz=*/ (int)numValues,
+        /*csrcVal=*/          A_values->data<float>(),  // second arg
+        /*csrcRowPtr=*/ (int*)A_offsets->data<IndexType>(),
+        /*csrcColInd=*/ (int*)A_indices->data<IndexType>(),
+        /*cscVal=*/    At_values->data<float>(),  // transposed version goes here
+        /*cscRowInd=*/ At_indices->data<int>(),
+        /*cscColPtr=*/ At_offsets->data<int>(),
+        /*copyValues=*/ CUSPARSE_ACTION_NUMERIC,
+        /*idxBase=*/ CUSPARSE_INDEX_BASE_ZERO));
+    CUSPARSE_CHECK(cusparseSgemmi(cusparseHandle,
+        /*m=*/ colsB, // #rows of A = #cols of row-major B
+        /*n=*/ rowsC, // #cols of B and C = #rows of row-major C
+        /*k=*/ rowsB, // #cols of A = #rows of row-major B
+        /*nnz=*/ (int)numValues,
+        &alpha,
+        /*A=*/ B->data(),
+        /*lda=*/ colsB, // stride
+        /*cscValB=*/    At_values->data<float>(),  // second arg, transposed
+        /*cscRowPtrB=*/ At_offsets->data<int>(),
+        /*cscColIndB=*/ At_indices->data<int>(),
+        &beta,
+        C->data(),
+        /*ldc=*/ colsC)); // stride
+    allocator->free(At_values);
+    allocator->free(At_indices);
+    allocator->free(At_offsets);
+  }
+  else {
+    CUSPARSE_CHECK(cusparseSgemmi(cusparseHandle,
+        /*m=*/ colsB, // #rows of A = #cols of row-major B
+        /*n=*/ rowsC, // #cols of B and C = #rows of row-major C
+        /*k=*/ rowsB, // #cols of A = #rows of row-major B
+        /*nnz=*/ (int)numValues,
+        &alpha,
+        /*A=*/ B->data(),
+        /*lda=*/ colsB, // stride
+        /*cscValB=*/          A_values->data<float>(),  // second arg
+        /*cscRowPtrB=*/ (int*)A_offsets->data<IndexType>(),
+        /*cscColIndB=*/ (int*)A_indices->data<IndexType>(),
+        &beta,
+        C->data(),
+        /*ldc=*/ colsC)); // stride
+  }
+#if 0
+  // Incorrect code that assumes col-major matrices. Reuse that later for dense x sparse.
+  cusparseMatDescr_t descrA;
+  CUSPARSE_CHECK(cusparseCreateMatDescr(&descrA));
+  cusparseSetMatType     (descrA, CUSPARSE_MATRIX_TYPE_GENERAL);
+  cusparseSetMatIndexBase(descrA, CUSPARSE_INDEX_BASE_ZERO);
+  CUSPARSE_CHECK(cusparseScsrmm(cusparseHandle,
+      transA ? CUSPARSE_OPERATION_TRANSPOSE : CUSPARSE_OPERATION_NON_TRANSPOSE,
+      /*m=*/ rowsA, // #rows of sparse A
+      /*n=*/ colsB, // #cols of dense B and C
+      /*k=*/ colsA, // #cols of sparse A
+      /*nnz=*/ (int)numValues,
+      &alpha, descrA,
+      /*csrValA=*/          A_values->data<float>(),
+      /*csrRowPtrA=*/ (int*)A_offsets->data<IndexType>(),
+      /*csrColIndA=*/ (int*)A_indices->data<IndexType>(),
+      B->data(),
+      /*ldb=*/ rowsB,
+      &beta,
+      C->data(),
+      /*ldc=*/ rowsC));
+  cusparseDestroyMatDescr(descrA);
+#endif
+}
+
 }  // namespace gpu
 }  // namespace marian
diff --git a/src/tensors/gpu/prod.h b/src/tensors/gpu/prod.h
old mode 100644
new mode 100755
index ce1f6cdca..35bde4f79
--- a/src/tensors/gpu/prod.h
+++ b/src/tensors/gpu/prod.h
@@ -33,5 +33,13 @@ void ProdBatched(marian::Tensor C,
                  bool transB,
                  float beta = 0,
                  float scalar = 1);
+void CSRProd(marian::Tensor C,
+             Ptr<Allocator> allocator,
+             const marian::Tensor& A_values,
+             const marian::Tensor& A_indices,
+             const marian::Tensor& A_offsets,
+             const marian::Tensor& B,
+             bool transA,
+             float beta = 0);
 }  // namespace gpu
 }  // namespace marian
diff --git a/src/tensors/tensor_operators.h b/src/tensors/tensor_operators.h
index 2cac284ab..a9bd58480 100755
--- a/src/tensors/tensor_operators.h
+++ b/src/tensors/tensor_operators.h
@@ -77,6 +77,7 @@ void Reduce(Functor functor, marian::Tensor out, Tensors... tensors) {
 // clang-format off
 DISPATCH7(Prod, marian::Tensor, const marian::Tensor&, const marian::Tensor&, bool, bool, float, float)
 DISPATCH8(ProdBatched, marian::Tensor, Ptr<Allocator>, const marian::Tensor, const marian::Tensor, bool, bool, float, float)
+DISPATCH8(CSRProd, marian::Tensor, Ptr<Allocator>, const marian::Tensor&, const marian::Tensor&, const marian::Tensor&, const marian::Tensor&, bool, float)
 
 DISPATCH2(Softmax, marian::Tensor, marian::Tensor)
 DISPATCH3(SoftmaxGrad, marian::Tensor, marian::Tensor, marian::Tensor)
diff --git a/src/tests/attention_tests.cpp b/src/tests/attention_tests.cpp
index 1f4ae5b6d..9b1b61859 100755
--- a/src/tests/attention_tests.cpp
+++ b/src/tests/attention_tests.cpp
@@ -53,15 +53,15 @@ void tests(DeviceType type) {
     auto mask = graph->constant({dimTime, dimBatch, 1},
                                 inits::from_vector(vMask));
 
-    auto rnn = rnn::rnn(graph)         //
+    auto rnn = rnn::rnn()         //
           ("prefix", "rnntest")        //
           ("type", "gru")              //
           ("dimInput", 16)             //
           ("dimState", 8)              //
-          .push_back(rnn::cell(graph)) //
-          .construct();
+          .push_back(rnn::cell()) //
+          .construct(graph);
 
-    auto context = rnn.construct()->transduce(input, mask);
+    auto context = rnn->transduce(input, mask);
 
     auto encState = New<EncoderState>(context, mask, nullptr);
 
diff --git a/src/tests/operator_tests.cpp b/src/tests/operator_tests.cpp
index 0ece15a23..954f4adce 100755
--- a/src/tests/operator_tests.cpp
+++ b/src/tests/operator_tests.cpp
@@ -15,7 +15,7 @@ void tests(DeviceType device) {
   graph->setDevice({0, device});
   graph->reserveWorkspaceMB(16);
 
-  std::vector<float> values;
+  std::vector<float> values, values2;
 
   SECTION("scalar multiplication") {
     graph->clear();
@@ -302,18 +302,75 @@ void tests(DeviceType device) {
     graph->clear();
     values.clear();
 
-    std::vector<float> vA({1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12});
-    std::vector<float> vB({1, 2, 3, 4, 5, 6});
-    std::vector<float> vC({22, 28, 49, 64, 76, 100, 103, 136});
+    std::vector<float> vA({1, 2, 3,
+                           4, 5, 6,
+                           7, 8, 9,
+                           10, 11, 12});
+    std::vector<float> vB({1, 2,
+                           3, 4,
+                           5, 6});
+    std::vector<float> vC({22, 28,
+                           49, 64,
+                           76, 100,
+                           103, 136});
 
     auto A = graph->param("A", {2, 2, 3}, inits::from_vector(vA));
     auto B = graph->param("B", {3, 2}, inits::from_vector(vB));
     auto C = dot(A, B);
-    graph->forward();
+
+    // CSR dot product
+    std::vector<float> vS({1, 0, 0, 1,
+                           0, 0, 1, 1.5});
+    std::vector<float> vR({1, 2, 3, 1.2, 5.6,
+                           4, 5, 6, 2.3, 6.7,
+                           7, 8, 9, 3.4, 7.8,
+                           1, 1, 2, 4.5, 8.9});
+    auto S = graph->param("S", { 2, 4 }, inits::from_vector(vS));
+    auto R = graph->param("R", { 4, 5 }, inits::from_vector(vR));
+    std::vector<float> SV;    // create CSR version of S
+    std::vector<IndexType> SI, SO;
+    SO.push_back((IndexType)SI.size());
+    for (IndexType i = 0; i < S->shape()[0]; i++) {
+      for (IndexType j = 0; j < S->shape()[1]; j++) {
+        auto k = 4 * i + j;
+        if (vS[k] != 0) {
+          SV.push_back(vS[k]);
+          SI.push_back(j);
+        }
+      }
+      SO.push_back((IndexType)SI.size());
+    }
+    auto SxRs = csr_dot(
+          S->shape(),
+          graph->constant({(int)SV.size()}, inits::from_vector(SV), Type::float32),
+          graph->constant({(int)SI.size()}, inits::from_vector(SI), Type::uint32),
+          graph->constant({(int)SO.size()}, inits::from_vector(SO), Type::uint32),
+          R);
+    auto SxRd = dot(S, R);
+    auto STxRs = csr_dot(   // and transpose; use result of previous since dimensions match
+          S->shape(),
+          graph->constant({(int)SV.size()}, inits::from_vector(SV), Type::float32),
+          graph->constant({(int)SI.size()}, inits::from_vector(SI), Type::uint32),
+          graph->constant({(int)SO.size()}, inits::from_vector(SO), Type::uint32),
+          SxRd, /*transA=*/true);
+    auto STxRd = dot(S, SxRd, /*transA=*/true);
 
     CHECK(C->shape() == Shape({2, 2, 2}));
+    CHECK(SxRs->shape() == SxRd->shape());
+    CHECK(STxRs->shape() == STxRd->shape());
+
+    graph->forward();
+
     C->val()->get(values);
     CHECK(values == vC);
+
+    SxRd->val()->get(values2); // dense
+    SxRs->val()->get(values);  // sparse
+    CHECK(values == values2);  // must be the same
+
+    STxRd->val()->get(values2);
+    STxRs->val()->get(values);
+    CHECK(values == values2);
   }
 
   SECTION("affine transformation") {
diff --git a/src/tests/rnn_tests.cpp b/src/tests/rnn_tests.cpp
index 145828788..8310f2332 100755
--- a/src/tests/rnn_tests.cpp
+++ b/src/tests/rnn_tests.cpp
@@ -43,15 +43,15 @@ void tests(DeviceType type) {
     auto input = graph->constant({4, 1, 4},
                                  inits::glorot_uniform);
 
-    auto rnn = rnn::rnn(graph)         //
-          ("prefix", "rnntest")        //
-          ("type", "tanh")             //
-          ("dimInput", 4)              //
-          ("dimState", 4)              //
-          .push_back(rnn::cell(graph)) //
-          .construct();
+    auto rnn = rnn::rnn()         //
+          ("prefix", "rnntest")   //
+          ("type", "tanh")        //
+          ("dimInput", 4)         //
+          ("dimState", 4)         //
+          .push_back(rnn::cell()) //
+          .construct(graph);
 
-    auto output = rnn.construct()->transduce(input);
+    auto output = rnn->transduce(input);
 
     graph->forward();
 
@@ -117,7 +117,7 @@ void tests(DeviceType type) {
       auto backward = type == "alternating" ? rnn::dir::alternating_backward
                                             : rnn::dir::backward;
 
-      auto rnnFw = rnn::rnn(graph)           //
+      auto rnnFw = rnn::rnn()                //
           ("type", cellType)                 //
           ("direction", forward)             //
           ("dimInput", dimEmb)               //
@@ -126,7 +126,7 @@ void tests(DeviceType type) {
           ("skip", skip);
 
       for(int i = 1; i <= first; ++i) {
-        auto stacked = rnn::stacked_cell(graph);
+        auto stacked = rnn::stacked_cell();
         for(int j = 1; j <= cellDepth; ++j) {
           std::string paramPrefix = prefix + "_bi";
           if(i > 1)
@@ -134,21 +134,21 @@ void tests(DeviceType type) {
           if(i > 1 || j > 1)
             paramPrefix += "_cell" + std::to_string(j);
 
-          stacked.push_back(rnn::cell(graph)("prefix", paramPrefix));
+          stacked.push_back(rnn::cell()("prefix", paramPrefix));
         }
         rnnFw.push_back(stacked);
       }
 
-      auto rnnBw = rnn::rnn(graph)            //
-          ("type", cellType)                  //
-          ("direction", backward)             //
-          ("dimInput", dimEmb)                //
-          ("dimState", dimRnn)                //
-          ("layer-normalization", layerNorm)  //
+      auto rnnBw = rnn::rnn()                //
+          ("type", cellType)                 //
+          ("direction", backward)            //
+          ("dimInput", dimEmb)               //
+          ("dimState", dimRnn)               //
+          ("layer-normalization", layerNorm) //
           ("skip", skip);
 
       for(int i = 1; i <= first; ++i) {
-        auto stacked = rnn::stacked_cell(graph);
+        auto stacked = rnn::stacked_cell();
         for(int j = 1; j <= cellDepth; ++j) {
           std::string paramPrefix = prefix + "_bi_r";
           if(i > 1)
@@ -156,13 +156,13 @@ void tests(DeviceType type) {
           if(i > 1 || j > 1)
             paramPrefix += "_cell" + std::to_string(j);
 
-          stacked.push_back(rnn::cell(graph)("prefix", paramPrefix));
+          stacked.push_back(rnn::cell()("prefix", paramPrefix));
         }
         rnnBw.push_back(stacked);
       }
 
-      auto context = concatenate({rnnFw.construct()->transduce(input, mask),
-                                  rnnBw.construct()->transduce(input, mask)},
+      auto context = concatenate({rnnFw.construct(graph)->transduce(input, mask),
+                                  rnnBw.construct(graph)->transduce(input, mask)},
                                   /*axis =*/ input->shape().size() - 1);
 
       if(second > 0) {
@@ -170,25 +170,25 @@ void tests(DeviceType type) {
         // previous bidirectional RNN through multiple layers
 
         // construct RNN first
-        auto rnnUni = rnn::rnn(graph)           //
-            ("type", cellType)                  //
-            ("dimInput", 2 * dimRnn)            //
-            ("dimState", dimRnn)                //
-            ("layer-normalization", layerNorm)  //
+        auto rnnUni = rnn::rnn()               //
+            ("type", cellType)                 //
+            ("dimInput", 2 * dimRnn)           //
+            ("dimState", dimRnn)               //
+            ("layer-normalization", layerNorm) //
             ("skip", skip);
 
         for(int i = first + 1; i <= second + first; ++i) {
-          auto stacked = rnn::stacked_cell(graph);
+          auto stacked = rnn::stacked_cell();
           for(int j = 1; j <= cellDepth; ++j) {
             std::string paramPrefix = prefix + "_l" + std::to_string(i) + "_cell"
                                       + std::to_string(j);
-            stacked.push_back(rnn::cell(graph)("prefix", paramPrefix));
+            stacked.push_back(rnn::cell()("prefix", paramPrefix));
           }
           rnnUni.push_back(stacked);
         }
 
         // transduce context to new context
-        context = rnnUni.construct()->transduce(context);
+        context = rnnUni.construct(graph)->transduce(context);
       }
       return context;
     };
diff --git a/src/training/communicator_nccl.h b/src/training/communicator_nccl.h
index dbfe2d877..55b485287 100755
--- a/src/training/communicator_nccl.h
+++ b/src/training/communicator_nccl.h
@@ -191,7 +191,7 @@ class NCCLCommunicator : public ICommunicator {
     groupEnd();
 
     mpiBarrier(); // (synchronize the log messages)
-    LOG(info, "NCCLCommunicator constructed successfully.");
+    LOG(info, "[comm] NCCLCommunicator constructed successfully.");
     mpiBarrier(); // (synchronize the log messages)
   }
 

From 4edc577b70e2d529469dfd770d7c306a70e7fcea Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Fri, 11 Jan 2019 19:50:10 -0800
Subject: [PATCH 114/838] bug fix: decoder should also use factors for the
 input

---
 src/layers/generic.h |  7 ++-----
 src/models/decoder.h | 43 ++++++++++++++++---------------------------
 2 files changed, 18 insertions(+), 32 deletions(-)

diff --git a/src/layers/generic.h b/src/layers/generic.h
index 25b51dc13..092567c94 100755
--- a/src/layers/generic.h
+++ b/src/layers/generic.h
@@ -177,17 +177,14 @@ class Output : public LayerBase, public IUnaryLayer {
 class Embedding : public LayerBase, public IEmbeddingLayer {
   Expr E_;
   Ptr<class EmbeddingFactorMapping> embeddingFactorMapping_;
+  Expr multiRows(const std::vector<IndexType>& data) const;
 public:
   Embedding(Ptr<ExpressionGraph> graph, Ptr<Options> options);
 
   std::tuple<Expr/*embeddings*/, Expr/*mask*/> apply(Ptr<data::SubBatch> subBatch) const override final;
 
   // special version used in decoding
-  Expr apply(const std::vector<IndexType>& embIdx, int dimBatch, int dimBeam) const override final {
-    int dimEmb = E_->shape()[-1];
-    auto selectedEmbs = rows(E_, embIdx);
-    return reshape(selectedEmbs, { dimBeam, 1, dimBatch, dimEmb });
-  }
+  Expr apply(const std::vector<IndexType>& embIdx, int dimBatch, int dimBeam) const override final;
 };
 
 class ULREmbedding : public LayerBase, public IEmbeddingLayer {
diff --git a/src/models/decoder.h b/src/models/decoder.h
index abf6bbd1e..9b7aad9c3 100755
--- a/src/models/decoder.h
+++ b/src/models/decoder.h
@@ -15,6 +15,7 @@ class DecoderBase {
   std::string prefix_{"decoder"};
   bool inference_{false};
   size_t batchIndex_{1};
+  std::vector<Ptr<IEmbeddingLayer>> embedding_; // @TODO: find a more grammattical name
 
   Ptr<data::Shortlist> shortlist_;
 
@@ -32,17 +33,13 @@ class DecoderBase {
 
   virtual Ptr<DecoderState> step(Ptr<ExpressionGraph>, Ptr<DecoderState>) = 0;
 
-  std::vector<Ptr<IEmbeddingLayer>> embedding_; // @TODO: move away, also rename
-  virtual void embeddingsFromBatch(Ptr<ExpressionGraph> graph,
-                                   Ptr<DecoderState> state,
-                                   Ptr<data::CorpusBatch> batch) {
-
-    int dimVoc = opt<std::vector<int>>("dim-vocabs")[batchIndex_];
-    int dimEmb = opt<int>("dim-emb");
-
+  void lazyCreateEmbedding(Ptr<ExpressionGraph> graph) {
     // @TODO: code dup with EncoderTransformer
-    if (embedding_.empty() || !embedding_[batchIndex_]) { // lazy
-      embedding_.resize(batch->sets());
+    if (embedding_.size() <= batchIndex_ || !embedding_[batchIndex_]) { // lazy
+      if (embedding_.size() <= batchIndex_)
+        embedding_.resize(batchIndex_ + 1);
+      int dimVoc = opt<std::vector<int>>("dim-vocabs")[batchIndex_];
+      int dimEmb = opt<int>("dim-emb");
       auto embFactory = embedding()("dimVocab", dimVoc)("dimEmb", dimEmb);
       if(opt<bool>("tied-embeddings-src") || opt<bool>("tied-embeddings-all"))
         embFactory("prefix", "Wemb");
@@ -61,9 +58,14 @@ class DecoderBase {
       }
       embedding_[batchIndex_] = embFactory.construct(graph);
     }
+  }
 
+  virtual void embeddingsFromBatch(Ptr<ExpressionGraph> graph,
+                                   Ptr<DecoderState> state,
+                                   Ptr<data::CorpusBatch> batch) {
     auto subBatch = (*batch)[batchIndex_];
 
+    lazyCreateEmbedding(graph);
     Expr y, yMask; std::tie
     (y, yMask) = embedding_[batchIndex_]->apply(subBatch);
 
@@ -86,26 +88,13 @@ class DecoderBase {
                                         const std::vector<IndexType>& embIdx,
                                         int dimBatch,
                                         int dimBeam) {
-    int dimTrgEmb = opt<int>("dim-emb");
-    int dimTrgVoc = opt<std::vector<int>>("dim-vocabs")[batchIndex_];
-
     Expr selectedEmbs;
     if(embIdx.empty()) {
-      selectedEmbs = graph->constant({1, 1, dimBatch, dimTrgEmb}, inits::zeros);
+      int dimEmb = opt<int>("dim-emb");
+      selectedEmbs = graph->constant({1, 1, dimBatch, dimEmb}, inits::zeros);
     } else {
-      // embeddings are loaded from model during translation, no fixing required
-      auto yEmbFactory = embedding()  //
-          ("dimVocab", dimTrgVoc)     //
-          ("dimEmb", dimTrgEmb);
-  
-      if(opt<bool>("tied-embeddings-src") || opt<bool>("tied-embeddings-all"))
-        yEmbFactory("prefix", "Wemb");
-      else
-        yEmbFactory("prefix", prefix_ + "_Wemb");
-  
-      auto yEmb = yEmbFactory.construct(graph);
-
-      selectedEmbs = yEmb->apply(embIdx, dimBatch, dimBeam);
+      lazyCreateEmbedding(graph);
+      selectedEmbs = embedding_[batchIndex_]->apply(embIdx, dimBatch, dimBeam);
     }
     state->setTargetEmbeddings(selectedEmbs);
   }

From 569b6639e23d34fd291efc78219bc77f0cdcca08 Mon Sep 17 00:00:00 2001
From: Marcin Junczys-Dowmunt <Marcin.Junczys-Dowmunt@microsoft.com>
Date: Sat, 12 Jan 2019 21:25:08 -0800
Subject: [PATCH 115/838] only scale trigonometric emeddings

---
 src/models/transformer.h | 15 +++++++--------
 1 file changed, 7 insertions(+), 8 deletions(-)

diff --git a/src/models/transformer.h b/src/models/transformer.h
index d57f23b95..3791d3c80 100755
--- a/src/models/transformer.h
+++ b/src/models/transformer.h
@@ -67,9 +67,11 @@ class Transformer : public EncoderOrDecoderBase {
       auto signal = rows(posEmbFactory, graph_->indices(positions));
       signal = reshape(signal, {dimWords, 1, dimEmb});
       embeddings = embeddings + signal;
-    } else {
+    } else {      
       auto signal = graph_->constant({dimWords, 1, dimEmb},
                                      inits::positions(start));
+      // according to paper embeddings are scaled up by \sqrt(d_m)
+      embeddings = std::sqrt((float)dimEmb) * embeddings;
       embeddings = embeddings + signal;
     }
 
@@ -537,7 +539,6 @@ class EncoderTransformer : public Transformer<EncoderBase> {
   }
 
   virtual Ptr<EncoderState> apply(Ptr<data::CorpusBatch> batch) {
-    int dimEmb = opt<int>("dim-emb");
     int dimBatch = (int)batch->size();
     int dimSrcWords = (int)(*batch)[batchIndex_]->batchWidth();
     // create the embedding matrix, considering tying and some other options
@@ -559,15 +560,13 @@ class EncoderTransformer : public Transformer<EncoderBase> {
       int srcWords = batchEmbeddings->shape()[-3];
       batchEmbeddings = dropout(batchEmbeddings, dropoutSrc, {srcWords, 1, 1});
     }
-    // according to paper embeddings are scaled up by \sqrt(d_m)
-    auto scaledEmbeddings = std::sqrt((float)dimEmb) * batchEmbeddings;
-
-    scaledEmbeddings = addSpecialEmbeddings(scaledEmbeddings, /*start=*/0, batch);
+    
+    batchEmbeddings = addSpecialEmbeddings(batchEmbeddings, /*start=*/0, batch);
 
     // reorganize batch and timestep
-    scaledEmbeddings = atleast_nd(scaledEmbeddings, 4);
+    batchEmbeddings = atleast_nd(batchEmbeddings, 4);
     batchMask = atleast_nd(batchMask, 4);
-    auto layer = transposeTimeBatch(scaledEmbeddings); // [-4: beam depth=1, -3: batch size, -2: max length, -1: vector dim]
+    auto layer = transposeTimeBatch(batchEmbeddings); // [-4: beam depth=1, -3: batch size, -2: max length, -1: vector dim]
     auto layerMask
       = reshape(transposeTimeBatch(batchMask), {1, dimBatch, 1, dimSrcWords}); // [-4: beam depth=1, -3: batch size, -2: vector dim=1, -1: max length]
 

From fe56ba3f33c39231fe1661dd1df2603ee36c336c Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Sun, 13 Jan 2019 10:34:08 -0800
Subject: [PATCH 116/838] first shot at factored embeddings

---
 src/layers/generic.cpp   | 180 +++++++++++++++++++++++++++++++++++++++
 src/models/transformer.h |   8 ++
 2 files changed, 188 insertions(+)
 create mode 100755 src/layers/generic.cpp

diff --git a/src/layers/generic.cpp b/src/layers/generic.cpp
new file mode 100755
index 000000000..2b18a0255
--- /dev/null
+++ b/src/layers/generic.cpp
@@ -0,0 +1,180 @@
+#include "marian.h"
+
+#include "layers/generic.h"
+
+namespace marian {
+  struct CSRSparseTensor { // simplistic for now
+    Shape shape;
+    Expr values;  // [k_i..k_{i+1}-1] -> value at [i,j]
+    Expr indices; // [k_i..k_{i+1}-1] -> j of non-null value
+    Expr offsets; // [i] -> k_i
+  };
+
+  class EmbeddingFactorMapping {
+    Vocab factorVocab_;                        // [factor name] -> factor index = row of E_
+    Shape mappingShape;
+    std::vector<std::vector<Word>> factorMap_; // [word index] -> set of factor indices
+    std::vector<int> factorRefCounts_;         // [factor index] -> how often this factor is referenced in factorMap_
+  public:
+    // mapPath = path to file with entries in order of vocab entries of the form
+    //   WORD FACTOR1 FACTOR2 FACTOR3...
+    // listPath = path to file that lists all FACTOR names
+    // vocab = original vocabulary
+    // Note: The WORD field in the map file is redundant. It is required for consistency checking only.
+    // Note: Presently, this implementation has the following short-comings
+    //  - we do not group factors (to normalize the probs); instead we assume that the global softmax will normalize correctly
+    //  - we do not handle binary features differently; we'd need to apply sigmoid(x) / sigmoid(-x)
+    EmbeddingFactorMapping(const std::string& mapPath, const std::string& factorVocabPath, const std::string& vocabPath) :
+        factorVocab_(New<Options>(), 0) {
+      // Note: We misuse the Vocab class a little.
+      // But it means that the factorVocab_ must contain </s> and "<unk>".
+      Vocab vocab(New<Options>(), 0);
+      vocab.load(vocabPath);
+      factorVocab_.load(factorVocabPath);
+      // load and parse factorMap
+      factorMap_.resize(vocab.size());
+      factorRefCounts_.resize(vocab.size());
+      std::vector<std::string> tokens;
+      io::InputFileStream in(mapPath);
+      std::string line;
+      size_t numTotalFactors = 0;
+      for (Word v = 0; io::getline(in, line); v++) {
+        tokens.clear(); // @BUGBUG: should be done in split()
+        utils::splitAny(line, tokens, " \t");
+        ABORT_IF(tokens.size() < 2 || tokens.front() != vocab[v], "Factor map must list words in same order as vocab, and have at least one factor per word", mapPath);
+        for (size_t i = 1; i < tokens.size(); i++) {
+          auto u = factorVocab_[tokens[i]];
+          auto& m = factorMap_[v];
+          m.push_back(u);
+          factorRefCounts_[u]++;
+        }
+        numTotalFactors += tokens.size() - 1;
+      }
+      LOG(info, "[embedding] Factored-embedding map read with total/unique of {}/{} factors for {} words", numTotalFactors, factorVocab_.size(), vocab.size());
+      mappingShape = Shape({ (int)vocab.size(), (int)factorVocab_.size() });
+    }
+
+    size_t factorVocabSize() const { return factorVocab_.size(); }
+
+    // create a CSR matrix M[V,U] from indices[] with
+    // M[v,u] = 1/c(u) if factor u is a factor of word v, and c(u) is how often u is referenced
+    std::tuple<Shape, std::vector<float>/*weights*/, std::vector<IndexType>/*indices*/, std::vector<IndexType>/*offsets*/> csr_rows(const std::vector<IndexType>& words) const {
+      std::vector<float> weights;
+      std::vector<IndexType> indices;
+      std::vector<IndexType> offsets;
+      offsets.reserve(words.size() + 1);
+      indices.reserve(words.size()); // (at least this many)
+      // loop over all input words, and select the corresponding set of unit indices into CSR format
+      offsets.push_back((IndexType)indices.size());
+      for (auto v : words) {
+        const auto& m = factorMap_[v];
+        for (auto u : m) {
+          indices.push_back(u);
+          weights.push_back(1.0f/(float)factorRefCounts_[u]);
+        }
+        offsets.push_back((IndexType)indices.size()); // next matrix row begins at this offset
+      }
+      return
+          std::tuple<Shape, std::vector<float>/*weights*/, std::vector<IndexType>/*indices*/, std::vector<IndexType>/*offsets*/> // (needed for unknown reasons)
+          { mappingShape, weights, indices, offsets };
+    }
+  };
+
+  Embedding::Embedding(Ptr<ExpressionGraph> graph, Ptr<Options> options) : LayerBase(graph, options) {
+    std::string name = opt<std::string>("prefix");
+    int dimVoc = opt<int>("dimVocab");
+    int dimEmb = opt<int>("dimEmb");
+
+    bool fixed = opt<bool>("fixed", false);
+
+    if (options_->has("embedding-factors") && !embeddingFactorMapping_) { // (lazy init)
+      std::vector<std::string> paths = opt<std::vector<std::string>>("embedding-factors");
+      ABORT_IF(paths.size() != 2, "--embedding-factors expects two paths");
+      embeddingFactorMapping_ = New<EmbeddingFactorMapping>(paths[0], paths[1], opt<std::string>("vocab"));
+      dimVoc = (int)embeddingFactorMapping_->factorVocabSize();
+    }
+
+    NodeInitializer initFunc = inits::glorot_uniform;
+    if (options_->has("embFile")) {
+      std::string file = opt<std::string>("embFile");
+      if (!file.empty()) {
+        bool norm = opt<bool>("normalization", false);
+        initFunc = inits::from_word2vec(file, dimVoc, dimEmb, norm);
+      }
+    }
+
+    E_ = graph_->param(name, {dimVoc, dimEmb}, initFunc, fixed);
+  }
+
+  // helper to embed a sequence of words (given as indices) via factored embeddings
+  /*private*/ Expr Embedding::multiRows(const std::vector<IndexType>& data) const
+  {
+    auto graph = E_->graph();
+    Shape shape; std::vector<float> weights;  std::vector<IndexType> indices, offsets; std::tie
+    (shape, weights, indices, offsets) = embeddingFactorMapping_->csr_rows(data);
+    // multi-hot factor vectors are represented as a sparse CSR matrix
+    // [row index = word position index] -> set of factor indices for word at this position
+    return csr_dot( // the CSR matrix is passed in pieces
+        Shape({(int)offsets.size()-1/*=rows of CSR*/, E_->shape()[0]}),
+        graph->constant({(int)indices.size()}, inits::from_vector(weights), Type::float32),
+        graph->constant({(int)indices.size()}, inits::from_vector(indices), Type::uint32),
+        graph->constant({(int)offsets.size()}, inits::from_vector(offsets), Type::uint32),
+        E_);
+  }
+
+  std::tuple<Expr/*embeddings*/, Expr/*mask*/> Embedding::apply(Ptr<data::SubBatch> subBatch) const /*override final*/ {
+    auto graph = E_->graph();
+    int dimBatch = (int)subBatch->batchSize();
+    int dimEmb = E_->shape()[-1];
+    int dimWords = (int)subBatch->batchWidth();
+
+    // factored embeddings:
+    //  - regular:
+    //     - y = x @ E    x:[B x 1ofV] ; E:[V x D] ; y:[B x D]
+    //  - factored:
+    //     - u = x @ M    one-hot to U-dimensional multi-hot (all factors in one concatenated space)
+    //        - each row of M contains the set of factors for one word => we want a CSR matrix
+    //     - y = (x @ M) @ E   (x:[B x 1ofV] ; M:[V x U]) ; E:[U x D] ; y:[B x D]
+    //  - first compute x @ M on the CPU
+    //     - (Uvalues, Uindices, Uoffsets) = csr_rows(Mvalues, Mindices, Moffsets, subBatch->data()):
+    //        - shape (U, specifically) not actually needed here
+    //     - foreach input x[i]
+    //        - locate row M[i,*]
+    //        - copy through its index values (std::vector<push_back>)
+    //     - create a matching ones vector (we can keep growing)
+    //     - convert to GPU-side CSR matrix. CSR matrix now has #rows equal to len(x)
+    //     - CSR matrix product with E
+    //     - csr_dot(Uvalues, Uindices, Uoffsets, E_, transposeU)
+    //        - double-check if all dimensions are specified. Probably not for transpose (which would be like csc_dot()).
+    //  - weighting:
+    //     - core factors' gradients are sums over all words that use the factors;
+    //        - core factors' embeddings move very fast
+    //        - words will need to make up for the move; rare words cannot
+    //     - so, we multiply each factor with 1/refCount
+    //        - core factors get weighed down a lot
+    //        - no impact on gradients, as Adam makes up for it; embeddings still move fast just as before
+    //        - but forward pass weighs them down, so that all factors are in a similar numeric range
+    //        - if it is required to be in a different range, the embeddings can still learn that, but more slowly
+
+    Expr chosenEmbeddings;
+    if (embeddingFactorMapping_)
+      chosenEmbeddings = multiRows(subBatch->data());
+    else
+      chosenEmbeddings = rows(E_, subBatch->data());
+
+    auto batchEmbeddings = reshape(chosenEmbeddings, { dimWords, dimBatch, dimEmb });
+    auto batchMask = graph->constant({ dimWords, dimBatch, 1 },
+                                     inits::from_vector(subBatch->mask()));
+    return std::make_tuple(batchEmbeddings, batchMask);
+  }
+
+  Expr Embedding::apply(const std::vector<IndexType>& embIdx, int dimBatch, int dimBeam) const /*override final*/ {
+    int dimEmb = E_->shape()[-1];
+    Expr chosenEmbeddings;
+    if (embeddingFactorMapping_)
+      chosenEmbeddings = multiRows(embIdx);
+    else
+      chosenEmbeddings = rows(E_, embIdx);
+    return reshape(chosenEmbeddings, { dimBeam, 1, dimBatch, dimEmb });
+  }
+}  // namespace marian
diff --git a/src/models/transformer.h b/src/models/transformer.h
index 48335d1d0..99999e8f6 100755
--- a/src/models/transformer.h
+++ b/src/models/transformer.h
@@ -627,6 +627,14 @@ class DecoderTransformer : public Transformer<DecoderBase> {
       if(opt<bool>("tied-embeddings-all") || opt<bool>("tied-embeddings-src"))
         tiedPrefix = "Wemb";
       layerOut.tieTransposed(tiedPrefix);
+      // factored embeddings, simplistic version (which just adds the logits, like multiplying probs)
+      //  z = h @ W        // h:[B x D] ; W:[D x V] -> [B x V]
+      // with factors:
+      //  z = h @ W @ M'        // h:[B x D] ; W:[D x U] ; M':[U x V]  -> [B x V]
+      // i.e. multiOutput():
+      //  output = dot_csr(output, M, transB=true)
+      // Should biases be done afterwards? Or maybe at two places?
+      // note: need to also specify output factors separately if not tied-embeddings or tied-embeddings-all
     }
 
     if(shortlist_)

From e37a1f8a7dc71192ffbc9c7cbe7c00b13a9b16f8 Mon Sep 17 00:00:00 2001
From: Roman Grundkiewicz <rgrundki@exseed.ed.ac.uk>
Date: Tue, 15 Jan 2019 12:22:04 +0000
Subject: [PATCH 117/838] Rename variable

---
 src/common/cli_wrapper.h     | 1 -
 src/common/config_parser.cpp | 6 +++---
 2 files changed, 3 insertions(+), 4 deletions(-)

diff --git a/src/common/cli_wrapper.h b/src/common/cli_wrapper.h
index a748c560e..95eb3590d 100755
--- a/src/common/cli_wrapper.h
+++ b/src/common/cli_wrapper.h
@@ -92,7 +92,6 @@ class CLIWrapper {
   // Command-line argument parser
   Ptr<CLI::App> app_;
 
-
   // Name of the default option group
   std::string defaultGroup_{""};
   // Name of the current option group
diff --git a/src/common/config_parser.cpp b/src/common/config_parser.cpp
index 1e75e7590..eef2e5b8f 100755
--- a/src/common/config_parser.cpp
+++ b/src/common/config_parser.cpp
@@ -763,14 +763,14 @@ void ConfigParser::parseOptions(int argc, char** argv, bool doValidate) {
   config_.remove("config");
 
   if(!get<std::string>("dump-config").empty() && get<std::string>("dump-config") != "false") {
-    auto type = get<std::string>("dump-config");
+    auto dumpMode = get<std::string>("dump-config");
     config_.remove("dump-config");
 
-    if(type == "explain") {
+    if(dumpMode == "explain") {
       cli.parseAliases();
     }
 
-    bool minimal = (type == "minimal" || type == "explain");
+    bool minimal = (dumpMode == "minimal" || dumpMode == "explain");
     std::cout << cli.dumpConfig(minimal) << std::endl;
     exit(0);
   }

From cc1c663e1c02a90b4e0267d0a50cc0e3f2616e06 Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Tue, 15 Jan 2019 12:53:50 -0800
Subject: [PATCH 118/838] bug fixes of factored source embeddings

---
 src/graph/node_operators_binary.h |  6 +++++-
 src/layers/generic.cpp            | 20 +++++++++++++-------
 src/tensors/tensor.h              |  4 ++++
 3 files changed, 22 insertions(+), 8 deletions(-)

diff --git a/src/graph/node_operators_binary.h b/src/graph/node_operators_binary.h
index 357c24b69..860199d8e 100755
--- a/src/graph/node_operators_binary.h
+++ b/src/graph/node_operators_binary.h
@@ -428,6 +428,8 @@ class CSRDotNodeOp : public NaryNodeOp {
     ABORT_IF(A_offsets->shape()[0] - 1 != A_shape[0],
         "Sparse matrix offset vector has incorrect size");
     auto outShape = B->shape();
+    ABORT_IF((transA ? A_shape[0] : A_shape[1] != B->shape()[0]),
+        "Matrix product requires dimensions to match");
     outShape.set(0, transA ? A_shape[1] : A_shape[0]);
     return outShape;
   }
@@ -442,7 +444,9 @@ class CSRDotNodeOp : public NaryNodeOp {
   }
 
   NodeOps backwardOps() override {
-    return {nullptr, // can't backprop into the sparse matrix (the gradient is dense)
+    return {nullptr, // can't backprop into the sparse matrix pieces (the gradient is dense)
+            nullptr,
+            nullptr,
             NodeOp(CSRProd(child(3)->grad(), // child(3) = B
                            graph()->allocator(),
                            child(0)->val(), child(1)->val(), child(2)->val(), // children(0..2) = A
diff --git a/src/layers/generic.cpp b/src/layers/generic.cpp
index 2b18a0255..f651a3ce6 100755
--- a/src/layers/generic.cpp
+++ b/src/layers/generic.cpp
@@ -12,7 +12,6 @@ namespace marian {
 
   class EmbeddingFactorMapping {
     Vocab factorVocab_;                        // [factor name] -> factor index = row of E_
-    Shape mappingShape;
     std::vector<std::vector<Word>> factorMap_; // [word index] -> set of factor indices
     std::vector<int> factorRefCounts_;         // [factor index] -> how often this factor is referenced in factorMap_
   public:
@@ -33,7 +32,7 @@ namespace marian {
       factorVocab_.load(factorVocabPath);
       // load and parse factorMap
       factorMap_.resize(vocab.size());
-      factorRefCounts_.resize(vocab.size());
+      factorRefCounts_.resize(factorVocab_.size());
       std::vector<std::string> tokens;
       io::InputFileStream in(mapPath);
       std::string line;
@@ -51,7 +50,6 @@ namespace marian {
         numTotalFactors += tokens.size() - 1;
       }
       LOG(info, "[embedding] Factored-embedding map read with total/unique of {}/{} factors for {} words", numTotalFactors, factorVocab_.size(), vocab.size());
-      mappingShape = Shape({ (int)vocab.size(), (int)factorVocab_.size() });
     }
 
     size_t factorVocabSize() const { return factorVocab_.size(); }
@@ -70,13 +68,13 @@ namespace marian {
         const auto& m = factorMap_[v];
         for (auto u : m) {
           indices.push_back(u);
-          weights.push_back(1.0f/(float)factorRefCounts_[u]);
+          weights.push_back(1.0f/*/(float)factorRefCounts_[u]*/);
         }
         offsets.push_back((IndexType)indices.size()); // next matrix row begins at this offset
       }
       return
           std::tuple<Shape, std::vector<float>/*weights*/, std::vector<IndexType>/*indices*/, std::vector<IndexType>/*offsets*/> // (needed for unknown reasons)
-          { mappingShape, weights, indices, offsets };
+          { Shape({(int)words.size(), (int)factorVocab_.size()}), weights, indices, offsets };
     }
   };
 
@@ -112,11 +110,19 @@ namespace marian {
     auto graph = E_->graph();
     Shape shape; std::vector<float> weights;  std::vector<IndexType> indices, offsets; std::tie
     (shape, weights, indices, offsets) = embeddingFactorMapping_->csr_rows(data);
+#if 0   // tests for special case of nop-op
+    ABORT_IF(data != indices, "bang");
+    for (size_t i = 0; i < offsets.size(); i++)
+      ABORT_IF(offsets[i] != i, "boom");
+    for (size_t i = 0; i < weights.size(); i++)
+      ABORT_IF(weights[i] != 1, "oops");
+#endif
     // multi-hot factor vectors are represented as a sparse CSR matrix
     // [row index = word position index] -> set of factor indices for word at this position
+    ABORT_IF(shape != Shape({(int)offsets.size()-1/*=rows of CSR*/, E_->shape()[0]}), "shape mismatch??");
     return csr_dot( // the CSR matrix is passed in pieces
-        Shape({(int)offsets.size()-1/*=rows of CSR*/, E_->shape()[0]}),
-        graph->constant({(int)indices.size()}, inits::from_vector(weights), Type::float32),
+        shape,
+        graph->constant({(int)weights.size()}, inits::from_vector(weights), Type::float32),
         graph->constant({(int)indices.size()}, inits::from_vector(indices), Type::uint32),
         graph->constant({(int)offsets.size()}, inits::from_vector(offsets), Type::uint32),
         E_);
diff --git a/src/tensors/tensor.h b/src/tensors/tensor.h
index acc7e54c8..f77259cb9 100755
--- a/src/tensors/tensor.h
+++ b/src/tensors/tensor.h
@@ -140,6 +140,10 @@ class TensorBase : public std::enable_shared_from_this<TensorBase> {
 
   template <typename T>
   void set(const T* begin, const T* end) {
+    ABORT_IF(end - begin != shape_.elements(),
+             "Vector size ({}) and underlying type ({}) do not match",
+             end - begin,
+             std::string(shape_));
     ABORT_IF(!matchType<T>(type_),
              "Requested type ({}) and underlying type ({}) do not match",
              request<T>(),

From 9733db11fbbcebf2e7e3e7f840f3e496c6198407 Mon Sep 17 00:00:00 2001
From: Marcin Junczys-Dowmunt <Marcin.Junczys-Dowmunt@microsoft.com>
Date: Tue, 15 Jan 2019 13:14:12 -0800
Subject: [PATCH 119/838] use ce-sum by default everywhere, but fake display as
 ce-mean

---
 src/layers/loss.cpp                         | 119 ++------
 src/layers/loss.h                           | 283 ++++++++++++++++----
 src/models/costs.h                          |  87 +++---
 src/models/encoder_classifier.h             |  28 +-
 src/models/encoder_decoder.cpp              |  10 +-
 src/models/encoder_decoder.h                |  30 +--
 src/models/model_base.h                     |   7 +-
 src/models/transformer.h                    |  16 +-
 src/rescorer/rescorer.h                     |   6 +-
 src/training/graph_group_async.cpp          |   2 +-
 src/training/graph_group_multinode.cpp      |   2 +-
 src/training/graph_group_multinode_sync.cpp |   2 +-
 src/training/graph_group_singleton.cpp      |   2 +-
 src/training/graph_group_sync.cpp           |  29 +-
 src/training/scheduler.h                    | 148 +++++-----
 src/training/training_state.h               |   2 +
 src/training/validator.h                    |  20 +-
 17 files changed, 446 insertions(+), 347 deletions(-)

diff --git a/src/layers/loss.cpp b/src/layers/loss.cpp
index 4dbbb99ee..024b884c4 100755
--- a/src/layers/loss.cpp
+++ b/src/layers/loss.cpp
@@ -2,119 +2,38 @@
 
 namespace marian {
 
-Ptr<LossBase> LossFactory(Ptr<Options> options, bool inference) {
+Ptr<LabelwiseLoss> newLoss(Ptr<Options> options, bool inference) {
   float smoothing = inference ? 0.f : options->get<float>("label-smoothing");
   std::string costType = options->get<std::string>("cost-type", "ce-mean");
   if(costType == "ce-mean" || costType == "cross-entropy") {
-    return New<CrossEntropyMeanLoss>(smoothing);
+    return New<CrossEntropyLoss>(smoothing);
   } else if(costType == "ce-mean-words") {
-    return New<CrossEntropyMeanWordsLoss>(smoothing);
+    return New<CrossEntropyLoss>(smoothing);
   } else if(costType == "ce-sum") {
-    return New<CrossEntropySumLoss>(smoothing);
+    return New<CrossEntropyLoss>(smoothing);
   } else if(costType == "perplexity") {
-    return New<PerplexityLoss>(smoothing);
+    return New<CrossEntropyLoss>(smoothing);
   } else if(costType == "ce-rescore") {
-    return New<CrossEntropyRescoreLoss>(smoothing);
+    return New<RescorerLoss>();
   } else if(costType == "ce-rescore-mean") {
-    return New<CrossEntropyRescoreMeanLoss>(smoothing);
+    return New<RescorerLoss>();
   } else {  // same as ce-mean
-    return New<CrossEntropyMeanLoss>(smoothing);
+    return New<CrossEntropyLoss>(smoothing);
   }
 }
 
-Expr LossBase::getCrossEntropy(Expr logits,
-                               Expr indices,
-                               Expr mask,
-                               Expr weights) {
-  auto ce = cross_entropy(logits, indices);
+Ptr<MultiRationalLoss> newMultiLoss(Ptr<Options> options) {
+    std::string multiLossType = options->get<std::string>("multi-loss-type", "sum");
+    if(multiLossType == "sum")
+      return New<SumMultiRationalLoss>();
+    else if(multiLossType == "scaled")
+      return New<ScaledMultiRationalLoss>();
+    else if(multiLossType == "normal")
+      return New<MeanMultiRationalLoss>();
+    else
+      ABORT("Unknown multi-loss-type {}", multiLossType);
 
-  if(smoothing_ > 0) {
-    // @TODO: add this to CE kernels instead
-    auto ceq = mean(logsoftmax(logits), /*axis=*/ -1);
-    ce = (1 - smoothing_) * ce - smoothing_ * ceq;
-  }
-
-  if(mask)
-    ce = ce * mask;
-
-  if(weights)
-    ce = ce * weights;
-
-  return ce;
-}
-
-Expr CrossEntropyMeanLoss::getCost(Expr logits,
-                                   Expr indices,
-                                   Expr mask,
-                                   Expr weights) {
-  auto ce = getCrossEntropy(logits, indices, mask, weights);
-  // Time axis (words): -3
-  // Batch axis (sentences): -2
-  // if(weights) {
-  //   return sum(sum(ce, /*axis =*/ -3) /*axis =*/ -2);
-  //          / sum(mean(mask * weights, /*axis =*/ -3) /*axis =*/ -2);
-  // }
-  // else {
-    return mean(sum(ce, /*axis =*/ -3), /*axis =*/ -2);
-  // }
-}
-
-Expr CrossEntropyMeanWordsLoss::getCost(Expr logits,
-                                        Expr indices,
-                                        Expr mask,
-                                        Expr weights) {
-  auto ce = getCrossEntropy(logits, indices, mask, weights);
-  if(mask) {
-    return sum(sum(ce, /*axis =*/ -3), /*axis =*/ -2) // sum CE over all words in the batch
-           / sum(sum(mask, /*axis =*/ -3), /*axis =*/ -2); // divide by number of words (sum over mask)
-  } else {
-    return sum(sum(ce, /*axis =*/ -3), /*axis =*/ -2) // sum CE over all words in the batch
-           / ce->shape().elements(); // no mask, hence divide by all number of all elements
-  }
-
-}
-
-Expr CrossEntropySumLoss::getCost(Expr logits,
-                                  Expr indices,
-                                  Expr mask,
-                                  Expr weights) {
-  auto ce = getCrossEntropy(logits, indices, mask, weights);
-  return sum(sum(ce, /*axis =*/ -3), /*axis =*/ -2);
-}
-
-Expr PerplexityLoss::getCost(Expr logits,
-                             Expr indices,
-                             Expr mask,
-                             Expr weights) {
-  auto ce = getCrossEntropy(logits, indices, mask, weights);
-  if(mask) {
-    return exp(sum(sum(ce, /*axis =*/ -3), /*axis =*/ -2) // sum CE over all words in the batch
-               / sum(sum(mask, /*axis =*/ -3), /*axis =*/ -2)); // divide by number of words (sum over mask)
-  } else {
-    return exp(sum(sum(ce, /*axis =*/ -3), /*axis =*/ -2) // sum CE over all words in the batch
-               / ce->shape().elements()); // divide by number of words (sum over mask)
-  }
-}
-
-Expr CrossEntropyRescoreLoss::getCost(Expr logits,
-                                      Expr indices,
-                                      Expr mask,
-                                      Expr weights) {
-  auto ce = getCrossEntropy(logits, indices, mask, weights);
-  return -sum(ce, /*axis =*/ -3);
-}
-
-Expr CrossEntropyRescoreMeanLoss::getCost(Expr logits,
-                                          Expr indices,
-                                          Expr mask,
-                                          Expr weights) {
-  auto ce = getCrossEntropy(logits, indices, mask, weights);
-  // divide by number of words in sentence
-  if(mask) {
-    return -sum(ce, /*axis =*/ -3) / sum(mask, /*axis =*/ -3);
-  } else {
-    return -sum(ce, /*axis =*/ -3) / ce->shape()[-3];
-  }
+    return nullptr;
 }
 
 }  // namespace marian
diff --git a/src/layers/loss.h b/src/layers/loss.h
index ebf711470..eaa98da04 100644
--- a/src/layers/loss.h
+++ b/src/layers/loss.h
@@ -3,74 +3,263 @@
 #include "marian.h"
 
 namespace marian {
-class LossBase {
+
+class RationalLoss {
 protected:
-  float smoothing_;
+  Expr loss_;
+  Expr labels_;
+
+  RationalLoss() = default; // protected
 
 public:
-  explicit LossBase(float smoothing = 0) : smoothing_(smoothing){};
-
-  Expr getCrossEntropy(Expr logits, Expr indices, Expr mask, Expr weights);
-  virtual Expr getCost(Expr logits,
-                       Expr indices,
-                       Expr mask,
-                       Expr weights = nullptr)
-      = 0;
+  RationalLoss(Expr loss, Expr labels)
+  : loss_(loss), labels_(labels) {}
+
+  RationalLoss(const RationalLoss& other)
+  : loss_(other.loss_), labels_(other.labels_) {}
+
+  virtual ~RationalLoss() = default;
+
+  Expr loss() const { return loss_; }
+
+  template <typename T>
+  void loss(std::vector<T>& losses) const {
+    ABORT_IF(!loss_, "Loss has not been defined");
+    loss_->val()->get(losses);
+  }
+
+  template <typename T>
+  T loss() const { // this will fail if loss is not a single value
+    ABORT_IF(!loss_, "Loss has not been defined");
+    return loss_->val()->scalar<T>();
+  }
+
+  Expr labels() const { return labels_; }
+
+  template <typename T>
+  void labels(std::vector<T>& labels) const {
+    ABORT_IF(!labels_, "Labels have not been defined");
+    labels_->val()->get(labels);
+  }
+
+  template <typename T>
+  T labels() const { // this will fail if loss is not a single value
+    ABORT_IF(!labels_, "Labels have not been defined");
+    return labels_->val()->scalar<T>();
+  }
+
+  size_t size() const {
+    ABORT_IF(!labels_, "Labels have not been defined");
+    return labels_->shape().elements();
+  }
 };
 
-/*
- * @brief The cross entropy loss function
- *
- * A sum over words and average over sentences
- */
-class CrossEntropyMeanLoss : public LossBase {
+// POD for accumulating loss values after backprop
+struct StaticLoss {
+  float loss;
+  float labels;
+
+  StaticLoss() : loss(0.f), labels(0.f) {}
+
+  StaticLoss(const RationalLoss& rl)
+  : loss(rl.loss<float>()), labels(rl.labels<float>()) {}
+
+  StaticLoss& operator +=(const StaticLoss& other) {
+    loss = loss + other.loss;
+    labels = labels + other.labels;
+    return *this;
+  }
+};
+
+class MultiRationalLoss : public RationalLoss {
+protected:
+  std::vector<RationalLoss> partialLosses_;
+
+  virtual Expr accumulateLoss(const RationalLoss& current) = 0;
+  virtual Expr accumulateLabels(const RationalLoss& current) = 0;
+
 public:
-  explicit CrossEntropyMeanLoss(float smoothing = 0) : LossBase(smoothing){};
-  Expr getCost(Expr logits, Expr indices, Expr mask, Expr weights) override;
+  MultiRationalLoss() : RationalLoss() {}
+
+  MultiRationalLoss(const RationalLoss& rl)
+    : RationalLoss(rl.loss(), rl.labels()) {
+    partialLosses_.push_back(rl);
+  }
+
+  void push_back(const RationalLoss& current) {
+    loss_   = accumulateLoss(current);
+    labels_ = accumulateLabels(current);
+    partialLosses_.push_back(current);
+  }
+
+  const RationalLoss& operator[](size_t i) {
+    return partialLosses_[i];
+  }
+
+  auto begin() -> decltype(partialLosses_.begin()) const {
+    return partialLosses_.begin();
+  }
+
+  auto end() -> decltype(partialLosses_.end()) const {
+    return partialLosses_.end();
+  }
+
+  size_t size() const {
+    return partialLosses_.size();
+  }
+
 };
 
-/*
- * @brief The cross entropy loss function as an average over target tokens
- */
-class CrossEntropyMeanWordsLoss : public LossBase {
+class SumMultiRationalLoss : public MultiRationalLoss {
+private:
+  virtual Expr accumulateLoss(const RationalLoss& current) override {
+    if(loss_)
+      return loss_ + current.loss();
+    else
+      return current.loss();
+  }
+
+  virtual Expr accumulateLabels(const RationalLoss& current) override {
+    if(labels_)
+      return labels_ + current.labels();
+    else
+      return current.labels();
+  }
+
 public:
-  explicit CrossEntropyMeanWordsLoss(float smoothing = 0)
-      : LossBase(smoothing){};
-  Expr getCost(Expr logits, Expr indices, Expr mask, Expr weights) override;
+  SumMultiRationalLoss() : MultiRationalLoss() {}
+  SumMultiRationalLoss(const RationalLoss& rl) : MultiRationalLoss(rl) {}
 };
 
-/*
- * @brief The cross entropy loss function as a sum over target tokens
- */
-class CrossEntropySumLoss : public LossBase {
+class MeanMultiRationalLoss : public MultiRationalLoss {
+private:
+  virtual Expr accumulateLoss(const RationalLoss& current) override {
+    if(loss_)
+      return loss_ + current.loss() / current.labels();
+    else
+      return current.loss() / current.labels();
+  }
+
+  virtual Expr accumulateLabels(const RationalLoss& current) override {
+    if(labels_)
+      return labels_ + 1.f; // broadcast to size
+    else
+      return current.labels() / current.labels(); // 1, but with correct size
+  }
+
+public:
+  MeanMultiRationalLoss() : MultiRationalLoss() {}
+  MeanMultiRationalLoss(const RationalLoss& rl) : MultiRationalLoss(rl) {}
+};
+
+class ScaledMultiRationalLoss : public MultiRationalLoss {
+private:
+  virtual Expr accumulateLoss(const RationalLoss& current) override {
+    if(loss_) {
+      const auto& first = partialLosses_.front();
+      return loss_ + first.labels() * (current.loss() / current.labels()); // scale up/down to match scale of first loss
+    } else {
+      return current.loss(); // first reference loss, keeps to scale with this one
+    }
+  }
+
+  virtual Expr accumulateLabels(const RationalLoss& current) override {
+    if(labels_) {
+      const auto& first = partialLosses_.front();
+      return labels_ + first.labels() / current.labels(); // fractional label counts are OK
+    } else {
+      return current.labels();
+    }
+  }
+
 public:
-  explicit CrossEntropySumLoss(float smoothing = 0) : LossBase(smoothing){};
-  Expr getCost(Expr logits, Expr indices, Expr mask, Expr weights) override;
+  ScaledMultiRationalLoss() : MultiRationalLoss() {}
+  ScaledMultiRationalLoss(const RationalLoss& rl) : MultiRationalLoss(rl) {}
 };
 
-/*
- * @brief The perplexity loss function
- */
-class PerplexityLoss : public LossBase {
+Ptr<MultiRationalLoss> newMultiLoss(Ptr<Options> options);
+
+//***********************************************************************************//
+// This needs some to be refactored. Currentl easiest route for backwards compat. 
+
+class LabelwiseLoss {
+protected:
+  std::vector<int> axes_;
+
+  virtual Expr compute(Expr logits, Expr labelIndices,
+                       Expr mask = nullptr, Expr labelWeights = nullptr) = 0;
+
+  RationalLoss reduce(Expr loss, Expr labels) {
+    ABORT_IF(!loss, "Loss has not been computed");
+    ABORT_IF(!labels, "Labels have not been computed");
+
+    Expr lossSum   = loss;
+    Expr labelsSum = labels;
+    for(int i = 0; i < axes_.size(); ++i) {
+      lossSum   = sum(lossSum, axes_[i]);
+      labelsSum = sum(labelsSum, axes_[i]);
+    }
+
+    return RationalLoss(lossSum, labelsSum);
+  }
+
 public:
-  explicit PerplexityLoss(float smoothing = 0) : LossBase(smoothing){};
-  Expr getCost(Expr logits, Expr indices, Expr mask, Expr weights) override;
+  LabelwiseLoss(const std::vector<int>& axes)
+  : axes_(axes) { }
+
+  RationalLoss apply(Expr logits, Expr labelIndices,
+                     Expr mask = nullptr, Expr labelWeights = nullptr) {
+    Expr loss = compute(logits, labelIndices, mask, labelWeights);
+
+    Expr labels;
+    if(mask)
+      labels = mask;
+    else
+      labels = constant_like(loss, inits::ones); // we have no mask, assume all items are labels
+
+    return reduce(loss, labels);
+  }
 };
 
-/*
- * @brief The cross entropy loss function that keeps sentence-level costs
- */
-class CrossEntropyRescoreLoss : public LossBase {
+class CrossEntropyLoss : public LabelwiseLoss {
 public:
-  explicit CrossEntropyRescoreLoss(float smoothing = 0) : LossBase(smoothing){};
-  Expr getCost(Expr logits, Expr indices, Expr mask, Expr weights) override;
+  CrossEntropyLoss(float smoothing)
+  : LabelwiseLoss(/*axes=*/{-2, -3}), // cross-entropy already reduces over axis -1
+    smoothing_(smoothing) {}
+
+  CrossEntropyLoss(const std::vector<int>& axes, float smoothing)
+  : LabelwiseLoss(axes), // cross-entropy already reduces over axis -1
+    smoothing_(smoothing) {}
+
+protected:
+  float smoothing_;
+
+  Expr compute(Expr logits, Expr labelIndices,
+               Expr mask = nullptr, Expr labelWeights = nullptr) override {
+    Expr ce = cross_entropy(logits, labelIndices);
+
+    if(smoothing_ > 0) {
+      // @TODO: add this to CE kernels instead
+      Expr ceq = mean(logsoftmax(logits), /*axis=*/ -1);
+      ce = (1 - smoothing_) * ce - smoothing_ * ceq;
+    }
+
+    if(mask)
+      ce = ce * mask;
+
+    if(labelWeights)
+      ce = ce * labelWeights;
+
+    return ce;
+  }
 };
 
-class CrossEntropyRescoreMeanLoss : public LossBase {
+class RescorerLoss : public CrossEntropyLoss {
 public:
-  explicit CrossEntropyRescoreMeanLoss(float smoothing = 0) : LossBase(smoothing){};
-  Expr getCost(Expr logits, Expr indices, Expr mask, Expr weights) override;
+  // sentence-wise CE, hence reduce only over time axis.
+  RescorerLoss() : CrossEntropyLoss(/*axes=*/{-3}, /*smoothing=*/0.f) {}
 };
 
-Ptr<LossBase> LossFactory(Ptr<Options> options, bool inference);
+Ptr<LabelwiseLoss> newLoss(Ptr<Options> options, bool inference);
+
 }  // namespace marian
diff --git a/src/models/costs.h b/src/models/costs.h
index da190e9ca..7f5f0743e 100755
--- a/src/models/costs.h
+++ b/src/models/costs.h
@@ -12,11 +12,10 @@ namespace models {
 
 class CostBase {
 public:
-  virtual Expr apply(Ptr<ModelBase> model,
-                     Ptr<ExpressionGraph> graph,
-                     Ptr<data::Batch> batch,
-                     bool clearGraph = true)
-      = 0;
+  virtual Ptr<MultiRationalLoss> apply(Ptr<ModelBase> model,
+                                       Ptr<ExpressionGraph> graph,
+                                       Ptr<data::Batch> batch,
+                                       bool clearGraph = true) = 0;
 };
 
 class EncoderDecoderCE : public CostBase {
@@ -25,13 +24,13 @@ class EncoderDecoderCE : public CostBase {
 
   bool inference_{false};
   bool toBeWeighted_{false};
-  Ptr<LossBase> loss_;
+  Ptr<LabelwiseLoss> loss_;
   Ptr<WeightingBase> weighter_;
 
 public:
   EncoderDecoderCE(Ptr<Options> options)
       : options_(options), inference_(options->get<bool>("inference", false)) {
-    loss_ = LossFactory(options_, inference_);
+    loss_ = newLoss(options_, inference_);
 
     toBeWeighted_
         = (options_->has("data-weighting") && !inference_)
@@ -41,7 +40,7 @@ class EncoderDecoderCE : public CostBase {
       weighter_ = WeightingFactory(options_);
   }
 
-  Expr apply(Ptr<ModelBase> model,
+  Ptr<MultiRationalLoss> apply(Ptr<ModelBase> model,
              Ptr<ExpressionGraph> graph,
              Ptr<data::Batch> batch,
              bool clearGraph = true) override {
@@ -54,11 +53,16 @@ class EncoderDecoderCE : public CostBase {
     if(toBeWeighted_)
       weights = weighter_->getWeights(graph, corpusBatch);
 
-    Expr cost;
-    cost = loss_->getCost(state->getLogProbs(),
-                          state->getTargetIndices(),
-                          state->getTargetMask(),
-                          weights);
+    // multi-objective training
+    Ptr<MultiRationalLoss> multiLoss = newMultiLoss(options_);
+
+    // @TODO: adapt to multi-objective training
+    auto partialLoss = loss_->apply(state->getLogProbs(),
+                                    state->getTargetIndices(),
+                                    state->getTargetMask(),
+                                    weights);
+    multiLoss->push_back(partialLoss);
+  
 
     if(options_->get("guided-alignment", std::string("none")) != "none" && !inference_) {
       auto alignments = encdec->getDecoders()[0]->getAlignments();
@@ -66,9 +70,12 @@ class EncoderDecoderCE : public CostBase {
 
       auto att = concatenate(alignments, /*axis =*/ -1);
 
-      return cost + guidedAlignmentCost(graph, corpusBatch, options_, att);
+      // @TODO: represent this as an multi-objective training loss, which it actually is
+      // return multiLoss.loss() + guidedAlignmentCost(graph, corpusBatch, options_, att);
+      ABORT("Fix me");
+      return multiLoss;
     } else {
-      return cost;
+      return multiLoss;
     }
   }
 };
@@ -77,15 +84,15 @@ class EncoderClassifierCE : public CostBase {
 protected:
   Ptr<Options> options_;
   bool inference_{false};
-  Ptr<LossBase> loss_;
+  Ptr<LabelwiseLoss> loss_;
 
 public:
   EncoderClassifierCE(Ptr<Options> options)
       : options_(options), inference_(options->get<bool>("inference", false)) {
-    loss_ = LossFactory(options_, inference_);
+    loss_ = newLoss(options_, inference_);
   }
 
-  Expr apply(Ptr<ModelBase> model,
+  Ptr<MultiRationalLoss> apply(Ptr<ModelBase> model,
              Ptr<ExpressionGraph> graph,
              Ptr<data::Batch> batch,
              bool clearGraph = true) override {
@@ -94,21 +101,17 @@ class EncoderClassifierCE : public CostBase {
     auto corpusBatch = std::static_pointer_cast<data::CorpusBatch>(batch);
 
     auto states = enccls->apply(graph, corpusBatch, clearGraph);
-
-    Expr cost = loss_->getCost(states[0]->getLogProbs(),
-                               states[0]->getTargetIndices(),
-                               /*mask=*/nullptr,
-                               /*weights=*/nullptr);
-
+    
     // multi-objective training
-    for(int i = 1; i < states.size(); ++i) {
-      cost = cost + loss_->getCost(states[i]->getLogProbs(),
-                                   states[i]->getTargetIndices(),
-                                   /*mask=*/nullptr,
-                                   /*weights=*/nullptr);
+    Ptr<MultiRationalLoss> multiLoss = newMultiLoss(options_);
+    for(int i = 0; i < states.size(); ++i) {
+      auto partialLoss = loss_->apply(states[i]->getLogProbs(),
+                                      states[i]->getTargetIndices(),
+                                      /*mask=*/nullptr,
+                                      /*weights=*/nullptr);
+      multiLoss->push_back(partialLoss);
     }
-
-    return cost;
+    return multiLoss;
   }
 };
 
@@ -135,9 +138,9 @@ class Trainer : public ModelBase {
     model_->save(graph, name, saveTranslatorConfig);
   }
 
-  virtual Expr build(Ptr<ExpressionGraph> graph,
-                     Ptr<data::Batch> batch,
-                     bool clearGraph = true) override {
+  virtual Ptr<RationalLoss> build(Ptr<ExpressionGraph> graph,
+                                  Ptr<data::Batch> batch,
+                                  bool clearGraph = true) override {
     return cost_->apply(model_, graph, batch, clearGraph);
   };
 
@@ -156,7 +159,7 @@ class LogSoftmaxStep : public CostStep {
   virtual Ptr<DecoderState> apply(Ptr<DecoderState> state) override {
     // decoder needs normalized probabilities (note: skipped if beam 1 and --skip-cost)
     auto logits = state->getLogProbs();
-    
+
     auto logprobs = logsoftmax(logits);
 
     state->setLogProbs(logprobs);
@@ -171,7 +174,7 @@ class GumbelSoftmaxStep : public CostStep {
 public:
   virtual Ptr<DecoderState> apply(Ptr<DecoderState> state) override {
     auto logits = state->getLogProbs();
-    
+
     auto logprobs = logsoftmax(logits + constant_like(logits, inits::gumbel));
 
     state->setLogProbs(logprobs);
@@ -211,9 +214,9 @@ class Stepwise : public EncoderDecoderBase {
 
   virtual void clear(Ptr<ExpressionGraph> graph) override { encdec_->clear(graph); }
 
-  virtual Expr build(Ptr<ExpressionGraph> graph,
-                     Ptr<data::Batch> batch,
-                     bool clearGraph = true) override {
+  virtual Ptr<RationalLoss> build(Ptr<ExpressionGraph> graph,
+                                  Ptr<data::Batch> batch,
+                                  bool clearGraph = true) override {
     auto corpusBatch = std::static_pointer_cast<data::CorpusBatch>(batch);
     return build(graph, corpusBatch, clearGraph);
   }
@@ -234,9 +237,9 @@ class Stepwise : public EncoderDecoderBase {
     return cost_->apply(nextState);
   }
 
-  virtual Expr build(Ptr<ExpressionGraph> /*graph*/,
-                     Ptr<data::CorpusBatch> /*batch*/,
-                     bool /*clearGraph*/ = true) override {
+  virtual Ptr<RationalLoss> build(Ptr<ExpressionGraph> /*graph*/,
+                                  Ptr<data::CorpusBatch> /*batch*/,
+                                  bool /*clearGraph*/ = true) override {
     ABORT("Wrong wrapper. Use models::Trainer or models::Scorer");
     return nullptr;
   }
diff --git a/src/models/encoder_classifier.h b/src/models/encoder_classifier.h
index 0bf048b93..9c794144c 100644
--- a/src/models/encoder_classifier.h
+++ b/src/models/encoder_classifier.h
@@ -33,15 +33,13 @@ class EncoderClassifierBase : public models::ModelBase {
   virtual std::vector<Ptr<ClassifierState>> apply(Ptr<ExpressionGraph>, Ptr<data::CorpusBatch>, bool) = 0;
 
 
-  virtual Expr build(Ptr<ExpressionGraph> graph,
-                     Ptr<data::Batch> batch,
-                     bool clearGraph = true) override
-      = 0;
+  virtual Ptr<RationalLoss> build(Ptr<ExpressionGraph> graph,
+                                  Ptr<data::Batch> batch,
+                                  bool clearGraph = true) override = 0;
 
-  virtual Expr build(Ptr<ExpressionGraph> graph,
-                     Ptr<data::CorpusBatch> batch,
-                     bool clearGraph = true)
-      = 0;
+  virtual Ptr<RationalLoss> build(Ptr<ExpressionGraph> graph,
+                                  Ptr<data::CorpusBatch> batch,
+                                  bool clearGraph = true) = 0;
 
   virtual Ptr<Options> getOptions() = 0;
 };
@@ -194,17 +192,17 @@ class EncoderClassifier : public EncoderClassifierBase {
     return classifierStates;
   }
 
-  virtual Expr build(Ptr<ExpressionGraph> graph,
-                     Ptr<data::CorpusBatch> batch,
-                     bool clearGraph = true) override {
+  virtual Ptr<RationalLoss> build(Ptr<ExpressionGraph> graph,
+                                  Ptr<data::CorpusBatch> batch,
+                                  bool clearGraph = true) override {
     auto states = apply(graph, batch, clearGraph);
     // returns raw logits
-    return states[0]->getLogProbs();
+    return New<RationalLoss>(states[0]->getLogProbs(), nullptr); // @TODO: this should explode
   }
 
-  virtual Expr build(Ptr<ExpressionGraph> graph,
-                     Ptr<data::Batch> batch,
-                     bool clearGraph = true) override {
+  virtual Ptr<RationalLoss> build(Ptr<ExpressionGraph> graph,
+                                  Ptr<data::Batch> batch,
+                                  bool clearGraph = true) override {
     auto corpusBatch = std::static_pointer_cast<data::CorpusBatch>(batch);
     return build(graph, corpusBatch, clearGraph);
   }
diff --git a/src/models/encoder_decoder.cpp b/src/models/encoder_decoder.cpp
index 047d960d1..70e71c836 100755
--- a/src/models/encoder_decoder.cpp
+++ b/src/models/encoder_decoder.cpp
@@ -183,16 +183,16 @@ Ptr<DecoderState> EncoderDecoder::stepAll(Ptr<ExpressionGraph> graph,
   return nextState;
 }
 
-Expr EncoderDecoder::build(Ptr<ExpressionGraph> graph,
-                           Ptr<data::CorpusBatch> batch,
-                           bool clearGraph) {
+Ptr<RationalLoss> EncoderDecoder::build(Ptr<ExpressionGraph> graph,
+                                        Ptr<data::CorpusBatch> batch,
+                                        bool clearGraph) {
   auto state = stepAll(graph, batch, clearGraph);
 
   // returns raw logits
-  return state->getLogProbs();
+  return New<RationalLoss>(state->getLogProbs(), state->getTargetMask()); // @TODO: hacky hack hack
 }
 
-Expr EncoderDecoder::build(Ptr<ExpressionGraph> graph,
+Ptr<RationalLoss> EncoderDecoder::build(Ptr<ExpressionGraph> graph,
                            Ptr<data::Batch> batch,
                            bool clearGraph) {
   auto corpusBatch = std::static_pointer_cast<data::CorpusBatch>(batch);
diff --git a/src/models/encoder_decoder.h b/src/models/encoder_decoder.h
index 0ea89b4f6..a33de385f 100644
--- a/src/models/encoder_decoder.h
+++ b/src/models/encoder_decoder.h
@@ -28,14 +28,14 @@ class EncoderDecoderBase : public models::ModelBase {
 
   virtual void clear(Ptr<ExpressionGraph> graph) override = 0;
 
-  virtual Expr build(Ptr<ExpressionGraph> graph,
-                     Ptr<data::Batch> batch,
-                     bool clearGraph = true) override
-      = 0;
+  virtual Ptr<RationalLoss> build(Ptr<ExpressionGraph> graph,
+                                  Ptr<data::Batch> batch,
+                                  bool clearGraph = true) override = 0;
 
-  virtual Ptr<DecoderState> startState(Ptr<ExpressionGraph> graph,
-                                       Ptr<data::CorpusBatch> batch)
-      = 0;
+  virtual Ptr<RationalLoss> build(Ptr<ExpressionGraph> graph,
+                                  Ptr<data::CorpusBatch> batch,
+                                  bool clearGraph = true) = 0;  virtual Ptr<DecoderState> startState(Ptr<ExpressionGraph> graph,
+                                       Ptr<data::CorpusBatch> batch) = 0;
 
   virtual Ptr<DecoderState> step(Ptr<ExpressionGraph> graph,
                                  Ptr<DecoderState> state,
@@ -45,10 +45,6 @@ class EncoderDecoderBase : public models::ModelBase {
                                  int beamSize)
       = 0;
 
-  virtual Expr build(Ptr<ExpressionGraph> graph,
-                     Ptr<data::CorpusBatch> batch,
-                     bool clearGraph = true)
-      = 0;
 
   virtual Ptr<Options> getOptions() = 0;
 
@@ -158,13 +154,13 @@ class EncoderDecoder : public EncoderDecoderBase {
                                     Ptr<data::CorpusBatch> batch,
                                     bool clearGraph = true);
 
-  virtual Expr build(Ptr<ExpressionGraph> graph,
-                     Ptr<data::CorpusBatch> batch,
-                     bool clearGraph = true) override;
+  virtual Ptr<RationalLoss> build(Ptr<ExpressionGraph> graph,
+                                  Ptr<data::CorpusBatch> batch,
+                                  bool clearGraph = true) override;
 
-  virtual Expr build(Ptr<ExpressionGraph> graph,
-                     Ptr<data::Batch> batch,
-                     bool clearGraph = true) override;
+  virtual Ptr<RationalLoss> build(Ptr<ExpressionGraph> graph,
+                                  Ptr<data::Batch> batch,
+                                  bool clearGraph = true) override;
 };
 
 }  // namespace marian
diff --git a/src/models/model_base.h b/src/models/model_base.h
index 47039841a..0701a8b4b 100644
--- a/src/models/model_base.h
+++ b/src/models/model_base.h
@@ -2,6 +2,7 @@
 
 #include <string>
 #include "marian.h"
+#include "layers/loss.h"
 
 namespace marian {
 namespace models {
@@ -26,9 +27,9 @@ class ModelBase {
                     bool saveTranslatorConfig = false)
       = 0;
 
-  virtual Expr build(Ptr<ExpressionGraph> graph,
-                     Ptr<data::Batch> batch,
-                     bool clearGraph = true)
+  virtual Ptr<RationalLoss> build(Ptr<ExpressionGraph> graph,
+                                  Ptr<data::Batch> batch,
+                                  bool clearGraph = true)
       = 0;
 
   virtual void clear(Ptr<ExpressionGraph> graph) = 0;
diff --git a/src/models/transformer.h b/src/models/transformer.h
index 3791d3c80..a5fba81aa 100755
--- a/src/models/transformer.h
+++ b/src/models/transformer.h
@@ -55,19 +55,25 @@ class Transformer : public EncoderOrDecoderBase {
     if(learnedPosEmbeddings) {
       int maxLength = opt<int>("max-length");
 
+      // Hack for translating with length longer than trained embeddings
+      Expr seenEmb = graph_->get("Wpos");
+      int numPos = seenEmb ? seenEmb->shape()[-2] : maxLength;
+
       auto posEmbFactory = embedding(graph_)
                             ("prefix", "Wpos") // share positional embeddings across all encoders/decorders
-                            ("dimVocab", maxLength)
+                            ("dimVocab", numPos)
                             ("dimEmb", dimEmb)
                             .construct();
 
-      std::vector<IndexType> positions(dimWords);
-      std::iota(positions.begin(), positions.end(), 0); // fill with increasing numbers until current length
+      // fill with increasing numbers until current length or maxPos
+      std::vector<IndexType> positions(dimWords, numPos - 1);
+      for(int i = 0; i < std::min(dimWords, numPos); ++i)
+        positions[i] = i;
 
       auto signal = rows(posEmbFactory, graph_->indices(positions));
       signal = reshape(signal, {dimWords, 1, dimEmb});
       embeddings = embeddings + signal;
-    } else {      
+    } else {
       auto signal = graph_->constant({dimWords, 1, dimEmb},
                                      inits::positions(start));
       // according to paper embeddings are scaled up by \sqrt(d_m)
@@ -560,7 +566,7 @@ class EncoderTransformer : public Transformer<EncoderBase> {
       int srcWords = batchEmbeddings->shape()[-3];
       batchEmbeddings = dropout(batchEmbeddings, dropoutSrc, {srcWords, 1, 1});
     }
-    
+
     batchEmbeddings = addSpecialEmbeddings(batchEmbeddings, /*start=*/0, batch);
 
     // reorganize batch and timestep
diff --git a/src/rescorer/rescorer.h b/src/rescorer/rescorer.h
index fa456856a..64ed3cde2 100644
--- a/src/rescorer/rescorer.h
+++ b/src/rescorer/rescorer.h
@@ -29,7 +29,7 @@ class Rescorer {
     builder_->load(graph, modelFile);
   }
 
-  Expr build(Ptr<ExpressionGraph> graph, Ptr<data::CorpusBatch> batch) {
+  Ptr<RationalLoss> build(Ptr<ExpressionGraph> graph, Ptr<data::CorpusBatch> batch) {
     return builder_->build(graph, batch);
   }
 
@@ -126,13 +126,13 @@ class Rescore : public ModelTask {
 
           // @TODO: normalize by length as in normalize
           // Once we have Frank's concept of ce-sum with sample size by words we will return a pair
-          // here which will make it trivial to report all variants. 
+          // here which will make it trivial to report all variants.
           auto costNode = builder->build(graph, batch);
 
           graph->forward();
 
           std::vector<float> scores;
-          costNode->val()->get(scores);
+          costNode->loss(scores);
 
           // soft alignments for each sentence in the batch
           std::vector<data::SoftAlignment> aligns(batch->size());
diff --git a/src/training/graph_group_async.cpp b/src/training/graph_group_async.cpp
index 3195c5624..b529089af 100755
--- a/src/training/graph_group_async.cpp
+++ b/src/training/graph_group_async.cpp
@@ -211,7 +211,7 @@ void AsyncGraphGroup::execute(Ptr<data::Batch> batch) {
     }
 
     graph->forward();
-    cost += costNode->scalar();
+    cost += costNode->loss<float>();
     graph->backward();
 
     Tensor gradients;
diff --git a/src/training/graph_group_multinode.cpp b/src/training/graph_group_multinode.cpp
index bac76b945..8adafd89a 100755
--- a/src/training/graph_group_multinode.cpp
+++ b/src/training/graph_group_multinode.cpp
@@ -537,7 +537,7 @@ void MultiNodeGraphGroup::execute(Ptr<data::Batch> batch) {
     }
 
     graph->forward();
-    cost += costNode->scalar();
+    cost += costNode->loss<float>();
     num_seen_words += batch->words();
     num_seen_sentences += batch->size();
     graph->backward();
diff --git a/src/training/graph_group_multinode_sync.cpp b/src/training/graph_group_multinode_sync.cpp
index 05abd6d5f..f32fb0d28 100755
--- a/src/training/graph_group_multinode_sync.cpp
+++ b/src/training/graph_group_multinode_sync.cpp
@@ -195,7 +195,7 @@ void MultiNodeGraphGroupSync::execute(Ptr<data::Batch> fullBatch) {
       graph->forward();
       {
         std::lock_guard<std::mutex> guard(sumCostMutex_);
-        cost += costNode->scalar();
+        cost += costNode->loss<float>();
         num_seen_words += batch->words();
         num_seen_sentences += batch->size();
       }
diff --git a/src/training/graph_group_singleton.cpp b/src/training/graph_group_singleton.cpp
index ac6ef5d0c..46084c787 100755
--- a/src/training/graph_group_singleton.cpp
+++ b/src/training/graph_group_singleton.cpp
@@ -13,7 +13,7 @@ void SingletonGraph::execute(Ptr<data::Batch> batch) {
   auto costNode = builder_->build(graph_, batch);
 
   graph_->forward();
-  float cost = costNode->scalar();
+  float cost = costNode->loss<float>();
   graph_->backward();
 
   // Get batch stats
diff --git a/src/training/graph_group_sync.cpp b/src/training/graph_group_sync.cpp
index c3b025e31..dafd61246 100755
--- a/src/training/graph_group_sync.cpp
+++ b/src/training/graph_group_sync.cpp
@@ -134,7 +134,8 @@ void SyncGraphGroup::update(Ptr<data::Batch> batch) /*override*/ {
 
   // Compute gradients
   // This happens in multiple steps in case of delay_ > 1.
-  std::vector<float> localDeviceCosts(devices_.size(), 0.f); // [local device index] aggregate cost for each local device
+  std::vector<StaticLoss> localDeviceCosts(devices_.size());
+
   for (size_t t = 0; t < delay_; t++) {
     // Execute single forward/backward step
     auto forwardBackward = [&](size_t localDeviceIndex, size_t /*begin*/, size_t /*end*/) {
@@ -143,11 +144,12 @@ void SyncGraphGroup::update(Ptr<data::Batch> batch) /*override*/ {
 
       if(subBatch) {
         timer::Timer timer;
-        auto costNode = builders_[localDeviceIndex]->build(graph, subBatch);
+        auto rationalLoss = builders_[localDeviceIndex]->build(graph, subBatch);
         //LOG(info, timer.format(2, "after build: %ws"));
         graph->forward();
         //LOG(info, timer.format(2, "after forward (no sync): %ws"));
-        localDeviceCosts[localDeviceIndex] += costNode->scalar();
+        localDeviceCosts[localDeviceIndex] += *rationalLoss; // converts dynamic RationalLoss to StaticLoss
+
         graph->backward(/*zero=*/t == 0); // only reset gradients to 0 if t = 0
         //LOG(info, timer.format(2, "after backward (no sync): %ws"));
         //localDeviceCosts[localDeviceIndex] += costNode->scalar(); // moved here for time measurements; @TODO: move this back
@@ -168,15 +170,6 @@ void SyncGraphGroup::update(Ptr<data::Batch> batch) /*override*/ {
     auto curGrad = graphs_[idx]->params()->grads()->subtensor(begin, end-begin);
     auto curParam = graphs_[idx]->params()->vals()->subtensor(begin, end-begin);
 
-    // if individual gradients were averages, then need to average again over all subBatches
-    auto div = subBatches.size();
-    if (options_->get<std::string>("cost-type") == "ce-sum")
-      div = 1;
-    if(div != 1) {
-      using namespace functional;
-      Element(_1 = _1 / (float)div, curGrad);
-    }
-
     // actual model update
     shardOpt_[idx]->update(curParam, curGrad);
 
@@ -196,17 +189,13 @@ void SyncGraphGroup::update(Ptr<data::Batch> batch) /*override*/ {
   //LOG(info, timer.format(2, "after allGather (has sync): %ws"));
 
   // cost across all local devices (scheduler will aggregate cross-process)
-  float localCost = 0;
+  StaticLoss localLoss;
   for(auto& c : localDeviceCosts) // localDeviceCosts is already summed up over delay steps
-    localCost += c;
-
-  // if localCost is average-based, we need to turn the sum over devices into an average as well
-  if(options_->get<std::string>("cost-type") != "ce-sum")
-    localCost /= numSubBatches;
+    localLoss += c;
 
   if(scheduler_) {
-    // track and log localCost
-    scheduler_->update(localCost, subBatches, mpi_);
+    // track and log localLoss, @TODO: rather pass StaticLoss object
+    scheduler_->update(localLoss.loss, localLoss.labels, subBatches, mpi_);
 
     // save intermediate model (and optimizer state) to file
     if(scheduler_->saving())
diff --git a/src/training/scheduler.h b/src/training/scheduler.h
index 1e9491cc8..d35749901 100755
--- a/src/training/scheduler.h
+++ b/src/training/scheduler.h
@@ -44,6 +44,33 @@ class Scheduler : public TrainingObserver {
     return baselr;
   }
 
+  std::string displayLoss(std::string lossType, bool dispLabelCounts, Ptr<TrainingState> state) {
+    std::stringstream ss;
+    ss << "Cost ";
+    ss << std::setprecision(8) << std::fixed;
+
+    // @TODO: put a single loss formatting funciton into loss.h and reuse here to avoid code duplication
+    // @TODO: use dispLabelCounts with any display type?
+    if(lossType == "ce-mean-words")
+      ss << state->costSum / state->costCount;
+    else if(lossType == "ce-sum" && dispLabelCounts)
+      ss << state->costSum / state->costCount
+         << " * "
+         << utils::withCommas(state->costCount)
+         << " after "
+         << utils::withCommas(state->labelsTotal);
+    else if(lossType == "ce-sum" && !dispLabelCounts)
+      ss << state->costSum;
+    else if(lossType == "perplexity")
+      ss << std::exp(state->costSum / state->costCount);
+    else if(lossType == "cross-entropy" || lossType == "ce-mean") // backwards-compat, @TODO: get rid of this?
+      ss << state->costSum / state->samplesCount;
+    else
+      ABORT("Unknown loss type {}", lossType);
+
+    return ss.str();
+  }
+
 public:
   Scheduler(Ptr<Options> options, Ptr<TrainingState> state)
       : options_(options), state_(state),
@@ -160,53 +187,44 @@ class Scheduler : public TrainingObserver {
   }
 
   void update(float cost, Ptr<data::Batch> batch) {
-    update(cost, std::vector<Ptr<data::Batch>>({batch}));
+    ABORT("Fix me");
+    update(cost, batch->words(-1), std::vector<Ptr<data::Batch>>({batch}));
   }
 
-  void update(float cost, const std::vector<Ptr<data::Batch>>& batches, Ptr<IMPIWrapper> mpi = nullptr) {
+  void update(float cost, float labels, const std::vector<Ptr<data::Batch>>& batches, Ptr<IMPIWrapper> mpi = nullptr) {
     state_->rememberPreviousProgress(); // note: epoch increases happen at the wrong place, hence -freq parameters do not support epoch units
     state_->validated = false;
 
     size_t batchSize = 0;    // number of sentences in batch
-    size_t batchLabels = 0;  // number of target words in batch
+    size_t batchLabels = (size_t)labels;  // number of target words in batch
+    size_t batchWords  = 0;
 
     for(const auto& batch : batches) {
       if (batch) { // (nullptr is allowed as result of split)
-        batchSize   += batch->size();
-        batchLabels += batch->words(dispIndex_);
+        batchSize  += batch->size();
+        batchWords += batch->words(dispIndex_);
       }
     }
 
     // extrapolate cost across MPI processes, so that we have numbers in the right range
     // When doing the actual log, we then aggregate across MPI processes to get the accurate number.
     if (mpi)
-      cost *= mpi->numMPIProcesses(); // @BUGBUG: this is presently correct for ce-sum, but possibly not the av-based losses
+      cost *= mpi->numMPIProcesses();
 
-    // reconstruct sum cost, for displaying epoch-level averages instead of minibatch-level
-    auto costType = options_->get<std::string>("cost-type");
-    auto dispLabelCounts = options_->get<bool>(
-        "disp-label-counts");  // if true then show as "cost per label * number of labels"
-    if(dispLabelCounts) {
-      auto count =  // what was cost normalized with originally?
-          /*if*/ (costType == "ce-sum") ?
-            1
-          /*else if*/ : ((costType == "ce-mean-words") ?
-            batchLabels
-          /*else*/ :  // all others: treat like ce-mean (not correct for some)
-            batchSize);
-      state_->costSum   += cost * count; // aggregate sum cost since last display
-      state_->costCount += batchLabels;  // cost gets normalized w.r.t. this in display
-    } else {               // (back compat)
-      state_->costSum   += cost * batchSize;
-      state_->costCount += batchSize;
-    }
+    state_->costSum      += cost;        // aggregate sum cost since last display
+    state_->costCount    += batchLabels; // cost gets normalized w.r.t. this in display
+    state_->samplesCount += batchSize;
 
-    state_->wordsDisp    += batchLabels; // words at given input processed since last display, for speed display
+    state_->wordsDisp    += batchWords;  // words at given input processed since last display, for speed display
     state_->samplesEpoch += batchSize;   // sentences processed in this epoch
     state_->labelsTotal  += batchLabels; // total labels processed
 
     state_->newBatch();
 
+    // reconstruct sum cost, for displaying epoch-level averages instead of minibatch-level
+    auto lossType = options_->get<std::string>("cost-type");
+    auto dispLabelCounts = options_->get<bool>("disp-label-counts");  // if true then show as "cost per label * number of labels"
+
     if(state_->enteredNewPeriodOf(options_->get<std::string>("disp-freq")) ||
        state_->batches <= options_->get<size_t>("disp-first")) {
       // if MPI then aggregate precise cost across workers
@@ -216,62 +234,37 @@ class Scheduler : public TrainingObserver {
         mpi->allReduce(&state_->costSum, &state_->costSum, 1, MPI_FLOAT, MPI_SUM);
         //LOG(info, "all-reduced cost to {}", state_->costSum);
       }
-      if (mpi && mpi->myMPIRank() != 0)
-        ; // skip the report on alternate worker processes
-      else if(dispLabelCounts) {
-        if(options_->get<bool>("lr-report")) {  // if true then show the learning rate
-          LOG(info,
-              "Ep. {} : Up. {} : Sen. {} : Cost {:.8f} * {} after {} : Time {:.2f}s : {:.2f} "
-              "words/s : L.r. {:.4e}",
-              state_->epochs,
-              state_->batches,
-              utils::withCommas(state_->samplesEpoch),
-              state_->costSum / state_->costCount,
-              utils::withCommas(state_->costCount),  // show cost as "av * count"
-              utils::withCommas(state_->labelsTotal),
-              timer_.elapsed(),
-              state_->wordsDisp / timer_.elapsed(),
-              state_->eta);
-        } else {
-          LOG(info,
-              "Ep. {} : Up. {} : Sen. {} : Cost {:.8f} * {} after {} : Time {:.2f}s : {:.2f} "
-              "words/s",
-              state_->epochs,
-              state_->batches,
-              utils::withCommas(state_->samplesEpoch),
-              state_->costSum / state_->costCount,
-              utils::withCommas(state_->costCount),
-              utils::withCommas(state_->labelsTotal),
-              timer_.elapsed(),
-              state_->wordsDisp / timer_.elapsed());
-        }
+      if (mpi && mpi->myMPIRank() != 0) {
+        // skip the report on alternate worker processes
+      } else if(options_->get<bool>("lr-report")) {
+        LOG(info,
+            "Ep. {} : Up. {} : Sen. {} : {} : Time {:.2f}s : {:.2f} words/s : L.r. {:.4e}",
+            state_->epochs,
+            state_->batches,
+            utils::withCommas(state_->samplesEpoch),
+            displayLoss(lossType, dispLabelCounts, state_),
+            timer_.elapsed(),
+            state_->wordsDisp / timer_.elapsed(),
+            state_->eta);
       } else {
-        if(options_->get<bool>("lr-report")) {
-          LOG(info,
-              "Ep. {} : Up. {} : Sen. {} : Cost {:.8f} : Time {:.2f}s : {:.2f} words/s : L.r. {:.4e}",
-              state_->epochs,
-              state_->batches,
-              utils::withCommas(state_->samplesEpoch),
-              state_->costSum / state_->costCount,
-              timer_.elapsed(),
-              state_->wordsDisp / timer_.elapsed(),
-              state_->eta);
-        } else {
-          LOG(info,
-              "Ep. {} : Up. {} : Sen. {} : Cost {:.8f} : Time {:.2f}s : {:.2f} words/s",
-              state_->epochs,
-              state_->batches,
-              utils::withCommas(state_->samplesEpoch),
-              state_->costSum / state_->costCount,
-              timer_.elapsed(),
-              state_->wordsDisp / timer_.elapsed());
-        }
+        LOG(info,
+            "Ep. {} : Up. {} : Sen. {} : {} : Time {:.2f}s : {:.2f} words/s",
+            state_->epochs,
+            state_->batches,
+            utils::withCommas(state_->samplesEpoch),
+            displayLoss(lossType, dispLabelCounts, state_),
+            timer_.elapsed(),
+            state_->wordsDisp / timer_.elapsed());
       }
+
+
       timer_.start();
-      state_->costSum = 0;
-      state_->costCount = 0;
-      state_->wordsDisp = 0;
+      state_->costSum      = 0;
+      state_->costCount    = 0;
+      state_->samplesCount = 0;
+      state_->wordsDisp    = 0;
     }
+
     // progress heartbeat for MS-internal Philly compute cluster
     // This environment variable exists when running on the cluster.
     if((!mpi || mpi->myMPIRank() == 0) && getenv("PHILLY_JOB_ID")
@@ -293,6 +286,7 @@ class Scheduler : public TrainingObserver {
       state_->samplesEpoch = 0;
       state_->costSum = 0;
       state_->costCount = 0;
+      state_->samplesCount = 0;
       state_->wordsDisp = 0;
     }
 
diff --git a/src/training/training_state.h b/src/training/training_state.h
index 2deefe471..d4ecdb6cb 100755
--- a/src/training/training_state.h
+++ b/src/training/training_state.h
@@ -99,6 +99,8 @@ class TrainingState {
   size_t costCount{0};
   // Number of words seen since last display, for speed measurement
   size_t wordsDisp{0};
+  // Number of samples/sentences seen since last display
+  size_t samplesCount{0};
 
   // The state of the random number generator from a batch generator
   std::string seedBatch;
diff --git a/src/training/validator.h b/src/training/validator.h
index f22dd56e0..052b7e15b 100755
--- a/src/training/validator.h
+++ b/src/training/validator.h
@@ -190,13 +190,14 @@ class CrossEntropyValidator : public Validator<data::Corpus> {
           }
 
           builder->clear(graph);
-          auto costNode = builder->build(graph, batch);
+          auto loss = builder->build(graph, batch);
           graph->forward();
 
           std::unique_lock<std::mutex> lock(mutex_);
-          cost += costNode->scalar();
+          cost  += loss->loss<float>();
+          words += (size_t)loss->labels<float>();
+
           samples += batch->size();
-          words += batch->back()->batchWords();
         };
 
         taskBarrier.push_back(threadPool_.enqueue(task, batchId));
@@ -254,7 +255,7 @@ class AccuracyValidator : public Validator<data::Corpus> {
             graph = graphs[id % graphs.size()];
           }
 
-          // Future: requires argmax implementation and integer arithmetics
+          // @TODO: requires argmax implementation and integer arithmetics
           // builder->clear(graph);
           // auto predicted = argmax(builder->build(graph, batch), /*axis*/-1);
           // auto labels    = graph->indices(batch->back()->data());
@@ -263,20 +264,21 @@ class AccuracyValidator : public Validator<data::Corpus> {
 
           // std::unique_lock<std::mutex> lock(mutex_);
           // totalLabels += labels->shape().elements();
-          // correct     += correct->scalar<int>();
+          // correct     += correct->scalar<IndexType>();
 
           builder->clear(graph);
-          auto logits = builder->build(graph, batch);
+          Expr logits = builder->build(graph, batch)->loss();
           graph->forward();
 
           std::vector<float> vLogits;
           logits->val()->get(vLogits);
-          const auto& labels = batch->back()->data();
+
+          const auto& groundTruth = batch->back()->data();
 
           IndexType cols = logits->shape()[-1];
 
           size_t thisCorrect = 0;
-          size_t thisLabels  = labels.size();
+          size_t thisLabels  = groundTruth.size();
 
           for(int i = 0; i < thisLabels; ++i) {
             // CPU-side Argmax
@@ -289,7 +291,7 @@ class AccuracyValidator : public Validator<data::Corpus> {
                 bestIndex = j;
               }
             }
-            thisCorrect += (size_t)(bestIndex == labels[i]);
+            thisCorrect += (size_t)(bestIndex == groundTruth[i]);
           }
 
           std::unique_lock<std::mutex> lock(mutex_);

From 31f4f721b41b8a867808babaf975092ea04c4d5a Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Tue, 15 Jan 2019 13:25:36 -0800
Subject: [PATCH 120/838] scaffolding for new operation dot_csr(); renamed
 prod.cu to prod.cpp, since it no longer has CUDA code

---
 src/CMakeLists.txt                    |   2 +-
 src/graph/expression_operators.cpp    |   8 +-
 src/graph/expression_operators.h      |   1 +
 src/graph/node_operators_binary.h     |  37 ++++----
 src/tensors/cpu/prod.cpp              |   3 +-
 src/tensors/gpu/{prod.cu => prod.cpp} | 119 +++++++++++++-------------
 src/tensors/gpu/prod.h                |   1 +
 src/tensors/tensor_operators.h        |   2 +-
 vs/Marian.vcxproj                     |   2 +-
 vs/Marian.vcxproj.filters             |   6 +-
 10 files changed, 98 insertions(+), 83 deletions(-)
 rename src/tensors/gpu/{prod.cu => prod.cpp} (74%)

diff --git a/src/CMakeLists.txt b/src/CMakeLists.txt
index 03524117b..8c757f100 100644
--- a/src/CMakeLists.txt
+++ b/src/CMakeLists.txt
@@ -103,7 +103,7 @@ if(CUDA_FOUND)
 cuda_add_library(marian_cuda
   tensors/gpu/device.cu
   tensors/gpu/algorithm.cu
-  tensors/gpu/prod.cu
+  tensors/gpu/prod.cpp
   tensors/gpu/element.cu
   tensors/gpu/add.cu
   tensors/gpu/tensor_operators.cu
diff --git a/src/graph/expression_operators.cpp b/src/graph/expression_operators.cpp
index 6828bf103..4694351b2 100755
--- a/src/graph/expression_operators.cpp
+++ b/src/graph/expression_operators.cpp
@@ -421,7 +421,13 @@ Expr affine(Expr a, Expr b, Expr bias, bool transA, bool transB, float scale) {
 // A[i,j] is at A_values[A_offsets[i]+k], where k is position of j in A_indices[A_offsets[i]:A_offsets[i+1]]
 // @TODO: Define a proper sparse tensor type.
 Expr csr_dot(const Shape& A_shape, Expr A_values, Expr A_indices, Expr A_offsets, Expr B, bool transA /*= false*/) {
-  return Expression<CSRDotNodeOp>(A_shape, A_values, A_indices, A_offsets, B, transA);
+  return Expression<CSRDotNodeOp>(A_shape, A_values, A_indices, A_offsets, B, transA, /*swapOperands=*/false);
+}
+
+// multiply a matrix A with a CSR matrix B
+// @TODO: Define a proper sparse tensor type.
+Expr dot_csr(Expr A, const Shape& B_shape, Expr B_values, Expr B_indices, Expr B_offsets, bool transB /*= false*/) {
+  return Expression<CSRDotNodeOp>(B_shape, B_values, B_indices, B_offsets, A, transB, /*swapOperands=*/true);
 }
 
 // swap the last two axes
diff --git a/src/graph/expression_operators.h b/src/graph/expression_operators.h
index c9a66daec..e2c1d9226 100755
--- a/src/graph/expression_operators.h
+++ b/src/graph/expression_operators.h
@@ -113,6 +113,7 @@ Expr affine(Expr a,
             float scalar = 1.f);
 
 Expr csr_dot(const Shape& A_shape, Expr Avalues, Expr Aindices, Expr Aoffsets, Expr B, bool transA = false);
+Expr dot_csr(Expr A, const Shape& B_shape, Expr B_values, Expr B_indices, Expr B_offsets, bool transB = false);
 
 Expr transpose(Expr a);
 Expr transpose(Expr a, const std::vector<int>& axes);
diff --git a/src/graph/node_operators_binary.h b/src/graph/node_operators_binary.h
index 357c24b69..19cf0253d 100755
--- a/src/graph/node_operators_binary.h
+++ b/src/graph/node_operators_binary.h
@@ -410,44 +410,49 @@ class DotBatchedNodeOp : public NaryNodeOp {
 };
 
 class CSRDotNodeOp : public NaryNodeOp {
-  bool transA_;
+  bool transS_;
+  bool swapOperands_;
 public:
-  CSRDotNodeOp(const Shape& A_shape, Expr A_values, Expr A_indices, Expr A_offsets, Expr B, bool transA)
-      : NaryNodeOp({ A_values, A_indices, A_offsets, B }, newShape(A_shape, A_values, A_indices, A_offsets, B, transA)), transA_(transA) {
-    matchOrAbort<IndexType>(A_indices->value_type());
-    matchOrAbort<IndexType>(A_offsets->value_type());
+  CSRDotNodeOp(const Shape& S_shape, Expr S_values, Expr S_indices, Expr S_offsets, Expr D, bool transS, bool swapOperands)
+      : NaryNodeOp({ S_values, S_indices, S_offsets, D }, newShape(S_shape, S_values, S_indices, S_offsets, D, transS, swapOperands)), transS_(transS) {
+    matchOrAbort<IndexType>(S_indices->value_type());
+    matchOrAbort<IndexType>(S_offsets->value_type());
   }
 
-  Shape newShape(const Shape& A_shape, Expr A_values, Expr A_indices, Expr A_offsets, Expr B, bool transA) {
-    ABORT_IF(A_values->shape().size() != 1 || A_indices->shape().size() != 1 || A_offsets->shape().size() != 1,
+  Shape newShape(const Shape& S_shape, Expr S_values, Expr S_indices, Expr S_offsets, Expr D, bool transS, bool swapOperands) {
+    ABORT_IF(S_values->shape().size() != 1 || S_indices->shape().size() != 1 || S_offsets->shape().size() != 1,
         "Sparse matrix components must all be vectors");
-    ABORT_IF(A_values->shape() != A_indices->shape(),
+    ABORT_IF(S_values->shape() != S_indices->shape(),
         "Sparse matrix values and indices must have the same shape");
-    ABORT_IF(A_shape.size() != 2,
+    ABORT_IF(S_shape.size() != 2,
         "Sparse matrix must have rank 2");
-    ABORT_IF(A_offsets->shape()[0] - 1 != A_shape[0],
+    ABORT_IF(S_offsets->shape()[0] - 1 != S_shape[0],
         "Sparse matrix offset vector has incorrect size");
-    auto outShape = B->shape();
-    outShape.set(0, transA ? A_shape[1] : A_shape[0]);
+    ABORT_IF(swapOperands, "swapOperands not yet implemented");
+    auto outShape = D->shape();
+    outShape.set(0, transS ? S_shape[1] : S_shape[0]);
     return outShape;
   }
 
   NodeOps forwardOps() override {
-    // C = dot(A, B)
+    // C = dot(D, S) if swapOperands else
+    // C = dot(A, D)
     return {NodeOp(CSRProd(val_,
                            graph()->allocator(),
                            child(0)->val(), child(1)->val(), child(2)->val(),
                            child(3)->val(),
-                           /*transA=*/transA_, /*beta=*/0))};
+                           /*transS=*/transS_, /*swapOperands=*/swapOperands_, /*beta=*/0))};
   }
 
   NodeOps backwardOps() override {
     return {nullptr, // can't backprop into the sparse matrix (the gradient is dense)
-            NodeOp(CSRProd(child(3)->grad(), // child(3) = B
+            nullptr,
+            nullptr,
+            NodeOp(CSRProd(child(3)->grad(), // child(3) = D
                            graph()->allocator(),
                            child(0)->val(), child(1)->val(), child(2)->val(), // children(0..2) = A
                            adj_,
-                           /*transA=*/!transA_, /*beta=*/1))};
+                           /*transS=*/!transS_, /*swapOperands=*/swapOperands_, /*beta=*/1))};
   }
 
   const std::string type() override { return "csr_dot"; }
diff --git a/src/tensors/cpu/prod.cpp b/src/tensors/cpu/prod.cpp
index f17d23f84..408e47913 100755
--- a/src/tensors/cpu/prod.cpp
+++ b/src/tensors/cpu/prod.cpp
@@ -174,8 +174,9 @@ void CSRProd(marian::Tensor C,
              const marian::Tensor& A_offsets,
              const marian::Tensor& B,
              bool transA,
+             bool swapOperands,
              float beta) {
-  C, A_values, A_indices, A_offsets, B, transA, beta;
+  C, A_values, A_indices, A_offsets, B, transA, swapOperands, beta;
   ABORT("CSRProd is not yet implemented for CPU");
 }
 
diff --git a/src/tensors/gpu/prod.cu b/src/tensors/gpu/prod.cpp
similarity index 74%
rename from src/tensors/gpu/prod.cu
rename to src/tensors/gpu/prod.cpp
index e816ecf10..0d86cbbd6 100755
--- a/src/tensors/gpu/prod.cu
+++ b/src/tensors/gpu/prod.cpp
@@ -95,6 +95,7 @@ void Prod(marian::Tensor C,
 #endif
 }
 
+#if 0 // @TODO: remove, then rename from .cu to .cpp
 __global__ void gAddBias(float* out,
                          const float* bias,
                          size_t length,
@@ -108,7 +109,6 @@ __global__ void gAddBias(float* out,
   }
 }
 
-#if 0 // @TODO: remove, then rename from .cu to .cpp
 void AddBias(marian::Tensor C, const marian::Tensor bias) {
   cudaSetDevice(C->getDeviceId().no);
 
@@ -229,84 +229,85 @@ void ProdBatched(marian::Tensor C,
 
 void CSRProd(marian::Tensor C,
              Ptr<Allocator> allocator,
-             const marian::Tensor& A_values,
-             const marian::Tensor& A_indices,
-             const marian::Tensor& A_offsets,
-             const marian::Tensor& B,
-             bool transA,
+             const marian::Tensor& S_values,
+             const marian::Tensor& S_indices,
+             const marian::Tensor& S_offsets,
+             const marian::Tensor& D,
+             bool transS,
+             bool swapOperands,
              float beta) {
   cudaSetDevice(C->getDeviceId().no);
   auto cusparseHandle = std::static_pointer_cast<gpu::Backend>(C->getBackend())
                               ->getCusparseHandle();
   // dimensions
   const auto& shapeC = C->shape();
-  const auto& shapeB = B->shape();
+  const auto& shapeD = D->shape();
+  ABORT_IF(swapOperands, "swapOperands not yet implemented");
   auto rowsC = shapeC[0];
   auto colsC = shapeC.elements() / rowsC;
-  auto rowsB = shapeB[0];
-  auto colsB = shapeB.elements() / rowsB;
-  auto rowsA = transA ? rowsB : rowsC;
-  auto colsA = transA ? rowsC : rowsB;
-  ABORT_IF((transA ? colsA : rowsA) != rowsC || (transA ? rowsA : colsA) != rowsB || colsB != colsC, "Inconsistent dimensions in CSR product");
+  auto rowsD = shapeD[0];
+  auto colsD = shapeD.elements() / rowsD;
+  auto rowsS = transS ? rowsD : rowsC;
+  auto colsS = transS ? rowsC : rowsD;
+  ABORT_IF((transS ? colsS : rowsS) != rowsC || (transS ? rowsS : colsS) != rowsD || colsD != colsC, "Inconsistent dimensions in CSR product");
   // sparse arrays
-  auto numValues  = A_values->shape().elements();
-  auto numOffsets = A_offsets->shape().elements() - 1; // -1 since last value is length
-  ABORT_IF(numOffsets != (transA ? rowsB : rowsC), "CSR offset array dimension mismatch: n={}, transA={}, rowsB={}, rowsC={}", numOffsets,transA, rowsB, rowsC);
-  ABORT_IF(numOffsets != (transA ? rowsB : rowsC), "CSR offset array dimension mismatch");
-  ABORT_IF(A_values->shape() != A_indices->shape(), "CSR values and indices must have the same size");
+  auto numValues  = S_values->shape().elements();
+  auto numOffsets = S_offsets->shape().elements() - 1; // -1 since last value is length
+  ABORT_IF(numOffsets != rowsS, "CSR offset array dimension mismatch");
+  ABORT_IF(S_values->shape() != S_indices->shape(), "CSR values and indices must have the same size");
   float alpha = 1;
   // Marian uses row-major storage, but CUSPARSE/CUBLAS assume column-major.
-  // Hence, we compute C = spA * B as C' = B' * spA'. where B' and C' are
-  // column-major views on the data of B and C, and likewise, spA' is
+  // Hence, we compute C = S * D as C' = D' * S'. where D' and C' are
+  // column-major views on the data of D and C, and likewise, S' is
   // the CSR matrix reinterpreted as a CSC matrix.
-  if (transA) {
+  if (transS) {
     // cusparse does not support this specific version of transpose; do it explicitly
-    auto At_values  = allocator->alloc<float>(numValues);
-    auto At_indices = allocator->alloc<int>(numValues);
-    auto At_offsets = allocator->alloc<int>(colsA + 1);
+    auto St_values  = allocator->alloc<float>(numValues);
+    auto St_indices = allocator->alloc<int>(numValues);
+    auto St_offsets = allocator->alloc<int>(colsS + 1);
     // transpose the second argument
     CUSPARSE_CHECK(cusparseScsr2csc(cusparseHandle,
-        /*m=*/ rowsA, // number of rows of matrix
-        /*n=*/ colsA, // number of columns of matrix
+        /*m=*/ rowsS, // number of rows of matrix
+        /*n=*/ colsS, // number of columns of matrix
         /*nnz=*/ (int)numValues,
-        /*csrcVal=*/          A_values->data<float>(),  // second arg
-        /*csrcRowPtr=*/ (int*)A_offsets->data<IndexType>(),
-        /*csrcColInd=*/ (int*)A_indices->data<IndexType>(),
-        /*cscVal=*/    At_values->data<float>(),  // transposed version goes here
-        /*cscRowInd=*/ At_indices->data<int>(),
-        /*cscColPtr=*/ At_offsets->data<int>(),
+        /*csrcVal=*/          S_values->data<float>(),  // second arg
+        /*csrcRowPtr=*/ (int*)S_offsets->data<IndexType>(),
+        /*csrcColInd=*/ (int*)S_indices->data<IndexType>(),
+        /*cscVal=*/    St_values->data<float>(),  // transposed version goes here
+        /*cscRowInd=*/ St_indices->data<int>(),
+        /*cscColPtr=*/ St_offsets->data<int>(),
         /*copyValues=*/ CUSPARSE_ACTION_NUMERIC,
         /*idxBase=*/ CUSPARSE_INDEX_BASE_ZERO));
     CUSPARSE_CHECK(cusparseSgemmi(cusparseHandle,
-        /*m=*/ colsB, // #rows of A = #cols of row-major B
-        /*n=*/ rowsC, // #cols of B and C = #rows of row-major C
-        /*k=*/ rowsB, // #cols of A = #rows of row-major B
+        /*m=*/ colsD, // #rows of A = #cols of row-major D
+        /*n=*/ rowsC, // #cols of D and C = #rows of row-major C
+        /*k=*/ rowsD, // #cols of A = #rows of row-major D
         /*nnz=*/ (int)numValues,
         &alpha,
-        /*A=*/ B->data(),
-        /*lda=*/ colsB, // stride
-        /*cscValB=*/    At_values->data<float>(),  // second arg, transposed
-        /*cscRowPtrB=*/ At_offsets->data<int>(),
-        /*cscColIndB=*/ At_indices->data<int>(),
+        /*A=*/ D->data(),
+        /*lda=*/ colsD, // stride
+        /*cscValB=*/    St_values->data<float>(),  // second arg, transposed
+        /*cscRowPtrB=*/ St_offsets->data<int>(),
+        /*cscColIndB=*/ St_indices->data<int>(),
         &beta,
         C->data(),
         /*ldc=*/ colsC)); // stride
-    allocator->free(At_values);
-    allocator->free(At_indices);
-    allocator->free(At_offsets);
+    allocator->free(St_values);
+    allocator->free(St_indices);
+    allocator->free(St_offsets);
   }
   else {
     CUSPARSE_CHECK(cusparseSgemmi(cusparseHandle,
-        /*m=*/ colsB, // #rows of A = #cols of row-major B
-        /*n=*/ rowsC, // #cols of B and C = #rows of row-major C
-        /*k=*/ rowsB, // #cols of A = #rows of row-major B
+        /*m=*/ colsD, // #rows of A = #cols of row-major D
+        /*n=*/ rowsC, // #cols of D and C = #rows of row-major C
+        /*k=*/ rowsD, // #cols of A = #rows of row-major D
         /*nnz=*/ (int)numValues,
         &alpha,
-        /*A=*/ B->data(),
-        /*lda=*/ colsB, // stride
-        /*cscValB=*/          A_values->data<float>(),  // second arg
-        /*cscRowPtrB=*/ (int*)A_offsets->data<IndexType>(),
-        /*cscColIndB=*/ (int*)A_indices->data<IndexType>(),
+        /*A=*/ D->data(),
+        /*lda=*/ colsD, // stride
+        /*cscValB=*/          S_values->data<float>(),  // second arg
+        /*cscRowPtrB=*/ (int*)S_offsets->data<IndexType>(),
+        /*cscColIndB=*/ (int*)S_indices->data<IndexType>(),
         &beta,
         C->data(),
         /*ldc=*/ colsC)); // stride
@@ -318,17 +319,17 @@ void CSRProd(marian::Tensor C,
   cusparseSetMatType     (descrA, CUSPARSE_MATRIX_TYPE_GENERAL);
   cusparseSetMatIndexBase(descrA, CUSPARSE_INDEX_BASE_ZERO);
   CUSPARSE_CHECK(cusparseScsrmm(cusparseHandle,
-      transA ? CUSPARSE_OPERATION_TRANSPOSE : CUSPARSE_OPERATION_NON_TRANSPOSE,
-      /*m=*/ rowsA, // #rows of sparse A
-      /*n=*/ colsB, // #cols of dense B and C
-      /*k=*/ colsA, // #cols of sparse A
+      transS ? CUSPARSE_OPERATION_TRANSPOSE : CUSPARSE_OPERATION_NON_TRANSPOSE,
+      /*m=*/ rowsS, // #rows of sparse A
+      /*n=*/ colsD, // #cols of dense D and C
+      /*k=*/ colsS, // #cols of sparse A
       /*nnz=*/ (int)numValues,
       &alpha, descrA,
-      /*csrValA=*/          A_values->data<float>(),
-      /*csrRowPtrA=*/ (int*)A_offsets->data<IndexType>(),
-      /*csrColIndA=*/ (int*)A_indices->data<IndexType>(),
-      B->data(),
-      /*ldb=*/ rowsB,
+      /*csrValA=*/          S_values->data<float>(),
+      /*csrRowPtrA=*/ (int*)S_offsets->data<IndexType>(),
+      /*csrColIndA=*/ (int*)S_indices->data<IndexType>(),
+      D->data(),
+      /*ldb=*/ rowsD,
       &beta,
       C->data(),
       /*ldc=*/ rowsC));
diff --git a/src/tensors/gpu/prod.h b/src/tensors/gpu/prod.h
index 35bde4f79..b7177649f 100755
--- a/src/tensors/gpu/prod.h
+++ b/src/tensors/gpu/prod.h
@@ -40,6 +40,7 @@ void CSRProd(marian::Tensor C,
              const marian::Tensor& A_offsets,
              const marian::Tensor& B,
              bool transA,
+             bool swapOperands,
              float beta = 0);
 }  // namespace gpu
 }  // namespace marian
diff --git a/src/tensors/tensor_operators.h b/src/tensors/tensor_operators.h
index a9bd58480..f7de2f205 100755
--- a/src/tensors/tensor_operators.h
+++ b/src/tensors/tensor_operators.h
@@ -77,7 +77,7 @@ void Reduce(Functor functor, marian::Tensor out, Tensors... tensors) {
 // clang-format off
 DISPATCH7(Prod, marian::Tensor, const marian::Tensor&, const marian::Tensor&, bool, bool, float, float)
 DISPATCH8(ProdBatched, marian::Tensor, Ptr<Allocator>, const marian::Tensor, const marian::Tensor, bool, bool, float, float)
-DISPATCH8(CSRProd, marian::Tensor, Ptr<Allocator>, const marian::Tensor&, const marian::Tensor&, const marian::Tensor&, const marian::Tensor&, bool, float)
+DISPATCH9(CSRProd, marian::Tensor, Ptr<Allocator>, const marian::Tensor&, const marian::Tensor&, const marian::Tensor&, const marian::Tensor&, bool, bool, float)
 
 DISPATCH2(Softmax, marian::Tensor, marian::Tensor)
 DISPATCH3(SoftmaxGrad, marian::Tensor, marian::Tensor, marian::Tensor)
diff --git a/vs/Marian.vcxproj b/vs/Marian.vcxproj
index 64faded63..a6c560d3a 100755
--- a/vs/Marian.vcxproj
+++ b/vs/Marian.vcxproj
@@ -1088,7 +1088,7 @@
       <ExcludedFromBuild Condition="'$(Configuration)|$(Platform)'=='Debug|x64'">true</ExcludedFromBuild>
     </None>
     <None Include="..\src\tensors\gpu\element.inc" />
-    <None Include="..\src\tensors\gpu\prod.cu">
+    <None Include="..\src\tensors\gpu\prod.cpp">
       <ExcludedFromBuild Condition="'$(Configuration)|$(Platform)'=='Debug|x64'">true</ExcludedFromBuild>
     </None>
     <None Include="..\src\tensors\gpu\sparse.cu">
diff --git a/vs/Marian.vcxproj.filters b/vs/Marian.vcxproj.filters
index 9e5cbf5f4..d9e56843f 100755
--- a/vs/Marian.vcxproj.filters
+++ b/vs/Marian.vcxproj.filters
@@ -1711,9 +1711,6 @@
     <None Include="..\src\tensors\gpu\element.inc">
       <Filter>tensors\gpu</Filter>
     </None>
-    <None Include="..\src\tensors\gpu\prod.cu">
-      <Filter>tensors\gpu</Filter>
-    </None>
     <None Include="..\src\tensors\gpu\sparse.cu">
       <Filter>tensors\gpu</Filter>
     </None>
@@ -1849,6 +1846,9 @@
     <None Include="..\src\examples\README.md">
       <Filter>examples</Filter>
     </None>
+    <None Include="..\src\tensors\gpu\prod.cpp">
+      <Filter>tensors\gpu</Filter>
+    </None>
   </ItemGroup>
   <ItemGroup>
     <Text Include="..\src\3rd_party\sentencepiece\src\CMakeLists.txt">

From 4b73840d547438de529ce579c7966d6a1364c775 Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Tue, 15 Jan 2019 13:29:35 -0800
Subject: [PATCH 121/838] ported one check from another branch

---
 src/graph/node_operators_binary.h | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/src/graph/node_operators_binary.h b/src/graph/node_operators_binary.h
index 19cf0253d..1aaad51b3 100755
--- a/src/graph/node_operators_binary.h
+++ b/src/graph/node_operators_binary.h
@@ -428,8 +428,10 @@ class CSRDotNodeOp : public NaryNodeOp {
         "Sparse matrix must have rank 2");
     ABORT_IF(S_offsets->shape()[0] - 1 != S_shape[0],
         "Sparse matrix offset vector has incorrect size");
-    ABORT_IF(swapOperands, "swapOperands not yet implemented");
     auto outShape = D->shape();
+    ABORT_IF(swapOperands, "swapOperands not yet implemented");
+    ABORT_IF((transS ? S_shape[0] : S_shape[1] != D->shape()[0]),
+             "Matrix product requires dimensions to match");
     outShape.set(0, transS ? S_shape[1] : S_shape[0]);
     return outShape;
   }

From 99a4e12425d0f734940f0e2215e7c02ac3731f9e Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Tue, 15 Jan 2019 13:43:34 -0800
Subject: [PATCH 122/838] CSRDotNodeOp::newShape() now handles swapOperands

---
 src/graph/node_operators_binary.h | 5 ++---
 1 file changed, 2 insertions(+), 3 deletions(-)

diff --git a/src/graph/node_operators_binary.h b/src/graph/node_operators_binary.h
index 1aaad51b3..a76df2a54 100755
--- a/src/graph/node_operators_binary.h
+++ b/src/graph/node_operators_binary.h
@@ -429,10 +429,9 @@ class CSRDotNodeOp : public NaryNodeOp {
     ABORT_IF(S_offsets->shape()[0] - 1 != S_shape[0],
         "Sparse matrix offset vector has incorrect size");
     auto outShape = D->shape();
-    ABORT_IF(swapOperands, "swapOperands not yet implemented");
-    ABORT_IF((transS ? S_shape[0] : S_shape[1] != D->shape()[0]),
+    ABORT_IF(S_shape[transS == swapOperands ? 1 : 0] != outShape[-(int)swapOperands],
              "Matrix product requires dimensions to match");
-    outShape.set(0, transS ? S_shape[1] : S_shape[0]);
+    outShape.set(-(int)swapOperands, S_shape[transS != swapOperands]);
     return outShape;
   }
 

From acf4e860f0fd4cc4925e2c7248f594ab2476d778 Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Tue, 15 Jan 2019 14:14:51 -0800
Subject: [PATCH 123/838] CSRProd now determines dimensions for swapOperands
 case

---
 src/graph/node_operators_binary.h |  4 +-
 src/tensors/gpu/prod.cpp          | 61 +++++++++++++++----------------
 2 files changed, 31 insertions(+), 34 deletions(-)

diff --git a/src/graph/node_operators_binary.h b/src/graph/node_operators_binary.h
index a76df2a54..e61cb1e32 100755
--- a/src/graph/node_operators_binary.h
+++ b/src/graph/node_operators_binary.h
@@ -409,6 +409,8 @@ class DotBatchedNodeOp : public NaryNodeOp {
   const std::string color() override { return "orange"; }
 };
 
+// Note: To reduce code duplication, we use the same NodeOp for C = op(S) x D and C = D x op(S).
+// Set swapOperands to select the latter.
 class CSRDotNodeOp : public NaryNodeOp {
   bool transS_;
   bool swapOperands_;
@@ -436,8 +438,6 @@ class CSRDotNodeOp : public NaryNodeOp {
   }
 
   NodeOps forwardOps() override {
-    // C = dot(D, S) if swapOperands else
-    // C = dot(A, D)
     return {NodeOp(CSRProd(val_,
                            graph()->allocator(),
                            child(0)->val(), child(1)->val(), child(2)->val(),
diff --git a/src/tensors/gpu/prod.cpp b/src/tensors/gpu/prod.cpp
index 0d86cbbd6..9758a4c43 100755
--- a/src/tensors/gpu/prod.cpp
+++ b/src/tensors/gpu/prod.cpp
@@ -239,32 +239,40 @@ void CSRProd(marian::Tensor C,
   cudaSetDevice(C->getDeviceId().no);
   auto cusparseHandle = std::static_pointer_cast<gpu::Backend>(C->getBackend())
                               ->getCusparseHandle();
-  // dimensions
+  // interpret tensor dimensions as matrix dimensions
   const auto& shapeC = C->shape();
   const auto& shapeD = D->shape();
-  ABORT_IF(swapOperands, "swapOperands not yet implemented");
-  auto rowsC = shapeC[0];
+  // If swapOperands, S and D are swapped (C = D x S instead of C = S x D).
+  // In that case, in the next 6 lines, please read all dimensions as if they were reversed in order.
+  auto rowsC = shapeC[-(int)swapOperands];
   auto colsC = shapeC.elements() / rowsC;
-  auto rowsD = shapeD[0];
+  auto rowsD = shapeD[-(int)swapOperands];
   auto colsD = shapeD.elements() / rowsD;
-  auto rowsS = transS ? rowsD : rowsC;
-  auto colsS = transS ? rowsC : rowsD;
-  ABORT_IF((transS ? colsS : rowsS) != rowsC || (transS ? rowsS : colsS) != rowsD || colsD != colsC, "Inconsistent dimensions in CSR product");
+  auto rowsS = transS != swapOperands ? rowsD : rowsC;
+  auto colsS = transS != swapOperands ? rowsC : rowsD;
+  ABORT_IF(colsD != colsC, "Inconsistent outer dimensions in CSR product");
+  if (swapOperands) { // make rowsX actual row dimensions again, likewise colsX
+    std::swap(rowsC, colsC);
+    std::swap(rowsD, colsD);
+    std::swap(rowsS, colsS);
+  }
   // sparse arrays
   auto numValues  = S_values->shape().elements();
   auto numOffsets = S_offsets->shape().elements() - 1; // -1 since last value is length
-  ABORT_IF(numOffsets != rowsS, "CSR offset array dimension mismatch");
+  ABORT_IF(numOffsets != rowsS, "Unexpected number of rows in CSR argument");
   ABORT_IF(S_values->shape() != S_indices->shape(), "CSR values and indices must have the same size");
   float alpha = 1;
   // Marian uses row-major storage, but CUSPARSE/CUBLAS assume column-major.
   // Hence, we compute C = S * D as C' = D' * S'. where D' and C' are
   // column-major views on the data of D and C, and likewise, S' is
   // the CSR matrix reinterpreted as a CSC matrix.
+  Ptr<MemoryPiece> St_values, St_indices, St_offsets;
   if (transS) {
-    // cusparse does not support this specific version of transpose; do it explicitly
-    auto St_values  = allocator->alloc<float>(numValues);
-    auto St_indices = allocator->alloc<int>(numValues);
-    auto St_offsets = allocator->alloc<int>(colsS + 1);
+    // Cusparse gemmi() does not support this specific version of transpose, and csrmm() is non-deterministic.
+    // Hence, we transpose the matrix explicitly.
+    St_values  = allocator->alloc<float>(numValues);
+    St_indices = allocator->alloc<int>(numValues);
+    St_offsets = allocator->alloc<int>(colsS + 1);
     // transpose the second argument
     CUSPARSE_CHECK(cusparseScsr2csc(cusparseHandle,
         /*m=*/ rowsS, // number of rows of matrix
@@ -278,23 +286,9 @@ void CSRProd(marian::Tensor C,
         /*cscColPtr=*/ St_offsets->data<int>(),
         /*copyValues=*/ CUSPARSE_ACTION_NUMERIC,
         /*idxBase=*/ CUSPARSE_INDEX_BASE_ZERO));
-    CUSPARSE_CHECK(cusparseSgemmi(cusparseHandle,
-        /*m=*/ colsD, // #rows of A = #cols of row-major D
-        /*n=*/ rowsC, // #cols of D and C = #rows of row-major C
-        /*k=*/ rowsD, // #cols of A = #rows of row-major D
-        /*nnz=*/ (int)numValues,
-        &alpha,
-        /*A=*/ D->data(),
-        /*lda=*/ colsD, // stride
-        /*cscValB=*/    St_values->data<float>(),  // second arg, transposed
-        /*cscRowPtrB=*/ St_offsets->data<int>(),
-        /*cscColIndB=*/ St_indices->data<int>(),
-        &beta,
-        C->data(),
-        /*ldc=*/ colsC)); // stride
-    allocator->free(St_values);
-    allocator->free(St_indices);
-    allocator->free(St_offsets);
+  }
+  if (swapOperands) {
+    ABORT("CSRProd does not yet implement swapOperands");
   }
   else {
     CUSPARSE_CHECK(cusparseSgemmi(cusparseHandle,
@@ -305,13 +299,16 @@ void CSRProd(marian::Tensor C,
         &alpha,
         /*A=*/ D->data(),
         /*lda=*/ colsD, // stride
-        /*cscValB=*/          S_values->data<float>(),  // second arg
-        /*cscRowPtrB=*/ (int*)S_offsets->data<IndexType>(),
-        /*cscColIndB=*/ (int*)S_indices->data<IndexType>(),
+        /*cscValB=*/    transS ? St_values ->data<float>() :       S_values ->data<float>(),  // second arg
+        /*cscRowPtrB=*/ transS ? St_offsets->data<int>()   : (int*)S_offsets->data<IndexType>(),
+        /*cscColIndB=*/ transS ? St_indices->data<int>()   : (int*)S_indices->data<IndexType>(),
         &beta,
         C->data(),
         /*ldc=*/ colsC)); // stride
   }
+  if(St_values ) allocator->free(St_values );
+  if(St_indices) allocator->free(St_indices);
+  if(St_offsets) allocator->free(St_offsets);
 #if 0
   // Incorrect code that assumes col-major matrices. Reuse that later for dense x sparse.
   cusparseMatDescr_t descrA;

From feed1c6edaf1d5c7f7d463217f151aa38e4026d4 Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Tue, 15 Jan 2019 14:35:25 -0800
Subject: [PATCH 124/838] clarified some comments

---
 src/tensors/gpu/prod.cpp | 15 ++++++++-------
 1 file changed, 8 insertions(+), 7 deletions(-)

diff --git a/src/tensors/gpu/prod.cpp b/src/tensors/gpu/prod.cpp
index 9758a4c43..0156c8602 100755
--- a/src/tensors/gpu/prod.cpp
+++ b/src/tensors/gpu/prod.cpp
@@ -227,6 +227,8 @@ void ProdBatched(marian::Tensor C,
   allocator->free(mp_cptr);
 }
 
+// C = op(S) x D if not swapOperands else C = D x op(S)
+// op(S) = S if not transA else S^T
 void CSRProd(marian::Tensor C,
              Ptr<Allocator> allocator,
              const marian::Tensor& S_values,
@@ -262,10 +264,6 @@ void CSRProd(marian::Tensor C,
   ABORT_IF(numOffsets != rowsS, "Unexpected number of rows in CSR argument");
   ABORT_IF(S_values->shape() != S_indices->shape(), "CSR values and indices must have the same size");
   float alpha = 1;
-  // Marian uses row-major storage, but CUSPARSE/CUBLAS assume column-major.
-  // Hence, we compute C = S * D as C' = D' * S'. where D' and C' are
-  // column-major views on the data of D and C, and likewise, S' is
-  // the CSR matrix reinterpreted as a CSC matrix.
   Ptr<MemoryPiece> St_values, St_indices, St_offsets;
   if (transS) {
     // Cusparse gemmi() does not support this specific version of transpose, and csrmm() is non-deterministic.
@@ -288,13 +286,16 @@ void CSRProd(marian::Tensor C,
         /*idxBase=*/ CUSPARSE_INDEX_BASE_ZERO));
   }
   if (swapOperands) {
+    // C = D x S
     ABORT("CSRProd does not yet implement swapOperands");
   }
   else {
+    // C = S x D for row-major matrices
+    // Implemented via cusparse as C' = D' x S' where C' and D' are column-major.
     CUSPARSE_CHECK(cusparseSgemmi(cusparseHandle,
-        /*m=*/ colsD, // #rows of A = #cols of row-major D
-        /*n=*/ rowsC, // #cols of D and C = #rows of row-major C
-        /*k=*/ rowsD, // #cols of A = #rows of row-major D
+        /*m=*/ colsD, // #rows of first (col-major) factor = #cols of row-major D
+        /*n=*/ rowsC, // #cols of second (sparse) factor and (col-major) result = #rows of row-major C
+        /*k=*/ rowsD, // #cols of first (col-major) factor = #rows of row-major D
         /*nnz=*/ (int)numValues,
         &alpha,
         /*A=*/ D->data(),

From 41d43039980b9a4b7ac2aad591ed98c6f7eb1207 Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Tue, 15 Jan 2019 15:06:45 -0800
Subject: [PATCH 125/838] added tests for dot_csr()

---
 src/graph/node_operators_binary.h |  8 ++---
 src/tests/operator_tests.cpp      | 58 ++++++++++++++++++++-----------
 2 files changed, 42 insertions(+), 24 deletions(-)

diff --git a/src/graph/node_operators_binary.h b/src/graph/node_operators_binary.h
index e61cb1e32..fa2bd350f 100755
--- a/src/graph/node_operators_binary.h
+++ b/src/graph/node_operators_binary.h
@@ -42,7 +42,7 @@ class DotNodeOp : public NaryNodeOp {
     Shape outShape = shapeA;
     outShape.set(outShape.size() - 1, shapeB[shapeB.size() - 1]);
     ABORT_IF(shapeA[shapeA.size() - 1] != shapeB[shapeB.size() - 2],
-             "Matrix product requires dimensions to match");
+             "Matrix product requires inner dimensions to match");
     return outShape;
   }
 
@@ -165,7 +165,7 @@ class AffineNodeOp : public NaryNodeOp {
     Shape outShape = shapeA;
     outShape.set(outShape.size() - 1, shapeB[shapeB.size() - 1]);
     ABORT_IF(shapeA[shapeA.size() - 1] != shapeB[shapeB.size() - 2],
-             "Matrix product requires dimensions to match");
+             "Matrix product requires inner dimensions to match");
     return outShape;
   }
 
@@ -309,7 +309,7 @@ class DotBatchedNodeOp : public NaryNodeOp {
     Shape outShape = shapeA;
     outShape.set(-1, shapeB[-1]);
     ABORT_IF(shapeA[-1] != shapeB[-2],
-             "Batched matrix product requires dimensions to match");
+             "Batched matrix product requires inner dimensions to match");
     return outShape;
   }
 
@@ -432,7 +432,7 @@ class CSRDotNodeOp : public NaryNodeOp {
         "Sparse matrix offset vector has incorrect size");
     auto outShape = D->shape();
     ABORT_IF(S_shape[transS == swapOperands ? 1 : 0] != outShape[-(int)swapOperands],
-             "Matrix product requires dimensions to match");
+             "Matrix product requires inner dimensions to match {} {} {} {}", transS, swapOperands, std::string(S_shape), std::string(outShape));
     outShape.set(-(int)swapOperands, S_shape[transS != swapOperands]);
     return outShape;
   }
diff --git a/src/tests/operator_tests.cpp b/src/tests/operator_tests.cpp
index 954f4adce..e4610dd92 100755
--- a/src/tests/operator_tests.cpp
+++ b/src/tests/operator_tests.cpp
@@ -318,15 +318,16 @@ void tests(DeviceType device) {
     auto B = graph->param("B", {3, 2}, inits::from_vector(vB));
     auto C = dot(A, B);
 
-    // CSR dot product
-    std::vector<float> vS({1, 0, 0, 1,
+    // CSR dot product, tested against dense product on the same values
+    std::vector<float> vS({1, 0, 0, 1,          // sparse
                            0, 0, 1, 1.5});
-    std::vector<float> vR({1, 2, 3, 1.2, 5.6,
+    std::vector<float> vD({1, 2, 3, 1.2, 5.6,   // dense
                            4, 5, 6, 2.3, 6.7,
                            7, 8, 9, 3.4, 7.8,
                            1, 1, 2, 4.5, 8.9});
-    auto S = graph->param("S", { 2, 4 }, inits::from_vector(vS));
-    auto R = graph->param("R", { 4, 5 }, inits::from_vector(vR));
+    auto S  = graph->param("S",  { 2, 4 }, inits::from_vector(vS));
+    auto D  = graph->param("D",  { 4, 5 }, inits::from_vector(vD));
+    auto DT = graph->param("DT", { 5, 4 }, inits::from_vector(vD)); // example matrix with transposed dimensions
     std::vector<float> SV;    // create CSR version of S
     std::vector<IndexType> SI, SO;
     SO.push_back((IndexType)SI.size());
@@ -340,37 +341,54 @@ void tests(DeviceType device) {
       }
       SO.push_back((IndexType)SI.size());
     }
-    auto SxRs = csr_dot(
+
+    auto SxDd    = dot(S, D);
+    auto STxSxDd = dot(S, SxDd, /*transA=*/true);
+    auto SxDs = csr_dot( // sparse x dense
+          S->shape(),
+          graph->constant({(int)SV.size()}, inits::from_vector(SV), Type::float32),
+          graph->constant({(int)SI.size()}, inits::from_vector(SI), Type::uint32),
+          graph->constant({(int)SO.size()}, inits::from_vector(SO), Type::uint32),
+          D);
+    auto STxSxDs = csr_dot(   // transpose(sparse) x dense; we use result of previous since dimensions match
           S->shape(),
           graph->constant({(int)SV.size()}, inits::from_vector(SV), Type::float32),
           graph->constant({(int)SI.size()}, inits::from_vector(SI), Type::uint32),
           graph->constant({(int)SO.size()}, inits::from_vector(SO), Type::uint32),
-          R);
-    auto SxRd = dot(S, R);
-    auto STxRs = csr_dot(   // and transpose; use result of previous since dimensions match
+          SxDd, /*transS=*/true);
+
+    auto DTxSTd   = dot(DT,     S, /*transA=*/false, /*transB=*/true);
+    auto DTxSTxSd = dot(DTxSTd, S);
+    auto DTxSTs = dot_csr( // dense x sparse
+          DT,
           S->shape(),
           graph->constant({(int)SV.size()}, inits::from_vector(SV), Type::float32),
           graph->constant({(int)SI.size()}, inits::from_vector(SI), Type::uint32),
           graph->constant({(int)SO.size()}, inits::from_vector(SO), Type::uint32),
-          SxRd, /*transA=*/true);
-    auto STxRd = dot(S, SxRd, /*transA=*/true);
+          /*transS=*/true);
+    auto DTxSTxSs = dot_csr( // dense x transpose(sparse)
+          DTxSTd,
+          S->shape(),
+          graph->constant({(int)SV.size()}, inits::from_vector(SV), Type::float32),
+          graph->constant({(int)SI.size()}, inits::from_vector(SI), Type::uint32),
+          graph->constant({(int)SO.size()}, inits::from_vector(SO), Type::uint32));
 
     CHECK(C->shape() == Shape({2, 2, 2}));
-    CHECK(SxRs->shape() == SxRd->shape());
-    CHECK(STxRs->shape() == STxRd->shape());
+    CHECK(SxDs->shape() == SxDd->shape());
+    CHECK(STxSxDs->shape() == STxSxDd->shape());
+    CHECK(DTxSTs->shape() == DTxSTd->shape());
+    CHECK(DTxSTxSs->shape() == DTxSTxSd->shape());
 
     graph->forward();
 
     C->val()->get(values);
     CHECK(values == vC);
 
-    SxRd->val()->get(values2); // dense
-    SxRs->val()->get(values);  // sparse
-    CHECK(values == values2);  // must be the same
-
-    STxRd->val()->get(values2);
-    STxRs->val()->get(values);
-    CHECK(values == values2);
+    // dense and sparse operation results must be the same
+    SxDd    ->val()->get(values2); SxDs    ->val()->get(values); CHECK(values == values2);
+    STxSxDd ->val()->get(values2); STxSxDs ->val()->get(values); CHECK(values == values2);
+    DTxSTd  ->val()->get(values2); DTxSTs  ->val()->get(values); CHECK(values == values2);
+    DTxSTxSd->val()->get(values2); DTxSTxSs->val()->get(values); CHECK(values == values2);
   }
 
   SECTION("affine transformation") {

From 1ffc8825ebb4714ec73d0f96d023d4f65431849f Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Tue, 15 Jan 2019 16:31:37 -0800
Subject: [PATCH 126/838] bug fix: CSRDotNodeOp should remember swapOperands
 arg; dot_csr() implemented and passes tests

---
 src/graph/node_operators_binary.h |  3 +-
 src/tensors/gpu/prod.cpp          | 68 +++++++++++++++----------------
 2 files changed, 35 insertions(+), 36 deletions(-)

diff --git a/src/graph/node_operators_binary.h b/src/graph/node_operators_binary.h
index fa2bd350f..4ebda8d38 100755
--- a/src/graph/node_operators_binary.h
+++ b/src/graph/node_operators_binary.h
@@ -416,7 +416,8 @@ class CSRDotNodeOp : public NaryNodeOp {
   bool swapOperands_;
 public:
   CSRDotNodeOp(const Shape& S_shape, Expr S_values, Expr S_indices, Expr S_offsets, Expr D, bool transS, bool swapOperands)
-      : NaryNodeOp({ S_values, S_indices, S_offsets, D }, newShape(S_shape, S_values, S_indices, S_offsets, D, transS, swapOperands)), transS_(transS) {
+      : NaryNodeOp({ S_values, S_indices, S_offsets, D }, newShape(S_shape, S_values, S_indices, S_offsets, D, transS, swapOperands)),
+                   transS_(transS), swapOperands_(swapOperands){
     matchOrAbort<IndexType>(S_indices->value_type());
     matchOrAbort<IndexType>(S_offsets->value_type());
   }
diff --git a/src/tensors/gpu/prod.cpp b/src/tensors/gpu/prod.cpp
index 0156c8602..cb795258b 100755
--- a/src/tensors/gpu/prod.cpp
+++ b/src/tensors/gpu/prod.cpp
@@ -250,8 +250,8 @@ void CSRProd(marian::Tensor C,
   auto colsC = shapeC.elements() / rowsC;
   auto rowsD = shapeD[-(int)swapOperands];
   auto colsD = shapeD.elements() / rowsD;
-  auto rowsS = transS != swapOperands ? rowsD : rowsC;
-  auto colsS = transS != swapOperands ? rowsC : rowsD;
+  auto rowsS = transS ? rowsD : rowsC;
+  auto colsS = transS ? rowsC : rowsD;
   ABORT_IF(colsD != colsC, "Inconsistent outer dimensions in CSR product");
   if (swapOperands) { // make rowsX actual row dimensions again, likewise colsX
     std::swap(rowsC, colsC);
@@ -265,9 +265,9 @@ void CSRProd(marian::Tensor C,
   ABORT_IF(S_values->shape() != S_indices->shape(), "CSR values and indices must have the same size");
   float alpha = 1;
   Ptr<MemoryPiece> St_values, St_indices, St_offsets;
-  if (transS) {
+  if (transS != swapOperands) {
     // Cusparse gemmi() does not support this specific version of transpose, and csrmm() is non-deterministic.
-    // Hence, we transpose the matrix explicitly.
+    // Hence, we transpose the matrix explicitly. Note that gemmi() expects a CSC, while csrmm() a CSR.
     St_values  = allocator->alloc<float>(numValues);
     St_indices = allocator->alloc<int>(numValues);
     St_offsets = allocator->alloc<int>(colsS + 1);
@@ -279,30 +279,51 @@ void CSRProd(marian::Tensor C,
         /*csrcVal=*/          S_values->data<float>(),  // second arg
         /*csrcRowPtr=*/ (int*)S_offsets->data<IndexType>(),
         /*csrcColInd=*/ (int*)S_indices->data<IndexType>(),
-        /*cscVal=*/    St_values->data<float>(),  // transposed version goes here
+        /*cscVal=*/    St_values ->data<float>(),  // transposed version goes here
         /*cscRowInd=*/ St_indices->data<int>(),
         /*cscColPtr=*/ St_offsets->data<int>(),
         /*copyValues=*/ CUSPARSE_ACTION_NUMERIC,
         /*idxBase=*/ CUSPARSE_INDEX_BASE_ZERO));
+    std::swap(rowsS, colsS); // dims of the explicitly transposed object
   }
   if (swapOperands) {
-    // C = D x S
-    ABORT("CSRProd does not yet implement swapOperands");
+    // C = D x S for row-major matrices
+    // Implemented via cusparse as C' = S' x D' ("csrmm") where C' and D' are column-major, and S' is CSC.
+    cusparseMatDescr_t descrA;
+    CUSPARSE_CHECK(cusparseCreateMatDescr(&descrA));
+    cusparseSetMatType     (descrA, CUSPARSE_MATRIX_TYPE_GENERAL);
+    cusparseSetMatIndexBase(descrA, CUSPARSE_INDEX_BASE_ZERO);
+    CUSPARSE_CHECK(cusparseScsrmm(cusparseHandle,
+        CUSPARSE_OPERATION_NON_TRANSPOSE, // (we explicitly transposed above)
+        /*m=*/ rowsS, // #rows of first (CSR) factor (the transpose was done explicitly)
+        /*n=*/ rowsC, // #cols of second (col-major) factor and (col-major) result = #rows of row-major C
+        /*k=*/ colsS, // #cols of first (CSR) factor
+        /*nnz=*/ (int)numValues,
+        &alpha, descrA,
+        /*csrValA=*/    St_values  ? St_values ->data<float>() :       S_values ->data<float>(),  // second arg
+        /*csrRowPtrA=*/ St_offsets ? St_offsets->data<int>()   : (int*)S_offsets->data<IndexType>(),
+        /*csrColIndA=*/ St_indices ? St_indices->data<int>()   : (int*)S_indices->data<IndexType>(),
+        D->data(),
+        /*ldb=*/ colsD, // stride
+        &beta,
+        C->data(),
+        /*ldc=*/ colsC)); // stride
+    cusparseDestroyMatDescr(descrA);
   }
   else {
     // C = S x D for row-major matrices
-    // Implemented via cusparse as C' = D' x S' where C' and D' are column-major.
+    // Implemented via cusparse as C' = D' x S' ("gemmi") where C' and D' are column-major.
     CUSPARSE_CHECK(cusparseSgemmi(cusparseHandle,
         /*m=*/ colsD, // #rows of first (col-major) factor = #cols of row-major D
-        /*n=*/ rowsC, // #cols of second (sparse) factor and (col-major) result = #rows of row-major C
+        /*n=*/ rowsC, // #cols of second (CSC) factor and (col-major) result = #rows of row-major C
         /*k=*/ rowsD, // #cols of first (col-major) factor = #rows of row-major D
         /*nnz=*/ (int)numValues,
         &alpha,
         /*A=*/ D->data(),
         /*lda=*/ colsD, // stride
-        /*cscValB=*/    transS ? St_values ->data<float>() :       S_values ->data<float>(),  // second arg
-        /*cscRowPtrB=*/ transS ? St_offsets->data<int>()   : (int*)S_offsets->data<IndexType>(),
-        /*cscColIndB=*/ transS ? St_indices->data<int>()   : (int*)S_indices->data<IndexType>(),
+        /*cscValB=*/    St_values  ? St_values ->data<float>() :       S_values ->data<float>(),  // second arg
+        /*cscRowPtrB=*/ St_offsets ? St_offsets->data<int>()   : (int*)S_offsets->data<IndexType>(),
+        /*cscColIndB=*/ St_indices ? St_indices->data<int>()   : (int*)S_indices->data<IndexType>(),
         &beta,
         C->data(),
         /*ldc=*/ colsC)); // stride
@@ -310,29 +331,6 @@ void CSRProd(marian::Tensor C,
   if(St_values ) allocator->free(St_values );
   if(St_indices) allocator->free(St_indices);
   if(St_offsets) allocator->free(St_offsets);
-#if 0
-  // Incorrect code that assumes col-major matrices. Reuse that later for dense x sparse.
-  cusparseMatDescr_t descrA;
-  CUSPARSE_CHECK(cusparseCreateMatDescr(&descrA));
-  cusparseSetMatType     (descrA, CUSPARSE_MATRIX_TYPE_GENERAL);
-  cusparseSetMatIndexBase(descrA, CUSPARSE_INDEX_BASE_ZERO);
-  CUSPARSE_CHECK(cusparseScsrmm(cusparseHandle,
-      transS ? CUSPARSE_OPERATION_TRANSPOSE : CUSPARSE_OPERATION_NON_TRANSPOSE,
-      /*m=*/ rowsS, // #rows of sparse A
-      /*n=*/ colsD, // #cols of dense D and C
-      /*k=*/ colsS, // #cols of sparse A
-      /*nnz=*/ (int)numValues,
-      &alpha, descrA,
-      /*csrValA=*/          S_values->data<float>(),
-      /*csrRowPtrA=*/ (int*)S_offsets->data<IndexType>(),
-      /*csrColIndA=*/ (int*)S_indices->data<IndexType>(),
-      D->data(),
-      /*ldb=*/ rowsD,
-      &beta,
-      C->data(),
-      /*ldc=*/ rowsC));
-  cusparseDestroyMatDescr(descrA);
-#endif
 }
 
 }  // namespace gpu

From 2b6ed113b615b0d51accfe714e630dc3997e137b Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Tue, 15 Jan 2019 16:40:54 -0800
Subject: [PATCH 127/838] (comments)

---
 src/tensors/gpu/prod.cpp | 14 ++++++++------
 1 file changed, 8 insertions(+), 6 deletions(-)

diff --git a/src/tensors/gpu/prod.cpp b/src/tensors/gpu/prod.cpp
index cb795258b..d13081728 100755
--- a/src/tensors/gpu/prod.cpp
+++ b/src/tensors/gpu/prod.cpp
@@ -267,7 +267,8 @@ void CSRProd(marian::Tensor C,
   Ptr<MemoryPiece> St_values, St_indices, St_offsets;
   if (transS != swapOperands) {
     // Cusparse gemmi() does not support this specific version of transpose, and csrmm() is non-deterministic.
-    // Hence, we transpose the matrix explicitly. Note that gemmi() expects a CSC, while csrmm() a CSR.
+    // Hence, we transpose the matrix explicitly.
+    // Note that gemmi() expects a CSC, while csrmm() a CSR; hence, the strange condition (transS != swapOperands) above.
     St_values  = allocator->alloc<float>(numValues);
     St_indices = allocator->alloc<int>(numValues);
     St_offsets = allocator->alloc<int>(colsS + 1);
@@ -276,7 +277,7 @@ void CSRProd(marian::Tensor C,
         /*m=*/ rowsS, // number of rows of matrix
         /*n=*/ colsS, // number of columns of matrix
         /*nnz=*/ (int)numValues,
-        /*csrcVal=*/          S_values->data<float>(),  // second arg
+        /*csrcVal=*/          S_values ->data<float>(),
         /*csrcRowPtr=*/ (int*)S_offsets->data<IndexType>(),
         /*csrcColInd=*/ (int*)S_indices->data<IndexType>(),
         /*cscVal=*/    St_values ->data<float>(),  // transposed version goes here
@@ -284,11 +285,12 @@ void CSRProd(marian::Tensor C,
         /*cscColPtr=*/ St_offsets->data<int>(),
         /*copyValues=*/ CUSPARSE_ACTION_NUMERIC,
         /*idxBase=*/ CUSPARSE_INDEX_BASE_ZERO));
-    std::swap(rowsS, colsS); // dims of the explicitly transposed object
+    std::swap(rowsS, colsS); // these variables now represent the dims of the explicitly transposed object
   }
   if (swapOperands) {
     // C = D x S for row-major matrices
-    // Implemented via cusparse as C' = S' x D' ("csrmm") where C' and D' are column-major, and S' is CSC.
+    // Implemented via cusparse as C' = S' x D' ("csrmm") where C' and D' are column-major,
+    // and S' is CSR (if not transS then we make a transposed copy).
     cusparseMatDescr_t descrA;
     CUSPARSE_CHECK(cusparseCreateMatDescr(&descrA));
     cusparseSetMatType     (descrA, CUSPARSE_MATRIX_TYPE_GENERAL);
@@ -300,7 +302,7 @@ void CSRProd(marian::Tensor C,
         /*k=*/ colsS, // #cols of first (CSR) factor
         /*nnz=*/ (int)numValues,
         &alpha, descrA,
-        /*csrValA=*/    St_values  ? St_values ->data<float>() :       S_values ->data<float>(),  // second arg
+        /*csrValA=*/    St_values  ? St_values ->data<float>() :       S_values ->data<float>(),
         /*csrRowPtrA=*/ St_offsets ? St_offsets->data<int>()   : (int*)S_offsets->data<IndexType>(),
         /*csrColIndA=*/ St_indices ? St_indices->data<int>()   : (int*)S_indices->data<IndexType>(),
         D->data(),
@@ -321,7 +323,7 @@ void CSRProd(marian::Tensor C,
         &alpha,
         /*A=*/ D->data(),
         /*lda=*/ colsD, // stride
-        /*cscValB=*/    St_values  ? St_values ->data<float>() :       S_values ->data<float>(),  // second arg
+        /*cscValB=*/    St_values  ? St_values ->data<float>() :       S_values ->data<float>(),
         /*cscRowPtrB=*/ St_offsets ? St_offsets->data<int>()   : (int*)S_offsets->data<IndexType>(),
         /*cscColIndB=*/ St_indices ? St_indices->data<int>()   : (int*)S_indices->data<IndexType>(),
         &beta,

From 148540310694d0d80218952202c0cb2357b0e6d7 Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Tue, 15 Jan 2019 16:42:37 -0800
Subject: [PATCH 128/838] (removed a debug message)

---
 src/graph/node_operators_binary.h | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/graph/node_operators_binary.h b/src/graph/node_operators_binary.h
index 4ebda8d38..eb20033d9 100755
--- a/src/graph/node_operators_binary.h
+++ b/src/graph/node_operators_binary.h
@@ -433,7 +433,7 @@ class CSRDotNodeOp : public NaryNodeOp {
         "Sparse matrix offset vector has incorrect size");
     auto outShape = D->shape();
     ABORT_IF(S_shape[transS == swapOperands ? 1 : 0] != outShape[-(int)swapOperands],
-             "Matrix product requires inner dimensions to match {} {} {} {}", transS, swapOperands, std::string(S_shape), std::string(outShape));
+             "Matrix product requires inner dimensions to match");
     outShape.set(-(int)swapOperands, S_shape[transS != swapOperands]);
     return outShape;
   }

From d6d281f84f3c02ce23b5d78e388bea7d3f1a4c8a Mon Sep 17 00:00:00 2001
From: Marcin Junczys-Dowmunt <Marcin.Junczys-Dowmunt@microsoft.com>
Date: Tue, 15 Jan 2019 22:43:00 -0800
Subject: [PATCH 129/838] fix first batch of regression tests

---
 src/common/config_parser.cpp  |  4 +++-
 src/data/vocab.cpp            |  9 ++++-----
 src/layers/loss.cpp           |  7 ++++---
 src/layers/loss.h             | 12 ++++--------
 src/models/costs.h            |  7 +++++--
 src/models/transformer.h      | 13 ++++---------
 src/training/scheduler.h      | 26 ++++++++++++++++----------
 src/training/training_state.h |  9 ++++++---
 8 files changed, 46 insertions(+), 41 deletions(-)

diff --git a/src/common/config_parser.cpp b/src/common/config_parser.cpp
index e745e99d5..cfc7774a9 100755
--- a/src/common/config_parser.cpp
+++ b/src/common/config_parser.cpp
@@ -250,8 +250,10 @@ void ConfigParser::addOptionsModel(cli::CLIWrapper& cli) {
 void ConfigParser::addOptionsTraining(cli::CLIWrapper& cli) {
   cli.switchGroup("Training options");
   // clang-format off
-  cli.add<std::string>("--cost-type",
+  cli.add<std::string>("--cost-type", // @TODO: rename to loss-type
       "Optimization criterion: ce-mean, ce-mean-words, ce-sum, perplexity", "ce-mean");
+  cli.add<std::string>("--multi-loss-type",
+      "How to accumulate multi-objective losses: sum, scaled, mean", "sum");
   cli.add<bool>("--overwrite",
       "Do not create model checkpoints, only overwrite main model file with last checkpoint. "
       "Reduces disk usage");
diff --git a/src/data/vocab.cpp b/src/data/vocab.cpp
index 1e6a341b2..97963e4d9 100755
--- a/src/data/vocab.cpp
+++ b/src/data/vocab.cpp
@@ -13,11 +13,10 @@ Ptr<VocabBase> createVocab(const std::string& vocabPath, Ptr<Options> options, s
   if(vocab) {
     return vocab; // this is defined which means that a sentencepiece vocabulary could be created, so return it
   } else {
-    auto inputType = options->get<std::vector<std::string>>("input-types")[batchIndex];
-    if(inputType == "labels")
-      return createLabelsVocab();
-    else
-      return createDefaultVocab();
+    // check type of input, if not given, assume "sequence"
+    auto inputTypes = options->get<std::vector<std::string>>("input-types", {});
+    std::string inputType = inputTypes.size() > batchIndex ? inputTypes[batchIndex] : "sequence";
+    return inputType == "labels" ? createLabelsVocab() : createDefaultVocab();
   }
 }
 
diff --git a/src/layers/loss.cpp b/src/layers/loss.cpp
index 024b884c4..f904f73ce 100755
--- a/src/layers/loss.cpp
+++ b/src/layers/loss.cpp
@@ -2,6 +2,7 @@
 
 namespace marian {
 
+// @TODO, simplify this. Currently here for back-compat
 Ptr<LabelwiseLoss> newLoss(Ptr<Options> options, bool inference) {
   float smoothing = inference ? 0.f : options->get<float>("label-smoothing");
   std::string costType = options->get<std::string>("cost-type", "ce-mean");
@@ -24,11 +25,11 @@ Ptr<LabelwiseLoss> newLoss(Ptr<Options> options, bool inference) {
 
 Ptr<MultiRationalLoss> newMultiLoss(Ptr<Options> options) {
     std::string multiLossType = options->get<std::string>("multi-loss-type", "sum");
-    if(multiLossType == "sum")
+    if(multiLossType == "sum")         // sum of sums
       return New<SumMultiRationalLoss>();
-    else if(multiLossType == "scaled")
+    else if(multiLossType == "scaled") // sum of scaled sums, first element is reference scale
       return New<ScaledMultiRationalLoss>();
-    else if(multiLossType == "normal")
+    else if(multiLossType == "mean")   // sum of means
       return New<MeanMultiRationalLoss>();
     else
       ABORT("Unknown multi-loss-type {}", multiLossType);
diff --git a/src/layers/loss.h b/src/layers/loss.h
index eaa98da04..3e876a3bd 100644
--- a/src/layers/loss.h
+++ b/src/layers/loss.h
@@ -81,9 +81,8 @@ class MultiRationalLoss : public RationalLoss {
 public:
   MultiRationalLoss() : RationalLoss() {}
 
-  MultiRationalLoss(const RationalLoss& rl)
-    : RationalLoss(rl.loss(), rl.labels()) {
-    partialLosses_.push_back(rl);
+  MultiRationalLoss(const RationalLoss& rl) : RationalLoss() {
+    this->push_back(rl);
   }
 
   void push_back(const RationalLoss& current) {
@@ -211,11 +210,8 @@ class LabelwiseLoss {
                      Expr mask = nullptr, Expr labelWeights = nullptr) {
     Expr loss = compute(logits, labelIndices, mask, labelWeights);
 
-    Expr labels;
-    if(mask)
-      labels = mask;
-    else
-      labels = constant_like(loss, inits::ones); // we have no mask, assume all items are labels
+    Expr labels = mask ? mask                              // mask can be used as element-wise label count with broadcasting
+                       : constant_like(loss, inits::ones); // we have no mask, assume all items are labels
 
     return reduce(loss, labels);
   }
diff --git a/src/models/costs.h b/src/models/costs.h
index 7f5f0743e..70bbe1768 100755
--- a/src/models/costs.h
+++ b/src/models/costs.h
@@ -10,6 +10,10 @@
 namespace marian {
 namespace models {
 
+// @TODO: this whole file is an unholy mess and needs to be refactored.
+// Using MultiRationalLoss is a first improvement, but we can probably
+// unify classifier and decoder costs. Also rethink step-wise cost.
+
 class CostBase {
 public:
   virtual Ptr<MultiRationalLoss> apply(Ptr<ModelBase> model,
@@ -56,14 +60,13 @@ class EncoderDecoderCE : public CostBase {
     // multi-objective training
     Ptr<MultiRationalLoss> multiLoss = newMultiLoss(options_);
 
-    // @TODO: adapt to multi-objective training
+    // @TODO: adapt to multi-objective training with multiple decoders
     auto partialLoss = loss_->apply(state->getLogProbs(),
                                     state->getTargetIndices(),
                                     state->getTargetMask(),
                                     weights);
     multiLoss->push_back(partialLoss);
   
-
     if(options_->get("guided-alignment", std::string("none")) != "none" && !inference_) {
       auto alignments = encdec->getDecoders()[0]->getAlignments();
       ABORT_IF(alignments.empty(), "Model does not seem to support alignments");
diff --git a/src/models/transformer.h b/src/models/transformer.h
index a5fba81aa..7cbb8d5d9 100755
--- a/src/models/transformer.h
+++ b/src/models/transformer.h
@@ -52,6 +52,7 @@ class Transformer : public EncoderOrDecoderBase {
     int dimWords = input->shape()[-3];
 
     Expr embeddings = input;
+
     if(learnedPosEmbeddings) {
       int maxLength = opt<int>("max-length");
 
@@ -84,11 +85,9 @@ class Transformer : public EncoderOrDecoderBase {
     return embeddings;
   }
 
-  virtual Expr addSpecialEmbeddings(Expr input, int start = 0, Ptr<data::CorpusBatch> batch = nullptr) const {
-    batch;
+  virtual Expr addSpecialEmbeddings(Expr input, int start = 0, Ptr<data::CorpusBatch> /*batch*/ = nullptr) const {
     bool learnedPosEmbeddings = opt<bool>("transformer-learned-positions", false);
-    input = addPositionalEmbeddings(input, start, learnedPosEmbeddings);
-    return input;
+    return addPositionalEmbeddings(input, start, learnedPosEmbeddings);
   }
 
   Expr triangleMask(int length) const {
@@ -706,20 +705,16 @@ class DecoderTransformer : public Transformer<DecoderBase> {
 
     //************************************************************************//
 
-    int dimEmb = embeddings->shape()[-1];
     int dimBeam = 1;
     if(embeddings->shape().size() > 3)
       dimBeam = embeddings->shape()[-4];
 
-    // according to paper embeddings are scaled by \sqrt(d_m)
-    auto scaledEmbeddings = std::sqrt((float)dimEmb) * embeddings;
-
     // set current target token position during decoding or training. At training
     // this should be 0. During translation the current length of the translation.
     // Used for position embeddings and creating new decoder states.
     int startPos = (int)state->getPosition();
 
-    scaledEmbeddings = addSpecialEmbeddings(scaledEmbeddings, startPos);
+    auto scaledEmbeddings = addSpecialEmbeddings(embeddings, startPos);
     scaledEmbeddings = atleast_nd(scaledEmbeddings, 4);
 
     // reorganize batch and timestep
diff --git a/src/training/scheduler.h b/src/training/scheduler.h
index d35749901..b03a4ec99 100755
--- a/src/training/scheduler.h
+++ b/src/training/scheduler.h
@@ -49,7 +49,7 @@ class Scheduler : public TrainingObserver {
     ss << "Cost ";
     ss << std::setprecision(8) << std::fixed;
 
-    // @TODO: put a single loss formatting funciton into loss.h and reuse here to avoid code duplication
+    // @TODO: put a single loss formatting function into loss.h and reuse here to avoid code duplication
     // @TODO: use dispLabelCounts with any display type?
     if(lossType == "ce-mean-words")
       ss << state->costSum / state->costCount;
@@ -60,11 +60,11 @@ class Scheduler : public TrainingObserver {
          << " after "
          << utils::withCommas(state->labelsTotal);
     else if(lossType == "ce-sum" && !dispLabelCounts)
-      ss << state->costSum;
+      ss << state->costSum / state->updatesDisp; // average over batches
     else if(lossType == "perplexity")
       ss << std::exp(state->costSum / state->costCount);
     else if(lossType == "cross-entropy" || lossType == "ce-mean") // backwards-compat, @TODO: get rid of this?
-      ss << state->costSum / state->samplesCount;
+      ss << state->costSum / state->samplesDisp;
     else
       ABORT("Unknown loss type {}", lossType);
 
@@ -187,7 +187,7 @@ class Scheduler : public TrainingObserver {
   }
 
   void update(float cost, Ptr<data::Batch> batch) {
-    ABORT("Fix me");
+    // @TODO: Check me and eventually remove me, currently here for back-compat.
     update(cost, batch->words(-1), std::vector<Ptr<data::Batch>>({batch}));
   }
 
@@ -213,9 +213,11 @@ class Scheduler : public TrainingObserver {
 
     state_->costSum      += cost;        // aggregate sum cost since last display
     state_->costCount    += batchLabels; // cost gets normalized w.r.t. this in display
-    state_->samplesCount += batchSize;
 
+    state_->updatesDisp  += 1;
+    state_->samplesDisp  += batchSize;
     state_->wordsDisp    += batchWords;  // words at given input processed since last display, for speed display
+
     state_->samplesEpoch += batchSize;   // sentences processed in this epoch
     state_->labelsTotal  += batchLabels; // total labels processed
 
@@ -261,7 +263,9 @@ class Scheduler : public TrainingObserver {
       timer_.start();
       state_->costSum      = 0;
       state_->costCount    = 0;
-      state_->samplesCount = 0;
+
+      state_->updatesDisp  = 0;
+      state_->samplesDisp  = 0;
       state_->wordsDisp    = 0;
     }
 
@@ -284,10 +288,12 @@ class Scheduler : public TrainingObserver {
 
     if(options_->get<bool>("no-restore-corpus")) {
       state_->samplesEpoch = 0;
-      state_->costSum = 0;
-      state_->costCount = 0;
-      state_->samplesCount = 0;
-      state_->wordsDisp = 0;
+      state_->costSum      = 0;
+      state_->costCount    = 0;
+
+      state_->updatesDisp  = 0;
+      state_->samplesDisp  = 0;
+      state_->wordsDisp    = 0;
     }
 
     state_->newLoad();
diff --git a/src/training/training_state.h b/src/training/training_state.h
index d4ecdb6cb..6cd5686af 100755
--- a/src/training/training_state.h
+++ b/src/training/training_state.h
@@ -94,13 +94,16 @@ class TrainingState {
 
   // Sum of costs since last display
   float costSum{0};
-  // Number of words/labels/samples (depending on cost-type) aggregated in
+  // Number of labels aggregated in
   // costSum since last display
   size_t costCount{0};
-  // Number of words seen since last display, for speed measurement
+
+  // Number of words seen since last display
   size_t wordsDisp{0};
   // Number of samples/sentences seen since last display
-  size_t samplesCount{0};
+  size_t samplesDisp{0};
+  // Number of updates seen since last display
+  size_t updatesDisp{0};
 
   // The state of the random number generator from a batch generator
   std::string seedBatch;

From 9a090af595a136cec7344265e9c43640edfc374d Mon Sep 17 00:00:00 2001
From: Marcin Junczys-Dowmunt <Marcin.Junczys-Dowmunt@microsoft.com>
Date: Tue, 15 Jan 2019 23:01:25 -0800
Subject: [PATCH 130/838] fix rescorer loss

---
 src/layers/loss.h        | 16 +++++++++++-----
 src/models/transformer.h |  1 +
 2 files changed, 12 insertions(+), 5 deletions(-)

diff --git a/src/layers/loss.h b/src/layers/loss.h
index 3e876a3bd..99abcd029 100644
--- a/src/layers/loss.h
+++ b/src/layers/loss.h
@@ -179,7 +179,7 @@ class ScaledMultiRationalLoss : public MultiRationalLoss {
 Ptr<MultiRationalLoss> newMultiLoss(Ptr<Options> options);
 
 //***********************************************************************************//
-// This needs some to be refactored. Currentl easiest route for backwards compat. 
+// This needs some to be refactored. Currentl easiest route for backwards compat.
 
 class LabelwiseLoss {
 protected:
@@ -206,8 +206,8 @@ class LabelwiseLoss {
   LabelwiseLoss(const std::vector<int>& axes)
   : axes_(axes) { }
 
-  RationalLoss apply(Expr logits, Expr labelIndices,
-                     Expr mask = nullptr, Expr labelWeights = nullptr) {
+  virtual RationalLoss apply(Expr logits, Expr labelIndices,
+                             Expr mask = nullptr, Expr labelWeights = nullptr) {
     Expr loss = compute(logits, labelIndices, mask, labelWeights);
 
     Expr labels = mask ? mask                              // mask can be used as element-wise label count with broadcasting
@@ -230,7 +230,7 @@ class CrossEntropyLoss : public LabelwiseLoss {
 protected:
   float smoothing_;
 
-  Expr compute(Expr logits, Expr labelIndices,
+  virtual Expr compute(Expr logits, Expr labelIndices,
                Expr mask = nullptr, Expr labelWeights = nullptr) override {
     Expr ce = cross_entropy(logits, labelIndices);
 
@@ -252,8 +252,14 @@ class CrossEntropyLoss : public LabelwiseLoss {
 
 class RescorerLoss : public CrossEntropyLoss {
 public:
-  // sentence-wise CE, hence reduce only over time axis.
+  // sentence-wise CE, hence reduce only over time axis. CE reduces over last axis (-1)
   RescorerLoss() : CrossEntropyLoss(/*axes=*/{-3}, /*smoothing=*/0.f) {}
+
+  virtual RationalLoss apply(Expr logits, Expr labelIndices,
+                             Expr mask = nullptr, Expr labelWeights = nullptr) override {
+    auto ce = CrossEntropyLoss::apply(logits, labelIndices, mask, labelWeights);
+    return RationalLoss(-ce.loss(), ce.labels()); // we report logprobs, hence negate
+  }
 };
 
 Ptr<LabelwiseLoss> newLoss(Ptr<Options> options, bool inference);
diff --git a/src/models/transformer.h b/src/models/transformer.h
index 7cbb8d5d9..c72d5a06e 100755
--- a/src/models/transformer.h
+++ b/src/models/transformer.h
@@ -71,6 +71,7 @@ class Transformer : public EncoderOrDecoderBase {
       for(int i = 0; i < std::min(dimWords, numPos); ++i)
         positions[i] = i;
 
+      // @TODO : test if embeddings should be scaled here too!
       auto signal = rows(posEmbFactory, graph_->indices(positions));
       signal = reshape(signal, {dimWords, 1, dimEmb});
       embeddings = embeddings + signal;

From 637f50837b7dc33c733b354525f50eb75c5f2de1 Mon Sep 17 00:00:00 2001
From: Marcin Junczys-Dowmunt <Marcin.Junczys-Dowmunt@microsoft.com>
Date: Tue, 15 Jan 2019 23:14:48 -0800
Subject: [PATCH 131/838] add comments

---
 src/layers/loss.h | 6 ++++--
 1 file changed, 4 insertions(+), 2 deletions(-)

diff --git a/src/layers/loss.h b/src/layers/loss.h
index 99abcd029..1f9543b44 100644
--- a/src/layers/loss.h
+++ b/src/layers/loss.h
@@ -54,7 +54,8 @@ class RationalLoss {
   }
 };
 
-// POD for accumulating loss values after backprop
+// POD for accumulating loss values after forward/backward and to-be-used in
+// Scheduler for updating statistics.
 struct StaticLoss {
   float loss;
   float labels;
@@ -179,7 +180,8 @@ class ScaledMultiRationalLoss : public MultiRationalLoss {
 Ptr<MultiRationalLoss> newMultiLoss(Ptr<Options> options);
 
 //***********************************************************************************//
-// This needs some to be refactored. Currentl easiest route for backwards compat.
+// This needs some to be refactored. Currently easiest route for backwards compat, but
+// still feels somewhat hacky.
 
 class LabelwiseLoss {
 protected:

From d0e380cd623a15dc694705ce93f95296b3cdec07 Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Wed, 16 Jan 2019 08:08:04 -0800
Subject: [PATCH 132/838] first version of factored outputs (closed vocab)

---
 src/layers/generic.cpp   | 118 +++++++++++++++++++++++++++++----------
 src/layers/generic.h     |  32 ++---------
 src/models/transformer.h |  14 +++--
 3 files changed, 105 insertions(+), 59 deletions(-)

diff --git a/src/layers/generic.cpp b/src/layers/generic.cpp
index f651a3ce6..82c5bc7c9 100755
--- a/src/layers/generic.cpp
+++ b/src/layers/generic.cpp
@@ -11,10 +11,13 @@ namespace marian {
   };
 
   class EmbeddingFactorMapping {
-    Vocab factorVocab_;                        // [factor name] -> factor index = row of E_
-    std::vector<std::vector<Word>> factorMap_; // [word index] -> set of factor indices
-    std::vector<int> factorRefCounts_;         // [factor index] -> how often this factor is referenced in factorMap_
   public:
+    struct CSRData {
+      Shape shape;
+      std::vector<float> weights;
+      std::vector<IndexType> indices;
+      std::vector<IndexType> offsets;
+    };
     // mapPath = path to file with entries in order of vocab entries of the form
     //   WORD FACTOR1 FACTOR2 FACTOR3...
     // listPath = path to file that lists all FACTOR names
@@ -23,13 +26,19 @@ namespace marian {
     // Note: Presently, this implementation has the following short-comings
     //  - we do not group factors (to normalize the probs); instead we assume that the global softmax will normalize correctly
     //  - we do not handle binary features differently; we'd need to apply sigmoid(x) / sigmoid(-x)
-    EmbeddingFactorMapping(const std::string& mapPath, const std::string& factorVocabPath, const std::string& vocabPath) :
-        factorVocab_(New<Options>(), 0) {
+    EmbeddingFactorMapping(Ptr<Options> options) : factorVocab_(New<Options>(), 0) {
+      std::vector<std::string> paths = options->get<std::vector<std::string>>("embedding-factors");
+      ABORT_IF(paths.size() != 2, "--embedding-factors expects two paths");
+      auto mapPath = paths[0];
+      auto factorVocabPath = paths[1];
+      auto vocabPath = options->get<std::string>("vocab");
+
       // Note: We misuse the Vocab class a little.
-      // But it means that the factorVocab_ must contain </s> and "<unk>".
+      // Specifically, it means that the factorVocab_ must contain </s> and "<unk>".
       Vocab vocab(New<Options>(), 0);
       vocab.load(vocabPath);
       factorVocab_.load(factorVocabPath);
+
       // load and parse factorMap
       factorMap_.resize(vocab.size());
       factorRefCounts_.resize(factorVocab_.size());
@@ -50,13 +59,18 @@ namespace marian {
         numTotalFactors += tokens.size() - 1;
       }
       LOG(info, "[embedding] Factored-embedding map read with total/unique of {}/{} factors for {} words", numTotalFactors, factorVocab_.size(), vocab.size());
+
+      // create the factor matrix
+      std::vector<IndexType> data(vocab.size());
+      std::iota(data.begin(), data.end(), 0);
+      factorMatrix_ = csr_rows(data); // [V x U]
     }
 
     size_t factorVocabSize() const { return factorVocab_.size(); }
 
     // create a CSR matrix M[V,U] from indices[] with
     // M[v,u] = 1/c(u) if factor u is a factor of word v, and c(u) is how often u is referenced
-    std::tuple<Shape, std::vector<float>/*weights*/, std::vector<IndexType>/*indices*/, std::vector<IndexType>/*offsets*/> csr_rows(const std::vector<IndexType>& words) const {
+    CSRData csr_rows(const std::vector<IndexType>& words) const {
       std::vector<float> weights;
       std::vector<IndexType> indices;
       std::vector<IndexType> offsets;
@@ -72,12 +86,69 @@ namespace marian {
         }
         offsets.push_back((IndexType)indices.size()); // next matrix row begins at this offset
       }
-      return
-          std::tuple<Shape, std::vector<float>/*weights*/, std::vector<IndexType>/*indices*/, std::vector<IndexType>/*offsets*/> // (needed for unknown reasons)
-          { Shape({(int)words.size(), (int)factorVocab_.size()}), weights, indices, offsets };
+      return { Shape({(int)words.size(), (int)factorVocab_.size()}), weights, indices, offsets };
     }
+
+    const CSRData& getFactorMatrix() const { return factorMatrix_; }
+  private:
+    Vocab factorVocab_;                        // [factor name] -> factor index = row of E_
+    std::vector<std::vector<Word>> factorMap_; // [word index] -> set of factor indices
+    std::vector<int> factorRefCounts_;         // [factor index] -> how often this factor is referenced in factorMap_
+    CSRData factorMatrix_;                     // [v,u] (sparse) -> =1 if u is factor of v
   };
 
+  namespace mlp {
+    /*private*/ void Output::lazyConstruct(int inputDim) {
+      // We must construct lazily since we won't know tying nor input dim in constructor.
+      if (W_)
+        return;
+
+      auto name = options_->get<std::string>("prefix");
+      auto dim = options_->get<int>("dim");
+
+      if (options_->has("embedding-factors")) {
+        ABORT_IF(shortlist_, "Shortlists are presently not compatible with factored embeddings");
+        embeddingFactorMapping_ = New<EmbeddingFactorMapping>(options_);
+        dim = (int)embeddingFactorMapping_->factorVocabSize();
+        LOG(info, "[embedding] Factored outputs enabled");
+      }
+
+      if(tiedParam_) {
+        transposeW_ = true;
+        W_ = tiedParam_;
+        if(shortlist_)
+          W_ = rows(W_, shortlist_->indices());
+      } else {
+        W_ = graph_->param(name + "_W",
+                           {inputDim, dim},
+                           inits::glorot_uniform);
+        if(shortlist_)
+          W_ = cols(W_, shortlist_->indices());
+      }
+
+      b_ = graph_->param(name + "_b", {1, dim}, inits::zeros);
+      if(shortlist_)
+        b_ = cols(b_, shortlist_->indices());
+    }
+
+    Expr Output::apply(Expr input) /*override*/ {
+      lazyConstruct(input->shape()[-1]);
+      auto y = affine(input, W_, b_, false, transposeW_);
+      if (embeddingFactorMapping_) {
+        auto graph = input->graph();
+        auto factorMatrix = embeddingFactorMapping_->getFactorMatrix(); // [V x U]
+        y = dot_csr( // the CSR matrix is passed in pieces
+            y,  // [B x U]
+            factorMatrix.shape,
+            graph->constant({(int)factorMatrix.weights.size()}, inits::from_vector(factorMatrix.weights), Type::float32),
+            graph->constant({(int)factorMatrix.indices.size()}, inits::from_vector(factorMatrix.indices), Type::uint32),
+            graph->constant({(int)factorMatrix.offsets.size()}, inits::from_vector(factorMatrix.offsets), Type::uint32),
+            /*transB=*/ true); // -> [B x V]
+      }
+      return y;
+    }
+  }
+
   Embedding::Embedding(Ptr<ExpressionGraph> graph, Ptr<Options> options) : LayerBase(graph, options) {
     std::string name = opt<std::string>("prefix");
     int dimVoc = opt<int>("dimVocab");
@@ -85,11 +156,10 @@ namespace marian {
 
     bool fixed = opt<bool>("fixed", false);
 
-    if (options_->has("embedding-factors") && !embeddingFactorMapping_) { // (lazy init)
-      std::vector<std::string> paths = opt<std::vector<std::string>>("embedding-factors");
-      ABORT_IF(paths.size() != 2, "--embedding-factors expects two paths");
-      embeddingFactorMapping_ = New<EmbeddingFactorMapping>(paths[0], paths[1], opt<std::string>("vocab"));
+    if (options_->has("embedding-factors")) {
+      embeddingFactorMapping_ = New<EmbeddingFactorMapping>(options_);
       dimVoc = (int)embeddingFactorMapping_->factorVocabSize();
+      LOG(info, "[embedding] Factored embeddings enabled");
     }
 
     NodeInitializer initFunc = inits::glorot_uniform;
@@ -108,23 +178,15 @@ namespace marian {
   /*private*/ Expr Embedding::multiRows(const std::vector<IndexType>& data) const
   {
     auto graph = E_->graph();
-    Shape shape; std::vector<float> weights;  std::vector<IndexType> indices, offsets; std::tie
-    (shape, weights, indices, offsets) = embeddingFactorMapping_->csr_rows(data);
-#if 0   // tests for special case of nop-op
-    ABORT_IF(data != indices, "bang");
-    for (size_t i = 0; i < offsets.size(); i++)
-      ABORT_IF(offsets[i] != i, "boom");
-    for (size_t i = 0; i < weights.size(); i++)
-      ABORT_IF(weights[i] != 1, "oops");
-#endif
+    auto factoredData = embeddingFactorMapping_->csr_rows(data);
     // multi-hot factor vectors are represented as a sparse CSR matrix
     // [row index = word position index] -> set of factor indices for word at this position
-    ABORT_IF(shape != Shape({(int)offsets.size()-1/*=rows of CSR*/, E_->shape()[0]}), "shape mismatch??");
+    ABORT_IF(factoredData.shape != Shape({(int)factoredData.offsets.size()-1/*=rows of CSR*/, E_->shape()[0]}), "shape mismatch??");
     return csr_dot( // the CSR matrix is passed in pieces
-        shape,
-        graph->constant({(int)weights.size()}, inits::from_vector(weights), Type::float32),
-        graph->constant({(int)indices.size()}, inits::from_vector(indices), Type::uint32),
-        graph->constant({(int)offsets.size()}, inits::from_vector(offsets), Type::uint32),
+        factoredData.shape,
+        graph->constant({(int)factoredData.weights.size()}, inits::from_vector(factoredData.weights), Type::float32),
+        graph->constant({(int)factoredData.indices.size()}, inits::from_vector(factoredData.indices), Type::uint32),
+        graph->constant({(int)factoredData.offsets.size()}, inits::from_vector(factoredData.offsets), Type::uint32),
         E_);
   }
 
diff --git a/src/layers/generic.h b/src/layers/generic.h
index 092567c94..1ac9764f9 100755
--- a/src/layers/generic.h
+++ b/src/layers/generic.h
@@ -54,6 +54,8 @@ struct IEmbeddingLayer {
   virtual Expr apply(const std::vector<IndexType>& embIdx, int dimBatch, int dimBeam) const = 0;
 };
 
+class EmbeddingFactorMapping;
+
 namespace mlp {
 
 class Dense : public LayerBase, public IUnaryLayer {
@@ -126,11 +128,13 @@ class Output : public LayerBase, public IUnaryLayer {
 private:
   Expr tiedParam_;
   Ptr<data::Shortlist> shortlist_;
+  Ptr<EmbeddingFactorMapping > embeddingFactorMapping_;
 
   Expr W_;
   Expr b_;
   bool transposeW_{false};
 
+  void lazyConstruct(int inputDim);
 public:
   Output(Ptr<ExpressionGraph> graph, Ptr<Options> options)
       : LayerBase(graph, options) {}
@@ -141,31 +145,7 @@ class Output : public LayerBase, public IUnaryLayer {
 
   void setShortlist(Ptr<data::Shortlist> shortlist) { shortlist_ = shortlist; }
 
-  Expr apply(Expr input) override {
-    if(!W_) {
-      auto name = options_->get<std::string>("prefix");
-      auto dim = options_->get<int>("dim");
-
-      if(tiedParam_) {
-        transposeW_ = true;
-        W_ = tiedParam_;
-        if(shortlist_)
-          W_ = rows(W_, shortlist_->indices());
-      } else {
-        W_ = graph_->param(name + "_W",
-                           {input->shape()[-1], dim},
-                           inits::glorot_uniform);
-        if(shortlist_)
-          W_ = cols(W_, shortlist_->indices());
-      }
-
-      b_ = graph_->param(name + "_b", {1, dim}, inits::zeros);
-      if(shortlist_)
-        b_ = cols(b_, shortlist_->indices());
-    }
-
-    return affine(input, W_, b_, false, transposeW_);
-  }
+  Expr apply(Expr input) override;
 
   virtual Expr apply(const std::vector<Expr>& /*inputs*/) override {
     ABORT("Not implemented");
@@ -176,7 +156,7 @@ class Output : public LayerBase, public IUnaryLayer {
 
 class Embedding : public LayerBase, public IEmbeddingLayer {
   Expr E_;
-  Ptr<class EmbeddingFactorMapping> embeddingFactorMapping_;
+  Ptr<EmbeddingFactorMapping> embeddingFactorMapping_;
   Expr multiRows(const std::vector<IndexType>& data) const;
 public:
   Embedding(Ptr<ExpressionGraph> graph, Ptr<Options> options);
diff --git a/src/models/transformer.h b/src/models/transformer.h
index 99999e8f6..0557ec19b 100755
--- a/src/models/transformer.h
+++ b/src/models/transformer.h
@@ -627,19 +627,23 @@ class DecoderTransformer : public Transformer<DecoderBase> {
       if(opt<bool>("tied-embeddings-all") || opt<bool>("tied-embeddings-src"))
         tiedPrefix = "Wemb";
       layerOut.tieTransposed(tiedPrefix);
+    }
+
+    if(shortlist_)
+      layerOut.setShortlist(shortlist_);
+
+    if (options_->has("embedding-factors")) {
       // factored embeddings, simplistic version (which just adds the logits, like multiplying probs)
       //  z = h @ W        // h:[B x D] ; W:[D x V] -> [B x V]
       // with factors:
       //  z = h @ W @ M'        // h:[B x D] ; W:[D x U] ; M':[U x V]  -> [B x V]
       // i.e. multiOutput():
       //  output = dot_csr(output, M, transB=true)
-      // Should biases be done afterwards? Or maybe at two places?
-      // note: need to also specify output factors separately if not tied-embeddings or tied-embeddings-all
+      // @BUGBUG: need to specify output factors separately if not tied-embeddings or tied-embeddings-all
+      layerOut("embedding-factors", opt<std::vector<std::string>>("embedding-factors"));
+      layerOut("vocab", opt<std::vector<std::string>>("vocabs")[batchIndex_]);
     }
 
-    if(shortlist_)
-      layerOut.setShortlist(shortlist_);
-
     // [-4: beam depth=1, -3: max length, -2: batch size, -1: vocab dim]
     // assemble layers into MLP and apply to embeddings, decoder context and
     // aligned source context

From f6a183d23e7e7c4502d1a311f96dfd9b931d72a0 Mon Sep 17 00:00:00 2001
From: Marcin Junczys-Dowmunt <Marcin.Junczys-Dowmunt@microsoft.com>
Date: Wed, 16 Jan 2019 08:49:32 -0800
Subject: [PATCH 133/838] use rational loss for guided alignment

---
 src/layers/guided_alignment.h | 70 +++++++++++++++--------------------
 src/layers/loss.cpp           |  1 +
 src/layers/loss.h             |  4 +-
 src/models/costs.h            |  6 +--
 src/training/scheduler.h      |  1 +
 5 files changed, 36 insertions(+), 46 deletions(-)

diff --git a/src/layers/guided_alignment.h b/src/layers/guided_alignment.h
index df8607cae..9fefab2ca 100755
--- a/src/layers/guided_alignment.h
+++ b/src/layers/guided_alignment.h
@@ -1,53 +1,41 @@
 #pragma once
 
-#include "marian.h"
+#include "layers/loss.h"
 
 namespace marian {
 
-static inline Expr guidedAlignmentCost(Ptr<ExpressionGraph> graph,
-                                       Ptr<data::CorpusBatch> batch,
-                                       Ptr<Options> options,
-                                       Expr att) {
-  int dimBatch = att->shape()[-2];
-  int dimSrc = att->shape()[-3];
-  int dimTrg = att->shape()[-1];
+static inline RationalLoss guidedAlignmentCost(Ptr<ExpressionGraph> graph,
+                                               Ptr<data::CorpusBatch> batch,
+                                               Ptr<Options> options,
+                                               Expr attention) {
 
-  //debug(att, "Attention");
-
-  auto aln = graph->constant(att->shape(),
-                             inits::from_vector(batch->getGuidedAlignment()));
-
-  //debug(aln, "Alignment");
-
-  std::string guidedCostType
-      = options->get<std::string>("guided-alignment-cost");
-
-  std::string costType = options->get<std::string>("cost-type");
-
-  int div = 1;
-  if(costType == "ce-mean-words") {
-    div = dimBatch * dimSrc * dimTrg;
-  } else if(costType == "perplexity") {
-    div = dimBatch * dimSrc * dimTrg;
-  } else if(costType == "ce-sum") {
-    div = 1;
-  } else {
-    div = dimBatch;
-  }
-
-  Expr alnCost;
+  // @TODO: change "cost" to "loss"
+  std::string guidedLossType = options->get<std::string>("guided-alignment-cost");
+  float guidedScalar = options->get<float>("guided-alignment-weight");
+  
   float epsilon = 1e-6f;
-  if(guidedCostType == "mse") {
-    alnCost = sum(flatten(square(att - aln))) / (float)(2 * div);
-  } else if(guidedCostType == "mult") {
-    alnCost = -log(sum(flatten(att * aln)) + epsilon) / (float)div;
-  } else if(guidedCostType == "ce") {
-    alnCost = -sum(flatten(aln * log(att + epsilon))) / (float)div;
+  Expr alignment = constant_like(attention, inits::from_vector(batch->getGuidedAlignment()));
+  Expr alignmentLoss; // sum up loss over all attention/alignment positions
+  if(guidedLossType == "mse") {
+    alignmentLoss = sum(flatten(square(attention - alignment))) / 2.f;
+  } else if(guidedLossType == "mult") {
+    alignmentLoss = -log(sum(flatten(attention * alignment)) + epsilon);
+  } else if(guidedLossType == "ce") {
+    alignmentLoss = -sum(flatten(alignment * log(attention + epsilon)));
   } else {
-    ABORT("Unknown alignment cost type");
+    ABORT("Unknown alignment cost type: {}", guidedLossType);
   }
+  
+  alignmentLoss = guidedScalar * alignmentLoss; // weigh by scalar
 
-  float guidedScalar = options->get<float>("guided-alignment-weight");
-  return guidedScalar * alnCost;
+  // every position is a label as they should all agree
+  float numLabels = alignment->shape().elements();
+  
+  // create label node
+  Expr labels = graph->constant({1}, inits::from_value(numLabels));
+  labels = guidedScalar * labels; // also weigh by scalar so labels and cost are in the same domain
+
+  return RationalLoss(alignmentLoss, labels);
 }
+
 }  // namespace marian
diff --git a/src/layers/loss.cpp b/src/layers/loss.cpp
index f904f73ce..06f8dc59d 100755
--- a/src/layers/loss.cpp
+++ b/src/layers/loss.cpp
@@ -17,6 +17,7 @@ Ptr<LabelwiseLoss> newLoss(Ptr<Options> options, bool inference) {
   } else if(costType == "ce-rescore") {
     return New<RescorerLoss>();
   } else if(costType == "ce-rescore-mean") {
+    ABORT("Check me");
     return New<RescorerLoss>();
   } else {  // same as ce-mean
     return New<CrossEntropyLoss>(smoothing);
diff --git a/src/layers/loss.h b/src/layers/loss.h
index 1f9543b44..8c8d170c0 100644
--- a/src/layers/loss.h
+++ b/src/layers/loss.h
@@ -1,6 +1,6 @@
 #pragma once
 
-#include "marian.h"
+#include "graph/expression_operators.h"
 
 namespace marian {
 
@@ -233,7 +233,7 @@ class CrossEntropyLoss : public LabelwiseLoss {
   float smoothing_;
 
   virtual Expr compute(Expr logits, Expr labelIndices,
-               Expr mask = nullptr, Expr labelWeights = nullptr) override {
+                       Expr mask = nullptr, Expr labelWeights = nullptr) override {
     Expr ce = cross_entropy(logits, labelIndices);
 
     if(smoothing_ > 0) {
diff --git a/src/models/costs.h b/src/models/costs.h
index 70bbe1768..aaab3890c 100755
--- a/src/models/costs.h
+++ b/src/models/costs.h
@@ -73,9 +73,9 @@ class EncoderDecoderCE : public CostBase {
 
       auto att = concatenate(alignments, /*axis =*/ -1);
 
-      // @TODO: represent this as an multi-objective training loss, which it actually is
-      // return multiLoss.loss() + guidedAlignmentCost(graph, corpusBatch, options_, att);
-      ABORT("Fix me");
+      auto alignmentLoss = guidedAlignmentCost(graph, corpusBatch, options_, att);
+      multiLoss->push_back(alignmentLoss);
+      
       return multiLoss;
     } else {
       return multiLoss;
diff --git a/src/training/scheduler.h b/src/training/scheduler.h
index b03a4ec99..41b765e45 100755
--- a/src/training/scheduler.h
+++ b/src/training/scheduler.h
@@ -51,6 +51,7 @@ class Scheduler : public TrainingObserver {
 
     // @TODO: put a single loss formatting function into loss.h and reuse here to avoid code duplication
     // @TODO: use dispLabelCounts with any display type?
+    // @TODO: bugbug cost-type ce-mean-words with multi-loss-type mean divides too much in display
     if(lossType == "ce-mean-words")
       ss << state->costSum / state->costCount;
     else if(lossType == "ce-sum" && dispLabelCounts)

From 041dcccc54d2cdb08699fc83748c510cd72ceb5c Mon Sep 17 00:00:00 2001
From: Marcin Junczys-Dowmunt <Marcin.Junczys-Dowmunt@microsoft.com>
Date: Wed, 16 Jan 2019 08:58:08 -0800
Subject: [PATCH 134/838] add comments

---
 src/layers/guided_alignment.h | 14 +++++++++-----
 1 file changed, 9 insertions(+), 5 deletions(-)

diff --git a/src/layers/guided_alignment.h b/src/layers/guided_alignment.h
index 9fefab2ca..451647826 100755
--- a/src/layers/guided_alignment.h
+++ b/src/layers/guided_alignment.h
@@ -9,6 +9,10 @@ static inline RationalLoss guidedAlignmentCost(Ptr<ExpressionGraph> graph,
                                                Ptr<Options> options,
                                                Expr attention) {
 
+  // @TODO: there should be positional masking here ... on the other hand, positions that are not 
+  // in a sentence should always agree (both being 0). Lack of masking affects label count only which is 
+  // probably negligible?
+
   // @TODO: change "cost" to "loss"
   std::string guidedLossType = options->get<std::string>("guided-alignment-cost");
   float guidedScalar = options->get<float>("guided-alignment-weight");
@@ -28,12 +32,12 @@ static inline RationalLoss guidedAlignmentCost(Ptr<ExpressionGraph> graph,
   
   alignmentLoss = guidedScalar * alignmentLoss; // weigh by scalar
 
-  // every position is a label as they should all agree
-  float numLabels = alignment->shape().elements();
+  // every position is a label as they should all agree, see caveat at the top.
+  size_t numLabels = alignment->shape().elements();
   
-  // create label node
-  Expr labels = graph->constant({1}, inits::from_value(numLabels));
-  labels = guidedScalar * labels; // also weigh by scalar so labels and cost are in the same domain
+  // Create label node, also weigh by scalar so labels and cost are in the same domain.
+  // Fractional label counts are OK
+  Expr labels = graph->constant({1}, inits::from_value(guidedScalar * numLabels)); // @TODO: introduce graph->value(...) ?
 
   return RationalLoss(alignmentLoss, labels);
 }

From 1e205c242441d5c09ab8ea2393520517de9dc2ae Mon Sep 17 00:00:00 2001
From: Marcin Junczys-Dowmunt <Marcin.Junczys-Dowmunt@microsoft.com>
Date: Wed, 16 Jan 2019 09:31:29 -0800
Subject: [PATCH 135/838] add comments

---
 src/models/costs.h | 20 +++++++++++++++-----
 1 file changed, 15 insertions(+), 5 deletions(-)

diff --git a/src/models/costs.h b/src/models/costs.h
index aaab3890c..40d490b53 100755
--- a/src/models/costs.h
+++ b/src/models/costs.h
@@ -14,6 +14,11 @@ namespace models {
 // Using MultiRationalLoss is a first improvement, but we can probably
 // unify classifier and decoder costs. Also rethink step-wise cost.
 
+// @TODO: inheritance and polymorphism is used here in a rather unclear way.
+// E.g. returns Ptr<MultiRationalLoss> which should be Ptr<RationalLoss>?
+// Other functions return RationalLoss directly without Ptr<...>, but also 
+// they do not need polymorphism here. 
+
 class CostBase {
 public:
   virtual Ptr<MultiRationalLoss> apply(Ptr<ModelBase> model,
@@ -28,6 +33,8 @@ class EncoderDecoderCE : public CostBase {
 
   bool inference_{false};
   bool toBeWeighted_{false};
+
+  // @TODO: single loss seems wrong
   Ptr<LabelwiseLoss> loss_;
   Ptr<WeightingBase> weighter_;
 
@@ -68,14 +75,14 @@ class EncoderDecoderCE : public CostBase {
     multiLoss->push_back(partialLoss);
   
     if(options_->get("guided-alignment", std::string("none")) != "none" && !inference_) {
-      auto alignments = encdec->getDecoders()[0]->getAlignments();
-      ABORT_IF(alignments.empty(), "Model does not seem to support alignments");
+      auto attentionVectors = encdec->getDecoders()[0]->getAlignments();
+      ABORT_IF(attentionVectors.empty(), "Model does not seem to support alignments");
 
-      auto att = concatenate(alignments, /*axis =*/ -1);
+      auto attention = concatenate(attentionVectors, /*axis =*/ -1);
 
-      auto alignmentLoss = guidedAlignmentCost(graph, corpusBatch, options_, att);
+      auto alignmentLoss = guidedAlignmentCost(graph, corpusBatch, options_, attention);
       multiLoss->push_back(alignmentLoss);
-      
+
       return multiLoss;
     } else {
       return multiLoss;
@@ -87,6 +94,9 @@ class EncoderClassifierCE : public CostBase {
 protected:
   Ptr<Options> options_;
   bool inference_{false};
+
+  // @TODO: single loss seems wrong, especially since we support multiple objectives here,
+  // also not sure this needs to be a member at all. 
   Ptr<LabelwiseLoss> loss_;
 
 public:

From 87e8d7526c14187dfb229ec2a4a284dd01e57029 Mon Sep 17 00:00:00 2001
From: Marcin Junczys-Dowmunt <Marcin.Junczys-Dowmunt@microsoft.com>
Date: Wed, 16 Jan 2019 10:39:29 -0800
Subject: [PATCH 136/838] use rationalLoss for updates

---
 src/training/graph_group_async.cpp          | 17 ++++++++---------
 src/training/graph_group_multinode.cpp      | 16 +++++++---------
 src/training/graph_group_multinode_sync.cpp | 15 ++++++---------
 src/training/graph_group_singleton.cpp      |  6 ++----
 src/training/graph_group_sync.cpp           | 13 ++++++-------
 src/training/scheduler.h                    | 21 ++++++++++-----------
 6 files changed, 39 insertions(+), 49 deletions(-)

diff --git a/src/training/graph_group_async.cpp b/src/training/graph_group_async.cpp
index b529089af..5ac456229 100755
--- a/src/training/graph_group_async.cpp
+++ b/src/training/graph_group_async.cpp
@@ -192,7 +192,7 @@ void AsyncGraphGroup::execute(Ptr<data::Batch> batch) {
     thread_local size_t num_seen_words = 0;
     thread_local size_t num_seen_sentences = 0;
     thread_local int t_id = 0;
-    thread_local float cost = 0;
+    thread_local StaticLoss loss;
 
     thread_local Tensor accGradients;
     thread_local Ptr<TensorAllocator> accAlloc;
@@ -204,14 +204,14 @@ void AsyncGraphGroup::execute(Ptr<data::Batch> batch) {
       builder = builders_[i++];
     }
 
-    auto costNode = builder->build(graph, batch);
+    auto lossNode = builder->build(graph, batch);
 
     if(t % optimizerDelay_ == 0) {
       fetchParams(graph->params()->vals(), params_, t_id);
     }
 
     graph->forward();
-    cost += costNode->loss<float>();
+    loss += *lossNode;
     graph->backward();
 
     Tensor gradients;
@@ -249,23 +249,22 @@ void AsyncGraphGroup::execute(Ptr<data::Batch> batch) {
       // Wait until the thread that wants to do validation is finished.
       pool_->wait_for_one(lock);
 
-      if(options_->get<std::string>("cost-type") != "ce-sum")
-        cost /= optimizerDelay_;
-
       if(optimizerDelay_ > 1) {
         std::vector<size_t> fakeLength = {1, 1};
         std::vector<Ptr<Vocab>> vocabs;
         auto fb = data::CorpusBatch::fakeBatch(fakeLength, vocabs, num_seen_sentences, NULL);
         fb->front()->setWords(num_seen_words);
-        scheduler_->update(cost, fb);
+        
+        scheduler_->update(loss, fb);
 
         num_seen_words = 0;
         num_seen_sentences = 0;
       } else {
-        scheduler_->update(cost, batch);
+        scheduler_->update(loss, batch);
       }
 
-      cost = 0;
+      loss.loss = 0;
+      loss.labels = 0;
 
       if(scheduler_->saving() || scheduler_->validating()) {
         // Wait with validation or saving until all other threads are done with
diff --git a/src/training/graph_group_multinode.cpp b/src/training/graph_group_multinode.cpp
index 8adafd89a..b5c01836d 100755
--- a/src/training/graph_group_multinode.cpp
+++ b/src/training/graph_group_multinode.cpp
@@ -516,7 +516,7 @@ void MultiNodeGraphGroup::execute(Ptr<data::Batch> batch) {
     thread_local size_t my_id = 0;
     thread_local size_t t = 0;
     // only for scheduler statistic
-    thread_local float cost = 0;
+    thread_local StaticLoss loss;
     thread_local size_t num_seen_words = 0;
     thread_local size_t num_seen_sentences = 0;
 
@@ -527,7 +527,7 @@ void MultiNodeGraphGroup::execute(Ptr<data::Batch> batch) {
       builder = clientBuilders_[i++];
     }
 
-    auto costNode = builder->build(graph, batch);
+    auto lossNode = builder->build(graph, batch);
 
     if(t == 0) {
       mpi_->barrier();
@@ -537,7 +537,7 @@ void MultiNodeGraphGroup::execute(Ptr<data::Batch> batch) {
     }
 
     graph->forward();
-    cost += costNode->loss<float>();
+    loss += *lossNode;
     num_seen_words += batch->words();
     num_seen_sentences += batch->size();
     graph->backward();
@@ -592,22 +592,20 @@ void MultiNodeGraphGroup::execute(Ptr<data::Batch> batch) {
       // Wait until the thread that wants to do validation is finished.
       clientThreadPool_->wait_for_one(lock);
 
-      if(options_->get<std::string>("cost-type") != "ce-sum")
-        cost /= tau_;
-
       if(tau_ > 1) {
         std::vector<size_t> fakeLength = {1, 1};
         std::vector<Ptr<Vocab>> vocabs;
         auto fb = data::CorpusBatch::fakeBatch(fakeLength, vocabs, num_seen_sentences, NULL);
         fb->front()->setWords(num_seen_words);
-        scheduler_->update(cost, fb);
+        scheduler_->update(loss, fb);
       } else {
-        scheduler_->update(cost, batch);
+        scheduler_->update(loss, batch);
       }
 
       num_seen_words = 0;
       num_seen_sentences = 0;
-      cost = 0;
+      loss.loss = 0; 
+      loss.labels = 0;
 
       if((scheduler_->saving() || scheduler_->validating())) {
         // Wait with validation or saving until all other threads are done with
diff --git a/src/training/graph_group_multinode_sync.cpp b/src/training/graph_group_multinode_sync.cpp
index f32fb0d28..7d7f2d00b 100755
--- a/src/training/graph_group_multinode_sync.cpp
+++ b/src/training/graph_group_multinode_sync.cpp
@@ -175,7 +175,7 @@ void MultiNodeGraphGroupSync::execute(Ptr<data::Batch> fullBatch) {
 
   static int t = 0;
 
-  static float cost = 0;
+  static StaticLoss loss;
   static size_t num_seen_words = 0;
   static size_t num_seen_sentences = 0;
 
@@ -185,7 +185,7 @@ void MultiNodeGraphGroupSync::execute(Ptr<data::Batch> fullBatch) {
       auto graph = clientGraphs_[my_id];
       auto builder = clientBuilders_[my_id];
 
-      auto costNode = builder->build(graph, batch);
+      auto lossNode = builder->build(graph, batch);
 
       if(t == 0) {
         if(my_id != 0)
@@ -195,7 +195,7 @@ void MultiNodeGraphGroupSync::execute(Ptr<data::Batch> fullBatch) {
       graph->forward();
       {
         std::lock_guard<std::mutex> guard(sumCostMutex_);
-        cost += costNode->loss<float>();
+        loss += *lossNode;
         num_seen_words += batch->words();
         num_seen_sentences += batch->size();
       }
@@ -219,21 +219,18 @@ void MultiNodeGraphGroupSync::execute(Ptr<data::Batch> fullBatch) {
 
   // Run scheduler (if enabled)
   if(t % tau_ == 0 && scheduler_) {
-    if(options_->get<std::string>("cost-type") != "ce-sum")
-      cost /= (tau_ * devices_.size());
-
     if(tau_ > 1) {
       std::vector<size_t> fakeLength = {1, 1};
       auto fb = data::CorpusBatch::fakeBatch(fakeLength, std::vector<Ptr<Vocab>>(), num_seen_sentences, NULL);
       fb->front()->setWords(num_seen_words);
-      scheduler_->update(cost, fb);
+      scheduler_->update(loss, fb);
     } else {
-      scheduler_->update(cost, fullBatch);
+      scheduler_->update(loss, fullBatch);
     }
 
     num_seen_words = 0;
     num_seen_sentences = 0;
-    cost = 0;
+    loss = StaticLoss();
 
     if((scheduler_->saving() || scheduler_->validating())) {
       // wait until other nodes are ready
diff --git a/src/training/graph_group_singleton.cpp b/src/training/graph_group_singleton.cpp
index 46084c787..210834081 100755
--- a/src/training/graph_group_singleton.cpp
+++ b/src/training/graph_group_singleton.cpp
@@ -10,10 +10,8 @@ void SingletonGraph::setScheduler(Ptr<Scheduler> scheduler) {
 }
 
 void SingletonGraph::execute(Ptr<data::Batch> batch) {
-  auto costNode = builder_->build(graph_, batch);
-
+  auto lossNode = builder_->build(graph_, batch);
   graph_->forward();
-  float cost = costNode->loss<float>();
   graph_->backward();
 
   // Get batch stats
@@ -34,7 +32,7 @@ void SingletonGraph::execute(Ptr<data::Batch> batch) {
   }
 
   if(scheduler_) {
-    scheduler_->update(cost, batch);
+    scheduler_->update(*lossNode, batch);
 
     if(scheduler_->validating()) {
       if(mvAvg_) {
diff --git a/src/training/graph_group_sync.cpp b/src/training/graph_group_sync.cpp
index dafd61246..e6b01c54c 100755
--- a/src/training/graph_group_sync.cpp
+++ b/src/training/graph_group_sync.cpp
@@ -134,7 +134,7 @@ void SyncGraphGroup::update(Ptr<data::Batch> batch) /*override*/ {
 
   // Compute gradients
   // This happens in multiple steps in case of delay_ > 1.
-  std::vector<StaticLoss> localDeviceCosts(devices_.size());
+  std::vector<StaticLoss> localDeviceLosses(devices_.size());
 
   for (size_t t = 0; t < delay_; t++) {
     // Execute single forward/backward step
@@ -148,11 +148,11 @@ void SyncGraphGroup::update(Ptr<data::Batch> batch) /*override*/ {
         //LOG(info, timer.format(2, "after build: %ws"));
         graph->forward();
         //LOG(info, timer.format(2, "after forward (no sync): %ws"));
-        localDeviceCosts[localDeviceIndex] += *rationalLoss; // converts dynamic RationalLoss to StaticLoss
+        localDeviceLosses[localDeviceIndex] += *rationalLoss; // converts dynamic RationalLoss to StaticLoss
 
         graph->backward(/*zero=*/t == 0); // only reset gradients to 0 if t = 0
         //LOG(info, timer.format(2, "after backward (no sync): %ws"));
-        //localDeviceCosts[localDeviceIndex] += costNode->scalar(); // moved here for time measurements; @TODO: move this back
+        //localDeviceLosses[localDeviceIndex] += costNode->scalar(); // moved here for time measurements; @TODO: move this back
         //LOG(info, timer.format(2, "after scalar() (that's a sync): %ws"));
       }
       else { // empty batch: execute do-nothing fw-bw step for proper inits and resets
@@ -190,12 +190,11 @@ void SyncGraphGroup::update(Ptr<data::Batch> batch) /*override*/ {
 
   // cost across all local devices (scheduler will aggregate cross-process)
   StaticLoss localLoss;
-  for(auto& c : localDeviceCosts) // localDeviceCosts is already summed up over delay steps
-    localLoss += c;
+  for(auto& l : localDeviceLosses) // localDeviceLosses is already summed up over delay steps
+    localLoss += l;
 
   if(scheduler_) {
-    // track and log localLoss, @TODO: rather pass StaticLoss object
-    scheduler_->update(localLoss.loss, localLoss.labels, subBatches, mpi_);
+    scheduler_->update(localLoss, subBatches, mpi_);
 
     // save intermediate model (and optimizer state) to file
     if(scheduler_->saving())
diff --git a/src/training/scheduler.h b/src/training/scheduler.h
index 41b765e45..7fc51da37 100755
--- a/src/training/scheduler.h
+++ b/src/training/scheduler.h
@@ -4,6 +4,7 @@
 #include "training/training_state.h"
 #include "training/validator.h"
 #include "training/communicator.h"
+#include "layers/loss.h"
 
 namespace marian {
 
@@ -187,17 +188,15 @@ class Scheduler : public TrainingObserver {
     return 0;
   }
 
-  void update(float cost, Ptr<data::Batch> batch) {
-    // @TODO: Check me and eventually remove me, currently here for back-compat.
-    update(cost, batch->words(-1), std::vector<Ptr<data::Batch>>({batch}));
+  void update(StaticLoss rationalLoss, Ptr<data::Batch> batch) {
+    update(rationalLoss, std::vector<Ptr<data::Batch>>({batch}));
   }
 
-  void update(float cost, float labels, const std::vector<Ptr<data::Batch>>& batches, Ptr<IMPIWrapper> mpi = nullptr) {
+  void update(StaticLoss rationalLoss, const std::vector<Ptr<data::Batch>>& batches, Ptr<IMPIWrapper> mpi = nullptr) {
     state_->rememberPreviousProgress(); // note: epoch increases happen at the wrong place, hence -freq parameters do not support epoch units
     state_->validated = false;
 
     size_t batchSize = 0;    // number of sentences in batch
-    size_t batchLabels = (size_t)labels;  // number of target words in batch
     size_t batchWords  = 0;
 
     for(const auto& batch : batches) {
@@ -210,17 +209,17 @@ class Scheduler : public TrainingObserver {
     // extrapolate cost across MPI processes, so that we have numbers in the right range
     // When doing the actual log, we then aggregate across MPI processes to get the accurate number.
     if (mpi)
-      cost *= mpi->numMPIProcesses();
+      rationalLoss.loss *= mpi->numMPIProcesses();
 
-    state_->costSum      += cost;        // aggregate sum cost since last display
-    state_->costCount    += batchLabels; // cost gets normalized w.r.t. this in display
+    state_->costSum      += rationalLoss.loss;   // aggregate sum cost since last display
+    state_->costCount    += rationalLoss.labels; // cost gets normalized w.r.t. this in display
 
     state_->updatesDisp  += 1;
     state_->samplesDisp  += batchSize;
-    state_->wordsDisp    += batchWords;  // words at given input processed since last display, for speed display
+    state_->wordsDisp    += batchWords;          // words at given input processed since last display, for speed display
 
-    state_->samplesEpoch += batchSize;   // sentences processed in this epoch
-    state_->labelsTotal  += batchLabels; // total labels processed
+    state_->samplesEpoch += batchSize;           // sentences processed in this epoch
+    state_->labelsTotal  += rationalLoss.labels; // total labels processed
 
     state_->newBatch();
 

From 874274377406e77c0249a42ca9631c75713598c5 Mon Sep 17 00:00:00 2001
From: Marcin Junczys-Dowmunt <Marcin.Junczys-Dowmunt@microsoft.com>
Date: Wed, 16 Jan 2019 14:30:46 -0800
Subject: [PATCH 137/838] mnist now passes reg tests

---
 src/examples/mnist/model.h     | 38 ++++++++++++++++++++++++++--------
 src/examples/mnist/training.h  |  2 +-
 src/examples/mnist/validator.h |  6 +++---
 src/training/training.h        |  2 +-
 4 files changed, 34 insertions(+), 14 deletions(-)

diff --git a/src/examples/mnist/model.h b/src/examples/mnist/model.h
index 71de15ec4..44f41e6ac 100755
--- a/src/examples/mnist/model.h
+++ b/src/examples/mnist/model.h
@@ -8,20 +8,23 @@
 #include "graph/expression_graph.h"
 #include "models/costs.h"
 #include "models/model_base.h"
+#include "layers/loss.h"
 
 #include "examples/mnist/dataset.h"
 
 namespace marian {
 namespace models {
 
+// @TODO: looking at this file, simplify the new RationalLoss idea. Here it gets too complicated
+
 class MNISTCrossEntropyCost : public CostBase {
 public:
   MNISTCrossEntropyCost() {}
 
-  Expr apply(Ptr<ModelBase> model,
-             Ptr<ExpressionGraph> graph,
-             Ptr<data::Batch> batch,
-             bool clearGraph = true) override {
+  Ptr<MultiRationalLoss> apply(Ptr<ModelBase> model,
+                               Ptr<ExpressionGraph> graph,
+                               Ptr<data::Batch> batch,
+                               bool clearGraph = true) override {
     auto top = model->build(graph, batch, clearGraph);
 
     auto vfLabels = std::static_pointer_cast<data::DataBatch>(batch)->labels();
@@ -31,7 +34,16 @@ class MNISTCrossEntropyCost : public CostBase {
     auto labels = graph->indices(vLabels);
 
     // Define a top-level node for training
-    return mean(cross_entropy(top, labels), /*axis =*/ 0);
+    // use CE loss
+
+    auto loss = sum(cross_entropy(top->loss(), labels), /*axis =*/ 0);
+    auto labelsNum = graph->constant({1}, inits::from_value((float)vLabels.size()));
+
+    // @TODO: simplify this
+    auto multiLoss = New<SumMultiRationalLoss>();
+    multiLoss->push_back({loss, labelsNum});
+
+    return multiLoss;
   }
 };
 
@@ -39,12 +51,16 @@ class MNISTLogsoftmax : public CostBase {
 public:
   MNISTLogsoftmax() {}
 
-  Expr apply(Ptr<ModelBase> model,
+  Ptr<MultiRationalLoss> apply(Ptr<ModelBase> model,
              Ptr<ExpressionGraph> graph,
              Ptr<data::Batch> batch,
              bool clearGraph = true) override {
     auto top = model->build(graph, batch, clearGraph);
-    return logsoftmax(top);
+    
+    // @TODO: simplify this
+    auto multiLoss = New<SumMultiRationalLoss>();
+    multiLoss->push_back({logsoftmax(top->loss()), top->labels()});
+    return multiLoss;
   }
 };
 
@@ -56,10 +72,14 @@ class MnistFeedForwardNet : public ModelBase {
   MnistFeedForwardNet(Ptr<Options> options, Args... args)
       : options_(options), inference_(options->get<bool>("inference", false)) {}
 
-  virtual Expr build(Ptr<ExpressionGraph> graph,
+  virtual Ptr<RationalLoss> build(Ptr<ExpressionGraph> graph,
                      Ptr<data::Batch> batch,
                      bool /*clean*/ = false) override {
-    return construct(graph, batch, inference_);
+    
+    auto loss   = construct(graph, batch, inference_);
+    auto labels = graph->constant({(int)batch->size(), 1}, inits::from_value(1.f));
+
+    return New<RationalLoss>(loss, labels);
   }
 
   void load(Ptr<ExpressionGraph> /*graph*/, const std::string& /*name*/, bool) override {
diff --git a/src/examples/mnist/training.h b/src/examples/mnist/training.h
index 64ccfcbd0..9e10bdd7e 100755
--- a/src/examples/mnist/training.h
+++ b/src/examples/mnist/training.h
@@ -28,7 +28,7 @@ class TrainMNIST : public ModelTask {
     // Prepare scheduler with validators
     auto trainState = New<TrainingState>(options_->get<float>("learn-rate"));
     auto scheduler = New<Scheduler>(options_, trainState);
-    scheduler->addValidator(New<AccuracyValidator>(options_));
+    scheduler->addValidator(New<MNISTAccuracyValidator>(options_));
 
     // Multi-node training
     auto mpi = initMPI(/*multiThreaded=*/false);
diff --git a/src/examples/mnist/validator.h b/src/examples/mnist/validator.h
index 907994ee7..f38a95cec 100644
--- a/src/examples/mnist/validator.h
+++ b/src/examples/mnist/validator.h
@@ -12,9 +12,9 @@ using namespace marian;
 
 namespace marian {
 
-class AccuracyValidator : public Validator<data::MNISTData> {
+class MNISTAccuracyValidator : public Validator<data::MNISTData> {
 public:
-  AccuracyValidator(Ptr<Options> options) : Validator(std::vector<Ptr<Vocab>>(), options, false) {
+  MNISTAccuracyValidator(Ptr<Options> options) : Validator(std::vector<Ptr<Vocab>>(), options, false) {
     createBatchGenerator(/*isTranslating=*/false);
     builder_ = models::from_options(options, models::usage::scoring);
   }
@@ -35,7 +35,7 @@ class AccuracyValidator : public Validator<data::MNISTData> {
       graphs[0]->forward();
 
       std::vector<float> scores;
-      probs->val()->get(scores);
+      probs->loss(scores);
 
       correct += countCorrect(scores, batch->labels());
       samples += batch->size();
diff --git a/src/training/training.h b/src/training/training.h
index b07d4c6e8..d0623cfde 100755
--- a/src/training/training.h
+++ b/src/training/training.h
@@ -43,7 +43,7 @@ class Train : public ModelTask {
       LOG(info,
           "[batching] Collecting statistics for batch fitting with step size {}",
           options_->get<size_t>("mini-batch-fit-step"));
-      // @TODO this should receive a function object that can generate a fake batch
+      // @TODO this should receive a function object that can generate a fake batch;
       // that way vocabs would not be exposed. 
       auto model = New<ModelWrapper>(options_, mpi);
       model->setScheduler(scheduler); // collectStats() needs to know about dynamic MB scaling

From 9a196afbfbf82d1f5de3e2c79e5e6046b1ea3fd8 Mon Sep 17 00:00:00 2001
From: Marcin Junczys-Dowmunt <Marcin.Junczys-Dowmunt@microsoft.com>
Date: Wed, 16 Jan 2019 15:05:37 -0800
Subject: [PATCH 138/838] fix missing restore parameters

---
 src/training/training_state.h | 11 ++++++++---
 1 file changed, 8 insertions(+), 3 deletions(-)

diff --git a/src/training/training_state.h b/src/training/training_state.h
index 814b270ae..99c1621d5 100755
--- a/src/training/training_state.h
+++ b/src/training/training_state.h
@@ -237,9 +237,11 @@ class TrainingState {
     warmupStart = SchedulingParameter::parse(config["warmup-start"].as<std::string>());
 
     costSum = config["cost-sum"].as<float>();
-    // (different serialization name for back compat)
-    costCount = config["disp-samples"].as<size_t>();
+    costCount = config["cost-count"].as<size_t>();
+
     wordsDisp = config["disp-words"].as<size_t>();
+    samplesDisp = config["disp-samples"].as<size_t>();
+    updatesDisp = config["disp-updates"].as<size_t>();
 
     seedBatch = config["seed-batch"].as<std::string>();
     seedCorpus = config["seed-corpus"].as<std::string>();
@@ -270,7 +272,10 @@ class TrainingState {
     config["warmup-start"] = std::string(warmupStart);
 
     config["cost-sum"] = costSum;
-    config["disp-samples"] = costCount;
+    config["cost-count"] = costCount;
+
+    config["disp-updates"] = updatesDisp;
+    config["disp-samples"] = samplesDisp;
     config["disp-words"] = wordsDisp;
 
     config["seed-batch"] = seedBatch;

From b13e5aa550e3b2dce855d79b3a4d37a2513568e5 Mon Sep 17 00:00:00 2001
From: Marcin Junczys-Dowmunt <Marcin.Junczys-Dowmunt@microsoft.com>
Date: Thu, 17 Jan 2019 08:05:23 -0800
Subject: [PATCH 139/838] comments in loss

---
 src/layers/loss.h | 131 ++++++++++++++++++++++++++++++++++++----------
 1 file changed, 103 insertions(+), 28 deletions(-)

diff --git a/src/layers/loss.h b/src/layers/loss.h
index 8c8d170c0..850dc6b78 100644
--- a/src/layers/loss.h
+++ b/src/layers/loss.h
@@ -4,6 +4,18 @@
 
 namespace marian {
 
+/**
+ * We represent loss as pair of expressions, where loss_ is usually a sum
+ * of all accumulated loss values per label and labels_ is the total number
+ * of labels over which the loss was collected.
+ *
+ * These two values can then be used to represent various cost variants -
+ * for instance label-wise cross-entropy or perplexity. Optimization is
+ * only performed with regard to the summed loss_.
+ *
+ * Since both, loss_ and labels_ are dynamic graph nodes they can be further
+ * combined into larger structures. See multi-objective losses below.
+ */
 class RationalLoss {
 protected:
   Expr loss_;
@@ -15,6 +27,10 @@ class RationalLoss {
   RationalLoss(Expr loss, Expr labels)
   : loss_(loss), labels_(labels) {}
 
+  RationalLoss(Expr loss, float labels)
+  : loss_(loss),
+    labels_(loss->graph()->constant({1}, inits::from_value(labels))) {}
+
   RationalLoss(const RationalLoss& other)
   : loss_(other.loss_), labels_(other.labels_) {}
 
@@ -54,16 +70,20 @@ class RationalLoss {
   }
 };
 
-// POD for accumulating loss values after forward/backward and to-be-used in
-// Scheduler for updating statistics.
+/**
+ * POD for accumulating loss values after forward/backward used in
+ * Scheduler for updating statistics. This can only be used after a
+ * successful forward step in a computation graph that owns the assigned
+ * RationalLoss object.
+ */
 struct StaticLoss {
   float loss;
   float labels;
 
   StaticLoss() : loss(0.f), labels(0.f) {}
 
-  StaticLoss(const RationalLoss& rl)
-  : loss(rl.loss<float>()), labels(rl.labels<float>()) {}
+  StaticLoss(const RationalLoss& dynamic)
+  : loss(dynamic.loss<float>()), labels(dynamic.labels<float>()) {}
 
   StaticLoss& operator +=(const StaticLoss& other) {
     loss = loss + other.loss;
@@ -72,11 +92,22 @@ struct StaticLoss {
   }
 };
 
+/**
+ * Base class for multi-objective losses which is a list of RationalLoss
+ * but also defines how to accumulate that list into a single RationalLoss
+ */
 class MultiRationalLoss : public RationalLoss {
 protected:
   std::vector<RationalLoss> partialLosses_;
 
+  /**
+   * Accumulation rule for losses
+   */
   virtual Expr accumulateLoss(const RationalLoss& current) = 0;
+
+  /**
+   * Accumulation rule for labels
+   */
   virtual Expr accumulateLabels(const RationalLoss& current) = 0;
 
 public:
@@ -110,6 +141,11 @@ class MultiRationalLoss : public RationalLoss {
 
 };
 
+/**
+ * Simple sum of losses.
+ * Using this makes sense when the two loss types are similar in scale and
+ * number of labels. For instance two decoders over similarly sized vocabularies
+ */
 class SumMultiRationalLoss : public MultiRationalLoss {
 private:
   virtual Expr accumulateLoss(const RationalLoss& current) override {
@@ -131,27 +167,20 @@ class SumMultiRationalLoss : public MultiRationalLoss {
   SumMultiRationalLoss(const RationalLoss& rl) : MultiRationalLoss(rl) {}
 };
 
-class MeanMultiRationalLoss : public MultiRationalLoss {
-private:
-  virtual Expr accumulateLoss(const RationalLoss& current) override {
-    if(loss_)
-      return loss_ + current.loss() / current.labels();
-    else
-      return current.loss() / current.labels();
-  }
-
-  virtual Expr accumulateLabels(const RationalLoss& current) override {
-    if(labels_)
-      return labels_ + 1.f; // broadcast to size
-    else
-      return current.labels() / current.labels(); // 1, but with correct size
-  }
-
-public:
-  MeanMultiRationalLoss() : MultiRationalLoss() {}
-  MeanMultiRationalLoss(const RationalLoss& rl) : MultiRationalLoss(rl) {}
-};
-
+/**
+ * Scaled sum of losses.
+ * This can weigh losses equally by choosing the first loss_0 as a reference
+ * and scaling all remaining losses loss_i by labels_0 / labels_i. Labels are
+ * summed up by the same rule. By this we simulate a sum of losses at similar
+ * scales. Dividing by scaled label counts yields a value close to an equally
+ * weighted sum of means.
+ *
+ * L = sum_i^N L_i + N/M sum_j^M L_j
+ *
+ * We set labels to N. When reporting L/N this is equvalient to sum of means.
+ * Compare to sum of means below where N is factored into the loss, but labels
+ * are set to 1.
+ */
 class ScaledMultiRationalLoss : public MultiRationalLoss {
 private:
   virtual Expr accumulateLoss(const RationalLoss& current) override {
@@ -165,10 +194,9 @@ class ScaledMultiRationalLoss : public MultiRationalLoss {
 
   virtual Expr accumulateLabels(const RationalLoss& current) override {
     if(labels_) {
-      const auto& first = partialLosses_.front();
-      return labels_ + first.labels() / current.labels(); // fractional label counts are OK
+      return labels_; // Keep first label count // or: labels_ + first.labels() / current.labels();
     } else {
-      return current.labels();
+      return current.labels(); // This is the first loss
     }
   }
 
@@ -177,12 +205,50 @@ class ScaledMultiRationalLoss : public MultiRationalLoss {
   ScaledMultiRationalLoss(const RationalLoss& rl) : MultiRationalLoss(rl) {}
 };
 
+/**
+ * Sum of mean losses.
+ * Not really a rational loss as labels are factored into loss. Contribution of
+ * losses is equal, same as for ScaledMultiRationalLoss, just divided by different
+ * number of labels. See:
+ *
+ * L = (1/N sum_i^N L_i + 1/M sum_j^M L_j) = (sum_i^N L_i + N/M sum_j^M L_j) / N
+ *
+ * We set labels to 1. During reporting, we would see the same numbers, but gradients
+ * are scaled diffrently which may result in different learning curves.
+ */
+class MeanMultiRationalLoss : public MultiRationalLoss {
+private:
+  virtual Expr accumulateLoss(const RationalLoss& current) override {
+    if(loss_)
+      return loss_ + current.loss() / current.labels();
+    else
+      return current.loss() / current.labels();
+  }
+
+  virtual Expr accumulateLabels(const RationalLoss& current) override {
+    if(labels_)
+      return labels_; // keep the existing '1'
+    else
+      return current.labels()->graph()->ones({1}); // just '1' as labels are factored into loss_
+  }
+
+public:
+  MeanMultiRationalLoss() : MultiRationalLoss() {}
+  MeanMultiRationalLoss(const RationalLoss& rl) : MultiRationalLoss(rl) {}
+};
+
+/**
+ * Factory for multi-objective rational loss functions
+ */
 Ptr<MultiRationalLoss> newMultiLoss(Ptr<Options> options);
 
 //***********************************************************************************//
 // This needs some to be refactored. Currently easiest route for backwards compat, but
 // still feels somewhat hacky.
 
+/**
+ * Computes loss per label and then reduces to RationalLoss
+ */
 class LabelwiseLoss {
 protected:
   std::vector<int> axes_;
@@ -219,6 +285,9 @@ class LabelwiseLoss {
   }
 };
 
+/**
+ * Cross entropy loss across last axis, summed up over batch and time dimensions
+ */
 class CrossEntropyLoss : public LabelwiseLoss {
 public:
   CrossEntropyLoss(float smoothing)
@@ -252,6 +321,9 @@ class CrossEntropyLoss : public LabelwiseLoss {
   }
 };
 
+/**
+ * Cross entropy in rescorer used for computing sentences-level log probabilities
+ */
 class RescorerLoss : public CrossEntropyLoss {
 public:
   // sentence-wise CE, hence reduce only over time axis. CE reduces over last axis (-1)
@@ -264,6 +336,9 @@ class RescorerLoss : public CrossEntropyLoss {
   }
 };
 
+/**
+ * Factory for label-wise loss functions
+ */
 Ptr<LabelwiseLoss> newLoss(Ptr<Options> options, bool inference);
 
 }  // namespace marian

From c26f3bd1563711c18720dbfeef83b88d91093f23 Mon Sep 17 00:00:00 2001
From: Marcin Junczys-Dowmunt <Marcin.Junczys-Dowmunt@microsoft.com>
Date: Thu, 17 Jan 2019 08:42:28 -0800
Subject: [PATCH 140/838] more comments

---
 src/graph/node_initializers.h | 12 ++++-
 src/models/bert.h             | 98 +++++++++++++++++++++++++----------
 2 files changed, 82 insertions(+), 28 deletions(-)

diff --git a/src/graph/node_initializers.h b/src/graph/node_initializers.h
index 185ab1191..c7576cbb2 100755
--- a/src/graph/node_initializers.h
+++ b/src/graph/node_initializers.h
@@ -42,8 +42,6 @@ NodeInitializer from_vector(const std::vector<IndexType>& v);
 
 NodeInitializer from_item(const io::Item& item);
 
-NodeInitializer positions(int start);
-
 NodeInitializer from_sparse_vector(
     std::pair<std::vector<size_t>, std::vector<float>>& v);
 
@@ -53,6 +51,16 @@ NodeInitializer from_word2vec(const std::string& file,
                               int dimVoc,
                               int dimEmb,
                               bool normalize = false);
+
+/**
+ * Computes Google's trigonometric positional embeddings 
+ * starting from position 'start' taking into account
+ * batch and time dimensions of the tensor.
+ * 
+ * Expected layout tensor layout {time, batch, model}
+ */
+NodeInitializer positions(int start);
+
 }  // namespace inits
 
 }  // namespace marian
diff --git a/src/models/bert.h b/src/models/bert.h
index e74b9f633..615cbd570 100644
--- a/src/models/bert.h
+++ b/src/models/bert.h
@@ -6,8 +6,22 @@
 #include "data/rng_engine.h"
 
 namespace marian {
+
+/** 
+ * This file contains nearly all BERT-related code and adds BERT-funtionality
+ * on top of existing classes like TansformerEncoder and Classifier. 
+ */
+
 namespace data {
 
+/**
+ * BERT-specific mini-batch that computes masking for Masked LM training.
+ * Expects symbols [MASK], [SEP], [CLS] to be present in vocabularies unless
+ * other symbols are specified on the command line.
+ * 
+ * This takes a normal CorpusBatch and extends it with additional data. Luckily
+ * all the BERT-functionality can be inferred from a CorpusBatch alone.
+ */
 class BertBatch : public CorpusBatch {
 private:
   std::mt19937& eng_;
@@ -20,14 +34,20 @@ class BertBatch : public CorpusBatch {
   std::string sepSymbol_;
   std::string clsSymbol_;
 
+  // Selects a random word from the vocabulary
   std::unique_ptr<std::uniform_int_distribution<Word>> randomWord_;
+
+  // Selects a random integer between 0 and 99
   std::unique_ptr<std::uniform_int_distribution<int>> randomPercent_;
 
+  // Word ids of words that should not be masked, e.g. separators, padding
   std::unordered_set<Word> dontMask_;
 
+  // Masking function
   Word maskOut(Word word, Word mask) {
     auto subBatch = subBatches_.front();
 
+    // @TODO: turn those threshold into parameters, adjustable from command line
     int r = (*randomPercent_)(eng_);
     if (r < 10) { // for 10% of cases return same word
       return word;
@@ -43,6 +63,9 @@ class BertBatch : public CorpusBatch {
   }
 
 public:
+
+  // Takes a corpus batch, random engine (for deterministic behavior) and the masking percentage.
+  // Also sets special vocabulary items given on command line.
   BertBatch(Ptr<CorpusBatch> batch,
             std::mt19937& engine,
             float maskFraction,
@@ -54,11 +77,15 @@ class BertBatch : public CorpusBatch {
 
     auto subBatch = subBatches_.front();
 
+    // Initialize to sample random vocab id
     randomWord_.reset(new std::uniform_int_distribution<Word>(0, subBatch->vocab()->size()));
+
+    // Intialize to sample random percentage
     randomPercent_.reset(new std::uniform_int_distribution<int>(0, 100));
 
     auto& words = subBatch->data();
 
+    // Get word id of special symbols
     Word maskId  = (*subBatch->vocab())[maskSymbol_];
     Word clsId   = (*subBatch->vocab())[clsSymbol_];
     Word sepId   = (*subBatch->vocab())[sepSymbol_];
@@ -82,7 +109,7 @@ class BertBatch : public CorpusBatch {
     for(int i = 0; i < words.size(); ++i) // collect words among which we will mask
       if(dontMask_.count(words[i]) == 0)  // do not add indices of special words
         selected.push_back(i);
-    std::shuffle(selected.begin(), selected.end(), eng_);
+    std::shuffle(selected.begin(), selected.end(), eng_); // randomize positions
     selected.resize(std::ceil(selected.size() * maskFraction)); // select first x percent from shuffled indices
 
     for(int i : selected) {
@@ -94,14 +121,17 @@ class BertBatch : public CorpusBatch {
     int dimBatch = subBatch->batchSize();
     int dimWords = subBatch->batchWidth();
 
-    sentenceIndices_.resize(words.size());
-    std::vector<IndexType> sentPos(dimBatch, 0);
-    for(int i = 0; i < dimWords; ++i) {
-      for(int j = 0; j < dimBatch; ++j) {
+    // create indices for BERT sentence embeddings A and B
+    sentenceIndices_.resize(words.size()); // each word is either in sentence A or B
+    std::vector<IndexType> sentPos(dimBatch, 0); // initialize each batch entry with being A [0]
+    for(int i = 0; i < dimWords; ++i) {   // advance word-wise
+      for(int j = 0; j < dimBatch; ++j) { // scan batch-wise
         int k = i * dimBatch + j;
-        sentenceIndices_[k] = sentPos[j];
-        if(words[k] == sepId)
-          sentPos[j]++;
+        sentenceIndices_[k] = sentPos[j]; // set to current sentence position for batch entry
+        if(words[k] == sepId) {           // if current word is a separator 
+          sentPos[j]++;                   // then increase sentence position for batch entry (probably to B [1])
+          ABORT_IF(sentPos[i] > 1, "Currently we only support sequences of up to two sentences in BERT, not {}", sentPos[i]);
+        }
       }
     }
   }
@@ -113,6 +143,10 @@ class BertBatch : public CorpusBatch {
 
 }
 
+/**
+ * BERT-specific version of EncoderClassifier, mostly here to automatically convert a
+ * CorpusBatch to BertBatch. 
+ */
 class BertEncoderClassifier : public EncoderClassifier, public data::RNGEngine {
 public:
   BertEncoderClassifier(Ptr<Options> options)
@@ -135,7 +169,11 @@ class BertEncoderClassifier : public EncoderClassifier, public data::RNGEngine {
   }
 };
 
-// @TODO: this should be in transformer.h
+/**
+ * BERT-specific modifications to EncoderTransformer
+ * Actually all that is needed is to intercept the creation of special embeddings,
+ * here sentence embeddings for sentence A and B. 
+ */
 class BertEncoder : public EncoderTransformer {
 public:
   BertEncoder(Ptr<Options> options) : EncoderTransformer(options) {}
@@ -155,7 +193,7 @@ class BertEncoder : public EncoderTransformer {
     if(learnedPosEmbeddings) {
       auto sentenceEmbeddings = embedding()
                                ("prefix", "Wsent")
-                               ("dimVocab", 2) // sentence A or sentence B
+                               ("dimVocab", 2) // sentence A or sentence B, @TODO: should rather be a parameter
                                ("dimEmb", dimEmb)
                                .construct(graph_);
       signal = sentenceEmbeddings->apply(bertBatch->bertSentenceIndices(), {dimWords, dimBatch, dimEmb});
@@ -173,17 +211,20 @@ class BertEncoder : public EncoderTransformer {
   virtual Expr addSpecialEmbeddings(Expr input, int start = 0, Ptr<data::CorpusBatch> batch = nullptr) const override {
     bool learnedPosEmbeddings = opt<bool>("transformer-learned-positions", true);
     input = addPositionalEmbeddings(input, start, learnedPosEmbeddings);
-    input = addSentenceEmbeddings(input, batch, learnedPosEmbeddings);
+    input = addSentenceEmbeddings(input, batch, learnedPosEmbeddings); // @TODO: separately set learnable pos and sent embeddings
     return input;
   }
 };
 
-// Can be used for next sentence prediction task
+/**
+ * BERT-specific classifier 
+ * Can be used for next sentence prediction task or other fine-tuned down-stream tasks
+ * Does not actually need a BertBatch, works with CorpusBatch
+ */
 class BertClassifier : public ClassifierBase {
 public:
   BertClassifier(Ptr<Options> options) : ClassifierBase(options) {}
 
-  // The batch has been filled with external classifier labels, @TODO: figure out how to do that
   Ptr<ClassifierState> apply(Ptr<ExpressionGraph> graph, Ptr<data::CorpusBatch> batch, const std::vector<Ptr<EncoderState>>& encoderStates) override {
     ABORT_IF(encoderStates.size() != 1, "Currently we only support a single encoder BERT model");
 
@@ -193,12 +234,12 @@ class BertClassifier : public ClassifierBase {
     int dimModel = classEmbeddings->shape()[-1];
     int dimTrgCls = opt<std::vector<int>>("dim-vocabs")[batchIndex_]; // Target vocab is used as class labels
 
-    auto output = mlp::mlp()                                     //
-                    .push_back(mlp::dense()                      //
+    auto output = mlp::mlp()                                          //
+                    .push_back(mlp::dense()                           //
                                  ("prefix", prefix_ + "_ff_logit_l1") //
                                  ("dim", dimModel)                    //
                                  ("activation", mlp::act::tanh))      // @TODO: do we actually need this?
-                    .push_back(mlp::output()                     //
+                    .push_back(mlp::output()                          //
                                  ("dim", dimTrgCls))                  //
                                  ("prefix", prefix_ + "_ff_logit_l2") //
                     .construct(graph);
@@ -208,7 +249,7 @@ class BertClassifier : public ClassifierBase {
     auto state = New<ClassifierState>();
     state->setLogProbs(logits);
 
-    // filled externally, for BERT these are NextSentence prediction labels
+    // Filled externally, for BERT these are NextSentence prediction labels
     const auto& classLabels = (*batch)[batchIndex_]->data();
     state->setTargetIndices(graph->indices(classLabels));
 
@@ -218,7 +259,12 @@ class BertClassifier : public ClassifierBase {
   virtual void clear() override {}
 };
 
-// This is a model that pretrains BERT for classification
+/**
+ * This is a model that pretrains BERT for classification.
+ * This is also a Classifier, but compared to the one above needs the BERT-specific information from BertBatch
+ * as this is self-generating its labels from the source. Labels are dynamically created as complements of the
+ * masking process. 
+ */
 class BertMaskedLM : public ClassifierBase {
 public:
   BertMaskedLM(Ptr<Options> options) : ClassifierBase(options) {}
@@ -242,21 +288,21 @@ class BertMaskedLM : public ClassifierBase {
 
     int dimVoc = opt<std::vector<int>>("dim-vocabs")[batchIndex_];
 
-    auto layerTanh = mlp::dense()    //
+    auto layerTanh = mlp::dense()                         //
         ("prefix", prefix_ + "_ff_logit_maskedlm_out_l1") //
-        ("dim", dimModel)                 //
-        ("activation", mlp::act::tanh);   //
-    auto layerOut = mlp::output()    //
+        ("dim", dimModel)                                 //
+        ("activation", mlp::act::tanh);                   // @TODO: again, check if this layer is present in original code
+    auto layerOut = mlp::output()                         //
         ("prefix", prefix_ + "_ff_logit_maskedlm_out_l2") //
         ("dim", dimVoc);                  //
-    layerOut.tieTransposed("Wemb"); // We are a BERT model, hence tie with input
+    layerOut.tieTransposed("Wemb"); // We are a BERT model, hence tie with input, @TODO: check if this is actually what Google does
 
     // [-4: beam depth=1, -3: max length, -2: batch size, -1: vocab dim]
     // assemble layers into MLP and apply to embeddings, decoder context and
     // aligned source context
-    auto output = mlp::mlp()                      //
-        .push_back(layerTanh)                          // @TODO: do we actually need this?
-        .push_back(layerOut)                           //
+    auto output = mlp::mlp()  //
+        .push_back(layerTanh) // 
+        .push_back(layerOut)  //
         .construct(graph);
 
     auto logits = output->apply(maskedEmbeddings);

From 43eb1b2b4d2f80bb5224c29db7453eb13eea80d8 Mon Sep 17 00:00:00 2001
From: Marcin Junczys-Dowmunt <Marcin.Junczys-Dowmunt@microsoft.com>
Date: Thu, 17 Jan 2019 08:52:38 -0800
Subject: [PATCH 141/838] more comments

---
 src/models/bert.h               |  6 ++++--
 src/models/classifier.h         |  4 ++++
 src/models/encoder_classifier.h | 12 +++++++++---
 src/models/model_factory.cpp    | 13 +++++--------
 4 files changed, 22 insertions(+), 13 deletions(-)

diff --git a/src/models/bert.h b/src/models/bert.h
index 615cbd570..f02b30c1a 100644
--- a/src/models/bert.h
+++ b/src/models/bert.h
@@ -156,7 +156,7 @@ class BertEncoderClassifier : public EncoderClassifier, public data::RNGEngine {
     // intercept batch and anotate with BERT-specific concepts
     auto bertBatch = New<data::BertBatch>(batch,
                                           eng_,
-                                          opt<float>("bert-masking-fraction"),
+                                          opt<float>("bert-masking-fraction", 0.15f), // 15% by default according to paper
                                           opt<std::string>("bert-mask-symbol"),
                                           opt<std::string>("bert-sep-symbol"),
                                           opt<std::string>("bert-class-symbol"));
@@ -219,7 +219,9 @@ class BertEncoder : public EncoderTransformer {
 /**
  * BERT-specific classifier 
  * Can be used for next sentence prediction task or other fine-tuned down-stream tasks
- * Does not actually need a BertBatch, works with CorpusBatch
+ * Does not actually need a BertBatch, works with CorpusBatch.
+ * 
+ * @TODO: This is in fact so generic that we might move it out of here as the typical classifier implementation
  */
 class BertClassifier : public ClassifierBase {
 public:
diff --git a/src/models/classifier.h b/src/models/classifier.h
index 61c4d64e3..86e01841e 100644
--- a/src/models/classifier.h
+++ b/src/models/classifier.h
@@ -7,6 +7,10 @@
 
 namespace marian {
 
+/**
+ * Simple base class for Classifiers
+ * Currently only implementations are in bert.h
+ */
 class ClassifierBase {
 protected:
   Ptr<Options> options_;
diff --git a/src/models/encoder_classifier.h b/src/models/encoder_classifier.h
index 9c794144c..43ed9e1ad 100644
--- a/src/models/encoder_classifier.h
+++ b/src/models/encoder_classifier.h
@@ -9,6 +9,14 @@
 
 namespace marian {
 
+/**
+ * Combines sequence encoders with generic classifiers
+ * Can be used to train sequence classifiers like language detection, BERT-next-sentence-prediction etc.
+ * Already has support for multi-objective training. 
+ * 
+ * @TODO: this should probably be unified somehow with EncoderDecoder which could allow for deocder/classifier
+ * multi-objective training.
+ */
 class EncoderClassifierBase : public models::ModelBase {
 public:
   virtual ~EncoderClassifierBase() {}
@@ -76,8 +84,6 @@ class EncoderClassifier : public EncoderClassifierBase {
     return std::string(out.c_str());
   }
 
-//   virtual void createClassifierConfig(const std::string& name) {}
-
 public:
   typedef data::Corpus dataset_type;
 
@@ -197,7 +203,7 @@ class EncoderClassifier : public EncoderClassifierBase {
                                   bool clearGraph = true) override {
     auto states = apply(graph, batch, clearGraph);
     // returns raw logits
-    return New<RationalLoss>(states[0]->getLogProbs(), nullptr); // @TODO: this should explode
+    return New<RationalLoss>(states[0]->getLogProbs(), nullptr); // @TODO: Check if this is actually used
   }
 
   virtual Ptr<RationalLoss> build(Ptr<ExpressionGraph> graph,
diff --git a/src/models/model_factory.cpp b/src/models/model_factory.cpp
index 0059aac88..a6ceb9988 100755
--- a/src/models/model_factory.cpp
+++ b/src/models/model_factory.cpp
@@ -36,11 +36,9 @@ Ptr<EncoderBase> EncoderFactory::construct(Ptr<ExpressionGraph> graph) {
 #endif
 
   if(options_->get<std::string>("type") == "transformer")
-    // return New<EncoderTransformer>(options_);
     return NewEncoderTransformer(options_);
 
   if(options_->get<std::string>("type") == "bert-encoder")
-    // return New<EncoderTransformer>(options_);
     return New<BertEncoder>(options_);
 
   ABORT("Unknown encoder type");
@@ -50,7 +48,6 @@ Ptr<DecoderBase> DecoderFactory::construct(Ptr<ExpressionGraph> graph) {
   if(options_->get<std::string>("type") == "s2s")
     return New<DecoderS2S>(options_);
   if(options_->get<std::string>("type") == "transformer")
-    // return New<DecoderTransformer>(options_);
     return NewDecoderTransformer(options_);
   ABORT("Unknown decoder type");
 }
@@ -229,12 +226,12 @@ Ptr<ModelBase> by_type(std::string type, usage use, Ptr<Options> options) {
             .construct(graph);
   }
 
-  if(type == "bert") {
+  if(type == "bert") {                           // for full BERT training
     return models::encoder_classifier()(options) //
         ("usage", use)                           //
         .push_back(models::encoder()             //
-                    ("type", "bert-encoder")     // @TODO: replace with 'bert-encoder'
-                    ("index", 0))                // close to original transformer encoder
+                    ("type", "bert-encoder")     // close to original transformer encoder
+                    ("index", 0))                // 
         .push_back(models::classifier()          //
                     ("type", "bert-masked-lm")   //
                     ("index", 0))                // multi-task learning with MaskedLM
@@ -244,11 +241,11 @@ Ptr<ModelBase> by_type(std::string type, usage use, Ptr<Options> options) {
         .construct(graph);
   }
 
-  if(type == "bert-classifier") {
+  if(type == "bert-classifier") {                // for BERT fine-tuning on non-BERT classification task
     return models::encoder_classifier()(options) //
         ("usage", use)                           //
         .push_back(models::encoder()             //
-                    ("type", "transformer")      //
+                    ("type", "bert-encoder")     //
                     ("index", 0))                // close to original transformer encoder
         .push_back(models::classifier()          //
                     ("type", "bert-classifier")  //

From 8ad7ef2748924d40627b67213dfe8d0a5c554473 Mon Sep 17 00:00:00 2001
From: Marcin Junczys-Dowmunt <Marcin.Junczys-Dowmunt@microsoft.com>
Date: Thu, 17 Jan 2019 09:02:09 -0800
Subject: [PATCH 142/838] more comments

---
 src/models/states.h      | 4 ++++
 src/training/scheduler.h | 4 ++++
 2 files changed, 8 insertions(+)

diff --git a/src/models/states.h b/src/models/states.h
index cb94e4bb7..8e3972210 100755
--- a/src/models/states.h
+++ b/src/models/states.h
@@ -100,6 +100,10 @@ class DecoderState {
   virtual void blacklist(Expr /*totalCosts*/, Ptr<data::CorpusBatch> /*batch*/) {}
 };
 
+/**
+ * Classifier output based on DecoderState
+ * @TODO: should be unified with DecoderState or not be used at all as Classifier do not really have stateful output.
+ */
 class ClassifierState {
 private:
   Expr logProbs_;
diff --git a/src/training/scheduler.h b/src/training/scheduler.h
index d316c08e7..12ad1ae4d 100755
--- a/src/training/scheduler.h
+++ b/src/training/scheduler.h
@@ -257,6 +257,10 @@ class Scheduler : public TrainingObserver {
     update(rationalLoss, /*numReadBatches=*/1, /*batchSize=*/batch->size(), /*batchLabels=*/batch->wordsTrg());
   }
 
+  // @TODO: go back to function which takes batch as an argument? The current arguments make it hard to choose
+  // which subbatch should be used for speed display. For sequence-classifiers it's more interesting to see the 
+  // source-words consumed rather than the labels. We have a CLI option '--disp-label-index' (bad name?) which is 
+  // now defunct.
   void update(StaticLoss rationalLoss, 
               size_t numReadBatches, // number of batches read by the reader (for seeking in case of restart)
               size_t batchSize,      // total number of sentences in batch

From 97bc272118022334e342795bbfbf10a812cf8887 Mon Sep 17 00:00:00 2001
From: Marcin Junczys-Dowmunt <Marcin.Junczys-Dowmunt@microsoft.com>
Date: Thu, 17 Jan 2019 09:11:05 -0800
Subject: [PATCH 143/838] add StaticLoss to CE validation

---
 src/training/validator.h | 23 ++++++++++-------------
 1 file changed, 10 insertions(+), 13 deletions(-)

diff --git a/src/training/validator.h b/src/training/validator.h
index 078fb4f3b..bce37da28 100755
--- a/src/training/validator.h
+++ b/src/training/validator.h
@@ -168,11 +168,10 @@ class CrossEntropyValidator : public Validator<data::Corpus> {
 protected:
   virtual float validateBG(const std::vector<Ptr<ExpressionGraph>>& graphs) override {
     auto ctype = options_->get<std::string>("cost-type");
-    options_->set("cost-type", "ce-sum");
+    options_->set("cost-type", "ce-sum"); // @TODO: check if still needed, most likely not.
 
-    float cost = 0;
+    StaticLoss loss;
     size_t samples = 0;
-    size_t words = 0;
     size_t batchId = 0;
 
     {
@@ -180,7 +179,7 @@ class CrossEntropyValidator : public Validator<data::Corpus> {
 
       TaskBarrier taskBarrier;
       for(auto batch : *batchGenerator_) {
-        auto task = [=, &cost, &samples, &words](size_t id) {
+        auto task = [=, &loss, &samples](size_t id) {
           thread_local Ptr<ExpressionGraph> graph;
           thread_local auto builder = models::from_options(options_, models::usage::scoring);
 
@@ -189,13 +188,11 @@ class CrossEntropyValidator : public Validator<data::Corpus> {
           }
 
           builder->clear(graph);
-          auto loss = builder->build(graph, batch);
+          auto dynamicLoss = builder->build(graph, batch);
           graph->forward();
 
           std::unique_lock<std::mutex> lock(mutex_);
-          cost  += loss->loss<float>();
-          words += (size_t)loss->labels<float>();
-
+          loss  += *dynamicLoss;
           samples += batch->size();
         };
 
@@ -206,16 +203,16 @@ class CrossEntropyValidator : public Validator<data::Corpus> {
     }
 
     // get back to the original cost type
-    options_->set("cost-type", ctype);
+    options_->set("cost-type", ctype); // @TODO: check if still needed, most likely not.
 
     if(ctype == "perplexity")
-      return std::exp(cost / words);
+      return std::exp(loss.loss / loss.labels);
     if(ctype == "ce-mean-words")
-      return cost / words;
+      return loss.loss / loss.labels;
     if(ctype == "ce-sum")
-      return cost;
+      return loss.loss;
     else
-      return cost / samples;
+      return loss.loss / samples; // @TODO: back-compat, to be removed
   }
 };
 

From 175e7e90adddfc7753f400d52defb0265d9e5442 Mon Sep 17 00:00:00 2001
From: Marcin Junczys-Dowmunt <Marcin.Junczys-Dowmunt@microsoft.com>
Date: Thu, 17 Jan 2019 09:20:53 -0800
Subject: [PATCH 144/838] fix index

---
 src/models/bert.h | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/models/bert.h b/src/models/bert.h
index f02b30c1a..ffc599549 100644
--- a/src/models/bert.h
+++ b/src/models/bert.h
@@ -130,7 +130,7 @@ class BertBatch : public CorpusBatch {
         sentenceIndices_[k] = sentPos[j]; // set to current sentence position for batch entry
         if(words[k] == sepId) {           // if current word is a separator 
           sentPos[j]++;                   // then increase sentence position for batch entry (probably to B [1])
-          ABORT_IF(sentPos[i] > 1, "Currently we only support sequences of up to two sentences in BERT, not {}", sentPos[i]);
+          ABORT_IF(sentPos[j] > 1, "Currently we only support sequences of up to two sentences in BERT, not {}", sentPos[i]);
         }
       }
     }

From d912f9171d04ffa1f6793f16fbcd0e55bb8f84f0 Mon Sep 17 00:00:00 2001
From: Marcin Junczys-Dowmunt <Marcin.Junczys-Dowmunt@microsoft.com>
Date: Thu, 17 Jan 2019 09:21:17 -0800
Subject: [PATCH 145/838] fix index 2

---
 src/models/bert.h | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/models/bert.h b/src/models/bert.h
index ffc599549..66b40dd92 100644
--- a/src/models/bert.h
+++ b/src/models/bert.h
@@ -130,7 +130,7 @@ class BertBatch : public CorpusBatch {
         sentenceIndices_[k] = sentPos[j]; // set to current sentence position for batch entry
         if(words[k] == sepId) {           // if current word is a separator 
           sentPos[j]++;                   // then increase sentence position for batch entry (probably to B [1])
-          ABORT_IF(sentPos[j] > 1, "Currently we only support sequences of up to two sentences in BERT, not {}", sentPos[i]);
+          ABORT_IF(sentPos[j] > 1, "Currently we only support sequences of up to two sentences in BERT, not {}", sentPos[j]);
         }
       }
     }

From 95b23d6492660559d3e5f2812896d454bbbef998 Mon Sep 17 00:00:00 2001
From: Marcin Junczys-Dowmunt <Marcin.Junczys-Dowmunt@microsoft.com>
Date: Thu, 17 Jan 2019 09:25:35 -0800
Subject: [PATCH 146/838] move sentence number check in BERT before assignment

---
 src/models/bert.h | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/models/bert.h b/src/models/bert.h
index 66b40dd92..904f6c716 100644
--- a/src/models/bert.h
+++ b/src/models/bert.h
@@ -127,10 +127,10 @@ class BertBatch : public CorpusBatch {
     for(int i = 0; i < dimWords; ++i) {   // advance word-wise
       for(int j = 0; j < dimBatch; ++j) { // scan batch-wise
         int k = i * dimBatch + j;
+        ABORT_IF(sentPos[j] > 1, "Currently we only support sequences of up to two sentences in BERT, not {}", sentPos[j] + 1);
         sentenceIndices_[k] = sentPos[j]; // set to current sentence position for batch entry
         if(words[k] == sepId) {           // if current word is a separator 
           sentPos[j]++;                   // then increase sentence position for batch entry (probably to B [1])
-          ABORT_IF(sentPos[j] > 1, "Currently we only support sequences of up to two sentences in BERT, not {}", sentPos[j]);
         }
       }
     }

From d5720460e134d8bbb4e476106429d8befe9160ab Mon Sep 17 00:00:00 2001
From: Marcin Junczys-Dowmunt <Marcin.Junczys-Dowmunt@microsoft.com>
Date: Thu, 17 Jan 2019 09:36:03 -0800
Subject: [PATCH 147/838] restrict max sentence embedding index to 1 for now

---
 src/models/bert.h | 8 +++++---
 1 file changed, 5 insertions(+), 3 deletions(-)

diff --git a/src/models/bert.h b/src/models/bert.h
index 904f6c716..9c5c05bc2 100644
--- a/src/models/bert.h
+++ b/src/models/bert.h
@@ -121,15 +121,17 @@ class BertBatch : public CorpusBatch {
     int dimBatch = subBatch->batchSize();
     int dimWords = subBatch->batchWidth();
 
+    int maxSentPos = 1; // Currently only two sentences allowed, if another separator is seen do not increase position index beyond 1
+    // @TODO: make this configurable, see below for NextSentencePredictions task where we also restrict to 2.
+
     // create indices for BERT sentence embeddings A and B
     sentenceIndices_.resize(words.size()); // each word is either in sentence A or B
     std::vector<IndexType> sentPos(dimBatch, 0); // initialize each batch entry with being A [0]
     for(int i = 0; i < dimWords; ++i) {   // advance word-wise
       for(int j = 0; j < dimBatch; ++j) { // scan batch-wise
         int k = i * dimBatch + j;
-        ABORT_IF(sentPos[j] > 1, "Currently we only support sequences of up to two sentences in BERT, not {}", sentPos[j] + 1);
-        sentenceIndices_[k] = sentPos[j]; // set to current sentence position for batch entry
-        if(words[k] == sepId) {           // if current word is a separator 
+        sentenceIndices_[k] = sentPos[j]; // set to current sentence position for batch entry, max position 1.
+        if(words[k] == sepId && sentPos[j] < maxSentPos) { // if current word is a separator and not beyond range
           sentPos[j]++;                   // then increase sentence position for batch entry (probably to B [1])
         }
       }

From 699b710b7fa342dbf4c91dd2f7a70370f31ca75c Mon Sep 17 00:00:00 2001
From: Marcin Junczys-Dowmunt <Marcin.Junczys-Dowmunt@microsoft.com>
Date: Thu, 17 Jan 2019 10:13:32 -0800
Subject: [PATCH 148/838] add third sentence embedding for padding

---
 src/models/bert.h | 7 ++++---
 1 file changed, 4 insertions(+), 3 deletions(-)

diff --git a/src/models/bert.h b/src/models/bert.h
index 9c5c05bc2..0591b3c08 100644
--- a/src/models/bert.h
+++ b/src/models/bert.h
@@ -121,7 +121,8 @@ class BertBatch : public CorpusBatch {
     int dimBatch = subBatch->batchSize();
     int dimWords = subBatch->batchWidth();
 
-    int maxSentPos = 1; // Currently only two sentences allowed, if another separator is seen do not increase position index beyond 1
+    int maxSentPos = 2; // Currently only two sentences allowed A at [0] and B at [1] and padding at [2]
+    // If another separator is seen do not increase position index beyond 2 but use padding.
     // @TODO: make this configurable, see below for NextSentencePredictions task where we also restrict to 2.
 
     // create indices for BERT sentence embeddings A and B
@@ -132,7 +133,7 @@ class BertBatch : public CorpusBatch {
         int k = i * dimBatch + j;
         sentenceIndices_[k] = sentPos[j]; // set to current sentence position for batch entry, max position 1.
         if(words[k] == sepId && sentPos[j] < maxSentPos) { // if current word is a separator and not beyond range
-          sentPos[j]++;                   // then increase sentence position for batch entry (probably to B [1])
+          sentPos[j]++;                   // then increase sentence position for batch entry (to B [1])
         }
       }
     }
@@ -195,7 +196,7 @@ class BertEncoder : public EncoderTransformer {
     if(learnedPosEmbeddings) {
       auto sentenceEmbeddings = embedding()
                                ("prefix", "Wsent")
-                               ("dimVocab", 2) // sentence A or sentence B, @TODO: should rather be a parameter
+                               ("dimVocab", 3) // sentence A or sentence B plus padding, @TODO: should rather be a parameter
                                ("dimEmb", dimEmb)
                                .construct(graph_);
       signal = sentenceEmbeddings->apply(bertBatch->bertSentenceIndices(), {dimWords, dimBatch, dimEmb});

From afcaf54651154b6afbee955553f655fe2e62fcac Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Thu, 17 Jan 2019 15:16:01 -0800
Subject: [PATCH 149/838] added --embedding-factors option also to decode and
 score commands

---
 src/common/config_parser.cpp | 4 ++++
 src/layers/generic.cpp       | 3 +++
 2 files changed, 7 insertions(+)

diff --git a/src/common/config_parser.cpp b/src/common/config_parser.cpp
index e301fb30b..776042858 100755
--- a/src/common/config_parser.cpp
+++ b/src/common/config_parser.cpp
@@ -469,6 +469,8 @@ void ConfigParser::addOptionsTranslation(cli::CLIWrapper& cli) {
       "stdout");
   cli.add<std::vector<std::string>>("--vocabs,-v",
       "Paths to vocabulary files have to correspond to --input");
+  cli.add_nondefault<std::vector<std::string>>("--embedding-factors",
+      "Paths to (factor map, factor list) file for factored embeddings");
   // decoding options
   cli.add<size_t>("--beam-size,-b",
       "Beam size used during search with validating translator",
@@ -531,6 +533,8 @@ void ConfigParser::addOptionsScoring(cli::CLIWrapper& cli) {
       "Paths to vocabulary files have to correspond to --train-sets. "
       "If this parameter is not supplied we look for vocabulary files source.{yml,json} and target.{yml,json}. "
       "If these files do not exists they are created");
+  cli.add_nondefault<std::vector<std::string>>("--embedding-factors",
+      "Paths to (factor map, factor list) file for factored embeddings");
   cli.add<bool>("--n-best",
       "Score n-best list instead of plain text corpus");
   cli.add<std::string>("--n-best-feature",
diff --git a/src/layers/generic.cpp b/src/layers/generic.cpp
index 82c5bc7c9..92f6c59ee 100755
--- a/src/layers/generic.cpp
+++ b/src/layers/generic.cpp
@@ -133,7 +133,9 @@ namespace marian {
 
     Expr Output::apply(Expr input) /*override*/ {
       lazyConstruct(input->shape()[-1]);
+
       auto y = affine(input, W_, b_, false, transposeW_);
+
       if (embeddingFactorMapping_) {
         auto graph = input->graph();
         auto factorMatrix = embeddingFactorMapping_->getFactorMatrix(); // [V x U]
@@ -145,6 +147,7 @@ namespace marian {
             graph->constant({(int)factorMatrix.offsets.size()}, inits::from_vector(factorMatrix.offsets), Type::uint32),
             /*transB=*/ true); // -> [B x V]
       }
+
       return y;
     }
   }

From e8403817ae9db20f4563c3085710acf42d9fb177 Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Thu, 17 Jan 2019 15:52:27 -0800
Subject: [PATCH 150/838] mlp::Output can now be reset per batch (with correct
 caching of shortlist), and therefore no longer needs to be recreated for each
 batch

---
 src/layers/generic.h     | 67 ++++++++++++++++++++++++++++------------
 src/models/transformer.h | 27 +++++++---------
 2 files changed, 58 insertions(+), 36 deletions(-)

diff --git a/src/layers/generic.h b/src/layers/generic.h
index a9e2be016..5c604205b 100755
--- a/src/layers/generic.h
+++ b/src/layers/generic.h
@@ -124,47 +124,74 @@ class Dense : public LayerBase, public IUnaryLayer {
 
 class Output : public LayerBase, public IUnaryLayer {
 private:
-  Expr tiedParam_;
-  Ptr<data::Shortlist> shortlist_;
-
-  Expr W_;
+  Expr W_;  // parameters held by this layer
   Expr b_;
+  Expr cachedShortW_;   // short-listed version, cached (cleared by clear())
+  Expr cachedShortb_;   // these match the current value of shortlist_
+
+  // optional parameters set/updated after construction
+  Expr tiedParam_;
   bool transposeW_{false};
+  Ptr<data::Shortlist> shortlist_;
 
 public:
   Output(Ptr<ExpressionGraph> graph, Ptr<Options> options)
-      : LayerBase(graph, options) {}
+      : LayerBase(graph, options) {
+    clear();
+  }
 
   void tieTransposed(Expr tied) {
-    tiedParam_ = tied;
+    if (W_)
+      ABORT_IF(tiedParam_.get() != tied.get(), "Tied output projection cannot be changed once weights have been created");
+    else
+      tiedParam_ = tied;
   }
 
-  void setShortlist(Ptr<data::Shortlist> shortlist) { shortlist_ = shortlist; }
+  void setShortlist(Ptr<data::Shortlist> shortlist) {
+    if (shortlist_)
+      ABORT_IF(shortlist.get() != shortlist_.get(), "Output shortlist cannot be changed except after clear()");
+    else {
+      ABORT_IF(cachedShortW_ || cachedShortb_, "No shortlist but cached parameters??");
+      shortlist_ = shortlist;
+    }
+    // cachedShortW_ and cachedShortb_ will be created lazily inside apply()
+  }
+
+  // this is expected to be called in sync with graph->clear(), which invalidates
+  // cachedShortW_ and cachedShortb_ in the graph's short-term cache
+  void clear() {
+    shortlist_ = nullptr;
+    cachedShortW_ = nullptr;
+    cachedShortb_ = nullptr;
+  }
 
   Expr apply(Expr input) override {
-    if(!W_) {
+    if(!W_) { // create lazily because we need input's dimension
       auto name = options_->get<std::string>("prefix");
       auto dim = options_->get<int>("dim");
 
       if(tiedParam_) {
-        transposeW_ = true;
         W_ = tiedParam_;
-        if(shortlist_)
-          W_ = rows(W_, shortlist_->indices());
+        transposeW_ = true;
       } else {
-        W_ = graph_->param(name + "_W",
-                           {input->shape()[-1], dim},
-                           inits::glorot_uniform);
-        if(shortlist_)
-          W_ = cols(W_, shortlist_->indices());
+        W_ = graph_->param(name + "_W", {input->shape()[-1], dim}, inits::glorot_uniform);
+        transposeW_ = false;
       }
-
       b_ = graph_->param(name + "_b", {1, dim}, inits::zeros);
-      if(shortlist_)
-        b_ = cols(b_, shortlist_->indices());
     }
 
-    return affine(input, W_, b_, false, transposeW_);
+    if (shortlist_) {
+      if (!cachedShortW_) { // short versions of parameters are cached within one batch, then clear()ed
+        if(transposeW_)
+          cachedShortW_ = rows(W_, shortlist_->indices());
+        else
+          cachedShortW_ = cols(W_, shortlist_->indices());
+        cachedShortb_ = cols(b_, shortlist_->indices());
+      }
+      return affine(input, cachedShortW_, cachedShortb_, false, transposeW_);
+    }
+    else
+      return affine(input, W_, b_, false, transposeW_);
   }
 
   virtual Expr apply(const std::vector<Expr>& /*inputs*/) override {
diff --git a/src/models/transformer.h b/src/models/transformer.h
index d91dfb7f8..048ac03ae 100755
--- a/src/models/transformer.h
+++ b/src/models/transformer.h
@@ -601,17 +601,17 @@ class TransformerState : public DecoderState {
 
 class DecoderTransformer : public Transformer<DecoderBase> {
 private:
-  Ptr<mlp::MLP> output_;
+  Ptr<mlp::Output> output_;
 
 private:
-  void LazyCreateOutputLayer()
+  void lazyCreateOutputLayer()
   {
     if(output_) // create it lazily
       return;
 
     int dimTrgVoc = opt<std::vector<int>>("dim-vocabs")[batchIndex_];
 
-    auto layerOut = mlp::output()              //
+    auto outputFactory = mlp::output()         //
         ("prefix", prefix_ + "_ff_logit_out")  //
         ("dim", dimTrgVoc);
 
@@ -619,18 +619,10 @@ class DecoderTransformer : public Transformer<DecoderBase> {
       std::string tiedPrefix = prefix_ + "_Wemb";
       if(opt<bool>("tied-embeddings-all") || opt<bool>("tied-embeddings-src"))
         tiedPrefix = "Wemb";
-      layerOut.tieTransposed(tiedPrefix);
+      outputFactory.tieTransposed(tiedPrefix);
     }
 
-    if(shortlist_)
-      layerOut.setShortlist(shortlist_);
-
-    // [-4: beam depth=1, -3: max length, -2: batch size, -1: vocab dim]
-    // assemble layers into MLP and apply to embeddings, decoder context and
-    // aligned source context
-    output_ = mlp::mlp()                //
-                  .push_back(layerOut)  //
-                  .construct(graph_);
+    output_ = std::dynamic_pointer_cast<mlp::Output>(outputFactory.construct(graph_)); // (construct() returns only the underlying interface)
   }
 
 public:
@@ -662,7 +654,7 @@ class DecoderTransformer : public Transformer<DecoderBase> {
   virtual Ptr<DecoderState> step(Ptr<ExpressionGraph> graph,
                                  Ptr<DecoderState> state) override {
     ABORT_IF(graph != graph_, "An inconsistent graph parameter was passed to step()");
-    LazyCreateOutputLayer();
+    lazyCreateOutputLayer();
     return step(state);
   }
 
@@ -818,7 +810,9 @@ class DecoderTransformer : public Transformer<DecoderBase> {
     //************************************************************************//
 
     // final feed-forward layer (output)
-    Expr logits = output_->apply(decoderContext); // [-4: beam depth=1, -3: max length, -2: batch size, -1: vocab dim]
+    if(shortlist_)
+      output_->setShortlist(shortlist_);
+    Expr logits = output_->apply(decoderContext); // [-4: beam depth=1, -3: max length, -2: batch size, -1: vocab or shortlist dim]
 
     // return unormalized(!) probabilities
     Ptr<DecoderState> nextState;
@@ -840,7 +834,8 @@ class DecoderTransformer : public Transformer<DecoderBase> {
   }
 
   void clear() override {
-    output_ = nullptr;
+    if (output_)
+      output_->clear();
     cache_.clear();
     alignments_.clear();
   }

From a2ade1537877e89f0d527064737c95e38a8a104d Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Thu, 17 Jan 2019 18:33:42 -0800
Subject: [PATCH 151/838] (comment)

---
 src/layers/generic.cpp | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/src/layers/generic.cpp b/src/layers/generic.cpp
index 0bc20434a..e99a5410f 100755
--- a/src/layers/generic.cpp
+++ b/src/layers/generic.cpp
@@ -28,10 +28,11 @@ namespace marian {
     // Factors are grouped
     //  - user specifies list-factor prefixes; all factors beginning with that prefix are in the same group
     //  - factors within a group as multi-class and normalized that way
-    //  - groups of size 1 are interpreted as sigmoids
+    //  - groups of size 1 are interpreted as sigmoids, multiply with P(u) / P(u-1)
     //  - one prefix must not contain another
     //  - all factors not matching a prefix get lumped into yet another class (the lemmas)
     //  - factor vocab must be sorted such that all groups are consecutive
+    //  - result of Output layer is nevertheless logits, not a normalized probability, due to the sigmoid entries
     EmbeddingFactorMapping(Ptr<Options> options) : factorVocab_(New<Options>(), 0) {
       std::vector<std::string> paths = options->get<std::vector<std::string>>("embedding-factors");
       ABORT_IF(paths.size() != 2, "--embedding-factors expects two paths");

From 272253c2dc48458804682ef3061717af1a6a25bb Mon Sep 17 00:00:00 2001
From: Marcin Junczys-Dowmunt <Marcin.Junczys-Dowmunt@microsoft.com>
Date: Thu, 17 Jan 2019 20:23:40 -0800
Subject: [PATCH 152/838] address code review comments, part 1

---
 src/common/config_parser.cpp    |  8 +--
 src/data/corpus.cpp             |  2 +-
 src/data/corpus_base.cpp        | 63 +++++++++------------
 src/data/corpus_base.h          | 18 +++---
 src/data/corpus_nbest.cpp       |  4 +-
 src/data/corpus_sqlite.cpp      |  2 +-
 src/data/default_vocab.cpp      |  6 +-
 src/data/vocab.cpp              |  4 +-
 src/examples/mnist/model.h      |  9 +--
 src/graph/node_initializers.cpp |  3 +-
 src/graph/node_initializers.h   | 11 +++-
 src/layers/generic.h            |  5 +-
 src/layers/guided_alignment.h   |  4 +-
 src/layers/loss.cpp             | 12 +---
 src/layers/loss.h               |  4 +-
 src/models/bert.h               | 97 ++++++++++++++++-----------------
 src/models/classifier.h         |  3 +-
 src/models/costs.h              | 14 +++--
 src/models/encoder_classifier.h |  7 ++-
 src/models/encoder_decoder.cpp  |  1 +
 src/models/transformer.h        |  6 +-
 src/training/graph_group.h      |  4 +-
 src/training/validator.h        |  1 +
 23 files changed, 140 insertions(+), 148 deletions(-)

diff --git a/src/common/config_parser.cpp b/src/common/config_parser.cpp
index de7b52a51..e192b995a 100755
--- a/src/common/config_parser.cpp
+++ b/src/common/config_parser.cpp
@@ -145,7 +145,7 @@ void ConfigParser::addOptionsModel(cli::CLIWrapper& cli) {
       "Train right-to-left model");
   cli.add<std::vector<std::string>>("--input-types",
       "Provide type of input data if different than 'sequence'. "
-      "Possible values: sequence, labels. You need to provide one type per input.",
+      "Possible values: sequence, class. You need to provide one type per input.",
       {});
   cli.add<bool>("--best-deep",
       "Use Edinburgh deep RNN configuration (s2s)");
@@ -200,8 +200,8 @@ void ConfigParser::addOptionsModel(cli::CLIWrapper& cli) {
   cli.add<std::string>("--transformer-postprocess",
       "Operation after each transformer layer: d = dropout, a = add, n = normalize",
       "dan");
-  cli.add<bool>("--transformer-learned-positions",
-      "Use learned positional embeddings instead of trigonometric embeddings");
+  cli.add<bool>("--transformer-train-positions",
+      "Train positional embeddings instead of using static sinusoidal embeddings");
 
   cli.add<std::string>("--bert-mask-symbol", "Masking symbol for BERT masked-LM training", "[MASK]");
   cli.add<std::string>("--bert-sep-symbol", "Sentence separator symbol for BERT next sentence prediction training", "[SEP]");
@@ -289,7 +289,7 @@ void ConfigParser::addOptionsTraining(cli::CLIWrapper& cli) {
   cli.add<bool>("--disp-label-counts",
       "Display label counts when logging loss progress");
   cli.add<int>("--disp-label-index",
-      "Display label counts based on i-th sub-batch (-1 is last)", -1);
+      "Display label counts based on i-th input stream (-1 is last)", -1);
   cli.add<std::string/*SchedulerPeriod*/>("--save-freq",
       "Save model file every  arg  updates (append 't' for every  arg  target labels)",
       "10000u");
diff --git a/src/data/corpus.cpp b/src/data/corpus.cpp
index 7d1f27544..7f51b12ba 100755
--- a/src/data/corpus.cpp
+++ b/src/data/corpus.cpp
@@ -55,7 +55,7 @@ SentenceTuple Corpus::next() {
       } else if(i > 0 && i == weightFileIdx_) {
         addWeightsToSentenceTuple(line, tup);
       } else {
-        addWordsToSentenceTuple(line, i, tup, addEOS_[i]);
+        addWordsToSentenceTuple(line, i, tup);
       }
     }
 
diff --git a/src/data/corpus_base.cpp b/src/data/corpus_base.cpp
index b35bb032b..b34bacef9 100755
--- a/src/data/corpus_base.cpp
+++ b/src/data/corpus_base.cpp
@@ -44,22 +44,7 @@ CorpusBase::CorpusBase(const std::vector<std::string>& paths,
     ABORT_IF(files_.back()->empty(), "File '{}' is empty", path);
   }
 
-  addEOS_.resize(paths_.size(), true);
-  // @TODo: think if this should be checked and processed here or in a validation step in config?
-  auto inputTypes = options_->get<std::vector<std::string>>("input-types", {}); // empty list by default
-  ABORT_IF(inputTypes.size() > 0 && inputTypes.size() != paths_.size(), 
-           "Input types are specified ({}) you need to specify one per input ({})", 
-           inputTypes.size(), 
-           paths_.size());
-  // Currently input types affects only EOS symbol
-  for(int i = 0; i < inputTypes.size(); ++i)
-    if(inputTypes[i] == "labels")
-      addEOS_[i] = false;
-    else if(inputTypes[i] == "sequence")
-      addEOS_[i] = true;
-    else
-      ABORT("Unknown input type {}: {}", i, inputTypes[i]);
-
+  initEOS();
 }
 
 CorpusBase::CorpusBase(Ptr<Options> options, bool translate)
@@ -74,9 +59,7 @@ CorpusBase::CorpusBase(Ptr<Options> options, bool translate)
   else
     paths_ = options_->get<std::vector<std::string>>("input");
 
-  addEOS_.resize(paths_.size(), true);
-  // @TODo: think if this should be checked and processed here or in a validation step in config?
-  auto inputTypes = options_->get<std::vector<std::string>>("input-types", {}); // empty list by default
+  initEOS();
 
   std::vector<std::string> vocabPaths;
   if(!options_->get<std::vector<std::string>>("vocabs").empty())
@@ -150,19 +133,6 @@ CorpusBase::CorpusBase(Ptr<Options> options, bool translate)
         vocabs_.emplace_back(vocab);
       }
     }
-
-    ABORT_IF(inputTypes.size() > 0 && inputTypes.size() != paths_.size(), 
-           "Input types are specified ({}) you need to specify one per input ({})", 
-            inputTypes.size(), 
-            paths_.size());
-    // Currently input types affects only EOS symbol
-    for(int i = 0; i < inputTypes.size(); ++i)
-      if(inputTypes[i] == "labels")
-        addEOS_[i] = false;
-      else if(inputTypes[i] == "sequence")
-        addEOS_[i] = true;
-      else
-        ABORT("Unknown input type {}: {}", i, inputTypes[i]);
   }
 
   if(translate) {
@@ -222,14 +192,13 @@ CorpusBase::CorpusBase(Ptr<Options> options, bool translate)
 }
 
 void CorpusBase::addWordsToSentenceTuple(const std::string& line,
-                                         size_t i,
-                                         SentenceTuple& tup,
-                                         bool addEOS) const {
+                                         size_t batchIndex,
+                                         SentenceTuple& tup) const {
 
   // This turns a string in to a sequence of numerical word ids. Depending
   // on the vocabulary type, this can be non-trivial, e.g. when SentencePiece
   // is used.
-  Words words = vocabs_[i]->encode(line, /*addEOS =*/ addEOS, inference_);
+  Words words = vocabs_[batchIndex]->encode(line, /*addEOS =*/ addEOS_[batchIndex], inference_);
 
   if(words.empty())
     words.push_back(0);
@@ -314,5 +283,27 @@ void CorpusBase::addWeightsToBatch(Ptr<CorpusBatch> batch,
 
   batch->setDataWeights(weights);
 }
+
+void CorpusBase::initEOS() {
+  // Labels fed into sub-batches that are just class-labels, not sequence labels do not require to
+  // add a EOS symbol. Hence decision to add EOS is now based on input stream positions and correspoding
+  // input type. 
+  
+  addEOS_.resize(paths_.size(), true);
+  // @TODO: think if this should be checked and processed here or in a validation step in config?
+  auto inputTypes = options_->get<std::vector<std::string>>("input-types", {}); // empty list by default
+  ABORT_IF(inputTypes.size() > 0 && inputTypes.size() != paths_.size(), 
+          "Input types have been specified ({}), you need to specify one per input ({})", 
+          inputTypes.size(), 
+          paths_.size());
+  for(int i = 0; i < inputTypes.size(); ++i)
+    if(inputTypes[i] == "class")
+      addEOS_[i] = false;
+    else if(inputTypes[i] == "sequence")
+      addEOS_[i] = true;
+    else
+      ABORT("Unknown input type {}: {}", i, inputTypes[i]);
+}
+
 }  // namespace data
 }  // namespace marian
diff --git a/src/data/corpus_base.h b/src/data/corpus_base.h
index e925f2dd0..f1ac99aef 100755
--- a/src/data/corpus_base.h
+++ b/src/data/corpus_base.h
@@ -316,16 +316,16 @@ class CorpusBatch : public Batch {
                                     Ptr<Options> options) {
     std::vector<Ptr<SubBatch>> batches;
 
-    size_t idx = 0;
+    size_t batchIndex = 0;
     for(auto len : lengths) {
-      auto sb = New<SubBatch>(batchSize, len, vocabs[idx]);
+      auto sb = New<SubBatch>(batchSize, len, vocabs[batchIndex]);
       // set word indices to different values to avoid same hashes
       // rand() is OK, this does not affect state in any way
       std::transform(sb->data().begin(), sb->data().end(), sb->data().begin(),
-                     [&](Word) { return rand() % vocabs[idx]->size(); }); 
+                     [&](Word) { return rand() % vocabs[batchIndex]->size(); }); 
       // mask: no items ask being masked out
       std::fill(sb->mask().begin(), sb->mask().end(), 1.f);
-      idx++;
+      batchIndex++;
 
       batches.push_back(sb);
     }
@@ -543,14 +543,18 @@ class CorpusBase
    */
   size_t alignFileIdx_{0};
 
+  /**
+   * @brief Determine if EOS symbol should be added to input
+   */
+  void initEOS();
+
   /**
    * @brief Helper function converting a line of text into words using the i-th
    * vocabulary and adding them to the sentence tuple.
    */
   void addWordsToSentenceTuple(const std::string& line,
-                               size_t i,
-                               SentenceTuple& tup,
-                               bool addEOS) const;
+                               size_t batchIndex,
+                               SentenceTuple& tup) const;
   /**
    * @brief Helper function parsing a line with word alignments and adding them
    * to the sentence tuple.
diff --git a/src/data/corpus_nbest.cpp b/src/data/corpus_nbest.cpp
index 4fd560992..328c3c0d7 100644
--- a/src/data/corpus_nbest.cpp
+++ b/src/data/corpus_nbest.cpp
@@ -61,9 +61,9 @@ SentenceTuple CorpusNBest::next() {
                    "Too few lines in input {}",
                    i);
         }
-        addWordsToSentenceTuple(lastLines_[i], i, tup, addEOS_[i]);
+        addWordsToSentenceTuple(lastLines_[i], i, tup);
       }
-      addWordsToSentenceTuple(curr_text, last, tup, addEOS_[last]);
+      addWordsToSentenceTuple(curr_text, last, tup);
       lastNum_ = curr_num;
     }
 
diff --git a/src/data/corpus_sqlite.cpp b/src/data/corpus_sqlite.cpp
index 7127aafda..cbab750eb 100644
--- a/src/data/corpus_sqlite.cpp
+++ b/src/data/corpus_sqlite.cpp
@@ -120,7 +120,7 @@ SentenceTuple CorpusSQLite::next() {
       } else if(i > 0 && i == weightFileIdx_) {
         addWeightsToSentenceTuple(line, tup);
       } else {
-        addWordsToSentenceTuple(line, i, tup, addEOS_[i]);
+        addWordsToSentenceTuple(line, i, tup);
       }
     }
 
diff --git a/src/data/default_vocab.cpp b/src/data/default_vocab.cpp
index 96ae61e64..4557830c1 100755
--- a/src/data/default_vocab.cpp
+++ b/src/data/default_vocab.cpp
@@ -285,7 +285,7 @@ class DefaultVocab : public VocabBase {
 
 // This is a vocabulary class that does not enforce </s> or <unk>. 
 // This is used for class lists in a classifier. 
-class LabelsVocab : public DefaultVocab {
+class ClassVocab : public DefaultVocab {
 private:
   virtual void addRequiredVocabulary(const std::string& vocabPath, bool isJson) override {} // Do nothing.
 };
@@ -294,8 +294,8 @@ Ptr<VocabBase> createDefaultVocab() {
   return New<DefaultVocab>();
 }
 
-Ptr<VocabBase> createLabelsVocab() {
-  return New<LabelsVocab>();
+Ptr<VocabBase> createClassVocab() {
+  return New<ClassVocab>();
 }
 
 }
diff --git a/src/data/vocab.cpp b/src/data/vocab.cpp
index 97963e4d9..266b14322 100755
--- a/src/data/vocab.cpp
+++ b/src/data/vocab.cpp
@@ -4,7 +4,7 @@
 namespace marian {
 
 Ptr<VocabBase> createDefaultVocab();
-Ptr<VocabBase> createLabelsVocab();
+Ptr<VocabBase> createClassVocab();
 Ptr<VocabBase> createSentencePieceVocab(const std::string& /*vocabPath*/, Ptr<Options>, size_t /*batchIndex*/);
 
 // @TODO: make each vocab peek on type
@@ -16,7 +16,7 @@ Ptr<VocabBase> createVocab(const std::string& vocabPath, Ptr<Options> options, s
     // check type of input, if not given, assume "sequence"
     auto inputTypes = options->get<std::vector<std::string>>("input-types", {});
     std::string inputType = inputTypes.size() > batchIndex ? inputTypes[batchIndex] : "sequence";
-    return inputType == "labels" ? createLabelsVocab() : createDefaultVocab();
+    return inputType == "class" ? createClassVocab() : createDefaultVocab();
   }
 }
 
diff --git a/src/examples/mnist/model.h b/src/examples/mnist/model.h
index 44f41e6ac..876830186 100755
--- a/src/examples/mnist/model.h
+++ b/src/examples/mnist/model.h
@@ -37,13 +37,8 @@ class MNISTCrossEntropyCost : public CostBase {
     // use CE loss
 
     auto loss = sum(cross_entropy(top->loss(), labels), /*axis =*/ 0);
-    auto labelsNum = graph->constant({1}, inits::from_value((float)vLabels.size()));
-
-    // @TODO: simplify this
-    auto multiLoss = New<SumMultiRationalLoss>();
-    multiLoss->push_back({loss, labelsNum});
-
-    return multiLoss;
+    
+    return New<SumMultiRationalLoss>(RationalLoss({loss, (float)vLabels.size()}));
   }
 };
 
diff --git a/src/graph/node_initializers.cpp b/src/graph/node_initializers.cpp
index 84f60044f..da38d2ae8 100755
--- a/src/graph/node_initializers.cpp
+++ b/src/graph/node_initializers.cpp
@@ -157,7 +157,8 @@ NodeInitializer from_item(const io::Item& item) {
   }
 }
 
-NodeInitializer positions(int start) {
+// Computes Google's sinusoidal position embeddings 
+NodeInitializer sinusoidalPositionEmbeddings(int start) {
   return [start](Tensor t) {
     int dimEmb   = t->shape()[-1];
     int dimWords = t->size() / dimEmb;
diff --git a/src/graph/node_initializers.h b/src/graph/node_initializers.h
index c7576cbb2..254f02ae2 100755
--- a/src/graph/node_initializers.h
+++ b/src/graph/node_initializers.h
@@ -53,13 +53,18 @@ NodeInitializer from_word2vec(const std::string& file,
                               bool normalize = false);
 
 /**
- * Computes Google's trigonometric positional embeddings 
+ * Computes Google's sinusoidal position embeddings 
  * starting from position 'start' taking into account
  * batch and time dimensions of the tensor.
  * 
- * Expected layout tensor layout {time, batch, model}
+ * Expected tensor layout {-2: time, -1: model}
+ * 
+ * Usually gets later reshaped to {time, 1, model} and 
+ * added with a broadcast to learned embeddings. Positional
+ * embeddings are the same for each batch entry and change 
+ * over time.
  */
-NodeInitializer positions(int start);
+NodeInitializer sinusoidalPositionEmbeddings(int start);
 
 }  // namespace inits
 
diff --git a/src/layers/generic.h b/src/layers/generic.h
index 7f4164bee..5a9435084 100755
--- a/src/layers/generic.h
+++ b/src/layers/generic.h
@@ -202,9 +202,8 @@ class Embedding : public LayerBase, public IEmbeddingLayer {
     int dimBatch = (int)subBatch->batchSize();
     int dimEmb = E_->shape()[-1];
     int dimWords = (int)subBatch->batchWidth();
-    // @TODO: merge this with below. Currently can't only due to the extra beam dimension
-    auto chosenEmbeddings = rows(E_, subBatch->data());
-    auto batchEmbeddings = reshape(chosenEmbeddings, { dimWords, dimBatch, dimEmb });
+
+    auto batchEmbeddings = apply(subBatch->data(), { dimWords, dimBatch, dimEmb });
     auto batchMask = graph->constant({ dimWords, dimBatch, 1 },
                                      inits::from_vector(subBatch->mask()));
     return std::make_tuple(batchEmbeddings, batchMask);
diff --git a/src/layers/guided_alignment.h b/src/layers/guided_alignment.h
index 451647826..8c912ddfc 100755
--- a/src/layers/guided_alignment.h
+++ b/src/layers/guided_alignment.h
@@ -37,9 +37,7 @@ static inline RationalLoss guidedAlignmentCost(Ptr<ExpressionGraph> graph,
   
   // Create label node, also weigh by scalar so labels and cost are in the same domain.
   // Fractional label counts are OK
-  Expr labels = graph->constant({1}, inits::from_value(guidedScalar * numLabels)); // @TODO: introduce graph->value(...) ?
-
-  return RationalLoss(alignmentLoss, labels);
+  return RationalLoss(alignmentLoss, guidedScalar * numLabels);
 }
 
 }  // namespace marian
diff --git a/src/layers/loss.cpp b/src/layers/loss.cpp
index 06f8dc59d..86cd99d19 100755
--- a/src/layers/loss.cpp
+++ b/src/layers/loss.cpp
@@ -6,15 +6,8 @@ namespace marian {
 Ptr<LabelwiseLoss> newLoss(Ptr<Options> options, bool inference) {
   float smoothing = inference ? 0.f : options->get<float>("label-smoothing");
   std::string costType = options->get<std::string>("cost-type", "ce-mean");
-  if(costType == "ce-mean" || costType == "cross-entropy") {
-    return New<CrossEntropyLoss>(smoothing);
-  } else if(costType == "ce-mean-words") {
-    return New<CrossEntropyLoss>(smoothing);
-  } else if(costType == "ce-sum") {
-    return New<CrossEntropyLoss>(smoothing);
-  } else if(costType == "perplexity") {
-    return New<CrossEntropyLoss>(smoothing);
-  } else if(costType == "ce-rescore") {
+  
+  if(costType == "ce-rescore") {
     return New<RescorerLoss>();
   } else if(costType == "ce-rescore-mean") {
     ABORT("Check me");
@@ -24,6 +17,7 @@ Ptr<LabelwiseLoss> newLoss(Ptr<Options> options, bool inference) {
   }
 }
 
+// see loss.h for detailed explanations of each class
 Ptr<MultiRationalLoss> newMultiLoss(Ptr<Options> options) {
     std::string multiLossType = options->get<std::string>("multi-loss-type", "sum");
     if(multiLossType == "sum")         // sum of sums
diff --git a/src/layers/loss.h b/src/layers/loss.h
index 850dc6b78..a82ca60fd 100644
--- a/src/layers/loss.h
+++ b/src/layers/loss.h
@@ -18,8 +18,8 @@ namespace marian {
  */
 class RationalLoss {
 protected:
-  Expr loss_;
-  Expr labels_;
+  Expr loss_;   // numerator
+  Expr labels_; // denominator
 
   RationalLoss() = default; // protected
 
diff --git a/src/models/bert.h b/src/models/bert.h
index 0591b3c08..72d744e9c 100644
--- a/src/models/bert.h
+++ b/src/models/bert.h
@@ -7,9 +7,9 @@
 
 namespace marian {
 
-/** 
+/**
  * This file contains nearly all BERT-related code and adds BERT-funtionality
- * on top of existing classes like TansformerEncoder and Classifier. 
+ * on top of existing classes like TansformerEncoder and Classifier.
  */
 
 namespace data {
@@ -17,15 +17,13 @@ namespace data {
 /**
  * BERT-specific mini-batch that computes masking for Masked LM training.
  * Expects symbols [MASK], [SEP], [CLS] to be present in vocabularies unless
- * other symbols are specified on the command line.
- * 
+ * other symbols are specified in the config.
+ *
  * This takes a normal CorpusBatch and extends it with additional data. Luckily
  * all the BERT-functionality can be inferred from a CorpusBatch alone.
  */
 class BertBatch : public CorpusBatch {
 private:
-  std::mt19937& eng_;
-
   std::vector<IndexType> maskedPositions_;
   std::vector<IndexType> maskedWords_;
   std::vector<IndexType> sentenceIndices_;
@@ -38,23 +36,24 @@ class BertBatch : public CorpusBatch {
   std::unique_ptr<std::uniform_int_distribution<Word>> randomWord_;
 
   // Selects a random integer between 0 and 99
-  std::unique_ptr<std::uniform_int_distribution<int>> randomPercent_;
+  std::unique_ptr<std::uniform_real_distribution<float>> randomPercent_;
 
   // Word ids of words that should not be masked, e.g. separators, padding
   std::unordered_set<Word> dontMask_;
 
-  // Masking function
-  Word maskOut(Word word, Word mask) {
+  // Masking function, i.e. replaces a chosen word with either
+  // a [MASK] symbol, itself or a random word
+  Word maskOut(Word word, Word mask, std::mt19937& engine) {
     auto subBatch = subBatches_.front();
 
     // @TODO: turn those threshold into parameters, adjustable from command line
-    int r = (*randomPercent_)(eng_);
-    if (r < 10) { // for 10% of cases return same word
+    float r = (*randomPercent_)(engine);
+    if (r < 0.1f) { // for 10% of cases return same word
       return word;
-    } else if (r < 20) { // for 10% return random word
-      Word randWord = (*randomWord_)(eng_);
-      if(dontMask_.count(randWord) > 0) // the random word is a forbidden word
-        return mask;                    // hence return mask symbol
+    } else if (r < 0.2f) { // for 10% return random word
+      Word randWord = (*randomWord_)(engine);
+      if(dontMask_.count(randWord) > 0) // some words, e.g. [CLS] or </s>, may not be used as random words
+        return mask;                    // for those, return the mask symbol instead
       else
         return randWord;                // else return the random word
     } else { // for 80% of words apply mask symbol
@@ -72,36 +71,38 @@ class BertBatch : public CorpusBatch {
             const std::string& maskSymbol,
             const std::string& sepSymbol,
             const std::string& clsSymbol)
-    : CorpusBatch(*batch), eng_(engine),
+    : CorpusBatch(*batch),
       maskSymbol_(maskSymbol), sepSymbol_(sepSymbol), clsSymbol_(clsSymbol) {
 
+    // BERT expects a textual first stream and a second stream with class labels
     auto subBatch = subBatches_.front();
+    const auto& vocab = *subBatch->vocab();
 
     // Initialize to sample random vocab id
-    randomWord_.reset(new std::uniform_int_distribution<Word>(0, subBatch->vocab()->size()));
+    randomWord_.reset(new std::uniform_int_distribution<Word>(0, vocab.size()));
 
     // Intialize to sample random percentage
-    randomPercent_.reset(new std::uniform_int_distribution<int>(0, 100));
+    randomPercent_.reset(new std::uniform_real_distribution<float>(0.f, 1.f));
 
     auto& words = subBatch->data();
 
     // Get word id of special symbols
-    Word maskId  = (*subBatch->vocab())[maskSymbol_];
-    Word clsId   = (*subBatch->vocab())[clsSymbol_];
-    Word sepId   = (*subBatch->vocab())[sepSymbol_];
+    Word maskId  = vocab[maskSymbol_];
+    Word clsId   = vocab[clsSymbol_];
+    Word sepId   = vocab[sepSymbol_];
 
-    ABORT_IF(maskId == subBatch->vocab()->getUnkId(),
+    ABORT_IF(maskId == vocab.getUnkId(),
              "BERT masking symbol {} not found in vocabulary", maskSymbol_);
 
-    ABORT_IF(sepId == subBatch->vocab()->getUnkId(),
+    ABORT_IF(sepId == vocab.getUnkId(),
              "BERT separator symbol {} not found in vocabulary", sepSymbol_);
 
-    ABORT_IF(clsId == subBatch->vocab()->getUnkId(),
+    ABORT_IF(clsId == vocab.getUnkId(),
              "BERT class symbol {} not found in vocabulary", clsSymbol_);
 
     dontMask_.insert(clsId); // don't mask class token
     dontMask_.insert(sepId); // don't mask separator token
-    dontMask_.insert(subBatch->vocab()->getEosId()); // don't mask </s>
+    dontMask_.insert(vocab.getEosId()); // don't mask </s>
     // it's ok to mask <unk>
 
     std::vector<int> selected;
@@ -109,13 +110,13 @@ class BertBatch : public CorpusBatch {
     for(int i = 0; i < words.size(); ++i) // collect words among which we will mask
       if(dontMask_.count(words[i]) == 0)  // do not add indices of special words
         selected.push_back(i);
-    std::shuffle(selected.begin(), selected.end(), eng_); // randomize positions
+    std::shuffle(selected.begin(), selected.end(), engine); // randomize positions
     selected.resize(std::ceil(selected.size() * maskFraction)); // select first x percent from shuffled indices
 
     for(int i : selected) {
-      maskedPositions_.push_back(i);        // where is the original word?
-      maskedWords_.push_back(words[i]);     // what is the original word?
-      words[i] = maskOut(words[i], maskId); // mask that position
+      maskedPositions_.push_back(i);                // where is the original word?
+      maskedWords_.push_back(words[i]);             // what is the original word?
+      words[i] = maskOut(words[i], maskId, engine); // mask that position
     }
 
     int dimBatch = subBatch->batchSize();
@@ -148,15 +149,15 @@ class BertBatch : public CorpusBatch {
 
 /**
  * BERT-specific version of EncoderClassifier, mostly here to automatically convert a
- * CorpusBatch to BertBatch. 
+ * CorpusBatch to BertBatch.
  */
-class BertEncoderClassifier : public EncoderClassifier, public data::RNGEngine {
+class BertEncoderClassifier : public EncoderClassifier, public data::RNGEngine { // @TODO: this random engine is not being serialized right now
 public:
   BertEncoderClassifier(Ptr<Options> options)
   : EncoderClassifier(options) {}
 
   std::vector<Ptr<ClassifierState>> apply(Ptr<ExpressionGraph> graph, Ptr<data::CorpusBatch> batch, bool clearGraph) override {
-    // intercept batch and anotate with BERT-specific concepts
+    // intercept batch and annotate with BERT-specific concepts
     auto bertBatch = New<data::BertBatch>(batch,
                                           eng_,
                                           opt<float>("bert-masking-fraction", 0.15f), // 15% by default according to paper
@@ -175,7 +176,7 @@ class BertEncoderClassifier : public EncoderClassifier, public data::RNGEngine {
 /**
  * BERT-specific modifications to EncoderTransformer
  * Actually all that is needed is to intercept the creation of special embeddings,
- * here sentence embeddings for sentence A and B. 
+ * here sentence embeddings for sentence A and B.
  */
 class BertEncoder : public EncoderTransformer {
 public:
@@ -201,9 +202,9 @@ class BertEncoder : public EncoderTransformer {
                                .construct(graph_);
       signal = sentenceEmbeddings->apply(bertBatch->bertSentenceIndices(), {dimWords, dimBatch, dimEmb});
     } else {
-      // @TODO: factory for postional embeddings?
-      // trigonometric positions, no backprob
-      auto sentenceEmbeddingsExpr = graph_->constant({2, dimEmb}, inits::positions(0));
+      // @TODO: factory for positional embeddings?
+      // constant sinusoidal position embeddings, no backprob
+      auto sentenceEmbeddingsExpr = graph_->constant({2, dimEmb}, inits::sinusoidalPositionEmbeddings(0));
       signal = rows(sentenceEmbeddingsExpr, bertBatch->bertSentenceIndices());
       signal = reshape(signal, {dimWords, dimBatch, dimEmb});
     }
@@ -212,18 +213,18 @@ class BertEncoder : public EncoderTransformer {
   }
 
   virtual Expr addSpecialEmbeddings(Expr input, int start = 0, Ptr<data::CorpusBatch> batch = nullptr) const override {
-    bool learnedPosEmbeddings = opt<bool>("transformer-learned-positions", true);
-    input = addPositionalEmbeddings(input, start, learnedPosEmbeddings);
-    input = addSentenceEmbeddings(input, batch, learnedPosEmbeddings); // @TODO: separately set learnable pos and sent embeddings
+    bool trainPosEmbeddings = opt<bool>("transformer-train-positions", true);
+    input = addPositionalEmbeddings(input, start, trainPosEmbeddings);
+    input = addSentenceEmbeddings(input, batch, trainPosEmbeddings); // @TODO: separately set learnable pos and sent embeddings
     return input;
   }
 };
 
 /**
- * BERT-specific classifier 
+ * BERT-specific classifier
  * Can be used for next sentence prediction task or other fine-tuned down-stream tasks
  * Does not actually need a BertBatch, works with CorpusBatch.
- * 
+ *
  * @TODO: This is in fact so generic that we might move it out of here as the typical classifier implementation
  */
 class BertClassifier : public ClassifierBase {
@@ -266,9 +267,8 @@ class BertClassifier : public ClassifierBase {
 
 /**
  * This is a model that pretrains BERT for classification.
- * This is also a Classifier, but compared to the one above needs the BERT-specific information from BertBatch
- * as this is self-generating its labels from the source. Labels are dynamically created as complements of the
- * masking process. 
+ * This is also a Classifier, but compared to the BertClassifier above needs the BERT-specific information from BertBatch
+ * as this is self-generating its labels from the source. Labels are dynamically created as complements of the masking process.
  */
 class BertMaskedLM : public ClassifierBase {
 public:
@@ -277,7 +277,7 @@ class BertMaskedLM : public ClassifierBase {
   Ptr<ClassifierState> apply(Ptr<ExpressionGraph> graph, Ptr<data::CorpusBatch> batch, const std::vector<Ptr<EncoderState>>& encoderStates) override {
     Ptr<data::BertBatch> bertBatch = std::dynamic_pointer_cast<data::BertBatch>(batch);
 
-    ABORT_IF(!bertBatch, "Batch could not be converted to batch for BERT training");
+    ABORT_IF(!bertBatch, "Batch must be BertBatch for BERT training");
     ABORT_IF(encoderStates.size() != 1, "Currently we only support a single encoder BERT model");
 
     auto context = encoderStates[0]->getContext();
@@ -289,7 +289,7 @@ class BertMaskedLM : public ClassifierBase {
     int dimBatch = context->shape()[-2];
     int dimTime  = context->shape()[-3];
 
-    auto maskedEmbeddings = rows(reshape(context, {dimBatch * dimTime, dimModel}), bertMaskedPositions); // subselect stuff that has actually been masked out;
+    auto maskedEmbeddings = rows(reshape(context, {dimBatch * dimTime, dimModel}), bertMaskedPositions); // subselect stuff that has actually been masked out
 
     int dimVoc = opt<std::vector<int>>("dim-vocabs")[batchIndex_];
 
@@ -302,15 +302,14 @@ class BertMaskedLM : public ClassifierBase {
         ("dim", dimVoc);                  //
     layerOut.tieTransposed("Wemb"); // We are a BERT model, hence tie with input, @TODO: check if this is actually what Google does
 
-    // [-4: beam depth=1, -3: max length, -2: batch size, -1: vocab dim]
     // assemble layers into MLP and apply to embeddings, decoder context and
     // aligned source context
     auto output = mlp::mlp()  //
-        .push_back(layerTanh) // 
+        .push_back(layerTanh) //
         .push_back(layerOut)  //
         .construct(graph);
 
-    auto logits = output->apply(maskedEmbeddings);
+    auto logits = output->apply(maskedEmbeddings); // [-4: beam depth=1, -3: max length, -2: batch size, -1: vocab dim]
 
     auto state = New<ClassifierState>();
     state->setLogProbs(logits);
diff --git a/src/models/classifier.h b/src/models/classifier.h
index 86e01841e..dae9bcbb2 100644
--- a/src/models/classifier.h
+++ b/src/models/classifier.h
@@ -8,7 +8,7 @@
 namespace marian {
 
 /**
- * Simple base class for Classifiers
+ * Simple base class for Classifiers to be used in EncoderClassifier framework
  * Currently only implementations are in bert.h
  */
 class ClassifierBase {
@@ -34,6 +34,7 @@ class ClassifierBase {
     return options_->get<T>(key);
   }
 
+  // Should be used to clear any batch-wise temporary objects if present
   virtual void clear() = 0;
 };
 
diff --git a/src/models/costs.h b/src/models/costs.h
index 40d490b53..bfd62da7b 100755
--- a/src/models/costs.h
+++ b/src/models/costs.h
@@ -82,14 +82,14 @@ class EncoderDecoderCE : public CostBase {
 
       auto alignmentLoss = guidedAlignmentCost(graph, corpusBatch, options_, attention);
       multiLoss->push_back(alignmentLoss);
-
-      return multiLoss;
-    } else {
-      return multiLoss;
     }
+    
+    return multiLoss;
+    
   }
 };
 
+// Wraps an EncoderClassifier so it can produce a cost from raw logits. @TODO: Needs refactoring
 class EncoderClassifierCE : public CostBase {
 protected:
   Ptr<Options> options_;
@@ -284,7 +284,8 @@ inline Ptr<ModelBase> add_cost(Ptr<EncoderDecoder> encdec,
       else
         return New<Stepwise>(encdec, New<LogSoftmaxStep>());
     case usage::raw:
-    default: return encdec;
+    default: 
+      return encdec;
   }
 }
 
@@ -298,7 +299,8 @@ inline Ptr<ModelBase> add_cost(Ptr<EncoderClassifier> enccls,
     case usage::translation:
       ABORT("Classifier cannot be used for translation");
     case usage::raw:
-    default: return enccls;
+    default: 
+      return enccls;
   }
 }
 
diff --git a/src/models/encoder_classifier.h b/src/models/encoder_classifier.h
index 43ed9e1ad..bc3d8f9f8 100644
--- a/src/models/encoder_classifier.h
+++ b/src/models/encoder_classifier.h
@@ -12,8 +12,8 @@ namespace marian {
 /**
  * Combines sequence encoders with generic classifiers
  * Can be used to train sequence classifiers like language detection, BERT-next-sentence-prediction etc.
- * Already has support for multi-objective training. 
- * 
+ * Already has support for multi-objective training.
+ *
  * @TODO: this should probably be unified somehow with EncoderDecoder which could allow for deocder/classifier
  * multi-objective training.
  */
@@ -128,6 +128,7 @@ class EncoderClassifier : public EncoderClassifierBase {
     modelFeatures_.insert("transformer-decoder-autoreg");
     modelFeatures_.insert("transformer-tied-layers");
     modelFeatures_.insert("transformer-guided-alignment-layer");
+    modelFeatures_.insert("transformer-train-positions");
   }
 
   virtual Ptr<Options> getOptions() override { return options_; }
@@ -194,7 +195,7 @@ class EncoderClassifier : public EncoderClassifierBase {
     std::vector<Ptr<ClassifierState>> classifierStates;
     for(auto& classifier : classifiers_)
       classifierStates.push_back(classifier->apply(graph, batch, encoderStates));
-    
+
     return classifierStates;
   }
 
diff --git a/src/models/encoder_decoder.cpp b/src/models/encoder_decoder.cpp
index 70e71c836..c4b96cc30 100755
--- a/src/models/encoder_decoder.cpp
+++ b/src/models/encoder_decoder.cpp
@@ -44,6 +44,7 @@ EncoderDecoder::EncoderDecoder(Ptr<Options> options)
   modelFeatures_.insert("transformer-decoder-autoreg");
   modelFeatures_.insert("transformer-tied-layers");
   modelFeatures_.insert("transformer-guided-alignment-layer");
+  modelFeatures_.insert("transformer-train-positions");
 }
 
 std::vector<Ptr<EncoderBase>>& EncoderDecoder::getEncoders() {
diff --git a/src/models/transformer.h b/src/models/transformer.h
index b3cb8e45f..8f54a2213 100755
--- a/src/models/transformer.h
+++ b/src/models/transformer.h
@@ -76,7 +76,7 @@ class Transformer : public EncoderOrDecoderBase {
       embeddings = embeddings + signal;
     } else {
       auto signal = graph_->constant({dimWords, 1, dimEmb},
-                                     inits::positions(start));
+                                     inits::sinusoidalPositionEmbeddings(start));
       // according to paper embeddings are scaled up by \sqrt(d_m)
       embeddings = std::sqrt((float)dimEmb) * embeddings;
       embeddings = embeddings + signal;
@@ -86,8 +86,8 @@ class Transformer : public EncoderOrDecoderBase {
   }
 
   virtual Expr addSpecialEmbeddings(Expr input, int start = 0, Ptr<data::CorpusBatch> /*batch*/ = nullptr) const {
-    bool learnedPosEmbeddings = opt<bool>("transformer-learned-positions", false);
-    return addPositionalEmbeddings(input, start, learnedPosEmbeddings);
+    bool trainPosEmbeddings = opt<bool>("transformer-train-positions", false);
+    return addPositionalEmbeddings(input, start, trainPosEmbeddings);
   }
 
   Expr triangleMask(int length) const {
diff --git a/src/training/graph_group.h b/src/training/graph_group.h
index e9ad041a8..404789a6f 100755
--- a/src/training/graph_group.h
+++ b/src/training/graph_group.h
@@ -71,11 +71,11 @@ class GraphGroup {
     size_t maxLength = options_->get<size_t>("max-length");
     maxLength = (size_t)(std::ceil(maxLength / (float)step) * step);
 
-    // restrict maximum length for labels to 1
+    // restrict maximum length for class labels to 1
     std::vector<size_t> localMaxes(numFiles, maxLength);
     auto inputTypes = options_->get<std::vector<std::string>>("input-types", {});
     for(int i = 0; i < inputTypes.size(); ++i)
-      if(inputTypes[i] == "labels")
+      if(inputTypes[i] == "class")
         localMaxes[i] = 1;
     
 
diff --git a/src/training/validator.h b/src/training/validator.h
index bce37da28..358d3e4a5 100755
--- a/src/training/validator.h
+++ b/src/training/validator.h
@@ -216,6 +216,7 @@ class CrossEntropyValidator : public Validator<data::Corpus> {
   }
 };
 
+// Used for validating with classifiers. Compute prediction accuary versus groundtruth for a set of classes
 class AccuracyValidator : public Validator<data::Corpus> {
 public:
   AccuracyValidator(std::vector<Ptr<Vocab>> vocabs, Ptr<Options> options)

From a38d82217b4b29eed4a93bce9d8e943c3ddfdead Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Fri, 18 Jan 2019 09:31:57 -0800
Subject: [PATCH 153/838] (comments)

---
 src/layers/generic.cpp | 8 +++++++-
 src/layers/loss.cpp    | 1 +
 2 files changed, 8 insertions(+), 1 deletion(-)

diff --git a/src/layers/generic.cpp b/src/layers/generic.cpp
index e99a5410f..347d4a833 100755
--- a/src/layers/generic.cpp
+++ b/src/layers/generic.cpp
@@ -33,6 +33,12 @@ namespace marian {
     //  - all factors not matching a prefix get lumped into yet another class (the lemmas)
     //  - factor vocab must be sorted such that all groups are consecutive
     //  - result of Output layer is nevertheless logits, not a normalized probability, due to the sigmoid entries
+    // Factor normalization
+    //  - f = h * E' + b                    // hidden state projected onto all factors
+    //  - f: [B x U]                        // U = number of factor units
+    //  - normalization terms Z: [B x G]    // G = number of groups
+    //  - factor group matrix F: [U x G]    // [u,g] 1 if factor u is in group g (one-hot); computed once
+    //  - z = f - Z * F' = affine(Z, F, f, transB=true, alpha=-1)   // This does it with only one extra copy
     EmbeddingFactorMapping(Ptr<Options> options) : factorVocab_(New<Options>(), 0) {
       std::vector<std::string> paths = options->get<std::vector<std::string>>("embedding-factors");
       ABORT_IF(paths.size() != 2, "--embedding-factors expects two paths");
@@ -96,7 +102,7 @@ namespace marian {
         groupCounts[g]++;
       }
       for (size_t g = 0; g < numGroups; g++) { // detect non-overlapping groups
-        ABORT_IF(groupRanges_[g].second - groupRanges_[g].first != groupCounts[g], "Factor group '{}' members are not consecutive in the factor vocabulary", groupPrefixes[g]);
+        //ABORT_IF(groupRanges_[g].second - groupRanges_[g].first != groupCounts[g], "Factor group '{}' members are not consecutive in the factor vocabulary", groupPrefixes[g]);
         LOG(info, "[embedding] Factor group '{}' has {} members ({})",
             groupPrefixes[g], groupCounts[g], groupCounts[g] == 1 ? "sigmoid" : "softmax");
       }
diff --git a/src/layers/loss.cpp b/src/layers/loss.cpp
index 03b796823..d9102456a 100755
--- a/src/layers/loss.cpp
+++ b/src/layers/loss.cpp
@@ -31,6 +31,7 @@ Expr LossBase::getCrossEntropy(Expr logits,
   if(smoothing_ > 0) {
     // @TODO: add this to CE kernels instead
     auto ceq = mean(logsoftmax(logits), /*axis=*/ -1);
+    //auto ceq = mean(logits, /*axis=*/ -1) - logsum(logits), /*axis=*/ -1);
     ce = (1 - smoothing_) * ce - smoothing_ * ceq;
   }
 

From 4cc6219f185fa91a02e33fbbec94fa4e4348001d Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Fri, 18 Jan 2019 09:52:12 -0800
Subject: [PATCH 154/838] towards unifying reduction operators

---
 src/graph/expression_operators.cpp |  9 ++-
 src/graph/node_operators_unary.h   | 90 +++++++++++-------------------
 2 files changed, 41 insertions(+), 58 deletions(-)

diff --git a/src/graph/expression_operators.cpp b/src/graph/expression_operators.cpp
index 4694351b2..9e2772bcd 100755
--- a/src/graph/expression_operators.cpp
+++ b/src/graph/expression_operators.cpp
@@ -278,11 +278,16 @@ Expr select(Expr a, const std::vector<IndexType>& indices, int axis) {
 }
 
 Expr sum(Expr a, int ax) {
-  return Expression<SumNodeOp>(a, ax);
+  return Expression<SumNodeOp>(a, ax, ReduceNodeOpCode::sum);
+}
+
+// log(sum(exp(a)))
+Expr logSumExp(Expr a, int ax) {
+  return Expression<SumNodeOp>(a, ax, ReduceNodeOpCode::logSumExp);
 }
 
 Expr mean(Expr a, int ax) {
-  return Expression<MeanNodeOp>(a, ax);
+  return Expression<SumNodeOp>(a, ax, ReduceNodeOpCode::mean);
 }
 
 Expr scalar_product(Expr a, Expr b, int ax) {
diff --git a/src/graph/node_operators_unary.h b/src/graph/node_operators_unary.h
index 462d81cf7..1716292c9 100755
--- a/src/graph/node_operators_unary.h
+++ b/src/graph/node_operators_unary.h
@@ -412,20 +412,40 @@ struct LogSoftmaxNodeOp : public UnaryNodeOp {
   const std::string type() override { return "logsoftmax"; }
 };
 
+enum class ReduceNodeOpCode {
+  sum, mean, min, max, logSumExp
+};
+
 struct SumNodeOp : public UnaryNodeOp {
   int axis_;
+  ReduceNodeOpCode opCode_;
 
-  SumNodeOp(Expr a, int axis) : UnaryNodeOp(a, newShape(a, axis)) {}
+  SumNodeOp(Expr a, int axis, ReduceNodeOpCode opCode)
+      : UnaryNodeOp(a, newShape(a, axis)), opCode_(opCode){}
 
   NodeOps forwardOps() override {
     using namespace functional;
 
-    return {NodeOp(Reduce(_1, val_, child(0)->val()))};
+    switch (opCode_) {
+    case ReduceNodeOpCode::sum:
+      return {NodeOp(Reduce(_1, val_, child(0)->val()))};
+    case ReduceNodeOpCode::mean:
+      return {NodeOp(Reduce(_1, (float)val_->shape().elements() / (float)child(0)->shape().elements(), val_, child(0)->val()))};
+    default:
+      ABORT("Unexpected reduction op-code {}", (int)opCode_);
+    }
   }
 
   NodeOps backwardOps() override {
     using namespace functional;
-    return {NodeOp(Add(_1, child(0)->grad(), adj_))};
+    switch (opCode_) {
+    case ReduceNodeOpCode::sum:
+      return {NodeOp(Add(_1, child(0)->grad(), adj_))};
+    case ReduceNodeOpCode::mean:
+      return {NodeOp(Add(_1, (float)val_->shape().elements() / (float)child(0)->shape().elements(), child(0)->grad(), adj_))};
+    default:
+      ABORT("Unexpected reduction op-code {}", (int)opCode_);
+    }
   }
 
   Shape newShape(Expr a, int axis) {
@@ -436,59 +456,16 @@ struct SumNodeOp : public UnaryNodeOp {
     return shape;
   }
 
-  const std::string type() override { return "sum"; }
-
-  const std::string color() override { return "orange"; }
-
-  virtual size_t hash() override {
-    if(!hash_) {
-      hash_ = NaryNodeOp::hash();
-      util::hash_combine(hash_, axis_);
+  const std::string type() override {
+    switch (opCode_) {
+    case ReduceNodeOpCode::sum:
+      return "sum";
+    case ReduceNodeOpCode::mean:
+      return "mean";
+    default:
+      ABORT("Unexpected reduction op-code {}", (int)opCode_);
     }
-    return hash_;
-  }
-
-  virtual bool equal(Expr node) override {
-    if(!NaryNodeOp::equal(node))
-      return false;
-    Ptr<SumNodeOp> cnode = std::dynamic_pointer_cast<SumNodeOp>(node);
-    if(!cnode)
-      return false;
-    if(axis_ != cnode->axis_)
-      return false;
-    return true;
   }
-};
-
-struct MeanNodeOp : public UnaryNodeOp {
-  int axis_;
-
-  MeanNodeOp(Expr a, int axis) : UnaryNodeOp(a, newShape(a, axis)) {}
-
-  NodeOps forwardOps() override {
-    using namespace functional;
-    int left = child(0)->shape().elements() / val_->shape().elements();
-    float scale = 1.f / left;
-
-    return {NodeOp(Reduce(_1, scale, val_, child(0)->val()))};
-  }
-
-  NodeOps backwardOps() override {
-    using namespace functional;
-    int left = child(0)->shape().elements() / val_->shape().elements();
-    float scale = 1.f / left;
-
-    return {NodeOp(Add(_1, scale, child(0)->grad(), adj_))};
-  }
-
-  Shape newShape(Expr a, int axis) {
-    Shape shape = a->shape();
-    axis_ = shape.axis(axis);
-    shape.set(axis_, 1);
-    return shape;
-  }
-
-  const std::string type() override { return "mean"; }
 
   const std::string color() override { return "orange"; }
 
@@ -496,6 +473,7 @@ struct MeanNodeOp : public UnaryNodeOp {
     if(!hash_) {
       hash_ = NaryNodeOp::hash();
       util::hash_combine(hash_, axis_);
+      util::hash_combine(hash_, (int)opCode_);
     }
     return hash_;
   }
@@ -503,10 +481,10 @@ struct MeanNodeOp : public UnaryNodeOp {
   virtual bool equal(Expr node) override {
     if(!NaryNodeOp::equal(node))
       return false;
-    Ptr<MeanNodeOp> cnode = std::dynamic_pointer_cast<MeanNodeOp>(node);
+    Ptr<SumNodeOp> cnode = std::dynamic_pointer_cast<SumNodeOp>(node);
     if(!cnode)
       return false;
-    if(axis_ != cnode->axis_)
+    if(axis_ != cnode->axis_ || opCode_ != cnode->opCode_)
       return false;
     return true;
   }

From 4a8aa185db03073b8b7316f5bdf597c316b911cf Mon Sep 17 00:00:00 2001
From: Marcin Junczys-Dowmunt <Marcin.Junczys-Dowmunt@microsoft.com>
Date: Fri, 18 Jan 2019 10:11:40 -0800
Subject: [PATCH 155/838] address more comments, part 2

---
 src/examples/mnist/model.h                  |  2 +-
 src/layers/loss.h                           | 62 +++++++++++++++------
 src/models/encoder_decoder.h                |  4 +-
 src/models/transformer.h                    |  8 ++-
 src/models/transformer_factory.h            |  1 +
 src/rescorer/rescorer.h                     | 28 +++++-----
 src/tensors/cpu/tensor_operators.cpp        | 31 +++++------
 src/training/graph_group.h                  |  2 +-
 src/training/graph_group_async.cpp          |  7 +--
 src/training/graph_group_multinode.cpp      |  3 +-
 src/training/graph_group_multinode_sync.cpp |  2 +-
 src/training/scheduler.h                    | 12 ++--
 12 files changed, 98 insertions(+), 64 deletions(-)

diff --git a/src/examples/mnist/model.h b/src/examples/mnist/model.h
index 876830186..788df5810 100755
--- a/src/examples/mnist/model.h
+++ b/src/examples/mnist/model.h
@@ -71,7 +71,7 @@ class MnistFeedForwardNet : public ModelBase {
                      Ptr<data::Batch> batch,
                      bool /*clean*/ = false) override {
     
-    auto loss   = construct(graph, batch, inference_);
+    auto loss   = construct(graph, batch, inference_); // @TODO: unify nomenclature, e.g. rather use apply
     auto labels = graph->constant({(int)batch->size(), 1}, inits::from_value(1.f));
 
     return New<RationalLoss>(loss, labels);
diff --git a/src/layers/loss.h b/src/layers/loss.h
index a82ca60fd..2975864ca 100644
--- a/src/layers/loss.h
+++ b/src/layers/loss.h
@@ -29,7 +29,7 @@ class RationalLoss {
 
   RationalLoss(Expr loss, float labels)
   : loss_(loss),
-    labels_(loss->graph()->constant({1}, inits::from_value(labels))) {}
+    labels_(constant_like(loss, inits::from_value(labels))) {}
 
   RationalLoss(const RationalLoss& other)
   : loss_(other.loss_), labels_(other.labels_) {}
@@ -64,6 +64,8 @@ class RationalLoss {
     return labels_->val()->scalar<T>();
   }
 
+  // @TODO: add a funtion for returning maybe ratio?
+
   size_t size() const {
     ABORT_IF(!labels_, "Labels have not been defined");
     return labels_->shape().elements();
@@ -90,8 +92,14 @@ struct StaticLoss {
     labels = labels + other.labels;
     return *this;
   }
+
+  void reset() {
+    loss = 0.f;
+    labels = 0.f;
+  }
 };
 
+// @TODO: overthink interface
 /**
  * Base class for multi-objective losses which is a list of RationalLoss
  * but also defines how to accumulate that list into a single RationalLoss
@@ -214,7 +222,7 @@ class ScaledMultiRationalLoss : public MultiRationalLoss {
  * L = (1/N sum_i^N L_i + 1/M sum_j^M L_j) = (sum_i^N L_i + N/M sum_j^M L_j) / N
  *
  * We set labels to 1. During reporting, we would see the same numbers, but gradients
- * are scaled diffrently which may result in different learning curves.
+ * are scaled differently which may result in different learning curves.
  */
 class MeanMultiRationalLoss : public MultiRationalLoss {
 private:
@@ -243,11 +251,11 @@ class MeanMultiRationalLoss : public MultiRationalLoss {
 Ptr<MultiRationalLoss> newMultiLoss(Ptr<Options> options);
 
 //***********************************************************************************//
-// This needs some to be refactored. Currently easiest route for backwards compat, but
+// This needs to be refactored. Currently easiest route for backwards compat, but
 // still feels somewhat hacky.
 
 /**
- * Computes loss per label and then reduces to RationalLoss
+ * Computes loss per given groundtruth label and then reduces to RationalLoss
  */
 class LabelwiseLoss {
 protected:
@@ -256,6 +264,7 @@ class LabelwiseLoss {
   virtual Expr compute(Expr logits, Expr labelIndices,
                        Expr mask = nullptr, Expr labelWeights = nullptr) = 0;
 
+  // label counts are available, reduce together with loss to obtain counts
   RationalLoss reduce(Expr loss, Expr labels) {
     ABORT_IF(!loss, "Loss has not been computed");
     ABORT_IF(!labels, "Labels have not been computed");
@@ -270,6 +279,19 @@ class LabelwiseLoss {
     return RationalLoss(lossSum, labelsSum);
   }
 
+  // label counts are not available, assume every element of tensor corresponds to label count 1
+  RationalLoss reduce(Expr loss) {
+    ABORT_IF(!loss, "Loss has not been computed");
+
+    Expr  lossSum    = loss;
+    for(int i = 0; i < axes_.size(); ++i)
+      lossSum = sum(lossSum, axes_[i]);
+
+    // reduction factor tells how over how many labels we reduced in total.
+    float reducedLabels = (float)loss->shape().elements() / (float)lossSum->shape().elements(); 
+    return RationalLoss(lossSum, reducedLabels);
+  }
+
 public:
   LabelwiseLoss(const std::vector<int>& axes)
   : axes_(axes) { }
@@ -278,10 +300,10 @@ class LabelwiseLoss {
                              Expr mask = nullptr, Expr labelWeights = nullptr) {
     Expr loss = compute(logits, labelIndices, mask, labelWeights);
 
-    Expr labels = mask ? mask                              // mask can be used as element-wise label count with broadcasting
-                       : constant_like(loss, inits::ones); // we have no mask, assume all items are labels
-
-    return reduce(loss, labels);
+    if(mask)
+      return reduce(loss, mask); // mask can be used as element-wise label count with broadcasting
+    else
+      return reduce(loss); // we have no mask, assume all items are labels
   }
 };
 
@@ -290,25 +312,33 @@ class LabelwiseLoss {
  */
 class CrossEntropyLoss : public LabelwiseLoss {
 public:
-  CrossEntropyLoss(float smoothing)
+  CrossEntropyLoss(float labelSmoothing)
   : LabelwiseLoss(/*axes=*/{-2, -3}), // cross-entropy already reduces over axis -1
-    smoothing_(smoothing) {}
+    labelSmoothing_(labelSmoothing) {}
 
-  CrossEntropyLoss(const std::vector<int>& axes, float smoothing)
+  CrossEntropyLoss(const std::vector<int>& axes, float labelSmoothing)
   : LabelwiseLoss(axes), // cross-entropy already reduces over axis -1
-    smoothing_(smoothing) {}
+    labelSmoothing_(labelSmoothing) {}
 
 protected:
-  float smoothing_;
+  float labelSmoothing_; // interpolation factor for label smoothing, see below
 
   virtual Expr compute(Expr logits, Expr labelIndices,
                        Expr mask = nullptr, Expr labelWeights = nullptr) override {
     Expr ce = cross_entropy(logits, labelIndices);
 
-    if(smoothing_ > 0) {
+    if(labelSmoothing_ > 0) {
       // @TODO: add this to CE kernels instead
+      
+      // Label smoothing (see https://arxiv.org/pdf/1512.00567.pdf, section 7)
+      // We compute smoothed H(q',p) = (1 - eps) * H(q,p) + eps * H(u,p) where H(q,p) is the normal cross-entropy
+      // and H(u,p) penalizes deviation of p from u, u being uniform distribution over vocab V => u_v = 1/|V|.
+      // H(u,p) = - \sum_{v \in V} u_v * \log p_v = - 1/|V| \sum_{v \in V} \log \softmax_v => -mean(logsoftmax(logits))
+      // ceq = -H(u,p) - avoid one kernel call by negating in the interpolation below
       Expr ceq = mean(logsoftmax(logits), /*axis=*/ -1);
-      ce = (1 - smoothing_) * ce - smoothing_ * ceq;
+
+      // H(q',p) = (1 - eps) * H(q,p) - eps * -H(u,p)
+      ce = (1 - labelSmoothing_) * ce - labelSmoothing_ * ceq;
     }
 
     if(mask)
@@ -332,7 +362,7 @@ class RescorerLoss : public CrossEntropyLoss {
   virtual RationalLoss apply(Expr logits, Expr labelIndices,
                              Expr mask = nullptr, Expr labelWeights = nullptr) override {
     auto ce = CrossEntropyLoss::apply(logits, labelIndices, mask, labelWeights);
-    return RationalLoss(-ce.loss(), ce.labels()); // we report logprobs, hence negate
+    return RationalLoss(ce.loss(), ce.labels());
   }
 };
 
diff --git a/src/models/encoder_decoder.h b/src/models/encoder_decoder.h
index a33de385f..b55e8bd1d 100644
--- a/src/models/encoder_decoder.h
+++ b/src/models/encoder_decoder.h
@@ -34,7 +34,9 @@ class EncoderDecoderBase : public models::ModelBase {
 
   virtual Ptr<RationalLoss> build(Ptr<ExpressionGraph> graph,
                                   Ptr<data::CorpusBatch> batch,
-                                  bool clearGraph = true) = 0;  virtual Ptr<DecoderState> startState(Ptr<ExpressionGraph> graph,
+                                  bool clearGraph = true) = 0;  
+                                  
+  virtual Ptr<DecoderState> startState(Ptr<ExpressionGraph> graph,
                                        Ptr<data::CorpusBatch> batch) = 0;
 
   virtual Ptr<DecoderState> step(Ptr<ExpressionGraph> graph,
diff --git a/src/models/transformer.h b/src/models/transformer.h
index 8f54a2213..b29ffc052 100755
--- a/src/models/transformer.h
+++ b/src/models/transformer.h
@@ -57,10 +57,14 @@ class Transformer : public EncoderOrDecoderBase {
       int maxLength = opt<int>("max-length");
 
       // Hack for translating with length longer than trained embeddings
+      // We check if the embedding matrix "Wpos" already exist so we can
+      // check the number of positions in that loaded parameter. 
+      // We then have to restict the maximum length to the maximum positon
+      // and positions beyond this will be the maximum position.
       Expr seenEmb = graph_->get("Wpos");
       int numPos = seenEmb ? seenEmb->shape()[-2] : maxLength;
 
-      auto posEmbFactory = embedding()
+      auto embeddingLayer = embedding()
                             ("prefix", "Wpos") // share positional embeddings across all encoders/decorders
                             ("dimVocab", numPos)
                             ("dimEmb", dimEmb)
@@ -72,7 +76,7 @@ class Transformer : public EncoderOrDecoderBase {
         positions[i] = i;
 
       // @TODO : test if embeddings should be scaled here too!
-      auto signal = posEmbFactory->apply(positions, {dimWords, 1, dimEmb});
+      auto signal = embeddingLayer->apply(positions, {dimWords, 1, dimEmb});
       embeddings = embeddings + signal;
     } else {
       auto signal = graph_->constant({dimWords, 1, dimEmb},
diff --git a/src/models/transformer_factory.h b/src/models/transformer_factory.h
index 16ce91bdc..c2a7e13bb 100755
--- a/src/models/transformer_factory.h
+++ b/src/models/transformer_factory.h
@@ -9,6 +9,7 @@
 //#include "layers/factory.h"
 
 namespace marian {
+// @TODO: find out why static is required here to get to compile
 static Ptr<EncoderBase> NewEncoderTransformer(Ptr<Options> options);
 static Ptr<DecoderBase> NewDecoderTransformer(Ptr<Options> options);
 }  // namespace marian
diff --git a/src/rescorer/rescorer.h b/src/rescorer/rescorer.h
index 64ed3cde2..2c145c002 100644
--- a/src/rescorer/rescorer.h
+++ b/src/rescorer/rescorer.h
@@ -105,7 +105,7 @@ class Rescore : public ModelTask {
 
     std::string summary = summarize ? options_->get<std::string>("summary") : "cross-entropy";
 
-    float sumCost = 0;
+    float sumLoss = 0;
     size_t sumWords = 0;
     size_t sumSamples = 0;
     size_t batchId = 0;
@@ -115,7 +115,7 @@ class Rescore : public ModelTask {
       ThreadPool pool(graphs_.size(), graphs_.size());
 
       for(auto batch : *batchGenerator) {
-        auto task = [=, &sumCost, &sumWords, &sumSamples, &smutex](size_t id) {
+        auto task = [=, &sumLoss, &sumWords, &sumSamples, &smutex](size_t id) {
           thread_local Ptr<ExpressionGraph> graph;
           thread_local Ptr<Model> builder;
 
@@ -127,12 +127,12 @@ class Rescore : public ModelTask {
           // @TODO: normalize by length as in normalize
           // Once we have Frank's concept of ce-sum with sample size by words we will return a pair
           // here which will make it trivial to report all variants.
-          auto costNode = builder->build(graph, batch);
+          auto dynamicLoss = builder->build(graph, batch);
 
           graph->forward();
 
           std::vector<float> scores;
-          costNode->loss(scores);
+          dynamicLoss->loss(scores);
 
           // soft alignments for each sentence in the batch
           std::vector<data::SoftAlignment> aligns(batch->size());
@@ -142,13 +142,15 @@ class Rescore : public ModelTask {
 
           std::unique_lock<std::mutex> lock(smutex);
           for(auto s : scores)
-            sumCost += s;
+            sumLoss += s; 
           sumWords += batch->back()->batchWords();
           sumSamples += batch->size();
 
           if(!summarize) {
             for(size_t i = 0; i < batch->size(); ++i) {
-              output->Write((long)batch->getSentenceIds()[i], scores[i], aligns[i]);
+              output->Write((long)batch->getSentenceIds()[i], 
+                             -1.f * scores[i], // report logProb while score is CE, hence negate
+                             aligns[i]);
             }
           }
 
@@ -168,22 +170,22 @@ class Rescore : public ModelTask {
     }
 
     if(normalize) {
-      LOG(info, "Total normalized log probs {} : Total sentences {} : Total words {}", sumCost, sumSamples, sumWords);
+      LOG(info, "Total normalized log probs {} : Total sentences {} : Total words {}", sumLoss, sumSamples, sumWords);
       LOG(warn, "Sum of normalized log probs is a sum of averages");
     } else {
-      LOG(info, "Total log probs {} : Total sentences {} : Total words {}", sumCost, sumSamples, sumWords);
+      LOG(info, "Total log probs {} : Total sentences {} : Total words {}", sumLoss, sumSamples, sumWords);
     }
 
-    if(summarize) {
+    if(summarize) { // @TODO: use one function from loss
       float cost = 0;
       if(summary == "perplexity")
-        cost = std::exp(-(float)sumCost / (float)sumWords);
+        cost = std::exp(sumLoss / (float)sumWords);
       else if(summary == "ce-sum")
-        cost = -sumCost;
+        cost = sumLoss;
       else if(summary == "ce-mean-words")
-        cost = -(float)sumCost / (float)sumWords;
+        cost = sumLoss / (float)sumWords;
       else
-        cost = -sumCost / sumSamples;
+        cost = sumLoss / sumSamples;
 
       LOG(info, "Reporting {} summary", summary);
       std::cout << cost << std::endl;
diff --git a/src/tensors/cpu/tensor_operators.cpp b/src/tensors/cpu/tensor_operators.cpp
index 4cc5daf0d..e7fefdcfe 100755
--- a/src/tensors/cpu/tensor_operators.cpp
+++ b/src/tensors/cpu/tensor_operators.cpp
@@ -661,8 +661,8 @@ void GRUFastBackward(std::vector<Tensor> outputs,
   }
 }
 
-void CrossEntropyPick(Tensor out, Tensor in, Tensor pick) {
-  matchOrAbort<IndexType>(pick->type());
+void CrossEntropyPick(Tensor out, Tensor in, Tensor labelIndices) {
+  matchOrAbort<IndexType>(labelIndices->type());
 
   // Shape& outShape = out_->shape();
   Shape& inShape = in->shape();
@@ -685,31 +685,28 @@ void CrossEntropyPick(Tensor out, Tensor in, Tensor pick) {
       sum += std::exp(sp[i] - max);
     }
 
-    // cross-entropy
-    IndexType i = pick->data<IndexType>()[j];
+    // Groundtruth label index
+    IndexType i = labelIndices->data<IndexType>()[j];
     // This appears to be safe i.e. that i >= 0 && i < cols is known
     out->data()[j] = std::log(sum) - sp[i] + max;
   }
 }
 
-void CrossEntropyPickBackward(Tensor out_,
-                              Tensor adj_,
-                              Tensor a,
-                              Tensor pick_) {
+void CrossEntropyPickBackward(Tensor out,
+                              Tensor adj,
+                              Tensor in,
+                              Tensor labelIndices) {
 
-  matchOrAbort<IndexType>(pick_->type());
-  float* out = out_->data();
-  Shape& outShape = out_->shape();
-  const float* adj = adj_->data();
-  const float* in = a->data();
+  matchOrAbort<IndexType>(labelIndices->type());
+  Shape& outShape = out->shape();
 
   int rows = outShape.elements() / outShape.back();
   int cols = outShape.back();
 
 #pragma omp parallel for
   for(int j = 0; j < rows; ++j) {
-    const float* sp = in + j * cols;
-    float* so = out + j * cols;
+    const float* sp = in->data() + j * cols;
+    float* so = out->data() + j * cols;
 
     float max = sp[0];
     for(int i = 1; i < cols; ++i) {
@@ -723,8 +720,8 @@ void CrossEntropyPickBackward(Tensor out_,
 
     // cross-entropy
     for(int i = 0; i < cols; ++i) {
-      float sub = (float)(i == (int)pick_->data<IndexType>()[j]);
-      so[i] += adj[j] * (std::exp(sp[i] - max) / sum - sub);
+      float sub = (float)(i == (int)labelIndices->data<IndexType>()[j]); // delta, true if label index and column index match
+      so[i] += adj->data()[j] * (std::exp(sp[i] - max) / sum - sub);
     }
   }
 }
diff --git a/src/training/graph_group.h b/src/training/graph_group.h
index 404789a6f..f4617d782 100755
--- a/src/training/graph_group.h
+++ b/src/training/graph_group.h
@@ -71,7 +71,7 @@ class GraphGroup {
     size_t maxLength = options_->get<size_t>("max-length");
     maxLength = (size_t)(std::ceil(maxLength / (float)step) * step);
 
-    // restrict maximum length for class labels to 1
+    // this should be only one class label per line on input, hence restricting length to 1
     std::vector<size_t> localMaxes(numFiles, maxLength);
     auto inputTypes = options_->get<std::vector<std::string>>("input-types", {});
     for(int i = 0; i < inputTypes.size(); ++i)
diff --git a/src/training/graph_group_async.cpp b/src/training/graph_group_async.cpp
index 6291c2ed7..c15adf3f6 100755
--- a/src/training/graph_group_async.cpp
+++ b/src/training/graph_group_async.cpp
@@ -206,14 +206,14 @@ void AsyncGraphGroup::execute(Ptr<data::Batch> batch) {
       builder = builders_[i++];
     }
 
-    auto lossNode = builder->build(graph, batch);
+    Ptr<RationalLoss> dynamicLoss = builder->build(graph, batch);
 
     if(t % optimizerDelay_ == 0) {
       fetchParams(graph->params()->vals(), params_, t_id);
     }
 
     graph->forward();
-    loss += *lossNode;
+    loss += *dynamicLoss;
     graph->backward();
 
     Tensor gradients;
@@ -265,8 +265,7 @@ void AsyncGraphGroup::execute(Ptr<data::Batch> batch) {
         scheduler_->update(loss, batch);
       }
 
-      loss.loss = 0;
-      loss.labels = 0;
+      loss.reset();
 
       if(scheduler_->saving() || scheduler_->validating()) {
         // Wait with validation or saving until all other threads are done with
diff --git a/src/training/graph_group_multinode.cpp b/src/training/graph_group_multinode.cpp
index b5c01836d..255b5a2a5 100755
--- a/src/training/graph_group_multinode.cpp
+++ b/src/training/graph_group_multinode.cpp
@@ -604,8 +604,7 @@ void MultiNodeGraphGroup::execute(Ptr<data::Batch> batch) {
 
       num_seen_words = 0;
       num_seen_sentences = 0;
-      loss.loss = 0; 
-      loss.labels = 0;
+      loss.reset();
 
       if((scheduler_->saving() || scheduler_->validating())) {
         // Wait with validation or saving until all other threads are done with
diff --git a/src/training/graph_group_multinode_sync.cpp b/src/training/graph_group_multinode_sync.cpp
index 7d7f2d00b..904d614f9 100755
--- a/src/training/graph_group_multinode_sync.cpp
+++ b/src/training/graph_group_multinode_sync.cpp
@@ -230,7 +230,7 @@ void MultiNodeGraphGroupSync::execute(Ptr<data::Batch> fullBatch) {
 
     num_seen_words = 0;
     num_seen_sentences = 0;
-    loss = StaticLoss();
+    loss.reset();
 
     if((scheduler_->saving() || scheduler_->validating())) {
       // wait until other nodes are ready
diff --git a/src/training/scheduler.h b/src/training/scheduler.h
index 12ad1ae4d..4e54f70b5 100755
--- a/src/training/scheduler.h
+++ b/src/training/scheduler.h
@@ -68,10 +68,10 @@ class Scheduler : public TrainingObserver {
     state.updateEta(baselr);
   }
 
-  std::string displayLoss(std::string lossType, 
-                          bool dispLabelCounts, 
-                          size_t batchLabels, 
-                          Ptr<TrainingState> state) {
+  std::string formatLoss(std::string lossType, 
+                         bool dispLabelCounts, 
+                         size_t batchLabels, 
+                         Ptr<TrainingState> state) {
     std::stringstream ss;
     ss << "Cost ";
     ss << std::setprecision(8) << std::fixed;
@@ -308,7 +308,7 @@ class Scheduler : public TrainingObserver {
             state_->epochs,
             state_->batches,
             utils::withCommas(state_->samplesEpoch),
-            displayLoss(lossType, dispLabelCounts, batchLabels, state_),
+            formatLoss(lossType, dispLabelCounts, batchLabels, state_),
             timer_.elapsed(),
             state_->wordsDisp / timer_.elapsed(),
             state_->eta);
@@ -318,7 +318,7 @@ class Scheduler : public TrainingObserver {
             state_->epochs,
             state_->batches,
             utils::withCommas(state_->samplesEpoch),
-            displayLoss(lossType, dispLabelCounts, 0, state_), // ignore batchLabels
+            formatLoss(lossType, dispLabelCounts, 0, state_), // ignore batchLabels
             timer_.elapsed(),
             state_->wordsDisp / timer_.elapsed());
       }

From 5c413f6e49215c523379c64fca8bb2bf7d1ed706 Mon Sep 17 00:00:00 2001
From: Marcin Junczys-Dowmunt <Marcin.Junczys-Dowmunt@microsoft.com>
Date: Fri, 18 Jan 2019 10:40:40 -0800
Subject: [PATCH 156/838] address remaining comments from code review

---
 src/examples/mnist/model.h |  6 +--
 src/layers/loss.h          | 89 ++++++++++++++++++++------------------
 src/tensors/gpu/add.cu     |  5 ---
 src/training/scheduler.h   |  4 +-
 src/training/validator.h   |  4 +-
 5 files changed, 54 insertions(+), 54 deletions(-)

diff --git a/src/examples/mnist/model.h b/src/examples/mnist/model.h
index 788df5810..3b5be8778 100755
--- a/src/examples/mnist/model.h
+++ b/src/examples/mnist/model.h
@@ -54,7 +54,7 @@ class MNISTLogsoftmax : public CostBase {
     
     // @TODO: simplify this
     auto multiLoss = New<SumMultiRationalLoss>();
-    multiLoss->push_back({logsoftmax(top->loss()), top->labels()});
+    multiLoss->push_back({logsoftmax(top->loss()), top->count()});
     return multiLoss;
   }
 };
@@ -72,9 +72,9 @@ class MnistFeedForwardNet : public ModelBase {
                      bool /*clean*/ = false) override {
     
     auto loss   = construct(graph, batch, inference_); // @TODO: unify nomenclature, e.g. rather use apply
-    auto labels = graph->constant({(int)batch->size(), 1}, inits::from_value(1.f));
+    auto count = graph->constant({(int)batch->size(), 1}, inits::from_value(1.f));
 
-    return New<RationalLoss>(loss, labels);
+    return New<RationalLoss>(loss, count);
   }
 
   void load(Ptr<ExpressionGraph> /*graph*/, const std::string& /*name*/, bool) override {
diff --git a/src/layers/loss.h b/src/layers/loss.h
index 2975864ca..52f2155e1 100644
--- a/src/layers/loss.h
+++ b/src/layers/loss.h
@@ -6,33 +6,33 @@ namespace marian {
 
 /**
  * We represent loss as pair of expressions, where loss_ is usually a sum
- * of all accumulated loss values per label and labels_ is the total number
+ * of all accumulated loss values per label and count_ is the total number
  * of labels over which the loss was collected.
  *
  * These two values can then be used to represent various cost variants -
  * for instance label-wise cross-entropy or perplexity. Optimization is
  * only performed with regard to the summed loss_.
  *
- * Since both, loss_ and labels_ are dynamic graph nodes they can be further
+ * Since both, loss_ and count_ are dynamic graph nodes they can be further
  * combined into larger structures. See multi-objective losses below.
  */
 class RationalLoss {
 protected:
   Expr loss_;   // numerator
-  Expr labels_; // denominator
+  Expr count_; // denominator
 
   RationalLoss() = default; // protected
 
 public:
-  RationalLoss(Expr loss, Expr labels)
-  : loss_(loss), labels_(labels) {}
+  RationalLoss(Expr loss, Expr count)
+  : loss_(loss), count_(count) {}
 
-  RationalLoss(Expr loss, float labels)
+  RationalLoss(Expr loss, float count)
   : loss_(loss),
-    labels_(constant_like(loss, inits::from_value(labels))) {}
+    count_(constant_like(loss, inits::from_value(count))) {}
 
   RationalLoss(const RationalLoss& other)
-  : loss_(other.loss_), labels_(other.labels_) {}
+  : loss_(other.loss_), count_(other.count_) {}
 
   virtual ~RationalLoss() = default;
 
@@ -50,25 +50,25 @@ class RationalLoss {
     return loss_->val()->scalar<T>();
   }
 
-  Expr labels() const { return labels_; }
+  Expr count() const { return count_; }
 
   template <typename T>
-  void labels(std::vector<T>& labels) const {
-    ABORT_IF(!labels_, "Labels have not been defined");
-    labels_->val()->get(labels);
+  void count(std::vector<T>& labels) const {
+    ABORT_IF(!count_, "Labels have not been defined");
+    count_->val()->get(labels);
   }
 
   template <typename T>
-  T labels() const { // this will fail if loss is not a single value
-    ABORT_IF(!labels_, "Labels have not been defined");
-    return labels_->val()->scalar<T>();
+  T count() const { // this will fail if loss is not a single value
+    ABORT_IF(!count_, "Labels have not been defined");
+    return count_->val()->scalar<T>();
   }
 
   // @TODO: add a funtion for returning maybe ratio?
 
   size_t size() const {
-    ABORT_IF(!labels_, "Labels have not been defined");
-    return labels_->shape().elements();
+    ABORT_IF(!count_, "Labels have not been defined");
+    return count_->shape().elements();
   }
 };
 
@@ -80,26 +80,25 @@ class RationalLoss {
  */
 struct StaticLoss {
   float loss;
-  float labels;
+  float count;
 
-  StaticLoss() : loss(0.f), labels(0.f) {}
+  StaticLoss() : loss(0.f), count(0.f) {}
 
   StaticLoss(const RationalLoss& dynamic)
-  : loss(dynamic.loss<float>()), labels(dynamic.labels<float>()) {}
+  : loss(dynamic.loss<float>()), count(dynamic.count<float>()) {}
 
   StaticLoss& operator +=(const StaticLoss& other) {
     loss = loss + other.loss;
-    labels = labels + other.labels;
+    count = count + other.count;
     return *this;
   }
 
   void reset() {
     loss = 0.f;
-    labels = 0.f;
+    count = 0.f;
   }
 };
 
-// @TODO: overthink interface
 /**
  * Base class for multi-objective losses which is a list of RationalLoss
  * but also defines how to accumulate that list into a single RationalLoss
@@ -110,13 +109,19 @@ class MultiRationalLoss : public RationalLoss {
 
   /**
    * Accumulation rule for losses
+   * In the default case this would just be a sum, see SumMultiRationalLoss, but there are 
+   * special cases like ScaledMultiRationalLoss or MeanMultiRationalLoss (see below) where
+   * the accumulation is more complex.
    */
   virtual Expr accumulateLoss(const RationalLoss& current) = 0;
 
   /**
    * Accumulation rule for labels
+   * Similar as above, the naive case is summation, but for instance MeanMultiRationalLoss
+   * is including all label counts in the loss hence label counts are always just 1 which is
+   * passed throught without summation or other modifications.
    */
-  virtual Expr accumulateLabels(const RationalLoss& current) = 0;
+  virtual Expr accumulateCount(const RationalLoss& current) = 0;
 
 public:
   MultiRationalLoss() : RationalLoss() {}
@@ -127,7 +132,7 @@ class MultiRationalLoss : public RationalLoss {
 
   void push_back(const RationalLoss& current) {
     loss_   = accumulateLoss(current);
-    labels_ = accumulateLabels(current);
+    count_ = accumulateCount(current);
     partialLosses_.push_back(current);
   }
 
@@ -163,11 +168,11 @@ class SumMultiRationalLoss : public MultiRationalLoss {
       return current.loss();
   }
 
-  virtual Expr accumulateLabels(const RationalLoss& current) override {
-    if(labels_)
-      return labels_ + current.labels();
+  virtual Expr accumulateCount(const RationalLoss& current) override {
+    if(count_)
+      return count_ + current.count();
     else
-      return current.labels();
+      return current.count();
   }
 
 public:
@@ -178,7 +183,7 @@ class SumMultiRationalLoss : public MultiRationalLoss {
 /**
  * Scaled sum of losses.
  * This can weigh losses equally by choosing the first loss_0 as a reference
- * and scaling all remaining losses loss_i by labels_0 / labels_i. Labels are
+ * and scaling all remaining losses loss_i by count_0 / count_i. Labels are
  * summed up by the same rule. By this we simulate a sum of losses at similar
  * scales. Dividing by scaled label counts yields a value close to an equally
  * weighted sum of means.
@@ -194,17 +199,17 @@ class ScaledMultiRationalLoss : public MultiRationalLoss {
   virtual Expr accumulateLoss(const RationalLoss& current) override {
     if(loss_) {
       const auto& first = partialLosses_.front();
-      return loss_ + first.labels() * (current.loss() / current.labels()); // scale up/down to match scale of first loss
+      return loss_ + first.count() * (current.loss() / current.count()); // scale up/down to match scale of first loss
     } else {
       return current.loss(); // first reference loss, keeps to scale with this one
     }
   }
 
-  virtual Expr accumulateLabels(const RationalLoss& current) override {
-    if(labels_) {
-      return labels_; // Keep first label count // or: labels_ + first.labels() / current.labels();
+  virtual Expr accumulateCount(const RationalLoss& current) override {
+    if(count_) {
+      return count_; // Keep first label count // or: count_ + first.count() / current.count();
     } else {
-      return current.labels(); // This is the first loss
+      return current.count(); // This is the first loss
     }
   }
 
@@ -228,16 +233,16 @@ class MeanMultiRationalLoss : public MultiRationalLoss {
 private:
   virtual Expr accumulateLoss(const RationalLoss& current) override {
     if(loss_)
-      return loss_ + current.loss() / current.labels();
+      return loss_ + current.loss() / current.count();
     else
-      return current.loss() / current.labels();
+      return current.loss() / current.count();
   }
 
-  virtual Expr accumulateLabels(const RationalLoss& current) override {
-    if(labels_)
-      return labels_; // keep the existing '1'
+  virtual Expr accumulateCount(const RationalLoss& current) override {
+    if(count_)
+      return count_; // keep the existing '1'
     else
-      return current.labels()->graph()->ones({1}); // just '1' as labels are factored into loss_
+      return current.count()->graph()->ones({1}); // just '1' as labels are factored into loss_
   }
 
 public:
@@ -362,7 +367,7 @@ class RescorerLoss : public CrossEntropyLoss {
   virtual RationalLoss apply(Expr logits, Expr labelIndices,
                              Expr mask = nullptr, Expr labelWeights = nullptr) override {
     auto ce = CrossEntropyLoss::apply(logits, labelIndices, mask, labelWeights);
-    return RationalLoss(ce.loss(), ce.labels());
+    return RationalLoss(ce.loss(), ce.count());
   }
 };
 
diff --git a/src/tensors/gpu/add.cu b/src/tensors/gpu/add.cu
index 2431948ee..81af79408 100755
--- a/src/tensors/gpu/add.cu
+++ b/src/tensors/gpu/add.cu
@@ -1,8 +1,3 @@
-/* All or part of this file was contributed by Intel under license:
- *   Copyright (C) 2017-2018 Intel Corporation
- *   SPDX-License-Identifier: MIT
- */
-
 #include "tensors/gpu/add.h"
 
 #include "tensors/gpu/cuda_helpers.h"
diff --git a/src/training/scheduler.h b/src/training/scheduler.h
index 4e54f70b5..891a2c0e7 100755
--- a/src/training/scheduler.h
+++ b/src/training/scheduler.h
@@ -277,14 +277,14 @@ class Scheduler : public TrainingObserver {
       rationalLoss.loss *= mpi->numMPIProcesses();
 
     state_->costSum      += rationalLoss.loss;   // aggregate sum cost since last display
-    state_->costCount    += rationalLoss.labels; // cost gets normalized w.r.t. this in display
+    state_->costCount    += rationalLoss.count; // cost gets normalized w.r.t. this in display
 
     state_->updatesDisp  += 1;
     state_->samplesDisp  += batchSize;
     state_->wordsDisp    += batchLabels;  //@TODO: this is wrong        // words at given input processed since last display, for speed display
 
     state_->samplesEpoch += batchSize;           // sentences processed in this epoch
-    state_->labelsTotal  += rationalLoss.labels; // total labels processed
+    state_->labelsTotal  += rationalLoss.count; // total labels processed
 
     state_->newUpdate(numReadBatches);
 
diff --git a/src/training/validator.h b/src/training/validator.h
index 358d3e4a5..d09859b20 100755
--- a/src/training/validator.h
+++ b/src/training/validator.h
@@ -206,9 +206,9 @@ class CrossEntropyValidator : public Validator<data::Corpus> {
     options_->set("cost-type", ctype); // @TODO: check if still needed, most likely not.
 
     if(ctype == "perplexity")
-      return std::exp(loss.loss / loss.labels);
+      return std::exp(loss.loss / loss.count);
     if(ctype == "ce-mean-words")
-      return loss.loss / loss.labels;
+      return loss.loss / loss.count;
     if(ctype == "ce-sum")
       return loss.loss;
     else

From fb656e566a06899b02aaf52a63dceac46a1c31b8 Mon Sep 17 00:00:00 2001
From: Marcin Junczys-Dowmunt <Marcin.Junczys-Dowmunt@microsoft.com>
Date: Fri, 18 Jan 2019 10:45:07 -0800
Subject: [PATCH 157/838] cleanup comments a bit

---
 src/layers/loss.h | 33 +++++++++++++++++----------------
 1 file changed, 17 insertions(+), 16 deletions(-)

diff --git a/src/layers/loss.h b/src/layers/loss.h
index 52f2155e1..e92364762 100644
--- a/src/layers/loss.h
+++ b/src/layers/loss.h
@@ -18,7 +18,7 @@ namespace marian {
  */
 class RationalLoss {
 protected:
-  Expr loss_;   // numerator
+  Expr loss_;  // numerator
   Expr count_; // denominator
 
   RationalLoss() = default; // protected
@@ -79,8 +79,8 @@ class RationalLoss {
  * RationalLoss object.
  */
 struct StaticLoss {
-  float loss;
-  float count;
+  float loss;  // numerator
+  float count; // denominator
 
   StaticLoss() : loss(0.f), count(0.f) {}
 
@@ -100,6 +100,7 @@ struct StaticLoss {
 };
 
 /**
+ * @brief Base class for multi-objective losses
  * Base class for multi-objective losses which is a list of RationalLoss
  * but also defines how to accumulate that list into a single RationalLoss
  */
@@ -108,18 +109,18 @@ class MultiRationalLoss : public RationalLoss {
   std::vector<RationalLoss> partialLosses_;
 
   /**
-   * Accumulation rule for losses
+   * @brief Accumulation rule for losses
    * In the default case this would just be a sum, see SumMultiRationalLoss, but there are 
-   * special cases like ScaledMultiRationalLoss or MeanMultiRationalLoss (see below) where
-   * the accumulation is more complex.
+   * special cases like ScaledMultiRationalLoss (scale other loses according to first label count) 
+   * or MeanMultiRationalLoss (sum of means) where the accumulation is more complex.
    */
   virtual Expr accumulateLoss(const RationalLoss& current) = 0;
 
   /**
-   * Accumulation rule for labels
+   * @brief Accumulation rule for labels
    * Similar as above, the naive case is summation, but for instance MeanMultiRationalLoss
    * is including all label counts in the loss hence label counts are always just 1 which is
-   * passed throught without summation or other modifications.
+   * passed through without summation or other modifications.
    */
   virtual Expr accumulateCount(const RationalLoss& current) = 0;
 
@@ -155,7 +156,7 @@ class MultiRationalLoss : public RationalLoss {
 };
 
 /**
- * Simple sum of losses.
+ * @brief Simple sum of losses.
  * Using this makes sense when the two loss types are similar in scale and
  * number of labels. For instance two decoders over similarly sized vocabularies
  */
@@ -181,7 +182,7 @@ class SumMultiRationalLoss : public MultiRationalLoss {
 };
 
 /**
- * Scaled sum of losses.
+ * @brief Scaled sum of losses.
  * This can weigh losses equally by choosing the first loss_0 as a reference
  * and scaling all remaining losses loss_i by count_0 / count_i. Labels are
  * summed up by the same rule. By this we simulate a sum of losses at similar
@@ -219,7 +220,7 @@ class ScaledMultiRationalLoss : public MultiRationalLoss {
 };
 
 /**
- * Sum of mean losses.
+ * @brief Sum of mean losses.
  * Not really a rational loss as labels are factored into loss. Contribution of
  * losses is equal, same as for ScaledMultiRationalLoss, just divided by different
  * number of labels. See:
@@ -251,7 +252,7 @@ class MeanMultiRationalLoss : public MultiRationalLoss {
 };
 
 /**
- * Factory for multi-objective rational loss functions
+ * @brief Factory for multi-objective rational loss functions
  */
 Ptr<MultiRationalLoss> newMultiLoss(Ptr<Options> options);
 
@@ -260,7 +261,7 @@ Ptr<MultiRationalLoss> newMultiLoss(Ptr<Options> options);
 // still feels somewhat hacky.
 
 /**
- * Computes loss per given groundtruth label and then reduces to RationalLoss
+ * @brief Computes loss per given groundtruth label and then reduces to RationalLoss
  */
 class LabelwiseLoss {
 protected:
@@ -313,7 +314,7 @@ class LabelwiseLoss {
 };
 
 /**
- * Cross entropy loss across last axis, summed up over batch and time dimensions
+ * @brief Cross entropy loss across last axis, summed up over batch and time dimensions
  */
 class CrossEntropyLoss : public LabelwiseLoss {
 public:
@@ -357,7 +358,7 @@ class CrossEntropyLoss : public LabelwiseLoss {
 };
 
 /**
- * Cross entropy in rescorer used for computing sentences-level log probabilities
+ * @brief Cross entropy in rescorer used for computing sentences-level log probabilities
  */
 class RescorerLoss : public CrossEntropyLoss {
 public:
@@ -372,7 +373,7 @@ class RescorerLoss : public CrossEntropyLoss {
 };
 
 /**
- * Factory for label-wise loss functions
+ * @brief Factory for label-wise loss functions
  */
 Ptr<LabelwiseLoss> newLoss(Ptr<Options> options, bool inference);
 

From 184b64475d8c93fabf9f66d906b1eec889a5bb1d Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Fri, 18 Jan 2019 11:17:05 -0800
Subject: [PATCH 158/838] first shot at extending Reduce() with a redunction
 functor (CPU only so far)

---
 src/functional/tmp.h               | 27 ++++++++--------
 src/graph/expression_operators.cpp | 32 +++++++++++++++----
 src/graph/node_operators_unary.h   | 51 ++++++++++++++++++++++--------
 src/tensors/cpu/add.h              | 37 +++++++++++-----------
 src/tensors/tensor_operators.h     | 20 +++++++++++-
 5 files changed, 116 insertions(+), 51 deletions(-)

diff --git a/src/functional/tmp.h b/src/functional/tmp.h
index 083836603..7c8f6fa11 100755
--- a/src/functional/tmp.h
+++ b/src/functional/tmp.h
@@ -118,55 +118,56 @@ __HDI__ float apply(Functor functor,
 
 /******************************************************************************/
 
+// @TODO: Rename this. It is a reduction loop.
 template <size_t n, size_t N, size_t K>
 struct Loop {
-  template <class Functor>
+  template <class Functor, class AggFunctor>
   __HDI__ static float result(
-      Functor functor,
+      Functor functor, float aggInit, AggFunctor aggFunctor,
       functional::Array<functional::Tensor<float>, K>& in,
       const functional::Array<int, K>& pAcc,
       const functional::Array<int, N>& length,
       const functional::Array<int, N>& dim) {
-    float sum = 0;
+    float agg = aggInit;
     functional::Array<int, K> acc;
     for(int i = 0; i < length[N - n]; ++i) {
       for(size_t j = 0; j < K; ++j) {
         acc[j] = pAcc[j] + (dim[N - n] + i) * in[j].shape().bstride(N - n);
       }
-      sum += Loop<n - 1, N, K>::result(functor, in, acc, length, dim);
+      agg = aggFunctor(agg, Loop<n - 1, N, K>::result(functor, aggInit, aggFunctor, in, acc, length, dim));
     }
-    return sum;
+    return agg;
   }
 };
 
 template <size_t N, size_t K>
 struct Loop<1, N, K> {
-  template <class Functor>
+  template <class Functor, class AggFunctor>
   __HDI__ static float result(
-      Functor functor,
+      Functor functor, float aggInit, AggFunctor aggFunctor,
       functional::Array<functional::Tensor<float>, K>& in,
       const functional::Array<int, K>& pAcc,
       const functional::Array<int, N>& length,
       const functional::Array<int, N>& dim) {
-    float sum = 0;
+    float agg = aggInit;
     functional::Array<int, K> acc;
     for(int i = 0; i < length[N - 1]; ++i) {
       for(size_t j = 0; j < K; ++j) {
         acc[j] = pAcc[j] + (dim[N - 1] + i) * in[j].shape().bstride(N - 1);
       }
-      sum += apply<K>(functor, in, acc);
+      agg = aggFunctor(agg, apply<K>(functor, in, acc));
     }
-    return sum;
+    return agg;
   }
 };
 
-template <size_t N, size_t K, class Functor>
-__HDI__ float loops(Functor functor,
+template <size_t N, size_t K, class Functor, class AggFunctor>
+__HDI__ float loops(Functor functor, float aggInit, AggFunctor aggFunctor,
                     functional::Array<functional::Tensor<float>, K>& in,
                     const functional::Array<int, N>& length,
                     const functional::Array<int, N>& dim) {
   functional::Array<int, K> acc = {0};
-  return Loop<N, N, K>::result(functor, in, acc, length, dim);
+  return Loop<N, N, K>::result(functor, aggInit, aggFunctor, in, acc, length, dim);
 }
 }  // namespace functional
 }  // namespace marian
diff --git a/src/graph/expression_operators.cpp b/src/graph/expression_operators.cpp
index 9e2772bcd..f3e1b75f7 100755
--- a/src/graph/expression_operators.cpp
+++ b/src/graph/expression_operators.cpp
@@ -278,16 +278,36 @@ Expr select(Expr a, const std::vector<IndexType>& indices, int axis) {
 }
 
 Expr sum(Expr a, int ax) {
-  return Expression<SumNodeOp>(a, ax, ReduceNodeOpCode::sum);
+  return Expression<ReduceNodeOp>(a, ax, ReduceNodeOpCode::sum);
 }
 
-// log(sum(exp(a)))
-Expr logSumExp(Expr a, int ax) {
-  return Expression<SumNodeOp>(a, ax, ReduceNodeOpCode::logSumExp);
+Expr mean(Expr a, int ax) {
+  return Expression<ReduceNodeOp>(a, ax, ReduceNodeOpCode::mean);
 }
 
-Expr mean(Expr a, int ax) {
-  return Expression<SumNodeOp>(a, ax, ReduceNodeOpCode::mean);
+Expr std(Expr a, int ax) {
+  return Expression<ReduceNodeOp>(a, ax, ReduceNodeOpCode::std);
+}
+
+Expr var(Expr a, int ax) {
+  return Expression<ReduceNodeOp>(a, ax, ReduceNodeOpCode::var);
+}
+
+Expr max(Expr a, int ax) {
+  return Expression<ReduceNodeOp>(a, ax, ReduceNodeOpCode::max);
+}
+
+Expr min(Expr a, int ax) {
+  return Expression<ReduceNodeOp>(a, ax, ReduceNodeOpCode::min);
+}
+
+Expr prod(Expr a, int ax) {
+  return Expression<ReduceNodeOp>(a, ax, ReduceNodeOpCode::prod);
+}
+
+// log(sum(exp(a)))
+Expr logsumexp(Expr a, int ax) {
+  return Expression<ReduceNodeOp>(a, ax, ReduceNodeOpCode::logSumExp);
 }
 
 Expr scalar_product(Expr a, Expr b, int ax) {
diff --git a/src/graph/node_operators_unary.h b/src/graph/node_operators_unary.h
index 1716292c9..70a47d5e7 100755
--- a/src/graph/node_operators_unary.h
+++ b/src/graph/node_operators_unary.h
@@ -413,15 +413,19 @@ struct LogSoftmaxNodeOp : public UnaryNodeOp {
 };
 
 enum class ReduceNodeOpCode {
-  sum, mean, min, max, logSumExp
+  sum, mean, std, var, min, max, prod, logSumExp
 };
 
-struct SumNodeOp : public UnaryNodeOp {
+struct ReduceNodeOp : public UnaryNodeOp {
   int axis_;
   ReduceNodeOpCode opCode_;
+  int reducedDim_; // dimension of axis being reduced, e.g. used in mean()
 
-  SumNodeOp(Expr a, int axis, ReduceNodeOpCode opCode)
-      : UnaryNodeOp(a, newShape(a, axis)), opCode_(opCode){}
+  ReduceNodeOp(Expr a, int axis, ReduceNodeOpCode opCode)
+      : UnaryNodeOp(a, newShape(a, axis)), opCode_(opCode)
+  {
+    reducedDim_ = child(0)->shape().elements() / val_->shape().elements(); // e.g. used in mean()
+  }
 
   NodeOps forwardOps() override {
     using namespace functional;
@@ -430,7 +434,20 @@ struct SumNodeOp : public UnaryNodeOp {
     case ReduceNodeOpCode::sum:
       return {NodeOp(Reduce(_1, val_, child(0)->val()))};
     case ReduceNodeOpCode::mean:
-      return {NodeOp(Reduce(_1, (float)val_->shape().elements() / (float)child(0)->shape().elements(), val_, child(0)->val()))};
+      return {NodeOp(Reduce(_1, 1.0f / (float)reducedDim_, val_, child(0)->val()))};
+    case ReduceNodeOpCode::std:
+      return {NodeOp(Reduce(_1 * _1, 1.0f / (float)reducedDim_, val_, child(0)->val());
+                     Element(_1 = sqrt(_1), val_, val_))};
+    case ReduceNodeOpCode::var:
+      return {NodeOp(Reduce(_1 * _1, 1.0f / (float)reducedDim_, val_, child(0)->val()))};
+    case ReduceNodeOpCode::min:
+      return {NodeOp(Reduce(_1, FLT_MAX, min(_1,_2), val_, child(0)->val()))};
+    case ReduceNodeOpCode::max:
+      return {NodeOp(Reduce(_1, -FLT_MAX, max(_1,_2), val_, child(0)->val()))};
+    case ReduceNodeOpCode::prod:
+      return {NodeOp(Reduce(_1, 1.0f, _1 * _2, val_, child(0)->val()))};
+    case ReduceNodeOpCode::logSumExp:
+      return {NodeOp(Reduce(_1, -FLT_MAX, logaddexp(_1,_2), val_, child(0)->val()))};
     default:
       ABORT("Unexpected reduction op-code {}", (int)opCode_);
     }
@@ -442,7 +459,12 @@ struct SumNodeOp : public UnaryNodeOp {
     case ReduceNodeOpCode::sum:
       return {NodeOp(Add(_1, child(0)->grad(), adj_))};
     case ReduceNodeOpCode::mean:
-      return {NodeOp(Add(_1, (float)val_->shape().elements() / (float)child(0)->shape().elements(), child(0)->grad(), adj_))};
+      return {NodeOp(Add(_1, 1.0f / (float)reducedDim_, child(0)->grad(), adj_))};
+    //case ReduceNodeOpCode::std:
+    //case ReduceNodeOpCode::var:
+    //case ReduceNodeOpCode::min:
+    //case ReduceNodeOpCode::max:
+    //case ReduceNodeOpCode::logSumExp:
     default:
       ABORT("Unexpected reduction op-code {}", (int)opCode_);
     }
@@ -458,12 +480,15 @@ struct SumNodeOp : public UnaryNodeOp {
 
   const std::string type() override {
     switch (opCode_) {
-    case ReduceNodeOpCode::sum:
-      return "sum";
-    case ReduceNodeOpCode::mean:
-      return "mean";
-    default:
-      ABORT("Unexpected reduction op-code {}", (int)opCode_);
+    case ReduceNodeOpCode::sum:       return "sum";
+    case ReduceNodeOpCode::mean:      return "mean";
+    case ReduceNodeOpCode::std:       return "std";
+    case ReduceNodeOpCode::var:       return "var";
+    case ReduceNodeOpCode::min:       return "min";
+    case ReduceNodeOpCode::max:       return "max";
+    case ReduceNodeOpCode::prod:      return "prod";
+    case ReduceNodeOpCode::logSumExp: return "logSumExp";
+    default: ABORT("Unexpected reduction op-code {}", (int)opCode_);
     }
   }
 
@@ -481,7 +506,7 @@ struct SumNodeOp : public UnaryNodeOp {
   virtual bool equal(Expr node) override {
     if(!NaryNodeOp::equal(node))
       return false;
-    Ptr<SumNodeOp> cnode = std::dynamic_pointer_cast<SumNodeOp>(node);
+    Ptr<ReduceNodeOp> cnode = std::dynamic_pointer_cast<ReduceNodeOp>(node);
     if(!cnode)
       return false;
     if(axis_ != cnode->axis_ || opCode_ != cnode->opCode_)
diff --git a/src/tensors/cpu/add.h b/src/tensors/cpu/add.h
index 38a0684dd..4bae5bb59 100755
--- a/src/tensors/cpu/add.h
+++ b/src/tensors/cpu/add.h
@@ -15,8 +15,8 @@ namespace marian {
 
 namespace cpu {
 
-template <size_t K, class Functor>
-void gAddGeneric(Functor functor,
+template <size_t K, class Functor, class AggFunctor>
+void gAggregateGeneric(Functor functor, float aggInit, AggFunctor aggFunctor,
                  const functional::Shape full,
                  functional::Tensor<float> out,
                  functional::Array<functional::Tensor<float>, K> ins,
@@ -34,16 +34,16 @@ void gAddGeneric(Functor functor,
   functional::Array<int, N> dims;
   for(int index = 0; index < outLength; ++index) {
     if(same) {
-      out[index] += functional::apply(functor, ins, index) * scale;
+      out[index] = aggFunctor(out[index], functional::apply(functor, ins, index) * scale);
     } else {
       out.shape().dims(index, dims);
-      out[index] += functional::loops(functor, ins, len, dims) * scale;
+      out[index] = aggFunctor(out[index], functional::loops(functor, aggInit, aggFunctor, ins, len, dims) * scale);
     }
   }
 }
 
-template <size_t K, class Functor>
-void gAddEqual(Functor functor,
+template <size_t K, class Functor, class AggFunctor>
+void gAggregateEqual(Functor functor, AggFunctor aggFunctor,
                functional::Tensor<float> out,
                functional::Array<functional::Tensor<float>, K> ins,
                float scale,
@@ -61,12 +61,12 @@ void gAddEqual(Functor functor,
         indices[i] = ins[i].shape().bindex(dims);
     }
 
-    out[index] += functional::apply(functor, ins, indices) * scale;
+    out[index] = aggFunctor(out[index], functional::apply(functor, ins, indices) * scale);
   }
 }
 
-template <size_t K, class Functor>
-void gAddReduce(Functor functor,
+template <size_t K, class Functor, class AggFunctor>
+void gAggregateReduce(Functor functor, float aggInit, AggFunctor aggFunctor,
                 const functional::Shape full,
                 functional::Tensor<float> out,
                 functional::Array<functional::Tensor<float>, K> ins,
@@ -79,10 +79,10 @@ void gAddReduce(Functor functor,
     same = same && ins[i].shape().elements() == full.elements();
 
   for(int j = 0; j < rows; ++j) {
-    float sum = 0;
+    float colSum = aggInit;
     if(same) {
       for(int id = 0; id < cols; ++id)
-        sum += functional::apply(functor, ins, j * cols + id);
+        colSum = aggFunctor(colSum, functional::apply(functor, ins, j * cols + id));
     } else {
       functional::Array<int, functional::Shape::size()> dims;
       for(int id = 0; id < cols; ++id) {
@@ -90,15 +90,15 @@ void gAddReduce(Functor functor,
         functional::Array<int, K> indices;
         for(size_t i = 0; i < K; ++i)
           indices[i] = ins[i].shape().bindex(dims);
-        sum += functional::apply(functor, ins, indices);
+        colSum = aggFunctor(colSum, functional::apply(functor, ins, indices));
       }
     }
-    out[j] += sum * scale;
+    out[j] = aggFunctor(out[j], colSum * scale);
   }
 }
 
-template <class Functor, class... Tensors>
-void Add(Functor functor, float scale, marian::Tensor out, Tensors... tensors) {
+template <class Functor, class AggFunctor, class... Tensors>
+void Aggregate(Functor functor, float aggInit, AggFunctor aggFunctor, float scale, marian::Tensor out, Tensors... tensors) {
   auto full = marian::Shape::broadcast({out, tensors...});
 
   //int length = out->shape().elements();
@@ -111,15 +111,16 @@ void Add(Functor functor, float scale, marian::Tensor out, Tensors... tensors) {
   if(full.back() != 1 && out->shape().back() == 1) {
     //size_t m = full.elements() / length;
     //size_t k = full.back();
-    cpu::gAddReduce(functor, full, gOut, gIns, scale);
+    cpu::gAggregateReduce(functor, aggInit, aggFunctor, full, gOut, gIns, scale);
   } else if(out->shape() == full) {
     bool broadcast = false;
     for(size_t i = 0; i < K; ++i)
       broadcast = broadcast || gOut.shape() != gIns[i].shape();
-    cpu::gAddEqual(functor, gOut, gIns, scale, broadcast);
+    cpu::gAggregateEqual(functor, aggFunctor, gOut, gIns, scale, broadcast);
   } else {
-    cpu::gAddGeneric(functor, full, gOut, gIns, scale);
+    cpu::gAggregateGeneric(functor, aggInit, aggFunctor, full, gOut, gIns, scale);
   }
 }
+
 }  // namespace cpu
 }  // namespace marian
diff --git a/src/tensors/tensor_operators.h b/src/tensors/tensor_operators.h
index f7de2f205..b2f31adc0 100755
--- a/src/tensors/tensor_operators.h
+++ b/src/tensors/tensor_operators.h
@@ -51,7 +51,7 @@ void Add(Functor functor, float scale, marian::Tensor out, Tensors... tensors) {
     gpu::Add(functor, scale, out, tensors...);
   else
 #endif
-    cpu::Add(functor, scale, out, tensors...);
+    cpu::Aggregate(functor, 0.0f, functional::_1 + functional::_2, scale, out, tensors...);
 }
 
 template <class Functor, class... Tensors>
@@ -59,6 +59,16 @@ void Add(Functor functor, marian::Tensor out, Tensors... tensors) {
   Add(functor, 1, out, tensors...);
 }
 
+template <class Functor, class AggFunctor, class... Tensors>
+void Aggregate(Functor functor, float aggInit, AggFunctor aggFunctor, marian::Tensor out, Tensors... tensors) {
+#ifdef CUDA_FOUND
+  if(out->getBackend()->getDeviceId().type == DeviceType::gpu)
+    gpu::Aggregate(functor, aggInit, aggFunctor, out, tensors...);
+  else
+#endif
+    cpu::Aggregate(functor, aggInit, aggFunctor, 1.0f, out, tensors...);
+}
+
 template <class Functor, class... Tensors>
 void Reduce(Functor functor,
             float scale,
@@ -74,6 +84,14 @@ void Reduce(Functor functor, marian::Tensor out, Tensors... tensors) {
   Add(functor, out, tensors...);
 }
 
+template <class Functor, class AggFunctor, class... Tensors>
+void Reduce(Functor functor, float aggInit, AggFunctor aggFunctor,
+            marian::Tensor out,
+            Tensors... tensors) {
+  out->set(aggInit);
+  Aggregate(functor, aggInit, aggFunctor, out, tensors...);
+}
+
 // clang-format off
 DISPATCH7(Prod, marian::Tensor, const marian::Tensor&, const marian::Tensor&, bool, bool, float, float)
 DISPATCH8(ProdBatched, marian::Tensor, Ptr<Allocator>, const marian::Tensor, const marian::Tensor, bool, bool, float, float)

From ecffcc4cfee8c21be1265e1921ba7c3425c0ece1 Mon Sep 17 00:00:00 2001
From: Marcin Junczys-Dowmunt <Marcin.Junczys-Dowmunt@microsoft.com>
Date: Fri, 18 Jan 2019 13:51:22 -0800
Subject: [PATCH 159/838] small changes to make BERT fine-tuning work

---
 src/data/corpus_base.cpp     | 25 +++++++++++-----
 src/data/corpus_base.h       |  2 +-
 src/models/bert.h            | 58 +++++++++++++++++++++++++++++-------
 src/models/model_factory.cpp |  4 +++
 4 files changed, 70 insertions(+), 19 deletions(-)

diff --git a/src/data/corpus_base.cpp b/src/data/corpus_base.cpp
index b34bacef9..b6bd7042a 100755
--- a/src/data/corpus_base.cpp
+++ b/src/data/corpus_base.cpp
@@ -44,7 +44,7 @@ CorpusBase::CorpusBase(const std::vector<std::string>& paths,
     ABORT_IF(files_.back()->empty(), "File '{}' is empty", path);
   }
 
-  initEOS();
+  initEOS(/*training=*/true);
 }
 
 CorpusBase::CorpusBase(Ptr<Options> options, bool translate)
@@ -59,7 +59,7 @@ CorpusBase::CorpusBase(Ptr<Options> options, bool translate)
   else
     paths_ = options_->get<std::vector<std::string>>("input");
 
-  initEOS();
+  initEOS(training);
 
   std::vector<std::string> vocabPaths;
   if(!options_->get<std::vector<std::string>>("vocabs").empty())
@@ -284,7 +284,7 @@ void CorpusBase::addWeightsToBatch(Ptr<CorpusBatch> batch,
   batch->setDataWeights(weights);
 }
 
-void CorpusBase::initEOS() {
+void CorpusBase::initEOS(bool training = true) {
   // Labels fed into sub-batches that are just class-labels, not sequence labels do not require to
   // add a EOS symbol. Hence decision to add EOS is now based on input stream positions and correspoding
   // input type. 
@@ -292,11 +292,20 @@ void CorpusBase::initEOS() {
   addEOS_.resize(paths_.size(), true);
   // @TODO: think if this should be checked and processed here or in a validation step in config?
   auto inputTypes = options_->get<std::vector<std::string>>("input-types", {}); // empty list by default
-  ABORT_IF(inputTypes.size() > 0 && inputTypes.size() != paths_.size(), 
-          "Input types have been specified ({}), you need to specify one per input ({})", 
-          inputTypes.size(), 
-          paths_.size());
-  for(int i = 0; i < inputTypes.size(); ++i)
+  
+  // make sure there is an input type for each path
+  ABORT_IF(inputTypes.size() > 0 && inputTypes.size() < paths_.size(), 
+           "Input types have been specified ({}), you need to specify one per input ({})", 
+           inputTypes.size(), 
+           paths_.size());
+
+  // make sure there is an equal number of input types and paths when training
+  ABORT_IF(training && inputTypes.size() > 0 && inputTypes.size() != paths_.size(), 
+           "Input types have been specified ({}), you need to specify one per input ({})", 
+           inputTypes.size(), 
+           paths_.size());
+  
+  for(int i = 0; i < paths_.size(); ++i)
     if(inputTypes[i] == "class")
       addEOS_[i] = false;
     else if(inputTypes[i] == "sequence")
diff --git a/src/data/corpus_base.h b/src/data/corpus_base.h
index f1ac99aef..85e26d717 100755
--- a/src/data/corpus_base.h
+++ b/src/data/corpus_base.h
@@ -546,7 +546,7 @@ class CorpusBase
   /**
    * @brief Determine if EOS symbol should be added to input
    */
-  void initEOS();
+  void initEOS(bool training);
 
   /**
    * @brief Helper function converting a line of text into words using the i-th
diff --git a/src/models/bert.h b/src/models/bert.h
index 72d744e9c..288d06c8c 100644
--- a/src/models/bert.h
+++ b/src/models/bert.h
@@ -119,6 +119,28 @@ class BertBatch : public CorpusBatch {
       words[i] = maskOut(words[i], maskId, engine); // mask that position
     }
 
+    annotateSentenceIndices();
+  }
+
+  BertBatch(Ptr<CorpusBatch> batch,
+            const std::string& sepSymbol,
+            const std::string& clsSymbol)
+    : CorpusBatch(*batch),
+      maskSymbol_("dummy"), sepSymbol_(sepSymbol), clsSymbol_(clsSymbol) {
+    annotateSentenceIndices();
+  }
+
+  void annotateSentenceIndices() {
+    // BERT expects a textual first stream and a second stream with class labels
+    auto subBatch = subBatches_.front();
+    const auto& vocab = *subBatch->vocab();
+    auto& words = subBatch->data();
+
+    // Get word id of special symbols
+    Word sepId   = vocab[sepSymbol_];
+    ABORT_IF(sepId == vocab.getUnkId(),
+             "BERT separator symbol {} not found in vocabulary", sepSymbol_);
+
     int dimBatch = subBatch->batchSize();
     int dimWords = subBatch->batchWidth();
 
@@ -157,13 +179,25 @@ class BertEncoderClassifier : public EncoderClassifier, public data::RNGEngine {
   : EncoderClassifier(options) {}
 
   std::vector<Ptr<ClassifierState>> apply(Ptr<ExpressionGraph> graph, Ptr<data::CorpusBatch> batch, bool clearGraph) override {
+    std::string modelType = opt<std::string>("type");
+
     // intercept batch and annotate with BERT-specific concepts
-    auto bertBatch = New<data::BertBatch>(batch,
-                                          eng_,
-                                          opt<float>("bert-masking-fraction", 0.15f), // 15% by default according to paper
-                                          opt<std::string>("bert-mask-symbol"),
-                                          opt<std::string>("bert-sep-symbol"),
-                                          opt<std::string>("bert-class-symbol"));
+    Ptr<data::BertBatch> bertBatch;
+    if(modelType == "bert") { // full BERT pre-training
+      bertBatch = New<data::BertBatch>(batch,
+                                       eng_,
+                                       opt<float>("bert-masking-fraction", 0.15f), // 15% by default according to paper
+                                       opt<std::string>("bert-mask-symbol"),
+                                       opt<std::string>("bert-sep-symbol"),
+                                       opt<std::string>("bert-class-symbol"));
+    } else if(modelType == "bert-classifier") { // we are probably fine-tuning a BERT model for a classification task
+      bertBatch = New<data::BertBatch>(batch, 
+                                       opt<std::string>("bert-sep-symbol"),
+                                       opt<std::string>("bert-class-symbol")); // only annotate sentence separators
+    } else {
+      ABORT("Unknown BERT-style model: {}", modelType);
+    }
+
     return EncoderClassifier::apply(graph, bertBatch, clearGraph);
   }
 
@@ -185,9 +219,9 @@ class BertEncoder : public EncoderTransformer {
   Expr addSentenceEmbeddings(Expr embeddings,
                              Ptr<data::CorpusBatch> batch,
                              bool learnedPosEmbeddings) const {
+    
     Ptr<data::BertBatch> bertBatch = std::dynamic_pointer_cast<data::BertBatch>(batch);
-
-    ABORT_IF(!bertBatch, "Batch could not be converted for BERT training");
+    ABORT_IF(!bertBatch, "Batch must be BertBatch for BERT training or fine-tuning");
 
     int dimEmb = embeddings->shape()[-1];
     int dimBatch = embeddings->shape()[-2];
@@ -240,14 +274,18 @@ class BertClassifier : public ClassifierBase {
     int dimModel = classEmbeddings->shape()[-1];
     int dimTrgCls = opt<std::vector<int>>("dim-vocabs")[batchIndex_]; // Target vocab is used as class labels
 
+    std::string finetune = "";
+    if(opt<std::string>("original-type") == "bert-classifier") // seems we are fine-tuning
+      finetune = "_finetune"; // change name so we do not relead BERT output layers for fine-tuning
+
     auto output = mlp::mlp()                                          //
                     .push_back(mlp::dense()                           //
-                                 ("prefix", prefix_ + "_ff_logit_l1") //
+                                 ("prefix", prefix_ + finetune + "_ff_logit_l1") //
                                  ("dim", dimModel)                    //
                                  ("activation", mlp::act::tanh))      // @TODO: do we actually need this?
                     .push_back(mlp::output()                          //
                                  ("dim", dimTrgCls))                  //
-                                 ("prefix", prefix_ + "_ff_logit_l2") //
+                                 ("prefix", prefix_ + finetune + "_ff_logit_l2") //
                     .construct(graph);
 
     auto logits = output->apply(classEmbeddings); // class logits for each batch entry
diff --git a/src/models/model_factory.cpp b/src/models/model_factory.cpp
index a6ceb9988..66f2db928 100755
--- a/src/models/model_factory.cpp
+++ b/src/models/model_factory.cpp
@@ -84,6 +84,8 @@ Ptr<ModelBase> EncoderClassifierFactory::construct(Ptr<ExpressionGraph> graph) {
   Ptr<EncoderClassifier> enccls;
   if(options_->get<std::string>("type") == "bert") {
     enccls = New<BertEncoderClassifier>(options_);
+  } else if(options_->get<std::string>("type") == "bert-classifier") {
+    enccls = New<BertEncoderClassifier>(options_);
   } else {
     enccls = New<EncoderClassifier>(options_);
   }
@@ -228,6 +230,7 @@ Ptr<ModelBase> by_type(std::string type, usage use, Ptr<Options> options) {
 
   if(type == "bert") {                           // for full BERT training
     return models::encoder_classifier()(options) //
+        ("original-type", "bert")                // so we can query this
         ("usage", use)                           //
         .push_back(models::encoder()             //
                     ("type", "bert-encoder")     // close to original transformer encoder
@@ -243,6 +246,7 @@ Ptr<ModelBase> by_type(std::string type, usage use, Ptr<Options> options) {
 
   if(type == "bert-classifier") {                // for BERT fine-tuning on non-BERT classification task
     return models::encoder_classifier()(options) //
+        ("original-type", "bert-classifier")     // so we can query this
         ("usage", use)                           //
         .push_back(models::encoder()             //
                     ("type", "bert-encoder")     //

From fb1b0cb1f7ded54e9921c8df7886a1e3304171d6 Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Fri, 18 Jan 2019 14:29:34 -0800
Subject: [PATCH 160/838] (towards GPU aggregator)

---
 src/functional/tmp.h             | 14 ++++++++++++++
 src/graph/node_operators_unary.h |  6 +++---
 src/tensors/gpu/add.h            |  2 ++
 3 files changed, 19 insertions(+), 3 deletions(-)
 mode change 100644 => 100755 src/tensors/gpu/add.h

diff --git a/src/functional/tmp.h b/src/functional/tmp.h
index 7c8f6fa11..059f36ec4 100755
--- a/src/functional/tmp.h
+++ b/src/functional/tmp.h
@@ -169,5 +169,19 @@ __HDI__ float loops(Functor functor, float aggInit, AggFunctor aggFunctor,
   functional::Array<int, K> acc = {0};
   return Loop<N, N, K>::result(functor, aggInit, aggFunctor, in, acc, length, dim);
 }
+
+
+// dummy until changed Add to Agg
+template <size_t N, size_t K, class Functor>
+__HDI__ float loops(Functor functor,
+                    functional::Array<functional::Tensor<float>, K>& in,
+                    const functional::Array<int, N>& length,
+                    const functional::Array<int, N>& dim) {
+    (void)functor; (void)in; (void)length; (void)dim;
+    return 0;
+}
+
+
+
 }  // namespace functional
 }  // namespace marian
diff --git a/src/graph/node_operators_unary.h b/src/graph/node_operators_unary.h
index 70a47d5e7..818aed0c6 100755
--- a/src/graph/node_operators_unary.h
+++ b/src/graph/node_operators_unary.h
@@ -441,13 +441,13 @@ struct ReduceNodeOp : public UnaryNodeOp {
     case ReduceNodeOpCode::var:
       return {NodeOp(Reduce(_1 * _1, 1.0f / (float)reducedDim_, val_, child(0)->val()))};
     case ReduceNodeOpCode::min:
-      return {NodeOp(Reduce(_1, FLT_MAX, min(_1,_2), val_, child(0)->val()))};
+      return {NodeOp(Reduce(_1, std::numeric_limits<float>::max(), min(_1,_2), val_, child(0)->val()))};
     case ReduceNodeOpCode::max:
-      return {NodeOp(Reduce(_1, -FLT_MAX, max(_1,_2), val_, child(0)->val()))};
+      return {NodeOp(Reduce(_1, std::numeric_limits<float>::lowest(), max(_1,_2), val_, child(0)->val()))};
     case ReduceNodeOpCode::prod:
       return {NodeOp(Reduce(_1, 1.0f, _1 * _2, val_, child(0)->val()))};
     case ReduceNodeOpCode::logSumExp:
-      return {NodeOp(Reduce(_1, -FLT_MAX, logaddexp(_1,_2), val_, child(0)->val()))};
+      return {NodeOp(Reduce(_1, std::numeric_limits<float>::lowest(), logaddexp(_1,_2), val_, child(0)->val()))};
     default:
       ABORT("Unexpected reduction op-code {}", (int)opCode_);
     }
diff --git a/src/tensors/gpu/add.h b/src/tensors/gpu/add.h
old mode 100644
new mode 100755
index e5e22d88d..dbb2cf719
--- a/src/tensors/gpu/add.h
+++ b/src/tensors/gpu/add.h
@@ -8,5 +8,7 @@ namespace gpu {
 
 template <class Functor, class... Tensors>
 void Add(Functor functor, float scale, marian::Tensor out, Tensors... tensors);
+template <class Functor, class AggFunctor, class... Tensors>
+void Aggregate(Functor functor, float initAgg, AggFunctor aggFunctor, marian::Tensor out, Tensors... tensors);
 }
 }  // namespace marian

From 19cdedf7f888bd0befd0b6e8099473706ab21e73 Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Fri, 18 Jan 2019 15:04:02 -0800
Subject: [PATCH 161/838] resolved a template ambiguity, still not compiling on
 gcc for now

---
 src/graph/node_operators_unary.h | 21 +++++++++++----------
 src/tensors/gpu/add.inc          |  6 ++++++
 src/tensors/gpu/element.inc      |  1 +
 src/tensors/tensor_operators.h   |  2 +-
 4 files changed, 19 insertions(+), 11 deletions(-)
 mode change 100644 => 100755 src/tensors/gpu/add.inc

diff --git a/src/graph/node_operators_unary.h b/src/graph/node_operators_unary.h
index 818aed0c6..27ca6f0d4 100755
--- a/src/graph/node_operators_unary.h
+++ b/src/graph/node_operators_unary.h
@@ -437,17 +437,17 @@ struct ReduceNodeOp : public UnaryNodeOp {
       return {NodeOp(Reduce(_1, 1.0f / (float)reducedDim_, val_, child(0)->val()))};
     case ReduceNodeOpCode::std:
       return {NodeOp(Reduce(_1 * _1, 1.0f / (float)reducedDim_, val_, child(0)->val());
-                     Element(_1 = sqrt(_1), val_, val_))};
+                     Element(_1 = sqrt(_1), val_))};
     case ReduceNodeOpCode::var:
       return {NodeOp(Reduce(_1 * _1, 1.0f / (float)reducedDim_, val_, child(0)->val()))};
     case ReduceNodeOpCode::min:
-      return {NodeOp(Reduce(_1, std::numeric_limits<float>::max(), min(_1,_2), val_, child(0)->val()))};
+      return {NodeOp(Reduce(_1, min(_1,_2), std::numeric_limits<float>::max(), val_, child(0)->val()))};
     case ReduceNodeOpCode::max:
-      return {NodeOp(Reduce(_1, std::numeric_limits<float>::lowest(), max(_1,_2), val_, child(0)->val()))};
+      return {NodeOp(Reduce(_1, max(_1,_2), std::numeric_limits<float>::lowest(), val_, child(0)->val()))};
     case ReduceNodeOpCode::prod:
-      return {NodeOp(Reduce(_1, 1.0f, _1 * _2, val_, child(0)->val()))};
+      return {NodeOp(Reduce(_1, _1 * _2, 1.0f, val_, child(0)->val()))};
     case ReduceNodeOpCode::logSumExp:
-      return {NodeOp(Reduce(_1, std::numeric_limits<float>::lowest(), logaddexp(_1,_2), val_, child(0)->val()))};
+      return {NodeOp(Reduce(_1, logaddexp(_1,_2), std::numeric_limits<float>::lowest(), val_, child(0)->val()))};
     default:
       ABORT("Unexpected reduction op-code {}", (int)opCode_);
     }
@@ -460,11 +460,12 @@ struct ReduceNodeOp : public UnaryNodeOp {
       return {NodeOp(Add(_1, child(0)->grad(), adj_))};
     case ReduceNodeOpCode::mean:
       return {NodeOp(Add(_1, 1.0f / (float)reducedDim_, child(0)->grad(), adj_))};
-    //case ReduceNodeOpCode::std:
-    //case ReduceNodeOpCode::var:
-    //case ReduceNodeOpCode::min:
-    //case ReduceNodeOpCode::max:
-    //case ReduceNodeOpCode::logSumExp:
+    case ReduceNodeOpCode::std:
+    case ReduceNodeOpCode::var:
+    case ReduceNodeOpCode::min:
+    case ReduceNodeOpCode::max:
+    case ReduceNodeOpCode::logSumExp:
+      ABORT("Reduction op-code for {} not yet implemented", type());
     default:
       ABORT("Unexpected reduction op-code {}", (int)opCode_);
     }
diff --git a/src/tensors/gpu/add.inc b/src/tensors/gpu/add.inc
old mode 100644
new mode 100755
index 27f35b954..c2aeffb4b
--- a/src/tensors/gpu/add.inc
+++ b/src/tensors/gpu/add.inc
@@ -1,3 +1,4 @@
+// see element.inc for instructions on how to maintain this
 using namespace functional;
 template void Add<BinaryFunctor<elem::Mult, BinaryFunctor<elem::Mult, Capture, Assignee<1>>, Assignee<2>>, marian::Tensor, marian::Tensor>(BinaryFunctor<elem::Mult, BinaryFunctor<elem::Mult, Capture, Assignee<1>>, Assignee<2>>, float, marian::Tensor, marian::Tensor, marian::Tensor);
 template void Add<BinaryFunctor<elem::Mult, BinaryFunctor<elem::Mult, Capture, BinaryFunctor<elem::Div, Capture, Assignee<1>>>, Assignee<2>>, marian::Tensor, marian::Tensor>(BinaryFunctor<elem::Mult, BinaryFunctor<elem::Mult, Capture, BinaryFunctor<elem::Div, Capture, Assignee<1>>>, Assignee<2>>, float, marian::Tensor, marian::Tensor, marian::Tensor);
@@ -22,3 +23,8 @@ template void Add<BinaryFunctor<elem::Mult, BinaryFunctor<elem::Gt, Assignee<2>,
 template void Add<BinaryFunctor<elem::Mult, BinaryFunctor<elem::Leq, Assignee<2>, Assignee<3>>, Assignee<1>>, marian::Tensor, marian::Tensor, marian::Tensor>(BinaryFunctor<elem::Mult, BinaryFunctor<elem::Leq, Assignee<2>, Assignee<3>>, Assignee<1>>, float, marian::Tensor, marian::Tensor, marian::Tensor, marian::Tensor);
 template void Add<BinaryFunctor<elem::Mult, Assignee<1>, UnaryFunctor<elem::Sigmoid, BinaryFunctor<elem::Minus, Assignee<2>, Assignee<3>>>>, marian::Tensor, marian::Tensor, marian::Tensor>(BinaryFunctor<elem::Mult, Assignee<1>, UnaryFunctor<elem::Sigmoid, BinaryFunctor<elem::Minus, Assignee<2>, Assignee<3>>>>, float, marian::Tensor, marian::Tensor, marian::Tensor, marian::Tensor);
 template void Add<BinaryFunctor<elem::Div, Assignee<1>, Assignee<2> >, std::shared_ptr<marian::TensorBase>, std::shared_ptr<marian::TensorBase> >(BinaryFunctor<elem::Div, Assignee<1>, Assignee<2> >, float, std::shared_ptr<marian::TensorBase>, std::shared_ptr<marian::TensorBase>, std::shared_ptr<marian::TensorBase>);
+template void marian::gpu::Add<marian::functional::BinaryFunctor<marian::functional::elem::Mult, marian::functional::Assignee<1>, marian::functional::Assignee<1> >, std::shared_ptr<marian::TensorBase> >(marian::functional::BinaryFunctor<marian::functional::elem::Mult, marian::functional::Assignee<1>, marian::functional::Assignee<1> >, float, std::shared_ptr<marian::TensorBase>, std::shared_ptr<marian::TensorBase>);
+template void marian::gpu::Aggregate<marian::functional::Assignee<1>, marian::functional::BinaryFunctor<marian::functional::elem::Minimum, marian::functional::Assignee<1>, marian::functional::Assignee<2> >, std::shared_ptr<marian::TensorBase> >(marian::functional::Assignee<1>, float, marian::functional::BinaryFunctor<marian::functional::elem::Minimum, marian::functional::Assignee<1>, marian::functional::Assignee<2> >, std::shared_ptr<marian::TensorBase>, std::shared_ptr<marian::TensorBase>);
+template void marian::gpu::Aggregate<marian::functional::Assignee<1>, marian::functional::BinaryFunctor<marian::functional::elem::Maximum, marian::functional::Assignee<1>, marian::functional::Assignee<2> >, std::shared_ptr<marian::TensorBase> >(marian::functional::Assignee<1>, float, marian::functional::BinaryFunctor<marian::functional::elem::Maximum, marian::functional::Assignee<1>, marian::functional::Assignee<2> >, std::shared_ptr<marian::TensorBase>, std::shared_ptr<marian::TensorBase>);
+template void marian::gpu::Aggregate<marian::functional::Assignee<1>, marian::functional::BinaryFunctor<marian::functional::elem::Mult, marian::functional::Assignee<1>, marian::functional::Assignee<2> >, std::shared_ptr<marian::TensorBase> >(marian::functional::Assignee<1>, float, marian::functional::BinaryFunctor<marian::functional::elem::Mult, marian::functional::Assignee<1>, marian::functional::Assignee<2> >, std::shared_ptr<marian::TensorBase>, std::shared_ptr<marian::TensorBase>);
+template void marian::gpu::Aggregate<marian::functional::Assignee<1>, marian::functional::BinaryFunctor<marian::functional::elem::LogAddExp, marian::functional::Assignee<1>, marian::functional::Assignee<2> >, std::shared_ptr<marian::TensorBase> >(marian::functional::Assignee<1>, float, marian::functional::BinaryFunctor<marian::functional::elem::LogAddExp, marian::functional::Assignee<1>, marian::functional::Assignee<2> >, std::shared_ptr<marian::TensorBase>, std::shared_ptr<marian::TensorBase>);
diff --git a/src/tensors/gpu/element.inc b/src/tensors/gpu/element.inc
index 66f763018..f3cdea282 100755
--- a/src/tensors/gpu/element.inc
+++ b/src/tensors/gpu/element.inc
@@ -55,6 +55,7 @@ template void Element<Assign<Var<1>, BinaryFunctor<elem::Minus, Assignee<1>, Bin
 template void Element<Assign<Var<1>, BinaryFunctor<elem::Minus, Assignee<1>, BinaryFunctor<elem::Mult, Capture, BinaryFunctor<elem::Plus, BinaryFunctor<elem::Div, BinaryFunctor<elem::Div, Assignee<2>, Capture>, BinaryFunctor<elem::Plus, UnaryFunctor<elem::Sqrt, BinaryFunctor<elem::Plus, BinaryFunctor<elem::Div, UnaryFunctor<elem::sReLU, BinaryFunctor<elem::Plus, BinaryFunctor<elem::Minus, Assignee<3>, BinaryFunctor<elem::Div, BinaryFunctor<elem::Mult, BinaryFunctor<elem::Mult, Capture, Assignee<5> >, Assignee<4> >, Capture> >, BinaryFunctor<elem::Div, BinaryFunctor<elem::Mult, BinaryFunctor<elem::Mult, Capture, Assignee<4> >, Assignee<4> >, Capture> > >, Capture>, BinaryFunctor<elem::Div, BinaryFunctor<elem::Mult, Assignee<4>, Assignee<4> >, Capture> > >, Capture> >, BinaryFunctor<elem::Mult, Capture, Assignee<1> > > > > >, std::shared_ptr<marian::TensorBase>, std::shared_ptr<marian::TensorBase>, std::shared_ptr<marian::TensorBase>, std::shared_ptr<marian::TensorBase> >(Assign<Var<1>, BinaryFunctor<elem::Minus, Assignee<1>, BinaryFunctor<elem::Mult, Capture, BinaryFunctor<elem::Plus, BinaryFunctor<elem::Div, BinaryFunctor<elem::Div, Assignee<2>, Capture>, BinaryFunctor<elem::Plus, UnaryFunctor<elem::Sqrt, BinaryFunctor<elem::Plus, BinaryFunctor<elem::Div, UnaryFunctor<elem::sReLU, BinaryFunctor<elem::Plus, BinaryFunctor<elem::Minus, Assignee<3>, BinaryFunctor<elem::Div, BinaryFunctor<elem::Mult, BinaryFunctor<elem::Mult, Capture, Assignee<5> >, Assignee<4> >, Capture> >, BinaryFunctor<elem::Div, BinaryFunctor<elem::Mult, BinaryFunctor<elem::Mult, Capture, Assignee<4> >, Assignee<4> >, Capture> > >, Capture>, BinaryFunctor<elem::Div, BinaryFunctor<elem::Mult, Assignee<4>, Assignee<4> >, Capture> > >, Capture> >, BinaryFunctor<elem::Mult, Capture, Assignee<1> > > > > >, std::shared_ptr<marian::TensorBase>, std::shared_ptr<marian::TensorBase>, std::shared_ptr<marian::TensorBase>, std::shared_ptr<marian::TensorBase>, std::shared_ptr<marian::TensorBase>);
 template void Element<Assign<Var<1>, BinaryFunctor<elem::Minus, Assignee<1>, BinaryFunctor<elem::Mult, Capture, BinaryFunctor<elem::Plus, BinaryFunctor<elem::Div, BinaryFunctor<elem::Div, Assignee<2>, Capture>, BinaryFunctor<elem::Plus, UnaryFunctor<elem::Sqrt, BinaryFunctor<elem::Div, Assignee<3>, Capture> >, Capture> >, BinaryFunctor<elem::Mult, Capture, Assignee<1> > > > > >, std::shared_ptr<marian::TensorBase>, std::shared_ptr<marian::TensorBase>, std::shared_ptr<marian::TensorBase>, std::shared_ptr<marian::TensorBase> >(Assign<Var<1>, BinaryFunctor<elem::Minus, Assignee<1>, BinaryFunctor<elem::Mult, Capture, BinaryFunctor<elem::Plus, BinaryFunctor<elem::Div, BinaryFunctor<elem::Div, Assignee<2>, Capture>, BinaryFunctor<elem::Plus, UnaryFunctor<elem::Sqrt, BinaryFunctor<elem::Div, Assignee<3>, Capture> >, Capture> >, BinaryFunctor<elem::Mult, Capture, Assignee<1> > > > > >, std::shared_ptr<marian::TensorBase>, std::shared_ptr<marian::TensorBase>, std::shared_ptr<marian::TensorBase>, std::shared_ptr<marian::TensorBase>, std::shared_ptr<marian::TensorBase>);
 template void marian::gpu::Element<marian::functional::Assign<marian::functional::Var<1>, marian::functional::BinaryFunctor<marian::functional::elem::NEq, marian::functional::BinaryFunctor<marian::functional::elem::Eq, marian::functional::BinaryFunctor<marian::functional::elem::Minus, marian::functional::BinaryFunctor<marian::functional::elem::Gt, marian::functional::Assignee<2>, marian::functional::Assignee<3> >, marian::functional::BinaryFunctor<marian::functional::elem::Lt, marian::functional::Assignee<2>, marian::functional::Assignee<3> > >, marian::functional::Capture>, marian::functional::Capture> >, std::shared_ptr<marian::TensorBase>, std::shared_ptr<marian::TensorBase> >(marian::functional::Assign<marian::functional::Var<1>, marian::functional::BinaryFunctor<marian::functional::elem::NEq, marian::functional::BinaryFunctor<marian::functional::elem::Eq, marian::functional::BinaryFunctor<marian::functional::elem::Minus, marian::functional::BinaryFunctor<marian::functional::elem::Gt, marian::functional::Assignee<2>, marian::functional::Assignee<3> >, marian::functional::BinaryFunctor<marian::functional::elem::Lt, marian::functional::Assignee<2>, marian::functional::Assignee<3> > >, marian::functional::Capture>, marian::functional::Capture> >, std::shared_ptr<marian::TensorBase>, std::shared_ptr<marian::TensorBase>, std::shared_ptr<marian::TensorBase>);
+template void marian::gpu::Element<marian::functional::Assign<marian::functional::Var<1>, marian::functional::UnaryFunctor<marian::functional::elem::Sqrt, marian::functional::Assignee<1> > >>(marian::functional::Assign<marian::functional::Var<1>, marian::functional::UnaryFunctor<marian::functional::elem::Sqrt, marian::functional::Assignee<1> > >, std::shared_ptr<marian::TensorBase>);
 // How to add new specializations:
 // When you use a new specialization, it will cause a link error of this form (example):
 //   .../src/tensors/tensor_operators.h:41: undefined reference to `void marian::gpu::Element<marian::functional::Assign< ... > ( ... )'
diff --git a/src/tensors/tensor_operators.h b/src/tensors/tensor_operators.h
index b2f31adc0..dd32ead60 100755
--- a/src/tensors/tensor_operators.h
+++ b/src/tensors/tensor_operators.h
@@ -85,7 +85,7 @@ void Reduce(Functor functor, marian::Tensor out, Tensors... tensors) {
 }
 
 template <class Functor, class AggFunctor, class... Tensors>
-void Reduce(Functor functor, float aggInit, AggFunctor aggFunctor,
+void Reduce(Functor functor, AggFunctor aggFunctor, float aggInit,
             marian::Tensor out,
             Tensors... tensors) {
   out->set(aggInit);

From ef456f27f67eef7cad3552a83f0556fbb199b7c8 Mon Sep 17 00:00:00 2001
From: Marcin Junczys-Dowmunt <Marcin.Junczys-Dowmunt@microsoft.com>
Date: Fri, 18 Jan 2019 15:17:56 -0800
Subject: [PATCH 162/838] make mnist run again

---
 src/examples/mnist/model.h | 5 +++--
 src/layers/loss.h          | 6 +++---
 2 files changed, 6 insertions(+), 5 deletions(-)

diff --git a/src/examples/mnist/model.h b/src/examples/mnist/model.h
index 3b5be8778..0b07f99a0 100755
--- a/src/examples/mnist/model.h
+++ b/src/examples/mnist/model.h
@@ -37,8 +37,9 @@ class MNISTCrossEntropyCost : public CostBase {
     // use CE loss
 
     auto loss = sum(cross_entropy(top->loss(), labels), /*axis =*/ 0);
-    
-    return New<SumMultiRationalLoss>(RationalLoss({loss, (float)vLabels.size()}));
+    auto multiLoss = New<SumMultiRationalLoss>();
+    multiLoss->push_back({loss, (float)vLabels.size()});
+    return multiLoss;
   }
 };
 
diff --git a/src/layers/loss.h b/src/layers/loss.h
index e92364762..4a28de7b2 100644
--- a/src/layers/loss.h
+++ b/src/layers/loss.h
@@ -128,12 +128,12 @@ class MultiRationalLoss : public RationalLoss {
   MultiRationalLoss() : RationalLoss() {}
 
   MultiRationalLoss(const RationalLoss& rl) : RationalLoss() {
-    this->push_back(rl);
+    push_back(rl);
   }
 
-  void push_back(const RationalLoss& current) {
+  virtual void push_back(const RationalLoss& current) {
     loss_   = accumulateLoss(current);
-    count_ = accumulateCount(current);
+    count_ =  accumulateCount(current);
     partialLosses_.push_back(current);
   }
 

From 53481ab6a55bbc2b4265fa32efc41ef45fdb690e Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Fri, 18 Jan 2019 17:16:17 -0800
Subject: [PATCH 163/838] towards gpu::Aggregate()

---
 src/tensors/gpu/add.cu         | 66 ++++++++++++++++++++++++++++------
 src/tensors/gpu/add.h          |  2 +-
 src/tensors/gpu/add.inc        |  8 ++---
 src/tensors/tensor_operators.h |  2 +-
 4 files changed, 61 insertions(+), 17 deletions(-)

diff --git a/src/tensors/gpu/add.cu b/src/tensors/gpu/add.cu
index 2431948ee..42800b9f6 100755
--- a/src/tensors/gpu/add.cu
+++ b/src/tensors/gpu/add.cu
@@ -16,8 +16,8 @@ namespace marian {
 
 namespace gpu {
 
-template <size_t K, class Functor>
-__global__ void gAddGeneric(Functor functor,
+template <size_t K, class Functor, class AggFunctor>
+__global__ void gAggregateGeneric(Functor functor, AggFunctor aggFunctor,
                             const functional::Shape full,
                             functional::Tensor<float> out,
                             functional::Array<functional::Tensor<float>, K> ins,
@@ -46,8 +46,8 @@ __global__ void gAddGeneric(Functor functor,
   }
 }
 
-template <size_t K, class Functor>
-__global__ void gAddEqual(Functor functor,
+template <size_t K, class Functor, class AggFunctor>
+__global__ void gAggregateEqual(Functor functor, AggFunctor aggFunctor,
                           functional::Tensor<float> out,
                           functional::Array<functional::Tensor<float>, K> ins,
                           float scale,
@@ -72,8 +72,8 @@ __global__ void gAddEqual(Functor functor,
   }
 }
 
-template <size_t K, class Functor>
-__global__ void gAddReduce(Functor functor,
+template <size_t K, class Functor, class AggFunctor>
+__global__ void gAggregateReduce(Functor functor, float aggInit, AggFunctor aggFunctor,
                            const functional::Shape full,
                            functional::Tensor<float> out,
                            functional::Array<functional::Tensor<float>, K> ins,
@@ -92,7 +92,7 @@ __global__ void gAddReduce(Functor functor,
       float* _sum = _share + blockDim.x;
 
       if(same) {
-        _sum[threadIdx.x] = 0;
+        _sum[threadIdx.x] = aggInit;
         for(int tid = 0; tid < cols; tid += blockDim.x) {
           int id = tid + threadIdx.x;
           if(id < cols)
@@ -100,7 +100,7 @@ __global__ void gAddReduce(Functor functor,
         }
       } else {
         functional::Array<int, functional::Shape::size()> dims;
-        _sum[threadIdx.x] = 0;
+        _sum[threadIdx.x] = aggInit;
 
         for(int tid = 0; tid < cols; tid += blockDim.x) {
           int id = tid + threadIdx.x;
@@ -129,6 +129,48 @@ __global__ void gAddReduce(Functor functor,
   }
 }
 
+template <class Functor, class AggFunctor, class... Tensors>
+void Aggregate(Functor functor, float aggInit, AggFunctor aggFunctor, float scale, marian::Tensor out, Tensors... tensors) {
+  cudaSetDevice(out->getDeviceId().no);
+
+  auto full = marian::Shape::broadcast({out, tensors...});
+
+  int length = out->shape().elements();
+
+  constexpr size_t K = sizeof...(Tensors);
+
+  functional::Tensor<float> gOut = out;
+  functional::Array<functional::Tensor<float>, K> gIns = {tensors...};
+
+  if(full.back() != 1 && out->shape().back() == 1) {
+    size_t m = full.elements() / length;
+    size_t k = full.back();
+
+    int blocks = std::min(MAX_BLOCKS, (int)m);
+    int threads = std::min(MAX_THREADS, (int)k);
+    int shared = sizeof(float) * threads * 2;
+
+    gAggregateReduce<<<blocks, threads, shared>>>(functor, aggInit, aggFunctor, full, gOut, gIns, scale);
+
+  } else if(out->shape() == full) {
+    int threads = std::min(MAX_THREADS, length);
+    int blocks
+        = std::min(MAX_BLOCKS, length / threads + (length % threads != 0));
+
+    bool broadcast = false;
+    for(int i = 0; i < K; ++i)
+      broadcast = broadcast || gOut.shape() != gIns[i].shape();
+    gAggregateEqual<<<blocks, threads>>>(functor, aggFunctor, gOut, gIns, scale, broadcast);
+  } else {
+    int threads = std::min(MAX_THREADS, length);
+    int blocks
+        = std::min(MAX_BLOCKS, length / threads + (length % threads != 0));
+
+    gAggregateGeneric<<<blocks, threads>>>(functor, aggFunctor, full, gOut, gIns, scale);
+  }
+}
+
+// @TODO: this is a duplicate; can be removed, but need to redo all the add.inc entries...
 template <class Functor, class... Tensors>
 void Add(Functor functor, float scale, marian::Tensor out, Tensors... tensors) {
   cudaSetDevice(out->getDeviceId().no);
@@ -142,6 +184,8 @@ void Add(Functor functor, float scale, marian::Tensor out, Tensors... tensors) {
   functional::Tensor<float> gOut = out;
   functional::Array<functional::Tensor<float>, K> gIns = {tensors...};
 
+  auto addFunctor = functional::_1 + functional::_2;
+
   if(full.back() != 1 && out->shape().back() == 1) {
     size_t m = full.elements() / length;
     size_t k = full.back();
@@ -150,7 +194,7 @@ void Add(Functor functor, float scale, marian::Tensor out, Tensors... tensors) {
     int threads = std::min(MAX_THREADS, (int)k);
     int shared = sizeof(float) * threads * 2;
 
-    gAddReduce<<<blocks, threads, shared>>>(functor, full, gOut, gIns, scale);
+    gAggregateReduce<<<blocks, threads, shared>>>(functor, 0, addFunctor, full, gOut, gIns, scale);
 
   } else if(out->shape() == full) {
     int threads = std::min(MAX_THREADS, length);
@@ -160,13 +204,13 @@ void Add(Functor functor, float scale, marian::Tensor out, Tensors... tensors) {
     bool broadcast = false;
     for(int i = 0; i < K; ++i)
       broadcast = broadcast || gOut.shape() != gIns[i].shape();
-    gAddEqual<<<blocks, threads>>>(functor, gOut, gIns, scale, broadcast);
+    gAggregateEqual<<<blocks, threads>>>(functor, addFunctor, gOut, gIns, scale, broadcast);
   } else {
     int threads = std::min(MAX_THREADS, length);
     int blocks
         = std::min(MAX_BLOCKS, length / threads + (length % threads != 0));
 
-    gAddGeneric<<<blocks, threads>>>(functor, full, gOut, gIns, scale);
+    gAggregateGeneric<<<blocks, threads>>>(functor, addFunctor, full, gOut, gIns, scale);
   }
 }
 
diff --git a/src/tensors/gpu/add.h b/src/tensors/gpu/add.h
index dbb2cf719..21e0bb96c 100755
--- a/src/tensors/gpu/add.h
+++ b/src/tensors/gpu/add.h
@@ -9,6 +9,6 @@ namespace gpu {
 template <class Functor, class... Tensors>
 void Add(Functor functor, float scale, marian::Tensor out, Tensors... tensors);
 template <class Functor, class AggFunctor, class... Tensors>
-void Aggregate(Functor functor, float initAgg, AggFunctor aggFunctor, marian::Tensor out, Tensors... tensors);
+void Aggregate(Functor functor, float initAgg, AggFunctor aggFunctor, float scale, marian::Tensor out, Tensors... tensors);
 }
 }  // namespace marian
diff --git a/src/tensors/gpu/add.inc b/src/tensors/gpu/add.inc
index c2aeffb4b..41bbe9a52 100755
--- a/src/tensors/gpu/add.inc
+++ b/src/tensors/gpu/add.inc
@@ -24,7 +24,7 @@ template void Add<BinaryFunctor<elem::Mult, BinaryFunctor<elem::Leq, Assignee<2>
 template void Add<BinaryFunctor<elem::Mult, Assignee<1>, UnaryFunctor<elem::Sigmoid, BinaryFunctor<elem::Minus, Assignee<2>, Assignee<3>>>>, marian::Tensor, marian::Tensor, marian::Tensor>(BinaryFunctor<elem::Mult, Assignee<1>, UnaryFunctor<elem::Sigmoid, BinaryFunctor<elem::Minus, Assignee<2>, Assignee<3>>>>, float, marian::Tensor, marian::Tensor, marian::Tensor, marian::Tensor);
 template void Add<BinaryFunctor<elem::Div, Assignee<1>, Assignee<2> >, std::shared_ptr<marian::TensorBase>, std::shared_ptr<marian::TensorBase> >(BinaryFunctor<elem::Div, Assignee<1>, Assignee<2> >, float, std::shared_ptr<marian::TensorBase>, std::shared_ptr<marian::TensorBase>, std::shared_ptr<marian::TensorBase>);
 template void marian::gpu::Add<marian::functional::BinaryFunctor<marian::functional::elem::Mult, marian::functional::Assignee<1>, marian::functional::Assignee<1> >, std::shared_ptr<marian::TensorBase> >(marian::functional::BinaryFunctor<marian::functional::elem::Mult, marian::functional::Assignee<1>, marian::functional::Assignee<1> >, float, std::shared_ptr<marian::TensorBase>, std::shared_ptr<marian::TensorBase>);
-template void marian::gpu::Aggregate<marian::functional::Assignee<1>, marian::functional::BinaryFunctor<marian::functional::elem::Minimum, marian::functional::Assignee<1>, marian::functional::Assignee<2> >, std::shared_ptr<marian::TensorBase> >(marian::functional::Assignee<1>, float, marian::functional::BinaryFunctor<marian::functional::elem::Minimum, marian::functional::Assignee<1>, marian::functional::Assignee<2> >, std::shared_ptr<marian::TensorBase>, std::shared_ptr<marian::TensorBase>);
-template void marian::gpu::Aggregate<marian::functional::Assignee<1>, marian::functional::BinaryFunctor<marian::functional::elem::Maximum, marian::functional::Assignee<1>, marian::functional::Assignee<2> >, std::shared_ptr<marian::TensorBase> >(marian::functional::Assignee<1>, float, marian::functional::BinaryFunctor<marian::functional::elem::Maximum, marian::functional::Assignee<1>, marian::functional::Assignee<2> >, std::shared_ptr<marian::TensorBase>, std::shared_ptr<marian::TensorBase>);
-template void marian::gpu::Aggregate<marian::functional::Assignee<1>, marian::functional::BinaryFunctor<marian::functional::elem::Mult, marian::functional::Assignee<1>, marian::functional::Assignee<2> >, std::shared_ptr<marian::TensorBase> >(marian::functional::Assignee<1>, float, marian::functional::BinaryFunctor<marian::functional::elem::Mult, marian::functional::Assignee<1>, marian::functional::Assignee<2> >, std::shared_ptr<marian::TensorBase>, std::shared_ptr<marian::TensorBase>);
-template void marian::gpu::Aggregate<marian::functional::Assignee<1>, marian::functional::BinaryFunctor<marian::functional::elem::LogAddExp, marian::functional::Assignee<1>, marian::functional::Assignee<2> >, std::shared_ptr<marian::TensorBase> >(marian::functional::Assignee<1>, float, marian::functional::BinaryFunctor<marian::functional::elem::LogAddExp, marian::functional::Assignee<1>, marian::functional::Assignee<2> >, std::shared_ptr<marian::TensorBase>, std::shared_ptr<marian::TensorBase>);
+template void marian::gpu::Aggregate<marian::functional::Assignee<1>, marian::functional::BinaryFunctor<marian::functional::elem::Minimum, marian::functional::Assignee<1>, marian::functional::Assignee<2> >, std::shared_ptr<marian::TensorBase> >(marian::functional::Assignee<1>, float, marian::functional::BinaryFunctor<marian::functional::elem::Minimum, marian::functional::Assignee<1>, marian::functional::Assignee<2> >, float, std::shared_ptr<marian::TensorBase>, std::shared_ptr<marian::TensorBase>);
+template void marian::gpu::Aggregate<marian::functional::Assignee<1>, marian::functional::BinaryFunctor<marian::functional::elem::Maximum, marian::functional::Assignee<1>, marian::functional::Assignee<2> >, std::shared_ptr<marian::TensorBase> >(marian::functional::Assignee<1>, float, marian::functional::BinaryFunctor<marian::functional::elem::Maximum, marian::functional::Assignee<1>, marian::functional::Assignee<2> >, float, std::shared_ptr<marian::TensorBase>, std::shared_ptr<marian::TensorBase>);
+template void marian::gpu::Aggregate<marian::functional::Assignee<1>, marian::functional::BinaryFunctor<marian::functional::elem::Mult, marian::functional::Assignee<1>, marian::functional::Assignee<2> >, std::shared_ptr<marian::TensorBase> >(marian::functional::Assignee<1>, float, marian::functional::BinaryFunctor<marian::functional::elem::Mult, marian::functional::Assignee<1>, marian::functional::Assignee<2> >, float, std::shared_ptr<marian::TensorBase>, std::shared_ptr<marian::TensorBase>);
+template void marian::gpu::Aggregate<marian::functional::Assignee<1>, marian::functional::BinaryFunctor<marian::functional::elem::LogAddExp, marian::functional::Assignee<1>, marian::functional::Assignee<2> >, std::shared_ptr<marian::TensorBase> >(marian::functional::Assignee<1>, float, marian::functional::BinaryFunctor<marian::functional::elem::LogAddExp, marian::functional::Assignee<1>, marian::functional::Assignee<2> >, float, std::shared_ptr<marian::TensorBase>, std::shared_ptr<marian::TensorBase>);
diff --git a/src/tensors/tensor_operators.h b/src/tensors/tensor_operators.h
index dd32ead60..8dd17a5ce 100755
--- a/src/tensors/tensor_operators.h
+++ b/src/tensors/tensor_operators.h
@@ -63,7 +63,7 @@ template <class Functor, class AggFunctor, class... Tensors>
 void Aggregate(Functor functor, float aggInit, AggFunctor aggFunctor, marian::Tensor out, Tensors... tensors) {
 #ifdef CUDA_FOUND
   if(out->getBackend()->getDeviceId().type == DeviceType::gpu)
-    gpu::Aggregate(functor, aggInit, aggFunctor, out, tensors...);
+    gpu::Aggregate(functor, aggInit, aggFunctor, 1.0f, out, tensors...);
   else
 #endif
     cpu::Aggregate(functor, aggInit, aggFunctor, 1.0f, out, tensors...);

From 7ca0460bea75fe11978f594642e1dd353f0727f9 Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Fri, 18 Jan 2019 17:27:04 -0800
Subject: [PATCH 164/838] completed gpu::Aggregate()

---
 src/functional/tmp.h   | 14 --------------
 src/tensors/gpu/add.cu | 20 ++++++++++----------
 2 files changed, 10 insertions(+), 24 deletions(-)

diff --git a/src/functional/tmp.h b/src/functional/tmp.h
index 059f36ec4..7c8f6fa11 100755
--- a/src/functional/tmp.h
+++ b/src/functional/tmp.h
@@ -169,19 +169,5 @@ __HDI__ float loops(Functor functor, float aggInit, AggFunctor aggFunctor,
   functional::Array<int, K> acc = {0};
   return Loop<N, N, K>::result(functor, aggInit, aggFunctor, in, acc, length, dim);
 }
-
-
-// dummy until changed Add to Agg
-template <size_t N, size_t K, class Functor>
-__HDI__ float loops(Functor functor,
-                    functional::Array<functional::Tensor<float>, K>& in,
-                    const functional::Array<int, N>& length,
-                    const functional::Array<int, N>& dim) {
-    (void)functor; (void)in; (void)length; (void)dim;
-    return 0;
-}
-
-
-
 }  // namespace functional
 }  // namespace marian
diff --git a/src/tensors/gpu/add.cu b/src/tensors/gpu/add.cu
index 42800b9f6..32c12783c 100755
--- a/src/tensors/gpu/add.cu
+++ b/src/tensors/gpu/add.cu
@@ -17,7 +17,7 @@ namespace marian {
 namespace gpu {
 
 template <size_t K, class Functor, class AggFunctor>
-__global__ void gAggregateGeneric(Functor functor, AggFunctor aggFunctor,
+__global__ void gAggregateGeneric(Functor functor, float aggInit, AggFunctor aggFunctor,
                             const functional::Shape full,
                             functional::Tensor<float> out,
                             functional::Array<functional::Tensor<float>, K> ins,
@@ -37,10 +37,10 @@ __global__ void gAggregateGeneric(Functor functor, AggFunctor aggFunctor,
     int index = bid + blockDim.x * blockIdx.x + threadIdx.x;
     if(index < outLength) {
       if(same) {
-        out[index] += functional::apply(functor, ins, index) * scale;
+        out[index] = aggFunctor(out[index], functional::apply(functor, ins, index) * scale);
       } else {
         out.shape().dims(index, dims);
-        out[index] += functional::loops(functor, ins, len, dims) * scale;
+        out[index] = aggFunctor(out[index], functional::loops(functor, aggInit, aggFunctor, ins, len, dims) * scale);
       }
     }
   }
@@ -67,7 +67,7 @@ __global__ void gAggregateEqual(Functor functor, AggFunctor aggFunctor,
           indices[i] = ins[i].shape().bindex(dims);
       }
 
-      out[index] += functional::apply(functor, ins, indices) * scale;
+      out[index] = aggFunctor(out[index], functional::apply(functor, ins, indices) * scale);
     }
   }
 }
@@ -96,7 +96,7 @@ __global__ void gAggregateReduce(Functor functor, float aggInit, AggFunctor aggF
         for(int tid = 0; tid < cols; tid += blockDim.x) {
           int id = tid + threadIdx.x;
           if(id < cols)
-            _sum[threadIdx.x] += functional::apply(functor, ins, j * cols + id);
+            _sum[threadIdx.x] = aggFunctor(_sum[threadIdx.x], functional::apply(functor, ins, j * cols + id));
         }
       } else {
         functional::Array<int, functional::Shape::size()> dims;
@@ -109,7 +109,7 @@ __global__ void gAggregateReduce(Functor functor, float aggInit, AggFunctor aggF
             functional::Array<int, K> indices;
             for(int i = 0; i < K; ++i)
               indices[i] = ins[i].shape().bindex(dims);
-            _sum[threadIdx.x] += functional::apply(functor, ins, indices);
+            _sum[threadIdx.x] = aggFunctor(_sum[threadIdx.x], functional::apply(functor, ins, indices));
           }
         }
       }
@@ -119,12 +119,12 @@ __global__ void gAggregateReduce(Functor functor, float aggInit, AggFunctor aggF
         __syncthreads();
         int skip = (len + 1) >> 1;
         if(threadIdx.x < (len >> 1)) {
-          _sum[threadIdx.x] += _sum[threadIdx.x + skip];
+          _sum[threadIdx.x] = aggFunctor(_sum[threadIdx.x], _sum[threadIdx.x + skip]);
         }
         len = (len + 1) >> 1;
       }
       __syncthreads();
-      out[j] += _sum[0] * scale;
+      out[j] = aggFunctor(out[j], _sum[0] * scale);
     }
   }
 }
@@ -166,7 +166,7 @@ void Aggregate(Functor functor, float aggInit, AggFunctor aggFunctor, float scal
     int blocks
         = std::min(MAX_BLOCKS, length / threads + (length % threads != 0));
 
-    gAggregateGeneric<<<blocks, threads>>>(functor, aggFunctor, full, gOut, gIns, scale);
+    gAggregateGeneric<<<blocks, threads>>>(functor, aggInit, aggFunctor, full, gOut, gIns, scale);
   }
 }
 
@@ -210,7 +210,7 @@ void Add(Functor functor, float scale, marian::Tensor out, Tensors... tensors) {
     int blocks
         = std::min(MAX_BLOCKS, length / threads + (length % threads != 0));
 
-    gAggregateGeneric<<<blocks, threads>>>(functor, addFunctor, full, gOut, gIns, scale);
+    gAggregateGeneric<<<blocks, threads>>>(functor, 0, addFunctor, full, gOut, gIns, scale);
   }
 }
 

From f211fffbe38b5d172a1671d155558802d78cfdfe Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Fri, 18 Jan 2019 17:44:23 -0800
Subject: [PATCH 165/838] (minor bug fix)

---
 src/graph/node_operators_unary.h | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/src/graph/node_operators_unary.h b/src/graph/node_operators_unary.h
index 27ca6f0d4..ed5c4c0ce 100755
--- a/src/graph/node_operators_unary.h
+++ b/src/graph/node_operators_unary.h
@@ -424,7 +424,8 @@ struct ReduceNodeOp : public UnaryNodeOp {
   ReduceNodeOp(Expr a, int axis, ReduceNodeOpCode opCode)
       : UnaryNodeOp(a, newShape(a, axis)), opCode_(opCode)
   {
-    reducedDim_ = child(0)->shape().elements() / val_->shape().elements(); // e.g. used in mean()
+    reducedDim_ = a->shape()[axis]; // e.g. used in mean()
+    ABORT_IF(reducedDim_ != a->shape().elements() / shape().elements(), "bug in determining reducedDim");
   }
 
   NodeOps forwardOps() override {

From 1932ada095126da267282ba9c81ecacf94ec8069 Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Fri, 18 Jan 2019 18:42:56 -0800
Subject: [PATCH 166/838] added tests for all reduction operators

---
 src/graph/expression_operators.cpp |  4 +--
 src/graph/expression_operators.h   |  9 +++--
 src/graph/node_operators_unary.h   | 20 +++++++----
 src/tests/operator_tests.cpp       | 54 +++++++++++++++++++++---------
 4 files changed, 61 insertions(+), 26 deletions(-)

diff --git a/src/graph/expression_operators.cpp b/src/graph/expression_operators.cpp
index f3e1b75f7..4e5ac5787 100755
--- a/src/graph/expression_operators.cpp
+++ b/src/graph/expression_operators.cpp
@@ -286,11 +286,11 @@ Expr mean(Expr a, int ax) {
 }
 
 Expr std(Expr a, int ax) {
-  return Expression<ReduceNodeOp>(a, ax, ReduceNodeOpCode::std);
+  return Expression<ReduceNodeOp>(a - mean(a,ax), ax, ReduceNodeOpCode::rms);
 }
 
 Expr var(Expr a, int ax) {
-  return Expression<ReduceNodeOp>(a, ax, ReduceNodeOpCode::var);
+  return Expression<ReduceNodeOp>(a - mean(a, ax), ax, ReduceNodeOpCode::sumSqr);
 }
 
 Expr max(Expr a, int ax) {
diff --git a/src/graph/expression_operators.h b/src/graph/expression_operators.h
index e2c1d9226..51d4dfc0d 100755
--- a/src/graph/expression_operators.h
+++ b/src/graph/expression_operators.h
@@ -151,6 +151,13 @@ Expr select(Expr a, const std::vector<IndexType>& indices, int axis);
 /*********************************************************/
 
 Expr sum(Expr a, int ax = 0);
+Expr mean(Expr a, int ax = 0);
+Expr std(Expr a, int ax);
+Expr var(Expr a, int ax);
+Expr max(Expr a, int ax);
+Expr min(Expr a, int ax);
+Expr prod(Expr a, int ax);
+Expr logsumexp(Expr a, int ax);
 
 Expr softmax(Expr x, int axis = -1);
 
@@ -160,8 +167,6 @@ Expr softmax(Expr a, Expr zeroOneMask, int axis = -1);
 
 Expr logsoftmax(Expr a);
 
-Expr mean(Expr a, int ax = 0);
-
 Expr cross_entropy(Expr a, Expr b);
 
 Expr scalar_product(Expr a, Expr b, int ax = 0);
diff --git a/src/graph/node_operators_unary.h b/src/graph/node_operators_unary.h
index ed5c4c0ce..086906a1c 100755
--- a/src/graph/node_operators_unary.h
+++ b/src/graph/node_operators_unary.h
@@ -413,7 +413,7 @@ struct LogSoftmaxNodeOp : public UnaryNodeOp {
 };
 
 enum class ReduceNodeOpCode {
-  sum, mean, std, var, min, max, prod, logSumExp
+  sum, mean, rms, sumSqr, min, max, prod, logSumExp
 };
 
 struct ReduceNodeOp : public UnaryNodeOp {
@@ -436,10 +436,10 @@ struct ReduceNodeOp : public UnaryNodeOp {
       return {NodeOp(Reduce(_1, val_, child(0)->val()))};
     case ReduceNodeOpCode::mean:
       return {NodeOp(Reduce(_1, 1.0f / (float)reducedDim_, val_, child(0)->val()))};
-    case ReduceNodeOpCode::std:
+    case ReduceNodeOpCode::rms:
       return {NodeOp(Reduce(_1 * _1, 1.0f / (float)reducedDim_, val_, child(0)->val());
                      Element(_1 = sqrt(_1), val_))};
-    case ReduceNodeOpCode::var:
+    case ReduceNodeOpCode::sumSqr:
       return {NodeOp(Reduce(_1 * _1, 1.0f / (float)reducedDim_, val_, child(0)->val()))};
     case ReduceNodeOpCode::min:
       return {NodeOp(Reduce(_1, min(_1,_2), std::numeric_limits<float>::max(), val_, child(0)->val()))};
@@ -461,12 +461,18 @@ struct ReduceNodeOp : public UnaryNodeOp {
       return {NodeOp(Add(_1, child(0)->grad(), adj_))};
     case ReduceNodeOpCode::mean:
       return {NodeOp(Add(_1, 1.0f / (float)reducedDim_, child(0)->grad(), adj_))};
-    case ReduceNodeOpCode::std:
-    case ReduceNodeOpCode::var:
+    case ReduceNodeOpCode::rms:
+    case ReduceNodeOpCode::sumSqr:
     case ReduceNodeOpCode::min:
     case ReduceNodeOpCode::max:
     case ReduceNodeOpCode::logSumExp:
       ABORT("Reduction op-code for {} not yet implemented", type());
+// NDArrayView::NumericOperation({ const_cast<NDArrayView*>(outputGradientValue)->shared_from_this(),
+//                                 const_cast<NDArrayView*>( inputValues[0]    )->shared_from_this(),
+//                                 const_cast<NDArrayView*>(outputValue        )->shared_from_this() }, alpha,
+//                               Microsoft::MSR::CNTK::ElementWiseOperator::opElementwiseProductWithExpOfDiff,
+//                               gradient, beta,
+//                               Microsoft::MSR::CNTK::ElementWiseOperator::opSum);
     default:
       ABORT("Unexpected reduction op-code {}", (int)opCode_);
     }
@@ -484,8 +490,8 @@ struct ReduceNodeOp : public UnaryNodeOp {
     switch (opCode_) {
     case ReduceNodeOpCode::sum:       return "sum";
     case ReduceNodeOpCode::mean:      return "mean";
-    case ReduceNodeOpCode::std:       return "std";
-    case ReduceNodeOpCode::var:       return "var";
+    case ReduceNodeOpCode::rms:      return "rms";
+    case ReduceNodeOpCode::sumSqr:      return "sumSqr";
     case ReduceNodeOpCode::min:       return "min";
     case ReduceNodeOpCode::max:       return "max";
     case ReduceNodeOpCode::prod:      return "prod";
diff --git a/src/tests/operator_tests.cpp b/src/tests/operator_tests.cpp
index e4610dd92..a6e298ea1 100755
--- a/src/tests/operator_tests.cpp
+++ b/src/tests/operator_tests.cpp
@@ -204,12 +204,19 @@ void tests(DeviceType device) {
     graph->clear();
     values.clear();
 
-    std::vector<float> vA({1, 2, 3, 4, 5, 6, 7, 8});
-    std::vector<float> vS1({6, 8, 10, 12});
-    std::vector<float> vS2({10, 26});
-
-    std::vector<float> vW({2.77778f, 6.77778f});
-
+    std::vector<float> vA({1, 6, 3, 8,
+                           5, 2, 7, 4});
+    // import numpy as np
+    // a = np.array([[1, 6, 3, 8], [5, 2, 7, 4]])
+    std::vector<float> vS1({6, 8, 10, 12});              // s1 = np.sum(a, axis=0)
+    std::vector<float> vS2({18, 18});                    // np.sum(a, axis = 1)
+    std::vector<float> vS4({2.6925824f, 1.80277564f});   // np.std(a, axis = 1)
+    std::vector<float> vV5({7.25, 3.25});                // np.var(a, axis = 1)
+    std::vector<float> vM6({8, 7});                      // np.max(a, axis = 1)
+    std::vector<float> vM7({1, 2});                      // np.min(a, axis = 1)
+    std::vector<float> vP8({144, 280});                  // np.prod(a, axis = 1)
+    std::vector<float> vL9({8.13364336f, 7.17551536f});  // np.log(np.sum(np.exp(a), axis=1))
+    std::vector<float> vW({5.0f, 4.55555556f});          // np.mean(a*s1,axis=-1) / np.mean(s1,axis=-1)
 
     auto a = graph->constant({2, 4}, inits::from_vector(vA));
 
@@ -218,6 +225,14 @@ void tests(DeviceType device) {
 
     auto m3 = mean(s1, /*axis=*/ 1);
 
+    auto s4 = marian::std(a, /*axis=*/ 1);
+    auto v5 = var(a, /*axis=*/ 1);
+
+    auto m6 = max(a, /*axis=*/ 1);
+    auto m7 = min(a, /*axis=*/ 1);
+    auto p8 = prod(a, /*axis=*/ 1);
+    auto l9 = logsumexp(a, /*axis=*/ 1);
+
     auto sp = scalar_product(s2, s2, /*axis=*/ 0);
 
     auto wa = weighted_average(a, s1, /*axis=*/ -1);
@@ -227,21 +242,30 @@ void tests(DeviceType device) {
     CHECK(s1->shape() == Shape({1, 4}));
     CHECK(s2->shape() == Shape({2, 1}));
     CHECK(m3->shape() == Shape({1, 1}));
+    CHECK(s4->shape() == Shape({2, 1}));
+    CHECK(v5->shape() == Shape({2, 1}));
+    CHECK(m6->shape() == Shape({2, 1}));
+    CHECK(m7->shape() == Shape({2, 1}));
+    CHECK(p8->shape() == Shape({2, 1}));
+    CHECK(l9->shape() == Shape({2, 1}));
     CHECK(sp->shape() == Shape({1, 1}));
     CHECK(wa->shape() == Shape({2, 1}));
 
-    s1->val()->get(values);
-    CHECK( values == vS1 );
+    s1->val()->get(values); CHECK(values == vS1);
+    s2->val()->get(values); CHECK(values == vS2);
 
-    s2->val()->get(values);
-    CHECK( values == vS2 );
+    CHECK(m3->val()->scalar() == 9);
 
-    CHECK( m3->val()->scalar() == 9 );
-    CHECK( sp->val()->scalar() == 776 );
+    s4->val()->get(values); CHECK(std::equal(values.begin(), values.end(), vS4.begin(), floatApprox));
+    v5->val()->get(values); CHECK(values == vV5);
+    m6->val()->get(values); CHECK(values == vM6);
+    m7->val()->get(values); CHECK(values == vM7);
+    p8->val()->get(values); CHECK(values == vP8);
+    l9->val()->get(values); CHECK(std::equal(values.begin(), values.end(), vL9.begin(), floatApprox));
 
-    wa->val()->get(values);
-    CHECK( std::equal(values.begin(), values.end(),
-                      vW.begin(), floatApprox) );
+    CHECK(sp->val()->scalar() == 648);
+
+    wa->val()->get(values); CHECK(std::equal(values.begin(), values.end(), vW.begin(), floatApprox));
   }
 
   SECTION("concatenation") {

From 7ce226739248bf3479c409159b4685a2bbb9dc8c Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Fri, 18 Jan 2019 19:08:40 -0800
Subject: [PATCH 167/838] added gradients for min(), max(), and logsumexp()

---
 src/graph/node_operators_unary.h | 17 +++++++++--------
 src/tensors/gpu/add.inc          |  2 ++
 2 files changed, 11 insertions(+), 8 deletions(-)

diff --git a/src/graph/node_operators_unary.h b/src/graph/node_operators_unary.h
index 086906a1c..2b9395436 100755
--- a/src/graph/node_operators_unary.h
+++ b/src/graph/node_operators_unary.h
@@ -463,16 +463,17 @@ struct ReduceNodeOp : public UnaryNodeOp {
       return {NodeOp(Add(_1, 1.0f / (float)reducedDim_, child(0)->grad(), adj_))};
     case ReduceNodeOpCode::rms:
     case ReduceNodeOpCode::sumSqr:
+      ABORT("Gradient of reduction op-code for {} not yet implemented", type());
     case ReduceNodeOpCode::min:
     case ReduceNodeOpCode::max:
-    case ReduceNodeOpCode::logSumExp:
-      ABORT("Reduction op-code for {} not yet implemented", type());
-// NDArrayView::NumericOperation({ const_cast<NDArrayView*>(outputGradientValue)->shared_from_this(),
-//                                 const_cast<NDArrayView*>( inputValues[0]    )->shared_from_this(),
-//                                 const_cast<NDArrayView*>(outputValue        )->shared_from_this() }, alpha,
-//                               Microsoft::MSR::CNTK::ElementWiseOperator::opElementwiseProductWithExpOfDiff,
-//                               gradient, beta,
-//                               Microsoft::MSR::CNTK::ElementWiseOperator::opSum);
+      return {NodeOp(Add((_1 == _2) * _3,  // adj_ gets routed into the min/max value  --@REVIEW: is this correct?
+                         child(0)->grad(), child(0)->val(), val_, adj_))};
+    case ReduceNodeOpCode::logSumExp:       // y = log(sum_j exp(x_j))
+      return {NodeOp(Add(_1 * exp(_2 - _3), // dJ/dx_i = dJ/dy * 1/(sum_j exp(x_j)) exp(x_i) = dJ/dy * exp(x_i - y))
+                         child(0)->grad(),  // out = dJ/dx_i
+                         adj_,              // _1 = dJ/dy
+                         child(0)->val(),   // _2 = x_i
+                         val_))};           // _3 = y
     default:
       ABORT("Unexpected reduction op-code {}", (int)opCode_);
     }
diff --git a/src/tensors/gpu/add.inc b/src/tensors/gpu/add.inc
index 41bbe9a52..2dac53996 100755
--- a/src/tensors/gpu/add.inc
+++ b/src/tensors/gpu/add.inc
@@ -28,3 +28,5 @@ template void marian::gpu::Aggregate<marian::functional::Assignee<1>, marian::fu
 template void marian::gpu::Aggregate<marian::functional::Assignee<1>, marian::functional::BinaryFunctor<marian::functional::elem::Maximum, marian::functional::Assignee<1>, marian::functional::Assignee<2> >, std::shared_ptr<marian::TensorBase> >(marian::functional::Assignee<1>, float, marian::functional::BinaryFunctor<marian::functional::elem::Maximum, marian::functional::Assignee<1>, marian::functional::Assignee<2> >, float, std::shared_ptr<marian::TensorBase>, std::shared_ptr<marian::TensorBase>);
 template void marian::gpu::Aggregate<marian::functional::Assignee<1>, marian::functional::BinaryFunctor<marian::functional::elem::Mult, marian::functional::Assignee<1>, marian::functional::Assignee<2> >, std::shared_ptr<marian::TensorBase> >(marian::functional::Assignee<1>, float, marian::functional::BinaryFunctor<marian::functional::elem::Mult, marian::functional::Assignee<1>, marian::functional::Assignee<2> >, float, std::shared_ptr<marian::TensorBase>, std::shared_ptr<marian::TensorBase>);
 template void marian::gpu::Aggregate<marian::functional::Assignee<1>, marian::functional::BinaryFunctor<marian::functional::elem::LogAddExp, marian::functional::Assignee<1>, marian::functional::Assignee<2> >, std::shared_ptr<marian::TensorBase> >(marian::functional::Assignee<1>, float, marian::functional::BinaryFunctor<marian::functional::elem::LogAddExp, marian::functional::Assignee<1>, marian::functional::Assignee<2> >, float, std::shared_ptr<marian::TensorBase>, std::shared_ptr<marian::TensorBase>);
+template void marian::gpu::Add<marian::functional::BinaryFunctor<marian::functional::elem::Mult, marian::functional::BinaryFunctor<marian::functional::elem::Eq, marian::functional::Assignee<1>, marian::functional::Assignee<2> >, marian::functional::Assignee<3> >, std::shared_ptr<marian::TensorBase>, std::shared_ptr<marian::TensorBase>, std::shared_ptr<marian::TensorBase> >(marian::functional::BinaryFunctor<marian::functional::elem::Mult, marian::functional::BinaryFunctor<marian::functional::elem::Eq, marian::functional::Assignee<1>, marian::functional::Assignee<2> >, marian::functional::Assignee<3> >, float, std::shared_ptr<marian::TensorBase>, std::shared_ptr<marian::TensorBase>, std::shared_ptr<marian::TensorBase>, std::shared_ptr<marian::TensorBase>);
+template void marian::gpu::Add<marian::functional::BinaryFunctor<marian::functional::elem::Mult, marian::functional::Assignee<1>, marian::functional::UnaryFunctor<marian::functional::elem::Exp, marian::functional::BinaryFunctor<marian::functional::elem::Minus, marian::functional::Assignee<2>, marian::functional::Assignee<3> > > >, std::shared_ptr<marian::TensorBase>, std::shared_ptr<marian::TensorBase>, std::shared_ptr<marian::TensorBase> >(marian::functional::BinaryFunctor<marian::functional::elem::Mult, marian::functional::Assignee<1>, marian::functional::UnaryFunctor<marian::functional::elem::Exp, marian::functional::BinaryFunctor<marian::functional::elem::Minus, marian::functional::Assignee<2>, marian::functional::Assignee<3> > > >, float, std::shared_ptr<marian::TensorBase>, std::shared_ptr<marian::TensorBase>, std::shared_ptr<marian::TensorBase>, std::shared_ptr<marian::TensorBase>);

From 1c115ad6a9bfece4f778a25be0f7d3df59d23ac2 Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Fri, 18 Jan 2019 19:37:13 -0800
Subject: [PATCH 168/838] added gradients for std() and var()

---
 src/graph/node_operators_unary.h | 30 +++++++++++++++++-------------
 src/tensors/gpu/add.inc          |  2 ++
 2 files changed, 19 insertions(+), 13 deletions(-)

diff --git a/src/graph/node_operators_unary.h b/src/graph/node_operators_unary.h
index 2b9395436..18120485b 100755
--- a/src/graph/node_operators_unary.h
+++ b/src/graph/node_operators_unary.h
@@ -461,19 +461,23 @@ struct ReduceNodeOp : public UnaryNodeOp {
       return {NodeOp(Add(_1, child(0)->grad(), adj_))};
     case ReduceNodeOpCode::mean:
       return {NodeOp(Add(_1, 1.0f / (float)reducedDim_, child(0)->grad(), adj_))};
-    case ReduceNodeOpCode::rms:
-    case ReduceNodeOpCode::sumSqr:
-      ABORT("Gradient of reduction op-code for {} not yet implemented", type());
-    case ReduceNodeOpCode::min:
-    case ReduceNodeOpCode::max:
-      return {NodeOp(Add((_1 == _2) * _3,  // adj_ gets routed into the min/max value  --@REVIEW: is this correct?
-                         child(0)->grad(), child(0)->val(), val_, adj_))};
-    case ReduceNodeOpCode::logSumExp:       // y = log(sum_j exp(x_j))
-      return {NodeOp(Add(_1 * exp(_2 - _3), // dJ/dx_i = dJ/dy * 1/(sum_j exp(x_j)) exp(x_i) = dJ/dy * exp(x_i - y))
-                         child(0)->grad(),  // out = dJ/dx_i
-                         adj_,              // _1 = dJ/dy
-                         child(0)->val(),   // _2 = x_i
-                         val_))};           // _3 = y
+    case ReduceNodeOpCode::rms: // WARNING: UNTESTED!!
+      // y = (sum_j x_j^2)^0.5
+      // dJ/dx_i = dJ/dy * 0.5 (sum_j x_j^2)^-0.5 * 2 x_i = dJ/dy * x_i / y  --@REVIEW: is this correct?
+      // @TODO: do we need protection against div by 0? L'hospital rule?
+      return {NodeOp(Add(_1 * _2 / _3, child(0)->grad(), adj_, child(0)->val(), val_))};
+    case ReduceNodeOpCode::sumSqr: // WARNING: UNTESTED!!
+      // y = sum_j x_j^2
+      // dJ/dx_i = dJ/dy * sum_j dx_j^2/dx_i = dJ/dy * 2 dx_i  --@REVIEW: is this correct?
+      return {NodeOp(Add(_1 * 2.0f * _2, child(0)->grad(), adj_, child(0)->val()))};
+    case ReduceNodeOpCode::min:  // WARNING: UNTESTED!!
+    case ReduceNodeOpCode::max:  // WARNING: UNTESTED!!
+      // adj_ gets routed into the min/max value  --@REVIEW: is this correct?
+      return {NodeOp(Add((_1 == _2) * _3, child(0)->grad(), child(0)->val(), val_, adj_))};
+    case ReduceNodeOpCode::logSumExp:
+      // y = log(sum_j exp(x_j))
+       // dJ/dx_i = dJ/dy * 1/(sum_j exp(x_j)) exp(x_i) = dJ/dy * exp(x_i - y))  --@REVIEW: is this correct?
+      return {NodeOp(Add(_1 * exp(_2 - _3), child(0)->grad(), adj_, child(0)->val(), val_))};
     default:
       ABORT("Unexpected reduction op-code {}", (int)opCode_);
     }
diff --git a/src/tensors/gpu/add.inc b/src/tensors/gpu/add.inc
index 2dac53996..69244dce0 100755
--- a/src/tensors/gpu/add.inc
+++ b/src/tensors/gpu/add.inc
@@ -30,3 +30,5 @@ template void marian::gpu::Aggregate<marian::functional::Assignee<1>, marian::fu
 template void marian::gpu::Aggregate<marian::functional::Assignee<1>, marian::functional::BinaryFunctor<marian::functional::elem::LogAddExp, marian::functional::Assignee<1>, marian::functional::Assignee<2> >, std::shared_ptr<marian::TensorBase> >(marian::functional::Assignee<1>, float, marian::functional::BinaryFunctor<marian::functional::elem::LogAddExp, marian::functional::Assignee<1>, marian::functional::Assignee<2> >, float, std::shared_ptr<marian::TensorBase>, std::shared_ptr<marian::TensorBase>);
 template void marian::gpu::Add<marian::functional::BinaryFunctor<marian::functional::elem::Mult, marian::functional::BinaryFunctor<marian::functional::elem::Eq, marian::functional::Assignee<1>, marian::functional::Assignee<2> >, marian::functional::Assignee<3> >, std::shared_ptr<marian::TensorBase>, std::shared_ptr<marian::TensorBase>, std::shared_ptr<marian::TensorBase> >(marian::functional::BinaryFunctor<marian::functional::elem::Mult, marian::functional::BinaryFunctor<marian::functional::elem::Eq, marian::functional::Assignee<1>, marian::functional::Assignee<2> >, marian::functional::Assignee<3> >, float, std::shared_ptr<marian::TensorBase>, std::shared_ptr<marian::TensorBase>, std::shared_ptr<marian::TensorBase>, std::shared_ptr<marian::TensorBase>);
 template void marian::gpu::Add<marian::functional::BinaryFunctor<marian::functional::elem::Mult, marian::functional::Assignee<1>, marian::functional::UnaryFunctor<marian::functional::elem::Exp, marian::functional::BinaryFunctor<marian::functional::elem::Minus, marian::functional::Assignee<2>, marian::functional::Assignee<3> > > >, std::shared_ptr<marian::TensorBase>, std::shared_ptr<marian::TensorBase>, std::shared_ptr<marian::TensorBase> >(marian::functional::BinaryFunctor<marian::functional::elem::Mult, marian::functional::Assignee<1>, marian::functional::UnaryFunctor<marian::functional::elem::Exp, marian::functional::BinaryFunctor<marian::functional::elem::Minus, marian::functional::Assignee<2>, marian::functional::Assignee<3> > > >, float, std::shared_ptr<marian::TensorBase>, std::shared_ptr<marian::TensorBase>, std::shared_ptr<marian::TensorBase>, std::shared_ptr<marian::TensorBase>);
+template void marian::gpu::Add<marian::functional::BinaryFunctor<marian::functional::elem::Div, marian::functional::BinaryFunctor<marian::functional::elem::Mult, marian::functional::Assignee<1>, marian::functional::Assignee<2> >, marian::functional::Assignee<3> >, std::shared_ptr<marian::TensorBase>, std::shared_ptr<marian::TensorBase>, std::shared_ptr<marian::TensorBase> >(marian::functional::BinaryFunctor<marian::functional::elem::Div, marian::functional::BinaryFunctor<marian::functional::elem::Mult, marian::functional::Assignee<1>, marian::functional::Assignee<2> >, marian::functional::Assignee<3> >, float, std::shared_ptr<marian::TensorBase>, std::shared_ptr<marian::TensorBase>, std::shared_ptr<marian::TensorBase>, std::shared_ptr<marian::TensorBase>);
+template void marian::gpu::Add<marian::functional::BinaryFunctor<marian::functional::elem::Mult, marian::functional::BinaryFunctor<marian::functional::elem::Mult, marian::functional::Assignee<1>, marian::functional::Capture>, marian::functional::Assignee<2> >, std::shared_ptr<marian::TensorBase>, std::shared_ptr<marian::TensorBase> >(marian::functional::BinaryFunctor<marian::functional::elem::Mult, marian::functional::BinaryFunctor<marian::functional::elem::Mult, marian::functional::Assignee<1>, marian::functional::Capture>, marian::functional::Assignee<2> >, float, std::shared_ptr<marian::TensorBase>, std::shared_ptr<marian::TensorBase>, std::shared_ptr<marian::TensorBase>);

From 731aab417a1453ef1936aba8f5c8d1fe5a65a3e6 Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Fri, 18 Jan 2019 19:39:23 -0800
Subject: [PATCH 169/838] bugbug: ReduceNodeOpCode::sumSqr should be meanSqr

---
 src/graph/expression_operators.cpp | 2 +-
 src/graph/node_operators_unary.h   | 8 ++++----
 2 files changed, 5 insertions(+), 5 deletions(-)

diff --git a/src/graph/expression_operators.cpp b/src/graph/expression_operators.cpp
index 4e5ac5787..75fac34a0 100755
--- a/src/graph/expression_operators.cpp
+++ b/src/graph/expression_operators.cpp
@@ -290,7 +290,7 @@ Expr std(Expr a, int ax) {
 }
 
 Expr var(Expr a, int ax) {
-  return Expression<ReduceNodeOp>(a - mean(a, ax), ax, ReduceNodeOpCode::sumSqr);
+  return Expression<ReduceNodeOp>(a - mean(a, ax), ax, ReduceNodeOpCode::meanSqr);
 }
 
 Expr max(Expr a, int ax) {
diff --git a/src/graph/node_operators_unary.h b/src/graph/node_operators_unary.h
index 18120485b..03c19f19c 100755
--- a/src/graph/node_operators_unary.h
+++ b/src/graph/node_operators_unary.h
@@ -413,7 +413,7 @@ struct LogSoftmaxNodeOp : public UnaryNodeOp {
 };
 
 enum class ReduceNodeOpCode {
-  sum, mean, rms, sumSqr, min, max, prod, logSumExp
+  sum, mean, rms, meanSqr, min, max, prod, logSumExp
 };
 
 struct ReduceNodeOp : public UnaryNodeOp {
@@ -439,7 +439,7 @@ struct ReduceNodeOp : public UnaryNodeOp {
     case ReduceNodeOpCode::rms:
       return {NodeOp(Reduce(_1 * _1, 1.0f / (float)reducedDim_, val_, child(0)->val());
                      Element(_1 = sqrt(_1), val_))};
-    case ReduceNodeOpCode::sumSqr:
+    case ReduceNodeOpCode::meanSqr:
       return {NodeOp(Reduce(_1 * _1, 1.0f / (float)reducedDim_, val_, child(0)->val()))};
     case ReduceNodeOpCode::min:
       return {NodeOp(Reduce(_1, min(_1,_2), std::numeric_limits<float>::max(), val_, child(0)->val()))};
@@ -466,7 +466,7 @@ struct ReduceNodeOp : public UnaryNodeOp {
       // dJ/dx_i = dJ/dy * 0.5 (sum_j x_j^2)^-0.5 * 2 x_i = dJ/dy * x_i / y  --@REVIEW: is this correct?
       // @TODO: do we need protection against div by 0? L'hospital rule?
       return {NodeOp(Add(_1 * _2 / _3, child(0)->grad(), adj_, child(0)->val(), val_))};
-    case ReduceNodeOpCode::sumSqr: // WARNING: UNTESTED!!
+    case ReduceNodeOpCode::meanSqr: // WARNING: UNTESTED!!
       // y = sum_j x_j^2
       // dJ/dx_i = dJ/dy * sum_j dx_j^2/dx_i = dJ/dy * 2 dx_i  --@REVIEW: is this correct?
       return {NodeOp(Add(_1 * 2.0f * _2, child(0)->grad(), adj_, child(0)->val()))};
@@ -496,7 +496,7 @@ struct ReduceNodeOp : public UnaryNodeOp {
     case ReduceNodeOpCode::sum:       return "sum";
     case ReduceNodeOpCode::mean:      return "mean";
     case ReduceNodeOpCode::rms:      return "rms";
-    case ReduceNodeOpCode::sumSqr:      return "sumSqr";
+    case ReduceNodeOpCode::meanSqr:      return "meanSqr";
     case ReduceNodeOpCode::min:       return "min";
     case ReduceNodeOpCode::max:       return "max";
     case ReduceNodeOpCode::prod:      return "prod";

From c97facfe6b92c1085aad5ddc6560f746ce98416c Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Fri, 18 Jan 2019 19:46:49 -0800
Subject: [PATCH 170/838] (fixed an indentation)

---
 src/graph/node_operators_unary.h | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/src/graph/node_operators_unary.h b/src/graph/node_operators_unary.h
index 03c19f19c..ef3654ea8 100755
--- a/src/graph/node_operators_unary.h
+++ b/src/graph/node_operators_unary.h
@@ -495,8 +495,8 @@ struct ReduceNodeOp : public UnaryNodeOp {
     switch (opCode_) {
     case ReduceNodeOpCode::sum:       return "sum";
     case ReduceNodeOpCode::mean:      return "mean";
-    case ReduceNodeOpCode::rms:      return "rms";
-    case ReduceNodeOpCode::meanSqr:      return "meanSqr";
+    case ReduceNodeOpCode::rms:       return "rms";
+    case ReduceNodeOpCode::meanSqr:   return "meanSqr";
     case ReduceNodeOpCode::min:       return "min";
     case ReduceNodeOpCode::max:       return "max";
     case ReduceNodeOpCode::prod:      return "prod";

From 406e289820531691ef6b21b10b2277fd3cb22a51 Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Sat, 19 Jan 2019 10:30:11 -0800
Subject: [PATCH 171/838] switched to memory-saving implementation of smoothing

---
 src/graph/node_operators_unary.h | 2 +-
 src/layers/loss.cpp              | 6 +++++-
 2 files changed, 6 insertions(+), 2 deletions(-)

diff --git a/src/graph/node_operators_unary.h b/src/graph/node_operators_unary.h
index ef3654ea8..a576fb3d9 100755
--- a/src/graph/node_operators_unary.h
+++ b/src/graph/node_operators_unary.h
@@ -476,7 +476,7 @@ struct ReduceNodeOp : public UnaryNodeOp {
       return {NodeOp(Add((_1 == _2) * _3, child(0)->grad(), child(0)->val(), val_, adj_))};
     case ReduceNodeOpCode::logSumExp:
       // y = log(sum_j exp(x_j))
-       // dJ/dx_i = dJ/dy * 1/(sum_j exp(x_j)) exp(x_i) = dJ/dy * exp(x_i - y))  --@REVIEW: is this correct?
+      // dJ/dx_i = dJ/dy * 1/(sum_j exp(x_j)) exp(x_i) = dJ/dy * exp(x_i - y))  --@REVIEW: is this correct?
       return {NodeOp(Add(_1 * exp(_2 - _3), child(0)->grad(), adj_, child(0)->val(), val_))};
     default:
       ABORT("Unexpected reduction op-code {}", (int)opCode_);
diff --git a/src/layers/loss.cpp b/src/layers/loss.cpp
index d9102456a..e9709c691 100755
--- a/src/layers/loss.cpp
+++ b/src/layers/loss.cpp
@@ -27,11 +27,15 @@ Expr LossBase::getCrossEntropy(Expr logits,
                                Expr mask,
                                Expr weights) {
   auto ce = cross_entropy(logits, indices);
+  //auto ce = rows(logits, indices) - logsumexp(logits, /*axis=*/ -1);
 
   if(smoothing_ > 0) {
     // @TODO: add this to CE kernels instead
+#if 0
     auto ceq = mean(logsoftmax(logits), /*axis=*/ -1);
-    //auto ceq = mean(logits, /*axis=*/ -1) - logsum(logits), /*axis=*/ -1);
+#else   // alternative that is cheaper memory-wise
+    auto ceq = mean(logits, /*axis=*/ -1) - logsumexp(logits, /*axis=*/ -1);
+#endif
     ce = (1 - smoothing_) * ce - smoothing_ * ceq;
   }
 

From 3ceb4fac81ed1aaca7b45f01ba2caa7aa8bb60c8 Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Sat, 19 Jan 2019 14:36:02 -0800
Subject: [PATCH 172/838] towards shared denominator for smoothed CE

---
 src/layers/loss.cpp | 13 ++++++++++---
 1 file changed, 10 insertions(+), 3 deletions(-)

diff --git a/src/layers/loss.cpp b/src/layers/loss.cpp
index e9709c691..e87cac865 100755
--- a/src/layers/loss.cpp
+++ b/src/layers/loss.cpp
@@ -26,18 +26,25 @@ Expr LossBase::getCrossEntropy(Expr logits,
                                Expr indices,
                                Expr mask,
                                Expr weights) {
-  auto ce = cross_entropy(logits, indices);
-  //auto ce = rows(logits, indices) - logsumexp(logits, /*axis=*/ -1);
 
+  Expr ce;
   if(smoothing_ > 0) {
     // @TODO: add this to CE kernels instead
 #if 0
+    ce = cross_entropy(logits, indices);
     auto ceq = mean(logsoftmax(logits), /*axis=*/ -1);
+    ce = (1 - smoothing_) * ce - smoothing_ * ceq;
 #else   // alternative that is cheaper memory-wise
+    ce = cross_entropy(logits, indices);
     auto ceq = mean(logits, /*axis=*/ -1) - logsumexp(logits, /*axis=*/ -1);
+    //auto ceq = mean(logits, /*axis=*/ -1) - Z;
+    //ce = (1 - smoothing_) * cols(logits, indices)   // ce term
+    //     - smoothing_ * mean(logits, /*axis=*/ -1)  // smoothing term
+    //     - logsumexp(logits, /*axis=*/ -1);         // denominator
 #endif
-    ce = (1 - smoothing_) * ce - smoothing_ * ceq;
   }
+  else
+      ce = cross_entropy(logits, indices);
 
   if(mask)
     ce = ce * mask;

From bc7957ad323d4f874acf4bfcc694b4d986495232 Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Sat, 19 Jan 2019 15:09:39 -0800
Subject: [PATCH 173/838] towards PyTorch names for select() (gather(),
 index_select())

---
 src/graph/expression_graph.h       |  2 +-
 src/graph/expression_operators.cpp | 44 +++++++++++++++---------------
 src/graph/expression_operators.h   | 27 +++++++++++-------
 src/graph/node_operators_binary.h  | 16 ++++++-----
 src/models/transformer.h           |  4 +--
 src/rnn/types.h                    |  1 +
 src/tests/operator_tests.cpp       | 21 +++++++-------
 7 files changed, 63 insertions(+), 52 deletions(-)

diff --git a/src/graph/expression_graph.h b/src/graph/expression_graph.h
index bf4f256d3..fe8361618 100755
--- a/src/graph/expression_graph.h
+++ b/src/graph/expression_graph.h
@@ -371,7 +371,7 @@ class ExpressionGraph : public std::enable_shared_from_this<ExpressionGraph> {
                     Type::uint32);
   }
   // this version sets up the shape such that the indices are in a given axis
-  // Use this if you want to pass these indices to select().
+  // Use this if you want to pass these indices to gather().
   // indexee shape = (3, 2, 5, 2); axis = 1 -> resulting shape = (1, size of indicesVector, 1, 1)
   Expr indices(const std::vector<IndexType>& indicesVector, Expr indexee, int axis = -1) {
     Shape shape;
diff --git a/src/graph/expression_operators.cpp b/src/graph/expression_operators.cpp
index 4694351b2..54e68960a 100755
--- a/src/graph/expression_operators.cpp
+++ b/src/graph/expression_operators.cpp
@@ -245,36 +245,40 @@ Expr constant_like(Expr a, const NodeInitializer& init) {
   return graph->constant(shape, init);
 }
 
+Expr gather(Expr a, Expr indices, int axis) {
+  return Expression<GatherNodeOp>(a, indices, axis);
+}
+
+Expr index_select(Expr a, const std::vector<IndexType>& indices, int axis) {
+  auto indexExpr = a->graph()->indices(indices, a, axis);
+  return gather(a, indexExpr, axis);
+}
+
 Expr rows(Expr a, Expr indices) {
-  // @TODO:: replace with `select(a, indices, -2)`
-  // as soon as select is efficient enough
-  return Expression<RowsNodeOp>(a, indices);
+    // @TODO:: replace with `index_select(a, indices, -2)`
+    // as soon as select is efficient enough
+    return Expression<RowsNodeOp>(a, indices);
 }
 
 Expr rows(Expr a, const std::vector<IndexType>& indices) {
-  auto indexExpr = a->graph()->indices(indices);
-  return rows(a, indexExpr);
+    auto indexExpr = a->graph()->indices(indices);
+    return rows(a, indexExpr);
 }
 
-
 Expr cols(Expr a, Expr indices) {
-  // @TODO:: replace with `select(a, indices, -1)`
-  // as soon as select is efficient enough
-  return Expression<ColsNodeOp>(a, indices);
+    // @TODO:: replace with `index_select(a, indices, -1)`
+    // as soon as select is efficient enough
+    return Expression<ColsNodeOp>(a, indices);
 }
 
 Expr cols(Expr a, const std::vector<IndexType>& indices) {
-  auto indexExpr = a->graph()->indices(indices);
-  return cols(a, indexExpr);
-}
-
-Expr select(Expr a, Expr indices, int axis) {
-  return Expression<SelectNodeOp>(a, indices, axis);
+    auto indexExpr = a->graph()->indices(indices);
+    return cols(a, indexExpr);
 }
 
-Expr select(Expr a, const std::vector<IndexType>& indices, int axis) {
-  auto indexExpr = a->graph()->indices(indices, a, axis);
-  return select(a, indexExpr, axis);
+Expr sliceView(Expr a, const Slice& slice, int axis) { // numpy __getitem__ semantics
+  // @TODO: If not memory-consecutive then fall back to index_select
+  return Expression<SliceViewNodeOp>(a, slice, axis);
 }
 
 Expr sum(Expr a, int ax) {
@@ -462,10 +466,6 @@ Expr swapAxes(Expr x, int axis1, int axis2)
   return transpose(x, axes);
 }
 
-Expr sliceView(Expr a, const Slice& slice, int axis) { // numpy __getitem__ semantics
-  return Expression<SliceViewNodeOp>(a, slice, axis);
-}
-
 Expr cross_entropy(Expr a, Expr indices) {
   return Expression<CrossEntropyNodeOp>(a, indices);
 }
diff --git a/src/graph/expression_operators.h b/src/graph/expression_operators.h
index e2c1d9226..f5379246d 100755
--- a/src/graph/expression_operators.h
+++ b/src/graph/expression_operators.h
@@ -139,14 +139,29 @@ Expr flatten_2d(Expr a);
 
 Expr stopGradient(Expr a);
 
+Expr gather(Expr a, Expr indices, int axis);
+Expr index_select(Expr a, const std::vector<IndexType>& indices, int axis);
+static inline Expr index_select(Expr a, int index, int axis) {
+  // Until Marian supports strides, use this for indexing non-memory-consecutive
+  // slices, while sliceView() can be used for memory-consecutive ones.
+  return index_select(a, std::vector<IndexType>({(IndexType)index}), axis);
+}
+
 Expr rows(Expr a, Expr indices);
 Expr rows(Expr a, const std::vector<IndexType>& indices);
 
 Expr cols(Expr a, Expr indices);
 Expr cols(Expr a, const std::vector<IndexType>& indices);
 
-Expr select(Expr a, Expr indices, int axis);
-Expr select(Expr a, const std::vector<IndexType>& indices, int axis);
+Expr sliceView(Expr a, const Slice& slice, int axis);
+
+static inline Expr narrow(Expr a, size_t start, size_t length, int axis) { // PyTorch name
+  return sliceView(a, Slice((int)start, (int)(start + length)), axis);
+}
+
+static inline Expr step(Expr a, int step, int axis) {
+  return sliceView(a, Slice(step), axis);
+}
 
 /*********************************************************/
 
@@ -168,14 +183,6 @@ Expr scalar_product(Expr a, Expr b, int ax = 0);
 
 Expr weighted_average(Expr in, Expr weights, int ax = 0);
 
-Expr sliceView(Expr a, const Slice& slice, int axis);
-static inline Expr narrow(Expr a, size_t start, size_t length, int axis) { // PyTorch name
-  return sliceView(a, Slice((int)start, (int)(start + length)), axis);
-}
-static inline Expr step(Expr a, int step, int axis) {
-  return sliceView(a, Slice(step), axis);
-}
-
 Expr sqrt(Expr a, float eps = 0.f);
 Expr square(Expr a);
 
diff --git a/src/graph/node_operators_binary.h b/src/graph/node_operators_binary.h
index eb20033d9..10541b9de 100755
--- a/src/graph/node_operators_binary.h
+++ b/src/graph/node_operators_binary.h
@@ -540,8 +540,8 @@ struct RowsNodeOp : public NaryNodeOp {
   const std::string color() override { return "orange"; }
 };
 
-// This operation indexes a tensor along an axis.
-// This is similar to the common gather() operation in other toolkits.
+// This operation gathers elements of a tensor along an axis.
+// This is like PyTorch gather(), except that Marian also implements broadcasting.
 // For example, this can be used for:
 //  - Same index applied to all batch items (today's select()):
 //    'index' has 1 in the axes that match batch axes in the input, and axis set to the one axis that gets selected over.
@@ -574,12 +574,14 @@ struct RowsNodeOp : public NaryNodeOp {
 //  out[i][j][k] = input[i][index[i][j][k]][k]  # if dim == 1
 //  out[i][j][k] = input[i][j][index[i][j][k]]  # if dim == 2
 // If 'a' and 'indices' do not have the same rank, then negative 'axis' is
-// interpreted relative to 'a', and 'indices' must have the resulting axis.
-// Broadcasting is supported as usual.
+// interpreted relative to 'a'; then both shapes are left-padded to the same rank;
+// and indexing happens along the axis that corresponds to 'axis' in the padded shapes.
+// Broadcasting is supported as usual. This way, this function can implement both
+// batched and non-batched selection.
 // @TODO: The current implementation does not support batched indices (third scenario above).
 //        I.e. all axes of 'indices' except 'axis' must have dimension 1.
-struct SelectNodeOp : public NaryNodeOp {
-  SelectNodeOp(Expr a, Expr indices, int axis)
+struct GatherNodeOp : public NaryNodeOp {
+  GatherNodeOp(Expr a, Expr indices, int axis)
       : NaryNodeOp({a, indices}, newShape(a, indices, axis), a->value_type()),
         axis_(a->shape().axis(axis)) {
     matchOrAbort<IndexType>(indices->value_type());
@@ -628,7 +630,7 @@ struct SelectNodeOp : public NaryNodeOp {
   virtual bool equal(Expr node) override {
     if(!NaryNodeOp::equal(node))
       return false;
-    Ptr<SelectNodeOp> cnode = std::dynamic_pointer_cast<SelectNodeOp>(node);
+    Ptr<GatherNodeOp> cnode = std::dynamic_pointer_cast<GatherNodeOp>(node);
     if(!cnode)
       return false;
     if(axis_ != cnode->axis_)
diff --git a/src/models/transformer.h b/src/models/transformer.h
index d91dfb7f8..166c68722 100755
--- a/src/models/transformer.h
+++ b/src/models/transformer.h
@@ -178,7 +178,7 @@ class Transformer : public EncoderOrDecoderBase {
 
   void collectOneHead(Expr weights, int dimBeam) {
     // select first head, this is arbitrary as the choice does not really matter
-    auto head0 = select(weights, std::vector<IndexType>({0}), -3); // @TODO: implement an index() or slice() operator and use that
+    auto head0 = index_select(weights, 0, -3);
 
     int dimBatchBeam = head0->shape()[-4];
     int srcWords = head0->shape()[-1];
@@ -194,7 +194,7 @@ class Transformer : public EncoderOrDecoderBase {
     // @TODO: make splitting obsolete
     alignments_.clear();
     for(int i = 0; i < trgWords; ++i) {
-      alignments_.push_back(select(head0, std::vector<IndexType>({(IndexType)i}), -1)); // [tgt index][-4: beam depth, -3: max src length, -2: batch size, -1: 1]
+      alignments_.push_back(index_select(head0, i, -1)); // [tgt index][-4: beam depth, -3: max src length, -2: batch size, -1: 1]
     }
   }
 
diff --git a/src/rnn/types.h b/src/rnn/types.h
index 672c600bb..bd44b98bb 100755
--- a/src/rnn/types.h
+++ b/src/rnn/types.h
@@ -34,6 +34,7 @@ struct State {
 
     ABORT_IF(dimTime != 1 && !isBatchMajor, "unexpected time extent for RNN state"); // (the reshape()/rows() trick won't work in this case)
     int numCols = isBatchMajor ? dimDepth * dimTime : dimDepth;
+    // @TODO: Can this complex operation be more easily written using index_select()?
     sel = reshape(sel, { sel->shape().elements() / numCols, numCols }); // [beamSize * dimBatch, dimDepth] or [beamSize * dimBatch, dimTime * dimDepth]
     sel = rows(sel, selIdx);
     sel = reshape(sel, { beamSize, isBatchMajor ? dimBatch : dimTime, isBatchMajor ? dimTime : dimBatch, dimDepth });
diff --git a/src/tests/operator_tests.cpp b/src/tests/operator_tests.cpp
index e4610dd92..8873655fa 100755
--- a/src/tests/operator_tests.cpp
+++ b/src/tests/operator_tests.cpp
@@ -632,15 +632,16 @@ void tests(DeviceType device) {
     std::vector<float> vS3({7, -8, 9, -10, 11, -12});
 
     auto A = graph->param("4x3", {4,3}, inits::from_vector(in));
-    auto B1 = select(A, Indices({0}), 0);
-    auto B2 = select(A, Indices({0}), 1);
-    auto B3 = select(A, Indices({1}), -1);
-    auto B4 = select(A, Indices({0, 1}), 0);
+    // @TODO: split this into index_select() and gather()
+    auto B1 = index_select(A, Indices({0}), 0);
+    auto B2 = index_select(A, Indices({0}), 1);
+    auto B3 = index_select(A, Indices({1}), -1);
+    auto B4 = index_select(A, Indices({0, 1}), 0);
 
     auto C = graph->param("2x3x2", {2, 3, 2}, inits::from_vector(in));
-    auto D1 = select(C, Indices({0}), 0);
-    auto D2 = select(C, Indices({2}), -2);
-    auto D3 = select(C, Indices({0,2}), 1);
+    auto D1 = index_select(C, Indices({0}), 0);
+    auto D2 = index_select(C, Indices({2}), -2);
+    auto D3 = index_select(C, Indices({0,2}), 1);
 
     auto S1 = step(A, 2, 0);
     auto S2 = narrow(A, 1, 2, 0);
@@ -693,7 +694,7 @@ void tests(DeviceType device) {
     CHECK(values == vS3);
   }
 
-  SECTION("rows/cols as select operations") {
+  SECTION("rows/cols as index_select operations") {
     graph->clear();
     values.clear();
     std::vector<float> values2;
@@ -703,9 +704,9 @@ void tests(DeviceType device) {
 
     auto A = graph->param("4x3", {4, 3}, inits::from_vector(vA));
     auto B1 = rows(A, idx);
-    auto B2 = select(A, idx, 0);
+    auto B2 = index_select(A, idx, 0);
     auto C1 = cols(A, idx);
-    auto C2 = select(A, idx, 1);
+    auto C2 = index_select(A, idx, 1);
     graph->forward();
 
     CHECK(B1->shape() == B2->shape());

From a14a6239490be8f0c9e397a9b5ecea209147d75f Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Sat, 19 Jan 2019 15:16:22 -0800
Subject: [PATCH 174/838] gather() (formerly select(tensor)) now requires the
 same rank for input and indices

---
 src/graph/node_operators_binary.h | 20 +++++++-------------
 1 file changed, 7 insertions(+), 13 deletions(-)

diff --git a/src/graph/node_operators_binary.h b/src/graph/node_operators_binary.h
index 10541b9de..b7c6d7064 100755
--- a/src/graph/node_operators_binary.h
+++ b/src/graph/node_operators_binary.h
@@ -541,7 +541,7 @@ struct RowsNodeOp : public NaryNodeOp {
 };
 
 // This operation gathers elements of a tensor along an axis.
-// This is like PyTorch gather(), except that Marian also implements broadcasting.
+// This is like PyTorch gather().
 // For example, this can be used for:
 //  - Same index applied to all batch items (today's select()):
 //    'index' has 1 in the axes that match batch axes in the input, and axis set to the one axis that gets selected over.
@@ -573,11 +573,7 @@ struct RowsNodeOp : public NaryNodeOp {
 //  out[i][j][k] = input[index[i][j][k]][j][k]  # if dim == 0
 //  out[i][j][k] = input[i][index[i][j][k]][k]  # if dim == 1
 //  out[i][j][k] = input[i][j][index[i][j][k]]  # if dim == 2
-// If 'a' and 'indices' do not have the same rank, then negative 'axis' is
-// interpreted relative to 'a'; then both shapes are left-padded to the same rank;
-// and indexing happens along the axis that corresponds to 'axis' in the padded shapes.
-// Broadcasting is supported as usual. This way, this function can implement both
-// batched and non-batched selection.
+// 'a' and 'indices' must have the same rank.
 // @TODO: The current implementation does not support batched indices (third scenario above).
 //        I.e. all axes of 'indices' except 'axis' must have dimension 1.
 struct GatherNodeOp : public NaryNodeOp {
@@ -598,16 +594,14 @@ struct GatherNodeOp : public NaryNodeOp {
   }
 
   Shape newShape(Expr a, Expr indices, int axis) {
-    axis = a->shape().axis(axis);
-    auto indicesRank = indices->shape().size();
-    ABORT_IF(axis >= indicesRank, "Axis {} is invalid for indices shape {}", axis, std::string(indices->shape()));
     Shape shape = a->shape();
-    if (shape.size() < indicesRank) // pad
-      shape.resize(indicesRank);
+    auto rank = shape.size();
+    ABORT_IF(rank != indices->shape().size(), "Mismatching shapes for input ({}) and indices ({})", std::string(shape), std::string(indices->shape()));
+    axis = a->shape().axis(axis);
     shape.set(axis, indices->shape()[axis]);
 #if 1 // presently, this implementation does not support batched indices
-    for (size_t i = 0; i < indicesRank; ++i) {
-      ABORT_IF(indices->shape()[i] != 1 && i + shape.size() - indicesRank != axis,
+    for (size_t i = 0; i < rank; ++i) {
+      ABORT_IF(indices->shape()[i] != 1 && i != axis,
                "Presently, select() does not implement batched indices");
     }
 #endif

From 6fc4d4b87f047e2b24fb5d5bfe3dbd68165382c9 Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Sat, 19 Jan 2019 16:53:34 -0800
Subject: [PATCH 175/838] now routing rows() and cols() via index_select(),
 which then redistributes them to RowsNodeOp or ColsNodeOp; tests updated
 accordingly; bug fix: missed an axis normalization; bug fix: ReshapeNodeOp
 should pass on the value_type as to allow reshaping IndexType tensors

---
 src/graph/expression_operators.cpp | 45 +++++++++++++-----------------
 src/graph/expression_operators.h   | 24 +++++++++++-----
 src/graph/node_operators_binary.h  |  5 ++--
 src/graph/node_operators_unary.h   | 10 +++----
 src/tests/operator_tests.cpp       | 20 ++++++-------
 5 files changed, 53 insertions(+), 51 deletions(-)

diff --git a/src/graph/expression_operators.cpp b/src/graph/expression_operators.cpp
index 54e68960a..27eef3eb4 100755
--- a/src/graph/expression_operators.cpp
+++ b/src/graph/expression_operators.cpp
@@ -249,35 +249,30 @@ Expr gather(Expr a, Expr indices, int axis) {
   return Expression<GatherNodeOp>(a, indices, axis);
 }
 
-Expr index_select(Expr a, const std::vector<IndexType>& indices, int axis) {
-  auto indexExpr = a->graph()->indices(indices, a, axis);
-  return gather(a, indexExpr, axis);
-}
-
-Expr rows(Expr a, Expr indices) {
-    // @TODO:: replace with `index_select(a, indices, -2)`
-    // as soon as select is efficient enough
-    return Expression<RowsNodeOp>(a, indices);
-}
-
-Expr rows(Expr a, const std::vector<IndexType>& indices) {
-    auto indexExpr = a->graph()->indices(indices);
-    return rows(a, indexExpr);
-}
-
-Expr cols(Expr a, Expr indices) {
-    // @TODO:: replace with `index_select(a, indices, -1)`
-    // as soon as select is efficient enough
-    return Expression<ColsNodeOp>(a, indices);
+Expr index_select(Expr a, Expr indices, int axis) {
+  ABORT_IF(indices->shape().size() != 1, "Indices must be a 1D tensor");
+  // We have specialized kernels for non-batched indexing of first or last axis of a 2D tensor.
+  auto rank = a->shape().size();
+  if (rank == 2) {
+    if (axis == 0)
+      return Expression<RowsNodeOp>(a, indices);
+    else if (axis == -1 || axis == 1)
+      return Expression<ColsNodeOp>(a, indices);
+  }
+  // Delegate to gather() for any other axis or non-matrix input.
+  Shape shape;
+  shape.resize(a->shape().size());
+  shape.set(axis, indices->shape()[0]);
+  indices = reshape(indices, shape); // move index to axis
+  return gather(a, indices, axis);
 }
-
-Expr cols(Expr a, const std::vector<IndexType>& indices) {
-    auto indexExpr = a->graph()->indices(indices);
-    return cols(a, indexExpr);
+Expr index_select(Expr a, const std::vector<IndexType>& indices, int axis) {
+  auto indexExpr = a->graph()->indices(indices);
+  return index_select(a, indexExpr, axis);
 }
 
 Expr sliceView(Expr a, const Slice& slice, int axis) { // numpy __getitem__ semantics
-  // @TODO: If not memory-consecutive then fall back to index_select
+  // @TODO: If not memory-consecutive then fall back to index_select(a, slice, axis)
   return Expression<SliceViewNodeOp>(a, slice, axis);
 }
 
diff --git a/src/graph/expression_operators.h b/src/graph/expression_operators.h
index f5379246d..6eba2b476 100755
--- a/src/graph/expression_operators.h
+++ b/src/graph/expression_operators.h
@@ -140,18 +140,28 @@ Expr flatten_2d(Expr a);
 Expr stopGradient(Expr a);
 
 Expr gather(Expr a, Expr indices, int axis);
+
+Expr index_select(Expr a, Expr indices, int axis);
+
+// convenience wrappers for index_select()
 Expr index_select(Expr a, const std::vector<IndexType>& indices, int axis);
-static inline Expr index_select(Expr a, int index, int axis) {
+static inline Expr index_select(Expr a, int index, int axis) { // scalar index version
   // Until Marian supports strides, use this for indexing non-memory-consecutive
   // slices, while sliceView() can be used for memory-consecutive ones.
   return index_select(a, std::vector<IndexType>({(IndexType)index}), axis);
 }
-
-Expr rows(Expr a, Expr indices);
-Expr rows(Expr a, const std::vector<IndexType>& indices);
-
-Expr cols(Expr a, Expr indices);
-Expr cols(Expr a, const std::vector<IndexType>& indices);
+static inline Expr rows(Expr a, Expr indices) {
+  return index_select(a, indices, 0);
+}
+static inline Expr rows(Expr a, const std::vector<IndexType>& indices) {
+  return index_select(a, indices, 0);
+}
+static inline Expr cols(Expr a, Expr indices) {
+  return index_select(a, indices, -1);
+}
+static inline Expr cols(Expr a, const std::vector<IndexType>& indices) {
+  return index_select(a, indices, -1);
+}
 
 Expr sliceView(Expr a, const Slice& slice, int axis);
 
diff --git a/src/graph/node_operators_binary.h b/src/graph/node_operators_binary.h
index b7c6d7064..8abba3c61 100755
--- a/src/graph/node_operators_binary.h
+++ b/src/graph/node_operators_binary.h
@@ -514,7 +514,7 @@ struct ScalarProductNodeOp : public NaryNodeOp {
 struct RowsNodeOp : public NaryNodeOp {
   RowsNodeOp(Expr a, Expr indices)
       : NaryNodeOp({a, indices}, newShape(a, indices->shape().elements())) {
-      matchOrAbort<IndexType>(indices->value_type());
+    matchOrAbort<IndexType>(indices->value_type());
   }
 
   NodeOps forwardOps() override {
@@ -526,7 +526,6 @@ struct RowsNodeOp : public NaryNodeOp {
     return {NodeOp(PasteRows(child(0)->grad(), adj_, child(1)->val()))};
   }
 
-  template <class... Args>
   Shape newShape(Expr a, size_t num) {
     Shape shape = a->shape();
     ABORT_IF(shape.size() != 2,
@@ -595,6 +594,7 @@ struct GatherNodeOp : public NaryNodeOp {
 
   Shape newShape(Expr a, Expr indices, int axis) {
     Shape shape = a->shape();
+    axis = shape.axis(axis);
     auto rank = shape.size();
     ABORT_IF(rank != indices->shape().size(), "Mismatching shapes for input ({}) and indices ({})", std::string(shape), std::string(indices->shape()));
     axis = a->shape().axis(axis);
@@ -649,7 +649,6 @@ struct ColsNodeOp : public NaryNodeOp {
     return {NodeOp(PasteCols(child(0)->grad(), adj_, child(1)->val()))};
   }
 
-  template <class... Args>
   Shape newShape(Expr a, size_t num) {
     Shape shape = a->shape();
     shape.set(1, num);
diff --git a/src/graph/node_operators_unary.h b/src/graph/node_operators_unary.h
index 462d81cf7..610928b2c 100755
--- a/src/graph/node_operators_unary.h
+++ b/src/graph/node_operators_unary.h
@@ -635,7 +635,6 @@ struct TransposeNodeOp : public UnaryNodeOp {
     return {NodeOp(TransposeNDGrad(child(0)->grad(), adj_, axesBw_))};
   }
 
-  template <class... Args>
   Shape newShape(Expr a, const std::vector<int>& axes) {
     Shape shape = a->shape();
 
@@ -680,8 +679,7 @@ class ReshapeNodeOp : public UnaryNodeOp {
   Expr reshapee_;
 
 public:
-  template <typename... Args>
-  ReshapeNodeOp(Expr a, Shape shape) : UnaryNodeOp(a, shape), reshapee_(a) {
+  ReshapeNodeOp(Expr a, Shape shape) : UnaryNodeOp(a, shape, a->value_type()), reshapee_(a) {
     Node::destroy_ = false;
   }
 
@@ -700,14 +698,14 @@ class ReshapeNodeOp : public UnaryNodeOp {
   Tensor& val() override {
     auto childVal = reshapee_->val();
     val_.reset(
-        new TensorBase(childVal->memory(), shape(), childVal->getBackend()));
+        new TensorBase(childVal->memory(), shape(), childVal->type(), childVal->getBackend()));
     return val_;
   };
 
   Tensor& grad() override {
     auto childGrad = reshapee_->grad();
     adj_.reset(
-        new TensorBase(childGrad->memory(), shape(), childGrad->getBackend()));
+        new TensorBase(childGrad->memory(), shape(), childGrad->type(), childGrad->getBackend()));
     return adj_;
   };
 
@@ -748,7 +746,7 @@ class SliceViewNodeOp : public UnaryNodeOp {
 
 public:
   SliceViewNodeOp(Expr a, Slice slice, int axis)
-      : UnaryNodeOp(a, newShape(a, slice, axis)), viewedNode_(a), slice_(slice), axis_(axis) {
+      : UnaryNodeOp(a, newShape(a, slice, axis), a->value_type()), viewedNode_(a), slice_(slice), axis_(axis) {
     Node::destroy_ = false;
     auto byteStride = a->shape().stride(axis) * sizeOf(value_type());
     byteOffset_ = slice.begin * byteStride;
diff --git a/src/tests/operator_tests.cpp b/src/tests/operator_tests.cpp
index 8873655fa..f37189d61 100755
--- a/src/tests/operator_tests.cpp
+++ b/src/tests/operator_tests.cpp
@@ -634,13 +634,13 @@ void tests(DeviceType device) {
     auto A = graph->param("4x3", {4,3}, inits::from_vector(in));
     // @TODO: split this into index_select() and gather()
     auto B1 = index_select(A, Indices({0}), 0);
-    auto B2 = index_select(A, Indices({0}), 1);
-    auto B3 = index_select(A, Indices({1}), -1);
+    auto B2 = index_select(A, 0, 1);
+    auto B3 = index_select(A, 1, -1);
     auto B4 = index_select(A, Indices({0, 1}), 0);
 
     auto C = graph->param("2x3x2", {2, 3, 2}, inits::from_vector(in));
-    auto D1 = index_select(C, Indices({0}), 0);
-    auto D2 = index_select(C, Indices({2}), -2);
+    auto D1 = index_select(C, 0, 0);
+    auto D2 = index_select(C, 2, -2);
     auto D3 = index_select(C, Indices({0,2}), 1);
 
     auto S1 = step(A, 2, 0);
@@ -694,19 +694,19 @@ void tests(DeviceType device) {
     CHECK(values == vS3);
   }
 
-  SECTION("rows/cols as index_select operations") {
+  SECTION("rows/cols as gather operations") {
     graph->clear();
     values.clear();
     std::vector<float> values2;
 
     std::vector<float> vA({0, .3333, -.2, -.3, 0, 4.5, 5.2, -10, 101.45, -100.05, 0, 1.05e-5});
-    std::vector<IndexType> idx({0, 2});
+    std::vector<IndexType> indices({0, 2});
 
     auto A = graph->param("4x3", {4, 3}, inits::from_vector(vA));
-    auto B1 = rows(A, idx);
-    auto B2 = index_select(A, idx, 0);
-    auto C1 = cols(A, idx);
-    auto C2 = index_select(A, idx, 1);
+    auto B1 = rows(A, indices);
+    auto B2 = gather(A, graph->indices(indices, A, 0), 0);
+    auto C1 = cols(A, indices);
+    auto C2 = gather(A, graph->indices(indices, A, 1), 1);
     graph->forward();
 
     CHECK(B1->shape() == B2->shape());

From 55c07775e2832270a926ab6082fd7ca774c7ffc4 Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Sat, 19 Jan 2019 17:12:22 -0800
Subject: [PATCH 176/838] bug fix: SliceViewNodeOp should forward value_type()
 correctly

---
 src/graph/node_operators_unary.h | 4 ++--
 src/tests/operator_tests.cpp     | 1 -
 2 files changed, 2 insertions(+), 3 deletions(-)

diff --git a/src/graph/node_operators_unary.h b/src/graph/node_operators_unary.h
index 610928b2c..51433f933 100755
--- a/src/graph/node_operators_unary.h
+++ b/src/graph/node_operators_unary.h
@@ -781,14 +781,14 @@ class SliceViewNodeOp : public UnaryNodeOp {
   Tensor& val() override {
     auto childVal = viewedNode_->val();
     auto mem = New<MemoryPiece>(childVal->memory()->data() + byteOffset_, byteSize_);
-    val_.reset(new TensorBase(mem, shape(), childVal->getBackend()));
+    val_.reset(new TensorBase(mem, shape(), childVal->type(), childVal->getBackend()));
     return val_;
   };
 
   Tensor& grad() override {
     auto childGrad = viewedNode_->grad();
     auto mem = New<MemoryPiece>(childGrad->memory()->data() + byteOffset_, byteSize_);
-    adj_.reset(new TensorBase(mem, shape(), childGrad->getBackend()));
+    adj_.reset(new TensorBase(mem, shape(), childGrad->type(), childGrad->getBackend()));
     return adj_;
   };
 
diff --git a/src/tests/operator_tests.cpp b/src/tests/operator_tests.cpp
index f37189d61..839ab4313 100755
--- a/src/tests/operator_tests.cpp
+++ b/src/tests/operator_tests.cpp
@@ -632,7 +632,6 @@ void tests(DeviceType device) {
     std::vector<float> vS3({7, -8, 9, -10, 11, -12});
 
     auto A = graph->param("4x3", {4,3}, inits::from_vector(in));
-    // @TODO: split this into index_select() and gather()
     auto B1 = index_select(A, Indices({0}), 0);
     auto B2 = index_select(A, 0, 1);
     auto B3 = index_select(A, 1, -1);

From dad3afe8a5baeda015b4f13748de5be196742148 Mon Sep 17 00:00:00 2001
From: Marcin Junczys-Dowmunt <Marcin.Junczys-Dowmunt@microsoft.com>
Date: Sun, 20 Jan 2019 18:59:40 -0800
Subject: [PATCH 177/838] various small fixes

---
 src/data/default_vocab.cpp         | 44 ++++++++++++++++++++++++------
 src/graph/expression_operators.cpp | 10 ++++---
 src/graph/node_initializers.cpp    | 36 +++++++++++++++++++++++-
 src/graph/node_initializers.h      | 12 ++++----
 src/layers/generic.h               | 17 ++++++++----
 src/models/bert.h                  | 16 ++++-------
 src/models/model_factory.cpp       |  6 ++--
 src/models/transformer.h           | 13 +++++----
 8 files changed, 111 insertions(+), 43 deletions(-)

diff --git a/src/data/default_vocab.cpp b/src/data/default_vocab.cpp
index 4557830c1..f5d009b2a 100755
--- a/src/data/default_vocab.cpp
+++ b/src/data/default_vocab.cpp
@@ -16,7 +16,7 @@
 namespace marian {
 
 class DefaultVocab : public VocabBase {
-private:
+protected:
   typedef std::map<std::string, Word> Str2Id;
   Str2Id str2id_;
 
@@ -36,7 +36,7 @@ class DefaultVocab : public VocabBase {
     VocabFreqOrderer(const std::unordered_map<std::string, size_t>& counter)
             : counter_(counter) {}
 
-    // order first by decreasing frequency, 
+    // order first by decreasing frequency,
     // if frequencies are the same order lexicographically by vocabulary string
     bool operator()(const std::string& a, const std::string& b) const {
       return counter_.at(a) > counter_.at(b) || (counter_.at(a) == counter_.at(b) && a < b);
@@ -157,7 +157,7 @@ class DefaultVocab : public VocabBase {
               "Vocabulary file '{}' exists. Not overwriting",
               path.string());
     }
-    
+
     std::unordered_map<std::string, size_t> counter;
     for(const auto& trainPath : trainPaths)
       addCounts(counter, trainPath);
@@ -221,9 +221,9 @@ class DefaultVocab : public VocabBase {
     }
   }
 
-  void create(const std::string& vocabPath,
-              const std::unordered_map<std::string, size_t>& counter,
-              size_t maxSize = 0) {
+  virtual void create(const std::string& vocabPath,
+                      const std::unordered_map<std::string, size_t>& counter,
+                      size_t maxSize = 0) {
 
     std::vector<std::string> vocabVec;
     for(auto& p : counter)
@@ -283,11 +283,37 @@ class DefaultVocab : public VocabBase {
   };
 };
 
-// This is a vocabulary class that does not enforce </s> or <unk>. 
-// This is used for class lists in a classifier. 
+// This is a vocabulary class that does not enforce </s> or <unk>.
+// This is used for class lists in a classifier.
 class ClassVocab : public DefaultVocab {
 private:
-  virtual void addRequiredVocabulary(const std::string& vocabPath, bool isJson) override {} // Do nothing.
+  // Do nothing.
+  virtual void addRequiredVocabulary(const std::string& vocabPath, bool isJson) override {}
+
+  // Not adding special class labels, only seen classes.
+  virtual void create(const std::string& vocabPath,
+                      const std::unordered_map<std::string, size_t>& counter,
+                      size_t maxSize = 0) override {
+
+    std::vector<std::string> vocabVec;
+    for(auto& p : counter)
+      vocabVec.push_back(p.first);
+    std::sort(vocabVec.begin(), vocabVec.end(), VocabFreqOrderer(counter));
+
+    ABORT_IF(maxSize != 0 && vocabVec.size() != maxSize,
+             "Class vocab maxSize given ({}) has to match class vocab size ({})",
+             maxSize, vocabVec.size());
+
+    YAML::Node vocabYaml;
+    for(size_t i = 0; i < vocabVec.size(); ++i)
+      vocabYaml.force_insert(vocabVec[i], i);
+
+    std::unique_ptr<io::OutputFileStream> vocabStrm(
+      vocabPath == "stdout" ? new io::OutputFileStream(std::cout)
+                            : new io::OutputFileStream(vocabPath)
+    );
+    *vocabStrm << vocabYaml;
+  }
 };
 
 Ptr<VocabBase> createDefaultVocab() {
diff --git a/src/graph/expression_operators.cpp b/src/graph/expression_operators.cpp
index 4694351b2..ecda67751 100755
--- a/src/graph/expression_operators.cpp
+++ b/src/graph/expression_operators.cpp
@@ -474,8 +474,9 @@ Expr plus(const std::vector<Expr>&) {
   ABORT("Not implemented");
 }
 
-Expr swish(const std::vector<Expr>&) {
-  ABORT("Not implemented");
+Expr swish(const std::vector<Expr>& nodes) {
+  ABORT_IF(nodes.size() > 1, "Not implemented");
+  return swish(nodes[0]);
 }
 
 Expr tanh(const std::vector<Expr>& nodes) {
@@ -486,8 +487,9 @@ Expr sigmoid(const std::vector<Expr>&) {
   ABORT("Not implemented");
 }
 
-Expr relu(const std::vector<Expr>&) {
-  ABORT("Not implemented");
+Expr relu(const std::vector<Expr>& nodes) {
+  ABORT_IF(nodes.size() > 1, "Not implemented");
+  return relu(nodes[0]);
 }
 
 Expr leakyrelu(const std::vector<Expr>&) {
diff --git a/src/graph/node_initializers.cpp b/src/graph/node_initializers.cpp
index da38d2ae8..f405102ec 100755
--- a/src/graph/node_initializers.cpp
+++ b/src/graph/node_initializers.cpp
@@ -50,16 +50,50 @@ NodeInitializer normal(float mean, float stddev) {
   };
 }
 
+// @TODO: replace with parametered version below
 void glorot_uniform(Tensor tensor) {
   float scale = sqrtf(6.0f / (tensor->shape()[-2] + tensor->shape()[-1]));
   uniform(-scale, scale)(tensor);
 }
 
+NodeInitializer glorot_uniform2(bool fanIn, bool fanOut) {
+  return [fanIn, fanOut](Tensor tensor) {
+    float scale = 1.f;
+    if(fanIn && fanOut)
+      scale = sqrtf(6.0f / (tensor->shape()[-2] + tensor->shape()[-1]));
+    else if(!fanIn && fanOut)
+      scale = sqrtf(3.0f / tensor->shape()[-1]);
+    else if(fanIn && !fanOut)
+      scale = sqrtf(3.0f / tensor->shape()[-2]);
+    else
+      ABORT("You need to set fanIn or fanOut or both to true");
+
+    uniform(-scale, scale)(tensor);
+  };
+}
+
+// @TODO: replace with parametered version below
 void glorot_normal(Tensor tensor) {
   float scale = sqrtf(2.0f / (tensor->shape()[-2] + tensor->shape()[-1]));
   normal(0.f, scale)(tensor);
 }
 
+NodeInitializer glorot_normal2(bool fanIn, bool fanOut) {
+  return [fanIn, fanOut](Tensor tensor) {
+    float scale = 1.f;
+    if(fanIn && fanOut)
+      scale = sqrtf(2.0f / (tensor->shape()[-2] + tensor->shape()[-1]));
+    else if(!fanIn && fanOut)
+      scale = sqrtf(1.0f / tensor->shape()[-1]);
+    else if(fanIn && !fanOut)
+      scale = sqrtf(1.0f / tensor->shape()[-2]);
+    else
+      ABORT("You need to set fanIn or fanOut or both to true");
+
+    normal(0.f, scale)(tensor);
+  };
+}
+
 NodeInitializer bernoulli(float prob, float scale) {
   return [prob, scale](Tensor tensor) {
     Bernoulli(tensor, prob, scale);
@@ -157,7 +191,7 @@ NodeInitializer from_item(const io::Item& item) {
   }
 }
 
-// Computes Google's sinusoidal position embeddings 
+// Computes Google's sinusoidal position embeddings
 NodeInitializer sinusoidalPositionEmbeddings(int start) {
   return [start](Tensor t) {
     int dimEmb   = t->shape()[-1];
diff --git a/src/graph/node_initializers.h b/src/graph/node_initializers.h
index 254f02ae2..fbb073482 100755
--- a/src/graph/node_initializers.h
+++ b/src/graph/node_initializers.h
@@ -26,8 +26,10 @@ NodeInitializer normal(float mean = 0.f, float stddev = 1.f);
 NodeInitializer uniform(float a = 0.f, float b = 1.f);
 
 void glorot_uniform(Tensor t);
+NodeInitializer glorot_uniform2(bool fanIn = true, bool fanOut = true);
 
 void glorot_normal(Tensor t);
+NodeInitializer glorot_normal2(bool fanIn = true, bool fanOut = true);
 
 NodeInitializer bernoulli(float p, float scale = 1.f);
 
@@ -53,15 +55,15 @@ NodeInitializer from_word2vec(const std::string& file,
                               bool normalize = false);
 
 /**
- * Computes Google's sinusoidal position embeddings 
+ * Computes Google's sinusoidal position embeddings
  * starting from position 'start' taking into account
  * batch and time dimensions of the tensor.
- * 
+ *
  * Expected tensor layout {-2: time, -1: model}
- * 
- * Usually gets later reshaped to {time, 1, model} and 
+ *
+ * Usually gets later reshaped to {time, 1, model} and
  * added with a broadcast to learned embeddings. Positional
- * embeddings are the same for each batch entry and change 
+ * embeddings are the same for each batch entry and change
  * over time.
  */
 NodeInitializer sinusoidalPositionEmbeddings(int start);
diff --git a/src/layers/generic.h b/src/layers/generic.h
index 074494910..062b59cc8 100755
--- a/src/layers/generic.h
+++ b/src/layers/generic.h
@@ -50,7 +50,7 @@ struct IUnaryLayer {
 // Embedding from corpus sub-batch to (emb, mask)
 struct IEmbeddingLayer {
   virtual std::tuple<Expr/*embeddings*/, Expr/*mask*/> apply(Ptr<data::SubBatch> subBatch) const = 0;
-  
+
   // alternative version from index vector, and with batch dims/shape
   virtual Expr apply(const std::vector<IndexType>& embIdx, const Shape& shape) const = 0;
 };
@@ -212,7 +212,9 @@ class Embedding : public LayerBase, public IEmbeddingLayer {
 
     bool fixed = opt<bool>("fixed", false);
 
-    NodeInitializer initFunc = inits::glorot_uniform;
+    // Embedding layer initialization should depend only on embedding size, hence fanIn=false
+    NodeInitializer initFunc = inits::glorot_uniform2(/*fanIn=*/false, /*fanOut=*/true);
+
     if (options_->has("embFile")) {
       std::string file = opt<std::string>("embFile");
       if (!file.empty()) {
@@ -253,7 +255,10 @@ class ULREmbedding : public LayerBase, public IEmbeddingLayer {
     int dimEmb = opt<int>("dimEmb");
     int dimUlrEmb =  opt<int>("dimUlrEmb"); // ULR mono embed size
     bool fixed = opt<bool>("fixed", false);
-    NodeInitializer initFunc = inits::glorot_uniform;
+
+    // Embedding layer initialization should depend only on embedding size, hence fanIn=false
+    NodeInitializer initFunc = inits::glorot_uniform2(/*fanIn=*/false, /*fanOut=*/true);
+
     std::string queryFile = opt<std::string>("ulrQueryFile");
     std::string keyFile = opt<std::string>("ulrKeysFile");
     bool trainTrans = opt<bool>("ulrTrainTransform", false);
@@ -326,15 +331,15 @@ class ULREmbedding : public LayerBase, public IEmbeddingLayer {
     auto qt = dot(queryEmbeddings, ulrTransform, false, false);  //A: transform embeddings based on similarity A :  dimUlrEmb*dimUlrEmb
     auto sqrtDim=std::sqrt((float)queryEmbeddings->shape()[-1]);
     qt = qt/sqrtDim;  // normalize accordin to embed size to avoid dot prodcut growing large in magnitude with larger embeds sizes
-    auto z = dot(qt, keyEmbed, false, true);      // query-key similarity 
+    auto z = dot(qt, keyEmbed, false, true);      // query-key similarity
     float dropProb = this->options_->get<float>("ulr-dropout", 0.0f);  // default no dropout
     z = dropout(z, dropProb);
     float tau = this->options_->get<float>("ulr-softmax-temperature", 1.0f);  // default no temperature
     // temperature in softmax is to control randomness of predictions
     // high temperature Softmax outputs are more close to each other
-    // low temperatures the softmax become more similar to  "hardmax" 
+    // low temperatures the softmax become more similar to  "hardmax"
     auto weights = softmax(z / tau);  // assume default  is dim=-1, what about temprature? - scaler ??
-    auto chosenEmbeddings = dot(weights, uniEmbed);  // AVERAGE 
+    auto chosenEmbeddings = dot(weights, uniEmbed);  // AVERAGE
     auto chosenEmbeddings_mix = srcEmbeddings + alpha * chosenEmbeddings;  // this should be elementwise  broadcast
     auto batchEmbeddings = reshape(chosenEmbeddings_mix, { dimWords, dimBatch, dimEmb });
     auto graph = ulrEmbeddings_.front()->graph();
diff --git a/src/models/bert.h b/src/models/bert.h
index 288d06c8c..849144b1a 100644
--- a/src/models/bert.h
+++ b/src/models/bert.h
@@ -191,7 +191,7 @@ class BertEncoderClassifier : public EncoderClassifier, public data::RNGEngine {
                                        opt<std::string>("bert-sep-symbol"),
                                        opt<std::string>("bert-class-symbol"));
     } else if(modelType == "bert-classifier") { // we are probably fine-tuning a BERT model for a classification task
-      bertBatch = New<data::BertBatch>(batch, 
+      bertBatch = New<data::BertBatch>(batch,
                                        opt<std::string>("bert-sep-symbol"),
                                        opt<std::string>("bert-class-symbol")); // only annotate sentence separators
     } else {
@@ -219,7 +219,7 @@ class BertEncoder : public EncoderTransformer {
   Expr addSentenceEmbeddings(Expr embeddings,
                              Ptr<data::CorpusBatch> batch,
                              bool learnedPosEmbeddings) const {
-    
+
     Ptr<data::BertBatch> bertBatch = std::dynamic_pointer_cast<data::BertBatch>(batch);
     ABORT_IF(!bertBatch, "Batch must be BertBatch for BERT training or fine-tuning");
 
@@ -274,18 +274,14 @@ class BertClassifier : public ClassifierBase {
     int dimModel = classEmbeddings->shape()[-1];
     int dimTrgCls = opt<std::vector<int>>("dim-vocabs")[batchIndex_]; // Target vocab is used as class labels
 
-    std::string finetune = "";
-    if(opt<std::string>("original-type") == "bert-classifier") // seems we are fine-tuning
-      finetune = "_finetune"; // change name so we do not relead BERT output layers for fine-tuning
-
     auto output = mlp::mlp()                                          //
                     .push_back(mlp::dense()                           //
-                                 ("prefix", prefix_ + finetune + "_ff_logit_l1") //
+                                 ("prefix", prefix_ + "_ff_logit_l1") //
                                  ("dim", dimModel)                    //
                                  ("activation", mlp::act::tanh))      // @TODO: do we actually need this?
                     .push_back(mlp::output()                          //
                                  ("dim", dimTrgCls))                  //
-                                 ("prefix", prefix_ + finetune + "_ff_logit_l2") //
+                                 ("prefix", prefix_ + "_ff_logit_l2") //
                     .construct(graph);
 
     auto logits = output->apply(classEmbeddings); // class logits for each batch entry
@@ -332,11 +328,11 @@ class BertMaskedLM : public ClassifierBase {
     int dimVoc = opt<std::vector<int>>("dim-vocabs")[batchIndex_];
 
     auto layerTanh = mlp::dense()                         //
-        ("prefix", prefix_ + "_ff_logit_maskedlm_out_l1") //
+        ("prefix", prefix_ + "_ff_logit_l1") //
         ("dim", dimModel)                                 //
         ("activation", mlp::act::tanh);                   // @TODO: again, check if this layer is present in original code
     auto layerOut = mlp::output()                         //
-        ("prefix", prefix_ + "_ff_logit_maskedlm_out_l2") //
+        ("prefix", prefix_ + "_ff_logit_l2") //
         ("dim", dimVoc);                  //
     layerOut.tieTransposed("Wemb"); // We are a BERT model, hence tie with input, @TODO: check if this is actually what Google does
 
diff --git a/src/models/model_factory.cpp b/src/models/model_factory.cpp
index 66f2db928..9eb1931ca 100755
--- a/src/models/model_factory.cpp
+++ b/src/models/model_factory.cpp
@@ -234,11 +234,13 @@ Ptr<ModelBase> by_type(std::string type, usage use, Ptr<Options> options) {
         ("usage", use)                           //
         .push_back(models::encoder()             //
                     ("type", "bert-encoder")     // close to original transformer encoder
-                    ("index", 0))                // 
+                    ("index", 0))                //
         .push_back(models::classifier()          //
+                    ("prefix", "masked-lm")      // prefix for parameter names
                     ("type", "bert-masked-lm")   //
                     ("index", 0))                // multi-task learning with MaskedLM
         .push_back(models::classifier()          //
+                    ("prefix", "next-sentence")  // prefix for parameter names
                     ("type", "bert-classifier")  //
                     ("index", 1))                // next sentence prediction
         .construct(graph);
@@ -246,7 +248,7 @@ Ptr<ModelBase> by_type(std::string type, usage use, Ptr<Options> options) {
 
   if(type == "bert-classifier") {                // for BERT fine-tuning on non-BERT classification task
     return models::encoder_classifier()(options) //
-        ("original-type", "bert-classifier")     // so we can query this
+        ("original-type", "bert-classifier")     // so we can query this if needed
         ("usage", use)                           //
         .push_back(models::encoder()             //
                     ("type", "bert-encoder")     //
diff --git a/src/models/transformer.h b/src/models/transformer.h
index bbdaa1343..a1cf4cb58 100755
--- a/src/models/transformer.h
+++ b/src/models/transformer.h
@@ -47,18 +47,18 @@ class Transformer : public EncoderOrDecoderBase {
 
   static Expr transposeTimeBatch(Expr input) { return transpose(input, {0, 2, 1, 3}); }
 
-  Expr addPositionalEmbeddings(Expr input, int start = 0, bool learnedPosEmbeddings = false) const {
+  Expr addPositionalEmbeddings(Expr input, int start = 0, bool trainPosEmbeddings = false) const {
     int dimEmb   = input->shape()[-1];
     int dimWords = input->shape()[-3];
 
     Expr embeddings = input;
 
-    if(learnedPosEmbeddings) {
+    if(trainPosEmbeddings) {
       int maxLength = opt<int>("max-length");
 
       // Hack for translating with length longer than trained embeddings
       // We check if the embedding matrix "Wpos" already exist so we can
-      // check the number of positions in that loaded parameter. 
+      // check the number of positions in that loaded parameter.
       // We then have to restict the maximum length to the maximum positon
       // and positions beyond this will be the maximum position.
       Expr seenEmb = graph_->get("Wpos");
@@ -75,14 +75,15 @@ class Transformer : public EncoderOrDecoderBase {
       for(int i = 0; i < std::min(dimWords, numPos); ++i)
         positions[i] = i;
 
-      // @TODO : test if embeddings should be scaled here too!
       auto signal = embeddingLayer->apply(positions, {dimWords, 1, dimEmb});
       embeddings = embeddings + signal;
     } else {
-      auto signal = graph_->constant({dimWords, 1, dimEmb},
-                                     inits::sinusoidalPositionEmbeddings(start));
+      // @TODO : test if embeddings should be scaled when trainable
       // according to paper embeddings are scaled up by \sqrt(d_m)
       embeddings = std::sqrt((float)dimEmb) * embeddings;
+
+      auto signal = graph_->constant({dimWords, 1, dimEmb},
+                                     inits::sinusoidalPositionEmbeddings(start));
       embeddings = embeddings + signal;
     }
 

From 5197012386427b1c75cd44c153a3661af82d2d30 Mon Sep 17 00:00:00 2001
From: Marcin Junczys-Dowmunt <Marcin.Junczys-Dowmunt@microsoft.com>
Date: Sun, 20 Jan 2019 19:24:06 -0800
Subject: [PATCH 178/838] remove disp-label-index for now

---
 src/common/config_parser.cpp |  4 ++--
 src/training/scheduler.h     | 17 +++++++----------
 2 files changed, 9 insertions(+), 12 deletions(-)

diff --git a/src/common/config_parser.cpp b/src/common/config_parser.cpp
index e192b995a..338d4b82c 100755
--- a/src/common/config_parser.cpp
+++ b/src/common/config_parser.cpp
@@ -288,8 +288,8 @@ void ConfigParser::addOptionsTraining(cli::CLIWrapper& cli) {
       "Display nformation for the first  arg  updates");
   cli.add<bool>("--disp-label-counts",
       "Display label counts when logging loss progress");
-  cli.add<int>("--disp-label-index",
-      "Display label counts based on i-th input stream (-1 is last)", -1);
+//   cli.add<int>("--disp-label-index",
+//       "Display label counts based on i-th input stream (-1 is last)", -1);
   cli.add<std::string/*SchedulerPeriod*/>("--save-freq",
       "Save model file every  arg  updates (append 't' for every  arg  target labels)",
       "10000u");
diff --git a/src/training/scheduler.h b/src/training/scheduler.h
index 891a2c0e7..65bb88207 100755
--- a/src/training/scheduler.h
+++ b/src/training/scheduler.h
@@ -15,7 +15,6 @@ class Scheduler : public TrainingObserver {
   std::vector<Ptr<ValidatorBase>> validators_;
 
   bool first_{true};
-  int dispIndex_{-1};
 
   timer::Timer timer_, heartBeatTimer_;
 
@@ -68,9 +67,9 @@ class Scheduler : public TrainingObserver {
     state.updateEta(baselr);
   }
 
-  std::string formatLoss(std::string lossType, 
-                         bool dispLabelCounts, 
-                         size_t batchLabels, 
+  std::string formatLoss(std::string lossType,
+                         bool dispLabelCounts,
+                         size_t batchLabels,
                          Ptr<TrainingState> state) {
     std::stringstream ss;
     ss << "Cost ";
@@ -139,8 +138,7 @@ class Scheduler : public TrainingObserver {
   }
 
   Scheduler(Ptr<Options> options, Ptr<TrainingState> state)
-      : options_(options), state_(state), 
-        dispIndex_{options_->get<int>("disp-label-index", -1)} {
+      : options_(options), state_(state) {
     ABORT_IF(state_->factor != 1, "state.factor unexpectedly not 1 at this point??");
     updateLearningRate(*state);
   }
@@ -258,10 +256,9 @@ class Scheduler : public TrainingObserver {
   }
 
   // @TODO: go back to function which takes batch as an argument? The current arguments make it hard to choose
-  // which subbatch should be used for speed display. For sequence-classifiers it's more interesting to see the 
-  // source-words consumed rather than the labels. We have a CLI option '--disp-label-index' (bad name?) which is 
-  // now defunct.
-  void update(StaticLoss rationalLoss, 
+  // which subbatch should be used for speed display. For sequence-classifiers it's more interesting to see the
+  // source-words consumed rather than the labels.
+  void update(StaticLoss rationalLoss,
               size_t numReadBatches, // number of batches read by the reader (for seeking in case of restart)
               size_t batchSize,      // total number of sentences in batch
               size_t batchLabels,    // total number of target words in batch

From 1712ccb35490cbee5f31e394bb9a20200ff147f8 Mon Sep 17 00:00:00 2001
From: Marcin Junczys-Dowmunt <Marcin.Junczys-Dowmunt@microsoft.com>
Date: Sun, 20 Jan 2019 19:57:39 -0800
Subject: [PATCH 179/838] add missing validation check on input types

---
 src/data/corpus_base.cpp | 39 ++++++++++++++++++++++-----------------
 1 file changed, 22 insertions(+), 17 deletions(-)

diff --git a/src/data/corpus_base.cpp b/src/data/corpus_base.cpp
index b6bd7042a..74987882f 100755
--- a/src/data/corpus_base.cpp
+++ b/src/data/corpus_base.cpp
@@ -105,7 +105,7 @@ CorpusBase::CorpusBase(Ptr<Options> options, bool translate)
         std::set<std::string> paths; // contains all paths that are used for training the vocabulary
         size_t size;                 // contains the maximum vocabulary size
       };
-      
+
       // Group training files based on vocabulary path. If the same
       // vocab path corresponds to different training files, this means
       // that a single vocab should combine tokens from all files.
@@ -124,7 +124,7 @@ CorpusBase::CorpusBase(Ptr<Options> options, bool translate)
         auto pathsAndSize = groupVocab[vocabPaths[i]];
         std::vector<std::string> groupedPaths(pathsAndSize.paths.begin(), pathsAndSize.paths.end());
         size_t vocSize = vocab->loadOrCreate(vocabPaths[i], groupedPaths, pathsAndSize.size);
-        
+
         // TODO: this is not nice as it modifies the option object and needs to expose the changes
         // outside the corpus as models need to know about the vocabulary size; extract the vocab
         // creation functionality from the class.
@@ -287,31 +287,36 @@ void CorpusBase::addWeightsToBatch(Ptr<CorpusBatch> batch,
 void CorpusBase::initEOS(bool training = true) {
   // Labels fed into sub-batches that are just class-labels, not sequence labels do not require to
   // add a EOS symbol. Hence decision to add EOS is now based on input stream positions and correspoding
-  // input type. 
-  
+  // input type.
+
   addEOS_.resize(paths_.size(), true);
   // @TODO: think if this should be checked and processed here or in a validation step in config?
   auto inputTypes = options_->get<std::vector<std::string>>("input-types", {}); // empty list by default
-  
+
   // make sure there is an input type for each path
-  ABORT_IF(inputTypes.size() > 0 && inputTypes.size() < paths_.size(), 
-           "Input types have been specified ({}), you need to specify one per input ({})", 
-           inputTypes.size(), 
+  ABORT_IF(inputTypes.size() > 0 && inputTypes.size() < paths_.size(),
+           "Input types have been specified ({}), you need to specify one per input ({})",
+           inputTypes.size(),
            paths_.size());
 
   // make sure there is an equal number of input types and paths when training
-  ABORT_IF(training && inputTypes.size() > 0 && inputTypes.size() != paths_.size(), 
-           "Input types have been specified ({}), you need to specify one per input ({})", 
-           inputTypes.size(), 
+  ABORT_IF(training && inputTypes.size() > 0 && inputTypes.size() != paths_.size(),
+           "Input types have been specified ({}), you need to specify one per input ({})",
+           inputTypes.size(),
            paths_.size());
-  
+
   for(int i = 0; i < paths_.size(); ++i)
-    if(inputTypes[i] == "class")
-      addEOS_[i] = false;
-    else if(inputTypes[i] == "sequence")
+    if(inputTypes.size() > i) {
+      if(inputTypes[i] == "class")
+        addEOS_[i] = false;
+      else if(inputTypes[i] == "sequence")
+        addEOS_[i] = true;
+      else
+        ABORT("Unknown input type {}: {}", i, inputTypes[i]);
+    } else {
+      // No input type specified, assuming "sequence"
       addEOS_[i] = true;
-    else
-      ABORT("Unknown input type {}: {}", i, inputTypes[i]);
+    }
 }
 
 }  // namespace data

From e5e85e426a6b76c0411256c0cc4b9764e34f7e93 Mon Sep 17 00:00:00 2001
From: Marcin Junczys-Dowmunt <Marcin.Junczys-Dowmunt@microsoft.com>
Date: Sun, 20 Jan 2019 23:41:39 -0800
Subject: [PATCH 180/838] formatting

---
 src/models/transformer.h      | 2 +-
 src/optimizers/optimizers.cpp | 9 +++------
 2 files changed, 4 insertions(+), 7 deletions(-)

diff --git a/src/models/transformer.h b/src/models/transformer.h
index a1cf4cb58..057c7cca9 100755
--- a/src/models/transformer.h
+++ b/src/models/transformer.h
@@ -285,7 +285,7 @@ class Transformer : public EncoderOrDecoderBase {
       auto Wk = graph_->param(prefix + "_Wk", {dimModel, dimModel}, inits::glorot_uniform);
       auto bk = graph_->param(prefix + "_bk", {1,        dimModel}, inits::zeros);
 
-      kh = affine(keys,Wk, bk);      // [-4: beam depth, -3: batch size, -2: max length, -1: vector dim]
+      kh = affine(keys, Wk, bk);     // [-4: beam depth, -3: batch size, -2: max length, -1: vector dim]
       kh = SplitHeads(kh, dimHeads); // [-4: batch size, -3: num heads, -2: max length, -1: split vector dim]
       cache_[prefix + "_keys"] = kh;
     }
diff --git a/src/optimizers/optimizers.cpp b/src/optimizers/optimizers.cpp
index 685c917f9..e1de4068a 100755
--- a/src/optimizers/optimizers.cpp
+++ b/src/optimizers/optimizers.cpp
@@ -55,7 +55,6 @@ void Adagrad::load(const std::string& name,
 
   std::vector<float> vGt;
 
-  // @TODO: use new IO
   auto items = io::loadItems(name);
   for(auto item : items) {
     // get the size of gt_
@@ -64,8 +63,7 @@ void Adagrad::load(const std::string& name,
     // extract data into vectors
     if(item.name == "adagrad_gt") {
       vGt.resize(totalSize);
-      std::copy(
-          (float*)item.data(), ((float*)item.data()) + totalSize, vGt.begin());
+      std::copy((float*)item.data(), ((float*)item.data()) + totalSize, vGt.begin());
     }
   }
   if(vGt.empty()) {
@@ -79,7 +77,7 @@ void Adagrad::load(const std::string& name,
     if(!opt->gt_) {
       if(!opt->alloc_)
         opt->alloc_ = New<TensorAllocator>(backends[localDeviceIndex]);
-      auto size = end-begin;
+      auto size = end - begin;
       opt->alloc_->reserveExact(sizeof(float) * size);
       opt->alloc_->allocate(opt->gt_, {1, (int)size});
     }
@@ -111,8 +109,7 @@ void Adagrad::save(const std::string& name,
   item.shape = Shape({1, (int)vGt.size()});
   item.type = Type::float32;
   item.bytes.resize(vGt.size() * sizeOf(item.type));
-  std::copy(
-      (char*)vGt.data(), (char*)(vGt.data() + vGt.size()), item.bytes.begin());
+  std::copy((char*)vGt.data(), (char*)(vGt.data() + vGt.size()), item.bytes.begin());
 
   io::saveItems(name, {item});
 }

From e94b627b949f9b4a1a2f11d5811749ac27eb8478 Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Mon, 21 Jan 2019 12:47:25 -0800
Subject: [PATCH 181/838] towards explicit normalization of output factors

---
 src/layers/generic.cpp | 49 +++++++++++++++++++++++++++++++-----------
 src/layers/loss.cpp    |  3 ++-
 2 files changed, 38 insertions(+), 14 deletions(-)

diff --git a/src/layers/generic.cpp b/src/layers/generic.cpp
index 347d4a833..c0812ae7b 100755
--- a/src/layers/generic.cpp
+++ b/src/layers/generic.cpp
@@ -101,10 +101,20 @@ namespace marian {
             groupRanges_[g].second = u + 1;
         groupCounts[g]++;
       }
+      // determine if a factor needs explicit softmax normalization
+      groupNeedsNormalization_.resize(numGroups, false);
       for (size_t g = 0; g < numGroups; g++) { // detect non-overlapping groups
-        //ABORT_IF(groupRanges_[g].second - groupRanges_[g].first != groupCounts[g], "Factor group '{}' members are not consecutive in the factor vocabulary", groupPrefixes[g]);
         LOG(info, "[embedding] Factor group '{}' has {} members ({})",
             groupPrefixes[g], groupCounts[g], groupCounts[g] == 1 ? "sigmoid" : "softmax");
+        // any factor that is not referenced in all words and is not a sigmoid needs normalization
+        if (g == 0) // @TODO: For now we assume that the main factor is used in all words. Test this.
+          continue;
+        if (groupCounts[g] == 1) // sigmoid factors have no normalizer
+          continue;
+        groupNeedsNormalization_[g] = true; // needed
+        ABORT_IF(groupRanges_[g].second - groupRanges_[g].first != groupCounts[g],
+                 "Factor group '{}' members should be consecutive in the factor vocabulary", groupPrefixes[g]);
+        LOG(info, "[embedding] Factor group '{}' needs needs explicit normalization ({}..{})", groupPrefixes[g], groupRanges_[g].first, groupRanges_[g].second-1);
       }
 
       // create the factor matrix
@@ -143,7 +153,9 @@ namespace marian {
     std::vector<int> factorRefCounts_;                   // [factor index] -> how often this factor is referenced in factorMap_
     CSRData factorMatrix_;                               // [v,u] (sparse) -> =1 if u is factor of v
     std::vector<size_t> factorGroups_;                   // [u] -> group id of factor u
+  public: // @TODO: temporarily; later factor this properly
     std::vector<std::pair<size_t, size_t>> groupRanges_; // [group id] -> (u_begin,u_end) index range of factors u for this group. These don't overlap.
+    std::vector<bool> groupNeedsNormalization_;          // [group id] -> true if explicit softmax normalization is necessary
   };
 
   namespace mlp {
@@ -186,23 +198,34 @@ namespace marian {
         }
         return affine(input, cachedShortW_, cachedShortb_, false, transposeW_);
       }
-      else {
-        auto y = affine(input, W_, b_, false, transposeW_);
+      else if (embeddingFactorMapping_) {
+        auto y = affine(input, W_, b_, false, transposeW_); // [B... x U]
 
-        if (embeddingFactorMapping_) { // note: presently mutually incompatible with shortlist_
-          auto graph = input->graph();
-          auto factorMatrix = embeddingFactorMapping_->getFactorMatrix(); // [V x U]
-          y = dot_csr( // the CSR matrix is passed in pieces
-              y,  // [B x U]
-              factorMatrix.shape,
-              graph->constant({(int)factorMatrix.weights.size()}, inits::from_vector(factorMatrix.weights), Type::float32),
-              graph->constant({(int)factorMatrix.indices.size()}, inits::from_vector(factorMatrix.indices), Type::uint32),
-              graph->constant({(int)factorMatrix.offsets.size()}, inits::from_vector(factorMatrix.offsets), Type::uint32),
-              /*transB=*/ true); // -> [B x V]
+        auto graph = input->graph();
+        auto factorMatrix = embeddingFactorMapping_->getFactorMatrix(); // [V x U]
+        y = dot_csr( // the CSR matrix is passed in pieces
+            y,  // [B x U]
+            factorMatrix.shape,
+            graph->constant({(int)factorMatrix.weights.size()}, inits::from_vector(factorMatrix.weights), Type::float32),
+            graph->constant({(int)factorMatrix.indices.size()}, inits::from_vector(factorMatrix.indices), Type::uint32),
+            graph->constant({(int)factorMatrix.offsets.size()}, inits::from_vector(factorMatrix.offsets), Type::uint32),
+            /*transB=*/ true); // -> [B x V]
+
+        // apply normalization factors
+        const auto& groupRanges = embeddingFactorMapping_->groupRanges_; // @TODO: factor this properly
+        auto numGroups = groupRanges.size();
+        for (size_t g = 0; g < numGroups; g++) {
+          if (!embeddingFactorMapping_->groupNeedsNormalization_[g])
+              continue;
+          auto range = groupRanges[g];
+          // need to compute log denominator over y[range] and subtract it from y[range]
+          auto groupLogits = narrow(y, range.first, range.second, /*axis=*/-1);
         }
 
         return y;
       }
+      else
+        return affine(input, W_, b_, false, transposeW_);
     }
   }
 
diff --git a/src/layers/loss.cpp b/src/layers/loss.cpp
index e87cac865..d11e4384c 100755
--- a/src/layers/loss.cpp
+++ b/src/layers/loss.cpp
@@ -37,6 +37,7 @@ Expr LossBase::getCrossEntropy(Expr logits,
 #else   // alternative that is cheaper memory-wise
     ce = cross_entropy(logits, indices);
     auto ceq = mean(logits, /*axis=*/ -1) - logsumexp(logits, /*axis=*/ -1);
+    ce = (1 - smoothing_) * ce - smoothing_ * ceq;
     //auto ceq = mean(logits, /*axis=*/ -1) - Z;
     //ce = (1 - smoothing_) * cols(logits, indices)   // ce term
     //     - smoothing_ * mean(logits, /*axis=*/ -1)  // smoothing term
@@ -44,7 +45,7 @@ Expr LossBase::getCrossEntropy(Expr logits,
 #endif
   }
   else
-      ce = cross_entropy(logits, indices);
+    ce = cross_entropy(logits, indices);
 
   if(mask)
     ce = ce * mask;

From e8448f6506df3420c0eba4cdcfcd43d1f2b309ff Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Mon, 21 Jan 2019 14:39:04 -0800
Subject: [PATCH 182/838] further disentangling of index operators

---
 src/graph/expression_operators.cpp |  33 +++++++-
 src/graph/expression_operators.h   |  24 +++---
 src/graph/node_operators_binary.h  |  20 +++--
 src/models/transformer.h           |   2 +-
 src/tests/operator_tests.cpp       | 116 ++++++++++++++---------------
 5 files changed, 112 insertions(+), 83 deletions(-)

diff --git a/src/graph/expression_operators.cpp b/src/graph/expression_operators.cpp
index 27eef3eb4..81310b438 100755
--- a/src/graph/expression_operators.cpp
+++ b/src/graph/expression_operators.cpp
@@ -245,10 +245,12 @@ Expr constant_like(Expr a, const NodeInitializer& init) {
   return graph->constant(shape, init);
 }
 
+// gather() -- gather arbitrary elements along an axis; batched or non-batched
 Expr gather(Expr a, Expr indices, int axis) {
   return Expression<GatherNodeOp>(a, indices, axis);
 }
 
+// index_select() -- gather arbitrary elements along an axis; unbatched (indices are specified as a 1D vector)
 Expr index_select(Expr a, Expr indices, int axis) {
   ABORT_IF(indices->shape().size() != 1, "Indices must be a 1D tensor");
   // We have specialized kernels for non-batched indexing of first or last axis of a 2D tensor.
@@ -271,11 +273,38 @@ Expr index_select(Expr a, const std::vector<IndexType>& indices, int axis) {
   return index_select(a, indexExpr, axis);
 }
 
-Expr sliceView(Expr a, const Slice& slice, int axis) { // numpy __getitem__ semantics
-  // @TODO: If not memory-consecutive then fall back to index_select(a, slice, axis)
+static Expr sliceCopy(Expr a, const Slice& slice, int axis) { // copy a Slice via gather()
+  ABORT_IF(slice.stride < 0, "Negative strides are not supported yet");
+  ABORT_IF(slice.begin == slice.end, "Empty slices are not allowed"); // @TODO: Or are they?
+  std::vector<IndexType> indices;
+  indices.reserve((slice.end - slice.begin - 1) / slice.stride + 1);
+  for (int i = slice.begin; i < slice.end; i += slice.stride)
+    indices.push_back((IndexType)i);
+  return gather(a, a->graph()->indices(indices, a, axis), axis);
+}
+
+static Expr sliceView(Expr a, const Slice& slice, int axis) { // view a slice (must be memory-consecutive)
   return Expression<SliceViewNodeOp>(a, slice, axis);
 }
 
+// slice() -- gather a slice along an axis (step size > 1 allowed)
+Expr slice(Expr a, Slice slice, int axis) { // numpy __getslice__ semantics, but with axis parameter
+  const auto& shape = a->shape();
+  axis  = shape.axis(axis);         // normalize negative axis
+  slice = shape.slice(slice, axis); // normalize negative slice values
+  if (slice.begin == 0 && slice.end == shape[axis] && slice.stride == 1)
+    return a; // it's a no-op
+#if 1 // until strided views are supported, non-consecutive slices are implemented via gather()
+  if (slice.stride != 1)
+    return sliceCopy(a, slice, axis);
+  for (int i = 0; i < axis; ++i) {
+    if (shape[i] != 1)  // this makes it non-consecutive
+      return sliceCopy(a, slice, axis);
+  }
+#endif
+  return sliceView(a, slice, axis);
+}
+
 Expr sum(Expr a, int ax) {
   return Expression<SumNodeOp>(a, ax);
 }
diff --git a/src/graph/expression_operators.h b/src/graph/expression_operators.h
index 6eba2b476..5902f2af2 100755
--- a/src/graph/expression_operators.h
+++ b/src/graph/expression_operators.h
@@ -145,32 +145,28 @@ Expr index_select(Expr a, Expr indices, int axis);
 
 // convenience wrappers for index_select()
 Expr index_select(Expr a, const std::vector<IndexType>& indices, int axis);
-static inline Expr index_select(Expr a, int index, int axis) { // scalar index version
-  // Until Marian supports strides, use this for indexing non-memory-consecutive
-  // slices, while sliceView() can be used for memory-consecutive ones.
-  return index_select(a, std::vector<IndexType>({(IndexType)index}), axis);
-}
 static inline Expr rows(Expr a, Expr indices) {
   return index_select(a, indices, 0);
 }
-static inline Expr rows(Expr a, const std::vector<IndexType>& indices) {
-  return index_select(a, indices, 0);
+static inline Expr rows(Expr a, const std::vector<IndexType>& indexVector) {
+  return index_select(a, indexVector, 0);
 }
 static inline Expr cols(Expr a, Expr indices) {
   return index_select(a, indices, -1);
 }
-static inline Expr cols(Expr a, const std::vector<IndexType>& indices) {
-  return index_select(a, indices, -1);
+static inline Expr cols(Expr a, const std::vector<IndexType>& indexVector) {
+  return index_select(a, indexVector, -1);
 }
 
-Expr sliceView(Expr a, const Slice& slice, int axis);
+Expr slice(Expr a, Slice slice, int axis);
 
-static inline Expr narrow(Expr a, size_t start, size_t length, int axis) { // PyTorch name
-  return sliceView(a, Slice((int)start, (int)(start + length)), axis);
+// convenience wrappers for slice()
+static inline Expr step(Expr a, int step, int axis) { // @TODO: name is too narrow
+  return slice(a, Slice(step), axis);
 }
 
-static inline Expr step(Expr a, int step, int axis) {
-  return sliceView(a, Slice(step), axis);
+static inline Expr narrow(Expr a, size_t start, size_t length, int axis) { // PyTorch name
+  return slice(a, Slice((int)start, (int)(start + length)), axis);
 }
 
 /*********************************************************/
diff --git a/src/graph/node_operators_binary.h b/src/graph/node_operators_binary.h
index 8abba3c61..22109fbc6 100755
--- a/src/graph/node_operators_binary.h
+++ b/src/graph/node_operators_binary.h
@@ -542,7 +542,7 @@ struct RowsNodeOp : public NaryNodeOp {
 // This operation gathers elements of a tensor along an axis.
 // This is like PyTorch gather().
 // For example, this can be used for:
-//  - Same index applied to all batch items (today's select()):
+//  - Same index applied to all batch items:
 //    'index' has 1 in the axes that match batch axes in the input, and axis set to the one axis that gets selected over.
 //    Example: Selecting Transformer head 0, i.e. return a[:,1,:,:]
 //      axis = -3
@@ -557,7 +557,7 @@ struct RowsNodeOp : public NaryNodeOp {
 //      idx: (#(B*S)#, 1)        B=batch size, S=source length, idx values are in range 0..V-1
 //      out: ( (B*S) , E)        out[b, s, e] == e[/*0,*/ idx[b, s, 0], e]
 //  - Batched selection (x-ent scenario): Both 'index' and 'data' have matching batch axes.
-//    Example: Cross-entropy loss as -select(logSoftmax(logits), groundTruth, axis=-1):
+//    Example: Cross-entropy loss as -gather(logSoftmax(logits), groundTruth, axis=-1):
 //      axis = -1
 //      lp : (B, T,  V )        B=batch size, T=trg length, V=vocab size
 //      idx: (B, T, #1#)        idx values are in range 0..V-1
@@ -596,19 +596,23 @@ struct GatherNodeOp : public NaryNodeOp {
     Shape shape = a->shape();
     axis = shape.axis(axis);
     auto rank = shape.size();
-    ABORT_IF(rank != indices->shape().size(), "Mismatching shapes for input ({}) and indices ({})", std::string(shape), std::string(indices->shape()));
+    ABORT_IF(rank != indices->shape().size(), "Mismatching ranks for input ({}) and indices ({})", std::string(shape), std::string(indices->shape()));
     axis = a->shape().axis(axis);
     shape.set(axis, indices->shape()[axis]);
-#if 1 // presently, this implementation does not support batched indices
     for (size_t i = 0; i < rank; ++i) {
-      ABORT_IF(indices->shape()[i] != 1 && i != axis,
-               "Presently, select() does not implement batched indices");
-    }
+      if (i != axis) {
+        ABORT_IF(indices->shape()[i] != shape[i] && indices->shape()[i] != 1,
+            "Dimensions must match or broadcast for input ({}) and indices ({})", std::string(shape), std::string(indices->shape()));
+#if 1 // presently, this implementation does not support batched indices
+        ABORT_IF(indices->shape()[i] != 1,
+            "Presently, gather() does not implement batched indices");
 #endif
+      }
+    }
     return shape;
   }
 
-  const std::string type() override { return "select"; }
+  const std::string type() override { return "gather"; }
 
   const std::string color() override { return "orange"; }
 
diff --git a/src/models/transformer.h b/src/models/transformer.h
index 166c68722..3c540c12a 100755
--- a/src/models/transformer.h
+++ b/src/models/transformer.h
@@ -194,7 +194,7 @@ class Transformer : public EncoderOrDecoderBase {
     // @TODO: make splitting obsolete
     alignments_.clear();
     for(int i = 0; i < trgWords; ++i) {
-      alignments_.push_back(index_select(head0, i, -1)); // [tgt index][-4: beam depth, -3: max src length, -2: batch size, -1: 1]
+      alignments_.push_back(marian::step(head0, i, -1)); // [tgt index][-4: beam depth, -3: max src length, -2: batch size, -1: 1]
     }
   }
 
diff --git a/src/tests/operator_tests.cpp b/src/tests/operator_tests.cpp
index 839ab4313..34d8b03c9 100755
--- a/src/tests/operator_tests.cpp
+++ b/src/tests/operator_tests.cpp
@@ -613,84 +613,84 @@ void tests(DeviceType device) {
     CHECK( values == values2 );
   }
 
-  SECTION("select, step, sliceView operators") {
-    using Indices = std::vector<IndexType>;
+  SECTION("select, step, slice operators") {
+    using IndexVector = std::vector<IndexType>;
 
     graph->clear();
     values.clear();
 
-    std::vector<float> in({1, -2, 3, -4, 5, -6, 7, -8, 9, -10, 11, -12});
+    std::vector<float> vA({  1, -2,   3,
+                            -4,  5,  -6,
+                             7, -8,   9,
+                           -10, 11, -12});
+    std::vector<float> vC({ 1,  -2, // C = np.array([1, -2, 3, -4, 5, -6, 7, -8, 9, -10, 11, -12]).reshape((2, 3, 2))
+                            3,  -4,
+                            5,  -6,
+
+                            7,  -8,
+                            9, -10,
+                           11, -12 });
     std::vector<float> vB1({1, -2, 3});
     std::vector<float> vB2({1, -4, 7, -10});
     std::vector<float> vB3({-2, 5, -8, 11});
     std::vector<float> vB4({1, -2, 3, -4, 5, -6});
     std::vector<float> vD1(vB4);
     std::vector<float> vD2({5, -6, 11, -12});
-    std::vector<float> vD3({1, -2, 5, -6, 7, -8, 11, -12});
+    std::vector<float> vD3({1, -2, 5, -6, 7, -8, 11, -12}); // C[:,(0,2),:]
+    //std::vector<float> vD4({5, -6, 3, -4, 7, -8, 11, -12}); // [C[0,(2,1),:],C[1,(0,2),:]]
     std::vector<float> vS1({7, -8, 9});
     std::vector<float> vS2({-4, 5, -6, 7, -8, 9});
     std::vector<float> vS3({7, -8, 9, -10, 11, -12});
 
-    auto A = graph->param("4x3", {4,3}, inits::from_vector(in));
-    auto B1 = index_select(A, Indices({0}), 0);
-    auto B2 = index_select(A, 0, 1);
-    auto B3 = index_select(A, 1, -1);
-    auto B4 = index_select(A, Indices({0, 1}), 0);
-
-    auto C = graph->param("2x3x2", {2, 3, 2}, inits::from_vector(in));
-    auto D1 = index_select(C, 0, 0);
-    auto D2 = index_select(C, 2, -2);
-    auto D3 = index_select(C, Indices({0,2}), 1);
+    auto A = graph->param("4x3", {4,3}, inits::from_vector(vA));
+    auto B1a = index_select(A, IndexVector({0}), 0); // always uses gather()
+    auto B1b = step(A, 0, 0);                        // memory-consecutive view
+    auto B2  = step(A, 0, 1);                        // not memory-consecutive
+    auto B3  = step(A, 1, -1);
+    auto B4a = index_select(A, IndexVector({0, 1}), 0);
+    auto B4b = slice(A, Slice(0, 2), 0); // this is memory-consecutive
+    auto B5  = slice(A, Slice(0, 4), 0); // this is a no-op
+    CHECK(B1a->type() == "rows");      // actually optimized to rows()
+    CHECK(B1b->type() == "sliceView"); // must use view
+    CHECK(B2->type() == "gather");     // cannot use view
+    CHECK(B4a->type() == "rows");
+    CHECK(B4b->type() == "sliceView"); // must use view
+    CHECK(B5.get() == A.get());        // must be no-op
+
+    auto C = graph->param("2x3x2", {2, 3, 2}, inits::from_vector(vC));
+    auto D1 = step(C, 0, 0);
+    auto D2 = step(C, 2, -2);
+    auto D3 = index_select(C, IndexVector({0, 2}), 1); // C[:,(0,2),:]
+    CHECK(D1->type() == "sliceView");
+    CHECK(D2->type() == "gather");
+    // enable this once gather() supports batched indices:
+    //auto D4 = gather(C, graph->constant({2, 2, 1}, // [C[0,(2,1),:],C[1,(0,2),:]]
+    //                                    inits::from_vector(std::vector<IndexType>{
+    //                                      2, 1,
+    //                                      0, 2 }),
+    //                                    Type::uint32), 1);
 
     auto S1 = step(A, 2, 0);
     auto S2 = narrow(A, 1, 2, 0);
-    auto S3 = sliceView(A, Slice(-2, Slice::END), 0);
+    auto S3 = slice(A, Slice(-2, Slice::END), 0);
 
     graph->forward();
 
-    CHECK(B1->shape() == Shape({1, 3}));
-    B1->val()->get(values);
-    CHECK( values == vB1 );
-
-    CHECK(B2->shape() == Shape({4, 1}));
-    B2->val()->get(values);
-    CHECK( values == vB2 );
-
-    CHECK(B3->shape() == Shape({4, 1}));
-    B3->val()->get(values);
-    CHECK( values == vB3 );
-
-    CHECK(B4->shape() == Shape({2, 3}));
-    B4->val()->get(values);
-    CHECK( values == vB4 );
-
-    values.clear();
-
-    CHECK(D1->shape() == Shape({1, 3, 2}));
-    D1->val()->get(values);
-    CHECK( values == vD1 );
-
-    CHECK(D2->shape() == Shape({2, 1, 2}));
-    D2->val()->get(values);
-    CHECK( values == vD2 );
-
-    CHECK(D3->shape() == Shape({2, 2, 2}));
-    D3->val()->get(values);
-    CHECK( values == vD3 );
-
-    values.clear();
-
-    CHECK(S1->shape() == Shape({1,3}));
-    S1->val()->get(values);
-    CHECK(values == vS1);
-
-    CHECK(S2->shape() == Shape({2,3}));
-    S2->val()->get(values);
-    CHECK(values == vS2);
-
-    CHECK(S3->shape() == Shape({2,3}));
-    S3->val()->get(values);
-    CHECK(values == vS3);
+    CHECK(B1a->shape() == Shape({1, 3})); B1a->val()->get(values); CHECK( values == vB1 );
+    CHECK(B1b->shape() == Shape({1, 3})); B1b->val()->get(values); CHECK( values == vB1 );
+    CHECK(B2->shape() == Shape({4, 1})); B2->val()->get(values); CHECK( values == vB2 );
+    CHECK(B3->shape() == Shape({4, 1})); B3->val()->get(values); CHECK( values == vB3 );
+    CHECK(B4a->shape() == Shape({2, 3})); B4a->val()->get(values); CHECK( values == vB4 );
+    CHECK(B4b->shape() == Shape({2, 3})); B4b->val()->get(values); CHECK( values == vB4 );
+
+    CHECK(D1->shape() == Shape({1, 3, 2})); D1->val()->get(values); CHECK( values == vD1 );
+    CHECK(D2->shape() == Shape({2, 1, 2})); D2->val()->get(values); CHECK( values == vD2 );
+    CHECK(D3->shape() == Shape({2, 2, 2})); D3->val()->get(values); CHECK( values == vD3 );
+    //CHECK(D4->shape() == Shape({2, 2, 2})); D4->val()->get(values); CHECK( values == vD4 );
+
+    CHECK(S1->shape() == Shape({1,3})); S1->val()->get(values); CHECK(values == vS1);
+    CHECK(S2->shape() == Shape({2,3})); S2->val()->get(values); CHECK(values == vS2);
+    CHECK(S3->shape() == Shape({2,3})); S3->val()->get(values); CHECK(values == vS3);
   }
 
   SECTION("rows/cols as gather operations") {

From f5dbb61fb47cf1da713768536aa8b3f00c46c5da Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Mon, 21 Jan 2019 15:18:11 -0800
Subject: [PATCH 183/838] now normalizes to factor logits

---
 src/layers/generic.cpp | 37 ++++++++++++++++++++++++-------------
 1 file changed, 24 insertions(+), 13 deletions(-)

diff --git a/src/layers/generic.cpp b/src/layers/generic.cpp
index c0812ae7b..d61d8216c 100755
--- a/src/layers/generic.cpp
+++ b/src/layers/generic.cpp
@@ -199,29 +199,40 @@ namespace marian {
         return affine(input, cachedShortW_, cachedShortb_, false, transposeW_);
       }
       else if (embeddingFactorMapping_) {
-        auto y = affine(input, W_, b_, false, transposeW_); // [B... x U]
-
         auto graph = input->graph();
-        auto factorMatrix = embeddingFactorMapping_->getFactorMatrix(); // [V x U]
-        y = dot_csr( // the CSR matrix is passed in pieces
-            y,  // [B x U]
-            factorMatrix.shape,
-            graph->constant({(int)factorMatrix.weights.size()}, inits::from_vector(factorMatrix.weights), Type::float32),
-            graph->constant({(int)factorMatrix.indices.size()}, inits::from_vector(factorMatrix.indices), Type::uint32),
-            graph->constant({(int)factorMatrix.offsets.size()}, inits::from_vector(factorMatrix.offsets), Type::uint32),
-            /*transB=*/ true); // -> [B x V]
+        auto y = affine(input, W_, b_, false, transposeW_); // [B... x U]
 
-        // apply normalization factors
+        // denominators (only for groups that don't normalize out naturally by the final softmax())
         const auto& groupRanges = embeddingFactorMapping_->groupRanges_; // @TODO: factor this properly
         auto numGroups = groupRanges.size();
         for (size_t g = 0; g < numGroups; g++) {
-          if (!embeddingFactorMapping_->groupNeedsNormalization_[g])
+          if (!embeddingFactorMapping_->groupNeedsNormalization_[g]) // @TODO: if we ever need it, we can combine multiple
               continue;
           auto range = groupRanges[g];
           // need to compute log denominator over y[range] and subtract it from y[range]
-          auto groupLogits = narrow(y, range.first, range.second, /*axis=*/-1);
+          auto groupY = slice(y, Slice((int)range.first, (int)range.second), /*axis=*/-1); // [B... x Ug]
+          auto groupZ = logsumexp(groupY, /*axis=*/-1); // [B... x 1]
+          // y: [B... x U]
+          // m: [1 x U]         // ones at positions of group members
+          auto yDim = y->shape()[-1];
+          std::vector<float> mVec(yDim, 0.0f);
+          for (size_t i = range.first; i < range.second; i++)
+            mVec[i] = 1.0f;
+          auto m = graph->constant({1, yDim}, inits::from_vector(mVec));
+          auto Z = dot(groupZ, m);
+          y = y - Z;
         }
 
+        // sum up the unit logits across factors for each target word
+        auto factorMatrix = embeddingFactorMapping_->getFactorMatrix(); // [V x U]
+        y = dot_csr(
+            y,  // [B x U]
+            factorMatrix.shape,
+            graph->constant({(int)factorMatrix.weights.size()}, inits::from_vector(factorMatrix.weights), Type::float32),
+            graph->constant({(int)factorMatrix.indices.size()}, inits::from_vector(factorMatrix.indices), Type::uint32),
+            graph->constant({(int)factorMatrix.offsets.size()}, inits::from_vector(factorMatrix.offsets), Type::uint32),
+            /*transB=*/ true); // -> [B x V]
+
         return y;
       }
       else

From fa38017e781898c2cd18a6cfdfe9a728522d12d8 Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Mon, 21 Jan 2019 15:55:45 -0800
Subject: [PATCH 184/838] (minor refactoring)

---
 src/layers/generic.cpp | 20 +++++++-------------
 1 file changed, 7 insertions(+), 13 deletions(-)

diff --git a/src/layers/generic.cpp b/src/layers/generic.cpp
index d61d8216c..3f138d87e 100755
--- a/src/layers/generic.cpp
+++ b/src/layers/generic.cpp
@@ -33,12 +33,6 @@ namespace marian {
     //  - all factors not matching a prefix get lumped into yet another class (the lemmas)
     //  - factor vocab must be sorted such that all groups are consecutive
     //  - result of Output layer is nevertheless logits, not a normalized probability, due to the sigmoid entries
-    // Factor normalization
-    //  - f = h * E' + b                    // hidden state projected onto all factors
-    //  - f: [B x U]                        // U = number of factor units
-    //  - normalization terms Z: [B x G]    // G = number of groups
-    //  - factor group matrix F: [U x G]    // [u,g] 1 if factor u is in group g (one-hot); computed once
-    //  - z = f - Z * F' = affine(Z, F, f, transB=true, alpha=-1)   // This does it with only one extra copy
     EmbeddingFactorMapping(Ptr<Options> options) : factorVocab_(New<Options>(), 0) {
       std::vector<std::string> paths = options->get<std::vector<std::string>>("embedding-factors");
       ABORT_IF(paths.size() != 2, "--embedding-factors expects two paths");
@@ -200,7 +194,7 @@ namespace marian {
       }
       else if (embeddingFactorMapping_) {
         auto graph = input->graph();
-        auto y = affine(input, W_, b_, false, transposeW_); // [B... x U]
+        auto y = affine(input, W_, b_, false, transposeW_); // [B... x U] factor logits
 
         // denominators (only for groups that don't normalize out naturally by the final softmax())
         const auto& groupRanges = embeddingFactorMapping_->groupRanges_; // @TODO: factor this properly
@@ -209,17 +203,17 @@ namespace marian {
           if (!embeddingFactorMapping_->groupNeedsNormalization_[g]) // @TODO: if we ever need it, we can combine multiple
               continue;
           auto range = groupRanges[g];
-          // need to compute log denominator over y[range] and subtract it from y[range]
-          auto groupY = slice(y, Slice((int)range.first, (int)range.second), /*axis=*/-1); // [B... x Ug]
-          auto groupZ = logsumexp(groupY, /*axis=*/-1); // [B... x 1]
           // y: [B... x U]
           // m: [1 x U]         // ones at positions of group members
           auto yDim = y->shape()[-1];
-          std::vector<float> mVec(yDim, 0.0f);
+          std::vector<float> mVec(yDim, 0.0f); // @TODO: This vector should be produced by embeddingFactorMapping_
           for (size_t i = range.first; i < range.second; i++)
             mVec[i] = 1.0f;
-          auto m = graph->constant({1, yDim}, inits::from_vector(mVec));
-          auto Z = dot(groupZ, m);
+          // need to compute log denominator over y[range] and subtract it from y[range]
+          auto groupY = slice(y, Slice((int)range.first, (int)range.second), /*axis=*/-1); // [B... x Ug]
+          auto groupZ = logsumexp(groupY, /*axis=*/-1); // [B... x 1]
+          auto m = graph->constant({ 1, (int)mVec.size() }, inits::from_vector(mVec)); // [1 x U]
+          auto Z = dot(groupZ, m); // [B... x U]
           y = y - Z;
         }
 

From d0d0b14736851201d6297e9c911046fd92837077 Mon Sep 17 00:00:00 2001
From: Marcin Junczys-Dowmunt <Marcin.Junczys-Dowmunt@microsoft.com>
Date: Mon, 21 Jan 2019 22:27:21 -0800
Subject: [PATCH 185/838] mimic bert output layer for masked lm

---
 src/graph/expression_operators.cpp |  5 +--
 src/models/bert.h                  | 49 +++++++++++++++++++-----------
 2 files changed, 34 insertions(+), 20 deletions(-)

diff --git a/src/graph/expression_operators.cpp b/src/graph/expression_operators.cpp
index ecda67751..8fa36719f 100755
--- a/src/graph/expression_operators.cpp
+++ b/src/graph/expression_operators.cpp
@@ -470,8 +470,9 @@ Expr cross_entropy(Expr a, Expr indices) {
   return Expression<CrossEntropyNodeOp>(a, indices);
 }
 
-Expr plus(const std::vector<Expr>&) {
-  ABORT("Not implemented");
+Expr plus(const std::vector<Expr>& nodes) {
+  ABORT_IF(nodes.size() > 1, "Not implemented");
+  return nodes[0];
 }
 
 Expr swish(const std::vector<Expr>& nodes) {
diff --git a/src/models/bert.h b/src/models/bert.h
index 849144b1a..4bf281dbc 100644
--- a/src/models/bert.h
+++ b/src/models/bert.h
@@ -323,27 +323,40 @@ class BertMaskedLM : public ClassifierBase {
     int dimBatch = context->shape()[-2];
     int dimTime  = context->shape()[-3];
 
-    auto maskedEmbeddings = rows(reshape(context, {dimBatch * dimTime, dimModel}), bertMaskedPositions); // subselect stuff that has actually been masked out
+    auto maskedContext = rows(reshape(context, {dimBatch * dimTime, dimModel}), bertMaskedPositions); // subselect stuff that has actually been masked out
 
     int dimVoc = opt<std::vector<int>>("dim-vocabs")[batchIndex_];
 
-    auto layerTanh = mlp::dense()                         //
-        ("prefix", prefix_ + "_ff_logit_l1") //
-        ("dim", dimModel)                                 //
-        ("activation", mlp::act::tanh);                   // @TODO: again, check if this layer is present in original code
-    auto layerOut = mlp::output()                         //
-        ("prefix", prefix_ + "_ff_logit_l2") //
-        ("dim", dimVoc);                  //
-    layerOut.tieTransposed("Wemb"); // We are a BERT model, hence tie with input, @TODO: check if this is actually what Google does
-
-    // assemble layers into MLP and apply to embeddings, decoder context and
-    // aligned source context
-    auto output = mlp::mlp()  //
-        .push_back(layerTanh) //
-        .push_back(layerOut)  //
-        .construct(graph);
-
-    auto logits = output->apply(maskedEmbeddings); // [-4: beam depth=1, -3: max length, -2: batch size, -1: vocab dim]
+    std::string activationType = opt<std::string>("transformer-ffn-activation");
+    mlp::act activation;
+    if(activationType == "relu")
+      activation = mlp::act::ReLU;
+    else if(activationType == "swish")
+      activation = mlp::act::swish;
+    else
+      ABORT("Activation function {} not supported in BERT masked LM", activationType);
+
+    auto layer1 = mlp::mlp()
+      .push_back(mlp::dense()
+                 ("prefix", prefix_ + "_ff_logit_l1")
+                 ("dim", dimModel)
+                 ("activation", activation))
+                 .construct(graph);
+
+    auto intermediate = layer1->apply(maskedContext);
+
+    auto gamma = graph->param(prefix_ + "_ff_ln_scale", {1, dimModel}, inits::ones);
+    auto beta  = graph->param(prefix_ + "_ff_ln_bias",  {1, dimModel}, inits::zeros);
+    intermediate = layerNorm(intermediate, gamma, beta);
+
+    auto layer2 = mlp::mlp()
+      .push_back(mlp::output()
+                 ("prefix", prefix_ + "_ff_logit_l2")
+                 ("dim", dimVoc)
+                 .tieTransposed("Wemb"))
+      .construct(graph);
+
+    auto logits = layer2->apply(intermediate); // [-4: beam depth=1, -3: max length, -2: batch size, -1: vocab dim]
 
     auto state = New<ClassifierState>();
     state->setLogProbs(logits);

From c1c175f99522da1611c0847c6fc3152d423a24fa Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Tue, 22 Jan 2019 14:21:42 -0800
Subject: [PATCH 186/838] added a log-linear weight to @C factor (#ifdef'ed out
 for now)

---
 src/layers/generic.cpp | 6 ++++++
 1 file changed, 6 insertions(+)

diff --git a/src/layers/generic.cpp b/src/layers/generic.cpp
index 3f138d87e..2941b689d 100755
--- a/src/layers/generic.cpp
+++ b/src/layers/generic.cpp
@@ -215,6 +215,12 @@ namespace marian {
           auto m = graph->constant({ 1, (int)mVec.size() }, inits::from_vector(mVec)); // [1 x U]
           auto Z = dot(groupZ, m); // [B... x U]
           y = y - Z;
+#if 0
+          // and a log-linear weight
+          auto name = options_->get<std::string>("prefix");
+          auto llWeight = graph->param(name + "_llWeight_" + std::to_string(g), {}, inits::from_value(1.0f));
+          y = y * ((llWeight  - 1) * m + 1);
+#endif
         }
 
         // sum up the unit logits across factors for each target word

From 0a62d1e2d051105cb25a83cc3ab348e4b19c50df Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Tue, 22 Jan 2019 15:08:32 -0800
Subject: [PATCH 187/838] changed index operations' parameter lists to match
 PyTorch parameter order (axis before arg)

---
 src/common/shape.h                 |  2 +-
 src/graph/expression_operators.cpp | 30 +++++++++++-----------
 src/graph/expression_operators.h   | 25 ++++++++++---------
 src/graph/node_operators_binary.h  |  6 ++---
 src/graph/node_operators_unary.h   |  6 ++---
 src/models/transformer.h           |  4 +--
 src/rnn/rnn.h                      |  4 +--
 src/tests/operator_tests.cpp       | 40 +++++++++++++++---------------
 8 files changed, 59 insertions(+), 58 deletions(-)

diff --git a/src/common/shape.h b/src/common/shape.h
index 20fb26070..c8a4bdd3d 100755
--- a/src/common/shape.h
+++ b/src/common/shape.h
@@ -17,7 +17,7 @@ struct Slice // Python-like slice/index descriptor
   Slice(int b, int e, int s) : begin(b), end(e), stride(s) {}
   Slice(int b, int e) : Slice(b, e, 1) {}
   Slice() : Slice(0, END) {}
-  Slice(int i) : Slice(i, i + 1) {}
+  explicit Slice(int i) : Slice(i, i + 1) {}
   Slice(const Slice& other) : Slice(other.begin, other.end, other.stride) {}
   const Slice& operator=(const Slice& other) { begin = other.begin; end = other.end; stride = other.stride; return *this; }
   const Slice& operator=(int i) { begin = i; end = i + 1; stride = 1; return *this; }
diff --git a/src/graph/expression_operators.cpp b/src/graph/expression_operators.cpp
index 81310b438..db820062a 100755
--- a/src/graph/expression_operators.cpp
+++ b/src/graph/expression_operators.cpp
@@ -246,17 +246,17 @@ Expr constant_like(Expr a, const NodeInitializer& init) {
 }
 
 // gather() -- gather arbitrary elements along an axis; batched or non-batched
-Expr gather(Expr a, Expr indices, int axis) {
-  return Expression<GatherNodeOp>(a, indices, axis);
+Expr gather(Expr a, int axis, Expr indices) {
+  return Expression<GatherNodeOp>(a, axis, indices);
 }
 
 // index_select() -- gather arbitrary elements along an axis; unbatched (indices are specified as a 1D vector)
-Expr index_select(Expr a, Expr indices, int axis) {
+Expr index_select(Expr a, int axis, Expr indices) {
   ABORT_IF(indices->shape().size() != 1, "Indices must be a 1D tensor");
   // We have specialized kernels for non-batched indexing of first or last axis of a 2D tensor.
   auto rank = a->shape().size();
   if (rank == 2) {
-    if (axis == 0)
+    if (axis == 0 || axis == 2)
       return Expression<RowsNodeOp>(a, indices);
     else if (axis == -1 || axis == 1)
       return Expression<ColsNodeOp>(a, indices);
@@ -266,29 +266,29 @@ Expr index_select(Expr a, Expr indices, int axis) {
   shape.resize(a->shape().size());
   shape.set(axis, indices->shape()[0]);
   indices = reshape(indices, shape); // move index to axis
-  return gather(a, indices, axis);
+  return gather(a, axis, indices);
 }
-Expr index_select(Expr a, const std::vector<IndexType>& indices, int axis) {
+Expr index_select(Expr a, int axis, const std::vector<IndexType>& indices) {
   auto indexExpr = a->graph()->indices(indices);
-  return index_select(a, indexExpr, axis);
+  return index_select(a, axis, indexExpr);
 }
 
-static Expr sliceCopy(Expr a, const Slice& slice, int axis) { // copy a Slice via gather()
+static Expr sliceCopy(Expr a, int axis, const Slice& slice) { // copy a Slice via gather()
   ABORT_IF(slice.stride < 0, "Negative strides are not supported yet");
   ABORT_IF(slice.begin == slice.end, "Empty slices are not allowed"); // @TODO: Or are they?
   std::vector<IndexType> indices;
   indices.reserve((slice.end - slice.begin - 1) / slice.stride + 1);
   for (int i = slice.begin; i < slice.end; i += slice.stride)
     indices.push_back((IndexType)i);
-  return gather(a, a->graph()->indices(indices, a, axis), axis);
+  return gather(a, axis, a->graph()->indices(indices, a, axis));
 }
 
-static Expr sliceView(Expr a, const Slice& slice, int axis) { // view a slice (must be memory-consecutive)
-  return Expression<SliceViewNodeOp>(a, slice, axis);
+static Expr sliceView(Expr a, int axis, const Slice& slice) { // view a slice (must be memory-consecutive)
+  return Expression<SliceViewNodeOp>(a, axis, slice);
 }
 
 // slice() -- gather a slice along an axis (step size > 1 allowed)
-Expr slice(Expr a, Slice slice, int axis) { // numpy __getslice__ semantics, but with axis parameter
+Expr slice(Expr a, int axis, Slice slice) { // numpy __getslice__ semantics, but with axis parameter
   const auto& shape = a->shape();
   axis  = shape.axis(axis);         // normalize negative axis
   slice = shape.slice(slice, axis); // normalize negative slice values
@@ -296,13 +296,13 @@ Expr slice(Expr a, Slice slice, int axis) { // numpy __getslice__ semantics, but
     return a; // it's a no-op
 #if 1 // until strided views are supported, non-consecutive slices are implemented via gather()
   if (slice.stride != 1)
-    return sliceCopy(a, slice, axis);
+    return sliceCopy(a, axis, slice);
   for (int i = 0; i < axis; ++i) {
     if (shape[i] != 1)  // this makes it non-consecutive
-      return sliceCopy(a, slice, axis);
+      return sliceCopy(a, axis, slice);
   }
 #endif
-  return sliceView(a, slice, axis);
+  return sliceView(a, axis, slice);
 }
 
 Expr sum(Expr a, int ax) {
diff --git a/src/graph/expression_operators.h b/src/graph/expression_operators.h
index 5902f2af2..58149bdeb 100755
--- a/src/graph/expression_operators.h
+++ b/src/graph/expression_operators.h
@@ -139,34 +139,35 @@ Expr flatten_2d(Expr a);
 
 Expr stopGradient(Expr a);
 
-Expr gather(Expr a, Expr indices, int axis);
+Expr gather(Expr a, int axis, Expr indices);
 
-Expr index_select(Expr a, Expr indices, int axis);
+// Warning: Don't try to pass a scalar literal 0 as indices; it will compile but pass nullptr...
+Expr index_select(Expr a, int axis, Expr indices);
 
 // convenience wrappers for index_select()
-Expr index_select(Expr a, const std::vector<IndexType>& indices, int axis);
+Expr index_select(Expr a, int axis, const std::vector<IndexType>& indices);
 static inline Expr rows(Expr a, Expr indices) {
-  return index_select(a, indices, 0);
+  return index_select(a, 0, indices);
 }
 static inline Expr rows(Expr a, const std::vector<IndexType>& indexVector) {
-  return index_select(a, indexVector, 0);
+  return index_select(a, 0, indexVector);
 }
 static inline Expr cols(Expr a, Expr indices) {
-  return index_select(a, indices, -1);
+  return index_select(a, -1, indices);
 }
 static inline Expr cols(Expr a, const std::vector<IndexType>& indexVector) {
-  return index_select(a, indexVector, -1);
+  return index_select(a, -1, indexVector);
 }
 
-Expr slice(Expr a, Slice slice, int axis);
+Expr slice(Expr a, int axis, Slice slice);
 
 // convenience wrappers for slice()
-static inline Expr step(Expr a, int step, int axis) { // @TODO: name is too narrow
-  return slice(a, Slice(step), axis);
+static inline Expr slice(Expr a, int axis, int index) { // single index  @NOTE: This was formerlly called step()
+  return slice(a, axis, Slice(index));
 }
 
-static inline Expr narrow(Expr a, size_t start, size_t length, int axis) { // PyTorch name
-  return slice(a, Slice((int)start, (int)(start + length)), axis);
+static inline Expr narrow(Expr a, int axis, size_t start, size_t length) { // PyTorch name
+  return slice(a, axis, Slice((int)start, (int)(start + length)));
 }
 
 /*********************************************************/
diff --git a/src/graph/node_operators_binary.h b/src/graph/node_operators_binary.h
index 22109fbc6..10e2ca763 100755
--- a/src/graph/node_operators_binary.h
+++ b/src/graph/node_operators_binary.h
@@ -576,8 +576,8 @@ struct RowsNodeOp : public NaryNodeOp {
 // @TODO: The current implementation does not support batched indices (third scenario above).
 //        I.e. all axes of 'indices' except 'axis' must have dimension 1.
 struct GatherNodeOp : public NaryNodeOp {
-  GatherNodeOp(Expr a, Expr indices, int axis)
-      : NaryNodeOp({a, indices}, newShape(a, indices, axis), a->value_type()),
+  GatherNodeOp(Expr a, int axis, Expr indices)
+      : NaryNodeOp({a, indices}, newShape(a, axis, indices), a->value_type()),
         axis_(a->shape().axis(axis)) {
     matchOrAbort<IndexType>(indices->value_type());
   }
@@ -592,7 +592,7 @@ struct GatherNodeOp : public NaryNodeOp {
         Insert(child(0)->grad(), adj_, child(1)->val(), axis_))};
   }
 
-  Shape newShape(Expr a, Expr indices, int axis) {
+  Shape newShape(Expr a, int axis, Expr indices) {
     Shape shape = a->shape();
     axis = shape.axis(axis);
     auto rank = shape.size();
diff --git a/src/graph/node_operators_unary.h b/src/graph/node_operators_unary.h
index 51433f933..7dbaec468 100755
--- a/src/graph/node_operators_unary.h
+++ b/src/graph/node_operators_unary.h
@@ -745,15 +745,15 @@ class SliceViewNodeOp : public UnaryNodeOp {
   size_t byteOffset_, byteSize_; // viewed segment in bytes (memory-consecutive)
 
 public:
-  SliceViewNodeOp(Expr a, Slice slice, int axis)
-      : UnaryNodeOp(a, newShape(a, slice, axis), a->value_type()), viewedNode_(a), slice_(slice), axis_(axis) {
+  SliceViewNodeOp(Expr a, int axis, Slice slice)
+      : UnaryNodeOp(a, newShape(a, axis, slice), a->value_type()), viewedNode_(a), slice_(slice), axis_(axis) {
     Node::destroy_ = false;
     auto byteStride = a->shape().stride(axis) * sizeOf(value_type());
     byteOffset_ = slice.begin * byteStride;
     byteSize_ = shape()[axis] * byteStride;
   }
 
-  static Shape newShape(Expr a, Slice& slice, int& axis) { // note: normalizes slice and axis in-place
+  static Shape newShape(Expr a, int& axis, Slice& slice) { // note: normalizes slice and axis in-place
     const auto& shape = a->shape();
     axis  = shape.axis(axis);         // normalize negative axis
     slice = shape.slice(slice, axis); // normalize negative slice values
diff --git a/src/models/transformer.h b/src/models/transformer.h
index 3c540c12a..968d481b4 100755
--- a/src/models/transformer.h
+++ b/src/models/transformer.h
@@ -178,7 +178,7 @@ class Transformer : public EncoderOrDecoderBase {
 
   void collectOneHead(Expr weights, int dimBeam) {
     // select first head, this is arbitrary as the choice does not really matter
-    auto head0 = index_select(weights, 0, -3);
+    auto head0 = slice(weights, -3, 0);
 
     int dimBatchBeam = head0->shape()[-4];
     int srcWords = head0->shape()[-1];
@@ -194,7 +194,7 @@ class Transformer : public EncoderOrDecoderBase {
     // @TODO: make splitting obsolete
     alignments_.clear();
     for(int i = 0; i < trgWords; ++i) {
-      alignments_.push_back(marian::step(head0, i, -1)); // [tgt index][-4: beam depth, -3: max src length, -2: batch size, -1: 1]
+      alignments_.push_back(slice(head0, -1, i)); // [tgt index][-4: beam depth, -3: max src length, -2: batch size, -1: 1]
     }
   }
 
diff --git a/src/rnn/rnn.h b/src/rnn/rnn.h
index 74a535d37..c9131d87a 100755
--- a/src/rnn/rnn.h
+++ b/src/rnn/rnn.h
@@ -75,11 +75,11 @@ class SingleLayerRNN : public BaseRNN {
 
       std::vector<Expr> steps(xWs.size());
       std::transform(xWs.begin(), xWs.end(), steps.begin(), [j](Expr e) {
-        return step(e, j, -3);
+        return slice(e, -3, j);
       });
 
       if(mask)
-        state = cell_->applyState(steps, state, step(mask, j, -3));
+        state = cell_->applyState(steps, state, slice(mask, -3, j));
       else
         state = cell_->applyState(steps, state);
 
diff --git a/src/tests/operator_tests.cpp b/src/tests/operator_tests.cpp
index 34d8b03c9..08fdd8ded 100755
--- a/src/tests/operator_tests.cpp
+++ b/src/tests/operator_tests.cpp
@@ -643,13 +643,13 @@ void tests(DeviceType device) {
     std::vector<float> vS3({7, -8, 9, -10, 11, -12});
 
     auto A = graph->param("4x3", {4,3}, inits::from_vector(vA));
-    auto B1a = index_select(A, IndexVector({0}), 0); // always uses gather()
-    auto B1b = step(A, 0, 0);                        // memory-consecutive view
-    auto B2  = step(A, 0, 1);                        // not memory-consecutive
-    auto B3  = step(A, 1, -1);
-    auto B4a = index_select(A, IndexVector({0, 1}), 0);
-    auto B4b = slice(A, Slice(0, 2), 0); // this is memory-consecutive
-    auto B5  = slice(A, Slice(0, 4), 0); // this is a no-op
+    auto B1a = index_select(A, 0, IndexVector({0})); // always uses gather()
+    auto B1b = slice(A,  0, 0);                        // memory-consecutive view
+    auto B2  = slice(A,  1, 0);                        // not memory-consecutive
+    auto B3  = slice(A, -1, 1);
+    auto B4a = index_select(A, 0, IndexVector({0, 1}));
+    auto B4b = slice(A, 0, Slice(0, 2)); // this is memory-consecutive
+    auto B5  = slice(A, 0, Slice(0, 4)); // this is a no-op
     CHECK(B1a->type() == "rows");      // actually optimized to rows()
     CHECK(B1b->type() == "sliceView"); // must use view
     CHECK(B2->type() == "gather");     // cannot use view
@@ -658,21 +658,21 @@ void tests(DeviceType device) {
     CHECK(B5.get() == A.get());        // must be no-op
 
     auto C = graph->param("2x3x2", {2, 3, 2}, inits::from_vector(vC));
-    auto D1 = step(C, 0, 0);
-    auto D2 = step(C, 2, -2);
-    auto D3 = index_select(C, IndexVector({0, 2}), 1); // C[:,(0,2),:]
+    auto D1 = slice(C,  0, 0);
+    auto D2 = slice(C, -2, 2);
+    auto D3 = index_select(C, 1, IndexVector({0, 2})); // C[:,(0,2),:]
     CHECK(D1->type() == "sliceView");
     CHECK(D2->type() == "gather");
     // enable this once gather() supports batched indices:
-    //auto D4 = gather(C, graph->constant({2, 2, 1}, // [C[0,(2,1),:],C[1,(0,2),:]]
-    //                                    inits::from_vector(std::vector<IndexType>{
-    //                                      2, 1,
-    //                                      0, 2 }),
-    //                                    Type::uint32), 1);
+    //auto D4 = gather(C, 1, graph->constant({2, 2, 1}, // [C[0,(2,1),:],C[1,(0,2),:]]
+    //                                       inits::from_vector(std::vector<IndexType>{
+    //                                         2, 1,
+    //                                         0, 2 }),
+    //                                       Type::uint32));
 
-    auto S1 = step(A, 2, 0);
-    auto S2 = narrow(A, 1, 2, 0);
-    auto S3 = slice(A, Slice(-2, Slice::END), 0);
+    auto S1 = slice(A, 0, 2);
+    auto S2 = narrow(A, 0, 1, 2);
+    auto S3 = slice(A, 0, Slice(-2, Slice::END));
 
     graph->forward();
 
@@ -703,9 +703,9 @@ void tests(DeviceType device) {
 
     auto A = graph->param("4x3", {4, 3}, inits::from_vector(vA));
     auto B1 = rows(A, indices);
-    auto B2 = gather(A, graph->indices(indices, A, 0), 0);
+    auto B2 = gather(A, 0, graph->indices(indices, A, 0));
     auto C1 = cols(A, indices);
-    auto C2 = gather(A, graph->indices(indices, A, 1), 1);
+    auto C2 = gather(A, 1, graph->indices(indices, A, 1));
     graph->forward();
 
     CHECK(B1->shape() == B2->shape());

From 7ae9709043cdcc4f9bf38e9519f06e9eccaf58eb Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Tue, 22 Jan 2019 15:23:11 -0800
Subject: [PATCH 188/838] fixed a typo

---
 src/graph/expression_operators.cpp | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/graph/expression_operators.cpp b/src/graph/expression_operators.cpp
index db820062a..826bd9f0e 100755
--- a/src/graph/expression_operators.cpp
+++ b/src/graph/expression_operators.cpp
@@ -256,7 +256,7 @@ Expr index_select(Expr a, int axis, Expr indices) {
   // We have specialized kernels for non-batched indexing of first or last axis of a 2D tensor.
   auto rank = a->shape().size();
   if (rank == 2) {
-    if (axis == 0 || axis == 2)
+    if (axis == 0 || axis == -2)
       return Expression<RowsNodeOp>(a, indices);
     else if (axis == -1 || axis == 1)
       return Expression<ColsNodeOp>(a, indices);

From 73a4648ba6f388c28160d464a915ff2ad7096a21 Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Tue, 22 Jan 2019 15:31:47 -0800
Subject: [PATCH 189/838] fix-up after last merge

---
 src/layers/generic.cpp | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/layers/generic.cpp b/src/layers/generic.cpp
index 2941b689d..1cff35ce2 100755
--- a/src/layers/generic.cpp
+++ b/src/layers/generic.cpp
@@ -210,7 +210,7 @@ namespace marian {
           for (size_t i = range.first; i < range.second; i++)
             mVec[i] = 1.0f;
           // need to compute log denominator over y[range] and subtract it from y[range]
-          auto groupY = slice(y, Slice((int)range.first, (int)range.second), /*axis=*/-1); // [B... x Ug]
+          auto groupY = slice(y, /*axis=*/-1, Slice((int)range.first, (int)range.second)); // [B... x Ug]
           auto groupZ = logsumexp(groupY, /*axis=*/-1); // [B... x 1]
           auto m = graph->constant({ 1, (int)mVec.size() }, inits::from_vector(mVec)); // [1 x U]
           auto Z = dot(groupZ, m); // [B... x U]

From 196deea12733685a11c9266153d72b921ec0919b Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Tue, 22 Jan 2019 17:37:49 -0800
Subject: [PATCH 190/838] fixed build break after last merge

---
 src/models/bert.h | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/src/models/bert.h b/src/models/bert.h
index 4bf281dbc..c642abac4 100644
--- a/src/models/bert.h
+++ b/src/models/bert.h
@@ -269,7 +269,7 @@ class BertClassifier : public ClassifierBase {
     ABORT_IF(encoderStates.size() != 1, "Currently we only support a single encoder BERT model");
 
     auto context = encoderStates[0]->getContext();
-    auto classEmbeddings = step(context, /*i=*/0, /*axis=*/-3); // [CLS] symbol is first symbol in each sequence
+    auto classEmbeddings = slice(context, /*axis=*/-3, /*i=*/0); // [CLS] symbol is first symbol in each sequence
 
     int dimModel = classEmbeddings->shape()[-1];
     int dimTrgCls = opt<std::vector<int>>("dim-vocabs")[batchIndex_]; // Target vocab is used as class labels
@@ -368,4 +368,4 @@ class BertMaskedLM : public ClassifierBase {
   virtual void clear() override {}
 };
 
-}
\ No newline at end of file
+}

From 92443a6b508a950585d18acee4027c2d832fc56c Mon Sep 17 00:00:00 2001
From: Marcin Junczys-Dowmunt <Marcin.Junczys-Dowmunt@microsoft.com>
Date: Tue, 22 Jan 2019 22:02:37 -0800
Subject: [PATCH 191/838] remove debug message

---
 src/optimizers/optimizers.cpp | 2 --
 1 file changed, 2 deletions(-)

diff --git a/src/optimizers/optimizers.cpp b/src/optimizers/optimizers.cpp
index e1de4068a..1580f7da5 100755
--- a/src/optimizers/optimizers.cpp
+++ b/src/optimizers/optimizers.cpp
@@ -148,8 +148,6 @@ void Adam::updateImpl(Tensor params, Tensor grads, size_t actualMBSize, size_t r
   denom1_ = (beta1 * denom1_) + (1 - beta1); // momentum smoothing
   denom2_ = (beta2 * denom2_) + (1 - beta2); // RMS normalization
 
-  LOG_ONCE(info, "[adam] First update: Tref = {}, T = {}, eta = {} -> {}, beta = {}, {}", Tref, T, eta_, eta, beta1, beta2);
-
   // numerators. Divide by T to convert ce-sum gradient to avg gradient.
   using namespace functional;
   Element(_1 = ((float)beta1 * _1) + float((1 - beta1) / T    ) *  _2,       mt_, grads); // momentum smoothing. At steady state: =smoothed avg gradient

From ed5b034fe03c9228dba401d5b78537d1ced0dc54 Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Wed, 23 Jan 2019 12:48:26 -0800
Subject: [PATCH 192/838] changed MAX_VOCAB_SIZE to 500k

---
 src/layers/generic.cpp        | 3 +++
 src/translator/nth_element.cu | 2 +-
 2 files changed, 4 insertions(+), 1 deletion(-)

diff --git a/src/layers/generic.cpp b/src/layers/generic.cpp
index 1cff35ce2..d44c6bb13 100755
--- a/src/layers/generic.cpp
+++ b/src/layers/generic.cpp
@@ -196,6 +196,7 @@ namespace marian {
         auto graph = input->graph();
         auto y = affine(input, W_, b_, false, transposeW_); // [B... x U] factor logits
 
+#if 0
         // denominators (only for groups that don't normalize out naturally by the final softmax())
         const auto& groupRanges = embeddingFactorMapping_->groupRanges_; // @TODO: factor this properly
         auto numGroups = groupRanges.size();
@@ -212,6 +213,7 @@ namespace marian {
           // need to compute log denominator over y[range] and subtract it from y[range]
           auto groupY = slice(y, /*axis=*/-1, Slice((int)range.first, (int)range.second)); // [B... x Ug]
           auto groupZ = logsumexp(groupY, /*axis=*/-1); // [B... x 1]
+          //auto groupZ = slice(groupY - logsoftmax(groupY), /*axis=*/-1, 0); // [B... x 1]
           auto m = graph->constant({ 1, (int)mVec.size() }, inits::from_vector(mVec)); // [1 x U]
           auto Z = dot(groupZ, m); // [B... x U]
           y = y - Z;
@@ -222,6 +224,7 @@ namespace marian {
           y = y * ((llWeight  - 1) * m + 1);
 #endif
         }
+#endif
 
         // sum up the unit logits across factors for each target word
         auto factorMatrix = embeddingFactorMapping_->getFactorMatrix(); // [V x U]
diff --git a/src/translator/nth_element.cu b/src/translator/nth_element.cu
index d5377f90c..1a7ff902f 100755
--- a/src/translator/nth_element.cu
+++ b/src/translator/nth_element.cu
@@ -416,7 +416,7 @@ private:
 
   DeviceId deviceId_;
 
-  const int MAX_VOCAB_SIZE = 100000;
+  const int MAX_VOCAB_SIZE = 500000;
 
   const int BLOCK_SIZE = 512;
   const int NUM_BLOCKS;

From f100a8649af3b2f6f3357c9ed769a5206f41e856 Mon Sep 17 00:00:00 2001
From: Kenneth Heafield <github@kheafield.com>
Date: Wed, 23 Jan 2019 23:20:46 +0000
Subject: [PATCH 193/838] C++ source is should not have execute bits

---
 .gitignore                                     | 0
 src/3rd_party/ExceptionWithCallStack.cpp       | 0
 src/3rd_party/any_type.h                       | 0
 src/3rd_party/catch.hpp                        | 0
 src/3rd_party/cnpy/cnpy.cpp                    | 0
 src/3rd_party/cnpy/cnpy.h                      | 0
 src/3rd_party/spdlog/details/format.h          | 0
 src/3rd_party/spdlog/details/logger_impl.h     | 0
 src/3rd_party/spdlog/logger.h                  | 0
 src/3rd_party/spdlog/tests/catch.hpp           | 0
 src/3rd_party/threadpool.h                     | 0
 src/3rd_party/yaml-cpp/binary_renamed.cpp      | 0
 src/3rd_party/yaml-cpp/collectionstack.h       | 0
 src/3rd_party/yaml-cpp/dll.h                   | 0
 src/3rd_party/yaml-cpp/emitterstate.cpp        | 0
 src/3rd_party/yaml-cpp/emitterstate.h          | 0
 src/3rd_party/yaml-cpp/node/convert.h          | 0
 src/3rd_party/yaml-cpp/node/node.h             | 0
 src/3rd_party/yaml-cpp/node_data.cpp           | 0
 src/3rd_party/yaml-cpp/scanner.cpp             | 0
 src/3rd_party/yaml-cpp/scantoken.cpp           | 0
 src/3rd_party/yaml-cpp/singledocparser.cpp     | 0
 src/command/marian_conv.cpp                    | 0
 src/command/marian_main.cpp                    | 0
 src/command/marian_train.cpp                   | 0
 src/command/marian_vocab.cpp                   | 0
 src/common/cli_helper.h                        | 0
 src/common/cli_wrapper.cpp                     | 0
 src/common/cli_wrapper.h                       | 0
 src/common/compile_time_crc32.h                | 0
 src/common/config.cpp                          | 0
 src/common/config.h                            | 0
 src/common/config_parser.cpp                   | 0
 src/common/config_parser.h                     | 0
 src/common/config_validator.cpp                | 0
 src/common/definitions.h                       | 0
 src/common/file_stream.h                       | 0
 src/common/filesystem.h                        | 0
 src/common/io.cpp                              | 0
 src/common/logging.cpp                         | 0
 src/common/logging.h                           | 0
 src/common/options.h                           | 0
 src/common/project_version.h.in                | 0
 src/common/shape.h                             | 0
 src/common/timer.h                             | 0
 src/common/types.h                             | 0
 src/common/utils.cpp                           | 0
 src/common/utils.h                             | 0
 src/common/version.cpp                         | 0
 src/common/version.h                           | 0
 src/data/alignment.cpp                         | 0
 src/data/alignment.h                           | 0
 src/data/batch.h                               | 0
 src/data/batch_generator.h                     | 0
 src/data/batch_stats.h                         | 0
 src/data/corpus.cpp                            | 0
 src/data/corpus.h                              | 0
 src/data/corpus_base.cpp                       | 0
 src/data/corpus_base.h                         | 0
 src/data/corpus_nbest.h                        | 0
 src/data/corpus_sqlite.h                       | 0
 src/data/dataset.h                             | 0
 src/data/default_vocab.cpp                     | 0
 src/data/rng_engine.h                          | 0
 src/data/sentencepiece_vocab.cpp               | 0
 src/data/shortlist.h                           | 0
 src/data/text_input.cpp                        | 0
 src/data/text_input.h                          | 0
 src/data/vocab.cpp                             | 0
 src/data/vocab.h                               | 0
 src/examples/mnist/dataset.h                   | 0
 src/examples/mnist/model.h                     | 0
 src/functional/approx.h                        | 0
 src/functional/defs.h                          | 0
 src/functional/functional.h                    | 0
 src/functional/operands.h                      | 0
 src/functional/predicates.h                    | 0
 src/functional/shape.h                         | 0
 src/graph/chainable.h                          | 0
 src/graph/expression_graph.cpp                 | 0
 src/graph/expression_graph.h                   | 0
 src/graph/expression_operators.cpp             | 0
 src/graph/node.cpp                             | 0
 src/graph/node.h                               | 0
 src/graph/node_initializers.cpp                | 0
 src/graph/node_initializers.h                  | 0
 src/graph/node_operators_binary.h              | 0
 src/graph/parameters.h                         | 0
 src/layers/generic.h                           | 0
 src/layers/guided_alignment.h                  | 0
 src/layers/loss.cpp                            | 0
 src/layers/weight.cpp                          | 0
 src/layers/weight.h                            | 0
 src/layers/word2vec_reader.h                   | 0
 src/marian.h                                   | 0
 src/microsoft/quicksand.cpp                    | 0
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 src/models/amun.h                              | 0
 src/models/costs.h                             | 0
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 src/models/states.h                            | 0
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 src/tensors/cpu/add.h                          | 0
 src/tensors/cpu/prod.cpp                       | 0
 src/tensors/cpu/sharp/int_gemm.h               | 0
 src/tensors/cpu/tensor_operators.cpp           | 0
 src/tensors/device.h                           | 0
 src/tensors/gpu/algorithm.cu                   | 0
 src/tensors/gpu/backend.h                      | 0
 src/tensors/gpu/cuda_helpers.h                 | 0
 src/tensors/gpu/device.cu                      | 0
 src/tensors/rand.cpp                           | 0
 src/tensors/tensor.h                           | 0
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 src/training/graph_group_async_drop.cpp        | 0
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 src/training/graph_group_multinode_sync.cpp    | 0
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 src/translator/helpers.cpp                     | 0
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 src/translator/history.h                       | 0
 src/translator/hypothesis.h                    | 0
 src/translator/nth_element.cpp                 | 0
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diff --git a/src/tensors/gpu/cuda_helpers.h b/src/tensors/gpu/cuda_helpers.h
old mode 100755
new mode 100644
diff --git a/src/tensors/gpu/device.cu b/src/tensors/gpu/device.cu
old mode 100755
new mode 100644
diff --git a/src/tensors/rand.cpp b/src/tensors/rand.cpp
old mode 100755
new mode 100644
diff --git a/src/tensors/tensor.h b/src/tensors/tensor.h
old mode 100755
new mode 100644
diff --git a/src/tensors/tensor_allocator.h b/src/tensors/tensor_allocator.h
old mode 100755
new mode 100644
diff --git a/src/tensors/tensor_operators.h b/src/tensors/tensor_operators.h
old mode 100755
new mode 100644
diff --git a/src/training/communicator.cpp b/src/training/communicator.cpp
old mode 100755
new mode 100644
diff --git a/src/training/communicator.h b/src/training/communicator.h
old mode 100755
new mode 100644
diff --git a/src/training/communicator_nccl.h b/src/training/communicator_nccl.h
old mode 100755
new mode 100644
diff --git a/src/training/exponential_smoothing.h b/src/training/exponential_smoothing.h
old mode 100755
new mode 100644
diff --git a/src/training/gradient_dropping/sparse_tensor.h b/src/training/gradient_dropping/sparse_tensor.h
old mode 100755
new mode 100644
diff --git a/src/training/graph_group.h b/src/training/graph_group.h
old mode 100755
new mode 100644
diff --git a/src/training/graph_group_async.cpp b/src/training/graph_group_async.cpp
old mode 100755
new mode 100644
diff --git a/src/training/graph_group_async.h b/src/training/graph_group_async.h
old mode 100755
new mode 100644
diff --git a/src/training/graph_group_async_drop.cpp b/src/training/graph_group_async_drop.cpp
old mode 100755
new mode 100644
diff --git a/src/training/graph_group_async_drop.h b/src/training/graph_group_async_drop.h
old mode 100755
new mode 100644
diff --git a/src/training/graph_group_multinode.cpp b/src/training/graph_group_multinode.cpp
old mode 100755
new mode 100644
diff --git a/src/training/graph_group_multinode.h b/src/training/graph_group_multinode.h
old mode 100755
new mode 100644
diff --git a/src/training/graph_group_multinode_sync.cpp b/src/training/graph_group_multinode_sync.cpp
old mode 100755
new mode 100644
diff --git a/src/training/graph_group_multinode_sync.h b/src/training/graph_group_multinode_sync.h
old mode 100755
new mode 100644
diff --git a/src/training/graph_group_singleton.cpp b/src/training/graph_group_singleton.cpp
old mode 100755
new mode 100644
diff --git a/src/training/graph_group_singleton.h b/src/training/graph_group_singleton.h
old mode 100755
new mode 100644
diff --git a/src/training/graph_group_sync.cpp b/src/training/graph_group_sync.cpp
old mode 100755
new mode 100644
diff --git a/src/training/graph_group_sync.h b/src/training/graph_group_sync.h
old mode 100755
new mode 100644
diff --git a/src/training/scheduler.h b/src/training/scheduler.h
old mode 100755
new mode 100644
diff --git a/src/training/training.h b/src/training/training.h
old mode 100755
new mode 100644
diff --git a/src/training/training_state.h b/src/training/training_state.h
old mode 100755
new mode 100644
diff --git a/src/training/validator.h b/src/training/validator.h
old mode 100755
new mode 100644
diff --git a/src/translator/beam_search.h b/src/translator/beam_search.h
old mode 100755
new mode 100644
diff --git a/src/translator/helpers.cpp b/src/translator/helpers.cpp
old mode 100755
new mode 100644
diff --git a/src/translator/helpers.h b/src/translator/helpers.h
old mode 100755
new mode 100644
diff --git a/src/translator/history.h b/src/translator/history.h
old mode 100755
new mode 100644
diff --git a/src/translator/hypothesis.h b/src/translator/hypothesis.h
old mode 100755
new mode 100644
diff --git a/src/translator/nth_element.cpp b/src/translator/nth_element.cpp
old mode 100755
new mode 100644
diff --git a/src/translator/nth_element.cu b/src/translator/nth_element.cu
old mode 100755
new mode 100644
diff --git a/src/translator/nth_element.h b/src/translator/nth_element.h
old mode 100755
new mode 100644
diff --git a/src/translator/output_collector.cpp b/src/translator/output_collector.cpp
old mode 100755
new mode 100644
diff --git a/src/translator/output_collector.h b/src/translator/output_collector.h
old mode 100755
new mode 100644
diff --git a/src/translator/output_printer.cpp b/src/translator/output_printer.cpp
old mode 100755
new mode 100644
diff --git a/src/translator/output_printer.h b/src/translator/output_printer.h
old mode 100755
new mode 100644
diff --git a/src/translator/scorers.cpp b/src/translator/scorers.cpp
old mode 100755
new mode 100644
diff --git a/src/translator/scorers.h b/src/translator/scorers.h
old mode 100755
new mode 100644
diff --git a/src/translator/translator.h b/src/translator/translator.h
old mode 100755
new mode 100644
diff --git a/vs/Marian.sln b/vs/Marian.sln
old mode 100755
new mode 100644
diff --git a/vs/Marian.vcxproj b/vs/Marian.vcxproj
old mode 100755
new mode 100644
diff --git a/vs/Marian.vcxproj.filters b/vs/Marian.vcxproj.filters
old mode 100755
new mode 100644

From 8ced995144d35f7c179a6f06b16a8fffa96117f5 Mon Sep 17 00:00:00 2001
From: Roman Grundkiewicz <rgrundki@exseed.ed.ac.uk>
Date: Thu, 24 Jan 2019 18:44:26 +0000
Subject: [PATCH 194/838] Add scripts generating shortlists

---
 scripts/shortlist/.gitignore             |  3 +
 scripts/shortlist/README.md              |  8 ++
 scripts/shortlist/generate_shortlists.pl | 97 ++++++++++++++++++++++++
 scripts/shortlist/install.sh             | 25 ++++++
 4 files changed, 133 insertions(+)
 create mode 100644 scripts/shortlist/.gitignore
 create mode 100644 scripts/shortlist/README.md
 create mode 100755 scripts/shortlist/generate_shortlists.pl
 create mode 100755 scripts/shortlist/install.sh

diff --git a/scripts/shortlist/.gitignore b/scripts/shortlist/.gitignore
new file mode 100644
index 000000000..bf0d379e4
--- /dev/null
+++ b/scripts/shortlist/.gitignore
@@ -0,0 +1,3 @@
+bin
+fast_align
+extract-lex
diff --git a/scripts/shortlist/README.md b/scripts/shortlist/README.md
new file mode 100644
index 000000000..30bf10154
--- /dev/null
+++ b/scripts/shortlist/README.md
@@ -0,0 +1,8 @@
+`install.sh` is a helper script that downloads and compiles fastalign and extract-lex, and copies
+required binaries into _./bin_.
+
+Shortlist files (_lex.s2t_ and _lex.t2s_) can be created using `generate_shortlists.pl`, for
+example:
+
+    perl generate_shortlists.pl --bindir ./bin -s corpus.bpe.src -t corpus.bpe.tgt
+
diff --git a/scripts/shortlist/generate_shortlists.pl b/scripts/shortlist/generate_shortlists.pl
new file mode 100755
index 000000000..309eeef86
--- /dev/null
+++ b/scripts/shortlist/generate_shortlists.pl
@@ -0,0 +1,97 @@
+#!/usr/bin/env perl
+
+use strict;
+use Getopt::Long;
+use FindBin qw($Bin);
+use File::Temp qw(tempdir tempfile);
+use POSIX;
+
+my $PID = $$;
+$SIG{TERM} = $SIG{INT} = $SIG{QUIT} = sub { die; };
+
+my $BINDIR = "$Bin/bin";
+my $SRC;
+my $TRG;
+my $OUTPUT = "lex";
+my $THREADS = 8;
+my $PARALLEL = 0;
+my $HELP;
+
+GetOptions(
+    "b|bindir=s" => \$BINDIR,
+    "s|source=s" => \$SRC,
+    "t|target=s" => \$TRG,
+    "o|output=s" => \$OUTPUT,
+    "threads=i" => \$THREADS,
+    "parallel" => \$PARALLEL,
+    "h|help" => \$HELP,
+);
+
+if($HELP) {
+    print "Usage: perl $0 -b bindir -s corpus.src -t corpus.tgt [-o outputprefix] [--threads 8] [--parallel]\n";
+    exit 0;
+}
+
+die "--bindir arg is required" if not defined $BINDIR;
+die "-s|--source arg is required" if not defined $SRC;
+die "-t|--target arg is required" if not defined $TRG;
+die "-o|--output arg is required" if not defined $OUTPUT;
+
+for my $app (qw(fast_align atools extract_lex)) {
+    die "Could not find $app in $BINDIR" if not -e "$BINDIR/$app";
+}
+
+my $TEMPDIR = tempdir(CLEANUP => 1);
+
+my (undef, $CORPUS)  = tempfile(DIR => $TEMPDIR);
+my (undef, $ALN_S2T) = tempfile(DIR => $TEMPDIR);
+my (undef, $ALN_T2S) = tempfile(DIR => $TEMPDIR);
+my (undef, $ALN_GDF) = tempfile(DIR => $TEMPDIR);
+
+execute("paste $SRC $TRG | sed 's/\\t/ ||| /' > $CORPUS");
+
+my @COMMANDS = (
+    "OMP_NUM_THREADS=$THREADS $BINDIR/fast_align -vdo -i $CORPUS > $ALN_S2T",
+    "OMP_NUM_THREADS=$THREADS $BINDIR/fast_align -vdor -i $CORPUS > $ALN_T2S"
+);
+
+my @PIDS;
+for my $c (@COMMANDS) {
+    if ($PARALLEL) {
+        my $pid = fork();
+        if (!$pid) {
+            execute($c);
+            exit(0);
+        } else {
+            push(@PIDS, $pid);
+            print "Forked process $pid\n";
+        }
+    } else {
+        execute($c);
+    }
+}
+if ($PARALLEL) {
+    waitpid($_, 0) foreach(@PIDS);
+}
+
+execute("$BINDIR/atools -c grow-diag-final -i $ALN_S2T -j $ALN_T2S > $ALN_GDF");
+execute("$BINDIR/extract_lex $TRG $SRC $ALN_GDF $OUTPUT.s2t $OUTPUT.t2s");
+
+sub execute {
+    my $command = shift;
+    logMessage("Executing:\t$command");
+    my $ret = system($command);
+    if ($ret != 0) {
+        logMessage("Command '$command' finished with return status $ret");
+        logMessage("Aborting and killing parent process");
+        kill(2, $PID);
+        die;
+    }
+}
+
+sub logMessage {
+    my $message = shift;
+    my $time = POSIX::strftime("%m/%d/%Y %H:%M:%S", localtime());
+    my $log_message = $time."\t$message\n";
+    print STDERR $log_message;
+}
diff --git a/scripts/shortlist/install.sh b/scripts/shortlist/install.sh
new file mode 100755
index 000000000..49b2171a4
--- /dev/null
+++ b/scripts/shortlist/install.sh
@@ -0,0 +1,25 @@
+#!/bin/bash -v
+
+mkdir -p bin
+
+# download and compile fast_align
+if [ ! -e bin/fast_align ]; then
+    git clone https://github.com/clab/fast_align
+    mkdir -p fast_align/build
+    cd fast_align/build
+    cmake ..
+    make -j4
+    cp fast_align atools ../../bin
+    cd ../../
+fi
+
+# download and compile extract-lex
+if [ ! -e bin/extract_lex ]; then
+    git clone https://github.com/marian-nmt/extract-lex
+    mkdir -p extract-lex/build
+    cd extract-lex/build
+    cmake ..
+    make -j4
+    cp extract_lex ../../bin
+    cd ../../
+fi

From 99000874b4784981e42d42d7f3c204bd04e35050 Mon Sep 17 00:00:00 2001
From: Kenneth Heafield <github@kheafield.com>
Date: Thu, 24 Jan 2019 20:20:26 +0000
Subject: [PATCH 195/838] Add cblas to libs when compiling without MKL

Otherwise on gentoo and CSD3 (without MKL module loaded), I get
missing cblas_gemm link errors
---
 CMakeLists.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/CMakeLists.txt b/CMakeLists.txt
index b54766ae9..1c96f4fa2 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -271,7 +271,7 @@ if(COMPILE_CPU)
     find_package(BLAS)
     if(BLAS_FOUND)
       include_directories(${BLAS_INCLUDE_DIR})
-      set(EXT_LIBS ${EXT_LIBS} ${BLAS_LIBRARIES})
+      set(EXT_LIBS ${EXT_LIBS} ${BLAS_LIBRARIES} cblas)
       add_definitions(-DBLAS_FOUND=1)
     endif(BLAS_FOUND)
   endif(MKL_FOUND)

From 2bc18347fa68d8769b4a62652de9605ce4163e60 Mon Sep 17 00:00:00 2001
From: Marcin Junczys-Dowmunt <Marcin.Junczys-Dowmunt@microsoft.com>
Date: Thu, 24 Jan 2019 17:22:44 -0800
Subject: [PATCH 196/838] changelog added information about first BERT attempts

---
 CHANGELOG.md | 1 +
 1 file changed, 1 insertion(+)

diff --git a/CHANGELOG.md b/CHANGELOG.md
index 02df12175..3c0dfbfa9 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -9,6 +9,7 @@ and this project adheres to [Semantic Versioning](http://semver.org/spec/v2.0.0.
 
 ### Added
 - Automatic detection of CPU intrisics when building with -arch=native 
+- First version of BERT-training and BERT-classifier, currently not compatible with TF models
 
 ### Fixed
 - Windows build with recent changes

From 960a6b373716ec239721df78c19c6e8c011ecce5 Mon Sep 17 00:00:00 2001
From: Marcin Junczys-Dowmunt <Marcin.JunczysDowmunt@microsoft.com>
Date: Thu, 24 Jan 2019 18:55:01 -0800
Subject: [PATCH 197/838] update changelog

---
 CHANGELOG.md | 1 +
 1 file changed, 1 insertion(+)

diff --git a/CHANGELOG.md b/CHANGELOG.md
index 3c0dfbfa9..98e89ff77 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -10,6 +10,7 @@ and this project adheres to [Semantic Versioning](http://semver.org/spec/v2.0.0.
 ### Added
 - Automatic detection of CPU intrisics when building with -arch=native 
 - First version of BERT-training and BERT-classifier, currently not compatible with TF models
+- New reduction operators
 
 ### Fixed
 - Windows build with recent changes

From c9be3d0a8ca7b35ee21d9e3a90dc34b63b41f204 Mon Sep 17 00:00:00 2001
From: Marcin Junczys-Dowmunt <Marcin.JunczysDowmunt@microsoft.com>
Date: Thu, 24 Jan 2019 19:14:12 -0800
Subject: [PATCH 198/838] make CUDNN files compile

---
 src/layers/convolution.cpp   | 8 +++++---
 src/models/model_factory.cpp | 2 +-
 2 files changed, 6 insertions(+), 4 deletions(-)

diff --git a/src/layers/convolution.cpp b/src/layers/convolution.cpp
index 225058e7c..9453d7f01 100644
--- a/src/layers/convolution.cpp
+++ b/src/layers/convolution.cpp
@@ -4,9 +4,11 @@
 namespace marian {
 
 #ifdef CUDNN
-Convolution::Convolution(Ptr<ExpressionGraph> graph) : Factory(graph) {}
+Convolution::Convolution(Ptr<ExpressionGraph> graph) {}
 
 Expr Convolution::apply(Expr x) {
+  auto graph = x->graph();
+
   auto prefix = opt<std::string>("prefix");
   auto kernelDims = opt<std::pair<int, int>>("kernel-dims");
   auto kernelNum = opt<int>("kernel-num");
@@ -15,11 +17,11 @@ Expr Convolution::apply(Expr x) {
 
   int layerIn = x->shape()[1];
   auto kernel
-      = graph_->param(prefix + "_conv_kernels",
+      = graph->param(prefix + "_conv_kernels",
                       {layerIn, kernelNum, kernelDims.first, kernelDims.second},
                       inits::glorot_uniform);
 
-  auto bias = graph_->param(
+  auto bias = graph->param(
       prefix + "_conv_bias", {1, kernelNum, 1, 1}, inits::zeros);
 
   std::vector<Expr> nodes = {x, kernel, bias};
diff --git a/src/models/model_factory.cpp b/src/models/model_factory.cpp
index 9eb1931ca..946b8a3ab 100755
--- a/src/models/model_factory.cpp
+++ b/src/models/model_factory.cpp
@@ -290,7 +290,7 @@ Ptr<ModelBase> by_type(std::string type, usage use, Ptr<Options> options) {
         ("original-type", type)
             .push_back(models::encoder()("type", "char-s2s"))
             .push_back(models::decoder()("type", "s2s"))
-            .construct();
+            .construct(graph);
   }
 #endif
 

From fbc49d0dd6b2115613cdd73aa52374d0ca984714 Mon Sep 17 00:00:00 2001
From: Marcin Junczys-Dowmunt <Marcin.JunczysDowmunt@microsoft.com>
Date: Thu, 24 Jan 2019 20:29:44 -0800
Subject: [PATCH 199/838] disable CSR operator test

---
 src/tests/operator_tests.cpp | 122 +++++++++++++++++------------------
 1 file changed, 61 insertions(+), 61 deletions(-)

diff --git a/src/tests/operator_tests.cpp b/src/tests/operator_tests.cpp
index 2607f41ff..e17eca9dc 100755
--- a/src/tests/operator_tests.cpp
+++ b/src/tests/operator_tests.cpp
@@ -343,65 +343,65 @@ void tests(DeviceType device) {
     auto C = dot(A, B);
 
     // CSR dot product, tested against dense product on the same values
-    std::vector<float> vS({1, 0, 0, 1,          // sparse
-                           0, 0, 1, 1.5});
-    std::vector<float> vD({1, 2, 3, 1.2, 5.6,   // dense
-                           4, 5, 6, 2.3, 6.7,
-                           7, 8, 9, 3.4, 7.8,
-                           1, 1, 2, 4.5, 8.9});
-    auto S  = graph->param("S",  { 2, 4 }, inits::from_vector(vS));
-    auto D  = graph->param("D",  { 4, 5 }, inits::from_vector(vD));
-    auto DT = graph->param("DT", { 5, 4 }, inits::from_vector(vD)); // example matrix with transposed dimensions
-    std::vector<float> SV;    // create CSR version of S
-    std::vector<IndexType> SI, SO;
-    SO.push_back((IndexType)SI.size());
-    for (IndexType i = 0; i < S->shape()[0]; i++) {
-      for (IndexType j = 0; j < S->shape()[1]; j++) {
-        auto k = 4 * i + j;
-        if (vS[k] != 0) {
-          SV.push_back(vS[k]);
-          SI.push_back(j);
-        }
-      }
-      SO.push_back((IndexType)SI.size());
-    }
-
-    auto SxDd    = dot(S, D);
-    auto STxSxDd = dot(S, SxDd, /*transA=*/true);
-    auto SxDs = csr_dot( // sparse x dense
-          S->shape(),
-          graph->constant({(int)SV.size()}, inits::from_vector(SV), Type::float32),
-          graph->constant({(int)SI.size()}, inits::from_vector(SI), Type::uint32),
-          graph->constant({(int)SO.size()}, inits::from_vector(SO), Type::uint32),
-          D);
-    auto STxSxDs = csr_dot(   // transpose(sparse) x dense; we use result of previous since dimensions match
-          S->shape(),
-          graph->constant({(int)SV.size()}, inits::from_vector(SV), Type::float32),
-          graph->constant({(int)SI.size()}, inits::from_vector(SI), Type::uint32),
-          graph->constant({(int)SO.size()}, inits::from_vector(SO), Type::uint32),
-          SxDd, /*transS=*/true);
-
-    auto DTxSTd   = dot(DT,     S, /*transA=*/false, /*transB=*/true);
-    auto DTxSTxSd = dot(DTxSTd, S);
-    auto DTxSTs = dot_csr( // dense x sparse
-          DT,
-          S->shape(),
-          graph->constant({(int)SV.size()}, inits::from_vector(SV), Type::float32),
-          graph->constant({(int)SI.size()}, inits::from_vector(SI), Type::uint32),
-          graph->constant({(int)SO.size()}, inits::from_vector(SO), Type::uint32),
-          /*transS=*/true);
-    auto DTxSTxSs = dot_csr( // dense x transpose(sparse)
-          DTxSTd,
-          S->shape(),
-          graph->constant({(int)SV.size()}, inits::from_vector(SV), Type::float32),
-          graph->constant({(int)SI.size()}, inits::from_vector(SI), Type::uint32),
-          graph->constant({(int)SO.size()}, inits::from_vector(SO), Type::uint32));
+    // std::vector<float> vS({1, 0, 0, 1,          // sparse
+    //                        0, 0, 1, 1.5});
+    // std::vector<float> vD({1, 2, 3, 1.2, 5.6,   // dense
+    //                        4, 5, 6, 2.3, 6.7,
+    //                        7, 8, 9, 3.4, 7.8,
+    //                        1, 1, 2, 4.5, 8.9});
+    // auto S  = graph->param("S",  { 2, 4 }, inits::from_vector(vS));
+    // auto D  = graph->param("D",  { 4, 5 }, inits::from_vector(vD));
+    // auto DT = graph->param("DT", { 5, 4 }, inits::from_vector(vD)); // example matrix with transposed dimensions
+    // std::vector<float> SV;    // create CSR version of S
+    // std::vector<IndexType> SI, SO;
+    // SO.push_back((IndexType)SI.size());
+    // for (IndexType i = 0; i < S->shape()[0]; i++) {
+    //   for (IndexType j = 0; j < S->shape()[1]; j++) {
+    //     auto k = 4 * i + j;
+    //     if (vS[k] != 0) {
+    //       SV.push_back(vS[k]);
+    //       SI.push_back(j);
+    //     }
+    //   }
+    //   SO.push_back((IndexType)SI.size());
+    // }
+
+    // auto SxDd    = dot(S, D);
+    // auto STxSxDd = dot(S, SxDd, /*transA=*/true);
+    // auto SxDs = csr_dot( // sparse x dense
+    //       S->shape(),
+    //       graph->constant({(int)SV.size()}, inits::from_vector(SV), Type::float32),
+    //       graph->constant({(int)SI.size()}, inits::from_vector(SI), Type::uint32),
+    //       graph->constant({(int)SO.size()}, inits::from_vector(SO), Type::uint32),
+    //       D);
+    // auto STxSxDs = csr_dot(   // transpose(sparse) x dense; we use result of previous since dimensions match
+    //       S->shape(),
+    //       graph->constant({(int)SV.size()}, inits::from_vector(SV), Type::float32),
+    //       graph->constant({(int)SI.size()}, inits::from_vector(SI), Type::uint32),
+    //       graph->constant({(int)SO.size()}, inits::from_vector(SO), Type::uint32),
+    //       SxDd, /*transS=*/true);
+
+    // auto DTxSTd   = dot(DT,     S, /*transA=*/false, /*transB=*/true);
+    // auto DTxSTxSd = dot(DTxSTd, S);
+    // auto DTxSTs = dot_csr( // dense x sparse
+    //       DT,
+    //       S->shape(),
+    //       graph->constant({(int)SV.size()}, inits::from_vector(SV), Type::float32),
+    //       graph->constant({(int)SI.size()}, inits::from_vector(SI), Type::uint32),
+    //       graph->constant({(int)SO.size()}, inits::from_vector(SO), Type::uint32),
+    //       /*transS=*/true);
+    // auto DTxSTxSs = dot_csr( // dense x transpose(sparse)
+    //       DTxSTd,
+    //       S->shape(),
+    //       graph->constant({(int)SV.size()}, inits::from_vector(SV), Type::float32),
+    //       graph->constant({(int)SI.size()}, inits::from_vector(SI), Type::uint32),
+    //       graph->constant({(int)SO.size()}, inits::from_vector(SO), Type::uint32));
 
     CHECK(C->shape() == Shape({2, 2, 2}));
-    CHECK(SxDs->shape() == SxDd->shape());
-    CHECK(STxSxDs->shape() == STxSxDd->shape());
-    CHECK(DTxSTs->shape() == DTxSTd->shape());
-    CHECK(DTxSTxSs->shape() == DTxSTxSd->shape());
+    // CHECK(SxDs->shape() == SxDd->shape());
+    // CHECK(STxSxDs->shape() == STxSxDd->shape());
+    // CHECK(DTxSTs->shape() == DTxSTd->shape());
+    // CHECK(DTxSTxSs->shape() == DTxSTxSd->shape());
 
     graph->forward();
 
@@ -409,10 +409,10 @@ void tests(DeviceType device) {
     CHECK(values == vC);
 
     // dense and sparse operation results must be the same
-    SxDd    ->val()->get(values2); SxDs    ->val()->get(values); CHECK(values == values2);
-    STxSxDd ->val()->get(values2); STxSxDs ->val()->get(values); CHECK(values == values2);
-    DTxSTd  ->val()->get(values2); DTxSTs  ->val()->get(values); CHECK(values == values2);
-    DTxSTxSd->val()->get(values2); DTxSTxSs->val()->get(values); CHECK(values == values2);
+    // SxDd    ->val()->get(values2); SxDs    ->val()->get(values); CHECK(values == values2);
+    // STxSxDd ->val()->get(values2); STxSxDs ->val()->get(values); CHECK(values == values2);
+    // DTxSTd  ->val()->get(values2); DTxSTs  ->val()->get(values); CHECK(values == values2);
+    // DTxSTxSd->val()->get(values2); DTxSTxSs->val()->get(values); CHECK(values == values2);
   }
 
   SECTION("affine transformation") {

From a8336cbac030fba374806796d0a1d87a09d69274 Mon Sep 17 00:00:00 2001
From: Roman Grundkiewicz <rgrundki@exseed.ed.ac.uk>
Date: Fri, 25 Jan 2019 10:09:56 +0000
Subject: [PATCH 200/838] Update README

---
 README.md | 56 ++++++++++++++++---------------------------------------
 1 file changed, 16 insertions(+), 40 deletions(-)

diff --git a/README.md b/README.md
index 47f79ca71..6fcc45b0b 100644
--- a/README.md
+++ b/README.md
@@ -8,33 +8,18 @@ Marian
 [![License: MIT](https://img.shields.io/badge/License-MIT-blue.svg)](./LICENSE.md)
 [![Twitter](https://img.shields.io/twitter/follow/marian_nmt.svg?style=social)](https://twitter.com/intent/follow?screen_name=marian_nmt)
 
- <p>
-  <b>Marian</b> is an efficient Neural Machine Translation framework written
-  in pure C++ with minimal dependencies. 
-
-  Named in honour of Marian Rejewski, a Polish mathematician and cryptologist.
-
-  <!--It has mainly been developed at the
-  Adam Mickiewicz University in Poznań (AMU) and at the University of Edinburgh.-->
-  </p>
-
-  <!--p>
-  It is currently being deployed in
-  multiple European projects and is the main translation and training engine
-  behind the neural MT launch at the
-  <a href="http://www.wipo.int/pressroom/en/articles/2016/article_0014.html">World Intellectual Property Organization</a>.
-  </p-->
-
-  <p>
-  Main features:
-  <ul>
-    <li> Fast multi-gpu training and translation </li>
-    <li> Compatible with Nematus and DL4MT </li>
-    <li> Efficient pure C++ implementation </li>
-    <li> Permissive open source license (MIT) </li>
-    <li> <a href="https://marian-nmt.github.io/features/"> more details... </a> </li>
-  </ul>
-  </p>
+*Marian* is an efficient Neural Machine Translation framework written in pure
+C++ with minimal dependencies.
+
+Named in honour of Marian Rejewski, a Polish mathematician and cryptologist.
+
+Main features:
+
+- Efficient pure C++ implementation
+- Fast multi-GPU training and GPU/CPU translation
+- State-of-the-art NMT architectures: deep RNN and transformer
+- Permissive open source license (MIT)
+- [more detail...](https://marian-nmt.github.io/features)
 
 If you use this, please cite:
 
@@ -59,20 +44,11 @@ Machine Translation in C++ (http://www.aclweb.org/anthology/P18-4020)
         url       = {http://www.aclweb.org/anthology/P18-4020}
     }
 
-<!--
-## Compilation
-
-```
-cd marian-dev
-mkdir -p build
-cd build
-cmake .. -DCMAKE_BUILD_TYPE=Release
-make -j
-```
--->
-
 ## Amun
-The handwritten decoder for RNN models compatible with Marian and Nematus has been superseded by the Marian decoder. The code is available in a separate repository: https://github.com/marian-nmt/amun
+
+The handwritten decoder for RNN models compatible with Marian and Nematus has
+been superseded by the Marian decoder. The code is available in a separate
+repository: https://github.com/marian-nmt/amun
 
 ## Website
 

From a6c9b59228b28f914a7e529f9c42a8f6b8a24fba Mon Sep 17 00:00:00 2001
From: Roman Grundkiewicz <rgrundki@exseed.ed.ac.uk>
Date: Fri, 25 Jan 2019 10:10:44 +0000
Subject: [PATCH 201/838] Bump version

---
 VERSION | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/VERSION b/VERSION
index 12751ca7b..f0b77c197 100644
--- a/VERSION
+++ b/VERSION
@@ -1 +1 @@
-v1.7.6
+v1.7.7

From c6211c9276b3fc89d8b89f3ff26163c990cf1aa2 Mon Sep 17 00:00:00 2001
From: Roman Grundkiewicz <rgrundki@exseed.ed.ac.uk>
Date: Fri, 25 Jan 2019 11:05:58 +0000
Subject: [PATCH 202/838] Rename --problem to --task

---
 src/common/config_parser.cpp | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/src/common/config_parser.cpp b/src/common/config_parser.cpp
index eef2e5b8f..967305511 100755
--- a/src/common/config_parser.cpp
+++ b/src/common/config_parser.cpp
@@ -399,7 +399,7 @@ void ConfigParser::addOptionsTraining(cli::CLIWrapper& cli) {
   // add ULR settings
   addSuboptionsULR(cli);
 
-  cli.add<std::vector<std::string>>("--problem",
+  cli.add<std::vector<std::string>>("--task",
      "Use predefined set of options. Possible values: transformer, transformer-big");
   // clang-format on
 }
@@ -672,7 +672,7 @@ void ConfigParser::addAliases(cli::CLIWrapper& cli) {
     config["skip"] = true;
   });
 
-  cli.alias("problem", "transformer", [](YAML::Node& config) {
+  cli.alias("task", "transformer", [](YAML::Node& config) {
     config["type"] = "transformer";
     config["enc-depth"] = 6;
     config["dec-depth"] = 6;
@@ -686,7 +686,7 @@ void ConfigParser::addAliases(cli::CLIWrapper& cli) {
     config["clip-norm"] = 5;
   });
 
-  cli.alias("problem", "transformer-big", [](YAML::Node& config) {
+  cli.alias("task", "transformer-big", [](YAML::Node& config) {
     config["type"] = "transformer";
     config["enc-depth"] = 6;
     config["dec-depth"] = 6;

From 8ef1caa0ec154934dd943dddc54ee72e875cac64 Mon Sep 17 00:00:00 2001
From: Roman Grundkiewicz <rgrundki@exseed.ed.ac.uk>
Date: Sat, 26 Jan 2019 10:31:49 +0000
Subject: [PATCH 203/838] Rename nonempty to hasAndNotEmpty; fix some comments

---
 src/common/options.h                      |  2 +-
 src/common/utils.cpp                      |  9 ++++-----
 src/data/corpus.h                         |  2 +-
 src/data/corpus_base.cpp                  |  2 +-
 src/data/corpus_base.h                    | 11 ++++++-----
 src/data/corpus_sqlite.h                  |  2 +-
 src/layers/weight.cpp                     |  2 +-
 src/microsoft/quicksand.cpp               |  4 ++--
 src/models/costs.h                        | 19 +++++++++----------
 src/models/decoder.h                      |  6 +++---
 src/models/s2s.h                          |  2 +-
 src/models/transformer.h                  |  4 ++--
 src/rescorer/rescorer.h                   |  4 ++--
 src/tests/cli.cpp                         |  2 +-
 src/training/graph_group_async.cpp        |  4 ++--
 src/training/graph_group_multinode.h      |  2 +-
 src/training/graph_group_multinode_sync.h |  2 +-
 src/training/graph_group_singleton.h      |  2 +-
 src/training/graph_group_sync.cpp         |  2 +-
 src/training/scheduler.h                  |  3 +--
 src/training/training.h                   |  2 +-
 src/training/validator.h                  | 13 +++++++------
 src/translator/beam_search.h              |  2 +-
 src/translator/scorers.cpp                |  4 ++--
 src/translator/translator.h               |  2 +-
 25 files changed, 54 insertions(+), 55 deletions(-)

diff --git a/src/common/options.h b/src/common/options.h
index b413f7db3..5a3f4eb70 100755
--- a/src/common/options.h
+++ b/src/common/options.h
@@ -102,7 +102,7 @@ class Options {
    *
    * @return true if the option is defined and is a nonempty sequence or string
    */
-  bool nonempty(const std::string& key) const {
+  bool hasAndNotEmpty(const std::string& key) const {
     if(!has(key)) {
       return false;
     }
diff --git a/src/common/utils.cpp b/src/common/utils.cpp
index 068ce9c2c..8a726fe57 100755
--- a/src/common/utils.cpp
+++ b/src/common/utils.cpp
@@ -147,8 +147,7 @@ std::string toUpper(const std::string& s) {
 
 double parseDouble(std::string s) {
   double res;
-  char c;  // dummy char -- if we succeed to parse this, then there were extraneous characters after
-           // the number
+  char c;  // dummy char -- if we succeed to parse this, then there were extraneous characters after the number
   auto rc = sscanf(s.c_str(), "%lf%c", &res, &c);
   ABORT_IF(rc != 1, "Mal-formed number: {}", s);
   return res;
@@ -160,9 +159,9 @@ double parseNumber(std::string param) {
   double factor = 1.;
   if(!param.empty() && param.back() >= 'A') {
     switch(param.back()) {
-      case 'k': factor = 1.e3; break;
-      case 'M': factor = 1.e6; break;
-      case 'G': factor = 1.e9; break;
+      case 'k': factor = 1.e3;  break;
+      case 'M': factor = 1.e6;  break;
+      case 'G': factor = 1.e9;  break;
       case 'T': factor = 1.e12; break;
       default: ABORT("Invalid or unsupported unit prefix '{}' in {}", param.back(), param);
     }
diff --git a/src/data/corpus.h b/src/data/corpus.h
index 7c8a0a9cb..b459eb87a 100755
--- a/src/data/corpus.h
+++ b/src/data/corpus.h
@@ -98,7 +98,7 @@ class Corpus : public CorpusBase {
 
     if(options_->get("guided-alignment", std::string("none")) != "none" && alignFileIdx_)
       addAlignmentsToBatch(batch, batchVector);
-    if(options_->nonempty("data-weighting") && weightFileIdx_)
+    if(options_->hasAndNotEmpty("data-weighting") && weightFileIdx_)
       addWeightsToBatch(batch, batchVector);
 
     return batch;
diff --git a/src/data/corpus_base.cpp b/src/data/corpus_base.cpp
index 867b540ab..94168a33a 100755
--- a/src/data/corpus_base.cpp
+++ b/src/data/corpus_base.cpp
@@ -178,7 +178,7 @@ CorpusBase::CorpusBase(Ptr<Options> options, bool translate)
     ABORT_IF(files_.back()->empty(), "File with alignments '{}' is empty", path);
   }
 
-  if(training && options_->nonempty("data-weighting")) {
+  if(training && options_->hasAndNotEmpty("data-weighting")) {
     auto path = options_->get<std::string>("data-weighting");
 
     ABORT_IF(!filesystem::exists(path), "Weight file does not exist");
diff --git a/src/data/corpus_base.h b/src/data/corpus_base.h
index e496e50ed..81c1e6aa0 100755
--- a/src/data/corpus_base.h
+++ b/src/data/corpus_base.h
@@ -311,7 +311,7 @@ class CorpusBatch : public Batch {
    * @return Fake batch of the same size as the real batch.
    */
   static Ptr<CorpusBatch> fakeBatch(const std::vector<size_t>& lengths,
-                                    const std::vector<Ptr<Vocab>>& vocabs, 
+                                    const std::vector<Ptr<Vocab>>& vocabs,
                                     size_t batchSize,
                                     Ptr<Options> options) {
     std::vector<Ptr<SubBatch>> batches;
@@ -322,7 +322,7 @@ class CorpusBatch : public Batch {
       // set word indices to different values to avoid same hashes
       // rand() is OK, this does not affect state in any way
       std::transform(sb->data().begin(), sb->data().end(), sb->data().begin(),
-                     [&](Word) { return rand() % vocabs[batchIndex]->size(); }); 
+                     [&](Word) { return rand() % vocabs[batchIndex]->size(); });
       // mask: no items ask being masked out
       std::fill(sb->mask().begin(), sb->mask().end(), 1.f);
       batchIndex++;
@@ -341,7 +341,7 @@ class CorpusBatch : public Batch {
       batch->setGuidedAlignment(std::move(alignment));
     }
 
-    if(options->nonempty("data-weighting")) {
+    if(options->hasAndNotEmpty("data-weighting")) {
       auto weightsSize = batchSize;
       if(options->get<std::string>("data-weighting-type") != "sentence")
         weightsSize *= lengths.back();
@@ -521,9 +521,10 @@ class CorpusBase
 
   /**
    * brief Determines if a EOS symbol should be added. By default this is true for any sequence,
-   * but should be false for instance for classifier labels. This is set per input stream, hence a vector.
+   * but should be false for instance for classifier labels. This is set per input stream, hence a
+   * vector.
    */
-  std::vector<bool> addEOS_; 
+  std::vector<bool> addEOS_;
 
   size_t pos_{0};
 
diff --git a/src/data/corpus_sqlite.h b/src/data/corpus_sqlite.h
index ba40ecb49..0da2a8647 100755
--- a/src/data/corpus_sqlite.h
+++ b/src/data/corpus_sqlite.h
@@ -104,7 +104,7 @@ class CorpusSQLite : public CorpusBase {
 
     if(options_->has("guided-alignment") && alignFileIdx_)
       addAlignmentsToBatch(batch, batchVector);
-    if(options_->nonempty("data-weighting") && weightFileIdx_)
+    if(options_->hasAndNotEmpty("data-weighting") && weightFileIdx_)
       addWeightsToBatch(batch, batchVector);
 
     return batch;
diff --git a/src/layers/weight.cpp b/src/layers/weight.cpp
index 98e97e4e9..50d749a21 100755
--- a/src/layers/weight.cpp
+++ b/src/layers/weight.cpp
@@ -3,7 +3,7 @@
 namespace marian {
 
 Ptr<WeightingBase> WeightingFactory(Ptr<Options> options) {
-  ABORT_IF(!options->nonempty("data-weighting"),
+  ABORT_IF(!options->hasAndNotEmpty("data-weighting"),
            "No data-weighting specified in options");
   return New<DataWeighting>(options->get<std::string>("data-weighting-type"));
 }
diff --git a/src/microsoft/quicksand.cpp b/src/microsoft/quicksand.cpp
index 2a82263f7..959e44311 100755
--- a/src/microsoft/quicksand.cpp
+++ b/src/microsoft/quicksand.cpp
@@ -43,7 +43,7 @@ class BeamSearchDecoder : public IBeamSearchDecoder {
                     const std::vector<const void*>& ptrs,
                     Word eos)
       : IBeamSearchDecoder(options, ptrs, eos) {
-    
+
     // setting 16-bit optimization to false for now. Re-enable with better caching or pre-computation
     graph_ = New<ExpressionGraph>(/*inference=*/true, /*optimize=*/false);
 
@@ -136,7 +136,7 @@ class BeamSearchDecoder : public IBeamSearchDecoder {
         auto score = std::get<2>(result);
         // determine alignment if present
         AlignmentSets alignmentSets;
-        if (options_->nonempty("alignment"))
+        if (options_->hasAndNotEmpty("alignment"))
         {
           float alignmentThreshold;
           auto alignment = options_->get<std::string>("alignment"); // @TODO: this logic now exists three times in Marian
diff --git a/src/models/costs.h b/src/models/costs.h
index 42e2220dd..2aba6fe09 100755
--- a/src/models/costs.h
+++ b/src/models/costs.h
@@ -16,8 +16,8 @@ namespace models {
 
 // @TODO: inheritance and polymorphism is used here in a rather unclear way.
 // E.g. returns Ptr<MultiRationalLoss> which should be Ptr<RationalLoss>?
-// Other functions return RationalLoss directly without Ptr<...>, but also 
-// they do not need polymorphism here. 
+// Other functions return RationalLoss directly without Ptr<...>, but also
+// they do not need polymorphism here.
 
 class CostBase {
 public:
@@ -44,7 +44,7 @@ class EncoderDecoderCE : public CostBase {
     loss_ = newLoss(options_, inference_);
 
     toBeWeighted_
-        = (options_->nonempty("data-weighting") && !inference_)
+        = (options_->hasAndNotEmpty("data-weighting") && !inference_)
           || (options_->has("dynamic-weighting") && options_->get<bool>("dynamic-weighting")
               && !inference_);
     if(toBeWeighted_)
@@ -73,7 +73,7 @@ class EncoderDecoderCE : public CostBase {
                                     state->getTargetMask(),
                                     weights);
     multiLoss->push_back(partialLoss);
-  
+
     if(options_->get("guided-alignment", std::string("none")) != "none" && !inference_) {
       auto attentionVectors = encdec->getDecoders()[0]->getAlignments();
       ABORT_IF(attentionVectors.empty(), "Model does not seem to support alignments");
@@ -83,9 +83,8 @@ class EncoderDecoderCE : public CostBase {
       auto alignmentLoss = guidedAlignmentCost(graph, corpusBatch, options_, attention);
       multiLoss->push_back(alignmentLoss);
     }
-    
+
     return multiLoss;
-    
   }
 };
 
@@ -96,7 +95,7 @@ class EncoderClassifierCE : public CostBase {
   bool inference_{false};
 
   // @TODO: single loss seems wrong, especially since we support multiple objectives here,
-  // also not sure this needs to be a member at all. 
+  // also not sure this needs to be a member at all.
   Ptr<LabelwiseLoss> loss_;
 
 public:
@@ -114,7 +113,7 @@ class EncoderClassifierCE : public CostBase {
     auto corpusBatch = std::static_pointer_cast<data::CorpusBatch>(batch);
 
     auto states = enccls->apply(graph, corpusBatch, clearGraph);
-    
+
     // multi-objective training
     Ptr<MultiRationalLoss> multiLoss = newMultiLoss(options_);
     for(int i = 0; i < states.size(); ++i) {
@@ -284,7 +283,7 @@ inline Ptr<ModelBase> add_cost(Ptr<EncoderDecoder> encdec,
       else
         return New<Stepwise>(encdec, New<LogSoftmaxStep>());
     case usage::raw:
-    default: 
+    default:
       return encdec;
   }
 }
@@ -299,7 +298,7 @@ inline Ptr<ModelBase> add_cost(Ptr<EncoderClassifier> enccls,
     case usage::translation:
       ABORT("Classifier cannot be used for translation");
     case usage::raw:
-    default: 
+    default:
       return enccls;
   }
 }
diff --git a/src/models/decoder.h b/src/models/decoder.h
index 079cf66fb..bc0c91191 100755
--- a/src/models/decoder.h
+++ b/src/models/decoder.h
@@ -52,7 +52,7 @@ class DecoderBase {
     if(options_->has("embedding-fix-trg"))
       yEmbFactory("fixed", opt<bool>("embedding-fix-trg"));
 
-    if(options_->nonempty("embedding-vectors")) {
+    if(options_->hasAndNotEmpty("embedding-vectors")) {
       auto embFiles = opt<std::vector<std::string>>("embedding-vectors");
       yEmbFactory("embFile", embFiles[batchIndex_])  //
           ("normalization", opt<bool>("embedding-normalization"));
@@ -95,12 +95,12 @@ class DecoderBase {
       auto yEmbFactory = embedding()  //
           ("dimVocab", dimTrgVoc)     //
           ("dimEmb", dimTrgEmb);
-  
+
       if(opt<bool>("tied-embeddings-src") || opt<bool>("tied-embeddings-all"))
         yEmbFactory("prefix", "Wemb");
       else
         yEmbFactory("prefix", prefix_ + "_Wemb");
-  
+
       auto yEmb = yEmbFactory.construct(graph);
 
       selectedEmbs = yEmb->apply(embIdx, {dimBeam, 1, dimBatch, dimTrgEmb});
diff --git a/src/models/s2s.h b/src/models/s2s.h
index 873227194..35846e783 100755
--- a/src/models/s2s.h
+++ b/src/models/s2s.h
@@ -136,7 +136,7 @@ class EncoderS2S : public EncoderBase {
     if(options_->has("embedding-fix-src"))
       embFactory("fixed", opt<bool>("embedding-fix-src"));
 
-    if(options_->nonempty("embedding-vectors")) {
+    if(options_->hasAndNotEmpty("embedding-vectors")) {
       auto embFiles = opt<std::vector<std::string>>("embedding-vectors");
       embFactory                              //
           ("embFile", embFiles[batchIndex_])  //
diff --git a/src/models/transformer.h b/src/models/transformer.h
index 87ce85cc4..f98ba1a91 100755
--- a/src/models/transformer.h
+++ b/src/models/transformer.h
@@ -533,7 +533,7 @@ class EncoderTransformer : public Transformer<EncoderBase> {
       embFactory("prefix", prefix_ + "_Wemb");
     if(options_->has("embedding-fix-src"))
       embFactory("fixed", opt<bool>("embedding-fix-src"));
-    if(options_->nonempty("embedding-vectors")) {
+    if(options_->hasAndNotEmpty("embedding-vectors")) {
       auto embFiles = opt<std::vector<std::string>>("embedding-vectors");
       embFactory("embFile", embFiles[subBatchIndex])
                 ("normalization", opt<bool>("embedding-normalization"));
@@ -797,7 +797,7 @@ class DecoderTransformer : public Transformer<DecoderBase> {
           // decoding or scoring return the attention weights of one head of the last layer.
           // @TODO: maybe allow to return average or max over all heads?
           bool saveAttentionWeights = false;
-          if(j == 0 && (options_->get("guided-alignment", std::string("none")) != "none" || options_->nonempty("alignment"))) {
+          if(j == 0 && (options_->get("guided-alignment", std::string("none")) != "none" || options_->hasAndNotEmpty("alignment"))) {
             size_t attLayer = decDepth - 1;
             std::string gaStr = options_->get<std::string>("transformer-guided-alignment-layer", "last");
             if(gaStr != "last")
diff --git a/src/rescorer/rescorer.h b/src/rescorer/rescorer.h
index 25abe97d9..ee2a3c9a5 100644
--- a/src/rescorer/rescorer.h
+++ b/src/rescorer/rescorer.h
@@ -49,10 +49,10 @@ class Rescore : public ModelTask {
 
 public:
   Rescore(Ptr<Options> options) : options_(options) {
-    ABORT_IF(options_->nonempty("summary") && options_->nonempty("alignment"),
+    ABORT_IF(options_->hasAndNotEmpty("summary") && options_->hasAndNotEmpty("alignment"),
              "Alignments can not be produced with summarized score");
 
-    ABORT_IF(options_->nonempty("summary") && options_->get<bool>("normalize"),
+    ABORT_IF(options_->hasAndNotEmpty("summary") && options_->get<bool>("normalize"),
              "Normalization by length cannot be used with summary scores");
 
     options_->set("inference", true);
diff --git a/src/tests/cli.cpp b/src/tests/cli.cpp
index c3210514b..f829041ea 100644
--- a/src/tests/cli.cpp
+++ b/src/tests/cli.cpp
@@ -69,7 +69,7 @@ int main(int argc, char** argv) {
   std::cout << emit.c_str() << std::endl;
 
   std::cout << "===" << std::endl;
-  std::cout << "vec/str.nonempty? " << options->nonempty("vec") << " " << options->nonempty("str") << std::endl;
+  std::cout << "vec/str.hasAndNotEmpty? " << options->hasAndNotEmpty("vec") << " " << options->hasAndNotEmpty("str") << std::endl;
   std::cout << "vec/str.has?      " << options->has("vec") << " " << options->has("str") << std::endl;
 
   return 0;
diff --git a/src/training/graph_group_async.cpp b/src/training/graph_group_async.cpp
index e2fb0aad6..f1a01cdfa 100755
--- a/src/training/graph_group_async.cpp
+++ b/src/training/graph_group_async.cpp
@@ -256,7 +256,7 @@ void AsyncGraphGroup::execute(Ptr<data::Batch> batch) {
         std::vector<Ptr<Vocab>> vocabs;
         auto fb = data::CorpusBatch::fakeBatch(fakeLength, vocabs, num_seen_sentences, NULL);
         fb->front()->setWords(num_seen_words);
-        
+
         scheduler_->update(loss, fb);
 
         num_seen_words = 0;
@@ -325,7 +325,7 @@ void AsyncGraphGroup::load() {
             setFn(i, data.begin() + begin, data.begin() + end);
           }
         });
-    } else if(options_->nonempty("pretrained-model")) {
+    } else if(options_->hasAndNotEmpty("pretrained-model")) {
       std::string nameInit = options_->get<std::string>("pretrained-model");
       LOG(info,
           "Initialize model weights with the pre-trained model {}",
diff --git a/src/training/graph_group_multinode.h b/src/training/graph_group_multinode.h
index f98f56316..340fdcbb3 100755
--- a/src/training/graph_group_multinode.h
+++ b/src/training/graph_group_multinode.h
@@ -397,7 +397,7 @@ class MultiNodeGraphGroup : public MultiNodeGraphGroupBase {
         size_t i = 0;
         for(auto graph : clientGraphs_)
           clientBuilders_[i++]->load(graph, name);
-      } else if(options_->nonempty("pretrained-model")) {
+      } else if(options_->hasAndNotEmpty("pretrained-model")) {
         std::string init = options_->get<std::string>("pretrained-model");
         LOG(info,
             "Initialize model weights with the pre-trained model {}",
diff --git a/src/training/graph_group_multinode_sync.h b/src/training/graph_group_multinode_sync.h
index c94bc50c0..ff1fe4e97 100755
--- a/src/training/graph_group_multinode_sync.h
+++ b/src/training/graph_group_multinode_sync.h
@@ -163,7 +163,7 @@ class MultiNodeGraphGroupSync : public MultiNodeGraphGroupBase {
         size_t i = 0;
         for(auto graph : clientGraphs_)
           clientBuilders_[i++]->load(graph, name);
-      } else if(options_->nonempty("pretrained-model")) {
+      } else if(options_->hasAndNotEmpty("pretrained-model")) {
         std::string init = options_->get<std::string>("pretrained-model");
         LOG(info,
             "Initialize model weights with the pre-trained model {}",
diff --git a/src/training/graph_group_singleton.h b/src/training/graph_group_singleton.h
index a750d6b35..01ffb1eb2 100755
--- a/src/training/graph_group_singleton.h
+++ b/src/training/graph_group_singleton.h
@@ -70,7 +70,7 @@ class SingletonGraph : public GraphGroup, public ExponentialSmoothing {
           /*scatterStateFn=*/[&](const std::vector<float>& data, const OptimizerBase::ScatterStateSetFunc& setFn) {
             setFn(/*localDeviceIndex=*/0, data.begin(), data.end());
           });
-      } else if(options_->nonempty("pretrained-model")) {
+      } else if(options_->hasAndNotEmpty("pretrained-model")) {
         std::string init = options_->get<std::string>("pretrained-model");
         LOG(info,
             "Initialize model weights with the pre-trained model {}",
diff --git a/src/training/graph_group_sync.cpp b/src/training/graph_group_sync.cpp
index 47879ab9d..c02a34afa 100755
--- a/src/training/graph_group_sync.cpp
+++ b/src/training/graph_group_sync.cpp
@@ -453,7 +453,7 @@ void SyncGraphGroup::load() /*override*/ {
           comm_->scatterState(optimizerStateVector, setShardFn);
         });
       LOG(info, "[training] Model reloaded from {}", name);
-    } else if(options_->nonempty("pretrained-model")) {
+    } else if(options_->hasAndNotEmpty("pretrained-model")) {
       std::string nameInit = options_->get<std::string>("pretrained-model");
       LOG(info,
           "[training] Initializing model weights with the pre-trained model {}",
diff --git a/src/training/scheduler.h b/src/training/scheduler.h
index b72d8e31e..8f5c3bd90 100755
--- a/src/training/scheduler.h
+++ b/src/training/scheduler.h
@@ -282,8 +282,7 @@ class Scheduler : public TrainingObserver {
 
     state_->updatesDisp  += 1;
     state_->samplesDisp  += batchSize;
-    state_->wordsDisp    += batchLabels;        // words at given input processed since last display, for speed display
-                                                // @TODO: this is wrong
+    state_->wordsDisp    += batchLabels;        // @TODO: this is wrong // words at given input processed since last display, for speed display
     state_->samplesEpoch += batchSize;          // sentences processed in this epoch
     state_->labelsTotal  += rationalLoss.count; // total labels processed
 
diff --git a/src/training/training.h b/src/training/training.h
index 485318367..8863c165d 100755
--- a/src/training/training.h
+++ b/src/training/training.h
@@ -51,7 +51,7 @@ class Train : public ModelTask {
       LOG(info, "[batching] Done. Typical MB size is {} target words", stats->estimateTypicalTrgWords());
     }
 
-    if((options_->nonempty("valid-sets") || options_->nonempty("valid-script-path"))
+    if((options_->hasAndNotEmpty("valid-sets") || options_->hasAndNotEmpty("valid-script-path"))
        && SchedulingParameter::parse(options_->get<std::string>("valid-freq"))) {
       for(auto validator : Validators(dataset->getVocabs(), options_))
         scheduler->addValidator(validator);
diff --git a/src/training/validator.h b/src/training/validator.h
index 6c82d36ea..fea1be75f 100755
--- a/src/training/validator.h
+++ b/src/training/validator.h
@@ -420,7 +420,8 @@ class ScriptValidator : public Validator<data::Corpus> {
       : Validator(vocabs, options, false) {
     builder_ = models::from_options(options_, models::usage::raw);
 
-    ABORT_IF(!options_->nonempty("valid-script-path"), "valid-script metric but no script given");
+    ABORT_IF(!options_->hasAndNotEmpty("valid-script-path"),
+             "valid-script metric but no script given");
   }
 
   virtual float validate(const std::vector<Ptr<ExpressionGraph>>& graphs) override {
@@ -451,7 +452,7 @@ class TranslationValidator : public Validator<data::Corpus> {
         quiet_(options_->get<bool>("quiet-translation")) {
     builder_ = models::from_options(options_, models::usage::translation);
 
-    if(!options_->nonempty("valid-script-path"))
+    if(!options_->hasAndNotEmpty("valid-script-path"))
       LOG_VALID(warn, "No post-processing script given for validating translator");
 
     createBatchGenerator(/*isTranslating=*/true);
@@ -482,7 +483,7 @@ class TranslationValidator : public Validator<data::Corpus> {
     std::string fileName;
     Ptr<io::TemporaryFile> tempFile;
 
-    if(options_->nonempty("valid-translation-output")) {
+    if(options_->hasAndNotEmpty("valid-translation-output")) {
       fileName = options_->get<std::string>("valid-translation-output");
     } else {
       tempFile.reset(new io::TemporaryFile(options_->get<std::string>("tempdir"), false));
@@ -500,7 +501,7 @@ class TranslationValidator : public Validator<data::Corpus> {
       auto printer = New<OutputPrinter>(options_, vocabs_.back());
       // @TODO: This can be simplified. If there is no "valid-translation-output", fileName already
       // contains the name of temporary file that should be used?
-      auto collector = options_->nonempty("valid-translation-output")
+      auto collector = options_->hasAndNotEmpty("valid-translation-output")
                            ? New<OutputCollector>(fileName)
                            : New<OutputCollector>(*tempFile);
 
@@ -555,7 +556,7 @@ class TranslationValidator : public Validator<data::Corpus> {
     float val = 0.0f;
 
     // Run post-processing script if given
-    if(options_->nonempty("valid-script-path")) {
+    if(options_->hasAndNotEmpty("valid-script-path")) {
       auto command = options_->get<std::string>("valid-script-path") + " " + fileName;
       auto valStr = utils::exec(command);
       val = (float)std::atof(valStr.c_str());
@@ -639,7 +640,7 @@ class BleuValidator : public Validator<data::Corpus> {
       auto printer = New<OutputPrinter>(options_, vocabs_.back());
 
       Ptr<OutputCollector> collector;
-      if(options_->nonempty("valid-translation-output")) {
+      if(options_->hasAndNotEmpty("valid-translation-output")) {
         auto fileName = options_->get<std::string>("valid-translation-output");
         collector = New<OutputCollector>(fileName); // for debugging
       }
diff --git a/src/translator/beam_search.h b/src/translator/beam_search.h
index 50bf5ccbc..17ba6d573 100755
--- a/src/translator/beam_search.h
+++ b/src/translator/beam_search.h
@@ -42,7 +42,7 @@ class BeamSearch {
     Beams newBeams(beams.size());
 
     std::vector<float> align;
-    if(options_->nonempty("alignment"))
+    if(options_->hasAndNotEmpty("alignment"))
       // Use alignments from the first scorer, even if ensemble
       align = scorers_[0]->getAlignment();
 
diff --git a/src/translator/scorers.cpp b/src/translator/scorers.cpp
index 6c0031bff..7a07a8cd5 100755
--- a/src/translator/scorers.cpp
+++ b/src/translator/scorers.cpp
@@ -53,7 +53,7 @@ std::vector<Ptr<Scorer>> createScorers(Ptr<Options> options) {
   auto models = options->get<std::vector<std::string>>("models");
 
   std::vector<float> weights(models.size(), 1.f);
-  if(options->nonempty("weights"))
+  if(options->hasAndNotEmpty("weights"))
     weights = options->get<std::vector<float>>("weights");
 
   size_t i = 0;
@@ -83,7 +83,7 @@ std::vector<Ptr<Scorer>> createScorers(Ptr<Options> options, const std::vector<c
   std::vector<Ptr<Scorer>> scorers;
 
   std::vector<float> weights(ptrs.size(), 1.f);
-  if(options->nonempty("weights"))
+  if(options->hasAndNotEmpty("weights"))
     weights = options->get<std::vector<float>>("weights");
 
   size_t i = 0;
diff --git a/src/translator/translator.h b/src/translator/translator.h
index 877312b81..18371a3de 100755
--- a/src/translator/translator.h
+++ b/src/translator/translator.h
@@ -41,7 +41,7 @@ class Translate : public ModelTask {
     trgVocab_->load(vocabs.back());
     auto srcVocab = corpus_->getVocabs()[0];
 
-    if(options_->nonempty("shortlist"))
+    if(options_->hasAndNotEmpty("shortlist"))
       shortlistGenerator_ = New<data::LexicalShortlistGenerator>(
           options_, srcVocab, trgVocab_, 0, 1, vocabs.front() == vocabs.back());
 

From 79368b121f29a0c12beada95473655220ead8827 Mon Sep 17 00:00:00 2001
From: Marcin Junczys-Dowmunt <Marcin.JunczysDowmunt@microsoft.com>
Date: Sat, 26 Jan 2019 14:14:57 -0800
Subject: [PATCH 204/838] add gelu activation

---
 src/graph/expression_operators.cpp |  9 ++++++++
 src/graph/expression_operators.h   |  3 +++
 src/graph/node_operators_unary.h   | 35 ++++++++++++++++++++++++------
 src/layers/loss.h                  |  1 +
 src/models/bert.h                  | 26 +++++++++++-----------
 src/models/transformer.h           |  2 ++
 src/tensors/gpu/add.inc            |  1 +
 src/tensors/gpu/element.inc        |  2 ++
 8 files changed, 59 insertions(+), 20 deletions(-)

diff --git a/src/graph/expression_operators.cpp b/src/graph/expression_operators.cpp
index c0640edbf..e558ffd08 100644
--- a/src/graph/expression_operators.cpp
+++ b/src/graph/expression_operators.cpp
@@ -51,6 +51,10 @@ Expr swish(Expr a) {
   return Expression<SwishNodeOp>(a);
 }
 
+Expr gelu(Expr a) {
+  return Expression<SwishNodeOp>(a, 1.702f);
+}
+
 Expr operator-(Expr a) {
   return Expression<NegNodeOp>(a);
 };
@@ -529,6 +533,11 @@ Expr swish(const std::vector<Expr>& nodes) {
   return swish(nodes[0]);
 }
 
+Expr gelu(const std::vector<Expr>& nodes) {
+  ABORT_IF(nodes.size() > 1, "Not implemented");
+  return gelu(nodes[0]);
+}
+
 Expr tanh(const std::vector<Expr>& nodes) {
   return Expression<TanhNodeOp>(nodes);
 }
diff --git a/src/graph/expression_operators.h b/src/graph/expression_operators.h
index 78aed8347..0205fe864 100755
--- a/src/graph/expression_operators.h
+++ b/src/graph/expression_operators.h
@@ -17,6 +17,9 @@ Expr sigmoid(const std::vector<Expr>&);
 Expr swish(Expr a);
 Expr swish(const std::vector<Expr>&);
 
+Expr gelu(Expr a);
+Expr gelu(const std::vector<Expr>&);
+
 Expr tanh(const std::vector<Expr>&);
 
 template <typename... Args>
diff --git a/src/graph/node_operators_unary.h b/src/graph/node_operators_unary.h
index 63756da3a..190fa947f 100755
--- a/src/graph/node_operators_unary.h
+++ b/src/graph/node_operators_unary.h
@@ -343,31 +343,52 @@ struct PReLUNodeOp : public UnaryNodeOp {
  * in an expression graph.
  *
  * This node implements the activation function
- * \f$ f(x) = x \cdot \sigma(x) \f$
+ * \f$ f(x) = x \cdot \sigma(bx) \f$
  * and its derivative
- * \f$ f^\prime(x) = f(x) + \sigma(x)(1 - f(x)) \f$ .
+ * \f$ f^\prime(x) = bf(x) + \sigma(bx)(1 - bf(x)) \f$ .
  *
  */
 struct SwishNodeOp : public UnaryNodeOp {
-  SwishNodeOp(Expr a) : UnaryNodeOp(a) {}
+  SwishNodeOp(Expr a, float b = 1.f) : UnaryNodeOp(a), b_{b} {}
 
   NodeOps forwardOps() override {
     using namespace functional;
-    return {NodeOp(Element(_1 = _2 * sigmoid(_2), val_, child(0)->val()))};
+    return {NodeOp(Element(_1 = _2 * sigmoid(b_ * _2), val_, child(0)->val()))};
   }
 
   NodeOps backwardOps() override {
     using namespace functional;
-    // dJ/dx += dJ/df * ( f(x) + sigma(x) * (1 - f(x)) )
-    return {NodeOp(Add(_1 * (_3 + sigmoid(_2) * (1.f - _3)),
+    // dJ/dx += dJ/df * (b*f(x) + sigmoid(b*x) * (1 - b*f(x)))
+    return {NodeOp(Add(_1 * (b_ * _3 + sigmoid(b_ * _2) * (1.f - (b_ * _3))),
                        child(0)->grad(),  // dJ/dx
                        adj_,              // _1 := dJ/df
                        child(0)->val(),   // _2 := x
-                       val_               // _3 := f(x) = x*sigma(x)
+                       val_               // _3 := f(x) = x*sigmoid(b*x)
                        ))};
   }
 
   const std::string type() override { return "swish"; }
+
+  virtual size_t hash() override {
+    if(!hash_) {
+      hash_ = NaryNodeOp::hash();
+      util::hash_combine(hash_, b_);
+    }
+    return hash_;
+  }
+
+  virtual bool equal(Expr node) override {
+    if(!NaryNodeOp::equal(node))
+      return false;
+    Ptr<SwishNodeOp> cnode = std::dynamic_pointer_cast<SwishNodeOp>(node);
+    if(!cnode)
+      return false;
+    if(b_ != cnode->b_)
+      return false;
+    return true;
+  }
+
+  float b_;
 };
 
 struct SoftmaxNodeOp : public UnaryNodeOp {
diff --git a/src/layers/loss.h b/src/layers/loss.h
index 4a28de7b2..2ac4ae78d 100644
--- a/src/layers/loss.h
+++ b/src/layers/loss.h
@@ -331,6 +331,7 @@ class CrossEntropyLoss : public LabelwiseLoss {
 
   virtual Expr compute(Expr logits, Expr labelIndices,
                        Expr mask = nullptr, Expr labelWeights = nullptr) override {
+    logits = atleast_3d(logits); // safeguard against 2d classifier output, adds 1 on the left, non-op.
     Expr ce = cross_entropy(logits, labelIndices);
 
     if(labelSmoothing_ > 0) {
diff --git a/src/models/bert.h b/src/models/bert.h
index c642abac4..b4d2a34c2 100644
--- a/src/models/bert.h
+++ b/src/models/bert.h
@@ -144,7 +144,7 @@ class BertBatch : public CorpusBatch {
     int dimBatch = subBatch->batchSize();
     int dimWords = subBatch->batchWidth();
 
-    int maxSentPos = 2; // Currently only two sentences allowed A at [0] and B at [1] and padding at [2]
+    int maxSentPos = 1; // Currently only two sentences allowed A at [0] and B at [1] and padding at [2]
     // If another separator is seen do not increase position index beyond 2 but use padding.
     // @TODO: make this configurable, see below for NextSentencePredictions task where we also restrict to 2.
 
@@ -231,7 +231,7 @@ class BertEncoder : public EncoderTransformer {
     if(learnedPosEmbeddings) {
       auto sentenceEmbeddings = embedding()
                                ("prefix", "Wsent")
-                               ("dimVocab", 3) // sentence A or sentence B plus padding, @TODO: should rather be a parameter
+                               ("dimVocab", 2) // sentence A or sentence B plus padding, @TODO: should rather be a parameter
                                ("dimEmb", dimEmb)
                                .construct(graph_);
       signal = sentenceEmbeddings->apply(bertBatch->bertSentenceIndices(), {dimWords, dimBatch, dimEmb});
@@ -327,24 +327,24 @@ class BertMaskedLM : public ClassifierBase {
 
     int dimVoc = opt<std::vector<int>>("dim-vocabs")[batchIndex_];
 
-    std::string activationType = opt<std::string>("transformer-ffn-activation");
-    mlp::act activation;
-    if(activationType == "relu")
-      activation = mlp::act::ReLU;
-    else if(activationType == "swish")
-      activation = mlp::act::swish;
-    else
-      ABORT("Activation function {} not supported in BERT masked LM", activationType);
-
     auto layer1 = mlp::mlp()
       .push_back(mlp::dense()
                  ("prefix", prefix_ + "_ff_logit_l1")
-                 ("dim", dimModel)
-                 ("activation", activation))
+                 ("dim", dimModel))
                  .construct(graph);
 
     auto intermediate = layer1->apply(maskedContext);
 
+    std::string activationType = opt<std::string>("transformer-ffn-activation");
+    if(activationType == "relu")
+      intermediate = relu(intermediate);
+    else if(activationType == "swish")
+      intermediate = swish(intermediate);
+    else if(activationType == "gelu")
+      intermediate = gelu(intermediate);
+    else
+      ABORT("Activation function {} not supported in BERT masked LM", activationType);
+
     auto gamma = graph->param(prefix_ + "_ff_ln_scale", {1, dimModel}, inits::ones);
     auto beta  = graph->param(prefix_ + "_ff_ln_bias",  {1, dimModel}, inits::zeros);
     intermediate = layerNorm(intermediate, gamma, beta);
diff --git a/src/models/transformer.h b/src/models/transformer.h
index 01201df96..02ce2f6ed 100644
--- a/src/models/transformer.h
+++ b/src/models/transformer.h
@@ -375,6 +375,8 @@ class Transformer : public EncoderOrDecoderBase {
       return (ActivationFunction*)relu;
     else if (actName == "swish")
       return (ActivationFunction*)swish;
+    else if (actName == "gelu")
+      return (ActivationFunction*)gelu;
     ABORT("Invalid activation name '{}'", actName);
   }
 
diff --git a/src/tensors/gpu/add.inc b/src/tensors/gpu/add.inc
index 69244dce0..b70c59d0a 100755
--- a/src/tensors/gpu/add.inc
+++ b/src/tensors/gpu/add.inc
@@ -32,3 +32,4 @@ template void marian::gpu::Add<marian::functional::BinaryFunctor<marian::functio
 template void marian::gpu::Add<marian::functional::BinaryFunctor<marian::functional::elem::Mult, marian::functional::Assignee<1>, marian::functional::UnaryFunctor<marian::functional::elem::Exp, marian::functional::BinaryFunctor<marian::functional::elem::Minus, marian::functional::Assignee<2>, marian::functional::Assignee<3> > > >, std::shared_ptr<marian::TensorBase>, std::shared_ptr<marian::TensorBase>, std::shared_ptr<marian::TensorBase> >(marian::functional::BinaryFunctor<marian::functional::elem::Mult, marian::functional::Assignee<1>, marian::functional::UnaryFunctor<marian::functional::elem::Exp, marian::functional::BinaryFunctor<marian::functional::elem::Minus, marian::functional::Assignee<2>, marian::functional::Assignee<3> > > >, float, std::shared_ptr<marian::TensorBase>, std::shared_ptr<marian::TensorBase>, std::shared_ptr<marian::TensorBase>, std::shared_ptr<marian::TensorBase>);
 template void marian::gpu::Add<marian::functional::BinaryFunctor<marian::functional::elem::Div, marian::functional::BinaryFunctor<marian::functional::elem::Mult, marian::functional::Assignee<1>, marian::functional::Assignee<2> >, marian::functional::Assignee<3> >, std::shared_ptr<marian::TensorBase>, std::shared_ptr<marian::TensorBase>, std::shared_ptr<marian::TensorBase> >(marian::functional::BinaryFunctor<marian::functional::elem::Div, marian::functional::BinaryFunctor<marian::functional::elem::Mult, marian::functional::Assignee<1>, marian::functional::Assignee<2> >, marian::functional::Assignee<3> >, float, std::shared_ptr<marian::TensorBase>, std::shared_ptr<marian::TensorBase>, std::shared_ptr<marian::TensorBase>, std::shared_ptr<marian::TensorBase>);
 template void marian::gpu::Add<marian::functional::BinaryFunctor<marian::functional::elem::Mult, marian::functional::BinaryFunctor<marian::functional::elem::Mult, marian::functional::Assignee<1>, marian::functional::Capture>, marian::functional::Assignee<2> >, std::shared_ptr<marian::TensorBase>, std::shared_ptr<marian::TensorBase> >(marian::functional::BinaryFunctor<marian::functional::elem::Mult, marian::functional::BinaryFunctor<marian::functional::elem::Mult, marian::functional::Assignee<1>, marian::functional::Capture>, marian::functional::Assignee<2> >, float, std::shared_ptr<marian::TensorBase>, std::shared_ptr<marian::TensorBase>, std::shared_ptr<marian::TensorBase>);
+template void marian::gpu::Add<marian::functional::BinaryFunctor<marian::functional::elem::Mult, marian::functional::Assignee<1>, marian::functional::BinaryFunctor<marian::functional::elem::Plus, marian::functional::BinaryFunctor<marian::functional::elem::Mult, marian::functional::Capture, marian::functional::Assignee<3> >, marian::functional::BinaryFunctor<marian::functional::elem::Mult, marian::functional::UnaryFunctor<marian::functional::elem::Sigmoid, marian::functional::BinaryFunctor<marian::functional::elem::Mult, marian::functional::Capture, marian::functional::Assignee<2> > >, marian::functional::BinaryFunctor<marian::functional::elem::Minus, marian::functional::Capture, marian::functional::BinaryFunctor<marian::functional::elem::Mult, marian::functional::Capture, marian::functional::Assignee<3> > > > > >, std::shared_ptr<marian::TensorBase>, std::shared_ptr<marian::TensorBase>, std::shared_ptr<marian::TensorBase> >(marian::functional::BinaryFunctor<marian::functional::elem::Mult, marian::functional::Assignee<1>, marian::functional::BinaryFunctor<marian::functional::elem::Plus, marian::functional::BinaryFunctor<marian::functional::elem::Mult, marian::functional::Capture, marian::functional::Assignee<3> >, marian::functional::BinaryFunctor<marian::functional::elem::Mult, marian::functional::UnaryFunctor<marian::functional::elem::Sigmoid, marian::functional::BinaryFunctor<marian::functional::elem::Mult, marian::functional::Capture, marian::functional::Assignee<2> > >, marian::functional::BinaryFunctor<marian::functional::elem::Minus, marian::functional::Capture, marian::functional::BinaryFunctor<marian::functional::elem::Mult, marian::functional::Capture, marian::functional::Assignee<3> > > > > >, float, std::shared_ptr<marian::TensorBase>, std::shared_ptr<marian::TensorBase>, std::shared_ptr<marian::TensorBase>, std::shared_ptr<marian::TensorBase>);
\ No newline at end of file
diff --git a/src/tensors/gpu/element.inc b/src/tensors/gpu/element.inc
index f3cdea282..364866fab 100755
--- a/src/tensors/gpu/element.inc
+++ b/src/tensors/gpu/element.inc
@@ -56,6 +56,8 @@ template void Element<Assign<Var<1>, BinaryFunctor<elem::Minus, Assignee<1>, Bin
 template void Element<Assign<Var<1>, BinaryFunctor<elem::Minus, Assignee<1>, BinaryFunctor<elem::Mult, Capture, BinaryFunctor<elem::Plus, BinaryFunctor<elem::Div, BinaryFunctor<elem::Div, Assignee<2>, Capture>, BinaryFunctor<elem::Plus, UnaryFunctor<elem::Sqrt, BinaryFunctor<elem::Div, Assignee<3>, Capture> >, Capture> >, BinaryFunctor<elem::Mult, Capture, Assignee<1> > > > > >, std::shared_ptr<marian::TensorBase>, std::shared_ptr<marian::TensorBase>, std::shared_ptr<marian::TensorBase>, std::shared_ptr<marian::TensorBase> >(Assign<Var<1>, BinaryFunctor<elem::Minus, Assignee<1>, BinaryFunctor<elem::Mult, Capture, BinaryFunctor<elem::Plus, BinaryFunctor<elem::Div, BinaryFunctor<elem::Div, Assignee<2>, Capture>, BinaryFunctor<elem::Plus, UnaryFunctor<elem::Sqrt, BinaryFunctor<elem::Div, Assignee<3>, Capture> >, Capture> >, BinaryFunctor<elem::Mult, Capture, Assignee<1> > > > > >, std::shared_ptr<marian::TensorBase>, std::shared_ptr<marian::TensorBase>, std::shared_ptr<marian::TensorBase>, std::shared_ptr<marian::TensorBase>, std::shared_ptr<marian::TensorBase>);
 template void marian::gpu::Element<marian::functional::Assign<marian::functional::Var<1>, marian::functional::BinaryFunctor<marian::functional::elem::NEq, marian::functional::BinaryFunctor<marian::functional::elem::Eq, marian::functional::BinaryFunctor<marian::functional::elem::Minus, marian::functional::BinaryFunctor<marian::functional::elem::Gt, marian::functional::Assignee<2>, marian::functional::Assignee<3> >, marian::functional::BinaryFunctor<marian::functional::elem::Lt, marian::functional::Assignee<2>, marian::functional::Assignee<3> > >, marian::functional::Capture>, marian::functional::Capture> >, std::shared_ptr<marian::TensorBase>, std::shared_ptr<marian::TensorBase> >(marian::functional::Assign<marian::functional::Var<1>, marian::functional::BinaryFunctor<marian::functional::elem::NEq, marian::functional::BinaryFunctor<marian::functional::elem::Eq, marian::functional::BinaryFunctor<marian::functional::elem::Minus, marian::functional::BinaryFunctor<marian::functional::elem::Gt, marian::functional::Assignee<2>, marian::functional::Assignee<3> >, marian::functional::BinaryFunctor<marian::functional::elem::Lt, marian::functional::Assignee<2>, marian::functional::Assignee<3> > >, marian::functional::Capture>, marian::functional::Capture> >, std::shared_ptr<marian::TensorBase>, std::shared_ptr<marian::TensorBase>, std::shared_ptr<marian::TensorBase>);
 template void marian::gpu::Element<marian::functional::Assign<marian::functional::Var<1>, marian::functional::UnaryFunctor<marian::functional::elem::Sqrt, marian::functional::Assignee<1> > >>(marian::functional::Assign<marian::functional::Var<1>, marian::functional::UnaryFunctor<marian::functional::elem::Sqrt, marian::functional::Assignee<1> > >, std::shared_ptr<marian::TensorBase>);
+template void marian::gpu::Element<marian::functional::Assign<marian::functional::Var<1>, marian::functional::BinaryFunctor<marian::functional::elem::Mult, marian::functional::Assignee<2>, marian::functional::UnaryFunctor<marian::functional::elem::Sigmoid, marian::functional::BinaryFunctor<marian::functional::elem::Mult, marian::functional::Capture, marian::functional::Assignee<2> > > > >, std::shared_ptr<marian::TensorBase> >(marian::functional::Assign<marian::functional::Var<1>, marian::functional::BinaryFunctor<marian::functional::elem::Mult, marian::functional::Assignee<2>, marian::functional::UnaryFunctor<marian::functional::elem::Sigmoid, marian::functional::BinaryFunctor<marian::functional::elem::Mult, marian::functional::Capture, marian::functional::Assignee<2> > > > >, std::shared_ptr<marian::TensorBase>, std::shared_ptr<marian::TensorBase>);
+
 // How to add new specializations:
 // When you use a new specialization, it will cause a link error of this form (example):
 //   .../src/tensors/tensor_operators.h:41: undefined reference to `void marian::gpu::Element<marian::functional::Assign< ... > ( ... )'

From 664e87624a9242d7fd64dad1149ecd730cc38cd5 Mon Sep 17 00:00:00 2001
From: Marcin Junczys-Dowmunt <Marcin.JunczysDowmunt@microsoft.com>
Date: Sat, 26 Jan 2019 14:33:27 -0800
Subject: [PATCH 205/838] first try for bert to marian conversion script

---
 scripts/bert/bert4marian.py | 131 ++++++++++++++++++++++++++++++++++++
 1 file changed, 131 insertions(+)
 create mode 100755 scripts/bert/bert4marian.py

diff --git a/scripts/bert/bert4marian.py b/scripts/bert/bert4marian.py
new file mode 100755
index 000000000..1a151b5e8
--- /dev/null
+++ b/scripts/bert/bert4marian.py
@@ -0,0 +1,131 @@
+#!/usr/bin/env python3
+
+import tensorflow as tf
+import numpy as np
+import sys
+import yaml
+import argparse
+
+parser = argparse.ArgumentParser(description='Convert Tensorflow BERT model to Marian weight file.')
+parser.add_argument('--bert_prefix', help='Prefix for Tensorflow BERT checkpoint', required=True)
+parser.add_argument('--bert_config', help='Path to Tensorflow BERT JSON config', required=True)
+parser.add_argument('--marian', help='Output path for Marian weight file', required=True)
+args = parser.parse_args()
+
+print("Loading TensorFlow config from %s" % (args.bert_config,))
+bertConfig = yaml.load(open(args.bert_config))
+bertConfigYamlStr = yaml.dump(bertConfig, default_flow_style=False)
+print(bertConfigYamlStr)
+
+print("Loading TensorFlow model from %s" % (args.bert_prefix,))
+
+# Collect tensors from TF model as numpy matrices
+tfModel = dict()
+with tf.Session() as sess:
+    preloader = tf.train.import_meta_graph(args.bert_prefix + ".meta")
+    preloader.restore(sess, args.bert_prefix)
+    vars = tf.global_variables()
+    for v in vars:
+        if len(v.shape) > 0:
+            if "adam" not in v.name: # ignore adam parameters
+                print(v.name, v.shape)
+                tfModel[v.name] = sess.run(v.name) # get numpy matrix
+
+# Prepare Marian model config
+config = dict()
+config["type"] = "bert"
+config["input-types"] = ["sequence", "class"]
+config["tied-embeddings-all"] = True
+config["dim-emb"] = tfModel["bert/embeddings/word_embeddings:0"].shape[-1]
+config["dim-vocabs"] = [ tfModel["bert/embeddings/word_embeddings:0"].shape[0],
+                         tfModel["cls/seq_relationship/output_weights:0"].shape[0] ]
+
+config["transformer-dim-ffn"] = tfModel["bert/encoder/layer_0/intermediate/dense/kernel:0"].shape[-1]
+config["transformer-ffn-activation"] = bertConfig["hidden_act"]
+config["transformer-ffn-depth"] = 2
+config["transformer-heads"] = bertConfig["num_attention_heads"]
+config["transformer-train-positions"] = True
+config["transformer-token-types"] = tfModel["bert/embeddings/token_type_embeddings:0"].shape[0]
+config["transformer-preprocess"] = ""
+config["transformer-postprocess"] = "dan"
+config["transformer-postprocess-emb"] = "nd"
+config["version"] = "bert4marian.py conversion"
+
+# check number of layers
+found = True
+config["enc-depth"] = 0;
+while found:
+    found = False
+    for key in tfModel:
+        if "bert/encoder/layer_" + str(config["enc-depth"]) in key:
+            config["enc-depth"] += 1
+            found = True
+            break
+
+configYamlStr = yaml.dump(config, default_flow_style=False)
+desc = list(configYamlStr)
+npDesc = np.chararray((len(desc),))
+npDesc[:] = desc
+npDesc.dtype = np.int8
+
+marianModel = dict()
+marianModel["special:model.yml"] = npDesc
+
+# Map model weights here #
+# Embedding layers
+marianModel["Wemb"]  = tfModel["bert/embeddings/word_embeddings:0"]
+marianModel["Wpos"]  = tfModel["bert/embeddings/position_embeddings:0"]
+marianModel["Wsent"] = tfModel["bert/embeddings/token_type_embeddings:0"]
+marianModel["encoder_emb_ln_scale_pre"] = tfModel["bert/embeddings/LayerNorm/gamma:0"]
+marianModel["encoder_emb_ln_bias_pre"]  = tfModel["bert/embeddings/LayerNorm/beta:0"]
+
+for layer in range(config["enc-depth"]):
+    marianPrefix = "encoder_l%s" % (layer + 1,)
+    tfPrefix  = "bert/encoder/layer_%s" % (layer,)
+
+    # Attention
+    marianModel[marianPrefix + "_self_Wq"] = tfModel[tfPrefix + "/attention/self/query/kernel:0"]
+    marianModel[marianPrefix + "_self_bq"] = tfModel[tfPrefix + "/attention/self/query/bias:0"]
+
+    marianModel[marianPrefix + "_self_Wk"] = tfModel[tfPrefix + "/attention/self/key/kernel:0"]
+    marianModel[marianPrefix + "_self_bk"] = tfModel[tfPrefix + "/attention/self/key/bias:0"]
+
+    marianModel[marianPrefix + "_self_Wv"] = tfModel[tfPrefix + "/attention/self/value/kernel:0"]
+    marianModel[marianPrefix + "_self_bv"] = tfModel[tfPrefix + "/attention/self/value/bias:0"]
+
+    marianModel[marianPrefix + "_self_Wo"] = tfModel[tfPrefix + "/attention/output/dense/kernel:0"]
+    marianModel[marianPrefix + "_self_bo"] = tfModel[tfPrefix + "/attention/output/dense/bias:0"]
+
+    marianModel[marianPrefix + "_self_Wo_ln_scale"] = tfModel[tfPrefix + "/attention/output/LayerNorm/gamma:0"]
+    marianModel[marianPrefix + "_self_Wo_ln_bias"]  = tfModel[tfPrefix + "/attention/output/LayerNorm/beta:0"]
+
+    # FFN
+    marianModel[marianPrefix + "_ffn_W1"] = tfModel[tfPrefix + "/intermediate/dense/kernel:0"]
+    marianModel[marianPrefix + "_ffn_b1"] = tfModel[tfPrefix + "/intermediate/dense/bias:0"]
+
+    marianModel[marianPrefix + "_ffn_W2"] = tfModel[tfPrefix + "/output/dense/kernel:0"]
+    marianModel[marianPrefix + "_ffn_b2"] = tfModel[tfPrefix + "/output/dense/bias:0"]
+
+    marianModel[marianPrefix + "_ffn_ffn_ln_scale"] = tfModel[tfPrefix + "/output/LayerNorm/gamma:0"]
+    marianModel[marianPrefix + "_ffn_ffn_ln_bias"]  = tfModel[tfPrefix + "/output/LayerNorm/beta:0"]
+
+    # Masked-LM output layer
+    marianModel["masked-lm_ff_logit_l1_W"] = tfModel["cls/predictions/transform/dense/kernel:0"]
+    marianModel["masked-lm_ff_logit_l1_b"] = tfModel["cls/predictions/transform/dense/bias:0"]
+
+    marianModel["masked-lm_ff_ln_scale"] = tfModel["cls/predictions/transform/LayerNorm/gamma:0"]
+    marianModel["masked-lm_ff_ln_bias"] = tfModel["cls/predictions/transform/LayerNorm/beta:0"]
+
+    marianModel["masked-lm_ff_logit_l2_b"] = tfModel["cls/predictions/output_bias:0"]
+
+    # Next Sentence classifier
+    marianModel["next-sentence_ff_logit_l1_W"] = tfModel["bert/pooler/dense/kernel:0"]
+    marianModel["next-sentence_ff_logit_l1_b"] = tfModel["bert/pooler/dense/bias:0"]
+
+    marianModel["next-sentence_ff_logit_l2_W"] = np.transpose(tfModel["cls/seq_relationship/output_weights:0"]) # transpose?!
+    marianModel["next-sentence_ff_logit_l2_b"] = tfModel["cls/seq_relationship/output_bias:0"]
+
+print("\nMarian config:")
+print(configYamlStr)
+print("Saving Marian model to %s" % (args.marian,))
+np.savez(args.marian, **marianModel)

From cb8c249ec6be4881362a1952b761334ea34cca10 Mon Sep 17 00:00:00 2001
From: Marcin Junczys-Dowmunt <Marcin.JunczysDowmunt@microsoft.com>
Date: Sat, 26 Jan 2019 15:24:55 -0800
Subject: [PATCH 206/838] added checking of number of layers

---
 scripts/bert/bert4marian.py | 3 +++
 1 file changed, 3 insertions(+)

diff --git a/scripts/bert/bert4marian.py b/scripts/bert/bert4marian.py
index 1a151b5e8..e9c5824ca 100755
--- a/scripts/bert/bert4marian.py
+++ b/scripts/bert/bert4marian.py
@@ -62,6 +62,9 @@
             found = True
             break
 
+if config["enc-depth"] != bertConfig["num_hidden_layers"]:
+    sys.exit("Number of layers in JSON config (%s) and number of layers found in checkpoint (%s) do not match!" % (config["enc-depth"], bertConfig["num_hidden_layers"]))
+
 configYamlStr = yaml.dump(config, default_flow_style=False)
 desc = list(configYamlStr)
 npDesc = np.chararray((len(desc),))

From 83fbd248d00e89277bed70d00bba0316101c7d0d Mon Sep 17 00:00:00 2001
From: Marcin Junczys-Dowmunt <Marcin.JunczysDowmunt@microsoft.com>
Date: Sun, 27 Jan 2019 12:43:04 -0800
Subject: [PATCH 207/838] rename bert-specific options

---
 scripts/bert/bert4marian.py     |   7 +-
 src/common/config_parser.cpp    |   5 +-
 src/models/bert.h               |  33 +++--
 src/models/encoder_classifier.h |   5 +-
 src/models/encoder_decoder.cpp  |  49 ++++---
 src/training/validator.h        |   3 +-
 src/translator/classification.h | 241 ++++++++++++++++++++++++++++++++
 7 files changed, 303 insertions(+), 40 deletions(-)
 create mode 100644 src/translator/classification.h

diff --git a/scripts/bert/bert4marian.py b/scripts/bert/bert4marian.py
index e9c5824ca..12b012a4c 100755
--- a/scripts/bert/bert4marian.py
+++ b/scripts/bert/bert4marian.py
@@ -44,11 +44,12 @@
 config["transformer-ffn-activation"] = bertConfig["hidden_act"]
 config["transformer-ffn-depth"] = 2
 config["transformer-heads"] = bertConfig["num_attention_heads"]
-config["transformer-train-positions"] = True
-config["transformer-token-types"] = tfModel["bert/embeddings/token_type_embeddings:0"].shape[0]
+config["transformer-train-position-embeddings"] = True
 config["transformer-preprocess"] = ""
 config["transformer-postprocess"] = "dan"
 config["transformer-postprocess-emb"] = "nd"
+config["bert-train-type-embeddings"] = True
+config["bert-type-vocab-size"] = tfModel["bert/embeddings/token_type_embeddings:0"].shape[0]
 config["version"] = "bert4marian.py conversion"
 
 # check number of layers
@@ -78,7 +79,7 @@
 # Embedding layers
 marianModel["Wemb"]  = tfModel["bert/embeddings/word_embeddings:0"]
 marianModel["Wpos"]  = tfModel["bert/embeddings/position_embeddings:0"]
-marianModel["Wsent"] = tfModel["bert/embeddings/token_type_embeddings:0"]
+marianModel["Wtype"] = tfModel["bert/embeddings/token_type_embeddings:0"]
 marianModel["encoder_emb_ln_scale_pre"] = tfModel["bert/embeddings/LayerNorm/gamma:0"]
 marianModel["encoder_emb_ln_bias_pre"]  = tfModel["bert/embeddings/LayerNorm/beta:0"]
 
diff --git a/src/common/config_parser.cpp b/src/common/config_parser.cpp
index 338d4b82c..73be7cd71 100644
--- a/src/common/config_parser.cpp
+++ b/src/common/config_parser.cpp
@@ -200,13 +200,16 @@ void ConfigParser::addOptionsModel(cli::CLIWrapper& cli) {
   cli.add<std::string>("--transformer-postprocess",
       "Operation after each transformer layer: d = dropout, a = add, n = normalize",
       "dan");
-  cli.add<bool>("--transformer-train-positions",
+  cli.add<bool>("--transformer-train-position-embeddings",
       "Train positional embeddings instead of using static sinusoidal embeddings");
 
   cli.add<std::string>("--bert-mask-symbol", "Masking symbol for BERT masked-LM training", "[MASK]");
   cli.add<std::string>("--bert-sep-symbol", "Sentence separator symbol for BERT next sentence prediction training", "[SEP]");
   cli.add<std::string>("--bert-class-symbol", "Class symbol BERT classifier training", "[CLS]");
   cli.add<float>("--bert-masking-fraction", "Fraction of masked out tokens during training", 0.15);
+  cli.add<bool>("--bert-train-type-embeddings", "Train bert type embeddings, set to false to use static sinusoidal embeddings", true);
+  cli.add<int>("--bert-type-vocab-size", "Size of BERT type vocab (sentence A and B)", 2);
+
 #ifdef CUDNN
   cli.add<int>("--char-stride",
       "Width of max-pooling layer after convolution layer in char-s2s model",
diff --git a/src/models/bert.h b/src/models/bert.h
index b4d2a34c2..d41bde86f 100644
--- a/src/models/bert.h
+++ b/src/models/bert.h
@@ -70,7 +70,8 @@ class BertBatch : public CorpusBatch {
             float maskFraction,
             const std::string& maskSymbol,
             const std::string& sepSymbol,
-            const std::string& clsSymbol)
+            const std::string& clsSymbol,
+            int dimTypeVocab)
     : CorpusBatch(*batch),
       maskSymbol_(maskSymbol), sepSymbol_(sepSymbol), clsSymbol_(clsSymbol) {
 
@@ -119,18 +120,19 @@ class BertBatch : public CorpusBatch {
       words[i] = maskOut(words[i], maskId, engine); // mask that position
     }
 
-    annotateSentenceIndices();
+    annotateSentenceIndices(dimTypeVocab);
   }
 
   BertBatch(Ptr<CorpusBatch> batch,
             const std::string& sepSymbol,
-            const std::string& clsSymbol)
+            const std::string& clsSymbol,
+            int dimTypeVocab)
     : CorpusBatch(*batch),
       maskSymbol_("dummy"), sepSymbol_(sepSymbol), clsSymbol_(clsSymbol) {
-    annotateSentenceIndices();
+    annotateSentenceIndices(dimTypeVocab);
   }
 
-  void annotateSentenceIndices() {
+  void annotateSentenceIndices(int dimTypeVocab) {
     // BERT expects a textual first stream and a second stream with class labels
     auto subBatch = subBatches_.front();
     const auto& vocab = *subBatch->vocab();
@@ -144,7 +146,7 @@ class BertBatch : public CorpusBatch {
     int dimBatch = subBatch->batchSize();
     int dimWords = subBatch->batchWidth();
 
-    int maxSentPos = 1; // Currently only two sentences allowed A at [0] and B at [1] and padding at [2]
+    int maxSentPos = dimTypeVocab; // Currently only two sentences allowed A at [0] and B at [1] and padding at [2]
     // If another separator is seen do not increase position index beyond 2 but use padding.
     // @TODO: make this configurable, see below for NextSentencePredictions task where we also restrict to 2.
 
@@ -180,6 +182,7 @@ class BertEncoderClassifier : public EncoderClassifier, public data::RNGEngine {
 
   std::vector<Ptr<ClassifierState>> apply(Ptr<ExpressionGraph> graph, Ptr<data::CorpusBatch> batch, bool clearGraph) override {
     std::string modelType = opt<std::string>("type");
+    int dimTypeVocab = opt<int>("bert-type-vocab-size");
 
     // intercept batch and annotate with BERT-specific concepts
     Ptr<data::BertBatch> bertBatch;
@@ -189,11 +192,13 @@ class BertEncoderClassifier : public EncoderClassifier, public data::RNGEngine {
                                        opt<float>("bert-masking-fraction", 0.15f), // 15% by default according to paper
                                        opt<std::string>("bert-mask-symbol"),
                                        opt<std::string>("bert-sep-symbol"),
-                                       opt<std::string>("bert-class-symbol"));
+                                       opt<std::string>("bert-class-symbol"),
+                                       dimTypeVocab);
     } else if(modelType == "bert-classifier") { // we are probably fine-tuning a BERT model for a classification task
       bertBatch = New<data::BertBatch>(batch,
                                        opt<std::string>("bert-sep-symbol"),
-                                       opt<std::string>("bert-class-symbol")); // only annotate sentence separators
+                                       opt<std::string>("bert-class-symbol"),
+                                       dimTypeVocab); // only annotate sentence separators
     } else {
       ABORT("Unknown BERT-style model: {}", modelType);
     }
@@ -219,7 +224,6 @@ class BertEncoder : public EncoderTransformer {
   Expr addSentenceEmbeddings(Expr embeddings,
                              Ptr<data::CorpusBatch> batch,
                              bool learnedPosEmbeddings) const {
-
     Ptr<data::BertBatch> bertBatch = std::dynamic_pointer_cast<data::BertBatch>(batch);
     ABORT_IF(!bertBatch, "Batch must be BertBatch for BERT training or fine-tuning");
 
@@ -227,11 +231,13 @@ class BertEncoder : public EncoderTransformer {
     int dimBatch = embeddings->shape()[-2];
     int dimWords = embeddings->shape()[-3];
 
+    int dimTypeVocab = opt<int>("bert-type-vocab-size", 2);
+
     Expr signal;
     if(learnedPosEmbeddings) {
       auto sentenceEmbeddings = embedding()
-                               ("prefix", "Wsent")
-                               ("dimVocab", 2) // sentence A or sentence B plus padding, @TODO: should rather be a parameter
+                               ("prefix", "Wtype")
+                               ("dimVocab", dimTypeVocab) // sentence A or sentence B
                                ("dimEmb", dimEmb)
                                .construct(graph_);
       signal = sentenceEmbeddings->apply(bertBatch->bertSentenceIndices(), {dimWords, dimBatch, dimEmb});
@@ -247,9 +253,10 @@ class BertEncoder : public EncoderTransformer {
   }
 
   virtual Expr addSpecialEmbeddings(Expr input, int start = 0, Ptr<data::CorpusBatch> batch = nullptr) const override {
-    bool trainPosEmbeddings = opt<bool>("transformer-train-positions", true);
+    bool trainPosEmbeddings = opt<bool>("transformer-train-position-embeddings", true);
+    bool trainTypeEmbeddings = opt<bool>("bert-train-type-embeddings", true);
     input = addPositionalEmbeddings(input, start, trainPosEmbeddings);
-    input = addSentenceEmbeddings(input, batch, trainPosEmbeddings); // @TODO: separately set learnable pos and sent embeddings
+    input = addSentenceEmbeddings(input, batch, trainTypeEmbeddings);
     return input;
   }
 };
diff --git a/src/models/encoder_classifier.h b/src/models/encoder_classifier.h
index bc3d8f9f8..23977a6c5 100644
--- a/src/models/encoder_classifier.h
+++ b/src/models/encoder_classifier.h
@@ -92,6 +92,7 @@ class EncoderClassifier : public EncoderClassifierBase {
     : options_(options),
       prefix_(options->get<std::string>("prefix", "")),
       inference_(options->get<bool>("inference", false)) {
+
     modelFeatures_ = {"type",
                     "dim-vocabs",
                     "dim-emb",
@@ -128,7 +129,9 @@ class EncoderClassifier : public EncoderClassifierBase {
     modelFeatures_.insert("transformer-decoder-autoreg");
     modelFeatures_.insert("transformer-tied-layers");
     modelFeatures_.insert("transformer-guided-alignment-layer");
-    modelFeatures_.insert("transformer-train-positions");
+    modelFeatures_.insert("transformer-train-position-embeddings");
+    modelFeatures_.insert("bert-train-type-embeddings");
+    modelFeatures_.insert("bert-type-vocab-size");
   }
 
   virtual Ptr<Options> getOptions() override { return options_; }
diff --git a/src/models/encoder_decoder.cpp b/src/models/encoder_decoder.cpp
index c4b96cc30..817475bba 100644
--- a/src/models/encoder_decoder.cpp
+++ b/src/models/encoder_decoder.cpp
@@ -8,26 +8,31 @@ EncoderDecoder::EncoderDecoder(Ptr<Options> options)
     : options_(options),
       prefix_(options->get<std::string>("prefix", "")),
       inference_(options->get<bool>("inference", false)) {
-  modelFeatures_ = {"type",
-                    "dim-vocabs",
-                    "dim-emb",
-                    "dim-rnn",
-                    "enc-cell",
-                    "enc-type",
-                    "enc-cell-depth",
-                    "enc-depth",
-                    "dec-depth",
-                    "dec-cell",
-                    "dec-cell-base-depth",
-                    "dec-cell-high-depth",
-                    "skip",
-                    "layer-normalization",
-                    "right-left",
-                    "input-types",
-                    "special-vocab",
-                    "tied-embeddings",
-                    "tied-embeddings-src",
-                    "tied-embeddings-all"};
+
+  std::vector<std::string> encoderDecoderModelFeatures =
+    {"type",
+     "dim-vocabs",
+     "dim-emb",
+     "dim-rnn",
+     "enc-cell",
+     "enc-type",
+     "enc-cell-depth",
+     "enc-depth",
+     "dec-depth",
+     "dec-cell",
+     "dec-cell-base-depth",
+     "dec-cell-high-depth",
+     "skip",
+     "layer-normalization",
+     "right-left",
+     "input-types",
+     "special-vocab",
+     "tied-embeddings",
+     "tied-embeddings-src",
+     "tied-embeddings-all"};
+
+  for(auto feature : encoderDecoderModelFeatures)
+    modelFeatures_.insert(feature);
 
   modelFeatures_.insert("transformer-heads");
   modelFeatures_.insert("transformer-no-projection");
@@ -44,7 +49,9 @@ EncoderDecoder::EncoderDecoder(Ptr<Options> options)
   modelFeatures_.insert("transformer-decoder-autoreg");
   modelFeatures_.insert("transformer-tied-layers");
   modelFeatures_.insert("transformer-guided-alignment-layer");
-  modelFeatures_.insert("transformer-train-positions");
+  modelFeatures_.insert("transformer-train-position-embeddings");
+  modelFeatures_.insert("bert-train-type-embeddings");
+  modelFeatures_.insert("bert-type-vocab-size");
 }
 
 std::vector<Ptr<EncoderBase>>& EncoderDecoder::getEncoders() {
diff --git a/src/training/validator.h b/src/training/validator.h
index d09859b20..48ab88fea 100644
--- a/src/training/validator.h
+++ b/src/training/validator.h
@@ -359,7 +359,8 @@ class BertAccuracyValidator : public Validator<data::Corpus> {
                                                 options_->get<float>("bert-masking-fraction"),
                                                 options_->get<std::string>("bert-mask-symbol"),
                                                 options_->get<std::string>("bert-sep-symbol"),
-                                                options_->get<std::string>("bert-class-symbol"));
+                                                options_->get<std::string>("bert-class-symbol"),
+                                                options_->get<int>("bert-type-vocab-size"));
 
           builder->clear(graph);
           auto classifierStates = std::dynamic_pointer_cast<BertEncoderClassifier>(builder)->apply(graph, bertBatch, true);
diff --git a/src/translator/classification.h b/src/translator/classification.h
new file mode 100644
index 000000000..28a8a82a8
--- /dev/null
+++ b/src/translator/classification.h
@@ -0,0 +1,241 @@
+#pragma once
+#include <algorithm>
+
+#include "marian.h"
+#include "translator/history.h"
+#include "translator/scorers.h"
+
+#include "translator/helpers.h"
+#include "translator/nth_element.h"
+
+namespace marian {
+
+class Classification {
+private:
+  Ptr<Options> options_;
+  std::vector<Ptr<Scorer>> scorers_;
+  size_t topN_{1};
+
+public:
+  Classification(Ptr<Options> options)
+      : options_(options),
+        scorers_(scorers),
+        topN_{options_->get<size_t>("beam-size")} // misuse beam-size for topN display
+        {} 
+
+  Beams toHyps(const std::vector<unsigned int> keys,
+               const std::vector<float> pathScores,
+               size_t labelNum,
+               const Beams& beams,
+               std::vector<Ptr<ScorerState>>& states,
+               size_t topN,
+               bool first,
+               Ptr<data::CorpusBatch> batch) {
+    Beams newBeams(beams.size());
+
+    std::vector<float> align;
+    if(options_->has("alignment"))
+      // Use alignments from the first scorer, even if ensemble
+      align = scorers_[0]->getAlignment();
+
+    for(size_t i = 0; i < keys.size(); ++i) {
+      // Keys contains indices to vocab items in the entire beam.
+      // Values can be between 0 and topN * number of lables.
+      Word embIdx = (Word)(keys[i] % labelNum);
+      auto beamIdx = i / topN;
+
+      // Retrieve short list for final softmax (based on words aligned
+      // to source sentences). If short list has been set, map the indices
+      // in the sub-selected vocabulary matrix back to their original positions.
+      auto shortlist = scorers_[0]->getShortlist();
+      if(shortlist)
+        embIdx = shortlist->reverseMap(embIdx); // @TODO: should reverseMap accept a size_t or a Word?
+
+      if(newBeams[beamIdx].size() < beams[beamIdx].size()) {
+        auto& beam = beams[beamIdx];
+        auto& newBeam = newBeams[beamIdx];
+
+        auto hypIdx = (IndexType)(keys[i] / labelNum);
+        float pathScore = pathScores[i];
+
+        auto hypIdxTrans
+            = IndexType((hypIdx / topN) + (hypIdx % topN) * beams.size());
+        if(first)
+          hypIdxTrans = hypIdx;
+
+        size_t beamHypIdx = hypIdx % topN;
+        if(beamHypIdx >= (int)beam.size())
+          beamHypIdx = beamHypIdx % beam.size();
+
+        if(first)
+          beamHypIdx = 0;
+
+        auto hyp = New<Hypothesis>(beam[beamHypIdx], embIdx, hypIdxTrans, pathScore);
+
+        // Set score breakdown for n-best lists
+        if(options_->get<bool>("n-best")) {
+          std::vector<float> breakDown(states.size(), 0);
+          beam[beamHypIdx]->GetScoreBreakdown().resize(states.size(), 0);
+          for(size_t j = 0; j < states.size(); ++j) {
+            size_t key = embIdx + hypIdxTrans * labelNum;
+            breakDown[j] = states[j]->breakDown(key)
+                           + beam[beamHypIdx]->GetScoreBreakdown()[j];
+          }
+          hyp->GetScoreBreakdown() = breakDown;
+        }
+
+        newBeam.push_back(hyp);
+      }
+    }
+    return newBeams;
+  }
+
+  // main decoding function
+  Histories search(Ptr<ExpressionGraph> graph, Ptr<data::CorpusBatch> batch) {
+    int dimBatch = (int)batch->size();
+
+    Histories histories;
+    for(int i = 0; i < dimBatch; ++i) {
+      size_t sentId = batch->getSentenceIds()[i];
+      auto history = New<History>(sentId,
+                                  options_->get<float>("normalize"),
+                                  options_->get<float>("word-penalty"));
+      histories.push_back(history);
+    }
+
+    auto getNBestList = createGetNBestListFn(topN_, dimBatch, graph->getDeviceId());
+
+    Beams beams(dimBatch);        // [batchIndex][beamIndex] is one sentence hypothesis
+    for(auto& beam : beams)
+      beam.resize(topN_, New<Hypothesis>());
+
+    bool first = true;
+    bool final = false;
+
+    for(int i = 0; i < dimBatch; ++i)
+      histories[i]->Add(beams[i], trgEosId_);
+
+    std::vector<Ptr<ScorerState>> states;
+
+    for(auto scorer : scorers_) {
+      scorer->clear(graph);
+    }
+
+    for(auto scorer : scorers_) {
+      states.push_back(scorer->apply(graph, batch));
+    }
+
+    // main loop over output tokens
+    do {
+      //**********************************************************************
+      // create constant containing previous path scores for current beam
+      // also create mapping of hyp indices, which are not 1:1 if sentences complete
+      std::vector<IndexType> hypIndices; // [beamIndex * activeBatchSize + batchIndex] backpointers, concatenated over beam positions. Used for reordering hypotheses
+      std::vector<IndexType> embIndices;
+      Expr prevPathScores; // [beam, 1, 1, 1]
+      if(first) {
+        // no scores yet
+        prevPathScores = graph->constant({1, 1, 1, 1}, inits::from_value(0));
+      } else {
+        std::vector<float> beamScores;
+
+        dimBatch = (int)batch->size();
+
+        for(size_t i = 0; i < localBeamSize; ++i) {
+          for(size_t j = 0; j < beams.size(); ++j) { // loop over batch entries (active sentences)
+            auto& beam = beams[j];
+            if(i < beam.size()) {
+              auto hyp = beam[i];
+              hypIndices.push_back((IndexType)hyp->GetPrevStateIndex()); // backpointer
+              embIndices.push_back(hyp->GetWord());
+              beamScores.push_back(hyp->GetPathScore());
+            } else {  // dummy hypothesis
+              hypIndices.push_back(0);
+              embIndices.push_back(0);  // (unused)
+              beamScores.push_back(-9999);
+            }
+          }
+        }
+
+        prevPathScores = graph->constant({(int)localBeamSize, 1, dimBatch, 1},
+                                    inits::from_vector(beamScores));
+      }
+
+      //**********************************************************************
+      // prepare scores for beam search
+      auto pathScores = prevPathScores;
+
+      for(size_t i = 0; i < scorers_.size(); ++i) {
+        states[i] = scorers_[i]->step(
+            graph, states[i], hypIndices, embIndices, dimBatch, (int)localBeamSize);
+
+        if(scorers_[i]->getWeight() != 1.f)
+          pathScores = pathScores + scorers_[i]->getWeight() * states[i]->getLogProbs();
+        else
+          pathScores = pathScores + states[i]->getLogProbs();
+      }
+
+      // make beams continuous
+      if(dimBatch > 1 && localBeamSize > 1)
+        pathScores = transpose(pathScores, {2, 1, 0, 3}); // check if this is needed for classification, rather not, beamSize and topN is badly defined here
+
+      if(first)
+        graph->forward();
+      else
+        graph->forwardNext();
+
+      //**********************************************************************
+      // suppress specific symbols if not at right positions
+      if(trgUnkId_ != -1 && options_->has("allow-unk")
+         && !options_->get<bool>("allow-unk"))
+        suppressWord(pathScores, trgUnkId_);
+      for(auto state : states)
+        state->blacklist(pathScores, batch);
+
+      //**********************************************************************
+      // perform beam search and pruning
+      std::vector<unsigned int> outKeys;
+      std::vector<float> outPathScores;
+
+      std::vector<size_t> beamSizes(dimBatch, localBeamSize);
+      getNBestList(beamSizes, pathScores->val(), outPathScores, outKeys, first);
+
+      int dimTrgVoc = pathScores->shape()[-1];
+      beams = toHyps(outKeys,
+                     outPathScores,
+                     dimTrgVoc,
+                     beams,
+                     states,
+                     localBeamSize,
+                     first,
+                     batch);
+
+      auto prunedBeams = pruneBeam(beams);
+      for(int i = 0; i < dimBatch; ++i) {
+        if(!beams[i].empty()) {
+          final = final
+                  || histories[i]->size()
+                         >= options_->get<float>("max-length-factor")
+                                * batch->front()->batchWidth();
+          histories[i]->Add(
+              beams[i], trgEosId_, prunedBeams[i].empty() || final);
+        }
+      }
+      beams = prunedBeams;
+
+      // determine beam size for next sentence, as max over still-active sentences
+      if(!first) {
+        size_t maxBeam = 0;
+        for(auto& beam : beams)
+          if(beam.size() > maxBeam)
+            maxBeam = beam.size();
+        localBeamSize = maxBeam;
+      }
+      first = false;
+
+    } while(localBeamSize != 0 && !final); // end of main loop over output tokens
+
+    return histories;
+  }
+};
+}  // namespace marian

From abe94674710ca7d4535157bb2315f5a954e3a7e3 Mon Sep 17 00:00:00 2001
From: Marcin Junczys-Dowmunt <Marcin.JunczysDowmunt@microsoft.com>
Date: Sun, 27 Jan 2019 23:03:46 -0800
Subject: [PATCH 208/838] hacky Nan handling

---
 scripts/bert/bert4marian.py          |  1 +
 src/tensors/cpu/tensor_operators.cpp |  9 +++--
 src/tensors/gpu/algorithm.cu         |  5 +--
 src/tensors/gpu/tensor_operators.cu  | 51 ++++++++++++++++++++++------
 src/tensors/tensor_operators.h       |  2 ++
 src/training/graph_group_sync.cpp    | 11 ++++--
 6 files changed, 62 insertions(+), 17 deletions(-)

diff --git a/scripts/bert/bert4marian.py b/scripts/bert/bert4marian.py
index 12b012a4c..5ccc46595 100755
--- a/scripts/bert/bert4marian.py
+++ b/scripts/bert/bert4marian.py
@@ -113,6 +113,7 @@
     marianModel[marianPrefix + "_ffn_ffn_ln_scale"] = tfModel[tfPrefix + "/output/LayerNorm/gamma:0"]
     marianModel[marianPrefix + "_ffn_ffn_ln_bias"]  = tfModel[tfPrefix + "/output/LayerNorm/beta:0"]
 
+    # Training objectives
     # Masked-LM output layer
     marianModel["masked-lm_ff_logit_l1_W"] = tfModel["cls/predictions/transform/dense/kernel:0"]
     marianModel["masked-lm_ff_logit_l1_b"] = tfModel["cls/predictions/transform/dense/bias:0"]
diff --git a/src/tensors/cpu/tensor_operators.cpp b/src/tensors/cpu/tensor_operators.cpp
index e7fefdcfe..5ee1d3400 100644
--- a/src/tensors/cpu/tensor_operators.cpp
+++ b/src/tensors/cpu/tensor_operators.cpp
@@ -14,6 +14,11 @@ namespace marian {
 
 namespace cpu {
 
+void IsNan(const Tensor in, Ptr<Allocator> allocator, bool& isNan, bool& isInf) {
+  ABORT("Not implemented");
+}
+
+
 inline float stableSigmoid(float x) {
   if(x >= 0) {
     float z = expf(-x);
@@ -394,7 +399,7 @@ void CopyRows(Tensor out_,
   for(size_t j = 0; j < rows; ++j) {
     size_t dst = j;
 
-    // @TODO: consider moving type checking to this function 
+    // @TODO: consider moving type checking to this function
     // instead of matchOrAbort above
     size_t src = (size_t)indices->data<IndexType>()[j];
 
@@ -494,7 +499,7 @@ void Select(Tensor out,
 
   functional::Array<int, functional::Shape::size()> dims;
   int axisCPU = (int)(axis + functional::Shape::size() - out->shape().size());
-  
+
   for(int index = 0; index < length; ++index) {
     outShape.dims(index, dims);
     dims[axisCPU] = (int)indices->data<IndexType>()[dims[axisCPU]];
diff --git a/src/tensors/gpu/algorithm.cu b/src/tensors/gpu/algorithm.cu
index bdf66bac8..40709866c 100644
--- a/src/tensors/gpu/algorithm.cu
+++ b/src/tensors/gpu/algorithm.cu
@@ -55,6 +55,7 @@ void fill(Ptr<Backend> backend, T* begin, T* end, T value) {
   CUDA_CHECK(cudaStreamSynchronize(0));
 }
 
+template void fill<bool>(Ptr<Backend>, bool*, bool*, bool);
 template void fill<int8_t>(Ptr<Backend>, int8_t*, int8_t*, int8_t);
 template void fill<int16_t>(Ptr<Backend>, int16_t*, int16_t*, int16_t);
 template void fill<int32_t>(Ptr<Backend>, int32_t*, int32_t*, int32_t);
@@ -84,7 +85,7 @@ __global__ void gSwap(T* d_v1, T* d_v2, int size) {
   if(index < size) {
     T temp = d_v1[index];
     d_v1[index] = d_v2[index];
-    d_v2[index] = temp;  
+    d_v2[index] = temp;
   }
 }
 
@@ -93,7 +94,7 @@ void swap_ranges(Ptr<Backend> backend, T* begin, T* end, T* dest) {
   int size = end - begin;
   if (size == 0)
     return;
-  
+
   CUDA_CHECK(cudaSetDevice(backend->getDeviceId().no));
   int threadsPerBlock = std::min(MAX_THREADS, size);
   int blocks = (size / threadsPerBlock) + (size % threadsPerBlock != 0); // @TODO: (size+threadsPerBlock-1)/threadsPerBlock or CeilDiv(a,b)
diff --git a/src/tensors/gpu/tensor_operators.cu b/src/tensors/gpu/tensor_operators.cu
index 34da4c688..01ee303dd 100644
--- a/src/tensors/gpu/tensor_operators.cu
+++ b/src/tensors/gpu/tensor_operators.cu
@@ -27,14 +27,43 @@ __device__ inline float stableSigmoid(float x) {
   }
 }
 
-bool IsNan(Tensor in) {
-  // cudaSetDevice(in->getDeviceId().no);
-  // thrust::device_ptr<float> begin = thrust::device_pointer_cast(in->data());
-  // thrust::device_ptr<float> end
-  //    = thrust::device_pointer_cast(in->data() + in->size());
-  // return thrust::transform_reduce(
-  //    begin, end, isnan_test(), 0, thrust::plus<bool>());
-  return false;
+template <typename T>
+__global__ void gIsNan(const T* in, int length, bool* isNan, bool* isInf) {
+  for(int bid = 0; bid < length; bid += blockDim.x * gridDim.x) {
+    int index = bid + blockDim.x * blockIdx.x + threadIdx.x;
+    if(index < length) {
+      if(isnan((float)in[index])) *isNan = true;
+      if(isinf((float)in[index])) *isInf = true;
+      //if(isinf2(in[index])) *isInf = true;
+    }
+  }
+}
+
+void IsNan(const Tensor in, Ptr<Allocator> allocator, bool& isNan, bool& isInf) {
+  cudaSetDevice(in->getDeviceId().no);
+
+  int length = in->size();
+
+  int threads = std::min(MAX_THREADS, length);
+  int blocks = std::min(MAX_BLOCKS, length / threads + (length % threads != 0));
+
+  auto mem = allocator->alloc<bool>(2);
+  bool* dIsNan = &mem->data<bool>()[0];
+  bool* dIsInf = &mem->data<bool>()[1];
+  fill(in->getBackend(), dIsNan, dIsNan + 2, false);
+
+  if(in->type() == Type::float32) {
+    gIsNan<<<blocks, threads>>>(in->data<float>(), length, dIsNan, dIsInf);
+  } else {
+    ABORT("IsNan for type {} not implemented", in->type());
+  }
+
+  CudaCopy(dIsNan, dIsNan + 1, &isNan);
+  CudaCopy(dIsInf, dIsInf + 1, &isInf);
+
+  allocator->free(mem);
+
+  cudaStreamSynchronize(0);
 }
 
 void ConcatCont(Tensor out, const std::vector<Tensor>& inputs, int axis) {
@@ -1176,9 +1205,9 @@ __global__ void gCrossEntropyPick(float* out,
 }
 
 // In each j-th row, take the corresponding j-th label index i from indices and compute:
-// For each vocabulary item v, the only non-zero element in a row in the sum is the item 
-// that matches the label indexed by i (the picked element). 
-// C = sum_{v in V}(-logsoftmax(A) * delta(v, i) = -logsoftmax(A)[i] 
+// For each vocabulary item v, the only non-zero element in a row in the sum is the item
+// that matches the label indexed by i (the picked element).
+// C = sum_{v in V}(-logsoftmax(A) * delta(v, i) = -logsoftmax(A)[i]
 void CrossEntropyPick(Tensor out, Tensor in, Tensor indices) {
   matchOrAbort<IndexType>(indices->type());
 
diff --git a/src/tensors/tensor_operators.h b/src/tensors/tensor_operators.h
index 8dd17a5ce..7f0f75943 100644
--- a/src/tensors/tensor_operators.h
+++ b/src/tensors/tensor_operators.h
@@ -34,6 +34,8 @@ void copy(Ptr<Backend> backend, const InIt beg, const InIt end, OutIt it) {
     std::copy(beg, end, it);
 }
 
+DISPATCH4(IsNan, const Tensor, Ptr<Allocator>, bool&, bool&);
+
 template <class Functor, class... Tensors>
 void Element(Functor functor, marian::Tensor out, Tensors... tensors) {
 #ifdef CUDA_FOUND
diff --git a/src/training/graph_group_sync.cpp b/src/training/graph_group_sync.cpp
index 1ec5c3d68..85bf13f1f 100644
--- a/src/training/graph_group_sync.cpp
+++ b/src/training/graph_group_sync.cpp
@@ -356,12 +356,19 @@ void SyncGraphGroup::update(std::vector<Ptr<data::Batch>> subBatches, size_t num
 
       auto rationalLoss = builders_[localDeviceIndex]->build(graph, subBatch);
       graph->forward();
-      
+
       StaticLoss tempLoss = *rationalLoss; // needed for overstuff
       tempLoss.loss /= (float)overstuff; // @TODO: @fseide: scale only loss? should this scale labels too?
 
       localDeviceLosses[localDeviceIndex] += tempLoss;
       graph->backward(/*zero=*/false); // (gradients are reset before we get here)
+
+      bool hasNan = false, hasInf = false;
+      IsNan(graph->params()->grads(), graph->allocator(), hasNan, hasInf);
+      if(hasNan || hasInf) {
+        LOG(warn, "Seen Nan ({}) or Inf ({}) in gradient, zeroing gradient", hasNan, hasInf);
+        graph->params()->grads()->set(0.f);
+      }
     }
   });
   // At this point, each device on each MPI process has a gradient aggregated over a subset of the sub-batches.
@@ -521,7 +528,7 @@ void SyncGraphGroup::save(bool final) /*override*/ {
       return comm_->gatherState(getShardFn);
     },
     isMainProcess());
-  
+
   barrier(); // (for better grouping of log messages)
 }
 

From 0fe0f18e851b3c42abefb4db8c71a477237b6143 Mon Sep 17 00:00:00 2001
From: Marcin Junczys-Dowmunt <Marcin.JunczysDowmunt@microsoft.com>
Date: Mon, 28 Jan 2019 08:25:45 -0800
Subject: [PATCH 209/838] more nan handling code copied from fp16 branch

---
 src/graph/expression_graph.cpp       | 81 ++++++++++++++++++++++++++--
 src/graph/expression_graph.h         | 80 +++++++++------------------
 src/tensors/cpu/tensor_operators.cpp |  2 +-
 src/tensors/gpu/tensor_operators.cu  | 17 +++---
 src/tensors/tensor_operators.h       |  2 +-
 src/training/graph_group_sync.cpp    | 10 ++--
 6 files changed, 120 insertions(+), 72 deletions(-)

diff --git a/src/graph/expression_graph.cpp b/src/graph/expression_graph.cpp
index b3c237d1e..19c3fc4e9 100644
--- a/src/graph/expression_graph.cpp
+++ b/src/graph/expression_graph.cpp
@@ -24,11 +24,86 @@ Expr ExpressionGraph::dropout(float prob, const Shape& shape) {
   return constant(shape, inits::dropout(prob));
 }
 
-void ExpressionGraph::checkNan(Tensor t) {
-  ABORT_IF(throwNaN_, "Not implemented"); t;
-  // ABORT_IF(throwNaN_ && IsNan(t), "Tensor has NaN");
+void ExpressionGraph::checkNan(Tensor t, bool& isNan, bool& isInf, bool zero) {
+  IsNan(t, allocator(), isNan, isInf, zero);
 }
 
+void ExpressionGraph::backward(bool zero, float clipValue) {
+  if(topNodes_.size() > 1) {
+    LOG(critical, "There are more ({}) than one top most nodes for backward pass:", topNodes_.size());
+    for(auto node : topNodes_) {
+      LOG(critical,
+          "\tType: {}, Shape: {}, Name: {}, Id: {}, Hash: {}",
+          node->type(),
+          node->shape(),
+          node->name(),
+          node->getId(),
+          node->hash());
+    }
+    ABORT("Aborting");
+  }
+
+  params_->allocateBackward();
+  if(zero)
+    params_->set_zero_adjoint();
+
+  for(auto&& v : topNodes_)
+    v->init_dependent();
+
+  // named_.clear();
+  topNodes_.clear();
+
+  tensors_->clearShorttermMemory();
+
+  while(!nodesBackward_.empty()) {
+    auto v = nodesBackward_.back();
+    nodesBackward_.pop_back();
+
+    for(auto&& child : v->children()) {
+      if(child->trainable() && child->type() != "param")
+        child->set_zero_adjoint();
+    }
+
+    if(v->trainable()) {
+      v->backward();
+      if(clipValue != 0) {
+        using namespace functional;
+        Element(_1 = clip(_1, clipValue), v->grad());
+      }
+    }
+      
+
+    if(throwNan_) {
+      for(auto&& child : v->children()) {
+        if(child->trainable()) {
+          bool isNan = false, isInf = false;
+          checkNan(child->grad(), isNan, isInf);
+          if(isNan || isInf) {
+            LOG(critical, "Detected NaN ({}) or Inf ({}) in gradient (backward pass) of child node", isNan, isInf);
+            LOG(critical, "Child - Type: {}, Shape: {}, Name: {}, Id: {}, Hash: {}",
+                child->type(), child->shape(), child->name(), child->getId(), child->hash());
+            LOG(critical, "Value debug: {}", child->val()->debug());
+            LOG(critical, "Grad debug: {}", child->grad()->debug());
+            LOG(critical, "Parent - Type: {}, Shape: {}, Name: {}, Id: {}, Hash: {}",
+                v->type(), v->shape(), v->name(), v->getId(), v->hash());
+            LOG(critical, "Value debug: {}", v->val()->debug());
+            LOG(critical, "Grad debug: {}", v->grad()->debug());
+            ABORT("Aborting");
+          }
+        }
+      }
+    }
+
+    if(v->trainable() && v->marked_for_debug()) {
+      LOG(info, "Debug Grad: {} op={}", v->debug_message(), v->type());
+      LOG(info, v->grad()->debug());
+    }
+
+    v->children().clear();
+  }
+}
+
+
 void ExpressionGraph::save(std::vector<io::Item>& ioItems) {
   for(auto p : params()->getMap()) {
     std::string pName = p.first;
diff --git a/src/graph/expression_graph.h b/src/graph/expression_graph.h
index fe8361618..24eb88a6a 100644
--- a/src/graph/expression_graph.h
+++ b/src/graph/expression_graph.h
@@ -136,7 +136,7 @@ class ExpressionGraph : public std::enable_shared_from_this<ExpressionGraph> {
   bool reloaded_{false};
   std::string namespace_;
 
-  bool throwNaN_{false};
+  bool throwNan_{false};
 
 protected:
   // Delete, copy and move constructors
@@ -217,7 +217,7 @@ class ExpressionGraph : public std::enable_shared_from_this<ExpressionGraph> {
     forwardNext();
   }
 
-  void checkNan(Tensor t);
+  void checkNan(Tensor t, bool& isNan, bool& isInf, bool zero = false);
 
   void forwardNext() {
     // @TODO: check if allocation works properly
@@ -229,12 +229,27 @@ class ExpressionGraph : public std::enable_shared_from_this<ExpressionGraph> {
       v->init();
       v->forward();
 
-      checkNan(v->val());
+      if(v->trainable() && throwNan_) {
+        bool isNan = false, isInf = false;
+        checkNan(v->val(), isNan, isInf);
+        if(isNan || isInf) {
+          LOG(critical, "Detected NaN ({}) or Inf ({}) in value (forward pass)", isNan, isInf);
+          LOG(critical, "\tType: {}, Shape: {}, Name: {}, Id: {}, Hash: {}",
+              v->type(), v->shape(), v->name(), v->getId(), v->hash());
+          LOG(critical, "Value debug {}", v->val()->debug());
+          LOG(critical, "Children: {}", v->children().size());
+          for(auto&& child : v->children()) {
+            LOG(critical, "\tType: {}, Shape: {}, Name: {}, Id: {}, Hash: {}",
+              child->type(), child->shape(), child->name(), child->getId(), child->hash());
+            LOG(critical, "Value debug {}", child->val()->debug());
+          }
+          ABORT("Aborting");
+        }
+      }
 
       if(v->marked_for_debug()) {
-        std::cerr << "Debug: " << v->debug_message() << " op=" << v->type()
-                  << std::endl;
-        std::cerr << v->val()->debug() << std::endl;
+        LOG(info, "Debug: {} op={}", v->debug_message(), v->type());
+        LOG(info, v->val()->debug());
       }
 
       if(inferenceOnly_)
@@ -243,55 +258,7 @@ class ExpressionGraph : public std::enable_shared_from_this<ExpressionGraph> {
     }
   }
 
-  void backward(bool zero = true) {
-    if(topNodes_.size() > 1) {
-      LOG(critical, "There are more ({}) than one top most node for backward step:", topNodes_.size());
-      for(auto node : topNodes_) {
-        LOG(critical,
-            "\tType: {}, Shape: {}, Name: {}, Id: {}, Hash: {}",
-            node->type(),
-            node->shape(),
-            node->name(),
-            node->getId(),
-            node->hash());
-      }
-      ABORT("Aborting");
-    }
-
-    params_->allocateBackward();
-    if(zero)
-      params_->set_zero_adjoint();
-
-    for(auto&& v : topNodes_)
-      v->init_dependent();
-
-    // named_.clear();
-    topNodes_.clear();
-
-    tensors_->clearShorttermMemory();
-
-    while(!nodesBackward_.empty()) {
-      auto v = nodesBackward_.back();
-      nodesBackward_.pop_back();
-
-      for(auto&& child : v->children()) {
-        if(child->trainable() && child->type() != "param")
-          child->set_zero_adjoint();
-      }
-
-      if(v->trainable())
-        v->backward();
-
-      checkNan(v->grad());
-
-      if(v->trainable() && v->marked_for_debug()) {
-        std::cerr << "Debug Grad: " << v->debug_message() << std::endl;
-        std::cerr << v->grad()->debug() << std::endl;
-      }
-
-      v->children().clear();
-    }
-  }
+  void backward(bool zero = true, float clipValue = 0.f);
 
   std::string graphviz() {
     std::stringstream ss;
@@ -460,7 +427,8 @@ class ExpressionGraph : public std::enable_shared_from_this<ExpressionGraph> {
 
   void setReloaded(bool reloaded) { reloaded_ = reloaded; }
 
-  void setThrowNaN(bool throwNaN) { throwNaN_ = throwNaN; }
+  void setThrowNan(bool throwNan) { throwNan_ = throwNan; }
+  bool getThrowNan() { return throwNan_; }
 
 public:
   // convert all parameters into an array of IoItem elements, for loading
diff --git a/src/tensors/cpu/tensor_operators.cpp b/src/tensors/cpu/tensor_operators.cpp
index 5ee1d3400..d7d019f7d 100644
--- a/src/tensors/cpu/tensor_operators.cpp
+++ b/src/tensors/cpu/tensor_operators.cpp
@@ -14,7 +14,7 @@ namespace marian {
 
 namespace cpu {
 
-void IsNan(const Tensor in, Ptr<Allocator> allocator, bool& isNan, bool& isInf) {
+void IsNan(const Tensor in, Ptr<Allocator> allocator, bool& isNan, bool& isInf, bool zero) {
   ABORT("Not implemented");
 }
 
diff --git a/src/tensors/gpu/tensor_operators.cu b/src/tensors/gpu/tensor_operators.cu
index 01ee303dd..8e256bc78 100644
--- a/src/tensors/gpu/tensor_operators.cu
+++ b/src/tensors/gpu/tensor_operators.cu
@@ -28,18 +28,23 @@ __device__ inline float stableSigmoid(float x) {
 }
 
 template <typename T>
-__global__ void gIsNan(const T* in, int length, bool* isNan, bool* isInf) {
+__global__ void gIsNan(T* in, int length, bool* isNan, bool* isInf, bool zero) {
   for(int bid = 0; bid < length; bid += blockDim.x * gridDim.x) {
     int index = bid + blockDim.x * blockIdx.x + threadIdx.x;
     if(index < length) {
-      if(isnan((float)in[index])) *isNan = true;
-      if(isinf((float)in[index])) *isInf = true;
-      //if(isinf2(in[index])) *isInf = true;
+      if(isnan((float)in[index])) {
+        if(zero) in[index] = (T)0.f;
+        *isNan = true; 
+      }
+      else if(isinf((float)in[index])) { 
+        if(zero) in[index] = (T)0.f;
+        *isInf = true;
+      }
     }
   }
 }
 
-void IsNan(const Tensor in, Ptr<Allocator> allocator, bool& isNan, bool& isInf) {
+void IsNan(Tensor in, Ptr<Allocator> allocator, bool& isNan, bool& isInf, bool zero) {
   cudaSetDevice(in->getDeviceId().no);
 
   int length = in->size();
@@ -53,7 +58,7 @@ void IsNan(const Tensor in, Ptr<Allocator> allocator, bool& isNan, bool& isInf)
   fill(in->getBackend(), dIsNan, dIsNan + 2, false);
 
   if(in->type() == Type::float32) {
-    gIsNan<<<blocks, threads>>>(in->data<float>(), length, dIsNan, dIsInf);
+    gIsNan<<<blocks, threads>>>(in->data<float>(), length, dIsNan, dIsInf, zero);
   } else {
     ABORT("IsNan for type {} not implemented", in->type());
   }
diff --git a/src/tensors/tensor_operators.h b/src/tensors/tensor_operators.h
index 7f0f75943..5ee4c3031 100644
--- a/src/tensors/tensor_operators.h
+++ b/src/tensors/tensor_operators.h
@@ -34,7 +34,7 @@ void copy(Ptr<Backend> backend, const InIt beg, const InIt end, OutIt it) {
     std::copy(beg, end, it);
 }
 
-DISPATCH4(IsNan, const Tensor, Ptr<Allocator>, bool&, bool&);
+DISPATCH5(IsNan, const Tensor, Ptr<Allocator>, bool&, bool&, bool);
 
 template <class Functor, class... Tensors>
 void Element(Functor functor, marian::Tensor out, Tensors... tensors) {
diff --git a/src/training/graph_group_sync.cpp b/src/training/graph_group_sync.cpp
index 85bf13f1f..782ced8b3 100644
--- a/src/training/graph_group_sync.cpp
+++ b/src/training/graph_group_sync.cpp
@@ -358,16 +358,15 @@ void SyncGraphGroup::update(std::vector<Ptr<data::Batch>> subBatches, size_t num
       graph->forward();
 
       StaticLoss tempLoss = *rationalLoss; // needed for overstuff
-      tempLoss.loss /= (float)overstuff; // @TODO: @fseide: scale only loss? should this scale labels too?
+      tempLoss.loss /= (float)overstuff; 
 
       localDeviceLosses[localDeviceIndex] += tempLoss;
       graph->backward(/*zero=*/false); // (gradients are reset before we get here)
 
       bool hasNan = false, hasInf = false;
-      IsNan(graph->params()->grads(), graph->allocator(), hasNan, hasInf);
+      IsNan(graph->params()->grads(), graph->allocator(), hasNan, hasInf, /*zero=*/true);
       if(hasNan || hasInf) {
-        LOG(warn, "Seen Nan ({}) or Inf ({}) in gradient, zeroing gradient", hasNan, hasInf);
-        graph->params()->grads()->set(0.f);
+        LOG(warn, "Seen Nan ({}) or Inf ({}) in gradient, zeroed offending gradient", hasNan, hasInf);
       }
     }
   });
@@ -407,7 +406,8 @@ void SyncGraphGroup::update(std::vector<Ptr<data::Batch>> subBatches, size_t num
   // cost across all local devices (scheduler will aggregate cross-process)
   StaticLoss localLoss;
   for(auto& l : localDeviceLosses) // localDeviceLosses is already summed up over delay steps
-    localLoss += l;
+    if(std::isfinite((float)l.loss))
+      localLoss += l;
 
   if(scheduler_) {
     // track and log localLoss

From ddb5472717523d891f7b495f7b8f553e7ce3096c Mon Sep 17 00:00:00 2001
From: Marcin Junczys-Dowmunt <Marcin.JunczysDowmunt@microsoft.com>
Date: Mon, 28 Jan 2019 08:44:26 -0800
Subject: [PATCH 210/838] more aggressive nan checking

---
 src/common/config_parser.cpp      | 30 ++++++++++++++++--------------
 src/training/graph_group_sync.cpp |  8 +++++++-
 2 files changed, 23 insertions(+), 15 deletions(-)

diff --git a/src/common/config_parser.cpp b/src/common/config_parser.cpp
index 73be7cd71..58fd4090e 100644
--- a/src/common/config_parser.cpp
+++ b/src/common/config_parser.cpp
@@ -54,31 +54,33 @@ void ConfigParser::addOptionsGeneral(cli::CLIWrapper& cli) {
 
   // clang-format off
   cli.add<std::vector<std::string>>("--config,-c",
-     "Configuration file(s). If multiple, later overrides earlier");
+    "Configuration file(s). If multiple, later overrides earlier");
   cli.add<size_t>("--workspace,-w",
-      "Preallocate  arg  MB of work space",
-      defaultWorkspace);
+    "Preallocate  arg  MB of work space",
+    defaultWorkspace);
   cli.add_nondefault<std::string>("--log",
-     "Log training process information to file given by  arg");
+    "Log training process information to file given by  arg");
   cli.add<std::string>("--log-level",
-     "Set verbosity level of logging: trace, debug, info, warn, err(or), critical, off",
-     "info");
+    "Set verbosity level of logging: trace, debug, info, warn, err(or), critical, off",
+    "info");
   cli.add_nondefault<std::string>("--log-time-zone",
-     "Set time zone for the date shown on logging");
+    "Set time zone for the date shown on logging");
   cli.add<bool>("--quiet",
-     "Suppress all logging to stderr. Logging to files still works");
+    "Suppress all logging to stderr. Logging to files still works");
   cli.add<bool>("--quiet-translation",
-     "Suppress logging for translation");
+    "Suppress logging for translation");
   cli.add<size_t>("--seed",
-     "Seed for all random number generators. 0 means initialize randomly");
+    "Seed for all random number generators. 0 means initialize randomly");  
+  cli.add<bool>("--check-nan",
+    "Check for NaNs or Infs in forward and backward pass. Will abort when found.");
   cli.add<float>("--clip-gemm",
-     "If not 0 clip GEMM input values to +/- arg");
+    "If not 0 clip GEMM input values to +/- arg");
   cli.add<bool>("--interpolate-env-vars",
-     "allow the use of environment variables in paths, of the form ${VAR_NAME}");
+    "allow the use of environment variables in paths, of the form ${VAR_NAME}");
   cli.add<bool>("--relative-paths",
-     "All paths are relative to the config file location");
+    "All paths are relative to the config file location");
   cli.add_nondefault<std::string>("--dump-config",
-     "Dump current (modified) configuration to stdout and exit. Possible values: full, minimal")
+    "Dump current (modified) configuration to stdout and exit. Possible values: full, minimal")
     ->implicit_val("full");
   // clang-format on
 }
diff --git a/src/training/graph_group_sync.cpp b/src/training/graph_group_sync.cpp
index 782ced8b3..c26cb9ff5 100644
--- a/src/training/graph_group_sync.cpp
+++ b/src/training/graph_group_sync.cpp
@@ -10,6 +10,10 @@ SyncGraphGroup::SyncGraphGroup(Ptr<Options> config, Ptr<IMPIWrapper> mpi)
   for(auto device : devices_) {
     auto graph = New<ExpressionGraph>();
     graph->setDevice(device);
+
+    if(options_->get<bool>("check-nan"))
+      graph->setThrowNan(true);
+
     graph->reserveWorkspaceMB(options_->get<size_t>("workspace"));
     graph->getBackend()->setClip(options_->get<float>("clip-gemm"));
 
@@ -366,7 +370,7 @@ void SyncGraphGroup::update(std::vector<Ptr<data::Batch>> subBatches, size_t num
       bool hasNan = false, hasInf = false;
       IsNan(graph->params()->grads(), graph->allocator(), hasNan, hasInf, /*zero=*/true);
       if(hasNan || hasInf) {
-        LOG(warn, "Seen Nan ({}) or Inf ({}) in gradient, zeroed offending gradient", hasNan, hasInf);
+        LOG(warn, "Seen Nan ({}) or Inf ({}) in gradient, zeroed out offending gradient", hasNan, hasInf);
       }
     }
   });
@@ -408,6 +412,8 @@ void SyncGraphGroup::update(std::vector<Ptr<data::Batch>> subBatches, size_t num
   for(auto& l : localDeviceLosses) // localDeviceLosses is already summed up over delay steps
     if(std::isfinite((float)l.loss))
       localLoss += l;
+    else 
+      LOG(warn, "Seen non-finite loss, offending gradients have been zeroed out");
 
   if(scheduler_) {
     // track and log localLoss

From 93763c30e6c430d21e9631415916c48d19784c18 Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Mon, 28 Jan 2019 10:02:53 -0800
Subject: [PATCH 211/838] now always normalizes factors

---
 src/data/default_vocab.cpp |  3 ++-
 src/layers/generic.cpp     | 12 +++++++-----
 src/layers/loss.cpp        |  5 +++++
 src/models/costs.h         |  2 ++
 vs/Marian.vcxproj          |  4 ++++
 vs/Marian.vcxproj.filters  |  3 +++
 6 files changed, 23 insertions(+), 6 deletions(-)

diff --git a/src/data/default_vocab.cpp b/src/data/default_vocab.cpp
index e344eadf9..500dbda99 100755
--- a/src/data/default_vocab.cpp
+++ b/src/data/default_vocab.cpp
@@ -107,7 +107,8 @@ class DefaultVocab : public VocabBase {
         ABORT_IF(line.empty(),
                 "DefaultVocabulary file {} must not contain empty lines",
                 vocabPath);
-        vocab.insert({line, (Word)vocab.size()});
+        auto wasInserted = vocab.insert({line, (Word)vocab.size()}).second;
+        ABORT_IF(!wasInserted, "Duplicate vocabulary entry {}", line);
       }
       ABORT_IF(in.bad(), "DefaultVocabulary file {} could not be read", vocabPath);
     }
diff --git a/src/layers/generic.cpp b/src/layers/generic.cpp
index d44c6bb13..60cbcf295 100755
--- a/src/layers/generic.cpp
+++ b/src/layers/generic.cpp
@@ -100,10 +100,12 @@ namespace marian {
       for (size_t g = 0; g < numGroups; g++) { // detect non-overlapping groups
         LOG(info, "[embedding] Factor group '{}' has {} members ({})",
             groupPrefixes[g], groupCounts[g], groupCounts[g] == 1 ? "sigmoid" : "softmax");
-        // any factor that is not referenced in all words and is not a sigmoid needs normalization
-        if (g == 0) // @TODO: For now we assume that the main factor is used in all words. Test this.
+        if (groupCounts[g] == 0) // factor group is unused  --@TODO: once this is not hard-coded, this is an error condition
           continue;
-        if (groupCounts[g] == 1) // sigmoid factors have no normalizer
+        // any factor that is not referenced in all words and is not a sigmoid needs normalization
+        //if (g == 0) // @TODO: For now we assume that the main factor is used in all words. Test this.
+        //  continue;
+        if (groupCounts[g] == 1) // sigmoid factors have no normalizer  --@BUGBUG: This is not even possible I think. We need the counter-class.
           continue;
         groupNeedsNormalization_[g] = true; // needed
         ABORT_IF(groupRanges_[g].second - groupRanges_[g].first != groupCounts[g],
@@ -196,7 +198,7 @@ namespace marian {
         auto graph = input->graph();
         auto y = affine(input, W_, b_, false, transposeW_); // [B... x U] factor logits
 
-#if 0
+#if 1
         // denominators (only for groups that don't normalize out naturally by the final softmax())
         const auto& groupRanges = embeddingFactorMapping_->groupRanges_; // @TODO: factor this properly
         auto numGroups = groupRanges.size();
@@ -217,7 +219,7 @@ namespace marian {
           auto m = graph->constant({ 1, (int)mVec.size() }, inits::from_vector(mVec)); // [1 x U]
           auto Z = dot(groupZ, m); // [B... x U]
           y = y - Z;
-#if 0
+#if 1
           // and a log-linear weight
           auto name = options_->get<std::string>("prefix");
           auto llWeight = graph->param(name + "_llWeight_" + std::to_string(g), {}, inits::from_value(1.0f));
diff --git a/src/layers/loss.cpp b/src/layers/loss.cpp
index d11e4384c..5ca493f1d 100755
--- a/src/layers/loss.cpp
+++ b/src/layers/loss.cpp
@@ -29,6 +29,11 @@ Expr LossBase::getCrossEntropy(Expr logits,
 
   Expr ce;
   if(smoothing_ > 0) {
+    // ce = sum_i y^_i log y_i(z)_i
+    // with smoothing:
+    // ce' = sum_i ((1-smoothing_) y^_i + smoothing_/N) log y_i(z)_i
+    //     = (1-smoothing_) sum_i y^_i log y_i(z)_i + smoothing_ mean_i log y_i(z)_i
+    //     = (1-smoothing_) ce + smoothing_ mean_i log y_i(z)_i
     // @TODO: add this to CE kernels instead
 #if 0
     ce = cross_entropy(logits, indices);
diff --git a/src/models/costs.h b/src/models/costs.h
index d38a445e8..a38fca915 100755
--- a/src/models/costs.h
+++ b/src/models/costs.h
@@ -108,6 +108,8 @@ typedef Trainer Scorer;
 
 class CostStep {
 public:
+  // @BUGBUG: This is not a function application. Rather, it updates 'state' in-place.
+  // Suggest to call it updateState, and not return the state object.
   virtual Ptr<DecoderState> apply(Ptr<DecoderState> state) = 0;
 };
 
diff --git a/vs/Marian.vcxproj b/vs/Marian.vcxproj
index a82c879e5..e96cede5e 100755
--- a/vs/Marian.vcxproj
+++ b/vs/Marian.vcxproj
@@ -1112,6 +1112,10 @@
       <ExcludedFromBuild Condition="'$(Configuration)|$(Platform)'=='Release|x64'">true</ExcludedFromBuild>
     </None>
     <None Include="..\src\training\communicator_nccl.h" />
+    <None Include="..\src\translator\nth_element.cu">
+      <ExcludedFromBuild Condition="'$(Configuration)|$(Platform)'=='Debug|x64'">true</ExcludedFromBuild>
+      <ExcludedFromBuild Condition="'$(Configuration)|$(Platform)'=='Release|x64'">true</ExcludedFromBuild>
+    </None>
     <None Include=".editorConfig" />
   </ItemGroup>
   <ItemGroup>
diff --git a/vs/Marian.vcxproj.filters b/vs/Marian.vcxproj.filters
index a7c2331e7..3a0fe4902 100755
--- a/vs/Marian.vcxproj.filters
+++ b/vs/Marian.vcxproj.filters
@@ -1852,6 +1852,9 @@
     <None Include="..\src\tensors\gpu\prod.cpp">
       <Filter>tensors\gpu</Filter>
     </None>
+    <None Include="..\src\translator\nth_element.cu">
+      <Filter>translator</Filter>
+    </None>
   </ItemGroup>
   <ItemGroup>
     <Text Include="..\src\3rd_party\sentencepiece\src\CMakeLists.txt">

From 7f159ab64aa17c28e97a9afa9cd458568e46ea9d Mon Sep 17 00:00:00 2001
From: Marcin Junczys-Dowmunt <Marcin.JunczysDowmunt@microsoft.com>
Date: Mon, 28 Jan 2019 10:43:34 -0800
Subject: [PATCH 212/838] dump matrices for nan

---
 src/graph/expression_graph.cpp | 62 ++++++++++++++++++++++++++++++++++
 src/graph/expression_graph.h   | 39 +--------------------
 2 files changed, 63 insertions(+), 38 deletions(-)

diff --git a/src/graph/expression_graph.cpp b/src/graph/expression_graph.cpp
index 19c3fc4e9..4e9e9a103 100644
--- a/src/graph/expression_graph.cpp
+++ b/src/graph/expression_graph.cpp
@@ -28,6 +28,68 @@ void ExpressionGraph::checkNan(Tensor t, bool& isNan, bool& isInf, bool zero) {
   IsNan(t, allocator(), isNan, isInf, zero);
 }
 
+io::Item itemFromTensor(Tensor t, const std::string name, Ptr<Backend> backend) {
+  io::Item item;
+  item.name  = name;
+  item.shape = t->shape();
+  item.type  = t->type();
+
+  size_t bytesWithoutPadding = t->shape().elements() * sizeOf(t->type());
+  item.bytes.resize(bytesWithoutPadding);
+  copy(backend,
+        (char*)t->data(),
+        (char*)t->data() + bytesWithoutPadding,
+        item.bytes.data());
+  return item;
+}
+
+void ExpressionGraph::forwardNext() {
+  // @TODO: check if allocation works properly
+  tensors_->clearShorttermMemory();
+
+  while(!nodesForward_.empty()) {
+    auto v = nodesForward_.front();
+    v->allocate();
+    v->init();
+    v->forward();
+
+    if(v->trainable() && throwNan_) {
+      bool isNan = false, isInf = false;
+      checkNan(v->val(), isNan, isInf);
+      if(isNan || isInf) {
+        std::vector<io::Item> ioItems;
+        LOG(critical, "Detected NaN ({}) or Inf ({}) in value (forward pass)", isNan, isInf);
+        LOG(critical, "\tType: {}, Shape: {}, Name: {}, Id: {}, Hash: {}",
+            v->type(), v->shape(), v->name(), v->getId(), v->hash());
+        LOG(critical, "Value debug {}", v->val()->debug());
+
+        ioItems.push_back(itemFromTensor(v->val(), "value", backend_));
+
+        LOG(critical, "Children: {}", v->children().size());
+        for(auto&& child : v->children()) {
+          LOG(critical, "\tType: {}, Shape: {}, Name: {}, Id: {}, Hash: {}",
+            child->type(), child->shape(), child->name(), child->getId(), child->hash());
+          LOG(critical, "Value debug {}", child->val()->debug());
+          ioItems.push_back(itemFromTensor(v->val(), "child_" + std::to_string(child->hash()), backend_));
+        }
+
+        io::saveItems("dump-for-nans.npz", ioItems);
+
+        ABORT("Aborting");
+      }
+    }
+
+    if(v->marked_for_debug()) {
+      LOG(info, "Debug: {} op={}", v->debug_message(), v->type());
+      LOG(info, v->val()->debug());
+    }
+
+    if(inferenceOnly_)
+      v->children().clear();
+    nodesForward_.pop_front();
+  }
+}
+
 void ExpressionGraph::backward(bool zero, float clipValue) {
   if(topNodes_.size() > 1) {
     LOG(critical, "There are more ({}) than one top most nodes for backward pass:", topNodes_.size());
diff --git a/src/graph/expression_graph.h b/src/graph/expression_graph.h
index 24eb88a6a..389c6e3e4 100644
--- a/src/graph/expression_graph.h
+++ b/src/graph/expression_graph.h
@@ -219,44 +219,7 @@ class ExpressionGraph : public std::enable_shared_from_this<ExpressionGraph> {
 
   void checkNan(Tensor t, bool& isNan, bool& isInf, bool zero = false);
 
-  void forwardNext() {
-    // @TODO: check if allocation works properly
-    tensors_->clearShorttermMemory();
-
-    while(!nodesForward_.empty()) {
-      auto v = nodesForward_.front();
-      v->allocate();
-      v->init();
-      v->forward();
-
-      if(v->trainable() && throwNan_) {
-        bool isNan = false, isInf = false;
-        checkNan(v->val(), isNan, isInf);
-        if(isNan || isInf) {
-          LOG(critical, "Detected NaN ({}) or Inf ({}) in value (forward pass)", isNan, isInf);
-          LOG(critical, "\tType: {}, Shape: {}, Name: {}, Id: {}, Hash: {}",
-              v->type(), v->shape(), v->name(), v->getId(), v->hash());
-          LOG(critical, "Value debug {}", v->val()->debug());
-          LOG(critical, "Children: {}", v->children().size());
-          for(auto&& child : v->children()) {
-            LOG(critical, "\tType: {}, Shape: {}, Name: {}, Id: {}, Hash: {}",
-              child->type(), child->shape(), child->name(), child->getId(), child->hash());
-            LOG(critical, "Value debug {}", child->val()->debug());
-          }
-          ABORT("Aborting");
-        }
-      }
-
-      if(v->marked_for_debug()) {
-        LOG(info, "Debug: {} op={}", v->debug_message(), v->type());
-        LOG(info, v->val()->debug());
-      }
-
-      if(inferenceOnly_)
-        v->children().clear();
-      nodesForward_.pop_front();
-    }
-  }
+  void forwardNext();
 
   void backward(bool zero = true, float clipValue = 0.f);
 

From a327956d314e43f052ad0716c7a7419096553088 Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Mon, 28 Jan 2019 10:59:01 -0800
Subject: [PATCH 213/838] refactoring: Output layer, and therefore all Models,
 now return logits as a Logits object instead of an Exp, in preparation for
 optimizing CE loss for factored embeddings

---
 src/examples/mnist/model.h                  |  6 ++---
 src/layers/constructors.h                   | 23 ++++++++++++++++--
 src/layers/generic.cpp                      |  2 +-
 src/layers/generic.h                        | 26 ++++++++++++++++++---
 src/models/costs.h                          | 15 ++++++------
 src/models/encoder_decoder.cpp              |  4 ++--
 src/models/encoder_decoder.h                |  8 +++----
 src/models/model_base.h                     |  3 ++-
 src/models/s2s.h                            |  7 +++++-
 src/models/states.h                         |  9 +++----
 src/models/transformer.h                    |  8 +++----
 src/rescorer/rescorer.h                     |  2 +-
 src/training/graph_group_async.cpp          |  2 +-
 src/training/graph_group_multinode.cpp      |  2 +-
 src/training/graph_group_multinode_sync.cpp |  2 +-
 src/training/graph_group_singleton.cpp      |  2 +-
 src/training/graph_group_sync.cpp           |  2 +-
 src/training/validator.h                    |  2 +-
 src/translator/scorers.h                    |  2 +-
 19 files changed, 87 insertions(+), 40 deletions(-)
 mode change 100644 => 100755 src/models/encoder_decoder.h
 mode change 100644 => 100755 src/models/model_base.h
 mode change 100644 => 100755 src/rescorer/rescorer.h

diff --git a/src/examples/mnist/model.h b/src/examples/mnist/model.h
index 71de15ec4..2f66f215f 100755
--- a/src/examples/mnist/model.h
+++ b/src/examples/mnist/model.h
@@ -22,7 +22,7 @@ class MNISTCrossEntropyCost : public CostBase {
              Ptr<ExpressionGraph> graph,
              Ptr<data::Batch> batch,
              bool clearGraph = true) override {
-    auto top = model->build(graph, batch, clearGraph);
+    auto top = model->build(graph, batch, clearGraph).getLogits();
 
     auto vfLabels = std::static_pointer_cast<data::DataBatch>(batch)->labels();
 
@@ -43,7 +43,7 @@ class MNISTLogsoftmax : public CostBase {
              Ptr<ExpressionGraph> graph,
              Ptr<data::Batch> batch,
              bool clearGraph = true) override {
-    auto top = model->build(graph, batch, clearGraph);
+    auto top = model->build(graph, batch, clearGraph).getLogits();
     return logsoftmax(top);
   }
 };
@@ -56,7 +56,7 @@ class MnistFeedForwardNet : public ModelBase {
   MnistFeedForwardNet(Ptr<Options> options, Args... args)
       : options_(options), inference_(options->get<bool>("inference", false)) {}
 
-  virtual Expr build(Ptr<ExpressionGraph> graph,
+  virtual Logits build(Ptr<ExpressionGraph> graph,
                      Ptr<data::Batch> batch,
                      bool /*clean*/ = false) override {
     return construct(graph, batch, inference_);
diff --git a/src/layers/constructors.h b/src/layers/constructors.h
index 5ed7f3f53..d1aef42e5 100755
--- a/src/layers/constructors.h
+++ b/src/layers/constructors.h
@@ -51,7 +51,26 @@ typedef Accumulator<DenseFactory> dense;
 /**
  * Factory for output layers, can be used in a multi-layer network factory.
  */
-class OutputFactory : public LayerFactory {
+struct LogitLayerFactory : public Factory {
+  LogitLayerFactory() : Factory() {}
+  LogitLayerFactory(const LogitLayerFactory&) = default;
+  LogitLayerFactory(LogitLayerFactory&&) = default;
+
+  virtual ~LogitLayerFactory() {}
+
+  template <typename Cast>
+  inline Ptr<Cast> as() {
+    return std::dynamic_pointer_cast<Cast>(shared_from_this());
+  }
+
+  template <typename Cast>
+  inline bool is() {
+    return as<Cast>() != nullptr;
+  }
+
+  virtual Ptr<IUnaryLogitLayer> construct(Ptr<ExpressionGraph> graph) = 0;
+};
+class OutputFactory : public LogitLayerFactory {
 protected:
   std::string tiedTransposedName_;
   Ptr<data::Shortlist> shortlist_;
@@ -67,7 +86,7 @@ class OutputFactory : public LayerFactory {
     return Accumulator<OutputFactory>(*this);
   }
 
-  Ptr<IUnaryLayer> construct(Ptr<ExpressionGraph> graph) override {
+  Ptr<IUnaryLogitLayer> construct(Ptr<ExpressionGraph> graph) override {
     auto output = New<Output>(graph, options_);
     output->tieTransposed(graph->get(tiedTransposedName_));
     output->setShortlist(shortlist_);
diff --git a/src/layers/generic.cpp b/src/layers/generic.cpp
index 60cbcf295..06b109537 100755
--- a/src/layers/generic.cpp
+++ b/src/layers/generic.cpp
@@ -181,7 +181,7 @@ namespace marian {
       b_ = graph_->param(name + "_b", {1, dim}, inits::zeros);
     }
 
-    Expr Output::apply(Expr input) /*override*/ {
+    Logits Output::apply(Expr input) /*override*/ {
       lazyConstruct(input->shape()[-1]);
 
       if (shortlist_) {
diff --git a/src/layers/generic.h b/src/layers/generic.h
index d70c7f06e..6eb60cb37 100755
--- a/src/layers/generic.h
+++ b/src/layers/generic.h
@@ -54,6 +54,26 @@ struct IEmbeddingLayer {
   virtual Expr apply(const std::vector<IndexType>& embIdx, int dimBatch, int dimBeam) const = 0;
 };
 
+// @HACK: Frank's quick implementation of factored outputs. To be re-thought once it works.
+// Output layer returns a Logits object, which is able to compute some things on the fly
+// for factored embeddings.
+class Logits {
+public:
+    Logits(Expr logits) : logits_(logits) {
+    }
+    Expr getLogits() const {
+      return logits_;
+    }
+private:
+    Expr logits_;
+};
+
+// Unary function that returns a Logits object
+struct IUnaryLogitLayer {
+  virtual Logits apply(Expr) = 0;
+  virtual Logits apply(const std::vector<Expr>&) = 0;
+};
+
 class EmbeddingFactorMapping;
 
 namespace mlp {
@@ -124,7 +144,7 @@ class Dense : public LayerBase, public IUnaryLayer {
   Expr apply(Expr input) override { return apply(std::vector<Expr>({input})); }
 };
 
-class Output : public LayerBase, public IUnaryLayer {
+class Output : public LayerBase, public IUnaryLogitLayer {
 private:
   Expr W_;  // parameters held by this layer
   Expr b_;
@@ -169,9 +189,9 @@ class Output : public LayerBase, public IUnaryLayer {
     cachedShortb_ = nullptr;
   }
 
-  Expr apply(Expr input) override;
+  Logits apply(Expr input) override;
 
-  virtual Expr apply(const std::vector<Expr>& /*inputs*/) override {
+  virtual Logits apply(const std::vector<Expr>& /*inputs*/) override {
     ABORT("Not implemented");
   };
 };
diff --git a/src/models/costs.h b/src/models/costs.h
index a38fca915..5231e0ce0 100755
--- a/src/models/costs.h
+++ b/src/models/costs.h
@@ -54,7 +54,7 @@ class EncoderDecoderCE : public CostBase {
       weights = weighter_->getWeights(graph, corpusBatch);
 
     Expr cost;
-    cost = loss_->getCost(state->getLogProbs(),
+    cost = loss_->getCost(state->getLogProbs().getLogits(),
                           state->getTargetIndices(),
                           state->getTargetMask(),
                           weights);
@@ -95,7 +95,7 @@ class Trainer : public ModelBase {
     model_->save(graph, name, saveTranslatorConfig);
   }
 
-  virtual Expr build(Ptr<ExpressionGraph> graph,
+  virtual Logits build(Ptr<ExpressionGraph> graph,
                      Ptr<data::Batch> batch,
                      bool clearGraph = true) override {
     return cost_->apply(model_, graph, batch, clearGraph);
@@ -117,7 +117,8 @@ class LogSoftmaxStep : public CostStep {
 public:
   virtual Ptr<DecoderState> apply(Ptr<DecoderState> state) override {
     // decoder needs normalized probabilities (note: skipped if beam 1 and --skip-cost)
-    auto logits = state->getLogProbs();
+    // @TODO: @HACK must know about individual parts; make it a loop
+    auto logits = state->getLogProbs().getLogits();
     
     auto logprobs = logsoftmax(logits);
 
@@ -132,7 +133,8 @@ class LogSoftmaxStep : public CostStep {
 class GumbelSoftmaxStep : public CostStep {
 public:
   virtual Ptr<DecoderState> apply(Ptr<DecoderState> state) override {
-    auto logits = state->getLogProbs();
+    // @TODO: @HACK must know about individual parts; make it a loop
+    auto logits = state->getLogProbs().getLogits();
     
     auto logprobs = logsoftmax(logits + constant_like(logits, inits::gumbel));
 
@@ -173,7 +175,7 @@ class Stepwise : public EncoderDecoderBase {
 
   virtual void clear(Ptr<ExpressionGraph> graph) override { encdec_->clear(graph); }
 
-  virtual Expr build(Ptr<ExpressionGraph> graph,
+  virtual Logits build(Ptr<ExpressionGraph> graph,
                      Ptr<data::Batch> batch,
                      bool clearGraph = true) override {
     auto corpusBatch = std::static_pointer_cast<data::CorpusBatch>(batch);
@@ -196,11 +198,10 @@ class Stepwise : public EncoderDecoderBase {
     return cost_->apply(nextState);
   }
 
-  virtual Expr build(Ptr<ExpressionGraph> /*graph*/,
+  virtual Logits build(Ptr<ExpressionGraph> /*graph*/,
                      Ptr<data::CorpusBatch> /*batch*/,
                      bool /*clearGraph*/ = true) override {
     ABORT("Wrong wrapper. Use models::Trainer or models::Scorer");
-    return nullptr;
   }
 
   virtual Ptr<Options> getOptions() override { return encdec_->getOptions(); };
diff --git a/src/models/encoder_decoder.cpp b/src/models/encoder_decoder.cpp
index 3edb7b335..267943834 100755
--- a/src/models/encoder_decoder.cpp
+++ b/src/models/encoder_decoder.cpp
@@ -182,7 +182,7 @@ Ptr<DecoderState> EncoderDecoder::stepAll(Ptr<ExpressionGraph> graph,
   return nextState;
 }
 
-Expr EncoderDecoder::build(Ptr<ExpressionGraph> graph,
+Logits EncoderDecoder::build(Ptr<ExpressionGraph> graph,
                            Ptr<data::CorpusBatch> batch,
                            bool clearGraph) {
   auto state = stepAll(graph, batch, clearGraph);
@@ -191,7 +191,7 @@ Expr EncoderDecoder::build(Ptr<ExpressionGraph> graph,
   return state->getLogProbs();
 }
 
-Expr EncoderDecoder::build(Ptr<ExpressionGraph> graph,
+Logits EncoderDecoder::build(Ptr<ExpressionGraph> graph,
                            Ptr<data::Batch> batch,
                            bool clearGraph) {
   auto corpusBatch = std::static_pointer_cast<data::CorpusBatch>(batch);
diff --git a/src/models/encoder_decoder.h b/src/models/encoder_decoder.h
old mode 100644
new mode 100755
index 5818c2370..5a897750b
--- a/src/models/encoder_decoder.h
+++ b/src/models/encoder_decoder.h
@@ -28,7 +28,7 @@ class EncoderDecoderBase : public models::ModelBase {
 
   virtual void clear(Ptr<ExpressionGraph> graph) override = 0;
 
-  virtual Expr build(Ptr<ExpressionGraph> graph,
+  virtual Logits build(Ptr<ExpressionGraph> graph,
                      Ptr<data::Batch> batch,
                      bool clearGraph = true) override
       = 0;
@@ -45,7 +45,7 @@ class EncoderDecoderBase : public models::ModelBase {
                                  int beamSize)
       = 0;
 
-  virtual Expr build(Ptr<ExpressionGraph> graph,
+  virtual Logits build(Ptr<ExpressionGraph> graph,
                      Ptr<data::CorpusBatch> batch,
                      bool clearGraph = true)
       = 0;
@@ -158,11 +158,11 @@ class EncoderDecoder : public EncoderDecoderBase {
                                     Ptr<data::CorpusBatch> batch,
                                     bool clearGraph = true);
 
-  virtual Expr build(Ptr<ExpressionGraph> graph,
+  virtual Logits build(Ptr<ExpressionGraph> graph,
                      Ptr<data::CorpusBatch> batch,
                      bool clearGraph = true) override;
 
-  virtual Expr build(Ptr<ExpressionGraph> graph,
+  virtual Logits build(Ptr<ExpressionGraph> graph,
                      Ptr<data::Batch> batch,
                      bool clearGraph = true) override;
 };
diff --git a/src/models/model_base.h b/src/models/model_base.h
old mode 100644
new mode 100755
index 47039841a..aa4ae5545
--- a/src/models/model_base.h
+++ b/src/models/model_base.h
@@ -2,6 +2,7 @@
 
 #include <string>
 #include "marian.h"
+#include "layers/generic.h" // @HACK for Frank's factored embeddings/Logits class
 
 namespace marian {
 namespace models {
@@ -26,7 +27,7 @@ class ModelBase {
                     bool saveTranslatorConfig = false)
       = 0;
 
-  virtual Expr build(Ptr<ExpressionGraph> graph,
+  virtual Logits build(Ptr<ExpressionGraph> graph,
                      Ptr<data::Batch> batch,
                      bool clearGraph = true)
       = 0;
diff --git a/src/models/s2s.h b/src/models/s2s.h
index 2f4a45797..57cabe0fc 100755
--- a/src/models/s2s.h
+++ b/src/models/s2s.h
@@ -278,7 +278,7 @@ class DecoderS2S : public DecoderBase {
     }
 
     rnn::States startStates(opt<size_t>("dec-depth"), {start, start});
-    return New<DecoderState>(startStates, nullptr, encStates, batch);
+    return New<DecoderState>(startStates, Logits(nullptr), encStates, batch);
   }
 
   virtual Ptr<DecoderState> step(Ptr<ExpressionGraph> graph,
@@ -347,12 +347,17 @@ class DecoderS2S : public DecoderBase {
       if(shortlist_)
         last.setShortlist(shortlist_);
 
+#if 1
+      hidden; last;
+      ABORT("@TODO: adapt s2s to Logits return type");
+#else
       // assemble layers into MLP and apply to embeddings, decoder context and
       // aligned source context
       output_ = mlp::mlp()              //
                     .push_back(hidden)  //
                     .push_back(last)
                     .construct(graph);
+#endif
     }
 
     Expr logits;
diff --git a/src/models/states.h b/src/models/states.h
index 964617340..26f66625d 100755
--- a/src/models/states.h
+++ b/src/models/states.h
@@ -1,6 +1,7 @@
 #pragma once
 
 #include "marian.h"
+#include "layers/generic.h" // @HACK: for factored embeddings only so far
 #include "rnn/types.h"
 
 namespace marian {
@@ -29,7 +30,7 @@ class EncoderState {
 class DecoderState {
 protected:
   rnn::States states_; // states of individual decoder layers
-  Expr logProbs_;
+  Logits logProbs_;
   std::vector<Ptr<EncoderState>> encStates_;
   Ptr<data::CorpusBatch> batch_;
 
@@ -42,7 +43,7 @@ class DecoderState {
 
 public:
   DecoderState(const rnn::States& states,
-               Expr logProbs,
+               Logits logProbs,
                const std::vector<Ptr<EncoderState>>& encStates,
                Ptr<data::CorpusBatch> batch)
       : states_(states), logProbs_(logProbs), encStates_(encStates), batch_(batch) {}
@@ -52,8 +53,8 @@ class DecoderState {
     return encStates_;
   }
 
-  virtual Expr getLogProbs() const { return logProbs_; }
-  virtual void setLogProbs(Expr logProbs) { logProbs_ = logProbs; }
+  virtual Logits getLogProbs() const { return logProbs_; }
+  virtual void setLogProbs(Logits logProbs) { logProbs_ = logProbs; }
 
   // @TODO: should this be a constructor? Then derived classes can call this without the New<> in the loop
   virtual Ptr<DecoderState> select(const std::vector<IndexType>& selIdx,
diff --git a/src/models/transformer.h b/src/models/transformer.h
index f6aa4ff1e..595215da1 100755
--- a/src/models/transformer.h
+++ b/src/models/transformer.h
@@ -589,7 +589,7 @@ class EncoderTransformer : public Transformer<EncoderBase> {
 class TransformerState : public DecoderState {
 public:
   TransformerState(const rnn::States& states,
-                   Expr logProbs,
+                   Logits logProbs,
                    const std::vector<Ptr<EncoderState>>& encStates,
                    Ptr<data::CorpusBatch> batch)
       : DecoderState(states, logProbs, encStates, batch) {}
@@ -662,11 +662,11 @@ class DecoderTransformer : public Transformer<DecoderBase> {
       rnn::States startStates(opt<size_t>("dec-depth"), {start, start});
 
       // don't use TransformerState for RNN layers
-      return New<DecoderState>(startStates, nullptr, encStates, batch);
+      return New<DecoderState>(startStates, Logits(nullptr), encStates, batch);
     }
     else {
       rnn::States startStates;
-      return New<TransformerState>(startStates, nullptr, encStates, batch);
+      return New<TransformerState>(startStates, Logits(nullptr), encStates, batch);
     }
   }
 
@@ -831,7 +831,7 @@ class DecoderTransformer : public Transformer<DecoderBase> {
     // final feed-forward layer (output)
     if(shortlist_)
       output_->setShortlist(shortlist_);
-    Expr logits = output_->apply(decoderContext); // [-4: beam depth=1, -3: max length, -2: batch size, -1: vocab or shortlist dim]
+    auto logits = output_->apply(decoderContext); // [-4: beam depth=1, -3: max length, -2: batch size, -1: vocab or shortlist dim]
 
     // return unormalized(!) probabilities
     Ptr<DecoderState> nextState;
diff --git a/src/rescorer/rescorer.h b/src/rescorer/rescorer.h
old mode 100644
new mode 100755
index fa456856a..d74738cc0
--- a/src/rescorer/rescorer.h
+++ b/src/rescorer/rescorer.h
@@ -30,7 +30,7 @@ class Rescorer {
   }
 
   Expr build(Ptr<ExpressionGraph> graph, Ptr<data::CorpusBatch> batch) {
-    return builder_->build(graph, batch);
+    return builder_->build(graph, batch).getLogits();
   }
 
   data::SoftAlignment getAlignment() {
diff --git a/src/training/graph_group_async.cpp b/src/training/graph_group_async.cpp
index ca7f7b487..dddd7cddd 100755
--- a/src/training/graph_group_async.cpp
+++ b/src/training/graph_group_async.cpp
@@ -206,7 +206,7 @@ void AsyncGraphGroup::execute(Ptr<data::Batch> batch) {
       builder = builders_[i++];
     }
 
-    auto costNode = builder->build(graph, batch);
+    auto costNode = builder->build(graph, batch).getLogits();
 
     if(t % optimizerDelay_ == 0) {
       fetchParams(graph->params()->vals(), params_, t_id);
diff --git a/src/training/graph_group_multinode.cpp b/src/training/graph_group_multinode.cpp
index f5cbafd36..0aed1533c 100755
--- a/src/training/graph_group_multinode.cpp
+++ b/src/training/graph_group_multinode.cpp
@@ -527,7 +527,7 @@ void MultiNodeGraphGroup::execute(Ptr<data::Batch> batch) {
       builder = clientBuilders_[i++];
     }
 
-    auto costNode = builder->build(graph, batch);
+    auto costNode = builder->build(graph, batch).getLogits();
 
     if(t == 0) {
       mpi_->barrier();
diff --git a/src/training/graph_group_multinode_sync.cpp b/src/training/graph_group_multinode_sync.cpp
index 328657057..62fb2765b 100755
--- a/src/training/graph_group_multinode_sync.cpp
+++ b/src/training/graph_group_multinode_sync.cpp
@@ -185,7 +185,7 @@ void MultiNodeGraphGroupSync::execute(Ptr<data::Batch> fullBatch) {
       auto graph = clientGraphs_[my_id];
       auto builder = clientBuilders_[my_id];
 
-      auto costNode = builder->build(graph, batch);
+      auto costNode = builder->build(graph, batch).getLogits();
 
       if(t == 0) {
         if(my_id != 0)
diff --git a/src/training/graph_group_singleton.cpp b/src/training/graph_group_singleton.cpp
index ac6ef5d0c..28b81eaad 100755
--- a/src/training/graph_group_singleton.cpp
+++ b/src/training/graph_group_singleton.cpp
@@ -10,7 +10,7 @@ void SingletonGraph::setScheduler(Ptr<Scheduler> scheduler) {
 }
 
 void SingletonGraph::execute(Ptr<data::Batch> batch) {
-  auto costNode = builder_->build(graph_, batch);
+  auto costNode = builder_->build(graph_, batch).getLogits();
 
   graph_->forward();
   float cost = costNode->scalar();
diff --git a/src/training/graph_group_sync.cpp b/src/training/graph_group_sync.cpp
index c00a4fc2f..85fd6d978 100755
--- a/src/training/graph_group_sync.cpp
+++ b/src/training/graph_group_sync.cpp
@@ -354,7 +354,7 @@ void SyncGraphGroup::update(std::vector<Ptr<data::Batch>> subBatches, size_t num
       if (!subBatch)
         break;
 
-      auto costNode = builders_[localDeviceIndex]->build(graph, subBatch);
+      auto costNode = builders_[localDeviceIndex]->build(graph, subBatch).getLogits();
       graph->forward();
       localDeviceCosts[localDeviceIndex] += costNode->scalar() / (float)overstuff;
       graph->backward(/*zero=*/false); // (gradients are reset before we get here)
diff --git a/src/training/validator.h b/src/training/validator.h
index 2b76d6578..d222443db 100755
--- a/src/training/validator.h
+++ b/src/training/validator.h
@@ -188,7 +188,7 @@ class CrossEntropyValidator : public Validator<data::Corpus> {
           }
 
           builder->clear(graph);
-          auto costNode = builder->build(graph, batch);
+          auto costNode = builder->build(graph, batch).getLogits();
           graph->forward();
 
           std::unique_lock<std::mutex> lock(mutex_);
diff --git a/src/translator/scorers.h b/src/translator/scorers.h
index 16a066b05..befaeb8fd 100755
--- a/src/translator/scorers.h
+++ b/src/translator/scorers.h
@@ -57,7 +57,7 @@ class ScorerWrapperState : public ScorerState {
 
   virtual Ptr<DecoderState> getState() { return state_; }
 
-  virtual Expr getLogProbs() override { return state_->getLogProbs(); };
+  virtual Expr getLogProbs() override { return state_->getLogProbs().getLogits(); };
 
   virtual void blacklist(Expr totalCosts, Ptr<data::CorpusBatch> batch) override {
     state_->blacklist(totalCosts, batch);

From badb8a624c1f55d9d7fe22a48a59234535fba1c4 Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Mon, 28 Jan 2019 11:25:15 -0800
Subject: [PATCH 214/838] minor fixes of last commit

---
 src/examples/mnist/validator.h | 2 +-
 src/layers/generic.h           | 4 ++++
 src/models/states.h            | 2 +-
 3 files changed, 6 insertions(+), 2 deletions(-)
 mode change 100644 => 100755 src/examples/mnist/validator.h

diff --git a/src/examples/mnist/validator.h b/src/examples/mnist/validator.h
old mode 100644
new mode 100755
index 907994ee7..7d668bcc3
--- a/src/examples/mnist/validator.h
+++ b/src/examples/mnist/validator.h
@@ -31,7 +31,7 @@ class AccuracyValidator : public Validator<data::MNISTData> {
     size_t samples = 0;
 
     for(auto batch : *batchGenerator_) {
-      auto probs = builder_->build(graphs[0], batch, true);
+      auto probs = builder_->build(graphs[0], batch, true).getLogits();
       graphs[0]->forward();
 
       std::vector<float> scores;
diff --git a/src/layers/generic.h b/src/layers/generic.h
index 6eb60cb37..dd64e7bcf 100755
--- a/src/layers/generic.h
+++ b/src/layers/generic.h
@@ -58,12 +58,16 @@ struct IEmbeddingLayer {
 // Output layer returns a Logits object, which is able to compute some things on the fly
 // for factored embeddings.
 class Logits {
+    Logits& operator=(const Logits& other) = default;
 public:
     Logits(Expr logits) : logits_(logits) {
     }
     Expr getLogits() const {
       return logits_;
     }
+    void assign(const Logits& other) { // @TODO: forbid changing the number of contributions
+      *this = other;
+    }
 private:
     Expr logits_;
 };
diff --git a/src/models/states.h b/src/models/states.h
index 26f66625d..05c0a5a93 100755
--- a/src/models/states.h
+++ b/src/models/states.h
@@ -54,7 +54,7 @@ class DecoderState {
   }
 
   virtual Logits getLogProbs() const { return logProbs_; }
-  virtual void setLogProbs(Logits logProbs) { logProbs_ = logProbs; }
+  virtual void setLogProbs(Logits logProbs) { logProbs_.assign(logProbs); }
 
   // @TODO: should this be a constructor? Then derived classes can call this without the New<> in the loop
   virtual Ptr<DecoderState> select(const std::vector<IndexType>& selIdx,

From 7d45598a0689aa7f3900d9c5a1c0aca536ee1a3b Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Mon, 28 Jan 2019 11:41:30 -0800
Subject: [PATCH 215/838] towards multiple factors in Logits object

---
 src/layers/generic.h | 23 ++++++++++++++++++++---
 1 file changed, 20 insertions(+), 3 deletions(-)

diff --git a/src/layers/generic.h b/src/layers/generic.h
index dd64e7bcf..8021dcfee 100755
--- a/src/layers/generic.h
+++ b/src/layers/generic.h
@@ -60,16 +60,33 @@ struct IEmbeddingLayer {
 class Logits {
     Logits& operator=(const Logits& other) = default;
 public:
-    Logits(Expr logits) : logits_(logits) {
+    Logits(Expr logits) {
+      if (logits)
+        logits_.push_back(logits);
     }
     Expr getLogits() const {
-      return logits_;
+      ABORT_IF(empty(), "Attempted to read out logits on empty Logits object");
+      // lazily compute logits
+      // @BUGBUG: This is completely wrong, we must use the factor information and map the dimensions
+      Expr res;
+      for (size_t i = 0; i < getNumFactors(); i++) {
+        auto logits = logits_[i];
+        if (!weights_.empty() && weights_[i]) // log-linear weights
+          logits = logits * weights_[i];
+        res = res ? res + logits : logits;
+      }
+      return res;
     }
     void assign(const Logits& other) { // @TODO: forbid changing the number of contributions
+      ABORT_IF(!empty() && getNumFactors() != other.getNumFactors(),
+               "Logits assignment cannot change number of factors");
       *this = other;
     }
+    size_t getNumFactors() const { return logits_.size(); }
+    bool empty() const { return logits_.empty(); }
 private:
-    Expr logits_;
+    std::vector<Expr> logits_;
+    std::vector<Expr> weights_;
 };
 
 // Unary function that returns a Logits object

From 6c69514de3fef7bc530a663c03b32024c163fa74 Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Mon, 28 Jan 2019 13:07:32 -0800
Subject: [PATCH 216/838] mVec is now precomputed (in factored embeddings)

---
 src/layers/generic.cpp | 29 +++++++++--------------------
 1 file changed, 9 insertions(+), 20 deletions(-)

diff --git a/src/layers/generic.cpp b/src/layers/generic.cpp
index 06b109537..813a5eb42 100755
--- a/src/layers/generic.cpp
+++ b/src/layers/generic.cpp
@@ -95,22 +95,19 @@ namespace marian {
             groupRanges_[g].second = u + 1;
         groupCounts[g]++;
       }
-      // determine if a factor needs explicit softmax normalization
-      groupNeedsNormalization_.resize(numGroups, false);
+      // create the flag vectors for normalization   --@TODO: maybe we won't need them anymore
+      mVecs_.resize(numGroups);
       for (size_t g = 0; g < numGroups; g++) { // detect non-overlapping groups
         LOG(info, "[embedding] Factor group '{}' has {} members ({})",
             groupPrefixes[g], groupCounts[g], groupCounts[g] == 1 ? "sigmoid" : "softmax");
         if (groupCounts[g] == 0) // factor group is unused  --@TODO: once this is not hard-coded, this is an error condition
           continue;
-        // any factor that is not referenced in all words and is not a sigmoid needs normalization
-        //if (g == 0) // @TODO: For now we assume that the main factor is used in all words. Test this.
-        //  continue;
-        if (groupCounts[g] == 1) // sigmoid factors have no normalizer  --@BUGBUG: This is not even possible I think. We need the counter-class.
-          continue;
-        groupNeedsNormalization_[g] = true; // needed
         ABORT_IF(groupRanges_[g].second - groupRanges_[g].first != groupCounts[g],
                  "Factor group '{}' members should be consecutive in the factor vocabulary", groupPrefixes[g]);
-        LOG(info, "[embedding] Factor group '{}' needs needs explicit normalization ({}..{})", groupPrefixes[g], groupRanges_[g].first, groupRanges_[g].second-1);
+        auto& mVec = mVecs_[g];
+        mVec.resize(numFactors, 0.0f);
+        for (size_t i = groupRanges_[g].first; i < groupRanges_[g].second; i++)
+          mVec[i] = 1.0f;
       }
 
       // create the factor matrix
@@ -151,7 +148,7 @@ namespace marian {
     std::vector<size_t> factorGroups_;                   // [u] -> group id of factor u
   public: // @TODO: temporarily; later factor this properly
     std::vector<std::pair<size_t, size_t>> groupRanges_; // [group id] -> (u_begin,u_end) index range of factors u for this group. These don't overlap.
-    std::vector<bool> groupNeedsNormalization_;          // [group id] -> true if explicit softmax normalization is necessary
+    std::vector<std::vector<float>> mVecs_;              // [group id][u] -> 1 if factor is member of group
   };
 
   namespace mlp {
@@ -198,35 +195,27 @@ namespace marian {
         auto graph = input->graph();
         auto y = affine(input, W_, b_, false, transposeW_); // [B... x U] factor logits
 
-#if 1
         // denominators (only for groups that don't normalize out naturally by the final softmax())
         const auto& groupRanges = embeddingFactorMapping_->groupRanges_; // @TODO: factor this properly
         auto numGroups = groupRanges.size();
+        const auto& mVecs = embeddingFactorMapping_->mVecs_;
         for (size_t g = 0; g < numGroups; g++) {
-          if (!embeddingFactorMapping_->groupNeedsNormalization_[g]) // @TODO: if we ever need it, we can combine multiple
-              continue;
           auto range = groupRanges[g];
           // y: [B... x U]
           // m: [1 x U]         // ones at positions of group members
-          auto yDim = y->shape()[-1];
-          std::vector<float> mVec(yDim, 0.0f); // @TODO: This vector should be produced by embeddingFactorMapping_
-          for (size_t i = range.first; i < range.second; i++)
-            mVec[i] = 1.0f;
           // need to compute log denominator over y[range] and subtract it from y[range]
           auto groupY = slice(y, /*axis=*/-1, Slice((int)range.first, (int)range.second)); // [B... x Ug]
           auto groupZ = logsumexp(groupY, /*axis=*/-1); // [B... x 1]
           //auto groupZ = slice(groupY - logsoftmax(groupY), /*axis=*/-1, 0); // [B... x 1]
+          const auto& mVec = mVecs[g];
           auto m = graph->constant({ 1, (int)mVec.size() }, inits::from_vector(mVec)); // [1 x U]
           auto Z = dot(groupZ, m); // [B... x U]
           y = y - Z;
-#if 1
           // and a log-linear weight
           auto name = options_->get<std::string>("prefix");
           auto llWeight = graph->param(name + "_llWeight_" + std::to_string(g), {}, inits::from_value(1.0f));
           y = y * ((llWeight  - 1) * m + 1);
-#endif
         }
-#endif
 
         // sum up the unit logits across factors for each target word
         auto factorMatrix = embeddingFactorMapping_->getFactorMatrix(); // [V x U]

From 685945f7ce1cf9b08b1ebe78b4b8e9b36b8007e0 Mon Sep 17 00:00:00 2001
From: Marcin Junczys-Dowmunt <Marcin.JunczysDowmunt@microsoft.com>
Date: Mon, 28 Jan 2019 13:55:55 -0800
Subject: [PATCH 217/838] option to dump nodes at runtime to npz

---
 src/graph/chainable.h          |  2 ++
 src/graph/expression_graph.cpp |  2 +-
 src/graph/node.cpp             | 19 +++++++++++++++++++
 src/graph/node.h               |  2 ++
 4 files changed, 24 insertions(+), 1 deletion(-)

diff --git a/src/graph/chainable.h b/src/graph/chainable.h
index 2679843ee..3aa81d3cc 100644
--- a/src/graph/chainable.h
+++ b/src/graph/chainable.h
@@ -96,6 +96,8 @@ class Chainable {
   virtual const std::string& name() const = 0;
 
   virtual void debug(const std::string& message) = 0;
+  virtual void dump(const std::string& filename) = 0;
+
   virtual bool marked_for_debug() = 0;
   virtual const std::string& debug_message() = 0;
 
diff --git a/src/graph/expression_graph.cpp b/src/graph/expression_graph.cpp
index 4e9e9a103..4df97a82f 100644
--- a/src/graph/expression_graph.cpp
+++ b/src/graph/expression_graph.cpp
@@ -70,7 +70,7 @@ void ExpressionGraph::forwardNext() {
           LOG(critical, "\tType: {}, Shape: {}, Name: {}, Id: {}, Hash: {}",
             child->type(), child->shape(), child->name(), child->getId(), child->hash());
           LOG(critical, "Value debug {}", child->val()->debug());
-          ioItems.push_back(itemFromTensor(v->val(), "child_" + std::to_string(child->hash()), backend_));
+          ioItems.push_back(itemFromTensor(child->val(), "child_" + std::to_string(child->hash()), backend_));
         }
 
         io::saveItems("dump-for-nans.npz", ioItems);
diff --git a/src/graph/node.cpp b/src/graph/node.cpp
index c11531da7..bdb501166 100644
--- a/src/graph/node.cpp
+++ b/src/graph/node.cpp
@@ -2,6 +2,8 @@
 #include "graph/auto_tuner.h"
 #include "graph/expression_graph.h"
 #include "tensors/backend.h"
+#include "tensors/tensor_operators.h"
+#include "common/io.h"
 
 namespace marian {
 
@@ -83,4 +85,21 @@ void Node::record(Ptr<AutoTunerRecorder> recorder,
   recorderHash_ = recorderHash;
   recorderStop_ = stop;
 }
+
+void Node::dump(const std::string& filename) {
+  io::Item item;
+  item.name  = "dump";
+  item.shape = val_->shape();
+  item.type  = val_->type();
+
+  size_t bytesWithoutPadding = val_->shape().elements() * sizeOf(val_->type());
+  item.bytes.resize(bytesWithoutPadding);
+  copy(graph()->getBackend(),
+        (char*)val_->data(),
+        (char*)val_->data() + bytesWithoutPadding,
+        item.bytes.data());
+  
+  std::vector<io::Item> items({item});
+  io::saveItems(filename, items);
+}
 }  // namespace marian
diff --git a/src/graph/node.h b/src/graph/node.h
index 1397e74b0..defefd5b5 100644
--- a/src/graph/node.h
+++ b/src/graph/node.h
@@ -100,6 +100,8 @@ class Node : public Chainable<Tensor>,
   virtual bool marked_for_debug() override { return markedForDebug_; }
   virtual const std::string& debug_message() override { return debugMessage_; }
 
+  virtual void dump(const std::string& filename) override;
+
   virtual size_t allocate() override;
 
   virtual void free() override;

From 555fb80386b1fd716f8aaf1fa56cab1e8ea8df26 Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Mon, 28 Jan 2019 13:59:34 -0800
Subject: [PATCH 218/838] now using explicit slices for factors

---
 src/layers/generic.cpp | 24 ++++++++++++++++++++++--
 1 file changed, 22 insertions(+), 2 deletions(-)

diff --git a/src/layers/generic.cpp b/src/layers/generic.cpp
index 813a5eb42..fe4e71e7c 100755
--- a/src/layers/generic.cpp
+++ b/src/layers/generic.cpp
@@ -193,11 +193,30 @@ namespace marian {
       }
       else if (embeddingFactorMapping_) {
         auto graph = input->graph();
-        auto y = affine(input, W_, b_, false, transposeW_); // [B... x U] factor logits
 
-        // denominators (only for groups that don't normalize out naturally by the final softmax())
+        // project each factor
         const auto& groupRanges = embeddingFactorMapping_->groupRanges_; // @TODO: factor this properly
         auto numGroups = groupRanges.size();
+        std::vector<Expr> groupYs(numGroups);
+#if 1
+        for (size_t g = 0; g < numGroups; g++) {
+          auto range = groupRanges[g];
+          ABORT_IF(g > 0 && groupRanges[g].first != groupRanges[g-1].second, "Factor groups must be consecutive"); // we could sort groupYs though
+          // slice this group's section out of W_
+          // @TODO: This is highly inefficient if not tied. We should always transpose Output's matrix.
+          auto groupW = slice(W_, transposeW_ ? 0 : -1, Slice((int)range.first, (int)range.second));
+          //LOG(info, "slice() -> {}, {}", groupW->type(), std::string(groupW->shape()));
+          auto groupB = slice(b_, -1, Slice((int)range.first, (int)range.second)); // @TODO: b_ should be a vector, not a matrix
+          auto groupY = affine(input, groupW, groupB, false, transposeW_); // [B... x U] factor logits
+          groupYs[g] = groupY;
+        }
+        auto y = concatenate(groupYs, /*axis=*/ -1);
+#else
+        auto y = affine(input, W_, b_, false, transposeW_); // [B... x U] factor logits
+#endif
+        // @TODO: next, do normalization per group
+
+        // denominators
         const auto& mVecs = embeddingFactorMapping_->mVecs_;
         for (size_t g = 0; g < numGroups; g++) {
           auto range = groupRanges[g];
@@ -215,6 +234,7 @@ namespace marian {
           auto name = options_->get<std::string>("prefix");
           auto llWeight = graph->param(name + "_llWeight_" + std::to_string(g), {}, inits::from_value(1.0f));
           y = y * ((llWeight  - 1) * m + 1);
+          // @BUGBUG: Global softmax no longer normalizes, due to words that lack some factors.
         }
 
         // sum up the unit logits across factors for each target word

From 8061b0fdb5db291a8a2f16a2aaaf04da1d965550 Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Mon, 28 Jan 2019 14:16:38 -0800
Subject: [PATCH 219/838] normalization now also done on slices

---
 src/layers/generic.cpp | 25 ++++++++++++++++++-------
 1 file changed, 18 insertions(+), 7 deletions(-)

diff --git a/src/layers/generic.cpp b/src/layers/generic.cpp
index fe4e71e7c..ff9884a86 100755
--- a/src/layers/generic.cpp
+++ b/src/layers/generic.cpp
@@ -198,6 +198,7 @@ namespace marian {
         const auto& groupRanges = embeddingFactorMapping_->groupRanges_; // @TODO: factor this properly
         auto numGroups = groupRanges.size();
         std::vector<Expr> groupYs(numGroups);
+        std::vector<Expr> groupLLWeights(numGroups);
 #if 1
         for (size_t g = 0; g < numGroups; g++) {
           auto range = groupRanges[g];
@@ -208,6 +209,14 @@ namespace marian {
           //LOG(info, "slice() -> {}, {}", groupW->type(), std::string(groupW->shape()));
           auto groupB = slice(b_, -1, Slice((int)range.first, (int)range.second)); // @TODO: b_ should be a vector, not a matrix
           auto groupY = affine(input, groupW, groupB, false, transposeW_); // [B... x U] factor logits
+          // normalize
+          groupY = logsoftmax(groupY);
+          // log-linear weight   --@TODO: pre-create in constructor
+          auto name = options_->get<std::string>("prefix");
+          groupLLWeights[g] = graph->param(name + "_llWeight_" + std::to_string(g), {}, inits::from_value(1.0f));
+          groupY = groupY * groupLLWeights[g];
+          // @BUGBUG: Global softmax no longer normalizes, due to words that lack some factors.
+          // @TODO: normalize again. Do I need the first normalization?
           groupYs[g] = groupY;
         }
         auto y = concatenate(groupYs, /*axis=*/ -1);
@@ -216,6 +225,7 @@ namespace marian {
 #endif
         // @TODO: next, do normalization per group
 
+#if 0
         // denominators
         const auto& mVecs = embeddingFactorMapping_->mVecs_;
         for (size_t g = 0; g < numGroups; g++) {
@@ -223,19 +233,20 @@ namespace marian {
           // y: [B... x U]
           // m: [1 x U]         // ones at positions of group members
           // need to compute log denominator over y[range] and subtract it from y[range]
-          auto groupY = slice(y, /*axis=*/-1, Slice((int)range.first, (int)range.second)); // [B... x Ug]
-          auto groupZ = logsumexp(groupY, /*axis=*/-1); // [B... x 1]
-          //auto groupZ = slice(groupY - logsoftmax(groupY), /*axis=*/-1, 0); // [B... x 1]
+          //auto groupY = slice(y, /*axis=*/-1, Slice((int)range.first, (int)range.second)); // [B... x Ug]
+          //auto groupZ = logsumexp(groupY, /*axis=*/-1); // [B... x 1]
+          ////auto groupZ = slice(groupY - logsoftmax(groupY), /*axis=*/-1, 0); // [B... x 1]
           const auto& mVec = mVecs[g];
           auto m = graph->constant({ 1, (int)mVec.size() }, inits::from_vector(mVec)); // [1 x U]
-          auto Z = dot(groupZ, m); // [B... x U]
-          y = y - Z;
+          //auto Z = dot(groupZ, m); // [B... x U]
+          //y = y - Z;
           // and a log-linear weight
           auto name = options_->get<std::string>("prefix");
-          auto llWeight = graph->param(name + "_llWeight_" + std::to_string(g), {}, inits::from_value(1.0f));
-          y = y * ((llWeight  - 1) * m + 1);
+          auto groupLLWeights[g] = graph->param(name + "_llWeight_" + std::to_string(g), {}, inits::from_value(1.0f));
+          y = y * ((groupLLWeights[g] - 1) * m + 1);
           // @BUGBUG: Global softmax no longer normalizes, due to words that lack some factors.
         }
+#endif
 
         // sum up the unit logits across factors for each target word
         auto factorMatrix = embeddingFactorMapping_->getFactorMatrix(); // [V x U]

From 57813fdef94bd5f3dbd1bcd40cefdc287292b5a0 Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Mon, 28 Jan 2019 14:42:35 -0800
Subject: [PATCH 220/838] moved factored-output combination into the Logits
 class

---
 src/layers/generic.cpp | 88 ++++++++++++++++++++++--------------------
 src/layers/generic.h   | 26 ++++---------
 2 files changed, 55 insertions(+), 59 deletions(-)

diff --git a/src/layers/generic.cpp b/src/layers/generic.cpp
index ff9884a86..c8d370c32 100755
--- a/src/layers/generic.cpp
+++ b/src/layers/generic.cpp
@@ -151,6 +151,52 @@ namespace marian {
     std::vector<std::vector<float>> mVecs_;              // [group id][u] -> 1 if factor is member of group
   };
 
+  Expr Logits::getLogits() const {
+    ABORT_IF(empty(), "Attempted to read out logits on empty Logits object");
+    if (!embeddingFactorMapping_) {
+      ABORT_IF(logits_.size() != 1, "Factors without factor mappings??");
+      return logits_.front();
+    }
+
+    // lazily compute combined logits from factors
+    auto y = concatenate(logits_, /*axis=*/ -1);
+
+    // sum up the unit logits across factors for each target word
+    auto graph = y->graph();
+    auto factorMatrix = embeddingFactorMapping_->getFactorMatrix(); // [V x U]
+    y = dot_csr(
+        y,  // [B x U]
+        factorMatrix.shape,
+        graph->constant({(int)factorMatrix.weights.size()}, inits::from_vector(factorMatrix.weights), Type::float32),
+        graph->constant({(int)factorMatrix.indices.size()}, inits::from_vector(factorMatrix.indices), Type::uint32),
+        graph->constant({(int)factorMatrix.offsets.size()}, inits::from_vector(factorMatrix.offsets), Type::uint32),
+        /*transB=*/ true); // -> [B x V]
+
+    return y;
+#if 0
+        // denominators
+        const auto& mVecs = embeddingFactorMapping_->mVecs_;
+        for (size_t g = 0; g < numGroups; g++) {
+          auto range = groupRanges[g];
+          // y: [B... x U]
+          // m: [1 x U]         // ones at positions of group members
+          // need to compute log denominator over y[range] and subtract it from y[range]
+          //auto groupY = slice(y, /*axis=*/-1, Slice((int)range.first, (int)range.second)); // [B... x Ug]
+          //auto groupZ = logsumexp(groupY, /*axis=*/-1); // [B... x 1]
+          ////auto groupZ = slice(groupY - logsoftmax(groupY), /*axis=*/-1, 0); // [B... x 1]
+          const auto& mVec = mVecs[g];
+          auto m = graph->constant({ 1, (int)mVec.size() }, inits::from_vector(mVec)); // [1 x U]
+          //auto Z = dot(groupZ, m); // [B... x U]
+          //y = y - Z;
+          // and a log-linear weight
+          auto name = options_->get<std::string>("prefix");
+          auto groupLLWeights[g] = graph->param(name + "_llWeight_" + std::to_string(g), {}, inits::from_value(1.0f));
+          y = y * ((groupLLWeights[g] - 1) * m + 1);
+          // @BUGBUG: Global softmax no longer normalizes, due to words that lack some factors.
+        }
+#endif
+  }
+
   namespace mlp {
     /*private*/ void Output::lazyConstruct(int inputDim) {
       // We must construct lazily since we won't know tying nor input dim in constructor.
@@ -199,7 +245,6 @@ namespace marian {
         auto numGroups = groupRanges.size();
         std::vector<Expr> groupYs(numGroups);
         std::vector<Expr> groupLLWeights(numGroups);
-#if 1
         for (size_t g = 0; g < numGroups; g++) {
           auto range = groupRanges[g];
           ABORT_IF(g > 0 && groupRanges[g].first != groupRanges[g-1].second, "Factor groups must be consecutive"); // we could sort groupYs though
@@ -219,46 +264,7 @@ namespace marian {
           // @TODO: normalize again. Do I need the first normalization?
           groupYs[g] = groupY;
         }
-        auto y = concatenate(groupYs, /*axis=*/ -1);
-#else
-        auto y = affine(input, W_, b_, false, transposeW_); // [B... x U] factor logits
-#endif
-        // @TODO: next, do normalization per group
-
-#if 0
-        // denominators
-        const auto& mVecs = embeddingFactorMapping_->mVecs_;
-        for (size_t g = 0; g < numGroups; g++) {
-          auto range = groupRanges[g];
-          // y: [B... x U]
-          // m: [1 x U]         // ones at positions of group members
-          // need to compute log denominator over y[range] and subtract it from y[range]
-          //auto groupY = slice(y, /*axis=*/-1, Slice((int)range.first, (int)range.second)); // [B... x Ug]
-          //auto groupZ = logsumexp(groupY, /*axis=*/-1); // [B... x 1]
-          ////auto groupZ = slice(groupY - logsoftmax(groupY), /*axis=*/-1, 0); // [B... x 1]
-          const auto& mVec = mVecs[g];
-          auto m = graph->constant({ 1, (int)mVec.size() }, inits::from_vector(mVec)); // [1 x U]
-          //auto Z = dot(groupZ, m); // [B... x U]
-          //y = y - Z;
-          // and a log-linear weight
-          auto name = options_->get<std::string>("prefix");
-          auto groupLLWeights[g] = graph->param(name + "_llWeight_" + std::to_string(g), {}, inits::from_value(1.0f));
-          y = y * ((groupLLWeights[g] - 1) * m + 1);
-          // @BUGBUG: Global softmax no longer normalizes, due to words that lack some factors.
-        }
-#endif
-
-        // sum up the unit logits across factors for each target word
-        auto factorMatrix = embeddingFactorMapping_->getFactorMatrix(); // [V x U]
-        y = dot_csr(
-            y,  // [B x U]
-            factorMatrix.shape,
-            graph->constant({(int)factorMatrix.weights.size()}, inits::from_vector(factorMatrix.weights), Type::float32),
-            graph->constant({(int)factorMatrix.indices.size()}, inits::from_vector(factorMatrix.indices), Type::uint32),
-            graph->constant({(int)factorMatrix.offsets.size()}, inits::from_vector(factorMatrix.offsets), Type::uint32),
-            /*transB=*/ true); // -> [B x V]
-
-        return y;
+        return Logits(std::move(groupYs), embeddingFactorMapping_);
       }
       else
         return affine(input, W_, b_, false, transposeW_);
diff --git a/src/layers/generic.h b/src/layers/generic.h
index 8021dcfee..70bb75aab 100755
--- a/src/layers/generic.h
+++ b/src/layers/generic.h
@@ -54,30 +54,22 @@ struct IEmbeddingLayer {
   virtual Expr apply(const std::vector<IndexType>& embIdx, int dimBatch, int dimBeam) const = 0;
 };
 
+class EmbeddingFactorMapping;
+
 // @HACK: Frank's quick implementation of factored outputs. To be re-thought once it works.
 // Output layer returns a Logits object, which is able to compute some things on the fly
 // for factored embeddings.
 class Logits {
     Logits& operator=(const Logits& other) = default;
 public:
-    Logits(Expr logits) {
+    Logits(Expr logits) { // single-output constructor
       if (logits)
         logits_.push_back(logits);
     }
-    Expr getLogits() const {
-      ABORT_IF(empty(), "Attempted to read out logits on empty Logits object");
-      // lazily compute logits
-      // @BUGBUG: This is completely wrong, we must use the factor information and map the dimensions
-      Expr res;
-      for (size_t i = 0; i < getNumFactors(); i++) {
-        auto logits = logits_[i];
-        if (!weights_.empty() && weights_[i]) // log-linear weights
-          logits = logits * weights_[i];
-        res = res ? res + logits : logits;
-      }
-      return res;
-    }
-    void assign(const Logits& other) { // @TODO: forbid changing the number of contributions
+    Logits(std::vector<Expr>&& logits, Ptr<EmbeddingFactorMapping> embeddingFactorMapping) // factored-output constructor
+      : logits_(std::move(logits)), embeddingFactorMapping_(embeddingFactorMapping) {}
+    Expr getLogits() const;
+    void assign(const Logits& other) {
       ABORT_IF(!empty() && getNumFactors() != other.getNumFactors(),
                "Logits assignment cannot change number of factors");
       *this = other;
@@ -86,7 +78,7 @@ class Logits {
     bool empty() const { return logits_.empty(); }
 private:
     std::vector<Expr> logits_;
-    std::vector<Expr> weights_;
+    Ptr<EmbeddingFactorMapping> embeddingFactorMapping_;
 };
 
 // Unary function that returns a Logits object
@@ -95,8 +87,6 @@ struct IUnaryLogitLayer {
   virtual Logits apply(const std::vector<Expr>&) = 0;
 };
 
-class EmbeddingFactorMapping;
-
 namespace mlp {
 
 class Dense : public LayerBase, public IUnaryLayer {

From 20a0d26e4c607579fb7d89f99d4e7cc20261abe8 Mon Sep 17 00:00:00 2001
From: Marcin Junczys-Dowmunt <Marcin.JunczysDowmunt@microsoft.com>
Date: Mon, 28 Jan 2019 14:52:10 -0800
Subject: [PATCH 221/838] dump nodes recursively

---
 src/graph/expression_graph.cpp | 12 ++++++++----
 1 file changed, 8 insertions(+), 4 deletions(-)

diff --git a/src/graph/expression_graph.cpp b/src/graph/expression_graph.cpp
index 4df97a82f..3cf9f82fb 100644
--- a/src/graph/expression_graph.cpp
+++ b/src/graph/expression_graph.cpp
@@ -43,6 +43,12 @@ io::Item itemFromTensor(Tensor t, const std::string name, Ptr<Backend> backend)
   return item;
 }
 
+void recChildren(Expr node, std::vector<io::Item>& items, Ptr<Backend> backend) {
+  items.push_back(itemFromTensor(node->val(), "node" + std::to_string(node->getId()), backend));
+  for(auto&& child : node->children())
+    recChildren(child, items, backend);
+}
+
 void ExpressionGraph::forwardNext() {
   // @TODO: check if allocation works properly
   tensors_->clearShorttermMemory();
@@ -57,22 +63,20 @@ void ExpressionGraph::forwardNext() {
       bool isNan = false, isInf = false;
       checkNan(v->val(), isNan, isInf);
       if(isNan || isInf) {
-        std::vector<io::Item> ioItems;
         LOG(critical, "Detected NaN ({}) or Inf ({}) in value (forward pass)", isNan, isInf);
         LOG(critical, "\tType: {}, Shape: {}, Name: {}, Id: {}, Hash: {}",
             v->type(), v->shape(), v->name(), v->getId(), v->hash());
         LOG(critical, "Value debug {}", v->val()->debug());
 
-        ioItems.push_back(itemFromTensor(v->val(), "value", backend_));
-
         LOG(critical, "Children: {}", v->children().size());
         for(auto&& child : v->children()) {
           LOG(critical, "\tType: {}, Shape: {}, Name: {}, Id: {}, Hash: {}",
             child->type(), child->shape(), child->name(), child->getId(), child->hash());
           LOG(critical, "Value debug {}", child->val()->debug());
-          ioItems.push_back(itemFromTensor(child->val(), "child_" + std::to_string(child->hash()), backend_));
         }
 
+        std::vector<io::Item> ioItems;
+        recChildren(v, ioItems, backend_);
         io::saveItems("dump-for-nans.npz", ioItems);
 
         ABORT("Aborting");

From 9ce31fa3fd96dd59aaa4b56602576dcfcbad2746 Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Mon, 28 Jan 2019 15:10:28 -0800
Subject: [PATCH 222/838] moved crossEntropy() to Logits class

---
 src/layers/generic.cpp | 29 +++++++++++++++++++++++++++++
 src/layers/generic.h   |  1 +
 src/layers/loss.cpp    | 40 ++++++++--------------------------------
 src/layers/loss.h      | 17 +++++++++--------
 src/models/costs.h     |  2 +-
 5 files changed, 48 insertions(+), 41 deletions(-)
 mode change 100644 => 100755 src/layers/loss.h

diff --git a/src/layers/generic.cpp b/src/layers/generic.cpp
index c8d370c32..633b0f62f 100755
--- a/src/layers/generic.cpp
+++ b/src/layers/generic.cpp
@@ -151,6 +151,35 @@ namespace marian {
     std::vector<std::vector<float>> mVecs_;              // [group id][u] -> 1 if factor is member of group
   };
 
+  Expr Logits::crossEntropy(Expr indices, float smoothing) const {
+    auto logits = getLogits();
+    Expr ce;
+    if(smoothing > 0) {
+      // ce = sum_i y^_i log y_i(z)_i
+      // with smoothing:
+      // ce' = sum_i ((1-smoothing_) y^_i + smoothing_/N) log y_i(z)_i
+      //     = (1-smoothing_) sum_i y^_i log y_i(z)_i + smoothing_ mean_i log y_i(z)_i
+      //     = (1-smoothing_) ce + smoothing_ mean_i log y_i(z)_i
+      // @TODO: add this to CE kernels instead
+#if 0
+      ce = cross_entropy(logits, indices);
+      auto ceq = mean(logsoftmax(logits), /*axis=*/ -1);
+      ce = (1 - smoothing_) * ce - smoothing_ * ceq;
+#else   // alternative that is cheaper memory-wise
+      ce = cross_entropy(logits, indices);
+      auto ceq = mean(logits, /*axis=*/ -1) - logsumexp(logits, /*axis=*/ -1);
+      ce = (1 - smoothing) * ce - smoothing * ceq;
+      //auto ceq = mean(logits, /*axis=*/ -1) - Z;
+      //ce = (1 - smoothing_) * cols(logits, indices)   // ce term
+      //     - smoothing_ * mean(logits, /*axis=*/ -1)  // smoothing term
+      //     - logsumexp(logits, /*axis=*/ -1);         // denominator
+#endif
+    }
+    else
+      ce = cross_entropy(logits, indices);
+    return ce;
+  }
+
   Expr Logits::getLogits() const {
     ABORT_IF(empty(), "Attempted to read out logits on empty Logits object");
     if (!embeddingFactorMapping_) {
diff --git a/src/layers/generic.h b/src/layers/generic.h
index 70bb75aab..d33ab03cf 100755
--- a/src/layers/generic.h
+++ b/src/layers/generic.h
@@ -69,6 +69,7 @@ class Logits {
     Logits(std::vector<Expr>&& logits, Ptr<EmbeddingFactorMapping> embeddingFactorMapping) // factored-output constructor
       : logits_(std::move(logits)), embeddingFactorMapping_(embeddingFactorMapping) {}
     Expr getLogits() const;
+    Expr crossEntropy(Expr indices,  float smoothing) const;
     void assign(const Logits& other) {
       ABORT_IF(!empty() && getNumFactors() != other.getNumFactors(),
                "Logits assignment cannot change number of factors");
diff --git a/src/layers/loss.cpp b/src/layers/loss.cpp
index 5ca493f1d..12e69d028 100755
--- a/src/layers/loss.cpp
+++ b/src/layers/loss.cpp
@@ -22,35 +22,11 @@ Ptr<LossBase> LossFactory(Ptr<Options> options, bool inference) {
   }
 }
 
-Expr LossBase::getCrossEntropy(Expr logits,
+Expr LossBase::getCrossEntropy(const Logits& logits1,
                                Expr indices,
                                Expr mask,
                                Expr weights) {
-
-  Expr ce;
-  if(smoothing_ > 0) {
-    // ce = sum_i y^_i log y_i(z)_i
-    // with smoothing:
-    // ce' = sum_i ((1-smoothing_) y^_i + smoothing_/N) log y_i(z)_i
-    //     = (1-smoothing_) sum_i y^_i log y_i(z)_i + smoothing_ mean_i log y_i(z)_i
-    //     = (1-smoothing_) ce + smoothing_ mean_i log y_i(z)_i
-    // @TODO: add this to CE kernels instead
-#if 0
-    ce = cross_entropy(logits, indices);
-    auto ceq = mean(logsoftmax(logits), /*axis=*/ -1);
-    ce = (1 - smoothing_) * ce - smoothing_ * ceq;
-#else   // alternative that is cheaper memory-wise
-    ce = cross_entropy(logits, indices);
-    auto ceq = mean(logits, /*axis=*/ -1) - logsumexp(logits, /*axis=*/ -1);
-    ce = (1 - smoothing_) * ce - smoothing_ * ceq;
-    //auto ceq = mean(logits, /*axis=*/ -1) - Z;
-    //ce = (1 - smoothing_) * cols(logits, indices)   // ce term
-    //     - smoothing_ * mean(logits, /*axis=*/ -1)  // smoothing term
-    //     - logsumexp(logits, /*axis=*/ -1);         // denominator
-#endif
-  }
-  else
-    ce = cross_entropy(logits, indices);
+  auto ce = logits1.crossEntropy(indices, smoothing_);
 
   if(mask)
     ce = ce * mask;
@@ -61,7 +37,7 @@ Expr LossBase::getCrossEntropy(Expr logits,
   return ce;
 }
 
-Expr CrossEntropyMeanLoss::getCost(Expr logits,
+Expr CrossEntropyMeanLoss::getCost(const Logits& logits,
                                    Expr indices,
                                    Expr mask,
                                    Expr weights) {
@@ -77,7 +53,7 @@ Expr CrossEntropyMeanLoss::getCost(Expr logits,
   // }
 }
 
-Expr CrossEntropyMeanWordsLoss::getCost(Expr logits,
+Expr CrossEntropyMeanWordsLoss::getCost(const Logits& logits,
                                         Expr indices,
                                         Expr mask,
                                         Expr weights) {
@@ -92,7 +68,7 @@ Expr CrossEntropyMeanWordsLoss::getCost(Expr logits,
   // }
 }
 
-Expr CrossEntropySumLoss::getCost(Expr logits,
+Expr CrossEntropySumLoss::getCost(const Logits& logits,
                                   Expr indices,
                                   Expr mask,
                                   Expr weights) {
@@ -106,7 +82,7 @@ Expr CrossEntropySumLoss::getCost(Expr logits,
   // }
 }
 
-Expr PerplexityLoss::getCost(Expr logits,
+Expr PerplexityLoss::getCost(const Logits& logits,
                              Expr indices,
                              Expr mask,
                              Expr weights) {
@@ -121,7 +97,7 @@ Expr PerplexityLoss::getCost(Expr logits,
   // }
 }
 
-Expr CrossEntropyRescoreLoss::getCost(Expr logits,
+Expr CrossEntropyRescoreLoss::getCost(const Logits& logits,
                                       Expr indices,
                                       Expr mask,
                                       Expr weights) {
@@ -129,7 +105,7 @@ Expr CrossEntropyRescoreLoss::getCost(Expr logits,
   return -sum(ce, /*axis =*/ -3);
 }
 
-Expr CrossEntropyRescoreMeanLoss::getCost(Expr logits,
+Expr CrossEntropyRescoreMeanLoss::getCost(const Logits& logits,
                                           Expr indices,
                                           Expr mask,
                                           Expr weights) {
diff --git a/src/layers/loss.h b/src/layers/loss.h
old mode 100644
new mode 100755
index ebf711470..ccd00ed3a
--- a/src/layers/loss.h
+++ b/src/layers/loss.h
@@ -1,6 +1,7 @@
 #pragma once
 
 #include "marian.h"
+#include "layers/generic.h" // @HACK for Frank's factoring hack
 
 namespace marian {
 class LossBase {
@@ -10,8 +11,8 @@ class LossBase {
 public:
   explicit LossBase(float smoothing = 0) : smoothing_(smoothing){};
 
-  Expr getCrossEntropy(Expr logits, Expr indices, Expr mask, Expr weights);
-  virtual Expr getCost(Expr logits,
+  Expr getCrossEntropy(const Logits& logits, Expr indices, Expr mask, Expr weights);
+  virtual Expr getCost(const Logits& logits,
                        Expr indices,
                        Expr mask,
                        Expr weights = nullptr)
@@ -26,7 +27,7 @@ class LossBase {
 class CrossEntropyMeanLoss : public LossBase {
 public:
   explicit CrossEntropyMeanLoss(float smoothing = 0) : LossBase(smoothing){};
-  Expr getCost(Expr logits, Expr indices, Expr mask, Expr weights) override;
+  Expr getCost(const Logits& logits, Expr indices, Expr mask, Expr weights) override;
 };
 
 /*
@@ -36,7 +37,7 @@ class CrossEntropyMeanWordsLoss : public LossBase {
 public:
   explicit CrossEntropyMeanWordsLoss(float smoothing = 0)
       : LossBase(smoothing){};
-  Expr getCost(Expr logits, Expr indices, Expr mask, Expr weights) override;
+  Expr getCost(const Logits& logits, Expr indices, Expr mask, Expr weights) override;
 };
 
 /*
@@ -45,7 +46,7 @@ class CrossEntropyMeanWordsLoss : public LossBase {
 class CrossEntropySumLoss : public LossBase {
 public:
   explicit CrossEntropySumLoss(float smoothing = 0) : LossBase(smoothing){};
-  Expr getCost(Expr logits, Expr indices, Expr mask, Expr weights) override;
+  Expr getCost(const Logits& logits, Expr indices, Expr mask, Expr weights) override;
 };
 
 /*
@@ -54,7 +55,7 @@ class CrossEntropySumLoss : public LossBase {
 class PerplexityLoss : public LossBase {
 public:
   explicit PerplexityLoss(float smoothing = 0) : LossBase(smoothing){};
-  Expr getCost(Expr logits, Expr indices, Expr mask, Expr weights) override;
+  Expr getCost(const Logits& logits, Expr indices, Expr mask, Expr weights) override;
 };
 
 /*
@@ -63,13 +64,13 @@ class PerplexityLoss : public LossBase {
 class CrossEntropyRescoreLoss : public LossBase {
 public:
   explicit CrossEntropyRescoreLoss(float smoothing = 0) : LossBase(smoothing){};
-  Expr getCost(Expr logits, Expr indices, Expr mask, Expr weights) override;
+  Expr getCost(const Logits& logits, Expr indices, Expr mask, Expr weights) override;
 };
 
 class CrossEntropyRescoreMeanLoss : public LossBase {
 public:
   explicit CrossEntropyRescoreMeanLoss(float smoothing = 0) : LossBase(smoothing){};
-  Expr getCost(Expr logits, Expr indices, Expr mask, Expr weights) override;
+  Expr getCost(const Logits& logits, Expr indices, Expr mask, Expr weights) override;
 };
 
 Ptr<LossBase> LossFactory(Ptr<Options> options, bool inference);
diff --git a/src/models/costs.h b/src/models/costs.h
index 5231e0ce0..efa3faa34 100755
--- a/src/models/costs.h
+++ b/src/models/costs.h
@@ -54,7 +54,7 @@ class EncoderDecoderCE : public CostBase {
       weights = weighter_->getWeights(graph, corpusBatch);
 
     Expr cost;
-    cost = loss_->getCost(state->getLogProbs().getLogits(),
+    cost = loss_->getCost(state->getLogProbs(),
                           state->getTargetIndices(),
                           state->getTargetMask(),
                           weights);

From 226721ffe9af43dca44a5dba663c955f03108bbf Mon Sep 17 00:00:00 2001
From: Marcin Junczys-Dowmunt <Marcin.JunczysDowmunt@microsoft.com>
Date: Mon, 28 Jan 2019 15:55:29 -0800
Subject: [PATCH 223/838] full graph below nide

---
 src/graph/expression_graph.cpp | 9 +++++----
 1 file changed, 5 insertions(+), 4 deletions(-)

diff --git a/src/graph/expression_graph.cpp b/src/graph/expression_graph.cpp
index 3cf9f82fb..5b4df3395 100644
--- a/src/graph/expression_graph.cpp
+++ b/src/graph/expression_graph.cpp
@@ -43,10 +43,11 @@ io::Item itemFromTensor(Tensor t, const std::string name, Ptr<Backend> backend)
   return item;
 }
 
-void recChildren(Expr node, std::vector<io::Item>& items, Ptr<Backend> backend) {
-  items.push_back(itemFromTensor(node->val(), "node" + std::to_string(node->getId()), backend));
+void recChildren(Expr node, const std::string& parent, std::vector<io::Item>& items, Ptr<Backend> backend) {
+  std::string name = node->type() + "_" + std::to_string(node->getId()) + "_p:" + parent;
+  items.push_back(itemFromTensor(node->val(), name, backend));
   for(auto&& child : node->children())
-    recChildren(child, items, backend);
+    recChildren(child, std::to_string(node->getId()), items, backend);
 }
 
 void ExpressionGraph::forwardNext() {
@@ -76,7 +77,7 @@ void ExpressionGraph::forwardNext() {
         }
 
         std::vector<io::Item> ioItems;
-        recChildren(v, ioItems, backend_);
+        recChildren(v, "root", ioItems, backend_);
         io::saveItems("dump-for-nans.npz", ioItems);
 
         ABORT("Aborting");

From c46ebb0c68e4eab546177678100b7695cc09db09 Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Mon, 28 Jan 2019 21:42:53 -0800
Subject: [PATCH 224/838] optimized factored loss, also moved CE back to
 loss.cp via; bug fix: RowsNodeOp should propagate value type

---
 src/graph/node_operators_binary.h    |   4 +-
 src/layers/generic.cpp               | 119 +++++++++++++++------------
 src/layers/generic.h                 |   2 +-
 src/layers/loss.cpp                  |  16 +++-
 src/tensors/cpu/tensor_operators.cpp |   1 +
 5 files changed, 86 insertions(+), 56 deletions(-)

diff --git a/src/graph/node_operators_binary.h b/src/graph/node_operators_binary.h
index 7d0908237..ab2f2efef 100755
--- a/src/graph/node_operators_binary.h
+++ b/src/graph/node_operators_binary.h
@@ -513,7 +513,7 @@ struct ScalarProductNodeOp : public NaryNodeOp {
 
 struct RowsNodeOp : public NaryNodeOp {
   RowsNodeOp(Expr a, Expr indices)
-      : NaryNodeOp({a, indices}, newShape(a, indices->shape().elements())) {
+      : NaryNodeOp({a, indices}, newShape(a, indices->shape().elements()), a->value_type()) {
     matchOrAbort<IndexType>(indices->value_type());
   }
 
@@ -641,7 +641,7 @@ struct GatherNodeOp : public NaryNodeOp {
 
 struct ColsNodeOp : public NaryNodeOp {
   ColsNodeOp(Expr a, Expr indices)
-    : NaryNodeOp({a, indices}, newShape(a, indices->shape().elements())) {
+    : NaryNodeOp({a, indices}, newShape(a, indices->shape().elements()), a->value_type()) {
     matchOrAbort<IndexType>(indices->value_type());
   }
 
diff --git a/src/layers/generic.cpp b/src/layers/generic.cpp
index 633b0f62f..2a3d3db7c 100755
--- a/src/layers/generic.cpp
+++ b/src/layers/generic.cpp
@@ -44,11 +44,12 @@ namespace marian {
       // Specifically, it means that the factorVocab_ must contain </s> and "<unk>".
       Vocab vocab(New<Options>(), 0);
       vocab.load(vocabPath);
+      auto vocabSize = vocab.size();
       factorVocab_.load(factorVocabPath);
       Word numFactors = (Word)factorVocab_.size();
 
       // load and parse factorMap
-      factorMap_.resize(vocab.size());
+      factorMap_.resize(vocabSize);
       factorRefCounts_.resize(numFactors);
       std::vector<std::string> tokens;
       io::InputFileStream in(mapPath);
@@ -60,13 +61,12 @@ namespace marian {
         ABORT_IF(tokens.size() < 2 || tokens.front() != vocab[v], "Factor map must list words in same order as vocab, and have at least one factor per word", mapPath);
         for (size_t i = 1; i < tokens.size(); i++) {
           auto u = factorVocab_[tokens[i]];
-          auto& m = factorMap_[v];
-          m.push_back(u);
+          factorMap_[v].push_back(u);
           factorRefCounts_[u]++;
         }
         numTotalFactors += tokens.size() - 1;
       }
-      LOG(info, "[embedding] Factored-embedding map read with total/unique of {}/{} factors for {} words", numTotalFactors, numFactors, vocab.size());
+      LOG(info, "[embedding] Factored-embedding map read with total/unique of {}/{} factors for {} words", numTotalFactors, numFactors, vocabSize);
 
       // form groups
       // @TODO: hard-coded for these initial experiments
@@ -85,6 +85,7 @@ namespace marian {
             factorGroups_[u] = g;
           }
       }
+      // determine group index ranges
       groupRanges_.resize(numGroups, { SIZE_MAX, (size_t)0 });
       std::vector<size_t> groupCounts(numGroups); // number of group members
       for (Word u = 0; u < numFactors; u++) { // determine ranges; these must be non-overlapping, verified via groupCounts
@@ -95,8 +96,18 @@ namespace marian {
             groupRanges_[g].second = u + 1;
         groupCounts[g]++;
       }
-      // create the flag vectors for normalization   --@TODO: maybe we won't need them anymore
-      mVecs_.resize(numGroups);
+      // create mappings needed for normalization in factored outputs
+      factorMasks_  .resize(numGroups, std::vector<float>(vocabSize, 0));     // [g][v] 1.0 if word v has factor g
+      factorIndices_.resize(numGroups, std::vector<IndexType>(vocabSize, 0)); // [g][v] index of factor (or any valid index if it does not have it; we use 0)
+      for (Word v = 0; v < vocabSize; v++) {
+        for (auto u : factorMap_[v]) {
+          auto g = factorGroups_[u]; // convert u to relative u within factor group range
+          ABORT_IF(u < groupRanges_[g].first || u >= groupRanges_[g].second, "Invalid factorGroups_ entry??");
+          factorIndices_[g][v] = (IndexType)(u - groupRanges_[g].first);
+          factorMasks_[g][v] = 1.0f;
+        }
+      }
+      //mVecs_.resize(numGroups); // @TODO: no longer needed, delete soon
       for (size_t g = 0; g < numGroups; g++) { // detect non-overlapping groups
         LOG(info, "[embedding] Factor group '{}' has {} members ({})",
             groupPrefixes[g], groupCounts[g], groupCounts[g] == 1 ? "sigmoid" : "softmax");
@@ -104,16 +115,16 @@ namespace marian {
           continue;
         ABORT_IF(groupRanges_[g].second - groupRanges_[g].first != groupCounts[g],
                  "Factor group '{}' members should be consecutive in the factor vocabulary", groupPrefixes[g]);
-        auto& mVec = mVecs_[g];
-        mVec.resize(numFactors, 0.0f);
-        for (size_t i = groupRanges_[g].first; i < groupRanges_[g].second; i++)
-          mVec[i] = 1.0f;
+        //auto& mVec = mVecs_[g];
+        //mVec.resize(numFactors, 0.0f);
+        //for (size_t i = groupRanges_[g].first; i < groupRanges_[g].second; i++)
+        //  mVec[i] = 1.0f;
       }
 
-      // create the factor matrix
-      std::vector<IndexType> data(vocab.size());
+      // create the global factor matrix, which is used for factored embeddings
+      std::vector<IndexType> data(vocabSize);
       std::iota(data.begin(), data.end(), 0);
-      factorMatrix_ = csr_rows(data); // [V x U]
+      globalFactorMatrix_ = csr_rows(data); // [V x U]
     }
 
     size_t factorVocabSize() const { return factorVocab_.size(); }
@@ -139,45 +150,52 @@ namespace marian {
       return { Shape({(int)words.size(), (int)factorVocab_.size()}), weights, indices, offsets };
     }
 
-    const CSRData& getFactorMatrix() const { return factorMatrix_; } // [v,u] (sparse) -> =1 if u is factor of v
+    const CSRData& getGlobalFactorMatrix() const { return globalFactorMatrix_; }   // [v,u] (sparse) -> =1 if u is factor of v
+    size_t getNumGroups() const { return groupRanges_.size(); }
+    std::pair<size_t, size_t>     getGroupRange(size_t g)    const { return groupRanges_[g]; }   // [g] -> (u_begin, u_end)
+    const std::vector<float>&     getFactorMasks(size_t g)   const { return factorMasks_[g]; }   // [g][v] 1.0 if word v has factor g
+    const std::vector<IndexType>& getFactorIndices(size_t g) const { return factorIndices_[g]; } // [g][v] local index u_g = u - u_g,begin of factor g for word v; 0 if not a factor
   private:
     Vocab factorVocab_;                                  // [factor name] -> factor index = row of E_
-    std::vector<std::vector<Word>> factorMap_;           // [word index] -> set of factor indices
-    std::vector<int> factorRefCounts_;                   // [factor index] -> how often this factor is referenced in factorMap_
-    CSRData factorMatrix_;                               // [v,u] (sparse) -> =1 if u is factor of v
+    std::vector<std::vector<Word>> factorMap_;           // [word index v] -> set of factor indices u
+    std::vector<int> factorRefCounts_;                   // [factor index u] -> how often factor u is referenced in factorMap_
+    CSRData globalFactorMatrix_;                         // [v,u] (sparse) -> =1 if u is factor of v
     std::vector<size_t> factorGroups_;                   // [u] -> group id of factor u
-  public: // @TODO: temporarily; later factor this properly
-    std::vector<std::pair<size_t, size_t>> groupRanges_; // [group id] -> (u_begin,u_end) index range of factors u for this group. These don't overlap.
-    std::vector<std::vector<float>> mVecs_;              // [group id][u] -> 1 if factor is member of group
+    std::vector<std::pair<size_t, size_t>> groupRanges_; // [group id g] -> (u_begin,u_end) index range of factors u for this group. These don't overlap.
+    std::vector<std::vector<float>>     factorMasks_;    // [g][v] 1.0 if word v has factor g
+    std::vector<std::vector<IndexType>> factorIndices_;  // [g][v] relative index u - u_begin of factor g (or any valid index if it does not have it; we use 0)
+  //public: // @TODO: temporarily; later factor this properly
+    //std::vector<std::vector<float>> mVecs_;              // [group id][u] -> 1 if factor is member of group
   };
 
-  Expr Logits::crossEntropy(Expr indices, float smoothing) const {
-    auto logits = getLogits();
-    Expr ce;
-    if(smoothing > 0) {
-      // ce = sum_i y^_i log y_i(z)_i
-      // with smoothing:
-      // ce' = sum_i ((1-smoothing_) y^_i + smoothing_/N) log y_i(z)_i
-      //     = (1-smoothing_) sum_i y^_i log y_i(z)_i + smoothing_ mean_i log y_i(z)_i
-      //     = (1-smoothing_) ce + smoothing_ mean_i log y_i(z)_i
-      // @TODO: add this to CE kernels instead
-#if 0
-      ce = cross_entropy(logits, indices);
-      auto ceq = mean(logsoftmax(logits), /*axis=*/ -1);
-      ce = (1 - smoothing_) * ce - smoothing_ * ceq;
-#else   // alternative that is cheaper memory-wise
-      ce = cross_entropy(logits, indices);
-      auto ceq = mean(logits, /*axis=*/ -1) - logsumexp(logits, /*axis=*/ -1);
-      ce = (1 - smoothing) * ce - smoothing * ceq;
-      //auto ceq = mean(logits, /*axis=*/ -1) - Z;
-      //ce = (1 - smoothing_) * cols(logits, indices)   // ce term
-      //     - smoothing_ * mean(logits, /*axis=*/ -1)  // smoothing term
-      //     - logsumexp(logits, /*axis=*/ -1);         // denominator
-#endif
+  Expr Logits::getLoss(Expr indices, const std::function<Expr(Expr/*logits*/, Expr/*indices*/)>& lossFn) const {
+    ABORT_IF(empty(), "Attempted to read out logits on empty Logits object");
+    if (!embeddingFactorMapping_) {
+      ABORT_IF(logits_.size() != 1, "Factors without factor mappings??");
+      return lossFn(logits_.front(), indices);
     }
-    else
-      ce = cross_entropy(logits, indices);
-    return ce;
+
+    // accumulate all CEs for all words that have the factor
+    // Memory-wise, this is cheap, all temp objects below are batches of scalars or lookup vectors.
+    auto graph = indices->graph();
+    Expr loss;
+    auto numGroups = embeddingFactorMapping_->getNumGroups();
+    for (size_t g = 0; g < numGroups; g++) {
+      indices; // [B... * 1] all batch items flattened
+      auto factorMaskVector  = embeddingFactorMapping_->getFactorMasks(g);   // [v] 1.0 if v has factor of group g
+      auto factorIndexVector = embeddingFactorMapping_->getFactorIndices(g); // [v] index of factor for word v in group p; must be 0 if factor is not used
+      auto factorMaskMatrix  = graph->constant({(int)factorMaskVector.size(),  1}, inits::from_vector(factorMaskVector),  Type::float32); // [V x 1]
+      auto factorIndexMatrix = graph->constant({(int)factorIndexVector.size(), 1}, inits::from_vector(factorIndexVector), Type::uint32);  // [V x 1(Ug)]
+      auto factorIndex  = rows(factorIndexMatrix, indices); // [B... * 1(Ug)] map word indices to factor indices (indices into factorLogits)
+      auto factorMask   = rows(factorMaskMatrix,  indices); // [B... * 1]     flag whether word has the factor in the first place
+      auto factorLogits = logits_[g];                       // [B... * Ug]
+      // @TODO: no need to normalize factors before here
+      // For each location in [B...] select [indices[B...]]. If not using factor, select [0] and mask it out next.
+      auto factorLoss = lossFn(factorLogits, factorIndex);  // [B... x 1]
+      factorLoss = factorLoss * reshape(factorMask, factorLoss->shape()); // mask out factor for words that do not have that factor
+      loss = loss ? (loss + factorLoss) : factorLoss; // [B... x 1]
+    }
+    return loss;
   }
 
   Expr Logits::getLogits() const {
@@ -192,7 +210,7 @@ namespace marian {
 
     // sum up the unit logits across factors for each target word
     auto graph = y->graph();
-    auto factorMatrix = embeddingFactorMapping_->getFactorMatrix(); // [V x U]
+    auto factorMatrix = embeddingFactorMapping_->getGlobalFactorMatrix(); // [V x U]
     y = dot_csr(
         y,  // [B x U]
         factorMatrix.shape,
@@ -270,13 +288,12 @@ namespace marian {
         auto graph = input->graph();
 
         // project each factor
-        const auto& groupRanges = embeddingFactorMapping_->groupRanges_; // @TODO: factor this properly
-        auto numGroups = groupRanges.size();
+        auto numGroups = embeddingFactorMapping_->getNumGroups();
         std::vector<Expr> groupYs(numGroups);
         std::vector<Expr> groupLLWeights(numGroups);
         for (size_t g = 0; g < numGroups; g++) {
-          auto range = groupRanges[g];
-          ABORT_IF(g > 0 && groupRanges[g].first != groupRanges[g-1].second, "Factor groups must be consecutive"); // we could sort groupYs though
+          auto range = embeddingFactorMapping_->getGroupRange(g);
+          ABORT_IF(g > 0 && range.first != embeddingFactorMapping_->getGroupRange(g-1).second, "Factor groups must be consecutive"); // we could sort groupYs though
           // slice this group's section out of W_
           // @TODO: This is highly inefficient if not tied. We should always transpose Output's matrix.
           auto groupW = slice(W_, transposeW_ ? 0 : -1, Slice((int)range.first, (int)range.second));
diff --git a/src/layers/generic.h b/src/layers/generic.h
index d33ab03cf..2bc958b73 100755
--- a/src/layers/generic.h
+++ b/src/layers/generic.h
@@ -69,7 +69,7 @@ class Logits {
     Logits(std::vector<Expr>&& logits, Ptr<EmbeddingFactorMapping> embeddingFactorMapping) // factored-output constructor
       : logits_(std::move(logits)), embeddingFactorMapping_(embeddingFactorMapping) {}
     Expr getLogits() const;
-    Expr crossEntropy(Expr indices,  float smoothing) const;
+    Expr getLoss(Expr indices, const std::function<Expr(Expr/*logits*/,Expr/*indices*/)>& lossFn) const;
     void assign(const Logits& other) {
       ABORT_IF(!empty() && getNumFactors() != other.getNumFactors(),
                "Logits assignment cannot change number of factors");
diff --git a/src/layers/loss.cpp b/src/layers/loss.cpp
index 12e69d028..3493e7c85 100755
--- a/src/layers/loss.cpp
+++ b/src/layers/loss.cpp
@@ -22,11 +22,23 @@ Ptr<LossBase> LossFactory(Ptr<Options> options, bool inference) {
   }
 }
 
-Expr LossBase::getCrossEntropy(const Logits& logits1,
+Expr LossBase::getCrossEntropy(const Logits& logits,
                                Expr indices,
                                Expr mask,
                                Expr weights) {
-  auto ce = logits1.crossEntropy(indices, smoothing_);
+  auto ce = logits.getLoss(indices, [&](Expr logits, Expr indices) {
+    Expr ce = cross_entropy(logits, indices);
+    if (smoothing_ > 0) {
+      // ce = sum_i y^_i log y_i(z)_i
+      // with smoothing:
+      // ce' = sum_i ((1-smoothing_) y^_i + smoothing_/N) log y_i(z)_i
+      //     = (1-smoothing_) sum_i y^_i log y_i(z)_i + smoothing_ mean_i log y_i(z)_i
+      //     = (1-smoothing_) ce + smoothing_ mean_i log y_i(z)_i
+      auto ceq = mean(logits, /*axis=*/ -1) - logsumexp(logits, /*axis=*/ -1);
+      ce = (1 - smoothing_) * ce - smoothing_ * ceq;
+    }
+    return ce;
+  });
 
   if(mask)
     ce = ce * mask;
diff --git a/src/tensors/cpu/tensor_operators.cpp b/src/tensors/cpu/tensor_operators.cpp
index a3b6bf9a6..f38c1006b 100755
--- a/src/tensors/cpu/tensor_operators.cpp
+++ b/src/tensors/cpu/tensor_operators.cpp
@@ -387,6 +387,7 @@ void CopyRows(Tensor out_,
   size_t cols = in_->shape()[-1];
   size_t rows = indices->size();
 
+  // note: may also be applied to IndexType; works by luck. Fix with fp16
   float* out = out_->data();
   const float* in = in_->data();
 

From bd98139d2140a11d8057fb529910d5cb280a3794 Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Mon, 28 Jan 2019 22:46:51 -0800
Subject: [PATCH 225/838] added normalization to getLogits()

---
 src/layers/generic.cpp | 6 +++++-
 src/layers/generic.h   | 4 ++--
 2 files changed, 7 insertions(+), 3 deletions(-)

diff --git a/src/layers/generic.cpp b/src/layers/generic.cpp
index 2a3d3db7c..4b43f8e85 100755
--- a/src/layers/generic.cpp
+++ b/src/layers/generic.cpp
@@ -169,6 +169,7 @@ namespace marian {
   };
 
   Expr Logits::getLoss(Expr indices, const std::function<Expr(Expr/*logits*/, Expr/*indices*/)>& lossFn) const {
+    LOG_ONCE(info, "[logits] getLoss() for {} factors", logits_.size());
     ABORT_IF(empty(), "Attempted to read out logits on empty Logits object");
     if (!embeddingFactorMapping_) {
       ABORT_IF(logits_.size() != 1, "Factors without factor mappings??");
@@ -206,7 +207,10 @@ namespace marian {
     }
 
     // lazily compute combined logits from factors
-    auto y = concatenate(logits_, /*axis=*/ -1);
+    std::vector<Expr> logProbs(logits_.size());
+    for (size_t g = 0; g < logits_.size(); g++)
+        logProbs[g] = logsoftmax(logits_[g]);
+    auto y = concatenate(logProbs, /*axis=*/ -1);
 
     // sum up the unit logits across factors for each target word
     auto graph = y->graph();
diff --git a/src/layers/generic.h b/src/layers/generic.h
index 2bc958b73..fcc97b88d 100755
--- a/src/layers/generic.h
+++ b/src/layers/generic.h
@@ -71,8 +71,8 @@ class Logits {
     Expr getLogits() const;
     Expr getLoss(Expr indices, const std::function<Expr(Expr/*logits*/,Expr/*indices*/)>& lossFn) const;
     void assign(const Logits& other) {
-      ABORT_IF(!empty() && getNumFactors() != other.getNumFactors(),
-               "Logits assignment cannot change number of factors");
+      //ABORT_IF(!empty() && getNumFactors() != other.getNumFactors(),
+      //         "Logits assignment cannot change number of factors");
       *this = other;
     }
     size_t getNumFactors() const { return logits_.size(); }

From 85408e3537e7cb076a6550ccb1a5daa55eaa8699 Mon Sep 17 00:00:00 2001
From: Marcin Junczys-Dowmunt <Marcin.JunczysDowmunt@microsoft.com>
Date: Mon, 28 Jan 2019 22:55:15 -0800
Subject: [PATCH 226/838] temporarily use strided gemm

---
 src/tensors/gpu/prod.cpp | 82 +++++++++++++++++++++++-----------------
 1 file changed, 47 insertions(+), 35 deletions(-)

diff --git a/src/tensors/gpu/prod.cpp b/src/tensors/gpu/prod.cpp
index d13081728..f282cc839 100755
--- a/src/tensors/gpu/prod.cpp
+++ b/src/tensors/gpu/prod.cpp
@@ -178,53 +178,65 @@ void ProdBatched(marian::Tensor C,
   int strideC = n * m;
   int batchC = std::max(batchA, batchB);
 
-  std::vector<const float*> aptr;
-  std::vector<const float*> bptr;
-  std::vector<float*> cptr;
-
-  for(int i = 0; i < batchC; i++) {
-    aptr.push_back(A->data() + (i % batchA) * strideA);
-    bptr.push_back(B->data() + (i % batchB) * strideB);
-    cptr.push_back(C->data() + i * strideC);
-  }
+  // std::vector<const float*> aptr;
+  // std::vector<const float*> bptr;
+  // std::vector<float*> cptr;
+
+  // for(int i = 0; i < batchC; i++) {
+  //   aptr.push_back(A->data() + (i % batchA) * strideA);
+  //   bptr.push_back(B->data() + (i % batchB) * strideB);
+  //   cptr.push_back(C->data() + i * strideC);
+  // }
 
-  auto mp_aptr = allocator->alloc<const float*>(aptr.size());
-  CudaCopy(
-      aptr.data(), aptr.data() + aptr.size(), mp_aptr->data<const float*>());
+  // auto mp_aptr = allocator->alloc<const float*>(aptr.size());
+  // CudaCopy(aptr.data(), aptr.data() + aptr.size(), mp_aptr->data<const float*>());
 
-  auto mp_bptr = allocator->alloc<const float*>(bptr.size());
-  CudaCopy(
-      bptr.data(), bptr.data() + bptr.size(), mp_bptr->data<const float*>());
+  // auto mp_bptr = allocator->alloc<const float*>(bptr.size());
+  // CudaCopy(bptr.data(), bptr.data() + bptr.size(), mp_bptr->data<const float*>());
 
-  auto mp_cptr = allocator->alloc<float*>(cptr.size());
-  CudaCopy(cptr.data(), cptr.data() + cptr.size(), mp_cptr->data<float*>());
+  // auto mp_cptr = allocator->alloc<float*>(cptr.size());
+  // CudaCopy(cptr.data(), cptr.data() + cptr.size(), mp_cptr->data<float*>());
 
 #if CUDA_VERSION >= 9000
   setTensorMode(cublasHandle);
   //cublasSetMathMode(cublasHandle, CUBLAS_TENSOR_OP_MATH);
 #endif
-  CUBLAS_CHECK(cublasSgemmBatched(cublasHandle,
-                     opB,
-                     opA,
-                     n,
-                     m,
-                     k,
-                     &alpha,
-                     mp_bptr->data<const float*>(),
-                     ldb,
-                     mp_aptr->data<const float*>(),
-                     lda,
-                     &beta,
-                     mp_cptr->data<float*>(),
-                     ldc,
-                     batchC));
+  // CUBLAS_CHECK(cublasSgemmBatched(cublasHandle,
+  //                    opB,
+  //                    opA,
+  //                    n,
+  //                    m,
+  //                    k,
+  //                    &alpha,
+  //                    mp_bptr->data<const float*>(),
+  //                    ldb,
+  //                    mp_aptr->data<const float*>(),
+  //                    lda,
+  //                    &beta,
+  //                    mp_cptr->data<float*>(),
+  //                    ldc,
+  //                    batchC));
+
+cublasSgemmStridedBatched(cublasHandle,
+                      opB, opA,
+                      n, m, k,
+                      &alpha,
+                      B->data(), ldb, strideB,
+                      A->data(), lda, strideA,
+                      &beta,
+                      C->data(), ldc, strideC,
+                      batchC);
+
+
 #if CUDA_VERSION >= 9000
   cublasSetMathMode(cublasHandle, CUBLAS_DEFAULT_MATH);
 #endif
 
-  allocator->free(mp_aptr);
-  allocator->free(mp_bptr);
-  allocator->free(mp_cptr);
+  // allocator->free(mp_aptr);
+  // allocator->free(mp_bptr);
+  // allocator->free(mp_cptr);
+
+  cudaStreamSynchronize(0);
 }
 
 // C = op(S) x D if not swapOperands else C = D x op(S)

From 6235641f5966053e9befae2d0a6abb92bcaf29e3 Mon Sep 17 00:00:00 2001
From: Marcin Junczys-Dowmunt <Marcin.JunczysDowmunt@microsoft.com>
Date: Tue, 29 Jan 2019 09:13:23 -0800
Subject: [PATCH 227/838] correct usage of shared memory

---
 src/tensors/gpu/tensor_operators.cu | 22 +++++++++++-----------
 1 file changed, 11 insertions(+), 11 deletions(-)

diff --git a/src/tensors/gpu/tensor_operators.cu b/src/tensors/gpu/tensor_operators.cu
index 8e256bc78..ae1e02653 100644
--- a/src/tensors/gpu/tensor_operators.cu
+++ b/src/tensors/gpu/tensor_operators.cu
@@ -420,7 +420,7 @@ __global__ void gSoftmax(float* out,
 
       extern __shared__ float _share[];
 
-      float* _max = _share + blockDim.x;
+      float* _max = _share;
       _max[threadIdx.x] = -CUDA_FLT_MAX;  // mask
       for(int tid = 0; tid < cols; tid += blockDim.x) {
         int id = tid + threadIdx.x;
@@ -503,7 +503,7 @@ __global__ void gLogSoftmax(float* out,
 
       extern __shared__ float _share[];
 
-      float* _max = _share + blockDim.x;
+      float* _max = _share;
       _max[threadIdx.x] = sp[threadIdx.x];
       for(int tid = 0; tid < cols; tid += blockDim.x) {
         int id = tid + threadIdx.x;
@@ -584,7 +584,7 @@ __global__ void gSoftmaxGrad(float* grad,
     int j = bid + blockIdx.x;
     if(j < rows) {
       extern __shared__ float _share[];
-      float* _sum = _share + blockDim.x;
+      float* _sum = _share;
 
       float* gradRow = grad + j * cols;
       const float* adjRow = adj + j * cols;
@@ -629,7 +629,7 @@ void SoftmaxGrad(Tensor grad, Tensor adj, Tensor val) {
 
   int blocks = std::min(MAX_BLOCKS, m);
   int threads = std::min(MAX_THREADS, k);
-  int shared = sizeof(float) * threads * 2;
+  int shared = sizeof(float) * threads;
   gSoftmaxGrad<<<blocks, threads, shared>>>(
       grad->data(), adj->data(), val->data(), m, k);
 }
@@ -643,7 +643,7 @@ __global__ void gLogSoftmaxGrad(float* grad,
     int j = bid + blockIdx.x;
     if(j < rows) {
       extern __shared__ float _share[];
-      float* _sum = _share + blockDim.x;
+      float* _sum = _share;
 
       float* gradRow = grad + j * cols;
       const float* adjRow = adj + j * cols;
@@ -686,7 +686,7 @@ void LogSoftmaxGrad(Tensor grad, Tensor adj, Tensor val) {
 
   int blocks = std::min(MAX_BLOCKS, m);
   int threads = std::min(MAX_THREADS, k);
-  int shared = sizeof(float) * threads * 2;
+  int shared = sizeof(float) * threads;
   gLogSoftmaxGrad<<<blocks, threads, shared>>>(
       grad->data(), adj->data(), val->data(), m, k);
 }
@@ -1153,7 +1153,7 @@ __global__ void gCrossEntropyPick(float* out,
       const float* sp = in + j * cols;
 
       extern __shared__ float _share[];
-      float* _max = _share + blockDim.x;
+      float* _max = _share;
 
       _max[threadIdx.x] = sp[threadIdx.x];
       for(int tid = 1; tid < cols; tid += blockDim.x) {
@@ -1243,7 +1243,7 @@ __global__ void gCrossEntropyPickBackward(float* out,
       float* so = out + j * cols;
 
       extern __shared__ float _share[];
-      float* _max = _share + blockDim.x;
+      float* _max = _share;
 
       _max[threadIdx.x] = sp[threadIdx.x];
       for(int tid = 1; tid < cols; tid += blockDim.x) {
@@ -1358,7 +1358,7 @@ __global__ void gAtt(float* out,
       const float* stateRow = state + ((j / (b * t)) * b + j % b) * cols;
 
       extern __shared__ float _share[];
-      float* _sum = _share + blockDim.x;
+      float* _sum = _share;
 
       _sum[threadIdx.x] = 0.0;
       for(int tid = 0; tid < cols; tid += blockDim.x) {
@@ -1395,7 +1395,7 @@ void Att(Tensor out, Tensor va, Tensor context, Tensor state) {
 
   int blocks = std::min(MAX_BLOCKS, (int)m);
   int threads = std::min(MAX_THREADS, (int)k);
-  int shared = sizeof(float) * threads * 2;
+  int shared = sizeof(float) * threads;
 
   gAtt<<<blocks, threads, shared>>>(
       out->data(), va->data(), context->data(), state->data(), m, k, b, t);
@@ -1485,7 +1485,7 @@ __global__ void gLNormalization(float* out,
       float* so = out + j * cols;
       const float* sp = in + j * cols;
 
-      float* _sum = _share + blockDim.x;
+      float* _sum = _share;
       _sum[threadIdx.x] = 0.0f;
       for(int tid = 0; tid < cols; tid += blockDim.x) {
         int id = tid + threadIdx.x;

From db66fc70a5a10a917fcbd6d3c32bcc5b04b62418 Mon Sep 17 00:00:00 2001
From: Marcin Junczys-Dowmunt <Marcin.JunczysDowmunt@microsoft.com>
Date: Tue, 29 Jan 2019 09:22:55 -0800
Subject: [PATCH 228/838] fix shared memory

---
 src/tensors/gpu/add.cu | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/src/tensors/gpu/add.cu b/src/tensors/gpu/add.cu
index f8f93b372..9e0c8e687 100755
--- a/src/tensors/gpu/add.cu
+++ b/src/tensors/gpu/add.cu
@@ -84,7 +84,7 @@ __global__ void gAggregateReduce(Functor functor, float aggInit, AggFunctor aggF
     int j = bid + blockIdx.x;
     if(j < rows) {
       extern __shared__ float _share[];
-      float* _sum = _share + blockDim.x;
+      float* _sum = _share;
 
       if(same) {
         _sum[threadIdx.x] = aggInit;
@@ -143,7 +143,7 @@ void Aggregate(Functor functor, float aggInit, AggFunctor aggFunctor, float scal
 
     int blocks = std::min(MAX_BLOCKS, (int)m);
     int threads = std::min(MAX_THREADS, (int)k);
-    int shared = sizeof(float) * threads * 2;
+    int shared = sizeof(float) * threads;
 
     gAggregateReduce<<<blocks, threads, shared>>>(functor, aggInit, aggFunctor, full, gOut, gIns, scale);
 
@@ -187,7 +187,7 @@ void Add(Functor functor, float scale, marian::Tensor out, Tensors... tensors) {
 
     int blocks = std::min(MAX_BLOCKS, (int)m);
     int threads = std::min(MAX_THREADS, (int)k);
-    int shared = sizeof(float) * threads * 2;
+    int shared = sizeof(float) * threads;
 
     gAggregateReduce<<<blocks, threads, shared>>>(functor, 0, addFunctor, full, gOut, gIns, scale);
 

From 0acb9d2e72563b5cb9d2896ac1d4580f4bb66b63 Mon Sep 17 00:00:00 2001
From: Marcin Junczys-Dowmunt <Marcin.JunczysDowmunt@microsoft.com>
Date: Tue, 29 Jan 2019 09:26:30 -0800
Subject: [PATCH 229/838] restore prod.cpp

---
 src/tensors/gpu/prod.cpp | 82 +++++++++++++++++-----------------------
 1 file changed, 35 insertions(+), 47 deletions(-)

diff --git a/src/tensors/gpu/prod.cpp b/src/tensors/gpu/prod.cpp
index f282cc839..d13081728 100755
--- a/src/tensors/gpu/prod.cpp
+++ b/src/tensors/gpu/prod.cpp
@@ -178,65 +178,53 @@ void ProdBatched(marian::Tensor C,
   int strideC = n * m;
   int batchC = std::max(batchA, batchB);
 
-  // std::vector<const float*> aptr;
-  // std::vector<const float*> bptr;
-  // std::vector<float*> cptr;
-
-  // for(int i = 0; i < batchC; i++) {
-  //   aptr.push_back(A->data() + (i % batchA) * strideA);
-  //   bptr.push_back(B->data() + (i % batchB) * strideB);
-  //   cptr.push_back(C->data() + i * strideC);
-  // }
+  std::vector<const float*> aptr;
+  std::vector<const float*> bptr;
+  std::vector<float*> cptr;
+
+  for(int i = 0; i < batchC; i++) {
+    aptr.push_back(A->data() + (i % batchA) * strideA);
+    bptr.push_back(B->data() + (i % batchB) * strideB);
+    cptr.push_back(C->data() + i * strideC);
+  }
 
-  // auto mp_aptr = allocator->alloc<const float*>(aptr.size());
-  // CudaCopy(aptr.data(), aptr.data() + aptr.size(), mp_aptr->data<const float*>());
+  auto mp_aptr = allocator->alloc<const float*>(aptr.size());
+  CudaCopy(
+      aptr.data(), aptr.data() + aptr.size(), mp_aptr->data<const float*>());
 
-  // auto mp_bptr = allocator->alloc<const float*>(bptr.size());
-  // CudaCopy(bptr.data(), bptr.data() + bptr.size(), mp_bptr->data<const float*>());
+  auto mp_bptr = allocator->alloc<const float*>(bptr.size());
+  CudaCopy(
+      bptr.data(), bptr.data() + bptr.size(), mp_bptr->data<const float*>());
 
-  // auto mp_cptr = allocator->alloc<float*>(cptr.size());
-  // CudaCopy(cptr.data(), cptr.data() + cptr.size(), mp_cptr->data<float*>());
+  auto mp_cptr = allocator->alloc<float*>(cptr.size());
+  CudaCopy(cptr.data(), cptr.data() + cptr.size(), mp_cptr->data<float*>());
 
 #if CUDA_VERSION >= 9000
   setTensorMode(cublasHandle);
   //cublasSetMathMode(cublasHandle, CUBLAS_TENSOR_OP_MATH);
 #endif
-  // CUBLAS_CHECK(cublasSgemmBatched(cublasHandle,
-  //                    opB,
-  //                    opA,
-  //                    n,
-  //                    m,
-  //                    k,
-  //                    &alpha,
-  //                    mp_bptr->data<const float*>(),
-  //                    ldb,
-  //                    mp_aptr->data<const float*>(),
-  //                    lda,
-  //                    &beta,
-  //                    mp_cptr->data<float*>(),
-  //                    ldc,
-  //                    batchC));
-
-cublasSgemmStridedBatched(cublasHandle,
-                      opB, opA,
-                      n, m, k,
-                      &alpha,
-                      B->data(), ldb, strideB,
-                      A->data(), lda, strideA,
-                      &beta,
-                      C->data(), ldc, strideC,
-                      batchC);
-
-
+  CUBLAS_CHECK(cublasSgemmBatched(cublasHandle,
+                     opB,
+                     opA,
+                     n,
+                     m,
+                     k,
+                     &alpha,
+                     mp_bptr->data<const float*>(),
+                     ldb,
+                     mp_aptr->data<const float*>(),
+                     lda,
+                     &beta,
+                     mp_cptr->data<float*>(),
+                     ldc,
+                     batchC));
 #if CUDA_VERSION >= 9000
   cublasSetMathMode(cublasHandle, CUBLAS_DEFAULT_MATH);
 #endif
 
-  // allocator->free(mp_aptr);
-  // allocator->free(mp_bptr);
-  // allocator->free(mp_cptr);
-
-  cudaStreamSynchronize(0);
+  allocator->free(mp_aptr);
+  allocator->free(mp_bptr);
+  allocator->free(mp_cptr);
 }
 
 // C = op(S) x D if not swapOperands else C = D x op(S)

From 6a663f60baa868fa1659a0cea014df26f89623c4 Mon Sep 17 00:00:00 2001
From: Marcin Junczys-Dowmunt <Marcin.JunczysDowmunt@microsoft.com>
Date: Tue, 29 Jan 2019 09:27:23 -0800
Subject: [PATCH 230/838] remove classification.h

---
 src/translator/classification.h | 241 --------------------------------
 1 file changed, 241 deletions(-)
 delete mode 100644 src/translator/classification.h

diff --git a/src/translator/classification.h b/src/translator/classification.h
deleted file mode 100644
index 28a8a82a8..000000000
--- a/src/translator/classification.h
+++ /dev/null
@@ -1,241 +0,0 @@
-#pragma once
-#include <algorithm>
-
-#include "marian.h"
-#include "translator/history.h"
-#include "translator/scorers.h"
-
-#include "translator/helpers.h"
-#include "translator/nth_element.h"
-
-namespace marian {
-
-class Classification {
-private:
-  Ptr<Options> options_;
-  std::vector<Ptr<Scorer>> scorers_;
-  size_t topN_{1};
-
-public:
-  Classification(Ptr<Options> options)
-      : options_(options),
-        scorers_(scorers),
-        topN_{options_->get<size_t>("beam-size")} // misuse beam-size for topN display
-        {} 
-
-  Beams toHyps(const std::vector<unsigned int> keys,
-               const std::vector<float> pathScores,
-               size_t labelNum,
-               const Beams& beams,
-               std::vector<Ptr<ScorerState>>& states,
-               size_t topN,
-               bool first,
-               Ptr<data::CorpusBatch> batch) {
-    Beams newBeams(beams.size());
-
-    std::vector<float> align;
-    if(options_->has("alignment"))
-      // Use alignments from the first scorer, even if ensemble
-      align = scorers_[0]->getAlignment();
-
-    for(size_t i = 0; i < keys.size(); ++i) {
-      // Keys contains indices to vocab items in the entire beam.
-      // Values can be between 0 and topN * number of lables.
-      Word embIdx = (Word)(keys[i] % labelNum);
-      auto beamIdx = i / topN;
-
-      // Retrieve short list for final softmax (based on words aligned
-      // to source sentences). If short list has been set, map the indices
-      // in the sub-selected vocabulary matrix back to their original positions.
-      auto shortlist = scorers_[0]->getShortlist();
-      if(shortlist)
-        embIdx = shortlist->reverseMap(embIdx); // @TODO: should reverseMap accept a size_t or a Word?
-
-      if(newBeams[beamIdx].size() < beams[beamIdx].size()) {
-        auto& beam = beams[beamIdx];
-        auto& newBeam = newBeams[beamIdx];
-
-        auto hypIdx = (IndexType)(keys[i] / labelNum);
-        float pathScore = pathScores[i];
-
-        auto hypIdxTrans
-            = IndexType((hypIdx / topN) + (hypIdx % topN) * beams.size());
-        if(first)
-          hypIdxTrans = hypIdx;
-
-        size_t beamHypIdx = hypIdx % topN;
-        if(beamHypIdx >= (int)beam.size())
-          beamHypIdx = beamHypIdx % beam.size();
-
-        if(first)
-          beamHypIdx = 0;
-
-        auto hyp = New<Hypothesis>(beam[beamHypIdx], embIdx, hypIdxTrans, pathScore);
-
-        // Set score breakdown for n-best lists
-        if(options_->get<bool>("n-best")) {
-          std::vector<float> breakDown(states.size(), 0);
-          beam[beamHypIdx]->GetScoreBreakdown().resize(states.size(), 0);
-          for(size_t j = 0; j < states.size(); ++j) {
-            size_t key = embIdx + hypIdxTrans * labelNum;
-            breakDown[j] = states[j]->breakDown(key)
-                           + beam[beamHypIdx]->GetScoreBreakdown()[j];
-          }
-          hyp->GetScoreBreakdown() = breakDown;
-        }
-
-        newBeam.push_back(hyp);
-      }
-    }
-    return newBeams;
-  }
-
-  // main decoding function
-  Histories search(Ptr<ExpressionGraph> graph, Ptr<data::CorpusBatch> batch) {
-    int dimBatch = (int)batch->size();
-
-    Histories histories;
-    for(int i = 0; i < dimBatch; ++i) {
-      size_t sentId = batch->getSentenceIds()[i];
-      auto history = New<History>(sentId,
-                                  options_->get<float>("normalize"),
-                                  options_->get<float>("word-penalty"));
-      histories.push_back(history);
-    }
-
-    auto getNBestList = createGetNBestListFn(topN_, dimBatch, graph->getDeviceId());
-
-    Beams beams(dimBatch);        // [batchIndex][beamIndex] is one sentence hypothesis
-    for(auto& beam : beams)
-      beam.resize(topN_, New<Hypothesis>());
-
-    bool first = true;
-    bool final = false;
-
-    for(int i = 0; i < dimBatch; ++i)
-      histories[i]->Add(beams[i], trgEosId_);
-
-    std::vector<Ptr<ScorerState>> states;
-
-    for(auto scorer : scorers_) {
-      scorer->clear(graph);
-    }
-
-    for(auto scorer : scorers_) {
-      states.push_back(scorer->apply(graph, batch));
-    }
-
-    // main loop over output tokens
-    do {
-      //**********************************************************************
-      // create constant containing previous path scores for current beam
-      // also create mapping of hyp indices, which are not 1:1 if sentences complete
-      std::vector<IndexType> hypIndices; // [beamIndex * activeBatchSize + batchIndex] backpointers, concatenated over beam positions. Used for reordering hypotheses
-      std::vector<IndexType> embIndices;
-      Expr prevPathScores; // [beam, 1, 1, 1]
-      if(first) {
-        // no scores yet
-        prevPathScores = graph->constant({1, 1, 1, 1}, inits::from_value(0));
-      } else {
-        std::vector<float> beamScores;
-
-        dimBatch = (int)batch->size();
-
-        for(size_t i = 0; i < localBeamSize; ++i) {
-          for(size_t j = 0; j < beams.size(); ++j) { // loop over batch entries (active sentences)
-            auto& beam = beams[j];
-            if(i < beam.size()) {
-              auto hyp = beam[i];
-              hypIndices.push_back((IndexType)hyp->GetPrevStateIndex()); // backpointer
-              embIndices.push_back(hyp->GetWord());
-              beamScores.push_back(hyp->GetPathScore());
-            } else {  // dummy hypothesis
-              hypIndices.push_back(0);
-              embIndices.push_back(0);  // (unused)
-              beamScores.push_back(-9999);
-            }
-          }
-        }
-
-        prevPathScores = graph->constant({(int)localBeamSize, 1, dimBatch, 1},
-                                    inits::from_vector(beamScores));
-      }
-
-      //**********************************************************************
-      // prepare scores for beam search
-      auto pathScores = prevPathScores;
-
-      for(size_t i = 0; i < scorers_.size(); ++i) {
-        states[i] = scorers_[i]->step(
-            graph, states[i], hypIndices, embIndices, dimBatch, (int)localBeamSize);
-
-        if(scorers_[i]->getWeight() != 1.f)
-          pathScores = pathScores + scorers_[i]->getWeight() * states[i]->getLogProbs();
-        else
-          pathScores = pathScores + states[i]->getLogProbs();
-      }
-
-      // make beams continuous
-      if(dimBatch > 1 && localBeamSize > 1)
-        pathScores = transpose(pathScores, {2, 1, 0, 3}); // check if this is needed for classification, rather not, beamSize and topN is badly defined here
-
-      if(first)
-        graph->forward();
-      else
-        graph->forwardNext();
-
-      //**********************************************************************
-      // suppress specific symbols if not at right positions
-      if(trgUnkId_ != -1 && options_->has("allow-unk")
-         && !options_->get<bool>("allow-unk"))
-        suppressWord(pathScores, trgUnkId_);
-      for(auto state : states)
-        state->blacklist(pathScores, batch);
-
-      //**********************************************************************
-      // perform beam search and pruning
-      std::vector<unsigned int> outKeys;
-      std::vector<float> outPathScores;
-
-      std::vector<size_t> beamSizes(dimBatch, localBeamSize);
-      getNBestList(beamSizes, pathScores->val(), outPathScores, outKeys, first);
-
-      int dimTrgVoc = pathScores->shape()[-1];
-      beams = toHyps(outKeys,
-                     outPathScores,
-                     dimTrgVoc,
-                     beams,
-                     states,
-                     localBeamSize,
-                     first,
-                     batch);
-
-      auto prunedBeams = pruneBeam(beams);
-      for(int i = 0; i < dimBatch; ++i) {
-        if(!beams[i].empty()) {
-          final = final
-                  || histories[i]->size()
-                         >= options_->get<float>("max-length-factor")
-                                * batch->front()->batchWidth();
-          histories[i]->Add(
-              beams[i], trgEosId_, prunedBeams[i].empty() || final);
-        }
-      }
-      beams = prunedBeams;
-
-      // determine beam size for next sentence, as max over still-active sentences
-      if(!first) {
-        size_t maxBeam = 0;
-        for(auto& beam : beams)
-          if(beam.size() > maxBeam)
-            maxBeam = beam.size();
-        localBeamSize = maxBeam;
-      }
-      first = false;
-
-    } while(localBeamSize != 0 && !final); // end of main loop over output tokens
-
-    return histories;
-  }
-};
-}  // namespace marian

From 49c139d950184c777d02417ed2586349d853e862 Mon Sep 17 00:00:00 2001
From: Marcin Junczys-Dowmunt <Marcin.JunczysDowmunt@microsoft.com>
Date: Tue, 29 Jan 2019 09:35:53 -0800
Subject: [PATCH 231/838] remove debug artifacts

---
 src/common/config_parser.cpp      |   2 -
 src/graph/chainable.h             |   2 -
 src/graph/expression_graph.cpp    | 148 +-----------------------------
 src/graph/expression_graph.h      |  81 ++++++++++++++--
 src/graph/node.cpp                |  19 ----
 src/graph/node.h                  |   2 -
 src/training/graph_group_sync.cpp |  21 +----
 7 files changed, 82 insertions(+), 193 deletions(-)

diff --git a/src/common/config_parser.cpp b/src/common/config_parser.cpp
index 58fd4090e..3db06cc2b 100644
--- a/src/common/config_parser.cpp
+++ b/src/common/config_parser.cpp
@@ -71,8 +71,6 @@ void ConfigParser::addOptionsGeneral(cli::CLIWrapper& cli) {
     "Suppress logging for translation");
   cli.add<size_t>("--seed",
     "Seed for all random number generators. 0 means initialize randomly");  
-  cli.add<bool>("--check-nan",
-    "Check for NaNs or Infs in forward and backward pass. Will abort when found.");
   cli.add<float>("--clip-gemm",
     "If not 0 clip GEMM input values to +/- arg");
   cli.add<bool>("--interpolate-env-vars",
diff --git a/src/graph/chainable.h b/src/graph/chainable.h
index 3aa81d3cc..2679843ee 100644
--- a/src/graph/chainable.h
+++ b/src/graph/chainable.h
@@ -96,8 +96,6 @@ class Chainable {
   virtual const std::string& name() const = 0;
 
   virtual void debug(const std::string& message) = 0;
-  virtual void dump(const std::string& filename) = 0;
-
   virtual bool marked_for_debug() = 0;
   virtual const std::string& debug_message() = 0;
 
diff --git a/src/graph/expression_graph.cpp b/src/graph/expression_graph.cpp
index 5b4df3395..b3c237d1e 100644
--- a/src/graph/expression_graph.cpp
+++ b/src/graph/expression_graph.cpp
@@ -24,153 +24,11 @@ Expr ExpressionGraph::dropout(float prob, const Shape& shape) {
   return constant(shape, inits::dropout(prob));
 }
 
-void ExpressionGraph::checkNan(Tensor t, bool& isNan, bool& isInf, bool zero) {
-  IsNan(t, allocator(), isNan, isInf, zero);
+void ExpressionGraph::checkNan(Tensor t) {
+  ABORT_IF(throwNaN_, "Not implemented"); t;
+  // ABORT_IF(throwNaN_ && IsNan(t), "Tensor has NaN");
 }
 
-io::Item itemFromTensor(Tensor t, const std::string name, Ptr<Backend> backend) {
-  io::Item item;
-  item.name  = name;
-  item.shape = t->shape();
-  item.type  = t->type();
-
-  size_t bytesWithoutPadding = t->shape().elements() * sizeOf(t->type());
-  item.bytes.resize(bytesWithoutPadding);
-  copy(backend,
-        (char*)t->data(),
-        (char*)t->data() + bytesWithoutPadding,
-        item.bytes.data());
-  return item;
-}
-
-void recChildren(Expr node, const std::string& parent, std::vector<io::Item>& items, Ptr<Backend> backend) {
-  std::string name = node->type() + "_" + std::to_string(node->getId()) + "_p:" + parent;
-  items.push_back(itemFromTensor(node->val(), name, backend));
-  for(auto&& child : node->children())
-    recChildren(child, std::to_string(node->getId()), items, backend);
-}
-
-void ExpressionGraph::forwardNext() {
-  // @TODO: check if allocation works properly
-  tensors_->clearShorttermMemory();
-
-  while(!nodesForward_.empty()) {
-    auto v = nodesForward_.front();
-    v->allocate();
-    v->init();
-    v->forward();
-
-    if(v->trainable() && throwNan_) {
-      bool isNan = false, isInf = false;
-      checkNan(v->val(), isNan, isInf);
-      if(isNan || isInf) {
-        LOG(critical, "Detected NaN ({}) or Inf ({}) in value (forward pass)", isNan, isInf);
-        LOG(critical, "\tType: {}, Shape: {}, Name: {}, Id: {}, Hash: {}",
-            v->type(), v->shape(), v->name(), v->getId(), v->hash());
-        LOG(critical, "Value debug {}", v->val()->debug());
-
-        LOG(critical, "Children: {}", v->children().size());
-        for(auto&& child : v->children()) {
-          LOG(critical, "\tType: {}, Shape: {}, Name: {}, Id: {}, Hash: {}",
-            child->type(), child->shape(), child->name(), child->getId(), child->hash());
-          LOG(critical, "Value debug {}", child->val()->debug());
-        }
-
-        std::vector<io::Item> ioItems;
-        recChildren(v, "root", ioItems, backend_);
-        io::saveItems("dump-for-nans.npz", ioItems);
-
-        ABORT("Aborting");
-      }
-    }
-
-    if(v->marked_for_debug()) {
-      LOG(info, "Debug: {} op={}", v->debug_message(), v->type());
-      LOG(info, v->val()->debug());
-    }
-
-    if(inferenceOnly_)
-      v->children().clear();
-    nodesForward_.pop_front();
-  }
-}
-
-void ExpressionGraph::backward(bool zero, float clipValue) {
-  if(topNodes_.size() > 1) {
-    LOG(critical, "There are more ({}) than one top most nodes for backward pass:", topNodes_.size());
-    for(auto node : topNodes_) {
-      LOG(critical,
-          "\tType: {}, Shape: {}, Name: {}, Id: {}, Hash: {}",
-          node->type(),
-          node->shape(),
-          node->name(),
-          node->getId(),
-          node->hash());
-    }
-    ABORT("Aborting");
-  }
-
-  params_->allocateBackward();
-  if(zero)
-    params_->set_zero_adjoint();
-
-  for(auto&& v : topNodes_)
-    v->init_dependent();
-
-  // named_.clear();
-  topNodes_.clear();
-
-  tensors_->clearShorttermMemory();
-
-  while(!nodesBackward_.empty()) {
-    auto v = nodesBackward_.back();
-    nodesBackward_.pop_back();
-
-    for(auto&& child : v->children()) {
-      if(child->trainable() && child->type() != "param")
-        child->set_zero_adjoint();
-    }
-
-    if(v->trainable()) {
-      v->backward();
-      if(clipValue != 0) {
-        using namespace functional;
-        Element(_1 = clip(_1, clipValue), v->grad());
-      }
-    }
-      
-
-    if(throwNan_) {
-      for(auto&& child : v->children()) {
-        if(child->trainable()) {
-          bool isNan = false, isInf = false;
-          checkNan(child->grad(), isNan, isInf);
-          if(isNan || isInf) {
-            LOG(critical, "Detected NaN ({}) or Inf ({}) in gradient (backward pass) of child node", isNan, isInf);
-            LOG(critical, "Child - Type: {}, Shape: {}, Name: {}, Id: {}, Hash: {}",
-                child->type(), child->shape(), child->name(), child->getId(), child->hash());
-            LOG(critical, "Value debug: {}", child->val()->debug());
-            LOG(critical, "Grad debug: {}", child->grad()->debug());
-            LOG(critical, "Parent - Type: {}, Shape: {}, Name: {}, Id: {}, Hash: {}",
-                v->type(), v->shape(), v->name(), v->getId(), v->hash());
-            LOG(critical, "Value debug: {}", v->val()->debug());
-            LOG(critical, "Grad debug: {}", v->grad()->debug());
-            ABORT("Aborting");
-          }
-        }
-      }
-    }
-
-    if(v->trainable() && v->marked_for_debug()) {
-      LOG(info, "Debug Grad: {} op={}", v->debug_message(), v->type());
-      LOG(info, v->grad()->debug());
-    }
-
-    v->children().clear();
-  }
-}
-
-
 void ExpressionGraph::save(std::vector<io::Item>& ioItems) {
   for(auto p : params()->getMap()) {
     std::string pName = p.first;
diff --git a/src/graph/expression_graph.h b/src/graph/expression_graph.h
index 389c6e3e4..fe8361618 100644
--- a/src/graph/expression_graph.h
+++ b/src/graph/expression_graph.h
@@ -136,7 +136,7 @@ class ExpressionGraph : public std::enable_shared_from_this<ExpressionGraph> {
   bool reloaded_{false};
   std::string namespace_;
 
-  bool throwNan_{false};
+  bool throwNaN_{false};
 
 protected:
   // Delete, copy and move constructors
@@ -217,11 +217,81 @@ class ExpressionGraph : public std::enable_shared_from_this<ExpressionGraph> {
     forwardNext();
   }
 
-  void checkNan(Tensor t, bool& isNan, bool& isInf, bool zero = false);
+  void checkNan(Tensor t);
 
-  void forwardNext();
+  void forwardNext() {
+    // @TODO: check if allocation works properly
+    tensors_->clearShorttermMemory();
 
-  void backward(bool zero = true, float clipValue = 0.f);
+    while(!nodesForward_.empty()) {
+      auto v = nodesForward_.front();
+      v->allocate();
+      v->init();
+      v->forward();
+
+      checkNan(v->val());
+
+      if(v->marked_for_debug()) {
+        std::cerr << "Debug: " << v->debug_message() << " op=" << v->type()
+                  << std::endl;
+        std::cerr << v->val()->debug() << std::endl;
+      }
+
+      if(inferenceOnly_)
+        v->children().clear();
+      nodesForward_.pop_front();
+    }
+  }
+
+  void backward(bool zero = true) {
+    if(topNodes_.size() > 1) {
+      LOG(critical, "There are more ({}) than one top most node for backward step:", topNodes_.size());
+      for(auto node : topNodes_) {
+        LOG(critical,
+            "\tType: {}, Shape: {}, Name: {}, Id: {}, Hash: {}",
+            node->type(),
+            node->shape(),
+            node->name(),
+            node->getId(),
+            node->hash());
+      }
+      ABORT("Aborting");
+    }
+
+    params_->allocateBackward();
+    if(zero)
+      params_->set_zero_adjoint();
+
+    for(auto&& v : topNodes_)
+      v->init_dependent();
+
+    // named_.clear();
+    topNodes_.clear();
+
+    tensors_->clearShorttermMemory();
+
+    while(!nodesBackward_.empty()) {
+      auto v = nodesBackward_.back();
+      nodesBackward_.pop_back();
+
+      for(auto&& child : v->children()) {
+        if(child->trainable() && child->type() != "param")
+          child->set_zero_adjoint();
+      }
+
+      if(v->trainable())
+        v->backward();
+
+      checkNan(v->grad());
+
+      if(v->trainable() && v->marked_for_debug()) {
+        std::cerr << "Debug Grad: " << v->debug_message() << std::endl;
+        std::cerr << v->grad()->debug() << std::endl;
+      }
+
+      v->children().clear();
+    }
+  }
 
   std::string graphviz() {
     std::stringstream ss;
@@ -390,8 +460,7 @@ class ExpressionGraph : public std::enable_shared_from_this<ExpressionGraph> {
 
   void setReloaded(bool reloaded) { reloaded_ = reloaded; }
 
-  void setThrowNan(bool throwNan) { throwNan_ = throwNan; }
-  bool getThrowNan() { return throwNan_; }
+  void setThrowNaN(bool throwNaN) { throwNaN_ = throwNaN; }
 
 public:
   // convert all parameters into an array of IoItem elements, for loading
diff --git a/src/graph/node.cpp b/src/graph/node.cpp
index bdb501166..c11531da7 100644
--- a/src/graph/node.cpp
+++ b/src/graph/node.cpp
@@ -2,8 +2,6 @@
 #include "graph/auto_tuner.h"
 #include "graph/expression_graph.h"
 #include "tensors/backend.h"
-#include "tensors/tensor_operators.h"
-#include "common/io.h"
 
 namespace marian {
 
@@ -85,21 +83,4 @@ void Node::record(Ptr<AutoTunerRecorder> recorder,
   recorderHash_ = recorderHash;
   recorderStop_ = stop;
 }
-
-void Node::dump(const std::string& filename) {
-  io::Item item;
-  item.name  = "dump";
-  item.shape = val_->shape();
-  item.type  = val_->type();
-
-  size_t bytesWithoutPadding = val_->shape().elements() * sizeOf(val_->type());
-  item.bytes.resize(bytesWithoutPadding);
-  copy(graph()->getBackend(),
-        (char*)val_->data(),
-        (char*)val_->data() + bytesWithoutPadding,
-        item.bytes.data());
-  
-  std::vector<io::Item> items({item});
-  io::saveItems(filename, items);
-}
 }  // namespace marian
diff --git a/src/graph/node.h b/src/graph/node.h
index defefd5b5..1397e74b0 100644
--- a/src/graph/node.h
+++ b/src/graph/node.h
@@ -100,8 +100,6 @@ class Node : public Chainable<Tensor>,
   virtual bool marked_for_debug() override { return markedForDebug_; }
   virtual const std::string& debug_message() override { return debugMessage_; }
 
-  virtual void dump(const std::string& filename) override;
-
   virtual size_t allocate() override;
 
   virtual void free() override;
diff --git a/src/training/graph_group_sync.cpp b/src/training/graph_group_sync.cpp
index c26cb9ff5..1ec5c3d68 100644
--- a/src/training/graph_group_sync.cpp
+++ b/src/training/graph_group_sync.cpp
@@ -10,10 +10,6 @@ SyncGraphGroup::SyncGraphGroup(Ptr<Options> config, Ptr<IMPIWrapper> mpi)
   for(auto device : devices_) {
     auto graph = New<ExpressionGraph>();
     graph->setDevice(device);
-
-    if(options_->get<bool>("check-nan"))
-      graph->setThrowNan(true);
-
     graph->reserveWorkspaceMB(options_->get<size_t>("workspace"));
     graph->getBackend()->setClip(options_->get<float>("clip-gemm"));
 
@@ -360,18 +356,12 @@ void SyncGraphGroup::update(std::vector<Ptr<data::Batch>> subBatches, size_t num
 
       auto rationalLoss = builders_[localDeviceIndex]->build(graph, subBatch);
       graph->forward();
-
+      
       StaticLoss tempLoss = *rationalLoss; // needed for overstuff
-      tempLoss.loss /= (float)overstuff; 
+      tempLoss.loss /= (float)overstuff; // @TODO: @fseide: scale only loss? should this scale labels too?
 
       localDeviceLosses[localDeviceIndex] += tempLoss;
       graph->backward(/*zero=*/false); // (gradients are reset before we get here)
-
-      bool hasNan = false, hasInf = false;
-      IsNan(graph->params()->grads(), graph->allocator(), hasNan, hasInf, /*zero=*/true);
-      if(hasNan || hasInf) {
-        LOG(warn, "Seen Nan ({}) or Inf ({}) in gradient, zeroed out offending gradient", hasNan, hasInf);
-      }
     }
   });
   // At this point, each device on each MPI process has a gradient aggregated over a subset of the sub-batches.
@@ -410,10 +400,7 @@ void SyncGraphGroup::update(std::vector<Ptr<data::Batch>> subBatches, size_t num
   // cost across all local devices (scheduler will aggregate cross-process)
   StaticLoss localLoss;
   for(auto& l : localDeviceLosses) // localDeviceLosses is already summed up over delay steps
-    if(std::isfinite((float)l.loss))
-      localLoss += l;
-    else 
-      LOG(warn, "Seen non-finite loss, offending gradients have been zeroed out");
+    localLoss += l;
 
   if(scheduler_) {
     // track and log localLoss
@@ -534,7 +521,7 @@ void SyncGraphGroup::save(bool final) /*override*/ {
       return comm_->gatherState(getShardFn);
     },
     isMainProcess());
-
+  
   barrier(); // (for better grouping of log messages)
 }
 

From 2d70ecd787cb3a29ab97809d4ccc7444312e1d60 Mon Sep 17 00:00:00 2001
From: Marcin Junczys-Dowmunt <Marcin.JunczysDowmunt@microsoft.com>
Date: Tue, 29 Jan 2019 10:17:18 -0800
Subject: [PATCH 232/838] update changelog

---
 CHANGELOG.md | 1 +
 1 file changed, 1 insertion(+)

diff --git a/CHANGELOG.md b/CHANGELOG.md
index 98e89ff77..c409a9d2b 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -18,6 +18,7 @@ and this project adheres to [Semantic Versioning](http://semver.org/spec/v2.0.0.
 - Fixed handling of "dump-config: false" in YAML config
 - Errors due to warnings
 - Fixed issue concerning failed saving with single GPU training and --sync-sgd option. 
+- Fixed NaN problem when training with Tensor Cores on Volta GPUs
 
 ### Changed
 - Add zlib source to Marian's source tree, builds now as object lib

From 095338af8d4f37d1443415f8517411be2598149b Mon Sep 17 00:00:00 2001
From: Marcin Junczys-Dowmunt <Marcin.JunczysDowmunt@microsoft.com>
Date: Tue, 29 Jan 2019 10:19:06 -0800
Subject: [PATCH 233/838] duplicate vocab entry handling

---
 src/data/default_vocab.cpp | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/src/data/default_vocab.cpp b/src/data/default_vocab.cpp
index f5d009b2a..38c0c323d 100644
--- a/src/data/default_vocab.cpp
+++ b/src/data/default_vocab.cpp
@@ -107,7 +107,9 @@ class DefaultVocab : public VocabBase {
         ABORT_IF(line.empty(),
                 "DefaultVocabulary file {} must not contain empty lines",
                 vocabPath);
-        vocab.insert({line, (Word)vocab.size()});
+        auto wasInserted = vocab.insert({line, (Word)vocab.size()}).second;
+        ABORT_IF(!wasInserted, "Duplicate vocabulary entry {}", line);
+
       }
       ABORT_IF(in.bad(), "DefaultVocabulary file {} could not be read", vocabPath);
     }

From 6068674029e39d95c89c3d6c23d31f99fb9122cf Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Tue, 29 Jan 2019 15:40:21 -0800
Subject: [PATCH 234/838] minor cleanup of factored outputs; renamed toUpper()
 to utf8ToUpper(), and it now handles UTF8 correctly

---
 src/common/config_parser.cpp      |  2 ++
 src/common/utils.cpp              | 26 +++++++++++++++++-----
 src/common/utils.h                |  2 +-
 src/data/batch_generator.h        |  6 ++---
 src/data/corpus.cpp               | 10 +++++++--
 src/data/corpus.h                 |  4 ++++
 src/layers/generic.cpp            | 37 ++++++++++++++++++-------------
 src/layers/loss.cpp               | 12 +++++-----
 src/training/graph_group_sync.cpp |  2 +-
 9 files changed, 67 insertions(+), 34 deletions(-)

diff --git a/src/common/config_parser.cpp b/src/common/config_parser.cpp
index 776042858..777481991 100755
--- a/src/common/config_parser.cpp
+++ b/src/common/config_parser.cpp
@@ -615,6 +615,8 @@ void ConfigParser::addSuboptionsBatching(cli::CLIWrapper& cli) {
 
   cli.add<bool>("--shuffle-in-ram",
       "Keep shuffled corpus in RAM, do not write to temp file");
+  cli.add<size_t>("--all-caps-every",
+      "When forming minibatches, preprocess every Nth line on the fly to all-caps. Assumes UTF-8");
 
   cli.add<int>("--mini-batch-words-ref",
       "If given, the following hyper parameters are adjusted as-if we had this mini-batch size: "
diff --git a/src/common/utils.cpp b/src/common/utils.cpp
index 252afa547..73c719e25 100755
--- a/src/common/utils.cpp
+++ b/src/common/utils.cpp
@@ -10,6 +10,8 @@
 #ifdef __unix__
 #include <unistd.h>
 #endif
+#include <codecvt>
+#include <locale>
 
 namespace marian {
 namespace utils {
@@ -154,12 +156,24 @@ bool endsWith(const std::string& text, const std::string& suffix) {
          && !text.compare(text.size() - suffix.size(), suffix.size(), suffix);
 }
 
-std::string toUpper(const std::string& s) {
-  std::locale loc;
-  std::string res; res.reserve(s.capacity());
-  for (auto c : s) // @BUGBUG: This won't work with UTF-8 characters.
-    res.push_back((char)std::toupper(c, loc));
-  return res;
+static std::wstring utf8ToWString(std::string const& s) {
+  std::wstring_convert<std::codecvt_utf8<wchar_t>> converter;
+  return converter.from_bytes(s);
+}
+
+static std::string toUTF8String(std::wstring const& s) {
+  std::wstring_convert<std::codecvt_utf8<wchar_t>> converter;
+  return converter.to_bytes(s);
+}
+
+std::locale const utf8("en_US.UTF-8");
+
+// convert a UTF-8 string to all-caps
+std::string utf8ToUpper(const std::string& s) {
+  auto ws = utf8ToWString(s);
+  for (auto& c : ws)
+    c = std::toupper(c, utf8);
+  return toUTF8String(ws);
 }
 
 double parseDouble(std::string s) {
diff --git a/src/common/utils.h b/src/common/utils.h
index d76d07fa7..777c97763 100755
--- a/src/common/utils.h
+++ b/src/common/utils.h
@@ -39,7 +39,7 @@ std::string withCommas(size_t n);
 bool beginsWith(const std::string& text, const std::string& prefix);
 bool endsWith(const std::string& text, const std::string& suffix);
 
-std::string toUpper(const std::string& s);
+std::string utf8ToUpper(const std::string& s);
 double parseDouble(std::string s);
 double parseNumber(std::string s);
 
diff --git a/src/data/batch_generator.h b/src/data/batch_generator.h
index ce673cd2a..4ec03d4d4 100755
--- a/src/data/batch_generator.h
+++ b/src/data/batch_generator.h
@@ -130,9 +130,9 @@ class BatchGenerator : public RNGEngine {
     while(current_ != data_->end() && maxiBatch->size() < maxSize) { // loop over data
       maxiBatch->push(*current_);
       sets = current_->size();
-        // do not consume more than required for the maxi batch as this causes
-        // that line-by-line translation is delayed by one sentence
-        bool last = maxiBatch->size() == maxSize;
+      // do not consume more than required for the maxi batch as this causes
+      // that line-by-line translation is delayed by one sentence
+      bool last = maxiBatch->size() == maxSize;
       if(!last)
         ++current_; // this actually reads the next line and pre-processes it
     }
diff --git a/src/data/corpus.cpp b/src/data/corpus.cpp
index 7f51b12ba..158635b15 100755
--- a/src/data/corpus.cpp
+++ b/src/data/corpus.cpp
@@ -10,12 +10,17 @@ namespace marian {
 namespace data {
 
 Corpus::Corpus(Ptr<Options> options, bool translate /*= false*/)
-    : CorpusBase(options, translate), shuffleInRAM_(options_->get<bool>("shuffle-in-ram")) {}
+    : CorpusBase(options, translate), shuffleInRAM_(options_->get<bool>("shuffle-in-ram")), allCapsEvery_(options_->get<bool>("all-caps-every")) {}
 
 Corpus::Corpus(std::vector<std::string> paths,
                std::vector<Ptr<Vocab>> vocabs,
                Ptr<Options> options)
-    : CorpusBase(paths, vocabs, options), shuffleInRAM_(options_->get<bool>("shuffle-in-ram")) {}
+    : CorpusBase(paths, vocabs, options), shuffleInRAM_(options_->get<bool>("shuffle-in-ram")), allCapsEvery_(options_->get<bool>("all-caps-every") {}
+
+void Corpus::preprocessLine(std::string& line, size_t streamId) {
+  if (allCapsEvery_ != 0 && pos_ % allCapsEvery_ == 0)
+    line = utils::toAllCapsUTF8(line);
+}
 
 SentenceTuple Corpus::next() {
   for (;;) { // (this is a retry loop for skipping invalid sentences)
@@ -55,6 +60,7 @@ SentenceTuple Corpus::next() {
       } else if(i > 0 && i == weightFileIdx_) {
         addWeightsToSentenceTuple(line, tup);
       } else {
+        preprocessLine(line, i);
         addWordsToSentenceTuple(line, i, tup);
       }
     }
diff --git a/src/data/corpus.h b/src/data/corpus.h
index 59d82ef55..3416f5b47 100755
--- a/src/data/corpus.h
+++ b/src/data/corpus.h
@@ -27,6 +27,10 @@ class Corpus : public CorpusBase {
 
   void shuffleData(const std::vector<std::string>& paths);
 
+  // for pre-processing
+  size_t allCapsEvery_{0};
+  void preprocessLine(std::string& line, size_t streamId);
+
 public:
   // @TODO: check if translate can be replaced by an option in options
   Corpus(Ptr<Options> options, bool translate = false);
diff --git a/src/layers/generic.cpp b/src/layers/generic.cpp
index 4b43f8e85..2ff6d34cc 100755
--- a/src/layers/generic.cpp
+++ b/src/layers/generic.cpp
@@ -107,6 +107,13 @@ namespace marian {
           factorMasks_[g][v] = 1.0f;
         }
       }
+      //for (Word v = 0; v < vocabSize; v++) {
+      //  LOG(info, "'{}': {}*{} {}*{} {}*{} {}*{}", vocab[v],
+      //      factorMasks_[0][v], factorIndices_[0][v],
+      //      factorMasks_[1][v], factorIndices_[1][v],
+      //      factorMasks_[2][v], factorIndices_[2][v],
+      //      factorMasks_[3][v], factorIndices_[3][v]);
+      //}
       //mVecs_.resize(numGroups); // @TODO: no longer needed, delete soon
       for (size_t g = 0; g < numGroups; g++) { // detect non-overlapping groups
         LOG(info, "[embedding] Factor group '{}' has {} members ({})",
@@ -193,7 +200,9 @@ namespace marian {
       // @TODO: no need to normalize factors before here
       // For each location in [B...] select [indices[B...]]. If not using factor, select [0] and mask it out next.
       auto factorLoss = lossFn(factorLogits, factorIndex);  // [B... x 1]
-      factorLoss = factorLoss * reshape(factorMask, factorLoss->shape()); // mask out factor for words that do not have that factor
+      auto xxx = reshape(factorMask, factorLoss->shape());
+      //factorLoss->debug("factorLoss");
+      factorLoss = factorLoss * xxx;// reshape(factorMask, factorLoss->shape()); // mask out factor for words that do not have that factor
       loss = loss ? (loss + factorLoss) : factorLoss; // [B... x 1]
     }
     return loss;
@@ -293,28 +302,24 @@ namespace marian {
 
         // project each factor
         auto numGroups = embeddingFactorMapping_->getNumGroups();
-        std::vector<Expr> groupYs(numGroups);
-        std::vector<Expr> groupLLWeights(numGroups);
+        std::vector<Expr> allLogits(numGroups);
         for (size_t g = 0; g < numGroups; g++) {
           auto range = embeddingFactorMapping_->getGroupRange(g);
           ABORT_IF(g > 0 && range.first != embeddingFactorMapping_->getGroupRange(g-1).second, "Factor groups must be consecutive"); // we could sort groupYs though
           // slice this group's section out of W_
           // @TODO: This is highly inefficient if not tied. We should always transpose Output's matrix.
-          auto groupW = slice(W_, transposeW_ ? 0 : -1, Slice((int)range.first, (int)range.second));
-          //LOG(info, "slice() -> {}, {}", groupW->type(), std::string(groupW->shape()));
-          auto groupB = slice(b_, -1, Slice((int)range.first, (int)range.second)); // @TODO: b_ should be a vector, not a matrix
-          auto groupY = affine(input, groupW, groupB, false, transposeW_); // [B... x U] factor logits
-          // normalize
-          groupY = logsoftmax(groupY);
-          // log-linear weight   --@TODO: pre-create in constructor
+          auto factorW = slice(W_, transposeW_ ? 0 : -1, Slice((int)range.first, (int)range.second));
+          auto factorB = slice(b_, -1, Slice((int)range.first, (int)range.second)); // @TODO: b_ should be a vector, not a matrix
+          auto factorLogits = affine(input, factorW, factorB, false, transposeW_); // [B... x U] factor logits
+          // log-linear weight
+          // @TODO: The weight should not be needed, since it could learn it into the embeddings. Maybe it doesn't.
+          // @TODO? If we move the weight before affine(), it would use less memory at least for the main factor.
           auto name = options_->get<std::string>("prefix");
-          groupLLWeights[g] = graph->param(name + "_llWeight_" + std::to_string(g), {}, inits::from_value(1.0f));
-          groupY = groupY * groupLLWeights[g];
-          // @BUGBUG: Global softmax no longer normalizes, due to words that lack some factors.
-          // @TODO: normalize again. Do I need the first normalization?
-          groupYs[g] = groupY;
+          auto llWeight = graph->param(name + "_llWeight_" + std::to_string(g), {}, inits::from_value(1.0f));
+          factorLogits = factorLogits * llWeight; // -a constant, which is OK for logits
+          allLogits[g] = factorLogits;
         }
-        return Logits(std::move(groupYs), embeddingFactorMapping_);
+        return Logits(std::move(allLogits), embeddingFactorMapping_);
       }
       else
         return affine(input, W_, b_, false, transposeW_);
diff --git a/src/layers/loss.cpp b/src/layers/loss.cpp
index 3493e7c85..5c03ea5c1 100755
--- a/src/layers/loss.cpp
+++ b/src/layers/loss.cpp
@@ -26,16 +26,18 @@ Expr LossBase::getCrossEntropy(const Logits& logits,
                                Expr indices,
                                Expr mask,
                                Expr weights) {
+  // logits may be factored; in that case, the getLoss() function computes one loss for each, and sums them up
   auto ce = logits.getLoss(indices, [&](Expr logits, Expr indices) {
     Expr ce = cross_entropy(logits, indices);
     if (smoothing_ > 0) {
-      // ce = sum_i y^_i log y_i(z)_i
+      // ce = -sum_i y^_i log y_i(z)
       // with smoothing:
-      // ce' = sum_i ((1-smoothing_) y^_i + smoothing_/N) log y_i(z)_i
-      //     = (1-smoothing_) sum_i y^_i log y_i(z)_i + smoothing_ mean_i log y_i(z)_i
-      //     = (1-smoothing_) ce + smoothing_ mean_i log y_i(z)_i
+      // ce' = -sum_i ((1-smoothing_) y^_i + smoothing_/N) log y_i(z)
+      //     = -(1-smoothing_) sum_i y^_i log y_i(z) - smoothing_ mean_i log y_i(z)
+      //     = (1-smoothing_) ce - smoothing_ mean_i log y_i(z)
       auto ceq = mean(logits, /*axis=*/ -1) - logsumexp(logits, /*axis=*/ -1);
-      ce = (1 - smoothing_) * ce - smoothing_ * ceq;
+      //ce = (1 - smoothing_) * ce - smoothing_ * ceq;
+      ce = ce - smoothing_ * (ce + ceq); // writing it this way saves one op :)
     }
     return ce;
   });
diff --git a/src/training/graph_group_sync.cpp b/src/training/graph_group_sync.cpp
index 85fd6d978..a4d616165 100755
--- a/src/training/graph_group_sync.cpp
+++ b/src/training/graph_group_sync.cpp
@@ -23,7 +23,7 @@ SyncGraphGroup::SyncGraphGroup(Ptr<Options> config, Ptr<IMPIWrapper> mpi)
   // Rather, it is assumed that the communicator knows to reduce unnecessary transfers to no-ops.
   comm_ = createCommunicator(graphs_, /*noNccl=*/options_->get<bool>("no-nccl", false), /*mpi=*/mpi_);
 
-  auto type = utils::toUpper(devices_.front().typeAsString()) + "s";
+  auto type = utils::utf8ToUpper(devices_.front().typeAsString()) + "s";
   if (mpi_->numMPIProcesses() > 1)
     LOG(info, "[training] Using {} {}, distributed over {} MPI processes", mpi_->numMPIProcesses() * devices_.size(), type, mpi_->numMPIProcesses());
   else

From 1f7967b4b08ee28453bf7a4ace3e2acf68f789c3 Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Tue, 29 Jan 2019 15:51:17 -0800
Subject: [PATCH 235/838] fixed minor glitch in previous commit

---
 src/data/corpus.cpp | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/src/data/corpus.cpp b/src/data/corpus.cpp
index 158635b15..d83067a60 100755
--- a/src/data/corpus.cpp
+++ b/src/data/corpus.cpp
@@ -15,11 +15,11 @@ Corpus::Corpus(Ptr<Options> options, bool translate /*= false*/)
 Corpus::Corpus(std::vector<std::string> paths,
                std::vector<Ptr<Vocab>> vocabs,
                Ptr<Options> options)
-    : CorpusBase(paths, vocabs, options), shuffleInRAM_(options_->get<bool>("shuffle-in-ram")), allCapsEvery_(options_->get<bool>("all-caps-every") {}
+    : CorpusBase(paths, vocabs, options), shuffleInRAM_(options_->get<bool>("shuffle-in-ram")), allCapsEvery_(options_->get<bool>("all-caps-every")) {}
 
-void Corpus::preprocessLine(std::string& line, size_t streamId) {
+void Corpus::preprocessLine(std::string& line, size_t /*streamId*/) {
   if (allCapsEvery_ != 0 && pos_ % allCapsEvery_ == 0)
-    line = utils::toAllCapsUTF8(line);
+    line = utils::utf8ToUpper(line);
 }
 
 SentenceTuple Corpus::next() {

From 86a212bf787d87704b3a50c22bf68a0b0ffc3570 Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Tue, 29 Jan 2019 16:00:47 -0800
Subject: [PATCH 236/838] reverted that CE refactoring, too risky right now

---
 src/data/corpus.cpp |  4 +++-
 src/layers/loss.cpp | 14 +++++++-------
 2 files changed, 10 insertions(+), 8 deletions(-)

diff --git a/src/data/corpus.cpp b/src/data/corpus.cpp
index d83067a60..bd5711b24 100755
--- a/src/data/corpus.cpp
+++ b/src/data/corpus.cpp
@@ -18,8 +18,10 @@ Corpus::Corpus(std::vector<std::string> paths,
     : CorpusBase(paths, vocabs, options), shuffleInRAM_(options_->get<bool>("shuffle-in-ram")), allCapsEvery_(options_->get<bool>("all-caps-every")) {}
 
 void Corpus::preprocessLine(std::string& line, size_t /*streamId*/) {
-  if (allCapsEvery_ != 0 && pos_ % allCapsEvery_ == 0)
+  if (allCapsEvery_ != 0 && pos_ % allCapsEvery_ == 0) {
     line = utils::utf8ToUpper(line);
+    LOG_ONCE(info, "all-caps'ed line to {}", line);
+  }
 }
 
 SentenceTuple Corpus::next() {
diff --git a/src/layers/loss.cpp b/src/layers/loss.cpp
index 5c03ea5c1..ecd92e793 100755
--- a/src/layers/loss.cpp
+++ b/src/layers/loss.cpp
@@ -30,14 +30,14 @@ Expr LossBase::getCrossEntropy(const Logits& logits,
   auto ce = logits.getLoss(indices, [&](Expr logits, Expr indices) {
     Expr ce = cross_entropy(logits, indices);
     if (smoothing_ > 0) {
-      // ce = -sum_i y^_i log y_i(z)
+      // ce = -sum_i y^_i log y_i(h)
       // with smoothing:
-      // ce' = -sum_i ((1-smoothing_) y^_i + smoothing_/N) log y_i(z)
-      //     = -(1-smoothing_) sum_i y^_i log y_i(z) - smoothing_ mean_i log y_i(z)
-      //     = (1-smoothing_) ce - smoothing_ mean_i log y_i(z)
-      auto ceq = mean(logits, /*axis=*/ -1) - logsumexp(logits, /*axis=*/ -1);
-      //ce = (1 - smoothing_) * ce - smoothing_ * ceq;
-      ce = ce - smoothing_ * (ce + ceq); // writing it this way saves one op :)
+      // ce' = -sum_i ((1-smoothing_) y^_i + smoothing_/N) log y_i(h)
+      //     = -(1-smoothing_) sum_i y^_i log y_i(h) - smoothing_ mean_i log y_i(h)
+      //     = (1-smoothing_) ce - smoothing_ mean_i log y_i(h)
+      auto ceqNeg = mean(logits, /*axis=*/ -1) - logsumexp(logits, /*axis=*/ -1);
+      ce = (1 - smoothing_) * ce - smoothing_ * ceqNeg;
+      //ce = ce - smoothing_ * (ce + ceqNeg); // writing it this way saves one op :)
     }
     return ce;
   });

From 37d5d3a1ce05b3a43acd459bfb68bb2cb039c880 Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Tue, 29 Jan 2019 16:10:29 -0800
Subject: [PATCH 237/838] made locale a local variable

---
 src/common/utils.cpp | 3 +--
 src/data/corpus.cpp  | 4 ++--
 2 files changed, 3 insertions(+), 4 deletions(-)

diff --git a/src/common/utils.cpp b/src/common/utils.cpp
index 73c719e25..3914452b4 100755
--- a/src/common/utils.cpp
+++ b/src/common/utils.cpp
@@ -166,10 +166,9 @@ static std::string toUTF8String(std::wstring const& s) {
   return converter.to_bytes(s);
 }
 
-std::locale const utf8("en_US.UTF-8");
-
 // convert a UTF-8 string to all-caps
 std::string utf8ToUpper(const std::string& s) {
+  std::locale const utf8("en_US.UTF-8");
   auto ws = utf8ToWString(s);
   for (auto& c : ws)
     c = std::toupper(c, utf8);
diff --git a/src/data/corpus.cpp b/src/data/corpus.cpp
index bd5711b24..b39a7fa5a 100755
--- a/src/data/corpus.cpp
+++ b/src/data/corpus.cpp
@@ -10,12 +10,12 @@ namespace marian {
 namespace data {
 
 Corpus::Corpus(Ptr<Options> options, bool translate /*= false*/)
-    : CorpusBase(options, translate), shuffleInRAM_(options_->get<bool>("shuffle-in-ram")), allCapsEvery_(options_->get<bool>("all-caps-every")) {}
+    : CorpusBase(options, translate), shuffleInRAM_(options_->get<bool>("shuffle-in-ram")), allCapsEvery_(options_->get<size_t>("all-caps-every")) {}
 
 Corpus::Corpus(std::vector<std::string> paths,
                std::vector<Ptr<Vocab>> vocabs,
                Ptr<Options> options)
-    : CorpusBase(paths, vocabs, options), shuffleInRAM_(options_->get<bool>("shuffle-in-ram")), allCapsEvery_(options_->get<bool>("all-caps-every")) {}
+    : CorpusBase(paths, vocabs, options), shuffleInRAM_(options_->get<bool>("shuffle-in-ram")), allCapsEvery_(options_->get<size_t>("all-caps-every")) {}
 
 void Corpus::preprocessLine(std::string& line, size_t /*streamId*/) {
   if (allCapsEvery_ != 0 && pos_ % allCapsEvery_ == 0) {

From f46ffcb002f7b148a05d19cd15ab41adb5e4b1fb Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Wed, 30 Jan 2019 08:08:32 -0800
Subject: [PATCH 238/838] utf8ToUpper() now works for a subset of characters
 without locales installed; added missing __syncthreads() to softmax variants

---
 src/common/utils.cpp                | 46 +++++++++++++++++++++++++++--
 src/data/corpus.cpp                 |  7 +++--
 src/tensors/gpu/tensor_operators.cu |  4 +++
 3 files changed, 52 insertions(+), 5 deletions(-)
 mode change 100644 => 100755 src/tensors/gpu/tensor_operators.cu

diff --git a/src/common/utils.cpp b/src/common/utils.cpp
index 3914452b4..59cc4cc88 100755
--- a/src/common/utils.cpp
+++ b/src/common/utils.cpp
@@ -11,7 +11,7 @@
 #include <unistd.h>
 #endif
 #include <codecvt>
-#include <locale>
+#include <cwctype>
 
 namespace marian {
 namespace utils {
@@ -167,13 +167,53 @@ static std::string toUTF8String(std::wstring const& s) {
 }
 
 // convert a UTF-8 string to all-caps
+#if 0
+// @BUGBUG: This does not work for non-ASCII characters.
 std::string utf8ToUpper(const std::string& s) {
-  std::locale const utf8("en_US.UTF-8");
   auto ws = utf8ToWString(s);
   for (auto& c : ws)
-    c = std::toupper(c, utf8);
+    c = std::towupper(c);
   return toUTF8String(ws);
 }
+#else
+struct UTF8Mapper : std::map<wchar_t, wchar_t> { // Hack because Philly does not have UTF-8 locale installed
+  UTF8Mapper() {
+    /*
+      env LC_ALL=en_US.UTF-8 sed 's/\(.\)/\1\n/g'   TEXT_FILE_CONTAINING_ALL_CHARS > l
+      env LC_ALL=en_US.UTF-8 sed 's/\(.\)/\U\1\n/g' TEXT_FILE_CONTAINING_ALL_CHARS > u
+      paste l u | env LC_ALL=en_US.UTF-8 sort -u > x
+      cat x | awk '{if($1 != $2){print}}' > y
+      cat y | tr -d '\r' \
+        | od -w10000 -t x1 \
+        | head -1 \
+        | sed -e 's/^0000000 /{{".x/g' -e 's/ 09 /",".x/g' -e 's/ 0a /"},{".x/g' -e 's/ 0a$/"}/' -e 's/ /.x/g' \
+        | tr '.' '\\' \
+        | xclip
+    */
+    std::vector<std::pair<std::string, std::string>> map8{ {"\xc9\x92","\xe2\xb1\xb0"},{"\x61","\x41"},{"\xc3\xa1","\xc3\x81"},{"\xc3\xa0","\xc3\x80"},{"\xe1\xba\xaf","\xe1\xba\xae"},{"\xe1\xba\xb1","\xe1\xba\xb0"},{"\xe1\xba\xb5","\xe1\xba\xb4"},{"\xe1\xba\xb3","\xe1\xba\xb2"},{"\xe1\xba\xb7","\xe1\xba\xb6"},{"\xc4\x83","\xc4\x82"},{"\xe1\xba\xa5","\xe1\xba\xa4"},{"\xe1\xba\xa7","\xe1\xba\xa6"},{"\xe1\xba\xab","\xe1\xba\xaa"},{"\xe1\xba\xa9","\xe1\xba\xa8"},{"\xe1\xba\xad","\xe1\xba\xac"},{"\xc3\xa2","\xc3\x82"},{"\xc7\x8e","\xc7\x8d"},{"\xc7\xbb","\xc7\xba"},{"\xc3\xa5","\xc3\x85"},{"\xc7\x9f","\xc7\x9e"},{"\xc3\xa4","\xc3\x84"},{"\xc3\xa3","\xc3\x83"},{"\xc4\x85","\xc4\x84"},{"\xc4\x81","\xc4\x80"},{"\xe1\xba\xa3","\xe1\xba\xa2"},{"\xc8\x83","\xc8\x82"},{"\xe1\xba\xa1","\xe1\xba\xa0"},{"\xc7\xa3","\xc7\xa2"},{"\xc3\xa6","\xc3\x86"},{"\x62","\x42"},{"\xe1\xb8\x87","\xe1\xb8\x86"},{"\x63","\x43"},{"\xc4\x87","\xc4\x86"},{"\xc4\x89","\xc4\x88"},{"\xc4\x8d","\xc4\x8c"},{"\xc4\x8b","\xc4\x8a"},{"\xc3\xa7","\xc3\x87"},{"\x64","\x44"},{"\xc4\x8f","\xc4\x8e"},{"\xc4\x91","\xc4\x90"},{"\xe1\xb8\x91","\xe1\xb8\x90"},{"\xe1\xb8\x8d","\xe1\xb8\x8c"},{"\xe1\xb8\x8f","\xe1\xb8\x8e"},{"\xc3\xb0","\xc3\x90"},{"\x65","\x45"},{"\xc3\xa9","\xc3\x89"},{"\xc3\xa8","\xc3\x88"},{"\xc4\x95","\xc4\x94"},{"\xe1\xba\xbf","\xe1\xba\xbe"},{"\xe1\xbb\x81","\xe1\xbb\x80"},{"\xe1\xbb\x85","\xe1\xbb\x84"},{"\xe1\xbb\x83","\xe1\xbb\x82"},{"\xe1\xbb\x87","\xe1\xbb\x86"},{"\xc3\xaa","\xc3\x8a"},{"\xc4\x9b","\xc4\x9a"},{"\xc3\xab","\xc3\x8b"},{"\xe1\xba\xbd","\xe1\xba\xbc"},{"\xc4\x97","\xc4\x96"},{"\xc4\x99","\xc4\x98"},{"\xe1\xb8\x97","\xe1\xb8\x96"},{"\xc4\x93","\xc4\x92"},{"\xe1\xba\xbb","\xe1\xba\xba"},{"\xc8\x87","\xc8\x86"},{"\xe1\xba\xb9","\xe1\xba\xb8"},{"\xc7\x9d","\xc6\x8e"},{"\x66","\x46"},{"\x67","\x47"},{"\xc7\xb5","\xc7\xb4"},{"\xc4\x9f","\xc4\x9e"},{"\xc4\x9d","\xc4\x9c"},{"\xc7\xa7","\xc7\xa6"},{"\xc4\xa1","\xc4\xa0"},{"\xc4\xa3","\xc4\xa2"},{"\xc9\xa0","\xc6\x93"},{"\x68","\x48"},{"\xc4\xa5","\xc4\xa4"},{"\xc4\xa7","\xc4\xa6"},{"\xe1\xb8\xa9","\xe1\xb8\xa8"},{"\xe1\xb8\xa5","\xe1\xb8\xa4"},{"\xe1\xb8\xab","\xe1\xb8\xaa"},{"\x69","\x49"},{"\xc4\xb1","\x49"},{"\xc3\xad","\xc3\x8d"},{"\xc3\xac","\xc3\x8c"},{"\xc4\xad","\xc4\xac"},{"\xc3\xae","\xc3\x8e"},{"\xc7\x90","\xc7\x8f"},{"\xc3\xaf","\xc3\x8f"},{"\xc4\xa9","\xc4\xa8"},{"\xc4\xaf","\xc4\xae"},{"\xc4\xab","\xc4\xaa"},{"\xe1\xbb\x89","\xe1\xbb\x88"},{"\xc8\x8b","\xc8\x8a"},{"\xe1\xbb\x8b","\xe1\xbb\x8a"},{"\x6a","\x4a"},{"\xc4\xb5","\xc4\xb4"},{"\x6b","\x4b"},{"\xe1\xb8\xb1","\xe1\xb8\xb0"},{"\xc4\xb7","\xc4\xb6"},{"\xe1\xb8\xb3","\xe1\xb8\xb2"},{"\xc6\x99","\xc6\x98"},{"\x6c","\x4c"},{"\xc4\xba","\xc4\xb9"},{"\xc4\xbe","\xc4\xbd"},{"\xc5\x82","\xc5\x81"},{"\xc4\xbc","\xc4\xbb"},{"\xe1\xb8\xb7","\xe1\xb8\xb6"},{"\x6d","\x4d"},{"\xe1\xb8\xbf","\xe1\xb8\xbe"},{"\xe1\xb9\x83","\xe1\xb9\x82"},{"\xc5\x8b","\xc5\x8a"},{"\x6e","\x4e"},{"\xc5\x84","\xc5\x83"},{"\xc5\x88","\xc5\x87"},{"\xc3\xb1","\xc3\x91"},{"\xe1\xb9\x85","\xe1\xb9\x84"},{"\xc5\x86","\xc5\x85"},{"\xe1\xb9\x87","\xe1\xb9\x86"},{"\xe1\xb9\x89","\xe1\xb9\x88"},{"\xc5\x93","\xc5\x92"},{"\x6f","\x4f"},{"\xc3\xb3","\xc3\x93"},{"\xc3\xb2","\xc3\x92"},{"\xc5\x8f","\xc5\x8e"},{"\xe1\xbb\x91","\xe1\xbb\x90"},{"\xe1\xbb\x93","\xe1\xbb\x92"},{"\xe1\xbb\x95","\xe1\xbb\x94"},{"\xe1\xbb\x99","\xe1\xbb\x98"},{"\xc3\xb4","\xc3\x94"},{"\xc7\x92","\xc7\x91"},{"\xc3\xb6","\xc3\x96"},{"\xc5\x91","\xc5\x90"},{"\xc3\xb5","\xc3\x95"},{"\xc3\xb8","\xc3\x98"},{"\xc7\xab","\xc7\xaa"},{"\xc5\x8d","\xc5\x8c"},{"\xe1\xbb\x8f","\xe1\xbb\x8e"},{"\xc8\x8f","\xc8\x8e"},{"\xe1\xbb\x8d","\xe1\xbb\x8c"},{"\xe1\xbb\x9b","\xe1\xbb\x9a"},{"\xe1\xbb\x9d","\xe1\xbb\x9c"},{"\xe1\xbb\xa1","\xe1\xbb\xa0"},{"\xe1\xbb\x9f","\xe1\xbb\x9e"},{"\xe1\xbb\xa3","\xe1\xbb\xa2"},{"\xc6\xa1","\xc6\xa0"},{"\xc9\x94","\xc6\x86"},{"\x70","\x50"},{"\xe1\xb9\x95","\xe1\xb9\x94"},{"\x71","\x51"},{"\x72","\x52"},{"\xc5\x95","\xc5\x94"},{"\xc5\x99","\xc5\x98"},{"\xc5\x97","\xc5\x96"},{"\xe1\xb9\x9b","\xe1\xb9\x9a"},{"\xe1\xb9\x9f","\xe1\xb9\x9e"},{"\x73","\x53"},{"\xc5\x9b","\xc5\x9a"},{"\xc5\x9d","\xc5\x9c"},{"\xc5\xa1","\xc5\xa0"},{"\xc5\x9f","\xc5\x9e"},{"\xe1\xb9\xa3","\xe1\xb9\xa2"},{"\x74","\x54"},{"\xc5\xa5","\xc5\xa4"},{"\xc5\xa3","\xc5\xa2"},{"\xe1\xb9\xad","\xe1\xb9\xac"},{"\xe1\xb9\xaf","\xe1\xb9\xae"},{"\xc8\x95","\xc8\x94"},{"\x75","\x55"},{"\xc3\xba","\xc3\x9a"},{"\xc3\xb9","\xc3\x99"},{"\xc5\xad","\xc5\xac"},{"\xc3\xbb","\xc3\x9b"},{"\xc7\x94","\xc7\x93"},{"\xc5\xaf","\xc5\xae"},{"\xc7\x98","\xc7\x97"},{"\xc7\x9c","\xc7\x9b"},{"\xc3\xbc","\xc3\x9c"},{"\xc5\xb1","\xc5\xb0"},{"\xc5\xa9","\xc5\xa8"},{"\xc5\xb3","\xc5\xb2"},{"\xc5\xab","\xc5\xaa"},{"\xe1\xbb\xa7","\xe1\xbb\xa6"},{"\xe1\xbb\xa5","\xe1\xbb\xa4"},{"\xe1\xb9\xb3","\xe1\xb9\xb2"},{"\xe1\xbb\xa9","\xe1\xbb\xa8"},{"\xe1\xbb\xab","\xe1\xbb\xaa"},{"\xe1\xbb\xaf","\xe1\xbb\xae"},{"\xe1\xbb\xad","\xe1\xbb\xac"},{"\xe1\xbb\xb1","\xe1\xbb\xb0"},{"\xc6\xb0","\xc6\xaf"},{"\x76","\x56"},{"\x77","\x57"},{"\xc5\xb5","\xc5\xb4"},{"\x78","\x58"},{"\xe1\xba\x8b","\xe1\xba\x8a"},{"\x79","\x59"},{"\xc3\xbd","\xc3\x9d"},{"\xe1\xbb\xb3","\xe1\xbb\xb2"},{"\xc5\xb7","\xc5\xb6"},{"\xc3\xbf","\xc5\xb8"},{"\xe1\xbb\xb9","\xe1\xbb\xb8"},{"\x7a","\x5a"},{"\xc5\xba","\xc5\xb9"},{"\xc5\xbe","\xc5\xbd"},{"\xc5\xbc","\xc5\xbb"},{"\xc6\xb6","\xc6\xb5"},{"\xe1\xba\x93","\xe1\xba\x92"},{"\xe1\xba\x95","\xe1\xba\x94"},{"\xc8\xa5","\xc8\xa4"},{"\xc3\xbe","\xc3\x9e"},{"\xca\x92","\xc6\xb7"},{"\xce\xb1","\xce\x91"},{"\xce\xac","\xce\x86"},{"\xce\xb2","\xce\x92"},{"\xce\xb3","\xce\x93"},{"\xce\xb4","\xce\x94"},{"\xce\xb5","\xce\x95"},{"\xce\xad","\xce\x88"},{"\xce\xb6","\xce\x96"},{"\xce\xb7","\xce\x97"},{"\xce\xae","\xce\x89"},{"\xce\xb8","\xce\x98"},{"\xce\xb9","\xce\x99"},{"\xce\xaf","\xce\x8a"},{"\xcf\x8a","\xce\xaa"},{"\xce\xba","\xce\x9a"},{"\xce\xbb","\xce\x9b"},{"\xce\xbc","\xce\x9c"},{"\xce\xbd","\xce\x9d"},{"\xce\xbe","\xce\x9e"},{"\xce\xbf","\xce\x9f"},{"\xcf\x8c","\xce\x8c"},{"\xcf\x80","\xce\xa0"},{"\xcf\x83","\xce\xa3"},{"\xcf\x82","\xce\xa3"},{"\xcf\x84","\xce\xa4"},{"\xcf\x85","\xce\xa5"},{"\xcf\x8d","\xce\x8e"},{"\xcf\x8b","\xce\xab"},{"\xcf\x86","\xce\xa6"},{"\xcf\x87","\xce\xa7"},{"\xcf\x88","\xce\xa8"},{"\xcf\x89","\xce\xa9"},{"\xcf\x8e","\xce\x8f"},{"\xd0\xb0","\xd0\x90"},{"\xd3\x93","\xd3\x92"},{"\xd3\x95","\xd3\x94"},{"\xd0\xb1","\xd0\x91"},{"\xd0\xb2","\xd0\x92"},{"\xd0\xb3","\xd0\x93"},{"\xd2\x93","\xd2\x92"},{"\xd2\x91","\xd2\x90"},{"\xd0\xb4","\xd0\x94"},{"\xd1\x93","\xd0\x83"},{"\xd1\x92","\xd0\x82"},{"\xd0\xb5","\xd0\x95"},{"\xd1\x90","\xd0\x80"},{"\xd3\x99","\xd3\x98"},{"\xd1\x94","\xd0\x84"},{"\xd1\x91","\xd0\x81"},{"\xd0\xb6","\xd0\x96"},{"\xd0\xb7","\xd0\x97"},{"\xd2\x99","\xd2\x98"},{"\xd1\x95","\xd0\x85"},{"\xd0\xb8","\xd0\x98"},{"\xd3\xa3","\xd3\xa2"},{"\xd1\x96","\xd0\x86"},{"\xd1\x97","\xd0\x87"},{"\xd0\xb9","\xd0\x99"},{"\xd1\x98","\xd0\x88"},{"\xd0\xba","\xd0\x9a"},{"\xd2\x9b","\xd2\x9a"},{"\xd3\x84","\xd3\x83"},{"\xd2\xa1","\xd2\xa0"},{"\xd0\xbb","\xd0\x9b"},{"\xd1\x99","\xd0\x89"},{"\xd0\xbc","\xd0\x9c"},{"\xd0\xbd","\xd0\x9d"},{"\xd2\xa3","\xd2\xa2"},{"\xd1\x9a","\xd0\x8a"},{"\xd0\xbe","\xd0\x9e"},{"\xd3\xa7","\xd3\xa6"},{"\xd3\xa9","\xd3\xa8"},{"\xd0\xbf","\xd0\x9f"},{"\xd1\x80","\xd0\xa0"},{"\xd1\x81","\xd0\xa1"},{"\xd2\xab","\xd2\xaa"},{"\xd1\x82","\xd0\xa2"},{"\xd1\x9c","\xd0\x8c"},{"\xd1\x9b","\xd0\x8b"},{"\xd1\x83","\xd0\xa3"},{"\xd3\xb1","\xd3\xb0"},{"\xd2\xb1","\xd2\xb0"},{"\xd2\xaf","\xd2\xae"},{"\xd1\x9e","\xd0\x8e"},{"\xd1\x84","\xd0\xa4"},{"\xd1\x85","\xd0\xa5"},{"\xd2\xb3","\xd2\xb2"},{"\xd2\xbb","\xd2\xba"},{"\xd1\x86","\xd0\xa6"},{"\xd1\x87","\xd0\xa7"},{"\xd1\x9f","\xd0\x8f"},{"\xd1\x88","\xd0\xa8"},{"\xd1\x89","\xd0\xa9"},{"\xd1\x8a","\xd0\xaa"},{"\xd1\x8b","\xd0\xab"},{"\xd1\x8c","\xd0\xac"},{"\xd1\x8d","\xd0\xad"},{"\xd1\x8e","\xd0\xae"},{"\xd1\x8f","\xd0\xaf"},{"\xd5\xa1","\xd4\xb1"},{"\xd5\xa3","\xd4\xb3"},{"\xd5\xa5","\xd4\xb5"},{"\xd5\xab","\xd4\xbb"},{"\xd5\xac","\xd4\xbc"},{"\xd5\xb2","\xd5\x82"},{"\xd5\xb8","\xd5\x88"},{"\xd5\xbd","\xd5\x8d"},{"\xd5\xbe","\xd5\x8e"},{"\xd5\xbf","\xd5\x8f"},{"\xd6\x80","\xd5\x90"},{"\xd6\x81","\xd5\x91"} };
+    for (auto p8 : map8) {
+      auto from = utf8ToWString(p8.first);
+      auto to   = utf8ToWString(p8.second);
+      ABORT_IF(from.size() != 1 || to.size() != 1, "Incorrect character encoding??");
+      insert(std::make_pair(from.front(), to.front()));
+    }
+  }
+  wchar_t towupper(wchar_t c) const {
+    auto iter = find(c);
+    if (iter == end())
+      return c;
+    else
+      return iter->second;
+  }
+};
+std::string utf8ToUpper(const std::string& s) {
+  static UTF8Mapper mapper;
+  auto ws = utf8ToWString(s);
+  for (auto& c : ws)
+    c = mapper.towupper(c);
+  return toUTF8String(ws);
+}
+#endif
 
 double parseDouble(std::string s) {
   double res;
diff --git a/src/data/corpus.cpp b/src/data/corpus.cpp
index b39a7fa5a..07993f485 100755
--- a/src/data/corpus.cpp
+++ b/src/data/corpus.cpp
@@ -17,10 +17,13 @@ Corpus::Corpus(std::vector<std::string> paths,
                Ptr<Options> options)
     : CorpusBase(paths, vocabs, options), shuffleInRAM_(options_->get<bool>("shuffle-in-ram")), allCapsEvery_(options_->get<size_t>("all-caps-every")) {}
 
-void Corpus::preprocessLine(std::string& line, size_t /*streamId*/) {
+void Corpus::preprocessLine(std::string& line, size_t streamId) {
   if (allCapsEvery_ != 0 && pos_ % allCapsEvery_ == 0) {
     line = utils::utf8ToUpper(line);
-    LOG_ONCE(info, "all-caps'ed line to {}", line);
+    if (streamId == 0)
+      LOG_ONCE(info, "[data] source all-caps'ed line to {}", line);
+    else
+      LOG_ONCE(info, "[data] target all-caps'ed line to {}", line);
   }
 }
 
diff --git a/src/tensors/gpu/tensor_operators.cu b/src/tensors/gpu/tensor_operators.cu
old mode 100644
new mode 100755
index 34da4c688..b68d24c3e
--- a/src/tensors/gpu/tensor_operators.cu
+++ b/src/tensors/gpu/tensor_operators.cu
@@ -438,6 +438,7 @@ __global__ void gSoftmax(float* out,
           so[id] = so[id] / _sum[0];
         }
       }
+      __syncthreads();
     }
   }
 }
@@ -521,6 +522,7 @@ __global__ void gLogSoftmax(float* out,
         if(id < cols)
           so[id] -= __logf(_sum[0]);
       }
+      __syncthreads();
     }
   }
 }
@@ -580,6 +582,7 @@ __global__ void gSoftmaxGrad(float* grad,
             gradRow[id] += val;
         }
       }
+      __syncthreads();
     }
   }
 }
@@ -636,6 +639,7 @@ __global__ void gLogSoftmaxGrad(float* grad,
         if(id < cols)
           gradRow[id] += adjRow[id] - (expf(valRow[id]) * _sum[0]);
       }
+      __syncthreads();
     }
   }
 }

From 5d01d870537e8853f28604e36367ccb6b1904234 Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Wed, 30 Jan 2019 13:48:20 -0800
Subject: [PATCH 239/838] disabled the LL weights for factors again, as they
 can now be learned directly

---
 src/layers/generic.cpp | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/src/layers/generic.cpp b/src/layers/generic.cpp
index 2ff6d34cc..fb618686d 100755
--- a/src/layers/generic.cpp
+++ b/src/layers/generic.cpp
@@ -314,9 +314,9 @@ namespace marian {
           // log-linear weight
           // @TODO: The weight should not be needed, since it could learn it into the embeddings. Maybe it doesn't.
           // @TODO? If we move the weight before affine(), it would use less memory at least for the main factor.
-          auto name = options_->get<std::string>("prefix");
-          auto llWeight = graph->param(name + "_llWeight_" + std::to_string(g), {}, inits::from_value(1.0f));
-          factorLogits = factorLogits * llWeight; // -a constant, which is OK for logits
+          //auto name = options_->get<std::string>("prefix");
+          //auto llWeight = graph->param(name + "_llWeight_" + std::to_string(g), {}, inits::from_value(1.0f));
+          //factorLogits = factorLogits * llWeight; // -a constant, which is OK for logits
           allLogits[g] = factorLogits;
         }
         return Logits(std::move(allLogits), embeddingFactorMapping_);

From b117996c0357b7a372756c08b4d87287dc1dd8fb Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Wed, 30 Jan 2019 15:29:16 -0800
Subject: [PATCH 240/838] Output now creates its own W matrix in transposed
 form

---
 src/common/config_parser.cpp    |   2 +-
 src/data/corpus_base.h          |   2 +-
 src/data/default_vocab.cpp      |   2 +-
 src/graph/expression_graph.h    |   7 +
 src/graph/node_initializers.cpp |   2 +-
 src/layers/generic.h            |  45 ++--
 src/models/bert.h               |  10 +-
 src/models/encoder_classifier.h | 436 ++++++++++++++++----------------
 src/rnn/cells.h                 |   0
 src/training/scheduler.h        |   8 +-
 src/training/validator.h        |   2 +-
 11 files changed, 262 insertions(+), 254 deletions(-)
 mode change 100644 => 100755 src/models/bert.h
 mode change 100644 => 100755 src/models/encoder_classifier.h
 mode change 100644 => 100755 src/rnn/cells.h

diff --git a/src/common/config_parser.cpp b/src/common/config_parser.cpp
index 338d4b82c..49d96ca1a 100755
--- a/src/common/config_parser.cpp
+++ b/src/common/config_parser.cpp
@@ -206,7 +206,7 @@ void ConfigParser::addOptionsModel(cli::CLIWrapper& cli) {
   cli.add<std::string>("--bert-mask-symbol", "Masking symbol for BERT masked-LM training", "[MASK]");
   cli.add<std::string>("--bert-sep-symbol", "Sentence separator symbol for BERT next sentence prediction training", "[SEP]");
   cli.add<std::string>("--bert-class-symbol", "Class symbol BERT classifier training", "[CLS]");
-  cli.add<float>("--bert-masking-fraction", "Fraction of masked out tokens during training", 0.15);
+  cli.add<float>("--bert-masking-fraction", "Fraction of masked out tokens during training", 0.15f);
 #ifdef CUDNN
   cli.add<int>("--char-stride",
       "Width of max-pooling layer after convolution layer in char-s2s model",
diff --git a/src/data/corpus_base.h b/src/data/corpus_base.h
index 85e26d717..725059520 100755
--- a/src/data/corpus_base.h
+++ b/src/data/corpus_base.h
@@ -322,7 +322,7 @@ class CorpusBatch : public Batch {
       // set word indices to different values to avoid same hashes
       // rand() is OK, this does not affect state in any way
       std::transform(sb->data().begin(), sb->data().end(), sb->data().begin(),
-                     [&](Word) { return rand() % vocabs[batchIndex]->size(); }); 
+                     [&](Word) -> Word { return rand() % vocabs[batchIndex]->size(); });
       // mask: no items ask being masked out
       std::fill(sb->mask().begin(), sb->mask().end(), 1.f);
       batchIndex++;
diff --git a/src/data/default_vocab.cpp b/src/data/default_vocab.cpp
index f5d009b2a..36a32328f 100755
--- a/src/data/default_vocab.cpp
+++ b/src/data/default_vocab.cpp
@@ -288,7 +288,7 @@ class DefaultVocab : public VocabBase {
 class ClassVocab : public DefaultVocab {
 private:
   // Do nothing.
-  virtual void addRequiredVocabulary(const std::string& vocabPath, bool isJson) override {}
+  virtual void addRequiredVocabulary(const std::string& vocabPath, bool isJson) override { vocabPath; isJson; }
 
   // Not adding special class labels, only seen classes.
   virtual void create(const std::string& vocabPath,
diff --git a/src/graph/expression_graph.h b/src/graph/expression_graph.h
index fe8361618..22908d96b 100755
--- a/src/graph/expression_graph.h
+++ b/src/graph/expression_graph.h
@@ -316,6 +316,13 @@ class ExpressionGraph : public std::enable_shared_from_this<ExpressionGraph> {
     dot.close();
   }
 
+  Expr tryFindParam(const std::string& pname) const {
+    std::string name = pname;
+    if(!namespace_.empty())
+      name = namespace_ + "::" + name;
+    return params_->get(name);
+  }
+
   Expr param(const std::string& pname,
              const Shape& shape,
              const NodeInitializer& init,
diff --git a/src/graph/node_initializers.cpp b/src/graph/node_initializers.cpp
index f405102ec..b2550c548 100755
--- a/src/graph/node_initializers.cpp
+++ b/src/graph/node_initializers.cpp
@@ -195,7 +195,7 @@ NodeInitializer from_item(const io::Item& item) {
 NodeInitializer sinusoidalPositionEmbeddings(int start) {
   return [start](Tensor t) {
     int dimEmb   = t->shape()[-1];
-    int dimWords = t->size() / dimEmb;
+    int dimWords = (int)t->size() / dimEmb;
 
     float numTimescales = (float)dimEmb / 2;
     float logTimescaleIncrement = std::log(10000.f) / (numTimescales - 1.f);
diff --git a/src/layers/generic.h b/src/layers/generic.h
index 062b59cc8..8e03efd08 100755
--- a/src/layers/generic.h
+++ b/src/layers/generic.h
@@ -125,14 +125,14 @@ class Dense : public LayerBase, public IUnaryLayer {
 
 class Output : public LayerBase, public IUnaryLayer {
 private:
-  Expr W_;  // parameters held by this layer
+  // parameters held by this layer
+  Expr Wt_; // weight matrix is stored transposed for efficiency
   Expr b_;
-  Expr cachedShortW_;   // short-listed version, cached (cleared by clear())
+  Expr cachedShortWt_;  // short-listed version, cached (cleared by clear())
   Expr cachedShortb_;   // these match the current value of shortlist_
 
   // optional parameters set/updated after construction
   Expr tiedParam_;
-  bool transposeW_{false};
   Ptr<data::Shortlist> shortlist_;
 
 public:
@@ -142,7 +142,7 @@ class Output : public LayerBase, public IUnaryLayer {
   }
 
   void tieTransposed(Expr tied) {
-    if (W_)
+    if (Wt_)
       ABORT_IF(tiedParam_.get() != tied.get(), "Tied output projection cannot be changed once weights have been created");
     else
       tiedParam_ = tied;
@@ -152,47 +152,46 @@ class Output : public LayerBase, public IUnaryLayer {
     if (shortlist_)
       ABORT_IF(shortlist.get() != shortlist_.get(), "Output shortlist cannot be changed except after clear()");
     else {
-      ABORT_IF(cachedShortW_ || cachedShortb_, "No shortlist but cached parameters??");
+      ABORT_IF(cachedShortWt_ || cachedShortb_, "No shortlist but cached parameters??");
       shortlist_ = shortlist;
     }
-    // cachedShortW_ and cachedShortb_ will be created lazily inside apply()
+    // cachedShortWt_ and cachedShortb_ will be created lazily inside apply()
   }
 
   // this is expected to be called in sync with graph->clear(), which invalidates
-  // cachedShortW_ and cachedShortb_ in the graph's short-term cache
+  // cachedShortWt_ and cachedShortb_ in the graph's short-term cache
   void clear() {
     shortlist_ = nullptr;
-    cachedShortW_ = nullptr;
-    cachedShortb_ = nullptr;
+    cachedShortWt_ = nullptr;
+    cachedShortb_  = nullptr;
   }
 
   Expr apply(Expr input) override {
-    if(!W_) { // create lazily because we need input's dimension
+    if(!Wt_) { // create lazily because we need input's dimension
       auto name = options_->get<std::string>("prefix");
       auto dim = options_->get<int>("dim");
 
       if(tiedParam_) {
-        W_ = tiedParam_;
-        transposeW_ = true;
+        Wt_ = tiedParam_;
       } else {
-        W_ = graph_->param(name + "_W", {input->shape()[-1], dim}, inits::glorot_uniform);
-        transposeW_ = false;
+        auto W = graph_->tryFindParam(name + "_W"); // support of legacy models that did not transpose
+        if (W)
+          Wt_ = transpose(W); // legacy
+        else // this is the regular case:
+          Wt_ = graph_->param(name + "_Wt", {input->shape()[-1], dim}, inits::glorot_uniform);
       }
       b_ = graph_->param(name + "_b", {1, dim}, inits::zeros);
     }
 
     if (shortlist_) {
-      if (!cachedShortW_) { // short versions of parameters are cached within one batch, then clear()ed
-        if(transposeW_)
-          cachedShortW_ = rows(W_, shortlist_->indices());
-        else
-          cachedShortW_ = cols(W_, shortlist_->indices());
-        cachedShortb_ = cols(b_, shortlist_->indices());
+      if (!cachedShortWt_) { // short versions of parameters are cached within one batch, then clear()ed
+        cachedShortWt_ = rows(Wt_, shortlist_->indices());
+        cachedShortb_  = cols(b_ , shortlist_->indices());
       }
-      return affine(input, cachedShortW_, cachedShortb_, false, transposeW_);
+      return affine(input, cachedShortWt_, cachedShortb_, false, /*transB=*/true);
     }
     else
-      return affine(input, W_, b_, false, transposeW_);
+      return affine(input, Wt_, b_, false, /*transB=*/true);
   }
 
   virtual Expr apply(const std::vector<Expr>& /*inputs*/) override {
@@ -349,7 +348,7 @@ class ULREmbedding : public LayerBase, public IEmbeddingLayer {
   }
 
   Expr apply(const std::vector<IndexType>& embIdx, const Shape& shape) const override final {
-    shape;
+    embIdx; shape;
     ABORT("not implemented"); // @TODO: implement me
   }
 };
diff --git a/src/models/bert.h b/src/models/bert.h
old mode 100644
new mode 100755
index c642abac4..c56eb2977
--- a/src/models/bert.h
+++ b/src/models/bert.h
@@ -79,7 +79,7 @@ class BertBatch : public CorpusBatch {
     const auto& vocab = *subBatch->vocab();
 
     // Initialize to sample random vocab id
-    randomWord_.reset(new std::uniform_int_distribution<Word>(0, vocab.size()));
+    randomWord_.reset(new std::uniform_int_distribution<Word>(0, (Word)vocab.size()));
 
     // Intialize to sample random percentage
     randomPercent_.reset(new std::uniform_real_distribution<float>(0.f, 1.f));
@@ -111,7 +111,7 @@ class BertBatch : public CorpusBatch {
       if(dontMask_.count(words[i]) == 0)  // do not add indices of special words
         selected.push_back(i);
     std::shuffle(selected.begin(), selected.end(), engine); // randomize positions
-    selected.resize(std::ceil(selected.size() * maskFraction)); // select first x percent from shuffled indices
+    selected.resize((size_t)std::ceil(selected.size() * maskFraction)); // select first x percent from shuffled indices
 
     for(int i : selected) {
       maskedPositions_.push_back(i);                // where is the original word?
@@ -141,10 +141,10 @@ class BertBatch : public CorpusBatch {
     ABORT_IF(sepId == vocab.getUnkId(),
              "BERT separator symbol {} not found in vocabulary", sepSymbol_);
 
-    int dimBatch = subBatch->batchSize();
-    int dimWords = subBatch->batchWidth();
+    int dimBatch = (int)subBatch->batchSize();
+    int dimWords = (int)subBatch->batchWidth();
 
-    int maxSentPos = 2; // Currently only two sentences allowed A at [0] and B at [1] and padding at [2]
+    const size_t maxSentPos = 2; // Currently only two sentences allowed A at [0] and B at [1] and padding at [2]
     // If another separator is seen do not increase position index beyond 2 but use padding.
     // @TODO: make this configurable, see below for NextSentencePredictions task where we also restrict to 2.
 
diff --git a/src/models/encoder_classifier.h b/src/models/encoder_classifier.h
old mode 100644
new mode 100755
index bc3d8f9f8..c593ccfd3
--- a/src/models/encoder_classifier.h
+++ b/src/models/encoder_classifier.h
@@ -1,218 +1,218 @@
-#pragma once
-
-#include "marian.h"
-
-#include "models/encoder.h"
-#include "models/classifier.h"
-#include "models/model_base.h"
-#include "models/states.h"
-
-namespace marian {
-
-/**
- * Combines sequence encoders with generic classifiers
- * Can be used to train sequence classifiers like language detection, BERT-next-sentence-prediction etc.
- * Already has support for multi-objective training.
- *
- * @TODO: this should probably be unified somehow with EncoderDecoder which could allow for deocder/classifier
- * multi-objective training.
- */
-class EncoderClassifierBase : public models::ModelBase {
-public:
-  virtual ~EncoderClassifierBase() {}
-
-  virtual void load(Ptr<ExpressionGraph> graph,
-                    const std::string& name,
-                    bool markedReloaded = true) override
-      = 0;
-
-  virtual void mmap(Ptr<ExpressionGraph> graph,
-                    const void* ptr,
-                    bool markedReloaded = true)
-      = 0;
-
-  virtual void save(Ptr<ExpressionGraph> graph,
-                    const std::string& name,
-                    bool saveTranslatorConfig = false) override
-      = 0;
-
-  virtual void clear(Ptr<ExpressionGraph> graph) override = 0;
-
-  virtual std::vector<Ptr<ClassifierState>> apply(Ptr<ExpressionGraph>, Ptr<data::CorpusBatch>, bool) = 0;
-
-
-  virtual Ptr<RationalLoss> build(Ptr<ExpressionGraph> graph,
-                                  Ptr<data::Batch> batch,
-                                  bool clearGraph = true) override = 0;
-
-  virtual Ptr<RationalLoss> build(Ptr<ExpressionGraph> graph,
-                                  Ptr<data::CorpusBatch> batch,
-                                  bool clearGraph = true) = 0;
-
-  virtual Ptr<Options> getOptions() = 0;
-};
-
-class EncoderClassifier : public EncoderClassifierBase {
-protected:
-  Ptr<Options> options_;
-
-  std::string prefix_;
-
-  std::vector<Ptr<EncoderBase>> encoders_;
-  std::vector<Ptr<ClassifierBase>> classifiers_;
-
-  bool inference_{false};
-
-  std::set<std::string> modelFeatures_;
-
-  Config::YamlNode getModelParameters() {
-    Config::YamlNode modelParams;
-    for(auto& key : modelFeatures_)
-        modelParams[key] = options_->getYaml()[key];
-
-    if(options_->has("original-type"))
-        modelParams["type"] = options_->getYaml()["original-type"];
-
-    modelParams["version"] = buildVersion();
-    return modelParams;
-  }
-
-  std::string getModelParametersAsString() {
-    auto yaml = getModelParameters();
-    YAML::Emitter out;
-    cli::OutputYaml(yaml, out);
-    return std::string(out.c_str());
-  }
-
-public:
-  typedef data::Corpus dataset_type;
-
-  // @TODO: lots of code-duplication with EncoderDecoder
-  EncoderClassifier(Ptr<Options> options)
-    : options_(options),
-      prefix_(options->get<std::string>("prefix", "")),
-      inference_(options->get<bool>("inference", false)) {
-    modelFeatures_ = {"type",
-                    "dim-vocabs",
-                    "dim-emb",
-                    "dim-rnn",
-                    "enc-cell",
-                    "enc-type",
-                    "enc-cell-depth",
-                    "enc-depth",
-                    "dec-depth",
-                    "dec-cell",
-                    "dec-cell-base-depth",
-                    "dec-cell-high-depth",
-                    "skip",
-                    "layer-normalization",
-                    "right-left",
-                    "input-types",
-                    "special-vocab",
-                    "tied-embeddings",
-                    "tied-embeddings-src",
-                    "tied-embeddings-all"};
-
-    modelFeatures_.insert("transformer-heads");
-    modelFeatures_.insert("transformer-no-projection");
-    modelFeatures_.insert("transformer-dim-ffn");
-    modelFeatures_.insert("transformer-ffn-depth");
-    modelFeatures_.insert("transformer-ffn-activation");
-    modelFeatures_.insert("transformer-dim-aan");
-    modelFeatures_.insert("transformer-aan-depth");
-    modelFeatures_.insert("transformer-aan-activation");
-    modelFeatures_.insert("transformer-aan-nogate");
-    modelFeatures_.insert("transformer-preprocess");
-    modelFeatures_.insert("transformer-postprocess");
-    modelFeatures_.insert("transformer-postprocess-emb");
-    modelFeatures_.insert("transformer-decoder-autoreg");
-    modelFeatures_.insert("transformer-tied-layers");
-    modelFeatures_.insert("transformer-guided-alignment-layer");
-    modelFeatures_.insert("transformer-train-positions");
-  }
-
-  virtual Ptr<Options> getOptions() override { return options_; }
-
-  std::vector<Ptr<EncoderBase>>& getEncoders() { return encoders_; }
-  std::vector<Ptr<ClassifierBase>>& getClassifiers() { return classifiers_; }
-
-  void push_back(Ptr<EncoderBase> encoder) { encoders_.push_back(encoder); }
-  void push_back(Ptr<ClassifierBase> classifier) { classifiers_.push_back(classifier); }
-
-  void load(Ptr<ExpressionGraph> graph,
-            const std::string& name,
-            bool markedReloaded) override {
-    graph->load(name, markedReloaded && !opt<bool>("ignore-model-config", false));
-  }
-
-  void mmap(Ptr<ExpressionGraph> graph,
-            const void* ptr,
-            bool markedReloaded) override {
-    graph->mmap(ptr, markedReloaded && !opt<bool>("ignore-model-config", false));
-  }
-
-  void save(Ptr<ExpressionGraph> graph,
-            const std::string& name,
-            bool saveModelConfig) override {
-    LOG(info, "Saving model weights and runtime parameters to {}", name);
-    graph->save(name , getModelParametersAsString());
-  }
-
-  void clear(Ptr<ExpressionGraph> graph) override {
-    graph->clear();
-
-    for(auto& enc : encoders_)
-      enc->clear();
-    for(auto& cls : classifiers_)
-      cls->clear();
-  }
-
-  template <typename T>
-  T opt(const std::string& key) {
-    return options_->get<T>(key);
-  }
-
-  template <typename T>
-  T opt(const std::string& key, const T& def) {
-    return options_->get<T>(key, def);
-  }
-
-  template <typename T>
-  void set(std::string key, T value) {
-    options_->set(key, value);
-  }
-
-  /*********************************************************************/
-
-  virtual std::vector<Ptr<ClassifierState>> apply(Ptr<ExpressionGraph> graph, Ptr<data::CorpusBatch> batch, bool clearGraph) override {
-    if(clearGraph)
-      clear(graph);
-
-    std::vector<Ptr<EncoderState>> encoderStates;
-    for(auto& encoder : encoders_)
-        encoderStates.push_back(encoder->build(graph, batch));
-
-    std::vector<Ptr<ClassifierState>> classifierStates;
-    for(auto& classifier : classifiers_)
-      classifierStates.push_back(classifier->apply(graph, batch, encoderStates));
-
-    return classifierStates;
-  }
-
-  virtual Ptr<RationalLoss> build(Ptr<ExpressionGraph> graph,
-                                  Ptr<data::CorpusBatch> batch,
-                                  bool clearGraph = true) override {
-    auto states = apply(graph, batch, clearGraph);
-    // returns raw logits
-    return New<RationalLoss>(states[0]->getLogProbs(), nullptr); // @TODO: Check if this is actually used
-  }
-
-  virtual Ptr<RationalLoss> build(Ptr<ExpressionGraph> graph,
-                                  Ptr<data::Batch> batch,
-                                  bool clearGraph = true) override {
-    auto corpusBatch = std::static_pointer_cast<data::CorpusBatch>(batch);
-    return build(graph, corpusBatch, clearGraph);
-  }
-};
-
-}  // namespace marian
+#pragma once
+
+#include "marian.h"
+
+#include "models/encoder.h"
+#include "models/classifier.h"
+#include "models/model_base.h"
+#include "models/states.h"
+
+namespace marian {
+
+/**
+ * Combines sequence encoders with generic classifiers
+ * Can be used to train sequence classifiers like language detection, BERT-next-sentence-prediction etc.
+ * Already has support for multi-objective training.
+ *
+ * @TODO: this should probably be unified somehow with EncoderDecoder which could allow for deocder/classifier
+ * multi-objective training.
+ */
+class EncoderClassifierBase : public models::ModelBase {
+public:
+  virtual ~EncoderClassifierBase() {}
+
+  virtual void load(Ptr<ExpressionGraph> graph,
+                    const std::string& name,
+                    bool markedReloaded = true) override
+      = 0;
+
+  virtual void mmap(Ptr<ExpressionGraph> graph,
+                    const void* ptr,
+                    bool markedReloaded = true)
+      = 0;
+
+  virtual void save(Ptr<ExpressionGraph> graph,
+                    const std::string& name,
+                    bool saveTranslatorConfig = false) override
+      = 0;
+
+  virtual void clear(Ptr<ExpressionGraph> graph) override = 0;
+
+  virtual std::vector<Ptr<ClassifierState>> apply(Ptr<ExpressionGraph>, Ptr<data::CorpusBatch>, bool) = 0;
+
+
+  virtual Ptr<RationalLoss> build(Ptr<ExpressionGraph> graph,
+                                  Ptr<data::Batch> batch,
+                                  bool clearGraph = true) override = 0;
+
+  virtual Ptr<RationalLoss> build(Ptr<ExpressionGraph> graph,
+                                  Ptr<data::CorpusBatch> batch,
+                                  bool clearGraph = true) = 0;
+
+  virtual Ptr<Options> getOptions() = 0;
+};
+
+class EncoderClassifier : public EncoderClassifierBase {
+protected:
+  Ptr<Options> options_;
+
+  std::string prefix_;
+
+  std::vector<Ptr<EncoderBase>> encoders_;
+  std::vector<Ptr<ClassifierBase>> classifiers_;
+
+  bool inference_{false};
+
+  std::set<std::string> modelFeatures_;
+
+  Config::YamlNode getModelParameters() {
+    Config::YamlNode modelParams;
+    for(auto& key : modelFeatures_)
+        modelParams[key] = options_->getYaml()[key];
+
+    if(options_->has("original-type"))
+        modelParams["type"] = options_->getYaml()["original-type"];
+
+    modelParams["version"] = buildVersion();
+    return modelParams;
+  }
+
+  std::string getModelParametersAsString() {
+    auto yaml = getModelParameters();
+    YAML::Emitter out;
+    cli::OutputYaml(yaml, out);
+    return std::string(out.c_str());
+  }
+
+public:
+  typedef data::Corpus dataset_type;
+
+  // @TODO: lots of code-duplication with EncoderDecoder
+  EncoderClassifier(Ptr<Options> options)
+    : options_(options),
+      prefix_(options->get<std::string>("prefix", "")),
+      inference_(options->get<bool>("inference", false)) {
+    modelFeatures_ = {"type",
+                    "dim-vocabs",
+                    "dim-emb",
+                    "dim-rnn",
+                    "enc-cell",
+                    "enc-type",
+                    "enc-cell-depth",
+                    "enc-depth",
+                    "dec-depth",
+                    "dec-cell",
+                    "dec-cell-base-depth",
+                    "dec-cell-high-depth",
+                    "skip",
+                    "layer-normalization",
+                    "right-left",
+                    "input-types",
+                    "special-vocab",
+                    "tied-embeddings",
+                    "tied-embeddings-src",
+                    "tied-embeddings-all"};
+
+    modelFeatures_.insert("transformer-heads");
+    modelFeatures_.insert("transformer-no-projection");
+    modelFeatures_.insert("transformer-dim-ffn");
+    modelFeatures_.insert("transformer-ffn-depth");
+    modelFeatures_.insert("transformer-ffn-activation");
+    modelFeatures_.insert("transformer-dim-aan");
+    modelFeatures_.insert("transformer-aan-depth");
+    modelFeatures_.insert("transformer-aan-activation");
+    modelFeatures_.insert("transformer-aan-nogate");
+    modelFeatures_.insert("transformer-preprocess");
+    modelFeatures_.insert("transformer-postprocess");
+    modelFeatures_.insert("transformer-postprocess-emb");
+    modelFeatures_.insert("transformer-decoder-autoreg");
+    modelFeatures_.insert("transformer-tied-layers");
+    modelFeatures_.insert("transformer-guided-alignment-layer");
+    modelFeatures_.insert("transformer-train-positions");
+  }
+
+  virtual Ptr<Options> getOptions() override { return options_; }
+
+  std::vector<Ptr<EncoderBase>>& getEncoders() { return encoders_; }
+  std::vector<Ptr<ClassifierBase>>& getClassifiers() { return classifiers_; }
+
+  void push_back(Ptr<EncoderBase> encoder) { encoders_.push_back(encoder); }
+  void push_back(Ptr<ClassifierBase> classifier) { classifiers_.push_back(classifier); }
+
+  void load(Ptr<ExpressionGraph> graph,
+            const std::string& name,
+            bool markedReloaded) override {
+    graph->load(name, markedReloaded && !opt<bool>("ignore-model-config", false));
+  }
+
+  void mmap(Ptr<ExpressionGraph> graph,
+            const void* ptr,
+            bool markedReloaded) override {
+    graph->mmap(ptr, markedReloaded && !opt<bool>("ignore-model-config", false));
+  }
+
+  void save(Ptr<ExpressionGraph> graph,
+            const std::string& name,
+            bool /*saveModelConfig*/) override {
+    LOG(info, "Saving model weights and runtime parameters to {}", name);
+    graph->save(name , getModelParametersAsString());
+  }
+
+  void clear(Ptr<ExpressionGraph> graph) override {
+    graph->clear();
+
+    for(auto& enc : encoders_)
+      enc->clear();
+    for(auto& cls : classifiers_)
+      cls->clear();
+  }
+
+  template <typename T>
+  T opt(const std::string& key) {
+    return options_->get<T>(key);
+  }
+
+  template <typename T>
+  T opt(const std::string& key, const T& def) {
+    return options_->get<T>(key, def);
+  }
+
+  template <typename T>
+  void set(std::string key, T value) {
+    options_->set(key, value);
+  }
+
+  /*********************************************************************/
+
+  virtual std::vector<Ptr<ClassifierState>> apply(Ptr<ExpressionGraph> graph, Ptr<data::CorpusBatch> batch, bool clearGraph) override {
+    if(clearGraph)
+      clear(graph);
+
+    std::vector<Ptr<EncoderState>> encoderStates;
+    for(auto& encoder : encoders_)
+        encoderStates.push_back(encoder->build(graph, batch));
+
+    std::vector<Ptr<ClassifierState>> classifierStates;
+    for(auto& classifier : classifiers_)
+      classifierStates.push_back(classifier->apply(graph, batch, encoderStates));
+
+    return classifierStates;
+  }
+
+  virtual Ptr<RationalLoss> build(Ptr<ExpressionGraph> graph,
+                                  Ptr<data::CorpusBatch> batch,
+                                  bool clearGraph = true) override {
+    auto states = apply(graph, batch, clearGraph);
+    // returns raw logits
+    return New<RationalLoss>(states[0]->getLogProbs(), nullptr); // @TODO: Check if this is actually used
+  }
+
+  virtual Ptr<RationalLoss> build(Ptr<ExpressionGraph> graph,
+                                  Ptr<data::Batch> batch,
+                                  bool clearGraph = true) override {
+    auto corpusBatch = std::static_pointer_cast<data::CorpusBatch>(batch);
+    return build(graph, corpusBatch, clearGraph);
+  }
+};
+
+}  // namespace marian
diff --git a/src/rnn/cells.h b/src/rnn/cells.h
old mode 100644
new mode 100755
diff --git a/src/training/scheduler.h b/src/training/scheduler.h
index 65bb88207..8cc787b3d 100755
--- a/src/training/scheduler.h
+++ b/src/training/scheduler.h
@@ -273,15 +273,17 @@ class Scheduler : public TrainingObserver {
     if (mpi)
       rationalLoss.loss *= mpi->numMPIProcesses();
 
+    // @BUGBUG: rationalLoss.count is float, not a count. Possible solution: make (costSum, costCount) a StaticLoss object as well
     state_->costSum      += rationalLoss.loss;   // aggregate sum cost since last display
-    state_->costCount    += rationalLoss.count; // cost gets normalized w.r.t. this in display
+    state_->costCount    += (size_t)rationalLoss.count; // cost gets normalized w.r.t. this in display
 
     state_->updatesDisp  += 1;
     state_->samplesDisp  += batchSize;
     state_->wordsDisp    += batchLabels;  //@TODO: this is wrong        // words at given input processed since last display, for speed display
 
-    state_->samplesEpoch += batchSize;           // sentences processed in this epoch
-    state_->labelsTotal  += rationalLoss.count; // total labels processed
+    state_->samplesEpoch += batchSize;          // sentences processed in this epoch
+    // @BUGBUG: rationalLoss.count is float, not a count
+    state_->labelsTotal  += (size_t)rationalLoss.count; // total labels processed
 
     state_->newUpdate(numReadBatches);
 
diff --git a/src/training/validator.h b/src/training/validator.h
index d09859b20..a819968b6 100755
--- a/src/training/validator.h
+++ b/src/training/validator.h
@@ -352,7 +352,7 @@ class BertAccuracyValidator : public Validator<data::Corpus> {
           }
 
           if(!engine)
-            engine.reset(new std::mt19937(Config::seed + id));
+            engine.reset(new std::mt19937((unsigned int)(Config::seed + id)));
 
           auto bertBatch = New<data::BertBatch>(batch,
                                                 *engine,

From 7b649c00e0d08083818235533b770cc46c66716e Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Thu, 31 Jan 2019 14:45:51 -0800
Subject: [PATCH 241/838] In VS Project, changed transformer.h from CPP file to
 a true header, and added the stub, to fix a compilation warning

---
 src/models/transformer_factory.h |  8 ++------
 src/models/transformer_stub.cpp  |  0
 vs/Marian.vcxproj                |  3 ++-
 vs/Marian.vcxproj.filters        | 13 +++++++++----
 4 files changed, 13 insertions(+), 11 deletions(-)
 mode change 100644 => 100755 src/models/transformer_stub.cpp

diff --git a/src/models/transformer_factory.h b/src/models/transformer_factory.h
index c2a7e13bb..fe9ee3ee6 100755
--- a/src/models/transformer_factory.h
+++ b/src/models/transformer_factory.h
@@ -4,12 +4,8 @@
 
 #include "models/decoder.h"
 #include "models/encoder.h"
-//#include "models/states.h"
-//#include "layers/constructors.h"
-//#include "layers/factory.h"
 
 namespace marian {
-// @TODO: find out why static is required here to get to compile
-static Ptr<EncoderBase> NewEncoderTransformer(Ptr<Options> options);
-static Ptr<DecoderBase> NewDecoderTransformer(Ptr<Options> options);
+Ptr<EncoderBase> NewEncoderTransformer(Ptr<Options> options);
+Ptr<DecoderBase> NewDecoderTransformer(Ptr<Options> options);
 }  // namespace marian
diff --git a/src/models/transformer_stub.cpp b/src/models/transformer_stub.cpp
old mode 100644
new mode 100755
diff --git a/vs/Marian.vcxproj b/vs/Marian.vcxproj
index a6c560d3a..7f82324be 100755
--- a/vs/Marian.vcxproj
+++ b/vs/Marian.vcxproj
@@ -580,6 +580,7 @@
     <ClCompile Include="..\src\microsoft\quicksand.cpp">
       <ExcludedFromBuild Condition="'$(Configuration)|$(Platform)'=='Debug|x64'">false</ExcludedFromBuild>
     </ClCompile>
+    <ClCompile Include="..\src\models\transformer_stub.cpp" />
     <ClCompile Include="..\src\rescorer\score_collector.cpp" />
     <ClCompile Include="..\src\tensors\backend.cpp" />
     <ClCompile Include="..\src\tensors\cpu\device.cpp" />
@@ -905,7 +906,7 @@
     <ClInclude Include="..\src\models\nematus.h" />
     <ClInclude Include="..\src\models\s2s.h" />
     <ClInclude Include="..\src\models\states.h" />
-    <ClCompile Include="..\src\models\transformer.h" />
+    <ClInclude Include="..\src\models\transformer.h" />
     <ClInclude Include="..\src\models\experimental\lex_probs.h" />
     <ClInclude Include="..\src\models\transformer_factory.h" />
     <ClInclude Include="..\src\optimizers\clippers.h" />
diff --git a/vs/Marian.vcxproj.filters b/vs/Marian.vcxproj.filters
index d9e56843f..1e30aa196 100755
--- a/vs/Marian.vcxproj.filters
+++ b/vs/Marian.vcxproj.filters
@@ -202,9 +202,6 @@
     <ClCompile Include="..\src\tensors\cpu\sharp\sse_gemm.cpp">
       <Filter>tensors\cpu\sharp</Filter>
     </ClCompile>
-    <ClCompile Include="..\src\models\transformer.h">
-      <Filter>models</Filter>
-    </ClCompile>
     <ClCompile Include="..\src\common\io.cpp">
       <Filter>common</Filter>
     </ClCompile>
@@ -481,6 +478,9 @@
     <ClCompile Include="..\src\examples\iris\iris.cpp">
       <Filter>examples\iris</Filter>
     </ClCompile>
+    <ClCompile Include="..\src\models\transformer_stub.cpp">
+      <Filter>models</Filter>
+    </ClCompile>
   </ItemGroup>
   <ItemGroup>
     <ClInclude Include="..\src\marian.h" />
@@ -1390,7 +1390,6 @@
     <ClInclude Include="..\src\3rd_party\sentencepiece\src\word_model_trainer.h">
       <Filter>3rd_party\sentencepiece\src</Filter>
     </ClInclude>
-    <ClInclude Include="..\src\command\marian_train.cpp" />
     <ClInclude Include="..\src\3rd_party\nccl\src\collectives\collectives.h">
       <Filter>3rd_party\nccl\src\collectives</Filter>
     </ClInclude>
@@ -1517,6 +1516,12 @@
     <ClInclude Include="..\src\examples\mnist\validator.h">
       <Filter>examples\mnist</Filter>
     </ClInclude>
+    <ClInclude Include="..\src\command\marian_train.cpp">
+      <Filter>command</Filter>
+    </ClInclude>
+    <ClInclude Include="..\src\models\transformer.h">
+      <Filter>models</Filter>
+    </ClInclude>
   </ItemGroup>
   <ItemGroup>
     <Filter Include="3rd_party">

From acee183116876c26f3852e9b13c82e130c513437 Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Thu, 31 Jan 2019 14:55:45 -0800
Subject: [PATCH 242/838] towards fixing the bug that bert.h includes
 transformer.h

---
 src/models/bert.h               |  4 +++-
 src/models/transformer.h        | 11 -----------
 src/models/transformer_stub.cpp | 13 +++++++++++++
 vs/Marian.vcxproj               |  1 +
 vs/Marian.vcxproj.filters       |  3 +++
 5 files changed, 20 insertions(+), 12 deletions(-)

diff --git a/src/models/bert.h b/src/models/bert.h
index c56eb2977..2ea725143 100755
--- a/src/models/bert.h
+++ b/src/models/bert.h
@@ -2,7 +2,7 @@
 
 #include "data/corpus_base.h"
 #include "models/encoder_classifier.h"
-#include "models/transformer.h"
+#include "models/transformer.h"   // @BUGBUG: transformer.h is large and was meant to be compiled separately
 #include "data/rng_engine.h"
 
 namespace marian {
@@ -211,6 +211,8 @@ class BertEncoderClassifier : public EncoderClassifier, public data::RNGEngine {
  * BERT-specific modifications to EncoderTransformer
  * Actually all that is needed is to intercept the creation of special embeddings,
  * here sentence embeddings for sentence A and B.
+ * @BUGBUG: transformer.h was meant to be compiled separately. I.e., one cannot derive from it.
+ *          Is there a way to maybe instead include a reference in here, instead of deriving from it?
  */
 class BertEncoder : public EncoderTransformer {
 public:
diff --git a/src/models/transformer.h b/src/models/transformer.h
index 01201df96..1db455c9e 100755
--- a/src/models/transformer.h
+++ b/src/models/transformer.h
@@ -862,17 +862,6 @@ class DecoderTransformer : public Transformer<DecoderBase> {
   }
 };
 
-// factory functions
-Ptr<EncoderBase> NewEncoderTransformer(Ptr<Options> options)
-{
-  return New<EncoderTransformer>(options);
-}
-
-Ptr<DecoderBase> NewDecoderTransformer(Ptr<Options> options)
-{
-  return New<DecoderTransformer>(options);
-}
-
 // clang-format on
 
 }  // namespace marian
diff --git a/src/models/transformer_stub.cpp b/src/models/transformer_stub.cpp
index 420b77810..c30921914 100755
--- a/src/models/transformer_stub.cpp
+++ b/src/models/transformer_stub.cpp
@@ -2,3 +2,16 @@
 // This is meant to speed-up builds, and to support Ctrl-F7 to rebuild.
 
 #include "models/transformer.h"
+
+namespace marian {
+// factory functions
+Ptr<EncoderBase> NewEncoderTransformer(Ptr<Options> options)
+{
+  return New<EncoderTransformer>(options);
+}
+
+Ptr<DecoderBase> NewDecoderTransformer(Ptr<Options> options)
+{
+  return New<DecoderTransformer>(options);
+}
+}  // namespace marian
diff --git a/vs/Marian.vcxproj b/vs/Marian.vcxproj
index 7f82324be..c899c242f 100755
--- a/vs/Marian.vcxproj
+++ b/vs/Marian.vcxproj
@@ -895,6 +895,7 @@
     <ClInclude Include="..\src\layers\word2vec_reader.h" />
     <ClInclude Include="..\src\microsoft\quicksand.h" />
     <ClInclude Include="..\src\models\amun.h" />
+    <ClInclude Include="..\src\models\bert.h" />
     <ClInclude Include="..\src\models\char_s2s.h" />
     <ClInclude Include="..\src\models\costs.h" />
     <ClInclude Include="..\src\models\decoder.h" />
diff --git a/vs/Marian.vcxproj.filters b/vs/Marian.vcxproj.filters
index 1e30aa196..947bcece8 100755
--- a/vs/Marian.vcxproj.filters
+++ b/vs/Marian.vcxproj.filters
@@ -1522,6 +1522,9 @@
     <ClInclude Include="..\src\models\transformer.h">
       <Filter>models</Filter>
     </ClInclude>
+    <ClInclude Include="..\src\models\bert.h">
+      <Filter>models</Filter>
+    </ClInclude>
   </ItemGroup>
   <ItemGroup>
     <Filter Include="3rd_party">

From 3312ed91116bf6ad7aeb0ed1b8516d888aa8b840 Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Thu, 31 Jan 2019 20:42:42 -0800
Subject: [PATCH 243/838] abstracted type Word into a class, in prep for
 factored decoding

---
 src/common/definitions.h       |  2 +-
 src/data/corpus_base.h         |  4 +-
 src/data/default_vocab.cpp     | 20 ++++----
 src/data/shortlist.h           | 86 +++++++++++++++++-----------------
 src/data/types.h               | 36 ++++++++++++--
 src/data/vocab.cpp             |  4 ++
 src/data/vocab.h               |  4 +-
 src/layers/generic.h           | 26 ++++++----
 src/layers/word2vec_reader.h   |  6 +--
 src/microsoft/quicksand.cpp    |  4 +-
 src/models/bert.h              | 14 +++---
 src/models/costs.h             |  4 +-
 src/models/decoder.h           |  8 ++--
 src/models/encoder_decoder.cpp |  4 +-
 src/models/encoder_decoder.h   |  4 +-
 src/models/transformer.h       |  4 +-
 src/tests/attention_tests.cpp  |  2 +-
 src/tests/rnn_tests.cpp        |  2 +-
 src/training/validator.h       |  4 +-
 src/translator/beam_search.h   | 14 +++---
 src/translator/scorers.h       |  6 +--
 21 files changed, 150 insertions(+), 108 deletions(-)
 mode change 100644 => 100755 src/data/types.h
 mode change 100644 => 100755 src/models/encoder_decoder.h

diff --git a/src/common/definitions.h b/src/common/definitions.h
index 101200fff..21ee816fc 100755
--- a/src/common/definitions.h
+++ b/src/common/definitions.h
@@ -1,7 +1,7 @@
 #pragma once
 
 #include "common/logging.h"
-#include "shape.h"
+#include "shape.h" // @TODO: why not common/shape.h?
 
 #include <functional>
 #include <iostream>
diff --git a/src/data/corpus_base.h b/src/data/corpus_base.h
index 725059520..6614d7d2c 100755
--- a/src/data/corpus_base.h
+++ b/src/data/corpus_base.h
@@ -110,7 +110,7 @@ class SentenceTuple {
  */
 class SubBatch {
 private:
-  std::vector<Word> indices_;
+  Words indices_;
   std::vector<float> mask_;
 
   size_t size_;
@@ -142,7 +142,7 @@ class SubBatch {
    * idx_{w,0},idx_{w,1},\dots,idx_{w,s}\f$, where \f$w\f$ is the number of
    * words (width) and \f$s\f$ is the number of sentences (size).
    */
-  std::vector<Word>& data() { return indices_; }
+  Words& data() { return indices_; }
   /**
    * @brief Flat masking vector; 0 is used for masked words.
    *
diff --git a/src/data/default_vocab.cpp b/src/data/default_vocab.cpp
index 36a32328f..b791ec52a 100755
--- a/src/data/default_vocab.cpp
+++ b/src/data/default_vocab.cpp
@@ -23,8 +23,8 @@ class DefaultVocab : public VocabBase {
   typedef std::vector<std::string> Id2Str;
   Id2Str id2str_;
 
-  Word eosId_ = (Word)-1;
-  Word unkId_ = (Word)-1;
+  Word eosId_ = Word::NONE;
+  Word unkId_ = Word::NONE;
 
   std::vector<std::string> suffixes_ = { ".yml", ".yaml", ".json" };
 
@@ -97,7 +97,7 @@ class DefaultVocab : public VocabBase {
     if(isJson) {
       YAML::Node vocabNode = YAML::Load(io::InputFileStream(vocabPath));
       for(auto&& pair : vocabNode)
-        vocab.insert({pair.first.as<std::string>(), pair.second.as<Word>()});
+        vocab.insert({pair.first.as<std::string>(), Word(pair.second.as<IndexType>())});
     }
     // read from flat text file
     else {
@@ -107,7 +107,7 @@ class DefaultVocab : public VocabBase {
         ABORT_IF(line.empty(),
                 "DefaultVocabulary file {} must not contain empty lines",
                 vocabPath);
-        vocab.insert({line, (Word)vocab.size()});
+        vocab.insert({line, Word(vocab.size())});
       }
       ABORT_IF(in.bad(), "DefaultVocabulary file {} could not be read", vocabPath);
     }
@@ -131,8 +131,8 @@ class DefaultVocab : public VocabBase {
 
   // for fakeBatch()
   virtual void createFake() override {
-    eosId_ = insertWord(DEFAULT_EOS_ID, DEFAULT_EOS_STR);
-    unkId_ = insertWord(DEFAULT_UNK_ID, DEFAULT_UNK_STR);
+    eosId_ = insertWord(Word::DEFAULT_EOS_ID, DEFAULT_EOS_STR);
+    unkId_ = insertWord(Word::DEFAULT_UNK_ID, DEFAULT_UNK_STR);
   }
 
   virtual void create(const std::string& vocabPath,
@@ -195,8 +195,8 @@ class DefaultVocab : public VocabBase {
               str);
       return iter->second;
     };
-    eosId_ = getRequiredWordId(DEFAULT_EOS_STR, NEMATUS_EOS_STR, DEFAULT_EOS_ID);
-    unkId_ = getRequiredWordId(DEFAULT_UNK_STR, NEMATUS_UNK_STR, DEFAULT_UNK_ID);
+    eosId_ = getRequiredWordId(DEFAULT_EOS_STR, NEMATUS_EOS_STR, Word::DEFAULT_EOS_ID);
+    unkId_ = getRequiredWordId(DEFAULT_UNK_STR, NEMATUS_UNK_STR, Word::DEFAULT_UNK_ID);
   }
 
   void addCounts(std::unordered_map<std::string, size_t>& counter,
@@ -232,8 +232,8 @@ class DefaultVocab : public VocabBase {
     std::sort(vocabVec.begin(), vocabVec.end(), VocabFreqOrderer(counter));
 
     YAML::Node vocabYaml;
-    vocabYaml.force_insert(DEFAULT_EOS_STR, DEFAULT_EOS_ID);
-    vocabYaml.force_insert(DEFAULT_UNK_STR, DEFAULT_UNK_ID);
+    vocabYaml.force_insert(DEFAULT_EOS_STR, Word::DEFAULT_EOS_ID.getWordIndex());
+    vocabYaml.force_insert(DEFAULT_UNK_STR, Word::DEFAULT_UNK_ID.getWordIndex());
 
     Word maxSpec = 1;
     auto vocabSize = vocabVec.size();
diff --git a/src/data/shortlist.h b/src/data/shortlist.h
index 55fe4f5bc..50dac8d54 100755
--- a/src/data/shortlist.h
+++ b/src/data/shortlist.h
@@ -14,21 +14,21 @@ namespace data {
 
 class Shortlist {
 private:
-  std::vector<Word> indices_;
-  std::vector<Word> mappedIndices_;
-  std::vector<Word> reverseMap_;
+  std::vector<WordIndex> indices_;
+  std::vector<WordIndex> mappedIndices_;
+  std::vector<WordIndex> reverseMap_;
 
 public:
-  Shortlist(const std::vector<Word>& indices,
-            const std::vector<Word>& mappedIndices,
-            const std::vector<Word>& reverseMap)
+  Shortlist(const std::vector<WordIndex>& indices,
+            const std::vector<WordIndex>& mappedIndices,
+            const std::vector<WordIndex>& reverseMap)
       : indices_(indices),
         mappedIndices_(mappedIndices),
         reverseMap_(reverseMap) {}
 
-  std::vector<Word>& indices() { return indices_; }
-  std::vector<Word>& mappedIndices() { return mappedIndices_; }
-  Word reverseMap(Word idx) { return reverseMap_[idx]; }
+  std::vector<WordIndex>& indices() { return indices_; }
+  std::vector<WordIndex>& mappedIndices() { return mappedIndices_; }
+  WordIndex reverseMap(WordIndex idx) { return reverseMap_[idx]; }
 };
 
 class ShortlistGenerator {
@@ -73,37 +73,37 @@ class SampledShortlistGenerator : public ShortlistGenerator {
     auto trgBatch = (*batch)[trgIdx_];
 
     // add firstNum most frequent words
-    std::unordered_set<Word> idxSet;
-    for(Word i = 0; i < firstNum_ && i < maxVocab_; ++i)
+    std::unordered_set<WordIndex> idxSet;
+    for(WordIndex i = 0; i < firstNum_ && i < maxVocab_; ++i)
       idxSet.insert(i);
 
     // add all words from ground truth
     for(auto i : trgBatch->data())
-      idxSet.insert(i);
+      idxSet.insert(i.getWordIndex());
 
     // add all words from source
     if(shared_)
       for(auto i : srcBatch->data())
-        idxSet.insert(i);
+        idxSet.insert(i.getWordIndex());
 
     std::uniform_int_distribution<> dis((int)firstNum_, (int)maxVocab_);
     while(idxSet.size() < total_ && idxSet.size() < maxVocab_)
       idxSet.insert(dis(gen_));
 
     // turn into vector and sort (selected indices)
-    std::vector<Word> idx(idxSet.begin(), idxSet.end());
+    std::vector<WordIndex> idx(idxSet.begin(), idxSet.end());
     std::sort(idx.begin(), idx.end());
 
     // assign new shifted position
-    std::unordered_map<Word, Word> pos;
-    std::vector<Word> reverseMap;
+    std::unordered_map<WordIndex, WordIndex> pos;
+    std::vector<WordIndex> reverseMap;
 
-    for(Word i = 0; i < idx.size(); ++i) {
+    for(WordIndex i = 0; i < idx.size(); ++i) {
       pos[idx[i]] = i;
       reverseMap.push_back(idx[i]);
     }
 
-    std::vector<Word> mapped;
+    std::vector<WordIndex> mapped;
     for(auto i : trgBatch->data()) {
       // mapped postions for cross-entropy
       mapped.push_back(pos[i]);
@@ -126,7 +126,7 @@ class LexicalShortlistGenerator : public ShortlistGenerator {
   size_t firstNum_{100};
   size_t bestNum_{100};
 
-  std::vector<std::unordered_map<Word, float>> data_;
+  std::vector<std::unordered_map<WordIndex, float>> data_; // [WordIndex src] -> [WordIndex tgt] -> P_trans(tgt|src) --@TODO: rename data_ accordingly
 
   void load(const std::string& fname) {
     io::InputFileStream in(fname);
@@ -138,8 +138,8 @@ class LexicalShortlistGenerator : public ShortlistGenerator {
       if(src == "NULL" || trg == "NULL")
         continue;
 
-      Word sId = (*srcVocab_)[src];
-      Word tId = (*trgVocab_)[trg];
+      auto sId = (*srcVocab_)[src].getWordIndex();
+      auto tId = (*trgVocab_)[trg].getWordIndex();
 
       if(data_.size() <= sId)
         data_.resize(sId + 1);
@@ -150,12 +150,12 @@ class LexicalShortlistGenerator : public ShortlistGenerator {
   void prune(float threshold = 0.f) {
     size_t i = 0;
     for(auto& probs : data_) {
-      std::vector<std::pair<float, Word>> sorter;
+      std::vector<std::pair<float, WordIndex>> sorter;
       for(auto& it : probs)
-        sorter.emplace_back(it.second, (Word)it.first);
+        sorter.emplace_back(it.second, it.first);
 
       std::sort(
-          sorter.begin(), sorter.end(), std::greater<std::pair<float, Word>>());
+          sorter.begin(), sorter.end(), std::greater<std::pair<float, WordIndex>>()); // sort by prob
 
       probs.clear();
       for(auto& it : sorter) {
@@ -211,14 +211,14 @@ class LexicalShortlistGenerator : public ShortlistGenerator {
     // Dump top most frequent words from target vocabulary
     LOG(info, "[data] Saving shortlist dump to {}", prefix + ".{top,dic}");
     io::OutputFileStream outTop(prefix + ".top");
-    for(Word i = 0; i < firstNum_ && i < trgVocab_->size(); ++i)
+    for(WordIndex i = 0; i < firstNum_ && i < trgVocab_->size(); ++i)
       outTop << (*trgVocab_)[i] << std::endl;
 
     // Dump translation pairs from dictionary
     io::OutputFileStream outDic(prefix + ".dic");
-    for(Word srcId = 0; srcId < data_.size(); srcId++) {
-      for(auto& it : data_[srcId]) { // @TODO: change data_.first from size_t to Word
-        Word trgId = (Word)it.first;
+    for(WordIndex srcId = 0; srcId < data_.size(); srcId++) {
+      for(auto& it : data_[srcId]) {
+        auto trgId = it.first;
         outDic << (*srcVocab_)[srcId] << "\t" << (*trgVocab_)[trgId] << std::endl;
       }
     }
@@ -229,16 +229,16 @@ class LexicalShortlistGenerator : public ShortlistGenerator {
     // auto trgBatch = (*batch)[trgIdx_];
 
     // add firstNum most frequent words
-    std::unordered_set<Word> idxSet;
-    for(Word i = 0; i < firstNum_ && i < trgVocab_->size(); ++i)
+    std::unordered_set<WordIndex> idxSet;
+    for(WordIndex i = 0; i < firstNum_ && i < trgVocab_->size(); ++i)
       idxSet.insert(i);
 
     // add all words from ground truth
     // for(auto i : trgBatch->data())
-    //  idxSet.insert(i);
+    //  idxSet.insert(i.getWordIndex());
 
     // collect unique words form source
-    std::unordered_set<Word> srcSet;
+    std::unordered_set<WordIndex> srcSet;
     for(auto i : srcBatch->data())
       srcSet.insert(i);
 
@@ -247,23 +247,23 @@ class LexicalShortlistGenerator : public ShortlistGenerator {
       if(shared_)
         idxSet.insert(i);
       for(auto& it : data_[i])
-        idxSet.insert((Word)it.first); // @TODO: change it.first to Word
+        idxSet.insert(it.first);
     }
 
     // turn into vector and sort (selected indices)
-    std::vector<Word> idx(idxSet.begin(), idxSet.end());
+    std::vector<WordIndex> idx(idxSet.begin(), idxSet.end());
     std::sort(idx.begin(), idx.end());
 
     // assign new shifted position
-    // std::unordered_map<Word, Word> pos;
-    std::vector<Word> reverseMap;
+    // std::unordered_map<WordIndex, WordIndex> pos;
+    std::vector<WordIndex> reverseMap;
 
-    for(Word i = 0; i < idx.size(); ++i) {
+    for(WordIndex i = 0; i < idx.size(); ++i) {
       // pos[idx[i]] = i;
       reverseMap.push_back(idx[i]);
     }
 
-    std::vector<Word> mapped;
+    std::vector<WordIndex> mapped;
     // for(auto i : trgBatch->data()) {
     // mapped postions for cross-entropy
     // mapped.push_back(pos[i]);
@@ -275,21 +275,21 @@ class LexicalShortlistGenerator : public ShortlistGenerator {
 
 class FakeShortlistGenerator : public ShortlistGenerator {
 private:
-  std::vector<Word> idx_;
-  std::vector<Word> reverseIdx_;
+  std::vector<WordIndex> idx_;
+  std::vector<WordIndex> reverseIdx_;
 
 public:
-  FakeShortlistGenerator(const std::unordered_set<Word>& idxSet)
+  FakeShortlistGenerator(const std::unordered_set<WordIndex>& idxSet)
       : idx_(idxSet.begin(), idxSet.end()) {
     std::sort(idx_.begin(), idx_.end());
     // assign new shifted position
-    for(Word i = 0; i < idx_.size(); ++i) {
+    for(WordIndex i = 0; i < idx_.size(); ++i) {
       reverseIdx_.push_back(idx_[i]);
     }
   }
 
   Ptr<Shortlist> generate(Ptr<data::CorpusBatch> /*batch*/) override {
-    std::vector<Word> tmp;
+    std::vector<WordIndex> tmp;
     return New<Shortlist>(idx_, tmp, reverseIdx_);
   }
 };
diff --git a/src/data/types.h b/src/data/types.h
old mode 100644
new mode 100755
index 2bda6ecea..549b439ea
--- a/src/data/types.h
+++ b/src/data/types.h
@@ -11,14 +11,36 @@
 namespace marian {
 
 // Type for all vocabulary items, based on IndexType
-typedef IndexType Word;
+typedef IndexType WordIndex;    // WordIndex is used for words or tokens arranged in consecutive order
+class Word {                    // Word is an abstraction of a unique id, not necessarily consecutive
+  WordIndex wordId_;
+public:
+  // back compat with WordIndex
+  Word(std::size_t wordId) : wordId_((WordIndex)wordId) {} // @TODO: make explicit, or make private
+  operator WordIndex() const { return getWordIndex(); }
+
+  // needed for STL containers
+  Word() : wordId_((WordIndex)-1) {}
+  bool operator==(const Word& other) const { return wordId_ == other.wordId_; }
+  std::size_t hash() const { return std::hash<WordIndex>{}(wordId_); }
+
+  // main methods and constants
+  static Word From(std::size_t wordId) { return Word(wordId); }
+  const WordIndex& getWordIndex() const { return wordId_; }
+
+  static Word NONE; // @TODO: decide whether we need this, in additional Word()
+  // EOS and UNK are placed in these positions in Marian-generated vocabs
+  static Word DEFAULT_EOS_ID;
+  static Word DEFAULT_UNK_ID;
+};
 
 // Sequence of vocabulary items
 typedef std::vector<Word> Words;
 
-// EOS and UNK are placed in these positions in Marian-generated vocabs
-const Word DEFAULT_EOS_ID = 0;
-const Word DEFAULT_UNK_ID = 1;
+// Helper to map a Word vector to a WordIndex vector
+static inline std::vector<WordIndex> toWordIndexVector(const Words& words) {
+  return std::vector<WordIndex>(words.begin(), words.end());
+}
 
 // names of EOS and UNK symbols
 const std::string DEFAULT_EOS_STR = "</s>";
@@ -29,3 +51,9 @@ const std::string NEMATUS_EOS_STR = "eos";
 const std::string NEMATUS_UNK_STR = "UNK";
 
 }  // namespace marian
+
+namespace std {
+  template<> struct hash<marian::Word> {
+    std::size_t  operator()(const marian::Word& s) const noexcept { return s.hash(); }
+  };
+}
diff --git a/src/data/vocab.cpp b/src/data/vocab.cpp
index 266b14322..8118ea24f 100755
--- a/src/data/vocab.cpp
+++ b/src/data/vocab.cpp
@@ -3,6 +3,10 @@
 
 namespace marian {
 
+Word Word::NONE = Word();
+Word Word::DEFAULT_EOS_ID = Word(0);
+Word Word::DEFAULT_UNK_ID = Word(1);
+
 Ptr<VocabBase> createDefaultVocab();
 Ptr<VocabBase> createClassVocab();
 Ptr<VocabBase> createSentencePieceVocab(const std::string& /*vocabPath*/, Ptr<Options>, size_t /*batchIndex*/);
diff --git a/src/data/vocab.h b/src/data/vocab.h
index af4ea71fe..e82673ecc 100755
--- a/src/data/vocab.h
+++ b/src/data/vocab.h
@@ -42,8 +42,8 @@ class Vocab {
   // string token to token id
   Word operator[](const std::string& word) const;
 
-  // token id to string token
-  const std::string& operator[](Word id) const;
+  // token index to string token
+  const std::string& operator[](Word word) const;
 
   // line of text to list of token ids, can perform tokenization
   Words encode(const std::string& line,
diff --git a/src/layers/generic.h b/src/layers/generic.h
index 8e03efd08..55d42f555 100755
--- a/src/layers/generic.h
+++ b/src/layers/generic.h
@@ -51,8 +51,10 @@ struct IUnaryLayer {
 struct IEmbeddingLayer {
   virtual std::tuple<Expr/*embeddings*/, Expr/*mask*/> apply(Ptr<data::SubBatch> subBatch) const = 0;
 
-  // alternative version from index vector, and with batch dims/shape
-  virtual Expr apply(const std::vector<IndexType>& embIdx, const Shape& shape) const = 0;
+  virtual Expr apply(const Words& embIdx, const Shape& shape) const = 0;
+
+  // alternative from indices directly
+  virtual Expr applyIndices(const std::vector<WordIndex>& embIdx, const Shape& shape) const = 0;
 };
 
 namespace mlp {
@@ -237,8 +239,11 @@ class Embedding : public LayerBase, public IEmbeddingLayer {
     return std::make_tuple(batchEmbeddings, batchMask);
   }
 
-  // special version used in decoding or for learned positional embeddings
-  Expr apply(const std::vector<IndexType>& embIdx, const Shape& shape) const override final {
+  Expr apply(const Words& words, const Shape& shape) const override final {
+    return applyIndices(toWordIndexVector(words), shape);
+  }
+
+  Expr applyIndices(const std::vector<WordIndex>& embIdx, const Shape& shape) const override final {
     auto selectedEmbs = rows(E_, embIdx);
     return reshape(selectedEmbs, shape);
   }
@@ -324,9 +329,10 @@ class ULREmbedding : public LayerBase, public IEmbeddingLayer {
     // note all above can be precombuted and serialized if A is not trainiable and during decoding (TBD)
     // here we need to handle the mini-batch
     // extract raws corresponding to Xs in this minibatch from Q
-    auto queryEmbeddings = rows(queryEmbed, subBatch->data());
-    auto srcEmbeddings = rows(srcEmbed, subBatch->data());   // extract trainable src embeddings
-    auto alpha = rows(ulrSharable, subBatch->data());  // extract sharable flags
+    auto embIdx = toWordIndexVector(subBatch->data());
+    auto queryEmbeddings = rows(queryEmbed, embIdx);
+    auto srcEmbeddings = rows(srcEmbed, embIdx);   // extract trainable src embeddings
+    auto alpha = rows(ulrSharable, embIdx);  // extract sharable flags
     auto qt = dot(queryEmbeddings, ulrTransform, false, false);  //A: transform embeddings based on similarity A :  dimUlrEmb*dimUlrEmb
     auto sqrtDim=std::sqrt((float)queryEmbeddings->shape()[-1]);
     qt = qt/sqrtDim;  // normalize accordin to embed size to avoid dot prodcut growing large in magnitude with larger embeds sizes
@@ -347,7 +353,11 @@ class ULREmbedding : public LayerBase, public IEmbeddingLayer {
     return std::make_tuple(batchEmbeddings, batchMask);
   }
 
-  Expr apply(const std::vector<IndexType>& embIdx, const Shape& shape) const override final {
+  Expr apply(const Words& words, const Shape& shape) const override final {
+    return applyIndices(toWordIndexVector(words), shape);
+  }
+
+  Expr applyIndices(const std::vector<WordIndex>& embIdx, const Shape& shape) const override final {
     embIdx; shape;
     ABORT("not implemented"); // @TODO: implement me
   }
diff --git a/src/layers/word2vec_reader.h b/src/layers/word2vec_reader.h
index a7e855927..88c695aa9 100755
--- a/src/layers/word2vec_reader.h
+++ b/src/layers/word2vec_reader.h
@@ -33,12 +33,12 @@ class Word2VecReader {
              "Unexpected length of embedding vectors");
 
     // Read embedding vectors into a map
-    std::unordered_map<Word, std::vector<float>> word2vec;
+    std::unordered_map<WordIndex, std::vector<float>> word2vec;
     while(io::getline(embFile, line)) {
       values.clear();
       utils::split(line, values);
 
-      Word word = std::stoi(values.front());
+      WordIndex word = std::stoi(values.front());
       if(word >= (size_t)dimVoc)
         continue;
 
@@ -54,7 +54,7 @@ class Word2VecReader {
     embs.reserve(dimVoc * dimEmb);
 
     // Populate output vector with embedding
-    for(Word word = 0; word < (Word)dimVoc; ++word) {
+    for(WordIndex word = 0; word < (WordIndex)dimVoc; ++word) {
       // For words not occuring in the file use uniform distribution
       if(word2vec.find(word) == word2vec.end()) {
         auto randVals = randomEmbeddings(dimVoc, dimEmb);
diff --git a/src/microsoft/quicksand.cpp b/src/microsoft/quicksand.cpp
index 962bda182..3d3c8a508 100755
--- a/src/microsoft/quicksand.cpp
+++ b/src/microsoft/quicksand.cpp
@@ -151,10 +151,10 @@ class BeamSearchDecoder : public IBeamSearchDecoder {
           // convert to QuickSAND format
           alignmentSets.resize(words.size());
           for (const auto& p : align) // @TODO: Does the feature_model param max_alignment_links apply here?
-              alignmentSets[p.tgtPos].insert({p.srcPos, p.prob});
+            alignmentSets[p.tgtPos].insert({p.srcPos, p.prob});
         }
         // form hypothesis to return
-        qsNbest.emplace_back(words, std::move(alignmentSets), score);
+        qsNbest.emplace_back(toWordIndexVector(words), std::move(alignmentSets), score);
       }
       qsNbestBatch.push_back(qsNbest);
     }
diff --git a/src/models/bert.h b/src/models/bert.h
index 2ea725143..e40b858ab 100755
--- a/src/models/bert.h
+++ b/src/models/bert.h
@@ -25,7 +25,7 @@ namespace data {
 class BertBatch : public CorpusBatch {
 private:
   std::vector<IndexType> maskedPositions_;
-  std::vector<IndexType> maskedWords_;
+  Words maskedWords_;
   std::vector<IndexType> sentenceIndices_;
 
   std::string maskSymbol_;
@@ -33,7 +33,7 @@ class BertBatch : public CorpusBatch {
   std::string clsSymbol_;
 
   // Selects a random word from the vocabulary
-  std::unique_ptr<std::uniform_int_distribution<Word>> randomWord_;
+  std::unique_ptr<std::uniform_int_distribution<WordIndex>> randomWord_;
 
   // Selects a random integer between 0 and 99
   std::unique_ptr<std::uniform_real_distribution<float>> randomPercent_;
@@ -79,7 +79,7 @@ class BertBatch : public CorpusBatch {
     const auto& vocab = *subBatch->vocab();
 
     // Initialize to sample random vocab id
-    randomWord_.reset(new std::uniform_int_distribution<Word>(0, (Word)vocab.size()));
+    randomWord_.reset(new std::uniform_int_distribution<WordIndex>(0, (WordIndex)vocab.size()));
 
     // Intialize to sample random percentage
     randomPercent_.reset(new std::uniform_real_distribution<float>(0.f, 1.f));
@@ -163,7 +163,7 @@ class BertBatch : public CorpusBatch {
   }
 
   const std::vector<IndexType>& bertMaskedPositions() { return maskedPositions_; }
-  const std::vector<IndexType>& bertMaskedWords()     { return maskedWords_; }
+  const Words& bertMaskedWords() { return maskedWords_; }
   const std::vector<IndexType>& bertSentenceIndices() { return sentenceIndices_; }
 };
 
@@ -236,7 +236,7 @@ class BertEncoder : public EncoderTransformer {
                                ("dimVocab", 3) // sentence A or sentence B plus padding, @TODO: should rather be a parameter
                                ("dimEmb", dimEmb)
                                .construct(graph_);
-      signal = sentenceEmbeddings->apply(bertBatch->bertSentenceIndices(), {dimWords, dimBatch, dimEmb});
+      signal = sentenceEmbeddings->applyIndices(bertBatch->bertSentenceIndices(), {dimWords, dimBatch, dimEmb});
     } else {
       // @TODO: factory for positional embeddings?
       // constant sinusoidal position embeddings, no backprob
@@ -293,7 +293,7 @@ class BertClassifier : public ClassifierBase {
 
     // Filled externally, for BERT these are NextSentence prediction labels
     const auto& classLabels = (*batch)[batchIndex_]->data();
-    state->setTargetIndices(graph->indices(classLabels));
+    state->setTargetIndices(graph->indices(toWordIndexVector(classLabels)));
 
     return state;
   }
@@ -319,7 +319,7 @@ class BertMaskedLM : public ClassifierBase {
     auto context = encoderStates[0]->getContext();
 
     auto bertMaskedPositions = graph->indices(bertBatch->bertMaskedPositions()); // positions in batch of masked entries
-    auto bertMaskedWords     = graph->indices(bertBatch->bertMaskedWords());   // vocab ids of entries that have been masked
+    auto bertMaskedWords     = graph->indices(toWordIndexVector(bertBatch->bertMaskedWords()));   // vocab ids of entries that have been masked
 
     int dimModel = context->shape()[-1];
     int dimBatch = context->shape()[-2];
diff --git a/src/models/costs.h b/src/models/costs.h
index bfd62da7b..d186db4dc 100755
--- a/src/models/costs.h
+++ b/src/models/costs.h
@@ -242,11 +242,11 @@ class Stepwise : public EncoderDecoderBase {
   virtual Ptr<DecoderState> step(Ptr<ExpressionGraph> graph,
                                  Ptr<DecoderState> state,
                                  const std::vector<IndexType>& hypIndices,
-                                 const std::vector<IndexType>& embIndices,
+                                 const Words& words,
                                  int dimBatch,
                                  int beamSize) override {
     auto nextState = encdec_->step(
-        graph, state, hypIndices, embIndices, dimBatch, beamSize);
+        graph, state, hypIndices, words, dimBatch, beamSize);
     return cost_->apply(nextState);
   }
 
diff --git a/src/models/decoder.h b/src/models/decoder.h
index 53e383797..c5295621a 100755
--- a/src/models/decoder.h
+++ b/src/models/decoder.h
@@ -69,7 +69,7 @@ class DecoderBase {
     if(shortlist_) {
       yData = graph->indices(shortlist_->mappedIndices());
     } else {
-      yData = graph->indices(subBatch->data());
+      yData = graph->indices(toWordIndexVector(subBatch->data()));
     }
 
     auto yShifted = shift(y, {1, 0, 0});
@@ -81,14 +81,14 @@ class DecoderBase {
 
   virtual void embeddingsFromPrediction(Ptr<ExpressionGraph> graph,
                                         Ptr<DecoderState> state,
-                                        const std::vector<IndexType>& embIdx,
+                                        const Words& words,
                                         int dimBatch,
                                         int dimBeam) {
     int dimTrgEmb = opt<int>("dim-emb");
     int dimTrgVoc = opt<std::vector<int>>("dim-vocabs")[batchIndex_];
 
     Expr selectedEmbs;
-    if(embIdx.empty()) {
+    if(words.empty()) {
       selectedEmbs = graph->constant({1, 1, dimBatch, dimTrgEmb}, inits::zeros);
     } else {
       // embeddings are loaded from model during translation, no fixing required
@@ -103,7 +103,7 @@ class DecoderBase {
   
       auto yEmb = yEmbFactory.construct(graph);
 
-      selectedEmbs = yEmb->apply(embIdx, {dimBeam, 1, dimBatch, dimTrgEmb});
+      selectedEmbs = yEmb->apply(words, {dimBeam, 1, dimBatch, dimTrgEmb});
     }
     state->setTargetEmbeddings(selectedEmbs);
   }
diff --git a/src/models/encoder_decoder.cpp b/src/models/encoder_decoder.cpp
index c4b96cc30..ca9669fde 100755
--- a/src/models/encoder_decoder.cpp
+++ b/src/models/encoder_decoder.cpp
@@ -152,7 +152,7 @@ Ptr<DecoderState> EncoderDecoder::startState(Ptr<ExpressionGraph> graph,
 Ptr<DecoderState> EncoderDecoder::step(Ptr<ExpressionGraph> graph,
                                        Ptr<DecoderState> state,
                                        const std::vector<IndexType>& hypIndices, // [beamIndex * activeBatchSize + batchIndex]
-                                       const std::vector<IndexType>& embIndices, // [beamIndex * activeBatchSize + batchIndex]
+                                       const Words& words,                       // [beamIndex * activeBatchSize + batchIndex]
                                        int dimBatch,
                                        int beamSize) {
   // create updated state that reflects reordering and dropping of hypotheses
@@ -160,7 +160,7 @@ Ptr<DecoderState> EncoderDecoder::step(Ptr<ExpressionGraph> graph,
 
   // Fill stte with embeddings based on last prediction
   decoders_[0]->embeddingsFromPrediction(
-      graph, state, embIndices, dimBatch, beamSize);
+      graph, state, words, dimBatch, beamSize);
   auto nextState = decoders_[0]->step(graph, state);
 
   return nextState;
diff --git a/src/models/encoder_decoder.h b/src/models/encoder_decoder.h
old mode 100644
new mode 100755
index b55e8bd1d..12c58a356
--- a/src/models/encoder_decoder.h
+++ b/src/models/encoder_decoder.h
@@ -42,7 +42,7 @@ class EncoderDecoderBase : public models::ModelBase {
   virtual Ptr<DecoderState> step(Ptr<ExpressionGraph> graph,
                                  Ptr<DecoderState> state,
                                  const std::vector<IndexType>& hypIndices,
-                                 const std::vector<IndexType>& embIndices,
+                                 const Words& words,
                                  int dimBatch,
                                  int beamSize)
       = 0;
@@ -148,7 +148,7 @@ class EncoderDecoder : public EncoderDecoderBase {
   virtual Ptr<DecoderState> step(Ptr<ExpressionGraph> graph,
                                  Ptr<DecoderState> state,
                                  const std::vector<IndexType>& hypIndices,
-                                 const std::vector<IndexType>& embIndices,
+                                 const Words& words,
                                  int dimBatch,
                                  int beamSize) override;
 
diff --git a/src/models/transformer.h b/src/models/transformer.h
index 1db455c9e..52b07241d 100755
--- a/src/models/transformer.h
+++ b/src/models/transformer.h
@@ -73,9 +73,9 @@ class Transformer : public EncoderOrDecoderBase {
       // fill with increasing numbers until current length or maxPos
       std::vector<IndexType> positions(dimWords, numPos - 1);
       for(int i = 0; i < std::min(dimWords, numPos); ++i)
-        positions[i] = i;
+        positions[i] = i; // @TODO: use std::iota()?
 
-      auto signal = embeddingLayer->apply(positions, {dimWords, 1, dimEmb});
+      auto signal = embeddingLayer->applyIndices(positions, {dimWords, 1, dimEmb});
       embeddings = embeddings + signal;
     } else {
       // @TODO : test if embeddings should be scaled when trainable
diff --git a/src/tests/attention_tests.cpp b/src/tests/attention_tests.cpp
index 9b1b61859..8dfd1844c 100755
--- a/src/tests/attention_tests.cpp
+++ b/src/tests/attention_tests.cpp
@@ -49,7 +49,7 @@ void tests(DeviceType type) {
                             {128, dimEmb},
                             inits::glorot_uniform);
 
-    auto input = reshape(rows(emb, vWords), {dimTime, dimBatch, dimEmb});
+    auto input = reshape(rows(emb, toWordIndexVector(vWords)), {dimTime, dimBatch, dimEmb});
     auto mask = graph->constant({dimTime, dimBatch, 1},
                                 inits::from_vector(vMask));
 
diff --git a/src/tests/rnn_tests.cpp b/src/tests/rnn_tests.cpp
index 726f5ab54..97a7848c2 100755
--- a/src/tests/rnn_tests.cpp
+++ b/src/tests/rnn_tests.cpp
@@ -202,7 +202,7 @@ void tests(DeviceType type) {
                             {128, dimEmb},
                             inits::glorot_uniform);
 
-    auto input = reshape(rows(emb, vWords), {dimTime, dimBatch, dimEmb});
+    auto input = reshape(rows(emb, toWordIndexVector(vWords)), {dimTime, dimBatch, dimEmb});
     auto mask = graph->constant({dimTime, dimBatch, 1},
                                 inits::from_vector(vMask));
 
diff --git a/src/training/validator.h b/src/training/validator.h
index a819968b6..657c1fbcb 100755
--- a/src/training/validator.h
+++ b/src/training/validator.h
@@ -371,7 +371,7 @@ class BertAccuracyValidator : public Validator<data::Corpus> {
           auto sentenceLogits = classifierStates[1]->getLogProbs();
           const auto& sentenceLabels = bertBatch->back()->data();
 
-          auto count = [=, &correct, &totalLabels](Expr logits, const std::vector<IndexType>& labels) {
+          auto count = [=, &correct, &totalLabels](Expr logits, const Words& labels) {
             IndexType cols = logits->shape()[-1];
             size_t thisCorrect = 0;
             size_t thisLabels  = labels.size();
@@ -390,7 +390,7 @@ class BertAccuracyValidator : public Validator<data::Corpus> {
                   bestIndex = j;
                 }
               }
-              thisCorrect += (size_t)(bestIndex == labels[i]);
+              thisCorrect += (size_t)(bestIndex == labels[i].getWordIndex());
             }
 
             std::unique_lock<std::mutex> lock(mutex_);
diff --git a/src/translator/beam_search.h b/src/translator/beam_search.h
index 798312fed..5643d96d6 100755
--- a/src/translator/beam_search.h
+++ b/src/translator/beam_search.h
@@ -15,14 +15,14 @@ class BeamSearch {
   Ptr<Options> options_;
   std::vector<Ptr<Scorer>> scorers_;
   size_t beamSize_;
-  Word trgEosId_ = (Word)-1;
-  Word trgUnkId_ = (Word)-1;
+  Word trgEosId_{Word::NONE};
+  Word trgUnkId_{Word::NONE};
 
 public:
   BeamSearch(Ptr<Options> options,
              const std::vector<Ptr<Scorer>>& scorers,
              Word trgEosId,
-             Word trgUnkId = -1)
+             Word trgUnkId = Word::NONE)
       : options_(options),
         scorers_(scorers),
         beamSize_(options_->has("beam-size")
@@ -197,7 +197,7 @@ class BeamSearch {
       // create constant containing previous path scores for current beam
       // also create mapping of hyp indices, which are not 1:1 if sentences complete
       std::vector<IndexType> hypIndices; // [beamIndex * activeBatchSize + batchIndex] backpointers, concatenated over beam positions. Used for reordering hypotheses
-      std::vector<IndexType> embIndices;
+      Words predWords;
       Expr prevPathScores; // [beam, 1, 1, 1]
       if(first) {
         // no scores yet
@@ -213,11 +213,11 @@ class BeamSearch {
             if(i < beam.size()) {
               auto hyp = beam[i];
               hypIndices.push_back((IndexType)hyp->GetPrevStateIndex()); // backpointer
-              embIndices.push_back(hyp->GetWord());
+              predWords.push_back(hyp->GetWord());
               beamScores.push_back(hyp->GetPathScore());
             } else {  // dummy hypothesis
               hypIndices.push_back(0);
-              embIndices.push_back(0);  // (unused)
+              predWords.push_back(Word::NONE);  // (unused)
               beamScores.push_back(-9999);
             }
           }
@@ -233,7 +233,7 @@ class BeamSearch {
 
       for(size_t i = 0; i < scorers_.size(); ++i) {
         states[i] = scorers_[i]->step(
-            graph, states[i], hypIndices, embIndices, dimBatch, (int)localBeamSize);
+            graph, states[i], hypIndices, predWords, dimBatch, (int)localBeamSize);
 
         if(scorers_[i]->getWeight() != 1.f)
           pathScores = pathScores + scorers_[i]->getWeight() * states[i]->getLogProbs();
diff --git a/src/translator/scorers.h b/src/translator/scorers.h
index 16a066b05..1795c1661 100755
--- a/src/translator/scorers.h
+++ b/src/translator/scorers.h
@@ -35,7 +35,7 @@ class Scorer {
   virtual Ptr<ScorerState> step(Ptr<ExpressionGraph>,
                                 Ptr<ScorerState>,
                                 const std::vector<IndexType>&,
-                                const std::vector<IndexType>&,
+                                const Words&,
                                 int dimBatch,
                                 int beamSize)
       = 0;
@@ -111,12 +111,12 @@ class ScorerWrapper : public Scorer {
   virtual Ptr<ScorerState> step(Ptr<ExpressionGraph> graph,
                                 Ptr<ScorerState> state,
                                 const std::vector<IndexType>& hypIndices,
-                                const std::vector<IndexType>& embIndices,
+                                const Words& words,
                                 int dimBatch,
                                 int beamSize) override {
     graph->switchParams(getName());
     auto wrapperState = std::dynamic_pointer_cast<ScorerWrapperState>(state);
-    auto newState = encdec_->step(graph, wrapperState->getState(), hypIndices, embIndices, dimBatch, beamSize);
+    auto newState = encdec_->step(graph, wrapperState->getState(), hypIndices, words, dimBatch, beamSize);
     return New<ScorerWrapperState>(newState);
   }
 

From f9e63da391c0542af9ed2c87a038060aff178525 Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Thu, 31 Jan 2019 22:24:26 -0800
Subject: [PATCH 244/838] removed auto-conversion between Word and WordIndex,
 and dealt with fallout

---
 src/data/corpus_base.cpp      |  4 ++--
 src/data/corpus_base.h        |  6 +++---
 src/data/default_vocab.cpp    | 30 ++++++++++++++++--------------
 src/data/shortlist.h          | 18 +++++++++---------
 src/data/text_input.cpp       |  2 +-
 src/data/types.h              | 19 +++++++++++--------
 src/microsoft/quicksand.cpp   |  4 ++--
 src/models/bert.h             |  2 +-
 src/tests/attention_tests.cpp |  4 ++--
 src/tests/rnn_tests.cpp       |  4 ++--
 src/training/validator.h      |  8 ++++----
 src/translator/beam_search.h  |  8 ++++----
 src/translator/helpers.cpp    |  2 +-
 src/translator/helpers.cu     |  2 +-
 src/translator/hypothesis.h   |  2 +-
 vs/Marian.vcxproj             |  2 ++
 vs/Marian.vcxproj.filters     |  6 ++++++
 17 files changed, 68 insertions(+), 55 deletions(-)
 mode change 100644 => 100755 src/translator/helpers.cu

diff --git a/src/data/corpus_base.cpp b/src/data/corpus_base.cpp
index 74987882f..6e291a289 100755
--- a/src/data/corpus_base.cpp
+++ b/src/data/corpus_base.cpp
@@ -201,11 +201,11 @@ void CorpusBase::addWordsToSentenceTuple(const std::string& line,
   Words words = vocabs_[batchIndex]->encode(line, /*addEOS =*/ addEOS_[batchIndex], inference_);
 
   if(words.empty())
-    words.push_back(0);
+    words.push_back(Word::NONE);
 
   if(maxLengthCrop_ && words.size() > maxLength_) {
     words.resize(maxLength_);
-    words.back() = 0;
+    words.back() = Word::NONE;
   }
 
   if(rightLeft_)
diff --git a/src/data/corpus_base.h b/src/data/corpus_base.h
index 6614d7d2c..79ac12532 100755
--- a/src/data/corpus_base.h
+++ b/src/data/corpus_base.h
@@ -128,7 +128,7 @@ class SubBatch {
    * @param width Number of words in the longest sentence
    */
   SubBatch(size_t size, size_t width, const Ptr<Vocab>& vocab)
-      : indices_(size * width, 0),
+      : indices_(size * width, Word::NONE),
         mask_(size * width, 0),
         size_(size),
         width_(width),
@@ -322,7 +322,7 @@ class CorpusBatch : public Batch {
       // set word indices to different values to avoid same hashes
       // rand() is OK, this does not affect state in any way
       std::transform(sb->data().begin(), sb->data().end(), sb->data().begin(),
-                     [&](Word) -> Word { return rand() % vocabs[batchIndex]->size(); });
+                     [&](Word) -> Word { return Word::fromWordIndex(rand() % vocabs[batchIndex]->size()); });
       // mask: no items ask being masked out
       std::fill(sb->mask().begin(), sb->mask().end(), 1.f);
       batchIndex++;
@@ -484,7 +484,7 @@ class CorpusBatch : public Batch {
           if (vocab)
             std::cerr << (*vocab)[w] << " ";
           else
-            std::cerr << w << " "; // if not loaded then print numeric id instead
+            std::cerr << w.toString() << " "; // if not loaded then print numeric id instead
         }
         std::cerr << std::endl;
       }
diff --git a/src/data/default_vocab.cpp b/src/data/default_vocab.cpp
index b791ec52a..8c02014cd 100755
--- a/src/data/default_vocab.cpp
+++ b/src/data/default_vocab.cpp
@@ -72,8 +72,8 @@ class DefaultVocab : public VocabBase {
   virtual Word getEosId() const override { return eosId_; }
   virtual Word getUnkId() const override { return unkId_; }
 
-
-  const std::string& operator[](Word id) const override {
+  const std::string& operator[](Word word) const override {
+    auto id = word.toWordIndex();
     ABORT_IF(id >= id2str_.size(), "Unknown word id: {}", id);
     return id2str_[id];
   }
@@ -97,7 +97,7 @@ class DefaultVocab : public VocabBase {
     if(isJson) {
       YAML::Node vocabNode = YAML::Load(io::InputFileStream(vocabPath));
       for(auto&& pair : vocabNode)
-        vocab.insert({pair.first.as<std::string>(), Word(pair.second.as<IndexType>())});
+        vocab.insert({pair.first.as<std::string>(), Word::fromWordIndex(pair.second.as<IndexType>())});
     }
     // read from flat text file
     else {
@@ -107,7 +107,7 @@ class DefaultVocab : public VocabBase {
         ABORT_IF(line.empty(),
                 "DefaultVocabulary file {} must not contain empty lines",
                 vocabPath);
-        vocab.insert({line, Word(vocab.size())});
+        vocab.insert({line, Word::fromWordIndex(vocab.size())});
       }
       ABORT_IF(in.bad(), "DefaultVocabulary file {} could not be read", vocabPath);
     }
@@ -118,7 +118,7 @@ class DefaultVocab : public VocabBase {
       auto id = pair.second;
 
       // note: this requires ids to be sorted by frequency
-      if(!maxSize || id < (Word)maxSize) {
+      if(!maxSize || id.toWordIndex() < maxSize) {
         insertWord(id, str);
       }
     }
@@ -171,12 +171,13 @@ class DefaultVocab : public VocabBase {
     // The name backCompatStr is alternatively accepted for Yaml vocabs if id
     // equals backCompatId.
     auto getRequiredWordId = [&](const std::string& str,
-                                const std::string& backCompatStr,
-                                Word backCompatId) {
+                                 const std::string& backCompatStr,
+                                 Word backCompatWord) -> Word {
       // back compat with Nematus Yaml dicts
       if(isJson) {
         // if word id 0 or 1 is either empty or has the Nematus-convention string,
         // then use it
+        auto backCompatId = backCompatWord.toWordIndex();
         if(backCompatId < id2str_.size()
           && (id2str_[backCompatId].empty()
               || id2str_[backCompatId] == backCompatStr)) {
@@ -185,7 +186,7 @@ class DefaultVocab : public VocabBase {
               backCompatStr,
               backCompatId,
               str);
-          return backCompatId;
+          return backCompatWord;
         }
       }
       auto iter = str2id_.find(str);
@@ -232,10 +233,10 @@ class DefaultVocab : public VocabBase {
     std::sort(vocabVec.begin(), vocabVec.end(), VocabFreqOrderer(counter));
 
     YAML::Node vocabYaml;
-    vocabYaml.force_insert(DEFAULT_EOS_STR, Word::DEFAULT_EOS_ID.getWordIndex());
-    vocabYaml.force_insert(DEFAULT_UNK_STR, Word::DEFAULT_UNK_ID.getWordIndex());
+    vocabYaml.force_insert(DEFAULT_EOS_STR, Word::DEFAULT_EOS_ID.toWordIndex());
+    vocabYaml.force_insert(DEFAULT_UNK_STR, Word::DEFAULT_UNK_ID.toWordIndex());
 
-    Word maxSpec = 1;
+    WordIndex maxSpec = 1;
     auto vocabSize = vocabVec.size();
     if(maxSize > maxSpec)
       vocabSize = std::min(maxSize - maxSpec - 1, vocabVec.size());
@@ -274,12 +275,13 @@ class DefaultVocab : public VocabBase {
   }
 
   // helper to insert a word into str2id_[] and id2str_[]
-  Word insertWord(Word id, const std::string& str) {
-    str2id_[str] = id;
+  Word insertWord(Word word, const std::string& str) {
+    str2id_[str] = word;
+    auto id = word.toWordIndex();
     if(id >= id2str_.size())
       id2str_.resize(id + 1);
     id2str_[id] = str;
-    return id;
+    return word;
   };
 };
 
diff --git a/src/data/shortlist.h b/src/data/shortlist.h
index 50dac8d54..ded1ed5e1 100755
--- a/src/data/shortlist.h
+++ b/src/data/shortlist.h
@@ -79,12 +79,12 @@ class SampledShortlistGenerator : public ShortlistGenerator {
 
     // add all words from ground truth
     for(auto i : trgBatch->data())
-      idxSet.insert(i.getWordIndex());
+      idxSet.insert(i.toWordIndex());
 
     // add all words from source
     if(shared_)
       for(auto i : srcBatch->data())
-        idxSet.insert(i.getWordIndex());
+        idxSet.insert(i.toWordIndex());
 
     std::uniform_int_distribution<> dis((int)firstNum_, (int)maxVocab_);
     while(idxSet.size() < total_ && idxSet.size() < maxVocab_)
@@ -106,7 +106,7 @@ class SampledShortlistGenerator : public ShortlistGenerator {
     std::vector<WordIndex> mapped;
     for(auto i : trgBatch->data()) {
       // mapped postions for cross-entropy
-      mapped.push_back(pos[i]);
+      mapped.push_back(pos[i.toWordIndex()]);
     }
 
     return New<Shortlist>(idx, mapped, reverseMap);
@@ -138,8 +138,8 @@ class LexicalShortlistGenerator : public ShortlistGenerator {
       if(src == "NULL" || trg == "NULL")
         continue;
 
-      auto sId = (*srcVocab_)[src].getWordIndex();
-      auto tId = (*trgVocab_)[trg].getWordIndex();
+      auto sId = (*srcVocab_)[src].toWordIndex();
+      auto tId = (*trgVocab_)[trg].toWordIndex();
 
       if(data_.size() <= sId)
         data_.resize(sId + 1);
@@ -212,14 +212,14 @@ class LexicalShortlistGenerator : public ShortlistGenerator {
     LOG(info, "[data] Saving shortlist dump to {}", prefix + ".{top,dic}");
     io::OutputFileStream outTop(prefix + ".top");
     for(WordIndex i = 0; i < firstNum_ && i < trgVocab_->size(); ++i)
-      outTop << (*trgVocab_)[i] << std::endl;
+      outTop << (*trgVocab_)[Word::fromWordIndex(i)] << std::endl;
 
     // Dump translation pairs from dictionary
     io::OutputFileStream outDic(prefix + ".dic");
     for(WordIndex srcId = 0; srcId < data_.size(); srcId++) {
       for(auto& it : data_[srcId]) {
         auto trgId = it.first;
-        outDic << (*srcVocab_)[srcId] << "\t" << (*trgVocab_)[trgId] << std::endl;
+        outDic << (*srcVocab_)[Word::fromWordIndex(srcId)] << "\t" << (*trgVocab_)[Word::fromWordIndex(trgId)] << std::endl;
       }
     }
   }
@@ -235,12 +235,12 @@ class LexicalShortlistGenerator : public ShortlistGenerator {
 
     // add all words from ground truth
     // for(auto i : trgBatch->data())
-    //  idxSet.insert(i.getWordIndex());
+    //  idxSet.insert(i.toWordIndex());
 
     // collect unique words form source
     std::unordered_set<WordIndex> srcSet;
     for(auto i : srcBatch->data())
-      srcSet.insert(i);
+      srcSet.insert(i.toWordIndex());
 
     // add aligned target words
     for(auto i : srcSet) {
diff --git a/src/data/text_input.cpp b/src/data/text_input.cpp
index 78fbc7af0..9c694c836 100755
--- a/src/data/text_input.cpp
+++ b/src/data/text_input.cpp
@@ -48,7 +48,7 @@ SentenceTuple TextInput::next() {
       if(io::getline(dummyStream, line)) {
         Words words = vocabs_[i]->encode(line, /*addEOS =*/ true, /*inference =*/ inference_);
         if(words.empty())
-          words.push_back(0);
+          words.push_back(Word::NONE);
         tup.push_back(words);
       }
     }
diff --git a/src/data/types.h b/src/data/types.h
index 549b439ea..dedbf11ce 100755
--- a/src/data/types.h
+++ b/src/data/types.h
@@ -14,20 +14,20 @@ namespace marian {
 typedef IndexType WordIndex;    // WordIndex is used for words or tokens arranged in consecutive order
 class Word {                    // Word is an abstraction of a unique id, not necessarily consecutive
   WordIndex wordId_;
+  explicit Word(std::size_t wordId) : wordId_((WordIndex)wordId) {}
 public:
-  // back compat with WordIndex
-  Word(std::size_t wordId) : wordId_((WordIndex)wordId) {} // @TODO: make explicit, or make private
-  operator WordIndex() const { return getWordIndex(); }
+  static Word fromWordIndex(std::size_t wordId) { return Word(wordId); }
+  const WordIndex& toWordIndex() const { return wordId_; }
+  std::string toString() const { return std::to_string(wordId_); }
 
   // needed for STL containers
   Word() : wordId_((WordIndex)-1) {}
   bool operator==(const Word& other) const { return wordId_ == other.wordId_; }
+  bool operator!=(const Word& other) const { return !(*this == other); }
+  bool operator<(const Word& other) const { return wordId_ < other.wordId_; }
   std::size_t hash() const { return std::hash<WordIndex>{}(wordId_); }
 
-  // main methods and constants
-  static Word From(std::size_t wordId) { return Word(wordId); }
-  const WordIndex& getWordIndex() const { return wordId_; }
-
+  // constants
   static Word NONE; // @TODO: decide whether we need this, in additional Word()
   // EOS and UNK are placed in these positions in Marian-generated vocabs
   static Word DEFAULT_EOS_ID;
@@ -39,7 +39,10 @@ typedef std::vector<Word> Words;
 
 // Helper to map a Word vector to a WordIndex vector
 static inline std::vector<WordIndex> toWordIndexVector(const Words& words) {
-  return std::vector<WordIndex>(words.begin(), words.end());
+  std::vector<WordIndex> res;
+  std::transform(words.begin(), words.end(), std::back_inserter(res),
+                 [](const Word& word) -> WordIndex { return word.toWordIndex(); });
+  return res;
 }
 
 // names of EOS and UNK symbols
diff --git a/src/microsoft/quicksand.cpp b/src/microsoft/quicksand.cpp
index 3d3c8a508..63f05e738 100755
--- a/src/microsoft/quicksand.cpp
+++ b/src/microsoft/quicksand.cpp
@@ -109,7 +109,7 @@ class BeamSearchDecoder : public IBeamSearchDecoder {
         const auto& sent = qsBatch[j];
         if(i < sent.size()) {
           size_t idx = i * batchSize + j;
-          subBatch->data()[idx] = (unsigned int)sent[i];
+          subBatch->data()[idx] = marian::Word::fromWordIndex(sent[i]);
           subBatch->mask()[idx] = 1;
         }
       }
@@ -122,7 +122,7 @@ class BeamSearchDecoder : public IBeamSearchDecoder {
     batch->setSentenceIds(sentIds);
 
     // decode
-    auto search = New<BeamSearch>(options_, scorers_, eos_);
+    auto search = New<BeamSearch>(options_, scorers_, marian::Word::fromWordIndex(eos_));
     Histories histories = search->search(graph_, batch);
 
     // convert to QuickSAND format
diff --git a/src/models/bert.h b/src/models/bert.h
index e40b858ab..84b74cc33 100755
--- a/src/models/bert.h
+++ b/src/models/bert.h
@@ -51,7 +51,7 @@ class BertBatch : public CorpusBatch {
     if (r < 0.1f) { // for 10% of cases return same word
       return word;
     } else if (r < 0.2f) { // for 10% return random word
-      Word randWord = (*randomWord_)(engine);
+      Word randWord = Word::fromWordIndex((*randomWord_)(engine));
       if(dontMask_.count(randWord) > 0) // some words, e.g. [CLS] or </s>, may not be used as random words
         return mask;                    // for those, return the mask symbol instead
       else
diff --git a/src/tests/attention_tests.cpp b/src/tests/attention_tests.cpp
index 8dfd1844c..6bd31069d 100755
--- a/src/tests/attention_tests.cpp
+++ b/src/tests/attention_tests.cpp
@@ -12,7 +12,7 @@ void tests(DeviceType type) {
 
   Config::seed = 1234;
 
-  Words vWords = {
+  std::vector<WordIndex> vWords = {
     43, 2, 83, 78,
     6, 38, 80, 40,
     40, 70, 26, 60,
@@ -49,7 +49,7 @@ void tests(DeviceType type) {
                             {128, dimEmb},
                             inits::glorot_uniform);
 
-    auto input = reshape(rows(emb, toWordIndexVector(vWords)), {dimTime, dimBatch, dimEmb});
+    auto input = reshape(rows(emb, vWords), {dimTime, dimBatch, dimEmb});
     auto mask = graph->constant({dimTime, dimBatch, 1},
                                 inits::from_vector(vMask));
 
diff --git a/src/tests/rnn_tests.cpp b/src/tests/rnn_tests.cpp
index 97a7848c2..2b4fda0b9 100755
--- a/src/tests/rnn_tests.cpp
+++ b/src/tests/rnn_tests.cpp
@@ -9,7 +9,7 @@ using namespace marian;
 void tests(DeviceType type) {
   auto floatApprox = [](float x, float y) { return x == Approx(y).epsilon(0.01); };
 
-  Words vWords = {
+  std::vector<WordIndex> vWords = {
     43, 2, 83, 78,
     6, 38, 80, 40,
     40, 70, 26, 60,
@@ -202,7 +202,7 @@ void tests(DeviceType type) {
                             {128, dimEmb},
                             inits::glorot_uniform);
 
-    auto input = reshape(rows(emb, toWordIndexVector(vWords)), {dimTime, dimBatch, dimEmb});
+    auto input = reshape(rows(emb, vWords), {dimTime, dimBatch, dimEmb});
     auto mask = graph->constant({dimTime, dimBatch, 1},
                                 inits::from_vector(vMask));
 
diff --git a/src/training/validator.h b/src/training/validator.h
index 657c1fbcb..3abb8a9c9 100755
--- a/src/training/validator.h
+++ b/src/training/validator.h
@@ -279,16 +279,16 @@ class AccuracyValidator : public Validator<data::Corpus> {
 
           for(int i = 0; i < thisLabels; ++i) {
             // CPU-side Argmax
-            IndexType bestIndex = 0;
+            Word bestWord = Word::NONE;
             float bestValue = std::numeric_limits<float>::lowest();
             for(IndexType j = 0; j < cols; ++j) {
               float currValue = vLogits[i * cols + j];
               if(currValue > bestValue) {
                 bestValue = currValue;
-                bestIndex = j;
+                bestWord = Word::fromWordIndex(j);
               }
             }
-            thisCorrect += (size_t)(bestIndex == groundTruth[i]);
+            thisCorrect += (size_t)(bestWord == groundTruth[i]);
           }
 
           std::unique_lock<std::mutex> lock(mutex_);
@@ -390,7 +390,7 @@ class BertAccuracyValidator : public Validator<data::Corpus> {
                   bestIndex = j;
                 }
               }
-              thisCorrect += (size_t)(bestIndex == labels[i].getWordIndex());
+              thisCorrect += (size_t)(bestIndex == labels[i].toWordIndex());
             }
 
             std::unique_lock<std::mutex> lock(mutex_);
diff --git a/src/translator/beam_search.h b/src/translator/beam_search.h
index 5643d96d6..09d382bfd 100755
--- a/src/translator/beam_search.h
+++ b/src/translator/beam_search.h
@@ -49,7 +49,7 @@ class BeamSearch {
     for(size_t i = 0; i < keys.size(); ++i) {
       // Keys contains indices to vocab items in the entire beam.
       // Values can be between 0 and beamSize * vocabSize.
-      Word embIdx = (Word)(keys[i] % vocabSize);
+      Word embIdx = Word::fromWordIndex(keys[i] % vocabSize);
       auto beamIdx = i / beamSize;
 
       // Retrieve short list for final softmax (based on words aligned
@@ -57,7 +57,7 @@ class BeamSearch {
       // in the sub-selected vocabulary matrix back to their original positions.
       auto shortlist = scorers_[0]->getShortlist();
       if(shortlist)
-        embIdx = shortlist->reverseMap(embIdx); // @TODO: should reverseMap accept a size_t or a Word?
+        embIdx = Word::fromWordIndex(shortlist->reverseMap(embIdx.toWordIndex())); // @TODO: should reverseMap accept a size_t or a Word?
 
       if(newBeams[beamIdx].size() < beams[beamIdx].size()) {
         auto& beam = beams[beamIdx];
@@ -85,7 +85,7 @@ class BeamSearch {
           std::vector<float> breakDown(states.size(), 0);
           beam[beamHypIdx]->GetScoreBreakdown().resize(states.size(), 0);
           for(size_t j = 0; j < states.size(); ++j) {
-            size_t key = embIdx + hypIdxTrans * vocabSize;
+            size_t key = embIdx.toWordIndex() + hypIdxTrans * vocabSize;
             breakDown[j] = states[j]->breakDown(key)
                            + beam[beamHypIdx]->GetScoreBreakdown()[j];
           }
@@ -252,7 +252,7 @@ class BeamSearch {
 
       //**********************************************************************
       // suppress specific symbols if not at right positions
-      if(trgUnkId_ != -1 && options_->has("allow-unk")
+      if(trgUnkId_ != Word::NONE && options_->has("allow-unk")
          && !options_->get<bool>("allow-unk"))
         suppressWord(pathScores, trgUnkId_);
       for(auto state : states)
diff --git a/src/translator/helpers.cpp b/src/translator/helpers.cpp
index ccc450401..f131398c2 100755
--- a/src/translator/helpers.cpp
+++ b/src/translator/helpers.cpp
@@ -25,7 +25,7 @@ void SetColumn(Tensor in_, size_t col, float value) {
 }
 
 void suppressWord(Expr logProbs, Word id) {
-  SetColumn(logProbs->val(), id, std::numeric_limits<float>::lowest());
+  SetColumn(logProbs->val(), id.toWordIndex(), std::numeric_limits<float>::lowest());
 }
 }  // namespace cpu
 
diff --git a/src/translator/helpers.cu b/src/translator/helpers.cu
old mode 100644
new mode 100755
index 8b40d8db7..d23c7500f
--- a/src/translator/helpers.cu
+++ b/src/translator/helpers.cu
@@ -38,7 +38,7 @@ void SetColumn(Tensor in_, size_t col, float value) {
 }
 
 void suppressWord(Expr probs, Word id) {
-  SetColumn(probs->val(), id, std::numeric_limits<float>::lowest());
+  SetColumn(probs->val(), id.toWordIndex(), std::numeric_limits<float>::lowest());
 }
 }  // namespace gpu
 }  // namespace marian
diff --git a/src/translator/hypothesis.h b/src/translator/hypothesis.h
index c13ac716b..eaeeaa497 100755
--- a/src/translator/hypothesis.h
+++ b/src/translator/hypothesis.h
@@ -8,7 +8,7 @@ namespace marian {
 
 class Hypothesis {
 public:
-  Hypothesis() : prevHyp_(nullptr), prevIndex_(0), word_(0), pathScore_(0.0) {}
+  Hypothesis() : prevHyp_(nullptr), prevIndex_(0), word_(Word::NONE), pathScore_(0.0) {}
 
   Hypothesis(const Ptr<Hypothesis> prevHyp,
              Word word,
diff --git a/vs/Marian.vcxproj b/vs/Marian.vcxproj
index c899c242f..9e161fefe 100755
--- a/vs/Marian.vcxproj
+++ b/vs/Marian.vcxproj
@@ -1113,6 +1113,8 @@
       <ExcludedFromBuild Condition="'$(Configuration)|$(Platform)'=='Release|x64'">true</ExcludedFromBuild>
     </None>
     <None Include="..\src\training\communicator_nccl.h" />
+    <None Include="..\src\translator\helpers.cu" />
+    <None Include="..\src\translator\nth_element.cu" />
     <None Include=".editorConfig" />
   </ItemGroup>
   <ItemGroup>
diff --git a/vs/Marian.vcxproj.filters b/vs/Marian.vcxproj.filters
index 947bcece8..f690d6cc6 100755
--- a/vs/Marian.vcxproj.filters
+++ b/vs/Marian.vcxproj.filters
@@ -1857,6 +1857,12 @@
     <None Include="..\src\tensors\gpu\prod.cpp">
       <Filter>tensors\gpu</Filter>
     </None>
+    <None Include="..\src\translator\helpers.cu">
+      <Filter>translator</Filter>
+    </None>
+    <None Include="..\src\translator\nth_element.cu">
+      <Filter>translator</Filter>
+    </None>
   </ItemGroup>
   <ItemGroup>
     <Text Include="..\src\3rd_party\sentencepiece\src\CMakeLists.txt">

From 72c1a3b985448301de522e6e62b8a497efc77f7e Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Thu, 31 Jan 2019 22:32:56 -0800
Subject: [PATCH 245/838] now distinguishing between Word::NONE and Word::ZERO
 which is a valid index meant to be masked out

---
 src/data/corpus_base.cpp     | 4 ++--
 src/data/corpus_base.h       | 2 +-
 src/data/text_input.cpp      | 2 +-
 src/data/types.h             | 1 +
 src/data/vocab.cpp           | 1 +
 src/translator/beam_search.h | 2 +-
 src/translator/hypothesis.h  | 2 +-
 7 files changed, 8 insertions(+), 6 deletions(-)

diff --git a/src/data/corpus_base.cpp b/src/data/corpus_base.cpp
index 6e291a289..8253e5100 100755
--- a/src/data/corpus_base.cpp
+++ b/src/data/corpus_base.cpp
@@ -201,11 +201,11 @@ void CorpusBase::addWordsToSentenceTuple(const std::string& line,
   Words words = vocabs_[batchIndex]->encode(line, /*addEOS =*/ addEOS_[batchIndex], inference_);
 
   if(words.empty())
-    words.push_back(Word::NONE);
+    words.push_back(Word::ZERO);
 
   if(maxLengthCrop_ && words.size() > maxLength_) {
     words.resize(maxLength_);
-    words.back() = Word::NONE;
+    words.back() = Word::ZERO;  // @TODO: What does this do? Meant as sent-end token??
   }
 
   if(rightLeft_)
diff --git a/src/data/corpus_base.h b/src/data/corpus_base.h
index 79ac12532..683f41937 100755
--- a/src/data/corpus_base.h
+++ b/src/data/corpus_base.h
@@ -128,7 +128,7 @@ class SubBatch {
    * @param width Number of words in the longest sentence
    */
   SubBatch(size_t size, size_t width, const Ptr<Vocab>& vocab)
-      : indices_(size * width, Word::NONE),
+      : indices_(size * width, Word::ZERO), // note: for gaps, we must use a valid index
         mask_(size * width, 0),
         size_(size),
         width_(width),
diff --git a/src/data/text_input.cpp b/src/data/text_input.cpp
index 9c694c836..59f16f1bf 100755
--- a/src/data/text_input.cpp
+++ b/src/data/text_input.cpp
@@ -48,7 +48,7 @@ SentenceTuple TextInput::next() {
       if(io::getline(dummyStream, line)) {
         Words words = vocabs_[i]->encode(line, /*addEOS =*/ true, /*inference =*/ inference_);
         if(words.empty())
-          words.push_back(Word::NONE);
+          words.push_back(Word::ZERO); // @TODO: What is this for? @BUGBUG: addEOS=true, so this can never happen, right?
         tup.push_back(words);
       }
     }
diff --git a/src/data/types.h b/src/data/types.h
index dedbf11ce..d204beb64 100755
--- a/src/data/types.h
+++ b/src/data/types.h
@@ -29,6 +29,7 @@ class Word {                    // Word is an abstraction of a unique id, not ne
 
   // constants
   static Word NONE; // @TODO: decide whether we need this, in additional Word()
+  static Word ZERO; // an invalid word that nevertheless can safely be looked up (and then masked out)
   // EOS and UNK are placed in these positions in Marian-generated vocabs
   static Word DEFAULT_EOS_ID;
   static Word DEFAULT_UNK_ID;
diff --git a/src/data/vocab.cpp b/src/data/vocab.cpp
index 8118ea24f..7844ecfa4 100755
--- a/src/data/vocab.cpp
+++ b/src/data/vocab.cpp
@@ -4,6 +4,7 @@
 namespace marian {
 
 Word Word::NONE = Word();
+Word Word::ZERO = Word(0);
 Word Word::DEFAULT_EOS_ID = Word(0);
 Word Word::DEFAULT_UNK_ID = Word(1);
 
diff --git a/src/translator/beam_search.h b/src/translator/beam_search.h
index 09d382bfd..c80865f73 100755
--- a/src/translator/beam_search.h
+++ b/src/translator/beam_search.h
@@ -217,7 +217,7 @@ class BeamSearch {
               beamScores.push_back(hyp->GetPathScore());
             } else {  // dummy hypothesis
               hypIndices.push_back(0);
-              predWords.push_back(Word::NONE);  // (unused)
+              predWords.push_back(Word::ZERO);  // (unused)
               beamScores.push_back(-9999);
             }
           }
diff --git a/src/translator/hypothesis.h b/src/translator/hypothesis.h
index eaeeaa497..ab114470e 100755
--- a/src/translator/hypothesis.h
+++ b/src/translator/hypothesis.h
@@ -8,7 +8,7 @@ namespace marian {
 
 class Hypothesis {
 public:
-  Hypothesis() : prevHyp_(nullptr), prevIndex_(0), word_(Word::NONE), pathScore_(0.0) {}
+  Hypothesis() : prevHyp_(nullptr), prevIndex_(0), word_(Word::ZERO), pathScore_(0.0) {}
 
   Hypothesis(const Ptr<Hypothesis> prevHyp,
              Word word,

From 75604a140130b35653be65a64fff043c92e65569 Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Fri, 1 Feb 2019 15:41:26 -0800
Subject: [PATCH 246/838] addressed PR feedback

---
 src/graph/expression_graph.h |  9 +--------
 src/layers/generic.h         | 17 ++++++++++-------
 2 files changed, 11 insertions(+), 15 deletions(-)

diff --git a/src/graph/expression_graph.h b/src/graph/expression_graph.h
index 22908d96b..dfd00a9a5 100755
--- a/src/graph/expression_graph.h
+++ b/src/graph/expression_graph.h
@@ -316,13 +316,6 @@ class ExpressionGraph : public std::enable_shared_from_this<ExpressionGraph> {
     dot.close();
   }
 
-  Expr tryFindParam(const std::string& pname) const {
-    std::string name = pname;
-    if(!namespace_.empty())
-      name = namespace_ + "::" + name;
-    return params_->get(name);
-  }
-
   Expr param(const std::string& pname,
              const Shape& shape,
              const NodeInitializer& init,
@@ -407,7 +400,7 @@ class ExpressionGraph : public std::enable_shared_from_this<ExpressionGraph> {
     auto e = params_->get(name);
     if(e)
       return e;
-    return Expr();
+    return Expr();  // @TODO: how is this different from just returning 'e'?
   }
 
   Ptr<Parameters>& params() { return params_; }
diff --git a/src/layers/generic.h b/src/layers/generic.h
index 8e03efd08..d050a55f0 100755
--- a/src/layers/generic.h
+++ b/src/layers/generic.h
@@ -128,6 +128,7 @@ class Output : public LayerBase, public IUnaryLayer {
   // parameters held by this layer
   Expr Wt_; // weight matrix is stored transposed for efficiency
   Expr b_;
+  bool isLegacyUntransposedW{false}; // legacy-model emulation: W is stored in non-transposed form
   Expr cachedShortWt_;  // short-listed version, cached (cleared by clear())
   Expr cachedShortb_;   // these match the current value of shortlist_
 
@@ -174,9 +175,11 @@ class Output : public LayerBase, public IUnaryLayer {
       if(tiedParam_) {
         Wt_ = tiedParam_;
       } else {
-        auto W = graph_->tryFindParam(name + "_W"); // support of legacy models that did not transpose
-        if (W)
-          Wt_ = transpose(W); // legacy
+        auto W = graph_->get(name + "_W"); // support of legacy models that did not transpose
+        if (W) {
+          Wt_ = W;
+          isLegacyUntransposedW = true;
+        }
         else // this is the regular case:
           Wt_ = graph_->param(name + "_Wt", {input->shape()[-1], dim}, inits::glorot_uniform);
       }
@@ -185,13 +188,13 @@ class Output : public LayerBase, public IUnaryLayer {
 
     if (shortlist_) {
       if (!cachedShortWt_) { // short versions of parameters are cached within one batch, then clear()ed
-        cachedShortWt_ = rows(Wt_, shortlist_->indices());
-        cachedShortb_  = cols(b_ , shortlist_->indices());
+        cachedShortWt_ = index_select(Wt_, isLegacyUntransposedW ? -1 : 0, shortlist_->indices());
+        cachedShortb_  = index_select(b_ ,                             -1, shortlist_->indices());
       }
-      return affine(input, cachedShortWt_, cachedShortb_, false, /*transB=*/true);
+      return affine(input, cachedShortWt_, cachedShortb_, false, /*transB=*/isLegacyUntransposedW ? false : true);
     }
     else
-      return affine(input, Wt_, b_, false, /*transB=*/true);
+      return affine(input, Wt_, b_, false, /*transB=*/isLegacyUntransposedW ? false : true);
   }
 
   virtual Expr apply(const std::vector<Expr>& /*inputs*/) override {

From f07af897d9586f37fc72b9b374198837dc646022 Mon Sep 17 00:00:00 2001
From: Alham Fikri Aji <afaji321@gmail.com>
Date: Sat, 2 Feb 2019 08:25:38 +0000
Subject: [PATCH 247/838] reset gradients in default comm.

---
 src/training/communicator.h | 10 ++++++++++
 1 file changed, 10 insertions(+)

diff --git a/src/training/communicator.h b/src/training/communicator.h
index 4190401a8..6ccce204f 100644
--- a/src/training/communicator.h
+++ b/src/training/communicator.h
@@ -175,7 +175,17 @@ class DefaultCommunicator : public ICommunicator {
       }
     };
 
+    // reset gradients outside current shard
+    auto reset = [this, shardSize](size_t idx, size_t begin, size_t end) {
+      auto grad = graphs_[idx]->params()->grads();
+      if (begin > 0)
+        grad->subtensor(0, begin)->set(0);
+      if (end < grad->size())
+        grad->subtensor(end, grad->size()-end)->set(0);
+    };
+
     foreach(scatter);
+    foreach(reset);
   }
 
   void allGatherParams() const override {

From 3e9c10e95c3bb41b3ca35a55c5489c058f0f3a61 Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Sun, 3 Feb 2019 14:50:40 -0800
Subject: [PATCH 248/838] legacy mode for Output must still call param() to get
 it into the graph

---
 src/layers/generic.h | 5 ++---
 1 file changed, 2 insertions(+), 3 deletions(-)

diff --git a/src/layers/generic.h b/src/layers/generic.h
index d050a55f0..5841b8047 100755
--- a/src/layers/generic.h
+++ b/src/layers/generic.h
@@ -175,9 +175,8 @@ class Output : public LayerBase, public IUnaryLayer {
       if(tiedParam_) {
         Wt_ = tiedParam_;
       } else {
-        auto W = graph_->get(name + "_W"); // support of legacy models that did not transpose
-        if (W) {
-          Wt_ = W;
+        if (graph_->get(name + "_W")) { // support of legacy models that did not transpose
+          Wt_ = graph_->param(name + "_Wt", {dim, input->shape()[-1]}, inits::glorot_uniform);
           isLegacyUntransposedW = true;
         }
         else // this is the regular case:

From 7832b3be911ea6a77692f592422cb11b4c35a1a4 Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Sun, 3 Feb 2019 14:50:57 -0800
Subject: [PATCH 249/838] legacy mode for Output must still call param() to get
 it into the graph

---
 src/layers/generic.h | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/layers/generic.h b/src/layers/generic.h
index 5841b8047..6e12bc073 100755
--- a/src/layers/generic.h
+++ b/src/layers/generic.h
@@ -176,7 +176,7 @@ class Output : public LayerBase, public IUnaryLayer {
         Wt_ = tiedParam_;
       } else {
         if (graph_->get(name + "_W")) { // support of legacy models that did not transpose
-          Wt_ = graph_->param(name + "_Wt", {dim, input->shape()[-1]}, inits::glorot_uniform);
+          Wt_ = graph_->param(name + "_W", {dim, input->shape()[-1]}, inits::glorot_uniform);
           isLegacyUntransposedW = true;
         }
         else // this is the regular case:

From 0c14096d7f0a44185db2ba6b327b041f33be23c1 Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Sun, 3 Feb 2019 15:51:41 -0800
Subject: [PATCH 250/838] (comments)

---
 src/graph/expression_operators.cpp |  7 ++++++-
 src/layers/generic.cpp             | 18 +++++-------------
 2 files changed, 11 insertions(+), 14 deletions(-)

diff --git a/src/graph/expression_operators.cpp b/src/graph/expression_operators.cpp
index 6a07611dc..b74b822ed 100755
--- a/src/graph/expression_operators.cpp
+++ b/src/graph/expression_operators.cpp
@@ -250,7 +250,12 @@ Expr gather(Expr a, int axis, Expr indices) {
   return Expression<GatherNodeOp>(a, axis, indices);
 }
 
-// index_select() -- gather arbitrary elements along an axis; unbatched (indices are specified as a 1D vector)
+// index_select() -- gather arbitrary elements along an axis from an unbatched
+// input 'a'. Indices are specified as a 1D vector.
+// This is used e.g. for embedding lookup.
+// Note: To use a batch of index vectors, reshape them into a single vector,
+// call index_select(), then reshape the result back. Reshapes are cheap.
+// This function has the same semantics as PyTorch operation of the same name.
 Expr index_select(Expr a, int axis, Expr indices) {
   ABORT_IF(indices->shape().size() != 1, "Indices must be a 1D tensor");
   // We have specialized kernels for non-batched indexing of first or last axis of a 2D tensor.
diff --git a/src/layers/generic.cpp b/src/layers/generic.cpp
index fb618686d..5fa5d3bf0 100755
--- a/src/layers/generic.cpp
+++ b/src/layers/generic.cpp
@@ -197,12 +197,10 @@ namespace marian {
       auto factorIndex  = rows(factorIndexMatrix, indices); // [B... * 1(Ug)] map word indices to factor indices (indices into factorLogits)
       auto factorMask   = rows(factorMaskMatrix,  indices); // [B... * 1]     flag whether word has the factor in the first place
       auto factorLogits = logits_[g];                       // [B... * Ug]
-      // @TODO: no need to normalize factors before here
       // For each location in [B...] select [indices[B...]]. If not using factor, select [0] and mask it out next.
       auto factorLoss = lossFn(factorLogits, factorIndex);  // [B... x 1]
-      auto xxx = reshape(factorMask, factorLoss->shape());
       //factorLoss->debug("factorLoss");
-      factorLoss = factorLoss * xxx;// reshape(factorMask, factorLoss->shape()); // mask out factor for words that do not have that factor
+      factorLoss = factorLoss * reshape(factorMask, factorLoss->shape()); // mask out factor for words that do not have that factor
       loss = loss ? (loss + factorLoss) : factorLoss; // [B... x 1]
     }
     return loss;
@@ -215,10 +213,10 @@ namespace marian {
       return logits_.front();
     }
 
-    // lazily compute combined logits from factors
+    // compute normalized factor log probs
     std::vector<Expr> logProbs(logits_.size());
     for (size_t g = 0; g < logits_.size(); g++)
-        logProbs[g] = logsoftmax(logits_[g]);
+      logProbs[g] = logsoftmax(logits_[g]);
     auto y = concatenate(logProbs, /*axis=*/ -1);
 
     // sum up the unit logits across factors for each target word
@@ -300,7 +298,7 @@ namespace marian {
       else if (embeddingFactorMapping_) {
         auto graph = input->graph();
 
-        // project each factor
+        // project each factor separately
         auto numGroups = embeddingFactorMapping_->getNumGroups();
         std::vector<Expr> allLogits(numGroups);
         for (size_t g = 0; g < numGroups; g++) {
@@ -309,14 +307,8 @@ namespace marian {
           // slice this group's section out of W_
           // @TODO: This is highly inefficient if not tied. We should always transpose Output's matrix.
           auto factorW = slice(W_, transposeW_ ? 0 : -1, Slice((int)range.first, (int)range.second));
-          auto factorB = slice(b_, -1, Slice((int)range.first, (int)range.second)); // @TODO: b_ should be a vector, not a matrix
+          auto factorB = slice(b_, -1, Slice((int)range.first, (int)range.second)); // @TODO: b_ should be a vector, not a matrix; but shotlists use cols() in, which requires a matrix
           auto factorLogits = affine(input, factorW, factorB, false, transposeW_); // [B... x U] factor logits
-          // log-linear weight
-          // @TODO: The weight should not be needed, since it could learn it into the embeddings. Maybe it doesn't.
-          // @TODO? If we move the weight before affine(), it would use less memory at least for the main factor.
-          //auto name = options_->get<std::string>("prefix");
-          //auto llWeight = graph->param(name + "_llWeight_" + std::to_string(g), {}, inits::from_value(1.0f));
-          //factorLogits = factorLogits * llWeight; // -a constant, which is OK for logits
           allLogits[g] = factorLogits;
         }
         return Logits(std::move(allLogits), embeddingFactorMapping_);

From 37e6e680e12797c80947cfc4f1515cfafc8c3b56 Mon Sep 17 00:00:00 2001
From: Marcin Junczys-Dowmunt <Marcin.JunczysDowmunt@microsoft.com>
Date: Mon, 4 Feb 2019 12:49:10 -0800
Subject: [PATCH 251/838] invert dims when parameter tying is not used

---
 src/layers/generic.h | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/src/layers/generic.h b/src/layers/generic.h
index 6e12bc073..d13a920c3 100755
--- a/src/layers/generic.h
+++ b/src/layers/generic.h
@@ -176,11 +176,11 @@ class Output : public LayerBase, public IUnaryLayer {
         Wt_ = tiedParam_;
       } else {
         if (graph_->get(name + "_W")) { // support of legacy models that did not transpose
-          Wt_ = graph_->param(name + "_W", {dim, input->shape()[-1]}, inits::glorot_uniform);
+          Wt_ = graph_->param(name + "_W", {input->shape()[-1], dim}, inits::glorot_uniform);
           isLegacyUntransposedW = true;
         }
         else // this is the regular case:
-          Wt_ = graph_->param(name + "_Wt", {input->shape()[-1], dim}, inits::glorot_uniform);
+          Wt_ = graph_->param(name + "_Wt", {dim, input->shape()[-1]}, inits::glorot_uniform);
       }
       b_ = graph_->param(name + "_b", {1, dim}, inits::zeros);
     }

From 4634c5c660a6bc5690103c42936f39308d7568ce Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Mon, 4 Feb 2019 13:15:46 -0800
Subject: [PATCH 252/838] brought back loss-function aggregation over factors

---
 src/layers/generic.cpp | 45 ++++++++++++++++++------------------------
 src/layers/generic.h   | 13 ++++--------
 src/layers/loss.h      | 39 ++++++++++++++++++------------------
 3 files changed, 43 insertions(+), 54 deletions(-)

diff --git a/src/layers/generic.cpp b/src/layers/generic.cpp
index 94b40d19e..5a614621c 100755
--- a/src/layers/generic.cpp
+++ b/src/layers/generic.cpp
@@ -1,6 +1,7 @@
 #include "marian.h"
 
 #include "layers/generic.h"
+#include "layers/loss.h"
 
 using std::size_t; // not sure why this is needed
 
@@ -175,12 +176,16 @@ namespace marian {
     //std::vector<std::vector<float>> mVecs_;              // [group id][u] -> 1 if factor is member of group
   };
 
-  Ptr<RationalLoss> Logits::applyLossFunction(Expr indices, const std::function<Ptr<RationalLoss>(Ptr<RationalLoss>/*logits*/, Expr/*indices*/)>& lossFn) const {
+  Logits::Logits(Expr logits) : Logits(New<RationalLoss>(logits, nullptr)) {} // single-output constructor from Expr only (RationalLoss has no count)
+
+  // This function assumes that the object holds one or more factor logits.
+  // It applies the supplied loss function to each, and then returns the aggregate loss over all factors.
+  Expr Logits::applyLossFunction(Expr indices, const std::function<Expr(Expr/*logits*/, Expr/*indices*/)>& lossFn) const {
     LOG_ONCE(info, "[logits] applyLossFunction() for {} factors", logits_.size());
     ABORT_IF(empty(), "Attempted to read out logits on empty Logits object");
     if (!embeddingFactorMapping_) {
       ABORT_IF(logits_.size() != 1, "Factors without factor mappings??");
-      return lossFn(logits_.front(), indices);
+      return lossFn(logits_.front()->loss(), indices);
     }
 
     // accumulate all CEs for all words that have the factor
@@ -198,13 +203,14 @@ namespace marian {
       auto factorMask   = rows(factorMaskMatrix,  indices); // [B... * 1]     flag whether word has the factor in the first place
       auto factorLogits = logits_[g];                       // [B... * Ug]
       // For each location in [B...] select [indices[B...]]. If not using factor, select [0] and mask it out next.
-      auto factorLoss = lossFn(factorLogits, factorIndex)->loss(); // [B... x 1]
+      auto factorLoss = lossFn(factorLogits->loss(), factorIndex); // [B... x 1]
       factorLoss = factorLoss * reshape(factorMask, factorLoss->shape()); // mask out factor for words that do not have that factor
       loss = loss ? (loss + factorLoss) : factorLoss; // [B... x 1]
     }
-    return New<RationalLoss>(loss, logits_.front()->count());
+    return loss;
   }
 
+  // This function assumes this object holds a single factor that represents a rational loss (with count).
   Ptr<RationalLoss> Logits::getRationalLoss() const {
     //return New<RationalLoss>(getLogits(), logits_.front()->count());
     ABORT_IF(logits_.size() != 1 || embeddingFactorMapping_, "getRationalLoss() cannot be used on multi-factor outputs");
@@ -212,6 +218,8 @@ namespace marian {
     return logits_.front();
   }
 
+  // This function assumes that the object holds one or more factor logits, which are summed up
+  // into output-vocab logits according to the factored model (with correct normalization of factors).
   Expr Logits::getLogits() const {
     ABORT_IF(empty(), "Attempted to read out logits on empty Logits object");
     if (!embeddingFactorMapping_) {
@@ -237,28 +245,13 @@ namespace marian {
         /*transB=*/ true); // -> [B x V]
 
     return y;
-#if 0
-        // denominators
-        const auto& mVecs = embeddingFactorMapping_->mVecs_;
-        for (size_t g = 0; g < numGroups; g++) {
-          auto range = groupRanges[g];
-          // y: [B... x U]
-          // m: [1 x U]         // ones at positions of group members
-          // need to compute log denominator over y[range] and subtract it from y[range]
-          //auto groupY = slice(y, /*axis=*/-1, Slice((int)range.first, (int)range.second)); // [B... x Ug]
-          //auto groupZ = logsumexp(groupY, /*axis=*/-1); // [B... x 1]
-          ////auto groupZ = slice(groupY - logsoftmax(groupY), /*axis=*/-1, 0); // [B... x 1]
-          const auto& mVec = mVecs[g];
-          auto m = graph->constant({ 1, (int)mVec.size() }, inits::from_vector(mVec)); // [1 x U]
-          //auto Z = dot(groupZ, m); // [B... x U]
-          //y = y - Z;
-          // and a log-linear weight
-          auto name = options_->get<std::string>("prefix");
-          auto groupLLWeights[g] = graph->param(name + "_llWeight_" + std::to_string(g), {}, inits::from_value(1.0f));
-          y = y * ((groupLLWeights[g] - 1) * m + 1);
-          // @BUGBUG: Global softmax no longer normalizes, due to words that lack some factors.
-        }
-#endif
+  }
+
+  Logits Logits::withCounts(const Expr& count) const { // create new Logits with 'count' implanted into all logits_
+    std::vector<Ptr<RationalLoss>> newLogits;
+    for (const auto& l : logits_)
+      newLogits.emplace_back(New<RationalLoss>(l->loss(), count));
+    return Logits(std::move(newLogits), embeddingFactorMapping_);
   }
 
   namespace mlp {
diff --git a/src/layers/generic.h b/src/layers/generic.h
index 651eeca5b..f335cbf99 100755
--- a/src/layers/generic.h
+++ b/src/layers/generic.h
@@ -4,7 +4,6 @@
 
 #include "data/shortlist.h"
 #include "layers/factory.h"
-#include "layers/loss.h"
 
 namespace marian {
 namespace mlp {
@@ -63,6 +62,7 @@ class EmbeddingFactorMapping;
 // @HACK: Frank's quick implementation of factored outputs. To be re-thought once it works.
 // Output layer returns a Logits object, which is able to compute some things on the fly
 // for factored embeddings.
+class RationalLoss;
 class Logits {
     Logits& operator=(const Logits& other) = default;
 public:
@@ -70,12 +70,12 @@ class Logits {
     Logits(Ptr<RationalLoss> logits) { // single-output constructor
       logits_.push_back(logits);
     }
-    Logits(Expr logits) : Logits(New<RationalLoss>(logits, nullptr)) {} // single-output constructor from Expr only (RationalLoss has no count)
+    Logits(Expr logits); // single-output constructor from Expr only (RationalLoss has no count)
     Logits(std::vector<Ptr<RationalLoss>>&& logits, Ptr<EmbeddingFactorMapping> embeddingFactorMapping) // factored-output constructor
       : logits_(std::move(logits)), embeddingFactorMapping_(embeddingFactorMapping) {}
     Expr getLogits() const; // assume it holds logits: get them, possibly aggregating over factors
     Ptr<RationalLoss> getRationalLoss() const; // assume it holds a loss: get that
-    Ptr<RationalLoss> applyLossFunction(Expr indices, const std::function<Ptr<RationalLoss>(Ptr<RationalLoss>/*logits*/,Expr/*indices*/)>& lossFn) const;
+    Expr applyLossFunction(Expr indices, const std::function<Expr(Expr/*logits*/,Expr/*indices*/)>& lossFn) const;
     void assign(const Logits& other) {
       //ABORT_IF(!empty() && getNumFactors() != other.getNumFactors(),
       //         "Logits assignment cannot change number of factors");
@@ -83,12 +83,7 @@ class Logits {
     }
     size_t getNumFactors() const { return logits_.size(); }
     bool empty() const { return logits_.empty(); }
-    Logits withCounts(const Expr& count) const { // create new Logits with 'count' implanted into all logits_
-      std::vector<Ptr<RationalLoss>> newLogits;
-      for (const auto& l : logits_)
-        newLogits.emplace_back(New<RationalLoss>(l->loss(), count));
-      return Logits(std::move(newLogits), embeddingFactorMapping_);
-    }
+    Logits withCounts(const Expr& count) const; // create new Logits with 'count' implanted into all logits_
 private:
     // @HACK: The interplay between Logits and RationalLoss is weird. Here, we allow RationalLoss with count == nullptr.
     std::vector<Ptr<RationalLoss>> logits_;
diff --git a/src/layers/loss.h b/src/layers/loss.h
index ea126c2ad..66a702c7d 100755
--- a/src/layers/loss.h
+++ b/src/layers/loss.h
@@ -1,6 +1,7 @@
 #pragma once
 
 #include "graph/expression_operators.h"
+#include "layers/generic.h" // for Logits (Frank's factor hack)
 
 namespace marian {
 
@@ -269,7 +270,7 @@ class LabelwiseLoss {
 protected:
   std::vector<int> axes_;
 
-  virtual Expr compute(Expr logits, Expr labelIndices,
+  virtual Expr compute(Logits logits, Expr labelIndices,
                        Expr mask = nullptr, Expr labelWeights = nullptr) = 0;
 
   // label counts are available, reduce together with loss to obtain counts
@@ -304,7 +305,7 @@ class LabelwiseLoss {
   LabelwiseLoss(const std::vector<int>& axes)
   : axes_(axes) { }
 
-  virtual RationalLoss apply(Expr logits, Expr labelIndices,
+  virtual RationalLoss apply(Logits logits, Expr labelIndices,
                              Expr mask = nullptr, Expr labelWeights = nullptr) {
     Expr loss = compute(logits, labelIndices, mask, labelWeights);
 
@@ -331,23 +332,23 @@ class CrossEntropyLoss : public LabelwiseLoss {
 protected:
   float labelSmoothing_; // interpolation factor for label smoothing, see below
 
-  virtual Expr compute(Expr logits, Expr labelIndices,
+  virtual Expr compute(Logits logits, Expr labelIndices,
                        Expr mask = nullptr, Expr labelWeights = nullptr) override {
-    Expr ce = cross_entropy(logits, labelIndices);
-
-    if(labelSmoothing_ > 0) {
-      // @TODO: add this to CE kernels instead
-      
-      // Label smoothing (see https://arxiv.org/pdf/1512.00567.pdf, section 7)
-      // We compute smoothed H(q',p) = (1 - eps) * H(q,p) + eps * H(u,p) where H(q,p) is the normal cross-entropy
-      // and H(u,p) penalizes deviation of p from u, u being uniform distribution over vocab V => u_v = 1/|V|.
-      // H(u,p) = - \sum_{v \in V} u_v * \log p_v = - 1/|V| \sum_{v \in V} \log \softmax_v => -mean(logsoftmax(logits))
-      // ceq = -H(u,p) - avoid one kernel call by negating in the interpolation below
-      Expr ceq = mean(logsoftmax(logits), /*axis=*/ -1);
-
-      // H(q',p) = (1 - eps) * H(q,p) - eps * -H(u,p)
-      ce = (1 - labelSmoothing_) * ce - labelSmoothing_ * ceq;
-    }
+    // logits may be factored; in that case, the getLoss() function computes one loss for each, and sums them up
+    auto ce = logits.applyLossFunction(labelIndices, [&](Expr logits, Expr indices) {
+      Expr ce = cross_entropy(logits, indices);
+      if (labelSmoothing_ > 0) {
+        // ce = -sum_i y^_i log y_i(h)
+        // with smoothing:
+        // ce' = -sum_i ((1-labelSmoothing_) y^_i + labelSmoothing_/N) log y_i(h)
+        //     = -(1-labelSmoothing_) sum_i y^_i log y_i(h) - labelSmoothing_ mean_i log y_i(h)
+        //     = (1-labelSmoothing_) ce - labelSmoothing_ mean_i log y_i(h)
+        auto ceqNeg = mean(logits, /*axis=*/ -1) - logsumexp(logits, /*axis=*/ -1);
+        ce = (1 - labelSmoothing_) * ce - labelSmoothing_ * ceqNeg;
+        //ce = ce - labelSmoothing_ * (ce + ceqNeg); // writing it this way saves one op :)
+      }
+      return ce;
+    });
 
     if(mask)
       ce = ce * mask;
@@ -367,7 +368,7 @@ class RescorerLoss : public CrossEntropyLoss {
   // sentence-wise CE, hence reduce only over time axis. CE reduces over last axis (-1)
   RescorerLoss() : CrossEntropyLoss(/*axes=*/{-3}, /*smoothing=*/0.f) {}
 
-  virtual RationalLoss apply(Expr logits, Expr labelIndices,
+  virtual RationalLoss apply(Logits logits, Expr labelIndices,
                              Expr mask = nullptr, Expr labelWeights = nullptr) override {
     auto ce = CrossEntropyLoss::apply(logits, labelIndices, mask, labelWeights);
     return RationalLoss(ce.loss(), ce.count());

From 5da979a893faacc2b44f300e51f70b05352522e2 Mon Sep 17 00:00:00 2001
From: Marcin Junczys-Dowmunt <Marcin.JunczysDowmunt@microsoft.com>
Date: Mon, 4 Feb 2019 13:27:10 -0800
Subject: [PATCH 253/838] initialize output weights taking into account fanIn
 and fanOut for glorot_uniform

---
 src/layers/generic.h | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/src/layers/generic.h b/src/layers/generic.h
index d13a920c3..2aa047865 100755
--- a/src/layers/generic.h
+++ b/src/layers/generic.h
@@ -176,11 +176,11 @@ class Output : public LayerBase, public IUnaryLayer {
         Wt_ = tiedParam_;
       } else {
         if (graph_->get(name + "_W")) { // support of legacy models that did not transpose
-          Wt_ = graph_->param(name + "_W", {input->shape()[-1], dim}, inits::glorot_uniform);
+          Wt_ = graph_->param(name + "_W", {input->shape()[-1], dim}, inits::glorot_uniform2(/*fanIn=*/true, /*fanOut=*/false)); // @TODO: unify initializers, already done in other branch
           isLegacyUntransposedW = true;
         }
         else // this is the regular case:
-          Wt_ = graph_->param(name + "_Wt", {dim, input->shape()[-1]}, inits::glorot_uniform);
+          Wt_ = graph_->param(name + "_Wt", {dim, input->shape()[-1]}, inits::glorot_uniform2(/*fanIn=*/false, /*fanOut=*/true)); // @TODO: unify initializers, already done in other branch
       }
       b_ = graph_->param(name + "_b", {1, dim}, inits::zeros);
     }

From ed0be0026f80768c8272ff44651662a750d58a80 Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Mon, 4 Feb 2019 14:01:07 -0800
Subject: [PATCH 254/838] rolled back to original initialization of Embedding
 matrices, for comparability

---
 src/layers/generic.cpp | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/src/layers/generic.cpp b/src/layers/generic.cpp
index 5a614621c..2c85e8b40 100755
--- a/src/layers/generic.cpp
+++ b/src/layers/generic.cpp
@@ -332,7 +332,8 @@ namespace marian {
     }
 
     // Embedding layer initialization should depend only on embedding size, hence fanIn=false
-    NodeInitializer initFunc = inits::glorot_uniform2(/*fanIn=*/false, /*fanOut=*/true);
+    //NodeInitializer initFunc = inits::glorot_uniform2(/*fanIn=*/false, /*fanOut=*/true);
+    NodeInitializer initFunc = inits::glorot_uniform;
     if (options_->has("embFile")) {
       std::string file = opt<std::string>("embFile");
       if (!file.empty()) {

From 57e3d782e0b65703ba7743377ca2ce6c351071a9 Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Mon, 4 Feb 2019 16:10:47 -0800
Subject: [PATCH 255/838] renamed VocabBase to IVocab

---
 src/data/default_vocab.cpp       | 6 +++---
 src/data/sentencepiece_vocab.cpp | 4 ++--
 src/data/vocab.cpp               | 8 ++++----
 src/data/vocab.h                 | 4 ++--
 src/data/vocab_base.h            | 2 +-
 5 files changed, 12 insertions(+), 12 deletions(-)
 mode change 100644 => 100755 src/data/vocab_base.h

diff --git a/src/data/default_vocab.cpp b/src/data/default_vocab.cpp
index a0db64edd..bb91fab36 100755
--- a/src/data/default_vocab.cpp
+++ b/src/data/default_vocab.cpp
@@ -15,7 +15,7 @@
 
 namespace marian {
 
-class DefaultVocab : public VocabBase {
+class DefaultVocab : public IVocab {
 protected:
   typedef std::map<std::string, Word> Str2Id;
   Str2Id str2id_;
@@ -319,11 +319,11 @@ class ClassVocab : public DefaultVocab {
   }
 };
 
-Ptr<VocabBase> createDefaultVocab() {
+Ptr<IVocab> createDefaultVocab() {
   return New<DefaultVocab>();
 }
 
-Ptr<VocabBase> createClassVocab() {
+Ptr<IVocab> createClassVocab() {
   return New<ClassVocab>();
 }
 
diff --git a/src/data/sentencepiece_vocab.cpp b/src/data/sentencepiece_vocab.cpp
index 4457dc064..9af727565 100755
--- a/src/data/sentencepiece_vocab.cpp
+++ b/src/data/sentencepiece_vocab.cpp
@@ -19,7 +19,7 @@ namespace marian {
 #ifdef USE_SENTENCEPIECE
 
 // Wrapper around https://github.com/google/sentencepiece
-class SentencePieceVocab : public VocabBase {
+class SentencePieceVocab : public IVocab {
 private:
   // Actual SentencePiece processor object
   UPtr<sentencepiece::SentencePieceProcessor> spm_;
@@ -252,7 +252,7 @@ class SentencePieceVocab : public VocabBase {
 };
 #endif // USE_SENTENCEPIECE
 
-Ptr<VocabBase> createSentencePieceVocab(const std::string& vocabPath, Ptr<Options> options, size_t batchIndex) {
+Ptr<IVocab> createSentencePieceVocab(const std::string& vocabPath, Ptr<Options> options, size_t batchIndex) {
   bool isSentencePiece = regex::regex_search(vocabPath, regex::regex("\\.(spm)$"));
   if(isSentencePiece) {
 #ifdef USE_SENTENCEPIECE
diff --git a/src/data/vocab.cpp b/src/data/vocab.cpp
index 7844ecfa4..8094c699c 100755
--- a/src/data/vocab.cpp
+++ b/src/data/vocab.cpp
@@ -8,12 +8,12 @@ Word Word::ZERO = Word(0);
 Word Word::DEFAULT_EOS_ID = Word(0);
 Word Word::DEFAULT_UNK_ID = Word(1);
 
-Ptr<VocabBase> createDefaultVocab();
-Ptr<VocabBase> createClassVocab();
-Ptr<VocabBase> createSentencePieceVocab(const std::string& /*vocabPath*/, Ptr<Options>, size_t /*batchIndex*/);
+Ptr<IVocab> createDefaultVocab();
+Ptr<IVocab> createClassVocab();
+Ptr<IVocab> createSentencePieceVocab(const std::string& /*vocabPath*/, Ptr<Options>, size_t /*batchIndex*/);
 
 // @TODO: make each vocab peek on type
-Ptr<VocabBase> createVocab(const std::string& vocabPath, Ptr<Options> options, size_t batchIndex) {
+Ptr<IVocab> createVocab(const std::string& vocabPath, Ptr<Options> options, size_t batchIndex) {
   auto vocab = createSentencePieceVocab(vocabPath, options, batchIndex);
   if(vocab) {
     return vocab; // this is defined which means that a sentencepiece vocabulary could be created, so return it
diff --git a/src/data/vocab.h b/src/data/vocab.h
index e82673ecc..afe79d1f0 100755
--- a/src/data/vocab.h
+++ b/src/data/vocab.h
@@ -7,7 +7,7 @@
 
 namespace marian {
 
-class VocabBase;
+class IVocab;
 
 // Wrapper around vocabulary types. Can choose underlying
 // vocabulary implementation (vImpl_) based on speficied path
@@ -17,7 +17,7 @@ class VocabBase;
 // * SentencePiece with suffix *.spm (works, but has to be created outside Marian)
 class Vocab {
 private:
-  Ptr<VocabBase> vImpl_;
+  Ptr<IVocab> vImpl_;
   Ptr<Options> options_;
   size_t batchIndex_;
 
diff --git a/src/data/vocab_base.h b/src/data/vocab_base.h
old mode 100644
new mode 100755
index 2050792f3..9a82131e8
--- a/src/data/vocab_base.h
+++ b/src/data/vocab_base.h
@@ -7,7 +7,7 @@
 
 namespace marian {
 
-class VocabBase {
+class IVocab {
 public:
   virtual size_t load(const std::string& vocabPath, size_t maxSize = 0) = 0;
 

From 41117fb4326d60d18f2fbc48a26f446d66ee4543 Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Mon, 4 Feb 2019 16:34:26 -0800
Subject: [PATCH 256/838] abstracted Expr State::targetIndices_ to Words
 targetWords_, in prep for factored word representations

---
 src/layers/loss.h              | 14 ++++++++------
 src/models/bert.h              |  8 ++++----
 src/models/costs.h             |  4 ++--
 src/models/decoder.h           | 14 +++++++-------
 src/models/encoder_decoder.cpp |  2 +-
 src/models/states.h            | 16 ++++++----------
 vs/Marian.vcxproj              |  1 +
 vs/Marian.vcxproj.filters      |  3 +++
 8 files changed, 32 insertions(+), 30 deletions(-)
 mode change 100644 => 100755 src/layers/loss.h

diff --git a/src/layers/loss.h b/src/layers/loss.h
old mode 100644
new mode 100755
index 4a28de7b2..112d0b52d
--- a/src/layers/loss.h
+++ b/src/layers/loss.h
@@ -1,6 +1,7 @@
 #pragma once
 
 #include "graph/expression_operators.h"
+#include "data/types.h"
 
 namespace marian {
 
@@ -267,7 +268,7 @@ class LabelwiseLoss {
 protected:
   std::vector<int> axes_;
 
-  virtual Expr compute(Expr logits, Expr labelIndices,
+  virtual Expr compute(Expr logits, const Words& labels,
                        Expr mask = nullptr, Expr labelWeights = nullptr) = 0;
 
   // label counts are available, reduce together with loss to obtain counts
@@ -302,9 +303,9 @@ class LabelwiseLoss {
   LabelwiseLoss(const std::vector<int>& axes)
   : axes_(axes) { }
 
-  virtual RationalLoss apply(Expr logits, Expr labelIndices,
+  virtual RationalLoss apply(Expr logits, const Words& labels,
                              Expr mask = nullptr, Expr labelWeights = nullptr) {
-    Expr loss = compute(logits, labelIndices, mask, labelWeights);
+    Expr loss = compute(logits, labels, mask, labelWeights);
 
     if(mask)
       return reduce(loss, mask); // mask can be used as element-wise label count with broadcasting
@@ -329,8 +330,9 @@ class CrossEntropyLoss : public LabelwiseLoss {
 protected:
   float labelSmoothing_; // interpolation factor for label smoothing, see below
 
-  virtual Expr compute(Expr logits, Expr labelIndices,
+  virtual Expr compute(Expr logits, const Words& labels,
                        Expr mask = nullptr, Expr labelWeights = nullptr) override {
+    auto labelIndices = logits->graph()->indices(labels);
     Expr ce = cross_entropy(logits, labelIndices);
 
     if(labelSmoothing_ > 0) {
@@ -365,9 +367,9 @@ class RescorerLoss : public CrossEntropyLoss {
   // sentence-wise CE, hence reduce only over time axis. CE reduces over last axis (-1)
   RescorerLoss() : CrossEntropyLoss(/*axes=*/{-3}, /*smoothing=*/0.f) {}
 
-  virtual RationalLoss apply(Expr logits, Expr labelIndices,
+  virtual RationalLoss apply(Expr logits, const Words& labels,
                              Expr mask = nullptr, Expr labelWeights = nullptr) override {
-    auto ce = CrossEntropyLoss::apply(logits, labelIndices, mask, labelWeights);
+    auto ce = CrossEntropyLoss::apply(logits, labels, mask, labelWeights);
     return RationalLoss(ce.loss(), ce.count());
   }
 };
diff --git a/src/models/bert.h b/src/models/bert.h
index c56eb2977..0c54717df 100755
--- a/src/models/bert.h
+++ b/src/models/bert.h
@@ -291,7 +291,7 @@ class BertClassifier : public ClassifierBase {
 
     // Filled externally, for BERT these are NextSentence prediction labels
     const auto& classLabels = (*batch)[batchIndex_]->data();
-    state->setTargetIndices(graph->indices(classLabels));
+    state->setTargetWords(classLabels);
 
     return state;
   }
@@ -316,8 +316,8 @@ class BertMaskedLM : public ClassifierBase {
 
     auto context = encoderStates[0]->getContext();
 
-    auto bertMaskedPositions = graph->indices(bertBatch->bertMaskedPositions()); // positions in batch of masked entries
-    auto bertMaskedWords     = graph->indices(bertBatch->bertMaskedWords());   // vocab ids of entries that have been masked
+    auto bertMaskedPositions    = graph->indices(bertBatch->bertMaskedPositions()); // positions in batch of masked entries
+    const auto& bertMaskedWords = bertBatch->bertMaskedWords();   // vocab ids of entries that have been masked
 
     int dimModel = context->shape()[-1];
     int dimBatch = context->shape()[-2];
@@ -360,7 +360,7 @@ class BertMaskedLM : public ClassifierBase {
 
     auto state = New<ClassifierState>();
     state->setLogProbs(logits);
-    state->setTargetIndices(bertMaskedWords);
+    state->setTargetWords(bertMaskedWords);
 
     return state;
   }
diff --git a/src/models/costs.h b/src/models/costs.h
index bfd62da7b..528bf838f 100755
--- a/src/models/costs.h
+++ b/src/models/costs.h
@@ -69,7 +69,7 @@ class EncoderDecoderCE : public CostBase {
 
     // @TODO: adapt to multi-objective training with multiple decoders
     auto partialLoss = loss_->apply(state->getLogProbs(),
-                                    state->getTargetIndices(),
+                                    state->getTargetWords(),
                                     state->getTargetMask(),
                                     weights);
     multiLoss->push_back(partialLoss);
@@ -119,7 +119,7 @@ class EncoderClassifierCE : public CostBase {
     Ptr<MultiRationalLoss> multiLoss = newMultiLoss(options_);
     for(int i = 0; i < states.size(); ++i) {
       auto partialLoss = loss_->apply(states[i]->getLogProbs(),
-                                      states[i]->getTargetIndices(),
+                                      states[i]->getTargetWords(),
                                       /*mask=*/nullptr,
                                       /*weights=*/nullptr);
       multiLoss->push_back(partialLoss);
diff --git a/src/models/decoder.h b/src/models/decoder.h
index 53e383797..c8181fddb 100755
--- a/src/models/decoder.h
+++ b/src/models/decoder.h
@@ -65,18 +65,18 @@ class DecoderBase {
     Expr y, yMask; std::tie
     (y, yMask) = yEmb->apply(subBatch);
 
-    Expr yData;
-    if(shortlist_) {
-      yData = graph->indices(shortlist_->mappedIndices());
-    } else {
-      yData = graph->indices(subBatch->data());
-    }
+    const Words& data =
+      /*if*/ (shortlist_) ?
+        shortlist_->mappedIndices()
+      /*else*/ :
+        subBatch->data();
+    Expr yData = graph->indices(data);
 
     auto yShifted = shift(y, {1, 0, 0});
 
     state->setTargetEmbeddings(yShifted);
     state->setTargetMask(yMask);
-    state->setTargetIndices(yData);
+    state->setTargetWords(data);
   }
 
   virtual void embeddingsFromPrediction(Ptr<ExpressionGraph> graph,
diff --git a/src/models/encoder_decoder.cpp b/src/models/encoder_decoder.cpp
index c4b96cc30..af73ee55f 100755
--- a/src/models/encoder_decoder.cpp
+++ b/src/models/encoder_decoder.cpp
@@ -179,7 +179,7 @@ Ptr<DecoderState> EncoderDecoder::stepAll(Ptr<ExpressionGraph> graph,
   decoders_[0]->embeddingsFromBatch(graph, state, batch);
   auto nextState = decoders_[0]->step(graph, state);
   nextState->setTargetMask(state->getTargetMask());
-  nextState->setTargetIndices(state->getTargetIndices());
+  nextState->setTargetWords(state->getTargetWords());
 
   return nextState;
 }
diff --git a/src/models/states.h b/src/models/states.h
index 8e3972210..81ca693d5 100755
--- a/src/models/states.h
+++ b/src/models/states.h
@@ -35,7 +35,7 @@ class DecoderState {
 
   Expr targetEmbeddings_;
   Expr targetMask_;
-  Expr targetIndices_;
+  Words targetWords_;
 
   // Keep track of current target token position during translation
   size_t position_{0};
@@ -75,11 +75,8 @@ class DecoderState {
     targetEmbeddings_ = targetEmbeddings;
   }
 
-  virtual Expr getTargetIndices() const { return targetIndices_; };
-
-  virtual void setTargetIndices(Expr targetIndices) {
-    targetIndices_ = targetIndices;
-  }
+  virtual const Words& getTargetWords() const { return targetWords_; };
+  virtual void setTargetWords(const Words& targetWords) { targetWords_ = targetWords; }
 
   virtual Expr getTargetMask() const { return targetMask_; };
 
@@ -111,15 +108,14 @@ class ClassifierState {
   Ptr<data::CorpusBatch> batch_;
 
   Expr targetMask_;
-  Expr targetIndices_;
+  Words targetWords_;
 
 public:
   virtual Expr getLogProbs() const { return logProbs_; }
   virtual void setLogProbs(Expr logProbs) { logProbs_ = logProbs; }
 
-  virtual Expr getTargetIndices() const { return targetIndices_; };
-
-  virtual void setTargetIndices(Expr targetIndices) { targetIndices_ = targetIndices; }
+  virtual const Words& getTargetWords() const { return targetWords_; };
+  virtual void setTargetWords(const Words& targetWords) { targetWords_ = targetWords; }
 
   virtual Expr getTargetMask() const { return targetMask_; };
 
diff --git a/vs/Marian.vcxproj b/vs/Marian.vcxproj
index a6c560d3a..3cb727a33 100755
--- a/vs/Marian.vcxproj
+++ b/vs/Marian.vcxproj
@@ -894,6 +894,7 @@
     <ClInclude Include="..\src\layers\word2vec_reader.h" />
     <ClInclude Include="..\src\microsoft\quicksand.h" />
     <ClInclude Include="..\src\models\amun.h" />
+    <ClInclude Include="..\src\models\bert.h" />
     <ClInclude Include="..\src\models\char_s2s.h" />
     <ClInclude Include="..\src\models\costs.h" />
     <ClInclude Include="..\src\models\decoder.h" />
diff --git a/vs/Marian.vcxproj.filters b/vs/Marian.vcxproj.filters
index d9e56843f..a5c6985c3 100755
--- a/vs/Marian.vcxproj.filters
+++ b/vs/Marian.vcxproj.filters
@@ -1517,6 +1517,9 @@
     <ClInclude Include="..\src\examples\mnist\validator.h">
       <Filter>examples\mnist</Filter>
     </ClInclude>
+    <ClInclude Include="..\src\models\bert.h">
+      <Filter>models</Filter>
+    </ClInclude>
   </ItemGroup>
   <ItemGroup>
     <Filter Include="3rd_party">

From 1b9dad403d59ce3bbc72f6268c68c0711fc08880 Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Mon, 4 Feb 2019 17:09:34 -0800
Subject: [PATCH 257/838] renamed VocabBase to IVocab, since it is a pure
 interface

---
 src/data/default_vocab.cpp       | 6 +++---
 src/data/sentencepiece_vocab.cpp | 4 ++--
 src/data/vocab.cpp               | 8 ++++----
 src/data/vocab.h                 | 4 ++--
 src/data/vocab_base.h            | 2 +-
 vs/Marian.vcxproj                | 1 +
 vs/Marian.vcxproj.filters        | 5 ++++-
 7 files changed, 17 insertions(+), 13 deletions(-)
 mode change 100644 => 100755 src/data/vocab_base.h

diff --git a/src/data/default_vocab.cpp b/src/data/default_vocab.cpp
index 8c02014cd..8eb0c6571 100755
--- a/src/data/default_vocab.cpp
+++ b/src/data/default_vocab.cpp
@@ -15,7 +15,7 @@
 
 namespace marian {
 
-class DefaultVocab : public VocabBase {
+class DefaultVocab : public IVocab {
 protected:
   typedef std::map<std::string, Word> Str2Id;
   Str2Id str2id_;
@@ -318,11 +318,11 @@ class ClassVocab : public DefaultVocab {
   }
 };
 
-Ptr<VocabBase> createDefaultVocab() {
+Ptr<IVocab> createDefaultVocab() {
   return New<DefaultVocab>();
 }
 
-Ptr<VocabBase> createClassVocab() {
+Ptr<IVocab> createClassVocab() {
   return New<ClassVocab>();
 }
 
diff --git a/src/data/sentencepiece_vocab.cpp b/src/data/sentencepiece_vocab.cpp
index 4457dc064..9af727565 100755
--- a/src/data/sentencepiece_vocab.cpp
+++ b/src/data/sentencepiece_vocab.cpp
@@ -19,7 +19,7 @@ namespace marian {
 #ifdef USE_SENTENCEPIECE
 
 // Wrapper around https://github.com/google/sentencepiece
-class SentencePieceVocab : public VocabBase {
+class SentencePieceVocab : public IVocab {
 private:
   // Actual SentencePiece processor object
   UPtr<sentencepiece::SentencePieceProcessor> spm_;
@@ -252,7 +252,7 @@ class SentencePieceVocab : public VocabBase {
 };
 #endif // USE_SENTENCEPIECE
 
-Ptr<VocabBase> createSentencePieceVocab(const std::string& vocabPath, Ptr<Options> options, size_t batchIndex) {
+Ptr<IVocab> createSentencePieceVocab(const std::string& vocabPath, Ptr<Options> options, size_t batchIndex) {
   bool isSentencePiece = regex::regex_search(vocabPath, regex::regex("\\.(spm)$"));
   if(isSentencePiece) {
 #ifdef USE_SENTENCEPIECE
diff --git a/src/data/vocab.cpp b/src/data/vocab.cpp
index 7844ecfa4..8094c699c 100755
--- a/src/data/vocab.cpp
+++ b/src/data/vocab.cpp
@@ -8,12 +8,12 @@ Word Word::ZERO = Word(0);
 Word Word::DEFAULT_EOS_ID = Word(0);
 Word Word::DEFAULT_UNK_ID = Word(1);
 
-Ptr<VocabBase> createDefaultVocab();
-Ptr<VocabBase> createClassVocab();
-Ptr<VocabBase> createSentencePieceVocab(const std::string& /*vocabPath*/, Ptr<Options>, size_t /*batchIndex*/);
+Ptr<IVocab> createDefaultVocab();
+Ptr<IVocab> createClassVocab();
+Ptr<IVocab> createSentencePieceVocab(const std::string& /*vocabPath*/, Ptr<Options>, size_t /*batchIndex*/);
 
 // @TODO: make each vocab peek on type
-Ptr<VocabBase> createVocab(const std::string& vocabPath, Ptr<Options> options, size_t batchIndex) {
+Ptr<IVocab> createVocab(const std::string& vocabPath, Ptr<Options> options, size_t batchIndex) {
   auto vocab = createSentencePieceVocab(vocabPath, options, batchIndex);
   if(vocab) {
     return vocab; // this is defined which means that a sentencepiece vocabulary could be created, so return it
diff --git a/src/data/vocab.h b/src/data/vocab.h
index e82673ecc..afe79d1f0 100755
--- a/src/data/vocab.h
+++ b/src/data/vocab.h
@@ -7,7 +7,7 @@
 
 namespace marian {
 
-class VocabBase;
+class IVocab;
 
 // Wrapper around vocabulary types. Can choose underlying
 // vocabulary implementation (vImpl_) based on speficied path
@@ -17,7 +17,7 @@ class VocabBase;
 // * SentencePiece with suffix *.spm (works, but has to be created outside Marian)
 class Vocab {
 private:
-  Ptr<VocabBase> vImpl_;
+  Ptr<IVocab> vImpl_;
   Ptr<Options> options_;
   size_t batchIndex_;
 
diff --git a/src/data/vocab_base.h b/src/data/vocab_base.h
old mode 100644
new mode 100755
index 2050792f3..9a82131e8
--- a/src/data/vocab_base.h
+++ b/src/data/vocab_base.h
@@ -7,7 +7,7 @@
 
 namespace marian {
 
-class VocabBase {
+class IVocab {
 public:
   virtual size_t load(const std::string& vocabPath, size_t maxSize = 0) = 0;
 
diff --git a/vs/Marian.vcxproj b/vs/Marian.vcxproj
index 9e161fefe..0c12f5d34 100755
--- a/vs/Marian.vcxproj
+++ b/vs/Marian.vcxproj
@@ -700,6 +700,7 @@
     <ClInclude Include="..\src\common\timer.h" />
     <ClInclude Include="..\src\common\types.h" />
     <ClInclude Include="..\src\common\version.h" />
+    <ClInclude Include="..\src\data\vocab_base.h" />
     <ClInclude Include="..\src\examples\mnist\dataset.h" />
     <ClInclude Include="..\src\examples\mnist\model.h" />
     <ClInclude Include="..\src\examples\mnist\model_lenet.h" />
diff --git a/vs/Marian.vcxproj.filters b/vs/Marian.vcxproj.filters
index f26b0bf70..85cb3736e 100755
--- a/vs/Marian.vcxproj.filters
+++ b/vs/Marian.vcxproj.filters
@@ -1525,6 +1525,9 @@
     <ClInclude Include="..\src\models\bert.h">
       <Filter>models</Filter>
     </ClInclude>
+    <ClInclude Include="..\src\data\vocab_base.h">
+      <Filter>data</Filter>
+    </ClInclude>
   </ItemGroup>
   <ItemGroup>
     <Filter Include="3rd_party">
@@ -1878,4 +1881,4 @@
       <Filter>examples</Filter>
     </Text>
   </ItemGroup>
-</Project>
+</Project>
\ No newline at end of file

From 79db5b174992129f7f40d5f3e7fd9af448d688e6 Mon Sep 17 00:00:00 2001
From: Marcin Junczys-Dowmunt <Marcin.JunczysDowmunt@microsoft.com>
Date: Mon, 4 Feb 2019 18:25:44 -0800
Subject: [PATCH 258/838] add ULR parameters to model saving

---
 src/models/encoder_classifier.h | 4 ++++
 src/models/encoder_decoder.cpp  | 4 ++++
 2 files changed, 8 insertions(+)

diff --git a/src/models/encoder_classifier.h b/src/models/encoder_classifier.h
index c593ccfd3..738bae029 100755
--- a/src/models/encoder_classifier.h
+++ b/src/models/encoder_classifier.h
@@ -129,6 +129,10 @@ class EncoderClassifier : public EncoderClassifierBase {
     modelFeatures_.insert("transformer-tied-layers");
     modelFeatures_.insert("transformer-guided-alignment-layer");
     modelFeatures_.insert("transformer-train-positions");
+
+    modelFeatures_.insert("ulr");
+    modelFeatures_.insert("ulr-trainable-transformation");
+    modelFeatures_.insert("ulr-dim-emb");
   }
 
   virtual Ptr<Options> getOptions() override { return options_; }
diff --git a/src/models/encoder_decoder.cpp b/src/models/encoder_decoder.cpp
index c4b96cc30..d39acaf2e 100755
--- a/src/models/encoder_decoder.cpp
+++ b/src/models/encoder_decoder.cpp
@@ -45,6 +45,10 @@ EncoderDecoder::EncoderDecoder(Ptr<Options> options)
   modelFeatures_.insert("transformer-tied-layers");
   modelFeatures_.insert("transformer-guided-alignment-layer");
   modelFeatures_.insert("transformer-train-positions");
+
+  modelFeatures_.insert("ulr");
+  modelFeatures_.insert("ulr-trainable-transformation");
+  modelFeatures_.insert("ulr-dim-emb");
 }
 
 std::vector<Ptr<EncoderBase>>& EncoderDecoder::getEncoders() {

From 5fe528256181f5970c1415e81ee936c5082bcfd2 Mon Sep 17 00:00:00 2001
From: Marcin Junczys-Dowmunt <Marcin.JunczysDowmunt@microsoft.com>
Date: Mon, 4 Feb 2019 18:38:18 -0800
Subject: [PATCH 259/838] rm crlr

---
 src/models/encoder_classifier.h | 442 ++++++++++++++++----------------
 1 file changed, 221 insertions(+), 221 deletions(-)

diff --git a/src/models/encoder_classifier.h b/src/models/encoder_classifier.h
index 23977a6c5..1f5855af1 100644
--- a/src/models/encoder_classifier.h
+++ b/src/models/encoder_classifier.h
@@ -1,221 +1,221 @@
-#pragma once
-
-#include "marian.h"
-
-#include "models/encoder.h"
-#include "models/classifier.h"
-#include "models/model_base.h"
-#include "models/states.h"
-
-namespace marian {
-
-/**
- * Combines sequence encoders with generic classifiers
- * Can be used to train sequence classifiers like language detection, BERT-next-sentence-prediction etc.
- * Already has support for multi-objective training.
- *
- * @TODO: this should probably be unified somehow with EncoderDecoder which could allow for deocder/classifier
- * multi-objective training.
- */
-class EncoderClassifierBase : public models::ModelBase {
-public:
-  virtual ~EncoderClassifierBase() {}
-
-  virtual void load(Ptr<ExpressionGraph> graph,
-                    const std::string& name,
-                    bool markedReloaded = true) override
-      = 0;
-
-  virtual void mmap(Ptr<ExpressionGraph> graph,
-                    const void* ptr,
-                    bool markedReloaded = true)
-      = 0;
-
-  virtual void save(Ptr<ExpressionGraph> graph,
-                    const std::string& name,
-                    bool saveTranslatorConfig = false) override
-      = 0;
-
-  virtual void clear(Ptr<ExpressionGraph> graph) override = 0;
-
-  virtual std::vector<Ptr<ClassifierState>> apply(Ptr<ExpressionGraph>, Ptr<data::CorpusBatch>, bool) = 0;
-
-
-  virtual Ptr<RationalLoss> build(Ptr<ExpressionGraph> graph,
-                                  Ptr<data::Batch> batch,
-                                  bool clearGraph = true) override = 0;
-
-  virtual Ptr<RationalLoss> build(Ptr<ExpressionGraph> graph,
-                                  Ptr<data::CorpusBatch> batch,
-                                  bool clearGraph = true) = 0;
-
-  virtual Ptr<Options> getOptions() = 0;
-};
-
-class EncoderClassifier : public EncoderClassifierBase {
-protected:
-  Ptr<Options> options_;
-
-  std::string prefix_;
-
-  std::vector<Ptr<EncoderBase>> encoders_;
-  std::vector<Ptr<ClassifierBase>> classifiers_;
-
-  bool inference_{false};
-
-  std::set<std::string> modelFeatures_;
-
-  Config::YamlNode getModelParameters() {
-    Config::YamlNode modelParams;
-    for(auto& key : modelFeatures_)
-        modelParams[key] = options_->getYaml()[key];
-
-    if(options_->has("original-type"))
-        modelParams["type"] = options_->getYaml()["original-type"];
-
-    modelParams["version"] = buildVersion();
-    return modelParams;
-  }
-
-  std::string getModelParametersAsString() {
-    auto yaml = getModelParameters();
-    YAML::Emitter out;
-    cli::OutputYaml(yaml, out);
-    return std::string(out.c_str());
-  }
-
-public:
-  typedef data::Corpus dataset_type;
-
-  // @TODO: lots of code-duplication with EncoderDecoder
-  EncoderClassifier(Ptr<Options> options)
-    : options_(options),
-      prefix_(options->get<std::string>("prefix", "")),
-      inference_(options->get<bool>("inference", false)) {
-
-    modelFeatures_ = {"type",
-                    "dim-vocabs",
-                    "dim-emb",
-                    "dim-rnn",
-                    "enc-cell",
-                    "enc-type",
-                    "enc-cell-depth",
-                    "enc-depth",
-                    "dec-depth",
-                    "dec-cell",
-                    "dec-cell-base-depth",
-                    "dec-cell-high-depth",
-                    "skip",
-                    "layer-normalization",
-                    "right-left",
-                    "input-types",
-                    "special-vocab",
-                    "tied-embeddings",
-                    "tied-embeddings-src",
-                    "tied-embeddings-all"};
-
-    modelFeatures_.insert("transformer-heads");
-    modelFeatures_.insert("transformer-no-projection");
-    modelFeatures_.insert("transformer-dim-ffn");
-    modelFeatures_.insert("transformer-ffn-depth");
-    modelFeatures_.insert("transformer-ffn-activation");
-    modelFeatures_.insert("transformer-dim-aan");
-    modelFeatures_.insert("transformer-aan-depth");
-    modelFeatures_.insert("transformer-aan-activation");
-    modelFeatures_.insert("transformer-aan-nogate");
-    modelFeatures_.insert("transformer-preprocess");
-    modelFeatures_.insert("transformer-postprocess");
-    modelFeatures_.insert("transformer-postprocess-emb");
-    modelFeatures_.insert("transformer-decoder-autoreg");
-    modelFeatures_.insert("transformer-tied-layers");
-    modelFeatures_.insert("transformer-guided-alignment-layer");
-    modelFeatures_.insert("transformer-train-position-embeddings");
-    modelFeatures_.insert("bert-train-type-embeddings");
-    modelFeatures_.insert("bert-type-vocab-size");
-  }
-
-  virtual Ptr<Options> getOptions() override { return options_; }
-
-  std::vector<Ptr<EncoderBase>>& getEncoders() { return encoders_; }
-  std::vector<Ptr<ClassifierBase>>& getClassifiers() { return classifiers_; }
-
-  void push_back(Ptr<EncoderBase> encoder) { encoders_.push_back(encoder); }
-  void push_back(Ptr<ClassifierBase> classifier) { classifiers_.push_back(classifier); }
-
-  void load(Ptr<ExpressionGraph> graph,
-            const std::string& name,
-            bool markedReloaded) override {
-    graph->load(name, markedReloaded && !opt<bool>("ignore-model-config", false));
-  }
-
-  void mmap(Ptr<ExpressionGraph> graph,
-            const void* ptr,
-            bool markedReloaded) override {
-    graph->mmap(ptr, markedReloaded && !opt<bool>("ignore-model-config", false));
-  }
-
-  void save(Ptr<ExpressionGraph> graph,
-            const std::string& name,
-            bool saveModelConfig) override {
-    LOG(info, "Saving model weights and runtime parameters to {}", name);
-    graph->save(name , getModelParametersAsString());
-  }
-
-  void clear(Ptr<ExpressionGraph> graph) override {
-    graph->clear();
-
-    for(auto& enc : encoders_)
-      enc->clear();
-    for(auto& cls : classifiers_)
-      cls->clear();
-  }
-
-  template <typename T>
-  T opt(const std::string& key) {
-    return options_->get<T>(key);
-  }
-
-  template <typename T>
-  T opt(const std::string& key, const T& def) {
-    return options_->get<T>(key, def);
-  }
-
-  template <typename T>
-  void set(std::string key, T value) {
-    options_->set(key, value);
-  }
-
-  /*********************************************************************/
-
-  virtual std::vector<Ptr<ClassifierState>> apply(Ptr<ExpressionGraph> graph, Ptr<data::CorpusBatch> batch, bool clearGraph) override {
-    if(clearGraph)
-      clear(graph);
-
-    std::vector<Ptr<EncoderState>> encoderStates;
-    for(auto& encoder : encoders_)
-        encoderStates.push_back(encoder->build(graph, batch));
-
-    std::vector<Ptr<ClassifierState>> classifierStates;
-    for(auto& classifier : classifiers_)
-      classifierStates.push_back(classifier->apply(graph, batch, encoderStates));
-
-    return classifierStates;
-  }
-
-  virtual Ptr<RationalLoss> build(Ptr<ExpressionGraph> graph,
-                                  Ptr<data::CorpusBatch> batch,
-                                  bool clearGraph = true) override {
-    auto states = apply(graph, batch, clearGraph);
-    // returns raw logits
-    return New<RationalLoss>(states[0]->getLogProbs(), nullptr); // @TODO: Check if this is actually used
-  }
-
-  virtual Ptr<RationalLoss> build(Ptr<ExpressionGraph> graph,
-                                  Ptr<data::Batch> batch,
-                                  bool clearGraph = true) override {
-    auto corpusBatch = std::static_pointer_cast<data::CorpusBatch>(batch);
-    return build(graph, corpusBatch, clearGraph);
-  }
-};
-
-}  // namespace marian
+#pragma once
+
+#include "marian.h"
+
+#include "models/encoder.h"
+#include "models/classifier.h"
+#include "models/model_base.h"
+#include "models/states.h"
+
+namespace marian {
+
+/**
+ * Combines sequence encoders with generic classifiers
+ * Can be used to train sequence classifiers like language detection, BERT-next-sentence-prediction etc.
+ * Already has support for multi-objective training.
+ *
+ * @TODO: this should probably be unified somehow with EncoderDecoder which could allow for deocder/classifier
+ * multi-objective training.
+ */
+class EncoderClassifierBase : public models::ModelBase {
+public:
+  virtual ~EncoderClassifierBase() {}
+
+  virtual void load(Ptr<ExpressionGraph> graph,
+                    const std::string& name,
+                    bool markedReloaded = true) override
+      = 0;
+
+  virtual void mmap(Ptr<ExpressionGraph> graph,
+                    const void* ptr,
+                    bool markedReloaded = true)
+      = 0;
+
+  virtual void save(Ptr<ExpressionGraph> graph,
+                    const std::string& name,
+                    bool saveTranslatorConfig = false) override
+      = 0;
+
+  virtual void clear(Ptr<ExpressionGraph> graph) override = 0;
+
+  virtual std::vector<Ptr<ClassifierState>> apply(Ptr<ExpressionGraph>, Ptr<data::CorpusBatch>, bool) = 0;
+
+
+  virtual Ptr<RationalLoss> build(Ptr<ExpressionGraph> graph,
+                                  Ptr<data::Batch> batch,
+                                  bool clearGraph = true) override = 0;
+
+  virtual Ptr<RationalLoss> build(Ptr<ExpressionGraph> graph,
+                                  Ptr<data::CorpusBatch> batch,
+                                  bool clearGraph = true) = 0;
+
+  virtual Ptr<Options> getOptions() = 0;
+};
+
+class EncoderClassifier : public EncoderClassifierBase {
+protected:
+  Ptr<Options> options_;
+
+  std::string prefix_;
+
+  std::vector<Ptr<EncoderBase>> encoders_;
+  std::vector<Ptr<ClassifierBase>> classifiers_;
+
+  bool inference_{false};
+
+  std::set<std::string> modelFeatures_;
+
+  Config::YamlNode getModelParameters() {
+    Config::YamlNode modelParams;
+    for(auto& key : modelFeatures_)
+        modelParams[key] = options_->getYaml()[key];
+
+    if(options_->has("original-type"))
+        modelParams["type"] = options_->getYaml()["original-type"];
+
+    modelParams["version"] = buildVersion();
+    return modelParams;
+  }
+
+  std::string getModelParametersAsString() {
+    auto yaml = getModelParameters();
+    YAML::Emitter out;
+    cli::OutputYaml(yaml, out);
+    return std::string(out.c_str());
+  }
+
+public:
+  typedef data::Corpus dataset_type;
+
+  // @TODO: lots of code-duplication with EncoderDecoder
+  EncoderClassifier(Ptr<Options> options)
+    : options_(options),
+      prefix_(options->get<std::string>("prefix", "")),
+      inference_(options->get<bool>("inference", false)) {
+
+    modelFeatures_ = {"type",
+                    "dim-vocabs",
+                    "dim-emb",
+                    "dim-rnn",
+                    "enc-cell",
+                    "enc-type",
+                    "enc-cell-depth",
+                    "enc-depth",
+                    "dec-depth",
+                    "dec-cell",
+                    "dec-cell-base-depth",
+                    "dec-cell-high-depth",
+                    "skip",
+                    "layer-normalization",
+                    "right-left",
+                    "input-types",
+                    "special-vocab",
+                    "tied-embeddings",
+                    "tied-embeddings-src",
+                    "tied-embeddings-all"};
+
+    modelFeatures_.insert("transformer-heads");
+    modelFeatures_.insert("transformer-no-projection");
+    modelFeatures_.insert("transformer-dim-ffn");
+    modelFeatures_.insert("transformer-ffn-depth");
+    modelFeatures_.insert("transformer-ffn-activation");
+    modelFeatures_.insert("transformer-dim-aan");
+    modelFeatures_.insert("transformer-aan-depth");
+    modelFeatures_.insert("transformer-aan-activation");
+    modelFeatures_.insert("transformer-aan-nogate");
+    modelFeatures_.insert("transformer-preprocess");
+    modelFeatures_.insert("transformer-postprocess");
+    modelFeatures_.insert("transformer-postprocess-emb");
+    modelFeatures_.insert("transformer-decoder-autoreg");
+    modelFeatures_.insert("transformer-tied-layers");
+    modelFeatures_.insert("transformer-guided-alignment-layer");
+    modelFeatures_.insert("transformer-train-position-embeddings");
+    modelFeatures_.insert("bert-train-type-embeddings");
+    modelFeatures_.insert("bert-type-vocab-size");
+  }
+
+  virtual Ptr<Options> getOptions() override { return options_; }
+
+  std::vector<Ptr<EncoderBase>>& getEncoders() { return encoders_; }
+  std::vector<Ptr<ClassifierBase>>& getClassifiers() { return classifiers_; }
+
+  void push_back(Ptr<EncoderBase> encoder) { encoders_.push_back(encoder); }
+  void push_back(Ptr<ClassifierBase> classifier) { classifiers_.push_back(classifier); }
+
+  void load(Ptr<ExpressionGraph> graph,
+            const std::string& name,
+            bool markedReloaded) override {
+    graph->load(name, markedReloaded && !opt<bool>("ignore-model-config", false));
+  }
+
+  void mmap(Ptr<ExpressionGraph> graph,
+            const void* ptr,
+            bool markedReloaded) override {
+    graph->mmap(ptr, markedReloaded && !opt<bool>("ignore-model-config", false));
+  }
+
+  void save(Ptr<ExpressionGraph> graph,
+            const std::string& name,
+            bool saveModelConfig) override {
+    LOG(info, "Saving model weights and runtime parameters to {}", name);
+    graph->save(name , getModelParametersAsString());
+  }
+
+  void clear(Ptr<ExpressionGraph> graph) override {
+    graph->clear();
+
+    for(auto& enc : encoders_)
+      enc->clear();
+    for(auto& cls : classifiers_)
+      cls->clear();
+  }
+
+  template <typename T>
+  T opt(const std::string& key) {
+    return options_->get<T>(key);
+  }
+
+  template <typename T>
+  T opt(const std::string& key, const T& def) {
+    return options_->get<T>(key, def);
+  }
+
+  template <typename T>
+  void set(std::string key, T value) {
+    options_->set(key, value);
+  }
+
+  /*********************************************************************/
+
+  virtual std::vector<Ptr<ClassifierState>> apply(Ptr<ExpressionGraph> graph, Ptr<data::CorpusBatch> batch, bool clearGraph) override {
+    if(clearGraph)
+      clear(graph);
+
+    std::vector<Ptr<EncoderState>> encoderStates;
+    for(auto& encoder : encoders_)
+        encoderStates.push_back(encoder->build(graph, batch));
+
+    std::vector<Ptr<ClassifierState>> classifierStates;
+    for(auto& classifier : classifiers_)
+      classifierStates.push_back(classifier->apply(graph, batch, encoderStates));
+
+    return classifierStates;
+  }
+
+  virtual Ptr<RationalLoss> build(Ptr<ExpressionGraph> graph,
+                                  Ptr<data::CorpusBatch> batch,
+                                  bool clearGraph = true) override {
+    auto states = apply(graph, batch, clearGraph);
+    // returns raw logits
+    return New<RationalLoss>(states[0]->getLogProbs(), nullptr); // @TODO: Check if this is actually used
+  }
+
+  virtual Ptr<RationalLoss> build(Ptr<ExpressionGraph> graph,
+                                  Ptr<data::Batch> batch,
+                                  bool clearGraph = true) override {
+    auto corpusBatch = std::static_pointer_cast<data::CorpusBatch>(batch);
+    return build(graph, corpusBatch, clearGraph);
+  }
+};
+
+}  // namespace marian

From d121ba4726e843ab50bf35b81150586d5a581c7f Mon Sep 17 00:00:00 2001
From: Marcin Junczys-Dowmunt <Marcin.JunczysDowmunt@microsoft.com>
Date: Mon, 4 Feb 2019 20:26:46 -0800
Subject: [PATCH 260/838] address code review comments

---
 scripts/bert/bert4marian.py          |  18 ++++
 src/layers/loss.h                    |  13 ++-
 src/tensors/cpu/tensor_operators.cpp |   1 -
 src/tensors/gpu/tensor_operators.cu  |  92 +++++++++++-------
 src/tests/operator_tests.cpp         | 137 ++++++++++++++-------------
 5 files changed, 158 insertions(+), 103 deletions(-)

diff --git a/scripts/bert/bert4marian.py b/scripts/bert/bert4marian.py
index 5ccc46595..8070c0fe9 100755
--- a/scripts/bert/bert4marian.py
+++ b/scripts/bert/bert4marian.py
@@ -1,4 +1,22 @@
 #!/usr/bin/env python3
+"""
+This script takes a Tensorflow BERT checkpoint and a model description in a JSON file and converts
+it to a Marian weight file with numpy weights and an internal YAML description.
+
+This works with checkpoints from https://github.com/google-research/bert
+
+Assmung a BERT checkpoint like this:
+drwxr-xr-x 2 marcinjd marcinjd 4.0K Nov 23 16:39 .
+-rw-r--r-- 1 marcinjd marcinjd  521 Nov 23 16:38 bert_config.json
+-rw-r--r-- 1 marcinjd marcinjd 682M Nov 23 16:39 bert_model.ckpt.data-00000-of-00001
+-rw-r--r-- 1 marcinjd marcinjd 8.5K Nov 23 16:39 bert_model.ckpt.index
+-rw-r--r-- 1 marcinjd marcinjd 888K Nov 23 16:39 bert_model.ckpt.meta
+-rw-r--r-- 1 marcinjd marcinjd 973K Nov 23 16:37 vocab.txt
+
+usage:
+
+./bert.py --bert_prefix bert_model.ckpt  --bert_config bert_config.json --marian bert.npz
+"""
 
 import tensorflow as tf
 import numpy as np
diff --git a/src/layers/loss.h b/src/layers/loss.h
index 2ac4ae78d..32be5618e 100644
--- a/src/layers/loss.h
+++ b/src/layers/loss.h
@@ -110,8 +110,8 @@ class MultiRationalLoss : public RationalLoss {
 
   /**
    * @brief Accumulation rule for losses
-   * In the default case this would just be a sum, see SumMultiRationalLoss, but there are 
-   * special cases like ScaledMultiRationalLoss (scale other loses according to first label count) 
+   * In the default case this would just be a sum, see SumMultiRationalLoss, but there are
+   * special cases like ScaledMultiRationalLoss (scale other loses according to first label count)
    * or MeanMultiRationalLoss (sum of means) where the accumulation is more complex.
    */
   virtual Expr accumulateLoss(const RationalLoss& current) = 0;
@@ -294,7 +294,7 @@ class LabelwiseLoss {
       lossSum = sum(lossSum, axes_[i]);
 
     // reduction factor tells how over how many labels we reduced in total.
-    float reducedLabels = (float)loss->shape().elements() / (float)lossSum->shape().elements(); 
+    float reducedLabels = (float)loss->shape().elements() / (float)lossSum->shape().elements();
     return RationalLoss(lossSum, reducedLabels);
   }
 
@@ -331,12 +331,15 @@ class CrossEntropyLoss : public LabelwiseLoss {
 
   virtual Expr compute(Expr logits, Expr labelIndices,
                        Expr mask = nullptr, Expr labelWeights = nullptr) override {
-    logits = atleast_3d(logits); // safeguard against 2d classifier output, adds 1 on the left, non-op.
+    logits = atleast_3d(logits); // we always assuma a time and batch dimension exists.
+    // for bert training or classification the time dimension is lot.
+    // Here safeguard against 2d classifier output, adds 1 on the left, non-op.
+
     Expr ce = cross_entropy(logits, labelIndices);
 
     if(labelSmoothing_ > 0) {
       // @TODO: add this to CE kernels instead
-      
+
       // Label smoothing (see https://arxiv.org/pdf/1512.00567.pdf, section 7)
       // We compute smoothed H(q',p) = (1 - eps) * H(q,p) + eps * H(u,p) where H(q,p) is the normal cross-entropy
       // and H(u,p) penalizes deviation of p from u, u being uniform distribution over vocab V => u_v = 1/|V|.
diff --git a/src/tensors/cpu/tensor_operators.cpp b/src/tensors/cpu/tensor_operators.cpp
index d7d019f7d..72dbd131c 100644
--- a/src/tensors/cpu/tensor_operators.cpp
+++ b/src/tensors/cpu/tensor_operators.cpp
@@ -18,7 +18,6 @@ void IsNan(const Tensor in, Ptr<Allocator> allocator, bool& isNan, bool& isInf,
   ABORT("Not implemented");
 }
 
-
 inline float stableSigmoid(float x) {
   if(x >= 0) {
     float z = expf(-x);
diff --git a/src/tensors/gpu/tensor_operators.cu b/src/tensors/gpu/tensor_operators.cu
index ae1e02653..28f57f762 100644
--- a/src/tensors/gpu/tensor_operators.cu
+++ b/src/tensors/gpu/tensor_operators.cu
@@ -34,9 +34,9 @@ __global__ void gIsNan(T* in, int length, bool* isNan, bool* isInf, bool zero) {
     if(index < length) {
       if(isnan((float)in[index])) {
         if(zero) in[index] = (T)0.f;
-        *isNan = true; 
+        *isNan = true;
       }
-      else if(isinf((float)in[index])) { 
+      else if(isinf((float)in[index])) {
         if(zero) in[index] = (T)0.f;
         *isInf = true;
       }
@@ -406,30 +406,41 @@ void TransposeNDGrad(Tensor out, Tensor in, const std::vector<int>& vAxis) {
   }
 }
 
+// Computes the softmax
+// in - input tensor
+// out - output tensor
+// we compute the softmax over the the cols (last dimension)
+// rows are time, batch or beam dimensions
+// number of threads is number of cols or MAX_THREADS
+// number of blocks is number of rows or MAX_BLOCKS
 __global__ void gSoftmax(float* out,
                          functional::Shape outShape,
                          const float* in) {
   int rows = outShape.elements() / outShape.back();
   int cols = outShape.back();
 
-  for(int bid = 0; bid < rows; bid += gridDim.x) {
-    int j = bid + blockIdx.x;
-    if(j < rows) {
-      float* so = out + j * cols;
+  for(int bid = 0; bid < rows; bid += gridDim.x) { // loop over blocks of rows
+    int j = bid + blockIdx.x; // blockIdx.x - row index (within block of rows)
+    if(j < rows) { // compute softmax over one row, row elements distributed over threads
+      float* so = out + j * cols; // pointer to row input data
       const float* sp = in + j * cols;
 
       extern __shared__ float _share[];
 
+      // determine max (used below to improve numeric stability)
       float* _max = _share;
-      _max[threadIdx.x] = -CUDA_FLT_MAX;  // mask
-      for(int tid = 0; tid < cols; tid += blockDim.x) {
-        int id = tid + threadIdx.x;
-        if(id < cols) {
-          if(sp[id] > _max[threadIdx.x])
-            _max[threadIdx.x] = sp[id];
+      _max[threadIdx.x] = -CUDA_FLT_MAX; // [threadIdx.x = relative column index within a block of columns]
+      // find max over column indices that have the same relative column index (=threadIdx.x) across all blocks of columns
+      for(int tid = 0; tid < cols; tid += blockDim.x) { // loop over blocks of columns, blockDim.x = index of block of columns
+        // threadIdx.x = column index within block of columns; we reduce over columns within a block, then over blocks
+        int i = tid + threadIdx.x;
+        if(i < cols) {
+          if(sp[i] > _max[threadIdx.x])
+            _max[threadIdx.x] = sp[i];
         }
       }
       __syncthreads();
+      // max over columns within a column block via tree reduction
       int len = blockDim.x;
       while(len != 1) {
         __syncthreads();
@@ -443,20 +454,22 @@ __global__ void gSoftmax(float* out,
       }
       __syncthreads();
       float max = _max[0];
-      __syncthreads();
-
-      float* _sum = _share + blockDim.x;
+      __syncthreads(); // @TODO: do we need this?
 
+      // compute denominator
+      float* _sum = _share;
       _sum[threadIdx.x] = 0.0;
       for(int tid = 0; tid < cols; tid += blockDim.x) {
-        int id = tid + threadIdx.x;
-        if(id < cols) {
-          float ex = __expf(sp[id] - max);
-          so[id] = ex;
+        int i = tid + threadIdx.x;
+        if(i < cols) {
+          // @TODO: is it faster to cache the result of expf() in GPU RAM, or would it be faster to recompute it below?
+          float ex = __expf(sp[i] - max);
+          so[i] = ex;
           _sum[threadIdx.x] += ex;
         }
       }
       __syncthreads();
+      // now reduce over all columns within the block
       len = blockDim.x;
       while(len != 1) {
         __syncthreads();
@@ -466,13 +479,17 @@ __global__ void gSoftmax(float* out,
         len = (len + 1) >> 1;
       }
       __syncthreads();
+
+      // produce final output data
+      float sum = _sum[0];
       for(int tid = 0; tid < cols; tid += blockDim.x) {
-        int id = tid + threadIdx.x;
-        if(id < cols) {
-          so[id] = so[id] / _sum[0];
+        int i = tid + threadIdx.x;
+        if(i < cols) {
+          so[i] = so[i] / sum;
         }
       }
     }
+    __syncthreads();
   }
 }
 
@@ -484,11 +501,12 @@ void Softmax(Tensor out, Tensor in) {
 
   int blocks = std::min(MAX_BLOCKS, (int)m);
   int threads = std::min(MAX_THREADS, (int)k);
-  int shared = sizeof(float) * threads * 2;
+  int shared = sizeof(float) * threads;
 
   gSoftmax<<<blocks, threads, shared>>>(out->data(), out->shape(), in->data());
 }
 
+// @TODO: refactor to reuse code from softmax, add comments
 __global__ void gLogSoftmax(float* out,
                             const functional::Shape outShape,
                             const float* in) {
@@ -528,7 +546,7 @@ __global__ void gLogSoftmax(float* out,
       float max = _max[0];
       __syncthreads();
 
-      float* _sum = _share + blockDim.x;
+      float* _sum = _share;
 
       _sum[threadIdx.x] = 0.0;
       for(int tid = 0; tid < cols; tid += blockDim.x) {
@@ -553,9 +571,10 @@ __global__ void gLogSoftmax(float* out,
       for(int tid = 0; tid < cols; tid += blockDim.x) {
         int id = tid + threadIdx.x;
         if(id < cols)
-          so[id] -= __logf(_sum[0]);
+          so[id] = __logf(_sum[0]);
       }
     }
+    __syncthreads();
   }
 }
 
@@ -567,7 +586,7 @@ void LogSoftmax(Tensor out, Tensor in) {
 
   int blocks = std::min(MAX_BLOCKS, (int)m);
   int threads = std::min(MAX_THREADS, (int)k);
-  int shared = sizeof(float) * threads * 2;
+  int shared = sizeof(float) * threads;
 
   gLogSoftmax<<<blocks, threads, shared>>>(
       out->data(), out->shape(), in->data());
@@ -615,9 +634,11 @@ __global__ void gSoftmaxGrad(float* grad,
         }
       }
     }
+    __syncthreads();
   }
 }
 
+// @TODO: refactor with logsoftmax, add math
 void SoftmaxGrad(Tensor grad, Tensor adj, Tensor val) {
   cudaSetDevice(adj->getDeviceId().no);
   // grad and val are both m-by-k matrices, passed as input.
@@ -671,6 +692,7 @@ __global__ void gLogSoftmaxGrad(float* grad,
           gradRow[id] += adjRow[id] - (expf(valRow[id]) * _sum[0]);
       }
     }
+    __syncthreads();
   }
 }
 
@@ -1179,7 +1201,7 @@ __global__ void gCrossEntropyPick(float* out,
       float max = _max[0];
       __syncthreads();
 
-      float* _sum = _share + blockDim.x;
+      float* _sum = _share;
       _sum[threadIdx.x] = 0.0;
       for(int tid = 0; tid < cols; tid += blockDim.x) {
         int id = tid + threadIdx.x;
@@ -1206,6 +1228,7 @@ __global__ void gCrossEntropyPick(float* out,
         }
       }
     }
+    __syncthreads();
   }
 }
 
@@ -1223,7 +1246,7 @@ void CrossEntropyPick(Tensor out, Tensor in, Tensor indices) {
 
   int blocks = std::min(MAX_BLOCKS, (int)rows);
   int threads = std::min(MAX_THREADS, (int)cols);
-  int shared = sizeof(float) * threads * 2;
+  int shared = sizeof(float) * threads;
 
   gCrossEntropyPick<<<blocks, threads, shared>>>(
       out->data(), out->shape(), in->data(), in->shape(), indices->data<IndexType>());
@@ -1269,7 +1292,7 @@ __global__ void gCrossEntropyPickBackward(float* out,
       float max = _max[0];
       __syncthreads();
 
-      float* _sum = _share + blockDim.x;
+      float* _sum = _share;
       _sum[threadIdx.x] = 0.0;
       for(int tid = 0; tid < cols; tid += blockDim.x) {
         int id = tid + threadIdx.x;
@@ -1298,6 +1321,7 @@ __global__ void gCrossEntropyPickBackward(float* out,
         }
       }
     }
+    __syncthreads();
   }
 }
 
@@ -1311,7 +1335,7 @@ void CrossEntropyPickBackward(Tensor out, Tensor adj, Tensor a, Tensor indices)
 
   int blocks = std::min(MAX_BLOCKS, (int)rows);
   int threads = std::min(MAX_THREADS, (int)cols);
-  int shared = sizeof(float) * threads * 2;
+  int shared = sizeof(float) * threads;
 
   gCrossEntropyPickBackward<<<blocks, threads, shared>>>(
       out->data(), out->shape(), adj->data(), a->data(), indices->data<IndexType>());
@@ -1380,8 +1404,8 @@ __global__ void gAtt(float* out,
       }
       __syncthreads();
       out[j] = _sum[0];
-      __syncthreads();
     }
+    __syncthreads();
   }
 }
 
@@ -1507,7 +1531,7 @@ __global__ void gLNormalization(float* out,
       float mean = _sum[0] / cols;
       __syncthreads();
 
-      float* _sqSum = _share + blockDim.x;
+      float* _sqSum = _share;
 
       _sqSum[threadIdx.x] = 0.0;
       for(int tid = 0; tid < cols; tid += blockDim.x) {
@@ -1540,6 +1564,7 @@ __global__ void gLNormalization(float* out,
         }
       }
     }
+    __syncthreads();
   }
 }
 
@@ -1555,7 +1580,7 @@ void LayerNormalization(Tensor out,
 
   int blocks = std::min(MAX_BLOCKS, (int)rows);
   int threads = std::min(MAX_THREADS, (int)cols);
-  int shared = 2 * threads * sizeof(float);
+  int shared = threads * sizeof(float);
 
   gLNormalization<<<blocks, threads, shared>>>(out->data(),
                                                in->data(),
@@ -1665,6 +1690,7 @@ __global__ void gLayerNormalizationGrad(float* gradX,
         }
       }
     }
+    __syncthreads();
   }
 }
 
diff --git a/src/tests/operator_tests.cpp b/src/tests/operator_tests.cpp
index e17eca9dc..360da61b3 100755
--- a/src/tests/operator_tests.cpp
+++ b/src/tests/operator_tests.cpp
@@ -342,77 +342,86 @@ void tests(DeviceType device) {
     auto B = graph->param("B", {3, 2}, inits::from_vector(vB));
     auto C = dot(A, B);
 
-    // CSR dot product, tested against dense product on the same values
-    // std::vector<float> vS({1, 0, 0, 1,          // sparse
-    //                        0, 0, 1, 1.5});
-    // std::vector<float> vD({1, 2, 3, 1.2, 5.6,   // dense
-    //                        4, 5, 6, 2.3, 6.7,
-    //                        7, 8, 9, 3.4, 7.8,
-    //                        1, 1, 2, 4.5, 8.9});
-    // auto S  = graph->param("S",  { 2, 4 }, inits::from_vector(vS));
-    // auto D  = graph->param("D",  { 4, 5 }, inits::from_vector(vD));
-    // auto DT = graph->param("DT", { 5, 4 }, inits::from_vector(vD)); // example matrix with transposed dimensions
-    // std::vector<float> SV;    // create CSR version of S
-    // std::vector<IndexType> SI, SO;
-    // SO.push_back((IndexType)SI.size());
-    // for (IndexType i = 0; i < S->shape()[0]; i++) {
-    //   for (IndexType j = 0; j < S->shape()[1]; j++) {
-    //     auto k = 4 * i + j;
-    //     if (vS[k] != 0) {
-    //       SV.push_back(vS[k]);
-    //       SI.push_back(j);
-    //     }
-    //   }
-    //   SO.push_back((IndexType)SI.size());
-    // }
-
-    // auto SxDd    = dot(S, D);
-    // auto STxSxDd = dot(S, SxDd, /*transA=*/true);
-    // auto SxDs = csr_dot( // sparse x dense
-    //       S->shape(),
-    //       graph->constant({(int)SV.size()}, inits::from_vector(SV), Type::float32),
-    //       graph->constant({(int)SI.size()}, inits::from_vector(SI), Type::uint32),
-    //       graph->constant({(int)SO.size()}, inits::from_vector(SO), Type::uint32),
-    //       D);
-    // auto STxSxDs = csr_dot(   // transpose(sparse) x dense; we use result of previous since dimensions match
-    //       S->shape(),
-    //       graph->constant({(int)SV.size()}, inits::from_vector(SV), Type::float32),
-    //       graph->constant({(int)SI.size()}, inits::from_vector(SI), Type::uint32),
-    //       graph->constant({(int)SO.size()}, inits::from_vector(SO), Type::uint32),
-    //       SxDd, /*transS=*/true);
-
-    // auto DTxSTd   = dot(DT,     S, /*transA=*/false, /*transB=*/true);
-    // auto DTxSTxSd = dot(DTxSTd, S);
-    // auto DTxSTs = dot_csr( // dense x sparse
-    //       DT,
-    //       S->shape(),
-    //       graph->constant({(int)SV.size()}, inits::from_vector(SV), Type::float32),
-    //       graph->constant({(int)SI.size()}, inits::from_vector(SI), Type::uint32),
-    //       graph->constant({(int)SO.size()}, inits::from_vector(SO), Type::uint32),
-    //       /*transS=*/true);
-    // auto DTxSTxSs = dot_csr( // dense x transpose(sparse)
-    //       DTxSTd,
-    //       S->shape(),
-    //       graph->constant({(int)SV.size()}, inits::from_vector(SV), Type::float32),
-    //       graph->constant({(int)SI.size()}, inits::from_vector(SI), Type::uint32),
-    //       graph->constant({(int)SO.size()}, inits::from_vector(SO), Type::uint32));
-
     CHECK(C->shape() == Shape({2, 2, 2}));
-    // CHECK(SxDs->shape() == SxDd->shape());
-    // CHECK(STxSxDs->shape() == STxSxDd->shape());
-    // CHECK(DTxSTs->shape() == DTxSTd->shape());
-    // CHECK(DTxSTxSs->shape() == DTxSTxSd->shape());
 
     graph->forward();
 
     C->val()->get(values);
     CHECK(values == vC);
+  }
 
-    // dense and sparse operation results must be the same
-    // SxDd    ->val()->get(values2); SxDs    ->val()->get(values); CHECK(values == values2);
-    // STxSxDd ->val()->get(values2); STxSxDs ->val()->get(values); CHECK(values == values2);
-    // DTxSTd  ->val()->get(values2); DTxSTs  ->val()->get(values); CHECK(values == values2);
-    // DTxSTxSd->val()->get(values2); DTxSTxSs->val()->get(values); CHECK(values == values2);
+  if(device == DeviceType::gpu) {
+    SECTION("csr-dot product") {
+      graph->clear();
+      values.clear();
+      // CSR dot product, tested against dense product on the same values
+      std::vector<float> vS({1, 0, 0, 1,          // sparse
+                            0, 0, 1, 1.5});
+      std::vector<float> vD({1, 2, 3, 1.2, 5.6,   // dense
+                            4, 5, 6, 2.3, 6.7,
+                            7, 8, 9, 3.4, 7.8,
+                            1, 1, 2, 4.5, 8.9});
+      auto S  = graph->param("S",  { 2, 4 }, inits::from_vector(vS));
+      auto D  = graph->param("D",  { 4, 5 }, inits::from_vector(vD));
+      auto DT = graph->param("DT", { 5, 4 }, inits::from_vector(vD)); // example matrix with transposed dimensions
+      std::vector<float> SV;    // create CSR version of S
+      std::vector<IndexType> SI, SO;
+      SO.push_back((IndexType)SI.size());
+      for (IndexType i = 0; i < S->shape()[0]; i++) {
+        for (IndexType j = 0; j < S->shape()[1]; j++) {
+          auto k = 4 * i + j;
+          if (vS[k] != 0) {
+            SV.push_back(vS[k]);
+            SI.push_back(j);
+          }
+        }
+        SO.push_back((IndexType)SI.size());
+      }
+
+      auto SxDd    = dot(S, D);
+      auto STxSxDd = dot(S, SxDd, /*transA=*/true);
+      auto SxDs = csr_dot( // sparse x dense
+            S->shape(),
+            graph->constant({(int)SV.size()}, inits::from_vector(SV), Type::float32),
+            graph->constant({(int)SI.size()}, inits::from_vector(SI), Type::uint32),
+            graph->constant({(int)SO.size()}, inits::from_vector(SO), Type::uint32),
+            D);
+      auto STxSxDs = csr_dot(   // transpose(sparse) x dense; we use result of previous since dimensions match
+            S->shape(),
+            graph->constant({(int)SV.size()}, inits::from_vector(SV), Type::float32),
+            graph->constant({(int)SI.size()}, inits::from_vector(SI), Type::uint32),
+            graph->constant({(int)SO.size()}, inits::from_vector(SO), Type::uint32),
+            SxDd, /*transS=*/true);
+
+      auto DTxSTd   = dot(DT,     S, /*transA=*/false, /*transB=*/true);
+      auto DTxSTxSd = dot(DTxSTd, S);
+      auto DTxSTs = dot_csr( // dense x sparse
+            DT,
+            S->shape(),
+            graph->constant({(int)SV.size()}, inits::from_vector(SV), Type::float32),
+            graph->constant({(int)SI.size()}, inits::from_vector(SI), Type::uint32),
+            graph->constant({(int)SO.size()}, inits::from_vector(SO), Type::uint32),
+            /*transS=*/true);
+      auto DTxSTxSs = dot_csr( // dense x transpose(sparse)
+            DTxSTd,
+            S->shape(),
+            graph->constant({(int)SV.size()}, inits::from_vector(SV), Type::float32),
+            graph->constant({(int)SI.size()}, inits::from_vector(SI), Type::uint32),
+            graph->constant({(int)SO.size()}, inits::from_vector(SO), Type::uint32));
+
+      CHECK(SxDs->shape() == SxDd->shape());
+      CHECK(STxSxDs->shape() == STxSxDd->shape());
+      CHECK(DTxSTs->shape() == DTxSTd->shape());
+      CHECK(DTxSTxSs->shape() == DTxSTxSd->shape());
+
+      graph->forward();
+
+      // dense and sparse operation results must be the same
+      SxDd    ->val()->get(values2); SxDs    ->val()->get(values); CHECK(values == values2);
+      STxSxDd ->val()->get(values2); STxSxDs ->val()->get(values); CHECK(values == values2);
+      DTxSTd  ->val()->get(values2); DTxSTs  ->val()->get(values); CHECK(values == values2);
+      DTxSTxSd->val()->get(values2); DTxSTxSs->val()->get(values); CHECK(values == values2);
+    }
   }
 
   SECTION("affine transformation") {

From 9f6e756e360a89e1e76343772df074468d1f5a59 Mon Sep 17 00:00:00 2001
From: Marcin Junczys-Dowmunt <Marcin.JunczysDowmunt@microsoft.com>
Date: Mon, 4 Feb 2019 20:56:13 -0800
Subject: [PATCH 261/838] fix merge

---
 src/common/config_parser.cpp | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/src/common/config_parser.cpp b/src/common/config_parser.cpp
index 144c3e159..0d1863562 100644
--- a/src/common/config_parser.cpp
+++ b/src/common/config_parser.cpp
@@ -59,12 +59,12 @@ void ConfigParser::addOptionsGeneral(cli::CLIWrapper& cli) {
   cli.add<size_t>("--workspace,-w",
     "Preallocate  arg  MB of work space",
     defaultWorkspace);
-  cli.add_nondefault<std::string>("--log",
+  cli.add<std::string>("--log",
     "Log training process information to file given by  arg");
   cli.add<std::string>("--log-level",
     "Set verbosity level of logging: trace, debug, info, warn, err(or), critical, off",
     "info");
-  cli.add_nondefault<std::string>("--log-time-zone",
+  cli.add<std::string>("--log-time-zone",
     "Set time zone for the date shown on logging");
   cli.add<bool>("--quiet",
     "Suppress all logging to stderr. Logging to files still works");
@@ -78,7 +78,7 @@ void ConfigParser::addOptionsGeneral(cli::CLIWrapper& cli) {
     "allow the use of environment variables in paths, of the form ${VAR_NAME}");
   cli.add<bool>("--relative-paths",
     "All paths are relative to the config file location");
-  cli.add_nondefault<std::string>("--dump-config",
+  cli.add<std::string>("--dump-config",
     "Dump current (modified) configuration to stdout and exit. Possible values: full, minimal")
     ->implicit_val("full");
   // clang-format on

From 160568effaafc8410b217d3ec1a68d04fdbc3725 Mon Sep 17 00:00:00 2001
From: Marcin Junczys-Dowmunt <Marcin.JunczysDowmunt@microsoft.com>
Date: Mon, 4 Feb 2019 21:18:58 -0800
Subject: [PATCH 262/838] add missing -

---
 src/tensors/gpu/add.cu              | 1 +
 src/tensors/gpu/tensor_operators.cu | 2 +-
 2 files changed, 2 insertions(+), 1 deletion(-)

diff --git a/src/tensors/gpu/add.cu b/src/tensors/gpu/add.cu
index 9e0c8e687..e9474d048 100755
--- a/src/tensors/gpu/add.cu
+++ b/src/tensors/gpu/add.cu
@@ -121,6 +121,7 @@ __global__ void gAggregateReduce(Functor functor, float aggInit, AggFunctor aggF
       __syncthreads();
       out[j] = aggFunctor(out[j], _sum[0] * scale);
     }
+    __syncthreads();
   }
 }
 
diff --git a/src/tensors/gpu/tensor_operators.cu b/src/tensors/gpu/tensor_operators.cu
index 28f57f762..a7edcdf59 100644
--- a/src/tensors/gpu/tensor_operators.cu
+++ b/src/tensors/gpu/tensor_operators.cu
@@ -571,7 +571,7 @@ __global__ void gLogSoftmax(float* out,
       for(int tid = 0; tid < cols; tid += blockDim.x) {
         int id = tid + threadIdx.x;
         if(id < cols)
-          so[id] = __logf(_sum[0]);
+          so[id] -= __logf(_sum[0]);
       }
     }
     __syncthreads();

From d7db8d8534e3a6bceb4f594c417e04011d85a1f5 Mon Sep 17 00:00:00 2001
From: Marcin Junczys-Dowmunt <Marcin.JunczysDowmunt@microsoft.com>
Date: Mon, 4 Feb 2019 22:12:17 -0800
Subject: [PATCH 263/838] update regression tests

---
 regression-tests | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/regression-tests b/regression-tests
index febdc3f56..466d85a2e 160000
--- a/regression-tests
+++ b/regression-tests
@@ -1 +1 @@
-Subproject commit febdc3f56f75929b1f7b5b38a4b9b96ea8f648e7
+Subproject commit 466d85a2e0504a981508c7f9c8d1639c87c63b90

From 10361f81d3d5c337f7554c17f4b6fb9beaf8b882 Mon Sep 17 00:00:00 2001
From: Marcin Junczys-Dowmunt <Marcin.JunczysDowmunt@microsoft.com>
Date: Mon, 4 Feb 2019 22:39:48 -0800
Subject: [PATCH 264/838] update nccl version to 4

---
 src/3rd_party/nccl | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/3rd_party/nccl b/src/3rd_party/nccl
index d6297d250..8e3a3f7c5 160000
--- a/src/3rd_party/nccl
+++ b/src/3rd_party/nccl
@@ -1 +1 @@
-Subproject commit d6297d250433715c283d17f1969cfcb50d2b6531
+Subproject commit 8e3a3f7c5b520babff49cec54a866fa3eda3a3b6

From 4ab941b90992ed19ad1392d48efde28db552d20d Mon Sep 17 00:00:00 2001
From: Marcin Junczys-Dowmunt <Marcin.JunczysDowmunt@microsoft.com>
Date: Mon, 4 Feb 2019 23:07:00 -0800
Subject: [PATCH 265/838] use ExternalProject for installation

---
 CMakeLists.txt               | 37 +++++-------------------------------
 src/3rd_party/CMakeLists.txt | 32 +++++++++++++++++++++++++++++++
 src/CMakeLists.txt           |  5 +++++
 3 files changed, 42 insertions(+), 32 deletions(-)

diff --git a/CMakeLists.txt b/CMakeLists.txt
index b54766ae9..340c15864 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -32,7 +32,7 @@ message(STATUS "Project version: ${PROJECT_VERSION_STRING_FULL}")
 
 execute_process(COMMAND git submodule update --init --recursive --no-fetch
                 WORKING_DIRECTORY ${CMAKE_CURRENT_SOURCE_DIR})
- 
+
 # Set compilation flags
 if(MSVC)
 # These are used in src/CMakeLists.txt on a per-target basis
@@ -42,7 +42,7 @@ if(MSVC)
   # C4310: cast truncates constant value
   # C4324: 'marian::cpu::int16::`anonymous-namespace'::ScatterPut': structure was padded due to alignment specifier
   set(DISABLE_GLOBALLY "/wd\"4310\" /wd\"4324\"")
-  
+
   set(INTRINSICS "/arch:AVX")
 
   # Or maybe use these?
@@ -57,7 +57,7 @@ if(MSVC)
   set(CMAKE_EXE_LINKER_FLAGS    "${CMAKE_EXE_LINKER_FLAGS} /DEBUG /LTCG:incremental /INCREMENTAL:NO /NODEFAULTLIB:MSVCRT /ignore:4049")
   set(CMAKE_STATIC_LINKER_FLAGS "${CMAKE_STATIC_LINKER_FLAGS} /LTCG:incremental")
 
-  find_library(SHLWAPI Shlwapi.lib) 
+  find_library(SHLWAPI Shlwapi.lib)
   set(EXT_LIBS ${EXT_LIBS} SHLWAPI)
 else()
 
@@ -172,37 +172,10 @@ endif(USE_STATIC_LIBS)
   # Cmake. This is also fairly untested, let's hope it does not explode.
   # @TODO: Make sure it does not use pre-installed NCCL headers
   if(USE_NCCL)
-    # define and set the include dir for the generated nccl.h header
-    set(NCCL_HEADER_LOCATION "${CMAKE_CURRENT_BINARY_DIR}/nccl/include")
-    include_directories(${NCCL_HEADER_LOCATION})
-
-    # set the path for the generated static lib
-    set(NCCL_LIB_STATIC "${CMAKE_CURRENT_BINARY_DIR}/nccl/lib/libnccl_static.a")
-    set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -DUSE_NCCL")
-
-    LIST(APPEND CUDA_NVCC_FLAGS -DUSE_NCCL; )
-
-    # disables compilation for sm_30 to avoid ptxas warning... that's general Kepler support. But K80s are supported for instance by sm_35
-    set(GENCODE "-gencode=arch=compute_35,code=sm_35 -gencode=arch=compute_50,code=sm_50 -gencode=arch=compute_60,code=sm_60 -gencode=arch=compute_61,code=sm_61")
-
-    # We build using NVidia's custom makefile, for that we pass a number of variables from CMake.
-    # Sets output to the chosen build folder, i.e. where the binaries and objects are generated.
-    # Also passes CUDA location from FindCUDA, sets c++ compiler to the same one CMake uses.
-    add_custom_command(OUTPUT ${NCCL_LIB_STATIC}
-                       COMMAND ${CMAKE_MAKE_PROGRAM} src.build
-                       BUILDDIR=${CMAKE_CURRENT_BINARY_DIR}/nccl
-                       CUDA_HOME=${CUDA_TOOLKIT_ROOT_DIR}
-                       CUDA8_GENCODE=${GENCODE}
-                       CXX=${CMAKE_CXX_COMPILER}
-                       WORKING_DIRECTORY ${CMAKE_CURRENT_SOURCE_DIR}/src/3rd_party/nccl)
-    add_custom_target(nccl_target DEPENDS ${NCCL_LIB_STATIC})
     add_library(nccl STATIC IMPORTED)
-    set_target_properties(nccl PROPERTIES IMPORTED_LOCATION ${NCCL_LIB_STATIC})
-    add_dependencies(nccl nccl_target)
     set(EXT_LIBS ${EXT_LIBS} nccl)
-
-    # adds the resulting files to be removed by `make clean`
-    set_directory_properties(PROPERTY ADDITIONAL_MAKE_CLEAN_FILES ${CMAKE_CURRENT_BINARY_DIR}/nccl)
+    set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -DUSE_NCCL")
+    LIST(APPEND CUDA_NVCC_FLAGS -DUSE_NCCL; )
   endif(USE_NCCL)
 
 if(USE_STATIC_LIBS)
diff --git a/src/3rd_party/CMakeLists.txt b/src/3rd_party/CMakeLists.txt
index 8548d9b8b..87f79a438 100644
--- a/src/3rd_party/CMakeLists.txt
+++ b/src/3rd_party/CMakeLists.txt
@@ -38,3 +38,35 @@ include_directories(./pathie-cpp/include)
 
 include_directories(./zlib)
 
+include(ExternalProject)
+
+set(INSTALLS "") # this will contain a list of 3rd part dependencies that we install locally
+if(CUDA_FOUND)
+  if(USE_NCCL)
+
+    # disables compilation for sm_30 to avoid ptxas warning... that's general Kepler support. But K80s are supported for instance by sm_35
+    set(GENCODE "-gencode=arch=compute_35,code=sm_35 -gencode=arch=compute_50,code=sm_50 -gencode=arch=compute_60,code=sm_60 -gencode=arch=compute_61,code=sm_61")
+
+    # install nccl in ${CMAKE_BINARY_DIR}/local similar to /usr/local linux installation
+    ExternalProject_Add(nccl_install
+      SOURCE_DIR ${CMAKE_CURRENT_SOURCE_DIR}/nccl
+      BINARY_DIR ${CMAKE_CURRENT_SOURCE_DIR}/nccl
+      CONFIGURE_COMMAND ""
+      BUILD_COMMAND
+        $(MAKE) -f ${CMAKE_CURRENT_SOURCE_DIR}/nccl/Makefile src.build
+        BUILDDIR=${CMAKE_BINARY_DIR}/local CUDA_HOME=${CUDA_TOOLKIT_ROOT_DIR}
+        CUDA8_GENCODE=${GENCODE} CXX=${CMAKE_CXX_COMPILER}
+      INSTALL_COMMAND "")
+
+    set_target_properties(nccl PROPERTIES IMPORTED_LOCATION ${CMAKE_BINARY_DIR}/local/lib/libnccl_static.a)
+    add_dependencies(nccl nccl_install)
+    set(INSTALLS ${INSTALLS} nccl_install)
+
+  endif(USE_NCCL)
+endif(CUDA_FOUND)
+
+# @TODO: do the same for SentencePiece, Protobuf etc.
+# make clean will clean "${CMAKE_BINARY_DIR}/local"
+set_directory_properties(PROPERTY ADDITIONAL_MAKE_CLEAN_FILES ${CMAKE_BINARY_DIR}/local)
+
+add_custom_target(3rd_party_installs DEPENDS ${INSTALLS})
diff --git a/src/CMakeLists.txt b/src/CMakeLists.txt
index 8c757f100..fd489e3e1 100644
--- a/src/CMakeLists.txt
+++ b/src/CMakeLists.txt
@@ -4,6 +4,7 @@ include_directories(.)
 include_directories(3rd_party)
 include_directories(3rd_party/SQLiteCpp/include)
 include_directories(3rd_party/sentencepiece)
+include_directories(${CMAKE_BINARY_DIR}/local/include)
 
 add_library(marian STATIC
   common/version.cpp
@@ -98,6 +99,8 @@ add_custom_command(OUTPUT ${CMAKE_CURRENT_SOURCE_DIR}/common/git_revision.h
 )
 add_custom_target(marian_version DEPENDS ${CMAKE_CURRENT_SOURCE_DIR}/common/git_revision.h)
 add_dependencies(marian marian_version) # marian must depend on it so that it gets created first
+# make sure all local dependencies are installed first before this is built
+add_dependencies(marian 3rd_party_installs)
 
 if(CUDA_FOUND)
 cuda_add_library(marian_cuda
@@ -115,6 +118,8 @@ cuda_add_library(marian_cuda
   STATIC)
 
   target_compile_options(marian_cuda PUBLIC ${ALL_WARNINGS})
+  # make sure all local dependencies are installed first before this is built
+  add_dependencies(marian_cuda 3rd_party_installs)
 endif(CUDA_FOUND)
 
 set_target_properties(marian PROPERTIES LIBRARY_OUTPUT_DIRECTORY "${CMAKE_BINARY_DIR}")

From 31ca4ac876f2a44e70fb1208238eaeb206deb2db Mon Sep 17 00:00:00 2001
From: Marcin Junczys-Dowmunt <Marcin.JunczysDowmunt@microsoft.com>
Date: Mon, 4 Feb 2019 23:14:10 -0800
Subject: [PATCH 266/838] update changelog

---
 CHANGELOG.md | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/CHANGELOG.md b/CHANGELOG.md
index c409a9d2b..ebe04c36a 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -11,6 +11,7 @@ and this project adheres to [Semantic Versioning](http://semver.org/spec/v2.0.0.
 - Automatic detection of CPU intrisics when building with -arch=native 
 - First version of BERT-training and BERT-classifier, currently not compatible with TF models
 - New reduction operators
+- Use Cmake's ExternalProject to build NCCL and potentially other external libs. 
 
 ### Fixed
 - Windows build with recent changes
@@ -21,6 +22,7 @@ and this project adheres to [Semantic Versioning](http://semver.org/spec/v2.0.0.
 - Fixed NaN problem when training with Tensor Cores on Volta GPUs
 
 ### Changed
+- Update NCCL to 2.4.2
 - Add zlib source to Marian's source tree, builds now as object lib
 - -DUSE_STATIC_LIBS=on now also looks for static versions of CUDA libraries
 - Include NCCL build from github.com/marian-nmt/nccl and compile within source tree

From f91ee5351c21f9a29d1be7ef6ae2da62a6e37936 Mon Sep 17 00:00:00 2001
From: Marcin Junczys-Dowmunt <Marcin.JunczysDowmunt@microsoft.com>
Date: Tue, 5 Feb 2019 08:04:43 -0800
Subject: [PATCH 267/838] move pointer for reg tests

---
 regression-tests   | 2 +-
 src/3rd_party/nccl | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/regression-tests b/regression-tests
index 466d85a2e..142eadddb 160000
--- a/regression-tests
+++ b/regression-tests
@@ -1 +1 @@
-Subproject commit 466d85a2e0504a981508c7f9c8d1639c87c63b90
+Subproject commit 142eadddbe04493c1024b42586030b72e9cb7ea2
diff --git a/src/3rd_party/nccl b/src/3rd_party/nccl
index d6297d250..8e3a3f7c5 160000
--- a/src/3rd_party/nccl
+++ b/src/3rd_party/nccl
@@ -1 +1 @@
-Subproject commit d6297d250433715c283d17f1969cfcb50d2b6531
+Subproject commit 8e3a3f7c5b520babff49cec54a866fa3eda3a3b6

From 8d7a0284090d758abf79d6a917eb84d68cf2a9b7 Mon Sep 17 00:00:00 2001
From: Marcin Junczys-Dowmunt <Marcin.JunczysDowmunt@microsoft.com>
Date: Tue, 5 Feb 2019 08:09:32 -0800
Subject: [PATCH 268/838] move nccl pointer back

---
 src/3rd_party/nccl | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/3rd_party/nccl b/src/3rd_party/nccl
index 8e3a3f7c5..d6297d250 160000
--- a/src/3rd_party/nccl
+++ b/src/3rd_party/nccl
@@ -1 +1 @@
-Subproject commit 8e3a3f7c5b520babff49cec54a866fa3eda3a3b6
+Subproject commit d6297d250433715c283d17f1969cfcb50d2b6531

From b7d245945fb6b74201ab449bc3f73ee9db847bc9 Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Tue, 5 Feb 2019 11:45:55 -0800
Subject: [PATCH 269/838] added factored_vocab stubs

---
 src/CMakeLists.txt          |   1 +
 src/data/factored_vocab.cpp | 131 ++++++++++++++++++++++++++++++++++++
 src/data/factored_vocab.h   |  73 ++++++++++++++++++++
 vs/Marian.vcxproj           |   2 +
 vs/Marian.vcxproj.filters   |   6 ++
 5 files changed, 213 insertions(+)
 create mode 100755 src/data/factored_vocab.cpp
 create mode 100755 src/data/factored_vocab.h

diff --git a/src/CMakeLists.txt b/src/CMakeLists.txt
index 4c16aa1da..90dde706a 100644
--- a/src/CMakeLists.txt
+++ b/src/CMakeLists.txt
@@ -21,6 +21,7 @@ add_library(marian STATIC
   data/vocab.cpp
   data/default_vocab.cpp
   data/sentencepiece_vocab.cpp
+  data/factored_vocab.cpp
   data/corpus_base.cpp
   data/corpus.cpp
   data/corpus_sqlite.cpp
diff --git a/src/data/factored_vocab.cpp b/src/data/factored_vocab.cpp
new file mode 100755
index 000000000..df203cbcd
--- /dev/null
+++ b/src/data/factored_vocab.cpp
@@ -0,0 +1,131 @@
+#if 0
+#include "data/vocab.h"
+#include "data/vocab_base.h"
+
+namespace marian {
+
+Word Word::NONE = Word();
+Word Word::ZERO = Word(0);
+Word Word::DEFAULT_EOS_ID = Word(0);
+Word Word::DEFAULT_UNK_ID = Word(1);
+
+Ptr<IVocab> createDefaultVocab();
+Ptr<IVocab> createClassVocab();
+Ptr<IVocab> createSentencePieceVocab(const std::string& /*vocabPath*/, Ptr<Options>, size_t /*batchIndex*/);
+
+// @TODO: make each vocab peek on type
+Ptr<IVocab> createVocab(const std::string& vocabPath, Ptr<Options> options, size_t batchIndex) {
+  auto vocab = createSentencePieceVocab(vocabPath, options, batchIndex);
+  if(vocab) {
+    return vocab; // this is defined which means that a sentencepiece vocabulary could be created, so return it
+  } else {
+    // check type of input, if not given, assume "sequence"
+    auto inputTypes = options->get<std::vector<std::string>>("input-types", {});
+    std::string inputType = inputTypes.size() > batchIndex ? inputTypes[batchIndex] : "sequence";
+    return inputType == "class" ? createClassVocab() : createDefaultVocab();
+  }
+}
+
+size_t Vocab::loadOrCreate(const std::string& vocabPath,
+                           const std::vector<std::string>& trainPaths,
+                           size_t maxSize) {
+  size_t size = 0;
+  if(vocabPath.empty()) {
+    // No vocabulary path was given, attempt to first find a vocabulary
+    // for trainPaths[0] + possible suffixes. If not found attempt to create
+    // as trainPaths[0] + canonical suffix.
+    // Only search based on first path, maybe disable this at all?
+
+    LOG(info,
+        "No vocabulary path given; "
+        "trying to find default vocabulary based on data path {}",
+        trainPaths[0]);
+
+    vImpl_ = createDefaultVocab();
+    size = vImpl_->findAndLoad(trainPaths[0], maxSize);
+
+    if(size == 0) {
+      auto newVocabPath = trainPaths[0] + vImpl_->canonicalExtension();
+      LOG(info,
+          "No vocabulary path given; "
+          "trying to create vocabulary based on data paths {}",
+          utils::join(trainPaths, ", "));
+      create(newVocabPath, trainPaths, maxSize);
+      size = load(newVocabPath, maxSize);
+    }
+  } else {
+    if(!filesystem::exists(vocabPath)) {
+      // Vocabulary path was given, but no vocabulary present,
+      // attempt to create in specified location.
+      create(vocabPath, trainPaths, maxSize);
+    }
+    // Vocabulary path exists, attempting to load
+    size = load(vocabPath, maxSize);
+  }
+  LOG(info, "[data] Setting vocabulary size for input {} to {}", batchIndex_, size);
+  return size;
+}
+
+size_t Vocab::load(const std::string& vocabPath, size_t maxSize) {
+  if(!vImpl_)
+    vImpl_ = createVocab(vocabPath, options_, batchIndex_);
+  return vImpl_->load(vocabPath, (int)maxSize);
+}
+
+void Vocab::create(const std::string& vocabPath,
+                   const std::vector<std::string>& trainPaths,
+                   size_t maxSize) {
+  if(!vImpl_)
+    vImpl_ = createVocab(vocabPath, options_, batchIndex_);
+  vImpl_->create(vocabPath, trainPaths, maxSize);
+}
+
+void Vocab::create(const std::string& vocabPath,
+                   const std::string& trainPath,
+                   size_t maxSize) {
+  create(vocabPath, std::vector<std::string>({trainPath}), maxSize);
+}
+
+void Vocab::createFake() {
+  if(!vImpl_)
+    vImpl_ = createDefaultVocab(); // DefaultVocab is OK here
+  vImpl_->createFake();
+}
+
+// string token to token id
+Word Vocab::operator[](const std::string& word) const {
+  return vImpl_->operator[](word);
+}
+
+// token id to string token
+const std::string& Vocab::operator[](Word id) const {
+  return vImpl_->operator[](id);
+}
+
+// line of text to list of token ids, can perform tokenization
+Words Vocab::encode(const std::string& line,
+              bool addEOS,
+              bool inference) const {
+  return vImpl_->encode(line, addEOS, inference);
+}
+
+// list of token ids to single line, can perform detokenization
+std::string Vocab::decode(const Words& sentence,
+                    bool ignoreEOS) const {
+  return vImpl_->decode(sentence, ignoreEOS);
+}
+
+// number of vocabulary items
+size_t Vocab::size() const { return vImpl_->size(); }
+
+// number of vocabulary items
+std::string Vocab::type() const { return vImpl_->type(); }
+
+// return EOS symbol id
+Word Vocab::getEosId() const { return vImpl_->getEosId(); }
+
+// return UNK symbol id
+Word Vocab::getUnkId() const { return vImpl_->getUnkId(); }
+
+}  // namespace marian
+#endif
diff --git a/src/data/factored_vocab.h b/src/data/factored_vocab.h
new file mode 100755
index 000000000..afe79d1f0
--- /dev/null
+++ b/src/data/factored_vocab.h
@@ -0,0 +1,73 @@
+#pragma once
+
+#include "common/definitions.h"
+#include "data/types.h"
+#include "common/options.h"
+#include "common/file_stream.h"
+
+namespace marian {
+
+class IVocab;
+
+// Wrapper around vocabulary types. Can choose underlying
+// vocabulary implementation (vImpl_) based on speficied path
+// and suffix.
+// Vocabulary implementations can currently be:
+// * DefaultVocabulary for YAML (*.yml and *.yaml) and TXT (any other non-specific ending)
+// * SentencePiece with suffix *.spm (works, but has to be created outside Marian)
+class Vocab {
+private:
+  Ptr<IVocab> vImpl_;
+  Ptr<Options> options_;
+  size_t batchIndex_;
+
+public:
+  Vocab(Ptr<Options> options, size_t batchIndex)
+  : options_(options), batchIndex_(batchIndex) {}
+
+  size_t loadOrCreate(const std::string& vocabPath,
+                      const std::vector<std::string>& trainPaths,
+                      size_t maxSize = 0);
+
+  size_t load(const std::string& vocabPath, size_t maxSize = 0);
+
+  void create(const std::string& vocabPath,
+              const std::vector<std::string>& trainPaths,
+              size_t maxSize);
+
+  void create(const std::string& vocabPath,
+              const std::string& trainPath,
+              size_t maxSize);
+
+  // string token to token id
+  Word operator[](const std::string& word) const;
+
+  // token index to string token
+  const std::string& operator[](Word word) const;
+
+  // line of text to list of token ids, can perform tokenization
+  Words encode(const std::string& line,
+               bool addEOS = true,
+               bool inference = false) const;
+
+  // list of token ids to single line, can perform detokenization
+  std::string decode(const Words& sentence,
+                     bool ignoreEOS = true) const;
+
+  // number of vocabulary items
+  size_t size() const;
+
+  // number of vocabulary items
+  std::string type() const;
+
+  // return EOS symbol id
+  Word getEosId() const;
+
+  // return UNK symbol id
+  Word getUnkId() const;
+
+  // create fake vocabulary for collecting batch statistics
+  void createFake();
+};
+
+}  // namespace marian
diff --git a/vs/Marian.vcxproj b/vs/Marian.vcxproj
index 5c037f321..6bc733022 100755
--- a/vs/Marian.vcxproj
+++ b/vs/Marian.vcxproj
@@ -556,6 +556,7 @@
     <ClCompile Include="..\src\common\version.cpp" />
     <ClCompile Include="..\src\data\alignment.cpp" />
     <ClCompile Include="..\src\data\default_vocab.cpp" />
+    <ClCompile Include="..\src\data\factored_vocab.cpp" />
     <ClCompile Include="..\src\data\sentencepiece_vocab.cpp" />
     <ClCompile Include="..\src\data\vocab.cpp" />
     <ClCompile Include="..\src\data\corpus_base.cpp" />
@@ -701,6 +702,7 @@
     <ClInclude Include="..\src\common\timer.h" />
     <ClInclude Include="..\src\common\types.h" />
     <ClInclude Include="..\src\common\version.h" />
+    <ClInclude Include="..\src\data\factored_vocab.h" />
     <ClInclude Include="..\src\data\vocab_base.h" />
     <ClInclude Include="..\src\examples\mnist\dataset.h" />
     <ClInclude Include="..\src\examples\mnist\model.h" />
diff --git a/vs/Marian.vcxproj.filters b/vs/Marian.vcxproj.filters
index 307424729..168c3480f 100755
--- a/vs/Marian.vcxproj.filters
+++ b/vs/Marian.vcxproj.filters
@@ -484,6 +484,9 @@
     <ClCompile Include="..\src\layers\generic.cpp">
       <Filter>layers</Filter>
     </ClCompile>
+    <ClCompile Include="..\src\data\factored_vocab.cpp">
+      <Filter>data</Filter>
+    </ClCompile>
   </ItemGroup>
   <ItemGroup>
     <ClInclude Include="..\src\marian.h" />
@@ -1531,6 +1534,9 @@
     <ClInclude Include="..\src\data\vocab_base.h">
       <Filter>data</Filter>
     </ClInclude>
+    <ClInclude Include="..\src\data\factored_vocab.h">
+      <Filter>data</Filter>
+    </ClInclude>
   </ItemGroup>
   <ItemGroup>
     <Filter Include="3rd_party">

From 37655127b4c3684cbbc44f483d0804416423dead Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Tue, 5 Feb 2019 11:52:32 -0800
Subject: [PATCH 270/838] moved CSRSparseTensor and EmbeddingFactorMapping from
 generic.cpp to the new factored_vocab.h

---
 src/data/factored_vocab.h | 226 ++++++++++++++++++++++++++++----------
 src/layers/generic.cpp    | 176 ++---------------------------
 2 files changed, 172 insertions(+), 230 deletions(-)

diff --git a/src/data/factored_vocab.h b/src/data/factored_vocab.h
index afe79d1f0..e05d9b73b 100755
--- a/src/data/factored_vocab.h
+++ b/src/data/factored_vocab.h
@@ -1,73 +1,177 @@
+// Implementation of an IVocab that represents a factored representation.
+// This is accessed via the IVocab interface, and also by Embedding and Output
+// layers directly.
+
 #pragma once
 
 #include "common/definitions.h"
 #include "data/types.h"
-#include "common/options.h"
-#include "common/file_stream.h"
 
 namespace marian {
 
 class IVocab;
-
-// Wrapper around vocabulary types. Can choose underlying
-// vocabulary implementation (vImpl_) based on speficied path
-// and suffix.
-// Vocabulary implementations can currently be:
-// * DefaultVocabulary for YAML (*.yml and *.yaml) and TXT (any other non-specific ending)
-// * SentencePiece with suffix *.spm (works, but has to be created outside Marian)
-class Vocab {
-private:
-  Ptr<IVocab> vImpl_;
-  Ptr<Options> options_;
-  size_t batchIndex_;
-
+
+class EmbeddingFactorMapping {
 public:
-  Vocab(Ptr<Options> options, size_t batchIndex)
-  : options_(options), batchIndex_(batchIndex) {}
-
-  size_t loadOrCreate(const std::string& vocabPath,
-                      const std::vector<std::string>& trainPaths,
-                      size_t maxSize = 0);
-
-  size_t load(const std::string& vocabPath, size_t maxSize = 0);
-
-  void create(const std::string& vocabPath,
-              const std::vector<std::string>& trainPaths,
-              size_t maxSize);
-
-  void create(const std::string& vocabPath,
-              const std::string& trainPath,
-              size_t maxSize);
-
-  // string token to token id
-  Word operator[](const std::string& word) const;
-
-  // token index to string token
-  const std::string& operator[](Word word) const;
-
-  // line of text to list of token ids, can perform tokenization
-  Words encode(const std::string& line,
-               bool addEOS = true,
-               bool inference = false) const;
-
-  // list of token ids to single line, can perform detokenization
-  std::string decode(const Words& sentence,
-                     bool ignoreEOS = true) const;
-
-  // number of vocabulary items
-  size_t size() const;
-
-  // number of vocabulary items
-  std::string type() const;
-
-  // return EOS symbol id
-  Word getEosId() const;
-
-  // return UNK symbol id
-  Word getUnkId() const;
-
-  // create fake vocabulary for collecting batch statistics
-  void createFake();
+  struct CSRData {
+    Shape shape;
+    std::vector<float> weights;
+    std::vector<IndexType> indices;
+    std::vector<IndexType> offsets;
+  };
+  // mapPath = path to file with entries in order of vocab entries of the form
+  //   WORD FACTOR1 FACTOR2 FACTOR3...
+  // listPath = path to file that lists all FACTOR names
+  // vocab = original vocabulary
+  // Note: The WORD field in the map file is redundant. It is required for consistency checking only.
+  // Factors are grouped
+  //  - user specifies list-factor prefixes; all factors beginning with that prefix are in the same group
+  //  - factors within a group as multi-class and normalized that way
+  //  - groups of size 1 are interpreted as sigmoids, multiply with P(u) / P(u-1)
+  //  - one prefix must not contain another
+  //  - all factors not matching a prefix get lumped into yet another class (the lemmas)
+  //  - factor vocab must be sorted such that all groups are consecutive
+  //  - result of Output layer is nevertheless logits, not a normalized probability, due to the sigmoid entries
+  EmbeddingFactorMapping(Ptr<Options> options) : factorVocab_(New<Options>(), 0) {
+    std::vector<std::string> paths = options->get<std::vector<std::string>>("embedding-factors");
+    ABORT_IF(paths.size() != 2, "--embedding-factors expects two paths");
+    auto mapPath = paths[0];
+    auto factorVocabPath = paths[1];
+    auto vocabPath = options->get<std::string>("vocab");
+
+    // Note: We misuse the Vocab class a little.
+    // Specifically, it means that the factorVocab_ must contain </s> and "<unk>".
+    Vocab vocab(New<Options>(), 0);
+    vocab.load(vocabPath);
+    auto vocabSize = vocab.size();
+    factorVocab_.load(factorVocabPath);
+    auto numFactors = factorVocab_.size();
+
+    // load and parse factorMap
+    factorMap_.resize(vocabSize);
+    factorRefCounts_.resize(numFactors);
+    std::vector<std::string> tokens;
+    io::InputFileStream in(mapPath);
+    std::string line;
+    size_t numTotalFactors = 0;
+    for (WordIndex v = 0; io::getline(in, line); v++) {
+      tokens.clear(); // @BUGBUG: should be done in split()
+      utils::splitAny(line, tokens, " \t");
+      ABORT_IF(tokens.size() < 2 || tokens.front() != vocab[Word::fromWordIndex(v)], "Factor map must list words in same order as vocab, and have at least one factor per word", mapPath);
+      for (size_t i = 1; i < tokens.size(); i++) {
+        auto u = factorVocab_[tokens[i]].toWordIndex();
+        factorMap_[v].push_back(u);
+        factorRefCounts_[u]++;
+      }
+      numTotalFactors += tokens.size() - 1;
+    }
+    LOG(info, "[embedding] Factored-embedding map read with total/unique of {}/{} factors for {} words", numTotalFactors, numFactors, vocabSize);
+
+    // form groups
+    // @TODO: hard-coded for these initial experiments
+    std::vector<std::string> groupPrefixes = {
+      "@C",
+      "@GL", "@GR"
+    };
+    groupPrefixes.insert(groupPrefixes.begin(), "(unassigned)");     // first group is fallback for normal words (the string is only used for messages)
+    size_t numGroups = groupPrefixes.size();
+    factorGroups_.resize(numFactors, 0);
+    for (size_t g = 1; g < groupPrefixes.size(); g++) { // set group labels; what does not match any prefix will stay in group 0
+      const auto& groupPrefix = groupPrefixes[g];
+      for (WordIndex u = 0; u < numFactors; u++)
+        if (utils::beginsWith(factorVocab_[Word::fromWordIndex(u)], groupPrefix)) {
+          ABORT_IF(factorGroups_[u] != 0, "Factor {} matches multiple groups, incl. {}", factorVocab_[Word::fromWordIndex(u)], groupPrefix);
+          factorGroups_[u] = g;
+        }
+    }
+    // determine group index ranges
+    groupRanges_.resize(numGroups, { SIZE_MAX, (size_t)0 });
+    std::vector<size_t> groupCounts(numGroups); // number of group members
+    for (WordIndex u = 0; u < numFactors; u++) { // determine ranges; these must be non-overlapping, verified via groupCounts
+      auto g = factorGroups_[u];
+      if (groupRanges_[g].first > u)
+          groupRanges_[g].first = u;
+      if (groupRanges_[g].second < u + 1)
+          groupRanges_[g].second = u + 1;
+      groupCounts[g]++;
+    }
+    // create mappings needed for normalization in factored outputs
+    factorMasks_  .resize(numGroups, std::vector<float>(vocabSize, 0));     // [g][v] 1.0 if word v has factor g
+    factorIndices_.resize(numGroups, std::vector<IndexType>(vocabSize, 0)); // [g][v] index of factor (or any valid index if it does not have it; we use 0)
+    for (WordIndex v = 0; v < vocabSize; v++) {
+      for (auto u : factorMap_[v]) {
+        auto g = factorGroups_[u]; // convert u to relative u within factor group range
+        ABORT_IF(u < groupRanges_[g].first || u >= groupRanges_[g].second, "Invalid factorGroups_ entry??");
+        factorIndices_[g][v] = (IndexType)(u - groupRanges_[g].first);
+        factorMasks_[g][v] = 1.0f;
+      }
+    }
+    //for (Word v = 0; v < vocabSize; v++) {
+    //  LOG(info, "'{}': {}*{} {}*{} {}*{} {}*{}", vocab[v],
+    //      factorMasks_[0][v], factorIndices_[0][v],
+    //      factorMasks_[1][v], factorIndices_[1][v],
+    //      factorMasks_[2][v], factorIndices_[2][v],
+    //      factorMasks_[3][v], factorIndices_[3][v]);
+    //}
+    //mVecs_.resize(numGroups); // @TODO: no longer needed, delete soon
+    for (size_t g = 0; g < numGroups; g++) { // detect non-overlapping groups
+      LOG(info, "[embedding] Factor group '{}' has {} members ({})",
+          groupPrefixes[g], groupCounts[g], groupCounts[g] == 1 ? "sigmoid" : "softmax");
+      if (groupCounts[g] == 0) // factor group is unused  --@TODO: once this is not hard-coded, this is an error condition
+        continue;
+      ABORT_IF(groupRanges_[g].second - groupRanges_[g].first != groupCounts[g],
+               "Factor group '{}' members should be consecutive in the factor vocabulary", groupPrefixes[g]);
+      //auto& mVec = mVecs_[g];
+      //mVec.resize(numFactors, 0.0f);
+      //for (size_t i = groupRanges_[g].first; i < groupRanges_[g].second; i++)
+      //  mVec[i] = 1.0f;
+    }
+
+    // create the global factor matrix, which is used for factored embeddings
+    std::vector<IndexType> data(vocabSize);
+    std::iota(data.begin(), data.end(), 0);
+    globalFactorMatrix_ = csr_rows(data); // [V x U]
+  }
+
+  size_t factorVocabSize() const { return factorVocab_.size(); }
+
+  // create a CSR matrix M[V,U] from indices[] with
+  // M[v,u] = 1/c(u) if factor u is a factor of word v, and c(u) is how often u is referenced
+  CSRData csr_rows(const std::vector<IndexType>& words) const {
+    std::vector<float> weights;
+    std::vector<IndexType> indices;
+    std::vector<IndexType> offsets;
+    offsets.reserve(words.size() + 1);
+    indices.reserve(words.size()); // (at least this many)
+    // loop over all input words, and select the corresponding set of unit indices into CSR format
+    offsets.push_back((IndexType)indices.size());
+    for (auto v : words) {
+      const auto& m = factorMap_[v];
+      for (auto u : m) {
+        indices.push_back(u);
+        weights.push_back(1.0f/*/(float)factorRefCounts_[u]*/);
+      }
+      offsets.push_back((IndexType)indices.size()); // next matrix row begins at this offset
+    }
+    return { Shape({(int)words.size(), (int)factorVocab_.size()}), weights, indices, offsets };
+  }
+
+  const CSRData& getGlobalFactorMatrix() const { return globalFactorMatrix_; }   // [v,u] (sparse) -> =1 if u is factor of v
+  size_t getNumGroups() const { return groupRanges_.size(); }
+  std::pair<size_t, size_t>     getGroupRange(size_t g)    const { return groupRanges_[g]; }   // [g] -> (u_begin, u_end)
+  const std::vector<float>&     getFactorMasks(size_t g)   const { return factorMasks_[g]; }   // [g][v] 1.0 if word v has factor g
+  const std::vector<IndexType>& getFactorIndices(size_t g) const { return factorIndices_[g]; } // [g][v] local index u_g = u - u_g,begin of factor g for word v; 0 if not a factor
+private:
+  Vocab factorVocab_;                                  // [factor name] -> factor index = row of E_
+  std::vector<std::vector<WordIndex>> factorMap_;      // [word index v] -> set of factor indices u
+  std::vector<int> factorRefCounts_;                   // [factor index u] -> how often factor u is referenced in factorMap_
+  CSRData globalFactorMatrix_;                         // [v,u] (sparse) -> =1 if u is factor of v
+  std::vector<size_t> factorGroups_;                   // [u] -> group id of factor u
+  std::vector<std::pair<size_t, size_t>> groupRanges_; // [group id g] -> (u_begin,u_end) index range of factors u for this group. These don't overlap.
+  std::vector<std::vector<float>>     factorMasks_;    // [g][v] 1.0 if word v has factor g
+  std::vector<std::vector<IndexType>> factorIndices_;  // [g][v] relative index u - u_begin of factor g (or any valid index if it does not have it; we use 0)
+//public: // @TODO: temporarily; later factor this properly
+  //std::vector<std::vector<float>> mVecs_;              // [group id][u] -> 1 if factor is member of group
 };
 
 }  // namespace marian
diff --git a/src/layers/generic.cpp b/src/layers/generic.cpp
index 5844b3fb6..8ce29c9a9 100755
--- a/src/layers/generic.cpp
+++ b/src/layers/generic.cpp
@@ -2,179 +2,17 @@
 
 #include "layers/generic.h"
 #include "layers/loss.h"
+#include "data/factored_vocab.h"
 
 using std::size_t; // not sure why this is needed
 
 namespace marian {
-  struct CSRSparseTensor { // simplistic for now
-    Shape shape;
-    Expr values;  // [k_i..k_{i+1}-1] -> value at [i,j]
-    Expr indices; // [k_i..k_{i+1}-1] -> j of non-null value
-    Expr offsets; // [i] -> k_i
-  };
-
-  class EmbeddingFactorMapping {
-  public:
-    struct CSRData {
-      Shape shape;
-      std::vector<float> weights;
-      std::vector<IndexType> indices;
-      std::vector<IndexType> offsets;
-    };
-    // mapPath = path to file with entries in order of vocab entries of the form
-    //   WORD FACTOR1 FACTOR2 FACTOR3...
-    // listPath = path to file that lists all FACTOR names
-    // vocab = original vocabulary
-    // Note: The WORD field in the map file is redundant. It is required for consistency checking only.
-    // Factors are grouped
-    //  - user specifies list-factor prefixes; all factors beginning with that prefix are in the same group
-    //  - factors within a group as multi-class and normalized that way
-    //  - groups of size 1 are interpreted as sigmoids, multiply with P(u) / P(u-1)
-    //  - one prefix must not contain another
-    //  - all factors not matching a prefix get lumped into yet another class (the lemmas)
-    //  - factor vocab must be sorted such that all groups are consecutive
-    //  - result of Output layer is nevertheless logits, not a normalized probability, due to the sigmoid entries
-    EmbeddingFactorMapping(Ptr<Options> options) : factorVocab_(New<Options>(), 0) {
-      std::vector<std::string> paths = options->get<std::vector<std::string>>("embedding-factors");
-      ABORT_IF(paths.size() != 2, "--embedding-factors expects two paths");
-      auto mapPath = paths[0];
-      auto factorVocabPath = paths[1];
-      auto vocabPath = options->get<std::string>("vocab");
-
-      // Note: We misuse the Vocab class a little.
-      // Specifically, it means that the factorVocab_ must contain </s> and "<unk>".
-      Vocab vocab(New<Options>(), 0);
-      vocab.load(vocabPath);
-      auto vocabSize = vocab.size();
-      factorVocab_.load(factorVocabPath);
-      auto numFactors = factorVocab_.size();
-
-      // load and parse factorMap
-      factorMap_.resize(vocabSize);
-      factorRefCounts_.resize(numFactors);
-      std::vector<std::string> tokens;
-      io::InputFileStream in(mapPath);
-      std::string line;
-      size_t numTotalFactors = 0;
-      for (WordIndex v = 0; io::getline(in, line); v++) {
-        tokens.clear(); // @BUGBUG: should be done in split()
-        utils::splitAny(line, tokens, " \t");
-        ABORT_IF(tokens.size() < 2 || tokens.front() != vocab[Word::fromWordIndex(v)], "Factor map must list words in same order as vocab, and have at least one factor per word", mapPath);
-        for (size_t i = 1; i < tokens.size(); i++) {
-          auto u = factorVocab_[tokens[i]].toWordIndex();
-          factorMap_[v].push_back(u);
-          factorRefCounts_[u]++;
-        }
-        numTotalFactors += tokens.size() - 1;
-      }
-      LOG(info, "[embedding] Factored-embedding map read with total/unique of {}/{} factors for {} words", numTotalFactors, numFactors, vocabSize);
-
-      // form groups
-      // @TODO: hard-coded for these initial experiments
-      std::vector<std::string> groupPrefixes = {
-        "@C",
-        "@GL", "@GR"
-      };
-      groupPrefixes.insert(groupPrefixes.begin(), "(unassigned)");     // first group is fallback for normal words (the string is only used for messages)
-      size_t numGroups = groupPrefixes.size();
-      factorGroups_.resize(numFactors, 0);
-      for (size_t g = 1; g < groupPrefixes.size(); g++) { // set group labels; what does not match any prefix will stay in group 0
-        const auto& groupPrefix = groupPrefixes[g];
-        for (WordIndex u = 0; u < numFactors; u++)
-          if (utils::beginsWith(factorVocab_[Word::fromWordIndex(u)], groupPrefix)) {
-            ABORT_IF(factorGroups_[u] != 0, "Factor {} matches multiple groups, incl. {}", factorVocab_[Word::fromWordIndex(u)], groupPrefix);
-            factorGroups_[u] = g;
-          }
-      }
-      // determine group index ranges
-      groupRanges_.resize(numGroups, { SIZE_MAX, (size_t)0 });
-      std::vector<size_t> groupCounts(numGroups); // number of group members
-      for (WordIndex u = 0; u < numFactors; u++) { // determine ranges; these must be non-overlapping, verified via groupCounts
-        auto g = factorGroups_[u];
-        if (groupRanges_[g].first > u)
-            groupRanges_[g].first = u;
-        if (groupRanges_[g].second < u + 1)
-            groupRanges_[g].second = u + 1;
-        groupCounts[g]++;
-      }
-      // create mappings needed for normalization in factored outputs
-      factorMasks_  .resize(numGroups, std::vector<float>(vocabSize, 0));     // [g][v] 1.0 if word v has factor g
-      factorIndices_.resize(numGroups, std::vector<IndexType>(vocabSize, 0)); // [g][v] index of factor (or any valid index if it does not have it; we use 0)
-      for (WordIndex v = 0; v < vocabSize; v++) {
-        for (auto u : factorMap_[v]) {
-          auto g = factorGroups_[u]; // convert u to relative u within factor group range
-          ABORT_IF(u < groupRanges_[g].first || u >= groupRanges_[g].second, "Invalid factorGroups_ entry??");
-          factorIndices_[g][v] = (IndexType)(u - groupRanges_[g].first);
-          factorMasks_[g][v] = 1.0f;
-        }
-      }
-      //for (Word v = 0; v < vocabSize; v++) {
-      //  LOG(info, "'{}': {}*{} {}*{} {}*{} {}*{}", vocab[v],
-      //      factorMasks_[0][v], factorIndices_[0][v],
-      //      factorMasks_[1][v], factorIndices_[1][v],
-      //      factorMasks_[2][v], factorIndices_[2][v],
-      //      factorMasks_[3][v], factorIndices_[3][v]);
-      //}
-      //mVecs_.resize(numGroups); // @TODO: no longer needed, delete soon
-      for (size_t g = 0; g < numGroups; g++) { // detect non-overlapping groups
-        LOG(info, "[embedding] Factor group '{}' has {} members ({})",
-            groupPrefixes[g], groupCounts[g], groupCounts[g] == 1 ? "sigmoid" : "softmax");
-        if (groupCounts[g] == 0) // factor group is unused  --@TODO: once this is not hard-coded, this is an error condition
-          continue;
-        ABORT_IF(groupRanges_[g].second - groupRanges_[g].first != groupCounts[g],
-                 "Factor group '{}' members should be consecutive in the factor vocabulary", groupPrefixes[g]);
-        //auto& mVec = mVecs_[g];
-        //mVec.resize(numFactors, 0.0f);
-        //for (size_t i = groupRanges_[g].first; i < groupRanges_[g].second; i++)
-        //  mVec[i] = 1.0f;
-      }
-
-      // create the global factor matrix, which is used for factored embeddings
-      std::vector<IndexType> data(vocabSize);
-      std::iota(data.begin(), data.end(), 0);
-      globalFactorMatrix_ = csr_rows(data); // [V x U]
-    }
-
-    size_t factorVocabSize() const { return factorVocab_.size(); }
-
-    // create a CSR matrix M[V,U] from indices[] with
-    // M[v,u] = 1/c(u) if factor u is a factor of word v, and c(u) is how often u is referenced
-    CSRData csr_rows(const std::vector<IndexType>& words) const {
-      std::vector<float> weights;
-      std::vector<IndexType> indices;
-      std::vector<IndexType> offsets;
-      offsets.reserve(words.size() + 1);
-      indices.reserve(words.size()); // (at least this many)
-      // loop over all input words, and select the corresponding set of unit indices into CSR format
-      offsets.push_back((IndexType)indices.size());
-      for (auto v : words) {
-        const auto& m = factorMap_[v];
-        for (auto u : m) {
-          indices.push_back(u);
-          weights.push_back(1.0f/*/(float)factorRefCounts_[u]*/);
-        }
-        offsets.push_back((IndexType)indices.size()); // next matrix row begins at this offset
-      }
-      return { Shape({(int)words.size(), (int)factorVocab_.size()}), weights, indices, offsets };
-    }
-
-    const CSRData& getGlobalFactorMatrix() const { return globalFactorMatrix_; }   // [v,u] (sparse) -> =1 if u is factor of v
-    size_t getNumGroups() const { return groupRanges_.size(); }
-    std::pair<size_t, size_t>     getGroupRange(size_t g)    const { return groupRanges_[g]; }   // [g] -> (u_begin, u_end)
-    const std::vector<float>&     getFactorMasks(size_t g)   const { return factorMasks_[g]; }   // [g][v] 1.0 if word v has factor g
-    const std::vector<IndexType>& getFactorIndices(size_t g) const { return factorIndices_[g]; } // [g][v] local index u_g = u - u_g,begin of factor g for word v; 0 if not a factor
-  private:
-    Vocab factorVocab_;                                  // [factor name] -> factor index = row of E_
-    std::vector<std::vector<WordIndex>> factorMap_;      // [word index v] -> set of factor indices u
-    std::vector<int> factorRefCounts_;                   // [factor index u] -> how often factor u is referenced in factorMap_
-    CSRData globalFactorMatrix_;                         // [v,u] (sparse) -> =1 if u is factor of v
-    std::vector<size_t> factorGroups_;                   // [u] -> group id of factor u
-    std::vector<std::pair<size_t, size_t>> groupRanges_; // [group id g] -> (u_begin,u_end) index range of factors u for this group. These don't overlap.
-    std::vector<std::vector<float>>     factorMasks_;    // [g][v] 1.0 if word v has factor g
-    std::vector<std::vector<IndexType>> factorIndices_;  // [g][v] relative index u - u_begin of factor g (or any valid index if it does not have it; we use 0)
-  //public: // @TODO: temporarily; later factor this properly
-    //std::vector<std::vector<float>> mVecs_;              // [group id][u] -> 1 if factor is member of group
-  };
+  //struct CSRSparseTensor { // simplistic for now
+  //  Shape shape;
+  //  Expr values;  // [k_i..k_{i+1}-1] -> value at [i,j]
+  //  Expr indices; // [k_i..k_{i+1}-1] -> j of non-null value
+  //  Expr offsets; // [i] -> k_i
+  //};
 
   Logits::Logits(Expr logits) : Logits(New<RationalLoss>(logits, nullptr)) {} // single-output constructor from Expr only (RationalLoss has no count)
 

From c962433c9479b66137921c481e3df688f93abbd7 Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Tue, 5 Feb 2019 12:12:59 -0800
Subject: [PATCH 271/838] renamed EmbeddingFactorMapping to FactoredVocab

---
 src/data/factored_vocab.cpp | 18 ++++++++++++++---
 src/data/factored_vocab.h   |  7 +++++--
 src/data/vocab.cpp          |  4 ----
 src/data/vocab_base.h       |  9 ++++++++-
 src/layers/generic.cpp      | 40 ++++++++++++++++++-------------------
 src/layers/generic.h        | 12 +++++------
 6 files changed, 54 insertions(+), 36 deletions(-)

diff --git a/src/data/factored_vocab.cpp b/src/data/factored_vocab.cpp
index df203cbcd..e8cdda168 100755
--- a/src/data/factored_vocab.cpp
+++ b/src/data/factored_vocab.cpp
@@ -1,9 +1,13 @@
-#if 0
-#include "data/vocab.h"
 #include "data/vocab_base.h"
+#include "common/definitions.h"
+#include "data/types.h"
+#include "common/options.h"
+#include "common/regex.h"
+#include "data/factored_vocab.h"
 
 namespace marian {
 
+#if 0
 Word Word::NONE = Word();
 Word Word::ZERO = Word(0);
 Word Word::DEFAULT_EOS_ID = Word(0);
@@ -126,6 +130,14 @@ Word Vocab::getEosId() const { return vImpl_->getEosId(); }
 
 // return UNK symbol id
 Word Vocab::getUnkId() const { return vImpl_->getUnkId(); }
+#endif
+
+Ptr<FactoredVocab> createFactoredPieceVocab(const std::string& vocabPath, Ptr<Options> options) {
+  bool isSentencePiece = regex::regex_search(vocabPath, regex::regex("\\.(fm)$"));
+  if(isSentencePiece)
+    return New<FactoredVocab>(options);
+  else
+    return nullptr;
+}
 
 }  // namespace marian
-#endif
diff --git a/src/data/factored_vocab.h b/src/data/factored_vocab.h
index e05d9b73b..59712f6e4 100755
--- a/src/data/factored_vocab.h
+++ b/src/data/factored_vocab.h
@@ -6,12 +6,15 @@
 
 #include "common/definitions.h"
 #include "data/types.h"
+#include "data/vocab.h"
+
+#include <numeric> // for std::iota()
 
 namespace marian {
 
 class IVocab;
 
-class EmbeddingFactorMapping {
+class FactoredVocab {
 public:
   struct CSRData {
     Shape shape;
@@ -32,7 +35,7 @@ class EmbeddingFactorMapping {
   //  - all factors not matching a prefix get lumped into yet another class (the lemmas)
   //  - factor vocab must be sorted such that all groups are consecutive
   //  - result of Output layer is nevertheless logits, not a normalized probability, due to the sigmoid entries
-  EmbeddingFactorMapping(Ptr<Options> options) : factorVocab_(New<Options>(), 0) {
+  FactoredVocab(Ptr<Options> options) : factorVocab_(New<Options>(), 0) {
     std::vector<std::string> paths = options->get<std::vector<std::string>>("embedding-factors");
     ABORT_IF(paths.size() != 2, "--embedding-factors expects two paths");
     auto mapPath = paths[0];
diff --git a/src/data/vocab.cpp b/src/data/vocab.cpp
index 8094c699c..d2e4b046e 100755
--- a/src/data/vocab.cpp
+++ b/src/data/vocab.cpp
@@ -8,10 +8,6 @@ Word Word::ZERO = Word(0);
 Word Word::DEFAULT_EOS_ID = Word(0);
 Word Word::DEFAULT_UNK_ID = Word(1);
 
-Ptr<IVocab> createDefaultVocab();
-Ptr<IVocab> createClassVocab();
-Ptr<IVocab> createSentencePieceVocab(const std::string& /*vocabPath*/, Ptr<Options>, size_t /*batchIndex*/);
-
 // @TODO: make each vocab peek on type
 Ptr<IVocab> createVocab(const std::string& vocabPath, Ptr<Options> options, size_t batchIndex) {
   auto vocab = createSentencePieceVocab(vocabPath, options, batchIndex);
diff --git a/src/data/vocab_base.h b/src/data/vocab_base.h
index 9a82131e8..20f39c5ec 100755
--- a/src/data/vocab_base.h
+++ b/src/data/vocab_base.h
@@ -46,4 +46,11 @@ class IVocab {
   virtual void createFake() = 0;
 };
 
-}
\ No newline at end of file
+class Options;
+Ptr<IVocab> createDefaultVocab();
+Ptr<IVocab> createClassVocab();
+Ptr<IVocab> createSentencePieceVocab(const std::string& vocabPath, Ptr<Options>, size_t batchIndex);
+class FactoredVocab;
+Ptr<FactoredVocab> createFactoredVocab(const std::string& vocabPath, Ptr<Options>);
+
+}
diff --git a/src/layers/generic.cpp b/src/layers/generic.cpp
index 8ce29c9a9..95a7bda78 100755
--- a/src/layers/generic.cpp
+++ b/src/layers/generic.cpp
@@ -25,7 +25,7 @@ namespace marian {
 
     LOG_ONCE(info, "[logits] applyLossFunction() for {} factors", logits_.size());
     ABORT_IF(empty(), "Attempted to read out logits on empty Logits object");
-    if (!embeddingFactorMapping_) {
+    if (!factoredVocab_) {
       ABORT_IF(logits_.size() != 1, "Factors without factor mappings??");
       return lossFn(logits_.front()->loss(), indices);
     }
@@ -33,11 +33,11 @@ namespace marian {
     // accumulate all CEs for all words that have the factor
     // Memory-wise, this is cheap, all temp objects below are batches of scalars or lookup vectors.
     Expr loss;
-    auto numGroups = embeddingFactorMapping_->getNumGroups();
+    auto numGroups = factoredVocab_->getNumGroups();
     for (size_t g = 0; g < numGroups; g++) {
       indices; // [B... * 1] all batch items flattened
-      auto factorMaskVector  = embeddingFactorMapping_->getFactorMasks(g);   // [v] 1.0 if v has factor of group g
-      auto factorIndexVector = embeddingFactorMapping_->getFactorIndices(g); // [v] index of factor for word v in group p; must be 0 if factor is not used
+      auto factorMaskVector  = factoredVocab_->getFactorMasks(g);   // [v] 1.0 if v has factor of group g
+      auto factorIndexVector = factoredVocab_->getFactorIndices(g); // [v] index of factor for word v in group p; must be 0 if factor is not used
       auto factorMaskMatrix  = graph->constant({(int)factorMaskVector.size(),  1}, inits::from_vector(factorMaskVector),  Type::float32); // [V x 1]
       auto factorIndexMatrix = graph->constant({(int)factorIndexVector.size(), 1}, inits::from_vector(factorIndexVector), Type::uint32);  // [V x 1(Ug)]
       auto factorIndex  = rows(factorIndexMatrix, indices); // [B... * 1(Ug)] map word indices to factor indices (indices into factorLogits)
@@ -54,7 +54,7 @@ namespace marian {
   // This function assumes this object holds a single factor that represents a rational loss (with count).
   Ptr<RationalLoss> Logits::getRationalLoss() const {
     //return New<RationalLoss>(getLogits(), logits_.front()->count());
-    ABORT_IF(logits_.size() != 1 || embeddingFactorMapping_, "getRationalLoss() cannot be used on multi-factor outputs");
+    ABORT_IF(logits_.size() != 1 || factoredVocab_, "getRationalLoss() cannot be used on multi-factor outputs");
     ABORT_IF(!logits_.front()->count(), "getRationalLoss() used on rational loss without count");
     return logits_.front();
   }
@@ -63,7 +63,7 @@ namespace marian {
   // into output-vocab logits according to the factored model (with correct normalization of factors).
   Expr Logits::getLogits() const {
     ABORT_IF(empty(), "Attempted to read out logits on empty Logits object");
-    if (!embeddingFactorMapping_) {
+    if (!factoredVocab_) {
       ABORT_IF(logits_.size() != 1, "Factors without factor mappings??");
       return logits_.front()->loss();
     }
@@ -76,7 +76,7 @@ namespace marian {
 
     // sum up the unit logits across factors for each target word
     auto graph = y->graph();
-    auto factorMatrix = embeddingFactorMapping_->getGlobalFactorMatrix(); // [V x U]
+    auto factorMatrix = factoredVocab_->getGlobalFactorMatrix(); // [V x U]
     y = dot_csr(
         y,  // [B x U]
         factorMatrix.shape,
@@ -92,7 +92,7 @@ namespace marian {
     std::vector<Ptr<RationalLoss>> newLogits;
     for (const auto& l : logits_)
       newLogits.emplace_back(New<RationalLoss>(l->loss(), count));
-    return Logits(std::move(newLogits), embeddingFactorMapping_);
+    return Logits(std::move(newLogits), factoredVocab_);
   }
 
   namespace mlp {
@@ -106,8 +106,8 @@ namespace marian {
 
       if (options_->has("embedding-factors")) {
         ABORT_IF(shortlist_, "Shortlists are presently not compatible with factored embeddings");
-        embeddingFactorMapping_ = New<EmbeddingFactorMapping>(options_);
-        numOutputClasses = (int)embeddingFactorMapping_->factorVocabSize();
+        factoredVocab_ = New<FactoredVocab>(options_);
+        numOutputClasses = (int)factoredVocab_->factorVocabSize();
         LOG(info, "[embedding] Factored outputs enabled");
       }
 
@@ -135,15 +135,15 @@ namespace marian {
         }
         return affine(input, cachedShortWt_, cachedShortb_, false, /*transB=*/isLegacyUntransposedW ? false : true);
       }
-      else if (embeddingFactorMapping_) {
+      else if (factoredVocab_) {
         auto graph = input->graph();
 
         // project each factor separately
-        auto numGroups = embeddingFactorMapping_->getNumGroups();
+        auto numGroups = factoredVocab_->getNumGroups();
         std::vector<Ptr<RationalLoss>> allLogits(numGroups);
         for (size_t g = 0; g < numGroups; g++) {
-          auto range = embeddingFactorMapping_->getGroupRange(g);
-          ABORT_IF(g > 0 && range.first != embeddingFactorMapping_->getGroupRange(g-1).second, "Factor groups must be consecutive"); // we could sort groupYs though
+          auto range = factoredVocab_->getGroupRange(g);
+          ABORT_IF(g > 0 && range.first != factoredVocab_->getGroupRange(g-1).second, "Factor groups must be consecutive"); // we could sort groupYs though
           // slice this group's section out of W_
           // @TODO: This is highly inefficient if not tied. We should always transpose Output's matrix.
           auto factorWt = slice(Wt_, isLegacyUntransposedW ? -1 : 0, Slice((int)range.first, (int)range.second));
@@ -152,7 +152,7 @@ namespace marian {
           auto factorLogits = affine(input, factorWt, factorB, false, /*transB=*/isLegacyUntransposedW ? false : true); // [B... x U] factor logits
           allLogits[g] = New<RationalLoss>(factorLogits, nullptr);
         }
-        return Logits(std::move(allLogits), embeddingFactorMapping_);
+        return Logits(std::move(allLogits), factoredVocab_);
       }
       else
         return affine(input, Wt_, b_, false, /*transB=*/isLegacyUntransposedW ? false : true);
@@ -167,8 +167,8 @@ namespace marian {
     bool fixed = opt<bool>("fixed", false);
 
     if (options_->has("embedding-factors")) {
-      embeddingFactorMapping_ = New<EmbeddingFactorMapping>(options_);
-      dimVoc = (int)embeddingFactorMapping_->factorVocabSize();
+      factoredVocab_ = New<FactoredVocab>(options_);
+      dimVoc = (int)factoredVocab_->factorVocabSize();
       LOG(info, "[embedding] Factored embeddings enabled");
     }
 
@@ -190,7 +190,7 @@ namespace marian {
   /*private*/ Expr Embedding::multiRows(const std::vector<IndexType>& data) const
   {
     auto graph = E_->graph();
-    auto factoredData = embeddingFactorMapping_->csr_rows(data);
+    auto factoredData = factoredVocab_->csr_rows(data);
     // multi-hot factor vectors are represented as a sparse CSR matrix
     // [row index = word position index] -> set of factor indices for word at this position
     ABORT_IF(factoredData.shape != Shape({(int)factoredData.offsets.size()-1/*=rows of CSR*/, E_->shape()[0]}), "shape mismatch??");
@@ -240,7 +240,7 @@ namespace marian {
     auto batchEmbeddings = apply(subBatch->data(), {dimWords, dimBatch, dimEmb});
 #else
     Expr selectedEmbs;
-    if (embeddingFactorMapping_)
+    if (factoredVocab_)
       selectedEmbs = multiRows(subBatch->data());
     else
       selectedEmbs = rows(E_, subBatch->data());
@@ -257,7 +257,7 @@ namespace marian {
 
   Expr Embedding::applyIndices(const std::vector<WordIndex>& embIdx, const Shape& shape) const /*override final*/ {
     Expr selectedEmbs;
-    if (embeddingFactorMapping_)
+    if (factoredVocab_)
       selectedEmbs = multiRows(embIdx);
     else
       selectedEmbs = rows(E_, embIdx);
diff --git a/src/layers/generic.h b/src/layers/generic.h
index a3d184f0f..c5a199384 100755
--- a/src/layers/generic.h
+++ b/src/layers/generic.h
@@ -57,7 +57,7 @@ struct IEmbeddingLayer {
   virtual Expr applyIndices(const std::vector<WordIndex>& embIdx, const Shape& shape) const = 0;
 };
 
-class EmbeddingFactorMapping;
+class FactoredVocab;
 
 // @HACK: Frank's quick implementation of factored outputs. To be re-thought once it works.
 // Output layer returns a Logits object, which is able to compute some things on the fly
@@ -71,8 +71,8 @@ class Logits {
       logits_.push_back(logits);
     }
     Logits(Expr logits); // single-output constructor from Expr only (RationalLoss has no count)
-    Logits(std::vector<Ptr<RationalLoss>>&& logits, Ptr<EmbeddingFactorMapping> embeddingFactorMapping) // factored-output constructor
-      : logits_(std::move(logits)), embeddingFactorMapping_(embeddingFactorMapping) {}
+    Logits(std::vector<Ptr<RationalLoss>>&& logits, Ptr<FactoredVocab> embeddingFactorMapping) // factored-output constructor
+      : logits_(std::move(logits)), factoredVocab_(embeddingFactorMapping) {}
     Expr getLogits() const; // assume it holds logits: get them, possibly aggregating over factors
     Ptr<RationalLoss> getRationalLoss() const; // assume it holds a loss: get that
     Expr applyLossFunction(const Words& labels, const std::function<Expr(Expr/*logits*/,Expr/*indices*/)>& lossFn) const;
@@ -87,7 +87,7 @@ class Logits {
 private:
     // @HACK: The interplay between Logits and RationalLoss is weird. Here, we allow RationalLoss with count == nullptr.
     std::vector<Ptr<RationalLoss>> logits_;
-    Ptr<EmbeddingFactorMapping> embeddingFactorMapping_;
+    Ptr<FactoredVocab> factoredVocab_;
 };
 
 // Unary function that returns a Logits object
@@ -172,7 +172,7 @@ class Output : public LayerBase, public IUnaryLogitLayer {
   bool isLegacyUntransposedW{false}; // legacy-model emulation: W is stored in non-transposed form
   Expr cachedShortWt_;  // short-listed version, cached (cleared by clear())
   Expr cachedShortb_;   // these match the current value of shortlist_
-  Ptr<EmbeddingFactorMapping > embeddingFactorMapping_;
+  Ptr<FactoredVocab > factoredVocab_;
 
   // optional parameters set/updated after construction
   Expr tiedParam_;
@@ -221,7 +221,7 @@ class Output : public LayerBase, public IUnaryLogitLayer {
 
 class Embedding : public LayerBase, public IEmbeddingLayer {
   Expr E_;
-  Ptr<EmbeddingFactorMapping> embeddingFactorMapping_;
+  Ptr<FactoredVocab> factoredVocab_;
   Expr multiRows(const std::vector<IndexType>& data) const;
 public:
   Embedding(Ptr<ExpressionGraph> graph, Ptr<Options> options);

From 69850f16d0ff1703924994fa531d31b8fcc7c7e8 Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Tue, 5 Feb 2019 13:38:26 -0800
Subject: [PATCH 272/838] towards turning FactoredVocab into an actual IVocab

---
 src/data/factored_vocab.cpp |  8 ++++----
 src/data/factored_vocab.h   | 16 +++++++---------
 src/layers/generic.cpp      | 10 ++++++++--
 3 files changed, 19 insertions(+), 15 deletions(-)

diff --git a/src/data/factored_vocab.cpp b/src/data/factored_vocab.cpp
index e8cdda168..0f1bf5b5f 100755
--- a/src/data/factored_vocab.cpp
+++ b/src/data/factored_vocab.cpp
@@ -132,10 +132,10 @@ Word Vocab::getEosId() const { return vImpl_->getEosId(); }
 Word Vocab::getUnkId() const { return vImpl_->getUnkId(); }
 #endif
 
-Ptr<FactoredVocab> createFactoredPieceVocab(const std::string& vocabPath, Ptr<Options> options) {
-  bool isSentencePiece = regex::regex_search(vocabPath, regex::regex("\\.(fm)$"));
-  if(isSentencePiece)
-    return New<FactoredVocab>(options);
+Ptr<FactoredVocab> createFactoredVocab(const std::string& vocabPath, Ptr<Options> options) {
+  bool isFactoredVocab = regex::regex_search(vocabPath, regex::regex("\\.(fm)$"));
+  if(isFactoredVocab)
+    return New<FactoredVocab>(vocabPath, options);
   else
     return nullptr;
 }
diff --git a/src/data/factored_vocab.h b/src/data/factored_vocab.h
index 59712f6e4..36ccb006f 100755
--- a/src/data/factored_vocab.h
+++ b/src/data/factored_vocab.h
@@ -7,14 +7,13 @@
 #include "common/definitions.h"
 #include "data/types.h"
 #include "data/vocab.h"
+#include "data/vocab_base.h"
 
 #include <numeric> // for std::iota()
 
 namespace marian {
 
-class IVocab;
-
-class FactoredVocab {
+class FactoredVocab /*: public IVocab*/ {
 public:
   struct CSRData {
     Shape shape;
@@ -35,12 +34,11 @@ class FactoredVocab {
   //  - all factors not matching a prefix get lumped into yet another class (the lemmas)
   //  - factor vocab must be sorted such that all groups are consecutive
   //  - result of Output layer is nevertheless logits, not a normalized probability, due to the sigmoid entries
-  FactoredVocab(Ptr<Options> options) : factorVocab_(New<Options>(), 0) {
-    std::vector<std::string> paths = options->get<std::vector<std::string>>("embedding-factors");
-    ABORT_IF(paths.size() != 2, "--embedding-factors expects two paths");
-    auto mapPath = paths[0];
-    auto factorVocabPath = paths[1];
-    auto vocabPath = options->get<std::string>("vocab");
+  FactoredVocab(const std::string& factoredVocabPath, Ptr<Options> options) : factorVocab_(New<Options>(), 0) {
+    auto mapPath = factoredVocabPath;
+    auto factorVocabPath = mapPath;
+    factorVocabPath.back() = 'l'; // map .fm to .fl
+    auto vocabPath = options->get<std::string>("vocab");    // @TODO: This should go away; esp. to allow per-stream vocabs
 
     // Note: We misuse the Vocab class a little.
     // Specifically, it means that the factorVocab_ must contain </s> and "<unk>".
diff --git a/src/layers/generic.cpp b/src/layers/generic.cpp
index 95a7bda78..135f32219 100755
--- a/src/layers/generic.cpp
+++ b/src/layers/generic.cpp
@@ -105,8 +105,11 @@ namespace marian {
       auto numOutputClasses = options_->get<int>("dim");
 
       if (options_->has("embedding-factors")) {
+        std::vector<std::string> paths = options_->get<std::vector<std::string>>("embedding-factors");
+        factoredVocab_ = createFactoredVocab(paths[0], options_);
+      }
+      if (factoredVocab_) {
         ABORT_IF(shortlist_, "Shortlists are presently not compatible with factored embeddings");
-        factoredVocab_ = New<FactoredVocab>(options_);
         numOutputClasses = (int)factoredVocab_->factorVocabSize();
         LOG(info, "[embedding] Factored outputs enabled");
       }
@@ -167,7 +170,10 @@ namespace marian {
     bool fixed = opt<bool>("fixed", false);
 
     if (options_->has("embedding-factors")) {
-      factoredVocab_ = New<FactoredVocab>(options_);
+      std::vector<std::string> paths = options_->get<std::vector<std::string>>("embedding-factors");
+      factoredVocab_ = createFactoredVocab(paths[0], options_);
+    }
+    if (factoredVocab_) {
       dimVoc = (int)factoredVocab_->factorVocabSize();
       LOG(info, "[embedding] Factored embeddings enabled");
     }

From 299fdcc4a77d324d461778ac97aa625c97bea4a4 Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Tue, 5 Feb 2019 14:06:43 -0800
Subject: [PATCH 273/838] FactoredVocab is now an IVocab

---
 src/data/corpus.cpp              |  2 +-
 src/data/factored_vocab.cpp      | 30 +++++++++++++++++++++++++++-
 src/data/factored_vocab.h        | 34 +++++++++++++++++++++++++++++---
 src/data/sentencepiece_vocab.cpp |  1 -
 src/data/vocab_base.h            |  2 +-
 src/layers/generic.cpp           |  4 ++++
 6 files changed, 66 insertions(+), 7 deletions(-)

diff --git a/src/data/corpus.cpp b/src/data/corpus.cpp
index 07993f485..77a33bf98 100755
--- a/src/data/corpus.cpp
+++ b/src/data/corpus.cpp
@@ -18,7 +18,7 @@ Corpus::Corpus(std::vector<std::string> paths,
     : CorpusBase(paths, vocabs, options), shuffleInRAM_(options_->get<bool>("shuffle-in-ram")), allCapsEvery_(options_->get<size_t>("all-caps-every")) {}
 
 void Corpus::preprocessLine(std::string& line, size_t streamId) {
-  if (allCapsEvery_ != 0 && pos_ % allCapsEvery_ == 0) {
+  if (allCapsEvery_ != 0 && pos_ % allCapsEvery_ == 0 && !inference_) {
     line = utils::utf8ToUpper(line);
     if (streamId == 0)
       LOG_ONCE(info, "[data] source all-caps'ed line to {}", line);
diff --git a/src/data/factored_vocab.cpp b/src/data/factored_vocab.cpp
index 0f1bf5b5f..0ad6bfa84 100755
--- a/src/data/factored_vocab.cpp
+++ b/src/data/factored_vocab.cpp
@@ -132,10 +132,38 @@ Word Vocab::getEosId() const { return vImpl_->getEosId(); }
 Word Vocab::getUnkId() const { return vImpl_->getUnkId(); }
 #endif
 
+/*virtual*/ Word FactoredVocab::operator[](const std::string& word) const /*override final*/ {
+  word;
+  return Word();
+}
+
+/*virtual*/ Words FactoredVocab::encode(const std::string& line, bool addEOS /*= true*/, bool inference /*= false*/) const /*override final*/ {
+  line; addEOS; inference;
+  return {};
+}
+
+/*virtual*/ std::string FactoredVocab::decode(const Words& sentence, bool ignoreEos /*= true*/) const /*override final*/ {
+  sentence; ignoreEos;
+  return {};
+}
+
+/*virtual*/ const std::string& FactoredVocab::operator[](Word id) const /*override final*/ {
+  id;
+  static std::string x;
+  return x;
+}
+
+/*virtual*/ size_t FactoredVocab::size() const /*override final*/ {
+  return 0;
+}
+
+/*virtual*/ void FactoredVocab::createFake() /*override final*/ {
+}
+
 Ptr<FactoredVocab> createFactoredVocab(const std::string& vocabPath, Ptr<Options> options) {
   bool isFactoredVocab = regex::regex_search(vocabPath, regex::regex("\\.(fm)$"));
   if(isFactoredVocab)
-    return New<FactoredVocab>(vocabPath, options);
+    return New<FactoredVocab>(options);
   else
     return nullptr;
 }
diff --git a/src/data/factored_vocab.h b/src/data/factored_vocab.h
index 36ccb006f..e30b58483 100755
--- a/src/data/factored_vocab.h
+++ b/src/data/factored_vocab.h
@@ -13,7 +13,7 @@
 
 namespace marian {
 
-class FactoredVocab /*: public IVocab*/ {
+class FactoredVocab : public IVocab {
 public:
   struct CSRData {
     Shape shape;
@@ -21,6 +21,11 @@ class FactoredVocab /*: public IVocab*/ {
     std::vector<IndexType> indices;
     std::vector<IndexType> offsets;
   };
+
+  FactoredVocab(Ptr<Options> options) : options_(options), factorVocab_(New<Options>(), 0) { }
+
+  // from IVocab:
+
   // mapPath = path to file with entries in order of vocab entries of the form
   //   WORD FACTOR1 FACTOR2 FACTOR3...
   // listPath = path to file that lists all FACTOR names
@@ -34,11 +39,12 @@ class FactoredVocab /*: public IVocab*/ {
   //  - all factors not matching a prefix get lumped into yet another class (the lemmas)
   //  - factor vocab must be sorted such that all groups are consecutive
   //  - result of Output layer is nevertheless logits, not a normalized probability, due to the sigmoid entries
-  FactoredVocab(const std::string& factoredVocabPath, Ptr<Options> options) : factorVocab_(New<Options>(), 0) {
+  virtual size_t load(const std::string& factoredVocabPath, size_t maxSizeUnused = 0) override final {
+    ABORT_IF(maxSizeUnused != 0, "Factored vocabulary does not allow on-the-fly clipping to a maximum vocab size");
     auto mapPath = factoredVocabPath;
     auto factorVocabPath = mapPath;
     factorVocabPath.back() = 'l'; // map .fm to .fl
-    auto vocabPath = options->get<std::string>("vocab");    // @TODO: This should go away; esp. to allow per-stream vocabs
+    auto vocabPath = options_->get<std::string>("vocab");    // @TODO: This should go away; esp. to allow per-stream vocabs
 
     // Note: We misuse the Vocab class a little.
     // Specifically, it means that the factorVocab_ must contain </s> and "<unk>".
@@ -132,8 +138,23 @@ class FactoredVocab /*: public IVocab*/ {
     std::vector<IndexType> data(vocabSize);
     std::iota(data.begin(), data.end(), 0);
     globalFactorMatrix_ = csr_rows(data); // [V x U]
+
+    return factorVocabSize(); // @TODO: return the actual virtual unrolled vocab size, which eventually we will know here
   }
 
+  virtual void create(const std::string& vocabPath, const std::vector<std::string>& trainPaths, size_t maxSize) override final { vocabPath, trainPaths, maxSize; ABORT("Factored vocab cannot be created on the fly"); }
+  virtual const std::string& canonicalExtension() const override final { return suffixes()[0]; }
+  virtual const std::vector<std::string>& suffixes() const override final { const static std::vector<std::string> exts{".fm"}; return exts; }
+  virtual Word operator[](const std::string& word) const override final;
+  virtual Words encode(const std::string& line, bool addEOS = true, bool inference = false) const override final;
+  virtual std::string decode(const Words& sentence, bool ignoreEos = true) const override final;
+  virtual const std::string& operator[](Word id) const override final;
+  virtual size_t size() const override final;
+  virtual std::string type() const override final { return "FactoredVocab"; }
+  virtual Word getEosId() const override { return eosId_; }
+  virtual Word getUnkId() const override { return unkId_; }
+  virtual void createFake() override final;
+
   size_t factorVocabSize() const { return factorVocab_.size(); }
 
   // create a CSR matrix M[V,U] from indices[] with
@@ -162,7 +183,14 @@ class FactoredVocab /*: public IVocab*/ {
   std::pair<size_t, size_t>     getGroupRange(size_t g)    const { return groupRanges_[g]; }   // [g] -> (u_begin, u_end)
   const std::vector<float>&     getFactorMasks(size_t g)   const { return factorMasks_[g]; }   // [g][v] 1.0 if word v has factor g
   const std::vector<IndexType>& getFactorIndices(size_t g) const { return factorIndices_[g]; } // [g][v] local index u_g = u - u_g,begin of factor g for word v; 0 if not a factor
+
 private:
+  Ptr<Options> options_;
+  // main vocab
+  Word eosId_ = Word::NONE;
+  Word unkId_ = Word::NONE;
+
+  // factors
   Vocab factorVocab_;                                  // [factor name] -> factor index = row of E_
   std::vector<std::vector<WordIndex>> factorMap_;      // [word index v] -> set of factor indices u
   std::vector<int> factorRefCounts_;                   // [factor index u] -> how often factor u is referenced in factorMap_
diff --git a/src/data/sentencepiece_vocab.cpp b/src/data/sentencepiece_vocab.cpp
index 9af727565..2f419738d 100755
--- a/src/data/sentencepiece_vocab.cpp
+++ b/src/data/sentencepiece_vocab.cpp
@@ -126,7 +126,6 @@ class SentencePieceVocab : public IVocab {
             alpha_,
             batchIndex_);
     }
-
   }
 
   virtual const std::string& canonicalExtension() const override { return suffixes_[0]; }
diff --git a/src/data/vocab_base.h b/src/data/vocab_base.h
index 20f39c5ec..b993233e6 100755
--- a/src/data/vocab_base.h
+++ b/src/data/vocab_base.h
@@ -19,7 +19,7 @@ class IVocab {
   virtual const std::string& canonicalExtension() const = 0;
   virtual const std::vector<std::string>& suffixes() const = 0;
 
-  size_t findAndLoad(const std::string& path, size_t maxSize) {
+  size_t findAndLoad(const std::string& path, size_t maxSize) { // @TODO: Only used in one place; just inline it there -> true interface
     for(auto suffix : suffixes())
       if(filesystem::exists(path + suffix))
         return load(path + suffix, maxSize);
diff --git a/src/layers/generic.cpp b/src/layers/generic.cpp
index 135f32219..57b6d8374 100755
--- a/src/layers/generic.cpp
+++ b/src/layers/generic.cpp
@@ -109,6 +109,8 @@ namespace marian {
         factoredVocab_ = createFactoredVocab(paths[0], options_);
       }
       if (factoredVocab_) {
+        std::vector<std::string> paths = options_->get<std::vector<std::string>>("embedding-factors");
+        factoredVocab_->load(paths[0]);
         ABORT_IF(shortlist_, "Shortlists are presently not compatible with factored embeddings");
         numOutputClasses = (int)factoredVocab_->factorVocabSize();
         LOG(info, "[embedding] Factored outputs enabled");
@@ -174,6 +176,8 @@ namespace marian {
       factoredVocab_ = createFactoredVocab(paths[0], options_);
     }
     if (factoredVocab_) {
+      std::vector<std::string> paths = options_->get<std::vector<std::string>>("embedding-factors");
+      factoredVocab_->load(paths[0]);
       dimVoc = (int)factoredVocab_->factorVocabSize();
       LOG(info, "[embedding] Factored embeddings enabled");
     }

From 2398fbbf2a5f9f9cdb981cc3c642146b5204ce6b Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Tue, 5 Feb 2019 14:11:42 -0800
Subject: [PATCH 274/838] moved FactoredVocab code from header to CPP

---
 src/data/factored_vocab.cpp | 248 +++++++++++++++++++-----------------
 src/data/factored_vocab.h   | 146 +--------------------
 2 files changed, 136 insertions(+), 258 deletions(-)

diff --git a/src/data/factored_vocab.cpp b/src/data/factored_vocab.cpp
index 0ad6bfa84..3bae39237 100755
--- a/src/data/factored_vocab.cpp
+++ b/src/data/factored_vocab.cpp
@@ -7,131 +7,122 @@
 
 namespace marian {
 
-#if 0
-Word Word::NONE = Word();
-Word Word::ZERO = Word(0);
-Word Word::DEFAULT_EOS_ID = Word(0);
-Word Word::DEFAULT_UNK_ID = Word(1);
-
-Ptr<IVocab> createDefaultVocab();
-Ptr<IVocab> createClassVocab();
-Ptr<IVocab> createSentencePieceVocab(const std::string& /*vocabPath*/, Ptr<Options>, size_t /*batchIndex*/);
-
-// @TODO: make each vocab peek on type
-Ptr<IVocab> createVocab(const std::string& vocabPath, Ptr<Options> options, size_t batchIndex) {
-  auto vocab = createSentencePieceVocab(vocabPath, options, batchIndex);
-  if(vocab) {
-    return vocab; // this is defined which means that a sentencepiece vocabulary could be created, so return it
-  } else {
-    // check type of input, if not given, assume "sequence"
-    auto inputTypes = options->get<std::vector<std::string>>("input-types", {});
-    std::string inputType = inputTypes.size() > batchIndex ? inputTypes[batchIndex] : "sequence";
-    return inputType == "class" ? createClassVocab() : createDefaultVocab();
-  }
-}
-
-size_t Vocab::loadOrCreate(const std::string& vocabPath,
-                           const std::vector<std::string>& trainPaths,
-                           size_t maxSize) {
-  size_t size = 0;
-  if(vocabPath.empty()) {
-    // No vocabulary path was given, attempt to first find a vocabulary
-    // for trainPaths[0] + possible suffixes. If not found attempt to create
-    // as trainPaths[0] + canonical suffix.
-    // Only search based on first path, maybe disable this at all?
-
-    LOG(info,
-        "No vocabulary path given; "
-        "trying to find default vocabulary based on data path {}",
-        trainPaths[0]);
-
-    vImpl_ = createDefaultVocab();
-    size = vImpl_->findAndLoad(trainPaths[0], maxSize);
-
-    if(size == 0) {
-      auto newVocabPath = trainPaths[0] + vImpl_->canonicalExtension();
-      LOG(info,
-          "No vocabulary path given; "
-          "trying to create vocabulary based on data paths {}",
-          utils::join(trainPaths, ", "));
-      create(newVocabPath, trainPaths, maxSize);
-      size = load(newVocabPath, maxSize);
+// mapPath = path to file with entries in order of vocab entries of the form
+//   WORD FACTOR1 FACTOR2 FACTOR3...
+// listPath = path to file that lists all FACTOR names
+// vocab = original vocabulary
+// Note: The WORD field in the map file is redundant. It is required for consistency checking only.
+// Factors are grouped
+//  - user specifies list-factor prefixes; all factors beginning with that prefix are in the same group
+//  - factors within a group as multi-class and normalized that way
+//  - groups of size 1 are interpreted as sigmoids, multiply with P(u) / P(u-1)
+//  - one prefix must not contain another
+//  - all factors not matching a prefix get lumped into yet another class (the lemmas)
+//  - factor vocab must be sorted such that all groups are consecutive
+//  - result of Output layer is nevertheless logits, not a normalized probability, due to the sigmoid entries
+/*virtual*/ size_t FactoredVocab::load(const std::string& factoredVocabPath, size_t maxSizeUnused /*= 0*/) /*override final*/ {
+  ABORT_IF(maxSizeUnused != 0, "Factored vocabulary does not allow on-the-fly clipping to a maximum vocab size");
+  auto mapPath = factoredVocabPath;
+  auto factorVocabPath = mapPath;
+  factorVocabPath.back() = 'l'; // map .fm to .fl
+  auto vocabPath = options_->get<std::string>("vocab");    // @TODO: This should go away; esp. to allow per-stream vocabs
+
+  // Note: We misuse the Vocab class a little.
+  // Specifically, it means that the factorVocab_ must contain </s> and "<unk>".
+  Vocab vocab(New<Options>(), 0);
+  vocab.load(vocabPath);
+  auto vocabSize = vocab.size();
+  factorVocab_.load(factorVocabPath);
+  auto numFactors = factorVocab_.size();
+
+  // load and parse factorMap
+  factorMap_.resize(vocabSize);
+  factorRefCounts_.resize(numFactors);
+  std::vector<std::string> tokens;
+  io::InputFileStream in(mapPath);
+  std::string line;
+  size_t numTotalFactors = 0;
+  for (WordIndex v = 0; io::getline(in, line); v++) {
+    tokens.clear(); // @BUGBUG: should be done in split()
+    utils::splitAny(line, tokens, " \t");
+    ABORT_IF(tokens.size() < 2 || tokens.front() != vocab[Word::fromWordIndex(v)], "Factor map must list words in same order as vocab, and have at least one factor per word", mapPath);
+    for (size_t i = 1; i < tokens.size(); i++) {
+      auto u = factorVocab_[tokens[i]].toWordIndex();
+      factorMap_[v].push_back(u);
+      factorRefCounts_[u]++;
     }
-  } else {
-    if(!filesystem::exists(vocabPath)) {
-      // Vocabulary path was given, but no vocabulary present,
-      // attempt to create in specified location.
-      create(vocabPath, trainPaths, maxSize);
+    numTotalFactors += tokens.size() - 1;
+  }
+  LOG(info, "[embedding] Factored-embedding map read with total/unique of {}/{} factors for {} words", numTotalFactors, numFactors, vocabSize);
+
+  // form groups
+  // @TODO: hard-coded for these initial experiments
+  std::vector<std::string> groupPrefixes = {
+    "@C",
+    "@GL", "@GR"
+  };
+  groupPrefixes.insert(groupPrefixes.begin(), "(unassigned)");     // first group is fallback for normal words (the string is only used for messages)
+  size_t numGroups = groupPrefixes.size();
+  factorGroups_.resize(numFactors, 0);
+  for (size_t g = 1; g < groupPrefixes.size(); g++) { // set group labels; what does not match any prefix will stay in group 0
+    const auto& groupPrefix = groupPrefixes[g];
+    for (WordIndex u = 0; u < numFactors; u++)
+      if (utils::beginsWith(factorVocab_[Word::fromWordIndex(u)], groupPrefix)) {
+        ABORT_IF(factorGroups_[u] != 0, "Factor {} matches multiple groups, incl. {}", factorVocab_[Word::fromWordIndex(u)], groupPrefix);
+        factorGroups_[u] = g;
+      }
+  }
+  // determine group index ranges
+  groupRanges_.resize(numGroups, { SIZE_MAX, (size_t)0 });
+  std::vector<size_t> groupCounts(numGroups); // number of group members
+  for (WordIndex u = 0; u < numFactors; u++) { // determine ranges; these must be non-overlapping, verified via groupCounts
+    auto g = factorGroups_[u];
+    if (groupRanges_[g].first > u)
+        groupRanges_[g].first = u;
+    if (groupRanges_[g].second < u + 1)
+        groupRanges_[g].second = u + 1;
+    groupCounts[g]++;
+  }
+  // create mappings needed for normalization in factored outputs
+  factorMasks_  .resize(numGroups, std::vector<float>(vocabSize, 0));     // [g][v] 1.0 if word v has factor g
+  factorIndices_.resize(numGroups, std::vector<IndexType>(vocabSize, 0)); // [g][v] index of factor (or any valid index if it does not have it; we use 0)
+  for (WordIndex v = 0; v < vocabSize; v++) {
+    for (auto u : factorMap_[v]) {
+      auto g = factorGroups_[u]; // convert u to relative u within factor group range
+      ABORT_IF(u < groupRanges_[g].first || u >= groupRanges_[g].second, "Invalid factorGroups_ entry??");
+      factorIndices_[g][v] = (IndexType)(u - groupRanges_[g].first);
+      factorMasks_[g][v] = 1.0f;
     }
-    // Vocabulary path exists, attempting to load
-    size = load(vocabPath, maxSize);
   }
-  LOG(info, "[data] Setting vocabulary size for input {} to {}", batchIndex_, size);
-  return size;
-}
-
-size_t Vocab::load(const std::string& vocabPath, size_t maxSize) {
-  if(!vImpl_)
-    vImpl_ = createVocab(vocabPath, options_, batchIndex_);
-  return vImpl_->load(vocabPath, (int)maxSize);
-}
-
-void Vocab::create(const std::string& vocabPath,
-                   const std::vector<std::string>& trainPaths,
-                   size_t maxSize) {
-  if(!vImpl_)
-    vImpl_ = createVocab(vocabPath, options_, batchIndex_);
-  vImpl_->create(vocabPath, trainPaths, maxSize);
-}
-
-void Vocab::create(const std::string& vocabPath,
-                   const std::string& trainPath,
-                   size_t maxSize) {
-  create(vocabPath, std::vector<std::string>({trainPath}), maxSize);
-}
-
-void Vocab::createFake() {
-  if(!vImpl_)
-    vImpl_ = createDefaultVocab(); // DefaultVocab is OK here
-  vImpl_->createFake();
-}
-
-// string token to token id
-Word Vocab::operator[](const std::string& word) const {
-  return vImpl_->operator[](word);
-}
-
-// token id to string token
-const std::string& Vocab::operator[](Word id) const {
-  return vImpl_->operator[](id);
-}
+  //for (Word v = 0; v < vocabSize; v++) {
+  //  LOG(info, "'{}': {}*{} {}*{} {}*{} {}*{}", vocab[v],
+  //      factorMasks_[0][v], factorIndices_[0][v],
+  //      factorMasks_[1][v], factorIndices_[1][v],
+  //      factorMasks_[2][v], factorIndices_[2][v],
+  //      factorMasks_[3][v], factorIndices_[3][v]);
+  //}
+  //mVecs_.resize(numGroups); // @TODO: no longer needed, delete soon
+  for (size_t g = 0; g < numGroups; g++) { // detect non-overlapping groups
+    LOG(info, "[embedding] Factor group '{}' has {} members ({})",
+        groupPrefixes[g], groupCounts[g], groupCounts[g] == 1 ? "sigmoid" : "softmax");
+    if (groupCounts[g] == 0) // factor group is unused  --@TODO: once this is not hard-coded, this is an error condition
+      continue;
+    ABORT_IF(groupRanges_[g].second - groupRanges_[g].first != groupCounts[g],
+             "Factor group '{}' members should be consecutive in the factor vocabulary", groupPrefixes[g]);
+    //auto& mVec = mVecs_[g];
+    //mVec.resize(numFactors, 0.0f);
+    //for (size_t i = groupRanges_[g].first; i < groupRanges_[g].second; i++)
+    //  mVec[i] = 1.0f;
+  }
 
-// line of text to list of token ids, can perform tokenization
-Words Vocab::encode(const std::string& line,
-              bool addEOS,
-              bool inference) const {
-  return vImpl_->encode(line, addEOS, inference);
-}
+  // create the global factor matrix, which is used for factored embeddings
+  std::vector<IndexType> data(vocabSize);
+  std::iota(data.begin(), data.end(), 0);
+  globalFactorMatrix_ = csr_rows(data); // [V x U]
 
-// list of token ids to single line, can perform detokenization
-std::string Vocab::decode(const Words& sentence,
-                    bool ignoreEOS) const {
-  return vImpl_->decode(sentence, ignoreEOS);
+  return factorVocabSize(); // @TODO: return the actual virtual unrolled vocab size, which eventually we will know here
 }
 
-// number of vocabulary items
-size_t Vocab::size() const { return vImpl_->size(); }
-
-// number of vocabulary items
-std::string Vocab::type() const { return vImpl_->type(); }
-
-// return EOS symbol id
-Word Vocab::getEosId() const { return vImpl_->getEosId(); }
-
-// return UNK symbol id
-Word Vocab::getUnkId() const { return vImpl_->getUnkId(); }
-#endif
-
 /*virtual*/ Word FactoredVocab::operator[](const std::string& word) const /*override final*/ {
   word;
   return Word();
@@ -160,6 +151,27 @@ Word Vocab::getUnkId() const { return vImpl_->getUnkId(); }
 /*virtual*/ void FactoredVocab::createFake() /*override final*/ {
 }
 
+// create a CSR matrix M[V,U] from indices[] with
+// M[v,u] = 1/c(u) if factor u is a factor of word v, and c(u) is how often u is referenced
+FactoredVocab::CSRData FactoredVocab::csr_rows(const std::vector<IndexType>& words) const {
+  std::vector<float> weights;
+  std::vector<IndexType> indices;
+  std::vector<IndexType> offsets;
+  offsets.reserve(words.size() + 1);
+  indices.reserve(words.size()); // (at least this many)
+  // loop over all input words, and select the corresponding set of unit indices into CSR format
+  offsets.push_back((IndexType)indices.size());
+  for (auto v : words) {
+    const auto& m = factorMap_[v];
+    for (auto u : m) {
+      indices.push_back(u);
+      weights.push_back(1.0f/*/(float)factorRefCounts_[u]*/);
+    }
+    offsets.push_back((IndexType)indices.size()); // next matrix row begins at this offset
+  }
+  return { Shape({(int)words.size(), (int)factorVocab_.size()}), weights, indices, offsets };
+}
+
 Ptr<FactoredVocab> createFactoredVocab(const std::string& vocabPath, Ptr<Options> options) {
   bool isFactoredVocab = regex::regex_search(vocabPath, regex::regex("\\.(fm)$"));
   if(isFactoredVocab)
diff --git a/src/data/factored_vocab.h b/src/data/factored_vocab.h
index e30b58483..bb4be3600 100755
--- a/src/data/factored_vocab.h
+++ b/src/data/factored_vocab.h
@@ -25,122 +25,7 @@ class FactoredVocab : public IVocab {
   FactoredVocab(Ptr<Options> options) : options_(options), factorVocab_(New<Options>(), 0) { }
 
   // from IVocab:
-
-  // mapPath = path to file with entries in order of vocab entries of the form
-  //   WORD FACTOR1 FACTOR2 FACTOR3...
-  // listPath = path to file that lists all FACTOR names
-  // vocab = original vocabulary
-  // Note: The WORD field in the map file is redundant. It is required for consistency checking only.
-  // Factors are grouped
-  //  - user specifies list-factor prefixes; all factors beginning with that prefix are in the same group
-  //  - factors within a group as multi-class and normalized that way
-  //  - groups of size 1 are interpreted as sigmoids, multiply with P(u) / P(u-1)
-  //  - one prefix must not contain another
-  //  - all factors not matching a prefix get lumped into yet another class (the lemmas)
-  //  - factor vocab must be sorted such that all groups are consecutive
-  //  - result of Output layer is nevertheless logits, not a normalized probability, due to the sigmoid entries
-  virtual size_t load(const std::string& factoredVocabPath, size_t maxSizeUnused = 0) override final {
-    ABORT_IF(maxSizeUnused != 0, "Factored vocabulary does not allow on-the-fly clipping to a maximum vocab size");
-    auto mapPath = factoredVocabPath;
-    auto factorVocabPath = mapPath;
-    factorVocabPath.back() = 'l'; // map .fm to .fl
-    auto vocabPath = options_->get<std::string>("vocab");    // @TODO: This should go away; esp. to allow per-stream vocabs
-
-    // Note: We misuse the Vocab class a little.
-    // Specifically, it means that the factorVocab_ must contain </s> and "<unk>".
-    Vocab vocab(New<Options>(), 0);
-    vocab.load(vocabPath);
-    auto vocabSize = vocab.size();
-    factorVocab_.load(factorVocabPath);
-    auto numFactors = factorVocab_.size();
-
-    // load and parse factorMap
-    factorMap_.resize(vocabSize);
-    factorRefCounts_.resize(numFactors);
-    std::vector<std::string> tokens;
-    io::InputFileStream in(mapPath);
-    std::string line;
-    size_t numTotalFactors = 0;
-    for (WordIndex v = 0; io::getline(in, line); v++) {
-      tokens.clear(); // @BUGBUG: should be done in split()
-      utils::splitAny(line, tokens, " \t");
-      ABORT_IF(tokens.size() < 2 || tokens.front() != vocab[Word::fromWordIndex(v)], "Factor map must list words in same order as vocab, and have at least one factor per word", mapPath);
-      for (size_t i = 1; i < tokens.size(); i++) {
-        auto u = factorVocab_[tokens[i]].toWordIndex();
-        factorMap_[v].push_back(u);
-        factorRefCounts_[u]++;
-      }
-      numTotalFactors += tokens.size() - 1;
-    }
-    LOG(info, "[embedding] Factored-embedding map read with total/unique of {}/{} factors for {} words", numTotalFactors, numFactors, vocabSize);
-
-    // form groups
-    // @TODO: hard-coded for these initial experiments
-    std::vector<std::string> groupPrefixes = {
-      "@C",
-      "@GL", "@GR"
-    };
-    groupPrefixes.insert(groupPrefixes.begin(), "(unassigned)");     // first group is fallback for normal words (the string is only used for messages)
-    size_t numGroups = groupPrefixes.size();
-    factorGroups_.resize(numFactors, 0);
-    for (size_t g = 1; g < groupPrefixes.size(); g++) { // set group labels; what does not match any prefix will stay in group 0
-      const auto& groupPrefix = groupPrefixes[g];
-      for (WordIndex u = 0; u < numFactors; u++)
-        if (utils::beginsWith(factorVocab_[Word::fromWordIndex(u)], groupPrefix)) {
-          ABORT_IF(factorGroups_[u] != 0, "Factor {} matches multiple groups, incl. {}", factorVocab_[Word::fromWordIndex(u)], groupPrefix);
-          factorGroups_[u] = g;
-        }
-    }
-    // determine group index ranges
-    groupRanges_.resize(numGroups, { SIZE_MAX, (size_t)0 });
-    std::vector<size_t> groupCounts(numGroups); // number of group members
-    for (WordIndex u = 0; u < numFactors; u++) { // determine ranges; these must be non-overlapping, verified via groupCounts
-      auto g = factorGroups_[u];
-      if (groupRanges_[g].first > u)
-          groupRanges_[g].first = u;
-      if (groupRanges_[g].second < u + 1)
-          groupRanges_[g].second = u + 1;
-      groupCounts[g]++;
-    }
-    // create mappings needed for normalization in factored outputs
-    factorMasks_  .resize(numGroups, std::vector<float>(vocabSize, 0));     // [g][v] 1.0 if word v has factor g
-    factorIndices_.resize(numGroups, std::vector<IndexType>(vocabSize, 0)); // [g][v] index of factor (or any valid index if it does not have it; we use 0)
-    for (WordIndex v = 0; v < vocabSize; v++) {
-      for (auto u : factorMap_[v]) {
-        auto g = factorGroups_[u]; // convert u to relative u within factor group range
-        ABORT_IF(u < groupRanges_[g].first || u >= groupRanges_[g].second, "Invalid factorGroups_ entry??");
-        factorIndices_[g][v] = (IndexType)(u - groupRanges_[g].first);
-        factorMasks_[g][v] = 1.0f;
-      }
-    }
-    //for (Word v = 0; v < vocabSize; v++) {
-    //  LOG(info, "'{}': {}*{} {}*{} {}*{} {}*{}", vocab[v],
-    //      factorMasks_[0][v], factorIndices_[0][v],
-    //      factorMasks_[1][v], factorIndices_[1][v],
-    //      factorMasks_[2][v], factorIndices_[2][v],
-    //      factorMasks_[3][v], factorIndices_[3][v]);
-    //}
-    //mVecs_.resize(numGroups); // @TODO: no longer needed, delete soon
-    for (size_t g = 0; g < numGroups; g++) { // detect non-overlapping groups
-      LOG(info, "[embedding] Factor group '{}' has {} members ({})",
-          groupPrefixes[g], groupCounts[g], groupCounts[g] == 1 ? "sigmoid" : "softmax");
-      if (groupCounts[g] == 0) // factor group is unused  --@TODO: once this is not hard-coded, this is an error condition
-        continue;
-      ABORT_IF(groupRanges_[g].second - groupRanges_[g].first != groupCounts[g],
-               "Factor group '{}' members should be consecutive in the factor vocabulary", groupPrefixes[g]);
-      //auto& mVec = mVecs_[g];
-      //mVec.resize(numFactors, 0.0f);
-      //for (size_t i = groupRanges_[g].first; i < groupRanges_[g].second; i++)
-      //  mVec[i] = 1.0f;
-    }
-
-    // create the global factor matrix, which is used for factored embeddings
-    std::vector<IndexType> data(vocabSize);
-    std::iota(data.begin(), data.end(), 0);
-    globalFactorMatrix_ = csr_rows(data); // [V x U]
-
-    return factorVocabSize(); // @TODO: return the actual virtual unrolled vocab size, which eventually we will know here
-  }
+  virtual size_t load(const std::string& factoredVocabPath, size_t maxSizeUnused = 0) override final;
 
   virtual void create(const std::string& vocabPath, const std::vector<std::string>& trainPaths, size_t maxSize) override final { vocabPath, trainPaths, maxSize; ABORT("Factored vocab cannot be created on the fly"); }
   virtual const std::string& canonicalExtension() const override final { return suffixes()[0]; }
@@ -155,28 +40,10 @@ class FactoredVocab : public IVocab {
   virtual Word getUnkId() const override { return unkId_; }
   virtual void createFake() override final;
 
+  // factor-specific. These methods are consumed by Output and Embedding.
   size_t factorVocabSize() const { return factorVocab_.size(); }
 
-  // create a CSR matrix M[V,U] from indices[] with
-  // M[v,u] = 1/c(u) if factor u is a factor of word v, and c(u) is how often u is referenced
-  CSRData csr_rows(const std::vector<IndexType>& words) const {
-    std::vector<float> weights;
-    std::vector<IndexType> indices;
-    std::vector<IndexType> offsets;
-    offsets.reserve(words.size() + 1);
-    indices.reserve(words.size()); // (at least this many)
-    // loop over all input words, and select the corresponding set of unit indices into CSR format
-    offsets.push_back((IndexType)indices.size());
-    for (auto v : words) {
-      const auto& m = factorMap_[v];
-      for (auto u : m) {
-        indices.push_back(u);
-        weights.push_back(1.0f/*/(float)factorRefCounts_[u]*/);
-      }
-      offsets.push_back((IndexType)indices.size()); // next matrix row begins at this offset
-    }
-    return { Shape({(int)words.size(), (int)factorVocab_.size()}), weights, indices, offsets };
-  }
+  CSRData csr_rows(const std::vector<IndexType>& words) const;
 
   const CSRData& getGlobalFactorMatrix() const { return globalFactorMatrix_; }   // [v,u] (sparse) -> =1 if u is factor of v
   size_t getNumGroups() const { return groupRanges_.size(); }
@@ -186,9 +53,10 @@ class FactoredVocab : public IVocab {
 
 private:
   Ptr<Options> options_;
+
   // main vocab
-  Word eosId_ = Word::NONE;
-  Word unkId_ = Word::NONE;
+  Word eosId_{};
+  Word unkId_{};
 
   // factors
   Vocab factorVocab_;                                  // [factor name] -> factor index = row of E_
@@ -199,8 +67,6 @@ class FactoredVocab : public IVocab {
   std::vector<std::pair<size_t, size_t>> groupRanges_; // [group id g] -> (u_begin,u_end) index range of factors u for this group. These don't overlap.
   std::vector<std::vector<float>>     factorMasks_;    // [g][v] 1.0 if word v has factor g
   std::vector<std::vector<IndexType>> factorIndices_;  // [g][v] relative index u - u_begin of factor g (or any valid index if it does not have it; we use 0)
-//public: // @TODO: temporarily; later factor this properly
-  //std::vector<std::vector<float>> mVecs_;              // [group id][u] -> 1 if factor is member of group
 };
 
 }  // namespace marian

From e770dfa78572c643e0b29e2dfe76baab058a0174 Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Tue, 5 Feb 2019 14:21:00 -0800
Subject: [PATCH 275/838] FactoredVocab now actually implements all IVocab
 methods

---
 src/data/factored_vocab.cpp | 29 ++++++++++-------------------
 src/data/factored_vocab.h   |  4 ++--
 2 files changed, 12 insertions(+), 21 deletions(-)

diff --git a/src/data/factored_vocab.cpp b/src/data/factored_vocab.cpp
index 3bae39237..61e6ae142 100755
--- a/src/data/factored_vocab.cpp
+++ b/src/data/factored_vocab.cpp
@@ -29,9 +29,8 @@ namespace marian {
 
   // Note: We misuse the Vocab class a little.
   // Specifically, it means that the factorVocab_ must contain </s> and "<unk>".
-  Vocab vocab(New<Options>(), 0);
-  vocab.load(vocabPath);
-  auto vocabSize = vocab.size();
+  vocab_.load(vocabPath);
+  auto vocabSize = vocab_.size();
   factorVocab_.load(factorVocabPath);
   auto numFactors = factorVocab_.size();
 
@@ -45,7 +44,7 @@ namespace marian {
   for (WordIndex v = 0; io::getline(in, line); v++) {
     tokens.clear(); // @BUGBUG: should be done in split()
     utils::splitAny(line, tokens, " \t");
-    ABORT_IF(tokens.size() < 2 || tokens.front() != vocab[Word::fromWordIndex(v)], "Factor map must list words in same order as vocab, and have at least one factor per word", mapPath);
+    ABORT_IF(tokens.size() < 2 || tokens.front() != vocab_[Word::fromWordIndex(v)], "Factor map must list words in same order as vocab, and have at least one factor per word", mapPath);
     for (size_t i = 1; i < tokens.size(); i++) {
       auto u = factorVocab_[tokens[i]].toWordIndex();
       factorMap_[v].push_back(u);
@@ -109,10 +108,6 @@ namespace marian {
       continue;
     ABORT_IF(groupRanges_[g].second - groupRanges_[g].first != groupCounts[g],
              "Factor group '{}' members should be consecutive in the factor vocabulary", groupPrefixes[g]);
-    //auto& mVec = mVecs_[g];
-    //mVec.resize(numFactors, 0.0f);
-    //for (size_t i = groupRanges_[g].first; i < groupRanges_[g].second; i++)
-    //  mVec[i] = 1.0f;
   }
 
   // create the global factor matrix, which is used for factored embeddings
@@ -120,35 +115,31 @@ namespace marian {
   std::iota(data.begin(), data.end(), 0);
   globalFactorMatrix_ = csr_rows(data); // [V x U]
 
-  return factorVocabSize(); // @TODO: return the actual virtual unrolled vocab size, which eventually we will know here
+  return vocabSize; // @TODO: return the actual virtual unrolled vocab size, which eventually we will know here
 }
 
 /*virtual*/ Word FactoredVocab::operator[](const std::string& word) const /*override final*/ {
-  word;
-  return Word();
+  return vocab_[word];
 }
 
 /*virtual*/ Words FactoredVocab::encode(const std::string& line, bool addEOS /*= true*/, bool inference /*= false*/) const /*override final*/ {
-  line; addEOS; inference;
-  return {};
+  return vocab_.encode(line, addEOS, inference);
 }
 
 /*virtual*/ std::string FactoredVocab::decode(const Words& sentence, bool ignoreEos /*= true*/) const /*override final*/ {
-  sentence; ignoreEos;
-  return {};
+  return vocab_.decode(sentence, ignoreEos);
 }
 
 /*virtual*/ const std::string& FactoredVocab::operator[](Word id) const /*override final*/ {
-  id;
-  static std::string x;
-  return x;
+  return vocab_[id];
 }
 
 /*virtual*/ size_t FactoredVocab::size() const /*override final*/ {
-  return 0;
+  return vocab_.size();
 }
 
 /*virtual*/ void FactoredVocab::createFake() /*override final*/ {
+  return vocab_.createFake();
 }
 
 // create a CSR matrix M[V,U] from indices[] with
diff --git a/src/data/factored_vocab.h b/src/data/factored_vocab.h
index bb4be3600..b2fa81749 100755
--- a/src/data/factored_vocab.h
+++ b/src/data/factored_vocab.h
@@ -22,11 +22,10 @@ class FactoredVocab : public IVocab {
     std::vector<IndexType> offsets;
   };
 
-  FactoredVocab(Ptr<Options> options) : options_(options), factorVocab_(New<Options>(), 0) { }
+  FactoredVocab(Ptr<Options> options) : options_(options), vocab_(New<Options>(), 0), factorVocab_(New<Options>(), 0) { }
 
   // from IVocab:
   virtual size_t load(const std::string& factoredVocabPath, size_t maxSizeUnused = 0) override final;
-
   virtual void create(const std::string& vocabPath, const std::vector<std::string>& trainPaths, size_t maxSize) override final { vocabPath, trainPaths, maxSize; ABORT("Factored vocab cannot be created on the fly"); }
   virtual const std::string& canonicalExtension() const override final { return suffixes()[0]; }
   virtual const std::vector<std::string>& suffixes() const override final { const static std::vector<std::string> exts{".fm"}; return exts; }
@@ -57,6 +56,7 @@ class FactoredVocab : public IVocab {
   // main vocab
   Word eosId_{};
   Word unkId_{};
+  Vocab vocab_;
 
   // factors
   Vocab factorVocab_;                                  // [factor name] -> factor index = row of E_

From 14f2a7904005ddeb5c5f46c4c986c8998ade63da Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Tue, 5 Feb 2019 14:44:22 -0800
Subject: [PATCH 276/838] FactoredVocab is now picked up as regular vocab

---
 src/data/factored_vocab.cpp |  8 +++++---
 src/data/vocab.cpp          | 18 +++++++++++-------
 src/data/vocab_base.h       |  3 +--
 src/layers/generic.cpp      |  4 ++--
 4 files changed, 19 insertions(+), 14 deletions(-)

diff --git a/src/data/factored_vocab.cpp b/src/data/factored_vocab.cpp
index 61e6ae142..79e28b3d3 100755
--- a/src/data/factored_vocab.cpp
+++ b/src/data/factored_vocab.cpp
@@ -21,11 +21,11 @@ namespace marian {
 //  - factor vocab must be sorted such that all groups are consecutive
 //  - result of Output layer is nevertheless logits, not a normalized probability, due to the sigmoid entries
 /*virtual*/ size_t FactoredVocab::load(const std::string& factoredVocabPath, size_t maxSizeUnused /*= 0*/) /*override final*/ {
-  ABORT_IF(maxSizeUnused != 0, "Factored vocabulary does not allow on-the-fly clipping to a maximum vocab size");
   auto mapPath = factoredVocabPath;
   auto factorVocabPath = mapPath;
   factorVocabPath.back() = 'l'; // map .fm to .fl
-  auto vocabPath = options_->get<std::string>("vocab");    // @TODO: This should go away; esp. to allow per-stream vocabs
+  auto vocabPath = factorVocabPath;
+  vocabPath[vocabPath.size() - 2] = 'w'; // map .fl to .wl  --@TODO: This should go away; esp. to allow per-stream vocabs
 
   // Note: We misuse the Vocab class a little.
   // Specifically, it means that the factorVocab_ must contain </s> and "<unk>".
@@ -115,10 +115,12 @@ namespace marian {
   std::iota(data.begin(), data.end(), 0);
   globalFactorMatrix_ = csr_rows(data); // [V x U]
 
+  ABORT_IF(maxSizeUnused != 0 && maxSizeUnused != vocabSize, "Factored vocabulary does not allow on-the-fly clipping to a maximum vocab size (to {})", maxSizeUnused);
   return vocabSize; // @TODO: return the actual virtual unrolled vocab size, which eventually we will know here
 }
 
 /*virtual*/ Word FactoredVocab::operator[](const std::string& word) const /*override final*/ {
+  ABORT("operator[] called indeed");
   return vocab_[word];
 }
 
@@ -163,7 +165,7 @@ FactoredVocab::CSRData FactoredVocab::csr_rows(const std::vector<IndexType>& wor
   return { Shape({(int)words.size(), (int)factorVocab_.size()}), weights, indices, offsets };
 }
 
-Ptr<FactoredVocab> createFactoredVocab(const std::string& vocabPath, Ptr<Options> options) {
+Ptr<IVocab> createFactoredVocab(const std::string& vocabPath, Ptr<Options> options) {
   bool isFactoredVocab = regex::regex_search(vocabPath, regex::regex("\\.(fm)$"));
   if(isFactoredVocab)
     return New<FactoredVocab>(options);
diff --git a/src/data/vocab.cpp b/src/data/vocab.cpp
index d2e4b046e..fbb7eac67 100755
--- a/src/data/vocab.cpp
+++ b/src/data/vocab.cpp
@@ -10,15 +10,19 @@ Word Word::DEFAULT_UNK_ID = Word(1);
 
 // @TODO: make each vocab peek on type
 Ptr<IVocab> createVocab(const std::string& vocabPath, Ptr<Options> options, size_t batchIndex) {
+  // try SentencePiece
   auto vocab = createSentencePieceVocab(vocabPath, options, batchIndex);
-  if(vocab) {
+  if(vocab)
     return vocab; // this is defined which means that a sentencepiece vocabulary could be created, so return it
-  } else {
-    // check type of input, if not given, assume "sequence"
-    auto inputTypes = options->get<std::vector<std::string>>("input-types", {});
-    std::string inputType = inputTypes.size() > batchIndex ? inputTypes[batchIndex] : "sequence";
-    return inputType == "class" ? createClassVocab() : createDefaultVocab();
-  }
+  // try factored
+  vocab = createFactoredVocab(vocabPath, options);
+  if (vocab)
+    return vocab;
+  // regular vocab
+  // check type of input, if not given, assume "sequence"
+  auto inputTypes = options->get<std::vector<std::string>>("input-types", {});
+  std::string inputType = inputTypes.size() > batchIndex ? inputTypes[batchIndex] : "sequence";
+  return inputType == "class" ? createClassVocab() : createDefaultVocab();
 }
 
 size_t Vocab::loadOrCreate(const std::string& vocabPath,
diff --git a/src/data/vocab_base.h b/src/data/vocab_base.h
index b993233e6..aa43b96f1 100755
--- a/src/data/vocab_base.h
+++ b/src/data/vocab_base.h
@@ -50,7 +50,6 @@ class Options;
 Ptr<IVocab> createDefaultVocab();
 Ptr<IVocab> createClassVocab();
 Ptr<IVocab> createSentencePieceVocab(const std::string& vocabPath, Ptr<Options>, size_t batchIndex);
-class FactoredVocab;
-Ptr<FactoredVocab> createFactoredVocab(const std::string& vocabPath, Ptr<Options>);
+Ptr<IVocab> createFactoredVocab(const std::string& vocabPath, Ptr<Options>);
 
 }
diff --git a/src/layers/generic.cpp b/src/layers/generic.cpp
index 57b6d8374..5a79ada38 100755
--- a/src/layers/generic.cpp
+++ b/src/layers/generic.cpp
@@ -106,7 +106,7 @@ namespace marian {
 
       if (options_->has("embedding-factors")) {
         std::vector<std::string> paths = options_->get<std::vector<std::string>>("embedding-factors");
-        factoredVocab_ = createFactoredVocab(paths[0], options_);
+        factoredVocab_ = std::static_pointer_cast<FactoredVocab>(createFactoredVocab(paths[0], options_));
       }
       if (factoredVocab_) {
         std::vector<std::string> paths = options_->get<std::vector<std::string>>("embedding-factors");
@@ -173,7 +173,7 @@ namespace marian {
 
     if (options_->has("embedding-factors")) {
       std::vector<std::string> paths = options_->get<std::vector<std::string>>("embedding-factors");
-      factoredVocab_ = createFactoredVocab(paths[0], options_);
+      factoredVocab_ = std::static_pointer_cast<FactoredVocab>(createFactoredVocab(paths[0], options_));
     }
     if (factoredVocab_) {
       std::vector<std::string> paths = options_->get<std::vector<std::string>>("embedding-factors");

From 3a8d8ae71ce93228ea60b159431d82c7b1d30850 Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Tue, 5 Feb 2019 14:49:10 -0800
Subject: [PATCH 277/838] bug fix: factored vocab should forward base vocab's
 EOS and UNK

---
 src/data/factored_vocab.cpp | 8 +++++---
 1 file changed, 5 insertions(+), 3 deletions(-)

diff --git a/src/data/factored_vocab.cpp b/src/data/factored_vocab.cpp
index 79e28b3d3..3f13c8579 100755
--- a/src/data/factored_vocab.cpp
+++ b/src/data/factored_vocab.cpp
@@ -100,7 +100,6 @@ namespace marian {
   //      factorMasks_[2][v], factorIndices_[2][v],
   //      factorMasks_[3][v], factorIndices_[3][v]);
   //}
-  //mVecs_.resize(numGroups); // @TODO: no longer needed, delete soon
   for (size_t g = 0; g < numGroups; g++) { // detect non-overlapping groups
     LOG(info, "[embedding] Factor group '{}' has {} members ({})",
         groupPrefixes[g], groupCounts[g], groupCounts[g] == 1 ? "sigmoid" : "softmax");
@@ -114,9 +113,12 @@ namespace marian {
   std::vector<IndexType> data(vocabSize);
   std::iota(data.begin(), data.end(), 0);
   globalFactorMatrix_ = csr_rows(data); // [V x U]
+
+  eosId_ = vocab_.getEosId();
+  unkId_ = vocab_.getUnkId();
 
-  ABORT_IF(maxSizeUnused != 0 && maxSizeUnused != vocabSize, "Factored vocabulary does not allow on-the-fly clipping to a maximum vocab size (to {})", maxSizeUnused);
-  return vocabSize; // @TODO: return the actual virtual unrolled vocab size, which eventually we will know here
+  ABORT_IF(maxSizeUnused != 0 && maxSizeUnused != size(), "Factored vocabulary does not allow on-the-fly clipping to a maximum vocab size (to {})", maxSizeUnused);
+  return size();
 }
 
 /*virtual*/ Word FactoredVocab::operator[](const std::string& word) const /*override final*/ {

From a0e6411a824731f40966e3c60c6c6551ec3a2cd7 Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Tue, 5 Feb 2019 15:10:03 -0800
Subject: [PATCH 278/838] removed option --embedding-factors, instead just
 specify a .fm file as vocab

---
 src/common/config_parser.cpp |  7 -------
 src/data/factored_vocab.cpp  | 17 +++++++++++++++--
 src/data/factored_vocab.h    |  5 ++---
 src/data/vocab.cpp           |  2 +-
 src/data/vocab_base.h        |  2 +-
 src/layers/generic.cpp       | 14 ++------------
 src/models/decoder.h         |  5 +----
 src/models/transformer.h     | 17 ++---------------
 8 files changed, 24 insertions(+), 45 deletions(-)

diff --git a/src/common/config_parser.cpp b/src/common/config_parser.cpp
index 422432e9d..252810531 100755
--- a/src/common/config_parser.cpp
+++ b/src/common/config_parser.cpp
@@ -30,7 +30,6 @@ const std::set<std::string> PATHS = {
   "train-sets",
   "vocabs",
   "embedding-vectors",
-  "embedding-factors",
   "valid-sets",
   "valid-script-path",
   "valid-log",
@@ -400,8 +399,6 @@ void ConfigParser::addOptionsTraining(cli::CLIWrapper& cli) {
      "Fix source embeddings. Affects all encoders");
   cli.add<bool>("--embedding-fix-trg",
      "Fix target embeddings. Affects all decoders");
-  cli.add_nondefault<std::vector<std::string>>("--embedding-factors",
-     "Paths to (factor map, factor list) file for factored embeddings");
 
   cli.add<bool>("--multi-node",
      "Enable asynchronous multi-node training through MPI (and legacy sync if combined with --sync-sgd)");
@@ -483,8 +480,6 @@ void ConfigParser::addOptionsTranslation(cli::CLIWrapper& cli) {
       "stdout");
   cli.add<std::vector<std::string>>("--vocabs,-v",
       "Paths to vocabulary files have to correspond to --input");
-  cli.add_nondefault<std::vector<std::string>>("--embedding-factors",
-      "Paths to (factor map, factor list) file for factored embeddings");
   // decoding options
   cli.add<size_t>("--beam-size,-b",
       "Beam size used during search with validating translator",
@@ -547,8 +542,6 @@ void ConfigParser::addOptionsScoring(cli::CLIWrapper& cli) {
       "Paths to vocabulary files have to correspond to --train-sets. "
       "If this parameter is not supplied we look for vocabulary files source.{yml,json} and target.{yml,json}. "
       "If these files do not exists they are created");
-  cli.add_nondefault<std::vector<std::string>>("--embedding-factors",
-      "Paths to (factor map, factor list) file for factored embeddings");
   cli.add<bool>("--n-best",
       "Score n-best list instead of plain text corpus");
   cli.add<std::string>("--n-best-feature",
diff --git a/src/data/factored_vocab.cpp b/src/data/factored_vocab.cpp
index 3f13c8579..2c1591cbe 100755
--- a/src/data/factored_vocab.cpp
+++ b/src/data/factored_vocab.cpp
@@ -167,10 +167,23 @@ FactoredVocab::CSRData FactoredVocab::csr_rows(const std::vector<IndexType>& wor
   return { Shape({(int)words.size(), (int)factorVocab_.size()}), weights, indices, offsets };
 }
 
-Ptr<IVocab> createFactoredVocab(const std::string& vocabPath, Ptr<Options> options) {
+// Helper to construct and load a FactordVocab from a path is given (non-empty) and if it specifies a factored vocab.
+// This is used by the Embedding and Output layers.
+/*static*/ Ptr<FactoredVocab> FactoredVocab::tryCreateAndLoad(const std::string& path) {
+  Ptr<FactoredVocab> res;
+  if (!path.empty()) {
+    res = std::static_pointer_cast<FactoredVocab>(createFactoredVocab(path)); // this checks the file extension
+    if (res)
+      res->load(path); // or throw
+  }
+  return res;
+}
+
+// Note: This does not actually load it, only checks the path for the type.
+Ptr<IVocab> createFactoredVocab(const std::string& vocabPath) {
   bool isFactoredVocab = regex::regex_search(vocabPath, regex::regex("\\.(fm)$"));
   if(isFactoredVocab)
-    return New<FactoredVocab>(options);
+    return New<FactoredVocab>();
   else
     return nullptr;
 }
diff --git a/src/data/factored_vocab.h b/src/data/factored_vocab.h
index b2fa81749..848bf6001 100755
--- a/src/data/factored_vocab.h
+++ b/src/data/factored_vocab.h
@@ -22,7 +22,7 @@ class FactoredVocab : public IVocab {
     std::vector<IndexType> offsets;
   };
 
-  FactoredVocab(Ptr<Options> options) : options_(options), vocab_(New<Options>(), 0), factorVocab_(New<Options>(), 0) { }
+  FactoredVocab() : vocab_(New<Options>(), 0), factorVocab_(New<Options>(), 0) { }
 
   // from IVocab:
   virtual size_t load(const std::string& factoredVocabPath, size_t maxSizeUnused = 0) override final;
@@ -50,9 +50,8 @@ class FactoredVocab : public IVocab {
   const std::vector<float>&     getFactorMasks(size_t g)   const { return factorMasks_[g]; }   // [g][v] 1.0 if word v has factor g
   const std::vector<IndexType>& getFactorIndices(size_t g) const { return factorIndices_[g]; } // [g][v] local index u_g = u - u_g,begin of factor g for word v; 0 if not a factor
 
+  static Ptr<FactoredVocab> tryCreateAndLoad(const std::string& path); // load from "vocab" option if it specifies a factored vocab
 private:
-  Ptr<Options> options_;
-
   // main vocab
   Word eosId_{};
   Word unkId_{};
diff --git a/src/data/vocab.cpp b/src/data/vocab.cpp
index fbb7eac67..63947647d 100755
--- a/src/data/vocab.cpp
+++ b/src/data/vocab.cpp
@@ -15,7 +15,7 @@ Ptr<IVocab> createVocab(const std::string& vocabPath, Ptr<Options> options, size
   if(vocab)
     return vocab; // this is defined which means that a sentencepiece vocabulary could be created, so return it
   // try factored
-  vocab = createFactoredVocab(vocabPath, options);
+  vocab = createFactoredVocab(vocabPath);
   if (vocab)
     return vocab;
   // regular vocab
diff --git a/src/data/vocab_base.h b/src/data/vocab_base.h
index aa43b96f1..bca9db957 100755
--- a/src/data/vocab_base.h
+++ b/src/data/vocab_base.h
@@ -50,6 +50,6 @@ class Options;
 Ptr<IVocab> createDefaultVocab();
 Ptr<IVocab> createClassVocab();
 Ptr<IVocab> createSentencePieceVocab(const std::string& vocabPath, Ptr<Options>, size_t batchIndex);
-Ptr<IVocab> createFactoredVocab(const std::string& vocabPath, Ptr<Options>);
+Ptr<IVocab> createFactoredVocab(const std::string& vocabPath);
 
 }
diff --git a/src/layers/generic.cpp b/src/layers/generic.cpp
index 5a79ada38..41e0071e0 100755
--- a/src/layers/generic.cpp
+++ b/src/layers/generic.cpp
@@ -104,13 +104,8 @@ namespace marian {
       auto name = options_->get<std::string>("prefix");
       auto numOutputClasses = options_->get<int>("dim");
 
-      if (options_->has("embedding-factors")) {
-        std::vector<std::string> paths = options_->get<std::vector<std::string>>("embedding-factors");
-        factoredVocab_ = std::static_pointer_cast<FactoredVocab>(createFactoredVocab(paths[0], options_));
-      }
+      factoredVocab_ = FactoredVocab::tryCreateAndLoad(options_->get<std::string>("vocab", ""));
       if (factoredVocab_) {
-        std::vector<std::string> paths = options_->get<std::vector<std::string>>("embedding-factors");
-        factoredVocab_->load(paths[0]);
         ABORT_IF(shortlist_, "Shortlists are presently not compatible with factored embeddings");
         numOutputClasses = (int)factoredVocab_->factorVocabSize();
         LOG(info, "[embedding] Factored outputs enabled");
@@ -171,13 +166,8 @@ namespace marian {
 
     bool fixed = opt<bool>("fixed", false);
 
-    if (options_->has("embedding-factors")) {
-      std::vector<std::string> paths = options_->get<std::vector<std::string>>("embedding-factors");
-      factoredVocab_ = std::static_pointer_cast<FactoredVocab>(createFactoredVocab(paths[0], options_));
-    }
+    factoredVocab_ = FactoredVocab::tryCreateAndLoad(options_->get<std::string>("vocab", ""));
     if (factoredVocab_) {
-      std::vector<std::string> paths = options_->get<std::vector<std::string>>("embedding-factors");
-      factoredVocab_->load(paths[0]);
       dimVoc = (int)factoredVocab_->factorVocabSize();
       LOG(info, "[embedding] Factored embeddings enabled");
     }
diff --git a/src/models/decoder.h b/src/models/decoder.h
index 64058344c..8f3db3d72 100755
--- a/src/models/decoder.h
+++ b/src/models/decoder.h
@@ -54,10 +54,7 @@ class DecoderBase {
         embFactory("embFile", embFiles[batchIndex_])  //
             ("normalization", opt<bool>("embedding-normalization"));
       }
-      if (options_->has("embedding-factors")) {
-        embFactory("embedding-factors", opt<std::vector<std::string>>("embedding-factors"));
-        embFactory("vocab", opt<std::vector<std::string>>("vocabs")[batchIndex_]);
-      }
+      embFactory("vocab", opt<std::vector<std::string>>("vocabs")[batchIndex_]); // for factored embeddings
       embedding_[batchIndex_] = embFactory.construct(graph);
     }
   }
diff --git a/src/models/transformer.h b/src/models/transformer.h
index a6275f258..48b22e113 100755
--- a/src/models/transformer.h
+++ b/src/models/transformer.h
@@ -537,10 +537,7 @@ class EncoderTransformer : public Transformer<EncoderBase> {
       embFactory("embFile", embFiles[subBatchIndex])
                 ("normalization", opt<bool>("embedding-normalization"));
     }
-    if (options_->has("embedding-factors")) {
-      embFactory("embedding-factors", opt<std::vector<std::string>>("embedding-factors"));
-      embFactory("vocab", opt<std::vector<std::string>>("vocabs")[subBatchIndex]);
-    }
+    embFactory("vocab", opt<std::vector<std::string>>("vocabs")[subBatchIndex]); // for factored embeddings
     return embFactory.construct(graph_);
   }
 
@@ -655,17 +652,7 @@ class DecoderTransformer : public Transformer<DecoderBase> {
       outputFactory.tieTransposed(tiedPrefix);
     }
 
-    if (options_->has("embedding-factors")) {
-      // factored embeddings, simplistic version (which just adds the logits, like multiplying probs)
-      //  z = h @ W        // h:[B x D] ; W:[D x V] -> [B x V]
-      // with factors:
-      //  z = h @ W @ M'        // h:[B x D] ; W:[D x U] ; M':[U x V]  -> [B x V]
-      // i.e. multiOutput():
-      //  output = dot_csr(output, M, transB=true)
-      // @BUGBUG: need to specify output factors separately if not tied-embeddings or tied-embeddings-all
-      outputFactory("embedding-factors", opt<std::vector<std::string>>("embedding-factors"));
-      outputFactory("vocab", opt<std::vector<std::string>>("vocabs")[batchIndex_]);
-    }
+    outputFactory("vocab", opt<std::vector<std::string>>("vocabs")[batchIndex_]); // for factored outputs
 
     output_ = std::dynamic_pointer_cast<mlp::Output>(outputFactory.construct(graph_)); // (construct() returns only the underlying interface)
   }

From 0380a2b3ef89a28894add3b3e9a1b06f30f24558 Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Tue, 5 Feb 2019 15:53:04 -0800
Subject: [PATCH 279/838] FactoredVocab no longer uses Vocab for the factors

---
 src/data/factored_vocab.cpp | 15 ++++++++++-----
 src/data/factored_vocab.h   | 30 ++++++++++++++++++++++++++++--
 2 files changed, 38 insertions(+), 7 deletions(-)

diff --git a/src/data/factored_vocab.cpp b/src/data/factored_vocab.cpp
index 2c1591cbe..7adee5132 100755
--- a/src/data/factored_vocab.cpp
+++ b/src/data/factored_vocab.cpp
@@ -31,7 +31,13 @@ namespace marian {
   // Specifically, it means that the factorVocab_ must contain </s> and "<unk>".
   vocab_.load(vocabPath);
   auto vocabSize = vocab_.size();
-  factorVocab_.load(factorVocabPath);
+
+  std::string line;
+
+  // load factor vocabulary
+  io::InputFileStream fin(factorVocabPath);
+  for (WordIndex v = 0; io::getline(fin, line); v++)
+    factorVocab_.add(line);
   auto numFactors = factorVocab_.size();
 
   // load and parse factorMap
@@ -39,14 +45,13 @@ namespace marian {
   factorRefCounts_.resize(numFactors);
   std::vector<std::string> tokens;
   io::InputFileStream in(mapPath);
-  std::string line;
   size_t numTotalFactors = 0;
   for (WordIndex v = 0; io::getline(in, line); v++) {
     tokens.clear(); // @BUGBUG: should be done in split()
     utils::splitAny(line, tokens, " \t");
     ABORT_IF(tokens.size() < 2 || tokens.front() != vocab_[Word::fromWordIndex(v)], "Factor map must list words in same order as vocab, and have at least one factor per word", mapPath);
     for (size_t i = 1; i < tokens.size(); i++) {
-      auto u = factorVocab_[tokens[i]].toWordIndex();
+      auto u = factorVocab_[tokens[i]];
       factorMap_[v].push_back(u);
       factorRefCounts_[u]++;
     }
@@ -66,8 +71,8 @@ namespace marian {
   for (size_t g = 1; g < groupPrefixes.size(); g++) { // set group labels; what does not match any prefix will stay in group 0
     const auto& groupPrefix = groupPrefixes[g];
     for (WordIndex u = 0; u < numFactors; u++)
-      if (utils::beginsWith(factorVocab_[Word::fromWordIndex(u)], groupPrefix)) {
-        ABORT_IF(factorGroups_[u] != 0, "Factor {} matches multiple groups, incl. {}", factorVocab_[Word::fromWordIndex(u)], groupPrefix);
+      if (utils::beginsWith(factorVocab_[u], groupPrefix)) {
+        ABORT_IF(factorGroups_[u] != 0, "Factor {} matches multiple groups, incl. {}", factorVocab_[u], groupPrefix);
         factorGroups_[u] = g;
       }
   }
diff --git a/src/data/factored_vocab.h b/src/data/factored_vocab.h
index 848bf6001..d9292e429 100755
--- a/src/data/factored_vocab.h
+++ b/src/data/factored_vocab.h
@@ -22,7 +22,7 @@ class FactoredVocab : public IVocab {
     std::vector<IndexType> offsets;
   };
 
-  FactoredVocab() : vocab_(New<Options>(), 0), factorVocab_(New<Options>(), 0) { }
+  FactoredVocab() : vocab_(New<Options>(), 0) { }
 
   // from IVocab:
   virtual size_t load(const std::string& factoredVocabPath, size_t maxSizeUnused = 0) override final;
@@ -52,13 +52,39 @@ class FactoredVocab : public IVocab {
 
   static Ptr<FactoredVocab> tryCreateAndLoad(const std::string& path); // load from "vocab" option if it specifies a factored vocab
 private:
+  class WordLUT { // map between strings and WordIndex
+    std::map<std::string, WordIndex> str2index_;
+    std::vector<std::string> index2str_;
+  public:
+    void add(const std::string& word) {
+      auto index = (WordIndex)index2str_.size();
+      auto wasInserted = str2index_.insert(std::make_pair(word, index)).second;
+      ABORT_IF(!wasInserted, "Duplicate vocab entry for '{}'", word);
+      index2str_.push_back(word);
+    }
+    const std::string& operator[](WordIndex index) const { return index2str_[index]; }
+    WordIndex operator[](const std::string& word) const {
+      auto iter = str2index_.find(word);
+      ABORT_IF(iter == str2index_.end(), "Token '{}' not found in vocabulary", word);
+      return iter->second;
+    }
+    bool tryFind(const std::string& word, WordIndex& index) const {
+      auto iter = str2index_.find(word);
+      if (iter == str2index_.end())
+        return false;
+      index = iter->second;
+      return true;
+    }
+    size_t size() const { return index2str_.size(); }
+  };
+
   // main vocab
   Word eosId_{};
   Word unkId_{};
   Vocab vocab_;
 
   // factors
-  Vocab factorVocab_;                                  // [factor name] -> factor index = row of E_
+  WordLUT factorVocab_;                                // [factor name] -> factor index = row of E_
   std::vector<std::vector<WordIndex>> factorMap_;      // [word index v] -> set of factor indices u
   std::vector<int> factorRefCounts_;                   // [factor index u] -> how often factor u is referenced in factorMap_
   CSRData globalFactorMatrix_;                         // [v,u] (sparse) -> =1 if u is factor of v

From 1143ab97dfa1f3bd2032f6f0c7643632aa638756 Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Tue, 5 Feb 2019 16:15:16 -0800
Subject: [PATCH 280/838] FactoredVocab no longer uses Vocab objects under the
 hood

---
 src/data/default_vocab.cpp  |  1 -
 src/data/factored_vocab.cpp | 58 +++++++++++++++++++++++--------------
 src/data/factored_vocab.h   | 19 +++++++-----
 3 files changed, 49 insertions(+), 29 deletions(-)

diff --git a/src/data/default_vocab.cpp b/src/data/default_vocab.cpp
index bb91fab36..be653fb8f 100755
--- a/src/data/default_vocab.cpp
+++ b/src/data/default_vocab.cpp
@@ -62,7 +62,6 @@ class DefaultVocab : public IVocab {
   }
 
   std::string decode(const Words& sentence, bool ignoreEOS) const override {
-    std::string line;
     auto tokens = (*this)(sentence, ignoreEOS);
     return utils::join(tokens, " ");
   }
diff --git a/src/data/factored_vocab.cpp b/src/data/factored_vocab.cpp
index 7adee5132..cc7c7febd 100755
--- a/src/data/factored_vocab.cpp
+++ b/src/data/factored_vocab.cpp
@@ -27,18 +27,13 @@ namespace marian {
   auto vocabPath = factorVocabPath;
   vocabPath[vocabPath.size() - 2] = 'w'; // map .fl to .wl  --@TODO: This should go away; esp. to allow per-stream vocabs
 
-  // Note: We misuse the Vocab class a little.
-  // Specifically, it means that the factorVocab_ must contain </s> and "<unk>".
-  vocab_.load(vocabPath);
-  auto vocabSize = vocab_.size();
-
   std::string line;
 
+  // load main vocabulary   --@TODO: This will go away soon.
+  auto vocabSize = vocab_.load(vocabPath);
+
   // load factor vocabulary
-  io::InputFileStream fin(factorVocabPath);
-  for (WordIndex v = 0; io::getline(fin, line); v++)
-    factorVocab_.add(line);
-  auto numFactors = factorVocab_.size();
+  auto numFactors = factorVocab_.load(factorVocabPath);
 
   // load and parse factorMap
   factorMap_.resize(vocabSize);
@@ -49,7 +44,7 @@ namespace marian {
   for (WordIndex v = 0; io::getline(in, line); v++) {
     tokens.clear(); // @BUGBUG: should be done in split()
     utils::splitAny(line, tokens, " \t");
-    ABORT_IF(tokens.size() < 2 || tokens.front() != vocab_[Word::fromWordIndex(v)], "Factor map must list words in same order as vocab, and have at least one factor per word", mapPath);
+    ABORT_IF(tokens.size() < 2 || tokens.front() != vocab_[v], "Factor map must list words in same order as vocab, and have at least one factor per word", mapPath);
     for (size_t i = 1; i < tokens.size(); i++) {
       auto u = factorVocab_[tokens[i]];
       factorMap_[v].push_back(u);
@@ -118,37 +113,58 @@ namespace marian {
   std::vector<IndexType> data(vocabSize);
   std::iota(data.begin(), data.end(), 0);
   globalFactorMatrix_ = csr_rows(data); // [V x U]
-
-  eosId_ = vocab_.getEosId();
-  unkId_ = vocab_.getUnkId();
+
+  // </s> and <unk> must exist in the vocabulary
+  eosId_ = Word::fromWordIndex(vocab_[DEFAULT_EOS_STR]);
+  unkId_ = Word::fromWordIndex(vocab_[DEFAULT_UNK_STR]);
 
   ABORT_IF(maxSizeUnused != 0 && maxSizeUnused != size(), "Factored vocabulary does not allow on-the-fly clipping to a maximum vocab size (to {})", maxSizeUnused);
   return size();
 }
 
 /*virtual*/ Word FactoredVocab::operator[](const std::string& word) const /*override final*/ {
-  ABORT("operator[] called indeed");
-  return vocab_[word];
+  WordIndex index;
+  bool found = vocab_.tryFind(word, index);
+  if (found)
+    return Word::fromWordIndex(index);
+  else
+    return getUnkId();
 }
 
-/*virtual*/ Words FactoredVocab::encode(const std::string& line, bool addEOS /*= true*/, bool inference /*= false*/) const /*override final*/ {
-  return vocab_.encode(line, addEOS, inference);
+/*virtual*/ Words FactoredVocab::encode(const std::string& line, bool addEOS /*= true*/, bool /*inference*/ /*= false*/) const /*override final*/ {
+  std::vector<std::string> lineTokens;
+  utils::split(line, lineTokens, " ");
+  Words res; res.reserve(lineTokens.size() + addEOS);
+  for (const auto& tok : lineTokens)
+    res.push_back((*this)[tok]);
+  if (addEOS)
+    res.push_back(getEosId());
+  return res;
 }
 
-/*virtual*/ std::string FactoredVocab::decode(const Words& sentence, bool ignoreEos /*= true*/) const /*override final*/ {
-  return vocab_.decode(sentence, ignoreEos);
+/*virtual*/ std::string FactoredVocab::decode(const Words& sentence, bool ignoreEOS /*= true*/) const /*override final*/ {
+  std::vector<std::string> decoded;
+  decoded.reserve(sentence.size());
+  for(auto w : sentence) {
+    if((w != getEosId() || !ignoreEOS))
+      decoded.push_back((*this)[w]);
+  }
+  return utils::join(decoded, " ");
 }
 
 /*virtual*/ const std::string& FactoredVocab::operator[](Word id) const /*override final*/ {
-  return vocab_[id];
+  return vocab_[id.toWordIndex()];
 }
 
 /*virtual*/ size_t FactoredVocab::size() const /*override final*/ {
   return vocab_.size();
 }
 
+// This creates a fake vocabulary fro use in fakeBatch().
+// @TODO: This may become more complex.
 /*virtual*/ void FactoredVocab::createFake() /*override final*/ {
-  return vocab_.createFake();
+  eosId_ = Word::fromWordIndex(vocab_.add(DEFAULT_EOS_STR));
+  unkId_ = Word::fromWordIndex(vocab_.add(DEFAULT_UNK_STR));
 }
 
 // create a CSR matrix M[V,U] from indices[] with
diff --git a/src/data/factored_vocab.h b/src/data/factored_vocab.h
index d9292e429..09ac98c78 100755
--- a/src/data/factored_vocab.h
+++ b/src/data/factored_vocab.h
@@ -6,7 +6,6 @@
 
 #include "common/definitions.h"
 #include "data/types.h"
-#include "data/vocab.h"
 #include "data/vocab_base.h"
 
 #include <numeric> // for std::iota()
@@ -22,8 +21,6 @@ class FactoredVocab : public IVocab {
     std::vector<IndexType> offsets;
   };
 
-  FactoredVocab() : vocab_(New<Options>(), 0) { }
-
   // from IVocab:
   virtual size_t load(const std::string& factoredVocabPath, size_t maxSizeUnused = 0) override final;
   virtual void create(const std::string& vocabPath, const std::vector<std::string>& trainPaths, size_t maxSize) override final { vocabPath, trainPaths, maxSize; ABORT("Factored vocab cannot be created on the fly"); }
@@ -35,8 +32,8 @@ class FactoredVocab : public IVocab {
   virtual const std::string& operator[](Word id) const override final;
   virtual size_t size() const override final;
   virtual std::string type() const override final { return "FactoredVocab"; }
-  virtual Word getEosId() const override { return eosId_; }
-  virtual Word getUnkId() const override { return unkId_; }
+  virtual Word getEosId() const override final { return eosId_; }
+  virtual Word getUnkId() const override final { return unkId_; }
   virtual void createFake() override final;
 
   // factor-specific. These methods are consumed by Output and Embedding.
@@ -56,11 +53,12 @@ class FactoredVocab : public IVocab {
     std::map<std::string, WordIndex> str2index_;
     std::vector<std::string> index2str_;
   public:
-    void add(const std::string& word) {
+    WordIndex add(const std::string& word) {
       auto index = (WordIndex)index2str_.size();
       auto wasInserted = str2index_.insert(std::make_pair(word, index)).second;
       ABORT_IF(!wasInserted, "Duplicate vocab entry for '{}'", word);
       index2str_.push_back(word);
+      return index;
     }
     const std::string& operator[](WordIndex index) const { return index2str_[index]; }
     WordIndex operator[](const std::string& word) const {
@@ -76,12 +74,19 @@ class FactoredVocab : public IVocab {
       return true;
     }
     size_t size() const { return index2str_.size(); }
+    size_t load(const std::string& path) {
+      std::string line;
+      io::InputFileStream in(path);
+      for (WordIndex v = 0; io::getline(in, line); v++)
+        add(line);
+      return size();
+    }
   };
 
   // main vocab
   Word eosId_{};
   Word unkId_{};
-  Vocab vocab_;
+  WordLUT vocab_;
 
   // factors
   WordLUT factorVocab_;                                // [factor name] -> factor index = row of E_

From 91b1403c155ea9698d5d128ce4d9746529946ae8 Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Tue, 5 Feb 2019 16:22:32 -0800
Subject: [PATCH 281/838] FactorVocab no longer requires the .wl file, as it
 can know everything from the .fm file

---
 src/common/utils.cpp        |  6 ++++--
 src/data/factored_vocab.cpp | 22 +++++++++-------------
 2 files changed, 13 insertions(+), 15 deletions(-)

diff --git a/src/common/utils.cpp b/src/common/utils.cpp
index 59cc4cc88..179cd4d4d 100755
--- a/src/common/utils.cpp
+++ b/src/common/utils.cpp
@@ -30,9 +30,10 @@ void trimLeft(std::string& s) {
 
 // @TODO: use more functions from CLI instead of own implementations
 void split(const std::string& line,
-           std::vector<std::string>& pieces,
+           /*out*/ std::vector<std::string>& pieces,
            const std::string& del /*= " "*/,
            bool keepEmpty) {
+  pieces.clear();
   size_t begin = 0;
   size_t pos = 0;
   std::string token;
@@ -61,9 +62,10 @@ std::vector<std::string> split(const std::string& line,
 
 // @TODO: splitAny() shares all but 2 expressions with split(). Merge them.
 void splitAny(const std::string& line,
-              std::vector<std::string>& pieces,
+              /*out*/ std::vector<std::string>& pieces,
               const std::string& del /*= " "*/,
               bool keepEmpty) {
+  pieces.clear();
   size_t begin = 0;
   size_t pos = 0;
   std::string token;
diff --git a/src/data/factored_vocab.cpp b/src/data/factored_vocab.cpp
index cc7c7febd..f48cfe6f8 100755
--- a/src/data/factored_vocab.cpp
+++ b/src/data/factored_vocab.cpp
@@ -24,34 +24,30 @@ namespace marian {
   auto mapPath = factoredVocabPath;
   auto factorVocabPath = mapPath;
   factorVocabPath.back() = 'l'; // map .fm to .fl
-  auto vocabPath = factorVocabPath;
-  vocabPath[vocabPath.size() - 2] = 'w'; // map .fl to .wl  --@TODO: This should go away; esp. to allow per-stream vocabs
-
-  std::string line;
-
-  // load main vocabulary   --@TODO: This will go away soon.
-  auto vocabSize = vocab_.load(vocabPath);
 
   // load factor vocabulary
   auto numFactors = factorVocab_.load(factorVocabPath);
+  factorRefCounts_.resize(numFactors);
 
   // load and parse factorMap
-  factorMap_.resize(vocabSize);
-  factorRefCounts_.resize(numFactors);
   std::vector<std::string> tokens;
-  io::InputFileStream in(mapPath);
+  std::string line;
   size_t numTotalFactors = 0;
+  io::InputFileStream in(mapPath);
   for (WordIndex v = 0; io::getline(in, line); v++) {
-    tokens.clear(); // @BUGBUG: should be done in split()
     utils::splitAny(line, tokens, " \t");
-    ABORT_IF(tokens.size() < 2 || tokens.front() != vocab_[v], "Factor map must list words in same order as vocab, and have at least one factor per word", mapPath);
+    vocab_.add(tokens.front());
+    ABORT_IF(tokens.size() < 2, "Factor map must have at least one factor per word", mapPath);
+    std::vector<WordIndex> factors;
     for (size_t i = 1; i < tokens.size(); i++) {
       auto u = factorVocab_[tokens[i]];
-      factorMap_[v].push_back(u);
+      factors.push_back(u);
       factorRefCounts_[u]++;
     }
+    factorMap_.emplace_back(std::move(factors));
     numTotalFactors += tokens.size() - 1;
   }
+  auto vocabSize = vocab_.size();
   LOG(info, "[embedding] Factored-embedding map read with total/unique of {}/{} factors for {} words", numTotalFactors, numFactors, vocabSize);
 
   // form groups

From 67dd472b0abbe80c56a1e1a107236b3590fc4470 Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Tue, 5 Feb 2019 21:40:51 -0800
Subject: [PATCH 282/838] some refactoring of FactoredVocab

---
 src/data/factored_vocab.cpp | 107 ++++++++++++++++++++----------------
 src/data/factored_vocab.h   |  32 ++++++++---
 src/layers/generic.cpp      |   1 -
 3 files changed, 86 insertions(+), 54 deletions(-)

diff --git a/src/data/factored_vocab.cpp b/src/data/factored_vocab.cpp
index f48cfe6f8..d31339a11 100755
--- a/src/data/factored_vocab.cpp
+++ b/src/data/factored_vocab.cpp
@@ -26,42 +26,60 @@ namespace marian {
   factorVocabPath.back() = 'l'; // map .fm to .fl
 
   // load factor vocabulary
-  auto numFactors = factorVocab_.load(factorVocabPath);
-  factorRefCounts_.resize(numFactors);
+  factorVocab_.load(factorVocabPath);
+  groupPrefixes_ = { "(lemma)", "@C", "@GL", "@GR" }; // @TODO: hard-coded for these initial experiments
+
+  // construct mapping tables for factors
+  constructGroupInfoFromFactorVocab();
 
   // load and parse factorMap
+  auto factorVocabSize = factorVocab_.size();
+  factorRefCounts_.resize(factorVocabSize);
   std::vector<std::string> tokens;
   std::string line;
   size_t numTotalFactors = 0;
   io::InputFileStream in(mapPath);
   for (WordIndex v = 0; io::getline(in, line); v++) {
+    // parse the line, of the form WORD FACTOR1 FACTOR2 FACTOR1 ...
+    // where FACTOR1 is the lemma, a factor that all words have.
+    // Not every word has all other factors, so the n-th item is not always the same factor.
     utils::splitAny(line, tokens, " \t");
-    vocab_.add(tokens.front());
     ABORT_IF(tokens.size() < 2, "Factor map must have at least one factor per word", mapPath);
     std::vector<WordIndex> factors;
-    for (size_t i = 1; i < tokens.size(); i++) {
+    for (size_t i = 1/*first factor*/; i < tokens.size(); i++) {
       auto u = factorVocab_[tokens[i]];
       factors.push_back(u);
       factorRefCounts_[u]++;
     }
     factorMap_.emplace_back(std::move(factors));
+    // add to vocab
+    WordIndex index = v;
+    // @TODO: map factors to non-dense integer
+    vocab_.add(tokens.front(), index);
     numTotalFactors += tokens.size() - 1;
   }
-  auto vocabSize = vocab_.size();
-  LOG(info, "[embedding] Factored-embedding map read with total/unique of {}/{} factors for {} words", numTotalFactors, numFactors, vocabSize);
+  LOG(info, "[embedding] Factored-embedding map read with total/unique of {}/{} factors for {} valid words (in space of {})",
+      numTotalFactors, factorVocabSize, vocab_.numValid(), vocab_.size());
+
+  // create mappings needed for normalization in factored outputs
+  constructNormalizationInfoForVocab();
+
+  // </s> and <unk> must exist in the vocabulary
+  eosId_ = Word::fromWordIndex(vocab_[DEFAULT_EOS_STR]);
+  unkId_ = Word::fromWordIndex(vocab_[DEFAULT_UNK_STR]);
+
+  ABORT_IF(maxSizeUnused != 0 && maxSizeUnused != size(), "Factored vocabulary does not allow on-the-fly clipping to a maximum vocab size (to {})", maxSizeUnused);
+  return size();
+}
 
+void FactoredVocab::constructGroupInfoFromFactorVocab() {
   // form groups
-  // @TODO: hard-coded for these initial experiments
-  std::vector<std::string> groupPrefixes = {
-    "@C",
-    "@GL", "@GR"
-  };
-  groupPrefixes.insert(groupPrefixes.begin(), "(unassigned)");     // first group is fallback for normal words (the string is only used for messages)
-  size_t numGroups = groupPrefixes.size();
-  factorGroups_.resize(numFactors, 0);
-  for (size_t g = 1; g < groupPrefixes.size(); g++) { // set group labels; what does not match any prefix will stay in group 0
-    const auto& groupPrefix = groupPrefixes[g];
-    for (WordIndex u = 0; u < numFactors; u++)
+  size_t numGroups = groupPrefixes_.size();
+  size_t factorVocabSize = factorVocab_.size();
+  factorGroups_.resize(factorVocabSize, 0);
+  for (size_t g = 1; g < groupPrefixes_.size(); g++) { // set group labels; what does not match any prefix will stay in group 0
+    const auto& groupPrefix = groupPrefixes_[g];
+    for (WordIndex u = 0; u < factorVocabSize; u++)
       if (utils::beginsWith(factorVocab_[u], groupPrefix)) {
         ABORT_IF(factorGroups_[u] != 0, "Factor {} matches multiple groups, incl. {}", factorVocab_[u], groupPrefix);
         factorGroups_[u] = g;
@@ -70,7 +88,7 @@ namespace marian {
   // determine group index ranges
   groupRanges_.resize(numGroups, { SIZE_MAX, (size_t)0 });
   std::vector<size_t> groupCounts(numGroups); // number of group members
-  for (WordIndex u = 0; u < numFactors; u++) { // determine ranges; these must be non-overlapping, verified via groupCounts
+  for (WordIndex u = 0; u < factorVocabSize; u++) { // determine ranges; these must be non-overlapping, verified via groupCounts
     auto g = factorGroups_[u];
     if (groupRanges_[g].first > u)
         groupRanges_[g].first = u;
@@ -78,7 +96,19 @@ namespace marian {
         groupRanges_[g].second = u + 1;
     groupCounts[g]++;
   }
+  for (size_t g = 0; g < numGroups; g++) { // detect non-overlapping groups
+    LOG(info, "[embedding] Factor group '{}' has {} members", groupPrefixes_[g], groupCounts[g]);
+    if (groupCounts[g] == 0) // factor group is unused  --@TODO: once this is not hard-coded, this is an error condition
+      continue;
+    ABORT_IF(groupRanges_[g].second - groupRanges_[g].first != groupCounts[g],
+             "Factor group '{}' members should be consecutive in the factor vocabulary", groupPrefixes_[g]);
+  }
+}
+
+void FactoredVocab::constructNormalizationInfoForVocab() {
   // create mappings needed for normalization in factored outputs
+  size_t numGroups = groupPrefixes_.size();
+  size_t vocabSize = vocab_.size();
   factorMasks_  .resize(numGroups, std::vector<float>(vocabSize, 0));     // [g][v] 1.0 if word v has factor g
   factorIndices_.resize(numGroups, std::vector<IndexType>(vocabSize, 0)); // [g][v] index of factor (or any valid index if it does not have it; we use 0)
   for (WordIndex v = 0; v < vocabSize; v++) {
@@ -96,26 +126,11 @@ namespace marian {
   //      factorMasks_[2][v], factorIndices_[2][v],
   //      factorMasks_[3][v], factorIndices_[3][v]);
   //}
-  for (size_t g = 0; g < numGroups; g++) { // detect non-overlapping groups
-    LOG(info, "[embedding] Factor group '{}' has {} members ({})",
-        groupPrefixes[g], groupCounts[g], groupCounts[g] == 1 ? "sigmoid" : "softmax");
-    if (groupCounts[g] == 0) // factor group is unused  --@TODO: once this is not hard-coded, this is an error condition
-      continue;
-    ABORT_IF(groupRanges_[g].second - groupRanges_[g].first != groupCounts[g],
-             "Factor group '{}' members should be consecutive in the factor vocabulary", groupPrefixes[g]);
-  }
 
-  // create the global factor matrix, which is used for factored embeddings
+  // create the global factor matrix, which is used for getLogits()
   std::vector<IndexType> data(vocabSize);
   std::iota(data.begin(), data.end(), 0);
   globalFactorMatrix_ = csr_rows(data); // [V x U]
-
-  // </s> and <unk> must exist in the vocabulary
-  eosId_ = Word::fromWordIndex(vocab_[DEFAULT_EOS_STR]);
-  unkId_ = Word::fromWordIndex(vocab_[DEFAULT_UNK_STR]);
-
-  ABORT_IF(maxSizeUnused != 0 && maxSizeUnused != size(), "Factored vocabulary does not allow on-the-fly clipping to a maximum vocab size (to {})", maxSizeUnused);
-  return size();
 }
 
 /*virtual*/ Word FactoredVocab::operator[](const std::string& word) const /*override final*/ {
@@ -127,6 +142,14 @@ namespace marian {
     return getUnkId();
 }
 
+/*virtual*/ const std::string& FactoredVocab::operator[](Word id) const /*override final*/ {
+  return vocab_[id.toWordIndex()];
+}
+
+/*virtual*/ size_t FactoredVocab::size() const /*override final*/ {
+  return vocab_.size();
+}
+
 /*virtual*/ Words FactoredVocab::encode(const std::string& line, bool addEOS /*= true*/, bool /*inference*/ /*= false*/) const /*override final*/ {
   std::vector<std::string> lineTokens;
   utils::split(line, lineTokens, " ");
@@ -148,19 +171,11 @@ namespace marian {
   return utils::join(decoded, " ");
 }
 
-/*virtual*/ const std::string& FactoredVocab::operator[](Word id) const /*override final*/ {
-  return vocab_[id.toWordIndex()];
-}
-
-/*virtual*/ size_t FactoredVocab::size() const /*override final*/ {
-  return vocab_.size();
-}
-
 // This creates a fake vocabulary fro use in fakeBatch().
 // @TODO: This may become more complex.
 /*virtual*/ void FactoredVocab::createFake() /*override final*/ {
-  eosId_ = Word::fromWordIndex(vocab_.add(DEFAULT_EOS_STR));
-  unkId_ = Word::fromWordIndex(vocab_.add(DEFAULT_UNK_STR));
+  eosId_ = Word::fromWordIndex(vocab_.add(DEFAULT_EOS_STR, 0));
+  unkId_ = Word::fromWordIndex(vocab_.add(DEFAULT_UNK_STR, 1));
 }
 
 // create a CSR matrix M[V,U] from indices[] with
@@ -173,8 +188,8 @@ FactoredVocab::CSRData FactoredVocab::csr_rows(const std::vector<IndexType>& wor
   indices.reserve(words.size()); // (at least this many)
   // loop over all input words, and select the corresponding set of unit indices into CSR format
   offsets.push_back((IndexType)indices.size());
-  for (auto v : words) {
-    const auto& m = factorMap_[v];
+  for (auto w : words) {
+    const auto& m = factorMap_[w];
     for (auto u : m) {
       indices.push_back(u);
       weights.push_back(1.0f/*/(float)factorRefCounts_[u]*/);
diff --git a/src/data/factored_vocab.h b/src/data/factored_vocab.h
index 09ac98c78..c941093be 100755
--- a/src/data/factored_vocab.h
+++ b/src/data/factored_vocab.h
@@ -41,31 +41,43 @@ class FactoredVocab : public IVocab {
 
   CSRData csr_rows(const std::vector<IndexType>& words) const;
 
-  const CSRData& getGlobalFactorMatrix() const { return globalFactorMatrix_; }   // [v,u] (sparse) -> =1 if u is factor of v
+  const CSRData& getGlobalFactorMatrix() const { return globalFactorMatrix_; }   // [v,u] (sparse) -> =1 if u is factor of v  --only used in getLogits()
   size_t getNumGroups() const { return groupRanges_.size(); }
   std::pair<size_t, size_t>     getGroupRange(size_t g)    const { return groupRanges_[g]; }   // [g] -> (u_begin, u_end)
   const std::vector<float>&     getFactorMasks(size_t g)   const { return factorMasks_[g]; }   // [g][v] 1.0 if word v has factor g
   const std::vector<IndexType>& getFactorIndices(size_t g) const { return factorIndices_[g]; } // [g][v] local index u_g = u - u_g,begin of factor g for word v; 0 if not a factor
 
   static Ptr<FactoredVocab> tryCreateAndLoad(const std::string& path); // load from "vocab" option if it specifies a factored vocab
+private:
+  void constructGroupInfoFromFactorVocab();
+  void constructNormalizationInfoForVocab();
 private:
   class WordLUT { // map between strings and WordIndex
     std::map<std::string, WordIndex> str2index_;
     std::vector<std::string> index2str_;
   public:
-    WordIndex add(const std::string& word) {
-      auto index = (WordIndex)index2str_.size();
+    WordIndex add(const std::string& word, WordIndex index) {
+      ABORT_IF(word.empty(), "Attempted to add the empty word to a dictionary");
       auto wasInserted = str2index_.insert(std::make_pair(word, index)).second;
       ABORT_IF(!wasInserted, "Duplicate vocab entry for '{}'", word);
-      index2str_.push_back(word);
+      while (index2str_.size() <= index)
+        index2str_.emplace_back(); // @TODO: what's the right way to get linear complexity in steps?
+      if (!index2str_[index].empty())
+        ABORT_IF(!wasInserted, "Duplicate vocab entry for index {} (new: '{}'; existing: '{}')", index, word, index2str_[index]);
+      index2str_[index] = word;
       return index;
     }
-    const std::string& operator[](WordIndex index) const { return index2str_[index]; }
+    const std::string& operator[](WordIndex index) const {
+      const auto& word = index2str_[index];
+      ABORT_IF(word.empty(), "Invalid access to dictionary gap item");
+      return word;
+    }
     WordIndex operator[](const std::string& word) const {
       auto iter = str2index_.find(word);
       ABORT_IF(iter == str2index_.end(), "Token '{}' not found in vocabulary", word);
       return iter->second;
     }
+    bool isGap(WordIndex index) const { return index2str_[index].empty(); }
     bool tryFind(const std::string& word, WordIndex& index) const {
       auto iter = str2index_.find(word);
       if (iter == str2index_.end())
@@ -73,12 +85,17 @@ class FactoredVocab : public IVocab {
       index = iter->second;
       return true;
     }
-    size_t size() const { return index2str_.size(); }
+    void resize(size_t num) {
+      ABORT_IF(num < index2str_.size(), "Word table cannot be shrunk");
+      index2str_.resize(num); // gets filled up with gap items (empty strings)
+    }
+    size_t size() const { return index2str_.size(); } // nominal size including gap items
+    size_t numValid() const { return str2index_.size(); } // actual non-gaps items
     size_t load(const std::string& path) {
       std::string line;
       io::InputFileStream in(path);
       for (WordIndex v = 0; io::getline(in, line); v++)
-        add(line);
+        add(line, v);
       return size();
     }
   };
@@ -90,6 +107,7 @@ class FactoredVocab : public IVocab {
 
   // factors
   WordLUT factorVocab_;                                // [factor name] -> factor index = row of E_
+  std::vector<std::string> groupPrefixes_;             // [group id g] shared prefix of factors (used for grouping)
   std::vector<std::vector<WordIndex>> factorMap_;      // [word index v] -> set of factor indices u
   std::vector<int> factorRefCounts_;                   // [factor index u] -> how often factor u is referenced in factorMap_
   CSRData globalFactorMatrix_;                         // [v,u] (sparse) -> =1 if u is factor of v
diff --git a/src/layers/generic.cpp b/src/layers/generic.cpp
index 41e0071e0..815b43ae1 100755
--- a/src/layers/generic.cpp
+++ b/src/layers/generic.cpp
@@ -53,7 +53,6 @@ namespace marian {
 
   // This function assumes this object holds a single factor that represents a rational loss (with count).
   Ptr<RationalLoss> Logits::getRationalLoss() const {
-    //return New<RationalLoss>(getLogits(), logits_.front()->count());
     ABORT_IF(logits_.size() != 1 || factoredVocab_, "getRationalLoss() cannot be used on multi-factor outputs");
     ABORT_IF(!logits_.front()->count(), "getRationalLoss() used on rational loss without count");
     return logits_.front();

From 0eb5de7719e9716bf379d51d62c4c83607b61be8 Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Tue, 5 Feb 2019 22:15:28 -0800
Subject: [PATCH 283/838] FactoredVocab with gap items are now handled
 correctly in getLogits()

---
 src/data/factored_vocab.cpp | 20 +++++++++++++++-----
 src/data/factored_vocab.h   |  3 +++
 src/layers/generic.cpp      |  4 ++++
 3 files changed, 22 insertions(+), 5 deletions(-)

diff --git a/src/data/factored_vocab.cpp b/src/data/factored_vocab.cpp
index d31339a11..21a0eefa8 100755
--- a/src/data/factored_vocab.cpp
+++ b/src/data/factored_vocab.cpp
@@ -33,6 +33,9 @@ namespace marian {
   constructGroupInfoFromFactorVocab();
 
   // load and parse factorMap
+  auto elements = factorShape_.elements();
+  vocab_.resize(elements);
+  factorMap_.resize(elements);
   auto factorVocabSize = factorVocab_.size();
   factorRefCounts_.resize(factorVocabSize);
   std::vector<std::string> tokens;
@@ -51,15 +54,15 @@ namespace marian {
       factors.push_back(u);
       factorRefCounts_[u]++;
     }
-    factorMap_.emplace_back(std::move(factors));
-    // add to vocab
     WordIndex index = v;
     // @TODO: map factors to non-dense integer
+    factorMap_[index] = std::move(factors);
+    // add to vocab
     vocab_.add(tokens.front(), index);
     numTotalFactors += tokens.size() - 1;
   }
   LOG(info, "[embedding] Factored-embedding map read with total/unique of {}/{} factors for {} valid words (in space of {})",
-      numTotalFactors, factorVocabSize, vocab_.numValid(), vocab_.size());
+      numTotalFactors, factorVocabSize, vocab_.numValid(), size());
 
   // create mappings needed for normalization in factored outputs
   constructNormalizationInfoForVocab();
@@ -68,7 +71,11 @@ namespace marian {
   eosId_ = Word::fromWordIndex(vocab_[DEFAULT_EOS_STR]);
   unkId_ = Word::fromWordIndex(vocab_[DEFAULT_UNK_STR]);
 
-  ABORT_IF(maxSizeUnused != 0 && maxSizeUnused != size(), "Factored vocabulary does not allow on-the-fly clipping to a maximum vocab size (to {})", maxSizeUnused);
+#if 1   // dim-vocabs stores numValid() in legacy model files, and would now have been size()
+  if (maxSizeUnused == vocab_.numValid())
+    maxSizeUnused = vocab_.size();
+#endif
+  ABORT_IF(maxSizeUnused != 0 && maxSizeUnused != size(), "Factored vocabulary does not allow on-the-fly clipping to a maximum vocab size (from {} to {})", size(), maxSizeUnused);
   return size();
 }
 
@@ -87,7 +94,7 @@ void FactoredVocab::constructGroupInfoFromFactorVocab() {
   }
   // determine group index ranges
   groupRanges_.resize(numGroups, { SIZE_MAX, (size_t)0 });
-  std::vector<size_t> groupCounts(numGroups); // number of group members
+  std::vector<int> groupCounts(numGroups); // number of group members
   for (WordIndex u = 0; u < factorVocabSize; u++) { // determine ranges; these must be non-overlapping, verified via groupCounts
     auto g = factorGroups_[u];
     if (groupRanges_[g].first > u)
@@ -103,6 +110,7 @@ void FactoredVocab::constructGroupInfoFromFactorVocab() {
     ABORT_IF(groupRanges_[g].second - groupRanges_[g].first != groupCounts[g],
              "Factor group '{}' members should be consecutive in the factor vocabulary", groupPrefixes_[g]);
   }
+  factorShape_ = Shape(std::move(groupCounts));
 }
 
 void FactoredVocab::constructNormalizationInfoForVocab() {
@@ -111,12 +119,14 @@ void FactoredVocab::constructNormalizationInfoForVocab() {
   size_t vocabSize = vocab_.size();
   factorMasks_  .resize(numGroups, std::vector<float>(vocabSize, 0));     // [g][v] 1.0 if word v has factor g
   factorIndices_.resize(numGroups, std::vector<IndexType>(vocabSize, 0)); // [g][v] index of factor (or any valid index if it does not have it; we use 0)
+  gapLogMask_.resize(vocabSize, -1e8f);
   for (WordIndex v = 0; v < vocabSize; v++) {
     for (auto u : factorMap_[v]) {
       auto g = factorGroups_[u]; // convert u to relative u within factor group range
       ABORT_IF(u < groupRanges_[g].first || u >= groupRanges_[g].second, "Invalid factorGroups_ entry??");
       factorIndices_[g][v] = (IndexType)(u - groupRanges_[g].first);
       factorMasks_[g][v] = 1.0f;
+      gapLogMask_[v] = 0.0f; // valid entry
     }
   }
   //for (Word v = 0; v < vocabSize; v++) {
diff --git a/src/data/factored_vocab.h b/src/data/factored_vocab.h
index c941093be..25e9bd733 100755
--- a/src/data/factored_vocab.h
+++ b/src/data/factored_vocab.h
@@ -46,6 +46,7 @@ class FactoredVocab : public IVocab {
   std::pair<size_t, size_t>     getGroupRange(size_t g)    const { return groupRanges_[g]; }   // [g] -> (u_begin, u_end)
   const std::vector<float>&     getFactorMasks(size_t g)   const { return factorMasks_[g]; }   // [g][v] 1.0 if word v has factor g
   const std::vector<IndexType>& getFactorIndices(size_t g) const { return factorIndices_[g]; } // [g][v] local index u_g = u - u_g,begin of factor g for word v; 0 if not a factor
+  const std::vector<float>& getGapLogMask() const { return gapLogMask_; } // [v] -inf if v is a gap entry, else 0
 
   static Ptr<FactoredVocab> tryCreateAndLoad(const std::string& path); // load from "vocab" option if it specifies a factored vocab
 private:
@@ -113,8 +114,10 @@ class FactoredVocab : public IVocab {
   CSRData globalFactorMatrix_;                         // [v,u] (sparse) -> =1 if u is factor of v
   std::vector<size_t> factorGroups_;                   // [u] -> group id of factor u
   std::vector<std::pair<size_t, size_t>> groupRanges_; // [group id g] -> (u_begin,u_end) index range of factors u for this group. These don't overlap.
+  Shape factorShape_;                                  // [g] number of factors in each factor group
   std::vector<std::vector<float>>     factorMasks_;    // [g][v] 1.0 if word v has factor g
   std::vector<std::vector<IndexType>> factorIndices_;  // [g][v] relative index u - u_begin of factor g (or any valid index if it does not have it; we use 0)
+  std::vector<float> gapLogMask_;                      // [v] -1e8 if this is a gap, else 0
 };
 
 }  // namespace marian
diff --git a/src/layers/generic.cpp b/src/layers/generic.cpp
index 815b43ae1..c2aab49a9 100755
--- a/src/layers/generic.cpp
+++ b/src/layers/generic.cpp
@@ -84,6 +84,10 @@ namespace marian {
         graph->constant({(int)factorMatrix.offsets.size()}, inits::from_vector(factorMatrix.offsets), Type::uint32),
         /*transB=*/ true); // -> [B x V]
 
+    // mask out gaps
+    auto gapLogMask = factoredVocab_->getGapLogMask(); // [V]
+    y = y + graph->constant({ (int)gapLogMask.size() }, inits::from_vector(gapLogMask), Type::float32);
+
     return y;
   }
 

From 250de94e0bdc115a28973a67177754d97a78c76f Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Tue, 5 Feb 2019 22:35:34 -0800
Subject: [PATCH 284/838] FactoredVocab now uses the non-dense numeric id to
 represent the factors directly in the id

---
 src/data/factored_vocab.cpp | 15 +++++++++++++--
 src/data/factored_vocab.h   |  1 +
 2 files changed, 14 insertions(+), 2 deletions(-)

diff --git a/src/data/factored_vocab.cpp b/src/data/factored_vocab.cpp
index 21a0eefa8..6973a56ff 100755
--- a/src/data/factored_vocab.cpp
+++ b/src/data/factored_vocab.cpp
@@ -54,11 +54,17 @@ namespace marian {
       factors.push_back(u);
       factorRefCounts_[u]++;
     }
-    WordIndex index = v;
+    size_t index = 0;
+    for (auto u : factors) {
+      auto g = factorGroups_[u]; // convert u to relative u within factor group range
+      ABORT_IF(u < groupRanges_[g].first || u >= groupRanges_[g].second, "Invalid factorGroups_ entry??");
+      auto factorIndex = u - groupRanges_[g].first;
+      index += factorIndex * factorStrides_[g];
+    }
     // @TODO: map factors to non-dense integer
     factorMap_[index] = std::move(factors);
     // add to vocab
-    vocab_.add(tokens.front(), index);
+    vocab_.add(tokens.front(), (WordIndex)index);
     numTotalFactors += tokens.size() - 1;
   }
   LOG(info, "[embedding] Factored-embedding map read with total/unique of {}/{} factors for {} valid words (in space of {})",
@@ -111,6 +117,11 @@ void FactoredVocab::constructGroupInfoFromFactorVocab() {
              "Factor group '{}' members should be consecutive in the factor vocabulary", groupPrefixes_[g]);
   }
   factorShape_ = Shape(std::move(groupCounts));
+  factorStrides_.resize(factorShape_.size(), 1);
+  for (size_t g = factorStrides_.size() - 1; g --> 0; )
+    factorStrides_[g] = factorStrides_[g + 1] * (size_t)factorShape_[g + 1];
+  for (auto str : factorStrides_)
+    LOG(info, "stride {}", str);
 }
 
 void FactoredVocab::constructNormalizationInfoForVocab() {
diff --git a/src/data/factored_vocab.h b/src/data/factored_vocab.h
index 25e9bd733..dd35b4993 100755
--- a/src/data/factored_vocab.h
+++ b/src/data/factored_vocab.h
@@ -115,6 +115,7 @@ class FactoredVocab : public IVocab {
   std::vector<size_t> factorGroups_;                   // [u] -> group id of factor u
   std::vector<std::pair<size_t, size_t>> groupRanges_; // [group id g] -> (u_begin,u_end) index range of factors u for this group. These don't overlap.
   Shape factorShape_;                                  // [g] number of factors in each factor group
+  std::vector<size_t> factorStrides_;                  // [g] stride for factor dimension
   std::vector<std::vector<float>>     factorMasks_;    // [g][v] 1.0 if word v has factor g
   std::vector<std::vector<IndexType>> factorIndices_;  // [g][v] relative index u - u_begin of factor g (or any valid index if it does not have it; we use 0)
   std::vector<float> gapLogMask_;                      // [v] -1e8 if this is a gap, else 0

From 7f66068bd51fe9f27bfbb9cb63ba07c4b0e68c26 Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Tue, 5 Feb 2019 22:45:23 -0800
Subject: [PATCH 285/838] fixed an internal check

---
 src/data/factored_vocab.h | 3 +--
 1 file changed, 1 insertion(+), 2 deletions(-)

diff --git a/src/data/factored_vocab.h b/src/data/factored_vocab.h
index dd35b4993..c7086825e 100755
--- a/src/data/factored_vocab.h
+++ b/src/data/factored_vocab.h
@@ -63,8 +63,7 @@ class FactoredVocab : public IVocab {
       ABORT_IF(!wasInserted, "Duplicate vocab entry for '{}'", word);
       while (index2str_.size() <= index)
         index2str_.emplace_back(); // @TODO: what's the right way to get linear complexity in steps?
-      if (!index2str_[index].empty())
-        ABORT_IF(!wasInserted, "Duplicate vocab entry for index {} (new: '{}'; existing: '{}')", index, word, index2str_[index]);
+      ABORT_IF(!index2str_[index].empty(), "Duplicate vocab entry for index {} (new: '{}'; existing: '{}')", index, word, index2str_[index]);
       index2str_[index] = word;
       return index;
     }

From 4744df010a5e3024f76dcf27ecacb5cb78d7ea2b Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Tue, 5 Feb 2019 22:54:25 -0800
Subject: [PATCH 286/838] removed FactoredVocab::createFake(), as it is not
 used

---
 src/data/factored_vocab.cpp | 9 ---------
 src/data/factored_vocab.h   | 2 +-
 2 files changed, 1 insertion(+), 10 deletions(-)

diff --git a/src/data/factored_vocab.cpp b/src/data/factored_vocab.cpp
index 6973a56ff..5afb5b989 100755
--- a/src/data/factored_vocab.cpp
+++ b/src/data/factored_vocab.cpp
@@ -120,8 +120,6 @@ void FactoredVocab::constructGroupInfoFromFactorVocab() {
   factorStrides_.resize(factorShape_.size(), 1);
   for (size_t g = factorStrides_.size() - 1; g --> 0; )
     factorStrides_[g] = factorStrides_[g + 1] * (size_t)factorShape_[g + 1];
-  for (auto str : factorStrides_)
-    LOG(info, "stride {}", str);
 }
 
 void FactoredVocab::constructNormalizationInfoForVocab() {
@@ -192,13 +190,6 @@ void FactoredVocab::constructNormalizationInfoForVocab() {
   return utils::join(decoded, " ");
 }
 
-// This creates a fake vocabulary fro use in fakeBatch().
-// @TODO: This may become more complex.
-/*virtual*/ void FactoredVocab::createFake() /*override final*/ {
-  eosId_ = Word::fromWordIndex(vocab_.add(DEFAULT_EOS_STR, 0));
-  unkId_ = Word::fromWordIndex(vocab_.add(DEFAULT_UNK_STR, 1));
-}
-
 // create a CSR matrix M[V,U] from indices[] with
 // M[v,u] = 1/c(u) if factor u is a factor of word v, and c(u) is how often u is referenced
 FactoredVocab::CSRData FactoredVocab::csr_rows(const std::vector<IndexType>& words) const {
diff --git a/src/data/factored_vocab.h b/src/data/factored_vocab.h
index c7086825e..8d41d363a 100755
--- a/src/data/factored_vocab.h
+++ b/src/data/factored_vocab.h
@@ -34,7 +34,7 @@ class FactoredVocab : public IVocab {
   virtual std::string type() const override final { return "FactoredVocab"; }
   virtual Word getEosId() const override final { return eosId_; }
   virtual Word getUnkId() const override final { return unkId_; }
-  virtual void createFake() override final;
+  virtual void createFake() override final { ABORT("[data] Fake FactoredVocab vocabulary not supported"); }
 
   // factor-specific. These methods are consumed by Output and Embedding.
   size_t factorVocabSize() const { return factorVocab_.size(); }

From d263f743ab2bb81e6634848ebb80bd5e2dd495a5 Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Tue, 5 Feb 2019 23:03:51 -0800
Subject: [PATCH 287/838] bug fix (merge error): LabelwiseLoss should  be
 passed the correct Logits object

---
 src/models/costs.h | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/models/costs.h b/src/models/costs.h
index ebef2d4e6..f3fc1923e 100755
--- a/src/models/costs.h
+++ b/src/models/costs.h
@@ -68,7 +68,7 @@ class EncoderDecoderCE : public CostBase {
     Ptr<MultiRationalLoss> multiLoss = newMultiLoss(options_);
 
     // @TODO: adapt to multi-objective training with multiple decoders
-    auto partialLoss = loss_->apply(state->getLogProbs().getLogits(),
+    auto partialLoss = loss_->apply(state->getLogProbs(),
                                     state->getTargetWords(),
                                     state->getTargetMask(),
                                     weights);

From 86c412c81d5b2947d29ad9620c6828cb15da9ede Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Wed, 6 Feb 2019 08:17:37 -0800
Subject: [PATCH 288/838] ScorerWrapper::getLogProbs() now also returns Logits,
 in prep for factored decoding

---
 src/data/factored_vocab.cpp        |  2 +-
 src/graph/expression_operators.cpp | 32 +++++++++++++++++++++++-------
 src/layers/generic.h               |  1 +
 src/training/validator.h           |  2 +-
 src/translator/beam_search.h       |  5 +----
 src/translator/scorers.h           |  6 +++---
 6 files changed, 32 insertions(+), 16 deletions(-)

diff --git a/src/data/factored_vocab.cpp b/src/data/factored_vocab.cpp
index 5afb5b989..622e19392 100755
--- a/src/data/factored_vocab.cpp
+++ b/src/data/factored_vocab.cpp
@@ -146,7 +146,7 @@ void FactoredVocab::constructNormalizationInfoForVocab() {
   //      factorMasks_[3][v], factorIndices_[3][v]);
   //}
 
-  // create the global factor matrix, which is used for getLogits()
+  // create the global factor matrix, which is used for getLogits() only
   std::vector<IndexType> data(vocabSize);
   std::iota(data.begin(), data.end(), 0);
   globalFactorMatrix_ = csr_rows(data); // [V x U]
diff --git a/src/graph/expression_operators.cpp b/src/graph/expression_operators.cpp
index dd3f2741b..866c4b2c7 100755
--- a/src/graph/expression_operators.cpp
+++ b/src/graph/expression_operators.cpp
@@ -131,31 +131,49 @@ Expr le(Expr a, float b) { return Expression<CmpNodeOp>(a, a->graph()->constant(
 /*********************************************************/
 
 Expr operator+(Expr a, float b) {
-  return Expression<ScalarAddNodeOp>(a, b);
+  if (b == 0)
+    return a;
+  else
+    return Expression<ScalarAddNodeOp>(a, b);
 }
 
 Expr operator+(float a, Expr b) {
-  return Expression<ScalarAddNodeOp>(b, a);
+  if (a == 0)
+    return b;
+  else
+    return Expression<ScalarAddNodeOp>(b, a);
 }
 
 Expr operator-(Expr a, float b) {
-  return Expression<ScalarAddNodeOp>(a, -b);
+  if (b == 0)
+    return a;
+  else
+    return Expression<ScalarAddNodeOp>(a, -b);
 }
 
 Expr operator-(float a, Expr b) {
-  return Expression<ScalarAddNodeOp>(-b, a);
+  if (a == 0)
+    return -b;
+  else
+    return Expression<ScalarAddNodeOp>(-b, a);
 }
 
 Expr operator*(float a, Expr b) {
-  return Expression<ScalarMultNodeOp>(b, a);
+  if (a == 1.0f)
+    return b;
+  else
+    return Expression<ScalarMultNodeOp>(b, a);
 }
 
 Expr operator*(Expr a, float b) {
-  return Expression<ScalarMultNodeOp>(a, b);
+  if (b == 1.0f)
+    return a;
+  else
+    return Expression<ScalarMultNodeOp>(a, b);
 }
 
 Expr operator/(Expr a, float b) {
-  return Expression<ScalarMultNodeOp>(a, 1.f / b);
+  return a * (1.f / b);
 }
 
 // TODO: efficient version of this without constant()
diff --git a/src/layers/generic.h b/src/layers/generic.h
index c5a199384..35183213e 100755
--- a/src/layers/generic.h
+++ b/src/layers/generic.h
@@ -76,6 +76,7 @@ class Logits {
     Expr getLogits() const; // assume it holds logits: get them, possibly aggregating over factors
     Ptr<RationalLoss> getRationalLoss() const; // assume it holds a loss: get that
     Expr applyLossFunction(const Words& labels, const std::function<Expr(Expr/*logits*/,Expr/*indices*/)>& lossFn) const;
+    float getLogitAt(size_t i) const { return getLogits()->val()->get(i); } // @TODO: avoid the fully expanded logits
     void assign(const Logits& other) {
       //ABORT_IF(!empty() && getNumFactors() != other.getNumFactors(),
       //         "Logits assignment cannot change number of factors");
diff --git a/src/training/validator.h b/src/training/validator.h
index 677eec8f9..138b98683 100755
--- a/src/training/validator.h
+++ b/src/training/validator.h
@@ -216,7 +216,7 @@ class CrossEntropyValidator : public Validator<data::Corpus> {
   }
 };
 
-// Used for validating with classifiers. Compute prediction accuary versus groundtruth for a set of classes
+// Used for validating with classifiers. Compute prediction accuracy versus ground truth for a set of classes
 class AccuracyValidator : public Validator<data::Corpus> {
 public:
   AccuracyValidator(std::vector<Ptr<Vocab>> vocabs, Ptr<Options> options)
diff --git a/src/translator/beam_search.h b/src/translator/beam_search.h
index c80865f73..6641f1f45 100755
--- a/src/translator/beam_search.h
+++ b/src/translator/beam_search.h
@@ -235,10 +235,7 @@ class BeamSearch {
         states[i] = scorers_[i]->step(
             graph, states[i], hypIndices, predWords, dimBatch, (int)localBeamSize);
 
-        if(scorers_[i]->getWeight() != 1.f)
-          pathScores = pathScores + scorers_[i]->getWeight() * states[i]->getLogProbs();
-        else
-          pathScores = pathScores + states[i]->getLogProbs();
+        pathScores = pathScores + scorers_[i]->getWeight() * states[i]->getLogProbs().getLogits();
       }
 
       // make beams continuous
diff --git a/src/translator/scorers.h b/src/translator/scorers.h
index 3f49a18c2..1b7c4e3c9 100755
--- a/src/translator/scorers.h
+++ b/src/translator/scorers.h
@@ -9,9 +9,9 @@ namespace marian {
 
 class ScorerState {
 public:
-  virtual Expr getLogProbs() = 0;
+  virtual Logits getLogProbs() = 0;
 
-  virtual float breakDown(size_t i) { return getLogProbs()->val()->get(i); }
+  virtual float breakDown(size_t i) { return getLogProbs().getLogitAt(i); }
 
   virtual void blacklist(Expr /*totalCosts*/, Ptr<data::CorpusBatch> /*batch*/){};
 };
@@ -57,7 +57,7 @@ class ScorerWrapperState : public ScorerState {
 
   virtual Ptr<DecoderState> getState() { return state_; }
 
-  virtual Expr getLogProbs() override { return state_->getLogProbs().getLogits(); };
+  virtual Logits getLogProbs() override { return state_->getLogProbs(); };
 
   virtual void blacklist(Expr totalCosts, Ptr<data::CorpusBatch> batch) override {
     state_->blacklist(totalCosts, batch);

From 492bf28a54bc7926355690706f82970c5446bacf Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Wed, 6 Feb 2019 09:10:01 -0800
Subject: [PATCH 289/838] towards converting Word <-> factors

---
 src/data/factored_vocab.cpp | 77 +++++++++++++++++++++++++++++++------
 src/data/factored_vocab.h   |  9 +++++
 2 files changed, 74 insertions(+), 12 deletions(-)

diff --git a/src/data/factored_vocab.cpp b/src/data/factored_vocab.cpp
index 622e19392..e9165d0b5 100755
--- a/src/data/factored_vocab.cpp
+++ b/src/data/factored_vocab.cpp
@@ -31,6 +31,7 @@ namespace marian {
 
   // construct mapping tables for factors
   constructGroupInfoFromFactorVocab();
+  constructFactorIndexConversion();
 
   // load and parse factorMap
   auto elements = factorShape_.elements();
@@ -48,23 +49,17 @@ namespace marian {
     // Not every word has all other factors, so the n-th item is not always the same factor.
     utils::splitAny(line, tokens, " \t");
     ABORT_IF(tokens.size() < 2, "Factor map must have at least one factor per word", mapPath);
-    std::vector<WordIndex> factors;
+    std::vector<WordIndex> factorUnits;
     for (size_t i = 1/*first factor*/; i < tokens.size(); i++) {
       auto u = factorVocab_[tokens[i]];
-      factors.push_back(u);
+      factorUnits.push_back(u);
       factorRefCounts_[u]++;
     }
-    size_t index = 0;
-    for (auto u : factors) {
-      auto g = factorGroups_[u]; // convert u to relative u within factor group range
-      ABORT_IF(u < groupRanges_[g].first || u >= groupRanges_[g].second, "Invalid factorGroups_ entry??");
-      auto factorIndex = u - groupRanges_[g].first;
-      index += factorIndex * factorStrides_[g];
-    }
+    auto index = factorUnits2wordIndex(factorUnits);
     // @TODO: map factors to non-dense integer
-    factorMap_[index] = std::move(factors);
+    factorMap_[index] = std::move(factorUnits);
     // add to vocab
-    vocab_.add(tokens.front(), (WordIndex)index);
+    vocab_.add(tokens.front(), index);
     numTotalFactors += tokens.size() - 1;
   }
   LOG(info, "[embedding] Factored-embedding map read with total/unique of {}/{} factors for {} valid words (in space of {})",
@@ -116,12 +111,70 @@ void FactoredVocab::constructGroupInfoFromFactorVocab() {
     ABORT_IF(groupRanges_[g].second - groupRanges_[g].first != groupCounts[g],
              "Factor group '{}' members should be consecutive in the factor vocabulary", groupPrefixes_[g]);
   }
-  factorShape_ = Shape(std::move(groupCounts));
+  // we map between factors and flat WordIndex like indexing a tensor
+  constructFactorIndexConversion();
+}
+
+void FactoredVocab::constructFactorIndexConversion() {
+  std::vector<int> shape;
+  for (const auto& r : groupRanges_)
+    shape.push_back((int)(r.second - r.first + 1)); // +1 to reserve the last value for either "factor not used" or "factor not present"
+  factorShape_ = Shape(std::move(shape));
   factorStrides_.resize(factorShape_.size(), 1);
   for (size_t g = factorStrides_.size() - 1; g --> 0; )
     factorStrides_[g] = factorStrides_[g + 1] * (size_t)factorShape_[g + 1];
 }
 
+// encode factors into a Word struct
+Word FactoredVocab::factors2word(const std::vector<size_t>& factorIndices /* [numGroups] */) {
+  size_t index = 0;
+  size_t numGroups = getNumGroups();
+  ABORT_IF(factorIndices.size() != numGroups, "Factor indices array size must be same as number of factor groups");
+  for (size_t g = 0; g < numGroups; g++) {
+    auto factorIndex = factorIndices[g];
+    ABORT_IF(factorIndex >= (size_t)factorShape_[g], "Factor index out of range");
+    index += factorIndex * factorStrides_[g];
+  }
+  return Word::fromWordIndex(index);
+}
+
+// like factors2word, except that factors are expressed as global unit indices, and result is just the WordIndex
+// @BUGBUG: need to encode factors that are not used ==> change to unroll explicitly, then call factors2Word()
+WordIndex FactoredVocab::factorUnits2wordIndex(const std::vector<WordIndex>& factorUnits) {
+  size_t index = 0;
+  for (auto u : factorUnits) {
+    auto g = factorGroups_[u]; // convert u to relative u within factor group range
+    ABORT_IF(u < groupRanges_[g].first || u >= groupRanges_[g].second, "Invalid factorGroups_ entry??");
+    auto factorIndex = u - groupRanges_[g].first;
+    index += factorIndex * factorStrides_[g];
+  }
+  return (WordIndex)index;
+}
+
+void FactoredVocab::word2factors(Word word, std::vector<size_t>& factorIndices /* [numGroups] */) {
+  word;
+  size_t numGroups = getNumGroups();
+  factorIndices.resize(numGroups);
+  ABORT("Not implemented");
+}
+
+size_t FactoredVocab::getFactor(Word word, size_t groupIndex) {
+  word; groupIndex;
+  //size_t factorIndex = 0;
+  ABORT("Not implemented");
+}
+
+// @TODO: or just map to sentinel values for FACTOR_NOT_APPLICABLE and FACTOR_NOT_SPECIFIED
+bool FactoredVocab::hasFactor(Word word, size_t groupIndex) {
+  word; groupIndex;
+  ABORT("Not implemented");
+}
+
+std::pair<WordIndex, bool> FactoredVocab::getFactorUnit(Word word, size_t groupIndex) {
+  word; groupIndex;
+  ABORT("Not implemented");
+}
+
 void FactoredVocab::constructNormalizationInfoForVocab() {
   // create mappings needed for normalization in factored outputs
   size_t numGroups = groupPrefixes_.size();
diff --git a/src/data/factored_vocab.h b/src/data/factored_vocab.h
index 8d41d363a..94bc024f8 100755
--- a/src/data/factored_vocab.h
+++ b/src/data/factored_vocab.h
@@ -48,9 +48,18 @@ class FactoredVocab : public IVocab {
   const std::vector<IndexType>& getFactorIndices(size_t g) const { return factorIndices_[g]; } // [g][v] local index u_g = u - u_g,begin of factor g for word v; 0 if not a factor
   const std::vector<float>& getGapLogMask() const { return gapLogMask_; } // [v] -inf if v is a gap entry, else 0
 
+  // convert representations
+  Word factors2word(const std::vector<size_t>& factors);
+  WordIndex factorUnits2wordIndex(const std::vector<WordIndex>& factorUnits);
+  void word2factors(Word word, std::vector<size_t>& factors);
+  size_t getFactor(Word word, size_t groupIndex);
+  bool hasFactor(Word word, size_t groupIndex);
+  std::pair<WordIndex, bool> getFactorUnit(Word word, size_t groupIndex);
+
   static Ptr<FactoredVocab> tryCreateAndLoad(const std::string& path); // load from "vocab" option if it specifies a factored vocab
 private:
   void constructGroupInfoFromFactorVocab();
+  void constructFactorIndexConversion();
   void constructNormalizationInfoForVocab();
 private:
   class WordLUT { // map between strings and WordIndex

From 267805c7408a3ec85eaf00631c6bf3c840ebe29e Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Wed, 6 Feb 2019 09:22:01 -0800
Subject: [PATCH 290/838] Word representation of factors now includes a
 sentinel for 'unspecified' and

---
 src/data/factored_vocab.cpp | 21 ++++++++++-----------
 src/data/factored_vocab.h   |  5 +++--
 2 files changed, 13 insertions(+), 13 deletions(-)

diff --git a/src/data/factored_vocab.cpp b/src/data/factored_vocab.cpp
index e9165d0b5..4fc5dccde 100755
--- a/src/data/factored_vocab.cpp
+++ b/src/data/factored_vocab.cpp
@@ -115,6 +115,7 @@ void FactoredVocab::constructGroupInfoFromFactorVocab() {
   constructFactorIndexConversion();
 }
 
+// create factorShape_ and factorStrides_, for mapping between flat (non-dense) ids and factor arrays
 void FactoredVocab::constructFactorIndexConversion() {
   std::vector<int> shape;
   for (const auto& r : groupRanges_)
@@ -132,23 +133,27 @@ Word FactoredVocab::factors2word(const std::vector<size_t>& factorIndices /* [nu
   ABORT_IF(factorIndices.size() != numGroups, "Factor indices array size must be same as number of factor groups");
   for (size_t g = 0; g < numGroups; g++) {
     auto factorIndex = factorIndices[g];
-    ABORT_IF(factorIndex >= (size_t)factorShape_[g], "Factor index out of range");
+    if (factorIndex == FACTOR_NOT_APPLICABLE || factorIndex == FACTOR_NOT_SPECIFIED)
+      factorIndex = (size_t)factorShape_[g] - 1; // sentinel for "unused" or "not specified"
+    else
+      ABORT_IF(factorIndex >= (size_t)factorShape_[g] - 1, "Factor index out of range");
     index += factorIndex * factorStrides_[g];
   }
   return Word::fromWordIndex(index);
 }
 
 // like factors2word, except that factors are expressed as global unit indices, and result is just the WordIndex
-// @BUGBUG: need to encode factors that are not used ==> change to unroll explicitly, then call factors2Word()
+// This is only used during initialization, so it's OK if it is a little inefficient.
 WordIndex FactoredVocab::factorUnits2wordIndex(const std::vector<WordIndex>& factorUnits) {
-  size_t index = 0;
+  // convert to fully unrolled factors representation
+  std::vector<size_t> factorIndices(getNumGroups(), FACTOR_NOT_APPLICABLE); // default for unused factors
   for (auto u : factorUnits) {
     auto g = factorGroups_[u]; // convert u to relative u within factor group range
     ABORT_IF(u < groupRanges_[g].first || u >= groupRanges_[g].second, "Invalid factorGroups_ entry??");
     auto factorIndex = u - groupRanges_[g].first;
-    index += factorIndex * factorStrides_[g];
+    factorIndices[g] = factorIndex;
   }
-  return (WordIndex)index;
+  return factors2word(factorIndices).toWordIndex();
 }
 
 void FactoredVocab::word2factors(Word word, std::vector<size_t>& factorIndices /* [numGroups] */) {
@@ -164,12 +169,6 @@ size_t FactoredVocab::getFactor(Word word, size_t groupIndex) {
   ABORT("Not implemented");
 }
 
-// @TODO: or just map to sentinel values for FACTOR_NOT_APPLICABLE and FACTOR_NOT_SPECIFIED
-bool FactoredVocab::hasFactor(Word word, size_t groupIndex) {
-  word; groupIndex;
-  ABORT("Not implemented");
-}
-
 std::pair<WordIndex, bool> FactoredVocab::getFactorUnit(Word word, size_t groupIndex) {
   word; groupIndex;
   ABORT("Not implemented");
diff --git a/src/data/factored_vocab.h b/src/data/factored_vocab.h
index 94bc024f8..6c5358a22 100755
--- a/src/data/factored_vocab.h
+++ b/src/data/factored_vocab.h
@@ -50,17 +50,18 @@ class FactoredVocab : public IVocab {
 
   // convert representations
   Word factors2word(const std::vector<size_t>& factors);
-  WordIndex factorUnits2wordIndex(const std::vector<WordIndex>& factorUnits);
   void word2factors(Word word, std::vector<size_t>& factors);
   size_t getFactor(Word word, size_t groupIndex);
-  bool hasFactor(Word word, size_t groupIndex);
   std::pair<WordIndex, bool> getFactorUnit(Word word, size_t groupIndex);
+  static constexpr size_t FACTOR_NOT_APPLICABLE = (SIZE_MAX - 1);
+  static constexpr size_t FACTOR_NOT_SPECIFIED  = (SIZE_MAX - 2);
 
   static Ptr<FactoredVocab> tryCreateAndLoad(const std::string& path); // load from "vocab" option if it specifies a factored vocab
 private:
   void constructGroupInfoFromFactorVocab();
   void constructFactorIndexConversion();
   void constructNormalizationInfoForVocab();
+  WordIndex factorUnits2wordIndex(const std::vector<WordIndex>& factorUnits);
 private:
   class WordLUT { // map between strings and WordIndex
     std::map<std::string, WordIndex> str2index_;

From 03f19da26c1b525925f537c694864688cfb09379 Mon Sep 17 00:00:00 2001
From: Marcin Junczys-Dowmunt <Marcin.JunczysDowmunt@microsoft.com>
Date: Wed, 6 Feb 2019 10:24:53 -0800
Subject: [PATCH 291/838] Remove old comment

---
 CMakeLists.txt | 3 ---
 1 file changed, 3 deletions(-)

diff --git a/CMakeLists.txt b/CMakeLists.txt
index 340c15864..022b97019 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -168,9 +168,6 @@ endif(USE_STATIC_LIBS)
     list(APPEND CUDA_NVCC_FLAGS -DBOOST_PP_VARIADICS=0; )
   endif()
 
-  # We compile NCCL ourselves, using the NVidia Makefile rather than CMake, this requires to pass a couple of parameters from
-  # Cmake. This is also fairly untested, let's hope it does not explode.
-  # @TODO: Make sure it does not use pre-installed NCCL headers
   if(USE_NCCL)
     add_library(nccl STATIC IMPORTED)
     set(EXT_LIBS ${EXT_LIBS} nccl)

From 0af135fe18f6c74353ca93c4f1f1f4631594c00d Mon Sep 17 00:00:00 2001
From: Marcin Junczys-Dowmunt <Marcin.JunczysDowmunt@microsoft.com>
Date: Wed, 6 Feb 2019 10:25:51 -0800
Subject: [PATCH 292/838] Update VERSION

---
 VERSION | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/VERSION b/VERSION
index f0b77c197..257d138aa 100644
--- a/VERSION
+++ b/VERSION
@@ -1 +1 @@
-v1.7.7
+v1.7.8

From 46e9565c9a06e54f4f05f50f722da726ae437df0 Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Wed, 6 Feb 2019 10:33:41 -0800
Subject: [PATCH 293/838] bug fix: </s> and <unk> ids should not be
 misinterpreted as factor indices

---
 src/data/factored_vocab.cpp | 48 ++++++++++++++++++++++++++++++-------
 src/data/factored_vocab.h   |  3 ++-
 2 files changed, 41 insertions(+), 10 deletions(-)

diff --git a/src/data/factored_vocab.cpp b/src/data/factored_vocab.cpp
index 4fc5dccde..b72db7196 100755
--- a/src/data/factored_vocab.cpp
+++ b/src/data/factored_vocab.cpp
@@ -55,12 +55,14 @@ namespace marian {
       factorUnits.push_back(u);
       factorRefCounts_[u]++;
     }
-    auto index = factorUnits2wordIndex(factorUnits);
+    auto index = factorUnits2word(factorUnits).toWordIndex();
     // @TODO: map factors to non-dense integer
     factorMap_[index] = std::move(factorUnits);
     // add to vocab
     vocab_.add(tokens.front(), index);
     numTotalFactors += tokens.size() - 1;
+    if (v % 5000 == 0)
+      LOG(info, "{} -> {}", tokens.front(), word2string(Word::fromWordIndex(index)));
   }
   LOG(info, "[embedding] Factored-embedding map read with total/unique of {}/{} factors for {} valid words (in space of {})",
       numTotalFactors, factorVocabSize, vocab_.numValid(), size());
@@ -71,6 +73,7 @@ namespace marian {
   // </s> and <unk> must exist in the vocabulary
   eosId_ = Word::fromWordIndex(vocab_[DEFAULT_EOS_STR]);
   unkId_ = Word::fromWordIndex(vocab_[DEFAULT_UNK_STR]);
+  LOG(info, "eos: {}; unk: {}", word2string(eosId_), word2string(unkId_));
 
 #if 1   // dim-vocabs stores numValid() in legacy model files, and would now have been size()
   if (maxSizeUnused == vocab_.numValid())
@@ -133,7 +136,7 @@ Word FactoredVocab::factors2word(const std::vector<size_t>& factorIndices /* [nu
   ABORT_IF(factorIndices.size() != numGroups, "Factor indices array size must be same as number of factor groups");
   for (size_t g = 0; g < numGroups; g++) {
     auto factorIndex = factorIndices[g];
-    if (factorIndex == FACTOR_NOT_APPLICABLE || factorIndex == FACTOR_NOT_SPECIFIED)
+    if (factorIndex == FACTOR_NOT_APPLICABLE || factorIndex == FACTOR_NOT_SPECIFIED) // @TODO: check validity. If word has the factor, then N/A is invalid
       factorIndex = (size_t)factorShape_[g] - 1; // sentinel for "unused" or "not specified"
     else
       ABORT_IF(factorIndex >= (size_t)factorShape_[g] - 1, "Factor index out of range");
@@ -144,7 +147,7 @@ Word FactoredVocab::factors2word(const std::vector<size_t>& factorIndices /* [nu
 
 // like factors2word, except that factors are expressed as global unit indices, and result is just the WordIndex
 // This is only used during initialization, so it's OK if it is a little inefficient.
-WordIndex FactoredVocab::factorUnits2wordIndex(const std::vector<WordIndex>& factorUnits) {
+Word FactoredVocab::factorUnits2word(const std::vector<WordIndex>& factorUnits) {
   // convert to fully unrolled factors representation
   std::vector<size_t> factorIndices(getNumGroups(), FACTOR_NOT_APPLICABLE); // default for unused factors
   for (auto u : factorUnits) {
@@ -153,20 +156,47 @@ WordIndex FactoredVocab::factorUnits2wordIndex(const std::vector<WordIndex>& fac
     auto factorIndex = u - groupRanges_[g].first;
     factorIndices[g] = factorIndex;
   }
-  return factors2word(factorIndices).toWordIndex();
+  return factors2word(factorIndices);
 }
 
 void FactoredVocab::word2factors(Word word, std::vector<size_t>& factorIndices /* [numGroups] */) {
-  word;
   size_t numGroups = getNumGroups();
   factorIndices.resize(numGroups);
-  ABORT("Not implemented");
+  for (size_t g = 0; g < numGroups; g++) {
+    auto factorIndex = getFactor(word, g);
+    factorIndices[g] = factorIndex;
+  }
+#if 1
+  auto test = factors2word(factorIndices);
+  ABORT_IF(test != word, "Word <-> factor conversion broken??");
+#endif
+}
+
+std::string FactoredVocab::word2string(Word word) {
+  std::vector<size_t> factorIndices;
+  word2factors(word, factorIndices);
+  std::string res;
+  size_t numGroups = getNumGroups();
+  for (size_t g = 0; g < numGroups; g++) {
+    res.append(res.empty() ? "(" : ", ");
+    auto factorIndex = factorIndices[g];
+    switch (factorIndex) {
+    case FACTOR_NOT_APPLICABLE: res.append("n/a"); break;
+    case FACTOR_NOT_SPECIFIED: res.append("?"); break;
+    default: res.append(factorVocab_[(WordIndex)(factorIndex + groupRanges_[g].first)]); break;
+    }
+  }
+  return res + ")";
 }
 
 size_t FactoredVocab::getFactor(Word word, size_t groupIndex) {
-  word; groupIndex;
-  //size_t factorIndex = 0;
-  ABORT("Not implemented");
+  size_t index = word.toWordIndex();
+  index = index / factorStrides_[groupIndex];
+  index = index % (size_t)factorShape_[groupIndex];
+  if (index == (size_t)factorShape_[groupIndex] - 1) {
+    index = FACTOR_NOT_APPLICABLE; // @BUGBUG: We should check here which one it is.
+  }
+  return index;
 }
 
 std::pair<WordIndex, bool> FactoredVocab::getFactorUnit(Word word, size_t groupIndex) {
diff --git a/src/data/factored_vocab.h b/src/data/factored_vocab.h
index 6c5358a22..e2ae02785 100755
--- a/src/data/factored_vocab.h
+++ b/src/data/factored_vocab.h
@@ -61,7 +61,8 @@ class FactoredVocab : public IVocab {
   void constructGroupInfoFromFactorVocab();
   void constructFactorIndexConversion();
   void constructNormalizationInfoForVocab();
-  WordIndex factorUnits2wordIndex(const std::vector<WordIndex>& factorUnits);
+  Word factorUnits2word(const std::vector<WordIndex>& factorUnits);
+  std::string word2string(Word word);
 private:
   class WordLUT { // map between strings and WordIndex
     std::map<std::string, WordIndex> str2index_;

From f88eb0d3687575cfe55f908ddad4617c9dc68ee2 Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Wed, 6 Feb 2019 20:25:43 -0800
Subject: [PATCH 294/838] commenting and minor refactoring of beam search

---
 src/common/options.h                 |   2 +-
 src/graph/expression_operators.cpp   |  39 ++++--
 src/microsoft/quicksand.cpp          |   2 +-
 src/models/transformer.h             |  11 --
 src/models/transformer_factory.h     |   5 +-
 src/models/transformer_stub.cpp      |  16 ++-
 src/tensors/cpu/tensor_operators.cpp |   1 +
 src/training/validator.h             |   6 +-
 src/translator/beam_search.h         | 174 ++++++++++++++++-----------
 src/translator/history.h             |  23 ++--
 src/translator/hypothesis.h          |  15 ++-
 src/translator/output_printer.h      |   8 +-
 src/translator/translator.h          |   4 +-
 vs/Marian.vcxproj                    |   4 +-
 vs/Marian.vcxproj.filters            |  12 +-
 15 files changed, 194 insertions(+), 128 deletions(-)
 mode change 100644 => 100755 src/common/options.h
 mode change 100644 => 100755 src/graph/expression_operators.cpp
 mode change 100644 => 100755 src/microsoft/quicksand.cpp
 mode change 100644 => 100755 src/models/transformer.h
 mode change 100644 => 100755 src/models/transformer_factory.h
 mode change 100644 => 100755 src/models/transformer_stub.cpp
 mode change 100644 => 100755 src/tensors/cpu/tensor_operators.cpp
 mode change 100644 => 100755 src/training/validator.h
 mode change 100644 => 100755 src/translator/beam_search.h
 mode change 100644 => 100755 src/translator/history.h
 mode change 100644 => 100755 src/translator/hypothesis.h
 mode change 100644 => 100755 src/translator/output_printer.h
 mode change 100644 => 100755 src/translator/translator.h
 mode change 100644 => 100755 vs/Marian.vcxproj
 mode change 100644 => 100755 vs/Marian.vcxproj.filters

diff --git a/src/common/options.h b/src/common/options.h
old mode 100644
new mode 100755
index 5a3f4eb70..643456c93
--- a/src/common/options.h
+++ b/src/common/options.h
@@ -111,7 +111,7 @@ class Options {
     }
     try {
       return !options_[key].as<std::string>().empty();
-    } catch(const YAML::BadConversion& e) {
+    } catch(const YAML::BadConversion&) {
       ABORT("Option '{}' is neither a sequence nor a text");
     }
     return false;
diff --git a/src/graph/expression_operators.cpp b/src/graph/expression_operators.cpp
old mode 100644
new mode 100755
index e558ffd08..8a79cbe0c
--- a/src/graph/expression_operators.cpp
+++ b/src/graph/expression_operators.cpp
@@ -135,31 +135,49 @@ Expr le(Expr a, float b) { return Expression<CmpNodeOp>(a, a->graph()->constant(
 /*********************************************************/
 
 Expr operator+(Expr a, float b) {
-  return Expression<ScalarAddNodeOp>(a, b);
+  if (b == 0)
+    return a;
+  else
+    return Expression<ScalarAddNodeOp>(a, b);
 }
 
 Expr operator+(float a, Expr b) {
-  return Expression<ScalarAddNodeOp>(b, a);
+  if (a == 0)
+    return b;
+  else
+    return Expression<ScalarAddNodeOp>(b, a);
 }
 
 Expr operator-(Expr a, float b) {
-  return Expression<ScalarAddNodeOp>(a, -b);
+  if (b == 0)
+    return a;
+  else
+    return Expression<ScalarAddNodeOp>(a, -b);
 }
 
 Expr operator-(float a, Expr b) {
-  return Expression<ScalarAddNodeOp>(-b, a);
+  if (a == 0)
+    return -b;
+  else
+    return Expression<ScalarAddNodeOp>(-b, a);
 }
 
 Expr operator*(float a, Expr b) {
-  return Expression<ScalarMultNodeOp>(b, a);
+  if (a == 1.0f)
+    return b;
+  else
+    return Expression<ScalarMultNodeOp>(b, a);
 }
 
 Expr operator*(Expr a, float b) {
-  return Expression<ScalarMultNodeOp>(a, b);
+  if (b == 1.0f)
+    return a;
+  else
+    return Expression<ScalarMultNodeOp>(a, b);
 }
 
 Expr operator/(Expr a, float b) {
-  return Expression<ScalarMultNodeOp>(a, 1.f / b);
+  return a * (1.f / b);
 }
 
 // TODO: efficient version of this without constant()
@@ -254,7 +272,12 @@ Expr gather(Expr a, int axis, Expr indices) {
   return Expression<GatherNodeOp>(a, axis, indices);
 }
 
-// index_select() -- gather arbitrary elements along an axis; unbatched (indices are specified as a 1D vector)
+// index_select() -- gather arbitrary elements along an axis from an unbatched
+// input 'a'. Indices are specified as a 1D vector.
+// This is used e.g. for embedding lookup.
+// Note: To use a batch of index vectors, reshape them into a single vector,
+// call index_select(), then reshape the result back. Reshapes are cheap.
+// This function has the same semantics as PyTorch operation of the same name.
 Expr index_select(Expr a, int axis, Expr indices) {
   ABORT_IF(indices->shape().size() != 1, "Indices must be a 1D tensor");
   // We have specialized kernels for non-batched indexing of first or last axis of a 2D tensor.
diff --git a/src/microsoft/quicksand.cpp b/src/microsoft/quicksand.cpp
old mode 100644
new mode 100755
index 959e44311..2c9fc2a3f
--- a/src/microsoft/quicksand.cpp
+++ b/src/microsoft/quicksand.cpp
@@ -129,7 +129,7 @@ class BeamSearchDecoder : public IBeamSearchDecoder {
     QSNBestBatch qsNbestBatch;
     for(const auto& history : histories) { // loop over batch entries
       QSNBest qsNbest;
-      NBestList nbestHyps = history->NBest(SIZE_MAX); // request as many N as we have
+      NBestList nbestHyps = history->nBest(SIZE_MAX); // request as many N as we have
       for (const Result& result : nbestHyps) { // loop over N-best entries
         // get hypothesis word sequence and normalized sentence score
         auto words = std::get<0>(result);
diff --git a/src/models/transformer.h b/src/models/transformer.h
old mode 100644
new mode 100755
index 1083efe29..97faf13ce
--- a/src/models/transformer.h
+++ b/src/models/transformer.h
@@ -864,17 +864,6 @@ class DecoderTransformer : public Transformer<DecoderBase> {
   }
 };
 
-// factory functions
-Ptr<EncoderBase> NewEncoderTransformer(Ptr<Options> options)
-{
-  return New<EncoderTransformer>(options);
-}
-
-Ptr<DecoderBase> NewDecoderTransformer(Ptr<Options> options)
-{
-  return New<DecoderTransformer>(options);
-}
-
 // clang-format on
 
 }  // namespace marian
diff --git a/src/models/transformer_factory.h b/src/models/transformer_factory.h
old mode 100644
new mode 100755
index c2a7e13bb..aa31e4d15
--- a/src/models/transformer_factory.h
+++ b/src/models/transformer_factory.h
@@ -9,7 +9,6 @@
 //#include "layers/factory.h"
 
 namespace marian {
-// @TODO: find out why static is required here to get to compile
-static Ptr<EncoderBase> NewEncoderTransformer(Ptr<Options> options);
-static Ptr<DecoderBase> NewDecoderTransformer(Ptr<Options> options);
+Ptr<EncoderBase> NewEncoderTransformer(Ptr<Options> options);
+Ptr<DecoderBase> NewDecoderTransformer(Ptr<Options> options);
 }  // namespace marian
diff --git a/src/models/transformer_stub.cpp b/src/models/transformer_stub.cpp
old mode 100644
new mode 100755
index 420b77810..1e7b19f68
--- a/src/models/transformer_stub.cpp
+++ b/src/models/transformer_stub.cpp
@@ -1,4 +1,14 @@
-// TODO: This is a wrapper around transformer.h. We kept the .H name to minimize confusing git, until this is code-reviewed.
-// This is meant to speed-up builds, and to support Ctrl-F7 to rebuild.
-
 #include "models/transformer.h"
+
+namespace marian {
+// factory functions
+Ptr<EncoderBase> NewEncoderTransformer(Ptr<Options> options)
+{
+  return New<EncoderTransformer>(options);
+}
+
+Ptr<DecoderBase> NewDecoderTransformer(Ptr<Options> options)
+{
+  return New<DecoderTransformer>(options);
+}
+}  // namespace marian
diff --git a/src/tensors/cpu/tensor_operators.cpp b/src/tensors/cpu/tensor_operators.cpp
old mode 100644
new mode 100755
index 72dbd131c..840d132ca
--- a/src/tensors/cpu/tensor_operators.cpp
+++ b/src/tensors/cpu/tensor_operators.cpp
@@ -15,6 +15,7 @@ namespace marian {
 namespace cpu {
 
 void IsNan(const Tensor in, Ptr<Allocator> allocator, bool& isNan, bool& isInf, bool zero) {
+  isNan; isInf; zero;
   ABORT("Not implemented");
 }
 
diff --git a/src/training/validator.h b/src/training/validator.h
old mode 100644
new mode 100755
index 1bbebb91c..cf67299ee
--- a/src/training/validator.h
+++ b/src/training/validator.h
@@ -535,7 +535,7 @@ class TranslationValidator : public Validator<data::Corpus> {
             std::stringstream best1;
             std::stringstream bestn;
             printer->print(history, best1, bestn);
-            collector->Write((long)history->GetLineNum(),
+            collector->Write((long)history->getLineNum(),
                              best1.str(),
                              bestn.str(),
                              options_->get<bool>("n-best"));
@@ -677,14 +677,14 @@ class BleuValidator : public Validator<data::Corpus> {
           size_t no = 0;
           std::lock_guard<std::mutex> statsLock(mutex_);
           for(auto history : histories) {
-            auto result = history->Top();
+            auto result = history->top();
             const auto& words = std::get<0>(result);
             updateStats(stats, words, batch, no, vocabs_.back()->getEosId());
 
             std::stringstream best1;
             std::stringstream bestn;
             printer->print(history, best1, bestn);
-            collector->Write((long)history->GetLineNum(),
+            collector->Write((long)history->getLineNum(),
                              best1.str(),
                              bestn.str(),
                              /*nbest=*/ false);
diff --git a/src/translator/beam_search.h b/src/translator/beam_search.h
old mode 100644
new mode 100755
index 17ba6d573..5272c217a
--- a/src/translator/beam_search.h
+++ b/src/translator/beam_search.h
@@ -35,10 +35,10 @@ class BeamSearch {
                const std::vector<float> pathScores,
                size_t vocabSize,
                const Beams& beams,
-               std::vector<Ptr<ScorerState>>& states,
+               const std::vector<Ptr<ScorerState>>& states,
                size_t beamSize,
                bool first,
-               Ptr<data::CorpusBatch> batch) {
+               Ptr<data::CorpusBatch> batch) const {
     Beams newBeams(beams.size());
 
     std::vector<float> align;
@@ -46,46 +46,49 @@ class BeamSearch {
       // Use alignments from the first scorer, even if ensemble
       align = scorers_[0]->getAlignment();
 
-    for(size_t i = 0; i < keys.size(); ++i) {
+    for(size_t i = 0; i < keys.size(); ++i) { // keys: [beamSize, ?] (flattened)
       // Keys contains indices to vocab items in the entire beam.
       // Values can be between 0 and beamSize * vocabSize.
-      Word embIdx = (Word)(keys[i] % vocabSize);
       auto beamIdx = i / beamSize;
 
-      // Retrieve short list for final softmax (based on words aligned
-      // to source sentences). If short list has been set, map the indices
-      // in the sub-selected vocabulary matrix back to their original positions.
-      auto shortlist = scorers_[0]->getShortlist();
-      if(shortlist)
-        embIdx = shortlist->reverseMap(embIdx); // @TODO: should reverseMap accept a size_t or a Word?
-
       if(newBeams[beamIdx].size() < beams[beamIdx].size()) {
-        auto& beam = beams[beamIdx];
+        Word wordIdx = (Word)(keys[i] % vocabSize);
+        // Retrieve short list for final softmax (based on words aligned
+        // to source sentences). If short list has been set, map the indices
+        // in the sub-selected vocabulary matrix back to their original positions.
+        auto shortlist = scorers_[0]->getShortlist();
+        if(shortlist)
+          wordIdx = shortlist->reverseMap(wordIdx); // @TODO: should reverseMap accept a size_t or a Word?
+
+        const auto& beam = beams[beamIdx];
         auto& newBeam = newBeams[beamIdx];
 
-        auto hypIdx = (IndexType)(keys[i] / vocabSize);
-        float pathScore = pathScores[i];
+        const float pathScore = pathScores[i];
+
+        // keys[i] = offset into row-major cube of dims [whatIsThis, beamSize, vocabSize]
+        // deconstruct into individual indices
+        const auto hypIdx = (IndexType)(keys[i] / vocabSize);
+        const auto whatIsThis = (hypIdx / beamSize); // @TODO: is this batchIdx?
+        size_t beamHypIdx = hypIdx % beamSize;
 
-        auto hypIdxTrans
-            = IndexType((hypIdx / beamSize) + (hypIdx % beamSize) * beams.size());
+        auto hypIdxTrans = IndexType(whatIsThis + beamHypIdx * beams.size());
         if(first)
           hypIdxTrans = hypIdx;
 
-        size_t beamHypIdx = hypIdx % beamSize;
-        if(beamHypIdx >= (int)beam.size())
+        if(beamHypIdx >= (int)beam.size())  // @TODO: What is this condition? Cf. beamHypIdx = hypIdx % beamSize
           beamHypIdx = beamHypIdx % beam.size();
 
         if(first)
           beamHypIdx = 0;
 
-        auto hyp = New<Hypothesis>(beam[beamHypIdx], embIdx, hypIdxTrans, pathScore);
+        auto hyp = New<Hypothesis>(beam[beamHypIdx], wordIdx, hypIdxTrans, pathScore);
 
         // Set score breakdown for n-best lists
         if(options_->get<bool>("n-best")) {
           std::vector<float> breakDown(states.size(), 0);
           beam[beamHypIdx]->GetScoreBreakdown().resize(states.size(), 0);
           for(size_t j = 0; j < states.size(); ++j) {
-            size_t key = embIdx + hypIdxTrans * vocabSize;
+            size_t key = wordIdx + hypIdxTrans * vocabSize;
             breakDown[j] = states[j]->breakDown(key)
                            + beam[beamHypIdx]->GetScoreBreakdown()[j];
           }
@@ -108,7 +111,7 @@ class BeamSearch {
       const std::vector<float> alignAll,
       Ptr<data::CorpusBatch> batch,
       int beamHypIdx,
-      int beamIdx) {
+      int beamIdx) const {
     // Let's B be the beam size, N be the number of batched sentences,
     // and L the number of words in the longest sentence in the batch.
     // The alignment vector:
@@ -140,12 +143,13 @@ class BeamSearch {
     return align;
   }
 
-  Beams pruneBeam(const Beams& beams) {
+  // remove all beam entries that have reached EOS
+  Beams purgeBeams(const Beams& beams) {
     Beams newBeams;
     for(auto beam : beams) {
       Beam newBeam;
       for(auto hyp : beam) {
-        if(hyp->GetWord() != trgEosId_) {
+        if(hyp->getWord() != trgEosId_) {
           newBeam.push_back(hyp);
         }
       }
@@ -154,32 +158,29 @@ class BeamSearch {
     return newBeams;
   }
 
+  //**********************************************************************
   // main decoding function
   Histories search(Ptr<ExpressionGraph> graph, Ptr<data::CorpusBatch> batch) {
     int dimBatch = (int)batch->size();
 
-    Histories histories;
+    Histories histories(dimBatch);
     for(int i = 0; i < dimBatch; ++i) {
       size_t sentId = batch->getSentenceIds()[i];
-      auto history = New<History>(sentId,
+      histories[i] = New<History>(sentId,
                                   options_->get<float>("normalize"),
                                   options_->get<float>("word-penalty"));
-      histories.push_back(history);
     }
 
     size_t localBeamSize = beamSize_; // max over beam sizes of active sentence hypotheses
 
     auto getNBestList = createGetNBestListFn(localBeamSize, dimBatch, graph->getDeviceId());
 
-    Beams beams(dimBatch);        // [batchIndex][beamIndex] is one sentence hypothesis
+    Beams beams(dimBatch);        // array [dimBatch] of array [localBeamSize] of Hypothesis
     for(auto& beam : beams)
       beam.resize(localBeamSize, New<Hypothesis>());
 
-    bool first = true;
-    bool final = false;
-
     for(int i = 0; i < dimBatch; ++i)
-      histories[i]->Add(beams[i], trgEosId_);
+      histories[i]->add(beams[i], trgEosId_);
 
     std::vector<Ptr<ScorerState>> states;
 
@@ -191,33 +192,48 @@ class BeamSearch {
       states.push_back(scorer->startState(graph, batch));
     }
 
-    // main loop over output tokens
-    do {
+    // the decoder maintains the following state:
+    //  - histories : array [dimBatch] of History
+    //    with History : vector [t] of array [localBeamSize] of Hypothesis
+    //    with Hypothesis : (last word, aggregate score, prev Hypothesis)
+    //     - search grid
+    //     - stores traceback information
+    //     - gets added to in each output time step
+    //     - the final version is the return value of this function
+    //  - beams : array [dimBatch] of  array [localBeamSize] of Hypothesis
+    //     - current output time step's set of active hypotheses, aka active search space
+    //     - gets replaced at the end of each output time step
+    //  - states[.] : ScorerState
+    //     - NN state
+    //     - one per scorer, e.g. 2 for ensemble of 2
+
+    // main loop over output time steps
+    for(bool first = true; ; first = false) {
       //**********************************************************************
       // create constant containing previous path scores for current beam
-      // also create mapping of hyp indices, which are not 1:1 if sentences complete
-      std::vector<IndexType> hypIndices; // [beamIndex * activeBatchSize + batchIndex] backpointers, concatenated over beam positions. Used for reordering hypotheses
-      std::vector<IndexType> embIndices;
-      Expr prevPathScores; // [beam, 1, 1, 1]
+      // Also create mapping of hyp indices, which are not 1:1 if sentences complete.
+      std::vector<IndexType> hypIndices; // [localBeamsize, 1, dimBatch, 1] (flattened) index of beam index that each of the new top N originated from
+      std::vector<Word> prevWords;      // [localBeamsize, 1, dimBatch, 1] (flattened) predecessor word
+      Expr prevPathScores;               // [localBeamSize, 1, dimBatch, 1], where the last axis broadcasts into vocab size when adding pathScores
       if(first) {
         // no scores yet
         prevPathScores = graph->constant({1, 1, 1, 1}, inits::from_value(0));
       } else {
-        std::vector<float> beamScores;
-
         dimBatch = (int)batch->size();
+        ABORT_IF(dimBatch != beams.size(), "Dimensions mismatch??");
 
-        for(size_t i = 0; i < localBeamSize; ++i) {
-          for(size_t j = 0; j < beams.size(); ++j) { // loop over batch entries (active sentences)
-            auto& beam = beams[j];
-            if(i < beam.size()) {
-              auto hyp = beam[i];
-              hypIndices.push_back((IndexType)hyp->GetPrevStateIndex()); // backpointer
-              embIndices.push_back(hyp->GetWord());
-              beamScores.push_back(hyp->GetPathScore());
+        std::vector<float> beamScores;
+        for(size_t beamIndex = 0; beamIndex < localBeamSize; ++beamIndex) {
+          for(int batchIndex = 0; batchIndex < dimBatch; ++batchIndex) { // loop over batch entries (active sentences)
+            auto& beam = beams[batchIndex];
+            if(beamIndex < beam.size()) {
+              auto hyp = beam[beamIndex];
+              hypIndices.push_back((IndexType)hyp->getPrevStateIndex()); // backpointer
+              prevWords .push_back(hyp->getWord());
+              beamScores.push_back(hyp->getPathScore());
             } else {  // dummy hypothesis
               hypIndices.push_back(0);
-              embIndices.push_back(0);  // (unused)
+              prevWords .push_back(Word{});  // (unused)
               beamScores.push_back(-9999);
             }
           }
@@ -232,18 +248,23 @@ class BeamSearch {
       auto pathScores = prevPathScores;
 
       for(size_t i = 0; i < scorers_.size(); ++i) {
-        states[i] = scorers_[i]->step(
-            graph, states[i], hypIndices, embIndices, dimBatch, (int)localBeamSize);
-
-        if(scorers_[i]->getWeight() != 1.f)
-          pathScores = pathScores + scorers_[i]->getWeight() * states[i]->getLogProbs();
-        else
-          pathScores = pathScores + states[i]->getLogProbs();
+        // compute output probabilities for current output time step
+        //  - uses hypIndices[index in beam, 1, batch index, 1] and embIndices[index in beam, 1, batch index, 1] to reorder hypotheses
+        //  - returns new NN state for use in next output time step
+        //  - returns vector of prediction probabilities over output vocab via newState
+        auto newState = scorers_[i]->step(
+            graph, states[i], hypIndices, prevWords, dimBatch, (int)localBeamSize);
+
+        // expand all hypotheses, [localBeamSize, 1, dimBatch, 1] -> [localBeamSize, 1, dimBatch, dimVocab]
+        pathScores = pathScores + scorers_[i]->getWeight() * newState->getLogProbs();
+
+        // update state in-place for next output time step
+        states[i] = newState;
       }
 
       // make beams continuous
       if(dimBatch > 1 && localBeamSize > 1)
-        pathScores = transpose(pathScores, {2, 1, 0, 3});
+        pathScores = transpose(pathScores, {2, 1, 0, 3}); // -> [dimBatch, 1, localBeamSize, dimVocab]
 
       if(first)
         graph->forward();
@@ -260,12 +281,15 @@ class BeamSearch {
 
       //**********************************************************************
       // perform beam search and pruning
+
+      // find N best amongst the (localBeamSize * dimVocab) hypotheses
+      const std::vector<size_t> beamSizes(dimBatch, localBeamSize);
       std::vector<unsigned int> outKeys;
       std::vector<float> outPathScores;
-
-      std::vector<size_t> beamSizes(dimBatch, localBeamSize);
       getNBestList(beamSizes, pathScores->val(), outPathScores, outKeys, first);
+      // outPathScores and outKeys contain pathScores and their original indices in N-best order
 
+      // convert N-best sets to updated search space
       int dimTrgVoc = pathScores->shape()[-1];
       beams = toHyps(outKeys,
                      outPathScores,
@@ -276,20 +300,28 @@ class BeamSearch {
                      first,
                      batch);
 
-      auto prunedBeams = pruneBeam(beams);
+      // remove all hyps that end in EOS
+      auto purgedBeams = purgeBeams(beams); // @TODO: rename; this is not pruning
+
+      // add updated search space to search grid for traceback
+      bool maxLengthReached = false;
       for(int i = 0; i < dimBatch; ++i) {
+        // if this batch entry has surviving hyps then add them to the traceback grid
         if(!beams[i].empty()) {
-          final = final
-                  || histories[i]->size()
-                         >= options_->get<float>("max-length-factor")
-                                * batch->front()->batchWidth();
-          histories[i]->Add(
-              beams[i], trgEosId_, prunedBeams[i].empty() || final);
+          if (histories[i]->size() >= options_->get<float>("max-length-factor") * batch->front()->batchWidth())
+            maxLengthReached = true;
+          histories[i]->add(beams[i], trgEosId_, purgedBeams[i].empty() || maxLengthReached);
         }
       }
-      beams = prunedBeams;
+      if (maxLengthReached) // early exit if max length limit was reached
+        break;
+
+      // this is the search space for the next output time step
+      beams = purgedBeams;
 
-      // determine beam size for next sentence, as max over still-active sentences
+      // determine beam size for next output time step, as max over still-active sentences
+      // E.g. if all batch entries are down from beam 5 to no more than 4 surviving hyps, then
+      // switch to beam of 4 for all. If all are done, then beam ends up being 0, and we are done.
       if(!first) {
         size_t maxBeam = 0;
         for(auto& beam : beams)
@@ -297,11 +329,11 @@ class BeamSearch {
             maxBeam = beam.size();
         localBeamSize = maxBeam;
       }
-      first = false;
-
-    } while(localBeamSize != 0 && !final); // end of main loop over output tokens
+      if (localBeamSize == 0) // done if all batch entries have reached EOS on all beam entries
+        break;
+    } // end of main loop over output tokens
 
-    return histories;
+    return histories; // [dimBatch][t][N best hyps]
   }
 };
 }  // namespace marian
diff --git a/src/translator/history.h b/src/translator/history.h
old mode 100644
new mode 100755
index e70b80c6f..dcf5e5854
--- a/src/translator/history.h
+++ b/src/translator/history.h
@@ -19,18 +19,17 @@ class History {
     float normalizedPathScore; // length-normalized sentence score
   };
 
+  float lengthPenalty(size_t length) { return std::pow((float)length, alpha_); }
+  float wordPenalty(size_t length) { return wp_ * (float)length; }
 public:
   History(size_t lineNo, float alpha = 1.f, float wp_ = 0.f);
 
-  float LengthPenalty(size_t length) { return std::pow((float)length, alpha_); }
-  float WordPenalty(size_t length) { return wp_ * (float)length; }
-
-  void Add(const Beam& beam, Word trgEosId, bool last = false) {
+  void add(const Beam& beam, Word trgEosId, bool last = false) {
     if(beam.back()->GetPrevHyp() != nullptr) {
       for(size_t j = 0; j < beam.size(); ++j)
-        if(beam[j]->GetWord() == trgEosId || last) {
-          float pathScore = (beam[j]->GetPathScore() - WordPenalty(history_.size()))
-                       / LengthPenalty(history_.size());
+        if(beam[j]->getWord() == trgEosId || last) {
+          float pathScore =
+              (beam[j]->getPathScore() - wordPenalty(history_.size())) / lengthPenalty(history_.size());
           topHyps_.push({history_.size(), j, pathScore});
           // std::cerr << "Add " << history_.size() << " " << j << " " << pathScore
           // << std::endl;
@@ -41,7 +40,7 @@ class History {
 
   size_t size() const { return history_.size(); } // number of time steps
 
-  NBestList NBest(size_t n) const {
+  NBestList nBest(size_t n) const {
     NBestList nbest;
     for (auto topHypsCopy = topHyps_; nbest.size() < n && !topHypsCopy.empty(); topHypsCopy.pop()) {
       auto bestHypCoord = topHypsCopy.top();
@@ -55,15 +54,15 @@ class History {
       // trace back best path
       Words targetWords = bestHyp->TracebackWords();
 
-      // note: bestHyp->GetPathScore() is not normalized, while bestHypCoord.normalizedPathScore is
+      // note: bestHyp->getPathScore() is not normalized, while bestHypCoord.normalizedPathScore is
       nbest.emplace_back(targetWords, bestHyp, bestHypCoord.normalizedPathScore);
     }
     return nbest;
   }
 
-  Result Top() const { return NBest(1)[0]; }
+  Result top() const { return nBest(1)[0]; }
 
-  size_t GetLineNum() const { return lineNo_; }
+  size_t getLineNum() const { return lineNo_; }
 
 private:
   std::vector<Beam> history_; // [time step][index into beam] search grid
@@ -73,5 +72,5 @@ class History {
   float wp_;
 };
 
-typedef std::vector<Ptr<History>> Histories;
+typedef std::vector<Ptr<History>> Histories; // [batchDim]
 }  // namespace marian
diff --git a/src/translator/hypothesis.h b/src/translator/hypothesis.h
old mode 100644
new mode 100755
index c13ac716b..ddb475143
--- a/src/translator/hypothesis.h
+++ b/src/translator/hypothesis.h
@@ -6,6 +6,11 @@
 
 namespace marian {
 
+// one single (possibly partial) hypothesis in beam search
+// key elements:
+//  - the word that this hyp ends with
+//  - the aggregate score up to and including the word
+//  - back pointer to previous hypothesis for traceback
 class Hypothesis {
 public:
   Hypothesis() : prevHyp_(nullptr), prevIndex_(0), word_(0), pathScore_(0.0) {}
@@ -18,11 +23,11 @@ class Hypothesis {
 
   const Ptr<Hypothesis> GetPrevHyp() const { return prevHyp_; }
 
-  Word GetWord() const { return word_; }
+  Word getWord() const { return word_; }
 
-  IndexType GetPrevStateIndex() const { return prevIndex_; }
+  IndexType getPrevStateIndex() const { return prevIndex_; }
 
-  float GetPathScore() const { return pathScore_; }
+  float getPathScore() const { return pathScore_; }
 
   std::vector<float>& GetScoreBreakdown() { return scoreBreakdown_; }
   std::vector<float>& GetAlignment() { return alignment_; }
@@ -34,8 +39,8 @@ class Hypothesis {
   {
       Words targetWords;
       for (auto hyp = this; hyp->GetPrevHyp(); hyp = hyp->GetPrevHyp().get()) {
-          targetWords.push_back(hyp->GetWord());
-          // std::cerr << hyp->GetWord() << " " << hyp << std::endl;
+          targetWords.push_back(hyp->getWord());
+          // std::cerr << hyp->getWord() << " " << hyp << std::endl;
       }
       std::reverse(targetWords.begin(), targetWords.end());
       return targetWords;
diff --git a/src/translator/output_printer.h b/src/translator/output_printer.h
old mode 100644
new mode 100755
index b27b591cf..38ed7b9c6
--- a/src/translator/output_printer.h
+++ b/src/translator/output_printer.h
@@ -24,7 +24,7 @@ class OutputPrinter {
 
   template <class OStream>
   void print(Ptr<History> history, OStream& best1, OStream& bestn) {
-    const auto& nbl = history->NBest(nbest_);
+    const auto& nbl = history->nBest(nbest_);
 
     for(size_t i = 0; i < nbl.size(); ++i) {
       const auto& result = nbl[i];
@@ -35,14 +35,14 @@ class OutputPrinter {
         std::reverse(words.begin(), words.end());
 
       std::string translation = vocab_->decode(words);
-      bestn << history->GetLineNum() << " ||| " << translation;
+      bestn << history->getLineNum() << " ||| " << translation;
 
       if(!alignment_.empty())
         bestn << " ||| " << getAlignment(hypo);
 
       bestn << " |||";
       if(hypo->GetScoreBreakdown().empty()) {
-        bestn << " F0=" << hypo->GetPathScore();
+        bestn << " F0=" << hypo->getPathScore();
       } else {
         for(size_t j = 0; j < hypo->GetScoreBreakdown().size(); ++j) {
           bestn << " F" << j << "= " << hypo->GetScoreBreakdown()[j];
@@ -58,7 +58,7 @@ class OutputPrinter {
         bestn << std::flush;
     }
 
-    auto result = history->Top();
+    auto result = history->top();
     auto words = std::get<0>(result);
 
     if(reverse_)
diff --git a/src/translator/translator.h b/src/translator/translator.h
old mode 100644
new mode 100755
index 18371a3de..e79b52716
--- a/src/translator/translator.h
+++ b/src/translator/translator.h
@@ -106,7 +106,7 @@ class Translate : public ModelTask {
           std::stringstream best1;
           std::stringstream bestn;
           printer->print(history, best1, bestn);
-          collector->Write((long)history->GetLineNum(),
+          collector->Write((long)history->getLineNum(),
                            best1.str(),
                            bestn.str(),
                            options_->get<bool>("n-best"));
@@ -211,7 +211,7 @@ class TranslateService : public ModelServiceTask {
             std::stringstream best1;
             std::stringstream bestn;
             printer->print(history, best1, bestn);
-            collector->add((long)history->GetLineNum(), best1.str(), bestn.str());
+            collector->add((long)history->getLineNum(), best1.str(), bestn.str());
           }
         };
 
diff --git a/vs/Marian.vcxproj b/vs/Marian.vcxproj
old mode 100644
new mode 100755
index a6c560d3a..c899c242f
--- a/vs/Marian.vcxproj
+++ b/vs/Marian.vcxproj
@@ -580,6 +580,7 @@
     <ClCompile Include="..\src\microsoft\quicksand.cpp">
       <ExcludedFromBuild Condition="'$(Configuration)|$(Platform)'=='Debug|x64'">false</ExcludedFromBuild>
     </ClCompile>
+    <ClCompile Include="..\src\models\transformer_stub.cpp" />
     <ClCompile Include="..\src\rescorer\score_collector.cpp" />
     <ClCompile Include="..\src\tensors\backend.cpp" />
     <ClCompile Include="..\src\tensors\cpu\device.cpp" />
@@ -894,6 +895,7 @@
     <ClInclude Include="..\src\layers\word2vec_reader.h" />
     <ClInclude Include="..\src\microsoft\quicksand.h" />
     <ClInclude Include="..\src\models\amun.h" />
+    <ClInclude Include="..\src\models\bert.h" />
     <ClInclude Include="..\src\models\char_s2s.h" />
     <ClInclude Include="..\src\models\costs.h" />
     <ClInclude Include="..\src\models\decoder.h" />
@@ -905,7 +907,7 @@
     <ClInclude Include="..\src\models\nematus.h" />
     <ClInclude Include="..\src\models\s2s.h" />
     <ClInclude Include="..\src\models\states.h" />
-    <ClCompile Include="..\src\models\transformer.h" />
+    <ClInclude Include="..\src\models\transformer.h" />
     <ClInclude Include="..\src\models\experimental\lex_probs.h" />
     <ClInclude Include="..\src\models\transformer_factory.h" />
     <ClInclude Include="..\src\optimizers\clippers.h" />
diff --git a/vs/Marian.vcxproj.filters b/vs/Marian.vcxproj.filters
old mode 100644
new mode 100755
index d9e56843f..b9036c1fd
--- a/vs/Marian.vcxproj.filters
+++ b/vs/Marian.vcxproj.filters
@@ -202,9 +202,6 @@
     <ClCompile Include="..\src\tensors\cpu\sharp\sse_gemm.cpp">
       <Filter>tensors\cpu\sharp</Filter>
     </ClCompile>
-    <ClCompile Include="..\src\models\transformer.h">
-      <Filter>models</Filter>
-    </ClCompile>
     <ClCompile Include="..\src\common\io.cpp">
       <Filter>common</Filter>
     </ClCompile>
@@ -481,6 +478,9 @@
     <ClCompile Include="..\src\examples\iris\iris.cpp">
       <Filter>examples\iris</Filter>
     </ClCompile>
+    <ClCompile Include="..\src\models\transformer_stub.cpp">
+      <Filter>models</Filter>
+    </ClCompile>
   </ItemGroup>
   <ItemGroup>
     <ClInclude Include="..\src\marian.h" />
@@ -1517,6 +1517,12 @@
     <ClInclude Include="..\src\examples\mnist\validator.h">
       <Filter>examples\mnist</Filter>
     </ClInclude>
+    <ClInclude Include="..\src\models\bert.h">
+      <Filter>models</Filter>
+    </ClInclude>
+    <ClInclude Include="..\src\models\transformer.h">
+      <Filter>models</Filter>
+    </ClInclude>
   </ItemGroup>
   <ItemGroup>
     <Filter Include="3rd_party">

From 9b54e7f1caa86b16da84638a20566e8d5451c170 Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Wed, 6 Feb 2019 23:50:33 -0800
Subject: [PATCH 295/838] further simplification/commenting of beam search

---
 src/graph/expression_operators.cpp |   2 +-
 src/microsoft/quicksand.cpp        |   2 +-
 src/translator/beam_search.h       | 241 +++++++++++++++--------------
 src/translator/history.h           |   4 +-
 src/translator/hypothesis.h        |  27 ++--
 src/translator/output_printer.cpp  |   6 +-
 src/translator/output_printer.h    |   6 +-
 src/translator/scorers.h           |   6 +-
 8 files changed, 151 insertions(+), 143 deletions(-)
 mode change 100644 => 100755 src/translator/output_printer.cpp
 mode change 100644 => 100755 src/translator/scorers.h

diff --git a/src/graph/expression_operators.cpp b/src/graph/expression_operators.cpp
index 8a79cbe0c..5c37beef3 100755
--- a/src/graph/expression_operators.cpp
+++ b/src/graph/expression_operators.cpp
@@ -536,7 +536,7 @@ Expr swapAxes(Expr x, int axis1, int axis2)
     return x;
   // TODO: This is code dup from transpose(x). Implement transpose(x) as swapAxes(x, 0, 1)
   std::vector<int> axes(x->shape().size());
-  for (int i = 0; i < axes.size(); ++i)
+  for (int i = 0; i < axes.size(); ++i) // @TODO: use std::iota()
     axes[i] = i;
   std::swap(axes[axis1], axes[axis2]);
   return transpose(x, axes);
diff --git a/src/microsoft/quicksand.cpp b/src/microsoft/quicksand.cpp
index 2c9fc2a3f..98b9fdd59 100755
--- a/src/microsoft/quicksand.cpp
+++ b/src/microsoft/quicksand.cpp
@@ -147,7 +147,7 @@ class BeamSearchDecoder : public IBeamSearchDecoder {
           else
             alignmentThreshold = std::max(std::stof(alignment), 0.f);
           auto hyp = std::get<1>(result);
-          data::WordAlignment align = data::ConvertSoftAlignToHardAlign(hyp->TracebackAlignment(), alignmentThreshold);
+          data::WordAlignment align = data::ConvertSoftAlignToHardAlign(hyp->tracebackAlignment(), alignmentThreshold);
           // convert to QuickSAND format
           alignmentSets.resize(words.size());
           for (const auto& p : align) // @TODO: Does the feature_model param max_alignment_links apply here?
diff --git a/src/translator/beam_search.h b/src/translator/beam_search.h
index 5272c217a..f825b8531 100755
--- a/src/translator/beam_search.h
+++ b/src/translator/beam_search.h
@@ -31,74 +31,83 @@ class BeamSearch {
         trgEosId_(trgEosId),
         trgUnkId_(trgUnkId) {}
 
-  Beams toHyps(const std::vector<unsigned int> keys,
-               const std::vector<float> pathScores,
-               size_t vocabSize,
+  // combine new expandedPathScores and previous beams into new set of beams
+  Beams toHyps(const std::vector<unsigned int> nBestKeys, // [dimBatch, beamSize] flattened -> ((batchIdx, beamHypIdx) flattened, word idx) flattened
+               const std::vector<float> nBestPathScores,  // [dimBatch, beamSize] flattened
+               const size_t vocabSize,
                const Beams& beams,
-               const std::vector<Ptr<ScorerState>>& states,
-               size_t beamSize,
-               bool first,
-               Ptr<data::CorpusBatch> batch) const {
-    Beams newBeams(beams.size());
+               const std::vector<Ptr<ScorerState /*const*/>>& states,
+               const size_t beamSize,
+               const bool first,
+               Ptr<data::CorpusBatch /*const*/> batch) const {
+    const auto dimBatch = beams.size();
+    Beams newBeams(dimBatch);
 
     std::vector<float> align;
     if(options_->hasAndNotEmpty("alignment"))
       // Use alignments from the first scorer, even if ensemble
       align = scorers_[0]->getAlignment();
 
-    for(size_t i = 0; i < keys.size(); ++i) { // keys: [beamSize, ?] (flattened)
+    for(size_t i = 0; i < nBestKeys.size(); ++i) { // [dimBatch, beamSize] flattened
       // Keys contains indices to vocab items in the entire beam.
       // Values can be between 0 and beamSize * vocabSize.
-      auto beamIdx = i / beamSize;
+      const auto batchIdx = i / beamSize; // and i % beamSize is the beam hyp index
+      const auto& beam = beams[batchIdx];
+      auto& newBeam = newBeams[batchIdx];
+
+      if(newBeam.size() < beam.size()) {
+        const float pathScore = nBestPathScores[i];
+        const auto  key       = nBestKeys[i]; // key = pathScore's location, as ((batchIdx, beamHypIdx) flattened, word idx) flattened
+
+        // decompose key into individual indices
+        Word       wordIdx = (Word)(key % vocabSize);
+        const auto hypIdx  =       (key / vocabSize);
+#if 1
+        // further decompose hypIdx, taking into account that the very first entry had beam size 1
+        // and compose a new hypIdx that assumes actual beamSize
+        const auto keyBatchIdx   = hypIdx / (first ? 1 : beamSize);
+        const auto keyBeamHypIdx = hypIdx % (first ? 1 : beamSize);
+        const auto hypIdxTrans = keyBeamHypIdx * dimBatch + keyBatchIdx;
+        ABORT_IF(keyBeamHypIdx >= (int)beam.size(), "Beam hyp index exceeds beam size??"); // not possible, as beamSize = max(beams[.].size())
+#else
+        const auto keyBatchIdx = hypIdx / beamSize; // @REVIEW: is this actually keyBatchIdx?
+        size_t keyBeamHypIdx = hypIdx % beamSize;
+
+        auto hypIdxTrans = keyBatchIdx + keyBeamHypIdx * dimBatch;
+        if(first)
+          hypIdxTrans = hypIdx; // == keyBeamHypIdx + keyBatchIdx * beamSize? or was beamSize=1, and keyBeamHypIdx = 0?
+
+        ABORT_IF(keyBeamHypIdx >= (int)beam.size(), "Beam hyp index exceeds beam size??");
+        //if(keyBeamHypIdx >= (int)beam.size())  // @TODO: What is this condition? Cf. keyBeamHypIdx = hypIdx % beamSize; beamSize = max(beams[.].size())
+        //  keyBeamHypIdx = keyBeamHypIdx % beam.size();
 
-      if(newBeams[beamIdx].size() < beams[beamIdx].size()) {
-        Word wordIdx = (Word)(keys[i] % vocabSize);
+        if(first)
+          keyBeamHypIdx = 0;
+#endif
         // Retrieve short list for final softmax (based on words aligned
         // to source sentences). If short list has been set, map the indices
         // in the sub-selected vocabulary matrix back to their original positions.
         auto shortlist = scorers_[0]->getShortlist();
         if(shortlist)
           wordIdx = shortlist->reverseMap(wordIdx); // @TODO: should reverseMap accept a size_t or a Word?
+        // now wordIdx is a regular Word again
 
-        const auto& beam = beams[beamIdx];
-        auto& newBeam = newBeams[beamIdx];
-
-        const float pathScore = pathScores[i];
-
-        // keys[i] = offset into row-major cube of dims [whatIsThis, beamSize, vocabSize]
-        // deconstruct into individual indices
-        const auto hypIdx = (IndexType)(keys[i] / vocabSize);
-        const auto whatIsThis = (hypIdx / beamSize); // @TODO: is this batchIdx?
-        size_t beamHypIdx = hypIdx % beamSize;
-
-        auto hypIdxTrans = IndexType(whatIsThis + beamHypIdx * beams.size());
-        if(first)
-          hypIdxTrans = hypIdx;
-
-        if(beamHypIdx >= (int)beam.size())  // @TODO: What is this condition? Cf. beamHypIdx = hypIdx % beamSize
-          beamHypIdx = beamHypIdx % beam.size();
-
-        if(first)
-          beamHypIdx = 0;
-
-        auto hyp = New<Hypothesis>(beam[beamHypIdx], wordIdx, hypIdxTrans, pathScore);
+        auto hyp = New<Hypothesis>(beam[keyBeamHypIdx], wordIdx, (IndexType)hypIdxTrans, pathScore);
 
         // Set score breakdown for n-best lists
         if(options_->get<bool>("n-best")) {
           std::vector<float> breakDown(states.size(), 0);
-          beam[beamHypIdx]->GetScoreBreakdown().resize(states.size(), 0);
+          beam[keyBeamHypIdx]->getScoreBreakdown().resize(states.size(), 0); // @TODO: Why? Can we just guard the read-out below, then make it const? Or getScoreBreakdown(j)?
           for(size_t j = 0; j < states.size(); ++j) {
-            size_t key = wordIdx + hypIdxTrans * vocabSize;
-            breakDown[j] = states[j]->breakDown(key)
-                           + beam[beamHypIdx]->GetScoreBreakdown()[j];
+            size_t key1 = hypIdxTrans * vocabSize + wordIdx;
+            breakDown[j] = states[j]->breakDown(key1) + beam[keyBeamHypIdx]->getScoreBreakdown()[j];
           }
-          hyp->GetScoreBreakdown() = breakDown;
+          hyp->setScoreBreakdown(breakDown);
         }
 
         // Set alignments
         if(!align.empty()) {
-          hyp->SetAlignment(
-              getAlignmentsForHypothesis(align, batch, (int)beamHypIdx, (int)beamIdx));
+          hyp->setAlignment(getAlignmentsForHypothesis(align, batch, (int)keyBeamHypIdx, (int)batchIdx));
         }
 
         newBeam.push_back(hyp);
@@ -161,7 +170,13 @@ class BeamSearch {
   //**********************************************************************
   // main decoding function
   Histories search(Ptr<ExpressionGraph> graph, Ptr<data::CorpusBatch> batch) {
-    int dimBatch = (int)batch->size();
+    const int dimBatch = (int)batch->size();
+
+    auto getNBestList = createGetNBestListFn(beamSize_, dimBatch, graph->getDeviceId());
+
+    for(auto scorer : scorers_) {
+      scorer->clear(graph);
+    }
 
     Histories histories(dimBatch);
     for(int i = 0; i < dimBatch; ++i) {
@@ -171,27 +186,20 @@ class BeamSearch {
                                   options_->get<float>("word-penalty"));
     }
 
-    size_t localBeamSize = beamSize_; // max over beam sizes of active sentence hypotheses
-
-    auto getNBestList = createGetNBestListFn(localBeamSize, dimBatch, graph->getDeviceId());
-
-    Beams beams(dimBatch);        // array [dimBatch] of array [localBeamSize] of Hypothesis
-    for(auto& beam : beams)
-      beam.resize(localBeamSize, New<Hypothesis>());
-
-    for(int i = 0; i < dimBatch; ++i)
-      histories[i]->add(beams[i], trgEosId_);
-
+    // start states
     std::vector<Ptr<ScorerState>> states;
-
-    for(auto scorer : scorers_) {
-      scorer->clear(graph);
-    }
-
     for(auto scorer : scorers_) {
       states.push_back(scorer->startState(graph, batch));
     }
 
+    Beams beams(dimBatch, Beam(beamSize_, New<Hypothesis>())); // array [dimBatch] of array [localBeamSize] of Hypothesis
+    //Beams beams(dimBatch); // array [dimBatch] of array [localBeamSize] of Hypothesis
+    //for(auto& beam : beams)
+    //  beam.resize(beamSize_, New<Hypothesis>());
+
+    for(int i = 0; i < dimBatch; ++i)
+      histories[i]->add(beams[i], trgEosId_);
+
     // the decoder maintains the following state:
     //  - histories : array [dimBatch] of History
     //    with History : vector [t] of array [localBeamSize] of Hypothesis
@@ -206,104 +214,116 @@ class BeamSearch {
     //  - states[.] : ScorerState
     //     - NN state
     //     - one per scorer, e.g. 2 for ensemble of 2
+    //     - gets replaced at the end of each output time step
 
     // main loop over output time steps
-    for(bool first = true; ; first = false) {
+    for (size_t t = 0; ; t++) {
+      ABORT_IF(dimBatch != beams.size(), "Lost a batch entry??");
+      // determine beam size for next output time step, as max over still-active sentences
+      // E.g. if all batch entries are down from beam 5 to no more than 4 surviving hyps, then
+      // switch to beam of 4 for all. If all are done, then beam ends up being 0, and we are done.
+      size_t localBeamSize = 0; // @TODO: is there some std::algorithm for this?
+      for(auto& beam : beams)
+        if(beam.size() > localBeamSize)
+          localBeamSize = beam.size();
+
+      // done if all batch entries have reached EOS on all beam entries
+      if (localBeamSize == 0)
+        break;
+
       //**********************************************************************
       // create constant containing previous path scores for current beam
       // Also create mapping of hyp indices, which are not 1:1 if sentences complete.
-      std::vector<IndexType> hypIndices; // [localBeamsize, 1, dimBatch, 1] (flattened) index of beam index that each of the new top N originated from
-      std::vector<Word> prevWords;      // [localBeamsize, 1, dimBatch, 1] (flattened) predecessor word
-      Expr prevPathScores;               // [localBeamSize, 1, dimBatch, 1], where the last axis broadcasts into vocab size when adding pathScores
-      if(first) {
-        // no scores yet
+      std::vector<IndexType> hypIndices; // [localBeamsize, 1, dimBatch, 1] (flattened) index of hyp that the new top N originated from
+      std::vector<Word> prevWords;       // [localBeamsize, 1, dimBatch, 1] (flattened) predecessor word
+      Expr prevPathScores;               // [localBeamSize, 1, dimBatch, 1], where the last axis broadcasts into vocab size when adding expandedPathScores
+      if(t == 0) { // no scores yet
         prevPathScores = graph->constant({1, 1, 1, 1}, inits::from_value(0));
       } else {
-        dimBatch = (int)batch->size();
-        ABORT_IF(dimBatch != beams.size(), "Dimensions mismatch??");
-
-        std::vector<float> beamScores;
+        std::vector<float> prevScores;
         for(size_t beamIndex = 0; beamIndex < localBeamSize; ++beamIndex) {
           for(int batchIndex = 0; batchIndex < dimBatch; ++batchIndex) { // loop over batch entries (active sentences)
             auto& beam = beams[batchIndex];
             if(beamIndex < beam.size()) {
               auto hyp = beam[beamIndex];
-              hypIndices.push_back((IndexType)hyp->getPrevStateIndex()); // backpointer
+              hypIndices.push_back((IndexType)hyp->getPrevStateIndex()); // index where to find prev hyp (beamHypIdx, batchIdx), =beamHypIdx * dimBatch + batchIdx
               prevWords .push_back(hyp->getWord());
-              beamScores.push_back(hyp->getPathScore());
-            } else {  // dummy hypothesis
+              prevScores.push_back(hyp->getPathScore());
+            } else {  // pad to localBeamSize (dummy hypothesis)
               hypIndices.push_back(0);
               prevWords .push_back(Word{});  // (unused)
-              beamScores.push_back(-9999);
+              prevScores.push_back(-9999);
             }
           }
         }
-
         prevPathScores = graph->constant({(int)localBeamSize, 1, dimBatch, 1},
-                                    inits::from_vector(beamScores));
+                                    inits::from_vector(prevScores));
       }
 
       //**********************************************************************
-      // prepare scores for beam search
-      auto pathScores = prevPathScores;
-
+      // compute expanded path scores with word prediction probs from all scorers
+      auto expandedPathScores = prevPathScores; // will become [localBeamSize, 1, dimBatch, dimVocab]
       for(size_t i = 0; i < scorers_.size(); ++i) {
         // compute output probabilities for current output time step
-        //  - uses hypIndices[index in beam, 1, batch index, 1] and embIndices[index in beam, 1, batch index, 1] to reorder hypotheses
+        //  - uses hypIndices[index in beam, 1, batch index, 1] to reorder hypotheses
+        //  - adds prevWords [index in beam, 1, batch index, 1] to the decoder model's target history
+        //  - performs one step of the decoder model
         //  - returns new NN state for use in next output time step
         //  - returns vector of prediction probabilities over output vocab via newState
         auto newState = scorers_[i]->step(
             graph, states[i], hypIndices, prevWords, dimBatch, (int)localBeamSize);
 
         // expand all hypotheses, [localBeamSize, 1, dimBatch, 1] -> [localBeamSize, 1, dimBatch, dimVocab]
-        pathScores = pathScores + scorers_[i]->getWeight() * newState->getLogProbs();
+        expandedPathScores = expandedPathScores + scorers_[i]->getWeight() * newState->getLogProbs();
 
         // update state in-place for next output time step
         states[i] = newState;
       }
 
       // make beams continuous
-      if(dimBatch > 1 && localBeamSize > 1)
-        pathScores = transpose(pathScores, {2, 1, 0, 3}); // -> [dimBatch, 1, localBeamSize, dimVocab]
+      expandedPathScores = swapAxes(expandedPathScores, 0, 2); // -> [dimBatch, 1, localBeamSize, dimVocab]
+      //if(dimBatch > 1 && localBeamSize > 1)
+      //  expandedPathScores = transpose(expandedPathScores, {2, 1, 0, 3}); // -> [dimBatch, 1, localBeamSize, dimVocab]
 
-      if(first)
+      // perform NN computation
+      if(t == 0)
         graph->forward();
       else
         graph->forwardNext();
 
       //**********************************************************************
       // suppress specific symbols if not at right positions
-      if(trgUnkId_ != -1 && options_->has("allow-unk")
-         && !options_->get<bool>("allow-unk"))
-        suppressWord(pathScores, trgUnkId_);
+      if(trgUnkId_ != -1 && options_->has("allow-unk") && !options_->get<bool>("allow-unk"))
+        suppressWord(expandedPathScores, trgUnkId_);
       for(auto state : states)
-        state->blacklist(pathScores, batch);
+        state->blacklist(expandedPathScores, batch);
 
       //**********************************************************************
-      // perform beam search and pruning
+      // perform beam search
 
       // find N best amongst the (localBeamSize * dimVocab) hypotheses
-      const std::vector<size_t> beamSizes(dimBatch, localBeamSize);
-      std::vector<unsigned int> outKeys;
-      std::vector<float> outPathScores;
-      getNBestList(beamSizes, pathScores->val(), outPathScores, outKeys, first);
-      // outPathScores and outKeys contain pathScores and their original indices in N-best order
-
-      // convert N-best sets to updated search space
-      int dimTrgVoc = pathScores->shape()[-1];
-      beams = toHyps(outKeys,
-                     outPathScores,
-                     dimTrgVoc,
+      std::vector<unsigned int> nBestKeys; // [dimBatch, localBeamSize] flattened -> ((batchIdx, beamHypIdx) flattened, word idx) flattened
+      std::vector<float> nBestPathScores;  // [dimBatch, localBeamSize] flattened
+      getNBestList(/*beamSizes=*/std::vector<size_t>(dimBatch, localBeamSize), // output layout of (nBestPathScores, nBestKeys)  --@REVIEW: correct?
+                   /*in*/ expandedPathScores->val(),                           // [dimBatch, 1, localBeamSize, dimVocab or dimShortlist]
+                   /*out*/ nBestPathScores, /*out*/ nBestKeys,
+                   /*first=*/t == 0); // @TODO: Why is this passed? To know that the beam size is 1 for first step, for flattened hyp index?
+      // Now, nBestPathScores contain N-best expandedPathScores, and nBestKeys for each their original location (batchIdx, beamHypIdx, word).
+
+      // combine N-best sets with existing search space (beams) to updated search space
+      beams = toHyps(nBestKeys, nBestPathScores,
+                     /*dimTrgVoc=*/expandedPathScores->shape()[-1],
                      beams,
-                     states,
-                     localBeamSize,
-                     first,
+                     states,           // used for keeping track of per-ensemble-member path score
+                     localBeamSize,    // used in the encoding of the (batchIdx, beamHypIdx, word) tuples
+                     /*first=*/t == 0, // used to indicate originating beamSize of 1
                      batch);
 
       // remove all hyps that end in EOS
-      auto purgedBeams = purgeBeams(beams); // @TODO: rename; this is not pruning
+      // The position of a hyp in the beam may change.
+      const auto purgedBeams = purgeBeams(beams);
 
-      // add updated search space to search grid for traceback
+      // add updated search space (beams) to search grid (histories) for traceback
       bool maxLengthReached = false;
       for(int i = 0; i < dimBatch; ++i) {
         // if this batch entry has surviving hyps then add them to the traceback grid
@@ -318,20 +338,7 @@ class BeamSearch {
 
       // this is the search space for the next output time step
       beams = purgedBeams;
-
-      // determine beam size for next output time step, as max over still-active sentences
-      // E.g. if all batch entries are down from beam 5 to no more than 4 surviving hyps, then
-      // switch to beam of 4 for all. If all are done, then beam ends up being 0, and we are done.
-      if(!first) {
-        size_t maxBeam = 0;
-        for(auto& beam : beams)
-          if(beam.size() > maxBeam)
-            maxBeam = beam.size();
-        localBeamSize = maxBeam;
-      }
-      if (localBeamSize == 0) // done if all batch entries have reached EOS on all beam entries
-        break;
-    } // end of main loop over output tokens
+    } // end of main loop over output time steps
 
     return histories; // [dimBatch][t][N best hyps]
   }
diff --git a/src/translator/history.h b/src/translator/history.h
index dcf5e5854..ff37fb6e2 100755
--- a/src/translator/history.h
+++ b/src/translator/history.h
@@ -25,7 +25,7 @@ class History {
   History(size_t lineNo, float alpha = 1.f, float wp_ = 0.f);
 
   void add(const Beam& beam, Word trgEosId, bool last = false) {
-    if(beam.back()->GetPrevHyp() != nullptr) {
+    if(beam.back()->getPrevHyp() != nullptr) {
       for(size_t j = 0; j < beam.size(); ++j)
         if(beam[j]->getWord() == trgEosId || last) {
           float pathScore =
@@ -52,7 +52,7 @@ class History {
       // std::cerr << "h: " << start << " " << j << " " << c << std::endl;
 
       // trace back best path
-      Words targetWords = bestHyp->TracebackWords();
+      Words targetWords = bestHyp->tracebackWords();
 
       // note: bestHyp->getPathScore() is not normalized, while bestHypCoord.normalizedPathScore is
       nbest.emplace_back(targetWords, bestHyp, bestHypCoord.normalizedPathScore);
diff --git a/src/translator/hypothesis.h b/src/translator/hypothesis.h
index ddb475143..452b3349b 100755
--- a/src/translator/hypothesis.h
+++ b/src/translator/hypothesis.h
@@ -17,11 +17,11 @@ class Hypothesis {
 
   Hypothesis(const Ptr<Hypothesis> prevHyp,
              Word word,
-             IndexType prevIndex,
+             IndexType prevIndex, // (beamHypIdx, batchIdx) flattened as beamHypIdx * dimBatch + batchIdx
              float pathScore)
       : prevHyp_(prevHyp), prevIndex_(prevIndex), word_(word), pathScore_(pathScore) {}
 
-  const Ptr<Hypothesis> GetPrevHyp() const { return prevHyp_; }
+  const Ptr<Hypothesis> getPrevHyp() const { return prevHyp_; }
 
   Word getWord() const { return word_; }
 
@@ -29,16 +29,17 @@ class Hypothesis {
 
   float getPathScore() const { return pathScore_; }
 
-  std::vector<float>& GetScoreBreakdown() { return scoreBreakdown_; }
-  std::vector<float>& GetAlignment() { return alignment_; }
+  std::vector<float>& getScoreBreakdown() { return scoreBreakdown_; }
+  void setScoreBreakdown(const std::vector<float>& scoreBreaddown) { scoreBreakdown_ = scoreBreaddown; }
 
-  void SetAlignment(const std::vector<float>& align) { alignment_ = align; };
+  const std::vector<float>& getAlignment() { return alignment_; }
+  void setAlignment(const std::vector<float>& align) { alignment_ = align; };
 
   // helpers to trace back paths referenced from this hypothesis
-  Words TracebackWords()
+  Words tracebackWords()
   {
       Words targetWords;
-      for (auto hyp = this; hyp->GetPrevHyp(); hyp = hyp->GetPrevHyp().get()) {
+      for (auto hyp = this; hyp->getPrevHyp(); hyp = hyp->getPrevHyp().get()) {
           targetWords.push_back(hyp->getWord());
           // std::cerr << hyp->getWord() << " " << hyp << std::endl;
       }
@@ -48,11 +49,11 @@ class Hypothesis {
 
   // get soft alignments for each target word starting from the hyp one
   typedef data::SoftAlignment SoftAlignment;
-  SoftAlignment TracebackAlignment()
+  SoftAlignment tracebackAlignment()
   {
       SoftAlignment align;
-      for (auto hyp = this; hyp->GetPrevHyp(); hyp = hyp->GetPrevHyp().get()) {
-          align.push_back(hyp->GetAlignment());
+      for (auto hyp = this; hyp->getPrevHyp(); hyp = hyp->getPrevHyp().get()) {
+          align.push_back(hyp->getAlignment());
       }
       std::reverse(align.begin(), align.end());
       return align;
@@ -64,12 +65,12 @@ class Hypothesis {
   const Word word_;
   const float pathScore_;
 
-  std::vector<float> scoreBreakdown_;
+  std::vector<float> scoreBreakdown_; // [num scorers]
   std::vector<float> alignment_;
 };
 
-typedef std::vector<Ptr<Hypothesis>> Beam;                // Beam = vector of hypotheses
-typedef std::vector<Beam> Beams;                          // Beams = vector of vector of hypotheses
+typedef std::vector<Ptr<Hypothesis>> Beam;                // Beam = vector [beamSize] of hypotheses
+typedef std::vector<Beam> Beams;                          // Beams = vector [batchDim] of vector [beamSize] of hypotheses
 typedef std::tuple<Words, Ptr<Hypothesis>, float> Result; // (word ids for hyp, hyp, normalized sentence score for hyp)
 typedef std::vector<Result> NBestList;                    // sorted vector of (word ids, hyp, sent score) tuples
 }  // namespace marian
diff --git a/src/translator/output_printer.cpp b/src/translator/output_printer.cpp
old mode 100644
new mode 100755
index 000af7892..05167898d
--- a/src/translator/output_printer.cpp
+++ b/src/translator/output_printer.cpp
@@ -6,9 +6,9 @@ std::string OutputPrinter::getAlignment(const Ptr<Hypothesis>& hyp) {
   data::SoftAlignment align;
   auto last = hyp;
   // get soft alignments for each target word starting from the last one
-  while(last->GetPrevHyp().get() != nullptr) {
-    align.push_back(last->GetAlignment());
-    last = last->GetPrevHyp();
+  while(last->getPrevHyp().get() != nullptr) {
+    align.push_back(last->getAlignment());
+    last = last->getPrevHyp();
   }
 
   // reverse alignments
diff --git a/src/translator/output_printer.h b/src/translator/output_printer.h
index 38ed7b9c6..83ef9c0ca 100755
--- a/src/translator/output_printer.h
+++ b/src/translator/output_printer.h
@@ -41,11 +41,11 @@ class OutputPrinter {
         bestn << " ||| " << getAlignment(hypo);
 
       bestn << " |||";
-      if(hypo->GetScoreBreakdown().empty()) {
+      if(hypo->getScoreBreakdown().empty()) {
         bestn << " F0=" << hypo->getPathScore();
       } else {
-        for(size_t j = 0; j < hypo->GetScoreBreakdown().size(); ++j) {
-          bestn << " F" << j << "= " << hypo->GetScoreBreakdown()[j];
+        for(size_t j = 0; j < hypo->getScoreBreakdown().size(); ++j) {
+          bestn << " F" << j << "= " << hypo->getScoreBreakdown()[j];
         }
       }
 
diff --git a/src/translator/scorers.h b/src/translator/scorers.h
old mode 100644
new mode 100755
index 16a066b05..36591fe83
--- a/src/translator/scorers.h
+++ b/src/translator/scorers.h
@@ -9,9 +9,9 @@ namespace marian {
 
 class ScorerState {
 public:
-  virtual Expr getLogProbs() = 0;
+  virtual Expr getLogProbs() const = 0;
 
-  virtual float breakDown(size_t i) { return getLogProbs()->val()->get(i); }
+  float breakDown(size_t i) const { return getLogProbs()->val()->get(i); }
 
   virtual void blacklist(Expr /*totalCosts*/, Ptr<data::CorpusBatch> /*batch*/){};
 };
@@ -57,7 +57,7 @@ class ScorerWrapperState : public ScorerState {
 
   virtual Ptr<DecoderState> getState() { return state_; }
 
-  virtual Expr getLogProbs() override { return state_->getLogProbs(); };
+  virtual Expr getLogProbs() const override { return state_->getLogProbs(); };
 
   virtual void blacklist(Expr totalCosts, Ptr<data::CorpusBatch> batch) override {
     state_->blacklist(totalCosts, batch);

From 0462847998bb8039d3674c2c251a12fc0cc7cbb4 Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Thu, 7 Feb 2019 10:02:26 -0800
Subject: [PATCH 296/838] minor refactoring

---
 src/data/factored_vocab.cpp  | 40 +++++++++++++++++++++++++++++
 src/data/factored_vocab.h    | 49 +++++++-----------------------------
 src/translator/beam_search.h | 15 ++++++++---
 3 files changed, 60 insertions(+), 44 deletions(-)

diff --git a/src/data/factored_vocab.cpp b/src/data/factored_vocab.cpp
index b72db7196..21711422f 100755
--- a/src/data/factored_vocab.cpp
+++ b/src/data/factored_vocab.cpp
@@ -305,6 +305,46 @@ FactoredVocab::CSRData FactoredVocab::csr_rows(const std::vector<IndexType>& wor
   return res;
 }
 
+// WordLUT
+WordIndex FactoredVocab::WordLUT::add(const std::string& word, WordIndex index) {
+  ABORT_IF(word.empty(), "Attempted to add the empty word to a dictionary");
+  auto wasInserted = str2index_.insert(std::make_pair(word, index)).second;
+  ABORT_IF(!wasInserted, "Duplicate vocab entry for '{}'", word);
+  while (index2str_.size() <= index)
+    index2str_.emplace_back(); // @TODO: what's the right way to get linear complexity in steps?
+  ABORT_IF(!index2str_[index].empty(), "Duplicate vocab entry for index {} (new: '{}'; existing: '{}')", index, word, index2str_[index]);
+  index2str_[index] = word;
+  return index;
+}
+const std::string& FactoredVocab::WordLUT::operator[](WordIndex index) const {
+  const auto& word = index2str_[index];
+  ABORT_IF(word.empty(), "Invalid access to dictionary gap item");
+  return word;
+}
+WordIndex FactoredVocab::WordLUT::operator[](const std::string& word) const {
+  auto iter = str2index_.find(word);
+  ABORT_IF(iter == str2index_.end(), "Token '{}' not found in vocabulary", word);
+  return iter->second;
+}
+bool FactoredVocab::WordLUT::tryFind(const std::string& word, WordIndex& index) const {
+  auto iter = str2index_.find(word);
+  if (iter == str2index_.end())
+    return false;
+  index = iter->second;
+  return true;
+}
+void FactoredVocab::WordLUT::resize(size_t num) {
+  ABORT_IF(num < index2str_.size(), "Word table cannot be shrunk");
+  index2str_.resize(num); // gets filled up with gap items (empty strings)
+}
+size_t FactoredVocab::WordLUT::load(const std::string& path) {
+  std::string line;
+  io::InputFileStream in(path);
+  for (WordIndex v = 0; io::getline(in, line); v++)
+    add(line, v);
+  return size();
+}
+
 // Note: This does not actually load it, only checks the path for the type.
 Ptr<IVocab> createFactoredVocab(const std::string& vocabPath) {
   bool isFactoredVocab = regex::regex_search(vocabPath, regex::regex("\\.(fm)$"));
diff --git a/src/data/factored_vocab.h b/src/data/factored_vocab.h
index e2ae02785..ea37101b2 100755
--- a/src/data/factored_vocab.h
+++ b/src/data/factored_vocab.h
@@ -1,6 +1,7 @@
 // Implementation of an IVocab that represents a factored representation.
-// This is accessed via the IVocab interface, and also by Embedding and Output
-// layers directly.
+// This is accessed via the IVocab interface for the base vocab functionality,
+// and via dynamic_cast to FactoredVocab for factored-specific things used by
+// the Embedding and Output layers.
 
 #pragma once
 
@@ -68,47 +69,15 @@ class FactoredVocab : public IVocab {
     std::map<std::string, WordIndex> str2index_;
     std::vector<std::string> index2str_;
   public:
-    WordIndex add(const std::string& word, WordIndex index) {
-      ABORT_IF(word.empty(), "Attempted to add the empty word to a dictionary");
-      auto wasInserted = str2index_.insert(std::make_pair(word, index)).second;
-      ABORT_IF(!wasInserted, "Duplicate vocab entry for '{}'", word);
-      while (index2str_.size() <= index)
-        index2str_.emplace_back(); // @TODO: what's the right way to get linear complexity in steps?
-      ABORT_IF(!index2str_[index].empty(), "Duplicate vocab entry for index {} (new: '{}'; existing: '{}')", index, word, index2str_[index]);
-      index2str_[index] = word;
-      return index;
-    }
-    const std::string& operator[](WordIndex index) const {
-      const auto& word = index2str_[index];
-      ABORT_IF(word.empty(), "Invalid access to dictionary gap item");
-      return word;
-    }
-    WordIndex operator[](const std::string& word) const {
-      auto iter = str2index_.find(word);
-      ABORT_IF(iter == str2index_.end(), "Token '{}' not found in vocabulary", word);
-      return iter->second;
-    }
+    WordIndex add(const std::string& word, WordIndex index);
+    const std::string& operator[](WordIndex index) const;
+    WordIndex operator[](const std::string& word) const;
     bool isGap(WordIndex index) const { return index2str_[index].empty(); }
-    bool tryFind(const std::string& word, WordIndex& index) const {
-      auto iter = str2index_.find(word);
-      if (iter == str2index_.end())
-        return false;
-      index = iter->second;
-      return true;
-    }
-    void resize(size_t num) {
-      ABORT_IF(num < index2str_.size(), "Word table cannot be shrunk");
-      index2str_.resize(num); // gets filled up with gap items (empty strings)
-    }
+    bool tryFind(const std::string& word, WordIndex& index) const;
+    void resize(size_t num);
     size_t size() const { return index2str_.size(); } // nominal size including gap items
     size_t numValid() const { return str2index_.size(); } // actual non-gaps items
-    size_t load(const std::string& path) {
-      std::string line;
-      io::InputFileStream in(path);
-      for (WordIndex v = 0; io::getline(in, line); v++)
-        add(line, v);
-      return size();
-    }
+    size_t load(const std::string& path);
   };
 
   // main vocab
diff --git a/src/translator/beam_search.h b/src/translator/beam_search.h
index 0520ed0cd..df38a7e61 100755
--- a/src/translator/beam_search.h
+++ b/src/translator/beam_search.h
@@ -4,6 +4,7 @@
 #include "marian.h"
 #include "translator/history.h"
 #include "translator/scorers.h"
+#include "data/factored_vocab.h"
 
 #include "translator/helpers.h"
 #include "translator/nth_element.h"
@@ -68,7 +69,7 @@ class BeamSearch {
         const auto keyBatchIdx   = hypIdx / (first ? 1 : beamSize);
         const auto keyBeamHypIdx = hypIdx % (first ? 1 : beamSize);
         const auto hypIdxTrans = keyBeamHypIdx * dimBatch + keyBatchIdx;
-        ABORT_IF(keyBeamHypIdx >= (int)beam.size(), "Beam hyp index exceeds beam size??"); // not possible, as beamSize = max(beams[.].size())
+        ABORT_IF(keyBeamHypIdx >= (int)beam.size(), "Beam hyp index exceeds beam size??"); // @TODO: is this possible? Should be, but does not seem to trigger.
 #else
         const auto keyBatchIdx = hypIdx / beamSize; // @REVIEW: is this actually keyBatchIdx?
         size_t keyBeamHypIdx = hypIdx % beamSize;
@@ -92,7 +93,7 @@ class BeamSearch {
           wordIdx = shortlist->reverseMap(wordIdx); // @TODO: should reverseMap accept a size_t or a Word?
         // now wordIdx is a regular Word again
 
-        auto hyp = New<Hypothesis>(beam[keyBeamHypIdx], wordIdx, (IndexType)hypIdxTrans, pathScore);
+        auto hyp = New<Hypothesis>(beam[keyBeamHypIdx], Word::fromWordIndex(wordIdx), (IndexType)hypIdxTrans, pathScore);
 
         // Set score breakdown for n-best lists
         if(options_->get<bool>("n-best")) {
@@ -170,6 +171,12 @@ class BeamSearch {
   //**********************************************************************
   // main decoding function
   Histories search(Ptr<ExpressionGraph> graph, Ptr<data::CorpusBatch> batch) {
+    // @TODO: EOS id does not need to be stored in this object, since it is available from vocab()
+    ABORT_IF(batch->back()->vocab()->getEosId() == trgEosId_);
+
+    auto factoredVocab = std::dynamic_pointer_cast<FactoredVocab>(batch->back()->vocab());
+    size_t numFactors = factoredVocab ? factoredVocab->getNumGroups() : 1;
+
     const int dimBatch = (int)batch->size();
 
     auto getNBestList = createGetNBestListFn(beamSize_, dimBatch, graph->getDeviceId());
@@ -281,8 +288,8 @@ class BeamSearch {
       }
 
       // make beams continuous
-      expandedPathScores = swapAxes(expandedPathScores, 0, 2); // -> [dimBatch, 1, localBeamSize, dimVocab]
-      //if(dimBatch > 1 && localBeamSize > 1)
+      if(dimBatch > 1 && localBeamSize > 1)
+        expandedPathScores = swapAxes(expandedPathScores, 0, 2); // -> [dimBatch, 1, localBeamSize, dimVocab]
       //  expandedPathScores = transpose(expandedPathScores, {2, 1, 0, 3}); // -> [dimBatch, 1, localBeamSize, dimVocab]
 
       // perform NN computation

From 15158deef0ae34ac4347fde416a545f049178792 Mon Sep 17 00:00:00 2001
From: Roman Grundkiewicz <rgrundki@exseed.ed.ac.uk>
Date: Thu, 7 Feb 2019 18:16:52 +0000
Subject: [PATCH 297/838] Add comments and refactorize

---
 src/common/cli_wrapper.cpp   | 83 +++++++++++++++++++++---------------
 src/common/cli_wrapper.h     | 40 +++++++++++------
 src/common/config_parser.cpp |  7 ++-
 3 files changed, 81 insertions(+), 49 deletions(-)

diff --git a/src/common/cli_wrapper.cpp b/src/common/cli_wrapper.cpp
index 1bf822196..161a61d8f 100644
--- a/src/common/cli_wrapper.cpp
+++ b/src/common/cli_wrapper.cpp
@@ -129,37 +129,50 @@ void CLIWrapper::parse(int argc, char **argv) {
 }
 
 void CLIWrapper::parseAliases() {
+  // Exit if no aliases defined
   if(aliases_.empty())
     return;
 
-  std::set<std::string> aliasKeys;
-  for(const auto& alias : aliases_) {
+  // Names of all triggered/expanded aliases will be collected and removed from the config at the
+  // end.  We keep them in a set as multiple aliases can be defined for one option name (aka `key`).
+  std::set<std::string> parsedAliases;
+
+  // Iterate all known aliases, each alias has a key, value, and config
+  for(const auto &alias : aliases_) {
+    // Check if the alias option exists in the config (it may come from command line or a config
+    // file)
     if(config_[alias.key]) {
+      // Check if the option in the config stores the value required to expand the alias. If so,
+      // expand the alias.
+      // Two cases:
+      //  * the option is a sequence: extract it as a vector of strings and look for the value
+      //  * otherwise: compare values as strings
       bool expand = false;
       if(config_[alias.key].IsSequence()) {
-        // Note: options values are always extracted as vectors and compared as strings
         auto aliasOpts = config_[alias.key].as<std::vector<std::string>>();
         expand = std::find(aliasOpts.begin(), aliasOpts.end(), alias.value) != aliasOpts.end();
       } else {
-        // Note: options values are always compared as strings
         expand = config_[alias.key].as<std::string>() == alias.value;
       }
 
       if(expand) {
+        // Update global config options with the config associated with the alias. Abort if the
+        // alias contains an undefined option
         updateConfig(alias.config,
                      "Unknown option(s) in alias '" + alias.key + ": " + alias.value + "'");
       }
-      aliasKeys.insert(alias.key);
+
+      // Collect the alias option as they will be removed at the end
+      parsedAliases.insert(alias.key);
     }
   }
 
-  // Remove aliases from the config
-  for(const auto& key : aliasKeys) {
+  // Remove aliases from the global config to avoid redundancy when dumping/reading config files
+  for(const auto &key : parsedAliases) {
     config_.remove(key);
   }
 }
 
-
 std::string CLIWrapper::failureMessage(const CLI::App *app, const CLI::Error &e) {
   std::string header = "Error: " + std::string(e.what()) + "\n";
   if(app->get_help_ptr() != nullptr)
@@ -167,48 +180,50 @@ std::string CLIWrapper::failureMessage(const CLI::App *app, const CLI::Error &e)
   return header;
 }
 
-void CLIWrapper::updateConfig(const YAML::Node &config, const std::string& errorMsg) {
-  std::vector<std::string> invalidKeys;
+void CLIWrapper::updateConfig(const YAML::Node &config, const std::string &errorMsg) {
   auto cmdOptions = getParsedOptionNames();
+  // Keep track of unrecognized options from the provided config
+  std::vector<std::string> unknownOpts;
 
+  // Iterate incoming options: they need to be merged into the global config
   for(auto it : config) {
     auto key = it.first.as<std::string>();
-    // skip options specified via command-line to allow overwriting them
+
+    // Skip options specified via command-line to allow overwriting them
     if(cmdOptions.count(key))
       continue;
-    // skip options that might exist in config files generated by older versions of Marian
+    // Skip options that might exist in config files generated by older versions of Marian
     if(DEPRECIATED_OPTIONS.count(key))
       continue;
 
+    // Check if an incoming option has been defined in CLI
     if(options_.count(key)) {
-      if(config_[key]) { // it exists, so this is a default value, hence it has a node type
-        if(config_[key].Type() != it.second.Type()) { // types don't match, handle this
-          // default value is a sequence and incoming node is a scalar, hence we can upcast to
-          // single element sequence
-          if(config_[key].Type() == YAML::NodeType::Sequence
-             && it.second.Type() == YAML::NodeType::Scalar) {
-            // create single element sequence
-            YAML::Node sequence;
-            sequence.push_back(YAML::Clone(it.second));
-            config_[key] = sequence; // overwrite to replace default values
-            options_[key].modified = true;
-          } else { // Cannot convert other non-matching types, e.g. scalar <- list should fail
-            ABORT("Cannot convert values for the option: " + key);
-          }
-        } else { // types match, go ahead
-          config_[key] = YAML::Clone(it.second);
-          options_[key].modified = true;
-        }
-      } else {
+      // Check if the option exists in the global config and types match
+      if(config_[key] && config_[key].Type() == it.second.Type()) {
         config_[key] = YAML::Clone(it.second);
         options_[key].modified = true;
+        // If types doesn't match, try to convert
+      } else {
+        // Default value is a sequence and incoming node is a scalar, hence we can upcast to
+        // single element sequence
+        if(config_[key].Type() == YAML::NodeType::Sequence
+           && it.second.Type() == YAML::NodeType::Scalar) {
+          // create single element sequence
+          YAML::Node sequence;
+          sequence.push_back(YAML::Clone(it.second));
+          config_[key] = sequence;  // overwrite to replace default values
+          options_[key].modified = true;
+        } else {
+          // Cannot convert other non-matching types, e.g. scalar <- list should fail
+          ABORT("Cannot convert values for the option: " + key);
+        }
       }
-    } else {
-      invalidKeys.push_back(key);
+    } else {  // an unknown option
+      unknownOpts.push_back(key);
     }
   }
 
-  ABORT_IF(!invalidKeys.empty(), errorMsg + ": " + utils::join(invalidKeys, ", "));
+  ABORT_IF(!unknownOpts.empty(), errorMsg + ": " + utils::join(unknownOpts, ", "));
 }
 
 std::string CLIWrapper::dumpConfig(bool skipDefault /*= false*/) const {
diff --git a/src/common/cli_wrapper.h b/src/common/cli_wrapper.h
index 95eb3590d..f0ca51c34 100644
--- a/src/common/cli_wrapper.h
+++ b/src/common/cli_wrapper.h
@@ -51,7 +51,7 @@ class CLIFormatter : public CLI::Formatter {
 // @TODO: in this file review the use of naked pointers. We use Ptr<Type> anywhere else,
 // what's up with that?
 
-// The helper structure storing an option object, the associated variable and creation index
+// Helper structure storing an option object, the associated variable and creation index
 struct CLIOptionTuple {
   CLI::Option *opt;
   Ptr<any_type> var;
@@ -59,11 +59,12 @@ struct CLIOptionTuple {
   bool modified{false};
 };
 
-// Helper structure used for aliases and storing an option key, value, and YAML node
+// Helper structure used for aliases storing an option key, value, and options to be expanded in the
+// form of a YAML config
 struct CLIAliasTuple {
-  std::string key;
-  std::string value;
-  YAML::Node config;
+  std::string key;    // alias option name
+  std::string value;  // value for the alias option indicating that it should be expanded
+  YAML::Node config;  // config with options that the alias adds
 };
 
 /**
@@ -200,13 +201,24 @@ class CLIWrapper {
   }
 
   /**
-   * @brief Define an alias that is a shortcut for a set of options
+   * @brief Transform the option into an alias that is a shortcut for a set of options
    *
-   * Option values are compared as std::string.
+   * An alias sets one or more options to predefined values. The options expanded by the alias are
+   * provided as a function setting a temporary YAML config.
    *
-   * @param key Option name
+   * The alias option has to be first defined using `add<T>()`. Otherwise, the program will abort.
+   *
+   * Defining more than one alias for the same `key` but different `value` is allowed.
+   *
+   * Option values are compared as std::string. If the alias option is a vector, the alias will be
+   * triggered if `value` exists in that vector at least once.
+   *
+   * Options set directly via command line have precedence over options defined in an alias, i.e. an
+   * option added via alias can be overwritten by setting a specific option via command line.
+   *
+   * @param key Alias option name
    * @param value Option value that trigger the alias
-   * @param fun Function initializing options
+   * @param fun Function setting a temporary YAML config with options expanded by alias
    */
   void alias(const std::string &key,
              const std::string &value,
@@ -231,7 +243,8 @@ class CLIWrapper {
   /**
    * @brief Expand aliases based on arguments parsed with parse(int, char**)
    *
-   * Should be called after parse(int, char**) to take an effect.
+   * Should be called after parse(int, char**) to take an effect.  If any alias tries to expand an
+   * undefined option, the method will abort.
    *
    * All options defined as aliases are removed from the config object.
    */
@@ -240,10 +253,9 @@ class CLIWrapper {
   /*
    * @brief Overwrite values for unparsed options
    *
-   * Default values are overwritten with the options from the config provided, while parsed
-   * command-line options remain unchanged.
-   * This should be a preferred way of updating config options as the class keeps track of options,
-   * which values have changed.
+   * Default values are overwritten with the options from the provided config, while parsed
+   * command-line options remain unchanged.  This should be a preferred way of updating config
+   * options as the class keeps track of options, which values have changed.
    *
    * @param config YAML config with new default values for options
    * @param errorMsg error message printed if config contains undefined keys. The message is
diff --git a/src/common/config_parser.cpp b/src/common/config_parser.cpp
index b50dbcd02..cd8cc2db9 100644
--- a/src/common/config_parser.cpp
+++ b/src/common/config_parser.cpp
@@ -694,8 +694,9 @@ void ConfigParser::addSuboptionsULR(cli::CLIWrapper& cli) {
 }
 
 void ConfigParser::addAliases(cli::CLIWrapper& cli) {
-  // The order of aliases does matter as later options overwrite earlier
+  // The order of aliases does matter as later options in the command line overwrite earlier
 
+  // Options reconstructing the BiDeep architecture proposed in http://www.aclweb.org/anthology/W17-4710
   cli.alias("best-deep", "true", [](YAML::Node& config) {
     config["layer-normalization"] = true;
     config["tied-embeddings"] = true;
@@ -708,6 +709,8 @@ void ConfigParser::addAliases(cli::CLIWrapper& cli) {
     config["skip"] = true;
   });
 
+  // Architecture and proposed training settings for a Transformer BASE model introduced in
+  // https://papers.nips.cc/paper/7181-attention-is-all-you-need.pdf
   cli.alias("task", "transformer", [](YAML::Node& config) {
     config["type"] = "transformer";
     config["enc-depth"] = 6;
@@ -722,6 +725,8 @@ void ConfigParser::addAliases(cli::CLIWrapper& cli) {
     config["clip-norm"] = 5;
   });
 
+  // Architecture and proposed training settings for a Transformer BIG model introduced in
+  // https://papers.nips.cc/paper/7181-attention-is-all-you-need.pdf
   cli.alias("task", "transformer-big", [](YAML::Node& config) {
     config["type"] = "transformer";
     config["enc-depth"] = 6;

From 86189dd66da78e54c10fb57295c20f75ef321fc5 Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Thu, 7 Feb 2019 10:54:27 -0800
Subject: [PATCH 298/838] new option --english-title-case-every

---
 src/common/config_parser.cpp |  2 ++
 src/common/utils.cpp         | 30 ++++++++++++++++++++++++++++++
 src/common/utils.h           |  2 ++
 src/data/corpus.cpp          | 21 +++++++++++++++++++--
 src/data/corpus.h            |  1 +
 src/data/vocab.h             |  4 ++++
 src/translator/beam_search.h |  5 +++--
 7 files changed, 61 insertions(+), 4 deletions(-)
 mode change 100644 => 100755 src/common/config_parser.cpp
 mode change 100644 => 100755 src/common/utils.h
 mode change 100644 => 100755 src/data/corpus.cpp
 mode change 100644 => 100755 src/data/corpus.h
 mode change 100644 => 100755 src/data/vocab.h

diff --git a/src/common/config_parser.cpp b/src/common/config_parser.cpp
old mode 100644
new mode 100755
index d346e94b4..54c1f6a66
--- a/src/common/config_parser.cpp
+++ b/src/common/config_parser.cpp
@@ -632,6 +632,8 @@ void ConfigParser::addSuboptionsBatching(cli::CLIWrapper& cli) {
       "Keep shuffled corpus in RAM, do not write to temp file");
   cli.add<size_t>("--all-caps-every",
       "When forming minibatches, preprocess every Nth line on the fly to all-caps. Assumes UTF-8");
+  cli.add<size_t>("--english-title-case-every",
+      "When forming minibatches, preprocess every Nth line on the fly to title-case. Assumes English (ASCII only)");
 
   cli.add<int>("--mini-batch-words-ref",
       "If given, the following hyper parameters are adjusted as-if we had this mini-batch size: "
diff --git a/src/common/utils.cpp b/src/common/utils.cpp
index cd10be230..e12022362 100755
--- a/src/common/utils.cpp
+++ b/src/common/utils.cpp
@@ -7,6 +7,7 @@
 #include <iostream>
 #include <sstream>
 #include <string>
+#include <set>
 #ifdef __unix__
 #include <unistd.h>
 #endif
@@ -202,6 +203,35 @@ std::string utf8ToUpper(const std::string& s) {
 }
 #endif
 
+// convert an English sentence to title case
+// Since title case is an English thing, we only consider ASCII characters.
+std::string toEnglishTitleCase(const std::string& s) {
+  auto res = s;
+  // process token by token
+  const std::string wordStartChars = "abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ";
+  const std::string wordInternalChars = wordStartChars + "'"; // don't title-case letters after word-internal apostrophe
+  const std::set<std::string> exceptions = { // from moses-scripts/scripts/recaser/detruecase.perl
+    "a","after","against","al-.+","and","any","as","at","be","because","between","by","during","el-.+","for","from","his","in","is","its","last","not","of","off","on","than","the","their","this","to","was","were","which","will","with"
+  };
+  // These are tokenization heuristics, which may be incomplete.
+  size_t epos = 0;
+  for(size_t pos = epos; pos < res.size(); pos = epos) {
+    // locate the next word
+    pos = res.find_first_of(wordStartChars, pos); // find first letter
+    if (pos == std::string::npos)
+      break;
+    epos = res.find_first_not_of(wordInternalChars, pos + 1); // find first non-letter
+    if (epos == std::string::npos)
+      epos = res.size();
+    auto word = res.substr(pos, epos - pos);
+    // upper-case the word, unless it is in the exception list
+    if (res[pos] >= 'a' && res[pos] <= 'z' && exceptions.find(word) == exceptions.end()) {
+      res[pos] -= 'A' - 'a';
+    }
+  }
+  return res;
+}
+
 double parseDouble(std::string s) {
   double res;
   char c;  // dummy char -- if we succeed to parse this, then there were extraneous characters after the number
diff --git a/src/common/utils.h b/src/common/utils.h
old mode 100644
new mode 100755
index ffd9454c3..23d6ffed4
--- a/src/common/utils.h
+++ b/src/common/utils.h
@@ -39,6 +39,8 @@ bool beginsWith(const std::string& text, const std::string& prefix);
 bool endsWith(const std::string& text, const std::string& suffix);
 
 std::string utf8ToUpper(const std::string& s);
+std::string toEnglishTitleCase(const std::string& s);
+
 double parseDouble(std::string s);
 double parseNumber(std::string s);
 
diff --git a/src/data/corpus.cpp b/src/data/corpus.cpp
old mode 100644
new mode 100755
index 53a50614b..7e8a5aa0f
--- a/src/data/corpus.cpp
+++ b/src/data/corpus.cpp
@@ -10,12 +10,18 @@ namespace marian {
 namespace data {
 
 Corpus::Corpus(Ptr<Options> options, bool translate /*= false*/)
-    : CorpusBase(options, translate), shuffleInRAM_(options_->get<bool>("shuffle-in-ram")), allCapsEvery_(options_->get<size_t>("all-caps-every")) {}
+    : CorpusBase(options, translate),
+        shuffleInRAM_(options_->get<bool>("shuffle-in-ram")),
+        allCapsEvery_(options_->get<size_t>("all-caps-every")),
+        titleCaseEvery_(options_->get<size_t>("english-title-case-every")) {}
 
 Corpus::Corpus(std::vector<std::string> paths,
                std::vector<Ptr<Vocab>> vocabs,
                Ptr<Options> options)
-    : CorpusBase(paths, vocabs, options), shuffleInRAM_(options_->get<bool>("shuffle-in-ram")), allCapsEvery_(options_->get<size_t>("all-caps-every")) {}
+    : CorpusBase(paths, vocabs, options),
+        shuffleInRAM_(options_->get<bool>("shuffle-in-ram")),
+        allCapsEvery_(options_->get<size_t>("all-caps-every")),
+        titleCaseEvery_(options_->get<size_t>("english-title-case-every")) {}
 
 void Corpus::preprocessLine(std::string& line, size_t streamId) {
   if (allCapsEvery_ != 0 && pos_ % allCapsEvery_ == 0 && !inference_) {
@@ -25,6 +31,17 @@ void Corpus::preprocessLine(std::string& line, size_t streamId) {
     else
       LOG_ONCE(info, "[data] target all-caps'ed line to {}", line);
   }
+  else if (titleCaseEvery_ != 0 && pos_ % titleCaseEvery_ == 1 && !inference_
+      
+      &&   streamId == 0       // @HACK: Hard-coding EN-X direction for now; needs an option in the future
+      
+      ) {
+    line = utils::toEnglishTitleCase(line);
+    if (streamId == 0)
+      LOG_ONCE(info, "[data] source English-title-case'd line to {}", line);
+    else
+      LOG_ONCE(info, "[data] target English-title-case'd line to {}", line);
+  }
 }
 
 SentenceTuple Corpus::next() {
diff --git a/src/data/corpus.h b/src/data/corpus.h
old mode 100644
new mode 100755
index 7edced620..c5af7220b
--- a/src/data/corpus.h
+++ b/src/data/corpus.h
@@ -29,6 +29,7 @@ class Corpus : public CorpusBase {
 
   // for pre-processing
   size_t allCapsEvery_{0};
+  size_t titleCaseEvery_{0};
   void preprocessLine(std::string& line, size_t streamId);
 
 public:
diff --git a/src/data/vocab.h b/src/data/vocab.h
old mode 100644
new mode 100755
index afe79d1f0..dd746ba9a
--- a/src/data/vocab.h
+++ b/src/data/vocab.h
@@ -68,6 +68,10 @@ class Vocab {
 
   // create fake vocabulary for collecting batch statistics
   void createFake();
+
+  // give access to base implementation. Returns null if not the requested type.
+  template<class VocabType> // e.g. FactoredVocab
+  Ptr<VocabType> tryAs() const { return std::dynamic_pointer_cast<VocabType>(vImpl_); }
 };
 
 }  // namespace marian
diff --git a/src/translator/beam_search.h b/src/translator/beam_search.h
index df38a7e61..1dc05d193 100755
--- a/src/translator/beam_search.h
+++ b/src/translator/beam_search.h
@@ -172,10 +172,11 @@ class BeamSearch {
   // main decoding function
   Histories search(Ptr<ExpressionGraph> graph, Ptr<data::CorpusBatch> batch) {
     // @TODO: EOS id does not need to be stored in this object, since it is available from vocab()
-    ABORT_IF(batch->back()->vocab()->getEosId() == trgEosId_);
+    ABORT_IF(batch->back()->vocab()->getEosId() != trgEosId_, "Batch uses different EOS token than was passed to BeamSearch originally");
 
-    auto factoredVocab = std::dynamic_pointer_cast<FactoredVocab>(batch->back()->vocab());
+    auto factoredVocab = batch->back()->vocab()->tryAs<FactoredVocab>();
     size_t numFactors = factoredVocab ? factoredVocab->getNumGroups() : 1;
+    numFactors;
 
     const int dimBatch = (int)batch->size();
 

From 16a35b12eacda00a0055a93eb6d1696e71b1b744 Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Thu, 7 Feb 2019 13:44:33 -0800
Subject: [PATCH 299/838] bug fix: change to has(embedding-vectors option)
 check regressed in last merge; some fixes to toEnglishTitleCase()

---
 src/common/utils.cpp | 14 ++++++++++----
 src/data/corpus.cpp  |  8 ++++----
 src/models/decoder.h |  2 +-
 3 files changed, 15 insertions(+), 9 deletions(-)

diff --git a/src/common/utils.cpp b/src/common/utils.cpp
index e12022362..e4f6aa904 100755
--- a/src/common/utils.cpp
+++ b/src/common/utils.cpp
@@ -213,6 +213,7 @@ std::string toEnglishTitleCase(const std::string& s) {
   const std::set<std::string> exceptions = { // from moses-scripts/scripts/recaser/detruecase.perl
     "a","after","against","al-.+","and","any","as","at","be","because","between","by","during","el-.+","for","from","his","in","is","its","last","not","of","off","on","than","the","their","this","to","was","were","which","will","with"
   };
+  const std::set<char> wordPredChars = {' ', '"', '\'', '-'}; // only capitalize words if following these characters (to avoid upper-casing word-internal SPM units)
   // These are tokenization heuristics, which may be incomplete.
   size_t epos = 0;
   for(size_t pos = epos; pos < res.size(); pos = epos) {
@@ -224,10 +225,15 @@ std::string toEnglishTitleCase(const std::string& s) {
     if (epos == std::string::npos)
       epos = res.size();
     auto word = res.substr(pos, epos - pos);
-    // upper-case the word, unless it is in the exception list
-    if (res[pos] >= 'a' && res[pos] <= 'z' && exceptions.find(word) == exceptions.end()) {
-      res[pos] -= 'A' - 'a';
-    }
+    // further checks of the word
+    if (res[pos] < 'a' || res[pos] > 'z') // skip if already upper-case
+      continue;
+    if (pos > 0 && wordPredChars.find(res[pos-1]) == wordPredChars.end()) // skip if unexpected char before the word
+      continue;
+    if (exceptions.find(word) != exceptions.end()) // skip if in the exception list
+      continue;
+    // upper-case it
+    res[pos] -= 'a' - 'A';
   }
   return res;
 }
diff --git a/src/data/corpus.cpp b/src/data/corpus.cpp
index 7e8a5aa0f..44fa064d8 100755
--- a/src/data/corpus.cpp
+++ b/src/data/corpus.cpp
@@ -27,9 +27,9 @@ void Corpus::preprocessLine(std::string& line, size_t streamId) {
   if (allCapsEvery_ != 0 && pos_ % allCapsEvery_ == 0 && !inference_) {
     line = utils::utf8ToUpper(line);
     if (streamId == 0)
-      LOG_ONCE(info, "[data] source all-caps'ed line to {}", line);
+      LOG_ONCE(info, "[data] Source all-caps'ed line to: {}", line);
     else
-      LOG_ONCE(info, "[data] target all-caps'ed line to {}", line);
+      LOG_ONCE(info, "[data] Target all-caps'ed line to: {}", line);
   }
   else if (titleCaseEvery_ != 0 && pos_ % titleCaseEvery_ == 1 && !inference_
       
@@ -38,9 +38,9 @@ void Corpus::preprocessLine(std::string& line, size_t streamId) {
       ) {
     line = utils::toEnglishTitleCase(line);
     if (streamId == 0)
-      LOG_ONCE(info, "[data] source English-title-case'd line to {}", line);
+      LOG_ONCE(info, "[data] Source English-title-case'd line to: {}", line);
     else
-      LOG_ONCE(info, "[data] target English-title-case'd line to {}", line);
+      LOG_ONCE(info, "[data] Target English-title-case'd line to: {}", line);
   }
 }
 
diff --git a/src/models/decoder.h b/src/models/decoder.h
index 8f3db3d72..9258cf541 100755
--- a/src/models/decoder.h
+++ b/src/models/decoder.h
@@ -49,7 +49,7 @@ class DecoderBase {
         embFactory("prefix", prefix_ + "_Wemb");
       if(options_->has("embedding-fix-trg"))
         embFactory("fixed", opt<bool>("embedding-fix-trg"));
-      if(options_->has("embedding-vectors")) {
+      if(options_->hasAndNotEmpty("embedding-vectors")) {
         auto embFiles = opt<std::vector<std::string>>("embedding-vectors");
         embFactory("embFile", embFiles[batchIndex_])  //
             ("normalization", opt<bool>("embedding-normalization"));

From e994eeca5523ed85a484ffbaf4cef0620adacbf9 Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Thu, 7 Feb 2019 16:40:18 -0800
Subject: [PATCH 300/838] reimplemented Logits::applyLossFunction() to directly
 use factored labels

---
 src/data/factored_vocab.h |  1 +
 src/layers/generic.cpp    | 65 +++++++++++++++++++++++++++++++--------
 src/layers/generic.h      | 21 ++++++++++++-
 3 files changed, 74 insertions(+), 13 deletions(-)
 mode change 100644 => 100755 src/layers/generic.h

diff --git a/src/data/factored_vocab.h b/src/data/factored_vocab.h
index ea37101b2..762e21d52 100755
--- a/src/data/factored_vocab.h
+++ b/src/data/factored_vocab.h
@@ -56,6 +56,7 @@ class FactoredVocab : public IVocab {
   std::pair<WordIndex, bool> getFactorUnit(Word word, size_t groupIndex);
   static constexpr size_t FACTOR_NOT_APPLICABLE = (SIZE_MAX - 1);
   static constexpr size_t FACTOR_NOT_SPECIFIED  = (SIZE_MAX - 2);
+  static bool isFactorValid(size_t factorIndex) { return factorIndex < FACTOR_NOT_SPECIFIED; }
 
   static Ptr<FactoredVocab> tryCreateAndLoad(const std::string& path); // load from "vocab" option if it specifies a factored vocab
 private:
diff --git a/src/layers/generic.cpp b/src/layers/generic.cpp
index c2aab49a9..18f095875 100755
--- a/src/layers/generic.cpp
+++ b/src/layers/generic.cpp
@@ -16,35 +16,54 @@ namespace marian {
 
   Logits::Logits(Expr logits) : Logits(New<RationalLoss>(logits, nullptr)) {} // single-output constructor from Expr only (RationalLoss has no count)
 
+  Ptr<ExpressionGraph> Logits::graph() const {
+    ABORT_IF(logits_.empty(), "Empty logits object??");
+    return logits_.front()->loss()->graph();
+  }
+
   // This function assumes that the object holds one or more factor logits.
   // It applies the supplied loss function to each, and then returns the aggregate loss over all factors.
   Expr Logits::applyLossFunction(const Words& labels, const std::function<Expr(Expr/*logits*/, Expr/*indices*/)>& lossFn) const {
-    ABORT_IF(logits_.empty(), "Empty logits object??");
-    auto graph = logits_.front()->loss()->graph();
-    Expr indices = graph->indices(toWordIndexVector(labels));
-
     LOG_ONCE(info, "[logits] applyLossFunction() for {} factors", logits_.size());
     ABORT_IF(empty(), "Attempted to read out logits on empty Logits object");
+
+    auto firstLogits = logits_.front()->loss();
+    ABORT_IF(labels.size() * firstLogits->shape()[-1] != firstLogits->shape().elements(), "Labels not matching logits shape??");
+
+    // base case (no factors)
     if (!factoredVocab_) {
       ABORT_IF(logits_.size() != 1, "Factors without factor mappings??");
-      return lossFn(logits_.front()->loss(), indices);
+      return lossFn(firstLogits, indices(toWordIndexVector(labels)));
     }
 
+    auto numGroups = factoredVocab_->getNumGroups();
+
+    // split labels into individual factor labels
+    // @TODO: Is there value in having factorize() return Exprs? That would allow to move some stuff to GPU.
+    auto allMaskedFactoredLabels = factorize(labels); // [numGroups][labels.size()] = [numGroups][B... flattened]
+
+    //Expr indices = this->indices(toWordIndexVector(labels));
     // accumulate all CEs for all words that have the factor
     // Memory-wise, this is cheap, all temp objects below are batches of scalars or lookup vectors.
     Expr loss;
-    auto numGroups = factoredVocab_->getNumGroups();
     for (size_t g = 0; g < numGroups; g++) {
+      const auto& maskedFactoredLabels = allMaskedFactoredLabels[g]; // array of (word index, mask)
+#if 1
+      auto factorIndices = indices (maskedFactoredLabels.indices); // [B... flattened] factor-label indices, or 0 if factor does not apply
+      auto factorMask    = constant(maskedFactoredLabels.masks);   // [B... flattened] loss values get multiplied with 0 for labels that don't have this factor
+#else // @TODO: if this^^ works, we can remove the stuff below (quite a bit code)
+      maskedFactoredLabels;
       indices; // [B... * 1] all batch items flattened
       auto factorMaskVector  = factoredVocab_->getFactorMasks(g);   // [v] 1.0 if v has factor of group g
       auto factorIndexVector = factoredVocab_->getFactorIndices(g); // [v] index of factor for word v in group p; must be 0 if factor is not used
-      auto factorMaskMatrix  = graph->constant({(int)factorMaskVector.size(),  1}, inits::from_vector(factorMaskVector),  Type::float32); // [V x 1]
-      auto factorIndexMatrix = graph->constant({(int)factorIndexVector.size(), 1}, inits::from_vector(factorIndexVector), Type::uint32);  // [V x 1(Ug)]
-      auto factorIndex  = rows(factorIndexMatrix, indices); // [B... * 1(Ug)] map word indices to factor indices (indices into factorLogits)
-      auto factorMask   = rows(factorMaskMatrix,  indices); // [B... * 1]     flag whether word has the factor in the first place
-      auto factorLogits = logits_[g];                       // [B... * Ug]
+      auto factorMaskMatrix  = constant({(int)factorMaskVector.size(),  1}, factorMaskVector);  // [V x 1]
+      auto factorIndexMatrix = constant({(int)factorIndexVector.size(), 1}, factorIndexVector); // [V x 1(Ug)]
+      auto factorIndices = rows(factorIndexMatrix, indices); // [B... * 1(Ug)] map word indices to factor indices (indices into factorLogits)
+      auto factorMask    = rows(factorMaskMatrix,  indices); // [B... * 1]     flag whether word has the factor in the first place
+#endif
+      auto factorLogits = logits_[g];                       // [B... * Ug] label-wise loss values (not aggregated yet)
       // For each location in [B...] select [indices[B...]]. If not using factor, select [0] and mask it out next.
-      auto factorLoss = lossFn(factorLogits->loss(), factorIndex); // [B... x 1]
+      auto factorLoss = lossFn(factorLogits->loss(), factorIndices); // [B... x 1]
       factorLoss = factorLoss * reshape(factorMask, factorLoss->shape()); // mask out factor for words that do not have that factor
       loss = loss ? (loss + factorLoss) : factorLoss; // [B... x 1]
     }
@@ -91,6 +110,28 @@ namespace marian {
     return y;
   }
 
+  void Logits::MaskedFactorIndices::push_back(size_t factorIndex) {
+    bool isValid = FactoredVocab::isFactorValid(factorIndex);
+    indices.push_back(isValid ? (WordIndex)factorIndex : 0);
+    masks.push_back((float)isValid);
+  }
+
+  std::vector<Logits::MaskedFactorIndices> Logits::factorize(const Words& words) const { // [numGroups][words.size()] -> breaks encoded Word into individual factor indices
+    if (!factoredVocab_) {
+      ABORT_IF(logits_.size() != 1, "Factors without factor mappings??");
+      return {MaskedFactorIndices(words)};
+    }
+    auto numGroups = factoredVocab_->getNumGroups();
+    std::vector<MaskedFactorIndices> res(numGroups);
+    for (size_t g = 0; g < numGroups; g++) {
+      auto& resg = res[g];
+      resg.reserve(words.size());
+      for (const auto& word : words)
+        resg.push_back(factoredVocab_->getFactor(word, g));
+    }
+    return res;
+  }
+
   Logits Logits::withCounts(const Expr& count) const { // create new Logits with 'count' implanted into all logits_
     std::vector<Ptr<RationalLoss>> newLogits;
     for (const auto& l : logits_)
diff --git a/src/layers/generic.h b/src/layers/generic.h
old mode 100644
new mode 100755
index 35183213e..2958162f2
--- a/src/layers/generic.h
+++ b/src/layers/generic.h
@@ -76,8 +76,19 @@ class Logits {
     Expr getLogits() const; // assume it holds logits: get them, possibly aggregating over factors
     Ptr<RationalLoss> getRationalLoss() const; // assume it holds a loss: get that
     Expr applyLossFunction(const Words& labels, const std::function<Expr(Expr/*logits*/,Expr/*indices*/)>& lossFn) const;
+
+    struct MaskedFactorIndices {
+      std::vector<WordIndex> indices; // factor index, or 0 if masked
+      std::vector<float> masks;
+      void reserve(size_t n) { indices.reserve(n); masks.reserve(n); }
+      void push_back(size_t factorIndex); // push back into both arrays, setting mask and index to 0 for invalid entries
+      MaskedFactorIndices() {}
+      MaskedFactorIndices(const Words& words) { indices = toWordIndexVector(words); } // we can leave masks uninitialized for this special use case
+    };
+    std::vector<MaskedFactorIndices> factorize(const Words& words) const; // breaks encoded Word into individual factor indices
     float getLogitAt(size_t i) const { return getLogits()->val()->get(i); } // @TODO: avoid the fully expanded logits
-    void assign(const Logits& other) {
+
+    void assign(const Logits& other) { // @TODO: we can remove this
       //ABORT_IF(!empty() && getNumFactors() != other.getNumFactors(),
       //         "Logits assignment cannot change number of factors");
       *this = other;
@@ -86,6 +97,14 @@ class Logits {
     bool empty() const { return logits_.empty(); }
     Logits withCounts(const Expr& count) const; // create new Logits with 'count' implanted into all logits_
 private:
+    // helper functions
+    Ptr<ExpressionGraph> graph() const;
+    Expr constant(const Shape& shape, const std::vector<float>&    data) const { return graph()->constant(shape, inits::from_vector(data), Type::float32); }
+    Expr constant(const Shape& shape, const std::vector<uint32_t>& data) const { return graph()->constant(shape, inits::from_vector(data), Type::uint32);  }
+    template<typename T> Expr constant(const std::vector<T>& data) const { return constant(Shape{(int)data.size()}, data); } // same as constant() but assuming vector
+    Expr indices(const std::vector<uint32_t>& data) const { return graph()->indices(data); } // actually the same as constant(data) for this data type
+private:
+    // members
     // @HACK: The interplay between Logits and RationalLoss is weird. Here, we allow RationalLoss with count == nullptr.
     std::vector<Ptr<RationalLoss>> logits_;
     Ptr<FactoredVocab> factoredVocab_;

From 518f3c33c6db779078f96da89563737ff7032b0b Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Thu, 7 Feb 2019 16:48:57 -0800
Subject: [PATCH 301/838] removed getFactorIndices() and getFactorMasks(),
 since they are no longer needed after last change

---
 src/data/factored_vocab.cpp | 10 +++++-----
 src/data/factored_vocab.h   |  8 ++++----
 src/layers/generic.cpp      |  5 ++---
 src/layers/generic.h        |  2 +-
 4 files changed, 12 insertions(+), 13 deletions(-)

diff --git a/src/data/factored_vocab.cpp b/src/data/factored_vocab.cpp
index 21711422f..153741e35 100755
--- a/src/data/factored_vocab.cpp
+++ b/src/data/factored_vocab.cpp
@@ -206,17 +206,17 @@ std::pair<WordIndex, bool> FactoredVocab::getFactorUnit(Word word, size_t groupI
 
 void FactoredVocab::constructNormalizationInfoForVocab() {
   // create mappings needed for normalization in factored outputs
-  size_t numGroups = groupPrefixes_.size();
+  //size_t numGroups = groupPrefixes_.size();
   size_t vocabSize = vocab_.size();
-  factorMasks_  .resize(numGroups, std::vector<float>(vocabSize, 0));     // [g][v] 1.0 if word v has factor g
-  factorIndices_.resize(numGroups, std::vector<IndexType>(vocabSize, 0)); // [g][v] index of factor (or any valid index if it does not have it; we use 0)
+  //factorMasks_  .resize(numGroups, std::vector<float>(vocabSize, 0));     // [g][v] 1.0 if word v has factor g
+  //factorIndices_.resize(numGroups, std::vector<IndexType>(vocabSize, 0)); // [g][v] index of factor (or any valid index if it does not have it; we use 0)
   gapLogMask_.resize(vocabSize, -1e8f);
   for (WordIndex v = 0; v < vocabSize; v++) {
     for (auto u : factorMap_[v]) {
       auto g = factorGroups_[u]; // convert u to relative u within factor group range
       ABORT_IF(u < groupRanges_[g].first || u >= groupRanges_[g].second, "Invalid factorGroups_ entry??");
-      factorIndices_[g][v] = (IndexType)(u - groupRanges_[g].first);
-      factorMasks_[g][v] = 1.0f;
+      //factorIndices_[g][v] = (IndexType)(u - groupRanges_[g].first);
+      //factorMasks_[g][v] = 1.0f;
       gapLogMask_[v] = 0.0f; // valid entry
     }
   }
diff --git a/src/data/factored_vocab.h b/src/data/factored_vocab.h
index 762e21d52..de2bd34f1 100755
--- a/src/data/factored_vocab.h
+++ b/src/data/factored_vocab.h
@@ -45,8 +45,8 @@ class FactoredVocab : public IVocab {
   const CSRData& getGlobalFactorMatrix() const { return globalFactorMatrix_; }   // [v,u] (sparse) -> =1 if u is factor of v  --only used in getLogits()
   size_t getNumGroups() const { return groupRanges_.size(); }
   std::pair<size_t, size_t>     getGroupRange(size_t g)    const { return groupRanges_[g]; }   // [g] -> (u_begin, u_end)
-  const std::vector<float>&     getFactorMasks(size_t g)   const { return factorMasks_[g]; }   // [g][v] 1.0 if word v has factor g
-  const std::vector<IndexType>& getFactorIndices(size_t g) const { return factorIndices_[g]; } // [g][v] local index u_g = u - u_g,begin of factor g for word v; 0 if not a factor
+  //const std::vector<float>&     getFactorMasks(size_t g)   const { return factorMasks_[g]; }   // [g][v] 1.0 if word v has factor g
+  //const std::vector<IndexType>& getFactorIndices(size_t g) const { return factorIndices_[g]; } // [g][v] local index u_g = u - u_g,begin of factor g for word v; 0 if not a factor
   const std::vector<float>& getGapLogMask() const { return gapLogMask_; } // [v] -inf if v is a gap entry, else 0
 
   // convert representations
@@ -96,8 +96,8 @@ class FactoredVocab : public IVocab {
   std::vector<std::pair<size_t, size_t>> groupRanges_; // [group id g] -> (u_begin,u_end) index range of factors u for this group. These don't overlap.
   Shape factorShape_;                                  // [g] number of factors in each factor group
   std::vector<size_t> factorStrides_;                  // [g] stride for factor dimension
-  std::vector<std::vector<float>>     factorMasks_;    // [g][v] 1.0 if word v has factor g
-  std::vector<std::vector<IndexType>> factorIndices_;  // [g][v] relative index u - u_begin of factor g (or any valid index if it does not have it; we use 0)
+  //std::vector<std::vector<float>>     factorMasks_;    // [g][v] 1.0 if word v has factor g
+  //std::vector<std::vector<IndexType>> factorIndices_;  // [g][v] relative index u - u_begin of factor g (or any valid index if it does not have it; we use 0)
   std::vector<float> gapLogMask_;                      // [v] -1e8 if this is a gap, else 0
 };
 
diff --git a/src/layers/generic.cpp b/src/layers/generic.cpp
index 18f095875..80398ee08 100755
--- a/src/layers/generic.cpp
+++ b/src/layers/generic.cpp
@@ -39,8 +39,7 @@ namespace marian {
     auto numGroups = factoredVocab_->getNumGroups();
 
     // split labels into individual factor labels
-    // @TODO: Is there value in having factorize() return Exprs? That would allow to move some stuff to GPU.
-    auto allMaskedFactoredLabels = factorize(labels); // [numGroups][labels.size()] = [numGroups][B... flattened]
+    auto allMaskedFactoredLabels = factorizeWords(labels); // [numGroups][labels.size()] = [numGroups][B... flattened]
 
     //Expr indices = this->indices(toWordIndexVector(labels));
     // accumulate all CEs for all words that have the factor
@@ -116,7 +115,7 @@ namespace marian {
     masks.push_back((float)isValid);
   }
 
-  std::vector<Logits::MaskedFactorIndices> Logits::factorize(const Words& words) const { // [numGroups][words.size()] -> breaks encoded Word into individual factor indices
+  std::vector<Logits::MaskedFactorIndices> Logits::factorizeWords(const Words& words) const { // [numGroups][words.size()] -> breaks encoded Word into individual factor indices
     if (!factoredVocab_) {
       ABORT_IF(logits_.size() != 1, "Factors without factor mappings??");
       return {MaskedFactorIndices(words)};
diff --git a/src/layers/generic.h b/src/layers/generic.h
index 2958162f2..e6fd5c30b 100755
--- a/src/layers/generic.h
+++ b/src/layers/generic.h
@@ -85,7 +85,7 @@ class Logits {
       MaskedFactorIndices() {}
       MaskedFactorIndices(const Words& words) { indices = toWordIndexVector(words); } // we can leave masks uninitialized for this special use case
     };
-    std::vector<MaskedFactorIndices> factorize(const Words& words) const; // breaks encoded Word into individual factor indices
+    std::vector<MaskedFactorIndices> factorizeWords(const Words& words) const; // breaks encoded Word into individual factor indices
     float getLogitAt(size_t i) const { return getLogits()->val()->get(i); } // @TODO: avoid the fully expanded logits
 
     void assign(const Logits& other) { // @TODO: we can remove this

From 5d46e103cd11a20ab060ad52ac793d75cc2cf34c Mon Sep 17 00:00:00 2001
From: Roman Grundkiewicz <rgrundki@exseed.ed.ac.uk>
Date: Fri, 8 Feb 2019 14:24:21 +0000
Subject: [PATCH 302/838] Add and clean comments

---
 src/common/cli_wrapper.cpp |  25 ++++----
 src/common/cli_wrapper.h   | 118 ++++++++++++++-----------------------
 2 files changed, 59 insertions(+), 84 deletions(-)

diff --git a/src/common/cli_wrapper.cpp b/src/common/cli_wrapper.cpp
index 161a61d8f..863dc02ad 100644
--- a/src/common/cli_wrapper.cpp
+++ b/src/common/cli_wrapper.cpp
@@ -167,19 +167,12 @@ void CLIWrapper::parseAliases() {
     }
   }
 
-  // Remove aliases from the global config to avoid redundancy when dumping/reading config files
+  // Remove aliases from the global config to avoid redundancy when writing/reading config files
   for(const auto &key : parsedAliases) {
     config_.remove(key);
   }
 }
 
-std::string CLIWrapper::failureMessage(const CLI::App *app, const CLI::Error &e) {
-  std::string header = "Error: " + std::string(e.what()) + "\n";
-  if(app->get_help_ptr() != nullptr)
-    header += "Run with " + app->get_help_ptr()->get_name() + " for more information.\n";
-  return header;
-}
-
 void CLIWrapper::updateConfig(const YAML::Node &config, const std::string &errorMsg) {
   auto cmdOptions = getParsedOptionNames();
   // Keep track of unrecognized options from the provided config
@@ -226,18 +219,21 @@ void CLIWrapper::updateConfig(const YAML::Node &config, const std::string &error
   ABORT_IF(!unknownOpts.empty(), errorMsg + ": " + utils::join(unknownOpts, ", "));
 }
 
-std::string CLIWrapper::dumpConfig(bool skipDefault /*= false*/) const {
+std::string CLIWrapper::dumpConfig(bool skipUnmodified /*= false*/) const {
   YAML::Emitter out;
   out << YAML::Comment("Marian configuration file generated at " + timer::currentDate()
                        + " with version " + buildVersion());
   out << YAML::BeginMap;
   std::string comment;
+  // Iterate option names in the same order as they have been created
   for(const auto &key : getOrderedOptionNames()) {
-    // do not proceed keys that are removed from config_
+    // Do not dump options that were removed from config_
     if(!config_[key])
       continue;
-    if(skipDefault && !options_.at(key).modified)
+    // Do not dump options that were not passed via the command line
+    if(skipUnmodified && !options_.at(key).modified)
       continue;
+    // Put the group name as a comment before the first option in the group
     auto group = options_.at(key).opt->get_group();
     if(comment != group) {
       if(!comment.empty())
@@ -274,5 +270,12 @@ std::vector<std::string> CLIWrapper::getOrderedOptionNames() const {
   return keys;
 }
 
+std::string CLIWrapper::failureMessage(const CLI::App *app, const CLI::Error &e) {
+  std::string header = "Error: " + std::string(e.what()) + "\n";
+  if(app->get_help_ptr() != nullptr)
+    header += "Run with " + app->get_help_ptr()->get_name() + " for more information.\n";
+  return header;
+}
+
 }  // namespace cli
 }  // namespace marian
diff --git a/src/common/cli_wrapper.h b/src/common/cli_wrapper.h
index f0ca51c34..851abfe8d 100644
--- a/src/common/cli_wrapper.h
+++ b/src/common/cli_wrapper.h
@@ -16,29 +16,7 @@ namespace marian {
 class Options;
 
 namespace cli {
-
-// Try to determine the width of the terminal
-//
-// TODO: make use of it in the current CLI or remove. This is an old code used
-// for boost::program_options and might not be needed anymore.
-//static uint16_t guess_terminal_width(uint16_t max_width = 0,
-//                                     uint16_t default_width = 180);
-
-// TODO: use validators in ConfigParser
-namespace validators {
-const CLI::detail::ExistingFileValidator file_exists;
-const CLI::detail::ExistingDirectoryValidator dir_exists;
-const CLI::detail::ExistingPathValidator path_exists;
-
-const CLI::detail::NonexistentPathValidator path_not_exists;
-
-typedef CLI::Range range;
-}
-
-/**
- * The helper class for cli::CLIWrapper handling formatting of options and their
- * descriptions.
- */
+// The helper class for cli::CLIWrapper handling formatting of options and their descriptions.
 class CLIFormatter : public CLI::Formatter {
 public:
   CLIFormatter(size_t columnWidth, size_t screenWidth);
@@ -48,65 +26,61 @@ class CLIFormatter : public CLI::Formatter {
   size_t screenWidth_{0};
 };
 
-// @TODO: in this file review the use of naked pointers. We use Ptr<Type> anywhere else,
-// what's up with that?
 
-// Helper structure storing an option object, the associated variable and creation index
+/**
+ * Helper tuple storing an option object, the associated variable and creation index
+ *
+ * Note: a bare pointer is used for the CLI::Option as this comes from the CLI11 library. Removing
+ * it would need deep modifications in the 3rd party library, that is not desirable.
+ */
 struct CLIOptionTuple {
-  CLI::Option *opt;
-  Ptr<any_type> var;
-  size_t idx{0};
-  bool modified{false};
+  CLI::Option *opt;     // a pointer to an option object from CLI11
+  Ptr<any_type> var;    // value assigned to the option via command-line
+  size_t idx{0};        // order in which the option was created
+  bool modified{false}; // whether the option occurred as a command-line argument or not
 };
 
-// Helper structure used for aliases storing an option key, value, and options to be expanded in the
-// form of a YAML config
+// Helper tuple for aliases storing an option key, value, and options to be expanded
 struct CLIAliasTuple {
   std::string key;    // alias option name
   std::string value;  // value for the alias option indicating that it should be expanded
   YAML::Node config;  // config with options that the alias adds
 };
 
+
 /**
  * @brief The class used to define and parse command-line arguments.
  *
- * It is a wrapper around https://github.com/CLIUtils/CLI11 that stores defined
- * command-line arguments in a YAML object.
+ * It is a wrapper around https://github.com/CLIUtils/CLI11 that stores defined command-line
+ * arguments in a YAML object.
  *
- * Usage outline: first call add() methods to create all the options; then call
- * parse(argv, argc) to parse command line and get defined options and their
- * values in a YAML object. The object can be also obtained later by calling
+ * Usage outline: first call add() methods to create all the options; then call parse(argv, argc) to
+ * parse command line and get defined options and their values in a YAML object; finally call
+ * parseAliases() to expand alias options. The config object can be also obtained later by calling
  * getConfig().
  *
- * Options are organized in option groups. Each option group has a header that
- * preceeds all options in the group. The header for the default option group
- * can be set from the class constructor.
+ * Options are organized in option groups. Each option group has a header that preceeds all options
+ * in the group. The header for the default option group can be set from the class constructor.
  */
 class CLIWrapper {
 private:
   // Map with option names and option tuples
   std::unordered_map<std::string, CLIOptionTuple> options_;
-  // Counter for created options
+  // Counter for created options to keep track of order in which options were created
   size_t counter_{0};
-  // List of alias tuples
-  std::vector<CLIAliasTuple> aliases_;
-  // Command-line argument parser
-  Ptr<CLI::App> app_;
+  std::vector<CLIAliasTuple> aliases_;  // List of alias tuples
+
+  Ptr<CLI::App> app_;                   // Command-line argument parser from CLI11
 
-  // Name of the default option group
-  std::string defaultGroup_{""};
-  // Name of the current option group
-  std::string currentGroup_{""};
+  std::string defaultGroup_{""};        // Name of the default option group
+  std::string currentGroup_{""};        // Name of the current option group
 
-  // Reference to the main config object
-  YAML::Node &config_;
+  YAML::Node &config_;                  // Reference to the main config object
 
   // Option for --version flag. This is a special flag and similarly to --help,
   // the key "version" will be not added into the YAML config
   CLI::Option *optVersion_;
 
-  static std::string failureMessage(const CLI::App *app, const CLI::Error &e);
-
   // Extract option name from a comma-separated list of long and short options, e.g. 'help' from
   // '--help,-h'
   std::string keyName(const std::string &args) const {
@@ -116,6 +90,13 @@ class CLIWrapper {
         .front();                                                       // get first long name
   }
 
+  // Get names of options passed via command-line
+  std::unordered_set<std::string> getParsedOptionNames() const;
+  // Get option names in the same order as they are created
+  std::vector<std::string> getOrderedOptionNames() const;
+
+  static std::string failureMessage(const CLI::App *app, const CLI::Error &e);
+
 public:
   /**
    * @brief Create an instance of the command-line argument parser
@@ -127,8 +108,7 @@ class CLIWrapper {
    * @param header Header text for the main option group
    * @param footer Text displayed after the list of options
    * @param columnWidth Width of the column with option names
-   * @param screenWidth Maximum allowed width for help messages, 0 means no
-   *  limit
+   * @param screenWidth Maximum allowed width for help messages, 0 means no limit
    */
   CLIWrapper(YAML::Node &config,
              const std::string &description = "",
@@ -138,8 +118,7 @@ class CLIWrapper {
              size_t screenWidth = 0);
 
   /**
-   * @brief Create an instance of the command-line argument parser,
-   * short-cuft for Options object.
+   * @brief Create an instance of the command-line argument parser, short-cut for Options object.
    *
    * @see Other constructor
    */
@@ -173,13 +152,11 @@ class CLIWrapper {
   }
 
   /**
-   * @brief Define an option without an explicit default value. The implicit
-   * default value is T()
+   * @brief Define an option without an explicit default value. The implicit default value is T()
    *
-   * The option will be defined in the config file even if not given as a
-   * command-line argument. The implicit default value for a boolean or numeric
-   * option is 0, for a string is an empty string, and for a vector is an empty
-   * vector.
+   * The option will be defined in the config file even if not given as a command-line argument. The
+   * implicit default value for a boolean or numeric option is 0, for a string is an empty string,
+   * and for a vector is an empty vector.
    *
    * Implicit default values will *NOT* appear in help messages.
    *
@@ -188,8 +165,7 @@ class CLIWrapper {
    *
    * @return Option object
    *
-   * TODO: require to always state the default value creating the parser as this
-   * will be clearer
+   * @TODO: require to always state the default value creating the parser as this will be clearer
    */
   template <typename T>
   CLI::Option *add(const std::string &args, const std::string &help) {
@@ -244,9 +220,10 @@ class CLIWrapper {
    * @brief Expand aliases based on arguments parsed with parse(int, char**)
    *
    * Should be called after parse(int, char**) to take an effect.  If any alias tries to expand an
-   * undefined option, the method will abort.
+   * undefined option, the method will abort the program.
    *
-   * All options defined as aliases are removed from the config object.
+   * All options defined as aliases are removed from the global config object to avoid redundancy
+   * when options are dumped (explicitly or implicitly) to a config file.
    */
   void parseAliases();
 
@@ -264,14 +241,9 @@ class CLIWrapper {
   void updateConfig(const YAML::Node &config, const std::string &errorMsg);
 
   // Get textual YAML representation of the config
-  std::string dumpConfig(bool skipDefault = false) const;
+  std::string dumpConfig(bool skipUnmodified = false) const;
 
 private:
-  // Get names of options passed via command-line
-  std::unordered_set<std::string> getParsedOptionNames() const;
-  // Get option names in the same order as they are created
-  std::vector<std::string> getOrderedOptionNames() const;
-
   template <typename T,
             // options with numeric and string-like values
             CLI::enable_if_t<!CLI::is_bool<T>::value && !CLI::is_vector<T>::value,

From 3c0e38382c2b8241f4dd57e3dd0baa9fd03f10b5 Mon Sep 17 00:00:00 2001
From: Roman Grundkiewicz <rgrundki@exseed.ed.ac.uk>
Date: Fri, 8 Feb 2019 15:59:23 +0000
Subject: [PATCH 303/838] Introduce option priority

---
 src/common/cli_wrapper.cpp   | 27 +++++++++++++--------------
 src/common/cli_wrapper.h     | 24 ++++++++++++++----------
 src/common/config_parser.cpp |  2 +-
 3 files changed, 28 insertions(+), 25 deletions(-)

diff --git a/src/common/cli_wrapper.cpp b/src/common/cli_wrapper.cpp
index 863dc02ad..320edeca5 100644
--- a/src/common/cli_wrapper.cpp
+++ b/src/common/cli_wrapper.cpp
@@ -133,10 +133,6 @@ void CLIWrapper::parseAliases() {
   if(aliases_.empty())
     return;
 
-  // Names of all triggered/expanded aliases will be collected and removed from the config at the
-  // end.  We keep them in a set as multiple aliases can be defined for one option name (aka `key`).
-  std::set<std::string> parsedAliases;
-
   // Iterate all known aliases, each alias has a key, value, and config
   for(const auto &alias : aliases_) {
     // Check if the alias option exists in the config (it may come from command line or a config
@@ -157,23 +153,22 @@ void CLIWrapper::parseAliases() {
 
       if(expand) {
         // Update global config options with the config associated with the alias. Abort if the
-        // alias contains an undefined option
+        // alias contains an undefined option.
         updateConfig(alias.config,
+                     // Priority of each expanded option is the same as the priority of the alias
+                     options_[alias.key].priority,
                      "Unknown option(s) in alias '" + alias.key + ": " + alias.value + "'");
       }
-
-      // Collect the alias option as they will be removed at the end
-      parsedAliases.insert(alias.key);
     }
   }
 
   // Remove aliases from the global config to avoid redundancy when writing/reading config files
-  for(const auto &key : parsedAliases) {
-    config_.remove(key);
+  for(const auto &alias : aliases_) {
+    config_.remove(alias.key);
   }
 }
 
-void CLIWrapper::updateConfig(const YAML::Node &config, const std::string &errorMsg) {
+void CLIWrapper::updateConfig(const YAML::Node &config, int priority, const std::string &errorMsg) {
   auto cmdOptions = getParsedOptionNames();
   // Keep track of unrecognized options from the provided config
   std::vector<std::string> unknownOpts;
@@ -191,10 +186,14 @@ void CLIWrapper::updateConfig(const YAML::Node &config, const std::string &error
 
     // Check if an incoming option has been defined in CLI
     if(options_.count(key)) {
+      // Do not proceed if the priority of incoming option is not greater than the existing option
+      if(priority <= options_[key].priority) {
+        continue;
+      }
       // Check if the option exists in the global config and types match
       if(config_[key] && config_[key].Type() == it.second.Type()) {
         config_[key] = YAML::Clone(it.second);
-        options_[key].modified = true;
+        options_[key].priority = priority;
         // If types doesn't match, try to convert
       } else {
         // Default value is a sequence and incoming node is a scalar, hence we can upcast to
@@ -205,7 +204,7 @@ void CLIWrapper::updateConfig(const YAML::Node &config, const std::string &error
           YAML::Node sequence;
           sequence.push_back(YAML::Clone(it.second));
           config_[key] = sequence;  // overwrite to replace default values
-          options_[key].modified = true;
+          options_[key].priority = priority;
         } else {
           // Cannot convert other non-matching types, e.g. scalar <- list should fail
           ABORT("Cannot convert values for the option: " + key);
@@ -231,7 +230,7 @@ std::string CLIWrapper::dumpConfig(bool skipUnmodified /*= false*/) const {
     if(!config_[key])
       continue;
     // Do not dump options that were not passed via the command line
-    if(skipUnmodified && !options_.at(key).modified)
+    if(skipUnmodified && options_.at(key).priority == 0)
       continue;
     // Put the group name as a comment before the first option in the group
     auto group = options_.at(key).opt->get_group();
diff --git a/src/common/cli_wrapper.h b/src/common/cli_wrapper.h
index 851abfe8d..ec3cd47b4 100644
--- a/src/common/cli_wrapper.h
+++ b/src/common/cli_wrapper.h
@@ -37,7 +37,7 @@ struct CLIOptionTuple {
   CLI::Option *opt;     // a pointer to an option object from CLI11
   Ptr<any_type> var;    // value assigned to the option via command-line
   size_t idx{0};        // order in which the option was created
-  bool modified{false}; // whether the option occurred as a command-line argument or not
+  int priority{0};      // priority: 0 - default value, 1 - from config, 2 - from command line
 };
 
 // Helper tuple for aliases storing an option key, value, and options to be expanded
@@ -227,18 +227,22 @@ class CLIWrapper {
    */
   void parseAliases();
 
-  /*
-   * @brief Overwrite values for unparsed options
+  /**
+   * @brief Overwrite options with lower priority
+   *
+   * Values for options with lower priority than the provided priority remain unchanged. This allows
+   * for overwritting default options by options from config files, or both by options provided in
+   * the command line.
    *
-   * Default values are overwritten with the options from the provided config, while parsed
-   * command-line options remain unchanged.  This should be a preferred way of updating config
-   * options as the class keeps track of options, which values have changed.
+   * This should be a preferred way of updating config options as the class keeps track of options,
+   * which values have changed.
    *
    * @param config YAML config with new default values for options
+   * @param priority priority of incoming options
    * @param errorMsg error message printed if config contains undefined keys. The message is
    *   appended with ": <comma-separated list of invalid options>"
    */
-  void updateConfig(const YAML::Node &config, const std::string &errorMsg);
+  void updateConfig(const YAML::Node &config, int priority, const std::string &errorMsg);
 
   // Get textual YAML representation of the config
   std::string dumpConfig(bool skipUnmodified = false) const;
@@ -263,7 +267,7 @@ class CLIWrapper {
 
     // callback function collecting a command-line argument
     CLI::callback_t fun = [this, key](CLI::results_t res) {
-      options_[key].modified = true;
+      options_[key].priority = 2;
       // get variable associated with the option
       auto &var = options_[key].var->as<T>();
       // store parser result in var
@@ -310,7 +314,7 @@ class CLIWrapper {
 
     // callback function collecting command-line arguments
     CLI::callback_t fun = [this, key](CLI::results_t res) {
-      options_[key].modified = true;
+      options_[key].priority = 2;
       // get vector variable associated with the option
       auto &vec = options_[key].var->as<T>();
       vec.clear();
@@ -367,7 +371,7 @@ class CLIWrapper {
 
     // callback function setting the flag
     CLI::callback_t fun = [this, key](CLI::results_t res) {
-      options_[key].modified = true;
+      options_[key].priority = 2;
       // get parser result, it is safe as boolean options have an implicit value
       auto val = res[0];
       auto ret = true;
diff --git a/src/common/config_parser.cpp b/src/common/config_parser.cpp
index cd8cc2db9..cf6951b6f 100644
--- a/src/common/config_parser.cpp
+++ b/src/common/config_parser.cpp
@@ -787,7 +787,7 @@ void ConfigParser::parseOptions(int argc, char** argv, bool doValidate) {
   auto configPaths = findConfigPaths();
   if(!configPaths.empty()) {
     auto config = loadConfigFiles(configPaths);
-    cli.updateConfig(config, "There are option(s) in a config file that are not expected");
+    cli.updateConfig(config, 1, "There are option(s) in a config file that are not expected");
   }
 
   if(get<bool>("interpolate-env-vars")) {

From 20948750cb5e3cb6d46b2c681f446ed75a43bc12 Mon Sep 17 00:00:00 2001
From: Roman Grundkiewicz <rgrundki@exseed.ed.ac.uk>
Date: Fri, 8 Feb 2019 16:10:46 +0000
Subject: [PATCH 304/838] Add enum cli::Priority

---
 src/common/cli_wrapper.cpp   |  4 ++--
 src/common/cli_wrapper.h     | 36 +++++++++++++++++++-----------------
 src/common/config_parser.cpp |  4 +++-
 src/common/config_parser.h   |  1 +
 4 files changed, 25 insertions(+), 20 deletions(-)

diff --git a/src/common/cli_wrapper.cpp b/src/common/cli_wrapper.cpp
index 320edeca5..3bae8c01b 100644
--- a/src/common/cli_wrapper.cpp
+++ b/src/common/cli_wrapper.cpp
@@ -168,7 +168,7 @@ void CLIWrapper::parseAliases() {
   }
 }
 
-void CLIWrapper::updateConfig(const YAML::Node &config, int priority, const std::string &errorMsg) {
+void CLIWrapper::updateConfig(const YAML::Node &config, cli::Priority priority, const std::string &errorMsg) {
   auto cmdOptions = getParsedOptionNames();
   // Keep track of unrecognized options from the provided config
   std::vector<std::string> unknownOpts;
@@ -230,7 +230,7 @@ std::string CLIWrapper::dumpConfig(bool skipUnmodified /*= false*/) const {
     if(!config_[key])
       continue;
     // Do not dump options that were not passed via the command line
-    if(skipUnmodified && options_.at(key).priority == 0)
+    if(skipUnmodified && options_.at(key).priority == cli::Priority::DefaultValue)
       continue;
     // Put the group name as a comment before the first option in the group
     auto group = options_.at(key).opt->get_group();
diff --git a/src/common/cli_wrapper.h b/src/common/cli_wrapper.h
index ec3cd47b4..4347cb329 100644
--- a/src/common/cli_wrapper.h
+++ b/src/common/cli_wrapper.h
@@ -16,37 +16,39 @@ namespace marian {
 class Options;
 
 namespace cli {
-// The helper class for cli::CLIWrapper handling formatting of options and their descriptions.
-class CLIFormatter : public CLI::Formatter {
-public:
-  CLIFormatter(size_t columnWidth, size_t screenWidth);
-  virtual std::string make_option_desc(const CLI::Option *) const override;
-
-private:
-  size_t screenWidth_{0};
-};
 
+// Option priority
+enum struct Priority { DefaultValue = 0, ConfigFile = 1, CommandLine = 2 };
 
 /**
  * Helper tuple storing an option object, the associated variable and creation index
  *
- * Note: a bare pointer is used for the CLI::Option as this comes from the CLI11 library. Removing
- * it would need deep modifications in the 3rd party library, that is not desirable.
+ * Note: bare pointers are used for CLI::Option objects as this comes from the CLI11 library.
+ * Removing it would require deep modifications in the 3rd party library, what we want to avoid.
  */
 struct CLIOptionTuple {
   CLI::Option *opt;     // a pointer to an option object from CLI11
   Ptr<any_type> var;    // value assigned to the option via command-line
   size_t idx{0};        // order in which the option was created
-  int priority{0};      // priority: 0 - default value, 1 - from config, 2 - from command line
+  Priority priority{cli::Priority::DefaultValue};
 };
 
-// Helper tuple for aliases storing an option key, value, and options to be expanded
+// Helper tuple for aliases storing the alias name, value, and options to be expanded
 struct CLIAliasTuple {
   std::string key;    // alias option name
   std::string value;  // value for the alias option indicating that it should be expanded
   YAML::Node config;  // config with options that the alias adds
 };
 
+// The helper class for cli::CLIWrapper handling formatting of options and their descriptions.
+class CLIFormatter : public CLI::Formatter {
+public:
+  CLIFormatter(size_t columnWidth, size_t screenWidth);
+  virtual std::string make_option_desc(const CLI::Option *) const override;
+
+private:
+  size_t screenWidth_{0};
+};
 
 /**
  * @brief The class used to define and parse command-line arguments.
@@ -242,7 +244,7 @@ class CLIWrapper {
    * @param errorMsg error message printed if config contains undefined keys. The message is
    *   appended with ": <comma-separated list of invalid options>"
    */
-  void updateConfig(const YAML::Node &config, int priority, const std::string &errorMsg);
+  void updateConfig(const YAML::Node &config, cli::Priority priority, const std::string &errorMsg);
 
   // Get textual YAML representation of the config
   std::string dumpConfig(bool skipUnmodified = false) const;
@@ -267,7 +269,7 @@ class CLIWrapper {
 
     // callback function collecting a command-line argument
     CLI::callback_t fun = [this, key](CLI::results_t res) {
-      options_[key].priority = 2;
+      options_[key].priority = cli::Priority::CommandLine;
       // get variable associated with the option
       auto &var = options_[key].var->as<T>();
       // store parser result in var
@@ -314,7 +316,7 @@ class CLIWrapper {
 
     // callback function collecting command-line arguments
     CLI::callback_t fun = [this, key](CLI::results_t res) {
-      options_[key].priority = 2;
+      options_[key].priority = cli::Priority::CommandLine;
       // get vector variable associated with the option
       auto &vec = options_[key].var->as<T>();
       vec.clear();
@@ -371,7 +373,7 @@ class CLIWrapper {
 
     // callback function setting the flag
     CLI::callback_t fun = [this, key](CLI::results_t res) {
-      options_[key].priority = 2;
+      options_[key].priority = cli::Priority::CommandLine;
       // get parser result, it is safe as boolean options have an implicit value
       auto val = res[0];
       auto ret = true;
diff --git a/src/common/config_parser.cpp b/src/common/config_parser.cpp
index cf6951b6f..6fce1bef1 100644
--- a/src/common/config_parser.cpp
+++ b/src/common/config_parser.cpp
@@ -787,7 +787,9 @@ void ConfigParser::parseOptions(int argc, char** argv, bool doValidate) {
   auto configPaths = findConfigPaths();
   if(!configPaths.empty()) {
     auto config = loadConfigFiles(configPaths);
-    cli.updateConfig(config, 1, "There are option(s) in a config file that are not expected");
+    cli.updateConfig(config,
+                     cli::Priority::ConfigFile,
+                     "There are option(s) in a config file that are not expected");
   }
 
   if(get<bool>("interpolate-env-vars")) {
diff --git a/src/common/config_parser.h b/src/common/config_parser.h
index d6822cc1a..ef2aee950 100644
--- a/src/common/config_parser.h
+++ b/src/common/config_parser.h
@@ -14,6 +14,7 @@
 namespace marian {
 
 namespace cli {
+// CLI mode
 enum struct mode { training, translation, scoring, server };
 }  // namespace cli
 

From 614876be4159331f7cfc6dc146d6770d652c0405 Mon Sep 17 00:00:00 2001
From: Roman Grundkiewicz <rgrundki@exseed.ed.ac.uk>
Date: Fri, 8 Feb 2019 16:17:25 +0000
Subject: [PATCH 305/838] Rename --dump-config explain to --dump-config expand

---
 src/common/config_parser.cpp | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/src/common/config_parser.cpp b/src/common/config_parser.cpp
index 6fce1bef1..766b5e0a7 100644
--- a/src/common/config_parser.cpp
+++ b/src/common/config_parser.cpp
@@ -79,7 +79,7 @@ void ConfigParser::addOptionsGeneral(cli::CLIWrapper& cli) {
   cli.add<bool>("--relative-paths",
     "All paths are relative to the config file location");
   cli.add<std::string>("--dump-config",
-    "Dump current (modified) configuration to stdout and exit. Possible values: full, minimal")
+    "Dump current (modified) configuration to stdout and exit. Possible values: full, minimal, expand")
     ->implicit_val("full");
   // clang-format on
 }
@@ -809,11 +809,11 @@ void ConfigParser::parseOptions(int argc, char** argv, bool doValidate) {
     auto dumpMode = get<std::string>("dump-config");
     config_.remove("dump-config");
 
-    if(dumpMode == "explain") {
+    if(dumpMode == "expand") {
       cli.parseAliases();
     }
 
-    bool minimal = (dumpMode == "minimal" || dumpMode == "explain");
+    bool minimal = (dumpMode == "minimal" || dumpMode == "expand");
     std::cout << cli.dumpConfig(minimal) << std::endl;
     exit(0);
   }

From a848e2dce5ee39e8132f218b932f552c994208f7 Mon Sep 17 00:00:00 2001
From: Roman Grundkiewicz <rgrundki@exseed.ed.ac.uk>
Date: Fri, 8 Feb 2019 16:41:10 +0000
Subject: [PATCH 306/838] Rename dump_

---
 src/common/config_parser.cpp    | 2 --
 src/common/config_validator.cpp | 6 +++---
 src/common/config_validator.h   | 6 ++++--
 3 files changed, 7 insertions(+), 7 deletions(-)

diff --git a/src/common/config_parser.cpp b/src/common/config_parser.cpp
index 766b5e0a7..de6b9b6aa 100644
--- a/src/common/config_parser.cpp
+++ b/src/common/config_parser.cpp
@@ -797,8 +797,6 @@ void ConfigParser::parseOptions(int argc, char** argv, bool doValidate) {
   }
 
   if(doValidate) {
-    // TODO: Do not check some constraints if --dump-config, e.g. -t
-    // this aborts the program on first validation error
     ConfigValidator(config_).validateOptions(mode_);
   }
 
diff --git a/src/common/config_validator.cpp b/src/common/config_validator.cpp
index 1566e1cfc..61365c39c 100644
--- a/src/common/config_validator.cpp
+++ b/src/common/config_validator.cpp
@@ -12,8 +12,8 @@ bool ConfigValidator::has(const std::string& key) const {
 
 ConfigValidator::ConfigValidator(const YAML::Node& config)
     : config_(config),
-      dump_(config["dump-config"] && !config["dump-config"].as<std::string>().empty()
-            && config["dump-config"].as<std::string>() != "false") {}
+      dumpConfigOnly_(config["dump-config"] && !config["dump-config"].as<std::string>().empty()
+                      && config["dump-config"].as<std::string>() != "false") {}
 
 ConfigValidator::~ConfigValidator() {}
 
@@ -59,7 +59,7 @@ void ConfigValidator::validateOptionsTranslation() const {
 
 void ConfigValidator::validateOptionsParallelData() const {
   // Do not check these constraints if only goal is to dump config
-  if(dump_)
+  if(dumpConfigOnly_)
     return;
 
   auto trainSets = get<std::vector<std::string>>("train-sets");
diff --git a/src/common/config_validator.h b/src/common/config_validator.h
index fb40ea6c6..0e73a9e39 100644
--- a/src/common/config_validator.h
+++ b/src/common/config_validator.h
@@ -8,15 +8,17 @@ namespace marian {
 class ConfigValidator {
 private:
   const YAML::Node& config_;
-  bool dump_{false};
 
   bool has(const std::string& key) const;
-
   template <typename T>
   T get(const std::string& key) const {
     return config_[key].as<T>();
   }
 
+  // The option --dump-config is used, so alleviate some constraints, e.g. we don't want to require
+  // --train-sets or --vocabs
+  bool dumpConfigOnly_{false};
+
   void validateOptionsTranslation() const;
   void validateOptionsParallelData() const;
   void validateOptionsScoring() const;

From cf5578b66e36456ac918410071238141d1fbb827 Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Fri, 8 Feb 2019 09:00:51 -0800
Subject: [PATCH 307/838] (minor cleanup)

---
 src/layers/generic.cpp       |  8 +++++++-
 src/layers/generic.h         | 12 +++---------
 src/models/states.h          |  2 +-
 src/translator/beam_search.h |  2 +-
 4 files changed, 12 insertions(+), 12 deletions(-)
 mode change 100644 => 100755 src/models/states.h

diff --git a/src/layers/generic.cpp b/src/layers/generic.cpp
index 80398ee08..e36d3e213 100755
--- a/src/layers/generic.cpp
+++ b/src/layers/generic.cpp
@@ -76,13 +76,19 @@ namespace marian {
     return logits_.front();
   }
 
+  // get logits for one factor group
+  Expr Logits::getFactoredLogits(size_t groupIndex) const {
+    ABORT_IF(empty(), "Attempted to read out logits on empty Logits object");
+    return logits_[groupIndex]->loss();
+  }
+
   // This function assumes that the object holds one or more factor logits, which are summed up
   // into output-vocab logits according to the factored model (with correct normalization of factors).
   Expr Logits::getLogits() const {
     ABORT_IF(empty(), "Attempted to read out logits on empty Logits object");
     if (!factoredVocab_) {
       ABORT_IF(logits_.size() != 1, "Factors without factor mappings??");
-      return logits_.front()->loss();
+      return getFactoredLogits(0);
     }
 
     // compute normalized factor log probs
diff --git a/src/layers/generic.h b/src/layers/generic.h
index e6fd5c30b..6befea76e 100755
--- a/src/layers/generic.h
+++ b/src/layers/generic.h
@@ -64,7 +64,6 @@ class FactoredVocab;
 // for factored embeddings.
 class RationalLoss;
 class Logits {
-    Logits& operator=(const Logits& other) = default;
 public:
     Logits() {}
     Logits(Ptr<RationalLoss> logits) { // single-output constructor
@@ -74,6 +73,7 @@ class Logits {
     Logits(std::vector<Ptr<RationalLoss>>&& logits, Ptr<FactoredVocab> embeddingFactorMapping) // factored-output constructor
       : logits_(std::move(logits)), factoredVocab_(embeddingFactorMapping) {}
     Expr getLogits() const; // assume it holds logits: get them, possibly aggregating over factors
+    Expr getFactoredLogits(size_t groupIndex) const; // get logits for only one factor group
     Ptr<RationalLoss> getRationalLoss() const; // assume it holds a loss: get that
     Expr applyLossFunction(const Words& labels, const std::function<Expr(Expr/*logits*/,Expr/*indices*/)>& lossFn) const;
 
@@ -87,13 +87,7 @@ class Logits {
     };
     std::vector<MaskedFactorIndices> factorizeWords(const Words& words) const; // breaks encoded Word into individual factor indices
     float getLogitAt(size_t i) const { return getLogits()->val()->get(i); } // @TODO: avoid the fully expanded logits
-
-    void assign(const Logits& other) { // @TODO: we can remove this
-      //ABORT_IF(!empty() && getNumFactors() != other.getNumFactors(),
-      //         "Logits assignment cannot change number of factors");
-      *this = other;
-    }
-    size_t getNumFactors() const { return logits_.size(); }
+    size_t getNumFactorGroups() const { return logits_.size(); }
     bool empty() const { return logits_.empty(); }
     Logits withCounts(const Expr& count) const; // create new Logits with 'count' implanted into all logits_
 private:
@@ -106,7 +100,7 @@ class Logits {
 private:
     // members
     // @HACK: The interplay between Logits and RationalLoss is weird. Here, we allow RationalLoss with count == nullptr.
-    std::vector<Ptr<RationalLoss>> logits_;
+    std::vector<Ptr<RationalLoss>> logits_; // [group id][B..., num factors in group]
     Ptr<FactoredVocab> factoredVocab_;
 };
 
diff --git a/src/models/states.h b/src/models/states.h
old mode 100644
new mode 100755
index c33cddc3c..f17038cca
--- a/src/models/states.h
+++ b/src/models/states.h
@@ -54,7 +54,7 @@ class DecoderState {
   }
 
   virtual Logits getLogProbs() const { return logProbs_; }
-  virtual void setLogProbs(Logits logProbs) { logProbs_.assign(logProbs); }
+  virtual void setLogProbs(Logits logProbs) { logProbs_ = logProbs; }
 
   // @TODO: should this be a constructor? Then derived classes can call this without the New<> in the loop
   virtual Ptr<DecoderState> select(const std::vector<IndexType>& selIdx,
diff --git a/src/translator/beam_search.h b/src/translator/beam_search.h
index 1dc05d193..336441f58 100755
--- a/src/translator/beam_search.h
+++ b/src/translator/beam_search.h
@@ -242,7 +242,7 @@ class BeamSearch {
       //**********************************************************************
       // create constant containing previous path scores for current beam
       // Also create mapping of hyp indices, which are not 1:1 if sentences complete.
-      std::vector<IndexType> hypIndices; // [localBeamsize, 1, dimBatch, 1] (flattened) index of hyp that the new top N originated from
+      std::vector<IndexType> hypIndices; // [localBeamsize, 1, dimBatch, 1] (flattened) index of prev hyp that the current hyp originated from
       std::vector<Word> prevWords;       // [localBeamsize, 1, dimBatch, 1] (flattened) predecessor word
       Expr prevPathScores;               // [localBeamSize, 1, dimBatch, 1], where the last axis broadcasts into vocab size when adding expandedPathScores
       if(t == 0) { // no scores yet

From fc60cf8c38849a996d5f0439fb068186d32a9dd0 Mon Sep 17 00:00:00 2001
From: Roman Grundkiewicz <rgrundki@exseed.ed.ac.uk>
Date: Fri, 8 Feb 2019 17:25:11 +0000
Subject: [PATCH 308/838] Move addAliases() to separate file

---
 src/CMakeLists.txt           |  1 +
 src/common/aliases.cpp       | 75 ++++++++++++++++++++++++++++++++++++
 src/common/config_parser.cpp | 56 ---------------------------
 3 files changed, 76 insertions(+), 56 deletions(-)
 create mode 100644 src/common/aliases.cpp

diff --git a/src/CMakeLists.txt b/src/CMakeLists.txt
index fd489e3e1..4549b020c 100644
--- a/src/CMakeLists.txt
+++ b/src/CMakeLists.txt
@@ -14,6 +14,7 @@ add_library(marian STATIC
   common/cli_wrapper.cpp
   common/config.cpp
   common/config_parser.cpp
+  common/aliases.cpp
   common/config_validator.cpp
   common/binary.cpp
   common/io.cpp
diff --git a/src/common/aliases.cpp b/src/common/aliases.cpp
new file mode 100644
index 000000000..11ca27fde
--- /dev/null
+++ b/src/common/aliases.cpp
@@ -0,0 +1,75 @@
+#include "common/config_parser.h"
+#include "common/definitions.h"
+
+namespace marian {
+
+/**
+ * Add all aliases
+ *
+ * An alias is a shortcut option for a predefined set of options. It is triggered if the option has
+ * the requested value. The alias option has to be first defined using cli.add<T>(). Defining
+ * multiple aliases for the same option name but with different value is allowed.
+ *
+ * Values are compared as std::string. If the alias option is a vector, the alias will be triggered
+ * if the requested value exists in that vector at least once.
+ *
+ * @see CLIWrapper::alias()
+ *
+ * The order of alias definitions *does* matter: options from later aliases override earlier
+ * regardless of its order in the command line or config file.
+ */
+void ConfigParser::addAliases(cli::CLIWrapper& cli) {
+  // Options setting the BiDeep architecture proposed in http://www.aclweb.org/anthology/W17-4710
+  cli.alias("best-deep", "true", [](YAML::Node& config) {
+    config["layer-normalization"] = true;
+    config["tied-embeddings"] = true;
+    config["enc-type"] = "alternating";
+    config["enc-cell-depth"] = 2;
+    config["enc-depth"] = 4;
+    config["dec-cell-base-depth"] = 4;
+    config["dec-cell-high-depth"] = 2;
+    config["dec-depth"] = 4;
+    config["skip"] = true;
+  });
+
+  // Architecture and proposed training settings for a Transformer BASE model introduced in
+  // https://papers.nips.cc/paper/7181-attention-is-all-you-need.pdf
+  cli.alias("task", "transformer", [](YAML::Node& config) {
+    config["type"] = "transformer";
+    config["enc-depth"] = 6;
+    config["dec-depth"] = 6;
+    config["transformer-heads"] = 8;
+    config["learn-rate"] = 0.0003;
+    config["cost-type"] = "ce-mean-words";
+    config["lr-warmup"] = 16000;
+    config["lr-decay-inv-sqrt"] = 16000;
+    config["transformer-dropout"] = 0.1;
+    config["label-smoothing"] = 0.1;
+    config["clip-norm"] = 5;
+  });
+
+  // Architecture and proposed training settings for a Transformer BIG model introduced in
+  // https://papers.nips.cc/paper/7181-attention-is-all-you-need.pdf
+  cli.alias("task", "transformer-big", [](YAML::Node& config) {
+    config["type"] = "transformer";
+    config["enc-depth"] = 6;
+    config["dec-depth"] = 6;
+    config["dim-emb"] = 1024;
+    config["transformer-dim-ffn"] = 4096;
+    config["transformer-heads"] = 16;
+    config["transformer-postprocess"] = "dan";
+    config["transformer-preprocess"] = "d";
+    config["transformer-ffn-activation"] = "relu";
+    config["learn-rate"] = 0.0002;
+    config["cost-type"] = "ce-mean-words";
+    config["lr-warmup"] = 8000;
+    config["lr-decay-inv-sqrt"] = 8000;
+    config["transformer-dropout"] = 0.1;
+    config["transformer-dropout-attention"] = 0.1;
+    config["transformer-dropout-ffn"] = 0.1;
+    config["label-smoothing"] = 0.1;
+    config["clip-norm"] = 5;
+  });
+}
+
+}  // namespace marian
diff --git a/src/common/config_parser.cpp b/src/common/config_parser.cpp
index de6b9b6aa..fdf86da8b 100644
--- a/src/common/config_parser.cpp
+++ b/src/common/config_parser.cpp
@@ -693,62 +693,6 @@ void ConfigParser::addSuboptionsULR(cli::CLIWrapper& cli) {
   // clang-format on
 }
 
-void ConfigParser::addAliases(cli::CLIWrapper& cli) {
-  // The order of aliases does matter as later options in the command line overwrite earlier
-
-  // Options reconstructing the BiDeep architecture proposed in http://www.aclweb.org/anthology/W17-4710
-  cli.alias("best-deep", "true", [](YAML::Node& config) {
-    config["layer-normalization"] = true;
-    config["tied-embeddings"] = true;
-    config["enc-type"] = "alternating";
-    config["enc-cell-depth"] = 2;
-    config["enc-depth"] = 4;
-    config["dec-cell-base-depth"] = 4;
-    config["dec-cell-high-depth"] = 2;
-    config["dec-depth"] = 4;
-    config["skip"] = true;
-  });
-
-  // Architecture and proposed training settings for a Transformer BASE model introduced in
-  // https://papers.nips.cc/paper/7181-attention-is-all-you-need.pdf
-  cli.alias("task", "transformer", [](YAML::Node& config) {
-    config["type"] = "transformer";
-    config["enc-depth"] = 6;
-    config["dec-depth"] = 6;
-    config["transformer-heads"] = 8;
-    config["learn-rate"] = 0.0003;
-    config["cost-type"] = "ce-mean-words";
-    config["lr-warmup"] = 16000;
-    config["lr-decay-inv-sqrt"] = 16000;
-    config["transformer-dropout"] = 0.1;
-    config["label-smoothing"] = 0.1;
-    config["clip-norm"] = 5;
-  });
-
-  // Architecture and proposed training settings for a Transformer BIG model introduced in
-  // https://papers.nips.cc/paper/7181-attention-is-all-you-need.pdf
-  cli.alias("task", "transformer-big", [](YAML::Node& config) {
-    config["type"] = "transformer";
-    config["enc-depth"] = 6;
-    config["dec-depth"] = 6;
-    config["dim-emb"] = 1024;
-    config["transformer-dim-ffn"] = 4096;
-    config["transformer-heads"] = 16;
-    config["transformer-postprocess"] = "dan";
-    config["transformer-preprocess"] = "d";
-    config["transformer-ffn-activation"] = "relu";
-    config["learn-rate"] = 0.0002;
-    config["cost-type"] = "ce-mean-words";
-    config["lr-warmup"] = 8000;
-    config["lr-decay-inv-sqrt"] = 8000;
-    config["transformer-dropout"] = 0.1;
-    config["transformer-dropout-attention"] = 0.1;
-    config["transformer-dropout-ffn"] = 0.1;
-    config["label-smoothing"] = 0.1;
-    config["clip-norm"] = 5;
-  });
-}
-
 void ConfigParser::parseOptions(int argc, char** argv, bool doValidate) {
   cli::CLIWrapper cli(config_,
                       "Marian: Fast Neural Machine Translation in C++",

From 785a660fe08ecfefeda76efbf170ebbec84f4208 Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Fri, 8 Feb 2019 09:36:54 -0800
Subject: [PATCH 309/838] Hypothesis::prexIndex_ changed to no longer contain
 the batch index (it does not belong here)

---
 src/translator/beam_search.h | 47 ++++++++++++------------------------
 src/translator/hypothesis.h  | 10 ++++----
 2 files changed, 21 insertions(+), 36 deletions(-)

diff --git a/src/translator/beam_search.h b/src/translator/beam_search.h
index 336441f58..6cc220d47 100755
--- a/src/translator/beam_search.h
+++ b/src/translator/beam_search.h
@@ -63,28 +63,13 @@ class BeamSearch {
         // decompose key into individual indices
         WordIndex  wordIdx = (WordIndex)(key % vocabSize);
         const auto hypIdx  =            (key / vocabSize);
-#if 1
         // further decompose hypIdx, taking into account that the very first entry had beam size 1
-        // and compose a new hypIdx that assumes actual beamSize
-        const auto keyBatchIdx   = hypIdx / (first ? 1 : beamSize);
-        const auto keyBeamHypIdx = hypIdx % (first ? 1 : beamSize);
-        const auto hypIdxTrans = keyBeamHypIdx * dimBatch + keyBatchIdx;
-        ABORT_IF(keyBeamHypIdx >= (int)beam.size(), "Beam hyp index exceeds beam size??"); // @TODO: is this possible? Should be, but does not seem to trigger.
-#else
-        const auto keyBatchIdx = hypIdx / beamSize; // @REVIEW: is this actually keyBatchIdx?
-        size_t keyBeamHypIdx = hypIdx % beamSize;
-
-        auto hypIdxTrans = keyBatchIdx + keyBeamHypIdx * dimBatch;
-        if(first)
-          hypIdxTrans = hypIdx; // == keyBeamHypIdx + keyBatchIdx * beamSize? or was beamSize=1, and keyBeamHypIdx = 0?
-
-        ABORT_IF(keyBeamHypIdx >= (int)beam.size(), "Beam hyp index exceeds beam size??");
-        //if(keyBeamHypIdx >= (int)beam.size())  // @TODO: What is this condition? Cf. keyBeamHypIdx = hypIdx % beamSize; beamSize = max(beams[.].size())
-        //  keyBeamHypIdx = keyBeamHypIdx % beam.size();
-
-        if(first)
-          keyBeamHypIdx = 0;
-#endif
+        // and compose a new hypIdx that assumes actual beamSize and refers to a transposed object
+        const auto beamHypIdx = hypIdx % (first ? 1 : beamSize);
+        const auto batchIdx1  = hypIdx / (first ? 1 : beamSize); // (only for checking; must be same as batchIdx)
+        ABORT_IF(batchIdx1 != batchIdx || beamHypIdx >= (int)beam.size(), "Inconsistent (beamHypIdx, batchIdx) value in key??");
+        const auto hypIdxTrans = beamHypIdx * dimBatch + batchIdx;
+
         // Retrieve short list for final softmax (based on words aligned
         // to source sentences). If short list has been set, map the indices
         // in the sub-selected vocabulary matrix back to their original positions.
@@ -93,22 +78,22 @@ class BeamSearch {
           wordIdx = shortlist->reverseMap(wordIdx); // @TODO: should reverseMap accept a size_t or a Word?
         // now wordIdx is a regular Word again
 
-        auto hyp = New<Hypothesis>(beam[keyBeamHypIdx], Word::fromWordIndex(wordIdx), (IndexType)hypIdxTrans, pathScore);
+        auto hyp = New<Hypothesis>(beam[beamHypIdx], Word::fromWordIndex(wordIdx), beamHypIdx, pathScore);
 
         // Set score breakdown for n-best lists
         if(options_->get<bool>("n-best")) {
           std::vector<float> breakDown(states.size(), 0);
-          beam[keyBeamHypIdx]->getScoreBreakdown().resize(states.size(), 0); // @TODO: Why? Can we just guard the read-out below, then make it const? Or getScoreBreakdown(j)?
+          beam[beamHypIdx]->getScoreBreakdown().resize(states.size(), 0); // @TODO: Why? Can we just guard the read-out below, then make it const? Or getScoreBreakdown(j)?
           for(size_t j = 0; j < states.size(); ++j) {
             size_t key1 = hypIdxTrans * vocabSize + wordIdx;
-            breakDown[j] = states[j]->breakDown(key1) + beam[keyBeamHypIdx]->getScoreBreakdown()[j];
+            breakDown[j] = states[j]->breakDown(key1) + beam[beamHypIdx]->getScoreBreakdown()[j];
           }
           hyp->setScoreBreakdown(breakDown);
         }
 
         // Set alignments
         if(!align.empty()) {
-          hyp->setAlignment(getAlignmentsForHypothesis(align, batch, (int)keyBeamHypIdx, (int)batchIdx));
+          hyp->setAlignment(getAlignmentsForHypothesis(align, batch, (int)beamHypIdx, (int)batchIdx));
         }
 
         newBeam.push_back(hyp);
@@ -249,12 +234,12 @@ class BeamSearch {
         prevPathScores = graph->constant({1, 1, 1, 1}, inits::from_value(0));
       } else {
         std::vector<float> prevScores;
-        for(size_t beamIndex = 0; beamIndex < localBeamSize; ++beamIndex) {
-          for(int batchIndex = 0; batchIndex < dimBatch; ++batchIndex) { // loop over batch entries (active sentences)
-            auto& beam = beams[batchIndex];
-            if(beamIndex < beam.size()) {
-              auto hyp = beam[beamIndex];
-              hypIndices.push_back((IndexType)hyp->getPrevStateIndex()); // index where to find prev hyp (beamHypIdx, batchIdx), =beamHypIdx * dimBatch + batchIdx
+        for(size_t beamHypIdx = 0; beamHypIdx < localBeamSize; ++beamHypIdx) {
+          for(int batchIdx = 0; batchIdx < dimBatch; ++batchIdx) { // loop over batch entries (active sentences)
+            auto& beam = beams[batchIdx];
+            if(beamHypIdx < beam.size()) {
+              auto hyp = beam[beamHypIdx];
+              hypIndices.push_back((IndexType)(hyp->getPrevStateIndex() * dimBatch + batchIdx)); // flattened index for index_select() operation
               prevWords .push_back(hyp->getWord());
               prevScores.push_back(hyp->getPathScore());
             } else {  // pad to localBeamSize (dummy hypothesis)
diff --git a/src/translator/hypothesis.h b/src/translator/hypothesis.h
index 90d5543cd..5b6ff6b00 100755
--- a/src/translator/hypothesis.h
+++ b/src/translator/hypothesis.h
@@ -13,19 +13,19 @@ namespace marian {
 //  - back pointer to previous hypothesis for traceback
 class Hypothesis {
 public:
-  Hypothesis() : prevHyp_(nullptr), prevIndex_(0), word_(Word::ZERO), pathScore_(0.0) {}
+  Hypothesis() : prevHyp_(nullptr), prevBeamHypIdx_(0), word_(Word::ZERO), pathScore_(0.0) {}
 
   Hypothesis(const Ptr<Hypothesis> prevHyp,
              Word word,
-             IndexType prevIndex, // (beamHypIdx, batchIdx) flattened as beamHypIdx * dimBatch + batchIdx
+             size_t prevBeamHypIdx, // beam-hyp index that this hypothesis originated from
              float pathScore)
-      : prevHyp_(prevHyp), prevIndex_(prevIndex), word_(word), pathScore_(pathScore) {}
+      : prevHyp_(prevHyp), prevBeamHypIdx_(prevBeamHypIdx), word_(word), pathScore_(pathScore) {}
 
   const Ptr<Hypothesis> getPrevHyp() const { return prevHyp_; }
 
   Word getWord() const { return word_; }
 
-  IndexType getPrevStateIndex() const { return prevIndex_; }
+  size_t getPrevStateIndex() const { return prevBeamHypIdx_; }
 
   float getPathScore() const { return pathScore_; }
 
@@ -61,7 +61,7 @@ class Hypothesis {
 
 private:
   const Ptr<Hypothesis> prevHyp_;
-  const IndexType prevIndex_;
+  const size_t prevBeamHypIdx_;
   const Word word_;
   const float pathScore_;
 

From 46576dade4dc3f70238c06e9ee0ca801d25db662 Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Fri, 8 Feb 2019 10:23:54 -0800
Subject: [PATCH 310/838] bug fix (?): breakDown() call in toHyp() should use
 wordIdx before de-shortlisting

---
 src/translator/beam_search.h | 88 ++++++++++++++++++------------------
 src/translator/hypothesis.h  | 10 ++--
 2 files changed, 51 insertions(+), 47 deletions(-)

diff --git a/src/translator/beam_search.h b/src/translator/beam_search.h
index 6cc220d47..4a68ba6bc 100755
--- a/src/translator/beam_search.h
+++ b/src/translator/beam_search.h
@@ -41,63 +41,65 @@ class BeamSearch {
                const size_t beamSize,
                const bool first,
                Ptr<data::CorpusBatch /*const*/> batch) const {
-    const auto dimBatch = beams.size();
-    Beams newBeams(dimBatch);
-
     std::vector<float> align;
     if(options_->hasAndNotEmpty("alignment"))
       // Use alignments from the first scorer, even if ensemble
       align = scorers_[0]->getAlignment();
 
+    const auto dimBatch = beams.size();
+    Beams newBeams(dimBatch);
+
     for(size_t i = 0; i < nBestKeys.size(); ++i) { // [dimBatch, beamSize] flattened
       // Keys contains indices to vocab items in the entire beam.
       // Values can be between 0 and beamSize * vocabSize.
-      const auto batchIdx = i / beamSize; // and i % beamSize is the beam hyp index
+      const float pathScore = nBestPathScores[i];
+      const auto  key       = nBestKeys[i]; // key = pathScore's location, as (batchIdx, beamHypIdx, word idx) flattened
+
+      // decompose key into individual indices (batchIdx, beamHypIdx, wordIdx)
+      const auto wordIdx    = (WordIndex)(key % vocabSize);
+      const auto beamHypIdx =            (key / vocabSize) % (first ? 1 : beamSize);
+      const auto batchIdx   =            (key / vocabSize) / (first ? 1 : beamSize);
+
+      ABORT_IF(i / beamSize != batchIdx, "Inconsistent batchIdx value in key??");
+
       const auto& beam = beams[batchIdx];
       auto& newBeam = newBeams[batchIdx];
 
-      if(newBeam.size() < beam.size()) {
-        const float pathScore = nBestPathScores[i];
-        const auto  key       = nBestKeys[i]; // key = pathScore's location, as ((batchIdx, beamHypIdx) flattened, word idx) flattened
-
-        // decompose key into individual indices
-        WordIndex  wordIdx = (WordIndex)(key % vocabSize);
-        const auto hypIdx  =            (key / vocabSize);
-        // further decompose hypIdx, taking into account that the very first entry had beam size 1
-        // and compose a new hypIdx that assumes actual beamSize and refers to a transposed object
-        const auto beamHypIdx = hypIdx % (first ? 1 : beamSize);
-        const auto batchIdx1  = hypIdx / (first ? 1 : beamSize); // (only for checking; must be same as batchIdx)
-        ABORT_IF(batchIdx1 != batchIdx || beamHypIdx >= (int)beam.size(), "Inconsistent (beamHypIdx, batchIdx) value in key??");
-        const auto hypIdxTrans = beamHypIdx * dimBatch + batchIdx;
-
-        // Retrieve short list for final softmax (based on words aligned
-        // to source sentences). If short list has been set, map the indices
-        // in the sub-selected vocabulary matrix back to their original positions.
-        auto shortlist = scorers_[0]->getShortlist();
-        if(shortlist)
-          wordIdx = shortlist->reverseMap(wordIdx); // @TODO: should reverseMap accept a size_t or a Word?
-        // now wordIdx is a regular Word again
-
-        auto hyp = New<Hypothesis>(beam[beamHypIdx], Word::fromWordIndex(wordIdx), beamHypIdx, pathScore);
-
-        // Set score breakdown for n-best lists
-        if(options_->get<bool>("n-best")) {
-          std::vector<float> breakDown(states.size(), 0);
-          beam[beamHypIdx]->getScoreBreakdown().resize(states.size(), 0); // @TODO: Why? Can we just guard the read-out below, then make it const? Or getScoreBreakdown(j)?
-          for(size_t j = 0; j < states.size(); ++j) {
-            size_t key1 = hypIdxTrans * vocabSize + wordIdx;
-            breakDown[j] = states[j]->breakDown(key1) + beam[beamHypIdx]->getScoreBreakdown()[j];
-          }
-          hyp->setScoreBreakdown(breakDown);
-        }
-
-        // Set alignments
-        if(!align.empty()) {
-          hyp->setAlignment(getAlignmentsForHypothesis(align, batch, (int)beamHypIdx, (int)batchIdx));
+      if (newBeam.size() >= beam.size()) // @TODO: Why this condition? It does happen. Why?
+        continue;
+
+      ABORT_IF(beamHypIdx >= (int)beam.size(), "Out of bounds beamHypIdx value in key??");
+
+      // map wordIdx to word
+      // If short list has been set, then wordIdx is an index into the short-listed word set,
+      // rather than the true word index.
+      auto shortlist = scorers_[0]->getShortlist();
+      Word word =
+        /*if*/(shortlist) ?
+          Word::fromWordIndex(shortlist->reverseMap(wordIdx))
+        /*else*/:
+          Word::fromWordIndex(wordIdx);
+
+      auto hyp = New<Hypothesis>(beam[beamHypIdx], word, /*experimental dummy*/0, beamHypIdx, pathScore);
+
+      // Set score breakdown for n-best lists
+      if(options_->get<bool>("n-best")) {
+        std::vector<float> breakDown(states.size(), 0);
+        beam[beamHypIdx]->getScoreBreakdown().resize(states.size(), 0); // @TODO: Why? Can we just guard the read-out below, then make it const? Or getScoreBreakdown(j)?
+        for(size_t j = 0; j < states.size(); ++j) {
+          size_t flattenedLogitIndex = (beamHypIdx * dimBatch + batchIdx) * vocabSize + wordIdx;  // (beam idx, batch idx, word idx)
+          // @BUGBUG: This ^^ used to use wordIdx after de-shortlisting, which seems wrong. If it was right though, change wordIdx to word.toWordIndex().
+          breakDown[j] = states[j]->breakDown(flattenedLogitIndex) + beam[beamHypIdx]->getScoreBreakdown()[j];
         }
+        hyp->setScoreBreakdown(breakDown);
+      }
 
-        newBeam.push_back(hyp);
+      // Set alignments
+      if(!align.empty()) {
+        hyp->setAlignment(getAlignmentsForHypothesis(align, batch, (int)beamHypIdx, (int)batchIdx));
       }
+
+      newBeam.push_back(hyp);
     }
     return newBeams;
   }
diff --git a/src/translator/hypothesis.h b/src/translator/hypothesis.h
index 5b6ff6b00..755a5b890 100755
--- a/src/translator/hypothesis.h
+++ b/src/translator/hypothesis.h
@@ -13,13 +13,14 @@ namespace marian {
 //  - back pointer to previous hypothesis for traceback
 class Hypothesis {
 public:
-  Hypothesis() : prevHyp_(nullptr), prevBeamHypIdx_(0), word_(Word::ZERO), pathScore_(0.0) {}
+  Hypothesis() : prevHyp_(nullptr), beamHypIdx_(0), prevBeamHypIdx_(0), word_(Word::ZERO), pathScore_(0.0) {}
 
   Hypothesis(const Ptr<Hypothesis> prevHyp,
              Word word,
+             size_t beamHypIdx, // EXPERIMENTAL, UNUSED. which beam does this come from? (beamHypIdx, word) indexes the logit tensor
              size_t prevBeamHypIdx, // beam-hyp index that this hypothesis originated from
              float pathScore)
-      : prevHyp_(prevHyp), prevBeamHypIdx_(prevBeamHypIdx), word_(word), pathScore_(pathScore) {}
+      : prevHyp_(prevHyp), beamHypIdx_(beamHypIdx), prevBeamHypIdx_(prevBeamHypIdx), word_(word), pathScore_(pathScore) {}
 
   const Ptr<Hypothesis> getPrevHyp() const { return prevHyp_; }
 
@@ -40,8 +41,8 @@ class Hypothesis {
   {
       Words targetWords;
       for (auto hyp = this; hyp->getPrevHyp(); hyp = hyp->getPrevHyp().get()) {
-          targetWords.push_back(hyp->getWord());
-          // std::cerr << hyp->getWord() << " " << hyp << std::endl;
+        targetWords.push_back(hyp->getWord());
+        // std::cerr << hyp->getWord() << " " << hyp << std::endl;
       }
       std::reverse(targetWords.begin(), targetWords.end());
       return targetWords;
@@ -61,6 +62,7 @@ class Hypothesis {
 
 private:
   const Ptr<Hypothesis> prevHyp_;
+  const size_t beamHypIdx_; // EXPERIMENTAL, UNUSED
   const size_t prevBeamHypIdx_;
   const Word word_;
   const float pathScore_;

From 18a86e5f460256a475ab6575c07c6eda3ab41fc6 Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Fri, 8 Feb 2019 10:57:37 -0800
Subject: [PATCH 311/838] removed some experimental code

---
 src/translator/beam_search.h | 20 ++++++++++----------
 src/translator/hypothesis.h  |  6 ++----
 2 files changed, 12 insertions(+), 14 deletions(-)

diff --git a/src/translator/beam_search.h b/src/translator/beam_search.h
index 4a68ba6bc..1d2a3f520 100755
--- a/src/translator/beam_search.h
+++ b/src/translator/beam_search.h
@@ -43,8 +43,7 @@ class BeamSearch {
                Ptr<data::CorpusBatch /*const*/> batch) const {
     std::vector<float> align;
     if(options_->hasAndNotEmpty("alignment"))
-      // Use alignments from the first scorer, even if ensemble
-      align = scorers_[0]->getAlignment();
+      align = scorers_[0]->getAlignment(); // use alignments from the first scorer, even if ensemble
 
     const auto dimBatch = beams.size();
     Beams newBeams(dimBatch);
@@ -53,7 +52,7 @@ class BeamSearch {
       // Keys contains indices to vocab items in the entire beam.
       // Values can be between 0 and beamSize * vocabSize.
       const float pathScore = nBestPathScores[i];
-      const auto  key       = nBestKeys[i]; // key = pathScore's location, as (batchIdx, beamHypIdx, word idx) flattened
+      const auto  key       = nBestKeys[i]; // key = pathScore's tensor location, as (batchIdx, beamHypIdx, word idx) flattened
 
       // decompose key into individual indices (batchIdx, beamHypIdx, wordIdx)
       const auto wordIdx    = (WordIndex)(key % vocabSize);
@@ -80,16 +79,17 @@ class BeamSearch {
         /*else*/:
           Word::fromWordIndex(wordIdx);
 
-      auto hyp = New<Hypothesis>(beam[beamHypIdx], word, /*experimental dummy*/0, beamHypIdx, pathScore);
+      auto hyp = New<Hypothesis>(beam[beamHypIdx], word, beamHypIdx, pathScore);
 
       // Set score breakdown for n-best lists
       if(options_->get<bool>("n-best")) {
         std::vector<float> breakDown(states.size(), 0);
         beam[beamHypIdx]->getScoreBreakdown().resize(states.size(), 0); // @TODO: Why? Can we just guard the read-out below, then make it const? Or getScoreBreakdown(j)?
         for(size_t j = 0; j < states.size(); ++j) {
-          size_t flattenedLogitIndex = (beamHypIdx * dimBatch + batchIdx) * vocabSize + wordIdx;  // (beam idx, batch idx, word idx)
+          size_t flattenedLogitIndex = (beamHypIdx * dimBatch + batchIdx) * vocabSize + wordIdx;  // (beam idx, batch idx, word idx); note: beam and batch are transposed, compared to 'key'
           // @BUGBUG: This ^^ used to use wordIdx after de-shortlisting, which seems wrong. If it was right though, change wordIdx to word.toWordIndex().
           breakDown[j] = states[j]->breakDown(flattenedLogitIndex) + beam[beamHypIdx]->getScoreBreakdown()[j];
+          // @TODO: pass those 3 indices directly into breakDown
         }
         hyp->setScoreBreakdown(breakDown);
       }
@@ -229,9 +229,9 @@ class BeamSearch {
       //**********************************************************************
       // create constant containing previous path scores for current beam
       // Also create mapping of hyp indices, which are not 1:1 if sentences complete.
-      std::vector<IndexType> hypIndices; // [localBeamsize, 1, dimBatch, 1] (flattened) index of prev hyp that the current hyp originated from
-      std::vector<Word> prevWords;       // [localBeamsize, 1, dimBatch, 1] (flattened) predecessor word
-      Expr prevPathScores;               // [localBeamSize, 1, dimBatch, 1], where the last axis broadcasts into vocab size when adding expandedPathScores
+      std::vector<IndexType> hypIndices; // [localBeamsize, 1, dimBatch, 1] (flattened) tensor index ((beamHypIdx, batchIdx), flattened) of prev hyp that a hyp originated from, for reordering
+      std::vector<Word> prevWords;       // [localBeamsize, 1, dimBatch, 1] (flattened) word that a hyp ended in
+      Expr prevPathScores;               // [localBeamSize, 1, dimBatch, 1], path score that a hyp ended in (last axis will broadcast into vocab size when adding expandedPathScores)
       if(t == 0) { // no scores yet
         prevPathScores = graph->constant({1, 1, 1, 1}, inits::from_value(0));
       } else {
@@ -241,7 +241,7 @@ class BeamSearch {
             auto& beam = beams[batchIdx];
             if(beamHypIdx < beam.size()) {
               auto hyp = beam[beamHypIdx];
-              hypIndices.push_back((IndexType)(hyp->getPrevStateIndex() * dimBatch + batchIdx)); // flattened index for index_select() operation
+              hypIndices.push_back((IndexType)(hyp->getPrevStateIndex() * dimBatch + batchIdx)); // (beamHypIdx, batchIdx), flattened, for index_select() operation
               prevWords .push_back(hyp->getWord());
               prevScores.push_back(hyp->getPathScore());
             } else {  // pad to localBeamSize (dummy hypothesis)
@@ -260,7 +260,7 @@ class BeamSearch {
       auto expandedPathScores = prevPathScores; // will become [localBeamSize, 1, dimBatch, dimVocab]
       for(size_t i = 0; i < scorers_.size(); ++i) {
         // compute output probabilities for current output time step
-        //  - uses hypIndices[index in beam, 1, batch index, 1] to reorder hypotheses
+        //  - uses hypIndices[index in beam, 1, batch index, 1] to reorder decoder state to reflect the top-N in beams[][]
         //  - adds prevWords [index in beam, 1, batch index, 1] to the decoder model's target history
         //  - performs one step of the decoder model
         //  - returns new NN state for use in next output time step
diff --git a/src/translator/hypothesis.h b/src/translator/hypothesis.h
index 755a5b890..28cac2765 100755
--- a/src/translator/hypothesis.h
+++ b/src/translator/hypothesis.h
@@ -13,14 +13,13 @@ namespace marian {
 //  - back pointer to previous hypothesis for traceback
 class Hypothesis {
 public:
-  Hypothesis() : prevHyp_(nullptr), beamHypIdx_(0), prevBeamHypIdx_(0), word_(Word::ZERO), pathScore_(0.0) {}
+  Hypothesis() : prevHyp_(nullptr), prevBeamHypIdx_(0), word_(Word::ZERO), pathScore_(0.0) {}
 
   Hypothesis(const Ptr<Hypothesis> prevHyp,
              Word word,
-             size_t beamHypIdx, // EXPERIMENTAL, UNUSED. which beam does this come from? (beamHypIdx, word) indexes the logit tensor
              size_t prevBeamHypIdx, // beam-hyp index that this hypothesis originated from
              float pathScore)
-      : prevHyp_(prevHyp), beamHypIdx_(beamHypIdx), prevBeamHypIdx_(prevBeamHypIdx), word_(word), pathScore_(pathScore) {}
+      : prevHyp_(prevHyp), prevBeamHypIdx_(prevBeamHypIdx), word_(word), pathScore_(pathScore) {}
 
   const Ptr<Hypothesis> getPrevHyp() const { return prevHyp_; }
 
@@ -62,7 +61,6 @@ class Hypothesis {
 
 private:
   const Ptr<Hypothesis> prevHyp_;
-  const size_t beamHypIdx_; // EXPERIMENTAL, UNUSED
   const size_t prevBeamHypIdx_;
   const Word word_;
   const float pathScore_;

From 7ff1aab91f97162e159b0833158ccb8a708318aa Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Fri, 8 Feb 2019 11:00:01 -0800
Subject: [PATCH 312/838] removed some experimental code

---
 src/translator/beam_search.h | 3 +--
 1 file changed, 1 insertion(+), 2 deletions(-)

diff --git a/src/translator/beam_search.h b/src/translator/beam_search.h
index 1d2a3f520..e206832ec 100755
--- a/src/translator/beam_search.h
+++ b/src/translator/beam_search.h
@@ -87,9 +87,8 @@ class BeamSearch {
         beam[beamHypIdx]->getScoreBreakdown().resize(states.size(), 0); // @TODO: Why? Can we just guard the read-out below, then make it const? Or getScoreBreakdown(j)?
         for(size_t j = 0; j < states.size(); ++j) {
           size_t flattenedLogitIndex = (beamHypIdx * dimBatch + batchIdx) * vocabSize + wordIdx;  // (beam idx, batch idx, word idx); note: beam and batch are transposed, compared to 'key'
-          // @BUGBUG: This ^^ used to use wordIdx after de-shortlisting, which seems wrong. If it was right though, change wordIdx to word.toWordIndex().
           breakDown[j] = states[j]->breakDown(flattenedLogitIndex) + beam[beamHypIdx]->getScoreBreakdown()[j];
-          // @TODO: pass those 3 indices directly into breakDown
+          // @TODO: pass those 3 indices directly into breakDown (state knows the dimensions)
         }
         hyp->setScoreBreakdown(breakDown);
       }

From 791e2c7af561a388c65d557ecb77ab5494595170 Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Fri, 8 Feb 2019 11:35:57 -0800
Subject: [PATCH 313/838] towards factored decoding

---
 src/translator/beam_search.h | 98 +++++++++++++++++++++++-------------
 1 file changed, 62 insertions(+), 36 deletions(-)

diff --git a/src/translator/beam_search.h b/src/translator/beam_search.h
index e206832ec..13fa84227 100755
--- a/src/translator/beam_search.h
+++ b/src/translator/beam_search.h
@@ -40,7 +40,8 @@ class BeamSearch {
                const std::vector<Ptr<ScorerState /*const*/>>& states,
                const size_t beamSize,
                const bool first,
-               Ptr<data::CorpusBatch /*const*/> batch) const {
+               Ptr<data::CorpusBatch /*const*/> batch, // for alignments only
+               Ptr<FactoredVocab/*const*/> factoredVocab) const {
     std::vector<float> align;
     if(options_->hasAndNotEmpty("alignment"))
       align = scorers_[0]->getAlignment(); // use alignments from the first scorer, even if ensemble
@@ -70,14 +71,14 @@ class BeamSearch {
       ABORT_IF(beamHypIdx >= (int)beam.size(), "Out of bounds beamHypIdx value in key??");
 
       // map wordIdx to word
+      Word word;
       // If short list has been set, then wordIdx is an index into the short-listed word set,
       // rather than the true word index.
       auto shortlist = scorers_[0]->getShortlist();
-      Word word =
-        /*if*/(shortlist) ?
-          Word::fromWordIndex(shortlist->reverseMap(wordIdx))
-        /*else*/:
-          Word::fromWordIndex(wordIdx);
+      if (shortlist)
+        word = Word::fromWordIndex(shortlist->reverseMap(wordIdx));
+      else
+        word = Word::fromWordIndex(wordIdx);
 
       auto hyp = New<Hypothesis>(beam[beamHypIdx], word, beamHypIdx, pathScore);
 
@@ -161,8 +162,10 @@ class BeamSearch {
     ABORT_IF(batch->back()->vocab()->getEosId() != trgEosId_, "Batch uses different EOS token than was passed to BeamSearch originally");
 
     auto factoredVocab = batch->back()->vocab()->tryAs<FactoredVocab>();
-    size_t numFactors = factoredVocab ? factoredVocab->getNumGroups() : 1;
-    numFactors;
+#if 1   // use '1' here to disable factored decoding, e.g. for comparisons
+    factoredVocab.reset();
+#endif
+    size_t numFactorGroups = factoredVocab ? factoredVocab->getNumGroups() : 1;
 
     const int dimBatch = (int)batch->size();
 
@@ -213,6 +216,10 @@ class BeamSearch {
     // main loop over output time steps
     for (size_t t = 0; ; t++) {
       ABORT_IF(dimBatch != beams.size(), "Lost a batch entry??");
+
+      // for factored vocabs, we do one factor at a time, but without updating the decoder model for secondary factors
+      auto factorGroup = t % numFactorGroups;
+
       // determine beam size for next output time step, as max over still-active sentences
       // E.g. if all batch entries are down from beam 5 to no more than 4 surviving hyps, then
       // switch to beam of 4 for all. If all are done, then beam ends up being 0, and we are done.
@@ -227,9 +234,9 @@ class BeamSearch {
 
       //**********************************************************************
       // create constant containing previous path scores for current beam
-      // Also create mapping of hyp indices, which are not 1:1 if sentences complete.
-      std::vector<IndexType> hypIndices; // [localBeamsize, 1, dimBatch, 1] (flattened) tensor index ((beamHypIdx, batchIdx), flattened) of prev hyp that a hyp originated from, for reordering
-      std::vector<Word> prevWords;       // [localBeamsize, 1, dimBatch, 1] (flattened) word that a hyp ended in
+      // Also create mapping of hyp indices, for reordering the decoder-state tensors.
+      std::vector<IndexType> hypIndices; // [localBeamsize, 1, dimBatch, 1] (flattened) tensor index ((beamHypIdx, batchIdx), flattened) of prev hyp that a hyp originated from
+      std::vector<Word> prevWords;       // [localBeamsize, 1, dimBatch, 1] (flattened) word that a hyp ended in, for advancing the decoder-model's history
       Expr prevPathScores;               // [localBeamSize, 1, dimBatch, 1], path score that a hyp ended in (last axis will broadcast into vocab size when adding expandedPathScores)
       if(t == 0) { // no scores yet
         prevPathScores = graph->constant({1, 1, 1, 1}, inits::from_value(0));
@@ -258,20 +265,39 @@ class BeamSearch {
       // compute expanded path scores with word prediction probs from all scorers
       auto expandedPathScores = prevPathScores; // will become [localBeamSize, 1, dimBatch, dimVocab]
       for(size_t i = 0; i < scorers_.size(); ++i) {
-        // compute output probabilities for current output time step
-        //  - uses hypIndices[index in beam, 1, batch index, 1] to reorder decoder state to reflect the top-N in beams[][]
-        //  - adds prevWords [index in beam, 1, batch index, 1] to the decoder model's target history
-        //  - performs one step of the decoder model
-        //  - returns new NN state for use in next output time step
-        //  - returns vector of prediction probabilities over output vocab via newState
-        auto newState = scorers_[i]->step(
-            graph, states[i], hypIndices, prevWords, dimBatch, (int)localBeamSize);
-
+        Expr logProbs, factorMasks;
+        if (factorGroup == 0) {
+          // compute output probabilities for current output time step
+          //  - uses hypIndices[index in beam, 1, batch index, 1] to reorder decoder state to reflect the top-N in beams[][]
+          //  - adds prevWords [index in beam, 1, batch index, 1] to the decoder model's target history
+          //  - performs one step of the decoder model
+          //  - returns new NN state for use in next output time step
+          //  - returns vector of prediction probabilities over output vocab via newState
+          // update state in-place for next output time step
+          states[i] = scorers_[i]->step(graph, states[i], hypIndices, prevWords, dimBatch, (int)localBeamSize);
+        }
+        else {
+            // add secondary factors
+            // For those, we don't update the decoder-model state in any way.
+            // Instead, we just keep expanding with the factors.
+            // Considerations:
+            //  - not all scores should get a factor
+            //    We need a [localBeamSize, 1, dimBatch, 1] tensor that knows whether a factor is applicable
+            //    by considering the lemma at each (beamHypIdx, batchIdx). prevWords is already in the right order.
+            //  - factors are incorporated one step at a time; so we will have temporary Word entries
+            //    in hyps with some factors set to FACTOR_NOT_SPECIFIED.
+            //  - we did not rearrange the tensors in the decoder model's state
+
+            // ...
+        }
         // expand all hypotheses, [localBeamSize, 1, dimBatch, 1] -> [localBeamSize, 1, dimBatch, dimVocab]
-        expandedPathScores = expandedPathScores + scorers_[i]->getWeight() * newState->getLogProbs().getLogits();
-
-        // update state in-place for next output time step
-        states[i] = newState;
+        if (numFactorGroups == 1)
+          logProbs = states[i]->getLogProbs().getLogits(); // [localBeamSize, 1, dimBatch, dimVocab]
+        else
+          logProbs = states[i]->getLogProbs().getFactoredLogits(factorGroup); // [localBeamSize, 1, dimBatch, dimVocab]
+        if (factorMasks)
+          logProbs = logProbs * factorMasks; // those hyps that don't have a factor get multiplied with 0
+        expandedPathScores = expandedPathScores + scorers_[i]->getWeight() * logProbs;
       }
 
       // make beams continuous
@@ -305,33 +331,33 @@ class BeamSearch {
       // Now, nBestPathScores contain N-best expandedPathScores, and nBestKeys for each their original location (batchIdx, beamHypIdx, word).
 
       // combine N-best sets with existing search space (beams) to updated search space
-      beams = toHyps(nBestKeys, nBestPathScores,
-                     /*dimTrgVoc=*/expandedPathScores->shape()[-1],
-                     beams,
-                     states,           // used for keeping track of per-ensemble-member path score
-                     localBeamSize,    // used in the encoding of the (batchIdx, beamHypIdx, word) tuples
-                     /*first=*/t == 0, // used to indicate originating beamSize of 1
-                     batch);
+      auto newBeams = toHyps(nBestKeys, nBestPathScores,
+                             /*dimTrgVoc=*/expandedPathScores->shape()[-1],
+                             beams,
+                             states,           // used for keeping track of per-ensemble-member path score
+                             localBeamSize,    // used in the encoding of the (batchIdx, beamHypIdx, word) tuples
+                             /*first=*/t == 0, // used to indicate originating beamSize of 1
+                             batch, factoredVocab);
 
       // remove all hyps that end in EOS
       // The position of a hyp in the beam may change.
-      const auto purgedBeams = purgeBeams(beams);
+      const auto purgedNewBeams = purgeBeams(newBeams);
 
-      // add updated search space (beams) to search grid (histories) for traceback
+      // add updated search space (newBeams) to search grid (histories) for traceback
       bool maxLengthReached = false;
       for(int i = 0; i < dimBatch; ++i) {
         // if this batch entry has surviving hyps then add them to the traceback grid
-        if(!beams[i].empty()) {
+        if(!newBeams[i].empty()) {
           if (histories[i]->size() >= options_->get<float>("max-length-factor") * batch->front()->batchWidth())
             maxLengthReached = true;
-          histories[i]->add(beams[i], trgEosId_, purgedBeams[i].empty() || maxLengthReached);
+          histories[i]->add(newBeams[i], trgEosId_, purgedNewBeams[i].empty() || maxLengthReached);
         }
       }
       if (maxLengthReached) // early exit if max length limit was reached
         break;
 
       // this is the search space for the next output time step
-      beams = purgedBeams;
+      beams = purgedNewBeams;
     } // end of main loop over output time steps
 
     return histories; // [dimBatch][t][N best hyps]

From d2db12a3ce88878eeb86b79149a631c1126e605a Mon Sep 17 00:00:00 2001
From: Ulrich Germann <ulrich.germann@gmail.com>
Date: Fri, 8 Feb 2019 19:41:22 +0000
Subject: [PATCH 314/838] Allow build dir to be outside the source tree

Currently, the call to git to record the git revision fails if the build
directory is outside the source tree. This fix allows the build directory
to be outside the source tree. However, compilation still works only if
the source is obtained via git and not from a .zip file.
---
 src/CMakeLists.txt | 1 +
 1 file changed, 1 insertion(+)

diff --git a/src/CMakeLists.txt b/src/CMakeLists.txt
index fd489e3e1..fbafc3b99 100644
--- a/src/CMakeLists.txt
+++ b/src/CMakeLists.txt
@@ -93,6 +93,7 @@ target_compile_options(marian PUBLIC ${ALL_WARNINGS})
 # [https://stackoverflow.com/questions/1435953/how-can-i-pass-git-sha1-to-compiler-as-definition-using-cmake]
 # Git updates .git/logs/HEAD file whenever you pull or commit something.
 add_custom_command(OUTPUT ${CMAKE_CURRENT_SOURCE_DIR}/common/git_revision.h
+  WORKING_DIRECTORY ${CMAKE_SOURCE_DIR}
   COMMAND git log -1 --pretty=format:\#define\ GIT_REVISION\ \"\%h\ \%ai\" > ${CMAKE_CURRENT_SOURCE_DIR}/common/git_revision.h
   DEPENDS ${CMAKE_SOURCE_DIR}/.git/logs/HEAD
   VERBATIM

From 82dc0ed93f56773d09f515d2372a7f511b4153cf Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Fri, 8 Feb 2019 11:59:45 -0800
Subject: [PATCH 315/838] towwrds factored decoding

---
 src/layers/generic.cpp       |  7 +++++++
 src/layers/generic.h         |  3 ++-
 src/translator/beam_search.h | 10 ++++++----
 3 files changed, 15 insertions(+), 5 deletions(-)

diff --git a/src/layers/generic.cpp b/src/layers/generic.cpp
index e36d3e213..1ebd87f5a 100755
--- a/src/layers/generic.cpp
+++ b/src/layers/generic.cpp
@@ -137,6 +137,13 @@ namespace marian {
     return res;
   }
 
+  // use first factor of each word to determine whether it has a specific factor
+  std::vector<float> Logits::getFactorMasks(const Words& words, size_t factorGroup) const { // 1.0 for words that do have this factor; else 0
+    std::vector<float> res;
+    ABORT("FINISH THIS");
+    return res;
+  }
+
   Logits Logits::withCounts(const Expr& count) const { // create new Logits with 'count' implanted into all logits_
     std::vector<Ptr<RationalLoss>> newLogits;
     for (const auto& l : logits_)
diff --git a/src/layers/generic.h b/src/layers/generic.h
index 6befea76e..85dec5692 100755
--- a/src/layers/generic.h
+++ b/src/layers/generic.h
@@ -86,7 +86,8 @@ class Logits {
       MaskedFactorIndices(const Words& words) { indices = toWordIndexVector(words); } // we can leave masks uninitialized for this special use case
     };
     std::vector<MaskedFactorIndices> factorizeWords(const Words& words) const; // breaks encoded Word into individual factor indices
-    float getLogitAt(size_t i) const { return getLogits()->val()->get(i); } // @TODO: avoid the fully expanded logits
+    std::vector<float> getFactorMasks(const Words& words, size_t factorGroup) const;
+    float getLogitAt(size_t i) const { return getLogits()->val()->get(i); } // used for breakDown() only; @TODO: avoid the fully expanded logits; pass separate indices instead of 'i'
     size_t getNumFactorGroups() const { return logits_.size(); }
     bool empty() const { return logits_.empty(); }
     Logits withCounts(const Expr& count) const; // create new Logits with 'count' implanted into all logits_
diff --git a/src/translator/beam_search.h b/src/translator/beam_search.h
index 13fa84227..36836acc7 100755
--- a/src/translator/beam_search.h
+++ b/src/translator/beam_search.h
@@ -257,8 +257,7 @@ class BeamSearch {
             }
           }
         }
-        prevPathScores = graph->constant({(int)localBeamSize, 1, dimBatch, 1},
-                                    inits::from_vector(prevScores));
+        prevPathScores = graph->constant({(int)localBeamSize, 1, dimBatch, 1}, inits::from_vector(prevScores));
       }
 
       //**********************************************************************
@@ -286,9 +285,12 @@ class BeamSearch {
             //    by considering the lemma at each (beamHypIdx, batchIdx). prevWords is already in the right order.
             //  - factors are incorporated one step at a time; so we will have temporary Word entries
             //    in hyps with some factors set to FACTOR_NOT_SPECIFIED.
+            // TODO:
             //  - we did not rearrange the tensors in the decoder model's state
-
-            // ...
+            //  - initial word should set lemma by all other factors as unspecified
+            //  - toHyp() should implant factors
+            auto factorMaskVector = states[i]->getLogProbs().getFactorMasks(prevWords, factorGroup);
+            factorMasks = graph->constant({(int)localBeamSize, 1, dimBatch, 1}, inits::from_vector(factorMaskVector));
         }
         // expand all hypotheses, [localBeamSize, 1, dimBatch, 1] -> [localBeamSize, 1, dimBatch, dimVocab]
         if (numFactorGroups == 1)

From b50830cc0c4e1e687328f8569f6977bc3c0837d2 Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Fri, 8 Feb 2019 20:22:51 -0800
Subject: [PATCH 316/838] added lots of checks to word/factor conversions; new
 methods lemma2Word(), addFactor()

---
 src/data/factored_vocab.cpp  | 102 ++++++++++++++++++++++++++---------
 src/data/factored_vocab.h    |  14 +++--
 src/layers/generic.cpp       |   1 +
 src/translator/beam_search.h |  14 +++--
 4 files changed, 98 insertions(+), 33 deletions(-)

diff --git a/src/data/factored_vocab.cpp b/src/data/factored_vocab.cpp
index 153741e35..2390adf63 100755
--- a/src/data/factored_vocab.cpp
+++ b/src/data/factored_vocab.cpp
@@ -39,6 +39,7 @@ namespace marian {
   factorMap_.resize(elements);
   auto factorVocabSize = factorVocab_.size();
   factorRefCounts_.resize(factorVocabSize);
+  lemmaHasFactorGroup_.resize(groupRanges_[0].second - groupRanges_[0].first);
   std::vector<std::string> tokens;
   std::string line;
   size_t numTotalFactors = 0;
@@ -46,7 +47,7 @@ namespace marian {
   for (WordIndex v = 0; io::getline(in, line); v++) {
     // parse the line, of the form WORD FACTOR1 FACTOR2 FACTOR1 ...
     // where FACTOR1 is the lemma, a factor that all words have.
-    // Not every word has all other factors, so the n-th item is not always the same factor.
+    // Not every word has all other factors, so the n-th item is not always in the same factor group.
     utils::splitAny(line, tokens, " \t");
     ABORT_IF(tokens.size() < 2, "Factor map must have at least one factor per word", mapPath);
     std::vector<WordIndex> factorUnits;
@@ -55,14 +56,29 @@ namespace marian {
       factorUnits.push_back(u);
       factorRefCounts_[u]++;
     }
-    auto index = factorUnits2word(factorUnits).toWordIndex();
-    // @TODO: map factors to non-dense integer
-    factorMap_[index] = std::move(factorUnits);
-    // add to vocab
-    vocab_.add(tokens.front(), index);
+    // convert to fully unrolled factors representation
+    std::vector<size_t> factorIndices(groupRanges_.size(), FACTOR_NOT_APPLICABLE); // default for unused factors
+    std::vector<bool> hasFactorGroupFlags(groupRanges_.size(), false);
+    for (auto u : factorUnits) {
+      factorIndices[factorGroups_[u]] = factorUnit2FactorIndex(u);
+      hasFactorGroupFlags[factorGroups_[u]] = true;
+    }
+    // record which lemma has what factor groups
+    ABORT_IF(!hasFactorGroupFlags[0], "Factor map does not specify a lemma (factor of first group) for word {}", tokens.front());
+    auto& lemmaFlags = lemmaHasFactorGroup_[factorIndices[0]];
+    if (lemmaFlags.empty())
+      lemmaFlags = std::move(hasFactorGroupFlags);
+    else
+      ABORT_IF(lemmaFlags != hasFactorGroupFlags, "Inconsistent factor groups used for word {}", tokens.front());
+    // map factors to non-dense integer
+    auto word = factors2word(factorIndices);
+    auto wordIndex = word.toWordIndex();
+    factorMap_[wordIndex] = std::move(factorUnits);
+    // add to vocab (the wordIndex are not dense, so the vocab will have holes)
+    vocab_.add(tokens.front(), wordIndex);
     numTotalFactors += tokens.size() - 1;
     if (v % 5000 == 0)
-      LOG(info, "{} -> {}", tokens.front(), word2string(Word::fromWordIndex(index)));
+      LOG(info, "{} -> {}", tokens.front(), word2string(word));
   }
   LOG(info, "[embedding] Factored-embedding map read with total/unique of {}/{} factors for {} valid words (in space of {})",
       numTotalFactors, factorVocabSize, vocab_.numValid(), size());
@@ -130,13 +146,18 @@ void FactoredVocab::constructFactorIndexConversion() {
 }
 
 // encode factors into a Word struct
-Word FactoredVocab::factors2word(const std::vector<size_t>& factorIndices /* [numGroups] */) {
+Word FactoredVocab::factors2word(const std::vector<size_t>& factorIndices /* [numGroups] */) const {
   size_t index = 0;
   size_t numGroups = getNumGroups();
   ABORT_IF(factorIndices.size() != numGroups, "Factor indices array size must be same as number of factor groups");
   for (size_t g = 0; g < numGroups; g++) {
     auto factorIndex = factorIndices[g];
-    if (factorIndex == FACTOR_NOT_APPLICABLE || factorIndex == FACTOR_NOT_SPECIFIED) // @TODO: check validity. If word has the factor, then N/A is invalid
+    if (factorIndex != FACTOR_NOT_SPECIFIED) { // check validity
+      auto factor0Index = factorIndices[0]; // lemma
+      ABORT_IF(factor0Index == FACTOR_NOT_SPECIFIED, "Without lemma, no other factor may be specified");
+      ABORT_IF(lemmaHasFactorGroup(factor0Index, g) == (factorIndex == FACTOR_NOT_APPLICABLE), "Lemma {} does not have factor group {}", factor0Index, g);
+    }
+    if (factorIndex == FACTOR_NOT_APPLICABLE || factorIndex == FACTOR_NOT_SPECIFIED)
       factorIndex = (size_t)factorShape_[g] - 1; // sentinel for "unused" or "not specified"
     else
       ABORT_IF(factorIndex >= (size_t)factorShape_[g] - 1, "Factor index out of range");
@@ -145,21 +166,41 @@ Word FactoredVocab::factors2word(const std::vector<size_t>& factorIndices /* [nu
   return Word::fromWordIndex(index);
 }
 
-// like factors2word, except that factors are expressed as global unit indices, and result is just the WordIndex
-// This is only used during initialization, so it's OK if it is a little inefficient.
-Word FactoredVocab::factorUnits2word(const std::vector<WordIndex>& factorUnits) {
-  // convert to fully unrolled factors representation
-  std::vector<size_t> factorIndices(getNumGroups(), FACTOR_NOT_APPLICABLE); // default for unused factors
-  for (auto u : factorUnits) {
-    auto g = factorGroups_[u]; // convert u to relative u within factor group range
-    ABORT_IF(u < groupRanges_[g].first || u >= groupRanges_[g].second, "Invalid factorGroups_ entry??");
-    auto factorIndex = u - groupRanges_[g].first;
-    factorIndices[g] = factorIndex;
+Word FactoredVocab::lemma2Word(size_t factor0Index) const {
+  size_t numGroups = getNumGroups();
+  std::vector<size_t> factorIndices;
+  factorIndices.reserve(numGroups);
+  factorIndices.push_back(factor0Index);
+  for (size_t g = 1; g < numGroups; g++) {
+    auto index = lemmaHasFactorGroup(factor0Index, g) ? FACTOR_NOT_SPECIFIED : FACTOR_NOT_APPLICABLE;
+    factorIndices.push_back(index);
   }
   return factors2word(factorIndices);
 }
 
-void FactoredVocab::word2factors(Word word, std::vector<size_t>& factorIndices /* [numGroups] */) {
+// replace a factor that is FACTOR_NOT_SPECIFIED by a specified one
+// This is used in beam search, where factors are searched one after another.
+Word FactoredVocab::addFactor(Word word, size_t groupIndex, size_t factorIndex) const {
+  ABORT_IF(groupIndex == 0, "Cannot add or change lemma in a partial Word");
+  ABORT_IF(!isFactorValid(factorIndex), "Cannot add unspecified or n/a factor to a partial Word");
+  std::vector<size_t> factorIndices;
+  word2factors(word, factorIndices);
+  auto factor0Index = factorIndices[0];
+  ABORT_IF(!isFactorValid(factor0Index), "Cannot add factor to a partial Word without lemma");
+  ABORT_IF(factorIndices[groupIndex] == FACTOR_NOT_APPLICABLE, "Cannot add a factor that the lemma does not have");
+  ABORT_IF(factorIndices[groupIndex] != FACTOR_NOT_SPECIFIED, "Cannot modify a specified factor in a partial Word");
+  factorIndices[groupIndex] = factorIndex;
+  return factors2word(factorIndices);
+}
+
+size_t FactoredVocab::factorUnit2FactorIndex(WordIndex u) const {
+  auto g = factorGroups_[u]; // convert u to relative u within factor group range
+  ABORT_IF(u < groupRanges_[g].first || u >= groupRanges_[g].second, "Invalid factorGroups_ entry??");
+  return u - groupRanges_[g].first;
+}
+
+
+void FactoredVocab::word2factors(Word word, std::vector<size_t>& factorIndices /* [numGroups] */) const {
   size_t numGroups = getNumGroups();
   factorIndices.resize(numGroups);
   for (size_t g = 0; g < numGroups; g++) {
@@ -189,17 +230,30 @@ std::string FactoredVocab::word2string(Word word) {
   return res + ")";
 }
 
-size_t FactoredVocab::getFactor(Word word, size_t groupIndex) {
+size_t FactoredVocab::getFactor(Word word, size_t groupIndex) const {
   size_t index = word.toWordIndex();
   index = index / factorStrides_[groupIndex];
   index = index % (size_t)factorShape_[groupIndex];
-  if (index == (size_t)factorShape_[groupIndex] - 1) {
-    index = FACTOR_NOT_APPLICABLE; // @BUGBUG: We should check here which one it is.
+  if (index == (size_t)factorShape_[groupIndex] - 1) { // special sentinel value for unspecified or not-applicable
+    if (groupIndex == 0) // lemma itself is always applicable, hence 'not specified'
+      index = FACTOR_NOT_SPECIFIED;
+    else { // not lemma: check whether lemma of word has this factor group
+      size_t factor0Index = word.toWordIndex() / factorStrides_[0];
+      if (lemmaHasFactorGroup(factor0Index, groupIndex))
+        index = FACTOR_NOT_SPECIFIED;
+      else
+        index = FACTOR_NOT_APPLICABLE;
+    }
+  }
+  else { // regular value: consistency check if lemma really has this factor group
+    size_t factor0Index = word.toWordIndex() / factorStrides_[0];
+    ABORT_IF(factor0Index == (size_t)factorShape_[0] - 1, "Word has specified factor but no lemma??");
+    ABORT_IF(!lemmaHasFactorGroup(factor0Index, groupIndex), "Word has a specified factor for a lemma that does not have that factor group??");
   }
   return index;
 }
 
-std::pair<WordIndex, bool> FactoredVocab::getFactorUnit(Word word, size_t groupIndex) {
+std::pair<WordIndex, bool> FactoredVocab::getFactorUnit(Word word, size_t groupIndex) const {
   word; groupIndex;
   ABORT("Not implemented");
 }
diff --git a/src/data/factored_vocab.h b/src/data/factored_vocab.h
index de2bd34f1..a5aefc726 100755
--- a/src/data/factored_vocab.h
+++ b/src/data/factored_vocab.h
@@ -50,10 +50,13 @@ class FactoredVocab : public IVocab {
   const std::vector<float>& getGapLogMask() const { return gapLogMask_; } // [v] -inf if v is a gap entry, else 0
 
   // convert representations
-  Word factors2word(const std::vector<size_t>& factors);
-  void word2factors(Word word, std::vector<size_t>& factors);
-  size_t getFactor(Word word, size_t groupIndex);
-  std::pair<WordIndex, bool> getFactorUnit(Word word, size_t groupIndex);
+  Word factors2word(const std::vector<size_t>& factors) const;
+  void word2factors(Word word, std::vector<size_t>& factors) const;
+  Word lemma2Word(size_t factor0Index) const;
+  Word addFactor(Word word, size_t groupIndex, size_t factor0Index) const;
+  size_t getFactor(Word word, size_t groupIndex) const;
+  std::pair<WordIndex, bool> getFactorUnit(Word word, size_t groupIndex) const;
+  bool lemmaHasFactorGroup(size_t factor0Index, size_t g) const { return lemmaHasFactorGroup_[factor0Index][g]; }
   static constexpr size_t FACTOR_NOT_APPLICABLE = (SIZE_MAX - 1);
   static constexpr size_t FACTOR_NOT_SPECIFIED  = (SIZE_MAX - 2);
   static bool isFactorValid(size_t factorIndex) { return factorIndex < FACTOR_NOT_SPECIFIED; }
@@ -63,7 +66,7 @@ class FactoredVocab : public IVocab {
   void constructGroupInfoFromFactorVocab();
   void constructFactorIndexConversion();
   void constructNormalizationInfoForVocab();
-  Word factorUnits2word(const std::vector<WordIndex>& factorUnits);
+  size_t factorUnit2FactorIndex(WordIndex u) const;
   std::string word2string(Word word);
 private:
   class WordLUT { // map between strings and WordIndex
@@ -94,6 +97,7 @@ class FactoredVocab : public IVocab {
   CSRData globalFactorMatrix_;                         // [v,u] (sparse) -> =1 if u is factor of v
   std::vector<size_t> factorGroups_;                   // [u] -> group id of factor u
   std::vector<std::pair<size_t, size_t>> groupRanges_; // [group id g] -> (u_begin,u_end) index range of factors u for this group. These don't overlap.
+  std::vector<std::vector<bool>>lemmaHasFactorGroup_;  // [factor 0 index][g] -> true if lemma has factor group
   Shape factorShape_;                                  // [g] number of factors in each factor group
   std::vector<size_t> factorStrides_;                  // [g] stride for factor dimension
   //std::vector<std::vector<float>>     factorMasks_;    // [g][v] 1.0 if word v has factor g
diff --git a/src/layers/generic.cpp b/src/layers/generic.cpp
index 1ebd87f5a..4157df3ad 100755
--- a/src/layers/generic.cpp
+++ b/src/layers/generic.cpp
@@ -139,6 +139,7 @@ namespace marian {
 
   // use first factor of each word to determine whether it has a specific factor
   std::vector<float> Logits::getFactorMasks(const Words& words, size_t factorGroup) const { // 1.0 for words that do have this factor; else 0
+    words; factorGroup;
     std::vector<float> res;
     ABORT("FINISH THIS");
     return res;
diff --git a/src/translator/beam_search.h b/src/translator/beam_search.h
index 36836acc7..ef5fa32f9 100755
--- a/src/translator/beam_search.h
+++ b/src/translator/beam_search.h
@@ -41,7 +41,7 @@ class BeamSearch {
                const size_t beamSize,
                const bool first,
                Ptr<data::CorpusBatch /*const*/> batch, // for alignments only
-               Ptr<FactoredVocab/*const*/> factoredVocab) const {
+               Ptr<FactoredVocab/*const*/> factoredVocab, size_t factorGroup) const {
     std::vector<float> align;
     if(options_->hasAndNotEmpty("alignment"))
       align = scorers_[0]->getAlignment(); // use alignments from the first scorer, even if ensemble
@@ -78,7 +78,12 @@ class BeamSearch {
       if (shortlist)
         word = Word::fromWordIndex(shortlist->reverseMap(wordIdx));
       else
+        // @TODO: implant factor here into existing one--what is the wordIdx?
+        //  - factoredVocab->expandFactoredWord(beam[beamHypIdx]->getWord(), factorIndex, groupIndex)
+        //     - if groupIndex = 0 then create a new partially set factor tuple with a lemma, and all others unspecified or not applicable
+        //     - if additional factor, then add it in if applicable
         word = Word::fromWordIndex(wordIdx);
+      factoredVocab; factorGroup;
 
       auto hyp = New<Hypothesis>(beam[beamHypIdx], word, beamHypIdx, pathScore);
 
@@ -287,8 +292,9 @@ class BeamSearch {
             //    in hyps with some factors set to FACTOR_NOT_SPECIFIED.
             // TODO:
             //  - we did not rearrange the tensors in the decoder model's state
-            //  - initial word should set lemma by all other factors as unspecified
-            //  - toHyp() should implant factors
+            //  - toHyps():
+            //     - initial word should set lemma by all other factors as unspecified
+            //     - implant factors for subsequent words
             auto factorMaskVector = states[i]->getLogProbs().getFactorMasks(prevWords, factorGroup);
             factorMasks = graph->constant({(int)localBeamSize, 1, dimBatch, 1}, inits::from_vector(factorMaskVector));
         }
@@ -339,7 +345,7 @@ class BeamSearch {
                              states,           // used for keeping track of per-ensemble-member path score
                              localBeamSize,    // used in the encoding of the (batchIdx, beamHypIdx, word) tuples
                              /*first=*/t == 0, // used to indicate originating beamSize of 1
-                             batch, factoredVocab);
+                             batch, factoredVocab, factorGroup);
 
       // remove all hyps that end in EOS
       // The position of a hyp in the beam may change.

From ff6b158ede57319670002a8325bba6dfb4eb0444 Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Fri, 8 Feb 2019 20:35:08 -0800
Subject: [PATCH 317/838] toHyp() now builds factored words

---
 src/data/factored_vocab.cpp  |  2 +-
 src/data/factored_vocab.h    |  2 +-
 src/translator/beam_search.h | 18 ++++++++++--------
 3 files changed, 12 insertions(+), 10 deletions(-)

diff --git a/src/data/factored_vocab.cpp b/src/data/factored_vocab.cpp
index 2390adf63..6bafb559e 100755
--- a/src/data/factored_vocab.cpp
+++ b/src/data/factored_vocab.cpp
@@ -180,7 +180,7 @@ Word FactoredVocab::lemma2Word(size_t factor0Index) const {
 
 // replace a factor that is FACTOR_NOT_SPECIFIED by a specified one
 // This is used in beam search, where factors are searched one after another.
-Word FactoredVocab::addFactor(Word word, size_t groupIndex, size_t factorIndex) const {
+Word FactoredVocab::expandFactoredWord(Word word, size_t groupIndex, size_t factorIndex) const {
   ABORT_IF(groupIndex == 0, "Cannot add or change lemma in a partial Word");
   ABORT_IF(!isFactorValid(factorIndex), "Cannot add unspecified or n/a factor to a partial Word");
   std::vector<size_t> factorIndices;
diff --git a/src/data/factored_vocab.h b/src/data/factored_vocab.h
index a5aefc726..67a6c3be5 100755
--- a/src/data/factored_vocab.h
+++ b/src/data/factored_vocab.h
@@ -53,7 +53,7 @@ class FactoredVocab : public IVocab {
   Word factors2word(const std::vector<size_t>& factors) const;
   void word2factors(Word word, std::vector<size_t>& factors) const;
   Word lemma2Word(size_t factor0Index) const;
-  Word addFactor(Word word, size_t groupIndex, size_t factor0Index) const;
+  Word expandFactoredWord(Word word, size_t groupIndex, size_t factorIndex) const;
   size_t getFactor(Word word, size_t groupIndex) const;
   std::pair<WordIndex, bool> getFactorUnit(Word word, size_t groupIndex) const;
   bool lemmaHasFactorGroup(size_t factor0Index, size_t g) const { return lemmaHasFactorGroup_[factor0Index][g]; }
diff --git a/src/translator/beam_search.h b/src/translator/beam_search.h
index ef5fa32f9..ed152b9c0 100755
--- a/src/translator/beam_search.h
+++ b/src/translator/beam_search.h
@@ -77,13 +77,16 @@ class BeamSearch {
       auto shortlist = scorers_[0]->getShortlist();
       if (shortlist)
         word = Word::fromWordIndex(shortlist->reverseMap(wordIdx));
+      else if (factoredVocab) {
+        // For factored decoding, the word is built over multiple decoding steps,
+        // starting with the lemma, then adding factors one by one.
+        if (factorGroup == 0)
+          word = factoredVocab->lemma2Word(wordIdx);
+        else
+          word = factoredVocab->expandFactoredWord(beam[beamHypIdx]->getPrevHyp()->getWord(), factorGroup, wordIdx);
+      }
       else
-        // @TODO: implant factor here into existing one--what is the wordIdx?
-        //  - factoredVocab->expandFactoredWord(beam[beamHypIdx]->getWord(), factorIndex, groupIndex)
-        //     - if groupIndex = 0 then create a new partially set factor tuple with a lemma, and all others unspecified or not applicable
-        //     - if additional factor, then add it in if applicable
         word = Word::fromWordIndex(wordIdx);
-      factoredVocab; factorGroup;
 
       auto hyp = New<Hypothesis>(beam[beamHypIdx], word, beamHypIdx, pathScore);
 
@@ -171,6 +174,8 @@ class BeamSearch {
     factoredVocab.reset();
 #endif
     size_t numFactorGroups = factoredVocab ? factoredVocab->getNumGroups() : 1;
+    if (numFactorGroups == 1) // if no factors then reset
+      factoredVocab.reset();
 
     const int dimBatch = (int)batch->size();
 
@@ -292,9 +297,6 @@ class BeamSearch {
             //    in hyps with some factors set to FACTOR_NOT_SPECIFIED.
             // TODO:
             //  - we did not rearrange the tensors in the decoder model's state
-            //  - toHyps():
-            //     - initial word should set lemma by all other factors as unspecified
-            //     - implant factors for subsequent words
             auto factorMaskVector = states[i]->getLogProbs().getFactorMasks(prevWords, factorGroup);
             factorMasks = graph->constant({(int)localBeamSize, 1, dimBatch, 1}, inits::from_vector(factorMaskVector));
         }

From 2f96d49b5969aa1d8a6fc4f1f8ba1b44d8a2235d Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Fri, 8 Feb 2019 22:08:25 -0800
Subject: [PATCH 318/838] added lots of checks to word/factor conversions; new
 methods lemma2Word(), addFactor()

---
 src/data/factored_vocab.cpp  |  9 +++++----
 src/data/factored_vocab.h    |  2 +-
 src/layers/generic.cpp       | 15 +++++++++++++--
 src/layers/generic.h         |  1 +
 src/models/costs.h           |  7 +------
 src/translator/beam_search.h |  5 +++--
 6 files changed, 24 insertions(+), 15 deletions(-)
 mode change 100644 => 100755 src/models/costs.h

diff --git a/src/data/factored_vocab.cpp b/src/data/factored_vocab.cpp
index 6bafb559e..fa4fca2e8 100755
--- a/src/data/factored_vocab.cpp
+++ b/src/data/factored_vocab.cpp
@@ -181,6 +181,7 @@ Word FactoredVocab::lemma2Word(size_t factor0Index) const {
 // replace a factor that is FACTOR_NOT_SPECIFIED by a specified one
 // This is used in beam search, where factors are searched one after another.
 Word FactoredVocab::expandFactoredWord(Word word, size_t groupIndex, size_t factorIndex) const {
+  LOG(info, "expand {} + [{}]={}", word2string(word), groupIndex, factorIndex);
   ABORT_IF(groupIndex == 0, "Cannot add or change lemma in a partial Word");
   ABORT_IF(!isFactorValid(factorIndex), "Cannot add unspecified or n/a factor to a partial Word");
   std::vector<size_t> factorIndices;
@@ -190,7 +191,8 @@ Word FactoredVocab::expandFactoredWord(Word word, size_t groupIndex, size_t fact
   ABORT_IF(factorIndices[groupIndex] == FACTOR_NOT_APPLICABLE, "Cannot add a factor that the lemma does not have");
   ABORT_IF(factorIndices[groupIndex] != FACTOR_NOT_SPECIFIED, "Cannot modify a specified factor in a partial Word");
   factorIndices[groupIndex] = factorIndex;
-  return factors2word(factorIndices);
+  word = factors2word(factorIndices);
+  LOG(info, "to {}", word2string(word));
 }
 
 size_t FactoredVocab::factorUnit2FactorIndex(WordIndex u) const {
@@ -213,7 +215,7 @@ void FactoredVocab::word2factors(Word word, std::vector<size_t>& factorIndices /
 #endif
 }
 
-std::string FactoredVocab::word2string(Word word) {
+std::string FactoredVocab::word2string(Word word) const {
   std::vector<size_t> factorIndices;
   word2factors(word, factorIndices);
   std::string res;
@@ -232,13 +234,13 @@ std::string FactoredVocab::word2string(Word word) {
 
 size_t FactoredVocab::getFactor(Word word, size_t groupIndex) const {
   size_t index = word.toWordIndex();
+  size_t factor0Index = index / factorStrides_[0];
   index = index / factorStrides_[groupIndex];
   index = index % (size_t)factorShape_[groupIndex];
   if (index == (size_t)factorShape_[groupIndex] - 1) { // special sentinel value for unspecified or not-applicable
     if (groupIndex == 0) // lemma itself is always applicable, hence 'not specified'
       index = FACTOR_NOT_SPECIFIED;
     else { // not lemma: check whether lemma of word has this factor group
-      size_t factor0Index = word.toWordIndex() / factorStrides_[0];
       if (lemmaHasFactorGroup(factor0Index, groupIndex))
         index = FACTOR_NOT_SPECIFIED;
       else
@@ -246,7 +248,6 @@ size_t FactoredVocab::getFactor(Word word, size_t groupIndex) const {
     }
   }
   else { // regular value: consistency check if lemma really has this factor group
-    size_t factor0Index = word.toWordIndex() / factorStrides_[0];
     ABORT_IF(factor0Index == (size_t)factorShape_[0] - 1, "Word has specified factor but no lemma??");
     ABORT_IF(!lemmaHasFactorGroup(factor0Index, groupIndex), "Word has a specified factor for a lemma that does not have that factor group??");
   }
diff --git a/src/data/factored_vocab.h b/src/data/factored_vocab.h
index 67a6c3be5..0d10f7278 100755
--- a/src/data/factored_vocab.h
+++ b/src/data/factored_vocab.h
@@ -67,7 +67,7 @@ class FactoredVocab : public IVocab {
   void constructFactorIndexConversion();
   void constructNormalizationInfoForVocab();
   size_t factorUnit2FactorIndex(WordIndex u) const;
-  std::string word2string(Word word);
+  std::string word2string(Word word) const;
 private:
   class WordLUT { // map between strings and WordIndex
     std::map<std::string, WordIndex> str2index_;
diff --git a/src/layers/generic.cpp b/src/layers/generic.cpp
index 4157df3ad..2686de20f 100755
--- a/src/layers/generic.cpp
+++ b/src/layers/generic.cpp
@@ -139,12 +139,23 @@ namespace marian {
 
   // use first factor of each word to determine whether it has a specific factor
   std::vector<float> Logits::getFactorMasks(const Words& words, size_t factorGroup) const { // 1.0 for words that do have this factor; else 0
-    words; factorGroup;
     std::vector<float> res;
-    ABORT("FINISH THIS");
+    res.reserve(words.size());
+    for (const auto& word : words) {
+      auto lemma = factoredVocab_->getFactor(word, 0);
+      res.push_back((float)factoredVocab_->lemmaHasFactorGroup(lemma, factorGroup));
+    }
     return res;
   }
 
+  Logits Logits::applyUnaryFunction(const std::function<Expr(Expr)>& f) const { // clone this but apply f to all loss values
+    std::vector<Ptr<RationalLoss>> newLogits;
+    for (const auto& l : logits_)
+      newLogits.emplace_back(New<RationalLoss>(f(l->loss()), l->count()));
+    return Logits(std::move(newLogits), factoredVocab_);
+  }
+
+  // @TODO: code dup with above; we can merge it into applyToRationalLoss()
   Logits Logits::withCounts(const Expr& count) const { // create new Logits with 'count' implanted into all logits_
     std::vector<Ptr<RationalLoss>> newLogits;
     for (const auto& l : logits_)
diff --git a/src/layers/generic.h b/src/layers/generic.h
index 85dec5692..4e25be784 100755
--- a/src/layers/generic.h
+++ b/src/layers/generic.h
@@ -76,6 +76,7 @@ class Logits {
     Expr getFactoredLogits(size_t groupIndex) const; // get logits for only one factor group
     Ptr<RationalLoss> getRationalLoss() const; // assume it holds a loss: get that
     Expr applyLossFunction(const Words& labels, const std::function<Expr(Expr/*logits*/,Expr/*indices*/)>& lossFn) const;
+    Logits applyUnaryFunction(const std::function<Expr(Expr)>& f) const; // clone this but apply f to all loss values
 
     struct MaskedFactorIndices {
       std::vector<WordIndex> indices; // factor index, or 0 if masked
diff --git a/src/models/costs.h b/src/models/costs.h
old mode 100644
new mode 100755
index 98f9300fc..2819e13a6
--- a/src/models/costs.h
+++ b/src/models/costs.h
@@ -172,12 +172,7 @@ class LogSoftmaxStep : public CostStep {
 public:
   virtual Ptr<DecoderState> apply(Ptr<DecoderState> state) override {
     // decoder needs normalized probabilities (note: skipped if beam 1 and --skip-cost)
-    // @TODO: @HACK must know about individual parts; make it a loop
-    auto logits = state->getLogProbs().getLogits();
-    
-    auto logprobs = logsoftmax(logits);
-
-    state->setLogProbs(logprobs);
+    state->setLogProbs(state->getLogProbs().applyUnaryFunction(logsoftmax));
     return state;
   }
 };
diff --git a/src/translator/beam_search.h b/src/translator/beam_search.h
index ed152b9c0..ee86cda11 100755
--- a/src/translator/beam_search.h
+++ b/src/translator/beam_search.h
@@ -59,6 +59,7 @@ class BeamSearch {
       const auto wordIdx    = (WordIndex)(key % vocabSize);
       const auto beamHypIdx =            (key / vocabSize) % (first ? 1 : beamSize);
       const auto batchIdx   =            (key / vocabSize) / (first ? 1 : beamSize);
+      LOG(info, "key = (batch {}, beam {}, word {})", batchIdx, beamHypIdx, wordIdx);
 
       ABORT_IF(i / beamSize != batchIdx, "Inconsistent batchIdx value in key??");
 
@@ -170,7 +171,7 @@ class BeamSearch {
     ABORT_IF(batch->back()->vocab()->getEosId() != trgEosId_, "Batch uses different EOS token than was passed to BeamSearch originally");
 
     auto factoredVocab = batch->back()->vocab()->tryAs<FactoredVocab>();
-#if 1   // use '1' here to disable factored decoding, e.g. for comparisons
+#if 0   // use '1' here to disable factored decoding, e.g. for comparisons
     factoredVocab.reset();
 #endif
     size_t numFactorGroups = factoredVocab ? factoredVocab->getNumGroups() : 1;
@@ -332,7 +333,7 @@ class BeamSearch {
       // perform beam search
 
       // find N best amongst the (localBeamSize * dimVocab) hypotheses
-      std::vector<unsigned int> nBestKeys; // [dimBatch, localBeamSize] flattened -> ((batchIdx, beamHypIdx) flattened, word idx) flattened
+      std::vector<unsigned int> nBestKeys; // [dimBatch, localBeamSize] flattened -> (batchIdx, beamHypIdx, word idx) flattened
       std::vector<float> nBestPathScores;  // [dimBatch, localBeamSize] flattened
       getNBestList(/*beamSizes=*/std::vector<size_t>(dimBatch, localBeamSize), // output layout of (nBestPathScores, nBestKeys)  --@REVIEW: correct?
                    /*in*/ expandedPathScores->val(),                           // [dimBatch, 1, localBeamSize, dimVocab or dimShortlist]

From 53c1dbe28732e5acb699b1c30367550d668a6df2 Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Sat, 9 Feb 2019 09:27:09 -0800
Subject: [PATCH 319/838] first step to actually include factor scores;
 word2string() can now print invalid items. Please igonore last commit, was am
 accident.

---
 src/data/factored_vocab.cpp  | 25 +++++++++++++++++--------
 src/data/factored_vocab.h    |  4 +++-
 src/translator/beam_search.h | 16 +++++++++++++---
 3 files changed, 33 insertions(+), 12 deletions(-)

diff --git a/src/data/factored_vocab.cpp b/src/data/factored_vocab.cpp
index fa4fca2e8..85818ac71 100755
--- a/src/data/factored_vocab.cpp
+++ b/src/data/factored_vocab.cpp
@@ -193,6 +193,7 @@ Word FactoredVocab::expandFactoredWord(Word word, size_t groupIndex, size_t fact
   factorIndices[groupIndex] = factorIndex;
   word = factors2word(factorIndices);
   LOG(info, "to {}", word2string(word));
+  return word;
 }
 
 size_t FactoredVocab::factorUnit2FactorIndex(WordIndex u) const {
@@ -216,18 +217,25 @@ void FactoredVocab::word2factors(Word word, std::vector<size_t>& factorIndices /
 }
 
 std::string FactoredVocab::word2string(Word word) const {
-  std::vector<size_t> factorIndices;
-  word2factors(word, factorIndices);
-  std::string res;
+  // this function has some code dup, so that we can bypass some checks for debugging
   size_t numGroups = getNumGroups();
+  size_t factor0Index = word.toWordIndex() / factorStrides_[0];
+  std::string res;
   for (size_t g = 0; g < numGroups; g++) {
     res.append(res.empty() ? "(" : ", ");
-    auto factorIndex = factorIndices[g];
-    switch (factorIndex) {
-    case FACTOR_NOT_APPLICABLE: res.append("n/a"); break;
-    case FACTOR_NOT_SPECIFIED: res.append("?"); break;
-    default: res.append(factorVocab_[(WordIndex)(factorIndex + groupRanges_[g].first)]); break;
+    size_t index = word.toWordIndex();
+    index = index / factorStrides_[g];
+    index = index % (size_t)factorShape_[g];
+    if (index == (size_t)factorShape_[g] - 1) { // special sentinel value for unspecified or not-applicable
+      if (factor0Index >= (size_t)factorShape_[0])
+        res.append("(lemma oob)");
+      else if (lemmaHasFactorGroup(factor0Index, g))
+        res.append("?");
+      else
+        res.append("n/a");
     }
+    else
+      res.append(factorVocab_[(WordIndex)(index + groupRanges_[g].first)]);
   }
   return res + ")";
 }
@@ -299,6 +307,7 @@ void FactoredVocab::constructNormalizationInfoForVocab() {
 }
 
 /*virtual*/ const std::string& FactoredVocab::operator[](Word id) const /*override final*/ {
+  LOG(info, "Looking up Word {}={}", id.toWordIndex(), word2string(id));
   return vocab_[id.toWordIndex()];
 }
 
diff --git a/src/data/factored_vocab.h b/src/data/factored_vocab.h
index 0d10f7278..c5581932a 100755
--- a/src/data/factored_vocab.h
+++ b/src/data/factored_vocab.h
@@ -54,20 +54,22 @@ class FactoredVocab : public IVocab {
   void word2factors(Word word, std::vector<size_t>& factors) const;
   Word lemma2Word(size_t factor0Index) const;
   Word expandFactoredWord(Word word, size_t groupIndex, size_t factorIndex) const;
+  bool canExpandFactoredWord(Word word, size_t groupIndex) const { return lemmaHasFactorGroup(getFactor(word, 0), groupIndex); }
   size_t getFactor(Word word, size_t groupIndex) const;
   std::pair<WordIndex, bool> getFactorUnit(Word word, size_t groupIndex) const;
   bool lemmaHasFactorGroup(size_t factor0Index, size_t g) const { return lemmaHasFactorGroup_[factor0Index][g]; }
+
   static constexpr size_t FACTOR_NOT_APPLICABLE = (SIZE_MAX - 1);
   static constexpr size_t FACTOR_NOT_SPECIFIED  = (SIZE_MAX - 2);
   static bool isFactorValid(size_t factorIndex) { return factorIndex < FACTOR_NOT_SPECIFIED; }
 
   static Ptr<FactoredVocab> tryCreateAndLoad(const std::string& path); // load from "vocab" option if it specifies a factored vocab
+  std::string word2string(Word word) const; // (diagnostics only)
 private:
   void constructGroupInfoFromFactorVocab();
   void constructFactorIndexConversion();
   void constructNormalizationInfoForVocab();
   size_t factorUnit2FactorIndex(WordIndex u) const;
-  std::string word2string(Word word) const;
 private:
   class WordLUT { // map between strings and WordIndex
     std::map<std::string, WordIndex> str2index_;
diff --git a/src/translator/beam_search.h b/src/translator/beam_search.h
index ee86cda11..aa74757b1 100755
--- a/src/translator/beam_search.h
+++ b/src/translator/beam_search.h
@@ -81,10 +81,18 @@ class BeamSearch {
       else if (factoredVocab) {
         // For factored decoding, the word is built over multiple decoding steps,
         // starting with the lemma, then adding factors one by one.
-        if (factorGroup == 0)
+        if (factorGroup == 0) {
           word = factoredVocab->lemma2Word(wordIdx);
-        else
-          word = factoredVocab->expandFactoredWord(beam[beamHypIdx]->getPrevHyp()->getWord(), factorGroup, wordIdx);
+          LOG(info, "new lemma {}={}", word.toWordIndex(), factoredVocab->word2string(word));
+        }
+        else {
+          LOG(info, "expand word {}={} with factor[{}] {}", beam[beamHypIdx]->getWord().toWordIndex(),
+              factoredVocab->word2string(beam[beamHypIdx]->getWord()), factorGroup, wordIdx);
+          word = beam[beamHypIdx]->getWord();
+          if (factoredVocab->canExpandFactoredWord(word, factorGroup))
+            word = factoredVocab->expandFactoredWord(word, factorGroup, wordIdx);
+          // @TODO: maybe factor the two above into a single function; for now, I want the extra checks
+        }
       }
       else
         word = Word::fromWordIndex(wordIdx);
@@ -298,6 +306,8 @@ class BeamSearch {
             //    in hyps with some factors set to FACTOR_NOT_SPECIFIED.
             // TODO:
             //  - we did not rearrange the tensors in the decoder model's state
+            for (auto word : prevWords)
+                LOG(info, "prevWords[]={}", factoredVocab->word2string(word));
             auto factorMaskVector = states[i]->getLogProbs().getFactorMasks(prevWords, factorGroup);
             factorMasks = graph->constant({(int)localBeamSize, 1, dimBatch, 1}, inits::from_vector(factorMaskVector));
         }

From bed1f902a165b052544216d8abec21add29ab1e1 Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Sat, 9 Feb 2019 09:36:27 -0800
Subject: [PATCH 320/838] (comments)

---
 src/translator/beam_search.h | 25 +++++++++++++------------
 1 file changed, 13 insertions(+), 12 deletions(-)

diff --git a/src/translator/beam_search.h b/src/translator/beam_search.h
index aa74757b1..a9fbd668c 100755
--- a/src/translator/beam_search.h
+++ b/src/translator/beam_search.h
@@ -217,6 +217,14 @@ class BeamSearch {
       histories[i]->add(beams[i], trgEosId_);
 
     // the decoder maintains the following state:
+    //  - beams : array [dimBatch] of  array [localBeamSize] of Hypothesis
+    //     - current output time step's set of active hypotheses, aka active search space
+    //     - gets replaced at the end of each output time step
+    //  - states[.] : ScorerState
+    //     - NN state
+    //     - one per scorer, e.g. 2 for ensemble of 2
+    //     - gets replaced in each output time step
+    // and it forms the following return value
     //  - histories : array [dimBatch] of History
     //    with History : vector [t] of array [localBeamSize] of Hypothesis
     //    with Hypothesis : (last word, aggregate score, prev Hypothesis)
@@ -224,19 +232,12 @@ class BeamSearch {
     //     - stores traceback information
     //     - gets added to in each output time step
     //     - the final version is the return value of this function
-    //  - beams : array [dimBatch] of  array [localBeamSize] of Hypothesis
-    //     - current output time step's set of active hypotheses, aka active search space
-    //     - gets replaced at the end of each output time step
-    //  - states[.] : ScorerState
-    //     - NN state
-    //     - one per scorer, e.g. 2 for ensemble of 2
-    //     - gets replaced at the end of each output time step
 
     // main loop over output time steps
     for (size_t t = 0; ; t++) {
       ABORT_IF(dimBatch != beams.size(), "Lost a batch entry??");
 
-      // for factored vocabs, we do one factor at a time, but without updating the decoder model for secondary factors
+      // for factored vocabs, we do one factor at a time, but without updating the scorer for secondary factors
       auto factorGroup = t % numFactorGroups;
 
       // determine beam size for next output time step, as max over still-active sentences
@@ -286,9 +287,9 @@ class BeamSearch {
         Expr logProbs, factorMasks;
         if (factorGroup == 0) {
           // compute output probabilities for current output time step
-          //  - uses hypIndices[index in beam, 1, batch index, 1] to reorder decoder state to reflect the top-N in beams[][]
-          //  - adds prevWords [index in beam, 1, batch index, 1] to the decoder model's target history
-          //  - performs one step of the decoder model
+          //  - uses hypIndices[index in beam, 1, batch index, 1] to reorder scorer state to reflect the top-N in beams[][]
+          //  - adds prevWords [index in beam, 1, batch index, 1] to the scorer's target history
+          //  - performs one step of the scorer
           //  - returns new NN state for use in next output time step
           //  - returns vector of prediction probabilities over output vocab via newState
           // update state in-place for next output time step
@@ -305,7 +306,7 @@ class BeamSearch {
             //  - factors are incorporated one step at a time; so we will have temporary Word entries
             //    in hyps with some factors set to FACTOR_NOT_SPECIFIED.
             // TODO:
-            //  - we did not rearrange the tensors in the decoder model's state
+            //  - we did not rearrange the tensors in the scorer's state
             for (auto word : prevWords)
                 LOG(info, "prevWords[]={}", factoredVocab->word2string(word));
             auto factorMaskVector = states[i]->getLogProbs().getFactorMasks(prevWords, factorGroup);

From 675a60875a8fcbc70862df352a0353948ec7d100 Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Sat, 9 Feb 2019 14:35:19 -0800
Subject: [PATCH 321/838] towards seleccting the correct pred hyp in factored
 beam search

---
 src/data/factored_vocab.cpp  | 14 +++++---
 src/layers/generic.cpp       |  9 ++++--
 src/layers/generic.h         |  2 +-
 src/rnn/types.h              |  2 +-
 src/translator/beam_search.h | 62 +++++++++++++++++++++---------------
 5 files changed, 55 insertions(+), 34 deletions(-)
 mode change 100644 => 100755 src/rnn/types.h

diff --git a/src/data/factored_vocab.cpp b/src/data/factored_vocab.cpp
index 85818ac71..c23c4d1dc 100755
--- a/src/data/factored_vocab.cpp
+++ b/src/data/factored_vocab.cpp
@@ -306,9 +306,15 @@ void FactoredVocab::constructNormalizationInfoForVocab() {
     return getUnkId();
 }
 
-/*virtual*/ const std::string& FactoredVocab::operator[](Word id) const /*override final*/ {
-  LOG(info, "Looking up Word {}={}", id.toWordIndex(), word2string(id));
-  return vocab_[id.toWordIndex()];
+/*virtual*/ const std::string& FactoredVocab::operator[](Word word) const /*overrworde final*/ {
+  LOG(info, "Looking up Word {}={}", word.toWordIndex(), word2string(word));
+#if 1 // @BUGBUG: our manually prepared dict does not contain @CI tags for single letters, but it's a valid factor
+  if (vocab_.isGap(word.toWordIndex())) {
+    LOG(info, "Factor combination {} missing in external dict, generating fake entry", word2string(word));
+    const_cast<WordLUT&>(vocab_).add("??" + word2string(word), word.toWordIndex());
+  }
+#endif
+  return vocab_[word.toWordIndex()];
 }
 
 /*virtual*/ size_t FactoredVocab::size() const /*override final*/ {
@@ -382,7 +388,7 @@ WordIndex FactoredVocab::WordLUT::add(const std::string& word, WordIndex index)
 }
 const std::string& FactoredVocab::WordLUT::operator[](WordIndex index) const {
   const auto& word = index2str_[index];
-  ABORT_IF(word.empty(), "Invalid access to dictionary gap item");
+  //ABORT_IF(word.empty(), "Invalid access to dictionary gap item");
   return word;
 }
 WordIndex FactoredVocab::WordLUT::operator[](const std::string& word) const {
diff --git a/src/layers/generic.cpp b/src/layers/generic.cpp
index 2686de20f..19ea9895d 100755
--- a/src/layers/generic.cpp
+++ b/src/layers/generic.cpp
@@ -3,6 +3,7 @@
 #include "layers/generic.h"
 #include "layers/loss.h"
 #include "data/factored_vocab.h"
+#include "rnn/types.h" // for State::select()
 
 using std::size_t; // not sure why this is needed
 
@@ -77,9 +78,13 @@ namespace marian {
   }
 
   // get logits for one factor group
-  Expr Logits::getFactoredLogits(size_t groupIndex) const {
+  Expr Logits::getFactoredLogits(size_t groupIndex, const std::vector<IndexType>& selIdx /*= {}*/, size_t beamSize /*= 0*/) const {
     ABORT_IF(empty(), "Attempted to read out logits on empty Logits object");
-    return logits_[groupIndex]->loss();
+    auto sel = logits_[groupIndex]->loss(); // [localBeamSize, 1, dimBatch, dimFactorVocab]
+    // if selIdx are given, then we must reshuffle accordingly
+    if (!selIdx.empty()) // use the same function that shuffles decoder state
+      sel = rnn::State::select(sel, selIdx, (int)beamSize, /*isBatchMajor=*/false);
+    return sel;
   }
 
   // This function assumes that the object holds one or more factor logits, which are summed up
diff --git a/src/layers/generic.h b/src/layers/generic.h
index 4e25be784..08d5cbbb9 100755
--- a/src/layers/generic.h
+++ b/src/layers/generic.h
@@ -73,7 +73,7 @@ class Logits {
     Logits(std::vector<Ptr<RationalLoss>>&& logits, Ptr<FactoredVocab> embeddingFactorMapping) // factored-output constructor
       : logits_(std::move(logits)), factoredVocab_(embeddingFactorMapping) {}
     Expr getLogits() const; // assume it holds logits: get them, possibly aggregating over factors
-    Expr getFactoredLogits(size_t groupIndex) const; // get logits for only one factor group
+    Expr getFactoredLogits(size_t groupIndex, const std::vector<IndexType>& hypIndices = {}, size_t beamSize = 0) const; // get logits for only one factor group, with optional reshuffle
     Ptr<RationalLoss> getRationalLoss() const; // assume it holds a loss: get that
     Expr applyLossFunction(const Words& labels, const std::function<Expr(Expr/*logits*/,Expr/*indices*/)>& lossFn) const;
     Logits applyUnaryFunction(const std::function<Expr(Expr)>& f) const; // clone this but apply f to all loss values
diff --git a/src/rnn/types.h b/src/rnn/types.h
old mode 100644
new mode 100755
index bd44b98bb..47424b759
--- a/src/rnn/types.h
+++ b/src/rnn/types.h
@@ -18,7 +18,7 @@ struct State {
             select(cell,   selIdx, beamSize, isBatchMajor) };
   }
 
-private:
+  // this function is also called by Logits
   static Expr select(Expr sel, // [beamSize, dimTime, dimBatch, dimDepth] or [beamSize, dimBatch, dimTime, dimDepth] (dimTime = 1 for RNN)
                      const std::vector<IndexType>& selIdx, // [beamIndex * activeBatchSize + batchIndex]
                      int beamSize, bool isBatchMajor)
diff --git a/src/translator/beam_search.h b/src/translator/beam_search.h
index a9fbd668c..2b241e7f3 100755
--- a/src/translator/beam_search.h
+++ b/src/translator/beam_search.h
@@ -72,6 +72,7 @@ class BeamSearch {
       ABORT_IF(beamHypIdx >= (int)beam.size(), "Out of bounds beamHypIdx value in key??");
 
       // map wordIdx to word
+      auto prevHyp = beam[beamHypIdx];
       Word word;
       // If short list has been set, then wordIdx is an index into the short-listed word set,
       // rather than the true word index.
@@ -92,12 +93,13 @@ class BeamSearch {
           if (factoredVocab->canExpandFactoredWord(word, factorGroup))
             word = factoredVocab->expandFactoredWord(word, factorGroup, wordIdx);
           // @TODO: maybe factor the two above into a single function; for now, I want the extra checks
+          prevHyp = prevHyp->getPrevHyp(); // short-circuit the backpointer, so that the traceback doesnot contain partially factored words
         }
       }
       else
         word = Word::fromWordIndex(wordIdx);
 
-      auto hyp = New<Hypothesis>(beam[beamHypIdx], word, beamHypIdx, pathScore);
+      auto hyp = New<Hypothesis>(prevHyp, word, beamHypIdx, pathScore);
 
       // Set score breakdown for n-best lists
       if(options_->get<bool>("n-best")) {
@@ -237,9 +239,6 @@ class BeamSearch {
     for (size_t t = 0; ; t++) {
       ABORT_IF(dimBatch != beams.size(), "Lost a batch entry??");
 
-      // for factored vocabs, we do one factor at a time, but without updating the scorer for secondary factors
-      auto factorGroup = t % numFactorGroups;
-
       // determine beam size for next output time step, as max over still-active sentences
       // E.g. if all batch entries are down from beam 5 to no more than 4 surviving hyps, then
       // switch to beam of 4 for all. If all are done, then beam ends up being 0, and we are done.
@@ -252,6 +251,11 @@ class BeamSearch {
       if (localBeamSize == 0)
         break;
 
+      // BEGIN FOR factorGroup = 0 .. numFactorGroups-1
+      // @TODO: use an explicit nested loop here for factors
+      // for factored vocabs, we do one factor at a time, but without updating the scorer for secondary factors
+      auto factorGroup = t % numFactorGroups;
+
       //**********************************************************************
       // create constant containing previous path scores for current beam
       // Also create mapping of hyp indices, for reordering the decoder-state tensors.
@@ -294,31 +298,31 @@ class BeamSearch {
           //  - returns vector of prediction probabilities over output vocab via newState
           // update state in-place for next output time step
           states[i] = scorers_[i]->step(graph, states[i], hypIndices, prevWords, dimBatch, (int)localBeamSize);
+          if (numFactorGroups == 1)
+            logProbs = states[i]->getLogProbs().getLogits(); // [localBeamSize, 1, dimBatch, dimVocab]
+          else
+            logProbs = states[i]->getLogProbs().getFactoredLogits(factorGroup); // [localBeamSize, 1, dimBatch, dimVocab]
         }
         else {
-            // add secondary factors
-            // For those, we don't update the decoder-model state in any way.
-            // Instead, we just keep expanding with the factors.
-            // Considerations:
-            //  - not all scores should get a factor
-            //    We need a [localBeamSize, 1, dimBatch, 1] tensor that knows whether a factor is applicable
-            //    by considering the lemma at each (beamHypIdx, batchIdx). prevWords is already in the right order.
-            //  - factors are incorporated one step at a time; so we will have temporary Word entries
-            //    in hyps with some factors set to FACTOR_NOT_SPECIFIED.
-            // TODO:
-            //  - we did not rearrange the tensors in the scorer's state
-            for (auto word : prevWords)
-                LOG(info, "prevWords[]={}", factoredVocab->word2string(word));
-            auto factorMaskVector = states[i]->getLogProbs().getFactorMasks(prevWords, factorGroup);
-            factorMasks = graph->constant({(int)localBeamSize, 1, dimBatch, 1}, inits::from_vector(factorMaskVector));
+          // add secondary factors
+          // For those, we don't update the decoder-model state in any way.
+          // Instead, we just keep expanding with the factors.
+          // Considerations:
+          //  - not all scores should get a factor
+          //    We need a [localBeamSize, 1, dimBatch, 1] tensor that knows whether a factor is applicable
+          //    by considering the lemma at each (beamHypIdx, batchIdx). prevWords is already in the right order.
+          //  - factors are incorporated one step at a time; so we will have temporary Word entries
+          //    in hyps with some factors set to FACTOR_NOT_SPECIFIED.
+          // TODO:
+          //  - we did not rearrange the tensors in the scorer's state
+          logProbs = states[i]->getLogProbs().getFactoredLogits(factorGroup, hypIndices, localBeamSize); // [localBeamSize, 1, dimBatch, dimVocab]
+          for (auto word : prevWords)
+            LOG(info, "prevWords[]={}", factoredVocab->word2string(word));
+          auto factorMaskVector = states[i]->getLogProbs().getFactorMasks(prevWords, factorGroup);
+          factorMasks = graph->constant({(int)localBeamSize, 1, dimBatch, 1}, inits::from_vector(factorMaskVector));
+          logProbs = logProbs * factorMasks; // those hyps that don't have a factor get multiplied with 0
         }
         // expand all hypotheses, [localBeamSize, 1, dimBatch, 1] -> [localBeamSize, 1, dimBatch, dimVocab]
-        if (numFactorGroups == 1)
-          logProbs = states[i]->getLogProbs().getLogits(); // [localBeamSize, 1, dimBatch, dimVocab]
-        else
-          logProbs = states[i]->getLogProbs().getFactoredLogits(factorGroup); // [localBeamSize, 1, dimBatch, dimVocab]
-        if (factorMasks)
-          logProbs = logProbs * factorMasks; // those hyps that don't have a factor get multiplied with 0
         expandedPathScores = expandedPathScores + scorers_[i]->getWeight() * logProbs;
       }
 
@@ -361,11 +365,17 @@ class BeamSearch {
                              /*first=*/t == 0, // used to indicate originating beamSize of 1
                              batch, factoredVocab, factorGroup);
 
+      if (factorGroup != numFactorGroups - 1) { // skip adding to history and skip purging for partially factored words
+        beams = newBeams;
+        continue;
+      }
+      // END FOR factorGroup = 0 .. numFactorGroups-1
+
       // remove all hyps that end in EOS
       // The position of a hyp in the beam may change.
       const auto purgedNewBeams = purgeBeams(newBeams);
 
-      // add updated search space (newBeams) to search grid (histories) for traceback
+      // add updated search space (newBeams) to our return value
       bool maxLengthReached = false;
       for(int i = 0; i < dimBatch; ++i) {
         // if this batch entry has surviving hyps then add them to the traceback grid

From 5b7334d9049af88b36b44baeb79ff96cdf5b6ac7 Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Sat, 9 Feb 2019 22:29:10 -0800
Subject: [PATCH 322/838] towards correct hyp index in Hypothesis

---
 src/translator/beam_search.h | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/src/translator/beam_search.h b/src/translator/beam_search.h
index 2b241e7f3..3dcdad616 100755
--- a/src/translator/beam_search.h
+++ b/src/translator/beam_search.h
@@ -73,6 +73,7 @@ class BeamSearch {
 
       // map wordIdx to word
       auto prevHyp = beam[beamHypIdx];
+      auto prevBeamHypIdx = beamHypIdx;
       Word word;
       // If short list has been set, then wordIdx is an index into the short-listed word set,
       // rather than the true word index.
@@ -93,13 +94,14 @@ class BeamSearch {
           if (factoredVocab->canExpandFactoredWord(word, factorGroup))
             word = factoredVocab->expandFactoredWord(word, factorGroup, wordIdx);
           // @TODO: maybe factor the two above into a single function; for now, I want the extra checks
+          prevBeamHypIdx = prevHyp->getPrevStateIndex();
           prevHyp = prevHyp->getPrevHyp(); // short-circuit the backpointer, so that the traceback doesnot contain partially factored words
         }
       }
       else
         word = Word::fromWordIndex(wordIdx);
 
-      auto hyp = New<Hypothesis>(prevHyp, word, beamHypIdx, pathScore);
+      auto hyp = New<Hypothesis>(prevHyp, word, prevBeamHypIdx, pathScore);
 
       // Set score breakdown for n-best lists
       if(options_->get<bool>("n-best")) {

From dd0a691974b13e5b391eb127954a9955f93f84ba Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Sun, 10 Feb 2019 13:44:28 -0800
Subject: [PATCH 323/838] two minor optimizations; temporarily prints scores in
 output  now

---
 src/graph/expression_operators.cpp | 2 ++
 src/translator/beam_search.h       | 4 ++++
 src/translator/output_printer.h    | 3 +++
 3 files changed, 9 insertions(+)

diff --git a/src/graph/expression_operators.cpp b/src/graph/expression_operators.cpp
index 5c37beef3..731220489 100755
--- a/src/graph/expression_operators.cpp
+++ b/src/graph/expression_operators.cpp
@@ -78,6 +78,8 @@ Expr softmax(Expr a, Expr zeroOneMask, int axis /*=-1*/) {
 }
 
 Expr logsoftmax(Expr a) {
+  if (a->type() == "logsoftmax") // logsoftmax(logsoftmax(x)) == logsoftmax(x)
+    return a;
   return Expression<LogSoftmaxNodeOp>(a);
 }
 
diff --git a/src/translator/beam_search.h b/src/translator/beam_search.h
index 3dcdad616..1df16b0e3 100755
--- a/src/translator/beam_search.h
+++ b/src/translator/beam_search.h
@@ -94,6 +94,8 @@ class BeamSearch {
           if (factoredVocab->canExpandFactoredWord(word, factorGroup))
             word = factoredVocab->expandFactoredWord(word, factorGroup, wordIdx);
           // @TODO: maybe factor the two above into a single function; for now, I want the extra checks
+          else
+            ABORT_IF(prevHyp->getPathScore() != pathScore, "Score changed despite factor not applicable??");
           prevBeamHypIdx = prevHyp->getPrevStateIndex();
           prevHyp = prevHyp->getPrevHyp(); // short-circuit the backpointer, so that the traceback doesnot contain partially factored words
         }
@@ -331,6 +333,8 @@ class BeamSearch {
       // make beams continuous
       if(dimBatch > 1 && localBeamSize > 1)
         expandedPathScores = swapAxes(expandedPathScores, 0, 2); // -> [dimBatch, 1, localBeamSize, dimVocab]
+      else // (avoid copy if we can)
+        expandedPathScores = reshape(expandedPathScores, {dimBatch, 1, (int)localBeamSize, expandedPathScores->shape()[-1]}); // -> [dimBatch, 1, localBeamSize, dimVocab]
       //  expandedPathScores = transpose(expandedPathScores, {2, 1, 0, 3}); // -> [dimBatch, 1, localBeamSize, dimVocab]
 
       // perform NN computation
diff --git a/src/translator/output_printer.h b/src/translator/output_printer.h
index 83ef9c0ca..3b8827f9a 100755
--- a/src/translator/output_printer.h
+++ b/src/translator/output_printer.h
@@ -71,6 +71,9 @@ class OutputPrinter {
       const auto& hypo = std::get<1>(result);
       best1 << " ||| " << getAlignment(hypo);
     }
+
+    best1 << " |sc=" << std::get<1>(result)->getPathScore();
+
     best1 << std::flush;
   }
 

From 3a347bf12ab5f3e552b8db73b831b1ddefc72c0f Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Sun, 10 Feb 2019 14:22:21 -0800
Subject: [PATCH 324/838] added lookahead for lemma score in decoding

---
 src/layers/generic.cpp       | 29 +++++++++++++++++++++++++++++
 src/layers/generic.h         |  1 +
 src/translator/beam_search.h |  1 +
 3 files changed, 31 insertions(+)

diff --git a/src/layers/generic.cpp b/src/layers/generic.cpp
index 19ea9895d..9bd91876c 100755
--- a/src/layers/generic.cpp
+++ b/src/layers/generic.cpp
@@ -81,6 +81,23 @@ namespace marian {
   Expr Logits::getFactoredLogits(size_t groupIndex, const std::vector<IndexType>& selIdx /*= {}*/, size_t beamSize /*= 0*/) const {
     ABORT_IF(empty(), "Attempted to read out logits on empty Logits object");
     auto sel = logits_[groupIndex]->loss(); // [localBeamSize, 1, dimBatch, dimFactorVocab]
+
+
+    // normalize for decoding:
+    //  - all secondary factors: subtract their max
+    //  - lemma: add all maxes of applicable factors
+    if (groupIndex > 0) {
+      sel = sel - max(sel, -1);
+    }
+    else {
+      auto numGroups = getNumFactorGroups();
+      for (size_t g = 1; g < numGroups; g++) {
+        auto factorMaxima = max(logits_[g]->loss(), -1);
+        auto factorMasks = constant(getFactorMasks(g));
+        sel = sel + factorMaxima * factorMasks; // those lemmas that don't have a factor get multiplied with 0
+      }
+    }
+
     // if selIdx are given, then we must reshuffle accordingly
     if (!selIdx.empty()) // use the same function that shuffles decoder state
       sel = rnn::State::select(sel, selIdx, (int)beamSize, /*isBatchMajor=*/false);
@@ -153,6 +170,18 @@ namespace marian {
     return res;
   }
 
+  // same but for lemma
+  std::vector<float> Logits::getFactorMasks(size_t factorGroup) const { // 1.0 for words that do have this factor; else 0
+    size_t numLemmas = factoredVocab_->getGroupRange(0).second - factoredVocab_->getGroupRange(0).first;
+    std::vector<float> res;
+    res.reserve(numLemmas);
+    // @TODO: we should rearange lemmaHasFactorGroup as vector[groups[lemma] of float; then move this into FactoredVocab
+    for (size_t lemma = 0; lemma < numLemmas; lemma++) {
+      res.push_back((float)factoredVocab_->lemmaHasFactorGroup(lemma, factorGroup));
+    }
+    return res;
+  }
+
   Logits Logits::applyUnaryFunction(const std::function<Expr(Expr)>& f) const { // clone this but apply f to all loss values
     std::vector<Ptr<RationalLoss>> newLogits;
     for (const auto& l : logits_)
diff --git a/src/layers/generic.h b/src/layers/generic.h
index 08d5cbbb9..9158fbd82 100755
--- a/src/layers/generic.h
+++ b/src/layers/generic.h
@@ -88,6 +88,7 @@ class Logits {
     };
     std::vector<MaskedFactorIndices> factorizeWords(const Words& words) const; // breaks encoded Word into individual factor indices
     std::vector<float> getFactorMasks(const Words& words, size_t factorGroup) const;
+    std::vector<float> getFactorMasks(size_t factorGroup) const;
     float getLogitAt(size_t i) const { return getLogits()->val()->get(i); } // used for breakDown() only; @TODO: avoid the fully expanded logits; pass separate indices instead of 'i'
     size_t getNumFactorGroups() const { return logits_.size(); }
     bool empty() const { return logits_.empty(); }
diff --git a/src/translator/beam_search.h b/src/translator/beam_search.h
index 1df16b0e3..f366a284c 100755
--- a/src/translator/beam_search.h
+++ b/src/translator/beam_search.h
@@ -356,6 +356,7 @@ class BeamSearch {
       // find N best amongst the (localBeamSize * dimVocab) hypotheses
       std::vector<unsigned int> nBestKeys; // [dimBatch, localBeamSize] flattened -> (batchIdx, beamHypIdx, word idx) flattened
       std::vector<float> nBestPathScores;  // [dimBatch, localBeamSize] flattened
+      // @TODO: getNBestList() API is redundant; input dimensions are known from expandedPathScores(); but no way to specify target N different from input N
       getNBestList(/*beamSizes=*/std::vector<size_t>(dimBatch, localBeamSize), // output layout of (nBestPathScores, nBestKeys)  --@REVIEW: correct?
                    /*in*/ expandedPathScores->val(),                           // [dimBatch, 1, localBeamSize, dimVocab or dimShortlist]
                    /*out*/ nBestPathScores, /*out*/ nBestKeys,

From d1bf38ebf4678da686336b0057b84b4df0b5ac3f Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Mon, 11 Feb 2019 17:31:30 -0800
Subject: [PATCH 325/838] no longer expanding non-applicable factors, as that
 blows stuff out of the beam

---
 src/common/logging.cpp       |  3 ++-
 src/data/factored_vocab.cpp  |  2 +-
 src/translator/beam_search.h | 49 ++++++++++++++++++++++++++++++------
 3 files changed, 45 insertions(+), 9 deletions(-)
 mode change 100644 => 100755 src/common/logging.cpp

diff --git a/src/common/logging.cpp b/src/common/logging.cpp
old mode 100644
new mode 100755
index 57b55de96..9c4b2951f
--- a/src/common/logging.cpp
+++ b/src/common/logging.cpp
@@ -77,7 +77,8 @@ void createLoggers(const marian::Config* config) {
   }
 
   bool quiet = config && config->get<bool>("quiet");
-  Logger general{createStderrLogger("general", "[%Y-%m-%d %T] %v", generalLogs, quiet)};
+  //Logger general{createStderrLogger("general", "[%Y-%m-%d %T] %v", generalLogs, quiet)};
+  Logger general{createStderrLogger("general", "%v", generalLogs, quiet)};
   Logger valid{createStderrLogger("valid", "[%Y-%m-%d %T] [valid] %v", validLogs, quiet)};
 
   if(config && config->has("log-level")) {
diff --git a/src/data/factored_vocab.cpp b/src/data/factored_vocab.cpp
index c23c4d1dc..1279b5fc4 100755
--- a/src/data/factored_vocab.cpp
+++ b/src/data/factored_vocab.cpp
@@ -307,7 +307,7 @@ void FactoredVocab::constructNormalizationInfoForVocab() {
 }
 
 /*virtual*/ const std::string& FactoredVocab::operator[](Word word) const /*overrworde final*/ {
-  LOG(info, "Looking up Word {}={}", word.toWordIndex(), word2string(word));
+  //LOG(info, "Looking up Word {}={}", word.toWordIndex(), word2string(word));
 #if 1 // @BUGBUG: our manually prepared dict does not contain @CI tags for single letters, but it's a valid factor
   if (vocab_.isGap(word.toWordIndex())) {
     LOG(info, "Factor combination {} missing in external dict, generating fake entry", word2string(word));
diff --git a/src/translator/beam_search.h b/src/translator/beam_search.h
index f366a284c..fbbc8ec2d 100755
--- a/src/translator/beam_search.h
+++ b/src/translator/beam_search.h
@@ -95,7 +95,8 @@ class BeamSearch {
             word = factoredVocab->expandFactoredWord(word, factorGroup, wordIdx);
           // @TODO: maybe factor the two above into a single function; for now, I want the extra checks
           else
-            ABORT_IF(prevHyp->getPathScore() != pathScore, "Score changed despite factor not applicable??");
+            continue; // skip if word does not have this factor
+            //ABORT_IF(prevHyp->getPathScore() != pathScore, "Score changed despite factor not applicable??");
           prevBeamHypIdx = prevHyp->getPrevStateIndex();
           prevHyp = prevHyp->getPrevHyp(); // short-circuit the backpointer, so that the traceback doesnot contain partially factored words
         }
@@ -124,6 +125,30 @@ class BeamSearch {
 
       newBeam.push_back(hyp);
     }
+
+    // also propagate factored hypotheses that do not get expanded in this step as they don't have this factor
+    if (factorGroup > 0) {
+      for (size_t batchIdx = 0; batchIdx < beams.size(); batchIdx++) {
+        const auto& beam = beams[batchIdx];
+        auto& newBeam = newBeams[batchIdx];
+        for (const auto& beamHyp : beam) {
+          auto word = beamHyp->getWord();
+          LOG(info, "Checking {}", factoredVocab->word2string(word));
+          if (factoredVocab->canExpandFactoredWord(word, factorGroup)) // handled above
+            continue;
+          LOG(info, "Expanded {}", factoredVocab->word2string(word));
+          newBeam.push_back(beamHyp);
+        }
+        if (newBeam.size() > beamSize) {
+          LOG(info, "Size {}, sorting...", newBeam.size());
+          std::nth_element(newBeam.begin(), newBeam.begin() + beamSize, newBeam.end(), [](Ptr<Hypothesis> a, Ptr<Hypothesis> b) {
+            return a->getPathScore() > b->getPathScore();
+          });
+          LOG(info, "Size {}, sorted...", newBeam.size());
+          newBeam.resize(beamSize); // @TODO: needed?
+        }
+      }
+    }
     return newBeams;
   }
 
@@ -266,10 +291,11 @@ class BeamSearch {
       std::vector<IndexType> hypIndices; // [localBeamsize, 1, dimBatch, 1] (flattened) tensor index ((beamHypIdx, batchIdx), flattened) of prev hyp that a hyp originated from
       std::vector<Word> prevWords;       // [localBeamsize, 1, dimBatch, 1] (flattened) word that a hyp ended in, for advancing the decoder-model's history
       Expr prevPathScores;               // [localBeamSize, 1, dimBatch, 1], path score that a hyp ended in (last axis will broadcast into vocab size when adding expandedPathScores)
+      std::vector<float> prevScores; // @TODO: remove here again
       if(t == 0) { // no scores yet
         prevPathScores = graph->constant({1, 1, 1, 1}, inits::from_value(0));
       } else {
-        std::vector<float> prevScores;
+        //std::vector<float> prevScores;
         for(size_t beamHypIdx = 0; beamHypIdx < localBeamSize; ++beamHypIdx) {
           for(int batchIdx = 0; batchIdx < dimBatch; ++batchIdx) { // loop over batch entries (active sentences)
             auto& beam = beams[batchIdx];
@@ -291,8 +317,8 @@ class BeamSearch {
       //**********************************************************************
       // compute expanded path scores with word prediction probs from all scorers
       auto expandedPathScores = prevPathScores; // will become [localBeamSize, 1, dimBatch, dimVocab]
+      Expr logProbs;
       for(size_t i = 0; i < scorers_.size(); ++i) {
-        Expr logProbs, factorMasks;
         if (factorGroup == 0) {
           // compute output probabilities for current output time step
           //  - uses hypIndices[index in beam, 1, batch index, 1] to reorder scorer state to reflect the top-N in beams[][]
@@ -301,11 +327,16 @@ class BeamSearch {
           //  - returns new NN state for use in next output time step
           //  - returns vector of prediction probabilities over output vocab via newState
           // update state in-place for next output time step
+          if (t > 0) for (size_t kk = 0; kk < prevWords.size(); kk++)
+            LOG(info, "prevWords[{},{}]={} -> {}", t/numFactorGroups, factorGroup,
+                factoredVocab ? factoredVocab->word2string(prevWords[kk]) : (*batch->back()->vocab())[prevWords[kk]],
+                prevScores[kk]);
           states[i] = scorers_[i]->step(graph, states[i], hypIndices, prevWords, dimBatch, (int)localBeamSize);
           if (numFactorGroups == 1)
             logProbs = states[i]->getLogProbs().getLogits(); // [localBeamSize, 1, dimBatch, dimVocab]
           else
             logProbs = states[i]->getLogProbs().getFactoredLogits(factorGroup); // [localBeamSize, 1, dimBatch, dimVocab]
+          logProbs->debug("logProbs");
         }
         else {
           // add secondary factors
@@ -320,14 +351,17 @@ class BeamSearch {
           // TODO:
           //  - we did not rearrange the tensors in the scorer's state
           logProbs = states[i]->getLogProbs().getFactoredLogits(factorGroup, hypIndices, localBeamSize); // [localBeamSize, 1, dimBatch, dimVocab]
-          for (auto word : prevWords)
-            LOG(info, "prevWords[]={}", factoredVocab->word2string(word));
+          for (size_t kk = 0; kk < prevWords.size(); kk++)
+            LOG(info, "prevWords[{},{}]={} -> {}", t/numFactorGroups, factorGroup, factoredVocab->word2string(prevWords[kk]), prevScores[kk]);
           auto factorMaskVector = states[i]->getLogProbs().getFactorMasks(prevWords, factorGroup);
-          factorMasks = graph->constant({(int)localBeamSize, 1, dimBatch, 1}, inits::from_vector(factorMaskVector));
-          logProbs = logProbs * factorMasks; // those hyps that don't have a factor get multiplied with 0
+          for (auto& m : factorMaskVector)
+            m = m ? 0.f : -9999.f; // block hyps that do not have the factor; these are short-circuited directly
+          auto logFactorMasks = graph->constant({(int)localBeamSize, 1, dimBatch, 1}, inits::from_vector(factorMaskVector));
+          logProbs = logProbs + logFactorMasks; // those hyps that don't have a factor get multiplied with 0
         }
         // expand all hypotheses, [localBeamSize, 1, dimBatch, 1] -> [localBeamSize, 1, dimBatch, dimVocab]
         expandedPathScores = expandedPathScores + scorers_[i]->getWeight() * logProbs;
+        logProbs->debug("logProbs");
       }
 
       // make beams continuous
@@ -336,6 +370,7 @@ class BeamSearch {
       else // (avoid copy if we can)
         expandedPathScores = reshape(expandedPathScores, {dimBatch, 1, (int)localBeamSize, expandedPathScores->shape()[-1]}); // -> [dimBatch, 1, localBeamSize, dimVocab]
       //  expandedPathScores = transpose(expandedPathScores, {2, 1, 0, 3}); // -> [dimBatch, 1, localBeamSize, dimVocab]
+      expandedPathScores->debug("expandedPathScores");
 
       // perform NN computation
       if(t == 0)

From 462820ab2ddea751fbdb9ba2935e5f64155f29da Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Mon, 11 Feb 2019 19:03:03 -0800
Subject: [PATCH 326/838] fixed a bounds check

---
 src/translator/beam_search.h | 14 +++++++++-----
 1 file changed, 9 insertions(+), 5 deletions(-)

diff --git a/src/translator/beam_search.h b/src/translator/beam_search.h
index fbbc8ec2d..f553d3bf3 100755
--- a/src/translator/beam_search.h
+++ b/src/translator/beam_search.h
@@ -19,6 +19,8 @@ class BeamSearch {
   Word trgEosId_{Word::NONE};
   Word trgUnkId_{Word::NONE};
 
+  static constexpr auto INVALID_PATH_SCORE = -9999;// .0f;
+
 public:
   BeamSearch(Ptr<Options> options,
              const std::vector<Ptr<Scorer>>& scorers,
@@ -69,7 +71,9 @@ class BeamSearch {
       if (newBeam.size() >= beam.size()) // @TODO: Why this condition? It does happen. Why?
         continue;
 
-      ABORT_IF(beamHypIdx >= (int)beam.size(), "Out of bounds beamHypIdx value in key??");
+      if (beamHypIdx >= (int)beam.size() && pathScore <= INVALID_PATH_SCORE) // (dummy slot)
+        continue;
+      ABORT_IF(beamHypIdx >= (int)beam.size(), "Out of bounds beamHypIdx value {} in key?? word={}, batch={}, pathScore={}", beamHypIdx, wordIdx, batchIdx, pathScore);
 
       // map wordIdx to word
       auto prevHyp = beam[beamHypIdx];
@@ -136,7 +140,7 @@ class BeamSearch {
           LOG(info, "Checking {}", factoredVocab->word2string(word));
           if (factoredVocab->canExpandFactoredWord(word, factorGroup)) // handled above
             continue;
-          LOG(info, "Expanded {}", factoredVocab->word2string(word));
+          LOG(info, "Forwarded {}", factoredVocab->word2string(word));
           newBeam.push_back(beamHyp);
         }
         if (newBeam.size() > beamSize) {
@@ -306,8 +310,8 @@ class BeamSearch {
               prevScores.push_back(hyp->getPathScore());
             } else {  // pad to localBeamSize (dummy hypothesis)
               hypIndices.push_back(0);
-              prevWords.push_back(Word::ZERO);  // (unused)
-              prevScores.push_back(-9999);
+              prevWords.push_back(trgEosId_);  // (unused, but must be valid)
+              prevScores.push_back(INVALID_PATH_SCORE);
             }
           }
         }
@@ -355,7 +359,7 @@ class BeamSearch {
             LOG(info, "prevWords[{},{}]={} -> {}", t/numFactorGroups, factorGroup, factoredVocab->word2string(prevWords[kk]), prevScores[kk]);
           auto factorMaskVector = states[i]->getLogProbs().getFactorMasks(prevWords, factorGroup);
           for (auto& m : factorMaskVector)
-            m = m ? 0.f : -9999.f; // block hyps that do not have the factor; these are short-circuited directly
+            m = m ? 0.f : INVALID_PATH_SCORE; // block hyps that do not have the factor; these are short-circuited directly
           auto logFactorMasks = graph->constant({(int)localBeamSize, 1, dimBatch, 1}, inits::from_vector(factorMaskVector));
           logProbs = logProbs + logFactorMasks; // those hyps that don't have a factor get multiplied with 0
         }

From 202b610f6f5f1bf9f33bbe9c122dc63f6ba097a0 Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Mon, 11 Feb 2019 19:53:16 -0800
Subject: [PATCH 327/838] commented out lots of debyug logging

---
 src/data/factored_vocab.cpp     |  4 ++--
 src/translator/beam_search.h    | 34 ++++++++++++++++-----------------
 src/translator/output_printer.h |  2 +-
 3 files changed, 20 insertions(+), 20 deletions(-)

diff --git a/src/data/factored_vocab.cpp b/src/data/factored_vocab.cpp
index 1279b5fc4..ea076af6a 100755
--- a/src/data/factored_vocab.cpp
+++ b/src/data/factored_vocab.cpp
@@ -181,7 +181,7 @@ Word FactoredVocab::lemma2Word(size_t factor0Index) const {
 // replace a factor that is FACTOR_NOT_SPECIFIED by a specified one
 // This is used in beam search, where factors are searched one after another.
 Word FactoredVocab::expandFactoredWord(Word word, size_t groupIndex, size_t factorIndex) const {
-  LOG(info, "expand {} + [{}]={}", word2string(word), groupIndex, factorIndex);
+  //LOG(info, "expand {} + [{}]={}", word2string(word), groupIndex, factorIndex);
   ABORT_IF(groupIndex == 0, "Cannot add or change lemma in a partial Word");
   ABORT_IF(!isFactorValid(factorIndex), "Cannot add unspecified or n/a factor to a partial Word");
   std::vector<size_t> factorIndices;
@@ -192,7 +192,7 @@ Word FactoredVocab::expandFactoredWord(Word word, size_t groupIndex, size_t fact
   ABORT_IF(factorIndices[groupIndex] != FACTOR_NOT_SPECIFIED, "Cannot modify a specified factor in a partial Word");
   factorIndices[groupIndex] = factorIndex;
   word = factors2word(factorIndices);
-  LOG(info, "to {}", word2string(word));
+  //LOG(info, "to {}", word2string(word));
   return word;
 }
 
diff --git a/src/translator/beam_search.h b/src/translator/beam_search.h
index f553d3bf3..e82a910a2 100755
--- a/src/translator/beam_search.h
+++ b/src/translator/beam_search.h
@@ -61,7 +61,7 @@ class BeamSearch {
       const auto wordIdx    = (WordIndex)(key % vocabSize);
       const auto beamHypIdx =            (key / vocabSize) % (first ? 1 : beamSize);
       const auto batchIdx   =            (key / vocabSize) / (first ? 1 : beamSize);
-      LOG(info, "key = (batch {}, beam {}, word {})", batchIdx, beamHypIdx, wordIdx);
+      //LOG(info, "key = (batch {}, beam {}, word {})", batchIdx, beamHypIdx, wordIdx);
 
       ABORT_IF(i / beamSize != batchIdx, "Inconsistent batchIdx value in key??");
 
@@ -89,11 +89,11 @@ class BeamSearch {
         // starting with the lemma, then adding factors one by one.
         if (factorGroup == 0) {
           word = factoredVocab->lemma2Word(wordIdx);
-          LOG(info, "new lemma {}={}", word.toWordIndex(), factoredVocab->word2string(word));
+          //LOG(info, "new lemma {}={}", word.toWordIndex(), factoredVocab->word2string(word));
         }
         else {
-          LOG(info, "expand word {}={} with factor[{}] {}", beam[beamHypIdx]->getWord().toWordIndex(),
-              factoredVocab->word2string(beam[beamHypIdx]->getWord()), factorGroup, wordIdx);
+          //LOG(info, "expand word {}={} with factor[{}] {}", beam[beamHypIdx]->getWord().toWordIndex(),
+          //    factoredVocab->word2string(beam[beamHypIdx]->getWord()), factorGroup, wordIdx);
           word = beam[beamHypIdx]->getWord();
           if (factoredVocab->canExpandFactoredWord(word, factorGroup))
             word = factoredVocab->expandFactoredWord(word, factorGroup, wordIdx);
@@ -137,18 +137,18 @@ class BeamSearch {
         auto& newBeam = newBeams[batchIdx];
         for (const auto& beamHyp : beam) {
           auto word = beamHyp->getWord();
-          LOG(info, "Checking {}", factoredVocab->word2string(word));
+          //LOG(info, "Checking {}", factoredVocab->word2string(word));
           if (factoredVocab->canExpandFactoredWord(word, factorGroup)) // handled above
             continue;
           LOG(info, "Forwarded {}", factoredVocab->word2string(word));
           newBeam.push_back(beamHyp);
         }
         if (newBeam.size() > beamSize) {
-          LOG(info, "Size {}, sorting...", newBeam.size());
+          //LOG(info, "Size {}, sorting...", newBeam.size());
           std::nth_element(newBeam.begin(), newBeam.begin() + beamSize, newBeam.end(), [](Ptr<Hypothesis> a, Ptr<Hypothesis> b) {
-            return a->getPathScore() > b->getPathScore();
+            return a->getPathScore() > b->getPathScore(); // (sort highest score first)
           });
-          LOG(info, "Size {}, sorted...", newBeam.size());
+          //LOG(info, "Size {}, sorted...", newBeam.size());
           newBeam.resize(beamSize); // @TODO: needed?
         }
       }
@@ -331,16 +331,16 @@ class BeamSearch {
           //  - returns new NN state for use in next output time step
           //  - returns vector of prediction probabilities over output vocab via newState
           // update state in-place for next output time step
-          if (t > 0) for (size_t kk = 0; kk < prevWords.size(); kk++)
-            LOG(info, "prevWords[{},{}]={} -> {}", t/numFactorGroups, factorGroup,
-                factoredVocab ? factoredVocab->word2string(prevWords[kk]) : (*batch->back()->vocab())[prevWords[kk]],
-                prevScores[kk]);
+          //if (t > 0) for (size_t kk = 0; kk < prevWords.size(); kk++)
+          //  LOG(info, "prevWords[{},{}]={} -> {}", t/numFactorGroups, factorGroup,
+          //      factoredVocab ? factoredVocab->word2string(prevWords[kk]) : (*batch->back()->vocab())[prevWords[kk]],
+          //      prevScores[kk]);
           states[i] = scorers_[i]->step(graph, states[i], hypIndices, prevWords, dimBatch, (int)localBeamSize);
           if (numFactorGroups == 1)
             logProbs = states[i]->getLogProbs().getLogits(); // [localBeamSize, 1, dimBatch, dimVocab]
           else
             logProbs = states[i]->getLogProbs().getFactoredLogits(factorGroup); // [localBeamSize, 1, dimBatch, dimVocab]
-          logProbs->debug("logProbs");
+          //logProbs->debug("logProbs");
         }
         else {
           // add secondary factors
@@ -355,8 +355,8 @@ class BeamSearch {
           // TODO:
           //  - we did not rearrange the tensors in the scorer's state
           logProbs = states[i]->getLogProbs().getFactoredLogits(factorGroup, hypIndices, localBeamSize); // [localBeamSize, 1, dimBatch, dimVocab]
-          for (size_t kk = 0; kk < prevWords.size(); kk++)
-            LOG(info, "prevWords[{},{}]={} -> {}", t/numFactorGroups, factorGroup, factoredVocab->word2string(prevWords[kk]), prevScores[kk]);
+          //for (size_t kk = 0; kk < prevWords.size(); kk++)
+          //  LOG(info, "prevWords[{},{}]={} -> {}", t/numFactorGroups, factorGroup, factoredVocab->word2string(prevWords[kk]), prevScores[kk]);
           auto factorMaskVector = states[i]->getLogProbs().getFactorMasks(prevWords, factorGroup);
           for (auto& m : factorMaskVector)
             m = m ? 0.f : INVALID_PATH_SCORE; // block hyps that do not have the factor; these are short-circuited directly
@@ -365,7 +365,7 @@ class BeamSearch {
         }
         // expand all hypotheses, [localBeamSize, 1, dimBatch, 1] -> [localBeamSize, 1, dimBatch, dimVocab]
         expandedPathScores = expandedPathScores + scorers_[i]->getWeight() * logProbs;
-        logProbs->debug("logProbs");
+        //logProbs->debug("logProbs");
       }
 
       // make beams continuous
@@ -374,7 +374,7 @@ class BeamSearch {
       else // (avoid copy if we can)
         expandedPathScores = reshape(expandedPathScores, {dimBatch, 1, (int)localBeamSize, expandedPathScores->shape()[-1]}); // -> [dimBatch, 1, localBeamSize, dimVocab]
       //  expandedPathScores = transpose(expandedPathScores, {2, 1, 0, 3}); // -> [dimBatch, 1, localBeamSize, dimVocab]
-      expandedPathScores->debug("expandedPathScores");
+      //expandedPathScores->debug("expandedPathScores");
 
       // perform NN computation
       if(t == 0)
diff --git a/src/translator/output_printer.h b/src/translator/output_printer.h
index 3b8827f9a..e2fc97fee 100755
--- a/src/translator/output_printer.h
+++ b/src/translator/output_printer.h
@@ -72,7 +72,7 @@ class OutputPrinter {
       best1 << " ||| " << getAlignment(hypo);
     }
 
-    best1 << " |sc=" << std::get<1>(result)->getPathScore();
+    //best1 << " |sc=" << std::get<1>(result)->getPathScore();
 
     best1 << std::flush;
   }

From c2232e9046c581ebc06dd4c2db8b44d648cd406e Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Mon, 11 Feb 2019 20:12:21 -0800
Subject: [PATCH 328/838] factored decoder now uses an explicit inner for loop
 over factors

---
 src/translator/beam_search.h | 40 ++++++++++++++++--------------------
 1 file changed, 18 insertions(+), 22 deletions(-)

diff --git a/src/translator/beam_search.h b/src/translator/beam_search.h
index e82a910a2..496fbb27e 100755
--- a/src/translator/beam_search.h
+++ b/src/translator/beam_search.h
@@ -140,7 +140,7 @@ class BeamSearch {
           //LOG(info, "Checking {}", factoredVocab->word2string(word));
           if (factoredVocab->canExpandFactoredWord(word, factorGroup)) // handled above
             continue;
-          LOG(info, "Forwarded {}", factoredVocab->word2string(word));
+          //LOG(info, "Forwarded {}", factoredVocab->word2string(word));
           newBeam.push_back(beamHyp);
         }
         if (newBeam.size() > beamSize) {
@@ -284,10 +284,11 @@ class BeamSearch {
       if (localBeamSize == 0)
         break;
 
+      for (size_t factorGroup = 0; factorGroup < numFactorGroups; factorGroup++) {
       // BEGIN FOR factorGroup = 0 .. numFactorGroups-1
       // @TODO: use an explicit nested loop here for factors
       // for factored vocabs, we do one factor at a time, but without updating the scorer for secondary factors
-      auto factorGroup = t % numFactorGroups;
+      //auto factorGroup = t % numFactorGroups;
 
       //**********************************************************************
       // create constant containing previous path scores for current beam
@@ -296,7 +297,7 @@ class BeamSearch {
       std::vector<Word> prevWords;       // [localBeamsize, 1, dimBatch, 1] (flattened) word that a hyp ended in, for advancing the decoder-model's history
       Expr prevPathScores;               // [localBeamSize, 1, dimBatch, 1], path score that a hyp ended in (last axis will broadcast into vocab size when adding expandedPathScores)
       std::vector<float> prevScores; // @TODO: remove here again
-      if(t == 0) { // no scores yet
+      if(t == 0 && factorGroup == 0) { // no scores yet
         prevPathScores = graph->constant({1, 1, 1, 1}, inits::from_value(0));
       } else {
         //std::vector<float> prevScores;
@@ -377,7 +378,7 @@ class BeamSearch {
       //expandedPathScores->debug("expandedPathScores");
 
       // perform NN computation
-      if(t == 0)
+      if(t == 0 && factorGroup == 0)
         graph->forward();
       else
         graph->forwardNext();
@@ -399,36 +400,31 @@ class BeamSearch {
       getNBestList(/*beamSizes=*/std::vector<size_t>(dimBatch, localBeamSize), // output layout of (nBestPathScores, nBestKeys)  --@REVIEW: correct?
                    /*in*/ expandedPathScores->val(),                           // [dimBatch, 1, localBeamSize, dimVocab or dimShortlist]
                    /*out*/ nBestPathScores, /*out*/ nBestKeys,
-                   /*first=*/t == 0); // @TODO: Why is this passed? To know that the beam size is 1 for first step, for flattened hyp index?
+                   /*first=*/t == 0 && factorGroup == 0); // @TODO: Why is this passed? To know that the beam size is 1 for first step, for flattened hyp index?
       // Now, nBestPathScores contain N-best expandedPathScores, and nBestKeys for each their original location (batchIdx, beamHypIdx, word).
 
       // combine N-best sets with existing search space (beams) to updated search space
-      auto newBeams = toHyps(nBestKeys, nBestPathScores,
-                             /*dimTrgVoc=*/expandedPathScores->shape()[-1],
-                             beams,
-                             states,           // used for keeping track of per-ensemble-member path score
-                             localBeamSize,    // used in the encoding of the (batchIdx, beamHypIdx, word) tuples
-                             /*first=*/t == 0, // used to indicate originating beamSize of 1
-                             batch, factoredVocab, factorGroup);
-
-      if (factorGroup != numFactorGroups - 1) { // skip adding to history and skip purging for partially factored words
-        beams = newBeams;
-        continue;
-      }
-      // END FOR factorGroup = 0 .. numFactorGroups-1
+      beams = toHyps(nBestKeys, nBestPathScores,
+                     /*dimTrgVoc=*/expandedPathScores->shape()[-1],
+                     beams,
+                     states,           // used for keeping track of per-ensemble-member path score
+                     localBeamSize,    // used in the encoding of the (batchIdx, beamHypIdx, word) tuples
+                     /*first=*/t == 0 && factorGroup == 0, // used to indicate originating beamSize of 1
+                     batch, factoredVocab, factorGroup);
+      } // END FOR factorGroup = 0 .. numFactorGroups-1
 
       // remove all hyps that end in EOS
       // The position of a hyp in the beam may change.
-      const auto purgedNewBeams = purgeBeams(newBeams);
+      const auto purgedNewBeams = purgeBeams(beams);
 
-      // add updated search space (newBeams) to our return value
+      // add updated search space (beams) to our return value
       bool maxLengthReached = false;
       for(int i = 0; i < dimBatch; ++i) {
         // if this batch entry has surviving hyps then add them to the traceback grid
-        if(!newBeams[i].empty()) {
+        if(!beams[i].empty()) {
           if (histories[i]->size() >= options_->get<float>("max-length-factor") * batch->front()->batchWidth())
             maxLengthReached = true;
-          histories[i]->add(newBeams[i], trgEosId_, purgedNewBeams[i].empty() || maxLengthReached);
+          histories[i]->add(beams[i], trgEosId_, purgedNewBeams[i].empty() || maxLengthReached);
         }
       }
       if (maxLengthReached) // early exit if max length limit was reached

From 4f11b0100ed82626627b1cc1de1c8756ea1e7c68 Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Mon, 11 Feb 2019 20:23:14 -0800
Subject: [PATCH 329/838] no longer need a log mask for logProbs, done with
 INVALID_PATH_SCORE instead

---
 src/layers/generic.cpp       | 26 +++++++++++++-------------
 src/layers/generic.h         |  2 +-
 src/translator/beam_search.h | 15 +++++++--------
 3 files changed, 21 insertions(+), 22 deletions(-)

diff --git a/src/layers/generic.cpp b/src/layers/generic.cpp
index 9bd91876c..df06398b0 100755
--- a/src/layers/generic.cpp
+++ b/src/layers/generic.cpp
@@ -159,19 +159,19 @@ namespace marian {
     return res;
   }
 
-  // use first factor of each word to determine whether it has a specific factor
-  std::vector<float> Logits::getFactorMasks(const Words& words, size_t factorGroup) const { // 1.0 for words that do have this factor; else 0
-    std::vector<float> res;
-    res.reserve(words.size());
-    for (const auto& word : words) {
-      auto lemma = factoredVocab_->getFactor(word, 0);
-      res.push_back((float)factoredVocab_->lemmaHasFactorGroup(lemma, factorGroup));
-    }
-    return res;
-  }
-
-  // same but for lemma
-  std::vector<float> Logits::getFactorMasks(size_t factorGroup) const { // 1.0 for words that do have this factor; else 0
+  //// use first factor of each word to determine whether it has a specific factor
+  //std::vector<float> Logits::getFactorMasks(const Words& words, size_t factorGroup) const { // 1.0 for words that do have this factor; else 0
+  //  std::vector<float> res;
+  //  res.reserve(words.size());
+  //  for (const auto& word : words) {
+  //    auto lemma = factoredVocab_->getFactor(word, 0);
+  //    res.push_back((float)factoredVocab_->lemmaHasFactorGroup(lemma, factorGroup));
+  //  }
+  //  return res;
+  //}
+
+  // return a vector of 1 or 0 indicating for each lemma whether it has a specific factor
+  std::vector<float> Logits::getFactorMasks(size_t factorGroup) const { // [lemmaIndex] -> 1.0 for words that do have this factor; else 0
     size_t numLemmas = factoredVocab_->getGroupRange(0).second - factoredVocab_->getGroupRange(0).first;
     std::vector<float> res;
     res.reserve(numLemmas);
diff --git a/src/layers/generic.h b/src/layers/generic.h
index 9158fbd82..532324641 100755
--- a/src/layers/generic.h
+++ b/src/layers/generic.h
@@ -87,7 +87,7 @@ class Logits {
       MaskedFactorIndices(const Words& words) { indices = toWordIndexVector(words); } // we can leave masks uninitialized for this special use case
     };
     std::vector<MaskedFactorIndices> factorizeWords(const Words& words) const; // breaks encoded Word into individual factor indices
-    std::vector<float> getFactorMasks(const Words& words, size_t factorGroup) const;
+    //std::vector<float> getFactorMasks(const Words& words, size_t factorGroup) const;
     std::vector<float> getFactorMasks(size_t factorGroup) const;
     float getLogitAt(size_t i) const { return getLogits()->val()->get(i); } // used for breakDown() only; @TODO: avoid the fully expanded logits; pass separate indices instead of 'i'
     size_t getNumFactorGroups() const { return logits_.size(); }
diff --git a/src/translator/beam_search.h b/src/translator/beam_search.h
index 496fbb27e..1caa9821e 100755
--- a/src/translator/beam_search.h
+++ b/src/translator/beam_search.h
@@ -308,7 +308,7 @@ class BeamSearch {
               auto hyp = beam[beamHypIdx];
               hypIndices.push_back((IndexType)(hyp->getPrevStateIndex() * dimBatch + batchIdx)); // (beamHypIdx, batchIdx), flattened, for index_select() operation
               prevWords .push_back(hyp->getWord());
-              prevScores.push_back(hyp->getPathScore());
+              prevScores.push_back((factoredVocab && !factoredVocab->canExpandFactoredWord(hyp->getWord(), factorGroup)) ? INVALID_PATH_SCORE : hyp->getPathScore());
             } else {  // pad to localBeamSize (dummy hypothesis)
               hypIndices.push_back(0);
               prevWords.push_back(trgEosId_);  // (unused, but must be valid)
@@ -353,16 +353,15 @@ class BeamSearch {
           //    by considering the lemma at each (beamHypIdx, batchIdx). prevWords is already in the right order.
           //  - factors are incorporated one step at a time; so we will have temporary Word entries
           //    in hyps with some factors set to FACTOR_NOT_SPECIFIED.
-          // TODO:
-          //  - we did not rearrange the tensors in the scorer's state
           logProbs = states[i]->getLogProbs().getFactoredLogits(factorGroup, hypIndices, localBeamSize); // [localBeamSize, 1, dimBatch, dimVocab]
+          // Note: pathScores was set to INVALID_PATH_SCORE if this path cannot expanded by this factor; toHyps() handles that special case on the side
           //for (size_t kk = 0; kk < prevWords.size(); kk++)
           //  LOG(info, "prevWords[{},{}]={} -> {}", t/numFactorGroups, factorGroup, factoredVocab->word2string(prevWords[kk]), prevScores[kk]);
-          auto factorMaskVector = states[i]->getLogProbs().getFactorMasks(prevWords, factorGroup);
-          for (auto& m : factorMaskVector)
-            m = m ? 0.f : INVALID_PATH_SCORE; // block hyps that do not have the factor; these are short-circuited directly
-          auto logFactorMasks = graph->constant({(int)localBeamSize, 1, dimBatch, 1}, inits::from_vector(factorMaskVector));
-          logProbs = logProbs + logFactorMasks; // those hyps that don't have a factor get multiplied with 0
+          //auto factorMaskVector = states[i]->getLogProbs().getFactorMasks(prevWords, factorGroup);
+          //for (auto& m : factorMaskVector)
+          //  m = m ? 0.f : INVALID_PATH_SCORE; // block hyps that do not have the factor; these are short-circuited directly
+          //auto logFactorMasks = graph->constant({(int)localBeamSize, 1, dimBatch, 1}, inits::from_vector(factorMaskVector));
+          //logProbs = logProbs + logFactorMasks; // those hyps that don't have a factor get multiplied with 0
         }
         // expand all hypotheses, [localBeamSize, 1, dimBatch, 1] -> [localBeamSize, 1, dimBatch, dimVocab]
         expandedPathScores = expandedPathScores + scorers_[i]->getWeight() * logProbs;

From b9a0856df8828ef5b5fea0e3dd9d729257d370e2 Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Mon, 11 Feb 2019 20:36:13 -0800
Subject: [PATCH 330/838] removed a redundant check for applicability of a
 factor in toHyps()

---
 src/translator/beam_search.h | 11 +++++++----
 1 file changed, 7 insertions(+), 4 deletions(-)

diff --git a/src/translator/beam_search.h b/src/translator/beam_search.h
index 1caa9821e..6dc378399 100755
--- a/src/translator/beam_search.h
+++ b/src/translator/beam_search.h
@@ -71,8 +71,10 @@ class BeamSearch {
       if (newBeam.size() >= beam.size()) // @TODO: Why this condition? It does happen. Why?
         continue;
 
-      if (beamHypIdx >= (int)beam.size() && pathScore <= INVALID_PATH_SCORE) // (dummy slot)
+      if (pathScore <= INVALID_PATH_SCORE) // (dummy slot or word that cannot be expanded by current factor)
         continue;
+      //if (beamHypIdx >= (int)beam.size() && pathScore <= INVALID_PATH_SCORE) // (dummy slot)
+      //  continue;
       ABORT_IF(beamHypIdx >= (int)beam.size(), "Out of bounds beamHypIdx value {} in key?? word={}, batch={}, pathScore={}", beamHypIdx, wordIdx, batchIdx, pathScore);
 
       // map wordIdx to word
@@ -95,11 +97,12 @@ class BeamSearch {
           //LOG(info, "expand word {}={} with factor[{}] {}", beam[beamHypIdx]->getWord().toWordIndex(),
           //    factoredVocab->word2string(beam[beamHypIdx]->getWord()), factorGroup, wordIdx);
           word = beam[beamHypIdx]->getWord();
-          if (factoredVocab->canExpandFactoredWord(word, factorGroup))
+          ABORT_IF(!factoredVocab->canExpandFactoredWord(word, factorGroup), "A word without this factor snuck through to here??");
+          //if (factoredVocab->canExpandFactoredWord(word, factorGroup))
             word = factoredVocab->expandFactoredWord(word, factorGroup, wordIdx);
           // @TODO: maybe factor the two above into a single function; for now, I want the extra checks
-          else
-            continue; // skip if word does not have this factor
+          //else
+          //  continue; // skip if word does not have this factor
             //ABORT_IF(prevHyp->getPathScore() != pathScore, "Score changed despite factor not applicable??");
           prevBeamHypIdx = prevHyp->getPrevStateIndex();
           prevHyp = prevHyp->getPrevHyp(); // short-circuit the backpointer, so that the traceback doesnot contain partially factored words

From a48f1daf0175f3c5757b539750bcf2d0084d43bb Mon Sep 17 00:00:00 2001
From: Marcin Junczys-Dowmunt <Marcin.JunczysDowmunt@microsoft.com>
Date: Mon, 11 Feb 2019 21:03:28 -0800
Subject: [PATCH 331/838] add comment

---
 src/tensors/allocator.h | 1 +
 1 file changed, 1 insertion(+)

diff --git a/src/tensors/allocator.h b/src/tensors/allocator.h
index 1e402ba67..6a5f883e9 100644
--- a/src/tensors/allocator.h
+++ b/src/tensors/allocator.h
@@ -126,6 +126,7 @@ class Allocator {
       throw AllocationException(available_, size);
     }
 
+    // @TODO: compact memory before re-allocation attempt, maybe by left shifting memory over currently largest gap
     while(it == gaps_.end()) {
       grow(step_);
       it = std::lower_bound(gaps_.begin(), gaps_.end(), Gap(nullptr, size));

From 9fb1e795ef65f227bffbeebabefed1034e9f5eb8 Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Mon, 11 Feb 2019 21:41:58 -0800
Subject: [PATCH 332/838] now skips if no factor expansion is possible in one
 step

---
 src/data/factored_vocab.h    |  2 +-
 src/translator/beam_search.h | 13 ++++++++++---
 2 files changed, 11 insertions(+), 4 deletions(-)

diff --git a/src/data/factored_vocab.h b/src/data/factored_vocab.h
index c5581932a..3a562fadb 100755
--- a/src/data/factored_vocab.h
+++ b/src/data/factored_vocab.h
@@ -44,7 +44,7 @@ class FactoredVocab : public IVocab {
 
   const CSRData& getGlobalFactorMatrix() const { return globalFactorMatrix_; }   // [v,u] (sparse) -> =1 if u is factor of v  --only used in getLogits()
   size_t getNumGroups() const { return groupRanges_.size(); }
-  std::pair<size_t, size_t>     getGroupRange(size_t g)    const { return groupRanges_[g]; }   // [g] -> (u_begin, u_end)
+  std::pair<size_t, size_t> getGroupRange(size_t g)    const { return groupRanges_[g]; }   // [g] -> (u_begin, u_end)
   //const std::vector<float>&     getFactorMasks(size_t g)   const { return factorMasks_[g]; }   // [g][v] 1.0 if word v has factor g
   //const std::vector<IndexType>& getFactorIndices(size_t g) const { return factorIndices_[g]; } // [g][v] local index u_g = u - u_g,begin of factor g for word v; 0 if not a factor
   const std::vector<float>& getGapLogMask() const { return gapLogMask_; } // [v] -inf if v is a gap entry, else 0
diff --git a/src/translator/beam_search.h b/src/translator/beam_search.h
index 6dc378399..3ff0b695b 100755
--- a/src/translator/beam_search.h
+++ b/src/translator/beam_search.h
@@ -299,9 +299,11 @@ class BeamSearch {
       std::vector<IndexType> hypIndices; // [localBeamsize, 1, dimBatch, 1] (flattened) tensor index ((beamHypIdx, batchIdx), flattened) of prev hyp that a hyp originated from
       std::vector<Word> prevWords;       // [localBeamsize, 1, dimBatch, 1] (flattened) word that a hyp ended in, for advancing the decoder-model's history
       Expr prevPathScores;               // [localBeamSize, 1, dimBatch, 1], path score that a hyp ended in (last axis will broadcast into vocab size when adding expandedPathScores)
+      bool anyCanExpand = false; // stays false if all hyps are invalid factor expansions
       std::vector<float> prevScores; // @TODO: remove here again
       if(t == 0 && factorGroup == 0) { // no scores yet
         prevPathScores = graph->constant({1, 1, 1, 1}, inits::from_value(0));
+        anyCanExpand = true;
       } else {
         //std::vector<float> prevScores;
         for(size_t beamHypIdx = 0; beamHypIdx < localBeamSize; ++beamHypIdx) {
@@ -309,9 +311,12 @@ class BeamSearch {
             auto& beam = beams[batchIdx];
             if(beamHypIdx < beam.size()) {
               auto hyp = beam[beamHypIdx];
+              auto word = hyp->getWord();
+              auto canExpand = (!factoredVocab || factoredVocab->canExpandFactoredWord(hyp->getWord(), factorGroup));
+              anyCanExpand |= canExpand;
               hypIndices.push_back((IndexType)(hyp->getPrevStateIndex() * dimBatch + batchIdx)); // (beamHypIdx, batchIdx), flattened, for index_select() operation
-              prevWords .push_back(hyp->getWord());
-              prevScores.push_back((factoredVocab && !factoredVocab->canExpandFactoredWord(hyp->getWord(), factorGroup)) ? INVALID_PATH_SCORE : hyp->getPathScore());
+              prevWords .push_back(word);
+              prevScores.push_back(canExpand ? hyp->getPathScore() : INVALID_PATH_SCORE);
             } else {  // pad to localBeamSize (dummy hypothesis)
               hypIndices.push_back(0);
               prevWords.push_back(trgEosId_);  // (unused, but must be valid)
@@ -321,6 +326,8 @@ class BeamSearch {
         }
         prevPathScores = graph->constant({(int)localBeamSize, 1, dimBatch, 1}, inits::from_vector(prevScores));
       }
+      if (!anyCanExpand) // all words cannot expand this factor: skip
+        continue;
 
       //**********************************************************************
       // compute expanded path scores with word prediction probs from all scorers
@@ -340,7 +347,7 @@ class BeamSearch {
           //      factoredVocab ? factoredVocab->word2string(prevWords[kk]) : (*batch->back()->vocab())[prevWords[kk]],
           //      prevScores[kk]);
           states[i] = scorers_[i]->step(graph, states[i], hypIndices, prevWords, dimBatch, (int)localBeamSize);
-          if (numFactorGroups == 1)
+          if (numFactorGroups == 1) // @TODO: this branch can go away
             logProbs = states[i]->getLogProbs().getLogits(); // [localBeamSize, 1, dimBatch, dimVocab]
           else
             logProbs = states[i]->getLogProbs().getFactoredLogits(factorGroup); // [localBeamSize, 1, dimBatch, dimVocab]

From d36b6f572aac5e8ffece84f76511848c809abb00 Mon Sep 17 00:00:00 2001
From: Marcin Junczys-Dowmunt <Marcin.JunczysDowmunt@microsoft.com>
Date: Mon, 11 Feb 2019 23:04:17 -0800
Subject: [PATCH 333/838] move regression test pointer

---
 regression-tests | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/regression-tests b/regression-tests
index febdc3f56..142eadddb 160000
--- a/regression-tests
+++ b/regression-tests
@@ -1 +1 @@
-Subproject commit febdc3f56f75929b1f7b5b38a4b9b96ea8f648e7
+Subproject commit 142eadddbe04493c1024b42586030b72e9cb7ea2

From 4cbbfaa4867d85568405b772d0493c6b8307efc0 Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Tue, 12 Feb 2019 11:27:27 -0800
Subject: [PATCH 334/838] pulled some further changes to beam_search.h from
 fseide/factoredembeddings

---
 src/translator/beam_search.h | 202 ++++++++++++++++-------------------
 1 file changed, 94 insertions(+), 108 deletions(-)

diff --git a/src/translator/beam_search.h b/src/translator/beam_search.h
index f825b8531..86da1e56a 100755
--- a/src/translator/beam_search.h
+++ b/src/translator/beam_search.h
@@ -18,6 +18,8 @@ class BeamSearch {
   Word trgEosId_ = (Word)-1;
   Word trgUnkId_ = (Word)-1;
 
+  static constexpr auto INVALID_PATH_SCORE = -9999;
+
 public:
   BeamSearch(Ptr<Options> options,
              const std::vector<Ptr<Scorer>>& scorers,
@@ -40,78 +42,66 @@ class BeamSearch {
                const size_t beamSize,
                const bool first,
                Ptr<data::CorpusBatch /*const*/> batch) const {
-    const auto dimBatch = beams.size();
-    Beams newBeams(dimBatch);
-
     std::vector<float> align;
     if(options_->hasAndNotEmpty("alignment"))
-      // Use alignments from the first scorer, even if ensemble
-      align = scorers_[0]->getAlignment();
+      align = scorers_[0]->getAlignment(); // use alignments from the first scorer, even if ensemble
+
+    const auto dimBatch = beams.size();
+    Beams newBeams(dimBatch);
 
     for(size_t i = 0; i < nBestKeys.size(); ++i) { // [dimBatch, beamSize] flattened
-      // Keys contains indices to vocab items in the entire beam.
-      // Values can be between 0 and beamSize * vocabSize.
-      const auto batchIdx = i / beamSize; // and i % beamSize is the beam hyp index
+      // Keys encode batchIdx, beamHypIdx, and word index in the entire beam.
+      // They can be between 0 and beamSize * vocabSize-1.
+      const auto  key       = nBestKeys[i];
+      const float pathScore = nBestPathScores[i]; // expanded path score for (batchIdx, beamHypIdx, word)
+
+      // decompose key into individual indices (batchIdx, beamHypIdx, wordIdx)
+      const auto wordIdx    = (Word)(key % vocabSize);
+      const auto beamHypIdx =       (key / vocabSize) % (first ? 1 : beamSize);
+      const auto batchIdx   =       (key / vocabSize) / (first ? 1 : beamSize);
+
+      ABORT_IF(i / beamSize != batchIdx, "Inconsistent batchIdx value in key??");
+
       const auto& beam = beams[batchIdx];
       auto& newBeam = newBeams[batchIdx];
 
-      if(newBeam.size() < beam.size()) {
-        const float pathScore = nBestPathScores[i];
-        const auto  key       = nBestKeys[i]; // key = pathScore's location, as ((batchIdx, beamHypIdx) flattened, word idx) flattened
-
-        // decompose key into individual indices
-        Word       wordIdx = (Word)(key % vocabSize);
-        const auto hypIdx  =       (key / vocabSize);
-#if 1
-        // further decompose hypIdx, taking into account that the very first entry had beam size 1
-        // and compose a new hypIdx that assumes actual beamSize
-        const auto keyBatchIdx   = hypIdx / (first ? 1 : beamSize);
-        const auto keyBeamHypIdx = hypIdx % (first ? 1 : beamSize);
-        const auto hypIdxTrans = keyBeamHypIdx * dimBatch + keyBatchIdx;
-        ABORT_IF(keyBeamHypIdx >= (int)beam.size(), "Beam hyp index exceeds beam size??"); // not possible, as beamSize = max(beams[.].size())
-#else
-        const auto keyBatchIdx = hypIdx / beamSize; // @REVIEW: is this actually keyBatchIdx?
-        size_t keyBeamHypIdx = hypIdx % beamSize;
-
-        auto hypIdxTrans = keyBatchIdx + keyBeamHypIdx * dimBatch;
-        if(first)
-          hypIdxTrans = hypIdx; // == keyBeamHypIdx + keyBatchIdx * beamSize? or was beamSize=1, and keyBeamHypIdx = 0?
-
-        ABORT_IF(keyBeamHypIdx >= (int)beam.size(), "Beam hyp index exceeds beam size??");
-        //if(keyBeamHypIdx >= (int)beam.size())  // @TODO: What is this condition? Cf. keyBeamHypIdx = hypIdx % beamSize; beamSize = max(beams[.].size())
-        //  keyBeamHypIdx = keyBeamHypIdx % beam.size();
-
-        if(first)
-          keyBeamHypIdx = 0;
-#endif
-        // Retrieve short list for final softmax (based on words aligned
-        // to source sentences). If short list has been set, map the indices
-        // in the sub-selected vocabulary matrix back to their original positions.
-        auto shortlist = scorers_[0]->getShortlist();
-        if(shortlist)
-          wordIdx = shortlist->reverseMap(wordIdx); // @TODO: should reverseMap accept a size_t or a Word?
-        // now wordIdx is a regular Word again
-
-        auto hyp = New<Hypothesis>(beam[keyBeamHypIdx], wordIdx, (IndexType)hypIdxTrans, pathScore);
-
-        // Set score breakdown for n-best lists
-        if(options_->get<bool>("n-best")) {
-          std::vector<float> breakDown(states.size(), 0);
-          beam[keyBeamHypIdx]->getScoreBreakdown().resize(states.size(), 0); // @TODO: Why? Can we just guard the read-out below, then make it const? Or getScoreBreakdown(j)?
-          for(size_t j = 0; j < states.size(); ++j) {
-            size_t key1 = hypIdxTrans * vocabSize + wordIdx;
-            breakDown[j] = states[j]->breakDown(key1) + beam[keyBeamHypIdx]->getScoreBreakdown()[j];
-          }
-          hyp->setScoreBreakdown(breakDown);
-        }
+      if (newBeam.size() >= beam.size()) // @TODO: Why this condition? It does happen. Why?
+        continue;
+      if (pathScore <= INVALID_PATH_SCORE) // (unused slot)
+        continue;
+
+      ABORT_IF(beamHypIdx >= (int)beam.size(), "Out of bounds beamHypIdx??");
 
-        // Set alignments
-        if(!align.empty()) {
-          hyp->setAlignment(getAlignmentsForHypothesis(align, batch, (int)keyBeamHypIdx, (int)batchIdx));
+      // Map wordIdx to word
+      Word word;
+      // If short list has been set, then wordIdx is an index into the short-listed word set,
+      // rather than the true word index.
+      auto shortlist = scorers_[0]->getShortlist();
+      if (shortlist)
+        word = shortlist->reverseMap(wordIdx);
+      else
+        word = wordIdx;
+
+      auto hyp = New<Hypothesis>(beam[beamHypIdx], word, beamHypIdx, pathScore);
+
+      // Set score breakdown for n-best lists
+      if(options_->get<bool>("n-best")) {
+        std::vector<float> breakDown(states.size(), 0);
+        beam[beamHypIdx]->getScoreBreakdown().resize(states.size(), 0); // @TODO: Why? Can we just guard the read-out below, then make it const? Or getScoreBreakdown(j)?
+        for(size_t j = 0; j < states.size(); ++j) {
+          size_t flattenedLogitIndex = (beamHypIdx * dimBatch + batchIdx) * vocabSize + wordIdx; // (beam idx, batch idx, word idx); note: beam and batch are transposed, compared to 'key'
+          breakDown[j] = states[j]->breakDown(flattenedLogitIndex) + beam[beamHypIdx]->getScoreBreakdown()[j];
+          // @TODO: pass those 3 indices directly into breakDown (state knows the dimensions)
         }
+        hyp->setScoreBreakdown(breakDown);
+      }
 
-        newBeam.push_back(hyp);
+      // Set alignments
+      if(!align.empty()) {
+        hyp->setAlignment(getAlignmentsForHypothesis(align, batch, (int)beamHypIdx, (int)batchIdx));
       }
+
+      newBeam.push_back(hyp);
     }
     return newBeams;
   }
@@ -170,6 +160,9 @@ class BeamSearch {
   //**********************************************************************
   // main decoding function
   Histories search(Ptr<ExpressionGraph> graph, Ptr<data::CorpusBatch> batch) {
+    ABORT_IF(batch->back()->vocab() && batch->back()->vocab()->getEosId() != trgEosId_,
+        "Batch uses different EOS token than was passed to BeamSearch originally");
+
     const int dimBatch = (int)batch->size();
 
     auto getNBestList = createGetNBestListFn(beamSize_, dimBatch, graph->getDeviceId());
@@ -200,21 +193,15 @@ class BeamSearch {
     for(int i = 0; i < dimBatch; ++i)
       histories[i]->add(beams[i], trgEosId_);
 
-    // the decoder maintains the following state:
-    //  - histories : array [dimBatch] of History
-    //    with History : vector [t] of array [localBeamSize] of Hypothesis
-    //    with Hypothesis : (last word, aggregate score, prev Hypothesis)
-    //     - search grid
-    //     - stores traceback information
-    //     - gets added to in each output time step
-    //     - the final version is the return value of this function
-    //  - beams : array [dimBatch] of  array [localBeamSize] of Hypothesis
+    // the decoder updates the following state information in each output time step:
+    //  - beams: array [dimBatch] of array [localBeamSize] of Hypothesis
     //     - current output time step's set of active hypotheses, aka active search space
-    //     - gets replaced at the end of each output time step
-    //  - states[.] : ScorerState
-    //     - NN state
-    //     - one per scorer, e.g. 2 for ensemble of 2
-    //     - gets replaced at the end of each output time step
+    //  - states[.]: ScorerState
+    //     - NN state; one per scorer, e.g. 2 for ensemble of 2
+    // and it forms the following return value
+    //  - histories: array [dimBatch] of History
+    //    with History: vector [t] of array [localBeamSize] of Hypothesis
+    //    with Hypothesis: (last word, aggregate score, prev Hypothesis)
 
     // main loop over output time steps
     for (size_t t = 0; ; t++) {
@@ -233,57 +220,54 @@ class BeamSearch {
 
       //**********************************************************************
       // create constant containing previous path scores for current beam
-      // Also create mapping of hyp indices, which are not 1:1 if sentences complete.
-      std::vector<IndexType> hypIndices; // [localBeamsize, 1, dimBatch, 1] (flattened) index of hyp that the new top N originated from
-      std::vector<Word> prevWords;       // [localBeamsize, 1, dimBatch, 1] (flattened) predecessor word
-      Expr prevPathScores;               // [localBeamSize, 1, dimBatch, 1], where the last axis broadcasts into vocab size when adding expandedPathScores
+      // Also create mapping of hyp indices, for reordering the decoder-state tensors.
+      std::vector<IndexType> hypIndices; // [localBeamsize, 1, dimBatch, 1] (flattened) tensor index ((beamHypIdx, batchIdx), flattened) of prev hyp that a hyp originated from
+      std::vector<Word> prevWords;       // [localBeamsize, 1, dimBatch, 1] (flattened) word that a hyp ended in, for advancing the decoder-model's history
+      Expr prevPathScores;               // [localBeamSize, 1, dimBatch, 1], path score that a hyp ended in (last axis will broadcast into vocab size when adding expandedPathScores)
       if(t == 0) { // no scores yet
         prevPathScores = graph->constant({1, 1, 1, 1}, inits::from_value(0));
       } else {
         std::vector<float> prevScores;
-        for(size_t beamIndex = 0; beamIndex < localBeamSize; ++beamIndex) {
-          for(int batchIndex = 0; batchIndex < dimBatch; ++batchIndex) { // loop over batch entries (active sentences)
-            auto& beam = beams[batchIndex];
-            if(beamIndex < beam.size()) {
-              auto hyp = beam[beamIndex];
-              hypIndices.push_back((IndexType)hyp->getPrevStateIndex()); // index where to find prev hyp (beamHypIdx, batchIdx), =beamHypIdx * dimBatch + batchIdx
+        for(size_t beamHypIdx = 0; beamHypIdx < localBeamSize; ++beamHypIdx) {
+          for(int batchIdx = 0; batchIdx < dimBatch; ++batchIdx) { // loop over batch entries (active sentences)
+            auto& beam = beams[batchIdx];
+            if(beamHypIdx < beam.size()) {
+              auto hyp = beam[beamHypIdx];
+              hypIndices.push_back((IndexType)(hyp->getPrevStateIndex() * dimBatch + batchIdx)); // (beamHypIdx, batchIdx), flattened, for index_select() operation
               prevWords .push_back(hyp->getWord());
               prevScores.push_back(hyp->getPathScore());
             } else {  // pad to localBeamSize (dummy hypothesis)
               hypIndices.push_back(0);
-              prevWords .push_back(Word{});  // (unused)
-              prevScores.push_back(-9999);
+              prevWords.push_back(trgEosId_);  // (unused, but let's use a valid value)
+              prevScores.push_back(INVALID_PATH_SCORE);
             }
           }
         }
-        prevPathScores = graph->constant({(int)localBeamSize, 1, dimBatch, 1},
-                                    inits::from_vector(prevScores));
+        prevPathScores = graph->constant({(int)localBeamSize, 1, dimBatch, 1}, inits::from_vector(prevScores));
       }
 
       //**********************************************************************
       // compute expanded path scores with word prediction probs from all scorers
       auto expandedPathScores = prevPathScores; // will become [localBeamSize, 1, dimBatch, dimVocab]
+      Expr logProbs;
       for(size_t i = 0; i < scorers_.size(); ++i) {
         // compute output probabilities for current output time step
-        //  - uses hypIndices[index in beam, 1, batch index, 1] to reorder hypotheses
-        //  - adds prevWords [index in beam, 1, batch index, 1] to the decoder model's target history
-        //  - performs one step of the decoder model
+        //  - uses hypIndices[index in beam, 1, batch index, 1] to reorder scorer state to reflect the top-N in beams[][]
+        //  - adds prevWords [index in beam, 1, batch index, 1] to the scorer's target history
+        //  - performs one step of the scorer
         //  - returns new NN state for use in next output time step
         //  - returns vector of prediction probabilities over output vocab via newState
-        auto newState = scorers_[i]->step(
-            graph, states[i], hypIndices, prevWords, dimBatch, (int)localBeamSize);
-
+        states[i] = scorers_[i]->step(graph, states[i], hypIndices, prevWords, dimBatch, (int)localBeamSize);
+        logProbs = states[i]->getLogProbs(); // [localBeamSize, 1, dimBatch, dimVocab]
         // expand all hypotheses, [localBeamSize, 1, dimBatch, 1] -> [localBeamSize, 1, dimBatch, dimVocab]
-        expandedPathScores = expandedPathScores + scorers_[i]->getWeight() * newState->getLogProbs();
-
-        // update state in-place for next output time step
-        states[i] = newState;
+        expandedPathScores = expandedPathScores + scorers_[i]->getWeight() * logProbs;
       }
 
       // make beams continuous
-      expandedPathScores = swapAxes(expandedPathScores, 0, 2); // -> [dimBatch, 1, localBeamSize, dimVocab]
-      //if(dimBatch > 1 && localBeamSize > 1)
-      //  expandedPathScores = transpose(expandedPathScores, {2, 1, 0, 3}); // -> [dimBatch, 1, localBeamSize, dimVocab]
+      if(dimBatch > 1 && localBeamSize > 1)
+        expandedPathScores = swapAxes(expandedPathScores, 0, 2); // -> [dimBatch, 1, localBeamSize, dimVocab]
+      else // (avoid copy if we can)
+        expandedPathScores = reshape(expandedPathScores, {dimBatch, 1, (int)localBeamSize, expandedPathScores->shape()[-1]});
 
       // perform NN computation
       if(t == 0)
@@ -302,13 +286,15 @@ class BeamSearch {
       // perform beam search
 
       // find N best amongst the (localBeamSize * dimVocab) hypotheses
-      std::vector<unsigned int> nBestKeys; // [dimBatch, localBeamSize] flattened -> ((batchIdx, beamHypIdx) flattened, word idx) flattened
+      std::vector<unsigned int> nBestKeys; // [dimBatch, localBeamSize] flattened -> (batchIdx, beamHypIdx, word idx) flattened
       std::vector<float> nBestPathScores;  // [dimBatch, localBeamSize] flattened
+      // @TODO: getNBestList() API is redundant; input dimensions are known from expandedPathScores(); but no way to specify target N different from input N
       getNBestList(/*beamSizes=*/std::vector<size_t>(dimBatch, localBeamSize), // output layout of (nBestPathScores, nBestKeys)  --@REVIEW: correct?
                    /*in*/ expandedPathScores->val(),                           // [dimBatch, 1, localBeamSize, dimVocab or dimShortlist]
                    /*out*/ nBestPathScores, /*out*/ nBestKeys,
                    /*first=*/t == 0); // @TODO: Why is this passed? To know that the beam size is 1 for first step, for flattened hyp index?
-      // Now, nBestPathScores contain N-best expandedPathScores, and nBestKeys for each their original location (batchIdx, beamHypIdx, word).
+      // Now, nBestPathScores contain N-best expandedPathScores for each batch and beam,
+      // and nBestKeys for each their original location (batchIdx, beamHypIdx, word).
 
       // combine N-best sets with existing search space (beams) to updated search space
       beams = toHyps(nBestKeys, nBestPathScores,
@@ -321,23 +307,23 @@ class BeamSearch {
 
       // remove all hyps that end in EOS
       // The position of a hyp in the beam may change.
-      const auto purgedBeams = purgeBeams(beams);
+      const auto purgedNewBeams = purgeBeams(beams);
 
-      // add updated search space (beams) to search grid (histories) for traceback
+      // add updated search space (beams) to our return value
       bool maxLengthReached = false;
       for(int i = 0; i < dimBatch; ++i) {
         // if this batch entry has surviving hyps then add them to the traceback grid
         if(!beams[i].empty()) {
           if (histories[i]->size() >= options_->get<float>("max-length-factor") * batch->front()->batchWidth())
             maxLengthReached = true;
-          histories[i]->add(beams[i], trgEosId_, purgedBeams[i].empty() || maxLengthReached);
+          histories[i]->add(beams[i], trgEosId_, purgedNewBeams[i].empty() || maxLengthReached);
         }
       }
       if (maxLengthReached) // early exit if max length limit was reached
         break;
 
       // this is the search space for the next output time step
-      beams = purgedBeams;
+      beams = purgedNewBeams;
     } // end of main loop over output time steps
 
     return histories; // [dimBatch][t][N best hyps]

From 168300c5d9b77b029d363b1117b79f56d071f680 Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Tue, 12 Feb 2019 12:06:59 -0800
Subject: [PATCH 335/838] towards cleanup of getNBestList()

---
 src/translator/beam_search.h   | 15 +++----
 src/translator/nth_element.cpp | 79 ++++++++++++++++++----------------
 src/translator/nth_element.cu  | 54 +++++++++++++----------
 src/translator/nth_element.h   |  4 +-
 4 files changed, 84 insertions(+), 68 deletions(-)
 mode change 100644 => 100755 src/translator/nth_element.cpp
 mode change 100644 => 100755 src/translator/nth_element.cu
 mode change 100644 => 100755 src/translator/nth_element.h

diff --git a/src/translator/beam_search.h b/src/translator/beam_search.h
index 86da1e56a..60659a22a 100755
--- a/src/translator/beam_search.h
+++ b/src/translator/beam_search.h
@@ -34,8 +34,8 @@ class BeamSearch {
         trgUnkId_(trgUnkId) {}
 
   // combine new expandedPathScores and previous beams into new set of beams
-  Beams toHyps(const std::vector<unsigned int> nBestKeys, // [dimBatch, beamSize] flattened -> ((batchIdx, beamHypIdx) flattened, word idx) flattened
-               const std::vector<float> nBestPathScores,  // [dimBatch, beamSize] flattened
+  Beams toHyps(const std::vector<unsigned int>& nBestKeys, // [dimBatch, beamSize] flattened -> ((batchIdx, beamHypIdx) flattened, word idx) flattened
+               const std::vector<float>& nBestPathScores,  // [dimBatch, beamSize] flattened
                const size_t vocabSize,
                const Beams& beams,
                const std::vector<Ptr<ScorerState /*const*/>>& states,
@@ -70,7 +70,7 @@ class BeamSearch {
       if (pathScore <= INVALID_PATH_SCORE) // (unused slot)
         continue;
 
-      ABORT_IF(beamHypIdx >= (int)beam.size(), "Out of bounds beamHypIdx??");
+      ABORT_IF(beamHypIdx >= beam.size(), "Out of bounds beamHypIdx??");
 
       // Map wordIdx to word
       Word word;
@@ -82,7 +82,7 @@ class BeamSearch {
       else
         word = wordIdx;
 
-      auto hyp = New<Hypothesis>(beam[beamHypIdx], word, beamHypIdx, pathScore);
+      auto hyp = New<Hypothesis>(beam[beamHypIdx], word, (IndexType)beamHypIdx, pathScore);
 
       // Set score breakdown for n-best lists
       if(options_->get<bool>("n-best")) {
@@ -239,7 +239,7 @@ class BeamSearch {
             } else {  // pad to localBeamSize (dummy hypothesis)
               hypIndices.push_back(0);
               prevWords.push_back(trgEosId_);  // (unused, but let's use a valid value)
-              prevScores.push_back(INVALID_PATH_SCORE);
+              prevScores.push_back((float)INVALID_PATH_SCORE);
             }
           }
         }
@@ -288,9 +288,8 @@ class BeamSearch {
       // find N best amongst the (localBeamSize * dimVocab) hypotheses
       std::vector<unsigned int> nBestKeys; // [dimBatch, localBeamSize] flattened -> (batchIdx, beamHypIdx, word idx) flattened
       std::vector<float> nBestPathScores;  // [dimBatch, localBeamSize] flattened
-      // @TODO: getNBestList() API is redundant; input dimensions are known from expandedPathScores(); but no way to specify target N different from input N
-      getNBestList(/*beamSizes=*/std::vector<size_t>(dimBatch, localBeamSize), // output layout of (nBestPathScores, nBestKeys)  --@REVIEW: correct?
-                   /*in*/ expandedPathScores->val(),                           // [dimBatch, 1, localBeamSize, dimVocab or dimShortlist]
+      getNBestList(/*in*/ expandedPathScores->val(),                           // [dimBatch, 1, localBeamSize, dimVocab or dimShortlist]
+                   /*N=*/localBeamSize,
                    /*out*/ nBestPathScores, /*out*/ nBestKeys,
                    /*first=*/t == 0); // @TODO: Why is this passed? To know that the beam size is 1 for first step, for flattened hyp index?
       // Now, nBestPathScores contain N-best expandedPathScores for each batch and beam,
diff --git a/src/translator/nth_element.cpp b/src/translator/nth_element.cpp
old mode 100644
new mode 100755
index 549833230..dbfc0a70d
--- a/src/translator/nth_element.cpp
+++ b/src/translator/nth_element.cpp
@@ -14,22 +14,18 @@ namespace marian {
 class NthElementCPU {
   std::vector<int> h_res_idx;
   std::vector<float> h_res;
-  size_t lastN;
+  size_t maxBeamSize_;
+  size_t maxBatchSize_;
+  //size_t lastN_;
 
 public:
-  NthElementCPU() = delete;
+  NthElementCPU() {}
   NthElementCPU(const NthElementCPU& copy) = delete;
 
-  NthElementCPU(size_t maxBeamSize, size_t maxBatchSize) {
-    size_t maxSize = maxBeamSize * maxBatchSize;
-    h_res.resize(maxSize);
-    h_res_idx.resize(maxSize);
-  }
-
 private:
-  void getNBestList(float* scores,
-                    const std::vector<int>& batchFirstElementIdxs,
-                    const std::vector<int>& cumulativeBeamSizes) {
+    void selectNBest(float* scores,
+                     const std::vector<int>& batchFirstElementIdxs,
+                     const std::vector<int>& cumulativeBeamSizes) {
     /* For each batch, select the max N elements, where N is the beam size for
      * this batch. Locally record these elements (their current value and index
      * in 'scores') before updating each element to a large negative value, such
@@ -62,23 +58,38 @@ class NthElementCPU {
   }
 
 public:
-  void getNBestList(const std::vector<size_t>& beamSizes,
-                                   Tensor scores,
-                                   std::vector<float>& outPathScores,
-                                   std::vector<unsigned>& outKeys,
-                                   const bool isFirst) {
+  // @BUGBUG: This API mixes input and output beam size.
+  void getNBestList(Tensor scores, // [dimBatch, 1, beamSize, dimVocab or dimShortlist]
+                    size_t N,
+                    std::vector<float>& outPathScores,
+                    std::vector<unsigned>& outKeys,
+                    const bool isFirst) {
+    const auto vocabSize = scores->shape()[-1];
+    const auto inputN    = scores->shape()[-2];
+    const auto dimBatch  = scores->shape()[-4];
+    ABORT_IF(inputN != (isFirst ? 1 : N), "Input tensor has wrong beam dim??");
+    ABORT_IF(dimBatch > maxBatchSize_, "GetNBestList(): actual batch size exceeds initialization parameter");
+    ABORT_IF(N > maxBeamSize_, "GetNBestList(): actual beam size exceeds initialization parameter"); // @TODO: or inputN?
+
+    const std::vector<size_t> beamSizes(dimBatch, N);
     std::vector<int> cumulativeBeamSizes(beamSizes.size() + 1, 0);
     std::vector<int> batchFirstElementIdxs(beamSizes.size() + 1, 0);
 
-    auto vocabSize = scores->shape()[-1];
-    for(int i = 0; i < beamSizes.size(); ++i) {
-      cumulativeBeamSizes[i + 1] = cumulativeBeamSizes[i] + (int)beamSizes[i];
-      batchFirstElementIdxs[i + 1]
-          += (isFirst ? i + 1 : cumulativeBeamSizes[i + 1]) * vocabSize;
+    for(int batchIdx = 0; batchIdx < beamSizes.size(); ++batchIdx) {
+      cumulativeBeamSizes[batchIdx + 1] = cumulativeBeamSizes[batchIdx] + (int)beamSizes[batchIdx];
+      ABORT_IF(cumulativeBeamSizes[batchIdx + 1] != (batchIdx + 1) * N, "cumulativeBeamSizes wrong??");
+      batchFirstElementIdxs[batchIdx + 1]
+          += (isFirst ? batchIdx + 1 : cumulativeBeamSizes[batchIdx + 1]) * vocabSize;
+      ABORT_IF((isFirst ? batchIdx + 1 : cumulativeBeamSizes[batchIdx + 1]) != (batchIdx + 1) * inputN, "inputN wrong??");
     }
 
-    getNBestList(scores->data(), batchFirstElementIdxs, cumulativeBeamSizes);
+    size_t maxSize = N * dimBatch;
+    h_res.resize(maxSize);
+    h_res_idx.resize(maxSize);
+
+    selectNBest(scores->data(), batchFirstElementIdxs, cumulativeBeamSizes);
     getPairs(cumulativeBeamSizes.back(), outKeys, outPathScores);
+    ABORT_IF(cumulativeBeamSizes.back() != dimBatch * N, "cumulativeBeamSizes.back() wrong??");
   }
 
 private:
@@ -87,14 +98,14 @@ class NthElementCPU {
                 std::vector<float>& outValues) {
     std::copy(h_res_idx.begin(), h_res_idx.begin() + number, std::back_inserter(outKeys));
     std::copy(h_res    .begin(), h_res    .begin() + number, std::back_inserter(outValues));
-    lastN = number;
+    //lastN_ = number;
   }
 
-  void getValueByKey(std::vector<float>& out, float* d_in) {
-    for(size_t i = 0; i < lastN; ++i) {
-      out[i] = d_in[h_res_idx[i]];
-    }
-  }
+  //void getValueByKey(std::vector<float>& out, float* d_in) {
+  //  for(size_t i = 0; i < lastN_; ++i) {
+  //    out[i] = d_in[h_res_idx[i]];
+  //  }
+  //}
 };
 
 #ifdef CUDA_FOUND
@@ -108,15 +119,11 @@ GetNBestListFn createGetNBestListFn(size_t beamSize, size_t dimBatch, DeviceId d
   if(deviceId.type == DeviceType::gpu)
     return createGetNBestListGPUFn(beamSize, dimBatch, deviceId);
 #else
-    deviceId; // (unused)
+  deviceId; beamSize; dimBatch; // (unused)
 #endif
-  auto nth = New<NthElementCPU>(beamSize, dimBatch);
-  return [nth](const std::vector<size_t>& beamSizes,
-      Tensor logProbs,
-      std::vector<float>& outCosts,
-      std::vector<unsigned>& outKeys,
-      const bool isFirst) {
-      return nth->getNBestList(beamSizes, logProbs, outCosts, outKeys, isFirst);
+  auto nth = New<NthElementCPU>();
+  return [nth](Tensor logProbs, size_t N, std::vector<float>& outCosts, std::vector<unsigned>& outKeys, const bool isFirst) {
+    return nth->getNBestList(logProbs, N, outCosts, outKeys, isFirst);
   };
 }
 
diff --git a/src/translator/nth_element.cu b/src/translator/nth_element.cu
old mode 100644
new mode 100755
index d5377f90c..ecf9daf5c
--- a/src/translator/nth_element.cu
+++ b/src/translator/nth_element.cu
@@ -279,6 +279,7 @@ public:
                 size_t maxBatchSize,
                 DeviceId deviceId)
       : deviceId_(deviceId),
+        maxBeamSize_(maxBeamSize), maxBatchSize_(maxBatchSize),
         NUM_BLOCKS(std::min(
             500,
             int(maxBeamSize* MAX_VOCAB_SIZE / (2 * BLOCK_SIZE))
@@ -316,9 +317,9 @@ public:
   }
 
 private:
-  void getNBestList(float* probs,
-                    const std::vector<int>& batchFirstElementIdxs,
-                    const std::vector<int>& cummulatedBeamSizes) {
+  void selectNBest(float* probs,
+                   const std::vector<int>& batchFirstElementIdxs,
+                   const std::vector<int>& cumulativeBeamSizes) {
     cudaSetDevice(deviceId_.no);
     CUDA_CHECK(cudaMemcpyAsync(d_batchPosition,
                                batchFirstElementIdxs.data(),
@@ -326,8 +327,8 @@ private:
                                cudaMemcpyHostToDevice,
                                /* stream_ */ 0));
     CUDA_CHECK(cudaMemcpyAsync(d_cumBeamSizes,
-                               cummulatedBeamSizes.data(),
-                               cummulatedBeamSizes.size() * sizeof(int),
+                               cumulativeBeamSizes.data(),
+                               cumulativeBeamSizes.size() * sizeof(int),
                                cudaMemcpyHostToDevice,
                                /* stream_ */ 0));
 
@@ -353,26 +354,37 @@ private:
   }
 
 public:
-  void getNBestList(const std::vector<size_t>& beamSizes,
-                    Tensor Probs,
+  // @BUGBUG: This API mixes input and output beam size.
+  void getNBestList(Tensor scores,
+                    size_t N,
                     std::vector<float>& outCosts,
                     std::vector<unsigned>& outKeys,
                     const bool isFirst) {
     cudaSetDevice(deviceId_.no);
 
-    std::vector<int> cummulatedBeamSizes(beamSizes.size() + 1, 0);
-    std::vector<int> batchFirstElementIdxs(beamSizes.size() + 1, 0);
+    const auto vocabSize = scores->shape()[-1];
+    const auto inputN    = scores->shape()[-2];
+    const auto dimBatch  = scores->shape()[-4];
+    ABORT_IF(inputN != (isFirst ? 1 : N), "Input tensor has wrong beam dim??"); // @TODO: Remove isFirst argument altogether
+    ABORT_IF(vocabSize > MAX_VOCAB_SIZE, "GetNBestList(): actual vocab size exceeds MAX_VOCAB_SIZE");
+    ABORT_IF(dimBatch > maxBatchSize_, "GetNBestList(): actual batch size exceeds initialization parameter");
+    ABORT_IF(N > maxBeamSize_, "GetNBestList(): actual beam size exceeds initialization parameter"); // @TODO: or inputN?
 
-    const size_t vocabSize = Probs->shape()[-1];
+    const std::vector<size_t> beamSizes(dimBatch, N);
+    std::vector<int> cumulativeBeamSizes(beamSizes.size() + 1, 0);
+    std::vector<int> batchFirstElementIdxs(beamSizes.size() + 1, 0);
 
-    for(size_t i = 0; i < beamSizes.size(); ++i) {
-      cummulatedBeamSizes[i + 1] = cummulatedBeamSizes[i] + beamSizes[i];
-      batchFirstElementIdxs[i + 1]
-          += ((isFirst) ? (i + 1) : cummulatedBeamSizes[i + 1]) * vocabSize;
+    for(size_t batchIdx = 0; batchIdx < beamSizes.size(); ++batchIdx) {
+      cumulativeBeamSizes[batchIdx + 1] = cumulativeBeamSizes[batchIdx] + beamSizes[batchIdx];
+      ABORT_IF(cumulativeBeamSizes[batchIdx + 1] != (batchIdx + 1) * N, "cumulativeBeamSizes wrong??");
+      batchFirstElementIdxs[batchIdx + 1]
+          += ((isFirst) ? (batchIdx + 1) : cumulativeBeamSizes[batchIdx + 1]) * vocabSize;
+      ABORT_IF((isFirst ? batchIdx + 1 : cumulativeBeamSizes[batchIdx + 1]) != (batchIdx + 1) * inputN, "inputN wrong??");
     }
 
-    getNBestList(Probs->data(), batchFirstElementIdxs, cummulatedBeamSizes);
-    getPairs(cummulatedBeamSizes.back(), outKeys, outCosts);
+    selectNBest(scores->data(), batchFirstElementIdxs, cumulativeBeamSizes);
+    getPairs(cumulativeBeamSizes.back(), outKeys, outCosts);
+    ABORT_IF(cumulativeBeamSizes.back() != dimBatch * N, "cumulativeBeamSizes.back() wrong??");
   }
 
 private:
@@ -417,6 +429,8 @@ private:
   DeviceId deviceId_;
 
   const int MAX_VOCAB_SIZE = 100000;
+  size_t maxBeamSize_;
+  size_t maxBatchSize_;
 
   const int BLOCK_SIZE = 512;
   const int NUM_BLOCKS;
@@ -440,12 +454,8 @@ private:
 // Returns a lambda with the same signature as the getNBestList() function.
 GetNBestListFn createGetNBestListGPUFn(size_t beamSize, size_t dimBatch, DeviceId deviceId) {
   auto nth = New<NthElementGPU>(beamSize, dimBatch, deviceId);
-  return [nth](const std::vector<size_t>& beamSizes,
-      Tensor logProbs,
-      std::vector<float>& outCosts,
-      std::vector<unsigned>& outKeys,
-      const bool isFirst) {
-      return nth->getNBestList(beamSizes, logProbs, outCosts, outKeys, isFirst);
+  return [nth](Tensor logProbs, size_t N, std::vector<float>& outCosts, std::vector<unsigned>& outKeys, const bool isFirst) {
+    return nth->getNBestList(logProbs, N, outCosts, outKeys, isFirst);
   };
 }
 
diff --git a/src/translator/nth_element.h b/src/translator/nth_element.h
old mode 100644
new mode 100755
index 91ea3792b..ca325ed0d
--- a/src/translator/nth_element.h
+++ b/src/translator/nth_element.h
@@ -10,8 +10,8 @@
 
 namespace marian {
 
-typedef std::function<void(const std::vector<size_t>& beamSizes,
-                           Tensor logProbs,
+typedef std::function<void(Tensor logProbs,
+                           size_t N,
                            std::vector<float>& outCosts,
                            std::vector<unsigned>& outKeys,
                            const bool isFirst)> GetNBestListFn;

From 699e48700d43104ce62894aadd211838fb648e62 Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Tue, 12 Feb 2019 12:07:45 -0800
Subject: [PATCH 336/838] minor fix

---
 src/translator/nth_element.cpp | 4 ----
 1 file changed, 4 deletions(-)

diff --git a/src/translator/nth_element.cpp b/src/translator/nth_element.cpp
index dbfc0a70d..f99b0be42 100755
--- a/src/translator/nth_element.cpp
+++ b/src/translator/nth_element.cpp
@@ -14,8 +14,6 @@ namespace marian {
 class NthElementCPU {
   std::vector<int> h_res_idx;
   std::vector<float> h_res;
-  size_t maxBeamSize_;
-  size_t maxBatchSize_;
   //size_t lastN_;
 
 public:
@@ -68,8 +66,6 @@ class NthElementCPU {
     const auto inputN    = scores->shape()[-2];
     const auto dimBatch  = scores->shape()[-4];
     ABORT_IF(inputN != (isFirst ? 1 : N), "Input tensor has wrong beam dim??");
-    ABORT_IF(dimBatch > maxBatchSize_, "GetNBestList(): actual batch size exceeds initialization parameter");
-    ABORT_IF(N > maxBeamSize_, "GetNBestList(): actual beam size exceeds initialization parameter"); // @TODO: or inputN?
 
     const std::vector<size_t> beamSizes(dimBatch, N);
     std::vector<int> cumulativeBeamSizes(beamSizes.size() + 1, 0);

From 43e389d7c703b7c393ff12827121f869b1a1e472 Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Tue, 12 Feb 2019 13:55:54 -0800
Subject: [PATCH 337/838] bug fix: reshape() must verify that #elements does
 not change; bug fix: beam search must reshape first step correctly

---
 src/graph/node_operators_unary.h |  2 ++
 src/models/encoder_decoder.cpp   |  5 ++---
 src/translator/beam_search.h     |  2 +-
 src/translator/nth_element.cpp   | 21 ++++++++++-----------
 4 files changed, 15 insertions(+), 15 deletions(-)
 mode change 100644 => 100755 src/models/encoder_decoder.cpp

diff --git a/src/graph/node_operators_unary.h b/src/graph/node_operators_unary.h
index 190fa947f..c5141092b 100755
--- a/src/graph/node_operators_unary.h
+++ b/src/graph/node_operators_unary.h
@@ -717,6 +717,8 @@ class ReshapeNodeOp : public UnaryNodeOp {
 
 public:
   ReshapeNodeOp(Expr a, Shape shape) : UnaryNodeOp(a, shape, a->value_type()), reshapee_(a) {
+    ABORT_IF(a->shape().elements() != shape.elements(),
+             "Reshape must not change the number of elements (from {} to {})", a->shape().toString(), shape.toString());
     Node::destroy_ = false;
   }
 
diff --git a/src/models/encoder_decoder.cpp b/src/models/encoder_decoder.cpp
old mode 100644
new mode 100755
index 4b072c73a..33af5d016
--- a/src/models/encoder_decoder.cpp
+++ b/src/models/encoder_decoder.cpp
@@ -170,9 +170,8 @@ Ptr<DecoderState> EncoderDecoder::step(Ptr<ExpressionGraph> graph,
   // create updated state that reflects reordering and dropping of hypotheses
   state = hypIndices.empty() ? state : state->select(hypIndices, beamSize);
 
-  // Fill stte with embeddings based on last prediction
-  decoders_[0]->embeddingsFromPrediction(
-      graph, state, embIndices, dimBatch, beamSize);
+  // Fill state with embeddings based on last prediction
+  decoders_[0]->embeddingsFromPrediction(graph, state, embIndices, dimBatch, beamSize);
   auto nextState = decoders_[0]->step(graph, state);
 
   return nextState;
diff --git a/src/translator/beam_search.h b/src/translator/beam_search.h
index 60659a22a..7e3cb385d 100755
--- a/src/translator/beam_search.h
+++ b/src/translator/beam_search.h
@@ -267,7 +267,7 @@ class BeamSearch {
       if(dimBatch > 1 && localBeamSize > 1)
         expandedPathScores = swapAxes(expandedPathScores, 0, 2); // -> [dimBatch, 1, localBeamSize, dimVocab]
       else // (avoid copy if we can)
-        expandedPathScores = reshape(expandedPathScores, {dimBatch, 1, (int)localBeamSize, expandedPathScores->shape()[-1]});
+        expandedPathScores = reshape(expandedPathScores, {expandedPathScores->shape()[-2], 1, expandedPathScores->shape()[-4], expandedPathScores->shape()[-1]});
 
       // perform NN computation
       if(t == 0)
diff --git a/src/translator/nth_element.cpp b/src/translator/nth_element.cpp
index f99b0be42..febf0739b 100755
--- a/src/translator/nth_element.cpp
+++ b/src/translator/nth_element.cpp
@@ -43,7 +43,7 @@ class NthElementCPU {
       std::vector<int>::iterator middle = begin + beamSize;
       std::vector<int>::iterator end = idxs.begin() + batchFirstElementIdxs[batchIdx + 1];
       std::partial_sort(
-          begin, middle, end, [=](int a, int b) { return scores[a] > scores[b]; });
+          begin, middle, end, [&](int a, int b) { return scores[a] > scores[b]; });
 
       while(begin != middle) {
         int idx = *begin++;
@@ -67,33 +67,32 @@ class NthElementCPU {
     const auto dimBatch  = scores->shape()[-4];
     ABORT_IF(inputN != (isFirst ? 1 : N), "Input tensor has wrong beam dim??");
 
-    const std::vector<size_t> beamSizes(dimBatch, N);
-    std::vector<int> cumulativeBeamSizes(beamSizes.size() + 1, 0);
-    std::vector<int> batchFirstElementIdxs(beamSizes.size() + 1, 0);
+    std::vector<int> cumulativeBeamSizes(dimBatch + 1, 0);
+    std::vector<int> batchFirstElementIdxs(dimBatch + 1, 0);
 
-    for(int batchIdx = 0; batchIdx < beamSizes.size(); ++batchIdx) {
-      cumulativeBeamSizes[batchIdx + 1] = cumulativeBeamSizes[batchIdx] + (int)beamSizes[batchIdx];
+    for(int batchIdx = 0; batchIdx < dimBatch; ++batchIdx) {
+      cumulativeBeamSizes[batchIdx + 1] = cumulativeBeamSizes[batchIdx] + (int)N;
       ABORT_IF(cumulativeBeamSizes[batchIdx + 1] != (batchIdx + 1) * N, "cumulativeBeamSizes wrong??");
       batchFirstElementIdxs[batchIdx + 1]
           += (isFirst ? batchIdx + 1 : cumulativeBeamSizes[batchIdx + 1]) * vocabSize;
       ABORT_IF((isFirst ? batchIdx + 1 : cumulativeBeamSizes[batchIdx + 1]) != (batchIdx + 1) * inputN, "inputN wrong??");
     }
+    ABORT_IF(cumulativeBeamSizes.back() != dimBatch * N, "cumulativeBeamSizes.back() wrong??");
 
     size_t maxSize = N * dimBatch;
     h_res.resize(maxSize);
     h_res_idx.resize(maxSize);
 
     selectNBest(scores->data(), batchFirstElementIdxs, cumulativeBeamSizes);
-    getPairs(cumulativeBeamSizes.back(), outKeys, outPathScores);
-    ABORT_IF(cumulativeBeamSizes.back() != dimBatch * N, "cumulativeBeamSizes.back() wrong??");
+    getPairs(/*cumulativeBeamSizes.back(),*/ outKeys, outPathScores);
   }
 
 private:
-  void getPairs(size_t number,
+  void getPairs(/*size_t number,*/
                 std::vector<unsigned>& outKeys,
                 std::vector<float>& outValues) {
-    std::copy(h_res_idx.begin(), h_res_idx.begin() + number, std::back_inserter(outKeys));
-    std::copy(h_res    .begin(), h_res    .begin() + number, std::back_inserter(outValues));
+    std::copy(h_res_idx.begin(), h_res_idx.end(), std::back_inserter(outKeys));
+    std::copy(h_res    .begin(), h_res    .end(), std::back_inserter(outValues));
     //lastN_ = number;
   }
 

From 94791594eb647f3e72fbe81d1e15020da20dd11f Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Tue, 12 Feb 2019 14:09:04 -0800
Subject: [PATCH 338/838] swapAxes() now optimizes for case where it can
 reshape() instead

---
 src/graph/expression_operators.cpp | 25 +++++++++++++++++++++----
 src/translator/beam_search.h       |  5 +----
 2 files changed, 22 insertions(+), 8 deletions(-)

diff --git a/src/graph/expression_operators.cpp b/src/graph/expression_operators.cpp
index 5c37beef3..43e6b1a2e 100755
--- a/src/graph/expression_operators.cpp
+++ b/src/graph/expression_operators.cpp
@@ -213,6 +213,8 @@ Expr repeat(Expr a, size_t repeats, int ax) {
 }
 
 Expr reshape(Expr a, Shape shape) {
+  if (a->shape() == shape)
+    return a;
   return Expression<ReshapeNodeOp>(a, shape);
 }
 
@@ -256,7 +258,7 @@ Expr flatten_2d(Expr a) {
 
 Expr stopGradient(Expr a) {
   // implemented as a dummy reshape that is not trainable
-  auto res = reshape(a, a->shape());
+  auto res = Expression<ReshapeNodeOp>(a, a->shape());
   res->setTrainable(false);
   return res;
 }
@@ -530,12 +532,27 @@ Expr transpose(Expr a, const std::vector<int>& axes) {
 
 Expr swapAxes(Expr x, int axis1, int axis2)
 {
-  axis1 = x->shape().axis(axis1);
-  axis2 = x->shape().axis(axis2);
+  const auto& shape = x->shape();
+  axis1 = shape.axis(axis1);
+  axis2 = shape.axis(axis2);
   if (axis1 == axis2)
     return x;
+  if (shape[axis1] == 1 || shape[axis2] == 1) { // can we use a reshape instead?
+    if (axis1 > axis2)
+      std::swap(axis1, axis2);
+    bool canReshape = true;
+    for (int ax = axis1 + 1; ax < axis2 && canReshape; ax++)
+      canReshape &= (shape[ax] == 1);
+    if (canReshape) {
+      auto newShape = shape;
+      newShape.set(axis1, shape[axis2]);
+      newShape.set(axis2, shape[axis1]);
+      //LOG(info, "SwapAxes() did a reshape from {} to {}", shape.toString(), newShape.toString());
+      return reshape(x, newShape);
+    }
+  }
   // TODO: This is code dup from transpose(x). Implement transpose(x) as swapAxes(x, 0, 1)
-  std::vector<int> axes(x->shape().size());
+  std::vector<int> axes(shape.size());
   for (int i = 0; i < axes.size(); ++i) // @TODO: use std::iota()
     axes[i] = i;
   std::swap(axes[axis1], axes[axis2]);
diff --git a/src/translator/beam_search.h b/src/translator/beam_search.h
index 7e3cb385d..2f529a0fd 100755
--- a/src/translator/beam_search.h
+++ b/src/translator/beam_search.h
@@ -264,10 +264,7 @@ class BeamSearch {
       }
 
       // make beams continuous
-      if(dimBatch > 1 && localBeamSize > 1)
-        expandedPathScores = swapAxes(expandedPathScores, 0, 2); // -> [dimBatch, 1, localBeamSize, dimVocab]
-      else // (avoid copy if we can)
-        expandedPathScores = reshape(expandedPathScores, {expandedPathScores->shape()[-2], 1, expandedPathScores->shape()[-4], expandedPathScores->shape()[-1]});
+      expandedPathScores = swapAxes(expandedPathScores, 0, 2); // -> [dimBatch, 1, localBeamSize, dimVocab]
 
       // perform NN computation
       if(t == 0)

From 9f6f0f12529ffc393fbf922051d4aaf834a83233 Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Tue, 12 Feb 2019 14:32:04 -0800
Subject: [PATCH 339/838] removed 'first' parameter from toHyps(); towards
 removing it from getNBestList(); renamed target embeddings to target-history
 embeddings

---
 src/models/decoder.h           |  4 ++--
 src/models/s2s.h               |  2 +-
 src/models/states.h            | 17 +++++----------
 src/models/transformer.h       |  4 ++--
 src/translator/beam_search.h   | 28 ++++++++++--------------
 src/translator/nth_element.cpp | 10 +++++++--
 src/translator/nth_element.cu  | 40 +++++++++++++++++++---------------
 7 files changed, 53 insertions(+), 52 deletions(-)
 mode change 100644 => 100755 src/models/decoder.h
 mode change 100644 => 100755 src/models/s2s.h
 mode change 100644 => 100755 src/models/states.h

diff --git a/src/models/decoder.h b/src/models/decoder.h
old mode 100644
new mode 100755
index bc0c91191..d0414d86b
--- a/src/models/decoder.h
+++ b/src/models/decoder.h
@@ -74,7 +74,7 @@ class DecoderBase {
 
     auto yShifted = shift(y, {1, 0, 0});
 
-    state->setTargetEmbeddings(yShifted);
+    state->setTargetHistoryEmbeddings(yShifted);
     state->setTargetMask(yMask);
     state->setTargetIndices(yData);
   }
@@ -105,7 +105,7 @@ class DecoderBase {
 
       selectedEmbs = yEmb->apply(embIdx, {dimBeam, 1, dimBatch, dimTrgEmb});
     }
-    state->setTargetEmbeddings(selectedEmbs);
+    state->setTargetHistoryEmbeddings(selectedEmbs);
   }
 
   virtual const std::vector<Expr> getAlignments(int /*i*/ = 0) { return {}; };
diff --git a/src/models/s2s.h b/src/models/s2s.h
old mode 100644
new mode 100755
index 35846e783..bc3626ac7
--- a/src/models/s2s.h
+++ b/src/models/s2s.h
@@ -283,7 +283,7 @@ class DecoderS2S : public DecoderBase {
   virtual Ptr<DecoderState> step(Ptr<ExpressionGraph> graph,
                                  Ptr<DecoderState> state) override {
 
-    auto embeddings = state->getTargetEmbeddings();
+    auto embeddings = state->getTargetHistoryEmbeddings();
 
     // dropout target words
     float dropoutTrg = inference_ ? 0 : opt<float>("dropout-trg");
diff --git a/src/models/states.h b/src/models/states.h
old mode 100644
new mode 100755
index 8e3972210..297c42eb9
--- a/src/models/states.h
+++ b/src/models/states.h
@@ -33,9 +33,9 @@ class DecoderState {
   std::vector<Ptr<EncoderState>> encStates_;
   Ptr<data::CorpusBatch> batch_;
 
-  Expr targetEmbeddings_;
+  Expr targetHistoryEmbeddings_; // decoder history (teacher-forced or from decoding), embedded
   Expr targetMask_;
-  Expr targetIndices_;
+  Expr targetIndices_;           // target labels
 
   // Keep track of current target token position during translation
   size_t position_{0};
@@ -69,20 +69,13 @@ class DecoderState {
 
   virtual const rnn::States& getStates() const { return states_; }
 
-  virtual Expr getTargetEmbeddings() const { return targetEmbeddings_; };
-
-  virtual void setTargetEmbeddings(Expr targetEmbeddings) {
-    targetEmbeddings_ = targetEmbeddings;
-  }
+  virtual Expr getTargetHistoryEmbeddings() const { return targetHistoryEmbeddings_; };
+  virtual void setTargetHistoryEmbeddings(Expr targetEmbeddings) { targetHistoryEmbeddings_ = targetEmbeddings; }
 
   virtual Expr getTargetIndices() const { return targetIndices_; };
-
-  virtual void setTargetIndices(Expr targetIndices) {
-    targetIndices_ = targetIndices;
-  }
+  virtual void setTargetIndices(Expr targetIndices) { targetIndices_ = targetIndices; }
 
   virtual Expr getTargetMask() const { return targetMask_; };
-
   virtual void setTargetMask(Expr targetMask) { targetMask_ = targetMask; }
 
   virtual const Words& getSourceWords() const {
diff --git a/src/models/transformer.h b/src/models/transformer.h
index 97faf13ce..c65ba5962 100755
--- a/src/models/transformer.h
+++ b/src/models/transformer.h
@@ -688,8 +688,8 @@ class DecoderTransformer : public Transformer<DecoderBase> {
   }
 
   Ptr<DecoderState> step(Ptr<DecoderState> state) {
-    auto embeddings  = state->getTargetEmbeddings(); // [-4: beam depth=1, -3: max length, -2: batch size, -1: vector dim]
-    auto decoderMask = state->getTargetMask();       // [max length, batch size, 1]  --this is a hypothesis
+    auto embeddings  = state->getTargetHistoryEmbeddings(); // [-4: beam depth=1, -3: max length, -2: batch size, -1: vector dim]
+    auto decoderMask = state->getTargetMask();              // [max length, batch size, 1]  --this is a hypothesis
 
     // dropout target words
     float dropoutTrg = inference_ ? 0 : opt<float>("dropout-trg");
diff --git a/src/translator/beam_search.h b/src/translator/beam_search.h
index 2f529a0fd..2954f06d7 100755
--- a/src/translator/beam_search.h
+++ b/src/translator/beam_search.h
@@ -36,11 +36,10 @@ class BeamSearch {
   // combine new expandedPathScores and previous beams into new set of beams
   Beams toHyps(const std::vector<unsigned int>& nBestKeys, // [dimBatch, beamSize] flattened -> ((batchIdx, beamHypIdx) flattened, word idx) flattened
                const std::vector<float>& nBestPathScores,  // [dimBatch, beamSize] flattened
-               const size_t vocabSize,
+               const size_t inputBeamSize, // for interpretation of nBestKeys
+               const size_t vocabSize,     // ditto.
                const Beams& beams,
                const std::vector<Ptr<ScorerState /*const*/>>& states,
-               const size_t beamSize,
-               const bool first,
                Ptr<data::CorpusBatch /*const*/> batch) const {
     std::vector<float> align;
     if(options_->hasAndNotEmpty("alignment"))
@@ -49,7 +48,7 @@ class BeamSearch {
     const auto dimBatch = beams.size();
     Beams newBeams(dimBatch);
 
-    for(size_t i = 0; i < nBestKeys.size(); ++i) { // [dimBatch, beamSize] flattened
+    for(size_t i = 0; i < nBestKeys.size(); ++i) {
       // Keys encode batchIdx, beamHypIdx, and word index in the entire beam.
       // They can be between 0 and beamSize * vocabSize-1.
       const auto  key       = nBestKeys[i];
@@ -57,10 +56,8 @@ class BeamSearch {
 
       // decompose key into individual indices (batchIdx, beamHypIdx, wordIdx)
       const auto wordIdx    = (Word)(key % vocabSize);
-      const auto beamHypIdx =       (key / vocabSize) % (first ? 1 : beamSize);
-      const auto batchIdx   =       (key / vocabSize) / (first ? 1 : beamSize);
-
-      ABORT_IF(i / beamSize != batchIdx, "Inconsistent batchIdx value in key??");
+      const auto beamHypIdx =       (key / vocabSize) % inputBeamSize;
+      const auto batchIdx   =       (key / vocabSize) / inputBeamSize;
 
       const auto& beam = beams[batchIdx];
       auto& newBeam = newBeams[batchIdx];
@@ -285,21 +282,20 @@ class BeamSearch {
       // find N best amongst the (localBeamSize * dimVocab) hypotheses
       std::vector<unsigned int> nBestKeys; // [dimBatch, localBeamSize] flattened -> (batchIdx, beamHypIdx, word idx) flattened
       std::vector<float> nBestPathScores;  // [dimBatch, localBeamSize] flattened
-      getNBestList(/*in*/ expandedPathScores->val(),                           // [dimBatch, 1, localBeamSize, dimVocab or dimShortlist]
-                   /*N=*/localBeamSize,
+      getNBestList(/*in*/ expandedPathScores->val(), // [dimBatch, 1, localBeamSize, dimVocab or dimShortlist]
+                   /*N=*/localBeamSize,              // desired beam size
                    /*out*/ nBestPathScores, /*out*/ nBestKeys,
-                   /*first=*/t == 0); // @TODO: Why is this passed? To know that the beam size is 1 for first step, for flattened hyp index?
+                   /*first=*/t == 0); // @TODO: this is only used for checking presently, and should be removed altogether
       // Now, nBestPathScores contain N-best expandedPathScores for each batch and beam,
       // and nBestKeys for each their original location (batchIdx, beamHypIdx, word).
 
       // combine N-best sets with existing search space (beams) to updated search space
       beams = toHyps(nBestKeys, nBestPathScores,
-                     /*dimTrgVoc=*/expandedPathScores->shape()[-1],
+                     /*inputBeamSize*/expandedPathScores->shape()[-2], // used for interpretation of keys
+                     /*vocabSize=*/expandedPathScores->shape()[-1],    // used for interpretation of keys
                      beams,
-                     states,           // used for keeping track of per-ensemble-member path score
-                     localBeamSize,    // used in the encoding of the (batchIdx, beamHypIdx, word) tuples
-                     /*first=*/t == 0, // used to indicate originating beamSize of 1
-                     batch);
+                     states,    // only used for keeping track of per-ensemble-member path score
+                     batch);    // only used for propagating alignment info
 
       // remove all hyps that end in EOS
       // The position of a hyp in the beam may change.
diff --git a/src/translator/nth_element.cpp b/src/translator/nth_element.cpp
index febf0739b..7d18555d8 100755
--- a/src/translator/nth_element.cpp
+++ b/src/translator/nth_element.cpp
@@ -56,7 +56,6 @@ class NthElementCPU {
   }
 
 public:
-  // @BUGBUG: This API mixes input and output beam size.
   void getNBestList(Tensor scores, // [dimBatch, 1, beamSize, dimVocab or dimShortlist]
                     size_t N,
                     std::vector<float>& outPathScores,
@@ -65,17 +64,24 @@ class NthElementCPU {
     const auto vocabSize = scores->shape()[-1];
     const auto inputN    = scores->shape()[-2];
     const auto dimBatch  = scores->shape()[-4];
-    ABORT_IF(inputN != (isFirst ? 1 : N), "Input tensor has wrong beam dim??");
+    ABORT_IF(inputN != (isFirst ? 1 : N), "Input tensor has wrong beam dim??"); // @TODO: Remove isFirst argument altogether
 
     std::vector<int> cumulativeBeamSizes(dimBatch + 1, 0);
     std::vector<int> batchFirstElementIdxs(dimBatch + 1, 0);
 
     for(int batchIdx = 0; batchIdx < dimBatch; ++batchIdx) {
+#if 1
+      cumulativeBeamSizes[batchIdx + 1] = (batchIdx + 1) * (int)N;
+      batchFirstElementIdxs[batchIdx + 1] += (batchIdx + 1) * inputN * vocabSize;
+      ABORT_IF(cumulativeBeamSizes[batchIdx + 1] != cumulativeBeamSizes[batchIdx] + (int)N, "cumulativeBeamSizes wrong??");
+      ABORT_IF((isFirst ? batchIdx + 1 : cumulativeBeamSizes[batchIdx + 1]) != (batchIdx + 1) * inputN, "inputN wrong??");
+#else
       cumulativeBeamSizes[batchIdx + 1] = cumulativeBeamSizes[batchIdx] + (int)N;
       ABORT_IF(cumulativeBeamSizes[batchIdx + 1] != (batchIdx + 1) * N, "cumulativeBeamSizes wrong??");
       batchFirstElementIdxs[batchIdx + 1]
           += (isFirst ? batchIdx + 1 : cumulativeBeamSizes[batchIdx + 1]) * vocabSize;
       ABORT_IF((isFirst ? batchIdx + 1 : cumulativeBeamSizes[batchIdx + 1]) != (batchIdx + 1) * inputN, "inputN wrong??");
+#endif
     }
     ABORT_IF(cumulativeBeamSizes.back() != dimBatch * N, "cumulativeBeamSizes.back() wrong??");
 
diff --git a/src/translator/nth_element.cu b/src/translator/nth_element.cu
index ecf9daf5c..b2f23d8c5 100755
--- a/src/translator/nth_element.cu
+++ b/src/translator/nth_element.cu
@@ -354,7 +354,6 @@ private:
   }
 
 public:
-  // @BUGBUG: This API mixes input and output beam size.
   void getNBestList(Tensor scores,
                     size_t N,
                     std::vector<float>& outCosts,
@@ -375,15 +374,22 @@ public:
     std::vector<int> batchFirstElementIdxs(beamSizes.size() + 1, 0);
 
     for(size_t batchIdx = 0; batchIdx < beamSizes.size(); ++batchIdx) {
+#if 1
+      cumulativeBeamSizes[batchIdx + 1] = (batchIdx + 1) * (int)N;
+      batchFirstElementIdxs[batchIdx + 1] += (batchIdx + 1) * inputN * vocabSize;
+      ABORT_IF(cumulativeBeamSizes[batchIdx + 1] != cumulativeBeamSizes[batchIdx] + (int)N, "cumulativeBeamSizes wrong??");
+      ABORT_IF((isFirst ? batchIdx + 1 : cumulativeBeamSizes[batchIdx + 1]) != (batchIdx + 1) * inputN, "inputN wrong??");
+#else
       cumulativeBeamSizes[batchIdx + 1] = cumulativeBeamSizes[batchIdx] + beamSizes[batchIdx];
       ABORT_IF(cumulativeBeamSizes[batchIdx + 1] != (batchIdx + 1) * N, "cumulativeBeamSizes wrong??");
       batchFirstElementIdxs[batchIdx + 1]
           += ((isFirst) ? (batchIdx + 1) : cumulativeBeamSizes[batchIdx + 1]) * vocabSize;
       ABORT_IF((isFirst ? batchIdx + 1 : cumulativeBeamSizes[batchIdx + 1]) != (batchIdx + 1) * inputN, "inputN wrong??");
+#endif
     }
 
     selectNBest(scores->data(), batchFirstElementIdxs, cumulativeBeamSizes);
-    getPairs(cumulativeBeamSizes.back(), outKeys, outCosts);
+    getPairs(dimBatch * N, outKeys, outCosts);
     ABORT_IF(cumulativeBeamSizes.back() != dimBatch * N, "cumulativeBeamSizes.back() wrong??");
   }
 
@@ -409,22 +415,22 @@ private:
       outValues.push_back(h_res[i]);
     }
 
-    lastN = number;
+    //lastN = number;
   }
 
-  void getValueByKey(std::vector<float>& out, float* d_in) {
-    cudaSetDevice(deviceId_.no);
-
-    gGetValueByKey<<<1, lastN, 0, /* stream_ */ 0>>>(
-        d_in, d_breakdown, h_res_idx, lastN);
-
-    CUDA_CHECK(cudaMemcpyAsync(out.data(),
-                               d_breakdown,
-                               lastN * sizeof(float),
-                               cudaMemcpyDeviceToHost,
-                               /* stream_ */ 0));
-    CUDA_CHECK(cudaStreamSynchronize(/* stream_ */ 0));
-  }
+  //void getValueByKey(std::vector<float>& out, float* d_in) {
+  //  cudaSetDevice(deviceId_.no);
+  //
+  //  gGetValueByKey<<<1, lastN, 0, /* stream_ */ 0>>>(
+  //      d_in, d_breakdown, h_res_idx, lastN);
+  //
+  //  CUDA_CHECK(cudaMemcpyAsync(out.data(),
+  //                             d_breakdown,
+  //                             lastN * sizeof(float),
+  //                             cudaMemcpyDeviceToHost,
+  //                             /* stream_ */ 0));
+  //  CUDA_CHECK(cudaStreamSynchronize(/* stream_ */ 0));
+  //}
 
   DeviceId deviceId_;
 
@@ -447,7 +453,7 @@ private:
   float* d_breakdown;
   int* d_batchPosition;
   int* d_cumBeamSizes;
-  size_t lastN;
+  //size_t lastN;
 };
 
 // factory function

From ceab6036b1227f01f589c379859bc75ee9acbbc8 Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Tue, 12 Feb 2019 17:01:51 -0800
Subject: [PATCH 340/838] fixed a merge issue

---
 src/translator/beam_search.h | 59 +++++++++++-------------------------
 1 file changed, 17 insertions(+), 42 deletions(-)

diff --git a/src/translator/beam_search.h b/src/translator/beam_search.h
index b752538ae..fb762d70b 100755
--- a/src/translator/beam_search.h
+++ b/src/translator/beam_search.h
@@ -41,8 +41,6 @@ class BeamSearch {
                const size_t vocabSize,     // ditto.
                const Beams& beams,
                const std::vector<Ptr<ScorerState /*const*/>>& states,
-               const size_t beamSize,
-               const bool first,
                Ptr<data::CorpusBatch /*const*/> batch, // for alignments only
                Ptr<FactoredVocab/*const*/> factoredVocab, size_t factorGroup) const {
     std::vector<float> align;
@@ -60,22 +58,18 @@ class BeamSearch {
 
       // decompose key into individual indices (batchIdx, beamHypIdx, wordIdx)
       const auto wordIdx    = (WordIndex)(key % vocabSize);
-      const auto beamHypIdx =            (key / vocabSize) % (first ? 1 : beamSize);
-      const auto batchIdx   =            (key / vocabSize) / (first ? 1 : beamSize);
+      const auto beamHypIdx =            (key / vocabSize) % inputBeamSize;
+      const auto batchIdx   =            (key / vocabSize) / inputBeamSize;
       //LOG(info, "key = (batch {}, beam {}, word {})", batchIdx, beamHypIdx, wordIdx);
 
-      ABORT_IF(i / beamSize != batchIdx, "Inconsistent batchIdx value in key??");
-
       const auto& beam = beams[batchIdx];
       auto& newBeam = newBeams[batchIdx];
 
-      if (newBeam.size() >= beam.size()) // @TODO: Why this condition? It does happen. Why?
+      if (newBeam.size() >= beam.size()) // getNBestList() generates N for all batch entries incl. those that already have a narrower beam
         continue;
-
       if (pathScore <= INVALID_PATH_SCORE) // (dummy slot or word that cannot be expanded by current factor)
         continue;
-      //if (beamHypIdx >= (int)beam.size() && pathScore <= INVALID_PATH_SCORE) // (dummy slot)
-      //  continue;
+
       ABORT_IF(beamHypIdx >= (int)beam.size(), "Out of bounds beamHypIdx value {} in key?? word={}, batch={}, pathScore={}", beamHypIdx, wordIdx, batchIdx, pathScore);
 
       // map wordIdx to word
@@ -99,12 +93,7 @@ class BeamSearch {
           //    factoredVocab->word2string(beam[beamHypIdx]->getWord()), factorGroup, wordIdx);
           word = beam[beamHypIdx]->getWord();
           ABORT_IF(!factoredVocab->canExpandFactoredWord(word, factorGroup), "A word without this factor snuck through to here??");
-          //if (factoredVocab->canExpandFactoredWord(word, factorGroup))
-            word = factoredVocab->expandFactoredWord(word, factorGroup, wordIdx);
-          // @TODO: maybe factor the two above into a single function; for now, I want the extra checks
-          //else
-          //  continue; // skip if word does not have this factor
-            //ABORT_IF(prevHyp->getPathScore() != pathScore, "Score changed despite factor not applicable??");
+          word = factoredVocab->expandFactoredWord(word, factorGroup, wordIdx);
           prevBeamHypIdx = prevHyp->getPrevStateIndex();
           prevHyp = prevHyp->getPrevHyp(); // short-circuit the backpointer, so that the traceback doesnot contain partially factored words
         }
@@ -147,13 +136,13 @@ class BeamSearch {
           //LOG(info, "Forwarded {}", factoredVocab->word2string(word));
           newBeam.push_back(beamHyp);
         }
-        if (newBeam.size() > beamSize) {
+        if (newBeam.size() > beam.size()) {
           //LOG(info, "Size {}, sorting...", newBeam.size());
-          std::nth_element(newBeam.begin(), newBeam.begin() + beamSize, newBeam.end(), [](Ptr<Hypothesis> a, Ptr<Hypothesis> b) {
+          std::nth_element(newBeam.begin(), newBeam.begin() + beam.size(), newBeam.end(), [](Ptr<Hypothesis> a, Ptr<Hypothesis> b) {
             return a->getPathScore() > b->getPathScore(); // (sort highest score first)
           });
           //LOG(info, "Size {}, sorted...", newBeam.size());
-          newBeam.resize(beamSize); // @TODO: needed?
+          newBeam.resize(beam.size());
         }
       }
     }
@@ -248,9 +237,6 @@ class BeamSearch {
     }
 
     Beams beams(dimBatch, Beam(beamSize_, New<Hypothesis>())); // array [dimBatch] of array [localBeamSize] of Hypothesis
-    //Beams beams(dimBatch); // array [dimBatch] of array [localBeamSize] of Hypothesis
-    //for(auto& beam : beams)
-    //  beam.resize(beamSize_, New<Hypothesis>());
 
     for(int i = 0; i < dimBatch; ++i)
       histories[i]->add(beams[i], trgEosId_);
@@ -282,10 +268,8 @@ class BeamSearch {
         break;
 
       for (size_t factorGroup = 0; factorGroup < numFactorGroups; factorGroup++) {
-      // BEGIN FOR factorGroup = 0 .. numFactorGroups-1
-      // @TODO: use an explicit nested loop here for factors
+      // Note: not indenting the block, for easier merging
       // for factored vocabs, we do one factor at a time, but without updating the scorer for secondary factors
-      //auto factorGroup = t % numFactorGroups;
 
       //**********************************************************************
       // create constant containing previous path scores for current beam
@@ -294,12 +278,11 @@ class BeamSearch {
       std::vector<Word> prevWords;       // [localBeamsize, 1, dimBatch, 1] (flattened) word that a hyp ended in, for advancing the decoder-model's history
       Expr prevPathScores;               // [localBeamSize, 1, dimBatch, 1], path score that a hyp ended in (last axis will broadcast into vocab size when adding expandedPathScores)
       bool anyCanExpand = false; // stays false if all hyps are invalid factor expansions
-      std::vector<float> prevScores; // @TODO: remove here again
       if(t == 0 && factorGroup == 0) { // no scores yet
         prevPathScores = graph->constant({1, 1, 1, 1}, inits::from_value(0));
         anyCanExpand = true;
       } else {
-        //std::vector<float> prevScores;
+        std::vector<float> prevScores;
         for(size_t beamHypIdx = 0; beamHypIdx < localBeamSize; ++beamHypIdx) {
           for(int batchIdx = 0; batchIdx < dimBatch; ++batchIdx) { // loop over batch entries (active sentences)
             auto& beam = beams[batchIdx];
@@ -351,21 +334,13 @@ class BeamSearch {
           // add secondary factors
           // For those, we don't update the decoder-model state in any way.
           // Instead, we just keep expanding with the factors.
-          // Considerations:
-          //  - not all scores should get a factor
-          //    We need a [localBeamSize, 1, dimBatch, 1] tensor that knows whether a factor is applicable
-          //    by considering the lemma at each (beamHypIdx, batchIdx). prevWords is already in the right order.
-          //  - factors are incorporated one step at a time; so we will have temporary Word entries
-          //    in hyps with some factors set to FACTOR_NOT_SPECIFIED.
+          // We will have temporary Word entries in hyps with some factors set to FACTOR_NOT_SPECIFIED.
+          // For some lemmas, a factor is not applicable. For those, the factor score is the same (zero)
+          // for all factor values. This would thus unnecessarily pollute the beam with identical copies,
+          // and push out other hypotheses. Hence, we exclude those here by setting the path score to
+          // INVALID_PATH_SCORE. Instead, toHyps() explicitly propagates those hyps by simply copying the
+          // previous hypothesis.
           logProbs = states[i]->getLogProbs().getFactoredLogits(factorGroup, hypIndices, localBeamSize); // [localBeamSize, 1, dimBatch, dimVocab]
-          // Note: pathScores was set to INVALID_PATH_SCORE if this path cannot expanded by this factor; toHyps() handles that special case on the side
-          //for (size_t kk = 0; kk < prevWords.size(); kk++)
-          //  LOG(info, "prevWords[{},{}]={} -> {}", t/numFactorGroups, factorGroup, factoredVocab->word2string(prevWords[kk]), prevScores[kk]);
-          //auto factorMaskVector = states[i]->getLogProbs().getFactorMasks(prevWords, factorGroup);
-          //for (auto& m : factorMaskVector)
-          //  m = m ? 0.f : INVALID_PATH_SCORE; // block hyps that do not have the factor; these are short-circuited directly
-          //auto logFactorMasks = graph->constant({(int)localBeamSize, 1, dimBatch, 1}, inits::from_vector(factorMaskVector));
-          //logProbs = logProbs + logFactorMasks; // those hyps that don't have a factor get multiplied with 0
         }
         // expand all hypotheses, [localBeamSize, 1, dimBatch, 1] -> [localBeamSize, 1, dimBatch, dimVocab]
         expandedPathScores = expandedPathScores + scorers_[i]->getWeight() * logProbs;
@@ -407,8 +382,8 @@ class BeamSearch {
                      beams,
                      states,    // used for keeping track of per-ensemble-member path score
                      batch,     // only used for propagating alignment info
-
                      factoredVocab, factorGroup);
+
       } // END FOR factorGroup = 0 .. numFactorGroups-1
 
       // remove all hyps that end in EOS

From febe660bb9c57e9e6ae9327f96dddcf1f4c23e3c Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Fri, 15 Feb 2019 18:19:12 -0800
Subject: [PATCH 341/838] SubBatch() now fills gaps with EOS, not Word::ZERO;
 FactoredVocab::csr_rows() now takes Words and directly breaks each out Word's
 factors without needing the factorMap_[]

---
 src/data/corpus_base.h      |  2 +-
 src/data/factored_vocab.cpp | 45 ++++++++++++++++++++++++++++---------
 src/data/factored_vocab.h   |  2 +-
 src/layers/generic.cpp      | 15 +++++++------
 src/layers/generic.h        |  2 +-
 5 files changed, 46 insertions(+), 20 deletions(-)
 mode change 100644 => 100755 src/data/corpus_base.h

diff --git a/src/data/corpus_base.h b/src/data/corpus_base.h
old mode 100644
new mode 100755
index 5034c3404..f47de0bb4
--- a/src/data/corpus_base.h
+++ b/src/data/corpus_base.h
@@ -128,7 +128,7 @@ class SubBatch {
    * @param width Number of words in the longest sentence
    */
   SubBatch(size_t size, size_t width, const Ptr<Vocab>& vocab)
-      : indices_(size * width, Word::ZERO), // note: for gaps, we must use a valid index
+      : indices_(size * width, vocab ? vocab->getEosId() : Word::ZERO), // note: for gaps, we must use a valid index
         mask_(size * width, 0),
         size_(size),
         width_(width),
diff --git a/src/data/factored_vocab.cpp b/src/data/factored_vocab.cpp
index ea076af6a..d7ccb892f 100755
--- a/src/data/factored_vocab.cpp
+++ b/src/data/factored_vocab.cpp
@@ -48,6 +48,7 @@ namespace marian {
     // parse the line, of the form WORD FACTOR1 FACTOR2 FACTOR1 ...
     // where FACTOR1 is the lemma, a factor that all words have.
     // Not every word has all other factors, so the n-th item is not always in the same factor group.
+    // @TODO: change to just use the .wl file, and manually split at @
     utils::splitAny(line, tokens, " \t");
     ABORT_IF(tokens.size() < 2, "Factor map must have at least one factor per word", mapPath);
     std::vector<WordIndex> factorUnits;
@@ -270,7 +271,7 @@ std::pair<WordIndex, bool> FactoredVocab::getFactorUnit(Word word, size_t groupI
 void FactoredVocab::constructNormalizationInfoForVocab() {
   // create mappings needed for normalization in factored outputs
   //size_t numGroups = groupPrefixes_.size();
-  size_t vocabSize = vocab_.size();
+  size_t vocabSize = size();
   //factorMasks_  .resize(numGroups, std::vector<float>(vocabSize, 0));     // [g][v] 1.0 if word v has factor g
   //factorIndices_.resize(numGroups, std::vector<IndexType>(vocabSize, 0)); // [g][v] index of factor (or any valid index if it does not have it; we use 0)
   gapLogMask_.resize(vocabSize, -1e8f);
@@ -292,8 +293,10 @@ void FactoredVocab::constructNormalizationInfoForVocab() {
   //}
 
   // create the global factor matrix, which is used for getLogits() only
-  std::vector<IndexType> data(vocabSize);
-  std::iota(data.begin(), data.end(), 0);
+  // For invalid words, this leaves empty matrix rows, which are later masked by adding gapLogMask.
+  Words data;
+  for (size_t v = 0; v < vocabSize; v++) // note: this loops over the entire vocab space, incl. gaps
+      data.push_back(Word::fromWordIndex(v));
   globalFactorMatrix_ = csr_rows(data); // [V x U]
 }
 
@@ -342,9 +345,10 @@ void FactoredVocab::constructNormalizationInfoForVocab() {
   return utils::join(decoded, " ");
 }
 
-// create a CSR matrix M[V,U] from indices[] with
+// create a CSR matrix M[V,U] from words[] with
 // M[v,u] = 1/c(u) if factor u is a factor of word v, and c(u) is how often u is referenced
-FactoredVocab::CSRData FactoredVocab::csr_rows(const std::vector<IndexType>& words) const {
+FactoredVocab::CSRData FactoredVocab::csr_rows(const Words& words) const {
+  auto numGroups = getNumGroups();
   std::vector<float> weights;
   std::vector<IndexType> indices;
   std::vector<IndexType> offsets;
@@ -352,11 +356,32 @@ FactoredVocab::CSRData FactoredVocab::csr_rows(const std::vector<IndexType>& wor
   indices.reserve(words.size()); // (at least this many)
   // loop over all input words, and select the corresponding set of unit indices into CSR format
   offsets.push_back((IndexType)indices.size());
-  for (auto w : words) {
-    const auto& m = factorMap_[w];
-    for (auto u : m) {
-      indices.push_back(u);
-      weights.push_back(1.0f/*/(float)factorRefCounts_[u]*/);
+  std::vector<size_t> factorIndices;
+  for (auto word : words) {
+    if (!vocab_.isGap(word.toWordIndex())) { // skip invalid combinations in the space (can only happen during initialization)  --@TODO: add a check?
+      word2factors(word, factorIndices);
+#if 0 // original code; enable this to try
+      numGroups;
+      const auto& m = factorMap_[word.toWordIndex()];
+      for (auto u : m) {
+        indices.push_back(u);
+#else
+#if 1 // special handling of the missing single capitalized letters
+      // @TODO: remove this once we use the factor-spec file
+      const auto& lemma = factorVocab_[(WordIndex)(factorIndices[0] + groupRanges_[0].first)];
+      if (lemma.size() == 1 && factorIndices[1]/*@C*/ == 1/*@CI*/) // skip one-letter factors with 
+          LOG(info, "Suppressing embedding for word {}", word2string(word));
+      else
+#endif
+      for (size_t g = 0; g < numGroups; g++) { // @TODO: make this faster by having a list of all factors to consider for a lemma?
+        auto factorIndex = factorIndices[g];
+        ABORT_IF(factorIndex == FACTOR_NOT_SPECIFIED, "Attempted to embed a word with a factor not specified");
+        if (factorIndex == FACTOR_NOT_APPLICABLE)
+          continue;
+        indices.push_back((IndexType)(factorIndex + groupRanges_[g].first)); // map to unit index
+#endif
+        weights.push_back(1.0f/*/(float)factorRefCounts_[u]*/);
+      }
     }
     offsets.push_back((IndexType)indices.size()); // next matrix row begins at this offset
   }
diff --git a/src/data/factored_vocab.h b/src/data/factored_vocab.h
index 3a562fadb..7b0e84d83 100755
--- a/src/data/factored_vocab.h
+++ b/src/data/factored_vocab.h
@@ -40,7 +40,7 @@ class FactoredVocab : public IVocab {
   // factor-specific. These methods are consumed by Output and Embedding.
   size_t factorVocabSize() const { return factorVocab_.size(); }
 
-  CSRData csr_rows(const std::vector<IndexType>& words) const;
+  CSRData csr_rows(const Words& words) const;
 
   const CSRData& getGlobalFactorMatrix() const { return globalFactorMatrix_; }   // [v,u] (sparse) -> =1 if u is factor of v  --only used in getLogits()
   size_t getNumGroups() const { return groupRanges_.size(); }
diff --git a/src/layers/generic.cpp b/src/layers/generic.cpp
index df06398b0..488747814 100755
--- a/src/layers/generic.cpp
+++ b/src/layers/generic.cpp
@@ -289,7 +289,7 @@ namespace marian {
   }
 
   // helper to embed a sequence of words (given as indices) via factored embeddings
-  /*private*/ Expr Embedding::multiRows(const std::vector<IndexType>& data) const
+  /*private*/ Expr Embedding::multiRows(const Words& data) const
   {
     auto graph = E_->graph();
     auto factoredData = factoredVocab_->csr_rows(data);
@@ -354,15 +354,16 @@ namespace marian {
   }
 
   Expr Embedding::apply(const Words& words, const Shape& shape) const /*override final*/ {
-    return applyIndices(toWordIndexVector(words), shape);
-  }
-
-  Expr Embedding::applyIndices(const std::vector<WordIndex>& embIdx, const Shape& shape) const /*override final*/ {
     Expr selectedEmbs;
     if (factoredVocab_)
-      selectedEmbs = multiRows(embIdx);
+      selectedEmbs = multiRows(words);
     else
-      selectedEmbs = rows(E_, embIdx);
+      selectedEmbs = rows(E_, toWordIndexVector(words));
     return reshape(selectedEmbs, shape);
   }
+
+  Expr Embedding::applyIndices(const std::vector<WordIndex>& embIdx, const Shape& shape) const /*override final*/ {
+    ABORT_IF(factoredVocab_ /*&& factoredVocab_->getNumGroups() > 1*/, "Embedding: applyIndices must not be used with a factored vocabulary");
+    return reshape(rows(E_, embIdx), shape);
+  }
 }  // namespace marian
diff --git a/src/layers/generic.h b/src/layers/generic.h
index 532324641..1ce773dcb 100755
--- a/src/layers/generic.h
+++ b/src/layers/generic.h
@@ -239,7 +239,7 @@ class Output : public LayerBase, public IUnaryLogitLayer {
 class Embedding : public LayerBase, public IEmbeddingLayer {
   Expr E_;
   Ptr<FactoredVocab> factoredVocab_;
-  Expr multiRows(const std::vector<IndexType>& data) const;
+  Expr multiRows(const Words& data) const;
 public:
   Embedding(Ptr<ExpressionGraph> graph, Ptr<Options> options);
 

From 1f4dc6e608af91e1225bfb66f668cf56d721a48d Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Fri, 15 Feb 2019 18:35:37 -0800
Subject: [PATCH 342/838] (refined an error msg)

---
 src/translator/nth_element.cu | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/src/translator/nth_element.cu b/src/translator/nth_element.cu
index 23fa2ff95..fb3d86095 100755
--- a/src/translator/nth_element.cu
+++ b/src/translator/nth_element.cu
@@ -365,9 +365,9 @@ public:
     const auto inputN    = scores->shape()[-2];
     const auto dimBatch  = scores->shape()[-4];
     ABORT_IF(inputN != (isFirst ? 1 : N), "Input tensor has wrong beam dim??"); // @TODO: Remove isFirst argument altogether
-    ABORT_IF(vocabSize > MAX_VOCAB_SIZE, "GetNBestList(): actual vocab size exceeds MAX_VOCAB_SIZE");
-    ABORT_IF(dimBatch > maxBatchSize_, "GetNBestList(): actual batch size exceeds initialization parameter");
-    ABORT_IF(N > maxBeamSize_, "GetNBestList(): actual beam size exceeds initialization parameter"); // @TODO: or inputN?
+    ABORT_IF(vocabSize > MAX_VOCAB_SIZE, "GetNBestList(): actual vocab size {} exceeds MAX_VOCAB_SIZE of {}", vocabSize, MAX_VOCAB_SIZE);
+    ABORT_IF(dimBatch > maxBatchSize_, "GetNBestList(): actual batch size {} exceeds initialization parameter {}", dimBatch, maxBatchSize_);
+    ABORT_IF(N > maxBeamSize_, "GetNBestList(): actual beam size {} exceeds initialization parameter {}", N, maxBeamSize_); // @TODO: or inputN?
 
     const std::vector<size_t> beamSizes(dimBatch, N);
     std::vector<int> cumulativeBeamSizes(beamSizes.size() + 1, 0);

From a93de15370e84864d60d0b7153e402b464314a03 Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Fri, 15 Feb 2019 19:40:54 -0800
Subject: [PATCH 343/838] now explicitly unrolls all factor combinations; had
 to temporarily disable a few checks for this

---
 src/data/factored_vocab.cpp | 59 +++++++++++++++++++++++++++----------
 src/data/factored_vocab.h   |  5 ++--
 2 files changed, 45 insertions(+), 19 deletions(-)

diff --git a/src/data/factored_vocab.cpp b/src/data/factored_vocab.cpp
index d7ccb892f..6b521332d 100755
--- a/src/data/factored_vocab.cpp
+++ b/src/data/factored_vocab.cpp
@@ -32,13 +32,13 @@ namespace marian {
   // construct mapping tables for factors
   constructGroupInfoFromFactorVocab();
   constructFactorIndexConversion();
+  auto numGroups = getNumGroups();
 
   // load and parse factorMap
-  auto elements = factorShape_.elements();
-  vocab_.resize(elements);
-  factorMap_.resize(elements);
+  auto vocabSize = factorShape_.elements(); // size of vocab space including gaps
+  vocab_.resize(vocabSize);
+  //factorMap_.resize(vocabSize);
   auto factorVocabSize = factorVocab_.size();
-  factorRefCounts_.resize(factorVocabSize);
   lemmaHasFactorGroup_.resize(groupRanges_[0].second - groupRanges_[0].first);
   std::vector<std::string> tokens;
   std::string line;
@@ -55,7 +55,6 @@ namespace marian {
     for (size_t i = 1/*first factor*/; i < tokens.size(); i++) {
       auto u = factorVocab_[tokens[i]];
       factorUnits.push_back(u);
-      factorRefCounts_[u]++;
     }
     // convert to fully unrolled factors representation
     std::vector<size_t> factorIndices(groupRanges_.size(), FACTOR_NOT_APPLICABLE); // default for unused factors
@@ -74,7 +73,7 @@ namespace marian {
     // map factors to non-dense integer
     auto word = factors2word(factorIndices);
     auto wordIndex = word.toWordIndex();
-    factorMap_[wordIndex] = std::move(factorUnits);
+    //factorMap_[wordIndex] = std::move(factorUnits);
     // add to vocab (the wordIndex are not dense, so the vocab will have holes)
     vocab_.add(tokens.front(), wordIndex);
     numTotalFactors += tokens.size() - 1;
@@ -84,6 +83,24 @@ namespace marian {
   LOG(info, "[embedding] Factored-embedding map read with total/unique of {}/{} factors for {} valid words (in space of {})",
       numTotalFactors, factorVocabSize, vocab_.numValid(), size());
 
+  // enumerate all combinations of factors for each lemma
+  // @TODO: switch to factor-spec, which no longer enumerates all combinations. Then don't set vocab string here.
+  for (size_t v = 0; v < vocabSize; v++) {
+    auto word = Word::fromWordIndex(v);
+    bool isValid = true;
+    for (size_t g = 0; isValid && g < numGroups; g++) {
+      auto factorIndex = getFactor(word, g);
+      // @TODO: we have a hack in getFactor() to return not-specified if factor is specified but not applicable, making it invalid
+      isValid = factorIndex != FACTOR_NOT_SPECIFIED; // FACTOR_NOT_APPLICABLE is a valid value
+    }
+    if (isValid != !vocab_.isGap((WordIndex)v))
+    {
+      //LOG(info, "WARNING: Factored vocab mismatch for {}: isValid={}, isGap={}", word2string(word), isValid, vocab_.isGap((WordIndex)v));
+      if (isValid) // add the missing word (albeit with a poor grapheme)
+        (*this)[word];
+    }
+  }
+
   // create mappings needed for normalization in factored outputs
   constructNormalizationInfoForVocab();
 
@@ -96,7 +113,8 @@ namespace marian {
   if (maxSizeUnused == vocab_.numValid())
     maxSizeUnused = vocab_.size();
 #endif
-  ABORT_IF(maxSizeUnused != 0 && maxSizeUnused != size(), "Factored vocabulary does not allow on-the-fly clipping to a maximum vocab size (from {} to {})", size(), maxSizeUnused);
+  //ABORT_IF(maxSizeUnused != 0 && maxSizeUnused != size(), "Factored vocabulary does not allow on-the-fly clipping to a maximum vocab size (from {} to {})", size(), maxSizeUnused);
+  // @TODO: ^^ disabled now that we are generating the full combination of factors; reenable once we have consistent setups again
   return size();
 }
 
@@ -258,15 +276,18 @@ size_t FactoredVocab::getFactor(Word word, size_t groupIndex) const {
   }
   else { // regular value: consistency check if lemma really has this factor group
     ABORT_IF(factor0Index == (size_t)factorShape_[0] - 1, "Word has specified factor but no lemma??");
-    ABORT_IF(!lemmaHasFactorGroup(factor0Index, groupIndex), "Word has a specified factor for a lemma that does not have that factor group??");
+    //ABORT_IF(!lemmaHasFactorGroup(factor0Index, groupIndex), "Word has a specified factor for a lemma that does not have that factor group??");
+    if (!lemmaHasFactorGroup(factor0Index, groupIndex))
+      index = FACTOR_NOT_SPECIFIED;
+    // @TODO: ^^ needed for determining all valid vocab entries; can we pass a flag in to allow this?
   }
   return index;
 }
 
-std::pair<WordIndex, bool> FactoredVocab::getFactorUnit(Word word, size_t groupIndex) const {
-  word; groupIndex;
-  ABORT("Not implemented");
-}
+//std::pair<WordIndex, bool> FactoredVocab::getFactorUnit(Word word, size_t groupIndex) const {
+//  word; groupIndex;
+//  ABORT("Not implemented");
+//}
 
 void FactoredVocab::constructNormalizationInfoForVocab() {
   // create mappings needed for normalization in factored outputs
@@ -276,6 +297,10 @@ void FactoredVocab::constructNormalizationInfoForVocab() {
   //factorIndices_.resize(numGroups, std::vector<IndexType>(vocabSize, 0)); // [g][v] index of factor (or any valid index if it does not have it; we use 0)
   gapLogMask_.resize(vocabSize, -1e8f);
   for (WordIndex v = 0; v < vocabSize; v++) {
+#if 1 // @TODO: TEST THIS again by disabling factored decoding in beam_search.h
+    if (!vocab_.isGap(v))
+      gapLogMask_[v] = 0.0f; // valid entry
+#else
     for (auto u : factorMap_[v]) {
       auto g = factorGroups_[u]; // convert u to relative u within factor group range
       ABORT_IF(u < groupRanges_[g].first || u >= groupRanges_[g].second, "Invalid factorGroups_ entry??");
@@ -283,6 +308,7 @@ void FactoredVocab::constructNormalizationInfoForVocab() {
       //factorMasks_[g][v] = 1.0f;
       gapLogMask_[v] = 0.0f; // valid entry
     }
+#endif
   }
   //for (Word v = 0; v < vocabSize; v++) {
   //  LOG(info, "'{}': {}*{} {}*{} {}*{} {}*{}", vocab[v],
@@ -296,7 +322,7 @@ void FactoredVocab::constructNormalizationInfoForVocab() {
   // For invalid words, this leaves empty matrix rows, which are later masked by adding gapLogMask.
   Words data;
   for (size_t v = 0; v < vocabSize; v++) // note: this loops over the entire vocab space, incl. gaps
-      data.push_back(Word::fromWordIndex(v));
+    data.push_back(Word::fromWordIndex(v));
   globalFactorMatrix_ = csr_rows(data); // [V x U]
 }
 
@@ -313,7 +339,7 @@ void FactoredVocab::constructNormalizationInfoForVocab() {
   //LOG(info, "Looking up Word {}={}", word.toWordIndex(), word2string(word));
 #if 1 // @BUGBUG: our manually prepared dict does not contain @CI tags for single letters, but it's a valid factor
   if (vocab_.isGap(word.toWordIndex())) {
-    LOG(info, "Factor combination {} missing in external dict, generating fake entry", word2string(word));
+    LOG_ONCE(info, "Factor combination {} missing in external dict, generating fake entry (only showing this warning once)", word2string(word));
     const_cast<WordLUT&>(vocab_).add("??" + word2string(word), word.toWordIndex());
   }
 #endif
@@ -367,10 +393,11 @@ FactoredVocab::CSRData FactoredVocab::csr_rows(const Words& words) const {
         indices.push_back(u);
 #else
 #if 1 // special handling of the missing single capitalized letters
+      // costs about 0.1 BLEU when original model never saw this combination (which is quite nicely low)
       // @TODO: remove this once we use the factor-spec file
       const auto& lemma = factorVocab_[(WordIndex)(factorIndices[0] + groupRanges_[0].first)];
       if (lemma.size() == 1 && factorIndices[1]/*@C*/ == 1/*@CI*/) // skip one-letter factors with 
-          LOG(info, "Suppressing embedding for word {}", word2string(word));
+          LOG_ONCE(info, "Suppressing embedding for word {} (only showing this warning once)", word2string(word));
       else
 #endif
       for (size_t g = 0; g < numGroups; g++) { // @TODO: make this faster by having a list of all factors to consider for a lemma?
@@ -380,7 +407,7 @@ FactoredVocab::CSRData FactoredVocab::csr_rows(const Words& words) const {
           continue;
         indices.push_back((IndexType)(factorIndex + groupRanges_[g].first)); // map to unit index
 #endif
-        weights.push_back(1.0f/*/(float)factorRefCounts_[u]*/);
+        weights.push_back(1.0f);
       }
     }
     offsets.push_back((IndexType)indices.size()); // next matrix row begins at this offset
diff --git a/src/data/factored_vocab.h b/src/data/factored_vocab.h
index 7b0e84d83..b17b184ae 100755
--- a/src/data/factored_vocab.h
+++ b/src/data/factored_vocab.h
@@ -56,7 +56,7 @@ class FactoredVocab : public IVocab {
   Word expandFactoredWord(Word word, size_t groupIndex, size_t factorIndex) const;
   bool canExpandFactoredWord(Word word, size_t groupIndex) const { return lemmaHasFactorGroup(getFactor(word, 0), groupIndex); }
   size_t getFactor(Word word, size_t groupIndex) const;
-  std::pair<WordIndex, bool> getFactorUnit(Word word, size_t groupIndex) const;
+  //std::pair<WordIndex, bool> getFactorUnit(Word word, size_t groupIndex) const;
   bool lemmaHasFactorGroup(size_t factor0Index, size_t g) const { return lemmaHasFactorGroup_[factor0Index][g]; }
 
   static constexpr size_t FACTOR_NOT_APPLICABLE = (SIZE_MAX - 1);
@@ -94,8 +94,7 @@ class FactoredVocab : public IVocab {
   // factors
   WordLUT factorVocab_;                                // [factor name] -> factor index = row of E_
   std::vector<std::string> groupPrefixes_;             // [group id g] shared prefix of factors (used for grouping)
-  std::vector<std::vector<WordIndex>> factorMap_;      // [word index v] -> set of factor indices u
-  std::vector<int> factorRefCounts_;                   // [factor index u] -> how often factor u is referenced in factorMap_
+  //std::vector<std::vector<WordIndex>> factorMap_;      // [word index v] -> set of factor indices u
   CSRData globalFactorMatrix_;                         // [v,u] (sparse) -> =1 if u is factor of v
   std::vector<size_t> factorGroups_;                   // [u] -> group id of factor u
   std::vector<std::pair<size_t, size_t>> groupRanges_; // [group id g] -> (u_begin,u_end) index range of factors u for this group. These don't overlap.

From 76d280580ed251cfa1e3f6ebbc9e2236b4911359 Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Fri, 15 Feb 2019 20:07:26 -0800
Subject: [PATCH 344/838] added a message if new units are added by expanding
 all factors

---
 src/data/factored_vocab.cpp | 11 ++++++++---
 1 file changed, 8 insertions(+), 3 deletions(-)

diff --git a/src/data/factored_vocab.cpp b/src/data/factored_vocab.cpp
index 6b521332d..89d92ff0a 100755
--- a/src/data/factored_vocab.cpp
+++ b/src/data/factored_vocab.cpp
@@ -85,6 +85,7 @@ namespace marian {
 
   // enumerate all combinations of factors for each lemma
   // @TODO: switch to factor-spec, which no longer enumerates all combinations. Then don't set vocab string here.
+  size_t numMissing = 0;
   for (size_t v = 0; v < vocabSize; v++) {
     auto word = Word::fromWordIndex(v);
     bool isValid = true;
@@ -93,13 +94,17 @@ namespace marian {
       // @TODO: we have a hack in getFactor() to return not-specified if factor is specified but not applicable, making it invalid
       isValid = factorIndex != FACTOR_NOT_SPECIFIED; // FACTOR_NOT_APPLICABLE is a valid value
     }
-    if (isValid != !vocab_.isGap((WordIndex)v))
-    {
+    if (isValid != !vocab_.isGap((WordIndex)v)) {
       //LOG(info, "WARNING: Factored vocab mismatch for {}: isValid={}, isGap={}", word2string(word), isValid, vocab_.isGap((WordIndex)v));
-      if (isValid) // add the missing word (albeit with a poor grapheme)
+      if (isValid) { // add the missing word (albeit with a poor grapheme)
         (*this)[word];
+        // @TODO: ^^disabled to test getLogits(), which no longer works with this enabled (I guess since model has not seen the new units)
+        numMissing++;
+      }
     }
   }
+  if (numMissing > 0)
+    LOG(info, "[embedding] completed {} factor combinations missing from the original vocab file", numMissing);
 
   // create mappings needed for normalization in factored outputs
   constructNormalizationInfoForVocab();

From 63214994d3e30b5b7f79de67ae48f628df16fda2 Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Sat, 16 Feb 2019 12:55:52 -0800
Subject: [PATCH 345/838] changed the canonical form of factored words to match
 the FactoredSegmenter tool

---
 src/data/factored_vocab.cpp | 16 +++++++++++-----
 1 file changed, 11 insertions(+), 5 deletions(-)

diff --git a/src/data/factored_vocab.cpp b/src/data/factored_vocab.cpp
index 89d92ff0a..d9fc22989 100755
--- a/src/data/factored_vocab.cpp
+++ b/src/data/factored_vocab.cpp
@@ -75,7 +75,11 @@ namespace marian {
     auto wordIndex = word.toWordIndex();
     //factorMap_[wordIndex] = std::move(factorUnits);
     // add to vocab (the wordIndex are not dense, so the vocab will have holes)
+    //if (tokens.front().front() == '<') // all others are auto-expanded
+    // for now add what we get, and then expand more below
     vocab_.add(tokens.front(), wordIndex);
+    if (tokens.front() != word2string(word))
+      LOG_ONCE(info, "[embedding] Word name in .wl file {} differs from canonical form {} (this warning is only shown once)", tokens.front(), word2string(word));
     numTotalFactors += tokens.size() - 1;
     if (v % 5000 == 0)
       LOG(info, "{} -> {}", tokens.front(), word2string(word));
@@ -246,7 +250,7 @@ std::string FactoredVocab::word2string(Word word) const {
   size_t factor0Index = word.toWordIndex() / factorStrides_[0];
   std::string res;
   for (size_t g = 0; g < numGroups; g++) {
-    res.append(res.empty() ? "(" : ", ");
+    //res.append(res.empty() ? "(" : ", ");
     size_t index = word.toWordIndex();
     index = index / factorStrides_[g];
     index = index % (size_t)factorShape_[g];
@@ -255,13 +259,14 @@ std::string FactoredVocab::word2string(Word word) const {
         res.append("(lemma oob)");
       else if (lemmaHasFactorGroup(factor0Index, g))
         res.append("?");
-      else
-        res.append("n/a");
+      //else
+      //  res.append("n/a");
     }
     else
       res.append(factorVocab_[(WordIndex)(index + groupRanges_[g].first)]);
   }
-  return res + ")";
+  //res.append(")");
+  return res;
 }
 
 size_t FactoredVocab::getFactor(Word word, size_t groupIndex) const {
@@ -345,7 +350,8 @@ void FactoredVocab::constructNormalizationInfoForVocab() {
 #if 1 // @BUGBUG: our manually prepared dict does not contain @CI tags for single letters, but it's a valid factor
   if (vocab_.isGap(word.toWordIndex())) {
     LOG_ONCE(info, "Factor combination {} missing in external dict, generating fake entry (only showing this warning once)", word2string(word));
-    const_cast<WordLUT&>(vocab_).add("??" + word2string(word), word.toWordIndex());
+    //const_cast<WordLUT&>(vocab_).add("??" + word2string(word), word.toWordIndex());
+    const_cast<WordLUT&>(vocab_).add(word2string(word), word.toWordIndex());
   }
 #endif
   return vocab_[word.toWordIndex()];

From f19def2803b836e6fbc2df5f217e4c245969c4be Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Sat, 16 Feb 2019 14:31:20 -0800
Subject: [PATCH 346/838] removed suppression of single-letter all-caps

---
 src/data/factored_vocab.cpp | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/data/factored_vocab.cpp b/src/data/factored_vocab.cpp
index d9fc22989..fce755c03 100755
--- a/src/data/factored_vocab.cpp
+++ b/src/data/factored_vocab.cpp
@@ -403,7 +403,7 @@ FactoredVocab::CSRData FactoredVocab::csr_rows(const Words& words) const {
       for (auto u : m) {
         indices.push_back(u);
 #else
-#if 1 // special handling of the missing single capitalized letters
+#if 0 // special handling of the missing single capitalized letters
       // costs about 0.1 BLEU when original model never saw this combination (which is quite nicely low)
       // @TODO: remove this once we use the factor-spec file
       const auto& lemma = factorVocab_[(WordIndex)(factorIndices[0] + groupRanges_[0].first)];

From e7a4e5c16d029419c47a4bca956d61b7e6034647 Mon Sep 17 00:00:00 2001
From: Ulrich Germann <ulrich.germann@gmail.com>
Date: Sat, 16 Feb 2019 22:47:09 +0000
Subject: [PATCH 347/838] Fix latex generation in doxygen.

---
 Doxyfile.in | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/Doxyfile.in b/Doxyfile.in
index 1761a2283..ba2fec096 100644
--- a/Doxyfile.in
+++ b/Doxyfile.in
@@ -1592,7 +1592,7 @@ PAPER_TYPE             = a4
 # If left blank no extra packages will be included.
 # This tag requires that the tag GENERATE_LATEX is set to YES.
 
-EXTRA_PACKAGES         =
+EXTRA_PACKAGES         = amsmath
 
 # The LATEX_HEADER tag can be used to specify a personal LaTeX header for the
 # generated LaTeX document. The header should contain everything until the first

From 980cc933aa332c7870d13aacc877bdf5045f3ed5 Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Sat, 16 Feb 2019 15:04:51 -0800
Subject: [PATCH 348/838] moved gen of rand word from fakeBatch() to
 IVocab::randWord(), where factored vocab can override

---
 src/data/corpus_base.h      |  2 +-
 src/data/factored_vocab.cpp | 15 +++++++++++++++
 src/data/factored_vocab.h   |  1 +
 src/data/vocab.cpp          |  4 ++++
 src/data/vocab.h            |  3 +++
 src/data/vocab_base.h       |  4 ++++
 src/training/graph_group.h  |  1 -
 7 files changed, 28 insertions(+), 2 deletions(-)
 mode change 100644 => 100755 src/data/vocab.cpp
 mode change 100644 => 100755 src/training/graph_group.h

diff --git a/src/data/corpus_base.h b/src/data/corpus_base.h
index f47de0bb4..652f4beb5 100755
--- a/src/data/corpus_base.h
+++ b/src/data/corpus_base.h
@@ -322,7 +322,7 @@ class CorpusBatch : public Batch {
       // set word indices to different values to avoid same hashes
       // rand() is OK, this does not affect state in any way
       std::transform(sb->data().begin(), sb->data().end(), sb->data().begin(),
-                     [&](Word) -> Word { return Word::fromWordIndex(rand() % vocabs[batchIndex]->size()); });
+                     [&](Word) -> Word { return vocabs[batchIndex]->randWord(); });
       // mask: no items ask being masked out
       std::fill(sb->mask().begin(), sb->mask().end(), 1.f);
       batchIndex++;
diff --git a/src/data/factored_vocab.cpp b/src/data/factored_vocab.cpp
index fce755c03..d887fde92 100755
--- a/src/data/factored_vocab.cpp
+++ b/src/data/factored_vocab.cpp
@@ -361,6 +361,21 @@ void FactoredVocab::constructNormalizationInfoForVocab() {
   return vocab_.size();
 }
 
+// generate a valid random factored word (used by collectStats())
+/*virtual*/ Word FactoredVocab::randWord() const /*override final*/ {
+  auto numGroups = getNumGroups();
+  std::vector<size_t> factorIndices; factorIndices.reserve(numGroups);
+  for (size_t g = 0; g < numGroups; g++) {
+    size_t factorIndex;
+    if (g == 0 || lemmaHasFactorGroup(factorIndices[0], g))
+      factorIndex = rand() % (factorShape_[g] - 1);
+    else
+      factorIndex = FACTOR_NOT_APPLICABLE;
+    factorIndices.push_back(factorIndex);
+  }
+  return factors2word(factorIndices);
+}
+
 /*virtual*/ Words FactoredVocab::encode(const std::string& line, bool addEOS /*= true*/, bool /*inference*/ /*= false*/) const /*override final*/ {
   std::vector<std::string> lineTokens;
   utils::split(line, lineTokens, " ");
diff --git a/src/data/factored_vocab.h b/src/data/factored_vocab.h
index b17b184ae..aff256512 100755
--- a/src/data/factored_vocab.h
+++ b/src/data/factored_vocab.h
@@ -36,6 +36,7 @@ class FactoredVocab : public IVocab {
   virtual Word getEosId() const override final { return eosId_; }
   virtual Word getUnkId() const override final { return unkId_; }
   virtual void createFake() override final { ABORT("[data] Fake FactoredVocab vocabulary not supported"); }
+  virtual Word randWord() const override final;
 
   // factor-specific. These methods are consumed by Output and Embedding.
   size_t factorVocabSize() const { return factorVocab_.size(); }
diff --git a/src/data/vocab.cpp b/src/data/vocab.cpp
old mode 100644
new mode 100755
index 63947647d..1bf113e86
--- a/src/data/vocab.cpp
+++ b/src/data/vocab.cpp
@@ -91,6 +91,10 @@ void Vocab::createFake() {
   vImpl_->createFake();
 }
 
+Word Vocab::randWord() {
+  return vImpl_->randWord();
+}
+
 // string token to token id
 Word Vocab::operator[](const std::string& word) const {
   return vImpl_->operator[](word);
diff --git a/src/data/vocab.h b/src/data/vocab.h
index dd746ba9a..c6f92c90a 100755
--- a/src/data/vocab.h
+++ b/src/data/vocab.h
@@ -69,6 +69,9 @@ class Vocab {
   // create fake vocabulary for collecting batch statistics
   void createFake();
 
+  // generate a fake word (using rand())
+  Word randWord();
+
   // give access to base implementation. Returns null if not the requested type.
   template<class VocabType> // e.g. FactoredVocab
   Ptr<VocabType> tryAs() const { return std::dynamic_pointer_cast<VocabType>(vImpl_); }
diff --git a/src/data/vocab_base.h b/src/data/vocab_base.h
index bca9db957..42d871bb5 100755
--- a/src/data/vocab_base.h
+++ b/src/data/vocab_base.h
@@ -44,6 +44,10 @@ class IVocab {
   virtual Word getUnkId() const = 0;
 
   virtual void createFake() = 0;
+
+  virtual Word randWord() const {
+    return Word::fromWordIndex(rand() % size());
+  }
 };
 
 class Options;
diff --git a/src/training/graph_group.h b/src/training/graph_group.h
old mode 100644
new mode 100755
index f4617d782..60b81a831
--- a/src/training/graph_group.h
+++ b/src/training/graph_group.h
@@ -77,7 +77,6 @@ class GraphGroup {
     for(int i = 0; i < inputTypes.size(); ++i)
       if(inputTypes[i] == "class")
         localMaxes[i] = 1;
-    
 
     size_t maxBatch = 512;
     bool fits = true;

From e87db82b153a766be940b042f78e3725276321c6 Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Mon, 18 Feb 2019 17:11:16 -0800
Subject: [PATCH 349/838] a few things I am trying to track down why it does
 not work

---
 src/common/logging.cpp      |  4 ++--
 src/data/corpus_base.h      |  3 +--
 src/data/factored_vocab.cpp | 17 +++++++++++++----
 3 files changed, 16 insertions(+), 8 deletions(-)

diff --git a/src/common/logging.cpp b/src/common/logging.cpp
index 9c4b2951f..85dade236 100755
--- a/src/common/logging.cpp
+++ b/src/common/logging.cpp
@@ -77,8 +77,8 @@ void createLoggers(const marian::Config* config) {
   }
 
   bool quiet = config && config->get<bool>("quiet");
-  //Logger general{createStderrLogger("general", "[%Y-%m-%d %T] %v", generalLogs, quiet)};
-  Logger general{createStderrLogger("general", "%v", generalLogs, quiet)};
+  Logger general{createStderrLogger("general", "[%Y-%m-%d %T] %v", generalLogs, quiet)};
+  //Logger general{createStderrLogger("general", "%v", generalLogs, quiet)};
   Logger valid{createStderrLogger("valid", "[%Y-%m-%d %T] [valid] %v", validLogs, quiet)};
 
   if(config && config->has("log-level")) {
diff --git a/src/data/corpus_base.h b/src/data/corpus_base.h
index 652f4beb5..8f836f357 100755
--- a/src/data/corpus_base.h
+++ b/src/data/corpus_base.h
@@ -319,8 +319,7 @@ class CorpusBatch : public Batch {
     size_t batchIndex = 0;
     for(auto len : lengths) {
       auto sb = New<SubBatch>(batchSize, len, vocabs[batchIndex]);
-      // set word indices to different values to avoid same hashes
-      // rand() is OK, this does not affect state in any way
+      // set word indices to random values (not actually needed with current version  --@marcinjd: please confirm)
       std::transform(sb->data().begin(), sb->data().end(), sb->data().begin(),
                      [&](Word) -> Word { return vocabs[batchIndex]->randWord(); });
       // mask: no items ask being masked out
diff --git a/src/data/factored_vocab.cpp b/src/data/factored_vocab.cpp
index d887fde92..4e81d7140 100755
--- a/src/data/factored_vocab.cpp
+++ b/src/data/factored_vocab.cpp
@@ -81,8 +81,8 @@ namespace marian {
     if (tokens.front() != word2string(word))
       LOG_ONCE(info, "[embedding] Word name in .wl file {} differs from canonical form {} (this warning is only shown once)", tokens.front(), word2string(word));
     numTotalFactors += tokens.size() - 1;
-    if (v % 5000 == 0)
-      LOG(info, "{} -> {}", tokens.front(), word2string(word));
+    //if (v % 5000 == 0)
+    //  LOG(info, "{} -> {}", tokens.front(), word2string(word));
   }
   LOG(info, "[embedding] Factored-embedding map read with total/unique of {}/{} factors for {} valid words (in space of {})",
       numTotalFactors, factorVocabSize, vocab_.numValid(), size());
@@ -101,7 +101,8 @@ namespace marian {
     if (isValid != !vocab_.isGap((WordIndex)v)) {
       //LOG(info, "WARNING: Factored vocab mismatch for {}: isValid={}, isGap={}", word2string(word), isValid, vocab_.isGap((WordIndex)v));
       if (isValid) { // add the missing word (albeit with a poor grapheme)
-        (*this)[word];
+        vocab_.add(word2string(word), word.toWordIndex());
+        //(*this)[word];
         // @TODO: ^^disabled to test getLogits(), which no longer works with this enabled (I guess since model has not seen the new units)
         numMissing++;
       }
@@ -342,7 +343,8 @@ void FactoredVocab::constructNormalizationInfoForVocab() {
   if (found)
     return Word::fromWordIndex(index);
   else
-    return getUnkId();
+    ABORT("Unknown word {}", word);
+    //return getUnkId();
 }
 
 /*virtual*/ const std::string& FactoredVocab::operator[](Word word) const /*overrworde final*/ {
@@ -436,6 +438,13 @@ FactoredVocab::CSRData FactoredVocab::csr_rows(const Words& words) const {
         weights.push_back(1.0f);
       }
     }
+#if 1
+    else {
+      // push a dummy entry. Not sure if this is needed.
+      indices.push_back(0);
+      weights.push_back(0.0f);
+    }
+#endif
     offsets.push_back((IndexType)indices.size()); // next matrix row begins at this offset
   }
   return { Shape({(int)words.size(), (int)factorVocab_.size()}), weights, indices, offsets };

From 7ece433b55ee618db9f374b3ea03842301ddd1c7 Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Tue, 19 Feb 2019 17:59:09 -0800
Subject: [PATCH 350/838] bug fix: suppressWord() should use the Unk lemma
 index; CPU selectNBest() can use a const*

---
 src/data/batch_generator.h     |  2 +-
 src/data/factored_vocab.h      |  1 +
 src/translator/beam_search.h   | 35 +++++++++++++++++++++++++---------
 src/translator/helpers.cpp     | 10 +++++-----
 src/translator/helpers.cu      |  4 ++--
 src/translator/helpers.h       |  6 +++---
 src/translator/nth_element.cpp | 22 ++++-----------------
 7 files changed, 42 insertions(+), 38 deletions(-)
 mode change 100644 => 100755 src/data/batch_generator.h
 mode change 100644 => 100755 src/translator/helpers.cpp
 mode change 100644 => 100755 src/translator/helpers.h

diff --git a/src/data/batch_generator.h b/src/data/batch_generator.h
old mode 100644
new mode 100755
index 4ec03d4d4..950a71b1f
--- a/src/data/batch_generator.h
+++ b/src/data/batch_generator.h
@@ -96,7 +96,7 @@ class BatchGenerator : public RNGEngine {
           a.rbegin(), a.rend(), b.rbegin(), b.rend(), itemCmp);
     };
 
-    auto cmpNone = [](const Sample& a, const Sample& b) { return &a < &b; }; // instead sort by address, so we have something to work with
+    auto cmpNone = [](const Sample& a, const Sample& b) { return a.getId() > b.getId(); }; // sort in order of original ids = original data order unless shuffling
 
     typedef std::function<bool(const Sample&, const Sample&)> cmp_type;
     typedef std::priority_queue<Sample, Samples, cmp_type> sample_queue;
diff --git a/src/data/factored_vocab.h b/src/data/factored_vocab.h
index aff256512..846821bc6 100755
--- a/src/data/factored_vocab.h
+++ b/src/data/factored_vocab.h
@@ -35,6 +35,7 @@ class FactoredVocab : public IVocab {
   virtual std::string type() const override final { return "FactoredVocab"; }
   virtual Word getEosId() const override final { return eosId_; }
   virtual Word getUnkId() const override final { return unkId_; }
+  WordIndex getUnkIndex() const { return (WordIndex)getFactor(getUnkId(), 0); } // used in decoding
   virtual void createFake() override final { ABORT("[data] Fake FactoredVocab vocabulary not supported"); }
   virtual Word randWord() const override final;
 
diff --git a/src/translator/beam_search.h b/src/translator/beam_search.h
index fb762d70b..ba366fff2 100755
--- a/src/translator/beam_search.h
+++ b/src/translator/beam_search.h
@@ -60,7 +60,7 @@ class BeamSearch {
       const auto wordIdx    = (WordIndex)(key % vocabSize);
       const auto beamHypIdx =            (key / vocabSize) % inputBeamSize;
       const auto batchIdx   =            (key / vocabSize) / inputBeamSize;
-      //LOG(info, "key = (batch {}, beam {}, word {})", batchIdx, beamHypIdx, wordIdx);
+      LOG(info, "key = (batch {}, beam {}, word {}) -> {}", batchIdx, beamHypIdx, wordIdx, pathScore);
 
       const auto& beam = beams[batchIdx];
       auto& newBeam = newBeams[batchIdx];
@@ -86,11 +86,11 @@ class BeamSearch {
         // starting with the lemma, then adding factors one by one.
         if (factorGroup == 0) {
           word = factoredVocab->lemma2Word(wordIdx);
-          //LOG(info, "new lemma {}={}", word.toWordIndex(), factoredVocab->word2string(word));
+          LOG(info, "new lemma {}={}", word.toWordIndex(), factoredVocab->word2string(word));
         }
         else {
-          //LOG(info, "expand word {}={} with factor[{}] {}", beam[beamHypIdx]->getWord().toWordIndex(),
-          //    factoredVocab->word2string(beam[beamHypIdx]->getWord()), factorGroup, wordIdx);
+          LOG(info, "expand word {}={} with factor[{}] {}", beam[beamHypIdx]->getWord().toWordIndex(),
+              factoredVocab->word2string(beam[beamHypIdx]->getWord()), factorGroup, wordIdx);
           word = beam[beamHypIdx]->getWord();
           ABORT_IF(!factoredVocab->canExpandFactoredWord(word, factorGroup), "A word without this factor snuck through to here??");
           word = factoredVocab->expandFactoredWord(word, factorGroup, wordIdx);
@@ -130,10 +130,10 @@ class BeamSearch {
         auto& newBeam = newBeams[batchIdx];
         for (const auto& beamHyp : beam) {
           auto word = beamHyp->getWord();
-          //LOG(info, "Checking {}", factoredVocab->word2string(word));
+          LOG(info, "Checking {}", factoredVocab->word2string(word));
           if (factoredVocab->canExpandFactoredWord(word, factorGroup)) // handled above
             continue;
-          //LOG(info, "Forwarded {}", factoredVocab->word2string(word));
+          LOG(info, "Forwarded {}", factoredVocab->word2string(word));
           newBeam.push_back(beamHyp);
         }
         if (newBeam.size() > beam.size()) {
@@ -141,7 +141,7 @@ class BeamSearch {
           std::nth_element(newBeam.begin(), newBeam.begin() + beam.size(), newBeam.end(), [](Ptr<Hypothesis> a, Ptr<Hypothesis> b) {
             return a->getPathScore() > b->getPathScore(); // (sort highest score first)
           });
-          //LOG(info, "Size {}, sorted...", newBeam.size());
+          LOG(info, "Size {}, sorted...", newBeam.size());
           newBeam.resize(beam.size());
         }
       }
@@ -217,6 +217,7 @@ class BeamSearch {
     const int dimBatch = (int)batch->size();
 
     auto getNBestList = createGetNBestListFn(beamSize_, dimBatch, graph->getDeviceId());
+    auto getNBestListCPU = createGetNBestListFn(beamSize_, dimBatch, DeviceId(0, DeviceType::cpu));
 
     for(auto scorer : scorers_) {
       scorer->clear(graph);
@@ -290,6 +291,7 @@ class BeamSearch {
               auto hyp = beam[beamHypIdx];
               auto word = hyp->getWord();
               auto canExpand = (!factoredVocab || factoredVocab->canExpandFactoredWord(hyp->getWord(), factorGroup));
+              LOG(info, "[{}, {}] Can expand {} with {} -> {}", batchIdx, beamHypIdx, (*batch->back()->vocab())[hyp->getWord()], factorGroup, canExpand);
               anyCanExpand |= canExpand;
               hypIndices.push_back((IndexType)(hyp->getPrevStateIndex() * dimBatch + batchIdx)); // (beamHypIdx, batchIdx), flattened, for index_select() operation
               prevWords .push_back(word);
@@ -348,6 +350,7 @@ class BeamSearch {
 
       // make beams continuous
       expandedPathScores = swapAxes(expandedPathScores, 0, 2); // -> [dimBatch, 1, localBeamSize, dimVocab]
+      expandedPathScores->debug("expandedPathScores");
 
       // perform NN computation
       if(t == 0 && factorGroup == 0)
@@ -357,14 +360,21 @@ class BeamSearch {
 
       //**********************************************************************
       // suppress specific symbols if not at right positions
-      if(trgUnkId_ != Word::NONE && options_->has("allow-unk") && !options_->get<bool>("allow-unk"))
-        suppressWord(expandedPathScores, trgUnkId_);
+      if(trgUnkId_ != Word::NONE && options_->has("allow-unk") && !options_->get<bool>("allow-unk") && factorGroup == 0)
+        suppressWord(expandedPathScores, factoredVocab ? factoredVocab->getUnkIndex() : trgUnkId_.toWordIndex());
       for(auto state : states)
         state->blacklist(expandedPathScores, batch);
 
       //**********************************************************************
       // perform beam search
 
+      std::vector<unsigned int> nBestKeysCPU; // [dimBatch, localBeamSize] flattened -> (batchIdx, beamHypIdx, word idx) flattened
+      std::vector<float> nBestPathScoresCPU;  // [dimBatch, localBeamSize] flattened
+      getNBestListCPU(/*in*/ expandedPathScores->val(), // [dimBatch, 1, localBeamSize, dimVocab or dimShortlist]
+          /*N=*/localBeamSize,              // desired beam size
+          /*out*/ nBestPathScoresCPU, /*out*/ nBestKeysCPU,
+          /*first=*/t == 0 && factorGroup == 0); // @TODO: this is only used for checking presently, and should be removed altogether
+
       // find N best amongst the (localBeamSize * dimVocab) hypotheses
       std::vector<unsigned int> nBestKeys; // [dimBatch, localBeamSize] flattened -> (batchIdx, beamHypIdx, word idx) flattened
       std::vector<float> nBestPathScores;  // [dimBatch, localBeamSize] flattened
@@ -374,6 +384,13 @@ class BeamSearch {
                    /*first=*/t == 0 && factorGroup == 0); // @TODO: this is only used for checking presently, and should be removed altogether
       // Now, nBestPathScores contain N-best expandedPathScores for each batch and beam,
       // and nBestKeys for each their original location (batchIdx, beamHypIdx, word).
+      ABORT_IF(nBestKeys.size() != nBestKeysCPU.size(), "Inconsistent getNth size");
+      for (size_t u = 0; u < nBestKeys.size(); u++) {
+          //if (nBestKeysCPU[u] != nBestKeys[u] || nBestPathScoresCPU[u] != nBestPathScores[u])
+              LOG(info, "Inconsistent result [{}]: {} vs {} , {} vs {}", u, nBestKeysCPU[u], nBestKeys[u], nBestPathScoresCPU[u], nBestPathScores[u]);
+      }
+      //nBestKeys = nBestKeysCPU;
+      //nBestPathScores = nBestPathScoresCPU;
 
       // combine N-best sets with existing search space (beams) to updated search space
       beams = toHyps(nBestKeys, nBestPathScores,
diff --git a/src/translator/helpers.cpp b/src/translator/helpers.cpp
old mode 100644
new mode 100755
index f131398c2..f4b75da0a
--- a/src/translator/helpers.cpp
+++ b/src/translator/helpers.cpp
@@ -24,18 +24,18 @@ void SetColumn(Tensor in_, size_t col, float value) {
   }
 }
 
-void suppressWord(Expr logProbs, Word id) {
-  SetColumn(logProbs->val(), id.toWordIndex(), std::numeric_limits<float>::lowest());
+void suppressWord(Expr logProbs, WordIndex wordIndex) {
+  SetColumn(logProbs->val(), wordIndex, std::numeric_limits<float>::lowest());
 }
 }  // namespace cpu
 
-void suppressWord(Expr logProbs, Word id) {
+void suppressWord(Expr logProbs, WordIndex wordIndex) {
   if(logProbs->val()->getBackend()->getDeviceId().type == DeviceType::cpu) {
-    cpu::suppressWord(logProbs, id);
+    cpu::suppressWord(logProbs, wordIndex);
   }
 #ifdef CUDA_FOUND
   else {
-    gpu::suppressWord(logProbs, id);
+    gpu::suppressWord(logProbs, wordIndex);
   }
 #endif
 }
diff --git a/src/translator/helpers.cu b/src/translator/helpers.cu
index d23c7500f..a5dfd2e89 100755
--- a/src/translator/helpers.cu
+++ b/src/translator/helpers.cu
@@ -37,8 +37,8 @@ void SetColumn(Tensor in_, size_t col, float value) {
   gSetColumn<<<nBlocks, nThreads>>>(in_->data(), nColumns, nRows, col, value);
 }
 
-void suppressWord(Expr probs, Word id) {
-  SetColumn(probs->val(), id.toWordIndex(), std::numeric_limits<float>::lowest());
+void suppressWord(Expr probs, WordIndex wordIndex) {
+  SetColumn(probs->val(), wordIndex, std::numeric_limits<float>::lowest());
 }
 }  // namespace gpu
 }  // namespace marian
diff --git a/src/translator/helpers.h b/src/translator/helpers.h
old mode 100644
new mode 100755
index d4ff3a946..71b1eb202
--- a/src/translator/helpers.h
+++ b/src/translator/helpers.h
@@ -11,13 +11,13 @@ namespace marian {
 
 namespace cpu {
 
-void suppressWord(Expr logProbs, Word id);
+void suppressWord(Expr logProbs, WordIndex wordIndex);
 }
 
 namespace gpu {
 
-void suppressWord(Expr logProbs, Word id);
+void suppressWord(Expr logProbs, WordIndex wordIndex);
 }
 
-void suppressWord(Expr logProbs, Word id);
+void suppressWord(Expr logProbs, WordIndex wordIndex);
 }  // namespace marian
diff --git a/src/translator/nth_element.cpp b/src/translator/nth_element.cpp
index 7d18555d8..4692a5057 100755
--- a/src/translator/nth_element.cpp
+++ b/src/translator/nth_element.cpp
@@ -21,15 +21,10 @@ class NthElementCPU {
   NthElementCPU(const NthElementCPU& copy) = delete;
 
 private:
-    void selectNBest(float* scores,
-                     const std::vector<int>& batchFirstElementIdxs,
-                     const std::vector<int>& cumulativeBeamSizes) {
-    /* For each batch, select the max N elements, where N is the beam size for
-     * this batch. Locally record these elements (their current value and index
-     * in 'scores') before updating each element to a large negative value, such
-     * that they won't be a maximum if we're called again on the same input.
-     */
-
+  // for each batch, select the max N elements, where N is the beam size for this batch.
+  void selectNBest(const float* scores,
+                   const std::vector<int>& batchFirstElementIdxs,
+                   const std::vector<int>& cumulativeBeamSizes) {
     int numProbs = batchFirstElementIdxs.back();
     std::vector<int> idxs(numProbs);
     std::iota(idxs.begin(), idxs.end(), 0);
@@ -49,7 +44,6 @@ class NthElementCPU {
         int idx = *begin++;
         h_res_idx[pos] = idx;
         h_res[pos] = scores[idx];
-        scores[idx] = std::numeric_limits<float>::lowest();
         ++pos;
       }
     }
@@ -70,18 +64,10 @@ class NthElementCPU {
     std::vector<int> batchFirstElementIdxs(dimBatch + 1, 0);
 
     for(int batchIdx = 0; batchIdx < dimBatch; ++batchIdx) {
-#if 1
       cumulativeBeamSizes[batchIdx + 1] = (batchIdx + 1) * (int)N;
       batchFirstElementIdxs[batchIdx + 1] += (batchIdx + 1) * inputN * vocabSize;
       ABORT_IF(cumulativeBeamSizes[batchIdx + 1] != cumulativeBeamSizes[batchIdx] + (int)N, "cumulativeBeamSizes wrong??");
       ABORT_IF((isFirst ? batchIdx + 1 : cumulativeBeamSizes[batchIdx + 1]) != (batchIdx + 1) * inputN, "inputN wrong??");
-#else
-      cumulativeBeamSizes[batchIdx + 1] = cumulativeBeamSizes[batchIdx] + (int)N;
-      ABORT_IF(cumulativeBeamSizes[batchIdx + 1] != (batchIdx + 1) * N, "cumulativeBeamSizes wrong??");
-      batchFirstElementIdxs[batchIdx + 1]
-          += (isFirst ? batchIdx + 1 : cumulativeBeamSizes[batchIdx + 1]) * vocabSize;
-      ABORT_IF((isFirst ? batchIdx + 1 : cumulativeBeamSizes[batchIdx + 1]) != (batchIdx + 1) * inputN, "inputN wrong??");
-#endif
     }
     ABORT_IF(cumulativeBeamSizes.back() != dimBatch * N, "cumulativeBeamSizes.back() wrong??");
 

From 8314387f9e52ac62d8d015cedc73589b377980bd Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Tue, 19 Feb 2019 18:39:48 -0800
Subject: [PATCH 351/838] removed CPU comparison

---
 src/data/factored_vocab.cpp  |  4 ++--
 src/translator/beam_search.h | 32 ++++++++------------------------
 2 files changed, 10 insertions(+), 26 deletions(-)

diff --git a/src/data/factored_vocab.cpp b/src/data/factored_vocab.cpp
index 4e81d7140..1949d3d90 100755
--- a/src/data/factored_vocab.cpp
+++ b/src/data/factored_vocab.cpp
@@ -351,7 +351,7 @@ void FactoredVocab::constructNormalizationInfoForVocab() {
   //LOG(info, "Looking up Word {}={}", word.toWordIndex(), word2string(word));
 #if 1 // @BUGBUG: our manually prepared dict does not contain @CI tags for single letters, but it's a valid factor
   if (vocab_.isGap(word.toWordIndex())) {
-    LOG_ONCE(info, "Factor combination {} missing in external dict, generating fake entry (only showing this warning once)", word2string(word));
+    LOG/*_ONCE*/(info, "Factor combination {} missing in external dict, generating fake entry (only showing this warning once)", word2string(word));
     //const_cast<WordLUT&>(vocab_).add("??" + word2string(word), word.toWordIndex());
     const_cast<WordLUT&>(vocab_).add(word2string(word), word.toWordIndex());
   }
@@ -466,7 +466,7 @@ FactoredVocab::CSRData FactoredVocab::csr_rows(const Words& words) const {
 WordIndex FactoredVocab::WordLUT::add(const std::string& word, WordIndex index) {
   ABORT_IF(word.empty(), "Attempted to add the empty word to a dictionary");
   auto wasInserted = str2index_.insert(std::make_pair(word, index)).second;
-  ABORT_IF(!wasInserted, "Duplicate vocab entry for '{}'", word);
+  ABORT_IF(!wasInserted, "Duplicate vocab entry for '{}', new index {} vs. existing index {}", word, index, str2index_[word]);
   while (index2str_.size() <= index)
     index2str_.emplace_back(); // @TODO: what's the right way to get linear complexity in steps?
   ABORT_IF(!index2str_[index].empty(), "Duplicate vocab entry for index {} (new: '{}'; existing: '{}')", index, word, index2str_[index]);
diff --git a/src/translator/beam_search.h b/src/translator/beam_search.h
index ba366fff2..a22fd5dfa 100755
--- a/src/translator/beam_search.h
+++ b/src/translator/beam_search.h
@@ -60,7 +60,7 @@ class BeamSearch {
       const auto wordIdx    = (WordIndex)(key % vocabSize);
       const auto beamHypIdx =            (key / vocabSize) % inputBeamSize;
       const auto batchIdx   =            (key / vocabSize) / inputBeamSize;
-      LOG(info, "key = (batch {}, beam {}, word {}) -> {}", batchIdx, beamHypIdx, wordIdx, pathScore);
+      //LOG(info, "key = (batch {}, beam {}, word {}) -> {}", batchIdx, beamHypIdx, wordIdx, pathScore);
 
       const auto& beam = beams[batchIdx];
       auto& newBeam = newBeams[batchIdx];
@@ -86,11 +86,11 @@ class BeamSearch {
         // starting with the lemma, then adding factors one by one.
         if (factorGroup == 0) {
           word = factoredVocab->lemma2Word(wordIdx);
-          LOG(info, "new lemma {}={}", word.toWordIndex(), factoredVocab->word2string(word));
+          //LOG(info, "new lemma {}={}", word.toWordIndex(), factoredVocab->word2string(word));
         }
         else {
-          LOG(info, "expand word {}={} with factor[{}] {}", beam[beamHypIdx]->getWord().toWordIndex(),
-              factoredVocab->word2string(beam[beamHypIdx]->getWord()), factorGroup, wordIdx);
+          //LOG(info, "expand word {}={} with factor[{}] {}", beam[beamHypIdx]->getWord().toWordIndex(),
+          //    factoredVocab->word2string(beam[beamHypIdx]->getWord()), factorGroup, wordIdx);
           word = beam[beamHypIdx]->getWord();
           ABORT_IF(!factoredVocab->canExpandFactoredWord(word, factorGroup), "A word without this factor snuck through to here??");
           word = factoredVocab->expandFactoredWord(word, factorGroup, wordIdx);
@@ -130,10 +130,10 @@ class BeamSearch {
         auto& newBeam = newBeams[batchIdx];
         for (const auto& beamHyp : beam) {
           auto word = beamHyp->getWord();
-          LOG(info, "Checking {}", factoredVocab->word2string(word));
+          //LOG(info, "Checking {}", factoredVocab->word2string(word));
           if (factoredVocab->canExpandFactoredWord(word, factorGroup)) // handled above
             continue;
-          LOG(info, "Forwarded {}", factoredVocab->word2string(word));
+          //LOG(info, "Forwarded {}", factoredVocab->word2string(word));
           newBeam.push_back(beamHyp);
         }
         if (newBeam.size() > beam.size()) {
@@ -141,7 +141,7 @@ class BeamSearch {
           std::nth_element(newBeam.begin(), newBeam.begin() + beam.size(), newBeam.end(), [](Ptr<Hypothesis> a, Ptr<Hypothesis> b) {
             return a->getPathScore() > b->getPathScore(); // (sort highest score first)
           });
-          LOG(info, "Size {}, sorted...", newBeam.size());
+          //LOG(info, "Size {}, sorted...", newBeam.size());
           newBeam.resize(beam.size());
         }
       }
@@ -217,7 +217,6 @@ class BeamSearch {
     const int dimBatch = (int)batch->size();
 
     auto getNBestList = createGetNBestListFn(beamSize_, dimBatch, graph->getDeviceId());
-    auto getNBestListCPU = createGetNBestListFn(beamSize_, dimBatch, DeviceId(0, DeviceType::cpu));
 
     for(auto scorer : scorers_) {
       scorer->clear(graph);
@@ -291,7 +290,7 @@ class BeamSearch {
               auto hyp = beam[beamHypIdx];
               auto word = hyp->getWord();
               auto canExpand = (!factoredVocab || factoredVocab->canExpandFactoredWord(hyp->getWord(), factorGroup));
-              LOG(info, "[{}, {}] Can expand {} with {} -> {}", batchIdx, beamHypIdx, (*batch->back()->vocab())[hyp->getWord()], factorGroup, canExpand);
+              //LOG(info, "[{}, {}] Can expand {} with {} -> {}", batchIdx, beamHypIdx, (*batch->back()->vocab())[hyp->getWord()], factorGroup, canExpand);
               anyCanExpand |= canExpand;
               hypIndices.push_back((IndexType)(hyp->getPrevStateIndex() * dimBatch + batchIdx)); // (beamHypIdx, batchIdx), flattened, for index_select() operation
               prevWords .push_back(word);
@@ -350,7 +349,6 @@ class BeamSearch {
 
       // make beams continuous
       expandedPathScores = swapAxes(expandedPathScores, 0, 2); // -> [dimBatch, 1, localBeamSize, dimVocab]
-      expandedPathScores->debug("expandedPathScores");
 
       // perform NN computation
       if(t == 0 && factorGroup == 0)
@@ -368,13 +366,6 @@ class BeamSearch {
       //**********************************************************************
       // perform beam search
 
-      std::vector<unsigned int> nBestKeysCPU; // [dimBatch, localBeamSize] flattened -> (batchIdx, beamHypIdx, word idx) flattened
-      std::vector<float> nBestPathScoresCPU;  // [dimBatch, localBeamSize] flattened
-      getNBestListCPU(/*in*/ expandedPathScores->val(), // [dimBatch, 1, localBeamSize, dimVocab or dimShortlist]
-          /*N=*/localBeamSize,              // desired beam size
-          /*out*/ nBestPathScoresCPU, /*out*/ nBestKeysCPU,
-          /*first=*/t == 0 && factorGroup == 0); // @TODO: this is only used for checking presently, and should be removed altogether
-
       // find N best amongst the (localBeamSize * dimVocab) hypotheses
       std::vector<unsigned int> nBestKeys; // [dimBatch, localBeamSize] flattened -> (batchIdx, beamHypIdx, word idx) flattened
       std::vector<float> nBestPathScores;  // [dimBatch, localBeamSize] flattened
@@ -384,13 +375,6 @@ class BeamSearch {
                    /*first=*/t == 0 && factorGroup == 0); // @TODO: this is only used for checking presently, and should be removed altogether
       // Now, nBestPathScores contain N-best expandedPathScores for each batch and beam,
       // and nBestKeys for each their original location (batchIdx, beamHypIdx, word).
-      ABORT_IF(nBestKeys.size() != nBestKeysCPU.size(), "Inconsistent getNth size");
-      for (size_t u = 0; u < nBestKeys.size(); u++) {
-          //if (nBestKeysCPU[u] != nBestKeys[u] || nBestPathScoresCPU[u] != nBestPathScores[u])
-              LOG(info, "Inconsistent result [{}]: {} vs {} , {} vs {}", u, nBestKeysCPU[u], nBestKeys[u], nBestPathScoresCPU[u], nBestPathScores[u]);
-      }
-      //nBestKeys = nBestKeysCPU;
-      //nBestPathScores = nBestPathScoresCPU;
 
       // combine N-best sets with existing search space (beams) to updated search space
       beams = toHyps(nBestKeys, nBestPathScores,

From 8d3b3cc96a84f42f7fdbfe82ca8c6cc5d7076b4f Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Tue, 19 Feb 2019 19:44:27 -0800
Subject: [PATCH 352/838] implemented all-caps'ing and title-casing for
 factored vocabs

---
 src/common/utils.cpp        | 12 +++++++++++-
 src/common/utils.h          |  2 ++
 src/data/corpus.cpp         |  4 ++--
 src/data/factored_vocab.cpp | 11 ++++++++++-
 src/data/factored_vocab.h   |  2 ++
 src/data/vocab.cpp          |  6 ++++++
 src/data/vocab.h            |  6 ++++++
 src/data/vocab_base.h       |  3 +++
 8 files changed, 42 insertions(+), 4 deletions(-)

diff --git a/src/common/utils.cpp b/src/common/utils.cpp
index e4f6aa904..eb464878e 100755
--- a/src/common/utils.cpp
+++ b/src/common/utils.cpp
@@ -211,7 +211,7 @@ std::string toEnglishTitleCase(const std::string& s) {
   const std::string wordStartChars = "abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ";
   const std::string wordInternalChars = wordStartChars + "'"; // don't title-case letters after word-internal apostrophe
   const std::set<std::string> exceptions = { // from moses-scripts/scripts/recaser/detruecase.perl
-    "a","after","against","al-.+","and","any","as","at","be","because","between","by","during","el-.+","for","from","his","in","is","its","last","not","of","off","on","than","the","their","this","to","was","were","which","will","with"
+    "a","after","against","al-.+","and","any","as","at","be","because","between","by","during","el-.+","for","from","his","in","is","its","last","not","of","off","on","than","the","their","this","to","was","were","which","will","with"
   };
   const std::set<char> wordPredChars = {' ', '"', '\'', '-'}; // only capitalize words if following these characters (to avoid upper-casing word-internal SPM units)
   // These are tokenization heuristics, which may be incomplete.
@@ -238,6 +238,16 @@ std::string toEnglishTitleCase(const std::string& s) {
   return res;
 }
 
+std::string findReplace(const std::string& in, const std::string& what, const std::string& withWhat, bool all /*= false*/) {
+  std::string res = in;
+  for(size_t pos = res.find(what); pos != std::string::npos; pos = res.find(what, pos + withWhat.length())) {
+    res.replace(pos, what.length(), withWhat);
+    if (!all)
+      break;
+  }
+  return res;
+}
+
 double parseDouble(std::string s) {
   double res;
   char c;  // dummy char -- if we succeed to parse this, then there were extraneous characters after the number
diff --git a/src/common/utils.h b/src/common/utils.h
index 23d6ffed4..8aa40f835 100755
--- a/src/common/utils.h
+++ b/src/common/utils.h
@@ -41,6 +41,8 @@ bool endsWith(const std::string& text, const std::string& suffix);
 std::string utf8ToUpper(const std::string& s);
 std::string toEnglishTitleCase(const std::string& s);
 
+std::string findReplace(const std::string& in, const std::string& what, const std::string& withWhat, bool all = false);
+
 double parseDouble(std::string s);
 double parseNumber(std::string s);
 
diff --git a/src/data/corpus.cpp b/src/data/corpus.cpp
index 44fa064d8..61d359c91 100755
--- a/src/data/corpus.cpp
+++ b/src/data/corpus.cpp
@@ -25,7 +25,7 @@ Corpus::Corpus(std::vector<std::string> paths,
 
 void Corpus::preprocessLine(std::string& line, size_t streamId) {
   if (allCapsEvery_ != 0 && pos_ % allCapsEvery_ == 0 && !inference_) {
-    line = utils::utf8ToUpper(line);
+    line = vocabs_[streamId]->toUpper(line);
     if (streamId == 0)
       LOG_ONCE(info, "[data] Source all-caps'ed line to: {}", line);
     else
@@ -36,7 +36,7 @@ void Corpus::preprocessLine(std::string& line, size_t streamId) {
       &&   streamId == 0       // @HACK: Hard-coding EN-X direction for now; needs an option in the future
       
       ) {
-    line = utils::toEnglishTitleCase(line);
+    line = vocabs_[streamId]->toEnglishTitleCase(line);
     if (streamId == 0)
       LOG_ONCE(info, "[data] Source English-title-case'd line to: {}", line);
     else
diff --git a/src/data/factored_vocab.cpp b/src/data/factored_vocab.cpp
index 1949d3d90..4c5b5acea 100755
--- a/src/data/factored_vocab.cpp
+++ b/src/data/factored_vocab.cpp
@@ -117,7 +117,7 @@ namespace marian {
   // </s> and <unk> must exist in the vocabulary
   eosId_ = Word::fromWordIndex(vocab_[DEFAULT_EOS_STR]);
   unkId_ = Word::fromWordIndex(vocab_[DEFAULT_UNK_STR]);
-  LOG(info, "eos: {}; unk: {}", word2string(eosId_), word2string(unkId_));
+  //LOG(info, "eos: {}; unk: {}", word2string(eosId_), word2string(unkId_));
 
 #if 1   // dim-vocabs stores numValid() in legacy model files, and would now have been size()
   if (maxSizeUnused == vocab_.numValid())
@@ -363,6 +363,15 @@ void FactoredVocab::constructNormalizationInfoForVocab() {
   return vocab_.size();
 }
 
+/*virtual*/ std::string FactoredVocab::toUpper(const std::string& line) const /*override final*/ {
+  return utils::findReplace(utils::findReplace(line, "@CI", "@CA", /*all=*/true), "@CN", "@CA", /*all=*/true);
+}
+
+/*virtual*/ std::string FactoredVocab::toEnglishTitleCase(const std::string& line) const /*override final*/ {
+  // @BUGBUG: does not handle the special words that should remain lower-case
+  return utils::findReplace(line, "@CN@GL-", "@CI@GL-", /*all=*/true);
+}
+
 // generate a valid random factored word (used by collectStats())
 /*virtual*/ Word FactoredVocab::randWord() const /*override final*/ {
   auto numGroups = getNumGroups();
diff --git a/src/data/factored_vocab.h b/src/data/factored_vocab.h
index 846821bc6..433a9d61a 100755
--- a/src/data/factored_vocab.h
+++ b/src/data/factored_vocab.h
@@ -35,6 +35,8 @@ class FactoredVocab : public IVocab {
   virtual std::string type() const override final { return "FactoredVocab"; }
   virtual Word getEosId() const override final { return eosId_; }
   virtual Word getUnkId() const override final { return unkId_; }
+  virtual std::string toUpper(const std::string& line) const override final;
+  virtual std::string toEnglishTitleCase(const std::string& line) const override final;
   WordIndex getUnkIndex() const { return (WordIndex)getFactor(getUnkId(), 0); } // used in decoding
   virtual void createFake() override final { ABORT("[data] Fake FactoredVocab vocabulary not supported"); }
   virtual Word randWord() const override final;
diff --git a/src/data/vocab.cpp b/src/data/vocab.cpp
index 1bf113e86..9c1039d81 100755
--- a/src/data/vocab.cpp
+++ b/src/data/vocab.cpp
@@ -130,4 +130,10 @@ Word Vocab::getEosId() const { return vImpl_->getEosId(); }
 // return UNK symbol id
 Word Vocab::getUnkId() const { return vImpl_->getUnkId(); }
 
+// for corpus augmentation: convert string to all-caps
+std::string Vocab::toUpper(const std::string& line) const { return vImpl_->toUpper(line); }
+
+// for corpus augmentation: convert string to title case
+std::string Vocab::toEnglishTitleCase(const std::string& line) const { return vImpl_->toEnglishTitleCase(line); }
+
 }  // namespace marian
diff --git a/src/data/vocab.h b/src/data/vocab.h
index c6f92c90a..81f97a618 100755
--- a/src/data/vocab.h
+++ b/src/data/vocab.h
@@ -66,6 +66,12 @@ class Vocab {
   // return UNK symbol id
   Word getUnkId() const;
 
+  // for corpus augmentation: convert string to all-caps
+  std::string toUpper(const std::string& line) const;
+
+  // for corpus augmentation: convert string to title case
+  std::string toEnglishTitleCase(const std::string& line) const;
+
   // create fake vocabulary for collecting batch statistics
   void createFake();
 
diff --git a/src/data/vocab_base.h b/src/data/vocab_base.h
index 42d871bb5..240626655 100755
--- a/src/data/vocab_base.h
+++ b/src/data/vocab_base.h
@@ -43,6 +43,9 @@ class IVocab {
   virtual Word getEosId() const = 0;
   virtual Word getUnkId() const = 0;
 
+  virtual std::string toUpper(const std::string& line) const { return utils::utf8ToUpper(line); }
+  virtual std::string toEnglishTitleCase(const std::string& line) const { return utils::toEnglishTitleCase(line); }
+
   virtual void createFake() = 0;
 
   virtual Word randWord() const {

From 03806ff60f17734fcded3ab7d517c8d5634f9423 Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Fri, 22 Feb 2019 12:38:23 -0800
Subject: [PATCH 353/838] split ModelBase into ModelBase and CriterionBase,
 which have different signatures

---
 src/common/options.h                   |  2 +-
 src/examples/mnist/validator.h         |  2 +-
 src/microsoft/quicksand.cpp            |  2 +-
 src/models/costs.h                     | 70 ++++++++++-----------
 src/models/model_base.h                | 21 +++++++
 src/models/model_factory.cpp           | 84 +++++++++++++++++---------
 src/models/model_factory.h             |  6 +-
 src/models/transformer.h               |  4 +-
 src/rescorer/rescorer.h                |  2 +-
 src/tensors/cpu/tensor_operators.cpp   |  1 +
 src/training/graph_group.h             |  6 +-
 src/training/graph_group_async.cpp     |  4 +-
 src/training/graph_group_async.h       |  2 +-
 src/training/graph_group_multinode.cpp |  2 +-
 src/training/graph_group_singleton.h   |  4 +-
 src/training/graph_group_sync.cpp      |  2 +-
 src/training/graph_group_sync.h        |  2 +-
 src/training/validator.h               | 22 +++----
 src/translator/beam_search.h           |  0
 src/translator/scorers.cpp             |  4 +-
 20 files changed, 146 insertions(+), 96 deletions(-)
 mode change 100644 => 100755 src/common/options.h
 mode change 100644 => 100755 src/examples/mnist/validator.h
 mode change 100644 => 100755 src/microsoft/quicksand.cpp
 mode change 100644 => 100755 src/models/costs.h
 mode change 100644 => 100755 src/models/model_base.h
 mode change 100644 => 100755 src/models/transformer.h
 mode change 100644 => 100755 src/rescorer/rescorer.h
 mode change 100644 => 100755 src/tensors/cpu/tensor_operators.cpp
 mode change 100644 => 100755 src/training/graph_group.h
 mode change 100644 => 100755 src/training/graph_group_async.cpp
 mode change 100644 => 100755 src/training/graph_group_async.h
 mode change 100644 => 100755 src/training/graph_group_multinode.cpp
 mode change 100644 => 100755 src/training/graph_group_singleton.h
 mode change 100644 => 100755 src/training/graph_group_sync.cpp
 mode change 100644 => 100755 src/training/graph_group_sync.h
 mode change 100644 => 100755 src/training/validator.h
 mode change 100644 => 100755 src/translator/beam_search.h
 mode change 100644 => 100755 src/translator/scorers.cpp

diff --git a/src/common/options.h b/src/common/options.h
old mode 100644
new mode 100755
index 5a3f4eb70..643456c93
--- a/src/common/options.h
+++ b/src/common/options.h
@@ -111,7 +111,7 @@ class Options {
     }
     try {
       return !options_[key].as<std::string>().empty();
-    } catch(const YAML::BadConversion& e) {
+    } catch(const YAML::BadConversion&) {
       ABORT("Option '{}' is neither a sequence nor a text");
     }
     return false;
diff --git a/src/examples/mnist/validator.h b/src/examples/mnist/validator.h
old mode 100644
new mode 100755
index f38a95cec..8a85915a1
--- a/src/examples/mnist/validator.h
+++ b/src/examples/mnist/validator.h
@@ -16,7 +16,7 @@ class MNISTAccuracyValidator : public Validator<data::MNISTData> {
 public:
   MNISTAccuracyValidator(Ptr<Options> options) : Validator(std::vector<Ptr<Vocab>>(), options, false) {
     createBatchGenerator(/*isTranslating=*/false);
-    builder_ = models::from_options(options, models::usage::scoring);
+    builder_ = models::createModelFromOptions(options, models::usage::scoring);
   }
 
   virtual void keepBest(const std::vector<Ptr<ExpressionGraph>>& graphs) override {
diff --git a/src/microsoft/quicksand.cpp b/src/microsoft/quicksand.cpp
old mode 100644
new mode 100755
index 959e44311..022b200d4
--- a/src/microsoft/quicksand.cpp
+++ b/src/microsoft/quicksand.cpp
@@ -72,7 +72,7 @@ class BeamSearchDecoder : public IBeamSearchDecoder {
 
       std::cerr << modelOpts->str() << std::flush;
 
-      auto encdec = models::from_options(modelOpts, models::usage::translation);
+      auto encdec = models::createModelFromOptions(modelOpts, models::usage::translation);
 
       if(io::isBin(models[i]) && ptrs_[i] != nullptr) {
         // if file ends in *.bin and has been mapped by QuickSAND
diff --git a/src/models/costs.h b/src/models/costs.h
old mode 100644
new mode 100755
index 2aba6fe09..eb9af6b0b
--- a/src/models/costs.h
+++ b/src/models/costs.h
@@ -127,7 +127,7 @@ class EncoderClassifierCE : public CostBase {
   }
 };
 
-class Trainer : public ModelBase {
+class Trainer : public CriterionBase {
 protected:
   Ptr<ModelBase> model_;
   Ptr<CostBase> cost_;
@@ -159,7 +159,40 @@ class Trainer : public ModelBase {
   virtual void clear(Ptr<ExpressionGraph> graph) override { model_->clear(graph); };
 };
 
-typedef Trainer Scorer;
+// @TODO: Name 'scorer' is ambiguous: Does it compute scores for all classes, or the loss value for the ground truth?
+//        Beam search uses it for the former meaning, while 'marian score' and validation in the latter.
+//        This class is for the former use. The latter is done using Trainer.
+class Scorer : public ModelBase {
+protected:
+  Ptr<ModelBase> model_;
+  Ptr<CostBase> cost_;
+
+public:
+  Scorer(Ptr<ModelBase> model, Ptr<CostBase> cost)
+      : model_(model), cost_(cost) {}
+
+  Ptr<ModelBase> getModel() { return model_; }
+
+  virtual void load(Ptr<ExpressionGraph> graph,
+                    const std::string& name,
+                    bool markedReloaded = true) override {
+    model_->load(graph, name, markedReloaded);
+  };
+
+  virtual void save(Ptr<ExpressionGraph> graph,
+                    const std::string& name,
+                    bool saveTranslatorConfig = false) override {
+    model_->save(graph, name, saveTranslatorConfig);
+  }
+
+  virtual Ptr<RationalLoss> build(Ptr<ExpressionGraph> graph,
+                                  Ptr<data::Batch> batch,
+                                  bool clearGraph = true) override {
+    return cost_->apply(model_, graph, batch, clearGraph);
+  };
+
+  virtual void clear(Ptr<ExpressionGraph> graph) override { model_->clear(graph); };
+};
 
 class CostStep {
 public:
@@ -270,38 +303,5 @@ class Stepwise : public EncoderDecoderBase {
   virtual data::SoftAlignment getAlignment() override { return encdec_->getAlignment(); }
 };
 
-inline Ptr<ModelBase> add_cost(Ptr<EncoderDecoder> encdec,
-                               Ptr<Options> options) {
-  switch(options->get<usage>("usage", usage::raw)) {
-    case usage::training:
-      return New<Trainer>(encdec, New<EncoderDecoderCE>(options));
-    case usage::scoring:
-      return New<Scorer>(encdec, New<EncoderDecoderCE>(options));
-    case usage::translation:
-      if(options->get<bool>("output-sampling", false))
-        return New<Stepwise>(encdec, New<GumbelSoftmaxStep>());
-      else
-        return New<Stepwise>(encdec, New<LogSoftmaxStep>());
-    case usage::raw:
-    default:
-      return encdec;
-  }
-}
-
-inline Ptr<ModelBase> add_cost(Ptr<EncoderClassifier> enccls,
-                               Ptr<Options> options) {
-  switch(options->get<usage>("usage", usage::raw)) {
-    case usage::training:
-      return New<Trainer>(enccls, New<EncoderClassifierCE>(options));
-    case usage::scoring:
-      return New<Scorer>(enccls, New<EncoderClassifierCE>(options));
-    case usage::translation:
-      ABORT("Classifier cannot be used for translation");
-    case usage::raw:
-    default:
-      return enccls;
-  }
-}
-
 }  // namespace models
 }  // namespace marian
diff --git a/src/models/model_base.h b/src/models/model_base.h
old mode 100644
new mode 100755
index 0701a8b4b..bbc7aca33
--- a/src/models/model_base.h
+++ b/src/models/model_base.h
@@ -16,6 +16,7 @@ YAML_REGISTER_TYPE(marian::models::usage, int)
 namespace marian {
 namespace models {
 
+// model = input -> predictions
 class ModelBase {
 public:
   virtual void load(Ptr<ExpressionGraph>,
@@ -35,5 +36,25 @@ class ModelBase {
   virtual void clear(Ptr<ExpressionGraph> graph) = 0;
 };
 
+// criterion = (input, reference) -> loss
+class CriterionBase {
+public:
+  virtual void load(Ptr<ExpressionGraph>,
+                    const std::string&,
+                    bool markReloaded = true)
+      = 0;
+  virtual void save(Ptr<ExpressionGraph>,
+                    const std::string&,
+                    bool saveTranslatorConfig = false)
+      = 0;
+
+  virtual Ptr<RationalLoss> build(Ptr<ExpressionGraph> graph,
+                                  Ptr<data::Batch> batch,
+                                  bool clearGraph = true)
+      = 0;
+
+  virtual void clear(Ptr<ExpressionGraph> graph) = 0;
+};
+
 }  // namespace models
 }  // namespace marian
diff --git a/src/models/model_factory.cpp b/src/models/model_factory.cpp
index 946b8a3ab..d7bd8d854 100755
--- a/src/models/model_factory.cpp
+++ b/src/models/model_factory.cpp
@@ -77,7 +77,7 @@ Ptr<ModelBase> EncoderDecoderFactory::construct(Ptr<ExpressionGraph> graph) {
   for(auto& df : decoders_)
     encdec->push_back(df(options_).construct(graph));
 
-  return add_cost(encdec, options_);
+  return encdec;
 }
 
 Ptr<ModelBase> EncoderClassifierFactory::construct(Ptr<ExpressionGraph> graph) {
@@ -96,10 +96,10 @@ Ptr<ModelBase> EncoderClassifierFactory::construct(Ptr<ExpressionGraph> graph) {
   for(auto& cf : classifiers_)
     enccls->push_back(cf(options_).construct(graph));
 
-  return add_cost(enccls, options_);
+  return enccls;
 }
 
-Ptr<ModelBase> by_type(std::string type, usage use, Ptr<Options> options) {
+Ptr<ModelBase> createBaseModelByType(std::string type, usage use, Ptr<Options> options) {
   Ptr<ExpressionGraph> graph = nullptr; // graph unknown at this stage
   // clang-format off
   if(type == "s2s" || type == "amun" || type == "nematus") {
@@ -259,31 +259,7 @@ Ptr<ModelBase> by_type(std::string type, usage use, Ptr<Options> options) {
         .construct(graph);
   }
 
-#ifdef COMPILE_EXAMPLES
-  // @TODO: examples should be compiled optionally
-  if(type == "mnist-ffnn") {
-    auto mnist = New<MnistFeedForwardNet>(options);
-    if(use == usage::scoring)
-      return New<Scorer>(mnist, New<MNISTLogsoftmax>());
-    else if(use == usage::training)
-      return New<Trainer>(mnist, New<MNISTCrossEntropyCost>());
-    else
-      return mnist;
-  }
-#endif
-
 #ifdef CUDNN
-#ifdef COMPILE_EXAMPLES
-  if(type == "mnist-lenet") {
-    auto mnist = New<MnistLeNet>(options);
-    if(use == usage::scoring)
-      return New<Scorer>(mnist, New<MNISTLogsoftmax>());
-    else if(use == usage::training)
-      return New<Trainer>(mnist, New<MNISTCrossEntropyCost>());
-    else
-      return mnist;
-  }
-#endif
   if(type == "char-s2s") {
     return models::encoder_decoder()(options)
         ("usage", use)
@@ -298,9 +274,59 @@ Ptr<ModelBase> by_type(std::string type, usage use, Ptr<Options> options) {
   ABORT("Unknown model type: {}", type);
 }
 
-Ptr<ModelBase> from_options(Ptr<Options> options, usage use) {
+Ptr<ModelBase> createModelFromOptions(Ptr<Options> options, usage use) {
   std::string type = options->get<std::string>("type");
-  return by_type(type, use, options);
+  auto baseModel = createBaseModelByType(type, use, options);
+
+  // add (log)softmax if requested
+  if (use == usage::translation) {
+    if(std::dynamic_pointer_cast<EncoderDecoder>(baseModel)) {
+      if(options->get<bool>("output-sampling", false))
+        return New<Stepwise>(std::dynamic_pointer_cast<EncoderDecoder>(baseModel), New<GumbelSoftmaxStep>());
+      else
+        return New<Stepwise>(std::dynamic_pointer_cast<EncoderDecoder>(baseModel), New<LogSoftmaxStep>());
+    }
+#ifdef COMPILE_EXAMPLES
+    // note: 'usage::translation' here means 'inference'
+    else if (std::dynamic_pointer_cast<MnistFeedForwardNet>(baseModel))
+      return New<Scorer>(baseModel, New<MNISTLogsoftmax>());
+#ifdef CUDNN
+    else if (std::dynamic_pointer_cast<MnistLeNet>(baseModel))
+      return New<Scorer>(baseModel, New<MNISTLogsoftmax>());
+#endif
+#endif
+    else
+      ABORT("'usage' parameter 'translation' cannot be applied to model type: {}", type);
+  }
+  else if (use == usage::raw)
+    return baseModel;
+  else
+    ABORT("'Usage' parameter must be 'translation' or 'raw'");
+}
+
+Ptr<CriterionBase> createCriterionFromOptions(Ptr<Options> options, usage use) {
+  std::string type = options->get<std::string>("type");
+  auto baseModel = createBaseModelByType(type, use, options);
+
+  // add cost function
+  ABORT_IF(use != usage::training && use != usage::scoring, "'Usage' parameter must be 'training' or 'scoring'");
+  // note: usage::scoring means "score the loss function", hence it uses a Trainer (not Scorer, which is for decoding)
+  // @TODO: Should we define a new class that does not compute gradients?
+  if (std::dynamic_pointer_cast<EncoderDecoder>(baseModel))
+    return New<Trainer>(baseModel, New<EncoderDecoderCE>(options));
+  else if (std::dynamic_pointer_cast<EncoderClassifier>(baseModel))
+    return New<Trainer>(baseModel, New<EncoderClassifierCE>(options));
+#ifdef COMPILE_EXAMPLES
+  // @TODO: examples should be compiled optionally
+  else if (std::dynamic_pointer_cast<MnistFeedForwardNet>(baseModel))
+    return New<Trainer>(baseModel, New<MNISTCrossEntropyCost>());
+#ifdef CUDNN
+  else if (std::dynamic_pointer_cast<MnistLeNet>(baseModel))
+    return New<Trainer>(baseModel, New<MNISTCrossEntropyCost>());
+#endif
+#endif
+  else
+    ABORT("Criterion function unknown for model type: {}", type);
 }
 
 }  // namespace models
diff --git a/src/models/model_factory.h b/src/models/model_factory.h
index be14dc6d9..9d089e294 100755
--- a/src/models/model_factory.h
+++ b/src/models/model_factory.h
@@ -85,8 +85,10 @@ class EncoderClassifierFactory : public Factory {
 
 typedef Accumulator<EncoderClassifierFactory> encoder_classifier;
 
-Ptr<ModelBase> by_type(std::string type, usage, Ptr<Options> options);
+Ptr<ModelBase> createBaseModelByType(std::string type, usage, Ptr<Options> options);
 
-Ptr<ModelBase> from_options(Ptr<Options> options, usage);
+Ptr<ModelBase> createModelFromOptions(Ptr<Options> options, usage);
+
+Ptr<CriterionBase> createCriterionFromOptions(Ptr<Options> options, usage);
 }  // namespace models
 }  // namespace marian
diff --git a/src/models/transformer.h b/src/models/transformer.h
old mode 100644
new mode 100755
index 1083efe29..9f5410f36
--- a/src/models/transformer.h
+++ b/src/models/transformer.h
@@ -865,12 +865,12 @@ class DecoderTransformer : public Transformer<DecoderBase> {
 };
 
 // factory functions
-Ptr<EncoderBase> NewEncoderTransformer(Ptr<Options> options)
+static inline Ptr<EncoderBase> NewEncoderTransformer(Ptr<Options> options)
 {
   return New<EncoderTransformer>(options);
 }
 
-Ptr<DecoderBase> NewDecoderTransformer(Ptr<Options> options)
+static inline Ptr<DecoderBase> NewDecoderTransformer(Ptr<Options> options)
 {
   return New<DecoderTransformer>(options);
 }
diff --git a/src/rescorer/rescorer.h b/src/rescorer/rescorer.h
old mode 100644
new mode 100755
index ee2a3c9a5..372fc17f6
--- a/src/rescorer/rescorer.h
+++ b/src/rescorer/rescorer.h
@@ -23,7 +23,7 @@ class Rescorer {
 
 public:
   Rescorer(Ptr<Options> options)
-      : builder_(models::from_options(options, models::usage::scoring)) {}
+      : builder_(models::createModelFromOptions(options, models::usage::scoring)) {}
 
   void load(Ptr<ExpressionGraph> graph, const std::string& modelFile) {
     builder_->load(graph, modelFile);
diff --git a/src/tensors/cpu/tensor_operators.cpp b/src/tensors/cpu/tensor_operators.cpp
old mode 100644
new mode 100755
index 72dbd131c..580be94ce
--- a/src/tensors/cpu/tensor_operators.cpp
+++ b/src/tensors/cpu/tensor_operators.cpp
@@ -15,6 +15,7 @@ namespace marian {
 namespace cpu {
 
 void IsNan(const Tensor in, Ptr<Allocator> allocator, bool& isNan, bool& isInf, bool zero) {
+  zero; isInf; isNan;
   ABORT("Not implemented");
 }
 
diff --git a/src/training/graph_group.h b/src/training/graph_group.h
old mode 100644
new mode 100755
index f4617d782..8e67ad724
--- a/src/training/graph_group.h
+++ b/src/training/graph_group.h
@@ -55,7 +55,7 @@ class GraphGroup {
    */
   // @TODO: Can this be made const? It seems wrong to have a stateful method that still returns a result.
   virtual Ptr<data::BatchStats> collectStats(Ptr<ExpressionGraph> graph,
-                                             Ptr<models::ModelBase> model,
+                                             Ptr<models::CriterionBase> model,
                                              const std::vector<Ptr<Vocab>>& vocabs,
                                              double multiplier = 1.) {
     auto stats = New<data::BatchStats>();
@@ -141,7 +141,7 @@ class MultiNodeGraphGroupBase : public GraphGroup {
   std::vector<size_t> devices_; // [num local GPUs]
 
   /** Graph builders for clients (which run forward and backward passes). */
-  std::vector<Ptr<models::ModelBase>> clientBuilders_;
+  std::vector<Ptr<models::CriterionBase>> clientBuilders_;
 
   /** Graphs of clients. One entry per GPU on this node. */
   std::vector<Ptr<ExpressionGraph>> clientGraphs_; // [num local GPUs]
@@ -161,7 +161,7 @@ class MultiNodeGraphGroupBase : public GraphGroup {
       clientGraphs_.push_back(New<ExpressionGraph>());
       clientGraphs_[i]->setDevice({ devices_[i], DeviceType::gpu });
       clientGraphs_[i]->reserveWorkspaceMB(options_->get<size_t>("workspace"));
-      clientBuilders_.push_back(models::from_options(options_, models::usage::training));
+      clientBuilders_.push_back(models::createCriterionFromOptions(options_, models::usage::training));
     }
   }
 
diff --git a/src/training/graph_group_async.cpp b/src/training/graph_group_async.cpp
old mode 100644
new mode 100755
index f1a01cdfa..780108f17
--- a/src/training/graph_group_async.cpp
+++ b/src/training/graph_group_async.cpp
@@ -23,7 +23,7 @@ AsyncGraphGroup::AsyncGraphGroup(Ptr<Options> config, Ptr<IMPIWrapper> mpi)
     graphs_.push_back(graph);
     shardOpt_.push_back(Optimizer(options_));
 
-    builders_.push_back(models::from_options(options_, models::usage::training));
+    builders_.push_back(models::createCriterionFromOptions(options_, models::usage::training));
   }
 }
 
@@ -189,7 +189,7 @@ void AsyncGraphGroup::execute(Ptr<data::Batch> batch) {
   auto task = [this](Ptr<data::Batch> batch) {
     static size_t i = 0;
     thread_local Ptr<ExpressionGraph> graph;
-    thread_local Ptr<models::ModelBase> builder;
+    thread_local Ptr<models::CriterionBase> builder;
     thread_local size_t t = 0;
     thread_local size_t num_seen_words = 0;
     thread_local size_t num_seen_sentences = 0;
diff --git a/src/training/graph_group_async.h b/src/training/graph_group_async.h
old mode 100644
new mode 100755
index cd85cc501..bb19bde40
--- a/src/training/graph_group_async.h
+++ b/src/training/graph_group_async.h
@@ -16,7 +16,7 @@ class AsyncGraphGroup : public GraphGroup, public ExponentialSmoothing {
 protected:
   bool first_{true};
 
-  std::vector<Ptr<models::ModelBase>> builders_;
+  std::vector<Ptr<models::CriterionBase>> builders_;
   std::vector<Ptr<ExpressionGraph>> graphs_;
   std::vector<DeviceId> devices_;
 
diff --git a/src/training/graph_group_multinode.cpp b/src/training/graph_group_multinode.cpp
old mode 100644
new mode 100755
index 255b5a2a5..4e7d931cf
--- a/src/training/graph_group_multinode.cpp
+++ b/src/training/graph_group_multinode.cpp
@@ -512,7 +512,7 @@ void MultiNodeGraphGroup::execute(Ptr<data::Batch> batch) {
   auto task = [this](Ptr<data::Batch> batch) {
     static size_t i = 0;
     thread_local Ptr<ExpressionGraph> graph;
-    thread_local Ptr<models::ModelBase> builder;
+    thread_local Ptr<models::CriterionBase> builder;
     thread_local size_t my_id = 0;
     thread_local size_t t = 0;
     // only for scheduler statistic
diff --git a/src/training/graph_group_singleton.h b/src/training/graph_group_singleton.h
old mode 100644
new mode 100755
index 01ffb1eb2..8aede537a
--- a/src/training/graph_group_singleton.h
+++ b/src/training/graph_group_singleton.h
@@ -16,7 +16,7 @@ class SingletonGraph : public GraphGroup, public ExponentialSmoothing {
   virtual void setScheduler(Ptr<Scheduler> scheduler) override;
 
 private:
-  Ptr<models::ModelBase> builder_;
+  Ptr<models::CriterionBase> builder_;
   Ptr<ExpressionGraph> graph_;
   Ptr<ExpressionGraph> graphAvg_;
 
@@ -37,7 +37,7 @@ class SingletonGraph : public GraphGroup, public ExponentialSmoothing {
     graph_->getBackend()->setClip(options_->get<float>("clip-gemm"));
     graph_->reserveWorkspaceMB(options_->get<size_t>("workspace"));
     opt_ = Optimizer(options_);
-    builder_ = models::from_options(options_, models::usage::training);
+    builder_ = models::createCriterionFromOptions(options_, models::usage::training);
   }
 
   void update(Ptr<data::Batch> batch) override {
diff --git a/src/training/graph_group_sync.cpp b/src/training/graph_group_sync.cpp
old mode 100644
new mode 100755
index c02a34afa..b97ac65a1
--- a/src/training/graph_group_sync.cpp
+++ b/src/training/graph_group_sync.cpp
@@ -15,7 +15,7 @@ SyncGraphGroup::SyncGraphGroup(Ptr<Options> config, Ptr<IMPIWrapper> mpi)
 
     graphs_.push_back(graph);
     shardOpt_.push_back(Optimizer(options_));
-    builders_.push_back(models::from_options(options_, models::usage::training));
+    builders_.push_back(models::createCriterionFromOptions(options_, models::usage::training));
   }
 
   // Note: We may well end up with only one MPI process or only one graph per worker.
diff --git a/src/training/graph_group_sync.h b/src/training/graph_group_sync.h
old mode 100644
new mode 100755
index af10d5863..a34bb114d
--- a/src/training/graph_group_sync.h
+++ b/src/training/graph_group_sync.h
@@ -14,7 +14,7 @@ class SyncGraphGroup : public GraphGroup, public ExponentialSmoothing {
   Ptr<IMPIWrapper> mpi_;    // [not null] all MPI-like communication goes through this (this is a dummy implementation if no MPI run)
 
   std::vector<DeviceId> devices_;                  // [deviceIndex]
-  std::vector<Ptr<models::ModelBase>> builders_;   // [deviceIndex]
+  std::vector<Ptr<models::CriterionBase>> builders_;   // [deviceIndex]
   std::vector<Ptr<ExpressionGraph>> graphs_;       // [deviceIndex]
 
   std::vector<Ptr<OptimizerBase>> shardOpt_;       // [deviceIndex]
diff --git a/src/training/validator.h b/src/training/validator.h
old mode 100644
new mode 100755
index 1bbebb91c..44a4f6f70
--- a/src/training/validator.h
+++ b/src/training/validator.h
@@ -159,7 +159,7 @@ class CrossEntropyValidator : public Validator<data::Corpus> {
     opts->merge(options);
     opts->set("inference", true);
     opts->set("cost-type", "ce-sum");
-    builder_ = models::from_options(opts, models::usage::scoring);
+    builder_ = models::createModelFromOptions(opts, models::usage::scoring);
   }
 
   std::string type() override { return options_->get<std::string>("cost-type"); }
@@ -180,7 +180,7 @@ class CrossEntropyValidator : public Validator<data::Corpus> {
       for(auto batch : *batchGenerator_) {
         auto task = [=, &loss, &samples](size_t id) {
           thread_local Ptr<ExpressionGraph> graph;
-          thread_local auto builder = models::from_options(options_, models::usage::scoring);
+          thread_local auto builder = models::createModelFromOptions(options_, models::usage::scoring);
 
           if(!graph) {
             graph = graphs[id % graphs.size()];
@@ -226,7 +226,7 @@ class AccuracyValidator : public Validator<data::Corpus> {
     Ptr<Options> opts = New<Options>();
     opts->merge(options);
     opts->set("inference", true);
-    builder_ = models::from_options(opts, models::usage::raw);
+    builder_ = models::createModelFromOptions(opts, models::usage::raw);
   }
 
   std::string type() override { return "accuracy"; }
@@ -245,7 +245,7 @@ class AccuracyValidator : public Validator<data::Corpus> {
       for(auto batch : *batchGenerator_) {
         auto task = [=, &correct, &totalLabels](size_t id) {
           thread_local Ptr<ExpressionGraph> graph;
-          thread_local auto builder = models::from_options(options_, models::usage::raw);
+          thread_local auto builder = models::createModelFromOptions(options_, models::usage::raw);
 
           if(!graph) {
             graph = graphs[id % graphs.size()];
@@ -319,7 +319,7 @@ class BertAccuracyValidator : public Validator<data::Corpus> {
     Ptr<Options> opts = New<Options>();
     opts->merge(options);
     opts->set("inference", true);
-    builder_ = models::from_options(opts, models::usage::raw);
+    builder_ = models::createModelFromOptions(opts, models::usage::raw);
   }
 
   std::string type() override {
@@ -343,7 +343,7 @@ class BertAccuracyValidator : public Validator<data::Corpus> {
       for(auto batch : *batchGenerator_) {
         auto task = [=, &correct, &totalLabels](size_t id) {
           thread_local Ptr<ExpressionGraph> graph;
-          thread_local auto builder = models::from_options(options_, models::usage::raw);
+          thread_local auto builder = models::createModelFromOptions(options_, models::usage::raw);
           thread_local std::unique_ptr<std::mt19937> engine;
 
           if(!graph) {
@@ -419,7 +419,7 @@ class ScriptValidator : public Validator<data::Corpus> {
 public:
   ScriptValidator(std::vector<Ptr<Vocab>> vocabs, Ptr<Options> options)
       : Validator(vocabs, options, false) {
-    builder_ = models::from_options(options_, models::usage::raw);
+    builder_ = models::createModelFromOptions(options_, models::usage::raw);
 
     ABORT_IF(!options_->hasAndNotEmpty("valid-script-path"),
              "valid-script metric but no script given");
@@ -451,7 +451,7 @@ class TranslationValidator : public Validator<data::Corpus> {
   TranslationValidator(std::vector<Ptr<Vocab>> vocabs, Ptr<Options> options)
       : Validator(vocabs, options, false),
         quiet_(options_->get<bool>("quiet-translation")) {
-    builder_ = models::from_options(options_, models::usage::translation);
+    builder_ = models::createModelFromOptions(options_, models::usage::translation);
 
     if(!options_->hasAndNotEmpty("valid-script-path"))
       LOG_VALID(warn, "No post-processing script given for validating translator");
@@ -475,7 +475,7 @@ class TranslationValidator : public Validator<data::Corpus> {
 
     std::vector<Ptr<Scorer>> scorers;
     for(auto graph : graphs) {
-      auto builder = models::from_options(options_, models::usage::translation);
+      auto builder = models::createModelFromOptions(options_, models::usage::translation);
       Ptr<Scorer> scorer = New<ScorerWrapper>(builder, "", 1.0f, model);
       scorers.push_back(scorer);
     }
@@ -587,7 +587,7 @@ class BleuValidator : public Validator<data::Corpus> {
       : Validator(vocabs, options, false),
         detok_(detok),
         quiet_(options_->get<bool>("quiet-translation")) {
-    builder_ = models::from_options(options_, models::usage::translation);
+    builder_ = models::createModelFromOptions(options_, models::usage::translation);
 
 #ifdef USE_SENTENCEPIECE
     auto vocab = vocabs_.back();
@@ -619,7 +619,7 @@ class BleuValidator : public Validator<data::Corpus> {
 
     std::vector<Ptr<Scorer>> scorers;
     for(auto graph : graphs) {
-      auto builder = models::from_options(options_, models::usage::translation);
+      auto builder = models::createModelFromOptions(options_, models::usage::translation);
       Ptr<Scorer> scorer = New<ScorerWrapper>(builder, "", 1.0f, model);
       scorers.push_back(scorer);
     }
diff --git a/src/translator/beam_search.h b/src/translator/beam_search.h
old mode 100644
new mode 100755
diff --git a/src/translator/scorers.cpp b/src/translator/scorers.cpp
old mode 100644
new mode 100755
index 7a07a8cd5..f49c64fbd
--- a/src/translator/scorers.cpp
+++ b/src/translator/scorers.cpp
@@ -17,7 +17,7 @@ Ptr<Scorer> scorerByType(const std::string& fname,
   }
 
   bool skipCost = options->get<bool>("skip-cost");
-  auto encdec = models::from_options(
+  auto encdec = models::createModelFromOptions(
       options, skipCost ? models::usage::raw : models::usage::translation);
 
   LOG(info, "Loading scorer of type {} as feature {}", type, fname);
@@ -39,7 +39,7 @@ Ptr<Scorer> scorerByType(const std::string& fname,
   }
 
   bool skipCost = options->get<bool>("skip-cost");
-  auto encdec = models::from_options(
+  auto encdec = models::createModelFromOptions(
       options, skipCost ? models::usage::raw : models::usage::translation);
 
   LOG(info, "Loading scorer of type {} as feature {}", type, fname);

From 23ece0040a491ff09e4181b5ab857beeb239607b Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Fri, 22 Feb 2019 13:21:45 -0800
Subject: [PATCH 354/838] ModelBase::build() now returns an Expr, not a
 RationalLoss

---
 src/examples/mnist/model.h      | 28 ++++++++++-----------------
 src/examples/mnist/validator.h  |  6 +++---
 src/models/costs.h              | 34 ++++++++++++++++++++-------------
 src/models/encoder_classifier.h | 26 ++++++++++++-------------
 src/models/encoder_decoder.cpp  | 10 +++++-----
 src/models/encoder_decoder.h    | 24 +++++++++++------------
 src/models/model_base.h         |  6 +++---
 src/rescorer/rescorer.h         |  6 +++---
 src/training/validator.cpp      |  4 ++--
 src/training/validator.h        | 26 ++++++++++++-------------
 vs/Marian.vcxproj               |  3 +++
 vs/Marian.vcxproj.filters       |  9 +++++++++
 12 files changed, 97 insertions(+), 85 deletions(-)
 mode change 100644 => 100755 src/examples/mnist/model.h
 mode change 100644 => 100755 src/models/encoder_decoder.cpp
 mode change 100644 => 100755 src/models/encoder_decoder.h
 mode change 100644 => 100755 src/training/validator.cpp
 mode change 100644 => 100755 vs/Marian.vcxproj
 mode change 100644 => 100755 vs/Marian.vcxproj.filters

diff --git a/src/examples/mnist/model.h b/src/examples/mnist/model.h
old mode 100644
new mode 100755
index 0b07f99a0..2a0e0e6a8
--- a/src/examples/mnist/model.h
+++ b/src/examples/mnist/model.h
@@ -36,27 +36,23 @@ class MNISTCrossEntropyCost : public CostBase {
     // Define a top-level node for training
     // use CE loss
 
-    auto loss = sum(cross_entropy(top->loss(), labels), /*axis =*/ 0);
+    auto loss = sum(cross_entropy(top, labels), /*axis =*/ 0);
     auto multiLoss = New<SumMultiRationalLoss>();
     multiLoss->push_back({loss, (float)vLabels.size()});
     return multiLoss;
   }
 };
 
-class MNISTLogsoftmax : public CostBase {
+class MNISTLogsoftmax : public LogProbBase {
 public:
   MNISTLogsoftmax() {}
 
-  Ptr<MultiRationalLoss> apply(Ptr<ModelBase> model,
+  Expr apply(Ptr<ModelBase> model,
              Ptr<ExpressionGraph> graph,
              Ptr<data::Batch> batch,
              bool clearGraph = true) override {
     auto top = model->build(graph, batch, clearGraph);
-    
-    // @TODO: simplify this
-    auto multiLoss = New<SumMultiRationalLoss>();
-    multiLoss->push_back({logsoftmax(top->loss()), top->count()});
-    return multiLoss;
+    return logsoftmax(top);
   }
 };
 
@@ -68,14 +64,11 @@ class MnistFeedForwardNet : public ModelBase {
   MnistFeedForwardNet(Ptr<Options> options, Args... args)
       : options_(options), inference_(options->get<bool>("inference", false)) {}
 
-  virtual Ptr<RationalLoss> build(Ptr<ExpressionGraph> graph,
+  virtual Expr build(Ptr<ExpressionGraph> graph,
                      Ptr<data::Batch> batch,
                      bool /*clean*/ = false) override {
     
-    auto loss   = construct(graph, batch, inference_); // @TODO: unify nomenclature, e.g. rather use apply
-    auto count = graph->constant({(int)batch->size(), 1}, inits::from_value(1.f));
-
-    return New<RationalLoss>(loss, count);
+    return apply(graph, batch, inference_);
   }
 
   void load(Ptr<ExpressionGraph> /*graph*/, const std::string& /*name*/, bool) override {
@@ -103,8 +96,7 @@ class MnistFeedForwardNet : public ModelBase {
   bool inference_{false};
 
   /**
-   * @brief Constructs an expression graph representing a feed-forward
-   * classifier.
+   * @brief Builds an expression graph representing a feed-forward classifier.
    *
    * @param dims number of nodes in each layer of the feed-forward classifier
    * @param batch a batch of training or testing examples
@@ -112,9 +104,9 @@ class MnistFeedForwardNet : public ModelBase {
    *
    * @return a shared pointer to the newly constructed expression graph
    */
-  virtual Expr construct(Ptr<ExpressionGraph> g,
-                         Ptr<data::Batch> batch,
-                         bool /*inference*/ = false) {
+  virtual Expr apply(Ptr<ExpressionGraph> g,
+                     Ptr<data::Batch> batch,
+                     bool /*inference*/ = false) {
     const std::vector<int> dims = {784, 2048, 2048, 10};
 
     // Start with an empty expression graph
diff --git a/src/examples/mnist/validator.h b/src/examples/mnist/validator.h
index 8a85915a1..b95a83dca 100755
--- a/src/examples/mnist/validator.h
+++ b/src/examples/mnist/validator.h
@@ -12,11 +12,11 @@ using namespace marian;
 
 namespace marian {
 
-class MNISTAccuracyValidator : public Validator<data::MNISTData> {
+class MNISTAccuracyValidator : public Validator<data::MNISTData, models::ModelBase> {
 public:
   MNISTAccuracyValidator(Ptr<Options> options) : Validator(std::vector<Ptr<Vocab>>(), options, false) {
     createBatchGenerator(/*isTranslating=*/false);
-    builder_ = models::createModelFromOptions(options, models::usage::scoring);
+    builder_ = models::createModelFromOptions(options, models::usage::translation);
   }
 
   virtual void keepBest(const std::vector<Ptr<ExpressionGraph>>& graphs) override {
@@ -35,7 +35,7 @@ class MNISTAccuracyValidator : public Validator<data::MNISTData> {
       graphs[0]->forward();
 
       std::vector<float> scores;
-      probs->loss(scores);
+      probs->val()->get(scores);
 
       correct += countCorrect(scores, batch->labels());
       samples += batch->size();
diff --git a/src/models/costs.h b/src/models/costs.h
index eb9af6b0b..8a057034b 100755
--- a/src/models/costs.h
+++ b/src/models/costs.h
@@ -159,17 +159,25 @@ class Trainer : public CriterionBase {
   virtual void clear(Ptr<ExpressionGraph> graph) override { model_->clear(graph); };
 };
 
+class LogProbBase {
+public:
+  virtual Expr apply(Ptr<ModelBase> model,
+                     Ptr<ExpressionGraph> graph,
+                     Ptr<data::Batch> batch,
+                     bool clearGraph = true) = 0;
+};
+
 // @TODO: Name 'scorer' is ambiguous: Does it compute scores for all classes, or the loss value for the ground truth?
 //        Beam search uses it for the former meaning, while 'marian score' and validation in the latter.
 //        This class is for the former use. The latter is done using Trainer.
 class Scorer : public ModelBase {
 protected:
   Ptr<ModelBase> model_;
-  Ptr<CostBase> cost_;
+  Ptr<LogProbBase> logProb_;
 
 public:
-  Scorer(Ptr<ModelBase> model, Ptr<CostBase> cost)
-      : model_(model), cost_(cost) {}
+  Scorer(Ptr<ModelBase> model, Ptr<LogProbBase> cost)
+      : model_(model), logProb_(cost) {}
 
   Ptr<ModelBase> getModel() { return model_; }
 
@@ -185,10 +193,10 @@ class Scorer : public ModelBase {
     model_->save(graph, name, saveTranslatorConfig);
   }
 
-  virtual Ptr<RationalLoss> build(Ptr<ExpressionGraph> graph,
-                                  Ptr<data::Batch> batch,
-                                  bool clearGraph = true) override {
-    return cost_->apply(model_, graph, batch, clearGraph);
+  virtual Expr build(Ptr<ExpressionGraph> graph,
+                     Ptr<data::Batch> batch,
+                     bool clearGraph = true) override {
+    return logProb_->apply(model_, graph, batch, clearGraph);
   };
 
   virtual void clear(Ptr<ExpressionGraph> graph) override { model_->clear(graph); };
@@ -259,9 +267,9 @@ class Stepwise : public EncoderDecoderBase {
 
   virtual void clear(Ptr<ExpressionGraph> graph) override { encdec_->clear(graph); }
 
-  virtual Ptr<RationalLoss> build(Ptr<ExpressionGraph> graph,
-                                  Ptr<data::Batch> batch,
-                                  bool clearGraph = true) override {
+  virtual Expr build(Ptr<ExpressionGraph> graph,
+                     Ptr<data::Batch> batch,
+                     bool clearGraph = true) override {
     auto corpusBatch = std::static_pointer_cast<data::CorpusBatch>(batch);
     return build(graph, corpusBatch, clearGraph);
   }
@@ -282,9 +290,9 @@ class Stepwise : public EncoderDecoderBase {
     return cost_->apply(nextState);
   }
 
-  virtual Ptr<RationalLoss> build(Ptr<ExpressionGraph> /*graph*/,
-                                  Ptr<data::CorpusBatch> /*batch*/,
-                                  bool /*clearGraph*/ = true) override {
+  virtual Expr build(Ptr<ExpressionGraph> /*graph*/,
+                     Ptr<data::CorpusBatch> /*batch*/,
+                     bool /*clearGraph*/ = true) override {
     ABORT("Wrong wrapper. Use models::Trainer or models::Scorer");
     return nullptr;
   }
diff --git a/src/models/encoder_classifier.h b/src/models/encoder_classifier.h
index f3e3645d0..15cbb7d7d 100755
--- a/src/models/encoder_classifier.h
+++ b/src/models/encoder_classifier.h
@@ -41,13 +41,13 @@ class EncoderClassifierBase : public models::ModelBase {
   virtual std::vector<Ptr<ClassifierState>> apply(Ptr<ExpressionGraph>, Ptr<data::CorpusBatch>, bool) = 0;
 
 
-  virtual Ptr<RationalLoss> build(Ptr<ExpressionGraph> graph,
-                                  Ptr<data::Batch> batch,
-                                  bool clearGraph = true) override = 0;
+  virtual Expr build(Ptr<ExpressionGraph> graph,
+                     Ptr<data::Batch> batch,
+                     bool clearGraph = true) override = 0;
 
-  virtual Ptr<RationalLoss> build(Ptr<ExpressionGraph> graph,
-                                  Ptr<data::CorpusBatch> batch,
-                                  bool clearGraph = true) = 0;
+  virtual Expr build(Ptr<ExpressionGraph> graph,
+                     Ptr<data::CorpusBatch> batch,
+                     bool clearGraph = true) = 0;
 
   virtual Ptr<Options> getOptions() = 0;
 };
@@ -206,17 +206,17 @@ class EncoderClassifier : public EncoderClassifierBase {
     return classifierStates;
   }
 
-  virtual Ptr<RationalLoss> build(Ptr<ExpressionGraph> graph,
-                                  Ptr<data::CorpusBatch> batch,
-                                  bool clearGraph = true) override {
+  virtual Expr build(Ptr<ExpressionGraph> graph,
+                          Ptr<data::CorpusBatch> batch,
+                          bool clearGraph = true) override {
     auto states = apply(graph, batch, clearGraph);
     // returns raw logits
-    return New<RationalLoss>(states[0]->getLogProbs(), nullptr); // @TODO: Check if this is actually used
+    return states[0]->getLogProbs();
   }
 
-  virtual Ptr<RationalLoss> build(Ptr<ExpressionGraph> graph,
-                                  Ptr<data::Batch> batch,
-                                  bool clearGraph = true) override {
+  virtual Expr build(Ptr<ExpressionGraph> graph,
+                          Ptr<data::Batch> batch,
+                          bool clearGraph = true) override {
     auto corpusBatch = std::static_pointer_cast<data::CorpusBatch>(batch);
     return build(graph, corpusBatch, clearGraph);
   }
diff --git a/src/models/encoder_decoder.cpp b/src/models/encoder_decoder.cpp
old mode 100644
new mode 100755
index 4b072c73a..926cae943
--- a/src/models/encoder_decoder.cpp
+++ b/src/models/encoder_decoder.cpp
@@ -196,16 +196,16 @@ Ptr<DecoderState> EncoderDecoder::stepAll(Ptr<ExpressionGraph> graph,
   return nextState;
 }
 
-Ptr<RationalLoss> EncoderDecoder::build(Ptr<ExpressionGraph> graph,
-                                        Ptr<data::CorpusBatch> batch,
-                                        bool clearGraph) {
+Expr EncoderDecoder::build(Ptr<ExpressionGraph> graph,
+                           Ptr<data::CorpusBatch> batch,
+                           bool clearGraph) {
   auto state = stepAll(graph, batch, clearGraph);
 
   // returns raw logits
-  return New<RationalLoss>(state->getLogProbs(), state->getTargetMask()); // @TODO: hacky hack hack
+  return state->getLogProbs();  // , state->getTargetMask()); // @TODO: hacky hack hack
 }
 
-Ptr<RationalLoss> EncoderDecoder::build(Ptr<ExpressionGraph> graph,
+Expr EncoderDecoder::build(Ptr<ExpressionGraph> graph,
                            Ptr<data::Batch> batch,
                            bool clearGraph) {
   auto corpusBatch = std::static_pointer_cast<data::CorpusBatch>(batch);
diff --git a/src/models/encoder_decoder.h b/src/models/encoder_decoder.h
old mode 100644
new mode 100755
index b55e8bd1d..b0f5b3145
--- a/src/models/encoder_decoder.h
+++ b/src/models/encoder_decoder.h
@@ -28,13 +28,13 @@ class EncoderDecoderBase : public models::ModelBase {
 
   virtual void clear(Ptr<ExpressionGraph> graph) override = 0;
 
-  virtual Ptr<RationalLoss> build(Ptr<ExpressionGraph> graph,
-                                  Ptr<data::Batch> batch,
-                                  bool clearGraph = true) override = 0;
+  virtual Expr build(Ptr<ExpressionGraph> graph,
+                     Ptr<data::Batch> batch,
+                     bool clearGraph = true) override = 0;
 
-  virtual Ptr<RationalLoss> build(Ptr<ExpressionGraph> graph,
-                                  Ptr<data::CorpusBatch> batch,
-                                  bool clearGraph = true) = 0;  
+  virtual Expr build(Ptr<ExpressionGraph> graph,
+                     Ptr<data::CorpusBatch> batch,
+                     bool clearGraph = true) = 0;  
                                   
   virtual Ptr<DecoderState> startState(Ptr<ExpressionGraph> graph,
                                        Ptr<data::CorpusBatch> batch) = 0;
@@ -156,13 +156,13 @@ class EncoderDecoder : public EncoderDecoderBase {
                                     Ptr<data::CorpusBatch> batch,
                                     bool clearGraph = true);
 
-  virtual Ptr<RationalLoss> build(Ptr<ExpressionGraph> graph,
-                                  Ptr<data::CorpusBatch> batch,
-                                  bool clearGraph = true) override;
+  virtual Expr build(Ptr<ExpressionGraph> graph,
+                          Ptr<data::CorpusBatch> batch,
+                          bool clearGraph = true) override;
 
-  virtual Ptr<RationalLoss> build(Ptr<ExpressionGraph> graph,
-                                  Ptr<data::Batch> batch,
-                                  bool clearGraph = true) override;
+  virtual Expr build(Ptr<ExpressionGraph> graph,
+                          Ptr<data::Batch> batch,
+                          bool clearGraph = true) override;
 };
 
 }  // namespace marian
diff --git a/src/models/model_base.h b/src/models/model_base.h
index bbc7aca33..0113a7ec0 100755
--- a/src/models/model_base.h
+++ b/src/models/model_base.h
@@ -28,9 +28,9 @@ class ModelBase {
                     bool saveTranslatorConfig = false)
       = 0;
 
-  virtual Ptr<RationalLoss> build(Ptr<ExpressionGraph> graph,
-                                  Ptr<data::Batch> batch,
-                                  bool clearGraph = true)
+  virtual Expr build(Ptr<ExpressionGraph> graph,
+                          Ptr<data::Batch> batch,
+                          bool clearGraph = true)
       = 0;
 
   virtual void clear(Ptr<ExpressionGraph> graph) = 0;
diff --git a/src/rescorer/rescorer.h b/src/rescorer/rescorer.h
index 372fc17f6..011b6b58c 100755
--- a/src/rescorer/rescorer.h
+++ b/src/rescorer/rescorer.h
@@ -19,11 +19,11 @@ using namespace data;
 
 class Rescorer {
 private:
-  Ptr<models::ModelBase> builder_;
+  Ptr<models::CriterionBase> builder_;
 
 public:
   Rescorer(Ptr<Options> options)
-      : builder_(models::createModelFromOptions(options, models::usage::scoring)) {}
+      : builder_(models::createCriterionFromOptions(options, models::usage::scoring)) {}
 
   void load(Ptr<ExpressionGraph> graph, const std::string& modelFile) {
     builder_->load(graph, modelFile);
@@ -34,7 +34,7 @@ class Rescorer {
   }
 
   data::SoftAlignment getAlignment() {
-    auto model = std::static_pointer_cast<models::Scorer>(builder_)->getModel();
+    auto model = std::static_pointer_cast<models::Trainer>(builder_)->getModel();
     return std::static_pointer_cast<EncoderDecoderBase>(model)->getAlignment();
   }
 };
diff --git a/src/training/validator.cpp b/src/training/validator.cpp
old mode 100644
new mode 100755
index 14b854ff3..f6b1c86a1
--- a/src/training/validator.cpp
+++ b/src/training/validator.cpp
@@ -2,10 +2,10 @@
 
 namespace marian {
 
-std::vector<Ptr<Validator<data::Corpus>>> Validators(
+std::vector<Ptr<ValidatorBase/*<data::Corpus>*/>> Validators(
     std::vector<Ptr<Vocab>> vocabs,
     Ptr<Options> config) {
-  std::vector<Ptr<Validator<data::Corpus>>> validators;
+  std::vector<Ptr<ValidatorBase/*<data::Corpus>*/>> validators;
 
   auto validMetrics = config->get<std::vector<std::string>>("valid-metrics");
 
diff --git a/src/training/validator.h b/src/training/validator.h
index 44a4f6f70..d49ded8f6 100755
--- a/src/training/validator.h
+++ b/src/training/validator.h
@@ -56,7 +56,7 @@ class ValidatorBase : public TrainingObserver {
   }
 };
 
-template <class DataSet>
+template <class DataSet, class BuilderType>
 class Validator : public ValidatorBase {
 public:
   Validator(std::vector<Ptr<Vocab>> vocabs, Ptr<Options> options, bool lowerIsBetter = true)
@@ -123,7 +123,7 @@ class Validator : public ValidatorBase {
 protected:
   std::vector<Ptr<Vocab>> vocabs_;
   Ptr<Options> options_;
-  Ptr<models::ModelBase> builder_;
+  Ptr<BuilderType> builder_;
   Ptr<data::BatchGenerator<DataSet>> batchGenerator_;
 
   virtual float validateBG(const std::vector<Ptr<ExpressionGraph>>&)
@@ -148,7 +148,7 @@ class Validator : public ValidatorBase {
   }
 };
 
-class CrossEntropyValidator : public Validator<data::Corpus> {
+class CrossEntropyValidator : public Validator<data::Corpus, models::CriterionBase> {
 public:
   CrossEntropyValidator(std::vector<Ptr<Vocab>> vocabs, Ptr<Options> options)
       : Validator(vocabs, options) {
@@ -159,7 +159,7 @@ class CrossEntropyValidator : public Validator<data::Corpus> {
     opts->merge(options);
     opts->set("inference", true);
     opts->set("cost-type", "ce-sum");
-    builder_ = models::createModelFromOptions(opts, models::usage::scoring);
+    builder_ = models::createCriterionFromOptions(opts, models::usage::scoring);
   }
 
   std::string type() override { return options_->get<std::string>("cost-type"); }
@@ -180,7 +180,7 @@ class CrossEntropyValidator : public Validator<data::Corpus> {
       for(auto batch : *batchGenerator_) {
         auto task = [=, &loss, &samples](size_t id) {
           thread_local Ptr<ExpressionGraph> graph;
-          thread_local auto builder = models::createModelFromOptions(options_, models::usage::scoring);
+          thread_local auto builder = models::createCriterionFromOptions(options_, models::usage::scoring);
 
           if(!graph) {
             graph = graphs[id % graphs.size()];
@@ -215,8 +215,8 @@ class CrossEntropyValidator : public Validator<data::Corpus> {
   }
 };
 
-// Used for validating with classifiers. Compute prediction accuary versus groundtruth for a set of classes
-class AccuracyValidator : public Validator<data::Corpus> {
+// Used for validating with classifiers. Compute prediction accuracy versus ground truth for a set of classes
+class AccuracyValidator : public Validator<data::Corpus, models::ModelBase> {
 public:
   AccuracyValidator(std::vector<Ptr<Vocab>> vocabs, Ptr<Options> options)
       : Validator(vocabs, options, /*lowerIsBetter=*/false) {
@@ -263,7 +263,7 @@ class AccuracyValidator : public Validator<data::Corpus> {
           // correct     += correct->scalar<IndexType>();
 
           builder->clear(graph);
-          Expr logits = builder->build(graph, batch)->loss();
+          Expr logits = builder->build(graph, batch);
           graph->forward();
 
           std::vector<float> vLogits;
@@ -305,7 +305,7 @@ class AccuracyValidator : public Validator<data::Corpus> {
   }
 };
 
-class BertAccuracyValidator : public Validator<data::Corpus> {
+class BertAccuracyValidator : public Validator<data::Corpus, models::ModelBase> {
 private:
   bool evalMaskedLM_{true};
 
@@ -415,7 +415,7 @@ class BertAccuracyValidator : public Validator<data::Corpus> {
 };
 
 
-class ScriptValidator : public Validator<data::Corpus> {
+class ScriptValidator : public Validator<data::Corpus, models::ModelBase> {
 public:
   ScriptValidator(std::vector<Ptr<Vocab>> vocabs, Ptr<Options> options)
       : Validator(vocabs, options, false) {
@@ -446,7 +446,7 @@ class ScriptValidator : public Validator<data::Corpus> {
   }
 };
 
-class TranslationValidator : public Validator<data::Corpus> {
+class TranslationValidator : public Validator<data::Corpus, models::ModelBase> {
 public:
   TranslationValidator(std::vector<Ptr<Vocab>> vocabs, Ptr<Options> options)
       : Validator(vocabs, options, false),
@@ -578,7 +578,7 @@ class TranslationValidator : public Validator<data::Corpus> {
 };
 
 // @TODO: combine with TranslationValidator (above) to avoid code duplication
-class BleuValidator : public Validator<data::Corpus> {
+class BleuValidator : public Validator<data::Corpus, models::ModelBase> {
 private:
   bool detok_{false};
 
@@ -844,7 +844,7 @@ class BleuValidator : public Validator<data::Corpus> {
  *
  * @return Vector of validator objects
  */
-std::vector<Ptr<Validator<data::Corpus>>> Validators(
+std::vector<Ptr<ValidatorBase/*<data::Corpus>*/>> Validators(
     std::vector<Ptr<Vocab>> vocabs,
     Ptr<Options> config);
 }  // namespace marian
diff --git a/vs/Marian.vcxproj b/vs/Marian.vcxproj
old mode 100644
new mode 100755
index a6c560d3a..8edada060
--- a/vs/Marian.vcxproj
+++ b/vs/Marian.vcxproj
@@ -894,10 +894,13 @@
     <ClInclude Include="..\src\layers\word2vec_reader.h" />
     <ClInclude Include="..\src\microsoft\quicksand.h" />
     <ClInclude Include="..\src\models\amun.h" />
+    <ClInclude Include="..\src\models\bert.h" />
     <ClInclude Include="..\src\models\char_s2s.h" />
+    <ClInclude Include="..\src\models\classifier.h" />
     <ClInclude Include="..\src\models\costs.h" />
     <ClInclude Include="..\src\models\decoder.h" />
     <ClInclude Include="..\src\models\encoder.h" />
+    <ClInclude Include="..\src\models\encoder_classifier.h" />
     <ClInclude Include="..\src\models\encoder_decoder.h" />
     <ClInclude Include="..\src\models\model_base.h" />
     <ClInclude Include="..\src\models\model_factory.h" />
diff --git a/vs/Marian.vcxproj.filters b/vs/Marian.vcxproj.filters
old mode 100644
new mode 100755
index d9e56843f..81d6d69d4
--- a/vs/Marian.vcxproj.filters
+++ b/vs/Marian.vcxproj.filters
@@ -1517,6 +1517,15 @@
     <ClInclude Include="..\src\examples\mnist\validator.h">
       <Filter>examples\mnist</Filter>
     </ClInclude>
+    <ClInclude Include="..\src\models\bert.h">
+      <Filter>models</Filter>
+    </ClInclude>
+    <ClInclude Include="..\src\models\classifier.h">
+      <Filter>models</Filter>
+    </ClInclude>
+    <ClInclude Include="..\src\models\encoder_classifier.h">
+      <Filter>models</Filter>
+    </ClInclude>
   </ItemGroup>
   <ItemGroup>
     <Filter Include="3rd_party">

From 359398dffb21271466abeae27f18c721956e8eb5 Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Fri, 22 Feb 2019 20:02:31 -0800
Subject: [PATCH 355/838] bug fix: cusparse sgemmi can't handle matrices wider
 than 65535; made Logits() constructor explicit; FactoredVocab::csr_rows() no
 longer inserts dummy zeros

---
 src/common/definitions.h        |  7 ++++
 src/common/types.h              |  4 +++
 src/data/factored_vocab.cpp     |  2 +-
 src/examples/mnist/model.h      |  2 +-
 src/layers/generic.cpp          |  4 +--
 src/layers/generic.h            |  4 +--
 src/models/costs.h              |  2 +-
 src/models/encoder_classifier.h |  2 +-
 src/models/s2s.h                |  2 +-
 src/tensors/gpu/prod.cpp        | 59 +++++++++++++++++++++++++++++++--
 10 files changed, 76 insertions(+), 12 deletions(-)
 mode change 100644 => 100755 src/common/definitions.h
 mode change 100644 => 100755 src/common/types.h

diff --git a/src/common/definitions.h b/src/common/definitions.h
old mode 100644
new mode 100755
index 21ee816fc..393a8b7fe
--- a/src/common/definitions.h
+++ b/src/common/definitions.h
@@ -13,6 +13,13 @@
 #define THREAD_GUARD(body) [&]() { body; }() // test if THREAD_GUARD is neccessary, remove if no problems occur.
 #define NodeOp(op) [=]() { op; }
 
+// helper macro to disable optimization (gcc only)
+#ifdef _GNUC_
+#define DONT_OPTIMIZE __attribute__((optimize("O0")))
+#else
+#define DONT_OPTIMIZE // silently ignore on Visual Studio, where this is less of a problem
+#endif
+
 namespace marian {
 
 // Type to be used for all index types, e.g. for integer tensors for rows operator.
diff --git a/src/common/types.h b/src/common/types.h
old mode 100644
new mode 100755
index f25520f02..3e9adf0e8
--- a/src/common/types.h
+++ b/src/common/types.h
@@ -73,6 +73,10 @@ template <> inline bool matchType<float>(Type type)  { return type == Type::floa
 template <> inline bool matchType<double>(Type type) { return type == Type::float64; }
 // clang-format on
 
+template <typename T> inline Type getType();
+template <> inline Type getType<float>() { return Type::float32; }
+template <> inline Type getType<uint32_t>() { return Type::uint32; }
+
 static inline std::ostream& operator<<(std::ostream& out, Type type) {
   switch(type) {
     case Type::int8: out << "int8"; break;
diff --git a/src/data/factored_vocab.cpp b/src/data/factored_vocab.cpp
index 4c5b5acea..bbae31127 100755
--- a/src/data/factored_vocab.cpp
+++ b/src/data/factored_vocab.cpp
@@ -447,7 +447,7 @@ FactoredVocab::CSRData FactoredVocab::csr_rows(const Words& words) const {
         weights.push_back(1.0f);
       }
     }
-#if 1
+#if 0 // @BUGBUG: No, this is wrong! The vector must be 1s, since we use it in backprop transposition.
     else {
       // push a dummy entry. Not sure if this is needed.
       indices.push_back(0);
diff --git a/src/examples/mnist/model.h b/src/examples/mnist/model.h
index 38a5d12c4..70122a0f0 100755
--- a/src/examples/mnist/model.h
+++ b/src/examples/mnist/model.h
@@ -68,7 +68,7 @@ class MnistFeedForwardNet : public ModelBase {
                      Ptr<data::Batch> batch,
                      bool /*clean*/ = false) override {
     
-    return apply(graph, batch, inference_);
+    return Logits(apply(graph, batch, inference_));
   }
 
   void load(Ptr<ExpressionGraph> /*graph*/, const std::string& /*name*/, bool) override {
diff --git a/src/layers/generic.cpp b/src/layers/generic.cpp
index 6e4544cf0..6b0ef93e2 100755
--- a/src/layers/generic.cpp
+++ b/src/layers/generic.cpp
@@ -235,7 +235,7 @@ namespace marian {
           cachedShortWt_ = index_select(Wt_, isLegacyUntransposedW ? -1 : 0, shortlist_->indices());
           cachedShortb_  = index_select(b_ ,                             -1, shortlist_->indices());
         }
-        return affine(input, cachedShortWt_, cachedShortb_, false, /*transB=*/isLegacyUntransposedW ? false : true);
+        return Logits(affine(input, cachedShortWt_, cachedShortb_, false, /*transB=*/isLegacyUntransposedW ? false : true));
       }
       else if (factoredVocab_) {
         auto graph = input->graph();
@@ -257,7 +257,7 @@ namespace marian {
         return Logits(std::move(allLogits), factoredVocab_);
       }
       else
-        return affine(input, Wt_, b_, false, /*transB=*/isLegacyUntransposedW ? false : true);
+        return Logits(affine(input, Wt_, b_, false, /*transB=*/isLegacyUntransposedW ? false : true));
     }
   }
 
diff --git a/src/layers/generic.h b/src/layers/generic.h
index 838ee8754..e83801aa2 100755
--- a/src/layers/generic.h
+++ b/src/layers/generic.h
@@ -66,10 +66,10 @@ class RationalLoss;
 class Logits {
 public:
     Logits() {}
-    Logits(Ptr<RationalLoss> logits) { // single-output constructor
+    explicit Logits(Ptr<RationalLoss> logits) { // single-output constructor
       logits_.push_back(logits);
     }
-    Logits(Expr logits); // single-output constructor from Expr only (RationalLoss has no count)
+    explicit Logits(Expr logits); // single-output constructor from Expr only (RationalLoss has no count)
     Logits(std::vector<Ptr<RationalLoss>>&& logits, Ptr<FactoredVocab> embeddingFactorMapping) // factored-output constructor
       : logits_(std::move(logits)), factoredVocab_(embeddingFactorMapping) {}
     Expr getLogits() const; // assume it holds logits: get them, possibly aggregating over factors
diff --git a/src/models/costs.h b/src/models/costs.h
index 2e4054bf5..306b29138 100755
--- a/src/models/costs.h
+++ b/src/models/costs.h
@@ -117,7 +117,7 @@ class EncoderClassifierCE : public CostBase {
     // multi-objective training
     Ptr<MultiRationalLoss> multiLoss = newMultiLoss(options_);
     for(int i = 0; i < states.size(); ++i) {
-      auto partialLoss = loss_->apply(states[i]->getLogProbs(),
+      auto partialLoss = loss_->apply(Logits(states[i]->getLogProbs()),
                                       states[i]->getTargetWords(),
                                       /*mask=*/nullptr,
                                       /*weights=*/nullptr);
diff --git a/src/models/encoder_classifier.h b/src/models/encoder_classifier.h
index d32160d01..f6ffbebfc 100755
--- a/src/models/encoder_classifier.h
+++ b/src/models/encoder_classifier.h
@@ -211,7 +211,7 @@ class EncoderClassifier : public EncoderClassifierBase {
                        bool clearGraph = true) override {
     auto states = apply(graph, batch, clearGraph);
     // returns raw logits
-    return states[0]->getLogProbs();
+    return Logits(states[0]->getLogProbs());
   }
 
   virtual Logits build(Ptr<ExpressionGraph> graph,
diff --git a/src/models/s2s.h b/src/models/s2s.h
index 8a18e346b..a63c98418 100755
--- a/src/models/s2s.h
+++ b/src/models/s2s.h
@@ -368,7 +368,7 @@ class DecoderS2S : public DecoderBase {
 
     // return unormalized(!) probabilities
     auto nextState = New<DecoderState>(
-        decoderStates, logits, state->getEncoderStates(), state->getBatch());
+        decoderStates, Logits(logits), state->getEncoderStates(), state->getBatch());
 
     // Advance current target token position by one
     nextState->setPosition(state->getPosition() + 1);
diff --git a/src/tensors/gpu/prod.cpp b/src/tensors/gpu/prod.cpp
index d13081728..f259f1c47 100755
--- a/src/tensors/gpu/prod.cpp
+++ b/src/tensors/gpu/prod.cpp
@@ -227,6 +227,40 @@ void ProdBatched(marian::Tensor C,
   allocator->free(mp_cptr);
 }
 
+// debugging helper   --@TODO: move it to some shared place?
+template<typename T>
+static std::vector<T> get(Ptr<MemoryPiece> memory, Ptr<marian::Backend> backend) {
+  size_t n = memory->size() / sizeof(T);
+  TensorBase t(memory, Shape({(int)n}), getType<T>(), backend);
+  std::vector<T> res;
+  t.get(res);
+  return res;
+}
+
+// bug in cuSparse: sparse matrix is limited to 65535 columns
+// This function is a drop-in replacement that handles it (by slicing).
+cusparseStatus_t
+static cusparseSgemmiEx(cusparseHandle_t handle, int m,
+  int n, // the offending number of columns of matrices B and C
+  int k, int nnz, const float *alpha, const float *A, int lda,
+  const float *cscValB, const int *cscColPtrB, const int *cscRowIndB, const float *beta,
+  float *C, int ldc)
+{
+  const int nMax = 65535; // max. number of columns allowed by cuSparse 10 implementation
+  for (int j0 = 0; j0 < n; j0 += 65535) { // loop over column slices, j0 = index of first column
+    // Call original function on a column slice.
+    // Replace all parameters that relate to the column slice.
+    // nnz does not need to be corrected.
+    auto n1 = std::min(n - j0, nMax);   // width of column slice is limited to max
+    auto C1 = C + j0 * ldc;             // column slice into result matrix C
+    auto cscColPtrB1 = cscColPtrB + j0; // column slice into sparse factor B
+    auto rc = cusparseSgemmi(handle, m, n1, k, nnz, alpha, A, lda, cscValB, cscColPtrB1, cscRowIndB, beta, C1, ldc);
+    if (rc != CUSPARSE_STATUS_SUCCESS)
+      return rc;
+  }
+  return CUSPARSE_STATUS_SUCCESS;
+}
+
 // C = op(S) x D if not swapOperands else C = D x op(S)
 // op(S) = S if not transA else S^T
 void CSRProd(marian::Tensor C,
@@ -315,7 +349,23 @@ void CSRProd(marian::Tensor C,
   else {
     // C = S x D for row-major matrices
     // Implemented via cusparse as C' = D' x S' ("gemmi") where C' and D' are column-major.
-    CUSPARSE_CHECK(cusparseSgemmi(cusparseHandle,
+    //if (St_values) {
+    //  auto vals = get<float>(St_values, C->getBackend());  vals.resize(numValues);
+    //  auto inds = get<IndexType>(St_indices, C->getBackend()); inds.resize(numValues);
+    //  auto offs = get<IndexType>(St_offsets, C->getBackend()); offs.resize(rowsS + 1);
+    //  LOG(info, "[{} x {}] = [{} x {}] * [{} x {}]", colsC, rowsC, colsD, rowsD, -1, offs.size() - 1);
+    //  for (auto v : vals)
+    //    ABORT_IF(v != 1, "v={}", v);
+    //  for (auto i : inds)
+    //    ABORT_IF(i >= rowsC, "i={}", i);
+    //  for (auto o : offs)
+    //    ABORT_IF(o > inds.size(), "o={}", o);
+    //  ABORT_IF(colsC != colsD, "0");
+    //  std::vector<float> dData; D->get(dData); ABORT_IF(dData.size() != colsD * rowsD, "1");
+    //  std::vector<float> cData; C->get(cData); ABORT_IF(cData.size() != colsC * rowsC, "2");
+    //  ABORT_IF(rowsC != rowsS, "3: {} != {}, asz={}", rowsC, rowsS, St_offsets->size()/4);
+    //}
+    CUSPARSE_CHECK(cusparseSgemmiEx(cusparseHandle,
         /*m=*/ colsD, // #rows of first (col-major) factor = #cols of row-major D
         /*n=*/ rowsC, // #cols of second (CSC) factor and (col-major) result = #rows of row-major C
         /*k=*/ rowsD, // #cols of first (col-major) factor = #rows of row-major D
@@ -324,11 +374,14 @@ void CSRProd(marian::Tensor C,
         /*A=*/ D->data(),
         /*lda=*/ colsD, // stride
         /*cscValB=*/    St_values  ? St_values ->data<float>() :       S_values ->data<float>(),
-        /*cscRowPtrB=*/ St_offsets ? St_offsets->data<int>()   : (int*)S_offsets->data<IndexType>(),
-        /*cscColIndB=*/ St_indices ? St_indices->data<int>()   : (int*)S_indices->data<IndexType>(),
+        /*cscColPtrB=*/ St_offsets ? St_offsets->data<int>()   : (int*)S_offsets->data<IndexType>(),
+        /*cscRowIndB=*/ St_indices ? St_indices->data<int>()   : (int*)S_indices->data<IndexType>(),
         &beta,
         C->data(),
         /*ldc=*/ colsC)); // stride
+    // Note: cuSparse 10 docs says this about cscColPtrB:
+    //   "integer array of k + 1 elements that contains the start of every row and the end of the last row plus one."
+    // This is wrong. It should be col instead of row, and n instead of k.
   }
   if(St_values ) allocator->free(St_values );
   if(St_indices) allocator->free(St_indices);

From a2d381d65c9330dadbab520b9d4f58a351c525fa Mon Sep 17 00:00:00 2001
From: Nikolay Bogoychev <nheart@gmail.com>
Date: Sun, 24 Feb 2019 19:59:26 +0000
Subject: [PATCH 356/838] A more intelligent way to detect cblas if it's not
 included in openblas

---
 CMakeLists.txt        |   2 +-
 cmake/FindCBLAS.cmake | 181 ++++++++++++++++++++++++++++++++++++++++++
 2 files changed, 182 insertions(+), 1 deletion(-)
 create mode 100644 cmake/FindCBLAS.cmake

diff --git a/CMakeLists.txt b/CMakeLists.txt
index b8537d20f..022b97019 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -241,7 +241,7 @@ if(COMPILE_CPU)
     find_package(BLAS)
     if(BLAS_FOUND)
       include_directories(${BLAS_INCLUDE_DIR})
-      set(EXT_LIBS ${EXT_LIBS} ${BLAS_LIBRARIES} cblas)
+      set(EXT_LIBS ${EXT_LIBS} ${BLAS_LIBRARIES})
       add_definitions(-DBLAS_FOUND=1)
     endif(BLAS_FOUND)
   endif(MKL_FOUND)
diff --git a/cmake/FindCBLAS.cmake b/cmake/FindCBLAS.cmake
new file mode 100644
index 000000000..e7426014e
--- /dev/null
+++ b/cmake/FindCBLAS.cmake
@@ -0,0 +1,181 @@
+# - Find CBLAS library
+#
+# This module finds an installed fortran library that implements the CBLAS 
+# linear-algebra interface (see http://www.netlib.org/blas/), with CBLAS
+# interface.
+#
+# This module sets the following variables:
+#  CBLAS_FOUND - set to true if a library implementing the CBLAS interface
+#    is found
+#  CBLAS_LINKER_FLAGS - uncached list of required linker flags (excluding -l
+#    and -L).
+#  CBLAS_LIBRARIES - uncached list of libraries (using full path name) to 
+#    link against to use CBLAS
+#  CBLAS_INCLUDE_DIR - path to includes
+#  CBLAS_INCLUDE_FILE - the file to be included to use CBLAS
+#
+
+## Based on https://github.com/Eyescale/CMake/blob/master/FindCBLAS.cmake
+
+INCLUDE(CheckFunctionExists)
+INCLUDE(CheckIncludeFile)
+
+MACRO(CHECK_ALL_LIBRARIES LIBRARIES _prefix _name _flags _list _include _search_include)
+  # This macro checks for the existence of the combination of fortran libraries
+  # given by _list.  If the combination is found, this macro checks (using the 
+  # Check_Fortran_Function_Exists macro) whether can link against that library
+  # combination using the name of a routine given by _name using the linker
+  # flags given by _flags.  If the combination of libraries is found and passes
+  # the link test, LIBRARIES is set to the list of complete library paths that
+  # have been found.  Otherwise, LIBRARIES is set to FALSE.
+  
+  # N.B. _prefix is the prefix applied to the names of all cached variables that
+  # are generated internally and marked advanced by this macro.
+
+  SET(__list)
+  FOREACH(_elem ${_list})
+    IF(__list)
+      SET(__list "${__list} - ${_elem}")
+    ELSE(__list)
+      SET(__list "${_elem}")
+    ENDIF(__list)
+  ENDFOREACH(_elem)
+  MESSAGE(STATUS "Checking for [${__list}]")
+  SET(_libraries_work TRUE)
+  SET(${LIBRARIES})
+  SET(_combined_name)
+  SET(_paths)
+  FOREACH(_library ${_list})
+    SET(_combined_name ${_combined_name}_${_library})
+
+    # did we find all the libraries in the _list until now?
+    # (we stop at the first unfound one)
+    IF(_libraries_work)      
+      IF(APPLE) 
+        FIND_LIBRARY(${_prefix}_${_library}_LIBRARY
+          NAMES ${_library}
+          PATHS /usr/local/lib /usr/lib /usr/local/lib64 /usr/lib64 ENV 
+          DYLD_LIBRARY_PATH 
+          )
+      ELSE(APPLE)
+        FIND_LIBRARY(${_prefix}_${_library}_LIBRARY
+          NAMES ${_library}
+          PATHS /usr/local/lib /usr/lib /usr/local/lib64 /usr/lib64 ENV 
+          LD_LIBRARY_PATH 
+          )
+      ENDIF(APPLE)
+      MARK_AS_ADVANCED(${_prefix}_${_library}_LIBRARY)
+      IF(${_prefix}_${_library}_LIBRARY)
+        GET_FILENAME_COMPONENT(_path ${${_prefix}_${_library}_LIBRARY} PATH)
+        LIST(APPEND _paths ${_path}/../include ${_path}/../../include)
+      ENDIF(${_prefix}_${_library}_LIBRARY)
+      SET(${LIBRARIES} ${${LIBRARIES}} ${${_prefix}_${_library}_LIBRARY})
+      SET(_libraries_work ${${_prefix}_${_library}_LIBRARY})
+    ENDIF(_libraries_work)
+  ENDFOREACH(_library ${_list})
+
+  # Test include
+  SET(_bug_search_include ${_search_include}) #CMAKE BUG!!! SHOULD NOT BE THAT
+  IF(_bug_search_include)
+    FIND_PATH(${_prefix}${_combined_name}_INCLUDE ${_include} ${_paths})
+    MARK_AS_ADVANCED(${_prefix}${_combined_name}_INCLUDE)
+    IF(${_prefix}${_combined_name}_INCLUDE)
+      MESSAGE(STATUS "Checking for [${__list}] -- includes found")
+      SET(${_prefix}_INCLUDE_DIR ${${_prefix}${_combined_name}_INCLUDE})
+      SET(${_prefix}_INCLUDE_FILE ${_include})
+    ELSE(${_prefix}${_combined_name}_INCLUDE)
+      MESSAGE(STATUS "Checking for [${__list}] -- includes not found")
+      SET(_libraries_work FALSE)
+    ENDIF(${_prefix}${_combined_name}_INCLUDE)
+  ELSE(_bug_search_include)
+    SET(${_prefix}_INCLUDE_DIR)
+    SET(${_prefix}_INCLUDE_FILE ${_include})
+  ENDIF(_bug_search_include)
+
+  IF(_libraries_work)
+    # Test this combination of libraries.
+    SET(CMAKE_REQUIRED_LIBRARIES ${_flags} ${${LIBRARIES}})
+    CHECK_FUNCTION_EXISTS(${_name} ${_prefix}${_combined_name}_WORKS)
+    SET(CMAKE_REQUIRED_LIBRARIES)
+    MARK_AS_ADVANCED(${_prefix}${_combined_name}_WORKS)
+    SET(_libraries_work ${${_prefix}${_combined_name}_WORKS})
+
+    IF(_libraries_work)
+      MESSAGE(STATUS "Checking for [${__list}] -- libraries found")
+    ENDIF(_libraries_work)
+
+  ENDIF(_libraries_work)
+  
+
+  IF(NOT _libraries_work)
+    SET(${LIBRARIES} FALSE)
+  ENDIF(NOT _libraries_work)
+
+ENDMACRO(CHECK_ALL_LIBRARIES)
+
+SET(CBLAS_LINKER_FLAGS)
+SET(CBLAS_LIBRARIES)
+
+# CBLAS in openBLAS
+IF(NOT CBLAS_LIBRARIES)
+  CHECK_ALL_LIBRARIES(
+    CBLAS_LIBRARIES
+    cblas
+    cblas_sgemm
+    ""
+    "openblas"
+    "cblas.h"
+    TRUE
+    )
+ENDIF(NOT CBLAS_LIBRARIES)
+
+#MESSAGE(STATUS ${openblas_INCLUDE_DIR})
+
+# CBLAS in CBLAS
+IF(NOT CBLAS_LIBRARIES)
+  CHECK_ALL_LIBRARIES(
+    CBLAS_LIBRARIES
+    cblas
+    cblas_sgemm
+    ""
+    "cblas"
+    "cblas.h"
+    TRUE
+    )
+ENDIF(NOT CBLAS_LIBRARIES)
+
+#MESSAGE(STATUS ${cblas_INCLUDE_DIR})
+
+# CBLAS in lapacke
+IF(NOT CBLAS_LIBRARIES)
+  CHECK_ALL_LIBRARIES(
+    CBLAS_LIBRARIES
+    cblas
+    cblas_sgemm
+    ""
+    "lapacke"
+    "cblas.h"
+    TRUE
+    )
+ENDIF(NOT CBLAS_LIBRARIES)
+
+#MESSAGE(STATUS ${lapacke_INCLUDE_DIR})
+
+IF(CBLAS_LIBRARIES)
+  SET(CBLAS_FOUND TRUE)
+ELSE(CBLAS_LIBRARIES)
+  SET(CBLAS_FOUND FALSE)
+ENDIF(CBLAS_LIBRARIES)
+
+IF(NOT CBLAS_FOUND AND CBLAS_FIND_REQUIRED)
+  MESSAGE(FATAL_ERROR "CBLAS library not found. Please specify library  location")
+ENDIF(NOT CBLAS_FOUND AND CBLAS_FIND_REQUIRED)
+
+IF(NOT CBLAS_FIND_QUIETLY)
+  IF(CBLAS_FOUND)
+    MESSAGE(STATUS "CBLAS library found: " ${CBLAS_LIBRARIES})
+    #MESSAGE(STATUS )
+  ELSE(CBLAS_FOUND)
+    MESSAGE(STATUS "CBLAS library not found. Please specify library location")
+  ENDIF(CBLAS_FOUND)
+ENDIF(NOT CBLAS_FIND_QUIETLY)

From f8943a78d14cee9bec67b6ebb669b328194120f7 Mon Sep 17 00:00:00 2001
From: Nikolay Bogoychev <nheart@gmail.com>
Date: Sun, 24 Feb 2019 20:29:54 +0000
Subject: [PATCH 357/838] Add the changes to CMakeLists.txt and add also
 support for finding the include directory

---
 CMakeLists.txt        | 9 ++++++---
 cmake/FindCBLAS.cmake | 9 +++++++--
 2 files changed, 13 insertions(+), 5 deletions(-)

diff --git a/CMakeLists.txt b/CMakeLists.txt
index 022b97019..f9011cd6a 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -240,9 +240,12 @@ if(COMPILE_CPU)
     set(BLA_VENDOR "OpenBLAS")
     find_package(BLAS)
     if(BLAS_FOUND)
-      include_directories(${BLAS_INCLUDE_DIR})
-      set(EXT_LIBS ${EXT_LIBS} ${BLAS_LIBRARIES})
-      add_definitions(-DBLAS_FOUND=1)
+      include(FindCBLAS)
+      if(CBLAS_FOUND)
+        include_directories(${BLAS_INCLUDE_DIR} ${CBLAS_INCLUDE_DIR})
+        set(EXT_LIBS ${EXT_LIBS} ${BLAS_LIBRARIES} ${CBLAS_LIBRARIES})
+        add_definitions(-DBLAS_FOUND=1)
+      endif(CBLAS_FOUND)
     endif(BLAS_FOUND)
   endif(MKL_FOUND)
 endif(COMPILE_CPU)
diff --git a/cmake/FindCBLAS.cmake b/cmake/FindCBLAS.cmake
index e7426014e..631da99b4 100644
--- a/cmake/FindCBLAS.cmake
+++ b/cmake/FindCBLAS.cmake
@@ -20,7 +20,7 @@
 INCLUDE(CheckFunctionExists)
 INCLUDE(CheckIncludeFile)
 
-MACRO(CHECK_ALL_LIBRARIES LIBRARIES _prefix _name _flags _list _include _search_include)
+MACRO(CHECK_ALL_LIBRARIES LIBRARIES INCLUDE _prefix _name _flags _list _include _search_include)
   # This macro checks for the existence of the combination of fortran libraries
   # given by _list.  If the combination is found, this macro checks (using the 
   # Check_Fortran_Function_Exists macro) whether can link against that library
@@ -83,6 +83,7 @@ MACRO(CHECK_ALL_LIBRARIES LIBRARIES _prefix _name _flags _list _include _search_
       MESSAGE(STATUS "Checking for [${__list}] -- includes found")
       SET(${_prefix}_INCLUDE_DIR ${${_prefix}${_combined_name}_INCLUDE})
       SET(${_prefix}_INCLUDE_FILE ${_include})
+      SET(${INCLUDE} ${${_prefix}_INCLUDE_DIR})
     ELSE(${_prefix}${_combined_name}_INCLUDE)
       MESSAGE(STATUS "Checking for [${__list}] -- includes not found")
       SET(_libraries_work FALSE)
@@ -115,11 +116,13 @@ ENDMACRO(CHECK_ALL_LIBRARIES)
 
 SET(CBLAS_LINKER_FLAGS)
 SET(CBLAS_LIBRARIES)
+SET(CBLAS_INCLUDE_DIR)
 
 # CBLAS in openBLAS
 IF(NOT CBLAS_LIBRARIES)
   CHECK_ALL_LIBRARIES(
     CBLAS_LIBRARIES
+    CBLAS_INCLUDE_DIR
     cblas
     cblas_sgemm
     ""
@@ -135,6 +138,7 @@ ENDIF(NOT CBLAS_LIBRARIES)
 IF(NOT CBLAS_LIBRARIES)
   CHECK_ALL_LIBRARIES(
     CBLAS_LIBRARIES
+    CBLAS_INCLUDE_DIR
     cblas
     cblas_sgemm
     ""
@@ -150,6 +154,7 @@ ENDIF(NOT CBLAS_LIBRARIES)
 IF(NOT CBLAS_LIBRARIES)
   CHECK_ALL_LIBRARIES(
     CBLAS_LIBRARIES
+    CBLAS_INCLUDE_DIR
     cblas
     cblas_sgemm
     ""
@@ -174,7 +179,7 @@ ENDIF(NOT CBLAS_FOUND AND CBLAS_FIND_REQUIRED)
 IF(NOT CBLAS_FIND_QUIETLY)
   IF(CBLAS_FOUND)
     MESSAGE(STATUS "CBLAS library found: " ${CBLAS_LIBRARIES})
-    #MESSAGE(STATUS )
+    MESSAGE(STATUS "cblas.h include directory: " ${CBLAS_INCLUDE_DIR})
   ELSE(CBLAS_FOUND)
     MESSAGE(STATUS "CBLAS library not found. Please specify library location")
   ENDIF(CBLAS_FOUND)

From 4834983770ea1771f2a6a665c912f89cd1de7357 Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Sun, 24 Feb 2019 18:26:29 -0800
Subject: [PATCH 358/838] added @WB and @WE factors

---
 src/data/factored_vocab.cpp | 5 +++--
 1 file changed, 3 insertions(+), 2 deletions(-)

diff --git a/src/data/factored_vocab.cpp b/src/data/factored_vocab.cpp
index bbae31127..00af9a1b5 100755
--- a/src/data/factored_vocab.cpp
+++ b/src/data/factored_vocab.cpp
@@ -27,7 +27,7 @@ namespace marian {
 
   // load factor vocabulary
   factorVocab_.load(factorVocabPath);
-  groupPrefixes_ = { "(lemma)", "@C", "@GL", "@GR" }; // @TODO: hard-coded for these initial experiments
+  groupPrefixes_ = { "(lemma)", "@C", "@GL", "@GR", "@WB", "@WE" }; // @TODO: hard-coded for these initial experiments
 
   // construct mapping tables for factors
   constructGroupInfoFromFactorVocab();
@@ -369,7 +369,8 @@ void FactoredVocab::constructNormalizationInfoForVocab() {
 
 /*virtual*/ std::string FactoredVocab::toEnglishTitleCase(const std::string& line) const /*override final*/ {
   // @BUGBUG: does not handle the special words that should remain lower-case
-  return utils::findReplace(line, "@CN@GL-", "@CI@GL-", /*all=*/true);
+  // note: this presently supports both @WB and @GL- (legacy)
+  return utils::findReplace(utils::findReplace(line, "@CN@WB", "@CI@WB", /*all=*/true), "@CN@GL-", "@CI@GL-", /*all=*/true);
 }
 
 // generate a valid random factored word (used by collectStats())

From ae6fc3c19c9cb064033dbc093c9c1fc2410b79db Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Mon, 25 Feb 2019 13:27:50 -0800
Subject: [PATCH 359/838] added @CB and @CE factor types

---
 src/data/factored_vocab.cpp | 6 ++++--
 vs/Marian.sln               | 0
 2 files changed, 4 insertions(+), 2 deletions(-)
 mode change 100644 => 100755 vs/Marian.sln

diff --git a/src/data/factored_vocab.cpp b/src/data/factored_vocab.cpp
index 00af9a1b5..7f2138daf 100755
--- a/src/data/factored_vocab.cpp
+++ b/src/data/factored_vocab.cpp
@@ -27,7 +27,7 @@ namespace marian {
 
   // load factor vocabulary
   factorVocab_.load(factorVocabPath);
-  groupPrefixes_ = { "(lemma)", "@C", "@GL", "@GR", "@WB", "@WE" }; // @TODO: hard-coded for these initial experiments
+  groupPrefixes_ = { "(lemma)", "@C", "@GL", "@GR", "@WB", "@WE", "@CB", "@CE" }; // @TODO: hard-coded for these initial experiments
 
   // construct mapping tables for factors
   constructGroupInfoFromFactorVocab();
@@ -137,7 +137,9 @@ void FactoredVocab::constructGroupInfoFromFactorVocab() {
     const auto& groupPrefix = groupPrefixes_[g];
     for (WordIndex u = 0; u < factorVocabSize; u++)
       if (utils::beginsWith(factorVocab_[u], groupPrefix)) {
-        ABORT_IF(factorGroups_[u] != 0, "Factor {} matches multiple groups, incl. {}", factorVocab_[u], groupPrefix);
+        //ABORT_IF(factorGroups_[u] != 0, "Factor {} matches multiple groups, incl. {}", factorVocab_[u], groupPrefix);
+        if(factorGroups_[u] != 0)
+          LOG(info, "Factor {} matches multiple groups, incl. {}, using {}", factorVocab_[u], groupPrefixes_[factorGroups_[u]], groupPrefix);
         factorGroups_[u] = g;
       }
   }
diff --git a/vs/Marian.sln b/vs/Marian.sln
old mode 100644
new mode 100755

From 842887cd661d089810bbcec2d135f0ea9ac75028 Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Mon, 25 Feb 2019 14:08:51 -0800
Subject: [PATCH 360/838] removed @WE and @CE since empty factors currently
 cause it to fail

---
 src/data/factored_vocab.cpp | 11 ++++++++++-
 src/layers/generic.cpp      |  4 +++-
 2 files changed, 13 insertions(+), 2 deletions(-)

diff --git a/src/data/factored_vocab.cpp b/src/data/factored_vocab.cpp
index 7f2138daf..2a759448a 100755
--- a/src/data/factored_vocab.cpp
+++ b/src/data/factored_vocab.cpp
@@ -27,7 +27,8 @@ namespace marian {
 
   // load factor vocabulary
   factorVocab_.load(factorVocabPath);
-  groupPrefixes_ = { "(lemma)", "@C", "@GL", "@GR", "@WB", "@WE", "@CB", "@CE" }; // @TODO: hard-coded for these initial experiments
+  groupPrefixes_ = { "(lemma)", "@C", "@GL", "@GR", "@WB",/* "@WE",*/ "@CB"/*, "@CE"*/ }; // @TODO: hard-coded for these initial experiments
+  // @TODO: add checks for empty factor groups until it stops crashing
 
   // construct mapping tables for factors
   constructGroupInfoFromFactorVocab();
@@ -154,6 +155,14 @@ void FactoredVocab::constructGroupInfoFromFactorVocab() {
         groupRanges_[g].second = u + 1;
     groupCounts[g]++;
   }
+  //for (size_t g = 0; g < numGroups; g++) { // fix up empty entries
+  //  LOG(info, "GROUP {}: {} {}", groupPrefixes_[g], groupRanges_[g].first, groupRanges_[g].second);
+  //  //if (groupCounts[g] == 0) {
+  //  //  ABORT("Group {} has no members", groupPrefixes_[g]);
+  //  //  //groupRanges_[g].first = g > 0 ? groupRanges_[g-1].second : 0;
+  //  //  //groupRanges_[g].second = groupRanges_[g].first;
+  //  //}
+  //}
   for (size_t g = 0; g < numGroups; g++) { // detect non-overlapping groups
     LOG(info, "[embedding] Factor group '{}' has {} members", groupPrefixes_[g], groupCounts[g]);
     if (groupCounts[g] == 0) // factor group is unused  --@TODO: once this is not hard-coded, this is an error condition
diff --git a/src/layers/generic.cpp b/src/layers/generic.cpp
index 6b0ef93e2..1c9a011a5 100755
--- a/src/layers/generic.cpp
+++ b/src/layers/generic.cpp
@@ -245,7 +245,9 @@ namespace marian {
         std::vector<Ptr<RationalLoss>> allLogits(numGroups);
         for (size_t g = 0; g < numGroups; g++) {
           auto range = factoredVocab_->getGroupRange(g);
-          ABORT_IF(g > 0 && range.first != factoredVocab_->getGroupRange(g-1).second, "Factor groups must be consecutive"); // we could sort groupYs though
+          //if (range.first == SIZE_MAX)
+          //  continue;
+          ABORT_IF(g > 0 && range.first != factoredVocab_->getGroupRange(g-1).second, "Factor groups must be consecutive (group {} vs predecessor)", g); // we could sort groupYs though
           // slice this group's section out of W_
           // @TODO: This is highly inefficient if not tied. We should always transpose Output's matrix.
           auto factorWt = slice(Wt_, isLegacyUntransposedW ? -1 : 0, Slice((int)range.first, (int)range.second));

From 4d57f87adf170c09fedbae87f46047748815699f Mon Sep 17 00:00:00 2001
From: Kenneth Heafield <github@kheafield.com>
Date: Fri, 1 Mar 2019 18:41:40 +0000
Subject: [PATCH 361/838] Add switch fallthroughs for marian-nmt/marian#190

---
 src/tensors/cpu/sharp/avx_gemm.cpp | 6 ++++++
 1 file changed, 6 insertions(+)

diff --git a/src/tensors/cpu/sharp/avx_gemm.cpp b/src/tensors/cpu/sharp/avx_gemm.cpp
index c41b73eb0..a1018c7df 100644
--- a/src/tensors/cpu/sharp/avx_gemm.cpp
+++ b/src/tensors/cpu/sharp/avx_gemm.cpp
@@ -495,6 +495,7 @@ void AVX_MatrixMult8(const __m512i *A,
         put.Write(C + (i + 4) * num_B_rows + j, Reduce16to32(sum5, sum6));
         put.Write(C + (i + 6) * num_B_rows + j, Reduce16to32(sum7));
       }
+      [[fallthrough]];
     case 6:
       for(int j = 0; j < num_B_rows; j++) {
         const __m512i *B_row = B + j * sse_width;
@@ -518,6 +519,7 @@ void AVX_MatrixMult8(const __m512i *A,
         put.Write(C + i * num_B_rows + j, Reduce16to32(sum1, sum2, sum3, sum4));
         put.Write(C + (i + 4) * num_B_rows + j, Reduce16to32(sum5, sum6));
       }
+      [[fallthrough]];
     case 5:
       for(int j = 0; j < num_B_rows; j++) {
         const __m512i *B_row = B + j * sse_width;
@@ -539,6 +541,7 @@ void AVX_MatrixMult8(const __m512i *A,
         put.Write(C + i * num_B_rows + j, Reduce16to32(sum1, sum2, sum3, sum4));
         put.Write(C + (i + 4) * num_B_rows + j, Reduce16to32(sum5));
       }
+      [[fallthrough]];
     case 4:
       for(int j = 0; j < num_B_rows; j++) {
         const __m512i *B_row = B + j * sse_width;
@@ -557,6 +560,7 @@ void AVX_MatrixMult8(const __m512i *A,
         }
         put.Write(C + i * num_B_rows + j, Reduce16to32(sum1, sum2, sum3, sum4));
       }
+      [[fallthrough]];
     case 3:
       for(int j = 0; j < num_B_rows; j++) {
         const __m512i *B_row = B + j * sse_width;
@@ -574,6 +578,7 @@ void AVX_MatrixMult8(const __m512i *A,
         put.Write(C + i * num_B_rows + j, Reduce16to32(sum1, sum2));
         put.Write(C + (i + 2) * num_B_rows + j, Reduce16to32(sum3));
       }
+      [[fallthrough]];
     case 2:
       for(int j = 0; j < num_B_rows; j++) {
         const __m512i *B_row = B + j * sse_width;
@@ -588,6 +593,7 @@ void AVX_MatrixMult8(const __m512i *A,
         }
         put.Write(C + i * num_B_rows + j, Reduce16to32(sum1, sum2));
       }
+      [[fallthrough]];
     case 1:
       for(int j = 0; j < num_B_rows; j++) {
         const __m512i *B_row = B + j * sse_width;

From f9132495319ec76cf3065d29272ed9ce9c627acf Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Mon, 11 Mar 2019 08:51:30 -0700
Subject: [PATCH 362/838] new parameter lemma-dim-emb (simple conditional for
 factors); some more robustness against unused factors

---
 src/common/config_parser.cpp    |  3 +++
 src/data/factored_vocab.cpp     | 22 +++++++---------
 src/layers/generic.cpp          | 46 ++++++++++++++++++++++++++++-----
 src/models/encoder_classifier.h |  1 +
 src/models/encoder_decoder.cpp  |  1 +
 src/models/transformer.h        |  1 +
 src/translator/beam_search.h    |  3 +++
 7 files changed, 58 insertions(+), 19 deletions(-)

diff --git a/src/common/config_parser.cpp b/src/common/config_parser.cpp
index 54c1f6a66..ee49b448f 100755
--- a/src/common/config_parser.cpp
+++ b/src/common/config_parser.cpp
@@ -121,6 +121,9 @@ void ConfigParser::addOptionsModel(cli::CLIWrapper& cli) {
   cli.add<int>("--dim-emb",
       "Size of embedding vector",
       512);
+  cli.add<int>("--lemma-dim-emb",
+      "Re-embedding dimension of lemma in factors",
+      0);
   cli.add<int>("--dim-rnn",
       "Size of rnn hidden state", 1024);
   cli.add<std::string>("--enc-type",
diff --git a/src/data/factored_vocab.cpp b/src/data/factored_vocab.cpp
index 2a759448a..2b0695fd2 100755
--- a/src/data/factored_vocab.cpp
+++ b/src/data/factored_vocab.cpp
@@ -27,8 +27,8 @@ namespace marian {
 
   // load factor vocabulary
   factorVocab_.load(factorVocabPath);
-  groupPrefixes_ = { "(lemma)", "@C", "@GL", "@GR", "@WB",/* "@WE",*/ "@CB"/*, "@CE"*/ }; // @TODO: hard-coded for these initial experiments
-  // @TODO: add checks for empty factor groups until it stops crashing
+  groupPrefixes_ = { "(lemma)", "@C", "@GL", "@GR", "@WB"/*, "@WE"*/, "@CB"/*, "@CE"*/ }; // @TODO: hard-coded for these initial experiments
+  // @TODO: add checks for empty factor groups until it stops crashing (training already works; decoder still crashes)
 
   // construct mapping tables for factors
   constructGroupInfoFromFactorVocab();
@@ -155,18 +155,13 @@ void FactoredVocab::constructGroupInfoFromFactorVocab() {
         groupRanges_[g].second = u + 1;
     groupCounts[g]++;
   }
-  //for (size_t g = 0; g < numGroups; g++) { // fix up empty entries
-  //  LOG(info, "GROUP {}: {} {}", groupPrefixes_[g], groupRanges_[g].first, groupRanges_[g].second);
-  //  //if (groupCounts[g] == 0) {
-  //  //  ABORT("Group {} has no members", groupPrefixes_[g]);
-  //  //  //groupRanges_[g].first = g > 0 ? groupRanges_[g-1].second : 0;
-  //  //  //groupRanges_[g].second = groupRanges_[g].first;
-  //  //}
-  //}
   for (size_t g = 0; g < numGroups; g++) { // detect non-overlapping groups
     LOG(info, "[embedding] Factor group '{}' has {} members", groupPrefixes_[g], groupCounts[g]);
-    if (groupCounts[g] == 0) // factor group is unused  --@TODO: once this is not hard-coded, this is an error condition
+    if (groupCounts[g] == 0) { // factor group is unused  --@TODO: once this is not hard-coded, this is an error condition
+      groupRanges_[g].first = g > 0 ? groupRanges_[g-1].second : 0; // fix up the entry
+      groupRanges_[g].second = groupRanges_[g].first;
       continue;
+    }
     ABORT_IF(groupRanges_[g].second - groupRanges_[g].first != groupCounts[g],
              "Factor group '{}' members should be consecutive in the factor vocabulary", groupPrefixes_[g]);
   }
@@ -354,8 +349,9 @@ void FactoredVocab::constructNormalizationInfoForVocab() {
   if (found)
     return Word::fromWordIndex(index);
   else
-    ABORT("Unknown word {}", word);
-    //return getUnkId();
+    //ABORT("Unknown word {} mapped to {}", word, word2string(getUnkId()));
+    LOG(info, "WARNING: Unknown word {} mapped to {}", word, word2string(getUnkId()));
+    return getUnkId();
 }
 
 /*virtual*/ const std::string& FactoredVocab::operator[](Word word) const /*overrworde final*/ {
diff --git a/src/layers/generic.cpp b/src/layers/generic.cpp
index 1c9a011a5..f59105932 100755
--- a/src/layers/generic.cpp
+++ b/src/layers/generic.cpp
@@ -47,6 +47,8 @@ namespace marian {
     // Memory-wise, this is cheap, all temp objects below are batches of scalars or lookup vectors.
     Expr loss;
     for (size_t g = 0; g < numGroups; g++) {
+      if (!logits_[g])
+        continue; // empty factor  --@TODO: handle this more nicely
       const auto& maskedFactoredLabels = allMaskedFactoredLabels[g]; // array of (word index, mask)
 #if 1
       auto factorIndices = indices (maskedFactoredLabels.indices); // [B... flattened] factor-label indices, or 0 if factor does not apply
@@ -242,19 +244,51 @@ namespace marian {
 
         // project each factor separately
         auto numGroups = factoredVocab_->getNumGroups();
-        std::vector<Ptr<RationalLoss>> allLogits(numGroups);
+        std::vector<Ptr<RationalLoss>> allLogits(numGroups, nullptr); // (note: null entries for absent factors)
+        Expr input1 = input;
+        Expr Plemma = nullptr;
         for (size_t g = 0; g < numGroups; g++) {
           auto range = factoredVocab_->getGroupRange(g);
-          //if (range.first == SIZE_MAX)
-          //  continue;
-          ABORT_IF(g > 0 && range.first != factoredVocab_->getGroupRange(g-1).second, "Factor groups must be consecutive (group {} vs predecessor)", g); // we could sort groupYs though
+          if (g > 0 && range.first == range.second) // empty entry
+            continue;
+          ABORT_IF(g > 0 && range.first != factoredVocab_->getGroupRange(g-1).second, "Factor groups must be consecutive (group {} vs predecessor)", g);
           // slice this group's section out of W_
-          // @TODO: This is highly inefficient if not tied. We should always transpose Output's matrix.
           auto factorWt = slice(Wt_, isLegacyUntransposedW ? -1 : 0, Slice((int)range.first, (int)range.second));
           auto factorB  = slice(b_,                              -1, Slice((int)range.first, (int)range.second));
           // @TODO: b_ should be a vector, not a matrix; but shotlists use cols() in, which requires a matrix
-          auto factorLogits = affine(input, factorWt, factorB, false, /*transB=*/isLegacyUntransposedW ? false : true); // [B... x U] factor logits
+          auto factorLogits = affine(input1, factorWt, factorB, false, /*transB=*/isLegacyUntransposedW ? false : true); // [B... x U] factor logits
+          // optionally add lemma-dependent bias
+          if (Plemma) { // [B... x U0]
+            int lemmaVocabDim = Plemma->shape()[-1];
+            int factorVocabDim = factorLogits->shape()[-1];
+            auto name = options_->get<std::string>("prefix");
+            Expr lemmaBt = graph_->param(name + "_lemmaBt_" + std::to_string(g), {factorVocabDim, lemmaVocabDim}, inits::zeros/*glorot_uniform*/); // [U x U0] U0=#lemmas one bias per class per lemma
+            auto b = dot(Plemma, lemmaBt, false, true); // [B... x U]
+            factorLogits = factorLogits + b;
+          }
           allLogits[g] = New<RationalLoss>(factorLogits, nullptr);
+          // optionally add a soft embedding of lemma back to create some lemma dependency
+          // @TODO: if this works, move it into lazyConstruct
+          const int lemmaDimEmb = options_->get<int>("lemma-dim-emb", 0);
+          if (lemmaDimEmb < 0 && g == 0) {
+            LOG_ONCE(info, "[embedding] using lemma-dependent bias");
+            factorLogits = logsoftmax(factorLogits); // explicitly, since we do that again later
+            auto z = /*stopGradient*/(factorLogits);
+            Plemma = exp(z); // [B... x U]
+          }
+          if (lemmaDimEmb > 0 && g == 0) {
+            LOG_ONCE(info, "[embedding] enabled re-embedding of lemma, at dim {}", lemmaDimEmb);
+            int lemmaVocabDim = factorLogits->shape()[-1];
+            int inputDim  = input1->shape()[-1];
+            auto name = options_->get<std::string>("prefix");
+            factorLogits = logsoftmax(factorLogits); // explicitly, since we do that again later
+            Expr lemmaEt = graph_->param(name + "_lemmaEt", { lemmaDimEmb, lemmaVocabDim }, inits::glorot_uniform); // [L x U] L=lemmaDimEmb; transposed for speed
+            auto e = dot(exp(factorLogits), lemmaEt, false, true); // [B... x L]
+            //e = tanh(e); // make it non-scale-preserving
+            Expr lemmaWt = inputDim == lemmaDimEmb ? nullptr : graph_->param(name + "_lemmaWt", { inputDim,  lemmaDimEmb }, inits::glorot_uniform); // [D x L] D=hidden-vector dimension
+            auto f = lemmaWt ? dot(e, lemmaWt, false, true) : e; // [B... x D]
+            input1 = input1 + f; // augment the original hidden vector with this additional information
+          }
         }
         return Logits(std::move(allLogits), factoredVocab_);
       }
diff --git a/src/models/encoder_classifier.h b/src/models/encoder_classifier.h
index f6ffbebfc..04a099861 100755
--- a/src/models/encoder_classifier.h
+++ b/src/models/encoder_classifier.h
@@ -136,6 +136,7 @@ class EncoderClassifier : public EncoderClassifierBase {
     modelFeatures_.insert("ulr");
     modelFeatures_.insert("ulr-trainable-transformation");
     modelFeatures_.insert("ulr-dim-emb");
+    modelFeatures_.insert("lemma-dim-emb");
   }
 
   virtual Ptr<Options> getOptions() override { return options_; }
diff --git a/src/models/encoder_decoder.cpp b/src/models/encoder_decoder.cpp
index 62c60ddb8..5c7053f46 100755
--- a/src/models/encoder_decoder.cpp
+++ b/src/models/encoder_decoder.cpp
@@ -57,6 +57,7 @@ EncoderDecoder::EncoderDecoder(Ptr<Options> options)
   modelFeatures_.insert("ulr");
   modelFeatures_.insert("ulr-trainable-transformation");
   modelFeatures_.insert("ulr-dim-emb");
+  modelFeatures_.insert("lemma-dim-emb");
 }
 
 std::vector<Ptr<EncoderBase>>& EncoderDecoder::getEncoders() {
diff --git a/src/models/transformer.h b/src/models/transformer.h
index 87bda0a89..0318674f3 100755
--- a/src/models/transformer.h
+++ b/src/models/transformer.h
@@ -655,6 +655,7 @@ class DecoderTransformer : public Transformer<DecoderBase> {
     }
 
     outputFactory("vocab", opt<std::vector<std::string>>("vocabs")[batchIndex_]); // for factored outputs
+    outputFactory("lemma-dim-emb", opt<int>("lemma-dim-emb", 0)); // for factored outputs
 
     output_ = std::dynamic_pointer_cast<mlp::Output>(outputFactory.construct(graph_)); // (construct() returns only the underlying interface)
   }
diff --git a/src/translator/beam_search.h b/src/translator/beam_search.h
index a22fd5dfa..43dd5f12a 100755
--- a/src/translator/beam_search.h
+++ b/src/translator/beam_search.h
@@ -109,7 +109,10 @@ class BeamSearch {
         beam[beamHypIdx]->getScoreBreakdown().resize(states.size(), 0); // @TODO: Why? Can we just guard the read-out below, then make it const? Or getScoreBreakdown(j)?
         for(size_t j = 0; j < states.size(); ++j) {
           size_t flattenedLogitIndex = (beamHypIdx * dimBatch + batchIdx) * vocabSize + wordIdx;  // (beam idx, batch idx, word idx); note: beam and batch are transposed, compared to 'key'
+          flattenedLogitIndex;
+#if 0   // @BUGBUG: This currently segfaults with factors.
           breakDown[j] = states[j]->breakDown(flattenedLogitIndex) + beam[beamHypIdx]->getScoreBreakdown()[j];
+#endif
           // @TODO: pass those 3 indices directly into breakDown (state knows the dimensions)
         }
         hyp->setScoreBreakdown(breakDown);

From 129253cbc60cf41e71a953cae59f3b8f36c210eb Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Mon, 11 Mar 2019 11:06:10 -0700
Subject: [PATCH 363/838] fixed build errors in SentencePiece

---
 src/data/sentencepiece_vocab.cpp | 18 +++++++++++-------
 src/models/transformer.h         |  2 +-
 2 files changed, 12 insertions(+), 8 deletions(-)
 mode change 100644 => 100755 src/data/sentencepiece_vocab.cpp

diff --git a/src/data/sentencepiece_vocab.cpp b/src/data/sentencepiece_vocab.cpp
old mode 100644
new mode 100755
index 2f419738d..b2b829019
--- a/src/data/sentencepiece_vocab.cpp
+++ b/src/data/sentencepiece_vocab.cpp
@@ -135,8 +135,8 @@ class SentencePieceVocab : public IVocab {
 
   virtual std::string type() const override { return "SentencePieceVocab"; }
 
-  virtual Word getEosId() const override { return (Word)spm_->eos_id(); }
-  virtual Word getUnkId() const override { return (Word)spm_->unk_id(); }
+  virtual Word getEosId() const override { return Word::fromWordIndex(spm_->eos_id()); }
+  virtual Word getUnkId() const override { return Word::fromWordIndex(spm_->unk_id()); }
 
   void create(const std::string& vocabPath,
               const std::vector<std::string>& trainPaths,
@@ -197,12 +197,12 @@ class SentencePieceVocab : public IVocab {
   }
 
   Word operator[](const std::string& token) const override {
-    return (Word)spm_->PieceToId(token);
+    return Word::fromWordIndex(spm_->PieceToId(token));
   }
 
   const std::string& operator[](Word id) const override {
-    ABORT_IF(id >= size(), "Unknown word id: ", id);
-    return spm_->IdToPiece(id);
+    ABORT_IF(id.toWordIndex() >= size(), "Unknown word id: ", id.toWordIndex());
+    return spm_->IdToPiece(id.toWordIndex());
   }
 
   Words encode(const std::string& line, bool addEOS, bool inference) const override {
@@ -212,7 +212,9 @@ class SentencePieceVocab : public IVocab {
     else
       spm_->SampleEncode(line, -1, alpha_, &spmIds);
 
-    Words words(spmIds.begin(), spmIds.end());
+    Words words; words.reserve(spmIds.size() + addEOS);
+    for (auto&& spmId : spmIds)
+      words.push_back(Word::fromWordIndex(spmId));
 
     if(addEOS)
       words.push_back(getEosId());
@@ -222,7 +224,9 @@ class SentencePieceVocab : public IVocab {
   std::string decode(const Words& sentence, bool /*ignoreEOS*/) const override {
     std::string line;
     // convert vector of Word to vector of int
-    std::vector<int> spmSentence(sentence.begin(), sentence.end());
+    std::vector<int> spmSentence; spmSentence.reserve(sentence.size());
+    for (auto&& word : sentence)
+      spmSentence.push_back(word.toWordIndex());
     spm_->Decode(spmSentence, &line);
     return line;
   }
diff --git a/src/models/transformer.h b/src/models/transformer.h
index 0318674f3..1817947ea 100755
--- a/src/models/transformer.h
+++ b/src/models/transformer.h
@@ -72,7 +72,7 @@ class Transformer : public EncoderOrDecoderBase {
       // fill with increasing numbers until current length or maxPos
       std::vector<IndexType> positions(dimWords, numPos - 1);
       for(int i = 0; i < std::min(dimWords, numPos); ++i)
-        positions[i] = i; // @TODO: use std::iota()?
+        positions[i] = i;
 
       auto signal = embeddingLayer->applyIndices(positions, {dimWords, 1, dimEmb});
       embeddings = embeddings + signal;

From 2c80015144edc95c1c82a694c3c6ddf086c589be Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Mon, 11 Mar 2019 11:08:15 -0700
Subject: [PATCH 364/838] fixed build errors in SentencePiece

---
 src/data/sentencepiece_vocab.cpp | 19 +++++++++++--------
 src/models/transformer.h         |  2 +-
 2 files changed, 12 insertions(+), 9 deletions(-)
 mode change 100644 => 100755 src/data/sentencepiece_vocab.cpp
 mode change 100644 => 100755 src/models/transformer.h

diff --git a/src/data/sentencepiece_vocab.cpp b/src/data/sentencepiece_vocab.cpp
old mode 100644
new mode 100755
index 9af727565..b2b829019
--- a/src/data/sentencepiece_vocab.cpp
+++ b/src/data/sentencepiece_vocab.cpp
@@ -126,7 +126,6 @@ class SentencePieceVocab : public IVocab {
             alpha_,
             batchIndex_);
     }
-
   }
 
   virtual const std::string& canonicalExtension() const override { return suffixes_[0]; }
@@ -136,8 +135,8 @@ class SentencePieceVocab : public IVocab {
 
   virtual std::string type() const override { return "SentencePieceVocab"; }
 
-  virtual Word getEosId() const override { return (Word)spm_->eos_id(); }
-  virtual Word getUnkId() const override { return (Word)spm_->unk_id(); }
+  virtual Word getEosId() const override { return Word::fromWordIndex(spm_->eos_id()); }
+  virtual Word getUnkId() const override { return Word::fromWordIndex(spm_->unk_id()); }
 
   void create(const std::string& vocabPath,
               const std::vector<std::string>& trainPaths,
@@ -198,12 +197,12 @@ class SentencePieceVocab : public IVocab {
   }
 
   Word operator[](const std::string& token) const override {
-    return (Word)spm_->PieceToId(token);
+    return Word::fromWordIndex(spm_->PieceToId(token));
   }
 
   const std::string& operator[](Word id) const override {
-    ABORT_IF(id >= size(), "Unknown word id: ", id);
-    return spm_->IdToPiece(id);
+    ABORT_IF(id.toWordIndex() >= size(), "Unknown word id: ", id.toWordIndex());
+    return spm_->IdToPiece(id.toWordIndex());
   }
 
   Words encode(const std::string& line, bool addEOS, bool inference) const override {
@@ -213,7 +212,9 @@ class SentencePieceVocab : public IVocab {
     else
       spm_->SampleEncode(line, -1, alpha_, &spmIds);
 
-    Words words(spmIds.begin(), spmIds.end());
+    Words words; words.reserve(spmIds.size() + addEOS);
+    for (auto&& spmId : spmIds)
+      words.push_back(Word::fromWordIndex(spmId));
 
     if(addEOS)
       words.push_back(getEosId());
@@ -223,7 +224,9 @@ class SentencePieceVocab : public IVocab {
   std::string decode(const Words& sentence, bool /*ignoreEOS*/) const override {
     std::string line;
     // convert vector of Word to vector of int
-    std::vector<int> spmSentence(sentence.begin(), sentence.end());
+    std::vector<int> spmSentence; spmSentence.reserve(sentence.size());
+    for (auto&& word : sentence)
+      spmSentence.push_back(word.toWordIndex());
     spm_->Decode(spmSentence, &line);
     return line;
   }
diff --git a/src/models/transformer.h b/src/models/transformer.h
old mode 100644
new mode 100755
index fcd2442aa..91d6c35f8
--- a/src/models/transformer.h
+++ b/src/models/transformer.h
@@ -73,7 +73,7 @@ class Transformer : public EncoderOrDecoderBase {
       // fill with increasing numbers until current length or maxPos
       std::vector<IndexType> positions(dimWords, numPos - 1);
       for(int i = 0; i < std::min(dimWords, numPos); ++i)
-        positions[i] = i; // @TODO: use std::iota()?
+        positions[i] = i;
 
       auto signal = embeddingLayer->applyIndices(positions, {dimWords, 1, dimEmb});
       embeddings = embeddings + signal;

From 95387b1eb65850a5074eccf52ab4851a5e41cea5 Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Mon, 11 Mar 2019 11:51:50 -0700
Subject: [PATCH 365/838] bug fix: MPI should only fail with unsupported
 threading mode if we actually run distributed

---
 src/training/communicator.cpp | 7 +++----
 1 file changed, 3 insertions(+), 4 deletions(-)
 mode change 100644 => 100755 src/training/communicator.cpp

diff --git a/src/training/communicator.cpp b/src/training/communicator.cpp
old mode 100644
new mode 100755
index 35835a0ed..6307cc8b1
--- a/src/training/communicator.cpp
+++ b/src/training/communicator.cpp
@@ -79,13 +79,12 @@ class MPIWrapper : public IMPIWrapper
     HANDLE_MPI_ERROR(MPI_Init_thread(&argc, &argvp, MPI_THREAD_MULTIPLE, &providedThreadingMode));
     MPI_Comm_set_errhandler(MPI_COMM_WORLD, MPI_ERRORS_RETURN); // have errors reported as return codes
 
-    ABORT_IF(
-      providedThreadingMode < requiredThreadingMode,
-      "Your version of MPI does not support multi-threaded communication.");
-
     MPI_Comm_size(MPI_COMM_WORLD, &comm_world_size_);
     MPI_Comm_rank(MPI_COMM_WORLD, &my_rank_);
 
+    ABORT_IF(comm_world_size_ > 1 && providedThreadingMode < requiredThreadingMode,
+      "Your version of MPI does not support multi-threaded communication.");
+
     // patch logging pattern to include the MPI rank, so that we can associate error messages with nodes
     if (numMPIProcesses() > 1) {
       std::string rankStr = std::to_string(MPIWrapper::myMPIRank());

From c91059774431ded8978cd683d2d2ce196f6b1ea7 Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Mon, 11 Mar 2019 13:33:42 -0700
Subject: [PATCH 366/838] bug fix: MNIST model creation somehow got lost in
 last refactoring

---
 src/models/model_factory.cpp | 91 ++++++++++++++++++++----------------
 1 file changed, 50 insertions(+), 41 deletions(-)

diff --git a/src/models/model_factory.cpp b/src/models/model_factory.cpp
index d7bd8d854..cee855ab6 100755
--- a/src/models/model_factory.cpp
+++ b/src/models/model_factory.cpp
@@ -106,12 +106,12 @@ Ptr<ModelBase> createBaseModelByType(std::string type, usage use, Ptr<Options> o
     return models::encoder_decoder()(options)
         ("usage", use)
         ("original-type", type)
-            .push_back(models::encoder()("type", "s2s"))
-            .push_back(models::decoder()("type", "s2s"))
-            .construct(graph);
+        .push_back(models::encoder()("type", "s2s"))
+        .push_back(models::decoder()("type", "s2s"))
+        .construct(graph);
   }
 
-  if(type == "transformer") {
+  else if(type == "transformer") {
     return models::encoder_decoder()(options)
         ("usage", use)
         .push_back(models::encoder()("type", "transformer"))
@@ -119,16 +119,16 @@ Ptr<ModelBase> createBaseModelByType(std::string type, usage use, Ptr<Options> o
         .construct(graph);
   }
 
-  if(type == "transformer_s2s") {
+  else if(type == "transformer_s2s") {
     return models::encoder_decoder()(options)
         ("usage", use)
         ("original-type", type)
-            .push_back(models::encoder()("type", "transformer"))
-            .push_back(models::decoder()("type", "s2s"))
-            .construct(graph);
+        .push_back(models::encoder()("type", "transformer"))
+        .push_back(models::decoder()("type", "s2s"))
+        .construct(graph);
   }
 
-  if(type == "lm") {
+  else if(type == "lm") {
     auto idx = options->has("index") ? options->get<size_t>("index") : 0;
     std::vector<int> dimVocabs = options->get<std::vector<int>>("dim-vocabs");
     int vocab = dimVocabs[0];
@@ -139,13 +139,13 @@ Ptr<ModelBase> createBaseModelByType(std::string type, usage use, Ptr<Options> o
         ("usage", use)
         ("type", "s2s")
         ("original-type", type)
-            .push_back(models::decoder()
-                       ("index", idx)
-                       ("dim-vocabs", dimVocabs))
-            .construct(graph);
+        .push_back(models::decoder()
+                   ("index", idx)
+                   ("dim-vocabs", dimVocabs))
+        .construct(graph);
   }
 
-  if(type == "multi-s2s") {
+  else if(type == "multi-s2s") {
     size_t numEncoders = 2;
     auto ms2sFactory = models::encoder_decoder()(options)
         ("usage", use)
@@ -162,7 +162,7 @@ Ptr<ModelBase> createBaseModelByType(std::string type, usage use, Ptr<Options> o
     return ms2sFactory.construct(graph);
   }
 
-  if(type == "shared-multi-s2s") {
+  else if(type == "shared-multi-s2s") {
     size_t numEncoders = 2;
     auto ms2sFactory = models::encoder_decoder()(options)
         ("usage", use)
@@ -179,7 +179,7 @@ Ptr<ModelBase> createBaseModelByType(std::string type, usage use, Ptr<Options> o
     return ms2sFactory.construct(graph);
   }
 
-  if(type == "multi-transformer") {
+  else if(type == "multi-transformer") {
     size_t numEncoders = 2;
     auto mtransFactory = models::encoder_decoder()(options)
         ("usage", use)
@@ -195,7 +195,7 @@ Ptr<ModelBase> createBaseModelByType(std::string type, usage use, Ptr<Options> o
     return mtransFactory.construct(graph);
   }
 
-  if(type == "shared-multi-transformer") {
+  else if(type == "shared-multi-transformer") {
     size_t numEncoders = 2;
     auto mtransFactory = models::encoder_decoder()(options)
         ("usage", use)
@@ -211,7 +211,7 @@ Ptr<ModelBase> createBaseModelByType(std::string type, usage use, Ptr<Options> o
     return mtransFactory.construct(graph);
   }
 
-  if(type == "lm-transformer") {
+  else if(type == "lm-transformer") {
     auto idx = options->has("index") ? options->get<size_t>("index") : 0;
     std::vector<int> dimVocabs = options->get<std::vector<int>>("dim-vocabs");
     int vocab = dimVocabs[0];
@@ -222,56 +222,65 @@ Ptr<ModelBase> createBaseModelByType(std::string type, usage use, Ptr<Options> o
         ("usage", use)
         ("type", "transformer")
         ("original-type", type)
-            .push_back(models::decoder()
-                       ("index", idx)
-                       ("dim-vocabs", dimVocabs))
-            .construct(graph);
+        .push_back(models::decoder()
+                   ("index", idx)
+                   ("dim-vocabs", dimVocabs))
+        .construct(graph);
   }
 
-  if(type == "bert") {                           // for full BERT training
+  else if(type == "bert") {                      // for full BERT training
     return models::encoder_classifier()(options) //
         ("original-type", "bert")                // so we can query this
         ("usage", use)                           //
         .push_back(models::encoder()             //
-                    ("type", "bert-encoder")     // close to original transformer encoder
-                    ("index", 0))                //
+                   ("type", "bert-encoder")      // close to original transformer encoder
+                   ("index", 0))                 //
         .push_back(models::classifier()          //
-                    ("prefix", "masked-lm")      // prefix for parameter names
-                    ("type", "bert-masked-lm")   //
-                    ("index", 0))                // multi-task learning with MaskedLM
+                   ("prefix", "masked-lm")       // prefix for parameter names
+                   ("type", "bert-masked-lm")    //
+                   ("index", 0))                 // multi-task learning with MaskedLM
         .push_back(models::classifier()          //
-                    ("prefix", "next-sentence")  // prefix for parameter names
-                    ("type", "bert-classifier")  //
-                    ("index", 1))                // next sentence prediction
+                   ("prefix", "next-sentence")   // prefix for parameter names
+                   ("type", "bert-classifier")   //
+                   ("index", 1))                 // next sentence prediction
         .construct(graph);
   }
 
-  if(type == "bert-classifier") {                // for BERT fine-tuning on non-BERT classification task
+  else if(type == "bert-classifier") {           // for BERT fine-tuning on non-BERT classification task
     return models::encoder_classifier()(options) //
         ("original-type", "bert-classifier")     // so we can query this if needed
         ("usage", use)                           //
         .push_back(models::encoder()             //
-                    ("type", "bert-encoder")     //
-                    ("index", 0))                // close to original transformer encoder
+                   ("type", "bert-encoder")      //
+                   ("index", 0))                 // close to original transformer encoder
         .push_back(models::classifier()          //
-                    ("type", "bert-classifier")  //
-                    ("index", 1))                // next sentence prediction
+                   ("type", "bert-classifier")   //
+                   ("index", 1))                 // next sentence prediction
         .construct(graph);
   }
 
+#ifdef COMPILE_EXAMPLES
+  else if(type == "mnist-ffnn")
+    return New<MnistFeedForwardNet>(options);
+#endif
 #ifdef CUDNN
-  if(type == "char-s2s") {
+#ifdef COMPILE_EXAMPLES
+  else if(type == "mnist-lenet")
+    return New<MnistLeNet>(options);
+#endif
+  else if(type == "char-s2s") {
     return models::encoder_decoder()(options)
         ("usage", use)
         ("original-type", type)
-            .push_back(models::encoder()("type", "char-s2s"))
-            .push_back(models::decoder()("type", "s2s"))
-            .construct(graph);
+        .push_back(models::encoder()("type", "char-s2s"))
+        .push_back(models::decoder()("type", "s2s"))
+        .construct(graph);
   }
 #endif
 
   // clang-format on
-  ABORT("Unknown model type: {}", type);
+  else
+    ABORT("Unknown model type: {}", type);
 }
 
 Ptr<ModelBase> createModelFromOptions(Ptr<Options> options, usage use) {

From 43d39d35e344d23c018ce570c7ce5712d83186e4 Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Mon, 11 Mar 2019 13:46:29 -0700
Subject: [PATCH 367/838] some renaming

---
 src/examples/mnist/model.h             | 10 +++---
 src/examples/mnist/validator.h         |  2 +-
 src/models/costs.h                     | 48 +++++++++++++-------------
 src/models/encoder_classifier.h        |  2 +-
 src/models/encoder_decoder.cpp         |  2 +-
 src/models/encoder_decoder.h           |  2 +-
 src/models/model_base.h                |  5 +--
 src/models/model_factory.cpp           | 10 +++---
 src/models/model_factory.h             | 10 +++---
 src/rescorer/rescorer.h                |  2 +-
 src/training/graph_group.h             |  4 +--
 src/training/graph_group_async.cpp     |  2 +-
 src/training/graph_group_async.h       |  2 +-
 src/training/graph_group_multinode.cpp |  2 +-
 src/training/graph_group_singleton.h   |  2 +-
 src/training/graph_group_sync.h        |  7 ++--
 src/training/validator.h               | 12 +++----
 src/translator/scorers.h               |  4 +--
 18 files changed, 64 insertions(+), 64 deletions(-)
 mode change 100644 => 100755 src/translator/scorers.h

diff --git a/src/examples/mnist/model.h b/src/examples/mnist/model.h
index 2a0e0e6a8..ffc04876f 100755
--- a/src/examples/mnist/model.h
+++ b/src/examples/mnist/model.h
@@ -17,11 +17,11 @@ namespace models {
 
 // @TODO: looking at this file, simplify the new RationalLoss idea. Here it gets too complicated
 
-class MNISTCrossEntropyCost : public CostBase {
+class MNISTCrossEntropyCost : public ICost {
 public:
   MNISTCrossEntropyCost() {}
 
-  Ptr<MultiRationalLoss> apply(Ptr<ModelBase> model,
+  Ptr<MultiRationalLoss> apply(Ptr<IModel> model,
                                Ptr<ExpressionGraph> graph,
                                Ptr<data::Batch> batch,
                                bool clearGraph = true) override {
@@ -43,11 +43,11 @@ class MNISTCrossEntropyCost : public CostBase {
   }
 };
 
-class MNISTLogsoftmax : public LogProbBase {
+class MNISTLogsoftmax : public ILogProb {
 public:
   MNISTLogsoftmax() {}
 
-  Expr apply(Ptr<ModelBase> model,
+  Expr apply(Ptr<IModel> model,
              Ptr<ExpressionGraph> graph,
              Ptr<data::Batch> batch,
              bool clearGraph = true) override {
@@ -56,7 +56,7 @@ class MNISTLogsoftmax : public LogProbBase {
   }
 };
 
-class MnistFeedForwardNet : public ModelBase {
+class MnistFeedForwardNet : public IModel {
 public:
   typedef data::MNISTData dataset_type;
 
diff --git a/src/examples/mnist/validator.h b/src/examples/mnist/validator.h
index b95a83dca..fa9eb3342 100755
--- a/src/examples/mnist/validator.h
+++ b/src/examples/mnist/validator.h
@@ -12,7 +12,7 @@ using namespace marian;
 
 namespace marian {
 
-class MNISTAccuracyValidator : public Validator<data::MNISTData, models::ModelBase> {
+class MNISTAccuracyValidator : public Validator<data::MNISTData, models::IModel> {
 public:
   MNISTAccuracyValidator(Ptr<Options> options) : Validator(std::vector<Ptr<Vocab>>(), options, false) {
     createBatchGenerator(/*isTranslating=*/false);
diff --git a/src/models/costs.h b/src/models/costs.h
index 28191aa2c..70a23952f 100755
--- a/src/models/costs.h
+++ b/src/models/costs.h
@@ -19,15 +19,15 @@ namespace models {
 // Other functions return RationalLoss directly without Ptr<...>, but also
 // they do not need polymorphism here.
 
-class CostBase {
+class ICost {
 public:
-  virtual Ptr<MultiRationalLoss> apply(Ptr<ModelBase> model,
+  virtual Ptr<MultiRationalLoss> apply(Ptr<IModel> model,
                                        Ptr<ExpressionGraph> graph,
                                        Ptr<data::Batch> batch,
                                        bool clearGraph = true) = 0;
 };
 
-class EncoderDecoderCE : public CostBase {
+class EncoderDecoderCE : public ICost {
 protected:
   Ptr<Options> options_;
 
@@ -51,7 +51,7 @@ class EncoderDecoderCE : public CostBase {
       weighter_ = WeightingFactory(options_);
   }
 
-  Ptr<MultiRationalLoss> apply(Ptr<ModelBase> model,
+  Ptr<MultiRationalLoss> apply(Ptr<IModel> model,
              Ptr<ExpressionGraph> graph,
              Ptr<data::Batch> batch,
              bool clearGraph = true) override {
@@ -89,7 +89,7 @@ class EncoderDecoderCE : public CostBase {
 };
 
 // Wraps an EncoderClassifier so it can produce a cost from raw logits. @TODO: Needs refactoring
-class EncoderClassifierCE : public CostBase {
+class EncoderClassifierCE : public ICost {
 protected:
   Ptr<Options> options_;
   bool inference_{false};
@@ -104,7 +104,7 @@ class EncoderClassifierCE : public CostBase {
     loss_ = newLoss(options_, inference_);
   }
 
-  Ptr<MultiRationalLoss> apply(Ptr<ModelBase> model,
+  Ptr<MultiRationalLoss> apply(Ptr<IModel> model,
              Ptr<ExpressionGraph> graph,
              Ptr<data::Batch> batch,
              bool clearGraph = true) override {
@@ -127,16 +127,16 @@ class EncoderClassifierCE : public CostBase {
   }
 };
 
-class Trainer : public CriterionBase {
+class Trainer : public ICriterionFunction {
 protected:
-  Ptr<ModelBase> model_;
-  Ptr<CostBase> cost_;
+  Ptr<IModel> model_;
+  Ptr<ICost> cost_;
 
 public:
-  Trainer(Ptr<ModelBase> model, Ptr<CostBase> cost)
+  Trainer(Ptr<IModel> model, Ptr<ICost> cost)
       : model_(model), cost_(cost) {}
 
-  Ptr<ModelBase> getModel() { return model_; }
+  Ptr<IModel> getModel() { return model_; }
 
   virtual void load(Ptr<ExpressionGraph> graph,
                     const std::string& name,
@@ -159,9 +159,9 @@ class Trainer : public CriterionBase {
   virtual void clear(Ptr<ExpressionGraph> graph) override { model_->clear(graph); };
 };
 
-class LogProbBase {
+class ILogProb {
 public:
-  virtual Expr apply(Ptr<ModelBase> model,
+  virtual Expr apply(Ptr<IModel> model,
                      Ptr<ExpressionGraph> graph,
                      Ptr<data::Batch> batch,
                      bool clearGraph = true) = 0;
@@ -170,16 +170,16 @@ class LogProbBase {
 // @TODO: Name 'scorer' is ambiguous: Does it compute scores for all classes, or the loss value for the ground truth?
 //        Beam search uses it for the former meaning, while 'marian score' and validation in the latter.
 //        This class is for the former use. The latter is done using Trainer.
-class Scorer : public ModelBase {
+class Scorer : public IModel {
 protected:
-  Ptr<ModelBase> model_;
-  Ptr<LogProbBase> logProb_;
+  Ptr<IModel> model_;
+  Ptr<ILogProb> logProb_;
 
 public:
-  Scorer(Ptr<ModelBase> model, Ptr<LogProbBase> cost)
+  Scorer(Ptr<IModel> model, Ptr<ILogProb> cost)
       : model_(model), logProb_(cost) {}
 
-  Ptr<ModelBase> getModel() { return model_; }
+  Ptr<IModel> getModel() { return model_; }
 
   virtual void load(Ptr<ExpressionGraph> graph,
                     const std::string& name,
@@ -202,12 +202,12 @@ class Scorer : public ModelBase {
   virtual void clear(Ptr<ExpressionGraph> graph) override { model_->clear(graph); };
 };
 
-class CostStep {
+class LogProbStep {
 public:
   virtual Ptr<DecoderState> apply(Ptr<DecoderState> state) = 0;
 };
 
-class LogSoftmaxStep : public CostStep {
+class LogSoftmaxStep : public LogProbStep {
 public:
   virtual Ptr<DecoderState> apply(Ptr<DecoderState> state) override {
     // decoder needs normalized probabilities (note: skipped if beam 1 and --skip-cost)
@@ -223,7 +223,7 @@ class LogSoftmaxStep : public CostStep {
 // Gumbel-max noising for sampling during beam-search
 // Seems to work well enough with beam-size=1. Turn on
 // with --output-sampling during translation with marian-decoder
-class GumbelSoftmaxStep : public CostStep {
+class GumbelSoftmaxStep : public LogProbStep {
 public:
   virtual Ptr<DecoderState> apply(Ptr<DecoderState> state) override {
     auto logits = state->getLogProbs();
@@ -235,16 +235,16 @@ class GumbelSoftmaxStep : public CostStep {
   }
 };
 
-// class to wrap an EncoderDecoderBase and a CostStep that are executed in sequence,
+// class to wrap an EncoderDecoderBase and a LogProbStep that are executed in sequence,
 // wrapped again in the EncoderDecoderBase interface
 // @TODO: seems we are conflating an interface defition with its implementation?
 class Stepwise : public EncoderDecoderBase {
 protected:
   Ptr<EncoderDecoderBase> encdec_;
-  Ptr<CostStep> cost_;
+  Ptr<LogProbStep> cost_;
 
 public:
-  Stepwise(Ptr<EncoderDecoderBase> encdec, Ptr<CostStep> cost)
+  Stepwise(Ptr<EncoderDecoderBase> encdec, Ptr<LogProbStep> cost)
       : encdec_(encdec), cost_(cost) {}
 
   virtual void load(Ptr<ExpressionGraph> graph,
diff --git a/src/models/encoder_classifier.h b/src/models/encoder_classifier.h
index 15cbb7d7d..2477c330c 100755
--- a/src/models/encoder_classifier.h
+++ b/src/models/encoder_classifier.h
@@ -17,7 +17,7 @@ namespace marian {
  * @TODO: this should probably be unified somehow with EncoderDecoder which could allow for deocder/classifier
  * multi-objective training.
  */
-class EncoderClassifierBase : public models::ModelBase {
+class EncoderClassifierBase : public models::IModel {
 public:
   virtual ~EncoderClassifierBase() {}
 
diff --git a/src/models/encoder_decoder.cpp b/src/models/encoder_decoder.cpp
index 398eb6d70..6eed8ab62 100755
--- a/src/models/encoder_decoder.cpp
+++ b/src/models/encoder_decoder.cpp
@@ -202,7 +202,7 @@ Expr EncoderDecoder::build(Ptr<ExpressionGraph> graph,
   auto state = stepAll(graph, batch, clearGraph);
 
   // returns raw logits
-  return state->getLogProbs();  // , state->getTargetMask()); // @TODO: hacky hack hack
+  return state->getLogProbs();
 }
 
 Expr EncoderDecoder::build(Ptr<ExpressionGraph> graph,
diff --git a/src/models/encoder_decoder.h b/src/models/encoder_decoder.h
index b0f5b3145..e3b6bc40b 100755
--- a/src/models/encoder_decoder.h
+++ b/src/models/encoder_decoder.h
@@ -9,7 +9,7 @@
 
 namespace marian {
 
-class EncoderDecoderBase : public models::ModelBase {
+class EncoderDecoderBase : public models::IModel {
 public:
   virtual void load(Ptr<ExpressionGraph> graph,
                     const std::string& name,
diff --git a/src/models/model_base.h b/src/models/model_base.h
index 0113a7ec0..c11869cc6 100755
--- a/src/models/model_base.h
+++ b/src/models/model_base.h
@@ -17,7 +17,7 @@ namespace marian {
 namespace models {
 
 // model = input -> predictions
-class ModelBase {
+class IModel {
 public:
   virtual void load(Ptr<ExpressionGraph>,
                     const std::string&,
@@ -37,7 +37,8 @@ class ModelBase {
 };
 
 // criterion = (input, reference) -> loss
-class CriterionBase {
+// @TODO: Is there a better name?
+class ICriterionFunction {
 public:
   virtual void load(Ptr<ExpressionGraph>,
                     const std::string&,
diff --git a/src/models/model_factory.cpp b/src/models/model_factory.cpp
index cee855ab6..9d660510b 100755
--- a/src/models/model_factory.cpp
+++ b/src/models/model_factory.cpp
@@ -60,7 +60,7 @@ Ptr<ClassifierBase> ClassifierFactory::construct(Ptr<ExpressionGraph> /*graph*/)
   ABORT("Unknown classifier type");
 }
 
-Ptr<ModelBase> EncoderDecoderFactory::construct(Ptr<ExpressionGraph> graph) {
+Ptr<IModel> EncoderDecoderFactory::construct(Ptr<ExpressionGraph> graph) {
   Ptr<EncoderDecoder> encdec;
 
   if(options_->get<std::string>("type") == "amun")
@@ -80,7 +80,7 @@ Ptr<ModelBase> EncoderDecoderFactory::construct(Ptr<ExpressionGraph> graph) {
   return encdec;
 }
 
-Ptr<ModelBase> EncoderClassifierFactory::construct(Ptr<ExpressionGraph> graph) {
+Ptr<IModel> EncoderClassifierFactory::construct(Ptr<ExpressionGraph> graph) {
   Ptr<EncoderClassifier> enccls;
   if(options_->get<std::string>("type") == "bert") {
     enccls = New<BertEncoderClassifier>(options_);
@@ -99,7 +99,7 @@ Ptr<ModelBase> EncoderClassifierFactory::construct(Ptr<ExpressionGraph> graph) {
   return enccls;
 }
 
-Ptr<ModelBase> createBaseModelByType(std::string type, usage use, Ptr<Options> options) {
+Ptr<IModel> createBaseModelByType(std::string type, usage use, Ptr<Options> options) {
   Ptr<ExpressionGraph> graph = nullptr; // graph unknown at this stage
   // clang-format off
   if(type == "s2s" || type == "amun" || type == "nematus") {
@@ -283,7 +283,7 @@ Ptr<ModelBase> createBaseModelByType(std::string type, usage use, Ptr<Options> o
     ABORT("Unknown model type: {}", type);
 }
 
-Ptr<ModelBase> createModelFromOptions(Ptr<Options> options, usage use) {
+Ptr<IModel> createModelFromOptions(Ptr<Options> options, usage use) {
   std::string type = options->get<std::string>("type");
   auto baseModel = createBaseModelByType(type, use, options);
 
@@ -313,7 +313,7 @@ Ptr<ModelBase> createModelFromOptions(Ptr<Options> options, usage use) {
     ABORT("'Usage' parameter must be 'translation' or 'raw'");
 }
 
-Ptr<CriterionBase> createCriterionFromOptions(Ptr<Options> options, usage use) {
+Ptr<ICriterionFunction> createCriterionFromOptions(Ptr<Options> options, usage use) {
   std::string type = options->get<std::string>("type");
   auto baseModel = createBaseModelByType(type, use, options);
 
diff --git a/src/models/model_factory.h b/src/models/model_factory.h
index 9d089e294..9fcbe091a 100755
--- a/src/models/model_factory.h
+++ b/src/models/model_factory.h
@@ -56,7 +56,7 @@ class EncoderDecoderFactory : public Factory {
     return Accumulator<EncoderDecoderFactory>(*this);
   }
 
-  virtual Ptr<ModelBase> construct(Ptr<ExpressionGraph> graph);
+  virtual Ptr<IModel> construct(Ptr<ExpressionGraph> graph);
 };
 
 typedef Accumulator<EncoderDecoderFactory> encoder_decoder;
@@ -80,15 +80,15 @@ class EncoderClassifierFactory : public Factory {
     return Accumulator<EncoderClassifierFactory>(*this);
   }
 
-  virtual Ptr<ModelBase> construct(Ptr<ExpressionGraph> graph);
+  virtual Ptr<IModel> construct(Ptr<ExpressionGraph> graph);
 };
 
 typedef Accumulator<EncoderClassifierFactory> encoder_classifier;
 
-Ptr<ModelBase> createBaseModelByType(std::string type, usage, Ptr<Options> options);
+Ptr<IModel> createBaseModelByType(std::string type, usage, Ptr<Options> options);
 
-Ptr<ModelBase> createModelFromOptions(Ptr<Options> options, usage);
+Ptr<IModel> createModelFromOptions(Ptr<Options> options, usage);
 
-Ptr<CriterionBase> createCriterionFromOptions(Ptr<Options> options, usage);
+Ptr<ICriterionFunction> createCriterionFromOptions(Ptr<Options> options, usage);
 }  // namespace models
 }  // namespace marian
diff --git a/src/rescorer/rescorer.h b/src/rescorer/rescorer.h
index 011b6b58c..14d2eae14 100755
--- a/src/rescorer/rescorer.h
+++ b/src/rescorer/rescorer.h
@@ -19,7 +19,7 @@ using namespace data;
 
 class Rescorer {
 private:
-  Ptr<models::CriterionBase> builder_;
+  Ptr<models::ICriterionFunction> builder_;
 
 public:
   Rescorer(Ptr<Options> options)
diff --git a/src/training/graph_group.h b/src/training/graph_group.h
index 8e67ad724..4ec6cdd11 100755
--- a/src/training/graph_group.h
+++ b/src/training/graph_group.h
@@ -55,7 +55,7 @@ class GraphGroup {
    */
   // @TODO: Can this be made const? It seems wrong to have a stateful method that still returns a result.
   virtual Ptr<data::BatchStats> collectStats(Ptr<ExpressionGraph> graph,
-                                             Ptr<models::CriterionBase> model,
+                                             Ptr<models::ICriterionFunction> model,
                                              const std::vector<Ptr<Vocab>>& vocabs,
                                              double multiplier = 1.) {
     auto stats = New<data::BatchStats>();
@@ -141,7 +141,7 @@ class MultiNodeGraphGroupBase : public GraphGroup {
   std::vector<size_t> devices_; // [num local GPUs]
 
   /** Graph builders for clients (which run forward and backward passes). */
-  std::vector<Ptr<models::CriterionBase>> clientBuilders_;
+  std::vector<Ptr<models::ICriterionFunction>> clientBuilders_;
 
   /** Graphs of clients. One entry per GPU on this node. */
   std::vector<Ptr<ExpressionGraph>> clientGraphs_; // [num local GPUs]
diff --git a/src/training/graph_group_async.cpp b/src/training/graph_group_async.cpp
index 780108f17..5679d81b2 100755
--- a/src/training/graph_group_async.cpp
+++ b/src/training/graph_group_async.cpp
@@ -189,7 +189,7 @@ void AsyncGraphGroup::execute(Ptr<data::Batch> batch) {
   auto task = [this](Ptr<data::Batch> batch) {
     static size_t i = 0;
     thread_local Ptr<ExpressionGraph> graph;
-    thread_local Ptr<models::CriterionBase> builder;
+    thread_local Ptr<models::ICriterionFunction> builder;
     thread_local size_t t = 0;
     thread_local size_t num_seen_words = 0;
     thread_local size_t num_seen_sentences = 0;
diff --git a/src/training/graph_group_async.h b/src/training/graph_group_async.h
index bb19bde40..0e27a6657 100755
--- a/src/training/graph_group_async.h
+++ b/src/training/graph_group_async.h
@@ -16,7 +16,7 @@ class AsyncGraphGroup : public GraphGroup, public ExponentialSmoothing {
 protected:
   bool first_{true};
 
-  std::vector<Ptr<models::CriterionBase>> builders_;
+  std::vector<Ptr<models::ICriterionFunction>> builders_;
   std::vector<Ptr<ExpressionGraph>> graphs_;
   std::vector<DeviceId> devices_;
 
diff --git a/src/training/graph_group_multinode.cpp b/src/training/graph_group_multinode.cpp
index 4e7d931cf..28c256414 100755
--- a/src/training/graph_group_multinode.cpp
+++ b/src/training/graph_group_multinode.cpp
@@ -512,7 +512,7 @@ void MultiNodeGraphGroup::execute(Ptr<data::Batch> batch) {
   auto task = [this](Ptr<data::Batch> batch) {
     static size_t i = 0;
     thread_local Ptr<ExpressionGraph> graph;
-    thread_local Ptr<models::CriterionBase> builder;
+    thread_local Ptr<models::ICriterionFunction> builder;
     thread_local size_t my_id = 0;
     thread_local size_t t = 0;
     // only for scheduler statistic
diff --git a/src/training/graph_group_singleton.h b/src/training/graph_group_singleton.h
index 8aede537a..4587695c6 100755
--- a/src/training/graph_group_singleton.h
+++ b/src/training/graph_group_singleton.h
@@ -16,7 +16,7 @@ class SingletonGraph : public GraphGroup, public ExponentialSmoothing {
   virtual void setScheduler(Ptr<Scheduler> scheduler) override;
 
 private:
-  Ptr<models::CriterionBase> builder_;
+  Ptr<models::ICriterionFunction> builder_;
   Ptr<ExpressionGraph> graph_;
   Ptr<ExpressionGraph> graphAvg_;
 
diff --git a/src/training/graph_group_sync.h b/src/training/graph_group_sync.h
index a34bb114d..b127296da 100755
--- a/src/training/graph_group_sync.h
+++ b/src/training/graph_group_sync.h
@@ -13,12 +13,11 @@ class SyncGraphGroup : public GraphGroup, public ExponentialSmoothing {
   Ptr<ICommunicator> comm_; // [not null] communicator, e.g. NCCLCommunicator
   Ptr<IMPIWrapper> mpi_;    // [not null] all MPI-like communication goes through this (this is a dummy implementation if no MPI run)
 
-  std::vector<DeviceId> devices_;                  // [deviceIndex]
-  std::vector<Ptr<models::CriterionBase>> builders_;   // [deviceIndex]
-  std::vector<Ptr<ExpressionGraph>> graphs_;       // [deviceIndex]
+  std::vector<DeviceId> devices_;                         // [deviceIndex]
+  std::vector<Ptr<models::ICriterionFunction>> builders_; // [deviceIndex]
+  std::vector<Ptr<ExpressionGraph>> graphs_;              // [deviceIndex]
 
   std::vector<Ptr<OptimizerBase>> shardOpt_;       // [deviceIndex]
-
   std::vector<Tensor> paramsAvg_;                  // [deviceIndex] exponentially smoothed parameters, sharded
   // @TODO: instead, create an array of ExponentialSmoothing objects, and don't use ExponentialSmoothing as a base class
   std::vector<Ptr<TensorAllocator>> paramsAllocs_; // [deviceIndex] we must hold a reference to the memory until this class dies
diff --git a/src/training/validator.h b/src/training/validator.h
index d49ded8f6..7c9b5e9a6 100755
--- a/src/training/validator.h
+++ b/src/training/validator.h
@@ -148,7 +148,7 @@ class Validator : public ValidatorBase {
   }
 };
 
-class CrossEntropyValidator : public Validator<data::Corpus, models::CriterionBase> {
+class CrossEntropyValidator : public Validator<data::Corpus, models::ICriterionFunction> {
 public:
   CrossEntropyValidator(std::vector<Ptr<Vocab>> vocabs, Ptr<Options> options)
       : Validator(vocabs, options) {
@@ -216,7 +216,7 @@ class CrossEntropyValidator : public Validator<data::Corpus, models::CriterionBa
 };
 
 // Used for validating with classifiers. Compute prediction accuracy versus ground truth for a set of classes
-class AccuracyValidator : public Validator<data::Corpus, models::ModelBase> {
+class AccuracyValidator : public Validator<data::Corpus, models::IModel> {
 public:
   AccuracyValidator(std::vector<Ptr<Vocab>> vocabs, Ptr<Options> options)
       : Validator(vocabs, options, /*lowerIsBetter=*/false) {
@@ -305,7 +305,7 @@ class AccuracyValidator : public Validator<data::Corpus, models::ModelBase> {
   }
 };
 
-class BertAccuracyValidator : public Validator<data::Corpus, models::ModelBase> {
+class BertAccuracyValidator : public Validator<data::Corpus, models::IModel> {
 private:
   bool evalMaskedLM_{true};
 
@@ -415,7 +415,7 @@ class BertAccuracyValidator : public Validator<data::Corpus, models::ModelBase>
 };
 
 
-class ScriptValidator : public Validator<data::Corpus, models::ModelBase> {
+class ScriptValidator : public Validator<data::Corpus, models::IModel> {
 public:
   ScriptValidator(std::vector<Ptr<Vocab>> vocabs, Ptr<Options> options)
       : Validator(vocabs, options, false) {
@@ -446,7 +446,7 @@ class ScriptValidator : public Validator<data::Corpus, models::ModelBase> {
   }
 };
 
-class TranslationValidator : public Validator<data::Corpus, models::ModelBase> {
+class TranslationValidator : public Validator<data::Corpus, models::IModel> {
 public:
   TranslationValidator(std::vector<Ptr<Vocab>> vocabs, Ptr<Options> options)
       : Validator(vocabs, options, false),
@@ -578,7 +578,7 @@ class TranslationValidator : public Validator<data::Corpus, models::ModelBase> {
 };
 
 // @TODO: combine with TranslationValidator (above) to avoid code duplication
-class BleuValidator : public Validator<data::Corpus, models::ModelBase> {
+class BleuValidator : public Validator<data::Corpus, models::IModel> {
 private:
   bool detok_{false};
 
diff --git a/src/translator/scorers.h b/src/translator/scorers.h
old mode 100644
new mode 100755
index 16a066b05..82e484d0e
--- a/src/translator/scorers.h
+++ b/src/translator/scorers.h
@@ -72,7 +72,7 @@ class ScorerWrapper : public Scorer {
   const void* ptr_;
 
 public:
-  ScorerWrapper(Ptr<models::ModelBase> encdec,
+  ScorerWrapper(Ptr<models::IModel> encdec,
                 const std::string& name,
                 float weight,
                 const std::string& fname)
@@ -81,7 +81,7 @@ class ScorerWrapper : public Scorer {
         fname_(fname),
         ptr_{0} {}
 
-  ScorerWrapper(Ptr<models::ModelBase> encdec,
+  ScorerWrapper(Ptr<models::IModel> encdec,
                 const std::string& name,
                 float weight,
                 const void* ptr)

From 980f2698c590bfb3974343a3ad490572ba307efd Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Mon, 11 Mar 2019 14:02:36 -0700
Subject: [PATCH 368/838] some renaming

---
 src/models/costs.h                   | 22 +++++++++++-----------
 src/models/model_factory.cpp         |  6 +++---
 src/models/model_factory.h           |  2 +-
 src/rescorer/rescorer.h              |  2 +-
 src/training/graph_group.h           |  2 +-
 src/training/graph_group_async.cpp   |  2 +-
 src/training/graph_group_singleton.h |  2 +-
 src/training/graph_group_sync.cpp    |  2 +-
 src/training/validator.h             |  4 ++--
 9 files changed, 22 insertions(+), 22 deletions(-)

diff --git a/src/models/costs.h b/src/models/costs.h
index 70a23952f..35ee24444 100755
--- a/src/models/costs.h
+++ b/src/models/costs.h
@@ -22,12 +22,12 @@ namespace models {
 class ICost {
 public:
   virtual Ptr<MultiRationalLoss> apply(Ptr<IModel> model,
-                                       Ptr<ExpressionGraph> graph,
+                                       Ptr<ExpressionGraph> graph, // @TODO: why needed? Can it be gotten from model?
                                        Ptr<data::Batch> batch,
                                        bool clearGraph = true) = 0;
 };
 
-class EncoderDecoderCE : public ICost {
+class EncoderDecoderCECost : public ICost {
 protected:
   Ptr<Options> options_;
 
@@ -39,7 +39,7 @@ class EncoderDecoderCE : public ICost {
   Ptr<WeightingBase> weighter_;
 
 public:
-  EncoderDecoderCE(Ptr<Options> options)
+  EncoderDecoderCECost(Ptr<Options> options)
       : options_(options), inference_(options->get<bool>("inference", false)) {
     loss_ = newLoss(options_, inference_);
 
@@ -89,7 +89,7 @@ class EncoderDecoderCE : public ICost {
 };
 
 // Wraps an EncoderClassifier so it can produce a cost from raw logits. @TODO: Needs refactoring
-class EncoderClassifierCE : public ICost {
+class EncoderClassifierCECost : public ICost {
 protected:
   Ptr<Options> options_;
   bool inference_{false};
@@ -99,7 +99,7 @@ class EncoderClassifierCE : public ICost {
   Ptr<LabelwiseLoss> loss_;
 
 public:
-  EncoderClassifierCE(Ptr<Options> options)
+  EncoderClassifierCECost(Ptr<Options> options)
       : options_(options), inference_(options->get<bool>("inference", false)) {
     loss_ = newLoss(options_, inference_);
   }
@@ -202,12 +202,12 @@ class Scorer : public IModel {
   virtual void clear(Ptr<ExpressionGraph> graph) override { model_->clear(graph); };
 };
 
-class LogProbStep {
+class ILogProbStep {
 public:
   virtual Ptr<DecoderState> apply(Ptr<DecoderState> state) = 0;
 };
 
-class LogSoftmaxStep : public LogProbStep {
+class LogSoftmaxStep : public ILogProbStep {
 public:
   virtual Ptr<DecoderState> apply(Ptr<DecoderState> state) override {
     // decoder needs normalized probabilities (note: skipped if beam 1 and --skip-cost)
@@ -223,7 +223,7 @@ class LogSoftmaxStep : public LogProbStep {
 // Gumbel-max noising for sampling during beam-search
 // Seems to work well enough with beam-size=1. Turn on
 // with --output-sampling during translation with marian-decoder
-class GumbelSoftmaxStep : public LogProbStep {
+class GumbelSoftmaxStep : public ILogProbStep {
 public:
   virtual Ptr<DecoderState> apply(Ptr<DecoderState> state) override {
     auto logits = state->getLogProbs();
@@ -235,16 +235,16 @@ class GumbelSoftmaxStep : public LogProbStep {
   }
 };
 
-// class to wrap an EncoderDecoderBase and a LogProbStep that are executed in sequence,
+// class to wrap an EncoderDecoderBase and a ILogProbStep that are executed in sequence,
 // wrapped again in the EncoderDecoderBase interface
 // @TODO: seems we are conflating an interface defition with its implementation?
 class Stepwise : public EncoderDecoderBase {
 protected:
   Ptr<EncoderDecoderBase> encdec_;
-  Ptr<LogProbStep> cost_;
+  Ptr<ILogProbStep> cost_;
 
 public:
-  Stepwise(Ptr<EncoderDecoderBase> encdec, Ptr<LogProbStep> cost)
+  Stepwise(Ptr<EncoderDecoderBase> encdec, Ptr<ILogProbStep> cost)
       : encdec_(encdec), cost_(cost) {}
 
   virtual void load(Ptr<ExpressionGraph> graph,
diff --git a/src/models/model_factory.cpp b/src/models/model_factory.cpp
index 9d660510b..b0cd02e0b 100755
--- a/src/models/model_factory.cpp
+++ b/src/models/model_factory.cpp
@@ -313,7 +313,7 @@ Ptr<IModel> createModelFromOptions(Ptr<Options> options, usage use) {
     ABORT("'Usage' parameter must be 'translation' or 'raw'");
 }
 
-Ptr<ICriterionFunction> createCriterionFromOptions(Ptr<Options> options, usage use) {
+Ptr<ICriterionFunction> createCriterionFunctionFromOptions(Ptr<Options> options, usage use) {
   std::string type = options->get<std::string>("type");
   auto baseModel = createBaseModelByType(type, use, options);
 
@@ -322,9 +322,9 @@ Ptr<ICriterionFunction> createCriterionFromOptions(Ptr<Options> options, usage u
   // note: usage::scoring means "score the loss function", hence it uses a Trainer (not Scorer, which is for decoding)
   // @TODO: Should we define a new class that does not compute gradients?
   if (std::dynamic_pointer_cast<EncoderDecoder>(baseModel))
-    return New<Trainer>(baseModel, New<EncoderDecoderCE>(options));
+    return New<Trainer>(baseModel, New<EncoderDecoderCECost>(options));
   else if (std::dynamic_pointer_cast<EncoderClassifier>(baseModel))
-    return New<Trainer>(baseModel, New<EncoderClassifierCE>(options));
+    return New<Trainer>(baseModel, New<EncoderClassifierCECost>(options));
 #ifdef COMPILE_EXAMPLES
   // @TODO: examples should be compiled optionally
   else if (std::dynamic_pointer_cast<MnistFeedForwardNet>(baseModel))
diff --git a/src/models/model_factory.h b/src/models/model_factory.h
index 9fcbe091a..8f3f07abe 100755
--- a/src/models/model_factory.h
+++ b/src/models/model_factory.h
@@ -89,6 +89,6 @@ Ptr<IModel> createBaseModelByType(std::string type, usage, Ptr<Options> options)
 
 Ptr<IModel> createModelFromOptions(Ptr<Options> options, usage);
 
-Ptr<ICriterionFunction> createCriterionFromOptions(Ptr<Options> options, usage);
+Ptr<ICriterionFunction> createCriterionFunctionFromOptions(Ptr<Options> options, usage);
 }  // namespace models
 }  // namespace marian
diff --git a/src/rescorer/rescorer.h b/src/rescorer/rescorer.h
index 14d2eae14..3917a901c 100755
--- a/src/rescorer/rescorer.h
+++ b/src/rescorer/rescorer.h
@@ -23,7 +23,7 @@ class Rescorer {
 
 public:
   Rescorer(Ptr<Options> options)
-      : builder_(models::createCriterionFromOptions(options, models::usage::scoring)) {}
+      : builder_(models::createCriterionFunctionFromOptions(options, models::usage::scoring)) {}
 
   void load(Ptr<ExpressionGraph> graph, const std::string& modelFile) {
     builder_->load(graph, modelFile);
diff --git a/src/training/graph_group.h b/src/training/graph_group.h
index 4ec6cdd11..33070f469 100755
--- a/src/training/graph_group.h
+++ b/src/training/graph_group.h
@@ -161,7 +161,7 @@ class MultiNodeGraphGroupBase : public GraphGroup {
       clientGraphs_.push_back(New<ExpressionGraph>());
       clientGraphs_[i]->setDevice({ devices_[i], DeviceType::gpu });
       clientGraphs_[i]->reserveWorkspaceMB(options_->get<size_t>("workspace"));
-      clientBuilders_.push_back(models::createCriterionFromOptions(options_, models::usage::training));
+      clientBuilders_.push_back(models::createCriterionFunctionFromOptions(options_, models::usage::training));
     }
   }
 
diff --git a/src/training/graph_group_async.cpp b/src/training/graph_group_async.cpp
index 5679d81b2..6ecaa1d9c 100755
--- a/src/training/graph_group_async.cpp
+++ b/src/training/graph_group_async.cpp
@@ -23,7 +23,7 @@ AsyncGraphGroup::AsyncGraphGroup(Ptr<Options> config, Ptr<IMPIWrapper> mpi)
     graphs_.push_back(graph);
     shardOpt_.push_back(Optimizer(options_));
 
-    builders_.push_back(models::createCriterionFromOptions(options_, models::usage::training));
+    builders_.push_back(models::createCriterionFunctionFromOptions(options_, models::usage::training));
   }
 }
 
diff --git a/src/training/graph_group_singleton.h b/src/training/graph_group_singleton.h
index 4587695c6..e2b1818cc 100755
--- a/src/training/graph_group_singleton.h
+++ b/src/training/graph_group_singleton.h
@@ -37,7 +37,7 @@ class SingletonGraph : public GraphGroup, public ExponentialSmoothing {
     graph_->getBackend()->setClip(options_->get<float>("clip-gemm"));
     graph_->reserveWorkspaceMB(options_->get<size_t>("workspace"));
     opt_ = Optimizer(options_);
-    builder_ = models::createCriterionFromOptions(options_, models::usage::training);
+    builder_ = models::createCriterionFunctionFromOptions(options_, models::usage::training);
   }
 
   void update(Ptr<data::Batch> batch) override {
diff --git a/src/training/graph_group_sync.cpp b/src/training/graph_group_sync.cpp
index b97ac65a1..ba5751691 100755
--- a/src/training/graph_group_sync.cpp
+++ b/src/training/graph_group_sync.cpp
@@ -15,7 +15,7 @@ SyncGraphGroup::SyncGraphGroup(Ptr<Options> config, Ptr<IMPIWrapper> mpi)
 
     graphs_.push_back(graph);
     shardOpt_.push_back(Optimizer(options_));
-    builders_.push_back(models::createCriterionFromOptions(options_, models::usage::training));
+    builders_.push_back(models::createCriterionFunctionFromOptions(options_, models::usage::training));
   }
 
   // Note: We may well end up with only one MPI process or only one graph per worker.
diff --git a/src/training/validator.h b/src/training/validator.h
index 7c9b5e9a6..1323b6758 100755
--- a/src/training/validator.h
+++ b/src/training/validator.h
@@ -159,7 +159,7 @@ class CrossEntropyValidator : public Validator<data::Corpus, models::ICriterionF
     opts->merge(options);
     opts->set("inference", true);
     opts->set("cost-type", "ce-sum");
-    builder_ = models::createCriterionFromOptions(opts, models::usage::scoring);
+    builder_ = models::createCriterionFunctionFromOptions(opts, models::usage::scoring);
   }
 
   std::string type() override { return options_->get<std::string>("cost-type"); }
@@ -180,7 +180,7 @@ class CrossEntropyValidator : public Validator<data::Corpus, models::ICriterionF
       for(auto batch : *batchGenerator_) {
         auto task = [=, &loss, &samples](size_t id) {
           thread_local Ptr<ExpressionGraph> graph;
-          thread_local auto builder = models::createCriterionFromOptions(options_, models::usage::scoring);
+          thread_local auto builder = models::createCriterionFunctionFromOptions(options_, models::usage::scoring);
 
           if(!graph) {
             graph = graphs[id % graphs.size()];

From 0d1c824342726b34935204c8d1cecbd7b37564f6 Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Mon, 11 Mar 2019 14:23:39 -0700
Subject: [PATCH 369/838] minor comment edits and a few renamings

---
 src/translator/beam_search.h   | 28 +++++++++++++---------------
 src/translator/hypothesis.h    | 12 ++++++------
 src/translator/nth_element.cpp | 12 +++++-------
 src/translator/nth_element.cu  | 20 ++++++++++----------
 4 files changed, 34 insertions(+), 38 deletions(-)

diff --git a/src/translator/beam_search.h b/src/translator/beam_search.h
index 2954f06d7..184fded0e 100755
--- a/src/translator/beam_search.h
+++ b/src/translator/beam_search.h
@@ -18,7 +18,7 @@ class BeamSearch {
   Word trgEosId_ = (Word)-1;
   Word trgUnkId_ = (Word)-1;
 
-  static constexpr auto INVALID_PATH_SCORE = -9999;
+  static constexpr auto INVALID_PATH_SCORE = -9999; // (@TODO: change to -9999.0 once C++ allows that)
 
 public:
   BeamSearch(Ptr<Options> options,
@@ -36,7 +36,7 @@ class BeamSearch {
   // combine new expandedPathScores and previous beams into new set of beams
   Beams toHyps(const std::vector<unsigned int>& nBestKeys, // [dimBatch, beamSize] flattened -> ((batchIdx, beamHypIdx) flattened, word idx) flattened
                const std::vector<float>& nBestPathScores,  // [dimBatch, beamSize] flattened
-               const size_t inputBeamSize, // for interpretation of nBestKeys
+               const size_t nBestBeamSize, // for interpretation of nBestKeys
                const size_t vocabSize,     // ditto.
                const Beams& beams,
                const std::vector<Ptr<ScorerState /*const*/>>& states,
@@ -46,23 +46,24 @@ class BeamSearch {
       align = scorers_[0]->getAlignment(); // use alignments from the first scorer, even if ensemble
 
     const auto dimBatch = beams.size();
-    Beams newBeams(dimBatch);
+    Beams newBeams(dimBatch);   // return value of this function goes here
 
     for(size_t i = 0; i < nBestKeys.size(); ++i) {
       // Keys encode batchIdx, beamHypIdx, and word index in the entire beam.
-      // They can be between 0 and beamSize * vocabSize-1.
+      // They can be between 0 and (vocabSize * nBestBeamSize * batchSize)-1.
+      // (beamHypIdx refers to the GPU tensors, *not* the beams[] array; they are not the same in case of purging)
       const auto  key       = nBestKeys[i];
       const float pathScore = nBestPathScores[i]; // expanded path score for (batchIdx, beamHypIdx, word)
 
       // decompose key into individual indices (batchIdx, beamHypIdx, wordIdx)
       const auto wordIdx    = (Word)(key % vocabSize);
-      const auto beamHypIdx =       (key / vocabSize) % inputBeamSize;
-      const auto batchIdx   =       (key / vocabSize) / inputBeamSize;
+      const auto beamHypIdx =       (key / vocabSize) % nBestBeamSize;
+      const auto batchIdx   =       (key / vocabSize) / nBestBeamSize;
 
       const auto& beam = beams[batchIdx];
       auto& newBeam = newBeams[batchIdx];
 
-      if (newBeam.size() >= beam.size()) // @TODO: Why this condition? It does happen. Why?
+      if (newBeam.size() >= beam.size()) // getNBestList() generates N for all batch entries incl. those that already have a narrower beam
         continue;
       if (pathScore <= INVALID_PATH_SCORE) // (unused slot)
         continue;
@@ -183,14 +184,11 @@ class BeamSearch {
     }
 
     Beams beams(dimBatch, Beam(beamSize_, New<Hypothesis>())); // array [dimBatch] of array [localBeamSize] of Hypothesis
-    //Beams beams(dimBatch); // array [dimBatch] of array [localBeamSize] of Hypothesis
-    //for(auto& beam : beams)
-    //  beam.resize(beamSize_, New<Hypothesis>());
 
     for(int i = 0; i < dimBatch; ++i)
       histories[i]->add(beams[i], trgEosId_);
 
-    // the decoder updates the following state information in each output time step:
+    // the decoding process updates the following state information in each output time step:
     //  - beams: array [dimBatch] of array [localBeamSize] of Hypothesis
     //     - current output time step's set of active hypotheses, aka active search space
     //  - states[.]: ScorerState
@@ -230,7 +228,7 @@ class BeamSearch {
             auto& beam = beams[batchIdx];
             if(beamHypIdx < beam.size()) {
               auto hyp = beam[beamHypIdx];
-              hypIndices.push_back((IndexType)(hyp->getPrevStateIndex() * dimBatch + batchIdx)); // (beamHypIdx, batchIdx), flattened, for index_select() operation
+              hypIndices.push_back((IndexType)(hyp->getPrevBeamHypIndex() * dimBatch + batchIdx)); // (beamHypIdx, batchIdx), flattened, for index_select() operation
               prevWords .push_back(hyp->getWord());
               prevScores.push_back(hyp->getPathScore());
             } else {  // pad to localBeamSize (dummy hypothesis)
@@ -291,11 +289,11 @@ class BeamSearch {
 
       // combine N-best sets with existing search space (beams) to updated search space
       beams = toHyps(nBestKeys, nBestPathScores,
-                     /*inputBeamSize*/expandedPathScores->shape()[-2], // used for interpretation of keys
+                     /*nBestBeamSize*/expandedPathScores->shape()[-2], // used for interpretation of keys
                      /*vocabSize=*/expandedPathScores->shape()[-1],    // used for interpretation of keys
                      beams,
-                     states,    // only used for keeping track of per-ensemble-member path score
-                     batch);    // only used for propagating alignment info
+                     states,    // only used with nbest, for keeping track of per-ensemble-member path score
+                     batch);    // only used when propagating alignment info
 
       // remove all hyps that end in EOS
       // The position of a hyp in the beam may change.
diff --git a/src/translator/hypothesis.h b/src/translator/hypothesis.h
index 452b3349b..aa94cbe65 100755
--- a/src/translator/hypothesis.h
+++ b/src/translator/hypothesis.h
@@ -6,30 +6,30 @@
 
 namespace marian {
 
-// one single (possibly partial) hypothesis in beam search
+// one single (partial or full) hypothesis in beam search
 // key elements:
 //  - the word that this hyp ends with
 //  - the aggregate score up to and including the word
 //  - back pointer to previous hypothesis for traceback
 class Hypothesis {
 public:
-  Hypothesis() : prevHyp_(nullptr), prevIndex_(0), word_(0), pathScore_(0.0) {}
+  Hypothesis() : prevHyp_(nullptr), prevBeamHypIdx_(0), word_(0), pathScore_(0.0) {}
 
   Hypothesis(const Ptr<Hypothesis> prevHyp,
              Word word,
              IndexType prevIndex, // (beamHypIdx, batchIdx) flattened as beamHypIdx * dimBatch + batchIdx
              float pathScore)
-      : prevHyp_(prevHyp), prevIndex_(prevIndex), word_(word), pathScore_(pathScore) {}
+      : prevHyp_(prevHyp), prevBeamHypIdx_(prevIndex), word_(word), pathScore_(pathScore) {}
 
   const Ptr<Hypothesis> getPrevHyp() const { return prevHyp_; }
 
   Word getWord() const { return word_; }
 
-  IndexType getPrevStateIndex() const { return prevIndex_; }
+  IndexType getPrevBeamHypIndex() const { return prevBeamHypIdx_; }
 
   float getPathScore() const { return pathScore_; }
 
-  std::vector<float>& getScoreBreakdown() { return scoreBreakdown_; }
+  std::vector<float>& getScoreBreakdown() { return scoreBreakdown_; } // @TODO: make this const
   void setScoreBreakdown(const std::vector<float>& scoreBreaddown) { scoreBreakdown_ = scoreBreaddown; }
 
   const std::vector<float>& getAlignment() { return alignment_; }
@@ -61,7 +61,7 @@ class Hypothesis {
 
 private:
   const Ptr<Hypothesis> prevHyp_;
-  const IndexType prevIndex_;
+  const IndexType prevBeamHypIdx_;
   const Word word_;
   const float pathScore_;
 
diff --git a/src/translator/nth_element.cpp b/src/translator/nth_element.cpp
index 7d18555d8..8dc1a44c8 100755
--- a/src/translator/nth_element.cpp
+++ b/src/translator/nth_element.cpp
@@ -21,14 +21,12 @@ class NthElementCPU {
   NthElementCPU(const NthElementCPU& copy) = delete;
 
 private:
-    void selectNBest(float* scores,
+    void selectNBest(const float* scores,
                      const std::vector<int>& batchFirstElementIdxs,
                      const std::vector<int>& cumulativeBeamSizes) {
-    /* For each batch, select the max N elements, where N is the beam size for
-     * this batch. Locally record these elements (their current value and index
-     * in 'scores') before updating each element to a large negative value, such
-     * that they won't be a maximum if we're called again on the same input.
-     */
+    // For each batch, select the max N elements, where N is the beam size for
+    // this batch. Locally record these elements (their current value and index
+    // in 'scores').
 
     int numProbs = batchFirstElementIdxs.back();
     std::vector<int> idxs(numProbs);
@@ -49,7 +47,7 @@ class NthElementCPU {
         int idx = *begin++;
         h_res_idx[pos] = idx;
         h_res[pos] = scores[idx];
-        scores[idx] = std::numeric_limits<float>::lowest();
+        //scores[idx] = std::numeric_limits<float>::lowest();
         ++pos;
       }
     }
diff --git a/src/translator/nth_element.cu b/src/translator/nth_element.cu
index b2f23d8c5..82aa6dd12 100755
--- a/src/translator/nth_element.cu
+++ b/src/translator/nth_element.cu
@@ -367,7 +367,7 @@ public:
     ABORT_IF(inputN != (isFirst ? 1 : N), "Input tensor has wrong beam dim??"); // @TODO: Remove isFirst argument altogether
     ABORT_IF(vocabSize > MAX_VOCAB_SIZE, "GetNBestList(): actual vocab size exceeds MAX_VOCAB_SIZE");
     ABORT_IF(dimBatch > maxBatchSize_, "GetNBestList(): actual batch size exceeds initialization parameter");
-    ABORT_IF(N > maxBeamSize_, "GetNBestList(): actual beam size exceeds initialization parameter"); // @TODO: or inputN?
+    ABORT_IF(std::max(N, (size_t)inputN) > maxBeamSize_, "GetNBestList(): actual beam size exceeds initialization parameter");
 
     const std::vector<size_t> beamSizes(dimBatch, N);
     std::vector<int> cumulativeBeamSizes(beamSizes.size() + 1, 0);
@@ -440,19 +440,19 @@ private:
 
   const int BLOCK_SIZE = 512;
   const int NUM_BLOCKS;
-  int* d_ind;
 
-  float* d_out;
+  int* d_ind;           // [maxBatchSize * NUM_BLOCKS]
+  float* d_out;         // [maxBatchSize * NUM_BLOCKS]
 
-  int* d_res_idx;
-  float* d_res;
+  int* d_res_idx;       // [maxBatchSize * maxBeamSize]
+  float* d_res;         // [maxBatchSize * maxBeamSize]
 
-  int* h_res_idx;
-  float* h_res;
+  int* h_res_idx;       // [maxBeamSize * maxBatchSize]
+  float* h_res;         // [maxBeamSize * maxBatchSize]
 
-  float* d_breakdown;
-  int* d_batchPosition;
-  int* d_cumBeamSizes;
+  float* d_breakdown;   // [maxBeamSize]
+  int* d_batchPosition; // [maxBatchSize + 1]
+  int* d_cumBeamSizes;  // [maxBatchSize + 1]
   //size_t lastN;
 };
 

From ec1c885f8e458a9aaf61cc544fb7e4f1eb993c53 Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Wed, 13 Mar 2019 09:26:25 -0700
Subject: [PATCH 370/838] fixed merge error

---
 src/translator/beam_search.h | 23 ++++++++++-------------
 1 file changed, 10 insertions(+), 13 deletions(-)

diff --git a/src/translator/beam_search.h b/src/translator/beam_search.h
index 708541d70..9a9ea50b0 100755
--- a/src/translator/beam_search.h
+++ b/src/translator/beam_search.h
@@ -48,23 +48,23 @@ class BeamSearch {
     const auto dimBatch = beams.size();
     Beams newBeams(dimBatch);
 
-    for(size_t i = 0; i < nBestKeys.size(); ++i) {
+    for(size_t i = 0; i < nBestKeys.size(); ++i) { // [dimBatch, beamSize] flattened
       // Keys encode batchIdx, beamHypIdx, and word index in the entire beam.
       // They can be between 0 and beamSize * vocabSize-1.
-      const auto  key       = nBestKeys[i];
-      const float pathScore = nBestPathScores[i]; // expanded path score for (batchIdx, beamHypIdx, word)
+      const float pathScore = nBestPathScores[i];
+      const auto  key       = nBestKeys[i]; // key = pathScore's tensor location, as (batchIdx, beamHypIdx, word idx) flattened
 
       // decompose key into individual indices (batchIdx, beamHypIdx, wordIdx)
-      const auto wordIdx    = (Word)(key % vocabSize);
-      const auto beamHypIdx =       (key / vocabSize) % inputBeamSize;
-      const auto batchIdx   =       (key / vocabSize) / inputBeamSize;
+      const auto wordIdx    = (WordIndex)(key % vocabSize);
+      const auto beamHypIdx =            (key / vocabSize) % inputBeamSize;
+      const auto batchIdx   =            (key / vocabSize) / inputBeamSize;
 
       const auto& beam = beams[batchIdx];
       auto& newBeam = newBeams[batchIdx];
 
-      if (newBeam.size() >= beam.size()) // @TODO: Why this condition? It does happen. Why?
+      if (newBeam.size() >= beam.size()) // getNBestList() generates N for all batch entries incl. those that already have a narrower beam
         continue;
-      if (pathScore <= INVALID_PATH_SCORE) // (unused slot)
+      if (pathScore <= INVALID_PATH_SCORE) // (dummy slot or word that cannot be expanded by current factor)
         continue;
 
       ABORT_IF(beamHypIdx >= beam.size(), "Out of bounds beamHypIdx??");
@@ -75,9 +75,9 @@ class BeamSearch {
       // rather than the true word index.
       auto shortlist = scorers_[0]->getShortlist();
       if (shortlist)
-        word = shortlist->reverseMap(wordIdx);
+        word = Word::fromWordIndex(shortlist->reverseMap(wordIdx));
       else
-        word = wordIdx;
+        word = Word::fromWordIndex(wordIdx);
 
       auto hyp = New<Hypothesis>(beam[beamHypIdx], word, (IndexType)beamHypIdx, pathScore);
 
@@ -183,9 +183,6 @@ class BeamSearch {
     }
 
     Beams beams(dimBatch, Beam(beamSize_, New<Hypothesis>())); // array [dimBatch] of array [localBeamSize] of Hypothesis
-    //Beams beams(dimBatch); // array [dimBatch] of array [localBeamSize] of Hypothesis
-    //for(auto& beam : beams)
-    //  beam.resize(beamSize_, New<Hypothesis>());
 
     for(int i = 0; i < dimBatch; ++i)
       histories[i]->add(beams[i], trgEosId_);

From 2069b0e909722b72e1b1cdf97ad3b24f7779bfd4 Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Thu, 14 Mar 2019 18:00:17 -0700
Subject: [PATCH 371/838] fixed a merge error in MNIST example

---
 src/examples/mnist/dataset.h | 12 ++++++++----
 1 file changed, 8 insertions(+), 4 deletions(-)
 mode change 100644 => 100755 src/examples/mnist/dataset.h

diff --git a/src/examples/mnist/dataset.h b/src/examples/mnist/dataset.h
old mode 100644
new mode 100755
index 4a4f5ae45..0e73840bf
--- a/src/examples/mnist/dataset.h
+++ b/src/examples/mnist/dataset.h
@@ -19,7 +19,12 @@ namespace data {
 
 typedef std::vector<float> Data;
 typedef std::vector<IndexType> Labels;
-typedef std::vector<Data> Example;
+struct Example : public std::vector<Data> { // a std::vector<Data> with a getId() method
+  size_t id;
+  size_t getId() const { return id; }
+  Example(std::vector<Data>&& data, size_t id) : std::vector<Data>(std::move(data)), id(id) {}
+  Example() : id(SIZE_MAX) {}
+};
 typedef std::vector<Example> Examples;
 
 typedef Examples::const_iterator ExampleIterator;
@@ -147,12 +152,11 @@ class MNISTData : public Dataset {
     ABORT_IF(features.size() != labels.size(), "Features do not match labels");
 
     for(size_t i = 0; i < features.size(); ++i) {
-      Example ex = {features[i], labels[i]};
-      examples_.emplace_back(ex);
+      examples_.emplace_back(std::vector<Data>{ features[i], labels[i] }, i);
     }
   }
 
-  Example next() override { return{ }; } //@TODO: this return was added to fix a warning. Is it correct?
+  Example next() override { return Example(); } //@TODO: this return was added to fix a warning. Is it correct?
 
 private:
   typedef unsigned char uchar;

From 93316a211927dfc6ca611c2681a043f3cedfa207 Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Fri, 15 Mar 2019 10:02:35 -0700
Subject: [PATCH 372/838] MLP can now handle a IUnaryLogitsLayer as its last
 layer; therefore, s2s model type now supports factored Logits

---
 src/layers/constructors.h | 114 ++++++++++++++++++++++++++------------
 src/layers/factory.h      |  10 ++++
 src/layers/generic.cpp    |   2 +-
 src/layers/generic.h      |  23 +++++---
 src/models/bert.h         |   4 +-
 src/models/s2s.h          |  13 ++---
 src/models/transformer.h  |   6 +-
 7 files changed, 112 insertions(+), 60 deletions(-)

diff --git a/src/layers/constructors.h b/src/layers/constructors.h
index d1aef42e5..f19a0922b 100755
--- a/src/layers/constructors.h
+++ b/src/layers/constructors.h
@@ -10,21 +10,21 @@ namespace mlp {
  * Base class for layer factories, can be used in a multi-layer network factory.
  */
 struct LayerFactory : public Factory {
-  LayerFactory() : Factory() {}
-  LayerFactory(const LayerFactory&) = default;
-  LayerFactory(LayerFactory&&) = default;
-
-  virtual ~LayerFactory() {}
-
-  template <typename Cast>
-  inline Ptr<Cast> as() {
-    return std::dynamic_pointer_cast<Cast>(shared_from_this());
-  }
-
-  template <typename Cast>
-  inline bool is() {
-    return as<Cast>() != nullptr;
-  }
+  //LayerFactory() : Factory() {}
+  //LayerFactory(const LayerFactory&) = default;
+  //LayerFactory(LayerFactory&&) = default;
+  //
+  //virtual ~LayerFactory() {}
+
+  //template <typename Cast>
+  //inline Ptr<Cast> as() {
+  //  return std::dynamic_pointer_cast<Cast>(shared_from_this());
+  //}
+  //
+  //template <typename Cast>
+  //inline bool is() {
+  //  return as<Cast>() != nullptr;
+  //}
 
   virtual Ptr<IUnaryLayer> construct(Ptr<ExpressionGraph> graph) = 0;
 };
@@ -52,21 +52,21 @@ typedef Accumulator<DenseFactory> dense;
  * Factory for output layers, can be used in a multi-layer network factory.
  */
 struct LogitLayerFactory : public Factory {
-  LogitLayerFactory() : Factory() {}
-  LogitLayerFactory(const LogitLayerFactory&) = default;
-  LogitLayerFactory(LogitLayerFactory&&) = default;
-
-  virtual ~LogitLayerFactory() {}
-
-  template <typename Cast>
-  inline Ptr<Cast> as() {
-    return std::dynamic_pointer_cast<Cast>(shared_from_this());
-  }
-
-  template <typename Cast>
-  inline bool is() {
-    return as<Cast>() != nullptr;
-  }
+  //LogitLayerFactory() : Factory() {}
+  //LogitLayerFactory(const LogitLayerFactory&) = default;
+  //LogitLayerFactory(LogitLayerFactory&&) = default;
+  //
+  //virtual ~LogitLayerFactory() {}
+  //
+  //template <typename Cast>
+  //inline Ptr<Cast> as() {
+  //  return std::dynamic_pointer_cast<Cast>(shared_from_this());
+  //}
+  //
+  //template <typename Cast>
+  //inline bool is() {
+  //  return as<Cast>() != nullptr;
+  //}
 
   virtual Ptr<IUnaryLogitLayer> construct(Ptr<ExpressionGraph> graph) = 0;
 };
@@ -108,7 +108,7 @@ typedef Accumulator<OutputFactory> output;
 /**
  * Multi-layer network, holds and applies layers.
  */
-class MLP {
+class MLP : public IUnaryLogitLayer {
 protected:
   Ptr<ExpressionGraph> graph_;
   Ptr<Options> options_;
@@ -119,10 +119,7 @@ class MLP {
   MLP(Ptr<ExpressionGraph> graph, Ptr<Options> options)
       : graph_(graph), options_(options) {}
 
-  template <typename... Args>
-  Expr apply(Args... args) {
-    std::vector<Expr> av = {args...};
-
+  Expr apply(const std::vector<Expr>& av) {
     Expr output;
     if(av.size() == 1)
       output = layers_[0]->apply(av[0]);
@@ -135,7 +132,32 @@ class MLP {
     return output;
   }
 
+  Logits applyAsLogits(const std::vector<Expr>& av) {
+    auto lastLayer = std::dynamic_pointer_cast<IUnaryLogitLayer>(layers_.back());
+    ABORT_IF(!lastLayer, "MLP::applyAsLogits() applied but last MLP layer is not IUnaryLogitLayer");
+    if (layers_.size() == 1) {
+      if (av.size() == 1)
+        return lastLayer->applyAsLogits(av[0]);
+      else
+        return lastLayer->applyAsLogits(av);
+    }
+    else {
+      Expr output;
+      if (av.size() == 1)
+        output = layers_[0]->apply(av[0]);
+      else
+        output = layers_[0]->apply(av);
+      for (size_t i = 1; i < layers_.size() - 1; ++i)
+        output = layers_[i]->apply(output);
+      return lastLayer->applyAsLogits(output);
+    }
+  }
+
+  Expr apply(Expr e) { return apply(std::vector<Expr>{ e }); }
+  Logits applyAsLogits(Expr e) { return applyAsLogits(std::vector<Expr>{ e }); }
+
   void push_back(Ptr<IUnaryLayer> layer) { layers_.push_back(layer); }
+  void push_back(Ptr<IUnaryLogitLayer> layer) { layers_.push_back(layer); }
 };
 
 /**
@@ -161,6 +183,28 @@ class MLPFactory : public Factory {
     layers_.push_back(New<LF>(lf));
     return Accumulator<MLPFactory>(*this);
   }
+
+  // special case for last layer, which may be a IUnaryLogitLayer. Requires some hackery
+private:
+  template<class WrappedFactory>
+  class AsLayerFactory : public LayerFactory {
+      WrappedFactory us;
+  public:
+      AsLayerFactory(const WrappedFactory& wrapped) : us(wrapped) {}
+      Ptr<IUnaryLayer> construct(Ptr<ExpressionGraph> graph) override final {
+          auto p = std::static_pointer_cast<IUnaryLayer>(us.construct(graph));
+          ABORT_IF(!p, "Attempted to cast a Factory to LayerFactory that isn't one");
+          return p;
+      }
+  };
+  template<class WrappedFactory>
+  static inline AsLayerFactory<WrappedFactory> asLayerFactory(const WrappedFactory& wrapped) { return wrapped; }
+public:
+  Accumulator<MLPFactory> push_back(const Accumulator<OutputFactory>& lf) {
+    push_back(AsLayerFactory<OutputFactory>(lf));
+    //layers_.push_back(New<AsLayerFactory<OutputFactory>>(asLayerFactory((OutputFactory&)lf)));
+    return Accumulator<MLPFactory>(*this);
+  }
 };
 
 // @TODO: change naming convention.
diff --git a/src/layers/factory.h b/src/layers/factory.h
index e3b742442..0aaf86a92 100755
--- a/src/layers/factory.h
+++ b/src/layers/factory.h
@@ -29,6 +29,16 @@ class Factory : public std::enable_shared_from_this<Factory> {
   T opt(const std::string& key, T defaultValue) {
     return options_->get<T>(key, defaultValue);
   }
+
+  template <typename Cast>
+  inline Ptr<Cast> as() {
+    return std::dynamic_pointer_cast<Cast>(shared_from_this());
+  }
+
+  template <typename Cast>
+  inline bool is() {
+    return as<Cast>() != nullptr;
+  }
 };
 
 // simplest form of Factory that just passes on options to the constructor of a layer type
diff --git a/src/layers/generic.cpp b/src/layers/generic.cpp
index f59105932..2f302ef12 100755
--- a/src/layers/generic.cpp
+++ b/src/layers/generic.cpp
@@ -229,7 +229,7 @@ namespace marian {
       b_ = graph_->param(name + "_b", {1, numOutputClasses}, inits::zeros);
     }
 
-    Logits Output::apply(Expr input) /*override*/ {
+    Logits Output::applyAsLogits(Expr input) /*override final*/ {
       lazyConstruct(input->shape()[-1]);
 
       if (shortlist_) {
diff --git a/src/layers/generic.h b/src/layers/generic.h
index e83801aa2..5b3e5ceab 100755
--- a/src/layers/generic.h
+++ b/src/layers/generic.h
@@ -44,7 +44,10 @@ class LayerBase {
 // Simplest layer interface: Unary function
 struct IUnaryLayer {
   virtual Expr apply(Expr) = 0;
-  virtual Expr apply(const std::vector<Expr>&) = 0;
+  virtual Expr apply(const std::vector<Expr>& es) {
+    ABORT_IF(es.size() > 1, "Not implemented"); // simple stub
+    return apply(es.front());
+  }
 };
 
 // Embedding from corpus sub-batch to (emb, mask)
@@ -108,9 +111,15 @@ class Logits {
 };
 
 // Unary function that returns a Logits object
-struct IUnaryLogitLayer {
-  virtual Logits apply(Expr) = 0;
-  virtual Logits apply(const std::vector<Expr>&) = 0;
+// Also implements IUnaryLayer, since Logits can be cast to Expr.
+struct IUnaryLogitLayer : public IUnaryLayer {
+  virtual Logits applyAsLogits(Expr) = 0;
+  virtual Logits applyAsLogits(const std::vector<Expr>& es) {
+    ABORT_IF(es.size() > 1, "Not implemented"); // simple stub
+    return applyAsLogits(es.front());
+  }
+  virtual Expr apply(Expr e) override { return applyAsLogits(e).getLogits(); }
+  virtual Expr apply(const std::vector<Expr>& es) override { return applyAsLogits(es).getLogits(); }
 };
 
 namespace mlp {
@@ -227,11 +236,7 @@ class Output : public LayerBase, public IUnaryLogitLayer {
     cachedShortb_  = nullptr;
   }
 
-  Logits apply(Expr input) override;
-
-  virtual Logits apply(const std::vector<Expr>& /*inputs*/) override {
-    ABORT("Not implemented");
-  };
+  Logits applyAsLogits(Expr input) override final;
 };
 
 }  // namespace mlp
diff --git a/src/models/bert.h b/src/models/bert.h
index b335b5eaf..fb2dab846 100755
--- a/src/models/bert.h
+++ b/src/models/bert.h
@@ -334,7 +334,7 @@ class BertMaskedLM : public ClassifierBase {
 
     auto maskedContext = rows(reshape(context, {dimBatch * dimTime, dimModel}), bertMaskedPositions); // subselect stuff that has actually been masked out
 
-//    int dimVoc = opt<std::vector<int>>("dim-vocabs")[batchIndex_];
+    int dimVoc = opt<std::vector<int>>("dim-vocabs")[batchIndex_];
 
     auto layer1 = mlp::mlp()
       .push_back(mlp::dense()
@@ -359,12 +359,10 @@ class BertMaskedLM : public ClassifierBase {
     intermediate = layerNorm(intermediate, gamma, beta);
 
     auto layer2 = mlp::mlp()
-#if 0 // @TODO: Not supported presently since Output has a different signature now
       .push_back(mlp::output()
                  ("prefix", prefix_ + "_ff_logit_l2")
                  ("dim", dimVoc)
                  .tieTransposed("Wemb"))
-#endif
       .construct(graph);
 
     auto logits = layer2->apply(intermediate); // [-4: beam depth=1, -3: max length, -2: batch size, -1: vocab dim]
diff --git a/src/models/s2s.h b/src/models/s2s.h
index a63c98418..03baff564 100755
--- a/src/models/s2s.h
+++ b/src/models/s2s.h
@@ -347,28 +347,23 @@ class DecoderS2S : public DecoderBase {
       if(shortlist_)
         last.setShortlist(shortlist_);
 
-#if 1
-      hidden; last;
-      ABORT("@TODO: adapt s2s to Logits return type");
-#else
       // assemble layers into MLP and apply to embeddings, decoder context and
       // aligned source context
       output_ = mlp::mlp()              //
                     .push_back(hidden)  //
                     .push_back(last)
                     .construct(graph);
-#endif
     }
 
-    Expr logits;
+    Logits logits;
     if(alignedContext)
-      logits = output_->apply(embeddings, decoderContext, alignedContext);
+      logits = output_->applyAsLogits({embeddings, decoderContext, alignedContext});
     else
-      logits = output_->apply(embeddings, decoderContext);
+      logits = output_->applyAsLogits({embeddings, decoderContext});
 
     // return unormalized(!) probabilities
     auto nextState = New<DecoderState>(
-        decoderStates, Logits(logits), state->getEncoderStates(), state->getBatch());
+      decoderStates, logits, state->getEncoderStates(), state->getBatch());
 
     // Advance current target token position by one
     nextState->setPosition(state->getPosition() + 1);
diff --git a/src/models/transformer.h b/src/models/transformer.h
index 1817947ea..588a79b41 100755
--- a/src/models/transformer.h
+++ b/src/models/transformer.h
@@ -841,16 +841,16 @@ class DecoderTransformer : public Transformer<DecoderBase> {
     // final feed-forward layer (output)
     if(shortlist_)
       output_->setShortlist(shortlist_);
-    auto logits = output_->apply(decoderContext); // [-4: beam depth=1, -3: max length, -2: batch size, -1: vocab or shortlist dim]
+    auto logits = output_->applyAsLogits(decoderContext); // [-4: beam depth=1, -3: max length, -2: batch size, -1: vocab or shortlist dim]
 
     // return unormalized(!) probabilities
     Ptr<DecoderState> nextState;
     if (opt<std::string>("transformer-decoder-autoreg", "self-attention") == "rnn") {
       nextState = New<DecoderState>(
-          decoderStates, logits, state->getEncoderStates(), state->getBatch());
+        decoderStates, logits, state->getEncoderStates(), state->getBatch());
     } else {
       nextState = New<TransformerState>(
-          decoderStates, logits, state->getEncoderStates(), state->getBatch());
+        decoderStates, logits, state->getEncoderStates(), state->getBatch());
     }
     nextState->setPosition(state->getPosition() + 1);
     return nextState;

From f309bbc799156f5de508595f9e155156ea9182f1 Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Fri, 15 Mar 2019 11:36:09 -0700
Subject: [PATCH 373/838] fixed gcc warnings

---
 src/layers/constructors.h | 9 +++++----
 1 file changed, 5 insertions(+), 4 deletions(-)

diff --git a/src/layers/constructors.h b/src/layers/constructors.h
index f19a0922b..953d01aa3 100755
--- a/src/layers/constructors.h
+++ b/src/layers/constructors.h
@@ -119,7 +119,7 @@ class MLP : public IUnaryLogitLayer {
   MLP(Ptr<ExpressionGraph> graph, Ptr<Options> options)
       : graph_(graph), options_(options) {}
 
-  Expr apply(const std::vector<Expr>& av) {
+  Expr apply(const std::vector<Expr>& av) override {
     Expr output;
     if(av.size() == 1)
       output = layers_[0]->apply(av[0]);
@@ -132,7 +132,8 @@ class MLP : public IUnaryLogitLayer {
     return output;
   }
 
-  Logits applyAsLogits(const std::vector<Expr>& av) {
+  Logits applyAsLogits(const std::vector<Expr>& av) override {
+    // same as apply() except  for the last layer, we invoke applyAsLogits(), which has a different return type
     auto lastLayer = std::dynamic_pointer_cast<IUnaryLogitLayer>(layers_.back());
     ABORT_IF(!lastLayer, "MLP::applyAsLogits() applied but last MLP layer is not IUnaryLogitLayer");
     if (layers_.size() == 1) {
@@ -153,8 +154,8 @@ class MLP : public IUnaryLogitLayer {
     }
   }
 
-  Expr apply(Expr e) { return apply(std::vector<Expr>{ e }); }
-  Logits applyAsLogits(Expr e) { return applyAsLogits(std::vector<Expr>{ e }); }
+  Expr apply(Expr e) override { return apply(std::vector<Expr>{ e }); }
+  Logits applyAsLogits(Expr e) override { return applyAsLogits(std::vector<Expr>{ e }); }
 
   void push_back(Ptr<IUnaryLayer> layer) { layers_.push_back(layer); }
   void push_back(Ptr<IUnaryLogitLayer> layer) { layers_.push_back(layer); }

From 213c9d14390cb67cb694cdf670189e44267fbe99 Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Sat, 16 Mar 2019 15:41:33 -0700
Subject: [PATCH 374/838] minor refactoring towards supporting .fsv

---
 src/data/factored_vocab.cpp | 42 ++++++++++++++++++-------------------
 src/data/factored_vocab.h   |  2 +-
 2 files changed, 22 insertions(+), 22 deletions(-)

diff --git a/src/data/factored_vocab.cpp b/src/data/factored_vocab.cpp
index 2b0695fd2..513903827 100755
--- a/src/data/factored_vocab.cpp
+++ b/src/data/factored_vocab.cpp
@@ -7,25 +7,10 @@
 
 namespace marian {
 
-// mapPath = path to file with entries in order of vocab entries of the form
-//   WORD FACTOR1 FACTOR2 FACTOR3...
-// listPath = path to file that lists all FACTOR names
-// vocab = original vocabulary
-// Note: The WORD field in the map file is redundant. It is required for consistency checking only.
-// Factors are grouped
-//  - user specifies list-factor prefixes; all factors beginning with that prefix are in the same group
-//  - factors within a group as multi-class and normalized that way
-//  - groups of size 1 are interpreted as sigmoids, multiply with P(u) / P(u-1)
-//  - one prefix must not contain another
-//  - all factors not matching a prefix get lumped into yet another class (the lemmas)
-//  - factor vocab must be sorted such that all groups are consecutive
-//  - result of Output layer is nevertheless logits, not a normalized probability, due to the sigmoid entries
-/*virtual*/ size_t FactoredVocab::load(const std::string& factoredVocabPath, size_t maxSizeUnused /*= 0*/) /*override final*/ {
-  auto mapPath = factoredVocabPath;
-  auto factorVocabPath = mapPath;
-  factorVocabPath.back() = 'l'; // map .fm to .fl
-
+/*virtual*/ size_t FactoredVocab::load(const std::string& modelPath, size_t maxSizeUnused /*= 0*/) /*override final*/ {
   // load factor vocabulary
+  auto factorVocabPath = modelPath;
+  factorVocabPath.back() = 'l'; // map .fm to .fl
   factorVocab_.load(factorVocabPath);
   groupPrefixes_ = { "(lemma)", "@C", "@GL", "@GR", "@WB"/*, "@WE"*/, "@CB"/*, "@CE"*/ }; // @TODO: hard-coded for these initial experiments
   // @TODO: add checks for empty factor groups until it stops crashing (training already works; decoder still crashes)
@@ -36,6 +21,16 @@ namespace marian {
   auto numGroups = getNumGroups();
 
   // load and parse factorMap
+  // modelPath = path to file with entries in order of vocab entries of the form
+  //   WORD FACTOR1 FACTOR2 FACTOR3...
+  // Factors are grouped
+  //  - user specifies list-factor prefixes; all factors beginning with that prefix are in the same group
+  //  - factors within a group as multi-class and normalized that way
+  //  - groups of size 1 are interpreted as sigmoids, multiply with P(u) / P(u-1)
+  //  - one prefix must not contain another
+  //  - all factors not matching a prefix get lumped into yet another class (the lemmas)
+  //  - factor vocab must be sorted such that all groups are consecutive
+  //  - result of Output layer is nevertheless logits, not a normalized probability, due to the sigmoid entries
   auto vocabSize = factorShape_.elements(); // size of vocab space including gaps
   vocab_.resize(vocabSize);
   //factorMap_.resize(vocabSize);
@@ -44,14 +39,14 @@ namespace marian {
   std::vector<std::string> tokens;
   std::string line;
   size_t numTotalFactors = 0;
-  io::InputFileStream in(mapPath);
+  io::InputFileStream in(modelPath);
   for (WordIndex v = 0; io::getline(in, line); v++) {
     // parse the line, of the form WORD FACTOR1 FACTOR2 FACTOR1 ...
     // where FACTOR1 is the lemma, a factor that all words have.
     // Not every word has all other factors, so the n-th item is not always in the same factor group.
     // @TODO: change to just use the .wl file, and manually split at @
     utils::splitAny(line, tokens, " \t");
-    ABORT_IF(tokens.size() < 2, "Factor map must have at least one factor per word", mapPath);
+    ABORT_IF(tokens.size() < 2, "Factor map must have at least one factor per word", modelPath);
     std::vector<WordIndex> factorUnits;
     for (size_t i = 1/*first factor*/; i < tokens.size(); i++) {
       auto u = factorVocab_[tokens[i]];
@@ -519,13 +514,18 @@ size_t FactoredVocab::WordLUT::load(const std::string& path) {
   return size();
 }
 
+const static std::vector<std::string> exts{ ".fsv", ".fm"/*legacy*/ };
+
 // Note: This does not actually load it, only checks the path for the type.
 Ptr<IVocab> createFactoredVocab(const std::string& vocabPath) {
-  bool isFactoredVocab = regex::regex_search(vocabPath, regex::regex("\\.(fm)$"));
+  bool isFactoredVocab = std::any_of(exts.begin(), exts.end(), [&](const std::string& ext) { return utils::endsWith(vocabPath, ext); });
   if(isFactoredVocab)
     return New<FactoredVocab>();
   else
     return nullptr;
 }
+/*virtual*/ const std::vector<std::string>& FactoredVocab::suffixes() const /*override final*/ {
+  return exts;
+}
 
 }  // namespace marian
diff --git a/src/data/factored_vocab.h b/src/data/factored_vocab.h
index 433a9d61a..c27efd74d 100755
--- a/src/data/factored_vocab.h
+++ b/src/data/factored_vocab.h
@@ -26,7 +26,7 @@ class FactoredVocab : public IVocab {
   virtual size_t load(const std::string& factoredVocabPath, size_t maxSizeUnused = 0) override final;
   virtual void create(const std::string& vocabPath, const std::vector<std::string>& trainPaths, size_t maxSize) override final { vocabPath, trainPaths, maxSize; ABORT("Factored vocab cannot be created on the fly"); }
   virtual const std::string& canonicalExtension() const override final { return suffixes()[0]; }
-  virtual const std::vector<std::string>& suffixes() const override final { const static std::vector<std::string> exts{".fm"}; return exts; }
+  virtual const std::vector<std::string>& suffixes() const override final;
   virtual Word operator[](const std::string& word) const override final;
   virtual Words encode(const std::string& line, bool addEOS = true, bool inference = false) const override final;
   virtual std::string decode(const Words& sentence, bool ignoreEos = true) const override final;

From ff65f6b741bb015f2db6e645ced42ab5a11a7d66 Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Sat, 16 Mar 2019 15:57:54 -0700
Subject: [PATCH 375/838] towards splitting off .fsv reading

---
 src/data/factored_vocab.cpp | 33 ++++++++++++++++++++++-----------
 1 file changed, 22 insertions(+), 11 deletions(-)

diff --git a/src/data/factored_vocab.cpp b/src/data/factored_vocab.cpp
index 513903827..2e2e71702 100755
--- a/src/data/factored_vocab.cpp
+++ b/src/data/factored_vocab.cpp
@@ -8,12 +8,26 @@
 namespace marian {
 
 /*virtual*/ size_t FactoredVocab::load(const std::string& modelPath, size_t maxSizeUnused /*= 0*/) /*override final*/ {
-  // load factor vocabulary
-  auto factorVocabPath = modelPath;
-  factorVocabPath.back() = 'l'; // map .fm to .fl
-  factorVocab_.load(factorVocabPath);
-  groupPrefixes_ = { "(lemma)", "@C", "@GL", "@GR", "@WB"/*, "@WE"*/, "@CB"/*, "@CE"*/ }; // @TODO: hard-coded for these initial experiments
-  // @TODO: add checks for empty factor groups until it stops crashing (training already works; decoder still crashes)
+  std::vector<std::vector<std::string>> factorMapTokenized;
+
+  if (utils::endsWith(modelPath, ".fsv")) {
+    ABORT("Not implemented yet");
+  } else {  // legacy for old configs
+    // load factor vocabulary
+    auto factorVocabPath = modelPath;
+    factorVocabPath.back() = 'l'; // map .fm to .fl
+    factorVocab_.load(factorVocabPath);
+    groupPrefixes_ = { "(lemma)", "@C", "@GL", "@GR", "@WB"/*, "@WE"*/, "@CB"/*, "@CE"*/ }; // @TODO: hard-coded for these initial experiments
+    // @TODO: add checks for empty factor groups until it stops crashing (training already works; decoder still crashes)
+
+    std::vector<std::string> tokBuf;
+    std::string line;
+    io::InputFileStream in(modelPath);
+    for (WordIndex v = 0; io::getline(in, line); v++) {
+      utils::splitAny(line, tokBuf, " \t");
+      factorMapTokenized.push_back(tokBuf);
+    }
+  }
 
   // construct mapping tables for factors
   constructGroupInfoFromFactorVocab();
@@ -36,16 +50,13 @@ namespace marian {
   //factorMap_.resize(vocabSize);
   auto factorVocabSize = factorVocab_.size();
   lemmaHasFactorGroup_.resize(groupRanges_[0].second - groupRanges_[0].first);
-  std::vector<std::string> tokens;
-  std::string line;
   size_t numTotalFactors = 0;
-  io::InputFileStream in(modelPath);
-  for (WordIndex v = 0; io::getline(in, line); v++) {
+  for (WordIndex v = 0; v < factorMapTokenized.size(); v++) {
+    const auto& tokens = factorMapTokenized[v];
     // parse the line, of the form WORD FACTOR1 FACTOR2 FACTOR1 ...
     // where FACTOR1 is the lemma, a factor that all words have.
     // Not every word has all other factors, so the n-th item is not always in the same factor group.
     // @TODO: change to just use the .wl file, and manually split at @
-    utils::splitAny(line, tokens, " \t");
     ABORT_IF(tokens.size() < 2, "Factor map must have at least one factor per word", modelPath);
     std::vector<WordIndex> factorUnits;
     for (size_t i = 1/*first factor*/; i < tokens.size(); i++) {

From 6c5bb6e54b7b82946108037c37cf4f05057d2d74 Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Sat, 16 Mar 2019 16:56:03 -0700
Subject: [PATCH 376/838] re-added a piece of code that got lost in an
 incorrect resolution of a merge conflict, works again

---
 src/translator/beam_search.h | 17 +++++++++++++++++
 1 file changed, 17 insertions(+)

diff --git a/src/translator/beam_search.h b/src/translator/beam_search.h
index 9673b99e2..569e7756f 100755
--- a/src/translator/beam_search.h
+++ b/src/translator/beam_search.h
@@ -81,6 +81,23 @@ class BeamSearch {
       auto shortlist = scorers_[0]->getShortlist();
       if (shortlist)
         word = Word::fromWordIndex(shortlist->reverseMap(wordIdx));
+      else if (factoredVocab) {
+        // For factored decoding, the word is built over multiple decoding steps,
+        // starting with the lemma, then adding factors one by one.
+        if (factorGroup == 0) {
+          word = factoredVocab->lemma2Word(wordIdx);
+          //LOG(info, "new lemma {}={}", word.toWordIndex(), factoredVocab->word2string(word));
+        }
+        else {
+          //LOG(info, "expand word {}={} with factor[{}] {}", beam[beamHypIdx]->getWord().toWordIndex(),
+          //    factoredVocab->word2string(beam[beamHypIdx]->getWord()), factorGroup, wordIdx);
+          word = beam[beamHypIdx]->getWord();
+          ABORT_IF(!factoredVocab->canExpandFactoredWord(word, factorGroup), "A word without this factor snuck through to here??");
+          word = factoredVocab->expandFactoredWord(word, factorGroup, wordIdx);
+          prevBeamHypIdx = prevHyp->getPrevStateIndex();
+          prevHyp = prevHyp->getPrevHyp(); // short-circuit the backpointer, so that the traceback doesnot contain partially factored words
+        }
+      }
       else
         word = Word::fromWordIndex(wordIdx);
 

From 87d7ae47597e07401220367b9f259d0f9ad21bad Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Sat, 16 Mar 2019 18:54:29 -0700
Subject: [PATCH 377/838] now parses .fsv files (for now by converting them
 internally to the legacy form)

---
 src/data/factored_vocab.cpp | 89 ++++++++++++++++++++++++++++++++++---
 src/data/factored_vocab.h   |  1 +
 2 files changed, 85 insertions(+), 5 deletions(-)

diff --git a/src/data/factored_vocab.cpp b/src/data/factored_vocab.cpp
index 2e2e71702..a235cef37 100755
--- a/src/data/factored_vocab.cpp
+++ b/src/data/factored_vocab.cpp
@@ -9,19 +9,98 @@ namespace marian {
 
 /*virtual*/ size_t FactoredVocab::load(const std::string& modelPath, size_t maxSizeUnused /*= 0*/) /*override final*/ {
   std::vector<std::vector<std::string>> factorMapTokenized;
-
+  std::string line;
+  std::vector<std::string> tokBuf;
   if (utils::endsWith(modelPath, ".fsv")) {
-    ABORT("Not implemented yet");
+    // this is a fake parser for the generic factor spec, which makes a few hard assumptions:
+    //     - all types starting with _ except _has_* are factor names
+    //     - X : _x makes surface form X part of prob distribution _x except for _has_*
+    //     - X : _has_x adds factor "x" to lemma X
+    //     - _x <-> form only allows "_x <->" or "_x <-> _has_x" (same x), and is otherwise unused
+    //     - _lemma is special
+    // The current version of the code just converts it internally to the legacy form.
+    // Once the legacy form is no longer needed, must of this can be simplified a lot.
+    io::InputFileStream in(modelPath);
+    WordIndex v = 0;
+    std::map<std::string,std::set<std::string>> factorTypeMap; // [type name] -> {factor-type names}
+    std::vector<std::string> deferredFactorVocab; // factor surface forms are presently expected to be at the end of factorVocab_, so collect them here first
+    while(io::getline(in, line)) {
+      utils::splitAny(line, tokBuf, " \t");
+      if (tokBuf.empty() || tokBuf[0][0] == '#') // skip comments and blank lines
+        continue;
+      const auto& lhs = tokBuf[0];
+      const auto& op = tokBuf.size() > 1 ? tokBuf[1] : "";
+      if (lhs[0] == '_') { // factor name
+        if (utils::beginsWith(lhs, "_has_")) {
+          const auto fName = lhs.substr(5); // skip _has_
+          ABORT_IF(factorTypeMap.find(fName) == factorTypeMap.end(), "Factor trait '{}' requires a factor named '{}' to exist", lhs, fName);
+          ABORT_IF(tokBuf.size() != 1, "Extraneous characters after factor trait: '{}'", line);
+          continue;
+        }
+        else if (op == "<->") {
+          ABORT_IF(lhs == "_lemma" && tokBuf.size() != 2, "Lemma factor distribution cannot be conditioned: '{}'", line);
+          ABORT_IF(lhs != "_lemma" && (tokBuf.size() != 3 || tokBuf[2] != "_has" + lhs), "Factor distribution can only be conditioned on nothing or on _has{}: '{}'", lhs, line);
+          continue;
+        }
+        else { // this declares a new factor
+          ABORT_IF(tokBuf.size() != 1, "Extraneous characters after factor declaration: '{}'", line);
+          const auto& fName = lhs.substr(1); // skip _
+          ABORT_IF(factorTypeMap.empty() && fName != "lemma", "First factor must be _lemma");
+          auto rv = factorTypeMap.insert(std::make_pair(fName, std::set<std::string>())); // create new factor
+          ABORT_IF(!rv.second, "Factor declared twice: '{}'", line);
+          groupPrefixes_.push_back(fName == "lemma" ? "(lemma)" : ("|" + fName));
+          continue;
+        }
+      }
+      else { // if not _ then it is a surface form
+        ABORT_IF(op != ":" || 2 >= tokBuf.size(), "Factor-lemma declaration should have the form LEMMA : _FACTOR, _has_FACTOR, _has_FACTOR... in '{}'", line);
+        ABORT_IF(tokBuf[2][0] != '_', "Factor name should begin with _ in '{}'", line);
+        ABORT_IF(utils::beginsWith(tokBuf[2], "_has_"), "The first factor after : must not begin with _has_ in '{}'", line);
+        // add to surface-form dictionary
+        const auto& fName = tokBuf[2].substr(1); // skip _
+        auto isLemma = fName == "lemma";
+        if (isLemma)
+          factorVocab_.add(lhs, v++); // note: each item can only be declared once
+        else
+          deferredFactorVocab.push_back(lhs);        // add surface form to its declared factor type
+        auto surfaceFormSet = factorTypeMap.find(fName); // set of surface forms for this factor
+        ABORT_IF(surfaceFormSet == factorTypeMap.end(), "Unknown factor name in '{}'", line);
+        auto rv = surfaceFormSet->second.insert(lhs); // insert surface form into its declared factor type
+        ABORT_IF(!rv.second, "Factor declared twice: '{}'", line);
+        auto tokenizedMapLine = isLemma ? std::vector<std::string>{ lhs, lhs } : std::vector<std::string>();
+        // associated factors
+        for (size_t i = 3; i < tokBuf.size(); i++) {
+          const auto& has = tokBuf[i];
+          ABORT_IF(!utils::beginsWith(has, "_has_"), "Factor associations must use the form _has_X in '{}'", line);
+          ABORT_IF(!isLemma, "Factor associations are only allowed when factor type is _lemma: '{}', line");
+          const auto& faName = has.substr(5); // skip _has_ and prepend |
+          // for tokenized map, we pick one example of the factor names
+          auto iter = factorTypeMap.find(faName);
+          ABORT_IF(iter == factorTypeMap.end(), "Invalid factor association {}, no such factor: '{}'", has, line);
+          const auto& factorNames = iter->second;
+          ABORT_IF(factorNames.empty(), "Factor association {} refers to empty factor type: '{}'", has, line);
+          const auto& oneFactorName = "|" + *factorNames.begin(); // pick the first entry as one example
+          tokenizedMapLine[0] += oneFactorName;
+          tokenizedMapLine.push_back(oneFactorName);
+        }
+        if (isLemma)
+          factorMapTokenized.push_back(std::move(tokenizedMapLine));
+        continue;
+      }
+      ABORT("Malformed .fsv input line {}", line); // we only get here for lines we could not process
+    }
+    for (auto factorTypeName : deferredFactorVocab)
+      factorVocab_.add("|" + factorTypeName, v++);
   } else {  // legacy for old configs
+    // legacy format: one factor map, one flat list of factor surface forms
     // load factor vocabulary
+    factorSeparator_ = '@';
     auto factorVocabPath = modelPath;
     factorVocabPath.back() = 'l'; // map .fm to .fl
     factorVocab_.load(factorVocabPath);
     groupPrefixes_ = { "(lemma)", "@C", "@GL", "@GR", "@WB"/*, "@WE"*/, "@CB"/*, "@CE"*/ }; // @TODO: hard-coded for these initial experiments
     // @TODO: add checks for empty factor groups until it stops crashing (training already works; decoder still crashes)
 
-    std::vector<std::string> tokBuf;
-    std::string line;
     io::InputFileStream in(modelPath);
     for (WordIndex v = 0; io::getline(in, line); v++) {
       utils::splitAny(line, tokBuf, " \t");
@@ -360,7 +439,7 @@ void FactoredVocab::constructNormalizationInfoForVocab() {
     return getUnkId();
 }
 
-/*virtual*/ const std::string& FactoredVocab::operator[](Word word) const /*overrworde final*/ {
+/*virtual*/ const std::string& FactoredVocab::operator[](Word word) const /*override final*/ {
   //LOG(info, "Looking up Word {}={}", word.toWordIndex(), word2string(word));
 #if 1 // @BUGBUG: our manually prepared dict does not contain @CI tags for single letters, but it's a valid factor
   if (vocab_.isGap(word.toWordIndex())) {
diff --git a/src/data/factored_vocab.h b/src/data/factored_vocab.h
index c27efd74d..220979e81 100755
--- a/src/data/factored_vocab.h
+++ b/src/data/factored_vocab.h
@@ -96,6 +96,7 @@ class FactoredVocab : public IVocab {
   WordLUT vocab_;
 
   // factors
+  char factorSeparator_ = '|';                         // separator symbol for parsing factored words
   WordLUT factorVocab_;                                // [factor name] -> factor index = row of E_
   std::vector<std::string> groupPrefixes_;             // [group id g] shared prefix of factors (used for grouping)
   //std::vector<std::vector<WordIndex>> factorMap_;      // [word index v] -> set of factor indices u

From ef5d086a546233ac1407fd8156151347f77671b1 Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Sat, 16 Mar 2019 19:41:52 -0700
Subject: [PATCH 378/838] bug fix: Output layer in s2s also should pass the
 extra vocab args to support factored embeddings

---
 src/models/s2s.h | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/src/models/s2s.h b/src/models/s2s.h
index 03baff564..cd9c663f2 100755
--- a/src/models/s2s.h
+++ b/src/models/s2s.h
@@ -343,6 +343,8 @@ class DecoderS2S : public DecoderBase {
           tiedPrefix = "Wemb";
         last.tieTransposed(tiedPrefix);
       }
+      last("vocab", opt<std::vector<std::string>>("vocabs")[batchIndex_]); // for factored outputs
+      last("lemma-dim-emb", opt<int>("lemma-dim-emb", 0)); // for factored outputs
 
       if(shortlist_)
         last.setShortlist(shortlist_);

From 90a9670a403700587161e2318e0cba107dcf7994 Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Sat, 16 Mar 2019 20:10:04 -0700
Subject: [PATCH 379/838] bug fix: S2SDecoder should not delete output_ in
 clear(), but instead call output_->clear()

---
 src/layers/constructors.h | 16 +++++++++++++++-
 src/layers/generic.h      | 11 ++++++++---
 src/models/s2s.h          | 12 +++++++-----
 3 files changed, 30 insertions(+), 9 deletions(-)

diff --git a/src/layers/constructors.h b/src/layers/constructors.h
index 953d01aa3..fc751dfc9 100755
--- a/src/layers/constructors.h
+++ b/src/layers/constructors.h
@@ -108,7 +108,7 @@ typedef Accumulator<OutputFactory> output;
 /**
  * Multi-layer network, holds and applies layers.
  */
-class MLP : public IUnaryLogitLayer {
+class MLP : public IUnaryLogitLayer, public IHasShortList {
 protected:
   Ptr<ExpressionGraph> graph_;
   Ptr<Options> options_;
@@ -159,6 +159,20 @@ class MLP : public IUnaryLogitLayer {
 
   void push_back(Ptr<IUnaryLayer> layer) { layers_.push_back(layer); }
   void push_back(Ptr<IUnaryLogitLayer> layer) { layers_.push_back(layer); }
+
+  void setShortlist(Ptr<data::Shortlist> shortlist) override final {
+    auto p = tryAsHasShortlist();
+    ABORT_IF(!p, "setShortlist() called on an MLP with an output layer that does not support short lists");
+    p->setShortlist(shortlist);
+  }
+
+  void clear() override final {
+    auto p = tryAsHasShortlist();
+    if (p)
+      p->clear();
+  }
+private:
+  Ptr<IHasShortList> tryAsHasShortlist() const { return std::dynamic_pointer_cast<IHasShortList>(layers_.back()); }
 };
 
 /**
diff --git a/src/layers/generic.h b/src/layers/generic.h
index 5b3e5ceab..40b8bb61a 100755
--- a/src/layers/generic.h
+++ b/src/layers/generic.h
@@ -50,6 +50,11 @@ struct IUnaryLayer {
   }
 };
 
+struct IHasShortList {
+  virtual void setShortlist(Ptr<data::Shortlist> shortlist) = 0;
+  virtual void clear() = 0;
+};
+
 // Embedding from corpus sub-batch to (emb, mask)
 struct IEmbeddingLayer {
   virtual std::tuple<Expr/*embeddings*/, Expr/*mask*/> apply(Ptr<data::SubBatch> subBatch) const = 0;
@@ -190,7 +195,7 @@ class Dense : public LayerBase, public IUnaryLayer {
   Expr apply(Expr input) override { return apply(std::vector<Expr>({input})); }
 };
 
-class Output : public LayerBase, public IUnaryLogitLayer {
+class Output : public LayerBase, public IUnaryLogitLayer, public IHasShortList {
 private:
   // parameters held by this layer
   Expr Wt_; // weight matrix is stored transposed for efficiency
@@ -218,7 +223,7 @@ class Output : public LayerBase, public IUnaryLogitLayer {
       tiedParam_ = tied;
   }
 
-  void setShortlist(Ptr<data::Shortlist> shortlist) {
+  void setShortlist(Ptr<data::Shortlist> shortlist) override final {
     if (shortlist_)
       ABORT_IF(shortlist.get() != shortlist_.get(), "Output shortlist cannot be changed except after clear()");
     else {
@@ -230,7 +235,7 @@ class Output : public LayerBase, public IUnaryLogitLayer {
 
   // this is expected to be called in sync with graph->clear(), which invalidates
   // cachedShortWt_ and cachedShortb_ in the graph's short-term cache
-  void clear() {
+  void clear() override final {
     shortlist_ = nullptr;
     cachedShortWt_ = nullptr;
     cachedShortb_  = nullptr;
diff --git a/src/models/s2s.h b/src/models/s2s.h
index cd9c663f2..cfd0d7e00 100755
--- a/src/models/s2s.h
+++ b/src/models/s2s.h
@@ -239,7 +239,8 @@ class DecoderS2S : public DecoderBase {
   }
 
 public:
-  DecoderS2S(Ptr<Options> options) : DecoderBase(options) {}
+  DecoderS2S(Ptr<Options> options) : DecoderBase(options) {
+  }
 
   virtual Ptr<DecoderState> startState(
       Ptr<ExpressionGraph> graph,
@@ -346,9 +347,6 @@ class DecoderS2S : public DecoderBase {
       last("vocab", opt<std::vector<std::string>>("vocabs")[batchIndex_]); // for factored outputs
       last("lemma-dim-emb", opt<int>("lemma-dim-emb", 0)); // for factored outputs
 
-      if(shortlist_)
-        last.setShortlist(shortlist_);
-
       // assemble layers into MLP and apply to embeddings, decoder context and
       // aligned source context
       output_ = mlp::mlp()              //
@@ -357,6 +355,9 @@ class DecoderS2S : public DecoderBase {
                     .construct(graph);
     }
 
+    if (shortlist_)
+      output_->setShortlist(shortlist_);
+
     Logits logits;
     if(alignedContext)
       logits = output_->applyAsLogits({embeddings, decoderContext, alignedContext});
@@ -381,7 +382,8 @@ class DecoderS2S : public DecoderBase {
 
   void clear() override {
     rnn_ = nullptr;
-    output_ = nullptr;
+    if (output_)
+      output_->clear();
   }
 };
 }  // namespace marian

From 51c277b72d1a6b9be0be42ae4af35a8e48893152 Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Sat, 16 Mar 2019 21:02:29 -0700
Subject: [PATCH 380/838] removed an overly cautious check

---
 src/layers/loss.cpp | 1 -
 1 file changed, 1 deletion(-)
 mode change 100644 => 100755 src/layers/loss.cpp

diff --git a/src/layers/loss.cpp b/src/layers/loss.cpp
old mode 100644
new mode 100755
index 86cd99d19..bd7ee0562
--- a/src/layers/loss.cpp
+++ b/src/layers/loss.cpp
@@ -10,7 +10,6 @@ Ptr<LabelwiseLoss> newLoss(Ptr<Options> options, bool inference) {
   if(costType == "ce-rescore") {
     return New<RescorerLoss>();
   } else if(costType == "ce-rescore-mean") {
-    ABORT("Check me");
     return New<RescorerLoss>();
   } else {  // same as ce-mean
     return New<CrossEntropyLoss>(smoothing);

From 1a5d9b0ad17a78666370dd013538efaa91910d58 Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Sun, 17 Mar 2019 16:56:35 -0700
Subject: [PATCH 381/838] bug fix: 'normalize' option in Rescorer must now
 explicitly divide by sentence length

---
 src/layers/loss.cpp     |  6 ++----
 src/layers/loss.h       | 20 ++++++++++++--------
 src/rescorer/rescorer.h | 24 +++++++++++++++++-------
 3 files changed, 31 insertions(+), 19 deletions(-)

diff --git a/src/layers/loss.cpp b/src/layers/loss.cpp
index bd7ee0562..61186fc1e 100755
--- a/src/layers/loss.cpp
+++ b/src/layers/loss.cpp
@@ -7,11 +7,9 @@ Ptr<LabelwiseLoss> newLoss(Ptr<Options> options, bool inference) {
   float smoothing = inference ? 0.f : options->get<float>("label-smoothing");
   std::string costType = options->get<std::string>("cost-type", "ce-mean");
   
-  if(costType == "ce-rescore") {
+  if(costType == "ce-rescore") { // returns per-batch-item scores (while ce-mean reduces over batch)
     return New<RescorerLoss>();
-  } else if(costType == "ce-rescore-mean") {
-    return New<RescorerLoss>();
-  } else {  // same as ce-mean
+  } else {  // same as ce-mean  --@TODO: better check all allowed values, and fail for invalid ones. E.g. what about ce-sum?
     return New<CrossEntropyLoss>(smoothing);
   }
 }
diff --git a/src/layers/loss.h b/src/layers/loss.h
index bcf9ca4ca..8607fc5d7 100755
--- a/src/layers/loss.h
+++ b/src/layers/loss.h
@@ -42,6 +42,7 @@ class RationalLoss {
 
   Expr loss() const { return loss_; }
 
+  // @TODO: remove this function, as it does not add too much value over loss()->get(...)
   template <typename T>
   void loss(std::vector<T>& losses) const {
     ABORT_IF(!loss_, "Loss has not been defined");
@@ -56,6 +57,7 @@ class RationalLoss {
 
   Expr count() const { return count_; }
 
+  // @TODO: remove this function, as it does not add too much value over count()->get(...)
   template <typename T>
   void count(std::vector<T>& labels) const {
     ABORT_IF(!count_, "Labels have not been defined");
@@ -369,14 +371,16 @@ class CrossEntropyLoss : public LabelwiseLoss {
  */
 class RescorerLoss : public CrossEntropyLoss {
 public:
-  // sentence-wise CE, hence reduce only over time axis. CE reduces over last axis (-1)
-  RescorerLoss() : CrossEntropyLoss(/*axes=*/{-3}, /*smoothing=*/0.f) {}
-
-  virtual RationalLoss apply(Logits logits, const Words& labels,
-                             Expr mask = nullptr, Expr labelWeights = nullptr) override {
-    auto ce = CrossEntropyLoss::apply(logits, labels, mask, labelWeights);
-    return RationalLoss(ce.loss(), ce.count());
-  }
+  // sentence-wise CE, hence reduce only over time axis
+  // This class differs from CrossEntropy in the different 'axes' setting, and that label smoothing is disabled.
+  RescorerLoss() : CrossEntropyLoss(/*axes=*/{-3} /*time axis*/, /*smoothing=*/0.f) {}
+
+  // @TODO: this adds nothing; remove
+  //virtual RationalLoss apply(Logits logits, const Words& labels,
+  //                           Expr mask = nullptr, Expr labelWeights = nullptr) override {
+  //  auto ce = CrossEntropyLoss::apply(logits, labels, mask, labelWeights);
+  //  return RationalLoss(ce.loss(), ce.count());
+  //}
 };
 
 /**
diff --git a/src/rescorer/rescorer.h b/src/rescorer/rescorer.h
index 3917a901c..8925391bd 100755
--- a/src/rescorer/rescorer.h
+++ b/src/rescorer/rescorer.h
@@ -56,8 +56,7 @@ class Rescore : public ModelTask {
              "Normalization by length cannot be used with summary scores");
 
     options_->set("inference", true);
-    // @TODO: make normalize here a float and pass into loss to compute the same way as in decoding
-    options_->set("cost-type", options_->get<bool>("normalize") ? "ce-rescore-mean" : "ce-rescore");
+    options_->set("cost-type", "ce-rescore"); // indicates that to keep separate per-batch-item scoresForSummary
 
     if(options_->get<bool>("n-best"))
       corpus_ = New<CorpusNBest>(options_);
@@ -102,6 +101,7 @@ class Rescore : public ModelTask {
     auto alignment = options_->get<std::string>("alignment", "");
     auto summary = options_->get<std::string>("summary", "");
     bool summarize = !summary.empty();
+    // @TODO: make normalize here a float and pass into loss to compute the same way as in decoding
     bool normalize = options_->get<bool>("normalize");
 
     float sumLoss = 0;
@@ -130,17 +130,27 @@ class Rescore : public ModelTask {
 
           graph->forward();
 
-          std::vector<float> scores;
-          dynamicLoss->loss(scores);
+          // get loss
+          std::vector<float> scoresForSummary;
+          dynamicLoss->loss(scoresForSummary);
+          std::vector<float> sentScores(scoresForSummary); // if '--normalize' then report scoresForSummary length-normalized
+          if (normalize) {
+            std::vector<float> sentLengths;
+            dynamicLoss->count(sentLengths);
+            for (size_t i = 0; i < scoresForSummary.size(); i++) {
+              if (sentScores[i] != 0) // (avoid 0/0)
+                sentScores[i] /= (sentLengths.size() == 1 ? sentLengths[0] : sentLengths[i]); // emulate broadcasting semantics
+            }
+          }
 
           // soft alignments for each sentence in the batch
-          std::vector<data::SoftAlignment> aligns(batch->size());
+          std::vector<data::SoftAlignment> aligns(batch->size()); // @TODO: do this resize inside getAlignmentsForBatch()
           if(!alignment.empty()) {
             getAlignmentsForBatch(builder->getAlignment(), batch, aligns);
           }
 
           std::unique_lock<std::mutex> lock(smutex);
-          for(auto s : scores)
+          for(auto s : scoresForSummary)
             sumLoss += s;
           sumWords += batch->back()->batchWords();
           sumSamples += batch->size();
@@ -148,7 +158,7 @@ class Rescore : public ModelTask {
           if(!summarize) {
             for(size_t i = 0; i < batch->size(); ++i) {
               output->Write((long)batch->getSentenceIds()[i],
-                             -1.f * scores[i], // report logProb while score is CE, hence negate
+                             -1.f * sentScores[i], // report logProb while score is CE, hence negate
                              aligns[i]);
             }
           }

From 45ba9477ba1a3c70a55e83a64b9d53358d1077c5 Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Sun, 17 Mar 2019 17:28:22 -0700
Subject: [PATCH 382/838] updated --all-caps-every and
 --english-title-case-every to new syntax

---
 src/data/factored_vocab.cpp | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/src/data/factored_vocab.cpp b/src/data/factored_vocab.cpp
index a235cef37..69172edea 100755
--- a/src/data/factored_vocab.cpp
+++ b/src/data/factored_vocab.cpp
@@ -456,13 +456,13 @@ void FactoredVocab::constructNormalizationInfoForVocab() {
 }
 
 /*virtual*/ std::string FactoredVocab::toUpper(const std::string& line) const /*override final*/ {
-  return utils::findReplace(utils::findReplace(line, "@CI", "@CA", /*all=*/true), "@CN", "@CA", /*all=*/true);
+  return utils::findReplace(utils::findReplace(utils::findReplace(utils::findReplace(line, "|ci", "|ca", /*all=*/true), "|cn", "|ca", /*all=*/true), "@CI", "@CA", /*all=*/true), "@CN", "@CA", /*all=*/true);
 }
 
 /*virtual*/ std::string FactoredVocab::toEnglishTitleCase(const std::string& line) const /*override final*/ {
   // @BUGBUG: does not handle the special words that should remain lower-case
   // note: this presently supports both @WB and @GL- (legacy)
-  return utils::findReplace(utils::findReplace(line, "@CN@WB", "@CI@WB", /*all=*/true), "@CN@GL-", "@CI@GL-", /*all=*/true);
+  return utils::findReplace(utils::findReplace(utils::findReplace(utils::findReplace(line, "|cn|wb", "|ci|wb", /*all=*/true), "|cn|gl-", "|ci|gl-", /*all=*/true), "@CN@WB", "@CI@WB", /*all=*/true), "@CN@GL-", "@CI@GL-", /*all=*/true);
 }
 
 // generate a valid random factored word (used by collectStats())

From 58feb01050b64af3189f146a138082bb90f95952 Mon Sep 17 00:00:00 2001
From: Roman Grundkiewicz <rgrundki@exseed.ed.ac.uk>
Date: Mon, 18 Mar 2019 12:07:42 +0000
Subject: [PATCH 383/838] Add comments

---
 src/3rd_party/CLI/App.hpp | 8 ++++++++
 1 file changed, 8 insertions(+)

diff --git a/src/3rd_party/CLI/App.hpp b/src/3rd_party/CLI/App.hpp
index fe430c16c..14ddd1e7f 100644
--- a/src/3rd_party/CLI/App.hpp
+++ b/src/3rd_party/CLI/App.hpp
@@ -1590,7 +1590,11 @@ class App {
         }
 
         // Unlimited vector parser
+        // RG: A negative number for the total number of expected values means that the option is a
+        // vector and accepts an unlimited number of values
         if(num < 0) {
+            // RG: We need to keep track if the vector option is empty and handle this separately as
+            // otherwise the parser will mark the command-line option as not set
             bool emptyVectorArgs = true;
             while(!args.empty() && _recognize(args.back()) == detail::Classifer::NONE) {
                 if(collected >= -num) {
@@ -1611,12 +1615,16 @@ class App {
             if(!args.empty() && _recognize(args.back()) == detail::Classifer::POSITIONAL_MARK)
                 args.pop_back();
 
+            // RG: Handle empty vector-like options
             if(emptyVectorArgs) {
+                // RG: Set implicit value(s) if the option has it (them)
                 if(op->get_implicit()) {
                     for(const auto& ival : detail::split_up(op->get_implicitval())) {
                         op->add_result(ival);
                         parse_order_.push_back(op.get());
                     }
+                // RG: Abort if there is a minimum number of values expected. Note: get_expected()
+                // equals to -N means at least N values are expected
                 } else if (op->get_expected() < 0) {
                     parse_order_.push_back(op.get());
                     throw ArgumentMismatch(op->get_name(), op->get_expected(), 0);

From 86033cb5c42061437a9575a0d0c5c7a1abadd974 Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Tue, 19 Mar 2019 10:16:04 -0700
Subject: [PATCH 384/838] new parameter --valid-script-args

---
 src/common/config_parser.cpp |  4 ++++
 src/common/utils.cpp         | 17 ++++++++++++++---
 src/common/utils.h           |  2 +-
 src/layers/loss.h            |  2 --
 src/training/validator.h     | 11 +++++++----
 5 files changed, 26 insertions(+), 10 deletions(-)

diff --git a/src/common/config_parser.cpp b/src/common/config_parser.cpp
index ee49b448f..46402e38e 100755
--- a/src/common/config_parser.cpp
+++ b/src/common/config_parser.cpp
@@ -33,6 +33,7 @@ const std::set<std::string> PATHS = {
   "embedding-vectors",
   "valid-sets",
   "valid-script-path",
+  "valid-script-args",
   "valid-log",
   "valid-translation-output",
   "input",            // except: stdin
@@ -471,6 +472,9 @@ void ConfigParser::addOptionsValidation(cli::CLIWrapper& cli) {
      " It should print a single score to stdout."
      " If the option is used with validating translation, the output"
      " translation file will be passed as a first argument");
+  cli.add<std::vector<std::string>>("--valid-script-args",
+      "Additional args passed to --valid-script-path. These are inserted"
+      " between the script path and the output translation-file path");
   cli.add<std::string>("--valid-translation-output",
      "Path to store the translation");
 
diff --git a/src/common/utils.cpp b/src/common/utils.cpp
index eb464878e..46870531a 100755
--- a/src/common/utils.cpp
+++ b/src/common/utils.cpp
@@ -93,19 +93,30 @@ std::string join(const std::vector<std::string>& words, const std::string& del /
   return ss.str();
 }
 
-std::string exec(const std::string& cmd) {
+// escapes a string for passing to popen, which uses /bin/sh to parse its argument string
+static std::string escapeForPOpen(const std::string& arg) {
+  // e.g. abc -> 'abc'; my file.txt -> 'my file.txt'; $10 -> '$10'; it's -> 'it'\''s'
+  return "'" + findReplace(arg, "'", "'\\''", /*all=*/ true) + "'";
+}
+
+std::string exec(const std::string& cmd, const std::vector<std::string>& args /*= {}*/, const std::string& arg /*= ""*/) {
   std::array<char, 128> buffer;
   std::string result;
 #ifdef _WIN32
 #define popen _popen
 #define pclose _pclose
 #endif
-  std::shared_ptr<std::FILE> pipe(popen(cmd.c_str(), "r"), pclose);
+  auto cmdLine = escapeForPOpen(cmd);
+  for (const auto& a : args) // @TODO: proper escaping
+    cmdLine += " " + escapeForPOpen(a);
+  if (!arg.empty())
+    cmdLine += " " + escapeForPOpen(arg);
+  std::shared_ptr<std::FILE> pipe(popen(cmdLine.c_str(), "r"), pclose);
   if(!pipe)
     ABORT("popen() failed!");
 
   while(!std::feof(pipe.get())) {
-    if(std::fgets(buffer.data(), 128, pipe.get()) != NULL)
+    if(std::fgets(buffer.data(), buffer.size(), pipe.get()) != NULL)
       result += buffer.data();
   }
   return result;
diff --git a/src/common/utils.h b/src/common/utils.h
index 8aa40f835..6226ed992 100755
--- a/src/common/utils.h
+++ b/src/common/utils.h
@@ -30,7 +30,7 @@ std::vector<std::string> splitAny(const std::string& line,
 
 std::string join(const std::vector<std::string>& words, const std::string& del = " ");
 
-std::string exec(const std::string& cmd);
+std::string exec(const std::string& cmd, const std::vector<std::string>& args = {}, const std::string& arg = "");
 
 std::pair<std::string, int> hostnameAndProcessId();
 
diff --git a/src/layers/loss.h b/src/layers/loss.h
index 8607fc5d7..22b97b1af 100755
--- a/src/layers/loss.h
+++ b/src/layers/loss.h
@@ -42,7 +42,6 @@ class RationalLoss {
 
   Expr loss() const { return loss_; }
 
-  // @TODO: remove this function, as it does not add too much value over loss()->get(...)
   template <typename T>
   void loss(std::vector<T>& losses) const {
     ABORT_IF(!loss_, "Loss has not been defined");
@@ -57,7 +56,6 @@ class RationalLoss {
 
   Expr count() const { return count_; }
 
-  // @TODO: remove this function, as it does not add too much value over count()->get(...)
   template <typename T>
   void count(std::vector<T>& labels) const {
     ABORT_IF(!count_, "Labels have not been defined");
diff --git a/src/training/validator.h b/src/training/validator.h
index 0e423008a..f7f586f9d 100755
--- a/src/training/validator.h
+++ b/src/training/validator.h
@@ -430,8 +430,8 @@ class ScriptValidator : public Validator<data::Corpus, models::IModel> {
     auto model = options_->get<std::string>("model");
     builder_->save(graphs[0], model + ".dev.npz", true);
 
-    auto command = options_->get<std::string>("valid-script-path");
-    auto valStr = utils::exec(command);
+    auto valStr = utils::exec(options_->get<std::string>("valid-script-path"),
+                              options_->get<std::vector<std::string>>("valid-script-args"));
     float val = (float)std::atof(valStr.c_str());
     updateStalled(graphs, val);
 
@@ -558,8 +558,11 @@ class TranslationValidator : public Validator<data::Corpus, models::IModel> {
 
     // Run post-processing script if given
     if(options_->hasAndNotEmpty("valid-script-path")) {
-      auto command = options_->get<std::string>("valid-script-path") + " " + fileName;
-      auto valStr = utils::exec(command);
+      //auto command = options_->get<std::string>("valid-script-path") + " " + fileName;
+      //auto valStr = utils::exec(command);
+      auto valStr = utils::exec(options_->get<std::string>("valid-script-path"),
+                                options_->get<std::vector<std::string>>("valid-script-args"),
+                                fileName);
       val = (float)std::atof(valStr.c_str());
       updateStalled(graphs, val);
     }

From a38ee501745d1f836fb9b0f74aa299243e95313b Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Sun, 17 Mar 2019 16:56:35 -0700
Subject: [PATCH 385/838] apply Frank's fix to rescorer

---
 src/layers/loss.cpp     |  7 ++-----
 src/layers/loss.h       | 13 +++++--------
 src/rescorer/rescorer.h | 24 +++++++++++++++++-------
 3 files changed, 24 insertions(+), 20 deletions(-)

diff --git a/src/layers/loss.cpp b/src/layers/loss.cpp
index 86cd99d19..61186fc1e 100644
--- a/src/layers/loss.cpp
+++ b/src/layers/loss.cpp
@@ -7,12 +7,9 @@ Ptr<LabelwiseLoss> newLoss(Ptr<Options> options, bool inference) {
   float smoothing = inference ? 0.f : options->get<float>("label-smoothing");
   std::string costType = options->get<std::string>("cost-type", "ce-mean");
   
-  if(costType == "ce-rescore") {
+  if(costType == "ce-rescore") { // returns per-batch-item scores (while ce-mean reduces over batch)
     return New<RescorerLoss>();
-  } else if(costType == "ce-rescore-mean") {
-    ABORT("Check me");
-    return New<RescorerLoss>();
-  } else {  // same as ce-mean
+  } else {  // same as ce-mean  --@TODO: better check all allowed values, and fail for invalid ones. E.g. what about ce-sum?
     return New<CrossEntropyLoss>(smoothing);
   }
 }
diff --git a/src/layers/loss.h b/src/layers/loss.h
index 3f2ec5446..0087188e0 100755
--- a/src/layers/loss.h
+++ b/src/layers/loss.h
@@ -39,6 +39,7 @@ class RationalLoss {
 
   Expr loss() const { return loss_; }
 
+  // @TODO: remove this function, as it does not add too much value over loss()->get(...)
   template <typename T>
   void loss(std::vector<T>& losses) const {
     ABORT_IF(!loss_, "Loss has not been defined");
@@ -53,6 +54,7 @@ class RationalLoss {
 
   Expr count() const { return count_; }
 
+  // @TODO: remove this function, as it does not add too much value over count()->get(...)
   template <typename T>
   void count(std::vector<T>& labels) const {
     ABORT_IF(!count_, "Labels have not been defined");
@@ -368,14 +370,9 @@ class CrossEntropyLoss : public LabelwiseLoss {
  */
 class RescorerLoss : public CrossEntropyLoss {
 public:
-  // sentence-wise CE, hence reduce only over time axis. CE reduces over last axis (-1)
-  RescorerLoss() : CrossEntropyLoss(/*axes=*/{-3}, /*smoothing=*/0.f) {}
-
-  virtual RationalLoss apply(Expr logits, const Words& labels,
-                             Expr mask = nullptr, Expr labelWeights = nullptr) override {
-    auto ce = CrossEntropyLoss::apply(logits, labels, mask, labelWeights);
-    return RationalLoss(ce.loss(), ce.count());
-  }
+  // sentence-wise CE, hence reduce only over time axis
+  // This class differs from CrossEntropy in the different 'axes' setting, and that label smoothing is disabled.
+  RescorerLoss() : CrossEntropyLoss(/*axes=*/{-3} /*time axis*/, /*smoothing=*/0.f) {}
 };
 
 /**
diff --git a/src/rescorer/rescorer.h b/src/rescorer/rescorer.h
index 3917a901c..8925391bd 100755
--- a/src/rescorer/rescorer.h
+++ b/src/rescorer/rescorer.h
@@ -56,8 +56,7 @@ class Rescore : public ModelTask {
              "Normalization by length cannot be used with summary scores");
 
     options_->set("inference", true);
-    // @TODO: make normalize here a float and pass into loss to compute the same way as in decoding
-    options_->set("cost-type", options_->get<bool>("normalize") ? "ce-rescore-mean" : "ce-rescore");
+    options_->set("cost-type", "ce-rescore"); // indicates that to keep separate per-batch-item scoresForSummary
 
     if(options_->get<bool>("n-best"))
       corpus_ = New<CorpusNBest>(options_);
@@ -102,6 +101,7 @@ class Rescore : public ModelTask {
     auto alignment = options_->get<std::string>("alignment", "");
     auto summary = options_->get<std::string>("summary", "");
     bool summarize = !summary.empty();
+    // @TODO: make normalize here a float and pass into loss to compute the same way as in decoding
     bool normalize = options_->get<bool>("normalize");
 
     float sumLoss = 0;
@@ -130,17 +130,27 @@ class Rescore : public ModelTask {
 
           graph->forward();
 
-          std::vector<float> scores;
-          dynamicLoss->loss(scores);
+          // get loss
+          std::vector<float> scoresForSummary;
+          dynamicLoss->loss(scoresForSummary);
+          std::vector<float> sentScores(scoresForSummary); // if '--normalize' then report scoresForSummary length-normalized
+          if (normalize) {
+            std::vector<float> sentLengths;
+            dynamicLoss->count(sentLengths);
+            for (size_t i = 0; i < scoresForSummary.size(); i++) {
+              if (sentScores[i] != 0) // (avoid 0/0)
+                sentScores[i] /= (sentLengths.size() == 1 ? sentLengths[0] : sentLengths[i]); // emulate broadcasting semantics
+            }
+          }
 
           // soft alignments for each sentence in the batch
-          std::vector<data::SoftAlignment> aligns(batch->size());
+          std::vector<data::SoftAlignment> aligns(batch->size()); // @TODO: do this resize inside getAlignmentsForBatch()
           if(!alignment.empty()) {
             getAlignmentsForBatch(builder->getAlignment(), batch, aligns);
           }
 
           std::unique_lock<std::mutex> lock(smutex);
-          for(auto s : scores)
+          for(auto s : scoresForSummary)
             sumLoss += s;
           sumWords += batch->back()->batchWords();
           sumSamples += batch->size();
@@ -148,7 +158,7 @@ class Rescore : public ModelTask {
           if(!summarize) {
             for(size_t i = 0; i < batch->size(); ++i) {
               output->Write((long)batch->getSentenceIds()[i],
-                             -1.f * scores[i], // report logProb while score is CE, hence negate
+                             -1.f * sentScores[i], // report logProb while score is CE, hence negate
                              aligns[i]);
             }
           }

From 8e8c4efc3bc86e5f18626949fd1fed3c924188ce Mon Sep 17 00:00:00 2001
From: Ulrich Germann <ugermann@inf.ed.ac.uk>
Date: Fri, 22 Mar 2019 21:07:07 +0000
Subject: [PATCH 386/838] Don't close named pipes during corpus reset.

Corpus::reset() closed and reopened input files for corpora. So files were opened once
during the construction of the Corpus instance, then closed and repopened during
data->reset() in BatchGenerator::prepare(false). With normal files that's not a problem,
but if the "file" is a named pipe, the closing triggers a SIGPIPE (broken pipe) on
the writing end of the pipe. With this commit, Corpus::reset() leaves open pipes
alone.
---
 src/common/filesystem.cpp | 17 +++++++++++++++++
 src/common/filesystem.h   |  4 +++-
 src/data/corpus.cpp       | 23 ++++++++++++++++-------
 3 files changed, 36 insertions(+), 8 deletions(-)
 create mode 100644 src/common/filesystem.cpp

diff --git a/src/common/filesystem.cpp b/src/common/filesystem.cpp
new file mode 100644
index 000000000..d5196e850
--- /dev/null
+++ b/src/common/filesystem.cpp
@@ -0,0 +1,17 @@
+#include "filesystem.h"
+
+#include <sys/types.h>
+#include <sys/stat.h>
+#include <unistd.h>
+
+namespace marian {
+namespace filesystem {
+
+bool is_fifo(char const* path) {
+  struct stat buf;
+  stat(path, &buf);
+  return S_ISFIFO(buf.st_mode);
+}
+
+} // end of namespace marian::filesystem
+} // end of namespace marian
diff --git a/src/common/filesystem.h b/src/common/filesystem.h
index 08d5995f1..2c700aa09 100644
--- a/src/common/filesystem.h
+++ b/src/common/filesystem.h
@@ -22,6 +22,8 @@
 namespace marian {
 namespace filesystem {
 
+  bool is_fifo(char const* path);
+
   class Path {
     private:
       Pathie::Path path;
@@ -97,4 +99,4 @@ namespace filesystem {
   using FilesystemError = Pathie::PathieError;
 
 }
-}
\ No newline at end of file
+}
diff --git a/src/data/corpus.cpp b/src/data/corpus.cpp
index fda8df95f..c851c3c70 100644
--- a/src/data/corpus.cpp
+++ b/src/data/corpus.cpp
@@ -4,6 +4,8 @@
 #include <random>
 
 #include "common/utils.h"
+#include "common/filesystem.h"
+
 #include "data/corpus.h"
 
 namespace marian {
@@ -44,6 +46,7 @@ SentenceTuple Corpus::next() {
       }
       else {
         bool gotLine = io::getline(*files_[i], line);
+        // LOG(debug,"[{}][{}] {}", i, pos_ - 1, line);
         if(!gotLine) {
           eofsHit++;
           continue;
@@ -85,16 +88,22 @@ void Corpus::shuffle() {
 // Call either reset() or shuffle().
 // @TODO: make shuffle() private, instad pass a shuffle() flag to reset(), to clarify mutual exclusiveness with shuffle()
 void Corpus::reset() {
-  files_.clear();
   corpusInRAM_.clear();
   ids_.clear();
   pos_ = 0;
-  for(auto& path : paths_) {
-    if(path == "stdin")
-      files_.emplace_back(new io::InputFileStream(std::cin));
-    else
-      files_.emplace_back(new io::InputFileStream(path));
-  }
+  for (size_t i = 0; i < paths_.size(); ++i)
+    {
+      if(paths_[i] == "stdin") {
+        files_[i].reset(new io::InputFileStream(std::cin));
+        // Probably not necessary, unless there are some buffers
+        // that we want flushed.
+      }
+      else if (!filesystem::is_fifo(paths_[i].c_str())) {
+        // Do NOT reset named pipes; that closes them and triggers a SIGPIPE
+        // (lost pipe) at the writing end.
+        files_[i].reset(new io::InputFileStream(paths_[i]));
+      }
+    }
 }
 
 void Corpus::restore(Ptr<TrainingState> ts) {

From e56685a21466817e9ed776979c819bbfd48a4693 Mon Sep 17 00:00:00 2001
From: Ulrich Germann <ulrich.germann@gmail.com>
Date: Sat, 23 Mar 2019 02:01:03 +0000
Subject: [PATCH 387/838] Add common/filesystem.cpp to CMakeLists.txt

---
 src/CMakeLists.txt | 1 +
 1 file changed, 1 insertion(+)

diff --git a/src/CMakeLists.txt b/src/CMakeLists.txt
index fbafc3b99..f7ab0a1c5 100644
--- a/src/CMakeLists.txt
+++ b/src/CMakeLists.txt
@@ -17,6 +17,7 @@ add_library(marian STATIC
   common/config_validator.cpp
   common/binary.cpp
   common/io.cpp
+  common/filesystem.cpp
 
   data/alignment.cpp
   data/vocab.cpp

From c098dd7e52f4dcc58699e1cc3cf595bdff55015b Mon Sep 17 00:00:00 2001
From: Ulrich Germann <ugermann@inf.ed.ac.uk>
Date: Tue, 26 Mar 2019 18:10:21 +0000
Subject: [PATCH 388/838] Updated fix after discussions with @frankseide.

---
 src/common/filesystem.cpp | 4 ++++
 src/common/filesystem.h   | 1 +
 src/data/corpus.cpp       | 9 +++++++--
 3 files changed, 12 insertions(+), 2 deletions(-)

diff --git a/src/common/filesystem.cpp b/src/common/filesystem.cpp
index d5196e850..81500a5e7 100644
--- a/src/common/filesystem.cpp
+++ b/src/common/filesystem.cpp
@@ -13,5 +13,9 @@ bool is_fifo(char const* path) {
   return S_ISFIFO(buf.st_mode);
 }
 
+bool is_fifo(std::string const& path) {
+  return is_fifo(path.c_str());
+}
+
 } // end of namespace marian::filesystem
 } // end of namespace marian
diff --git a/src/common/filesystem.h b/src/common/filesystem.h
index 2c700aa09..434c23ba9 100644
--- a/src/common/filesystem.h
+++ b/src/common/filesystem.h
@@ -23,6 +23,7 @@ namespace marian {
 namespace filesystem {
 
   bool is_fifo(char const* path);
+  bool is_fifo(std::string const& path);
 
   class Path {
     private:
diff --git a/src/data/corpus.cpp b/src/data/corpus.cpp
index c851c3c70..1218383c9 100644
--- a/src/data/corpus.cpp
+++ b/src/data/corpus.cpp
@@ -90,6 +90,8 @@ void Corpus::shuffle() {
 void Corpus::reset() {
   corpusInRAM_.clear();
   ids_.clear();
+  if (pos_ == 0) // no data read yet
+    return;
   pos_ = 0;
   for (size_t i = 0; i < paths_.size(); ++i)
     {
@@ -98,9 +100,12 @@ void Corpus::reset() {
         // Probably not necessary, unless there are some buffers
         // that we want flushed.
       }
-      else if (!filesystem::is_fifo(paths_[i].c_str())) {
+      else {
+        ABORT_IF(files_[i] and filesystem::is_fifo(paths_[i]),
+                 "File '", paths_[i], "' is a pipe and cannot be re-opened.");
         // Do NOT reset named pipes; that closes them and triggers a SIGPIPE
-        // (lost pipe) at the writing end.
+        // (lost pipe) at the writing end, which may do whatever it wants
+        // in this situation.
         files_[i].reset(new io::InputFileStream(paths_[i]));
       }
     }

From d7f7d29258df0a2c463b086bdb930e73cf669035 Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Wed, 27 Mar 2019 10:08:29 -0700
Subject: [PATCH 389/838] new validator bleu-detok-ms that treats CJT
 continuous-script characters as individual tokens

---
 src/command/marian_main.cpp  |  0
 src/common/config_parser.cpp |  2 +-
 src/common/utils.cpp         | 62 +++++++++++++++++++++-----------
 src/common/utils.h           |  4 +++
 src/training/validator.cpp   |  7 ++--
 src/training/validator.h     | 69 ++++++++++++++++++++++++++----------
 6 files changed, 101 insertions(+), 43 deletions(-)
 mode change 100644 => 100755 src/command/marian_main.cpp

diff --git a/src/command/marian_main.cpp b/src/command/marian_main.cpp
old mode 100644
new mode 100755
diff --git a/src/common/config_parser.cpp b/src/common/config_parser.cpp
index 46402e38e..ae09f2aa2 100755
--- a/src/common/config_parser.cpp
+++ b/src/common/config_parser.cpp
@@ -435,7 +435,7 @@ void ConfigParser::addOptionsValidation(cli::CLIWrapper& cli) {
       "10000u");
   cli.add<std::vector<std::string>>("--valid-metrics",
       "Metric to use during validation: cross-entropy, ce-mean-words, perplexity, valid-script, "
-      "translation, bleu, bleu-detok. Multiple metrics can be specified",
+      "translation, bleu, bleu-detok, bleu-detok-ms. Multiple metrics can be specified",
       {"cross-entropy"});
   cli.add<size_t>("--early-stopping",
      "Stop if the first validation metric does not improve for  arg  consecutive validation steps",
diff --git a/src/common/utils.cpp b/src/common/utils.cpp
index 46870531a..958dfc77a 100755
--- a/src/common/utils.cpp
+++ b/src/common/utils.cpp
@@ -116,7 +116,7 @@ std::string exec(const std::string& cmd, const std::vector<std::string>& args /*
     ABORT("popen() failed!");
 
   while(!std::feof(pipe.get())) {
-    if(std::fgets(buffer.data(), buffer.size(), pipe.get()) != NULL)
+    if(std::fgets(buffer.data(), (int)buffer.size(), pipe.get()) != NULL)
       result += buffer.data();
   }
   return result;
@@ -155,27 +155,46 @@ bool endsWith(const std::string& text, const std::string& suffix) {
          && !text.compare(text.size() - suffix.size(), suffix.size(), suffix);
 }
 
-static std::wstring utf8ToWString(std::string const& s) {
-  std::wstring_convert<std::codecvt_utf8<wchar_t>> converter;
+std::u32string utf8ToUnicodeString(std::string const& s) {
+#ifdef _MSC_VER // workaround for a known bug in VS CRT
+  std::wstring_convert<std::codecvt_utf8<unsigned int/*char32_t*/>, unsigned int/*char32_t*/> converter;
+  auto res = converter.from_bytes(s);
+  return std::u32string(res.begin(), res.end());
+#else
+  std::wstring_convert<std::codecvt_utf8<char32_t>, char32_t> converter;
   return converter.from_bytes(s);
+#endif
 }
 
-static std::string toUTF8String(std::wstring const& s) {
-  std::wstring_convert<std::codecvt_utf8<wchar_t>> converter;
+std::string utf8FromUnicodeString(const std::u32string& s) {
+#ifdef _MSC_VER // workaround for a known bug in VS CRT
+  std::wstring_convert<std::codecvt_utf8<unsigned int/*char32_t*/>, unsigned int/*char32_t*/> converter;
+  std::basic_string<unsigned int> si(s.begin(), s.end());
+  return converter.to_bytes(si);
+#else
+  std::wstring_convert<std::codecvt_utf8<char32_t>, char32_t> converter;
   return converter.to_bytes(s);
+#endif
 }
 
-// convert a UTF-8 string to all-caps
-#if 0
-// @BUGBUG: This does not work for non-ASCII characters.
-std::string utf8ToUpper(const std::string& s) {
-  auto ws = utf8ToWString(s);
-  for (auto& c : ws)
-    c = std::towupper(c);
-  return toUTF8String(ws);
+bool isContinuousScript(char32_t c) {
+  // currently, this table is hand-coded, and may need to be extended when the standard grows
+  auto in = [c](char32_t minVal, char32_t maxVal) { return c >= minVal && c <= maxVal; };
+  bool isHan = in(0x2E80, 0x2E99) || in(0x2E9B, 0x2EF3) || in(0x2F00, 0x2FD5) || in(0x3005, 0x3005) ||
+               in(0x3007, 0x3007) || in(0x3021, 0x3029) || in(0x3038, 0x303A) || in(0x303B, 0x303b) ||
+               in(0x3400, 0x4DB5) || in(0x4E00, 0x9FEF) || in(0xF900, 0xFA6D) || in(0xFA70, 0xFAD9) ||
+               in(0x20000, 0x2A6D6) || in(0x2A700, 0x2B734) || in(0x2B740, 0x2B81D) || in(0x2B820, 0x2CEA1) ||
+               in(0x2CEB0, 0x2EBE0) || in(0x2F800, 0x2FA1D) ||
+               in(0x3200, 0x32FF); // Enclosed CJK Letters and Months, https://en.wikipedia.org/wiki/Enclosed_CJK_Letters_and_Months
+  bool isKana = in(0x3040, 0x30FF) ||   // Hiragana, Katakana
+                in(0x1B000, 0x1B0FF) || // Kana supplement, https://en.wikipedia.org/wiki/Kana_Supplement
+                in(0x1B130, 0x1B16F);   // small Kana, https://en.wikipedia.org/wiki/Small_Kana_Extension
+  bool isThai = in(0x0E00, 0x0E7F); // https://en.wikipedia.org/wiki/Thai_(Unicode_block)
+  return isHan || isKana || isThai;
 }
-#else
-struct UTF8Mapper : std::map<wchar_t, wchar_t> { // Hack because Philly does not have UTF-8 locale installed
+
+// convert a UTF-8 string to all-caps
+struct UTF8Mapper : std::map<char32_t, char32_t> { // Hack because MS-internal Philly servers do not have UTF-8 locale installed
   UTF8Mapper() {
     /*
       env LC_ALL=en_US.UTF-8 sed 's/\(.\)/\1\n/g'   TEXT_FILE_CONTAINING_ALL_CHARS > l
@@ -191,13 +210,13 @@ struct UTF8Mapper : std::map<wchar_t, wchar_t> { // Hack because Philly does not
     */
     std::vector<std::pair<std::string, std::string>> map8{ {"\xc9\x92","\xe2\xb1\xb0"},{"\x61","\x41"},{"\xc3\xa1","\xc3\x81"},{"\xc3\xa0","\xc3\x80"},{"\xe1\xba\xaf","\xe1\xba\xae"},{"\xe1\xba\xb1","\xe1\xba\xb0"},{"\xe1\xba\xb5","\xe1\xba\xb4"},{"\xe1\xba\xb3","\xe1\xba\xb2"},{"\xe1\xba\xb7","\xe1\xba\xb6"},{"\xc4\x83","\xc4\x82"},{"\xe1\xba\xa5","\xe1\xba\xa4"},{"\xe1\xba\xa7","\xe1\xba\xa6"},{"\xe1\xba\xab","\xe1\xba\xaa"},{"\xe1\xba\xa9","\xe1\xba\xa8"},{"\xe1\xba\xad","\xe1\xba\xac"},{"\xc3\xa2","\xc3\x82"},{"\xc7\x8e","\xc7\x8d"},{"\xc7\xbb","\xc7\xba"},{"\xc3\xa5","\xc3\x85"},{"\xc7\x9f","\xc7\x9e"},{"\xc3\xa4","\xc3\x84"},{"\xc3\xa3","\xc3\x83"},{"\xc4\x85","\xc4\x84"},{"\xc4\x81","\xc4\x80"},{"\xe1\xba\xa3","\xe1\xba\xa2"},{"\xc8\x83","\xc8\x82"},{"\xe1\xba\xa1","\xe1\xba\xa0"},{"\xc7\xa3","\xc7\xa2"},{"\xc3\xa6","\xc3\x86"},{"\x62","\x42"},{"\xe1\xb8\x87","\xe1\xb8\x86"},{"\x63","\x43"},{"\xc4\x87","\xc4\x86"},{"\xc4\x89","\xc4\x88"},{"\xc4\x8d","\xc4\x8c"},{"\xc4\x8b","\xc4\x8a"},{"\xc3\xa7","\xc3\x87"},{"\x64","\x44"},{"\xc4\x8f","\xc4\x8e"},{"\xc4\x91","\xc4\x90"},{"\xe1\xb8\x91","\xe1\xb8\x90"},{"\xe1\xb8\x8d","\xe1\xb8\x8c"},{"\xe1\xb8\x8f","\xe1\xb8\x8e"},{"\xc3\xb0","\xc3\x90"},{"\x65","\x45"},{"\xc3\xa9","\xc3\x89"},{"\xc3\xa8","\xc3\x88"},{"\xc4\x95","\xc4\x94"},{"\xe1\xba\xbf","\xe1\xba\xbe"},{"\xe1\xbb\x81","\xe1\xbb\x80"},{"\xe1\xbb\x85","\xe1\xbb\x84"},{"\xe1\xbb\x83","\xe1\xbb\x82"},{"\xe1\xbb\x87","\xe1\xbb\x86"},{"\xc3\xaa","\xc3\x8a"},{"\xc4\x9b","\xc4\x9a"},{"\xc3\xab","\xc3\x8b"},{"\xe1\xba\xbd","\xe1\xba\xbc"},{"\xc4\x97","\xc4\x96"},{"\xc4\x99","\xc4\x98"},{"\xe1\xb8\x97","\xe1\xb8\x96"},{"\xc4\x93","\xc4\x92"},{"\xe1\xba\xbb","\xe1\xba\xba"},{"\xc8\x87","\xc8\x86"},{"\xe1\xba\xb9","\xe1\xba\xb8"},{"\xc7\x9d","\xc6\x8e"},{"\x66","\x46"},{"\x67","\x47"},{"\xc7\xb5","\xc7\xb4"},{"\xc4\x9f","\xc4\x9e"},{"\xc4\x9d","\xc4\x9c"},{"\xc7\xa7","\xc7\xa6"},{"\xc4\xa1","\xc4\xa0"},{"\xc4\xa3","\xc4\xa2"},{"\xc9\xa0","\xc6\x93"},{"\x68","\x48"},{"\xc4\xa5","\xc4\xa4"},{"\xc4\xa7","\xc4\xa6"},{"\xe1\xb8\xa9","\xe1\xb8\xa8"},{"\xe1\xb8\xa5","\xe1\xb8\xa4"},{"\xe1\xb8\xab","\xe1\xb8\xaa"},{"\x69","\x49"},{"\xc4\xb1","\x49"},{"\xc3\xad","\xc3\x8d"},{"\xc3\xac","\xc3\x8c"},{"\xc4\xad","\xc4\xac"},{"\xc3\xae","\xc3\x8e"},{"\xc7\x90","\xc7\x8f"},{"\xc3\xaf","\xc3\x8f"},{"\xc4\xa9","\xc4\xa8"},{"\xc4\xaf","\xc4\xae"},{"\xc4\xab","\xc4\xaa"},{"\xe1\xbb\x89","\xe1\xbb\x88"},{"\xc8\x8b","\xc8\x8a"},{"\xe1\xbb\x8b","\xe1\xbb\x8a"},{"\x6a","\x4a"},{"\xc4\xb5","\xc4\xb4"},{"\x6b","\x4b"},{"\xe1\xb8\xb1","\xe1\xb8\xb0"},{"\xc4\xb7","\xc4\xb6"},{"\xe1\xb8\xb3","\xe1\xb8\xb2"},{"\xc6\x99","\xc6\x98"},{"\x6c","\x4c"},{"\xc4\xba","\xc4\xb9"},{"\xc4\xbe","\xc4\xbd"},{"\xc5\x82","\xc5\x81"},{"\xc4\xbc","\xc4\xbb"},{"\xe1\xb8\xb7","\xe1\xb8\xb6"},{"\x6d","\x4d"},{"\xe1\xb8\xbf","\xe1\xb8\xbe"},{"\xe1\xb9\x83","\xe1\xb9\x82"},{"\xc5\x8b","\xc5\x8a"},{"\x6e","\x4e"},{"\xc5\x84","\xc5\x83"},{"\xc5\x88","\xc5\x87"},{"\xc3\xb1","\xc3\x91"},{"\xe1\xb9\x85","\xe1\xb9\x84"},{"\xc5\x86","\xc5\x85"},{"\xe1\xb9\x87","\xe1\xb9\x86"},{"\xe1\xb9\x89","\xe1\xb9\x88"},{"\xc5\x93","\xc5\x92"},{"\x6f","\x4f"},{"\xc3\xb3","\xc3\x93"},{"\xc3\xb2","\xc3\x92"},{"\xc5\x8f","\xc5\x8e"},{"\xe1\xbb\x91","\xe1\xbb\x90"},{"\xe1\xbb\x93","\xe1\xbb\x92"},{"\xe1\xbb\x95","\xe1\xbb\x94"},{"\xe1\xbb\x99","\xe1\xbb\x98"},{"\xc3\xb4","\xc3\x94"},{"\xc7\x92","\xc7\x91"},{"\xc3\xb6","\xc3\x96"},{"\xc5\x91","\xc5\x90"},{"\xc3\xb5","\xc3\x95"},{"\xc3\xb8","\xc3\x98"},{"\xc7\xab","\xc7\xaa"},{"\xc5\x8d","\xc5\x8c"},{"\xe1\xbb\x8f","\xe1\xbb\x8e"},{"\xc8\x8f","\xc8\x8e"},{"\xe1\xbb\x8d","\xe1\xbb\x8c"},{"\xe1\xbb\x9b","\xe1\xbb\x9a"},{"\xe1\xbb\x9d","\xe1\xbb\x9c"},{"\xe1\xbb\xa1","\xe1\xbb\xa0"},{"\xe1\xbb\x9f","\xe1\xbb\x9e"},{"\xe1\xbb\xa3","\xe1\xbb\xa2"},{"\xc6\xa1","\xc6\xa0"},{"\xc9\x94","\xc6\x86"},{"\x70","\x50"},{"\xe1\xb9\x95","\xe1\xb9\x94"},{"\x71","\x51"},{"\x72","\x52"},{"\xc5\x95","\xc5\x94"},{"\xc5\x99","\xc5\x98"},{"\xc5\x97","\xc5\x96"},{"\xe1\xb9\x9b","\xe1\xb9\x9a"},{"\xe1\xb9\x9f","\xe1\xb9\x9e"},{"\x73","\x53"},{"\xc5\x9b","\xc5\x9a"},{"\xc5\x9d","\xc5\x9c"},{"\xc5\xa1","\xc5\xa0"},{"\xc5\x9f","\xc5\x9e"},{"\xe1\xb9\xa3","\xe1\xb9\xa2"},{"\x74","\x54"},{"\xc5\xa5","\xc5\xa4"},{"\xc5\xa3","\xc5\xa2"},{"\xe1\xb9\xad","\xe1\xb9\xac"},{"\xe1\xb9\xaf","\xe1\xb9\xae"},{"\xc8\x95","\xc8\x94"},{"\x75","\x55"},{"\xc3\xba","\xc3\x9a"},{"\xc3\xb9","\xc3\x99"},{"\xc5\xad","\xc5\xac"},{"\xc3\xbb","\xc3\x9b"},{"\xc7\x94","\xc7\x93"},{"\xc5\xaf","\xc5\xae"},{"\xc7\x98","\xc7\x97"},{"\xc7\x9c","\xc7\x9b"},{"\xc3\xbc","\xc3\x9c"},{"\xc5\xb1","\xc5\xb0"},{"\xc5\xa9","\xc5\xa8"},{"\xc5\xb3","\xc5\xb2"},{"\xc5\xab","\xc5\xaa"},{"\xe1\xbb\xa7","\xe1\xbb\xa6"},{"\xe1\xbb\xa5","\xe1\xbb\xa4"},{"\xe1\xb9\xb3","\xe1\xb9\xb2"},{"\xe1\xbb\xa9","\xe1\xbb\xa8"},{"\xe1\xbb\xab","\xe1\xbb\xaa"},{"\xe1\xbb\xaf","\xe1\xbb\xae"},{"\xe1\xbb\xad","\xe1\xbb\xac"},{"\xe1\xbb\xb1","\xe1\xbb\xb0"},{"\xc6\xb0","\xc6\xaf"},{"\x76","\x56"},{"\x77","\x57"},{"\xc5\xb5","\xc5\xb4"},{"\x78","\x58"},{"\xe1\xba\x8b","\xe1\xba\x8a"},{"\x79","\x59"},{"\xc3\xbd","\xc3\x9d"},{"\xe1\xbb\xb3","\xe1\xbb\xb2"},{"\xc5\xb7","\xc5\xb6"},{"\xc3\xbf","\xc5\xb8"},{"\xe1\xbb\xb9","\xe1\xbb\xb8"},{"\x7a","\x5a"},{"\xc5\xba","\xc5\xb9"},{"\xc5\xbe","\xc5\xbd"},{"\xc5\xbc","\xc5\xbb"},{"\xc6\xb6","\xc6\xb5"},{"\xe1\xba\x93","\xe1\xba\x92"},{"\xe1\xba\x95","\xe1\xba\x94"},{"\xc8\xa5","\xc8\xa4"},{"\xc3\xbe","\xc3\x9e"},{"\xca\x92","\xc6\xb7"},{"\xce\xb1","\xce\x91"},{"\xce\xac","\xce\x86"},{"\xce\xb2","\xce\x92"},{"\xce\xb3","\xce\x93"},{"\xce\xb4","\xce\x94"},{"\xce\xb5","\xce\x95"},{"\xce\xad","\xce\x88"},{"\xce\xb6","\xce\x96"},{"\xce\xb7","\xce\x97"},{"\xce\xae","\xce\x89"},{"\xce\xb8","\xce\x98"},{"\xce\xb9","\xce\x99"},{"\xce\xaf","\xce\x8a"},{"\xcf\x8a","\xce\xaa"},{"\xce\xba","\xce\x9a"},{"\xce\xbb","\xce\x9b"},{"\xce\xbc","\xce\x9c"},{"\xce\xbd","\xce\x9d"},{"\xce\xbe","\xce\x9e"},{"\xce\xbf","\xce\x9f"},{"\xcf\x8c","\xce\x8c"},{"\xcf\x80","\xce\xa0"},{"\xcf\x83","\xce\xa3"},{"\xcf\x82","\xce\xa3"},{"\xcf\x84","\xce\xa4"},{"\xcf\x85","\xce\xa5"},{"\xcf\x8d","\xce\x8e"},{"\xcf\x8b","\xce\xab"},{"\xcf\x86","\xce\xa6"},{"\xcf\x87","\xce\xa7"},{"\xcf\x88","\xce\xa8"},{"\xcf\x89","\xce\xa9"},{"\xcf\x8e","\xce\x8f"},{"\xd0\xb0","\xd0\x90"},{"\xd3\x93","\xd3\x92"},{"\xd3\x95","\xd3\x94"},{"\xd0\xb1","\xd0\x91"},{"\xd0\xb2","\xd0\x92"},{"\xd0\xb3","\xd0\x93"},{"\xd2\x93","\xd2\x92"},{"\xd2\x91","\xd2\x90"},{"\xd0\xb4","\xd0\x94"},{"\xd1\x93","\xd0\x83"},{"\xd1\x92","\xd0\x82"},{"\xd0\xb5","\xd0\x95"},{"\xd1\x90","\xd0\x80"},{"\xd3\x99","\xd3\x98"},{"\xd1\x94","\xd0\x84"},{"\xd1\x91","\xd0\x81"},{"\xd0\xb6","\xd0\x96"},{"\xd0\xb7","\xd0\x97"},{"\xd2\x99","\xd2\x98"},{"\xd1\x95","\xd0\x85"},{"\xd0\xb8","\xd0\x98"},{"\xd3\xa3","\xd3\xa2"},{"\xd1\x96","\xd0\x86"},{"\xd1\x97","\xd0\x87"},{"\xd0\xb9","\xd0\x99"},{"\xd1\x98","\xd0\x88"},{"\xd0\xba","\xd0\x9a"},{"\xd2\x9b","\xd2\x9a"},{"\xd3\x84","\xd3\x83"},{"\xd2\xa1","\xd2\xa0"},{"\xd0\xbb","\xd0\x9b"},{"\xd1\x99","\xd0\x89"},{"\xd0\xbc","\xd0\x9c"},{"\xd0\xbd","\xd0\x9d"},{"\xd2\xa3","\xd2\xa2"},{"\xd1\x9a","\xd0\x8a"},{"\xd0\xbe","\xd0\x9e"},{"\xd3\xa7","\xd3\xa6"},{"\xd3\xa9","\xd3\xa8"},{"\xd0\xbf","\xd0\x9f"},{"\xd1\x80","\xd0\xa0"},{"\xd1\x81","\xd0\xa1"},{"\xd2\xab","\xd2\xaa"},{"\xd1\x82","\xd0\xa2"},{"\xd1\x9c","\xd0\x8c"},{"\xd1\x9b","\xd0\x8b"},{"\xd1\x83","\xd0\xa3"},{"\xd3\xb1","\xd3\xb0"},{"\xd2\xb1","\xd2\xb0"},{"\xd2\xaf","\xd2\xae"},{"\xd1\x9e","\xd0\x8e"},{"\xd1\x84","\xd0\xa4"},{"\xd1\x85","\xd0\xa5"},{"\xd2\xb3","\xd2\xb2"},{"\xd2\xbb","\xd2\xba"},{"\xd1\x86","\xd0\xa6"},{"\xd1\x87","\xd0\xa7"},{"\xd1\x9f","\xd0\x8f"},{"\xd1\x88","\xd0\xa8"},{"\xd1\x89","\xd0\xa9"},{"\xd1\x8a","\xd0\xaa"},{"\xd1\x8b","\xd0\xab"},{"\xd1\x8c","\xd0\xac"},{"\xd1\x8d","\xd0\xad"},{"\xd1\x8e","\xd0\xae"},{"\xd1\x8f","\xd0\xaf"},{"\xd5\xa1","\xd4\xb1"},{"\xd5\xa3","\xd4\xb3"},{"\xd5\xa5","\xd4\xb5"},{"\xd5\xab","\xd4\xbb"},{"\xd5\xac","\xd4\xbc"},{"\xd5\xb2","\xd5\x82"},{"\xd5\xb8","\xd5\x88"},{"\xd5\xbd","\xd5\x8d"},{"\xd5\xbe","\xd5\x8e"},{"\xd5\xbf","\xd5\x8f"},{"\xd6\x80","\xd5\x90"},{"\xd6\x81","\xd5\x91"} };
     for (auto p8 : map8) {
-      auto from = utf8ToWString(p8.first);
-      auto to   = utf8ToWString(p8.second);
+      auto from = utf8ToUnicodeString(p8.first);
+      auto to   = utf8ToUnicodeString(p8.second);
       ABORT_IF(from.size() != 1 || to.size() != 1, "Incorrect character encoding??");
       insert(std::make_pair(from.front(), to.front()));
     }
   }
-  wchar_t towupper(wchar_t c) const {
+  char32_t towupper(char32_t c) const {
     auto iter = find(c);
     if (iter == end())
       return c;
@@ -205,17 +224,18 @@ struct UTF8Mapper : std::map<wchar_t, wchar_t> { // Hack because Philly does not
       return iter->second;
   }
 };
+
 std::string utf8ToUpper(const std::string& s) {
   static UTF8Mapper mapper;
-  auto ws = utf8ToWString(s);
+  auto ws = utf8ToUnicodeString(s);
   for (auto& c : ws)
     c = mapper.towupper(c);
-  return toUTF8String(ws);
+  return utf8FromUnicodeString(ws);
 }
-#endif
 
 // convert an English sentence to title case
 // Since title case is an English thing, we only consider ASCII characters.
+// @BUGBUG: This only works for untokenized text, and is therefore useless.
 std::string toEnglishTitleCase(const std::string& s) {
   auto res = s;
   // process token by token
diff --git a/src/common/utils.h b/src/common/utils.h
index 6226ed992..651e91717 100755
--- a/src/common/utils.h
+++ b/src/common/utils.h
@@ -41,6 +41,10 @@ bool endsWith(const std::string& text, const std::string& suffix);
 std::string utf8ToUpper(const std::string& s);
 std::string toEnglishTitleCase(const std::string& s);
 
+std::u32string utf8ToUnicodeString(const std::string& s);
+std::string utf8FromUnicodeString(const std::u32string& s);
+bool isContinuousScript(char32_t c);
+
 std::string findReplace(const std::string& in, const std::string& what, const std::string& withWhat, bool all = false);
 
 double parseDouble(std::string s);
diff --git a/src/training/validator.cpp b/src/training/validator.cpp
index f6b1c86a1..4ce5a60fb 100755
--- a/src/training/validator.cpp
+++ b/src/training/validator.cpp
@@ -26,10 +26,13 @@ std::vector<Ptr<ValidatorBase/*<data::Corpus>*/>> Validators(
       auto validator = New<TranslationValidator>(vocabs, config);
       validators.push_back(validator);
     } else if(metric == "bleu") {
-      auto validator = New<BleuValidator>(vocabs, config, false);
+      auto validator = New<BleuValidator>(vocabs, config, BleuValidator::NoDetok);
       validators.push_back(validator);
     } else if(metric == "bleu-detok") {
-      auto validator = New<BleuValidator>(vocabs, config, true);
+      auto validator = New<BleuValidator>(vocabs, config, BleuValidator::DetokSacreBLEUWestern);
+      validators.push_back(validator);
+    } else if(metric == "bleu-detok-ms") {
+      auto validator = New<BleuValidator>(vocabs, config, BleuValidator::DetokMS);
       validators.push_back(validator);
     } else if(metric == "accuracy") {
       auto validator = New<AccuracyValidator>(vocabs, config);
diff --git a/src/training/validator.h b/src/training/validator.h
index f7f586f9d..c17b56070 100755
--- a/src/training/validator.h
+++ b/src/training/validator.h
@@ -446,6 +446,7 @@ class ScriptValidator : public Validator<data::Corpus, models::IModel> {
   }
 };
 
+// validator that translates and computes BLEU (or any metric) with an external script
 class TranslationValidator : public Validator<data::Corpus, models::IModel> {
 public:
   TranslationValidator(std::vector<Ptr<Vocab>> vocabs, Ptr<Options> options)
@@ -580,30 +581,26 @@ class TranslationValidator : public Validator<data::Corpus, models::IModel> {
   }
 };
 
+// validator that translates and computes BLEU internally, with or without decoding
 // @TODO: combine with TranslationValidator (above) to avoid code duplication
 class BleuValidator : public Validator<data::Corpus, models::IModel> {
-private:
-  bool detok_{false};
-
 public:
-  BleuValidator(std::vector<Ptr<Vocab>> vocabs, Ptr<Options> options, bool detok = false)
+  enum DetokType {
+    NoDetok,               // tokens are raw tokens as used for decoding
+    DetokSacreBLEUWestern, // tokens are SacreBleu-compatible, assuming Western languages and no text normalization
+    DetokMS                // MS-internal: tokens are DetokLatin1 except for continuous-script chars, which are counted as individual tokens
+  };
+
+  BleuValidator(std::vector<Ptr<Vocab>> vocabs, Ptr<Options> options, DetokType detok = NoDetok)
       : Validator(vocabs, options, false),
         detok_(detok),
         quiet_(options_->get<bool>("quiet-translation")) {
     builder_ = models::createModelFromOptions(options_, models::usage::translation);
 
-#ifdef USE_SENTENCEPIECE
     auto vocab = vocabs_.back();
-    ABORT_IF(detok_ && vocab->type() != "SentencePieceVocab",
-             "Detokenizing BLEU validator expects the target vocabulary to be SentencePieceVocab. "
+    ABORT_IF(detok_ != NoDetok && vocab->type() != "SentencePieceVocab" && vocab->type() != "FactoredVocab",
+             "Detokenizing BLEU validator expects the target vocabulary to be SentencePieceVocab or FactoredVocab. "
              "Current vocabulary type is {}", vocab->type());
-#else
-    ABORT_IF(detok_,
-             "Detokenizing BLEU validator expects the target vocabulary to be SentencePieceVocab. "
-             "Marian has not been compiled with SentencePieceVocab support");
-#endif
-
-    createBatchGenerator(/*isTranslating=*/true);
   }
 
   virtual float validate(const std::vector<Ptr<ExpressionGraph>>& graphs) override {
@@ -713,13 +710,19 @@ class BleuValidator : public Validator<data::Corpus, models::IModel> {
     return val;
   };
 
-  std::string type() override { return detok_ ? "bleu-detok" : "bleu"; }
+  // @TODO: why do we return this string, but not pass it to the constructor?
+  std::string type() override {
+    switch (detok_) {
+    case NoDetok:               return "bleu";
+    case DetokSacreBLEUWestern: return "bleu-detok";
+    case DetokMS:               return "bleu-detok-ms";
+    default: ABORT("Unexpected DetokType??");
+    }
+  }
 
 protected:
-  bool quiet_{false};
-
   // Tokenizer function adapted from multi-bleu-detok.pl, corresponds to sacreBLEU.py
-  std::string tokenize(const std::string& text) {
+  static std::string tokenizeSacreBLEUWestern(const std::string& text) {
     std::string normText = text;
 
     // language-independent part:
@@ -743,10 +746,34 @@ class BleuValidator : public Validator<data::Corpus, models::IModel> {
 
     return normText;
   }
+public:
+  static std::string tokenizeContinuousScript(const std::string& sUTF8) {
+    // We want BLEU-like scores that are comparable across different tokenization schemes.
+    // For continuous scripts )Chinese, Japanese, Thai), we would need a language-specific
+    // statistical word segmenter, which is outside the scope of Marian. As a practical
+    // compromise, we segment continuous-script sequences into individual characters, while
+    // leaving Western scripts as words. This way we can use the same settings for Western
+    // languages, where Marian would report SacreBLEU scores, and Asian languages, where
+    // scores are not standard but internally comparable across tokenization schemes.
+    auto in = utils::utf8ToUnicodeString(sUTF8);
+    auto out = in.substr(0, 0); // (out should be same type as in, don't want to bother with exact type)
+    for (auto c : in) {
+      auto isCS = utils::isContinuousScript(c);
+      if (isCS) // surround continuous-script chars by spaces on each side
+        out.push_back(' '); // (duplicate spaces are ignored when splitting later)
+      out.push_back(c);
+      if (isCS)
+        out.push_back(' ');
+    }
+    return utils::utf8FromUnicodeString(out);
+  }
 
   std::vector<std::string> decode(const Words& words, bool addEOS = false) {
     auto vocab = vocabs_.back();
-    auto tokens = utils::splitAny(tokenize(vocab->decode(words)), " ");
+    auto tokenString = tokenizeSacreBLEUWestern(vocab->decode(words));
+    if (detok_ == DetokMS) // score continuous-script sequences as individual characters
+      tokenString = tokenizeContinuousScript(tokenString);
+    auto tokens = utils::splitAny(tokenString, " ");
     if(addEOS)
       tokens.push_back("</s>");
     return tokens;
@@ -834,6 +861,10 @@ class BleuValidator : public Validator<data::Corpus, models::IModel> {
   virtual float validateBG(const std::vector<Ptr<ExpressionGraph>>& /*graphs*/) override {
     return 0;
   }
+
+private:
+  DetokType detok_;
+  bool quiet_{ false };
 };
 
 /**

From 5a10abd5b7b1dbcbcf363f636e5448019ef2057d Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Wed, 27 Mar 2019 10:25:53 -0700
Subject: [PATCH 390/838] temporarily interpreting 'bleu' validator as
 'bleu-detok-ms'. so that it is used without changing Flo

---
 src/training/validator.cpp | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/training/validator.cpp b/src/training/validator.cpp
index 4ce5a60fb..ed32964a7 100755
--- a/src/training/validator.cpp
+++ b/src/training/validator.cpp
@@ -26,7 +26,7 @@ std::vector<Ptr<ValidatorBase/*<data::Corpus>*/>> Validators(
       auto validator = New<TranslationValidator>(vocabs, config);
       validators.push_back(validator);
     } else if(metric == "bleu") {
-      auto validator = New<BleuValidator>(vocabs, config, BleuValidator::NoDetok);
+      auto validator = New<BleuValidator>(vocabs, config, BleuValidator::DetokMS); // TEMPORARILY until Flo is updated. Then change back to NoDetok
       validators.push_back(validator);
     } else if(metric == "bleu-detok") {
       auto validator = New<BleuValidator>(vocabs, config, BleuValidator::DetokSacreBLEUWestern);

From 4895cdb28fb0dbdedce25622ec3060a930ac0c75 Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Wed, 27 Mar 2019 10:28:01 -0700
Subject: [PATCH 391/838] (minor fix)

---
 src/training/validator.h | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/training/validator.h b/src/training/validator.h
index c17b56070..155a3721b 100755
--- a/src/training/validator.h
+++ b/src/training/validator.h
@@ -838,7 +838,7 @@ class BleuValidator : public Validator<data::Corpus, models::IModel> {
       ref.push_back(w);
     }
 
-    if(detok_)
+    if(detok_ != NoDetok)
       updateStats(stats, decode(cand, /*addEOS=*/ true), decode(ref));
     else
       updateStats(stats, cand, ref);

From a9e2447a2d7ed1f0c9cf208c4ed1ecfa5fddc3d4 Mon Sep 17 00:00:00 2001
From: Marcin Junczys-Dowmunt <Marcin.JunczysDowmunt@microsoft.com>
Date: Wed, 27 Mar 2019 13:27:25 -0700
Subject: [PATCH 392/838] write *.bin suffix whenever a model is saved and
 --model *.bin is specified

---
 src/training/graph_group_sync.cpp | 23 +++++++++++++++++------
 src/training/validator.h          | 10 ++++++++--
 2 files changed, 25 insertions(+), 8 deletions(-)

diff --git a/src/training/graph_group_sync.cpp b/src/training/graph_group_sync.cpp
index ba5751691..ba451a22f 100755
--- a/src/training/graph_group_sync.cpp
+++ b/src/training/graph_group_sync.cpp
@@ -75,7 +75,10 @@ void SyncGraphGroup::initialize(const Ptr<data::Batch>& exampleBatch) {
 void SyncGraphGroup::initializeAvg() {
   Ptr<ExpressionGraph> graphAvg; // CPU-side temp
   std::string name = options_->get<std::string>("model");
-  if(filesystem::exists(name + ".orig.npz")) {
+  std::string suffix = name.substr(name.size() - 4);
+  ABORT_IF(suffix != ".npz" && suffix != ".bin", "Unknown model suffix {}", suffix);
+
+  if(filesystem::exists(name + ".orig" + suffix)) {
     // Load the averaged parameters into a temporary graph
     graphAvg = New<ExpressionGraph>();
     graphAvg->setDevice({0, DeviceType::cpu});
@@ -436,9 +439,12 @@ void SyncGraphGroup::load() /*override*/ {
         scheduler_->load(name);
 
       std::string nameGraph = name;
-      if(mvAvg_ && filesystem::exists(name + ".orig.npz"))
+      std::string suffix = name.substr(name.size() - 4);
+      ABORT_IF(suffix != ".npz" && suffix != ".bin", "Unknown model suffix {}", suffix);
+
+      if(mvAvg_ && filesystem::exists(name + ".orig" + suffix))
         // Load the original parameters from model.npz.orig.npz
-        nameGraph += ".orig.npz";
+        nameGraph += ".orig" + suffix;
 
       size_t i = 0;
       for(auto graph : graphs_)
@@ -448,7 +454,7 @@ void SyncGraphGroup::load() /*override*/ {
       std::vector<Ptr<Backend>> backends;
       for(auto graph : graphs_)
         backends.push_back(graph->getBackend());
-      shardOpt_[0]->load(name + ".optimizer.npz", shardOpt_, backends,
+      shardOpt_[0]->load(name + ".optimizer.npz", shardOpt_, backends, // keep npz suffix for optimize checkpoint
         [&](const std::vector<float>& optimizerStateVector, const OptimizerBase::ScatterStateSetFunc& setShardFn) {
           comm_->scatterState(optimizerStateVector, setShardFn);
         });
@@ -480,13 +486,18 @@ void SyncGraphGroup::save(bool final) /*override*/ {
     swapParamsAvg();
   }
 
+  // @TODO: put all this in one place, in new branch this is already localized in one place and class, this is a quick hack which will be 
+  // done better after the next merge. Not doing this in other graph_groups as this would only make the merge harder. 
+  // Determine model suffix *.npz or *.bin, then use the same suffix for all following models saved.
   std::string name = options_->get<std::string>("model");
+  std::string suffix = name.substr(name.size() - 4);
+  ABORT_IF(suffix != ".npz" && suffix != ".bin", "Unknown model suffix {}", suffix);
 
   barrier(); // (for better grouping of log messages)
   // if smoothing then save original (unsmoothed) parameters as well
   if(mvAvg_ && paramsAvg_.size() > 0 && isMainProcess()) // only save from one MPI process
     // Save the original parameters in model.npz.orig.npz
-    builders_[0]->save(graphs_[0], name + ".orig.npz", true);
+    builders_[0]->save(graphs_[0], name + ".orig" + suffix, true);
 
   // Temporarily switch to the averaged parameters
   // Note: the smoothed model is sharded across GPUs, and across MPI processes if applicable. This brings it into MPI process[*].device[*]
@@ -500,7 +511,7 @@ void SyncGraphGroup::save(bool final) /*override*/ {
           = scheduler_ ? std::to_string(scheduler_->numberOfBatches())
                        : "unknown";
       std::string nameOverwrite = name;
-      nameOverwrite.replace(name.size() - 4, 4, ".iter" + numberOfBatches + ".npz"); // @TODO: use insert?
+      nameOverwrite.replace(name.size() - 4, 4, ".iter" + numberOfBatches + suffix); // @TODO: use insert?
       builders_[0]->save(graphs_[0], nameOverwrite);
     }
     // save main model file
diff --git a/src/training/validator.h b/src/training/validator.h
index 46c58825d..582c232f6 100755
--- a/src/training/validator.h
+++ b/src/training/validator.h
@@ -144,7 +144,10 @@ class Validator : public ValidatorBase {
 
   virtual void keepBest(const std::vector<Ptr<ExpressionGraph>>& graphs) {
     auto model = options_->get<std::string>("model");
-    builder_->save(graphs[0], model + ".best-" + type() + ".npz", true);
+    std::string suffix = model.substr(model.size() - 4);
+    ABORT_IF(suffix != ".npz" && suffix != ".bin", "Unknown model suffix {}", suffix);
+
+    builder_->save(graphs[0], model + ".best-" + type() + suffix, true);
   }
 };
 
@@ -428,7 +431,10 @@ class ScriptValidator : public Validator<data::Corpus, models::IModel> {
   virtual float validate(const std::vector<Ptr<ExpressionGraph>>& graphs) override {
     using namespace data;
     auto model = options_->get<std::string>("model");
-    builder_->save(graphs[0], model + ".dev.npz", true);
+    std::string suffix = model.substr(model.size() - 4);
+    ABORT_IF(suffix != ".npz" && suffix != ".bin", "Unknown model suffix {}", suffix);
+
+    builder_->save(graphs[0], model + ".dev" + suffix, true);
 
     auto command = options_->get<std::string>("valid-script-path");
     auto valStr = utils::exec(command);

From ff0bd5f9396cdedae9a4913c89c4e8e1fd94d202 Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Thu, 28 Mar 2019 17:13:25 -0700
Subject: [PATCH 393/838] bug fix: _GNUC_ should be __GNUC__;
 SentencePieceVocab now supports all-caps and title-casing; bleu-detok now
 supports FactoredVocab via the new IVocab::surfaceForm() method

---
 src/common/definitions.h         |  2 +-
 src/common/timer.h               |  4 ++--
 src/common/utils.cpp             | 33 ++++++++++++++++++---------
 src/common/utils.h               |  2 ++
 src/data/default_vocab.cpp       |  5 ++++
 src/data/factored_vocab.cpp      | 39 +++++++++++++++++++++++++++++++-
 src/data/factored_vocab.h        |  2 ++
 src/data/sentencepiece_vocab.cpp |  7 ++++++
 src/data/vocab.cpp               |  9 +++++++-
 src/data/vocab.h                 |  6 ++++-
 src/data/vocab_base.h            |  7 ++++--
 src/training/scheduler.h         |  0
 src/training/validator.cpp       |  7 ++----
 src/training/validator.h         | 32 ++++++++------------------
 14 files changed, 109 insertions(+), 46 deletions(-)
 mode change 100644 => 100755 src/common/timer.h
 mode change 100644 => 100755 src/training/scheduler.h

diff --git a/src/common/definitions.h b/src/common/definitions.h
index 393a8b7fe..3fdf6659f 100755
--- a/src/common/definitions.h
+++ b/src/common/definitions.h
@@ -14,7 +14,7 @@
 #define NodeOp(op) [=]() { op; }
 
 // helper macro to disable optimization (gcc only)
-#ifdef _GNUC_
+#ifdef __GNUC__
 #define DONT_OPTIMIZE __attribute__((optimize("O0")))
 #else
 #define DONT_OPTIMIZE // silently ignore on Visual Studio, where this is less of a problem
diff --git a/src/common/timer.h b/src/common/timer.h
old mode 100644
new mode 100755
index dfb91bf01..fd9307dfc
--- a/src/common/timer.h
+++ b/src/common/timer.h
@@ -1,11 +1,11 @@
 #pragma once
 
-#ifdef _GNUC_
+#ifdef __GNUC__
 #pragma GCC diagnostic push
 #pragma GCC diagnostic ignored "-Wsuggest-override"
 #endif
 #include <boost/timer/timer.hpp>
-#ifdef _GNUC_
+#ifdef __GNUC__
 #pragma GCC diagnostic pop
 #endif
 
diff --git a/src/common/utils.cpp b/src/common/utils.cpp
index 958dfc77a..aacc0c602 100755
--- a/src/common/utils.cpp
+++ b/src/common/utils.cpp
@@ -193,8 +193,9 @@ bool isContinuousScript(char32_t c) {
   return isHan || isKana || isThai;
 }
 
-// convert a UTF-8 string to all-caps
-struct UTF8Mapper : std::map<char32_t, char32_t> { // Hack because MS-internal Philly servers do not have UTF-8 locale installed
+// convert UTF-8 characters to lower or upper case
+struct UTF8Mapper { // can't use the standard lib functions because MS-internal Philly servers do not have UTF-8 locale installed
+  std::map<char32_t, char32_t> toUpperMap, toLowerMap;
   UTF8Mapper() {
     /*
       env LC_ALL=en_US.UTF-8 sed 's/\(.\)/\1\n/g'   TEXT_FILE_CONTAINING_ALL_CHARS > l
@@ -213,29 +214,39 @@ struct UTF8Mapper : std::map<char32_t, char32_t> { // Hack because MS-internal P
       auto from = utf8ToUnicodeString(p8.first);
       auto to   = utf8ToUnicodeString(p8.second);
       ABORT_IF(from.size() != 1 || to.size() != 1, "Incorrect character encoding??");
-      insert(std::make_pair(from.front(), to.front()));
+      toUpperMap.insert(std::make_pair(from.front(), to.front()));
+      toLowerMap.insert(std::make_pair(to.front(), from.front()));
     }
   }
-  char32_t towupper(char32_t c) const {
-    auto iter = find(c);
-    if (iter == end())
+  char32_t toUpperOrLower(char32_t c, bool toLower) const { return mapChar(toLower ? toLowerMap : toUpperMap, c); }
+private:
+  static char32_t mapChar(const std::map<char32_t, char32_t>& map, char32_t c) {
+    auto iter = map.find(c);
+    if (iter == map.end())
       return c;
     else
       return iter->second;
   }
 };
 
-std::string utf8ToUpper(const std::string& s) {
-  static UTF8Mapper mapper;
+// shared implementation of toUpper, toLower, and toCapitalized
+static std::string utf8ToUpperOrLower(const std::string& s, bool toLower, bool toInitCap) {
+  static UTF8Mapper utf8Mapper;
   auto ws = utf8ToUnicodeString(s);
-  for (auto& c : ws)
-    c = mapper.towupper(c);
+  for (auto& c : ws) {
+    c = utf8Mapper.toUpperOrLower(c, toLower);
+    if (toInitCap)
+      toLower = true;
+  }
   return utf8FromUnicodeString(ws);
 }
 
+std::string utf8ToUpper(const std::string& s)     { return utf8ToUpperOrLower(s, /*toLower=*/false, /*toInitCap=*/false); }
+std::string utf8ToLower(const std::string& s)     { return utf8ToUpperOrLower(s, /*toLower=*/true , /*toInitCap=*/false); }
+std::string utf8Capitalized(const std::string& s) { return utf8ToUpperOrLower(s, /*toLower=*/false, /*toInitCap=*/true ); }
+
 // convert an English sentence to title case
 // Since title case is an English thing, we only consider ASCII characters.
-// @BUGBUG: This only works for untokenized text, and is therefore useless.
 std::string toEnglishTitleCase(const std::string& s) {
   auto res = s;
   // process token by token
diff --git a/src/common/utils.h b/src/common/utils.h
index 651e91717..50ed8a580 100755
--- a/src/common/utils.h
+++ b/src/common/utils.h
@@ -39,6 +39,8 @@ bool beginsWith(const std::string& text, const std::string& prefix);
 bool endsWith(const std::string& text, const std::string& suffix);
 
 std::string utf8ToUpper(const std::string& s);
+std::string utf8ToLower(const std::string& s);
+std::string utf8Capitalized(const std::string& word); // capitalize the first character only
 std::string toEnglishTitleCase(const std::string& s);
 
 std::u32string utf8ToUnicodeString(const std::string& s);
diff --git a/src/data/default_vocab.cpp b/src/data/default_vocab.cpp
index 25aaff95f..b40c0fd92 100755
--- a/src/data/default_vocab.cpp
+++ b/src/data/default_vocab.cpp
@@ -44,6 +44,7 @@ class DefaultVocab : public IVocab {
   };
 
 public:
+  // @TODO: choose between 'virtual' and 'final'. Can we derive from this class?
   virtual const std::string& canonicalExtension() const override { return suffixes_[0]; }
   virtual const std::vector<std::string>& suffixes() const override { return suffixes_; }
 
@@ -65,6 +66,10 @@ class DefaultVocab : public IVocab {
     return utils::join(tokens, " ");
   }
 
+  std::string surfaceForm(const Words& sentence) const override {
+    ABORT("surfaceForm() not supported by this vocabulary type");
+  }
+
   virtual std::string type() const override { return "DefaultVocab"; }
 
   virtual Word getEosId() const override { return eosId_; }
diff --git a/src/data/factored_vocab.cpp b/src/data/factored_vocab.cpp
index 69172edea..6b1906c88 100755
--- a/src/data/factored_vocab.cpp
+++ b/src/data/factored_vocab.cpp
@@ -435,7 +435,7 @@ void FactoredVocab::constructNormalizationInfoForVocab() {
     return Word::fromWordIndex(index);
   else
     //ABORT("Unknown word {} mapped to {}", word, word2string(getUnkId()));
-    LOG(info, "WARNING: Unknown word {} mapped to {}", word, word2string(getUnkId()));
+    LOG_ONCE(info, "WARNING: Unknown word {} mapped to {}", word, word2string(getUnkId()));
     return getUnkId();
 }
 
@@ -501,6 +501,43 @@ void FactoredVocab::constructNormalizationInfoForVocab() {
   return utils::join(decoded, " ");
 }
 
+// interpret the capitalization and glue factors
+// This assumes a specific notation of factors, emulating our C# code for generating these factors:
+//  - | as separator symbol
+//  - capitalization factors are cn, ci, and ca
+//  - glue factors are gl+, gr+, wbn, wen, cbn, cen
+std::string FactoredVocab::surfaceForm(const Words& sentence) const /*override final*/ {
+  std::string res;
+  res.reserve(sentence.size() * 10);
+  bool prevHadGlueRight = true; // no space at sentence start
+  for(auto w : sentence) {
+    auto token = (*this)[w];
+    if (w == getEosId())
+      break;
+    auto tokens = utils::split(token, "|");
+    //std::cerr << token << " ";
+    const auto& lemma = tokens[0];
+    std::set<std::string> tokenSet(tokens.begin() + 1, tokens.end());
+    auto has = [&](const char* factor) { return tokenSet.find(factor) != tokenSet.end(); };
+    // spacing
+    bool hasGlueRight = has("gr+") || has("wen") || has("cen");
+    bool hasGlueLeft  = has("gl+") || has("wbn") || has("cbn");
+    bool insertSpaceBefore = !prevHadGlueRight && !hasGlueLeft;
+    if (insertSpaceBefore)
+      res.push_back(' ');
+    prevHadGlueRight = hasGlueRight;
+    // capitalization
+    std::string surfaceForm;
+    if      (has("ci")) surfaceForm = utils::utf8Capitalized(lemma);
+    else if (has("ca")) surfaceForm = utils::utf8ToUpper    (lemma);
+    else if (has("cn")) surfaceForm = utils::utf8ToLower    (lemma);
+    else                surfaceForm =                        lemma ;
+    res.append(surfaceForm);
+  }
+  //std::cerr << "\n" << res << "\n";
+  return res;
+}
+
 // create a CSR matrix M[V,U] from words[] with
 // M[v,u] = 1/c(u) if factor u is a factor of word v, and c(u) is how often u is referenced
 FactoredVocab::CSRData FactoredVocab::csr_rows(const Words& words) const {
diff --git a/src/data/factored_vocab.h b/src/data/factored_vocab.h
index 220979e81..d6bb8a21f 100755
--- a/src/data/factored_vocab.h
+++ b/src/data/factored_vocab.h
@@ -23,6 +23,7 @@ class FactoredVocab : public IVocab {
   };
 
   // from IVocab:
+  // @TODO: Why are these virtual and final at the same time? Seems we should remove all the 'virtual' here
   virtual size_t load(const std::string& factoredVocabPath, size_t maxSizeUnused = 0) override final;
   virtual void create(const std::string& vocabPath, const std::vector<std::string>& trainPaths, size_t maxSize) override final { vocabPath, trainPaths, maxSize; ABORT("Factored vocab cannot be created on the fly"); }
   virtual const std::string& canonicalExtension() const override final { return suffixes()[0]; }
@@ -30,6 +31,7 @@ class FactoredVocab : public IVocab {
   virtual Word operator[](const std::string& word) const override final;
   virtual Words encode(const std::string& line, bool addEOS = true, bool inference = false) const override final;
   virtual std::string decode(const Words& sentence, bool ignoreEos = true) const override final;
+  virtual std::string surfaceForm(const Words& sentence) const override final;
   virtual const std::string& operator[](Word id) const override final;
   virtual size_t size() const override final;
   virtual std::string type() const override final { return "FactoredVocab"; }
diff --git a/src/data/sentencepiece_vocab.cpp b/src/data/sentencepiece_vocab.cpp
index b2b829019..0e8b48034 100755
--- a/src/data/sentencepiece_vocab.cpp
+++ b/src/data/sentencepiece_vocab.cpp
@@ -231,6 +231,11 @@ class SentencePieceVocab : public IVocab {
     return line;
   }
 
+  std::string surfaceForm(const Words& sentence) const override {
+    // with SentencePiece, decoded form and surface form are identical
+    return decode(sentence, /*ignoreEOS=*/true);
+  }
+
   size_t size() const override {
     return spm_->GetPieceSize();
   }
@@ -252,6 +257,8 @@ class SentencePieceVocab : public IVocab {
     return spm_->GetPieceSize();
   }
 
+  std::string toUpper(const std::string& line) const override { return utils::utf8ToUpper(line); }
+  std::string toEnglishTitleCase(const std::string& line) const override { return utils::toEnglishTitleCase(line); }
 };
 #endif // USE_SENTENCEPIECE
 
diff --git a/src/data/vocab.cpp b/src/data/vocab.cpp
index 9c1039d81..2cafb3313 100755
--- a/src/data/vocab.cpp
+++ b/src/data/vocab.cpp
@@ -112,12 +112,19 @@ Words Vocab::encode(const std::string& line,
   return vImpl_->encode(line, addEOS, inference);
 }
 
-// list of token ids to single line, can perform detokenization
+// convert sequence of token ids to single line, can perform detokenization
 std::string Vocab::decode(const Words& sentence,
                     bool ignoreEOS) const {
   return vImpl_->decode(sentence, ignoreEOS);
 }
 
+// convert sequence of token its to surface form (incl. removng spaces, applying factors)
+// for in-process BLEU validation
+std::string Vocab::surfaceForm(const Words& sentence) const {
+  return vImpl_->surfaceForm(sentence);
+}
+
+
 // number of vocabulary items
 size_t Vocab::size() const { return vImpl_->size(); }
 
diff --git a/src/data/vocab.h b/src/data/vocab.h
index 81f97a618..1de93f5b4 100755
--- a/src/data/vocab.h
+++ b/src/data/vocab.h
@@ -50,10 +50,14 @@ class Vocab {
                bool addEOS = true,
                bool inference = false) const;
 
-  // list of token ids to single line, can perform detokenization
+  // convert sequence of token ids to single line, can perform detokenization
   std::string decode(const Words& sentence,
                      bool ignoreEOS = true) const;
 
+  // convert sequence of token its to surface form (incl. removng spaces, applying factors)
+  // for in-process BLEU validation
+  std::string surfaceForm(const Words& sentence) const;
+
   // number of vocabulary items
   size_t size() const;
 
diff --git a/src/data/vocab_base.h b/src/data/vocab_base.h
index 240626655..bf276f5de 100755
--- a/src/data/vocab_base.h
+++ b/src/data/vocab_base.h
@@ -34,6 +34,7 @@ class IVocab {
 
   virtual std::string decode(const Words& sentence,
                              bool ignoreEos = true) const = 0;
+  virtual std::string surfaceForm(const Words& sentence) const = 0;
 
   virtual const std::string& operator[](Word id) const = 0;
 
@@ -43,8 +44,10 @@ class IVocab {
   virtual Word getEosId() const = 0;
   virtual Word getUnkId() const = 0;
 
-  virtual std::string toUpper(const std::string& line) const { return utils::utf8ToUpper(line); }
-  virtual std::string toEnglishTitleCase(const std::string& line) const { return utils::toEnglishTitleCase(line); }
+  // without specific knowledge of tokenization, these two functions can do nothing
+  // Both SentencePieceVocab and FactoredSegmenterVocab
+  virtual std::string toUpper(const std::string& line) const { return line; }
+  virtual std::string toEnglishTitleCase(const std::string& line) const { return line; }
 
   virtual void createFake() = 0;
 
diff --git a/src/training/scheduler.h b/src/training/scheduler.h
old mode 100644
new mode 100755
diff --git a/src/training/validator.cpp b/src/training/validator.cpp
index ed32964a7..f6b1c86a1 100755
--- a/src/training/validator.cpp
+++ b/src/training/validator.cpp
@@ -26,13 +26,10 @@ std::vector<Ptr<ValidatorBase/*<data::Corpus>*/>> Validators(
       auto validator = New<TranslationValidator>(vocabs, config);
       validators.push_back(validator);
     } else if(metric == "bleu") {
-      auto validator = New<BleuValidator>(vocabs, config, BleuValidator::DetokMS); // TEMPORARILY until Flo is updated. Then change back to NoDetok
+      auto validator = New<BleuValidator>(vocabs, config, false);
       validators.push_back(validator);
     } else if(metric == "bleu-detok") {
-      auto validator = New<BleuValidator>(vocabs, config, BleuValidator::DetokSacreBLEUWestern);
-      validators.push_back(validator);
-    } else if(metric == "bleu-detok-ms") {
-      auto validator = New<BleuValidator>(vocabs, config, BleuValidator::DetokMS);
+      auto validator = New<BleuValidator>(vocabs, config, true);
       validators.push_back(validator);
     } else if(metric == "accuracy") {
       auto validator = New<AccuracyValidator>(vocabs, config);
diff --git a/src/training/validator.h b/src/training/validator.h
index 155a3721b..37a583b32 100755
--- a/src/training/validator.h
+++ b/src/training/validator.h
@@ -585,22 +585,18 @@ class TranslationValidator : public Validator<data::Corpus, models::IModel> {
 // @TODO: combine with TranslationValidator (above) to avoid code duplication
 class BleuValidator : public Validator<data::Corpus, models::IModel> {
 public:
-  enum DetokType {
-    NoDetok,               // tokens are raw tokens as used for decoding
-    DetokSacreBLEUWestern, // tokens are SacreBleu-compatible, assuming Western languages and no text normalization
-    DetokMS                // MS-internal: tokens are DetokLatin1 except for continuous-script chars, which are counted as individual tokens
-  };
-
-  BleuValidator(std::vector<Ptr<Vocab>> vocabs, Ptr<Options> options, DetokType detok = NoDetok)
+  BleuValidator(std::vector<Ptr<Vocab>> vocabs, Ptr<Options> options, bool detok = false)
       : Validator(vocabs, options, false),
         detok_(detok),
         quiet_(options_->get<bool>("quiet-translation")) {
     builder_ = models::createModelFromOptions(options_, models::usage::translation);
 
     auto vocab = vocabs_.back();
-    ABORT_IF(detok_ != NoDetok && vocab->type() != "SentencePieceVocab" && vocab->type() != "FactoredVocab",
+    ABORT_IF(detok_ && vocab->type() != "SentencePieceVocab" && vocab->type() != "FactoredVocab",
              "Detokenizing BLEU validator expects the target vocabulary to be SentencePieceVocab or FactoredVocab. "
              "Current vocabulary type is {}", vocab->type());
+
+    createBatchGenerator(/*isTranslating=*/true);
   }
 
   virtual float validate(const std::vector<Ptr<ExpressionGraph>>& graphs) override {
@@ -711,18 +707,11 @@ class BleuValidator : public Validator<data::Corpus, models::IModel> {
   };
 
   // @TODO: why do we return this string, but not pass it to the constructor?
-  std::string type() override {
-    switch (detok_) {
-    case NoDetok:               return "bleu";
-    case DetokSacreBLEUWestern: return "bleu-detok";
-    case DetokMS:               return "bleu-detok-ms";
-    default: ABORT("Unexpected DetokType??");
-    }
-  }
+  std::string type() override { return detok_ ? "bleu-detok" : "bleu"; }
 
 protected:
   // Tokenizer function adapted from multi-bleu-detok.pl, corresponds to sacreBLEU.py
-  static std::string tokenizeSacreBLEUWestern(const std::string& text) {
+  static std::string tokenize(const std::string& text) {
     std::string normText = text;
 
     // language-independent part:
@@ -770,9 +759,8 @@ class BleuValidator : public Validator<data::Corpus, models::IModel> {
 
   std::vector<std::string> decode(const Words& words, bool addEOS = false) {
     auto vocab = vocabs_.back();
-    auto tokenString = tokenizeSacreBLEUWestern(vocab->decode(words));
-    if (detok_ == DetokMS) // score continuous-script sequences as individual characters
-      tokenString = tokenizeContinuousScript(tokenString);
+    auto tokenString = tokenize(vocab->surfaceForm(words));
+    tokenString = tokenizeContinuousScript(tokenString);
     auto tokens = utils::splitAny(tokenString, " ");
     if(addEOS)
       tokens.push_back("</s>");
@@ -838,7 +826,7 @@ class BleuValidator : public Validator<data::Corpus, models::IModel> {
       ref.push_back(w);
     }
 
-    if(detok_ != NoDetok)
+    if(detok_)
       updateStats(stats, decode(cand, /*addEOS=*/ true), decode(ref));
     else
       updateStats(stats, cand, ref);
@@ -863,7 +851,7 @@ class BleuValidator : public Validator<data::Corpus, models::IModel> {
   }
 
 private:
-  DetokType detok_;
+  bool detok_;
   bool quiet_{ false };
 };
 

From aece1e3f91a23291249658c05e961a2b4f65dbed Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Thu, 28 Mar 2019 18:55:07 -0700
Subject: [PATCH 394/838] bug fix: surfaceForm() should also unescape \x and \u
 sequences

---
 src/common/utils.cpp        | 22 ++++++++++++++++++++++
 src/common/utils.h          |  2 ++
 src/data/default_vocab.cpp  |  1 +
 src/data/factored_vocab.cpp | 35 +++++++++++++++++++++++++++--------
 4 files changed, 52 insertions(+), 8 deletions(-)

diff --git a/src/common/utils.cpp b/src/common/utils.cpp
index aacc0c602..3fd0bdead 100755
--- a/src/common/utils.cpp
+++ b/src/common/utils.cpp
@@ -177,6 +177,28 @@ std::string utf8FromUnicodeString(const std::u32string& s) {
 #endif
 }
 
+std::u16string utf8ToUtf16String(std::string const& s) {
+#ifdef _MSC_VER // workaround for a known bug in VS CRT
+  std::wstring_convert<std::codecvt_utf8<wchar_t/*char16_t*/>, wchar_t/*char16_t*/> converter;
+  auto res = converter.from_bytes(s);
+  return std::u16string(res.begin(), res.end());
+#else
+  std::wstring_convert<std::codecvt_utf8<char16_t>, char16_t> converter;
+  return converter.from_bytes(s);
+#endif
+}
+
+std::string utf8FromUtf16String(const std::u16string& s) {
+#ifdef _MSC_VER // workaround for a known bug in VS CRT
+  std::wstring_convert<std::codecvt_utf8<wchar_t/*char16_t*/>, wchar_t/*char16_t*/> converter;
+  std::basic_string<wchar_t> si(s.begin(), s.end());
+  return converter.to_bytes(si);
+#else
+  std::wstring_convert<std::codecvt_utf8<char16_t>, char16_t> converter;
+  return converter.to_bytes(s);
+#endif
+}
+
 bool isContinuousScript(char32_t c) {
   // currently, this table is hand-coded, and may need to be extended when the standard grows
   auto in = [c](char32_t minVal, char32_t maxVal) { return c >= minVal && c <= maxVal; };
diff --git a/src/common/utils.h b/src/common/utils.h
index 50ed8a580..c3266bbf4 100755
--- a/src/common/utils.h
+++ b/src/common/utils.h
@@ -45,6 +45,8 @@ std::string toEnglishTitleCase(const std::string& s);
 
 std::u32string utf8ToUnicodeString(const std::string& s);
 std::string utf8FromUnicodeString(const std::u32string& s);
+std::u16string utf8ToUtf16String(const std::string& s);
+std::string utf8FromUtf16String(const std::u16string& s);
 bool isContinuousScript(char32_t c);
 
 std::string findReplace(const std::string& in, const std::string& what, const std::string& withWhat, bool all = false);
diff --git a/src/data/default_vocab.cpp b/src/data/default_vocab.cpp
index b40c0fd92..60bd8b10f 100755
--- a/src/data/default_vocab.cpp
+++ b/src/data/default_vocab.cpp
@@ -67,6 +67,7 @@ class DefaultVocab : public IVocab {
   }
 
   std::string surfaceForm(const Words& sentence) const override {
+    sentence;
     ABORT("surfaceForm() not supported by this vocabulary type");
   }
 
diff --git a/src/data/factored_vocab.cpp b/src/data/factored_vocab.cpp
index 6b1906c88..04f625389 100755
--- a/src/data/factored_vocab.cpp
+++ b/src/data/factored_vocab.cpp
@@ -501,6 +501,25 @@ void FactoredVocab::constructNormalizationInfoForVocab() {
   return utils::join(decoded, " ");
 }
 
+// helper to unescape \x.. and \u....
+static void unescapeHexEscapes(std::string& utf8Lemma) {
+  if (utf8Lemma.find('\\') == std::string::npos)
+    return; // nothing to do
+  auto lemma = utils::utf8ToUtf16String(utf8Lemma); // \u.... implies we must operate on UTF-16 level
+  auto pos = lemma.find('\\');
+  while (pos != std::string::npos) {
+    ABORT_IF(pos + 1 >= lemma.size() || (lemma[pos+1] != 'x' && lemma[pos + 1] != 'u'), "Malformed escape in factored encoding: {}", utf8Lemma);
+    int numDigits = 2 + (lemma[pos + 1] == 'u');
+    ABORT_IF(pos + 2 + numDigits > lemma.size(), "Malformed escape in factored encoding: {}", utf8Lemma);
+    auto digits = utils::utf8FromUtf16String(lemma.substr(pos + 2, numDigits));
+    auto c = std::strtoul(digits.c_str(), nullptr, 16);
+    lemma[pos] = (char16_t)c;
+    lemma.erase(pos + 1, 1 + numDigits);
+    pos = lemma.find('\\', pos+1);
+  }
+  utf8Lemma = utils::utf8FromUtf16String(lemma);
+}
+
 // interpret the capitalization and glue factors
 // This assumes a specific notation of factors, emulating our C# code for generating these factors:
 //  - | as separator symbol
@@ -511,12 +530,12 @@ std::string FactoredVocab::surfaceForm(const Words& sentence) const /*override f
   res.reserve(sentence.size() * 10);
   bool prevHadGlueRight = true; // no space at sentence start
   for(auto w : sentence) {
-    auto token = (*this)[w];
     if (w == getEosId())
       break;
+    auto token = (*this)[w];
     auto tokens = utils::split(token, "|");
     //std::cerr << token << " ";
-    const auto& lemma = tokens[0];
+    auto lemma = tokens[0];
     std::set<std::string> tokenSet(tokens.begin() + 1, tokens.end());
     auto has = [&](const char* factor) { return tokenSet.find(factor) != tokenSet.end(); };
     // spacing
@@ -527,12 +546,12 @@ std::string FactoredVocab::surfaceForm(const Words& sentence) const /*override f
       res.push_back(' ');
     prevHadGlueRight = hasGlueRight;
     // capitalization
-    std::string surfaceForm;
-    if      (has("ci")) surfaceForm = utils::utf8Capitalized(lemma);
-    else if (has("ca")) surfaceForm = utils::utf8ToUpper    (lemma);
-    else if (has("cn")) surfaceForm = utils::utf8ToLower    (lemma);
-    else                surfaceForm =                        lemma ;
-    res.append(surfaceForm);
+    unescapeHexEscapes(lemma); // unescape \x.. and \u....
+    if      (has("ci")) lemma = utils::utf8Capitalized(lemma);
+    else if (has("ca")) lemma = utils::utf8ToUpper    (lemma);
+    else if (has("cn")) lemma = utils::utf8ToLower    (lemma);
+    else                lemma =                        lemma ;
+    res.append(lemma);
   }
   //std::cerr << "\n" << res << "\n";
   return res;

From db3a73212eefaa0e261505f1a10500098878f378 Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Thu, 28 Mar 2019 20:09:26 -0700
Subject: [PATCH 395/838] temporarily setting detok_ to true for
 FactoredSegmenter vocabs

---
 src/training/validator.h | 4 ++++
 1 file changed, 4 insertions(+)

diff --git a/src/training/validator.h b/src/training/validator.h
index 37a583b32..e06f4b57e 100755
--- a/src/training/validator.h
+++ b/src/training/validator.h
@@ -592,6 +592,10 @@ class BleuValidator : public Validator<data::Corpus, models::IModel> {
     builder_ = models::createModelFromOptions(options_, models::usage::translation);
 
     auto vocab = vocabs_.back();
+#if 1 // hack for now, to get this feature when running under Flo
+    if (vocab->type() == "FactoredVocab")
+        detok_ = true; // always use bleu-detok
+#endif
     ABORT_IF(detok_ && vocab->type() != "SentencePieceVocab" && vocab->type() != "FactoredVocab",
              "Detokenizing BLEU validator expects the target vocabulary to be SentencePieceVocab or FactoredVocab. "
              "Current vocabulary type is {}", vocab->type());

From 9f3337b1023ac84e8070ec2917ccebe50493b2d9 Mon Sep 17 00:00:00 2001
From: Ulrich Germann <ugermann@inf.ed.ac.uk>
Date: Mon, 1 Apr 2019 17:02:46 +0100
Subject: [PATCH 396/838] Added explanatory comment on validate().

---
 src/training/graph_group_sync.cpp | 4 ++++
 1 file changed, 4 insertions(+)

diff --git a/src/training/graph_group_sync.cpp b/src/training/graph_group_sync.cpp
index c02a34afa..d6a1c3217 100644
--- a/src/training/graph_group_sync.cpp
+++ b/src/training/graph_group_sync.cpp
@@ -527,6 +527,10 @@ void SyncGraphGroup::save(bool final) /*override*/ {
 
 void SyncGraphGroup::finalize() /*override*/ {
   validate();
+  // Note to the uninitiated: validate() has nothing to do with
+  // validation on the validation set. It's just a sanity check. [UG]
+  // @TODO: rename validate() to avoid confusion with model
+  //        validation on withheld data. [UG]
   Base::finalize();
 }
 

From 3e6fb9921db0fcf95f23fd5415f73b7cbff08105 Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Fri, 5 Apr 2019 12:44:10 -0700
Subject: [PATCH 397/838] bug fix: GumbelSoftmaxStep should not apply sampling
 to factors

---
 src/layers/generic.cpp   | 10 ++++++++++
 src/layers/generic.h     |  1 +
 src/models/costs.h       | 11 +++++------
 src/training/validator.h |  2 +-
 4 files changed, 17 insertions(+), 7 deletions(-)

diff --git a/src/layers/generic.cpp b/src/layers/generic.cpp
index 2f302ef12..5bed72ec7 100755
--- a/src/layers/generic.cpp
+++ b/src/layers/generic.cpp
@@ -191,6 +191,16 @@ namespace marian {
     return Logits(std::move(newLogits), factoredVocab_);
   }
 
+  Logits Logits::applyUnaryFunctions(const std::function<Expr(Expr)>& f1, const std::function<Expr(Expr)>& fother) const {
+      std::vector<Ptr<RationalLoss>> newLogits;
+      bool first = true;
+      for (const auto& l : logits_) {
+        newLogits.emplace_back(New<RationalLoss>((first?f1:fother)(l->loss()), l->count())); // f1 for first, fother for all others
+        first = false;
+      }
+      return Logits(std::move(newLogits), factoredVocab_);
+  }
+
   // @TODO: code dup with above; we can merge it into applyToRationalLoss()
   Logits Logits::withCounts(const Expr& count) const { // create new Logits with 'count' implanted into all logits_
     std::vector<Ptr<RationalLoss>> newLogits;
diff --git a/src/layers/generic.h b/src/layers/generic.h
index 40b8bb61a..01c1c1721 100755
--- a/src/layers/generic.h
+++ b/src/layers/generic.h
@@ -85,6 +85,7 @@ class Logits {
     //Ptr<RationalLoss> getRationalLoss() const; // assume it holds a loss: get that
     Expr applyLossFunction(const Words& labels, const std::function<Expr(Expr/*logits*/,Expr/*indices*/)>& lossFn) const;
     Logits applyUnaryFunction(const std::function<Expr(Expr)>& f) const; // clone this but apply f to all loss values
+    Logits applyUnaryFunctions(const std::function<Expr(Expr)>& f1, const std::function<Expr(Expr)>& fother) const; // clone this but apply f1 to first and fother to to all other values
 
     struct MaskedFactorIndices {
       std::vector<WordIndex> indices; // factor index, or 0 if masked
diff --git a/src/models/costs.h b/src/models/costs.h
index c0d7ebf03..f56f6e05f 100755
--- a/src/models/costs.h
+++ b/src/models/costs.h
@@ -224,12 +224,11 @@ class LogSoftmaxStep : public ILogProbStep {
 class GumbelSoftmaxStep : public ILogProbStep {
 public:
   virtual Ptr<DecoderState> apply(Ptr<DecoderState> state) override {
-    // @TODO: @HACK must know about individual parts; make it a loop
-    state->setLogProbs(state->getLogProbs().applyUnaryFunction([](Expr logits){
-      //auto logits = state->getLogProbs().getLogits();
-      return logsoftmax(logits + constant_like(logits, inits::gumbel));
-    }));
-    //state->setLogProbs(logprobs);
+    state->setLogProbs(state->getLogProbs().applyUnaryFunctions(
+      [](Expr logits){ // lemma gets gumbelled
+        return logsoftmax(logits + constant_like(logits, inits::gumbel));
+      },
+      logsoftmax)); // factors don't
     return state;
   }
 };
diff --git a/src/training/validator.h b/src/training/validator.h
index e06f4b57e..a01f2fb17 100755
--- a/src/training/validator.h
+++ b/src/training/validator.h
@@ -594,7 +594,7 @@ class BleuValidator : public Validator<data::Corpus, models::IModel> {
     auto vocab = vocabs_.back();
 #if 1 // hack for now, to get this feature when running under Flo
     if (vocab->type() == "FactoredVocab")
-        detok_ = true; // always use bleu-detok
+      detok_ = true; // always use bleu-detok
 #endif
     ABORT_IF(detok_ && vocab->type() != "SentencePieceVocab" && vocab->type() != "FactoredVocab",
              "Detokenizing BLEU validator expects the target vocabulary to be SentencePieceVocab or FactoredVocab. "

From 5f9c7c00a316acc60beacf9bc7faa3913a329ead Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Thu, 11 Apr 2019 20:47:08 -0700
Subject: [PATCH 398/838] extended quicksand API to use our native vocabulary
 class

---
 src/3rd_party/pathie-cpp/src/path.cpp |  5 ++-
 src/microsoft/quicksand.cpp           | 54 +++++++++++++++++++++++----
 src/microsoft/quicksand.h             | 27 ++++++++++----
 3 files changed, 70 insertions(+), 16 deletions(-)

diff --git a/src/3rd_party/pathie-cpp/src/path.cpp b/src/3rd_party/pathie-cpp/src/path.cpp
index 12aa02703..6d9e8b63a 100755
--- a/src/3rd_party/pathie-cpp/src/path.cpp
+++ b/src/3rd_party/pathie-cpp/src/path.cpp
@@ -149,6 +149,8 @@ Path::Path(const std::vector<Path>& components)
  */
 void Path::sanitize()
 {
+  bool isWindowsUNCPath = m_path.c_str()[0] == '\\' && m_path.c_str()[1] == '\\'; // UNC path
+
   // Replace any backslashes \ with forward slashes /.
   size_t cur = string::npos;
   while ((cur = m_path.find("\\")) != string::npos) { // assignment intended
@@ -156,8 +158,9 @@ void Path::sanitize()
   }
 
   // Replace all double slashes // with a single one
+  // [fseide] except for the first position, which would be a Windows UNC path
   cur = string::npos;
-  while ((cur = m_path.find("//")) != string::npos) { // assignment intended
+  while ((cur = m_path.find("//", isWindowsUNCPath ? 1 : 0)) != string::npos) { // assignment intended
     m_path.replace(cur, 2, "/");
   }
 
diff --git a/src/microsoft/quicksand.cpp b/src/microsoft/quicksand.cpp
index 2d932f0b7..a213efe12 100755
--- a/src/microsoft/quicksand.cpp
+++ b/src/microsoft/quicksand.cpp
@@ -9,6 +9,7 @@
 #include "translator/beam_search.h"
 #include "translator/scorers.h"
 #include "data/alignment.h"
+#include "data/vocab_base.h"
 
 namespace marian {
 
@@ -31,6 +32,20 @@ Ptr<Options> newOptions() {
   return New<Options>();
 }
 
+class VocabWrapper : public IVocabWrapper {
+  Ptr<Vocab> pImpl_;
+public:
+  VocabWrapper(Ptr<Vocab> vocab) : pImpl_(vocab) {}
+  WordIndex encode(const std::string& word) const {
+    auto enc = pImpl_->encode(word, /*addEOS=*/false, /*inference=*/true);
+    ABORT_IF(enc.size() != 1, "QuickSAND passed an invalid word '{}' to Marian (empty or contains a space)", word);
+    return enc[0].toWordIndex();
+  }
+  size_t size() const { return pImpl_->size(); }
+  std::string decode(WordIndex id) const { return pImpl_->decode({ Word::fromWordIndex(id) }); }
+  Ptr<Vocab> getVocab() const { return pImpl_; }
+};
+
 class BeamSearchDecoder : public IBeamSearchDecoder {
 private:
   Ptr<ExpressionGraph> graph_;
@@ -38,12 +53,19 @@ class BeamSearchDecoder : public IBeamSearchDecoder {
 
   std::vector<Ptr<Scorer>> scorers_;
 
+  std::vector<Ptr<Vocab>> vocabs_;
+
 public:
   BeamSearchDecoder(Ptr<Options> options,
                     const std::vector<const void*>& ptrs,
-                    Word eos)
+                    const std::vector<Ptr<IVocabWrapper>>& vocabs,
+                    WordIndex eos)
       : IBeamSearchDecoder(options, ptrs, eos) {
 
+    // copy the vocabs
+    for (auto vi : vocabs)
+      vocabs_.push_back(std::dynamic_pointer_cast<VocabWrapper>(vi)->getVocab());
+
     // setting 16-bit optimization to false for now. Re-enable with better caching or pre-computation
     graph_ = New<ExpressionGraph>(/*inference=*/true, /*optimize=*/false);
 
@@ -94,7 +116,7 @@ class BeamSearchDecoder : public IBeamSearchDecoder {
 
   QSNBestBatch decode(const QSBatch& qsBatch,
                       size_t maxLength,
-                      const std::unordered_set<Word>& shortlist) override {
+                      const std::unordered_set<WordIndex>& shortlist) override {
     if(shortlist.size() > 0) {
       auto shortListGen = New<data::FakeShortlistGenerator>(shortlist);
       for(auto scorer : scorers_)
@@ -103,7 +125,7 @@ class BeamSearchDecoder : public IBeamSearchDecoder {
 
     // form source batch, by interleaving the words over sentences in the batch, and setting the mask
     size_t batchSize = qsBatch.size();
-    auto subBatch = New<data::SubBatch>(batchSize, maxLength, nullptr);
+    auto subBatch = New<data::SubBatch>(batchSize, maxLength, vocabs_[0]);
     for(size_t i = 0; i < maxLength; ++i) {
       for(size_t j = 0; j < batchSize; ++j) {
         const auto& sent = qsBatch[j];
@@ -114,8 +136,8 @@ class BeamSearchDecoder : public IBeamSearchDecoder {
         }
       }
     }
-    std::vector<Ptr<data::SubBatch>> subBatches;
-    subBatches.push_back(subBatch);
+    auto tgtSubBatch = New<data::SubBatch>(batchSize, 0, vocabs_[1]); // only holds a vocab, but data is dummy
+    std::vector<Ptr<data::SubBatch>> subBatches{ subBatch, tgtSubBatch };
     std::vector<size_t> sentIds(batchSize, 0);
 
     auto batch = New<data::CorpusBatch>(subBatches);
@@ -165,8 +187,26 @@ class BeamSearchDecoder : public IBeamSearchDecoder {
 
 Ptr<IBeamSearchDecoder> newDecoder(Ptr<Options> options,
                                    const std::vector<const void*>& ptrs,
-                                   Word eos) {
-  return New<BeamSearchDecoder>(options, ptrs, eos);
+                                   const std::vector<Ptr<IVocabWrapper>>& vocabs,
+                                   WordIndex eos) {
+  return New<BeamSearchDecoder>(options, ptrs, vocabs, eos);
+}
+
+std::vector<Ptr<IVocabWrapper>> loadVocabs(const std::vector<std::string>& vocabPaths) {
+  std::vector<Ptr<IVocabWrapper>> res(vocabPaths.size());
+  for (size_t i = 0; i < vocabPaths.size(); i++) {
+    if (i > 0 && vocabPaths[i] == vocabPaths[i-1]) {
+      res[i] = res[i-1];
+      LOG(info, "[data] Input {} sharing vocabulary with input {}", i, i-1);
+    }
+    else {
+      auto vocab = New<Vocab>(New<Options>(), i); // (empty options, since they are only used for creating vocabs)
+      auto size = vocab->load(vocabPaths[i]);
+      LOG(info, "[data] Loaded vocabulary size for input {} of size {}", i, size);
+      res[i] = New<VocabWrapper>(vocab);
+    }
+  }
+  return res;
 }
 
 }  // namespace quicksand
diff --git a/src/microsoft/quicksand.h b/src/microsoft/quicksand.h
index 93308fe98..77d13efa4 100644
--- a/src/microsoft/quicksand.h
+++ b/src/microsoft/quicksand.h
@@ -16,12 +16,12 @@ class Options;
 namespace quicksand {
 
 typedef uint32_t IndexType;
-typedef IndexType Word;
-typedef std::vector<Word> Words;
-typedef std::vector<Words> QSBatch;
+typedef IndexType WordIndex;
+typedef std::vector<WordIndex> WordIndices;
+typedef std::vector<WordIndices> QSBatch;
 typedef std::vector<std::set<std::pair<size_t, float>>> AlignmentSets; // [tgtPos] -> set of (srcPos, score)
 
-typedef std::tuple<Words, AlignmentSets, float> QSSentenceWithProb;
+typedef std::tuple<WordIndices, AlignmentSets, float> QSSentenceWithProb;
 typedef std::vector<QSSentenceWithProb> QSNBest;
 typedef std::vector<QSNBest> QSNBestBatch;
 
@@ -30,21 +30,28 @@ Ptr<Options> newOptions();
 template <class T>
 void set(Ptr<Options> options, const std::string& key, const T& value);
 
+class IVocabWrapper {
+public:
+  virtual WordIndex encode(const std::string& word) const = 0;
+  virtual size_t size() const = 0;
+  virtual std::string decode(WordIndex id) const = 0;
+};
+
 class IBeamSearchDecoder {
 protected:
   Ptr<Options> options_;
   std::vector<const void*> ptrs_;
-  Word eos_;
+  WordIndex eos_;
 
 public:
   IBeamSearchDecoder(Ptr<Options> options,
                      const std::vector<const void*>& ptrs,
-                     Word eos)
+                     WordIndex eos)
       : options_(options), ptrs_(ptrs), eos_(eos) {}
 
   virtual QSNBestBatch decode(const QSBatch& qsBatch,
                               size_t maxLength,
-                              const std::unordered_set<Word>& shortlist)
+                              const std::unordered_set<WordIndex>& shortlist)
       = 0;
 
   virtual void setWorkspace(uint8_t* data, size_t size) = 0;
@@ -52,7 +59,11 @@ class IBeamSearchDecoder {
 
 Ptr<IBeamSearchDecoder> newDecoder(Ptr<Options> options,
                                    const std::vector<const void*>& ptrs,
-                                   Word eos);
+                                   const std::vector<Ptr<IVocabWrapper>>& vocabs,
+                                   WordIndex eos);
+
+// load src and tgt vocabs
+std::vector<Ptr<IVocabWrapper>> loadVocabs(const std::vector<std::string>& vocabPaths);
 
 }  // namespace quicksand
 }  // namespace marian

From b9419a39b7728c1aaeb4f288df7e48d259db0006 Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Thu, 11 Apr 2019 20:47:08 -0700
Subject: [PATCH 399/838] extended quicksand API to use our native vocabulary
 class

---
 src/3rd_party/pathie-cpp/src/path.cpp |  5 ++-
 src/microsoft/quicksand.cpp           | 54 +++++++++++++++++++++++----
 src/microsoft/quicksand.h             | 27 ++++++++++----
 3 files changed, 70 insertions(+), 16 deletions(-)

diff --git a/src/3rd_party/pathie-cpp/src/path.cpp b/src/3rd_party/pathie-cpp/src/path.cpp
index 12aa02703..6d9e8b63a 100755
--- a/src/3rd_party/pathie-cpp/src/path.cpp
+++ b/src/3rd_party/pathie-cpp/src/path.cpp
@@ -149,6 +149,8 @@ Path::Path(const std::vector<Path>& components)
  */
 void Path::sanitize()
 {
+  bool isWindowsUNCPath = m_path.c_str()[0] == '\\' && m_path.c_str()[1] == '\\'; // UNC path
+
   // Replace any backslashes \ with forward slashes /.
   size_t cur = string::npos;
   while ((cur = m_path.find("\\")) != string::npos) { // assignment intended
@@ -156,8 +158,9 @@ void Path::sanitize()
   }
 
   // Replace all double slashes // with a single one
+  // [fseide] except for the first position, which would be a Windows UNC path
   cur = string::npos;
-  while ((cur = m_path.find("//")) != string::npos) { // assignment intended
+  while ((cur = m_path.find("//", isWindowsUNCPath ? 1 : 0)) != string::npos) { // assignment intended
     m_path.replace(cur, 2, "/");
   }
 
diff --git a/src/microsoft/quicksand.cpp b/src/microsoft/quicksand.cpp
index 2d932f0b7..a213efe12 100755
--- a/src/microsoft/quicksand.cpp
+++ b/src/microsoft/quicksand.cpp
@@ -9,6 +9,7 @@
 #include "translator/beam_search.h"
 #include "translator/scorers.h"
 #include "data/alignment.h"
+#include "data/vocab_base.h"
 
 namespace marian {
 
@@ -31,6 +32,20 @@ Ptr<Options> newOptions() {
   return New<Options>();
 }
 
+class VocabWrapper : public IVocabWrapper {
+  Ptr<Vocab> pImpl_;
+public:
+  VocabWrapper(Ptr<Vocab> vocab) : pImpl_(vocab) {}
+  WordIndex encode(const std::string& word) const {
+    auto enc = pImpl_->encode(word, /*addEOS=*/false, /*inference=*/true);
+    ABORT_IF(enc.size() != 1, "QuickSAND passed an invalid word '{}' to Marian (empty or contains a space)", word);
+    return enc[0].toWordIndex();
+  }
+  size_t size() const { return pImpl_->size(); }
+  std::string decode(WordIndex id) const { return pImpl_->decode({ Word::fromWordIndex(id) }); }
+  Ptr<Vocab> getVocab() const { return pImpl_; }
+};
+
 class BeamSearchDecoder : public IBeamSearchDecoder {
 private:
   Ptr<ExpressionGraph> graph_;
@@ -38,12 +53,19 @@ class BeamSearchDecoder : public IBeamSearchDecoder {
 
   std::vector<Ptr<Scorer>> scorers_;
 
+  std::vector<Ptr<Vocab>> vocabs_;
+
 public:
   BeamSearchDecoder(Ptr<Options> options,
                     const std::vector<const void*>& ptrs,
-                    Word eos)
+                    const std::vector<Ptr<IVocabWrapper>>& vocabs,
+                    WordIndex eos)
       : IBeamSearchDecoder(options, ptrs, eos) {
 
+    // copy the vocabs
+    for (auto vi : vocabs)
+      vocabs_.push_back(std::dynamic_pointer_cast<VocabWrapper>(vi)->getVocab());
+
     // setting 16-bit optimization to false for now. Re-enable with better caching or pre-computation
     graph_ = New<ExpressionGraph>(/*inference=*/true, /*optimize=*/false);
 
@@ -94,7 +116,7 @@ class BeamSearchDecoder : public IBeamSearchDecoder {
 
   QSNBestBatch decode(const QSBatch& qsBatch,
                       size_t maxLength,
-                      const std::unordered_set<Word>& shortlist) override {
+                      const std::unordered_set<WordIndex>& shortlist) override {
     if(shortlist.size() > 0) {
       auto shortListGen = New<data::FakeShortlistGenerator>(shortlist);
       for(auto scorer : scorers_)
@@ -103,7 +125,7 @@ class BeamSearchDecoder : public IBeamSearchDecoder {
 
     // form source batch, by interleaving the words over sentences in the batch, and setting the mask
     size_t batchSize = qsBatch.size();
-    auto subBatch = New<data::SubBatch>(batchSize, maxLength, nullptr);
+    auto subBatch = New<data::SubBatch>(batchSize, maxLength, vocabs_[0]);
     for(size_t i = 0; i < maxLength; ++i) {
       for(size_t j = 0; j < batchSize; ++j) {
         const auto& sent = qsBatch[j];
@@ -114,8 +136,8 @@ class BeamSearchDecoder : public IBeamSearchDecoder {
         }
       }
     }
-    std::vector<Ptr<data::SubBatch>> subBatches;
-    subBatches.push_back(subBatch);
+    auto tgtSubBatch = New<data::SubBatch>(batchSize, 0, vocabs_[1]); // only holds a vocab, but data is dummy
+    std::vector<Ptr<data::SubBatch>> subBatches{ subBatch, tgtSubBatch };
     std::vector<size_t> sentIds(batchSize, 0);
 
     auto batch = New<data::CorpusBatch>(subBatches);
@@ -165,8 +187,26 @@ class BeamSearchDecoder : public IBeamSearchDecoder {
 
 Ptr<IBeamSearchDecoder> newDecoder(Ptr<Options> options,
                                    const std::vector<const void*>& ptrs,
-                                   Word eos) {
-  return New<BeamSearchDecoder>(options, ptrs, eos);
+                                   const std::vector<Ptr<IVocabWrapper>>& vocabs,
+                                   WordIndex eos) {
+  return New<BeamSearchDecoder>(options, ptrs, vocabs, eos);
+}
+
+std::vector<Ptr<IVocabWrapper>> loadVocabs(const std::vector<std::string>& vocabPaths) {
+  std::vector<Ptr<IVocabWrapper>> res(vocabPaths.size());
+  for (size_t i = 0; i < vocabPaths.size(); i++) {
+    if (i > 0 && vocabPaths[i] == vocabPaths[i-1]) {
+      res[i] = res[i-1];
+      LOG(info, "[data] Input {} sharing vocabulary with input {}", i, i-1);
+    }
+    else {
+      auto vocab = New<Vocab>(New<Options>(), i); // (empty options, since they are only used for creating vocabs)
+      auto size = vocab->load(vocabPaths[i]);
+      LOG(info, "[data] Loaded vocabulary size for input {} of size {}", i, size);
+      res[i] = New<VocabWrapper>(vocab);
+    }
+  }
+  return res;
 }
 
 }  // namespace quicksand
diff --git a/src/microsoft/quicksand.h b/src/microsoft/quicksand.h
index 93308fe98..77d13efa4 100644
--- a/src/microsoft/quicksand.h
+++ b/src/microsoft/quicksand.h
@@ -16,12 +16,12 @@ class Options;
 namespace quicksand {
 
 typedef uint32_t IndexType;
-typedef IndexType Word;
-typedef std::vector<Word> Words;
-typedef std::vector<Words> QSBatch;
+typedef IndexType WordIndex;
+typedef std::vector<WordIndex> WordIndices;
+typedef std::vector<WordIndices> QSBatch;
 typedef std::vector<std::set<std::pair<size_t, float>>> AlignmentSets; // [tgtPos] -> set of (srcPos, score)
 
-typedef std::tuple<Words, AlignmentSets, float> QSSentenceWithProb;
+typedef std::tuple<WordIndices, AlignmentSets, float> QSSentenceWithProb;
 typedef std::vector<QSSentenceWithProb> QSNBest;
 typedef std::vector<QSNBest> QSNBestBatch;
 
@@ -30,21 +30,28 @@ Ptr<Options> newOptions();
 template <class T>
 void set(Ptr<Options> options, const std::string& key, const T& value);
 
+class IVocabWrapper {
+public:
+  virtual WordIndex encode(const std::string& word) const = 0;
+  virtual size_t size() const = 0;
+  virtual std::string decode(WordIndex id) const = 0;
+};
+
 class IBeamSearchDecoder {
 protected:
   Ptr<Options> options_;
   std::vector<const void*> ptrs_;
-  Word eos_;
+  WordIndex eos_;
 
 public:
   IBeamSearchDecoder(Ptr<Options> options,
                      const std::vector<const void*>& ptrs,
-                     Word eos)
+                     WordIndex eos)
       : options_(options), ptrs_(ptrs), eos_(eos) {}
 
   virtual QSNBestBatch decode(const QSBatch& qsBatch,
                               size_t maxLength,
-                              const std::unordered_set<Word>& shortlist)
+                              const std::unordered_set<WordIndex>& shortlist)
       = 0;
 
   virtual void setWorkspace(uint8_t* data, size_t size) = 0;
@@ -52,7 +59,11 @@ class IBeamSearchDecoder {
 
 Ptr<IBeamSearchDecoder> newDecoder(Ptr<Options> options,
                                    const std::vector<const void*>& ptrs,
-                                   Word eos);
+                                   const std::vector<Ptr<IVocabWrapper>>& vocabs,
+                                   WordIndex eos);
+
+// load src and tgt vocabs
+std::vector<Ptr<IVocabWrapper>> loadVocabs(const std::vector<std::string>& vocabPaths);
 
 }  // namespace quicksand
 }  // namespace marian

From abbbc7f109b50136eb7b1ca6ac1b8d660b3fb8ca Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Thu, 11 Apr 2019 20:55:01 -0700
Subject: [PATCH 400/838] added stubs for compilation under Flo

---
 src/data/factored_vocab.cpp | 1 +
 src/layers/generic.cpp      | 1 +
 2 files changed, 2 insertions(+)
 create mode 100755 src/data/factored_vocab.cpp
 create mode 100755 src/layers/generic.cpp

diff --git a/src/data/factored_vocab.cpp b/src/data/factored_vocab.cpp
new file mode 100755
index 000000000..056696600
--- /dev/null
+++ b/src/data/factored_vocab.cpp
@@ -0,0 +1 @@
+// will be used in the future
\ No newline at end of file
diff --git a/src/layers/generic.cpp b/src/layers/generic.cpp
new file mode 100755
index 000000000..056696600
--- /dev/null
+++ b/src/layers/generic.cpp
@@ -0,0 +1 @@
+// will be used in the future
\ No newline at end of file

From 4c841cb8a23a9e8b2141f1e341ba7d0d3dae6132 Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Thu, 11 Apr 2019 21:03:19 -0700
Subject: [PATCH 401/838] (reordered two methods)

---
 src/microsoft/quicksand.cpp | 2 +-
 src/microsoft/quicksand.h   | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/src/microsoft/quicksand.cpp b/src/microsoft/quicksand.cpp
index a213efe12..1560ccda5 100755
--- a/src/microsoft/quicksand.cpp
+++ b/src/microsoft/quicksand.cpp
@@ -41,8 +41,8 @@ class VocabWrapper : public IVocabWrapper {
     ABORT_IF(enc.size() != 1, "QuickSAND passed an invalid word '{}' to Marian (empty or contains a space)", word);
     return enc[0].toWordIndex();
   }
-  size_t size() const { return pImpl_->size(); }
   std::string decode(WordIndex id) const { return pImpl_->decode({ Word::fromWordIndex(id) }); }
+  size_t size() const { return pImpl_->size(); }
   Ptr<Vocab> getVocab() const { return pImpl_; }
 };
 
diff --git a/src/microsoft/quicksand.h b/src/microsoft/quicksand.h
index 77d13efa4..22fe97b67 100644
--- a/src/microsoft/quicksand.h
+++ b/src/microsoft/quicksand.h
@@ -33,8 +33,8 @@ void set(Ptr<Options> options, const std::string& key, const T& value);
 class IVocabWrapper {
 public:
   virtual WordIndex encode(const std::string& word) const = 0;
-  virtual size_t size() const = 0;
   virtual std::string decode(WordIndex id) const = 0;
+  virtual size_t size() const = 0;
 };
 
 class IBeamSearchDecoder {

From ada3d1f0ed5ef6e6fa8b6266b4f87e9779a346f7 Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Thu, 11 Apr 2019 21:09:08 -0700
Subject: [PATCH 402/838] (minor prettification)

---
 src/3rd_party/pathie-cpp/src/path.cpp | 2 +-
 src/microsoft/quicksand.cpp           | 2 +-
 src/microsoft/quicksand.h             | 2 +-
 3 files changed, 3 insertions(+), 3 deletions(-)

diff --git a/src/3rd_party/pathie-cpp/src/path.cpp b/src/3rd_party/pathie-cpp/src/path.cpp
index 6d9e8b63a..07fba2711 100755
--- a/src/3rd_party/pathie-cpp/src/path.cpp
+++ b/src/3rd_party/pathie-cpp/src/path.cpp
@@ -149,7 +149,7 @@ Path::Path(const std::vector<Path>& components)
  */
 void Path::sanitize()
 {
-  bool isWindowsUNCPath = m_path.c_str()[0] == '\\' && m_path.c_str()[1] == '\\'; // UNC path
+  bool isWindowsUNCPath = m_path.size() >= 2 && (m_path[0] == '\\' && m_path[1] == '\\'); // UNC path
 
   // Replace any backslashes \ with forward slashes /.
   size_t cur = string::npos;
diff --git a/src/microsoft/quicksand.cpp b/src/microsoft/quicksand.cpp
index a213efe12..1560ccda5 100755
--- a/src/microsoft/quicksand.cpp
+++ b/src/microsoft/quicksand.cpp
@@ -41,8 +41,8 @@ class VocabWrapper : public IVocabWrapper {
     ABORT_IF(enc.size() != 1, "QuickSAND passed an invalid word '{}' to Marian (empty or contains a space)", word);
     return enc[0].toWordIndex();
   }
-  size_t size() const { return pImpl_->size(); }
   std::string decode(WordIndex id) const { return pImpl_->decode({ Word::fromWordIndex(id) }); }
+  size_t size() const { return pImpl_->size(); }
   Ptr<Vocab> getVocab() const { return pImpl_; }
 };
 
diff --git a/src/microsoft/quicksand.h b/src/microsoft/quicksand.h
index 77d13efa4..22fe97b67 100644
--- a/src/microsoft/quicksand.h
+++ b/src/microsoft/quicksand.h
@@ -33,8 +33,8 @@ void set(Ptr<Options> options, const std::string& key, const T& value);
 class IVocabWrapper {
 public:
   virtual WordIndex encode(const std::string& word) const = 0;
-  virtual size_t size() const = 0;
   virtual std::string decode(WordIndex id) const = 0;
+  virtual size_t size() const = 0;
 };
 
 class IBeamSearchDecoder {

From 4351229899811c322bbb61800f499b37cc23a9fe Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Fri, 12 Apr 2019 09:41:11 -0700
Subject: [PATCH 403/838] (updated the VS project)

---
 vs/Marian.vcxproj         | 1 -
 vs/Marian.vcxproj.filters | 3 ---
 2 files changed, 4 deletions(-)

diff --git a/vs/Marian.vcxproj b/vs/Marian.vcxproj
index e860f7e1d..e103ec45f 100755
--- a/vs/Marian.vcxproj
+++ b/vs/Marian.vcxproj
@@ -944,7 +944,6 @@
     <ClInclude Include="..\src\tensors\tensor.h" />
     <ClInclude Include="..\src\tensors\tensor_allocator.h" />
     <ClInclude Include="..\src\tensors\tensor_operators.h" />
-    <ClInclude Include="..\src\tensors\types.h" />
     <ClInclude Include="..\src\tensors\cpu\add.h" />
     <ClInclude Include="..\src\tensors\cpu\backend.h" />
     <ClInclude Include="..\src\tensors\cpu\element.h" />
diff --git a/vs/Marian.vcxproj.filters b/vs/Marian.vcxproj.filters
index 8da3d3d60..c8af656de 100755
--- a/vs/Marian.vcxproj.filters
+++ b/vs/Marian.vcxproj.filters
@@ -1117,9 +1117,6 @@
     <ClInclude Include="..\src\tensors\tensor_operators.h">
       <Filter>tensors</Filter>
     </ClInclude>
-    <ClInclude Include="..\src\tensors\types.h">
-      <Filter>tensors</Filter>
-    </ClInclude>
     <ClInclude Include="..\src\tensors\cpu\add.h">
       <Filter>tensors\cpu</Filter>
     </ClInclude>

From 01723b97b7cc89ca847ed98e3098b28601618681 Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Fri, 12 Apr 2019 09:47:16 -0700
Subject: [PATCH 404/838] made gcc happy

---
 src/microsoft/quicksand.cpp | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/src/microsoft/quicksand.cpp b/src/microsoft/quicksand.cpp
index a213efe12..6df9748bb 100755
--- a/src/microsoft/quicksand.cpp
+++ b/src/microsoft/quicksand.cpp
@@ -36,13 +36,13 @@ class VocabWrapper : public IVocabWrapper {
   Ptr<Vocab> pImpl_;
 public:
   VocabWrapper(Ptr<Vocab> vocab) : pImpl_(vocab) {}
-  WordIndex encode(const std::string& word) const {
+  WordIndex encode(const std::string& word) const override {
     auto enc = pImpl_->encode(word, /*addEOS=*/false, /*inference=*/true);
     ABORT_IF(enc.size() != 1, "QuickSAND passed an invalid word '{}' to Marian (empty or contains a space)", word);
     return enc[0].toWordIndex();
   }
-  size_t size() const { return pImpl_->size(); }
-  std::string decode(WordIndex id) const { return pImpl_->decode({ Word::fromWordIndex(id) }); }
+  size_t size() const override { return pImpl_->size(); }
+  std::string decode(WordIndex id) const override { return pImpl_->decode({ Word::fromWordIndex(id) }); }
   Ptr<Vocab> getVocab() const { return pImpl_; }
 };
 

From a4e56c63382fcc55c45cc9c744ef374904ab3a98 Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Fri, 12 Apr 2019 09:49:52 -0700
Subject: [PATCH 405/838] added missing override'

---
 src/microsoft/quicksand.cpp | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/src/microsoft/quicksand.cpp b/src/microsoft/quicksand.cpp
index 1560ccda5..f3ae8ad83 100755
--- a/src/microsoft/quicksand.cpp
+++ b/src/microsoft/quicksand.cpp
@@ -36,13 +36,13 @@ class VocabWrapper : public IVocabWrapper {
   Ptr<Vocab> pImpl_;
 public:
   VocabWrapper(Ptr<Vocab> vocab) : pImpl_(vocab) {}
-  WordIndex encode(const std::string& word) const {
+  WordIndex encode(const std::string& word) const override {
     auto enc = pImpl_->encode(word, /*addEOS=*/false, /*inference=*/true);
     ABORT_IF(enc.size() != 1, "QuickSAND passed an invalid word '{}' to Marian (empty or contains a space)", word);
     return enc[0].toWordIndex();
   }
-  std::string decode(WordIndex id) const { return pImpl_->decode({ Word::fromWordIndex(id) }); }
-  size_t size() const { return pImpl_->size(); }
+  std::string decode(WordIndex id) const override { return pImpl_->decode({ Word::fromWordIndex(id) }); }
+  size_t size() const override { return pImpl_->size(); }
   Ptr<Vocab> getVocab() const { return pImpl_; }
 };
 

From cb5a11ffef9ee095b02705e3940ab0248a8cff38 Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Fri, 12 Apr 2019 12:57:40 -0700
Subject: [PATCH 406/838] replaced the index2str_, which enumerated all factor
 combinations incl. invalid ones, with a map

---
 src/data/factored_vocab.cpp | 63 ++++++++++++++++++-------------------
 src/data/factored_vocab.h   | 30 +++++++++++++-----
 src/layers/generic.cpp      |  7 ++++-
 3 files changed, 59 insertions(+), 41 deletions(-)

diff --git a/src/data/factored_vocab.cpp b/src/data/factored_vocab.cpp
index 04f625389..589d17bc3 100755
--- a/src/data/factored_vocab.cpp
+++ b/src/data/factored_vocab.cpp
@@ -124,10 +124,9 @@ namespace marian {
   //  - all factors not matching a prefix get lumped into yet another class (the lemmas)
   //  - factor vocab must be sorted such that all groups are consecutive
   //  - result of Output layer is nevertheless logits, not a normalized probability, due to the sigmoid entries
-  auto vocabSize = factorShape_.elements(); // size of vocab space including gaps
-  vocab_.resize(vocabSize);
+  //vocab_.resize(vocabSize);
   //factorMap_.resize(vocabSize);
-  auto factorVocabSize = factorVocab_.size();
+  //auto factorVocabSize = this->factorVocabSize();
   lemmaHasFactorGroup_.resize(groupRanges_[0].second - groupRanges_[0].first);
   size_t numTotalFactors = 0;
   for (WordIndex v = 0; v < factorMapTokenized.size(); v++) {
@@ -171,12 +170,13 @@ namespace marian {
     //  LOG(info, "{} -> {}", tokens.front(), word2string(word));
   }
   LOG(info, "[embedding] Factored-embedding map read with total/unique of {}/{} factors for {} valid words (in space of {})",
-      numTotalFactors, factorVocabSize, vocab_.numValid(), size());
+      numTotalFactors, factorVocabSize(), vocab_.size()/*numValid()*/, size());
 
   // enumerate all combinations of factors for each lemma
   // @TODO: switch to factor-spec, which no longer enumerates all combinations. Then don't set vocab string here.
   size_t numMissing = 0;
-  for (size_t v = 0; v < vocabSize; v++) {
+  auto vocabSize = size(); // size of vocab space including gaps
+  for (size_t v = 0; v < vocabSize; v++) { // @BUGBUG:L This will run forever
     auto word = Word::fromWordIndex(v);
     bool isValid = true;
     for (size_t g = 0; isValid && g < numGroups; g++) {
@@ -197,8 +197,10 @@ namespace marian {
   if (numMissing > 0)
     LOG(info, "[embedding] completed {} factor combinations missing from the original vocab file", numMissing);
 
+#ifdef FACTOR_FULL_EXPANSION
   // create mappings needed for normalization in factored outputs
   constructNormalizationInfoForVocab();
+#endif
 
   // </s> and <unk> must exist in the vocabulary
   eosId_ = Word::fromWordIndex(vocab_[DEFAULT_EOS_STR]);
@@ -206,8 +208,8 @@ namespace marian {
   //LOG(info, "eos: {}; unk: {}", word2string(eosId_), word2string(unkId_));
 
 #if 1   // dim-vocabs stores numValid() in legacy model files, and would now have been size()
-  if (maxSizeUnused == vocab_.numValid())
-    maxSizeUnused = vocab_.size();
+  if (maxSizeUnused == vocab_.size()/*numValid()*/)
+    maxSizeUnused = vocabSize;
 #endif
   //ABORT_IF(maxSizeUnused != 0 && maxSizeUnused != size(), "Factored vocabulary does not allow on-the-fly clipping to a maximum vocab size (from {} to {})", size(), maxSizeUnused);
   // @TODO: ^^ disabled now that we are generating the full combination of factors; reenable once we have consistent setups again
@@ -217,7 +219,7 @@ namespace marian {
 void FactoredVocab::constructGroupInfoFromFactorVocab() {
   // form groups
   size_t numGroups = groupPrefixes_.size();
-  size_t factorVocabSize = factorVocab_.size();
+  size_t factorVocabSize = this->factorVocabSize();
   factorGroups_.resize(factorVocabSize, 0);
   for (size_t g = 1; g < groupPrefixes_.size(); g++) { // set group labels; what does not match any prefix will stay in group 0
     const auto& groupPrefix = groupPrefixes_[g];
@@ -391,6 +393,7 @@ size_t FactoredVocab::getFactor(Word word, size_t groupIndex) const {
 //  ABORT("Not implemented");
 //}
 
+#ifdef FACTOR_FULL_EXPANSION
 void FactoredVocab::constructNormalizationInfoForVocab() {
   // create mappings needed for normalization in factored outputs
   //size_t numGroups = groupPrefixes_.size();
@@ -427,6 +430,7 @@ void FactoredVocab::constructNormalizationInfoForVocab() {
     data.push_back(Word::fromWordIndex(v));
   globalFactorMatrix_ = csr_rows(data); // [V x U]
 }
+#endif
 
 /*virtual*/ Word FactoredVocab::operator[](const std::string& word) const /*override final*/ {
   WordIndex index;
@@ -441,7 +445,9 @@ void FactoredVocab::constructNormalizationInfoForVocab() {
 
 /*virtual*/ const std::string& FactoredVocab::operator[](Word word) const /*override final*/ {
   //LOG(info, "Looking up Word {}={}", word.toWordIndex(), word2string(word));
-#if 1 // @BUGBUG: our manually prepared dict does not contain @CI tags for single letters, but it's a valid factor
+#if 1 // @TODO: remove this
+  ABORT_IF(vocab_.isGap(word.toWordIndex()), "Invalid factor combination {}", word2string(word));
+#else // @BUGBUG: our manually prepared dict does not contain @CI tags for single letters, but it's a valid factor
   if (vocab_.isGap(word.toWordIndex())) {
     LOG/*_ONCE*/(info, "Factor combination {} missing in external dict, generating fake entry (only showing this warning once)", word2string(word));
     //const_cast<WordLUT&>(vocab_).add("??" + word2string(word), word.toWordIndex());
@@ -451,10 +457,6 @@ void FactoredVocab::constructNormalizationInfoForVocab() {
   return vocab_[word.toWordIndex()];
 }
 
-/*virtual*/ size_t FactoredVocab::size() const /*override final*/ {
-  return vocab_.size();
-}
-
 /*virtual*/ std::string FactoredVocab::toUpper(const std::string& line) const /*override final*/ {
   return utils::findReplace(utils::findReplace(utils::findReplace(utils::findReplace(line, "|ci", "|ca", /*all=*/true), "|cn", "|ca", /*all=*/true), "@CI", "@CA", /*all=*/true), "@CN", "@CA", /*all=*/true);
 }
@@ -596,16 +598,9 @@ FactoredVocab::CSRData FactoredVocab::csr_rows(const Words& words) const {
         weights.push_back(1.0f);
       }
     }
-#if 0 // @BUGBUG: No, this is wrong! The vector must be 1s, since we use it in backprop transposition.
-    else {
-      // push a dummy entry. Not sure if this is needed.
-      indices.push_back(0);
-      weights.push_back(0.0f);
-    }
-#endif
     offsets.push_back((IndexType)indices.size()); // next matrix row begins at this offset
   }
-  return { Shape({(int)words.size(), (int)factorVocab_.size()}), weights, indices, offsets };
+  return { Shape({(int)words.size(), (int)factorVocabSize()}), weights, indices, offsets };
 }
 
 // Helper to construct and load a FactordVocab from a path is given (non-empty) and if it specifies a factored vocab.
@@ -625,16 +620,20 @@ WordIndex FactoredVocab::WordLUT::add(const std::string& word, WordIndex index)
   ABORT_IF(word.empty(), "Attempted to add the empty word to a dictionary");
   auto wasInserted = str2index_.insert(std::make_pair(word, index)).second;
   ABORT_IF(!wasInserted, "Duplicate vocab entry for '{}', new index {} vs. existing index {}", word, index, str2index_[word]);
-  while (index2str_.size() <= index)
-    index2str_.emplace_back(); // @TODO: what's the right way to get linear complexity in steps?
-  ABORT_IF(!index2str_[index].empty(), "Duplicate vocab entry for index {} (new: '{}'; existing: '{}')", index, word, index2str_[index]);
-  index2str_[index] = word;
+  wasInserted = index2str_.insert(std::make_pair(index, word)).second;
+  ABORT_IF(!wasInserted, "Duplicate vocab entry for index {} (new: '{}'; existing: '{}')", index, word, index2str_[index]);
+  //if (vocabSize_ < index2str_.size())
+  //  vocabSize_ = index2str_.size();
   return index;
 }
+static const std::string g_emptyString;
 const std::string& FactoredVocab::WordLUT::operator[](WordIndex index) const {
-  const auto& word = index2str_[index];
-  //ABORT_IF(word.empty(), "Invalid access to dictionary gap item");
-  return word;
+  auto iter = index2str_.find(index);
+  //ABORT_IF(iter == index2str_.end(), "Invalid access to dictionary gap item");
+  if (iter == index2str_.end())
+    return g_emptyString; // (using a global since we return a reference)
+  else
+    return iter->second;
 }
 WordIndex FactoredVocab::WordLUT::operator[](const std::string& word) const {
   auto iter = str2index_.find(word);
@@ -648,10 +647,10 @@ bool FactoredVocab::WordLUT::tryFind(const std::string& word, WordIndex& index)
   index = iter->second;
   return true;
 }
-void FactoredVocab::WordLUT::resize(size_t num) {
-  ABORT_IF(num < index2str_.size(), "Word table cannot be shrunk");
-  index2str_.resize(num); // gets filled up with gap items (empty strings)
-}
+//void FactoredVocab::WordLUT::resize(size_t num) {
+//  ABORT_IF(num < size(), "Word table cannot be shrunk");
+//  vocabSize_ = num;
+//}
 size_t FactoredVocab::WordLUT::load(const std::string& path) {
   std::string line;
   io::InputFileStream in(path);
diff --git a/src/data/factored_vocab.h b/src/data/factored_vocab.h
index d6bb8a21f..c5bfee7f3 100755
--- a/src/data/factored_vocab.h
+++ b/src/data/factored_vocab.h
@@ -11,6 +11,8 @@
 
 #include <numeric> // for std::iota()
 
+#undef FACTOR_FULL_EXPANSION // define this to get full expansion. @TODO: infeasible for many factors; just delete this
+
 namespace marian {
 
 class FactoredVocab : public IVocab {
@@ -33,7 +35,7 @@ class FactoredVocab : public IVocab {
   virtual std::string decode(const Words& sentence, bool ignoreEos = true) const override final;
   virtual std::string surfaceForm(const Words& sentence) const override final;
   virtual const std::string& operator[](Word id) const override final;
-  virtual size_t size() const override final;
+  virtual size_t size() const override final { return factorShape_.elements(); } // virtual vocab size with all factor combinations including gaps
   virtual std::string type() const override final { return "FactoredVocab"; }
   virtual Word getEosId() const override final { return eosId_; }
   virtual Word getUnkId() const override final { return unkId_; }
@@ -44,16 +46,20 @@ class FactoredVocab : public IVocab {
   virtual Word randWord() const override final;
 
   // factor-specific. These methods are consumed by Output and Embedding.
-  size_t factorVocabSize() const { return factorVocab_.size(); }
+  size_t factorVocabSize() const { return factorVocab_.size(); } // total number of factors across all types
 
-  CSRData csr_rows(const Words& words) const;
+  CSRData csr_rows(const Words& words) const; // sparse matrix for summing up factors from the concatenated embedding matrix for each word
 
+#ifdef FACTOR_FULL_EXPANSION
   const CSRData& getGlobalFactorMatrix() const { return globalFactorMatrix_; }   // [v,u] (sparse) -> =1 if u is factor of v  --only used in getLogits()
+#endif
   size_t getNumGroups() const { return groupRanges_.size(); }
   std::pair<size_t, size_t> getGroupRange(size_t g)    const { return groupRanges_[g]; }   // [g] -> (u_begin, u_end)
   //const std::vector<float>&     getFactorMasks(size_t g)   const { return factorMasks_[g]; }   // [g][v] 1.0 if word v has factor g
   //const std::vector<IndexType>& getFactorIndices(size_t g) const { return factorIndices_[g]; } // [g][v] local index u_g = u - u_g,begin of factor g for word v; 0 if not a factor
+#ifdef FACTOR_FULL_EXPANSION
   const std::vector<float>& getGapLogMask() const { return gapLogMask_; } // [v] -inf if v is a gap entry, else 0
+#endif
 
   // convert representations
   Word factors2word(const std::vector<size_t>& factors) const;
@@ -74,21 +80,25 @@ class FactoredVocab : public IVocab {
 private:
   void constructGroupInfoFromFactorVocab();
   void constructFactorIndexConversion();
+#ifdef FACTOR_FULL_EXPANSION
   void constructNormalizationInfoForVocab();
+#endif
   size_t factorUnit2FactorIndex(WordIndex u) const;
 private:
   class WordLUT { // map between strings and WordIndex
     std::map<std::string, WordIndex> str2index_;
-    std::vector<std::string> index2str_;
+    std::map<WordIndex, std::string> index2str_;
+    //size_t vocabSize_; // total number of vocab items as set by user
   public:
     WordIndex add(const std::string& word, WordIndex index);
     const std::string& operator[](WordIndex index) const;
     WordIndex operator[](const std::string& word) const;
-    bool isGap(WordIndex index) const { return index2str_[index].empty(); }
+    bool isGap(WordIndex index) const { return index2str_.find(index) == index2str_.end(); }
     bool tryFind(const std::string& word, WordIndex& index) const;
-    void resize(size_t num);
-    size_t size() const { return index2str_.size(); } // nominal size including gap items
-    size_t numValid() const { return str2index_.size(); } // actual non-gaps items
+    //void resize(size_t num); // @TODO: remove this, and remove the distinction of size() and numValid()
+    //size_t size() const { return vocabSize_; } // nominal size including gap items
+    //size_t numValid() const { return str2index_.size(); } // actual non-gaps items
+    size_t size() const { return str2index_.size(); }
     size_t load(const std::string& path);
   };
 
@@ -102,7 +112,9 @@ class FactoredVocab : public IVocab {
   WordLUT factorVocab_;                                // [factor name] -> factor index = row of E_
   std::vector<std::string> groupPrefixes_;             // [group id g] shared prefix of factors (used for grouping)
   //std::vector<std::vector<WordIndex>> factorMap_;      // [word index v] -> set of factor indices u
+#ifdef FACTOR_FULL_EXPANSION
   CSRData globalFactorMatrix_;                         // [v,u] (sparse) -> =1 if u is factor of v
+#endif
   std::vector<size_t> factorGroups_;                   // [u] -> group id of factor u
   std::vector<std::pair<size_t, size_t>> groupRanges_; // [group id g] -> (u_begin,u_end) index range of factors u for this group. These don't overlap.
   std::vector<std::vector<bool>>lemmaHasFactorGroup_;  // [factor 0 index][g] -> true if lemma has factor group
@@ -110,7 +122,9 @@ class FactoredVocab : public IVocab {
   std::vector<size_t> factorStrides_;                  // [g] stride for factor dimension
   //std::vector<std::vector<float>>     factorMasks_;    // [g][v] 1.0 if word v has factor g
   //std::vector<std::vector<IndexType>> factorIndices_;  // [g][v] relative index u - u_begin of factor g (or any valid index if it does not have it; we use 0)
+#ifdef FACTOR_FULL_EXPANSION
   std::vector<float> gapLogMask_;                      // [v] -1e8 if this is a gap, else 0
+#endif
 };
 
 }  // namespace marian
diff --git a/src/layers/generic.cpp b/src/layers/generic.cpp
index 5bed72ec7..366b05f35 100755
--- a/src/layers/generic.cpp
+++ b/src/layers/generic.cpp
@@ -84,7 +84,6 @@ namespace marian {
     ABORT_IF(empty(), "Attempted to read out logits on empty Logits object");
     auto sel = logits_[groupIndex]->loss(); // [localBeamSize, 1, dimBatch, dimFactorVocab]
 
-
     // normalize for decoding:
     //  - all secondary factors: subtract their max
     //  - lemma: add all maxes of applicable factors
@@ -108,6 +107,8 @@ namespace marian {
 
   // This function assumes that the object holds one or more factor logits, which are summed up
   // into output-vocab logits according to the factored model (with correct normalization of factors).
+  // This is infeasible for realistic factor sets, and therefore only implemented for 1 factor.
+  // @TODO: remove altogether
   Expr Logits::getLogits() const {
     ABORT_IF(empty(), "Attempted to read out logits on empty Logits object");
     if (!factoredVocab_) {
@@ -115,6 +116,7 @@ namespace marian {
       return getFactoredLogits(0);
     }
 
+#ifdef FACTOR_FULL_EXPANSION
     // compute normalized factor log probs
     std::vector<Expr> logProbs(logits_.size());
     for (size_t g = 0; g < logits_.size(); g++)
@@ -137,6 +139,9 @@ namespace marian {
     y = y + graph->constant({ (int)gapLogMask.size() }, inits::from_vector(gapLogMask), Type::float32);
 
     return y;
+#else
+    ABORT("getLogits() no longer supported for actual factored vocab"); // because it is infeasible
+#endif
   }
 
   void Logits::MaskedFactorIndices::push_back(size_t factorIndex) {

From 3b927cd50756dd84bdd53880acc1f071b586b60b Mon Sep 17 00:00:00 2001
From: Vishal Chowdhary <vishalc@microsoft.com>
Date: Fri, 12 Apr 2019 15:59:43 -0700
Subject: [PATCH 407/838] added a missing #include for some VS version

---
 src/data/types.h | 1 +
 1 file changed, 1 insertion(+)

diff --git a/src/data/types.h b/src/data/types.h
index d204beb64..05cdf96cf 100755
--- a/src/data/types.h
+++ b/src/data/types.h
@@ -7,6 +7,7 @@
 #include <string>
 #include <unordered_map>
 #include <vector>
+#include <iterator>
 
 namespace marian {
 

From 0685248cfec3657225daaccbfd83106eddb079b6 Mon Sep 17 00:00:00 2001
From: Vishal Chowdhary <vishalc@microsoft.com>
Date: Fri, 12 Apr 2019 15:59:43 -0700
Subject: [PATCH 408/838] added a missing #include for some VS version

---
 src/data/types.h | 1 +
 1 file changed, 1 insertion(+)

diff --git a/src/data/types.h b/src/data/types.h
index d204beb64..05cdf96cf 100755
--- a/src/data/types.h
+++ b/src/data/types.h
@@ -7,6 +7,7 @@
 #include <string>
 #include <unordered_map>
 #include <vector>
+#include <iterator>
 
 namespace marian {
 

From 6d3b6d3fb0654356576e772f037206815ca902d1 Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Fri, 12 Apr 2019 18:10:07 -0700
Subject: [PATCH 409/838] added caching of FactoredVocab instances (keyed by
 pathname); Shape::elements() can now run as size_t

---
 src/common/shape.h          |  7 ++--
 src/data/factored_vocab.cpp | 81 +++++++++++++++++++++++++------------
 src/data/factored_vocab.h   |  3 +-
 3 files changed, 62 insertions(+), 29 deletions(-)
 mode change 100644 => 100755 src/common/shape.h

diff --git a/src/common/shape.h b/src/common/shape.h
old mode 100644
new mode 100755
index c8a4bdd3d..138871396
--- a/src/common/shape.h
+++ b/src/common/shape.h
@@ -100,10 +100,11 @@ struct Shape {
       return stride[size() + i];
   }
 
-  inline int elements() const {
-    int el = 1;
+  template<typename T = int> // using a template so that FactoredSegmenter, which uses this as well, can pass size_t
+  inline T elements() const {
+    T el = 1;
     for(auto s : shape_)
-      el *= s;
+      el *= (T)s;
     return el;
   }
 
diff --git a/src/data/factored_vocab.cpp b/src/data/factored_vocab.cpp
index 589d17bc3..56382bcec 100755
--- a/src/data/factored_vocab.cpp
+++ b/src/data/factored_vocab.cpp
@@ -7,7 +7,17 @@
 
 namespace marian {
 
+    DONT_OPTIMIZE
 /*virtual*/ size_t FactoredVocab::load(const std::string& modelPath, size_t maxSizeUnused /*= 0*/) /*override final*/ {
+  // If model has already been loaded, then assume this is a shared object, and skip loading it again.
+  // This can be multi-threaded, so must run under lock.
+  static std::mutex s_mtx;
+  std::lock_guard<std::mutex> criticalSection(s_mtx);
+  if (vocab_.size() != 0) {
+    LOG(info, "[vocab] Attempting to load model a second time; skipping (assuming shared vocab)");
+    return size();
+  }
+
   std::vector<std::vector<std::string>> factorMapTokenized;
   std::string line;
   std::vector<std::string> tokBuf;
@@ -142,7 +152,8 @@ namespace marian {
       factorUnits.push_back(u);
     }
     // convert to fully unrolled factors representation
-    std::vector<size_t> factorIndices(groupRanges_.size(), FACTOR_NOT_APPLICABLE); // default for unused factors
+    auto na = FACTOR_NOT_APPLICABLE; // (gcc compiler bug: sometimes it cannot find this if passed directly)
+    std::vector<size_t> factorIndices(groupRanges_.size(), na); // default for unused factors
     std::vector<bool> hasFactorGroupFlags(groupRanges_.size(), false);
     for (auto u : factorUnits) {
       factorIndices[factorGroups_[u]] = factorUnit2FactorIndex(u);
@@ -164,38 +175,37 @@ namespace marian {
     // for now add what we get, and then expand more below
     vocab_.add(tokens.front(), wordIndex);
     if (tokens.front() != word2string(word))
-      LOG_ONCE(info, "[embedding] Word name in .wl file {} differs from canonical form {} (this warning is only shown once)", tokens.front(), word2string(word));
+      LOG_ONCE(info, "[vocab] Word name in vocab file {} differs from canonical form {} (this warning is only shown once)", tokens.front(), word2string(word));
     numTotalFactors += tokens.size() - 1;
     //if (v % 5000 == 0)
     //  LOG(info, "{} -> {}", tokens.front(), word2string(word));
   }
-  LOG(info, "[embedding] Factored-embedding map read with total/unique of {}/{} factors for {} valid words (in space of {})",
-      numTotalFactors, factorVocabSize(), vocab_.size()/*numValid()*/, size());
+  LOG(info, "[vocab] Factored-embedding map read with total/unique of {}/{} factors from {} example words (in space of {})",
+      numTotalFactors, factorVocabSize(), vocab_.size()/*numValid()*/, utils::withCommas(size()));
+  vocab_.dumpToFile(modelPath + "_examples");
 
   // enumerate all combinations of factors for each lemma
   // @TODO: switch to factor-spec, which no longer enumerates all combinations. Then don't set vocab string here.
-  size_t numMissing = 0;
-  auto vocabSize = size(); // size of vocab space including gaps
-  for (size_t v = 0; v < vocabSize; v++) { // @BUGBUG:L This will run forever
-    auto word = Word::fromWordIndex(v);
+  //        This enumerates all possible combinations (incl. invalid ones), and stores all valid ones
+  auto virtualVocabSize = (WordIndex)size(); // size of vocab space including gaps
+  ABORT_IF((size_t)virtualVocabSize != size(), "Too many factors, virtual index space {} exceeds the bit limit of WordIndex type", utils::withCommas(size()));
+  LOG(info, "[vocab] Expanding all valid vocab entries out of {}...", utils::withCommas(size()));
+  for (WordIndex v = 0; v < virtualVocabSize; v++) { // @BUGBUG: This is SLOOOW. Need further changes to remove this altogether
+    // determine whether this bit combination is a thing
     bool isValid = true;
+    auto word = Word::fromWordIndex(v);
     for (size_t g = 0; isValid && g < numGroups; g++) {
       auto factorIndex = getFactor(word, g);
       // @TODO: we have a hack in getFactor() to return not-specified if factor is specified but not applicable, making it invalid
       isValid = factorIndex != FACTOR_NOT_SPECIFIED; // FACTOR_NOT_APPLICABLE is a valid value
     }
-    if (isValid != !vocab_.isGap((WordIndex)v)) {
-      //LOG(info, "WARNING: Factored vocab mismatch for {}: isValid={}, isGap={}", word2string(word), isValid, vocab_.isGap((WordIndex)v));
-      if (isValid) { // add the missing word (albeit with a poor grapheme)
-        vocab_.add(word2string(word), word.toWordIndex());
-        //(*this)[word];
-        // @TODO: ^^disabled to test getLogits(), which no longer works with this enabled (I guess since model has not seen the new units)
-        numMissing++;
-      }
-    }
+    if (isValid && vocab_.isGap(v)) // add if missing
+      vocab_.add(word2string(word), word.toWordIndex());
+    else if (!isValid && !vocab_.isGap(v))
+      LOG(info, "WARNING: Factored vocab mismatch for {}: isValid={}, isGap={}", word2string(word), isValid, vocab_.isGap(v));
   }
-  if (numMissing > 0)
-    LOG(info, "[embedding] completed {} factor combinations missing from the original vocab file", numMissing);
+  LOG(info, "[vocab] Completed, total {} valid combinations", vocab_.size()/*numValid()*/);
+  vocab_.dumpToFile(modelPath + "_expanded");
 
 #ifdef FACTOR_FULL_EXPANSION
   // create mappings needed for normalization in factored outputs
@@ -209,7 +219,7 @@ namespace marian {
 
 #if 1   // dim-vocabs stores numValid() in legacy model files, and would now have been size()
   if (maxSizeUnused == vocab_.size()/*numValid()*/)
-    maxSizeUnused = vocabSize;
+    maxSizeUnused = virtualVocabSize;
 #endif
   //ABORT_IF(maxSizeUnused != 0 && maxSizeUnused != size(), "Factored vocabulary does not allow on-the-fly clipping to a maximum vocab size (from {} to {})", size(), maxSizeUnused);
   // @TODO: ^^ disabled now that we are generating the full combination of factors; reenable once we have consistent setups again
@@ -243,7 +253,7 @@ void FactoredVocab::constructGroupInfoFromFactorVocab() {
     groupCounts[g]++;
   }
   for (size_t g = 0; g < numGroups; g++) { // detect non-overlapping groups
-    LOG(info, "[embedding] Factor group '{}' has {} members", groupPrefixes_[g], groupCounts[g]);
+    LOG(info, "[vocab] Factor group '{}' has {} members", groupPrefixes_[g], groupCounts[g]);
     if (groupCounts[g] == 0) { // factor group is unused  --@TODO: once this is not hard-coded, this is an error condition
       groupRanges_[g].first = g > 0 ? groupRanges_[g-1].second : 0; // fix up the entry
       groupRanges_[g].second = groupRanges_[g].first;
@@ -324,7 +334,7 @@ size_t FactoredVocab::factorUnit2FactorIndex(WordIndex u) const {
   return u - groupRanges_[g].first;
 }
 
-
+DONT_OPTIMIZE
 void FactoredVocab::word2factors(Word word, std::vector<size_t>& factorIndices /* [numGroups] */) const {
   size_t numGroups = getNumGroups();
   factorIndices.resize(numGroups);
@@ -334,7 +344,8 @@ void FactoredVocab::word2factors(Word word, std::vector<size_t>& factorIndices /
   }
 #if 1
   auto test = factors2word(factorIndices);
-  ABORT_IF(test != word, "Word <-> factor conversion broken??");
+  ABORT_IF(test != word, "Word <-> factor conversion broken?? {} vs{}, '{}' vs. '{}'",
+           test.toWordIndex(), word.toWordIndex(), word2string(test), word2string(word));
 #endif
 }
 
@@ -659,13 +670,33 @@ size_t FactoredVocab::WordLUT::load(const std::string& path) {
   return size();
 }
 
+void FactoredVocab::WordLUT::dumpToFile(const std::string& path) {
+  io::OutputFileStream out(path);
+  for (auto kvp : index2str_)
+    out << kvp.second << "\t" << utils::withCommas(kvp.first) << "\n";
+}
+
 const static std::vector<std::string> exts{ ".fsv", ".fm"/*legacy*/ };
 
 // Note: This does not actually load it, only checks the path for the type.
+// Since loading takes a while, we cache instances.
 Ptr<IVocab> createFactoredVocab(const std::string& vocabPath) {
+  // this can be multi-threaded, so must run under lock
+  static std::mutex s_mtx;
+  std::lock_guard<std::mutex> criticalSection(s_mtx);
+
   bool isFactoredVocab = std::any_of(exts.begin(), exts.end(), [&](const std::string& ext) { return utils::endsWith(vocabPath, ext); });
-  if(isFactoredVocab)
-    return New<FactoredVocab>();
+  if (isFactoredVocab) {
+    static std::map<std::string, Ptr<IVocab>> s_cache;
+    auto iter = s_cache.find(vocabPath);
+    if (iter != s_cache.end()) {
+      LOG(info, "[vocab] Reusing existing vocabulary object in memory");
+      return iter->second;
+    }
+    auto vocab = New<FactoredVocab>();
+    s_cache.insert(std::make_pair(vocabPath, vocab));
+    return vocab;
+  }
   else
     return nullptr;
 }
diff --git a/src/data/factored_vocab.h b/src/data/factored_vocab.h
index c5bfee7f3..43bd40b1b 100755
--- a/src/data/factored_vocab.h
+++ b/src/data/factored_vocab.h
@@ -35,7 +35,7 @@ class FactoredVocab : public IVocab {
   virtual std::string decode(const Words& sentence, bool ignoreEos = true) const override final;
   virtual std::string surfaceForm(const Words& sentence) const override final;
   virtual const std::string& operator[](Word id) const override final;
-  virtual size_t size() const override final { return factorShape_.elements(); } // virtual vocab size with all factor combinations including gaps
+  virtual size_t size() const override final { return factorShape_.elements<size_t>(); } // valid WordIndex range (representing all factor combinations including gaps); virtual and huge
   virtual std::string type() const override final { return "FactoredVocab"; }
   virtual Word getEosId() const override final { return eosId_; }
   virtual Word getUnkId() const override final { return unkId_; }
@@ -100,6 +100,7 @@ class FactoredVocab : public IVocab {
     //size_t numValid() const { return str2index_.size(); } // actual non-gaps items
     size_t size() const { return str2index_.size(); }
     size_t load(const std::string& path);
+    void dumpToFile(const std::string& path);
   };
 
   // main vocab

From c0e7c6998ea2a40cbaa8ac4cf0666f224ec29b53 Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Fri, 12 Apr 2019 18:58:53 -0700
Subject: [PATCH 410/838] vocab_ is now recursively created, without looping
 through the full sparse index space

---
 src/data/factored_vocab.cpp | 33 ++++++++++++++++++++++++++++++++-
 src/data/factored_vocab.h   |  1 +
 2 files changed, 33 insertions(+), 1 deletion(-)

diff --git a/src/data/factored_vocab.cpp b/src/data/factored_vocab.cpp
index 56382bcec..baac5448f 100755
--- a/src/data/factored_vocab.cpp
+++ b/src/data/factored_vocab.cpp
@@ -121,7 +121,6 @@ namespace marian {
   // construct mapping tables for factors
   constructGroupInfoFromFactorVocab();
   constructFactorIndexConversion();
-  auto numGroups = getNumGroups();
 
   // load and parse factorMap
   // modelPath = path to file with entries in order of vocab entries of the form
@@ -190,6 +189,11 @@ namespace marian {
   auto virtualVocabSize = (WordIndex)size(); // size of vocab space including gaps
   ABORT_IF((size_t)virtualVocabSize != size(), "Too many factors, virtual index space {} exceeds the bit limit of WordIndex type", utils::withCommas(size()));
   LOG(info, "[vocab] Expanding all valid vocab entries out of {}...", utils::withCommas(size()));
+  std::vector<size_t> factorIndices(getNumGroups());
+#if 1
+  rCompleteVocab(factorIndices, /*g=*/0);
+#else
+  auto numGroups = getNumGroups();
   for (WordIndex v = 0; v < virtualVocabSize; v++) { // @BUGBUG: This is SLOOOW. Need further changes to remove this altogether
     // determine whether this bit combination is a thing
     bool isValid = true;
@@ -204,6 +208,7 @@ namespace marian {
     else if (!isValid && !vocab_.isGap(v))
       LOG(info, "WARNING: Factored vocab mismatch for {}: isValid={}, isGap={}", word2string(word), isValid, vocab_.isGap(v));
   }
+#endif
   LOG(info, "[vocab] Completed, total {} valid combinations", vocab_.size()/*numValid()*/);
   vocab_.dumpToFile(modelPath + "_expanded");
 
@@ -226,6 +231,32 @@ namespace marian {
   return size();
 }
 
+// helper to add missing words to vocab_
+// factorIndices has been formed up to *ex*cluding position [g].
+void FactoredVocab::rCompleteVocab(std::vector<size_t>& factorIndices, size_t g) {
+  // reached the end
+  if (g == getNumGroups()) {
+    auto word = factors2word(factorIndices);
+    auto v = word.toWordIndex(); // by design, we only generate those that are still missing
+    //auto ws = word2string(word);
+    //ABORT_IF(!vocab_.isGap(v), "Incorrect vocab_ construction sequence?? {}", word2string(word));
+    if (vocab_.isGap(v)) // add if missing
+      vocab_.add(word2string(word), v);
+    return;
+  }
+  // try next factor
+  if (g == 0 || lemmaHasFactorGroup(factorIndices[0], g)) {
+    for (size_t g1 = 0; g1 < factorShape_[g] - 1; g1++) {
+      factorIndices[g] = g1;
+      rCompleteVocab(factorIndices, g + 1);
+    }
+  }
+  else {
+    factorIndices[g] = FACTOR_NOT_APPLICABLE;
+    rCompleteVocab(factorIndices, g + 1);
+  }
+}
+
 void FactoredVocab::constructGroupInfoFromFactorVocab() {
   // form groups
   size_t numGroups = groupPrefixes_.size();
diff --git a/src/data/factored_vocab.h b/src/data/factored_vocab.h
index 43bd40b1b..b51a3e725 100755
--- a/src/data/factored_vocab.h
+++ b/src/data/factored_vocab.h
@@ -80,6 +80,7 @@ class FactoredVocab : public IVocab {
 private:
   void constructGroupInfoFromFactorVocab();
   void constructFactorIndexConversion();
+  void rCompleteVocab(std::vector<size_t>& factorIndices, size_t g);
 #ifdef FACTOR_FULL_EXPANSION
   void constructNormalizationInfoForVocab();
 #endif

From 4be44d30f1b684e77196cec0a1a8711b18cdb4ff Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Fri, 12 Apr 2019 19:11:38 -0700
Subject: [PATCH 411/838] renamed isGap() to (not) contains()

---
 src/data/factored_vocab.cpp | 20 +++++++++-----------
 src/data/factored_vocab.h   |  2 +-
 2 files changed, 10 insertions(+), 12 deletions(-)

diff --git a/src/data/factored_vocab.cpp b/src/data/factored_vocab.cpp
index baac5448f..1ccba6088 100755
--- a/src/data/factored_vocab.cpp
+++ b/src/data/factored_vocab.cpp
@@ -203,10 +203,10 @@ namespace marian {
       // @TODO: we have a hack in getFactor() to return not-specified if factor is specified but not applicable, making it invalid
       isValid = factorIndex != FACTOR_NOT_SPECIFIED; // FACTOR_NOT_APPLICABLE is a valid value
     }
-    if (isValid && vocab_.isGap(v)) // add if missing
+    if (isValid && !vocab_.contains(v)) // add if missing
       vocab_.add(word2string(word), word.toWordIndex());
-    else if (!isValid && !vocab_.isGap(v))
-      LOG(info, "WARNING: Factored vocab mismatch for {}: isValid={}, isGap={}", word2string(word), isValid, vocab_.isGap(v));
+    else if (!isValid && vocab_.contains(v))
+      LOG(info, "WARNING: Factored vocab mismatch for {}: isValid={}, contains={}", word2string(word), isValid, vocab_.contains(v));
   }
 #endif
   LOG(info, "[vocab] Completed, total {} valid combinations", vocab_.size()/*numValid()*/);
@@ -237,10 +237,8 @@ void FactoredVocab::rCompleteVocab(std::vector<size_t>& factorIndices, size_t g)
   // reached the end
   if (g == getNumGroups()) {
     auto word = factors2word(factorIndices);
-    auto v = word.toWordIndex(); // by design, we only generate those that are still missing
-    //auto ws = word2string(word);
-    //ABORT_IF(!vocab_.isGap(v), "Incorrect vocab_ construction sequence?? {}", word2string(word));
-    if (vocab_.isGap(v)) // add if missing
+    auto v = word.toWordIndex();
+    if (!vocab_.contains(v)) // add if missing
       vocab_.add(word2string(word), v);
     return;
   }
@@ -445,7 +443,7 @@ void FactoredVocab::constructNormalizationInfoForVocab() {
   gapLogMask_.resize(vocabSize, -1e8f);
   for (WordIndex v = 0; v < vocabSize; v++) {
 #if 1 // @TODO: TEST THIS again by disabling factored decoding in beam_search.h
-    if (!vocab_.isGap(v))
+    if (vocab_.contains(v))
       gapLogMask_[v] = 0.0f; // valid entry
 #else
     for (auto u : factorMap_[v]) {
@@ -488,9 +486,9 @@ void FactoredVocab::constructNormalizationInfoForVocab() {
 /*virtual*/ const std::string& FactoredVocab::operator[](Word word) const /*override final*/ {
   //LOG(info, "Looking up Word {}={}", word.toWordIndex(), word2string(word));
 #if 1 // @TODO: remove this
-  ABORT_IF(vocab_.isGap(word.toWordIndex()), "Invalid factor combination {}", word2string(word));
+  ABORT_IF(!vocab_.contains(word.toWordIndex()), "Invalid factor combination {}", word2string(word));
 #else // @BUGBUG: our manually prepared dict does not contain @CI tags for single letters, but it's a valid factor
-  if (vocab_.isGap(word.toWordIndex())) {
+  if (!vocab_.contains(word.toWordIndex())) {
     LOG/*_ONCE*/(info, "Factor combination {} missing in external dict, generating fake entry (only showing this warning once)", word2string(word));
     //const_cast<WordLUT&>(vocab_).add("??" + word2string(word), word.toWordIndex());
     const_cast<WordLUT&>(vocab_).add(word2string(word), word.toWordIndex());
@@ -614,7 +612,7 @@ FactoredVocab::CSRData FactoredVocab::csr_rows(const Words& words) const {
   offsets.push_back((IndexType)indices.size());
   std::vector<size_t> factorIndices;
   for (auto word : words) {
-    if (!vocab_.isGap(word.toWordIndex())) { // skip invalid combinations in the space (can only happen during initialization)  --@TODO: add a check?
+    if (vocab_.contains(word.toWordIndex())) { // skip invalid combinations in the space (can only happen during initialization)  --@TODO: add a check?
       word2factors(word, factorIndices);
 #if 0 // original code; enable this to try
       numGroups;
diff --git a/src/data/factored_vocab.h b/src/data/factored_vocab.h
index b51a3e725..03c199c45 100755
--- a/src/data/factored_vocab.h
+++ b/src/data/factored_vocab.h
@@ -94,7 +94,7 @@ class FactoredVocab : public IVocab {
     WordIndex add(const std::string& word, WordIndex index);
     const std::string& operator[](WordIndex index) const;
     WordIndex operator[](const std::string& word) const;
-    bool isGap(WordIndex index) const { return index2str_.find(index) == index2str_.end(); }
+    bool contains(WordIndex index) const { return index2str_.find(index) != index2str_.end(); }
     bool tryFind(const std::string& word, WordIndex& index) const;
     //void resize(size_t num); // @TODO: remove this, and remove the distinction of size() and numValid()
     //size_t size() const { return vocabSize_; } // nominal size including gap items

From 78ad43dbf5d9adb93777b6d9a58cef19ea4edc22 Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Fri, 12 Apr 2019 19:13:45 -0700
Subject: [PATCH 412/838] removed dead code

---
 src/data/factored_vocab.cpp | 20 --------------------
 1 file changed, 20 deletions(-)

diff --git a/src/data/factored_vocab.cpp b/src/data/factored_vocab.cpp
index 1ccba6088..81a318501 100755
--- a/src/data/factored_vocab.cpp
+++ b/src/data/factored_vocab.cpp
@@ -7,7 +7,6 @@
 
 namespace marian {
 
-    DONT_OPTIMIZE
 /*virtual*/ size_t FactoredVocab::load(const std::string& modelPath, size_t maxSizeUnused /*= 0*/) /*override final*/ {
   // If model has already been loaded, then assume this is a shared object, and skip loading it again.
   // This can be multi-threaded, so must run under lock.
@@ -190,25 +189,7 @@ namespace marian {
   ABORT_IF((size_t)virtualVocabSize != size(), "Too many factors, virtual index space {} exceeds the bit limit of WordIndex type", utils::withCommas(size()));
   LOG(info, "[vocab] Expanding all valid vocab entries out of {}...", utils::withCommas(size()));
   std::vector<size_t> factorIndices(getNumGroups());
-#if 1
   rCompleteVocab(factorIndices, /*g=*/0);
-#else
-  auto numGroups = getNumGroups();
-  for (WordIndex v = 0; v < virtualVocabSize; v++) { // @BUGBUG: This is SLOOOW. Need further changes to remove this altogether
-    // determine whether this bit combination is a thing
-    bool isValid = true;
-    auto word = Word::fromWordIndex(v);
-    for (size_t g = 0; isValid && g < numGroups; g++) {
-      auto factorIndex = getFactor(word, g);
-      // @TODO: we have a hack in getFactor() to return not-specified if factor is specified but not applicable, making it invalid
-      isValid = factorIndex != FACTOR_NOT_SPECIFIED; // FACTOR_NOT_APPLICABLE is a valid value
-    }
-    if (isValid && !vocab_.contains(v)) // add if missing
-      vocab_.add(word2string(word), word.toWordIndex());
-    else if (!isValid && vocab_.contains(v))
-      LOG(info, "WARNING: Factored vocab mismatch for {}: isValid={}, contains={}", word2string(word), isValid, vocab_.contains(v));
-  }
-#endif
   LOG(info, "[vocab] Completed, total {} valid combinations", vocab_.size()/*numValid()*/);
   vocab_.dumpToFile(modelPath + "_expanded");
 
@@ -363,7 +344,6 @@ size_t FactoredVocab::factorUnit2FactorIndex(WordIndex u) const {
   return u - groupRanges_[g].first;
 }
 
-DONT_OPTIMIZE
 void FactoredVocab::word2factors(Word word, std::vector<size_t>& factorIndices /* [numGroups] */) const {
   size_t numGroups = getNumGroups();
   factorIndices.resize(numGroups);

From 2e8bac26e334fd1e559a13fcef2ef5209152146d Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Fri, 12 Apr 2019 19:41:40 -0700
Subject: [PATCH 413/838] FactoredVocab::size() now returns the active
 vocabulary (excluding gaps), not the virtual one (which may exceed int range)

---
 src/data/factored_vocab.cpp | 25 +++++++++++--------------
 src/data/factored_vocab.h   |  3 ++-
 2 files changed, 13 insertions(+), 15 deletions(-)

diff --git a/src/data/factored_vocab.cpp b/src/data/factored_vocab.cpp
index 81a318501..242857a29 100755
--- a/src/data/factored_vocab.cpp
+++ b/src/data/factored_vocab.cpp
@@ -8,11 +8,12 @@
 namespace marian {
 
 /*virtual*/ size_t FactoredVocab::load(const std::string& modelPath, size_t maxSizeUnused /*= 0*/) /*override final*/ {
+  maxSizeUnused;
   // If model has already been loaded, then assume this is a shared object, and skip loading it again.
   // This can be multi-threaded, so must run under lock.
   static std::mutex s_mtx;
   std::lock_guard<std::mutex> criticalSection(s_mtx);
-  if (vocab_.size() != 0) {
+  if (size() != 0) {
     LOG(info, "[vocab] Attempting to load model a second time; skipping (assuming shared vocab)");
     return size();
   }
@@ -179,15 +180,13 @@ namespace marian {
     //  LOG(info, "{} -> {}", tokens.front(), word2string(word));
   }
   LOG(info, "[vocab] Factored-embedding map read with total/unique of {}/{} factors from {} example words (in space of {})",
-      numTotalFactors, factorVocabSize(), vocab_.size()/*numValid()*/, utils::withCommas(size()));
+      numTotalFactors, factorVocabSize(), vocab_.size()/*numValid()*/, utils::withCommas(virtualVocabSize()));
   vocab_.dumpToFile(modelPath + "_examples");
 
   // enumerate all combinations of factors for each lemma
   // @TODO: switch to factor-spec, which no longer enumerates all combinations. Then don't set vocab string here.
   //        This enumerates all possible combinations (incl. invalid ones), and stores all valid ones
-  auto virtualVocabSize = (WordIndex)size(); // size of vocab space including gaps
-  ABORT_IF((size_t)virtualVocabSize != size(), "Too many factors, virtual index space {} exceeds the bit limit of WordIndex type", utils::withCommas(size()));
-  LOG(info, "[vocab] Expanding all valid vocab entries out of {}...", utils::withCommas(size()));
+  LOG(info, "[vocab] Expanding all valid vocab entries out of {}...", utils::withCommas(virtualVocabSize()));
   std::vector<size_t> factorIndices(getNumGroups());
   rCompleteVocab(factorIndices, /*g=*/0);
   LOG(info, "[vocab] Completed, total {} valid combinations", vocab_.size()/*numValid()*/);
@@ -203,10 +202,10 @@ namespace marian {
   unkId_ = Word::fromWordIndex(vocab_[DEFAULT_UNK_STR]);
   //LOG(info, "eos: {}; unk: {}", word2string(eosId_), word2string(unkId_));
 
-#if 1   // dim-vocabs stores numValid() in legacy model files, and would now have been size()
-  if (maxSizeUnused == vocab_.size()/*numValid()*/)
-    maxSizeUnused = virtualVocabSize;
-#endif
+//#if 1   // dim-vocabs stores numValid() in legacy model files, and would now have been size()
+//  if (maxSizeUnused == vocab_.size()/*numValid()*/)
+//    maxSizeUnused = virtualVocabSize;
+//#endif
   //ABORT_IF(maxSizeUnused != 0 && maxSizeUnused != size(), "Factored vocabulary does not allow on-the-fly clipping to a maximum vocab size (from {} to {})", size(), maxSizeUnused);
   // @TODO: ^^ disabled now that we are generating the full combination of factors; reenable once we have consistent setups again
   return size();
@@ -285,6 +284,8 @@ void FactoredVocab::constructFactorIndexConversion() {
   factorStrides_.resize(factorShape_.size(), 1);
   for (size_t g = factorStrides_.size() - 1; g --> 0; )
     factorStrides_[g] = factorStrides_[g + 1] * (size_t)factorShape_[g + 1];
+  ABORT_IF((WordIndex)virtualVocabSize() != virtualVocabSize(),
+      "Too many factors, virtual index space {} exceeds the bit limit of WordIndex type", utils::withCommas(virtualVocabSize()));
 }
 
 // encode factors into a Word struct
@@ -417,7 +418,7 @@ size_t FactoredVocab::getFactor(Word word, size_t groupIndex) const {
 void FactoredVocab::constructNormalizationInfoForVocab() {
   // create mappings needed for normalization in factored outputs
   //size_t numGroups = groupPrefixes_.size();
-  size_t vocabSize = size();
+  size_t vocabSize = virtualVocabSize();
   //factorMasks_  .resize(numGroups, std::vector<float>(vocabSize, 0));     // [g][v] 1.0 if word v has factor g
   //factorIndices_.resize(numGroups, std::vector<IndexType>(vocabSize, 0)); // [g][v] index of factor (or any valid index if it does not have it; we use 0)
   gapLogMask_.resize(vocabSize, -1e8f);
@@ -667,10 +668,6 @@ bool FactoredVocab::WordLUT::tryFind(const std::string& word, WordIndex& index)
   index = iter->second;
   return true;
 }
-//void FactoredVocab::WordLUT::resize(size_t num) {
-//  ABORT_IF(num < size(), "Word table cannot be shrunk");
-//  vocabSize_ = num;
-//}
 size_t FactoredVocab::WordLUT::load(const std::string& path) {
   std::string line;
   io::InputFileStream in(path);
diff --git a/src/data/factored_vocab.h b/src/data/factored_vocab.h
index 03c199c45..33ff75ae5 100755
--- a/src/data/factored_vocab.h
+++ b/src/data/factored_vocab.h
@@ -35,7 +35,7 @@ class FactoredVocab : public IVocab {
   virtual std::string decode(const Words& sentence, bool ignoreEos = true) const override final;
   virtual std::string surfaceForm(const Words& sentence) const override final;
   virtual const std::string& operator[](Word id) const override final;
-  virtual size_t size() const override final { return factorShape_.elements<size_t>(); } // valid WordIndex range (representing all factor combinations including gaps); virtual and huge
+  virtual size_t size() const override final { return vocab_.size(); } // active factored vocabulary size (counting all valid combinations but not gaps)
   virtual std::string type() const override final { return "FactoredVocab"; }
   virtual Word getEosId() const override final { return eosId_; }
   virtual Word getUnkId() const override final { return unkId_; }
@@ -47,6 +47,7 @@ class FactoredVocab : public IVocab {
 
   // factor-specific. These methods are consumed by Output and Embedding.
   size_t factorVocabSize() const { return factorVocab_.size(); } // total number of factors across all types
+  size_t virtualVocabSize() const { return factorShape_.elements<size_t>(); } // valid WordIndex range (representing all factor combinations including gaps); virtual and huge
 
   CSRData csr_rows(const Words& words) const; // sparse matrix for summing up factors from the concatenated embedding matrix for each word
 

From 621a7a0daf075a82b6c7a90cc1f9fccb48309e1b Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Sat, 13 Apr 2019 01:45:58 -0700
Subject: [PATCH 414/838] s2s encoder now supports FactoredVocab

---
 src/models/s2s.h | 13 ++++++++++---
 1 file changed, 10 insertions(+), 3 deletions(-)

diff --git a/src/models/s2s.h b/src/models/s2s.h
index cfd0d7e00..67b0817a4 100755
--- a/src/models/s2s.h
+++ b/src/models/s2s.h
@@ -119,12 +119,12 @@ class EncoderS2S : public EncoderBase {
     return context;
   }
 
-  Ptr<IEmbeddingLayer> createSourceEmbedding(Ptr<ExpressionGraph> graph) {
+  Ptr<IEmbeddingLayer> createSourceEmbeddingLayer(Ptr<ExpressionGraph> graph) {
     // create source embeddings
     int dimVoc = opt<std::vector<int>>("dim-vocabs")[batchIndex_];
     int dimEmb = opt<int>("dim-emb");
 
-    // @TODO: code dup with Decider and EncoderTransformer; actually diverged by now. Unify this.
+    // @TODO: code dup with Decoder and EncoderTransformer; actually diverged by now. Unify this.
     auto embFactory = embedding()  //
         ("dimVocab", dimVoc)       //
         ("dimEmb", dimEmb);
@@ -144,14 +144,21 @@ class EncoderS2S : public EncoderBase {
           ("normalization", opt<bool>("embedding-normalization"));
     }
 
+    embFactory("vocab", opt<std::vector<std::string>>("vocabs")[batchIndex_]); // for factored embeddings
     return embFactory.construct(graph);
   }
 
   EncoderS2S(Ptr<Options> options) : EncoderBase(options) {}
 
+  std::vector<Ptr<IEmbeddingLayer>> embedding_; // @TODO: move away, also rename
   virtual Ptr<EncoderState> build(Ptr<ExpressionGraph> graph,
                                   Ptr<data::CorpusBatch> batch) override {
-    auto embedding = createSourceEmbedding(graph);
+    // lazily create embedding layer
+    if (embedding_.empty() || !embedding_[batchIndex_]) { // lazy
+      embedding_.resize(batch->sets());
+      embedding_[batchIndex_] = createSourceEmbeddingLayer(graph);
+    }
+    auto embedding = embedding_[batchIndex_];
 
     // select embeddings that occur in the batch
     Expr batchEmbeddings, batchMask; std::tie

From 5fceec0d5ef5ccfdad2a9ce8e9defd12e1bec0bb Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Sat, 13 Apr 2019 14:03:40 -0700
Subject: [PATCH 415/838] temp hack: validator will write 'bleu' file not
 'bleu-detok', for Flo compat

---
 src/training/validator.h | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/training/validator.h b/src/training/validator.h
index a01f2fb17..1341834dc 100755
--- a/src/training/validator.h
+++ b/src/training/validator.h
@@ -711,7 +711,7 @@ class BleuValidator : public Validator<data::Corpus, models::IModel> {
   };
 
   // @TODO: why do we return this string, but not pass it to the constructor?
-  std::string type() override { return detok_ ? "bleu-detok" : "bleu"; }
+  std::string type() override { return /*detok_ ? "bleu-detok" :*/ "bleu"; }
 
 protected:
   // Tokenizer function adapted from multi-bleu-detok.pl, corresponds to sacreBLEU.py

From 87a88fb4d095402af87330fdbfe60a8607f02ad9 Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Sat, 13 Apr 2019 14:48:26 -0700
Subject: [PATCH 416/838] BleuValidator now always uses detokenized mode with
 FactoredSegmenter, even if not requested ()

---
 src/training/validator.h | 15 +++++++++------
 1 file changed, 9 insertions(+), 6 deletions(-)

diff --git a/src/training/validator.h b/src/training/validator.h
index 1341834dc..97c20181d 100755
--- a/src/training/validator.h
+++ b/src/training/validator.h
@@ -592,10 +592,6 @@ class BleuValidator : public Validator<data::Corpus, models::IModel> {
     builder_ = models::createModelFromOptions(options_, models::usage::translation);
 
     auto vocab = vocabs_.back();
-#if 1 // hack for now, to get this feature when running under Flo
-    if (vocab->type() == "FactoredVocab")
-      detok_ = true; // always use bleu-detok
-#endif
     ABORT_IF(detok_ && vocab->type() != "SentencePieceVocab" && vocab->type() != "FactoredVocab",
              "Detokenizing BLEU validator expects the target vocabulary to be SentencePieceVocab or FactoredVocab. "
              "Current vocabulary type is {}", vocab->type());
@@ -711,7 +707,7 @@ class BleuValidator : public Validator<data::Corpus, models::IModel> {
   };
 
   // @TODO: why do we return this string, but not pass it to the constructor?
-  std::string type() override { return /*detok_ ? "bleu-detok" :*/ "bleu"; }
+  std::string type() override { return detok_ ? "bleu-detok" : "bleu"; }
 
 protected:
   // Tokenizer function adapted from multi-bleu-detok.pl, corresponds to sacreBLEU.py
@@ -830,7 +826,14 @@ class BleuValidator : public Validator<data::Corpus, models::IModel> {
       ref.push_back(w);
     }
 
-    if(detok_)
+    bool detok = detok_;
+#if 1 // hack for now, to get this feature when running under Flo
+    if (vocabs_.back()->type() == "FactoredVocab") {
+      LOG_ONCE(info, "[valid] FactoredVocab implies using detokenized BLEU");
+      detok = true; // always use bleu-detok
+    }
+#endif
+    if(detok)
       updateStats(stats, decode(cand, /*addEOS=*/ true), decode(ref));
     else
       updateStats(stats, cand, ref);

From 59acad39637c1995c886efef5901fc701e6695e9 Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Sat, 13 Apr 2019 18:32:32 -0700
Subject: [PATCH 417/838] FactoredVocab::operator[string] now parses the token,
 to allow for varying factor order; quicksand encode/decode now call
 operator[]

---
 src/data/factored_vocab.cpp | 45 ++++++++++++++++++++++++++++---------
 src/data/factored_vocab.h   |  5 +++--
 src/microsoft/quicksand.cpp |  8 ++-----
 3 files changed, 40 insertions(+), 18 deletions(-)

diff --git a/src/data/factored_vocab.cpp b/src/data/factored_vocab.cpp
index 242857a29..890ae0415 100755
--- a/src/data/factored_vocab.cpp
+++ b/src/data/factored_vocab.cpp
@@ -172,25 +172,27 @@ namespace marian {
     // add to vocab (the wordIndex are not dense, so the vocab will have holes)
     //if (tokens.front().front() == '<') // all others are auto-expanded
     // for now add what we get, and then expand more below
-    vocab_.add(tokens.front(), wordIndex);
-    if (tokens.front() != word2string(word))
-      LOG_ONCE(info, "[vocab] Word name in vocab file {} differs from canonical form {} (this warning is only shown once)", tokens.front(), word2string(word));
+    auto wordString = word2string(word);
+    if (tokens.front() != wordString) // order may differ, since we formed the input based on the factors in the user file, which may be in any order
+      LOG_ONCE(info, "[vocab] Word name in vocab file {} differs from canonical form {} (this warning is only shown once)", tokens.front(), wordString);
+    vocab_.add(wordString, wordIndex);
     numTotalFactors += tokens.size() - 1;
     //if (v % 5000 == 0)
     //  LOG(info, "{} -> {}", tokens.front(), word2string(word));
   }
   LOG(info, "[vocab] Factored-embedding map read with total/unique of {}/{} factors from {} example words (in space of {})",
       numTotalFactors, factorVocabSize(), vocab_.size()/*numValid()*/, utils::withCommas(virtualVocabSize()));
-  vocab_.dumpToFile(modelPath + "_examples");
+  //vocab_.dumpToFile(modelPath + "_examples");
 
-  // enumerate all combinations of factors for each lemma
-  // @TODO: switch to factor-spec, which no longer enumerates all combinations. Then don't set vocab string here.
-  //        This enumerates all possible combinations (incl. invalid ones), and stores all valid ones
+  // enumerate all valid combinations of factors for each lemma and add them to vocab_
+  // Having vocab_ makes life easier, although it is not strictly needed. Typical expanded valid vocabs
+  // are on the order of 200k entries. If we ever go much larger, we'd want to elimimate vocab_
+  // and fully virtualize its function.
   LOG(info, "[vocab] Expanding all valid vocab entries out of {}...", utils::withCommas(virtualVocabSize()));
   std::vector<size_t> factorIndices(getNumGroups());
   rCompleteVocab(factorIndices, /*g=*/0);
   LOG(info, "[vocab] Completed, total {} valid combinations", vocab_.size()/*numValid()*/);
-  vocab_.dumpToFile(modelPath + "_expanded");
+  //vocab_.dumpToFile(modelPath + "_expanded");
 
 #ifdef FACTOR_FULL_EXPANSION
   // create mappings needed for normalization in factored outputs
@@ -384,6 +386,28 @@ std::string FactoredVocab::word2string(Word word) const {
   return res;
 }
 
+Word FactoredVocab::string2word(const std::string& w) const {
+  auto sep = std::string(1, factorSeparator_);
+  auto parts = utils::splitAny(w, sep);
+  auto na = FACTOR_NOT_APPLICABLE; // (gcc compiler bug: sometimes it cannot find this if passed directly)
+  std::vector<size_t> factorIndices(groupRanges_.size(), na); // default for unused factors
+  for (size_t i = 0; i < parts.size(); i++) {
+    WordIndex u;
+    bool found = factorVocab_.tryFind(i == 0 ? parts[i] : sep + parts[i], u);
+    if (!found) {
+      LOG_ONCE(info, "WARNING: Unknown factor '{}' in '{}'; mapping to '{}'", parts[i], w, word2string(getUnkId()));
+      return getUnkId();
+    }
+    // convert u to relative u within factor group range
+    auto g = factorGroups_[u];
+    ABORT_IF(u < groupRanges_[g].first || u >= groupRanges_[g].second, "Invalid factorGroups_ entry??");
+    factorIndices[g] = u - groupRanges_[g].first;
+  }
+  auto word = factors2word(factorIndices);
+  //auto w2 = word2string(word);
+  return word;
+}
+
 size_t FactoredVocab::getFactor(Word word, size_t groupIndex) const {
   size_t index = word.toWordIndex();
   size_t factor0Index = index / factorStrides_[0];
@@ -459,9 +483,10 @@ void FactoredVocab::constructNormalizationInfoForVocab() {
   if (found)
     return Word::fromWordIndex(index);
   else
+    return string2word(word);
     //ABORT("Unknown word {} mapped to {}", word, word2string(getUnkId()));
-    LOG_ONCE(info, "WARNING: Unknown word {} mapped to {}", word, word2string(getUnkId()));
-    return getUnkId();
+    //LOG_ONCE(info, "WARNING: Unknown word {} mapped to {}", word, word2string(getUnkId()));
+    //return getUnkId();
 }
 
 /*virtual*/ const std::string& FactoredVocab::operator[](Word word) const /*override final*/ {
diff --git a/src/data/factored_vocab.h b/src/data/factored_vocab.h
index 33ff75ae5..9a916aa82 100755
--- a/src/data/factored_vocab.h
+++ b/src/data/factored_vocab.h
@@ -77,7 +77,8 @@ class FactoredVocab : public IVocab {
   static bool isFactorValid(size_t factorIndex) { return factorIndex < FACTOR_NOT_SPECIFIED; }
 
   static Ptr<FactoredVocab> tryCreateAndLoad(const std::string& path); // load from "vocab" option if it specifies a factored vocab
-  std::string word2string(Word word) const; // (diagnostics only)
+  std::string word2string(Word word) const;
+  Word string2word(const std::string& w) const;
 private:
   void constructGroupInfoFromFactorVocab();
   void constructFactorIndexConversion();
@@ -120,7 +121,7 @@ class FactoredVocab : public IVocab {
 #endif
   std::vector<size_t> factorGroups_;                   // [u] -> group id of factor u
   std::vector<std::pair<size_t, size_t>> groupRanges_; // [group id g] -> (u_begin,u_end) index range of factors u for this group. These don't overlap.
-  std::vector<std::vector<bool>>lemmaHasFactorGroup_;  // [factor 0 index][g] -> true if lemma has factor group
+  std::vector<std::vector<bool>> lemmaHasFactorGroup_; // [factor 0 index][g] -> true if lemma has factor group
   Shape factorShape_;                                  // [g] number of factors in each factor group
   std::vector<size_t> factorStrides_;                  // [g] stride for factor dimension
   //std::vector<std::vector<float>>     factorMasks_;    // [g][v] 1.0 if word v has factor g
diff --git a/src/microsoft/quicksand.cpp b/src/microsoft/quicksand.cpp
index 6df9748bb..51f857b52 100755
--- a/src/microsoft/quicksand.cpp
+++ b/src/microsoft/quicksand.cpp
@@ -36,13 +36,9 @@ class VocabWrapper : public IVocabWrapper {
   Ptr<Vocab> pImpl_;
 public:
   VocabWrapper(Ptr<Vocab> vocab) : pImpl_(vocab) {}
-  WordIndex encode(const std::string& word) const override {
-    auto enc = pImpl_->encode(word, /*addEOS=*/false, /*inference=*/true);
-    ABORT_IF(enc.size() != 1, "QuickSAND passed an invalid word '{}' to Marian (empty or contains a space)", word);
-    return enc[0].toWordIndex();
-  }
+  WordIndex encode(const std::string& word) const override { return (*pImpl_)[word].toWordIndex(); }
+  std::string decode(WordIndex id) const override { return (*pImpl_)[Word::fromWordIndex(id)]; }
   size_t size() const override { return pImpl_->size(); }
-  std::string decode(WordIndex id) const override { return pImpl_->decode({ Word::fromWordIndex(id) }); }
   Ptr<Vocab> getVocab() const { return pImpl_; }
 };
 

From ee8c14cfbc685f4a25967a33ddbba533925b098f Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Sat, 13 Apr 2019 23:52:30 -0700
Subject: [PATCH 418/838] detokenizing validator now logs the detokenized
 sequence once

---
 src/data/factored_vocab.cpp | 2 +-
 src/training/validator.h    | 7 +++++++
 2 files changed, 8 insertions(+), 1 deletion(-)

diff --git a/src/data/factored_vocab.cpp b/src/data/factored_vocab.cpp
index 890ae0415..619fe9841 100755
--- a/src/data/factored_vocab.cpp
+++ b/src/data/factored_vocab.cpp
@@ -14,7 +14,7 @@ namespace marian {
   static std::mutex s_mtx;
   std::lock_guard<std::mutex> criticalSection(s_mtx);
   if (size() != 0) {
-    LOG(info, "[vocab] Attempting to load model a second time; skipping (assuming shared vocab)");
+    //LOG(info, "[vocab] Attempting to load model a second time; skipping (assuming shared vocab)");
     return size();
   }
 
diff --git a/src/training/validator.h b/src/training/validator.h
index f831f723c..78b9f9d33 100755
--- a/src/training/validator.h
+++ b/src/training/validator.h
@@ -834,11 +834,18 @@ class BleuValidator : public Validator<data::Corpus, models::IModel> {
 
     bool detok = detok_;
 #if 1 // hack for now, to get this feature when running under Flo
+    // Problem is that Flo pieces that pass 'bleu' do not know whether vocab is factored,
+    // hence cannot select 'bleu-detok'.
     if (vocabs_.back()->type() == "FactoredVocab") {
       LOG_ONCE(info, "[valid] FactoredVocab implies using detokenized BLEU");
       detok = true; // always use bleu-detok
     }
 #endif
+    if(detok) { // log the first detokenized string
+      LOG_ONCE(info, "[valid] First sentence's tokens after detokenization, as scored:");
+      LOG_ONCE(info, "[valid]  Hyp: {}", utils::join(decode(cand, /*addEOS=*/ true)));
+      LOG_ONCE(info, "[valid]  Ref: {}", utils::join(decode(ref)));
+    }
     if(detok)
       updateStats(stats, decode(cand, /*addEOS=*/ true), decode(ref));
     else

From 9f7d4fd530628845b3f91c5eacc2a64151899374 Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Mon, 15 Apr 2019 15:46:41 -0700
Subject: [PATCH 419/838] implemented cpu::CSRProd() for decoding

---
 src/tensors/cpu/prod.cpp | 73 ++++++++++++++++++++++++++++++++++++----
 1 file changed, 66 insertions(+), 7 deletions(-)

diff --git a/src/tensors/cpu/prod.cpp b/src/tensors/cpu/prod.cpp
index 408e47913..fd18ea1c2 100644
--- a/src/tensors/cpu/prod.cpp
+++ b/src/tensors/cpu/prod.cpp
@@ -169,15 +169,74 @@ void ProdWithBias(marian::Tensor C,
 
 void CSRProd(marian::Tensor C,
              Ptr<Allocator> /*allocator*/,
-             const marian::Tensor& A_values,
-             const marian::Tensor& A_indices,
-             const marian::Tensor& A_offsets,
-             const marian::Tensor& B,
-             bool transA,
+             const marian::Tensor& S_values,
+             const marian::Tensor& S_indices,
+             const marian::Tensor& S_offsets,
+             const marian::Tensor& D,
+             bool transS,
              bool swapOperands,
              float beta) {
-  C, A_values, A_indices, A_offsets, B, transA, swapOperands, beta;
-  ABORT("CSRProd is not yet implemented for CPU");
+  C, S_values, S_indices, S_offsets, D;
+
+  // Note: The CPU implementation currently only implements what's needed for decoding.
+
+  // interpret tensor dimensions as matrix dimensions
+  const auto& shapeC = C->shape();
+  const auto& shapeD = D->shape();
+  // If swapOperands, S and D are swapped (C = D x S instead of C = S x D).
+  // In that case, in the next 6 lines, please read all dimensions as if they were reversed in order.
+  auto rowsC = shapeC[-(int)swapOperands];
+  auto colsC = shapeC.elements() / rowsC;
+  auto rowsD = shapeD[-(int)swapOperands];
+  auto colsD = shapeD.elements() / rowsD;
+  auto rowsS = transS ? rowsD : rowsC;
+  auto colsS = transS ? rowsC : rowsD;
+  ABORT_IF(colsD != colsC, "Inconsistent outer dimensions in CSR product");
+  if (swapOperands) { // make rowsX actual row dimensions again, likewise colsX
+    std::swap(rowsC, colsC);
+    std::swap(rowsD, colsD);
+    std::swap(rowsS, colsS);
+  }
+  // sparse arrays
+  auto numValues  = S_values->shape().elements();
+  auto numOffsets = S_offsets->shape().elements() - 1; // -1 since last value is length
+  ABORT_IF(numOffsets != rowsS, "Unexpected number of rows in CSR argument");
+  ABORT_IF(S_values->shape() != S_indices->shape(), "CSR values and indices must have the same size");
+  if (!transS && !swapOperands) {
+    // C = S * D, where D = CSR matrix
+    const auto* offsets = S_offsets->data<IndexType>();
+    const auto* indices = S_indices->data<IndexType>();
+    const auto* values  = S_values->data<float>();
+    const auto* dataD   = D->data<float>();
+    auto*       dataC   = C->data<float>();
+    ABORT_IF(beta != 0 && beta != 1, "cpu::CSRProd only supports beta = 0 or 1");
+    for (size_t i = 0; i < rowsC; i++) {
+      auto add = (beta == 1); // first element: overwrite or add according to beta; subsequent elements: add
+      for (size_t kk = offsets[i]; kk < offsets[i + 1]; kk++) {
+        auto k = indices[kk];    // fetch the non-zero row
+        auto valS = values[kk]; // and the value from that row
+        // This code is written with the hope for good vectorization, and the hope
+        // that adding to memory will be done efficiently by the caching system.
+        if (valS == 1)
+          if (!add)
+            for (size_t j = 0; j < colsC; j++)
+              dataC[i * colsC/*==colsD*/ + j] = dataD[k * colsC/*==colsD*/ + j]; // this is a memcpy()
+          else
+            for (size_t j = 0; j < colsC; j++)
+              dataC[i * colsC/*==colsD*/ + j] += dataD[k * colsC/*==colsD*/ + j]; // this is a contiguous-vector addition
+        else
+          if (!add)
+            for (size_t j = 0; j < colsC; j++)
+              dataC[i * colsC/*==colsD*/ + j] = valS * dataD[k * colsC/*==colsD*/ + j];
+          else
+            for (size_t j = 0; j < colsC; j++)
+              dataC[i * colsC/*==colsD*/ + j] += valS * dataD[k * colsC/*==colsD*/ + j]; // notice the +=
+        add = true; // next iteration will add to existing result
+      }
+    }
+  }
+  else
+    ABORT("CSRProd for transS={}, swapOperands={} is not yet implemented for CPU", transS, swapOperands);
 }
 
 }  // namespace cpu

From 1f10a700e32a7826187fefd397e456b0ce208814 Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Mon, 15 Apr 2019 15:46:41 -0700
Subject: [PATCH 420/838] implemented cpu::CSRProd() for decoding

---
 src/tensors/cpu/prod.cpp | 73 ++++++++++++++++++++++++++++++++++++----
 1 file changed, 66 insertions(+), 7 deletions(-)

diff --git a/src/tensors/cpu/prod.cpp b/src/tensors/cpu/prod.cpp
index 408e47913..fd18ea1c2 100644
--- a/src/tensors/cpu/prod.cpp
+++ b/src/tensors/cpu/prod.cpp
@@ -169,15 +169,74 @@ void ProdWithBias(marian::Tensor C,
 
 void CSRProd(marian::Tensor C,
              Ptr<Allocator> /*allocator*/,
-             const marian::Tensor& A_values,
-             const marian::Tensor& A_indices,
-             const marian::Tensor& A_offsets,
-             const marian::Tensor& B,
-             bool transA,
+             const marian::Tensor& S_values,
+             const marian::Tensor& S_indices,
+             const marian::Tensor& S_offsets,
+             const marian::Tensor& D,
+             bool transS,
              bool swapOperands,
              float beta) {
-  C, A_values, A_indices, A_offsets, B, transA, swapOperands, beta;
-  ABORT("CSRProd is not yet implemented for CPU");
+  C, S_values, S_indices, S_offsets, D;
+
+  // Note: The CPU implementation currently only implements what's needed for decoding.
+
+  // interpret tensor dimensions as matrix dimensions
+  const auto& shapeC = C->shape();
+  const auto& shapeD = D->shape();
+  // If swapOperands, S and D are swapped (C = D x S instead of C = S x D).
+  // In that case, in the next 6 lines, please read all dimensions as if they were reversed in order.
+  auto rowsC = shapeC[-(int)swapOperands];
+  auto colsC = shapeC.elements() / rowsC;
+  auto rowsD = shapeD[-(int)swapOperands];
+  auto colsD = shapeD.elements() / rowsD;
+  auto rowsS = transS ? rowsD : rowsC;
+  auto colsS = transS ? rowsC : rowsD;
+  ABORT_IF(colsD != colsC, "Inconsistent outer dimensions in CSR product");
+  if (swapOperands) { // make rowsX actual row dimensions again, likewise colsX
+    std::swap(rowsC, colsC);
+    std::swap(rowsD, colsD);
+    std::swap(rowsS, colsS);
+  }
+  // sparse arrays
+  auto numValues  = S_values->shape().elements();
+  auto numOffsets = S_offsets->shape().elements() - 1; // -1 since last value is length
+  ABORT_IF(numOffsets != rowsS, "Unexpected number of rows in CSR argument");
+  ABORT_IF(S_values->shape() != S_indices->shape(), "CSR values and indices must have the same size");
+  if (!transS && !swapOperands) {
+    // C = S * D, where D = CSR matrix
+    const auto* offsets = S_offsets->data<IndexType>();
+    const auto* indices = S_indices->data<IndexType>();
+    const auto* values  = S_values->data<float>();
+    const auto* dataD   = D->data<float>();
+    auto*       dataC   = C->data<float>();
+    ABORT_IF(beta != 0 && beta != 1, "cpu::CSRProd only supports beta = 0 or 1");
+    for (size_t i = 0; i < rowsC; i++) {
+      auto add = (beta == 1); // first element: overwrite or add according to beta; subsequent elements: add
+      for (size_t kk = offsets[i]; kk < offsets[i + 1]; kk++) {
+        auto k = indices[kk];    // fetch the non-zero row
+        auto valS = values[kk]; // and the value from that row
+        // This code is written with the hope for good vectorization, and the hope
+        // that adding to memory will be done efficiently by the caching system.
+        if (valS == 1)
+          if (!add)
+            for (size_t j = 0; j < colsC; j++)
+              dataC[i * colsC/*==colsD*/ + j] = dataD[k * colsC/*==colsD*/ + j]; // this is a memcpy()
+          else
+            for (size_t j = 0; j < colsC; j++)
+              dataC[i * colsC/*==colsD*/ + j] += dataD[k * colsC/*==colsD*/ + j]; // this is a contiguous-vector addition
+        else
+          if (!add)
+            for (size_t j = 0; j < colsC; j++)
+              dataC[i * colsC/*==colsD*/ + j] = valS * dataD[k * colsC/*==colsD*/ + j];
+          else
+            for (size_t j = 0; j < colsC; j++)
+              dataC[i * colsC/*==colsD*/ + j] += valS * dataD[k * colsC/*==colsD*/ + j]; // notice the +=
+        add = true; // next iteration will add to existing result
+      }
+    }
+  }
+  else
+    ABORT("CSRProd for transS={}, swapOperands={} is not yet implemented for CPU", transS, swapOperands);
 }
 
 }  // namespace cpu

From 569cd1be5f3125869bf32c785f48ea1c2690519c Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Tue, 16 Apr 2019 14:47:55 -0700
Subject: [PATCH 421/838] increased logging for invalid factrors

---
 src/data/factored_vocab.cpp | 6 +++++-
 1 file changed, 5 insertions(+), 1 deletion(-)

diff --git a/src/data/factored_vocab.cpp b/src/data/factored_vocab.cpp
index 619fe9841..d168cb4bf 100755
--- a/src/data/factored_vocab.cpp
+++ b/src/data/factored_vocab.cpp
@@ -395,7 +395,11 @@ Word FactoredVocab::string2word(const std::string& w) const {
     WordIndex u;
     bool found = factorVocab_.tryFind(i == 0 ? parts[i] : sep + parts[i], u);
     if (!found) {
-      LOG_ONCE(info, "WARNING: Unknown factor '{}' in '{}'; mapping to '{}'", parts[i], w, word2string(getUnkId()));
+      static int logs = 100;
+      if (logs > 0) {
+        logs--;
+        LOG(info, "WARNING: Unknown factor '{}' in '{}'; mapping to '{}'", parts[i], w, word2string(getUnkId()));
+      }
       return getUnkId();
     }
     // convert u to relative u within factor group range

From 44518f6a5bec0ef49955f5c8adb27e48a46a3d3e Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Tue, 16 Apr 2019 21:34:44 -0700
Subject: [PATCH 422/838] fixed a gcc warning

---
 src/tensors/cpu/prod.cpp | 3 +--
 1 file changed, 1 insertion(+), 2 deletions(-)

diff --git a/src/tensors/cpu/prod.cpp b/src/tensors/cpu/prod.cpp
index fd18ea1c2..ac13ccee8 100644
--- a/src/tensors/cpu/prod.cpp
+++ b/src/tensors/cpu/prod.cpp
@@ -198,9 +198,8 @@ void CSRProd(marian::Tensor C,
     std::swap(rowsS, colsS);
   }
   // sparse arrays
-  auto numValues  = S_values->shape().elements();
   auto numOffsets = S_offsets->shape().elements() - 1; // -1 since last value is length
-  ABORT_IF(numOffsets != rowsS, "Unexpected number of rows in CSR argument");
+  ABORT_IF(numOffsets != rowsS, "Unexpected number of rows in CSR argument"); numOffsets;
   ABORT_IF(S_values->shape() != S_indices->shape(), "CSR values and indices must have the same size");
   if (!transS && !swapOperands) {
     // C = S * D, where D = CSR matrix

From b96d9b33ebf2593d63a971e3ab50ab3792999037 Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Tue, 16 Apr 2019 21:34:44 -0700
Subject: [PATCH 423/838] fixed a build breal

---
 src/tensors/cpu/prod.cpp | 62 ++++++++++++++++++++++++++++++++++++++--
 1 file changed, 60 insertions(+), 2 deletions(-)

diff --git a/src/tensors/cpu/prod.cpp b/src/tensors/cpu/prod.cpp
index 408e47913..ea8b1359d 100644
--- a/src/tensors/cpu/prod.cpp
+++ b/src/tensors/cpu/prod.cpp
@@ -176,8 +176,66 @@ void CSRProd(marian::Tensor C,
              bool transA,
              bool swapOperands,
              float beta) {
-  C, A_values, A_indices, A_offsets, B, transA, swapOperands, beta;
-  ABORT("CSRProd is not yet implemented for CPU");
+  C, S_values, S_indices, S_offsets, D;
+
+  // Note: The CPU implementation currently only implements what's needed for decoding.
+
+  // interpret tensor dimensions as matrix dimensions
+  const auto& shapeC = C->shape();
+  const auto& shapeD = D->shape();
+  // If swapOperands, S and D are swapped (C = D x S instead of C = S x D).
+  // In that case, in the next 6 lines, please read all dimensions as if they were reversed in order.
+  auto rowsC = shapeC[-(int)swapOperands];
+  auto colsC = shapeC.elements() / rowsC;
+  auto rowsD = shapeD[-(int)swapOperands];
+  auto colsD = shapeD.elements() / rowsD;
+  auto rowsS = transS ? rowsD : rowsC;
+  auto colsS = transS ? rowsC : rowsD;
+  ABORT_IF(colsD != colsC, "Inconsistent outer dimensions in CSR product");
+  if (swapOperands) { // make rowsX actual row dimensions again, likewise colsX
+    std::swap(rowsC, colsC);
+    std::swap(rowsD, colsD);
+    std::swap(rowsS, colsS);
+  }
+  // sparse arrays
+  auto numOffsets = S_offsets->shape().elements() - 1; // -1 since last value is length
+  ABORT_IF(numOffsets != rowsS, "Unexpected number of rows in CSR argument"); numOffsets;
+  ABORT_IF(S_values->shape() != S_indices->shape(), "CSR values and indices must have the same size");
+  if (!transS && !swapOperands) {
+    // C = S * D, where D = CSR matrix
+    const auto* offsets = S_offsets->data<IndexType>();
+    const auto* indices = S_indices->data<IndexType>();
+    const auto* values  = S_values->data<float>();
+    const auto* dataD   = D->data<float>();
+    auto*       dataC   = C->data<float>();
+    ABORT_IF(beta != 0 && beta != 1, "cpu::CSRProd only supports beta = 0 or 1");
+    for (size_t i = 0; i < rowsC; i++) {
+      auto add = (beta == 1); // first element: overwrite or add according to beta; subsequent elements: add
+      for (size_t kk = offsets[i]; kk < offsets[i + 1]; kk++) {
+        auto k = indices[kk];    // fetch the non-zero row
+        auto valS = values[kk]; // and the value from that row
+        // This code is written with the hope for good vectorization, and the hope
+        // that adding to memory will be done efficiently by the caching system.
+        if (valS == 1)
+          if (!add)
+            for (size_t j = 0; j < colsC; j++)
+              dataC[i * colsC/*==colsD*/ + j] = dataD[k * colsC/*==colsD*/ + j]; // this is a memcpy()
+          else
+            for (size_t j = 0; j < colsC; j++)
+              dataC[i * colsC/*==colsD*/ + j] += dataD[k * colsC/*==colsD*/ + j]; // this is a contiguous-vector addition
+        else
+          if (!add)
+            for (size_t j = 0; j < colsC; j++)
+              dataC[i * colsC/*==colsD*/ + j] = valS * dataD[k * colsC/*==colsD*/ + j];
+          else
+            for (size_t j = 0; j < colsC; j++)
+              dataC[i * colsC/*==colsD*/ + j] += valS * dataD[k * colsC/*==colsD*/ + j]; // notice the +=
+        add = true; // next iteration will add to existing result
+      }
+    }
+  }
+  else
+    ABORT("CSRProd for transS={}, swapOperands={} is not yet implemented for CPU", transS, swapOperands);
 }
 
 }  // namespace cpu

From a38d09f25d66eac90b817755ccd9e70feb3afb8a Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Tue, 16 Apr 2019 21:34:44 -0700
Subject: [PATCH 424/838] fixed a gcc warning

---
 src/tensors/cpu/prod.cpp | 3 +--
 1 file changed, 1 insertion(+), 2 deletions(-)

diff --git a/src/tensors/cpu/prod.cpp b/src/tensors/cpu/prod.cpp
index fd18ea1c2..ac13ccee8 100644
--- a/src/tensors/cpu/prod.cpp
+++ b/src/tensors/cpu/prod.cpp
@@ -198,9 +198,8 @@ void CSRProd(marian::Tensor C,
     std::swap(rowsS, colsS);
   }
   // sparse arrays
-  auto numValues  = S_values->shape().elements();
   auto numOffsets = S_offsets->shape().elements() - 1; // -1 since last value is length
-  ABORT_IF(numOffsets != rowsS, "Unexpected number of rows in CSR argument");
+  ABORT_IF(numOffsets != rowsS, "Unexpected number of rows in CSR argument"); numOffsets;
   ABORT_IF(S_values->shape() != S_indices->shape(), "CSR values and indices must have the same size");
   if (!transS && !swapOperands) {
     // C = S * D, where D = CSR matrix

From 1a0849c72c47d811921568a21bafbc13c60a89a3 Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Wed, 17 Apr 2019 16:07:56 -0700
Subject: [PATCH 425/838] bug fix: target embedding matrix must be created
 before output layer uses it (if tied)

---
 src/models/decoder.h | 25 +++++++++++++------------
 1 file changed, 13 insertions(+), 12 deletions(-)

diff --git a/src/models/decoder.h b/src/models/decoder.h
index 697aef3b6..62f52eabe 100755
--- a/src/models/decoder.h
+++ b/src/models/decoder.h
@@ -88,21 +88,22 @@ class DecoderBase {
     int dimTrgVoc = opt<std::vector<int>>("dim-vocabs")[batchIndex_];
 
     Expr selectedEmbs;
-    if(words.empty()) {
-      selectedEmbs = graph->constant({1, 1, dimBatch, dimTrgEmb}, inits::zeros);
-    } else {
-      // embeddings are loaded from model during translation, no fixing required
-      auto yEmbFactory = embedding()  //
-          ("dimVocab", dimTrgVoc)     //
-          ("dimEmb", dimTrgEmb);
 
-      if(opt<bool>("tied-embeddings-src") || opt<bool>("tied-embeddings-all"))
-        yEmbFactory("prefix", "Wemb");
-      else
-        yEmbFactory("prefix", prefix_ + "_Wemb");
+	// embeddings are loaded from model during translation, no fixing required
+    auto yEmbFactory = embedding()  //
+        ("dimVocab", dimTrgVoc)     //
+        ("dimEmb", dimTrgEmb);
 
-      auto yEmb = yEmbFactory.construct(graph);
+    if(opt<bool>("tied-embeddings-src") || opt<bool>("tied-embeddings-all"))
+      yEmbFactory("prefix", "Wemb");
+    else
+      yEmbFactory("prefix", prefix_ + "_Wemb");
+
+    auto yEmb = yEmbFactory.construct(graph);
 
+    if(words.empty()) {
+      selectedEmbs = graph->constant({1, 1, dimBatch, dimTrgEmb}, inits::zeros);
+    } else {
       selectedEmbs = yEmb->apply(words, {dimBeam, 1, dimBatch, dimTrgEmb});
     }
     state->setTargetHistoryEmbeddings(selectedEmbs);

From 4ed735dc7f808cb1532d501958861d53ada38e2e Mon Sep 17 00:00:00 2001
From: Ulrich Germann <ulrich.germann@gmail.com>
Date: Thu, 18 Apr 2019 20:53:51 +0100
Subject: [PATCH 426/838] Install signal handler for SIGTERM to save model
 before exiting.

---
 src/CMakeLists.txt         |  1 +
 src/training/scheduler.cpp | 32 +++++++++++++++++++++++++++++
 src/training/scheduler.h   | 42 ++++++++++++++++++++++++++++++--------
 3 files changed, 66 insertions(+), 9 deletions(-)
 create mode 100644 src/training/scheduler.cpp

diff --git a/src/CMakeLists.txt b/src/CMakeLists.txt
index fbafc3b99..c287169dc 100644
--- a/src/CMakeLists.txt
+++ b/src/CMakeLists.txt
@@ -78,6 +78,7 @@ add_library(marian STATIC
   training/graph_group_multinode_sync.cpp
   training/validator.cpp
   training/communicator.cpp
+  training/scheduler.cpp
 
   # this is only compiled to catch build errors, but not linked
   microsoft/quicksand.cpp
diff --git a/src/training/scheduler.cpp b/src/training/scheduler.cpp
new file mode 100644
index 000000000..f508f3069
--- /dev/null
+++ b/src/training/scheduler.cpp
@@ -0,0 +1,32 @@
+#include "scheduler.h"
+#include <signal.h>
+
+namespace marian {
+bool Scheduler::sigterm_{false};
+bool Scheduler::sigusr1_{false};
+bool Scheduler::sigusr2_{false};
+
+void
+Scheduler::
+signalHandler_(int sig) {
+  switch (sig) {
+  case SIGTERM: Scheduler::sigterm_ = true; break;
+  case SIGUSR1: Scheduler::sigusr1_ = true; break;
+  case SIGUSR2: Scheduler::sigusr2_ = true; break;
+  default:
+    ABORT("This signal handler should not have been installed for signal ",
+          strsignal(sig));
+  }
+}
+
+
+void
+Scheduler::
+installSignalHandlers_() {
+  // TODO: use sigaction instead of signal
+  signal(SIGTERM, Scheduler::signalHandler_);
+  signal(SIGUSR1, Scheduler::signalHandler_);
+  signal(SIGUSR2, Scheduler::signalHandler_);
+}
+
+}
diff --git a/src/training/scheduler.h b/src/training/scheduler.h
index 9c3cb7e2d..68ebf395a 100644
--- a/src/training/scheduler.h
+++ b/src/training/scheduler.h
@@ -19,6 +19,13 @@ class Scheduler : public TrainingObserver {
   timer::Timer timer_;
   timer::Timer heartBeatTimer_;
 
+  std::string validFreq_;
+  std::string saveFreq_;
+
+  static bool sigterm_, sigusr1_, sigusr2_;
+  void installSignalHandlers_();
+  static void signalHandler_(int sig);
+
   // determine scheduled LR decay factor (--lr-decay-inv-sqrt option)
   float getScheduledLRDecayFactor(const TrainingState& state) const {
     auto args = options_->get<std::vector<std::string>>("lr-decay-inv-sqrt");
@@ -105,6 +112,10 @@ class Scheduler : public TrainingObserver {
   }
 
 public:
+  bool gotSigTerm() const { return sigterm_; }
+  // bool gotSigUsr1() const { return sigusr1_; } // currently has no effect
+  // bool gotSigUsr2() const { return sigusr2_; } // currently has no effect
+
   // test if any parameters specify dynamic MB scaling
   bool isDynamicMBSizeScaling() const {
     auto mbWarmup = SchedulingParameter::parse(options_->get<std::string>("mini-batch-warmup"));
@@ -144,11 +155,17 @@ class Scheduler : public TrainingObserver {
 
   Scheduler(Ptr<Options> options, Ptr<TrainingState> state)
       : options_(options), state_(state) {
+    validFreq_ = options_->get<std::string>("valid-freq");
+    saveFreq_ = options_->get<std::string>("save-freq");
     ABORT_IF(state_->factor != 1, "state.factor unexpectedly not 1 at this point??");
     updateLearningRate(*state);
   }
 
-  bool keepGoing() {
+  bool keepGoing(bool checkForSigTerm=true) {
+
+    if (checkForSigTerm && sigterm_) // received signam SIGERM => exit gracefully
+      return false;
+
     // stop if it reached the maximum number of epochs
     size_t stopAfterEpochs = options_->get<size_t>("after-epochs");
     if(stopAfterEpochs > 0 && state_->epochs > stopAfterEpochs)
@@ -175,7 +192,12 @@ class Scheduler : public TrainingObserver {
   }
 
   void started() { LOG(info, "Training started"); }
-  void finished() { LOG(info, "Training finished"); }
+  void finished() {
+    if (keepGoing(false)) // false means: ignore sigterm flag
+      LOG(info, "Training finished");
+    else
+      LOG(info, "Training interrupted (SIGTERM).");
+  }
 
   void addValidator(Ptr<ValidatorBase> validator) {
     validators_.push_back(validator);
@@ -192,20 +214,22 @@ class Scheduler : public TrainingObserver {
 
   bool validating() {
     return (!validators_.empty()
-            && state_->enteredNewPeriodOf(options_->get<std::string>("valid-freq"))
+            && state_->enteredNewPeriodOf(validFreq_)
             && keepGoing());
   }
 
   bool saving() {
-    return state_->enteredNewPeriodOf(options_->get<std::string>("save-freq"));
+    return state_->enteredNewPeriodOf(saveFreq_);
   }
 
   void validate(const std::vector<Ptr<ExpressionGraph>>& graphs,
                 bool final = false) {
-    // Do not validate if already validated (for instance, after the model is
-    // loaded) or if validation is scheduled for another update
-    if(state_->validated
-       || (!state_->enteredNewPeriodOf(options_->get<std::string>("valid-freq")) && !final))
+    // SKIP VALIDATION IF
+    // - it's not time to validate anyway
+    // - we received SIGTERM
+    // - the current state has been validated (e.g., model was just loaded)
+    if((!state_->enteredNewPeriodOf(validFreq_) && !final)
+       || state_->validated || sigterm_)
       return;
 
     bool firstValidator = true;
@@ -236,7 +260,7 @@ class Scheduler : public TrainingObserver {
       }
 
       state_->validators[validator->type()]["last-best"]
-          = validator->lastBest();
+        = validator->lastBest();
       state_->validators[validator->type()]["stalled"] = validator->stalled();
 
       // notify training observers if the first validator did not improve

From 16917363598165a9fe5174cb318ca4aa1bd73470 Mon Sep 17 00:00:00 2001
From: Ulrich Germann <ugermann@inf.ed.ac.uk>
Date: Sun, 21 Apr 2019 22:27:40 +0100
Subject: [PATCH 427/838] BatchGenerator now remembers if it has just restored
 the corpus ...

... in accordance with a saved training progress state.
---
 src/data/batch_generator.h | 27 +++++++++++++++++++++++----
 1 file changed, 23 insertions(+), 4 deletions(-)

diff --git a/src/data/batch_generator.h b/src/data/batch_generator.h
index ce673cd2a..d013cdd56 100644
--- a/src/data/batch_generator.h
+++ b/src/data/batch_generator.h
@@ -84,7 +84,10 @@ class BatchGenerator : public RNGEngine {
   // this runs on a bg thread; sequencing is handled by caller, but locking is done in here
   std::deque<BatchPtr> fetchBatches() {
     typedef typename Sample::value_type Item;
-    auto itemCmp = [](const Item& sa, const Item& sb) { return sa.size() < sb.size(); }; // sort by element length, not content
+    auto itemCmp = [](const Item& sa, const Item& sb) {
+      // sort by element length, not content
+      return sa.size() < sb.size();
+    };
 
     auto cmpSrc = [itemCmp](const Sample& a, const Sample& b) {
       return std::lexicographical_compare(
@@ -96,7 +99,10 @@ class BatchGenerator : public RNGEngine {
           a.rbegin(), a.rend(), b.rbegin(), b.rend(), itemCmp);
     };
 
-    auto cmpNone = [](const Sample& a, const Sample& b) { return &a < &b; }; // instead sort by address, so we have something to work with
+    auto cmpNone = [](const Sample& a, const Sample& b) {
+      // sort by address, so we have something to work with
+      return &a < &b;
+    };
 
     typedef std::function<bool(const Sample&, const Sample&)> cmp_type;
     typedef std::priority_queue<Sample, Samples, cmp_type> sample_queue;
@@ -252,7 +258,7 @@ class BatchGenerator : public RNGEngine {
   BatchGenerator(Ptr<DataSet> data,
                  Ptr<Options> options,
                  Ptr<BatchStats> stats = nullptr)
-      : data_(data), options_(options), stats_(stats), threadPool_(1) {}
+    : data_(data), options_(options), stats_(stats), threadPool_(1) { }
 
   ~BatchGenerator() {
     if (futureBufferedBatches_.valid()) // bg thread holds a reference to 'this',
@@ -268,7 +274,11 @@ class BatchGenerator : public RNGEngine {
   }
 
   // @TODO: get rid of this function, begin() or constructor should figure this out
-  void prepare(bool shuffle = true) {
+  void prepare(bool shuffle) {
+    if(restored_) { // state was just restored, restore() calls prepare()
+      restored_ = false;
+      return;
+    }
     if(shuffle)
       data_->shuffle();
     else
@@ -276,6 +286,12 @@ class BatchGenerator : public RNGEngine {
     newlyPrepared_ = true;
 
     // @TODO: solve this better, maybe use options
+    // => for this to work best, we need to replace --no-shuffle by --shuffle
+    // which is true by default for train, false otherwise, or explicitly set
+    // --no-shuffle=true by default for translate, validate, score etc. [UG]
+    // for the time begin, let's stick with the explicit function parameter
+    // (I've disabled the default value, because it's prone to cause problems
+    // sooner or later; callers should know if they want to shuffle or not).
     shuffle_ = shuffle;
 
     // start the background pre-fetch operation
@@ -287,6 +303,8 @@ class BatchGenerator : public RNGEngine {
   bool restore(Ptr<TrainingState> state, bool shuffle) {
     if(state->epochs == 1 && state->batchesEpoch == 0)
       return false;
+    if (options_->get<bool>("no-restore-corpus"))
+      return false;
 
     LOG(info,
         "[data] Restoring the corpus state to epoch {}, batch {}",
@@ -302,6 +320,7 @@ class BatchGenerator : public RNGEngine {
     for(size_t i = 0; i < state->batchesEpoch; ++i)
       next();
 
+    restored_ = true;
     return true;
   }
 

From d5b7af42ef539e156354e039c022dadb2a5cc43c Mon Sep 17 00:00:00 2001
From: Ulrich Germann <ugermann@inf.ed.ac.uk>
Date: Sun, 21 Apr 2019 22:31:37 +0100
Subject: [PATCH 428/838] New factory function to open corpus with sqlite or
 not, depending on spec in Options.

---
 src/data/corpus.cpp | 19 +++++++++++++++++++
 src/data/corpus.h   |  2 ++
 2 files changed, 21 insertions(+)

diff --git a/src/data/corpus.cpp b/src/data/corpus.cpp
index fda8df95f..0bde62322 100644
--- a/src/data/corpus.cpp
+++ b/src/data/corpus.cpp
@@ -171,5 +171,24 @@ void Corpus::shuffleData(const std::vector<std::string>& paths) {
   }
   pos_ = 0;
 }
+
+Ptr<CorpusBase> prepareTrainingData(Ptr<Options> options) {
+  // factory function to set up the training corpus for training
+  // code moved here from Train::run() in training.h
+  Ptr<CorpusBase> dataset;
+#ifndef _MSC_VER // @TODO: include SqLite in Visual Studio project
+  if(!options->get<std::string>("sqlite").empty())
+    ABORT("SqLite presently not supported on Windows");
+#else
+  if(!options->get<std::string>("sqlite").empty())
+    dataset = New<CorpusSQLite>(options);
+#endif
+  else
+    dataset = New<Corpus>(options);
+  dataset->prepare();
+  return dataset;
+}
+
+
 }  // namespace data
 }  // namespace marian
diff --git a/src/data/corpus.h b/src/data/corpus.h
index b459eb87a..165b0b238 100644
--- a/src/data/corpus.h
+++ b/src/data/corpus.h
@@ -16,6 +16,8 @@
 namespace marian {
 namespace data {
 
+Ptr<CorpusBase> prepareTrainingData(Ptr<Options> options);
+
 class Corpus : public CorpusBase {
 private:
   std::vector<UPtr<io::TemporaryFile>> tempFiles_;

From e5027a2272710c029cf64164dc7d4b6989f3b3c4 Mon Sep 17 00:00:00 2001
From: Ulrich Germann <ugermann@inf.ed.ac.uk>
Date: Sun, 21 Apr 2019 22:33:59 +0100
Subject: [PATCH 429/838] New function to set up all validators in one go.

---
 src/training/scheduler.h | 14 ++++++++++++++
 1 file changed, 14 insertions(+)

diff --git a/src/training/scheduler.h b/src/training/scheduler.h
index 68ebf395a..4ffcbce95 100644
--- a/src/training/scheduler.h
+++ b/src/training/scheduler.h
@@ -4,6 +4,7 @@
 #include "training/training_state.h"
 #include "training/validator.h"
 #include "training/communicator.h"
+#include "data/vocab.h"
 #include "layers/loss.h"
 
 namespace marian {
@@ -199,6 +200,19 @@ class Scheduler : public TrainingObserver {
       LOG(info, "Training interrupted (SIGTERM).");
   }
 
+  void setupValidators(std::vector<Ptr<Vocab>>& vocabs) {
+    // setup validators if specified
+    if (!SchedulingParameter::parse(options_->get<std::string>("valid-freq")))
+      return; // no validation interval given
+
+    if(!options_->hasAndNotEmpty("valid-sets") &&
+       !options_->hasAndNotEmpty("valid-script-path"))
+      return; // no validators specified
+
+    for(auto validator : Validators(vocabs, options_))
+      addValidator(validator);
+  }
+
   void addValidator(Ptr<ValidatorBase> validator) {
     validators_.push_back(validator);
 

From b0d041b0eac87d732c6714683f9370189b1addc6 Mon Sep 17 00:00:00 2001
From: Ulrich Germann <ugermann@inf.ed.ac.uk>
Date: Sun, 21 Apr 2019 22:36:59 +0100
Subject: [PATCH 430/838] Refactoring for better readability.

---
 src/training/training.h | 80 +++++++++++++++++++----------------------
 1 file changed, 36 insertions(+), 44 deletions(-)

diff --git a/src/training/training.h b/src/training/training.h
index 8863c165d..fb674ba52 100644
--- a/src/training/training.h
+++ b/src/training/training.h
@@ -11,6 +11,27 @@
 
 namespace marian {
 
+template<class ModelWrapper>
+Ptr<data::BatchStats>
+miniBatchFit(Ptr<Options> options, Ptr<data::CorpusBase> data,
+             Ptr<Scheduler> scheduler, Ptr<IMPIWrapper> mpi) {
+  // collect stats for fitting minibatches to available memory
+  Ptr<data::BatchStats> stats;
+  if(options->get<bool>("mini-batch-fit")) {
+    LOG(info,
+        "[batching] Collecting statistics for batch fitting with step size {}",
+        options->get<size_t>("mini-batch-fit-step"));
+    // @TODO this should receive a function object that can generate a fake batch;
+    // that way vocabs would not be exposed.
+    auto model = New<ModelWrapper>(options, mpi);
+    model->setScheduler(scheduler); // collectStats() needs to know about dynamic MB scaling
+    stats = model->collectStats(data->getVocabs());
+    LOG(info, "[batching] Done. Typical MB size is {} target words",
+        stats->estimateTypicalTrgWords());
+  }
+  return stats;
+}
+
 template <class ModelWrapper>
 class Train : public ModelTask {
 private:
@@ -22,64 +43,35 @@ class Train : public ModelTask {
   void run() override {
     using namespace data;
 
-    Ptr<CorpusBase> dataset;
-    if(!options_->get<std::string>("sqlite").empty())
-#ifndef _MSC_VER // @TODO: include SqLite in Visual Studio project
-      dataset = New<CorpusSQLite>(options_);
-#else
-      ABORT("SqLite presently not supported on Windows");
-#endif
-    else
-      dataset = New<Corpus>(options_);
-
-    dataset->prepare();
-
+    // TRAINING RUN SETUP
+    Ptr<CorpusBase> dataset = prepareTrainingData(options_);
     auto trainState = New<TrainingState>(options_->get<float>("learn-rate"));
     auto scheduler = New<Scheduler>(options_, trainState);
-    auto mpi = initMPI(/*multiThreaded=*/!options_->get<bool>("sync-sgd")); // @TODO: do we need the multiThreaded distinction at all?
-
-    Ptr<BatchStats> stats;
-    if(options_->get<bool>("mini-batch-fit")) {
-      LOG(info,
-          "[batching] Collecting statistics for batch fitting with step size {}",
-          options_->get<size_t>("mini-batch-fit-step"));
-      // @TODO this should receive a function object that can generate a fake batch;
-      // that way vocabs would not be exposed.
-      auto model = New<ModelWrapper>(options_, mpi);
-      model->setScheduler(scheduler); // collectStats() needs to know about dynamic MB scaling
-      stats = model->collectStats(dataset->getVocabs());
-      LOG(info, "[batching] Done. Typical MB size is {} target words", stats->estimateTypicalTrgWords());
-    }
-
-    if((options_->hasAndNotEmpty("valid-sets") || options_->hasAndNotEmpty("valid-script-path"))
-       && SchedulingParameter::parse(options_->get<std::string>("valid-freq"))) {
-      for(auto validator : Validators(dataset->getVocabs(), options_))
-        scheduler->addValidator(validator);
-    }
+    auto mpi = initMPI(/*multiThreaded=*/!options_->get<bool>("sync-sgd"));
+    // @TODO: do we need the multiThreaded distinction in initMPI at all?
 
+    Ptr<BatchStats> stats
+      = miniBatchFit<ModelWrapper>(options_, dataset, scheduler, mpi);
     auto batchGenerator = New<CorpusBatchGenerator>(dataset, options_, stats);
-
+    scheduler->setupValidators(dataset->getVocabs());
     scheduler->registerTrainingObserver(batchGenerator);
 
     auto model = New<ModelWrapper>(options_, mpi);
     model->setScheduler(scheduler);
-    model->setTypicalTrgBatchWords(batchGenerator->estimateTypicalTrgBatchWords()); // needed for dynamic MB scaling
-    model->load();
-
-    // @TODO: shuffle_ as a private attribute in BG
-    auto shuffle = !options_->get<bool>("no-shuffle");
-    bool restored = !options_->get<bool>("no-restore-corpus")
-                    && batchGenerator->restore(trainState, shuffle);
+    model->setTypicalTrgBatchWords(batchGenerator->estimateTypicalTrgBatchWords());
+    // typical no. of trg wrds in batch is needed for dynamic MiniBatch scaling
+    model->load(); //!! ALSO CHANGES scheduler AND trainState ON A RESUMED RUN!
 
     // -- main training loop
     scheduler->started();
+    bool shuffle = !options_->get<bool>("no-shuffle");
+    batchGenerator->restore(trainState,shuffle);
     while(scheduler->keepGoing()) {
-      if(!restored)
-        batchGenerator->prepare(shuffle);
-      restored = false;
+      batchGenerator->prepare(shuffle);
 
       // main training loop for one epoch
-      for(auto batchIt = std::begin(*batchGenerator); // @TODO: try to use for(auto ...)
+      // @TODO: try to use for(auto ...)
+      for(auto batchIt = std::begin(*batchGenerator);
           batchIt != std::end(*batchGenerator);
           batchIt++) {
         if (!scheduler->keepGoing())

From a12f357de4b690b8bde854d216fc4b67292918b7 Mon Sep 17 00:00:00 2001
From: Ulrich Germann <ugermann@inf.ed.ac.uk>
Date: Mon, 22 Apr 2019 03:06:54 +0100
Subject: [PATCH 431/838] CMakeList.txt: More informative error message when
 appropriate CUDA library cannot be found.

---
 CMakeLists.txt | 10 +++++++++-
 1 file changed, 9 insertions(+), 1 deletion(-)

diff --git a/CMakeLists.txt b/CMakeLists.txt
index 022b97019..6dc13c0ff 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -180,7 +180,15 @@ if(USE_STATIC_LIBS)
 endif()
 
 else(CUDA_FOUND)
-  message(FATAL_ERROR "CUDA has not been found, set -DCOMPILE_CUDA=off to avoid this check and to compile the CPU version only")
+  message("
+Cannot find suitable CUDA libraries. Specify the path explicitly with
+  -DCUDA_TOOLKIT_ROOT_DIR=/path/to/appropriate/cuda/installation
+   (hint: try /usr/local/$(readlink /usr/local/cuda))
+OR compile the CPU-only version of Marian with
+  -DCOMPILE_CUDA=off
+")
+  message(FATAL_ERROR
+    "FATAL ERROR: No suitable CUDA library found.")
 endif(CUDA_FOUND)
 
 else(COMPILE_CUDA)

From f37958a43100957f1262919a2102e83f10eedd75 Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Mon, 22 Apr 2019 17:41:39 -0700
Subject: [PATCH 432/838] added shortlists to lemma factor

---
 src/common/binary.cpp        | 17 ++++++++-----
 src/common/file_stream.h     | 33 +++++++++++---------------
 src/data/factored_vocab.cpp  | 12 ++++++++++
 src/data/factored_vocab.h    |  1 +
 src/data/vocab.cpp           |  3 +++
 src/data/vocab.h             |  3 +++
 src/data/vocab_base.h        |  3 +++
 src/layers/generic.cpp       | 46 ++++++++++++++++++++++--------------
 src/layers/generic.h         |  4 ++--
 src/microsoft/quicksand.cpp  |  1 +
 src/microsoft/quicksand.h    |  1 +
 src/translator/beam_search.h | 15 +++++++-----
 12 files changed, 88 insertions(+), 51 deletions(-)

diff --git a/src/common/binary.cpp b/src/common/binary.cpp
index 983c15b58..d4f443985 100644
--- a/src/common/binary.cpp
+++ b/src/common/binary.cpp
@@ -68,15 +68,20 @@ void loadItems(const void* current, std::vector<io::Item>& items, bool mapped) {
 void loadItems(const std::string& fileName, std::vector<io::Item>& items) {
   // Read file into buffer
   size_t fileSize = filesystem::fileSize(fileName);
-  char* ptr = new char[fileSize];
+  std::vector<char> buf(fileSize);
+#if 1 // for some reason, the #else branch fails with "file not found" in the *read* operation (open succeeds)
+  FILE *f = fopen(fileName.c_str(), "rb");
+  ABORT_IF(f == nullptr, "Error {} ('{}') opening file '{}'", errno, strerror(errno), fileName);
+  auto rc = fread(buf.data(), sizeof(*buf.data()), buf.size(), f);
+  ABORT_IF(rc != buf.size(), "Error {} ('{}') reading file '{}'", errno, strerror(errno), fileName);
+  fclose(f);
+#else
   io::InputFileStream in(fileName);
-  in.read(ptr, fileSize);
+  in.read(buf.data(), buf.size());
+#endif
 
   // Load items from buffer without mapping
-  loadItems(ptr, items, false);
-
-  // Delete buffer
-  delete[] ptr;
+  loadItems(buf.data(), items, false);
 }
 
 io::Item getItem(const void* current, const std::string& varName) {
diff --git a/src/common/file_stream.h b/src/common/file_stream.h
index 1fe6d91d1..ec03b1565 100644
--- a/src/common/file_stream.h
+++ b/src/common/file_stream.h
@@ -139,10 +139,10 @@ class InputFileStream {
     ABORT_IF(!marian::filesystem::exists(file_), "File '{}' does not exist", file);
 
     if(file_.extension() == marian::filesystem::Path(".gz"))
-      // @TODO: consider make_unique for next refactoring
-      istream_.reset(new zstr::ifstream(file_.string()));
+      istream_ = std::make_unique<zstr::ifstream>(file_.string());
     else
-      istream_.reset(new std::ifstream(file_.string()));
+      istream_ = std::make_unique<std::ifstream>(file_.string());
+    ABORT_IF(fail(), "Error {} ({}) opening file '{}'", errno, strerror(errno), path());
   }
 
   InputFileStream(TemporaryFile& tempfile)
@@ -150,8 +150,8 @@ class InputFileStream {
     lseek(tempfile.getFileDescriptor(), 0, SEEK_SET);
 
     namespace bio = boost::iostreams;
-    fdsBuffer_.reset(new bio::stream_buffer<bio::file_descriptor_source>(fds_));
-    istream_.reset(new std::istream(fdsBuffer_.get()));
+    fdsBuffer_ = std::make_unique<bio::stream_buffer<bio::file_descriptor_source>>(fds_);
+    istream_ = std::make_unique<std::istream>(fdsBuffer_.get());
   }
 
   InputFileStream(std::istream& strm)
@@ -187,7 +187,7 @@ class InputFileStream {
   friend InputFileStream& operator>>(InputFileStream& stream, T& t) {
     *stream.istream_ >> t;
     // bad() seems to be correct here. Should not abort on EOF.
-    ABORT_IF(stream.bad(), "Error reading from file '{}'", stream.path());
+    ABORT_IF(stream.bad(), "Error {} ({}) reading from file '{}'", errno, strerror(errno), stream.path());
     return stream;
   }
 
@@ -195,7 +195,7 @@ class InputFileStream {
   size_t read(T* ptr, size_t num = 1) {
     istream_->read((char*)ptr, num * sizeof(T));
     // fail() seems to be correct here. Failure to read should abort.
-    ABORT_IF(fail(), "Error reading from file '{}'", path());
+    ABORT_IF(fail(), "Error {} ({}) reading from file '{}'", errno, strerror(errno), path());
     return num * sizeof(T);
   }
 
@@ -236,10 +236,9 @@ class OutputFileStream {
 public:
   OutputFileStream(const std::string& file) : file_(file) {
     if(file_.extension() == marian::filesystem::Path(".gz"))
-      ostream_.reset(new zstr::ofstream(file_.string()));
+      ostream_ = std::make_unique<zstr::ofstream>(file_.string());
     else
-      ostream_.reset(new std::ofstream(file_.string()));
-
+      ostream_ = std::make_unique<std::ofstream>(file_.string());
     ABORT_IF(!marian::filesystem::exists(file_), "File '{}' could not be opened", file);
   }
 
@@ -248,25 +247,21 @@ class OutputFileStream {
     lseek(tempfile.getFileDescriptor(), 0, SEEK_SET);
 
     namespace bio = boost::iostreams;
-    fdsBuffer_.reset(new bio::stream_buffer<bio::file_descriptor_sink>(fds_));
-    ostream_.reset(new std::ostream(fdsBuffer_.get()));
+    fdsBuffer_ = std::make_unique<bio::stream_buffer<bio::file_descriptor_sink>>(fds_);
+    ostream_ = std::make_unique<std::ostream>(fdsBuffer_.get());
   }
 
   OutputFileStream(std::ostream& strm) {
-    ostream_.reset(new std::ostream(strm.rdbuf()));
+    ostream_ = std::make_unique<std::ostream>(strm.rdbuf());
   }
 
   operator std::ostream&() { return *ostream_; }
 
   operator bool() { return (bool)*ostream_; }
 
-  bool bad() const {
-    return ostream_->bad();
-  }
+  bool bad() const { return ostream_->bad(); }
 
-  bool fail() const {
-    return ostream_->fail();
-  }
+  bool fail() const { return ostream_->fail(); }
 
   template <typename T>
   friend OutputFileStream& operator<<(OutputFileStream& stream, const T& t) {
diff --git a/src/data/factored_vocab.cpp b/src/data/factored_vocab.cpp
index d168cb4bf..509815f4e 100755
--- a/src/data/factored_vocab.cpp
+++ b/src/data/factored_vocab.cpp
@@ -517,6 +517,18 @@ void FactoredVocab::constructNormalizationInfoForVocab() {
   return utils::findReplace(utils::findReplace(utils::findReplace(utils::findReplace(line, "|cn|wb", "|ci|wb", /*all=*/true), "|cn|gl-", "|ci|gl-", /*all=*/true), "@CN@WB", "@CI@WB", /*all=*/true), "@CN@GL-", "@CI@GL-", /*all=*/true);
 }
 
+// convert word indices to indices of shortlist items
+// We only shortlist the lemmas, hence return the lemma index (offset to correctly index into the concatenated W matrix).
+// This strange pointer-based interface is for ease of interaction with our production environment.
+/*virtual*/ void FactoredVocab::transcodeToShortlistInPlace(WordIndex* ptr, size_t num) const {
+  for (; num-- > 0; ptr++) {
+    auto word = Word::fromWordIndex(*ptr);
+    auto wordString = word2string(word);
+    auto lemmaIndex = getFactor(word, 0) + groupRanges_[0].first;
+    *ptr = lemmaIndex;
+  }
+}
+
 // generate a valid random factored word (used by collectStats())
 /*virtual*/ Word FactoredVocab::randWord() const /*override final*/ {
   auto numGroups = getNumGroups();
diff --git a/src/data/factored_vocab.h b/src/data/factored_vocab.h
index 9a916aa82..3ff338e23 100755
--- a/src/data/factored_vocab.h
+++ b/src/data/factored_vocab.h
@@ -41,6 +41,7 @@ class FactoredVocab : public IVocab {
   virtual Word getUnkId() const override final { return unkId_; }
   virtual std::string toUpper(const std::string& line) const override final;
   virtual std::string toEnglishTitleCase(const std::string& line) const override final;
+  virtual void transcodeToShortlistInPlace(WordIndex* ptr, size_t num) const override final;
   WordIndex getUnkIndex() const { return (WordIndex)getFactor(getUnkId(), 0); } // used in decoding
   virtual void createFake() override final { ABORT("[data] Fake FactoredVocab vocabulary not supported"); }
   virtual Word randWord() const override final;
diff --git a/src/data/vocab.cpp b/src/data/vocab.cpp
index 2cafb3313..b48d5eca0 100755
--- a/src/data/vocab.cpp
+++ b/src/data/vocab.cpp
@@ -143,4 +143,7 @@ std::string Vocab::toUpper(const std::string& line) const { return vImpl_->toUpp
 // for corpus augmentation: convert string to title case
 std::string Vocab::toEnglishTitleCase(const std::string& line) const { return vImpl_->toEnglishTitleCase(line); }
 
+// for short-list generation
+void Vocab::transcodeToShortlistInPlace(WordIndex* ptr, size_t num) const { vImpl_->transcodeToShortlistInPlace(ptr, num); }
+
 }  // namespace marian
diff --git a/src/data/vocab.h b/src/data/vocab.h
index 1de93f5b4..a15e202f0 100755
--- a/src/data/vocab.h
+++ b/src/data/vocab.h
@@ -76,6 +76,9 @@ class Vocab {
   // for corpus augmentation: convert string to title case
   std::string toEnglishTitleCase(const std::string& line) const;
 
+  // for short-list generation
+  void transcodeToShortlistInPlace(WordIndex* ptr, size_t num) const;
+
   // create fake vocabulary for collecting batch statistics
   void createFake();
 
diff --git a/src/data/vocab_base.h b/src/data/vocab_base.h
index bf276f5de..238f9146b 100755
--- a/src/data/vocab_base.h
+++ b/src/data/vocab_base.h
@@ -49,6 +49,9 @@ class IVocab {
   virtual std::string toUpper(const std::string& line) const { return line; }
   virtual std::string toEnglishTitleCase(const std::string& line) const { return line; }
 
+  // this function is an identity mapping for default vocabularies, hence do nothing
+  virtual void transcodeToShortlistInPlace(WordIndex* ptr, size_t num) const { }
+
   virtual void createFake() = 0;
 
   virtual Word randWord() const {
diff --git a/src/layers/generic.cpp b/src/layers/generic.cpp
index 366b05f35..1724f2038 100755
--- a/src/layers/generic.cpp
+++ b/src/layers/generic.cpp
@@ -80,7 +80,8 @@ namespace marian {
   //}
 
   // get logits for one factor group
-  Expr Logits::getFactoredLogits(size_t groupIndex, const std::vector<IndexType>& selIdx /*= {}*/, size_t beamSize /*= 0*/) const {
+  // For groupIndex == 0, the function also requires the shortlist if there is one.
+  Expr Logits::getFactoredLogits(size_t groupIndex, Ptr<data::Shortlist> shortlist /*= nullptr*/, const std::vector<IndexType>& selIdx /*= {}*/, size_t beamSize /*= 0*/) const {
     ABORT_IF(empty(), "Attempted to read out logits on empty Logits object");
     auto sel = logits_[groupIndex]->loss(); // [localBeamSize, 1, dimBatch, dimFactorVocab]
 
@@ -94,7 +95,7 @@ namespace marian {
       auto numGroups = getNumFactorGroups();
       for (size_t g = 1; g < numGroups; g++) {
         auto factorMaxima = max(logits_[g]->loss(), -1);
-        auto factorMasks = constant(getFactorMasks(g));
+        auto factorMasks = constant(getFactorMasks(g, shortlist ? shortlist->indices() : std::vector<WordIndex>()));
         sel = sel + factorMaxima * factorMasks; // those lemmas that don't have a factor get multiplied with 0
       }
     }
@@ -178,13 +179,15 @@ namespace marian {
   //}
 
   // return a vector of 1 or 0 indicating for each lemma whether it has a specific factor
-  std::vector<float> Logits::getFactorMasks(size_t factorGroup) const { // [lemmaIndex] -> 1.0 for words that do have this factor; else 0
-    size_t numLemmas = factoredVocab_->getGroupRange(0).second - factoredVocab_->getGroupRange(0).first;
+  // If 'indices' is given, then return the masks for the indices; otherwise for all lemmas
+  std::vector<float> Logits::getFactorMasks(size_t factorGroup, const std::vector<WordIndex>& indices) const { // [lemmaIndex] -> 1.0 for words that do have this factor; else 0
+    size_t n = indices.empty() ? (factoredVocab_->getGroupRange(0).second - factoredVocab_->getGroupRange(0).first) : indices.size();
     std::vector<float> res;
-    res.reserve(numLemmas);
-    // @TODO: we should rearange lemmaHasFactorGroup as vector[groups[lemma] of float; then move this into FactoredVocab
-    for (size_t lemma = 0; lemma < numLemmas; lemma++) {
-      res.push_back((float)factoredVocab_->lemmaHasFactorGroup(lemma, factorGroup));
+    res.reserve(n);
+    // @TODO: we should rearrange lemmaHasFactorGroup as vector[groups[i] of float; then move this into FactoredVocab
+    for (size_t i = 0; i < n; i++) {
+      auto lemma = indices.empty() ? i : (indices[i] - factoredVocab_->getGroupRange(0).first);
+      res.push_back((float)factoredVocab_->lemmaHasFactorGroup(i, factorGroup));
     }
     return res;
   }
@@ -225,7 +228,6 @@ namespace marian {
 
       factoredVocab_ = FactoredVocab::tryCreateAndLoad(options_->get<std::string>("vocab", ""));
       if (factoredVocab_) {
-        ABORT_IF(shortlist_, "Shortlists are presently not compatible with factored embeddings");
         numOutputClasses = (int)factoredVocab_->factorVocabSize();
         LOG(info, "[embedding] Factored outputs enabled");
       }
@@ -247,14 +249,12 @@ namespace marian {
     Logits Output::applyAsLogits(Expr input) /*override final*/ {
       lazyConstruct(input->shape()[-1]);
 
-      if (shortlist_) {
-        if (!cachedShortWt_) { // short versions of parameters are cached within one batch, then clear()ed
-          cachedShortWt_ = index_select(Wt_, isLegacyUntransposedW ? -1 : 0, shortlist_->indices());
-          cachedShortb_  = index_select(b_ ,                             -1, shortlist_->indices());
-        }
-        return Logits(affine(input, cachedShortWt_, cachedShortb_, false, /*transB=*/isLegacyUntransposedW ? false : true));
+      if (shortlist_ && !cachedShortWt_) { // shortlisted versions of parameters are cached within one batch, then clear()ed
+        cachedShortWt_ = index_select(Wt_, isLegacyUntransposedW ? -1 : 0, shortlist_->indices());
+        cachedShortb_  = index_select(b_ ,                             -1, shortlist_->indices());
       }
-      else if (factoredVocab_) {
+
+      if (factoredVocab_) {
         auto graph = input->graph();
 
         // project each factor separately
@@ -268,8 +268,15 @@ namespace marian {
             continue;
           ABORT_IF(g > 0 && range.first != factoredVocab_->getGroupRange(g-1).second, "Factor groups must be consecutive (group {} vs predecessor)", g);
           // slice this group's section out of W_
-          auto factorWt = slice(Wt_, isLegacyUntransposedW ? -1 : 0, Slice((int)range.first, (int)range.second));
-          auto factorB  = slice(b_,                              -1, Slice((int)range.first, (int)range.second));
+          Expr factorWt, factorB;
+          if (g == 0 && shortlist_) {
+            factorWt = cachedShortWt_;
+            factorB  = cachedShortb_;
+          }
+          else {
+            factorWt = slice(Wt_, isLegacyUntransposedW ? -1 : 0, Slice((int)range.first, (int)range.second));
+            factorB  = slice(b_,                              -1, Slice((int)range.first, (int)range.second));
+          }
           // @TODO: b_ should be a vector, not a matrix; but shotlists use cols() in, which requires a matrix
           auto factorLogits = affine(input1, factorWt, factorB, false, /*transB=*/isLegacyUntransposedW ? false : true); // [B... x U] factor logits
           // optionally add lemma-dependent bias
@@ -285,6 +292,7 @@ namespace marian {
           // optionally add a soft embedding of lemma back to create some lemma dependency
           // @TODO: if this works, move it into lazyConstruct
           const int lemmaDimEmb = options_->get<int>("lemma-dim-emb", 0);
+          ABORT_IF(shortlist_ && lemmaDimEmb != 0, "Lemma re-embedding with short list is not yet implemented");
           if (lemmaDimEmb < 0 && g == 0) {
             LOG_ONCE(info, "[embedding] using lemma-dependent bias");
             factorLogits = logsoftmax(factorLogits); // explicitly, since we do that again later
@@ -307,6 +315,8 @@ namespace marian {
         }
         return Logits(std::move(allLogits), factoredVocab_);
       }
+      else if (shortlist_)
+        return Logits(affine(input, cachedShortWt_, cachedShortb_, false, /*transB=*/isLegacyUntransposedW ? false : true));
       else
         return Logits(affine(input, Wt_, b_, false, /*transB=*/isLegacyUntransposedW ? false : true));
     }
diff --git a/src/layers/generic.h b/src/layers/generic.h
index 01c1c1721..775fbbe01 100755
--- a/src/layers/generic.h
+++ b/src/layers/generic.h
@@ -81,7 +81,7 @@ class Logits {
     Logits(std::vector<Ptr<RationalLoss>>&& logits, Ptr<FactoredVocab> embeddingFactorMapping) // factored-output constructor
       : logits_(std::move(logits)), factoredVocab_(embeddingFactorMapping) {}
     Expr getLogits() const; // assume it holds logits: get them, possibly aggregating over factors
-    Expr getFactoredLogits(size_t groupIndex, const std::vector<IndexType>& hypIndices = {}, size_t beamSize = 0) const; // get logits for only one factor group, with optional reshuffle
+    Expr getFactoredLogits(size_t groupIndex, Ptr<data::Shortlist> shortlist = nullptr, const std::vector<IndexType>& hypIndices = {}, size_t beamSize = 0) const; // get logits for only one factor group, with optional reshuffle
     //Ptr<RationalLoss> getRationalLoss() const; // assume it holds a loss: get that
     Expr applyLossFunction(const Words& labels, const std::function<Expr(Expr/*logits*/,Expr/*indices*/)>& lossFn) const;
     Logits applyUnaryFunction(const std::function<Expr(Expr)>& f) const; // clone this but apply f to all loss values
@@ -97,7 +97,6 @@ class Logits {
     };
     std::vector<MaskedFactorIndices> factorizeWords(const Words& words) const; // breaks encoded Word into individual factor indices
     //std::vector<float> getFactorMasks(const Words& words, size_t factorGroup) const;
-    std::vector<float> getFactorMasks(size_t factorGroup) const;
     float getLogitAt(size_t i) const { return getLogits()->val()->get(i); } // used for breakDown() only; @TODO: avoid the fully expanded logits; pass separate indices instead of 'i'
     size_t getNumFactorGroups() const { return logits_.size(); }
     bool empty() const { return logits_.empty(); }
@@ -109,6 +108,7 @@ class Logits {
     Expr constant(const Shape& shape, const std::vector<uint32_t>& data) const { return graph()->constant(shape, inits::from_vector(data), Type::uint32);  }
     template<typename T> Expr constant(const std::vector<T>& data) const { return constant(Shape{(int)data.size()}, data); } // same as constant() but assuming vector
     Expr indices(const std::vector<uint32_t>& data) const { return graph()->indices(data); } // actually the same as constant(data) for this data type
+    std::vector<float> getFactorMasks(size_t factorGroup, const std::vector<WordIndex>& indices) const;
 private:
     // members
     // @HACK: The interplay between Logits and RationalLoss is weird. Here, we allow RationalLoss with count == nullptr.
diff --git a/src/microsoft/quicksand.cpp b/src/microsoft/quicksand.cpp
index 51f857b52..98ff2978c 100755
--- a/src/microsoft/quicksand.cpp
+++ b/src/microsoft/quicksand.cpp
@@ -39,6 +39,7 @@ class VocabWrapper : public IVocabWrapper {
   WordIndex encode(const std::string& word) const override { return (*pImpl_)[word].toWordIndex(); }
   std::string decode(WordIndex id) const override { return (*pImpl_)[Word::fromWordIndex(id)]; }
   size_t size() const override { return pImpl_->size(); }
+  void transcodeToShortlistInPlace(WordIndex* ptr, size_t num) const override { pImpl_->transcodeToShortlistInPlace(ptr, num); }
   Ptr<Vocab> getVocab() const { return pImpl_; }
 };
 
diff --git a/src/microsoft/quicksand.h b/src/microsoft/quicksand.h
index 22fe97b67..bc31e1515 100755
--- a/src/microsoft/quicksand.h
+++ b/src/microsoft/quicksand.h
@@ -35,6 +35,7 @@ class IVocabWrapper {
   virtual WordIndex encode(const std::string& word) const = 0;
   virtual std::string decode(WordIndex id) const = 0;
   virtual size_t size() const = 0;
+  virtual void transcodeToShortlistInPlace(WordIndex* ptr, size_t num) const = 0;
 };
 
 class IBeamSearchDecoder {
diff --git a/src/translator/beam_search.h b/src/translator/beam_search.h
index 569e7756f..5445e079b 100755
--- a/src/translator/beam_search.h
+++ b/src/translator/beam_search.h
@@ -79,13 +79,11 @@ class BeamSearch {
       // If short list has been set, then wordIdx is an index into the short-listed word set,
       // rather than the true word index.
       auto shortlist = scorers_[0]->getShortlist();
-      if (shortlist)
-        word = Word::fromWordIndex(shortlist->reverseMap(wordIdx));
-      else if (factoredVocab) {
+      if (factoredVocab) {
         // For factored decoding, the word is built over multiple decoding steps,
         // starting with the lemma, then adding factors one by one.
         if (factorGroup == 0) {
-          word = factoredVocab->lemma2Word(wordIdx);
+          word = factoredVocab->lemma2Word(shortlist ? shortlist->reverseMap(wordIdx) : wordIdx); // @BUGBUG: reverseMap is only correct if factoredVocab_->getGroupRange(0).first == 0
           //LOG(info, "new lemma {}={}", word.toWordIndex(), factoredVocab->word2string(word));
         }
         else {
@@ -98,6 +96,8 @@ class BeamSearch {
           prevHyp = prevHyp->getPrevHyp(); // short-circuit the backpointer, so that the traceback doesnot contain partially factored words
         }
       }
+      else if (shortlist)
+        word = Word::fromWordIndex(shortlist->reverseMap(wordIdx));
       else
         word = Word::fromWordIndex(wordIdx);
 
@@ -331,7 +331,10 @@ class BeamSearch {
           if (numFactorGroups == 1) // @TODO: this branch can go away
             logProbs = states[i]->getLogProbs().getLogits(); // [localBeamSize, 1, dimBatch, dimVocab]
           else
-            logProbs = states[i]->getLogProbs().getFactoredLogits(factorGroup); // [localBeamSize, 1, dimBatch, dimVocab]
+          {
+            auto shortlist = scorers_[i]->getShortlist();
+            logProbs = states[i]->getLogProbs().getFactoredLogits(factorGroup, shortlist); // [localBeamSize, 1, dimBatch, dimVocab]
+          }
           //logProbs->debug("logProbs");
         }
         else {
@@ -344,7 +347,7 @@ class BeamSearch {
           // and push out other hypotheses. Hence, we exclude those here by setting the path score to
           // INVALID_PATH_SCORE. Instead, toHyps() explicitly propagates those hyps by simply copying the
           // previous hypothesis.
-          logProbs = states[i]->getLogProbs().getFactoredLogits(factorGroup, hypIndices, localBeamSize); // [localBeamSize, 1, dimBatch, dimVocab]
+          logProbs = states[i]->getLogProbs().getFactoredLogits(factorGroup, /*shortlist=*/ nullptr, hypIndices, localBeamSize); // [localBeamSize, 1, dimBatch, dimVocab]
         }
         // expand all hypotheses, [localBeamSize, 1, dimBatch, 1] -> [localBeamSize, 1, dimBatch, dimVocab]
         expandedPathScores = expandedPathScores + scorers_[i]->getWeight() * logProbs;

From 1da8d5281f660637a118fd806d932e2aa42268bc Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Tue, 23 Apr 2019 13:58:29 -0700
Subject: [PATCH 433/838] (fixed newlines)

---
 src/translator/beam_search.h | 34 +++++++++++++++++-----------------
 1 file changed, 17 insertions(+), 17 deletions(-)

diff --git a/src/translator/beam_search.h b/src/translator/beam_search.h
index 5445e079b..35c2d741a 100755
--- a/src/translator/beam_search.h
+++ b/src/translator/beam_search.h
@@ -79,23 +79,23 @@ class BeamSearch {
       // If short list has been set, then wordIdx is an index into the short-listed word set,
       // rather than the true word index.
       auto shortlist = scorers_[0]->getShortlist();
-      if (factoredVocab) {
-        // For factored decoding, the word is built over multiple decoding steps,
-        // starting with the lemma, then adding factors one by one.
-        if (factorGroup == 0) {
-          word = factoredVocab->lemma2Word(shortlist ? shortlist->reverseMap(wordIdx) : wordIdx); // @BUGBUG: reverseMap is only correct if factoredVocab_->getGroupRange(0).first == 0
-          //LOG(info, "new lemma {}={}", word.toWordIndex(), factoredVocab->word2string(word));
-        }
-        else {
-          //LOG(info, "expand word {}={} with factor[{}] {}", beam[beamHypIdx]->getWord().toWordIndex(),
-          //    factoredVocab->word2string(beam[beamHypIdx]->getWord()), factorGroup, wordIdx);
-          word = beam[beamHypIdx]->getWord();
-          ABORT_IF(!factoredVocab->canExpandFactoredWord(word, factorGroup), "A word without this factor snuck through to here??");
-          word = factoredVocab->expandFactoredWord(word, factorGroup, wordIdx);
-          prevBeamHypIdx = prevHyp->getPrevStateIndex();
-          prevHyp = prevHyp->getPrevHyp(); // short-circuit the backpointer, so that the traceback doesnot contain partially factored words
-        }
-      }
+      if (factoredVocab) {
+        // For factored decoding, the word is built over multiple decoding steps,
+        // starting with the lemma, then adding factors one by one.
+        if (factorGroup == 0) {
+          word = factoredVocab->lemma2Word(shortlist ? shortlist->reverseMap(wordIdx) : wordIdx); // @BUGBUG: reverseMap is only correct if factoredVocab_->getGroupRange(0).first == 0
+          //LOG(info, "new lemma {}={}", word.toWordIndex(), factoredVocab->word2string(word));
+        }
+        else {
+          //LOG(info, "expand word {}={} with factor[{}] {}", beam[beamHypIdx]->getWord().toWordIndex(),
+          //    factoredVocab->word2string(beam[beamHypIdx]->getWord()), factorGroup, wordIdx);
+          word = beam[beamHypIdx]->getWord();
+          ABORT_IF(!factoredVocab->canExpandFactoredWord(word, factorGroup), "A word without this factor snuck through to here??");
+          word = factoredVocab->expandFactoredWord(word, factorGroup, wordIdx);
+          prevBeamHypIdx = prevHyp->getPrevStateIndex();
+          prevHyp = prevHyp->getPrevHyp(); // short-circuit the backpointer, so that the traceback doesnot contain partially factored words
+        }
+      }
       else if (shortlist)
         word = Word::fromWordIndex(shortlist->reverseMap(wordIdx));
       else

From 9825133b74a99ead8443ec68443bd902595a0a71 Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Tue, 23 Apr 2019 14:12:09 -0700
Subject: [PATCH 434/838] made gcc happy; bug fix: should use a correct lemma
 index

---
 src/common/file_stream.h | 9 +++++++++
 src/layers/generic.cpp   | 2 +-
 2 files changed, 10 insertions(+), 1 deletion(-)

diff --git a/src/common/file_stream.h b/src/common/file_stream.h
index ec03b1565..a950b0897 100644
--- a/src/common/file_stream.h
+++ b/src/common/file_stream.h
@@ -18,6 +18,15 @@
 #include <iostream>
 #include <memory>
 
+#ifdef __GNUC__ // not supported; maybe we just need to increment a standard flag in gcc/cmake?
+namespace std {
+  template<typename T, typename... Args>
+  std::unique_ptr<T> make_unique(Args&&... args) {
+    return std::unique_ptr<T>(new T(std::forward<Args>(args)...));
+  }
+}
+#endif
+
 #ifdef _MSC_VER
 #include <fcntl.h>
 #include <io.h>
diff --git a/src/layers/generic.cpp b/src/layers/generic.cpp
index 1724f2038..e5d35e719 100755
--- a/src/layers/generic.cpp
+++ b/src/layers/generic.cpp
@@ -187,7 +187,7 @@ namespace marian {
     // @TODO: we should rearrange lemmaHasFactorGroup as vector[groups[i] of float; then move this into FactoredVocab
     for (size_t i = 0; i < n; i++) {
       auto lemma = indices.empty() ? i : (indices[i] - factoredVocab_->getGroupRange(0).first);
-      res.push_back((float)factoredVocab_->lemmaHasFactorGroup(i, factorGroup));
+      res.push_back((float)factoredVocab_->lemmaHasFactorGroup(lemma, factorGroup));
     }
     return res;
   }

From d9f99641be6288201bfed90e5ab248f0c2138e77 Mon Sep 17 00:00:00 2001
From: Roman Grundkiewicz <rgrundki@exseed.ed.ac.uk>
Date: Thu, 25 Apr 2019 09:42:23 +0100
Subject: [PATCH 435/838] Fix compilation with CUDA 8.0

---
 src/tensors/gpu/prod.cpp | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/src/tensors/gpu/prod.cpp b/src/tensors/gpu/prod.cpp
index d13081728..32f577f75 100755
--- a/src/tensors/gpu/prod.cpp
+++ b/src/tensors/gpu/prod.cpp
@@ -12,6 +12,7 @@ namespace marian {
 
 namespace gpu {
 
+#if CUDA_VERSION >= 9000
 static void setTensorMode(cublasHandle_t cublasHandle) {
   static int mode = 0;  // 1: use TC; -1: do not use TC; 0: not set yet
   if (mode == 0) { // multi-thread note: this is sort-of thread-safe, since multiple threads would determine the same value
@@ -37,6 +38,7 @@ static void setTensorMode(cublasHandle_t cublasHandle) {
   }
   CUBLAS_CHECK(cublasSetMathMode(cublasHandle, mode > 0 ? CUBLAS_TENSOR_OP_MATH : CUBLAS_DEFAULT_MATH));
 }
+#endif
 
 void Prod(marian::Tensor C,
           const marian::Tensor& A,

From 68c0aaff9edafa294ecb6ae3a63888ae257037f0 Mon Sep 17 00:00:00 2001
From: Roman Grundkiewicz <rgrundki@exseed.ed.ac.uk>
Date: Thu, 25 Apr 2019 10:04:28 +0100
Subject: [PATCH 436/838] Add status badges for different CUDA versions

---
 README.md | 6 ++++--
 1 file changed, 4 insertions(+), 2 deletions(-)

diff --git a/README.md b/README.md
index 6fcc45b0b..6f7ab5d85 100644
--- a/README.md
+++ b/README.md
@@ -1,8 +1,10 @@
 Marian
 ======
 
-[![Build Status](https://img.shields.io/jenkins/s/http/vali.inf.ed.ac.uk/jenkins/view/marian/job/marian-dev.svg?label=CUDA)](http://vali.inf.ed.ac.uk/jenkins/job/marian-dev/)
-[![CPU Build Status](https://img.shields.io/jenkins/s/http/vali.inf.ed.ac.uk/jenkins/view/marian/job/marian-dev-cpu.svg?label=CPU)](http://vali.inf.ed.ac.uk/jenkins/job/marian-dev-cpu/)
+[![Build Status CUDA 8.0](https://img.shields.io/jenkins/s/http/vali.inf.ed.ac.uk/jenkins/view/marian/job/marian-dev-cuda-8.0.svg?label=CUDA 8.0)](http://vali.inf.ed.ac.uk/jenkins/job/marian-dev-cuda-8.0/)
+[![Build Status CUDA 9.2](https://img.shields.io/jenkins/s/http/vali.inf.ed.ac.uk/jenkins/view/marian/job/marian-dev.svg?label=CUDA 9.2)](http://vali.inf.ed.ac.uk/jenkins/job/marian-dev/)
+[![Build Status CUDA 10.0](https://img.shields.io/jenkins/s/http/vali.inf.ed.ac.uk/jenkins/view/marian/job/marian-dev-cuda-10.0.svg?label=CUDA 10.0)](http://vali.inf.ed.ac.uk/jenkins/job/marian-dev-cuda-10.0/)
+[![Build Status CPU](https://img.shields.io/jenkins/s/http/vali.inf.ed.ac.uk/jenkins/view/marian/job/marian-dev-cpu.svg?label=CPU)](http://vali.inf.ed.ac.uk/jenkins/job/marian-dev-cpu/)
 [![Tests Status](https://img.shields.io/jenkins/s/http/vali.inf.ed.ac.uk/jenkins/view/marian/job/marian-regression-tests.svg?label=tests)](http://vali.inf.ed.ac.uk/jenkins/job/marian-regression-tests/)
 [![Latest release](https://img.shields.io/github/release/marian-nmt/marian.svg?label=release)](https://github.com/marian-nmt/marian/releases)
 [![License: MIT](https://img.shields.io/badge/License-MIT-blue.svg)](./LICENSE.md)

From c1451ab02558d3c4563426f98737020af0fd6b38 Mon Sep 17 00:00:00 2001
From: Roman Grundkiewicz <rgrundki@exseed.ed.ac.uk>
Date: Thu, 25 Apr 2019 10:23:18 +0100
Subject: [PATCH 437/838] Add acknowledgement

---
 README.md | 1 +
 1 file changed, 1 insertion(+)

diff --git a/README.md b/README.md
index 6f7ab5d85..0047a0ca3 100644
--- a/README.md
+++ b/README.md
@@ -68,6 +68,7 @@ _Horizon 2020 Research and Innovation Programme_ under grant agreements
 645487 ([Modern MT](http://www.modernmt.eu); 2015-2017),
 644333 ([TraMOOC](http://tramooc.eu/); 2015-2017),
 644402 ([HiML](http://www.himl.eu/); 2015-2017),
+825303 ([Bergamot](https://browser.mt/); 2019-2021),
 the Amazon Academic Research Awards program,
 the World Intellectual Property Organization,
 and is based upon work supported in part by the Office of the Director of

From 34717b40d8aec3bd487eb083354f7824c0476836 Mon Sep 17 00:00:00 2001
From: Roman Grundkiewicz <rgrundki@exseed.ed.ac.uk>
Date: Thu, 25 Apr 2019 11:35:56 +0100
Subject: [PATCH 438/838] Fix badges

---
 README.md | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/README.md b/README.md
index 0047a0ca3..d96f7c898 100644
--- a/README.md
+++ b/README.md
@@ -1,9 +1,9 @@
 Marian
 ======
 
-[![Build Status CUDA 8.0](https://img.shields.io/jenkins/s/http/vali.inf.ed.ac.uk/jenkins/view/marian/job/marian-dev-cuda-8.0.svg?label=CUDA 8.0)](http://vali.inf.ed.ac.uk/jenkins/job/marian-dev-cuda-8.0/)
-[![Build Status CUDA 9.2](https://img.shields.io/jenkins/s/http/vali.inf.ed.ac.uk/jenkins/view/marian/job/marian-dev.svg?label=CUDA 9.2)](http://vali.inf.ed.ac.uk/jenkins/job/marian-dev/)
-[![Build Status CUDA 10.0](https://img.shields.io/jenkins/s/http/vali.inf.ed.ac.uk/jenkins/view/marian/job/marian-dev-cuda-10.0.svg?label=CUDA 10.0)](http://vali.inf.ed.ac.uk/jenkins/job/marian-dev-cuda-10.0/)
+[![Build Status CUDA 8.0](https://img.shields.io/jenkins/s/http/vali.inf.ed.ac.uk/jenkins/view/marian/job/marian-dev-cuda-8.0.svg?label=CUDA%208.0)](http://vali.inf.ed.ac.uk/jenkins/job/marian-dev-cuda-8.0/)
+[![Build Status CUDA 9.2](https://img.shields.io/jenkins/s/http/vali.inf.ed.ac.uk/jenkins/view/marian/job/marian-dev.svg?label=CUDA%209.2)](http://vali.inf.ed.ac.uk/jenkins/job/marian-dev/)
+[![Build Status CUDA 10.0](https://img.shields.io/jenkins/s/http/vali.inf.ed.ac.uk/jenkins/view/marian/job/marian-dev-cuda-10.0.svg?label=CUDA%2010.0)](http://vali.inf.ed.ac.uk/jenkins/job/marian-dev-cuda-10.0/)
 [![Build Status CPU](https://img.shields.io/jenkins/s/http/vali.inf.ed.ac.uk/jenkins/view/marian/job/marian-dev-cpu.svg?label=CPU)](http://vali.inf.ed.ac.uk/jenkins/job/marian-dev-cpu/)
 [![Tests Status](https://img.shields.io/jenkins/s/http/vali.inf.ed.ac.uk/jenkins/view/marian/job/marian-regression-tests.svg?label=tests)](http://vali.inf.ed.ac.uk/jenkins/job/marian-regression-tests/)
 [![Latest release](https://img.shields.io/github/release/marian-nmt/marian.svg?label=release)](https://github.com/marian-nmt/marian/releases)

From 403ef88c77db5f1d9acd3a69c2e88f761015fa28 Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Thu, 25 Apr 2019 12:38:11 -0700
Subject: [PATCH 439/838] some cleanup; addressed some PR feedback

---
 src/common/config_parser.cpp       |   2 +-
 src/common/logging.cpp             |   1 -
 src/common/utils.cpp               |   8 ++
 src/data/corpus.cpp                |   9 +-
 src/data/corpus.h                  |   4 +-
 src/data/factored_vocab.cpp        | 136 +++++++++++++----------------
 src/data/factored_vocab.h          |  14 +--
 src/data/vocab_base.h              |   2 +-
 src/graph/expression_operators.cpp |   1 +
 src/layers/constructors.h          |   2 +-
 src/layers/generic.h               |  15 ++--
 src/layers/loss.h                  |   2 +-
 src/models/bert.h                  |   4 +-
 src/models/decoder.h               |   8 +-
 src/models/model_base.h            |   2 +-
 src/tensors/gpu/prod.cpp           |  16 ----
 src/training/validator.h           |  10 ++-
 17 files changed, 104 insertions(+), 132 deletions(-)

diff --git a/src/common/config_parser.cpp b/src/common/config_parser.cpp
index ae09f2aa2..46402e38e 100755
--- a/src/common/config_parser.cpp
+++ b/src/common/config_parser.cpp
@@ -435,7 +435,7 @@ void ConfigParser::addOptionsValidation(cli::CLIWrapper& cli) {
       "10000u");
   cli.add<std::vector<std::string>>("--valid-metrics",
       "Metric to use during validation: cross-entropy, ce-mean-words, perplexity, valid-script, "
-      "translation, bleu, bleu-detok, bleu-detok-ms. Multiple metrics can be specified",
+      "translation, bleu, bleu-detok. Multiple metrics can be specified",
       {"cross-entropy"});
   cli.add<size_t>("--early-stopping",
      "Stop if the first validation metric does not improve for  arg  consecutive validation steps",
diff --git a/src/common/logging.cpp b/src/common/logging.cpp
index 85dade236..57b55de96 100755
--- a/src/common/logging.cpp
+++ b/src/common/logging.cpp
@@ -78,7 +78,6 @@ void createLoggers(const marian::Config* config) {
 
   bool quiet = config && config->get<bool>("quiet");
   Logger general{createStderrLogger("general", "[%Y-%m-%d %T] %v", generalLogs, quiet)};
-  //Logger general{createStderrLogger("general", "%v", generalLogs, quiet)};
   Logger valid{createStderrLogger("valid", "[%Y-%m-%d %T] [valid] %v", validLogs, quiet)};
 
   if(config && config->has("log-level")) {
diff --git a/src/common/utils.cpp b/src/common/utils.cpp
index 3fd0bdead..4cb872ae2 100755
--- a/src/common/utils.cpp
+++ b/src/common/utils.cpp
@@ -99,6 +99,12 @@ static std::string escapeForPOpen(const std::string& arg) {
   return "'" + findReplace(arg, "'", "'\\''", /*all=*/ true) + "'";
 }
 
+// execute an external command
+// The command is composed of three pieces:
+//  - the executable path, e.g. --valid-script-path
+//  - an optional array of arguments. Meant for options. E.g. --valid-script-args. Options with leading  - can only be passed via Yaml/Json.
+//  - one more optional single argument. Meant as the main filename argument.
+// Each item will be escaped for shell syntax.
 std::string exec(const std::string& cmd, const std::vector<std::string>& args /*= {}*/, const std::string& arg /*= ""*/) {
   std::array<char, 128> buffer;
   std::string result;
@@ -199,6 +205,8 @@ std::string utf8FromUtf16String(const std::u16string& s) {
 #endif
 }
 
+// test whether a Unicode code point is in continuous script (e.g. Chinese or Thai)
+// This is used for detok bleu scoring where we have CJT characters.
 bool isContinuousScript(char32_t c) {
   // currently, this table is hand-coded, and may need to be extended when the standard grows
   auto in = [c](char32_t minVal, char32_t maxVal) { return c >= minVal && c <= maxVal; };
diff --git a/src/data/corpus.cpp b/src/data/corpus.cpp
index 61d359c91..1679e1a28 100755
--- a/src/data/corpus.cpp
+++ b/src/data/corpus.cpp
@@ -31,11 +31,10 @@ void Corpus::preprocessLine(std::string& line, size_t streamId) {
     else
       LOG_ONCE(info, "[data] Target all-caps'ed line to: {}", line);
   }
-  else if (titleCaseEvery_ != 0 && pos_ % titleCaseEvery_ == 1 && !inference_
-      
-      &&   streamId == 0       // @HACK: Hard-coding EN-X direction for now; needs an option in the future
-      
-      ) {
+  else if (titleCaseEvery_ != 0 && pos_ % titleCaseEvery_ == 1 && !inference_ && streamId == 0) {
+    // Only applied to stream 0 (source) since this feature is aimed at robustness against
+    // title case in the source (and not at translating into title case).
+    // Note: It is user's responsibility to not enable this if the source language is not English.
     line = vocabs_[streamId]->toEnglishTitleCase(line);
     if (streamId == 0)
       LOG_ONCE(info, "[data] Source English-title-case'd line to: {}", line);
diff --git a/src/data/corpus.h b/src/data/corpus.h
index c5af7220b..41272a811 100755
--- a/src/data/corpus.h
+++ b/src/data/corpus.h
@@ -28,8 +28,8 @@ class Corpus : public CorpusBase {
   void shuffleData(const std::vector<std::string>& paths);
 
   // for pre-processing
-  size_t allCapsEvery_{0};
-  size_t titleCaseEvery_{0};
+  size_t allCapsEvery_{0};   // if set, convert every N-th input sentence (after randomization) to all-caps (source and target)
+  size_t titleCaseEvery_{0}; // ditto for title case (source only)
   void preprocessLine(std::string& line, size_t streamId);
 
 public:
diff --git a/src/data/factored_vocab.cpp b/src/data/factored_vocab.cpp
index 509815f4e..f6208fece 100755
--- a/src/data/factored_vocab.cpp
+++ b/src/data/factored_vocab.cpp
@@ -1,3 +1,6 @@
+// This is the main implementation of factored models, which are driven by the vocabulary.
+// Decoding, embedding, and output layer call into the vocab to drive their behavior.
+
 #include "data/vocab_base.h"
 #include "common/definitions.h"
 #include "data/types.h"
@@ -5,6 +8,12 @@
 #include "common/regex.h"
 #include "data/factored_vocab.h"
 
+// @TODO: review all comments and clarify nomenclature:
+// * factor type (e.g. caps: |c* ); currently called a "group"
+// * factor name (e.g. all-caps: |ca )
+// * factor index (e.g. |ca is index 0 inside |ca |ci |cn)
+// * factor unit index (|ca is unit 41324 in joint factor vocab)
+
 namespace marian {
 
 /*virtual*/ size_t FactoredVocab::load(const std::string& modelPath, size_t maxSizeUnused /*= 0*/) /*override final*/ {
@@ -18,18 +27,19 @@ namespace marian {
     return size();
   }
 
+  // load factor-vocab file and parse it
   std::vector<std::vector<std::string>> factorMapTokenized;
   std::string line;
   std::vector<std::string> tokBuf;
-  if (utils::endsWith(modelPath, ".fsv")) {
+  if (utils::endsWith(modelPath, ".fsv")) { // @TODO: this extension check is only for backcompat; can be removed once we no longer support the old format
     // this is a fake parser for the generic factor spec, which makes a few hard assumptions:
-    //     - all types starting with _ except _has_* are factor names
-    //     - X : _x makes surface form X part of prob distribution _x except for _has_*
-    //     - X : _has_x adds factor "x" to lemma X
-    //     - _x <-> form only allows "_x <->" or "_x <-> _has_x" (same x), and is otherwise unused
-    //     - _lemma is special
-    // The current version of the code just converts it internally to the legacy form.
-    // Once the legacy form is no longer needed, must of this can be simplified a lot.
+    //     - all types starting with _ except _has_* are factor names
+    //     - X : _x makes surface form X part of prob distribution _x except for _has_*
+    //     - X : _has_x adds factor "x" to lemma X
+    //     - _x <-> form only allows "_x <->" or "_x <-> _has_x" (same x), and is otherwise unused
+    //     - _lemma is special
+    // The current version of the code just converts it internally to the legacy form.
+    // Once the legacy form is no longer needed, must of this can be simplified a lot.
     io::InputFileStream in(modelPath);
     WordIndex v = 0;
     std::map<std::string,std::set<std::string>> factorTypeMap; // [type name] -> {factor-type names}
@@ -122,7 +132,7 @@ namespace marian {
   constructGroupInfoFromFactorVocab();
   constructFactorIndexConversion();
 
-  // load and parse factorMap
+  // parse factorMap
   // modelPath = path to file with entries in order of vocab entries of the form
   //   WORD FACTOR1 FACTOR2 FACTOR3...
   // Factors are grouped
@@ -133,10 +143,9 @@ namespace marian {
   //  - all factors not matching a prefix get lumped into yet another class (the lemmas)
   //  - factor vocab must be sorted such that all groups are consecutive
   //  - result of Output layer is nevertheless logits, not a normalized probability, due to the sigmoid entries
-  //vocab_.resize(vocabSize);
-  //factorMap_.resize(vocabSize);
-  //auto factorVocabSize = this->factorVocabSize();
-  lemmaHasFactorGroup_.resize(groupRanges_[0].second - groupRanges_[0].first);
+  // For every lemma, the factor map contains one example. At the end of this loop, we have a vocabulary
+  // vocab_ that contains those examples, but not all possible combinations
+  lemmaHasFactorGroup_.resize(groupRanges_[0].second - groupRanges_[0].first); // group 0 is the lemmas; this difference is the number of lemma symbols
   size_t numTotalFactors = 0;
   for (WordIndex v = 0; v < factorMapTokenized.size(); v++) {
     const auto& tokens = factorMapTokenized[v];
@@ -145,7 +154,7 @@ namespace marian {
     // Not every word has all other factors, so the n-th item is not always in the same factor group.
     // @TODO: change to just use the .wl file, and manually split at @
     ABORT_IF(tokens.size() < 2, "Factor map must have at least one factor per word", modelPath);
-    std::vector<WordIndex> factorUnits;
+    std::vector<WordIndex> factorUnits; // units in the joint factor vocab that belong to a specific factor type
     for (size_t i = 1/*first factor*/; i < tokens.size(); i++) {
       auto u = factorVocab_[tokens[i]];
       factorUnits.push_back(u);
@@ -167,18 +176,13 @@ namespace marian {
       ABORT_IF(lemmaFlags != hasFactorGroupFlags, "Inconsistent factor groups used for word {}", tokens.front());
     // map factors to non-dense integer
     auto word = factors2word(factorIndices);
-    auto wordIndex = word.toWordIndex();
-    //factorMap_[wordIndex] = std::move(factorUnits);
     // add to vocab (the wordIndex are not dense, so the vocab will have holes)
-    //if (tokens.front().front() == '<') // all others are auto-expanded
     // for now add what we get, and then expand more below
     auto wordString = word2string(word);
     if (tokens.front() != wordString) // order may differ, since we formed the input based on the factors in the user file, which may be in any order
       LOG_ONCE(info, "[vocab] Word name in vocab file {} differs from canonical form {} (this warning is only shown once)", tokens.front(), wordString);
-    vocab_.add(wordString, wordIndex);
+    vocab_.add(wordString, word.toWordIndex());
     numTotalFactors += tokens.size() - 1;
-    //if (v % 5000 == 0)
-    //  LOG(info, "{} -> {}", tokens.front(), word2string(word));
   }
   LOG(info, "[vocab] Factored-embedding map read with total/unique of {}/{} factors from {} example words (in space of {})",
       numTotalFactors, factorVocabSize(), vocab_.size()/*numValid()*/, utils::withCommas(virtualVocabSize()));
@@ -204,12 +208,6 @@ namespace marian {
   unkId_ = Word::fromWordIndex(vocab_[DEFAULT_UNK_STR]);
   //LOG(info, "eos: {}; unk: {}", word2string(eosId_), word2string(unkId_));
 
-//#if 1   // dim-vocabs stores numValid() in legacy model files, and would now have been size()
-//  if (maxSizeUnused == vocab_.size()/*numValid()*/)
-//    maxSizeUnused = virtualVocabSize;
-//#endif
-  //ABORT_IF(maxSizeUnused != 0 && maxSizeUnused != size(), "Factored vocabulary does not allow on-the-fly clipping to a maximum vocab size (from {} to {})", size(), maxSizeUnused);
-  // @TODO: ^^ disabled now that we are generating the full combination of factors; reenable once we have consistent setups again
   return size();
 }
 
@@ -291,6 +289,10 @@ void FactoredVocab::constructFactorIndexConversion() {
 }
 
 // encode factors into a Word struct
+// inputs:
+//  - factorIndices[factorType] = factorIndex (e.g. 0 for |ca )
+// output:
+//  - representation as 'Word' (which is, in fact, a single big integer)
 Word FactoredVocab::factors2word(const std::vector<size_t>& factorIndices /* [numGroups] */) const {
   size_t index = 0;
   size_t numGroups = getNumGroups();
@@ -311,6 +313,10 @@ Word FactoredVocab::factors2word(const std::vector<size_t>& factorIndices /* [nu
   return Word::fromWordIndex(index);
 }
 
+// encode only a lemma into a 'Word'
+// The result is incomplete, in that the lemma likely has additional factors that are not yet specified.
+// Those are encoded as the value FACTOR_NOT_SPECIFIED. This function is used during beam search,
+// which starts with lemma scores, and then adds factors one by one to the path score.
 Word FactoredVocab::lemma2Word(size_t factor0Index) const {
   size_t numGroups = getNumGroups();
   std::vector<size_t> factorIndices;
@@ -341,12 +347,16 @@ Word FactoredVocab::expandFactoredWord(Word word, size_t groupIndex, size_t fact
   return word;
 }
 
+// factor unit: index of factor name in the joint factor vocabulary
+// factor index: relative index within factor type, e.g. 0 for |ca
 size_t FactoredVocab::factorUnit2FactorIndex(WordIndex u) const {
   auto g = factorGroups_[u]; // convert u to relative u within factor group range
   ABORT_IF(u < groupRanges_[g].first || u >= groupRanges_[g].second, "Invalid factorGroups_ entry??");
   return u - groupRanges_[g].first;
 }
 
+// split the 'Word' representation, which is really a single big integer, into the individual
+// factor indices for all factor types
 void FactoredVocab::word2factors(Word word, std::vector<size_t>& factorIndices /* [numGroups] */) const {
   size_t numGroups = getNumGroups();
   factorIndices.resize(numGroups);
@@ -361,13 +371,13 @@ void FactoredVocab::word2factors(Word word, std::vector<size_t>& factorIndices /
 #endif
 }
 
+// serialize 'Word' representation into its string form
 std::string FactoredVocab::word2string(Word word) const {
   // this function has some code dup, so that we can bypass some checks for debugging
   size_t numGroups = getNumGroups();
   size_t factor0Index = word.toWordIndex() / factorStrides_[0];
   std::string res;
   for (size_t g = 0; g < numGroups; g++) {
-    //res.append(res.empty() ? "(" : ", ");
     size_t index = word.toWordIndex();
     index = index / factorStrides_[g];
     index = index % (size_t)factorShape_[g];
@@ -376,16 +386,14 @@ std::string FactoredVocab::word2string(Word word) const {
         res.append("(lemma oob)");
       else if (lemmaHasFactorGroup(factor0Index, g))
         res.append("?");
-      //else
-      //  res.append("n/a");
     }
     else
       res.append(factorVocab_[(WordIndex)(index + groupRanges_[g].first)]);
   }
-  //res.append(")");
   return res;
 }
 
+// deserialize factored string form (e.g. HELLO|ci|wb) into its internal binary 'Word' representation
 Word FactoredVocab::string2word(const std::string& w) const {
   auto sep = std::string(1, factorSeparator_);
   auto parts = utils::splitAny(w, sep);
@@ -408,10 +416,10 @@ Word FactoredVocab::string2word(const std::string& w) const {
     factorIndices[g] = u - groupRanges_[g].first;
   }
   auto word = factors2word(factorIndices);
-  //auto w2 = word2string(word);
   return word;
 }
 
+// extract the factor index of a given factor type from the 'Word' representation
 size_t FactoredVocab::getFactor(Word word, size_t groupIndex) const {
   size_t index = word.toWordIndex();
   size_t factor0Index = index / factorStrides_[0];
@@ -437,11 +445,6 @@ size_t FactoredVocab::getFactor(Word word, size_t groupIndex) const {
   return index;
 }
 
-//std::pair<WordIndex, bool> FactoredVocab::getFactorUnit(Word word, size_t groupIndex) const {
-//  word; groupIndex;
-//  ABORT("Not implemented");
-//}
-
 #ifdef FACTOR_FULL_EXPANSION
 void FactoredVocab::constructNormalizationInfoForVocab() {
   // create mappings needed for normalization in factored outputs
@@ -482,35 +485,27 @@ void FactoredVocab::constructNormalizationInfoForVocab() {
 #endif
 
 /*virtual*/ Word FactoredVocab::operator[](const std::string& word) const /*override final*/ {
+  // @TODO: do away with vocab_ altogether, and just always parse.
   WordIndex index;
   bool found = vocab_.tryFind(word, index);
   if (found)
     return Word::fromWordIndex(index);
   else
     return string2word(word);
-    //ABORT("Unknown word {} mapped to {}", word, word2string(getUnkId()));
-    //LOG_ONCE(info, "WARNING: Unknown word {} mapped to {}", word, word2string(getUnkId()));
-    //return getUnkId();
 }
 
 /*virtual*/ const std::string& FactoredVocab::operator[](Word word) const /*override final*/ {
   //LOG(info, "Looking up Word {}={}", word.toWordIndex(), word2string(word));
-#if 1 // @TODO: remove this
   ABORT_IF(!vocab_.contains(word.toWordIndex()), "Invalid factor combination {}", word2string(word));
-#else // @BUGBUG: our manually prepared dict does not contain @CI tags for single letters, but it's a valid factor
-  if (!vocab_.contains(word.toWordIndex())) {
-    LOG/*_ONCE*/(info, "Factor combination {} missing in external dict, generating fake entry (only showing this warning once)", word2string(word));
-    //const_cast<WordLUT&>(vocab_).add("??" + word2string(word), word.toWordIndex());
-    const_cast<WordLUT&>(vocab_).add(word2string(word), word.toWordIndex());
-  }
-#endif
   return vocab_[word.toWordIndex()];
 }
 
+// convert a string representation of a token sequence to all-caps by changing all capitalization factors to |ca
 /*virtual*/ std::string FactoredVocab::toUpper(const std::string& line) const /*override final*/ {
   return utils::findReplace(utils::findReplace(utils::findReplace(utils::findReplace(line, "|ci", "|ca", /*all=*/true), "|cn", "|ca", /*all=*/true), "@CI", "@CA", /*all=*/true), "@CN", "@CA", /*all=*/true);
 }
 
+// convert a string representation of a token sequence to English title case by changing the capitalization factors to |ci
 /*virtual*/ std::string FactoredVocab::toEnglishTitleCase(const std::string& line) const /*override final*/ {
   // @BUGBUG: does not handle the special words that should remain lower-case
   // note: this presently supports both @WB and @GL- (legacy)
@@ -525,7 +520,7 @@ void FactoredVocab::constructNormalizationInfoForVocab() {
     auto word = Word::fromWordIndex(*ptr);
     auto wordString = word2string(word);
     auto lemmaIndex = getFactor(word, 0) + groupRanges_[0].first;
-    *ptr = lemmaIndex;
+    *ptr = (WordIndex)lemmaIndex;
   }
 }
 
@@ -544,6 +539,7 @@ void FactoredVocab::constructNormalizationInfoForVocab() {
   return factors2word(factorIndices);
 }
 
+// encode a string representation of an entire token sequence, as found in the corpus file, into a 'Word' array
 /*virtual*/ Words FactoredVocab::encode(const std::string& line, bool addEOS /*= true*/, bool /*inference*/ /*= false*/) const /*override final*/ {
   std::vector<std::string> lineTokens;
   utils::split(line, lineTokens, " ");
@@ -555,6 +551,7 @@ void FactoredVocab::constructNormalizationInfoForVocab() {
   return res;
 }
 
+// decode a 'Word' array into the external string representation of that token sequence, as written to output files
 /*virtual*/ std::string FactoredVocab::decode(const Words& sentence, bool ignoreEOS /*= true*/) const /*override final*/ {
   std::vector<std::string> decoded;
   decoded.reserve(sentence.size());
@@ -584,7 +581,8 @@ static void unescapeHexEscapes(std::string& utf8Lemma) {
   utf8Lemma = utils::utf8FromUtf16String(lemma);
 }
 
-// interpret the capitalization and glue factors
+// convert a 'Word' sequence to its final human-readable surface form
+// This interprets the capitalization and glue factors.
 // This assumes a specific notation of factors, emulating our C# code for generating these factors:
 //  - | as separator symbol
 //  - capitalization factors are cn, ci, and ca
@@ -592,7 +590,7 @@ static void unescapeHexEscapes(std::string& utf8Lemma) {
 std::string FactoredVocab::surfaceForm(const Words& sentence) const /*override final*/ {
   std::string res;
   res.reserve(sentence.size() * 10);
-  bool prevHadGlueRight = true; // no space at sentence start
+  bool prevHadGlueRight = true; // no space at sentence start
   for(auto w : sentence) {
     if (w == getEosId())
       break;
@@ -603,12 +601,12 @@ std::string FactoredVocab::surfaceForm(const Words& sentence) const /*override f
     std::set<std::string> tokenSet(tokens.begin() + 1, tokens.end());
     auto has = [&](const char* factor) { return tokenSet.find(factor) != tokenSet.end(); };
     // spacing
-    bool hasGlueRight = has("gr+") || has("wen") || has("cen");
-    bool hasGlueLeft  = has("gl+") || has("wbn") || has("cbn");
-    bool insertSpaceBefore = !prevHadGlueRight && !hasGlueLeft;
+    bool hasGlueRight = has("gr+") || has("wen") || has("cen");
+    bool hasGlueLeft  = has("gl+") || has("wbn") || has("cbn");
+    bool insertSpaceBefore = !prevHadGlueRight && !hasGlueLeft;
     if (insertSpaceBefore)
       res.push_back(' ');
-    prevHadGlueRight = hasGlueRight;
+    prevHadGlueRight = hasGlueRight;
     // capitalization
     unescapeHexEscapes(lemma); // unescape \x.. and \u....
     if      (has("ci")) lemma = utils::utf8Capitalized(lemma);
@@ -621,8 +619,13 @@ std::string FactoredVocab::surfaceForm(const Words& sentence) const /*override f
   return res;
 }
 
-// create a CSR matrix M[V,U] from words[] with
-// M[v,u] = 1/c(u) if factor u is a factor of word v, and c(u) is how often u is referenced
+// create a CSR matrix M[V,U] from words[] with M[v,u] = 1 if factor u is a factor of word v
+// This is used to form the embedding of a multi-factor token.
+// That embedding is a sum of the embeddings of the individual factors.
+// Those individual embeddings are assumed to be concatenated into one joint large embedding matrix.
+// The factor embeddings are summed up by multiplying the joint embedding matrix with a sparse matrix
+// that contains a 1 for all positions in the joint matrix that should be summed up.
+// This function creates that sparse matrix in CSR form.
 FactoredVocab::CSRData FactoredVocab::csr_rows(const Words& words) const {
   auto numGroups = getNumGroups();
   std::vector<float> weights;
@@ -636,27 +639,12 @@ FactoredVocab::CSRData FactoredVocab::csr_rows(const Words& words) const {
   for (auto word : words) {
     if (vocab_.contains(word.toWordIndex())) { // skip invalid combinations in the space (can only happen during initialization)  --@TODO: add a check?
       word2factors(word, factorIndices);
-#if 0 // original code; enable this to try
-      numGroups;
-      const auto& m = factorMap_[word.toWordIndex()];
-      for (auto u : m) {
-        indices.push_back(u);
-#else
-#if 0 // special handling of the missing single capitalized letters
-      // costs about 0.1 BLEU when original model never saw this combination (which is quite nicely low)
-      // @TODO: remove this once we use the factor-spec file
-      const auto& lemma = factorVocab_[(WordIndex)(factorIndices[0] + groupRanges_[0].first)];
-      if (lemma.size() == 1 && factorIndices[1]/*@C*/ == 1/*@CI*/) // skip one-letter factors with 
-          LOG_ONCE(info, "Suppressing embedding for word {} (only showing this warning once)", word2string(word));
-      else
-#endif
       for (size_t g = 0; g < numGroups; g++) { // @TODO: make this faster by having a list of all factors to consider for a lemma?
         auto factorIndex = factorIndices[g];
         ABORT_IF(factorIndex == FACTOR_NOT_SPECIFIED, "Attempted to embed a word with a factor not specified");
         if (factorIndex == FACTOR_NOT_APPLICABLE)
           continue;
         indices.push_back((IndexType)(factorIndex + groupRanges_[g].first)); // map to unit index
-#endif
         weights.push_back(1.0f);
       }
     }
@@ -684,15 +672,15 @@ WordIndex FactoredVocab::WordLUT::add(const std::string& word, WordIndex index)
   ABORT_IF(!wasInserted, "Duplicate vocab entry for '{}', new index {} vs. existing index {}", word, index, str2index_[word]);
   wasInserted = index2str_.insert(std::make_pair(index, word)).second;
   ABORT_IF(!wasInserted, "Duplicate vocab entry for index {} (new: '{}'; existing: '{}')", index, word, index2str_[index]);
-  //if (vocabSize_ < index2str_.size())
-  //  vocabSize_ = index2str_.size();
   return index;
 }
+
 static const std::string g_emptyString;
 const std::string& FactoredVocab::WordLUT::operator[](WordIndex index) const {
   auto iter = index2str_.find(index);
-  //ABORT_IF(iter == index2str_.end(), "Invalid access to dictionary gap item");
   if (iter == index2str_.end())
+    // returns an empty string for unknown index values
+    // @TODO: is that ever used ? If so, document.If not, remove this feature and let it fail.static const std::string g_emptyString;
     return g_emptyString; // (using a global since we return a reference)
   else
     return iter->second;
diff --git a/src/data/factored_vocab.h b/src/data/factored_vocab.h
index 3ff338e23..9e7734c43 100755
--- a/src/data/factored_vocab.h
+++ b/src/data/factored_vocab.h
@@ -9,8 +9,6 @@
 #include "data/types.h"
 #include "data/vocab_base.h"
 
-#include <numeric> // for std::iota()
-
 #undef FACTOR_FULL_EXPANSION // define this to get full expansion. @TODO: infeasible for many factors; just delete this
 
 namespace marian {
@@ -25,7 +23,6 @@ class FactoredVocab : public IVocab {
   };
 
   // from IVocab:
-  // @TODO: Why are these virtual and final at the same time? Seems we should remove all the 'virtual' here
   virtual size_t load(const std::string& factoredVocabPath, size_t maxSizeUnused = 0) override final;
   virtual void create(const std::string& vocabPath, const std::vector<std::string>& trainPaths, size_t maxSize) override final { vocabPath, trainPaths, maxSize; ABORT("Factored vocab cannot be created on the fly"); }
   virtual const std::string& canonicalExtension() const override final { return suffixes()[0]; }
@@ -57,8 +54,6 @@ class FactoredVocab : public IVocab {
 #endif
   size_t getNumGroups() const { return groupRanges_.size(); }
   std::pair<size_t, size_t> getGroupRange(size_t g)    const { return groupRanges_[g]; }   // [g] -> (u_begin, u_end)
-  //const std::vector<float>&     getFactorMasks(size_t g)   const { return factorMasks_[g]; }   // [g][v] 1.0 if word v has factor g
-  //const std::vector<IndexType>& getFactorIndices(size_t g) const { return factorIndices_[g]; } // [g][v] local index u_g = u - u_g,begin of factor g for word v; 0 if not a factor
 #ifdef FACTOR_FULL_EXPANSION
   const std::vector<float>& getGapLogMask() const { return gapLogMask_; } // [v] -inf if v is a gap entry, else 0
 #endif
@@ -70,7 +65,6 @@ class FactoredVocab : public IVocab {
   Word expandFactoredWord(Word word, size_t groupIndex, size_t factorIndex) const;
   bool canExpandFactoredWord(Word word, size_t groupIndex) const { return lemmaHasFactorGroup(getFactor(word, 0), groupIndex); }
   size_t getFactor(Word word, size_t groupIndex) const;
-  //std::pair<WordIndex, bool> getFactorUnit(Word word, size_t groupIndex) const;
   bool lemmaHasFactorGroup(size_t factor0Index, size_t g) const { return lemmaHasFactorGroup_[factor0Index][g]; }
 
   static constexpr size_t FACTOR_NOT_APPLICABLE = (SIZE_MAX - 1);
@@ -89,19 +83,16 @@ class FactoredVocab : public IVocab {
 #endif
   size_t factorUnit2FactorIndex(WordIndex u) const;
 private:
+  // @TODO: Should we move WordLUT to utils?
   class WordLUT { // map between strings and WordIndex
     std::map<std::string, WordIndex> str2index_;
     std::map<WordIndex, std::string> index2str_;
-    //size_t vocabSize_; // total number of vocab items as set by user
   public:
     WordIndex add(const std::string& word, WordIndex index);
     const std::string& operator[](WordIndex index) const;
     WordIndex operator[](const std::string& word) const;
     bool contains(WordIndex index) const { return index2str_.find(index) != index2str_.end(); }
     bool tryFind(const std::string& word, WordIndex& index) const;
-    //void resize(size_t num); // @TODO: remove this, and remove the distinction of size() and numValid()
-    //size_t size() const { return vocabSize_; } // nominal size including gap items
-    //size_t numValid() const { return str2index_.size(); } // actual non-gaps items
     size_t size() const { return str2index_.size(); }
     size_t load(const std::string& path);
     void dumpToFile(const std::string& path);
@@ -116,7 +107,6 @@ class FactoredVocab : public IVocab {
   char factorSeparator_ = '|';                         // separator symbol for parsing factored words
   WordLUT factorVocab_;                                // [factor name] -> factor index = row of E_
   std::vector<std::string> groupPrefixes_;             // [group id g] shared prefix of factors (used for grouping)
-  //std::vector<std::vector<WordIndex>> factorMap_;      // [word index v] -> set of factor indices u
 #ifdef FACTOR_FULL_EXPANSION
   CSRData globalFactorMatrix_;                         // [v,u] (sparse) -> =1 if u is factor of v
 #endif
@@ -125,8 +115,6 @@ class FactoredVocab : public IVocab {
   std::vector<std::vector<bool>> lemmaHasFactorGroup_; // [factor 0 index][g] -> true if lemma has factor group
   Shape factorShape_;                                  // [g] number of factors in each factor group
   std::vector<size_t> factorStrides_;                  // [g] stride for factor dimension
-  //std::vector<std::vector<float>>     factorMasks_;    // [g][v] 1.0 if word v has factor g
-  //std::vector<std::vector<IndexType>> factorIndices_;  // [g][v] relative index u - u_begin of factor g (or any valid index if it does not have it; we use 0)
 #ifdef FACTOR_FULL_EXPANSION
   std::vector<float> gapLogMask_;                      // [v] -1e8 if this is a gap, else 0
 #endif
diff --git a/src/data/vocab_base.h b/src/data/vocab_base.h
index 238f9146b..6b6869d88 100755
--- a/src/data/vocab_base.h
+++ b/src/data/vocab_base.h
@@ -50,7 +50,7 @@ class IVocab {
   virtual std::string toEnglishTitleCase(const std::string& line) const { return line; }
 
   // this function is an identity mapping for default vocabularies, hence do nothing
-  virtual void transcodeToShortlistInPlace(WordIndex* ptr, size_t num) const { }
+  virtual void transcodeToShortlistInPlace(WordIndex* ptr, size_t num) const { ptr; num; }
 
   virtual void createFake() = 0;
 
diff --git a/src/graph/expression_operators.cpp b/src/graph/expression_operators.cpp
index 7247c1001..396370bd1 100755
--- a/src/graph/expression_operators.cpp
+++ b/src/graph/expression_operators.cpp
@@ -79,6 +79,7 @@ Expr softmax(Expr a, Expr zeroOneMask, int axis /*=-1*/) {
 
 Expr logsoftmax(Expr a) {
   if (a->type() == "logsoftmax") // logsoftmax(logsoftmax(x)) == logsoftmax(x)
+    // @TODO: Is this correct? logsoftmax is idempotent in forward(), but not in backward()
     return a;
   return Expression<LogSoftmaxNodeOp>(a);
 }
diff --git a/src/layers/constructors.h b/src/layers/constructors.h
index fc751dfc9..ed2c029f8 100755
--- a/src/layers/constructors.h
+++ b/src/layers/constructors.h
@@ -135,7 +135,7 @@ class MLP : public IUnaryLogitLayer, public IHasShortList {
   Logits applyAsLogits(const std::vector<Expr>& av) override {
     // same as apply() except  for the last layer, we invoke applyAsLogits(), which has a different return type
     auto lastLayer = std::dynamic_pointer_cast<IUnaryLogitLayer>(layers_.back());
-    ABORT_IF(!lastLayer, "MLP::applyAsLogits() applied but last MLP layer is not IUnaryLogitLayer");
+    ABORT_IF(!lastLayer, "MLP::applyAsLogits() was called on an MLP whose last layer is not an IUnaryLogitLayer");
     if (layers_.size() == 1) {
       if (av.size() == 1)
         return lastLayer->applyAsLogits(av[0]);
diff --git a/src/layers/generic.h b/src/layers/generic.h
index 775fbbe01..7cf8ac6ce 100755
--- a/src/layers/generic.h
+++ b/src/layers/generic.h
@@ -67,9 +67,11 @@ struct IEmbeddingLayer {
 
 class FactoredVocab;
 
-// @HACK: Frank's quick implementation of factored outputs. To be re-thought once it works.
-// Output layer returns a Logits object, which is able to compute some things on the fly
-// for factored embeddings.
+// To support factors, any output projection (that is followed by a softmax) must
+// retain multiple outputs, one for each factor. Such layer returns not a single Expr,
+// but a Logits object that contains multiple.
+// This allows to compute softmax values in a factored manner, where we never create
+// a fully expanded list of all factor combinations.
 class RationalLoss;
 class Logits {
 public:
@@ -96,7 +98,6 @@ class Logits {
       MaskedFactorIndices(const Words& words) { indices = toWordIndexVector(words); } // we can leave masks uninitialized for this special use case
     };
     std::vector<MaskedFactorIndices> factorizeWords(const Words& words) const; // breaks encoded Word into individual factor indices
-    //std::vector<float> getFactorMasks(const Words& words, size_t factorGroup) const;
     float getLogitAt(size_t i) const { return getLogits()->val()->get(i); } // used for breakDown() only; @TODO: avoid the fully expanded logits; pass separate indices instead of 'i'
     size_t getNumFactorGroups() const { return logits_.size(); }
     bool empty() const { return logits_.empty(); }
@@ -111,13 +112,15 @@ class Logits {
     std::vector<float> getFactorMasks(size_t factorGroup, const std::vector<WordIndex>& indices) const;
 private:
     // members
-    // @HACK: The interplay between Logits and RationalLoss is weird. Here, we allow RationalLoss with count == nullptr.
-    std::vector<Ptr<RationalLoss>> logits_; // [group id][B..., num factors in group]  --@TODO: we don't use the RationalLoss component anymore, can be removed again
+    // @TODO: we don't use the RationalLoss component anymore, can be removed again, and replaced just by the Expr
+    std::vector<Ptr<RationalLoss>> logits_; // [group id][B..., num factors in group]
     Ptr<FactoredVocab> factoredVocab_;
 };
 
 // Unary function that returns a Logits object
 // Also implements IUnaryLayer, since Logits can be cast to Expr.
+// This interface is implemented by all layers that are of the form of a unary function
+// that returns multiple logits, to support factors.
 struct IUnaryLogitLayer : public IUnaryLayer {
   virtual Logits applyAsLogits(Expr) = 0;
   virtual Logits applyAsLogits(const std::vector<Expr>& es) {
diff --git a/src/layers/loss.h b/src/layers/loss.h
index b02aa78fa..554541cbd 100755
--- a/src/layers/loss.h
+++ b/src/layers/loss.h
@@ -341,7 +341,7 @@ class CrossEntropyLoss : public LabelwiseLoss {
     auto ce = logits.applyLossFunction(labels, [&](Expr logits, Expr indices) {
       logits = atleast_3d(logits); // we always assuma a time and batch dimension exists.
       // for bert training or classification the time dimension is lot.
-      // Here safeguard against 2d classifier output, adds 1 on the left, non-op.      Expr ce = cross_entropy(logits, indices);
+      // Here safeguard against 2d classifier output, adds 1 on the left, non-op.
       Expr ce = cross_entropy(logits, indices);
       if (labelSmoothing_ > 0) {
         // ce = -sum_i y^_i log y_i(h)
diff --git a/src/models/bert.h b/src/models/bert.h
index fb2dab846..61cff43bf 100755
--- a/src/models/bert.h
+++ b/src/models/bert.h
@@ -279,18 +279,16 @@ class BertClassifier : public ClassifierBase {
     auto classEmbeddings = slice(context, /*axis=*/-3, /*i=*/0); // [CLS] symbol is first symbol in each sequence
 
     int dimModel = classEmbeddings->shape()[-1];
-//    int dimTrgCls = opt<std::vector<int>>("dim-vocabs")[batchIndex_]; // Target vocab is used as class labels
+    int dimTrgCls = opt<std::vector<int>>("dim-vocabs")[batchIndex_]; // Target vocab is used as class labels
 
     auto output = mlp::mlp()                                          //
                     .push_back(mlp::dense()                           //
                                  ("prefix", prefix_ + "_ff_logit_l1") //
                                  ("dim", dimModel)                    //
                                  ("activation", mlp::act::tanh))      // @TODO: do we actually need this?
-#if 0 // @TODO: Not supported presently since Output has a different signature now
                     .push_back(mlp::output()                          //
                                  ("dim", dimTrgCls))                  //
                                  ("prefix", prefix_ + "_ff_logit_l2") //
-#endif
                     .construct(graph);
 
     auto logits = output->apply(classEmbeddings); // class logits for each batch entry
diff --git a/src/models/decoder.h b/src/models/decoder.h
index 962587d30..31baca9cb 100755
--- a/src/models/decoder.h
+++ b/src/models/decoder.h
@@ -15,7 +15,7 @@ class DecoderBase {
   std::string prefix_{"decoder"};
   bool inference_{false};
   size_t batchIndex_{1};
-  std::vector<Ptr<IEmbeddingLayer>> embedding_; // @TODO: find a more grammattical name
+  std::vector<Ptr<IEmbeddingLayer>> embedding_; // @TODO: find a more grammatical name
 
   Ptr<data::Shortlist> shortlist_;
 
@@ -35,7 +35,7 @@ class DecoderBase {
 
   virtual Ptr<DecoderState> step(Ptr<ExpressionGraph>, Ptr<DecoderState>) = 0;
 
-  void lazyCreateEmbedding(Ptr<ExpressionGraph> graph) {
+  void lazyCreateEmbeddingLayer(Ptr<ExpressionGraph> graph) {
     // @TODO: code dup with EncoderTransformer
     if (embedding_.size() <= batchIndex_ || !embedding_[batchIndex_]) { // lazy
       if (embedding_.size() <= batchIndex_)
@@ -64,7 +64,7 @@ class DecoderBase {
                                    Ptr<data::CorpusBatch> batch) {
     auto subBatch = (*batch)[batchIndex_];
 
-    lazyCreateEmbedding(graph);
+    lazyCreateEmbeddingLayer(graph);
     Expr y, yMask; std::tie
     (y, yMask) = embedding_[batchIndex_]->apply(subBatch);
 
@@ -87,7 +87,7 @@ class DecoderBase {
                                         const Words& words,
                                         int dimBatch,
                                         int dimBeam) {
-    lazyCreateEmbedding(graph);
+    lazyCreateEmbeddingLayer(graph);
     Expr selectedEmbs;
     int dimEmb = opt<int>("dim-emb");
     if(words.empty()) {
diff --git a/src/models/model_base.h b/src/models/model_base.h
index 0a64a1e1d..016f3b8e3 100755
--- a/src/models/model_base.h
+++ b/src/models/model_base.h
@@ -3,7 +3,7 @@
 #include <string>
 #include "marian.h"
 #include "layers/loss.h"
-#include "layers/generic.h" // @HACK for Frank's factored embeddings/Logits class
+#include "layers/generic.h"
 
 namespace marian {
 namespace models {
diff --git a/src/tensors/gpu/prod.cpp b/src/tensors/gpu/prod.cpp
index f259f1c47..74abfa219 100755
--- a/src/tensors/gpu/prod.cpp
+++ b/src/tensors/gpu/prod.cpp
@@ -349,22 +349,6 @@ void CSRProd(marian::Tensor C,
   else {
     // C = S x D for row-major matrices
     // Implemented via cusparse as C' = D' x S' ("gemmi") where C' and D' are column-major.
-    //if (St_values) {
-    //  auto vals = get<float>(St_values, C->getBackend());  vals.resize(numValues);
-    //  auto inds = get<IndexType>(St_indices, C->getBackend()); inds.resize(numValues);
-    //  auto offs = get<IndexType>(St_offsets, C->getBackend()); offs.resize(rowsS + 1);
-    //  LOG(info, "[{} x {}] = [{} x {}] * [{} x {}]", colsC, rowsC, colsD, rowsD, -1, offs.size() - 1);
-    //  for (auto v : vals)
-    //    ABORT_IF(v != 1, "v={}", v);
-    //  for (auto i : inds)
-    //    ABORT_IF(i >= rowsC, "i={}", i);
-    //  for (auto o : offs)
-    //    ABORT_IF(o > inds.size(), "o={}", o);
-    //  ABORT_IF(colsC != colsD, "0");
-    //  std::vector<float> dData; D->get(dData); ABORT_IF(dData.size() != colsD * rowsD, "1");
-    //  std::vector<float> cData; C->get(cData); ABORT_IF(cData.size() != colsC * rowsC, "2");
-    //  ABORT_IF(rowsC != rowsS, "3: {} != {}, asz={}", rowsC, rowsS, St_offsets->size()/4);
-    //}
     CUSPARSE_CHECK(cusparseSgemmiEx(cusparseHandle,
         /*m=*/ colsD, // #rows of first (col-major) factor = #cols of row-major D
         /*n=*/ rowsC, // #cols of second (CSC) factor and (col-major) result = #rows of row-major C
diff --git a/src/training/validator.h b/src/training/validator.h
index 78b9f9d33..37833b605 100755
--- a/src/training/validator.h
+++ b/src/training/validator.h
@@ -744,7 +744,7 @@ class BleuValidator : public Validator<data::Corpus, models::IModel> {
 public:
   static std::string tokenizeContinuousScript(const std::string& sUTF8) {
     // We want BLEU-like scores that are comparable across different tokenization schemes.
-    // For continuous scripts )Chinese, Japanese, Thai), we would need a language-specific
+    // For continuous scripts (Chinese, Japanese, Thai), we would need a language-specific
     // statistical word segmenter, which is outside the scope of Marian. As a practical
     // compromise, we segment continuous-script sequences into individual characters, while
     // leaving Western scripts as words. This way we can use the same settings for Western
@@ -765,8 +765,9 @@ class BleuValidator : public Validator<data::Corpus, models::IModel> {
 
   std::vector<std::string> decode(const Words& words, bool addEOS = false) {
     auto vocab = vocabs_.back();
-    auto tokenString = tokenize(vocab->surfaceForm(words));
-    tokenString = tokenizeContinuousScript(tokenString);
+    auto tokenString = vocab->surfaceForm(words);        // detokenize to surface form
+    tokenString = tokenize(tokenString);                 // tokenize according to SacreBLEU rules
+    tokenString = tokenizeContinuousScript(tokenString); // CJT scripts only: further break into characters
     auto tokens = utils::splitAny(tokenString, " ");
     if(addEOS)
       tokens.push_back("</s>");
@@ -836,6 +837,9 @@ class BleuValidator : public Validator<data::Corpus, models::IModel> {
 #if 1 // hack for now, to get this feature when running under Flo
     // Problem is that Flo pieces that pass 'bleu' do not know whether vocab is factored,
     // hence cannot select 'bleu-detok'.
+    // @TODO: We agreed that we will replace bleu-detok by bleu with an additional
+    // parameter to select the detokenization method, which will default to detok for FactoredSegmenter,
+    // and no-op for base vocab.
     if (vocabs_.back()->type() == "FactoredVocab") {
       LOG_ONCE(info, "[valid] FactoredVocab implies using detokenized BLEU");
       detok = true; // always use bleu-detok

From b570638533d145ebff0b3b5dccccfdd8ad3499f2 Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Fri, 26 Apr 2019 14:59:40 -0700
Subject: [PATCH 440/838] weirdo change of access permissions

---
 contrib/autoformat.sh                           | 0
 scripts/bert/bert4marian.py                     | 0
 scripts/embeddings/export_embeddings.py         | 0
 scripts/embeddings/prepare_corpus.py            | 0
 scripts/embeddings/process_word2vec.py          | 0
 scripts/server/client_example.py                | 0
 scripts/shortlist/generate_shortlists.pl        | 0
 scripts/shortlist/install.sh                    | 0
 src/3rd_party/ExceptionWithCallStack.h          | 0
 src/3rd_party/pathie-cpp/src/entry_iterator.cpp | 0
 src/3rd_party/pathie-cpp/src/path.cpp           | 0
 src/3rd_party/spdlog/astyle.sh                  | 0
 src/3rd_party/spdlog/bench/latency/compare.sh   | 0
 src/3rd_party/spdlog/tests/install_libcxx.sh    | 0
 src/command/marian_main.cpp                     | 0
 src/common/config_parser.cpp                    | 0
 src/common/definitions.h                        | 0
 src/common/logging.cpp                          | 0
 src/common/options.h                            | 0
 src/common/shape.h                              | 0
 src/common/timer.h                              | 0
 src/common/types.h                              | 0
 src/common/utils.cpp                            | 0
 src/common/utils.h                              | 0
 src/data/batch_generator.h                      | 0
 src/data/corpus.cpp                             | 0
 src/data/corpus.h                               | 0
 src/data/corpus_base.h                          | 0
 src/data/default_vocab.cpp                      | 0
 src/data/factored_vocab.cpp                     | 0
 src/data/factored_vocab.h                       | 0
 src/data/sentencepiece_vocab.cpp                | 0
 src/data/text_input.cpp                         | 0
 src/data/types.h                                | 0
 src/data/vocab.cpp                              | 0
 src/data/vocab.h                                | 0
 src/data/vocab_base.h                           | 0
 src/examples/mnist/dataset.h                    | 0
 src/examples/mnist/download.sh                  | 0
 src/examples/mnist/mnist_ffnn.cpp               | 0
 src/examples/mnist/model.h                      | 0
 src/examples/mnist/training.h                   | 0
 src/examples/mnist/validator.h                  | 0
 src/functional/tmp.h                            | 0
 src/graph/expression_operators.cpp              | 0
 src/graph/expression_operators.h                | 0
 src/graph/node_operators.h                      | 0
 src/graph/node_operators_unary.h                | 0
 src/layers/constructors.h                       | 0
 src/layers/factory.h                            | 0
 src/layers/generic.cpp                          | 0
 src/layers/generic.h                            | 0
 src/layers/loss.cpp                             | 0
 src/layers/loss.h                               | 0
 src/microsoft/quicksand.cpp                     | 0
 src/microsoft/quicksand.h                       | 0
 src/models/bert.h                               | 0
 src/models/char_s2s.h                           | 0
 src/models/costs.h                              | 0
 src/models/decoder.h                            | 0
 src/models/encoder_classifier.h                 | 0
 src/models/encoder_decoder.cpp                  | 0
 src/models/encoder_decoder.h                    | 0
 src/models/model_base.h                         | 0
 src/models/model_factory.cpp                    | 0
 src/models/model_factory.h                      | 0
 src/models/s2s.h                                | 0
 src/models/states.h                             | 0
 src/models/transformer.h                        | 0
 src/models/transformer_factory.h                | 0
 src/models/transformer_stub.cpp                 | 0
 src/rescorer/rescorer.h                         | 0
 src/rnn/attention_constructors.h                | 0
 src/rnn/cells.h                                 | 0
 src/rnn/types.h                                 | 0
 src/tensors/cpu/tensor_operators.cpp            | 0
 src/tensors/gpu/add.cu                          | 0
 src/tensors/gpu/add.h                           | 0
 src/tensors/gpu/add.inc                         | 0
 src/tensors/gpu/element.cu                      | 0
 src/tensors/gpu/element.inc                     | 0
 src/tensors/gpu/prod.cpp                        | 0
 src/tensors/gpu/prod.h                          | 0
 src/tensors/gpu/tensor_operators.cu             | 0
 src/tests/prod.cpp                              | 0
 src/tests/units/attention_tests.cpp             | 0
 src/tests/units/operator_tests.cpp              | 0
 src/tests/units/rnn_tests.cpp                   | 0
 src/training/communicator.cpp                   | 0
 src/training/graph_group.h                      | 0
 src/training/graph_group_async.cpp              | 0
 src/training/graph_group_async.h                | 0
 src/training/graph_group_multinode.cpp          | 0
 src/training/graph_group_multinode_sync.cpp     | 0
 src/training/graph_group_singleton.cpp          | 0
 src/training/graph_group_singleton.h            | 0
 src/training/graph_group_sync.cpp               | 0
 src/training/graph_group_sync.h                 | 0
 src/training/scheduler.h                        | 0
 src/training/validator.cpp                      | 0
 src/training/validator.h                        | 0
 src/translator/beam_search.h                    | 0
 src/translator/helpers.cpp                      | 0
 src/translator/helpers.cu                       | 0
 src/translator/helpers.h                        | 0
 src/translator/history.h                        | 0
 src/translator/hypothesis.h                     | 0
 src/translator/nth_element.cpp                  | 0
 src/translator/nth_element.cu                   | 0
 src/translator/nth_element.h                    | 0
 src/translator/output_printer.cpp               | 0
 src/translator/output_printer.h                 | 0
 src/translator/scorers.cpp                      | 0
 src/translator/scorers.h                        | 0
 src/translator/translator.h                     | 0
 vs/Marian.sln                                   | 0
 vs/Marian.vcxproj                               | 0
 vs/Marian.vcxproj.filters                       | 0
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diff --git a/contrib/autoformat.sh b/contrib/autoformat.sh
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diff --git a/scripts/bert/bert4marian.py b/scripts/bert/bert4marian.py
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diff --git a/scripts/embeddings/export_embeddings.py b/scripts/embeddings/export_embeddings.py
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diff --git a/scripts/embeddings/prepare_corpus.py b/scripts/embeddings/prepare_corpus.py
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diff --git a/src/data/default_vocab.cpp b/src/data/default_vocab.cpp
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diff --git a/src/data/factored_vocab.h b/src/data/factored_vocab.h
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diff --git a/src/data/sentencepiece_vocab.cpp b/src/data/sentencepiece_vocab.cpp
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diff --git a/src/data/text_input.cpp b/src/data/text_input.cpp
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diff --git a/src/examples/mnist/mnist_ffnn.cpp b/src/examples/mnist/mnist_ffnn.cpp
old mode 100755
new mode 100644
diff --git a/src/examples/mnist/model.h b/src/examples/mnist/model.h
old mode 100755
new mode 100644
diff --git a/src/examples/mnist/training.h b/src/examples/mnist/training.h
old mode 100755
new mode 100644
diff --git a/src/examples/mnist/validator.h b/src/examples/mnist/validator.h
old mode 100755
new mode 100644
diff --git a/src/functional/tmp.h b/src/functional/tmp.h
old mode 100755
new mode 100644
diff --git a/src/graph/expression_operators.cpp b/src/graph/expression_operators.cpp
old mode 100755
new mode 100644
diff --git a/src/graph/expression_operators.h b/src/graph/expression_operators.h
old mode 100755
new mode 100644
diff --git a/src/graph/node_operators.h b/src/graph/node_operators.h
old mode 100755
new mode 100644
diff --git a/src/graph/node_operators_unary.h b/src/graph/node_operators_unary.h
old mode 100755
new mode 100644
diff --git a/src/layers/constructors.h b/src/layers/constructors.h
old mode 100755
new mode 100644
diff --git a/src/layers/factory.h b/src/layers/factory.h
old mode 100755
new mode 100644
diff --git a/src/layers/generic.cpp b/src/layers/generic.cpp
old mode 100755
new mode 100644
diff --git a/src/layers/generic.h b/src/layers/generic.h
old mode 100755
new mode 100644
diff --git a/src/layers/loss.cpp b/src/layers/loss.cpp
old mode 100755
new mode 100644
diff --git a/src/layers/loss.h b/src/layers/loss.h
old mode 100755
new mode 100644
diff --git a/src/microsoft/quicksand.cpp b/src/microsoft/quicksand.cpp
old mode 100755
new mode 100644
diff --git a/src/microsoft/quicksand.h b/src/microsoft/quicksand.h
old mode 100755
new mode 100644
diff --git a/src/models/bert.h b/src/models/bert.h
old mode 100755
new mode 100644
diff --git a/src/models/char_s2s.h b/src/models/char_s2s.h
old mode 100755
new mode 100644
diff --git a/src/models/costs.h b/src/models/costs.h
old mode 100755
new mode 100644
diff --git a/src/models/decoder.h b/src/models/decoder.h
old mode 100755
new mode 100644
diff --git a/src/models/encoder_classifier.h b/src/models/encoder_classifier.h
old mode 100755
new mode 100644
diff --git a/src/models/encoder_decoder.cpp b/src/models/encoder_decoder.cpp
old mode 100755
new mode 100644
diff --git a/src/models/encoder_decoder.h b/src/models/encoder_decoder.h
old mode 100755
new mode 100644
diff --git a/src/models/model_base.h b/src/models/model_base.h
old mode 100755
new mode 100644
diff --git a/src/models/model_factory.cpp b/src/models/model_factory.cpp
old mode 100755
new mode 100644
diff --git a/src/models/model_factory.h b/src/models/model_factory.h
old mode 100755
new mode 100644
diff --git a/src/models/s2s.h b/src/models/s2s.h
old mode 100755
new mode 100644
diff --git a/src/models/states.h b/src/models/states.h
old mode 100755
new mode 100644
diff --git a/src/models/transformer.h b/src/models/transformer.h
old mode 100755
new mode 100644
diff --git a/src/models/transformer_factory.h b/src/models/transformer_factory.h
old mode 100755
new mode 100644
diff --git a/src/models/transformer_stub.cpp b/src/models/transformer_stub.cpp
old mode 100755
new mode 100644
diff --git a/src/rescorer/rescorer.h b/src/rescorer/rescorer.h
old mode 100755
new mode 100644
diff --git a/src/rnn/attention_constructors.h b/src/rnn/attention_constructors.h
old mode 100755
new mode 100644
diff --git a/src/rnn/cells.h b/src/rnn/cells.h
old mode 100755
new mode 100644
diff --git a/src/rnn/types.h b/src/rnn/types.h
old mode 100755
new mode 100644
diff --git a/src/tensors/cpu/tensor_operators.cpp b/src/tensors/cpu/tensor_operators.cpp
old mode 100755
new mode 100644
diff --git a/src/tensors/gpu/add.cu b/src/tensors/gpu/add.cu
old mode 100755
new mode 100644
diff --git a/src/tensors/gpu/add.h b/src/tensors/gpu/add.h
old mode 100755
new mode 100644
diff --git a/src/tensors/gpu/add.inc b/src/tensors/gpu/add.inc
old mode 100755
new mode 100644
diff --git a/src/tensors/gpu/element.cu b/src/tensors/gpu/element.cu
old mode 100755
new mode 100644
diff --git a/src/tensors/gpu/element.inc b/src/tensors/gpu/element.inc
old mode 100755
new mode 100644
diff --git a/src/tensors/gpu/prod.cpp b/src/tensors/gpu/prod.cpp
old mode 100755
new mode 100644
diff --git a/src/tensors/gpu/prod.h b/src/tensors/gpu/prod.h
old mode 100755
new mode 100644
diff --git a/src/tensors/gpu/tensor_operators.cu b/src/tensors/gpu/tensor_operators.cu
old mode 100755
new mode 100644
diff --git a/src/tests/prod.cpp b/src/tests/prod.cpp
old mode 100755
new mode 100644
diff --git a/src/tests/units/attention_tests.cpp b/src/tests/units/attention_tests.cpp
old mode 100755
new mode 100644
diff --git a/src/tests/units/operator_tests.cpp b/src/tests/units/operator_tests.cpp
old mode 100755
new mode 100644
diff --git a/src/tests/units/rnn_tests.cpp b/src/tests/units/rnn_tests.cpp
old mode 100755
new mode 100644
diff --git a/src/training/communicator.cpp b/src/training/communicator.cpp
old mode 100755
new mode 100644
diff --git a/src/training/graph_group.h b/src/training/graph_group.h
old mode 100755
new mode 100644
diff --git a/src/training/graph_group_async.cpp b/src/training/graph_group_async.cpp
old mode 100755
new mode 100644
diff --git a/src/training/graph_group_async.h b/src/training/graph_group_async.h
old mode 100755
new mode 100644
diff --git a/src/training/graph_group_multinode.cpp b/src/training/graph_group_multinode.cpp
old mode 100755
new mode 100644
diff --git a/src/training/graph_group_multinode_sync.cpp b/src/training/graph_group_multinode_sync.cpp
old mode 100755
new mode 100644
diff --git a/src/training/graph_group_singleton.cpp b/src/training/graph_group_singleton.cpp
old mode 100755
new mode 100644
diff --git a/src/training/graph_group_singleton.h b/src/training/graph_group_singleton.h
old mode 100755
new mode 100644
diff --git a/src/training/graph_group_sync.cpp b/src/training/graph_group_sync.cpp
old mode 100755
new mode 100644
diff --git a/src/training/graph_group_sync.h b/src/training/graph_group_sync.h
old mode 100755
new mode 100644
diff --git a/src/training/scheduler.h b/src/training/scheduler.h
old mode 100755
new mode 100644
diff --git a/src/training/validator.cpp b/src/training/validator.cpp
old mode 100755
new mode 100644
diff --git a/src/training/validator.h b/src/training/validator.h
old mode 100755
new mode 100644
diff --git a/src/translator/beam_search.h b/src/translator/beam_search.h
old mode 100755
new mode 100644
diff --git a/src/translator/helpers.cpp b/src/translator/helpers.cpp
old mode 100755
new mode 100644
diff --git a/src/translator/helpers.cu b/src/translator/helpers.cu
old mode 100755
new mode 100644
diff --git a/src/translator/helpers.h b/src/translator/helpers.h
old mode 100755
new mode 100644
diff --git a/src/translator/history.h b/src/translator/history.h
old mode 100755
new mode 100644
diff --git a/src/translator/hypothesis.h b/src/translator/hypothesis.h
old mode 100755
new mode 100644
diff --git a/src/translator/nth_element.cpp b/src/translator/nth_element.cpp
old mode 100755
new mode 100644
diff --git a/src/translator/nth_element.cu b/src/translator/nth_element.cu
old mode 100755
new mode 100644
diff --git a/src/translator/nth_element.h b/src/translator/nth_element.h
old mode 100755
new mode 100644
diff --git a/src/translator/output_printer.cpp b/src/translator/output_printer.cpp
old mode 100755
new mode 100644
diff --git a/src/translator/output_printer.h b/src/translator/output_printer.h
old mode 100755
new mode 100644
diff --git a/src/translator/scorers.cpp b/src/translator/scorers.cpp
old mode 100755
new mode 100644
diff --git a/src/translator/scorers.h b/src/translator/scorers.h
old mode 100755
new mode 100644
diff --git a/src/translator/translator.h b/src/translator/translator.h
old mode 100755
new mode 100644
diff --git a/vs/Marian.sln b/vs/Marian.sln
old mode 100755
new mode 100644
diff --git a/vs/Marian.vcxproj b/vs/Marian.vcxproj
old mode 100755
new mode 100644
diff --git a/vs/Marian.vcxproj.filters b/vs/Marian.vcxproj.filters
old mode 100755
new mode 100644

From 9ff0025fd52d11b4f1690183066a35cecec2efe6 Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Fri, 26 Apr 2019 20:33:11 -0700
Subject: [PATCH 441/838] torwards breakDown

---
 src/translator/beam_search.h | 11 ++++-------
 src/translator/hypothesis.h  |  2 +-
 2 files changed, 5 insertions(+), 8 deletions(-)

diff --git a/src/translator/beam_search.h b/src/translator/beam_search.h
index 35c2d741a..274cd4079 100755
--- a/src/translator/beam_search.h
+++ b/src/translator/beam_search.h
@@ -105,15 +105,12 @@ class BeamSearch {
 
       // Set score breakdown for n-best lists
       if(options_->get<bool>("n-best")) {
-        std::vector<float> breakDown(states.size(), 0);
-        beam[beamHypIdx]->getScoreBreakdown().resize(states.size(), 0); // @TODO: Why? Can we just guard the read-out below, then make it const? Or getScoreBreakdown(j)?
+        auto breakDown = beam[beamHypIdx]->getScoreBreakdown();
+        breakDown.resize(states.size(), 0); // reset to 0 if at start
         for(size_t j = 0; j < states.size(); ++j) {
           size_t flattenedLogitIndex = (beamHypIdx * dimBatch + batchIdx) * vocabSize + wordIdx;  // (beam idx, batch idx, word idx); note: beam and batch are transposed, compared to 'key'
-          flattenedLogitIndex;
-#if 0   // @BUGBUG: This currently segfaults with factors.
-          breakDown[j] = states[j]->breakDown(flattenedLogitIndex) + beam[beamHypIdx]->getScoreBreakdown()[j];
-#endif
-          // @TODO: pass those 3 indices directly into breakDown (state knows the dimensions)
+          // @TODO: push the index through into breakDown(), which has all dimensions
+          breakDown[j] += states[j]->breakDown(flattenedLogitIndex);
         }
         hyp->setScoreBreakdown(breakDown);
       }
diff --git a/src/translator/hypothesis.h b/src/translator/hypothesis.h
index cf19f59e2..cf5d9b601 100755
--- a/src/translator/hypothesis.h
+++ b/src/translator/hypothesis.h
@@ -29,7 +29,7 @@ class Hypothesis {
 
   float getPathScore() const { return pathScore_; }
 
-  std::vector<float>& getScoreBreakdown() { return scoreBreakdown_; } // @TODO: make this const
+  const std::vector<float>& getScoreBreakdown() { return scoreBreakdown_; }
   void setScoreBreakdown(const std::vector<float>& scoreBreaddown) { scoreBreakdown_ = scoreBreaddown; }
 
   const std::vector<float>& getAlignment() { return alignment_; }

From ac3f1a0a4f91e76527154d2347e5b11c89d2a24f Mon Sep 17 00:00:00 2001
From: Ulrich Germann <ugermann@inf.ed.ac.uk>
Date: Sat, 27 Apr 2019 22:34:25 +0000
Subject: [PATCH 442/838] Actually install signal handler for graceful
 shutdown; fix termination msg.

1. installSignalHandlers_() wasn't called in Scheduler::Scheduler().
   Now it is.
2. The termination message in Scheduler::finished() did not log
   termination vs. interrupt correctly. Fixed now.
---
 src/training/scheduler.cpp | 22 +++++++++++++++++-----
 src/training/scheduler.h   |  5 +++--
 2 files changed, 20 insertions(+), 7 deletions(-)

diff --git a/src/training/scheduler.cpp b/src/training/scheduler.cpp
index f508f3069..19d19f67e 100644
--- a/src/training/scheduler.cpp
+++ b/src/training/scheduler.cpp
@@ -1,5 +1,6 @@
 #include "scheduler.h"
 #include <signal.h>
+#include <cassert>
 
 namespace marian {
 bool Scheduler::sigterm_{false};
@@ -9,13 +10,24 @@ bool Scheduler::sigusr2_{false};
 void
 Scheduler::
 signalHandler_(int sig) {
+  // Note: sys_siglist[sig] or stdsignal() describe the effect (e.g.,
+  // 'Terminated' rather than provide the signal name (which are #define(s)
+  // in signal.h), so we have to do custom log messages here.
   switch (sig) {
-  case SIGTERM: Scheduler::sigterm_ = true; break;
-  case SIGUSR1: Scheduler::sigusr1_ = true; break;
-  case SIGUSR2: Scheduler::sigusr2_ = true; break;
+  case SIGTERM: // save models and exit
+    LOG(info, "[training] Scheduler received signal SIGTERM");
+    Scheduler::sigterm_ = true;
+    break;
+  case SIGUSR1: // currently has no effect
+    LOG(info, "[training] Scheduler received signal SIGUSR1");
+    Scheduler::sigusr1_ = true;
+    break;
+  case SIGUSR2: // currently has no effect
+    LOG(info, "[training] Scheduler received signal SIGUSR2");
+    Scheduler::sigusr2_ = true;
+    break;
   default:
-    ABORT("This signal handler should not have been installed for signal ",
-          strsignal(sig));
+    ABORT("No action defined for signal {}", sig);
   }
 }
 
diff --git a/src/training/scheduler.h b/src/training/scheduler.h
index 4ffcbce95..b888f35b0 100644
--- a/src/training/scheduler.h
+++ b/src/training/scheduler.h
@@ -160,6 +160,7 @@ class Scheduler : public TrainingObserver {
     saveFreq_ = options_->get<std::string>("save-freq");
     ABORT_IF(state_->factor != 1, "state.factor unexpectedly not 1 at this point??");
     updateLearningRate(*state);
+    installSignalHandlers_();
   }
 
   bool keepGoing(bool checkForSigTerm=true) {
@@ -195,9 +196,9 @@ class Scheduler : public TrainingObserver {
   void started() { LOG(info, "Training started"); }
   void finished() {
     if (keepGoing(false)) // false means: ignore sigterm flag
-      LOG(info, "Training finished");
-    else
       LOG(info, "Training interrupted (SIGTERM).");
+    else
+      LOG(info, "Training finished");
   }
 
   void setupValidators(std::vector<Ptr<Vocab>>& vocabs) {

From d00a28e49a898bc78694a6a15ef05c5ec39b5032 Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Sun, 28 Apr 2019 10:58:50 -0700
Subject: [PATCH 443/838] brought back score breakdown

---
 src/layers/generic.cpp       |  7 +++++++
 src/layers/generic.h         |  2 +-
 src/translator/beam_search.h | 11 ++++++++---
 src/translator/scorers.h     |  2 --
 4 files changed, 16 insertions(+), 6 deletions(-)

diff --git a/src/layers/generic.cpp b/src/layers/generic.cpp
index e5d35e719..a7456a80b 100644
--- a/src/layers/generic.cpp
+++ b/src/layers/generic.cpp
@@ -106,6 +106,13 @@ namespace marian {
     return sel;
   }
 
+  // used for breakDown() only
+  // Index is flattened
+  Tensor Logits::getFactoredLogitsTensor(size_t groupIndex) const {
+    ABORT_IF(empty(), "Attempted to read out logits on empty Logits object");
+    return logits_[groupIndex]->loss()->val();
+  }
+
   // This function assumes that the object holds one or more factor logits, which are summed up
   // into output-vocab logits according to the factored model (with correct normalization of factors).
   // This is infeasible for realistic factor sets, and therefore only implemented for 1 factor.
diff --git a/src/layers/generic.h b/src/layers/generic.h
index 7cf8ac6ce..9d787a3c3 100644
--- a/src/layers/generic.h
+++ b/src/layers/generic.h
@@ -98,7 +98,7 @@ class Logits {
       MaskedFactorIndices(const Words& words) { indices = toWordIndexVector(words); } // we can leave masks uninitialized for this special use case
     };
     std::vector<MaskedFactorIndices> factorizeWords(const Words& words) const; // breaks encoded Word into individual factor indices
-    float getLogitAt(size_t i) const { return getLogits()->val()->get(i); } // used for breakDown() only; @TODO: avoid the fully expanded logits; pass separate indices instead of 'i'
+    Tensor getFactoredLogitsTensor(size_t factorGroup) const; // used for breakDown() only
     size_t getNumFactorGroups() const { return logits_.size(); }
     bool empty() const { return logits_.empty(); }
     Logits withCounts(const Expr& count) const; // create new Logits with 'count' implanted into all logits_
diff --git a/src/translator/beam_search.h b/src/translator/beam_search.h
index 274cd4079..baa87cd88 100644
--- a/src/translator/beam_search.h
+++ b/src/translator/beam_search.h
@@ -90,7 +90,8 @@ class BeamSearch {
           //LOG(info, "expand word {}={} with factor[{}] {}", beam[beamHypIdx]->getWord().toWordIndex(),
           //    factoredVocab->word2string(beam[beamHypIdx]->getWord()), factorGroup, wordIdx);
           word = beam[beamHypIdx]->getWord();
-          ABORT_IF(!factoredVocab->canExpandFactoredWord(word, factorGroup), "A word without this factor snuck through to here??");
+          ABORT_IF(!factoredVocab->canExpandFactoredWord(word, factorGroup),
+                   "A word without this factor snuck through to here??");
           word = factoredVocab->expandFactoredWord(word, factorGroup, wordIdx);
           prevBeamHypIdx = prevHyp->getPrevStateIndex();
           prevHyp = prevHyp->getPrevHyp(); // short-circuit the backpointer, so that the traceback doesnot contain partially factored words
@@ -106,11 +107,15 @@ class BeamSearch {
       // Set score breakdown for n-best lists
       if(options_->get<bool>("n-best")) {
         auto breakDown = beam[beamHypIdx]->getScoreBreakdown();
+        ABORT_IF(factoredVocab && factorGroup > 0 && !factoredVocab->canExpandFactoredWord(word, factorGroup),
+                 "A word without this factor snuck through to here??");
         breakDown.resize(states.size(), 0); // reset to 0 if at start
         for(size_t j = 0; j < states.size(); ++j) {
+          auto lval = states[j]->getLogProbs().getFactoredLogitsTensor(factorGroup); // [localBeamSize, 1, dimBatch, dimFactorVocab]
           size_t flattenedLogitIndex = (beamHypIdx * dimBatch + batchIdx) * vocabSize + wordIdx;  // (beam idx, batch idx, word idx); note: beam and batch are transposed, compared to 'key'
-          // @TODO: push the index through into breakDown(), which has all dimensions
-          breakDown[j] += states[j]->breakDown(flattenedLogitIndex);
+          // @TODO: use a function on shape() to index, or new method val->at({i1, i2, i3, i4}) with broadcasting
+          ABORT_IF(lval->shape() != Shape({(int)beams.size(), 1, (int)dimBatch, (int)vocabSize}), "Unexpected shape of logits??");
+          breakDown[j] += lval->get(i);
         }
         hyp->setScoreBreakdown(breakDown);
       }
diff --git a/src/translator/scorers.h b/src/translator/scorers.h
index 389dae641..ef47f4313 100644
--- a/src/translator/scorers.h
+++ b/src/translator/scorers.h
@@ -11,8 +11,6 @@ class ScorerState {
 public:
   virtual Logits getLogProbs() const = 0;
 
-  virtual float breakDown(size_t i) const { return getLogProbs().getLogitAt(i); }
-
   virtual void blacklist(Expr /*totalCosts*/, Ptr<data::CorpusBatch> /*batch*/){};
 };
 

From 21269a2a85c861facc14f04784c3a75718628e76 Mon Sep 17 00:00:00 2001
From: Marcin Junczys-Dowmunt <Marcin.JunczysDowmunt@microsoft.com>
Date: Sun, 28 Apr 2019 19:24:34 -0700
Subject: [PATCH 444/838] fix node initializers

---
 src/layers/generic.cpp              | 11 ++++++-----
 src/tensors/gpu/tensor_operators.cu |  4 ----
 2 files changed, 6 insertions(+), 9 deletions(-)

diff --git a/src/layers/generic.cpp b/src/layers/generic.cpp
index e5d35e719..41160166b 100755
--- a/src/layers/generic.cpp
+++ b/src/layers/generic.cpp
@@ -236,11 +236,11 @@ namespace marian {
         Wt_ = tiedParam_;
       } else {
         if (graph_->get(name + "_W")) { // support of legacy models that did not transpose
-          Wt_ = graph_->param(name + "_W", {inputDim, numOutputClasses}, inits::glorot_uniform);
+          Wt_ = graph_->param(name + "_W", {inputDim, numOutputClasses}, inits::glorot_uniform2(true, false));
           isLegacyUntransposedW = true;
         }
         else // this is the regular case:
-          Wt_ = graph_->param(name + "_Wt", {numOutputClasses, inputDim}, inits::glorot_uniform);
+          Wt_ = graph_->param(name + "_Wt", {numOutputClasses, inputDim}, inits::glorot_uniform2(false, true));
       }
 
       b_ = graph_->param(name + "_b", {1, numOutputClasses}, inits::zeros);
@@ -336,9 +336,10 @@ namespace marian {
     }
 
     // Embedding layer initialization should depend only on embedding size, hence fanIn=false
-    //NodeInitializer initFunc = inits::glorot_uniform2(/*fanIn=*/false, /*fanOut=*/true);
-    NodeInitializer initFunc = inits::glorot_uniform;
-    if (options_->has("embFile")) {
+    NodeInitializer initFunc = inits::glorot_uniform2(/*fanIn=*/false, /*fanOut=*/true);
+    //NodeInitializer initFunc = inits::glorot_uniform;
+    
+if (options_->has("embFile")) {
       std::string file = opt<std::string>("embFile");
       if (!file.empty()) {
         bool norm = opt<bool>("normalization", false);
diff --git a/src/tensors/gpu/tensor_operators.cu b/src/tensors/gpu/tensor_operators.cu
index 60c6f8e3f..a7edcdf59 100755
--- a/src/tensors/gpu/tensor_operators.cu
+++ b/src/tensors/gpu/tensor_operators.cu
@@ -488,7 +488,6 @@ __global__ void gSoftmax(float* out,
           so[i] = so[i] / sum;
         }
       }
-      __syncthreads();
     }
     __syncthreads();
   }
@@ -574,7 +573,6 @@ __global__ void gLogSoftmax(float* out,
         if(id < cols)
           so[id] -= __logf(_sum[0]);
       }
-      __syncthreads();
     }
     __syncthreads();
   }
@@ -635,7 +633,6 @@ __global__ void gSoftmaxGrad(float* grad,
             gradRow[id] += val;
         }
       }
-      __syncthreads();
     }
     __syncthreads();
   }
@@ -694,7 +691,6 @@ __global__ void gLogSoftmaxGrad(float* grad,
         if(id < cols)
           gradRow[id] += adjRow[id] - (expf(valRow[id]) * _sum[0]);
       }
-      __syncthreads();
     }
     __syncthreads();
   }

From ef03692df83a829d491b156b41121a7e62a60dcc Mon Sep 17 00:00:00 2001
From: Ulrich Germann <ulrich.germann@gmail.com>
Date: Mon, 29 Apr 2019 20:16:06 +0100
Subject: [PATCH 445/838] Bug fixes related to (lack of) sqlite under Windows.

---
 src/data/corpus.cpp | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/src/data/corpus.cpp b/src/data/corpus.cpp
index 0bde62322..00195bb1c 100644
--- a/src/data/corpus.cpp
+++ b/src/data/corpus.cpp
@@ -5,6 +5,7 @@
 
 #include "common/utils.h"
 #include "data/corpus.h"
+#include "data/corpus_sqlite.h"
 
 namespace marian {
 namespace data {
@@ -176,11 +177,10 @@ Ptr<CorpusBase> prepareTrainingData(Ptr<Options> options) {
   // factory function to set up the training corpus for training
   // code moved here from Train::run() in training.h
   Ptr<CorpusBase> dataset;
-#ifndef _MSC_VER // @TODO: include SqLite in Visual Studio project
   if(!options->get<std::string>("sqlite").empty())
+#ifdef _MSC_VER // @TODO: include SqLite in Visual Studio project
     ABORT("SqLite presently not supported on Windows");
 #else
-  if(!options->get<std::string>("sqlite").empty())
     dataset = New<CorpusSQLite>(options);
 #endif
   else

From 081b843dc52d69a1ac306284983bef8d54e90196 Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Mon, 29 Apr 2019 16:03:48 -0700
Subject: [PATCH 446/838] bug fix: getAlignmentsForHypothesis() should not be
 called for all factors for each word

---
 src/data/corpus_base.h       |  2 +-
 src/translator/beam_search.h | 20 +++++++++++---------
 src/translator/scorers.h     |  2 +-
 3 files changed, 13 insertions(+), 11 deletions(-)
 mode change 100644 => 100755 src/data/corpus_base.h
 mode change 100644 => 100755 src/translator/beam_search.h
 mode change 100644 => 100755 src/translator/scorers.h

diff --git a/src/data/corpus_base.h b/src/data/corpus_base.h
old mode 100644
new mode 100755
index 8f836f357..f17df88da
--- a/src/data/corpus_base.h
+++ b/src/data/corpus_base.h
@@ -286,7 +286,7 @@ class CorpusBatch : public Batch {
   size_t sizeTrg() const override { return subBatches_.back()->batchSize(); }
 
   /**
-   * @brief The number of words for the longest sentence in the batch plus one.
+   * @brief The total number of words in the batch (not counting masked-out words).
    */
   size_t wordsTrg() const override { return subBatches_.back()->batchWords(); };
 
diff --git a/src/translator/beam_search.h b/src/translator/beam_search.h
old mode 100644
new mode 100755
index 274cd4079..db435b9bd
--- a/src/translator/beam_search.h
+++ b/src/translator/beam_search.h
@@ -45,7 +45,7 @@ class BeamSearch {
                Ptr<FactoredVocab/*const*/> factoredVocab, size_t factorGroup) const {
     std::vector<float> align;
     if(options_->hasAndNotEmpty("alignment"))
-      align = scorers_[0]->getAlignment(); // use alignments from the first scorer, even if ensemble
+      align = scorers_[0]->getAlignment(); // [beam depth, max src length, batch size, 1]; use alignments from the first scorer, even if ensemble
 
     const auto dimBatch = beams.size();
     Beams newBeams(dimBatch);   // return value of this function goes here
@@ -116,7 +116,7 @@ class BeamSearch {
       }
 
       // Set alignments
-      if(!align.empty()) {
+      if(!align.empty() && factorGroup == 0) {
         hyp->setAlignment(getAlignmentsForHypothesis(align, batch, (int)beamHypIdx, (int)batchIdx));
       }
 
@@ -150,10 +150,10 @@ class BeamSearch {
   }
 
   std::vector<float> getAlignmentsForHypothesis(
-      const std::vector<float> alignAll,
+      const std::vector<float> alignAll, // [beam depth, max src length, batch size, 1]
       Ptr<data::CorpusBatch> batch,
       int beamHypIdx,
-      int beamIdx) const {
+      int batchIdx) const {
     // Let's B be the beam size, N be the number of batched sentences,
     // and L the number of words in the longest sentence in the batch.
     // The alignment vector:
@@ -171,13 +171,15 @@ class BeamSearch {
     // in a single beam, i.e.:
     //   * [word1-batch1, word1-batch2, ..., word2-batch1, ...]
     //
-    size_t batchSize = batch->size();
-    size_t batchWidth = batch->width() * batchSize;
+    size_t batchSize = batch->size();                // number of sentences in batch
+    size_t batchWidth = batch->width();              // max src length
+    size_t batchWidthXSize = batchWidth * batchSize; // total number of words in the batch incl. padding
     std::vector<float> align;
 
-    for(size_t w = 0; w < batchWidth / batchSize; ++w) {
-      size_t a = ((batchWidth * beamHypIdx) + beamIdx) + (batchSize * w);
-      size_t m = a % batchWidth;
+    // loop over words of batch entry 'batchIdx' and beam entry 'beamHypIdx'
+    for(size_t w = 0; w < batchWidth; ++w) {
+      size_t a = ((batchWidthXSize * beamHypIdx) + batchIdx) + (batchSize * w);
+      size_t m = a % batchWidthXSize; // == batchIdx + (batchSize * w)
       if(batch->front()->mask()[m] != 0)
         align.emplace_back(alignAll[a]);
     }
diff --git a/src/translator/scorers.h b/src/translator/scorers.h
old mode 100644
new mode 100755
index 389dae641..952856ce8
--- a/src/translator/scorers.h
+++ b/src/translator/scorers.h
@@ -130,7 +130,7 @@ class ScorerWrapper : public Scorer {
   };
 
   virtual std::vector<float> getAlignment() override {
-    return encdec_->getAlignment().front();
+    return encdec_->getAlignment().front(); // [beam depth, max src length, batch size, 1]
   }
 };
 

From f3ea35cb15894f0001366361b315058f350c2a06 Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Mon, 29 Apr 2019 16:23:30 -0700
Subject: [PATCH 447/838] bug fix: score breakdown should use correct index and
 dimensions

---
 src/data/factored_vocab.cpp  |  5 +++--
 src/layers/constructors.h    | 38 +++++-------------------------------
 src/training/validator.h     |  1 +
 src/translator/beam_search.h | 13 ++++++------
 4 files changed, 16 insertions(+), 41 deletions(-)
 mode change 100644 => 100755 src/data/factored_vocab.cpp
 mode change 100644 => 100755 src/layers/constructors.h
 mode change 100644 => 100755 src/training/validator.h

diff --git a/src/data/factored_vocab.cpp b/src/data/factored_vocab.cpp
old mode 100644
new mode 100755
index f6208fece..3c7c8bc8e
--- a/src/data/factored_vocab.cpp
+++ b/src/data/factored_vocab.cpp
@@ -13,6 +13,7 @@
 // * factor name (e.g. all-caps: |ca )
 // * factor index (e.g. |ca is index 0 inside |ca |ci |cn)
 // * factor unit index (|ca is unit 41324 in joint factor vocab)
+// Also remove references to older outdated versions.
 
 namespace marian {
 
@@ -39,7 +40,7 @@ namespace marian {
     //     - _x <-> form only allows "_x <->" or "_x <-> _has_x" (same x), and is otherwise unused
     //     - _lemma is special
     // The current version of the code just converts it internally to the legacy form.
-    // Once the legacy form is no longer needed, must of this can be simplified a lot.
+    // @TODO: Once the legacy form is no longer needed, simplify this.
     io::InputFileStream in(modelPath);
     WordIndex v = 0;
     std::map<std::string,std::set<std::string>> factorTypeMap; // [type name] -> {factor-type names}
@@ -711,7 +712,7 @@ void FactoredVocab::WordLUT::dumpToFile(const std::string& path) {
     out << kvp.second << "\t" << utils::withCommas(kvp.first) << "\n";
 }
 
-const static std::vector<std::string> exts{ ".fsv", ".fm"/*legacy*/ };
+const static std::vector<std::string> exts{ ".fsv", ".fm"/*legacy*/ }; // @TODO: delete the legacy one
 
 // Note: This does not actually load it, only checks the path for the type.
 // Since loading takes a while, we cache instances.
diff --git a/src/layers/constructors.h b/src/layers/constructors.h
old mode 100644
new mode 100755
index ed2c029f8..54e1b3404
--- a/src/layers/constructors.h
+++ b/src/layers/constructors.h
@@ -10,22 +10,6 @@ namespace mlp {
  * Base class for layer factories, can be used in a multi-layer network factory.
  */
 struct LayerFactory : public Factory {
-  //LayerFactory() : Factory() {}
-  //LayerFactory(const LayerFactory&) = default;
-  //LayerFactory(LayerFactory&&) = default;
-  //
-  //virtual ~LayerFactory() {}
-
-  //template <typename Cast>
-  //inline Ptr<Cast> as() {
-  //  return std::dynamic_pointer_cast<Cast>(shared_from_this());
-  //}
-  //
-  //template <typename Cast>
-  //inline bool is() {
-  //  return as<Cast>() != nullptr;
-  //}
-
   virtual Ptr<IUnaryLayer> construct(Ptr<ExpressionGraph> graph) = 0;
 };
 
@@ -52,24 +36,10 @@ typedef Accumulator<DenseFactory> dense;
  * Factory for output layers, can be used in a multi-layer network factory.
  */
 struct LogitLayerFactory : public Factory {
-  //LogitLayerFactory() : Factory() {}
-  //LogitLayerFactory(const LogitLayerFactory&) = default;
-  //LogitLayerFactory(LogitLayerFactory&&) = default;
-  //
-  //virtual ~LogitLayerFactory() {}
-  //
-  //template <typename Cast>
-  //inline Ptr<Cast> as() {
-  //  return std::dynamic_pointer_cast<Cast>(shared_from_this());
-  //}
-  //
-  //template <typename Cast>
-  //inline bool is() {
-  //  return as<Cast>() != nullptr;
-  //}
-
   virtual Ptr<IUnaryLogitLayer> construct(Ptr<ExpressionGraph> graph) = 0;
 };
+
+// @TODO: In the long run, I hope we can get rid of the abstract factories altogether.
 class OutputFactory : public LogitLayerFactory {
 protected:
   std::string tiedTransposedName_;
@@ -199,7 +169,9 @@ class MLPFactory : public Factory {
     return Accumulator<MLPFactory>(*this);
   }
 
-  // special case for last layer, which may be a IUnaryLogitLayer. Requires some hackery
+  // Special case for last layer, which may be a IUnaryLogitLayer. Requires some hackery,
+  // which will go away if we get rid of the abstract factories, and instead just construct
+  // all layers immediately, which is my long-term goal for Marian.
 private:
   template<class WrappedFactory>
   class AsLayerFactory : public LayerFactory {
diff --git a/src/training/validator.h b/src/training/validator.h
old mode 100644
new mode 100755
index 37833b605..968fcf4ff
--- a/src/training/validator.h
+++ b/src/training/validator.h
@@ -597,6 +597,7 @@ class BleuValidator : public Validator<data::Corpus, models::IModel> {
         quiet_(options_->get<bool>("quiet-translation")) {
     builder_ = models::createModelFromOptions(options_, models::usage::translation);
 
+    // @TODO: replace bleu-detok by a separate parameter to enable (various forms of) detok
     auto vocab = vocabs_.back();
     ABORT_IF(detok_ && vocab->type() != "SentencePieceVocab" && vocab->type() != "FactoredVocab",
              "Detokenizing BLEU validator expects the target vocabulary to be SentencePieceVocab or FactoredVocab. "
diff --git a/src/translator/beam_search.h b/src/translator/beam_search.h
index 5e2808e09..3747ac90a 100755
--- a/src/translator/beam_search.h
+++ b/src/translator/beam_search.h
@@ -109,13 +109,13 @@ class BeamSearch {
         auto breakDown = beam[beamHypIdx]->getScoreBreakdown();
         ABORT_IF(factoredVocab && factorGroup > 0 && !factoredVocab->canExpandFactoredWord(word, factorGroup),
                  "A word without this factor snuck through to here??");
-        breakDown.resize(states.size(), 0); // reset to 0 if at start
+        breakDown.resize(states.size(), 0); // at start, this is empty, so this will set the initial score to 0
         for(size_t j = 0; j < states.size(); ++j) {
           auto lval = states[j]->getLogProbs().getFactoredLogitsTensor(factorGroup); // [localBeamSize, 1, dimBatch, dimFactorVocab]
           size_t flattenedLogitIndex = (beamHypIdx * dimBatch + batchIdx) * vocabSize + wordIdx;  // (beam idx, batch idx, word idx); note: beam and batch are transposed, compared to 'key'
           // @TODO: use a function on shape() to index, or new method val->at({i1, i2, i3, i4}) with broadcasting
-          ABORT_IF(lval->shape() != Shape({(int)beams.size(), 1, (int)dimBatch, (int)vocabSize}), "Unexpected shape of logits??");
-          breakDown[j] += lval->get(i);
+          ABORT_IF(lval->shape() != Shape({(int)beam.size(), 1, (int)dimBatch, (int)vocabSize}), "Unexpected shape of logits??");
+          breakDown[j] += lval->get(flattenedLogitIndex);
         }
         hyp->setScoreBreakdown(breakDown);
       }
@@ -128,7 +128,8 @@ class BeamSearch {
       newBeam.push_back(hyp);
     }
 
-    // also propagate factored hypotheses that do not get expanded in this step as they don't have this factor
+    // if factored vocab and this is not the first factor, we need to
+    // also propagate factored hypotheses that do not get expanded in this step because they don't have this factor
     if (factorGroup > 0) {
       for (size_t batchIdx = 0; batchIdx < beams.size(); batchIdx++) {
         const auto& beam = beams[batchIdx];
@@ -218,7 +219,7 @@ class BeamSearch {
     factoredVocab.reset();
 #endif
     size_t numFactorGroups = factoredVocab ? factoredVocab->getNumGroups() : 1;
-    if (numFactorGroups == 1) // if no factors then reset
+    if (numFactorGroups == 1) // if no factors then we didn't need this object in the first place
       factoredVocab.reset();
 
     const int dimBatch = (int)batch->size();
@@ -275,7 +276,7 @@ class BeamSearch {
         break;
 
       for (size_t factorGroup = 0; factorGroup < numFactorGroups; factorGroup++) {
-      // Note: not indenting the block, for easier merging
+      // @TODO: Indent the body of this loop. Not done for this commit for easier reviewing.
       // for factored vocabs, we do one factor at a time, but without updating the scorer for secondary factors
 
       //**********************************************************************

From 1ff9242f2b4380ccdf69ce303ac73fbf863add5f Mon Sep 17 00:00:00 2001
From: Marcin Junczys-Dowmunt <Marcin.JunczysDowmunt@microsoft.com>
Date: Mon, 29 Apr 2019 16:26:18 -0700
Subject: [PATCH 448/838] fix embeddings pre-loading

---
 scripts/embeddings/export_embeddings.py | 0
 src/common/utils.cpp                    | 1 +
 src/layers/word2vec_reader.h            | 3 ++-
 src/tensors/tensor.h                    | 2 +-
 src/translator/beam_search.h            | 5 +++--
 5 files changed, 7 insertions(+), 4 deletions(-)
 mode change 100644 => 100755 scripts/embeddings/export_embeddings.py

diff --git a/scripts/embeddings/export_embeddings.py b/scripts/embeddings/export_embeddings.py
old mode 100644
new mode 100755
diff --git a/src/common/utils.cpp b/src/common/utils.cpp
index 4cb872ae2..856aa640d 100644
--- a/src/common/utils.cpp
+++ b/src/common/utils.cpp
@@ -117,6 +117,7 @@ std::string exec(const std::string& cmd, const std::vector<std::string>& args /*
     cmdLine += " " + escapeForPOpen(a);
   if (!arg.empty())
     cmdLine += " " + escapeForPOpen(arg);
+  std::cerr << "###" << cmdLine << "###" << std::endl;
   std::shared_ptr<std::FILE> pipe(popen(cmdLine.c_str(), "r"), pclose);
   if(!pipe)
     ABORT("popen() failed!");
diff --git a/src/layers/word2vec_reader.h b/src/layers/word2vec_reader.h
index 88c695aa9..4bfc67091 100644
--- a/src/layers/word2vec_reader.h
+++ b/src/layers/word2vec_reader.h
@@ -64,13 +64,14 @@ class Word2VecReader {
       }
     }
 
+    embs.resize(dimVoc * dimEmb, 0); // @TODO: is it correct to zero out the remaining embeddings?
     return embs;
   }
 
 private:
   std::vector<float> randomEmbeddings(int dimVoc, int dimEmb) {
     std::vector<float> values;
-    values.reserve(dimEmb);
+    values.resize(dimEmb);
     // Glorot numal distribution
     float scale = sqrtf(2.0f / (dimVoc + dimEmb));
 
diff --git a/src/tensors/tensor.h b/src/tensors/tensor.h
index f77259cb9..9ec69effa 100644
--- a/src/tensors/tensor.h
+++ b/src/tensors/tensor.h
@@ -141,7 +141,7 @@ class TensorBase : public std::enable_shared_from_this<TensorBase> {
   template <typename T>
   void set(const T* begin, const T* end) {
     ABORT_IF(end - begin != shape_.elements(),
-             "Vector size ({}) and underlying type ({}) do not match",
+             "Vector size ({}) and underlying shape ({}) do not match",
              end - begin,
              std::string(shape_));
     ABORT_IF(!matchType<T>(type_),
diff --git a/src/translator/beam_search.h b/src/translator/beam_search.h
index baa87cd88..5a4a4ad60 100644
--- a/src/translator/beam_search.h
+++ b/src/translator/beam_search.h
@@ -114,8 +114,9 @@ class BeamSearch {
           auto lval = states[j]->getLogProbs().getFactoredLogitsTensor(factorGroup); // [localBeamSize, 1, dimBatch, dimFactorVocab]
           size_t flattenedLogitIndex = (beamHypIdx * dimBatch + batchIdx) * vocabSize + wordIdx;  // (beam idx, batch idx, word idx); note: beam and batch are transposed, compared to 'key'
           // @TODO: use a function on shape() to index, or new method val->at({i1, i2, i3, i4}) with broadcasting
-          ABORT_IF(lval->shape() != Shape({(int)beams.size(), 1, (int)dimBatch, (int)vocabSize}), "Unexpected shape of logits??");
-          breakDown[j] += lval->get(i);
+          ABORT_IF(lval->shape() != Shape({(int)nBestBeamSize, 1, (int)dimBatch, (int)vocabSize}), 
+                   "Unexpected shape of logits?? {} != {}", lval->shape(), Shape({(int)nBestBeamSize, 1, (int)dimBatch, (int)vocabSize}));
+          breakDown[j] += lval->get(flattenedLogitIndex);
         }
         hyp->setScoreBreakdown(breakDown);
       }

From a58214ba4d3cedbe191bf7836aae3b5b1d5e352f Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Mon, 29 Apr 2019 16:39:26 -0700
Subject: [PATCH 449/838] minor polish to previos commit

---
 src/common/utils.cpp                | 2 +-
 src/layers/generic.cpp              | 3 +--
 src/tensors/gpu/tensor_operators.cu | 2 +-
 3 files changed, 3 insertions(+), 4 deletions(-)

diff --git a/src/common/utils.cpp b/src/common/utils.cpp
index 856aa640d..0e406958a 100644
--- a/src/common/utils.cpp
+++ b/src/common/utils.cpp
@@ -117,7 +117,7 @@ std::string exec(const std::string& cmd, const std::vector<std::string>& args /*
     cmdLine += " " + escapeForPOpen(a);
   if (!arg.empty())
     cmdLine += " " + escapeForPOpen(arg);
-  std::cerr << "###" << cmdLine << "###" << std::endl;
+  //std::cerr << "###" << cmdLine << "###" << std::endl;
   std::shared_ptr<std::FILE> pipe(popen(cmdLine.c_str(), "r"), pclose);
   if(!pipe)
     ABORT("popen() failed!");
diff --git a/src/layers/generic.cpp b/src/layers/generic.cpp
index 4d04a66cb..d13d5871a 100644
--- a/src/layers/generic.cpp
+++ b/src/layers/generic.cpp
@@ -344,9 +344,8 @@ namespace marian {
 
     // Embedding layer initialization should depend only on embedding size, hence fanIn=false
     NodeInitializer initFunc = inits::glorot_uniform2(/*fanIn=*/false, /*fanOut=*/true);
-    //NodeInitializer initFunc = inits::glorot_uniform;
     
-if (options_->has("embFile")) {
+    if (options_->has("embFile")) {
       std::string file = opt<std::string>("embFile");
       if (!file.empty()) {
         bool norm = opt<bool>("normalization", false);
diff --git a/src/tensors/gpu/tensor_operators.cu b/src/tensors/gpu/tensor_operators.cu
index a7edcdf59..30c04c2fe 100644
--- a/src/tensors/gpu/tensor_operators.cu
+++ b/src/tensors/gpu/tensor_operators.cu
@@ -105,7 +105,7 @@ __global__ void gInsertCols(float* out,
                             size_t offset_in) {
   for(int bid = 0; bid < rows; bid += gridDim.x) {
     int j = bid + blockIdx.x;
-    if(j < rows) {
+    if(j < rows) { // @TODO: change to if j == rows then break, as that's what it means. In 4 functions in here.
       float* rowOut = out + j * cols_out + offset_out;
       const float* rowIn = in + j * cols_in + offset_in;
 

From cd329be400d3e1dafb3579f5d76e6215262d2324 Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Mon, 29 Apr 2019 17:52:23 -0700
Subject: [PATCH 450/838] weird mode change

---
 contrib/autoformat.sh                           | 0
 scripts/bert/bert4marian.py                     | 0
 scripts/embeddings/export_embeddings.py         | 0
 scripts/embeddings/prepare_corpus.py            | 0
 scripts/embeddings/process_word2vec.py          | 0
 scripts/server/client_example.py                | 0
 scripts/shortlist/generate_shortlists.pl        | 0
 scripts/shortlist/install.sh                    | 0
 src/3rd_party/ExceptionWithCallStack.h          | 0
 src/3rd_party/pathie-cpp/src/entry_iterator.cpp | 0
 src/3rd_party/pathie-cpp/src/path.cpp           | 0
 src/3rd_party/spdlog/astyle.sh                  | 0
 src/3rd_party/spdlog/bench/latency/compare.sh   | 0
 src/3rd_party/spdlog/tests/install_libcxx.sh    | 0
 src/command/marian_main.cpp                     | 0
 src/common/config_parser.cpp                    | 0
 src/common/definitions.h                        | 0
 src/common/logging.cpp                          | 0
 src/common/options.h                            | 0
 src/common/shape.h                              | 0
 src/common/timer.h                              | 0
 src/common/types.h                              | 0
 src/common/utils.cpp                            | 0
 src/common/utils.h                              | 0
 src/data/batch_generator.h                      | 0
 src/data/corpus.cpp                             | 0
 src/data/corpus.h                               | 0
 src/data/corpus_base.h                          | 0
 src/data/default_vocab.cpp                      | 0
 src/data/factored_vocab.cpp                     | 0
 src/data/factored_vocab.h                       | 0
 src/data/sentencepiece_vocab.cpp                | 0
 src/data/text_input.cpp                         | 0
 src/data/types.h                                | 0
 src/data/vocab.cpp                              | 0
 src/data/vocab.h                                | 0
 src/data/vocab_base.h                           | 0
 src/examples/mnist/dataset.h                    | 0
 src/examples/mnist/download.sh                  | 0
 src/examples/mnist/mnist_ffnn.cpp               | 0
 src/examples/mnist/model.h                      | 0
 src/examples/mnist/training.h                   | 0
 src/examples/mnist/validator.h                  | 0
 src/functional/tmp.h                            | 0
 src/graph/expression_operators.cpp              | 0
 src/graph/expression_operators.h                | 0
 src/graph/node_operators.h                      | 0
 src/graph/node_operators_unary.h                | 0
 src/layers/constructors.h                       | 0
 src/layers/factory.h                            | 0
 src/layers/generic.cpp                          | 0
 src/layers/generic.h                            | 0
 src/layers/loss.cpp                             | 0
 src/layers/loss.h                               | 0
 src/microsoft/quicksand.cpp                     | 0
 src/microsoft/quicksand.h                       | 0
 src/models/bert.h                               | 0
 src/models/char_s2s.h                           | 0
 src/models/costs.h                              | 0
 src/models/decoder.h                            | 0
 src/models/encoder_classifier.h                 | 0
 src/models/encoder_decoder.cpp                  | 0
 src/models/encoder_decoder.h                    | 0
 src/models/model_base.h                         | 0
 src/models/model_factory.cpp                    | 0
 src/models/model_factory.h                      | 0
 src/models/s2s.h                                | 0
 src/models/states.h                             | 0
 src/models/transformer.h                        | 0
 src/models/transformer_factory.h                | 0
 src/models/transformer_stub.cpp                 | 0
 src/rescorer/rescorer.h                         | 0
 src/rnn/attention_constructors.h                | 0
 src/rnn/cells.h                                 | 0
 src/rnn/types.h                                 | 0
 src/tensors/cpu/tensor_operators.cpp            | 0
 src/tensors/gpu/add.cu                          | 0
 src/tensors/gpu/add.h                           | 0
 src/tensors/gpu/add.inc                         | 0
 src/tensors/gpu/element.cu                      | 0
 src/tensors/gpu/element.inc                     | 0
 src/tensors/gpu/prod.cpp                        | 0
 src/tensors/gpu/prod.h                          | 0
 src/tensors/gpu/tensor_operators.cu             | 0
 src/tests/prod.cpp                              | 0
 src/tests/units/attention_tests.cpp             | 0
 src/tests/units/operator_tests.cpp              | 0
 src/tests/units/rnn_tests.cpp                   | 0
 src/training/communicator.cpp                   | 0
 src/training/graph_group.h                      | 0
 src/training/graph_group_async.cpp              | 0
 src/training/graph_group_async.h                | 0
 src/training/graph_group_multinode.cpp          | 0
 src/training/graph_group_multinode_sync.cpp     | 0
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diff --git a/contrib/autoformat.sh b/contrib/autoformat.sh
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From eacfd2de114c495917d9cdf85f91f410d0caff3c Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Mon, 29 Apr 2019 19:01:29 -0700
Subject: [PATCH 451/838] weird mode change back

---
 contrib/autoformat.sh                           | 0
 scripts/bert/bert4marian.py                     | 0
 scripts/embeddings/export_embeddings.py         | 0
 scripts/embeddings/prepare_corpus.py            | 0
 scripts/embeddings/process_word2vec.py          | 0
 scripts/server/client_example.py                | 0
 scripts/shortlist/generate_shortlists.pl        | 0
 scripts/shortlist/install.sh                    | 0
 src/3rd_party/ExceptionWithCallStack.h          | 0
 src/3rd_party/pathie-cpp/src/entry_iterator.cpp | 0
 src/3rd_party/pathie-cpp/src/path.cpp           | 0
 src/3rd_party/spdlog/astyle.sh                  | 0
 src/3rd_party/spdlog/bench/latency/compare.sh   | 0
 src/3rd_party/spdlog/tests/install_libcxx.sh    | 0
 src/command/marian_main.cpp                     | 0
 src/common/config_parser.cpp                    | 0
 src/common/definitions.h                        | 0
 src/common/logging.cpp                          | 0
 src/common/options.h                            | 0
 src/common/shape.h                              | 0
 src/common/timer.h                              | 0
 src/common/types.h                              | 0
 src/common/utils.cpp                            | 0
 src/common/utils.h                              | 0
 src/data/batch_generator.h                      | 0
 src/data/corpus.cpp                             | 0
 src/data/corpus.h                               | 0
 src/data/corpus_base.h                          | 0
 src/data/default_vocab.cpp                      | 0
 src/data/factored_vocab.cpp                     | 0
 src/data/factored_vocab.h                       | 0
 src/data/sentencepiece_vocab.cpp                | 0
 src/data/text_input.cpp                         | 0
 src/data/types.h                                | 0
 src/data/vocab.cpp                              | 0
 src/data/vocab.h                                | 0
 src/data/vocab_base.h                           | 0
 src/examples/mnist/dataset.h                    | 0
 src/examples/mnist/download.sh                  | 0
 src/examples/mnist/mnist_ffnn.cpp               | 0
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 src/examples/mnist/training.h                   | 0
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old mode 100755
new mode 100644
diff --git a/vs/Marian.vcxproj.filters b/vs/Marian.vcxproj.filters
old mode 100755
new mode 100644

From 0d217f765f1b8166bdaaece2f018aaca3d7cb15b Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Mon, 29 Apr 2019 19:42:48 -0700
Subject: [PATCH 452/838] disabled escaping

---
 src/common/utils.cpp | 5 ++++-
 1 file changed, 4 insertions(+), 1 deletion(-)

diff --git a/src/common/utils.cpp b/src/common/utils.cpp
index 0e406958a..4139729c4 100644
--- a/src/common/utils.cpp
+++ b/src/common/utils.cpp
@@ -96,7 +96,10 @@ std::string join(const std::vector<std::string>& words, const std::string& del /
 // escapes a string for passing to popen, which uses /bin/sh to parse its argument string
 static std::string escapeForPOpen(const std::string& arg) {
   // e.g. abc -> 'abc'; my file.txt -> 'my file.txt'; $10 -> '$10'; it's -> 'it'\''s'
-  return "'" + findReplace(arg, "'", "'\\''", /*all=*/ true) + "'";
+  return arg;
+  // @BUGBUG: This sometimes fails with "sh: 1: Syntax error: Unterminated quoted string",
+  // so since this is not super-critical, we will disable it for now.
+  //return "'" + findReplace(arg, "'", "'\\''", /*all=*/ true) + "'";
 }
 
 // execute an external command

From 209c1d4a4e05f10830ce9b3f436ea2ba7a7fdab6 Mon Sep 17 00:00:00 2001
From: Marcin Junczys-Dowmunt <Marcin.JunczysDowmunt@microsoft.com>
Date: Tue, 30 Apr 2019 12:52:13 -0700
Subject: [PATCH 453/838] add back execution rights for python/perl scripts

---
 scripts/bert/bert4marian.py              | 0
 scripts/contrib/fix_hard.py              | 0
 scripts/contrib/inject_ctt.py            | 0
 scripts/contrib/inject_model_params.py   | 0
 scripts/contrib/model_info.py            | 0
 scripts/embeddings/export_embeddings.py  | 0
 scripts/embeddings/prepare_corpus.py     | 0
 scripts/embeddings/process_word2vec.py   | 0
 scripts/server/client_example.py         | 0
 scripts/shortlist/generate_shortlists.pl | 0
 10 files changed, 0 insertions(+), 0 deletions(-)
 mode change 100644 => 100755 scripts/bert/bert4marian.py
 mode change 100644 => 100755 scripts/contrib/fix_hard.py
 mode change 100644 => 100755 scripts/contrib/inject_ctt.py
 mode change 100644 => 100755 scripts/contrib/inject_model_params.py
 mode change 100644 => 100755 scripts/contrib/model_info.py
 mode change 100644 => 100755 scripts/embeddings/export_embeddings.py
 mode change 100644 => 100755 scripts/embeddings/prepare_corpus.py
 mode change 100644 => 100755 scripts/embeddings/process_word2vec.py
 mode change 100644 => 100755 scripts/server/client_example.py
 mode change 100644 => 100755 scripts/shortlist/generate_shortlists.pl

diff --git a/scripts/bert/bert4marian.py b/scripts/bert/bert4marian.py
old mode 100644
new mode 100755
diff --git a/scripts/contrib/fix_hard.py b/scripts/contrib/fix_hard.py
old mode 100644
new mode 100755
diff --git a/scripts/contrib/inject_ctt.py b/scripts/contrib/inject_ctt.py
old mode 100644
new mode 100755
diff --git a/scripts/contrib/inject_model_params.py b/scripts/contrib/inject_model_params.py
old mode 100644
new mode 100755
diff --git a/scripts/contrib/model_info.py b/scripts/contrib/model_info.py
old mode 100644
new mode 100755
diff --git a/scripts/embeddings/export_embeddings.py b/scripts/embeddings/export_embeddings.py
old mode 100644
new mode 100755
diff --git a/scripts/embeddings/prepare_corpus.py b/scripts/embeddings/prepare_corpus.py
old mode 100644
new mode 100755
diff --git a/scripts/embeddings/process_word2vec.py b/scripts/embeddings/process_word2vec.py
old mode 100644
new mode 100755
diff --git a/scripts/server/client_example.py b/scripts/server/client_example.py
old mode 100644
new mode 100755
diff --git a/scripts/shortlist/generate_shortlists.pl b/scripts/shortlist/generate_shortlists.pl
old mode 100644
new mode 100755

From 7f9e1511313d03a838b967e233420c95df91e8f6 Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Tue, 30 Apr 2019 18:42:54 -0700
Subject: [PATCH 454/838] addressed PR feedbacjk

---
 src/common/definitions.h        |  2 ++
 src/tensors/gpu/prod.cpp        | 10 ----------
 src/tensors/memory_piece.h      |  1 +
 src/tensors/tensor.h            | 11 +++++++++++
 src/translator/output_printer.h |  2 --
 5 files changed, 14 insertions(+), 12 deletions(-)
 mode change 100644 => 100755 src/common/definitions.h
 mode change 100644 => 100755 src/tensors/gpu/prod.cpp
 mode change 100644 => 100755 src/tensors/memory_piece.h
 mode change 100644 => 100755 src/tensors/tensor.h
 mode change 100644 => 100755 src/translator/output_printer.h

diff --git a/src/common/definitions.h b/src/common/definitions.h
old mode 100644
new mode 100755
index 3fdf6659f..f46da2340
--- a/src/common/definitions.h
+++ b/src/common/definitions.h
@@ -14,6 +14,8 @@
 #define NodeOp(op) [=]() { op; }
 
 // helper macro to disable optimization (gcc only)
+// To use this, just insert DONT_OPTIMIZE right before the function definition
+// (e.g. where the "static" keyword would go).
 #ifdef __GNUC__
 #define DONT_OPTIMIZE __attribute__((optimize("O0")))
 #else
diff --git a/src/tensors/gpu/prod.cpp b/src/tensors/gpu/prod.cpp
old mode 100644
new mode 100755
index 74abfa219..48524f626
--- a/src/tensors/gpu/prod.cpp
+++ b/src/tensors/gpu/prod.cpp
@@ -227,16 +227,6 @@ void ProdBatched(marian::Tensor C,
   allocator->free(mp_cptr);
 }
 
-// debugging helper   --@TODO: move it to some shared place?
-template<typename T>
-static std::vector<T> get(Ptr<MemoryPiece> memory, Ptr<marian::Backend> backend) {
-  size_t n = memory->size() / sizeof(T);
-  TensorBase t(memory, Shape({(int)n}), getType<T>(), backend);
-  std::vector<T> res;
-  t.get(res);
-  return res;
-}
-
 // bug in cuSparse: sparse matrix is limited to 65535 columns
 // This function is a drop-in replacement that handles it (by slicing).
 cusparseStatus_t
diff --git a/src/tensors/memory_piece.h b/src/tensors/memory_piece.h
old mode 100644
new mode 100755
index c34352633..27446a6c1
--- a/src/tensors/memory_piece.h
+++ b/src/tensors/memory_piece.h
@@ -40,4 +40,5 @@ class MemoryPiece {
     return out;
   }
 };
+
 }  // namespace marian
diff --git a/src/tensors/tensor.h b/src/tensors/tensor.h
old mode 100644
new mode 100755
index 9ec69effa..e391bd75c
--- a/src/tensors/tensor.h
+++ b/src/tensors/tensor.h
@@ -412,4 +412,15 @@ class TensorBase : public std::enable_shared_from_this<TensorBase> {
 };
 
 typedef std::shared_ptr<TensorBase> Tensor;
+
+// debugging helper, to read out a memory piece into an STL vector (e.g. for inspection in debugger)
+template<typename T>
+static inline std::vector<T> get(Ptr<MemoryPiece> memory, Ptr<Backend> backend) {
+  size_t n = memory->size() / sizeof(T);
+  TensorBase t(memory, Shape({ (int)n }), getType<T>(), backend);
+  std::vector<T> res;
+  t.get(res);
+  return res;
+}
+
 }  // namespace marian
diff --git a/src/translator/output_printer.h b/src/translator/output_printer.h
old mode 100644
new mode 100755
index e2fc97fee..85a7585f4
--- a/src/translator/output_printer.h
+++ b/src/translator/output_printer.h
@@ -72,8 +72,6 @@ class OutputPrinter {
       best1 << " ||| " << getAlignment(hypo);
     }
 
-    //best1 << " |sc=" << std::get<1>(result)->getPathScore();
-
     best1 << std::flush;
   }
 

From b5e42c26d1d614eadb4be383756126e79195e908 Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Wed, 1 May 2019 13:28:50 -0700
Subject: [PATCH 455/838] bug fix: the overly cautious ABORT_IF on
 flattenedLogitIndex should be a little less cautious

---
 src/translator/beam_search.h | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)
 mode change 100644 => 100755 src/translator/beam_search.h

diff --git a/src/translator/beam_search.h b/src/translator/beam_search.h
old mode 100644
new mode 100755
index 4db173000..8016e76f7
--- a/src/translator/beam_search.h
+++ b/src/translator/beam_search.h
@@ -114,7 +114,8 @@ class BeamSearch {
           auto lval = states[j]->getLogProbs().getFactoredLogitsTensor(factorGroup); // [localBeamSize, 1, dimBatch, dimFactorVocab]
           size_t flattenedLogitIndex = (beamHypIdx * dimBatch + batchIdx) * vocabSize + wordIdx;  // (beam idx, batch idx, word idx); note: beam and batch are transposed, compared to 'key'
           // @TODO: use a function on shape() to index, or new method val->at({i1, i2, i3, i4}) with broadcasting
-          ABORT_IF(lval->shape() != Shape({(int)nBestBeamSize, 1, (int)dimBatch, (int)vocabSize}), 
+          ABORT_IF(lval->shape() != Shape({(int)nBestBeamSize, 1, (int)dimBatch, (int)vocabSize}) &&
+                   (beamHypIdx == 0 && lval->shape() != Shape({1, 1, (int)dimBatch, (int)vocabSize})),
                    "Unexpected shape of logits?? {} != {}", lval->shape(), Shape({(int)nBestBeamSize, 1, (int)dimBatch, (int)vocabSize}));
           breakDown[j] += lval->get(flattenedLogitIndex);
         }

From 8ba7d810dd11ebc7d95b5cb2c04064e6a6c89f47 Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Wed, 1 May 2019 13:29:15 -0700
Subject: [PATCH 456/838] (fixed a mode change)

---
 src/translator/beam_search.h | 0
 1 file changed, 0 insertions(+), 0 deletions(-)
 mode change 100755 => 100644 src/translator/beam_search.h

diff --git a/src/translator/beam_search.h b/src/translator/beam_search.h
old mode 100755
new mode 100644

From 7bd1311d87988fc5d46bccc243574da01e9ee16f Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Wed, 1 May 2019 14:11:03 -0700
Subject: [PATCH 457/838] addressed PR feedback

---
 src/common/config_parser.cpp       |  1 +
 src/common/utils.cpp               |  1 +
 src/data/vocab.h                   |  2 ++
 src/graph/expression_operators.cpp |  2 +-
 src/models/costs.h                 |  1 +
 src/models/decoder.h               | 14 +++++++-------
 src/models/s2s.h                   | 13 ++++++-------
 src/models/transformer.h           | 13 +++++++------
 src/training/graph_group_sync.cpp  |  6 +++---
 src/training/validator.h           |  6 ++++--
 10 files changed, 33 insertions(+), 26 deletions(-)
 mode change 100644 => 100755 src/common/config_parser.cpp
 mode change 100644 => 100755 src/common/utils.cpp
 mode change 100644 => 100755 src/data/vocab.h
 mode change 100644 => 100755 src/graph/expression_operators.cpp
 mode change 100644 => 100755 src/models/costs.h
 mode change 100644 => 100755 src/models/decoder.h
 mode change 100644 => 100755 src/models/s2s.h
 mode change 100644 => 100755 src/models/transformer.h
 mode change 100644 => 100755 src/training/graph_group_sync.cpp
 mode change 100644 => 100755 src/training/validator.h

diff --git a/src/common/config_parser.cpp b/src/common/config_parser.cpp
old mode 100644
new mode 100755
index 46402e38e..eaa9c9912
--- a/src/common/config_parser.cpp
+++ b/src/common/config_parser.cpp
@@ -637,6 +637,7 @@ void ConfigParser::addSuboptionsBatching(cli::CLIWrapper& cli) {
 
   cli.add<bool>("--shuffle-in-ram",
       "Keep shuffled corpus in RAM, do not write to temp file");
+  // @TODO: Consider making the next two options options of the vocab instead, to make it more local in scope.
   cli.add<size_t>("--all-caps-every",
       "When forming minibatches, preprocess every Nth line on the fly to all-caps. Assumes UTF-8");
   cli.add<size_t>("--english-title-case-every",
diff --git a/src/common/utils.cpp b/src/common/utils.cpp
old mode 100644
new mode 100755
index 4139729c4..de87b8a6a
--- a/src/common/utils.cpp
+++ b/src/common/utils.cpp
@@ -165,6 +165,7 @@ bool endsWith(const std::string& text, const std::string& suffix) {
          && !text.compare(text.size() - suffix.size(), suffix.size(), suffix);
 }
 
+// @TODO: sort these functions into a separate header.
 std::u32string utf8ToUnicodeString(std::string const& s) {
 #ifdef _MSC_VER // workaround for a known bug in VS CRT
   std::wstring_convert<std::codecvt_utf8<unsigned int/*char32_t*/>, unsigned int/*char32_t*/> converter;
diff --git a/src/data/vocab.h b/src/data/vocab.h
old mode 100644
new mode 100755
index a15e202f0..9a40ba16f
--- a/src/data/vocab.h
+++ b/src/data/vocab.h
@@ -71,6 +71,8 @@ class Vocab {
   Word getUnkId() const;
 
   // for corpus augmentation: convert string to all-caps
+  // @TODO: Consider a different implementation where this does not show on the vocab interface,
+  //        but instead as additional options passed to vocab instantiation.
   std::string toUpper(const std::string& line) const;
 
   // for corpus augmentation: convert string to title case
diff --git a/src/graph/expression_operators.cpp b/src/graph/expression_operators.cpp
old mode 100644
new mode 100755
index 396370bd1..396fa03e2
--- a/src/graph/expression_operators.cpp
+++ b/src/graph/expression_operators.cpp
@@ -79,7 +79,7 @@ Expr softmax(Expr a, Expr zeroOneMask, int axis /*=-1*/) {
 
 Expr logsoftmax(Expr a) {
   if (a->type() == "logsoftmax") // logsoftmax(logsoftmax(x)) == logsoftmax(x)
-    // @TODO: Is this correct? logsoftmax is idempotent in forward(), but not in backward()
+    // @TODO: Remove this. First add an ABORT() for a while to catch who relies on this, and if noone, then delete.
     return a;
   return Expression<LogSoftmaxNodeOp>(a);
 }
diff --git a/src/models/costs.h b/src/models/costs.h
old mode 100644
new mode 100755
index f56f6e05f..7aa5a5f3e
--- a/src/models/costs.h
+++ b/src/models/costs.h
@@ -214,6 +214,7 @@ class LogSoftmaxStep : public ILogProbStep {
   virtual Ptr<DecoderState> apply(Ptr<DecoderState> state) override {
     // decoder needs normalized probabilities (note: skipped if beam 1 and --skip-cost)
     state->setLogProbs(state->getLogProbs().applyUnaryFunction(logsoftmax));
+    // @TODO: This is becoming more and more opaque ^^. Can we simplify this?
     return state;
   }
 };
diff --git a/src/models/decoder.h b/src/models/decoder.h
old mode 100644
new mode 100755
index 31baca9cb..dd5a508a2
--- a/src/models/decoder.h
+++ b/src/models/decoder.h
@@ -15,7 +15,7 @@ class DecoderBase {
   std::string prefix_{"decoder"};
   bool inference_{false};
   size_t batchIndex_{1};
-  std::vector<Ptr<IEmbeddingLayer>> embedding_; // @TODO: find a more grammatical name
+  std::vector<Ptr<IEmbeddingLayer>> embeddingLayers_; // (lazily created)
 
   Ptr<data::Shortlist> shortlist_;
 
@@ -37,9 +37,9 @@ class DecoderBase {
 
   void lazyCreateEmbeddingLayer(Ptr<ExpressionGraph> graph) {
     // @TODO: code dup with EncoderTransformer
-    if (embedding_.size() <= batchIndex_ || !embedding_[batchIndex_]) { // lazy
-      if (embedding_.size() <= batchIndex_)
-        embedding_.resize(batchIndex_ + 1);
+    if (embeddingLayers_.size() <= batchIndex_ || !embeddingLayers_[batchIndex_]) { // lazy
+      if (embeddingLayers_.size() <= batchIndex_)
+        embeddingLayers_.resize(batchIndex_ + 1);
       int dimVoc = opt<std::vector<int>>("dim-vocabs")[batchIndex_];
       int dimEmb = opt<int>("dim-emb");
       auto embFactory = embedding()("dimVocab", dimVoc)("dimEmb", dimEmb);
@@ -55,7 +55,7 @@ class DecoderBase {
             ("normalization", opt<bool>("embedding-normalization"));
       }
       embFactory("vocab", opt<std::vector<std::string>>("vocabs")[batchIndex_]); // for factored embeddings
-      embedding_[batchIndex_] = embFactory.construct(graph);
+      embeddingLayers_[batchIndex_] = embFactory.construct(graph);
     }
   }
 
@@ -66,7 +66,7 @@ class DecoderBase {
 
     lazyCreateEmbeddingLayer(graph);
     Expr y, yMask; std::tie
-    (y, yMask) = embedding_[batchIndex_]->apply(subBatch);
+    (y, yMask) = embeddingLayers_[batchIndex_]->apply(subBatch);
 
     const Words& data =
       /*if*/ (shortlist_) ?
@@ -93,7 +93,7 @@ class DecoderBase {
     if(words.empty()) {
       selectedEmbs = graph->constant({1, 1, dimBatch, dimEmb}, inits::zeros);
     } else {
-      selectedEmbs = embedding_[batchIndex_]->apply(words, {dimBeam, 1, dimBatch, dimEmb});
+      selectedEmbs = embeddingLayers_[batchIndex_]->apply(words, {dimBeam, 1, dimBatch, dimEmb});
     }
     state->setTargetHistoryEmbeddings(selectedEmbs);
   }
diff --git a/src/models/s2s.h b/src/models/s2s.h
old mode 100644
new mode 100755
index 67b0817a4..4829e93de
--- a/src/models/s2s.h
+++ b/src/models/s2s.h
@@ -9,6 +9,7 @@
 namespace marian {
 
 class EncoderS2S : public EncoderBase {
+  std::vector<Ptr<IEmbeddingLayer>> embeddingLayers_; // (lazily created)
 public:
   Expr applyEncoderRNN(Ptr<ExpressionGraph> graph,
                        Expr embeddings,
@@ -150,15 +151,14 @@ class EncoderS2S : public EncoderBase {
 
   EncoderS2S(Ptr<Options> options) : EncoderBase(options) {}
 
-  std::vector<Ptr<IEmbeddingLayer>> embedding_; // @TODO: move away, also rename
   virtual Ptr<EncoderState> build(Ptr<ExpressionGraph> graph,
                                   Ptr<data::CorpusBatch> batch) override {
     // lazily create embedding layer
-    if (embedding_.empty() || !embedding_[batchIndex_]) { // lazy
-      embedding_.resize(batch->sets());
-      embedding_[batchIndex_] = createSourceEmbeddingLayer(graph);
+    if (embeddingLayers_.empty() || !embeddingLayers_[batchIndex_]) { // lazy
+      embeddingLayers_.resize(batch->sets());
+      embeddingLayers_[batchIndex_] = createSourceEmbeddingLayer(graph);
     }
-    auto embedding = embedding_[batchIndex_];
+    auto embedding = embeddingLayers_[batchIndex_];
 
     // select embeddings that occur in the batch
     Expr batchEmbeddings, batchMask; std::tie
@@ -246,8 +246,7 @@ class DecoderS2S : public DecoderBase {
   }
 
 public:
-  DecoderS2S(Ptr<Options> options) : DecoderBase(options) {
-  }
+  DecoderS2S(Ptr<Options> options) : DecoderBase(options) {}
 
   virtual Ptr<DecoderState> startState(
       Ptr<ExpressionGraph> graph,
diff --git a/src/models/transformer.h b/src/models/transformer.h
old mode 100644
new mode 100755
index 588a79b41..f36150edb
--- a/src/models/transformer.h
+++ b/src/models/transformer.h
@@ -502,6 +502,7 @@ class Transformer : public EncoderOrDecoderBase {
 };
 
 class EncoderTransformer : public Transformer<EncoderBase> {
+  std::vector<Ptr<IEmbeddingLayer>> embeddingLayers_; // (lazily created)
 public:
   EncoderTransformer(Ptr<Options> options) : Transformer(options) {}
   virtual ~EncoderTransformer() {}
@@ -549,7 +550,6 @@ class EncoderTransformer : public Transformer<EncoderBase> {
     return apply(batch);
   }
 
-  std::vector<Ptr<IEmbeddingLayer>> embedding_; // @TODO: move away, also rename
   Ptr<EncoderState> apply(Ptr<data::CorpusBatch> batch) {
     int dimBatch = (int)batch->size();
     int dimSrcWords = (int)(*batch)[batchIndex_]->batchWidth();
@@ -557,14 +557,14 @@ class EncoderTransformer : public Transformer<EncoderBase> {
     // embed the source words in the batch
     Expr batchEmbeddings, batchMask;
 
-    if (embedding_.empty() || !embedding_[batchIndex_]) { // lazy
-      embedding_.resize(batch->sets());
+    if (embeddingLayers_.empty() || !embeddingLayers_[batchIndex_]) { // lazy
+      embeddingLayers_.resize(batch->sets());
       if (options_->has("ulr") && options_->get<bool>("ulr") == true)
-        embedding_[batchIndex_] = createULREmbeddingLayer(); // embedding uses ULR
+        embeddingLayers_[batchIndex_] = createULREmbeddingLayer(); // embedding uses ULR
       else
-        embedding_[batchIndex_] = createSourceEmbeddingLayer(batchIndex_);
+        embeddingLayers_[batchIndex_] = createSourceEmbeddingLayer(batchIndex_);
     }
-    std::tie(batchEmbeddings, batchMask) = embedding_[batchIndex_]->apply((*batch)[batchIndex_]);
+    std::tie(batchEmbeddings, batchMask) = embeddingLayers_[batchIndex_]->apply((*batch)[batchIndex_]);
     // apply dropout over source words
     float dropoutSrc = inference_ ? 0 : opt<float>("dropout-src");
     if(dropoutSrc) {
@@ -636,6 +636,7 @@ class DecoderTransformer : public Transformer<DecoderBase> {
   Ptr<mlp::Output> output_;
 
 private:
+  // @TODO: move this out for sharing with other models
   void lazyCreateOutputLayer()
   {
     if(output_) // create it lazily
diff --git a/src/training/graph_group_sync.cpp b/src/training/graph_group_sync.cpp
old mode 100644
new mode 100755
index 86c503fb6..6c6da17bb
--- a/src/training/graph_group_sync.cpp
+++ b/src/training/graph_group_sync.cpp
@@ -23,11 +23,11 @@ SyncGraphGroup::SyncGraphGroup(Ptr<Options> config, Ptr<IMPIWrapper> mpi)
   // Rather, it is assumed that the communicator knows to reduce unnecessary transfers to no-ops.
   comm_ = createCommunicator(graphs_, /*noNccl=*/options_->get<bool>("no-nccl", false), /*mpi=*/mpi_);
 
-  auto type = utils::utf8ToUpper(devices_.front().typeAsString()) + "s";
+  auto formattedDeviceType = utils::utf8ToUpper(devices_.front().typeAsString()) + "s";
   if (mpi_->numMPIProcesses() > 1)
-    LOG(info, "[training] Using {} {}, distributed over {} MPI processes", mpi_->numMPIProcesses() * devices_.size(), type, mpi_->numMPIProcesses());
+    LOG(info, "[training] Using {} {}, distributed over {} MPI processes", mpi_->numMPIProcesses() * devices_.size(), formattedDeviceType, mpi_->numMPIProcesses());
   else
-    LOG(info, "[training] Using {} {}", devices_.size(), type);
+    LOG(info, "[training] Using {} {}", devices_.size(), formattedDeviceType);
 }
 
 void SyncGraphGroup::setScheduler(Ptr<Scheduler> scheduler) /*override*/ {
diff --git a/src/training/validator.h b/src/training/validator.h
old mode 100644
new mode 100755
index 968fcf4ff..7736318b1
--- a/src/training/validator.h
+++ b/src/training/validator.h
@@ -742,7 +742,7 @@ class BleuValidator : public Validator<data::Corpus, models::IModel> {
 
     return normText;
   }
-public:
+
   static std::string tokenizeContinuousScript(const std::string& sUTF8) {
     // We want BLEU-like scores that are comparable across different tokenization schemes.
     // For continuous scripts (Chinese, Japanese, Thai), we would need a language-specific
@@ -751,6 +751,7 @@ class BleuValidator : public Validator<data::Corpus, models::IModel> {
     // leaving Western scripts as words. This way we can use the same settings for Western
     // languages, where Marian would report SacreBLEU scores, and Asian languages, where
     // scores are not standard but internally comparable across tokenization schemes.
+    // @TODO: Check what sacrebleu.py is doing, and whether we can replicate that here faithfully.
     auto in = utils::utf8ToUnicodeString(sUTF8);
     auto out = in.substr(0, 0); // (out should be same type as in, don't want to bother with exact type)
     for (auto c : in) {
@@ -842,7 +843,8 @@ class BleuValidator : public Validator<data::Corpus, models::IModel> {
     // parameter to select the detokenization method, which will default to detok for FactoredSegmenter,
     // and no-op for base vocab.
     if (vocabs_.back()->type() == "FactoredVocab") {
-      LOG_ONCE(info, "[valid] FactoredVocab implies using detokenized BLEU");
+      if (!quiet_)
+        LOG_ONCE(info, "[valid] FactoredVocab implies using detokenized BLEU");
       detok = true; // always use bleu-detok
     }
 #endif

From cf379e9422b4ec187539b3fbd184c000b32ced19 Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Thu, 9 May 2019 18:51:02 -0700
Subject: [PATCH 458/838] implemented shortlists for lemma-dim-emb

---
 src/layers/generic.cpp | 26 ++++++++++++++++++--------
 src/layers/generic.h   |  9 ++++++---
 2 files changed, 24 insertions(+), 11 deletions(-)

diff --git a/src/layers/generic.cpp b/src/layers/generic.cpp
index d13d5871a..db23c7b56 100644
--- a/src/layers/generic.cpp
+++ b/src/layers/generic.cpp
@@ -251,6 +251,13 @@ namespace marian {
       }
 
       b_ = graph_->param(name + "_b", {1, numOutputClasses}, inits::zeros);
+
+      const int lemmaDimEmb = options_->get<int>("lemma-dim-emb", 0);
+      if (lemmaDimEmb > 0) {
+        auto range = factoredVocab_->getGroupRange(0);
+        auto lemmaVocabDim = (int)(range.second - range.first);
+        lemmaEt_ = graph_->param(name + "_lemmaEt", {lemmaDimEmb, lemmaVocabDim}, inits::glorot_uniform); // [L x U] L=lemmaDimEmb; transposed for speed
+      }
     }
 
     Logits Output::applyAsLogits(Expr input) /*override final*/ {
@@ -299,8 +306,8 @@ namespace marian {
           // optionally add a soft embedding of lemma back to create some lemma dependency
           // @TODO: if this works, move it into lazyConstruct
           const int lemmaDimEmb = options_->get<int>("lemma-dim-emb", 0);
-          ABORT_IF(shortlist_ && lemmaDimEmb != 0, "Lemma re-embedding with short list is not yet implemented");
           if (lemmaDimEmb < 0 && g == 0) {
+            ABORT_IF(shortlist_ && lemmaDimEmb != 0, "Lemma-dependent bias with short list is not yet implemented");
             LOG_ONCE(info, "[embedding] using lemma-dependent bias");
             factorLogits = logsoftmax(factorLogits); // explicitly, since we do that again later
             auto z = /*stopGradient*/(factorLogits);
@@ -308,16 +315,19 @@ namespace marian {
           }
           if (lemmaDimEmb > 0 && g == 0) {
             LOG_ONCE(info, "[embedding] enabled re-embedding of lemma, at dim {}", lemmaDimEmb);
-            int lemmaVocabDim = factorLogits->shape()[-1];
-            int inputDim  = input1->shape()[-1];
+            // compute softmax. We compute logsoftmax() separately because this way, computation will be reused later via CSE
+            factorLogits = logsoftmax(factorLogits);
+            // re-embedding lookup, soft-indexed by softmax
+            if (shortlist_ && !cachedShortLemmaEt_) // short-listed version of re-embedding matrix
+              cachedShortLemmaEt_ = index_select(lemmaEt_, -1, shortlist_->indices());
+            auto e = dot(exp(factorLogits), cachedShortLemmaEt_ ? cachedShortLemmaEt_ : lemmaEt_, false, true); // [B... x L]
+            // project it back to regular hidden dim
+            int inputDim = input1->shape()[-1];
             auto name = options_->get<std::string>("prefix");
-            factorLogits = logsoftmax(factorLogits); // explicitly, since we do that again later
-            Expr lemmaEt = graph_->param(name + "_lemmaEt", { lemmaDimEmb, lemmaVocabDim }, inits::glorot_uniform); // [L x U] L=lemmaDimEmb; transposed for speed
-            auto e = dot(exp(factorLogits), lemmaEt, false, true); // [B... x L]
-            //e = tanh(e); // make it non-scale-preserving
             Expr lemmaWt = inputDim == lemmaDimEmb ? nullptr : graph_->param(name + "_lemmaWt", { inputDim,  lemmaDimEmb }, inits::glorot_uniform); // [D x L] D=hidden-vector dimension
             auto f = lemmaWt ? dot(e, lemmaWt, false, true) : e; // [B... x D]
-            input1 = input1 + f; // augment the original hidden vector with this additional information
+            // augment the original hidden vector with this additional information
+            input1 = input1 + f;
           }
         }
         return Logits(std::move(allLogits), factoredVocab_);
diff --git a/src/layers/generic.h b/src/layers/generic.h
index 9d787a3c3..309b81c51 100644
--- a/src/layers/generic.h
+++ b/src/layers/generic.h
@@ -204,10 +204,12 @@ class Output : public LayerBase, public IUnaryLogitLayer, public IHasShortList {
   // parameters held by this layer
   Expr Wt_; // weight matrix is stored transposed for efficiency
   Expr b_;
+  Expr lemmaEt_; // re-embedding matrix for lemmas [lemmaDimEmb x lemmaVocabSize]
   bool isLegacyUntransposedW{false}; // legacy-model emulation: W is stored in non-transposed form
   Expr cachedShortWt_;  // short-listed version, cached (cleared by clear())
   Expr cachedShortb_;   // these match the current value of shortlist_
-  Ptr<FactoredVocab > factoredVocab_;
+  Expr cachedShortLemmaEt_;
+  Ptr<FactoredVocab> factoredVocab_;
 
   // optional parameters set/updated after construction
   Expr tiedParam_;
@@ -231,18 +233,19 @@ class Output : public LayerBase, public IUnaryLogitLayer, public IHasShortList {
     if (shortlist_)
       ABORT_IF(shortlist.get() != shortlist_.get(), "Output shortlist cannot be changed except after clear()");
     else {
-      ABORT_IF(cachedShortWt_ || cachedShortb_, "No shortlist but cached parameters??");
+      ABORT_IF(cachedShortWt_ || cachedShortb_ || cachedShortLemmaEt_, "No shortlist but cached parameters??");
       shortlist_ = shortlist;
     }
     // cachedShortWt_ and cachedShortb_ will be created lazily inside apply()
   }
 
   // this is expected to be called in sync with graph->clear(), which invalidates
-  // cachedShortWt_ and cachedShortb_ in the graph's short-term cache
+  // cachedShortWt_ etc. in the graph's short-term cache
   void clear() override final {
     shortlist_ = nullptr;
     cachedShortWt_ = nullptr;
     cachedShortb_  = nullptr;
+    cachedShortLemmaEt_ = nullptr;
   }
 
   Logits applyAsLogits(Expr input) override final;

From b9c06637368b38c6bc45f5cebe16b6199b4f2b04 Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Mon, 13 May 2019 15:39:32 -0700
Subject: [PATCH 459/838] added comments regarding SacreBLEU char BLEU

---
 src/common/utils.cpp | 35 +++++++++++++++++++++++++++++++----
 1 file changed, 31 insertions(+), 4 deletions(-)

diff --git a/src/common/utils.cpp b/src/common/utils.cpp
index de87b8a6a..80aee431e 100755
--- a/src/common/utils.cpp
+++ b/src/common/utils.cpp
@@ -217,15 +217,42 @@ bool isContinuousScript(char32_t c) {
   auto in = [c](char32_t minVal, char32_t maxVal) { return c >= minVal && c <= maxVal; };
   bool isHan = in(0x2E80, 0x2E99) || in(0x2E9B, 0x2EF3) || in(0x2F00, 0x2FD5) || in(0x3005, 0x3005) ||
                in(0x3007, 0x3007) || in(0x3021, 0x3029) || in(0x3038, 0x303A) || in(0x303B, 0x303b) ||
-               in(0x3400, 0x4DB5) || in(0x4E00, 0x9FEF) || in(0xF900, 0xFA6D) || in(0xFA70, 0xFAD9) ||
-               in(0x20000, 0x2A6D6) || in(0x2A700, 0x2B734) || in(0x2B740, 0x2B81D) || in(0x2B820, 0x2CEA1) ||
-               in(0x2CEB0, 0x2EBE0) || in(0x2F800, 0x2FA1D) ||
-               in(0x3200, 0x32FF); // Enclosed CJK Letters and Months, https://en.wikipedia.org/wiki/Enclosed_CJK_Letters_and_Months
+               in(0x3200, 0x32FF) || // Enclosed CJK Letters and Months, https://en.wikipedia.org/wiki/Enclosed_CJK_Letters_and_Months
+               in(0x3400, 0x4DB5) || // up to here, we have a few gaps compared to sacrebleu
+               in(0x4E00, 0x9FEF) || // sacrebleu: only up to 0x9fbb
+               in(0xF900, 0xFA6D) || in(0xFA70, 0xFAD9) || // similar to sacrebleu
+               in(0x20000, 0x2A6D6) ||
+               in(0x2A700, 0x2B734) || in(0x2B740, 0x2B81D) || in(0x2B820, 0x2CEA1) || in(0x2CEB0, 0x2EBE0) || // not in sacrebleu
+               in(0x2F800, 0x2FA1D);
   bool isKana = in(0x3040, 0x30FF) ||   // Hiragana, Katakana
                 in(0x1B000, 0x1B0FF) || // Kana supplement, https://en.wikipedia.org/wiki/Kana_Supplement
                 in(0x1B130, 0x1B16F);   // small Kana, https://en.wikipedia.org/wiki/Small_Kana_Extension
   bool isThai = in(0x0E00, 0x0E7F); // https://en.wikipedia.org/wiki/Thai_(Unicode_block)
   return isHan || isKana || isThai;
+  // Korean characters (Hangul syllables): 0xac00..0xd7a3
+  // Korean subwords (Hangul Jamo): 0x1100..0x11ff [https://en.wikipedia.org/wiki/Hangul_Jamo_(Unicode_block)]
+  // Sacrebleu uses characters units for Chinese characters; specifically, these ranges:
+  /* (ranges as used in sacrebleuy.py)
+        uchar >= u'\u2600' and uchar <= u'\u27bf'  ## missing above
+        uchar >= u'\u2e80'                         # CJK Radicals Supplement
+        uchar >= u'\u2f00' and uchar <= u'\u2fdf'  # Kangxi Radicals
+        uchar >= u'\u2ff0'                         # Chinese character structure
+        uchar >= u'\u3000' and uchar <= u'\u303f'  # CJK punctuation mark           ## 3040..30ff = Kana
+        uchar >= u'\u3100' and uchar <= u'\u312f'  # Phonetic symbols
+        uchar >= u'\u31a0'                         # Phonetic symbols (Taiwanese and Hakka expansion)
+        uchar >= u'\u31c0' and uchar <= u'\u31ef'  # CJK stroke
+        uchar >= u'\u3200' and uchar <= u'\u4db5'  # CJK Unified Ideographs Extension A, release 3.0
+        uchar >= u'\u4e00'                         # CJK Unified Ideographs, release 1.1
+        uchar >= u'\u9fa6' and uchar <= u'\u9fbb'  # CJK Unified Ideographs, release 4.1
+        uchar >= u'\uf900' and uchar <= u'\ufa2d'  # CJK Compatibility Ideographs, release 1.1
+        uchar >= u'\ufa30' and uchar <= u'\ufa6a'  # CJK Compatibility Ideographs, release 3.2
+        uchar >= u'\ufa70' and uchar <= u'\ufad9'  # CJK Compatibility Ideographs, release 4.1
+        uchar >= u'\ufe10' and uchar <= u'\ufe1f'  ## missing above
+        uchar >= u'\ufe30' and uchar <= u'\ufe4f'  ## missing above
+        uchar >= u'\uff00' and uchar <= u'\uffef'  # Full width ASCII, full width of English punctuation, half width Katakana, half wide half width kana, Korean alphabet
+        uchar >= u'\u20000' and uchar <= u'\u2a6d6'  # CJK Unified Ideographs Extension B, release 3.1
+        uchar >= u'\u2f800' and uchar <= u'\u2fa1d'  # CJK Compatibility Supplement, release 3.1
+  */
 }
 
 // convert UTF-8 characters to lower or upper case

From aced50d97a7c28f694f3ef04493daaca4e27423e Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Tue, 14 May 2019 13:52:47 -0700
Subject: [PATCH 460/838] some minor refactoring

---
 src/examples/mnist/model.h       |  2 +-
 src/graph/expression_graph.cpp   |  2 +-
 src/graph/expression_graph.h     |  2 +-
 src/graph/expression_operators.h |  2 +-
 src/layers/factory.h             |  2 +
 src/layers/generic.cpp           | 22 +++------
 src/models/char_s2s.h            |  1 -
 src/models/classifier.h          | 80 ++++++++++++++++----------------
 src/models/costs.h               |  6 +--
 src/models/decoder.h             | 18 +++++--
 src/models/encoder.h             |  9 ++--
 src/models/encoder_decoder.h     |  5 +-
 src/models/s2s.h                 | 10 +---
 src/models/transformer.h         |  8 ----
 src/rnn/attention.h              |  4 +-
 src/rnn/cells.h                  | 26 +++++------
 16 files changed, 94 insertions(+), 105 deletions(-)
 mode change 100644 => 100755 src/examples/mnist/model.h
 mode change 100644 => 100755 src/graph/expression_graph.cpp
 mode change 100644 => 100755 src/graph/expression_graph.h
 mode change 100644 => 100755 src/graph/expression_operators.h
 mode change 100644 => 100755 src/layers/factory.h
 mode change 100644 => 100755 src/layers/generic.cpp
 mode change 100644 => 100755 src/models/char_s2s.h
 mode change 100644 => 100755 src/models/classifier.h
 mode change 100644 => 100755 src/models/encoder.h
 mode change 100644 => 100755 src/models/encoder_decoder.h
 mode change 100644 => 100755 src/rnn/attention.h
 mode change 100644 => 100755 src/rnn/cells.h

diff --git a/src/examples/mnist/model.h b/src/examples/mnist/model.h
old mode 100644
new mode 100755
index 8e18aea1a..d03800f7b
--- a/src/examples/mnist/model.h
+++ b/src/examples/mnist/model.h
@@ -93,7 +93,7 @@ class MnistFeedForwardNet : public IModel {
 
 protected:
   Ptr<Options> options_;
-  bool inference_{false};
+  const bool inference_{false};
 
   /**
    * @brief Builds an expression graph representing a feed-forward classifier.
diff --git a/src/graph/expression_graph.cpp b/src/graph/expression_graph.cpp
old mode 100644
new mode 100755
index b3c237d1e..1df3af243
--- a/src/graph/expression_graph.cpp
+++ b/src/graph/expression_graph.cpp
@@ -20,7 +20,7 @@ void ExpressionGraph::setDevice(DeviceId deviceId, Ptr<Device> device) {
   }
 }
 
-Expr ExpressionGraph::dropout(float prob, const Shape& shape) {
+Expr ExpressionGraph::dropoutMask(float prob, const Shape& shape) {
   return constant(shape, inits::dropout(prob));
 }
 
diff --git a/src/graph/expression_graph.h b/src/graph/expression_graph.h
old mode 100644
new mode 100755
index dfd00a9a5..901d97245
--- a/src/graph/expression_graph.h
+++ b/src/graph/expression_graph.h
@@ -391,7 +391,7 @@ class ExpressionGraph : public std::enable_shared_from_this<ExpressionGraph> {
   }
 
   // prob = dropProb, e.g. 0.1 means 90% of values are kept
-  Expr dropout(float dropProb, const Shape& shape);
+  Expr dropoutMask(float dropProb, const Shape& shape);
 
   Expr get(std::string name) {
     if(!namespace_.empty())
diff --git a/src/graph/expression_operators.h b/src/graph/expression_operators.h
old mode 100644
new mode 100755
index 0205fe864..f8c7fd3c0
--- a/src/graph/expression_operators.h
+++ b/src/graph/expression_operators.h
@@ -214,7 +214,7 @@ static inline Expr dropout(Expr x, float dropProb, Shape shape) {
   if(dropProb == 0)
     return x;
   auto graph = x->graph();
-  auto mask = graph->dropout(dropProb, shape);
+  auto mask = graph->dropoutMask(dropProb, shape);
   return dropout(x, mask);
 }
 
diff --git a/src/layers/factory.h b/src/layers/factory.h
old mode 100644
new mode 100755
index 0aaf86a92..d3e1e3fa2
--- a/src/layers/factory.h
+++ b/src/layers/factory.h
@@ -70,10 +70,12 @@ class Accumulator : public BaseFactory {
     return *this;
   }
 
+#if 0
   Accumulator& operator()(Config::YamlNode yaml) {
     Factory::getOptions()->merge(yaml);
     return *this;
   }
+#endif
 
   Accumulator& operator()(Ptr<Options> options) {
     Factory::getOptions()->merge(options);
diff --git a/src/layers/generic.cpp b/src/layers/generic.cpp
old mode 100644
new mode 100755
index d13d5871a..ef3c21933
--- a/src/layers/generic.cpp
+++ b/src/layers/generic.cpp
@@ -364,12 +364,13 @@ namespace marian {
     // multi-hot factor vectors are represented as a sparse CSR matrix
     // [row index = word position index] -> set of factor indices for word at this position
     ABORT_IF(factoredData.shape != Shape({(int)factoredData.offsets.size()-1/*=rows of CSR*/, E_->shape()[0]}), "shape mismatch??");
-    return csr_dot( // the CSR matrix is passed in pieces
-        factoredData.shape,
-        graph->constant({(int)factoredData.weights.size()}, inits::from_vector(factoredData.weights), Type::float32),
-        graph->constant({(int)factoredData.indices.size()}, inits::from_vector(factoredData.indices), Type::uint32),
-        graph->constant({(int)factoredData.offsets.size()}, inits::from_vector(factoredData.offsets), Type::uint32),
-        E_);
+    // the CSR matrix is passed in pieces
+    auto weights = graph->constant({ (int)factoredData.weights.size() }, inits::from_vector(factoredData.weights), Type::float32);
+    auto indices = graph->constant({ (int)factoredData.indices.size() }, inits::from_vector(factoredData.indices), Type::uint32);
+    auto offsets = graph->constant({ (int)factoredData.offsets.size() }, inits::from_vector(factoredData.offsets), Type::uint32);
+    // apply dropout  --@TODO
+    // perform the product
+    return csr_dot(factoredData.shape, weights, indices, offsets, E_);
   }
 
   std::tuple<Expr/*embeddings*/, Expr/*mask*/> Embedding::apply(Ptr<data::SubBatch> subBatch) const /*override final*/ {
@@ -406,16 +407,7 @@ namespace marian {
     //        - but forward pass weighs them down, so that all factors are in a similar numeric range
     //        - if it is required to be in a different range, the embeddings can still learn that, but more slowly
 
-#if 1
     auto batchEmbeddings = apply(subBatch->data(), {dimWords, dimBatch, dimEmb});
-#else
-    Expr selectedEmbs;
-    if (factoredVocab_)
-      selectedEmbs = multiRows(subBatch->data());
-    else
-      selectedEmbs = rows(E_, subBatch->data());
-    auto batchEmbeddings = reshape(selectedEmbs, { dimWords, dimBatch, dimEmb });
-#endif
     auto batchMask = graph->constant({dimWords, dimBatch, 1},
                                      inits::from_vector(subBatch->mask()));
     return std::make_tuple(batchEmbeddings, batchMask);
diff --git a/src/models/char_s2s.h b/src/models/char_s2s.h
old mode 100644
new mode 100755
index 928c6a18d..f14bf8807
--- a/src/models/char_s2s.h
+++ b/src/models/char_s2s.h
@@ -18,7 +18,6 @@ class CharS2SEncoder : public EncoderS2S {
     // select embeddings that occur in the batch
     Expr batchEmbeddings, batchMask; std::tie
     (batchEmbeddings, batchMask) = embedding->apply(batch->front());
-
     // apply dropout over source words
     float dropProb = inference_ ? 0 : opt<float>("dropout-src");
     if(dropProb) {
diff --git a/src/models/classifier.h b/src/models/classifier.h
old mode 100644
new mode 100755
index dae9bcbb2..773299b4b
--- a/src/models/classifier.h
+++ b/src/models/classifier.h
@@ -1,41 +1,41 @@
-#pragma once
-
-#include "marian.h"
-#include "models/states.h"
-#include "layers/constructors.h"
-#include "layers/factory.h"
-
-namespace marian {
-
-/**
- * Simple base class for Classifiers to be used in EncoderClassifier framework
- * Currently only implementations are in bert.h
- */
-class ClassifierBase {
-protected:
-  Ptr<Options> options_;
-  std::string prefix_{"classifier"};
-  bool inference_{false};
-  size_t batchIndex_{0};
-
-public:
-  ClassifierBase(Ptr<Options> options)
-      : options_(options),
-        prefix_(options->get<std::string>("prefix", "classifier")),
-        inference_(options->get<bool>("inference", false)),
-        batchIndex_(options->get<size_t>("index", 1)) {} // assume that training input has batch index 0 and labels has 1
-
-  virtual ~ClassifierBase() {}
-
-  virtual Ptr<ClassifierState> apply(Ptr<ExpressionGraph>, Ptr<data::CorpusBatch>, const std::vector<Ptr<EncoderState>>&) = 0;
-
-  template <typename T>
-  T opt(const std::string& key) const {
-    return options_->get<T>(key);
-  }
-
-  // Should be used to clear any batch-wise temporary objects if present
-  virtual void clear() = 0;
-};
-
+#pragma once
+
+#include "marian.h"
+#include "models/states.h"
+#include "layers/constructors.h"
+#include "layers/factory.h"
+
+namespace marian {
+
+/**
+ * Simple base class for Classifiers to be used in EncoderClassifier framework
+ * Currently only implementations are in bert.h
+ */
+class ClassifierBase {
+protected:
+  Ptr<Options> options_;
+  const std::string prefix_{"classifier"};
+  const bool inference_{false};
+  const size_t batchIndex_{0};
+
+public:
+  ClassifierBase(Ptr<Options> options)
+      : options_(options),
+        prefix_(options->get<std::string>("prefix", "classifier")),
+        inference_(options->get<bool>("inference", false)),
+        batchIndex_(options->get<size_t>("index", 1)) {} // assume that training input has batch index 0 and labels has 1
+
+  virtual ~ClassifierBase() {}
+
+  virtual Ptr<ClassifierState> apply(Ptr<ExpressionGraph>, Ptr<data::CorpusBatch>, const std::vector<Ptr<EncoderState>>&) = 0;
+
+  template <typename T>
+  T opt(const std::string& key) const {
+    return options_->get<T>(key);
+  }
+
+  // Should be used to clear any batch-wise temporary objects if present
+  virtual void clear() = 0;
+};
+
 }
\ No newline at end of file
diff --git a/src/models/costs.h b/src/models/costs.h
index 7aa5a5f3e..c47092264 100755
--- a/src/models/costs.h
+++ b/src/models/costs.h
@@ -31,8 +31,8 @@ class EncoderDecoderCECost : public ICost {
 protected:
   Ptr<Options> options_;
 
-  bool inference_{false};
-  bool toBeWeighted_{false};
+  const bool inference_{false};
+  /*const*/ bool toBeWeighted_{false};
 
   // @TODO: single loss seems wrong
   Ptr<LabelwiseLoss> loss_;
@@ -92,7 +92,7 @@ class EncoderDecoderCECost : public ICost {
 class EncoderClassifierCECost : public ICost {
 protected:
   Ptr<Options> options_;
-  bool inference_{false};
+  const bool inference_{false};
 
   // @TODO: single loss seems wrong, especially since we support multiple objectives here,
   // also not sure this needs to be a member at all.
diff --git a/src/models/decoder.h b/src/models/decoder.h
index dd5a508a2..f149c4b5b 100755
--- a/src/models/decoder.h
+++ b/src/models/decoder.h
@@ -12,9 +12,9 @@ namespace marian {
 class DecoderBase {
 protected:
   Ptr<Options> options_;
-  std::string prefix_{"decoder"};
-  bool inference_{false};
-  size_t batchIndex_{1};
+  const std::string prefix_{"decoder"};
+  const bool inference_{false};
+  const size_t batchIndex_{1};
   std::vector<Ptr<IEmbeddingLayer>> embeddingLayers_; // (lazily created)
 
   Ptr<data::Shortlist> shortlist_;
@@ -67,6 +67,12 @@ class DecoderBase {
     lazyCreateEmbeddingLayer(graph);
     Expr y, yMask; std::tie
     (y, yMask) = embeddingLayers_[batchIndex_]->apply(subBatch);
+    // dropout target words
+    float dropoutTrg = inference_ ? 0 : opt<float>("dropout-trg");
+    if (dropoutTrg) {
+      int trgWords = y->shape()[-3];
+      y = dropout(y, dropoutTrg, {trgWords, 1, 1});
+    }
 
     const Words& data =
       /*if*/ (shortlist_) ?
@@ -94,6 +100,12 @@ class DecoderBase {
       selectedEmbs = graph->constant({1, 1, dimBatch, dimEmb}, inits::zeros);
     } else {
       selectedEmbs = embeddingLayers_[batchIndex_]->apply(words, {dimBeam, 1, dimBatch, dimEmb});
+      // dropout target words   --does not make sense here since this is always inference. Keep it regular though.
+      float dropoutTrg = inference_ ? 0 : opt<float>("dropout-trg");
+      if (dropoutTrg) {
+        int trgWords = selectedEmbs->shape()[-3];
+        selectedEmbs = dropout(selectedEmbs, dropoutTrg, { trgWords, 1, 1 });
+      }
     }
     state->setTargetHistoryEmbeddings(selectedEmbs);
   }
diff --git a/src/models/encoder.h b/src/models/encoder.h
old mode 100644
new mode 100755
index 7377ef9b9..a1f6f5311
--- a/src/models/encoder.h
+++ b/src/models/encoder.h
@@ -8,10 +8,11 @@ namespace marian {
 class EncoderBase {
 protected:
   Ptr<Options> options_;
-  std::string prefix_{"encoder"};
-  bool inference_{false};
-  size_t batchIndex_{0};
-public: 
+  const std::string prefix_{"encoder"};
+  const bool inference_{false};
+  const size_t batchIndex_{0};
+  std::vector<Ptr<IEmbeddingLayer>> embeddingLayers_; // (lazily created)
+public:
   EncoderBase(Ptr<Options> options)
       : options_(options),
         prefix_(options->get<std::string>("prefix", "encoder")),
diff --git a/src/models/encoder_decoder.h b/src/models/encoder_decoder.h
old mode 100644
new mode 100755
index 41702eceb..f09e4f2c3
--- a/src/models/encoder_decoder.h
+++ b/src/models/encoder_decoder.h
@@ -63,13 +63,12 @@ class EncoderDecoder : public EncoderDecoderBase {
   Ptr<Options> options_;
   Ptr<data::ShortlistGenerator> shortlistGenerator_;
 
-  std::string prefix_;
+  const std::string prefix_;
+  const bool inference_{ false };
 
   std::vector<Ptr<EncoderBase>> encoders_;
   std::vector<Ptr<DecoderBase>> decoders_;
 
-  bool inference_{false};
-
   std::set<std::string> modelFeatures_;
 
   Config::YamlNode getModelParameters();
diff --git a/src/models/s2s.h b/src/models/s2s.h
index 4829e93de..f7fa91dd2 100755
--- a/src/models/s2s.h
+++ b/src/models/s2s.h
@@ -9,7 +9,6 @@
 namespace marian {
 
 class EncoderS2S : public EncoderBase {
-  std::vector<Ptr<IEmbeddingLayer>> embeddingLayers_; // (lazily created)
 public:
   Expr applyEncoderRNN(Ptr<ExpressionGraph> graph,
                        Expr embeddings,
@@ -163,7 +162,6 @@ class EncoderS2S : public EncoderBase {
     // select embeddings that occur in the batch
     Expr batchEmbeddings, batchMask; std::tie
     (batchEmbeddings, batchMask) = embedding->apply((*batch)[batchIndex_]);
-
     // apply dropout over source words
     float dropProb = inference_ ? 0 : opt<float>("dropout-src");
     if(dropProb) {
@@ -188,6 +186,7 @@ class DecoderS2S : public DecoderBase {
   Ptr<rnn::RNN> constructDecoderRNN(Ptr<ExpressionGraph> graph,
                                     Ptr<DecoderState> state) {
     float dropoutRnn = inference_ ? 0 : opt<float>("dropout-rnn");
+
     auto rnn = rnn::rnn()                                          //
         ("type", opt<std::string>("dec-cell"))                     //
         ("dimInput", opt<int>("dim-emb"))                          //
@@ -293,13 +292,6 @@ class DecoderS2S : public DecoderBase {
 
     auto embeddings = state->getTargetHistoryEmbeddings();
 
-    // dropout target words
-    float dropoutTrg = inference_ ? 0 : opt<float>("dropout-trg");
-    if(dropoutTrg) {
-      int trgWords = embeddings->shape()[-3];
-      embeddings = dropout(embeddings, dropoutTrg, {trgWords, 1, 1});
-    }
-
     if(!rnn_)
       rnn_ = constructDecoderRNN(graph, state);
 
diff --git a/src/models/transformer.h b/src/models/transformer.h
index f36150edb..abfa88237 100755
--- a/src/models/transformer.h
+++ b/src/models/transformer.h
@@ -502,7 +502,6 @@ class Transformer : public EncoderOrDecoderBase {
 };
 
 class EncoderTransformer : public Transformer<EncoderBase> {
-  std::vector<Ptr<IEmbeddingLayer>> embeddingLayers_; // (lazily created)
 public:
   EncoderTransformer(Ptr<Options> options) : Transformer(options) {}
   virtual ~EncoderTransformer() {}
@@ -698,13 +697,6 @@ class DecoderTransformer : public Transformer<DecoderBase> {
     auto embeddings  = state->getTargetHistoryEmbeddings(); // [-4: beam depth=1, -3: max length, -2: batch size, -1: vector dim]
     auto decoderMask = state->getTargetMask();              // [max length, batch size, 1]  --this is a hypothesis
 
-    // dropout target words
-    float dropoutTrg = inference_ ? 0 : opt<float>("dropout-trg");
-    if(dropoutTrg) {
-      int trgWords = embeddings->shape()[-3];
-      embeddings = dropout(embeddings, dropoutTrg, {trgWords, 1, 1});
-    }
-
     //************************************************************************//
 
     int dimBeam = 1;
diff --git a/src/rnn/attention.h b/src/rnn/attention.h
old mode 100644
new mode 100755
index cbb16a340..964889d8d
--- a/src/rnn/attention.h
+++ b/src/rnn/attention.h
@@ -58,8 +58,8 @@ class GlobalAttention : public CellInput {
     ba_ = graph->param(prefix + "_b_att", {1, dimEncState}, inits::zeros);
 
     if(dropout_ > 0.0f) {
-      dropMaskContext_ = graph->dropout(dropout_, {1, dimEncState});
-      dropMaskState_ = graph->dropout(dropout_, {1, dimDecState});
+      dropMaskContext_ = graph->dropoutMask(dropout_, {1, dimEncState});
+      dropMaskState_   = graph->dropoutMask(dropout_, {1, dimDecState});
     }
 
     if(dropMaskContext_)
diff --git a/src/rnn/cells.h b/src/rnn/cells.h
old mode 100644
new mode 100755
index 12db6ea35..3b82c54ac
--- a/src/rnn/cells.h
+++ b/src/rnn/cells.h
@@ -46,8 +46,8 @@ class Tanh : public Cell {
 
     if(dropout_ > 0.0f) {
       if(dimInput)
-        dropMaskX_ = graph->dropout(dropout_, {1, dimInput});
-      dropMaskS_ = graph->dropout(dropout_, {1, dimState});
+        dropMaskX_ = graph->dropoutMask(dropout_, {1, dimInput});
+      dropMaskS_ = graph->dropoutMask(dropout_, {1, dimState});
     }
 
     if(layerNorm_) {
@@ -139,8 +139,8 @@ class ReLU : public Cell {
 
     if(dropout_ > 0.0f) {
       if(dimInput)
-        dropMaskX_ = graph->dropout(dropout_, {1, dimInput});
-      dropMaskS_ = graph->dropout(dropout_, {1, dimState});
+        dropMaskX_ = graph->dropoutMask(dropout_, {1, dimInput});
+      dropMaskS_ = graph->dropoutMask(dropout_, {1, dimState});
     }
 
     if(layerNorm_) {
@@ -256,8 +256,8 @@ class GRU : public Cell {
 
     if(dropout_ > 0.0f) {
       if(dimInput)
-        dropMaskX_ = graph->dropout(dropout_, {1, dimInput});
-      dropMaskS_ = graph->dropout(dropout_, {1, dimState});
+        dropMaskX_ = graph->dropoutMask(dropout_, {1, dimInput});
+      dropMaskS_ = graph->dropoutMask(dropout_, {1, dimState});
     }
 
     if(layerNorm_) {
@@ -420,8 +420,8 @@ class GRUNematus : public Cell {
 
     if(dropout_ > 0.0f) {
       if(dimInput)
-        dropMaskX_ = graph->dropout(dropout_, {1, dimInput});
-      dropMaskS_ = graph->dropout(dropout_, {1, dimState});
+        dropMaskX_ = graph->dropoutMask(dropout_, {1, dimInput});
+      dropMaskS_ = graph->dropoutMask(dropout_, {1, dimState});
     }
 
     if(layerNorm_) {
@@ -584,8 +584,8 @@ class FastLSTM : public Cell {
 
     if(dropout_ > 0.0f) {
       if(dimInput)
-        dropMaskX_ = graph->dropout(dropout_, {1, dimInput});
-      dropMaskS_ = graph->dropout(dropout_, {1, dimState});
+        dropMaskX_ = graph->dropoutMask(dropout_, {1, dimInput});
+      dropMaskS_ = graph->dropoutMask(dropout_, {1, dimState});
     }
 
     if(layerNorm_) {
@@ -930,7 +930,7 @@ class SRU : public Cell {
     br_ = graph->param(prefix + "_br", {1, dimInput}, inits::zeros);
 
     if(dropout_ > 0.0f) {
-      dropMaskX_ = graph->dropout(dropout_, {1, dimInput});
+      dropMaskX_ = graph->dropoutMask(dropout_, {1, dimInput});
     }
 
     if(layerNorm_) {
@@ -1021,7 +1021,7 @@ class SSRU : public Cell {
     bf_ = graph->param(prefix + "_bf", {1, dimInput}, inits::zeros);
 
     if(dropout_ > 0.0f) {
-      dropMaskX_ = graph->dropout(dropout_, {1, dimInput});
+      dropMaskX_ = graph->dropoutMask(dropout_, {1, dimInput});
     }
 
     if(layerNorm_) {
@@ -1104,7 +1104,7 @@ class SSRU : public Cell {
 //         prefix + "_bf", {1, dimInput}, inits::zeros);
 
 //     if(dropout_ > 0.0f) {
-//       dropMaskX_ = graph->dropout(dropout_, {1, dimInput});
+//       dropMaskX_ = graph->dropoutMask(dropout_, {1, dimInput});
 //     }
 //   }
 

From 55df8440275b74e19f8961348b63111f004562b6 Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Tue, 14 May 2019 14:38:56 -0700
Subject: [PATCH 461/838] refactored into EncoderDecoderLayerBase

---
 src/layers/generic.cpp   | 68 +++++++++++++++++++++++++++++++++++-----
 src/layers/generic.h     | 34 ++++++++++++++++++++
 src/models/decoder.h     | 51 ++----------------------------
 src/models/encoder.h     | 22 +++----------
 src/models/transformer.h | 41 ++----------------------
 5 files changed, 103 insertions(+), 113 deletions(-)
 mode change 100644 => 100755 src/layers/generic.h

diff --git a/src/layers/generic.cpp b/src/layers/generic.cpp
index ef3c21933..5249f46a1 100755
--- a/src/layers/generic.cpp
+++ b/src/layers/generic.cpp
@@ -1,20 +1,14 @@
 #include "marian.h"
 
 #include "layers/generic.h"
+#include "layers/constructors.h"
 #include "layers/loss.h"
 #include "data/factored_vocab.h"
 #include "rnn/types.h" // for State::select()
 
-using std::size_t; // not sure why this is needed
+//using std::size_t; // not sure why this is needed
 
 namespace marian {
-  //struct CSRSparseTensor { // simplistic for now
-  //  Shape shape;
-  //  Expr values;  // [k_i..k_{i+1}-1] -> value at [i,j]
-  //  Expr indices; // [k_i..k_{i+1}-1] -> j of non-null value
-  //  Expr offsets; // [i] -> k_i
-  //};
-
   Logits::Logits(Expr logits) : Logits(New<RationalLoss>(logits, nullptr)) {} // single-output constructor from Expr only (RationalLoss has no count)
 
   Ptr<ExpressionGraph> Logits::graph() const {
@@ -426,4 +420,62 @@ namespace marian {
     ABORT_IF(factoredVocab_ /*&& factoredVocab_->getNumGroups() > 1*/, "Embedding: applyIndices must not be used with a factored vocabulary");
     return reshape(rows(E_, embIdx), shape);
   }
+
+  Ptr<IEmbeddingLayer> EncoderDecoderLayerBase::createULREmbeddingLayer(Ptr<ExpressionGraph> graph) const {
+    // standard encoder word embeddings
+    int dimSrcVoc = opt<std::vector<int>>("dim-vocabs")[0];  //ULR multi-lingual src
+    int dimTgtVoc = opt<std::vector<int>>("dim-vocabs")[1];  //ULR monon tgt
+    int dimEmb = opt<int>("dim-emb");
+    int dimUlrEmb = opt<int>("ulr-dim-emb");
+    auto embFactory = ulr_embedding()("dimSrcVoc", dimSrcVoc)("dimTgtVoc", dimTgtVoc)
+                                     ("dimUlrEmb", dimUlrEmb)("dimEmb", dimEmb)
+                                     ("ulrTrainTransform", opt<bool>("ulr-trainable-transformation"))
+                                     ("ulrQueryFile", opt<std::string>("ulr-query-vectors"))
+                                     ("ulrKeysFile", opt<std::string>("ulr-keys-vectors"));
+    return embFactory.construct(graph);
+  }
+
+  Ptr<IEmbeddingLayer> EncoderDecoderLayerBase::createSourceEmbeddingLayer(Ptr<ExpressionGraph> graph, size_t subBatchIndex) const {
+    // standard encoder word embeddings
+    int dimVoc = opt<std::vector<int>>("dim-vocabs")[subBatchIndex];
+    int dimEmb = opt<int>("dim-emb");
+    auto embFactory = embedding()("dimVocab", dimVoc)("dimEmb", dimEmb);
+    if(opt<bool>("tied-embeddings-src") || opt<bool>("tied-embeddings-all"))
+      embFactory("prefix", "Wemb");
+    else
+      embFactory("prefix", prefix_ + "_Wemb");
+    if(options_->has("embedding-fix-src"))
+      embFactory("fixed", opt<bool>("embedding-fix-src"));
+    if(options_->hasAndNotEmpty("embedding-vectors")) {
+      auto embFiles = opt<std::vector<std::string>>("embedding-vectors");
+      embFactory("embFile", embFiles[subBatchIndex])
+                ("normalization", opt<bool>("embedding-normalization"));
+    }
+    embFactory("vocab", opt<std::vector<std::string>>("vocabs")[subBatchIndex]); // for factored embeddings
+    return embFactory.construct(graph);
+  }
+
+  void EncoderDecoderLayerBase::lazyCreateEmbeddingLayer(Ptr<ExpressionGraph> graph) {
+    // @TODO: code dup with above
+    if (embeddingLayers_.size() <= batchIndex_ || !embeddingLayers_[batchIndex_]) { // lazy
+      if (embeddingLayers_.size() <= batchIndex_)
+        embeddingLayers_.resize(batchIndex_ + 1);
+      int dimVoc = opt<std::vector<int>>("dim-vocabs")[batchIndex_];
+      int dimEmb = opt<int>("dim-emb");
+      auto embFactory = embedding()("dimVocab", dimVoc)("dimEmb", dimEmb);
+      if(opt<bool>("tied-embeddings-src") || opt<bool>("tied-embeddings-all"))
+        embFactory("prefix", "Wemb");
+      else
+        embFactory("prefix", prefix_ + "_Wemb");
+      if(options_->has("embedding-fix-trg"))
+        embFactory("fixed", opt<bool>("embedding-fix-trg"));
+      if(options_->hasAndNotEmpty("embedding-vectors")) {
+        auto embFiles = opt<std::vector<std::string>>("embedding-vectors");
+        embFactory("embFile", embFiles[batchIndex_])  //
+            ("normalization", opt<bool>("embedding-normalization"));
+      }
+      embFactory("vocab", opt<std::vector<std::string>>("vocabs")[batchIndex_]); // for factored embeddings
+      embeddingLayers_[batchIndex_] = embFactory.construct(graph);
+    }
+  }
 }  // namespace marian
diff --git a/src/layers/generic.h b/src/layers/generic.h
old mode 100644
new mode 100755
index 9d787a3c3..e581addcd
--- a/src/layers/generic.h
+++ b/src/layers/generic.h
@@ -65,6 +65,40 @@ struct IEmbeddingLayer {
   virtual Expr applyIndices(const std::vector<WordIndex>& embIdx, const Shape& shape) const = 0;
 };
 
+// base class for Encoder and Decoder classes, which have embeddings and a batch index (=stream index)
+// @TODO: also base this on LayerBase, which holds options_
+class EncoderDecoderLayerBase {
+protected:
+  Ptr<Options> options_;
+  const std::string prefix_;
+  const bool inference_{false};
+  const size_t batchIndex_{1};
+  std::vector<Ptr<IEmbeddingLayer>> embeddingLayers_; // (lazily created)
+
+  EncoderDecoderLayerBase(const std::string& prefix, size_t batchIndex, Ptr<Options> options) :
+      options_(options),
+      prefix_(options->get<std::string>("prefix", prefix)),
+      inference_(options->get<bool>("inference", false)),
+      batchIndex_(options->get<size_t>("index", batchIndex)) {}
+
+public:
+  template <typename T>
+  T opt(const std::string& key) const {
+    return options_->get<T>(key);
+  }
+
+  template <typename T>
+  T opt(const std::string& key, const T& def) {
+    return options_->get<T>(key, def);
+  }
+
+  virtual ~EncoderDecoderLayerBase() {}
+
+  void lazyCreateEmbeddingLayer(Ptr<ExpressionGraph> graph);
+  Ptr<IEmbeddingLayer> createULREmbeddingLayer(Ptr<ExpressionGraph> graph) const;
+  Ptr<IEmbeddingLayer> createSourceEmbeddingLayer(Ptr<ExpressionGraph> graph, size_t subBatchIndex) const;
+};
+
 class FactoredVocab;
 
 // To support factors, any output projection (that is followed by a softmax) must
diff --git a/src/models/decoder.h b/src/models/decoder.h
index f149c4b5b..658ee73d2 100755
--- a/src/models/decoder.h
+++ b/src/models/decoder.h
@@ -9,24 +9,13 @@
 
 namespace marian {
 
-class DecoderBase {
+class DecoderBase : public EncoderDecoderLayerBase {
 protected:
-  Ptr<Options> options_;
-  const std::string prefix_{"decoder"};
-  const bool inference_{false};
-  const size_t batchIndex_{1};
-  std::vector<Ptr<IEmbeddingLayer>> embeddingLayers_; // (lazily created)
-
   Ptr<data::Shortlist> shortlist_;
 
 public:
-  DecoderBase(Ptr<Options> options)
-      : options_(options),
-        prefix_(options->get<std::string>("prefix", "decoder")),
-        inference_(options->get<bool>("inference", false)),
-        batchIndex_(options->get<size_t>("index", 1)) {}
-
-  virtual ~DecoderBase() {}
+  DecoderBase(Ptr<Options> options) :
+    EncoderDecoderLayerBase("decoder", /*batchIndex=*/1, options) {}
 
   virtual Ptr<DecoderState> startState(Ptr<ExpressionGraph>,
                                        Ptr<data::CorpusBatch> batch,
@@ -35,30 +24,6 @@ class DecoderBase {
 
   virtual Ptr<DecoderState> step(Ptr<ExpressionGraph>, Ptr<DecoderState>) = 0;
 
-  void lazyCreateEmbeddingLayer(Ptr<ExpressionGraph> graph) {
-    // @TODO: code dup with EncoderTransformer
-    if (embeddingLayers_.size() <= batchIndex_ || !embeddingLayers_[batchIndex_]) { // lazy
-      if (embeddingLayers_.size() <= batchIndex_)
-        embeddingLayers_.resize(batchIndex_ + 1);
-      int dimVoc = opt<std::vector<int>>("dim-vocabs")[batchIndex_];
-      int dimEmb = opt<int>("dim-emb");
-      auto embFactory = embedding()("dimVocab", dimVoc)("dimEmb", dimEmb);
-      if(opt<bool>("tied-embeddings-src") || opt<bool>("tied-embeddings-all"))
-        embFactory("prefix", "Wemb");
-      else
-        embFactory("prefix", prefix_ + "_Wemb");
-      if(options_->has("embedding-fix-trg"))
-        embFactory("fixed", opt<bool>("embedding-fix-trg"));
-      if(options_->hasAndNotEmpty("embedding-vectors")) {
-        auto embFiles = opt<std::vector<std::string>>("embedding-vectors");
-        embFactory("embFile", embFiles[batchIndex_])  //
-            ("normalization", opt<bool>("embedding-normalization"));
-      }
-      embFactory("vocab", opt<std::vector<std::string>>("vocabs")[batchIndex_]); // for factored embeddings
-      embeddingLayers_[batchIndex_] = embFactory.construct(graph);
-    }
-  }
-
   virtual void embeddingsFromBatch(Ptr<ExpressionGraph> graph,
                                    Ptr<DecoderState> state,
                                    Ptr<data::CorpusBatch> batch) {
@@ -117,16 +82,6 @@ class DecoderBase {
     shortlist_ = shortlist;
   }
 
-  template <typename T>
-  T opt(const std::string& key) const {
-    return options_->get<T>(key);
-  }
-
-  template <typename T>
-  T opt(const std::string& key, const T& def) {
-    return options_->get<T>(key, def);
-  }
-
   virtual void clear() = 0;
 };
 
diff --git a/src/models/encoder.h b/src/models/encoder.h
index a1f6f5311..c6a3487a3 100755
--- a/src/models/encoder.h
+++ b/src/models/encoder.h
@@ -5,29 +5,15 @@
 
 namespace marian {
 
-class EncoderBase {
+class EncoderBase : public EncoderDecoderLayerBase {
 protected:
-  Ptr<Options> options_;
-  const std::string prefix_{"encoder"};
-  const bool inference_{false};
-  const size_t batchIndex_{0};
-  std::vector<Ptr<IEmbeddingLayer>> embeddingLayers_; // (lazily created)
 public:
-  EncoderBase(Ptr<Options> options)
-      : options_(options),
-        prefix_(options->get<std::string>("prefix", "encoder")),
-        inference_(options->get<bool>("inference", false)),
-        batchIndex_(options->get<size_t>("index", 0)) {}
-
-  virtual ~EncoderBase() {}
+  EncoderBase(Ptr<Options> options) :
+    EncoderDecoderLayerBase("encoder", /*batchIndex=*/0, options) {}
 
+  // @TODO: turn into an interface. Also see if we can get rid of the graph parameter.
   virtual Ptr<EncoderState> build(Ptr<ExpressionGraph>, Ptr<data::CorpusBatch>) = 0;
 
-  template <typename T>
-  T opt(const std::string& key) const {
-    return options_->get<T>(key);
-  }
-
   virtual void clear() = 0;
 };
 
diff --git a/src/models/transformer.h b/src/models/transformer.h
index abfa88237..5483e9765 100755
--- a/src/models/transformer.h
+++ b/src/models/transformer.h
@@ -506,43 +506,6 @@ class EncoderTransformer : public Transformer<EncoderBase> {
   EncoderTransformer(Ptr<Options> options) : Transformer(options) {}
   virtual ~EncoderTransformer() {}
 
-  // returns the embedding matrix based on options
-  // and based on batchIndex_.
-
-  Ptr<IEmbeddingLayer> createULREmbeddingLayer() const {
-    // standard encoder word embeddings
-    int dimSrcVoc = opt<std::vector<int>>("dim-vocabs")[0];  //ULR multi-lingual src
-    int dimTgtVoc = opt<std::vector<int>>("dim-vocabs")[1];  //ULR monon tgt
-    int dimEmb = opt<int>("dim-emb");
-    int dimUlrEmb = opt<int>("ulr-dim-emb");
-    auto embFactory = ulr_embedding()("dimSrcVoc", dimSrcVoc)("dimTgtVoc", dimTgtVoc)
-                                     ("dimUlrEmb", dimUlrEmb)("dimEmb", dimEmb)
-                                     ("ulrTrainTransform", opt<bool>("ulr-trainable-transformation"))
-                                     ("ulrQueryFile", opt<std::string>("ulr-query-vectors"))
-                                     ("ulrKeysFile", opt<std::string>("ulr-keys-vectors"));
-    return embFactory.construct(graph_);
-  }
-
-  Ptr<IEmbeddingLayer> createSourceEmbeddingLayer(size_t subBatchIndex) const {
-    // standard encoder word embeddings
-    int dimVoc = opt<std::vector<int>>("dim-vocabs")[subBatchIndex];
-    int dimEmb = opt<int>("dim-emb");
-    auto embFactory = embedding()("dimVocab", dimVoc)("dimEmb", dimEmb);
-    if(opt<bool>("tied-embeddings-src") || opt<bool>("tied-embeddings-all"))
-      embFactory("prefix", "Wemb");
-    else
-      embFactory("prefix", prefix_ + "_Wemb");
-    if(options_->has("embedding-fix-src"))
-      embFactory("fixed", opt<bool>("embedding-fix-src"));
-    if(options_->hasAndNotEmpty("embedding-vectors")) {
-      auto embFiles = opt<std::vector<std::string>>("embedding-vectors");
-      embFactory("embFile", embFiles[subBatchIndex])
-                ("normalization", opt<bool>("embedding-normalization"));
-    }
-    embFactory("vocab", opt<std::vector<std::string>>("vocabs")[subBatchIndex]); // for factored embeddings
-    return embFactory.construct(graph_);
-  }
-
   virtual Ptr<EncoderState> build(Ptr<ExpressionGraph> graph,
                                   Ptr<data::CorpusBatch> batch) override {
     graph_ = graph;
@@ -559,9 +522,9 @@ class EncoderTransformer : public Transformer<EncoderBase> {
     if (embeddingLayers_.empty() || !embeddingLayers_[batchIndex_]) { // lazy
       embeddingLayers_.resize(batch->sets());
       if (options_->has("ulr") && options_->get<bool>("ulr") == true)
-        embeddingLayers_[batchIndex_] = createULREmbeddingLayer(); // embedding uses ULR
+        embeddingLayers_[batchIndex_] = createULREmbeddingLayer(graph_); // embedding uses ULR
       else
-        embeddingLayers_[batchIndex_] = createSourceEmbeddingLayer(batchIndex_);
+        embeddingLayers_[batchIndex_] = createSourceEmbeddingLayer(graph_, batchIndex_);
     }
     std::tie(batchEmbeddings, batchMask) = embeddingLayers_[batchIndex_]->apply((*batch)[batchIndex_]);
     // apply dropout over source words

From 0c280de03ff208b9d6beefd1cbdf29bea2b052be Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Tue, 14 May 2019 14:48:36 -0700
Subject: [PATCH 462/838] merged two embedding-layer creation functions

---
 src/layers/generic.cpp | 25 ++++++-------------------
 src/layers/generic.h   |  4 +++-
 2 files changed, 9 insertions(+), 20 deletions(-)

diff --git a/src/layers/generic.cpp b/src/layers/generic.cpp
index 5249f46a1..aa31013aa 100755
--- a/src/layers/generic.cpp
+++ b/src/layers/generic.cpp
@@ -435,7 +435,9 @@ namespace marian {
     return embFactory.construct(graph);
   }
 
-  Ptr<IEmbeddingLayer> EncoderDecoderLayerBase::createSourceEmbeddingLayer(Ptr<ExpressionGraph> graph, size_t subBatchIndex) const {
+  Ptr<IEmbeddingLayer> EncoderDecoderLayerBase::createSourceEmbeddingLayer(Ptr<ExpressionGraph> graph,
+      size_t subBatchIndex,
+      const std::string& embeddingFixParamName /*= "embedding-fix-src"*/) const {
     // standard encoder word embeddings
     int dimVoc = opt<std::vector<int>>("dim-vocabs")[subBatchIndex];
     int dimEmb = opt<int>("dim-emb");
@@ -444,8 +446,8 @@ namespace marian {
       embFactory("prefix", "Wemb");
     else
       embFactory("prefix", prefix_ + "_Wemb");
-    if(options_->has("embedding-fix-src"))
-      embFactory("fixed", opt<bool>("embedding-fix-src"));
+    if(options_->has(embeddingFixParamName))
+      embFactory("fixed", opt<bool>(embeddingFixParamName));
     if(options_->hasAndNotEmpty("embedding-vectors")) {
       auto embFiles = opt<std::vector<std::string>>("embedding-vectors");
       embFactory("embFile", embFiles[subBatchIndex])
@@ -460,22 +462,7 @@ namespace marian {
     if (embeddingLayers_.size() <= batchIndex_ || !embeddingLayers_[batchIndex_]) { // lazy
       if (embeddingLayers_.size() <= batchIndex_)
         embeddingLayers_.resize(batchIndex_ + 1);
-      int dimVoc = opt<std::vector<int>>("dim-vocabs")[batchIndex_];
-      int dimEmb = opt<int>("dim-emb");
-      auto embFactory = embedding()("dimVocab", dimVoc)("dimEmb", dimEmb);
-      if(opt<bool>("tied-embeddings-src") || opt<bool>("tied-embeddings-all"))
-        embFactory("prefix", "Wemb");
-      else
-        embFactory("prefix", prefix_ + "_Wemb");
-      if(options_->has("embedding-fix-trg"))
-        embFactory("fixed", opt<bool>("embedding-fix-trg"));
-      if(options_->hasAndNotEmpty("embedding-vectors")) {
-        auto embFiles = opt<std::vector<std::string>>("embedding-vectors");
-        embFactory("embFile", embFiles[batchIndex_])  //
-            ("normalization", opt<bool>("embedding-normalization"));
-      }
-      embFactory("vocab", opt<std::vector<std::string>>("vocabs")[batchIndex_]); // for factored embeddings
-      embeddingLayers_[batchIndex_] = embFactory.construct(graph);
+      embeddingLayers_[batchIndex_] = createSourceEmbeddingLayer(graph, batchIndex_, "embedding-fix-trg");
     }
   }
 }  // namespace marian
diff --git a/src/layers/generic.h b/src/layers/generic.h
index e581addcd..b6d7c3faa 100755
--- a/src/layers/generic.h
+++ b/src/layers/generic.h
@@ -96,7 +96,9 @@ class EncoderDecoderLayerBase {
 
   void lazyCreateEmbeddingLayer(Ptr<ExpressionGraph> graph);
   Ptr<IEmbeddingLayer> createULREmbeddingLayer(Ptr<ExpressionGraph> graph) const;
-  Ptr<IEmbeddingLayer> createSourceEmbeddingLayer(Ptr<ExpressionGraph> graph, size_t subBatchIndex) const;
+  Ptr<IEmbeddingLayer> createSourceEmbeddingLayer(Ptr<ExpressionGraph> graph, size_t subBatchIndex,
+      const std::string& embeddingFixParamName = "embedding-fix-src") const;
+  Ptr<IEmbeddingLayer> createSourceEmbeddingLayer(Ptr<ExpressionGraph> graph);
 };
 
 class FactoredVocab;

From b06870c0473254b997afa76bdbe9f0561155b780 Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Tue, 14 May 2019 14:51:14 -0700
Subject: [PATCH 463/838] moved one functioin to base

---
 src/layers/generic.cpp | 30 +++++++++++++++++++++++++++++-
 src/models/s2s.h       | 29 -----------------------------
 2 files changed, 29 insertions(+), 30 deletions(-)

diff --git a/src/layers/generic.cpp b/src/layers/generic.cpp
index aa31013aa..66d7a1b79 100755
--- a/src/layers/generic.cpp
+++ b/src/layers/generic.cpp
@@ -457,8 +457,36 @@ namespace marian {
     return embFactory.construct(graph);
   }
 
+  Ptr<IEmbeddingLayer> EncoderDecoderLayerBase::createSourceEmbeddingLayer(Ptr<ExpressionGraph> graph) {
+    // create source embeddings
+    int dimVoc = opt<std::vector<int>>("dim-vocabs")[batchIndex_];
+    int dimEmb = opt<int>("dim-emb");
+
+    // @TODO: code dup with Decoder and EncoderTransformer; actually diverged by now. Unify this.
+    auto embFactory = embedding()  //
+        ("dimVocab", dimVoc)       //
+        ("dimEmb", dimEmb);
+
+    if(opt<bool>("tied-embeddings-src") || opt<bool>("tied-embeddings-all"))
+      embFactory("prefix", "Wemb");
+    else
+      embFactory("prefix", prefix_ + "_Wemb");
+
+    if(options_->has("embedding-fix-src"))
+      embFactory("fixed", opt<bool>("embedding-fix-src"));
+
+    if(options_->hasAndNotEmpty("embedding-vectors")) {
+      auto embFiles = opt<std::vector<std::string>>("embedding-vectors");
+      embFactory                              //
+          ("embFile", embFiles[batchIndex_])  //
+          ("normalization", opt<bool>("embedding-normalization"));
+    }
+
+    embFactory("vocab", opt<std::vector<std::string>>("vocabs")[batchIndex_]); // for factored embeddings
+    return embFactory.construct(graph);
+  }
+
   void EncoderDecoderLayerBase::lazyCreateEmbeddingLayer(Ptr<ExpressionGraph> graph) {
-    // @TODO: code dup with above
     if (embeddingLayers_.size() <= batchIndex_ || !embeddingLayers_[batchIndex_]) { // lazy
       if (embeddingLayers_.size() <= batchIndex_)
         embeddingLayers_.resize(batchIndex_ + 1);
diff --git a/src/models/s2s.h b/src/models/s2s.h
index f7fa91dd2..50305f471 100755
--- a/src/models/s2s.h
+++ b/src/models/s2s.h
@@ -119,35 +119,6 @@ class EncoderS2S : public EncoderBase {
     return context;
   }
 
-  Ptr<IEmbeddingLayer> createSourceEmbeddingLayer(Ptr<ExpressionGraph> graph) {
-    // create source embeddings
-    int dimVoc = opt<std::vector<int>>("dim-vocabs")[batchIndex_];
-    int dimEmb = opt<int>("dim-emb");
-
-    // @TODO: code dup with Decoder and EncoderTransformer; actually diverged by now. Unify this.
-    auto embFactory = embedding()  //
-        ("dimVocab", dimVoc)       //
-        ("dimEmb", dimEmb);
-
-    if(opt<bool>("tied-embeddings-src") || opt<bool>("tied-embeddings-all"))
-      embFactory("prefix", "Wemb");
-    else
-      embFactory("prefix", prefix_ + "_Wemb");
-
-    if(options_->has("embedding-fix-src"))
-      embFactory("fixed", opt<bool>("embedding-fix-src"));
-
-    if(options_->hasAndNotEmpty("embedding-vectors")) {
-      auto embFiles = opt<std::vector<std::string>>("embedding-vectors");
-      embFactory                              //
-          ("embFile", embFiles[batchIndex_])  //
-          ("normalization", opt<bool>("embedding-normalization"));
-    }
-
-    embFactory("vocab", opt<std::vector<std::string>>("vocabs")[batchIndex_]); // for factored embeddings
-    return embFactory.construct(graph);
-  }
-
   EncoderS2S(Ptr<Options> options) : EncoderBase(options) {}
 
   virtual Ptr<EncoderState> build(Ptr<ExpressionGraph> graph,

From 4c6a809a2e4f6efec72197e40f921ce9b4cac7cd Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Tue, 14 May 2019 14:52:50 -0700
Subject: [PATCH 464/838] reformatted

---
 src/layers/generic.cpp | 15 +++------------
 1 file changed, 3 insertions(+), 12 deletions(-)

diff --git a/src/layers/generic.cpp b/src/layers/generic.cpp
index 66d7a1b79..ff88ce23d 100755
--- a/src/layers/generic.cpp
+++ b/src/layers/generic.cpp
@@ -461,27 +461,18 @@ namespace marian {
     // create source embeddings
     int dimVoc = opt<std::vector<int>>("dim-vocabs")[batchIndex_];
     int dimEmb = opt<int>("dim-emb");
-
-    // @TODO: code dup with Decoder and EncoderTransformer; actually diverged by now. Unify this.
-    auto embFactory = embedding()  //
-        ("dimVocab", dimVoc)       //
-        ("dimEmb", dimEmb);
-
+    auto embFactory = embedding()("dimVocab", dimVoc)("dimEmb", dimEmb);
     if(opt<bool>("tied-embeddings-src") || opt<bool>("tied-embeddings-all"))
       embFactory("prefix", "Wemb");
     else
       embFactory("prefix", prefix_ + "_Wemb");
-
     if(options_->has("embedding-fix-src"))
       embFactory("fixed", opt<bool>("embedding-fix-src"));
-
     if(options_->hasAndNotEmpty("embedding-vectors")) {
       auto embFiles = opt<std::vector<std::string>>("embedding-vectors");
-      embFactory                              //
-          ("embFile", embFiles[batchIndex_])  //
-          ("normalization", opt<bool>("embedding-normalization"));
+      embFactory("embFile", embFiles[batchIndex_])
+                ("normalization", opt<bool>("embedding-normalization"));
     }
-
     embFactory("vocab", opt<std::vector<std::string>>("vocabs")[batchIndex_]); // for factored embeddings
     return embFactory.construct(graph);
   }

From 44df9c1c884b7bb78a8217fd63383019b995c81c Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Tue, 14 May 2019 14:54:59 -0700
Subject: [PATCH 465/838] merged a second lazy-create function

---
 src/layers/generic.cpp | 18 +-----------------
 1 file changed, 1 insertion(+), 17 deletions(-)

diff --git a/src/layers/generic.cpp b/src/layers/generic.cpp
index ff88ce23d..031c85dfa 100755
--- a/src/layers/generic.cpp
+++ b/src/layers/generic.cpp
@@ -458,23 +458,7 @@ namespace marian {
   }
 
   Ptr<IEmbeddingLayer> EncoderDecoderLayerBase::createSourceEmbeddingLayer(Ptr<ExpressionGraph> graph) {
-    // create source embeddings
-    int dimVoc = opt<std::vector<int>>("dim-vocabs")[batchIndex_];
-    int dimEmb = opt<int>("dim-emb");
-    auto embFactory = embedding()("dimVocab", dimVoc)("dimEmb", dimEmb);
-    if(opt<bool>("tied-embeddings-src") || opt<bool>("tied-embeddings-all"))
-      embFactory("prefix", "Wemb");
-    else
-      embFactory("prefix", prefix_ + "_Wemb");
-    if(options_->has("embedding-fix-src"))
-      embFactory("fixed", opt<bool>("embedding-fix-src"));
-    if(options_->hasAndNotEmpty("embedding-vectors")) {
-      auto embFiles = opt<std::vector<std::string>>("embedding-vectors");
-      embFactory("embFile", embFiles[batchIndex_])
-                ("normalization", opt<bool>("embedding-normalization"));
-    }
-    embFactory("vocab", opt<std::vector<std::string>>("vocabs")[batchIndex_]); // for factored embeddings
-    return embFactory.construct(graph);
+      return createSourceEmbeddingLayer(graph, batchIndex_, "embedding-fix-src");
   }
 
   void EncoderDecoderLayerBase::lazyCreateEmbeddingLayer(Ptr<ExpressionGraph> graph) {

From 22eb16281d96b66592736671158fc4981dd676bc Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Tue, 14 May 2019 15:11:38 -0700
Subject: [PATCH 466/838] removed a few parameters to
 createSourceEmbeddingLayer()

---
 src/layers/generic.cpp   | 22 ++++++++--------------
 src/layers/generic.h     |  8 ++++----
 src/models/decoder.h     |  2 +-
 src/models/encoder.h     |  2 +-
 src/models/s2s.h         |  1 +
 src/models/transformer.h |  2 +-
 6 files changed, 16 insertions(+), 21 deletions(-)

diff --git a/src/layers/generic.cpp b/src/layers/generic.cpp
index 031c85dfa..5b52059bf 100755
--- a/src/layers/generic.cpp
+++ b/src/layers/generic.cpp
@@ -417,7 +417,7 @@ namespace marian {
   }
 
   Expr Embedding::applyIndices(const std::vector<WordIndex>& embIdx, const Shape& shape) const /*override final*/ {
-    ABORT_IF(factoredVocab_ /*&& factoredVocab_->getNumGroups() > 1*/, "Embedding: applyIndices must not be used with a factored vocabulary");
+    ABORT_IF(factoredVocab_, "Embedding: applyIndices must not be used with a factored vocabulary");
     return reshape(rows(E_, embIdx), shape);
   }
 
@@ -435,37 +435,31 @@ namespace marian {
     return embFactory.construct(graph);
   }
 
-  Ptr<IEmbeddingLayer> EncoderDecoderLayerBase::createSourceEmbeddingLayer(Ptr<ExpressionGraph> graph,
-      size_t subBatchIndex,
-      const std::string& embeddingFixParamName /*= "embedding-fix-src"*/) const {
+  Ptr<IEmbeddingLayer> EncoderDecoderLayerBase::createSourceEmbeddingLayer(Ptr<ExpressionGraph> graph) const {
     // standard encoder word embeddings
-    int dimVoc = opt<std::vector<int>>("dim-vocabs")[subBatchIndex];
+    int dimVoc = opt<std::vector<int>>("dim-vocabs")[batchIndex_];
     int dimEmb = opt<int>("dim-emb");
     auto embFactory = embedding()("dimVocab", dimVoc)("dimEmb", dimEmb);
     if(opt<bool>("tied-embeddings-src") || opt<bool>("tied-embeddings-all"))
       embFactory("prefix", "Wemb");
     else
       embFactory("prefix", prefix_ + "_Wemb");
-    if(options_->has(embeddingFixParamName))
-      embFactory("fixed", opt<bool>(embeddingFixParamName));
+    if(options_->has(embeddingFixParamName_))
+      embFactory("fixed", opt<bool>(embeddingFixParamName_));
     if(options_->hasAndNotEmpty("embedding-vectors")) {
       auto embFiles = opt<std::vector<std::string>>("embedding-vectors");
-      embFactory("embFile", embFiles[subBatchIndex])
+      embFactory("embFile", embFiles[batchIndex_])
                 ("normalization", opt<bool>("embedding-normalization"));
     }
-    embFactory("vocab", opt<std::vector<std::string>>("vocabs")[subBatchIndex]); // for factored embeddings
+    embFactory("vocab", opt<std::vector<std::string>>("vocabs")[batchIndex_]); // for factored embeddings
     return embFactory.construct(graph);
   }
 
-  Ptr<IEmbeddingLayer> EncoderDecoderLayerBase::createSourceEmbeddingLayer(Ptr<ExpressionGraph> graph) {
-      return createSourceEmbeddingLayer(graph, batchIndex_, "embedding-fix-src");
-  }
-
   void EncoderDecoderLayerBase::lazyCreateEmbeddingLayer(Ptr<ExpressionGraph> graph) {
     if (embeddingLayers_.size() <= batchIndex_ || !embeddingLayers_[batchIndex_]) { // lazy
       if (embeddingLayers_.size() <= batchIndex_)
         embeddingLayers_.resize(batchIndex_ + 1);
-      embeddingLayers_[batchIndex_] = createSourceEmbeddingLayer(graph, batchIndex_, "embedding-fix-trg");
+      embeddingLayers_[batchIndex_] = createSourceEmbeddingLayer(graph);
     }
   }
 }  // namespace marian
diff --git a/src/layers/generic.h b/src/layers/generic.h
index b6d7c3faa..71d37e09e 100755
--- a/src/layers/generic.h
+++ b/src/layers/generic.h
@@ -71,13 +71,15 @@ class EncoderDecoderLayerBase {
 protected:
   Ptr<Options> options_;
   const std::string prefix_;
+  const std::string embeddingFixParamName_; // "embedding-fix-src" or "embedding-fix-trg"
   const bool inference_{false};
   const size_t batchIndex_{1};
   std::vector<Ptr<IEmbeddingLayer>> embeddingLayers_; // (lazily created)
 
-  EncoderDecoderLayerBase(const std::string& prefix, size_t batchIndex, Ptr<Options> options) :
+  EncoderDecoderLayerBase(const std::string& prefix, size_t batchIndex, Ptr<Options> options, const std::string& embeddingFixParamName) :
       options_(options),
       prefix_(options->get<std::string>("prefix", prefix)),
+      embeddingFixParamName_(embeddingFixParamName),
       inference_(options->get<bool>("inference", false)),
       batchIndex_(options->get<size_t>("index", batchIndex)) {}
 
@@ -96,9 +98,7 @@ class EncoderDecoderLayerBase {
 
   void lazyCreateEmbeddingLayer(Ptr<ExpressionGraph> graph);
   Ptr<IEmbeddingLayer> createULREmbeddingLayer(Ptr<ExpressionGraph> graph) const;
-  Ptr<IEmbeddingLayer> createSourceEmbeddingLayer(Ptr<ExpressionGraph> graph, size_t subBatchIndex,
-      const std::string& embeddingFixParamName = "embedding-fix-src") const;
-  Ptr<IEmbeddingLayer> createSourceEmbeddingLayer(Ptr<ExpressionGraph> graph);
+  Ptr<IEmbeddingLayer> createSourceEmbeddingLayer(Ptr<ExpressionGraph> graph) const;
 };
 
 class FactoredVocab;
diff --git a/src/models/decoder.h b/src/models/decoder.h
index 658ee73d2..48404efa3 100755
--- a/src/models/decoder.h
+++ b/src/models/decoder.h
@@ -15,7 +15,7 @@ class DecoderBase : public EncoderDecoderLayerBase {
 
 public:
   DecoderBase(Ptr<Options> options) :
-    EncoderDecoderLayerBase("decoder", /*batchIndex=*/1, options) {}
+    EncoderDecoderLayerBase("decoder", /*batchIndex=*/1, options, /*embeddingFixParamName=*/"embedding-fix-trg") {}
 
   virtual Ptr<DecoderState> startState(Ptr<ExpressionGraph>,
                                        Ptr<data::CorpusBatch> batch,
diff --git a/src/models/encoder.h b/src/models/encoder.h
index c6a3487a3..e9fb50334 100755
--- a/src/models/encoder.h
+++ b/src/models/encoder.h
@@ -9,7 +9,7 @@ class EncoderBase : public EncoderDecoderLayerBase {
 protected:
 public:
   EncoderBase(Ptr<Options> options) :
-    EncoderDecoderLayerBase("encoder", /*batchIndex=*/0, options) {}
+    EncoderDecoderLayerBase("encoder", /*batchIndex=*/0, options, /*embeddingFixParamName=*/"embedding-fix-src") {}
 
   // @TODO: turn into an interface. Also see if we can get rid of the graph parameter.
   virtual Ptr<EncoderState> build(Ptr<ExpressionGraph>, Ptr<data::CorpusBatch>) = 0;
diff --git a/src/models/s2s.h b/src/models/s2s.h
index 50305f471..924502e85 100755
--- a/src/models/s2s.h
+++ b/src/models/s2s.h
@@ -124,6 +124,7 @@ class EncoderS2S : public EncoderBase {
   virtual Ptr<EncoderState> build(Ptr<ExpressionGraph> graph,
                                   Ptr<data::CorpusBatch> batch) override {
     // lazily create embedding layer
+    // @TODO: use shared function in base class once disentangled
     if (embeddingLayers_.empty() || !embeddingLayers_[batchIndex_]) { // lazy
       embeddingLayers_.resize(batch->sets());
       embeddingLayers_[batchIndex_] = createSourceEmbeddingLayer(graph);
diff --git a/src/models/transformer.h b/src/models/transformer.h
index 5483e9765..9346d0cf8 100755
--- a/src/models/transformer.h
+++ b/src/models/transformer.h
@@ -524,7 +524,7 @@ class EncoderTransformer : public Transformer<EncoderBase> {
       if (options_->has("ulr") && options_->get<bool>("ulr") == true)
         embeddingLayers_[batchIndex_] = createULREmbeddingLayer(graph_); // embedding uses ULR
       else
-        embeddingLayers_[batchIndex_] = createSourceEmbeddingLayer(graph_, batchIndex_);
+        embeddingLayers_[batchIndex_] = createSourceEmbeddingLayer(graph_);
     }
     std::tie(batchEmbeddings, batchMask) = embeddingLayers_[batchIndex_]->apply((*batch)[batchIndex_]);
     // apply dropout over source words

From 13208380578a1666b480eca374190a1c9604919d Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Tue, 14 May 2019 15:23:07 -0700
Subject: [PATCH 467/838] streamlined lazy embedding creation

---
 src/layers/generic.cpp |  3 ++-
 src/layers/generic.h   |  2 +-
 src/models/decoder.h   |  7 +++----
 src/models/s2s.h       | 10 +---------
 4 files changed, 7 insertions(+), 15 deletions(-)

diff --git a/src/layers/generic.cpp b/src/layers/generic.cpp
index 5b52059bf..428ca5c4e 100755
--- a/src/layers/generic.cpp
+++ b/src/layers/generic.cpp
@@ -455,11 +455,12 @@ namespace marian {
     return embFactory.construct(graph);
   }
 
-  void EncoderDecoderLayerBase::lazyCreateEmbeddingLayer(Ptr<ExpressionGraph> graph) {
+  Ptr<IEmbeddingLayer> EncoderDecoderLayerBase::lazyCreateEmbeddingLayer(Ptr<ExpressionGraph> graph) {
     if (embeddingLayers_.size() <= batchIndex_ || !embeddingLayers_[batchIndex_]) { // lazy
       if (embeddingLayers_.size() <= batchIndex_)
         embeddingLayers_.resize(batchIndex_ + 1);
       embeddingLayers_[batchIndex_] = createSourceEmbeddingLayer(graph);
     }
+    return embeddingLayers_[batchIndex_];
   }
 }  // namespace marian
diff --git a/src/layers/generic.h b/src/layers/generic.h
index 71d37e09e..e2834be70 100755
--- a/src/layers/generic.h
+++ b/src/layers/generic.h
@@ -96,7 +96,7 @@ class EncoderDecoderLayerBase {
 
   virtual ~EncoderDecoderLayerBase() {}
 
-  void lazyCreateEmbeddingLayer(Ptr<ExpressionGraph> graph);
+  Ptr<IEmbeddingLayer> lazyCreateEmbeddingLayer(Ptr<ExpressionGraph> graph);
   Ptr<IEmbeddingLayer> createULREmbeddingLayer(Ptr<ExpressionGraph> graph) const;
   Ptr<IEmbeddingLayer> createSourceEmbeddingLayer(Ptr<ExpressionGraph> graph) const;
 };
diff --git a/src/models/decoder.h b/src/models/decoder.h
index 48404efa3..5bbff8936 100755
--- a/src/models/decoder.h
+++ b/src/models/decoder.h
@@ -29,9 +29,8 @@ class DecoderBase : public EncoderDecoderLayerBase {
                                    Ptr<data::CorpusBatch> batch) {
     auto subBatch = (*batch)[batchIndex_];
 
-    lazyCreateEmbeddingLayer(graph);
     Expr y, yMask; std::tie
-    (y, yMask) = embeddingLayers_[batchIndex_]->apply(subBatch);
+    (y, yMask) = lazyCreateEmbeddingLayer(graph)->apply(subBatch);
     // dropout target words
     float dropoutTrg = inference_ ? 0 : opt<float>("dropout-trg");
     if (dropoutTrg) {
@@ -58,13 +57,13 @@ class DecoderBase : public EncoderDecoderLayerBase {
                                         const Words& words,
                                         int dimBatch,
                                         int dimBeam) {
-    lazyCreateEmbeddingLayer(graph);
+    auto embeddingLayer = lazyCreateEmbeddingLayer(graph);
     Expr selectedEmbs;
     int dimEmb = opt<int>("dim-emb");
     if(words.empty()) {
       selectedEmbs = graph->constant({1, 1, dimBatch, dimEmb}, inits::zeros);
     } else {
-      selectedEmbs = embeddingLayers_[batchIndex_]->apply(words, {dimBeam, 1, dimBatch, dimEmb});
+      selectedEmbs = embeddingLayer->apply(words, {dimBeam, 1, dimBatch, dimEmb});
       // dropout target words   --does not make sense here since this is always inference. Keep it regular though.
       float dropoutTrg = inference_ ? 0 : opt<float>("dropout-trg");
       if (dropoutTrg) {
diff --git a/src/models/s2s.h b/src/models/s2s.h
index 924502e85..000177dbe 100755
--- a/src/models/s2s.h
+++ b/src/models/s2s.h
@@ -123,17 +123,9 @@ class EncoderS2S : public EncoderBase {
 
   virtual Ptr<EncoderState> build(Ptr<ExpressionGraph> graph,
                                   Ptr<data::CorpusBatch> batch) override {
-    // lazily create embedding layer
-    // @TODO: use shared function in base class once disentangled
-    if (embeddingLayers_.empty() || !embeddingLayers_[batchIndex_]) { // lazy
-      embeddingLayers_.resize(batch->sets());
-      embeddingLayers_[batchIndex_] = createSourceEmbeddingLayer(graph);
-    }
-    auto embedding = embeddingLayers_[batchIndex_];
-
     // select embeddings that occur in the batch
     Expr batchEmbeddings, batchMask; std::tie
-    (batchEmbeddings, batchMask) = embedding->apply((*batch)[batchIndex_]);
+    (batchEmbeddings, batchMask) = lazyCreateEmbeddingLayer(graph)->apply((*batch)[batchIndex_]);
     // apply dropout over source words
     float dropProb = inference_ ? 0 : opt<float>("dropout-src");
     if(dropProb) {

From 5fffeaa5a81f6b606e92d8bec882498264911d88 Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Tue, 14 May 2019 15:24:15 -0700
Subject: [PATCH 468/838] streamlined lazy embedding creation

---
 src/layers/generic.cpp | 2 +-
 src/layers/generic.h   | 2 +-
 src/models/decoder.h   | 4 ++--
 src/models/s2s.h       | 2 +-
 4 files changed, 5 insertions(+), 5 deletions(-)

diff --git a/src/layers/generic.cpp b/src/layers/generic.cpp
index 428ca5c4e..43637228b 100755
--- a/src/layers/generic.cpp
+++ b/src/layers/generic.cpp
@@ -455,7 +455,7 @@ namespace marian {
     return embFactory.construct(graph);
   }
 
-  Ptr<IEmbeddingLayer> EncoderDecoderLayerBase::lazyCreateEmbeddingLayer(Ptr<ExpressionGraph> graph) {
+  Ptr<IEmbeddingLayer> EncoderDecoderLayerBase::getEmbeddingLayer(Ptr<ExpressionGraph> graph) {
     if (embeddingLayers_.size() <= batchIndex_ || !embeddingLayers_[batchIndex_]) { // lazy
       if (embeddingLayers_.size() <= batchIndex_)
         embeddingLayers_.resize(batchIndex_ + 1);
diff --git a/src/layers/generic.h b/src/layers/generic.h
index e2834be70..07ca6be7f 100755
--- a/src/layers/generic.h
+++ b/src/layers/generic.h
@@ -96,7 +96,7 @@ class EncoderDecoderLayerBase {
 
   virtual ~EncoderDecoderLayerBase() {}
 
-  Ptr<IEmbeddingLayer> lazyCreateEmbeddingLayer(Ptr<ExpressionGraph> graph);
+  Ptr<IEmbeddingLayer> getEmbeddingLayer(Ptr<ExpressionGraph> graph);
   Ptr<IEmbeddingLayer> createULREmbeddingLayer(Ptr<ExpressionGraph> graph) const;
   Ptr<IEmbeddingLayer> createSourceEmbeddingLayer(Ptr<ExpressionGraph> graph) const;
 };
diff --git a/src/models/decoder.h b/src/models/decoder.h
index 5bbff8936..9ab097123 100755
--- a/src/models/decoder.h
+++ b/src/models/decoder.h
@@ -30,7 +30,7 @@ class DecoderBase : public EncoderDecoderLayerBase {
     auto subBatch = (*batch)[batchIndex_];
 
     Expr y, yMask; std::tie
-    (y, yMask) = lazyCreateEmbeddingLayer(graph)->apply(subBatch);
+    (y, yMask) = getEmbeddingLayer(graph)->apply(subBatch);
     // dropout target words
     float dropoutTrg = inference_ ? 0 : opt<float>("dropout-trg");
     if (dropoutTrg) {
@@ -57,7 +57,7 @@ class DecoderBase : public EncoderDecoderLayerBase {
                                         const Words& words,
                                         int dimBatch,
                                         int dimBeam) {
-    auto embeddingLayer = lazyCreateEmbeddingLayer(graph);
+    auto embeddingLayer = getEmbeddingLayer(graph);
     Expr selectedEmbs;
     int dimEmb = opt<int>("dim-emb");
     if(words.empty()) {
diff --git a/src/models/s2s.h b/src/models/s2s.h
index 000177dbe..dbc9a92fd 100755
--- a/src/models/s2s.h
+++ b/src/models/s2s.h
@@ -125,7 +125,7 @@ class EncoderS2S : public EncoderBase {
                                   Ptr<data::CorpusBatch> batch) override {
     // select embeddings that occur in the batch
     Expr batchEmbeddings, batchMask; std::tie
-    (batchEmbeddings, batchMask) = lazyCreateEmbeddingLayer(graph)->apply((*batch)[batchIndex_]);
+    (batchEmbeddings, batchMask) = getEmbeddingLayer(graph)->apply((*batch)[batchIndex_]);
     // apply dropout over source words
     float dropProb = inference_ ? 0 : opt<float>("dropout-src");
     if(dropProb) {

From d1e1867111ed1bb7eb546065ea61495a4f8b8c7f Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Tue, 14 May 2019 15:34:22 -0700
Subject: [PATCH 469/838] EncoderDecoderLayerBase now derive from LayerBase

---
 src/layers/generic.h     | 33 ++++++++++-----------------------
 src/models/transformer.h |  6 ++----
 2 files changed, 12 insertions(+), 27 deletions(-)

diff --git a/src/layers/generic.h b/src/layers/generic.h
index 07ca6be7f..61728fb5c 100755
--- a/src/layers/generic.h
+++ b/src/layers/generic.h
@@ -5,14 +5,12 @@
 #include "data/shortlist.h"
 #include "layers/factory.h"
 
-namespace marian {
-namespace mlp {
-/**
- * @brief Activation functions
- */
-enum struct act : int { linear, tanh, sigmoid, ReLU, LeakyReLU, PReLU, swish };
-}  // namespace mlp
-}  // namespace marian
+namespace marian { namespace mlp {
+  /**
+   * @brief Activation functions
+   */
+  enum struct act : int { linear, tanh, sigmoid, ReLU, LeakyReLU, PReLU, swish };
+}}
 
 YAML_REGISTER_TYPE(marian::mlp::act, int)
 
@@ -31,12 +29,12 @@ class LayerBase {
       : graph_(graph), options_(options) {}
 
   template <typename T>
-  T opt(const std::string key) {
+  T opt(const std::string key) const {
     return options_->get<T>(key);
   }
 
   template <typename T>
-  T opt(const std::string key, T defaultValue) {
+  T opt(const std::string key, const T& defaultValue) const {
     return options_->get<T>(key, defaultValue);
   }
 };
@@ -67,9 +65,8 @@ struct IEmbeddingLayer {
 
 // base class for Encoder and Decoder classes, which have embeddings and a batch index (=stream index)
 // @TODO: also base this on LayerBase, which holds options_
-class EncoderDecoderLayerBase {
+class EncoderDecoderLayerBase : public LayerBase {
 protected:
-  Ptr<Options> options_;
   const std::string prefix_;
   const std::string embeddingFixParamName_; // "embedding-fix-src" or "embedding-fix-trg"
   const bool inference_{false};
@@ -77,23 +74,13 @@ class EncoderDecoderLayerBase {
   std::vector<Ptr<IEmbeddingLayer>> embeddingLayers_; // (lazily created)
 
   EncoderDecoderLayerBase(const std::string& prefix, size_t batchIndex, Ptr<Options> options, const std::string& embeddingFixParamName) :
-      options_(options),
+      LayerBase(/*graph=*/nullptr, options), // @BUGBUG: we really should pass the graph in here
       prefix_(options->get<std::string>("prefix", prefix)),
       embeddingFixParamName_(embeddingFixParamName),
       inference_(options->get<bool>("inference", false)),
       batchIndex_(options->get<size_t>("index", batchIndex)) {}
 
 public:
-  template <typename T>
-  T opt(const std::string& key) const {
-    return options_->get<T>(key);
-  }
-
-  template <typename T>
-  T opt(const std::string& key, const T& def) {
-    return options_->get<T>(key, def);
-  }
-
   virtual ~EncoderDecoderLayerBase() {}
 
   Ptr<IEmbeddingLayer> getEmbeddingLayer(Ptr<ExpressionGraph> graph);
diff --git a/src/models/transformer.h b/src/models/transformer.h
index 9346d0cf8..a609265c5 100755
--- a/src/models/transformer.h
+++ b/src/models/transformer.h
@@ -24,8 +24,8 @@ class Transformer : public EncoderOrDecoderBase {
   typedef EncoderOrDecoderBase Base;
 
 protected:
-  using Base::options_; using Base::inference_; using Base::batchIndex_;
-  std::unordered_map<std::string, Expr> cache_;
+  using Base::options_; using Base::inference_; using Base::batchIndex_; using Base::graph_;
+  std::unordered_map<std::string, Expr> cache_;  // caching transformation of the encoder that should not be created again
 
   // attention weights produced by step()
   // If enabled, it is set once per batch during training, and once per step during translation.
@@ -37,8 +37,6 @@ class Transformer : public EncoderOrDecoderBase {
 
   template <typename T> T opt(const std::string& key, const T& def) const { Ptr<Options> options = options_; if (options->has(key)) return options->get<T>(key); else return def; }
 
-  Ptr<ExpressionGraph> graph_;
-
 public:
   Transformer(Ptr<Options> options)
     : EncoderOrDecoderBase(options) {

From baf6ce2e7a1428c0977bf230476b02796820298b Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Tue, 14 May 2019 15:53:47 -0700
Subject: [PATCH 470/838] made embedding-creation functions private

---
 src/layers/generic.cpp   | 37 ++++++++++++++++++++-----------------
 src/layers/generic.h     | 16 +++++++++++-----
 src/models/char_s2s.h    |  4 +---
 src/models/decoder.h     |  4 +++-
 src/models/encoder.h     |  4 +++-
 src/models/transformer.h | 10 ++--------
 6 files changed, 40 insertions(+), 35 deletions(-)

diff --git a/src/layers/generic.cpp b/src/layers/generic.cpp
index 43637228b..a484da7e4 100755
--- a/src/layers/generic.cpp
+++ b/src/layers/generic.cpp
@@ -421,21 +421,7 @@ namespace marian {
     return reshape(rows(E_, embIdx), shape);
   }
 
-  Ptr<IEmbeddingLayer> EncoderDecoderLayerBase::createULREmbeddingLayer(Ptr<ExpressionGraph> graph) const {
-    // standard encoder word embeddings
-    int dimSrcVoc = opt<std::vector<int>>("dim-vocabs")[0];  //ULR multi-lingual src
-    int dimTgtVoc = opt<std::vector<int>>("dim-vocabs")[1];  //ULR monon tgt
-    int dimEmb = opt<int>("dim-emb");
-    int dimUlrEmb = opt<int>("ulr-dim-emb");
-    auto embFactory = ulr_embedding()("dimSrcVoc", dimSrcVoc)("dimTgtVoc", dimTgtVoc)
-                                     ("dimUlrEmb", dimUlrEmb)("dimEmb", dimEmb)
-                                     ("ulrTrainTransform", opt<bool>("ulr-trainable-transformation"))
-                                     ("ulrQueryFile", opt<std::string>("ulr-query-vectors"))
-                                     ("ulrKeysFile", opt<std::string>("ulr-keys-vectors"));
-    return embFactory.construct(graph);
-  }
-
-  Ptr<IEmbeddingLayer> EncoderDecoderLayerBase::createSourceEmbeddingLayer(Ptr<ExpressionGraph> graph) const {
+  Ptr<IEmbeddingLayer> EncoderDecoderLayerBase::createEmbeddingLayer(Ptr<ExpressionGraph> graph) const {
     // standard encoder word embeddings
     int dimVoc = opt<std::vector<int>>("dim-vocabs")[batchIndex_];
     int dimEmb = opt<int>("dim-emb");
@@ -455,11 +441,28 @@ namespace marian {
     return embFactory.construct(graph);
   }
 
-  Ptr<IEmbeddingLayer> EncoderDecoderLayerBase::getEmbeddingLayer(Ptr<ExpressionGraph> graph) {
+  Ptr<IEmbeddingLayer> EncoderDecoderLayerBase::createULREmbeddingLayer(Ptr<ExpressionGraph> graph) const {
+    // standard encoder word embeddings
+    int dimSrcVoc = opt<std::vector<int>>("dim-vocabs")[0];  //ULR multi-lingual src
+    int dimTgtVoc = opt<std::vector<int>>("dim-vocabs")[1];  //ULR monon tgt
+    int dimEmb = opt<int>("dim-emb");
+    int dimUlrEmb = opt<int>("ulr-dim-emb");
+    auto embFactory = ulr_embedding()("dimSrcVoc", dimSrcVoc)("dimTgtVoc", dimTgtVoc)
+                                     ("dimUlrEmb", dimUlrEmb)("dimEmb", dimEmb)
+                                     ("ulrTrainTransform", opt<bool>("ulr-trainable-transformation"))
+                                     ("ulrQueryFile", opt<std::string>("ulr-query-vectors"))
+                                     ("ulrKeysFile", opt<std::string>("ulr-keys-vectors"));
+    return embFactory.construct(graph);
+  }
+
+  Ptr<IEmbeddingLayer> EncoderDecoderLayerBase::getEmbeddingLayer(Ptr<ExpressionGraph> graph, bool ulr) {
     if (embeddingLayers_.size() <= batchIndex_ || !embeddingLayers_[batchIndex_]) { // lazy
       if (embeddingLayers_.size() <= batchIndex_)
         embeddingLayers_.resize(batchIndex_ + 1);
-      embeddingLayers_[batchIndex_] = createSourceEmbeddingLayer(graph);
+      if (ulr)
+        embeddingLayers_[batchIndex_] = createULREmbeddingLayer(graph); // embedding uses ULR
+      else
+        embeddingLayers_[batchIndex_] = createEmbeddingLayer(graph);
     }
     return embeddingLayers_[batchIndex_];
   }
diff --git a/src/layers/generic.h b/src/layers/generic.h
index 61728fb5c..83bf73507 100755
--- a/src/layers/generic.h
+++ b/src/layers/generic.h
@@ -64,28 +64,34 @@ struct IEmbeddingLayer {
 };
 
 // base class for Encoder and Decoder classes, which have embeddings and a batch index (=stream index)
-// @TODO: also base this on LayerBase, which holds options_
 class EncoderDecoderLayerBase : public LayerBase {
 protected:
   const std::string prefix_;
+  const std::string dropoutParamName_; // "dropout-src" or "dropout-trg"
   const std::string embeddingFixParamName_; // "embedding-fix-src" or "embedding-fix-trg"
   const bool inference_{false};
   const size_t batchIndex_{1};
   std::vector<Ptr<IEmbeddingLayer>> embeddingLayers_; // (lazily created)
 
-  EncoderDecoderLayerBase(const std::string& prefix, size_t batchIndex, Ptr<Options> options, const std::string& embeddingFixParamName) :
+  EncoderDecoderLayerBase(const std::string& prefix, size_t batchIndex, Ptr<Options> options,
+        const std::string& dropoutParamName,
+        const std::string& embeddingFixParamName) :
       LayerBase(/*graph=*/nullptr, options), // @BUGBUG: we really should pass the graph in here
       prefix_(options->get<std::string>("prefix", prefix)),
+      dropoutParamName_(dropoutParamName),
       embeddingFixParamName_(embeddingFixParamName),
       inference_(options->get<bool>("inference", false)),
       batchIndex_(options->get<size_t>("index", batchIndex)) {}
 
-public:
   virtual ~EncoderDecoderLayerBase() {}
 
-  Ptr<IEmbeddingLayer> getEmbeddingLayer(Ptr<ExpressionGraph> graph);
+private:
+  Ptr<IEmbeddingLayer> createEmbeddingLayer(Ptr<ExpressionGraph> graph) const;
   Ptr<IEmbeddingLayer> createULREmbeddingLayer(Ptr<ExpressionGraph> graph) const;
-  Ptr<IEmbeddingLayer> createSourceEmbeddingLayer(Ptr<ExpressionGraph> graph) const;
+
+public:
+  // get embedding layer; lazily create on first call
+  Ptr<IEmbeddingLayer> getEmbeddingLayer(Ptr<ExpressionGraph> graph, bool ulr = false);
 };
 
 class FactoredVocab;
diff --git a/src/models/char_s2s.h b/src/models/char_s2s.h
index f14bf8807..d759d24f5 100755
--- a/src/models/char_s2s.h
+++ b/src/models/char_s2s.h
@@ -13,11 +13,9 @@ class CharS2SEncoder : public EncoderS2S {
 
   virtual Ptr<EncoderState> build(Ptr<ExpressionGraph> graph,
                                   Ptr<data::CorpusBatch> batch) override {
-    auto embedding = createSourceEmbedding(graph);
-
     // select embeddings that occur in the batch
     Expr batchEmbeddings, batchMask; std::tie
-    (batchEmbeddings, batchMask) = embedding->apply(batch->front());
+    (batchEmbeddings, batchMask) = getEmbeddingLayer(graph)->apply(batch->front());
     // apply dropout over source words
     float dropProb = inference_ ? 0 : opt<float>("dropout-src");
     if(dropProb) {
diff --git a/src/models/decoder.h b/src/models/decoder.h
index 9ab097123..b10f2a386 100755
--- a/src/models/decoder.h
+++ b/src/models/decoder.h
@@ -15,7 +15,9 @@ class DecoderBase : public EncoderDecoderLayerBase {
 
 public:
   DecoderBase(Ptr<Options> options) :
-    EncoderDecoderLayerBase("decoder", /*batchIndex=*/1, options, /*embeddingFixParamName=*/"embedding-fix-trg") {}
+    EncoderDecoderLayerBase("decoder", /*batchIndex=*/1, options,
+        /*dropoutParamName=*/"dropout-trg",
+        /*embeddingFixParamName=*/"embedding-fix-trg") {}
 
   virtual Ptr<DecoderState> startState(Ptr<ExpressionGraph>,
                                        Ptr<data::CorpusBatch> batch,
diff --git a/src/models/encoder.h b/src/models/encoder.h
index e9fb50334..3dc02237a 100755
--- a/src/models/encoder.h
+++ b/src/models/encoder.h
@@ -9,7 +9,9 @@ class EncoderBase : public EncoderDecoderLayerBase {
 protected:
 public:
   EncoderBase(Ptr<Options> options) :
-    EncoderDecoderLayerBase("encoder", /*batchIndex=*/0, options, /*embeddingFixParamName=*/"embedding-fix-src") {}
+    EncoderDecoderLayerBase("encoder", /*batchIndex=*/0, options,
+        /*dropoutParamName=*/"dropout-src",
+        /*embeddingFixParamName=*/"embedding-fix-src") {}
 
   // @TODO: turn into an interface. Also see if we can get rid of the graph parameter.
   virtual Ptr<EncoderState> build(Ptr<ExpressionGraph>, Ptr<data::CorpusBatch>) = 0;
diff --git a/src/models/transformer.h b/src/models/transformer.h
index a609265c5..4ea6d7283 100755
--- a/src/models/transformer.h
+++ b/src/models/transformer.h
@@ -517,14 +517,8 @@ class EncoderTransformer : public Transformer<EncoderBase> {
     // embed the source words in the batch
     Expr batchEmbeddings, batchMask;
 
-    if (embeddingLayers_.empty() || !embeddingLayers_[batchIndex_]) { // lazy
-      embeddingLayers_.resize(batch->sets());
-      if (options_->has("ulr") && options_->get<bool>("ulr") == true)
-        embeddingLayers_[batchIndex_] = createULREmbeddingLayer(graph_); // embedding uses ULR
-      else
-        embeddingLayers_[batchIndex_] = createSourceEmbeddingLayer(graph_);
-    }
-    std::tie(batchEmbeddings, batchMask) = embeddingLayers_[batchIndex_]->apply((*batch)[batchIndex_]);
+    auto embeddingLayer = getEmbeddingLayer(graph_, options_->has("ulr") && options_->get<bool>("ulr"));
+    std::tie(batchEmbeddings, batchMask) = embeddingLayer->apply((*batch)[batchIndex_]);
     // apply dropout over source words
     float dropoutSrc = inference_ ? 0 : opt<float>("dropout-src");
     if(dropoutSrc) {

From 23bf4c8f08f2a5882885a98e37b961b5c88a21b3 Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Tue, 14 May 2019 16:01:08 -0700
Subject: [PATCH 471/838] EncoderDecoderLayerBase now knows its dropout value

---
 src/layers/generic.cpp | 5 +++--
 src/layers/generic.h   | 6 ++++--
 2 files changed, 7 insertions(+), 4 deletions(-)

diff --git a/src/layers/generic.cpp b/src/layers/generic.cpp
index a484da7e4..1dd082007 100755
--- a/src/layers/generic.cpp
+++ b/src/layers/generic.cpp
@@ -421,7 +421,7 @@ namespace marian {
     return reshape(rows(E_, embIdx), shape);
   }
 
-  Ptr<IEmbeddingLayer> EncoderDecoderLayerBase::createEmbeddingLayer(Ptr<ExpressionGraph> graph) const {
+  /*private*/ Ptr<IEmbeddingLayer> EncoderDecoderLayerBase::createEmbeddingLayer(Ptr<ExpressionGraph> graph) const {
     // standard encoder word embeddings
     int dimVoc = opt<std::vector<int>>("dim-vocabs")[batchIndex_];
     int dimEmb = opt<int>("dim-emb");
@@ -441,7 +441,7 @@ namespace marian {
     return embFactory.construct(graph);
   }
 
-  Ptr<IEmbeddingLayer> EncoderDecoderLayerBase::createULREmbeddingLayer(Ptr<ExpressionGraph> graph) const {
+  /*private*/ Ptr<IEmbeddingLayer> EncoderDecoderLayerBase::createULREmbeddingLayer(Ptr<ExpressionGraph> graph) const {
     // standard encoder word embeddings
     int dimSrcVoc = opt<std::vector<int>>("dim-vocabs")[0];  //ULR multi-lingual src
     int dimTgtVoc = opt<std::vector<int>>("dim-vocabs")[1];  //ULR monon tgt
@@ -455,6 +455,7 @@ namespace marian {
     return embFactory.construct(graph);
   }
 
+  // get embedding layer for this encoder or decoder; lazily create it if not created yet
   Ptr<IEmbeddingLayer> EncoderDecoderLayerBase::getEmbeddingLayer(Ptr<ExpressionGraph> graph, bool ulr) {
     if (embeddingLayers_.size() <= batchIndex_ || !embeddingLayers_[batchIndex_]) { // lazy
       if (embeddingLayers_.size() <= batchIndex_)
diff --git a/src/layers/generic.h b/src/layers/generic.h
index 83bf73507..0cffd6a21 100755
--- a/src/layers/generic.h
+++ b/src/layers/generic.h
@@ -69,8 +69,9 @@ class EncoderDecoderLayerBase : public LayerBase {
   const std::string prefix_;
   const std::string dropoutParamName_; // "dropout-src" or "dropout-trg"
   const std::string embeddingFixParamName_; // "embedding-fix-src" or "embedding-fix-trg"
-  const bool inference_{false};
-  const size_t batchIndex_{1};
+  const bool inference_;
+  const float dropout_;
+  const size_t batchIndex_;
   std::vector<Ptr<IEmbeddingLayer>> embeddingLayers_; // (lazily created)
 
   EncoderDecoderLayerBase(const std::string& prefix, size_t batchIndex, Ptr<Options> options,
@@ -81,6 +82,7 @@ class EncoderDecoderLayerBase : public LayerBase {
       dropoutParamName_(dropoutParamName),
       embeddingFixParamName_(embeddingFixParamName),
       inference_(options->get<bool>("inference", false)),
+      dropout_(inference_ ? 0 : opt<float>(dropoutParamName)),
       batchIndex_(options->get<size_t>("index", batchIndex)) {}
 
   virtual ~EncoderDecoderLayerBase() {}

From 3a978be85b07eadc139a7095029e8e366621ed50 Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Tue, 14 May 2019 18:01:00 -0700
Subject: [PATCH 472/838] embedding dropout now exclusively implemented inside
 Embedding layer's apply() function

---
 src/layers/generic.cpp   |  5 ++++-
 src/layers/generic.h     |  7 +++++--
 src/models/char_s2s.h    |  6 ------
 src/models/decoder.h     | 17 ++---------------
 src/models/s2s.h         |  6 ------
 src/models/transformer.h |  6 ------
 6 files changed, 11 insertions(+), 36 deletions(-)

diff --git a/src/layers/generic.cpp b/src/layers/generic.cpp
index 1dd082007..ab2972faa 100755
--- a/src/layers/generic.cpp
+++ b/src/layers/generic.cpp
@@ -413,6 +413,7 @@ namespace marian {
       selectedEmbs = multiRows(words);
     else
       selectedEmbs = rows(E_, toWordIndexVector(words));
+    selectedEmbs = dropout(selectedEmbs, options_->get<float>("dropout", 0.0f), {selectedEmbs->shape()[-3], 1, 1});
     return reshape(selectedEmbs, shape);
   }
 
@@ -425,7 +426,7 @@ namespace marian {
     // standard encoder word embeddings
     int dimVoc = opt<std::vector<int>>("dim-vocabs")[batchIndex_];
     int dimEmb = opt<int>("dim-emb");
-    auto embFactory = embedding()("dimVocab", dimVoc)("dimEmb", dimEmb);
+    auto embFactory = embedding()("dimVocab", dimVoc)("dimEmb", dimEmb)("dropout", dropout_);
     if(opt<bool>("tied-embeddings-src") || opt<bool>("tied-embeddings-all"))
       embFactory("prefix", "Wemb");
     else
@@ -449,6 +450,8 @@ namespace marian {
     int dimUlrEmb = opt<int>("ulr-dim-emb");
     auto embFactory = ulr_embedding()("dimSrcVoc", dimSrcVoc)("dimTgtVoc", dimTgtVoc)
                                      ("dimUlrEmb", dimUlrEmb)("dimEmb", dimEmb)
+                                     ("ulr-dropout", opt<float>("ulr-dropout"))
+                                     ("dropout", dropout_)
                                      ("ulrTrainTransform", opt<bool>("ulr-trainable-transformation"))
                                      ("ulrQueryFile", opt<std::string>("ulr-query-vectors"))
                                      ("ulrKeysFile", opt<std::string>("ulr-keys-vectors"));
diff --git a/src/layers/generic.h b/src/layers/generic.h
index 0cffd6a21..74d761bb2 100755
--- a/src/layers/generic.h
+++ b/src/layers/generic.h
@@ -67,7 +67,6 @@ struct IEmbeddingLayer {
 class EncoderDecoderLayerBase : public LayerBase {
 protected:
   const std::string prefix_;
-  const std::string dropoutParamName_; // "dropout-src" or "dropout-trg"
   const std::string embeddingFixParamName_; // "embedding-fix-src" or "embedding-fix-trg"
   const bool inference_;
   const float dropout_;
@@ -79,7 +78,6 @@ class EncoderDecoderLayerBase : public LayerBase {
         const std::string& embeddingFixParamName) :
       LayerBase(/*graph=*/nullptr, options), // @BUGBUG: we really should pass the graph in here
       prefix_(options->get<std::string>("prefix", prefix)),
-      dropoutParamName_(dropoutParamName),
       embeddingFixParamName_(embeddingFixParamName),
       inference_(options->get<bool>("inference", false)),
       dropout_(inference_ ? 0 : opt<float>(dropoutParamName)),
@@ -281,6 +279,10 @@ class Output : public LayerBase, public IUnaryLogitLayer, public IHasShortList {
 
 }  // namespace mlp
 
+// A regular embedding layer.
+// Note that this also applies dropout if the option is passed (pass 0 when in inference mode).
+// It is best to not use Embedding directly, but rather via getEmbeddingLayer() in
+// EncoderDecoderLayerBase, which knows to pass on all required parameters from options.
 class Embedding : public LayerBase, public IEmbeddingLayer {
   Expr E_;
   Ptr<FactoredVocab> factoredVocab_;
@@ -396,6 +398,7 @@ class ULREmbedding : public LayerBase, public IEmbeddingLayer {
     auto graph = ulrEmbeddings_.front()->graph();
     auto batchMask = graph->constant({ dimWords, dimBatch, 1 },
                                      inits::from_vector(subBatch->mask()));
+    batchEmbeddings = dropout(batchEmbeddings, options_->get<float>("dropout", 0.0f), {batchEmbeddings->shape()[-3], 1, 1});
     return std::make_tuple(batchEmbeddings, batchMask);
   }
 
diff --git a/src/models/char_s2s.h b/src/models/char_s2s.h
index d759d24f5..85df8c5db 100755
--- a/src/models/char_s2s.h
+++ b/src/models/char_s2s.h
@@ -16,12 +16,6 @@ class CharS2SEncoder : public EncoderS2S {
     // select embeddings that occur in the batch
     Expr batchEmbeddings, batchMask; std::tie
     (batchEmbeddings, batchMask) = getEmbeddingLayer(graph)->apply(batch->front());
-    // apply dropout over source words
-    float dropProb = inference_ ? 0 : opt<float>("dropout-src");
-    if(dropProb) {
-      int srcWords = batchEmbeddings->shape()[-3];
-      batchEmbeddings = dropout(batchEmbeddings, dropProb, {srcWords, 1, 1});
-    }
 
     int dimEmb = opt<int>("dim-emb");
     auto convSizes = options_->get<std::vector<int>>("char-conv-filters-num");
diff --git a/src/models/decoder.h b/src/models/decoder.h
index b10f2a386..e4abcd07b 100755
--- a/src/models/decoder.h
+++ b/src/models/decoder.h
@@ -33,12 +33,6 @@ class DecoderBase : public EncoderDecoderLayerBase {
 
     Expr y, yMask; std::tie
     (y, yMask) = getEmbeddingLayer(graph)->apply(subBatch);
-    // dropout target words
-    float dropoutTrg = inference_ ? 0 : opt<float>("dropout-trg");
-    if (dropoutTrg) {
-      int trgWords = y->shape()[-3];
-      y = dropout(y, dropoutTrg, {trgWords, 1, 1});
-    }
 
     const Words& data =
       /*if*/ (shortlist_) ?
@@ -62,17 +56,10 @@ class DecoderBase : public EncoderDecoderLayerBase {
     auto embeddingLayer = getEmbeddingLayer(graph);
     Expr selectedEmbs;
     int dimEmb = opt<int>("dim-emb");
-    if(words.empty()) {
+    if(words.empty())
       selectedEmbs = graph->constant({1, 1, dimBatch, dimEmb}, inits::zeros);
-    } else {
+    else
       selectedEmbs = embeddingLayer->apply(words, {dimBeam, 1, dimBatch, dimEmb});
-      // dropout target words   --does not make sense here since this is always inference. Keep it regular though.
-      float dropoutTrg = inference_ ? 0 : opt<float>("dropout-trg");
-      if (dropoutTrg) {
-        int trgWords = selectedEmbs->shape()[-3];
-        selectedEmbs = dropout(selectedEmbs, dropoutTrg, { trgWords, 1, 1 });
-      }
-    }
     state->setTargetHistoryEmbeddings(selectedEmbs);
   }
 
diff --git a/src/models/s2s.h b/src/models/s2s.h
index dbc9a92fd..3d101c685 100755
--- a/src/models/s2s.h
+++ b/src/models/s2s.h
@@ -126,12 +126,6 @@ class EncoderS2S : public EncoderBase {
     // select embeddings that occur in the batch
     Expr batchEmbeddings, batchMask; std::tie
     (batchEmbeddings, batchMask) = getEmbeddingLayer(graph)->apply((*batch)[batchIndex_]);
-    // apply dropout over source words
-    float dropProb = inference_ ? 0 : opt<float>("dropout-src");
-    if(dropProb) {
-      int srcWords = batchEmbeddings->shape()[-3];
-      batchEmbeddings = dropout(batchEmbeddings, dropProb, {srcWords, 1, 1});
-    }
 
     Expr context = applyEncoderRNN(
         graph, batchEmbeddings, batchMask, opt<std::string>("enc-type"));
diff --git a/src/models/transformer.h b/src/models/transformer.h
index 4ea6d7283..973410db5 100755
--- a/src/models/transformer.h
+++ b/src/models/transformer.h
@@ -519,12 +519,6 @@ class EncoderTransformer : public Transformer<EncoderBase> {
 
     auto embeddingLayer = getEmbeddingLayer(graph_, options_->has("ulr") && options_->get<bool>("ulr"));
     std::tie(batchEmbeddings, batchMask) = embeddingLayer->apply((*batch)[batchIndex_]);
-    // apply dropout over source words
-    float dropoutSrc = inference_ ? 0 : opt<float>("dropout-src");
-    if(dropoutSrc) {
-      int srcWords = batchEmbeddings->shape()[-3];
-      batchEmbeddings = dropout(batchEmbeddings, dropoutSrc, {srcWords, 1, 1});
-    }
 
     batchEmbeddings = addSpecialEmbeddings(batchEmbeddings, /*start=*/0, batch);
 

From 6714c8b8dee6a62a403172e007a34074883e1d08 Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Tue, 14 May 2019 18:10:14 -0700
Subject: [PATCH 473/838] factored the 0/null check into the dropout()
 functions, reducing code dup

---
 src/graph/expression_operators.h |  5 ++++-
 src/models/transformer.h         |  7 ++-----
 src/rnn/attention.h              |  6 ++----
 src/rnn/cells.h                  | 36 ++++++++++++--------------------
 4 files changed, 21 insertions(+), 33 deletions(-)

diff --git a/src/graph/expression_operators.h b/src/graph/expression_operators.h
index f8c7fd3c0..a9433d2a6 100755
--- a/src/graph/expression_operators.h
+++ b/src/graph/expression_operators.h
@@ -207,7 +207,10 @@ Expr highway(Expr y, Expr x, Expr t);
 Expr highway(const std::string prefix, Expr x);
 
 static inline Expr dropout(Expr x, Expr mask) {
-  return x * mask;
+  if (mask)
+    return x * mask;
+  else
+    return x;
 }
 
 static inline Expr dropout(Expr x, float dropProb, Shape shape) {
diff --git a/src/models/transformer.h b/src/models/transformer.h
index 973410db5..ef932ae68 100755
--- a/src/models/transformer.h
+++ b/src/models/transformer.h
@@ -148,8 +148,7 @@ class Transformer : public EncoderOrDecoderBase {
     x = affine(x, W, b);
     if (actFn)
       x = actFn(x);
-    if (dropProb)
-      x = dropout(x, dropProb);
+    x = dropout(x, dropProb);
     return x;
   }
 
@@ -249,9 +248,7 @@ class Transformer : public EncoderOrDecoderBase {
       collectOneHead(weights, dimBeam);
 
     // optional dropout for attention weights
-    float dropProb
-        = inference_ ? 0 : opt<float>("transformer-dropout-attention");
-    weights = dropout(weights, dropProb);
+    weights = dropout(weights, inference_ ? 0 : opt<float>("transformer-dropout-attention"));
 
     // apply attention weights to values
     auto output = bdot(weights, v);   // [-4: beam depth * batch size, -3: num heads, -2: max tgt length, -1: split vector dim]
diff --git a/src/rnn/attention.h b/src/rnn/attention.h
index 964889d8d..154f63ef4 100755
--- a/src/rnn/attention.h
+++ b/src/rnn/attention.h
@@ -62,8 +62,7 @@ class GlobalAttention : public CellInput {
       dropMaskState_   = graph->dropoutMask(dropout_, {1, dimDecState});
     }
 
-    if(dropMaskContext_)
-      contextDropped_ = dropout(contextDropped_, dropMaskContext_);
+    contextDropped_ = dropout(contextDropped_, dropMaskContext_);
 
     if(layerNorm_) {
       if(nematusNorm_) {
@@ -113,8 +112,7 @@ class GlobalAttention : public CellInput {
     if(recState->shape().size() > 3)
       dimBeam = recState->shape()[-4];
 
-    if(dropMaskState_)
-      recState = dropout(recState, dropMaskState_);
+    recState = dropout(recState, dropMaskState_);
 
     auto mappedState = dot(recState, Wa_);
     if(layerNorm_) {
diff --git a/src/rnn/cells.h b/src/rnn/cells.h
index 3b82c54ac..36c3e24d1 100755
--- a/src/rnn/cells.h
+++ b/src/rnn/cells.h
@@ -72,8 +72,7 @@ class Tanh : public Cell {
     else
       input = inputs.front();
 
-    if(dropMaskX_)
-      input = dropout(input, dropMaskX_);
+    input = dropout(input, dropMaskX_);
 
     auto xW = dot(input, W_);
 
@@ -87,8 +86,7 @@ class Tanh : public Cell {
     Expr recState = state.output;
 
     auto stateDropped = recState;
-    if(dropMaskS_)
-      stateDropped = dropout(recState, dropMaskS_);
+    stateDropped = dropout(recState, dropMaskS_);
     auto sU = dot(stateDropped, U_);
     if(layerNorm_)
       sU = layerNorm(sU, gamma2_);
@@ -163,8 +161,7 @@ class ReLU : public Cell {
     else
       input = inputs.front();
 
-    if(dropMaskX_)
-      input = dropout(input, dropMaskX_);
+    input = dropout(input, dropMaskX_);
 
     auto xW = dot(input, W_);
 
@@ -178,8 +175,7 @@ class ReLU : public Cell {
     Expr recState = state.output;
 
     auto stateDropped = recState;
-    if(dropMaskS_)
-      stateDropped = dropout(recState, dropMaskS_);
+    stateDropped = dropout(recState, dropMaskS_);
     auto sU = dot(stateDropped, U_);
     if(layerNorm_)
       sU = layerNorm(sU, gamma2_);
@@ -284,8 +280,7 @@ class GRU : public Cell {
     else
       input = inputs[0];
 
-    if(dropMaskX_)
-      input = dropout(input, dropMaskX_);
+    input = dropout(input, dropMaskX_);
 
     auto xW = dot(input, W_);
     if(layerNorm_)
@@ -299,8 +294,7 @@ class GRU : public Cell {
                            Expr mask = nullptr) override {
     auto stateOrig = state.output;
     auto stateDropped = stateOrig;
-    if(dropMaskS_)
-      stateDropped = dropout(stateOrig, dropMaskS_);
+    stateDropped = dropout(stateOrig, dropMaskS_);
 
     auto sU = dot(stateDropped, U_);
     if(layerNorm_)
@@ -460,8 +454,7 @@ class GRUNematus : public Cell {
     else
       input = inputs[0];
 
-    if(dropMaskX_)
-      input = dropout(input, dropMaskX_);
+    input = dropout(input, dropMaskX_);
 
     Expr xW;
     if(layerNorm_) {
@@ -494,8 +487,7 @@ class GRUNematus : public Cell {
 
     auto stateOrig = state.output;
     auto stateDropped = stateOrig;
-    if(dropMaskS_)
-      stateDropped = dropout(stateOrig, dropMaskS_);
+    stateDropped = dropout(stateOrig, dropMaskS_);
 
     Expr sU;
     if(layerNorm_) {
@@ -612,8 +604,7 @@ class FastLSTM : public Cell {
     } else
       input = inputs.front();
 
-    if(dropMaskX_)
-      input = dropout(input, dropMaskX_);
+    input = dropout(input, dropMaskX_);
 
     auto xW = dot(input, W_);
 
@@ -630,8 +621,7 @@ class FastLSTM : public Cell {
     auto cellState = state.cell;
 
     auto recStateDropped = recState;
-    if(dropMaskS_)
-      recStateDropped = dropout(recState, dropMaskS_);
+    recStateDropped = dropout(recState, dropMaskS_);
 
     auto sU = dot(recStateDropped, U_);
 
@@ -954,7 +944,7 @@ class SRU : public Cell {
     else
       input = inputs.front();
 
-    auto inputDropped = dropMaskX_ ? dropout(input, dropMaskX_) : input;
+    auto inputDropped = dropout(input, dropMaskX_);
 
     Expr x, f, r;
     if(layerNorm_) {
@@ -1044,7 +1034,7 @@ class SSRU : public Cell {
     else
       input = inputs.front();
 
-    auto inputDropped = dropMaskX_ ? dropout(input, dropMaskX_) : input;
+    auto inputDropped = dropout(input, dropMaskX_);
 
     Expr x, f;
     if(layerNorm_) {
@@ -1121,7 +1111,7 @@ class SSRU : public Cell {
 //     else
 //       input = inputs.front();
 
-//     auto inputDropped = dropMaskX_ ? dropout(input, dropMaskX_) : input;
+//     auto inputDropped = dropout(input, dropMaskX_);
 
 //     auto x = dot(inputDropped, W_);
 //     auto f = affine(inputDropped, Wf_, bf_);

From 9a345a1d95161dace288c13464790b0017c91813 Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Tue, 14 May 2019 19:09:56 -0700
Subject: [PATCH 474/838] moved option setters from Accumulator<> to Factory
 (keeping the old one only for back compat)

---
 src/layers/constructors.h    |  2 +-
 src/layers/factory.h         | 74 ++++++++++++++++++++++++++----------
 src/layers/generic.cpp       | 54 +++++++++++++-------------
 src/layers/generic.h         |  2 +-
 src/models/char_s2s.h        |  3 +-
 src/models/decoder.h         | 11 ++++--
 src/models/encoder.h         |  1 -
 src/models/model_factory.cpp |  0
 src/models/s2s.h             |  5 ++-
 src/models/transformer.h     |  2 +-
 src/rnn/constructors.h       | 22 +++++------
 11 files changed, 106 insertions(+), 70 deletions(-)
 mode change 100644 => 100755 src/layers/constructors.h
 mode change 100644 => 100755 src/models/model_factory.cpp
 mode change 100644 => 100755 src/rnn/constructors.h

diff --git a/src/layers/constructors.h b/src/layers/constructors.h
old mode 100644
new mode 100755
index 54e1b3404..d5b1a881a
--- a/src/layers/constructors.h
+++ b/src/layers/constructors.h
@@ -157,7 +157,7 @@ class MLPFactory : public Factory {
   Ptr<MLP> construct(Ptr<ExpressionGraph> graph) {
     auto mlp = New<MLP>(graph, options_);
     for(auto layer : layers_) {
-      layer->getOptions()->merge(options_);
+      layer->mergeOpts(options_);
       mlp->push_back(layer->construct(graph));
     }
     return mlp;
diff --git a/src/layers/factory.h b/src/layers/factory.h
index d3e1e3fa2..f44f75feb 100755
--- a/src/layers/factory.h
+++ b/src/layers/factory.h
@@ -9,27 +9,59 @@ class Factory : public std::enable_shared_from_this<Factory> {
   Ptr<Options> options_;
 
 public:
+  // construct with empty options
   Factory() : options_(New<Options>()) {}
+  // construct with options
   Factory(Ptr<Options> options) : Factory() {
     options_->merge(options);
   }
+  // construct with one or more individual parameters
+  // Factory("var1", val1, "var2", val2, ...)
+  template <typename T, typename... Args>
+  Factory(const std::string& key, T value, Args&&... moreArgs) {
+    setOpts(key, value, std::forward<Args>(moreArgs)...);
+  }
+  // construct with options and one or more individual parameters
+  // Factory(options, "var1", val1, "var2", val2, ...)
+  template <typename... Args>
+  Factory(Ptr<Options> options, Args&&... moreArgs) : Factory(options) {
+    setOpts(std::forward<Args>(moreArgs)...);
+  }
 
   virtual ~Factory() {}
 
-  Ptr<Options> getOptions() { return options_; }
-
   std::string str() { return options_->str(); }
 
+  // retrieve an option
+  // auto val = opt<T>("var");
+  template <typename T>
+  T opt(const std::string& key) { return options_->get<T>(key); }
+
+  template <typename T>
+  T opt(const std::string& key, T defaultValue) { return options_->get<T>(key, defaultValue); }
+
+  // set a single option
+  // setOpt("var", val);
   template <typename T>
-  T opt(const std::string& key) {
-    return options_->get<T>(key);
+  void setOpt(const std::string& key, T value) {
+    options_->set(key, value);
   }
 
+  // set multiple options
+  // setOpts("var1", val1, "var2", val2, ...);
   template <typename T>
-  T opt(const std::string& key, T defaultValue) {
-    return options_->get<T>(key, defaultValue);
+  void setOpts(const std::string& key, T value) { options_->set(key, value); }
+
+  template <typename T, typename... Args>
+  void setOpts(const std::string& key, T value, Args&&... moreArgs) {
+    setOpt(key, value);
+    setOpts(std::forward<Args>(moreArgs)...); // recursively set the remaining args
   }
 
+  //void mergeOpts(const std::string& yaml) { options_->parse(yaml); }
+
+  void mergeOpts(Ptr<Options> options) { options_->merge(options); }
+
   template <typename Cast>
   inline Ptr<Cast> as() {
     return std::dynamic_pointer_cast<Cast>(shared_from_this());
@@ -44,41 +76,43 @@ class Factory : public std::enable_shared_from_this<Factory> {
 // simplest form of Factory that just passes on options to the constructor of a layer type
 template<class Class>
 struct ConstructingFactory : public Factory {
+  template <typename... Args>
+  ConstructingFactory(Args&&... moreArgs) : Factory(std::forward<Args>(moreArgs)...) {}
+
   Ptr<Class> construct(Ptr<ExpressionGraph> graph) {
     return New<Class>(graph, options_);
   }
 };
 
-template <class BaseFactory>
+template <class BaseFactory> // where BaseFactory : Factory
 class Accumulator : public BaseFactory {
   typedef BaseFactory Factory;
 
 public:
   Accumulator() : Factory() {}
+  Accumulator(Ptr<Options> options) : Factory(options) {}
+  template <typename... Args>
+  Accumulator(Ptr<Options> options, Args&&... moreArgs) : Factory(options, std::forward<Args>(moreArgs)...) {}
+  template <typename T, typename... Args>
+  Accumulator(const std::string& key, T value, Args&&... moreArgs) : Factory(key, value, std::forward<Args>(moreArgs)...) {}
   Accumulator(const Factory& factory) : Factory(factory) {}
   Accumulator(const Accumulator&) = default;
   Accumulator(Accumulator&&) = default;
 
+  // deprecated chaining syntax
   template <typename T>
   Accumulator& operator()(const std::string& key, T value) {
-    Factory::getOptions()->set(key, value);
-    return *this;
-  }
-
-  Accumulator& operator()(const std::string& yaml) {
-    Factory::getOptions()->parse(yaml);
+    Factory::setOpt(key, value);
     return *this;
   }
 
-#if 0
-  Accumulator& operator()(Config::YamlNode yaml) {
-    Factory::getOptions()->merge(yaml);
-    return *this;
-  }
-#endif
+  //Accumulator& operator()(const std::string& yaml) {
+  //  Factory::mergeOpts(yaml);
+  //  return *this;
+  //}
 
   Accumulator& operator()(Ptr<Options> options) {
-    Factory::getOptions()->merge(options);
+    Factory::mergeOpts(options);
     return *this;
   }
 
diff --git a/src/layers/generic.cpp b/src/layers/generic.cpp
index ab2972faa..853a345a5 100755
--- a/src/layers/generic.cpp
+++ b/src/layers/generic.cpp
@@ -422,51 +422,49 @@ namespace marian {
     return reshape(rows(E_, embIdx), shape);
   }
 
+  // standard encoder word embeddings
   /*private*/ Ptr<IEmbeddingLayer> EncoderDecoderLayerBase::createEmbeddingLayer(Ptr<ExpressionGraph> graph) const {
-    // standard encoder word embeddings
-    int dimVoc = opt<std::vector<int>>("dim-vocabs")[batchIndex_];
-    int dimEmb = opt<int>("dim-emb");
-    auto embFactory = embedding()("dimVocab", dimVoc)("dimEmb", dimEmb)("dropout", dropout_);
-    if(opt<bool>("tied-embeddings-src") || opt<bool>("tied-embeddings-all"))
-      embFactory("prefix", "Wemb");
-    else
-      embFactory("prefix", prefix_ + "_Wemb");
+    auto embFactory = EmbeddingFactory(
+        "dimVocab", opt<std::vector<int>>("dim-vocabs")[batchIndex_],
+        "dimEmb", opt<int>("dim-emb"),
+        "dropout", dropout_,
+        "prefix", (opt<bool>("tied-embeddings-src") || opt<bool>("tied-embeddings-all")) ? "Wemb" : prefix_ + "_Wemb",
+        "vocab", opt<std::vector<std::string>>("vocabs")[batchIndex_]); // for factored embeddings
     if(options_->has(embeddingFixParamName_))
-      embFactory("fixed", opt<bool>(embeddingFixParamName_));
+      embFactory.setOpt("fixed", opt<bool>(embeddingFixParamName_));
     if(options_->hasAndNotEmpty("embedding-vectors")) {
       auto embFiles = opt<std::vector<std::string>>("embedding-vectors");
-      embFactory("embFile", embFiles[batchIndex_])
-                ("normalization", opt<bool>("embedding-normalization"));
+      embFactory.setOpts(
+          "embFile", embFiles[batchIndex_],
+          "normalization", opt<bool>("embedding-normalization"));
     }
-    embFactory("vocab", opt<std::vector<std::string>>("vocabs")[batchIndex_]); // for factored embeddings
     return embFactory.construct(graph);
   }
 
+  // ULR word embeddings
   /*private*/ Ptr<IEmbeddingLayer> EncoderDecoderLayerBase::createULREmbeddingLayer(Ptr<ExpressionGraph> graph) const {
-    // standard encoder word embeddings
-    int dimSrcVoc = opt<std::vector<int>>("dim-vocabs")[0];  //ULR multi-lingual src
-    int dimTgtVoc = opt<std::vector<int>>("dim-vocabs")[1];  //ULR monon tgt
-    int dimEmb = opt<int>("dim-emb");
-    int dimUlrEmb = opt<int>("ulr-dim-emb");
-    auto embFactory = ulr_embedding()("dimSrcVoc", dimSrcVoc)("dimTgtVoc", dimTgtVoc)
-                                     ("dimUlrEmb", dimUlrEmb)("dimEmb", dimEmb)
-                                     ("ulr-dropout", opt<float>("ulr-dropout"))
-                                     ("dropout", dropout_)
-                                     ("ulrTrainTransform", opt<bool>("ulr-trainable-transformation"))
-                                     ("ulrQueryFile", opt<std::string>("ulr-query-vectors"))
-                                     ("ulrKeysFile", opt<std::string>("ulr-keys-vectors"));
-    return embFactory.construct(graph);
+    return ULREmbeddingFactory(
+        "dimSrcVoc", opt<std::vector<int>>("dim-vocabs")[0],  // ULR multi-lingual src
+        "dimTgtVoc", opt<std::vector<int>>("dim-vocabs")[1],  // ULR monon tgt
+        "dimUlrEmb", opt<int>("ulr-dim-emb"),
+        "dimEmb", opt<int>("dim-emb"),
+        "ulr-dropout", opt<float>("ulr-dropout"),
+        "dropout", dropout_,
+        "ulrTrainTransform", opt<bool>("ulr-trainable-transformation"),
+        "ulrQueryFile", opt<std::string>("ulr-query-vectors"),
+        "ulrKeysFile", opt<std::string>("ulr-keys-vectors"))
+        .construct(graph);
   }
 
   // get embedding layer for this encoder or decoder; lazily create it if not created yet
-  Ptr<IEmbeddingLayer> EncoderDecoderLayerBase::getEmbeddingLayer(Ptr<ExpressionGraph> graph, bool ulr) {
+  Ptr<IEmbeddingLayer> EncoderDecoderLayerBase::getEmbeddingLayer(bool ulr) {
     if (embeddingLayers_.size() <= batchIndex_ || !embeddingLayers_[batchIndex_]) { // lazy
       if (embeddingLayers_.size() <= batchIndex_)
         embeddingLayers_.resize(batchIndex_ + 1);
       if (ulr)
-        embeddingLayers_[batchIndex_] = createULREmbeddingLayer(graph); // embedding uses ULR
+        embeddingLayers_[batchIndex_] = createULREmbeddingLayer(graph_); // embedding uses ULR
       else
-        embeddingLayers_[batchIndex_] = createEmbeddingLayer(graph);
+        embeddingLayers_[batchIndex_] = createEmbeddingLayer(graph_);
     }
     return embeddingLayers_[batchIndex_];
   }
diff --git a/src/layers/generic.h b/src/layers/generic.h
index 74d761bb2..2e9d1c715 100755
--- a/src/layers/generic.h
+++ b/src/layers/generic.h
@@ -91,7 +91,7 @@ class EncoderDecoderLayerBase : public LayerBase {
 
 public:
   // get embedding layer; lazily create on first call
-  Ptr<IEmbeddingLayer> getEmbeddingLayer(Ptr<ExpressionGraph> graph, bool ulr = false);
+  Ptr<IEmbeddingLayer> getEmbeddingLayer(bool ulr = false);
 };
 
 class FactoredVocab;
diff --git a/src/models/char_s2s.h b/src/models/char_s2s.h
index 85df8c5db..06882dca8 100755
--- a/src/models/char_s2s.h
+++ b/src/models/char_s2s.h
@@ -13,9 +13,10 @@ class CharS2SEncoder : public EncoderS2S {
 
   virtual Ptr<EncoderState> build(Ptr<ExpressionGraph> graph,
                                   Ptr<data::CorpusBatch> batch) override {
+    graph_ = graph;
     // select embeddings that occur in the batch
     Expr batchEmbeddings, batchMask; std::tie
-    (batchEmbeddings, batchMask) = getEmbeddingLayer(graph)->apply(batch->front());
+    (batchEmbeddings, batchMask) = getEmbeddingLayer()->apply(batch->front());
 
     int dimEmb = opt<int>("dim-emb");
     auto convSizes = options_->get<std::vector<int>>("char-conv-filters-num");
diff --git a/src/models/decoder.h b/src/models/decoder.h
index e4abcd07b..2a89fadde 100755
--- a/src/models/decoder.h
+++ b/src/models/decoder.h
@@ -29,17 +29,19 @@ class DecoderBase : public EncoderDecoderLayerBase {
   virtual void embeddingsFromBatch(Ptr<ExpressionGraph> graph,
                                    Ptr<DecoderState> state,
                                    Ptr<data::CorpusBatch> batch) {
+    graph_ = graph;
+
     auto subBatch = (*batch)[batchIndex_];
 
     Expr y, yMask; std::tie
-    (y, yMask) = getEmbeddingLayer(graph)->apply(subBatch);
+    (y, yMask) = getEmbeddingLayer()->apply(subBatch);
 
     const Words& data =
       /*if*/ (shortlist_) ?
         shortlist_->mappedIndices()
       /*else*/ :
         subBatch->data();
-    Expr yData = graph->indices(toWordIndexVector(data));
+    Expr yData = graph_->indices(toWordIndexVector(data));
 
     auto yShifted = shift(y, {1, 0, 0});
 
@@ -53,11 +55,12 @@ class DecoderBase : public EncoderDecoderLayerBase {
                                         const Words& words,
                                         int dimBatch,
                                         int dimBeam) {
-    auto embeddingLayer = getEmbeddingLayer(graph);
+    graph_ = graph;
+    auto embeddingLayer = getEmbeddingLayer();
     Expr selectedEmbs;
     int dimEmb = opt<int>("dim-emb");
     if(words.empty())
-      selectedEmbs = graph->constant({1, 1, dimBatch, dimEmb}, inits::zeros);
+      selectedEmbs = graph_->constant({1, 1, dimBatch, dimEmb}, inits::zeros);
     else
       selectedEmbs = embeddingLayer->apply(words, {dimBeam, 1, dimBatch, dimEmb});
     state->setTargetHistoryEmbeddings(selectedEmbs);
diff --git a/src/models/encoder.h b/src/models/encoder.h
index 3dc02237a..acff064f9 100755
--- a/src/models/encoder.h
+++ b/src/models/encoder.h
@@ -6,7 +6,6 @@
 namespace marian {
 
 class EncoderBase : public EncoderDecoderLayerBase {
-protected:
 public:
   EncoderBase(Ptr<Options> options) :
     EncoderDecoderLayerBase("encoder", /*batchIndex=*/0, options,
diff --git a/src/models/model_factory.cpp b/src/models/model_factory.cpp
old mode 100644
new mode 100755
diff --git a/src/models/s2s.h b/src/models/s2s.h
index 3d101c685..081bf3e93 100755
--- a/src/models/s2s.h
+++ b/src/models/s2s.h
@@ -123,12 +123,13 @@ class EncoderS2S : public EncoderBase {
 
   virtual Ptr<EncoderState> build(Ptr<ExpressionGraph> graph,
                                   Ptr<data::CorpusBatch> batch) override {
+    graph_ = graph;
     // select embeddings that occur in the batch
     Expr batchEmbeddings, batchMask; std::tie
-    (batchEmbeddings, batchMask) = getEmbeddingLayer(graph)->apply((*batch)[batchIndex_]);
+    (batchEmbeddings, batchMask) = getEmbeddingLayer()->apply((*batch)[batchIndex_]);
 
     Expr context = applyEncoderRNN(
-        graph, batchEmbeddings, batchMask, opt<std::string>("enc-type"));
+        graph_, batchEmbeddings, batchMask, opt<std::string>("enc-type"));
 
     return New<EncoderState>(context, batchMask, batch);
   }
diff --git a/src/models/transformer.h b/src/models/transformer.h
index ef932ae68..8f84b67eb 100755
--- a/src/models/transformer.h
+++ b/src/models/transformer.h
@@ -514,7 +514,7 @@ class EncoderTransformer : public Transformer<EncoderBase> {
     // embed the source words in the batch
     Expr batchEmbeddings, batchMask;
 
-    auto embeddingLayer = getEmbeddingLayer(graph_, options_->has("ulr") && options_->get<bool>("ulr"));
+    auto embeddingLayer = getEmbeddingLayer(options_->has("ulr") && options_->get<bool>("ulr"));
     std::tie(batchEmbeddings, batchMask) = embeddingLayer->apply((*batch)[batchIndex_]);
 
     batchEmbeddings = addSpecialEmbeddings(batchEmbeddings, /*start=*/0, batch);
diff --git a/src/rnn/constructors.h b/src/rnn/constructors.h
old mode 100644
new mode 100755
index 3a5826d64..25b7d3544
--- a/src/rnn/constructors.h
+++ b/src/rnn/constructors.h
@@ -110,9 +110,9 @@ class StackedCellFactory : public CellFactory {
 
       if(sf->is<CellFactory>()) {
         auto cellFactory = sf->as<CellFactory>();
-        cellFactory->getOptions()->merge(options_);
+        cellFactory->mergeOpts(options_);
 
-        sf->getOptions()->set("dimInput", lastDimInput);
+        sf->setOpt("dimInput", lastDimInput);
         lastDimInput = 0;
 
         if(i == 0)
@@ -122,7 +122,7 @@ class StackedCellFactory : public CellFactory {
         stacked->push_back(cellFactory->construct(graph));
       } else {
         auto inputFactory = sf->as<InputFactory>();
-        inputFactory->getOptions()->merge(options_);
+        inputFactory->mergeOpts(options_);
         auto input = inputFactory->construct(graph);
         stacked->push_back(input);
         lastDimInput += input->dimOutput();
@@ -150,29 +150,29 @@ class RNNFactory : public Factory {
     for(size_t i = 0; i < layerFactories_.size(); ++i) {
       auto lf = layerFactories_[i];
 
-      lf->getOptions()->merge(options_);
+      lf->mergeOpts(options_);
       if(i > 0) {
         int dimInput
-            = layerFactories_[i - 1]->getOptions()->get<int>("dimState")
-              + lf->getOptions()->get<int>("dimInputExtra", 0);
+            = layerFactories_[i - 1]->opt<int>("dimState")
+              + lf->opt<int>("dimInputExtra", 0);
 
-        lf->getOptions()->set("dimInput", dimInput);
+        lf->setOpt("dimInput", dimInput);
       }
 
       if((rnn::dir)opt<int>("direction", (int)rnn::dir::forward)
          == rnn::dir::alternating_forward) {
         if(i % 2 == 0)
-          lf->getOptions()->set("direction", (int)rnn::dir::forward);
+          lf->setOpt("direction", (int)rnn::dir::forward);
         else
-          lf->getOptions()->set("direction", (int)rnn::dir::backward);
+          lf->setOpt("direction", (int)rnn::dir::backward);
       }
 
       if((rnn::dir)opt<int>("direction", (int)rnn::dir::forward)
          == rnn::dir::alternating_backward) {
         if(i % 2 == 1)
-          lf->getOptions()->set("direction", (int)rnn::dir::forward);
+          lf->setOpt("direction", (int)rnn::dir::forward);
         else
-          lf->getOptions()->set("direction", (int)rnn::dir::backward);
+          lf->setOpt("direction", (int)rnn::dir::backward);
       }
 
       rnn->push_back(lf->construct(graph));

From 80171f3ca61d07bfce365c0b0c58ac8aa69882ce Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Tue, 14 May 2019 19:41:06 -0700
Subject: [PATCH 475/838] removed Accumulator<> at some places, and used the
 new syntax at some as well

---
 src/layers/constructors.h |  5 +++--
 src/models/bert.h         |  6 +++---
 src/models/transformer.h  | 35 +++++++++++++++--------------------
 src/rnn/constructors.h    |  1 +
 4 files changed, 22 insertions(+), 25 deletions(-)
 mode change 100644 => 100755 src/models/bert.h

diff --git a/src/layers/constructors.h b/src/layers/constructors.h
index d5b1a881a..e6d013c3f 100755
--- a/src/layers/constructors.h
+++ b/src/layers/constructors.h
@@ -36,11 +36,13 @@ typedef Accumulator<DenseFactory> dense;
  * Factory for output layers, can be used in a multi-layer network factory.
  */
 struct LogitLayerFactory : public Factory {
+  using Factory::Factory;
   virtual Ptr<IUnaryLogitLayer> construct(Ptr<ExpressionGraph> graph) = 0;
 };
 
 // @TODO: In the long run, I hope we can get rid of the abstract factories altogether.
 class OutputFactory : public LogitLayerFactory {
+  using LogitLayerFactory::LogitLayerFactory;
 protected:
   std::string tiedTransposedName_;
   Ptr<data::Shortlist> shortlist_;
@@ -51,9 +53,8 @@ class OutputFactory : public LogitLayerFactory {
     return Accumulator<OutputFactory>(*this);
   }
 
-  Accumulator<OutputFactory> setShortlist(Ptr<data::Shortlist> shortlist) {
+  void setShortlist(Ptr<data::Shortlist> shortlist) {
     shortlist_ = shortlist;
-    return Accumulator<OutputFactory>(*this);
   }
 
   Ptr<IUnaryLogitLayer> construct(Ptr<ExpressionGraph> graph) override {
diff --git a/src/models/bert.h b/src/models/bert.h
old mode 100644
new mode 100755
index 61cff43bf..33f21fc82
--- a/src/models/bert.h
+++ b/src/models/bert.h
@@ -357,9 +357,9 @@ class BertMaskedLM : public ClassifierBase {
     intermediate = layerNorm(intermediate, gamma, beta);
 
     auto layer2 = mlp::mlp()
-      .push_back(mlp::output()
-                 ("prefix", prefix_ + "_ff_logit_l2")
-                 ("dim", dimVoc)
+      .push_back(mlp::output(
+                  "prefix", prefix_ + "_ff_logit_l2",
+                  "dim", dimVoc)
                  .tieTransposed("Wemb"))
       .construct(graph);
 
diff --git a/src/models/transformer.h b/src/models/transformer.h
index 8f84b67eb..8a6454e04 100755
--- a/src/models/transformer.h
+++ b/src/models/transformer.h
@@ -470,14 +470,14 @@ class Transformer : public EncoderOrDecoderBase {
                        int /*startPos*/) const {
     float dropoutRnn = inference_ ? 0.f : opt<float>("dropout-rnn");
 
-    auto rnn = rnn::rnn()                                          //
-        ("type", opt<std::string>("dec-cell"))                     //
-        ("prefix", prefix)                                         //
-        ("dimInput", opt<int>("dim-emb"))                          //
-        ("dimState", opt<int>("dim-emb"))                          //
-        ("dropout", dropoutRnn)                                    //
-        ("layer-normalization", opt<bool>("layer-normalization"))  //
-        .push_back(rnn::cell())                                    //
+    auto rnn = rnn::rnn(
+         "type", opt<std::string>("dec-cell"),
+         "prefix", prefix,
+         "dimInput", opt<int>("dim-emb"),
+         "dimState", opt<int>("dim-emb"),
+         "dropout", dropoutRnn,
+         "layer-normalization", opt<bool>("layer-normalization"))
+        .push_back(rnn::cell())
         .construct(graph_);
 
     float dropProb = inference_ ? 0 : opt<float>("transformer-dropout");
@@ -589,19 +589,14 @@ class DecoderTransformer : public Transformer<DecoderBase> {
 
     int dimTrgVoc = opt<std::vector<int>>("dim-vocabs")[batchIndex_];
 
-    auto outputFactory = mlp::output()         //
-        ("prefix", prefix_ + "_ff_logit_out")  //
-        ("dim", dimTrgVoc);
+    auto outputFactory = mlp::OutputFactory(
+        "prefix", prefix_ + "_ff_logit_out",
+        "dim", dimTrgVoc,
+        "vocab", opt<std::vector<std::string>>("vocabs")[batchIndex_], // for factored outputs
+        "lemma-dim-emb", opt<int>("lemma-dim-emb", 0)); // for factored outputs
 
-    if(opt<bool>("tied-embeddings") || opt<bool>("tied-embeddings-all")) {
-      std::string tiedPrefix = prefix_ + "_Wemb";
-      if(opt<bool>("tied-embeddings-all") || opt<bool>("tied-embeddings-src"))
-        tiedPrefix = "Wemb";
-      outputFactory.tieTransposed(tiedPrefix);
-    }
-
-    outputFactory("vocab", opt<std::vector<std::string>>("vocabs")[batchIndex_]); // for factored outputs
-    outputFactory("lemma-dim-emb", opt<int>("lemma-dim-emb", 0)); // for factored outputs
+    if(opt<bool>("tied-embeddings") || opt<bool>("tied-embeddings-all"))
+      outputFactory.tieTransposed(opt<bool>("tied-embeddings-all") || opt<bool>("tied-embeddings-src") ? "Wemb" : prefix_ + "_Wemb");
 
     output_ = std::dynamic_pointer_cast<mlp::Output>(outputFactory.construct(graph_)); // (construct() returns only the underlying interface)
   }
diff --git a/src/rnn/constructors.h b/src/rnn/constructors.h
index 25b7d3544..c8ddbb61c 100755
--- a/src/rnn/constructors.h
+++ b/src/rnn/constructors.h
@@ -141,6 +141,7 @@ class StackedCellFactory : public CellFactory {
 typedef Accumulator<StackedCellFactory> stacked_cell;
 
 class RNNFactory : public Factory {
+  using Factory::Factory;
 protected:
   std::vector<Ptr<CellFactory>> layerFactories_;
 

From 356f8042932e9dbebb397e1c73b5f5df410ef6ee Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Wed, 15 May 2019 11:43:20 -0700
Subject: [PATCH 476/838] minor bug fixes, to get last few changes to pass
 tests

---
 regression-tests                        | 2 +-
 scripts/embeddings/export_embeddings.py | 2 +-
 src/layers/factory.h                    | 2 +-
 src/layers/generic.cpp                  | 8 ++++++--
 src/models/transformer.h                | 8 ++++----
 src/tensors/cpu/prod.cpp                | 0
 src/tests/dropout.cpp                   | 4 ++--
 vs/Marian.vcxproj                       | 0
 8 files changed, 15 insertions(+), 11 deletions(-)
 mode change 100644 => 100755 src/tensors/cpu/prod.cpp
 mode change 100644 => 100755 vs/Marian.vcxproj

diff --git a/regression-tests b/regression-tests
index 142eadddb..71b473f29 160000
--- a/regression-tests
+++ b/regression-tests
@@ -1 +1 @@
-Subproject commit 142eadddbe04493c1024b42586030b72e9cb7ea2
+Subproject commit 71b473f29017933e68a513f7262044c46e39cccc
diff --git a/scripts/embeddings/export_embeddings.py b/scripts/embeddings/export_embeddings.py
index 1476e52c9..7aef07d5d 100755
--- a/scripts/embeddings/export_embeddings.py
+++ b/scripts/embeddings/export_embeddings.py
@@ -1,4 +1,4 @@
-#!/usr/bin/env python
+#!/usr/bin/env python3
 # -*- coding: utf-8 -*-
 
 from __future__ import print_function
diff --git a/src/layers/factory.h b/src/layers/factory.h
index f44f75feb..5ff5e8b8e 100755
--- a/src/layers/factory.h
+++ b/src/layers/factory.h
@@ -18,7 +18,7 @@ class Factory : public std::enable_shared_from_this<Factory> {
   // construct with one or more individual parameters
   // Factory("var1", val1, "var2", val2, ...)
   template <typename T, typename... Args>
-  Factory(const std::string& key, T value, Args&&... moreArgs) {
+  Factory(const std::string& key, T value, Args&&... moreArgs) : Factory() {
     setOpts(key, value, std::forward<Args>(moreArgs)...);
   }
   // construct with options and one or more individual parameters
diff --git a/src/layers/generic.cpp b/src/layers/generic.cpp
index 853a345a5..c3197db2b 100755
--- a/src/layers/generic.cpp
+++ b/src/layers/generic.cpp
@@ -413,13 +413,17 @@ namespace marian {
       selectedEmbs = multiRows(words);
     else
       selectedEmbs = rows(E_, toWordIndexVector(words));
+    selectedEmbs = reshape(selectedEmbs, shape);
     selectedEmbs = dropout(selectedEmbs, options_->get<float>("dropout", 0.0f), {selectedEmbs->shape()[-3], 1, 1});
-    return reshape(selectedEmbs, shape);
+    return selectedEmbs;
   }
 
   Expr Embedding::applyIndices(const std::vector<WordIndex>& embIdx, const Shape& shape) const /*override final*/ {
     ABORT_IF(factoredVocab_, "Embedding: applyIndices must not be used with a factored vocabulary");
-    return reshape(rows(E_, embIdx), shape);
+    auto selectedEmbs = rows(E_, embIdx);
+    selectedEmbs = reshape(selectedEmbs, shape);
+    selectedEmbs = dropout(selectedEmbs, options_->get<float>("dropout", 0.0f), { selectedEmbs->shape()[-3], 1, 1 });
+    return selectedEmbs;
   }
 
   // standard encoder word embeddings
diff --git a/src/models/transformer.h b/src/models/transformer.h
index 8a6454e04..8b1f97900 100755
--- a/src/models/transformer.h
+++ b/src/models/transformer.h
@@ -61,10 +61,10 @@ class Transformer : public EncoderOrDecoderBase {
       Expr seenEmb = graph_->get("Wpos");
       int numPos = seenEmb ? seenEmb->shape()[-2] : maxLength;
 
-      auto embeddingLayer = embedding()
-                            ("prefix", "Wpos") // share positional embeddings across all encoders/decorders
-                            ("dimVocab", numPos)
-                            ("dimEmb", dimEmb)
+      auto embeddingLayer = embedding(
+                             "prefix", "Wpos", // share positional embeddings across all encoders/decorders
+                             "dimVocab", numPos,
+                             "dimEmb", dimEmb)
                             .construct(graph_);
 
       // fill with increasing numbers until current length or maxPos
diff --git a/src/tensors/cpu/prod.cpp b/src/tensors/cpu/prod.cpp
old mode 100644
new mode 100755
diff --git a/src/tests/dropout.cpp b/src/tests/dropout.cpp
index c31ee6214..367029fe8 100644
--- a/src/tests/dropout.cpp
+++ b/src/tests/dropout.cpp
@@ -20,8 +20,8 @@ int main(int argc, char** argv) {
 
   for(int i = 0; i < 10; ++i) {
     g->clear();
-    auto mask1 = g->dropout(0.2, {10, 3072});
-    auto mask2 = g->dropout(0.3, {1, 3072});
+    auto mask1 = g->dropoutMask(0.2, {10, 3072});
+    auto mask2 = g->dropoutMask(0.3, {1, 3072});
     auto mask = mask1 + mask2;
     debug(mask1, "mask1");
     debug(mask2, "mask2");
diff --git a/vs/Marian.vcxproj b/vs/Marian.vcxproj
old mode 100644
new mode 100755

From 270fa7710f6bb2a8c3e7fec3c80bc0ce06933ebc Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Wed, 15 May 2019 13:41:58 -0700
Subject: [PATCH 477/838] updated submodule ref to regression-tests

---
 regression-tests | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/regression-tests b/regression-tests
index 71b473f29..658cf86e2 160000
--- a/regression-tests
+++ b/regression-tests
@@ -1 +1 @@
-Subproject commit 71b473f29017933e68a513f7262044c46e39cccc
+Subproject commit 658cf86e2ab4aa76b4ab3edabd750b93d2416f1a

From f3ff057537db5606ba15dbcc1adfa1b5e84e0a1c Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Wed, 15 May 2019 15:16:44 -0700
Subject: [PATCH 478/838] Options now accepts a list of key-value pairs

---
 src/common/options.h   | 24 +++++++++++++++++++++++
 src/layers/factory.h   |  5 -----
 src/layers/generic.cpp | 43 +++++++++++++++++++++---------------------
 src/layers/generic.h   |  4 ++--
 4 files changed, 47 insertions(+), 29 deletions(-)
 mode change 100644 => 100755 src/common/options.h

diff --git a/src/common/options.h b/src/common/options.h
old mode 100644
new mode 100755
index 643456c93..c5b07951e
--- a/src/common/options.h
+++ b/src/common/options.h
@@ -35,6 +35,22 @@ class Options {
   Options() {}
   Options(const Options& other) : options_(YAML::Clone(other.options_)) {}
 
+  // constructor with one or more key-value pairs
+  // New<Options>("var1", val1, "var2", val2, ...)
+  template <typename T, typename... Args>
+  Options(const std::string& key, T value, Args&&... moreArgs) : Options() {
+    set(key, value, std::forward<Args>(moreArgs)...);
+  }
+
+  // constructor that clones and zero or more updates
+  // options->with("var1", val1, "var2", val2, ...)
+  template <typename T, typename... Args>
+  Ptr<Options> with(Args&&... args) const {
+    auto options = New<Options>(*this);
+    options->set(std::forward<Args>(args)...);
+    return options;
+  }
+
   /**
    * @brief Return a copy of the object that can be safely modified.
    */
@@ -78,6 +94,14 @@ class Options {
     options_[key] = value;
   }
 
+  // set multiple
+  // options->set("var1", val1, "var2", val2, ...)
+  template <typename T, typename... Args>
+  void set(const std::string& key, T value, Args&&... moreArgs) {
+    set(key, value);
+    set(std::forward<Args>(moreArgs)...);
+  }
+
   template <typename T>
   T get(const std::string& key) {
     ABORT_IF(!has(key), "Required option '{}' has not been set", key);
diff --git a/src/layers/factory.h b/src/layers/factory.h
index 5ff5e8b8e..8173c854e 100755
--- a/src/layers/factory.h
+++ b/src/layers/factory.h
@@ -106,11 +106,6 @@ class Accumulator : public BaseFactory {
     return *this;
   }
 
-  //Accumulator& operator()(const std::string& yaml) {
-  //  Factory::mergeOpts(yaml);
-  //  return *this;
-  //}
-
   Accumulator& operator()(Ptr<Options> options) {
     Factory::mergeOpts(options);
     return *this;
diff --git a/src/layers/generic.cpp b/src/layers/generic.cpp
index c3197db2b..70e29a0d3 100755
--- a/src/layers/generic.cpp
+++ b/src/layers/generic.cpp
@@ -427,37 +427,36 @@ namespace marian {
   }
 
   // standard encoder word embeddings
-  /*private*/ Ptr<IEmbeddingLayer> EncoderDecoderLayerBase::createEmbeddingLayer(Ptr<ExpressionGraph> graph) const {
-    auto embFactory = EmbeddingFactory(
+  /*private*/ Ptr<IEmbeddingLayer> EncoderDecoderLayerBase::createEmbeddingLayer() const {
+    auto options = New<Options>(
         "dimVocab", opt<std::vector<int>>("dim-vocabs")[batchIndex_],
-        "dimEmb", opt<int>("dim-emb"),
-        "dropout", dropout_,
-        "prefix", (opt<bool>("tied-embeddings-src") || opt<bool>("tied-embeddings-all")) ? "Wemb" : prefix_ + "_Wemb",
-        "vocab", opt<std::vector<std::string>>("vocabs")[batchIndex_]); // for factored embeddings
+        "dimEmb",   opt<int>("dim-emb"),
+        "dropout",  dropout_,
+        "prefix",   (opt<bool>("tied-embeddings-src") || opt<bool>("tied-embeddings-all")) ? "Wemb" : prefix_ + "_Wemb",
+        "vocab",    opt<std::vector<std::string>>("vocabs")[batchIndex_]); // for factored embeddings
     if(options_->has(embeddingFixParamName_))
-      embFactory.setOpt("fixed", opt<bool>(embeddingFixParamName_));
+      options->set("fixed", opt<bool>(embeddingFixParamName_));
     if(options_->hasAndNotEmpty("embedding-vectors")) {
       auto embFiles = opt<std::vector<std::string>>("embedding-vectors");
-      embFactory.setOpts(
+      options->set(
           "embFile", embFiles[batchIndex_],
           "normalization", opt<bool>("embedding-normalization"));
     }
-    return embFactory.construct(graph);
+    return New<Embedding>(graph_, options);
   }
 
   // ULR word embeddings
-  /*private*/ Ptr<IEmbeddingLayer> EncoderDecoderLayerBase::createULREmbeddingLayer(Ptr<ExpressionGraph> graph) const {
-    return ULREmbeddingFactory(
-        "dimSrcVoc", opt<std::vector<int>>("dim-vocabs")[0],  // ULR multi-lingual src
-        "dimTgtVoc", opt<std::vector<int>>("dim-vocabs")[1],  // ULR monon tgt
-        "dimUlrEmb", opt<int>("ulr-dim-emb"),
-        "dimEmb", opt<int>("dim-emb"),
-        "ulr-dropout", opt<float>("ulr-dropout"),
-        "dropout", dropout_,
+  /*private*/ Ptr<IEmbeddingLayer> EncoderDecoderLayerBase::createULREmbeddingLayer() const {
+    return New<ULREmbedding>(graph_, New<Options>(
+        "dimSrcVoc",         opt<std::vector<int>>("dim-vocabs")[0],  // ULR multi-lingual src
+        "dimTgtVoc",         opt<std::vector<int>>("dim-vocabs")[1],  // ULR monon tgt
+        "dimUlrEmb",         opt<int>("ulr-dim-emb"),
+        "dimEmb",            opt<int>("dim-emb"),
+        "ulr-dropout",       opt<float>("ulr-dropout"),
+        "dropout",           dropout_,
         "ulrTrainTransform", opt<bool>("ulr-trainable-transformation"),
-        "ulrQueryFile", opt<std::string>("ulr-query-vectors"),
-        "ulrKeysFile", opt<std::string>("ulr-keys-vectors"))
-        .construct(graph);
+        "ulrQueryFile",      opt<std::string>("ulr-query-vectors"),
+        "ulrKeysFile",       opt<std::string>("ulr-keys-vectors")));
   }
 
   // get embedding layer for this encoder or decoder; lazily create it if not created yet
@@ -466,9 +465,9 @@ namespace marian {
       if (embeddingLayers_.size() <= batchIndex_)
         embeddingLayers_.resize(batchIndex_ + 1);
       if (ulr)
-        embeddingLayers_[batchIndex_] = createULREmbeddingLayer(graph_); // embedding uses ULR
+        embeddingLayers_[batchIndex_] = createULREmbeddingLayer(); // embedding uses ULR
       else
-        embeddingLayers_[batchIndex_] = createEmbeddingLayer(graph_);
+        embeddingLayers_[batchIndex_] = createEmbeddingLayer();
     }
     return embeddingLayers_[batchIndex_];
   }
diff --git a/src/layers/generic.h b/src/layers/generic.h
index 2e9d1c715..ea34f9379 100755
--- a/src/layers/generic.h
+++ b/src/layers/generic.h
@@ -86,8 +86,8 @@ class EncoderDecoderLayerBase : public LayerBase {
   virtual ~EncoderDecoderLayerBase() {}
 
 private:
-  Ptr<IEmbeddingLayer> createEmbeddingLayer(Ptr<ExpressionGraph> graph) const;
-  Ptr<IEmbeddingLayer> createULREmbeddingLayer(Ptr<ExpressionGraph> graph) const;
+  Ptr<IEmbeddingLayer> createEmbeddingLayer() const;
+  Ptr<IEmbeddingLayer> createULREmbeddingLayer() const;
 
 public:
   // get embedding layer; lazily create on first call

From ef1844d3eb0b464d48d02f24e595f66b99b5f8f0 Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Wed, 15 May 2019 15:51:58 -0700
Subject: [PATCH 479/838] dropout and embedding-fix are now passed as values to
 EncoderDecoderLayerBase; Factory(multiple) simplified; first example of
 options->with(); fixed default constructors of Factory classes

---
 src/common/options.h         |  2 +-
 src/layers/constructors.h    |  1 +
 src/layers/factory.h         | 18 ++++++------------
 src/layers/generic.cpp       |  9 +++++----
 src/layers/generic.h         | 16 ++++++++--------
 src/models/decoder.h         |  4 ++--
 src/models/encoder.h         |  4 ++--
 src/models/model_factory.cpp |  8 ++++----
 src/models/model_factory.h   | 21 +++++++++++++--------
 src/rnn/constructors.h       |  7 ++++---
 10 files changed, 46 insertions(+), 44 deletions(-)
 mode change 100644 => 100755 src/models/model_factory.h

diff --git a/src/common/options.h b/src/common/options.h
index c5b07951e..1337b2ece 100755
--- a/src/common/options.h
+++ b/src/common/options.h
@@ -44,7 +44,7 @@ class Options {
 
   // constructor that clones and zero or more updates
   // options->with("var1", val1, "var2", val2, ...)
-  template <typename T, typename... Args>
+  template <typename... Args>
   Ptr<Options> with(Args&&... args) const {
     auto options = New<Options>(*this);
     options->set(std::forward<Args>(args)...);
diff --git a/src/layers/constructors.h b/src/layers/constructors.h
index e6d013c3f..a2c38197f 100755
--- a/src/layers/constructors.h
+++ b/src/layers/constructors.h
@@ -151,6 +151,7 @@ class MLP : public IUnaryLogitLayer, public IHasShortList {
  * to accumulate options for later lazy construction.
  */
 class MLPFactory : public Factory {
+  using Factory::Factory;
 private:
   std::vector<Ptr<LayerFactory>> layers_;
 
diff --git a/src/layers/factory.h b/src/layers/factory.h
index 8173c854e..bb493f8a0 100755
--- a/src/layers/factory.h
+++ b/src/layers/factory.h
@@ -15,17 +15,17 @@ class Factory : public std::enable_shared_from_this<Factory> {
   Factory(Ptr<Options> options) : Factory() {
     options_->merge(options);
   }
-  // construct with one or more individual parameters
+  // construct with one or more individual option parameters
   // Factory("var1", val1, "var2", val2, ...)
   template <typename T, typename... Args>
   Factory(const std::string& key, T value, Args&&... moreArgs) : Factory() {
     setOpts(key, value, std::forward<Args>(moreArgs)...);
   }
-  // construct with options and one or more individual parameters
+  // construct with options and one or more individual option parameters
   // Factory(options, "var1", val1, "var2", val2, ...)
   template <typename... Args>
-  Factory(Ptr<Options> options, Args&&... moreArgs) : Factory(options) {
-    setOpts(std::forward<Args>(moreArgs)...);
+  Factory(Ptr<Options> options, Args&&... args) : Factory(options) {
+    setOpts(std::forward<Args>(args)...);
   }
 
   virtual ~Factory() {}
@@ -47,19 +47,13 @@ class Factory : public std::enable_shared_from_this<Factory> {
     options_->set(key, value);
   }
 
-  // set multiple options
+  // set one or more options at once
   // setOpts("var1", val1, "var2", val2, ...);
-  template <typename T>
-  void setOpts(const std::string& key, T value) { options_->set(key, value); }
-
   template <typename T, typename... Args>
   void setOpts(const std::string& key, T value, Args&&... moreArgs) {
-    setOpt(key, value);
-    setOpts(std::forward<Args>(moreArgs)...); // recursively set the remaining args
+    options_->set(key, value, std::forward<Args>(moreArgs)...);
   }
 
-  //void mergeOpts(const std::string& yaml) { options_->parse(yaml); }
-
   void mergeOpts(Ptr<Options> options) { options_->merge(options); }
 
   template <typename Cast>
diff --git a/src/layers/generic.cpp b/src/layers/generic.cpp
index 70e29a0d3..ffd9e9d8b 100755
--- a/src/layers/generic.cpp
+++ b/src/layers/generic.cpp
@@ -433,9 +433,8 @@ namespace marian {
         "dimEmb",   opt<int>("dim-emb"),
         "dropout",  dropout_,
         "prefix",   (opt<bool>("tied-embeddings-src") || opt<bool>("tied-embeddings-all")) ? "Wemb" : prefix_ + "_Wemb",
+        "fixed",    embeddingFix_,
         "vocab",    opt<std::vector<std::string>>("vocabs")[batchIndex_]); // for factored embeddings
-    if(options_->has(embeddingFixParamName_))
-      options->set("fixed", opt<bool>(embeddingFixParamName_));
     if(options_->hasAndNotEmpty("embedding-vectors")) {
       auto embFiles = opt<std::vector<std::string>>("embedding-vectors");
       options->set(
@@ -459,8 +458,10 @@ namespace marian {
         "ulrKeysFile",       opt<std::string>("ulr-keys-vectors")));
   }
 
-  // get embedding layer for this encoder or decoder; lazily create it if not created yet
-  Ptr<IEmbeddingLayer> EncoderDecoderLayerBase::getEmbeddingLayer(bool ulr) {
+  // get embedding layer for this encoder or decoder
+  // This is lazy mostly because the constructors of the consuming objects are not
+  // guaranteed presently to have access to their graph.
+  Ptr<IEmbeddingLayer> EncoderDecoderLayerBase::getEmbeddingLayer(bool ulr) const {
     if (embeddingLayers_.size() <= batchIndex_ || !embeddingLayers_[batchIndex_]) { // lazy
       if (embeddingLayers_.size() <= batchIndex_)
         embeddingLayers_.resize(batchIndex_ + 1);
diff --git a/src/layers/generic.h b/src/layers/generic.h
index ea34f9379..15195b683 100755
--- a/src/layers/generic.h
+++ b/src/layers/generic.h
@@ -67,20 +67,20 @@ struct IEmbeddingLayer {
 class EncoderDecoderLayerBase : public LayerBase {
 protected:
   const std::string prefix_;
-  const std::string embeddingFixParamName_; // "embedding-fix-src" or "embedding-fix-trg"
-  const bool inference_;
+  const bool embeddingFix_;
   const float dropout_;
+  const bool inference_;
   const size_t batchIndex_;
-  std::vector<Ptr<IEmbeddingLayer>> embeddingLayers_; // (lazily created)
+  mutable std::vector<Ptr<IEmbeddingLayer>> embeddingLayers_; // (lazily created)
 
   EncoderDecoderLayerBase(const std::string& prefix, size_t batchIndex, Ptr<Options> options,
-        const std::string& dropoutParamName,
-        const std::string& embeddingFixParamName) :
+        float dropout,
+        bool embeddingFix) :
       LayerBase(/*graph=*/nullptr, options), // @BUGBUG: we really should pass the graph in here
       prefix_(options->get<std::string>("prefix", prefix)),
-      embeddingFixParamName_(embeddingFixParamName),
+      embeddingFix_(embeddingFix),
+      dropout_(dropout),
       inference_(options->get<bool>("inference", false)),
-      dropout_(inference_ ? 0 : opt<float>(dropoutParamName)),
       batchIndex_(options->get<size_t>("index", batchIndex)) {}
 
   virtual ~EncoderDecoderLayerBase() {}
@@ -91,7 +91,7 @@ class EncoderDecoderLayerBase : public LayerBase {
 
 public:
   // get embedding layer; lazily create on first call
-  Ptr<IEmbeddingLayer> getEmbeddingLayer(bool ulr = false);
+  Ptr<IEmbeddingLayer> getEmbeddingLayer(bool ulr = false) const;
 };
 
 class FactoredVocab;
diff --git a/src/models/decoder.h b/src/models/decoder.h
index 2a89fadde..65c856d2f 100755
--- a/src/models/decoder.h
+++ b/src/models/decoder.h
@@ -16,8 +16,8 @@ class DecoderBase : public EncoderDecoderLayerBase {
 public:
   DecoderBase(Ptr<Options> options) :
     EncoderDecoderLayerBase("decoder", /*batchIndex=*/1, options,
-        /*dropoutParamName=*/"dropout-trg",
-        /*embeddingFixParamName=*/"embedding-fix-trg") {}
+        options->get<float>("dropout-trg", 0.0f),
+        options->get<bool>("embedding-fix-trg", false)) {}
 
   virtual Ptr<DecoderState> startState(Ptr<ExpressionGraph>,
                                        Ptr<data::CorpusBatch> batch,
diff --git a/src/models/encoder.h b/src/models/encoder.h
index acff064f9..baa90b460 100755
--- a/src/models/encoder.h
+++ b/src/models/encoder.h
@@ -9,8 +9,8 @@ class EncoderBase : public EncoderDecoderLayerBase {
 public:
   EncoderBase(Ptr<Options> options) :
     EncoderDecoderLayerBase("encoder", /*batchIndex=*/0, options,
-        /*dropoutParamName=*/"dropout-src",
-        /*embeddingFixParamName=*/"embedding-fix-src") {}
+        options->get<float>("dropout-src", 0.0f),
+        options->get<bool>("embedding-fix-src", false)) {}
 
   // @TODO: turn into an interface. Also see if we can get rid of the graph parameter.
   virtual Ptr<EncoderState> build(Ptr<ExpressionGraph>, Ptr<data::CorpusBatch>) = 0;
diff --git a/src/models/model_factory.cpp b/src/models/model_factory.cpp
index b0cd02e0b..3e50d4238 100755
--- a/src/models/model_factory.cpp
+++ b/src/models/model_factory.cpp
@@ -135,10 +135,10 @@ Ptr<IModel> createBaseModelByType(std::string type, usage use, Ptr<Options> opti
     dimVocabs.resize(idx + 1);
     std::fill(dimVocabs.begin(), dimVocabs.end(), vocab);
 
-    return models::encoder_decoder()(options)
-        ("usage", use)
-        ("type", "s2s")
-        ("original-type", type)
+    return models::encoder_decoder(options->with(
+         "usage", use,
+         "type", "s2s",
+         "original-type", type))
         .push_back(models::decoder()
                    ("index", idx)
                    ("dim-vocabs", dimVocabs))
diff --git a/src/models/model_factory.h b/src/models/model_factory.h
old mode 100644
new mode 100755
index 8f3f07abe..1dca104d5
--- a/src/models/model_factory.h
+++ b/src/models/model_factory.h
@@ -10,8 +10,9 @@ namespace marian {
 namespace models {
 
 class EncoderFactory : public Factory {
+  using Factory::Factory;
 public:
-  EncoderFactory(Ptr<ExpressionGraph> graph = nullptr) : Factory() {}
+  //EncoderFactory(Ptr<ExpressionGraph> graph = nullptr) : Factory() {}
 
   virtual Ptr<EncoderBase> construct(Ptr<ExpressionGraph> graph);
 };
@@ -19,8 +20,9 @@ class EncoderFactory : public Factory {
 typedef Accumulator<EncoderFactory> encoder;
 
 class DecoderFactory : public Factory {
+  using Factory::Factory;
 public:
-  DecoderFactory(Ptr<ExpressionGraph> graph = nullptr) : Factory() {}
+  //DecoderFactory(Ptr<ExpressionGraph> graph = nullptr) : Factory() {}
 
   virtual Ptr<DecoderBase> construct(Ptr<ExpressionGraph> graph);
 };
@@ -28,9 +30,10 @@ class DecoderFactory : public Factory {
 typedef Accumulator<DecoderFactory> decoder;
 
 class ClassifierFactory : public Factory {
+  using Factory::Factory;
 public:
-  ClassifierFactory(Ptr<ExpressionGraph> graph = nullptr) 
-     : Factory() {}
+  //ClassifierFactory(Ptr<ExpressionGraph> graph = nullptr) 
+  //   : Factory() {}
 
   virtual Ptr<ClassifierBase> construct(Ptr<ExpressionGraph> graph);
 };
@@ -38,13 +41,14 @@ class ClassifierFactory : public Factory {
 typedef Accumulator<ClassifierFactory> classifier;
 
 class EncoderDecoderFactory : public Factory {
+  using Factory::Factory;
 private:
   std::vector<encoder> encoders_;
   std::vector<decoder> decoders_;
 
 public:
-  EncoderDecoderFactory(Ptr<ExpressionGraph> graph = nullptr)
-      : Factory() {}
+  //EncoderDecoderFactory(Ptr<ExpressionGraph> graph = nullptr)
+  //    : Factory() {}
 
   Accumulator<EncoderDecoderFactory> push_back(encoder enc) {
     encoders_.push_back(enc);
@@ -62,13 +66,14 @@ class EncoderDecoderFactory : public Factory {
 typedef Accumulator<EncoderDecoderFactory> encoder_decoder;
 
 class EncoderClassifierFactory : public Factory {
+  using Factory::Factory;
 private:
   std::vector<encoder> encoders_;
   std::vector<classifier> classifiers_;
 
 public:
-  EncoderClassifierFactory(Ptr<ExpressionGraph> graph = nullptr)
-      : Factory() {}
+  //EncoderClassifierFactory(Ptr<ExpressionGraph> graph = nullptr)
+  //    : Factory() {}
 
   Accumulator<EncoderClassifierFactory> push_back(encoder enc) {
     encoders_.push_back(enc);
diff --git a/src/rnn/constructors.h b/src/rnn/constructors.h
index c8ddbb61c..6bac01865 100755
--- a/src/rnn/constructors.h
+++ b/src/rnn/constructors.h
@@ -8,9 +8,10 @@ namespace marian {
 namespace rnn {
 
 struct StackableFactory : public Factory {
-  StackableFactory() : Factory() {}
-  StackableFactory(const StackableFactory&) = default;
-  StackableFactory(StackableFactory&&) = default;
+  using Factory::Factory;
+  //StackableFactory() : Factory() {}
+  //StackableFactory(const StackableFactory&) = default;
+  //StackableFactory(StackableFactory&&) = default;
 
   virtual ~StackableFactory() {}
 

From dbe09c537247e59f5780e5bd20dcb843c483cb3e Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Wed, 15 May 2019 18:21:46 -0700
Subject: [PATCH 480/838] more examples of constructing a transformer directly

---
 src/layers/factory.h         | 19 +++++++++----------
 src/models/model_factory.cpp | 18 +++++++++++++-----
 src/models/model_factory.h   | 13 -------------
 src/rnn/constructors.h       | 34 ++++++++++++++++------------------
 4 files changed, 38 insertions(+), 46 deletions(-)

diff --git a/src/layers/factory.h b/src/layers/factory.h
index bb493f8a0..1f00210fe 100755
--- a/src/layers/factory.h
+++ b/src/layers/factory.h
@@ -27,6 +27,7 @@ class Factory : public std::enable_shared_from_this<Factory> {
   Factory(Ptr<Options> options, Args&&... args) : Factory(options) {
     setOpts(std::forward<Args>(args)...);
   }
+  Factory(const Factory& factory) = default;
 
   virtual ~Factory() {}
 
@@ -56,22 +57,20 @@ class Factory : public std::enable_shared_from_this<Factory> {
 
   void mergeOpts(Ptr<Options> options) { options_->merge(options); }
 
-  template <typename Cast>
-  inline Ptr<Cast> as() {
-    return std::dynamic_pointer_cast<Cast>(shared_from_this());
-  }
+  template <class Cast>
+  inline Ptr<Cast> as() { return std::dynamic_pointer_cast<Cast>(shared_from_this()); }
 
-  template <typename Cast>
-  inline bool is() {
-    return as<Cast>() != nullptr;
-  }
+  // @TODO: this fails with 'target type must be a pointer or reference to a defined class'
+  //template <class Cast>
+  //inline bool is() { return dynamic_cast<Cast>(this) != nullptr; }
+  template <class Cast>
+  inline bool is() { return std::dynamic_pointer_cast<Cast>(shared_from_this()) != nullptr; }
 };
 
 // simplest form of Factory that just passes on options to the constructor of a layer type
 template<class Class>
 struct ConstructingFactory : public Factory {
-  template <typename... Args>
-  ConstructingFactory(Args&&... moreArgs) : Factory(std::forward<Args>(moreArgs)...) {}
+  using Factory::Factory;
 
   Ptr<Class> construct(Ptr<ExpressionGraph> graph) {
     return New<Class>(graph, options_);
diff --git a/src/models/model_factory.cpp b/src/models/model_factory.cpp
index 3e50d4238..ce8fbf1fd 100755
--- a/src/models/model_factory.cpp
+++ b/src/models/model_factory.cpp
@@ -103,20 +103,28 @@ Ptr<IModel> createBaseModelByType(std::string type, usage use, Ptr<Options> opti
   Ptr<ExpressionGraph> graph = nullptr; // graph unknown at this stage
   // clang-format off
   if(type == "s2s" || type == "amun" || type == "nematus") {
-    return models::encoder_decoder()(options)
-        ("usage", use)
-        ("original-type", type)
+    return models::encoder_decoder(options->with(
+         "usage", use,
+         "original-type", type))
         .push_back(models::encoder()("type", "s2s"))
         .push_back(models::decoder()("type", "s2s"))
         .construct(graph);
   }
 
   else if(type == "transformer") {
-    return models::encoder_decoder()(options)
-        ("usage", use)
+#if 1
+    auto newOptions = options->with("usage", use);
+    auto res = New<EncoderDecoder>(/*graph,*/ newOptions); // @TODO: put EncoderDecoder on top of LayerBase
+    res->push_back(models::encoder(newOptions->with("type", "transformer")).construct(graph));
+    res->push_back(models::decoder(newOptions->with("type", "transformer")).construct(graph));
+    return res;
+#else
+    return models::encoder_decoder(options->with(
+         "usage", use))
         .push_back(models::encoder()("type", "transformer"))
         .push_back(models::decoder()("type", "transformer"))
         .construct(graph);
+#endif
   }
 
   else if(type == "transformer_s2s") {
diff --git a/src/models/model_factory.h b/src/models/model_factory.h
index 1dca104d5..1840df8f5 100755
--- a/src/models/model_factory.h
+++ b/src/models/model_factory.h
@@ -12,8 +12,6 @@ namespace models {
 class EncoderFactory : public Factory {
   using Factory::Factory;
 public:
-  //EncoderFactory(Ptr<ExpressionGraph> graph = nullptr) : Factory() {}
-
   virtual Ptr<EncoderBase> construct(Ptr<ExpressionGraph> graph);
 };
 
@@ -22,8 +20,6 @@ typedef Accumulator<EncoderFactory> encoder;
 class DecoderFactory : public Factory {
   using Factory::Factory;
 public:
-  //DecoderFactory(Ptr<ExpressionGraph> graph = nullptr) : Factory() {}
-
   virtual Ptr<DecoderBase> construct(Ptr<ExpressionGraph> graph);
 };
 
@@ -32,9 +28,6 @@ typedef Accumulator<DecoderFactory> decoder;
 class ClassifierFactory : public Factory {
   using Factory::Factory;
 public:
-  //ClassifierFactory(Ptr<ExpressionGraph> graph = nullptr) 
-  //   : Factory() {}
-
   virtual Ptr<ClassifierBase> construct(Ptr<ExpressionGraph> graph);
 };
 
@@ -47,9 +40,6 @@ class EncoderDecoderFactory : public Factory {
   std::vector<decoder> decoders_;
 
 public:
-  //EncoderDecoderFactory(Ptr<ExpressionGraph> graph = nullptr)
-  //    : Factory() {}
-
   Accumulator<EncoderDecoderFactory> push_back(encoder enc) {
     encoders_.push_back(enc);
     return Accumulator<EncoderDecoderFactory>(*this);
@@ -72,9 +62,6 @@ class EncoderClassifierFactory : public Factory {
   std::vector<classifier> classifiers_;
 
 public:
-  //EncoderClassifierFactory(Ptr<ExpressionGraph> graph = nullptr)
-  //    : Factory() {}
-
   Accumulator<EncoderClassifierFactory> push_back(encoder enc) {
     encoders_.push_back(enc);
     return Accumulator<EncoderClassifierFactory>(*this);
diff --git a/src/rnn/constructors.h b/src/rnn/constructors.h
index 6bac01865..beb1fce11 100755
--- a/src/rnn/constructors.h
+++ b/src/rnn/constructors.h
@@ -7,24 +7,22 @@
 namespace marian {
 namespace rnn {
 
-struct StackableFactory : public Factory {
-  using Factory::Factory;
-  //StackableFactory() : Factory() {}
-  //StackableFactory(const StackableFactory&) = default;
-  //StackableFactory(StackableFactory&&) = default;
-
-  virtual ~StackableFactory() {}
-
-  template <typename Cast>
-  inline Ptr<Cast> as() {
-    return std::dynamic_pointer_cast<Cast>(shared_from_this());
-  }
-
-  template <typename Cast>
-  inline bool is() {
-    return as<Cast>() != nullptr;
-  }
-};
+typedef Factory StackableFactory;
+//struct StackableFactory : public Factory {StackableFactory
+//  using Factory::Factory;
+//
+//  virtual ~StackableFactory() {}
+//
+//  template <typename Cast>
+//  inline Ptr<Cast> as() {
+//    return std::dynamic_pointer_cast<Cast>(shared_from_this());
+//  }
+//
+//  template <typename Cast>
+//  inline bool is() {
+//    return as<Cast>() != nullptr;
+//  }
+//};
 
 struct InputFactory : public StackableFactory {
   virtual Ptr<CellInput> construct(Ptr<ExpressionGraph> graph) = 0;

From d63da3db88336417de9f6e33ca4e8dca102ef278 Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Wed, 15 May 2019 18:26:48 -0700
Subject: [PATCH 481/838] further changed construction example of 'transformer'
 to direct creation of layer objects

---
 src/models/model_factory.cpp | 8 +++++---
 1 file changed, 5 insertions(+), 3 deletions(-)

diff --git a/src/models/model_factory.cpp b/src/models/model_factory.cpp
index ce8fbf1fd..e2d192a05 100755
--- a/src/models/model_factory.cpp
+++ b/src/models/model_factory.cpp
@@ -114,9 +114,11 @@ Ptr<IModel> createBaseModelByType(std::string type, usage use, Ptr<Options> opti
   else if(type == "transformer") {
 #if 1
     auto newOptions = options->with("usage", use);
-    auto res = New<EncoderDecoder>(/*graph,*/ newOptions); // @TODO: put EncoderDecoder on top of LayerBase
-    res->push_back(models::encoder(newOptions->with("type", "transformer")).construct(graph));
-    res->push_back(models::decoder(newOptions->with("type", "transformer")).construct(graph));
+    auto res = New<EncoderDecoder>(/*graph,*/ newOptions);
+    res->push_back(New<EncoderTransformer>(/*graph,*/ newOptions->with("type", "transformer")));
+    res->push_back(New<DecoderTransformer>(/*graph,*/ newOptions->with("type", "transformer")));
+    //res->push_back(models::encoder(newOptions->with("type", "transformer")).construct(graph));
+    //res->push_back(models::decoder(newOptions->with("type", "transformer")).construct(graph));
     return res;
 #else
     return models::encoder_decoder(options->with(

From 6d3736999a9f8e7cef63691f18594497ac9ae29f Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Wed, 15 May 2019 18:31:39 -0700
Subject: [PATCH 482/838] renamed EncoderDecoderBase to IEncoderDecoder

---
 src/models/costs.h           | 10 +++++-----
 src/models/encoder_decoder.h |  4 ++--
 src/rescorer/rescorer.h      |  2 +-
 src/translator/scorers.h     |  8 ++++----
 4 files changed, 12 insertions(+), 12 deletions(-)
 mode change 100644 => 100755 src/rescorer/rescorer.h
 mode change 100644 => 100755 src/translator/scorers.h

diff --git a/src/models/costs.h b/src/models/costs.h
index c47092264..6e0325fe1 100755
--- a/src/models/costs.h
+++ b/src/models/costs.h
@@ -234,16 +234,16 @@ class GumbelSoftmaxStep : public ILogProbStep {
   }
 };
 
-// class to wrap an EncoderDecoderBase and a ILogProbStep that are executed in sequence,
-// wrapped again in the EncoderDecoderBase interface
+// class to wrap an IEncoderDecoder and a ILogProbStep that are executed in sequence,
+// wrapped again in the IEncoderDecoder interface
 // @TODO: seems we are conflating an interface defition with its implementation?
-class Stepwise : public EncoderDecoderBase {
+class Stepwise : public IEncoderDecoder {
 protected:
-  Ptr<EncoderDecoderBase> encdec_;
+  Ptr<IEncoderDecoder> encdec_;
   Ptr<ILogProbStep> cost_;
 
 public:
-  Stepwise(Ptr<EncoderDecoderBase> encdec, Ptr<ILogProbStep> cost)
+  Stepwise(Ptr<IEncoderDecoder> encdec, Ptr<ILogProbStep> cost)
       : encdec_(encdec), cost_(cost) {}
 
   virtual void load(Ptr<ExpressionGraph> graph,
diff --git a/src/models/encoder_decoder.h b/src/models/encoder_decoder.h
index f09e4f2c3..b55649298 100755
--- a/src/models/encoder_decoder.h
+++ b/src/models/encoder_decoder.h
@@ -9,7 +9,7 @@
 
 namespace marian {
 
-class EncoderDecoderBase : public models::IModel {
+class IEncoderDecoder : public models::IModel {
 public:
   virtual void load(Ptr<ExpressionGraph> graph,
                     const std::string& name,
@@ -58,7 +58,7 @@ class EncoderDecoderBase : public models::IModel {
   virtual data::SoftAlignment getAlignment() = 0;
 };
 
-class EncoderDecoder : public EncoderDecoderBase {
+class EncoderDecoder : public IEncoderDecoder {
 protected:
   Ptr<Options> options_;
   Ptr<data::ShortlistGenerator> shortlistGenerator_;
diff --git a/src/rescorer/rescorer.h b/src/rescorer/rescorer.h
old mode 100644
new mode 100755
index 8925391bd..e4d01ec55
--- a/src/rescorer/rescorer.h
+++ b/src/rescorer/rescorer.h
@@ -35,7 +35,7 @@ class Rescorer {
 
   data::SoftAlignment getAlignment() {
     auto model = std::static_pointer_cast<models::Trainer>(builder_)->getModel();
-    return std::static_pointer_cast<EncoderDecoderBase>(model)->getAlignment();
+    return std::static_pointer_cast<IEncoderDecoder>(model)->getAlignment();
   }
 };
 
diff --git a/src/translator/scorers.h b/src/translator/scorers.h
old mode 100644
new mode 100755
index 8da8abb0c..33bb23abd
--- a/src/translator/scorers.h
+++ b/src/translator/scorers.h
@@ -62,10 +62,10 @@ class ScorerWrapperState : public ScorerState {
   }
 };
 
-// class to wrap EncoderDecoderBase in a Scorer interface
+// class to wrap IEncoderDecoder in a Scorer interface
 class ScorerWrapper : public Scorer {
 private:
-  Ptr<EncoderDecoderBase> encdec_;
+  Ptr<IEncoderDecoder> encdec_;
   std::string fname_;
   const void* ptr_;
 
@@ -75,7 +75,7 @@ class ScorerWrapper : public Scorer {
                 float weight,
                 const std::string& fname)
       : Scorer(name, weight),
-        encdec_(std::static_pointer_cast<EncoderDecoderBase>(encdec)),
+        encdec_(std::static_pointer_cast<IEncoderDecoder>(encdec)),
         fname_(fname),
         ptr_{0} {}
 
@@ -84,7 +84,7 @@ class ScorerWrapper : public Scorer {
                 float weight,
                 const void* ptr)
       : Scorer(name, weight),
-        encdec_(std::static_pointer_cast<EncoderDecoderBase>(encdec)),
+        encdec_(std::static_pointer_cast<IEncoderDecoder>(encdec)),
         ptr_{ptr} {}
 
   virtual void init(Ptr<ExpressionGraph> graph) override {

From 40ce6007812bd25673af3d4d45c0a0c53eb738e2 Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Wed, 15 May 2019 18:44:59 -0700
Subject: [PATCH 483/838] EncoderDecoder now derives from LayerBase

---
 src/models/amun.h              |  2 +-
 src/models/encoder_decoder.cpp |  4 ++--
 src/models/encoder_decoder.h   |  5 ++---
 src/models/model_factory.cpp   | 26 ++++++++++++--------------
 src/models/nematus.h           |  3 +--
 5 files changed, 18 insertions(+), 22 deletions(-)
 mode change 100644 => 100755 src/models/amun.h
 mode change 100644 => 100755 src/models/encoder_decoder.cpp
 mode change 100644 => 100755 src/models/nematus.h

diff --git a/src/models/amun.h b/src/models/amun.h
old mode 100644
new mode 100755
index 35b652068..6784587c8
--- a/src/models/amun.h
+++ b/src/models/amun.h
@@ -8,7 +8,7 @@ namespace marian {
 
 class Amun : public EncoderDecoder {
 public:
-  Amun(Ptr<Options> options) : EncoderDecoder(options) {
+  Amun(Ptr<ExpressionGraph> graph, Ptr<Options> options) : EncoderDecoder(graph, options) {
     ABORT_IF(opt<int>("enc-depth") > 1,
              "--type amun does not currently support multiple encoder "
              "layers, use --type s2s");
diff --git a/src/models/encoder_decoder.cpp b/src/models/encoder_decoder.cpp
old mode 100644
new mode 100755
index 862355ee2..2ac6ff6d9
--- a/src/models/encoder_decoder.cpp
+++ b/src/models/encoder_decoder.cpp
@@ -4,8 +4,8 @@
 
 namespace marian {
 
-EncoderDecoder::EncoderDecoder(Ptr<Options> options)
-    : options_(options),
+EncoderDecoder::EncoderDecoder(Ptr<ExpressionGraph> graph, Ptr<Options> options)
+    : LayerBase(graph, options),
       prefix_(options->get<std::string>("prefix", "")),
       inference_(options->get<bool>("inference", false)) {
 
diff --git a/src/models/encoder_decoder.h b/src/models/encoder_decoder.h
index b55649298..6ed069c68 100755
--- a/src/models/encoder_decoder.h
+++ b/src/models/encoder_decoder.h
@@ -58,9 +58,8 @@ class IEncoderDecoder : public models::IModel {
   virtual data::SoftAlignment getAlignment() = 0;
 };
 
-class EncoderDecoder : public IEncoderDecoder {
+class EncoderDecoder : public IEncoderDecoder, public LayerBase {
 protected:
-  Ptr<Options> options_;
   Ptr<data::ShortlistGenerator> shortlistGenerator_;
 
   const std::string prefix_;
@@ -79,7 +78,7 @@ class EncoderDecoder : public IEncoderDecoder {
 public:
   typedef data::Corpus dataset_type;
 
-  EncoderDecoder(Ptr<Options> options);
+  EncoderDecoder(Ptr<ExpressionGraph> graph, Ptr<Options> options);
 
   virtual Ptr<Options> getOptions() override { return options_; }
 
diff --git a/src/models/model_factory.cpp b/src/models/model_factory.cpp
index e2d192a05..f8f03077c 100755
--- a/src/models/model_factory.cpp
+++ b/src/models/model_factory.cpp
@@ -55,21 +55,20 @@ Ptr<DecoderBase> DecoderFactory::construct(Ptr<ExpressionGraph> graph) {
 Ptr<ClassifierBase> ClassifierFactory::construct(Ptr<ExpressionGraph> /*graph*/) {
   if(options_->get<std::string>("type") == "bert-masked-lm")
     return New<BertMaskedLM>(options_);
-  if(options_->get<std::string>("type") == "bert-classifier")
+  else if(options_->get<std::string>("type") == "bert-classifier")
     return New<BertClassifier>(options_);
-  ABORT("Unknown classifier type");
+  else
+    ABORT("Unknown classifier type");
 }
 
 Ptr<IModel> EncoderDecoderFactory::construct(Ptr<ExpressionGraph> graph) {
   Ptr<EncoderDecoder> encdec;
-
   if(options_->get<std::string>("type") == "amun")
-    encdec = New<Amun>(options_);
-  if(options_->get<std::string>("type") == "nematus")
-    encdec = New<Nematus>(options_);
-
-  if(!encdec)
-    encdec = New<EncoderDecoder>(options_);
+    encdec = New<Amun>(graph, options_);
+  else if(options_->get<std::string>("type") == "nematus")
+    encdec = New<Nematus>(graph, options_);
+  else
+    encdec = New<EncoderDecoder>(graph, options_);
 
   for(auto& ef : encoders_)
     encdec->push_back(ef(options_).construct(graph));
@@ -82,13 +81,12 @@ Ptr<IModel> EncoderDecoderFactory::construct(Ptr<ExpressionGraph> graph) {
 
 Ptr<IModel> EncoderClassifierFactory::construct(Ptr<ExpressionGraph> graph) {
   Ptr<EncoderClassifier> enccls;
-  if(options_->get<std::string>("type") == "bert") {
+  if(options_->get<std::string>("type") == "bert")
     enccls = New<BertEncoderClassifier>(options_);
-  } else if(options_->get<std::string>("type") == "bert-classifier") {
+  else if(options_->get<std::string>("type") == "bert-classifier")
     enccls = New<BertEncoderClassifier>(options_);
-  } else {
+  else
     enccls = New<EncoderClassifier>(options_);
-  }
 
   for(auto& ef : encoders_)
     enccls->push_back(ef(options_).construct(graph));
@@ -114,7 +112,7 @@ Ptr<IModel> createBaseModelByType(std::string type, usage use, Ptr<Options> opti
   else if(type == "transformer") {
 #if 1
     auto newOptions = options->with("usage", use);
-    auto res = New<EncoderDecoder>(/*graph,*/ newOptions);
+    auto res = New<EncoderDecoder>(graph, newOptions);
     res->push_back(New<EncoderTransformer>(/*graph,*/ newOptions->with("type", "transformer")));
     res->push_back(New<DecoderTransformer>(/*graph,*/ newOptions->with("type", "transformer")));
     //res->push_back(models::encoder(newOptions->with("type", "transformer")).construct(graph));
diff --git a/src/models/nematus.h b/src/models/nematus.h
old mode 100644
new mode 100755
index 88e9854be..d8e094ead
--- a/src/models/nematus.h
+++ b/src/models/nematus.h
@@ -8,8 +8,7 @@ namespace marian {
 
 class Nematus : public EncoderDecoder {
 public:
-  template <class... Args>
-  Nematus(Ptr<Options> options) : EncoderDecoder(options), nameMap_(createNameMap()) {
+  Nematus(Ptr<ExpressionGraph> graph, Ptr<Options> options) : EncoderDecoder(graph, options), nameMap_(createNameMap()) {
     ABORT_IF(options_->get<std::string>("enc-type") != "bidirectional",
              "--type nematus does not currently support other encoder "
              "type than bidirectional, use --type s2s");

From 442205fd6e473fe601277f808f80c2e63b6fd1cd Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Wed, 15 May 2019 18:59:40 -0700
Subject: [PATCH 484/838] more layers are based on LayerBase and get
 constructed as (graph, optioons)

---
 src/layers/generic.h             |  4 ++--
 src/models/bert.h                |  9 ++++++---
 src/models/classifier.h          |  7 ++++---
 src/models/decoder.h             |  4 ++--
 src/models/encoder.h             |  4 ++--
 src/models/model_factory.cpp     | 22 ++++++++++------------
 src/models/s2s.h                 |  6 ++++--
 src/models/transformer.h         | 13 +++++++------
 src/models/transformer_factory.h |  4 ++--
 src/models/transformer_stub.cpp  |  8 ++++----
 10 files changed, 43 insertions(+), 38 deletions(-)
 mode change 100644 => 100755 src/models/transformer_factory.h
 mode change 100644 => 100755 src/models/transformer_stub.cpp

diff --git a/src/layers/generic.h b/src/layers/generic.h
index 15195b683..14bf34e6c 100755
--- a/src/layers/generic.h
+++ b/src/layers/generic.h
@@ -73,10 +73,10 @@ class EncoderDecoderLayerBase : public LayerBase {
   const size_t batchIndex_;
   mutable std::vector<Ptr<IEmbeddingLayer>> embeddingLayers_; // (lazily created)
 
-  EncoderDecoderLayerBase(const std::string& prefix, size_t batchIndex, Ptr<Options> options,
+  EncoderDecoderLayerBase(Ptr<ExpressionGraph> graph, Ptr<Options> options, const std::string& prefix, size_t batchIndex,
         float dropout,
         bool embeddingFix) :
-      LayerBase(/*graph=*/nullptr, options), // @BUGBUG: we really should pass the graph in here
+      LayerBase(graph, options),
       prefix_(options->get<std::string>("prefix", prefix)),
       embeddingFix_(embeddingFix),
       dropout_(dropout),
diff --git a/src/models/bert.h b/src/models/bert.h
index 33f21fc82..682efa48b 100755
--- a/src/models/bert.h
+++ b/src/models/bert.h
@@ -218,8 +218,9 @@ class BertEncoderClassifier : public EncoderClassifier, public data::RNGEngine {
  *          Is there a way to maybe instead include a reference in here, instead of deriving from it?
  */
 class BertEncoder : public EncoderTransformer {
+  using EncoderTransformer::EncoderTransformer;
 public:
-  BertEncoder(Ptr<Options> options) : EncoderTransformer(options) {}
+  //BertEncoder(Ptr<Options> options) : EncoderTransformer(options) {}
 
   Expr addSentenceEmbeddings(Expr embeddings,
                              Ptr<data::CorpusBatch> batch,
@@ -269,8 +270,9 @@ class BertEncoder : public EncoderTransformer {
  * @TODO: This is in fact so generic that we might move it out of here as the typical classifier implementation
  */
 class BertClassifier : public ClassifierBase {
+  using ClassifierBase::ClassifierBase;
 public:
-  BertClassifier(Ptr<Options> options) : ClassifierBase(options) {}
+  //BertClassifier(Ptr<Options> options) : ClassifierBase(options) {}
 
   Ptr<ClassifierState> apply(Ptr<ExpressionGraph> graph, Ptr<data::CorpusBatch> batch, const std::vector<Ptr<EncoderState>>& encoderStates) override {
     ABORT_IF(encoderStates.size() != 1, "Currently we only support a single encoder BERT model");
@@ -312,8 +314,9 @@ class BertClassifier : public ClassifierBase {
  * as this is self-generating its labels from the source. Labels are dynamically created as complements of the masking process.
  */
 class BertMaskedLM : public ClassifierBase {
+  using ClassifierBase::ClassifierBase;
 public:
-  BertMaskedLM(Ptr<Options> options) : ClassifierBase(options) {}
+  //BertMaskedLM(Ptr<Options> options) : ClassifierBase(options) {}
 
   Ptr<ClassifierState> apply(Ptr<ExpressionGraph> graph, Ptr<data::CorpusBatch> batch, const std::vector<Ptr<EncoderState>>& encoderStates) override {
     Ptr<data::BertBatch> bertBatch = std::dynamic_pointer_cast<data::BertBatch>(batch);
diff --git a/src/models/classifier.h b/src/models/classifier.h
index 773299b4b..9faa907e1 100755
--- a/src/models/classifier.h
+++ b/src/models/classifier.h
@@ -11,7 +11,8 @@ namespace marian {
  * Simple base class for Classifiers to be used in EncoderClassifier framework
  * Currently only implementations are in bert.h
  */
-class ClassifierBase {
+class ClassifierBase :public LayerBase {
+  using LayerBase::LayerBase;
 protected:
   Ptr<Options> options_;
   const std::string prefix_{"classifier"};
@@ -19,8 +20,8 @@ class ClassifierBase {
   const size_t batchIndex_{0};
 
 public:
-  ClassifierBase(Ptr<Options> options)
-      : options_(options),
+  ClassifierBase(Ptr<ExpressionGraph> graph, Ptr<Options> options)
+      : LayerBase(graph, options),
         prefix_(options->get<std::string>("prefix", "classifier")),
         inference_(options->get<bool>("inference", false)),
         batchIndex_(options->get<size_t>("index", 1)) {} // assume that training input has batch index 0 and labels has 1
diff --git a/src/models/decoder.h b/src/models/decoder.h
index 65c856d2f..a79ef3731 100755
--- a/src/models/decoder.h
+++ b/src/models/decoder.h
@@ -14,8 +14,8 @@ class DecoderBase : public EncoderDecoderLayerBase {
   Ptr<data::Shortlist> shortlist_;
 
 public:
-  DecoderBase(Ptr<Options> options) :
-    EncoderDecoderLayerBase("decoder", /*batchIndex=*/1, options,
+  DecoderBase(Ptr<ExpressionGraph> graph, Ptr<Options> options) :
+    EncoderDecoderLayerBase(graph, options, "decoder", /*batchIndex=*/1,
         options->get<float>("dropout-trg", 0.0f),
         options->get<bool>("embedding-fix-trg", false)) {}
 
diff --git a/src/models/encoder.h b/src/models/encoder.h
index baa90b460..61bb0d88b 100755
--- a/src/models/encoder.h
+++ b/src/models/encoder.h
@@ -7,8 +7,8 @@ namespace marian {
 
 class EncoderBase : public EncoderDecoderLayerBase {
 public:
-  EncoderBase(Ptr<Options> options) :
-    EncoderDecoderLayerBase("encoder", /*batchIndex=*/0, options,
+  EncoderBase(Ptr<ExpressionGraph> graph, Ptr<Options> options) :
+    EncoderDecoderLayerBase(graph, options, "encoder", /*batchIndex=*/0,
         options->get<float>("dropout-src", 0.0f),
         options->get<bool>("embedding-fix-src", false)) {}
 
diff --git a/src/models/model_factory.cpp b/src/models/model_factory.cpp
index f8f03077c..098fc0a59 100755
--- a/src/models/model_factory.cpp
+++ b/src/models/model_factory.cpp
@@ -28,7 +28,7 @@ namespace models {
 
 Ptr<EncoderBase> EncoderFactory::construct(Ptr<ExpressionGraph> graph) {
   if(options_->get<std::string>("type") == "s2s")
-    return New<EncoderS2S>(options_);
+    return New<EncoderS2S>(graph, options_);
 
 #ifdef CUDNN
   if(options_->get<std::string>("type") == "char-s2s")
@@ -36,27 +36,27 @@ Ptr<EncoderBase> EncoderFactory::construct(Ptr<ExpressionGraph> graph) {
 #endif
 
   if(options_->get<std::string>("type") == "transformer")
-    return NewEncoderTransformer(options_);
+    return NewEncoderTransformer(graph, options_);
 
   if(options_->get<std::string>("type") == "bert-encoder")
-    return New<BertEncoder>(options_);
+    return New<BertEncoder>(graph, options_);
 
   ABORT("Unknown encoder type");
 }
 
 Ptr<DecoderBase> DecoderFactory::construct(Ptr<ExpressionGraph> graph) {
   if(options_->get<std::string>("type") == "s2s")
-    return New<DecoderS2S>(options_);
+    return New<DecoderS2S>(graph, options_);
   if(options_->get<std::string>("type") == "transformer")
-    return NewDecoderTransformer(options_);
+    return NewDecoderTransformer(graph, options_);
   ABORT("Unknown decoder type");
 }
 
-Ptr<ClassifierBase> ClassifierFactory::construct(Ptr<ExpressionGraph> /*graph*/) {
+Ptr<ClassifierBase> ClassifierFactory::construct(Ptr<ExpressionGraph> graph) {
   if(options_->get<std::string>("type") == "bert-masked-lm")
-    return New<BertMaskedLM>(options_);
+    return New<BertMaskedLM>(graph, options_);
   else if(options_->get<std::string>("type") == "bert-classifier")
-    return New<BertClassifier>(options_);
+    return New<BertClassifier>(graph, options_);
   else
     ABORT("Unknown classifier type");
 }
@@ -113,10 +113,8 @@ Ptr<IModel> createBaseModelByType(std::string type, usage use, Ptr<Options> opti
 #if 1
     auto newOptions = options->with("usage", use);
     auto res = New<EncoderDecoder>(graph, newOptions);
-    res->push_back(New<EncoderTransformer>(/*graph,*/ newOptions->with("type", "transformer")));
-    res->push_back(New<DecoderTransformer>(/*graph,*/ newOptions->with("type", "transformer")));
-    //res->push_back(models::encoder(newOptions->with("type", "transformer")).construct(graph));
-    //res->push_back(models::decoder(newOptions->with("type", "transformer")).construct(graph));
+    res->push_back(New<EncoderTransformer>(graph, newOptions->with("type", "transformer")));
+    res->push_back(New<DecoderTransformer>(graph, newOptions->with("type", "transformer")));
     return res;
 #else
     return models::encoder_decoder(options->with(
diff --git a/src/models/s2s.h b/src/models/s2s.h
index 081bf3e93..126580108 100755
--- a/src/models/s2s.h
+++ b/src/models/s2s.h
@@ -9,6 +9,7 @@
 namespace marian {
 
 class EncoderS2S : public EncoderBase {
+  using EncoderBase::EncoderBase;
 public:
   Expr applyEncoderRNN(Ptr<ExpressionGraph> graph,
                        Expr embeddings,
@@ -119,7 +120,7 @@ class EncoderS2S : public EncoderBase {
     return context;
   }
 
-  EncoderS2S(Ptr<Options> options) : EncoderBase(options) {}
+  //EncoderS2S(Ptr<Options> options) : EncoderBase(options) {}
 
   virtual Ptr<EncoderState> build(Ptr<ExpressionGraph> graph,
                                   Ptr<data::CorpusBatch> batch) override {
@@ -138,6 +139,7 @@ class EncoderS2S : public EncoderBase {
 };
 
 class DecoderS2S : public DecoderBase {
+  using DecoderBase::DecoderBase;
 private:
   Ptr<rnn::RNN> rnn_;
   Ptr<mlp::MLP> output_;
@@ -204,7 +206,7 @@ class DecoderS2S : public DecoderBase {
   }
 
 public:
-  DecoderS2S(Ptr<Options> options) : DecoderBase(options) {}
+  //DecoderS2S(Ptr<Options> options) : DecoderBase(options) {}
 
   virtual Ptr<DecoderState> startState(
       Ptr<ExpressionGraph> graph,
diff --git a/src/models/transformer.h b/src/models/transformer.h
index 8b1f97900..dabb8fb93 100755
--- a/src/models/transformer.h
+++ b/src/models/transformer.h
@@ -22,6 +22,7 @@ namespace marian {
 template<class EncoderOrDecoderBase>
 class Transformer : public EncoderOrDecoderBase {
   typedef EncoderOrDecoderBase Base;
+  using Base::Base;
 
 protected:
   using Base::options_; using Base::inference_; using Base::batchIndex_; using Base::graph_;
@@ -38,10 +39,6 @@ class Transformer : public EncoderOrDecoderBase {
   template <typename T> T opt(const std::string& key, const T& def) const { Ptr<Options> options = options_; if (options->has(key)) return options->get<T>(key); else return def; }
 
 public:
-  Transformer(Ptr<Options> options)
-    : EncoderOrDecoderBase(options) {
-  }
-
   static Expr transposeTimeBatch(Expr input) { return transpose(input, {0, 2, 1, 3}); }
 
   Expr addPositionalEmbeddings(Expr input, int start = 0, bool trainPosEmbeddings = false) const {
@@ -497,8 +494,10 @@ class Transformer : public EncoderOrDecoderBase {
 };
 
 class EncoderTransformer : public Transformer<EncoderBase> {
+  typedef Transformer<EncoderBase> Base;
+  using Base::Base;
 public:
-  EncoderTransformer(Ptr<Options> options) : Transformer(options) {}
+  //EncoderTransformer(Ptr<Options> options) : Transformer(options) {}
   virtual ~EncoderTransformer() {}
 
   virtual Ptr<EncoderState> build(Ptr<ExpressionGraph> graph,
@@ -577,6 +576,8 @@ class TransformerState : public DecoderState {
 };
 
 class DecoderTransformer : public Transformer<DecoderBase> {
+  typedef Transformer<DecoderBase> Base;
+  using Base::Base;
 private:
   Ptr<mlp::Output> output_;
 
@@ -602,7 +603,7 @@ class DecoderTransformer : public Transformer<DecoderBase> {
   }
 
 public:
-  DecoderTransformer(Ptr<Options> options) : Transformer(options) {}
+  //DecoderTransformer(Ptr<ExpressionGraph> graph, Ptr<Options> options) : Transformer(graph, options) {}
 
   virtual Ptr<DecoderState> startState(
       Ptr<ExpressionGraph> graph,
diff --git a/src/models/transformer_factory.h b/src/models/transformer_factory.h
old mode 100644
new mode 100755
index 488c7f5bf..b282d819c
--- a/src/models/transformer_factory.h
+++ b/src/models/transformer_factory.h
@@ -7,6 +7,6 @@
 #include "models/encoder.h"
 
 namespace marian {
-Ptr<EncoderBase> NewEncoderTransformer(Ptr<Options> options);
-Ptr<DecoderBase> NewDecoderTransformer(Ptr<Options> options);
+Ptr<EncoderBase> NewEncoderTransformer(Ptr<ExpressionGraph> graph, Ptr<Options> options);
+Ptr<DecoderBase> NewDecoderTransformer(Ptr<ExpressionGraph> graph, Ptr<Options> options);
 }  // namespace marian
diff --git a/src/models/transformer_stub.cpp b/src/models/transformer_stub.cpp
old mode 100644
new mode 100755
index 1e7b19f68..2e8d694fa
--- a/src/models/transformer_stub.cpp
+++ b/src/models/transformer_stub.cpp
@@ -2,13 +2,13 @@
 
 namespace marian {
 // factory functions
-Ptr<EncoderBase> NewEncoderTransformer(Ptr<Options> options)
+Ptr<EncoderBase> NewEncoderTransformer(Ptr<ExpressionGraph> graph, Ptr<Options> options)
 {
-  return New<EncoderTransformer>(options);
+  return New<EncoderTransformer>(graph, options);
 }
 
-Ptr<DecoderBase> NewDecoderTransformer(Ptr<Options> options)
+Ptr<DecoderBase> NewDecoderTransformer(Ptr<ExpressionGraph> graph, Ptr<Options> options)
 {
-  return New<DecoderTransformer>(options);
+  return New<DecoderTransformer>(graph, options);
 }
 }  // namespace marian

From e5af0f71c3812863636dcee836a0202314f0c1db Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Thu, 16 May 2019 09:30:28 -0700
Subject: [PATCH 485/838] bug fix: beam search must propagate alignments when
 expanding factors (2 lines change plus tens of comments added)

---
 src/data/alignment.h         |  4 +++-
 src/microsoft/quicksand.cpp  |  4 ++--
 src/microsoft/quicksand.h    |  2 +-
 src/models/costs.h           |  2 +-
 src/models/decoder.h         |  2 +-
 src/models/encoder_decoder.h | 12 +++++++-----
 src/models/transformer.h     | 12 ++++++------
 src/translator/beam_search.h | 32 ++++++++++++++++----------------
 src/translator/hypothesis.h  |  4 ++--
 src/translator/scorers.h     |  4 +++-
 10 files changed, 42 insertions(+), 36 deletions(-)

diff --git a/src/data/alignment.h b/src/data/alignment.h
index 49fbde767..1c68bb39e 100644
--- a/src/data/alignment.h
+++ b/src/data/alignment.h
@@ -52,7 +52,9 @@ class WordAlignment {
   std::string toString() const;
 };
 
-typedef std::vector<std::vector<float>> SoftAlignment;
+// soft alignment = P(src pos|trg pos) for each beam and batch index, stored in a flattened CPU-side array
+// Also used on QuickSAND boundary where beam and batch size is 1. Then it is simply [t][s] -> P(s|t)
+typedef std::vector<std::vector<float>> SoftAlignment; // [trg pos][beam depth * max src length * batch size]
 
 WordAlignment ConvertSoftAlignToHardAlign(SoftAlignment alignSoft,
                                           float threshold = 1.f);
diff --git a/src/microsoft/quicksand.cpp b/src/microsoft/quicksand.cpp
index 98ff2978c..440d0c116 100644
--- a/src/microsoft/quicksand.cpp
+++ b/src/microsoft/quicksand.cpp
@@ -169,8 +169,8 @@ class BeamSearchDecoder : public IBeamSearchDecoder {
           data::WordAlignment align = data::ConvertSoftAlignToHardAlign(hyp->tracebackAlignment(), alignmentThreshold);
           // convert to QuickSAND format
           alignmentSets.resize(words.size());
-          for (const auto& p : align) // @TODO: Does the feature_model param max_alignment_links apply here?
-            alignmentSets[p.tgtPos].insert({p.srcPos, p.prob});
+          for (const auto& p : align)
+            alignmentSets[p.tgtPos].insert({p.srcPos, p.prob}); // [trgPos] -> {(srcPos, P(srcPos|trgPos))}
         }
         // form hypothesis to return
         qsNbest.emplace_back(toWordIndexVector(words), std::move(alignmentSets), score);
diff --git a/src/microsoft/quicksand.h b/src/microsoft/quicksand.h
index bc31e1515..c4f539abd 100644
--- a/src/microsoft/quicksand.h
+++ b/src/microsoft/quicksand.h
@@ -19,7 +19,7 @@ typedef uint32_t IndexType;
 typedef IndexType WordIndex;
 typedef std::vector<WordIndex> WordIndices;
 typedef std::vector<WordIndices> QSBatch;
-typedef std::vector<std::set<std::pair<size_t, float>>> AlignmentSets; // [tgtPos] -> set of (srcPos, score)
+typedef std::vector<std::set<std::pair<size_t, float>>> AlignmentSets; // [trgPos] -> set of (srcPos, P(srcPos|trgPos))
 
 typedef std::tuple<WordIndices, AlignmentSets, float> QSSentenceWithProb;
 typedef std::vector<QSSentenceWithProb> QSNBest;
diff --git a/src/models/costs.h b/src/models/costs.h
index 7aa5a5f3e..6c54cde51 100755
--- a/src/models/costs.h
+++ b/src/models/costs.h
@@ -75,7 +75,7 @@ class EncoderDecoderCECost : public ICost {
     multiLoss->push_back(partialLoss);
 
     if(options_->get("guided-alignment", std::string("none")) != "none" && !inference_) {
-      auto attentionVectors = encdec->getDecoders()[0]->getAlignments();
+      auto attentionVectors = encdec->getDecoders()[0]->getAlignments(); // [tgt index][beam depth, max src length, batch size, 1]
       ABORT_IF(attentionVectors.empty(), "Model does not seem to support alignments");
 
       auto attention = concatenate(attentionVectors, /*axis =*/ -1);
diff --git a/src/models/decoder.h b/src/models/decoder.h
index dd5a508a2..984370e33 100755
--- a/src/models/decoder.h
+++ b/src/models/decoder.h
@@ -98,7 +98,7 @@ class DecoderBase {
     state->setTargetHistoryEmbeddings(selectedEmbs);
   }
 
-  virtual const std::vector<Expr> getAlignments(int /*i*/ = 0) { return {}; };
+  virtual const std::vector<Expr> getAlignments(int /*i*/ = 0) { return {}; }; // [tgt index][beam depth, max src length, batch size, 1]
 
   virtual Ptr<data::Shortlist> getShortlist() { return shortlist_; }
   virtual void setShortlist(Ptr<data::Shortlist> shortlist) {
diff --git a/src/models/encoder_decoder.h b/src/models/encoder_decoder.h
index 41702eceb..4cfd17712 100644
--- a/src/models/encoder_decoder.h
+++ b/src/models/encoder_decoder.h
@@ -130,13 +130,15 @@ class EncoderDecoder : public EncoderDecoderBase {
     return decoders_[0]->getShortlist();
   };
 
+  // convert alignment tensors that live GPU-side into a CPU-side vector of vectors
   virtual data::SoftAlignment getAlignment() override {
-    data::SoftAlignment aligns;
-    for(auto aln : decoders_[0]->getAlignments()) {
-      aligns.push_back({});
-      aln->val()->get(aligns.back());
+    data::SoftAlignment softAlignments;
+    auto alignments = decoders_[0]->getAlignments(); // [tgt index][beam depth, max src length, batch size, 1]
+    for(auto alignment : alignments) { // [beam depth, max src length, batch size, 1]
+      softAlignments.push_back({});
+      alignment->val()->get(softAlignments.back());
     }
-    return aligns;
+    return softAlignments; // [tgt index][beam depth * max src length * batch size]
   };
 
   /*********************************************************************/
diff --git a/src/models/transformer.h b/src/models/transformer.h
index f36150edb..b5c0ec092 100755
--- a/src/models/transformer.h
+++ b/src/models/transformer.h
@@ -206,20 +206,20 @@ class Transformer : public EncoderOrDecoderBase {
     auto head0 = slice(weights, -3, 0);
 
     int dimBatchBeam = head0->shape()[-4];
-    int srcWords = head0->shape()[-1];
-    int trgWords = head0->shape()[-2];
+    int srcWords = head0->shape()[-1]; // (max) length of src sequence
+    int trgWords = head0->shape()[-2]; // (max) length of trg sequence, or 1 in decoding
     int dimBatch = dimBatchBeam / dimBeam;
 
     // reshape and transpose to match the format guided_alignment expects
     head0 = reshape(head0, {dimBeam, dimBatch, trgWords, srcWords});
-    head0 = transpose(head0, {0, 3, 1, 2}); // [-4: beam depth, -3: max src length, -2: batch size, -1: max tgt length]
+    head0 = transpose(head0, {0, 3, 1, 2}); // [beam depth, max src length, batch size, max tgt length]
 
     // save only last alignment set. For training this will be all alignments,
     // for translation only the last one. Also split alignments by target words.
     // @TODO: make splitting obsolete
     alignments_.clear();
-    for(int i = 0; i < trgWords; ++i) {
-      alignments_.push_back(slice(head0, -1, i)); // [tgt index][-4: beam depth, -3: max src length, -2: batch size, -1: 1]
+    for(int i = 0; i < trgWords; ++i) { // loop over all trg positions. In decoding, there is only one.
+      alignments_.push_back(slice(head0, -1, i)); // [tgt index][beam depth, max src length, batch size, 1] P(src pos|trg pos, beam index, batch index)
     }
   }
 
@@ -860,7 +860,7 @@ class DecoderTransformer : public Transformer<DecoderBase> {
   // helper function for guided alignment
   // @TODO: const vector<> seems wrong. Either make it non-const or a const& (more efficient but dangerous)
   virtual const std::vector<Expr> getAlignments(int /*i*/ = 0) override {
-    return alignments_;
+    return alignments_; // [tgt index][beam depth, max src length, batch size, 1]
   }
 
   void clear() override {
diff --git a/src/translator/beam_search.h b/src/translator/beam_search.h
index 8016e76f7..393ff6dfe 100644
--- a/src/translator/beam_search.h
+++ b/src/translator/beam_search.h
@@ -44,8 +44,8 @@ class BeamSearch {
                Ptr<data::CorpusBatch /*const*/> batch, // for alignments only
                Ptr<FactoredVocab/*const*/> factoredVocab, size_t factorGroup) const {
     std::vector<float> align;
-    if(options_->hasAndNotEmpty("alignment"))
-      align = scorers_[0]->getAlignment(); // [beam depth, max src length, batch size, 1]; use alignments from the first scorer, even if ensemble
+    if(options_->hasAndNotEmpty("alignment") && factorGroup == 0)
+      align = scorers_[0]->getAlignment(); // [beam depth * max src length * batch size] -> P(s|t); use alignments from the first scorer, even if ensemble
 
     const auto dimBatch = beams.size();
     Beams newBeams(dimBatch);   // return value of this function goes here
@@ -73,8 +73,8 @@ class BeamSearch {
       ABORT_IF(beamHypIdx >= beam.size(), "Out of bounds beamHypIdx??");
 
       // map wordIdx to word
-      auto prevHyp = beam[beamHypIdx];
-      auto prevBeamHypIdx = beamHypIdx;
+      auto prevBeamHypIdx = beamHypIdx; // back pointer
+      auto prevHyp = beam[prevBeamHypIdx];
       Word word;
       // If short list has been set, then wordIdx is an index into the short-listed word set,
       // rather than the true word index.
@@ -94,7 +94,7 @@ class BeamSearch {
                    "A word without this factor snuck through to here??");
           word = factoredVocab->expandFactoredWord(word, factorGroup, wordIdx);
           prevBeamHypIdx = prevHyp->getPrevStateIndex();
-          prevHyp = prevHyp->getPrevHyp(); // short-circuit the backpointer, so that the traceback doesnot contain partially factored words
+          prevHyp = prevHyp->getPrevHyp(); // short-circuit the backpointer, so that the traceback does not contain partially factored words
         }
       }
       else if (shortlist)
@@ -123,9 +123,10 @@ class BeamSearch {
       }
 
       // Set alignments
-      if(!align.empty() && factorGroup == 0) {
+      if(!align.empty())
         hyp->setAlignment(getAlignmentsForHypothesis(align, batch, (int)beamHypIdx, (int)batchIdx));
-      }
+      else // not first factor: just copy
+        hyp->setAlignment(beam[beamHypIdx]->getAlignment());
 
       newBeam.push_back(hyp);
     }
@@ -157,8 +158,8 @@ class BeamSearch {
     return newBeams;
   }
 
-  std::vector<float> getAlignmentsForHypothesis(
-      const std::vector<float> alignAll, // [beam depth, max src length, batch size, 1]
+  std::vector<float> getAlignmentsForHypothesis( // -> P(s|t) for current t and given beam and batch dim
+      const std::vector<float> alignAll, // [beam depth, max src length, batch size, 1], flattened
       Ptr<data::CorpusBatch> batch,
       int beamHypIdx,
       int batchIdx) const {
@@ -179,19 +180,18 @@ class BeamSearch {
     // in a single beam, i.e.:
     //   * [word1-batch1, word1-batch2, ..., word2-batch1, ...]
     //
-    size_t batchSize = batch->size();                // number of sentences in batch
+    size_t batchSize  = batch->size();               // number of sentences in batch
     size_t batchWidth = batch->width();              // max src length
-    size_t batchWidthXSize = batchWidth * batchSize; // total number of words in the batch incl. padding
-    std::vector<float> align;
+    size_t batchWidthXSize = batchWidth * batchSize; // total number of words in the batch incl. padding = product of last 3 tensor dimensions
 
     // loop over words of batch entry 'batchIdx' and beam entry 'beamHypIdx'
-    for(size_t w = 0; w < batchWidth; ++w) {
-      size_t a = ((batchWidthXSize * beamHypIdx) + batchIdx) + (batchSize * w);
-      size_t m = a % batchWidthXSize; // == batchIdx + (batchSize * w)
+    std::vector<float> align;
+    for(size_t srcPos = 0; srcPos < batchWidth; ++srcPos) { // loop over source positions
+      size_t a = ((batchWidthXSize * beamHypIdx) + batchIdx) + (batchSize * srcPos); // = flatten [beam index, s, batch index, 0]
+      size_t m = a % batchWidthXSize; // == batchIdx + (batchSize * srcPos) = flatten [0, s, batch index, 0]
       if(batch->front()->mask()[m] != 0)
         align.emplace_back(alignAll[a]);
     }
-
     return align;
   }
 
diff --git a/src/translator/hypothesis.h b/src/translator/hypothesis.h
index cf5d9b601..c1d570313 100644
--- a/src/translator/hypothesis.h
+++ b/src/translator/hypothesis.h
@@ -47,7 +47,7 @@ class Hypothesis {
       return targetWords;
   }
 
-  // get soft alignments for each target word starting from the hyp one
+  // get soft alignments [t][s] -> P(s|t) for each target word starting from the hyp one
   typedef data::SoftAlignment SoftAlignment;
   SoftAlignment tracebackAlignment()
   {
@@ -56,7 +56,7 @@ class Hypothesis {
           align.push_back(hyp->getAlignment());
       }
       std::reverse(align.begin(), align.end());
-      return align;
+      return align; // [t][s] -> P(s|t)
   }
 
 private:
diff --git a/src/translator/scorers.h b/src/translator/scorers.h
index 8da8abb0c..97cc21bc0 100644
--- a/src/translator/scorers.h
+++ b/src/translator/scorers.h
@@ -128,7 +128,9 @@ class ScorerWrapper : public Scorer {
   };
 
   virtual std::vector<float> getAlignment() override {
-    return encdec_->getAlignment().front(); // [beam depth, max src length, batch size, 1]
+    // This is called during decoding, where alignments only exist for the last time step. Hence front().
+    // This makes as copy. @TODO: It should be OK to return this as a const&.
+    return encdec_->getAlignment().front(); // [beam depth * max src length * batch size]
   }
 };
 

From cfb1be65319da68c4f3d9bd33dad1c50bd2e52d8 Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Thu, 16 May 2019 18:14:24 -0700
Subject: [PATCH 486/838] bug fix: in case of truncation of input, it should
 append EOS not ZERO

---
 src/data/corpus_base.cpp | 5 ++---
 1 file changed, 2 insertions(+), 3 deletions(-)
 mode change 100644 => 100755 src/data/corpus_base.cpp

diff --git a/src/data/corpus_base.cpp b/src/data/corpus_base.cpp
old mode 100644
new mode 100755
index 8391fdc1f..d901f035e
--- a/src/data/corpus_base.cpp
+++ b/src/data/corpus_base.cpp
@@ -200,12 +200,11 @@ void CorpusBase::addWordsToSentenceTuple(const std::string& line,
   // is used.
   Words words = vocabs_[batchIndex]->encode(line, /*addEOS =*/ addEOS_[batchIndex], inference_);
 
-  if(words.empty())
-    words.push_back(Word::ZERO);
+  ABORT_IF(words.empty(), "Empty input sequences are presently untested");
 
   if(maxLengthCrop_ && words.size() > maxLength_) {
     words.resize(maxLength_);
-    words.back() = Word::ZERO;  // @TODO: What does this do? Meant as sent-end token??
+    words.back() = vocabs_[batchIndex]->getEosId();
   }
 
   if(rightLeft_)

From eb2e451152efdc57a120707125b75461c4f59445 Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Thu, 16 May 2019 18:14:24 -0700
Subject: [PATCH 487/838] bug fix: in case of truncation of input, it should
 append EOS not ZERO

---
 src/data/corpus_base.cpp | 5 ++---
 1 file changed, 2 insertions(+), 3 deletions(-)
 mode change 100644 => 100755 src/data/corpus_base.cpp

diff --git a/src/data/corpus_base.cpp b/src/data/corpus_base.cpp
old mode 100644
new mode 100755
index 8391fdc1f..d901f035e
--- a/src/data/corpus_base.cpp
+++ b/src/data/corpus_base.cpp
@@ -200,12 +200,11 @@ void CorpusBase::addWordsToSentenceTuple(const std::string& line,
   // is used.
   Words words = vocabs_[batchIndex]->encode(line, /*addEOS =*/ addEOS_[batchIndex], inference_);
 
-  if(words.empty())
-    words.push_back(Word::ZERO);
+  ABORT_IF(words.empty(), "Empty input sequences are presently untested");
 
   if(maxLengthCrop_ && words.size() > maxLength_) {
     words.resize(maxLength_);
-    words.back() = Word::ZERO;  // @TODO: What does this do? Meant as sent-end token??
+    words.back() = vocabs_[batchIndex]->getEosId();
   }
 
   if(rightLeft_)

From 4b984af4d9eca69a91bf294543695252f20f6316 Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Thu, 16 May 2019 18:19:23 -0700
Subject: [PATCH 488/838] bug fix: in case of truncation, EOS should only be
 added if addEOS_

---
 src/data/corpus_base.cpp | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/src/data/corpus_base.cpp b/src/data/corpus_base.cpp
index d901f035e..03633e828 100755
--- a/src/data/corpus_base.cpp
+++ b/src/data/corpus_base.cpp
@@ -204,7 +204,8 @@ void CorpusBase::addWordsToSentenceTuple(const std::string& line,
 
   if(maxLengthCrop_ && words.size() > maxLength_) {
     words.resize(maxLength_);
-    words.back() = vocabs_[batchIndex]->getEosId();
+    if(addEOS_[batchIndex])
+      words.back() = vocabs_[batchIndex]->getEosId();
   }
 
   if(rightLeft_)

From 7788d89651e27a4457f19d7a62548c58e29047de Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Tue, 21 May 2019 13:28:33 -0700
Subject: [PATCH 489/838] minor further refactoring; deleted some commented-out
 lines

---
 src/layers/factory.h     |  8 ++------
 src/layers/generic.cpp   | 14 +++++++-------
 src/models/bert.h        |  6 ------
 src/models/costs.h       |  1 +
 src/models/s2s.h         |  2 --
 src/models/transformer.h |  3 ---
 6 files changed, 10 insertions(+), 24 deletions(-)

diff --git a/src/layers/factory.h b/src/layers/factory.h
index 1f00210fe..bcb1db0c4 100755
--- a/src/layers/factory.h
+++ b/src/layers/factory.h
@@ -44,16 +44,12 @@ class Factory : public std::enable_shared_from_this<Factory> {
   // set a single option
   // setOpt("var", val);
   template <typename T>
-  void setOpt(const std::string& key, T value) {
-    options_->set(key, value);
-  }
+  void setOpt(const std::string& key, T value) { options_->set(key, value); }
 
   // set one or more options at once
   // setOpts("var1", val1, "var2", val2, ...);
   template <typename T, typename... Args>
-  void setOpts(const std::string& key, T value, Args&&... moreArgs) {
-    options_->set(key, value, std::forward<Args>(moreArgs)...);
-  }
+  void setOpts(const std::string& key, T value, Args&&... moreArgs) { options_->set(key, value, std::forward<Args>(moreArgs)...); }
 
   void mergeOpts(Ptr<Options> options) { options_->merge(options); }
 
diff --git a/src/layers/generic.cpp b/src/layers/generic.cpp
index abb2f69f8..9797667b8 100755
--- a/src/layers/generic.cpp
+++ b/src/layers/generic.cpp
@@ -418,14 +418,14 @@ namespace marian {
   }
 
   Expr Embedding::apply(const Words& words, const Shape& shape) const /*override final*/ {
-    Expr selectedEmbs;
-    if (factoredVocab_)
-      selectedEmbs = multiRows(words);
+    if (factoredVocab_) {
+      Expr selectedEmbs = multiRows(words);
+      selectedEmbs = reshape(selectedEmbs, shape);
+      selectedEmbs = dropout(selectedEmbs, options_->get<float>("dropout", 0.0f), { selectedEmbs->shape()[-3], 1, 1 });
+      return selectedEmbs;
+    }
     else
-      selectedEmbs = rows(E_, toWordIndexVector(words));
-    selectedEmbs = reshape(selectedEmbs, shape);
-    selectedEmbs = dropout(selectedEmbs, options_->get<float>("dropout", 0.0f), {selectedEmbs->shape()[-3], 1, 1});
-    return selectedEmbs;
+      return applyIndices(toWordIndexVector(words), shape);
   }
 
   Expr Embedding::applyIndices(const std::vector<WordIndex>& embIdx, const Shape& shape) const /*override final*/ {
diff --git a/src/models/bert.h b/src/models/bert.h
index 682efa48b..80e95df07 100755
--- a/src/models/bert.h
+++ b/src/models/bert.h
@@ -220,8 +220,6 @@ class BertEncoderClassifier : public EncoderClassifier, public data::RNGEngine {
 class BertEncoder : public EncoderTransformer {
   using EncoderTransformer::EncoderTransformer;
 public:
-  //BertEncoder(Ptr<Options> options) : EncoderTransformer(options) {}
-
   Expr addSentenceEmbeddings(Expr embeddings,
                              Ptr<data::CorpusBatch> batch,
                              bool learnedPosEmbeddings) const {
@@ -272,8 +270,6 @@ class BertEncoder : public EncoderTransformer {
 class BertClassifier : public ClassifierBase {
   using ClassifierBase::ClassifierBase;
 public:
-  //BertClassifier(Ptr<Options> options) : ClassifierBase(options) {}
-
   Ptr<ClassifierState> apply(Ptr<ExpressionGraph> graph, Ptr<data::CorpusBatch> batch, const std::vector<Ptr<EncoderState>>& encoderStates) override {
     ABORT_IF(encoderStates.size() != 1, "Currently we only support a single encoder BERT model");
 
@@ -316,8 +312,6 @@ class BertClassifier : public ClassifierBase {
 class BertMaskedLM : public ClassifierBase {
   using ClassifierBase::ClassifierBase;
 public:
-  //BertMaskedLM(Ptr<Options> options) : ClassifierBase(options) {}
-
   Ptr<ClassifierState> apply(Ptr<ExpressionGraph> graph, Ptr<data::CorpusBatch> batch, const std::vector<Ptr<EncoderState>>& encoderStates) override {
     Ptr<data::BertBatch> bertBatch = std::dynamic_pointer_cast<data::BertBatch>(batch);
 
diff --git a/src/models/costs.h b/src/models/costs.h
index 17c3d0d43..8b23df905 100755
--- a/src/models/costs.h
+++ b/src/models/costs.h
@@ -237,6 +237,7 @@ class GumbelSoftmaxStep : public ILogProbStep {
 // class to wrap an IEncoderDecoder and a ILogProbStep that are executed in sequence,
 // wrapped again in the IEncoderDecoder interface
 // @TODO: seems we are conflating an interface defition with its implementation?
+// @TODO: needs a better name. Stepwise is an adjective. Classes are things=nouns. StepwiseWhat?
 class Stepwise : public IEncoderDecoder {
 protected:
   Ptr<IEncoderDecoder> encdec_;
diff --git a/src/models/s2s.h b/src/models/s2s.h
index 126580108..f10c77bea 100755
--- a/src/models/s2s.h
+++ b/src/models/s2s.h
@@ -206,8 +206,6 @@ class DecoderS2S : public DecoderBase {
   }
 
 public:
-  //DecoderS2S(Ptr<Options> options) : DecoderBase(options) {}
-
   virtual Ptr<DecoderState> startState(
       Ptr<ExpressionGraph> graph,
       Ptr<data::CorpusBatch> batch,
diff --git a/src/models/transformer.h b/src/models/transformer.h
index 42713b521..4f0e8ea88 100755
--- a/src/models/transformer.h
+++ b/src/models/transformer.h
@@ -497,9 +497,6 @@ class EncoderTransformer : public Transformer<EncoderBase> {
   typedef Transformer<EncoderBase> Base;
   using Base::Base;
 public:
-  //EncoderTransformer(Ptr<Options> options) : Transformer(options) {}
-  virtual ~EncoderTransformer() {}
-
   virtual Ptr<EncoderState> build(Ptr<ExpressionGraph> graph,
                                   Ptr<data::CorpusBatch> batch) override {
     graph_ = graph;

From 31babb0fde09652fcf44970dd00c7ff213c284a7 Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Tue, 21 May 2019 14:58:48 -0700
Subject: [PATCH 490/838] implemented dropout for factored embeddings

---
 src/layers/generic.cpp | 17 ++++++++++-------
 src/layers/generic.h   |  2 +-
 2 files changed, 11 insertions(+), 8 deletions(-)

diff --git a/src/layers/generic.cpp b/src/layers/generic.cpp
index 9797667b8..30060432f 100755
--- a/src/layers/generic.cpp
+++ b/src/layers/generic.cpp
@@ -361,7 +361,7 @@ namespace marian {
   }
 
   // helper to embed a sequence of words (given as indices) via factored embeddings
-  /*private*/ Expr Embedding::multiRows(const Words& data) const
+  /*private*/ Expr Embedding::multiRows(const Words& data, float dropProb) const
   {
     auto graph = E_->graph();
     auto factoredData = factoredVocab_->csr_rows(data);
@@ -372,7 +372,9 @@ namespace marian {
     auto weights = graph->constant({ (int)factoredData.weights.size() }, inits::from_vector(factoredData.weights), Type::float32);
     auto indices = graph->constant({ (int)factoredData.indices.size() }, inits::from_vector(factoredData.indices), Type::uint32);
     auto offsets = graph->constant({ (int)factoredData.offsets.size() }, inits::from_vector(factoredData.offsets), Type::uint32);
-    // apply dropout  --@TODO
+    // apply dropout
+    // We apply it to the weights, i.e. factors get dropped out separately, but always as entire vectors.
+    weights = dropout(weights, dropProb);
     // perform the product
     return csr_dot(factoredData.shape, weights, indices, offsets, E_);
   }
@@ -419,9 +421,9 @@ namespace marian {
 
   Expr Embedding::apply(const Words& words, const Shape& shape) const /*override final*/ {
     if (factoredVocab_) {
-      Expr selectedEmbs = multiRows(words);
-      selectedEmbs = reshape(selectedEmbs, shape);
-      selectedEmbs = dropout(selectedEmbs, options_->get<float>("dropout", 0.0f), { selectedEmbs->shape()[-3], 1, 1 });
+      Expr selectedEmbs = multiRows(words, options_->get<float>("dropout", 0.0f));        // [(B*W) x E]
+      selectedEmbs = reshape(selectedEmbs, shape); // [W, B, E]
+      //selectedEmbs = dropout(selectedEmbs, options_->get<float>("dropout", 0.0f), { selectedEmbs->shape()[-3], 1, 1 }); // @TODO: replace with factor dropout
       return selectedEmbs;
     }
     else
@@ -430,8 +432,9 @@ namespace marian {
 
   Expr Embedding::applyIndices(const std::vector<WordIndex>& embIdx, const Shape& shape) const /*override final*/ {
     ABORT_IF(factoredVocab_, "Embedding: applyIndices must not be used with a factored vocabulary");
-    auto selectedEmbs = rows(E_, embIdx);
-    selectedEmbs = reshape(selectedEmbs, shape);
+    auto selectedEmbs = rows(E_, embIdx);        // [(B*W) x E]
+    selectedEmbs = reshape(selectedEmbs, shape); // [W, B, E]
+    // @BUGBUG: We should not broadcast along dimBatch=[-2]. Then we can also dropout before reshape() (test that separately)
     selectedEmbs = dropout(selectedEmbs, options_->get<float>("dropout", 0.0f), { selectedEmbs->shape()[-3], 1, 1 });
     return selectedEmbs;
   }
diff --git a/src/layers/generic.h b/src/layers/generic.h
index e6d700ef8..741ebc88d 100755
--- a/src/layers/generic.h
+++ b/src/layers/generic.h
@@ -289,7 +289,7 @@ class Output : public LayerBase, public IUnaryLogitLayer, public IHasShortList {
 class Embedding : public LayerBase, public IEmbeddingLayer {
   Expr E_;
   Ptr<FactoredVocab> factoredVocab_;
-  Expr multiRows(const Words& data) const;
+  Expr multiRows(const Words& data, float dropProb) const;
 public:
   Embedding(Ptr<ExpressionGraph> graph, Ptr<Options> options);
 

From ec2d66e85275313c206f760364724108f9508204 Mon Sep 17 00:00:00 2001
From: Roman Grundkiewicz <rgrundki@exseed.ed.ac.uk>
Date: Mon, 27 May 2019 14:52:11 +0100
Subject: [PATCH 491/838] Add execute permission

---
 scripts/contrib/model_info.py | 0
 1 file changed, 0 insertions(+), 0 deletions(-)
 mode change 100644 => 100755 scripts/contrib/model_info.py

diff --git a/scripts/contrib/model_info.py b/scripts/contrib/model_info.py
old mode 100644
new mode 100755

From 865f8634854709b84bdd5ec8baab8acfc305bc3d Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Fri, 31 May 2019 15:33:51 -0700
Subject: [PATCH 492/838] VS Project now builds with CUDA

---
 src/tensors/gpu/prod.cpp            |  44 ++++-----
 src/tensors/gpu/tensor_operators.cu |   0
 vs/Marian.vcxproj                   | 134 +++++++++++++++++-----------
 vs/Marian.vcxproj.filters           | 104 +++++++++++----------
 4 files changed, 157 insertions(+), 125 deletions(-)
 mode change 100644 => 100755 src/tensors/gpu/tensor_operators.cu
 mode change 100644 => 100755 vs/Marian.vcxproj.filters

diff --git a/src/tensors/gpu/prod.cpp b/src/tensors/gpu/prod.cpp
index 48524f626..dfcb813f4 100755
--- a/src/tensors/gpu/prod.cpp
+++ b/src/tensors/gpu/prod.cpp
@@ -1,4 +1,8 @@
 
+#ifdef _MSC_VER
+#pragma warning(disable: 4505) // warning C4505: '__float2half_rz': unreferenced local function has been removed (missing 'static inline')
+#endif
+
 #include <cublas_v2.h>
 #include <cusparse.h>
 
@@ -45,7 +49,7 @@ void Prod(marian::Tensor C,
           bool transB,
           float beta,
           float scalar) {
-  cudaSetDevice(C->getDeviceId().no);
+  cudaSetDevice((int)C->getDeviceId().no);
   float alpha = scalar;
 
   size_t m = A->shape().elements() / A->shape().back();
@@ -79,17 +83,17 @@ void Prod(marian::Tensor C,
   CUBLAS_CHECK(cublasSgemm(cublasHandle,
               opB,
               opA,
-              n,
-              m,
-              k,
+              (int)n,
+              (int)m,
+              (int)k,
               &alpha,
               B->data(),
-              ldb,
+              (int)ldb,
               A->data(),
-              lda,
+              (int)lda,
               &beta,
               C->data(),
-              ldc));
+              (int)ldc));
 #if CUDA_VERSION >= 9000
   cublasSetMathMode(cublasHandle, CUBLAS_DEFAULT_MATH);
 #endif
@@ -144,7 +148,7 @@ void ProdBatched(marian::Tensor C,
                  bool transB,
                  float beta,
                  float scalar) {
-  cudaSetDevice(C->getDeviceId().no);
+  cudaSetDevice((int)C->getDeviceId().no);
   float alpha = scalar;
 
   size_t batchA = A->shape().elements() / (A->shape()[-1] * A->shape()[-2]);
@@ -173,10 +177,10 @@ void ProdBatched(marian::Tensor C,
   auto cublasHandle = std::static_pointer_cast<gpu::Backend>(C->getBackend())
                           ->getCublasHandle();
 
-  int strideA = batchA == 1 ? 0 : m * k;
-  int strideB = batchB == 1 ? 0 : n * k;
-  int strideC = n * m;
-  int batchC = std::max(batchA, batchB);
+  auto strideA = batchA == 1 ? 0 : m * k;
+  auto strideB = batchB == 1 ? 0 : n * k;
+  auto strideC = n * m;
+  auto batchC = std::max(batchA, batchB);
 
   std::vector<const float*> aptr;
   std::vector<const float*> bptr;
@@ -206,18 +210,18 @@ void ProdBatched(marian::Tensor C,
   CUBLAS_CHECK(cublasSgemmBatched(cublasHandle,
                      opB,
                      opA,
-                     n,
-                     m,
-                     k,
+                     (int)n,
+                     (int)m,
+                     (int)k,
                      &alpha,
                      mp_bptr->data<const float*>(),
-                     ldb,
+                     (int)ldb,
                      mp_aptr->data<const float*>(),
-                     lda,
+                     (int)lda,
                      &beta,
                      mp_cptr->data<float*>(),
-                     ldc,
-                     batchC));
+                     (int)ldc,
+                     (int)batchC));
 #if CUDA_VERSION >= 9000
   cublasSetMathMode(cublasHandle, CUBLAS_DEFAULT_MATH);
 #endif
@@ -262,7 +266,7 @@ void CSRProd(marian::Tensor C,
              bool transS,
              bool swapOperands,
              float beta) {
-  cudaSetDevice(C->getDeviceId().no);
+  cudaSetDevice((int)C->getDeviceId().no);
   auto cusparseHandle = std::static_pointer_cast<gpu::Backend>(C->getBackend())
                               ->getCusparseHandle();
   // interpret tensor dimensions as matrix dimensions
diff --git a/src/tensors/gpu/tensor_operators.cu b/src/tensors/gpu/tensor_operators.cu
old mode 100644
new mode 100755
diff --git a/vs/Marian.vcxproj b/vs/Marian.vcxproj
index e103ec45f..5b8c32b75 100755
--- a/vs/Marian.vcxproj
+++ b/vs/Marian.vcxproj
@@ -32,23 +32,25 @@
     <CharacterSet>Unicode</CharacterSet>
   </PropertyGroup>
   <Import Project="$(VCTargetsPath)\Microsoft.Cpp.props" />
-  <ImportGroup Label="ExtensionSettings" />
+  <ImportGroup Label="ExtensionSettings">
+    <Import Project="$(VCTargetsPath)\BuildCustomizations\CUDA 10.1.props" />
+  </ImportGroup>
   <ImportGroup Label="PropertySheets">
     <Import Project="$(UserRootDir)\Microsoft.Cpp.$(Platform).user.props" Condition="exists('$(UserRootDir)\Microsoft.Cpp.$(Platform).user.props')" Label="LocalAppDataPlatform" />
   </ImportGroup>
   <PropertyGroup Label="UserMacros" />
   <PropertyGroup Condition="'$(Configuration)|$(Platform)'=='Debug|x64'">
     <LinkIncremental>true</LinkIncremental>
-    <IntDir>$(Platform)\$(Configuration)\Marian\</IntDir>
-    <IncludePath>..\src;..\src\3rd_party;%BOOST_INCLUDE_PATH%;%ZLIB_PATH%\include;%MKL_PATH%\include;$(VC_IncludePath);$(WindowsSDK_IncludePath);</IncludePath>
-    <LibraryPath>%BOOST_LIB_PATH%;%ZLIB_PATH%\lib;%MKL_PATH%\lib\intel64;$(VC_LibraryPath_x64);$(WindowsSDK_LibraryPath_x64);$(NETFXKitsDir)Lib\um\x64</LibraryPath>
+    <IntDir>$(SolutionDir)$(Platform)\$(Configuration)\Marian\</IntDir>
+    <IncludePath>%CUDA_PATH%\include;..\src;..\src\3rd_party;%BOOST_INCLUDE_PATH%;%ZLIB_PATH%\include;%MKL_PATH%\include;$(VC_IncludePath);$(WindowsSDK_IncludePath);</IncludePath>
+    <LibraryPath>%CUDA_PATH%\lib\x64;%BOOST_LIB_PATH%;%ZLIB_PATH%\lib;%MKL_PATH%\lib\intel64;$(VC_LibraryPath_x64);$(WindowsSDK_LibraryPath_x64);$(NETFXKitsDir)Lib\um\x64</LibraryPath>
   </PropertyGroup>
   <PropertyGroup Condition="'$(Configuration)|$(Platform)'=='Release|x64'">
     <LinkIncremental>false</LinkIncremental>
     <ExecutablePath>$(ExecutablePath)</ExecutablePath>
-    <IntDir>$(Platform)\$(Configuration)\Marian\</IntDir>
-    <IncludePath>..\src;..\src\3rd_party;%BOOST_INCLUDE_PATH%;%ZLIB_PATH%\include;%MKL_PATH%\include;$(VC_IncludePath);$(WindowsSDK_IncludePath);</IncludePath>
-    <LibraryPath>%BOOST_LIB_PATH%;%ZLIB_PATH%\lib;%MKL_PATH%\lib\intel64;$(VC_LibraryPath_x64);$(WindowsSDK_LibraryPath_x64);$(NETFXKitsDir)Lib\um\x64</LibraryPath>
+    <IntDir>$(SolutionDir)$(Platform)\$(Configuration)\Marian\</IntDir>
+    <IncludePath>%CUDA_PATH%\include;..\src;..\src\3rd_party;%BOOST_INCLUDE_PATH%;%ZLIB_PATH%\include;%MKL_PATH%\include;$(VC_IncludePath);$(WindowsSDK_IncludePath);</IncludePath>
+    <LibraryPath>%CUDA_PATH%\lib\x64;%BOOST_LIB_PATH%;%ZLIB_PATH%\lib;%MKL_PATH%\lib\intel64;$(VC_LibraryPath_x64);$(WindowsSDK_LibraryPath_x64);$(NETFXKitsDir)Lib\um\x64</LibraryPath>
   </PropertyGroup>
   <ItemDefinitionGroup>
     <ClCompile>
@@ -57,6 +59,9 @@
     <Link>
       <AdditionalLibraryDirectories>$(OutDir);$(SolutionDir)$(Platform)\$(Configuration);$(SolutionDir)$(Platform)\$(Configuration);$(MSMPI_LIB64)</AdditionalLibraryDirectories>
     </Link>
+    <CudaCompile>
+      <TargetMachinePlatform>64</TargetMachinePlatform>
+    </CudaCompile>
   </ItemDefinitionGroup>
   <ItemDefinitionGroup Condition="'$(Configuration)|$(Platform)'=='Debug|x64'">
     <ClCompile>
@@ -64,7 +69,7 @@
       </PrecompiledHeader>
       <WarningLevel>Level4</WarningLevel>
       <Optimization>Disabled</Optimization>
-      <PreprocessorDefinitions>MKL_FOUND=1; MPI_FOUND=1; BLAS_FOUND=1; MKL_ILP64; WIN32;_DEBUG;_CONSOLE;_LIB;%(PreprocessorDefinitions)</PreprocessorDefinitions>
+      <PreprocessorDefinitions>CUDA_FOUND=1; MKL_FOUND=1; MPI_FOUND=1; BLAS_FOUND=1; MKL_ILP64; WIN32;_DEBUG;_CONSOLE;_LIB;%(PreprocessorDefinitions)</PreprocessorDefinitions>
       <SDLCheck>true</SDLCheck>
       <TreatWarningAsError>true</TreatWarningAsError>
       <AdditionalOptions>/bigobj %(AdditionalOptions)</AdditionalOptions>
@@ -76,10 +81,20 @@
     <Link>
       <SubSystem>Console</SubSystem>
       <GenerateDebugInformation>true</GenerateDebugInformation>
-      <AdditionalDependencies>zlib.lib;msmpi.lib;mkl_intel_ilp64.lib;mkl_sequential.lib;mkl_core.lib;kernel32.lib;user32.lib;gdi32.lib;winspool.lib;comdlg32.lib;advapi32.lib;shell32.lib;ole32.lib;oleaut32.lib;uuid.lib;odbc32.lib;odbccp32.lib;shlwapi.lib;%(AdditionalDependencies)</AdditionalDependencies>
+      <AdditionalDependencies>cudart_static.lib;cublas.lib;cusparse.lib;curand.lib;zlib.lib;msmpi.lib;mkl_intel_ilp64.lib;mkl_sequential.lib;mkl_core.lib;kernel32.lib;user32.lib;gdi32.lib;winspool.lib;comdlg32.lib;advapi32.lib;shell32.lib;ole32.lib;oleaut32.lib;uuid.lib;odbc32.lib;odbccp32.lib;shlwapi.lib;%(AdditionalDependencies)</AdditionalDependencies>
       <StackReserveSize>100000000</StackReserveSize>
       <TreatLinkerWarningAsErrors>true</TreatLinkerWarningAsErrors>
     </Link>
+    <CudaCompile>
+      <Include>$(SolutionDir)..\src\;$(SolutionDir)..\src\3rd_party</Include>
+      <CodeGeneration>compute_50,sm_50</CodeGeneration>
+      <KeepDir>$(CudaIntDir)</KeepDir>
+      <CompileOut>$(IntDir)%(Filename)%(Extension).obj</CompileOut>
+      <Warning>W2</Warning>
+      <AdditionalCompilerOptions>
+      </AdditionalCompilerOptions>
+      <Defines>_SCL_SECURE_NO_WARNINGS</Defines>
+    </CudaCompile>
   </ItemDefinitionGroup>
   <ItemDefinitionGroup Condition="'$(Configuration)|$(Platform)'=='Release|x64'">
     <ClCompile>
@@ -89,7 +104,7 @@
       <Optimization>MaxSpeed</Optimization>
       <FunctionLevelLinking>true</FunctionLevelLinking>
       <IntrinsicFunctions>true</IntrinsicFunctions>
-      <PreprocessorDefinitions>MKL_FOUND=1; MPI_FOUND=1; BLAS_FOUND=1; MKL_ILP64; WIN32;NDEBUG;_CONSOLE;_LIB;%(PreprocessorDefinitions)</PreprocessorDefinitions>
+      <PreprocessorDefinitions>CUDA_FOUND=1; MKL_FOUND=1; MPI_FOUND=1; BLAS_FOUND=1; MKL_ILP64; WIN32;NDEBUG;_CONSOLE;_LIB;%(PreprocessorDefinitions)</PreprocessorDefinitions>
       <SDLCheck>true</SDLCheck>
       <FavorSizeOrSpeed>Speed</FavorSizeOrSpeed>
       <AdditionalOptions>/d2Zi+ /bigobj %(AdditionalOptions)</AdditionalOptions>
@@ -106,10 +121,18 @@
       <GenerateDebugInformation>true</GenerateDebugInformation>
       <EnableCOMDATFolding>true</EnableCOMDATFolding>
       <OptimizeReferences>true</OptimizeReferences>
-      <AdditionalDependencies>zlib.lib;msmpi.lib;mkl_intel_ilp64.lib;mkl_sequential.lib;mkl_core.lib;kernel32.lib;user32.lib;gdi32.lib;winspool.lib;comdlg32.lib;advapi32.lib;shell32.lib;ole32.lib;oleaut32.lib;uuid.lib;odbc32.lib;odbccp32.lib;shlwapi.lib;%(AdditionalDependencies)</AdditionalDependencies>
+      <AdditionalDependencies>cudart_static.lib;cublas.lib;cusparse.lib;curand.lib;zlib.lib;msmpi.lib;mkl_intel_ilp64.lib;mkl_sequential.lib;mkl_core.lib;kernel32.lib;user32.lib;gdi32.lib;winspool.lib;comdlg32.lib;advapi32.lib;shell32.lib;ole32.lib;oleaut32.lib;uuid.lib;odbc32.lib;odbccp32.lib;shlwapi.lib;%(AdditionalDependencies)</AdditionalDependencies>
       <StackReserveSize>100000000</StackReserveSize>
       <TreatLinkerWarningAsErrors>true</TreatLinkerWarningAsErrors>
     </Link>
+    <CudaCompile>
+      <Include>$(SolutionDir)..\src\;$(SolutionDir)..\src\3rd_party</Include>
+      <CodeGeneration>compute_50,sm_50</CodeGeneration>
+      <Warning>W2</Warning>
+      <AdditionalCompilerOptions>
+      </AdditionalCompilerOptions>
+      <Defines>_SCL_SECURE_NO_WARNINGS</Defines>
+    </CudaCompile>
   </ItemDefinitionGroup>
   <ItemGroup>
     <ClCompile Include="..\src\3rd_party\ExceptionWithCallStack.cpp" />
@@ -973,6 +996,7 @@
     <ClInclude Include="..\src\translator\printer.h" />
     <ClInclude Include="..\src\translator\scorers.h" />
     <ClInclude Include="..\src\translator\translator.h" />
+    <ClInclude Include="..\src\training\communicator_nccl.h" />
   </ItemGroup>
   <ItemGroup>
     <None Include="..\src\3rd_party\nccl\src\bootstrap.cu">
@@ -1062,6 +1086,50 @@
     <None Include="..\src\3rd_party\nccl\src\transport\shm.cu">
       <ExcludedFromBuild Condition="'$(Configuration)|$(Platform)'=='Release|x64'">true</ExcludedFromBuild>
     </None>
+  </ItemGroup>
+  <ItemGroup>
+    <CudaCompile Include="..\src\tensors\gpu\add.cu" />
+    <ClInclude Include="..\src\tensors\gpu\add.inc">
+      <ExcludedFromBuild Condition="'$(Configuration)|$(Platform)'=='Debug|x64'">true</ExcludedFromBuild>
+      <ExcludedFromBuild Condition="'$(Configuration)|$(Platform)'=='Release|x64'">true</ExcludedFromBuild>
+      <FileType>Document</FileType>
+    </ClInclude>
+    <CudaCompile Include="..\src\tensors\gpu\algorithm.cu" />
+    <CudaCompile Include="..\src\tensors\gpu\cudnn_wrappers.cu">
+      <ExcludedFromBuild Condition="'$(Configuration)|$(Platform)'=='Debug|x64'">false</ExcludedFromBuild>
+      <ExcludedFromBuild Condition="'$(Configuration)|$(Platform)'=='Release|x64'">false</ExcludedFromBuild>
+    </CudaCompile>
+    <CudaCompile Include="..\src\tensors\gpu\device.cu" />
+    <CudaCompile Include="..\src\tensors\gpu\element.cu">
+      <ExcludedFromBuild Condition="'$(Configuration)|$(Platform)'=='Debug|x64'">false</ExcludedFromBuild>
+      <ExcludedFromBuild Condition="'$(Configuration)|$(Platform)'=='Release|x64'">false</ExcludedFromBuild>
+    </CudaCompile>
+    <ClInclude Include="..\src\tensors\gpu\element.inc">
+      <ExcludedFromBuild Condition="'$(Configuration)|$(Platform)'=='Debug|x64'">true</ExcludedFromBuild>
+      <ExcludedFromBuild Condition="'$(Configuration)|$(Platform)'=='Release|x64'">true</ExcludedFromBuild>
+      <FileType>Document</FileType>
+    </ClInclude>
+    <ClCompile Include="..\src\tensors\gpu\prod.cpp">
+      <ExcludedFromBuild Condition="'$(Configuration)|$(Platform)'=='Debug|x64'">false</ExcludedFromBuild>
+      <ExcludedFromBuild Condition="'$(Configuration)|$(Platform)'=='Release|x64'">false</ExcludedFromBuild>
+      <ObjectFileName Condition="'$(Configuration)|$(Platform)'=='Debug|x64'">$(IntDir)%(RelativeDir)</ObjectFileName>
+      <ObjectFileName Condition="'$(Configuration)|$(Platform)'=='Release|x64'">$(IntDir)%(RelativeDir)</ObjectFileName>
+    </ClCompile>
+    <CudaCompile Include="..\src\tensors\gpu\sparse.cu">
+      <ExcludedFromBuild Condition="'$(Configuration)|$(Platform)'=='Debug|x64'">true</ExcludedFromBuild>
+      <ExcludedFromBuild Condition="'$(Configuration)|$(Platform)'=='Release|x64'">true</ExcludedFromBuild>
+    </CudaCompile>
+    <CudaCompile Include="..\src\tensors\gpu\tensor_operators.cu">
+      <ExcludedFromBuild Condition="'$(Configuration)|$(Platform)'=='Debug|x64'">false</ExcludedFromBuild>
+      <ExcludedFromBuild Condition="'$(Configuration)|$(Platform)'=='Release|x64'">false</ExcludedFromBuild>
+    </CudaCompile>
+    <CudaCompile Include="..\src\training\gradient_dropping\gpu\dropper.cu" />
+    <CudaCompile Include="..\src\training\gradient_dropping\gpu\sparse_algorithm.cu" />
+    <CudaCompile Include="..\src\translator\helpers.cu" />
+    <CudaCompile Include="..\src\translator\nth_element.cu" />
+  </ItemGroup>
+  <ItemGroup>
+    <None Include="..\src\tests\README.md" />
     <None Include="..\src\3rd_party\pathie-cpp\CHANGELOG" />
     <None Include="..\src\3rd_party\pathie-cpp\LICENSE" />
     <None Include="..\src\3rd_party\pathie-cpp\README.md" />
@@ -1077,50 +1145,11 @@
     <None Include="..\src\examples\iris\iris.data" />
     <None Include="..\src\examples\Makefile" />
     <None Include="..\src\examples\mnist\download.sh" />
-    <None Include="..\src\examples\README.md" />
-    <None Include="..\src\tensors\gpu\add.cu">
-      <ExcludedFromBuild Condition="'$(Configuration)|$(Platform)'=='Debug|x64'">true</ExcludedFromBuild>
-    </None>
-    <None Include="..\src\tensors\gpu\add.inc" />
-    <None Include="..\src\tensors\gpu\algorithm.cu">
-      <ExcludedFromBuild Condition="'$(Configuration)|$(Platform)'=='Debug|x64'">true</ExcludedFromBuild>
-    </None>
-    <None Include="..\src\tensors\gpu\cudnn_wrappers.cu">
-      <ExcludedFromBuild Condition="'$(Configuration)|$(Platform)'=='Debug|x64'">true</ExcludedFromBuild>
-    </None>
-    <None Include="..\src\tensors\gpu\device.cu">
-      <ExcludedFromBuild Condition="'$(Configuration)|$(Platform)'=='Debug|x64'">true</ExcludedFromBuild>
-    </None>
-    <None Include="..\src\tensors\gpu\element.cu">
-      <ExcludedFromBuild Condition="'$(Configuration)|$(Platform)'=='Debug|x64'">true</ExcludedFromBuild>
-    </None>
-    <None Include="..\src\tensors\gpu\element.inc" />
-    <None Include="..\src\tensors\gpu\prod.cpp">
-      <ExcludedFromBuild Condition="'$(Configuration)|$(Platform)'=='Debug|x64'">true</ExcludedFromBuild>
-    </None>
-    <None Include="..\src\tensors\gpu\sparse.cu">
-      <ExcludedFromBuild Condition="'$(Configuration)|$(Platform)'=='Debug|x64'">true</ExcludedFromBuild>
-    </None>
-    <None Include="..\src\tensors\gpu\tensor_operators.cu">
-      <ExcludedFromBuild Condition="'$(Configuration)|$(Platform)'=='Debug|x64'">true</ExcludedFromBuild>
-    </None>
-    <None Include="..\src\tests\conv_char_test.cu">
-      <ExcludedFromBuild Condition="'$(Configuration)|$(Platform)'=='Debug|x64'">true</ExcludedFromBuild>
-      <ExcludedFromBuild Condition="'$(Configuration)|$(Platform)'=='Release|x64'">true</ExcludedFromBuild>
-    </None>
-    <None Include="..\src\tests\conv_test.cu">
-      <ExcludedFromBuild Condition="'$(Configuration)|$(Platform)'=='Debug|x64'">true</ExcludedFromBuild>
-      <ExcludedFromBuild Condition="'$(Configuration)|$(Platform)'=='Release|x64'">true</ExcludedFromBuild>
-    </None>
-    <None Include="..\src\tests\README.md" />
-    <None Include="..\src\tests\tensor_test.cu">
+    <None Include="..\src\examples\README.md">
       <ExcludedFromBuild Condition="'$(Configuration)|$(Platform)'=='Debug|x64'">true</ExcludedFromBuild>
+      <FileType>Document</FileType>
       <ExcludedFromBuild Condition="'$(Configuration)|$(Platform)'=='Release|x64'">true</ExcludedFromBuild>
     </None>
-    <None Include="..\src\training\communicator_nccl.h" />
-    <None Include="..\src\translator\helpers.cu" />
-    <None Include="..\src\translator\nth_element.cu" />
-    <None Include=".editorConfig" />
   </ItemGroup>
   <ItemGroup>
     <Text Include="..\src\3rd_party\pathie-cpp\CMakeLists.txt" />
@@ -1133,5 +1162,6 @@
   </ItemGroup>
   <Import Project="$(VCTargetsPath)\Microsoft.Cpp.targets" />
   <ImportGroup Label="ExtensionTargets">
+    <Import Project="$(VCTargetsPath)\BuildCustomizations\CUDA 10.1.targets" />
   </ImportGroup>
 </Project>
\ No newline at end of file
diff --git a/vs/Marian.vcxproj.filters b/vs/Marian.vcxproj.filters
old mode 100644
new mode 100755
index c8af656de..d77aecd2a
--- a/vs/Marian.vcxproj.filters
+++ b/vs/Marian.vcxproj.filters
@@ -487,6 +487,9 @@
     <ClCompile Include="..\src\data\factored_vocab.cpp">
       <Filter>data</Filter>
     </ClCompile>
+    <ClCompile Include="..\src\tensors\gpu\prod.cpp">
+      <Filter>tensors\gpu</Filter>
+    </ClCompile>
   </ItemGroup>
   <ItemGroup>
     <ClInclude Include="..\src\marian.h" />
@@ -1543,6 +1546,15 @@
     <ClInclude Include="..\src\models\transformer.h">
       <Filter>models</Filter>
     </ClInclude>
+    <ClInclude Include="..\src\training\communicator_nccl.h">
+      <Filter>training</Filter>
+    </ClInclude>
+    <ClInclude Include="..\src\tensors\gpu\element.inc">
+      <Filter>tensors\gpu</Filter>
+    </ClInclude>
+    <ClInclude Include="..\src\tensors\gpu\add.inc">
+      <Filter>tensors\gpu</Filter>
+    </ClInclude>
   </ItemGroup>
   <ItemGroup>
     <Filter Include="3rd_party">
@@ -1712,36 +1724,11 @@
     </Filter>
   </ItemGroup>
   <ItemGroup>
-    <None Include=".editorConfig" />
-    <None Include="..\src\training\communicator_nccl.h">
-      <Filter>training</Filter>
-    </None>
-    <None Include="..\src\tensors\gpu\add.cu">
-      <Filter>tensors\gpu</Filter>
-    </None>
-    <None Include="..\src\tensors\gpu\add.inc">
-      <Filter>tensors\gpu</Filter>
-    </None>
-    <None Include="..\src\tensors\gpu\algorithm.cu">
-      <Filter>tensors\gpu</Filter>
-    </None>
-    <None Include="..\src\tensors\gpu\cudnn_wrappers.cu">
-      <Filter>tensors\gpu</Filter>
-    </None>
-    <None Include="..\src\tensors\gpu\device.cu">
-      <Filter>tensors\gpu</Filter>
-    </None>
-    <None Include="..\src\tensors\gpu\element.cu">
-      <Filter>tensors\gpu</Filter>
-    </None>
-    <None Include="..\src\tensors\gpu\element.inc">
-      <Filter>tensors\gpu</Filter>
-    </None>
-    <None Include="..\src\tensors\gpu\sparse.cu">
-      <Filter>tensors\gpu</Filter>
+    <None Include="..\src\3rd_party\nccl\src\bootstrap.cu">
+      <Filter>3rd_party\nccl\src</Filter>
     </None>
-    <None Include="..\src\tensors\gpu\tensor_operators.cu">
-      <Filter>tensors\gpu</Filter>
+    <None Include="..\src\3rd_party\nccl\src\init.cu">
+      <Filter>3rd_party\nccl\src</Filter>
     </None>
     <None Include="..\src\3rd_party\sentencepiece\src\sentencepiece.proto">
       <Filter>3rd_party\sentencepiece\src</Filter>
@@ -1749,12 +1736,6 @@
     <None Include="..\src\3rd_party\sentencepiece\src\sentencepiece_model.proto">
       <Filter>3rd_party\sentencepiece\src</Filter>
     </None>
-    <None Include="..\src\3rd_party\nccl\src\bootstrap.cu">
-      <Filter>3rd_party\nccl\src</Filter>
-    </None>
-    <None Include="..\src\3rd_party\nccl\src\init.cu">
-      <Filter>3rd_party\nccl\src</Filter>
-    </None>
     <None Include="..\src\3rd_party\nccl\src\Makefile">
       <Filter>3rd_party\nccl\src</Filter>
     </None>
@@ -1845,18 +1826,9 @@
     <None Include="..\src\3rd_party\pathie-cpp\README.md">
       <Filter>3rd_party\pathie-cpp</Filter>
     </None>
-    <None Include="..\src\tests\conv_char_test.cu">
-      <Filter>tests</Filter>
-    </None>
-    <None Include="..\src\tests\conv_test.cu">
-      <Filter>tests</Filter>
-    </None>
     <None Include="..\src\tests\README.md">
       <Filter>tests</Filter>
     </None>
-    <None Include="..\src\tests\tensor_test.cu">
-      <Filter>tests</Filter>
-    </None>
     <None Include="..\src\examples\mnist\download.sh">
       <Filter>examples\mnist</Filter>
     </None>
@@ -1872,15 +1844,6 @@
     <None Include="..\src\examples\README.md">
       <Filter>examples</Filter>
     </None>
-    <None Include="..\src\tensors\gpu\prod.cpp">
-      <Filter>tensors\gpu</Filter>
-    </None>
-    <None Include="..\src\translator\helpers.cu">
-      <Filter>translator</Filter>
-    </None>
-    <None Include="..\src\translator\nth_element.cu">
-      <Filter>translator</Filter>
-    </None>
   </ItemGroup>
   <ItemGroup>
     <Text Include="..\src\3rd_party\sentencepiece\src\CMakeLists.txt">
@@ -1896,4 +1859,39 @@
       <Filter>examples</Filter>
     </Text>
   </ItemGroup>
+  <ItemGroup>
+    <CudaCompile Include="..\src\tensors\gpu\add.cu">
+      <Filter>tensors\gpu</Filter>
+    </CudaCompile>
+    <CudaCompile Include="..\src\tensors\gpu\algorithm.cu">
+      <Filter>tensors\gpu</Filter>
+    </CudaCompile>
+    <CudaCompile Include="..\src\tensors\gpu\cudnn_wrappers.cu">
+      <Filter>tensors\gpu</Filter>
+    </CudaCompile>
+    <CudaCompile Include="..\src\tensors\gpu\device.cu">
+      <Filter>tensors\gpu</Filter>
+    </CudaCompile>
+    <CudaCompile Include="..\src\tensors\gpu\element.cu">
+      <Filter>tensors\gpu</Filter>
+    </CudaCompile>
+    <CudaCompile Include="..\src\translator\helpers.cu">
+      <Filter>translator</Filter>
+    </CudaCompile>
+    <CudaCompile Include="..\src\translator\nth_element.cu">
+      <Filter>translator</Filter>
+    </CudaCompile>
+    <CudaCompile Include="..\src\tensors\gpu\tensor_operators.cu">
+      <Filter>tensors\gpu</Filter>
+    </CudaCompile>
+    <CudaCompile Include="..\src\tensors\gpu\sparse.cu">
+      <Filter>tensors\gpu</Filter>
+    </CudaCompile>
+    <CudaCompile Include="..\src\training\gradient_dropping\gpu\dropper.cu">
+      <Filter>training\gradient_dropping\gpu</Filter>
+    </CudaCompile>
+    <CudaCompile Include="..\src\training\gradient_dropping\gpu\sparse_algorithm.cu">
+      <Filter>training\gradient_dropping\gpu</Filter>
+    </CudaCompile>
+  </ItemGroup>
 </Project>
\ No newline at end of file

From 75034f1a200d92c58dd947749f617370a5121a40 Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Mon, 3 Jun 2019 10:23:53 -0700
Subject: [PATCH 493/838] Now builds with VS 2017

---
 vs/Marian.vcxproj | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/vs/Marian.vcxproj b/vs/Marian.vcxproj
index 5b8c32b75..d28b467bc 100755
--- a/vs/Marian.vcxproj
+++ b/vs/Marian.vcxproj
@@ -15,19 +15,19 @@
     <Keyword>Win32Proj</Keyword>
     <RootNamespace>Marian</RootNamespace>
     <ProjectName>Marian</ProjectName>
-    <WindowsTargetPlatformVersion>8.1</WindowsTargetPlatformVersion>
+    <WindowsTargetPlatformVersion>10.0.17763.0</WindowsTargetPlatformVersion>
   </PropertyGroup>
   <Import Project="$(VCTargetsPath)\Microsoft.Cpp.Default.props" />
   <PropertyGroup Condition="'$(Configuration)|$(Platform)'=='Debug|x64'" Label="Configuration">
     <ConfigurationType>Application</ConfigurationType>
     <UseDebugLibraries>true</UseDebugLibraries>
-    <PlatformToolset>v140</PlatformToolset>
+    <PlatformToolset>v141</PlatformToolset>
     <CharacterSet>Unicode</CharacterSet>
   </PropertyGroup>
   <PropertyGroup Condition="'$(Configuration)|$(Platform)'=='Release|x64'" Label="Configuration">
     <ConfigurationType>Application</ConfigurationType>
     <UseDebugLibraries>false</UseDebugLibraries>
-    <PlatformToolset>v140</PlatformToolset>
+    <PlatformToolset>v141</PlatformToolset>
     <WholeProgramOptimization>true</WholeProgramOptimization>
     <CharacterSet>Unicode</CharacterSet>
   </PropertyGroup>

From bdc891b9b84266b0b98fcd4f10081f3bdebee27d Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Tue, 4 Jun 2019 09:37:32 -0700
Subject: [PATCH 494/838] refined an error message

---
 src/data/factored_vocab.cpp | 8 ++++++--
 1 file changed, 6 insertions(+), 2 deletions(-)
 mode change 100644 => 100755 src/data/factored_vocab.cpp

diff --git a/src/data/factored_vocab.cpp b/src/data/factored_vocab.cpp
old mode 100644
new mode 100755
index 3c7c8bc8e..37fbf4c72
--- a/src/data/factored_vocab.cpp
+++ b/src/data/factored_vocab.cpp
@@ -301,9 +301,13 @@ Word FactoredVocab::factors2word(const std::vector<size_t>& factorIndices /* [nu
   for (size_t g = 0; g < numGroups; g++) {
     auto factorIndex = factorIndices[g];
     if (factorIndex != FACTOR_NOT_SPECIFIED) { // check validity
-      auto factor0Index = factorIndices[0]; // lemma
+      auto factor0Index = factorIndices[0];    // lemma
       ABORT_IF(factor0Index == FACTOR_NOT_SPECIFIED, "Without lemma, no other factor may be specified");
-      ABORT_IF(lemmaHasFactorGroup(factor0Index, g) == (factorIndex == FACTOR_NOT_APPLICABLE), "Lemma {} does not have factor group {}", factor0Index, g);
+      ABORT_IF(lemmaHasFactorGroup(factor0Index, g) == (factorIndex == FACTOR_NOT_APPLICABLE),
+               "Lemma '{}' {} factor group '{}'",
+               factorVocab_[WordIndex(factor0Index + groupRanges_[0].first)],
+               lemmaHasFactorGroup(factor0Index, g) ? "needs" : "does not have",
+               groupPrefixes_[g]);
     }
     if (factorIndex == FACTOR_NOT_APPLICABLE || factorIndex == FACTOR_NOT_SPECIFIED)
       factorIndex = (size_t)factorShape_[g] - 1; // sentinel for "unused" or "not specified"

From 56e25e386f1301bb277544d638a76ff37b397d29 Mon Sep 17 00:00:00 2001
From: Young Jin Kim <youki@microsoft.com>
Date: Wed, 5 Jun 2019 09:04:07 -0700
Subject: [PATCH 495/838] fix a crash at the end caused by a wrong free

---
 src/tensors/cpu/device.cpp | 12 ++++++------
 1 file changed, 6 insertions(+), 6 deletions(-)

diff --git a/src/tensors/cpu/device.cpp b/src/tensors/cpu/device.cpp
index 04a79ae62..a9039b53a 100644
--- a/src/tensors/cpu/device.cpp
+++ b/src/tensors/cpu/device.cpp
@@ -9,12 +9,6 @@
 namespace marian {
 namespace cpu {
 
-Device::~Device() {
-  free(data_);
-  data_ = nullptr;
-  size_ = 0;
-}
-
 // allocate function for tensor reserve() below. 
 // Needed for AVX512, while not available on all compilers. It seems clang
 // does not have aligned_alloc for all cstlib versions. If AVX512 is not used
@@ -35,6 +29,12 @@ Device::~Device() {
 #define FREE(ptr) free(ptr)
 #endif
 
+Device::~Device() {
+  FREE(data_);
+  data_ = nullptr;
+  size_ = 0;
+}
+
 void Device::reserve(size_t size) {
   size = align(size);
   ABORT_IF(size < size_ || size == 0,

From 27defae8ce35b113038a5ed4973c6a82b04e7a16 Mon Sep 17 00:00:00 2001
From: Young Jin Kim <youki@microsoft.com>
Date: Wed, 5 Jun 2019 09:42:02 -0700
Subject: [PATCH 496/838] Some more changes - value deallocations,
 initializations

---
 src/tensors/cpu/device.cpp | 4 ++--
 src/tensors/device.h       | 4 ++--
 2 files changed, 4 insertions(+), 4 deletions(-)

diff --git a/src/tensors/cpu/device.cpp b/src/tensors/cpu/device.cpp
index a9039b53a..13c3bb074 100644
--- a/src/tensors/cpu/device.cpp
+++ b/src/tensors/cpu/device.cpp
@@ -30,9 +30,9 @@ namespace cpu {
 #endif
 
 Device::~Device() {
-  FREE(data_);
-  data_ = nullptr;
   size_ = 0;
+  data_ = nullptr;
+  FREE(data_);
 }
 
 void Device::reserve(size_t size) {
diff --git a/src/tensors/device.h b/src/tensors/device.h
index 68a9492eb..aa57019ad 100644
--- a/src/tensors/device.h
+++ b/src/tensors/device.h
@@ -11,8 +11,8 @@ class Device {
 protected:
   DeviceId deviceId_;
 
-  uint8_t* data_{0};
-  size_t size_{0};
+  uint8_t* data_;
+  size_t size_;
   size_t alignment_;
 
   size_t align(size_t size) {

From fb305877a61f6a8796370c070599ae4218530ff6 Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Wed, 5 Jun 2019 14:05:27 -0700
Subject: [PATCH 497/838] bug fix: FactoredSegmenter \u escapes should use 4
 digits, not 3

---
 src/common/config_parser.cpp | 2 +-
 src/data/factored_vocab.cpp  | 4 ++--
 2 files changed, 3 insertions(+), 3 deletions(-)

diff --git a/src/common/config_parser.cpp b/src/common/config_parser.cpp
index eaa9c9912..af0ec8cb0 100755
--- a/src/common/config_parser.cpp
+++ b/src/common/config_parser.cpp
@@ -668,7 +668,7 @@ void ConfigParser::addSuboptionsInputLength(cli::CLIWrapper& cli) {
       "Maximum length of a sentence in a training sentence pair",
       defaultMaxLength);
   cli.add<bool>("--max-length-crop",
-      "Crop a sentence to max-length instead of ommitting it if longer than max-length");
+      "Crop a sentence to max-length instead of omitting it if longer than max-length");
   // clang-format on
 }
 
diff --git a/src/data/factored_vocab.cpp b/src/data/factored_vocab.cpp
index 37fbf4c72..af40ea3ad 100755
--- a/src/data/factored_vocab.cpp
+++ b/src/data/factored_vocab.cpp
@@ -571,11 +571,11 @@ void FactoredVocab::constructNormalizationInfoForVocab() {
 static void unescapeHexEscapes(std::string& utf8Lemma) {
   if (utf8Lemma.find('\\') == std::string::npos)
     return; // nothing to do
-  auto lemma = utils::utf8ToUtf16String(utf8Lemma); // \u.... implies we must operate on UTF-16 level
+  auto lemma = utils::utf8ToUtf16String(utf8Lemma); // \u.... implies we must operate on UTF-16 level (not UCS-4)
   auto pos = lemma.find('\\');
   while (pos != std::string::npos) {
     ABORT_IF(pos + 1 >= lemma.size() || (lemma[pos+1] != 'x' && lemma[pos + 1] != 'u'), "Malformed escape in factored encoding: {}", utf8Lemma);
-    int numDigits = 2 + (lemma[pos + 1] == 'u');
+    int numDigits = 2 + 2 * (lemma[pos + 1] == 'u'); // 2 for \x, 4 for \u
     ABORT_IF(pos + 2 + numDigits > lemma.size(), "Malformed escape in factored encoding: {}", utf8Lemma);
     auto digits = utils::utf8FromUtf16String(lemma.substr(pos + 2, numDigits));
     auto c = std::strtoul(digits.c_str(), nullptr, 16);

From 3c5177ca74ce4c0557bb9bac19e4a93fd30018f3 Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Wed, 5 Jun 2019 14:13:42 -0700
Subject: [PATCH 498/838] now supports FactoredSegmenter's
 DistinguishInitialAndInternalPieces mode

---
 src/data/factored_vocab.cpp | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/src/data/factored_vocab.cpp b/src/data/factored_vocab.cpp
index af40ea3ad..68fd78f93 100755
--- a/src/data/factored_vocab.cpp
+++ b/src/data/factored_vocab.cpp
@@ -607,13 +607,15 @@ std::string FactoredVocab::surfaceForm(const Words& sentence) const /*override f
     auto has = [&](const char* factor) { return tokenSet.find(factor) != tokenSet.end(); };
     // spacing
     bool hasGlueRight = has("gr+") || has("wen") || has("cen");
-    bool hasGlueLeft  = has("gl+") || has("wbn") || has("cbn");
+    bool hasGlueLeft  = has("gl+") || has("wbn") || has("cbn") || has("wi");
     bool insertSpaceBefore = !prevHadGlueRight && !hasGlueLeft;
     if (insertSpaceBefore)
       res.push_back(' ');
     prevHadGlueRight = hasGlueRight;
     // capitalization
     unescapeHexEscapes(lemma); // unescape \x.. and \u....
+    if (utils::beginsWith(lemma, "\xE2\x96\x81"))  // remove leading _ (\u2581, for DistinguishInitialAndInternalPieces mode)
+        lemma = lemma.substr(3);
     if      (has("ci")) lemma = utils::utf8Capitalized(lemma);
     else if (has("ca")) lemma = utils::utf8ToUpper    (lemma);
     else if (has("cn")) lemma = utils::utf8ToLower    (lemma);

From 925d0f16d68eb5662b2e2196f6d4e2988f81b4fe Mon Sep 17 00:00:00 2001
From: Young Jin Kim <youki@microsoft.com>
Date: Thu, 6 Jun 2019 09:17:57 -0700
Subject: [PATCH 499/838] some more fixes

---
 src/tensors/cpu/device.cpp | 2 --
 src/tensors/device.h       | 6 +++---
 2 files changed, 3 insertions(+), 5 deletions(-)

diff --git a/src/tensors/cpu/device.cpp b/src/tensors/cpu/device.cpp
index 13c3bb074..941902a8f 100644
--- a/src/tensors/cpu/device.cpp
+++ b/src/tensors/cpu/device.cpp
@@ -30,8 +30,6 @@ namespace cpu {
 #endif
 
 Device::~Device() {
-  size_ = 0;
-  data_ = nullptr;
   FREE(data_);
 }
 
diff --git a/src/tensors/device.h b/src/tensors/device.h
index aa57019ad..0be6c076c 100644
--- a/src/tensors/device.h
+++ b/src/tensors/device.h
@@ -11,8 +11,8 @@ class Device {
 protected:
   DeviceId deviceId_;
 
-  uint8_t* data_;
-  size_t size_;
+  uint8_t* data_{0};
+  size_t size_{0};
   size_t alignment_;
 
   size_t align(size_t size) {
@@ -21,7 +21,7 @@ class Device {
 
 public:
   Device(DeviceId deviceId, size_t alignment = 256)
-      : deviceId_(deviceId), data_(0), size_(0), alignment_(alignment) {}
+      : deviceId_(deviceId), alignment_(alignment) {}
 
   virtual ~Device(){};
 

From d2d8ec041d443f906809b912a57a3d41b2c3a07c Mon Sep 17 00:00:00 2001
From: Young Jin Kim <youki@microsoft.com>
Date: Tue, 18 Jun 2019 16:59:11 -0700
Subject: [PATCH 500/838] Enable FBGEMM based packed GEMM on windows

---
 .gitmodules                           |   4 +
 CMakeLists.txt                        |  83 ++--
 src/3rd_party/CMakeLists.txt          |   5 +
 src/3rd_party/fbgemm                  |   1 +
 src/CMakeLists.txt                    |   2 +
 src/graph/auto_tuner.h                |   2 +-
 src/graph/expression_operators.cpp    |  96 +++--
 src/tensors/cpu/expanded_gemm.h       | 204 +++++++++
 src/tensors/cpu/sharp/avx_gemm.cpp    |   5 +
 src/tensors/cpu/sharp/packed_gemm.cpp | 585 ++++++++++++++++++++++++++
 src/tensors/cpu/sharp/packed_gemm.h   |  39 ++
 src/tensors/cpu/tensor_operators.cpp  |  63 +++
 vs/Marian.vcxproj                     |  28 +-
 vs/Marian.vcxproj.filters             |  57 +++
 14 files changed, 1107 insertions(+), 67 deletions(-)
 create mode 160000 src/3rd_party/fbgemm
 create mode 100644 src/tensors/cpu/expanded_gemm.h
 create mode 100644 src/tensors/cpu/sharp/packed_gemm.cpp
 create mode 100644 src/tensors/cpu/sharp/packed_gemm.h

diff --git a/.gitmodules b/.gitmodules
index efe6bc5ec..7f8ce6304 100644
--- a/.gitmodules
+++ b/.gitmodules
@@ -10,3 +10,7 @@
 [submodule "src/3rd_party/nccl"]
 	path = src/3rd_party/nccl
 	url = https://github.com/marian-nmt/nccl
+[submodule "src/3rd_party/fbgemm"]
+	path = src/3rd_party/fbgemm
+	url = https://machinetranslation.visualstudio.com/DefaultCollection/MachineTranslation/_git/FBGEMM-internal
+	branch = master
diff --git a/CMakeLists.txt b/CMakeLists.txt
index b54766ae9..7cb518c56 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -22,6 +22,7 @@ option(USE_MPI "Use MPI library" OFF)
 option(COMPILE_EXAMPLES "Compile examples" OFF)
 option(COMPILE_TESTS "Compile tests" OFF)
 option(COMPILE_SERVER "Compile marian-server" ON)
+option(USE_FBGEMM "Use FBGEMM" ON)
 
 # Project versioning
 find_package(Git QUIET)
@@ -59,44 +60,54 @@ if(MSVC)
 
   find_library(SHLWAPI Shlwapi.lib) 
   set(EXT_LIBS ${EXT_LIBS} SHLWAPI)
-else()
+else(MSVC)
+
+  # Detect support CPU instrinsics for the current platform. This will
+  # only by used with BUILD_ARCH=native. For overridden BUILD_ARCH we
+  # minimally use -msse4.1. This seems to work with MKL.
+  set(INTRINSICS "")
+  if(BUILD_ARCH STREQUAL "native")
+    message(STATUS "Checking support for CPU intrinsics")
+    include(FindSSE)
+    if(SSE2_FOUND)
+      message(STATUS "SSE2 support found")
+      set(INTRINSICS "${INTRINSICS} -msse2")
+    endif(SSE2_FOUND)
+    if(SSE3_FOUND)
+      message(STATUS "SSE3 support found")
+      set(INTRINSICS "${INTRINSICS} -msse3")
+    endif(SSE3_FOUND)
+    if(SSE4_1_FOUND)
+      message(STATUS "SSE4.1 support found")
+      set(INTRINSICS "${INTRINSICS} -msse4.1")
+    endif(SSE4_1_FOUND)
+    if(AVX_FOUND)
+      message(STATUS "AVX support found")
+      set(INTRINSICS "${INTRINSICS} -mavx")
+    endif(AVX_FOUND)
+    if(AVX2_FOUND)
+      message(STATUS "AVX2 support found")
+      set(INTRINSICS "${INTRINSICS} -mavx2")
+    endif(AVX2_FOUND)
+    if(AVX512_FOUND)
+      message(STATUS "AVX512 support found")
+      set(INTRINSICS "${INTRINSICS} -mavx512f")
+      list(APPEND INTRINSICS_NVCC -Xcompiler\ -mavx512f)
+    endif(AVX512_FOUND)
+  else()
+    set(INTRINSICS "-msse4.1")
+  endif()
 
-# Detect support CPU instrinsics for the current platform. This will
-# only by used with BUILD_ARCH=native. For overridden BUILD_ARCH we
-# minimally use -msse4.1. This seems to work with MKL.
-set(INTRINSICS "")
-if(BUILD_ARCH STREQUAL "native")
-  message(STATUS "Checking support for CPU intrinsics")
-  include(FindSSE)
-  if(SSE2_FOUND)
-    message(STATUS "SSE2 support found")
-    set(INTRINSICS "${INTRINSICS} -msse2")
-  endif(SSE2_FOUND)
-  if(SSE3_FOUND)
-    message(STATUS "SSE3 support found")
-    set(INTRINSICS "${INTRINSICS} -msse3")
-  endif(SSE3_FOUND)
-  if(SSE4_1_FOUND)
-    message(STATUS "SSE4.1 support found")
-    set(INTRINSICS "${INTRINSICS} -msse4.1")
-  endif(SSE4_1_FOUND)
-  if(AVX_FOUND)
-    message(STATUS "AVX support found")
-    set(INTRINSICS "${INTRINSICS} -mavx")
-  endif(AVX_FOUND)
-  if(AVX2_FOUND)
-    message(STATUS "AVX2 support found")
-    set(INTRINSICS "${INTRINSICS} -mavx2")
-  endif(AVX2_FOUND)
-else()
-  set(INTRINSICS "-msse4.1")
-endif()
+  if(USE_FBGEMM)
+    set(EXT_LIBS ${EXT_LIBS} fbgemm dl)
+    add_definitions(-DUSE_FBGEMM=1)
+  endif(USE_FBGEMM)
 
-set(DISABLE_GLOBALLY "-Wno-unused-result")
+  set(DISABLE_GLOBALLY "-Wno-unused-result")
 
-# These are used in src/CMakeLists.txt on a per-target basis
-list(APPEND ALL_WARNINGS -Wall; -Werror; -Wno-unused-result; -Wno-deprecated; -Wno-pragmas; -Wno-unused-parameter; -Wextra; -Wno-unused-function;
-                         -Wno-unused-value; -Wno-unknown-pragmas; -Wno-sign-compare; -Wno-missing-field-initializers;)
+  # These are used in src/CMakeLists.txt on a per-target basis
+  list(APPEND ALL_WARNINGS -Wall; -Werror; -Wno-unused-result; -Wno-deprecated; -Wno-pragmas; -Wno-unused-parameter; -Wextra; -Wno-unused-function;
+                          -Wno-unused-value; -Wno-unknown-pragmas; -Wno-sign-compare; -Wno-missing-field-initializers;)
 
   # This warning does not exist prior to gcc 5.0
   if(CMAKE_COMPILER_IS_GNUCC AND CMAKE_CXX_COMPILER_VERSION VERSION_GREATER 5.0)
@@ -111,7 +122,7 @@ list(APPEND ALL_WARNINGS -Wall; -Werror; -Wno-unused-result; -Wno-deprecated; -W
   set(CMAKE_CXX_FLAGS_PROFILE         "${CMAKE_CXX_FLAGS_RELEASE} -pg -g -rdynamic")
   set(CMAKE_CXX_FLAGS_PROFGEN         "${CMAKE_CXX_FLAGS_RELEASE} -fprofile-generate -fprofile-correction")
   set(CMAKE_CXX_FLAGS_PROFUSE         "${CMAKE_CXX_FLAGS_RELEASE} -fprofile-use -fprofile-correction")
-  endif()
+endif(MSVC)
 
 # Downloading SentencePiece if requested and set to compile with it.
 # Requires all the dependencies imposed by SentencePiece
diff --git a/src/3rd_party/CMakeLists.txt b/src/3rd_party/CMakeLists.txt
index 8548d9b8b..3dca348bc 100644
--- a/src/3rd_party/CMakeLists.txt
+++ b/src/3rd_party/CMakeLists.txt
@@ -5,6 +5,11 @@ add_subdirectory(./yaml-cpp)
 add_subdirectory(./SQLiteCpp)
 add_subdirectory(./pathie-cpp)
 add_subdirectory(./zlib)
+if(USE_FBGEMM)
+  set(FBGEMM_BUILD_TESTS OFF CACHE BOOL "Disable fbgemm tests")
+  set(FBGEMM_BUILD_BENCHMARKS OFF CACHE BOOL "Disable fbgemm benchmark")
+  add_subdirectory(./fbgemm)
+endif(USE_FBGEMM)
 
 if(USE_SENTENCEPIECE)
   if(USE_STATIC_LIBS)
diff --git a/src/3rd_party/fbgemm b/src/3rd_party/fbgemm
new file mode 160000
index 000000000..25c1595ce
--- /dev/null
+++ b/src/3rd_party/fbgemm
@@ -0,0 +1 @@
+Subproject commit 25c1595cebfc88775692660b1da7d0bf632112a8
diff --git a/src/CMakeLists.txt b/src/CMakeLists.txt
index 90dde706a..c3fcefefc 100644
--- a/src/CMakeLists.txt
+++ b/src/CMakeLists.txt
@@ -4,6 +4,7 @@ include_directories(.)
 include_directories(3rd_party)
 include_directories(3rd_party/SQLiteCpp/include)
 include_directories(3rd_party/sentencepiece)
+include_directories(3rd_party/fbgemm/include)
 
 add_library(marian STATIC
   common/version.cpp
@@ -40,6 +41,7 @@ add_library(marian STATIC
   tensors/cpu/sharp/int_gemm.cpp
   tensors/cpu/sharp/avx_gemm.cpp
   tensors/cpu/sharp/sse_gemm.cpp
+  tensors/cpu/sharp/packed_gemm.cpp
 
   graph/expression_graph.cpp
   graph/expression_operators.cpp
diff --git a/src/graph/auto_tuner.h b/src/graph/auto_tuner.h
index 7bf80c799..868a800e2 100644
--- a/src/graph/auto_tuner.h
+++ b/src/graph/auto_tuner.h
@@ -20,7 +20,7 @@ class AutoTuner : public AutoTunerRecorder {
 private:
   typedef std::function<Return(Args...)> Algorithm;
 
-  const size_t max = 100;
+  const size_t max = 50;
 
   UPtr<timer::CPUTimer> timer_;
 
diff --git a/src/graph/expression_operators.cpp b/src/graph/expression_operators.cpp
index 396fa03e2..42bb65e20 100755
--- a/src/graph/expression_operators.cpp
+++ b/src/graph/expression_operators.cpp
@@ -7,6 +7,7 @@
 
 #include "graph/auto_tuner.h"
 #include "tensors/cpu/int16.h"
+#include "tensors/cpu/expanded_gemm.h"
 
 namespace marian {
 
@@ -410,7 +411,7 @@ Expr affine(Expr a, Expr b, Expr bias, bool transA, bool transB, float scale) {
   float clipValue = a->graph()->getBackend()->getClip();
 
   if(a->graph()->isOptimized() && device == DeviceType::cpu) {
-    bool autotune = true;
+    bool autotune = false;
     if(autotune) {
       thread_local Ptr<AutoTuner<Expr>> tuner = New<AutoTuner<Expr>>();
 
@@ -431,60 +432,107 @@ Expr affine(Expr a, Expr b, Expr bias, bool transA, bool transB, float scale) {
       util::hash_combine(hash, transA);
       util::hash_combine(hash, transB);
 
+#if USE_FBGEMM
+      // Use Packed GEMM only if it's memoized
+      if(b->memoize()) {
+        // add third algorithm variant (Packed GEMM)
+        size_t hash1 = hash;
+        util::hash_combine(hash1, 1);
+        auto rec1 = [=](Expr e, bool stop = false) {
+          e->record(tuner, hash1, stop);
+          return e;
+        };
+
+        auto alg1 = [=]() {
+          auto packed = cpu::pack::pack(b, cpu::pack::PackMatrix::B, transB, clipValue);
+
+          return rec1(
+              cpu::pack::affine(
+              clip(a, clipValue), packed,
+              b->shape(),
+              bias,
+              transA,
+              transB,
+              scale),
+            true);
+        };
+        tuner->insert({hash1, alg1});
+      }
+#endif // USE_FBGEMM
+
       // add first algorithm variant (Int16)
-      size_t hash1 = hash;
-      util::hash_combine(hash1, 1);
-      auto rec1 = [=](Expr e, bool stop = false) {
-        e->record(tuner, hash1, stop);
+      size_t hash2 = hash;
+      util::hash_combine(hash2, 2);
+      auto rec2 = [=](Expr e, bool stop = false) {
+        e->record(tuner, hash2, stop);
         return e;
       };
-      auto alg1 = [=]() {
-        return rec1(
+      auto alg2 = [=]() {
+        return rec2(
             cpu::int16::affine(
-                rec1(cpu::int16::quantize(transA ? rec1(transpose(a)) : a,
+                rec2(cpu::int16::quantize(transA ? rec2(transpose(a)) : a,
                                           clipValue)),
                 cpu::int16::quantize(transB ? b : transpose(b), clipValue),
                 bias,
                 scale),
             true);
       };
-      tuner->insert({hash1, alg1});
+      tuner->insert({hash2, alg2});
 
       // add second algorithm variant (CBlas)
-      size_t hash2 = hash;
-      util::hash_combine(hash2, 2);
-      auto rec2 = [=](Expr e, bool stop = false) {
-        e->record(tuner, hash2, stop);
+      size_t hash3 = hash;
+      util::hash_combine(hash3, 3);
+      auto rec3 = [=](Expr e, bool stop = false) {
+        e->record(tuner, hash3, stop);
         return e;
       };
 
-      auto alg2 = [=]() {
+      auto alg3 = [=]() {
         auto ac = clip(a, clipValue);
         if(ac != a)
-          ac = rec2(ac);
+          ac = rec3(ac);
 
         auto bc = clip(b, clipValue);
         if(bc != b)
-          bc = rec2(bc);
+          bc = rec3(bc);
 
         int rows = ac->shape().elements() / ac->shape()[-1];
         Expr ones = ac->graph()->ones({rows, 1});
         std::vector<Expr> nodes = {ac, bc, bias, ones};
-        return rec2(Expression<AffineNodeOp>(nodes, transA, transB, scale),
+        return rec3(Expression<AffineNodeOp>(nodes, transA, transB, scale),
                     true);
       };
-      tuner->insert({hash2, alg2});
+      tuner->insert({hash3, alg3});
 
       // execute algorithm with autotuning
       return tuner->run();
 
     } else {
-      // cpu int16 version
-      return cpu::int16::affine(
-          cpu::int16::quantize(transA ? transpose(a) : a, clipValue),
-          cpu::int16::quantize(transB ? b : transpose(b), clipValue),
-          bias,
-          scale);
+      if(b->memoize()) {
+        auto packed = cpu::pack::pack(b, cpu::pack::PackMatrix::B, transB, clipValue);
+        // auto packed = transB ? 
+        //               cpu::pack::pack(transpose(b), cpu::pack::PackMatrix::B, false, clipValue) :
+        //               cpu::pack::pack(b, cpu::pack::PackMatrix::B, false, clipValue);
+
+        return cpu::pack::affine(
+            clip(a, clipValue), packed,
+            b->shape(),
+            bias,
+            transA,
+            transB,
+            scale);
+        // cpu int16 version
+        // return cpu::int16::affine(
+        //     cpu::int16::quantize(transA ? transpose(a) : a, clipValue),
+        //     cpu::int16::quantize(transB ? b : transpose(b), clipValue),
+        //     bias,
+        //     scale);
+      } else {
+        int rows = a->shape().elements() / a->shape()[-1];
+        Expr ones = a->graph()->ones({rows, 1});
+        std::vector<Expr> nodes = {clip(a, clipValue), clip(b, clipValue), bias, ones};
+        return Expression<AffineNodeOp>(nodes, transA, transB, scale);
+      }
     }
   } else {
     // general version, MKL, CBlas or CUDA
diff --git a/src/tensors/cpu/expanded_gemm.h b/src/tensors/cpu/expanded_gemm.h
new file mode 100644
index 000000000..33b677945
--- /dev/null
+++ b/src/tensors/cpu/expanded_gemm.h
@@ -0,0 +1,204 @@
+#pragma once
+
+#include "graph/node.h"
+#include "tensors/cpu/sharp/packed_gemm.h"
+
+#if USE_FBGEMM
+#include "3rd_party/fbgemm/include/fbgemm/FbgemmFP16.h"
+using namespace fbgemm;
+#endif  // USE_FBGEMM
+
+namespace marian {
+namespace cpu {
+namespace pack {
+
+enum class PackMatrix : uint8_t {
+  A = 0x00,
+  B = 0x01
+};
+
+struct PackNodeOp : public UnaryNodeOp {
+  PackMatrix packMat_;
+  bool transpose_;
+  int nrow_;
+  int ncol_;
+  int kernel_ncol_blocks_;
+  int brow_;
+  int bcol_;
+  int last_brow_;
+  int nbrow_;
+  int nbcol_;
+  uint64_t packsize_;
+
+  PackNodeOp(Expr a, PackMatrix packMat, bool transpose, float clipValue)
+      : UnaryNodeOp(a, newShape(a, transpose), Type::uint8),
+        packMat_(packMat),
+        transpose_(transpose) {
+    if(packMat != PackMatrix::B)
+      ABORT("Only prepacking of B (weight matrix) is supported");
+    if(clipValue != 0)
+      ABORT("Clipping is not supported");
+    if(!memoize_)
+      ABORT("Only memoizeable node is supported");
+  }
+
+  NodeOps forwardOps() override {
+    return {NodeOp(PackFp32(val_,
+                            child(0)->val(),
+                            transpose_,
+                            nrow_,
+                            ncol_,
+                            kernel_ncol_blocks_,
+                            brow_,
+                            bcol_,
+                            last_brow_,
+                            nbrow_,
+                            nbcol_,
+                            packsize_))
+    };
+  }
+
+  NodeOps backwardOps() override {
+    ABORT("Only used for inference");
+    return {NodeOp(0)};
+  }
+
+  const std::string type() override { return "packMat"; }
+
+  Shape newShape(Expr a, bool transpose) {
+#if USE_FBGEMM
+    auto shapeMat = a->shape();
+    // Should be 2D - weight matrix
+    ABORT_IF(shapeMat.size() != 2,
+             "Weight Matrix should be 2D");
+   if (true) {
+    // if (shapeMat[0] < 32000 && shapeMat[1] < 32000) {
+      nrow_ = transpose ? shapeMat[1] : shapeMat[0];
+      ncol_ = transpose ? shapeMat[0] : shapeMat[1];
+      kernel_ncol_blocks_ = 2;
+      brow_ = 512;
+      bcol_ = 8 * kernel_ncol_blocks_;
+      last_brow_ = nrow_ % brow_ == 0 ? brow_ : nrow_ % brow_;
+      nbrow_ = nrow_ % brow_ == 0 ? nrow_ / brow_ : (nrow_ + brow_) / brow_;
+      nbcol_ = ncol_ % bcol_ == 0 ? ncol_ / bcol_ : (ncol_ + bcol_) / bcol_;
+      const int padding = 1024;  // required by sw pipelined kernels
+      const int specialMem = 256;
+      packsize_ = ((nbrow_ * brow_) * (nbcol_ * bcol_)) * sizeof(fbgemm::float16) + padding + specialMem;
+    } else {
+      // use int 8 implementation
+      packsize_ = 1;
+    }
+
+    Shape outShape({(int)packsize_});
+
+    return outShape;
+#else // USE_FBGEMM
+    ABORT("Only FBGEMM based packed GEMM is supported");
+    return Shape();
+#endif  // USE_FBGEMM
+  }
+};
+
+class AffineNodeOp : public NaryNodeOp {
+private:
+  float scalar_;
+  int64_t m_;
+  int64_t n_;
+  int64_t k_;
+  bool transA_;
+  bool transB_;
+  size_t idx_;
+
+public:
+  AffineNodeOp(const std::vector<Expr>& nodes, Shape bShape, bool transA, bool transB, float scalar)
+      : NaryNodeOp(nodes, newShape(nodes[0], bShape, transA, transB), Type::float32),
+        scalar_(scalar) {
+    transA_ = transA;
+    transB_ = transB;
+    m_ = nodes[0]->shape().elements() / nodes[0]->shape()[-1];
+    k_ = nodes[0]->shape().back();
+    if(transA)
+      std::swap(m_, k_);
+
+    int64_t l = bShape.elements() / bShape[-1];
+    n_ = bShape[-1];
+    if(transB)
+      std::swap(l, n_);
+  }
+
+  Shape newShape(Expr a, Shape bShape, bool transA, bool transB) {
+    auto shapeA = a->shape();
+    if(transA) {
+      shapeA.set(shapeA.size() - 2, a->shape()[shapeA.size() - 1]);
+      shapeA.set(shapeA.size() - 1, a->shape()[shapeA.size() - 2]);
+    }
+
+    auto shapeB = bShape;
+    if(transB) {
+      shapeB.set(shapeB.size() - 2, bShape[shapeB.size() - 1]);
+      shapeB.set(shapeB.size() - 1, bShape[shapeB.size() - 2]);
+    }
+
+    Shape outShape = shapeA;
+    outShape.set(outShape.size() - 1, shapeB[shapeB.size() - 1]);
+    ABORT_IF(shapeA[shapeA.size() - 1] != shapeB[shapeB.size() - 2],
+             "Matrix product requires inner dimensions to match");
+    return outShape;
+  }
+
+  NodeOps forwardOps() override {
+    // if (n_ < 32000) {
+   if (true) {
+      return {
+        NodeOp(GemmPackFp32(val_,
+                                  child(0)->val(),
+                                  child(1)->val(),
+                                  child(2)->val(),
+                                  m_,
+                                  n_,
+                                  //k_,
+                                  //1,
+                                  //0,
+                                  transA_))
+                                  //transB_,
+                                  //idx_))
+      };
+    } else {
+      return {
+        NodeOp(GemmPackFp32(val_,
+                                  child(0)->val(),
+                                  child(1)->val(),
+                                  child(2)->val(),
+                                  m_,
+                                  n_,
+                                  //k_,
+                                  //1,
+                                  //0,
+                                  transA_);
+                                  //transB_,
+                                  //idx_);
+                AddBias(val_, child(2)->val()))
+      };
+    }
+  }
+
+  NodeOps backwardOps() override {
+    ABORT("Only used for inference");
+    return {NodeOp(0)};
+  }
+
+  const std::string type() override { return "affinePacked"; }
+};
+
+static inline Expr affine(Expr a, Expr b, Shape bShape, Expr c, bool transA, bool transB, float scalar) {
+  std::vector<Expr> nodes = {a, b, c};
+  return Expression<cpu::pack::AffineNodeOp>(nodes, bShape, transA, transB, scalar);
+}
+
+static inline Expr pack(Expr a, PackMatrix packMat, bool transpose, float clipValue) {
+  return Expression<cpu::pack::PackNodeOp>(a, packMat, transpose, clipValue);
+}
+
+}  // namespace pack
+}  // namespace cpu
+}  // namespace marian
diff --git a/src/tensors/cpu/sharp/avx_gemm.cpp b/src/tensors/cpu/sharp/avx_gemm.cpp
index c41b73eb0..572ac391a 100644
--- a/src/tensors/cpu/sharp/avx_gemm.cpp
+++ b/src/tensors/cpu/sharp/avx_gemm.cpp
@@ -10,6 +10,11 @@
 #include <cassert>
 #include <cstddef>
 
+#ifdef _MSC_VER
+#pragma warning(disable : 4310)
+#pragma warning(disable : 4324)
+#endif
+
 #ifdef __AVX512F__
 
 namespace marian {
diff --git a/src/tensors/cpu/sharp/packed_gemm.cpp b/src/tensors/cpu/sharp/packed_gemm.cpp
new file mode 100644
index 000000000..b6b7937f5
--- /dev/null
+++ b/src/tensors/cpu/sharp/packed_gemm.cpp
@@ -0,0 +1,585 @@
+#include "packed_gemm.h"
+#include "tensors/tensor_allocator.h"
+#include "tensors/tensor_operators.h"
+
+#include <emmintrin.h>
+#include <immintrin.h>
+#include <tmmintrin.h>
+#include <xmmintrin.h>
+#include <cassert>
+#include <cstddef>
+#include <unordered_map>
+//#include <chrono>
+
+#ifdef _MSC_VER
+#pragma warning(disable: 4505) // warning C4505: 'fbgemmAlignedAlloc' in fbgemm.h: unreferenced local function has been removed (missing 'static inline')
+#endif
+
+#if (USE_FBGEMM && MKL_FOUND)
+#include "3rd_party/fbgemm/include/fbgemm/FbgemmFP16.h"
+#include "3rd_party/fbgemm/include/fbgemm/QuantUtils.h"
+#include "3rd_party/fbgemm/include/fbgemm/Fbgemm.h"
+
+#include <mkl.h>
+#include <mkl_types.h>
+//#include "mkl_vsl.h"
+
+#ifdef _OPENMP
+#include <omp.h>
+#endif
+
+using namespace fbgemm;
+#endif // USE_FBGEMM && MKL_FOUND
+
+namespace marian {
+namespace cpu {
+namespace pack {
+
+//static float packingTime = 0;
+
+#if (USE_FBGEMM && MKL_FOUND)
+// initialize with a dummy
+static PackedGemmMatrixFP16 packedPlaceholder(1, 1, 1, 1, 1, 1, 1, 1);
+
+// temporary variable for int 8
+//static PackBMatrix<int8_t>* packedBint8 = nullptr;
+// transformer base wmt 2017
+// static float bqScale = 0.39/128;
+// static int32_t bqZeropoint = 0;
+// old student de-en
+//static float* bqScale;
+//static int32_t* bqZeropoint;
+// static float bqScale = 0.683/106;
+// static int32_t bqZeropoint = 21;
+// static float bqScale = 0.9672/128;
+// static int32_t bqZeropoint = 0;
+//static std::vector<int32_t>* col_offsets = nullptr;
+
+//inline void col_offsets_with_zero_pt_s8acc32_ref(
+//    bool transpose,
+//    int K,
+//    int N,
+//    const int8_t* Bint8,
+//    const int32_t* B_zero_point,
+//    int32_t* col_offsets,
+//    int ncols_per_quant_group) {
+//  for (int n = 0; n < N; ++n) {
+//    int32_t sum = 0;
+//    for (int k = 0; k < K; ++k) {
+//      sum += transpose ? Bint8[k + n * K] : Bint8[k * N + n];
+//    }
+//    col_offsets[n] = sum - B_zero_point[n / ncols_per_quant_group] * K;
+//  }
+//}
+
+// This is copied from FBGEMM code
+// A better way?
+// blocked row-major format address arithmetic
+inline uint64_t addr(const int r_,
+                     const int c_,
+                     const int brow_,
+                     const int bcol_,
+                     const int nbrow_,
+                     const int nbcol_,
+                     const int last_brow_) {
+  uint64_t r = (uint64_t)r_;
+  uint64_t c = (uint64_t)c_;
+
+  uint64_t block_row_id = r / brow_;
+  uint64_t brow_offset = (block_row_id * nbcol_) * (brow_ * bcol_);
+  uint64_t block_col_id = c / bcol_;
+  uint64_t bcol_offset
+      = block_col_id * ((block_row_id != nbrow_ - 1) ? (brow_ * bcol_) : (last_brow_ * bcol_));
+  uint64_t block_offset = brow_offset + bcol_offset;
+  uint64_t inblock_offset = r % brow_ * bcol_ + c % bcol_;
+
+  uint64_t index = block_offset + inblock_offset;
+  return index;
+}
+
+void PackFp32(marian::Tensor out,
+              const marian::Tensor in,
+              bool transpose,
+              int nrow,
+              int ncol,
+              int kernel_ncol_blocks,
+              int brow,
+              int bcol,
+              int last_brow,
+              int nbrow,
+              int nbcol,
+              uint64_t packsize) {
+  //auto t_start = std::chrono::high_resolution_clock::now();
+  // for the last embedding layer, pack it into int8
+  // if (in->shape().size() == 2 && (in->shape()[0] >= 32000 || in->shape()[1] >= 32000)) {
+ if (false) {
+#if 0 
+   if (packedBint8 == nullptr) {
+      int k = transpose ? in->shape()[1] : in->shape()[0];
+      int n = transpose ? in->shape()[0] : in->shape()[1];
+      // std::cout << "transpose: " << transpose << ", k: " << k << ", n: " << n << std::endl;
+      // std::cout << ", bqScale: " << bqScale << ", bqZeropoint: " << bqZeropoint << std::endl;
+      // two steps
+      // 0. quantize --> this should be done outside
+      int len = in->shape()[0]*in->shape()[1];
+
+      // 0-1. collect stats for each class
+      bqScale = new float[n];
+      bqZeropoint = new int32_t[n];
+
+      // int numBin = 20;
+      // float denum = 2/(float)numBin;
+
+      // int hist[numBin] = { 0, };
+
+      // Transposed only
+      float* data = in->data();
+      float val = 0;
+      for (int jj = 0; jj < n; jj++) {
+        float min = 1000000, max = -10000000;
+        for (int ii = 0; ii < k; ii++) {
+          val = data[jj*k + ii];
+          if (val < min) min = val;
+          if (val > max) max = val;
+          // hist[(int)((val + 1)/denum)]++;
+        }
+        bqScale[jj] = (max - min)/255;
+        bqZeropoint[jj] = (int32_t)(127 - max / bqScale[jj]);
+        // bqScale[jj] = (0.3 + 0.4)/255;
+        // bqZeropoint[jj] = (int32_t)(127 - 0.3 / bqScale[jj]);
+      }
+
+      // std::cout << "hist: ";
+      // for (int ii = 0; ii < numBin; ii++) {
+      //   std::cout << hist[ii] << ", ";
+      // }
+      // std::cout << std::endl;
+      // std::cout << "len: " << len << ", bqScale: " << bqScale << ", bqZeropoint: " << bqZeropoint << std::endl;
+      //int8_t quantized[len]; // aligned malloc?
+      int8_t* quantized = (int8_t*)aligned_alloc(256, len);
+      // std::cout << "len: " << len << ", bqScale: " << bqScale << ", bqZeropoint: " << bqZeropoint << std::endl;
+      for (int ii = 0; ii < n; ii++) {
+        TensorQuantizationParams bQuantParam;
+        // std::cout << "len: " << len << ", bqScale: " << bqScale << ", bqZeropoint: " << bqZeropoint << std::endl;
+        bQuantParam.scale = bqScale[ii];
+        bQuantParam.zero_point = bqZeropoint[ii];
+        bQuantParam.precision = 8;
+        // std::cout << "len: " << len << ", bqScale: " << bqScale << ", bqZeropoint: " << bqZeropoint << std::endl;
+
+        fbgemm::Quantize<int8_t>(in->data() + ii * k, quantized + ii * k, k, bQuantParam);
+      }
+      // std::cout << "original" << std::endl;
+      // for (int ii = 0; ii < n; ii++) {
+      //   for (int jj = 0; jj < 1; jj++) {
+      //     std::cout << in->data()[ii * k + jj] << ","; 
+      //   }
+      //   std::cout << std::endl;
+      // }
+      // std::cout << "quantized" << std::endl;
+      // for (int ii = 0; ii < 1; ii++) {
+      //   for (int jj = 0; jj < k; jj++) {
+      //     std::cout << (int32_t)quantized[ii * k + jj] << ","; 
+      //   }
+      //   std::cout << std::endl;
+      // }
+      // 1. compute column offsets
+      col_offsets = new std::vector<int32_t>(n);
+      col_offsets_with_zero_pt_s8acc32_ref(transpose, k, n, quantized, bqZeropoint, col_offsets->data(), 1);
+      // for (int ii = 0; ii < n; ii++) {
+      //   std::cout << (int32_t)col_offsets->data()[ii] << ","; 
+      // }
+      // std::cout << std::endl;
+      // std::cout << "calc offset done" << std::endl;
+      // 2. packing
+      // uint8_t* packedmem = aligned_alloc(256, len);
+      packedBint8 = new PackBMatrix<int8_t>(transpose ? matrix_op_t::Transpose : matrix_op_t::NoTranspose,
+                                            k, // in->shape()[0],
+                                            n, // in->shape()[1],
+                                            quantized,
+                                            in->shape()[1]);
+      // std::cout << "packing B done" << std::endl;
+      // int k = transpose ? in->shape()[1] : in->shape()[0];
+      // int n = transpose ? in->shape()[0] : in->shape()[1];
+      // std::cout << "transpose: " << transpose << ", k: " << k << ", n: " << n << std::endl;
+      // std::cout << ", bqScale: " << bqScale << ", bqZeropoint: " << bqZeropoint << std::endl;
+      // // two steps
+      // // 0. quantize --> this should be done outside
+      // int len = in->shape()[0]*in->shape()[1];
+      // std::cout << "len: " << len << ", bqScale: " << bqScale << ", bqZeropoint: " << bqZeropoint << std::endl;
+      // //int8_t quantized[len]; // aligned malloc?
+      // int8_t* quantized = (int8_t*)aligned_alloc(256, len);
+      // std::cout << "len: " << len << ", bqScale: " << bqScale << ", bqZeropoint: " << bqZeropoint << std::endl;
+      // TensorQuantizationParams bQuantParam;
+      // std::cout << "len: " << len << ", bqScale: " << bqScale << ", bqZeropoint: " << bqZeropoint << std::endl;
+      // bQuantParam.scale = bqScale;
+      // bQuantParam.zero_point = bqZeropoint;
+      // bQuantParam.precision = 8;
+      // std::cout << "len: " << len << ", bqScale: " << bqScale << ", bqZeropoint: " << bqZeropoint << std::endl;
+
+      // fbgemm::Quantize<int8_t>(in->data(), quantized, len, bQuantParam);
+      // std::cout << "original" << std::endl;
+      // // for (int ii = 0; ii < n; ii++) {
+      // //   for (int jj = 0; jj < 1; jj++) {
+      // //     std::cout << in->data()[ii * k + jj] << ","; 
+      // //   }
+      // //   std::cout << std::endl;
+      // // }
+      // std::cout << "quantized" << std::endl;
+      // // for (int ii = 0; ii < 1; ii++) {
+      // //   for (int jj = 0; jj < k; jj++) {
+      // //     std::cout << (int32_t)quantized[ii * k + jj] << ","; 
+      // //   }
+      // //   std::cout << std::endl;
+      // // }
+      // // 1. compute column offsets
+      // col_offsets = new std::vector<int32_t>(n);
+      // col_offsets_with_zero_pt_s8acc32_ref(k, n, n, quantized, &bqZeropoint, col_offsets->data(), n);
+      // // for (int ii = 0; ii < n; ii++) {
+      // //   std::cout << (int32_t)col_offsets->data()[ii] << ","; 
+      // // }
+      // std::cout << std::endl;
+      // std::cout << "calc offset done" << std::endl;
+      // 2. packing
+      // uint8_t* packedmem = aligned_alloc(256, len);
+      // packedBint8 = new PackBMatrix<int8_t>(transpose ? matrix_op_t::Transpose : matrix_op_t::NoTranspose,
+      //                                       in->shape()[0],
+      //                                       in->shape()[1],
+      //                                       quantized,
+      //                                       transpose ? in->shape()[1] : in->shape()[0]);
+      // std::cout << "packing B done" << std::endl;
+    }
+#endif
+  } else {
+    // initialize memory
+    uint8_t* outmemorg = out->data<uint8_t>();
+    for(auto i = 0; i < packsize; i++) {
+      outmemorg[i] = 0;
+    }
+    // save the other auxiliary variables
+    uint64_t* auxmemsize = (uint64_t*)outmemorg;
+    auxmemsize[0] = packsize;
+    int* auxmem = (int*)(auxmemsize + 1);
+    auxmem[0] = nrow;
+    auxmem[1] = ncol;
+    auxmem[2] = kernel_ncol_blocks;
+    auxmem[3] = brow;
+    auxmem[4] = bcol;
+    auxmem[5] = last_brow;
+    auxmem[6] = nbrow;
+    auxmem[7] = nbcol;
+    // cast to float16
+    fbgemm::float16* outmem = (fbgemm::float16*)(outmemorg + 256);
+    fbgemm::float16* dummy = new fbgemm::float16;
+    // pack the matrix
+    float* inmem = in->data();
+    for(int i = 0; i < nrow; i++) {
+      for(int j = 0; j < ncol; j++) {
+        outmem[addr(i, j, brow, bcol, nbrow, nbcol, last_brow)]
+            = tconv(!transpose ? inmem[i * ncol + j] : inmem[i + nrow * j], *dummy);
+      }
+    }
+    delete dummy;
+
+    //auto t_end = std::chrono::high_resolution_clock::now();
+    //packingTime += (float) std::chrono::duration<double, std::milli>(t_end-t_start).count();
+    //std::cout << "Packing time: " << packingTime << std::endl;
+  }
+
+  // std::cout << "B transposed: " << transpose << std::endl;
+  // for (int i = 0; i < in->shape().size(); i++) {
+  //   std::cout << "size " << i << ": " << in->shape()[i] << std::endl;
+  // }
+  // // compute statistics for quantization
+  // if (in->shape().size() == 2 && (in->shape()[0] >= 32000 || in->shape()[1] >= 32000)) {
+  //   float mins[ncol] = {0};
+  //   float maxs[ncol] = {0};
+  //   float means[ncol] = {0};
+  //   float stds[ncol] = {0};
+  //   for(int i = 0; i < nrow; i++) {
+  //     for(int j = 0; j < ncol; j++) {
+  //       float val = !transpose ? in->data()[i * ncol + j] : in->data()[i + nrow * j];
+  //       if (val < mins[j])
+  //         mins[j] = val;
+  //       if (val > maxs[j])
+  //         maxs[j] = val;
+        
+  //       means[j] += val;
+  //       stds[j] += val*val;
+  //     }
+  //   }
+  //   for(int j = 0; j < ncol; j++) {
+  //       std::cout << mins[j] << ", " << maxs[j] << ", " << means[j] << ", " << stds[j] << std::endl;
+  //   }
+  // }
+}
+
+void GemmPackFp32(marian::Tensor C,
+                  const marian::Tensor A,
+                  const marian::Tensor B,
+                  const marian::Tensor bias,
+                  const int64_t m,
+                  const int64_t n,
+                  int transA) {
+  // use int 8 packed gemm
+  // if (A->shape().size() == 4 && B->shape().size() == 1 && B->shape()[0] == 1) {
+ if (false) {
+    // quantize & pack A
+    // transformer base wmt 2017
+    // float ascale = 7.8/104;
+    // int32_t azeropoint = 151;
+    // old student de-en
+    // float ascale = 14.85/117;
+    // int32_t azeropoint = 138;
+
+#if 0
+    // compute range
+    float min_est=1000000, max_est=-10000000;
+    // VSLSSTaskPtr task;
+    // MKL_INT task_p, task_n, xstorage;
+
+    // /* Parameters of the task and initialization */
+    // task_p = 1;
+    // task_n = A->shape().elements();
+    // xstorage = VSL_SS_MATRIX_STORAGE_ROWS;
+    // min_est = max_est = A->data()[0];
+    // /* Create a task */
+    // vslsSSNewTask( &task, &task_p, &task_n, &xstorage, (float*)A->data(), 0, 0 );
+    // /* Initialize the task parameters */
+    // vslsSSEditTask( task, VSL_SS_ED_MIN, &min_est );
+    // vslsSSEditTask( task, VSL_SS_ED_MAX, &max_est );
+    // /* Compute the minimum and maximum values in observations */
+    // vslsSSCompute( task, VSL_SS_MIN|VSL_SS_MAX, VSL_SS_METHOD_FAST );
+    // /* Deallocate the task resources */
+    
+    // vslSSDeleteTask( &task );
+
+    int elem = A->shape().elements();
+    float* data = A->data();
+    for (int ii = 0; ii < elem; ii++) {
+      if (data[ii] < min_est) min_est = data[ii];
+      if (data[ii] > max_est) max_est = data[ii];
+    }
+ 
+    std::vector<int32_t> row_offset_buf(PackAWithQuantRowOffset<uint8_t>::rowOffsetBufferSize());
+
+    float ascale = (max_est - min_est)/255;
+    int32_t azeropoint = (int32_t)(255 - max_est / ascale);
+    PackAWithQuantRowOffset<uint8_t> packAN(
+        transA ? matrix_op_t::Transpose : matrix_op_t::NoTranspose,
+        transA ? k : m,
+        transA ? m : k,
+        A->data(),
+        transA ? m : k,
+        nullptr, /*buffer for packed matrix*/
+        ascale,
+        azeropoint,
+        1, /*groups*/
+        row_offset_buf.data());
+
+    DoNothing<float, float> doNothingObj{};
+    ReQuantizeForFloat<false, QuantizationGranularity::OUT_CHANNEL> outputProcObj(
+        doNothingObj,
+        ascale,
+        bqScale,
+        azeropoint,
+        bqZeropoint,
+        packAN.getRowOffsetBuffer(),
+        col_offsets->data(),
+        nullptr,
+        n);
+
+    // gemm
+    fbgemmPacked(
+        packAN,
+        *packedBint8,
+        C->data(),
+        (int32_t*)C->data(),
+        n,
+        outputProcObj,
+        0,
+        1);
+
+    // std::cout << "lowp gemm: " << std::endl;
+    // for (int ii = 0; ii < n; ii++) {
+    //   std::cout << C->data()[ii] << std::endl;
+    // }
+    // std::cout << std::endl;
+
+#endif
+  } else {
+    // row major
+    // keep the original mem
+    fbgemm::float16* pmat = packedPlaceholder.pmat_;
+    // retreive aux fields from the memory
+    uint64_t* packedmemSize = (uint64_t*)B->data();
+    packedPlaceholder.size_ = packedmemSize[0];
+    int* packedmemAux = (int*)(packedmemSize + 1);
+    packedPlaceholder.nrow_ = packedmemAux[0];
+    packedPlaceholder.ncol_ = packedmemAux[1];
+    packedPlaceholder.kernel_ncol_blocks_ = packedmemAux[2];
+    packedPlaceholder.brow_ = packedmemAux[3];
+    packedPlaceholder.bcol_ = packedmemAux[4];
+    packedPlaceholder.last_brow_ = packedmemAux[5];
+    packedPlaceholder.nbrow_ = packedmemAux[6];
+    packedPlaceholder.nbcol_ = packedmemAux[7];
+
+    // packed matrix
+    packedPlaceholder.pmat_ = (fbgemm::float16*)(B->data<uint8_t>() + 256);
+
+    for(int i = 0; i < m; ++i) {
+      mkl_somatcopy('R', 'N', 1, n, 1, bias->data(), n, C->data() + n * i, n);
+    }
+
+    if (true) {
+    // if (A->shape().size() == 4 && B->shape()[0] > 20480000) {
+#ifdef _OPENMP
+#pragma omp parallel
+#endif
+      {
+#ifdef _OPENMP
+        int num_threads = omp_get_num_threads();
+        int tid = omp_get_thread_num();
+#else
+        int num_threads = 1;
+        int tid = 0;
+#endif
+        fbgemm::cblas_gemm_compute(transA ? matrix_op_t::Transpose : matrix_op_t::NoTranspose,
+                          (int)m,
+                          A->data(),
+                          packedPlaceholder,
+                          1,
+                          C->data(),
+                          tid,
+                          num_threads);
+      }
+    } else {
+        fbgemm::cblas_gemm_compute(transA ? matrix_op_t::Transpose : matrix_op_t::NoTranspose,
+                          (int)m,
+                          A->data(),
+                          packedPlaceholder,
+                          1,
+                          C->data(),
+                          0,
+                          1);
+    }
+
+    // if (B->shape().size() == 1 && B->shape()[0] >= 32000 * 512) {
+    //   std::cout << "packed gemm: " << std::endl;
+    //   for (int ii = 0; ii < n; ii++) {
+    //     std::cout << C->data()[ii] << std::endl;
+    //   }
+    //   std::cout << std::endl;
+    // }
+
+    // return back the original mem
+    packedPlaceholder.pmat_ = pmat;
+  }
+
+  // std::cout << "A transposed: " << transA << std::endl;
+  // for (int i = 0; i < A->shape().size(); i++) {
+  //   std::cout << "size " << i << ": " << A->shape()[i] << std::endl;
+  // }
+  // compute statistics for quantization
+  // if (A->shape().size() == 4 && B->shape()[0] > 20480000) {
+  //   int bsize = A->shape().elements() / A->shape()[-1];
+  //   int hsize = A->shape()[3];
+  //   float mins[bsize] = {0};
+  //   float maxs[bsize] = {0};
+  //   float means[bsize] = {0};
+  //   float stds[bsize] = {0};
+  //   float* inmem = A->data();
+  //   for(int i = 0; i < bsize; i++) {
+  //     for(int j = 0; j < hsize; j++) {
+  //       float val = !transA ? inmem[i * hsize + j] : inmem[i + bsize * j];
+  //       if (val < mins[i])
+  //         mins[i] = val;
+  //       if (val > maxs[i])
+  //         maxs[i] = val;
+        
+  //       means[i] += val;
+  //       stds[i] += val*val;
+  //     }
+  //   }
+  //   for(int i = 0; i < bsize; i++) {
+  //       std::cout << mins[i] << ", " << maxs[i] << ", " << means[i] << ", " << stds[i] << std::endl;
+  //   }
+  // }
+}
+#else // USE_FBGEMM && MKL_FOUND
+void PackFp32(marian::Tensor out,
+              const marian::Tensor in,
+              bool tranpose,
+              int nrow,
+              int ncol,
+              int kernel_ncol_blocks,
+              int brow,
+              int bcol,
+              int last_brow,
+              int nbrow,
+              int nbcol,
+              uint64_t packsize) {
+                // does nothing. supports only FBGEMM based packed gemm at this moment.
+}
+void GemmPackFp32(marian::Tensor C,
+                  const marian::Tensor A,
+                  const marian::Tensor B,
+                  const marian::Tensor bias,
+                  const int64_t m,
+                  const int64_t n,
+                  const int64_t k,
+                  const float beta,
+                  const int layout,
+                  const int transA,
+                  const int transB,
+                  size_t idx) {
+                // does nothing. supports only FBGEMM based packed gemm at this moment.
+}
+#endif // USE_FBGEMM && MKL_FOUND
+
+// This operates on floats after processing so doesn't care about int8_t vs
+// int16_t.
+void AddBias(marian::Tensor C, const marian::Tensor Bias) {
+  float* y = C->data();
+  const float* x = C->data();
+  const float* bias = Bias->data();
+
+  int m = C->shape().elements() / C->shape()[-1];
+  int n = C->shape()[-1];
+#ifdef __AVX512F__
+  int n16 = n & ~15;
+#else
+  int n4 = (n / 4) * 4;
+#endif
+
+  for(int j = 0; j < m; ++j) {
+    int i = 0;
+#ifdef __AVX512F__
+    for(; i < n16; i += 16) {
+      __m512 ai = _mm512_loadu_ps(x + j * n + i);
+      __m512 bi = _mm512_loadu_ps(bias + i);
+      __m512 yi = _mm512_add_ps(ai, bi);
+      _mm512_storeu_ps(y + j * n + i, yi);
+    }
+#else
+    for(; i < n4; i += 4) {
+      __m128 ai = _mm_loadu_ps(x + j * n + i);
+      __m128 bi = _mm_loadu_ps(bias + i);
+      __m128 yi = _mm_add_ps(ai, bi);
+      _mm_storeu_ps(y + j * n + i, yi);
+    }
+#endif
+    for(; i < n; i++) {
+      y[j * n + i] = x[j * n + i] + bias[i];
+    }
+  }
+
+  // std::cout << "Output: " << std::endl;
+  // for (int ii = 0; ii < n; ii++) {
+  //   std::cout << y[ii] << ",";
+  // }
+  // std::cout << std::endl;
+}
+
+}  // namespace pack
+}  // namespace cpu
+}  // namespace marian
diff --git a/src/tensors/cpu/sharp/packed_gemm.h b/src/tensors/cpu/sharp/packed_gemm.h
new file mode 100644
index 000000000..428190254
--- /dev/null
+++ b/src/tensors/cpu/sharp/packed_gemm.h
@@ -0,0 +1,39 @@
+#pragma once
+
+#include "tensors/tensor.h"
+
+namespace marian {
+namespace cpu {
+namespace pack {
+
+void PackFp32(marian::Tensor out,
+              const marian::Tensor in,
+              bool transpose,
+              int nrow,
+              int ncol,
+              int kernel_ncol_blocks,
+              int brow,
+              int bcol,
+              int last_brow,
+              int nbrow,
+              int nbcol,
+              uint64_t packsize);
+
+void GemmPackFp32(marian::Tensor C,
+                  const marian::Tensor A,
+                  const marian::Tensor B,
+                  const marian::Tensor bias,
+                  const int64_t m,
+                  const int64_t n,
+                  // const int64_t k,
+                  // const float beta = 1,
+                  // const int layout = 0,
+                  const int transA = 0);
+                  //const int transB = 0,
+                  //const size_t idx = 0);
+
+void AddBias(marian::Tensor C, const marian::Tensor Bias);
+
+}  // namespace pack
+}  // namespace cpu
+}  // namespace marian
diff --git a/src/tensors/cpu/tensor_operators.cpp b/src/tensors/cpu/tensor_operators.cpp
index 0428ba8df..48885a15d 100644
--- a/src/tensors/cpu/tensor_operators.cpp
+++ b/src/tensors/cpu/tensor_operators.cpp
@@ -10,6 +10,10 @@
 #include "functional/functional.h"
 #include "functional/tensor.h"
 
+#if MKL_FOUND
+#include <mkl.h>
+#endif
+
 namespace marian {
 
 namespace cpu {
@@ -186,6 +190,61 @@ void Transpose0213(Tensor out, Tensor in) {
   }
 }
 
+template <bool add>
+void Transposexxx3(Tensor out, Tensor in, const std::vector<int>& vAxis) {
+#if MKL_FOUND
+  int innermost = in->shape()[-1];
+
+  int l1 = in->shape()[vAxis[0]];
+  int l2 = in->shape()[vAxis[1]];
+  int l3 = in->shape()[vAxis[2]];
+
+  int oi = 0, oj = 0, ok = 0;
+#pragma omp parallel for
+  for(int k = 0; k < l1; ++k) {
+    int shift = k * l2 * l3;
+    for(int j = 0; j < l2; ++j) {
+      for(int i = 0; i < l3; ++i) {
+        if(vAxis[0] == 0) {
+          if(vAxis[1] == 1) {
+            oi = i; oj = j; ok = k;
+          } else {
+            oi = j; oj = i; ok = k;
+          }
+        } else if(vAxis[0] == 1) {
+          if(vAxis[1] == 0) {
+            oi = i; oj = k; ok = j;
+          } else {
+            oi = j; oj = k; ok = i;
+          }
+        } else {
+          if(vAxis[1] == 0) {
+            oi = k; oj = i; ok = j;
+          } else {
+            oi = k; oj = j; ok = i;
+          }
+        }
+        int src = ok * in->shape()[1] * in->shape()[2] + oj * in->shape()[2] + oi;
+        int dst = l3 * j + shift + i;
+
+        const float* inRow = in->data() + src * innermost;
+        float* outRow = out->data() + dst * innermost;
+
+        if(!add) {
+          mkl_somatcopy('R', 'N', 1, innermost, 1.0f, inRow, innermost, outRow, innermost);
+        } else {
+          for(int ii = 0; ii < innermost; ++ii) {
+            outRow[ii] += inRow[ii];
+          }
+        }
+      }
+    }
+  }
+#else
+  // it shouldn't come into here. This function is called only when MKL is available.
+#endif  // MKL_FOUND
+}
+
 inline void transpose4x4_SSE(const float* A,
                              float* B,
                              const int lda,
@@ -262,6 +321,10 @@ void TransposeGeneric(Tensor out, Tensor in, const std::vector<int>& vAxis) {
 void TransposeND(Tensor out, Tensor in, const std::vector<int>& vAxis) {
   if(vAxis == std::vector<int>({0, 2, 1, 3}))
     Transpose0213<false>(out, in);
+#if MKL_FOUND
+  else if(vAxis.size() == 4 && vAxis[3] == 3)
+    Transposexxx3<false>(out, in, vAxis);
+#endif  // MKL_FOUND
   else if(vAxis == std::vector<int>({1, 0}) && in->shape()[-1] % 16 == 0
           && in->shape()[-2] % 16 == 0)
     Transpose10(out, in);
diff --git a/vs/Marian.vcxproj b/vs/Marian.vcxproj
index d28b467bc..8106f488c 100755
--- a/vs/Marian.vcxproj
+++ b/vs/Marian.vcxproj
@@ -49,8 +49,8 @@
     <LinkIncremental>false</LinkIncremental>
     <ExecutablePath>$(ExecutablePath)</ExecutablePath>
     <IntDir>$(SolutionDir)$(Platform)\$(Configuration)\Marian\</IntDir>
-    <IncludePath>%CUDA_PATH%\include;..\src;..\src\3rd_party;%BOOST_INCLUDE_PATH%;%ZLIB_PATH%\include;%MKL_PATH%\include;$(VC_IncludePath);$(WindowsSDK_IncludePath);</IncludePath>
-    <LibraryPath>%CUDA_PATH%\lib\x64;%BOOST_LIB_PATH%;%ZLIB_PATH%\lib;%MKL_PATH%\lib\intel64;$(VC_LibraryPath_x64);$(WindowsSDK_LibraryPath_x64);$(NETFXKitsDir)Lib\um\x64</LibraryPath>
+    <IncludePath>%MKL_PATH%\include;..\src\3rd_party\fbgemm\include;%CUDA_PATH%\include;..\src;..\src\3rd_party;%BOOST_INCLUDE_PATH%;%ZLIB_PATH%\include;$(VC_IncludePath);$(WindowsSDK_IncludePath);</IncludePath>
+    <LibraryPath>deps;%CUDA_PATH%\lib\x64;%BOOST_LIB_PATH%;%ZLIB_PATH%\lib;%MKL_PATH%\lib\intel64;$(VC_LibraryPath_x64);$(WindowsSDK_LibraryPath_x64);$(NETFXKitsDir)Lib\um\x64</LibraryPath>
   </PropertyGroup>
   <ItemDefinitionGroup>
     <ClCompile>
@@ -104,12 +104,12 @@
       <Optimization>MaxSpeed</Optimization>
       <FunctionLevelLinking>true</FunctionLevelLinking>
       <IntrinsicFunctions>true</IntrinsicFunctions>
-      <PreprocessorDefinitions>CUDA_FOUND=1; MKL_FOUND=1; MPI_FOUND=1; BLAS_FOUND=1; MKL_ILP64; WIN32;NDEBUG;_CONSOLE;_LIB;%(PreprocessorDefinitions)</PreprocessorDefinitions>
+      <PreprocessorDefinitions>FBGEMM_STATIC;USE_FBGEMM=1;CUDA_FOUND=1; MKL_FOUND=1; MPI_FOUND=1; BLAS_FOUND=1; MKL_ILP64; WIN32;NDEBUG;_CONSOLE;_LIB;%(PreprocessorDefinitions)</PreprocessorDefinitions>
       <SDLCheck>true</SDLCheck>
       <FavorSizeOrSpeed>Speed</FavorSizeOrSpeed>
-      <AdditionalOptions>/d2Zi+ /bigobj %(AdditionalOptions)</AdditionalOptions>
+      <AdditionalOptions>/d2Zi+ /bigobj %(AdditionalOptions) /arch:AVX2</AdditionalOptions>
       <TreatWarningAsError>true</TreatWarningAsError>
-      <RuntimeLibrary Condition="'$(Configuration)|$(Platform)'=='Release|x64'">MultiThreadedDLL</RuntimeLibrary>
+      <RuntimeLibrary Condition="'$(Configuration)|$(Platform)'=='Release|x64'">MultiThreaded</RuntimeLibrary>
       <RuntimeLibrary Condition="'$(Configuration)|$(Platform)'=='Release_NoOpt|x64'">MultiThreaded</RuntimeLibrary>
       <InlineFunctionExpansion>AnySuitable</InlineFunctionExpansion>
       <OmitFramePointers>true</OmitFramePointers>
@@ -121,7 +121,7 @@
       <GenerateDebugInformation>true</GenerateDebugInformation>
       <EnableCOMDATFolding>true</EnableCOMDATFolding>
       <OptimizeReferences>true</OptimizeReferences>
-      <AdditionalDependencies>cudart_static.lib;cublas.lib;cusparse.lib;curand.lib;zlib.lib;msmpi.lib;mkl_intel_ilp64.lib;mkl_sequential.lib;mkl_core.lib;kernel32.lib;user32.lib;gdi32.lib;winspool.lib;comdlg32.lib;advapi32.lib;shell32.lib;ole32.lib;oleaut32.lib;uuid.lib;odbc32.lib;odbccp32.lib;shlwapi.lib;%(AdditionalDependencies)</AdditionalDependencies>
+      <AdditionalDependencies>cudart_static.lib;cublas.lib;cusparse.lib;curand.lib;zlib.lib;msmpi.lib;mkl_intel_ilp64.lib;mkl_sequential.lib;mkl_core.lib;kernel32.lib;user32.lib;gdi32.lib;winspool.lib;comdlg32.lib;advapi32.lib;shell32.lib;ole32.lib;oleaut32.lib;uuid.lib;odbc32.lib;odbccp32.lib;shlwapi.lib;%(AdditionalDependencies);fbgemm.lib;cpuinfo.lib;clog.lib</AdditionalDependencies>
       <StackReserveSize>100000000</StackReserveSize>
       <TreatLinkerWarningAsErrors>true</TreatLinkerWarningAsErrors>
     </Link>
@@ -346,6 +346,19 @@
     <ClCompile Include="..\src\3rd_party\yaml-cpp\binary_renamed.cpp" />
     <ClCompile Include="..\src\3rd_party\yaml-cpp\yaml-node.cpp" />
     <ClInclude Include="..\src\3rd_party\ExceptionWithCallStack.h" />
+    <ClInclude Include="..\src\3rd_party\fbgemm\include\fbgemm\ConvUtils.h" />
+    <ClInclude Include="..\src\3rd_party\fbgemm\include\fbgemm\Fbgemm.h" />
+    <ClInclude Include="..\src\3rd_party\fbgemm\include\fbgemm\FbgemmBuild.h" />
+    <ClInclude Include="..\src\3rd_party\fbgemm\include\fbgemm\FbgemmFP16.h" />
+    <ClInclude Include="..\src\3rd_party\fbgemm\include\fbgemm\FbgemmI8DepthwiseAvx2.h" />
+    <ClInclude Include="..\src\3rd_party\fbgemm\include\fbgemm\FbgemmI8Spmdm.h" />
+    <ClInclude Include="..\src\3rd_party\fbgemm\include\fbgemm\OutputProcessing-inl.h" />
+    <ClInclude Include="..\src\3rd_party\fbgemm\include\fbgemm\PackingTraits-inl.h" />
+    <ClInclude Include="..\src\3rd_party\fbgemm\include\fbgemm\QuantUtils.h" />
+    <ClInclude Include="..\src\3rd_party\fbgemm\include\fbgemm\QuantUtilsAvx2.h" />
+    <ClInclude Include="..\src\3rd_party\fbgemm\include\fbgemm\Types.h" />
+    <ClInclude Include="..\src\3rd_party\fbgemm\include\fbgemm\Utils.h" />
+    <ClInclude Include="..\src\3rd_party\fbgemm\include\fbgemm\UtilsAvx2.h" />
     <ClInclude Include="..\src\3rd_party\nccl\src\collectives\collectives.h">
       <ExcludedFromBuild Condition="'$(Configuration)|$(Platform)'=='Release|x64'">true</ExcludedFromBuild>
     </ClInclude>
@@ -612,6 +625,7 @@
     <ClCompile Include="..\src\tensors\cpu\prod.cpp" />
     <ClCompile Include="..\src\tensors\cpu\sharp\avx_gemm.cpp" />
     <ClCompile Include="..\src\tensors\cpu\sharp\int_gemm.cpp" />
+    <ClCompile Include="..\src\tensors\cpu\sharp\packed_gemm.cpp" />
     <ClCompile Include="..\src\tensors\cpu\sharp\sse_gemm.cpp" />
     <ClCompile Include="..\src\tensors\cpu\tensor_operators.cpp" />
     <ClCompile Include="..\src\graph\expression_graph.cpp" />
@@ -951,7 +965,9 @@
     <ClInclude Include="..\src\rnn\types.h" />
     <ClInclude Include="..\src\tensors\allocator.h" />
     <ClInclude Include="..\src\tensors\backend.h" />
+    <ClInclude Include="..\src\tensors\cpu\expanded_gemm.h" />
     <ClInclude Include="..\src\tensors\cpu\sharp\int_gemm.h" />
+    <ClInclude Include="..\src\tensors\cpu\sharp\packed_gemm.h" />
     <ClInclude Include="..\src\tensors\device.h" />
     <ClInclude Include="..\src\tensors\dispatch.h" />
     <ClInclude Include="..\src\tensors\gpu\add.h" />
diff --git a/vs/Marian.vcxproj.filters b/vs/Marian.vcxproj.filters
index d77aecd2a..eaa6cfe25 100755
--- a/vs/Marian.vcxproj.filters
+++ b/vs/Marian.vcxproj.filters
@@ -490,6 +490,9 @@
     <ClCompile Include="..\src\tensors\gpu\prod.cpp">
       <Filter>tensors\gpu</Filter>
     </ClCompile>
+    <ClCompile Include="..\src\tensors\cpu\sharp\packed_gemm.cpp">
+      <Filter>tensors\cpu\sharp</Filter>
+    </ClCompile>
   </ItemGroup>
   <ItemGroup>
     <ClInclude Include="..\src\marian.h" />
@@ -1555,6 +1558,51 @@
     <ClInclude Include="..\src\tensors\gpu\add.inc">
       <Filter>tensors\gpu</Filter>
     </ClInclude>
+    <ClInclude Include="..\src\tensors\cpu\expanded_gemm.h">
+      <Filter>tensors\cpu</Filter>
+    </ClInclude>
+    <ClInclude Include="..\src\tensors\cpu\sharp\packed_gemm.h">
+      <Filter>tensors\cpu\sharp</Filter>
+    </ClInclude>
+    <ClInclude Include="..\src\3rd_party\fbgemm\include\fbgemm\ConvUtils.h">
+      <Filter>3rd_party\fbgemm\include\fbgemm</Filter>
+    </ClInclude>
+    <ClInclude Include="..\src\3rd_party\fbgemm\include\fbgemm\Fbgemm.h">
+      <Filter>3rd_party\fbgemm\include\fbgemm</Filter>
+    </ClInclude>
+    <ClInclude Include="..\src\3rd_party\fbgemm\include\fbgemm\FbgemmBuild.h">
+      <Filter>3rd_party\fbgemm\include\fbgemm</Filter>
+    </ClInclude>
+    <ClInclude Include="..\src\3rd_party\fbgemm\include\fbgemm\FbgemmFP16.h">
+      <Filter>3rd_party\fbgemm\include\fbgemm</Filter>
+    </ClInclude>
+    <ClInclude Include="..\src\3rd_party\fbgemm\include\fbgemm\FbgemmI8DepthwiseAvx2.h">
+      <Filter>3rd_party\fbgemm\include\fbgemm</Filter>
+    </ClInclude>
+    <ClInclude Include="..\src\3rd_party\fbgemm\include\fbgemm\FbgemmI8Spmdm.h">
+      <Filter>3rd_party\fbgemm\include\fbgemm</Filter>
+    </ClInclude>
+    <ClInclude Include="..\src\3rd_party\fbgemm\include\fbgemm\OutputProcessing-inl.h">
+      <Filter>3rd_party\fbgemm\include\fbgemm</Filter>
+    </ClInclude>
+    <ClInclude Include="..\src\3rd_party\fbgemm\include\fbgemm\PackingTraits-inl.h">
+      <Filter>3rd_party\fbgemm\include\fbgemm</Filter>
+    </ClInclude>
+    <ClInclude Include="..\src\3rd_party\fbgemm\include\fbgemm\QuantUtils.h">
+      <Filter>3rd_party\fbgemm\include\fbgemm</Filter>
+    </ClInclude>
+    <ClInclude Include="..\src\3rd_party\fbgemm\include\fbgemm\QuantUtilsAvx2.h">
+      <Filter>3rd_party\fbgemm\include\fbgemm</Filter>
+    </ClInclude>
+    <ClInclude Include="..\src\3rd_party\fbgemm\include\fbgemm\Types.h">
+      <Filter>3rd_party\fbgemm\include\fbgemm</Filter>
+    </ClInclude>
+    <ClInclude Include="..\src\3rd_party\fbgemm\include\fbgemm\Utils.h">
+      <Filter>3rd_party\fbgemm\include\fbgemm</Filter>
+    </ClInclude>
+    <ClInclude Include="..\src\3rd_party\fbgemm\include\fbgemm\UtilsAvx2.h">
+      <Filter>3rd_party\fbgemm\include\fbgemm</Filter>
+    </ClInclude>
   </ItemGroup>
   <ItemGroup>
     <Filter Include="3rd_party">
@@ -1722,6 +1770,15 @@
     <Filter Include="tests">
       <UniqueIdentifier>{a86d650a-2268-43d9-9d74-cb17cd6b534b}</UniqueIdentifier>
     </Filter>
+    <Filter Include="3rd_party\fbgemm">
+      <UniqueIdentifier>{4bb88f6d-7ddf-41e0-91be-a43dbcd0e9b0}</UniqueIdentifier>
+    </Filter>
+    <Filter Include="3rd_party\fbgemm\include">
+      <UniqueIdentifier>{6c2bef00-97a0-4881-a6f0-ded54b8520bf}</UniqueIdentifier>
+    </Filter>
+    <Filter Include="3rd_party\fbgemm\include\fbgemm">
+      <UniqueIdentifier>{95f7ce7c-c649-4d57-8d2a-d724bd75fe84}</UniqueIdentifier>
+    </Filter>
   </ItemGroup>
   <ItemGroup>
     <None Include="..\src\3rd_party\nccl\src\bootstrap.cu">

From e8ca9a37561efba2c86806518fd52d908e5ff1b7 Mon Sep 17 00:00:00 2001
From: Young Jin Kim <youki@microsoft.com>
Date: Tue, 18 Jun 2019 17:09:14 -0700
Subject: [PATCH 501/838] Update some fixed

---
 cmake/FindSSE.cmake                  | 23 ++++++++++++++++++++++-
 src/tensors/cpu/tensor_operators.cpp |  4 ++--
 2 files changed, 24 insertions(+), 3 deletions(-)

diff --git a/cmake/FindSSE.cmake b/cmake/FindSSE.cmake
index c152dd74f..e6d3e9caf 100644
--- a/cmake/FindSSE.cmake
+++ b/cmake/FindSSE.cmake
@@ -56,6 +56,14 @@ IF(CMAKE_SYSTEM_NAME MATCHES "Linux")
    ELSE (AVX2_TRUE)
       set(AVX2_FOUND false CACHE BOOL "AVX2 available on host")
    ENDIF (AVX2_TRUE)
+ 
+   STRING(REGEX REPLACE "^.*(avx512).*$" "\\1" SSE_THERE ${CPUINFO})
+   STRING(COMPARE EQUAL "avx512" "${SSE_THERE}" AVX512_TRUE)
+   IF (AVX512_TRUE)
+      set(AVX512_FOUND true CACHE BOOL "AVX512 available on host")
+   ELSE (AVX512_TRUE)
+      set(AVX512_FOUND false CACHE BOOL "AVX512 available on host")
+   ENDIF (AVX512_TRUE)
 
 ELSEIF(CMAKE_SYSTEM_NAME MATCHES "Darwin")
    EXEC_PROGRAM("/usr/sbin/sysctl -n machdep.cpu.features" OUTPUT_VARIABLE
@@ -108,6 +116,14 @@ ELSEIF(CMAKE_SYSTEM_NAME MATCHES "Darwin")
    ELSE (AVX2_TRUE)
       set(AVX2_FOUND false CACHE BOOL "AVX2 available on host")
    ENDIF (AVX2_TRUE)
+ 
+   STRING(REGEX REPLACE "^.*(avx512).*$" "\\1" SSE_THERE ${CPUINFO})
+   STRING(COMPARE EQUAL "avx512" "${SSE_THERE}" AVX512_TRUE)
+   IF (AVX512_TRUE)
+      set(AVX512_FOUND true CACHE BOOL "AVX512 available on host")
+   ELSE (AVX512_TRUE)
+      set(AVX512_FOUND false CACHE BOOL "AVX512 available on host")
+   ENDIF (AVX512_TRUE)
 
 ELSEIF(CMAKE_SYSTEM_NAME MATCHES "Windows")
    # TODO
@@ -117,6 +133,7 @@ ELSEIF(CMAKE_SYSTEM_NAME MATCHES "Windows")
    set(SSE4_1_FOUND false CACHE BOOL "SSE4.1 available on host")
    set(AVX_FOUND false CACHE BOOL "AVX available on host")
    set(AVX2_FOUND false CACHE BOOL "AVX2 available on host")
+   set(AVX512_FOUND false CACHE BOOL "AVX512 available on host")
 ELSE(CMAKE_SYSTEM_NAME MATCHES "Linux")
    set(SSE2_FOUND   true  CACHE BOOL "SSE2 available on host")
    set(SSE3_FOUND   false CACHE BOOL "SSE3 available on host")
@@ -124,6 +141,7 @@ ELSE(CMAKE_SYSTEM_NAME MATCHES "Linux")
    set(SSE4_1_FOUND false CACHE BOOL "SSE4.1 available on host")
    set(AVX_FOUND false CACHE BOOL "AVX available on host")
    set(AVX2_FOUND false CACHE BOOL "AVX2 available on host")
+   set(AVX512_FOUND false CACHE BOOL "AVX512 available on host")
 ENDIF(CMAKE_SYSTEM_NAME MATCHES "Linux")
 
 if(NOT SSE2_FOUND)
@@ -144,5 +162,8 @@ endif(NOT AVX_FOUND)
 if(NOT AVX2_FOUND)
       MESSAGE(STATUS "Could not find hardware support for AVX2 on this machine.")
 endif(NOT AVX2_FOUND)
+if(NOT AVX512_FOUND)
+      MESSAGE(STATUS "Could not find hardware support for AVX512 on this machine.")
+endif(NOT AVX512_FOUND)
 
-mark_as_advanced(SSE2_FOUND SSE3_FOUND SSSE3_FOUND SSE4_1_FOUND, AVX_FOUND, AVX2_FOUND)
+mark_as_advanced(SSE2_FOUND SSE3_FOUND SSSE3_FOUND SSE4_1_FOUND, AVX_FOUND, AVX2_FOUND, AVX512_FOUND)
diff --git a/src/tensors/cpu/tensor_operators.cpp b/src/tensors/cpu/tensor_operators.cpp
index 48885a15d..e6be8add0 100644
--- a/src/tensors/cpu/tensor_operators.cpp
+++ b/src/tensors/cpu/tensor_operators.cpp
@@ -191,7 +191,7 @@ void Transpose0213(Tensor out, Tensor in) {
 }
 
 template <bool add>
-void Transposexxx3(Tensor out, Tensor in, const std::vector<int>& vAxis) {
+void TransposeFirst3In4(Tensor out, Tensor in, const std::vector<int>& vAxis) {
 #if MKL_FOUND
   int innermost = in->shape()[-1];
 
@@ -323,7 +323,7 @@ void TransposeND(Tensor out, Tensor in, const std::vector<int>& vAxis) {
     Transpose0213<false>(out, in);
 #if MKL_FOUND
   else if(vAxis.size() == 4 && vAxis[3] == 3)
-    Transposexxx3<false>(out, in, vAxis);
+    TransposeFirst3In4<false>(out, in, vAxis);
 #endif  // MKL_FOUND
   else if(vAxis == std::vector<int>({1, 0}) && in->shape()[-1] % 16 == 0
           && in->shape()[-2] % 16 == 0)

From a71bcb5f7c9ae3b0514fbc53d85ad4e6349a8242 Mon Sep 17 00:00:00 2001
From: Young Jin Kim <youki@microsoft.com>
Date: Tue, 18 Jun 2019 17:34:06 -0700
Subject: [PATCH 502/838] clean up code

---
 src/graph/expression_operators.cpp    |  12 +-
 src/tensors/cpu/expanded_gemm.h       |  78 ++---
 src/tensors/cpu/sharp/packed_gemm.cpp | 476 ++++----------------------
 src/tensors/cpu/sharp/packed_gemm.h   |   9 +-
 4 files changed, 101 insertions(+), 474 deletions(-)

diff --git a/src/graph/expression_operators.cpp b/src/graph/expression_operators.cpp
index 42bb65e20..d7bfd19c2 100755
--- a/src/graph/expression_operators.cpp
+++ b/src/graph/expression_operators.cpp
@@ -444,10 +444,10 @@ Expr affine(Expr a, Expr b, Expr bias, bool transA, bool transB, float scale) {
         };
 
         auto alg1 = [=]() {
-          auto packed = cpu::pack::pack(b, cpu::pack::PackMatrix::B, transB, clipValue);
+          auto packed = cpu::variant::pack(b, cpu::variant::PackMatrix::B, transB, clipValue);
 
           return rec1(
-              cpu::pack::affine(
+              cpu::variant::affine(
               clip(a, clipValue), packed,
               b->shape(),
               bias,
@@ -509,12 +509,12 @@ Expr affine(Expr a, Expr b, Expr bias, bool transA, bool transB, float scale) {
 
     } else {
       if(b->memoize()) {
-        auto packed = cpu::pack::pack(b, cpu::pack::PackMatrix::B, transB, clipValue);
+        auto packed = cpu::variant::pack(b, cpu::variant::PackMatrix::B, transB, clipValue);
         // auto packed = transB ? 
-        //               cpu::pack::pack(transpose(b), cpu::pack::PackMatrix::B, false, clipValue) :
-        //               cpu::pack::pack(b, cpu::pack::PackMatrix::B, false, clipValue);
+        //               cpu::variant::pack(transpose(b), cpu::pack::PackMatrix::B, false, clipValue) :
+        //               cpu::variant::pack(b, cpu::pack::PackMatrix::B, false, clipValue);
 
-        return cpu::pack::affine(
+        return cpu::variant::affine(
             clip(a, clipValue), packed,
             b->shape(),
             bias,
diff --git a/src/tensors/cpu/expanded_gemm.h b/src/tensors/cpu/expanded_gemm.h
index 33b677945..03e58ae43 100644
--- a/src/tensors/cpu/expanded_gemm.h
+++ b/src/tensors/cpu/expanded_gemm.h
@@ -10,7 +10,7 @@ using namespace fbgemm;
 
 namespace marian {
 namespace cpu {
-namespace pack {
+namespace variant {
 
 enum class PackMatrix : uint8_t {
   A = 0x00,
@@ -71,23 +71,17 @@ struct PackNodeOp : public UnaryNodeOp {
     // Should be 2D - weight matrix
     ABORT_IF(shapeMat.size() != 2,
              "Weight Matrix should be 2D");
-   if (true) {
-    // if (shapeMat[0] < 32000 && shapeMat[1] < 32000) {
-      nrow_ = transpose ? shapeMat[1] : shapeMat[0];
-      ncol_ = transpose ? shapeMat[0] : shapeMat[1];
-      kernel_ncol_blocks_ = 2;
-      brow_ = 512;
-      bcol_ = 8 * kernel_ncol_blocks_;
-      last_brow_ = nrow_ % brow_ == 0 ? brow_ : nrow_ % brow_;
-      nbrow_ = nrow_ % brow_ == 0 ? nrow_ / brow_ : (nrow_ + brow_) / brow_;
-      nbcol_ = ncol_ % bcol_ == 0 ? ncol_ / bcol_ : (ncol_ + bcol_) / bcol_;
-      const int padding = 1024;  // required by sw pipelined kernels
-      const int specialMem = 256;
-      packsize_ = ((nbrow_ * brow_) * (nbcol_ * bcol_)) * sizeof(fbgemm::float16) + padding + specialMem;
-    } else {
-      // use int 8 implementation
-      packsize_ = 1;
-    }
+    nrow_ = transpose ? shapeMat[1] : shapeMat[0];
+    ncol_ = transpose ? shapeMat[0] : shapeMat[1];
+    kernel_ncol_blocks_ = 2;
+    brow_ = 512;
+    bcol_ = 8 * kernel_ncol_blocks_;
+    last_brow_ = nrow_ % brow_ == 0 ? brow_ : nrow_ % brow_;
+    nbrow_ = nrow_ % brow_ == 0 ? nrow_ / brow_ : (nrow_ + brow_) / brow_;
+    nbcol_ = ncol_ % bcol_ == 0 ? ncol_ / bcol_ : (ncol_ + bcol_) / bcol_;
+    const int padding = 1024;  // required by sw pipelined kernels
+    const int specialMem = 256;
+    packsize_ = ((nbrow_ * brow_) * (nbcol_ * bcol_)) * sizeof(fbgemm::float16) + padding + specialMem;
 
     Shape outShape({(int)packsize_});
 
@@ -147,39 +141,15 @@ class AffineNodeOp : public NaryNodeOp {
   }
 
   NodeOps forwardOps() override {
-    // if (n_ < 32000) {
-   if (true) {
-      return {
-        NodeOp(GemmPackFp32(val_,
-                                  child(0)->val(),
-                                  child(1)->val(),
-                                  child(2)->val(),
-                                  m_,
-                                  n_,
-                                  //k_,
-                                  //1,
-                                  //0,
-                                  transA_))
-                                  //transB_,
-                                  //idx_))
-      };
-    } else {
-      return {
-        NodeOp(GemmPackFp32(val_,
-                                  child(0)->val(),
-                                  child(1)->val(),
-                                  child(2)->val(),
-                                  m_,
-                                  n_,
-                                  //k_,
-                                  //1,
-                                  //0,
-                                  transA_);
-                                  //transB_,
-                                  //idx_);
-                AddBias(val_, child(2)->val()))
-      };
-    }
+    return {
+      NodeOp(GemmPackFp32(val_,
+                                child(0)->val(),
+                                child(1)->val(),
+                                child(2)->val(),
+                                m_,
+                                n_,
+                                transA_))
+    };
   }
 
   NodeOps backwardOps() override {
@@ -192,13 +162,13 @@ class AffineNodeOp : public NaryNodeOp {
 
 static inline Expr affine(Expr a, Expr b, Shape bShape, Expr c, bool transA, bool transB, float scalar) {
   std::vector<Expr> nodes = {a, b, c};
-  return Expression<cpu::pack::AffineNodeOp>(nodes, bShape, transA, transB, scalar);
+  return Expression<cpu::variant::AffineNodeOp>(nodes, bShape, transA, transB, scalar);
 }
 
 static inline Expr pack(Expr a, PackMatrix packMat, bool transpose, float clipValue) {
-  return Expression<cpu::pack::PackNodeOp>(a, packMat, transpose, clipValue);
+  return Expression<cpu::variant::PackNodeOp>(a, packMat, transpose, clipValue);
 }
 
-}  // namespace pack
+}  // namespace variant
 }  // namespace cpu
 }  // namespace marian
diff --git a/src/tensors/cpu/sharp/packed_gemm.cpp b/src/tensors/cpu/sharp/packed_gemm.cpp
index b6b7937f5..e3840b8c7 100644
--- a/src/tensors/cpu/sharp/packed_gemm.cpp
+++ b/src/tensors/cpu/sharp/packed_gemm.cpp
@@ -33,45 +33,12 @@ using namespace fbgemm;
 
 namespace marian {
 namespace cpu {
-namespace pack {
-
-//static float packingTime = 0;
+namespace variant {
 
 #if (USE_FBGEMM && MKL_FOUND)
 // initialize with a dummy
 static PackedGemmMatrixFP16 packedPlaceholder(1, 1, 1, 1, 1, 1, 1, 1);
 
-// temporary variable for int 8
-//static PackBMatrix<int8_t>* packedBint8 = nullptr;
-// transformer base wmt 2017
-// static float bqScale = 0.39/128;
-// static int32_t bqZeropoint = 0;
-// old student de-en
-//static float* bqScale;
-//static int32_t* bqZeropoint;
-// static float bqScale = 0.683/106;
-// static int32_t bqZeropoint = 21;
-// static float bqScale = 0.9672/128;
-// static int32_t bqZeropoint = 0;
-//static std::vector<int32_t>* col_offsets = nullptr;
-
-//inline void col_offsets_with_zero_pt_s8acc32_ref(
-//    bool transpose,
-//    int K,
-//    int N,
-//    const int8_t* Bint8,
-//    const int32_t* B_zero_point,
-//    int32_t* col_offsets,
-//    int ncols_per_quant_group) {
-//  for (int n = 0; n < N; ++n) {
-//    int32_t sum = 0;
-//    for (int k = 0; k < K; ++k) {
-//      sum += transpose ? Bint8[k + n * K] : Bint8[k * N + n];
-//    }
-//    col_offsets[n] = sum - B_zero_point[n / ncols_per_quant_group] * K;
-//  }
-//}
-
 // This is copied from FBGEMM code
 // A better way?
 // blocked row-major format address arithmetic
@@ -111,206 +78,39 @@ void PackFp32(marian::Tensor out,
               uint64_t packsize) {
   //auto t_start = std::chrono::high_resolution_clock::now();
   // for the last embedding layer, pack it into int8
-  // if (in->shape().size() == 2 && (in->shape()[0] >= 32000 || in->shape()[1] >= 32000)) {
- if (false) {
-#if 0 
-   if (packedBint8 == nullptr) {
-      int k = transpose ? in->shape()[1] : in->shape()[0];
-      int n = transpose ? in->shape()[0] : in->shape()[1];
-      // std::cout << "transpose: " << transpose << ", k: " << k << ", n: " << n << std::endl;
-      // std::cout << ", bqScale: " << bqScale << ", bqZeropoint: " << bqZeropoint << std::endl;
-      // two steps
-      // 0. quantize --> this should be done outside
-      int len = in->shape()[0]*in->shape()[1];
-
-      // 0-1. collect stats for each class
-      bqScale = new float[n];
-      bqZeropoint = new int32_t[n];
-
-      // int numBin = 20;
-      // float denum = 2/(float)numBin;
-
-      // int hist[numBin] = { 0, };
-
-      // Transposed only
-      float* data = in->data();
-      float val = 0;
-      for (int jj = 0; jj < n; jj++) {
-        float min = 1000000, max = -10000000;
-        for (int ii = 0; ii < k; ii++) {
-          val = data[jj*k + ii];
-          if (val < min) min = val;
-          if (val > max) max = val;
-          // hist[(int)((val + 1)/denum)]++;
-        }
-        bqScale[jj] = (max - min)/255;
-        bqZeropoint[jj] = (int32_t)(127 - max / bqScale[jj]);
-        // bqScale[jj] = (0.3 + 0.4)/255;
-        // bqZeropoint[jj] = (int32_t)(127 - 0.3 / bqScale[jj]);
-      }
-
-      // std::cout << "hist: ";
-      // for (int ii = 0; ii < numBin; ii++) {
-      //   std::cout << hist[ii] << ", ";
-      // }
-      // std::cout << std::endl;
-      // std::cout << "len: " << len << ", bqScale: " << bqScale << ", bqZeropoint: " << bqZeropoint << std::endl;
-      //int8_t quantized[len]; // aligned malloc?
-      int8_t* quantized = (int8_t*)aligned_alloc(256, len);
-      // std::cout << "len: " << len << ", bqScale: " << bqScale << ", bqZeropoint: " << bqZeropoint << std::endl;
-      for (int ii = 0; ii < n; ii++) {
-        TensorQuantizationParams bQuantParam;
-        // std::cout << "len: " << len << ", bqScale: " << bqScale << ", bqZeropoint: " << bqZeropoint << std::endl;
-        bQuantParam.scale = bqScale[ii];
-        bQuantParam.zero_point = bqZeropoint[ii];
-        bQuantParam.precision = 8;
-        // std::cout << "len: " << len << ", bqScale: " << bqScale << ", bqZeropoint: " << bqZeropoint << std::endl;
-
-        fbgemm::Quantize<int8_t>(in->data() + ii * k, quantized + ii * k, k, bQuantParam);
-      }
-      // std::cout << "original" << std::endl;
-      // for (int ii = 0; ii < n; ii++) {
-      //   for (int jj = 0; jj < 1; jj++) {
-      //     std::cout << in->data()[ii * k + jj] << ","; 
-      //   }
-      //   std::cout << std::endl;
-      // }
-      // std::cout << "quantized" << std::endl;
-      // for (int ii = 0; ii < 1; ii++) {
-      //   for (int jj = 0; jj < k; jj++) {
-      //     std::cout << (int32_t)quantized[ii * k + jj] << ","; 
-      //   }
-      //   std::cout << std::endl;
-      // }
-      // 1. compute column offsets
-      col_offsets = new std::vector<int32_t>(n);
-      col_offsets_with_zero_pt_s8acc32_ref(transpose, k, n, quantized, bqZeropoint, col_offsets->data(), 1);
-      // for (int ii = 0; ii < n; ii++) {
-      //   std::cout << (int32_t)col_offsets->data()[ii] << ","; 
-      // }
-      // std::cout << std::endl;
-      // std::cout << "calc offset done" << std::endl;
-      // 2. packing
-      // uint8_t* packedmem = aligned_alloc(256, len);
-      packedBint8 = new PackBMatrix<int8_t>(transpose ? matrix_op_t::Transpose : matrix_op_t::NoTranspose,
-                                            k, // in->shape()[0],
-                                            n, // in->shape()[1],
-                                            quantized,
-                                            in->shape()[1]);
-      // std::cout << "packing B done" << std::endl;
-      // int k = transpose ? in->shape()[1] : in->shape()[0];
-      // int n = transpose ? in->shape()[0] : in->shape()[1];
-      // std::cout << "transpose: " << transpose << ", k: " << k << ", n: " << n << std::endl;
-      // std::cout << ", bqScale: " << bqScale << ", bqZeropoint: " << bqZeropoint << std::endl;
-      // // two steps
-      // // 0. quantize --> this should be done outside
-      // int len = in->shape()[0]*in->shape()[1];
-      // std::cout << "len: " << len << ", bqScale: " << bqScale << ", bqZeropoint: " << bqZeropoint << std::endl;
-      // //int8_t quantized[len]; // aligned malloc?
-      // int8_t* quantized = (int8_t*)aligned_alloc(256, len);
-      // std::cout << "len: " << len << ", bqScale: " << bqScale << ", bqZeropoint: " << bqZeropoint << std::endl;
-      // TensorQuantizationParams bQuantParam;
-      // std::cout << "len: " << len << ", bqScale: " << bqScale << ", bqZeropoint: " << bqZeropoint << std::endl;
-      // bQuantParam.scale = bqScale;
-      // bQuantParam.zero_point = bqZeropoint;
-      // bQuantParam.precision = 8;
-      // std::cout << "len: " << len << ", bqScale: " << bqScale << ", bqZeropoint: " << bqZeropoint << std::endl;
-
-      // fbgemm::Quantize<int8_t>(in->data(), quantized, len, bQuantParam);
-      // std::cout << "original" << std::endl;
-      // // for (int ii = 0; ii < n; ii++) {
-      // //   for (int jj = 0; jj < 1; jj++) {
-      // //     std::cout << in->data()[ii * k + jj] << ","; 
-      // //   }
-      // //   std::cout << std::endl;
-      // // }
-      // std::cout << "quantized" << std::endl;
-      // // for (int ii = 0; ii < 1; ii++) {
-      // //   for (int jj = 0; jj < k; jj++) {
-      // //     std::cout << (int32_t)quantized[ii * k + jj] << ","; 
-      // //   }
-      // //   std::cout << std::endl;
-      // // }
-      // // 1. compute column offsets
-      // col_offsets = new std::vector<int32_t>(n);
-      // col_offsets_with_zero_pt_s8acc32_ref(k, n, n, quantized, &bqZeropoint, col_offsets->data(), n);
-      // // for (int ii = 0; ii < n; ii++) {
-      // //   std::cout << (int32_t)col_offsets->data()[ii] << ","; 
-      // // }
-      // std::cout << std::endl;
-      // std::cout << "calc offset done" << std::endl;
-      // 2. packing
-      // uint8_t* packedmem = aligned_alloc(256, len);
-      // packedBint8 = new PackBMatrix<int8_t>(transpose ? matrix_op_t::Transpose : matrix_op_t::NoTranspose,
-      //                                       in->shape()[0],
-      //                                       in->shape()[1],
-      //                                       quantized,
-      //                                       transpose ? in->shape()[1] : in->shape()[0]);
-      // std::cout << "packing B done" << std::endl;
-    }
-#endif
-  } else {
-    // initialize memory
-    uint8_t* outmemorg = out->data<uint8_t>();
-    for(auto i = 0; i < packsize; i++) {
-      outmemorg[i] = 0;
-    }
-    // save the other auxiliary variables
-    uint64_t* auxmemsize = (uint64_t*)outmemorg;
-    auxmemsize[0] = packsize;
-    int* auxmem = (int*)(auxmemsize + 1);
-    auxmem[0] = nrow;
-    auxmem[1] = ncol;
-    auxmem[2] = kernel_ncol_blocks;
-    auxmem[3] = brow;
-    auxmem[4] = bcol;
-    auxmem[5] = last_brow;
-    auxmem[6] = nbrow;
-    auxmem[7] = nbcol;
-    // cast to float16
-    fbgemm::float16* outmem = (fbgemm::float16*)(outmemorg + 256);
-    fbgemm::float16* dummy = new fbgemm::float16;
-    // pack the matrix
-    float* inmem = in->data();
-    for(int i = 0; i < nrow; i++) {
-      for(int j = 0; j < ncol; j++) {
-        outmem[addr(i, j, brow, bcol, nbrow, nbcol, last_brow)]
-            = tconv(!transpose ? inmem[i * ncol + j] : inmem[i + nrow * j], *dummy);
-      }
+  // initialize memory
+  uint8_t* outmemorg = out->data<uint8_t>();
+  for(auto i = 0; i < packsize; i++) {
+    outmemorg[i] = 0;
+  }
+  // save the other auxiliary variables
+  uint64_t* auxmemsize = (uint64_t*)outmemorg;
+  auxmemsize[0] = packsize;
+  int* auxmem = (int*)(auxmemsize + 1);
+  auxmem[0] = nrow;
+  auxmem[1] = ncol;
+  auxmem[2] = kernel_ncol_blocks;
+  auxmem[3] = brow;
+  auxmem[4] = bcol;
+  auxmem[5] = last_brow;
+  auxmem[6] = nbrow;
+  auxmem[7] = nbcol;
+  // cast to float16
+  fbgemm::float16* outmem = (fbgemm::float16*)(outmemorg + 256);
+  fbgemm::float16* dummy = new fbgemm::float16;
+  // pack the matrix
+  float* inmem = in->data();
+  for(int i = 0; i < nrow; i++) {
+    for(int j = 0; j < ncol; j++) {
+      outmem[addr(i, j, brow, bcol, nbrow, nbcol, last_brow)]
+          = tconv(!transpose ? inmem[i * ncol + j] : inmem[i + nrow * j], *dummy);
     }
-    delete dummy;
-
-    //auto t_end = std::chrono::high_resolution_clock::now();
-    //packingTime += (float) std::chrono::duration<double, std::milli>(t_end-t_start).count();
-    //std::cout << "Packing time: " << packingTime << std::endl;
   }
+  delete dummy;
 
-  // std::cout << "B transposed: " << transpose << std::endl;
-  // for (int i = 0; i < in->shape().size(); i++) {
-  //   std::cout << "size " << i << ": " << in->shape()[i] << std::endl;
-  // }
-  // // compute statistics for quantization
-  // if (in->shape().size() == 2 && (in->shape()[0] >= 32000 || in->shape()[1] >= 32000)) {
-  //   float mins[ncol] = {0};
-  //   float maxs[ncol] = {0};
-  //   float means[ncol] = {0};
-  //   float stds[ncol] = {0};
-  //   for(int i = 0; i < nrow; i++) {
-  //     for(int j = 0; j < ncol; j++) {
-  //       float val = !transpose ? in->data()[i * ncol + j] : in->data()[i + nrow * j];
-  //       if (val < mins[j])
-  //         mins[j] = val;
-  //       if (val > maxs[j])
-  //         maxs[j] = val;
-        
-  //       means[j] += val;
-  //       stds[j] += val*val;
-  //     }
-  //   }
-  //   for(int j = 0; j < ncol; j++) {
-  //       std::cout << mins[j] << ", " << maxs[j] << ", " << means[j] << ", " << stds[j] << std::endl;
-  //   }
-  // }
+  //auto t_end = std::chrono::high_resolution_clock::now();
+  //packingTime += (float) std::chrono::duration<double, std::milli>(t_end-t_start).count();
+  //std::cout << "Packing time: " << packingTime << std::endl;
 }
 
 void GemmPackFp32(marian::Tensor C,
@@ -320,190 +120,52 @@ void GemmPackFp32(marian::Tensor C,
                   const int64_t m,
                   const int64_t n,
                   int transA) {
-  // use int 8 packed gemm
-  // if (A->shape().size() == 4 && B->shape().size() == 1 && B->shape()[0] == 1) {
- if (false) {
-    // quantize & pack A
-    // transformer base wmt 2017
-    // float ascale = 7.8/104;
-    // int32_t azeropoint = 151;
-    // old student de-en
-    // float ascale = 14.85/117;
-    // int32_t azeropoint = 138;
-
-#if 0
-    // compute range
-    float min_est=1000000, max_est=-10000000;
-    // VSLSSTaskPtr task;
-    // MKL_INT task_p, task_n, xstorage;
-
-    // /* Parameters of the task and initialization */
-    // task_p = 1;
-    // task_n = A->shape().elements();
-    // xstorage = VSL_SS_MATRIX_STORAGE_ROWS;
-    // min_est = max_est = A->data()[0];
-    // /* Create a task */
-    // vslsSSNewTask( &task, &task_p, &task_n, &xstorage, (float*)A->data(), 0, 0 );
-    // /* Initialize the task parameters */
-    // vslsSSEditTask( task, VSL_SS_ED_MIN, &min_est );
-    // vslsSSEditTask( task, VSL_SS_ED_MAX, &max_est );
-    // /* Compute the minimum and maximum values in observations */
-    // vslsSSCompute( task, VSL_SS_MIN|VSL_SS_MAX, VSL_SS_METHOD_FAST );
-    // /* Deallocate the task resources */
-    
-    // vslSSDeleteTask( &task );
-
-    int elem = A->shape().elements();
-    float* data = A->data();
-    for (int ii = 0; ii < elem; ii++) {
-      if (data[ii] < min_est) min_est = data[ii];
-      if (data[ii] > max_est) max_est = data[ii];
-    }
- 
-    std::vector<int32_t> row_offset_buf(PackAWithQuantRowOffset<uint8_t>::rowOffsetBufferSize());
-
-    float ascale = (max_est - min_est)/255;
-    int32_t azeropoint = (int32_t)(255 - max_est / ascale);
-    PackAWithQuantRowOffset<uint8_t> packAN(
-        transA ? matrix_op_t::Transpose : matrix_op_t::NoTranspose,
-        transA ? k : m,
-        transA ? m : k,
-        A->data(),
-        transA ? m : k,
-        nullptr, /*buffer for packed matrix*/
-        ascale,
-        azeropoint,
-        1, /*groups*/
-        row_offset_buf.data());
-
-    DoNothing<float, float> doNothingObj{};
-    ReQuantizeForFloat<false, QuantizationGranularity::OUT_CHANNEL> outputProcObj(
-        doNothingObj,
-        ascale,
-        bqScale,
-        azeropoint,
-        bqZeropoint,
-        packAN.getRowOffsetBuffer(),
-        col_offsets->data(),
-        nullptr,
-        n);
-
-    // gemm
-    fbgemmPacked(
-        packAN,
-        *packedBint8,
-        C->data(),
-        (int32_t*)C->data(),
-        n,
-        outputProcObj,
-        0,
-        1);
-
-    // std::cout << "lowp gemm: " << std::endl;
-    // for (int ii = 0; ii < n; ii++) {
-    //   std::cout << C->data()[ii] << std::endl;
-    // }
-    // std::cout << std::endl;
-
-#endif
-  } else {
-    // row major
-    // keep the original mem
-    fbgemm::float16* pmat = packedPlaceholder.pmat_;
-    // retreive aux fields from the memory
-    uint64_t* packedmemSize = (uint64_t*)B->data();
-    packedPlaceholder.size_ = packedmemSize[0];
-    int* packedmemAux = (int*)(packedmemSize + 1);
-    packedPlaceholder.nrow_ = packedmemAux[0];
-    packedPlaceholder.ncol_ = packedmemAux[1];
-    packedPlaceholder.kernel_ncol_blocks_ = packedmemAux[2];
-    packedPlaceholder.brow_ = packedmemAux[3];
-    packedPlaceholder.bcol_ = packedmemAux[4];
-    packedPlaceholder.last_brow_ = packedmemAux[5];
-    packedPlaceholder.nbrow_ = packedmemAux[6];
-    packedPlaceholder.nbcol_ = packedmemAux[7];
-
-    // packed matrix
-    packedPlaceholder.pmat_ = (fbgemm::float16*)(B->data<uint8_t>() + 256);
-
-    for(int i = 0; i < m; ++i) {
-      mkl_somatcopy('R', 'N', 1, n, 1, bias->data(), n, C->data() + n * i, n);
-    }
+  // row major
+  // keep the original mem
+  fbgemm::float16* pmat = packedPlaceholder.pmat_;
+  // retreive aux fields from the memory
+  uint64_t* packedmemSize = (uint64_t*)B->data();
+  packedPlaceholder.size_ = packedmemSize[0];
+  int* packedmemAux = (int*)(packedmemSize + 1);
+  packedPlaceholder.nrow_ = packedmemAux[0];
+  packedPlaceholder.ncol_ = packedmemAux[1];
+  packedPlaceholder.kernel_ncol_blocks_ = packedmemAux[2];
+  packedPlaceholder.brow_ = packedmemAux[3];
+  packedPlaceholder.bcol_ = packedmemAux[4];
+  packedPlaceholder.last_brow_ = packedmemAux[5];
+  packedPlaceholder.nbrow_ = packedmemAux[6];
+  packedPlaceholder.nbcol_ = packedmemAux[7];
+
+  // packed matrix
+  packedPlaceholder.pmat_ = (fbgemm::float16*)(B->data<uint8_t>() + 256);
+
+  for(int i = 0; i < m; ++i) {
+    mkl_somatcopy('R', 'N', 1, n, 1, bias->data(), n, C->data() + n * i, n);
+  }
 
-    if (true) {
-    // if (A->shape().size() == 4 && B->shape()[0] > 20480000) {
 #ifdef _OPENMP
 #pragma omp parallel
 #endif
-      {
+  {
 #ifdef _OPENMP
-        int num_threads = omp_get_num_threads();
-        int tid = omp_get_thread_num();
+    int num_threads = omp_get_num_threads();
+    int tid = omp_get_thread_num();
 #else
-        int num_threads = 1;
-        int tid = 0;
+    int num_threads = 1;
+    int tid = 0;
 #endif
-        fbgemm::cblas_gemm_compute(transA ? matrix_op_t::Transpose : matrix_op_t::NoTranspose,
-                          (int)m,
-                          A->data(),
-                          packedPlaceholder,
-                          1,
-                          C->data(),
-                          tid,
-                          num_threads);
-      }
-    } else {
-        fbgemm::cblas_gemm_compute(transA ? matrix_op_t::Transpose : matrix_op_t::NoTranspose,
-                          (int)m,
-                          A->data(),
-                          packedPlaceholder,
-                          1,
-                          C->data(),
-                          0,
-                          1);
-    }
-
-    // if (B->shape().size() == 1 && B->shape()[0] >= 32000 * 512) {
-    //   std::cout << "packed gemm: " << std::endl;
-    //   for (int ii = 0; ii < n; ii++) {
-    //     std::cout << C->data()[ii] << std::endl;
-    //   }
-    //   std::cout << std::endl;
-    // }
-
-    // return back the original mem
-    packedPlaceholder.pmat_ = pmat;
+    fbgemm::cblas_gemm_compute(transA ? matrix_op_t::Transpose : matrix_op_t::NoTranspose,
+                      (int)m,
+                      A->data(),
+                      packedPlaceholder,
+                      1,
+                      C->data(),
+                      tid,
+                      num_threads);
   }
 
-  // std::cout << "A transposed: " << transA << std::endl;
-  // for (int i = 0; i < A->shape().size(); i++) {
-  //   std::cout << "size " << i << ": " << A->shape()[i] << std::endl;
-  // }
-  // compute statistics for quantization
-  // if (A->shape().size() == 4 && B->shape()[0] > 20480000) {
-  //   int bsize = A->shape().elements() / A->shape()[-1];
-  //   int hsize = A->shape()[3];
-  //   float mins[bsize] = {0};
-  //   float maxs[bsize] = {0};
-  //   float means[bsize] = {0};
-  //   float stds[bsize] = {0};
-  //   float* inmem = A->data();
-  //   for(int i = 0; i < bsize; i++) {
-  //     for(int j = 0; j < hsize; j++) {
-  //       float val = !transA ? inmem[i * hsize + j] : inmem[i + bsize * j];
-  //       if (val < mins[i])
-  //         mins[i] = val;
-  //       if (val > maxs[i])
-  //         maxs[i] = val;
-        
-  //       means[i] += val;
-  //       stds[i] += val*val;
-  //     }
-  //   }
-  //   for(int i = 0; i < bsize; i++) {
-  //       std::cout << mins[i] << ", " << maxs[i] << ", " << means[i] << ", " << stds[i] << std::endl;
-  //   }
-  // }
+  // return back the original mem
+  packedPlaceholder.pmat_ = pmat;
 }
 #else // USE_FBGEMM && MKL_FOUND
 void PackFp32(marian::Tensor out,
@@ -580,6 +242,6 @@ void AddBias(marian::Tensor C, const marian::Tensor Bias) {
   // std::cout << std::endl;
 }
 
-}  // namespace pack
+}  // namespace variant
 }  // namespace cpu
 }  // namespace marian
diff --git a/src/tensors/cpu/sharp/packed_gemm.h b/src/tensors/cpu/sharp/packed_gemm.h
index 428190254..4aef905d5 100644
--- a/src/tensors/cpu/sharp/packed_gemm.h
+++ b/src/tensors/cpu/sharp/packed_gemm.h
@@ -4,7 +4,7 @@
 
 namespace marian {
 namespace cpu {
-namespace pack {
+namespace variant {
 
 void PackFp32(marian::Tensor out,
               const marian::Tensor in,
@@ -25,15 +25,10 @@ void GemmPackFp32(marian::Tensor C,
                   const marian::Tensor bias,
                   const int64_t m,
                   const int64_t n,
-                  // const int64_t k,
-                  // const float beta = 1,
-                  // const int layout = 0,
                   const int transA = 0);
-                  //const int transB = 0,
-                  //const size_t idx = 0);
 
 void AddBias(marian::Tensor C, const marian::Tensor Bias);
 
-}  // namespace pack
+}  // namespace variant
 }  // namespace cpu
 }  // namespace marian

From 93b752c34968db0cd989899f131ccf4426002542 Mon Sep 17 00:00:00 2001
From: Young Jin Kim <youki@microsoft.com>
Date: Tue, 18 Jun 2019 17:52:21 -0700
Subject: [PATCH 503/838] fix for the case FBGEMM is not used

---
 src/graph/expression_operators.cpp    | 9 +++++++++
 src/tensors/cpu/sharp/packed_gemm.cpp | 9 ++-------
 2 files changed, 11 insertions(+), 7 deletions(-)

diff --git a/src/graph/expression_operators.cpp b/src/graph/expression_operators.cpp
index d7bfd19c2..88a6222de 100755
--- a/src/graph/expression_operators.cpp
+++ b/src/graph/expression_operators.cpp
@@ -508,6 +508,7 @@ Expr affine(Expr a, Expr b, Expr bias, bool transA, bool transB, float scale) {
       return tuner->run();
 
     } else {
+#if USE_FBGEMM
       if(b->memoize()) {
         auto packed = cpu::variant::pack(b, cpu::variant::PackMatrix::B, transB, clipValue);
         // auto packed = transB ? 
@@ -533,6 +534,14 @@ Expr affine(Expr a, Expr b, Expr bias, bool transA, bool transB, float scale) {
         std::vector<Expr> nodes = {clip(a, clipValue), clip(b, clipValue), bias, ones};
         return Expression<AffineNodeOp>(nodes, transA, transB, scale);
       }
+#else // USE_FBGEMM
+      // cpu int16 version
+      return cpu::int16::affine(
+          cpu::int16::quantize(transA ? transpose(a) : a, clipValue),
+          cpu::int16::quantize(transB ? b : transpose(b), clipValue),
+          bias,
+          scale);
+#endif  // USE_FBGEMM
     }
   } else {
     // general version, MKL, CBlas or CUDA
diff --git a/src/tensors/cpu/sharp/packed_gemm.cpp b/src/tensors/cpu/sharp/packed_gemm.cpp
index e3840b8c7..f351a96e2 100644
--- a/src/tensors/cpu/sharp/packed_gemm.cpp
+++ b/src/tensors/cpu/sharp/packed_gemm.cpp
@@ -170,7 +170,7 @@ void GemmPackFp32(marian::Tensor C,
 #else // USE_FBGEMM && MKL_FOUND
 void PackFp32(marian::Tensor out,
               const marian::Tensor in,
-              bool tranpose,
+              bool transpose,
               int nrow,
               int ncol,
               int kernel_ncol_blocks,
@@ -188,12 +188,7 @@ void GemmPackFp32(marian::Tensor C,
                   const marian::Tensor bias,
                   const int64_t m,
                   const int64_t n,
-                  const int64_t k,
-                  const float beta,
-                  const int layout,
-                  const int transA,
-                  const int transB,
-                  size_t idx) {
+                  int transA) {
                 // does nothing. supports only FBGEMM based packed gemm at this moment.
 }
 #endif // USE_FBGEMM && MKL_FOUND

From a6052e4202e74c515ad4ea34daef99ff2edb7ac0 Mon Sep 17 00:00:00 2001
From: Nikolay Bogoychev <nheart@gmail.com>
Date: Wed, 19 Jun 2019 17:54:23 +0100
Subject: [PATCH 504/838] Fix compilation on gcc 8

---
 src/3rd_party/zstr/strict_fstream.hpp | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/3rd_party/zstr/strict_fstream.hpp b/src/3rd_party/zstr/strict_fstream.hpp
index 21173c737..fbc641640 100644
--- a/src/3rd_party/zstr/strict_fstream.hpp
+++ b/src/3rd_party/zstr/strict_fstream.hpp
@@ -125,7 +125,7 @@ struct static_method_holder
             is_p->peek();
             peek_failed = is_p->fail();
         }
-        catch (std::ios_base::failure e) {}
+        catch (const std::ios_base::failure &e) {}
         if (peek_failed)
         {
             throw Exception(std::string("strict_fstream: open('")

From 56092425ed56adae33d459224a39f62b8f534182 Mon Sep 17 00:00:00 2001
From: Young Jin Kim <youki@microsoft.com>
Date: Wed, 19 Jun 2019 10:25:42 -0700
Subject: [PATCH 505/838] Add missing FBGEMM lib files

---
 vs/Marian.vcxproj   |   2 +-
 vs/libs/clog.lib    | Bin 0 -> 33970 bytes
 vs/libs/cpuinfo.lib | Bin 0 -> 244232 bytes
 vs/libs/fbgemm.lib  | Bin 0 -> 17549664 bytes
 4 files changed, 1 insertion(+), 1 deletion(-)
 create mode 100644 vs/libs/clog.lib
 create mode 100644 vs/libs/cpuinfo.lib
 create mode 100644 vs/libs/fbgemm.lib

diff --git a/vs/Marian.vcxproj b/vs/Marian.vcxproj
index 8106f488c..f2e3878b2 100755
--- a/vs/Marian.vcxproj
+++ b/vs/Marian.vcxproj
@@ -50,7 +50,7 @@
     <ExecutablePath>$(ExecutablePath)</ExecutablePath>
     <IntDir>$(SolutionDir)$(Platform)\$(Configuration)\Marian\</IntDir>
     <IncludePath>%MKL_PATH%\include;..\src\3rd_party\fbgemm\include;%CUDA_PATH%\include;..\src;..\src\3rd_party;%BOOST_INCLUDE_PATH%;%ZLIB_PATH%\include;$(VC_IncludePath);$(WindowsSDK_IncludePath);</IncludePath>
-    <LibraryPath>deps;%CUDA_PATH%\lib\x64;%BOOST_LIB_PATH%;%ZLIB_PATH%\lib;%MKL_PATH%\lib\intel64;$(VC_LibraryPath_x64);$(WindowsSDK_LibraryPath_x64);$(NETFXKitsDir)Lib\um\x64</LibraryPath>
+    <LibraryPath>libs;%CUDA_PATH%\lib\x64;%BOOST_LIB_PATH%;%ZLIB_PATH%\lib;%MKL_PATH%\lib\intel64;$(VC_LibraryPath_x64);$(WindowsSDK_LibraryPath_x64);$(NETFXKitsDir)Lib\um\x64</LibraryPath>
   </PropertyGroup>
   <ItemDefinitionGroup>
     <ClCompile>
diff --git a/vs/libs/clog.lib b/vs/libs/clog.lib
new file mode 100644
index 0000000000000000000000000000000000000000..8bac353445f48f0750ce76741f99502c5784199e
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HcmV?d00001


From edeea6060fc8f327eaaf2c5d8e91e0056326e6b6 Mon Sep 17 00:00:00 2001
From: Young Jin Kim <youki@microsoft.com>
Date: Wed, 19 Jun 2019 17:13:23 -0700
Subject: [PATCH 506/838] Add option for selecting GEMM type. Improve code
 based on the code review.

---
 src/common/config_parser.cpp          |  3 +
 src/common/definitions.h              |  9 +++
 src/graph/expression_graph.cpp        | 11 +++-
 src/graph/expression_graph.h          |  4 +-
 src/graph/expression_operators.cpp    | 80 +++++++++++++++---------
 src/microsoft/quicksand.cpp           |  3 +-
 src/rescorer/rescorer.h               |  3 +-
 src/tensors/cpu/sharp/packed_gemm.cpp | 90 +++++++++++++++++----------
 src/tensors/cpu/sharp/packed_gemm.h   | 22 +++----
 src/tensors/cpu/tensor_operators.cpp  | 12 +++-
 src/translator/translator.h           |  5 +-
 11 files changed, 159 insertions(+), 83 deletions(-)

diff --git a/src/common/config_parser.cpp b/src/common/config_parser.cpp
index eaa9c9912..7d706c863 100755
--- a/src/common/config_parser.cpp
+++ b/src/common/config_parser.cpp
@@ -525,6 +525,9 @@ void ConfigParser::addOptionsTranslation(cli::CLIWrapper& cli) {
       "Optimize speed aggressively sacrificing memory or precision");
   cli.add<bool>("--skip-cost",
       "Ignore model cost during translation, not recommended for beam-size > 1");
+  cli.add<std::string>("--gemm-type",
+      "Select GEMM options: auto, mkl, int16, packed, int8",
+      "auto");
 
   cli.add<std::vector<std::string>>("--shortlist",
      "Use softmax shortlist: path first best prune");
diff --git a/src/common/definitions.h b/src/common/definitions.h
index f46da2340..a0fc8b258 100755
--- a/src/common/definitions.h
+++ b/src/common/definitions.h
@@ -116,4 +116,13 @@ typedef Ptr<RunBase> RunBasePtr;
 
 
 const float NEMATUS_LN_EPS = 1e-5f;
+
+/* GEMM type enum */
+typedef enum { Auto     = 0,
+               Mkl      = 1,
+               Int16    = 2,
+               PackedFb = 10,
+               Int8Fb   = 11
+} GemmType;
+
 }  // namespace marian
diff --git a/src/graph/expression_graph.cpp b/src/graph/expression_graph.cpp
index 1df3af243..7a0c2b698 100755
--- a/src/graph/expression_graph.cpp
+++ b/src/graph/expression_graph.cpp
@@ -5,8 +5,15 @@
 
 namespace marian {
 
-ExpressionGraph::ExpressionGraph(bool inference, bool optimized)
-    : inferenceOnly_(inference), optimized_(optimized), backend_(nullptr) {}
+ExpressionGraph::ExpressionGraph(bool inference, bool optimized, std::string gemmType)
+    : inferenceOnly_(inference), optimized_(optimized), backend_(nullptr) {
+  if (gemmType == "auto") gemmType_ = GemmType::Auto;
+  else if (gemmType == "mkl") gemmType_ = GemmType::Mkl;
+  else if (gemmType == "int16") gemmType_ = GemmType::Int16;
+  else if (gemmType == "packed") gemmType_ = GemmType::PackedFb;
+  else if (gemmType == "int8") gemmType_ = GemmType::Int8Fb;
+  else ABORT("Unknow GEMM type");
+}
 
 void ExpressionGraph::setDevice(DeviceId deviceId, Ptr<Device> device) {
   if(!backend_) {
diff --git a/src/graph/expression_graph.h b/src/graph/expression_graph.h
index 901d97245..51e468e54 100755
--- a/src/graph/expression_graph.h
+++ b/src/graph/expression_graph.h
@@ -137,6 +137,7 @@ class ExpressionGraph : public std::enable_shared_from_this<ExpressionGraph> {
   std::string namespace_;
 
   bool throwNaN_{false};
+  GemmType gemmType_;
 
 protected:
   // Delete, copy and move constructors
@@ -148,7 +149,7 @@ class ExpressionGraph : public std::enable_shared_from_this<ExpressionGraph> {
    *
    * Constructor should be used as New<ExpressionGraph>()
    */
-  ExpressionGraph(bool inference = false, bool optimized = false);
+  ExpressionGraph(bool inference = false, bool optimized = false, std::string gemmType = "auto");
 
   void setInference(bool inference) { inferenceOnly_ = inference; }
   bool isInference() { return inferenceOnly_; }
@@ -167,6 +168,7 @@ class ExpressionGraph : public std::enable_shared_from_this<ExpressionGraph> {
 
   void setOptimized(bool optimized) { optimized_ = optimized; }
   bool isOptimized() { return (optimized_ && inferenceOnly_); }
+  GemmType getGemmType() { return gemmType_; } 
 
   void switchParams(const std::string& newNamespace) {
     namespace_ = newNamespace;
diff --git a/src/graph/expression_operators.cpp b/src/graph/expression_operators.cpp
index 88a6222de..fac864725 100755
--- a/src/graph/expression_operators.cpp
+++ b/src/graph/expression_operators.cpp
@@ -411,8 +411,8 @@ Expr affine(Expr a, Expr b, Expr bias, bool transA, bool transB, float scale) {
   float clipValue = a->graph()->getBackend()->getClip();
 
   if(a->graph()->isOptimized() && device == DeviceType::cpu) {
-    bool autotune = false;
-    if(autotune) {
+    GemmType gemmType = a->graph()->getGemmType();
+    if(gemmType == GemmType::Auto) {
       thread_local Ptr<AutoTuner<Expr>> tuner = New<AutoTuner<Expr>>();
 
       // start with new set of algorithms
@@ -508,40 +508,60 @@ Expr affine(Expr a, Expr b, Expr bias, bool transA, bool transB, float scale) {
       return tuner->run();
 
     } else {
-#if USE_FBGEMM
-      if(b->memoize()) {
-        auto packed = cpu::variant::pack(b, cpu::variant::PackMatrix::B, transB, clipValue);
-        // auto packed = transB ? 
-        //               cpu::variant::pack(transpose(b), cpu::pack::PackMatrix::B, false, clipValue) :
-        //               cpu::variant::pack(b, cpu::pack::PackMatrix::B, false, clipValue);
-
-        return cpu::variant::affine(
-            clip(a, clipValue), packed,
-            b->shape(),
+      if (gemmType == GemmType::Int16) {
+        // cpu int16 version
+        return cpu::int16::affine(
+            cpu::int16::quantize(transA ? transpose(a) : a, clipValue),
+            cpu::int16::quantize(transB ? b : transpose(b), clipValue),
             bias,
-            transA,
-            transB,
             scale);
-        // cpu int16 version
-        // return cpu::int16::affine(
-        //     cpu::int16::quantize(transA ? transpose(a) : a, clipValue),
-        //     cpu::int16::quantize(transB ? b : transpose(b), clipValue),
-        //     bias,
-        //     scale);
-      } else {
+      } else if (gemmType == GemmType::PackedFb) {
+#if USE_FBGEMM
+        if(b->memoize()) {
+          auto packed = cpu::variant::pack(b, cpu::variant::PackMatrix::B, transB, clipValue);
+          // auto packed = transB ? 
+          //               cpu::variant::pack(transpose(b), cpu::pack::PackMatrix::B, false, clipValue) :
+          //               cpu::variant::pack(b, cpu::pack::PackMatrix::B, false, clipValue);
+
+          return cpu::variant::affine(
+              clip(a, clipValue), packed,
+              b->shape(),
+              bias,
+              transA,
+              transB,
+              scale);
+          // cpu int16 version
+          // return cpu::int16::affine(
+          //     cpu::int16::quantize(transA ? transpose(a) : a, clipValue),
+          //     cpu::int16::quantize(transB ? b : transpose(b), clipValue),
+          //     bias,
+          //     scale);
+        } else {
+          int rows = a->shape().elements() / a->shape()[-1];
+          Expr ones = a->graph()->ones({rows, 1});
+          std::vector<Expr> nodes = {clip(a, clipValue), clip(b, clipValue), bias, ones};
+          return Expression<AffineNodeOp>(nodes, transA, transB, scale);
+        }
+#else
+        ABORT("Packed GEMM not implemented");
+#endif  // USE_FBGEMM
+
+      } else if (gemmType == GemmType::Mkl) {
+        // general version, MKL, CBlas or CUDA
+
+        // if clipValue > 0, the inputs will be clipped to range [-clipValue,
+        // clipValue] This is meant to keep values at the same range as used during
+        // training when optimizing for 8-bit integer products. Likely to be removed
+        // in the future when we explore better ways to handle this.
+
         int rows = a->shape().elements() / a->shape()[-1];
         Expr ones = a->graph()->ones({rows, 1});
-        std::vector<Expr> nodes = {clip(a, clipValue), clip(b, clipValue), bias, ones};
+        std::vector<Expr> nodes
+            = {clip(a, clipValue), clip(b, clipValue), bias, ones};
         return Expression<AffineNodeOp>(nodes, transA, transB, scale);
+      } else {
+        ABORT("Not implemented");
       }
-#else // USE_FBGEMM
-      // cpu int16 version
-      return cpu::int16::affine(
-          cpu::int16::quantize(transA ? transpose(a) : a, clipValue),
-          cpu::int16::quantize(transB ? b : transpose(b), clipValue),
-          bias,
-          scale);
-#endif  // USE_FBGEMM
     }
   } else {
     // general version, MKL, CBlas or CUDA
diff --git a/src/microsoft/quicksand.cpp b/src/microsoft/quicksand.cpp
index 440d0c116..74682f578 100644
--- a/src/microsoft/quicksand.cpp
+++ b/src/microsoft/quicksand.cpp
@@ -64,7 +64,8 @@ class BeamSearchDecoder : public IBeamSearchDecoder {
       vocabs_.push_back(std::dynamic_pointer_cast<VocabWrapper>(vi)->getVocab());
 
     // setting 16-bit optimization to false for now. Re-enable with better caching or pre-computation
-    graph_ = New<ExpressionGraph>(/*inference=*/true, /*optimize=*/false);
+    graph_ = New<ExpressionGraph>(/*inference=*/true, /*optimize=*/options->get<bool>("optimize"), 
+                                  /*gemmType=*/options->get<std::string>("gemm-type"));
 
     DeviceId deviceId{0, DeviceType::cpu};
     device_ = New<cpu::WrappedDevice>(deviceId);
diff --git a/src/rescorer/rescorer.h b/src/rescorer/rescorer.h
index e4d01ec55..b9f9f116e 100755
--- a/src/rescorer/rescorer.h
+++ b/src/rescorer/rescorer.h
@@ -67,7 +67,8 @@ class Rescore : public ModelTask {
     auto devices = Config::getDevices(options_);
 
     for(auto device : devices) {
-      auto graph = New<ExpressionGraph>(true, options_->get<bool>("optimize"));
+      auto graph = New<ExpressionGraph>(true, options_->get<bool>("optimize"), 
+                                        options_->get<std::string>("gemm-type"));
       graph->setDevice(device);
       graph->reserveWorkspaceMB(options_->get<size_t>("workspace"));
       graphs_.push_back(graph);
diff --git a/src/tensors/cpu/sharp/packed_gemm.cpp b/src/tensors/cpu/sharp/packed_gemm.cpp
index f351a96e2..c90956071 100644
--- a/src/tensors/cpu/sharp/packed_gemm.cpp
+++ b/src/tensors/cpu/sharp/packed_gemm.cpp
@@ -15,33 +15,47 @@
 #pragma warning(disable: 4505) // warning C4505: 'fbgemmAlignedAlloc' in fbgemm.h: unreferenced local function has been removed (missing 'static inline')
 #endif
 
-#if (USE_FBGEMM && MKL_FOUND)
+#if USE_FBGEMM
 #include "3rd_party/fbgemm/include/fbgemm/FbgemmFP16.h"
 #include "3rd_party/fbgemm/include/fbgemm/QuantUtils.h"
 #include "3rd_party/fbgemm/include/fbgemm/Fbgemm.h"
 
-#include <mkl.h>
-#include <mkl_types.h>
-//#include "mkl_vsl.h"
-
 #ifdef _OPENMP
 #include <omp.h>
 #endif
 
+#if MKL_FOUND
+#include <mkl.h>
+#include <mkl_types.h>
+#endif
+
 using namespace fbgemm;
-#endif // USE_FBGEMM && MKL_FOUND
+#endif // USE_FBGEMM
 
 namespace marian {
 namespace cpu {
-namespace variant {
+namespace variant { // Variants of GEMM implementations
 
-#if (USE_FBGEMM && MKL_FOUND)
+#if USE_FBGEMM
 // initialize with a dummy
 static PackedGemmMatrixFP16 packedPlaceholder(1, 1, 1, 1, 1, 1, 1, 1);
 
 // This is copied from FBGEMM code
 // A better way?
+// will be removed, when FBGEMM api is changed
 // blocked row-major format address arithmetic
+/**
+ * Returns the memory address in the packed (block formatted) matrix array of a specific element 
+ * indexed by the original non-packed array.
+ *
+ * @param r_ row index in the original matrix
+ * @param c_ column index in the original matrix
+ * @param brow_ row wide block index
+ * @param bcol_ column wide block index
+ * @param nbrow_ number of blocks in row
+ * @param nbcol_ number of blocks in column
+ * @param last_brow_ row number of the last block
+ */
 inline uint64_t addr(const int r_,
                      const int c_,
                      const int brow_,
@@ -66,16 +80,16 @@ inline uint64_t addr(const int r_,
 
 void PackFp32(marian::Tensor out,
               const marian::Tensor in,
-              bool transpose,
-              int nrow,
-              int ncol,
-              int kernel_ncol_blocks,
-              int brow,
-              int bcol,
-              int last_brow,
-              int nbrow,
-              int nbcol,
-              uint64_t packsize) {
+              const bool transpose,
+              const int nrow,
+              const int ncol,
+              const int kernel_ncol_blocks,
+              const int brow,
+              const int bcol,
+              const int last_brow,
+              const int nbrow,
+              const int nbcol,
+              const uint64_t packsize) {
   //auto t_start = std::chrono::high_resolution_clock::now();
   // for the last embedding layer, pack it into int8
   // initialize memory
@@ -86,7 +100,7 @@ void PackFp32(marian::Tensor out,
   // save the other auxiliary variables
   uint64_t* auxmemsize = (uint64_t*)outmemorg;
   auxmemsize[0] = packsize;
-  int* auxmem = (int*)(auxmemsize + 1);
+  int32_t* auxmem = (int32_t*)(auxmemsize + 1);
   auxmem[0] = nrow;
   auxmem[1] = ncol;
   auxmem[2] = kernel_ncol_blocks;
@@ -119,14 +133,14 @@ void GemmPackFp32(marian::Tensor C,
                   const marian::Tensor bias,
                   const int64_t m,
                   const int64_t n,
-                  int transA) {
+                  const int transA) {
   // row major
   // keep the original mem
   fbgemm::float16* pmat = packedPlaceholder.pmat_;
   // retreive aux fields from the memory
   uint64_t* packedmemSize = (uint64_t*)B->data();
   packedPlaceholder.size_ = packedmemSize[0];
-  int* packedmemAux = (int*)(packedmemSize + 1);
+  int32_t* packedmemAux = (int32_t*)(packedmemSize + 1);
   packedPlaceholder.nrow_ = packedmemAux[0];
   packedPlaceholder.ncol_ = packedmemAux[1];
   packedPlaceholder.kernel_ncol_blocks_ = packedmemAux[2];
@@ -139,9 +153,15 @@ void GemmPackFp32(marian::Tensor C,
   // packed matrix
   packedPlaceholder.pmat_ = (fbgemm::float16*)(B->data<uint8_t>() + 256);
 
+#if MKL_FOUND
   for(int i = 0; i < m; ++i) {
     mkl_somatcopy('R', 'N', 1, n, 1, bias->data(), n, C->data() + n * i, n);
   }
+#else
+  for(int i = 0; i < m; ++i) {
+    std::copy(bias->data(), bias->data() + n, C->data() + n * i);
+  }
+#endif
 
 #ifdef _OPENMP
 #pragma omp parallel
@@ -167,20 +187,21 @@ void GemmPackFp32(marian::Tensor C,
   // return back the original mem
   packedPlaceholder.pmat_ = pmat;
 }
-#else // USE_FBGEMM && MKL_FOUND
+#else // USE_FBGEMM
 void PackFp32(marian::Tensor out,
               const marian::Tensor in,
-              bool transpose,
-              int nrow,
-              int ncol,
-              int kernel_ncol_blocks,
-              int brow,
-              int bcol,
-              int last_brow,
-              int nbrow,
-              int nbcol,
-              uint64_t packsize) {
+              const bool transpose,
+              const int nrow,
+              const int ncol,
+              const int kernel_ncol_blocks,
+              const int brow,
+              const int bcol,
+              const int last_brow,
+              const int nbrow,
+              const int nbcol,
+              const uint64_t packsize) {
                 // does nothing. supports only FBGEMM based packed gemm at this moment.
+                ABORT("FBGEMM is needed to use packed GEMM.");
 }
 void GemmPackFp32(marian::Tensor C,
                   const marian::Tensor A,
@@ -188,10 +209,11 @@ void GemmPackFp32(marian::Tensor C,
                   const marian::Tensor bias,
                   const int64_t m,
                   const int64_t n,
-                  int transA) {
+                  const int transA) {
                 // does nothing. supports only FBGEMM based packed gemm at this moment.
+                ABORT("FBGEMM is needed to use packed GEMM.");
 }
-#endif // USE_FBGEMM && MKL_FOUND
+#endif // USE_FBGEMM
 
 // This operates on floats after processing so doesn't care about int8_t vs
 // int16_t.
diff --git a/src/tensors/cpu/sharp/packed_gemm.h b/src/tensors/cpu/sharp/packed_gemm.h
index 4aef905d5..87c1a75f8 100644
--- a/src/tensors/cpu/sharp/packed_gemm.h
+++ b/src/tensors/cpu/sharp/packed_gemm.h
@@ -4,20 +4,20 @@
 
 namespace marian {
 namespace cpu {
-namespace variant {
+namespace variant { // Variants of GEMM implementations
 
 void PackFp32(marian::Tensor out,
               const marian::Tensor in,
-              bool transpose,
-              int nrow,
-              int ncol,
-              int kernel_ncol_blocks,
-              int brow,
-              int bcol,
-              int last_brow,
-              int nbrow,
-              int nbcol,
-              uint64_t packsize);
+              const bool transpose,
+              const int nrow,
+              const int ncol,
+              const int kernel_ncol_blocks,
+              const int brow,
+              const int bcol,
+              const int last_brow,
+              const int nbrow,
+              const int nbcol,
+              const uint64_t packsize);
 
 void GemmPackFp32(marian::Tensor C,
                   const marian::Tensor A,
diff --git a/src/tensors/cpu/tensor_operators.cpp b/src/tensors/cpu/tensor_operators.cpp
index e6be8add0..2e9b31fc9 100644
--- a/src/tensors/cpu/tensor_operators.cpp
+++ b/src/tensors/cpu/tensor_operators.cpp
@@ -190,8 +190,16 @@ void Transpose0213(Tensor out, Tensor in) {
   }
 }
 
+/**
+ * Given a 4D array, transpose the initial 3 dimensions while keeping the last dimension.
+ *
+ * @param out output tensor
+ * @param in input tensor
+ * @param vAxis target (transposed) axes of each given axes
+ */
 template <bool add>
 void TransposeFirst3In4(Tensor out, Tensor in, const std::vector<int>& vAxis) {
+  ABORT_IF(vAxis.size() != 4, "This function handles only 4D arrays.");
 #if MKL_FOUND
   int innermost = in->shape()[-1];
 
@@ -199,7 +207,9 @@ void TransposeFirst3In4(Tensor out, Tensor in, const std::vector<int>& vAxis) {
   int l2 = in->shape()[vAxis[1]];
   int l3 = in->shape()[vAxis[2]];
 
-  int oi = 0, oj = 0, ok = 0;
+  // find the mapping between the transposed output dimensional indices (oi, oj, ok) 
+  // and original input dimensional indices (i, j, k)
+  int oi, oj, ok;
 #pragma omp parallel for
   for(int k = 0; k < l1; ++k) {
     int shift = k * l2 * l3;
diff --git a/src/translator/translator.h b/src/translator/translator.h
index e79b52716..7899957cf 100644
--- a/src/translator/translator.h
+++ b/src/translator/translator.h
@@ -55,7 +55,8 @@ class Translate : public ModelTask {
     size_t id = 0;
     for(auto device : devices) {
       auto task = [&](DeviceId device, size_t id) {
-        auto graph = New<ExpressionGraph>(true, options_->get<bool>("optimize"));
+        auto graph = New<ExpressionGraph>(true, options_->get<bool>("optimize"), 
+                                          options_->get<std::string>("gemm-type"));
         graph->setDevice(device);
         graph->getBackend()->setClip(options_->get<float>("clip-gemm"));
         graph->reserveWorkspaceMB(options_->get<size_t>("workspace"));
@@ -167,7 +168,7 @@ class TranslateService : public ModelServiceTask {
 
     // initialize scorers
     for(auto device : devices) {
-      auto graph = New<ExpressionGraph>(true, options_->get<bool>("optimize"));
+      auto graph = New<ExpressionGraph>(true, options_->get<bool>("optimize"), options_->get<std::string>("gemm-type"));
       graph->setDevice(device);
       graph->getBackend()->setClip(options_->get<float>("clip-gemm"));
       graph->reserveWorkspaceMB(options_->get<size_t>("workspace"));

From 1d80793b358d3a30126e69f892053a0a47181590 Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Thu, 20 Jun 2019 13:12:31 -0700
Subject: [PATCH 507/838] added a hacky version of hard-max for conditioning

---
 src/layers/generic.cpp | 21 +++++++++++++++++----
 1 file changed, 17 insertions(+), 4 deletions(-)

diff --git a/src/layers/generic.cpp b/src/layers/generic.cpp
index 30060432f..57aa282c5 100755
--- a/src/layers/generic.cpp
+++ b/src/layers/generic.cpp
@@ -246,8 +246,12 @@ namespace marian {
 
       b_ = graph_->param(name + "_b", {1, numOutputClasses}, inits::zeros);
 
-      const int lemmaDimEmb = options_->get<int>("lemma-dim-emb", 0);
+      /*const*/ int lemmaDimEmb = options_->get<int>("lemma-dim-emb", 0);
       if (lemmaDimEmb > 0) {
+#define HARDMAX_HACK
+#ifdef HARDMAX_HACK
+        lemmaDimEmb = lemmaDimEmb & 0xfffffffe;
+#endif
         auto range = factoredVocab_->getGroupRange(0);
         auto lemmaVocabDim = (int)(range.second - range.first);
         lemmaEt_ = graph_->param(name + "_lemmaEt", {lemmaDimEmb, lemmaVocabDim}, inits::glorot_uniform); // [L x U] L=lemmaDimEmb; transposed for speed
@@ -299,7 +303,7 @@ namespace marian {
           allLogits[g] = New<RationalLoss>(factorLogits, nullptr);
           // optionally add a soft embedding of lemma back to create some lemma dependency
           // @TODO: if this works, move it into lazyConstruct
-          const int lemmaDimEmb = options_->get<int>("lemma-dim-emb", 0);
+          /*const*/ int lemmaDimEmb = options_->get<int>("lemma-dim-emb", 0);
           if (lemmaDimEmb < 0 && g == 0) {
             ABORT_IF(shortlist_ && lemmaDimEmb != 0, "Lemma-dependent bias with short list is not yet implemented");
             LOG_ONCE(info, "[embedding] using lemma-dependent bias");
@@ -310,11 +314,20 @@ namespace marian {
           if (lemmaDimEmb > 0 && g == 0) {
             LOG_ONCE(info, "[embedding] enabled re-embedding of lemma, at dim {}", lemmaDimEmb);
             // compute softmax. We compute logsoftmax() separately because this way, computation will be reused later via CSE
-            factorLogits = logsoftmax(factorLogits);
+            auto factorLogSoftmax = logsoftmax(factorLogits);
+            auto factorSoftmax = exp(factorLogSoftmax);
+#ifdef HARDMAX_HACK
+            bool hardmax = (lemmaDimEmb & 1) != 0; // odd value triggers hardmax for now (for quick experimentation)
+            if (hardmax) {
+              lemmaDimEmb = lemmaDimEmb & 0xfffffffe;
+              LOG_ONCE(info, "[embedding] HARDMAX_HACK enabled. Actual dim is {}", lemmaDimEmb);
+              factorSoftmax = eq(factorSoftmax, max(factorSoftmax, -1));
+            }
+#endif
             // re-embedding lookup, soft-indexed by softmax
             if (shortlist_ && !cachedShortLemmaEt_) // short-listed version of re-embedding matrix
               cachedShortLemmaEt_ = index_select(lemmaEt_, -1, shortlist_->indices());
-            auto e = dot(exp(factorLogits), cachedShortLemmaEt_ ? cachedShortLemmaEt_ : lemmaEt_, false, true); // [B... x L]
+            auto e = dot(factorSoftmax, cachedShortLemmaEt_ ? cachedShortLemmaEt_ : lemmaEt_, false, true); // [B... x L]
             // project it back to regular hidden dim
             int inputDim = input1->shape()[-1];
             auto name = options_->get<std::string>("prefix");

From 3914032c9a4eca62f4449b383ea7d9220b096f65 Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Thu, 20 Jun 2019 18:58:32 -0700
Subject: [PATCH 508/838] workaround for a segfault

---
 src/graph/node_operators_unary.h | 4 ++++
 src/layers/generic.cpp           | 3 ++-
 2 files changed, 6 insertions(+), 1 deletion(-)
 mode change 100644 => 100755 src/graph/node_operators_unary.h

diff --git a/src/graph/node_operators_unary.h b/src/graph/node_operators_unary.h
old mode 100644
new mode 100755
index 36273d6fd..94200435d
--- a/src/graph/node_operators_unary.h
+++ b/src/graph/node_operators_unary.h
@@ -477,6 +477,10 @@ struct ReduceNodeOp : public UnaryNodeOp {
 
   NodeOps backwardOps() override {
     using namespace functional;
+#if 1 // @BUGBUG: This is a workaround for not correctly propagating non-trainable information. @TODO: Do this the right and general way.
+    if (adj_ == nullptr)
+      return {};
+#endif
     switch (opCode_) {
     case ReduceNodeOpCode::sum:
       return {NodeOp(Add(_1, child(0)->grad(), adj_))};
diff --git a/src/layers/generic.cpp b/src/layers/generic.cpp
index 57aa282c5..9fedaf25d 100755
--- a/src/layers/generic.cpp
+++ b/src/layers/generic.cpp
@@ -321,7 +321,8 @@ namespace marian {
             if (hardmax) {
               lemmaDimEmb = lemmaDimEmb & 0xfffffffe;
               LOG_ONCE(info, "[embedding] HARDMAX_HACK enabled. Actual dim is {}", lemmaDimEmb);
-              factorSoftmax = eq(factorSoftmax, max(factorSoftmax, -1));
+              auto maxVal = max(factorSoftmax, -1);
+              factorSoftmax = eq(factorSoftmax, maxVal);
             }
 #endif
             // re-embedding lookup, soft-indexed by softmax

From 4c41fec0ae1d385c8f5c1155e542dcb4a8ab3924 Mon Sep 17 00:00:00 2001
From: Young Jin Kim <youki@microsoft.com>
Date: Fri, 21 Jun 2019 09:59:52 -0700
Subject: [PATCH 509/838] QuickSAND env fix, Remove binary dependecy of FBGEMM
 and compile FBGEMM together, Update FBGEMM to the latest.

---
 src/3rd_party/fbgemm        |   2 +-
 src/microsoft/quicksand.cpp |   4 +-
 vs/Marian.vcxproj           | 377 +++++++++++++++++++++++++++++-
 vs/Marian.vcxproj.filters   | 450 ++++++++++++++++++++++++++++++++++++
 vs/libs/clog.lib            | Bin 33970 -> 0 bytes
 vs/libs/cpuinfo.lib         | Bin 244232 -> 0 bytes
 vs/libs/fbgemm.lib          | Bin 17549664 -> 0 bytes
 7 files changed, 818 insertions(+), 15 deletions(-)
 delete mode 100644 vs/libs/clog.lib
 delete mode 100644 vs/libs/cpuinfo.lib
 delete mode 100644 vs/libs/fbgemm.lib

diff --git a/src/3rd_party/fbgemm b/src/3rd_party/fbgemm
index 25c1595ce..c4269a772 160000
--- a/src/3rd_party/fbgemm
+++ b/src/3rd_party/fbgemm
@@ -1 +1 @@
-Subproject commit 25c1595cebfc88775692660b1da7d0bf632112a8
+Subproject commit c4269a772d2a1124cadbfe5bf5876c7cf2c1d256
diff --git a/src/microsoft/quicksand.cpp b/src/microsoft/quicksand.cpp
index 74682f578..9456c02e8 100644
--- a/src/microsoft/quicksand.cpp
+++ b/src/microsoft/quicksand.cpp
@@ -64,8 +64,8 @@ class BeamSearchDecoder : public IBeamSearchDecoder {
       vocabs_.push_back(std::dynamic_pointer_cast<VocabWrapper>(vi)->getVocab());
 
     // setting 16-bit optimization to false for now. Re-enable with better caching or pre-computation
-    graph_ = New<ExpressionGraph>(/*inference=*/true, /*optimize=*/options->get<bool>("optimize"), 
-                                  /*gemmType=*/options->get<std::string>("gemm-type"));
+    graph_ = New<ExpressionGraph>(/*inference=*/true, /*optimize=*/true, 
+                                  /*gemmType=*/"packed");
 
     DeviceId deviceId{0, DeviceType::cpu};
     device_ = New<cpu::WrappedDevice>(deviceId);
diff --git a/vs/Marian.vcxproj b/vs/Marian.vcxproj
index f2e3878b2..91d05867d 100755
--- a/vs/Marian.vcxproj
+++ b/vs/Marian.vcxproj
@@ -49,8 +49,8 @@
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+    </ClCompile>
+    <ClCompile Include="..\src\3rd_party\fbgemm\third_party\cpuinfo\src\init.c">
+      <TreatWarningAsError Condition="'$(Configuration)|$(Platform)'=='Release|x64'">false</TreatWarningAsError>
+      <TreatWarningAsError Condition="'$(Configuration)|$(Platform)'=='Debug|x64'">false</TreatWarningAsError>
+    </ClCompile>
+    <ClCompile Include="..\src\3rd_party\fbgemm\third_party\cpuinfo\src\x86\cache\descriptor.c">
+      <TreatWarningAsError Condition="'$(Configuration)|$(Platform)'=='Release|x64'">false</TreatWarningAsError>
+      <TreatWarningAsError Condition="'$(Configuration)|$(Platform)'=='Debug|x64'">false</TreatWarningAsError>
+    </ClCompile>
+    <ClCompile Include="..\src\3rd_party\fbgemm\third_party\cpuinfo\src\x86\cache\deterministic.c">
+      <TreatWarningAsError Condition="'$(Configuration)|$(Platform)'=='Release|x64'">false</TreatWarningAsError>
+      <TreatWarningAsError Condition="'$(Configuration)|$(Platform)'=='Debug|x64'">false</TreatWarningAsError>
+    </ClCompile>
+    <ClCompile Include="..\src\3rd_party\fbgemm\third_party\cpuinfo\src\x86\cache\init.c">
+      <TreatWarningAsError Condition="'$(Configuration)|$(Platform)'=='Release|x64'">false</TreatWarningAsError>
+      <TreatWarningAsError Condition="'$(Configuration)|$(Platform)'=='Debug|x64'">false</TreatWarningAsError>
+    </ClCompile>
+    <ClCompile Include="..\src\3rd_party\fbgemm\third_party\cpuinfo\src\x86\info.c">
+      <TreatWarningAsError Condition="'$(Configuration)|$(Platform)'=='Release|x64'">false</TreatWarningAsError>
+      <TreatWarningAsError Condition="'$(Configuration)|$(Platform)'=='Debug|x64'">false</TreatWarningAsError>
+    </ClCompile>
+    <ClCompile Include="..\src\3rd_party\fbgemm\third_party\cpuinfo\src\x86\init.c">
+      <TreatWarningAsError Condition="'$(Configuration)|$(Platform)'=='Release|x64'">false</TreatWarningAsError>
+      <TreatWarningAsError Condition="'$(Configuration)|$(Platform)'=='Debug|x64'">false</TreatWarningAsError>
+    </ClCompile>
+    <ClCompile Include="..\src\3rd_party\fbgemm\third_party\cpuinfo\src\x86\isa.c">
+      <TreatWarningAsError Condition="'$(Configuration)|$(Platform)'=='Release|x64'">false</TreatWarningAsError>
+      <TreatWarningAsError Condition="'$(Configuration)|$(Platform)'=='Debug|x64'">false</TreatWarningAsError>
+    </ClCompile>
+    <ClCompile Include="..\src\3rd_party\fbgemm\third_party\cpuinfo\src\x86\name.c">
+      <TreatWarningAsError Condition="'$(Configuration)|$(Platform)'=='Release|x64'">false</TreatWarningAsError>
+      <TreatWarningAsError Condition="'$(Configuration)|$(Platform)'=='Debug|x64'">false</TreatWarningAsError>
+    </ClCompile>
+    <ClCompile Include="..\src\3rd_party\fbgemm\third_party\cpuinfo\src\x86\topology.c">
+      <TreatWarningAsError Condition="'$(Configuration)|$(Platform)'=='Release|x64'">false</TreatWarningAsError>
+      <TreatWarningAsError Condition="'$(Configuration)|$(Platform)'=='Debug|x64'">false</TreatWarningAsError>
+    </ClCompile>
+    <ClCompile Include="..\src\3rd_party\fbgemm\third_party\cpuinfo\src\x86\uarch.c">
+      <TreatWarningAsError Condition="'$(Configuration)|$(Platform)'=='Release|x64'">false</TreatWarningAsError>
+      <TreatWarningAsError Condition="'$(Configuration)|$(Platform)'=='Debug|x64'">false</TreatWarningAsError>
+    </ClCompile>
+    <ClCompile Include="..\src\3rd_party\fbgemm\third_party\cpuinfo\src\x86\vendor.c">
+      <TreatWarningAsError Condition="'$(Configuration)|$(Platform)'=='Release|x64'">false</TreatWarningAsError>
+      <TreatWarningAsError Condition="'$(Configuration)|$(Platform)'=='Debug|x64'">false</TreatWarningAsError>
+    </ClCompile>
+    <ClCompile Include="..\src\3rd_party\fbgemm\third_party\cpuinfo\src\x86\windows\init.c">
+      <TreatWarningAsError Condition="'$(Configuration)|$(Platform)'=='Release|x64'">false</TreatWarningAsError>
+      <TreatWarningAsError Condition="'$(Configuration)|$(Platform)'=='Debug|x64'">false</TreatWarningAsError>
+    </ClCompile>
     <ClCompile Include="..\src\3rd_party\pathie-cpp\src\entry_iterator.cpp" />
     <ClCompile Include="..\src\3rd_party\pathie-cpp\src\errors.cpp" />
     <ClCompile Include="..\src\3rd_party\pathie-cpp\src\path.cpp" />
@@ -359,6 +649,66 @@
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     <ClInclude Include="..\src\3rd_party\fbgemm\include\fbgemm\UtilsAvx2.h" />
+    <ClInclude Include="..\src\3rd_party\fbgemm\src\ExecuteKernel.h" />
+    <ClInclude Include="..\src\3rd_party\fbgemm\src\ExecuteKernelGeneric.h" />
+    <ClInclude Include="..\src\3rd_party\fbgemm\src\ExecuteKernelU8S8.h" />
+    <ClInclude Include="..\src\3rd_party\fbgemm\src\FbgemmFP16UKernelsAvx2.h" />
+    <ClInclude Include="..\src\3rd_party\fbgemm\src\GenerateKernel.h" />
+    <ClInclude Include="..\src\3rd_party\fbgemm\src\GroupwiseConv.h" />
+    <ClInclude Include="..\src\3rd_party\fbgemm\src\OptimizedKernelsAvx2.h" />
+    <ClInclude Include="..\src\3rd_party\fbgemm\src\RefImplementations.h" />
+    <ClInclude Include="..\src\3rd_party\fbgemm\src\TransposeUtils.h" />
+    <ClInclude Include="..\src\3rd_party\fbgemm\third_party\asmjit\src\asmjit\arm.h" />
+    <ClInclude Include="..\src\3rd_party\fbgemm\third_party\asmjit\src\asmjit\asmjit.h" />
+    <ClInclude Include="..\src\3rd_party\fbgemm\third_party\asmjit\src\asmjit\asmjit_apibegin.h" />
+    <ClInclude Include="..\src\3rd_party\fbgemm\third_party\asmjit\src\asmjit\asmjit_apiend.h" />
+    <ClInclude Include="..\src\3rd_party\fbgemm\third_party\asmjit\src\asmjit\asmjit_build.h" />
+    <ClInclude Include="..\src\3rd_party\fbgemm\third_party\asmjit\src\asmjit\base.h" />
+    <ClInclude Include="..\src\3rd_party\fbgemm\third_party\asmjit\src\asmjit\base\arch.h" />
+    <ClInclude Include="..\src\3rd_party\fbgemm\third_party\asmjit\src\asmjit\base\assembler.h" />
+    <ClInclude Include="..\src\3rd_party\fbgemm\third_party\asmjit\src\asmjit\base\codebuilder.h" />
+    <ClInclude Include="..\src\3rd_party\fbgemm\third_party\asmjit\src\asmjit\base\codecompiler.h" />
+    <ClInclude Include="..\src\3rd_party\fbgemm\third_party\asmjit\src\asmjit\base\codeemitter.h" />
+    <ClInclude Include="..\src\3rd_party\fbgemm\third_party\asmjit\src\asmjit\base\codeholder.h" />
+    <ClInclude Include="..\src\3rd_party\fbgemm\third_party\asmjit\src\asmjit\base\constpool.h" />
+    <ClInclude Include="..\src\3rd_party\fbgemm\third_party\asmjit\src\asmjit\base\cpuinfo.h" />
+    <ClInclude Include="..\src\3rd_party\fbgemm\third_party\asmjit\src\asmjit\base\func.h" />
+    <ClInclude Include="..\src\3rd_party\fbgemm\third_party\asmjit\src\asmjit\base\globals.h" />
+    <ClInclude Include="..\src\3rd_party\fbgemm\third_party\asmjit\src\asmjit\base\inst.h" />
+    <ClInclude Include="..\src\3rd_party\fbgemm\third_party\asmjit\src\asmjit\base\logging.h" />
+    <ClInclude Include="..\src\3rd_party\fbgemm\third_party\asmjit\src\asmjit\base\misc_p.h" />
+    <ClInclude Include="..\src\3rd_party\fbgemm\third_party\asmjit\src\asmjit\base\operand.h" />
+    <ClInclude Include="..\src\3rd_party\fbgemm\third_party\asmjit\src\asmjit\base\osutils.h" />
+    <ClInclude Include="..\src\3rd_party\fbgemm\third_party\asmjit\src\asmjit\base\regalloc_p.h" />
+    <ClInclude Include="..\src\3rd_party\fbgemm\third_party\asmjit\src\asmjit\base\runtime.h" />
+    <ClInclude Include="..\src\3rd_party\fbgemm\third_party\asmjit\src\asmjit\base\simdtypes.h" />
+    <ClInclude Include="..\src\3rd_party\fbgemm\third_party\asmjit\src\asmjit\base\string.h" />
+    <ClInclude Include="..\src\3rd_party\fbgemm\third_party\asmjit\src\asmjit\base\utils.h" />
+    <ClInclude Include="..\src\3rd_party\fbgemm\third_party\asmjit\src\asmjit\base\vmem.h" />
+    <ClInclude Include="..\src\3rd_party\fbgemm\third_party\asmjit\src\asmjit\base\zone.h" />
+    <ClInclude Include="..\src\3rd_party\fbgemm\third_party\asmjit\src\asmjit\x86.h" />
+    <ClInclude Include="..\src\3rd_party\fbgemm\third_party\asmjit\src\asmjit\x86\x86assembler.h" />
+    <ClInclude Include="..\src\3rd_party\fbgemm\third_party\asmjit\src\asmjit\x86\x86builder.h" />
+    <ClInclude Include="..\src\3rd_party\fbgemm\third_party\asmjit\src\asmjit\x86\x86compiler.h" />
+    <ClInclude Include="..\src\3rd_party\fbgemm\third_party\asmjit\src\asmjit\x86\x86emitter.h" />
+    <ClInclude Include="..\src\3rd_party\fbgemm\third_party\asmjit\src\asmjit\x86\x86globals.h" />
+    <ClInclude Include="..\src\3rd_party\fbgemm\third_party\asmjit\src\asmjit\x86\x86inst.h" />
+    <ClInclude Include="..\src\3rd_party\fbgemm\third_party\asmjit\src\asmjit\x86\x86instimpl_p.h" />
+    <ClInclude Include="..\src\3rd_party\fbgemm\third_party\asmjit\src\asmjit\x86\x86internal_p.h" />
+    <ClInclude Include="..\src\3rd_party\fbgemm\third_party\asmjit\src\asmjit\x86\x86logging_p.h" />
+    <ClInclude Include="..\src\3rd_party\fbgemm\third_party\asmjit\src\asmjit\x86\x86misc.h" />
+    <ClInclude Include="..\src\3rd_party\fbgemm\third_party\asmjit\src\asmjit\x86\x86operand.h" />
+    <ClInclude Include="..\src\3rd_party\fbgemm\third_party\asmjit\src\asmjit\x86\x86regalloc_p.h" />
+    <ClInclude Include="..\src\3rd_party\fbgemm\third_party\cpuinfo\deps\clog\include\clog.h" />
+    <ClInclude Include="..\src\3rd_party\fbgemm\third_party\cpuinfo\include\cpuinfo-mock.h" />
+    <ClInclude Include="..\src\3rd_party\fbgemm\third_party\cpuinfo\include\cpuinfo.h" />
+    <ClInclude Include="..\src\3rd_party\fbgemm\third_party\cpuinfo\src\cpuinfo\common.h" />
+    <ClInclude Include="..\src\3rd_party\fbgemm\third_party\cpuinfo\src\cpuinfo\internal-api.h" />
+    <ClInclude Include="..\src\3rd_party\fbgemm\third_party\cpuinfo\src\cpuinfo\log.h" />
+    <ClInclude Include="..\src\3rd_party\fbgemm\third_party\cpuinfo\src\cpuinfo\utils.h" />
+    <ClInclude Include="..\src\3rd_party\fbgemm\third_party\cpuinfo\src\x86\api.h" />
+    <ClInclude Include="..\src\3rd_party\fbgemm\third_party\cpuinfo\src\x86\cpuid.h" />
+    <ClInclude Include="..\src\3rd_party\fbgemm\third_party\cpuinfo\src\x86\windows\api.h" />
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     <ClCompile Include="..\src\tensors\cpu\sharp\avx_gemm.cpp" />
     <ClCompile Include="..\src\tensors\cpu\sharp\int_gemm.cpp" />
-    <ClCompile Include="..\src\tensors\cpu\sharp\packed_gemm.cpp" />
+    <ClCompile Include="..\src\tensors\cpu\sharp\packed_gemm.cpp">
+      <TreatWarningAsError Condition="'$(Configuration)|$(Platform)'=='Release|x64'">false</TreatWarningAsError>
+      <TreatWarningAsError Condition="'$(Configuration)|$(Platform)'=='Debug|x64'">false</TreatWarningAsError>
+    </ClCompile>
     <ClCompile Include="..\src\tensors\cpu\sharp\sse_gemm.cpp" />
     <ClCompile Include="..\src\tensors\cpu\tensor_operators.cpp" />
     <ClCompile Include="..\src\graph\expression_graph.cpp" />
diff --git a/vs/Marian.vcxproj.filters b/vs/Marian.vcxproj.filters
index eaa6cfe25..23f5bdd4c 100755
--- a/vs/Marian.vcxproj.filters
+++ b/vs/Marian.vcxproj.filters
@@ -493,6 +493,222 @@
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       <Filter>tensors\cpu\sharp</Filter>
     </ClCompile>
+    <ClCompile Include="..\src\3rd_party\fbgemm\src\ExecuteKernel.cc">
+      <Filter>3rd_party\fbgemm\src</Filter>
+    </ClCompile>
+    <ClCompile Include="..\src\3rd_party\fbgemm\src\ExecuteKernelU8S8.cc">
+      <Filter>3rd_party\fbgemm\src</Filter>
+    </ClCompile>
+    <ClCompile Include="..\src\3rd_party\fbgemm\src\Fbgemm.cc">
+      <Filter>3rd_party\fbgemm\src</Filter>
+    </ClCompile>
+    <ClCompile Include="..\src\3rd_party\fbgemm\src\FbgemmConv.cc">
+      <Filter>3rd_party\fbgemm\src</Filter>
+    </ClCompile>
+    <ClCompile Include="..\src\3rd_party\fbgemm\src\FbgemmFP16.cc">
+      <Filter>3rd_party\fbgemm\src</Filter>
+    </ClCompile>
+    <ClCompile Include="..\src\3rd_party\fbgemm\src\FbgemmFP16UKernelsAvx2.cc">
+      <Filter>3rd_party\fbgemm\src</Filter>
+    </ClCompile>
+    <ClCompile Include="..\src\3rd_party\fbgemm\src\FbgemmI8DepthwiseAvx2.cc">
+      <Filter>3rd_party\fbgemm\src</Filter>
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+      <Filter>3rd_party\fbgemm\src</Filter>
+    </ClCompile>
+    <ClCompile Include="..\src\3rd_party\fbgemm\src\GenerateKernelU8S8S32ACC16.cc">
+      <Filter>3rd_party\fbgemm\src</Filter>
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+    <ClCompile Include="..\src\3rd_party\fbgemm\src\GenerateKernelU8S8S32ACC16Avx512.cc">
+      <Filter>3rd_party\fbgemm\src</Filter>
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+    <ClCompile Include="..\src\3rd_party\fbgemm\src\GenerateKernelU8S8S32ACC32.cc">
+      <Filter>3rd_party\fbgemm\src</Filter>
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+      <Filter>3rd_party\fbgemm\src</Filter>
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+      <Filter>3rd_party\fbgemm\src</Filter>
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+      <Filter>3rd_party\fbgemm\src</Filter>
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+      <Filter>3rd_party\fbgemm\src</Filter>
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+      <Filter>3rd_party\fbgemm\src</Filter>
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+      <Filter>3rd_party\fbgemm\src</Filter>
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+      <Filter>3rd_party\fbgemm\src</Filter>
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+      <Filter>3rd_party\fbgemm\src</Filter>
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+      <Filter>3rd_party\fbgemm\src</Filter>
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+      <Filter>3rd_party\fbgemm\src</Filter>
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+      <Filter>3rd_party\fbgemm\src</Filter>
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+      <Filter>3rd_party\fbgemm\src</Filter>
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+      <Filter>3rd_party\fbgemm\src</Filter>
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From 15cf6ba979e61086b4c8ee45a37249898bd44690 Mon Sep 17 00:00:00 2001
From: Young Jin Kim <youki@microsoft.com>
Date: Fri, 21 Jun 2019 10:44:25 -0700
Subject: [PATCH 510/838] fix a compile error in FBGEMM

---
 src/3rd_party/fbgemm | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/3rd_party/fbgemm b/src/3rd_party/fbgemm
index c4269a772..bc33ed947 160000
--- a/src/3rd_party/fbgemm
+++ b/src/3rd_party/fbgemm
@@ -1 +1 @@
-Subproject commit c4269a772d2a1124cadbfe5bf5876c7cf2c1d256
+Subproject commit bc33ed9474b4f944dc04a579c25919778eceb9d5

From 2dedcf811403a466f8a5d13b4ef3efe9d93199c1 Mon Sep 17 00:00:00 2001
From: Nikolay Bogoychev <nheart@gmail.com>
Date: Mon, 24 Jun 2019 17:46:31 +0100
Subject: [PATCH 511/838] Fix compilation on gcc 9

---
 src/common/filesystem.h | 3 ++-
 src/common/shape.h      | 2 ++
 2 files changed, 4 insertions(+), 1 deletion(-)

diff --git a/src/common/filesystem.h b/src/common/filesystem.h
index 08d5995f1..6d4616e3f 100644
--- a/src/common/filesystem.h
+++ b/src/common/filesystem.h
@@ -29,6 +29,7 @@ namespace filesystem {
     public:
       Path() {}
       Path(const Path& p) : path{p.path} {}
+      Path& operator=(const Path& p) = default;
       Path(const std::string& s) : path{s} {}
       Path(const Pathie::Path& p) : path{p} {}
 
@@ -97,4 +98,4 @@ namespace filesystem {
   using FilesystemError = Pathie::PathieError;
 
 }
-}
\ No newline at end of file
+}
diff --git a/src/common/shape.h b/src/common/shape.h
index c8a4bdd3d..9199adc4c 100644
--- a/src/common/shape.h
+++ b/src/common/shape.h
@@ -47,6 +47,8 @@ struct Shape {
     std::copy(shape.begin(), shape.end(), begin());
   }
 
+  Shape& operator=(const Shape& p) = default;
+
   inline size_t size() const { return shape_.size(); }
 
   void resize(size_t n) { shape_.resize(n, 1); }

From 457eef6f99d4268cc48f14e010027cd19044959f Mon Sep 17 00:00:00 2001
From: Young Jin Kim <youki@microsoft.com>
Date: Tue, 25 Jun 2019 15:37:18 -0700
Subject: [PATCH 512/838] Add thread_local to the thread specific variable. Add
 Gemm type check for the dot product.

---
 src/graph/expression_operators.cpp    | 3 ++-
 src/tensors/cpu/sharp/packed_gemm.cpp | 2 +-
 2 files changed, 3 insertions(+), 2 deletions(-)

diff --git a/src/graph/expression_operators.cpp b/src/graph/expression_operators.cpp
index fac864725..60296c87a 100755
--- a/src/graph/expression_operators.cpp
+++ b/src/graph/expression_operators.cpp
@@ -387,7 +387,8 @@ Expr dot(Expr a, Expr b, bool transA, bool transB, float scale) {
 
   // Currently only true when command line options
   // --optimize --cpu-thread=N with N > 0 are set.
-  if(a->graph()->isOptimized() && device == DeviceType::cpu) {
+  if(a->graph()->isOptimized() && device == DeviceType::cpu
+    && a->graph()->getGemmType() == GemmType::Int16) {
     // dotInt16 computes A * B.T, hence the transpose for B to get A * B
     // if transA = false and transB = false.
 
diff --git a/src/tensors/cpu/sharp/packed_gemm.cpp b/src/tensors/cpu/sharp/packed_gemm.cpp
index c90956071..9fa3b94c2 100644
--- a/src/tensors/cpu/sharp/packed_gemm.cpp
+++ b/src/tensors/cpu/sharp/packed_gemm.cpp
@@ -38,7 +38,7 @@ namespace variant { // Variants of GEMM implementations
 
 #if USE_FBGEMM
 // initialize with a dummy
-static PackedGemmMatrixFP16 packedPlaceholder(1, 1, 1, 1, 1, 1, 1, 1);
+static thread_local PackedGemmMatrixFP16 packedPlaceholder(1, 1, 1, 1, 1, 1, 1, 1);
 
 // This is copied from FBGEMM code
 // A better way?

From 10d126254f21da48856669865e7d2ebcf65abf3a Mon Sep 17 00:00:00 2001
From: Young Jin Kim <youki@microsoft.com>
Date: Tue, 25 Jun 2019 16:48:35 -0700
Subject: [PATCH 513/838] Move optimized and Gemm type optiions to the backend
 from the graph

---
 src/command/marian_conv.cpp        |  3 ++-
 src/graph/expression_graph.cpp     | 11 ++---------
 src/graph/expression_graph.h       |  9 +--------
 src/graph/expression_operators.cpp |  8 ++++----
 src/microsoft/quicksand.cpp        |  7 +++++--
 src/rescorer/rescorer.h            |  5 +++--
 src/tensors/backend.h              | 11 ++++++++---
 src/tensors/cpu/backend.h          | 18 ++++++++++++++++++
 src/tensors/gpu/backend.h          | 20 +++++++++++++++++---
 src/tests/prod.cpp                 |  7 +++++--
 src/translator/translator.h        |  9 ++++++---
 11 files changed, 71 insertions(+), 37 deletions(-)

diff --git a/src/command/marian_conv.cpp b/src/command/marian_conv.cpp
index 23350311e..aeda1e3ca 100644
--- a/src/command/marian_conv.cpp
+++ b/src/command/marian_conv.cpp
@@ -31,8 +31,9 @@ int main(int argc, char** argv) {
   marian::io::getYamlFromModel(config, "special:model.yml", modelFrom);
   configStr << config;
 
-  auto graph = New<ExpressionGraph>(true, false);
+  auto graph = New<ExpressionGraph>(true);
   graph->setDevice(CPU0);
+  graph->getBackend()->setOptimized(false);
 
   graph->load(modelFrom);
   graph->forward();
diff --git a/src/graph/expression_graph.cpp b/src/graph/expression_graph.cpp
index 7a0c2b698..bf1192351 100755
--- a/src/graph/expression_graph.cpp
+++ b/src/graph/expression_graph.cpp
@@ -5,15 +5,8 @@
 
 namespace marian {
 
-ExpressionGraph::ExpressionGraph(bool inference, bool optimized, std::string gemmType)
-    : inferenceOnly_(inference), optimized_(optimized), backend_(nullptr) {
-  if (gemmType == "auto") gemmType_ = GemmType::Auto;
-  else if (gemmType == "mkl") gemmType_ = GemmType::Mkl;
-  else if (gemmType == "int16") gemmType_ = GemmType::Int16;
-  else if (gemmType == "packed") gemmType_ = GemmType::PackedFb;
-  else if (gemmType == "int8") gemmType_ = GemmType::Int8Fb;
-  else ABORT("Unknow GEMM type");
-}
+ExpressionGraph::ExpressionGraph(bool inference)
+    : inferenceOnly_(inference), backend_(nullptr) {}
 
 void ExpressionGraph::setDevice(DeviceId deviceId, Ptr<Device> device) {
   if(!backend_) {
diff --git a/src/graph/expression_graph.h b/src/graph/expression_graph.h
index 51e468e54..563dc4a11 100755
--- a/src/graph/expression_graph.h
+++ b/src/graph/expression_graph.h
@@ -1,7 +1,6 @@
 #pragma once
 
 #include "common/config.h"
-#include "common/definitions.h"
 
 #include "tensors/backend.h"
 #include "tensors/tensor_allocator.h"
@@ -130,14 +129,12 @@ class ExpressionGraph : public std::enable_shared_from_this<ExpressionGraph> {
   std::unordered_map<size_t, std::vector<Expr>> memoized_;
 
   bool inferenceOnly_{false};
-  bool optimized_{false};
   Ptr<Backend> backend_;
 
   bool reloaded_{false};
   std::string namespace_;
 
   bool throwNaN_{false};
-  GemmType gemmType_;
 
 protected:
   // Delete, copy and move constructors
@@ -149,7 +146,7 @@ class ExpressionGraph : public std::enable_shared_from_this<ExpressionGraph> {
    *
    * Constructor should be used as New<ExpressionGraph>()
    */
-  ExpressionGraph(bool inference = false, bool optimized = false, std::string gemmType = "auto");
+  ExpressionGraph(bool inference = false);
 
   void setInference(bool inference) { inferenceOnly_ = inference; }
   bool isInference() { return inferenceOnly_; }
@@ -166,10 +163,6 @@ class ExpressionGraph : public std::enable_shared_from_this<ExpressionGraph> {
 
   Ptr<Backend> getBackend() { return backend_; }
 
-  void setOptimized(bool optimized) { optimized_ = optimized; }
-  bool isOptimized() { return (optimized_ && inferenceOnly_); }
-  GemmType getGemmType() { return gemmType_; } 
-
   void switchParams(const std::string& newNamespace) {
     namespace_ = newNamespace;
   }
diff --git a/src/graph/expression_operators.cpp b/src/graph/expression_operators.cpp
index 60296c87a..ae1e4b9f5 100755
--- a/src/graph/expression_operators.cpp
+++ b/src/graph/expression_operators.cpp
@@ -387,8 +387,8 @@ Expr dot(Expr a, Expr b, bool transA, bool transB, float scale) {
 
   // Currently only true when command line options
   // --optimize --cpu-thread=N with N > 0 are set.
-  if(a->graph()->isOptimized() && device == DeviceType::cpu
-    && a->graph()->getGemmType() == GemmType::Int16) {
+  if(a->graph()->getBackend()->isOptimized() && device == DeviceType::cpu
+    && a->graph()->getBackend()->getGemmType() == GemmType::Int16) {
     // dotInt16 computes A * B.T, hence the transpose for B to get A * B
     // if transA = false and transB = false.
 
@@ -411,8 +411,8 @@ Expr affine(Expr a, Expr b, Expr bias, bool transA, bool transB, float scale) {
 
   float clipValue = a->graph()->getBackend()->getClip();
 
-  if(a->graph()->isOptimized() && device == DeviceType::cpu) {
-    GemmType gemmType = a->graph()->getGemmType();
+  if(a->graph()->getBackend()->isOptimized() && device == DeviceType::cpu) {
+    GemmType gemmType = a->graph()->getBackend()->getGemmType();
     if(gemmType == GemmType::Auto) {
       thread_local Ptr<AutoTuner<Expr>> tuner = New<AutoTuner<Expr>>();
 
diff --git a/src/microsoft/quicksand.cpp b/src/microsoft/quicksand.cpp
index 9456c02e8..5809c18c0 100644
--- a/src/microsoft/quicksand.cpp
+++ b/src/microsoft/quicksand.cpp
@@ -64,13 +64,16 @@ class BeamSearchDecoder : public IBeamSearchDecoder {
       vocabs_.push_back(std::dynamic_pointer_cast<VocabWrapper>(vi)->getVocab());
 
     // setting 16-bit optimization to false for now. Re-enable with better caching or pre-computation
-    graph_ = New<ExpressionGraph>(/*inference=*/true, /*optimize=*/true, 
-                                  /*gemmType=*/"packed");
+    graph_ = New<ExpressionGraph>(/*inference=*/true);
 
     DeviceId deviceId{0, DeviceType::cpu};
     device_ = New<cpu::WrappedDevice>(deviceId);
     graph_->setDevice(deviceId, device_);
 
+    // Use packed GEMM for the production
+    graph_->getBackend()->setOptimized(true);
+    graph_->getBackend()->setGemmType("packed");
+
 #ifdef MKL_FOUND
     mkl_set_num_threads(options->get<int>("mkl-threads", 1));
 #endif
diff --git a/src/rescorer/rescorer.h b/src/rescorer/rescorer.h
index b9f9f116e..c4832078a 100755
--- a/src/rescorer/rescorer.h
+++ b/src/rescorer/rescorer.h
@@ -67,9 +67,10 @@ class Rescore : public ModelTask {
     auto devices = Config::getDevices(options_);
 
     for(auto device : devices) {
-      auto graph = New<ExpressionGraph>(true, options_->get<bool>("optimize"), 
-                                        options_->get<std::string>("gemm-type"));
+      auto graph = New<ExpressionGraph>(true);
       graph->setDevice(device);
+      graph->getBackend()->setOptimized(options_->get<bool>("optimize"));
+      graph->getBackend()->setGemmType(options_->get<std::string>("gemm-type"));
       graph->reserveWorkspaceMB(options_->get<size_t>("workspace"));
       graphs_.push_back(graph);
     }
diff --git a/src/tensors/backend.h b/src/tensors/backend.h
index 36e537dca..38e2317bc 100644
--- a/src/tensors/backend.h
+++ b/src/tensors/backend.h
@@ -16,9 +16,7 @@ class Backend {
 
 public:
   Backend(DeviceId deviceId, size_t seed)
-  : deviceId_(deviceId),
-    seed_(seed),
-    randomGenerator_(createRandomGenerator(seed, deviceId)) {}
+      : deviceId_(deviceId), seed_(seed), randomGenerator_(createRandomGenerator(seed, deviceId)) {}
 
   virtual DeviceId getDeviceId() { return deviceId_; };
   virtual Ptr<RandomGenerator> getRandomGenerator() { return randomGenerator_; }
@@ -29,6 +27,13 @@ class Backend {
 
   virtual void setClip(float clipValue) { clipValue_ = clipValue; }
   float getClip() { return clipValue_; }
+
+  // for CPU & inference only, sets to use optimized code for inference. Does nothing for GPU.
+  virtual void setOptimized(bool optimize) = 0;
+  virtual bool isOptimized() = 0;
+  // for CPU only, selects different GEMM types for the inference. Does nothing for GPU.
+  virtual void setGemmType(std::string gemmType) = 0;
+  virtual GemmType getGemmType() = 0;
 };
 
 Ptr<Backend> BackendByDeviceId(DeviceId deviceId, size_t seed);
diff --git a/src/tensors/cpu/backend.h b/src/tensors/cpu/backend.h
index 7264902e0..6441a2e15 100644
--- a/src/tensors/cpu/backend.h
+++ b/src/tensors/cpu/backend.h
@@ -10,10 +10,28 @@ namespace marian {
 namespace cpu {
 
 class Backend : public marian::Backend {
+protected:
+  bool optimized_{false};
+  GemmType gemmType_{GemmType::Auto};
+
 public:
   Backend(DeviceId deviceId, size_t seed) : marian::Backend(deviceId, seed) {}
   void setDevice() override {}
   void synchronize() override {}
+
+  // for CPU & inference only, sets to use optimized code for inference. Does nothing for GPU.
+  void setOptimized(bool optimize) { optimized_ = optimize; }
+  bool isOptimized() override { return optimized_; }
+  // for CPU only, selects different GEMM types for the inference. Does nothing for GPU.
+  void setGemmType(std::string gemmType) override {
+    if(gemmType == "auto") gemmType_ = GemmType::Auto;
+    else if(gemmType == "mkl") gemmType_ = GemmType::Mkl;
+    else if(gemmType == "int16") gemmType_ = GemmType::Int16;
+    else if(gemmType == "packed") gemmType_ = GemmType::PackedFb;
+    else if(gemmType == "int8") gemmType_ = GemmType::Int8Fb;
+    else ABORT("Unknow GEMM type");
+  }
+  GemmType getGemmType() override { return gemmType_; }
 };
 }  // namespace cpu
 }  // namespace marian
diff --git a/src/tensors/gpu/backend.h b/src/tensors/gpu/backend.h
index 87f6407c5..8e69d6d7d 100644
--- a/src/tensors/gpu/backend.h
+++ b/src/tensors/gpu/backend.h
@@ -1,13 +1,14 @@
 #pragma once
 
 #include "common/config.h"
-#include "tensors/backend.h" // note: this is one folder up
+#include "tensors/backend.h"  // note: this is one folder up
 #include "tensors/gpu/cuda_helpers.h"
+#include "common/logging.h"
 
-#include <cuda.h>
 #include <cublas_v2.h>
-#include <cusparse.h>
+#include <cuda.h>
 #include <curand.h>
+#include <cusparse.h>
 
 namespace marian {
 namespace gpu {
@@ -33,6 +34,19 @@ class Backend : public marian::Backend {
   cublasHandle_t getCublasHandle() { return cublasHandle_; }
   cusparseHandle_t getCusparseHandle() { return cusparseHandle_; }
 
+  // for CPU & inference only, sets to use optimized code for inference. Does nothing for GPU.
+  void setOptimized(bool optimize) override { ABORT("Not supported for GPU_{}", optimize); }
+  bool isOptimized() override {
+    ABORT("Not supported for GPU");
+    return false;
+  }
+  // for CPU only, selects different GEMM types for the inference. Does nothing for GPU.
+  void setGemmType(std::string gemmType) override { ABORT("Not supported for GPU_{}", gemmType); }
+  GemmType getGemmType() override {
+    ABORT("Not supported for GPU");
+    return GemmType::Auto;
+  }
+
 private:
   cublasHandle_t cublasHandle_;
   cusparseHandle_t cusparseHandle_;
diff --git a/src/tests/prod.cpp b/src/tests/prod.cpp
index c329d403e..49b683039 100644
--- a/src/tests/prod.cpp
+++ b/src/tests/prod.cpp
@@ -5,8 +5,9 @@ int main(int argc, char** argv) {
     using namespace marian;
 
     {
-        auto g = New<ExpressionGraph>(true, false);
+        auto g = New<ExpressionGraph>(true);
         g->setDevice({0, DeviceType::cpu});
+        g->getBackend()->setOptimized(false);
         g->reserveWorkspaceMB(2512);
 
         timer::AutoTimer timer;
@@ -37,8 +38,10 @@ int main(int argc, char** argv) {
     }
 
     {
-        auto g = New<ExpressionGraph>(true, true);
+        auto g = New<ExpressionGraph>(true);
         g->setDevice({0, DeviceType::cpu});
+        g->getBackend()->setOptimized(true);
+        g->getBackend()->setGemmType("auto");
         g->reserveWorkspaceMB(2512);
 
         timer::AutoTimer timer;
diff --git a/src/translator/translator.h b/src/translator/translator.h
index 7899957cf..ee08a2304 100644
--- a/src/translator/translator.h
+++ b/src/translator/translator.h
@@ -55,10 +55,11 @@ class Translate : public ModelTask {
     size_t id = 0;
     for(auto device : devices) {
       auto task = [&](DeviceId device, size_t id) {
-        auto graph = New<ExpressionGraph>(true, options_->get<bool>("optimize"), 
-                                          options_->get<std::string>("gemm-type"));
+        auto graph = New<ExpressionGraph>(true);
         graph->setDevice(device);
         graph->getBackend()->setClip(options_->get<float>("clip-gemm"));
+        graph->getBackend()->setOptimized(options_->get<bool>("optimize"));
+        graph->getBackend()->setGemmType(options_->get<std::string>("gemm-type"));
         graph->reserveWorkspaceMB(options_->get<size_t>("workspace"));
         graphs_[id] = graph;
 
@@ -168,9 +169,11 @@ class TranslateService : public ModelServiceTask {
 
     // initialize scorers
     for(auto device : devices) {
-      auto graph = New<ExpressionGraph>(true, options_->get<bool>("optimize"), options_->get<std::string>("gemm-type"));
+      auto graph = New<ExpressionGraph>(true);
       graph->setDevice(device);
       graph->getBackend()->setClip(options_->get<float>("clip-gemm"));
+      graph->getBackend()->setOptimized(options_->get<bool>("optimize"));
+      graph->getBackend()->setGemmType(options_->get<std::string>("gemm-type"));
       graph->reserveWorkspaceMB(options_->get<size_t>("workspace"));
       graphs_.push_back(graph);
 

From e176508a6be21ff65769dd4a50acffb77e89be3f Mon Sep 17 00:00:00 2001
From: Young Jin Kim <youki@microsoft.com>
Date: Tue, 25 Jun 2019 17:56:35 -0700
Subject: [PATCH 514/838] Move GEMM type enum to backend. And, change the names
 more descriptive

---
 src/common/definitions.h              |  8 -----
 src/graph/expression_operators.cpp    |  8 ++---
 src/tensors/backend.h                 |  8 +++++
 src/tensors/cpu/backend.h             | 10 +++---
 src/tensors/cpu/sharp/int_gemm.cpp    | 11 +++----
 src/tensors/cpu/sharp/packed_gemm.cpp | 44 ---------------------------
 src/tensors/cpu/sharp/packed_gemm.h   |  2 --
 7 files changed, 21 insertions(+), 70 deletions(-)

diff --git a/src/common/definitions.h b/src/common/definitions.h
index a0fc8b258..ea14a8e5c 100755
--- a/src/common/definitions.h
+++ b/src/common/definitions.h
@@ -117,12 +117,4 @@ typedef Ptr<RunBase> RunBasePtr;
 
 const float NEMATUS_LN_EPS = 1e-5f;
 
-/* GEMM type enum */
-typedef enum { Auto     = 0,
-               Mkl      = 1,
-               Int16    = 2,
-               PackedFb = 10,
-               Int8Fb   = 11
-} GemmType;
-
 }  // namespace marian
diff --git a/src/graph/expression_operators.cpp b/src/graph/expression_operators.cpp
index ae1e4b9f5..01b262a90 100755
--- a/src/graph/expression_operators.cpp
+++ b/src/graph/expression_operators.cpp
@@ -388,7 +388,7 @@ Expr dot(Expr a, Expr b, bool transA, bool transB, float scale) {
   // Currently only true when command line options
   // --optimize --cpu-thread=N with N > 0 are set.
   if(a->graph()->getBackend()->isOptimized() && device == DeviceType::cpu
-    && a->graph()->getBackend()->getGemmType() == GemmType::Int16) {
+     && a->graph()->getBackend()->getGemmType() == GemmType::IntrinsicInt16) {
     // dotInt16 computes A * B.T, hence the transpose for B to get A * B
     // if transA = false and transB = false.
 
@@ -509,14 +509,14 @@ Expr affine(Expr a, Expr b, Expr bias, bool transA, bool transB, float scale) {
       return tuner->run();
 
     } else {
-      if (gemmType == GemmType::Int16) {
+      if(gemmType == GemmType::IntrinsicInt16) {
         // cpu int16 version
         return cpu::int16::affine(
             cpu::int16::quantize(transA ? transpose(a) : a, clipValue),
             cpu::int16::quantize(transB ? b : transpose(b), clipValue),
             bias,
             scale);
-      } else if (gemmType == GemmType::PackedFb) {
+      } else if(gemmType == GemmType::FbFp16Packed) {
 #if USE_FBGEMM
         if(b->memoize()) {
           auto packed = cpu::variant::pack(b, cpu::variant::PackMatrix::B, transB, clipValue);
@@ -547,7 +547,7 @@ Expr affine(Expr a, Expr b, Expr bias, bool transA, bool transB, float scale) {
         ABORT("Packed GEMM not implemented");
 #endif  // USE_FBGEMM
 
-      } else if (gemmType == GemmType::Mkl) {
+      } else if(gemmType == GemmType::MklFp32) {
         // general version, MKL, CBlas or CUDA
 
         // if clipValue > 0, the inputs will be clipped to range [-clipValue,
diff --git a/src/tensors/backend.h b/src/tensors/backend.h
index 38e2317bc..d814bcc03 100644
--- a/src/tensors/backend.h
+++ b/src/tensors/backend.h
@@ -5,6 +5,14 @@
 
 namespace marian {
 
+// GEMM type enum
+typedef enum { Auto = 0,            // auto tuning between available GEMMs
+               MklFp32 = 1,         // MKL based GEMM, fp32
+               IntrinsicInt16 = 2,  // Intrinsic implementation of Int 16 GEMM
+               FbFp16Packed = 10,   // FBGEMM based fp16 GEMM with packing
+               FbInt8Packed = 11    // FBGEMM based int8 GEMM with packing
+} GemmType;
+
 class Backend {
 protected:
   DeviceId deviceId_;
diff --git a/src/tensors/cpu/backend.h b/src/tensors/cpu/backend.h
index 6441a2e15..5c791afe9 100644
--- a/src/tensors/cpu/backend.h
+++ b/src/tensors/cpu/backend.h
@@ -24,11 +24,11 @@ class Backend : public marian::Backend {
   bool isOptimized() override { return optimized_; }
   // for CPU only, selects different GEMM types for the inference. Does nothing for GPU.
   void setGemmType(std::string gemmType) override {
-    if(gemmType == "auto") gemmType_ = GemmType::Auto;
-    else if(gemmType == "mkl") gemmType_ = GemmType::Mkl;
-    else if(gemmType == "int16") gemmType_ = GemmType::Int16;
-    else if(gemmType == "packed") gemmType_ = GemmType::PackedFb;
-    else if(gemmType == "int8") gemmType_ = GemmType::Int8Fb;
+    if      (gemmType == "auto") gemmType_ = GemmType::Auto;
+    else if (gemmType == "mkl") gemmType_ = GemmType::MklFp32;
+    else if (gemmType == "int16") gemmType_ = GemmType::IntrinsicInt16;
+    else if (gemmType == "packed") gemmType_ = GemmType::FbFp16Packed;
+    else if (gemmType == "int8") gemmType_ = GemmType::FbInt8Packed;
     else ABORT("Unknow GEMM type");
   }
   GemmType getGemmType() override { return gemmType_; }
diff --git a/src/tensors/cpu/sharp/int_gemm.cpp b/src/tensors/cpu/sharp/int_gemm.cpp
index e04446bc4..cdb7cbf07 100644
--- a/src/tensors/cpu/sharp/int_gemm.cpp
+++ b/src/tensors/cpu/sharp/int_gemm.cpp
@@ -89,17 +89,13 @@ void AddBias(marian::Tensor C, const marian::Tensor Bias) {
   const float* x = C->data();
   const float* bias = Bias->data();
 
-  int m = C->shape().elements() / C->shape()[-1];
-  int n = C->shape()[-1];
-#ifdef __AVX512F__
-  int n16 = n & ~15;
-#else
-  int n4 = (n / 4) * 4;
-#endif
+  const int m = C->shape().elements() / C->shape()[-1];
+  const int n = C->shape()[-1];
 
   for(int j = 0; j < m; ++j) {
     int i = 0;
 #ifdef __AVX512F__
+    int n16 = n & ~15;
     for(; i < n16; i += 16) {
       __m512 ai = _mm512_loadu_ps(x + j * n + i);
       __m512 bi = _mm512_loadu_ps(bias + i);
@@ -107,6 +103,7 @@ void AddBias(marian::Tensor C, const marian::Tensor Bias) {
       _mm512_storeu_ps(y + j * n + i, yi);
     }
 #else
+    int n4 = (n / 4) * 4;
     for(; i < n4; i += 4) {
       __m128 ai = _mm_loadu_ps(x + j * n + i);
       __m128 bi = _mm_loadu_ps(bias + i);
diff --git a/src/tensors/cpu/sharp/packed_gemm.cpp b/src/tensors/cpu/sharp/packed_gemm.cpp
index 9fa3b94c2..fcca0206a 100644
--- a/src/tensors/cpu/sharp/packed_gemm.cpp
+++ b/src/tensors/cpu/sharp/packed_gemm.cpp
@@ -215,50 +215,6 @@ void GemmPackFp32(marian::Tensor C,
 }
 #endif // USE_FBGEMM
 
-// This operates on floats after processing so doesn't care about int8_t vs
-// int16_t.
-void AddBias(marian::Tensor C, const marian::Tensor Bias) {
-  float* y = C->data();
-  const float* x = C->data();
-  const float* bias = Bias->data();
-
-  int m = C->shape().elements() / C->shape()[-1];
-  int n = C->shape()[-1];
-#ifdef __AVX512F__
-  int n16 = n & ~15;
-#else
-  int n4 = (n / 4) * 4;
-#endif
-
-  for(int j = 0; j < m; ++j) {
-    int i = 0;
-#ifdef __AVX512F__
-    for(; i < n16; i += 16) {
-      __m512 ai = _mm512_loadu_ps(x + j * n + i);
-      __m512 bi = _mm512_loadu_ps(bias + i);
-      __m512 yi = _mm512_add_ps(ai, bi);
-      _mm512_storeu_ps(y + j * n + i, yi);
-    }
-#else
-    for(; i < n4; i += 4) {
-      __m128 ai = _mm_loadu_ps(x + j * n + i);
-      __m128 bi = _mm_loadu_ps(bias + i);
-      __m128 yi = _mm_add_ps(ai, bi);
-      _mm_storeu_ps(y + j * n + i, yi);
-    }
-#endif
-    for(; i < n; i++) {
-      y[j * n + i] = x[j * n + i] + bias[i];
-    }
-  }
-
-  // std::cout << "Output: " << std::endl;
-  // for (int ii = 0; ii < n; ii++) {
-  //   std::cout << y[ii] << ",";
-  // }
-  // std::cout << std::endl;
-}
-
 }  // namespace variant
 }  // namespace cpu
 }  // namespace marian
diff --git a/src/tensors/cpu/sharp/packed_gemm.h b/src/tensors/cpu/sharp/packed_gemm.h
index 87c1a75f8..433b731e9 100644
--- a/src/tensors/cpu/sharp/packed_gemm.h
+++ b/src/tensors/cpu/sharp/packed_gemm.h
@@ -27,8 +27,6 @@ void GemmPackFp32(marian::Tensor C,
                   const int64_t n,
                   const int transA = 0);
 
-void AddBias(marian::Tensor C, const marian::Tensor Bias);
-
 }  // namespace variant
 }  // namespace cpu
 }  // namespace marian

From 2672b085d700887d3db0bbbdfe6ee2e86e7b39e9 Mon Sep 17 00:00:00 2001
From: Young Jin Kim <youki@microsoft.com>
Date: Tue, 25 Jun 2019 21:11:20 -0700
Subject: [PATCH 515/838] Cleaning the code.

---
 src/common/config_parser.cpp       |  2 +-
 src/graph/expression_operators.cpp | 10 ++--------
 src/tensors/backend.h              |  2 +-
 src/tensors/cpu/backend.h          | 12 ++++++------
 4 files changed, 10 insertions(+), 16 deletions(-)

diff --git a/src/common/config_parser.cpp b/src/common/config_parser.cpp
index 7d706c863..bd6650c61 100755
--- a/src/common/config_parser.cpp
+++ b/src/common/config_parser.cpp
@@ -526,7 +526,7 @@ void ConfigParser::addOptionsTranslation(cli::CLIWrapper& cli) {
   cli.add<bool>("--skip-cost",
       "Ignore model cost during translation, not recommended for beam-size > 1");
   cli.add<std::string>("--gemm-type",
-      "Select GEMM options: auto, mkl, int16, packed, int8",
+      "Select GEMM options: auto, mklfp32, intrinint16, fp16packed, int8packed",
       "auto");
 
   cli.add<std::vector<std::string>>("--shortlist",
diff --git a/src/graph/expression_operators.cpp b/src/graph/expression_operators.cpp
index 01b262a90..56fb654a5 100755
--- a/src/graph/expression_operators.cpp
+++ b/src/graph/expression_operators.cpp
@@ -388,7 +388,7 @@ Expr dot(Expr a, Expr b, bool transA, bool transB, float scale) {
   // Currently only true when command line options
   // --optimize --cpu-thread=N with N > 0 are set.
   if(a->graph()->getBackend()->isOptimized() && device == DeviceType::cpu
-     && a->graph()->getBackend()->getGemmType() == GemmType::IntrinsicInt16) {
+     && a->graph()->getBackend()->getGemmType() == GemmType::IntrinInt16) {
     // dotInt16 computes A * B.T, hence the transpose for B to get A * B
     // if transA = false and transB = false.
 
@@ -509,7 +509,7 @@ Expr affine(Expr a, Expr b, Expr bias, bool transA, bool transB, float scale) {
       return tuner->run();
 
     } else {
-      if(gemmType == GemmType::IntrinsicInt16) {
+      if(gemmType == GemmType::IntrinInt16) {
         // cpu int16 version
         return cpu::int16::affine(
             cpu::int16::quantize(transA ? transpose(a) : a, clipValue),
@@ -531,12 +531,6 @@ Expr affine(Expr a, Expr b, Expr bias, bool transA, bool transB, float scale) {
               transA,
               transB,
               scale);
-          // cpu int16 version
-          // return cpu::int16::affine(
-          //     cpu::int16::quantize(transA ? transpose(a) : a, clipValue),
-          //     cpu::int16::quantize(transB ? b : transpose(b), clipValue),
-          //     bias,
-          //     scale);
         } else {
           int rows = a->shape().elements() / a->shape()[-1];
           Expr ones = a->graph()->ones({rows, 1});
diff --git a/src/tensors/backend.h b/src/tensors/backend.h
index d814bcc03..b664d6039 100644
--- a/src/tensors/backend.h
+++ b/src/tensors/backend.h
@@ -8,7 +8,7 @@ namespace marian {
 // GEMM type enum
 typedef enum { Auto = 0,            // auto tuning between available GEMMs
                MklFp32 = 1,         // MKL based GEMM, fp32
-               IntrinsicInt16 = 2,  // Intrinsic implementation of Int 16 GEMM
+               IntrinInt16 = 2,  // Intrinsic implementation of Int 16 GEMM
                FbFp16Packed = 10,   // FBGEMM based fp16 GEMM with packing
                FbInt8Packed = 11    // FBGEMM based int8 GEMM with packing
 } GemmType;
diff --git a/src/tensors/cpu/backend.h b/src/tensors/cpu/backend.h
index 5c791afe9..094e9331e 100644
--- a/src/tensors/cpu/backend.h
+++ b/src/tensors/cpu/backend.h
@@ -24,12 +24,12 @@ class Backend : public marian::Backend {
   bool isOptimized() override { return optimized_; }
   // for CPU only, selects different GEMM types for the inference. Does nothing for GPU.
   void setGemmType(std::string gemmType) override {
-    if      (gemmType == "auto") gemmType_ = GemmType::Auto;
-    else if (gemmType == "mkl") gemmType_ = GemmType::MklFp32;
-    else if (gemmType == "int16") gemmType_ = GemmType::IntrinsicInt16;
-    else if (gemmType == "packed") gemmType_ = GemmType::FbFp16Packed;
-    else if (gemmType == "int8") gemmType_ = GemmType::FbInt8Packed;
-    else ABORT("Unknow GEMM type");
+    if      (gemmType == "auto")        gemmType_ = GemmType::Auto;
+    else if (gemmType == "mklfp32")     gemmType_ = GemmType::MklFp32;
+    else if (gemmType == "intrinint16") gemmType_ = GemmType::IntrinInt16;
+    else if (gemmType == "fp16packed")  gemmType_ = GemmType::FbFp16Packed;
+    else if (gemmType == "int8packed")  gemmType_ = GemmType::FbInt8Packed;
+    else ABORT("Unknown GEMM type");
   }
   GemmType getGemmType() override { return gemmType_; }
 };

From d3c6e5f2bca5c14cdd5b81a0ac39ddabac085dc0 Mon Sep 17 00:00:00 2001
From: Young Jin Kim <youki@microsoft.com>
Date: Wed, 26 Jun 2019 11:36:02 -0700
Subject: [PATCH 516/838] Add some more comments and change variable names

---
 cmake/FindMKL.cmake                |  10 +--
 src/common/definitions.h           |   1 -
 src/graph/auto_tuner.h             |  17 +++-
 src/graph/expression_operators.cpp | 128 ++++++++++++++++-------------
 src/microsoft/quicksand.cpp        |   2 +-
 src/models/transformer_stub.cpp    |   2 +-
 src/tensors/cpu/backend.h          |   4 +-
 src/tensors/cpu/expanded_gemm.h    |   2 +-
 8 files changed, 95 insertions(+), 71 deletions(-)

diff --git a/cmake/FindMKL.cmake b/cmake/FindMKL.cmake
index 028161e32..a484e9ab4 100644
--- a/cmake/FindMKL.cmake
+++ b/cmake/FindMKL.cmake
@@ -53,11 +53,11 @@ else()
   set(COR_LIB "mkl_core")
 endif()
 
-if(MSVC)
-  set(ProgramFilesx86 "ProgramFiles(x86)")
-  set(INTEL_ROOT_DEFAULT $ENV{${ProgramFilesx86}}/IntelSWTools/compilers_and_libraries/windows)
-else()
-  set(INTEL_ROOT_DEFAULT "/opt/intel")
+if(MSVC)
+  set(ProgramFilesx86 "ProgramFiles(x86)")
+  set(INTEL_ROOT_DEFAULT $ENV{${ProgramFilesx86}}/IntelSWTools/compilers_and_libraries/windows)
+else()
+  set(INTEL_ROOT_DEFAULT "/opt/intel")
 endif()
 set(INTEL_ROOT ${INTEL_ROOT_DEFAULT} CACHE PATH "Folder contains intel libs")
 find_path(MKL_ROOT include/mkl.h PATHS $ENV{MKLROOT} ${INTEL_ROOT}/mkl
diff --git a/src/common/definitions.h b/src/common/definitions.h
index ea14a8e5c..f46da2340 100755
--- a/src/common/definitions.h
+++ b/src/common/definitions.h
@@ -116,5 +116,4 @@ typedef Ptr<RunBase> RunBasePtr;
 
 
 const float NEMATUS_LN_EPS = 1e-5f;
-
 }  // namespace marian
diff --git a/src/graph/auto_tuner.h b/src/graph/auto_tuner.h
index 868a800e2..6a0543320 100644
--- a/src/graph/auto_tuner.h
+++ b/src/graph/auto_tuner.h
@@ -20,15 +20,26 @@ class AutoTuner : public AutoTunerRecorder {
 private:
   typedef std::function<Return(Args...)> Algorithm;
 
-  const size_t max = 50;
+  // When the autotuner decides the fastest algorithm for a specific tensor operation (e.g. GEMM),
+  // the autotuner runs each algorithm at least this 'collectStatMax' number of times and
+  // collects the statistics.
+  const size_t collectStatMax = 50;
 
   UPtr<timer::CPUTimer> timer_;
 
+  // This structure holds a hash key an algorithm function (e.g. int16, packed gemm, mkl gemm)
+  // for a specific operation size
+  // hash: a unique hash key for each operation size
+  //      (e.g. m, n, k, transpose A, transpose B, bias size for GEMM)
+  // algorithm: a function that holds an algorithm
   struct HashedAlgorithm {
     size_t hash;
     Algorithm algorithm;
   };
 
+  // This structure represents the collected statistics.
+  // time: total accumulated time of this operator execution with the given algorithm
+  // runs: total time this algorithm was executed
   struct Stat {
     double time;
     size_t runs;
@@ -53,7 +64,7 @@ class AutoTuner : public AutoTunerRecorder {
         auto& stat = it->second;
 
         // collect more stats
-        if(stat.runs < max)
+        if(stat.runs < collectStatMax)
           return i;
 
         if(stat.time < bestTime) {
@@ -93,7 +104,7 @@ class AutoTuner : public AutoTunerRecorder {
 
       auto it = stats_.find(hash);
       if(it != stats_.end()) {
-        if(it->second.runs < max) {
+        if(it->second.runs < collectStatMax) {
           it->second.time += seconds.count();
           it->second.runs += 1;
         }
diff --git a/src/graph/expression_operators.cpp b/src/graph/expression_operators.cpp
index 56fb654a5..0f48d0b28 100755
--- a/src/graph/expression_operators.cpp
+++ b/src/graph/expression_operators.cpp
@@ -413,6 +413,10 @@ Expr affine(Expr a, Expr b, Expr bias, bool transA, bool transB, float scale) {
 
   if(a->graph()->getBackend()->isOptimized() && device == DeviceType::cpu) {
     GemmType gemmType = a->graph()->getBackend()->getGemmType();
+    // When gemmType is set to 'auto', an autotuner decides the best algorithm available.
+    // A new autotuner is created, then different kinds of algorithms are added to the autotuner.
+    // For each GEMM size, there is a unique hash key.
+    // (e.g. m, n, k, transpose A, transpose B, bias size for GEMM)
     if(gemmType == GemmType::Auto) {
       thread_local Ptr<AutoTuner<Expr>> tuner = New<AutoTuner<Expr>>();
 
@@ -434,76 +438,87 @@ Expr affine(Expr a, Expr b, Expr bias, bool transA, bool transB, float scale) {
       util::hash_combine(hash, transB);
 
 #if USE_FBGEMM
-      // Use Packed GEMM only if it's memoized
+      // Use Packed GEMM only if the node b in the graph is memoized.
+      // More specifically, packed GEMM is used only if the B matrix (weight) is constant.
+      // In general, 'memoized' means that the node is a constant variable or
+      // a combination of contant nodes which is also a constant variable
+      // when it's computed once.
+      // Those memoized nodes are cached to avoid duplicated computations.
       if(b->memoize()) {
-        // add third algorithm variant (Packed GEMM)
-        size_t hash1 = hash;
-        util::hash_combine(hash1, 1);
-        auto rec1 = [=](Expr e, bool stop = false) {
-          e->record(tuner, hash1, stop);
+        // add packed GEMM algorithm variant (Packed GEMM) to the autotuner
+        // Once an algorithm is added to the autotuner,
+        // autotuner runs all the added algorithms for a designated times.
+        // One algorithm is run per one this operation call
+        // and the stat for that algorithm is collected.
+        // When all the algorithms reach the maximum stat collection count,
+        // the autotuner decide the best algorithm, and keep using it afterward.
+        size_t hashPack = hash;
+        util::hash_combine(hashPack, 1);
+        auto recPack = [=](Expr e, bool stop = false) {
+          e->record(tuner, hashPack, stop);
           return e;
         };
 
-        auto alg1 = [=]() {
+        auto algPack = [=]() {
           auto packed = cpu::variant::pack(b, cpu::variant::PackMatrix::B, transB, clipValue);
 
-          return rec1(
-              cpu::variant::affine(
-              clip(a, clipValue), packed,
-              b->shape(),
-              bias,
-              transA,
-              transB,
-              scale),
+          return recPack(
+            cpu::variant::affine(clip(a, clipValue),
+                                 packed,
+                                 b->shape(),
+                                 bias,
+                                 transA,
+                                 transB,
+                                 scale),
             true);
         };
-        tuner->insert({hash1, alg1});
+        tuner->insert({hashPack, algPack});
       }
 #endif // USE_FBGEMM
 
-      // add first algorithm variant (Int16)
-      size_t hash2 = hash;
-      util::hash_combine(hash2, 2);
-      auto rec2 = [=](Expr e, bool stop = false) {
-        e->record(tuner, hash2, stop);
+      // add second algorithm variant (Int16) to the autotuner
+      size_t hashInt16 = hash;
+      util::hash_combine(hashInt16, 2);
+      auto recInt16 = [=](Expr e, bool stop = false) {
+        e->record(tuner, hashInt16, stop);
         return e;
       };
-      auto alg2 = [=]() {
-        return rec2(
-            cpu::int16::affine(
-                rec2(cpu::int16::quantize(transA ? rec2(transpose(a)) : a,
-                                          clipValue)),
-                cpu::int16::quantize(transB ? b : transpose(b), clipValue),
-                bias,
-                scale),
-            true);
+      auto algInt16 = [=]() {
+        return recInt16(
+          cpu::int16::affine(
+                recInt16(cpu::int16::quantize(transA ? recInt16(transpose(a)) : a,
+                                      clipValue)),
+            cpu::int16::quantize(transB ? b : transpose(b), clipValue),
+            bias,
+            scale),
+          true);
       };
-      tuner->insert({hash2, alg2});
+      tuner->insert({hashInt16, algInt16});
 
-      // add second algorithm variant (CBlas)
-      size_t hash3 = hash;
-      util::hash_combine(hash3, 3);
-      auto rec3 = [=](Expr e, bool stop = false) {
-        e->record(tuner, hash3, stop);
+      // add third algorithm variant (CBlas) to the autotuner
+      size_t hashCblas = hash;
+      util::hash_combine(hashCblas, 3);
+      auto recCblas = [=](Expr e, bool stop = false) {
+        e->record(tuner, hashCblas, stop);
         return e;
       };
 
-      auto alg3 = [=]() {
+      auto algCblas = [=]() {
         auto ac = clip(a, clipValue);
         if(ac != a)
-          ac = rec3(ac);
+          ac = recCblas(ac);
 
         auto bc = clip(b, clipValue);
         if(bc != b)
-          bc = rec3(bc);
+          bc = recCblas(bc);
 
         int rows = ac->shape().elements() / ac->shape()[-1];
         Expr ones = ac->graph()->ones({rows, 1});
         std::vector<Expr> nodes = {ac, bc, bias, ones};
-        return rec3(Expression<AffineNodeOp>(nodes, transA, transB, scale),
+        return recCblas(Expression<AffineNodeOp>(nodes, transA, transB, scale),
                     true);
       };
-      tuner->insert({hash3, alg3});
+      tuner->insert({hashCblas, algCblas});
 
       // execute algorithm with autotuning
       return tuner->run();
@@ -512,25 +527,22 @@ Expr affine(Expr a, Expr b, Expr bias, bool transA, bool transB, float scale) {
       if(gemmType == GemmType::IntrinInt16) {
         // cpu int16 version
         return cpu::int16::affine(
-            cpu::int16::quantize(transA ? transpose(a) : a, clipValue),
-            cpu::int16::quantize(transB ? b : transpose(b), clipValue),
-            bias,
-            scale);
+          cpu::int16::quantize(transA ? transpose(a) : a, clipValue),
+          cpu::int16::quantize(transB ? b : transpose(b), clipValue),
+          bias,
+          scale);
       } else if(gemmType == GemmType::FbFp16Packed) {
 #if USE_FBGEMM
         if(b->memoize()) {
           auto packed = cpu::variant::pack(b, cpu::variant::PackMatrix::B, transB, clipValue);
-          // auto packed = transB ? 
-          //               cpu::variant::pack(transpose(b), cpu::pack::PackMatrix::B, false, clipValue) :
-          //               cpu::variant::pack(b, cpu::pack::PackMatrix::B, false, clipValue);
-
-          return cpu::variant::affine(
-              clip(a, clipValue), packed,
-              b->shape(),
-              bias,
-              transA,
-              transB,
-              scale);
+
+          return cpu::variant::affine(clip(a, clipValue),
+                                      packed,
+                                      b->shape(),
+                                      bias,
+                                      transA,
+                                      transB,
+                                      scale);
         } else {
           int rows = a->shape().elements() / a->shape()[-1];
           Expr ones = a->graph()->ones({rows, 1});
@@ -538,7 +550,7 @@ Expr affine(Expr a, Expr b, Expr bias, bool transA, bool transB, float scale) {
           return Expression<AffineNodeOp>(nodes, transA, transB, scale);
         }
 #else
-        ABORT("Packed GEMM not implemented");
+        ABORT("Packed GEMM is not available in this build");
 #endif  // USE_FBGEMM
 
       } else if(gemmType == GemmType::MklFp32) {
@@ -555,7 +567,7 @@ Expr affine(Expr a, Expr b, Expr bias, bool transA, bool transB, float scale) {
             = {clip(a, clipValue), clip(b, clipValue), bias, ones};
         return Expression<AffineNodeOp>(nodes, transA, transB, scale);
       } else {
-        ABORT("Not implemented");
+        ABORT("GemmType..{} not available by affine()", gemmType);
       }
     }
   } else {
diff --git a/src/microsoft/quicksand.cpp b/src/microsoft/quicksand.cpp
index 5809c18c0..d46273b0e 100644
--- a/src/microsoft/quicksand.cpp
+++ b/src/microsoft/quicksand.cpp
@@ -72,7 +72,7 @@ class BeamSearchDecoder : public IBeamSearchDecoder {
 
     // Use packed GEMM for the production
     graph_->getBackend()->setOptimized(true);
-    graph_->getBackend()->setGemmType("packed");
+    graph_->getBackend()->setGemmType("fp16packed");
 
 #ifdef MKL_FOUND
     mkl_set_num_threads(options->get<int>("mkl-threads", 1));
diff --git a/src/models/transformer_stub.cpp b/src/models/transformer_stub.cpp
index 2e8d694fa..871ee0094 100755
--- a/src/models/transformer_stub.cpp
+++ b/src/models/transformer_stub.cpp
@@ -1,4 +1,4 @@
-#include "models/transformer.h"
+#include "models/transformer.h"
 
 namespace marian {
 // factory functions
diff --git a/src/tensors/cpu/backend.h b/src/tensors/cpu/backend.h
index 094e9331e..8d4adcfcb 100644
--- a/src/tensors/cpu/backend.h
+++ b/src/tensors/cpu/backend.h
@@ -27,9 +27,11 @@ class Backend : public marian::Backend {
     if      (gemmType == "auto")        gemmType_ = GemmType::Auto;
     else if (gemmType == "mklfp32")     gemmType_ = GemmType::MklFp32;
     else if (gemmType == "intrinint16") gemmType_ = GemmType::IntrinInt16;
+#if USE_FBGEMM
     else if (gemmType == "fp16packed")  gemmType_ = GemmType::FbFp16Packed;
     else if (gemmType == "int8packed")  gemmType_ = GemmType::FbInt8Packed;
-    else ABORT("Unknown GEMM type");
+#endif // USE_FBGEMM
+    else ABORT("Unknown GEMM type - '{}'", gemmType);
   }
   GemmType getGemmType() override { return gemmType_; }
 };
diff --git a/src/tensors/cpu/expanded_gemm.h b/src/tensors/cpu/expanded_gemm.h
index 03e58ae43..8ccdb4159 100644
--- a/src/tensors/cpu/expanded_gemm.h
+++ b/src/tensors/cpu/expanded_gemm.h
@@ -157,7 +157,7 @@ class AffineNodeOp : public NaryNodeOp {
     return {NodeOp(0)};
   }
 
-  const std::string type() override { return "affinePacked"; }
+  const std::string type() override { return "fp16packed"; }
 };
 
 static inline Expr affine(Expr a, Expr b, Shape bShape, Expr c, bool transA, bool transB, float scalar) {

From 79c5b955d4ca35733ee4ae021f2fb229e6b5962a Mon Sep 17 00:00:00 2001
From: Young Jin Kim <youki@microsoft.com>
Date: Wed, 26 Jun 2019 14:26:27 -0700
Subject: [PATCH 517/838] Added some more comments and clean the code

---
 src/tensors/cpu/expanded_gemm.h       | 41 ++++++++++++++++++++-------
 src/tensors/cpu/sharp/avx_gemm.cpp    |  5 ----
 src/tensors/cpu/sharp/packed_gemm.cpp |  6 ----
 src/tensors/cpu/tensor_operators.cpp  | 16 ++++++-----
 4 files changed, 40 insertions(+), 28 deletions(-)

diff --git a/src/tensors/cpu/expanded_gemm.h b/src/tensors/cpu/expanded_gemm.h
index 8ccdb4159..6944bceae 100644
--- a/src/tensors/cpu/expanded_gemm.h
+++ b/src/tensors/cpu/expanded_gemm.h
@@ -12,11 +12,26 @@ namespace marian {
 namespace cpu {
 namespace variant {
 
+// Enumeration for the Matrix used in pack functions
+// A matrix - 0, B matrix - 1
 enum class PackMatrix : uint8_t {
   A = 0x00,
   B = 0x01
 };
 
+// Pack a matrix into cache utilization efficient way (block format)
+// PackMatrix packMat_: the type of packed matrix - A or B matrix
+// bool transpose_: transpose
+// int nrow_: the number of rows
+// int ncol_: the number of columns
+// int kernel_ncol_blocks_: the number of column blocks
+// int brow_: the number of rows in a block
+// int bcol_: the number of columns in a block
+// int last_brow_: the number of rows in the last block
+// int nbrow_: row index in a block
+// int nbcol_: column index in a block
+// uint64_t packsize_: the size of the packed matrix
+//                    (the number of fp16 elements + padding (1024) + extra temporary memory (256))
 struct PackNodeOp : public UnaryNodeOp {
   PackMatrix packMat_;
   bool transpose_;
@@ -39,7 +54,7 @@ struct PackNodeOp : public UnaryNodeOp {
     if(clipValue != 0)
       ABORT("Clipping is not supported");
     if(!memoize_)
-      ABORT("Only memoizeable node is supported");
+      ABORT("Only constant weight node can be packed");
   }
 
   NodeOps forwardOps() override {
@@ -59,7 +74,7 @@ struct PackNodeOp : public UnaryNodeOp {
   }
 
   NodeOps backwardOps() override {
-    ABORT("Only used for inference");
+    ABORT("PackNodeOp only available for inference");
     return {NodeOp(0)};
   }
 
@@ -87,12 +102,19 @@ struct PackNodeOp : public UnaryNodeOp {
 
     return outShape;
 #else // USE_FBGEMM
-    ABORT("Only FBGEMM based packed GEMM is supported");
+    ABORT("Packed GEMM requires a build with USE_FBGEMM enabled");
     return Shape();
 #endif  // USE_FBGEMM
   }
 };
 
+// Affine transform (matrix multiplication) using packed B matrix
+// float scalar_: scalar multiplier
+// int64_t m_: m
+// int64_t n_: n
+// int64_t k_: k
+// bool transA_: transpose A
+// bool transB_: transpose B
 class AffineNodeOp : public NaryNodeOp {
 private:
   float scalar_;
@@ -101,7 +123,6 @@ class AffineNodeOp : public NaryNodeOp {
   int64_t k_;
   bool transA_;
   bool transB_;
-  size_t idx_;
 
 public:
   AffineNodeOp(const std::vector<Expr>& nodes, Shape bShape, bool transA, bool transB, float scalar)
@@ -143,12 +164,12 @@ class AffineNodeOp : public NaryNodeOp {
   NodeOps forwardOps() override {
     return {
       NodeOp(GemmPackFp32(val_,
-                                child(0)->val(),
-                                child(1)->val(),
-                                child(2)->val(),
-                                m_,
-                                n_,
-                                transA_))
+                          child(0)->val(),
+                          child(1)->val(),
+                          child(2)->val(),
+                          m_,
+                          n_,
+                          transA_))
     };
   }
 
diff --git a/src/tensors/cpu/sharp/avx_gemm.cpp b/src/tensors/cpu/sharp/avx_gemm.cpp
index 572ac391a..c41b73eb0 100644
--- a/src/tensors/cpu/sharp/avx_gemm.cpp
+++ b/src/tensors/cpu/sharp/avx_gemm.cpp
@@ -10,11 +10,6 @@
 #include <cassert>
 #include <cstddef>
 
-#ifdef _MSC_VER
-#pragma warning(disable : 4310)
-#pragma warning(disable : 4324)
-#endif
-
 #ifdef __AVX512F__
 
 namespace marian {
diff --git a/src/tensors/cpu/sharp/packed_gemm.cpp b/src/tensors/cpu/sharp/packed_gemm.cpp
index fcca0206a..3c69b9e0e 100644
--- a/src/tensors/cpu/sharp/packed_gemm.cpp
+++ b/src/tensors/cpu/sharp/packed_gemm.cpp
@@ -90,8 +90,6 @@ void PackFp32(marian::Tensor out,
               const int nbrow,
               const int nbcol,
               const uint64_t packsize) {
-  //auto t_start = std::chrono::high_resolution_clock::now();
-  // for the last embedding layer, pack it into int8
   // initialize memory
   uint8_t* outmemorg = out->data<uint8_t>();
   for(auto i = 0; i < packsize; i++) {
@@ -121,10 +119,6 @@ void PackFp32(marian::Tensor out,
     }
   }
   delete dummy;
-
-  //auto t_end = std::chrono::high_resolution_clock::now();
-  //packingTime += (float) std::chrono::duration<double, std::milli>(t_end-t_start).count();
-  //std::cout << "Packing time: " << packingTime << std::endl;
 }
 
 void GemmPackFp32(marian::Tensor C,
diff --git a/src/tensors/cpu/tensor_operators.cpp b/src/tensors/cpu/tensor_operators.cpp
index 2e9b31fc9..63ade0238 100644
--- a/src/tensors/cpu/tensor_operators.cpp
+++ b/src/tensors/cpu/tensor_operators.cpp
@@ -190,13 +190,14 @@ void Transpose0213(Tensor out, Tensor in) {
   }
 }
 
-/**
- * Given a 4D array, transpose the initial 3 dimensions while keeping the last dimension.
- *
- * @param out output tensor
- * @param in input tensor
- * @param vAxis target (transposed) axes of each given axes
- */
+// Given a 4D array, transpose (swap) the initial 3 dimensions while keeping the last dimension.
+// e.g. 1234 --> 2134, 1234 --> 3214 (4 is always kept).
+// This is an optimized version for swapping first 3 dimensions 
+// assuming the last dimension is large enough to get benefits from vectorized copy.
+//
+// @param out output tensor
+// @param in input tensor
+// @param vAxis target (transposed) axes of each given axes
 template <bool add>
 void TransposeFirst3In4(Tensor out, Tensor in, const std::vector<int>& vAxis) {
   ABORT_IF(vAxis.size() != 4, "This function handles only 4D arrays.");
@@ -252,6 +253,7 @@ void TransposeFirst3In4(Tensor out, Tensor in, const std::vector<int>& vAxis) {
   }
 #else
   // it shouldn't come into here. This function is called only when MKL is available.
+  ABORT("Should not get here");
 #endif  // MKL_FOUND
 }
 

From df71c5fc7bdba4b032b718d6daa13fb15a368e1d Mon Sep 17 00:00:00 2001
From: Young Jin Kim <youki@microsoft.com>
Date: Wed, 26 Jun 2019 14:37:26 -0700
Subject: [PATCH 518/838] Change unnecessary int64_t to size_t

---
 src/tensors/cpu/expanded_gemm.h       | 14 +++++++-------
 src/tensors/cpu/sharp/packed_gemm.cpp |  8 ++++----
 src/tensors/cpu/sharp/packed_gemm.h   |  4 ++--
 3 files changed, 13 insertions(+), 13 deletions(-)

diff --git a/src/tensors/cpu/expanded_gemm.h b/src/tensors/cpu/expanded_gemm.h
index 6944bceae..d3b03d514 100644
--- a/src/tensors/cpu/expanded_gemm.h
+++ b/src/tensors/cpu/expanded_gemm.h
@@ -110,17 +110,17 @@ struct PackNodeOp : public UnaryNodeOp {
 
 // Affine transform (matrix multiplication) using packed B matrix
 // float scalar_: scalar multiplier
-// int64_t m_: m
-// int64_t n_: n
-// int64_t k_: k
+// size_t m_: m
+// size_t n_: n
+// size_t k_: k
 // bool transA_: transpose A
 // bool transB_: transpose B
 class AffineNodeOp : public NaryNodeOp {
 private:
   float scalar_;
-  int64_t m_;
-  int64_t n_;
-  int64_t k_;
+  size_t m_;
+  size_t n_;
+  size_t k_;
   bool transA_;
   bool transB_;
 
@@ -135,7 +135,7 @@ class AffineNodeOp : public NaryNodeOp {
     if(transA)
       std::swap(m_, k_);
 
-    int64_t l = bShape.elements() / bShape[-1];
+    size_t l = bShape.elements() / bShape[-1];
     n_ = bShape[-1];
     if(transB)
       std::swap(l, n_);
diff --git a/src/tensors/cpu/sharp/packed_gemm.cpp b/src/tensors/cpu/sharp/packed_gemm.cpp
index 3c69b9e0e..15c70e4f5 100644
--- a/src/tensors/cpu/sharp/packed_gemm.cpp
+++ b/src/tensors/cpu/sharp/packed_gemm.cpp
@@ -125,8 +125,8 @@ void GemmPackFp32(marian::Tensor C,
                   const marian::Tensor A,
                   const marian::Tensor B,
                   const marian::Tensor bias,
-                  const int64_t m,
-                  const int64_t n,
+                  const size_t m,
+                  const size_t n,
                   const int transA) {
   // row major
   // keep the original mem
@@ -201,8 +201,8 @@ void GemmPackFp32(marian::Tensor C,
                   const marian::Tensor A,
                   const marian::Tensor B,
                   const marian::Tensor bias,
-                  const int64_t m,
-                  const int64_t n,
+                  const size_t m,
+                  const size_t n,
                   const int transA) {
                 // does nothing. supports only FBGEMM based packed gemm at this moment.
                 ABORT("FBGEMM is needed to use packed GEMM.");
diff --git a/src/tensors/cpu/sharp/packed_gemm.h b/src/tensors/cpu/sharp/packed_gemm.h
index 433b731e9..10943defd 100644
--- a/src/tensors/cpu/sharp/packed_gemm.h
+++ b/src/tensors/cpu/sharp/packed_gemm.h
@@ -23,8 +23,8 @@ void GemmPackFp32(marian::Tensor C,
                   const marian::Tensor A,
                   const marian::Tensor B,
                   const marian::Tensor bias,
-                  const int64_t m,
-                  const int64_t n,
+                  const size_t m,
+                  const size_t n,
                   const int transA = 0);
 
 }  // namespace variant

From 811ecd8371a53361da4d40fe665abd74b1fdb537 Mon Sep 17 00:00:00 2001
From: Young Jin Kim <youki@microsoft.com>
Date: Wed, 26 Jun 2019 14:57:22 -0700
Subject: [PATCH 519/838] Adding more comments.

---
 src/graph/expression_operators.cpp    |  2 +-
 src/tensors/backend.h                 |  2 +-
 src/tensors/cpu/expanded_gemm.h       |  6 +++---
 src/tensors/cpu/sharp/packed_gemm.cpp |  9 +++++++++
 src/tensors/cpu/sharp/packed_gemm.h   | 22 ++++++++++++++++++++++
 5 files changed, 36 insertions(+), 5 deletions(-)

diff --git a/src/graph/expression_operators.cpp b/src/graph/expression_operators.cpp
index 0f48d0b28..a90a69201 100755
--- a/src/graph/expression_operators.cpp
+++ b/src/graph/expression_operators.cpp
@@ -387,7 +387,7 @@ Expr dot(Expr a, Expr b, bool transA, bool transB, float scale) {
 
   // Currently only true when command line options
   // --optimize --cpu-thread=N with N > 0 are set.
-  if(a->graph()->getBackend()->isOptimized() && device == DeviceType::cpu
+  if(device == DeviceType::cpu && a->graph()->getBackend()->isOptimized()
      && a->graph()->getBackend()->getGemmType() == GemmType::IntrinInt16) {
     // dotInt16 computes A * B.T, hence the transpose for B to get A * B
     // if transA = false and transB = false.
diff --git a/src/tensors/backend.h b/src/tensors/backend.h
index b664d6039..43b901e91 100644
--- a/src/tensors/backend.h
+++ b/src/tensors/backend.h
@@ -8,7 +8,7 @@ namespace marian {
 // GEMM type enum
 typedef enum { Auto = 0,            // auto tuning between available GEMMs
                MklFp32 = 1,         // MKL based GEMM, fp32
-               IntrinInt16 = 2,  // Intrinsic implementation of Int 16 GEMM
+               IntrinInt16 = 2,     // Intrinsic implementation of Int 16 GEMM
                FbFp16Packed = 10,   // FBGEMM based fp16 GEMM with packing
                FbInt8Packed = 11    // FBGEMM based int8 GEMM with packing
 } GemmType;
diff --git a/src/tensors/cpu/expanded_gemm.h b/src/tensors/cpu/expanded_gemm.h
index d3b03d514..0c68ccfd7 100644
--- a/src/tensors/cpu/expanded_gemm.h
+++ b/src/tensors/cpu/expanded_gemm.h
@@ -110,9 +110,9 @@ struct PackNodeOp : public UnaryNodeOp {
 
 // Affine transform (matrix multiplication) using packed B matrix
 // float scalar_: scalar multiplier
-// size_t m_: m
-// size_t n_: n
-// size_t k_: k
+// size_t m_: the number of rows in A and C
+// size_t n_: the number of columns in B and C
+// size_t k_: the number of columns in A and the number of rows in C
 // bool transA_: transpose A
 // bool transB_: transpose B
 class AffineNodeOp : public NaryNodeOp {
diff --git a/src/tensors/cpu/sharp/packed_gemm.cpp b/src/tensors/cpu/sharp/packed_gemm.cpp
index 15c70e4f5..34b595936 100644
--- a/src/tensors/cpu/sharp/packed_gemm.cpp
+++ b/src/tensors/cpu/sharp/packed_gemm.cpp
@@ -98,6 +98,7 @@ void PackFp32(marian::Tensor out,
   // save the other auxiliary variables
   uint64_t* auxmemsize = (uint64_t*)outmemorg;
   auxmemsize[0] = packsize;
+  // save FBGEMM related parameters into the header of the allocated memory by marian
   int32_t* auxmem = (int32_t*)(auxmemsize + 1);
   auxmem[0] = nrow;
   auxmem[1] = ncol;
@@ -121,6 +122,14 @@ void PackFp32(marian::Tensor out,
   delete dummy;
 }
 
+// GEMM operation on the packed B matrix
+// C: output matrix
+// A: A matrix
+// B: B matrix (packed)
+// m: the number of rows in A and C
+// n: the number of columns in B and C
+// transA: transpose of A matrix
+// B is already packed. So, we don't need transB
 void GemmPackFp32(marian::Tensor C,
                   const marian::Tensor A,
                   const marian::Tensor B,
diff --git a/src/tensors/cpu/sharp/packed_gemm.h b/src/tensors/cpu/sharp/packed_gemm.h
index 10943defd..0946e551f 100644
--- a/src/tensors/cpu/sharp/packed_gemm.h
+++ b/src/tensors/cpu/sharp/packed_gemm.h
@@ -6,6 +6,20 @@ namespace marian {
 namespace cpu {
 namespace variant { // Variants of GEMM implementations
 
+// Pack a matrix into cache utilization efficient way (block format)
+// out: output tensor - packed format
+// in: input tensor - normal format
+// transpose: the matrix is transposed
+// nrow: the number of rows
+// ncol: the number of columns
+// kernel_ncol_blocks: the number of column blocks
+// brow: the number of rows in a block
+// bcol: the number of columns in a block
+// last_brow: the number of rows in the last block
+// nbrow: row index in a block
+// nbcol: column index in a block
+// packsize: the size of the packed matrix
+//          (the number of fp16 elements + padding (1024) + extra temporary memory (256))
 void PackFp32(marian::Tensor out,
               const marian::Tensor in,
               const bool transpose,
@@ -19,6 +33,14 @@ void PackFp32(marian::Tensor out,
               const int nbcol,
               const uint64_t packsize);
 
+// GEMM operation on the packed B matrix
+// C: output matrix
+// A: A matrix
+// B: B matrix (packed)
+// m: the number of rows in A and C
+// n: the number of columns in B and C
+// transA: transpose of A matrix
+// B is already packed. So, we don't need transB
 void GemmPackFp32(marian::Tensor C,
                   const marian::Tensor A,
                   const marian::Tensor B,

From 134c0c01126eb3835ed83e4328ce563363d7ed27 Mon Sep 17 00:00:00 2001
From: Young Jin Kim <youki@microsoft.com>
Date: Wed, 26 Jun 2019 15:23:19 -0700
Subject: [PATCH 520/838] Fix a potential memory corruption. And, added more
 comments.

---
 src/tensors/backend.h                 |  6 +++--
 src/tensors/cpu/sharp/packed_gemm.cpp | 38 ++++++++++++++-------------
 2 files changed, 24 insertions(+), 20 deletions(-)

diff --git a/src/tensors/backend.h b/src/tensors/backend.h
index 43b901e91..2e8d24c78 100644
--- a/src/tensors/backend.h
+++ b/src/tensors/backend.h
@@ -36,10 +36,12 @@ class Backend {
   virtual void setClip(float clipValue) { clipValue_ = clipValue; }
   float getClip() { return clipValue_; }
 
-  // for CPU & inference only, sets to use optimized code for inference. Does nothing for GPU.
+  // for CPU & inference only, sets to use optimized code for inference.
+  // The program aborts if these are called from GPU device.
   virtual void setOptimized(bool optimize) = 0;
   virtual bool isOptimized() = 0;
-  // for CPU only, selects different GEMM types for the inference. Does nothing for GPU.
+  // for CPU only, selects different GEMM types for the inference.
+  // The program aborts if these are called from GPU device.
   virtual void setGemmType(std::string gemmType) = 0;
   virtual GemmType getGemmType() = 0;
 };
diff --git a/src/tensors/cpu/sharp/packed_gemm.cpp b/src/tensors/cpu/sharp/packed_gemm.cpp
index 34b595936..0b3ad4607 100644
--- a/src/tensors/cpu/sharp/packed_gemm.cpp
+++ b/src/tensors/cpu/sharp/packed_gemm.cpp
@@ -99,15 +99,16 @@ void PackFp32(marian::Tensor out,
   uint64_t* auxmemsize = (uint64_t*)outmemorg;
   auxmemsize[0] = packsize;
   // save FBGEMM related parameters into the header of the allocated memory by marian
-  int32_t* auxmem = (int32_t*)(auxmemsize + 1);
-  auxmem[0] = nrow;
-  auxmem[1] = ncol;
-  auxmem[2] = kernel_ncol_blocks;
-  auxmem[3] = brow;
-  auxmem[4] = bcol;
-  auxmem[5] = last_brow;
-  auxmem[6] = nbrow;
-  auxmem[7] = nbcol;
+  int32_t header[8];
+  header[0] = nrow;
+  header[1] = ncol;
+  header[2] = kernel_ncol_blocks;
+  header[3] = brow;
+  header[4] = bcol;
+  header[5] = last_brow;
+  header[6] = nbrow;
+  header[7] = nbcol;
+  memcpy(auxmemsize + 1, header, sizeof(header));
   // cast to float16
   fbgemm::float16* outmem = (fbgemm::float16*)(outmemorg + 256);
   fbgemm::float16* dummy = new fbgemm::float16;
@@ -143,15 +144,16 @@ void GemmPackFp32(marian::Tensor C,
   // retreive aux fields from the memory
   uint64_t* packedmemSize = (uint64_t*)B->data();
   packedPlaceholder.size_ = packedmemSize[0];
-  int32_t* packedmemAux = (int32_t*)(packedmemSize + 1);
-  packedPlaceholder.nrow_ = packedmemAux[0];
-  packedPlaceholder.ncol_ = packedmemAux[1];
-  packedPlaceholder.kernel_ncol_blocks_ = packedmemAux[2];
-  packedPlaceholder.brow_ = packedmemAux[3];
-  packedPlaceholder.bcol_ = packedmemAux[4];
-  packedPlaceholder.last_brow_ = packedmemAux[5];
-  packedPlaceholder.nbrow_ = packedmemAux[6];
-  packedPlaceholder.nbcol_ = packedmemAux[7];
+  int32_t header[8];
+  memcpy(header, packedmemSize + 1, sizeof(header));
+  packedPlaceholder.nrow_ = header[0];
+  packedPlaceholder.ncol_ = header[1];
+  packedPlaceholder.kernel_ncol_blocks_ = header[2];
+  packedPlaceholder.brow_ = header[3];
+  packedPlaceholder.bcol_ = header[4];
+  packedPlaceholder.last_brow_ = header[5];
+  packedPlaceholder.nbrow_ = header[6];
+  packedPlaceholder.nbcol_ = header[7];
 
   // packed matrix
   packedPlaceholder.pmat_ = (fbgemm::float16*)(B->data<uint8_t>() + 256);

From 6927ee17391bf07678032087fd11374a18a8b684 Mon Sep 17 00:00:00 2001
From: Young Jin Kim <youki@microsoft.com>
Date: Wed, 26 Jun 2019 15:25:06 -0700
Subject: [PATCH 521/838] Added more comments.

---
 src/tensors/cpu/sharp/packed_gemm.cpp | 2 ++
 src/tensors/gpu/backend.h             | 6 ++++--
 2 files changed, 6 insertions(+), 2 deletions(-)

diff --git a/src/tensors/cpu/sharp/packed_gemm.cpp b/src/tensors/cpu/sharp/packed_gemm.cpp
index 0b3ad4607..71c2008e7 100644
--- a/src/tensors/cpu/sharp/packed_gemm.cpp
+++ b/src/tensors/cpu/sharp/packed_gemm.cpp
@@ -38,6 +38,8 @@ namespace variant { // Variants of GEMM implementations
 
 #if USE_FBGEMM
 // initialize with a dummy
+// In a multi marian instance setting (as a dynamic library),
+// different marian instances should not share this variable.
 static thread_local PackedGemmMatrixFP16 packedPlaceholder(1, 1, 1, 1, 1, 1, 1, 1);
 
 // This is copied from FBGEMM code
diff --git a/src/tensors/gpu/backend.h b/src/tensors/gpu/backend.h
index 8e69d6d7d..dd177aad4 100644
--- a/src/tensors/gpu/backend.h
+++ b/src/tensors/gpu/backend.h
@@ -34,13 +34,15 @@ class Backend : public marian::Backend {
   cublasHandle_t getCublasHandle() { return cublasHandle_; }
   cusparseHandle_t getCusparseHandle() { return cusparseHandle_; }
 
-  // for CPU & inference only, sets to use optimized code for inference. Does nothing for GPU.
+  // for CPU & inference only, sets to use optimized code for inference.
+  // The program aborts if these are called from GPU device.
   void setOptimized(bool optimize) override { ABORT("Not supported for GPU_{}", optimize); }
   bool isOptimized() override {
     ABORT("Not supported for GPU");
     return false;
   }
-  // for CPU only, selects different GEMM types for the inference. Does nothing for GPU.
+  // for CPU only, selects different GEMM types for the inference.
+  // The program aborts if these are called from GPU device.
   void setGemmType(std::string gemmType) override { ABORT("Not supported for GPU_{}", gemmType); }
   GemmType getGemmType() override {
     ABORT("Not supported for GPU");

From 5bb28f0a3ae4afa39827c9128416a42619319304 Mon Sep 17 00:00:00 2001
From: Young Jin Kim <youki@microsoft.com>
Date: Fri, 28 Jun 2019 04:56:26 -0700
Subject: [PATCH 522/838] Add more comments

---
 src/tensors/cpu/sharp/packed_gemm.cpp | 11 +++++++++++
 1 file changed, 11 insertions(+)

diff --git a/src/tensors/cpu/sharp/packed_gemm.cpp b/src/tensors/cpu/sharp/packed_gemm.cpp
index 71c2008e7..95d92b91e 100644
--- a/src/tensors/cpu/sharp/packed_gemm.cpp
+++ b/src/tensors/cpu/sharp/packed_gemm.cpp
@@ -38,6 +38,17 @@ namespace variant { // Variants of GEMM implementations
 
 #if USE_FBGEMM
 // initialize with a dummy
+// When this class is instantiated,
+// the actual packing operation is happening.If we create this instance every time we call GEMM,
+// we are doing packing every time and very slow.
+// In Caffe2, the operator is stateful and hold an instance of this.
+// But, we don't have any logic for this in marian. We can only cache a tensor (which means a memory chunk).
+// So, for now, we keep the packed memory on our own 1D tensor, then when we call GEMM,
+// we just reuse this instance again and again by replacing the class members (including memory pointer). Eventually,
+// I will add a new constructor to the class in FBGEMM which accepts
+// pre - allocated and pre - packed memory as a parameter.After it's done,
+// this temporary buffer will be removed.
+//
 // In a multi marian instance setting (as a dynamic library),
 // different marian instances should not share this variable.
 static thread_local PackedGemmMatrixFP16 packedPlaceholder(1, 1, 1, 1, 1, 1, 1, 1);

From 12a5b6d0916ac4b7e7ed2cb180f98f6ed8709a57 Mon Sep 17 00:00:00 2001
From: Young Jin Kim <youki@microsoft.com>
Date: Wed, 3 Jul 2019 10:20:33 -0700
Subject: [PATCH 523/838] Fixed indentations

---
 src/graph/expression_operators.cpp | 60 ++++++++++++++++--------------
 1 file changed, 33 insertions(+), 27 deletions(-)

diff --git a/src/graph/expression_operators.cpp b/src/graph/expression_operators.cpp
index a90a69201..97feeb4f3 100755
--- a/src/graph/expression_operators.cpp
+++ b/src/graph/expression_operators.cpp
@@ -463,14 +463,15 @@ Expr affine(Expr a, Expr b, Expr bias, bool transA, bool transB, float scale) {
           auto packed = cpu::variant::pack(b, cpu::variant::PackMatrix::B, transB, clipValue);
 
           return recPack(
-            cpu::variant::affine(clip(a, clipValue),
-                                 packed,
-                                 b->shape(),
-                                 bias,
-                                 transA,
-                                 transB,
-                                 scale),
-            true);
+              cpu::variant::affine(
+                  clip(a, clipValue),
+                  packed,
+                  b->shape(),
+                  bias,
+                  transA,
+                  transB,
+                  scale),
+              true);
         };
         tuner->insert({hashPack, algPack});
       }
@@ -485,13 +486,17 @@ Expr affine(Expr a, Expr b, Expr bias, bool transA, bool transB, float scale) {
       };
       auto algInt16 = [=]() {
         return recInt16(
-          cpu::int16::affine(
-                recInt16(cpu::int16::quantize(transA ? recInt16(transpose(a)) : a,
-                                      clipValue)),
-            cpu::int16::quantize(transB ? b : transpose(b), clipValue),
-            bias,
-            scale),
-          true);
+            cpu::int16::affine(
+                recInt16(
+                    cpu::int16::quantize(
+                        transA ? recInt16(transpose(a)) : a,
+                        clipValue)),
+                cpu::int16::quantize(
+                    transB ? b : transpose(b),
+                    clipValue),
+                bias,
+                scale),
+            true);
       };
       tuner->insert({hashInt16, algInt16});
 
@@ -516,7 +521,7 @@ Expr affine(Expr a, Expr b, Expr bias, bool transA, bool transB, float scale) {
         Expr ones = ac->graph()->ones({rows, 1});
         std::vector<Expr> nodes = {ac, bc, bias, ones};
         return recCblas(Expression<AffineNodeOp>(nodes, transA, transB, scale),
-                    true);
+                        true);
       };
       tuner->insert({hashCblas, algCblas});
 
@@ -527,22 +532,23 @@ Expr affine(Expr a, Expr b, Expr bias, bool transA, bool transB, float scale) {
       if(gemmType == GemmType::IntrinInt16) {
         // cpu int16 version
         return cpu::int16::affine(
-          cpu::int16::quantize(transA ? transpose(a) : a, clipValue),
-          cpu::int16::quantize(transB ? b : transpose(b), clipValue),
-          bias,
-          scale);
+            cpu::int16::quantize(transA ? transpose(a) : a, clipValue),
+            cpu::int16::quantize(transB ? b : transpose(b), clipValue),
+            bias,
+            scale);
       } else if(gemmType == GemmType::FbFp16Packed) {
 #if USE_FBGEMM
         if(b->memoize()) {
           auto packed = cpu::variant::pack(b, cpu::variant::PackMatrix::B, transB, clipValue);
 
-          return cpu::variant::affine(clip(a, clipValue),
-                                      packed,
-                                      b->shape(),
-                                      bias,
-                                      transA,
-                                      transB,
-                                      scale);
+          return cpu::variant::affine(
+              clip(a, clipValue),
+              packed,
+              b->shape(),
+              bias,
+              transA,
+              transB,
+              scale);
         } else {
           int rows = a->shape().elements() / a->shape()[-1];
           Expr ones = a->graph()->ones({rows, 1});

From 1b03ed688b5a9e7bd3db7ddb86f9b02efcc8a8b8 Mon Sep 17 00:00:00 2001
From: Young Jin Kim <youki@microsoft.com>
Date: Wed, 3 Jul 2019 11:13:34 -0700
Subject: [PATCH 524/838] Change #if to #ifdef for USE_FBGEMM and MKL_FOUND.
 Add override attribute to avoid a build warning on linux

---
 src/common/config_parser.cpp          | 2 +-
 src/graph/expression_operators.cpp    | 4 ++--
 src/tensors/cpu/backend.h             | 4 ++--
 src/tensors/cpu/expanded_gemm.h       | 4 ++--
 src/tensors/cpu/prod.cpp              | 2 +-
 src/tensors/cpu/sharp/packed_gemm.cpp | 8 ++++----
 src/tensors/cpu/tensor_operators.cpp  | 6 +++---
 7 files changed, 15 insertions(+), 15 deletions(-)

diff --git a/src/common/config_parser.cpp b/src/common/config_parser.cpp
index bd6650c61..2973e71fc 100755
--- a/src/common/config_parser.cpp
+++ b/src/common/config_parser.cpp
@@ -12,7 +12,7 @@
 #include <stdexcept>
 #include <string>
 
-#if MKL_FOUND
+#ifdef MKL_FOUND
 #include <mkl.h>
 #else
 #if BLAS_FOUND
diff --git a/src/graph/expression_operators.cpp b/src/graph/expression_operators.cpp
index 97feeb4f3..6b0ec4720 100755
--- a/src/graph/expression_operators.cpp
+++ b/src/graph/expression_operators.cpp
@@ -437,7 +437,7 @@ Expr affine(Expr a, Expr b, Expr bias, bool transA, bool transB, float scale) {
       util::hash_combine(hash, transA);
       util::hash_combine(hash, transB);
 
-#if USE_FBGEMM
+#ifdef USE_FBGEMM
       // Use Packed GEMM only if the node b in the graph is memoized.
       // More specifically, packed GEMM is used only if the B matrix (weight) is constant.
       // In general, 'memoized' means that the node is a constant variable or
@@ -537,7 +537,7 @@ Expr affine(Expr a, Expr b, Expr bias, bool transA, bool transB, float scale) {
             bias,
             scale);
       } else if(gemmType == GemmType::FbFp16Packed) {
-#if USE_FBGEMM
+#ifdef USE_FBGEMM
         if(b->memoize()) {
           auto packed = cpu::variant::pack(b, cpu::variant::PackMatrix::B, transB, clipValue);
 
diff --git a/src/tensors/cpu/backend.h b/src/tensors/cpu/backend.h
index 8d4adcfcb..ae1377534 100644
--- a/src/tensors/cpu/backend.h
+++ b/src/tensors/cpu/backend.h
@@ -20,14 +20,14 @@ class Backend : public marian::Backend {
   void synchronize() override {}
 
   // for CPU & inference only, sets to use optimized code for inference. Does nothing for GPU.
-  void setOptimized(bool optimize) { optimized_ = optimize; }
+  void setOptimized(bool optimize) override { optimized_ = optimize; }
   bool isOptimized() override { return optimized_; }
   // for CPU only, selects different GEMM types for the inference. Does nothing for GPU.
   void setGemmType(std::string gemmType) override {
     if      (gemmType == "auto")        gemmType_ = GemmType::Auto;
     else if (gemmType == "mklfp32")     gemmType_ = GemmType::MklFp32;
     else if (gemmType == "intrinint16") gemmType_ = GemmType::IntrinInt16;
-#if USE_FBGEMM
+#ifdef USE_FBGEMM
     else if (gemmType == "fp16packed")  gemmType_ = GemmType::FbFp16Packed;
     else if (gemmType == "int8packed")  gemmType_ = GemmType::FbInt8Packed;
 #endif // USE_FBGEMM
diff --git a/src/tensors/cpu/expanded_gemm.h b/src/tensors/cpu/expanded_gemm.h
index 0c68ccfd7..a9fd40617 100644
--- a/src/tensors/cpu/expanded_gemm.h
+++ b/src/tensors/cpu/expanded_gemm.h
@@ -3,7 +3,7 @@
 #include "graph/node.h"
 #include "tensors/cpu/sharp/packed_gemm.h"
 
-#if USE_FBGEMM
+#ifdef USE_FBGEMM
 #include "3rd_party/fbgemm/include/fbgemm/FbgemmFP16.h"
 using namespace fbgemm;
 #endif  // USE_FBGEMM
@@ -81,7 +81,7 @@ struct PackNodeOp : public UnaryNodeOp {
   const std::string type() override { return "packMat"; }
 
   Shape newShape(Expr a, bool transpose) {
-#if USE_FBGEMM
+#ifdef USE_FBGEMM
     auto shapeMat = a->shape();
     // Should be 2D - weight matrix
     ABORT_IF(shapeMat.size() != 2,
diff --git a/src/tensors/cpu/prod.cpp b/src/tensors/cpu/prod.cpp
index ac13ccee8..4c8bc7ac4 100755
--- a/src/tensors/cpu/prod.cpp
+++ b/src/tensors/cpu/prod.cpp
@@ -7,7 +7,7 @@
 #include "tensors/tensor.h"
 #include "tensors/tensor_allocator.h"
 
-#if MKL_FOUND
+#ifdef MKL_FOUND
 #include <mkl.h>
 #else
 #if BLAS_FOUND
diff --git a/src/tensors/cpu/sharp/packed_gemm.cpp b/src/tensors/cpu/sharp/packed_gemm.cpp
index 95d92b91e..f659ce4b5 100644
--- a/src/tensors/cpu/sharp/packed_gemm.cpp
+++ b/src/tensors/cpu/sharp/packed_gemm.cpp
@@ -15,7 +15,7 @@
 #pragma warning(disable: 4505) // warning C4505: 'fbgemmAlignedAlloc' in fbgemm.h: unreferenced local function has been removed (missing 'static inline')
 #endif
 
-#if USE_FBGEMM
+#ifdef USE_FBGEMM
 #include "3rd_party/fbgemm/include/fbgemm/FbgemmFP16.h"
 #include "3rd_party/fbgemm/include/fbgemm/QuantUtils.h"
 #include "3rd_party/fbgemm/include/fbgemm/Fbgemm.h"
@@ -24,7 +24,7 @@
 #include <omp.h>
 #endif
 
-#if MKL_FOUND
+#ifdef MKL_FOUND
 #include <mkl.h>
 #include <mkl_types.h>
 #endif
@@ -36,7 +36,7 @@ namespace marian {
 namespace cpu {
 namespace variant { // Variants of GEMM implementations
 
-#if USE_FBGEMM
+#ifdef USE_FBGEMM
 // initialize with a dummy
 // When this class is instantiated,
 // the actual packing operation is happening.If we create this instance every time we call GEMM,
@@ -171,7 +171,7 @@ void GemmPackFp32(marian::Tensor C,
   // packed matrix
   packedPlaceholder.pmat_ = (fbgemm::float16*)(B->data<uint8_t>() + 256);
 
-#if MKL_FOUND
+#ifdef MKL_FOUND
   for(int i = 0; i < m; ++i) {
     mkl_somatcopy('R', 'N', 1, n, 1, bias->data(), n, C->data() + n * i, n);
   }
diff --git a/src/tensors/cpu/tensor_operators.cpp b/src/tensors/cpu/tensor_operators.cpp
index 63ade0238..05e67e1f0 100644
--- a/src/tensors/cpu/tensor_operators.cpp
+++ b/src/tensors/cpu/tensor_operators.cpp
@@ -10,7 +10,7 @@
 #include "functional/functional.h"
 #include "functional/tensor.h"
 
-#if MKL_FOUND
+#ifdef MKL_FOUND
 #include <mkl.h>
 #endif
 
@@ -201,7 +201,7 @@ void Transpose0213(Tensor out, Tensor in) {
 template <bool add>
 void TransposeFirst3In4(Tensor out, Tensor in, const std::vector<int>& vAxis) {
   ABORT_IF(vAxis.size() != 4, "This function handles only 4D arrays.");
-#if MKL_FOUND
+#ifdef MKL_FOUND
   int innermost = in->shape()[-1];
 
   int l1 = in->shape()[vAxis[0]];
@@ -333,7 +333,7 @@ void TransposeGeneric(Tensor out, Tensor in, const std::vector<int>& vAxis) {
 void TransposeND(Tensor out, Tensor in, const std::vector<int>& vAxis) {
   if(vAxis == std::vector<int>({0, 2, 1, 3}))
     Transpose0213<false>(out, in);
-#if MKL_FOUND
+#ifdef MKL_FOUND
   else if(vAxis.size() == 4 && vAxis[3] == 3)
     TransposeFirst3In4<false>(out, in, vAxis);
 #endif  // MKL_FOUND

From 843c84ad717d8e84b47b8ce5549cd13e0b13c6f1 Mon Sep 17 00:00:00 2001
From: Young Jin Kim <youki@microsoft.com>
Date: Wed, 3 Jul 2019 15:00:04 -0700
Subject: [PATCH 525/838] Fix a bug for GPU inference

---
 src/graph/expression_operators.cpp | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/graph/expression_operators.cpp b/src/graph/expression_operators.cpp
index 6b0ec4720..a7bccaab2 100755
--- a/src/graph/expression_operators.cpp
+++ b/src/graph/expression_operators.cpp
@@ -411,7 +411,7 @@ Expr affine(Expr a, Expr b, Expr bias, bool transA, bool transB, float scale) {
 
   float clipValue = a->graph()->getBackend()->getClip();
 
-  if(a->graph()->getBackend()->isOptimized() && device == DeviceType::cpu) {
+  if(device == DeviceType::cpu && a->graph()->getBackend()->isOptimized()) {
     GemmType gemmType = a->graph()->getBackend()->getGemmType();
     // When gemmType is set to 'auto', an autotuner decides the best algorithm available.
     // A new autotuner is created, then different kinds of algorithms are added to the autotuner.

From d82bd9d2718e9eeb7627e82840d5547b60f56b74 Mon Sep 17 00:00:00 2001
From: Young Jin Kim <youki@microsoft.com>
Date: Wed, 3 Jul 2019 15:58:14 -0700
Subject: [PATCH 526/838] Add Wl block to avoid parallel dependencies in the
 unit tests

---
 cmake/FindMKL.cmake | 5 ++++-
 1 file changed, 4 insertions(+), 1 deletion(-)

diff --git a/cmake/FindMKL.cmake b/cmake/FindMKL.cmake
index a484e9ab4..4e8a99eee 100644
--- a/cmake/FindMKL.cmake
+++ b/cmake/FindMKL.cmake
@@ -89,7 +89,10 @@ find_library(MKL_CORE_LIBRARY
              NO_DEFAULT_PATH)
 
 set(MKL_INCLUDE_DIRS ${MKL_INCLUDE_DIR})
-set(MKL_LIBRARIES ${MKL_INTERFACE_LIBRARY} ${MKL_SEQUENTIAL_LAYER_LIBRARY} ${MKL_CORE_LIBRARY})
+# Added -Wl block to avoid circular dependencies.
+# https://stackoverflow.com/questions/5651869/what-are-the-start-group-and-end-group-command-line-options
+# https://software.intel.com/en-us/articles/intel-mkl-link-line-advisor
+set(MKL_LIBRARIES -Wl,--start-group ${MKL_INTERFACE_LIBRARY} ${MKL_SEQUENTIAL_LAYER_LIBRARY} ${MKL_CORE_LIBRARY} -Wl,--end-group)
 
 # message("1 ${MKL_INCLUDE_DIR}")
 # message("2 ${MKL_INTERFACE_LIBRARY}")

From cb376612ed3e26a6e1d9fea1fd8f81b9cb5bba9a Mon Sep 17 00:00:00 2001
From: Young Jin Kim <youki@microsoft.com>
Date: Wed, 3 Jul 2019 17:39:56 -0700
Subject: [PATCH 527/838] Fix backend functions bug

---
 src/rescorer/rescorer.h     |  6 ++++--
 src/tensors/backend.h       |  8 ++++----
 src/tensors/gpu/backend.h   | 19 +++++++++++--------
 src/translator/translator.h | 12 ++++++++----
 4 files changed, 27 insertions(+), 18 deletions(-)

diff --git a/src/rescorer/rescorer.h b/src/rescorer/rescorer.h
index c4832078a..f2b1a0fe3 100755
--- a/src/rescorer/rescorer.h
+++ b/src/rescorer/rescorer.h
@@ -69,8 +69,10 @@ class Rescore : public ModelTask {
     for(auto device : devices) {
       auto graph = New<ExpressionGraph>(true);
       graph->setDevice(device);
-      graph->getBackend()->setOptimized(options_->get<bool>("optimize"));
-      graph->getBackend()->setGemmType(options_->get<std::string>("gemm-type"));
+      if (device.type == DeviceType::cpu) {
+        graph->getBackend()->setOptimized(options_->get<bool>("optimize"));
+        graph->getBackend()->setGemmType(options_->get<std::string>("gemm-type"));
+      }
       graph->reserveWorkspaceMB(options_->get<size_t>("workspace"));
       graphs_.push_back(graph);
     }
diff --git a/src/tensors/backend.h b/src/tensors/backend.h
index 2e8d24c78..1fddb774c 100644
--- a/src/tensors/backend.h
+++ b/src/tensors/backend.h
@@ -36,12 +36,12 @@ class Backend {
   virtual void setClip(float clipValue) { clipValue_ = clipValue; }
   float getClip() { return clipValue_; }
 
-  // for CPU & inference only, sets to use optimized code for inference.
-  // The program aborts if these are called from GPU device.
+  // for CPU, sets to use optimized code for inference.
+  // for GPU, this is always false.
   virtual void setOptimized(bool optimize) = 0;
   virtual bool isOptimized() = 0;
-  // for CPU only, selects different GEMM types for the inference.
-  // The program aborts if these are called from GPU device.
+  // for CPU, selects different GEMM types for the inference.
+  // for GPU, there's no gemm type. so, it does nothing.
   virtual void setGemmType(std::string gemmType) = 0;
   virtual GemmType getGemmType() = 0;
 };
diff --git a/src/tensors/gpu/backend.h b/src/tensors/gpu/backend.h
index dd177aad4..8e1292d7d 100644
--- a/src/tensors/gpu/backend.h
+++ b/src/tensors/gpu/backend.h
@@ -34,18 +34,21 @@ class Backend : public marian::Backend {
   cublasHandle_t getCublasHandle() { return cublasHandle_; }
   cusparseHandle_t getCusparseHandle() { return cusparseHandle_; }
 
-  // for CPU & inference only, sets to use optimized code for inference.
-  // The program aborts if these are called from GPU device.
-  void setOptimized(bool optimize) override { ABORT("Not supported for GPU_{}", optimize); }
+  // for CPU, sets to use optimized code for inference.
+  // for GPU, this is always false.
+  void setOptimized(bool optimize) override {
+    LOG(info, "Not supported for GPU_{}", optimize);
+  }
   bool isOptimized() override {
-    ABORT("Not supported for GPU");
     return false;
   }
-  // for CPU only, selects different GEMM types for the inference.
-  // The program aborts if these are called from GPU device.
-  void setGemmType(std::string gemmType) override { ABORT("Not supported for GPU_{}", gemmType); }
+  // for CPU, selects different GEMM types for the inference.
+  // for GPU, there's no gemm type. so, it does nothing.
+  void setGemmType(std::string gemmType) override {
+    LOG(info, "Not supported for GPU_{}", gemmType);
+  }
   GemmType getGemmType() override {
-    ABORT("Not supported for GPU");
+    LOG(info, "Not supported for GPU");
     return GemmType::Auto;
   }
 
diff --git a/src/translator/translator.h b/src/translator/translator.h
index ee08a2304..d2518f5da 100644
--- a/src/translator/translator.h
+++ b/src/translator/translator.h
@@ -58,8 +58,10 @@ class Translate : public ModelTask {
         auto graph = New<ExpressionGraph>(true);
         graph->setDevice(device);
         graph->getBackend()->setClip(options_->get<float>("clip-gemm"));
-        graph->getBackend()->setOptimized(options_->get<bool>("optimize"));
-        graph->getBackend()->setGemmType(options_->get<std::string>("gemm-type"));
+        if (device.type == DeviceType::cpu) {
+          graph->getBackend()->setOptimized(options_->get<bool>("optimize"));
+          graph->getBackend()->setGemmType(options_->get<std::string>("gemm-type"));
+        }
         graph->reserveWorkspaceMB(options_->get<size_t>("workspace"));
         graphs_[id] = graph;
 
@@ -172,8 +174,10 @@ class TranslateService : public ModelServiceTask {
       auto graph = New<ExpressionGraph>(true);
       graph->setDevice(device);
       graph->getBackend()->setClip(options_->get<float>("clip-gemm"));
-      graph->getBackend()->setOptimized(options_->get<bool>("optimize"));
-      graph->getBackend()->setGemmType(options_->get<std::string>("gemm-type"));
+      if (device.type == DeviceType::cpu) {
+        graph->getBackend()->setOptimized(options_->get<bool>("optimize"));
+        graph->getBackend()->setGemmType(options_->get<std::string>("gemm-type"));
+      }
       graph->reserveWorkspaceMB(options_->get<size_t>("workspace"));
       graphs_.push_back(graph);
 

From 412afe3a93531bfb83697b8bc35bc0cdc2b43af5 Mon Sep 17 00:00:00 2001
From: Young Jin Kim <youki@microsoft.com>
Date: Thu, 4 Jul 2019 08:50:28 -0700
Subject: [PATCH 528/838] Add more comments and change #ifdef to #if back

---
 src/common/config_parser.cpp          |  2 +-
 src/graph/expression_operators.cpp    |  4 ++--
 src/microsoft/quicksand.cpp           |  4 ++--
 src/tensors/backend.h                 |  2 +-
 src/tensors/cpu/backend.h             |  2 +-
 src/tensors/cpu/expanded_gemm.h       |  4 ++--
 src/tensors/cpu/prod.cpp              |  2 +-
 src/tensors/cpu/sharp/packed_gemm.cpp | 11 ++++++-----
 src/tensors/cpu/tensor_operators.cpp  |  6 +++---
 src/tensors/gpu/backend.h             |  8 ++++----
 10 files changed, 23 insertions(+), 22 deletions(-)

diff --git a/src/common/config_parser.cpp b/src/common/config_parser.cpp
index 2973e71fc..bd6650c61 100755
--- a/src/common/config_parser.cpp
+++ b/src/common/config_parser.cpp
@@ -12,7 +12,7 @@
 #include <stdexcept>
 #include <string>
 
-#ifdef MKL_FOUND
+#if MKL_FOUND
 #include <mkl.h>
 #else
 #if BLAS_FOUND
diff --git a/src/graph/expression_operators.cpp b/src/graph/expression_operators.cpp
index a7bccaab2..0e762067a 100755
--- a/src/graph/expression_operators.cpp
+++ b/src/graph/expression_operators.cpp
@@ -437,7 +437,7 @@ Expr affine(Expr a, Expr b, Expr bias, bool transA, bool transB, float scale) {
       util::hash_combine(hash, transA);
       util::hash_combine(hash, transB);
 
-#ifdef USE_FBGEMM
+#if USE_FBGEMM
       // Use Packed GEMM only if the node b in the graph is memoized.
       // More specifically, packed GEMM is used only if the B matrix (weight) is constant.
       // In general, 'memoized' means that the node is a constant variable or
@@ -537,7 +537,7 @@ Expr affine(Expr a, Expr b, Expr bias, bool transA, bool transB, float scale) {
             bias,
             scale);
       } else if(gemmType == GemmType::FbFp16Packed) {
-#ifdef USE_FBGEMM
+#if USE_FBGEMM
         if(b->memoize()) {
           auto packed = cpu::variant::pack(b, cpu::variant::PackMatrix::B, transB, clipValue);
 
diff --git a/src/microsoft/quicksand.cpp b/src/microsoft/quicksand.cpp
index d46273b0e..2dc2efc4b 100644
--- a/src/microsoft/quicksand.cpp
+++ b/src/microsoft/quicksand.cpp
@@ -1,7 +1,7 @@
 #include "quicksand.h"
 #include "marian.h"
 
-#ifdef MKL_FOUND
+#if MKL_FOUND
 #include "mkl.h"
 #endif
 
@@ -74,7 +74,7 @@ class BeamSearchDecoder : public IBeamSearchDecoder {
     graph_->getBackend()->setOptimized(true);
     graph_->getBackend()->setGemmType("fp16packed");
 
-#ifdef MKL_FOUND
+#if MKL_FOUND
     mkl_set_num_threads(options->get<int>("mkl-threads", 1));
 #endif
 
diff --git a/src/tensors/backend.h b/src/tensors/backend.h
index 1fddb774c..1ac60bc79 100644
--- a/src/tensors/backend.h
+++ b/src/tensors/backend.h
@@ -37,7 +37,7 @@ class Backend {
   float getClip() { return clipValue_; }
 
   // for CPU, sets to use optimized code for inference.
-  // for GPU, this is always false.
+  // for GPU, this is invalid. for gpu, isOptimized() function always returns false.
   virtual void setOptimized(bool optimize) = 0;
   virtual bool isOptimized() = 0;
   // for CPU, selects different GEMM types for the inference.
diff --git a/src/tensors/cpu/backend.h b/src/tensors/cpu/backend.h
index ae1377534..dd496843a 100644
--- a/src/tensors/cpu/backend.h
+++ b/src/tensors/cpu/backend.h
@@ -27,7 +27,7 @@ class Backend : public marian::Backend {
     if      (gemmType == "auto")        gemmType_ = GemmType::Auto;
     else if (gemmType == "mklfp32")     gemmType_ = GemmType::MklFp32;
     else if (gemmType == "intrinint16") gemmType_ = GemmType::IntrinInt16;
-#ifdef USE_FBGEMM
+#if USE_FBGEMM
     else if (gemmType == "fp16packed")  gemmType_ = GemmType::FbFp16Packed;
     else if (gemmType == "int8packed")  gemmType_ = GemmType::FbInt8Packed;
 #endif // USE_FBGEMM
diff --git a/src/tensors/cpu/expanded_gemm.h b/src/tensors/cpu/expanded_gemm.h
index a9fd40617..0c68ccfd7 100644
--- a/src/tensors/cpu/expanded_gemm.h
+++ b/src/tensors/cpu/expanded_gemm.h
@@ -3,7 +3,7 @@
 #include "graph/node.h"
 #include "tensors/cpu/sharp/packed_gemm.h"
 
-#ifdef USE_FBGEMM
+#if USE_FBGEMM
 #include "3rd_party/fbgemm/include/fbgemm/FbgemmFP16.h"
 using namespace fbgemm;
 #endif  // USE_FBGEMM
@@ -81,7 +81,7 @@ struct PackNodeOp : public UnaryNodeOp {
   const std::string type() override { return "packMat"; }
 
   Shape newShape(Expr a, bool transpose) {
-#ifdef USE_FBGEMM
+#if USE_FBGEMM
     auto shapeMat = a->shape();
     // Should be 2D - weight matrix
     ABORT_IF(shapeMat.size() != 2,
diff --git a/src/tensors/cpu/prod.cpp b/src/tensors/cpu/prod.cpp
index 4c8bc7ac4..ac13ccee8 100755
--- a/src/tensors/cpu/prod.cpp
+++ b/src/tensors/cpu/prod.cpp
@@ -7,7 +7,7 @@
 #include "tensors/tensor.h"
 #include "tensors/tensor_allocator.h"
 
-#ifdef MKL_FOUND
+#if MKL_FOUND
 #include <mkl.h>
 #else
 #if BLAS_FOUND
diff --git a/src/tensors/cpu/sharp/packed_gemm.cpp b/src/tensors/cpu/sharp/packed_gemm.cpp
index f659ce4b5..28f798d40 100644
--- a/src/tensors/cpu/sharp/packed_gemm.cpp
+++ b/src/tensors/cpu/sharp/packed_gemm.cpp
@@ -15,7 +15,7 @@
 #pragma warning(disable: 4505) // warning C4505: 'fbgemmAlignedAlloc' in fbgemm.h: unreferenced local function has been removed (missing 'static inline')
 #endif
 
-#ifdef USE_FBGEMM
+#if USE_FBGEMM
 #include "3rd_party/fbgemm/include/fbgemm/FbgemmFP16.h"
 #include "3rd_party/fbgemm/include/fbgemm/QuantUtils.h"
 #include "3rd_party/fbgemm/include/fbgemm/Fbgemm.h"
@@ -24,7 +24,7 @@
 #include <omp.h>
 #endif
 
-#ifdef MKL_FOUND
+#if MKL_FOUND
 #include <mkl.h>
 #include <mkl_types.h>
 #endif
@@ -36,10 +36,10 @@ namespace marian {
 namespace cpu {
 namespace variant { // Variants of GEMM implementations
 
-#ifdef USE_FBGEMM
+#if USE_FBGEMM
 // initialize with a dummy
 // When this class is instantiated,
-// the actual packing operation is happening.If we create this instance every time we call GEMM,
+// the actual packing operation is happening. If we create this instance every time we call GEMM,
 // we are doing packing every time and very slow.
 // In Caffe2, the operator is stateful and hold an instance of this.
 // But, we don't have any logic for this in marian. We can only cache a tensor (which means a memory chunk).
@@ -48,6 +48,7 @@ namespace variant { // Variants of GEMM implementations
 // I will add a new constructor to the class in FBGEMM which accepts
 // pre - allocated and pre - packed memory as a parameter.After it's done,
 // this temporary buffer will be removed.
+// When constructing this dummy buffer, ones are used for all the parameters to allocate minimum amount of memory.
 //
 // In a multi marian instance setting (as a dynamic library),
 // different marian instances should not share this variable.
@@ -171,7 +172,7 @@ void GemmPackFp32(marian::Tensor C,
   // packed matrix
   packedPlaceholder.pmat_ = (fbgemm::float16*)(B->data<uint8_t>() + 256);
 
-#ifdef MKL_FOUND
+#if MKL_FOUND
   for(int i = 0; i < m; ++i) {
     mkl_somatcopy('R', 'N', 1, n, 1, bias->data(), n, C->data() + n * i, n);
   }
diff --git a/src/tensors/cpu/tensor_operators.cpp b/src/tensors/cpu/tensor_operators.cpp
index 05e67e1f0..63ade0238 100644
--- a/src/tensors/cpu/tensor_operators.cpp
+++ b/src/tensors/cpu/tensor_operators.cpp
@@ -10,7 +10,7 @@
 #include "functional/functional.h"
 #include "functional/tensor.h"
 
-#ifdef MKL_FOUND
+#if MKL_FOUND
 #include <mkl.h>
 #endif
 
@@ -201,7 +201,7 @@ void Transpose0213(Tensor out, Tensor in) {
 template <bool add>
 void TransposeFirst3In4(Tensor out, Tensor in, const std::vector<int>& vAxis) {
   ABORT_IF(vAxis.size() != 4, "This function handles only 4D arrays.");
-#ifdef MKL_FOUND
+#if MKL_FOUND
   int innermost = in->shape()[-1];
 
   int l1 = in->shape()[vAxis[0]];
@@ -333,7 +333,7 @@ void TransposeGeneric(Tensor out, Tensor in, const std::vector<int>& vAxis) {
 void TransposeND(Tensor out, Tensor in, const std::vector<int>& vAxis) {
   if(vAxis == std::vector<int>({0, 2, 1, 3}))
     Transpose0213<false>(out, in);
-#ifdef MKL_FOUND
+#if MKL_FOUND
   else if(vAxis.size() == 4 && vAxis[3] == 3)
     TransposeFirst3In4<false>(out, in, vAxis);
 #endif  // MKL_FOUND
diff --git a/src/tensors/gpu/backend.h b/src/tensors/gpu/backend.h
index 8e1292d7d..2a7672555 100644
--- a/src/tensors/gpu/backend.h
+++ b/src/tensors/gpu/backend.h
@@ -35,9 +35,9 @@ class Backend : public marian::Backend {
   cusparseHandle_t getCusparseHandle() { return cusparseHandle_; }
 
   // for CPU, sets to use optimized code for inference.
-  // for GPU, this is always false.
+  // for GPU, this is invalid. for gpu, isOptimized() function always returns false.
   void setOptimized(bool optimize) override {
-    LOG(info, "Not supported for GPU_{}", optimize);
+    LOG_ONCE(info, "setOptimized() not supported for GPU_{}", optimize);
   }
   bool isOptimized() override {
     return false;
@@ -45,10 +45,10 @@ class Backend : public marian::Backend {
   // for CPU, selects different GEMM types for the inference.
   // for GPU, there's no gemm type. so, it does nothing.
   void setGemmType(std::string gemmType) override {
-    LOG(info, "Not supported for GPU_{}", gemmType);
+    LOG_ONCE(info, "setGemmType() not supported for GPU_{}", gemmType);
   }
   GemmType getGemmType() override {
-    LOG(info, "Not supported for GPU");
+    LOG_ONCE(info, "getGemmType() not supported for GPU");
     return GemmType::Auto;
   }
 

From 222ed4040e879cab53a4ef2e326205d67f78b64e Mon Sep 17 00:00:00 2001
From: Marcin Junczys-Dowmunt <Marcin.JunczysDowmunt@microsoft.com>
Date: Thu, 4 Jul 2019 16:26:32 -0700
Subject: [PATCH 529/838] make compile with CUDNN and static libs, suppress
 CMAKE dev warning from 3rd party tools

---
 CMakeLists.txt                 | 1 -
 src/3rd_party/CMakeLists.txt   | 7 +++++++
 src/examples/CMakeLists.txt    | 2 +-
 src/models/char_s2s.h          | 4 ++--
 src/models/model_factory.cpp   | 2 +-
 src/tests/CMakeLists.txt       | 2 +-
 src/tests/units/CMakeLists.txt | 2 +-
 7 files changed, 13 insertions(+), 7 deletions(-)

diff --git a/CMakeLists.txt b/CMakeLists.txt
index 7cb518c56..65ee3367f 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -5,7 +5,6 @@ if (POLICY CMP0074)
   cmake_policy(SET CMP0074 NEW) # CMake 3.12
 endif ()
 
-
 project(marian CXX C)
 set(CMAKE_CXX_STANDARD 11)
 set(CMAKE_CXX_STANDARD_REQUIRED ON)
diff --git a/src/3rd_party/CMakeLists.txt b/src/3rd_party/CMakeLists.txt
index 3dca348bc..714882ef5 100644
--- a/src/3rd_party/CMakeLists.txt
+++ b/src/3rd_party/CMakeLists.txt
@@ -5,7 +5,14 @@ add_subdirectory(./yaml-cpp)
 add_subdirectory(./SQLiteCpp)
 add_subdirectory(./pathie-cpp)
 add_subdirectory(./zlib)
+
 if(USE_FBGEMM)
+  # @TODO: find out if this is somehow harmful. This is supppressing CMake warnings for CMAKE_SUPPRESS_DEVELOPER_WARNINGS
+  # meant to silence CMakeFiles of 3rd_party tools.
+  if(NOT DEFINED CMAKE_SUPPRESS_DEVELOPER_WARNINGS)
+    set(CMAKE_SUPPRESS_DEVELOPER_WARNINGS 1 CACHE INTERNAL "No dev warnings")
+  endif()
+
   set(FBGEMM_BUILD_TESTS OFF CACHE BOOL "Disable fbgemm tests")
   set(FBGEMM_BUILD_BENCHMARKS OFF CACHE BOOL "Disable fbgemm benchmark")
   add_subdirectory(./fbgemm)
diff --git a/src/examples/CMakeLists.txt b/src/examples/CMakeLists.txt
index 8740fa6b1..c904a3f3b 100644
--- a/src/examples/CMakeLists.txt
+++ b/src/examples/CMakeLists.txt
@@ -4,7 +4,7 @@ add_executable(mnist_example mnist/mnist_ffnn.cpp)
 foreach(exec iris_example mnist_example)
   target_link_libraries(${exec} marian ${EXT_LIBS})
   if(CUDA_FOUND)
-    target_link_libraries(${exec} marian marian_cuda ${EXT_LIBS})
+    target_link_libraries(${exec} marian ${EXT_LIBS} marian_cuda ${EXT_LIBS})
   endif(CUDA_FOUND)
   set_target_properties(${exec} PROPERTIES RUNTIME_OUTPUT_DIRECTORY "${CMAKE_BINARY_DIR}")
 endforeach(exec)
diff --git a/src/models/char_s2s.h b/src/models/char_s2s.h
index 06882dca8..bcd7af9b2 100755
--- a/src/models/char_s2s.h
+++ b/src/models/char_s2s.h
@@ -8,9 +8,9 @@
 namespace marian {
 
 class CharS2SEncoder : public EncoderS2S {
+  using EncoderS2S::EncoderS2S;
+  
 public:
-  CharS2SEncoder(Ptr<Options> options) : EncoderS2S(options) {}
-
   virtual Ptr<EncoderState> build(Ptr<ExpressionGraph> graph,
                                   Ptr<data::CorpusBatch> batch) override {
     graph_ = graph;
diff --git a/src/models/model_factory.cpp b/src/models/model_factory.cpp
index 098fc0a59..3d9e1e27b 100755
--- a/src/models/model_factory.cpp
+++ b/src/models/model_factory.cpp
@@ -32,7 +32,7 @@ Ptr<EncoderBase> EncoderFactory::construct(Ptr<ExpressionGraph> graph) {
 
 #ifdef CUDNN
   if(options_->get<std::string>("type") == "char-s2s")
-    return New<CharS2SEncoder>(options_);
+    return New<CharS2SEncoder>(graph, options_);
 #endif
 
   if(options_->get<std::string>("type") == "transformer")
diff --git a/src/tests/CMakeLists.txt b/src/tests/CMakeLists.txt
index 2df971407..e31ed1d4e 100644
--- a/src/tests/CMakeLists.txt
+++ b/src/tests/CMakeLists.txt
@@ -16,7 +16,7 @@ foreach(test ${APP_TESTS})
   add_executable("test_${test}" "${test}.cpp")
 
   if(CUDA_FOUND)
-    target_link_libraries("test_${test}" marian marian_cuda ${EXT_LIBS})
+    target_link_libraries("test_${test}" ${EXT_LIBS} marian ${EXT_LIBS} marian_cuda ${EXT_LIBS})
   else(CUDA_FOUND)
     target_link_libraries("test_${test}" marian ${EXT_LIBS})
   endif(CUDA_FOUND)
diff --git a/src/tests/units/CMakeLists.txt b/src/tests/units/CMakeLists.txt
index 7d842c427..e9e89d09f 100644
--- a/src/tests/units/CMakeLists.txt
+++ b/src/tests/units/CMakeLists.txt
@@ -10,7 +10,7 @@ foreach(test ${UNIT_TESTS})
   add_executable("run_${test}" run_tests.cpp "${test}.cpp")
 
   if(CUDA_FOUND)
-    target_link_libraries("run_${test}" marian marian_cuda ${EXT_LIBS} Catch)
+    target_link_libraries("run_${test}" ${EXT_LIBS} marian ${EXT_LIBS} marian_cuda ${EXT_LIBS} Catch)
   else(CUDA_FOUND)
     target_link_libraries("run_${test}" marian ${EXT_LIBS} Catch)
   endif(CUDA_FOUND)

From 4b3c6542a6687cf949f599cea5cb5959f91e3818 Mon Sep 17 00:00:00 2001
From: Young Jin Kim <youki@microsoft.com>
Date: Mon, 8 Jul 2019 10:13:02 -0700
Subject: [PATCH 530/838] Move fbgemm url under marian-nmt

---
 .gitmodules | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.gitmodules b/.gitmodules
index 7f8ce6304..b7c67befc 100644
--- a/.gitmodules
+++ b/.gitmodules
@@ -12,5 +12,5 @@
 	url = https://github.com/marian-nmt/nccl
 [submodule "src/3rd_party/fbgemm"]
 	path = src/3rd_party/fbgemm
-	url = https://machinetranslation.visualstudio.com/DefaultCollection/MachineTranslation/_git/FBGEMM-internal
+	url = https://github.com/marian-nmt/FBGEMM
 	branch = master

From c2074c98344ce4ea0da16b36ba293dad3d0f5e4e Mon Sep 17 00:00:00 2001
From: Young Jin Kim <youki@microsoft.com>
Date: Wed, 10 Jul 2019 15:49:35 -0700
Subject: [PATCH 531/838] Add dynamic AVX detection for packed GEMM

---
 src/3rd_party/fbgemm               |  2 +-
 src/graph/expression_operators.cpp | 10 ++++++++--
 2 files changed, 9 insertions(+), 3 deletions(-)

diff --git a/src/3rd_party/fbgemm b/src/3rd_party/fbgemm
index bc33ed947..49e8018ab 160000
--- a/src/3rd_party/fbgemm
+++ b/src/3rd_party/fbgemm
@@ -1 +1 @@
-Subproject commit bc33ed9474b4f944dc04a579c25919778eceb9d5
+Subproject commit 49e8018ab2397c175354317b35c6be6dd68f8932
diff --git a/src/graph/expression_operators.cpp b/src/graph/expression_operators.cpp
index 0e762067a..8f1fb54a5 100755
--- a/src/graph/expression_operators.cpp
+++ b/src/graph/expression_operators.cpp
@@ -9,6 +9,10 @@
 #include "tensors/cpu/int16.h"
 #include "tensors/cpu/expanded_gemm.h"
 
+#if USE_FBGEMM
+#include "fbgemm/Utils.h"
+#endif
+
 namespace marian {
 
 Expr debug(Expr a, const std::string& message) {
@@ -444,7 +448,8 @@ Expr affine(Expr a, Expr b, Expr bias, bool transA, bool transB, float scale) {
       // a combination of contant nodes which is also a constant variable
       // when it's computed once.
       // Those memoized nodes are cached to avoid duplicated computations.
-      if(b->memoize()) {
+      // 07/10/2019 - Use packed GEMM only if the cpu architecture supports AVX2
+      if(fbgemm::fbgemmHasAvx2Support() && b->memoize()) {
         // add packed GEMM algorithm variant (Packed GEMM) to the autotuner
         // Once an algorithm is added to the autotuner,
         // autotuner runs all the added algorithms for a designated times.
@@ -538,7 +543,8 @@ Expr affine(Expr a, Expr b, Expr bias, bool transA, bool transB, float scale) {
             scale);
       } else if(gemmType == GemmType::FbFp16Packed) {
 #if USE_FBGEMM
-        if(b->memoize()) {
+        // 07/10/2019 - Use packed GEMM only if the cpu architecture supports AVX2
+        if(fbgemm::fbgemmHasAvx2Support() && b->memoize()) {
           auto packed = cpu::variant::pack(b, cpu::variant::PackMatrix::B, transB, clipValue);
 
           return cpu::variant::affine(

From aedb3cb39d89a472288833a0a284e3ae705bbd89 Mon Sep 17 00:00:00 2001
From: Young Jin Kim <youki@microsoft.com>
Date: Wed, 10 Jul 2019 17:14:00 -0700
Subject: [PATCH 532/838] Add comments about cpu arch detection

---
 src/graph/expression_operators.cpp | 8 ++++++++
 1 file changed, 8 insertions(+)

diff --git a/src/graph/expression_operators.cpp b/src/graph/expression_operators.cpp
index 8f1fb54a5..b045c2588 100755
--- a/src/graph/expression_operators.cpp
+++ b/src/graph/expression_operators.cpp
@@ -449,6 +449,10 @@ Expr affine(Expr a, Expr b, Expr bias, bool transA, bool transB, float scale) {
       // when it's computed once.
       // Those memoized nodes are cached to avoid duplicated computations.
       // 07/10/2019 - Use packed GEMM only if the cpu architecture supports AVX2
+      // one of the fbgemm's sub modules, cpuinfo (https://github.com/pytorch/cpuinfo).
+      // It looks at the cpu register 
+      // (https://github.com/pytorch/cpuinfo/blob/master/src/x86/isa.c#L391),
+      // and this cpu lookup is executed only once and the state is kept in FBGEMM.
       if(fbgemm::fbgemmHasAvx2Support() && b->memoize()) {
         // add packed GEMM algorithm variant (Packed GEMM) to the autotuner
         // Once an algorithm is added to the autotuner,
@@ -544,6 +548,10 @@ Expr affine(Expr a, Expr b, Expr bias, bool transA, bool transB, float scale) {
       } else if(gemmType == GemmType::FbFp16Packed) {
 #if USE_FBGEMM
         // 07/10/2019 - Use packed GEMM only if the cpu architecture supports AVX2
+        // one of the fbgemm's sub modules, cpuinfo (https://github.com/pytorch/cpuinfo).
+        // It looks at the cpu register
+        // (https://github.com/pytorch/cpuinfo/blob/master/src/x86/isa.c#L391),
+        // and this cpu lookup is executed only once and the state is kept in FBGEMM.
         if(fbgemm::fbgemmHasAvx2Support() && b->memoize()) {
           auto packed = cpu::variant::pack(b, cpu::variant::PackMatrix::B, transB, clipValue);
 

From 03773a11d0ef3fef1413f7da6d818e7f01d3a0a0 Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Thu, 11 Jul 2019 12:27:40 -0700
Subject: [PATCH 533/838] BeamSearch() now takes a Vocab Ptr instead of EOS and
 UNK

---
 src/data/factored_vocab.cpp  | 3 ++-
 src/microsoft/quicksand.cpp  | 3 ++-
 src/training/validator.h     | 6 ++----
 src/translator/beam_search.h | 7 +++----
 src/translator/translator.h  | 4 ++--
 5 files changed, 11 insertions(+), 12 deletions(-)
 mode change 100644 => 100755 src/microsoft/quicksand.cpp
 mode change 100644 => 100755 src/translator/beam_search.h
 mode change 100644 => 100755 src/translator/translator.h

diff --git a/src/data/factored_vocab.cpp b/src/data/factored_vocab.cpp
index 68fd78f93..1f5361604 100755
--- a/src/data/factored_vocab.cpp
+++ b/src/data/factored_vocab.cpp
@@ -27,6 +27,7 @@ namespace marian {
     //LOG(info, "[vocab] Attempting to load model a second time; skipping (assuming shared vocab)");
     return size();
   }
+  LOG(info, "[vocab] Loading vocab spec file {}", modelPath);
 
   // load factor-vocab file and parse it
   std::vector<std::vector<std::string>> factorMapTokenized;
@@ -732,7 +733,7 @@ Ptr<IVocab> createFactoredVocab(const std::string& vocabPath) {
     static std::map<std::string, Ptr<IVocab>> s_cache;
     auto iter = s_cache.find(vocabPath);
     if (iter != s_cache.end()) {
-      LOG(info, "[vocab] Reusing existing vocabulary object in memory");
+      LOG(info, "[vocab] Reusing existing vocabulary object in memory (vocab size {})", iter->second->size());
       return iter->second;
     }
     auto vocab = New<FactoredVocab>();
diff --git a/src/microsoft/quicksand.cpp b/src/microsoft/quicksand.cpp
old mode 100644
new mode 100755
index 440d0c116..8285938e1
--- a/src/microsoft/quicksand.cpp
+++ b/src/microsoft/quicksand.cpp
@@ -141,7 +141,8 @@ class BeamSearchDecoder : public IBeamSearchDecoder {
     batch->setSentenceIds(sentIds);
 
     // decode
-    auto search = New<BeamSearch>(options_, scorers_, marian::Word::fromWordIndex(eos_));
+    ABORT_IF(marian::Word::fromWordIndex(eos_) != vocabs_[1]->getEosId(), "Inconsistent eos_ vs. vocabs_[1]"); // @TODO: do we still need eos_?
+    auto search = New<BeamSearch>(options_, scorers_, vocabs_[1]);
     Histories histories = search->search(graph_, batch);
 
     // convert to QuickSAND format
diff --git a/src/training/validator.h b/src/training/validator.h
index 7736318b1..330b889e4 100755
--- a/src/training/validator.h
+++ b/src/training/validator.h
@@ -534,8 +534,7 @@ class TranslationValidator : public Validator<data::Corpus, models::IModel> {
 
           auto search = New<BeamSearch>(options_,
                                         std::vector<Ptr<Scorer>>{scorer},
-                                        vocabs_.back()->getEosId(),
-                                        vocabs_.back()->getUnkId());
+                                        vocabs_.back());
           auto histories = search->search(graph, batch);
 
           for(auto history : histories) {
@@ -673,8 +672,7 @@ class BleuValidator : public Validator<data::Corpus, models::IModel> {
 
           auto search = New<BeamSearch>(options_,
                                         std::vector<Ptr<Scorer>>{scorer},
-                                        vocabs_.back()->getEosId(),
-                                        vocabs_.back()->getUnkId());
+                                        vocabs_.back());
           auto histories = search->search(graph, batch);
 
           size_t no = 0;
diff --git a/src/translator/beam_search.h b/src/translator/beam_search.h
old mode 100644
new mode 100755
index 393ff6dfe..5b792f8a4
--- a/src/translator/beam_search.h
+++ b/src/translator/beam_search.h
@@ -24,15 +24,14 @@ class BeamSearch {
 public:
   BeamSearch(Ptr<Options> options,
              const std::vector<Ptr<Scorer>>& scorers,
-             Word trgEosId,
-             Word trgUnkId = Word::NONE)
+             Ptr<Vocab> trgVocab)
       : options_(options),
         scorers_(scorers),
         beamSize_(options_->has("beam-size")
                       ? options_->get<size_t>("beam-size")
                       : 3),
-        trgEosId_(trgEosId),
-        trgUnkId_(trgUnkId) {}
+        trgEosId_(trgVocab->getEosId()),
+        trgUnkId_(trgVocab->getUnkId()) {}
 
   // combine new expandedPathScores and previous beams into new set of beams
   Beams toHyps(const std::vector<unsigned int>& nBestKeys, // [dimBatch, beamSize] flattened -> ((batchIdx, beamHypIdx) flattened, word idx) flattened
diff --git a/src/translator/translator.h b/src/translator/translator.h
old mode 100644
new mode 100755
index e79b52716..d73aee0ad
--- a/src/translator/translator.h
+++ b/src/translator/translator.h
@@ -99,7 +99,7 @@ class Translate : public ModelTask {
           scorers = scorers_[id % numDevices_];
         }
 
-        auto search = New<Search>(options_, scorers, trgVocab_->getEosId(), trgVocab_->getUnkId());
+        auto search = New<Search>(options_, scorers, trgVocab_);
         auto histories = search->search(graph, batch);
 
         for(auto history : histories) {
@@ -204,7 +204,7 @@ class TranslateService : public ModelServiceTask {
             scorers = scorers_[id % numDevices_];
           }
 
-          auto search = New<Search>(options_, scorers, trgVocab_->getEosId(), trgVocab_->getUnkId());
+          auto search = New<Search>(options_, scorers, trgVocab_);
           auto histories = search->search(graph, batch);
 
           for(auto history : histories) {

From 47d592cab0662dde8ba1d090896c0253efaeac37 Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Thu, 11 Jul 2019 12:42:14 -0700
Subject: [PATCH 534/838] removed EOS and UNK members from BeamSearch, replaced
 by direct access to trgVocab

---
 src/translator/beam_search.h | 26 ++++++++++++--------------
 1 file changed, 12 insertions(+), 14 deletions(-)

diff --git a/src/translator/beam_search.h b/src/translator/beam_search.h
index 5b792f8a4..d0a2cb11e 100755
--- a/src/translator/beam_search.h
+++ b/src/translator/beam_search.h
@@ -16,8 +16,7 @@ class BeamSearch {
   Ptr<Options> options_;
   std::vector<Ptr<Scorer>> scorers_;
   size_t beamSize_;
-  Word trgEosId_{Word::NONE};
-  Word trgUnkId_{Word::NONE};
+  Ptr<Vocab> trgVocab_;
 
   static constexpr auto INVALID_PATH_SCORE = -9999; // (@TODO: change to -9999.0 once C++ allows that)
 
@@ -30,8 +29,7 @@ class BeamSearch {
         beamSize_(options_->has("beam-size")
                       ? options_->get<size_t>("beam-size")
                       : 3),
-        trgEosId_(trgVocab->getEosId()),
-        trgUnkId_(trgVocab->getUnkId()) {}
+        trgVocab_(trgVocab) {}
 
   // combine new expandedPathScores and previous beams into new set of beams
   Beams toHyps(const std::vector<unsigned int>& nBestKeys, // [dimBatch, beamSize] flattened -> ((batchIdx, beamHypIdx) flattened, word idx) flattened
@@ -196,11 +194,12 @@ class BeamSearch {
 
   // remove all beam entries that have reached EOS
   Beams purgeBeams(const Beams& beams) {
+    const auto trgEosId = trgVocab_->getEosId();
     Beams newBeams;
     for(auto beam : beams) {
       Beam newBeam;
       for(auto hyp : beam) {
-        if(hyp->getWord() != trgEosId_) {
+        if(hyp->getWord() != trgEosId) {
           newBeam.push_back(hyp);
         }
       }
@@ -212,10 +211,7 @@ class BeamSearch {
   //**********************************************************************
   // main decoding function
   Histories search(Ptr<ExpressionGraph> graph, Ptr<data::CorpusBatch> batch) {
-    ABORT_IF(batch->back()->vocab() && batch->back()->vocab()->getEosId() != trgEosId_,
-        "Batch uses different EOS token than was passed to BeamSearch originally");
-
-    auto factoredVocab = batch->back()->vocab()->tryAs<FactoredVocab>();
+    auto factoredVocab = trgVocab_->tryAs<FactoredVocab>();
 #if 0   // use '1' here to disable factored decoding, e.g. for comparisons
     factoredVocab.reset();
 #endif
@@ -224,6 +220,8 @@ class BeamSearch {
       factoredVocab.reset();
 
     const int dimBatch = (int)batch->size();
+    const auto trgEosId = trgVocab_->getEosId();
+    const auto trgUnkId = trgVocab_->getUnkId();
 
     auto getNBestList = createGetNBestListFn(beamSize_, dimBatch, graph->getDeviceId());
 
@@ -248,7 +246,7 @@ class BeamSearch {
     Beams beams(dimBatch, Beam(beamSize_, New<Hypothesis>())); // array [dimBatch] of array [localBeamSize] of Hypothesis
 
     for(int i = 0; i < dimBatch; ++i)
-      histories[i]->add(beams[i], trgEosId_);
+      histories[i]->add(beams[i], trgEosId);
 
     // the decoding process updates the following state information in each output time step:
     //  - beams: array [dimBatch] of array [localBeamSize] of Hypothesis
@@ -306,7 +304,7 @@ class BeamSearch {
               prevScores.push_back(canExpand ? hyp->getPathScore() : INVALID_PATH_SCORE);
             } else {  // pad to localBeamSize (dummy hypothesis)
               hypIndices.push_back(0);
-              prevWords.push_back(trgEosId_);  // (unused, but must be valid)
+              prevWords.push_back(trgEosId);  // (unused, but must be valid)
               prevScores.push_back((float)INVALID_PATH_SCORE);
             }
           }
@@ -370,8 +368,8 @@ class BeamSearch {
 
       //**********************************************************************
       // suppress specific symbols if not at right positions
-      if(trgUnkId_ != Word::NONE && options_->has("allow-unk") && !options_->get<bool>("allow-unk") && factorGroup == 0)
-        suppressWord(expandedPathScores, factoredVocab ? factoredVocab->getUnkIndex() : trgUnkId_.toWordIndex());
+      if(trgUnkId != Word::NONE && options_->has("allow-unk") && !options_->get<bool>("allow-unk") && factorGroup == 0)
+        suppressWord(expandedPathScores, factoredVocab ? factoredVocab->getUnkIndex() : trgUnkId.toWordIndex());
       for(auto state : states)
         state->blacklist(expandedPathScores, batch);
 
@@ -410,7 +408,7 @@ class BeamSearch {
         if(!beams[i].empty()) {
           if (histories[i]->size() >= options_->get<float>("max-length-factor") * batch->front()->batchWidth())
             maxLengthReached = true;
-          histories[i]->add(beams[i], trgEosId_, purgedNewBeams[i].empty() || maxLengthReached);
+          histories[i]->add(beams[i], trgEosId, purgedNewBeams[i].empty() || maxLengthReached);
         }
       }
       if (maxLengthReached) // early exit if max length limit was reached

From 236d153ce4de973a374b827cbc3c4191fec4124a Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Thu, 11 Jul 2019 12:58:42 -0700
Subject: [PATCH 535/838] removed eos_ from QuickSAND API classes

---
 src/microsoft/quicksand.cpp | 12 ++++++------
 src/microsoft/quicksand.h   |  8 +++-----
 2 files changed, 9 insertions(+), 11 deletions(-)
 mode change 100644 => 100755 src/microsoft/quicksand.h

diff --git a/src/microsoft/quicksand.cpp b/src/microsoft/quicksand.cpp
index 8285938e1..2e2102de5 100755
--- a/src/microsoft/quicksand.cpp
+++ b/src/microsoft/quicksand.cpp
@@ -55,9 +55,8 @@ class BeamSearchDecoder : public IBeamSearchDecoder {
 public:
   BeamSearchDecoder(Ptr<Options> options,
                     const std::vector<const void*>& ptrs,
-                    const std::vector<Ptr<IVocabWrapper>>& vocabs,
-                    WordIndex eos)
-      : IBeamSearchDecoder(options, ptrs, eos) {
+                    const std::vector<Ptr<IVocabWrapper>>& vocabs)
+      : IBeamSearchDecoder(options, ptrs) {
 
     // copy the vocabs
     for (auto vi : vocabs)
@@ -141,7 +140,6 @@ class BeamSearchDecoder : public IBeamSearchDecoder {
     batch->setSentenceIds(sentIds);
 
     // decode
-    ABORT_IF(marian::Word::fromWordIndex(eos_) != vocabs_[1]->getEosId(), "Inconsistent eos_ vs. vocabs_[1]"); // @TODO: do we still need eos_?
     auto search = New<BeamSearch>(options_, scorers_, vocabs_[1]);
     Histories histories = search->search(graph_, batch);
 
@@ -186,8 +184,10 @@ class BeamSearchDecoder : public IBeamSearchDecoder {
 Ptr<IBeamSearchDecoder> newDecoder(Ptr<Options> options,
                                    const std::vector<const void*>& ptrs,
                                    const std::vector<Ptr<IVocabWrapper>>& vocabs,
-                                   WordIndex eos) {
-  return New<BeamSearchDecoder>(options, ptrs, vocabs, eos);
+                                   WordIndex eosDummy) { // @TODO: remove this parameter
+  ABORT_IF(marian::Word::fromWordIndex(eosDummy) != std::dynamic_pointer_cast<VocabWrapper>(vocabs[1])->getVocab()->getEosId(), "Inconsistent eos vs. vocabs_[1]");
+
+  return New<BeamSearchDecoder>(options, ptrs, vocabs/*, eos*/);
 }
 
 std::vector<Ptr<IVocabWrapper>> loadVocabs(const std::vector<std::string>& vocabPaths) {
diff --git a/src/microsoft/quicksand.h b/src/microsoft/quicksand.h
old mode 100644
new mode 100755
index c4f539abd..ffab6c0d4
--- a/src/microsoft/quicksand.h
+++ b/src/microsoft/quicksand.h
@@ -42,13 +42,11 @@ class IBeamSearchDecoder {
 protected:
   Ptr<Options> options_;
   std::vector<const void*> ptrs_;
-  WordIndex eos_;
 
 public:
   IBeamSearchDecoder(Ptr<Options> options,
-                     const std::vector<const void*>& ptrs,
-                     WordIndex eos)
-      : options_(options), ptrs_(ptrs), eos_(eos) {}
+                     const std::vector<const void*>& ptrs)
+      : options_(options), ptrs_(ptrs) {}
 
   virtual QSNBestBatch decode(const QSBatch& qsBatch,
                               size_t maxLength,
@@ -61,7 +59,7 @@ class IBeamSearchDecoder {
 Ptr<IBeamSearchDecoder> newDecoder(Ptr<Options> options,
                                    const std::vector<const void*>& ptrs,
                                    const std::vector<Ptr<IVocabWrapper>>& vocabs,
-                                   WordIndex eos);
+                                   WordIndex eos/*dummy --@TODO: remove*/);
 
 // load src and tgt vocabs
 std::vector<Ptr<IVocabWrapper>> loadVocabs(const std::vector<std::string>& vocabPaths);

From 993025327f83e47c0b996690f661d4b54f8d2146 Mon Sep 17 00:00:00 2001
From: Roman Grundkiewicz <rgrundki@exseed.ed.ac.uk>
Date: Fri, 19 Jul 2019 14:29:21 +0100
Subject: [PATCH 536/838] Add script for checkpoint averaging

---
 scripts/checkpoints/average.py | 45 ++++++++++++++++++++++++++++++++++
 1 file changed, 45 insertions(+)
 create mode 100755 scripts/checkpoints/average.py

diff --git a/scripts/checkpoints/average.py b/scripts/checkpoints/average.py
new file mode 100755
index 000000000..2127a9847
--- /dev/null
+++ b/scripts/checkpoints/average.py
@@ -0,0 +1,45 @@
+#!/usr/bin/env python
+
+from __future__ import print_function
+
+import os
+import sys
+import argparse
+
+import numpy as np
+
+# Parse arguments
+parser = argparse.ArgumentParser()
+parser.add_argument('-m', '--model', nargs='+', required=True,
+                    help="models to average")
+parser.add_argument('-o', '--output', required=True,
+                    help="output path")
+args = parser.parse_args()
+
+# *average* holds the model matrix
+average = dict()
+# No. of models.
+n = len(args.model)
+
+for filename in args.model:
+    print("Loading {}".format(filename))
+    with open(filename, "rb") as mfile:
+        # Loads matrix from model file
+        m = np.load(mfile)
+        for k in m:
+            if k != "history_errs":
+                # Initialize the key
+                if k not in average:
+                    average[k] = m[k]
+                # Add to the appropriate value
+                elif average[k].shape == m[k].shape and "special" not in k:
+                    average[k] += m[k]
+
+# Actual averaging
+for k in average:
+    if "special" not in k:
+        average[k] /= n
+
+# Save averaged model to file
+print("Saving to {}".format(args.output))
+np.savez(args.output, **average)

From 278ec02c4f0ced443c18ee116fc2e296e441b01e Mon Sep 17 00:00:00 2001
From: Roman Grundkiewicz <rgrundki@exseed.ed.ac.uk>
Date: Fri, 19 Jul 2019 15:13:18 +0100
Subject: [PATCH 537/838] Require CMake 3.12.2+ for CUDA 10.0+

---
 CMakeLists.txt | 13 +++++++++----
 1 file changed, 9 insertions(+), 4 deletions(-)

diff --git a/CMakeLists.txt b/CMakeLists.txt
index 022b97019..5521a6b09 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -142,6 +142,11 @@ endif()
 
 find_package(CUDA "8.0")
 if(CUDA_FOUND)
+  # CUDA >= 10.0 requires CMake >= 3.12.2
+  if((CUDA_VERSION VERSION_EQUAL "10.0" OR CUDA_VERSION VERSION_GREATER "10.0") AND (CMAKE_VERSION VERSION_LESS "3.12.2"))
+      message(FATAL_ERROR "CUDA 10.0+ requires CMake 3.12.2+, while you use CMake ${CMAKE_VERSION}")
+  endif()
+
   if(USE_STATIC_LIBS)
     find_library(CUDA_culibos_LIBRARY NAMES culibos PATHS ${CUDA_TOOLKIT_ROOT_DIR}/lib64)
     set(EXT_LIBS ${EXT_LIBS} ${CUDA_curand_LIBRARY} ${CUDA_cusparse_LIBRARY} ${CUDA_culibos_LIBRARY} ${CUDA_CUBLAS_LIBRARIES})
@@ -149,7 +154,7 @@ if(CUDA_FOUND)
   else(USE_STATIC_LIBS)
     set(EXT_LIBS ${EXT_LIBS} ${CUDA_curand_LIBRARY} ${CUDA_cusparse_LIBRARY} ${CUDA_CUBLAS_LIBRARIES})
     message(STATUS "Found CUDA libraries: ${CUDA_curand_LIBRARY} ${CUDA_cusparse_LIBRARY} ${CUDA_CUBLAS_LIBRARIES}")
-endif(USE_STATIC_LIBS)
+  endif(USE_STATIC_LIBS)
 
   if(USE_CUDNN)
     find_package(CUDNN "7.0")
@@ -175,9 +180,9 @@ endif(USE_STATIC_LIBS)
     LIST(APPEND CUDA_NVCC_FLAGS -DUSE_NCCL; )
   endif(USE_NCCL)
 
-if(USE_STATIC_LIBS)
-  set(CMAKE_FIND_LIBRARY_SUFFIXES ${_ORIG_CMAKE_FIND_LIBRARY_SUFFIXES})
-endif()
+  if(USE_STATIC_LIBS)
+    set(CMAKE_FIND_LIBRARY_SUFFIXES ${_ORIG_CMAKE_FIND_LIBRARY_SUFFIXES})
+  endif()
 
 else(CUDA_FOUND)
   message(FATAL_ERROR "CUDA has not been found, set -DCOMPILE_CUDA=off to avoid this check and to compile the CPU version only")

From bdb522046db759e3a2f067eeb82eca5b3e68ae69 Mon Sep 17 00:00:00 2001
From: Roman Grundkiewicz <rgrundki@exseed.ed.ac.uk>
Date: Wed, 7 Aug 2019 16:40:31 +0100
Subject: [PATCH 538/838] Fix misspellings

---
 src/data/default_vocab.cpp       | 2 +-
 src/data/sentencepiece_vocab.cpp | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/src/data/default_vocab.cpp b/src/data/default_vocab.cpp
index bcbd356ee..09b4c55ff 100644
--- a/src/data/default_vocab.cpp
+++ b/src/data/default_vocab.cpp
@@ -88,7 +88,7 @@ class DefaultVocab : public VocabBase {
         isJson ? "JSON/Yaml" : "text",
         vocabPath);
     ABORT_IF(!filesystem::exists(vocabPath),
-            "DefaultVocabulary file {} does not exits",
+            "DefaultVocabulary file {} does not exist",
             vocabPath);
 
     std::map<std::string, Word> vocab;
diff --git a/src/data/sentencepiece_vocab.cpp b/src/data/sentencepiece_vocab.cpp
index 4457dc064..7111b1c31 100644
--- a/src/data/sentencepiece_vocab.cpp
+++ b/src/data/sentencepiece_vocab.cpp
@@ -236,7 +236,7 @@ class SentencePieceVocab : public VocabBase {
     LOG(info, "[data] Loading SentencePiece vocabulary from file {}", vocabPath);
 
     ABORT_IF(!filesystem::exists(vocabPath),
-             "SentencePiece vocabulary file {} does not exits",
+             "SentencePiece vocabulary file {} does not exist",
              vocabPath);
 
     spm_.reset(new sentencepiece::SentencePieceProcessor());

From aea72867dfd25ba1421d6bd0f59d1581e54fbd34 Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Thu, 8 Aug 2019 16:46:04 -0700
Subject: [PATCH 539/838] lemmaEt should initialize with glorot_uniform2

---
 src/layers/generic.cpp       | 6 ++++--
 src/translator/beam_search.h | 7 ++++---
 vs/Marian.sln                | 0
 3 files changed, 8 insertions(+), 5 deletions(-)
 mode change 100644 => 100755 vs/Marian.sln

diff --git a/src/layers/generic.cpp b/src/layers/generic.cpp
index 9fedaf25d..17f526e28 100755
--- a/src/layers/generic.cpp
+++ b/src/layers/generic.cpp
@@ -254,7 +254,8 @@ namespace marian {
 #endif
         auto range = factoredVocab_->getGroupRange(0);
         auto lemmaVocabDim = (int)(range.second - range.first);
-        lemmaEt_ = graph_->param(name + "_lemmaEt", {lemmaDimEmb, lemmaVocabDim}, inits::glorot_uniform); // [L x U] L=lemmaDimEmb; transposed for speed
+        auto initFunc = inits::glorot_uniform2(/*fanIn=*/true, /*fanOut=*/false); // -> embedding vectors have roughly unit length
+        lemmaEt_ = graph_->param(name + "_lemmaEt", {lemmaDimEmb, lemmaVocabDim}, initFunc); // [L x U] L=lemmaDimEmb; transposed for speed
       }
     }
 
@@ -332,6 +333,7 @@ namespace marian {
             // project it back to regular hidden dim
             int inputDim = input1->shape()[-1];
             auto name = options_->get<std::string>("prefix");
+            // note: if the lemmaEt[:,w] have unit length (var = 1/L), then lemmaWt @ lemmaEt is also length 1
             Expr lemmaWt = inputDim == lemmaDimEmb ? nullptr : graph_->param(name + "_lemmaWt", { inputDim,  lemmaDimEmb }, inits::glorot_uniform); // [D x L] D=hidden-vector dimension
             auto f = lemmaWt ? dot(e, lemmaWt, false, true) : e; // [B... x D]
             // augment the original hidden vector with this additional information
@@ -361,7 +363,7 @@ namespace marian {
     }
 
     // Embedding layer initialization should depend only on embedding size, hence fanIn=false
-    NodeInitializer initFunc = inits::glorot_uniform2(/*fanIn=*/false, /*fanOut=*/true);
+    auto initFunc = inits::glorot_uniform2(/*fanIn=*/false, /*fanOut=*/true); // -> embedding vectors have roughly unit length
     
     if (options_->has("embFile")) {
       std::string file = opt<std::string>("embFile");
diff --git a/src/translator/beam_search.h b/src/translator/beam_search.h
index d0a2cb11e..2d596b2b8 100755
--- a/src/translator/beam_search.h
+++ b/src/translator/beam_search.h
@@ -81,11 +81,12 @@ class BeamSearch {
         // starting with the lemma, then adding factors one by one.
         if (factorGroup == 0) {
           word = factoredVocab->lemma2Word(shortlist ? shortlist->reverseMap(wordIdx) : wordIdx); // @BUGBUG: reverseMap is only correct if factoredVocab_->getGroupRange(0).first == 0
-          //LOG(info, "new lemma {}={}", word.toWordIndex(), factoredVocab->word2string(word));
+          //std::vector<size_t> factorIndices; factoredVocab->word2factors(word, factorIndices);
+          //LOG(info, "new lemma {},{}={} -> {}->{}", word.toWordIndex(), factorIndices[0], factoredVocab->word2string(word), prevHyp->getPathScore(), pathScore);
         }
         else {
-          //LOG(info, "expand word {}={} with factor[{}] {}", beam[beamHypIdx]->getWord().toWordIndex(),
-          //    factoredVocab->word2string(beam[beamHypIdx]->getWord()), factorGroup, wordIdx);
+          //LOG(info, "expand word {}={} with factor[{}] {} -> {}->{}", beam[beamHypIdx]->getWord().toWordIndex(),
+          //    factoredVocab->word2string(beam[beamHypIdx]->getWord()), factorGroup, wordIdx, prevHyp->getPathScore(), pathScore);
           word = beam[beamHypIdx]->getWord();
           ABORT_IF(!factoredVocab->canExpandFactoredWord(word, factorGroup),
                    "A word without this factor snuck through to here??");
diff --git a/vs/Marian.sln b/vs/Marian.sln
old mode 100644
new mode 100755

From 2fe10a5995b66332246efe516d5ab166e722088b Mon Sep 17 00:00:00 2001
From: Roman Grundkiewicz <rgrundki@exseed.ed.ac.uk>
Date: Fri, 9 Aug 2019 14:43:42 +0100
Subject: [PATCH 540/838] Check if vocabs exist in marian-scorer

---
 src/common/config_validator.cpp | 22 ++++++++++++++++------
 1 file changed, 16 insertions(+), 6 deletions(-)

diff --git a/src/common/config_validator.cpp b/src/common/config_validator.cpp
index 9345b0a56..f644a0aab 100644
--- a/src/common/config_validator.cpp
+++ b/src/common/config_validator.cpp
@@ -45,13 +45,18 @@ void ConfigValidator::validateOptionsTranslation() const {
   ABORT_IF(models.empty() && configs.empty(),
            "You need to provide at least one model file or a config file");
 
-  auto vocabs = get<std::vector<std::string>>("vocabs");
-  ABORT_IF(vocabs.empty(), "Translating, but vocabularies are not given!");
-
   for(const auto& modelFile : models) {
     filesystem::Path modelPath(modelFile);
     ABORT_IF(!filesystem::exists(modelPath), "Model file does not exist: " + modelFile);
   }
+
+  auto vocabs = get<std::vector<std::string>>("vocabs");
+  ABORT_IF(vocabs.empty(), "Translating, but vocabularies are not given");
+
+  for(const auto& vocabFile : vocabs) {
+    filesystem::Path vocabPath(vocabFile);
+    ABORT_IF(!filesystem::exists(vocabPath), "Vocabulary file does not exist: " + vocabFile);
+  }
 }
 
 void ConfigValidator::validateOptionsParallelData() const {
@@ -65,10 +70,15 @@ void ConfigValidator::validateOptionsParallelData() const {
 
 void ConfigValidator::validateOptionsScoring() const {
   filesystem::Path modelPath(get<std::string>("model"));
-
   ABORT_IF(!filesystem::exists(modelPath), "Model file does not exist: " + modelPath.string());
-  ABORT_IF(get<std::vector<std::string>>("vocabs").empty(),
-           "Scoring, but vocabularies are not given!");
+
+  auto vocabs = get<std::vector<std::string>>("vocabs");
+  ABORT_IF(vocabs.empty(), "Scoring, but vocabularies are not given");
+
+  for(const auto& vocabFile : vocabs) {
+    filesystem::Path vocabPath(vocabFile);
+    ABORT_IF(!filesystem::exists(vocabPath), "Vocabulary file does not exist: " + vocabFile);
+  }
 }
 
 void ConfigValidator::validateOptionsTraining() const {

From 90f47d05c5ee62b394b10d6422d2fbfb5fe2d81e Mon Sep 17 00:00:00 2001
From: Roman Grundkiewicz <rgrundki@exseed.ed.ac.uk>
Date: Fri, 9 Aug 2019 16:09:09 +0100
Subject: [PATCH 541/838] Update submodule for regression tests

---
 regression-tests | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/regression-tests b/regression-tests
index 142eadddb..db743d7ff 160000
--- a/regression-tests
+++ b/regression-tests
@@ -1 +1 @@
-Subproject commit 142eadddbe04493c1024b42586030b72e9cb7ea2
+Subproject commit db743d7fff2e07d9f839fbb3393dfbb3aabee2cb

From 9714a4f58a5deb30511119fcfc8203f6b26817aa Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Fri, 9 Aug 2019 19:21:42 -0700
Subject: [PATCH 542/838] support for |scl/|scu factors

---
 src/data/factored_vocab.cpp | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/src/data/factored_vocab.cpp b/src/data/factored_vocab.cpp
index 1f5361604..e9a684e35 100755
--- a/src/data/factored_vocab.cpp
+++ b/src/data/factored_vocab.cpp
@@ -508,14 +508,14 @@ void FactoredVocab::constructNormalizationInfoForVocab() {
 
 // convert a string representation of a token sequence to all-caps by changing all capitalization factors to |ca
 /*virtual*/ std::string FactoredVocab::toUpper(const std::string& line) const /*override final*/ {
-  return utils::findReplace(utils::findReplace(utils::findReplace(utils::findReplace(line, "|ci", "|ca", /*all=*/true), "|cn", "|ca", /*all=*/true), "@CI", "@CA", /*all=*/true), "@CN", "@CA", /*all=*/true);
+  return utils::findReplace(utils::findReplace(utils::findReplace(utils::findReplace(utils::findReplace(line, "|scl", "|scu", /*all=*/true), "|ci", "|ca", /*all=*/true), "|cn", "|ca", /*all=*/true), "@CI", "@CA", /*all=*/true), "@CN", "@CA", /*all=*/true);
 }
 
 // convert a string representation of a token sequence to English title case by changing the capitalization factors to |ci
 /*virtual*/ std::string FactoredVocab::toEnglishTitleCase(const std::string& line) const /*override final*/ {
   // @BUGBUG: does not handle the special words that should remain lower-case
   // note: this presently supports both @WB and @GL- (legacy)
-  return utils::findReplace(utils::findReplace(utils::findReplace(utils::findReplace(line, "|cn|wb", "|ci|wb", /*all=*/true), "|cn|gl-", "|ci|gl-", /*all=*/true), "@CN@WB", "@CI@WB", /*all=*/true), "@CN@GL-", "@CI@GL-", /*all=*/true);
+  return utils::findReplace(utils::findReplace(utils::findReplace(utils::findReplace(utils::findReplace(line, "|scl", "|scu", /*all=*/true), "|cn|wb", "|ci|wb", /*all=*/true), "|cn|gl-", "|ci|gl-", /*all=*/true), "@CN@WB", "@CI@WB", /*all=*/true), "@CN@GL-", "@CI@GL-", /*all=*/true);
 }
 
 // convert word indices to indices of shortlist items

From b49e68d23eff22466fb6c751e9d8b33a3abcd645 Mon Sep 17 00:00:00 2001
From: Roman Grundkiewicz <rgrundki@exseed.ed.ac.uk>
Date: Sat, 10 Aug 2019 20:12:42 +0100
Subject: [PATCH 543/838] Update aliases

---
 src/common/aliases.cpp | 66 +++++++++++++++++++++++++++++++++++++-----
 1 file changed, 58 insertions(+), 8 deletions(-)

diff --git a/src/common/aliases.cpp b/src/common/aliases.cpp
index 11ca27fde..47627ad65 100644
--- a/src/common/aliases.cpp
+++ b/src/common/aliases.cpp
@@ -30,45 +30,95 @@ void ConfigParser::addAliases(cli::CLIWrapper& cli) {
     config["dec-cell-high-depth"] = 2;
     config["dec-depth"] = 4;
     config["skip"] = true;
+
+    // Training specific options
+    config["learn-rate"] = 0.0003;
+    config["cost-type"] = "ce-mean-words";
+    config["lr-decay-inv-sqrt"] = 16000;
+    config["label-smoothing"] = 0.1;
+    config["clip-norm"] = 5;
+    config["sync-sgd"] = true;
+    config["exponential-smoothing"] = 1e-4;
+    config["mini-batch-fit"] = true;
+    config["mini-batch"] = 1000;
+    config["maxi-batch"] = 1000;
+    // config["workspace"] = 6500;
   });
 
-  // Architecture and proposed training settings for a Transformer BASE model introduced in
+  // Architecture and proposed training settings for a Transformer "base" model introduced in
   // https://papers.nips.cc/paper/7181-attention-is-all-you-need.pdf
-  cli.alias("task", "transformer", [](YAML::Node& config) {
+  cli.alias("task", "transformer-base", [](YAML::Node& config) {
+    // Model options
     config["type"] = "transformer";
     config["enc-depth"] = 6;
     config["dec-depth"] = 6;
+    config["dim-emb"] = 512;
+    config["tied-embeddings-all"] = true;
+    config["transformer-dim-ffn"] = 2048;
     config["transformer-heads"] = 8;
+    config["transformer-postprocess"] = "an";
+    config["transformer-preprocess"] = "d";
+    config["transformer-ffn-activation"] = "relu";
+    config["transformer-dropout"] = 0.1;
+
+    // Training specific options
     config["learn-rate"] = 0.0003;
     config["cost-type"] = "ce-mean-words";
     config["lr-warmup"] = 16000;
     config["lr-decay-inv-sqrt"] = 16000;
-    config["transformer-dropout"] = 0.1;
     config["label-smoothing"] = 0.1;
     config["clip-norm"] = 5;
+    config["sync-sgd"] = true;
+    config["exponential-smoothing"] = 1e-4;
+    config["max-length"] = 100;
+    config["mini-batch-fit"] = true;
+    config["mini-batch"] = 1000;
+    config["maxi-batch"] = 1000;
+    config["workspace"] = 9500;
+    config["optimizer-params"] = std::vector<float>({0.9, 0.98, 1e-09});
+
+    // Validation specific options
+    config["beam-size"] = 8;
+    config["valid-mini-batch"] = 16;
+    config["normalize"] = 1.0;
   });
 
-  // Architecture and proposed training settings for a Transformer BIG model introduced in
+  // Architecture and proposed training settings for a Transformer "big" model introduced in
   // https://papers.nips.cc/paper/7181-attention-is-all-you-need.pdf
   cli.alias("task", "transformer-big", [](YAML::Node& config) {
+    // Model options
     config["type"] = "transformer";
     config["enc-depth"] = 6;
     config["dec-depth"] = 6;
     config["dim-emb"] = 1024;
+    config["tied-embeddings-all"] = true;
     config["transformer-dim-ffn"] = 4096;
     config["transformer-heads"] = 16;
-    config["transformer-postprocess"] = "dan";
+    config["transformer-postprocess"] = "an";
     config["transformer-preprocess"] = "d";
     config["transformer-ffn-activation"] = "relu";
+    config["transformer-dropout"] = 0.1;
+
+    // Training specific options
     config["learn-rate"] = 0.0002;
     config["cost-type"] = "ce-mean-words";
     config["lr-warmup"] = 8000;
     config["lr-decay-inv-sqrt"] = 8000;
-    config["transformer-dropout"] = 0.1;
-    config["transformer-dropout-attention"] = 0.1;
-    config["transformer-dropout-ffn"] = 0.1;
     config["label-smoothing"] = 0.1;
     config["clip-norm"] = 5;
+    config["sync-sgd"] = true;
+    config["exponential-smoothing"] = 1e-4;
+    config["max-length"] = 100;
+    config["mini-batch-fit"] = true;
+    config["mini-batch"] = 1000;
+    config["maxi-batch"] = 1000;
+    config["workspace"] = 13000;
+    config["optimizer-params"] = std::vector<float>({0.9, 0.998, 1e-09});
+
+    // Validation specific options
+    config["beam-size"] = 8;
+    config["valid-mini-batch"] = 8;
+    config["normalize"] = 1.0;
   });
 }
 

From 56fcaaf8fd5a4b03e8a8a74a3ba0b62fa80f1ff2 Mon Sep 17 00:00:00 2001
From: Roman Grundkiewicz <rgrundki@exseed.ed.ac.uk>
Date: Sun, 11 Aug 2019 17:08:38 +0100
Subject: [PATCH 544/838] Change ERROR to WARNING if CUDA >10 and CMake <3.12

---
 CMakeLists.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/CMakeLists.txt b/CMakeLists.txt
index 5521a6b09..2995cb935 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -144,7 +144,7 @@ find_package(CUDA "8.0")
 if(CUDA_FOUND)
   # CUDA >= 10.0 requires CMake >= 3.12.2
   if((CUDA_VERSION VERSION_EQUAL "10.0" OR CUDA_VERSION VERSION_GREATER "10.0") AND (CMAKE_VERSION VERSION_LESS "3.12.2"))
-      message(FATAL_ERROR "CUDA 10.0+ requires CMake 3.12.2+, while you use CMake ${CMAKE_VERSION}")
+      message(WARNING "On some Unix systems CUDA 10.0+ requires CMake 3.12.2+; you use CMake ${CMAKE_VERSION}")
   endif()
 
   if(USE_STATIC_LIBS)

From c65c26d6a9fe2874cb0dd641550c78fa3f49993d Mon Sep 17 00:00:00 2001
From: Roman Grundkiewicz <rgrundki@exseed.ed.ac.uk>
Date: Sun, 11 Aug 2019 18:27:00 +0100
Subject: [PATCH 545/838] Fix test

---
 src/tests/cli.cpp | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/tests/cli.cpp b/src/tests/cli.cpp
index f829041ea..9c7c9a51c 100644
--- a/src/tests/cli.cpp
+++ b/src/tests/cli.cpp
@@ -49,7 +49,7 @@ int main(int argc, char** argv) {
     w->add<bool>("-x,--xbool", "false boolean option", true);
     w->add<std::string>("--a-very-long-option-name-for-testing-purposes", "A very long text a very long text a very long text a very long text a very long text a very long text");
     w->switchGroup();
-    w->add<std::string>("-f,--file", "help message")->check(validators::file_exists);
+    //w->add<std::string>("-f,--file", "help message")->check(validators::file_exists);
     //w.add<color>("-e,--enum", "help message for enum");
 
     w->parse(argc, argv);

From 52a8461fb32f003424262e05ef95b3a98cfc795f Mon Sep 17 00:00:00 2001
From: Marcin Junczys-Dowmunt <Marcin.JunczysDowmunt@microsoft.com>
Date: Mon, 12 Aug 2019 13:12:14 -0700
Subject: [PATCH 546/838] Fix CMakeLists.txt and src/3rd_party/CMakeLists.txt,
 bug with overlapping CXX flags

---
 CMakeLists.txt               | 16 ++++++++++------
 src/3rd_party/CMakeLists.txt | 14 ++++++++++++--
 2 files changed, 22 insertions(+), 8 deletions(-)

diff --git a/CMakeLists.txt b/CMakeLists.txt
index 2995cb935..c988042fe 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -103,12 +103,12 @@ list(APPEND ALL_WARNINGS -Wall; -Werror; -Wno-unused-result; -Wno-deprecated; -W
     list(APPEND ALL_WARNINGS -Wsuggest-override)
   endif()
 
-  set(CMAKE_CXX_FLAGS                 "-std=c++11 -O3 -Ofast -m64 -pthread -march=${BUILD_ARCH} ${INTRINSICS} -Wl,--no-as-needed -funroll-loops -ffinite-math-only -fPIC ${DISABLE_GLOBALLY}")
-  set(CMAKE_CXX_FLAGS_RELEASE         "${CMAKE_CXX_FLAGS} -g -rdynamic")
-  set(CMAKE_CXX_FLAGS_DEBUG           "-std=c++11 -g -rdynamic -O0 -pthread -Wl,--no-as-needed -fPIC -Wno-unused-result -Wno-deprecated -Wno-pragmas")
-  set(CMAKE_CXX_FLAGS_SLIM            "${CMAKE_CXX_FLAGS} -DNDEBUG")
-  set(CMAKE_CXX_FLAGS_RELWITHDEBINFO  "${CMAKE_CXX_FLAGS} -g -rdynamic")
-  set(CMAKE_CXX_FLAGS_PROFILE         "${CMAKE_CXX_FLAGS_RELEASE} -pg -g -rdynamic")
+  set(CMAKE_CXX_FLAGS                 "-std=c++11 -g -pthread -Wl,--no-as-needed -fPIC ${DISABLE_GLOBALLY}")
+  set(CMAKE_CXX_FLAGS_RELEASE         "-g -rdynamic -Ofast -m64 -march=${BUILD_ARCH} ${INTRINSICS} -funroll-loops -ffinite-math-only")
+  set(CMAKE_CXX_FLAGS_DEBUG           "-g -rdynamic -O0")
+  set(CMAKE_CXX_FLAGS_SLIM            "${CMAKE_CXX_FLAGS_RELEASE} -DNDEBUG")
+  set(CMAKE_CXX_FLAGS_RELWITHDEBINFO  "${CMAKE_CXX_FLAGS_RELEASE}")
+  set(CMAKE_CXX_FLAGS_PROFILE         "${CMAKE_CXX_FLAGS_RELEASE} -pg")
   set(CMAKE_CXX_FLAGS_PROFGEN         "${CMAKE_CXX_FLAGS_RELEASE} -fprofile-generate -fprofile-correction")
   set(CMAKE_CXX_FLAGS_PROFUSE         "${CMAKE_CXX_FLAGS_RELEASE} -fprofile-use -fprofile-correction")
   endif()
@@ -121,6 +121,10 @@ if(USE_SENTENCEPIECE)
   set(EXT_LIBS ${EXT_LIBS} sentencepiece sentencepiece_train)
 endif()
 
+if(NOT CMAKE_BUILD_TYPE)
+  message(WARNING "CMAKE_BUILD_TYPE not set; setting to Release")
+  set(CMAKE_BUILD_TYPE "Release")
+endif()
 
 # Find packages
 set(EXT_LIBS ${EXT_LIBS} ${CMAKE_DL_LIBS})
diff --git a/src/3rd_party/CMakeLists.txt b/src/3rd_party/CMakeLists.txt
index 87f79a438..17101c0bc 100644
--- a/src/3rd_party/CMakeLists.txt
+++ b/src/3rd_party/CMakeLists.txt
@@ -16,9 +16,19 @@ if(USE_SENTENCEPIECE)
     endif()
   endif()
 
-  set(SPM_ENABLE_SHARED OFF CACHE BOOL "Builds shared libaries in addition to static libraries." FORCE)
   set(SPM_ENABLE_TCMALLOC ON CACHE BOOL "Enable TCMalloc if available." FORCE)
-  set(SPM_TCMALLOC_STATIC ON CACHE BOOL "Link static library of TCMALLOC." FORCE)
+
+  if(USE_STATIC_LIBS)
+    message(WARNING "You are compiling SentencePiece binaries with -DUSE_STATIC_LIBS=on. \
+    This will cause spm_train to segfault. No need to worry if you do not intend to use that binary. \
+    Marian support for SentencePiece will work fine.")
+    
+    set(SPM_ENABLE_SHARED OFF CACHE BOOL "Builds shared libaries in addition to static libraries." FORCE)
+    set(SPM_TCMALLOC_STATIC ON CACHE BOOL "Link static library of TCMALLOC." FORCE)
+  else(USE_STATIC_LIBS)
+    set(SPM_ENABLE_SHARED ON CACHE BOOL "Builds shared libaries in addition to static libraries." FORCE)
+    set(SPM_TCMALLOC_STATIC OFF CACHE BOOL "Link static library of TCMALLOC." FORCE)
+  endif(USE_STATIC_LIBS)
 
   add_subdirectory(./sentencepiece)
   include_directories(./sentencepiece)

From 288dc27782f4ccd5a052dfd1217d01737d25e41e Mon Sep 17 00:00:00 2001
From: Marcin Junczys-Dowmunt <Marcin.JunczysDowmunt@microsoft.com>
Date: Mon, 12 Aug 2019 14:47:25 -0700
Subject: [PATCH 547/838] add intrinsics to common flags

---
 CMakeLists.txt | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/CMakeLists.txt b/CMakeLists.txt
index c988042fe..3c7839258 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -103,8 +103,8 @@ list(APPEND ALL_WARNINGS -Wall; -Werror; -Wno-unused-result; -Wno-deprecated; -W
     list(APPEND ALL_WARNINGS -Wsuggest-override)
   endif()
 
-  set(CMAKE_CXX_FLAGS                 "-std=c++11 -g -pthread -Wl,--no-as-needed -fPIC ${DISABLE_GLOBALLY}")
-  set(CMAKE_CXX_FLAGS_RELEASE         "-g -rdynamic -Ofast -m64 -march=${BUILD_ARCH} ${INTRINSICS} -funroll-loops -ffinite-math-only")
+  set(CMAKE_CXX_FLAGS                 "-std=c++11 -g -pthread -Wl,--no-as-needed -fPIC ${DISABLE_GLOBALLY} -march=${BUILD_ARCH} ${INTRINSICS}")
+  set(CMAKE_CXX_FLAGS_RELEASE         "-g -rdynamic -Ofast -m64 -funroll-loops -ffinite-math-only")
   set(CMAKE_CXX_FLAGS_DEBUG           "-g -rdynamic -O0")
   set(CMAKE_CXX_FLAGS_SLIM            "${CMAKE_CXX_FLAGS_RELEASE} -DNDEBUG")
   set(CMAKE_CXX_FLAGS_RELWITHDEBINFO  "${CMAKE_CXX_FLAGS_RELEASE}")

From 5c54fdd6a85fa1475a344085b71458217cccabd4 Mon Sep 17 00:00:00 2001
From: Marcin Junczys-Dowmunt <Marcin.JunczysDowmunt@microsoft.com>
Date: Mon, 12 Aug 2019 14:57:30 -0700
Subject: [PATCH 548/838] move debug symbols into specific build types

---
 CMakeLists.txt | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/CMakeLists.txt b/CMakeLists.txt
index 3c7839258..04ea42492 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -103,10 +103,10 @@ list(APPEND ALL_WARNINGS -Wall; -Werror; -Wno-unused-result; -Wno-deprecated; -W
     list(APPEND ALL_WARNINGS -Wsuggest-override)
   endif()
 
-  set(CMAKE_CXX_FLAGS                 "-std=c++11 -g -pthread -Wl,--no-as-needed -fPIC ${DISABLE_GLOBALLY} -march=${BUILD_ARCH} ${INTRINSICS}")
-  set(CMAKE_CXX_FLAGS_RELEASE         "-g -rdynamic -Ofast -m64 -funroll-loops -ffinite-math-only")
-  set(CMAKE_CXX_FLAGS_DEBUG           "-g -rdynamic -O0")
-  set(CMAKE_CXX_FLAGS_SLIM            "${CMAKE_CXX_FLAGS_RELEASE} -DNDEBUG")
+  set(CMAKE_CXX_FLAGS                 "-std=c++11 -pthread -Wl,--no-as-needed -fPIC ${DISABLE_GLOBALLY} -march=${BUILD_ARCH} ${INTRINSICS}")
+  set(CMAKE_CXX_FLAGS_RELEASE         "-Ofast -m64 -funroll-loops -ffinite-math-only -g -rdynamic")
+  set(CMAKE_CXX_FLAGS_DEBUG           "-O0 -g -rdynamic")
+  set(CMAKE_CXX_FLAGS_SLIM            "-Ofast -m64 -funroll-loops -ffinite-math-only -DNDEBUG")
   set(CMAKE_CXX_FLAGS_RELWITHDEBINFO  "${CMAKE_CXX_FLAGS_RELEASE}")
   set(CMAKE_CXX_FLAGS_PROFILE         "${CMAKE_CXX_FLAGS_RELEASE} -pg")
   set(CMAKE_CXX_FLAGS_PROFGEN         "${CMAKE_CXX_FLAGS_RELEASE} -fprofile-generate -fprofile-correction")

From c4b785d47a67ce26a3b02a502c29c7a73e3217eb Mon Sep 17 00:00:00 2001
From: Roman Grundkiewicz <rgrundki@exseed.ed.ac.uk>
Date: Wed, 14 Aug 2019 10:44:52 +0100
Subject: [PATCH 549/838] Abort if decoding LR and RL models together

---
 src/translator/scorers.cpp | 13 +++++++++++++
 1 file changed, 13 insertions(+)

diff --git a/src/translator/scorers.cpp b/src/translator/scorers.cpp
index 7a07a8cd5..c9ec114ae 100644
--- a/src/translator/scorers.cpp
+++ b/src/translator/scorers.cpp
@@ -56,6 +56,7 @@ std::vector<Ptr<Scorer>> createScorers(Ptr<Options> options) {
   if(options->hasAndNotEmpty("weights"))
     weights = options->get<std::vector<float>>("weights");
 
+  bool isPrevRightLeft = false;  // if the previous model was a right-to-left model
   size_t i = 0;
   for(auto model : models) {
     std::string fname = "F" + std::to_string(i);
@@ -72,6 +73,18 @@ std::vector<Ptr<Scorer>> createScorers(Ptr<Options> options) {
       LOG(warn, "No model settings found in model file");
     }
 
+    // check if there are left-to-right and right-to-left models together
+    if(models.size() > 1 && modelOptions->has("right-left")) {
+      if(i == 0) {
+        isPrevRightLeft = modelOptions->get<bool>("right-left");
+      } else {
+        // abort as soon as there are two consecutive models with opposite directions
+        ABORT_IF(isPrevRightLeft != modelOptions->get<bool>("right-left"),
+                 "A left-to-right and right-to-left model cannot be decoded together");
+        isPrevRightLeft = modelOptions->get<bool>("right-left");
+      }
+    }
+
     scorers.push_back(scorerByType(fname, weights[i], model, modelOptions));
     i++;
   }

From ff1201fcd53b85261122929197bfae4369edade3 Mon Sep 17 00:00:00 2001
From: Roman Grundkiewicz <rgrundki@exseed.ed.ac.uk>
Date: Mon, 12 Aug 2019 21:11:32 +0100
Subject: [PATCH 550/838] Fix #460 - relative paths in decoder config files

---
 src/common/filesystem.h        |  7 ++++++-
 src/models/amun.h              | 24 ++++++++++++++++-------
 src/models/encoder_decoder.cpp | 35 ++++++++++++++++++++++++++++------
 3 files changed, 52 insertions(+), 14 deletions(-)

diff --git a/src/common/filesystem.h b/src/common/filesystem.h
index 08d5995f1..180e0c4dc 100644
--- a/src/common/filesystem.h
+++ b/src/common/filesystem.h
@@ -78,6 +78,11 @@ namespace filesystem {
     return p.getImpl().absolute(base.getImpl()).expand();
   }
 
+  static inline Path relative(const Path& p, const Path& base) {
+    // create a path relative to the base path
+    return p.getImpl().absolute().expand().relative(base.getImpl().absolute().expand());
+  }
+
   static inline bool exists(const Path& p) {
     return p.getImpl().exists();
   }
@@ -97,4 +102,4 @@ namespace filesystem {
   using FilesystemError = Pathie::PathieError;
 
 }
-}
\ No newline at end of file
+}
diff --git a/src/models/amun.h b/src/models/amun.h
index 35b652068..8625d34ee 100644
--- a/src/models/amun.h
+++ b/src/models/amun.h
@@ -185,17 +185,27 @@ class Amun : public EncoderDecoder {
   void createAmunConfig(const std::string& name) {
     Config::YamlNode amun;
     auto vocabs = options_->get<std::vector<std::string>>("vocabs");
-    amun["source-vocab"] = vocabs[0];
-    amun["target-vocab"] = vocabs[1];
-    amun["devices"] = options_->get<std::vector<size_t>>("devices");
-    amun["normalize"] = opt<float>("normalize") > 0;
-    amun["beam-size"] = opt<size_t>("beam-size");
-    amun["relative-paths"] = false;
 
-    amun["scorers"]["F0"]["path"] = name;
+    if(options_->get<bool>("relative-paths")) {
+      amun["relative-paths"] = true;
+      auto dirPath = filesystem::Path{name}.parentPath();
+      amun["source-vocab"] = filesystem::relative(filesystem::Path{vocabs[0]}, dirPath).string();
+      amun["target-vocab"] = filesystem::relative(filesystem::Path{vocabs[1]}, dirPath).string();
+      amun["scorers"]["F0"]["path"] = filesystem::Path{name}.filename().string();
+    } else {
+      amun["relative-paths"] = false;
+      amun["source-vocab"] = vocabs[0];
+      amun["target-vocab"] = vocabs[1];
+      amun["scorers"]["F0"]["path"] = name;
+    }
+
     amun["scorers"]["F0"]["type"] = "Nematus";
     amun["weights"]["F0"] = 1.0f;
 
+    amun["devices"] = options_->get<std::vector<size_t>>("devices");
+    amun["normalize"] = opt<float>("normalize") > 0;
+    amun["beam-size"] = opt<size_t>("beam-size");
+
     io::OutputFileStream out(name + ".amun.yml");
     out << amun;
   }
diff --git a/src/models/encoder_decoder.cpp b/src/models/encoder_decoder.cpp
index 4b072c73a..faa4cc566 100644
--- a/src/models/encoder_decoder.cpp
+++ b/src/models/encoder_decoder.cpp
@@ -1,5 +1,6 @@
 #include "models/encoder_decoder.h"
 #include "common/cli_helper.h"
+#include "common/filesystem.h"
 #include "common/version.h"
 
 namespace marian {
@@ -77,18 +78,40 @@ void EncoderDecoder::push_back(Ptr<DecoderBase> decoder) {
 
 void EncoderDecoder::createDecoderConfig(const std::string& name) {
   Config::YamlNode decoder;
-  decoder["models"] = std::vector<std::string>({name});
-  decoder["vocabs"] = options_->get<std::vector<std::string>>("vocabs");
+
+  if(options_->get<bool>("relative-paths")) {
+    decoder["relative-paths"] = true;
+    // we can safely use a bare model file name here, because the config file is created in the same
+    // directory as the model file
+    auto modelFileName = filesystem::Path{name}.filename().string();
+    decoder["models"] = std::vector<std::string>({modelFileName});
+
+    // create relative paths to vocabs
+    auto dirPath = filesystem::Path{name}.parentPath();
+    std::vector<std::string> relativeVocabs;
+    const auto& vocabs = options_->get<std::vector<std::string>>("vocabs");
+    std::transform(
+        vocabs.begin(),
+        vocabs.end(),
+        std::back_inserter(relativeVocabs),
+        [&](const std::string& p) -> std::string {
+          return filesystem::relative(filesystem::Path{p}, dirPath).string();
+        });
+
+    decoder["vocabs"] = relativeVocabs;
+  } else {
+    decoder["relative-paths"] = false;
+    decoder["models"] = std::vector<std::string>({name});
+    decoder["vocabs"] = options_->get<std::vector<std::string>>("vocabs");
+  }
+
   decoder["beam-size"] = opt<size_t>("beam-size");
   decoder["normalize"] = opt<float>("normalize");
   decoder["word-penalty"] = opt<float>("word-penalty");
 
   decoder["mini-batch"] = opt<size_t>("valid-mini-batch");
   decoder["maxi-batch"] = opt<size_t>("valid-mini-batch") > 1 ? 100 : 1;
-  decoder["maxi-batch-sort"]
-      = opt<size_t>("valid-mini-batch") > 1 ? "src" : "none";
-
-  decoder["relative-paths"] = false;
+  decoder["maxi-batch-sort"] = opt<size_t>("valid-mini-batch") > 1 ? "src" : "none";
 
   io::OutputFileStream out(name + ".decoder.yml");
   out << decoder;

From bd3672cc7bf81f881928c10fb8fe6e7264619bde Mon Sep 17 00:00:00 2001
From: Roman Grundkiewicz <rgrundki@exseed.ed.ac.uk>
Date: Wed, 14 Aug 2019 12:26:10 +0100
Subject: [PATCH 551/838] Update error message

---
 src/translator/scorers.cpp | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/translator/scorers.cpp b/src/translator/scorers.cpp
index c9ec114ae..244b93e68 100644
--- a/src/translator/scorers.cpp
+++ b/src/translator/scorers.cpp
@@ -80,7 +80,7 @@ std::vector<Ptr<Scorer>> createScorers(Ptr<Options> options) {
       } else {
         // abort as soon as there are two consecutive models with opposite directions
         ABORT_IF(isPrevRightLeft != modelOptions->get<bool>("right-left"),
-                 "A left-to-right and right-to-left model cannot be decoded together");
+                 "Left-to-right and right-to-left models cannot be used together in ensembles");
         isPrevRightLeft = modelOptions->get<bool>("right-left");
       }
     }

From a3d7286ee1d48e427b869d319f6c15cda24fb6ba Mon Sep 17 00:00:00 2001
From: Roman Grundkiewicz <rgrundki@exseed.ed.ac.uk>
Date: Wed, 14 Aug 2019 12:32:38 +0100
Subject: [PATCH 552/838] Do not set devices in decoder config files

---
 src/models/amun.h | 2 --
 1 file changed, 2 deletions(-)

diff --git a/src/models/amun.h b/src/models/amun.h
index 8625d34ee..a0fff0ef6 100644
--- a/src/models/amun.h
+++ b/src/models/amun.h
@@ -201,8 +201,6 @@ class Amun : public EncoderDecoder {
 
     amun["scorers"]["F0"]["type"] = "Nematus";
     amun["weights"]["F0"] = 1.0f;
-
-    amun["devices"] = options_->get<std::vector<size_t>>("devices");
     amun["normalize"] = opt<float>("normalize") > 0;
     amun["beam-size"] = opt<size_t>("beam-size");
 

From d72f1bed104ea41fbe7c70deeae23854c989d472 Mon Sep 17 00:00:00 2001
From: Roman Grundkiewicz <rgrundki@exseed.ed.ac.uk>
Date: Wed, 14 Aug 2019 20:01:56 +0100
Subject: [PATCH 553/838] Update regression tests

---
 regression-tests | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/regression-tests b/regression-tests
index db743d7ff..504922d7f 160000
--- a/regression-tests
+++ b/regression-tests
@@ -1 +1 @@
-Subproject commit db743d7fff2e07d9f839fbb3393dfbb3aabee2cb
+Subproject commit 504922d7fae39c0d019ea40ceb211bc2230943f2

From ac22cf56bcfd3f099a91951c4d9c2baf314c72be Mon Sep 17 00:00:00 2001
From: Roman Grundkiewicz <rgrundki@exseed.ed.ac.uk>
Date: Wed, 14 Aug 2019 20:02:22 +0100
Subject: [PATCH 554/838] Update CHANGELOG and VERSION

---
 CHANGELOG.md | 19 +++++++++++++------
 VERSION      |  2 +-
 2 files changed, 14 insertions(+), 7 deletions(-)

diff --git a/CHANGELOG.md b/CHANGELOG.md
index ebe04c36a..f22074040 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -8,25 +8,32 @@ and this project adheres to [Semantic Versioning](http://semver.org/spec/v2.0.0.
 ## [Unreleased]
 
 ### Added
-- Automatic detection of CPU intrisics when building with -arch=native 
+- Alias options and new --task option
+- Automatic detection of CPU intrisics when building with -arch=native
 - First version of BERT-training and BERT-classifier, currently not compatible with TF models
 - New reduction operators
-- Use Cmake's ExternalProject to build NCCL and potentially other external libs. 
+- Use Cmake's ExternalProject to build NCCL and potentially other external libs
 
 ### Fixed
+- Issue with relative paths in automatically generated decoder config files
+- Bug with overlapping CXX flags and building spm_train executable
+- Compilation with gcc 8
+- Overwriting and unsetting vector options
 - Windows build with recent changes
 - Bug with read-ahead buffer
-- Fixed handling of "dump-config: false" in YAML config
+- Handling of "dump-config: false" in YAML config
 - Errors due to warnings
-- Fixed issue concerning failed saving with single GPU training and --sync-sgd option. 
-- Fixed NaN problem when training with Tensor Cores on Volta GPUs
+- Issue concerning failed saving with single GPU training and --sync-sgd option.
+- NaN problem when training with Tensor Cores on Volta GPUs
 
 ### Changed
+- Error message when using left-to-right and right-to-left models together in ensembles
+- Regression tests included as a submodule
 - Update NCCL to 2.4.2
 - Add zlib source to Marian's source tree, builds now as object lib
 - -DUSE_STATIC_LIBS=on now also looks for static versions of CUDA libraries
 - Include NCCL build from github.com/marian-nmt/nccl and compile within source tree
-- Set nearly all warnings as errors for Marian's own targets. Disable warnings for 3rd party.
+- Set nearly all warnings as errors for Marian's own targets. Disable warnings for 3rd party
 
 ## [1.7.0] - 2018-11-27
 
diff --git a/VERSION b/VERSION
index 257d138aa..b84efa430 100644
--- a/VERSION
+++ b/VERSION
@@ -1 +1 @@
-v1.7.8
+v1.7.12

From 9e039209a3e5f8eb235eb7365b9e5c2fff92dee2 Mon Sep 17 00:00:00 2001
From: Ulrich Germann <ulrich.germann@gmail.com>
Date: Wed, 14 Aug 2019 20:47:31 +0100
Subject: [PATCH 555/838] Facilitate custom options for executables.

Before, adding options required developers to add code to the ConfigParser class.
Now, an executable can add options for example like this:

```
int main(int argc, char* argv[]) {
  ConfigParser cp(cli::mode::translation);
  cp.addOption<int>("--port",                  // name of option
                    "Server Options",          // option group
                    "Port to use for server.", // help string
                    5678);                     // default option
  bool validate=true; // validate options?
  auto options = New<Options>(cp, argc, argv, validate);
  ...
}
```
---
 regression-tests             |   2 +-
 src/CMakeLists.txt           |   1 +
 src/common/cli_wrapper.cpp   |   8 ++-
 src/common/cli_wrapper.h     |   3 +-
 src/common/config.cpp        |  50 +++++++---------
 src/common/config.h          |  11 ++--
 src/common/config_parser.cpp | 113 +++++++++++++++++++++++------------
 src/common/config_parser.h   |  55 ++++++++++++++---
 src/common/options.cpp       |  69 +++++++++++++++++++++
 src/common/options.h         |  56 +++++------------
 10 files changed, 245 insertions(+), 123 deletions(-)
 create mode 100644 src/common/options.cpp

diff --git a/regression-tests b/regression-tests
index db743d7ff..142eadddb 160000
--- a/regression-tests
+++ b/regression-tests
@@ -1 +1 @@
-Subproject commit db743d7fff2e07d9f839fbb3393dfbb3aabee2cb
+Subproject commit 142eadddbe04493c1024b42586030b72e9cb7ea2
diff --git a/src/CMakeLists.txt b/src/CMakeLists.txt
index 28511780f..a4a5bba90 100644
--- a/src/CMakeLists.txt
+++ b/src/CMakeLists.txt
@@ -16,6 +16,7 @@ add_library(marian STATIC
   common/config_parser.cpp
   common/aliases.cpp
   common/config_validator.cpp
+  common/options.cpp
   common/binary.cpp
   common/io.cpp
 
diff --git a/src/common/cli_wrapper.cpp b/src/common/cli_wrapper.cpp
index 3bae8c01b..0477aa02b 100644
--- a/src/common/cli_wrapper.cpp
+++ b/src/common/cli_wrapper.cpp
@@ -110,8 +110,12 @@ CLIWrapper::CLIWrapper(Ptr<marian::Options> options,
 
 CLIWrapper::~CLIWrapper() {}
 
-void CLIWrapper::switchGroup(const std::string &name) {
-  currentGroup_ = name.empty() ? defaultGroup_ : name;
+// set current group to name, return previous group
+std::string CLIWrapper::switchGroup(std::string name) {
+  currentGroup_.swap(name);
+  if (currentGroup_.empty())
+    currentGroup_ = defaultGroup_;
+  return name;
 }
 
 void CLIWrapper::parse(int argc, char **argv) {
diff --git a/src/common/cli_wrapper.h b/src/common/cli_wrapper.h
index 4347cb329..f442842a5 100644
--- a/src/common/cli_wrapper.h
+++ b/src/common/cli_wrapper.h
@@ -212,8 +212,9 @@ class CLIWrapper {
    * Switch to different option group or to the default group if argument is empty.
    *
    * @param name Header of the option group
+   * @return Previous group.
    */
-  void switchGroup(const std::string &name = "");
+  std::string switchGroup(std::string name = "");
 
   // Parse command-line arguments. Handles --help and --version options
   void parse(int argc, char **argv);
diff --git a/src/common/config.cpp b/src/common/config.cpp
index ddfb5199e..38a41553f 100644
--- a/src/common/config.cpp
+++ b/src/common/config.cpp
@@ -1,4 +1,6 @@
 #include "common/config.h"
+#include "common/options.h"
+#include "common/config_parser.h"
 #include "common/file_stream.h"
 #include "common/logging.h"
 #include "common/utils.h"
@@ -14,35 +16,26 @@ namespace marian {
 // @TODO: keep seed in a single place, now it is kept here and in Config/Options
 size_t Config::seed = (size_t)time(0);
 
-Config::Config(int argc,
-               char** argv,
-               cli::mode mode,
-               bool validate /*= true*/) {
-  initialize(argc, argv, mode, validate);
+
+Config::Config(ConfigParser const& cp) {
+  initialize(cp);
 }
 
+Config::Config(int argc, char** argv, cli::mode mode, bool validate /*= true*/)
+  : Config(ConfigParser(argc, argv, mode, validate)) {}
+
 Config::Config(const Config& other) : config_(YAML::Clone(other.config_)) {}
 Config::Config(const Options& options) : config_(YAML::Clone(options.getYaml())) {}
 
-void Config::initialize(int argc, char** argv, cli::mode mode, bool validate) {
-  auto parser = ConfigParser(argc, argv, mode, validate);
-  config_ = parser.getConfig();
+void Config::initialize(ConfigParser const& cp) {
+  config_ = YAML::Clone(cp.getConfig());
+  cli::mode mode = cp.getMode();
 
   createLoggers(this);
 
   // echo version and command line
   LOG(info, "[marian] Marian {}", buildVersion());
-  std::string cmdLine;
-  for(int i = 0; i < argc; i++) {
-    std::string arg = argv[i];
-    std::string quote; // attempt to quote special chars
-    if(arg.empty() || arg.find_first_of(" #`\"'\\${}|&^?*!()%><") != std::string::npos)
-      quote = "'";
-    arg = regex::regex_replace(arg, regex::regex("'"), "'\\''");
-    if(!cmdLine.empty())
-      cmdLine.push_back(' ');
-    cmdLine += quote + arg + quote;
-  }
+  std::string cmdLine = cp.cmdLine();
   std::string hostname; int pid; std::tie
   (hostname, pid) = utils::hostnameAndProcessId();
   LOG(info, "[marian] Running on {} as process {} with command line:", hostname, pid);
@@ -262,14 +255,17 @@ std::vector<DeviceId> Config::getDevices(Ptr<Options> options,
   return devices;
 }
 
-Ptr<Options> parseOptions(int argc,
-                          char** argv,
-                          cli::mode mode,
-                          bool validate /*= true*/) {
-  auto config = New<Config>(argc, argv, mode, validate);
-  auto options = New<Options>();
-  options->merge(config->get());
-  return options;
+Ptr<Options>
+parseOptions(int argc, char** argv, cli::mode mode, bool validate){
+  ConfigParser cp(mode);
+  return New<Options>(cp, argc, argv, validate);
+}
+
+std::ostream& operator<<(std::ostream& out, const Config& config) {
+  YAML::Emitter outYaml;
+  cli::OutputYaml(config.get(), outYaml);
+  out << outYaml.c_str();
+  return out;
 }
 
 }  // namespace marian
diff --git a/src/common/config.h b/src/common/config.h
index 326dbcf1d..7a8e9f466 100644
--- a/src/common/config.h
+++ b/src/common/config.h
@@ -38,6 +38,7 @@ class Config {
 
   typedef YAML::Node YamlNode;
 
+  Config(ConfigParser const& cp);
   // TODO: remove mode from this class
   Config(int argc,
          char** argv,
@@ -47,7 +48,8 @@ class Config {
   Config(const Config& other);
   Config(const Options& options);
 
-  void initialize(int argc, char** argv, cli::mode mode, bool validate);
+  void initialize(ConfigParser const& cp);
+  // void initialize(int argc, char** argv, cli::mode mode, bool validate);
 
   bool has(const std::string& key) const;
 
@@ -83,12 +85,7 @@ class Config {
 
   void save(const std::string& name);
 
-  friend std::ostream& operator<<(std::ostream& out, const Config& config) {
-    YAML::Emitter outYaml;
-    cli::OutputYaml(config.get(), outYaml);
-    out << outYaml.c_str();
-    return out;
-  }
+  friend std::ostream& operator<<(std::ostream& out, const Config& config);
 
   static std::vector<DeviceId> getDevices(Ptr<Options> options,
                                           size_t myMPIRank = 0,
diff --git a/src/common/config_parser.cpp b/src/common/config_parser.cpp
index fdf86da8b..523f3a1db 100644
--- a/src/common/config_parser.cpp
+++ b/src/common/config_parser.cpp
@@ -5,8 +5,10 @@
 #include "common/config_validator.h"
 #include "common/file_stream.h"
 #include "common/logging.h"
+#include "common/options.h"
+#include "common/config.h"
 #include "common/utils.h"
-
+#include "common/regex.h"
 #include <algorithm>
 #include <set>
 #include <stdexcept>
@@ -48,6 +50,56 @@ const std::set<std::string> PATHS = {
 };
 // clang-format on
 
+std::string escapeCmdLine(int argc, char** argv){
+  std::string cmdLine;
+  for(int i = 0; i < argc; i++) {
+    std::string arg = argv[i];
+    std::string quote; // attempt to quote special chars
+    if(arg.empty() || arg.find_first_of(" #`\"'\\${}|&^?*!()%><") != std::string::npos)
+      quote = "'";
+    arg = regex::regex_replace(arg, regex::regex("'"), "'\\''");
+    if(!cmdLine.empty())
+      cmdLine.push_back(' ');
+    cmdLine += quote + arg + quote;
+  }
+  return cmdLine;
+}
+
+std::string const& ConfigParser::cmdLine() const {
+  return cmdLine_;
+}
+
+ConfigParser::ConfigParser(cli::mode mode)
+  : cli_(config_,"Marian: Fast Neural Machine Translation in C++",
+         "General options", "", 40),
+    // modeServer_(mode == cli::mode::server),
+    mode_(mode == cli::mode::server ? cli::mode::translation : mode) {
+
+  addOptionsGeneral(cli_);
+  if (mode == cli::mode::server)
+    addOptionsServer(cli_);
+  addOptionsModel(cli_);
+
+  // clang-format off
+  switch(mode_) {
+    case cli::mode::training:
+      addOptionsTraining(cli_);
+      addOptionsValidation(cli_);
+      break;
+    case cli::mode::translation:
+      addOptionsTranslation(cli_);
+      break;
+    case cli::mode::scoring:
+      addOptionsScoring(cli_);
+      break;
+    default:
+      ABORT("wrong CLI mode");
+      break;
+  }
+  // clang-format on
+
+}
+
 void ConfigParser::addOptionsGeneral(cli::CLIWrapper& cli) {
   int defaultWorkspace = (mode_ == cli::mode::translation) ? 512 : 2048;
 
@@ -86,14 +138,16 @@ void ConfigParser::addOptionsGeneral(cli::CLIWrapper& cli) {
 
 void ConfigParser::addOptionsServer(cli::CLIWrapper& cli) {
   // clang-format off
+  auto previous_group = cli.switchGroup("Server options");
   cli.add<size_t>("--port,-p",
       "Port number for web socket server",
       8080);
+  cli.switchGroup(previous_group);
   // clang-format on
 }
 
 void ConfigParser::addOptionsModel(cli::CLIWrapper& cli) {
-  cli.switchGroup("Model options");
+  auto previous_group = cli.switchGroup("Model options");
 
   // clang-format off
   if(mode_ == cli::mode::translation) {
@@ -253,11 +307,12 @@ void ConfigParser::addOptionsModel(cli::CLIWrapper& cli) {
     cli.add<float>("--transformer-dropout-ffn",
         "Dropout for transformer filter (0 = no dropout)");
   }
+  cli.switchGroup(previous_group);
   // clang-format on
 }
 
 void ConfigParser::addOptionsTraining(cli::CLIWrapper& cli) {
-  cli.switchGroup("Training options");
+  auto previous_group = cli.switchGroup("Training options");
   // clang-format off
   cli.add<std::string>("--cost-type", // @TODO: rename to loss-type
       "Optimization criterion: ce-mean, ce-mean-words, ce-sum, perplexity", "ce-mean");
@@ -421,11 +476,12 @@ void ConfigParser::addOptionsTraining(cli::CLIWrapper& cli) {
 
   cli.add<std::vector<std::string>>("--task",
      "Use predefined set of options. Possible values: transformer, transformer-big");
+  cli.switchGroup(previous_group);
   // clang-format on
 }
 
 void ConfigParser::addOptionsValidation(cli::CLIWrapper& cli) {
-  cli.switchGroup("Validation set options");
+  auto previous_group = cli.switchGroup("Validation set options");
 
   // clang-format off
   cli.add<std::vector<std::string>>("--valid-sets",
@@ -479,11 +535,12 @@ void ConfigParser::addOptionsValidation(cli::CLIWrapper& cli) {
       "Keep best model for each validation metric");
   cli.add<std::string>("--valid-log",
      "Log validation scores to file given by  arg");
+  cli.switchGroup(previous_group);
   // clang-format on
 }
 
 void ConfigParser::addOptionsTranslation(cli::CLIWrapper& cli) {
-  cli.switchGroup("Translator options");
+  auto previous_group = cli.switchGroup("Translator options");
 
   // clang-format off
   cli.add<std::vector<std::string>>("--input,-i",
@@ -534,11 +591,12 @@ void ConfigParser::addOptionsTranslation(cli::CLIWrapper& cli) {
   // add ULR settings
   addSuboptionsULR(cli);
 
+  cli.switchGroup(previous_group);
   // clang-format on
 }
 
 void ConfigParser::addOptionsScoring(cli::CLIWrapper& cli) {
-  cli.switchGroup("Scorer options");
+  auto previous_group = cli.switchGroup("Scorer options");
 
   // clang-format off
   cli.add<bool>("--no-reload",
@@ -572,6 +630,7 @@ void ConfigParser::addOptionsScoring(cli::CLIWrapper& cli) {
 
   cli.add<bool>("--optimize",
       "Optimize speed aggressively sacrificing memory or precision");
+  cli.switchGroup(previous_group);
   // clang-format on
 }
 
@@ -693,45 +752,21 @@ void ConfigParser::addSuboptionsULR(cli::CLIWrapper& cli) {
   // clang-format on
 }
 
-void ConfigParser::parseOptions(int argc, char** argv, bool doValidate) {
-  cli::CLIWrapper cli(config_,
-                      "Marian: Fast Neural Machine Translation in C++",
-                      "General options",
-                      "",
-                      40);
 
-  addOptionsGeneral(cli);
-  if(modeServer_)
-    addOptionsServer(cli);
-  addOptionsModel(cli);
+cli::mode ConfigParser::getMode() const { return mode_; }
 
-  // clang-format off
-  switch(mode_) {
-    case cli::mode::training:
-      addOptionsTraining(cli);
-      addOptionsValidation(cli);
-      addAliases(cli);
-      break;
-    case cli::mode::translation:
-      addOptionsTranslation(cli);
-      break;
-    case cli::mode::scoring:
-      addOptionsScoring(cli);
-      break;
-    default:
-      ABORT("wrong CLI mode");
-      break;
-  }
-  // clang-format on
+YAML::Node const&
+ConfigParser::parseOptions(int argc, char** argv, bool doValidate){
+  cmdLine_ = escapeCmdLine(argc,argv);
 
   // parse command-line options and fill wrapped YAML config
-  cli.parse(argc, argv);
+  cli_.parse(argc, argv);
 
   // get paths to extra config files
   auto configPaths = findConfigPaths();
   if(!configPaths.empty()) {
     auto config = loadConfigFiles(configPaths);
-    cli.updateConfig(config,
+    cli_.updateConfig(config,
                      cli::Priority::ConfigFile,
                      "There are option(s) in a config file that are not expected");
   }
@@ -741,6 +776,7 @@ void ConfigParser::parseOptions(int argc, char** argv, bool doValidate) {
   }
 
   if(doValidate) {
+    // this aborts the program on first validation error
     ConfigValidator(config_).validateOptions(mode_);
   }
 
@@ -760,7 +796,8 @@ void ConfigParser::parseOptions(int argc, char** argv, bool doValidate) {
     exit(0);
   }
 
-  cli.parseAliases();
+  cli_.parseAliases();
+  return getConfig();
 }
 
 std::vector<std::string> ConfigParser::findConfigPaths() {
@@ -821,7 +858,7 @@ YAML::Node ConfigParser::loadConfigFiles(const std::vector<std::string>& paths)
   return configAll;
 }
 
-YAML::Node ConfigParser::getConfig() const {
+YAML::Node const& ConfigParser::getConfig() const {
   return config_;
 }
 }  // namespace marian
diff --git a/src/common/config_parser.h b/src/common/config_parser.h
index ef2aee950..944b0fb45 100644
--- a/src/common/config_parser.h
+++ b/src/common/config_parser.h
@@ -1,3 +1,4 @@
+// -*- mode: c++; indent-tabs-mode: nil; tab-width: 2 -*-
 #pragma once
 
 #include "3rd_party/yaml-cpp/yaml.h"
@@ -14,7 +15,6 @@
 namespace marian {
 
 namespace cli {
-// CLI mode
 enum struct mode { training, translation, scoring, server };
 }  // namespace cli
 
@@ -25,12 +25,49 @@ enum struct mode { training, translation, scoring, server };
  */
 class ConfigParser {
 public:
+
+  ConfigParser(cli::mode mode);
+
   ConfigParser(int argc, char** argv, cli::mode mode, bool validate = false)
-      : modeServer_(mode == cli::mode::server),
-        mode_(mode == cli::mode::server ? cli::mode::translation : mode) {
+    : ConfigParser(mode) {
     parseOptions(argc, argv, validate);
   }
 
+  template<typename T>
+  ConfigParser&
+  addOption(const std::string& args,
+            const std::string& group,
+            const std::string& help,
+            const T val) {
+    std::string previous_group = cli_.switchGroup(group);
+    cli_.add<T>(args,help,val);
+    cli_.switchGroup(previous_group);
+    return *this;
+  }
+
+  template<typename T>
+  ConfigParser&
+  addOption(const std::string& args,
+            const std::string& group,
+            const std::string& help,
+            const T val, const T implicit_val) {
+    std::string previous_group = cli_.switchGroup(group);
+    cli_.add<T>(args,help,val)->implicit_val(implicit_val);
+    cli_.switchGroup(previous_group);
+    return *this;
+  }
+
+  template<typename T>
+  ConfigParser&
+  addOption(const std::string& args,
+            const std::string& group,
+            const std::string& help) {
+    std::string previous_group = cli_.switchGroup(group);
+    cli_.add<T>(args,help);
+    cli_.switchGroup(previous_group);
+    return *this;
+  }
+
   /**
    * @brief Parse command-line options
    *
@@ -47,15 +84,19 @@ class ConfigParser {
    * @param argc
    * @param argv
    * @param validate Do or do not validate parsed options
+   * @return (YAML::Node const&)config_
    */
-  void parseOptions(int argc, char** argv, bool validate);
-
-  YAML::Node getConfig() const;
 
+  YAML::Node const& parseOptions(int argc, char** argv, bool validate);
+  YAML::Node const& getConfig() const;
+  cli::mode getMode() const;
+  std::string const& cmdLine() const;
 private:
-  bool modeServer_;
+  // bool modeServer_;
+  cli::CLIWrapper cli_;
   cli::mode mode_;
   YAML::Node config_;
+  std::string cmdLine_;
 
   // Check if the config contains value for option key
   bool has(const std::string& key) const {
diff --git a/src/common/options.cpp b/src/common/options.cpp
new file mode 100644
index 000000000..3549b7a3b
--- /dev/null
+++ b/src/common/options.cpp
@@ -0,0 +1,69 @@
+// -*- mode: c++; indent-tabs-mode: nil; tab-width: 2 -*-
+#include "options.h"
+#include "common/config.h"
+
+namespace marian {
+  Options::Options() {}
+
+  Options::Options(const Options& other)
+    : options_(YAML::Clone(other.options_)) {}
+
+  Options::Options(ConfigParser& cp, int argc, char** argv, bool validate){
+    cp.parseOptions(argc, argv, validate);
+    merge(Config(cp).get());
+  }
+
+
+  Options Options::clone() const {
+    return Options(*this);
+  }
+
+  YAML::Node& Options::getYaml() {
+    return options_;
+  }
+
+  const YAML::Node& Options::getYaml() const {
+    return options_;
+  }
+
+  void Options::parse(const std::string& yaml) {
+    auto node = YAML::Load(yaml);
+    for(auto it : node)
+      options_[it.first.as<std::string>()] = YAML::Clone(it.second);
+  }
+
+  void Options::merge(const YAML::Node& node, bool overwrite) {
+    for(auto it : node)
+      if(overwrite || !options_[it.first.as<std::string>()])
+        options_[it.first.as<std::string>()] = YAML::Clone(it.second);
+  }
+
+  void Options::merge(Ptr<Options> options) {
+    merge(options->getYaml());
+  }
+
+  std::string Options::str() {
+    std::stringstream ss;
+    ss << options_;
+    return ss.str();
+  }
+
+  bool Options::hasAndNotEmpty(const std::string& key) const {
+    if(!has(key)) {
+      return false;
+    }
+    if(options_[key].IsSequence()) {
+      return options_[key].size() != 0;
+    }
+    try {
+      return !options_[key].as<std::string>().empty();
+    } catch(const YAML::BadConversion& e) {
+      ABORT("Option '{}' is neither a sequence nor a text");
+    }
+    return false;
+  }
+
+  bool Options::has(const std::string& key) const {
+    return options_[key];
+  }
+}
diff --git a/src/common/options.h b/src/common/options.h
index 5a3f4eb70..57654911a 100644
--- a/src/common/options.h
+++ b/src/common/options.h
@@ -3,6 +3,7 @@
 #include <sstream>
 #include <string>
 #include "common/definitions.h"
+#include "common/config_parser.h"
 
 #include "3rd_party/yaml-cpp/yaml.h"
 
@@ -32,22 +33,20 @@ class Options {
   YAML::Node options_;
 
 public:
-  Options() {}
-  Options(const Options& other) : options_(YAML::Clone(other.options_)) {}
+  Options();
+  Options(const Options& other);
+  Options(ConfigParser& cp, int argc, char** argv, bool validate);
 
   /**
    * @brief Return a copy of the object that can be safely modified.
    */
-  Options clone() const { return Options(*this); }
+  Options clone() const;
 
-  YAML::Node& getYaml() { return options_; }
-  const YAML::Node& getYaml() const { return options_; }
+  YAML::Node& getYaml();
 
-  void parse(const std::string& yaml) {
-    auto node = YAML::Load(yaml);
-    for(auto it : node)
-      options_[it.first.as<std::string>()] = YAML::Clone(it.second);
-  }
+  const YAML::Node& getYaml() const;
+
+  void parse(const std::string& yaml);
 
   /**
    * @brief Splice options from a YAML node
@@ -58,20 +57,10 @@ class Options {
    * @param node a YAML node to transfer the options from
    * @param overwrite overwrite all options
    */
-  void merge(YAML::Node& node, bool overwrite = false) {
-    for(auto it : node)
-      if(overwrite || !options_[it.first.as<std::string>()])
-        options_[it.first.as<std::string>()] = YAML::Clone(it.second);
-  }
+  void merge(const YAML::Node& node, bool overwrite = false);
+  void merge(Ptr<Options> options);
 
-  void merge(const YAML::Node& node, bool overwrite = false) { merge(node, overwrite); }
-  void merge(Ptr<Options> options) { merge(options->getYaml()); }
-
-  std::string str() {
-    std::stringstream ss;
-    ss << options_;
-    return ss.str();
-  }
+  std::string str();
 
   template <typename T>
   void set(const std::string& key, T value) {
@@ -79,13 +68,13 @@ class Options {
   }
 
   template <typename T>
-  T get(const std::string& key) {
+  T get(const std::string& key) const {
     ABORT_IF(!has(key), "Required option '{}' has not been set", key);
     return options_[key].as<T>();
   }
 
   template <typename T>
-  T get(const std::string& key, T defaultValue) {
+  T get(const std::string& key, T defaultValue) const {
     if(has(key))
       return options_[key].as<T>();
     else
@@ -102,22 +91,9 @@ class Options {
    *
    * @return true if the option is defined and is a nonempty sequence or string
    */
-  bool hasAndNotEmpty(const std::string& key) const {
-    if(!has(key)) {
-      return false;
-    }
-    if(options_[key].IsSequence()) {
-      return options_[key].size() != 0;
-    }
-    try {
-      return !options_[key].as<std::string>().empty();
-    } catch(const YAML::BadConversion& e) {
-      ABORT("Option '{}' is neither a sequence nor a text");
-    }
-    return false;
-  }
+  bool hasAndNotEmpty(const std::string& key) const;
 
-  bool has(const std::string& key) const { return options_[key]; }
+  bool has(const std::string& key) const;
 };
 
 }  // namespace marian

From d358e166b4e6118b68f96292248816d2574be2fb Mon Sep 17 00:00:00 2001
From: Ulrich Germann <ulrich.germann@gmail.com>
Date: Wed, 14 Aug 2019 21:01:21 +0100
Subject: [PATCH 556/838] Updated regression test to match master branch.

---
 regression-tests | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/regression-tests b/regression-tests
index 142eadddb..504922d7f 160000
--- a/regression-tests
+++ b/regression-tests
@@ -1 +1 @@
-Subproject commit 142eadddbe04493c1024b42586030b72e9cb7ea2
+Subproject commit 504922d7fae39c0d019ea40ceb211bc2230943f2

From 98e83d6d66c5d0d0f083fd7ab6a129053ee3709f Mon Sep 17 00:00:00 2001
From: Ulrich Germann <ulrich.germann@gmail.com>
Date: Wed, 14 Aug 2019 23:02:25 +0000
Subject: [PATCH 557/838] Bug fix.

---
 src/common/config_parser.cpp | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/src/common/config_parser.cpp b/src/common/config_parser.cpp
index 523f3a1db..cfa026eec 100644
--- a/src/common/config_parser.cpp
+++ b/src/common/config_parser.cpp
@@ -788,11 +788,11 @@ ConfigParser::parseOptions(int argc, char** argv, bool doValidate){
     config_.remove("dump-config");
 
     if(dumpMode == "expand") {
-      cli.parseAliases();
+      cli_.parseAliases();
     }
 
     bool minimal = (dumpMode == "minimal" || dumpMode == "expand");
-    std::cout << cli.dumpConfig(minimal) << std::endl;
+    std::cout << cli_.dumpConfig(minimal) << std::endl;
     exit(0);
   }
 

From 2539cc33706a1aac86f0006a37ef232108f8902a Mon Sep 17 00:00:00 2001
From: Ulrich Germann <ugermnann@inf.ed.ac.uk>
Date: Thu, 15 Aug 2019 18:26:39 +0000
Subject: [PATCH 558/838] Call addAliases() when initializing ConfigParser.

---
 src/common/config_parser.cpp | 1 +
 1 file changed, 1 insertion(+)

diff --git a/src/common/config_parser.cpp b/src/common/config_parser.cpp
index cfa026eec..1ca1b7252 100644
--- a/src/common/config_parser.cpp
+++ b/src/common/config_parser.cpp
@@ -85,6 +85,7 @@ ConfigParser::ConfigParser(cli::mode mode)
     case cli::mode::training:
       addOptionsTraining(cli_);
       addOptionsValidation(cli_);
+      addAliases(cli_);
       break;
     case cli::mode::translation:
       addOptionsTranslation(cli_);

From 789ab7a5210f9aa92850df2cf62f656a3dd63ff8 Mon Sep 17 00:00:00 2001
From: Ulrich Germann <ugermnann@inf.ed.ac.uk>
Date: Fri, 16 Aug 2019 12:12:45 +0000
Subject: [PATCH 559/838] Remove dependency of Options class on ConfigParser
 and Config.

ConfigParser::parseOptions() now returns Ptr<Options>.
---
 src/common/config.cpp        | 2 +-
 src/common/config_parser.cpp | 6 ++++--
 src/common/config_parser.h   | 2 +-
 src/common/options.cpp       | 7 -------
 src/common/options.h         | 3 +--
 5 files changed, 7 insertions(+), 13 deletions(-)

diff --git a/src/common/config.cpp b/src/common/config.cpp
index 38a41553f..21a11877d 100644
--- a/src/common/config.cpp
+++ b/src/common/config.cpp
@@ -258,7 +258,7 @@ std::vector<DeviceId> Config::getDevices(Ptr<Options> options,
 Ptr<Options>
 parseOptions(int argc, char** argv, cli::mode mode, bool validate){
   ConfigParser cp(mode);
-  return New<Options>(cp, argc, argv, validate);
+  return cp.parseOptions(argc, argv, validate);
 }
 
 std::ostream& operator<<(std::ostream& out, const Config& config) {
diff --git a/src/common/config_parser.cpp b/src/common/config_parser.cpp
index 1ca1b7252..aa500c7dc 100644
--- a/src/common/config_parser.cpp
+++ b/src/common/config_parser.cpp
@@ -756,7 +756,7 @@ void ConfigParser::addSuboptionsULR(cli::CLIWrapper& cli) {
 
 cli::mode ConfigParser::getMode() const { return mode_; }
 
-YAML::Node const&
+Ptr<Options>
 ConfigParser::parseOptions(int argc, char** argv, bool doValidate){
   cmdLine_ = escapeCmdLine(argc,argv);
 
@@ -798,7 +798,9 @@ ConfigParser::parseOptions(int argc, char** argv, bool doValidate){
   }
 
   cli_.parseAliases();
-  return getConfig();
+  auto opts = New<Options>();
+  opts->merge(Config(*this).get());
+  return opts;
 }
 
 std::vector<std::string> ConfigParser::findConfigPaths() {
diff --git a/src/common/config_parser.h b/src/common/config_parser.h
index 944b0fb45..379dbcb84 100644
--- a/src/common/config_parser.h
+++ b/src/common/config_parser.h
@@ -87,7 +87,7 @@ class ConfigParser {
    * @return (YAML::Node const&)config_
    */
 
-  YAML::Node const& parseOptions(int argc, char** argv, bool validate);
+  Ptr<Options> parseOptions(int argc, char** argv, bool validate);
   YAML::Node const& getConfig() const;
   cli::mode getMode() const;
   std::string const& cmdLine() const;
diff --git a/src/common/options.cpp b/src/common/options.cpp
index 3549b7a3b..5d425d19a 100644
--- a/src/common/options.cpp
+++ b/src/common/options.cpp
@@ -1,6 +1,5 @@
 // -*- mode: c++; indent-tabs-mode: nil; tab-width: 2 -*-
 #include "options.h"
-#include "common/config.h"
 
 namespace marian {
   Options::Options() {}
@@ -8,12 +7,6 @@ namespace marian {
   Options::Options(const Options& other)
     : options_(YAML::Clone(other.options_)) {}
 
-  Options::Options(ConfigParser& cp, int argc, char** argv, bool validate){
-    cp.parseOptions(argc, argv, validate);
-    merge(Config(cp).get());
-  }
-
-
   Options Options::clone() const {
     return Options(*this);
   }
diff --git a/src/common/options.h b/src/common/options.h
index 57654911a..4732458d8 100644
--- a/src/common/options.h
+++ b/src/common/options.h
@@ -3,7 +3,6 @@
 #include <sstream>
 #include <string>
 #include "common/definitions.h"
-#include "common/config_parser.h"
 
 #include "3rd_party/yaml-cpp/yaml.h"
 
@@ -35,7 +34,7 @@ class Options {
 public:
   Options();
   Options(const Options& other);
-  Options(ConfigParser& cp, int argc, char** argv, bool validate);
+  // Options(ConfigParser& cp, int argc, char** argv, bool validate);
 
   /**
    * @brief Return a copy of the object that can be safely modified.

From e099d094540c55c12b7fa1c383e8c567921af9ff Mon Sep 17 00:00:00 2001
From: Ulrich Germann <ugermann@inf.ed.ac.uk>
Date: Fri, 23 Aug 2019 17:10:21 +0100
Subject: [PATCH 560/838] Make Marian compile when it is a submodule in another
 repository

---
 src/CMakeLists.txt | 17 ++++++++++++++++-
 1 file changed, 16 insertions(+), 1 deletion(-)

diff --git a/src/CMakeLists.txt b/src/CMakeLists.txt
index 28511780f..67aad7400 100644
--- a/src/CMakeLists.txt
+++ b/src/CMakeLists.txt
@@ -93,10 +93,25 @@ target_compile_options(marian PUBLIC ${ALL_WARNINGS})
 # Generate git_revision.h to reflect current git revision information
 # [https://stackoverflow.com/questions/1435953/how-can-i-pass-git-sha1-to-compiler-as-definition-using-cmake]
 # Git updates .git/logs/HEAD file whenever you pull or commit something.
+
+# If Marian is checked out as a submodule in another repository,
+# there's no .git directory in ${CMAKE_SOURCE_DIR}. Instead .git is a
+# file that specifies the relative path from ${CMAKE_SOURCE_DIR} to
+# ./git/modules/<MARIAN_ROOT_DIR> in the root of the repository that
+# contains Marian as a submodule. We set MARIAN_GIT_DIR to the appropriate
+# path, depending on whether ${CMAKE_SOURCE_DIR}/.git is a directory or file.
+if(IS_DIRECTORY ${CMAKE_SOURCE_DIR}/.git) # not a submodule
+  set(MARIAN_GIT_DIR ${CMAKE_SOURCE_DIR}/.git)
+else(IS_DIRECTORY ${CMAKE_SOURCE_DIR}/.git)
+  file(READ ${CMAKE_SOURCE_DIR}/.git MARIAN_GIT_DIR)
+  string(REGEX REPLACE "gitdir: (.*)\n" "\\1" MARIAN_GIT_DIR ${MARIAN_GIT_DIR})
+  get_filename_component(MARIAN_GIT_DIR "${CMAKE_SOURCE_DIR}/${MARIAN_GIT_DIR}" ABSOLUTE)
+endif(IS_DIRECTORY ${CMAKE_SOURCE_DIR}/.git)
+
 add_custom_command(OUTPUT ${CMAKE_CURRENT_SOURCE_DIR}/common/git_revision.h
   WORKING_DIRECTORY ${CMAKE_SOURCE_DIR}
   COMMAND git log -1 --pretty=format:\#define\ GIT_REVISION\ \"\%h\ \%ai\" > ${CMAKE_CURRENT_SOURCE_DIR}/common/git_revision.h
-  DEPENDS ${CMAKE_SOURCE_DIR}/.git/logs/HEAD
+  DEPENDS ${MARIAN_GIT_DIR}/logs/HEAD
   VERBATIM
 )
 add_custom_target(marian_version DEPENDS ${CMAKE_CURRENT_SOURCE_DIR}/common/git_revision.h)

From 441aca40edb9b14c405d378b918e6dfb1ec0d153 Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Tue, 27 Aug 2019 14:52:19 -0700
Subject: [PATCH 561/838] added experimental option --factor-weight

---
 src/common/config_parser.cpp    |  2 ++
 src/data/factored_vocab.cpp     |  2 +-
 src/graph/node_initializers.cpp |  2 +-
 src/layers/loss.cpp             |  3 ++-
 src/layers/loss.h               | 18 ++++++++++++------
 src/models/transformer.h        |  2 +-
 6 files changed, 19 insertions(+), 10 deletions(-)
 mode change 100644 => 100755 src/graph/node_initializers.cpp
 mode change 100644 => 100755 src/layers/loss.cpp
 mode change 100644 => 100755 src/layers/loss.h

diff --git a/src/common/config_parser.cpp b/src/common/config_parser.cpp
index af0ec8cb0..46e4fa146 100755
--- a/src/common/config_parser.cpp
+++ b/src/common/config_parser.cpp
@@ -382,6 +382,8 @@ void ConfigParser::addOptionsTraining(cli::CLIWrapper& cli) {
 
   cli.add<double>("--label-smoothing",
      "Epsilon for label smoothing (0 to disable)");
+  cli.add<double>("--factor-weight",
+     "Weight for loss function for factors (factored vocab only) (1 to disable)", 1.0f);
   cli.add<float>("--clip-norm",
      "Clip gradient norm to  arg  (0 to disable)",
      1.f);
diff --git a/src/data/factored_vocab.cpp b/src/data/factored_vocab.cpp
index e9a684e35..3b12d9d39 100755
--- a/src/data/factored_vocab.cpp
+++ b/src/data/factored_vocab.cpp
@@ -4,9 +4,9 @@
 #include "data/vocab_base.h"
 #include "common/definitions.h"
 #include "data/types.h"
-#include "common/options.h"
 #include "common/regex.h"
 #include "data/factored_vocab.h"
+#include <set>
 
 // @TODO: review all comments and clarify nomenclature:
 // * factor type (e.g. caps: |c* ); currently called a "group"
diff --git a/src/graph/node_initializers.cpp b/src/graph/node_initializers.cpp
old mode 100644
new mode 100755
index b2550c548..233595da2
--- a/src/graph/node_initializers.cpp
+++ b/src/graph/node_initializers.cpp
@@ -62,7 +62,7 @@ NodeInitializer glorot_uniform2(bool fanIn, bool fanOut) {
     if(fanIn && fanOut)
       scale = sqrtf(6.0f / (tensor->shape()[-2] + tensor->shape()[-1]));
     else if(!fanIn && fanOut)
-      scale = sqrtf(3.0f / tensor->shape()[-1]);
+      scale = sqrtf(3.0f / tensor->shape()[-1]); // results in columns of matrix to be ~unit length
     else if(fanIn && !fanOut)
       scale = sqrtf(3.0f / tensor->shape()[-2]);
     else
diff --git a/src/layers/loss.cpp b/src/layers/loss.cpp
old mode 100644
new mode 100755
index 61186fc1e..5c4c99a9a
--- a/src/layers/loss.cpp
+++ b/src/layers/loss.cpp
@@ -5,12 +5,13 @@ namespace marian {
 // @TODO, simplify this. Currently here for back-compat
 Ptr<LabelwiseLoss> newLoss(Ptr<Options> options, bool inference) {
   float smoothing = inference ? 0.f : options->get<float>("label-smoothing");
+  float factorWeight = options->get<float>("factor-weight", 1.0f);
   std::string costType = options->get<std::string>("cost-type", "ce-mean");
   
   if(costType == "ce-rescore") { // returns per-batch-item scores (while ce-mean reduces over batch)
     return New<RescorerLoss>();
   } else {  // same as ce-mean  --@TODO: better check all allowed values, and fail for invalid ones. E.g. what about ce-sum?
-    return New<CrossEntropyLoss>(smoothing);
+    return New<CrossEntropyLoss>(smoothing, factorWeight);
   }
 }
 
diff --git a/src/layers/loss.h b/src/layers/loss.h
old mode 100644
new mode 100755
index 554541cbd..b64d14aa3
--- a/src/layers/loss.h
+++ b/src/layers/loss.h
@@ -324,25 +324,30 @@ class LabelwiseLoss {
  */
 class CrossEntropyLoss : public LabelwiseLoss {
 public:
-  CrossEntropyLoss(float labelSmoothing)
-  : LabelwiseLoss(/*axes=*/{-2, -3}), // cross-entropy already reduces over axis -1
-    labelSmoothing_(labelSmoothing) {}
+  CrossEntropyLoss(float labelSmoothing, float factorWeight)
+  : CrossEntropyLoss(/*axes=*/{-2, -3}, labelSmoothing, factorWeight) {} // cross-entropy already reduces over axis -1
 
-  CrossEntropyLoss(const std::vector<int>& axes, float labelSmoothing)
+  CrossEntropyLoss(const std::vector<int>& axes, float labelSmoothing, float factorWeight)
   : LabelwiseLoss(axes), // cross-entropy already reduces over axis -1
-    labelSmoothing_(labelSmoothing) {}
+    labelSmoothing_(labelSmoothing), factorWeight_(factorWeight) {}
 
 protected:
   float labelSmoothing_; // interpolation factor for label smoothing, see below
+  float factorWeight_;   // give extra weight to factors
 
   virtual Expr compute(Logits logits, const Words& labels,
                        Expr mask = nullptr, Expr labelWeights = nullptr) override {
     // logits may be factored; in that case, the getLoss() function computes one loss for each, and sums them up
+    int inFactor = false;
     auto ce = logits.applyLossFunction(labels, [&](Expr logits, Expr indices) {
       logits = atleast_3d(logits); // we always assuma a time and batch dimension exists.
       // for bert training or classification the time dimension is lot.
       // Here safeguard against 2d classifier output, adds 1 on the left, non-op.
       Expr ce = cross_entropy(logits, indices);
+      if (inFactor) {
+        LOG_ONCE("scaling factor losses with weight {}", factorWeight_);
+        ce = ce * factorWeight_;
+      }
       if (labelSmoothing_ > 0) {
         // ce = -sum_i y^_i log y_i(h)
         // with smoothing:
@@ -352,6 +357,7 @@ class CrossEntropyLoss : public LabelwiseLoss {
         auto ceqNeg = mean(logits, /*axis=*/ -1) - logsumexp(logits, /*axis=*/ -1);
         ce = (1 - labelSmoothing_) * ce - labelSmoothing_ * ceqNeg;
         //ce = ce - labelSmoothing_ * (ce + ceqNeg); // writing it this way saves one op :)
+        inFactor = true;
       }
       return ce;
     });
@@ -373,7 +379,7 @@ class RescorerLoss : public CrossEntropyLoss {
 public:
   // sentence-wise CE, hence reduce only over time axis
   // This class differs from CrossEntropy in the different 'axes' setting, and that label smoothing is disabled.
-  RescorerLoss() : CrossEntropyLoss(/*axes=*/{-3} /*time axis*/, /*smoothing=*/0.f) {}
+  RescorerLoss() : CrossEntropyLoss(/*axes=*/{-3} /*time axis*/, /*smoothing=*/0.f, /*factorWeight=*/1.0f) {}
 };
 
 /**
diff --git a/src/models/transformer.h b/src/models/transformer.h
index 4f0e8ea88..5eb64f24a 100755
--- a/src/models/transformer.h
+++ b/src/models/transformer.h
@@ -74,7 +74,7 @@ class Transformer : public EncoderOrDecoderBase {
     } else {
       // @TODO : test if embeddings should be scaled when trainable
       // according to paper embeddings are scaled up by \sqrt(d_m)
-      embeddings = std::sqrt((float)dimEmb) * embeddings;
+      embeddings = std::sqrt((float)dimEmb) * embeddings; // embeddings were initialized to unit length; so norms will be in order of sqrt(dimEmb)
 
       auto signal = graph_->constant({dimWords, 1, dimEmb},
                                      inits::sinusoidalPositionEmbeddings(start));

From 664f4a6da74b090d409540001a64a9b11514668c Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Wed, 28 Aug 2019 10:10:33 -0700
Subject: [PATCH 562/838] minor change for lemma-specific biases

---
 src/layers/generic.cpp | 21 +++++----------------
 1 file changed, 5 insertions(+), 16 deletions(-)

diff --git a/src/layers/generic.cpp b/src/layers/generic.cpp
index 17f526e28..97106dabe 100755
--- a/src/layers/generic.cpp
+++ b/src/layers/generic.cpp
@@ -42,22 +42,11 @@ namespace marian {
     Expr loss;
     for (size_t g = 0; g < numGroups; g++) {
       if (!logits_[g])
-        continue; // empty factor  --@TODO: handle this more nicely
+        continue; // empty factor  --@TODO: use an array of indices of non-empty logits_[]
       const auto& maskedFactoredLabels = allMaskedFactoredLabels[g]; // array of (word index, mask)
-#if 1
       auto factorIndices = indices (maskedFactoredLabels.indices); // [B... flattened] factor-label indices, or 0 if factor does not apply
       auto factorMask    = constant(maskedFactoredLabels.masks);   // [B... flattened] loss values get multiplied with 0 for labels that don't have this factor
-#else // @TODO: if this^^ works, we can remove the stuff below (quite a bit code)
-      maskedFactoredLabels;
-      indices; // [B... * 1] all batch items flattened
-      auto factorMaskVector  = factoredVocab_->getFactorMasks(g);   // [v] 1.0 if v has factor of group g
-      auto factorIndexVector = factoredVocab_->getFactorIndices(g); // [v] index of factor for word v in group p; must be 0 if factor is not used
-      auto factorMaskMatrix  = constant({(int)factorMaskVector.size(),  1}, factorMaskVector);  // [V x 1]
-      auto factorIndexMatrix = constant({(int)factorIndexVector.size(), 1}, factorIndexVector); // [V x 1(Ug)]
-      auto factorIndices = rows(factorIndexMatrix, indices); // [B... * 1(Ug)] map word indices to factor indices (indices into factorLogits)
-      auto factorMask    = rows(factorMaskMatrix,  indices); // [B... * 1]     flag whether word has the factor in the first place
-#endif
-      auto factorLogits = logits_[g];                       // [B... * Ug] label-wise loss values (not aggregated yet)
+      auto factorLogits  = logits_[g];                             // [B... * Ug] label-wise loss values (not aggregated yet)
       // For each location in [B...] select [indices[B...]]. If not using factor, select [0] and mask it out next.
       auto factorLoss = lossFn(factorLogits->loss(), factorIndices); // [B... x 1]
       factorLoss = factorLoss * reshape(factorMask, factorLoss->shape()); // mask out factor for words that do not have that factor
@@ -297,7 +286,7 @@ namespace marian {
             int lemmaVocabDim = Plemma->shape()[-1];
             int factorVocabDim = factorLogits->shape()[-1];
             auto name = options_->get<std::string>("prefix");
-            Expr lemmaBt = graph_->param(name + "_lemmaBt_" + std::to_string(g), {factorVocabDim, lemmaVocabDim}, inits::zeros/*glorot_uniform*/); // [U x U0] U0=#lemmas one bias per class per lemma
+            Expr lemmaBt = graph_->param(name + "_lemmaBt_" + std::to_string(g), {factorVocabDim, lemmaVocabDim}, inits::zeros); // [U x U0] U0=#lemmas one bias per class per lemma
             auto b = dot(Plemma, lemmaBt, false, true); // [B... x U]
             factorLogits = factorLogits + b;
           }
@@ -308,8 +297,8 @@ namespace marian {
           if (lemmaDimEmb < 0 && g == 0) {
             ABORT_IF(shortlist_ && lemmaDimEmb != 0, "Lemma-dependent bias with short list is not yet implemented");
             LOG_ONCE(info, "[embedding] using lemma-dependent bias");
-            factorLogits = logsoftmax(factorLogits); // explicitly, since we do that again later
-            auto z = /*stopGradient*/(factorLogits);
+            auto factorLogSoftmax = logsoftmax(factorLogits); // (we do that again later, CSE will kick in)
+            auto z = /*stopGradient*/(factorLogSoftmax);
             Plemma = exp(z); // [B... x U]
           }
           if (lemmaDimEmb > 0 && g == 0) {

From 32fcaf967cfb998ac193e882ed77f4a6009065e9 Mon Sep 17 00:00:00 2001
From: Marcin Junczys-Dowmunt <Marcin.JunczysDowmunt@microsoft.com>
Date: Tue, 3 Sep 2019 14:28:47 -0700
Subject: [PATCH 563/838] Add compiler flags for C programs to silence spurious
 warnings etc

---
 CMakeLists.txt | 20 +++++++++++++++-----
 1 file changed, 15 insertions(+), 5 deletions(-)

diff --git a/CMakeLists.txt b/CMakeLists.txt
index 4ef90b3ab..1191a674c 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -33,6 +33,11 @@ message(STATUS "Project version: ${PROJECT_VERSION_STRING_FULL}")
 execute_process(COMMAND git submodule update --init --recursive --no-fetch
                 WORKING_DIRECTORY ${CMAKE_CURRENT_SOURCE_DIR})
 
+if(NOT CMAKE_BUILD_TYPE)
+  message(WARNING "CMAKE_BUILD_TYPE not set; setting to Release")
+  set(CMAKE_BUILD_TYPE "Release")
+endif()
+
 # Set compilation flags
 if(MSVC)
 # These are used in src/CMakeLists.txt on a per-target basis
@@ -121,6 +126,16 @@ else(MSVC)
   set(CMAKE_CXX_FLAGS_PROFILE         "${CMAKE_CXX_FLAGS_RELEASE} -pg")
   set(CMAKE_CXX_FLAGS_PROFGEN         "${CMAKE_CXX_FLAGS_RELEASE} -fprofile-generate -fprofile-correction")
   set(CMAKE_CXX_FLAGS_PROFUSE         "${CMAKE_CXX_FLAGS_RELEASE} -fprofile-use -fprofile-correction")
+
+  # these need to be set separately
+  set(CMAKE_C_FLAGS                 "-pthread -Wl,--no-as-needed -fPIC ${DISABLE_GLOBALLY} -march=${BUILD_ARCH} ${INTRINSICS}")
+  set(CMAKE_C_FLAGS_RELEASE         "-O3 -m64 -funroll-loops -ffinite-math-only -g -rdynamic")
+  set(CMAKE_C_FLAGS_DEBUG           "-O0 -g -rdynamic")
+  set(CMAKE_C_FLAGS_SLIM            "-O3 -m64 -funroll-loops -ffinite-math-only -DNDEBUG")
+  set(CMAKE_C_FLAGS_RELWITHDEBINFO  "${CMAKE_C_FLAGS_RELEASE}")
+  set(CMAKE_C_FLAGS_PROFILE         "${CMAKE_C_FLAGS_RELEASE} -pg")
+  set(CMAKE_C_FLAGS_PROFGEN         "${CMAKE_C_FLAGS_RELEASE} -fprofile-generate -fprofile-correction")
+  set(CMAKE_C_FLAGS_PROFUSE         "${CMAKE_C_FLAGS_RELEASE} -fprofile-use -fprofile-correction")
 endif(MSVC)
 
 # Downloading SentencePiece if requested and set to compile with it.
@@ -131,11 +146,6 @@ if(USE_SENTENCEPIECE)
   set(EXT_LIBS ${EXT_LIBS} sentencepiece sentencepiece_train)
 endif()
 
-if(NOT CMAKE_BUILD_TYPE)
-  message(WARNING "CMAKE_BUILD_TYPE not set; setting to Release")
-  set(CMAKE_BUILD_TYPE "Release")
-endif()
-
 # Find packages
 set(EXT_LIBS ${EXT_LIBS} ${CMAKE_DL_LIBS})
 

From 21dfbb0165b1906d0915f14277e9e7cd40851819 Mon Sep 17 00:00:00 2001
From: Kenneth Heafield <github@kheafield.com>
Date: Wed, 4 Sep 2019 09:15:40 +0000
Subject: [PATCH 564/838] Switch to comment /* fall through */ as suggested by
 @rihardsk

---
 src/tensors/cpu/sharp/avx_gemm.cpp | 12 ++++++------
 1 file changed, 6 insertions(+), 6 deletions(-)

diff --git a/src/tensors/cpu/sharp/avx_gemm.cpp b/src/tensors/cpu/sharp/avx_gemm.cpp
index a1018c7df..61f75fea6 100644
--- a/src/tensors/cpu/sharp/avx_gemm.cpp
+++ b/src/tensors/cpu/sharp/avx_gemm.cpp
@@ -495,7 +495,7 @@ void AVX_MatrixMult8(const __m512i *A,
         put.Write(C + (i + 4) * num_B_rows + j, Reduce16to32(sum5, sum6));
         put.Write(C + (i + 6) * num_B_rows + j, Reduce16to32(sum7));
       }
-      [[fallthrough]];
+      /* fall through */
     case 6:
       for(int j = 0; j < num_B_rows; j++) {
         const __m512i *B_row = B + j * sse_width;
@@ -519,7 +519,7 @@ void AVX_MatrixMult8(const __m512i *A,
         put.Write(C + i * num_B_rows + j, Reduce16to32(sum1, sum2, sum3, sum4));
         put.Write(C + (i + 4) * num_B_rows + j, Reduce16to32(sum5, sum6));
       }
-      [[fallthrough]];
+      /* fall through */
     case 5:
       for(int j = 0; j < num_B_rows; j++) {
         const __m512i *B_row = B + j * sse_width;
@@ -541,7 +541,7 @@ void AVX_MatrixMult8(const __m512i *A,
         put.Write(C + i * num_B_rows + j, Reduce16to32(sum1, sum2, sum3, sum4));
         put.Write(C + (i + 4) * num_B_rows + j, Reduce16to32(sum5));
       }
-      [[fallthrough]];
+      /* fall through */
     case 4:
       for(int j = 0; j < num_B_rows; j++) {
         const __m512i *B_row = B + j * sse_width;
@@ -560,7 +560,7 @@ void AVX_MatrixMult8(const __m512i *A,
         }
         put.Write(C + i * num_B_rows + j, Reduce16to32(sum1, sum2, sum3, sum4));
       }
-      [[fallthrough]];
+      /* fall through */
     case 3:
       for(int j = 0; j < num_B_rows; j++) {
         const __m512i *B_row = B + j * sse_width;
@@ -578,7 +578,7 @@ void AVX_MatrixMult8(const __m512i *A,
         put.Write(C + i * num_B_rows + j, Reduce16to32(sum1, sum2));
         put.Write(C + (i + 2) * num_B_rows + j, Reduce16to32(sum3));
       }
-      [[fallthrough]];
+      /* fall through */
     case 2:
       for(int j = 0; j < num_B_rows; j++) {
         const __m512i *B_row = B + j * sse_width;
@@ -593,7 +593,7 @@ void AVX_MatrixMult8(const __m512i *A,
         }
         put.Write(C + i * num_B_rows + j, Reduce16to32(sum1, sum2));
       }
-      [[fallthrough]];
+      /* fall through */
     case 1:
       for(int j = 0; j < num_B_rows; j++) {
         const __m512i *B_row = B + j * sse_width;

From b78e75a05c8c2d6446ca79cda4326b6de5cd3a6f Mon Sep 17 00:00:00 2001
From: Ulrich Germann <ulrich.germann@gmail.com>
Date: Wed, 4 Sep 2019 17:45:31 +0100
Subject: [PATCH 565/838] Remove commented-out logging statement in
 src/data/corpus.cpp.

---
 src/data/corpus.cpp | 1 -
 1 file changed, 1 deletion(-)

diff --git a/src/data/corpus.cpp b/src/data/corpus.cpp
index 1218383c9..9cc871d72 100644
--- a/src/data/corpus.cpp
+++ b/src/data/corpus.cpp
@@ -46,7 +46,6 @@ SentenceTuple Corpus::next() {
       }
       else {
         bool gotLine = io::getline(*files_[i], line);
-        // LOG(debug,"[{}][{}] {}", i, pos_ - 1, line);
         if(!gotLine) {
           eofsHit++;
           continue;

From 337defed86cbf26de8014d5a9d9374263dc8ccb8 Mon Sep 17 00:00:00 2001
From: Ulrich Germann <ulrich.germann@gmail.com>
Date: Wed, 4 Sep 2019 18:29:38 +0100
Subject: [PATCH 566/838] Changes as requested by @emjotde in the code review.

---
 src/common/filesystem.cpp | 10 ++++++++++
 src/data/corpus.cpp       |  5 ++---
 2 files changed, 12 insertions(+), 3 deletions(-)

diff --git a/src/common/filesystem.cpp b/src/common/filesystem.cpp
index 81500a5e7..e1789e087 100644
--- a/src/common/filesystem.cpp
+++ b/src/common/filesystem.cpp
@@ -1,16 +1,26 @@
 #include "filesystem.h"
 
+#ifndef _MSC_VER
+// don't include these on Windows:
 #include <sys/types.h>
 #include <sys/stat.h>
 #include <unistd.h>
+#endif
 
 namespace marian {
 namespace filesystem {
 
 bool is_fifo(char const* path) {
+#ifdef _MSC_VER
+  // Pretend that Windows knows no named pipes. It does, by the way, but
+  // they seem to be different from pipes on Unix / Linux. See
+  // https://docs.microsoft.com/en-us/windows/win32/ipc/named-pipes
+  return false;
+#else
   struct stat buf;
   stat(path, &buf);
   return S_ISFIFO(buf.st_mode);
+#endif
 }
 
 bool is_fifo(std::string const& path) {
diff --git a/src/data/corpus.cpp b/src/data/corpus.cpp
index 9cc871d72..6f2048d84 100644
--- a/src/data/corpus.cpp
+++ b/src/data/corpus.cpp
@@ -92,15 +92,14 @@ void Corpus::reset() {
   if (pos_ == 0) // no data read yet
     return;
   pos_ = 0;
-  for (size_t i = 0; i < paths_.size(); ++i)
-    {
+  for (size_t i = 0; i < paths_.size(); ++i) {
       if(paths_[i] == "stdin") {
         files_[i].reset(new io::InputFileStream(std::cin));
         // Probably not necessary, unless there are some buffers
         // that we want flushed.
       }
       else {
-        ABORT_IF(files_[i] and filesystem::is_fifo(paths_[i]),
+        ABORT_IF(files_[i] && filesystem::is_fifo(paths_[i]),
                  "File '", paths_[i], "' is a pipe and cannot be re-opened.");
         // Do NOT reset named pipes; that closes them and triggers a SIGPIPE
         // (lost pipe) at the writing end, which may do whatever it wants

From b55c257af6f44a35d60b13592a832614020b565c Mon Sep 17 00:00:00 2001
From: Marcin Junczys-Dowmunt <Marcin.JunczysDowmunt@microsoft.com>
Date: Wed, 4 Sep 2019 12:48:44 -0700
Subject: [PATCH 567/838] address comments from PR

---
 scripts/checkpoints/average.py | 10 ++++++
 src/common/aliases.cpp         |  5 +--
 src/common/cli_wrapper.cpp     |  4 +--
 src/common/cli_wrapper.h       | 14 ++++----
 src/common/config_parser.cpp   |  2 +-
 src/models/encoder_decoder.cpp |  2 +-
 src/tensors/gpu/prod.cpp       | 66 ++++++----------------------------
 src/translator/scorers.cpp     |  2 +-
 8 files changed, 36 insertions(+), 69 deletions(-)

diff --git a/scripts/checkpoints/average.py b/scripts/checkpoints/average.py
index 2127a9847..53bff1862 100755
--- a/scripts/checkpoints/average.py
+++ b/scripts/checkpoints/average.py
@@ -1,4 +1,14 @@
 #!/usr/bin/env python
+"""
+This script takes multiple Marian *.npz model files and outputs an elementwise average of the model,
+meant to do check-point averaging from: 
+
+https://www.aclweb.org/anthology/W16-2316
+
+usage:
+
+./average.py -m model.1.npz model.2.npz --output model.avg.npz
+"""
 
 from __future__ import print_function
 
diff --git a/src/common/aliases.cpp b/src/common/aliases.cpp
index 47627ad65..0fef1e6d0 100644
--- a/src/common/aliases.cpp
+++ b/src/common/aliases.cpp
@@ -10,8 +10,9 @@ namespace marian {
  * the requested value. The alias option has to be first defined using cli.add<T>(). Defining
  * multiple aliases for the same option name but with different value is allowed.
  *
- * Values are compared as std::string. If the alias option is a vector, the alias will be triggered
- * if the requested value exists in that vector at least once.
+ * Aliases are key-value pairs by default. Values are compared as std::string. 
+ * If the command line option corresponding to the alias is a vector, the alias 
+ * will be triggered if the requested value exists in that vector at least once.
  *
  * @see CLIWrapper::alias()
  *
diff --git a/src/common/cli_wrapper.cpp b/src/common/cli_wrapper.cpp
index 3bae8c01b..07ef4bddb 100644
--- a/src/common/cli_wrapper.cpp
+++ b/src/common/cli_wrapper.cpp
@@ -168,7 +168,7 @@ void CLIWrapper::parseAliases() {
   }
 }
 
-void CLIWrapper::updateConfig(const YAML::Node &config, cli::Priority priority, const std::string &errorMsg) {
+void CLIWrapper::updateConfig(const YAML::Node &config, cli::OptionPriority priority, const std::string &errorMsg) {
   auto cmdOptions = getParsedOptionNames();
   // Keep track of unrecognized options from the provided config
   std::vector<std::string> unknownOpts;
@@ -230,7 +230,7 @@ std::string CLIWrapper::dumpConfig(bool skipUnmodified /*= false*/) const {
     if(!config_[key])
       continue;
     // Do not dump options that were not passed via the command line
-    if(skipUnmodified && options_.at(key).priority == cli::Priority::DefaultValue)
+    if(skipUnmodified && options_.at(key).priority == cli::OptionPriority::DefaultValue)
       continue;
     // Put the group name as a comment before the first option in the group
     auto group = options_.at(key).opt->get_group();
diff --git a/src/common/cli_wrapper.h b/src/common/cli_wrapper.h
index 4347cb329..8dcad9b14 100644
--- a/src/common/cli_wrapper.h
+++ b/src/common/cli_wrapper.h
@@ -18,7 +18,7 @@ class Options;
 namespace cli {
 
 // Option priority
-enum struct Priority { DefaultValue = 0, ConfigFile = 1, CommandLine = 2 };
+enum struct OptionPriority : int { DefaultValue = 0, ConfigFile = 1, CommandLine = 2 };
 
 /**
  * Helper tuple storing an option object, the associated variable and creation index
@@ -30,7 +30,7 @@ struct CLIOptionTuple {
   CLI::Option *opt;     // a pointer to an option object from CLI11
   Ptr<any_type> var;    // value assigned to the option via command-line
   size_t idx{0};        // order in which the option was created
-  Priority priority{cli::Priority::DefaultValue};
+  OptionPriority priority{cli::OptionPriority::DefaultValue};
 };
 
 // Helper tuple for aliases storing the alias name, value, and options to be expanded
@@ -179,7 +179,7 @@ class CLIWrapper {
   }
 
   /**
-   * @brief Transform the option into an alias that is a shortcut for a set of options
+   * @brief Transform a command line option into an alias. This alias will set other options later.
    *
    * An alias sets one or more options to predefined values. The options expanded by the alias are
    * provided as a function setting a temporary YAML config.
@@ -244,7 +244,7 @@ class CLIWrapper {
    * @param errorMsg error message printed if config contains undefined keys. The message is
    *   appended with ": <comma-separated list of invalid options>"
    */
-  void updateConfig(const YAML::Node &config, cli::Priority priority, const std::string &errorMsg);
+  void updateConfig(const YAML::Node &config, cli::OptionPriority priority, const std::string &errorMsg);
 
   // Get textual YAML representation of the config
   std::string dumpConfig(bool skipUnmodified = false) const;
@@ -269,7 +269,7 @@ class CLIWrapper {
 
     // callback function collecting a command-line argument
     CLI::callback_t fun = [this, key](CLI::results_t res) {
-      options_[key].priority = cli::Priority::CommandLine;
+      options_[key].priority = cli::OptionPriority::CommandLine;
       // get variable associated with the option
       auto &var = options_[key].var->as<T>();
       // store parser result in var
@@ -316,7 +316,7 @@ class CLIWrapper {
 
     // callback function collecting command-line arguments
     CLI::callback_t fun = [this, key](CLI::results_t res) {
-      options_[key].priority = cli::Priority::CommandLine;
+      options_[key].priority = cli::OptionPriority::CommandLine;
       // get vector variable associated with the option
       auto &vec = options_[key].var->as<T>();
       vec.clear();
@@ -373,7 +373,7 @@ class CLIWrapper {
 
     // callback function setting the flag
     CLI::callback_t fun = [this, key](CLI::results_t res) {
-      options_[key].priority = cli::Priority::CommandLine;
+      options_[key].priority = cli::OptionPriority::CommandLine;
       // get parser result, it is safe as boolean options have an implicit value
       auto val = res[0];
       auto ret = true;
diff --git a/src/common/config_parser.cpp b/src/common/config_parser.cpp
index 97383db4c..340d4daa6 100755
--- a/src/common/config_parser.cpp
+++ b/src/common/config_parser.cpp
@@ -747,7 +747,7 @@ void ConfigParser::parseOptions(int argc, char** argv, bool doValidate) {
   if(!configPaths.empty()) {
     auto config = loadConfigFiles(configPaths);
     cli.updateConfig(config,
-                     cli::Priority::ConfigFile,
+                     cli::OptionPriority::ConfigFile,
                      "There are option(s) in a config file that are not expected");
   }
 
diff --git a/src/models/encoder_decoder.cpp b/src/models/encoder_decoder.cpp
index 8c65685a1..2f29c045b 100755
--- a/src/models/encoder_decoder.cpp
+++ b/src/models/encoder_decoder.cpp
@@ -87,7 +87,7 @@ void EncoderDecoder::createDecoderConfig(const std::string& name) {
     auto modelFileName = filesystem::Path{name}.filename().string();
     decoder["models"] = std::vector<std::string>({modelFileName});
 
-    // create relative paths to vocabs
+    // create relative paths to vocabs with regard to saved model checkpoint
     auto dirPath = filesystem::Path{name}.parentPath();
     std::vector<std::string> relativeVocabs;
     const auto& vocabs = options_->get<std::vector<std::string>>("vocabs");
diff --git a/src/tensors/gpu/prod.cpp b/src/tensors/gpu/prod.cpp
index bb7e1c214..1b1e34189 100755
--- a/src/tensors/gpu/prod.cpp
+++ b/src/tensors/gpu/prod.cpp
@@ -16,8 +16,9 @@ namespace marian {
 
 namespace gpu {
 
-#if CUDA_VERSION >= 9000
 static void setTensorMode(cublasHandle_t cublasHandle) {
+  cublasHandle; // fool warnings
+#if CUDA_VERSION >= 9000
   static int mode = 0;  // 1: use TC; -1: do not use TC; 0: not set yet
   if (mode == 0) { // multi-thread note: this is sort-of thread-safe, since multiple threads would determine the same value
     const char* var = getenv("ENABLE_CUBLAS_TENSOR_OP_MATH_FP32");
@@ -41,8 +42,15 @@ static void setTensorMode(cublasHandle_t cublasHandle) {
       LOG(info, "[gpu] 16-bit TensorCores enabled for float32 matrix operations");
   }
   CUBLAS_CHECK(cublasSetMathMode(cublasHandle, mode > 0 ? CUBLAS_TENSOR_OP_MATH : CUBLAS_DEFAULT_MATH));
+#endif
 }
+
+static void unsetTensorMode(cublasHandle_t cublasHandle) {
+  cublasHandle; // fool warnings
+#if CUDA_VERSION >= 9000
+  CUBLAS_CHECK(cublasSetMathMode(cublasHandle, CUBLAS_DEFAULT_MATH));
 #endif
+}
 
 void Prod(marian::Tensor C,
           const marian::Tensor& A,
@@ -77,11 +85,7 @@ void Prod(marian::Tensor C,
   auto cublasHandle = std::static_pointer_cast<gpu::Backend>(C->getBackend())
                           ->getCublasHandle();
 
-#if CUDA_VERSION >= 9000
   setTensorMode(cublasHandle);
-  //cublasSetMathMode(cublasHandle, CUBLAS_TENSOR_OP_MATH);
-#endif
-
   CUBLAS_CHECK(cublasSgemm(cublasHandle,
               opB,
               opA,
@@ -96,52 +100,9 @@ void Prod(marian::Tensor C,
               &beta,
               C->data(),
               (int)ldc));
-#if CUDA_VERSION >= 9000
-  cublasSetMathMode(cublasHandle, CUBLAS_DEFAULT_MATH);
-#endif
-}
-
-#if 0 // @TODO: remove, then rename from .cu to .cpp
-__global__ void gAddBias(float* out,
-                         const float* bias,
-                         size_t length,
-                         size_t cols) {
-  for(int bid = 0; bid < length; bid += blockDim.x * gridDim.x) {
-    int index = bid + blockDim.x * blockIdx.x + threadIdx.x;
-    if(index < length) {
-      size_t index2 = index % cols;
-      out[index] += bias[index2];
-    }
-  }
-}
-
-void AddBias(marian::Tensor C, const marian::Tensor bias) {
-  cudaSetDevice(C->getDeviceId().no);
-
-  int length = C->shape().elements();
-  int cols = bias->shape().elements();
-
-  int threads = std::min(MAX_THREADS, length);
-  int blocks = std::min(MAX_BLOCKS, length / threads + (length % threads != 0));
-
-  gAddBias<<<blocks, threads>>>(C->data(), bias->data(), length, cols); // @TODO: CUDA_CHECK
-
-  CUDA_CHECK(cudaStreamSynchronize(0)); // @BUGBUG: Should not be here. Prod() also does not have this.
+  unsetTensorMode(cublasHandle);
 }
 
-void ProdWithBias(marian::Tensor C,
-                  const marian::Tensor& A,
-                  const marian::Tensor& B,
-                  const marian::Tensor& bias,
-                  bool transA,
-                  bool transB,
-                  float beta,
-                  float scalar) {
-  marian::gpu::Prod(C, A, B, transA, transB, beta, scalar);
-  marian::gpu::AddBias(C, bias);
-}
-#endif
-
 void ProdBatched(marian::Tensor C,
                  Ptr<Allocator> allocator,
                  const marian::Tensor A,
@@ -205,10 +166,7 @@ void ProdBatched(marian::Tensor C,
   auto mp_cptr = allocator->alloc<float*>(cptr.size());
   CudaCopy(cptr.data(), cptr.data() + cptr.size(), mp_cptr->data<float*>());
 
-#if CUDA_VERSION >= 9000
   setTensorMode(cublasHandle);
-  //cublasSetMathMode(cublasHandle, CUBLAS_TENSOR_OP_MATH);
-#endif
   CUBLAS_CHECK(cublasSgemmBatched(cublasHandle,
                      opB,
                      opA,
@@ -224,9 +182,7 @@ void ProdBatched(marian::Tensor C,
                      mp_cptr->data<float*>(),
                      (int)ldc,
                      (int)batchC));
-#if CUDA_VERSION >= 9000
-  cublasSetMathMode(cublasHandle, CUBLAS_DEFAULT_MATH);
-#endif
+  unsetTensorMode(cublasHandle);
 
   allocator->free(mp_aptr);
   allocator->free(mp_bptr);
diff --git a/src/translator/scorers.cpp b/src/translator/scorers.cpp
index b635e5829..4c4d908cc 100644
--- a/src/translator/scorers.cpp
+++ b/src/translator/scorers.cpp
@@ -73,7 +73,7 @@ std::vector<Ptr<Scorer>> createScorers(Ptr<Options> options) {
       LOG(warn, "No model settings found in model file");
     }
 
-    // check if there are left-to-right and right-to-left models together
+    // l2r and r2l cannot be used in the same ensemble
     if(models.size() > 1 && modelOptions->has("right-left")) {
       if(i == 0) {
         isPrevRightLeft = modelOptions->get<bool>("right-left");

From 8a780043d98a298166293615b1f3a616a9ef0135 Mon Sep 17 00:00:00 2001
From: Marcin Junczys-Dowmunt <Marcin.JunczysDowmunt@microsoft.com>
Date: Wed, 4 Sep 2019 13:36:21 -0700
Subject: [PATCH 568/838] update comment

---
 src/common/aliases.cpp | 14 ++++++++++----
 1 file changed, 10 insertions(+), 4 deletions(-)

diff --git a/src/common/aliases.cpp b/src/common/aliases.cpp
index 0fef1e6d0..936f1f3a2 100644
--- a/src/common/aliases.cpp
+++ b/src/common/aliases.cpp
@@ -6,11 +6,17 @@ namespace marian {
 /**
  * Add all aliases
  *
- * An alias is a shortcut option for a predefined set of options. It is triggered if the option has
- * the requested value. The alias option has to be first defined using cli.add<T>(). Defining
- * multiple aliases for the same option name but with different value is allowed.
+ * An alias is a command line option name and value pair that sets multiple other non-alias 
+ * (standard) command line options. And example would be `--task transformer-big` which -- 
+ * as a whole -- is an alias for setting options and hyperparameters that would be reasonable 
+ * for training a Google-style Transformer-Big model. Below key-value pairs 
+ * ("task", "transformer-base") and ("task", "transformer-big") are different aliases that result
+ * in different option sets to be defined.
+ * 
+ * The alias option has to be first defined using cli.add<T>(). Defining
+ * multiple aliases for the same option name but with different values is allowed.
  *
- * Aliases are key-value pairs by default. Values are compared as std::string. 
+ * As aliases are key-value pairs by default, values are compared as std::string. 
  * If the command line option corresponding to the alias is a vector, the alias 
  * will be triggered if the requested value exists in that vector at least once.
  *

From 5db4b9eed45ea4cdad19ed03fbec48c77d7d6a73 Mon Sep 17 00:00:00 2001
From: Marcin Junczys-Dowmunt <Marcin.JunczysDowmunt@microsoft.com>
Date: Wed, 4 Sep 2019 13:50:39 -0700
Subject: [PATCH 569/838] update version

---
 CHANGELOG.md | 7 +++++++
 VERSION      | 2 +-
 2 files changed, 8 insertions(+), 1 deletion(-)

diff --git a/CHANGELOG.md b/CHANGELOG.md
index f22074040..c77101528 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -7,12 +7,15 @@ and this project adheres to [Semantic Versioning](http://semver.org/spec/v2.0.0.
 
 ## [Unreleased]
 
+## [1.8.0] - 2019-09-04
+
 ### Added
 - Alias options and new --task option
 - Automatic detection of CPU intrisics when building with -arch=native
 - First version of BERT-training and BERT-classifier, currently not compatible with TF models
 - New reduction operators
 - Use Cmake's ExternalProject to build NCCL and potentially other external libs
+- Code for Factored Vocabulary, currently not usable yet without outside tools
 
 ### Fixed
 - Issue with relative paths in automatically generated decoder config files
@@ -25,6 +28,9 @@ and this project adheres to [Semantic Versioning](http://semver.org/spec/v2.0.0.
 - Errors due to warnings
 - Issue concerning failed saving with single GPU training and --sync-sgd option.
 - NaN problem when training with Tensor Cores on Volta GPUs
+- Fix pipe-handling
+- Fix compilation with GCC 9.1
+- Fix CMake build types
 
 ### Changed
 - Error message when using left-to-right and right-to-left models together in ensembles
@@ -34,6 +40,7 @@ and this project adheres to [Semantic Versioning](http://semver.org/spec/v2.0.0.
 - -DUSE_STATIC_LIBS=on now also looks for static versions of CUDA libraries
 - Include NCCL build from github.com/marian-nmt/nccl and compile within source tree
 - Set nearly all warnings as errors for Marian's own targets. Disable warnings for 3rd party
+- Refactored beam search
 
 ## [1.7.0] - 2018-11-27
 
diff --git a/VERSION b/VERSION
index b84efa430..804a616da 100644
--- a/VERSION
+++ b/VERSION
@@ -1 +1 @@
-v1.7.12
+v1.8.0

From c0a0f4add9a24412e9fdae1cd65fc722a4ace9fd Mon Sep 17 00:00:00 2001
From: Young Jin Kim <youki@microsoft.com>
Date: Wed, 4 Sep 2019 15:01:05 -0700
Subject: [PATCH 570/838] Fix windows build error

---
 src/common/aliases.cpp    | 4 ++--
 src/common/file_stream.h  | 8 ++++++++
 vs/Marian.vcxproj         | 1 +
 vs/Marian.vcxproj.filters | 3 +++
 4 files changed, 14 insertions(+), 2 deletions(-)

diff --git a/src/common/aliases.cpp b/src/common/aliases.cpp
index 936f1f3a2..50a6bc20a 100644
--- a/src/common/aliases.cpp
+++ b/src/common/aliases.cpp
@@ -82,7 +82,7 @@ void ConfigParser::addAliases(cli::CLIWrapper& cli) {
     config["mini-batch"] = 1000;
     config["maxi-batch"] = 1000;
     config["workspace"] = 9500;
-    config["optimizer-params"] = std::vector<float>({0.9, 0.98, 1e-09});
+    config["optimizer-params"] = std::vector<float>({0.9f, 0.98f, 1e-09f});
 
     // Validation specific options
     config["beam-size"] = 8;
@@ -120,7 +120,7 @@ void ConfigParser::addAliases(cli::CLIWrapper& cli) {
     config["mini-batch"] = 1000;
     config["maxi-batch"] = 1000;
     config["workspace"] = 13000;
-    config["optimizer-params"] = std::vector<float>({0.9, 0.998, 1e-09});
+    config["optimizer-params"] = std::vector<float>({0.9f, 0.998f, 1e-09f});
 
     // Validation specific options
     config["beam-size"] = 8;
diff --git a/src/common/file_stream.h b/src/common/file_stream.h
index a950b0897..4ce2d6ca0 100644
--- a/src/common/file_stream.h
+++ b/src/common/file_stream.h
@@ -8,12 +8,20 @@
 #pragma GCC diagnostic push
 #pragma GCC diagnostic ignored "-Wsuggest-override"
 #endif
+#ifdef _MSC_VER
+#pragma warning(push)
+#pragma warning(disable : 4101)
+#endif
 #include "3rd_party/zstr/zstr.hpp"
 #include <boost/iostreams/device/file_descriptor.hpp>
 #include <boost/iostreams/stream_buffer.hpp>
 #ifdef __GNUC__
 #pragma GCC diagnostic pop
 #endif
+#ifdef _MSC_VER
+#pragma warning(pop)
+#endif
+
 
 #include <iostream>
 #include <memory>
diff --git a/vs/Marian.vcxproj b/vs/Marian.vcxproj
index 91d05867d..0de76f656 100755
--- a/vs/Marian.vcxproj
+++ b/vs/Marian.vcxproj
@@ -930,6 +930,7 @@
       <ExcludedFromBuild Condition="'$(Configuration)|$(Platform)'=='Debug|x64'">true</ExcludedFromBuild>
       <ExcludedFromBuild Condition="'$(Configuration)|$(Platform)'=='Release|x64'">true</ExcludedFromBuild>
     </ClInclude>
+    <ClCompile Include="..\src\common\aliases.cpp" />
     <ClCompile Include="..\src\common\binary.cpp" />
     <ClCompile Include="..\src\common\cli_helper.cpp" />
     <ClCompile Include="..\src\common\cli_wrapper.cpp" />
diff --git a/vs/Marian.vcxproj.filters b/vs/Marian.vcxproj.filters
index 23f5bdd4c..f905636a7 100755
--- a/vs/Marian.vcxproj.filters
+++ b/vs/Marian.vcxproj.filters
@@ -709,6 +709,9 @@
     <ClCompile Include="..\src\3rd_party\fbgemm\third_party\cpuinfo\deps\clog\src\clog.c">
       <Filter>3rd_party\fbgemm\third_party\cpuinfo\deps\clog\src</Filter>
     </ClCompile>
+    <ClCompile Include="..\src\common\aliases.cpp">
+      <Filter>common</Filter>
+    </ClCompile>
   </ItemGroup>
   <ItemGroup>
     <ClInclude Include="..\src\marian.h" />

From 075e9ce934df3de0547039c6078665f92e1ee091 Mon Sep 17 00:00:00 2001
From: Marcin Junczys-Dowmunt <Marcin.JunczysDowmunt@microsoft.com>
Date: Wed, 4 Sep 2019 20:21:40 -0700
Subject: [PATCH 571/838] check shapes for concatenation

---
 src/graph/node_operators_binary.h | 16 ++++++++++++----
 1 file changed, 12 insertions(+), 4 deletions(-)

diff --git a/src/graph/node_operators_binary.h b/src/graph/node_operators_binary.h
index 07245391c..2cf49e36a 100644
--- a/src/graph/node_operators_binary.h
+++ b/src/graph/node_operators_binary.h
@@ -931,12 +931,21 @@ struct ConcatenateNodeOp : public NaryNodeOp {
   }
 
   Shape newShape(const std::vector<Expr>& nodes, int ax) {
-    Shape shape = nodes.back()->shape();
+    ABORT_IF(nodes.empty(), "No child nodes given");
+
+    Shape shape = nodes[0]->shape();
     ax_ = shape.axis(ax);
 
     int sum = 0;
-    for(auto child : nodes)
+    auto checkShape = shape;
+    for(auto child : nodes) {
+      checkShape.set(ax_, child->shape()[ax_]); // don't abort on different sizes on axis dim.
+      ABORT_IF(checkShape != child->shape(), 
+               "Child shapes {} and {} cannot be concatenated along axis {}", 
+               shape, child->shape(), ax);
+
       sum += child->shape()[ax_];
+    }
     shape.set(ax_, sum);
 
     return shape;
@@ -953,8 +962,7 @@ struct ConcatenateNodeOp : public NaryNodeOp {
     std::vector<Tensor> deconcatenees;
     for(size_t i = 0; i < children_.size(); ++i) {
       auto childPtr = child(i);
-      childPtr
-          ->set_zero_adjoint();  // @TODO: this is a hotfix, do this properly
+      childPtr->set_zero_adjoint();  // @TODO: this is a hotfix, do this properly
       deconcatenees.push_back(childPtr->grad());
     }
     Deconcatenate(deconcatenees, adj_, ax_);

From fe46e6b9d45fc0b0fbf1618c1e7ac90c1af0c984 Mon Sep 17 00:00:00 2001
From: Marcin Junczys-Dowmunt <Marcin.JunczysDowmunt@microsoft.com>
Date: Fri, 6 Sep 2019 09:26:09 -0700
Subject: [PATCH 572/838] better UNK-handling with shortlist

---
 src/data/shortlist.h         | 53 +++++++++++++-----------------------
 src/models/decoder.h         | 15 ++++++----
 src/translator/beam_search.h | 13 +++++++--
 3 files changed, 40 insertions(+), 41 deletions(-)

diff --git a/src/data/shortlist.h b/src/data/shortlist.h
index e08f9dfb4..519b2f735 100644
--- a/src/data/shortlist.h
+++ b/src/data/shortlist.h
@@ -8,27 +8,30 @@
 #include <unordered_map>
 #include <vector>
 #include <iostream>
+#include <algorithm>
 
 namespace marian {
 namespace data {
 
 class Shortlist {
 private:
-  std::vector<WordIndex> indices_;
-  Words mappedIndices_;
-  std::vector<WordIndex> reverseMap_;
+  std::vector<WordIndex> indices_;    // forward indices, used to select shortlisted indices, results in automatic down-shift
 
 public:
-  Shortlist(const std::vector<WordIndex>& indices,
-            const Words& mappedIndices,
-            const std::vector<WordIndex>& reverseMap)
-      : indices_(indices),
-        mappedIndices_(mappedIndices),
-        reverseMap_(reverseMap) {}
+  Shortlist(const std::vector<WordIndex>& indices)
+    : indices_(indices) {}
 
   const std::vector<WordIndex>& indices() const { return indices_; }
-  const Words& mappedIndices() const { return mappedIndices_; }
-  WordIndex reverseMap(WordIndex idx) { return reverseMap_[idx]; }
+  WordIndex reverseMap(int idx) { return indices_[idx]; }
+  
+  int tryForwardMap(WordIndex wIdx) {
+    auto first = std::lower_bound(indices_.begin(), indices_.end(), wIdx);
+    if(first != indices_.end() && *first == wIdx)         // check if element not less than wIdx has been found and if equal to wIdx
+      return (int)std::distance(indices_.begin(), first); // return coordinate if found
+    else
+      return -1;                                          // return -1 if not found
+  }
+
 };
 
 class ShortlistGenerator {
@@ -42,6 +45,8 @@ class ShortlistGenerator {
   }
 };
 
+
+/* Planned to use during training, currently disabled
 class SampledShortlistGenerator : public ShortlistGenerator {
 private:
   Ptr<Options> options_;
@@ -112,6 +117,7 @@ class SampledShortlistGenerator : public ShortlistGenerator {
     return New<Shortlist>(idx, mapped, reverseMap);
   }
 };
+*/
 
 class LexicalShortlistGenerator : public ShortlistGenerator {
 private:
@@ -253,43 +259,22 @@ class LexicalShortlistGenerator : public ShortlistGenerator {
     std::vector<WordIndex> idx(idxSet.begin(), idxSet.end());
     std::sort(idx.begin(), idx.end());
 
-    // assign new shifted position
-    // std::unordered_map<WordIndex, WordIndex> pos;
-    std::vector<WordIndex> reverseMap;
-
-    for(WordIndex i = 0; i < idx.size(); ++i) {
-      // pos[idx[i]] = i;
-      reverseMap.push_back(idx[i]);
-    }
-
-    Words mapped;
-    // for(auto i : trgBatch->data()) {
-    // mapped postions for cross-entropy
-    // mapped.push_back(pos[i]);
-    //}
-
-    return New<Shortlist>(idx, mapped, reverseMap);
+    return New<Shortlist>(idx);
   }
 };
 
 class FakeShortlistGenerator : public ShortlistGenerator {
 private:
   std::vector<WordIndex> idx_;
-  std::vector<WordIndex> reverseIdx_;
 
 public:
   FakeShortlistGenerator(const std::unordered_set<WordIndex>& idxSet)
       : idx_(idxSet.begin(), idxSet.end()) {
     std::sort(idx_.begin(), idx_.end());
-    // assign new shifted position
-    for(WordIndex i = 0; i < idx_.size(); ++i) {
-      reverseIdx_.push_back(idx_[i]);
-    }
   }
 
   Ptr<Shortlist> generate(Ptr<data::CorpusBatch> /*batch*/) override {
-    Words tmp;
-    return New<Shortlist>(idx_, tmp, reverseIdx_);
+    return New<Shortlist>(idx_);
   }
 };
 
diff --git a/src/models/decoder.h b/src/models/decoder.h
index ab5eb3503..905da97bb 100755
--- a/src/models/decoder.h
+++ b/src/models/decoder.h
@@ -36,11 +36,16 @@ class DecoderBase : public EncoderDecoderLayerBase {
     Expr y, yMask; std::tie
     (y, yMask) = getEmbeddingLayer()->apply(subBatch);
 
-    const Words& data =
-      /*if*/ (shortlist_) ?
-        shortlist_->mappedIndices()
-      /*else*/ :
-        subBatch->data();
+    // @TODO: during training there is currently no code path that leads to using a shortlist
+    // const Words& data =
+    //   /*if*/ (shortlist_) ?
+    //     shortlist_->mappedIndices()
+    //   /*else*/ :
+    //     subBatch->data();
+
+    ABORT_IF(shortlist_, "How did a shortlist make it into training?");
+    
+    const Words& data = subBatch->data();
     Expr yData = graph_->indices(toWordIndexVector(data));
 
     auto yShifted = shift(y, {1, 0, 0});
diff --git a/src/translator/beam_search.h b/src/translator/beam_search.h
index 2d596b2b8..5b9357704 100755
--- a/src/translator/beam_search.h
+++ b/src/translator/beam_search.h
@@ -369,8 +369,17 @@ class BeamSearch {
 
       //**********************************************************************
       // suppress specific symbols if not at right positions
-      if(trgUnkId != Word::NONE && options_->has("allow-unk") && !options_->get<bool>("allow-unk") && factorGroup == 0)
-        suppressWord(expandedPathScores, factoredVocab ? factoredVocab->getUnkIndex() : trgUnkId.toWordIndex());
+
+      int unkColId = -1;
+      if(trgUnkId != Word::NONE && !options_->get<bool>("allow-unk", false)) { // do we need to suppress unk?
+        unkColId = factoredVocab ? factoredVocab->getUnkIndex() : trgUnkId.toWordIndex(); // what's the raw index of unk?
+        
+        auto shortlist = scorers_[0]->getShortlist(); // first shortlist is generally ok, @TODO: make sure they are the same across scorers?
+        if(shortlist)
+          unkColId = shortlist->tryForwardMap(unkColId); // use shifted postion of unk in case of using a shortlist, shortlist may have removed unk which results in -1
+      }
+      if(unkColId != -1 && factorGroup == 0)
+        suppressWord(expandedPathScores, unkColId);
       for(auto state : states)
         state->blacklist(expandedPathScores, batch);
 

From 83e569a7075e3dd2ebd95b5698097c2ed37eb002 Mon Sep 17 00:00:00 2001
From: Marcin Junczys-Dowmunt <Marcin.JunczysDowmunt@microsoft.com>
Date: Fri, 6 Sep 2019 10:04:51 -0700
Subject: [PATCH 573/838] address code review comments

---
 src/data/shortlist.h         | 47 ++++++++++++++++++------------------
 src/models/decoder.h         | 14 ++++++-----
 src/translator/beam_search.h | 17 +++++++------
 3 files changed, 41 insertions(+), 37 deletions(-)

diff --git a/src/data/shortlist.h b/src/data/shortlist.h
index 519b2f735..a44268a90 100644
--- a/src/data/shortlist.h
+++ b/src/data/shortlist.h
@@ -15,7 +15,7 @@ namespace data {
 
 class Shortlist {
 private:
-  std::vector<WordIndex> indices_;    // forward indices, used to select shortlisted indices, results in automatic down-shift
+  std::vector<WordIndex> indices_;    // // [packed shortlist index] -> word index, used to select columns from output embeddings
 
 public:
   Shortlist(const std::vector<WordIndex>& indices)
@@ -46,7 +46,8 @@ class ShortlistGenerator {
 };
 
 
-/* Planned to use during training, currently disabled
+// Intended for use during training in the future, currently disabled
+#if 0
 class SampledShortlistGenerator : public ShortlistGenerator {
 private:
   Ptr<Options> options_;
@@ -78,25 +79,25 @@ class SampledShortlistGenerator : public ShortlistGenerator {
     auto trgBatch = (*batch)[trgIdx_];
 
     // add firstNum most frequent words
-    std::unordered_set<WordIndex> idxSet;
+    std::unordered_set<WordIndex> indexSet;
     for(WordIndex i = 0; i < firstNum_ && i < maxVocab_; ++i)
-      idxSet.insert(i);
+      indexSet.insert(i);
 
     // add all words from ground truth
     for(auto i : trgBatch->data())
-      idxSet.insert(i.toWordIndex());
+      indexSet.insert(i.toWordIndex());
 
     // add all words from source
     if(shared_)
       for(auto i : srcBatch->data())
-        idxSet.insert(i.toWordIndex());
+        indexSet.insert(i.toWordIndex());
 
     std::uniform_int_distribution<> dis((int)firstNum_, (int)maxVocab_);
-    while(idxSet.size() < total_ && idxSet.size() < maxVocab_)
-      idxSet.insert(dis(gen_));
+    while(indexSet.size() < total_ && indexSet.size() < maxVocab_)
+      indexSet.insert(dis(gen_));
 
     // turn into vector and sort (selected indices)
-    std::vector<WordIndex> idx(idxSet.begin(), idxSet.end());
+    std::vector<WordIndex> idx(indexSet.begin(), indexSet.end());
     std::sort(idx.begin(), idx.end());
 
     // assign new shifted position
@@ -117,7 +118,7 @@ class SampledShortlistGenerator : public ShortlistGenerator {
     return New<Shortlist>(idx, mapped, reverseMap);
   }
 };
-*/
+#endif
 
 class LexicalShortlistGenerator : public ShortlistGenerator {
 private:
@@ -234,13 +235,13 @@ class LexicalShortlistGenerator : public ShortlistGenerator {
     // auto trgBatch = (*batch)[trgIdx_];
 
     // add firstNum most frequent words
-    std::unordered_set<WordIndex> idxSet;
+    std::unordered_set<WordIndex> indexSet;
     for(WordIndex i = 0; i < firstNum_ && i < trgVocab_->size(); ++i)
-      idxSet.insert(i);
+      indexSet.insert(i);
 
     // add all words from ground truth
     // for(auto i : trgBatch->data())
-    //  idxSet.insert(i.toWordIndex());
+    //  indexSet.insert(i.toWordIndex());
 
     // collect unique words form source
     std::unordered_set<WordIndex> srcSet;
@@ -250,31 +251,31 @@ class LexicalShortlistGenerator : public ShortlistGenerator {
     // add aligned target words
     for(auto i : srcSet) {
       if(shared_)
-        idxSet.insert(i);
+        indexSet.insert(i);
       for(auto& it : data_[i])
-        idxSet.insert(it.first);
+        indexSet.insert(it.first);
     }
 
     // turn into vector and sort (selected indices)
-    std::vector<WordIndex> idx(idxSet.begin(), idxSet.end());
-    std::sort(idx.begin(), idx.end());
+    std::vector<WordIndex> indices(indexSet.begin(), indexSet.end());
+    std::sort(indices.begin(), indices.end());
 
-    return New<Shortlist>(idx);
+    return New<Shortlist>(indices);
   }
 };
 
 class FakeShortlistGenerator : public ShortlistGenerator {
 private:
-  std::vector<WordIndex> idx_;
+  std::vector<WordIndex> indices_;
 
 public:
-  FakeShortlistGenerator(const std::unordered_set<WordIndex>& idxSet)
-      : idx_(idxSet.begin(), idxSet.end()) {
-    std::sort(idx_.begin(), idx_.end());
+  FakeShortlistGenerator(const std::unordered_set<WordIndex>& indexSet)
+      : indices_(indexSet.begin(), indexSet.end()) {
+    std::sort(indices_.begin(), indices_.end());
   }
 
   Ptr<Shortlist> generate(Ptr<data::CorpusBatch> /*batch*/) override {
-    return New<Shortlist>(idx_);
+    return New<Shortlist>(indices_);
   }
 };
 
diff --git a/src/models/decoder.h b/src/models/decoder.h
index 905da97bb..786766f58 100755
--- a/src/models/decoder.h
+++ b/src/models/decoder.h
@@ -37,14 +37,16 @@ class DecoderBase : public EncoderDecoderLayerBase {
     (y, yMask) = getEmbeddingLayer()->apply(subBatch);
 
     // @TODO: during training there is currently no code path that leads to using a shortlist
-    // const Words& data =
-    //   /*if*/ (shortlist_) ?
-    //     shortlist_->mappedIndices()
-    //   /*else*/ :
-    //     subBatch->data();
+#if 0
+    const Words& data =
+      /*if*/ (shortlist_) ?
+        shortlist_->mappedIndices()
+      /*else*/ :
+        subBatch->data();
+#endif
 
     ABORT_IF(shortlist_, "How did a shortlist make it into training?");
-    
+
     const Words& data = subBatch->data();
     Expr yData = graph_->indices(toWordIndexVector(data));
 
diff --git a/src/translator/beam_search.h b/src/translator/beam_search.h
index 5b9357704..a6aa4c655 100755
--- a/src/translator/beam_search.h
+++ b/src/translator/beam_search.h
@@ -226,6 +226,15 @@ class BeamSearch {
 
     auto getNBestList = createGetNBestListFn(beamSize_, dimBatch, graph->getDeviceId());
 
+    int unkColId = -1;
+    if(trgUnkId != Word::NONE && !options_->get<bool>("allow-unk", false)) { // do we need to suppress unk?
+      unkColId = factoredVocab ? factoredVocab->getUnkIndex() : trgUnkId.toWordIndex(); // what's the raw index of unk?
+      
+      auto shortlist = scorers_[0]->getShortlist(); // first shortlist is generally ok, @TODO: make sure they are the same across scorers?
+      if(shortlist)
+        unkColId = shortlist->tryForwardMap(unkColId); // use shifted postion of unk in case of using a shortlist, shortlist may have removed unk which results in -1
+    }
+
     for(auto scorer : scorers_) {
       scorer->clear(graph);
     }
@@ -370,14 +379,6 @@ class BeamSearch {
       //**********************************************************************
       // suppress specific symbols if not at right positions
 
-      int unkColId = -1;
-      if(trgUnkId != Word::NONE && !options_->get<bool>("allow-unk", false)) { // do we need to suppress unk?
-        unkColId = factoredVocab ? factoredVocab->getUnkIndex() : trgUnkId.toWordIndex(); // what's the raw index of unk?
-        
-        auto shortlist = scorers_[0]->getShortlist(); // first shortlist is generally ok, @TODO: make sure they are the same across scorers?
-        if(shortlist)
-          unkColId = shortlist->tryForwardMap(unkColId); // use shifted postion of unk in case of using a shortlist, shortlist may have removed unk which results in -1
-      }
       if(unkColId != -1 && factorGroup == 0)
         suppressWord(expandedPathScores, unkColId);
       for(auto state : states)

From 1c5c824cdc308994b1f61875f1a9866ecd5afd00 Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Fri, 6 Sep 2019 10:30:25 -0700
Subject: [PATCH 574/838] bug fix of last commit, directly pushed to master as
 agreed by Marcin

---
 regression-tests             |  2 +-
 src/3rd_party/nccl           |  2 +-
 src/translator/beam_search.h | 18 +++++++++---------
 3 files changed, 11 insertions(+), 11 deletions(-)

diff --git a/regression-tests b/regression-tests
index 504922d7f..658cf86e2 160000
--- a/regression-tests
+++ b/regression-tests
@@ -1 +1 @@
-Subproject commit 504922d7fae39c0d019ea40ceb211bc2230943f2
+Subproject commit 658cf86e2ab4aa76b4ab3edabd750b93d2416f1a
diff --git a/src/3rd_party/nccl b/src/3rd_party/nccl
index 8e3a3f7c5..d6297d250 160000
--- a/src/3rd_party/nccl
+++ b/src/3rd_party/nccl
@@ -1 +1 @@
-Subproject commit 8e3a3f7c5b520babff49cec54a866fa3eda3a3b6
+Subproject commit d6297d250433715c283d17f1969cfcb50d2b6531
diff --git a/src/translator/beam_search.h b/src/translator/beam_search.h
index a6aa4c655..822da9842 100755
--- a/src/translator/beam_search.h
+++ b/src/translator/beam_search.h
@@ -226,15 +226,6 @@ class BeamSearch {
 
     auto getNBestList = createGetNBestListFn(beamSize_, dimBatch, graph->getDeviceId());
 
-    int unkColId = -1;
-    if(trgUnkId != Word::NONE && !options_->get<bool>("allow-unk", false)) { // do we need to suppress unk?
-      unkColId = factoredVocab ? factoredVocab->getUnkIndex() : trgUnkId.toWordIndex(); // what's the raw index of unk?
-      
-      auto shortlist = scorers_[0]->getShortlist(); // first shortlist is generally ok, @TODO: make sure they are the same across scorers?
-      if(shortlist)
-        unkColId = shortlist->tryForwardMap(unkColId); // use shifted postion of unk in case of using a shortlist, shortlist may have removed unk which results in -1
-    }
-
     for(auto scorer : scorers_) {
       scorer->clear(graph);
     }
@@ -258,6 +249,15 @@ class BeamSearch {
     for(int i = 0; i < dimBatch; ++i)
       histories[i]->add(beams[i], trgEosId);
 
+    // determine index of UNK in the log prob vectors if we want to suppress it in the decoding process
+    int unkColId = -1;
+    if (trgUnkId != Word::NONE && !options_->get<bool>("allow-unk", false)) { // do we need to suppress unk?
+        unkColId = factoredVocab ? factoredVocab->getUnkIndex() : trgUnkId.toWordIndex(); // what's the raw index of unk in the log prob vector?
+        auto shortlist = scorers_[0]->getShortlist();      // first shortlist is generally ok, @TODO: make sure they are the same across scorers?
+        if (shortlist)
+            unkColId = shortlist->tryForwardMap(unkColId); // use shifted postion of unk in case of using a shortlist, shortlist may have removed unk which results in -1
+    }
+
     // the decoding process updates the following state information in each output time step:
     //  - beams: array [dimBatch] of array [localBeamSize] of Hypothesis
     //     - current output time step's set of active hypotheses, aka active search space

From ee48f50f398539b3b82df80d80144b08e1003bae Mon Sep 17 00:00:00 2001
From: Marcin Junczys-Dowmunt <Marcin.JunczysDowmunt@microsoft.com>
Date: Fri, 6 Sep 2019 11:20:49 -0700
Subject: [PATCH 575/838] formatting

---
 regression-tests             | 2 +-
 src/3rd_party/nccl           | 2 +-
 src/translator/beam_search.h | 8 ++++----
 3 files changed, 6 insertions(+), 6 deletions(-)

diff --git a/regression-tests b/regression-tests
index 658cf86e2..504922d7f 160000
--- a/regression-tests
+++ b/regression-tests
@@ -1 +1 @@
-Subproject commit 658cf86e2ab4aa76b4ab3edabd750b93d2416f1a
+Subproject commit 504922d7fae39c0d019ea40ceb211bc2230943f2
diff --git a/src/3rd_party/nccl b/src/3rd_party/nccl
index d6297d250..8e3a3f7c5 160000
--- a/src/3rd_party/nccl
+++ b/src/3rd_party/nccl
@@ -1 +1 @@
-Subproject commit d6297d250433715c283d17f1969cfcb50d2b6531
+Subproject commit 8e3a3f7c5b520babff49cec54a866fa3eda3a3b6
diff --git a/src/translator/beam_search.h b/src/translator/beam_search.h
index 822da9842..16ee1d5f8 100755
--- a/src/translator/beam_search.h
+++ b/src/translator/beam_search.h
@@ -252,10 +252,10 @@ class BeamSearch {
     // determine index of UNK in the log prob vectors if we want to suppress it in the decoding process
     int unkColId = -1;
     if (trgUnkId != Word::NONE && !options_->get<bool>("allow-unk", false)) { // do we need to suppress unk?
-        unkColId = factoredVocab ? factoredVocab->getUnkIndex() : trgUnkId.toWordIndex(); // what's the raw index of unk in the log prob vector?
-        auto shortlist = scorers_[0]->getShortlist();      // first shortlist is generally ok, @TODO: make sure they are the same across scorers?
-        if (shortlist)
-            unkColId = shortlist->tryForwardMap(unkColId); // use shifted postion of unk in case of using a shortlist, shortlist may have removed unk which results in -1
+      unkColId = factoredVocab ? factoredVocab->getUnkIndex() : trgUnkId.toWordIndex(); // what's the raw index of unk in the log prob vector?
+      auto shortlist = scorers_[0]->getShortlist();      // first shortlist is generally ok, @TODO: make sure they are the same across scorers?
+      if (shortlist)
+        unkColId = shortlist->tryForwardMap(unkColId); // use shifted postion of unk in case of using a shortlist, shortlist may have removed unk which results in -1
     }
 
     // the decoding process updates the following state information in each output time step:

From f53488dce108082e8d449ff5604fefa8561869ab Mon Sep 17 00:00:00 2001
From: Ulrich Germann <ulrich.germann@gmail.com>
Date: Fri, 6 Sep 2019 19:48:13 +0100
Subject: [PATCH 576/838] Changes as requested by @snukky.

---
 src/common/config.cpp        |  4 ++--
 src/common/config.h          |  1 -
 src/common/config_parser.cpp | 10 ++++------
 src/common/config_parser.h   | 17 ++++++++++++++---
 src/common/options.cpp       |  1 -
 src/common/options.h         |  1 -
 6 files changed, 20 insertions(+), 14 deletions(-)

diff --git a/src/common/config.cpp b/src/common/config.cpp
index 21a11877d..ace01a208 100644
--- a/src/common/config.cpp
+++ b/src/common/config.cpp
@@ -1,11 +1,11 @@
 #include "common/config.h"
-#include "common/options.h"
 #include "common/config_parser.h"
 #include "common/file_stream.h"
 #include "common/logging.h"
+#include "common/options.h"
+#include "common/regex.h"
 #include "common/utils.h"
 #include "common/version.h"
-#include "common/regex.h"
 
 #include <algorithm>
 #include <set>
diff --git a/src/common/config.h b/src/common/config.h
index 7a8e9f466..d4784af73 100644
--- a/src/common/config.h
+++ b/src/common/config.h
@@ -49,7 +49,6 @@ class Config {
   Config(const Options& options);
 
   void initialize(ConfigParser const& cp);
-  // void initialize(int argc, char** argv, cli::mode mode, bool validate);
 
   bool has(const std::string& key) const;
 
diff --git a/src/common/config_parser.cpp b/src/common/config_parser.cpp
index aa500c7dc..cdd70a8ce 100644
--- a/src/common/config_parser.cpp
+++ b/src/common/config_parser.cpp
@@ -1,14 +1,13 @@
-#include "common/config_parser.h"
-
-#include "common/definitions.h"
 #include "common/cli_helper.h"
+#include "common/config.h"
+#include "common/config_parser.h"
 #include "common/config_validator.h"
+#include "common/definitions.h"
 #include "common/file_stream.h"
 #include "common/logging.h"
 #include "common/options.h"
-#include "common/config.h"
-#include "common/utils.h"
 #include "common/regex.h"
+#include "common/utils.h"
 #include <algorithm>
 #include <set>
 #include <stdexcept>
@@ -72,7 +71,6 @@ std::string const& ConfigParser::cmdLine() const {
 ConfigParser::ConfigParser(cli::mode mode)
   : cli_(config_,"Marian: Fast Neural Machine Translation in C++",
          "General options", "", 40),
-    // modeServer_(mode == cli::mode::server),
     mode_(mode == cli::mode::server ? cli::mode::translation : mode) {
 
   addOptionsGeneral(cli_);
diff --git a/src/common/config_parser.h b/src/common/config_parser.h
index 379dbcb84..652a1d249 100644
--- a/src/common/config_parser.h
+++ b/src/common/config_parser.h
@@ -1,4 +1,3 @@
-// -*- mode: c++; indent-tabs-mode: nil; tab-width: 2 -*-
 #pragma once
 
 #include "3rd_party/yaml-cpp/yaml.h"
@@ -22,6 +21,18 @@ enum struct mode { training, translation, scoring, server };
  * @brief Command-line options parser
  *
  * New options and aliases should be defined within `addOptions*` methods.
+ * ... unless they are specific to certain executables.
+ * In that case, use a pattern like this (e.g., for a server):
+ * int main(int argc, char* argv[]) {
+ *   ConfigParser cp(cli::mode::translation);
+ *   cp.addOption<int>("--port", // option name
+ *                     "Server Options", // option group name
+ *                     "Port for server.", // help string
+ *                     5678); // default value
+ *   auto opts = cp.parseOptions(argc,argv,true); // 'true' for validation
+ *   ...
+ *
+ *
  */
 class ConfigParser {
 public:
@@ -50,7 +61,8 @@ class ConfigParser {
   addOption(const std::string& args,
             const std::string& group,
             const std::string& help,
-            const T val, const T implicit_val) {
+            const T val,
+            const T implicit_val) {
     std::string previous_group = cli_.switchGroup(group);
     cli_.add<T>(args,help,val)->implicit_val(implicit_val);
     cli_.switchGroup(previous_group);
@@ -92,7 +104,6 @@ class ConfigParser {
   cli::mode getMode() const;
   std::string const& cmdLine() const;
 private:
-  // bool modeServer_;
   cli::CLIWrapper cli_;
   cli::mode mode_;
   YAML::Node config_;
diff --git a/src/common/options.cpp b/src/common/options.cpp
index 5d425d19a..37bfd5251 100644
--- a/src/common/options.cpp
+++ b/src/common/options.cpp
@@ -1,4 +1,3 @@
-// -*- mode: c++; indent-tabs-mode: nil; tab-width: 2 -*-
 #include "options.h"
 
 namespace marian {
diff --git a/src/common/options.h b/src/common/options.h
index 4732458d8..0abd5e72e 100644
--- a/src/common/options.h
+++ b/src/common/options.h
@@ -34,7 +34,6 @@ class Options {
 public:
   Options();
   Options(const Options& other);
-  // Options(ConfigParser& cp, int argc, char** argv, bool validate);
 
   /**
    * @brief Return a copy of the object that can be safely modified.

From e70f0b4fdd0601b0d07305190bfc90d13dccb886 Mon Sep 17 00:00:00 2001
From: Marcin Junczys-Dowmunt <Marcin.JunczysDowmunt@microsoft.com>
Date: Fri, 6 Sep 2019 14:37:40 -0700
Subject: [PATCH 577/838] CPU-side compilation with new pointers and automatic
 vectorization

---
 src/3rd_party/avx_mathfun.h                   | 721 ++++++++++++++++++
 .../half_float/HalfPrecisionFloatTest.cpp     | 115 +++
 src/3rd_party/half_float/Readme.md            |  43 ++
 src/3rd_party/half_float/stdint.h             | 222 ++++++
 src/3rd_party/half_float/umHalf.h             | 298 ++++++++
 src/3rd_party/half_float/umHalf.inl           | 495 ++++++++++++
 src/3rd_party/sse_mathfun.h                   | 711 +++++++++++++++++
 src/CMakeLists.txt                            |   1 +
 src/common/definitions.h                      |  30 +-
 src/common/intrusive_ptr.h                    | 227 ++++++
 src/common/types.h                            | 245 +++++-
 src/functional/approx.h                       |  20 +-
 src/functional/array.h                        |  31 +-
 src/functional/defs.h                         |  22 +-
 src/functional/floats.h                       |   2 +-
 src/functional/functional.h                   |   1 +
 src/functional/operands.h                     |  28 +-
 src/functional/operators.h                    | 575 ++++++++++++++
 src/functional/predicates.h                   | 140 ++--
 src/functional/shape.h                        | 230 +++++-
 src/functional/tensor.h                       | 201 ++++-
 src/functional/tmp.h                          | 138 ++--
 src/graph/chainable.h                         |   7 +-
 src/graph/expression_graph.h                  |   4 +-
 src/graph/node.cpp                            |  14 +-
 src/graph/node_initializers.cpp               |   2 +-
 src/graph/node_operators.cpp                  |   2 +-
 src/graph/node_operators_binary.h             |   2 +-
 src/graph/node_operators_unary.h              |  33 +-
 src/graph/parameters.h                        |  38 +-
 src/tensors/allocator.h                       |  27 +-
 src/tensors/cpu/element.h                     |  83 +-
 src/tensors/cpu/tensor_operators.cpp          |  66 +-
 src/tensors/memory_piece.h                    |  18 +-
 src/tensors/rand.h                            |   2 +-
 src/tensors/tensor.cpp                        | 130 ++++
 src/tensors/tensor.h                          | 335 +++-----
 src/tensors/tensor_allocator.h                |  17 +-
 38 files changed, 4677 insertions(+), 599 deletions(-)
 create mode 100644 src/3rd_party/avx_mathfun.h
 create mode 100644 src/3rd_party/half_float/HalfPrecisionFloatTest.cpp
 create mode 100644 src/3rd_party/half_float/Readme.md
 create mode 100644 src/3rd_party/half_float/stdint.h
 create mode 100644 src/3rd_party/half_float/umHalf.h
 create mode 100644 src/3rd_party/half_float/umHalf.inl
 create mode 100644 src/3rd_party/sse_mathfun.h
 create mode 100644 src/common/intrusive_ptr.h
 create mode 100644 src/functional/operators.h
 mode change 100644 => 100755 src/functional/tmp.h
 mode change 100755 => 100644 src/tensors/memory_piece.h
 create mode 100755 src/tensors/tensor.cpp
 mode change 100755 => 100644 src/tensors/tensor.h

diff --git a/src/3rd_party/avx_mathfun.h b/src/3rd_party/avx_mathfun.h
new file mode 100644
index 000000000..26b3132a8
--- /dev/null
+++ b/src/3rd_party/avx_mathfun.h
@@ -0,0 +1,721 @@
+/* 
+   AVX implementation of sin, cos, sincos, exp and log
+
+   Based on "sse_mathfun.h", by Julien Pommier
+   http://gruntthepeon.free.fr/ssemath/
+
+   Copyright (C) 2012 Giovanni Garberoglio
+   Interdisciplinary Laboratory for Computational Science (LISC)
+   Fondazione Bruno Kessler and University of Trento
+   via Sommarive, 18
+   I-38123 Trento (Italy)
+
+  This software is provided 'as-is', without any express or implied
+  warranty.  In no event will the authors be held liable for any damages
+  arising from the use of this software.
+
+  Permission is granted to anyone to use this software for any purpose,
+  including commercial applications, and to alter it and redistribute it
+  freely, subject to the following restrictions:
+
+  1. The origin of this software must not be misrepresented; you must not
+     claim that you wrote the original software. If you use this software
+     in a product, an acknowledgment in the product documentation would be
+     appreciated but is not required.
+  2. Altered source versions must be plainly marked as such, and must not be
+     misrepresented as being the original software.
+  3. This notice may not be removed or altered from any source distribution.
+
+  (this is the zlib license)
+*/
+
+#include <immintrin.h>
+
+/* yes I know, the top of this file is quite ugly */
+# define ALIGN32_BEG
+# define ALIGN32_END __attribute__((aligned(32)))
+
+/* __m128 is ugly to write */
+typedef __m256  v8sf; // vector of 8 float (avx)
+typedef __m256i v8si; // vector of 8 int   (avx)
+typedef __m128i v4si; // vector of 8 int   (avx)
+
+#define _PI32AVX_CONST(Name, Val)                                            \
+  static const ALIGN32_BEG int _pi32avx_##Name[4] ALIGN32_END = { Val, Val, Val, Val }
+
+_PI32AVX_CONST(1, 1);
+_PI32AVX_CONST(inv1, ~1);
+_PI32AVX_CONST(2, 2);
+_PI32AVX_CONST(4, 4);
+
+
+/* declare some AVX constants -- why can't I figure a better way to do that? */
+#define _PS256_CONST(Name, Val)                                            \
+  static const ALIGN32_BEG float _ps256_##Name[8] ALIGN32_END = { Val, Val, Val, Val, Val, Val, Val, Val }
+#define _PI32_CONST256(Name, Val)                                            \
+  static const ALIGN32_BEG int _pi32_256_##Name[8] ALIGN32_END = { Val, Val, Val, Val, Val, Val, Val, Val }
+#define _PS256_CONST_TYPE(Name, Type, Val)                                 \
+  static const ALIGN32_BEG Type _ps256_##Name[8] ALIGN32_END = { Val, Val, Val, Val, Val, Val, Val, Val }
+
+_PS256_CONST(1  , 1.0f);
+_PS256_CONST(0p5, 0.5f);
+/* the smallest non denormalized float number */
+_PS256_CONST_TYPE(min_norm_pos, int, 0x00800000);
+_PS256_CONST_TYPE(mant_mask, int, 0x7f800000);
+_PS256_CONST_TYPE(inv_mant_mask, int, ~0x7f800000);
+
+_PS256_CONST_TYPE(sign_mask, int, (int)0x80000000);
+_PS256_CONST_TYPE(inv_sign_mask, int, ~0x80000000);
+
+_PI32_CONST256(0, 0);
+_PI32_CONST256(1, 1);
+_PI32_CONST256(inv1, ~1);
+_PI32_CONST256(2, 2);
+_PI32_CONST256(4, 4);
+_PI32_CONST256(0x7f, 0x7f);
+
+_PS256_CONST(cephes_SQRTHF, 0.707106781186547524);
+_PS256_CONST(cephes_log_p0, 7.0376836292E-2);
+_PS256_CONST(cephes_log_p1, - 1.1514610310E-1);
+_PS256_CONST(cephes_log_p2, 1.1676998740E-1);
+_PS256_CONST(cephes_log_p3, - 1.2420140846E-1);
+_PS256_CONST(cephes_log_p4, + 1.4249322787E-1);
+_PS256_CONST(cephes_log_p5, - 1.6668057665E-1);
+_PS256_CONST(cephes_log_p6, + 2.0000714765E-1);
+_PS256_CONST(cephes_log_p7, - 2.4999993993E-1);
+_PS256_CONST(cephes_log_p8, + 3.3333331174E-1);
+_PS256_CONST(cephes_log_q1, -2.12194440e-4);
+_PS256_CONST(cephes_log_q2, 0.693359375);
+
+#ifndef __AVX2__
+
+typedef union imm_xmm_union {
+  v8si imm;
+  v4si xmm[2];
+} imm_xmm_union;
+
+#define COPY_IMM_TO_XMM(imm_, xmm0_, xmm1_) {    \
+    imm_xmm_union u __attribute__((aligned(32)));  \
+    u.imm = imm_;				   \
+    xmm0_ = u.xmm[0];                            \
+    xmm1_ = u.xmm[1];                            \
+}
+
+#define COPY_XMM_TO_IMM(xmm0_, xmm1_, imm_) {                       \
+    imm_xmm_union u __attribute__((aligned(32))); \
+    u.xmm[0]=xmm0_; u.xmm[1]=xmm1_; imm_ = u.imm; \
+  }
+
+
+#define AVX2_BITOP_USING_SSE2(fn) \
+static inline v8si avx2_mm256_##fn(v8si x, int a) \
+{ \
+  /* use SSE2 instruction to perform the bitop AVX2 */ \
+  v4si x1, x2; \
+  v8si ret; \
+  COPY_IMM_TO_XMM(x, x1, x2); \
+  x1 = _mm_##fn(x1,a); \
+  x2 = _mm_##fn(x2,a); \
+  COPY_XMM_TO_IMM(x1, x2, ret); \
+  return(ret); \
+}
+
+//#warning "Using SSE2 to perform AVX2 bitshift ops"
+AVX2_BITOP_USING_SSE2(slli_epi32)
+AVX2_BITOP_USING_SSE2(srli_epi32)
+
+#define AVX2_INTOP_USING_SSE2(fn) \
+static inline v8si avx2_mm256_##fn(v8si x, v8si y) \
+{ \
+  /* use SSE2 instructions to perform the AVX2 integer operation */ \
+  v4si x1, x2; \
+  v4si y1, y2; \
+  v8si ret; \
+  COPY_IMM_TO_XMM(x, x1, x2); \
+  COPY_IMM_TO_XMM(y, y1, y2); \
+  x1 = _mm_##fn(x1,y1); \
+  x2 = _mm_##fn(x2,y2); \
+  COPY_XMM_TO_IMM(x1, x2, ret); \
+  return(ret); \
+}
+
+//#warning "Using SSE2 to perform AVX2 integer ops"
+AVX2_INTOP_USING_SSE2(and_si128)
+AVX2_INTOP_USING_SSE2(andnot_si128)
+AVX2_INTOP_USING_SSE2(cmpeq_epi32)
+AVX2_INTOP_USING_SSE2(sub_epi32)
+AVX2_INTOP_USING_SSE2(add_epi32)
+#define avx2_mm256_and_si256 avx2_mm256_and_si128
+#define avx2_mm256_andnot_si256 avx2_mm256_andnot_si128
+#else
+#define avx2_mm256_slli_epi32 _mm256_slli_epi32
+#define avx2_mm256_srli_epi32 _mm256_srli_epi32
+#define avx2_mm256_and_si256 _mm256_and_si256
+#define avx2_mm256_andnot_si256 _mm256_andnot_si256
+#define avx2_mm256_cmpeq_epi32 _mm256_cmpeq_epi32
+#define avx2_mm256_sub_epi32 _mm256_sub_epi32
+#define avx2_mm256_add_epi32 _mm256_add_epi32
+#endif /* __AVX2__ */
+
+
+/* natural logarithm computed for 8 simultaneous float 
+   return NaN for x <= 0
+*/
+static inline v8sf log256_ps(v8sf x) {
+  v8si imm0;
+  v8sf one = *(v8sf*)_ps256_1;
+
+  //v8sf invalid_mask = _mm256_cmple_ps(x, _mm256_setzero_ps());
+  v8sf invalid_mask = _mm256_cmp_ps(x, _mm256_setzero_ps(), _CMP_LE_OS);
+
+  x = _mm256_max_ps(x, *(v8sf*)_ps256_min_norm_pos);  /* cut off denormalized stuff */
+
+  // can be done with AVX2
+  imm0 = avx2_mm256_srli_epi32(_mm256_castps_si256(x), 23);
+
+  /* keep only the fractional part */
+  x = _mm256_and_ps(x, *(v8sf*)_ps256_inv_mant_mask);
+  x = _mm256_or_ps(x, *(v8sf*)_ps256_0p5);
+
+  // this is again another AVX2 instruction
+  imm0 = avx2_mm256_sub_epi32(imm0, *(v8si*)_pi32_256_0x7f);
+  v8sf e = _mm256_cvtepi32_ps(imm0);
+
+  e = _mm256_add_ps(e, one);
+
+  /* part2: 
+     if( x < SQRTHF ) {
+       e -= 1;
+       x = x + x - 1.0;
+     } else { x = x - 1.0; }
+  */
+  //v8sf mask = _mm256_cmplt_ps(x, *(v8sf*)_ps256_cephes_SQRTHF);
+  v8sf mask = _mm256_cmp_ps(x, *(v8sf*)_ps256_cephes_SQRTHF, _CMP_LT_OS);
+  v8sf tmp = _mm256_and_ps(x, mask);
+  x = _mm256_sub_ps(x, one);
+  e = _mm256_sub_ps(e, _mm256_and_ps(one, mask));
+  x = _mm256_add_ps(x, tmp);
+
+  v8sf z = _mm256_mul_ps(x,x);
+
+  v8sf y = *(v8sf*)_ps256_cephes_log_p0;
+  y = _mm256_mul_ps(y, x);
+  y = _mm256_add_ps(y, *(v8sf*)_ps256_cephes_log_p1);
+  y = _mm256_mul_ps(y, x);
+  y = _mm256_add_ps(y, *(v8sf*)_ps256_cephes_log_p2);
+  y = _mm256_mul_ps(y, x);
+  y = _mm256_add_ps(y, *(v8sf*)_ps256_cephes_log_p3);
+  y = _mm256_mul_ps(y, x);
+  y = _mm256_add_ps(y, *(v8sf*)_ps256_cephes_log_p4);
+  y = _mm256_mul_ps(y, x);
+  y = _mm256_add_ps(y, *(v8sf*)_ps256_cephes_log_p5);
+  y = _mm256_mul_ps(y, x);
+  y = _mm256_add_ps(y, *(v8sf*)_ps256_cephes_log_p6);
+  y = _mm256_mul_ps(y, x);
+  y = _mm256_add_ps(y, *(v8sf*)_ps256_cephes_log_p7);
+  y = _mm256_mul_ps(y, x);
+  y = _mm256_add_ps(y, *(v8sf*)_ps256_cephes_log_p8);
+  y = _mm256_mul_ps(y, x);
+
+  y = _mm256_mul_ps(y, z);
+  
+  tmp = _mm256_mul_ps(e, *(v8sf*)_ps256_cephes_log_q1);
+  y = _mm256_add_ps(y, tmp);
+
+
+  tmp = _mm256_mul_ps(z, *(v8sf*)_ps256_0p5);
+  y = _mm256_sub_ps(y, tmp);
+
+  tmp = _mm256_mul_ps(e, *(v8sf*)_ps256_cephes_log_q2);
+  x = _mm256_add_ps(x, y);
+  x = _mm256_add_ps(x, tmp);
+  x = _mm256_or_ps(x, invalid_mask); // negative arg will be NAN
+  return x;
+}
+
+_PS256_CONST(exp_hi,	88.3762626647949f);
+_PS256_CONST(exp_lo,	-88.3762626647949f);
+
+_PS256_CONST(cephes_LOG2EF, 1.44269504088896341);
+_PS256_CONST(cephes_exp_C1, 0.693359375);
+_PS256_CONST(cephes_exp_C2, -2.12194440e-4);
+
+_PS256_CONST(cephes_exp_p0, 1.9875691500E-4);
+_PS256_CONST(cephes_exp_p1, 1.3981999507E-3);
+_PS256_CONST(cephes_exp_p2, 8.3334519073E-3);
+_PS256_CONST(cephes_exp_p3, 4.1665795894E-2);
+_PS256_CONST(cephes_exp_p4, 1.6666665459E-1);
+_PS256_CONST(cephes_exp_p5, 5.0000001201E-1);
+
+static inline v8sf exp256_ps(v8sf x) {
+  v8sf tmp = _mm256_setzero_ps(), fx;
+  v8si imm0;
+  v8sf one = *(v8sf*)_ps256_1;
+
+  x = _mm256_min_ps(x, *(v8sf*)_ps256_exp_hi);
+  x = _mm256_max_ps(x, *(v8sf*)_ps256_exp_lo);
+
+  /* express exp(x) as exp(g + n*log(2)) */
+  fx = _mm256_mul_ps(x, *(v8sf*)_ps256_cephes_LOG2EF);
+  fx = _mm256_add_ps(fx, *(v8sf*)_ps256_0p5);
+
+  /* how to perform a floorf with SSE: just below */
+  //imm0 = _mm256_cvttps_epi32(fx);
+  //tmp  = _mm256_cvtepi32_ps(imm0);
+  
+  tmp = _mm256_floor_ps(fx);
+
+  /* if greater, substract 1 */
+  //v8sf mask = _mm256_cmpgt_ps(tmp, fx);    
+  v8sf mask = _mm256_cmp_ps(tmp, fx, _CMP_GT_OS);    
+  mask = _mm256_and_ps(mask, one);
+  fx = _mm256_sub_ps(tmp, mask);
+
+  tmp = _mm256_mul_ps(fx, *(v8sf*)_ps256_cephes_exp_C1);
+  v8sf z = _mm256_mul_ps(fx, *(v8sf*)_ps256_cephes_exp_C2);
+  x = _mm256_sub_ps(x, tmp);
+  x = _mm256_sub_ps(x, z);
+
+  z = _mm256_mul_ps(x,x);
+  
+  v8sf y = *(v8sf*)_ps256_cephes_exp_p0;
+  y = _mm256_mul_ps(y, x);
+  y = _mm256_add_ps(y, *(v8sf*)_ps256_cephes_exp_p1);
+  y = _mm256_mul_ps(y, x);
+  y = _mm256_add_ps(y, *(v8sf*)_ps256_cephes_exp_p2);
+  y = _mm256_mul_ps(y, x);
+  y = _mm256_add_ps(y, *(v8sf*)_ps256_cephes_exp_p3);
+  y = _mm256_mul_ps(y, x);
+  y = _mm256_add_ps(y, *(v8sf*)_ps256_cephes_exp_p4);
+  y = _mm256_mul_ps(y, x);
+  y = _mm256_add_ps(y, *(v8sf*)_ps256_cephes_exp_p5);
+  y = _mm256_mul_ps(y, z);
+  y = _mm256_add_ps(y, x);
+  y = _mm256_add_ps(y, one);
+
+  /* build 2^n */
+  imm0 = _mm256_cvttps_epi32(fx);
+  // another two AVX2 instructions
+  imm0 = avx2_mm256_add_epi32(imm0, *(v8si*)_pi32_256_0x7f);
+  imm0 = avx2_mm256_slli_epi32(imm0, 23);
+  v8sf pow2n = _mm256_castsi256_ps(imm0);
+  y = _mm256_mul_ps(y, pow2n);
+  return y;
+}
+
+_PS256_CONST(minus_cephes_DP1, -0.78515625);
+_PS256_CONST(minus_cephes_DP2, -2.4187564849853515625e-4);
+_PS256_CONST(minus_cephes_DP3, -3.77489497744594108e-8);
+_PS256_CONST(sincof_p0, -1.9515295891E-4);
+_PS256_CONST(sincof_p1,  8.3321608736E-3);
+_PS256_CONST(sincof_p2, -1.6666654611E-1);
+_PS256_CONST(coscof_p0,  2.443315711809948E-005);
+_PS256_CONST(coscof_p1, -1.388731625493765E-003);
+_PS256_CONST(coscof_p2,  4.166664568298827E-002);
+_PS256_CONST(cephes_FOPI, 1.27323954473516); // 4 / M_PI
+
+
+/* evaluation of 8 sines at onces using AVX intrisics
+
+   The code is the exact rewriting of the cephes sinf function.
+   Precision is excellent as long as x < 8192 (I did not bother to
+   take into account the special handling they have for greater values
+   -- it does not return garbage for arguments over 8192, though, but
+   the extra precision is missing).
+
+   Note that it is such that sinf((float)M_PI) = 8.74e-8, which is the
+   surprising but correct result.
+
+*/
+static inline v8sf sin256_ps(v8sf x) { // any x
+  v8sf xmm1, xmm2 = _mm256_setzero_ps(), xmm3, sign_bit, y;
+  v8si imm0, imm2;
+
+#ifndef __AVX2__
+  v4si imm0_1, imm0_2;
+  v4si imm2_1, imm2_2;
+#endif
+
+  sign_bit = x;
+  /* take the absolute value */
+  x = _mm256_and_ps(x, *(v8sf*)_ps256_inv_sign_mask);
+  /* extract the sign bit (upper one) */
+  sign_bit = _mm256_and_ps(sign_bit, *(v8sf*)_ps256_sign_mask);
+  
+  /* scale by 4/Pi */
+  y = _mm256_mul_ps(x, *(v8sf*)_ps256_cephes_FOPI);
+
+  /*
+    Here we start a series of integer operations, which are in the
+    realm of AVX2.
+    If we don't have AVX, let's perform them using SSE2 directives
+  */
+
+#ifdef __AVX2__
+  /* store the integer part of y in mm0 */
+  imm2 = _mm256_cvttps_epi32(y);
+  /* j=(j+1) & (~1) (see the cephes sources) */
+  // another two AVX2 instruction
+  imm2 = avx2_mm256_add_epi32(imm2, *(v8si*)_pi32_256_1);
+  imm2 = avx2_mm256_and_si256(imm2, *(v8si*)_pi32_256_inv1);
+  y = _mm256_cvtepi32_ps(imm2);
+
+  /* get the swap sign flag */
+  imm0 = avx2_mm256_and_si256(imm2, *(v8si*)_pi32_256_4);
+  imm0 = avx2_mm256_slli_epi32(imm0, 29);
+  /* get the polynom selection mask 
+     there is one polynom for 0 <= x <= Pi/4
+     and another one for Pi/4<x<=Pi/2
+
+     Both branches will be computed.
+  */
+  imm2 = avx2_mm256_and_si256(imm2, *(v8si*)_pi32_256_2);
+  imm2 = avx2_mm256_cmpeq_epi32(imm2,*(v8si*)_pi32_256_0);
+#else
+  /* we use SSE2 routines to perform the integer ops */
+  COPY_IMM_TO_XMM(_mm256_cvttps_epi32(y),imm2_1,imm2_2);
+
+  imm2_1 = _mm_add_epi32(imm2_1, *(v4si*)_pi32avx_1);
+  imm2_2 = _mm_add_epi32(imm2_2, *(v4si*)_pi32avx_1);
+
+  imm2_1 = _mm_and_si128(imm2_1, *(v4si*)_pi32avx_inv1);
+  imm2_2 = _mm_and_si128(imm2_2, *(v4si*)_pi32avx_inv1);
+
+  COPY_XMM_TO_IMM(imm2_1,imm2_2,imm2);
+  y = _mm256_cvtepi32_ps(imm2);
+
+  imm0_1 = _mm_and_si128(imm2_1, *(v4si*)_pi32avx_4);
+  imm0_2 = _mm_and_si128(imm2_2, *(v4si*)_pi32avx_4);
+
+  imm0_1 = _mm_slli_epi32(imm0_1, 29);
+  imm0_2 = _mm_slli_epi32(imm0_2, 29);
+
+  COPY_XMM_TO_IMM(imm0_1, imm0_2, imm0);
+
+  imm2_1 = _mm_and_si128(imm2_1, *(v4si*)_pi32avx_2);
+  imm2_2 = _mm_and_si128(imm2_2, *(v4si*)_pi32avx_2);
+
+  imm2_1 = _mm_cmpeq_epi32(imm2_1, _mm_setzero_si128());
+  imm2_2 = _mm_cmpeq_epi32(imm2_2, _mm_setzero_si128());
+
+  COPY_XMM_TO_IMM(imm2_1, imm2_2, imm2);
+#endif
+ 
+  v8sf swap_sign_bit = _mm256_castsi256_ps(imm0);
+  v8sf poly_mask = _mm256_castsi256_ps(imm2);
+  sign_bit = _mm256_xor_ps(sign_bit, swap_sign_bit);
+
+  /* The magic pass: "Extended precision modular arithmetic" 
+     x = ((x - y * DP1) - y * DP2) - y * DP3; */
+  xmm1 = *(v8sf*)_ps256_minus_cephes_DP1;
+  xmm2 = *(v8sf*)_ps256_minus_cephes_DP2;
+  xmm3 = *(v8sf*)_ps256_minus_cephes_DP3;
+  xmm1 = _mm256_mul_ps(y, xmm1);
+  xmm2 = _mm256_mul_ps(y, xmm2);
+  xmm3 = _mm256_mul_ps(y, xmm3);
+  x = _mm256_add_ps(x, xmm1);
+  x = _mm256_add_ps(x, xmm2);
+  x = _mm256_add_ps(x, xmm3);
+
+  /* Evaluate the first polynom  (0 <= x <= Pi/4) */
+  y = *(v8sf*)_ps256_coscof_p0;
+  v8sf z = _mm256_mul_ps(x,x);
+
+  y = _mm256_mul_ps(y, z);
+  y = _mm256_add_ps(y, *(v8sf*)_ps256_coscof_p1);
+  y = _mm256_mul_ps(y, z);
+  y = _mm256_add_ps(y, *(v8sf*)_ps256_coscof_p2);
+  y = _mm256_mul_ps(y, z);
+  y = _mm256_mul_ps(y, z);
+  v8sf tmp = _mm256_mul_ps(z, *(v8sf*)_ps256_0p5);
+  y = _mm256_sub_ps(y, tmp);
+  y = _mm256_add_ps(y, *(v8sf*)_ps256_1);
+  
+  /* Evaluate the second polynom  (Pi/4 <= x <= 0) */
+
+  v8sf y2 = *(v8sf*)_ps256_sincof_p0;
+  y2 = _mm256_mul_ps(y2, z);
+  y2 = _mm256_add_ps(y2, *(v8sf*)_ps256_sincof_p1);
+  y2 = _mm256_mul_ps(y2, z);
+  y2 = _mm256_add_ps(y2, *(v8sf*)_ps256_sincof_p2);
+  y2 = _mm256_mul_ps(y2, z);
+  y2 = _mm256_mul_ps(y2, x);
+  y2 = _mm256_add_ps(y2, x);
+
+  /* select the correct result from the two polynoms */  
+  xmm3 = poly_mask;
+  y2 = _mm256_and_ps(xmm3, y2); //, xmm3);
+  y = _mm256_andnot_ps(xmm3, y);
+  y = _mm256_add_ps(y,y2);
+  /* update the sign */
+  y = _mm256_xor_ps(y, sign_bit);
+
+  return y;
+}
+
+/* almost the same as sin_ps */
+static inline v8sf cos256_ps(v8sf x) { // any x
+  v8sf xmm1, xmm2 = _mm256_setzero_ps(), xmm3, y;
+  v8si imm0, imm2;
+
+#ifndef __AVX2__
+  v4si imm0_1, imm0_2;
+  v4si imm2_1, imm2_2;
+#endif
+
+  /* take the absolute value */
+  x = _mm256_and_ps(x, *(v8sf*)_ps256_inv_sign_mask);
+  
+  /* scale by 4/Pi */
+  y = _mm256_mul_ps(x, *(v8sf*)_ps256_cephes_FOPI);
+  
+#ifdef __AVX2__
+  /* store the integer part of y in mm0 */
+  imm2 = _mm256_cvttps_epi32(y);
+  /* j=(j+1) & (~1) (see the cephes sources) */
+  imm2 = avx2_mm256_add_epi32(imm2, *(v8si*)_pi32_256_1);
+  imm2 = avx2_mm256_and_si256(imm2, *(v8si*)_pi32_256_inv1);
+  y = _mm256_cvtepi32_ps(imm2);
+  imm2 = avx2_mm256_sub_epi32(imm2, *(v8si*)_pi32_256_2);
+  
+  /* get the swap sign flag */
+  imm0 = avx2_mm256_andnot_si256(imm2, *(v8si*)_pi32_256_4);
+  imm0 = avx2_mm256_slli_epi32(imm0, 29);
+  /* get the polynom selection mask */
+  imm2 = avx2_mm256_and_si256(imm2, *(v8si*)_pi32_256_2);
+  imm2 = avx2_mm256_cmpeq_epi32(imm2, *(v8si*)_pi32_256_0);
+#else
+
+  /* we use SSE2 routines to perform the integer ops */
+  COPY_IMM_TO_XMM(_mm256_cvttps_epi32(y),imm2_1,imm2_2);
+
+  imm2_1 = _mm_add_epi32(imm2_1, *(v4si*)_pi32avx_1);
+  imm2_2 = _mm_add_epi32(imm2_2, *(v4si*)_pi32avx_1);
+
+  imm2_1 = _mm_and_si128(imm2_1, *(v4si*)_pi32avx_inv1);
+  imm2_2 = _mm_and_si128(imm2_2, *(v4si*)_pi32avx_inv1);
+
+  COPY_XMM_TO_IMM(imm2_1,imm2_2,imm2);
+  y = _mm256_cvtepi32_ps(imm2);
+
+  imm2_1 = _mm_sub_epi32(imm2_1, *(v4si*)_pi32avx_2);
+  imm2_2 = _mm_sub_epi32(imm2_2, *(v4si*)_pi32avx_2);
+
+  imm0_1 = _mm_andnot_si128(imm2_1, *(v4si*)_pi32avx_4);
+  imm0_2 = _mm_andnot_si128(imm2_2, *(v4si*)_pi32avx_4);
+
+  imm0_1 = _mm_slli_epi32(imm0_1, 29);
+  imm0_2 = _mm_slli_epi32(imm0_2, 29);
+
+  COPY_XMM_TO_IMM(imm0_1, imm0_2, imm0);
+
+  imm2_1 = _mm_and_si128(imm2_1, *(v4si*)_pi32avx_2);
+  imm2_2 = _mm_and_si128(imm2_2, *(v4si*)_pi32avx_2);
+
+  imm2_1 = _mm_cmpeq_epi32(imm2_1, _mm_setzero_si128());
+  imm2_2 = _mm_cmpeq_epi32(imm2_2, _mm_setzero_si128());
+
+  COPY_XMM_TO_IMM(imm2_1, imm2_2, imm2);
+#endif
+
+  v8sf sign_bit = _mm256_castsi256_ps(imm0);
+  v8sf poly_mask = _mm256_castsi256_ps(imm2);
+
+  /* The magic pass: "Extended precision modular arithmetic" 
+     x = ((x - y * DP1) - y * DP2) - y * DP3; */
+  xmm1 = *(v8sf*)_ps256_minus_cephes_DP1;
+  xmm2 = *(v8sf*)_ps256_minus_cephes_DP2;
+  xmm3 = *(v8sf*)_ps256_minus_cephes_DP3;
+  xmm1 = _mm256_mul_ps(y, xmm1);
+  xmm2 = _mm256_mul_ps(y, xmm2);
+  xmm3 = _mm256_mul_ps(y, xmm3);
+  x = _mm256_add_ps(x, xmm1);
+  x = _mm256_add_ps(x, xmm2);
+  x = _mm256_add_ps(x, xmm3);
+  
+  /* Evaluate the first polynom  (0 <= x <= Pi/4) */
+  y = *(v8sf*)_ps256_coscof_p0;
+  v8sf z = _mm256_mul_ps(x,x);
+
+  y = _mm256_mul_ps(y, z);
+  y = _mm256_add_ps(y, *(v8sf*)_ps256_coscof_p1);
+  y = _mm256_mul_ps(y, z);
+  y = _mm256_add_ps(y, *(v8sf*)_ps256_coscof_p2);
+  y = _mm256_mul_ps(y, z);
+  y = _mm256_mul_ps(y, z);
+  v8sf tmp = _mm256_mul_ps(z, *(v8sf*)_ps256_0p5);
+  y = _mm256_sub_ps(y, tmp);
+  y = _mm256_add_ps(y, *(v8sf*)_ps256_1);
+  
+  /* Evaluate the second polynom  (Pi/4 <= x <= 0) */
+
+  v8sf y2 = *(v8sf*)_ps256_sincof_p0;
+  y2 = _mm256_mul_ps(y2, z);
+  y2 = _mm256_add_ps(y2, *(v8sf*)_ps256_sincof_p1);
+  y2 = _mm256_mul_ps(y2, z);
+  y2 = _mm256_add_ps(y2, *(v8sf*)_ps256_sincof_p2);
+  y2 = _mm256_mul_ps(y2, z);
+  y2 = _mm256_mul_ps(y2, x);
+  y2 = _mm256_add_ps(y2, x);
+
+  /* select the correct result from the two polynoms */  
+  xmm3 = poly_mask;
+  y2 = _mm256_and_ps(xmm3, y2); //, xmm3);
+  y = _mm256_andnot_ps(xmm3, y);
+  y = _mm256_add_ps(y,y2);
+  /* update the sign */
+  y = _mm256_xor_ps(y, sign_bit);
+
+  return y;
+}
+
+/* since sin256_ps and cos256_ps are almost identical, sincos256_ps could replace both of them..
+   it is almost as fast, and gives you a free cosine with your sine */
+static inline void sincos256_ps(v8sf x, v8sf *s, v8sf *c) {
+
+  v8sf xmm1, xmm2, xmm3 = _mm256_setzero_ps(), sign_bit_sin, y;
+  v8si imm0, imm2, imm4;
+
+#ifndef __AVX2__
+  v4si imm0_1, imm0_2;
+  v4si imm2_1, imm2_2;
+  v4si imm4_1, imm4_2;
+#endif
+
+  sign_bit_sin = x;
+  /* take the absolute value */
+  x = _mm256_and_ps(x, *(v8sf*)_ps256_inv_sign_mask);
+  /* extract the sign bit (upper one) */
+  sign_bit_sin = _mm256_and_ps(sign_bit_sin, *(v8sf*)_ps256_sign_mask);
+  
+  /* scale by 4/Pi */
+  y = _mm256_mul_ps(x, *(v8sf*)_ps256_cephes_FOPI);
+
+#ifdef __AVX2__    
+  /* store the integer part of y in imm2 */
+  imm2 = _mm256_cvttps_epi32(y);
+
+  /* j=(j+1) & (~1) (see the cephes sources) */
+  imm2 = avx2_mm256_add_epi32(imm2, *(v8si*)_pi32_256_1);
+  imm2 = avx2_mm256_and_si256(imm2, *(v8si*)_pi32_256_inv1);
+
+  y = _mm256_cvtepi32_ps(imm2);
+  imm4 = imm2;
+
+  /* get the swap sign flag for the sine */
+  imm0 = avx2_mm256_and_si256(imm2, *(v8si*)_pi32_256_4);
+  imm0 = avx2_mm256_slli_epi32(imm0, 29);
+  //v8sf swap_sign_bit_sin = _mm256_castsi256_ps(imm0);
+
+  /* get the polynom selection mask for the sine*/
+  imm2 = avx2_mm256_and_si256(imm2, *(v8si*)_pi32_256_2);
+  imm2 = avx2_mm256_cmpeq_epi32(imm2, *(v8si*)_pi32_256_0);
+  //v8sf poly_mask = _mm256_castsi256_ps(imm2);
+#else
+  /* we use SSE2 routines to perform the integer ops */
+  COPY_IMM_TO_XMM(_mm256_cvttps_epi32(y),imm2_1,imm2_2);
+
+  imm2_1 = _mm_add_epi32(imm2_1, *(v4si*)_pi32avx_1);
+  imm2_2 = _mm_add_epi32(imm2_2, *(v4si*)_pi32avx_1);
+  
+  imm2_1 = _mm_and_si128(imm2_1, *(v4si*)_pi32avx_inv1);
+  imm2_2 = _mm_and_si128(imm2_2, *(v4si*)_pi32avx_inv1);
+
+  COPY_XMM_TO_IMM(imm2_1,imm2_2,imm2);
+  y = _mm256_cvtepi32_ps(imm2);
+
+  imm4_1 = imm2_1;
+  imm4_2 = imm2_2;
+
+  imm0_1 = _mm_and_si128(imm2_1, *(v4si*)_pi32avx_4);
+  imm0_2 = _mm_and_si128(imm2_2, *(v4si*)_pi32avx_4);
+  
+  imm0_1 = _mm_slli_epi32(imm0_1, 29);
+  imm0_2 = _mm_slli_epi32(imm0_2, 29);
+
+  COPY_XMM_TO_IMM(imm0_1, imm0_2, imm0);
+
+  imm2_1 = _mm_and_si128(imm2_1, *(v4si*)_pi32avx_2);
+  imm2_2 = _mm_and_si128(imm2_2, *(v4si*)_pi32avx_2);
+
+  imm2_1 = _mm_cmpeq_epi32(imm2_1, _mm_setzero_si128());
+  imm2_2 = _mm_cmpeq_epi32(imm2_2, _mm_setzero_si128());
+
+  COPY_XMM_TO_IMM(imm2_1, imm2_2, imm2);
+#endif
+  v8sf swap_sign_bit_sin = _mm256_castsi256_ps(imm0);
+  v8sf poly_mask = _mm256_castsi256_ps(imm2);
+
+  /* The magic pass: "Extended precision modular arithmetic" 
+     x = ((x - y * DP1) - y * DP2) - y * DP3; */
+  xmm1 = *(v8sf*)_ps256_minus_cephes_DP1;
+  xmm2 = *(v8sf*)_ps256_minus_cephes_DP2;
+  xmm3 = *(v8sf*)_ps256_minus_cephes_DP3;
+  xmm1 = _mm256_mul_ps(y, xmm1);
+  xmm2 = _mm256_mul_ps(y, xmm2);
+  xmm3 = _mm256_mul_ps(y, xmm3);
+  x = _mm256_add_ps(x, xmm1);
+  x = _mm256_add_ps(x, xmm2);
+  x = _mm256_add_ps(x, xmm3);
+
+#ifdef __AVX2__
+  imm4 = avx2_mm256_sub_epi32(imm4, *(v8si*)_pi32_256_2);
+  imm4 = avx2_mm256_andnot_si256(imm4, *(v8si*)_pi32_256_4);
+  imm4 = avx2_mm256_slli_epi32(imm4, 29);
+#else
+  imm4_1 = _mm_sub_epi32(imm4_1, *(v4si*)_pi32avx_2);
+  imm4_2 = _mm_sub_epi32(imm4_2, *(v4si*)_pi32avx_2);
+
+  imm4_1 = _mm_andnot_si128(imm4_1, *(v4si*)_pi32avx_4);
+  imm4_2 = _mm_andnot_si128(imm4_2, *(v4si*)_pi32avx_4);
+  
+  imm4_1 = _mm_slli_epi32(imm4_1, 29);
+  imm4_2 = _mm_slli_epi32(imm4_2, 29);
+
+  COPY_XMM_TO_IMM(imm4_1, imm4_2, imm4);
+#endif
+
+  v8sf sign_bit_cos = _mm256_castsi256_ps(imm4);
+
+  sign_bit_sin = _mm256_xor_ps(sign_bit_sin, swap_sign_bit_sin);
+  
+  /* Evaluate the first polynom  (0 <= x <= Pi/4) */
+  v8sf z = _mm256_mul_ps(x,x);
+  y = *(v8sf*)_ps256_coscof_p0;
+
+  y = _mm256_mul_ps(y, z);
+  y = _mm256_add_ps(y, *(v8sf*)_ps256_coscof_p1);
+  y = _mm256_mul_ps(y, z);
+  y = _mm256_add_ps(y, *(v8sf*)_ps256_coscof_p2);
+  y = _mm256_mul_ps(y, z);
+  y = _mm256_mul_ps(y, z);
+  v8sf tmp = _mm256_mul_ps(z, *(v8sf*)_ps256_0p5);
+  y = _mm256_sub_ps(y, tmp);
+  y = _mm256_add_ps(y, *(v8sf*)_ps256_1);
+  
+  /* Evaluate the second polynom  (Pi/4 <= x <= 0) */
+
+  v8sf y2 = *(v8sf*)_ps256_sincof_p0;
+  y2 = _mm256_mul_ps(y2, z);
+  y2 = _mm256_add_ps(y2, *(v8sf*)_ps256_sincof_p1);
+  y2 = _mm256_mul_ps(y2, z);
+  y2 = _mm256_add_ps(y2, *(v8sf*)_ps256_sincof_p2);
+  y2 = _mm256_mul_ps(y2, z);
+  y2 = _mm256_mul_ps(y2, x);
+  y2 = _mm256_add_ps(y2, x);
+
+  /* select the correct result from the two polynoms */  
+  xmm3 = poly_mask;
+  v8sf ysin2 = _mm256_and_ps(xmm3, y2);
+  v8sf ysin1 = _mm256_andnot_ps(xmm3, y);
+  y2 = _mm256_sub_ps(y2,ysin2);
+  y = _mm256_sub_ps(y, ysin1);
+
+  xmm1 = _mm256_add_ps(ysin1,ysin2);
+  xmm2 = _mm256_add_ps(y,y2);
+ 
+  /* update the sign */
+  *s = _mm256_xor_ps(xmm1, sign_bit_sin);
+  *c = _mm256_xor_ps(xmm2, sign_bit_cos);
+}
+
diff --git a/src/3rd_party/half_float/HalfPrecisionFloatTest.cpp b/src/3rd_party/half_float/HalfPrecisionFloatTest.cpp
new file mode 100644
index 000000000..281016191
--- /dev/null
+++ b/src/3rd_party/half_float/HalfPrecisionFloatTest.cpp
@@ -0,0 +1,115 @@
+
+
+
+#include "umHalf.h"
+#include <iostream>
+#include <assert.h>
+
+#define VALIDATE(x) if (!(x)){std::cout << "Failed: " <<  #x << std::endl;assert((x));}
+
+int main(int argc, char* argv[])
+{
+   half h = 1.f, h2 = 2.f;
+   --h2;
+   ++h2;
+   --h;
+   ++h;
+   h2 -= 1.f;
+   float f = h2, f2 = h;
+   VALIDATE(1.f == f && f == f2);
+
+   h = h2;
+   h2 = 15.5f;
+
+   f = h2, f2 = h;
+   VALIDATE(15.5f == f && 1.f == f2);
+   h2 *= h;
+   f = h2, f2 = h;
+   VALIDATE(15.5f == f && 1.f == f2);
+   h2 /= h;
+   f = h2, f2 = h;
+   VALIDATE(15.5f == f && 1.f == f2);
+   h2 += h;
+   f = h2, f2 = h;
+   VALIDATE(16.5f == f && 1.f == f2);
+   h++;h++;h++;
+   h2 = -h2;
+   h2 += 17.5f;
+   h2 *= h;
+   f = h2, f2 = h;
+   VALIDATE(4.f == f && 4.f == f2);	
+   VALIDATE(h == h2);
+   VALIDATE(h <= h2);
+   --h;
+   VALIDATE(h <= h2);
+  
+   h -= 250.f;
+   VALIDATE(h < h2);
+
+   h += 500.f;
+   VALIDATE(h > h2);
+   VALIDATE(h >= h2);
+
+   f = h2, f2 = h;
+   VALIDATE(h * h2 == (half)(f * f2));
+
+   // addition
+   // ****************************************************************************
+
+   // identical exponents
+   for (float f = 0.f; f < 1000.f; ++f)
+   {
+	  half one = f;
+	  half two = f;
+      half three = one + two;
+      f2 = three;
+      VALIDATE(f*2.f == f2);
+   }
+
+    // different exponents
+   for (float f = 0.f, fp = 1000.f; f < 500.f; ++f, --fp)
+   {
+	  half one = f;
+	  half two = fp;
+      half three = one + two;
+      f2 = three;
+      VALIDATE(f+fp == f2);
+   }
+
+   // very small numbers - this is already beyond the accuracy of 16 bit floats.
+   for (float f = 0.003f; f < 1000.f; f += 0.0005f)
+   {
+	  half one = f;
+	  half two = f;
+      half three = one + two;
+      f2 = three;
+	  float m = f*2.f;
+      VALIDATE(f2 > (m-0.05*m) && f2 < (m+0.05*m));
+   }
+
+ 
+   // subtraction
+   // ****************************************************************************
+
+   // identical exponents
+   for (float f = 0.f; f < 1000.f; ++f)
+   {
+	  half one = f;
+	  half two = f;
+      half three = one - two;
+      f2 = three;
+      VALIDATE(0.f == f2);
+   }
+
+    // different exponents
+   for (float f = 0.f, fp = 1000.f; f < 500.f; ++f, --fp)
+   {
+	  half one = f;
+	  half two = fp;
+      half three = one - two;
+      f2 = three;
+      VALIDATE(f-fp == f2);
+   }
+   return 0;		
+}
+
diff --git a/src/3rd_party/half_float/Readme.md b/src/3rd_party/half_float/Readme.md
new file mode 100644
index 000000000..dd20491ad
--- /dev/null
+++ b/src/3rd_party/half_float/Readme.md
@@ -0,0 +1,43 @@
+half_float
+========
+
+#### 16 bit floating-point data type for C++ ####
+
+Implements a `HalfFloat` class that implements all the common arithmetic operations for a 16 bit 
+floating-point type (10 bits mantissa, 5 bits exponent and one sign bit) and can thus be used (almost)
+interchangeably with regular `float`s. Not all operations have efficent implementations (some just convert to `float`, 
+compute the result and convert back again) - if in doubt, check out the source code.
+
+The implementation tries to adhere to IEEE 754 in that it supports NaN and Infinity, but fails in other points:
+
+ - no difference between qnan and snan
+ - no traps
+ - no well-defined rounding mode
+
+
+We also supply a specialization for `std::numeric_limits<half>` that `half` be usable in template code
+dependent on type traits.
+
+
+#### Usage ####
+
+     // get some halfs (half is a typedef for HalfFloat)
+     half a = 1.0f;
+     half b = 0.5f;
+     
+     // and have some FUN
+     half c = (a+b) / (a-b);
+     ++c;
+     
+     // now that we have a result in loosy precision,
+     // convert it back to double precision.
+     // if anybody asks, it's for the lulz.
+     double result = c;
+
+
+Credits to _Chris Maiwald_ for the conversion code to `double` and extensive testing.
+
+
+#### License ####
+
+3-clause BSD license: use it for anything, but give credit, don't blame us if your rocket crashes and don't advertise with it (who would).
\ No newline at end of file
diff --git a/src/3rd_party/half_float/stdint.h b/src/3rd_party/half_float/stdint.h
new file mode 100644
index 000000000..0cc9fd998
--- /dev/null
+++ b/src/3rd_party/half_float/stdint.h
@@ -0,0 +1,222 @@
+// ISO C9x  compliant stdint.h for Microsoft Visual Studio
+// Based on ISO/IEC 9899:TC2 Committee draft (May 6, 2005) WG14/N1124 
+// 
+//  Copyright (c) 2006 Alexander Chemeris
+// 
+// Redistribution and use in source and binary forms, with or without
+// modification, are permitted provided that the following conditions are met:
+// 
+//   1. Redistributions of source code must retain the above copyright notice,
+//      this list of conditions and the following disclaimer.
+// 
+//   2. Redistributions in binary form must reproduce the above copyright
+//      notice, this list of conditions and the following disclaimer in the
+//      documentation and/or other materials provided with the distribution.
+// 
+//   3. The name of the author may be used to endorse or promote products
+//      derived from this software without specific prior written permission.
+// 
+// THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS OR IMPLIED
+// WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF
+// MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO
+// EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
+// SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
+// PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS;
+// OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, 
+// WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR
+// OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF
+// ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+// 
+///////////////////////////////////////////////////////////////////////////////
+
+#ifndef _MSC_VER // [
+#error "Use this header only with Microsoft Visual C++ compilers!"
+#endif // _MSC_VER ]
+
+#ifndef _MSC_STDINT_H_ // [
+#define _MSC_STDINT_H_
+
+#if _MSC_VER > 1000
+#pragma once
+#endif
+
+#include <limits.h>
+
+// For Visual Studio 6 in C++ mode wrap <wchar.h> include with 'extern "C++" {}'
+// or compiler give many errors like this:
+//   error C2733: second C linkage of overloaded function 'wmemchr' not allowed
+#if (_MSC_VER < 1300) && defined(__cplusplus)
+   extern "C++" {
+#endif 
+#     include <wchar.h>
+#if (_MSC_VER < 1300) && defined(__cplusplus)
+   }
+#endif
+
+// 7.18.1 Integer types
+
+// 7.18.1.1 Exact-width integer types
+typedef __int8            int8_t;
+typedef __int16           int16_t;
+typedef __int32           int32_t;
+typedef __int64           int64_t;
+typedef unsigned __int8   uint8_t;
+typedef unsigned __int16  uint16_t;
+typedef unsigned __int32  uint32_t;
+typedef unsigned __int64  uint64_t;
+
+// 7.18.1.2 Minimum-width integer types
+typedef int8_t    int_least8_t;
+typedef int16_t   int_least16_t;
+typedef int32_t   int_least32_t;
+typedef int64_t   int_least64_t;
+typedef uint8_t   uint_least8_t;
+typedef uint16_t  uint_least16_t;
+typedef uint32_t  uint_least32_t;
+typedef uint64_t  uint_least64_t;
+
+// 7.18.1.3 Fastest minimum-width integer types
+typedef int8_t    int_fast8_t;
+typedef int16_t   int_fast16_t;
+typedef int32_t   int_fast32_t;
+typedef int64_t   int_fast64_t;
+typedef uint8_t   uint_fast8_t;
+typedef uint16_t  uint_fast16_t;
+typedef uint32_t  uint_fast32_t;
+typedef uint64_t  uint_fast64_t;
+
+// 7.18.1.4 Integer types capable of holding object pointers
+#ifdef _WIN64 // [
+   typedef __int64           intptr_t;
+   typedef unsigned __int64  uintptr_t;
+#else // _WIN64 ][
+   typedef int               intptr_t;
+   typedef unsigned int      uintptr_t;
+#endif // _WIN64 ]
+
+// 7.18.1.5 Greatest-width integer types
+typedef int64_t   intmax_t;
+typedef uint64_t  uintmax_t;
+
+
+// 7.18.2 Limits of specified-width integer types
+
+#if !defined(__cplusplus) || defined(__STDC_LIMIT_MACROS) // [   See footnote 220 at page 257 and footnote 221 at page 259
+
+// 7.18.2.1 Limits of exact-width integer types
+#define INT8_MIN     ((int8_t)_I8_MIN)
+#define INT8_MAX     _I8_MAX
+#define INT16_MIN    ((int16_t)_I16_MIN)
+#define INT16_MAX    _I16_MAX
+#define INT32_MIN    ((int32_t)_I32_MIN)
+#define INT32_MAX    _I32_MAX
+#define INT64_MIN    ((int64_t)_I64_MIN)
+#define INT64_MAX    _I64_MAX
+#define UINT8_MAX    _UI8_MAX
+#define UINT16_MAX   _UI16_MAX
+#define UINT32_MAX   _UI32_MAX
+#define UINT64_MAX   _UI64_MAX
+
+// 7.18.2.2 Limits of minimum-width integer types
+#define INT_LEAST8_MIN    INT8_MIN
+#define INT_LEAST8_MAX    INT8_MAX
+#define INT_LEAST16_MIN   INT16_MIN
+#define INT_LEAST16_MAX   INT16_MAX
+#define INT_LEAST32_MIN   INT32_MIN
+#define INT_LEAST32_MAX   INT32_MAX
+#define INT_LEAST64_MIN   INT64_MIN
+#define INT_LEAST64_MAX   INT64_MAX
+#define UINT_LEAST8_MAX   UINT8_MAX
+#define UINT_LEAST16_MAX  UINT16_MAX
+#define UINT_LEAST32_MAX  UINT32_MAX
+#define UINT_LEAST64_MAX  UINT64_MAX
+
+// 7.18.2.3 Limits of fastest minimum-width integer types
+#define INT_FAST8_MIN    INT8_MIN
+#define INT_FAST8_MAX    INT8_MAX
+#define INT_FAST16_MIN   INT16_MIN
+#define INT_FAST16_MAX   INT16_MAX
+#define INT_FAST32_MIN   INT32_MIN
+#define INT_FAST32_MAX   INT32_MAX
+#define INT_FAST64_MIN   INT64_MIN
+#define INT_FAST64_MAX   INT64_MAX
+#define UINT_FAST8_MAX   UINT8_MAX
+#define UINT_FAST16_MAX  UINT16_MAX
+#define UINT_FAST32_MAX  UINT32_MAX
+#define UINT_FAST64_MAX  UINT64_MAX
+
+// 7.18.2.4 Limits of integer types capable of holding object pointers
+#ifdef _WIN64 // [
+#  define INTPTR_MIN   INT64_MIN
+#  define INTPTR_MAX   INT64_MAX
+#  define UINTPTR_MAX  UINT64_MAX
+#else // _WIN64 ][
+#  define INTPTR_MIN   INT32_MIN
+#  define INTPTR_MAX   INT32_MAX
+#  define UINTPTR_MAX  UINT32_MAX
+#endif // _WIN64 ]
+
+// 7.18.2.5 Limits of greatest-width integer types
+#define INTMAX_MIN   INT64_MIN
+#define INTMAX_MAX   INT64_MAX
+#define UINTMAX_MAX  UINT64_MAX
+
+// 7.18.3 Limits of other integer types
+
+#ifdef _WIN64 // [
+#  define PTRDIFF_MIN  _I64_MIN
+#  define PTRDIFF_MAX  _I64_MAX
+#else  // _WIN64 ][
+#  define PTRDIFF_MIN  _I32_MIN
+#  define PTRDIFF_MAX  _I32_MAX
+#endif  // _WIN64 ]
+
+#define SIG_ATOMIC_MIN  INT_MIN
+#define SIG_ATOMIC_MAX  INT_MAX
+
+#ifndef SIZE_MAX // [
+#  ifdef _WIN64 // [
+#     define SIZE_MAX  _UI64_MAX
+#  else // _WIN64 ][
+#     define SIZE_MAX  _UI32_MAX
+#  endif // _WIN64 ]
+#endif // SIZE_MAX ]
+
+// WCHAR_MIN and WCHAR_MAX are also defined in <wchar.h>
+#ifndef WCHAR_MIN // [
+#  define WCHAR_MIN  0
+#endif  // WCHAR_MIN ]
+#ifndef WCHAR_MAX // [
+#  define WCHAR_MAX  _UI16_MAX
+#endif  // WCHAR_MAX ]
+
+#define WINT_MIN  0
+#define WINT_MAX  _UI16_MAX
+
+#endif // __STDC_LIMIT_MACROS ]
+
+
+// 7.18.4 Limits of other integer types
+
+#if !defined(__cplusplus) || defined(__STDC_CONSTANT_MACROS) // [   See footnote 224 at page 260
+
+// 7.18.4.1 Macros for minimum-width integer constants
+
+#define INT8_C(val)  val##i8
+#define INT16_C(val) val##i16
+#define INT32_C(val) val##i32
+#define INT64_C(val) val##i64
+
+#define UINT8_C(val)  val##ui8
+#define UINT16_C(val) val##ui16
+#define UINT32_C(val) val##ui32
+#define UINT64_C(val) val##ui64
+
+// 7.18.4.2 Macros for greatest-width integer constants
+#define INTMAX_C   INT64_C
+#define UINTMAX_C  UINT64_C
+
+#endif // __STDC_CONSTANT_MACROS ]
+
+
+#endif // _MSC_STDINT_H_ ]
\ No newline at end of file
diff --git a/src/3rd_party/half_float/umHalf.h b/src/3rd_party/half_float/umHalf.h
new file mode 100644
index 000000000..4c065c02d
--- /dev/null
+++ b/src/3rd_party/half_float/umHalf.h
@@ -0,0 +1,298 @@
+
+///////////////////////////////////////////////////////////////////////////////////
+/*
+Copyright (c) 2006-2008,
+Chris "Krishty" Maiwald, Alexander "Aramis" Gessler
+
+All rights reserved.
+
+Redistribution and use of this software in source and binary forms,
+with or without modification, are permitted provided that the following
+conditions are met:
+
+* Redistributions of source code must retain the above
+  copyright notice, this list of conditions and the
+  following disclaimer.
+
+* Redistributions in binary form must reproduce the above
+  copyright notice, this list of conditions and the
+  following disclaimer in the documentation and/or other
+  materials provided with the distribution.
+
+* Neither the name of the class, nor the names of its
+  contributors may be used to endorse or promote products
+  derived from this software without specific prior
+  written permission of the Development Team.
+
+THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
+"AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
+LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
+A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
+OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
+SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
+LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
+DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
+THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+(INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+*/
+///////////////////////////////////////////////////////////////////////////////////
+
+#ifndef UM_HALF_H_INCLUDED
+#define UM_HALF_H_INCLUDED
+
+#include <limits>
+#include <algorithm>
+
+#ifdef _MSC_VER
+#include "stdint.h"
+#else
+#include <stdint.h>
+#endif
+
+#undef min
+#undef max
+
+///////////////////////////////////////////////////////////////////////////////////
+/** 1. Represents a half-precision floating point value (16 bits) that behaves
+ *  nearly conformant to the IEE 754 standard for floating-point computations.
+ *
+ *  Not all operators have special implementations, most perform time-consuming
+ *  conversions from half to float and back again.
+ *  Differences to IEEE 754:
+ *  - no difference between qnan and snan
+ *  - no traps
+ *  - no well-defined rounding mode
+ */
+///////////////////////////////////////////////////////////////////////////////////
+class HalfFloat
+{
+	friend HalfFloat operator+ (HalfFloat, HalfFloat);
+	friend HalfFloat operator- (HalfFloat, HalfFloat);
+	friend HalfFloat operator* (HalfFloat, HalfFloat);
+	friend HalfFloat operator/ (HalfFloat, HalfFloat);
+
+public:
+
+	enum { BITS_MANTISSA = 10 };
+	enum { BITS_EXPONENT = 5 };
+
+	enum { MAX_EXPONENT_VALUE = 31 };
+	enum { BIAS = MAX_EXPONENT_VALUE/2 };
+
+	enum { MAX_EXPONENT = BIAS };
+	enum { MIN_EXPONENT = -BIAS };
+
+	enum { MAX_EXPONENT10 = 9 };
+	enum { MIN_EXPONENT10 = -9 };
+
+public:
+
+	/** Default constructor. Unitialized by default.
+	 */
+	inline HalfFloat() {}
+
+	/** Construction from an existing half
+	 */
+	inline HalfFloat(const HalfFloat& other)
+		: bits(other.GetBits())
+	{}
+
+	/** Construction from existing values for mantissa, sign
+	 *  and exponent. No validation is performed.
+	 *  @note The exponent is unsigned and biased by #BIAS
+	 */
+	inline HalfFloat(uint16_t _m,uint16_t _e,uint16_t _s);
+
+
+	/** Construction from a single-precision float
+	 */
+	inline HalfFloat(float other);
+
+	/** Conversion operator to convert from half to float
+	 */
+	inline operator float() const;
+
+	/** Assignment operator to assign another half to
+	 *  *this* object.
+	 */
+	inline HalfFloat& operator= (HalfFloat other);
+	inline HalfFloat& operator= (float other);
+
+	/** Comparison operators
+	 */
+	inline bool operator== (HalfFloat other) const;
+	inline bool operator!= (HalfFloat other) const;
+
+
+	/** Relational comparison operators
+	 */
+	inline bool operator<  (HalfFloat other) const;
+	inline bool operator>  (HalfFloat other) const;
+	inline bool operator<= (HalfFloat other) const;
+	inline bool operator>= (HalfFloat other) const;
+
+	inline bool operator<  (float other) const;
+	inline bool operator>  (float other) const;
+	inline bool operator<= (float other) const;
+	inline bool operator>= (float other) const;
+
+
+	/** Combined assignment operators
+	 */
+	inline HalfFloat& operator += (HalfFloat other);
+	inline HalfFloat& operator -= (HalfFloat other);
+	inline HalfFloat& operator *= (HalfFloat other);
+	inline HalfFloat& operator /= (HalfFloat other);
+
+	inline HalfFloat& operator += (float other);
+	inline HalfFloat& operator -= (float other);
+	inline HalfFloat& operator *= (float other);
+	inline HalfFloat& operator /= (float other);
+
+	/** Post and prefix increment operators
+	 */
+	inline HalfFloat& operator++();
+	inline HalfFloat operator++(int);
+
+	/** Post and prefix decrement operators
+	 */
+	inline HalfFloat& operator--();
+	inline HalfFloat operator--(int);
+
+	/** Unary minus operator
+	 */
+	inline HalfFloat operator-() const;
+
+
+	/** Provides direct access to the bits of a half float
+	 */
+	inline uint16_t GetBits() const;
+	inline uint16_t& GetBits();
+
+
+	/** Classification of floating-point types
+	 */
+	inline bool IsNaN() const;
+	inline bool IsInfinity() const;
+	inline bool IsDenorm() const;
+
+	/** Returns the sign of the floating-point value -
+	 *  true stands for positive.
+	 */
+	inline bool GetSign() const;
+
+public:
+
+	union
+	{
+		uint16_t bits;			// All bits
+		struct
+		{
+			uint16_t Frac : 10;	// mantissa
+			uint16_t Exp  : 5;		// exponent
+			uint16_t Sign : 1;		// sign
+		} IEEE;
+	};
+
+
+	union IEEESingle
+	{
+		float Float;
+		struct
+		{
+			uint32_t Frac : 23;
+			uint32_t Exp  : 8;
+			uint32_t Sign : 1;
+		} IEEE;
+	};
+};
+
+/** 2. Binary operations
+ */
+inline HalfFloat operator+ (HalfFloat one, HalfFloat two);
+inline HalfFloat operator- (HalfFloat one, HalfFloat two);
+inline HalfFloat operator* (HalfFloat one, HalfFloat two);
+inline HalfFloat operator/ (HalfFloat one, HalfFloat two);
+
+inline float operator+ (HalfFloat one, float two);
+inline float operator- (HalfFloat one, float two);
+inline float operator* (HalfFloat one, float two);
+inline float operator/ (HalfFloat one, float two);
+
+inline float operator+ (float one, HalfFloat two);
+inline float operator- (float one, HalfFloat two);
+inline float operator* (float one, HalfFloat two);
+inline float operator/ (float one, HalfFloat two);
+
+
+
+///////////////////////////////////////////////////////////////////////////////////
+/** 3. Specialization of std::numeric_limits for type half.
+ */
+///////////////////////////////////////////////////////////////////////////////////
+namespace std {
+template <>
+class numeric_limits<HalfFloat> {
+
+ public:
+
+	// General -- meaningful for all specializations.
+
+    static const bool is_specialized = true;
+    static HalfFloat min ()
+		{return HalfFloat(0,1,0);}
+    static HalfFloat max ()
+		{return HalfFloat(~0,HalfFloat::MAX_EXPONENT_VALUE-1,0);}
+    static const int radix = 2;
+    static const int digits = 10;   // conservative assumption
+    static const int digits10 = 2;  // conservative assumption
+	static const bool is_signed		= true;
+    static const bool is_integer	= true;
+    static const bool is_exact		= false;
+    static const bool traps			= false;
+    static const bool is_modulo		= false;
+    static const bool is_bounded	= true;
+
+	static const HalfFloat lowest() {
+	  return HalfFloat(~0,HalfFloat::MAX_EXPONENT_VALUE-1,~0);
+	}
+
+	// Floating point specific.
+
+    static HalfFloat epsilon ()
+		{return HalfFloat(0.00097656f);} // from OpenEXR, needs to be confirmed
+    static HalfFloat round_error ()
+		{return HalfFloat(0.00097656f/2);}
+    static const int min_exponent10 = HalfFloat::MIN_EXPONENT10;
+    static const int max_exponent10 = HalfFloat::MAX_EXPONENT10;
+    static const int min_exponent   = HalfFloat::MIN_EXPONENT;
+    static const int max_exponent   = HalfFloat::MAX_EXPONENT;
+
+    static const bool has_infinity			= true;
+    static const bool has_quiet_NaN			= true;
+    static const bool has_signaling_NaN		= true;
+    static const bool is_iec559				= false;
+    static const bool has_denorm			= denorm_present;
+    static const bool tinyness_before		= false;
+    static const float_round_style round_style = round_to_nearest;
+
+    static HalfFloat denorm_min ()
+		{return HalfFloat(1,0,1);}
+    static HalfFloat infinity ()
+		{return HalfFloat(0,HalfFloat::MAX_EXPONENT_VALUE,0);}
+    static HalfFloat quiet_NaN ()
+		{return HalfFloat(1,HalfFloat::MAX_EXPONENT_VALUE,0);}
+    static HalfFloat signaling_NaN ()
+		{return HalfFloat(1,HalfFloat::MAX_EXPONENT_VALUE,0);}
+ };
+} // end namespace std
+
+
+#include "umHalf.inl"
+
+#ifndef UM_HALF_NO_TYPEDEFS
+	typedef HalfFloat float16;
+#endif
+
+#endif // !! UM_HALF_H_INCLUDED
diff --git a/src/3rd_party/half_float/umHalf.inl b/src/3rd_party/half_float/umHalf.inl
new file mode 100644
index 000000000..4f065dad3
--- /dev/null
+++ b/src/3rd_party/half_float/umHalf.inl
@@ -0,0 +1,495 @@
+
+///////////////////////////////////////////////////////////////////////////////////
+/*
+Copyright (c) 2006-2008, Alexander Gessler
+
+All rights reserved.
+
+Redistribution and use of this software in source and binary forms,
+with or without modification, are permitted provided that the following
+conditions are met:
+
+* Redistributions of source code must retain the above
+  copyright notice, this list of conditions and the
+  following disclaimer.
+
+* Redistributions in binary form must reproduce the above
+  copyright notice, this list of conditions and the
+  following disclaimer in the documentation and/or other
+  materials provided with the distribution.
+
+* Neither the name of the ASSIMP team, nor the names of its
+  contributors may be used to endorse or promote products
+  derived from this software without specific prior
+  written permission of the ASSIMP Development Team.
+
+THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
+"AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
+LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
+A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
+OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
+SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
+LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
+DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
+THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+(INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+*/
+///////////////////////////////////////////////////////////////////////////////////
+
+#ifndef UM_HALF_INL_INCLUDED
+#define UM_HALF_INL_INCLUDED
+
+#ifdef _MSC_VER
+	#include <intrin.h>
+	#pragma intrinsic(_BitScanReverse)
+#endif
+
+// ------------------------------------------------------------------------------------------------
+inline HalfFloat::HalfFloat(float other)
+{
+	IEEESingle f;
+	f.Float = other;
+
+	IEEE.Sign = f.IEEE.Sign;
+
+	if ( !f.IEEE.Exp)
+	{
+		IEEE.Frac = 0;
+		IEEE.Exp = 0;
+	}
+	else if (f.IEEE.Exp==0xff)
+	{
+		// NaN or INF
+		IEEE.Frac = (f.IEEE.Frac!=0) ? 1 : 0;
+		IEEE.Exp = 31;
+	}
+	else
+	{
+		// regular number
+		int new_exp = f.IEEE.Exp-127;
+
+		if (new_exp<-24)
+		{ // this maps to 0
+			IEEE.Frac = 0;
+			IEEE.Exp = 0;
+		}
+
+		else if (new_exp<-14)
+		{
+			// this maps to a denorm
+			IEEE.Exp = 0;
+			unsigned int exp_val = (unsigned int) (-14 - new_exp);  // 2^-exp_val
+			switch (exp_val)
+			{
+			case 0:
+				IEEE.Frac = 0;
+				break;
+			case 1: IEEE.Frac = 512 + (f.IEEE.Frac>>14); break;
+			case 2: IEEE.Frac = 256 + (f.IEEE.Frac>>15); break;
+			case 3: IEEE.Frac = 128 + (f.IEEE.Frac>>16); break;
+			case 4: IEEE.Frac = 64 + (f.IEEE.Frac>>17); break;
+			case 5: IEEE.Frac = 32 + (f.IEEE.Frac>>18); break;
+			case 6: IEEE.Frac = 16 + (f.IEEE.Frac>>19); break;
+			case 7: IEEE.Frac = 8 + (f.IEEE.Frac>>20); break;
+			case 8: IEEE.Frac = 4 + (f.IEEE.Frac>>21); break;
+			case 9: IEEE.Frac = 2 + (f.IEEE.Frac>>22); break;
+			case 10: IEEE.Frac = 1; break;
+			}
+		}
+		else if (new_exp>15)
+		{ // map this value to infinity
+			IEEE.Frac = 0;
+			IEEE.Exp = 31;
+		}
+		else
+		{
+			IEEE.Exp = new_exp+15;
+			IEEE.Frac = (f.IEEE.Frac >> 13);
+		}
+	}
+}
+
+inline HalfFloat::HalfFloat(uint16_t _m,uint16_t _e,uint16_t _s)
+{
+  IEEE.Frac = _m;
+	IEEE.Exp = _e;
+	IEEE.Sign = _s;
+}
+// ------------------------------------------------------------------------------------------------
+HalfFloat::operator float() const
+{
+	IEEESingle sng;
+	sng.IEEE.Sign = IEEE.Sign;
+
+	if (!IEEE.Exp)
+	{
+		if (!IEEE.Frac)
+		{
+			sng.IEEE.Frac=0;
+			sng.IEEE.Exp=0;
+		}
+		else
+		{
+			const float half_denorm = (1.0f/16384.0f);
+			float mantissa = ((float)(IEEE.Frac)) / 1024.0f;
+			float sgn = (IEEE.Sign)? -1.0f :1.0f;
+			sng.Float = sgn*mantissa*half_denorm;
+		}
+	}
+	else if (31 == IEEE.Exp)
+	{
+		sng.IEEE.Exp = 0xff;
+		sng.IEEE.Frac = (IEEE.Frac!=0) ? 1 : 0;
+	}
+	else
+	{
+		sng.IEEE.Exp = IEEE.Exp+112;
+		sng.IEEE.Frac = (IEEE.Frac << 13);
+	}
+	return sng.Float;
+}
+
+// ------------------------------------------------------------------------------------------------
+inline bool HalfFloat::IsNaN() const
+{
+	return IEEE.Frac != 0 && IEEE.Exp == MAX_EXPONENT_VALUE;
+}
+// ------------------------------------------------------------------------------------------------
+inline bool HalfFloat::IsInfinity() const
+{
+	return IEEE.Frac == 0 && IEEE.Exp == MAX_EXPONENT_VALUE;
+}
+// ------------------------------------------------------------------------------------------------
+inline bool HalfFloat::IsDenorm() const
+{
+	return IEEE.Exp == 0;
+}
+// ------------------------------------------------------------------------------------------------
+inline bool HalfFloat::GetSign() const
+{
+	return IEEE.Sign == 0;
+}
+// ------------------------------------------------------------------------------------------------
+inline HalfFloat& HalfFloat::operator= (HalfFloat other)
+{
+	bits = other.GetBits();
+	return *this;
+}
+// ------------------------------------------------------------------------------------------------
+inline HalfFloat& HalfFloat::operator= (float other)
+{
+	*this = (HalfFloat)other;
+	return *this;
+}
+// ------------------------------------------------------------------------------------------------
+inline bool HalfFloat::operator== (HalfFloat other) const
+{
+	// +0 and -0 are considered to be equal
+	if (!(bits << 1u) && !(other.bits << 1u))return true;
+
+	return bits == other.bits && !this->IsNaN();
+}
+// ------------------------------------------------------------------------------------------------
+inline bool HalfFloat::operator!= (HalfFloat other) const
+{
+	// +0 and -0 are considered to be equal
+	if (!(bits << 1u) && !(other.bits << 1u))return false;
+
+	return bits != other.bits || this->IsNaN();
+}
+// ------------------------------------------------------------------------------------------------
+inline bool HalfFloat::operator<  (HalfFloat other) const
+{
+	// NaN comparisons are always false
+	if (this->IsNaN() || other.IsNaN())
+		return false;
+
+	// this works since the segment oder is s,e,m.
+	return (int16_t)this->bits < (int16_t)other.GetBits();
+}
+// ------------------------------------------------------------------------------------------------
+inline bool HalfFloat::operator>  (HalfFloat other) const
+{
+	// NaN comparisons are always false
+	if (this->IsNaN() || other.IsNaN())
+		return false;
+
+	// this works since the segment oder is s,e,m.
+	return (int16_t)this->bits > (int16_t)other.GetBits();
+}
+// ------------------------------------------------------------------------------------------------
+inline bool HalfFloat::operator<= (HalfFloat other) const
+{
+	return !(*this > other);
+}
+// ------------------------------------------------------------------------------------------------
+inline bool HalfFloat::operator>= (HalfFloat other) const
+{
+	return !(*this < other);
+}
+// ------------------------------------------------------------------------------------------------
+inline HalfFloat& HalfFloat::operator += (HalfFloat other)
+{
+	*this = (*this) + other;
+	return *this;
+}
+// ------------------------------------------------------------------------------------------------
+inline HalfFloat& HalfFloat::operator -= (HalfFloat other)
+{
+	*this = (*this) - other;
+	return *this;
+}
+// ------------------------------------------------------------------------------------------------
+inline HalfFloat& HalfFloat::operator *= (HalfFloat other)
+{
+	*this = (float)(*this) * (float)other;
+	return *this;
+}
+// ------------------------------------------------------------------------------------------------
+inline HalfFloat& HalfFloat::operator /= (HalfFloat other)
+{
+	*this = (float)(*this) / (float)other;
+	return *this;
+}
+// ------------------------------------------------------------------------------------------------
+inline HalfFloat& HalfFloat::operator += (float other)
+{
+	*this = (*this) + (HalfFloat)other;
+	return *this;
+}
+// ------------------------------------------------------------------------------------------------
+inline HalfFloat& HalfFloat::operator -= (float other)
+{
+	*this = (*this) - (HalfFloat)other;
+	return *this;
+}
+// ------------------------------------------------------------------------------------------------
+inline HalfFloat& HalfFloat::operator *= (float other)
+{
+	*this = (float)(*this) * other;
+	return *this;
+}
+// ------------------------------------------------------------------------------------------------
+inline HalfFloat& HalfFloat::operator /= (float other)
+{
+	*this = (float)(*this) / other;
+	return *this;
+}
+// ------------------------------------------------------------------------------------------------
+inline HalfFloat& HalfFloat::operator++()
+{
+	// setting the exponent to bias means using 0 as exponent - thus we
+	// can set the mantissa to any value we like, we'll always get 1.0
+	return this->operator+=(HalfFloat(0,BIAS,0));
+}
+// ------------------------------------------------------------------------------------------------
+inline HalfFloat HalfFloat::operator++(int)
+{
+	HalfFloat f = *this;
+	this->operator+=(HalfFloat(0,BIAS,0));
+	return f;
+}
+// ------------------------------------------------------------------------------------------------
+inline HalfFloat& HalfFloat::operator--()
+{
+	return this->operator-=(HalfFloat(0,BIAS,0));
+}
+// ------------------------------------------------------------------------------------------------
+inline HalfFloat HalfFloat::operator--(int)
+{
+	HalfFloat f = *this;
+	this->operator-=(HalfFloat(0,BIAS,0));
+	return f;
+}
+// ------------------------------------------------------------------------------------------------
+inline HalfFloat HalfFloat::operator-() const
+{
+	return HalfFloat(IEEE.Frac,IEEE.Exp,~IEEE.Sign);
+}
+// ------------------------------------------------------------------------------------------------
+inline uint16_t HalfFloat::GetBits() const
+{
+	return bits;
+}
+// ------------------------------------------------------------------------------------------------
+inline uint16_t& HalfFloat::GetBits()
+{
+	return bits;
+}
+// ------------------------------------------------------------------------------------------------
+inline HalfFloat operator+ (HalfFloat one, HalfFloat two)
+{
+#if (!defined HALFFLOAT_NO_CUSTOM_IMPLEMENTATIONS)
+
+	if (one.IEEE.Exp == HalfFloat::MAX_EXPONENT_VALUE)
+	{
+		// if one of the components is NaN the result becomes NaN, too.
+		if (0 != one.IEEE.Frac || two.IsNaN())
+			return HalfFloat(1,HalfFloat::MAX_EXPONENT_VALUE,0);
+
+		// otherwise this must be infinity
+		return HalfFloat(0,HalfFloat::MAX_EXPONENT_VALUE,one.IEEE.Sign | two.IEEE.Sign);
+	}
+	else if (two.IEEE.Exp == HalfFloat::MAX_EXPONENT_VALUE)
+	{
+		if (one.IsNaN() || 0 != two.IEEE.Frac)
+			return HalfFloat(1,HalfFloat::MAX_EXPONENT_VALUE,0);
+
+		return HalfFloat(0,HalfFloat::MAX_EXPONENT_VALUE,one.IEEE.Sign | two.IEEE.Sign);
+	}
+
+	HalfFloat out;
+	long m1,m2,temp;
+
+	// compute the difference between the two exponents. shifts with negative
+	// numbers are undefined, thus we need two code paths
+	register int expDiff = one.IEEE.Exp - two.IEEE.Exp;
+
+	if (0 == expDiff)
+	{
+		// the exponents are equal, thus we must just add the hidden bit
+		temp = two.IEEE.Exp;
+
+		if (0 == one.IEEE.Exp)m1 = one.IEEE.Frac;
+		else m1 = (int)one.IEEE.Frac | ( 1 << HalfFloat::BITS_MANTISSA );
+
+		if (0 == two.IEEE.Exp)m2 = two.IEEE.Frac;
+		else m2 = (int)two.IEEE.Frac | ( 1 << HalfFloat::BITS_MANTISSA );
+	}
+	else
+	{
+		if (expDiff < 0)
+		{
+			expDiff = -expDiff;
+			std::swap(one,two);
+		}
+
+		m1 = (int)one.IEEE.Frac | ( 1 << HalfFloat::BITS_MANTISSA );
+
+		if (0 == two.IEEE.Exp)m2 = two.IEEE.Frac;
+		else m2 = (int)two.IEEE.Frac | ( 1 << HalfFloat::BITS_MANTISSA );
+
+		if (expDiff < ((sizeof(long)<<3)-(HalfFloat::BITS_MANTISSA+1)))
+		{
+			m1 <<= expDiff;
+			temp = two.IEEE.Exp;
+		}
+		else
+		{
+			if (0 != two.IEEE.Exp)
+			{
+				// arithmetic underflow
+				if (expDiff > HalfFloat::BITS_MANTISSA)return HalfFloat(0,0,0);
+				else
+				{
+					m2 >>= expDiff;
+				}
+			}
+			temp = one.IEEE.Exp;
+		}
+	}
+
+	// convert from sign-bit to two's complement representation
+	if (one.IEEE.Sign)m1 = -m1;
+	if (two.IEEE.Sign)m2 = -m2;
+	m1 += m2;
+	if (m1 < 0)
+	{
+		out.IEEE.Sign = 1;
+		m1 = -m1;
+	}
+	else out.IEEE.Sign = 0;
+
+	// and renormalize the result to fit in a half
+	if (0 == m1)return HalfFloat(0,0,0);
+
+#ifdef _MSC_VER
+	_BitScanReverse((unsigned long*)&m2,m1);
+#else
+	m2 = __builtin_clz(m1);
+#endif
+	expDiff = m2 - HalfFloat::BITS_MANTISSA;
+	temp += expDiff;
+	if (expDiff >= HalfFloat::MAX_EXPONENT_VALUE)
+	{
+		// arithmetic overflow. return INF and keep the sign
+		return HalfFloat(0,HalfFloat::MAX_EXPONENT_VALUE,out.IEEE.Sign);
+	}
+	else if (temp <= 0)
+	{
+		// this maps to a denorm
+		m1 <<= (-expDiff-1);
+		temp = 0;
+	}
+	else
+	{
+		// rebuild the normalized representation, take care of the hidden bit
+		if (expDiff < 0)m1 <<= (-expDiff);
+		else m1 >>= expDiff; // m1 >= 0
+	}
+	out.IEEE.Frac = m1;
+	out.IEEE.Exp = temp;
+	return out;
+
+#else
+	return HalfFloat((float)one + (float)two);
+#endif
+}
+// ------------------------------------------------------------------------------------------------
+inline HalfFloat operator- (HalfFloat one, HalfFloat two)
+{
+	return HalfFloat(one + (-two));
+}
+// ------------------------------------------------------------------------------------------------
+inline HalfFloat operator* (HalfFloat one, HalfFloat two)
+{
+	return HalfFloat((float)one * (float)two);
+}
+// ------------------------------------------------------------------------------------------------
+inline HalfFloat operator/ (HalfFloat one, HalfFloat two)
+{
+	return HalfFloat((float)one / (float)two);
+}
+// ------------------------------------------------------------------------------------------------
+inline float operator+ (HalfFloat one, float two)
+{
+	return (float)one + two;
+}
+// ------------------------------------------------------------------------------------------------
+inline float operator- (HalfFloat one, float two)
+{
+	return (float)one - two;
+}
+// ------------------------------------------------------------------------------------------------
+inline float operator* (HalfFloat one, float two)
+{
+	return (float)one * two;
+}
+// ------------------------------------------------------------------------------------------------
+inline float operator/ (HalfFloat one, float two)
+{
+	return (float)one / two;
+}
+// ------------------------------------------------------------------------------------------------
+inline float operator+ (float one, HalfFloat two)
+{
+	return two + one;
+}
+// ------------------------------------------------------------------------------------------------
+inline float operator- (float one, HalfFloat two)
+{
+	return two - one;
+}
+// ------------------------------------------------------------------------------------------------
+inline float operator* (float one, HalfFloat two)
+{
+	return two * one;
+}
+// ------------------------------------------------------------------------------------------------
+inline float operator/ (float one, HalfFloat two)
+{
+	return two / one;
+}
+
+#endif //!! UM_HALF_INL_INCLUDED
diff --git a/src/3rd_party/sse_mathfun.h b/src/3rd_party/sse_mathfun.h
new file mode 100644
index 000000000..59f69414b
--- /dev/null
+++ b/src/3rd_party/sse_mathfun.h
@@ -0,0 +1,711 @@
+/* SIMD (SSE1+MMX or SSE2) implementation of sin, cos, exp and log
+
+   Inspired by Intel Approximate Math library, and based on the
+   corresponding algorithms of the cephes math library
+
+   The default is to use the SSE1 version. If you define USE_SSE2 the
+   the SSE2 intrinsics will be used in place of the MMX intrinsics. Do
+   not expect any significant performance improvement with SSE2.
+*/
+
+/* Copyright (C) 2007  Julien Pommier
+
+  This software is provided 'as-is', without any express or implied
+  warranty.  In no event will the authors be held liable for any damages
+  arising from the use of this software.
+
+  Permission is granted to anyone to use this software for any purpose,
+  including commercial applications, and to alter it and redistribute it
+  freely, subject to the following restrictions:
+
+  1. The origin of this software must not be misrepresented; you must not
+     claim that you wrote the original software. If you use this software
+     in a product, an acknowledgment in the product documentation would be
+     appreciated but is not required.
+  2. Altered source versions must be plainly marked as such, and must not be
+     misrepresented as being the original software.
+  3. This notice may not be removed or altered from any source distribution.
+
+  (this is the zlib license)
+*/
+
+#include <xmmintrin.h>
+
+/* yes I know, the top of this file is quite ugly */
+
+#ifdef _MSC_VER /* visual c++ */
+# define ALIGN16_BEG __declspec(align(16))
+# define ALIGN16_END 
+#else /* gcc or icc */
+# define ALIGN16_BEG
+# define ALIGN16_END __attribute__((aligned(16)))
+#endif
+
+/* __m128 is ugly to write */
+typedef __m128 v4sf;  // vector of 4 float (sse1)
+
+#ifdef USE_SSE2
+# include <emmintrin.h>
+typedef __m128i v4si; // vector of 4 int (sse2)
+#else
+typedef __m64 v2si;   // vector of 2 int (mmx)
+#endif
+
+/* declare some SSE constants -- why can't I figure a better way to do that? */
+#define _PS_CONST(Name, Val)                                            \
+  static const ALIGN16_BEG float _ps_##Name[4] ALIGN16_END = { Val, Val, Val, Val }
+#define _PI32_CONST(Name, Val)                                            \
+  static const ALIGN16_BEG int _pi32_##Name[4] ALIGN16_END = { Val, Val, Val, Val }
+#define _PS_CONST_TYPE(Name, Type, Val)                                 \
+  static const ALIGN16_BEG Type _ps_##Name[4] ALIGN16_END = { Val, Val, Val, Val }
+
+_PS_CONST(1  , 1.0f);
+_PS_CONST(0p5, 0.5f);
+/* the smallest non denormalized float number */
+_PS_CONST_TYPE(min_norm_pos, int, 0x00800000);
+_PS_CONST_TYPE(mant_mask, int, 0x7f800000);
+_PS_CONST_TYPE(inv_mant_mask, int, ~0x7f800000);
+
+_PS_CONST_TYPE(sign_mask, int, (int)0x80000000);
+_PS_CONST_TYPE(inv_sign_mask, int, ~0x80000000);
+
+_PI32_CONST(1, 1);
+_PI32_CONST(inv1, ~1);
+_PI32_CONST(2, 2);
+_PI32_CONST(4, 4);
+_PI32_CONST(0x7f, 0x7f);
+
+_PS_CONST(cephes_SQRTHF, 0.707106781186547524);
+_PS_CONST(cephes_log_p0, 7.0376836292E-2);
+_PS_CONST(cephes_log_p1, - 1.1514610310E-1);
+_PS_CONST(cephes_log_p2, 1.1676998740E-1);
+_PS_CONST(cephes_log_p3, - 1.2420140846E-1);
+_PS_CONST(cephes_log_p4, + 1.4249322787E-1);
+_PS_CONST(cephes_log_p5, - 1.6668057665E-1);
+_PS_CONST(cephes_log_p6, + 2.0000714765E-1);
+_PS_CONST(cephes_log_p7, - 2.4999993993E-1);
+_PS_CONST(cephes_log_p8, + 3.3333331174E-1);
+_PS_CONST(cephes_log_q1, -2.12194440e-4);
+_PS_CONST(cephes_log_q2, 0.693359375);
+
+#ifndef USE_SSE2
+typedef union xmm_mm_union {
+  __m128 xmm;
+  __m64 mm[2];
+} xmm_mm_union;
+
+#define COPY_XMM_TO_MM(xmm_, mm0_, mm1_) {          \
+    xmm_mm_union u; u.xmm = xmm_;                   \
+    mm0_ = u.mm[0];                                 \
+    mm1_ = u.mm[1];                                 \
+}
+
+#define COPY_MM_TO_XMM(mm0_, mm1_, xmm_) {                         \
+    xmm_mm_union u; u.mm[0]=mm0_; u.mm[1]=mm1_; xmm_ = u.xmm;      \
+  }
+
+#endif // USE_SSE2
+
+/* natural logarithm computed for 4 simultaneous float 
+   return NaN for x <= 0
+*/
+static inline v4sf log_ps(v4sf x) {
+#ifdef USE_SSE2
+  v4si emm0;
+#else
+  v2si mm0, mm1;
+#endif
+  v4sf one = *(v4sf*)_ps_1;
+
+  v4sf invalid_mask = _mm_cmple_ps(x, _mm_setzero_ps());
+
+  x = _mm_max_ps(x, *(v4sf*)_ps_min_norm_pos);  /* cut off denormalized stuff */
+
+#ifndef USE_SSE2
+  /* part 1: x = frexpf(x, &e); */
+  COPY_XMM_TO_MM(x, mm0, mm1);
+  mm0 = _mm_srli_pi32(mm0, 23);
+  mm1 = _mm_srli_pi32(mm1, 23);
+#else
+  emm0 = _mm_srli_epi32(_mm_castps_si128(x), 23);
+#endif
+  /* keep only the fractional part */
+  x = _mm_and_ps(x, *(v4sf*)_ps_inv_mant_mask);
+  x = _mm_or_ps(x, *(v4sf*)_ps_0p5);
+
+#ifndef USE_SSE2
+  /* now e=mm0:mm1 contain the really base-2 exponent */
+  mm0 = _mm_sub_pi32(mm0, *(v2si*)_pi32_0x7f);
+  mm1 = _mm_sub_pi32(mm1, *(v2si*)_pi32_0x7f);
+  v4sf e = _mm_cvtpi32x2_ps(mm0, mm1);
+  _mm_empty(); /* bye bye mmx */
+#else
+  emm0 = _mm_sub_epi32(emm0, *(v4si*)_pi32_0x7f);
+  v4sf e = _mm_cvtepi32_ps(emm0);
+#endif
+
+  e = _mm_add_ps(e, one);
+
+  /* part2: 
+     if( x < SQRTHF ) {
+       e -= 1;
+       x = x + x - 1.0;
+     } else { x = x - 1.0; }
+  */
+  v4sf mask = _mm_cmplt_ps(x, *(v4sf*)_ps_cephes_SQRTHF);
+  v4sf tmp = _mm_and_ps(x, mask);
+  x = _mm_sub_ps(x, one);
+  e = _mm_sub_ps(e, _mm_and_ps(one, mask));
+  x = _mm_add_ps(x, tmp);
+
+
+  v4sf z = _mm_mul_ps(x,x);
+
+  v4sf y = *(v4sf*)_ps_cephes_log_p0;
+  y = _mm_mul_ps(y, x);
+  y = _mm_add_ps(y, *(v4sf*)_ps_cephes_log_p1);
+  y = _mm_mul_ps(y, x);
+  y = _mm_add_ps(y, *(v4sf*)_ps_cephes_log_p2);
+  y = _mm_mul_ps(y, x);
+  y = _mm_add_ps(y, *(v4sf*)_ps_cephes_log_p3);
+  y = _mm_mul_ps(y, x);
+  y = _mm_add_ps(y, *(v4sf*)_ps_cephes_log_p4);
+  y = _mm_mul_ps(y, x);
+  y = _mm_add_ps(y, *(v4sf*)_ps_cephes_log_p5);
+  y = _mm_mul_ps(y, x);
+  y = _mm_add_ps(y, *(v4sf*)_ps_cephes_log_p6);
+  y = _mm_mul_ps(y, x);
+  y = _mm_add_ps(y, *(v4sf*)_ps_cephes_log_p7);
+  y = _mm_mul_ps(y, x);
+  y = _mm_add_ps(y, *(v4sf*)_ps_cephes_log_p8);
+  y = _mm_mul_ps(y, x);
+
+  y = _mm_mul_ps(y, z);
+  
+
+  tmp = _mm_mul_ps(e, *(v4sf*)_ps_cephes_log_q1);
+  y = _mm_add_ps(y, tmp);
+
+
+  tmp = _mm_mul_ps(z, *(v4sf*)_ps_0p5);
+  y = _mm_sub_ps(y, tmp);
+
+  tmp = _mm_mul_ps(e, *(v4sf*)_ps_cephes_log_q2);
+  x = _mm_add_ps(x, y);
+  x = _mm_add_ps(x, tmp);
+  x = _mm_or_ps(x, invalid_mask); // negative arg will be NAN
+  return x;
+}
+
+_PS_CONST(exp_hi,	88.3762626647949f);
+_PS_CONST(exp_lo,	-88.3762626647949f);
+
+_PS_CONST(cephes_LOG2EF, 1.44269504088896341);
+_PS_CONST(cephes_exp_C1, 0.693359375);
+_PS_CONST(cephes_exp_C2, -2.12194440e-4);
+
+_PS_CONST(cephes_exp_p0, 1.9875691500E-4);
+_PS_CONST(cephes_exp_p1, 1.3981999507E-3);
+_PS_CONST(cephes_exp_p2, 8.3334519073E-3);
+_PS_CONST(cephes_exp_p3, 4.1665795894E-2);
+_PS_CONST(cephes_exp_p4, 1.6666665459E-1);
+_PS_CONST(cephes_exp_p5, 5.0000001201E-1);
+
+static inline v4sf exp_ps(v4sf x) {
+  v4sf tmp = _mm_setzero_ps(), fx;
+#ifdef USE_SSE2
+  v4si emm0;
+#else
+  v2si mm0, mm1;
+#endif
+  v4sf one = *(v4sf*)_ps_1;
+
+  x = _mm_min_ps(x, *(v4sf*)_ps_exp_hi);
+  x = _mm_max_ps(x, *(v4sf*)_ps_exp_lo);
+
+  /* express exp(x) as exp(g + n*log(2)) */
+  fx = _mm_mul_ps(x, *(v4sf*)_ps_cephes_LOG2EF);
+  fx = _mm_add_ps(fx, *(v4sf*)_ps_0p5);
+
+  /* how to perform a floorf with SSE: just below */
+#ifndef USE_SSE2
+  /* step 1 : cast to int */
+  tmp = _mm_movehl_ps(tmp, fx);
+  mm0 = _mm_cvttps_pi32(fx);
+  mm1 = _mm_cvttps_pi32(tmp);
+  /* step 2 : cast back to float */
+  tmp = _mm_cvtpi32x2_ps(mm0, mm1);
+#else
+  emm0 = _mm_cvttps_epi32(fx);
+  tmp  = _mm_cvtepi32_ps(emm0);
+#endif
+  /* if greater, substract 1 */
+  v4sf mask = _mm_cmpgt_ps(tmp, fx);    
+  mask = _mm_and_ps(mask, one);
+  fx = _mm_sub_ps(tmp, mask);
+
+  tmp = _mm_mul_ps(fx, *(v4sf*)_ps_cephes_exp_C1);
+  v4sf z = _mm_mul_ps(fx, *(v4sf*)_ps_cephes_exp_C2);
+  x = _mm_sub_ps(x, tmp);
+  x = _mm_sub_ps(x, z);
+
+  z = _mm_mul_ps(x,x);
+  
+  v4sf y = *(v4sf*)_ps_cephes_exp_p0;
+  y = _mm_mul_ps(y, x);
+  y = _mm_add_ps(y, *(v4sf*)_ps_cephes_exp_p1);
+  y = _mm_mul_ps(y, x);
+  y = _mm_add_ps(y, *(v4sf*)_ps_cephes_exp_p2);
+  y = _mm_mul_ps(y, x);
+  y = _mm_add_ps(y, *(v4sf*)_ps_cephes_exp_p3);
+  y = _mm_mul_ps(y, x);
+  y = _mm_add_ps(y, *(v4sf*)_ps_cephes_exp_p4);
+  y = _mm_mul_ps(y, x);
+  y = _mm_add_ps(y, *(v4sf*)_ps_cephes_exp_p5);
+  y = _mm_mul_ps(y, z);
+  y = _mm_add_ps(y, x);
+  y = _mm_add_ps(y, one);
+
+  /* build 2^n */
+#ifndef USE_SSE2
+  z = _mm_movehl_ps(z, fx);
+  mm0 = _mm_cvttps_pi32(fx);
+  mm1 = _mm_cvttps_pi32(z);
+  mm0 = _mm_add_pi32(mm0, *(v2si*)_pi32_0x7f);
+  mm1 = _mm_add_pi32(mm1, *(v2si*)_pi32_0x7f);
+  mm0 = _mm_slli_pi32(mm0, 23); 
+  mm1 = _mm_slli_pi32(mm1, 23);
+  
+  v4sf pow2n; 
+  COPY_MM_TO_XMM(mm0, mm1, pow2n);
+  _mm_empty();
+#else
+  emm0 = _mm_cvttps_epi32(fx);
+  emm0 = _mm_add_epi32(emm0, *(v4si*)_pi32_0x7f);
+  emm0 = _mm_slli_epi32(emm0, 23);
+  v4sf pow2n = _mm_castsi128_ps(emm0);
+#endif
+  y = _mm_mul_ps(y, pow2n);
+  return y;
+}
+
+_PS_CONST(minus_cephes_DP1, -0.78515625);
+_PS_CONST(minus_cephes_DP2, -2.4187564849853515625e-4);
+_PS_CONST(minus_cephes_DP3, -3.77489497744594108e-8);
+_PS_CONST(sincof_p0, -1.9515295891E-4);
+_PS_CONST(sincof_p1,  8.3321608736E-3);
+_PS_CONST(sincof_p2, -1.6666654611E-1);
+_PS_CONST(coscof_p0,  2.443315711809948E-005);
+_PS_CONST(coscof_p1, -1.388731625493765E-003);
+_PS_CONST(coscof_p2,  4.166664568298827E-002);
+_PS_CONST(cephes_FOPI, 1.27323954473516); // 4 / M_PI
+
+
+/* evaluation of 4 sines at onces, using only SSE1+MMX intrinsics so
+   it runs also on old athlons XPs and the pentium III of your grand
+   mother.
+
+   The code is the exact rewriting of the cephes sinf function.
+   Precision is excellent as long as x < 8192 (I did not bother to
+   take into account the special handling they have for greater values
+   -- it does not return garbage for arguments over 8192, though, but
+   the extra precision is missing).
+
+   Note that it is such that sinf((float)M_PI) = 8.74e-8, which is the
+   surprising but correct result.
+
+   Performance is also surprisingly good, 1.33 times faster than the
+   macos vsinf SSE2 function, and 1.5 times faster than the
+   __vrs4_sinf of amd's ACML (which is only available in 64 bits). Not
+   too bad for an SSE1 function (with no special tuning) !
+   However the latter libraries probably have a much better handling of NaN,
+   Inf, denormalized and other special arguments..
+
+   On my core 1 duo, the execution of this function takes approximately 95 cycles.
+
+   From what I have observed on the experiments with Intel AMath lib, switching to an
+   SSE2 version would improve the perf by only 10%.
+
+   Since it is based on SSE intrinsics, it has to be compiled at -O2 to
+   deliver full speed.
+*/
+static inline v4sf sin_ps(v4sf x) { // any x
+  v4sf xmm1, xmm2 = _mm_setzero_ps(), xmm3, sign_bit, y;
+
+#ifdef USE_SSE2
+  v4si emm0, emm2;
+#else
+  v2si mm0, mm1, mm2, mm3;
+#endif
+  sign_bit = x;
+  /* take the absolute value */
+  x = _mm_and_ps(x, *(v4sf*)_ps_inv_sign_mask);
+  /* extract the sign bit (upper one) */
+  sign_bit = _mm_and_ps(sign_bit, *(v4sf*)_ps_sign_mask);
+  
+  /* scale by 4/Pi */
+  y = _mm_mul_ps(x, *(v4sf*)_ps_cephes_FOPI);
+
+#ifdef USE_SSE2
+  /* store the integer part of y in mm0 */
+  emm2 = _mm_cvttps_epi32(y);
+  /* j=(j+1) & (~1) (see the cephes sources) */
+  emm2 = _mm_add_epi32(emm2, *(v4si*)_pi32_1);
+  emm2 = _mm_and_si128(emm2, *(v4si*)_pi32_inv1);
+  y = _mm_cvtepi32_ps(emm2);
+
+  /* get the swap sign flag */
+  emm0 = _mm_and_si128(emm2, *(v4si*)_pi32_4);
+  emm0 = _mm_slli_epi32(emm0, 29);
+  /* get the polynom selection mask 
+     there is one polynom for 0 <= x <= Pi/4
+     and another one for Pi/4<x<=Pi/2
+
+     Both branches will be computed.
+  */
+  emm2 = _mm_and_si128(emm2, *(v4si*)_pi32_2);
+  emm2 = _mm_cmpeq_epi32(emm2, _mm_setzero_si128());
+  
+  v4sf swap_sign_bit = _mm_castsi128_ps(emm0);
+  v4sf poly_mask = _mm_castsi128_ps(emm2);
+  sign_bit = _mm_xor_ps(sign_bit, swap_sign_bit);
+  
+#else
+  /* store the integer part of y in mm0:mm1 */
+  xmm2 = _mm_movehl_ps(xmm2, y);
+  mm2 = _mm_cvttps_pi32(y);
+  mm3 = _mm_cvttps_pi32(xmm2);
+  /* j=(j+1) & (~1) (see the cephes sources) */
+  mm2 = _mm_add_pi32(mm2, *(v2si*)_pi32_1);
+  mm3 = _mm_add_pi32(mm3, *(v2si*)_pi32_1);
+  mm2 = _mm_and_si64(mm2, *(v2si*)_pi32_inv1);
+  mm3 = _mm_and_si64(mm3, *(v2si*)_pi32_inv1);
+  y = _mm_cvtpi32x2_ps(mm2, mm3);
+  /* get the swap sign flag */
+  mm0 = _mm_and_si64(mm2, *(v2si*)_pi32_4);
+  mm1 = _mm_and_si64(mm3, *(v2si*)_pi32_4);
+  mm0 = _mm_slli_pi32(mm0, 29);
+  mm1 = _mm_slli_pi32(mm1, 29);
+  /* get the polynom selection mask */
+  mm2 = _mm_and_si64(mm2, *(v2si*)_pi32_2);
+  mm3 = _mm_and_si64(mm3, *(v2si*)_pi32_2);
+  mm2 = _mm_cmpeq_pi32(mm2, _mm_setzero_si64());
+  mm3 = _mm_cmpeq_pi32(mm3, _mm_setzero_si64());
+  v4sf swap_sign_bit, poly_mask;
+  COPY_MM_TO_XMM(mm0, mm1, swap_sign_bit);
+  COPY_MM_TO_XMM(mm2, mm3, poly_mask);
+  sign_bit = _mm_xor_ps(sign_bit, swap_sign_bit);
+  _mm_empty(); /* good-bye mmx */
+#endif
+  
+  /* The magic pass: "Extended precision modular arithmetic" 
+     x = ((x - y * DP1) - y * DP2) - y * DP3; */
+  xmm1 = *(v4sf*)_ps_minus_cephes_DP1;
+  xmm2 = *(v4sf*)_ps_minus_cephes_DP2;
+  xmm3 = *(v4sf*)_ps_minus_cephes_DP3;
+  xmm1 = _mm_mul_ps(y, xmm1);
+  xmm2 = _mm_mul_ps(y, xmm2);
+  xmm3 = _mm_mul_ps(y, xmm3);
+  x = _mm_add_ps(x, xmm1);
+  x = _mm_add_ps(x, xmm2);
+  x = _mm_add_ps(x, xmm3);
+
+  /* Evaluate the first polynom  (0 <= x <= Pi/4) */
+  y = *(v4sf*)_ps_coscof_p0;
+  v4sf z = _mm_mul_ps(x,x);
+
+  y = _mm_mul_ps(y, z);
+  y = _mm_add_ps(y, *(v4sf*)_ps_coscof_p1);
+  y = _mm_mul_ps(y, z);
+  y = _mm_add_ps(y, *(v4sf*)_ps_coscof_p2);
+  y = _mm_mul_ps(y, z);
+  y = _mm_mul_ps(y, z);
+  v4sf tmp = _mm_mul_ps(z, *(v4sf*)_ps_0p5);
+  y = _mm_sub_ps(y, tmp);
+  y = _mm_add_ps(y, *(v4sf*)_ps_1);
+  
+  /* Evaluate the second polynom  (Pi/4 <= x <= 0) */
+
+  v4sf y2 = *(v4sf*)_ps_sincof_p0;
+  y2 = _mm_mul_ps(y2, z);
+  y2 = _mm_add_ps(y2, *(v4sf*)_ps_sincof_p1);
+  y2 = _mm_mul_ps(y2, z);
+  y2 = _mm_add_ps(y2, *(v4sf*)_ps_sincof_p2);
+  y2 = _mm_mul_ps(y2, z);
+  y2 = _mm_mul_ps(y2, x);
+  y2 = _mm_add_ps(y2, x);
+
+  /* select the correct result from the two polynoms */  
+  xmm3 = poly_mask;
+  y2 = _mm_and_ps(xmm3, y2); //, xmm3);
+  y = _mm_andnot_ps(xmm3, y);
+  y = _mm_add_ps(y,y2);
+  /* update the sign */
+  y = _mm_xor_ps(y, sign_bit);
+  return y;
+}
+
+/* almost the same as sin_ps */
+static inline v4sf cos_ps(v4sf x) { // any x
+  v4sf xmm1, xmm2 = _mm_setzero_ps(), xmm3, y;
+#ifdef USE_SSE2
+  v4si emm0, emm2;
+#else
+  v2si mm0, mm1, mm2, mm3;
+#endif
+  /* take the absolute value */
+  x = _mm_and_ps(x, *(v4sf*)_ps_inv_sign_mask);
+  
+  /* scale by 4/Pi */
+  y = _mm_mul_ps(x, *(v4sf*)_ps_cephes_FOPI);
+  
+#ifdef USE_SSE2
+  /* store the integer part of y in mm0 */
+  emm2 = _mm_cvttps_epi32(y);
+  /* j=(j+1) & (~1) (see the cephes sources) */
+  emm2 = _mm_add_epi32(emm2, *(v4si*)_pi32_1);
+  emm2 = _mm_and_si128(emm2, *(v4si*)_pi32_inv1);
+  y = _mm_cvtepi32_ps(emm2);
+
+  emm2 = _mm_sub_epi32(emm2, *(v4si*)_pi32_2);
+  
+  /* get the swap sign flag */
+  emm0 = _mm_andnot_si128(emm2, *(v4si*)_pi32_4);
+  emm0 = _mm_slli_epi32(emm0, 29);
+  /* get the polynom selection mask */
+  emm2 = _mm_and_si128(emm2, *(v4si*)_pi32_2);
+  emm2 = _mm_cmpeq_epi32(emm2, _mm_setzero_si128());
+  
+  v4sf sign_bit = _mm_castsi128_ps(emm0);
+  v4sf poly_mask = _mm_castsi128_ps(emm2);
+#else
+  /* store the integer part of y in mm0:mm1 */
+  xmm2 = _mm_movehl_ps(xmm2, y);
+  mm2 = _mm_cvttps_pi32(y);
+  mm3 = _mm_cvttps_pi32(xmm2);
+
+  /* j=(j+1) & (~1) (see the cephes sources) */
+  mm2 = _mm_add_pi32(mm2, *(v2si*)_pi32_1);
+  mm3 = _mm_add_pi32(mm3, *(v2si*)_pi32_1);
+  mm2 = _mm_and_si64(mm2, *(v2si*)_pi32_inv1);
+  mm3 = _mm_and_si64(mm3, *(v2si*)_pi32_inv1);
+
+  y = _mm_cvtpi32x2_ps(mm2, mm3);
+
+
+  mm2 = _mm_sub_pi32(mm2, *(v2si*)_pi32_2);
+  mm3 = _mm_sub_pi32(mm3, *(v2si*)_pi32_2);
+
+  /* get the swap sign flag in mm0:mm1 and the 
+     polynom selection mask in mm2:mm3 */
+
+  mm0 = _mm_andnot_si64(mm2, *(v2si*)_pi32_4);
+  mm1 = _mm_andnot_si64(mm3, *(v2si*)_pi32_4);
+  mm0 = _mm_slli_pi32(mm0, 29);
+  mm1 = _mm_slli_pi32(mm1, 29);
+
+  mm2 = _mm_and_si64(mm2, *(v2si*)_pi32_2);
+  mm3 = _mm_and_si64(mm3, *(v2si*)_pi32_2);
+
+  mm2 = _mm_cmpeq_pi32(mm2, _mm_setzero_si64());
+  mm3 = _mm_cmpeq_pi32(mm3, _mm_setzero_si64());
+
+  v4sf sign_bit, poly_mask;
+  COPY_MM_TO_XMM(mm0, mm1, sign_bit);
+  COPY_MM_TO_XMM(mm2, mm3, poly_mask);
+  _mm_empty(); /* good-bye mmx */
+#endif
+  /* The magic pass: "Extended precision modular arithmetic" 
+     x = ((x - y * DP1) - y * DP2) - y * DP3; */
+  xmm1 = *(v4sf*)_ps_minus_cephes_DP1;
+  xmm2 = *(v4sf*)_ps_minus_cephes_DP2;
+  xmm3 = *(v4sf*)_ps_minus_cephes_DP3;
+  xmm1 = _mm_mul_ps(y, xmm1);
+  xmm2 = _mm_mul_ps(y, xmm2);
+  xmm3 = _mm_mul_ps(y, xmm3);
+  x = _mm_add_ps(x, xmm1);
+  x = _mm_add_ps(x, xmm2);
+  x = _mm_add_ps(x, xmm3);
+  
+  /* Evaluate the first polynom  (0 <= x <= Pi/4) */
+  y = *(v4sf*)_ps_coscof_p0;
+  v4sf z = _mm_mul_ps(x,x);
+
+  y = _mm_mul_ps(y, z);
+  y = _mm_add_ps(y, *(v4sf*)_ps_coscof_p1);
+  y = _mm_mul_ps(y, z);
+  y = _mm_add_ps(y, *(v4sf*)_ps_coscof_p2);
+  y = _mm_mul_ps(y, z);
+  y = _mm_mul_ps(y, z);
+  v4sf tmp = _mm_mul_ps(z, *(v4sf*)_ps_0p5);
+  y = _mm_sub_ps(y, tmp);
+  y = _mm_add_ps(y, *(v4sf*)_ps_1);
+  
+  /* Evaluate the second polynom  (Pi/4 <= x <= 0) */
+
+  v4sf y2 = *(v4sf*)_ps_sincof_p0;
+  y2 = _mm_mul_ps(y2, z);
+  y2 = _mm_add_ps(y2, *(v4sf*)_ps_sincof_p1);
+  y2 = _mm_mul_ps(y2, z);
+  y2 = _mm_add_ps(y2, *(v4sf*)_ps_sincof_p2);
+  y2 = _mm_mul_ps(y2, z);
+  y2 = _mm_mul_ps(y2, x);
+  y2 = _mm_add_ps(y2, x);
+
+  /* select the correct result from the two polynoms */  
+  xmm3 = poly_mask;
+  y2 = _mm_and_ps(xmm3, y2); //, xmm3);
+  y = _mm_andnot_ps(xmm3, y);
+  y = _mm_add_ps(y,y2);
+  /* update the sign */
+  y = _mm_xor_ps(y, sign_bit);
+
+  return y;
+}
+
+/* since sin_ps and cos_ps are almost identical, sincos_ps could replace both of them..
+   it is almost as fast, and gives you a free cosine with your sine */
+static inline void sincos_ps(v4sf x, v4sf *s, v4sf *c) {
+  v4sf xmm1, xmm2, xmm3 = _mm_setzero_ps(), sign_bit_sin, y;
+#ifdef USE_SSE2
+  v4si emm0, emm2, emm4;
+#else
+  v2si mm0, mm1, mm2, mm3, mm4, mm5;
+#endif
+  sign_bit_sin = x;
+  /* take the absolute value */
+  x = _mm_and_ps(x, *(v4sf*)_ps_inv_sign_mask);
+  /* extract the sign bit (upper one) */
+  sign_bit_sin = _mm_and_ps(sign_bit_sin, *(v4sf*)_ps_sign_mask);
+  
+  /* scale by 4/Pi */
+  y = _mm_mul_ps(x, *(v4sf*)_ps_cephes_FOPI);
+    
+#ifdef USE_SSE2
+  /* store the integer part of y in emm2 */
+  emm2 = _mm_cvttps_epi32(y);
+
+  /* j=(j+1) & (~1) (see the cephes sources) */
+  emm2 = _mm_add_epi32(emm2, *(v4si*)_pi32_1);
+  emm2 = _mm_and_si128(emm2, *(v4si*)_pi32_inv1);
+  y = _mm_cvtepi32_ps(emm2);
+
+  emm4 = emm2;
+
+  /* get the swap sign flag for the sine */
+  emm0 = _mm_and_si128(emm2, *(v4si*)_pi32_4);
+  emm0 = _mm_slli_epi32(emm0, 29);
+  v4sf swap_sign_bit_sin = _mm_castsi128_ps(emm0);
+
+  /* get the polynom selection mask for the sine*/
+  emm2 = _mm_and_si128(emm2, *(v4si*)_pi32_2);
+  emm2 = _mm_cmpeq_epi32(emm2, _mm_setzero_si128());
+  v4sf poly_mask = _mm_castsi128_ps(emm2);
+#else
+  /* store the integer part of y in mm2:mm3 */
+  xmm3 = _mm_movehl_ps(xmm3, y);
+  mm2 = _mm_cvttps_pi32(y);
+  mm3 = _mm_cvttps_pi32(xmm3);
+
+  /* j=(j+1) & (~1) (see the cephes sources) */
+  mm2 = _mm_add_pi32(mm2, *(v2si*)_pi32_1);
+  mm3 = _mm_add_pi32(mm3, *(v2si*)_pi32_1);
+  mm2 = _mm_and_si64(mm2, *(v2si*)_pi32_inv1);
+  mm3 = _mm_and_si64(mm3, *(v2si*)_pi32_inv1);
+
+  y = _mm_cvtpi32x2_ps(mm2, mm3);
+
+  mm4 = mm2;
+  mm5 = mm3;
+
+  /* get the swap sign flag for the sine */
+  mm0 = _mm_and_si64(mm2, *(v2si*)_pi32_4);
+  mm1 = _mm_and_si64(mm3, *(v2si*)_pi32_4);
+  mm0 = _mm_slli_pi32(mm0, 29);
+  mm1 = _mm_slli_pi32(mm1, 29);
+  v4sf swap_sign_bit_sin;
+  COPY_MM_TO_XMM(mm0, mm1, swap_sign_bit_sin);
+
+  /* get the polynom selection mask for the sine */
+
+  mm2 = _mm_and_si64(mm2, *(v2si*)_pi32_2);
+  mm3 = _mm_and_si64(mm3, *(v2si*)_pi32_2);
+  mm2 = _mm_cmpeq_pi32(mm2, _mm_setzero_si64());
+  mm3 = _mm_cmpeq_pi32(mm3, _mm_setzero_si64());
+  v4sf poly_mask;
+  COPY_MM_TO_XMM(mm2, mm3, poly_mask);
+#endif
+
+  /* The magic pass: "Extended precision modular arithmetic" 
+     x = ((x - y * DP1) - y * DP2) - y * DP3; */
+  xmm1 = *(v4sf*)_ps_minus_cephes_DP1;
+  xmm2 = *(v4sf*)_ps_minus_cephes_DP2;
+  xmm3 = *(v4sf*)_ps_minus_cephes_DP3;
+  xmm1 = _mm_mul_ps(y, xmm1);
+  xmm2 = _mm_mul_ps(y, xmm2);
+  xmm3 = _mm_mul_ps(y, xmm3);
+  x = _mm_add_ps(x, xmm1);
+  x = _mm_add_ps(x, xmm2);
+  x = _mm_add_ps(x, xmm3);
+
+#ifdef USE_SSE2
+  emm4 = _mm_sub_epi32(emm4, *(v4si*)_pi32_2);
+  emm4 = _mm_andnot_si128(emm4, *(v4si*)_pi32_4);
+  emm4 = _mm_slli_epi32(emm4, 29);
+  v4sf sign_bit_cos = _mm_castsi128_ps(emm4);
+#else
+  /* get the sign flag for the cosine */
+  mm4 = _mm_sub_pi32(mm4, *(v2si*)_pi32_2);
+  mm5 = _mm_sub_pi32(mm5, *(v2si*)_pi32_2);
+  mm4 = _mm_andnot_si64(mm4, *(v2si*)_pi32_4);
+  mm5 = _mm_andnot_si64(mm5, *(v2si*)_pi32_4);
+  mm4 = _mm_slli_pi32(mm4, 29);
+  mm5 = _mm_slli_pi32(mm5, 29);
+  v4sf sign_bit_cos;
+  COPY_MM_TO_XMM(mm4, mm5, sign_bit_cos);
+  _mm_empty(); /* good-bye mmx */
+#endif
+
+  sign_bit_sin = _mm_xor_ps(sign_bit_sin, swap_sign_bit_sin);
+
+  
+  /* Evaluate the first polynom  (0 <= x <= Pi/4) */
+  v4sf z = _mm_mul_ps(x,x);
+  y = *(v4sf*)_ps_coscof_p0;
+
+  y = _mm_mul_ps(y, z);
+  y = _mm_add_ps(y, *(v4sf*)_ps_coscof_p1);
+  y = _mm_mul_ps(y, z);
+  y = _mm_add_ps(y, *(v4sf*)_ps_coscof_p2);
+  y = _mm_mul_ps(y, z);
+  y = _mm_mul_ps(y, z);
+  v4sf tmp = _mm_mul_ps(z, *(v4sf*)_ps_0p5);
+  y = _mm_sub_ps(y, tmp);
+  y = _mm_add_ps(y, *(v4sf*)_ps_1);
+  
+  /* Evaluate the second polynom  (Pi/4 <= x <= 0) */
+
+  v4sf y2 = *(v4sf*)_ps_sincof_p0;
+  y2 = _mm_mul_ps(y2, z);
+  y2 = _mm_add_ps(y2, *(v4sf*)_ps_sincof_p1);
+  y2 = _mm_mul_ps(y2, z);
+  y2 = _mm_add_ps(y2, *(v4sf*)_ps_sincof_p2);
+  y2 = _mm_mul_ps(y2, z);
+  y2 = _mm_mul_ps(y2, x);
+  y2 = _mm_add_ps(y2, x);
+
+  /* select the correct result from the two polynoms */  
+  xmm3 = poly_mask;
+  v4sf ysin2 = _mm_and_ps(xmm3, y2);
+  v4sf ysin1 = _mm_andnot_ps(xmm3, y);
+  y2 = _mm_sub_ps(y2,ysin2);
+  y = _mm_sub_ps(y, ysin1);
+
+  xmm1 = _mm_add_ps(ysin1,ysin2);
+  xmm2 = _mm_add_ps(y,y2);
+ 
+  /* update the sign */
+  *s = _mm_xor_ps(xmm1, sign_bit_sin);
+  *c = _mm_xor_ps(xmm2, sign_bit_cos);
+}
+
diff --git a/src/CMakeLists.txt b/src/CMakeLists.txt
index 810cd368f..a684bd6a2 100644
--- a/src/CMakeLists.txt
+++ b/src/CMakeLists.txt
@@ -37,6 +37,7 @@ add_library(marian STATIC
 
   tensors/backend.cpp
   tensors/rand.cpp
+  tensors/tensor.cpp
   tensors/cpu/device.cpp
   tensors/cpu/prod.cpp
   tensors/cpu/tensor_operators.cpp
diff --git a/src/common/definitions.h b/src/common/definitions.h
index f46da2340..b7de4ad14 100755
--- a/src/common/definitions.h
+++ b/src/common/definitions.h
@@ -1,7 +1,8 @@
 #pragma once
 
 #include "common/logging.h"
-#include "shape.h" // @TODO: why not common/shape.h?
+#include "common/shape.h"
+#include "common/intrusive_ptr.h"
 
 #include <functional>
 #include <iostream>
@@ -9,7 +10,6 @@
 #include <string>
 #include <vector>
 
-//#define THREAD_GUARD(body) std::thread([&]() { body; }).join()
 #define THREAD_GUARD(body) [&]() { body; }() // test if THREAD_GUARD is neccessary, remove if no problems occur.
 #define NodeOp(op) [=]() { op; }
 
@@ -31,11 +31,17 @@ namespace marian {
 typedef uint32_t IndexType;
 
 template <class T>
-using Ptr = std::shared_ptr<T>;
+using IPtr = IntrusivePtr<T>;
 
 template <class T>
 using UPtr = std::unique_ptr<T>;
 
+template <class T>
+using IWeak = T*;
+
+template <class T>
+using Ptr = std::shared_ptr<T>;
+
 template <class T>
 using Weak = std::weak_ptr<T>;
 
@@ -51,6 +57,18 @@ Ptr<T> New(Ptr<T> p) {
   return Ptr<T>(p);
 }
 
+/** @brief Creates InstrusivePtr of any type, passes all arguments to any available
+ * constructor */
+template <class T, typename... Args>
+IPtr<T> INew(Args&&... args) {
+  return IPtr<T>(new T(std::forward<Args>(args)...));
+}
+
+template <class T>
+IPtr<T> INew(Ptr<T> p) {
+  return IPtr<T>(p);
+}
+
 enum class DeviceType : size_t { gpu = 0, cpu = 1 };
 
 struct DeviceId {
@@ -98,12 +116,14 @@ const DeviceId GPU5{5, DeviceType::gpu};
 const DeviceId GPU6{6, DeviceType::gpu};
 const DeviceId GPU7{7, DeviceType::gpu};
 
+// These are many small objects, hence use IntrusivePtr
 class TensorBase;
-typedef Ptr<TensorBase> Tensor;
+typedef IPtr<TensorBase> Tensor;
 
+// These are many small objects, hence use IntrusivePtr
 template <class DataType>
 class Chainable;
-typedef Ptr<Chainable<Tensor>> Expr;
+typedef IPtr<Chainable<Tensor>> Expr;
 
 class OptimizerBase;
 typedef Ptr<OptimizerBase> OptimizerBasePtr;
diff --git a/src/common/intrusive_ptr.h b/src/common/intrusive_ptr.h
new file mode 100644
index 000000000..fa412a8b7
--- /dev/null
+++ b/src/common/intrusive_ptr.h
@@ -0,0 +1,227 @@
+#pragma once
+
+#include <cassert>
+#include <iostream>
+#include "common/logging.h"
+
+// Smart pointer class for small objects with reference counting but no thread-safety.
+// Inspired by boost::intrusive_ptr<T>.
+
+// Compared to std::shared_ptr this is small and cheap to construct and destroy.
+// Does not hold the counter, the pointed to class `T` needs to add
+// ENABLE_INTRUSIVE_PTR(T) into the body of the class (private section). This adds
+// the reference counters and count manipulation functions to the class.
+
+#define ENABLE_INTRUSIVE_PTR(type)           \
+  size_t references_{0};                     \
+                                             \
+  inline friend void intrusivePtrAddRef(type* x) {  \
+    if(x != 0)                               \
+      ++x->references_;                      \
+  }                                          \
+                                             \
+  inline friend void intrusivePtrRelease(type* x) { \
+    if(x != 0 && --x->references_ == 0) {    \
+      delete x;                              \
+      x = 0;                                 \
+    }                                        \
+  }                                          \
+                                             \
+  inline friend size_t references(type* x) {        \
+    return x->references_;                   \
+  }                                          \
+
+
+template<class T>
+class IntrusivePtr {
+private:
+  typedef IntrusivePtr this_type;
+
+public:
+  typedef T element_type;
+
+  IntrusivePtr() : ptr_(0) {};
+
+  IntrusivePtr(T* p)
+  : ptr_(p) {
+      if(ptr_ != 0)
+        intrusivePtrAddRef(ptr_);
+  }
+
+  template<class Y>
+  IntrusivePtr(const IntrusivePtr<Y>& rhs)
+  : ptr_(rhs.get()) {
+    if(ptr_ != 0)
+      intrusivePtrAddRef(ptr_);
+  }
+
+  IntrusivePtr(const IntrusivePtr& rhs)
+  : ptr_(rhs.ptr_) {
+    if(ptr_ != 0)
+      intrusivePtrAddRef(ptr_);
+  }
+
+  ~IntrusivePtr() {
+    if(ptr_ != 0)
+      intrusivePtrRelease(ptr_);
+  }
+
+  IntrusivePtr(IntrusivePtr&& rhs)
+  : ptr_(rhs.ptr_) {
+    rhs.ptr_ = 0;
+  }
+
+  inline size_t useCount() {
+    return references(ptr_);
+  }
+
+  inline IntrusivePtr& operator=(IntrusivePtr&& rhs) {
+    this_type(static_cast<IntrusivePtr&&>(rhs)).swap(*this);
+    return *this;
+  }
+
+  inline IntrusivePtr& operator=(const IntrusivePtr& rhs) {
+    this_type(rhs).swap(*this);
+    return *this;
+  }
+
+  template<class Y>
+  inline IntrusivePtr& operator=(const IntrusivePtr<Y>& rhs) {
+    this_type(rhs).swap(*this);
+    return *this;
+  }
+
+  inline void reset() {
+    this_type().swap(*this);
+  }
+
+  inline void reset(T* rhs) {
+    this_type(rhs).swap(*this);
+  }
+
+  inline T* get() const {
+    return ptr_;
+  }
+
+  inline T* detach() {
+    T* ret = ptr_;
+    ptr_ = 0;
+    return ret;
+  }
+
+  inline T& operator*() const {
+    //ABORT_IF(ptr_ == 0, "Null pointer in IntrusivePtr");
+    return *ptr_;
+  }
+
+  inline T* operator->() const {
+    //ABORT_IF(ptr_ == 0, "Null pointer in IntrusivePtr");
+    return ptr_;
+  }
+
+  inline explicit operator bool() const {
+    return ptr_ != 0;
+  }
+
+  inline bool operator!() const {
+    return ptr_ == 0;
+  }
+
+  inline void swap(IntrusivePtr& rhs) {
+    T* tmp = ptr_;
+    ptr_ = rhs.ptr_;
+    rhs.ptr_ = tmp;
+  }
+
+private:
+  T* ptr_;
+};
+
+template<class T, class U>
+inline bool operator==(const IntrusivePtr<T>& a, const IntrusivePtr<U>& b) {
+  return a.get() == b.get();
+}
+
+template<class T, class U>
+inline bool operator!=(const IntrusivePtr<T>& a, const IntrusivePtr<U>& b) {
+  return a.get() != b.get();
+}
+
+template<class T>
+inline bool operator==(const IntrusivePtr<T>& a, T* b) {
+  return a.get() == b;
+}
+
+template<class T>
+inline bool operator!=(const IntrusivePtr<T>& a, T* b) {
+  return a.get() != b;
+}
+
+template<class T>
+inline bool operator==(const IntrusivePtr<T>& a, std::nullptr_t) {
+  return a.get() == 0;
+}
+
+template<class T>
+inline bool operator!=(const IntrusivePtr<T>& a, std::nullptr_t) {
+  return a.get() != 0;
+}
+
+template<class T>
+inline bool operator==(T* a, const IntrusivePtr<T>& b) {
+  return b.get();
+}
+
+template<class T>
+inline bool operator!=(T* a, const IntrusivePtr<T>& b) {
+  return b.get();
+}
+
+template<class T, class U>
+inline bool operator<(const IntrusivePtr<T>& a, const IntrusivePtr<U>& b) {
+  return std::less<T*>()(a.get(), b.get());
+}
+
+template<class T>
+inline void swap(IntrusivePtr<T> & a, IntrusivePtr<T> & b) {
+  a.swap(b);
+}
+
+template<class E, class T, class Y>
+std::basic_ostream<E, T>& operator<<(std::basic_ostream<E, T>& os, const IntrusivePtr<Y>& p) {
+  os << p.get();
+  return os;
+}
+
+// compatibility functions to make std::*_pointer_cast<T> work, also for automatic hashing
+namespace std {
+  template<class T>
+  T* get_pointer(const IntrusivePtr<T>& p) {
+    return p.get();
+  }
+
+  template<class T, class U>
+  IntrusivePtr<T> static_pointer_cast(const IntrusivePtr<U>& p) {
+    return static_cast<T*>(p.get());
+  }
+
+  template<class T, class U>
+  IntrusivePtr<T> const_pointer_cast(const IntrusivePtr<U>& p) {
+    return const_cast<T*>(p.get());
+  }
+
+  template<class T, class U>
+  IntrusivePtr<T> dynamic_pointer_cast(const IntrusivePtr<U>& p) {
+    return dynamic_cast<T*>(p.get());
+  }
+
+  // IntrusivePtr<T> can be used as hash map key
+  template <class T> struct hash<IntrusivePtr<T>> {
+    size_t operator()(const IntrusivePtr<T>& x) const {
+      std::hash<size_t> hasher;
+      return hasher((size_t)x.get());
+    }
+  };
+}
+
+
diff --git a/src/common/types.h b/src/common/types.h
index 3e9adf0e8..abf1035bf 100644
--- a/src/common/types.h
+++ b/src/common/types.h
@@ -1,10 +1,125 @@
 #pragma once
 
+#include "half_float/umHalf.h"
+
 #include <iostream>
 #include <string>
+#include <functional>
+
+#ifndef __CUDA_ARCH__
+#include <immintrin.h>
+#endif
+
+#define DISPATCH_BY_TYPE0(type, func) \
+do { \
+  switch(type) { \
+    case Type::int8:    return func<int8_t  >(); \
+    case Type::int16:   return func<int16_t >(); \
+    case Type::int32:   return func<int32_t >(); \
+    case Type::int64:   return func<int64_t >(); \
+    case Type::uint8:   return func<uint8_t >(); \
+    case Type::uint16:  return func<uint16_t>(); \
+    case Type::uint32:  return func<uint32_t>(); \
+    case Type::uint64:  return func<uint64_t>(); \
+    case Type::float16: return func<float16 >(); \
+    case Type::float32: return func<float   >(); \
+    case Type::float64: return func<double  >(); \
+    default: ABORT("Unknown type {}", type); \
+  } \
+} while(0)
+
+#define DISPATCH_BY_TYPE1(type, func, arg1) \
+do { \
+  switch(type) { \
+    case Type::int8:    return func<int8_t  >(arg1); \
+    case Type::int16:   return func<int16_t >(arg1); \
+    case Type::int32:   return func<int32_t >(arg1); \
+    case Type::int64:   return func<int64_t >(arg1); \
+    case Type::uint8:   return func<uint8_t >(arg1); \
+    case Type::uint16:  return func<uint16_t>(arg1); \
+    case Type::uint32:  return func<uint32_t>(arg1); \
+    case Type::uint64:  return func<uint64_t>(arg1); \
+    case Type::float16: return func<float16 >(arg1); \
+    case Type::float32: return func<float   >(arg1); \
+    case Type::float64: return func<double  >(arg1); \
+    default: ABORT("Unknown type {}", type); \
+  } \
+} while(0)
+
+#define DISPATCH_BY_TYPE2(type, func, arg1, arg2) \
+do { \
+  switch(type) { \
+    case Type::int8    : return func<int8_t  >(arg1, arg2); \
+    case Type::int16   : return func<int16_t >(arg1, arg2); \
+    case Type::int32   : return func<int32_t >(arg1, arg2); \
+    case Type::int64   : return func<int64_t >(arg1, arg2); \
+    case Type::uint8   : return func<uint8_t >(arg1, arg2); \
+    case Type::uint16  : return func<uint16_t>(arg1, arg2); \
+    case Type::uint32  : return func<uint32_t>(arg1, arg2); \
+    case Type::uint64  : return func<uint64_t>(arg1, arg2); \
+    case Type::float16 : return func<float16 >(arg1, arg2); \
+    case Type::float32 : return func<float   >(arg1, arg2); \
+    case Type::float64 : return func<double  >(arg1, arg2); \
+    default: ABORT("Unknown type {}", type); \
+  } \
+} while(0)
 
 namespace marian {
 
+#ifndef __CUDA_ARCH__
+struct float32x4 {
+private:
+  __m128 f_;
+
+public:
+  float32x4() {}
+  float32x4(const __m128& f) : f_(f) {}
+  float32x4(const float& f) : f_(_mm_set1_ps(f)) {}
+
+  operator const __m128&() const { return f_; }
+  operator __m128&() { return f_; }
+
+  float operator[] (size_t i) const {
+    return *(((float*)&f_) + i);
+  }
+
+  friend std::ostream& operator<<(std::ostream& out, float32x4 f4) {
+    float* a = (float*)&f4;
+    out << "[" << a[0];
+    for(int i = 1; i < 4; i++)
+      out << " " << a[i];
+    out << "]";
+    return out;
+  }
+};
+
+struct float32x8 {
+private:
+  __m256 f_;
+
+public:
+  float32x8() {}
+  float32x8(const __m256& f) : f_(f) {}
+  float32x8(const float& f) : f_(_mm256_set1_ps(f)) {}
+
+  operator const __m256&() const { return f_; }
+  operator __m256&() { return f_; }
+
+  float operator[] (size_t i) const {
+    return *(((float*)&f_) + i);
+  }
+
+  friend std::ostream& operator<<(std::ostream& out, float32x8 f8) {
+    float* a = (float*)&f8;
+    out << "[" << a[0];
+    for(int i = 1; i < 8; i++)
+      out << " " << a[i];
+    out << "]";
+    return out;
+  }
+};
+#endif
+
 enum class TypeClass : size_t {
   signed_type = 0x100,
   unsigned_type = 0x200,
@@ -17,16 +132,17 @@ constexpr inline size_t operator+(TypeClass typeClass, size_t val) {
 }
 
 enum class Type : size_t {
-  int8 = TypeClass::signed_type + 1u,
+  int8  = TypeClass::signed_type + 1u,
   int16 = TypeClass::signed_type + 2u,
   int32 = TypeClass::signed_type + 4u,
   int64 = TypeClass::signed_type + 8u,
 
-  uint8 = TypeClass::unsigned_type + 1u,
+  uint8  = TypeClass::unsigned_type + 1u,
   uint16 = TypeClass::unsigned_type + 2u,
   uint32 = TypeClass::unsigned_type + 4u,
   uint64 = TypeClass::unsigned_type + 8u,
 
+  float16 = TypeClass::float_type + 2u,
   float32 = TypeClass::float_type + 4u,
   float64 = TypeClass::float_type + 8u
 };
@@ -59,38 +175,36 @@ template <typename T>
 inline bool matchType(Type type);
 
 // clang-format off
-template <> inline bool matchType<int8_t>(Type type)  { return type == Type::int8; }
-template <> inline bool matchType<int16_t>(Type type) { return type == Type::int16; }
-template <> inline bool matchType<int32_t>(Type type) { return type == Type::int32; }
-template <> inline bool matchType<int64_t>(Type type) { return type == Type::int64; }
+template <> inline bool matchType<int8_t>(Type type)   { return type == Type::int8; }
+template <> inline bool matchType<int16_t>(Type type)  { return type == Type::int16; }
+template <> inline bool matchType<int32_t>(Type type)  { return type == Type::int32; }
+template <> inline bool matchType<int64_t>(Type type)  { return type == Type::int64; }
 
 template <> inline bool matchType<uint8_t>(Type type)  { return type == Type::uint8; }
 template <> inline bool matchType<uint16_t>(Type type) { return type == Type::uint16; }
 template <> inline bool matchType<uint32_t>(Type type) { return type == Type::uint32; }
 template <> inline bool matchType<uint64_t>(Type type) { return type == Type::uint64; }
 
-template <> inline bool matchType<float>(Type type)  { return type == Type::float32; }
-template <> inline bool matchType<double>(Type type) { return type == Type::float64; }
+template <> inline bool matchType<float16>(Type type)  { return type == Type::float16; }
+template <> inline bool matchType<float>(Type type)    { return type == Type::float32; }
+template <> inline bool matchType<double>(Type type)   { return type == Type::float64; }
 // clang-format on
 
-template <typename T> inline Type getType();
-template <> inline Type getType<float>() { return Type::float32; }
-template <> inline Type getType<uint32_t>() { return Type::uint32; }
-
 static inline std::ostream& operator<<(std::ostream& out, Type type) {
   switch(type) {
-    case Type::int8: out << "int8"; break;
-    case Type::int16: out << "int16"; break;
-    case Type::int32: out << "int32"; break;
-    case Type::int64: out << "int64"; break;
-
-    case Type::uint8: out << "uint8"; break;
-    case Type::uint16: out << "uint16"; break;
-    case Type::uint32: out << "uint32"; break;
-    case Type::uint64: out << "uint64"; break;
-
-    case Type::float32: out << "float32"; break;
-    case Type::float64: out << "float64"; break;
+    case Type::int8    : out << "int8"; break;
+    case Type::int16   : out << "int16"; break;
+    case Type::int32   : out << "int32"; break;
+    case Type::int64   : out << "int64"; break;
+
+    case Type::uint8   : out << "uint8"; break;
+    case Type::uint16  : out << "uint16"; break;
+    case Type::uint32  : out << "uint32"; break;
+    case Type::uint64  : out << "uint64"; break;
+
+    case Type::float16 : out << "float16"; break;
+    case Type::float32 : out << "float32"; break;
+    case Type::float64 : out << "float64"; break;
   }
   return out;
 }
@@ -109,18 +223,89 @@ template <> inline std::string request<uint16_t>() { return "uint16"; }
 template <> inline std::string request<uint32_t>() { return "uint32"; }
 template <> inline std::string request<uint64_t>() { return "uint64"; }
 
-template <> inline std::string request<float>()  { return "float32"; }
-template <> inline std::string request<double>() { return "float64"; }
+template <> inline std::string request<float16>()  { return "float16"; }
+template <> inline std::string request<float>()    { return "float32"; }
+template <> inline std::string request<double>()   { return "float64"; }
 // clang-format on
 
+static Type inline typeFromString(const std::string& str) {
+  if(str == "int8")
+    return Type::int8;
+  if(str == "int16")
+    return Type::int16;
+  if(str == "int32")
+    return Type::int32;
+  if(str == "int64")
+    return Type::int64;
+
+  if(str == "uint8")
+    return Type::uint8;
+  if(str == "uint16")
+    return Type::uint16;
+  if(str == "uint32")
+    return Type::uint32;
+  if(str == "uint64")
+    return Type::uint64;
+
+  if(str == "float16")
+    return Type::float16;
+  if(str == "float32")
+    return Type::float32;
+  if(str == "float64")
+    return Type::float64;
+
+  ABORT("Unknown type {}", str);
+}
+
+template <typename T>
+inline Type typeId();
+
+template <> inline Type typeId<int8_t>()   { return Type::int8; }
+template <> inline Type typeId<int16_t>()  { return Type::int16; }
+template <> inline Type typeId<int32_t>()  { return Type::int32; }
+template <> inline Type typeId<int64_t>()  { return Type::int64; }
+
+template <> inline Type typeId<uint8_t>()  { return Type::uint8; }
+template <> inline Type typeId<uint16_t>() { return Type::uint16; }
+template <> inline Type typeId<uint32_t>() { return Type::uint32; }
+template <> inline Type typeId<uint64_t>() { return Type::uint64; }
+
+template <> inline Type typeId<float16>()  { return Type::float16; }
+template <> inline Type typeId<float>()    { return Type::float32; }
+template <> inline Type typeId<double>()   { return Type::float64; }
 
 // Abort if given C++ does not correspond to runtime type
 template <typename T>
 void matchOrAbort(Type type) {
-  ABORT_IF(!matchType<T>(type),
-           "Requested type ({}) and underlying type ({}) do not match",
-           request<T>(),
-           type);
+  // @TODO: hacky hack for WGNMT, turn this back on
+  // ABORT_IF(!matchType<T>(type),
+  //          "Requested type ({}) and underlying type ({}) do not match",
+  //          request<T>(),
+  //          type);
 }
 
+template <typename T>
+class NumericLimits {
+private:
+  template <typename L>
+  void setLimits() {
+    max = std::numeric_limits<L>::max();
+    min = std::numeric_limits<L>::min();
+    lowest = std::numeric_limits<L>::lowest();
+  }
+
+  void setLimits(Type type) {
+    DISPATCH_BY_TYPE0(type, setLimits);
+  }
+
+public:
+  T max;
+  T min;
+  T lowest;
+
+  NumericLimits(Type type) {
+    setLimits(type);
+  }
+};
+
 }  // namespace marian
diff --git a/src/functional/approx.h b/src/functional/approx.h
index 2989965db..233fd89d2 100644
--- a/src/functional/approx.h
+++ b/src/functional/approx.h
@@ -6,7 +6,7 @@ namespace marian {
 namespace functional {
 
 // approximate any unary float function within range with
-// piecewise linear functions in equal steps. 
+// piecewise linear functions in equal steps.
 //
 // Example:
 // static Approx<10, 0, 100> approxSigmoid(stableSigmoid);
@@ -14,12 +14,12 @@ namespace functional {
 //
 // Creates a functor for range [-10,10] with piecewise linear
 // approximations of a sigmoid, 100 pieces, step 0.2.
-// This is quite fast on the CPU. 
+// This is quite fast on the CPU.
 //
-// approxSigmoid.grad(x) computes the corresponding gradient. 
-// 
-// When used as a local variable, use static keyword to create 
-// only once. 
+// approxSigmoid.grad(x) computes the corresponding gradient.
+//
+// When used as a local variable, use static keyword to create
+// only once.
 
 template <int radius = 5, int offset = 0, int pieces = 10>
 struct Approx {
@@ -55,7 +55,7 @@ struct Approx {
 
   }
 
-  __HDI__ int index(float x) const {
+  HOST_DEVICE_INLINE int index(float x) const {
     if(x <= -radius)
       return 0;
     if(x < radius)  // +1 because 0 holds value for x < -radius
@@ -63,16 +63,16 @@ struct Approx {
     return pieces + 1;
   }
 
-  __HDI__ float domain(int i) const {
+  HOST_DEVICE_INLINE float domain(int i) const {
     return i * ((2.f * radius) / pieces) + offset - radius;
   }
 
-  __HDI__ float operator()(float x) const {
+  HOST_DEVICE_INLINE float operator()(float x) const {
     int i = index(x);
     return a[i] * x + b[i];
   }
 
-  __HDI__ float grad(float x) const {
+  HOST_DEVICE_INLINE float grad(float x) const {
     int i = index(x);
     return a[i];
   }
diff --git a/src/functional/array.h b/src/functional/array.h
index 2b73fc287..3c94ff032 100644
--- a/src/functional/array.h
+++ b/src/functional/array.h
@@ -9,28 +9,37 @@ namespace functional {
 template <typename T, size_t N>
 struct Array {
   typedef T value_type;
-
   T data_[N];
 
-  __HDI__ const T* data() const { return data_; }
+  HOST_DEVICE_INLINE const T* data() const { return data_; }
 
-  __HDI__ T* data() { return data_; }
+  HOST_DEVICE_INLINE T* data() { return data_; }
 
-  __HDI__ constexpr static size_t size() { return N; }
+  HOST_DEVICE_INLINE constexpr static size_t size() { return N; }
 
-  __HDI__ T& operator[](size_t i) { return data_[i]; }
-  __HDI__ const T& operator[](size_t i) const { return data_[i]; }
+  HOST_DEVICE_INLINE T& operator[](size_t i) { return data_[i]; }
+  HOST_DEVICE_INLINE const T& operator[](size_t i) const { return data_[i]; }
 
-  __HDI__ T* begin() { return data_; }
-  __HDI__ const T* begin() const { return data_; }
+  HOST_DEVICE_INLINE T* begin() { return data_; }
+  HOST_DEVICE_INLINE const T* begin() const { return data_; }
 
-  __HDI__ T* end() { return data_ + N; }
-  __HDI__ const T* end() const { return data_ + N; }
+  HOST_DEVICE_INLINE T* end() { return data_ + N; }
+  HOST_DEVICE_INLINE const T* end() const { return data_ + N; }
 
-  __HDI__ void fill(T val) {
+  HOST_DEVICE_INLINE void fill(T val) {
     for(int i = 0; i < N; ++i)
       data_[i] = val;
   }
+
+  HOST_DEVICE_INLINE T& back() { return data_[N - 1]; }
+  HOST_DEVICE_INLINE const T& back() const { return data_[N - 1]; }
+
+  HOST_DEVICE_INLINE bool operator==(const Array<T, N>& other) {
+    for(int i = 0; i < N; ++i)
+      if(data_[i] != other[i])
+        return false;
+    return true;
+  }
 };
 }  // namespace functional
 }  // namespace marian
diff --git a/src/functional/defs.h b/src/functional/defs.h
index 7ad627d9b..e423dc2a1 100644
--- a/src/functional/defs.h
+++ b/src/functional/defs.h
@@ -3,18 +3,20 @@
 #ifdef __CUDA_ARCH__
 
 #include <cuda.h>
-#define __H__ __host__
-#define __D__ __device__
-#define __HI__ __host__ inline
-#define __HD__ __host__ __device__
-#define __HDI__ __host__ __device__ inline
+#define HOST __host__
+#define DEVICE __device__
+#define DEVICE_INLINE __device__ inline
+#define HOST_INLINE __host__ inline
+#define HOST_DEVICE __host__ __device__
+#define HOST_DEVICE_INLINE __host__ __device__ inline
 
 #else
 
-#define __H__
-#define __D__
-#define __HI__ inline
-#define __HD__
-#define __HDI__ inline
+#define HOST
+#define DEVICE
+#define DEVICE_INLINE inline
+#define HOST_INLINE inline
+#define HOST_DEVICE
+#define HOST_DEVICE_INLINE inline
 
 #endif
diff --git a/src/functional/floats.h b/src/functional/floats.h
index 03e3f5404..2519d552e 100644
--- a/src/functional/floats.h
+++ b/src/functional/floats.h
@@ -83,7 +83,7 @@ struct F {
   static constexpr auto binary = V;
 
   template <typename... Args>
-  __HDI__ constexpr float operator()(Args&&... args) const {
+  HOST_DEVICE_INLINE constexpr float operator()(Args&&... args) const {
     return value;
   }
 
diff --git a/src/functional/functional.h b/src/functional/functional.h
index 435acbd19..f42eff951 100644
--- a/src/functional/functional.h
+++ b/src/functional/functional.h
@@ -2,6 +2,7 @@
 
 #include "functional/operands.h"
 #include "functional/predicates.h"
+#include "functional/operators.h"
 
 namespace marian {
 namespace functional {
diff --git a/src/functional/operands.h b/src/functional/operands.h
index 2d10a6a02..2bfa1cd04 100644
--- a/src/functional/operands.h
+++ b/src/functional/operands.h
@@ -13,7 +13,7 @@ using IsClass = typename std::enable_if<std::is_class<C>::value, C>::type;
 template <int N>
 struct Select {
   template <typename T, typename... Args>
-  __HDI__ static auto apply(T&& /*arg*/, Args&&... args)
+  HOST_DEVICE_INLINE static auto apply(T&& /*arg*/, Args&&... args)
       -> decltype(Select<N - 1>::apply(args...)) {
     return Select<N - 1>::apply(args...);
   }
@@ -22,7 +22,7 @@ struct Select {
 template <>
 struct Select<0> {
   template <typename T, typename... Args>
-  __HDI__ static T apply(T&& arg, Args&&... /*args*/) {
+  HOST_DEVICE_INLINE static T apply(T&& arg, Args&&... /*args*/) {
     return arg;
   }
 };
@@ -33,12 +33,12 @@ template <int V>
 struct C {
   static constexpr auto value = V;
 
-  template <typename... Args>
-  __HDI__ float operator()(Args&&... args) {
-    return V;
+  template <typename T, typename... Args>
+  HOST_DEVICE_INLINE T operator()(T&& /*arg*/, Args&&... /*args*/) {
+    return (T)V;
   }
 
-  std::string to_string() { return "C<" + std::to_string(V) + ">"; }
+  std::string to_string() const { return "C<" + std::to_string(V) + ">"; }
 };
 
 /******************************************************************************/
@@ -48,12 +48,12 @@ struct Capture {
 
   Capture(float val) : value(val){};
 
-  template <typename... Args>
-  __HDI__ float operator()(Args&&... /*args*/) {
-    return value;
+  template <typename T, typename... Args>
+  HOST_DEVICE_INLINE T operator()(const T& /*arg*/, const Args&... /*args*/) {
+    return T(value);
   }
 
-  std::string to_string() { return "Cap(" + std::to_string(value) + ")"; }
+  std::string to_string() const { return "Cap(" + std::to_string(value) + ")"; }
 };
 
 /******************************************************************************/
@@ -62,12 +62,12 @@ template <int N>
 struct Var {
   static constexpr auto index = N;
 
-  template <typename... Args>
-  __HDI__ float& operator()(Args&&... args) {
-    return Select<N - 1>::apply(args...);
+  template <typename T, typename... Args>
+  HOST_DEVICE_INLINE T& operator()(T&& arg, Args&&... args) {
+    return Select<N - 1>::apply(arg, args...);
   }
 
-  std::string to_string() { return "Var<" + std::to_string(N) + ">"; }
+  std::string to_string() const { return "Var<" + std::to_string(N) + ">"; }
 };
 }  // namespace functional
 }  // namespace marian
diff --git a/src/functional/operators.h b/src/functional/operators.h
new file mode 100644
index 000000000..4a210e134
--- /dev/null
+++ b/src/functional/operators.h
@@ -0,0 +1,575 @@
+#pragma once
+
+#include "common/types.h"
+#include <cmath>
+
+#ifdef __CUDA_ARCH__
+#include <cuda_fp16.h>
+#endif
+
+namespace marian {
+namespace functional {
+
+// General template, will be used for any type without specializations
+// and will fail with an abort message.
+template <typename T>
+struct Ops {
+  static HOST_DEVICE_INLINE T tanh(const T& x) { ABORT("Unknown type"); }
+  static HOST_DEVICE_INLINE T sin(const T& x)  { ABORT("Unknown type"); }
+  static HOST_DEVICE_INLINE T cos(const T& x)  { ABORT("Unknown type"); }
+  static HOST_DEVICE_INLINE T tan(const T& x)  { ABORT("Unknown type"); }
+  static HOST_DEVICE_INLINE T log(const T& x)  { ABORT("Unknown type"); }
+  static HOST_DEVICE_INLINE T exp(const T& x)  { ABORT("Unknown type"); }
+  static HOST_DEVICE_INLINE T abs(const T& x)  { ABORT("Unknown type"); }
+  static HOST_DEVICE_INLINE T sqrt(const T& x) { ABORT("Unknown type"); }
+  static HOST_DEVICE_INLINE T neg(const T& x)  { ABORT("Unknown type"); }
+  static HOST_DEVICE_INLINE T sgn(const T& x)  { ABORT("Unknown type"); }
+
+  static HOST_DEVICE_INLINE T add(const T& x, const T& y)  { ABORT("Unknown type"); }
+  static HOST_DEVICE_INLINE T sub(const T& x, const T& y)  { ABORT("Unknown type"); }
+  static HOST_DEVICE_INLINE T mul(const T& x, const T& y)  { ABORT("Unknown type"); }
+  static HOST_DEVICE_INLINE T div(const T& x, const T& y)  { ABORT("Unknown type"); }
+
+  static HOST_DEVICE_INLINE T max(const T& x, const T& y)  { ABORT("Unknown type"); }
+  static HOST_DEVICE_INLINE T min(const T& x, const T& y)  { ABORT("Unknown type"); }
+  static HOST_DEVICE_INLINE T pow(const T& x, const T& y)  { ABORT("Unknown type"); }
+
+  static HOST_DEVICE_INLINE T negate(const T& x)  { ABORT("Unknown type"); }
+  static HOST_DEVICE_INLINE T eq(const T& x, const T& y)   { ABORT("Unknown type"); }
+  static HOST_DEVICE_INLINE T neq(const T& x, const T& y)  { ABORT("Unknown type"); }
+  static HOST_DEVICE_INLINE T gt(const T& x, const T& y)   { ABORT("Unknown type"); }
+  static HOST_DEVICE_INLINE T lt(const T& x, const T& y)   { ABORT("Unknown type"); }
+  static HOST_DEVICE_INLINE T geq(const T& x, const T& y)  { ABORT("Unknown type"); }
+  static HOST_DEVICE_INLINE T leq(const T& x, const T& y)  { ABORT("Unknown type"); }
+  static HOST_DEVICE_INLINE T _and(const T& x, const T& y) { ABORT("Unknown type"); } // 'and' is used by gcc
+  static HOST_DEVICE_INLINE T _or(const T& x, const T& y)  { ABORT("Unknown type"); } // 'or' is used by gcc
+
+  // Neural Networks specific functions
+  static HOST_DEVICE_INLINE T sigmoid(const T& x)               { ABORT("Unknown type"); }
+  static HOST_DEVICE_INLINE T logaddexp(const T& x, const T& y) { ABORT("Unknown type"); }
+  static HOST_DEVICE_INLINE T clip(const T& x, const T& y)      { ABORT("Unknown type"); }
+  // derivative of Clip, cut-off function
+  static HOST_DEVICE_INLINE T bump(const T& x, const T& y)      { ABORT("Unknown type"); }
+  static HOST_DEVICE_INLINE T relu(const T& x)                  { ABORT("Unknown type"); }
+  static HOST_DEVICE_INLINE T reluBack(const T& x)              { ABORT("Unknown type"); }
+  static HOST_DEVICE_INLINE T prelu(const T& x, const T& y)     { ABORT("Unknown type"); }
+  static HOST_DEVICE_INLINE T preluBack(const T& x, const T& y) { ABORT("Unknown type"); }
+
+  static HOST_DEVICE_INLINE T if_then_else(const T& x, const T& y, const T& z) { ABORT("Unknown type"); }
+
+  static HOST_DEVICE_INLINE T sumReduce(const T& x) { ABORT("Unknown type"); }
+  static HOST_DEVICE_INLINE T maxReduce(const T& x) { ABORT("Unknown type"); }
+  static HOST_DEVICE_INLINE T minReduce(const T& x) { ABORT("Unknown type"); }
+};
+
+// Specialization for float
+template <>
+struct Ops<float> {
+  typedef float Single;
+
+  static HOST_DEVICE_INLINE float tanh(const float& x) { return tanhf(x); }
+  static HOST_DEVICE_INLINE float sin(const float& x)  { return sinf(x); }
+  static HOST_DEVICE_INLINE float cos(const float& x)  { return cosf(x); }
+  static HOST_DEVICE_INLINE float tan(const float& x)  { return tanf(x); }
+  static HOST_DEVICE_INLINE float log(const float& x)  { return logf(x); }
+  static HOST_DEVICE_INLINE float exp(const float& x)  { return expf(x); }
+  static HOST_DEVICE_INLINE float abs(const float& x)  { return fabs(x); }
+  static HOST_DEVICE_INLINE float sqrt(const float& x) { return sqrtf(x); }
+  static HOST_DEVICE_INLINE float neg(const float& x)  { return -x; }
+  static HOST_DEVICE_INLINE float sgn(const float& x)  { return (0 < x) - (x < 0); }
+
+  static HOST_DEVICE_INLINE float add(const float& x, const float& y)  { return x + y; }
+  static HOST_DEVICE_INLINE float sub(const float& x, const float& y)  { return x - y; }
+  static HOST_DEVICE_INLINE float mul(const float& x, const float& y)  { return x * y; }
+  static HOST_DEVICE_INLINE float div(const float& x, const float& y)  { return x / y; }
+
+  static HOST_DEVICE_INLINE float max(const float& x, const float& y)  { return x < y ? y : x; }
+  static HOST_DEVICE_INLINE float min(const float& x, const float& y)  { return x < y ? x : y; }
+  static HOST_DEVICE_INLINE float pow(const float& x, const float& y)  { return powf(x, y); }
+
+
+  static HOST_DEVICE_INLINE float negate(const float& x)  { return !(bool)x; }
+  static HOST_DEVICE_INLINE float eq(const float& x, const float& y)   { return x == y; }
+  static HOST_DEVICE_INLINE float neq(const float& x, const float& y)  { return x != y; }
+  static HOST_DEVICE_INLINE float gt(const float& x, const float& y)   { return x > y; }
+  static HOST_DEVICE_INLINE float lt(const float& x, const float& y)   { return x < y; }
+  static HOST_DEVICE_INLINE float geq(const float& x, const float& y)  { return x >= y; }
+  static HOST_DEVICE_INLINE float leq(const float& x, const float& y)  { return x <= y; }
+  static HOST_DEVICE_INLINE float and_(const float& x, const float& y) { return x && y; } // 'and' is used by gcc
+  static HOST_DEVICE_INLINE float or_(const float& x, const float& y)  { return x || y; } // 'or' is used by gcc
+
+  // Neural Networks specific functions
+  static HOST_DEVICE_INLINE float sigmoid(const float& x) {
+    return x > 0 ? (1.f / (1.f + exp(-x))) : (exp(x) / (1.f + exp(x)));
+  }
+
+  static HOST_DEVICE_INLINE float logaddexp(const float& x, const float& y) {
+    // Note: This may not be ideal for CUDA; cf. CNTK implementation
+    return x < y ? (y + log1pf(exp(x - y))) : (x + log1pf(exp(y - x)));
+  }
+
+  static HOST_DEVICE_INLINE float clip(const float& x, const float& y)  { return abs(x) >= y ? sgn(x) * y : x; }
+  // derivative of Clip, cut-off function
+  static HOST_DEVICE_INLINE float bump(const float& x, const float& y)  { return abs(x) >= y ? 0.f : 1.f; }
+
+  static HOST_DEVICE_INLINE float relu(const float& x)     { return x > 0.f ? x : 0.f; }
+  static HOST_DEVICE_INLINE float reluBack(const float& x) { return x > 0.f ? 1.f : 0.f; }
+
+  static HOST_DEVICE_INLINE float prelu(const float& x, const float& y)     { return x > 0.f ? x : x * y; }
+  static HOST_DEVICE_INLINE float preluBack(const float& x, const float& y) { return x > 0.f ? 1.f : y; }
+
+  static HOST_DEVICE_INLINE float if_then_else(const float& x, const float& y, const float& z) { return x ? y : z; }
+
+  static HOST_DEVICE_INLINE float sumReduce(const float& x) { return x; }
+  static HOST_DEVICE_INLINE float maxReduce(const float& x) { return x; }
+  static HOST_DEVICE_INLINE float minReduce(const float& x) { return x; }
+
+};
+
+// Specialization for double
+template <>
+struct Ops<double> {
+  typedef double Single;
+
+  static HOST_DEVICE_INLINE double tanh(const double& x) { return tanh(x); }
+  static HOST_DEVICE_INLINE double sin(const double& x)  { return sin(x); }
+  static HOST_DEVICE_INLINE double cos(const double& x)  { return cos(x); }
+  static HOST_DEVICE_INLINE double tan(const double& x)  { return tan(x); }
+  static HOST_DEVICE_INLINE double log(const double& x)  { return log(x); }
+  static HOST_DEVICE_INLINE double exp(const double& x)  { return exp(x); }
+  static HOST_DEVICE_INLINE double abs(const double& x)  { return abs(x); }
+  static HOST_DEVICE_INLINE double sqrt(const double& x) { return sqrt(x); }
+  static HOST_DEVICE_INLINE double neg(const double& x)  { return -x; }
+  static HOST_DEVICE_INLINE double sgn(const double& x)  { return (0 < x) - (x < 0); }
+
+  static HOST_DEVICE_INLINE double add(const double& x, const double& y)  { return x + y; }
+  static HOST_DEVICE_INLINE double sub(const double& x, const double& y)  { return x - y; }
+  static HOST_DEVICE_INLINE double mul(const double& x, const double& y)  { return x * y; }
+  static HOST_DEVICE_INLINE double div(const double& x, const double& y)  { return x / y; }
+
+  static HOST_DEVICE_INLINE double max(const double& x, const double& y)  { return x < y ? y : x; }
+  static HOST_DEVICE_INLINE double min(const double& x, const double& y)  { return x < y ? x : y; }
+  static HOST_DEVICE_INLINE double pow(const double& x, const double& y)  { return pow(x, y); }
+
+
+  static HOST_DEVICE_INLINE double negate(const double& x)  { return !(bool)x; }
+  static HOST_DEVICE_INLINE double eq(const double& x, const double& y)   { return x == y; }
+  static HOST_DEVICE_INLINE double neq(const double& x, const double& y)  { return x != y; }
+  static HOST_DEVICE_INLINE double gt(const double& x, const double& y)   { return x > y; }
+  static HOST_DEVICE_INLINE double lt(const double& x, const double& y)   { return x < y; }
+  static HOST_DEVICE_INLINE double geq(const double& x, const double& y)  { return x >= y; }
+  static HOST_DEVICE_INLINE double leq(const double& x, const double& y)  { return x <= y; }
+  static HOST_DEVICE_INLINE double and_(const double& x, const double& y) { return x && y; } // 'and' is used by gcc
+  static HOST_DEVICE_INLINE double or_(const double& x, const double& y)  { return x || y; } // 'or' is used by gcc
+
+  // Neural Networks specific functions
+  static HOST_DEVICE_INLINE double sigmoid(const double& x) {
+    return x > 0 ? (1.f / (1.f + exp(-x))) : (exp(x) / (1.f + exp(x)));
+  }
+
+  static HOST_DEVICE_INLINE double logaddexp(const double& x, const double& y) {
+    // Note: This may not be ideal for CUDA; cf. CNTK implementation
+    return x < y ? (y + log1p(exp(x - y))) : (x + log1p(exp(y - x)));
+  }
+
+  static HOST_DEVICE_INLINE double clip(const double& x, const double& y)  { return abs(x) >= y ? sgn(x) * y : x; }
+  // derivative of Clip, cut-off function
+  static HOST_DEVICE_INLINE double bump(const double& x, const double& y)  { return abs(x) >= y ? 0.f : 1.f; }
+
+  static HOST_DEVICE_INLINE double relu(const double& x)     { return x > 0.f ? x : 0.f; }
+  static HOST_DEVICE_INLINE double reluBack(const double& x) { return x > 0.f ? 1.f : 0.f; }
+
+  static HOST_DEVICE_INLINE double prelu(const double& x, const double& y)     { return x > 0.f ? x : x * y; }
+  static HOST_DEVICE_INLINE double preluBack(const double& x, const double& y) { return x > 0.f ? 1.f : y; }
+
+  static HOST_DEVICE_INLINE double if_then_else(const double& x, const double& y, const double& z) { return x ? y : z; }
+
+  static HOST_DEVICE_INLINE double sumReduce(const double& x) { return x; }
+  static HOST_DEVICE_INLINE double maxReduce(const double& x) { return x; }
+  static HOST_DEVICE_INLINE double minReduce(const double& x) { return x; }
+
+};
+
+#ifndef __CUDA_ARCH__
+
+#include "3rd_party/sse_mathfun.h"
+
+// Specialization for float32x8 (=__m128, CPU SSE intrisics)
+template <>
+struct Ops<float32x4> {
+  typedef float Single;
+
+  static inline float32x4 loop4(const std::function<float(const float&)>& f, const float32x4& x) {
+    float32x4 out;
+    for(int i = 0; i < 4; i++)
+      ((float*)&out)[i] = f(((const float*)&x)[i]);
+    return out;
+  }
+
+  static inline float32x4 loop4(const std::function<float(const float&, const float&)>& f, const float32x4& x, const float32x4& y) {
+    float32x4 out;
+    for(int i = 0; i < 4; i++)
+      ((float*)&out)[i] = f(((const float*)&x)[i], ((const float*)&y)[i]);
+    return out;
+  }
+
+  static inline float32x4 loop4(const std::function<float(const float&, const float&, const float&)>& f, const float32x4& x, const float32x4& y, const float32x4& z) {
+    float32x4 out;
+    for(int i = 0; i < 4; i++)
+      ((float*)&out)[i] = f(((const float*)&x)[i], ((const float*)&y)[i], ((const float*)&z)[i]);
+    return out;
+  }
+
+  // @TODO: why is this slow?
+  static inline float32x4 tanh(const float32x4& x) {
+    // ( e^x - e^-x )/( e^x + e^-x ) = (e^2x - 1) / (e^2x + 1)
+    float32x4 e2x = exp(mul(2.f, x));
+    return div(sub(e2x, 1.f), add(e2x, 1.f));
+  }
+
+  static inline float32x4 sin(const float32x4& x) { return sin_ps(x); }
+  static inline float32x4 cos(const float32x4& x) { return cos_ps(x); }
+  static inline float32x4 tan(const float32x4& x) { return div(sin(x), cos(x)); }
+  static inline float32x4 log(const float32x4& x) { return log_ps(x); }
+  static inline float32x4 exp(const float32x4& x) { return exp_ps(x); }
+
+  // @TODO: get rid of loop4 with proper intrisics
+  static inline float32x4 abs(const float32x4& x)  { return loop4(Ops<float>::abs, x); }
+  static inline float32x4 sqrt(const float32x4& x) { return _mm_sqrt_ps(x); }
+  static inline float32x4 neg(const float32x4& x)  { return sub(0.f, x); }
+
+  // @TODO: get rid of loop4 with proper intrisics
+  static inline float32x4 sgn(const float32x4& x)  { return loop4(Ops<float>::sgn, x); }
+
+  static inline float32x4 add(const float32x4& x, const float32x4& y) { return _mm_add_ps(x, y); }
+  static inline float32x4 sub(const float32x4& x, const float32x4& y) { return _mm_sub_ps(x, y); }
+  static inline float32x4 mul(const float32x4& x, const float32x4& y) { return _mm_mul_ps(x, y); }
+  static inline float32x4 div(const float32x4& x, const float32x4& y) { return _mm_div_ps(x, y); }
+
+  static inline float32x4 max(const float32x4& x, const float32x4& y) { return _mm_max_ps(x, y); }
+  static inline float32x4 min(const float32x4& x, const float32x4& y) { return _mm_min_ps(x, y); }
+  static inline float32x4 pow(const float32x4& x, const float32x4& y) { return exp(mul(y, log(x))); }
+
+  // @TODO: get rid of loop4 with proper intrisics
+  static inline float32x4 negate(float32x4& x)  { return loop4(Ops<float>::negate, x); }
+
+  static inline float32x4 eq(const float32x4& x, const float32x4& y)   { return loop4(Ops<float>::eq, x, y); }
+  static inline float32x4 neq(const float32x4& x, const float32x4& y)  { return loop4(Ops<float>::neq, x, y); }
+  static inline float32x4 gt(const float32x4& x, const float32x4& y)   { return loop4(Ops<float>::gt, x, y); }
+  static inline float32x4 lt(const float32x4& x, const float32x4& y)   { return loop4(Ops<float>::lt, x, y); }
+  static inline float32x4 geq(const float32x4& x, const float32x4& y)  { return loop4(Ops<float>::geq, x, y); }
+  static inline float32x4 leq(const float32x4& x, const float32x4& y)  { return loop4(Ops<float>::leq, x, y); }
+  static inline float32x4 and_(const float32x4& x, const float32x4& y) { return loop4(Ops<float>::and_, x, y); } // 'and' is used by gcc
+  static inline float32x4 or_(const float32x4& x, const float32x4& y)  { return loop4(Ops<float>::or_, x, y); } // 'or' is used by gcc
+
+  // Neural Networks specific functions
+  // @TODO: this is unsafe
+  static inline float32x4 sigmoid(const float32x4& x) {
+    float32x4 e = exp(x);
+    return div(e, add(1.f, e));
+  }
+
+  // // Neural Networks specific functions
+  // static HOST_DEVICE_INLINE float sigmoid(const float& x) {
+  //   return x > 0 ? (1.f / (1.f + exp(-x))) : (exp(x) / (1.f + exp(x)));
+  // }
+
+  static inline float32x4 logaddexp(const float32x4& x, const float32x4& y)  { return loop4(Ops<float>::logaddexp, x, y); }
+
+  static inline float32x4 clip(const float32x4& x, const float32x4& y)  { return loop4(Ops<float>::clip, x, y); }
+  static inline float32x4 bump(const float32x4& x, const float32x4& y)  { return loop4(Ops<float>::bump, x, y); }
+
+  static inline float32x4 relu(const float32x4& x)  { return max(0.f, x); }
+
+  static inline float32x4 reluBack(const float32x4& x)  { return loop4(Ops<float>::reluBack, x); }
+  static inline float32x4 prelu(const float32x4& x, const float32x4& y)  { return loop4(Ops<float>::prelu, x, y); }
+  static inline float32x4 preluBack(const float32x4& x, const float32x4& y)  { return loop4(Ops<float>::preluBack, x, y); }
+
+  static inline float32x4 if_then_else(const float32x4& x, const float32x4& y, const float32x4& z) { return loop4(Ops<float>::if_then_else, x, y, z);  }
+
+  static inline Single sumReduce(const float32x4& x) {
+    Single sum = 0;
+    for(int i = 0; i < 4; ++i)
+      sum = Ops<Single>::add(sum, x[i]);
+    return sum;
+  }
+
+  static inline Single maxReduce(const float32x4& x) {
+    Single maxs = x[0];
+    for(int i = 1; i < 4; ++i)
+      maxs = Ops<Single>::max(maxs, x[i]);
+    return maxs;
+  }
+
+  static inline Single minReduce(const float32x4& x) {
+    Single mins = x[0];
+    for(int i = 1; i < 4; ++i)
+      mins = Ops<Single>::min(mins, x[i]);
+    return mins;
+  }
+
+
+};
+
+#include "3rd_party/avx_mathfun.h"
+
+//*******************************************************************************************
+// Specialization for float32x8 (=__m256, CPU AVX intrisics)
+template <>
+struct Ops<float32x8> {
+  typedef float Single;
+
+
+  static inline float32x8 loop8(const std::function<float(const float&)>& f, const float32x8& x) {
+    float32x8 out;
+    for(int i = 0; i < 8; i++)
+      ((float*)&out)[i] = f(((const float*)&x)[i]);
+    return out;
+  }
+
+  static inline float32x8 loop8(const std::function<float(const float&, const float&)>& f, const float32x8& x, const float32x8& y) {
+    float32x8 out;
+    for(int i = 0; i < 8; i++)
+      ((float*)&out)[i] = f(((const float*)&x)[i], ((const float*)&y)[i]);
+    return out;
+  }
+
+  static inline float32x8 loop8(const std::function<float(const float&, const float&, const float&)>& f, const float32x8& x, const float32x8& y, const float32x8& z) {
+    float32x8 out;
+    for(int i = 0; i < 8; i++)
+      ((float*)&out)[i] = f(((const float*)&x)[i], ((const float*)&y)[i], ((const float*)&z)[i]);
+    return out;
+  }
+
+  static inline float32x8 tanh(const float32x8& x) { // ( e^x - e^-x )/( e^x + e^-x )
+    float32x8 e2x = exp(mul(2.f, x));
+    return div(sub(e2x, 1.f), add(e2x, 1.f));
+  }
+
+  static inline float32x8 sin(const float32x8& x) { return sin256_ps(x); }
+  static inline float32x8 cos(const float32x8& x) { return cos256_ps(x); }
+  static inline float32x8 tan(const float32x8& x) { return div(sin(x), cos(x)); } // @TODO: use sincos256_ps
+  static inline float32x8 log(const float32x8& x) { return log256_ps(x); }
+  static inline float32x8 exp(const float32x8& x) { return exp256_ps(x); }
+
+  // @TODO: get rid of loop8 with proper intrisics
+  static inline float32x8 abs(const float32x8& x)  { return loop8(Ops<float>::abs, x); }
+  static inline float32x8 sqrt(const float32x8& x) { return _mm256_sqrt_ps(x); }
+  static inline float32x8 neg(const float32x8& x)  { return sub(0.f, x); }
+
+  // @TODO: get rid of loop8 with proper intrisics
+  static inline float32x8 sgn(const float32x8& x)  { return loop8(Ops<float>::sgn, x); }
+
+  static inline float32x8 add(const float32x8& x, const float32x8& y) { return _mm256_add_ps(x, y); }
+  static inline float32x8 sub(const float32x8& x, const float32x8& y) { return _mm256_sub_ps(x, y); }
+  static inline float32x8 mul(const float32x8& x, const float32x8& y) { return _mm256_mul_ps(x, y); }
+  static inline float32x8 div(const float32x8& x, const float32x8& y) { return _mm256_div_ps(x, y); }
+
+  static inline float32x8 max(const float32x8& x, const float32x8& y) { return _mm256_max_ps(x, y); }
+  static inline float32x8 min(const float32x8& x, const float32x8& y) { return _mm256_min_ps(x, y); }
+  static inline float32x8 pow(const float32x8& x, const float32x8& y) { return exp(mul(y, log(x))); }
+
+  // @TODO: get rid of loop8 with proper intrisics
+  static inline float32x8 negate(float32x8& x)  { return loop8(Ops<float>::negate, x); }
+
+  static inline float32x8 eq(const float32x8& x, const float32x8& y)   { return loop8(Ops<float>::eq, x, y); }
+  static inline float32x8 neq(const float32x8& x, const float32x8& y)  { return loop8(Ops<float>::neq, x, y); }
+  static inline float32x8 gt(const float32x8& x, const float32x8& y)   { return loop8(Ops<float>::gt, x, y); }
+  static inline float32x8 lt(const float32x8& x, const float32x8& y)   { return loop8(Ops<float>::lt, x, y); }
+  static inline float32x8 geq(const float32x8& x, const float32x8& y)  { return loop8(Ops<float>::geq, x, y); }
+  static inline float32x8 leq(const float32x8& x, const float32x8& y)  { return loop8(Ops<float>::leq, x, y); }
+  static inline float32x8 and_(const float32x8& x, const float32x8& y) { return loop8(Ops<float>::and_, x, y); } // 'and' is used by gcc
+  static inline float32x8 or_(const float32x8& x, const float32x8& y)  { return loop8(Ops<float>::or_, x, y); } // 'or' is used by gcc
+
+
+  // Neural Networks specific functions
+  // @TODO: this is unsafe
+  static inline float32x8 sigmoid(const float32x8& x) {
+    float32x8 e = exp(x);
+    return div(e, add(1.f, e));
+  }
+
+  static inline float32x8 logaddexp(const float32x8& x, const float32x8& y)  { return loop8(Ops<float>::logaddexp, x, y); }
+
+  static inline float32x8 clip(const float32x8& x, const float32x8& y)  { return loop8(Ops<float>::clip, x, y); }
+  static inline float32x8 bump(const float32x8& x, const float32x8& y)  { return loop8(Ops<float>::bump, x, y); }
+
+  static inline float32x8 relu(const float32x8& x)  { return max(0.f, x); }
+
+  static inline float32x8 reluBack(const float32x8& x)  { return loop8(Ops<float>::reluBack, x); }
+  static inline float32x8 prelu(const float32x8& x, const float32x8& y)  { return loop8(Ops<float>::prelu, x, y); }
+  static inline float32x8 preluBack(const float32x8& x, const float32x8& y)  { return loop8(Ops<float>::preluBack, x, y); }
+
+  static inline float32x8 if_then_else(const float32x8& x, const float32x8& y, const float32x8& z) { return loop8(Ops<float>::if_then_else, x, y, z);  }
+
+  static inline Single sumReduce(const float32x8& x) {
+    Single sum = 0;
+    for(int i = 0; i < 8; ++i)
+      sum = Ops<Single>::add(sum, x[i]);
+    return sum;
+  }
+
+  static inline Single maxReduce(const float32x8& x) {
+    Single maxs = x[0];
+    for(int i = 1; i < 8; ++i)
+      maxs = Ops<Single>::max(maxs, x[i]);
+    return maxs;
+  }
+
+  static inline Single minReduce(const float32x8& x) {
+    Single mins = x[0];
+    for(int i = 1; i < 8; ++i)
+      mins = Ops<Single>::min(mins, x[i]);
+    return mins;
+  }
+};
+
+#endif
+
+#ifdef __CUDA_ARCH__
+
+// Specialization for half
+template <>
+struct Ops<half> {
+
+  static DEVICE_INLINE half sin(const half& x)  { return hsin(x); }
+  static DEVICE_INLINE half cos(const half& x)  { return hcos(x); }
+  static DEVICE_INLINE half tan(const half& x)  { return hsin(x) / hcos(x); }
+  static DEVICE_INLINE half log(const half& x)  { return hlog(x); }
+  static DEVICE_INLINE half exp(const half& x)  { return hexp(x); }
+  static DEVICE_INLINE half abs(const half& x)  { return fabs((float)x); }// @TODO half has this information somewhere in the struct, right?
+  static DEVICE_INLINE half sqrt(const half& x) { return hsqrt(x); }
+  static DEVICE_INLINE half neg(const half& x)  { return -x; }
+  static DEVICE_INLINE half sgn(const half& x)  { half zero = 0.f; return (zero < x) - (x < zero); } // @TODO half has this information somewhere in the struct, right?
+
+  static DEVICE_INLINE half add(const half& x, const half& y)  { return x + y; }
+  static DEVICE_INLINE half sub(const half& x, const half& y)  { return x - y; }
+  static DEVICE_INLINE half mul(const half& x, const half& y)  { return x * y; }
+  static DEVICE_INLINE half div(const half& x, const half& y)  { return x / y; }
+
+  static DEVICE_INLINE half max(const half& x, const half& y)  { return x < y ? y : x; }
+  static DEVICE_INLINE half min(const half& x, const half& y)  { return x < y ? x : y; }
+  static DEVICE_INLINE half pow(const half& x, const half& y)  { return exp(y * log(x)); }
+
+  static DEVICE_INLINE half negate(const half& x)  { return !(bool)x; }
+  static DEVICE_INLINE half eq(const half& x, const half& y)   { return x == y; }
+  static DEVICE_INLINE half neq(const half& x, const half& y)  { return x != y; }
+  static DEVICE_INLINE half gt(const half& x, const half& y)   { return x > y;  }
+  static DEVICE_INLINE half lt(const half& x, const half& y)   { return x < y;  }
+  static DEVICE_INLINE half geq(const half& x, const half& y)  { return x >= y; }
+  static DEVICE_INLINE half leq(const half& x, const half& y)  { return x <= y; }
+  static DEVICE_INLINE half and_(const half& x, const half& y) { return x && y; } // 'and' is used by gcc
+  static DEVICE_INLINE half or_(const half& x, const half& y)  { return x || y; } // 'or' is used by gcc
+
+  // Neural Networks specific functions
+  static DEVICE_INLINE half sigmoid(const half& x) {
+    const half zero = 0.f;
+    const half one  = 1.f;
+    return x > zero ? (one / (one + exp(-x))) : (exp(x) / (one + exp(x))); // safe sigmoid
+  }
+
+  static DEVICE_INLINE half tanh(const half& x) {
+    // tanh(x) = 2 * sigmoid(2 * x) - 1
+    const half one = 1.f;
+    const half two = 2.f;
+    return two * sigmoid(two * x) - one; // safe sigmoid => safe tanh
+  }
+
+  static DEVICE_INLINE half log1p(const half& x) {
+    return log(x + (half)1.f); // probably acceptable loss of precision, it's half anyway
+  }
+
+  static DEVICE_INLINE half logaddexp(const half& x, const half& y) {
+    // Note: This may not be ideal for CUDA; cf. CNTK implementation
+    return x < y ? (y + log1p(exp(x - y))) : (x + log1p(exp(y - x)));
+  }
+
+  static DEVICE_INLINE half clip(const half& x, const half& y)  { return abs(x) >= y ? sgn(x) * y : x; }
+
+  // derivative of Clip, cut-off function
+  static DEVICE_INLINE half bump(const half& x, const half& y)  {
+    const half zero = 0.f;
+    const half one =  1.f;
+    return abs(x) >= y ? zero : one;
+  }
+  static DEVICE_INLINE half relu(const half& x) {
+    const half zero = 0.f;
+    return x > zero ? x : zero;
+  }
+  static DEVICE_INLINE half reluBack(const half& x) {
+    const half zero = 0.f;
+    const half one =  1.f;
+    return x > zero ? one : zero;
+  }
+
+  static DEVICE_INLINE half prelu(const half& x, const half& y)     {
+    const half zero = 0.f;
+    return x > zero ? x : x * y;
+  }
+
+  static DEVICE_INLINE half preluBack(const half& x, const half& y) {
+    const half zero = 0.f;
+    const half one =  1.f;
+    return x > zero ? one : y;
+  }
+
+  static DEVICE_INLINE half if_then_else(const half& x, const half& y, const half& z) { return x ? y : z; }
+
+  static DEVICE_INLINE half sumReduce(const half& x) { return x; }
+  static DEVICE_INLINE half maxReduce(const half& x) { return x; }
+  static DEVICE_INLINE half minReduce(const half& x) { return x; }
+
+};
+
+#endif
+
+//*******************************************************************************************
+
+#include "functional/defs.h"
+#include "functional/predicates.h"
+
+UNARY(Tanh,    tanh,       Ops<ElementType>::tanh(x));
+UNARY(Sin,     sin,        Ops<ElementType>::sin(x));
+UNARY(Cos,     cos,        Ops<ElementType>::cos(x));
+UNARY(Tan,     tan,        Ops<ElementType>::tan(x));
+UNARY(Log,     log,        Ops<ElementType>::log(x));
+UNARY(Exp,     exp,        Ops<ElementType>::exp(x));
+UNARY(Abs,     abs,        Ops<ElementType>::abs(x));
+UNARY(Sqrt,    sqrt,       Ops<ElementType>::sqrt(x));
+UNARY(Neg,     operator-,  Ops<ElementType>::neg(x));
+UNARY(Sgn,     sgn,        Ops<ElementType>::sgn(x));
+
+BINARY(Plus,   operator+,  Ops<ElementType>::add(x, y));
+BINARY(Minus,  operator-,  Ops<ElementType>::sub(x, y));
+BINARY(Mult,   operator*,  Ops<ElementType>::mul(x, y));
+BINARY(Div,    operator/,  Ops<ElementType>::div(x, y));
+BINARY(Max,    max,        Ops<ElementType>::max(x, y));
+BINARY(Min,    min,        Ops<ElementType>::min(x, y));
+UNARY(Negate,  operator!,  Ops<ElementType>::negate(x));
+BINARY(Eq,     operator==, Ops<ElementType>::eq(x, y));
+BINARY(NEq,    operator!=, Ops<ElementType>::neq(x, y));
+BINARY(Gt,     operator>,  Ops<ElementType>::gt(x, y));
+BINARY(Lt,     operator<,  Ops<ElementType>::lt(x, y));
+BINARY(Geq,    operator>=, Ops<ElementType>::geq(x, y));
+BINARY(Leq,    operator<=, Ops<ElementType>::leq(x, y));
+BINARY(And,    operator&&, Ops<ElementType>::and_(x, y));
+BINARY(Or,     operator||, Ops<ElementType>::or_(x, y));
+BINARY(Pow,    pow,        Ops<ElementType>::pow(x, y));
+
+TERNARY(IfThenElse, if_then_else, Ops<ElementType>::if_then_else(x, y, z));
+
+// Neural Networks specific functions
+BINARY(Clip,       clip,      Ops<ElementType>::clip(x, y));
+// derivative of Clip, cut-off function
+BINARY(Bump,       bump,      Ops<ElementType>::bump(x, y));
+
+UNARY(Sigmoid,     sigmoid,   Ops<ElementType>::sigmoid(x));
+BINARY(LogAddExp,  logaddexp, Ops<ElementType>::logaddexp(x, y));
+UNARY(sReLU,       ReLU,      Ops<ElementType>::relu(x));
+UNARY(sReLUBack,   ReLUback,  Ops<ElementType>::reluBack(x));
+BINARY(sPReLU,     PReLU,     Ops<ElementType>::prelu(x, y));
+BINARY(sPReLUBack, PReLUback, Ops<ElementType>::preluBack(x, y));
+
+
+}
+}
\ No newline at end of file
diff --git a/src/functional/predicates.h b/src/functional/predicates.h
index 4d92a0224..420a88a39 100644
--- a/src/functional/predicates.h
+++ b/src/functional/predicates.h
@@ -1,7 +1,5 @@
 #pragma once
 
-#include <cmath>
-
 #include "functional/defs.h"
 #include "functional/operands.h"
 
@@ -15,12 +13,12 @@ struct UnaryFunctor {
   template <class Arg>
   UnaryFunctor(Arg a) : x(a) {}
 
-  template <typename... Args>
-  __HDI__ float operator()(Args&&... args) {
-    return Function::apply(x(args...));
+  template <typename T, typename... Args>
+  HOST_DEVICE_INLINE T operator()(T arg, Args&&... args) {
+    return Function::apply(x(arg, args...));
   }
 
-  std::string to_string() { return Function::n() + "<" + x.to_string() + ">"; }
+  std::string to_string() const { return Function::n() + "<" + x.to_string() + ">"; }
 };
 
 template <class Function, class X, class Y>
@@ -31,109 +29,57 @@ struct BinaryFunctor {
   template <class Arg1, class Arg2>
   BinaryFunctor(Arg1 arg1, Arg2 arg2) : x(arg1), y(arg2) {}
 
-  template <typename... Args>
-  __HDI__ float operator()(Args&&... args) {
-    return Function::apply(x(args...), y(args...));
+  template <typename T, typename... Args>
+  HOST_DEVICE_INLINE T operator()(T arg, Args&&... args) {
+    return Function::apply(x(arg, args...), y(arg, args...));
   }
 
-  std::string to_string() {
+  std::string to_string() const {
     return Function::n() + "<" + x.to_string() + "," + y.to_string() + ">";
   }
 };
 
-#define UNARY(name, name2, func)                         \
-  namespace elem {                                       \
-  struct name {                                          \
-    __HDI__ static float apply(float x) { return func; } \
-    static std::string n() { return #name; }             \
-  };                                                     \
-  }                                                      \
-  template <class X>                                     \
-  using name = UnaryFunctor<elem::name, X>;              \
-  template <typename X>                                  \
-  static inline name<IsClass<X>> name2(X x) {            \
-    return name<X>(x);                                   \
-  }                                                      \
+#define UNARY(name, name2, func)                                      \
+  namespace elem {                                                    \
+  struct name {                                                       \
+    template <typename ElementType>                                   \
+    HOST_DEVICE_INLINE static ElementType apply(const ElementType& x) { return func; } \
+    static std::string n() { return #name; }                          \
+  };                                                                  \
+  }                                                                   \
+  template <class X>                                                  \
+  using name = UnaryFunctor<elem::name, X>;                           \
+  template <typename X>                                               \
+  static inline name<IsClass<X>> name2(X x) {                         \
+    return name<X>(x);                                                \
+  }                                                                   \
   static inline name<Capture> name2(Capture x) { return name<Capture>(x); }
 
 #define BINARY(name, name2, func)                                 \
   namespace elem {                                                \
   struct name {                                                   \
-    __HDI__ static float apply(float x, float y) { return func; } \
+    template <typename ElementType>                               \
+    HOST_DEVICE_INLINE static ElementType apply(const ElementType& x,        \
+                                     const ElementType& y)        \
+      { return func; }                                            \
     static std::string n() { return #name; }                      \
   };                                                              \
   }                                                               \
   template <class X, class Y>                                     \
   using name = BinaryFunctor<elem::name, X, Y>;                   \
   template <class X, class Y>                                     \
-  name<IsClass<X>, IsClass<Y>> name2(X x, Y y) {                  \
+  name<IsClass<X>, IsClass<Y>> name2(const X& x, const Y& y) {    \
     return name<X, Y>(x, y);                                      \
   }                                                               \
   template <class Y>                                              \
-  name<Capture, IsClass<Y>> name2(Capture x, Y y) {               \
+  name<Capture, IsClass<Y>> name2(const Capture& x, const Y& y) { \
     return name<Capture, Y>(x, y);                                \
   }                                                               \
   template <class X>                                              \
-  name<IsClass<X>, Capture> name2(X x, Capture y) {               \
+  name<IsClass<X>, Capture> name2(const X& x, const Capture& y) { \
     return name<X, Capture>(x, y);                                \
   }
 
-UNARY(Tanh, tanh, tanhf(x));
-UNARY(Sin, sin, sinf(x));
-UNARY(Cos, cos, cosf(x));
-UNARY(Tan, tan, tanf(x));
-UNARY(Log, log, logf(x));
-UNARY(Exp, exp, expf(x));
-UNARY(Abs, abs, fabs(x));
-UNARY(Sqrt, sqrt, sqrtf(x));
-UNARY(Neg, operator-, - x);
-UNARY(Sigmoid,
-      sigmoid,
-      x > 0 ? (1.f / (1.f + expf(-x))) : (expf(x) / (1.f + expf(x))));
-
-BINARY(Plus, operator+, x + y);
-BINARY(Minus, operator-, x - y);
-BINARY(Mult, operator*, x* y);
-BINARY(Div, operator/, x / y);
-
-BINARY(LogAddExp,
-       logaddexp,
-       (/*if*/ (x < y) ?  // Note: This may not be ideal for CUDA; cf. CNTK implementation
-          (y + log1pf(expf(x - y)))
-        /*else*/ :
-          (x + log1pf(expf(y - x)))));
-BINARY(Maximum, max, (x > y) ? x : y);  // Note: std::max not available on CUDA it seems
-BINARY(Minimum, min, (x < y) ? x : y);
-
-UNARY(Negate, operator!, !x);
-BINARY(Eq, operator==, x == y);
-BINARY(NEq, operator!=, x != y);
-BINARY(Gt, operator>, x> y);
-BINARY(Lt, operator<, x<y);
-BINARY(Geq, operator>=, x >= y);
-BINARY(Leq, operator<=, x <= y);
-BINARY(And, operator&&, x&& y);
-BINARY(Or, operator||, x || y);
-
-template <typename T>
-__HDI__ T sgn(T val) {
-  return T((0 < val) - (val < 0));
-}
-
-UNARY(Sgn, sgn, sgn(x));
-
-BINARY(Pow, pow, pow(x, y));
-
-BINARY(Clip, clip, fabs(x) >= y ? sgn(x) * y : x);
-
-// derivative of Clip, cut-off function
-BINARY(Bump, bump, fabs(x) >= y ? 0.f : 1.f);
-
-UNARY(sReLU, ReLU, x > 0.f ? x : 0.f);
-UNARY(sReLUBack, ReLUback, x > 0.f ? 1.f : 0.f);
-BINARY(sPReLU, PReLU, x > 0.f ? x : x * y);
-BINARY(sPReLUBack, PReLUback, x > 0.f ? 1.f : y);
-
 template <class Function, class X, class Y, class Z>
 struct TernaryFunctor {
   X x;
@@ -144,7 +90,7 @@ struct TernaryFunctor {
   TernaryFunctor(Arg1 arg1, Arg2 arg2, Arg3 arg3) : x(arg1), y(arg2), z(arg3) {}
 
   template <typename... Args>
-  __HDI__ float operator()(Args&&... args) {
+  HOST_DEVICE_INLINE float operator()(Args&&... args) {
     return Function::apply(x(args...), y(args...), z(args...));
   }
 };
@@ -152,7 +98,11 @@ struct TernaryFunctor {
 #define TERNARY(name, name2, func)                                         \
   namespace elem {                                                         \
   struct name {                                                            \
-    __HDI__ static float apply(float x, float y, float z) { return func; } \
+    template <typename ElementType>                                        \
+    HOST_DEVICE_INLINE static ElementType apply(ElementType x,             \
+                                     ElementType y,                        \
+                                     ElementType z)                        \
+    { return func; }                                                       \
   };                                                                       \
   }                                                                        \
   template <class X, class Y, class Z>                                     \
@@ -182,8 +132,6 @@ struct TernaryFunctor {
     return name<Capture, Capture, Z>(x, y, z);                             \
   }
 
-TERNARY(IfThenElse, if_then_else, x ? y : z);
-
 template <class X, class Y>
 struct Assign {
   X x;
@@ -192,9 +140,13 @@ struct Assign {
   template <class Arg1, class Arg2>
   Assign(Arg1 arg1, Arg2 arg2) : x(arg1), y(arg2) {}
 
-  template <typename... Args>
-  __HDI__ float operator()(Args&&... args) {
-    return x(args...) = y(args...);
+  template <typename T, typename... Args>
+  HOST_DEVICE_INLINE T operator()(T&& arg, Args&&... args) {
+    return x(arg, args...) = y(arg, args...);
+  }
+
+  std::string to_string() const {
+    return "Assign<" + x.to_string() + "," + y.to_string() + ">";
   }
 };
 
@@ -205,9 +157,9 @@ struct Assignee {
   Assignee() {}
   Assignee(Var<N> v) : var(v) {}
 
-  template <typename... Args>
-  __HDI__ float& operator()(Args&&... args) {
-    return var(args...);
+  template <typename T, typename... Args>
+  HOST_DEVICE_INLINE T& operator()(T&& arg, Args&&... args) {
+    return var(arg, args...);
   }
 
   template <class X>
@@ -239,7 +191,7 @@ struct Assignee {
     return *this = *this / x;
   }
 
-  std::string to_string() { return var.to_string(); }
+  std::string to_string() const { return var.to_string(); }
 };
 
 /******************************************************************************/
diff --git a/src/functional/shape.h b/src/functional/shape.h
index 144c1458f..d3ec80938 100644
--- a/src/functional/shape.h
+++ b/src/functional/shape.h
@@ -13,6 +13,39 @@ namespace functional {
 
 #define CONST_SHAPE_DIMS 4
 
+const int MAX_INT = std::numeric_limits<int>::max();
+
+struct Slice {
+  static const int END{MAX_INT}; // fix
+
+  int begin{0};
+  int end{END};
+  int stride{1};
+
+  Slice(int b, int e, int s = 1)
+  : begin(b), end(e), stride(s) {}
+
+  Slice()
+  : begin(0), end(END), stride(1) {}
+
+  Slice(int i)
+  : begin(i), end(i + 1), stride(1) {}
+
+  Slice(const std::initializer_list<int>& l) {
+    std::vector<int> v(l);
+    switch(v.size()) {
+      case 0: begin = 0;    end = END;      stride = 1;    break;
+      case 1: begin = v[0]; end = v[0] + 1; stride = 1;    break;
+      case 2: begin = v[0]; end = v[1];     stride = 1;    break;
+      case 3: begin = v[0]; end = v[1];     stride = v[2]; break;
+      default:
+        ABORT("Too many elements in slice: {}", v.size());
+    }
+  }
+};
+
+const Slice All;
+
 /**
  * @brief Represents the size of each dimension in a tensor.
  */
@@ -22,19 +55,42 @@ struct ConstantShape {
   Array<int, N> shape_;
   Array<int, N> stride_;
   Array<int, N> bstride_;
+
   size_t elements_{1};
+  size_t offset_{0};
 
-  __HD__ ConstantShape() {
+  // @TODO: review all these constructors
+  HOST_DEVICE ConstantShape() {
     shape_.fill(1);
     stride_.fill(1);
     bstride_.fill(0);
   }
 
-  __HD__ ConstantShape(const ConstantShape& shape)
+  HOST_DEVICE ConstantShape(const ConstantShape& shape)
       : shape_(shape.shape_),
         stride_(shape.stride_),
         bstride_(shape.bstride_),
-        elements_(shape.elements_) {}
+        elements_(shape.elements_),
+        offset_(shape.offset_) {}
+
+  template <size_t M>
+  HOST_DEVICE ConstantShape(const Array<int, M>& shape) {
+    ABORT_IF(M > N, "Recompile with CONST_SHAPE_DIMS >= {}", M);
+
+    std::copy(shape.begin(), shape.end(), shape_.begin() + N - M);
+    if(N - M)
+      std::fill_n(shape_.begin(), N - M, 1);
+
+    updateStrides();
+    updateElements();
+  }
+
+  HOST_DEVICE ConstantShape(const Array<int, N>& shape,
+                       const Array<int, N>& stride,
+                       size_t offset)
+  : shape_(shape), stride_(stride), offset_(offset) {
+    updateElements();
+  }
 
   ConstantShape(const marian::Shape& shape) {
     size_t filled = shape.size();
@@ -45,11 +101,13 @@ struct ConstantShape {
     std::copy(shape.begin(), shape.end(), shape_.begin() + N - filled);
     if(N - filled)
       std::fill_n(shape_.begin(), N - filled, 1);
+
     updateStrides();
     updateElements();
   }
 
-  __HDI__ void updateStrides() {
+  // @TODO: do we need bstrides at all?
+  HOST_DEVICE_INLINE void updateStrides() {
     stride_[N - 1] = 1;
     bstride_[N - 1] = shape_[N - 1] == 1 ? 0 : stride_[N - 1];
 
@@ -59,67 +117,177 @@ struct ConstantShape {
     }
   }
 
-  __HDI__ void updateElements() {
+  HOST_DEVICE_INLINE void updateElements() {
     elements_ = 1;
     for(int i = 0; i < N; ++i)
       elements_ *= shape_[i];
   }
 
-  __HDI__ void set(int i, int dim) {
+  HOST_DEVICE_INLINE void set(int i, int dim) {
     shape_[i] = dim;
     updateStrides();
     updateElements();
   }
 
-  __HDI__ int dim(int i) { return shape_[i]; }
+  HOST_DEVICE_INLINE const int& dim(int i) const { return shape_[i]; }
 
-  __HDI__ int dim(int i) const {
-    return const_cast<ConstantShape&>(*this).dim(i);
-  }
+  HOST_DEVICE_INLINE const int& back() const { return dim(N - 1); }
 
-  __HDI__ int back() const { return dim(N - 1); }
+  HOST_DEVICE_INLINE const int& operator[](int i) const { return dim(i); }
 
-  __HDI__ int operator[](int i) { return dim(i); }
+  HOST_DEVICE_INLINE const int& stride(int i) const { return stride_[i]; }
 
-  __HDI__ int operator[](int i) const { return dim(i); }
+  HOST_DEVICE_INLINE const int& bstride(int i) const { return bstride_[i]; }
 
-  __HDI__ int stride(int i) const { return stride_[i]; }
+  HOST_DEVICE_INLINE static constexpr size_t size() { return N; }
 
-  __HDI__ int bstride(int i) const { return bstride_[i]; }
+  HOST_DEVICE_INLINE int elements() const { return (int)elements_; }
 
-  __HDI__ static constexpr size_t size() { return N; }
+  // The following functions iterate over shape dimensions and use resursive
+  // templates. They unroll over a compile-time defined number of dimensions.
 
-  __HDI__ int elements() const { return (int)elements_; }
+  // Struct for recurrent template calls over shape dimensions,
+  // version for K > 0
+  template <const int K, const int D> struct I {
+    HOST_DEVICE_INLINE static int index(const Array<int, D>& dims,
+                             const Array<int, D>& stride) {
+      return dims[K] * stride[K] + I<K-1, D>::index(dims, stride);
+    }
 
-  __HDI__ int index(const Array<int, N>& d) const {
-    int i = 0;
-    for(int j = 0; j < N; ++j)
-      i += d[j] * stride_[j];
-    return i;
+    HOST_DEVICE_INLINE static int index(int si,
+                             const Array<int, D>& shape,
+                             const Array<int, D>& stride) {
+      return (si % shape[K]) * stride[K] + I<K-1, D>::index(si / shape[K], shape, stride);
+    }
+
+    HOST_DEVICE_INLINE static void dims(int si,
+                             Array<int, D>& dims,
+                             const Array<int, D>& shape) {
+      dims[K] = si % shape[K];
+      I<K-1, D>::dims(si / shape[K], dims, shape);
+    }
+
+  };
+
+  // Struct for recurrent template calls over shape dimensions,
+  // specialization for K == 0
+  template <const int D> struct I<0, D> {
+    HOST_DEVICE_INLINE static int index(const Array<int, D>& dims,
+                             const Array<int, D>& stride) {
+      return dims[0] * stride[0];
+    }
+
+    HOST_DEVICE_INLINE static int index(int si,
+                             const Array<int, D>& shape,
+                             const Array<int, D>& stride) {
+      return (si % shape[0]) * stride[0];
+    }
+
+   HOST_DEVICE_INLINE static void dims(int si,
+                            Array<int, D>& dims,
+                            const Array<int, D>& shape) {
+      dims[0] = si % shape[0];
+    }
+  };
+
+  HOST_DEVICE_INLINE int index(const Array<int, N>& dims) const {
+    return offset_ + I<N-1, N>::index(dims, stride_);
   }
 
-  __HDI__ int bindex(const Array<int, N>& d) const {
-    int i = 0;
-    for(int j = 0; j < N; ++j)
-      i += d[j] * bstride_[j];
-    return i;
+  HOST_DEVICE_INLINE int index(int si) const {
+    return offset_ + I<N-1, N>::index(si, shape_, stride_);
   }
 
-  __HDI__ void dims(int i, Array<int, N>& d) const {
+  HOST_DEVICE_INLINE void dims(int si, Array<int, N>& dims) const {
+    I<N-1, N>::dims(si, dims, shape_);
+  }
+
+  HOST_DEVICE_INLINE int bindex(const Array<int, N>& dims) const {
+    int i = 0;
+    // ?? : return offset_ + I<N-1, N>::index(d, bstride_);
     for(int j = 0; j < N; ++j)
-      d[j] = (i / stride_[j]) % shape_[j];
+      i += dims[j] * bstride_[j];
+    return i;
   }
 
-  __HDI__ bool operator==(const ConstantShape& other) const {
+  // @TODO: should this check all the members?
+  HOST_DEVICE_INLINE bool operator==(const ConstantShape& other) const {
     for(int i = 0; i < N; ++i)
       if(shape_[i] != other[i])
         return false;
     return true;
   }
 
-  __HDI__ bool operator!=(const ConstantShape& other) const {
+  HOST_DEVICE_INLINE bool operator!=(const ConstantShape& other) const {
     return !(*this == other);
   }
+
+  std::string toString() const {
+    std::stringstream strm;
+    // @TODO: add more information
+    strm << "shape=" << (*this)[0];
+    for(int i = 1; i < size(); ++i)
+      strm << "x" << (*this)[i];
+    strm << " size=" << elements();
+    return strm.str();
+  }
+
+  // Performs numpy-like slicing on a given shape object. The number
+  // of slices corresponds to the number of dimensions.
+  HOST_DEVICE_INLINE ConstantShape<N> slice(const Array<Slice, N>& slices) {
+    // @TODO: add various checks
+    Array<int, N> offsets;
+    Array<int, N> shape;
+    Array<int, N> stride;
+    for(int i = 0; i < N; ++i) {
+      int beg = slices[i].begin;
+      // restrict maximum value to actual shape size if larger than shape size
+      int end = slices[i].end < shape_[i] ? slices[i].end : shape_[i];
+      int str = slices[i].stride;
+
+      // collect starting points for all coordinates
+      offsets[i] = beg;
+
+      // when calculating the new shape, take into account stride
+      // TODO: std::ceil does not work on the GPU
+      shape[i]   = std::ceil((end - beg) / (float) str);
+
+      // new stride is just old stride multiplied by slice stride
+      stride[i]  = str * stride_[i];
+    }
+
+    // map offset coordinates into single offset index
+    int offset = index(offsets);
+
+    return ConstantShape<N>(shape, stride, offset);
+  }
+
+// non-continguous slices cannot be reshaped! need to be copied
+//   template <const int D>
+//   HOST_DEVICE_INLINE ConstantShape<D> reshape(const ConstantShape<D>& other) const {
+//     // @TODO: add various checks
+// #ifndef __CUDA__ARCH__
+//     ABORT_IF(elements() != other.elements(),
+//              "Reshaping operation requires matching number of elements");
+// #endif
+
+//     Array<int, D> stride;
+//     for(int i = 0; i < D; ++i) {
+//       stride[i] = /*other.stride_[i] **/ stride_[i];
+//     }
+
+//     stride[D - 1] = stride_[N - 1];
+//     for(int i = 2; i < D + 1; ++i) {
+//       stride[D - i] = stride[D - i + 1] * stride_[N - i + 1] * shape_[D - i + 1];
+//     }
+
+//     return ConstantShape<D>(other.shape_, stride, offset_);
+//   }
+
+  friend std::ostream& operator<<(std::ostream& strm, const ConstantShape<N>& shape) {
+    strm << shape.toString();
+    return strm;
+  }
 };
 
 typedef ConstantShape<CONST_SHAPE_DIMS> Shape;
diff --git a/src/functional/tensor.h b/src/functional/tensor.h
index 45e2056c4..601550178 100644
--- a/src/functional/tensor.h
+++ b/src/functional/tensor.h
@@ -7,36 +7,205 @@
 namespace marian {
 namespace functional {
 
+// general case, do nothing. Usually the number of elements in a tensor is correctly mirrored in the
+// shape object. Only special multi-element types like float32x4 (4 floats), float32x8 (8 floats)
+// and half2 (2 half) require special handling. Similar for multi-element integer types to be added
+// later.
 template <typename T>
-struct Tensor {
+inline marian::Shape adapt(const marian::Shape& shape) {
+  return shape;
+}
+
+// modify last shape dimension to automatically map to a larger stride. We are moving now by 4 floats
+// at once and need to stop earlier. This is a shallow typecast to bascially an array of 4 floats.
+
+#ifndef __CUDA_ARCH__
+
+template <>
+inline marian::Shape adapt<float32x4>(const marian::Shape& shape) {
+  ABORT_IF(shape[-1] % 4 != 0,
+           "Last dim ({}) is not a multiple of 4 while converting to Tensor<float32x4>",
+           shape[-1]);
+
+  marian::Shape x4Shape = shape;
+  x4Shape.set(-1, shape[-1] / 4);
+  return x4Shape;
+}
+
+template <>
+inline marian::Shape adapt<float32x8>(const marian::Shape& shape) {
+  ABORT_IF(shape[-1] % 8 != 0,
+           "Last dim ({}) is not a multiple of 8 while converting to Tensor<float32x8>",
+           shape[-1]);
+
+  marian::Shape x8Shape = shape;
+  x8Shape.set(-1, shape[-1] / 8);
+  return x8Shape;
+}
+
+#endif
+
+template <typename T, const int D>
+struct View {
   T* data_;
-  functional::Shape shape_;
+  ConstantShape<D> shape_;
 
-  __HD__ Tensor() {}
+  HOST_DEVICE View() {}
 
-  __HD__ Tensor(T* ptr, const functional::Shape& shape)
+  HOST_DEVICE View(T* ptr, const ConstantShape<D>& shape)
       : data_(ptr), shape_(shape) {}
 
-  __H__ Tensor(marian::Tensor t) : data_(t->data()), shape_(t->shape()) {}
+  HOST View(marian::Tensor t) : data_(t->data<T>()), shape_(adapt<T>(t->shape())) {}
 
-  __HDI__ float& operator[](size_t i) { return data_[i]; }
-  __HDI__ const float& operator[](size_t i) const { return data_[i]; }
+  HOST_DEVICE_INLINE T& operator[](size_t i) {
+     return data_[shape_.index(i)];
+  }
 
-  __HDI__ float& operator[](
-      const functional::Array<int, functional::Shape::size()>& indices) {
-    return data_[shape_.index(indices)];
+  HOST_DEVICE_INLINE const T& operator[](size_t i) const {
+     return data_[shape_.index(i)];
   }
 
-  __HDI__ const float& operator[](
-      const functional::Array<int, functional::Shape::size()>& indices) const {
+  HOST_DEVICE_INLINE T& operator[](const Array<int, D>& indices) {
     return data_[shape_.index(indices)];
   }
 
-  __HDI__ T* data() { return data_; }
-  __HDI__ const T* data() const { return data_; }
+  HOST_DEVICE_INLINE const T& operator[](const Array<int, D>& indices) const {
+     return data_[shape_.index(indices)];
+  }
+
+  HOST_DEVICE_INLINE T* data() { return data_; }
+  HOST_DEVICE_INLINE const T* data() const { return data_; }
+
+  HOST_DEVICE_INLINE ConstantShape<D>& shape() { return shape_; }
+  HOST_DEVICE_INLINE const ConstantShape<D>& shape() const { return shape_; }
+
+  HOST_DEVICE_INLINE size_t size() const { return shape_.elements(); }
+
+  // @TODO: This is code duplication from marian::Tensor
+  std::string debug(int precision = 8, int dispCols = 5) {
+    std::stringstream strm;
+    assert(shape_.size());
+
+    strm << shape_;
+    strm << " type=" << request<T>();
+    strm << " ptr=" << (size_t)data_;
+    strm << std::endl;
+
+    size_t totSize = shape_.elements();
+    std::vector<T> values(totSize);
+    for(int i = 0; i < size(); ++i)
+      values[i] = operator[](i);
+
+    int colWidth  = precision + 4;
+    strm << std::fixed << std::setprecision(precision) << std::setfill(' ');
+
+    for(int i = 0; i < values.size(); ++i) {
+      Array<int, D> dims;
+      shape().dims(i, dims);
+
+      bool disp = true;
+      for(int j = 0; j < dims.size(); ++j)
+        disp = disp && (dims[j] < dispCols || dims[j] >= shape()[j] - dispCols);
 
-  __HDI__ Shape& shape() { return shape_; }
-  __HDI__ const Shape& shape() const { return shape_; }
+      if(disp) {
+        if(dims.back() == 0) {
+          bool par = true;
+          std::vector<std::string> p;
+          for(int j = (int)dims.size() - 1; j >= 0; --j) {
+            if(dims[j] != 0)
+              par = false;
+
+            p.push_back(par ? "[" : " ");
+          }
+          for(auto it = p.rbegin(); it != p.rend(); ++it)
+            strm << *it;
+          strm << " ";
+        }
+
+        strm << std::setw(colWidth);
+        strm << values[i];
+        strm << " ";
+
+        if(dims.back() + 1 == shape().back()) {
+          for(int j = (int)dims.size() - 1; j >= 0; --j) {
+            if(dims[j] + 1 != shape()[j])
+              break;
+            strm << "]";
+          }
+          strm << std::endl;
+        }
+
+        bool prev = true;
+        for(int j = (int)dims.size() - 1; j >= 0; --j) {
+          if(j < (int)dims.size() - 1)
+            prev = prev && dims[j + 1] + 1 == shape()[j + 1];
+          if(prev && dims[j] + 1 == dispCols && shape()[j] > 2 * dispCols) {
+            if(j < (int)dims.size() - 1)
+              for(int k = 0; k <= j; ++k)
+                strm << " ";
+            strm << "... ";
+            if(j < (int)dims.size() - 1)
+              strm << std::endl;
+            break;
+          }
+        }
+      }
+    }
+    strm << std::endl;
+    return strm.str();
+  }
 };
+
+template <typename T, const int D>
+HOST_DEVICE_INLINE View<T, D> slice(View<T, D> view, const Array<Slice, D>& slices) {
+  const auto& slicedShape = view.shape().slice(slices);
+  return View<T, D>(view.data(), slicedShape);
+}
+
+// template <typename T, const int D, class ...Slices>
+// View<T, D> slice(View<T, D> view,
+//                  const Slices&... slices) {
+//   return slice(view, {slices...});
+// }
+
+template <typename T>
+HOST_DEVICE_INLINE View<T, 1> slice(View<T, 1>& view,
+                         const Slice& slice0) {
+  return slice(view, {slice0});
+}
+
+template <typename T>
+HOST_DEVICE_INLINE View<T, 2> slice(View<T, 2>& view,
+                         const Slice& slice0,
+                         const Slice& slice1) {
+  return slice(view, {slice0, slice1});
+}
+
+template <typename T>
+HOST_DEVICE_INLINE View<T, 3> slice(View<T, 3>& view,
+                        const Slice& slice0,
+                        const Slice& slice1,
+                        const Slice& slice2) {
+  return slice(view, {slice0, slice1, slice2});
+}
+
+template <typename T>
+HOST_DEVICE_INLINE View<T, 4> slice(View<T, 4>& view,
+                         const Slice& slice0,
+                         const Slice& slice1,
+                         const Slice& slice2,
+                         const Slice& slice3) {
+  return slice(view, {slice0, slice1, slice2, slice3});
+}
+
+// template <typename T, const int D1, const int D2>
+// View<T, D2> reshape(View<T, D1>& view, const ConstantShape<D2>& shape) {
+//   auto reshaped = view.shape().reshape(shape);
+//   return View<T, D2>(view.data(), reshaped);
+// }
+
+template <typename T>
+using Tensor = View<T, CONST_SHAPE_DIMS>;
+
 }  // namespace functional
 }  // namespace marian
\ No newline at end of file
diff --git a/src/functional/tmp.h b/src/functional/tmp.h
old mode 100644
new mode 100755
index 7c8f6fa11..69be3391a
--- a/src/functional/tmp.h
+++ b/src/functional/tmp.h
@@ -12,106 +12,129 @@ struct FApply {};
 
 template <class Functor>
 struct FApply<1, Functor> {
-  __HDI__ static float apply(
+  template <typename ElementType>
+  HOST_DEVICE_INLINE static ElementType apply(
       Functor functor,
-      functional::Array<functional::Tensor<float>, 1>& in,
+      functional::Array<functional::Tensor<ElementType>, 1>& in,
       const functional::Array<int, 1>& indices) {
-    return functor(in[0][indices[0]]);
+    return functor(in[0].data()[indices[0]]);
   }
 
-  __HDI__ static float apply(
+  template <typename ElementType>
+  HOST_DEVICE_INLINE static ElementType apply(
       Functor functor,
-      functional::Array<functional::Tensor<float>, 1>& in,
+      functional::Array<functional::Tensor<ElementType>, 1>& in,
       int index) {
-    return functor(in[0][index]);
+    return functor(in[0].data()[index]);
   }
 };
 
 template <class Functor>
 struct FApply<2, Functor> {
-  __HDI__ static float apply(
+  template <typename ElementType>
+  HOST_DEVICE_INLINE static ElementType apply(
       Functor functor,
-      functional::Array<functional::Tensor<float>, 2>& in,
+      functional::Array<functional::Tensor<ElementType>, 2>& in,
       const functional::Array<int, 2>& indices) {
-    return functor(in[0][indices[0]], in[1][indices[1]]);
+    return functor(in[0].data()[indices[0]],
+                   in[1].data()[indices[1]]);
   }
 
-  __HDI__ static float apply(
+  template <typename ElementType>
+  HOST_DEVICE_INLINE static ElementType apply(
       Functor functor,
-      functional::Array<functional::Tensor<float>, 2>& in,
+      functional::Array<functional::Tensor<ElementType>, 2>& in,
       int index) {
-    return functor(in[0][index], in[1][index]);
+    return functor(in[0].data()[index],
+                   in[1].data()[index]);
   }
 };
 
 template <class Functor>
 struct FApply<3, Functor> {
-  __HDI__ static float apply(
+  template <typename ElementType>
+  HOST_DEVICE_INLINE static ElementType apply(
       Functor functor,
-      functional::Array<functional::Tensor<float>, 3>& in,
+      functional::Array<functional::Tensor<ElementType>, 3>& in,
       const functional::Array<int, 3>& indices) {
-    return functor(in[0][indices[0]], in[1][indices[1]], in[2][indices[2]]);
+    return functor(in[0].data()[indices[0]],
+                   in[1].data()[indices[1]],
+                   in[2].data()[indices[2]]);
   }
 
-  __HDI__ static float apply(
+  template <typename ElementType>
+  HOST_DEVICE_INLINE static ElementType apply(
       Functor functor,
-      functional::Array<functional::Tensor<float>, 3>& in,
+      functional::Array<functional::Tensor<ElementType>, 3>& in,
       int index) {
-    return functor(in[0][index], in[1][index], in[2][index]);
+    return functor(in[0].data()[index],
+                   in[1].data()[index],
+                   in[2].data()[index]);
   }
 };
 
 template <class Functor>
 struct FApply<4, Functor> {
-  __HDI__ static float apply(
+  template <typename ElementType>
+  HOST_DEVICE_INLINE static ElementType apply(
       Functor functor,
-      functional::Array<functional::Tensor<float>, 4>& in,
+      functional::Array<functional::Tensor<ElementType>, 4>& in,
       const functional::Array<int, 4>& indices) {
-    return functor(in[0][indices[0]],
-                   in[1][indices[1]],
-                   in[2][indices[2]],
-                   in[3][indices[3]]);
+    return functor(in[0].data()[indices[0]],
+                   in[1].data()[indices[1]],
+                   in[2].data()[indices[2]],
+                   in[3].data()[indices[3]]);
   }
 
-  __HDI__ static float apply(
+  template <typename ElementType>
+  HOST_DEVICE_INLINE static ElementType apply(
       Functor functor,
-      functional::Array<functional::Tensor<float>, 4>& in,
+      functional::Array<functional::Tensor<ElementType>, 4>& in,
       int index) {
-    return functor(in[0][index], in[1][index], in[2][index], in[3][index]);
+    return functor(in[0].data()[index],
+                   in[1].data()[index],
+                   in[2].data()[index],
+                   in[3].data()[index]);
   }
 };
 
 template <class Functor>
 struct FApply<5, Functor> {
-  __HDI__ static float apply(
+  template <typename ElementType>
+  HOST_DEVICE_INLINE static ElementType apply(
       Functor functor,
-      functional::Array<functional::Tensor<float>, 5>& in,
+      functional::Array<functional::Tensor<ElementType>, 5>& in,
       const functional::Array<int, 5>& indices) {
-    return functor(in[0][indices[0]],
-                   in[1][indices[1]],
-                   in[2][indices[2]],
-                   in[3][indices[3]],
-                   in[4][indices[4]]);
+    return functor(in[0].data()[indices[0]],
+                   in[1].data()[indices[1]],
+                   in[2].data()[indices[2]],
+                   in[3].data()[indices[3]],
+                   in[4].data()[indices[4]]);
   }
 
-  __HDI__ static float apply(
+  template <typename ElementType>
+  HOST_DEVICE_INLINE static ElementType apply(
       Functor functor,
-      functional::Array<functional::Tensor<float>, 5>& in,
+      functional::Array<functional::Tensor<ElementType>, 5>& in,
       int index) {
-    return functor(in[0][index], in[1][index], in[2][index], in[3][index], in[4][index]);
+    return functor(in[0].data()[index], 
+                   in[1].data()[index], 
+                   in[2].data()[index], 
+                   in[3].data()[index], 
+                   in[4].data()[index]);
   }
 };
 
-template <size_t K, class Functor>
-__HDI__ float apply(Functor functor,
-                    functional::Array<functional::Tensor<float>, K>& in,
+template <size_t K, class Functor, typename ElementType>
+HOST_DEVICE_INLINE ElementType apply(Functor functor,
+                    functional::Array<functional::Tensor<ElementType>, K>& in,
                     const functional::Array<int, K>& indices) {
   return FApply<K, Functor>::apply(functor, in, indices);
 }
 
-template <size_t K, class Functor>
-__HDI__ float apply(Functor functor,
-                    functional::Array<functional::Tensor<float>, K>& in,
+template <size_t K, class Functor, typename ElementType>
+HOST_DEVICE_INLINE ElementType apply(Functor functor,
+                    functional::Array<functional::Tensor<ElementType>, K>& in,
                     int index) {
   return FApply<K, Functor>::apply(functor, in, index);
 }
@@ -121,14 +144,14 @@ __HDI__ float apply(Functor functor,
 // @TODO: Rename this. It is a reduction loop.
 template <size_t n, size_t N, size_t K>
 struct Loop {
-  template <class Functor, class AggFunctor>
-  __HDI__ static float result(
-      Functor functor, float aggInit, AggFunctor aggFunctor,
-      functional::Array<functional::Tensor<float>, K>& in,
+  template <class Functor, class AggFunctor, typename ElementType, typename AccType>
+  HOST_DEVICE_INLINE static AccType result(
+      Functor functor, AccType aggInit, AggFunctor aggFunctor,
+      functional::Array<functional::Tensor<ElementType>, K>& in,
       const functional::Array<int, K>& pAcc,
       const functional::Array<int, N>& length,
       const functional::Array<int, N>& dim) {
-    float agg = aggInit;
+    AccType agg = aggInit;
     functional::Array<int, K> acc;
     for(int i = 0; i < length[N - n]; ++i) {
       for(size_t j = 0; j < K; ++j) {
@@ -142,28 +165,29 @@ struct Loop {
 
 template <size_t N, size_t K>
 struct Loop<1, N, K> {
-  template <class Functor, class AggFunctor>
-  __HDI__ static float result(
-      Functor functor, float aggInit, AggFunctor aggFunctor,
-      functional::Array<functional::Tensor<float>, K>& in,
+  template <class Functor, class AggFunctor, typename ElementType, typename AccType>
+  HOST_DEVICE_INLINE static AccType result(
+      Functor functor, AccType aggInit, AggFunctor aggFunctor,
+      functional::Array<functional::Tensor<ElementType>, K>& in,
       const functional::Array<int, K>& pAcc,
       const functional::Array<int, N>& length,
       const functional::Array<int, N>& dim) {
-    float agg = aggInit;
+    AccType agg = aggInit;
     functional::Array<int, K> acc;
     for(int i = 0; i < length[N - 1]; ++i) {
       for(size_t j = 0; j < K; ++j) {
         acc[j] = pAcc[j] + (dim[N - 1] + i) * in[j].shape().bstride(N - 1);
       }
-      agg = aggFunctor(agg, apply<K>(functor, in, acc));
+      agg = aggFunctor(agg, (AccType)apply<K>(functor, in, acc));
     }
     return agg;
   }
 };
 
-template <size_t N, size_t K, class Functor, class AggFunctor>
-__HDI__ float loops(Functor functor, float aggInit, AggFunctor aggFunctor,
-                    functional::Array<functional::Tensor<float>, K>& in,
+
+template <size_t N, size_t K, class Functor, class AggFunctor, typename ElementType, typename AccType>
+HOST_DEVICE_INLINE AccType loops(Functor functor, AccType aggInit, AggFunctor aggFunctor,
+                                     functional::Array<functional::Tensor<ElementType>, K>& in,
                     const functional::Array<int, N>& length,
                     const functional::Array<int, N>& dim) {
   functional::Array<int, K> acc = {0};
diff --git a/src/graph/chainable.h b/src/graph/chainable.h
index 2679843ee..f785c57c6 100644
--- a/src/graph/chainable.h
+++ b/src/graph/chainable.h
@@ -18,8 +18,8 @@ class Chainable;
  * A convenience type to represent a shared pointer to a Chainable<Tensor>
  * object.
  */
-typedef Ptr<Chainable<Tensor>> Expr;
-typedef Weak<Chainable<Tensor>> WExpr;
+typedef IPtr<Chainable<Tensor>> Expr;
+typedef IWeak<Chainable<Tensor>> WExpr;
 
 class ExpressionGraph;
 
@@ -50,6 +50,9 @@ class ExpressionGraph;
  */
 template <class DataType>
 class Chainable {
+private:
+ ENABLE_INTRUSIVE_PTR(Chainable<DataType>)
+
 public:
   Chainable() {}
   virtual ~Chainable(){};
diff --git a/src/graph/expression_graph.h b/src/graph/expression_graph.h
index 563dc4a11..28140a4e8 100755
--- a/src/graph/expression_graph.h
+++ b/src/graph/expression_graph.h
@@ -90,13 +90,13 @@ class Tensors {
     auto it = shortterm_->find(hash);
     if(it != shortterm_->end()) {
       for(auto foundWeak : it->second) {
-        auto found = foundWeak.lock();
+        auto found = foundWeak;
         if(node->equal(found)) {
           return found;
         }
       }
     }
-    (*shortterm_)[hash].push_back(node);
+    (*shortterm_)[hash].push_back(node.get());
     return nullptr;
   }
 
diff --git a/src/graph/node.cpp b/src/graph/node.cpp
index c11531da7..abbdacfd4 100644
--- a/src/graph/node.cpp
+++ b/src/graph/node.cpp
@@ -8,7 +8,7 @@ namespace marian {
 size_t Node::allocate() {
   size_t elements = 0;
   if(!val_) {
-    graph()->allocateForward(shared_from_this());
+    graph()->allocateForward(this);
     elements = val_->shape().elements();
   }
   return elements;
@@ -16,10 +16,14 @@ size_t Node::allocate() {
 
 void Node::free() {
   if(graph()) {
-    if(val_)
+    if(val_) {
       graph()->free(val_);
-    if(adj_)
+      val_ = nullptr;
+    }
+    if(adj_) {
       graph()->free(adj_);
+      adj_ = nullptr;
+    }
   }
 }
 
@@ -30,7 +34,7 @@ void Node::free() {
  */
 void Node::init_dependent() {
   if(!adj_) {
-    graph()->allocateBackward(shared_from_this());
+    graph()->allocateBackward(this);
     adj_->set(1.f);
   }
 }
@@ -43,7 +47,7 @@ void Node::init_dependent() {
  */
 void Node::set_zero_adjoint() {
   if(!adj_) {
-    graph()->allocateBackward(shared_from_this());
+    graph()->allocateBackward(this);
     adj_->set(0.f);
   }
 }
diff --git a/src/graph/node_initializers.cpp b/src/graph/node_initializers.cpp
index 233595da2..4b52ca0be 100755
--- a/src/graph/node_initializers.cpp
+++ b/src/graph/node_initializers.cpp
@@ -177,7 +177,7 @@ NodeInitializer from_item(const io::Item& item) {
                "Memory mapping only works for CPU tensors");
       ABORT_IF(!matchType<float>(t->type()),
                "Tensor type and type for mapping do not match");
-      auto mp = New<MemoryPiece>((uint8_t*)item.ptr, t->size() * sizeof(float));
+      auto mp = MemoryPiece::New((uint8_t*)item.ptr, t->size() * sizeof(float));
       t->reset(mp);
     };
   } else {
diff --git a/src/graph/node_operators.cpp b/src/graph/node_operators.cpp
index 00e0a3190..b5a37b9ba 100644
--- a/src/graph/node_operators.cpp
+++ b/src/graph/node_operators.cpp
@@ -8,7 +8,7 @@ namespace marian {
 size_t ConstantNode::allocate() {
   size_t elements = 0;
   if(!val_) {
-    graph()->allocateForward(shared_from_this());
+    graph()->allocateForward(this);
     elements = val_->shape().elements();
   }
   return elements;
diff --git a/src/graph/node_operators_binary.h b/src/graph/node_operators_binary.h
index 2cf49e36a..5dbf64c82 100644
--- a/src/graph/node_operators_binary.h
+++ b/src/graph/node_operators_binary.h
@@ -628,7 +628,7 @@ struct GatherNodeOp : public NaryNodeOp {
   virtual bool equal(Expr node) override {
     if(!NaryNodeOp::equal(node))
       return false;
-    Ptr<GatherNodeOp> cnode = std::dynamic_pointer_cast<GatherNodeOp>(node);
+    auto cnode = std::dynamic_pointer_cast<GatherNodeOp>(node);
     if(!cnode)
       return false;
     if(axis_ != cnode->axis_)
diff --git a/src/graph/node_operators_unary.h b/src/graph/node_operators_unary.h
index 94200435d..05a09df68 100755
--- a/src/graph/node_operators_unary.h
+++ b/src/graph/node_operators_unary.h
@@ -380,7 +380,7 @@ struct SwishNodeOp : public UnaryNodeOp {
   virtual bool equal(Expr node) override {
     if(!NaryNodeOp::equal(node))
       return false;
-    Ptr<SwishNodeOp> cnode = std::dynamic_pointer_cast<SwishNodeOp>(node);
+    auto cnode = std::dynamic_pointer_cast<SwishNodeOp>(node);
     if(!cnode)
       return false;
     if(b_ != cnode->b_)
@@ -544,7 +544,7 @@ struct ReduceNodeOp : public UnaryNodeOp {
   virtual bool equal(Expr node) override {
     if(!NaryNodeOp::equal(node))
       return false;
-    Ptr<ReduceNodeOp> cnode = std::dynamic_pointer_cast<ReduceNodeOp>(node);
+    auto cnode = std::dynamic_pointer_cast<ReduceNodeOp>(node);
     if(!cnode)
       return false;
     if(axis_ != cnode->axis_ || opCode_ != cnode->opCode_)
@@ -616,7 +616,7 @@ struct SqrtNodeOp : public UnaryNodeOp {
   virtual bool equal(Expr node) override {
     if(!NaryNodeOp::equal(node))
       return false;
-    Ptr<SqrtNodeOp> cnode = std::dynamic_pointer_cast<SqrtNodeOp>(node);
+    auto cnode = std::dynamic_pointer_cast<SqrtNodeOp>(node);
     if(!cnode)
       return false;
     if(epsilon_ != cnode->epsilon_)
@@ -701,8 +701,7 @@ struct TransposeNodeOp : public UnaryNodeOp {
   virtual bool equal(Expr node) override {
     if(!NaryNodeOp::equal(node))
       return false;
-    Ptr<TransposeNodeOp> cnode
-        = std::dynamic_pointer_cast<TransposeNodeOp>(node);
+    auto cnode = std::dynamic_pointer_cast<TransposeNodeOp>(node);
     if(!cnode)
       return false;
     if(axes_ != cnode->axes_)
@@ -740,15 +739,15 @@ class ReshapeNodeOp : public UnaryNodeOp {
 
   Tensor& val() override {
     auto childVal = reshapee_->val();
-    val_.reset(
-        new TensorBase(childVal->memory(), shape(), childVal->type(), childVal->getBackend()));
+    auto temp = TensorBase::New(childVal->memory(), shape(), childVal->type(), childVal->getBackend());
+    val_.swap(temp);
     return val_;
   };
 
   Tensor& grad() override {
     auto childGrad = reshapee_->grad();
-    adj_.reset(
-        new TensorBase(childGrad->memory(), shape(), childGrad->type(), childGrad->getBackend()));
+    auto temp = TensorBase::New(childGrad->memory(), shape(), childGrad->type(), childGrad->getBackend());
+    adj_.swap(temp);
     return adj_;
   };
 
@@ -769,7 +768,7 @@ class ReshapeNodeOp : public UnaryNodeOp {
   virtual bool equal(Expr node) override {
     if(!NaryNodeOp::equal(node))
       return false;
-    Ptr<ReshapeNodeOp> cnode = std::dynamic_pointer_cast<ReshapeNodeOp>(node);
+    auto cnode = std::dynamic_pointer_cast<ReshapeNodeOp>(node);
     if(!cnode)
       return false;
     if(shape() != cnode->shape())
@@ -823,15 +822,17 @@ class SliceViewNodeOp : public UnaryNodeOp {
 
   Tensor& val() override {
     auto childVal = viewedNode_->val();
-    auto mem = New<MemoryPiece>(childVal->memory()->data() + byteOffset_, byteSize_);
-    val_.reset(new TensorBase(mem, shape(), childVal->type(), childVal->getBackend()));
+    auto mem = MemoryPiece::New(childVal->memory()->data() + byteOffset_, byteSize_);
+    auto temp = TensorBase::New(mem, shape(), childVal->type(), childVal->getBackend());
+    val_.swap(temp);
     return val_;
   };
 
   Tensor& grad() override {
     auto childGrad = viewedNode_->grad();
-    auto mem = New<MemoryPiece>(childGrad->memory()->data() + byteOffset_, byteSize_);
-    adj_.reset(new TensorBase(mem, shape(), childGrad->type(), childGrad->getBackend()));
+    auto mem = MemoryPiece::New(childGrad->memory()->data() + byteOffset_, byteSize_);
+    auto temp = TensorBase::New(mem, shape(), childGrad->type(), childGrad->getBackend());
+    adj_.swap(temp);
     return adj_;
   };
 
@@ -853,7 +854,7 @@ class SliceViewNodeOp : public UnaryNodeOp {
   virtual bool equal(Expr node) override {
     if(!NaryNodeOp::equal(node))
       return false;
-    Ptr<SliceViewNodeOp> cnode = std::dynamic_pointer_cast<SliceViewNodeOp>(node);
+    auto cnode = std::dynamic_pointer_cast<SliceViewNodeOp>(node);
     if(!cnode)
       return false;
     if(slice_ != cnode->slice_)
@@ -895,7 +896,7 @@ struct ShiftNodeOp : public UnaryNodeOp {
   virtual bool equal(Expr node) override {
     if(!NaryNodeOp::equal(node))
       return false;
-    Ptr<ShiftNodeOp> cnode = std::dynamic_pointer_cast<ShiftNodeOp>(node);
+    auto cnode = std::dynamic_pointer_cast<ShiftNodeOp>(node);
     if(!cnode)
       return false;
     if(shift_ != cnode->shift_)
diff --git a/src/graph/parameters.h b/src/graph/parameters.h
index 32f88a1e4..b140a8144 100644
--- a/src/graph/parameters.h
+++ b/src/graph/parameters.h
@@ -19,10 +19,14 @@ class Parameters {
   Ptr<TensorAllocator> vals_;
   Ptr<TensorAllocator> grads_;
 
+  // A set of parameters should be of the same type
+  Type parameterType_{Type::float32};
+  bool parameterTypeSet_{false};
+
   size_t totalCapacity(Ptr<TensorAllocator> alloc) {
     size_t sum = 0;
     for(auto p : params_) {
-      sum += alloc->capacity(p->shape(), Type::float32);
+      sum += alloc->capacity(p->shape(), p->value_type());
     }
     return sum;
   }
@@ -46,9 +50,14 @@ class Parameters {
   size_t size() { return params_.size(); }
 
   void add(Expr p, const std::string& name) {
-    params_.push_back(p);
     ABORT_IF(named_.count(name), "Parameter '{}' already exists", name);
+    ABORT_IF(parameterTypeSet_ && p->value_type() != parameterType_,
+             "Requested parameter type ({}) is different from chosen parameter type ({})",
+             p->value_type(), parameterType_);
+    params_.push_back(p);
     named_[name] = p;
+    parameterType_ = p->value_type();
+    parameterTypeSet_ = true;
   }
 
   virtual void init(Ptr<Backend> backend) {
@@ -56,12 +65,21 @@ class Parameters {
     grads_ = New<TensorAllocator>(backend);
   }
 
+  virtual void init(Ptr<Backend> backend, Ptr<Device> device) {
+    vals_ = New<TensorAllocator>(backend, device);
+    grads_ = New<TensorAllocator>(backend, device);
+  }
+
   virtual void allocateForward() {
     if(!params_.empty() && vals_->size() == 0) {
       vals_->reserveExact(totalCapacity(vals_));
+
+      // sort parameters by name before allocation to make sure the memory layout after allocation is always the same
+      std::sort(params_.begin(), params_.end(), [](Expr n1, Expr n2){ return n1->name() < n2->name(); });
+
       for(auto p : params_) {
         if(!p->val()) {
-          vals_->allocate(p->val(), p->shape());
+          vals_->allocate(p->val(), p->shape(), p->value_type());
         }
       }
     }
@@ -69,18 +87,22 @@ class Parameters {
 
   virtual void allocateBackward() {
     if(!params_.empty() && grads_->size() == 0) {
+
+      // sort parameters by name before allocation to make sure the memory layout after allocation is always the same
+      std::sort(params_.begin(), params_.end(), [](Expr n1, Expr n2){ return n1->name() < n2->name(); });
+
       grads_->reserveExact(totalCapacity(grads_));
       for(auto p : params_)
         if(!p->grad())
-          grads_->allocate(p->grad(), p->shape());
+          grads_->allocate(p->grad(), p->shape(), p->value_type());
     }
   }
 
   virtual void set_zero_adjoint() { grads()->set(0.f); }
 
-  virtual Tensor vals() { return vals_->asTensor(); }
+  virtual Tensor vals() { return vals_->asTensor(parameterType_); }
 
-  virtual Tensor grads() { return grads_->asTensor(); }
+  virtual Tensor grads() { return grads_->asTensor(parameterType_); }
 
   virtual void clear() {
     params_.clear();
@@ -97,13 +119,13 @@ class MappedParameters : public Parameters {
 
 public:
   virtual void init(Ptr<Backend> backend) override { backend_ = backend; }
+  virtual void init(Ptr<Backend> backend, Ptr<Device>) override { init(backend); }
 
   virtual void allocateForward() override {
     if(!params_.empty()) {
       for(auto p : params_) {
         if(!p->val()) {
-          p->val() = Tensor(
-              new TensorBase(nullptr, p->shape(), Type::float32, backend_));
+          p->val() = TensorBase::New(nullptr, p->shape(), p->value_type(), backend_);
         }
       }
     }
diff --git a/src/tensors/allocator.h b/src/tensors/allocator.h
index 6a5f883e9..946138e58 100644
--- a/src/tensors/allocator.h
+++ b/src/tensors/allocator.h
@@ -88,11 +88,7 @@ class Allocator {
   bool throw_{false};
 
   std::set<Gap> gaps_;
-  std::unordered_map<uint8_t*, Ptr<MemoryPiece>> allocated_;
-
-  size_t align(size_t size) {
-    return (size_t)(ceil(size / (float)alignment_) * alignment_);
-  }
+  std::unordered_map<uint8_t*, MemoryPiece::PtrType> allocated_;
 
   void grow(size_t add) {
     add = align(add);
@@ -109,7 +105,7 @@ class Allocator {
                        gap.size()));
     insertGap(Gap(device_->data() + oldSize, add));
 
-    std::unordered_map<uint8_t*, Ptr<MemoryPiece>> oldAllocated;
+    std::unordered_map<uint8_t*, MemoryPiece::PtrType> oldAllocated;
     allocated_.swap(oldAllocated);
     for(auto it : oldAllocated) {
       uint8_t* newPtr = device_->data() + std::distance(oldData, it.first);
@@ -123,6 +119,7 @@ class Allocator {
     auto it = std::lower_bound(gaps_.begin(), gaps_.end(), Gap(nullptr, size));
 
     if(throw_ && it == gaps_.end()) {
+      //ABORT("Trying to allocate {}, but only {} available.", available_, size);
       throw AllocationException(available_, size);
     }
 
@@ -178,6 +175,10 @@ class Allocator {
     reserve(bytes);
   }
 
+  size_t align(size_t size) {
+    return (size_t)(ceil(size / (float)alignment_) * alignment_);
+  }
+
   void throwAtReallocation(bool throwRealloc) { throw_ = throwRealloc; }
 
   void reserve(size_t bytes) {
@@ -194,16 +195,16 @@ class Allocator {
 
   size_t capacity(size_t num, Type type) { return align(num * sizeOf(type)); }
 
-  Ptr<MemoryPiece> alloc(size_t num, Type type) {
+  MemoryPiece::PtrType alloc(size_t num, Type type) {
     return alloc(num * sizeOf(type));
   }
 
   template <typename T>
-  Ptr<MemoryPiece> alloc(size_t num) {
+  MemoryPiece::PtrType alloc(size_t num) {
     return alloc(capacity<T>(num));
   }
 
-  Ptr<MemoryPiece> alloc(size_t bytes) {
+  MemoryPiece::PtrType alloc(size_t bytes) {
     bytes = align(bytes);
     Gap gap = getGap(bytes);
 
@@ -212,7 +213,7 @@ class Allocator {
     }
 
     auto ptr = gap.data();
-    auto mp = New<MemoryPiece>(ptr, bytes);
+    auto mp = MemoryPiece::New(ptr, bytes);
     allocated_[ptr] = mp;
     return mp;
   }
@@ -234,7 +235,7 @@ class Allocator {
     return false;
   }
 
-  bool free(Ptr<MemoryPiece> mp) {
+  bool free(MemoryPiece::PtrType mp) {
     if(free(mp->data(), mp->size())) {
       mp->set(nullptr, 0);
       return true;
@@ -249,8 +250,8 @@ class Allocator {
     insertGap({device_->data(), device_->size()}, false);
   }
 
-  Ptr<MemoryPiece> memory() {
-    return New<MemoryPiece>(device_->data(), device_->size());
+  MemoryPiece::PtrType memory() {
+    return MemoryPiece::New(device_->data(), device_->size());
   }
 
   size_t size() { return device_->size(); }
diff --git a/src/tensors/cpu/element.h b/src/tensors/cpu/element.h
index ca554112f..86f0be85a 100644
--- a/src/tensors/cpu/element.h
+++ b/src/tensors/cpu/element.h
@@ -17,13 +17,16 @@ namespace cpu {
 // on strides, it correctly broadcasts to all dimensions without additional
 // computation.
 // Compiler optimizes this to single construct with nested(?) loops.
+
+namespace f = marian::functional;
+
 template <size_t I = 0>
 struct E {
-  template <size_t K, class Functor>
+  template <size_t numArg, class Functor, typename ElementType>
   static inline void element(
       const Functor& functor,
-      functional::Array<functional::Tensor<float>, K>& tensors,
-      functional::Array<int, K> indices) {
+      f::Array<f::Tensor<ElementType>, numArg>& tensors,
+      f::Array<int, numArg> indices) {
     const auto& shape = tensors[0].shape();
 
     // loop over outer-most dimension
@@ -34,8 +37,11 @@ struct E {
       // increase index for current dimension by stride or 0 if broadcasting.
       // bstride(i) is look-up value, either equal to stride if the
       // corresponding dim is larger 1 or 0 if the dim is 1.
-      for(size_t k = 0; k < K; ++k)
+      for(size_t k = 0; k < numArg; ++k) {
+        //int stride = tensors[k].shape().stride(I);
+        //indices[k] += stride == 1 ? 0 : stride;
         indices[k] += tensors[k].shape().bstride(I);
+      }
     }
   }
 };
@@ -43,31 +49,76 @@ struct E {
 // specialization for inner-most single element (recursive stopping criterion)
 // using const reference for indices here to avoid copying. No loop.
 template <>
-struct E<functional::Shape::size()> {
-  template <size_t K, class Functor>
+struct E<f::Shape::size()> {
+  template <size_t numArg, class Functor, typename ElementType>
   static inline void element(
       const Functor& functor,
-      functional::Array<functional::Tensor<float>, K>& tensors,
-      const functional::Array<int, K>& indices) {
+      f::Array<f::Tensor<ElementType>, numArg>& tensors,
+      const f::Array<int, numArg>& indices) {
     // just apply the function for all indexed elements across all tensors
-    tensors[0][indices[0]] = functional::apply(functor, tensors, indices);
+    // @TODO: use converting operator[] on tensor
+    tensors[0].data()[indices[0]] = f::apply(functor, tensors, indices);
   }
 };
 
-// main call to function executing element-wise operation
-template <class Functor, class... Tensors>
-void Element(const Functor& functor, marian::Tensor out, Tensors... tensors) {
-  constexpr size_t K = sizeof...(tensors) + 1;
-  functional::Array<functional::Tensor<float>, K> gTensors = {out, tensors...};
+template <typename ElementType, class Functor, class... Tensors>
+void element(const Functor& functor, marian::Tensor out, Tensors... tensors) {
 
-  // create and initialize indices to 0
-  functional::Array<int, K> indices;
+  // Number of input tensors + 1 (output tensor)
+  constexpr size_t argNum = sizeof...(tensors) + 1;
+  // create and initialize indices to 0, one index per tensor
+  f::Array<int, argNum> indices;
   indices.fill(0);
 
   // call elementwise operation going from outer-most dimension
   // to inner-most element.
+  f::Array<f::Tensor<ElementType>, argNum> gTensors = {out, tensors...};
   E<0>::element(functor, gTensors, indices);
 }
 
+template <class Functor, class... Tensors>
+void elementFloat(const Functor& functor, marian::Tensor out, Tensors... tensors) {
+#ifndef __CUDA_ARCH__
+  std::vector<marian::Tensor> ts({tensors...});
+  bool div8 = true;
+  bool div4 = true;
+
+  if(out->shape()[-1] % 8 != 0)
+    div8 = false;
+  if(out->shape()[-1] % 4 != 0)
+    div4 = false;
+  for(auto t : ts) {
+    if(t->shape()[-1] % 8 != 0)
+      div8 = false;
+    if(t->shape()[-1] % 4 != 0)
+      div4 = false;
+  }
+
+  if(div8) {
+    // std::cerr << "8: " << functor.to_string() << std::endl;
+    element<float32x8>(functor, out, tensors...);
+    return;
+  }
+
+  if(div4) {
+    // std::cerr << "4: " << functor.to_string() << std::endl;
+    element<float32x4>(functor, out, tensors...);
+    return;
+  }
+#endif
+  // std::cerr << "1: " << functor.to_string() << std::endl;
+  element<float>(functor, out, tensors...);
+}
+
+// main call to function executing element-wise operation
+template <class Functor, class... Tensors>
+void Element(const Functor& functor, marian::Tensor out, Tensors... tensors) {
+  switch(out->type()) {
+    case Type::float32: elementFloat(functor, out, tensors...); break;
+    //case Type::uint32:  element<uint32_t>(functor, out, tensors...); break;
+    default: ABORT("Unsupported type for element-wise operation"); break;
+  }
+}
+
 }  // namespace cpu
 }  // namespace marian
diff --git a/src/tensors/cpu/tensor_operators.cpp b/src/tensors/cpu/tensor_operators.cpp
index 63ade0238..300d46f7c 100644
--- a/src/tensors/cpu/tensor_operators.cpp
+++ b/src/tensors/cpu/tensor_operators.cpp
@@ -561,6 +561,35 @@ void PasteCols(Tensor out_,
   }
 }
 
+void Select2(Tensor out,
+             const Tensor in,
+             const Tensor indices) {
+
+  matchOrAbort<IndexType>(indices->type());
+
+  functional::Shape outShape = out->shape();
+  functional::Shape inShape = in->shape();
+  
+  auto idxData = indices->data<IndexType>();
+  auto odata = out->data();
+  const auto idata = in->data();
+
+  int size = outShape[3];
+
+  for(int k = 0; k < outShape[0]; ++k) {
+    for(int j = 0; j < outShape[1]; ++j) {
+      int outOffset = k * j * outShape[2] * size + j * outShape[2] * size;
+      int inOffset = k * j * inShape[2] * size + j * inShape[2] * size;
+      for(int i = 0; i < outShape[2]; ++i) {
+        auto idx = idxData[i];
+        int outIndex = outOffset +   i * size;
+        int inIndex  = inOffset  + idx * size;
+        std::copy(idata + inIndex, idata + inIndex + size, odata + outIndex);
+      }
+    }
+  }
+}
+
 void Select(Tensor out,
             const Tensor in,
             const Tensor indices,
@@ -576,13 +605,18 @@ void Select(Tensor out,
   functional::Array<int, functional::Shape::size()> dims;
   int axisCPU = (int)(axis + functional::Shape::size() - out->shape().size());
 
+  if(axisCPU == 2) // specialization for axis==2, assuming N=4
+    return Select2(out, in, indices);
+
+  auto odata = out->data();
+  const auto idata = in->data();
+
   for(int index = 0; index < length; ++index) {
     outShape.dims(index, dims);
     dims[axisCPU] = (int)indices->data<IndexType>()[dims[axisCPU]];
     int inIndex = inShape.index(dims);
-    out->data()[index] = in->data()[inIndex];
+    odata[index] = idata[inIndex];
   }
-
 }
 
 void Insert(Tensor out,
@@ -1309,29 +1343,13 @@ void LSTMOutputBackward(std::vector<Tensor> outputs,
   }
 }
 
-// void HighwayForward(Tensor out,
-//                    const Tensor in1,
-//                    const Tensor in2,
-//                    const Tensor t) {
-//  size_t length = out->shape().elements();
-//  for(size_t i = 0; i < length; ++i) {
-//    float sigma = stableSigmoid(t->data()[i]);
-//    out->data()[i] = sigma * in1->data()[i] + (1.f - sigma) * in2->data()[i];
-//  }
-//}
-
 void HighwayForward(Tensor out,
-                    const Tensor in1,
-                    const Tensor in2,
-                    const Tensor t) {
-  size_t length = out->shape().elements();
-
-  static functional::Approx<10, 0, 100> approxSigmoid(stableSigmoid);
-
-  for(size_t i = 0; i < length; ++i) {
-    float sigma = approxSigmoid(t->data()[i]);
-    out->data()[i] = sigma * in1->data()[i] + (1.f - sigma) * in2->data()[i];
-  }
+                   const Tensor in1,
+                   const Tensor in2,
+                   const Tensor t) {
+  using namespace functional;
+  cpu::Element(_1 = sigmoid(_2), out, t);
+  cpu::Element(_1 = _1 * _2 + (1.f - _1) * _3, out, in1, in2);
 }
 
 void HighwayBackward(Tensor /*out1*/,
diff --git a/src/tensors/memory_piece.h b/src/tensors/memory_piece.h
old mode 100755
new mode 100644
index 27446a6c1..cfe829492
--- a/src/tensors/memory_piece.h
+++ b/src/tensors/memory_piece.h
@@ -1,5 +1,7 @@
 #pragma once
 
+#include "common/definitions.h"
+
 #include <iostream>
 
 namespace marian {
@@ -9,9 +11,21 @@ class MemoryPiece {
   uint8_t* data_;
   size_t size_;
 
-public:
+  ENABLE_INTRUSIVE_PTR(MemoryPiece)
+  
+  // Contructor is private, use MemoryPiece::New(...)
   MemoryPiece(uint8_t* data, size_t size) : data_(data), size_(size) {}
 
+public:
+  // Use this whenever pointing to MemoryPiece
+  typedef IPtr<MemoryPiece> PtrType;
+
+  // Use this whenever creating a pointer to MemoryPiece
+  template <class ...Args>
+  static PtrType New(Args&& ...args) {
+    return PtrType(new MemoryPiece(std::forward<Args>(args)...));
+  }
+  
   uint8_t* data() const { return data_; }
   uint8_t* data() { return data_; }
 
@@ -39,6 +53,6 @@ class MemoryPiece {
         << " size: " << mp.size();
     return out;
   }
-};
 
+};
 }  // namespace marian
diff --git a/src/tensors/rand.h b/src/tensors/rand.h
index 568ac932f..f3e356fb6 100644
--- a/src/tensors/rand.h
+++ b/src/tensors/rand.h
@@ -7,7 +7,7 @@
 namespace marian {
 
 class TensorBase;
-typedef Ptr<TensorBase> Tensor;
+typedef IPtr<TensorBase> Tensor;
 
 class RandomGenerator {
 protected:
diff --git a/src/tensors/tensor.cpp b/src/tensors/tensor.cpp
new file mode 100755
index 000000000..76042d7a3
--- /dev/null
+++ b/src/tensors/tensor.cpp
@@ -0,0 +1,130 @@
+#include "tensors/tensor.h"
+#include "tensors/tensor_operators.h"
+
+namespace marian {
+
+template <typename T>
+std::string TensorBase::debug(int precision, int dispCols) {
+  // values
+  size_t totSize = shape_.elements();
+  std::vector<T> values(totSize);
+
+  get(values);
+
+  std::stringstream strm;
+  assert(shape_.size());
+  strm << shape_;
+  strm << " type=" << type_;
+  strm << " device=" << backend_->getDeviceId();
+  strm << " ptr=" << (size_t)memory_->data();
+  strm << " bytes=" << memory_->size();
+  strm << std::endl;
+
+  int colWidth  = precision + 4;
+
+  if(isFloat(type_))
+    strm << std::fixed << std::setprecision(precision) << std::setfill(' ');
+  else
+    strm << std::fixed << std::setprecision(0) << std::setfill(' ');
+
+  // double maxv = std::numeric_limits<double>::lowest();
+  // double minv = std::numeric_limits<double>::max();
+  // double l2Norm = 0.0;
+
+  for(int i = 0; i < values.size(); ++i) {
+    std::vector<int> dims;
+    shape().dims(i, dims);
+
+    // if((double)values[i] > maxv) maxv = values[i];
+    // if((double)values[i] < minv) minv = values[i];
+    // l2Norm += (double)values[i] * (double)values[i];
+
+    bool disp = true;
+    for(int j = 0; j < dims.size(); ++j)
+      disp = disp && (dims[j] < dispCols || dims[j] >= shape()[j] - dispCols);
+
+    if(disp) {
+      if(dims.back() == 0) {
+        bool par = true;
+        std::vector<std::string> p;
+        for(int j = (int)dims.size() - 1; j >= 0; --j) {
+          if(dims[j] != 0)
+            par = false;
+
+          p.push_back(par ? "[" : " ");
+        }
+        for(auto it = p.rbegin(); it != p.rend(); ++it)
+          strm << *it;
+        strm << " ";
+      }
+
+      strm << std::setw(colWidth);
+      if(isFloat(type_)) {
+        strm << (double)values[i];
+      } else if(isSignedInt(type_)) {
+        strm << (int64_t)values[i];
+      } else {
+        strm << (uint64_t)values[i];
+      }
+      strm << " ";
+
+      if(dims.back() + 1 == shape().back()) {
+        for(int j = (int)dims.size() - 1; j >= 0; --j) {
+          if(dims[j] + 1 != shape()[j])
+            break;
+          strm << "]";
+        }
+        strm << std::endl;
+      }
+
+      bool prev = true;
+      for(int j = (int)dims.size() - 1; j >= 0; --j) {
+        if(j < (int)dims.size() - 1)
+          prev = prev && dims[j + 1] + 1 == shape()[j + 1];
+        if(prev && dims[j] + 1 == dispCols && shape()[j] > 2 * dispCols) {
+          if(j < (int)dims.size() - 1)
+            for(int k = 0; k <= j; ++k)
+              strm << " ";
+          strm << "... ";
+          if(j < (int)dims.size() - 1)
+            strm << std::endl;
+          break;
+        }
+      }
+    }
+  }
+  strm << std::endl;
+  //strm << "min: " << minv << " max: " << maxv << " l2-norm: " << sqrt(l2Norm);
+
+  return strm.str();
+}
+
+template std::string TensorBase::debug<float>(int, int);
+template std::string TensorBase::debug<float16>(int, int);
+template std::string TensorBase::debug<double>(int, int);
+
+template std::string TensorBase::debug<uint8_t>(int, int);
+template std::string TensorBase::debug<uint16_t>(int, int);
+template std::string TensorBase::debug<uint32_t>(int, int);
+template std::string TensorBase::debug<uint64_t>(int, int);
+
+template std::string TensorBase::debug<int8_t>(int, int);
+template std::string TensorBase::debug<int16_t>(int, int);
+template std::string TensorBase::debug<int32_t>(int, int);
+template std::string TensorBase::debug<int64_t>(int, int);
+
+void TensorBase::get(io::Item& item, const std::string& name) {
+  item.name  = name;
+  item.shape = shape_;
+  item.type  = type_;
+
+  size_t bytesWithoutPadding = shape_.elements() * sizeOf(type_);
+  item.bytes.resize(bytesWithoutPadding);
+  copy(backend_,
+       memory_->data<char>(),
+       memory_->data<char>() + bytesWithoutPadding,
+       item.bytes.data());
+}
+
+}  // namespace marian
+
diff --git a/src/tensors/tensor.h b/src/tensors/tensor.h
old mode 100755
new mode 100644
index e391bd75c..c3585fca7
--- a/src/tensors/tensor.h
+++ b/src/tensors/tensor.h
@@ -3,6 +3,7 @@
 #include "common/definitions.h"
 #include "common/shape.h"
 #include "common/types.h"
+#include "common/io.h"
 #include "tensors/backend.h"
 #include "tensors/memory_piece.h"
 #ifdef CUDA_FOUND
@@ -17,31 +18,42 @@
 
 namespace marian {
 
-class TensorBase : public std::enable_shared_from_this<TensorBase> {
+class TensorBase {
 private:
-  Ptr<MemoryPiece> memory_;
+  MemoryPiece::PtrType memory_;
   Shape shape_;
   Type type_{Type::float32};
   Ptr<Backend> backend_;
 
-public:
-  TensorBase(Ptr<MemoryPiece> memory,
+  ENABLE_INTRUSIVE_PTR(TensorBase)
+
+  TensorBase(MemoryPiece::PtrType memory,
              Shape shape,
              Type type,
              Ptr<Backend> backend)
       : memory_(memory), shape_(shape), type_(type), backend_(backend) {}
 
-  TensorBase(Ptr<MemoryPiece> memory, Shape shape, Ptr<Backend> backend)
+  TensorBase(MemoryPiece::PtrType memory, Shape shape, Ptr<Backend> backend)
       : memory_(memory),
         shape_(shape),
         type_(Type::float32),
         backend_(backend) {}
 
-  ~TensorBase() {}
+public:
+  // Use this whenever pointing to MemoryPiece
+  typedef IPtr<TensorBase> PtrType;
+
+  // Use this whenever creating a pointer to MemoryPiece
+  template <class ...Args>
+  static PtrType New(Args&& ...args) {
+    return PtrType(new TensorBase(std::forward<Args>(args)...));
+  }
+
+  virtual ~TensorBase() {}
 
-  virtual void reset(Ptr<MemoryPiece> memory) { memory_ = memory; }
+  virtual void reset(MemoryPiece::PtrType memory) { memory_ = memory; }
 
-  virtual Ptr<MemoryPiece> memory() { return memory_; }
+  virtual MemoryPiece::PtrType memory() { return memory_; }
 
   virtual Type type() { return type_; }
 
@@ -56,47 +68,42 @@ class TensorBase : public std::enable_shared_from_this<TensorBase> {
 
   virtual size_t size() { return shape_.elements(); }
 
+  // this version of scalar will abort if numeric types do not match
   template <typename T>
   T scalar() {
-    ABORT_IF(!matchType<T>(type_),
-             "Requested type ({}) and underlying type ({}) do not match",
-             request<T>(),
-             type_);
-
     ABORT_IF(size() != 1, "Tensor is not a scalar");
     return get<T>(0);
   }
 
+  // this version converts all numeric types to float
   virtual float scalar() {
-    return scalar<float>();
+    DISPATCH_BY_TYPE0(type_, scalar);
   }
 
   Ptr<Backend> getBackend() { return backend_; }
   DeviceId getDeviceId() { return backend_->getDeviceId(); }
 
   Tensor subtensor(size_t offset, size_t size) {
-    auto mem = New<MemoryPiece>(memory_->data() + sizeOf(type_) * offset,
-                                sizeOf(type_) * size);
-    return New<TensorBase>(mem, Shape{1, (int)size}, backend_);
+    auto mem = MemoryPiece::New(memory_->data() + sizeOf(type_) * offset, sizeOf(type_) * size);
+    return TensorBase::New(mem, Shape{1, (int)size}, type(), backend_);
   }
 
   template <typename T>
   T get(size_t i) {
-    ABORT_IF(!matchType<T>(type_),
-             "Requested type ({}) and underlying type ({}) do not match",
-             request<T>(),
-             type_);
-
-    T temp = 0;
-    if(backend_->getDeviceId().type == DeviceType::cpu) {
-      std::copy(data<T>() + i, data<T>() + i + 1, &temp);
-    }
-#ifdef CUDA_FOUND
-    else {
-      gpu::copy(backend_, data<T>() + i, data<T>() + i + 1, &temp);
+    if(!matchType<T>(type_)) {
+      DISPATCH_BY_TYPE1(type_, get, i);
+    } else {
+      T temp = 0;
+      if(backend_->getDeviceId().type == DeviceType::cpu) {
+        std::copy(data<T>() + i, data<T>() + i + 1, &temp);
+      }
+  #ifdef CUDA_FOUND
+      else {
+        gpu::copy(backend_, data<T>() + i, data<T>() + i + 1, &temp);
+      }
+  #endif
+      return temp;
     }
-#endif
-    return temp;
   }
 
   float get(size_t i) {
@@ -104,50 +111,44 @@ class TensorBase : public std::enable_shared_from_this<TensorBase> {
   }
 
   template <typename T>
-  void set(size_t i, T value) {
+  void get(std::vector<T>& v) {
     ABORT_IF(!matchType<T>(type_),
              "Requested type ({}) and underlying type ({}) do not match",
              request<T>(),
              type_);
 
+    v.resize(size());
     if(backend_->getDeviceId().type == DeviceType::cpu) {
-      std::copy(&value, &value + 1, data<T>() + i);
+      std::copy(data<T>(), data<T>() + size(), v.data());
     }
 #ifdef CUDA_FOUND
     else {
-      gpu::copy(backend_, &value, &value + 1, data<T>() + i);
+      gpu::copy(backend_, data<T>(), data<T>() + size(), v.data());
     }
 #endif
   }
 
-  template <typename T>
-  void get(std::vector<T>& v) {
-    ABORT_IF(!matchType<T>(type_),
-             "Requested type ({}) and underlying type ({}) do not match",
-             request<T>(),
-             type_);
+  void get(io::Item& item, const std::string& name);
 
-    v.resize(size());
-    if(backend_->getDeviceId().type == DeviceType::cpu) {
-      std::copy(data<T>(), data<T>() + size(), v.data());
-    }
+  template <typename T>
+  void set(size_t i, T value) {
+    if(!matchType<T>(type_)) {
+      DISPATCH_BY_TYPE2(type_, set, i, value);
+    } else {
+      if(backend_->getDeviceId().type == DeviceType::cpu) {
+        std::copy(&value, &value + 1, data<T>() + i);
+      }
 #ifdef CUDA_FOUND
-    else {
-      gpu::copy(backend_, data<T>(), data<T>() + size(), v.data());
-    }
+      else {
+        gpu::copy(backend_, &value, &value + 1, data<T>() + i);
+      }
 #endif
+    }
   }
 
   template <typename T>
   void set(const T* begin, const T* end) {
-    ABORT_IF(end - begin != shape_.elements(),
-             "Vector size ({}) and underlying shape ({}) do not match",
-             end - begin,
-             std::string(shape_));
-    ABORT_IF(!matchType<T>(type_),
-             "Requested type ({}) and underlying type ({}) do not match",
-             request<T>(),
-             type_);
+    matchOrAbort<T>(type_);
 
     if(backend_->getDeviceId().type == DeviceType::cpu) {
       std::copy(begin, end, data<T>());
@@ -164,38 +165,54 @@ class TensorBase : public std::enable_shared_from_this<TensorBase> {
     set(v.data(), v.data() + v.size());
   }
 
-  template <typename T>
-  void set(T value) {
-    if(!matchType<T>(type_)) {
-      switch(type_) {
-        case Type::float32: set<float   >((float   )value); break;
-        case Type::float64: set<double  >((double  )value); break;
-        case Type::int8:    set<int8_t  >((int8_t  )value); break;
-        case Type::int16:   set<int16_t >((int16_t )value); break;
-        case Type::int32:   set<int32_t >((int32_t )value); break;
-        case Type::int64:   set<int64_t >((int64_t )value); break;
-        case Type::uint8:   set<uint8_t >((uint8_t )value); break;
-        case Type::uint16:  set<uint16_t>((uint16_t)value); break;
-        case Type::uint32:  set<uint32_t>((uint32_t)value); break;
-        case Type::uint64:  set<uint64_t>((uint64_t)value); break;
-        default:
-          ABORT(
-              "Requested type ({}) cannot be converted to underlying type ({})",
-              request<float>(),
-              type_);
-      }
-    }
+  // a binary copy with type checking
+  void set(const char* begin, const char* end, Type type) {
+    ABORT_IF(type_ != type,
+             "Tensor type ({}) and data type ({}) do not match",
+             type_,
+             type);
+
+    size_t dataSize = (end - begin) / sizeOf(type);
+    ABORT_IF(size() != dataSize,
+             "Tensor size ({}) and mapped size ({}) do not match",
+             size(),
+             dataSize);
 
     if(backend_->getDeviceId().type == DeviceType::cpu) {
-      std::fill(data<T>(), data<T>() + size(), value);
+      std::copy(begin, end, data<char>());
     }
 #ifdef CUDA_FOUND
     else {
-      gpu::fill(backend_, data<T>(), data<T>() + size(), value);
+      gpu::copy(backend_, begin, end, data<char>());
     }
 #endif
   }
 
+  void set(const std::vector<char>& v, Type type) {
+    set(v.data(), v.data() + v.size(), type);
+  }
+
+  void set(const io::Item& item) {
+    set(item.bytes.data(), item.bytes.data() + item.bytes.size(), item.type);
+  }
+
+  // For single values enable conversion to other numeric formats if possible
+  template <typename T>
+  void set(T value) {
+    if(!matchType<T>(type_)) {
+      DISPATCH_BY_TYPE1(type_, set, value);
+    } else {
+      if(backend_->getDeviceId().type == DeviceType::cpu) {
+        std::fill(data<T>(), data<T>() + size(), value);
+      }
+  #ifdef CUDA_FOUND
+      else {
+        gpu::fill(backend_, data<T>(), data<T>() + size(), value);
+      }
+  #endif
+    }
+  }
+
   void setSparse(const std::vector<size_t>& k, const std::vector<float>& v) {
     ABORT_IF(!matchType<float>(type_),
              "Requested type ({}) and underlying type ({}) do not match",
@@ -234,22 +251,7 @@ class TensorBase : public std::enable_shared_from_this<TensorBase> {
   }
 
   void copyFrom(Tensor in) {
-     switch(type_) {
-      case Type::int8:    copyFrom<int8_t>(in);  break;
-      case Type::int16:   copyFrom<int16_t>(in); break;
-      case Type::int32:   copyFrom<int32_t>(in); break;
-      case Type::int64:   copyFrom<int64_t>(in); break;
-
-      case Type::uint8:   copyFrom<uint8_t>(in);  break;
-      case Type::uint16:  copyFrom<uint16_t>(in); break;
-      case Type::uint32:  copyFrom<uint32_t>(in); break;
-      case Type::uint64:  copyFrom<uint64_t>(in); break;
-
-      case Type::float32: copyFrom<float>(in);  break;
-      case Type::float64: copyFrom<double>(in); break;
-
-      default: ABORT("Unknown type {}", type_);
-    }
+    DISPATCH_BY_TYPE1(type_, copyFrom, in);
   }
 
   // Swaps the contents of the current tensor with the argument tensor
@@ -284,143 +286,30 @@ class TensorBase : public std::enable_shared_from_this<TensorBase> {
   }
 
   void swap(Tensor swapee) {
-     switch(type_) {
-      case Type::int8:    swap<int8_t>(swapee);  break;
-      case Type::int16:   swap<int16_t>(swapee); break;
-      case Type::int32:   swap<int32_t>(swapee); break;
-      case Type::int64:   swap<int64_t>(swapee); break;
-
-      case Type::uint8:   swap<uint8_t>(swapee);  break;
-      case Type::uint16:  swap<uint16_t>(swapee); break;
-      case Type::uint32:  swap<uint32_t>(swapee); break;
-      case Type::uint64:  swap<uint64_t>(swapee); break;
-
-      case Type::float32: swap<float>(swapee);  break;
-      case Type::float64: swap<double>(swapee); break;
-
-      default: ABORT("Unknown type {}", type_);
-    }
+    DISPATCH_BY_TYPE1(type_, swap, swapee);
   }
-  
+
   template <typename T>
-  std::string debug() {
-    ABORT_IF(!matchType<T>(type_),
-             "Requested type ({}) and underlying type ({}) do not match",
-             request<T>(),
-             type_);
+  std::string debug(int precision = 8, int dispCols = 5);
 
-    std::stringstream strm;
-    assert(shape_.size());
-    strm << shape_;
-    strm << " type=" << type_;
-    strm << " device=" << backend_->getDeviceId();
-    strm << " ptr=" << (size_t)memory_->data();
-    strm << " bytes=" << memory_->size();
-    strm << std::endl;
-
-    // values
-    size_t totSize = shape_.elements();
-    std::vector<T> values(totSize);
-    get(values);
-
-    int dispCols = 5;
-    if(isFloat(type_))
-      strm << std::fixed << std::setprecision(8) << std::setfill(' ');
-    else
-      strm << std::fixed << std::setprecision(0) << std::setfill(' ');
-
-    for(int i = 0; i < values.size(); ++i) {
-      std::vector<int> dims;
-      shape().dims(i, dims);
-
-      bool disp = true;
-      for(int j = 0; j < dims.size(); ++j)
-        disp = disp && (dims[j] < dispCols || dims[j] >= shape()[j] - dispCols);
-
-      if(disp) {
-        if(dims.back() == 0) {
-          bool par = true;
-          std::vector<std::string> p;
-          for(int j = (int)dims.size() - 1; j >= 0; --j) {
-            if(dims[j] != 0)
-              par = false;
-
-            p.push_back(par ? "[" : " ");
-          }
-          for(auto it = p.rbegin(); it != p.rend(); ++it)
-            strm << *it;
-          strm << " ";
-        }
-
-        strm << std::setw(12);
-        if(isFloat(type_)) {
-          strm << (double)values[i];
-        } else if(isSignedInt(type_)) {
-          strm << (int64_t)values[i];
-        } else {
-          strm << (uint64_t)values[i];
-        }
-        strm << " ";
-
-        if(dims.back() + 1 == shape().back()) {
-          for(int j = (int)dims.size() - 1; j >= 0; --j) {
-            if(dims[j] + 1 != shape()[j])
-              break;
-            strm << "]";
-          }
-          strm << std::endl;
-        }
-
-        bool prev = true;
-        for(int j = (int)dims.size() - 1; j >= 0; --j) {
-          if(j < (int)dims.size() - 1)
-            prev = prev && dims[j + 1] + 1 == shape()[j + 1];
-          if(prev && dims[j] + 1 == dispCols && shape()[j] > 2 * dispCols) {
-            if(j < (int)dims.size() - 1)
-              for(int k = 0; k <= j; ++k)
-                strm << " ";
-            strm << "... ";
-            if(j < (int)dims.size() - 1)
-              strm << std::endl;
-            break;
-          }
-        }
-      }
-    }
-    strm << std::endl;
-    return strm.str();
+  std::string debug(int precision = 8, int dispCols = 5) {
+    DISPATCH_BY_TYPE2(type_, debug, precision, dispCols);
   }
 
-  std::string debug() {
-    switch(type_) {
-      case Type::int8: return debug<int8_t>();
-      case Type::int16: return debug<int16_t>();
-      case Type::int32: return debug<int32_t>();
-      case Type::int64: return debug<int64_t>();
-
-      case Type::uint8: return debug<uint8_t>();
-      case Type::uint16: return debug<uint16_t>();
-      case Type::uint32: return debug<uint32_t>();
-      case Type::uint64: return debug<uint64_t>();
-
-      case Type::float32: return debug<float>();
-      case Type::float64: return debug<double>();
-
-      default: ABORT("Unknown type {}", type_);
-    }
-  }
 };
 
-typedef std::shared_ptr<TensorBase> Tensor;
+typedef TensorBase::PtrType Tensor;
 
-// debugging helper, to read out a memory piece into an STL vector (e.g. for inspection in debugger)
-template<typename T>
-static inline std::vector<T> get(Ptr<MemoryPiece> memory, Ptr<Backend> backend) {
-  size_t n = memory->size() / sizeof(T);
-  TensorBase t(memory, Shape({ (int)n }), getType<T>(), backend);
-  std::vector<T> res;
-  t.get(res);
-  return res;
+template <class TensorType0, class ...TensorTypeRest>
+static inline void checkCommonType(TensorType0 first, TensorTypeRest ...rest) {
+  std::vector<Tensor> vTensors({first, rest...});
+  Type firstType = first->type();
+  for(int i = 1; i < vTensors.size(); ++i) {
+    ABORT_IF(vTensors[i]->type() != firstType,
+             "Type of tensor {} is different from type of tensor 0 ({} != {})",
+             i, vTensors[i]->type(), firstType);
+  }
 }
 
 }  // namespace marian
+
diff --git a/src/tensors/tensor_allocator.h b/src/tensors/tensor_allocator.h
index d65b50e7c..e908ec94b 100644
--- a/src/tensors/tensor_allocator.h
+++ b/src/tensors/tensor_allocator.h
@@ -44,6 +44,13 @@ class TensorAllocator {
     allocator_->reserve(mult * GROW);
   }
 
+  void reserveExact(const std::vector<size_t>& bytes) {
+    size_t total = 0;
+    for(auto part : bytes)
+      total += allocator_->align(part);
+    reserveExact(total);
+  }  
+
   void reserveExact(size_t bytes = 0) {
     size_t mbytes = bytes / MBYTE;
     if(mbytes == 0) {
@@ -70,19 +77,19 @@ class TensorAllocator {
     if(!t || t->shape() != shape) {
       int size = shape.elements();
       auto mem = allocator_->alloc(size, type);
-      t = Tensor(new TensorBase(mem, shape, type, backend_));
+      t = TensorBase::New(mem, shape, type, backend_);
     }
   }
 
   void free(const Tensor& t) { allocator_->free(t->memory()); }
 
-  Tensor asTensor() {
+  Tensor asTensor(Type type = Type::float32) {
     auto mem = allocator_->memory();
-    auto size = mem->size() / sizeof(float);
-    return Tensor(new TensorBase(mem, {1, (int)size}, backend_));
+    auto size = mem->size() / sizeOf(type);
+    return TensorBase::New(mem, Shape({1, (int)size}), type, backend_);
   }
 
-  size_t size() { return allocator_->size() / sizeof(float); }
+  size_t size(Type type = Type::float32) { return allocator_->size() / sizeOf(type); }
 
   Ptr<Allocator> allocator() { return allocator_; }
 };

From 9ed5c0078a30949ac1b6cc8494b4d34ec2c947f7 Mon Sep 17 00:00:00 2001
From: Marcin Junczys-Dowmunt <Marcin.JunczysDowmunt@microsoft.com>
Date: Fri, 6 Sep 2019 15:23:05 -0700
Subject: [PATCH 578/838] enable GPU compilation

---
 CMakeLists.txt                      |  15 +++-
 src/common/types.h                  |   9 +-
 src/tensors/gpu/add.cu              | 123 +++++++++++-----------------
 src/tensors/gpu/add.h               |   1 +
 src/tensors/gpu/add.inc             |  26 +++---
 src/tensors/gpu/algorithm.cu        |  30 ++++++-
 src/tensors/gpu/element.cu          |  39 ++++++---
 src/tensors/gpu/element.inc         |  31 ++++---
 src/tensors/gpu/prod.cpp            |   2 +-
 src/tensors/gpu/tensor_operators.cu |   5 +-
 10 files changed, 161 insertions(+), 120 deletions(-)
 mode change 100644 => 100755 src/tensors/gpu/add.cu
 mode change 100644 => 100755 src/tensors/gpu/add.h
 mode change 100644 => 100755 src/tensors/gpu/add.inc
 mode change 100644 => 100755 src/tensors/gpu/element.cu
 mode change 100644 => 100755 src/tensors/gpu/element.inc

diff --git a/CMakeLists.txt b/CMakeLists.txt
index 1191a674c..34d792d2c 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -70,28 +70,35 @@ else(MSVC)
   # only by used with BUILD_ARCH=native. For overridden BUILD_ARCH we
   # minimally use -msse4.1. This seems to work with MKL.
   set(INTRINSICS "")
+  list(APPEND INTRINSICS_NVCC)
+
   if(BUILD_ARCH STREQUAL "native")
     message(STATUS "Checking support for CPU intrinsics")
     include(FindSSE)
     if(SSE2_FOUND)
       message(STATUS "SSE2 support found")
       set(INTRINSICS "${INTRINSICS} -msse2")
+      list(APPEND INTRINSICS_NVCC -Xcompiler\ -msse2)
     endif(SSE2_FOUND)
     if(SSE3_FOUND)
       message(STATUS "SSE3 support found")
       set(INTRINSICS "${INTRINSICS} -msse3")
+      list(APPEND INTRINSICS_NVCC -Xcompiler\ -msse3)
     endif(SSE3_FOUND)
     if(SSE4_1_FOUND)
       message(STATUS "SSE4.1 support found")
       set(INTRINSICS "${INTRINSICS} -msse4.1")
+      list(APPEND INTRINSICS_NVCC -Xcompiler\ -msse4.1)
     endif(SSE4_1_FOUND)
     if(AVX_FOUND)
       message(STATUS "AVX support found")
       set(INTRINSICS "${INTRINSICS} -mavx")
+      list(APPEND INTRINSICS_NVCC -Xcompiler\ -mavx)
     endif(AVX_FOUND)
     if(AVX2_FOUND)
       message(STATUS "AVX2 support found")
       set(INTRINSICS "${INTRINSICS} -mavx2")
+      list(APPEND INTRINSICS_NVCC -Xcompiler\ -mavx2)
     endif(AVX2_FOUND)
     if(AVX512_FOUND)
       message(STATUS "AVX512 support found")
@@ -164,7 +171,7 @@ if(USE_STATIC_LIBS)
   endif()
 endif()
 
-find_package(CUDA "8.0")
+find_package(CUDA "10.0")
 if(CUDA_FOUND)
   # CUDA >= 10.0 requires CMake >= 3.12.2
   if((CUDA_VERSION VERSION_EQUAL "10.0" OR CUDA_VERSION VERSION_GREATER "10.0") AND (CMAKE_VERSION VERSION_LESS "3.12.2"))
@@ -216,14 +223,16 @@ else(COMPILE_CUDA)
   message(WARNING "COMPILE_CUDA=off : Building only CPU version")
 endif(COMPILE_CUDA)
 
+# TODO: make compatible with older CUDA versions
 if(CMAKE_BUILD_TYPE STREQUAL "Debug")
-  list(APPEND CUDA_NVCC_FLAGS --default-stream per-thread; -O0; -g; -arch=sm_30; -gencode=arch=compute_30,code=sm_30; -gencode=arch=compute_50,code=sm_50; -gencode=arch=compute_52,code=sm_52; -gencode=arch=compute_60,code=sm_60; -gencode=arch=compute_61,code=sm_61; -gencode=arch=compute_61,code=compute_61 ;)
+  list(APPEND CUDA_NVCC_FLAGS --default-stream per-thread; -O0; -g; --use_fast_math; -gencode=arch=compute_60,code=sm_60; -gencode=arch=compute_61,code=sm_61; -gencode=arch=compute_70,code=sm_70; -gencode=arch=compute_75,code=sm_75; -gencode=arch=compute_75,code=compute_75 ;)
 else(CMAKE_BUILD_TYPE STREQUAL "Debug")
-  list(APPEND CUDA_NVCC_FLAGS --default-stream per-thread; -O3; -g; --use_fast_math; -arch=sm_30; -gencode=arch=compute_30,code=sm_30; -gencode=arch=compute_50,code=sm_50; -gencode=arch=compute_52,code=sm_52; -gencode=arch=compute_60,code=sm_60; -gencode=arch=compute_61,code=sm_61; -gencode=arch=compute_61,code=compute_61 ;)
+  list(APPEND CUDA_NVCC_FLAGS --default-stream per-thread; -O3; -g; --use_fast_math; -gencode=arch=compute_60,code=sm_60; -gencode=arch=compute_61,code=sm_61; -gencode=arch=compute_70,code=sm_70; -gencode=arch=compute_75,code=sm_75; -gencode=arch=compute_75,code=compute_75 ;)
 endif(CMAKE_BUILD_TYPE STREQUAL "Debug")
 if(NOT MSVC)
   # @TODO: add warnings here too
   list(APPEND CUDA_NVCC_FLAGS -ccbin ${CMAKE_C_COMPILER}; -std=c++11; -Xcompiler\ -fPIC; -Xcompiler\ -Wno-unused-result; -Xcompiler\ -Wno-deprecated; -Xcompiler\ -Wno-pragmas; -Xcompiler\ -Wno-unused-value; -Xcompiler\ -Werror;)
+  list(APPEND CUDA_NVCC_FLAGS ${INTRINSICS_NVCC})
 else()
   list(APPEND CUDA_NVCC_FLAGS -Xcompiler\ /FS; )
 endif()
diff --git a/src/common/types.h b/src/common/types.h
index abf1035bf..991cd7807 100644
--- a/src/common/types.h
+++ b/src/common/types.h
@@ -277,11 +277,10 @@ template <> inline Type typeId<double>()   { return Type::float64; }
 // Abort if given C++ does not correspond to runtime type
 template <typename T>
 void matchOrAbort(Type type) {
-  // @TODO: hacky hack for WGNMT, turn this back on
-  // ABORT_IF(!matchType<T>(type),
-  //          "Requested type ({}) and underlying type ({}) do not match",
-  //          request<T>(),
-  //          type);
+  ABORT_IF(!matchType<T>(type),
+           "Requested type ({}) and underlying type ({}) do not match",
+           request<T>(),
+           type);
 }
 
 template <typename T>
diff --git a/src/tensors/gpu/add.cu b/src/tensors/gpu/add.cu
old mode 100644
new mode 100755
index e9474d048..92d364bf7
--- a/src/tensors/gpu/add.cu
+++ b/src/tensors/gpu/add.cu
@@ -11,12 +11,12 @@ namespace marian {
 
 namespace gpu {
 
-template <size_t K, class Functor, class AggFunctor>
-__global__ void gAggregateGeneric(Functor functor, float aggInit, AggFunctor aggFunctor,
-                            const functional::Shape full,
-                            functional::Tensor<float> out,
-                            functional::Array<functional::Tensor<float>, K> ins,
-                            float scale = 1.0) {
+template <size_t K, class Functor, class AggFunctor, typename T, typename AccType>
+__global__ void gAggregateGeneric(Functor functor, AccType aggInit, AggFunctor aggFunctor,
+                                  const functional::Shape full,
+                                  functional::Tensor<T> out,
+                                  functional::Array<functional::Tensor<T>, K> ins,
+                                  AccType scale = 1.0) {
   int outLength = out.shape().elements();
   bool same = outLength == full.elements();
   for(int i = 0; i < K; ++i)
@@ -32,21 +32,21 @@ __global__ void gAggregateGeneric(Functor functor, float aggInit, AggFunctor agg
     int index = bid + blockDim.x * blockIdx.x + threadIdx.x;
     if(index < outLength) {
       if(same) {
-        out[index] = aggFunctor(out[index], functional::apply(functor, ins, index) * scale);
+        out[index] = aggFunctor(out[index], functional::apply(functor, ins, index) * (T)scale);
       } else {
         out.shape().dims(index, dims);
-        out[index] = aggFunctor(out[index], functional::loops(functor, aggInit, aggFunctor, ins, len, dims) * scale);
+        out[index] = aggFunctor(out[index], (T)(functional::loops(functor, aggInit, aggFunctor, ins, len, dims) * scale));
       }
     }
   }
 }
 
-template <size_t K, class Functor, class AggFunctor>
+template <size_t K, class Functor, class AggFunctor, typename T, typename AccType>
 __global__ void gAggregateEqual(Functor functor, AggFunctor aggFunctor,
-                          functional::Tensor<float> out,
-                          functional::Array<functional::Tensor<float>, K> ins,
-                          float scale,
-                          bool broadcast) {
+                                functional::Tensor<T> out,
+                                functional::Array<functional::Tensor<T>, K> ins,
+                                AccType scale,
+                                bool broadcast) {
   int length = out.shape().elements();
   functional::Array<int, functional::Shape::size()> dims;
 
@@ -62,17 +62,17 @@ __global__ void gAggregateEqual(Functor functor, AggFunctor aggFunctor,
           indices[i] = ins[i].shape().bindex(dims);
       }
 
-      out[index] = aggFunctor(out[index], functional::apply(functor, ins, indices) * scale);
+      out[index] = aggFunctor(out[index], functional::apply(functor, ins, indices) * (T)scale);
     }
   }
 }
 
-template <size_t K, class Functor, class AggFunctor>
-__global__ void gAggregateReduce(Functor functor, float aggInit, AggFunctor aggFunctor,
-                           const functional::Shape full,
-                           functional::Tensor<float> out,
-                           functional::Array<functional::Tensor<float>, K> ins,
-                           float scale = 1.0) {
+template <size_t K, class Functor, class AggFunctor, typename T, typename AccType = float>
+__global__ void gAggregateReduce(Functor functor, AccType aggInit, AggFunctor aggFunctor,
+                                 const functional::Shape full,
+                                 functional::Tensor<T> out,
+                                 functional::Array<functional::Tensor<T>, K> ins,
+                                 AccType scale = 1.0) {
   int rows = full.elements() / full.back();
   int cols = full.back();
 
@@ -83,15 +83,17 @@ __global__ void gAggregateReduce(Functor functor, float aggInit, AggFunctor aggF
   for(int bid = 0; bid < rows; bid += gridDim.x) {
     int j = bid + blockIdx.x;
     if(j < rows) {
-      extern __shared__ float _share[];
-      float* _sum = _share;
+      // make sure shared memory is the same for different types
+      // by using bytes instead of type T
+      extern __shared__ uint8_t _sharedBytes[];
+      AccType* _sum = (AccType*)_sharedBytes;
 
       if(same) {
         _sum[threadIdx.x] = aggInit;
         for(int tid = 0; tid < cols; tid += blockDim.x) {
           int id = tid + threadIdx.x;
           if(id < cols)
-            _sum[threadIdx.x] = aggFunctor(_sum[threadIdx.x], functional::apply(functor, ins, j * cols + id));
+            _sum[threadIdx.x] = aggFunctor(_sum[threadIdx.x], (AccType)functional::apply(functor, ins, j * cols + id));
         }
       } else {
         functional::Array<int, functional::Shape::size()> dims;
@@ -104,7 +106,7 @@ __global__ void gAggregateReduce(Functor functor, float aggInit, AggFunctor aggF
             functional::Array<int, K> indices;
             for(int i = 0; i < K; ++i)
               indices[i] = ins[i].shape().bindex(dims);
-            _sum[threadIdx.x] = aggFunctor(_sum[threadIdx.x], functional::apply(functor, ins, indices));
+            _sum[threadIdx.x] = aggFunctor(_sum[threadIdx.x], (AccType)functional::apply(functor, ins, indices));
           }
         }
       }
@@ -119,14 +121,14 @@ __global__ void gAggregateReduce(Functor functor, float aggInit, AggFunctor aggF
         len = (len + 1) >> 1;
       }
       __syncthreads();
-      out[j] = aggFunctor(out[j], _sum[0] * scale);
+      out[j] = aggFunctor(out[j], (T)(_sum[0] * scale));
     }
     __syncthreads();
   }
 }
 
-template <class Functor, class AggFunctor, class... Tensors>
-void Aggregate(Functor functor, float aggInit, AggFunctor aggFunctor, float scale, marian::Tensor out, Tensors... tensors) {
+template <typename T, typename AccType, class Functor, class AggFunctor, class... Tensors>
+void AggregateTyped(Functor functor, AccType aggInit, AggFunctor aggFunctor, AccType scale, marian::Tensor out, Tensors... tensors) {
   cudaSetDevice(out->getDeviceId().no);
 
   auto full = marian::Shape::broadcast({out, tensors...});
@@ -135,8 +137,8 @@ void Aggregate(Functor functor, float aggInit, AggFunctor aggFunctor, float scal
 
   constexpr size_t K = sizeof...(Tensors);
 
-  functional::Tensor<float> gOut = out;
-  functional::Array<functional::Tensor<float>, K> gIns = {tensors...};
+  functional::Tensor<T> gOut = out;
+  functional::Array<functional::Tensor<T>, K> gIns = {tensors...};
 
   if(full.back() != 1 && out->shape().back() == 1) {
     size_t m = full.elements() / length;
@@ -144,14 +146,13 @@ void Aggregate(Functor functor, float aggInit, AggFunctor aggFunctor, float scal
 
     int blocks = std::min(MAX_BLOCKS, (int)m);
     int threads = std::min(MAX_THREADS, (int)k);
-    int shared = sizeof(float) * threads;
+    int shared = sizeof(AccType) * threads; 
 
-    gAggregateReduce<<<blocks, threads, shared>>>(functor, aggInit, aggFunctor, full, gOut, gIns, scale);
+    gAggregateReduce<K, Functor, AggFunctor, T, AccType><<<blocks, threads, shared>>>(functor, aggInit, aggFunctor, full, gOut, gIns, scale);
 
   } else if(out->shape() == full) {
     int threads = std::min(MAX_THREADS, length);
-    int blocks
-        = std::min(MAX_BLOCKS, length / threads + (length % threads != 0));
+    int blocks  = std::min(MAX_BLOCKS, length / threads + (length % threads != 0));
 
     bool broadcast = false;
     for(int i = 0; i < K; ++i)
@@ -159,55 +160,29 @@ void Aggregate(Functor functor, float aggInit, AggFunctor aggFunctor, float scal
     gAggregateEqual<<<blocks, threads>>>(functor, aggFunctor, gOut, gIns, scale, broadcast);
   } else {
     int threads = std::min(MAX_THREADS, length);
-    int blocks
-        = std::min(MAX_BLOCKS, length / threads + (length % threads != 0));
+    int blocks = std::min(MAX_BLOCKS, length / threads + (length % threads != 0));
 
     gAggregateGeneric<<<blocks, threads>>>(functor, aggInit, aggFunctor, full, gOut, gIns, scale);
   }
 }
 
-// @TODO: this is a duplicate; can be removed, but need to redo all the add.inc entries...
+template <class Functor, class AggFunctor, class... Tensors>
+void Aggregate(Functor functor, float aggInit, AggFunctor aggFunctor, float scale, marian::Tensor out, Tensors... tensors) {
+  if(out->type() == Type::float32) {
+    AggregateTyped<float, float>(functor, aggInit, aggFunctor, scale, out, tensors...);
+#if 0
+  } else if(out->type() == Type::float16) {
+    AggregateTyped<half,  float>(functor, aggInit, aggFunctor, scale, out, tensors...);
+#endif
+  } else {
+    ABORT("Type {} not yet supported", out->type());
+  }
+}
+
 template <class Functor, class... Tensors>
 void Add(Functor functor, float scale, marian::Tensor out, Tensors... tensors) {
-  cudaSetDevice(out->getDeviceId().no);
-
-  auto full = marian::Shape::broadcast({out, tensors...});
-
-  int length = out->shape().elements();
-
-  constexpr size_t K = sizeof...(Tensors);
-
-  functional::Tensor<float> gOut = out;
-  functional::Array<functional::Tensor<float>, K> gIns = {tensors...};
-
   auto addFunctor = functional::_1 + functional::_2;
-
-  if(full.back() != 1 && out->shape().back() == 1) {
-    size_t m = full.elements() / length;
-    size_t k = full.back();
-
-    int blocks = std::min(MAX_BLOCKS, (int)m);
-    int threads = std::min(MAX_THREADS, (int)k);
-    int shared = sizeof(float) * threads;
-
-    gAggregateReduce<<<blocks, threads, shared>>>(functor, 0, addFunctor, full, gOut, gIns, scale);
-
-  } else if(out->shape() == full) {
-    int threads = std::min(MAX_THREADS, length);
-    int blocks
-        = std::min(MAX_BLOCKS, length / threads + (length % threads != 0));
-
-    bool broadcast = false;
-    for(int i = 0; i < K; ++i)
-      broadcast = broadcast || gOut.shape() != gIns[i].shape();
-    gAggregateEqual<<<blocks, threads>>>(functor, addFunctor, gOut, gIns, scale, broadcast);
-  } else {
-    int threads = std::min(MAX_THREADS, length);
-    int blocks
-        = std::min(MAX_BLOCKS, length / threads + (length % threads != 0));
-
-    gAggregateGeneric<<<blocks, threads>>>(functor, 0, addFunctor, full, gOut, gIns, scale);
-  }
+  Aggregate(functor, 0.f, addFunctor, scale, out, tensors...);
 }
 
 #include "tensors/gpu/add.inc"
diff --git a/src/tensors/gpu/add.h b/src/tensors/gpu/add.h
old mode 100644
new mode 100755
index 21e0bb96c..33ef09663
--- a/src/tensors/gpu/add.h
+++ b/src/tensors/gpu/add.h
@@ -8,6 +8,7 @@ namespace gpu {
 
 template <class Functor, class... Tensors>
 void Add(Functor functor, float scale, marian::Tensor out, Tensors... tensors);
+
 template <class Functor, class AggFunctor, class... Tensors>
 void Aggregate(Functor functor, float initAgg, AggFunctor aggFunctor, float scale, marian::Tensor out, Tensors... tensors);
 }
diff --git a/src/tensors/gpu/add.inc b/src/tensors/gpu/add.inc
old mode 100644
new mode 100755
index b70c59d0a..df65805c6
--- a/src/tensors/gpu/add.inc
+++ b/src/tensors/gpu/add.inc
@@ -15,21 +15,23 @@ template void Add<BinaryFunctor<elem::Mult, Assignee<1>, BinaryFunctor<elem::Div
 template void Add<BinaryFunctor<elem::Mult, Assignee<1>, BinaryFunctor<elem::sPReLUBack, Assignee<2>, Capture>>, marian::Tensor, marian::Tensor>(BinaryFunctor<elem::Mult, Assignee<1>, BinaryFunctor<elem::sPReLUBack, Assignee<2>, Capture>>, float, marian::Tensor, marian::Tensor, marian::Tensor);
 template void Add<BinaryFunctor<elem::Mult, Assignee<1>, UnaryFunctor<elem::sReLUBack, Assignee<2>>>, marian::Tensor, marian::Tensor>(BinaryFunctor<elem::Mult, Assignee<1>, UnaryFunctor<elem::sReLUBack, Assignee<2>>>, float, marian::Tensor, marian::Tensor, marian::Tensor);
 template void Add<BinaryFunctor<elem::Mult, BinaryFunctor<elem::Mult, Assignee<1>, Assignee<2>>, BinaryFunctor<elem::Minus, Capture, Assignee<2>>>, marian::Tensor, marian::Tensor>(BinaryFunctor<elem::Mult, BinaryFunctor<elem::Mult, Assignee<1>, Assignee<2>>, BinaryFunctor<elem::Minus, Capture, Assignee<2>>>, float, marian::Tensor, marian::Tensor, marian::Tensor);
-template void Add<BinaryFunctor<elem::Mult, BinaryFunctor<elem::Bump, Assignee<1>, Capture>, Assignee<2>>, std::shared_ptr<marian::TensorBase>, std::shared_ptr<marian::TensorBase> >(BinaryFunctor<elem::Mult, BinaryFunctor<elem::Bump, Assignee<1>, Capture>, Assignee<2> >, float, std::shared_ptr<marian::TensorBase>, marian::Tensor, marian::Tensor);
+template void Add<BinaryFunctor<elem::Mult, BinaryFunctor<elem::Bump, Assignee<1>, Capture>, Assignee<2>>, marian::Tensor, marian::Tensor >(BinaryFunctor<elem::Mult, BinaryFunctor<elem::Bump, Assignee<1>, Capture>, Assignee<2> >, float, marian::Tensor, marian::Tensor, marian::Tensor);
 template void Add<BinaryFunctor<elem::Mult, BinaryFunctor<elem::Lt, Assignee<2>, Assignee<3>>, Assignee<1>>, marian::Tensor, marian::Tensor, marian::Tensor>(BinaryFunctor<elem::Mult, BinaryFunctor<elem::Lt, Assignee<2>, Assignee<3>>, Assignee<1>>, float, marian::Tensor, marian::Tensor, marian::Tensor, marian::Tensor);
 template void Add<BinaryFunctor<elem::Mult, BinaryFunctor<elem::Geq, Assignee<2>, Assignee<3>>, Assignee<1>>, marian::Tensor, marian::Tensor, marian::Tensor>(BinaryFunctor<elem::Mult, BinaryFunctor<elem::Geq, Assignee<2>, Assignee<3>>, Assignee<1>>, float, marian::Tensor, marian::Tensor, marian::Tensor, marian::Tensor);
 template void Add<BinaryFunctor<elem::Mult, Assignee<1>, UnaryFunctor<elem::Sigmoid, BinaryFunctor<elem::Minus, Assignee<3>, Assignee<2>>>>, marian::Tensor, marian::Tensor, marian::Tensor>(BinaryFunctor<elem::Mult, Assignee<1>, UnaryFunctor<elem::Sigmoid, BinaryFunctor<elem::Minus, Assignee<3>, Assignee<2>>>>, float, marian::Tensor, marian::Tensor, marian::Tensor, marian::Tensor);
 template void Add<BinaryFunctor<elem::Mult, BinaryFunctor<elem::Gt, Assignee<2>, Assignee<3>>, Assignee<1>>, marian::Tensor, marian::Tensor, marian::Tensor>(BinaryFunctor<elem::Mult, BinaryFunctor<elem::Gt, Assignee<2>, Assignee<3>>, Assignee<1>>, float, marian::Tensor, marian::Tensor, marian::Tensor, marian::Tensor);
 template void Add<BinaryFunctor<elem::Mult, BinaryFunctor<elem::Leq, Assignee<2>, Assignee<3>>, Assignee<1>>, marian::Tensor, marian::Tensor, marian::Tensor>(BinaryFunctor<elem::Mult, BinaryFunctor<elem::Leq, Assignee<2>, Assignee<3>>, Assignee<1>>, float, marian::Tensor, marian::Tensor, marian::Tensor, marian::Tensor);
 template void Add<BinaryFunctor<elem::Mult, Assignee<1>, UnaryFunctor<elem::Sigmoid, BinaryFunctor<elem::Minus, Assignee<2>, Assignee<3>>>>, marian::Tensor, marian::Tensor, marian::Tensor>(BinaryFunctor<elem::Mult, Assignee<1>, UnaryFunctor<elem::Sigmoid, BinaryFunctor<elem::Minus, Assignee<2>, Assignee<3>>>>, float, marian::Tensor, marian::Tensor, marian::Tensor, marian::Tensor);
-template void Add<BinaryFunctor<elem::Div, Assignee<1>, Assignee<2> >, std::shared_ptr<marian::TensorBase>, std::shared_ptr<marian::TensorBase> >(BinaryFunctor<elem::Div, Assignee<1>, Assignee<2> >, float, std::shared_ptr<marian::TensorBase>, std::shared_ptr<marian::TensorBase>, std::shared_ptr<marian::TensorBase>);
-template void marian::gpu::Add<marian::functional::BinaryFunctor<marian::functional::elem::Mult, marian::functional::Assignee<1>, marian::functional::Assignee<1> >, std::shared_ptr<marian::TensorBase> >(marian::functional::BinaryFunctor<marian::functional::elem::Mult, marian::functional::Assignee<1>, marian::functional::Assignee<1> >, float, std::shared_ptr<marian::TensorBase>, std::shared_ptr<marian::TensorBase>);
-template void marian::gpu::Aggregate<marian::functional::Assignee<1>, marian::functional::BinaryFunctor<marian::functional::elem::Minimum, marian::functional::Assignee<1>, marian::functional::Assignee<2> >, std::shared_ptr<marian::TensorBase> >(marian::functional::Assignee<1>, float, marian::functional::BinaryFunctor<marian::functional::elem::Minimum, marian::functional::Assignee<1>, marian::functional::Assignee<2> >, float, std::shared_ptr<marian::TensorBase>, std::shared_ptr<marian::TensorBase>);
-template void marian::gpu::Aggregate<marian::functional::Assignee<1>, marian::functional::BinaryFunctor<marian::functional::elem::Maximum, marian::functional::Assignee<1>, marian::functional::Assignee<2> >, std::shared_ptr<marian::TensorBase> >(marian::functional::Assignee<1>, float, marian::functional::BinaryFunctor<marian::functional::elem::Maximum, marian::functional::Assignee<1>, marian::functional::Assignee<2> >, float, std::shared_ptr<marian::TensorBase>, std::shared_ptr<marian::TensorBase>);
-template void marian::gpu::Aggregate<marian::functional::Assignee<1>, marian::functional::BinaryFunctor<marian::functional::elem::Mult, marian::functional::Assignee<1>, marian::functional::Assignee<2> >, std::shared_ptr<marian::TensorBase> >(marian::functional::Assignee<1>, float, marian::functional::BinaryFunctor<marian::functional::elem::Mult, marian::functional::Assignee<1>, marian::functional::Assignee<2> >, float, std::shared_ptr<marian::TensorBase>, std::shared_ptr<marian::TensorBase>);
-template void marian::gpu::Aggregate<marian::functional::Assignee<1>, marian::functional::BinaryFunctor<marian::functional::elem::LogAddExp, marian::functional::Assignee<1>, marian::functional::Assignee<2> >, std::shared_ptr<marian::TensorBase> >(marian::functional::Assignee<1>, float, marian::functional::BinaryFunctor<marian::functional::elem::LogAddExp, marian::functional::Assignee<1>, marian::functional::Assignee<2> >, float, std::shared_ptr<marian::TensorBase>, std::shared_ptr<marian::TensorBase>);
-template void marian::gpu::Add<marian::functional::BinaryFunctor<marian::functional::elem::Mult, marian::functional::BinaryFunctor<marian::functional::elem::Eq, marian::functional::Assignee<1>, marian::functional::Assignee<2> >, marian::functional::Assignee<3> >, std::shared_ptr<marian::TensorBase>, std::shared_ptr<marian::TensorBase>, std::shared_ptr<marian::TensorBase> >(marian::functional::BinaryFunctor<marian::functional::elem::Mult, marian::functional::BinaryFunctor<marian::functional::elem::Eq, marian::functional::Assignee<1>, marian::functional::Assignee<2> >, marian::functional::Assignee<3> >, float, std::shared_ptr<marian::TensorBase>, std::shared_ptr<marian::TensorBase>, std::shared_ptr<marian::TensorBase>, std::shared_ptr<marian::TensorBase>);
-template void marian::gpu::Add<marian::functional::BinaryFunctor<marian::functional::elem::Mult, marian::functional::Assignee<1>, marian::functional::UnaryFunctor<marian::functional::elem::Exp, marian::functional::BinaryFunctor<marian::functional::elem::Minus, marian::functional::Assignee<2>, marian::functional::Assignee<3> > > >, std::shared_ptr<marian::TensorBase>, std::shared_ptr<marian::TensorBase>, std::shared_ptr<marian::TensorBase> >(marian::functional::BinaryFunctor<marian::functional::elem::Mult, marian::functional::Assignee<1>, marian::functional::UnaryFunctor<marian::functional::elem::Exp, marian::functional::BinaryFunctor<marian::functional::elem::Minus, marian::functional::Assignee<2>, marian::functional::Assignee<3> > > >, float, std::shared_ptr<marian::TensorBase>, std::shared_ptr<marian::TensorBase>, std::shared_ptr<marian::TensorBase>, std::shared_ptr<marian::TensorBase>);
-template void marian::gpu::Add<marian::functional::BinaryFunctor<marian::functional::elem::Div, marian::functional::BinaryFunctor<marian::functional::elem::Mult, marian::functional::Assignee<1>, marian::functional::Assignee<2> >, marian::functional::Assignee<3> >, std::shared_ptr<marian::TensorBase>, std::shared_ptr<marian::TensorBase>, std::shared_ptr<marian::TensorBase> >(marian::functional::BinaryFunctor<marian::functional::elem::Div, marian::functional::BinaryFunctor<marian::functional::elem::Mult, marian::functional::Assignee<1>, marian::functional::Assignee<2> >, marian::functional::Assignee<3> >, float, std::shared_ptr<marian::TensorBase>, std::shared_ptr<marian::TensorBase>, std::shared_ptr<marian::TensorBase>, std::shared_ptr<marian::TensorBase>);
-template void marian::gpu::Add<marian::functional::BinaryFunctor<marian::functional::elem::Mult, marian::functional::BinaryFunctor<marian::functional::elem::Mult, marian::functional::Assignee<1>, marian::functional::Capture>, marian::functional::Assignee<2> >, std::shared_ptr<marian::TensorBase>, std::shared_ptr<marian::TensorBase> >(marian::functional::BinaryFunctor<marian::functional::elem::Mult, marian::functional::BinaryFunctor<marian::functional::elem::Mult, marian::functional::Assignee<1>, marian::functional::Capture>, marian::functional::Assignee<2> >, float, std::shared_ptr<marian::TensorBase>, std::shared_ptr<marian::TensorBase>, std::shared_ptr<marian::TensorBase>);
-template void marian::gpu::Add<marian::functional::BinaryFunctor<marian::functional::elem::Mult, marian::functional::Assignee<1>, marian::functional::BinaryFunctor<marian::functional::elem::Plus, marian::functional::BinaryFunctor<marian::functional::elem::Mult, marian::functional::Capture, marian::functional::Assignee<3> >, marian::functional::BinaryFunctor<marian::functional::elem::Mult, marian::functional::UnaryFunctor<marian::functional::elem::Sigmoid, marian::functional::BinaryFunctor<marian::functional::elem::Mult, marian::functional::Capture, marian::functional::Assignee<2> > >, marian::functional::BinaryFunctor<marian::functional::elem::Minus, marian::functional::Capture, marian::functional::BinaryFunctor<marian::functional::elem::Mult, marian::functional::Capture, marian::functional::Assignee<3> > > > > >, std::shared_ptr<marian::TensorBase>, std::shared_ptr<marian::TensorBase>, std::shared_ptr<marian::TensorBase> >(marian::functional::BinaryFunctor<marian::functional::elem::Mult, marian::functional::Assignee<1>, marian::functional::BinaryFunctor<marian::functional::elem::Plus, marian::functional::BinaryFunctor<marian::functional::elem::Mult, marian::functional::Capture, marian::functional::Assignee<3> >, marian::functional::BinaryFunctor<marian::functional::elem::Mult, marian::functional::UnaryFunctor<marian::functional::elem::Sigmoid, marian::functional::BinaryFunctor<marian::functional::elem::Mult, marian::functional::Capture, marian::functional::Assignee<2> > >, marian::functional::BinaryFunctor<marian::functional::elem::Minus, marian::functional::Capture, marian::functional::BinaryFunctor<marian::functional::elem::Mult, marian::functional::Capture, marian::functional::Assignee<3> > > > > >, float, std::shared_ptr<marian::TensorBase>, std::shared_ptr<marian::TensorBase>, std::shared_ptr<marian::TensorBase>, std::shared_ptr<marian::TensorBase>);
\ No newline at end of file
+template void Add<BinaryFunctor<elem::Div, Assignee<1>, Assignee<2> >, marian::Tensor, marian::Tensor >(BinaryFunctor<elem::Div, Assignee<1>, Assignee<2> >, float, marian::Tensor, marian::Tensor, marian::Tensor);
+
+template void marian::gpu::Add<marian::functional::BinaryFunctor<marian::functional::elem::Mult, marian::functional::Assignee<1>, marian::functional::Assignee<1> >, marian::Tensor >(marian::functional::BinaryFunctor<marian::functional::elem::Mult, marian::functional::Assignee<1>, marian::functional::Assignee<1> >, float, marian::Tensor, marian::Tensor);
+template void marian::gpu::Aggregate<marian::functional::Assignee<1>, marian::functional::BinaryFunctor<marian::functional::elem::Min, marian::functional::Assignee<1>, marian::functional::Assignee<2> >, marian::Tensor >(marian::functional::Assignee<1>, float, marian::functional::BinaryFunctor<marian::functional::elem::Min, marian::functional::Assignee<1>, marian::functional::Assignee<2> >, float, marian::Tensor, marian::Tensor);
+template void marian::gpu::Aggregate<marian::functional::Assignee<1>, marian::functional::BinaryFunctor<marian::functional::elem::Max, marian::functional::Assignee<1>, marian::functional::Assignee<2> >, marian::Tensor >(marian::functional::Assignee<1>, float, marian::functional::BinaryFunctor<marian::functional::elem::Max, marian::functional::Assignee<1>, marian::functional::Assignee<2> >, float, marian::Tensor, marian::Tensor);
+template void marian::gpu::Aggregate<marian::functional::Assignee<1>, marian::functional::BinaryFunctor<marian::functional::elem::Mult, marian::functional::Assignee<1>, marian::functional::Assignee<2> >, marian::Tensor >(marian::functional::Assignee<1>, float, marian::functional::BinaryFunctor<marian::functional::elem::Mult, marian::functional::Assignee<1>, marian::functional::Assignee<2> >, float, marian::Tensor, marian::Tensor);
+template void marian::gpu::Aggregate<marian::functional::Assignee<1>, marian::functional::BinaryFunctor<marian::functional::elem::LogAddExp, marian::functional::Assignee<1>, marian::functional::Assignee<2> >, marian::Tensor >(marian::functional::Assignee<1>, float, marian::functional::BinaryFunctor<marian::functional::elem::LogAddExp, marian::functional::Assignee<1>, marian::functional::Assignee<2> >, float, marian::Tensor, marian::Tensor);
+template void marian::gpu::Add<marian::functional::BinaryFunctor<marian::functional::elem::Mult, marian::functional::BinaryFunctor<marian::functional::elem::Eq, marian::functional::Assignee<1>, marian::functional::Assignee<2> >, marian::functional::Assignee<3> >, marian::Tensor, marian::Tensor, marian::Tensor >(marian::functional::BinaryFunctor<marian::functional::elem::Mult, marian::functional::BinaryFunctor<marian::functional::elem::Eq, marian::functional::Assignee<1>, marian::functional::Assignee<2> >, marian::functional::Assignee<3> >, float, marian::Tensor, marian::Tensor, marian::Tensor, marian::Tensor);
+template void marian::gpu::Add<marian::functional::BinaryFunctor<marian::functional::elem::Mult, marian::functional::Assignee<1>, marian::functional::UnaryFunctor<marian::functional::elem::Exp, marian::functional::BinaryFunctor<marian::functional::elem::Minus, marian::functional::Assignee<2>, marian::functional::Assignee<3> > > >, marian::Tensor, marian::Tensor, marian::Tensor >(marian::functional::BinaryFunctor<marian::functional::elem::Mult, marian::functional::Assignee<1>, marian::functional::UnaryFunctor<marian::functional::elem::Exp, marian::functional::BinaryFunctor<marian::functional::elem::Minus, marian::functional::Assignee<2>, marian::functional::Assignee<3> > > >, float, marian::Tensor, marian::Tensor, marian::Tensor, marian::Tensor);
+template void marian::gpu::Add<marian::functional::BinaryFunctor<marian::functional::elem::Div, marian::functional::BinaryFunctor<marian::functional::elem::Mult, marian::functional::Assignee<1>, marian::functional::Assignee<2> >, marian::functional::Assignee<3> >, marian::Tensor, marian::Tensor, marian::Tensor >(marian::functional::BinaryFunctor<marian::functional::elem::Div, marian::functional::BinaryFunctor<marian::functional::elem::Mult, marian::functional::Assignee<1>, marian::functional::Assignee<2> >, marian::functional::Assignee<3> >, float, marian::Tensor, marian::Tensor, marian::Tensor, marian::Tensor);
+template void marian::gpu::Add<marian::functional::BinaryFunctor<marian::functional::elem::Mult, marian::functional::BinaryFunctor<marian::functional::elem::Mult, marian::functional::Assignee<1>, marian::functional::Capture>, marian::functional::Assignee<2> >, marian::Tensor, marian::Tensor >(marian::functional::BinaryFunctor<marian::functional::elem::Mult, marian::functional::BinaryFunctor<marian::functional::elem::Mult, marian::functional::Assignee<1>, marian::functional::Capture>, marian::functional::Assignee<2> >, float, marian::Tensor, marian::Tensor, marian::Tensor);
+template void marian::gpu::Add<marian::functional::BinaryFunctor<marian::functional::elem::Mult, marian::functional::Assignee<1>, marian::functional::BinaryFunctor<marian::functional::elem::Plus, marian::functional::BinaryFunctor<marian::functional::elem::Mult, marian::functional::Capture, marian::functional::Assignee<3> >, marian::functional::BinaryFunctor<marian::functional::elem::Mult, marian::functional::UnaryFunctor<marian::functional::elem::Sigmoid, marian::functional::BinaryFunctor<marian::functional::elem::Mult, marian::functional::Capture, marian::functional::Assignee<2> > >, marian::functional::BinaryFunctor<marian::functional::elem::Minus, marian::functional::Capture, marian::functional::BinaryFunctor<marian::functional::elem::Mult, marian::functional::Capture, marian::functional::Assignee<3> > > > > >, marian::Tensor, marian::Tensor, marian::Tensor >(marian::functional::BinaryFunctor<marian::functional::elem::Mult, marian::functional::Assignee<1>, marian::functional::BinaryFunctor<marian::functional::elem::Plus, marian::functional::BinaryFunctor<marian::functional::elem::Mult, marian::functional::Capture, marian::functional::Assignee<3> >, marian::functional::BinaryFunctor<marian::functional::elem::Mult, marian::functional::UnaryFunctor<marian::functional::elem::Sigmoid, marian::functional::BinaryFunctor<marian::functional::elem::Mult, marian::functional::Capture, marian::functional::Assignee<2> > >, marian::functional::BinaryFunctor<marian::functional::elem::Minus, marian::functional::Capture, marian::functional::BinaryFunctor<marian::functional::elem::Mult, marian::functional::Capture, marian::functional::Assignee<3> > > > > >, float, marian::Tensor, marian::Tensor, marian::Tensor, marian::Tensor);
+
diff --git a/src/tensors/gpu/algorithm.cu b/src/tensors/gpu/algorithm.cu
index 40709866c..c3c8418bc 100644
--- a/src/tensors/gpu/algorithm.cu
+++ b/src/tensors/gpu/algorithm.cu
@@ -5,6 +5,8 @@
 #include "tensors/gpu/cuda_helpers.h"
 // clang-format on
 
+#include <cuda_fp16.h>
+
 namespace marian {
 namespace gpu {
 
@@ -27,6 +29,7 @@ template void copy<uint32_t>(Ptr<Backend>, const uint32_t*, const uint32_t*, uin
 template void copy<uint64_t>(Ptr<Backend>, const uint64_t*, const uint64_t*, uint64_t*);
 
 template void copy<char>(Ptr<Backend>, const char*, const char*, char*);
+template void copy<float16>(Ptr<Backend>, const float16*, const float16*, float16*);
 template void copy<float>(Ptr<Backend>, const float*, const float*, float*);
 template void copy<double>(Ptr<Backend>, const double*, const double*, double*);
 // clang-format on
@@ -55,6 +58,18 @@ void fill(Ptr<Backend> backend, T* begin, T* end, T value) {
   CUDA_CHECK(cudaStreamSynchronize(0));
 }
 
+template <>
+void fill<float16>(Ptr<Backend> backend, float16* begin, float16* end, float16 value) {
+  int size = end - begin;
+  if (size == 0)
+    return;
+  CUDA_CHECK(cudaSetDevice(backend->getDeviceId().no));
+  int threadsPerBlock = std::min(MAX_THREADS, size);
+  int blocks = (size / threadsPerBlock) + (size % threadsPerBlock != 0); // @TODO: (size+threadsPerBlock-1)/threadsPerBlock or CeilDiv(a,b)
+  gFill<<<blocks, threadsPerBlock>>>((__half*)begin, size, (__half)value);
+  CUDA_CHECK(cudaStreamSynchronize(0));
+}
+
 template void fill<bool>(Ptr<Backend>, bool*, bool*, bool);
 template void fill<int8_t>(Ptr<Backend>, int8_t*, int8_t*, int8_t);
 template void fill<int16_t>(Ptr<Backend>, int16_t*, int16_t*, int16_t);
@@ -102,7 +117,21 @@ void swap_ranges(Ptr<Backend> backend, T* begin, T* end, T* dest) {
   CUDA_CHECK(cudaStreamSynchronize(0));
 }
 
+template <>
+void swap_ranges<float16>(Ptr<Backend> backend, float16* begin, float16* end, float16* dest) {
+  int size = end - begin;
+  if (size == 0)
+    return;
+
+  CUDA_CHECK(cudaSetDevice(backend->getDeviceId().no));
+  int threadsPerBlock = std::min(MAX_THREADS, size);
+  int blocks = (size / threadsPerBlock) + (size % threadsPerBlock != 0); // @TODO: (size+threadsPerBlock-1)/threadsPerBlock or CeilDiv(a,b)
+  gSwap<<<blocks, threadsPerBlock>>>((__half*)begin, (__half*)dest, size);
+  CUDA_CHECK(cudaStreamSynchronize(0));
+}
+
 // clang-format off
+template void swap_ranges<char>(Ptr<Backend>, char*, char*, char*);
 template void swap_ranges<int8_t>(Ptr<Backend>, int8_t*, int8_t*, int8_t*);
 template void swap_ranges<int16_t>(Ptr<Backend>, int16_t*, int16_t*, int16_t*);
 template void swap_ranges<int32_t>(Ptr<Backend>, int32_t*, int32_t*, int32_t*);
@@ -113,7 +142,6 @@ template void swap_ranges<uint16_t>(Ptr<Backend>, uint16_t*, uint16_t*, uint16_t
 template void swap_ranges<uint32_t>(Ptr<Backend>, uint32_t*, uint32_t*, uint32_t*);
 template void swap_ranges<uint64_t>(Ptr<Backend>, uint64_t*, uint64_t*, uint64_t*);
 
-template void swap_ranges<char>(Ptr<Backend>, char*, char*, char*);
 template void swap_ranges<float>(Ptr<Backend>, float*, float*, float*);
 template void swap_ranges<double>(Ptr<Backend>, double*, double*, double*);
 // clang-format on
diff --git a/src/tensors/gpu/element.cu b/src/tensors/gpu/element.cu
old mode 100644
new mode 100755
index e48abc073..456958474
--- a/src/tensors/gpu/element.cu
+++ b/src/tensors/gpu/element.cu
@@ -9,10 +9,10 @@
 namespace marian {
 namespace gpu {
 
-template <size_t K, bool broadcast, class Functor>
+template <size_t K, bool broadcast, class Functor, typename T>
 __global__ void gElement(
     Functor functor,
-    functional::Array<functional::Tensor<float>, K> tensors) {
+    functional::Array<functional::Tensor<T>, K> tensors) {
   int length = tensors[0].shape().elements();
   functional::Array<int, functional::Shape::size()> dims;
   functional::Array<int, K> indices;
@@ -28,32 +28,51 @@ __global__ void gElement(
           indices[i] = tensors[i].shape().bindex(dims);
       }
 
-      tensors[0][index] = functional::apply(functor, tensors, indices);
+      tensors[0].data()[index] = functional::apply(functor, tensors, indices);
     }
   }
 }
 
-template <class Functor, class... Tensors>
-void Element(Functor functor, Tensor out, Tensors... tensors) {
-  cudaSetDevice(out->getDeviceId().no);
 
-  constexpr size_t K = sizeof...(tensors) + 1;
-  functional::Array<functional::Tensor<float>, K> gTensors = {out, tensors...};
+template <typename T, class Functor, class... Tensors>
+void ElementTyped(Functor functor, Tensor out, Tensors... tensors) {
+  //matchOrAbort<T>(out->type()); // @TODO: figure out undefined reference
 
-  int length = gTensors[0].shape().elements();
+  cudaSetDevice(out->getDeviceId().no);
+
+  int length = out->shape().elements();
   int threads = std::min(MAX_THREADS, length);
   int blocks = std::min(MAX_BLOCKS, length / threads + (length % threads != 0));
 
+  constexpr size_t K = sizeof...(tensors) + 1;
+  functional::Array<functional::Tensor<T>, K> gTensors = {out, tensors...};
+
   bool broadcast = false;
   for(int i = 1; i < K; ++i)
     broadcast = broadcast || gTensors[0].shape() != gTensors[i].shape();
-
   if(broadcast)
     gElement<K, true><<<blocks, threads>>>(functor, gTensors);
   else
     gElement<K, false><<<blocks, threads>>>(functor, gTensors);
 }
 
+template <class Functor, class... Tensors>
+void Element(Functor functor, Tensor out, Tensors... tensors) {
+  checkCommonType(out, tensors...);
+
+  if(out->type() == Type::float32) {
+    ElementTyped<float>(functor, out, tensors...);
+#if 0
+  } else if(out->type() == Type::float16) {
+    ElementTyped<half>(functor, out, tensors...);
+  } else if(out->type() == Type::float64) {
+    ElementTyped<double>(functor, out, tensors...);
+#endif
+  } else {
+    ABORT("Type {} not yet supported", out->type());
+  }
+}
+
 #include "tensors/gpu/element.inc"
 }  // namespace gpu
 }  // namespace marian
diff --git a/src/tensors/gpu/element.inc b/src/tensors/gpu/element.inc
old mode 100644
new mode 100755
index 364866fab..d7ce5b79a
--- a/src/tensors/gpu/element.inc
+++ b/src/tensors/gpu/element.inc
@@ -39,24 +39,29 @@ template void Element<Assign<Var<1>, TernaryFunctor<elem::IfThenElse, BinaryFunc
 template void Element<Assign<Var<1>, TernaryFunctor<elem::IfThenElse, BinaryFunctor<elem::Leq, UnaryFunctor<elem::Abs, Assignee<2>>, Capture>, Capture, Capture>>, marian::Tensor>(Assign<Var<1>, TernaryFunctor<elem::IfThenElse, BinaryFunctor<elem::Leq, UnaryFunctor<elem::Abs, Assignee<2>>, Capture>, Capture, Capture>>, marian::Tensor, marian::Tensor);
 template void Element<Assign<Var<1>, BinaryFunctor<elem::Clip, Assignee<2>, Capture>>, marian::Tensor>(Assign<Var<1>, BinaryFunctor<elem::Clip, Assignee<2>, Capture>>, marian::Tensor, marian::Tensor);
 template void Element<Assign<Var<1>, BinaryFunctor<elem::LogAddExp, Assignee<2>, Assignee<3>>>, marian::Tensor, marian::Tensor>(Assign<Var<1>, BinaryFunctor<elem::LogAddExp, Assignee<2>, Assignee<3>>>, marian::Tensor, marian::Tensor, marian::Tensor);
-template void Element<Assign<Var<1>, BinaryFunctor<elem::Maximum, Assignee<2>, Assignee<3>>>, marian::Tensor, marian::Tensor>(Assign<Var<1>, BinaryFunctor<elem::Maximum, Assignee<2>, Assignee<3>>>, marian::Tensor, marian::Tensor, marian::Tensor);
-template void Element<Assign<Var<1>, BinaryFunctor<elem::Minimum, Assignee<2>, Assignee<3>>>, marian::Tensor, marian::Tensor>(Assign<Var<1>, BinaryFunctor<elem::Minimum, Assignee<2>, Assignee<3>>>, marian::Tensor, marian::Tensor, marian::Tensor);
+template void Element<Assign<Var<1>, BinaryFunctor<elem::Max, Assignee<2>, Assignee<3>>>, marian::Tensor, marian::Tensor>(Assign<Var<1>, BinaryFunctor<elem::Max, Assignee<2>, Assignee<3>>>, marian::Tensor, marian::Tensor, marian::Tensor);
+template void Element<Assign<Var<1>, BinaryFunctor<elem::Min, Assignee<2>, Assignee<3>>>, marian::Tensor, marian::Tensor>(Assign<Var<1>, BinaryFunctor<elem::Min, Assignee<2>, Assignee<3>>>, marian::Tensor, marian::Tensor, marian::Tensor);
 template void Element<Assign<Var<1>, BinaryFunctor<elem::Div, Assignee<1>, Capture>>>(Assign<Var<1>, BinaryFunctor<elem::Div, Assignee<1>, Capture>>, marian::Tensor);
 template void Element<Assign<Var<1>, BinaryFunctor<elem::Minus, Assignee<1>, BinaryFunctor<elem::Mult, Capture, BinaryFunctor<elem::Plus, BinaryFunctor<elem::Div, BinaryFunctor<elem::Div, Assignee<2>, Capture>, BinaryFunctor<elem::Plus, UnaryFunctor<elem::Sqrt, BinaryFunctor<elem::Div, Assignee<3>, Capture>>, Capture>>, BinaryFunctor<elem::Mult, Capture, Assignee<1>>>>>>, marian::Tensor, marian::Tensor>(Assign<Var<1>, BinaryFunctor<elem::Minus, Assignee<1>, BinaryFunctor<elem::Mult, Capture, BinaryFunctor<elem::Plus, BinaryFunctor<elem::Div, BinaryFunctor<elem::Div, Assignee<2>, Capture>, BinaryFunctor<elem::Plus, UnaryFunctor<elem::Sqrt, BinaryFunctor<elem::Div, Assignee<3>, Capture>>, Capture>>, BinaryFunctor<elem::Mult, Capture, Assignee<1>>>>>>, marian::Tensor, marian::Tensor, marian::Tensor);
 template void Element<Assign<Var<1>, BinaryFunctor<elem::Plus, BinaryFunctor<elem::Mult, Capture, Assignee<1>>, Capture>>>(Assign<Var<1>, BinaryFunctor<elem::Plus, BinaryFunctor<elem::Mult, Capture, Assignee<1>>, Capture>>, marian::Tensor);
 template void Element<Assign<Var<1>, BinaryFunctor<elem::Div, BinaryFunctor<elem::Lt, Assignee<1>, Capture>, Capture>>>(Assign<Var<1>, BinaryFunctor<elem::Div, BinaryFunctor<elem::Lt, Assignee<1>, Capture>, Capture>>, marian::Tensor);
 template void Element<Assign<Var<1>, UnaryFunctor<elem::Neg, UnaryFunctor<elem::Log, UnaryFunctor<elem::Neg, UnaryFunctor<elem::Log, Assignee<1>>>>>>>(Assign<Var<1>, UnaryFunctor<elem::Neg, UnaryFunctor<elem::Log, UnaryFunctor<elem::Neg, UnaryFunctor<elem::Log, Assignee<1>>>>>>, marian::Tensor);
 template void Element<Assign<Var<1>, UnaryFunctor<elem::Neg, UnaryFunctor<elem::Log, BinaryFunctor<elem::Plus, UnaryFunctor<elem::Neg, UnaryFunctor<elem::Log, BinaryFunctor<elem::Plus, Assignee<1>, Capture>>>, Capture>>>>>(Assign<Var<1>, UnaryFunctor<elem::Neg, UnaryFunctor<elem::Log, BinaryFunctor<elem::Plus, UnaryFunctor<elem::Neg, UnaryFunctor<elem::Log, BinaryFunctor<elem::Plus, Assignee<1>, Capture>>>, Capture>>>>, marian::Tensor);
-template void Element<Assign<Var<1>, BinaryFunctor<elem::Mult, BinaryFunctor<elem::Lt, Assignee<1>, Capture>, Capture>>>(Assign<Var<1>, BinaryFunctor<elem::Mult, BinaryFunctor<elem::Lt, Assignee<1>, Capture>, Capture> >, marian::Tensor);
-template void Element<Assign<Var<1>, BinaryFunctor<elem::Minus, Assignee<1>, BinaryFunctor<elem::Mult, Capture, BinaryFunctor<elem::Plus, BinaryFunctor<elem::Div, BinaryFunctor<elem::Div, Assignee<2>, Capture>, BinaryFunctor<elem::Plus, UnaryFunctor<elem::Sqrt, BinaryFunctor<elem::Plus, BinaryFunctor<elem::Div, UnaryFunctor<elem::sReLU, BinaryFunctor<elem::Minus, Assignee<3>, BinaryFunctor<elem::Mult, BinaryFunctor<elem::Mult, Capture, Assignee<4> >, Assignee<4> > > >, Capture>, BinaryFunctor<elem::Mult, Assignee<4>, Assignee<4> > > >, Capture> >, BinaryFunctor<elem::Mult, Capture, Assignee<1> > > > > >, std::shared_ptr<marian::TensorBase>, std::shared_ptr<marian::TensorBase>, std::shared_ptr<marian::TensorBase> >(Assign<Var<1>, BinaryFunctor<elem::Minus, Assignee<1>, BinaryFunctor<elem::Mult, Capture, BinaryFunctor<elem::Plus, BinaryFunctor<elem::Div, BinaryFunctor<elem::Div, Assignee<2>, Capture>, BinaryFunctor<elem::Plus, UnaryFunctor<elem::Sqrt, BinaryFunctor<elem::Plus, BinaryFunctor<elem::Div, UnaryFunctor<elem::sReLU, BinaryFunctor<elem::Minus, Assignee<3>, BinaryFunctor<elem::Mult, BinaryFunctor<elem::Mult, Capture, Assignee<4> >, Assignee<4> > > >, Capture>, BinaryFunctor<elem::Mult, Assignee<4>, Assignee<4> > > >, Capture> >, BinaryFunctor<elem::Mult, Capture, Assignee<1> > > > > >, std::shared_ptr<marian::TensorBase>, std::shared_ptr<marian::TensorBase>, std::shared_ptr<marian::TensorBase>, std::shared_ptr<marian::TensorBase>);
-template void Element<Assign<Var<1>, BinaryFunctor<elem::Minus, Assignee<1>, BinaryFunctor<elem::Mult, Capture, BinaryFunctor<elem::Plus, BinaryFunctor<elem::Div, BinaryFunctor<elem::Div, Assignee<2>, Capture>, BinaryFunctor<elem::Plus, UnaryFunctor<elem::Sqrt, BinaryFunctor<elem::Plus, BinaryFunctor<elem::Div, UnaryFunctor<elem::sReLU, BinaryFunctor<elem::Minus, Assignee<3>, BinaryFunctor<elem::Div, BinaryFunctor<elem::Mult, BinaryFunctor<elem::Mult, Capture, Assignee<4> >, Assignee<4> >, Capture> > >, Capture>, BinaryFunctor<elem::Div, BinaryFunctor<elem::Mult, Assignee<4>, Assignee<4> >, Capture> > >, Capture> >, BinaryFunctor<elem::Mult, Capture, Assignee<1> > > > > >, std::shared_ptr<marian::TensorBase>, std::shared_ptr<marian::TensorBase>, std::shared_ptr<marian::TensorBase> >(Assign<Var<1>, BinaryFunctor<elem::Minus, Assignee<1>, BinaryFunctor<elem::Mult, Capture, BinaryFunctor<elem::Plus, BinaryFunctor<elem::Div, BinaryFunctor<elem::Div, Assignee<2>, Capture>, BinaryFunctor<elem::Plus, UnaryFunctor<elem::Sqrt, BinaryFunctor<elem::Plus, BinaryFunctor<elem::Div, UnaryFunctor<elem::sReLU, BinaryFunctor<elem::Minus, Assignee<3>, BinaryFunctor<elem::Div, BinaryFunctor<elem::Mult, BinaryFunctor<elem::Mult, Capture, Assignee<4> >, Assignee<4> >, Capture> > >, Capture>, BinaryFunctor<elem::Div, BinaryFunctor<elem::Mult, Assignee<4>, Assignee<4> >, Capture> > >, Capture> >, BinaryFunctor<elem::Mult, Capture, Assignee<1> > > > > >, std::shared_ptr<marian::TensorBase>, std::shared_ptr<marian::TensorBase>, std::shared_ptr<marian::TensorBase>, std::shared_ptr<marian::TensorBase>);
-template void Element<Assign<Var<1>, BinaryFunctor<elem::Minus, Assignee<1>, BinaryFunctor<elem::Mult, Capture, BinaryFunctor<elem::Plus, BinaryFunctor<elem::Div, BinaryFunctor<elem::Div, Assignee<2>, Capture>, BinaryFunctor<elem::Plus, UnaryFunctor<elem::Sqrt, BinaryFunctor<elem::Div, BinaryFunctor<elem::Mult, Assignee<4>, Assignee<4> >, Capture> >, Capture> >, BinaryFunctor<elem::Mult, Capture, Assignee<1> > > > > >, std::shared_ptr<marian::TensorBase>, std::shared_ptr<marian::TensorBase>, std::shared_ptr<marian::TensorBase> >(Assign<Var<1>, BinaryFunctor<elem::Minus, Assignee<1>, BinaryFunctor<elem::Mult, Capture, BinaryFunctor<elem::Plus, BinaryFunctor<elem::Div, BinaryFunctor<elem::Div, Assignee<2>, Capture>, BinaryFunctor<elem::Plus, UnaryFunctor<elem::Sqrt, BinaryFunctor<elem::Div, BinaryFunctor<elem::Mult, Assignee<4>, Assignee<4> >, Capture> >, Capture> >, BinaryFunctor<elem::Mult, Capture, Assignee<1> > > > > >, std::shared_ptr<marian::TensorBase>, std::shared_ptr<marian::TensorBase>, std::shared_ptr<marian::TensorBase>, std::shared_ptr<marian::TensorBase>);
-template void Element<Assign<Var<1>, BinaryFunctor<elem::Minus, Assignee<1>, BinaryFunctor<elem::Mult, Capture, BinaryFunctor<elem::Plus, BinaryFunctor<elem::Div, BinaryFunctor<elem::Div, Assignee<2>, Capture>, BinaryFunctor<elem::Plus, UnaryFunctor<elem::Sqrt, BinaryFunctor<elem::Div, Assignee<3>, Capture> >, Capture> >, BinaryFunctor<elem::Mult, Capture, Assignee<1> > > > > >, std::shared_ptr<marian::TensorBase>, std::shared_ptr<marian::TensorBase>, std::shared_ptr<marian::TensorBase> >(Assign<Var<1>, BinaryFunctor<elem::Minus, Assignee<1>, BinaryFunctor<elem::Mult, Capture, BinaryFunctor<elem::Plus, BinaryFunctor<elem::Div, BinaryFunctor<elem::Div, Assignee<2>, Capture>, BinaryFunctor<elem::Plus, UnaryFunctor<elem::Sqrt, BinaryFunctor<elem::Div, Assignee<3>, Capture> >, Capture> >, BinaryFunctor<elem::Mult, Capture, Assignee<1> > > > > >, std::shared_ptr<marian::TensorBase>, std::shared_ptr<marian::TensorBase>, std::shared_ptr<marian::TensorBase>, std::shared_ptr<marian::TensorBase>);
-template void Element<Assign<Var<1>, BinaryFunctor<elem::Minus, Assignee<1>, BinaryFunctor<elem::Mult, Capture, BinaryFunctor<elem::Plus, BinaryFunctor<elem::Div, BinaryFunctor<elem::Div, Assignee<2>, Capture>, BinaryFunctor<elem::Plus, UnaryFunctor<elem::Sqrt, BinaryFunctor<elem::Plus, BinaryFunctor<elem::Div, UnaryFunctor<elem::sReLU, BinaryFunctor<elem::Plus, BinaryFunctor<elem::Minus, Assignee<3>, BinaryFunctor<elem::Div, BinaryFunctor<elem::Mult, BinaryFunctor<elem::Mult, Capture, Assignee<5> >, Assignee<4> >, Capture> >, BinaryFunctor<elem::Div, BinaryFunctor<elem::Mult, BinaryFunctor<elem::Mult, Capture, Assignee<4> >, Assignee<4> >, Capture> > >, Capture>, BinaryFunctor<elem::Div, BinaryFunctor<elem::Mult, Assignee<4>, Assignee<4> >, Capture> > >, Capture> >, BinaryFunctor<elem::Mult, Capture, Assignee<1> > > > > >, std::shared_ptr<marian::TensorBase>, std::shared_ptr<marian::TensorBase>, std::shared_ptr<marian::TensorBase>, std::shared_ptr<marian::TensorBase> >(Assign<Var<1>, BinaryFunctor<elem::Minus, Assignee<1>, BinaryFunctor<elem::Mult, Capture, BinaryFunctor<elem::Plus, BinaryFunctor<elem::Div, BinaryFunctor<elem::Div, Assignee<2>, Capture>, BinaryFunctor<elem::Plus, UnaryFunctor<elem::Sqrt, BinaryFunctor<elem::Plus, BinaryFunctor<elem::Div, UnaryFunctor<elem::sReLU, BinaryFunctor<elem::Plus, BinaryFunctor<elem::Minus, Assignee<3>, BinaryFunctor<elem::Div, BinaryFunctor<elem::Mult, BinaryFunctor<elem::Mult, Capture, Assignee<5> >, Assignee<4> >, Capture> >, BinaryFunctor<elem::Div, BinaryFunctor<elem::Mult, BinaryFunctor<elem::Mult, Capture, Assignee<4> >, Assignee<4> >, Capture> > >, Capture>, BinaryFunctor<elem::Div, BinaryFunctor<elem::Mult, Assignee<4>, Assignee<4> >, Capture> > >, Capture> >, BinaryFunctor<elem::Mult, Capture, Assignee<1> > > > > >, std::shared_ptr<marian::TensorBase>, std::shared_ptr<marian::TensorBase>, std::shared_ptr<marian::TensorBase>, std::shared_ptr<marian::TensorBase>, std::shared_ptr<marian::TensorBase>);
-template void Element<Assign<Var<1>, BinaryFunctor<elem::Minus, Assignee<1>, BinaryFunctor<elem::Mult, Capture, BinaryFunctor<elem::Plus, BinaryFunctor<elem::Div, BinaryFunctor<elem::Div, Assignee<2>, Capture>, BinaryFunctor<elem::Plus, UnaryFunctor<elem::Sqrt, BinaryFunctor<elem::Div, Assignee<3>, Capture> >, Capture> >, BinaryFunctor<elem::Mult, Capture, Assignee<1> > > > > >, std::shared_ptr<marian::TensorBase>, std::shared_ptr<marian::TensorBase>, std::shared_ptr<marian::TensorBase>, std::shared_ptr<marian::TensorBase> >(Assign<Var<1>, BinaryFunctor<elem::Minus, Assignee<1>, BinaryFunctor<elem::Mult, Capture, BinaryFunctor<elem::Plus, BinaryFunctor<elem::Div, BinaryFunctor<elem::Div, Assignee<2>, Capture>, BinaryFunctor<elem::Plus, UnaryFunctor<elem::Sqrt, BinaryFunctor<elem::Div, Assignee<3>, Capture> >, Capture> >, BinaryFunctor<elem::Mult, Capture, Assignee<1> > > > > >, std::shared_ptr<marian::TensorBase>, std::shared_ptr<marian::TensorBase>, std::shared_ptr<marian::TensorBase>, std::shared_ptr<marian::TensorBase>, std::shared_ptr<marian::TensorBase>);
-template void marian::gpu::Element<marian::functional::Assign<marian::functional::Var<1>, marian::functional::BinaryFunctor<marian::functional::elem::NEq, marian::functional::BinaryFunctor<marian::functional::elem::Eq, marian::functional::BinaryFunctor<marian::functional::elem::Minus, marian::functional::BinaryFunctor<marian::functional::elem::Gt, marian::functional::Assignee<2>, marian::functional::Assignee<3> >, marian::functional::BinaryFunctor<marian::functional::elem::Lt, marian::functional::Assignee<2>, marian::functional::Assignee<3> > >, marian::functional::Capture>, marian::functional::Capture> >, std::shared_ptr<marian::TensorBase>, std::shared_ptr<marian::TensorBase> >(marian::functional::Assign<marian::functional::Var<1>, marian::functional::BinaryFunctor<marian::functional::elem::NEq, marian::functional::BinaryFunctor<marian::functional::elem::Eq, marian::functional::BinaryFunctor<marian::functional::elem::Minus, marian::functional::BinaryFunctor<marian::functional::elem::Gt, marian::functional::Assignee<2>, marian::functional::Assignee<3> >, marian::functional::BinaryFunctor<marian::functional::elem::Lt, marian::functional::Assignee<2>, marian::functional::Assignee<3> > >, marian::functional::Capture>, marian::functional::Capture> >, std::shared_ptr<marian::TensorBase>, std::shared_ptr<marian::TensorBase>, std::shared_ptr<marian::TensorBase>);
-template void marian::gpu::Element<marian::functional::Assign<marian::functional::Var<1>, marian::functional::UnaryFunctor<marian::functional::elem::Sqrt, marian::functional::Assignee<1> > >>(marian::functional::Assign<marian::functional::Var<1>, marian::functional::UnaryFunctor<marian::functional::elem::Sqrt, marian::functional::Assignee<1> > >, std::shared_ptr<marian::TensorBase>);
-template void marian::gpu::Element<marian::functional::Assign<marian::functional::Var<1>, marian::functional::BinaryFunctor<marian::functional::elem::Mult, marian::functional::Assignee<2>, marian::functional::UnaryFunctor<marian::functional::elem::Sigmoid, marian::functional::BinaryFunctor<marian::functional::elem::Mult, marian::functional::Capture, marian::functional::Assignee<2> > > > >, std::shared_ptr<marian::TensorBase> >(marian::functional::Assign<marian::functional::Var<1>, marian::functional::BinaryFunctor<marian::functional::elem::Mult, marian::functional::Assignee<2>, marian::functional::UnaryFunctor<marian::functional::elem::Sigmoid, marian::functional::BinaryFunctor<marian::functional::elem::Mult, marian::functional::Capture, marian::functional::Assignee<2> > > > >, std::shared_ptr<marian::TensorBase>, std::shared_ptr<marian::TensorBase>);
+template void Element<Assign<Var<1>, BinaryFunctor<elem::Mult, BinaryFunctor<elem::Lt, Assignee<1>, Capture>, Capture>>>(Assign<Var<1>, BinaryFunctor<elem::Mult, BinaryFunctor<elem::Lt, Assignee<1>, Capture>, Capture>>, marian::Tensor);
+template void Element<Assign<Var<1>, BinaryFunctor<elem::Minus, Assignee<1>, BinaryFunctor<elem::Mult, Capture, BinaryFunctor<elem::Plus, BinaryFunctor<elem::Div, BinaryFunctor<elem::Div, Assignee<2>, Capture>, BinaryFunctor<elem::Plus, UnaryFunctor<elem::Sqrt, BinaryFunctor<elem::Plus, BinaryFunctor<elem::Div, UnaryFunctor<elem::sReLU, BinaryFunctor<elem::Minus, Assignee<3>, BinaryFunctor<elem::Mult, BinaryFunctor<elem::Mult, Capture, Assignee<4>>, Assignee<4>>>>, Capture>, BinaryFunctor<elem::Mult, Assignee<4>, Assignee<4>>>>, Capture>>, BinaryFunctor<elem::Mult, Capture, Assignee<1>>>>>>, marian::Tensor, marian::Tensor, marian::Tensor>(Assign<Var<1>, BinaryFunctor<elem::Minus, Assignee<1>, BinaryFunctor<elem::Mult, Capture, BinaryFunctor<elem::Plus, BinaryFunctor<elem::Div, BinaryFunctor<elem::Div, Assignee<2>, Capture>, BinaryFunctor<elem::Plus, UnaryFunctor<elem::Sqrt, BinaryFunctor<elem::Plus, BinaryFunctor<elem::Div, UnaryFunctor<elem::sReLU, BinaryFunctor<elem::Minus, Assignee<3>, BinaryFunctor<elem::Mult, BinaryFunctor<elem::Mult, Capture, Assignee<4>>, Assignee<4>>>>, Capture>, BinaryFunctor<elem::Mult, Assignee<4>, Assignee<4>>>>, Capture>>, BinaryFunctor<elem::Mult, Capture, Assignee<1>>>>>>, marian::Tensor, marian::Tensor, marian::Tensor, marian::Tensor);
+template void Element<Assign<Var<1>, BinaryFunctor<elem::Minus, Assignee<1>, BinaryFunctor<elem::Mult, Capture, BinaryFunctor<elem::Plus, BinaryFunctor<elem::Div, BinaryFunctor<elem::Div, Assignee<2>, Capture>, BinaryFunctor<elem::Plus, UnaryFunctor<elem::Sqrt, BinaryFunctor<elem::Plus, BinaryFunctor<elem::Div, UnaryFunctor<elem::sReLU, BinaryFunctor<elem::Minus, Assignee<3>, BinaryFunctor<elem::Div, BinaryFunctor<elem::Mult, BinaryFunctor<elem::Mult, Capture, Assignee<4>>, Assignee<4>>, Capture>>>, Capture>, BinaryFunctor<elem::Div, BinaryFunctor<elem::Mult, Assignee<4>, Assignee<4>>, Capture>>>, Capture>>, BinaryFunctor<elem::Mult, Capture, Assignee<1>>>>>>, marian::Tensor, marian::Tensor, marian::Tensor>(Assign<Var<1>, BinaryFunctor<elem::Minus, Assignee<1>, BinaryFunctor<elem::Mult, Capture, BinaryFunctor<elem::Plus, BinaryFunctor<elem::Div, BinaryFunctor<elem::Div, Assignee<2>, Capture>, BinaryFunctor<elem::Plus, UnaryFunctor<elem::Sqrt, BinaryFunctor<elem::Plus, BinaryFunctor<elem::Div, UnaryFunctor<elem::sReLU, BinaryFunctor<elem::Minus, Assignee<3>, BinaryFunctor<elem::Div, BinaryFunctor<elem::Mult, BinaryFunctor<elem::Mult, Capture, Assignee<4>>, Assignee<4>>, Capture>>>, Capture>, BinaryFunctor<elem::Div, BinaryFunctor<elem::Mult, Assignee<4>, Assignee<4>>, Capture>>>, Capture>>, BinaryFunctor<elem::Mult, Capture, Assignee<1>>>>>>, marian::Tensor, marian::Tensor, marian::Tensor, marian::Tensor);
+template void Element<Assign<Var<1>, BinaryFunctor<elem::Minus, Assignee<1>, BinaryFunctor<elem::Mult, Capture, BinaryFunctor<elem::Plus, BinaryFunctor<elem::Div, BinaryFunctor<elem::Div, Assignee<2>, Capture>, BinaryFunctor<elem::Plus, UnaryFunctor<elem::Sqrt, BinaryFunctor<elem::Div, BinaryFunctor<elem::Mult, Assignee<4>, Assignee<4>>, Capture>>, Capture>>, BinaryFunctor<elem::Mult, Capture, Assignee<1>>>>>>, marian::Tensor, marian::Tensor, marian::Tensor>(Assign<Var<1>, BinaryFunctor<elem::Minus, Assignee<1>, BinaryFunctor<elem::Mult, Capture, BinaryFunctor<elem::Plus, BinaryFunctor<elem::Div, BinaryFunctor<elem::Div, Assignee<2>, Capture>, BinaryFunctor<elem::Plus, UnaryFunctor<elem::Sqrt, BinaryFunctor<elem::Div, BinaryFunctor<elem::Mult, Assignee<4>, Assignee<4>>, Capture>>, Capture>>, BinaryFunctor<elem::Mult, Capture, Assignee<1>>>>>>, marian::Tensor, marian::Tensor, marian::Tensor, marian::Tensor);
+template void Element<Assign<Var<1>, BinaryFunctor<elem::Minus, Assignee<1>, BinaryFunctor<elem::Mult, Capture, BinaryFunctor<elem::Plus, BinaryFunctor<elem::Div, BinaryFunctor<elem::Div, Assignee<2>, Capture>, BinaryFunctor<elem::Plus, UnaryFunctor<elem::Sqrt, BinaryFunctor<elem::Div, Assignee<3>, Capture>>, Capture>>, BinaryFunctor<elem::Mult, Capture, Assignee<1>>>>>>, marian::Tensor, marian::Tensor, marian::Tensor>(Assign<Var<1>, BinaryFunctor<elem::Minus, Assignee<1>, BinaryFunctor<elem::Mult, Capture, BinaryFunctor<elem::Plus, BinaryFunctor<elem::Div, BinaryFunctor<elem::Div, Assignee<2>, Capture>, BinaryFunctor<elem::Plus, UnaryFunctor<elem::Sqrt, BinaryFunctor<elem::Div, Assignee<3>, Capture>>, Capture>>, BinaryFunctor<elem::Mult, Capture, Assignee<1>>>>>>, marian::Tensor, marian::Tensor, marian::Tensor, marian::Tensor);
+template void Element<Assign<Var<1>, BinaryFunctor<elem::Minus, Assignee<1>, BinaryFunctor<elem::Mult, Capture, BinaryFunctor<elem::Plus, BinaryFunctor<elem::Div, BinaryFunctor<elem::Div, Assignee<2>, Capture>, BinaryFunctor<elem::Plus, UnaryFunctor<elem::Sqrt, BinaryFunctor<elem::Plus, BinaryFunctor<elem::Div, UnaryFunctor<elem::sReLU, BinaryFunctor<elem::Plus, BinaryFunctor<elem::Minus, Assignee<3>, BinaryFunctor<elem::Div, BinaryFunctor<elem::Mult, BinaryFunctor<elem::Mult, Capture, Assignee<5>>, Assignee<4>>, Capture>>, BinaryFunctor<elem::Div, BinaryFunctor<elem::Mult, BinaryFunctor<elem::Mult, Capture, Assignee<4>>, Assignee<4>>, Capture>>>, Capture>, BinaryFunctor<elem::Div, BinaryFunctor<elem::Mult, Assignee<4>, Assignee<4>>, Capture>>>, Capture>>, BinaryFunctor<elem::Mult, Capture, Assignee<1>>>>>>, marian::Tensor, marian::Tensor, marian::Tensor, marian::Tensor>(Assign<Var<1>, BinaryFunctor<elem::Minus, Assignee<1>, BinaryFunctor<elem::Mult, Capture, BinaryFunctor<elem::Plus, BinaryFunctor<elem::Div, BinaryFunctor<elem::Div, Assignee<2>, Capture>, BinaryFunctor<elem::Plus, UnaryFunctor<elem::Sqrt, BinaryFunctor<elem::Plus, BinaryFunctor<elem::Div, UnaryFunctor<elem::sReLU, BinaryFunctor<elem::Plus, BinaryFunctor<elem::Minus, Assignee<3>, BinaryFunctor<elem::Div, BinaryFunctor<elem::Mult, BinaryFunctor<elem::Mult, Capture, Assignee<5>>, Assignee<4>>, Capture>>, BinaryFunctor<elem::Div, BinaryFunctor<elem::Mult, BinaryFunctor<elem::Mult, Capture, Assignee<4>>, Assignee<4>>, Capture>>>, Capture>, BinaryFunctor<elem::Div, BinaryFunctor<elem::Mult, Assignee<4>, Assignee<4>>, Capture>>>, Capture>>, BinaryFunctor<elem::Mult, Capture, Assignee<1>>>>>>, marian::Tensor, marian::Tensor, marian::Tensor, marian::Tensor, marian::Tensor);
+template void Element<Assign<Var<1>, BinaryFunctor<elem::Minus, Assignee<1>, BinaryFunctor<elem::Mult, Capture, BinaryFunctor<elem::Plus, BinaryFunctor<elem::Div, BinaryFunctor<elem::Div, Assignee<2>, Capture>, BinaryFunctor<elem::Plus, UnaryFunctor<elem::Sqrt, BinaryFunctor<elem::Div, Assignee<3>, Capture>>, Capture>>, BinaryFunctor<elem::Mult, Capture, Assignee<1>>>>>>, marian::Tensor, marian::Tensor, marian::Tensor, marian::Tensor>(Assign<Var<1>, BinaryFunctor<elem::Minus, Assignee<1>, BinaryFunctor<elem::Mult, Capture, BinaryFunctor<elem::Plus, BinaryFunctor<elem::Div, BinaryFunctor<elem::Div, Assignee<2>, Capture>, BinaryFunctor<elem::Plus, UnaryFunctor<elem::Sqrt, BinaryFunctor<elem::Div, Assignee<3>, Capture>>, Capture>>, BinaryFunctor<elem::Mult, Capture, Assignee<1>>>>>>, marian::Tensor, marian::Tensor, marian::Tensor, marian::Tensor, marian::Tensor);
+
+template void marian::gpu::Element<marian::functional::Assign<marian::functional::Var<1>, marian::functional::BinaryFunctor<marian::functional::elem::NEq, marian::functional::BinaryFunctor<marian::functional::elem::Eq, marian::functional::BinaryFunctor<marian::functional::elem::Minus, marian::functional::BinaryFunctor<marian::functional::elem::Gt, marian::functional::Assignee<2>, marian::functional::Assignee<3> >, marian::functional::BinaryFunctor<marian::functional::elem::Lt, marian::functional::Assignee<2>, marian::functional::Assignee<3> > >, marian::functional::Capture>, marian::functional::Capture> >, marian::Tensor, marian::Tensor >(marian::functional::Assign<marian::functional::Var<1>, marian::functional::BinaryFunctor<marian::functional::elem::NEq, marian::functional::BinaryFunctor<marian::functional::elem::Eq, marian::functional::BinaryFunctor<marian::functional::elem::Minus, marian::functional::BinaryFunctor<marian::functional::elem::Gt, marian::functional::Assignee<2>, marian::functional::Assignee<3> >, marian::functional::BinaryFunctor<marian::functional::elem::Lt, marian::functional::Assignee<2>, marian::functional::Assignee<3> > >, marian::functional::Capture>, marian::functional::Capture> >, marian::Tensor, marian::Tensor, marian::Tensor);
+template void marian::gpu::Element<marian::functional::Assign<marian::functional::Var<1>, marian::functional::UnaryFunctor<marian::functional::elem::Sqrt, marian::functional::Assignee<1> > >>(marian::functional::Assign<marian::functional::Var<1>, marian::functional::UnaryFunctor<marian::functional::elem::Sqrt, marian::functional::Assignee<1> > >, marian::Tensor);
+template void marian::gpu::Element<marian::functional::Assign<marian::functional::Var<1>, marian::functional::BinaryFunctor<marian::functional::elem::Mult, marian::functional::Assignee<2>, marian::functional::UnaryFunctor<marian::functional::elem::Sigmoid, marian::functional::BinaryFunctor<marian::functional::elem::Mult, marian::functional::Capture, marian::functional::Assignee<2> > > > >, marian::Tensor >(marian::functional::Assign<marian::functional::Var<1>, marian::functional::BinaryFunctor<marian::functional::elem::Mult, marian::functional::Assignee<2>, marian::functional::UnaryFunctor<marian::functional::elem::Sigmoid, marian::functional::BinaryFunctor<marian::functional::elem::Mult, marian::functional::Capture, marian::functional::Assignee<2> > > > >, marian::Tensor, marian::Tensor);
+
+template void marian::gpu::Element<marian::functional::Assign<marian::functional::Var<1>, marian::functional::BinaryFunctor<marian::functional::elem::Plus, marian::functional::BinaryFunctor<marian::functional::elem::Mult, marian::functional::BinaryFunctor<marian::functional::elem::Lt, marian::functional::Assignee<1>, marian::functional::Capture>, marian::functional::Capture>, marian::functional::Capture> >>(marian::functional::Assign<marian::functional::Var<1>, marian::functional::BinaryFunctor<marian::functional::elem::Plus, marian::functional::BinaryFunctor<marian::functional::elem::Mult, marian::functional::BinaryFunctor<marian::functional::elem::Lt, marian::functional::Assignee<1>, marian::functional::Capture>, marian::functional::Capture>, marian::functional::Capture> >, marian::Tensor);
+
+template void marian::gpu::Element<marian::functional::Assign<marian::functional::Var<1>, marian::functional::BinaryFunctor<marian::functional::elem::Minus, marian::functional::Assignee<1>, marian::functional::BinaryFunctor<marian::functional::elem::Mult, marian::functional::Capture, marian::functional::BinaryFunctor<marian::functional::elem::Plus, marian::functional::BinaryFunctor<marian::functional::elem::Div, marian::functional::Assignee<2>, marian::functional::Capture>, marian::functional::BinaryFunctor<marian::functional::elem::Mult, marian::functional::Capture, marian::functional::Assignee<1> > > > > >, IntrusivePtr<marian::TensorBase> >(marian::functional::Assign<marian::functional::Var<1>, marian::functional::BinaryFunctor<marian::functional::elem::Minus, marian::functional::Assignee<1>, marian::functional::BinaryFunctor<marian::functional::elem::Mult, marian::functional::Capture, marian::functional::BinaryFunctor<marian::functional::elem::Plus, marian::functional::BinaryFunctor<marian::functional::elem::Div, marian::functional::Assignee<2>, marian::functional::Capture>, marian::functional::BinaryFunctor<marian::functional::elem::Mult, marian::functional::Capture, marian::functional::Assignee<1> > > > > >, marian::Tensor, marian::Tensor);
 
 // How to add new specializations:
 // When you use a new specialization, it will cause a link error of this form (example):
@@ -64,3 +69,5 @@ template void marian::gpu::Element<marian::functional::Assign<marian::functional
 // To fix this, copy the line with the error message in here and:
 //   - replace up to including "undefined reference to `" by "template"
 //   - replace final ' by a semicolon
+//   - replace 'IntrusivePtr<marian::TensorBase>' with 'marian::Tensor'
+
diff --git a/src/tensors/gpu/prod.cpp b/src/tensors/gpu/prod.cpp
index 1b1e34189..f2db243d4 100755
--- a/src/tensors/gpu/prod.cpp
+++ b/src/tensors/gpu/prod.cpp
@@ -250,7 +250,7 @@ void CSRProd(marian::Tensor C,
   ABORT_IF(numOffsets != rowsS, "Unexpected number of rows in CSR argument");
   ABORT_IF(S_values->shape() != S_indices->shape(), "CSR values and indices must have the same size");
   float alpha = 1;
-  Ptr<MemoryPiece> St_values, St_indices, St_offsets;
+  MemoryPiece::PtrType St_values, St_indices, St_offsets;
   if (transS != swapOperands) {
     // Cusparse gemmi() does not support this specific version of transpose, and csrmm() is non-deterministic.
     // Hence, we transpose the matrix explicitly.
diff --git a/src/tensors/gpu/tensor_operators.cu b/src/tensors/gpu/tensor_operators.cu
index 30c04c2fe..e68a0f3f2 100755
--- a/src/tensors/gpu/tensor_operators.cu
+++ b/src/tensors/gpu/tensor_operators.cu
@@ -1350,8 +1350,9 @@ float L2Norm(Tensor in) {
 
   uint8_t* data;
   cudaMalloc(&data, blocks * sizeof(float));
-  Tensor out(new TensorBase(New<MemoryPiece>(data, blocks * sizeof(float)),
-                            {1, blocks},
+  Tensor out(TensorBase::New(MemoryPiece::New(data, blocks * sizeof(float)),
+                            Shape({1, blocks}),
+                            Type::float32,
                             in->getBackend()));
 
   using namespace functional;

From d88c120de3676cbca9373e6f511e243fd27b8085 Mon Sep 17 00:00:00 2001
From: Marcin Junczys-Dowmunt <Marcin.JunczysDowmunt@microsoft.com>
Date: Sat, 7 Sep 2019 15:29:58 -0700
Subject: [PATCH 579/838] do not produce translations for empty input

---
 src/3rd_party/fbgemm         |  2 +-
 src/translator/beam_search.h | 15 +++++++++++++--
 2 files changed, 14 insertions(+), 3 deletions(-)

diff --git a/src/3rd_party/fbgemm b/src/3rd_party/fbgemm
index 49e8018ab..d8f740de7 160000
--- a/src/3rd_party/fbgemm
+++ b/src/3rd_party/fbgemm
@@ -1 +1 @@
-Subproject commit 49e8018ab2397c175354317b35c6be6dd68f8932
+Subproject commit d8f740de7689a449d58a8c9cb13b49c7998fde63
diff --git a/src/translator/beam_search.h b/src/translator/beam_search.h
index d0a2cb11e..739d94627 100755
--- a/src/translator/beam_search.h
+++ b/src/translator/beam_search.h
@@ -39,7 +39,8 @@ class BeamSearch {
                const Beams& beams,
                const std::vector<Ptr<ScorerState /*const*/>>& states,
                Ptr<data::CorpusBatch /*const*/> batch, // for alignments only
-               Ptr<FactoredVocab/*const*/> factoredVocab, size_t factorGroup) const {
+               Ptr<FactoredVocab/*const*/> factoredVocab, size_t factorGroup,
+               bool first) const {
     std::vector<float> align;
     if(options_->hasAndNotEmpty("alignment") && factorGroup == 0)
       align = scorers_[0]->getAlignment(); // [beam depth * max src length * batch size] -> P(s|t); use alignments from the first scorer, even if ensemble
@@ -99,6 +100,15 @@ class BeamSearch {
       else
         word = Word::fromWordIndex(wordIdx);
 
+      // Prune out all hyps for which the first input batch entry is <EOS>
+      // That way empty lines get mapped to empty lines 
+      if(first) {
+        const auto srcEosId = batch->front()->vocab()->getEosId();
+        const auto trgEosId = trgVocab_->getEosId();
+        if(batch->front()->data()[dimBatch] == srcEosId)
+          word = trgEosId;
+      }
+
       auto hyp = New<Hypothesis>(prevHyp, word, prevBeamHypIdx, pathScore);
 
       // Set score breakdown for n-best lists
@@ -393,7 +403,8 @@ class BeamSearch {
                      beams,
                      states,    // used for keeping track of per-ensemble-member path score
                      batch,     // only used for propagating alignment info
-                     factoredVocab, factorGroup);
+                     factoredVocab, factorGroup,
+                     /*first=*/t == 0 && factorGroup == 0);
 
       } // END FOR factorGroup = 0 .. numFactorGroups-1
 

From fe71fcd5ed482a3b05b5123434b654159f7ff38f Mon Sep 17 00:00:00 2001
From: Marcin Junczys-Dowmunt <Marcin.JunczysDowmunt@microsoft.com>
Date: Sat, 7 Sep 2019 15:41:55 -0700
Subject: [PATCH 580/838] no empty translations

---
 src/translator/beam_search.h | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/src/translator/beam_search.h b/src/translator/beam_search.h
index 98633df9c..64f387318 100755
--- a/src/translator/beam_search.h
+++ b/src/translator/beam_search.h
@@ -101,8 +101,8 @@ class BeamSearch {
       else
         word = Word::fromWordIndex(wordIdx);
 
-      // Prune out all hyps for which the first input batch entry is <EOS>
-      // That way empty lines get mapped to empty lines 
+      // Set word to <EOS> out for all hyps for which the first input batch entry is <EOS>.
+      // That way empty lines get mapped to empty lines.
       if(first) {
         const auto srcEosId = batch->front()->vocab()->getEosId();
         const auto trgEosId = trgVocab_->getEosId();

From a501f9512a7c52f69cb56bf90c6e9bf8a617c1d6 Mon Sep 17 00:00:00 2001
From: Marcin Junczys-Dowmunt <Marcin.JunczysDowmunt@microsoft.com>
Date: Sat, 7 Sep 2019 18:44:55 -0700
Subject: [PATCH 581/838] make it work for mini-batch > 1

---
 src/3rd_party/fbgemm         | 2 +-
 src/data/batch.h             | 2 +-
 src/data/corpus_base.h       | 4 ++--
 src/translator/beam_search.h | 8 +++++---
 4 files changed, 9 insertions(+), 7 deletions(-)

diff --git a/src/3rd_party/fbgemm b/src/3rd_party/fbgemm
index d8f740de7..49e8018ab 160000
--- a/src/3rd_party/fbgemm
+++ b/src/3rd_party/fbgemm
@@ -1 +1 @@
-Subproject commit d8f740de7689a449d58a8c9cb13b49c7998fde63
+Subproject commit 49e8018ab2397c175354317b35c6be6dd68f8932
diff --git a/src/data/batch.h b/src/data/batch.h
index f4b870177..7296ae135 100644
--- a/src/data/batch.h
+++ b/src/data/batch.h
@@ -17,7 +17,7 @@ class Batch {
   virtual size_t wordsTrg() const { return 0; };
   virtual size_t widthTrg() const { return 0; };
 
-  virtual void debug(){};
+  virtual void debug(bool printIndices = false) {};
 
   virtual std::vector<Ptr<Batch>> split(size_t n, size_t sizeLimit = SIZE_MAX) = 0;
 
diff --git a/src/data/corpus_base.h b/src/data/corpus_base.h
index f17df88da..6aea8ab4f 100644
--- a/src/data/corpus_base.h
+++ b/src/data/corpus_base.h
@@ -461,7 +461,7 @@ class CorpusBatch : public Batch {
   /**
    * @brief Prints the batch in a readable form on stderr for debugging.
    */
-  void debug() override {
+  void debug(bool printIndices_ = false) override {
     std::cerr << "batches: " << sets() << std::endl;
 
     if(!sentenceIds_.empty()) {
@@ -480,7 +480,7 @@ class CorpusBatch : public Batch {
         for(size_t j = 0; j < sb->batchSize(); j++) {
           size_t idx = i * sb->batchSize() + j;
           Word w = sb->data()[idx];
-          if (vocab)
+          if (vocab && !printIndices_)
             std::cerr << (*vocab)[w] << " ";
           else
             std::cerr << w.toString() << " "; // if not loaded then print numeric id instead
diff --git a/src/translator/beam_search.h b/src/translator/beam_search.h
index 64f387318..74efae627 100755
--- a/src/translator/beam_search.h
+++ b/src/translator/beam_search.h
@@ -52,8 +52,8 @@ class BeamSearch {
       // Keys encode batchIdx, beamHypIdx, and word index in the entire beam.
       // They can be between 0 and (vocabSize * nBestBeamSize * batchSize)-1.
       // (beamHypIdx refers to the GPU tensors, *not* the beams[] array; they are not the same in case of purging)
-      const auto  key       = nBestKeys[i];
-      const float pathScore = nBestPathScores[i]; // expanded path score for (batchIdx, beamHypIdx, word)
+      const auto key  = nBestKeys[i];
+      float pathScore = nBestPathScores[i]; // expanded path score for (batchIdx, beamHypIdx, word)
 
       // decompose key into individual indices (batchIdx, beamHypIdx, wordIdx)
       const auto wordIdx    = (WordIndex)(key % vocabSize);
@@ -106,8 +106,10 @@ class BeamSearch {
       if(first) {
         const auto srcEosId = batch->front()->vocab()->getEosId();
         const auto trgEosId = trgVocab_->getEosId();
-        if(batch->front()->data()[dimBatch] == srcEosId)
+        if(batch->front()->data()[batchIdx] == srcEosId) {
           word = trgEosId;
+          pathScore = 0.f;
+        }
       }
 
       auto hyp = New<Hypothesis>(prevHyp, word, prevBeamHypIdx, pathScore);

From baec145c859c42168cf683448350b9ad7c9ac947 Mon Sep 17 00:00:00 2001
From: Marcin Junczys-Dowmunt <Marcin.JunczysDowmunt@microsoft.com>
Date: Sat, 7 Sep 2019 18:48:15 -0700
Subject: [PATCH 582/838] fix comment

---
 src/translator/beam_search.h | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/translator/beam_search.h b/src/translator/beam_search.h
index 74efae627..07fd53239 100755
--- a/src/translator/beam_search.h
+++ b/src/translator/beam_search.h
@@ -101,7 +101,7 @@ class BeamSearch {
       else
         word = Word::fromWordIndex(wordIdx);
 
-      // Set word to <EOS> out for all hyps for which the first input batch entry is <EOS>.
+      // Set word to <EOS> output for all hyps for which the first input batch entry is <EOS>.
       // That way empty lines get mapped to empty lines.
       if(first) {
         const auto srcEosId = batch->front()->vocab()->getEosId();

From d9e44a23499657fea6e798ba0b5344ca44b761a7 Mon Sep 17 00:00:00 2001
From: Marcin Junczys-Dowmunt <Marcin.JunczysDowmunt@microsoft.com>
Date: Sat, 7 Sep 2019 18:51:42 -0700
Subject: [PATCH 583/838] fix typo in var name

---
 src/data/corpus_base.h | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/src/data/corpus_base.h b/src/data/corpus_base.h
index 6aea8ab4f..ded3e0a2c 100644
--- a/src/data/corpus_base.h
+++ b/src/data/corpus_base.h
@@ -461,7 +461,7 @@ class CorpusBatch : public Batch {
   /**
    * @brief Prints the batch in a readable form on stderr for debugging.
    */
-  void debug(bool printIndices_ = false) override {
+  void debug(bool printIndices = false) override {
     std::cerr << "batches: " << sets() << std::endl;
 
     if(!sentenceIds_.empty()) {
@@ -480,7 +480,7 @@ class CorpusBatch : public Batch {
         for(size_t j = 0; j < sb->batchSize(); j++) {
           size_t idx = i * sb->batchSize() + j;
           Word w = sb->data()[idx];
-          if (vocab && !printIndices_)
+          if (vocab && !printIndices)
             std::cerr << (*vocab)[w] << " ";
           else
             std::cerr << w.toString() << " "; // if not loaded then print numeric id instead

From 08c03cf83c127d321ac0bae96b45e832fea092d7 Mon Sep 17 00:00:00 2001
From: Marcin Junczys-Dowmunt <Marcin.JunczysDowmunt@microsoft.com>
Date: Sat, 7 Sep 2019 23:01:24 -0700
Subject: [PATCH 584/838] move regression tests forward

---
 regression-tests | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/regression-tests b/regression-tests
index 658cf86e2..232e21d91 160000
--- a/regression-tests
+++ b/regression-tests
@@ -1 +1 @@
-Subproject commit 658cf86e2ab4aa76b4ab3edabd750b93d2416f1a
+Subproject commit 232e21d912681e78969e93ed18030f6e7010f8d7

From 789b502ac9d0a873397d260ab0a0bb26b735fb1c Mon Sep 17 00:00:00 2001
From: Marcin Junczys-Dowmunt <Marcin.JunczysDowmunt@microsoft.com>
Date: Sat, 7 Sep 2019 23:05:37 -0700
Subject: [PATCH 585/838] update changelog

---
 CHANGELOG.md | 4 ++++
 1 file changed, 4 insertions(+)

diff --git a/CHANGELOG.md b/CHANGELOG.md
index c77101528..3b9255493 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -7,6 +7,10 @@ and this project adheres to [Semantic Versioning](http://semver.org/spec/v2.0.0.
 
 ## [Unreleased]
 
+### Fixed
+- Output empty line when input is empty line. Previous behavior might result in 
+  hallucinated outputs.
+
 ## [1.8.0] - 2019-09-04
 
 ### Added

From 83edc2bb755ed76ecdea425a3b639b9e2a016821 Mon Sep 17 00:00:00 2001
From: Marcin Junczys-Dowmunt <Marcin.JunczysDowmunt@microsoft.com>
Date: Sat, 7 Sep 2019 23:06:40 -0700
Subject: [PATCH 586/838] update version

---
 VERSION | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/VERSION b/VERSION
index 804a616da..2fb69e15c 100644
--- a/VERSION
+++ b/VERSION
@@ -1 +1 @@
-v1.8.0
+v1.8.1

From 633153199c40565c9afd35430835826ae2ea24b2 Mon Sep 17 00:00:00 2001
From: Tomasz Dwojak <t.dwojak@amu.edu.pl>
Date: Mon, 9 Sep 2019 22:19:00 +0200
Subject: [PATCH 587/838] Fix static build

---
 CMakeLists.txt | 9 ++++++++-
 1 file changed, 8 insertions(+), 1 deletion(-)

diff --git a/CMakeLists.txt b/CMakeLists.txt
index 1191a674c..73b8652b8 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -174,7 +174,14 @@ if(CUDA_FOUND)
   if(USE_STATIC_LIBS)
     find_library(CUDA_culibos_LIBRARY NAMES culibos PATHS ${CUDA_TOOLKIT_ROOT_DIR}/lib64)
     set(EXT_LIBS ${EXT_LIBS} ${CUDA_curand_LIBRARY} ${CUDA_cusparse_LIBRARY} ${CUDA_culibos_LIBRARY} ${CUDA_CUBLAS_LIBRARIES})
-    message(STATUS "Found CUDA libraries: ${CUDA_curand_LIBRARY} ${CUDA_cusparse_LIBRARY} ${CUDA_culibos_LIBRARY} ${CUDA_CUBLAS_LIBRARIES}")
+    set(CUDA_LIBS ${CUDA_curand_LIBRARY} ${CUDA_cusparse_LIBRARY} ${CUDA_culibos_LIBRARY} ${CUDA_CUBLAS_LIBRARIES})
+    # CUDA 10.1 introduces cublasLt library that is required on static build
+    if ((CUDA_VERSION VERSION_EQUAL "10.1" OR CUDA_VERSION VERSION_GREATER "10.1"))
+      find_library(CUDA_cublasLt_LIBRARY NAMES cublasLt PATHS ${CUDA_TOOLKIT_ROOT_DIR}/lib64)
+      set(EXT_LIBS ${EXT_LIBS} ${CUDA_cublasLt_LIBRARY})
+      set(CUDA_LIBS ${CUDA_LIBS} ${CUDA_cublasLt_LIBRARY})
+    endif()
+    message(STATUS "Found CUDA libraries: ${CUDA_LIBS}")
   else(USE_STATIC_LIBS)
     set(EXT_LIBS ${EXT_LIBS} ${CUDA_curand_LIBRARY} ${CUDA_cusparse_LIBRARY} ${CUDA_CUBLAS_LIBRARIES})
     message(STATUS "Found CUDA libraries: ${CUDA_curand_LIBRARY} ${CUDA_cusparse_LIBRARY} ${CUDA_CUBLAS_LIBRARIES}")

From cb63e4244174b4c8af0857b0f7d1a8bef5e476e1 Mon Sep 17 00:00:00 2001
From: Marcin Junczys-Dowmunt <Marcin.JunczysDowmunt@microsoft.com>
Date: Mon, 9 Sep 2019 13:44:54 -0700
Subject: [PATCH 588/838] fix problem with loading averaged parameters for
 models which overload Model::load

---
 regression-tests                  |  2 +-
 src/graph/expression_graph.cpp    | 16 +---------------
 src/graph/parameters.h            |  2 +-
 src/training/graph_group_sync.cpp |  7 ++++++-
 4 files changed, 9 insertions(+), 18 deletions(-)

diff --git a/regression-tests b/regression-tests
index 504922d7f..71b0506a5 160000
--- a/regression-tests
+++ b/regression-tests
@@ -1 +1 @@
-Subproject commit 504922d7fae39c0d019ea40ceb211bc2230943f2
+Subproject commit 71b0506a50e1f181e269fe90073a8ae470676dd1
diff --git a/src/graph/expression_graph.cpp b/src/graph/expression_graph.cpp
index bf1192351..8d7500f70 100755
--- a/src/graph/expression_graph.cpp
+++ b/src/graph/expression_graph.cpp
@@ -42,22 +42,8 @@ void ExpressionGraph::save(std::vector<io::Item>& ioItems) {
              "Only float32 supported at the moment");
 
     Tensor val = p.second->val();
-
     io::Item item;
-    item.name = pName;
-    item.shape = val->shape();
-    item.type = val->type();
-
-    // Use the actual memory as this will be aligned and padded.
-    // When memory mapping this is required. Shape keeps track of
-    // tensor size. Saving to *.npz will cut to size.
-    auto mem = val->memory();
-    item.bytes.resize(mem->size());
-    copy(backend_,
-         mem->data<char>(),
-         mem->data<char>() + mem->size(),
-         item.bytes.data());
-
+    val->get(item, pName);
     ioItems.emplace_back(std::move(item));
   }
 }
diff --git a/src/graph/parameters.h b/src/graph/parameters.h
index b140a8144..9a9206cf7 100644
--- a/src/graph/parameters.h
+++ b/src/graph/parameters.h
@@ -76,7 +76,7 @@ class Parameters {
 
       // sort parameters by name before allocation to make sure the memory layout after allocation is always the same
       std::sort(params_.begin(), params_.end(), [](Expr n1, Expr n2){ return n1->name() < n2->name(); });
-
+      
       for(auto p : params_) {
         if(!p->val()) {
           vals_->allocate(p->val(), p->shape(), p->value_type());
diff --git a/src/training/graph_group_sync.cpp b/src/training/graph_group_sync.cpp
index 6c6da17bb..57a54769d 100755
--- a/src/training/graph_group_sync.cpp
+++ b/src/training/graph_group_sync.cpp
@@ -82,7 +82,12 @@ void SyncGraphGroup::initializeAvg() {
     // Load the averaged parameters into a temporary graph
     graphAvg = New<ExpressionGraph>();
     graphAvg->setDevice({0, DeviceType::cpu});
-    graphAvg->load(name, false);
+
+    // load model through builder to activate model specific loading functions.
+    // This is important if a model is overloading Model::load(...) and e.g. 
+    // mapping matrix names as in Amun.h
+    auto builder = models::createCriterionFunctionFromOptions(options_, models::usage::training);
+    builder->load(graphAvg, name, false);
     graphAvg->forward(); // initialize parameters if needed
   }
 

From 29953c7186b328b62fc78290bc552efb46778651 Mon Sep 17 00:00:00 2001
From: Marcin Junczys-Dowmunt <Marcin.JunczysDowmunt@microsoft.com>
Date: Mon, 9 Sep 2019 15:11:16 -0700
Subject: [PATCH 589/838] handle EOS translation outside loop

---
 src/translator/beam_search.h | 38 +++++++++++++++++++-----------------
 1 file changed, 20 insertions(+), 18 deletions(-)

diff --git a/src/translator/beam_search.h b/src/translator/beam_search.h
index 07fd53239..70b4fb908 100755
--- a/src/translator/beam_search.h
+++ b/src/translator/beam_search.h
@@ -39,8 +39,7 @@ class BeamSearch {
                const Beams& beams,
                const std::vector<Ptr<ScorerState /*const*/>>& states,
                Ptr<data::CorpusBatch /*const*/> batch, // for alignments only
-               Ptr<FactoredVocab/*const*/> factoredVocab, size_t factorGroup,
-               bool first) const {
+               Ptr<FactoredVocab/*const*/> factoredVocab, size_t factorGroup) const {
     std::vector<float> align;
     if(options_->hasAndNotEmpty("alignment") && factorGroup == 0)
       align = scorers_[0]->getAlignment(); // [beam depth * max src length * batch size] -> P(s|t); use alignments from the first scorer, even if ensemble
@@ -101,17 +100,6 @@ class BeamSearch {
       else
         word = Word::fromWordIndex(wordIdx);
 
-      // Set word to <EOS> output for all hyps for which the first input batch entry is <EOS>.
-      // That way empty lines get mapped to empty lines.
-      if(first) {
-        const auto srcEosId = batch->front()->vocab()->getEosId();
-        const auto trgEosId = trgVocab_->getEosId();
-        if(batch->front()->data()[batchIdx] == srcEosId) {
-          word = trgEosId;
-          pathScore = 0.f;
-        }
-      }
-
       auto hyp = New<Hypothesis>(prevHyp, word, prevBeamHypIdx, pathScore);
 
       // Set score breakdown for n-best lists
@@ -256,10 +244,26 @@ class BeamSearch {
       states.push_back(scorer->startState(graph, batch));
     }
 
+    // create beam with null-hypotheses
     Beams beams(dimBatch, Beam(beamSize_, New<Hypothesis>())); // array [dimBatch] of array [localBeamSize] of Hypothesis
 
-    for(int i = 0; i < dimBatch; ++i)
-      histories[i]->add(beams[i], trgEosId);
+    const auto srcEosId = batch->front()->vocab()->getEosId();
+    for(int batchIdx = 0; batchIdx < dimBatch; ++batchIdx) {
+      auto& beam = beams[batchIdx];
+      histories[batchIdx]->add(beam, trgEosId); // add beams with null-hypotheses to histories
+
+      // Handle batch entries that consist only of source <EOS> i.e. these are empty lines
+      if(batch->front()->data()[batchIdx] == srcEosId) { // if input is empty, i.e. first word is source <EOS>
+        // create an target <EOS> hypothesis that extends the null-hypothesis
+        auto eosHyp = New<Hypothesis>(/*prevHyp=*/    beam[0], 
+                                      /*currWord=*/   trgEosId, 
+                                      /*prevHypIdx=*/ 0, 
+                                      /*pathScore=*/  0.f);
+        auto eosBeam = Beam(beamSize_, eosHyp);      // create a dummy beam filled with <EOS>-hyps
+        histories[batchIdx]->add(eosBeam, trgEosId); // push dummy <EOS>-beam to history
+        beam.clear(); // zero out current beam, so it does not get used for further symbols as empty beams get omitted/dummy-filled everywhere
+      }
+    }
 
     // determine index of UNK in the log prob vectors if we want to suppress it in the decoding process
     int unkColId = -1;
@@ -416,9 +420,7 @@ class BeamSearch {
                      beams,
                      states,    // used for keeping track of per-ensemble-member path score
                      batch,     // only used for propagating alignment info
-                     factoredVocab, factorGroup,
-                     /*first=*/t == 0 && factorGroup == 0);
-
+                     factoredVocab, factorGroup);
       } // END FOR factorGroup = 0 .. numFactorGroups-1
 
       // remove all hyps that end in EOS

From 3b6acb90b19982b832b382bf6c64ea5d329145c4 Mon Sep 17 00:00:00 2001
From: Marcin Junczys-Dowmunt <Marcin.JunczysDowmunt@microsoft.com>
Date: Mon, 9 Sep 2019 15:15:48 -0700
Subject: [PATCH 590/838] address commments

---
 src/translator/beam_search.h | 10 +++++-----
 1 file changed, 5 insertions(+), 5 deletions(-)

diff --git a/src/translator/beam_search.h b/src/translator/beam_search.h
index 70b4fb908..ae260e2f9 100755
--- a/src/translator/beam_search.h
+++ b/src/translator/beam_search.h
@@ -51,8 +51,8 @@ class BeamSearch {
       // Keys encode batchIdx, beamHypIdx, and word index in the entire beam.
       // They can be between 0 and (vocabSize * nBestBeamSize * batchSize)-1.
       // (beamHypIdx refers to the GPU tensors, *not* the beams[] array; they are not the same in case of purging)
-      const auto key  = nBestKeys[i];
-      float pathScore = nBestPathScores[i]; // expanded path score for (batchIdx, beamHypIdx, word)
+      const auto key        = nBestKeys[i];
+      const float pathScore = nBestPathScores[i]; // expanded path score for (batchIdx, beamHypIdx, word)
 
       // decompose key into individual indices (batchIdx, beamHypIdx, wordIdx)
       const auto wordIdx    = (WordIndex)(key % vocabSize);
@@ -244,17 +244,17 @@ class BeamSearch {
       states.push_back(scorer->startState(graph, batch));
     }
 
-    // create beam with null-hypotheses
+    // create one beam per batch entry with sentence-start hypothesis
     Beams beams(dimBatch, Beam(beamSize_, New<Hypothesis>())); // array [dimBatch] of array [localBeamSize] of Hypothesis
 
     const auto srcEosId = batch->front()->vocab()->getEosId();
     for(int batchIdx = 0; batchIdx < dimBatch; ++batchIdx) {
       auto& beam = beams[batchIdx];
-      histories[batchIdx]->add(beam, trgEosId); // add beams with null-hypotheses to histories
+      histories[batchIdx]->add(beam, trgEosId); // add beams with start-hypotheses to histories
 
       // Handle batch entries that consist only of source <EOS> i.e. these are empty lines
       if(batch->front()->data()[batchIdx] == srcEosId) { // if input is empty, i.e. first word is source <EOS>
-        // create an target <EOS> hypothesis that extends the null-hypothesis
+        // create an target <EOS> hypothesis that extends the start-hypothesis
         auto eosHyp = New<Hypothesis>(/*prevHyp=*/    beam[0], 
                                       /*currWord=*/   trgEosId, 
                                       /*prevHypIdx=*/ 0, 

From 40ab54f3e701e3612a9785db90ddec6c1b05ac4f Mon Sep 17 00:00:00 2001
From: Marcin Junczys-Dowmunt <Marcin.JunczysDowmunt@microsoft.com>
Date: Mon, 9 Sep 2019 15:17:34 -0700
Subject: [PATCH 591/838] address commments

---
 src/translator/beam_search.h | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/src/translator/beam_search.h b/src/translator/beam_search.h
index ae260e2f9..160d10462 100755
--- a/src/translator/beam_search.h
+++ b/src/translator/beam_search.h
@@ -250,17 +250,17 @@ class BeamSearch {
     const auto srcEosId = batch->front()->vocab()->getEosId();
     for(int batchIdx = 0; batchIdx < dimBatch; ++batchIdx) {
       auto& beam = beams[batchIdx];
-      histories[batchIdx]->add(beam, trgEosId); // add beams with start-hypotheses to histories
+      histories[batchIdx]->add(beam, trgEosId); // add beams with start-hypotheses to traceback grid
 
       // Handle batch entries that consist only of source <EOS> i.e. these are empty lines
-      if(batch->front()->data()[batchIdx] == srcEosId) { // if input is empty, i.e. first word is source <EOS>
+      if(batch->front()->data()[batchIdx] == srcEosId) {
         // create an target <EOS> hypothesis that extends the start-hypothesis
         auto eosHyp = New<Hypothesis>(/*prevHyp=*/    beam[0], 
                                       /*currWord=*/   trgEosId, 
                                       /*prevHypIdx=*/ 0, 
                                       /*pathScore=*/  0.f);
         auto eosBeam = Beam(beamSize_, eosHyp);      // create a dummy beam filled with <EOS>-hyps
-        histories[batchIdx]->add(eosBeam, trgEosId); // push dummy <EOS>-beam to history
+        histories[batchIdx]->add(eosBeam, trgEosId); // push dummy <EOS>-beam to traceback grid
         beam.clear(); // zero out current beam, so it does not get used for further symbols as empty beams get omitted/dummy-filled everywhere
       }
     }

From e1bd8b9e8d6e16a37cb093b925e92fefd20bd459 Mon Sep 17 00:00:00 2001
From: Marcin Junczys-Dowmunt <Marcin.JunczysDowmunt@microsoft.com>
Date: Mon, 9 Sep 2019 15:23:23 -0700
Subject: [PATCH 592/838] address remaining commments and missing indent

---
 src/data/corpus_base.h       |   3 +-
 src/translator/beam_search.h | 231 +++++++++++++++++------------------
 2 files changed, 117 insertions(+), 117 deletions(-)

diff --git a/src/data/corpus_base.h b/src/data/corpus_base.h
index ded3e0a2c..62bdb18dd 100644
--- a/src/data/corpus_base.h
+++ b/src/data/corpus_base.h
@@ -461,7 +461,8 @@ class CorpusBatch : public Batch {
   /**
    * @brief Prints the batch in a readable form on stderr for debugging.
    */
-  void debug(bool printIndices = false) override {
+  void debug(bool printIndices = false) override { // prints word string if subbatch has vocab and 
+                                                   // printIndices == false otherwise only numeric indices
     std::cerr << "batches: " << sets() << std::endl;
 
     if(!sentenceIds_.empty()) {
diff --git a/src/translator/beam_search.h b/src/translator/beam_search.h
index 160d10462..3b61bff4e 100755
--- a/src/translator/beam_search.h
+++ b/src/translator/beam_search.h
@@ -51,7 +51,7 @@ class BeamSearch {
       // Keys encode batchIdx, beamHypIdx, and word index in the entire beam.
       // They can be between 0 and (vocabSize * nBestBeamSize * batchSize)-1.
       // (beamHypIdx refers to the GPU tensors, *not* the beams[] array; they are not the same in case of purging)
-      const auto key        = nBestKeys[i];
+      const auto  key       = nBestKeys[i];
       const float pathScore = nBestPathScores[i]; // expanded path score for (batchIdx, beamHypIdx, word)
 
       // decompose key into individual indices (batchIdx, beamHypIdx, wordIdx)
@@ -254,7 +254,7 @@ class BeamSearch {
 
       // Handle batch entries that consist only of source <EOS> i.e. these are empty lines
       if(batch->front()->data()[batchIdx] == srcEosId) {
-        // create an target <EOS> hypothesis that extends the start-hypothesis
+        // create a target <EOS> hypothesis that extends the start-hypothesis
         auto eosHyp = New<Hypothesis>(/*prevHyp=*/    beam[0], 
                                       /*currWord=*/   trgEosId, 
                                       /*prevHypIdx=*/ 0, 
@@ -301,126 +301,125 @@ class BeamSearch {
         break;
 
       for (size_t factorGroup = 0; factorGroup < numFactorGroups; factorGroup++) {
-      // @TODO: Indent the body of this loop. Not done for this commit for easier reviewing.
-      // for factored vocabs, we do one factor at a time, but without updating the scorer for secondary factors
-
-      //**********************************************************************
-      // create constant containing previous path scores for current beam
-      // Also create mapping of hyp indices, for reordering the decoder-state tensors.
-      std::vector<IndexType> hypIndices; // [localBeamsize, 1, dimBatch, 1] (flattened) tensor index ((beamHypIdx, batchIdx), flattened) of prev hyp that a hyp originated from
-      std::vector<Word> prevWords;       // [localBeamsize, 1, dimBatch, 1] (flattened) word that a hyp ended in, for advancing the decoder-model's history
-      Expr prevPathScores;               // [localBeamSize, 1, dimBatch, 1], path score that a hyp ended in (last axis will broadcast into vocab size when adding expandedPathScores)
-      bool anyCanExpand = false; // stays false if all hyps are invalid factor expansions
-      if(t == 0 && factorGroup == 0) { // no scores yet
-        prevPathScores = graph->constant({1, 1, 1, 1}, inits::from_value(0));
-        anyCanExpand = true;
-      } else {
-        std::vector<float> prevScores;
-        for(size_t beamHypIdx = 0; beamHypIdx < localBeamSize; ++beamHypIdx) {
-          for(int batchIdx = 0; batchIdx < dimBatch; ++batchIdx) { // loop over batch entries (active sentences)
-            auto& beam = beams[batchIdx];
-            if(beamHypIdx < beam.size()) {
-              auto hyp = beam[beamHypIdx];
-              auto word = hyp->getWord();
-              auto canExpand = (!factoredVocab || factoredVocab->canExpandFactoredWord(hyp->getWord(), factorGroup));
-              //LOG(info, "[{}, {}] Can expand {} with {} -> {}", batchIdx, beamHypIdx, (*batch->back()->vocab())[hyp->getWord()], factorGroup, canExpand);
-              anyCanExpand |= canExpand;
-              hypIndices.push_back((IndexType)(hyp->getPrevStateIndex() * dimBatch + batchIdx)); // (beamHypIdx, batchIdx), flattened, for index_select() operation
-              prevWords .push_back(word);
-              prevScores.push_back(canExpand ? hyp->getPathScore() : INVALID_PATH_SCORE);
-            } else {  // pad to localBeamSize (dummy hypothesis)
-              hypIndices.push_back(0);
-              prevWords.push_back(trgEosId);  // (unused, but must be valid)
-              prevScores.push_back((float)INVALID_PATH_SCORE);
+        // for factored vocabs, we do one factor at a time, but without updating the scorer for secondary factors
+
+        //**********************************************************************
+        // create constant containing previous path scores for current beam
+        // Also create mapping of hyp indices, for reordering the decoder-state tensors.
+        std::vector<IndexType> hypIndices; // [localBeamsize, 1, dimBatch, 1] (flattened) tensor index ((beamHypIdx, batchIdx), flattened) of prev hyp that a hyp originated from
+        std::vector<Word> prevWords;       // [localBeamsize, 1, dimBatch, 1] (flattened) word that a hyp ended in, for advancing the decoder-model's history
+        Expr prevPathScores;               // [localBeamSize, 1, dimBatch, 1], path score that a hyp ended in (last axis will broadcast into vocab size when adding expandedPathScores)
+        bool anyCanExpand = false; // stays false if all hyps are invalid factor expansions
+        if(t == 0 && factorGroup == 0) { // no scores yet
+          prevPathScores = graph->constant({1, 1, 1, 1}, inits::from_value(0));
+          anyCanExpand = true;
+        } else {
+          std::vector<float> prevScores;
+          for(size_t beamHypIdx = 0; beamHypIdx < localBeamSize; ++beamHypIdx) {
+            for(int batchIdx = 0; batchIdx < dimBatch; ++batchIdx) { // loop over batch entries (active sentences)
+              auto& beam = beams[batchIdx];
+              if(beamHypIdx < beam.size()) {
+                auto hyp = beam[beamHypIdx];
+                auto word = hyp->getWord();
+                auto canExpand = (!factoredVocab || factoredVocab->canExpandFactoredWord(hyp->getWord(), factorGroup));
+                //LOG(info, "[{}, {}] Can expand {} with {} -> {}", batchIdx, beamHypIdx, (*batch->back()->vocab())[hyp->getWord()], factorGroup, canExpand);
+                anyCanExpand |= canExpand;
+                hypIndices.push_back((IndexType)(hyp->getPrevStateIndex() * dimBatch + batchIdx)); // (beamHypIdx, batchIdx), flattened, for index_select() operation
+                prevWords .push_back(word);
+                prevScores.push_back(canExpand ? hyp->getPathScore() : INVALID_PATH_SCORE);
+              } else {  // pad to localBeamSize (dummy hypothesis)
+                hypIndices.push_back(0);
+                prevWords.push_back(trgEosId);  // (unused, but must be valid)
+                prevScores.push_back((float)INVALID_PATH_SCORE);
+              }
             }
           }
+          prevPathScores = graph->constant({(int)localBeamSize, 1, dimBatch, 1}, inits::from_vector(prevScores));
         }
-        prevPathScores = graph->constant({(int)localBeamSize, 1, dimBatch, 1}, inits::from_vector(prevScores));
-      }
-      if (!anyCanExpand) // all words cannot expand this factor: skip
-        continue;
-
-      //**********************************************************************
-      // compute expanded path scores with word prediction probs from all scorers
-      auto expandedPathScores = prevPathScores; // will become [localBeamSize, 1, dimBatch, dimVocab]
-      Expr logProbs;
-      for(size_t i = 0; i < scorers_.size(); ++i) {
-        if (factorGroup == 0) {
-          // compute output probabilities for current output time step
-          //  - uses hypIndices[index in beam, 1, batch index, 1] to reorder scorer state to reflect the top-N in beams[][]
-          //  - adds prevWords [index in beam, 1, batch index, 1] to the scorer's target history
-          //  - performs one step of the scorer
-          //  - returns new NN state for use in next output time step
-          //  - returns vector of prediction probabilities over output vocab via newState
-          // update state in-place for next output time step
-          //if (t > 0) for (size_t kk = 0; kk < prevWords.size(); kk++)
-          //  LOG(info, "prevWords[{},{}]={} -> {}", t/numFactorGroups, factorGroup,
-          //      factoredVocab ? factoredVocab->word2string(prevWords[kk]) : (*batch->back()->vocab())[prevWords[kk]],
-          //      prevScores[kk]);
-          states[i] = scorers_[i]->step(graph, states[i], hypIndices, prevWords, dimBatch, (int)localBeamSize);
-          if (numFactorGroups == 1) // @TODO: this branch can go away
-            logProbs = states[i]->getLogProbs().getLogits(); // [localBeamSize, 1, dimBatch, dimVocab]
-          else
-          {
-            auto shortlist = scorers_[i]->getShortlist();
-            logProbs = states[i]->getLogProbs().getFactoredLogits(factorGroup, shortlist); // [localBeamSize, 1, dimBatch, dimVocab]
+        if (!anyCanExpand) // all words cannot expand this factor: skip
+          continue;
+
+        //**********************************************************************
+        // compute expanded path scores with word prediction probs from all scorers
+        auto expandedPathScores = prevPathScores; // will become [localBeamSize, 1, dimBatch, dimVocab]
+        Expr logProbs;
+        for(size_t i = 0; i < scorers_.size(); ++i) {
+          if (factorGroup == 0) {
+            // compute output probabilities for current output time step
+            //  - uses hypIndices[index in beam, 1, batch index, 1] to reorder scorer state to reflect the top-N in beams[][]
+            //  - adds prevWords [index in beam, 1, batch index, 1] to the scorer's target history
+            //  - performs one step of the scorer
+            //  - returns new NN state for use in next output time step
+            //  - returns vector of prediction probabilities over output vocab via newState
+            // update state in-place for next output time step
+            //if (t > 0) for (size_t kk = 0; kk < prevWords.size(); kk++)
+            //  LOG(info, "prevWords[{},{}]={} -> {}", t/numFactorGroups, factorGroup,
+            //      factoredVocab ? factoredVocab->word2string(prevWords[kk]) : (*batch->back()->vocab())[prevWords[kk]],
+            //      prevScores[kk]);
+            states[i] = scorers_[i]->step(graph, states[i], hypIndices, prevWords, dimBatch, (int)localBeamSize);
+            if (numFactorGroups == 1) // @TODO: this branch can go away
+              logProbs = states[i]->getLogProbs().getLogits(); // [localBeamSize, 1, dimBatch, dimVocab]
+            else
+            {
+              auto shortlist = scorers_[i]->getShortlist();
+              logProbs = states[i]->getLogProbs().getFactoredLogits(factorGroup, shortlist); // [localBeamSize, 1, dimBatch, dimVocab]
+            }
+            //logProbs->debug("logProbs");
           }
-          //logProbs->debug("logProbs");
-        }
-        else {
-          // add secondary factors
-          // For those, we don't update the decoder-model state in any way.
-          // Instead, we just keep expanding with the factors.
-          // We will have temporary Word entries in hyps with some factors set to FACTOR_NOT_SPECIFIED.
-          // For some lemmas, a factor is not applicable. For those, the factor score is the same (zero)
-          // for all factor values. This would thus unnecessarily pollute the beam with identical copies,
-          // and push out other hypotheses. Hence, we exclude those here by setting the path score to
-          // INVALID_PATH_SCORE. Instead, toHyps() explicitly propagates those hyps by simply copying the
-          // previous hypothesis.
-          logProbs = states[i]->getLogProbs().getFactoredLogits(factorGroup, /*shortlist=*/ nullptr, hypIndices, localBeamSize); // [localBeamSize, 1, dimBatch, dimVocab]
+          else {
+            // add secondary factors
+            // For those, we don't update the decoder-model state in any way.
+            // Instead, we just keep expanding with the factors.
+            // We will have temporary Word entries in hyps with some factors set to FACTOR_NOT_SPECIFIED.
+            // For some lemmas, a factor is not applicable. For those, the factor score is the same (zero)
+            // for all factor values. This would thus unnecessarily pollute the beam with identical copies,
+            // and push out other hypotheses. Hence, we exclude those here by setting the path score to
+            // INVALID_PATH_SCORE. Instead, toHyps() explicitly propagates those hyps by simply copying the
+            // previous hypothesis.
+            logProbs = states[i]->getLogProbs().getFactoredLogits(factorGroup, /*shortlist=*/ nullptr, hypIndices, localBeamSize); // [localBeamSize, 1, dimBatch, dimVocab]
+          }
+          // expand all hypotheses, [localBeamSize, 1, dimBatch, 1] -> [localBeamSize, 1, dimBatch, dimVocab]
+          expandedPathScores = expandedPathScores + scorers_[i]->getWeight() * logProbs;
         }
-        // expand all hypotheses, [localBeamSize, 1, dimBatch, 1] -> [localBeamSize, 1, dimBatch, dimVocab]
-        expandedPathScores = expandedPathScores + scorers_[i]->getWeight() * logProbs;
-      }
 
-      // make beams continuous
-      expandedPathScores = swapAxes(expandedPathScores, 0, 2); // -> [dimBatch, 1, localBeamSize, dimVocab]
-
-      // perform NN computation
-      if(t == 0 && factorGroup == 0)
-        graph->forward();
-      else
-        graph->forwardNext();
-
-      //**********************************************************************
-      // suppress specific symbols if not at right positions
-
-      if(unkColId != -1 && factorGroup == 0)
-        suppressWord(expandedPathScores, unkColId);
-      for(auto state : states)
-        state->blacklist(expandedPathScores, batch);
-
-      //**********************************************************************
-      // perform beam search
-
-      // find N best amongst the (localBeamSize * dimVocab) hypotheses
-      std::vector<unsigned int> nBestKeys; // [dimBatch, localBeamSize] flattened -> (batchIdx, beamHypIdx, word idx) flattened
-      std::vector<float> nBestPathScores;  // [dimBatch, localBeamSize] flattened
-      getNBestList(/*in*/ expandedPathScores->val(), // [dimBatch, 1, localBeamSize, dimVocab or dimShortlist]
-                   /*N=*/localBeamSize,              // desired beam size
-                   /*out*/ nBestPathScores, /*out*/ nBestKeys,
-                   /*first=*/t == 0 && factorGroup == 0); // @TODO: this is only used for checking presently, and should be removed altogether
-      // Now, nBestPathScores contain N-best expandedPathScores for each batch and beam,
-      // and nBestKeys for each their original location (batchIdx, beamHypIdx, word).
-
-      // combine N-best sets with existing search space (beams) to updated search space
-      beams = toHyps(nBestKeys, nBestPathScores,
-                     /*nBestBeamSize*/expandedPathScores->shape()[-2], // used for interpretation of keys
-                     /*vocabSize=*/expandedPathScores->shape()[-1],    // used for interpretation of keys
-                     beams,
-                     states,    // used for keeping track of per-ensemble-member path score
-                     batch,     // only used for propagating alignment info
-                     factoredVocab, factorGroup);
+        // make beams continuous
+        expandedPathScores = swapAxes(expandedPathScores, 0, 2); // -> [dimBatch, 1, localBeamSize, dimVocab]
+
+        // perform NN computation
+        if(t == 0 && factorGroup == 0)
+          graph->forward();
+        else
+          graph->forwardNext();
+
+        //**********************************************************************
+        // suppress specific symbols if not at right positions
+
+        if(unkColId != -1 && factorGroup == 0)
+          suppressWord(expandedPathScores, unkColId);
+        for(auto state : states)
+          state->blacklist(expandedPathScores, batch);
+
+        //**********************************************************************
+        // perform beam search
+
+        // find N best amongst the (localBeamSize * dimVocab) hypotheses
+        std::vector<unsigned int> nBestKeys; // [dimBatch, localBeamSize] flattened -> (batchIdx, beamHypIdx, word idx) flattened
+        std::vector<float> nBestPathScores;  // [dimBatch, localBeamSize] flattened
+        getNBestList(/*in*/ expandedPathScores->val(), // [dimBatch, 1, localBeamSize, dimVocab or dimShortlist]
+                    /*N=*/localBeamSize,              // desired beam size
+                    /*out*/ nBestPathScores, /*out*/ nBestKeys,
+                    /*first=*/t == 0 && factorGroup == 0); // @TODO: this is only used for checking presently, and should be removed altogether
+        // Now, nBestPathScores contain N-best expandedPathScores for each batch and beam,
+        // and nBestKeys for each their original location (batchIdx, beamHypIdx, word).
+
+        // combine N-best sets with existing search space (beams) to updated search space
+        beams = toHyps(nBestKeys, nBestPathScores,
+                      /*nBestBeamSize*/expandedPathScores->shape()[-2], // used for interpretation of keys
+                      /*vocabSize=*/expandedPathScores->shape()[-1],    // used for interpretation of keys
+                      beams,
+                      states,    // used for keeping track of per-ensemble-member path score
+                      batch,     // only used for propagating alignment info
+                      factoredVocab, factorGroup);
       } // END FOR factorGroup = 0 .. numFactorGroups-1
 
       // remove all hyps that end in EOS

From 80ae122518ac3ed60c49fedca1a3d300fdb19cab Mon Sep 17 00:00:00 2001
From: Marcin Junczys-Dowmunt <Marcin.JunczysDowmunt@microsoft.com>
Date: Mon, 9 Sep 2019 15:46:34 -0700
Subject: [PATCH 593/838] add IntrusivePtr to Hypothesis

---
 src/translator/beam_search.h      |  8 ++++----
 src/translator/history.h          |  2 +-
 src/translator/hypothesis.h       | 21 ++++++++++++++++-----
 src/translator/output_printer.cpp |  2 +-
 src/translator/output_printer.h   |  2 +-
 5 files changed, 23 insertions(+), 12 deletions(-)

diff --git a/src/translator/beam_search.h b/src/translator/beam_search.h
index 408a550d5..e014bb08c 100755
--- a/src/translator/beam_search.h
+++ b/src/translator/beam_search.h
@@ -100,7 +100,7 @@ class BeamSearch {
       else
         word = Word::fromWordIndex(wordIdx);
 
-      auto hyp = New<Hypothesis>(prevHyp, word, prevBeamHypIdx, pathScore);
+      auto hyp = Hypothesis::New(prevHyp, word, prevBeamHypIdx, pathScore);
 
       // Set score breakdown for n-best lists
       if(options_->get<bool>("n-best")) {
@@ -145,7 +145,7 @@ class BeamSearch {
         }
         if (newBeam.size() > beam.size()) {
           //LOG(info, "Size {}, sorting...", newBeam.size());
-          std::nth_element(newBeam.begin(), newBeam.begin() + beam.size(), newBeam.end(), [](Ptr<Hypothesis> a, Ptr<Hypothesis> b) {
+          std::nth_element(newBeam.begin(), newBeam.begin() + beam.size(), newBeam.end(), [](Hypothesis::PtrType a, Hypothesis::PtrType b) {
             return a->getPathScore() > b->getPathScore(); // (sort highest score first)
           });
           //LOG(info, "Size {}, sorted...", newBeam.size());
@@ -245,7 +245,7 @@ class BeamSearch {
     }
 
     // create one beam per batch entry with sentence-start hypothesis
-    Beams beams(dimBatch, Beam(beamSize_, New<Hypothesis>())); // array [dimBatch] of array [localBeamSize] of Hypothesis
+    Beams beams(dimBatch, Beam(beamSize_, Hypothesis::New())); // array [dimBatch] of array [localBeamSize] of Hypothesis
 
     const auto srcEosId = batch->front()->vocab()->getEosId();
     for(int batchIdx = 0; batchIdx < dimBatch; ++batchIdx) {
@@ -255,7 +255,7 @@ class BeamSearch {
       // Handle batch entries that consist only of source <EOS> i.e. these are empty lines
       if(batch->front()->data()[batchIdx] == srcEosId) {
         // create a target <EOS> hypothesis that extends the start-hypothesis
-        auto eosHyp = New<Hypothesis>(/*prevHyp=*/    beam[0], 
+        auto eosHyp = Hypothesis::New(/*prevHyp=*/    beam[0], 
                                       /*currWord=*/   trgEosId, 
                                       /*prevHypIdx=*/ 0, 
                                       /*pathScore=*/  0.f);
diff --git a/src/translator/history.h b/src/translator/history.h
index ff37fb6e2..8265fdb28 100644
--- a/src/translator/history.h
+++ b/src/translator/history.h
@@ -47,7 +47,7 @@ class History {
 
       const size_t start = bestHypCoord.i; // last time step of this hypothesis
       const size_t j     = bestHypCoord.j; // which beam entry
-      Ptr<Hypothesis> bestHyp = history_[start][j];
+      Hypothesis::PtrType bestHyp = history_[start][j];
       // float c = bestHypCoord.normalizedPathScore;
       // std::cerr << "h: " << start << " " << j << " " << c << std::endl;
 
diff --git a/src/translator/hypothesis.h b/src/translator/hypothesis.h
index c1d570313..6553278dc 100644
--- a/src/translator/hypothesis.h
+++ b/src/translator/hypothesis.h
@@ -13,15 +13,24 @@ namespace marian {
 //  - back pointer to previous hypothesis for traceback
 class Hypothesis {
 public:
+  typedef IPtr<Hypothesis> PtrType;
+  
   Hypothesis() : prevHyp_(nullptr), prevBeamHypIdx_(0), word_(Word::ZERO), pathScore_(0.0) {}
 
-  Hypothesis(const Ptr<Hypothesis> prevHyp,
+  Hypothesis(const PtrType prevHyp,
              Word word,
              size_t prevBeamHypIdx, // beam-hyp index that this hypothesis originated from
              float pathScore)
       : prevHyp_(prevHyp), prevBeamHypIdx_(prevBeamHypIdx), word_(word), pathScore_(pathScore) {}
 
-  const Ptr<Hypothesis> getPrevHyp() const { return prevHyp_; }
+
+ // Use this whenever creating a pointer to MemoryPiece
+ template <class ...Args>
+ static PtrType New(Args&& ...args) {
+   return PtrType(new Hypothesis(std::forward<Args>(args)...));
+ }
+
+  const PtrType getPrevHyp() const { return prevHyp_; }
 
   Word getWord() const { return word_; }
 
@@ -60,17 +69,19 @@ class Hypothesis {
   }
 
 private:
-  const Ptr<Hypothesis> prevHyp_;
+  const PtrType prevHyp_;
   const size_t prevBeamHypIdx_;
   const Word word_;
   const float pathScore_;
 
   std::vector<float> scoreBreakdown_; // [num scorers]
   std::vector<float> alignment_;
+
+  ENABLE_INTRUSIVE_PTR(Hypothesis)
 };
 
-typedef std::vector<Ptr<Hypothesis>> Beam;                // Beam = vector [beamSize] of hypotheses
+typedef std::vector<IPtr<Hypothesis>> Beam;                // Beam = vector [beamSize] of hypotheses
 typedef std::vector<Beam> Beams;                          // Beams = vector [batchDim] of vector [beamSize] of hypotheses
-typedef std::tuple<Words, Ptr<Hypothesis>, float> Result; // (word ids for hyp, hyp, normalized sentence score for hyp)
+typedef std::tuple<Words, IPtr<Hypothesis>, float> Result; // (word ids for hyp, hyp, normalized sentence score for hyp)
 typedef std::vector<Result> NBestList;                    // sorted vector of (word ids, hyp, sent score) tuples
 }  // namespace marian
diff --git a/src/translator/output_printer.cpp b/src/translator/output_printer.cpp
index 05167898d..7e2165ef8 100644
--- a/src/translator/output_printer.cpp
+++ b/src/translator/output_printer.cpp
@@ -2,7 +2,7 @@
 
 namespace marian {
 
-std::string OutputPrinter::getAlignment(const Ptr<Hypothesis>& hyp) {
+std::string OutputPrinter::getAlignment(const Hypothesis::PtrType& hyp) {
   data::SoftAlignment align;
   auto last = hyp;
   // get soft alignments for each target word starting from the last one
diff --git a/src/translator/output_printer.h b/src/translator/output_printer.h
index 85a7585f4..f37ff5b96 100755
--- a/src/translator/output_printer.h
+++ b/src/translator/output_printer.h
@@ -82,7 +82,7 @@ class OutputPrinter {
   std::string alignment_;
   float alignmentThreshold_{0.f};
 
-  std::string getAlignment(const Ptr<Hypothesis>& hyp);
+  std::string getAlignment(const Hypothesis::PtrType& hyp);
 
   float getAlignmentThreshold(const std::string& str) {
     try {

From 37bdad19e84311ad4c7a7f2507e5f32b925174b4 Mon Sep 17 00:00:00 2001
From: Marcin Junczys-Dowmunt <Marcin.JunczysDowmunt@microsoft.com>
Date: Mon, 9 Sep 2019 16:33:06 -0700
Subject: [PATCH 594/838] make constructor private

---
 src/tensors/tensor.h        | 1 +
 src/translator/hypothesis.h | 7 +++++--
 2 files changed, 6 insertions(+), 2 deletions(-)

diff --git a/src/tensors/tensor.h b/src/tensors/tensor.h
index c3585fca7..904f36b60 100644
--- a/src/tensors/tensor.h
+++ b/src/tensors/tensor.h
@@ -27,6 +27,7 @@ class TensorBase {
 
   ENABLE_INTRUSIVE_PTR(TensorBase)
 
+  // Constructors are private, use TensorBase::New(...)
   TensorBase(MemoryPiece::PtrType memory,
              Shape shape,
              Type type,
diff --git a/src/translator/hypothesis.h b/src/translator/hypothesis.h
index 6553278dc..a36296bd8 100644
--- a/src/translator/hypothesis.h
+++ b/src/translator/hypothesis.h
@@ -14,7 +14,10 @@ namespace marian {
 class Hypothesis {
 public:
   typedef IPtr<Hypothesis> PtrType;
-  
+
+private:
+  // Constructors are private, use Hypothesis::New(...)
+
   Hypothesis() : prevHyp_(nullptr), prevBeamHypIdx_(0), word_(Word::ZERO), pathScore_(0.0) {}
 
   Hypothesis(const PtrType prevHyp,
@@ -23,7 +26,7 @@ class Hypothesis {
              float pathScore)
       : prevHyp_(prevHyp), prevBeamHypIdx_(prevBeamHypIdx), word_(word), pathScore_(pathScore) {}
 
-
+public:
  // Use this whenever creating a pointer to MemoryPiece
  template <class ...Args>
  static PtrType New(Args&& ...args) {

From a7f7f710869a096500b59cc47cb01b011bcdd3fb Mon Sep 17 00:00:00 2001
From: Marcin Junczys-Dowmunt <Marcin.JunczysDowmunt@microsoft.com>
Date: Mon, 9 Sep 2019 16:39:36 -0700
Subject: [PATCH 595/838] Adapt changelog and version

---
 CHANGELOG.md | 9 +++++++++
 VERSION      | 2 +-
 2 files changed, 10 insertions(+), 1 deletion(-)

diff --git a/CHANGELOG.md b/CHANGELOG.md
index 3b9255493..3671c2b69 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -7,7 +7,16 @@ and this project adheres to [Semantic Versioning](http://semver.org/spec/v2.0.0.
 
 ## [Unreleased]
 
+### Added
+- Automatic vectorization of elementwise operations on CPU for tensors dims that 
+  are divible by 4 (AVX) and 8 (AVX2)
+- Replacing std::shared_ptr<T> with custom IntrusivePtr<T> for small objects like 
+  Tensors, Hypotheses and Expressions.
+- First steps towards integrating FP16 support, currently no-ops.
+
 ### Fixed
+- Sort parameters by lexicographical order during allocation to ensure consistent 
+  memory-layout during allocation, loading, saving.
 - Output empty line when input is empty line. Previous behavior might result in 
   hallucinated outputs.
 
diff --git a/VERSION b/VERSION
index 2fb69e15c..57f52717b 100644
--- a/VERSION
+++ b/VERSION
@@ -1 +1 @@
-v1.8.1
+v1.8.2

From 864fe82ad143c4a2767a55a944ee85ee6f93f002 Mon Sep 17 00:00:00 2001
From: Marcin Junczys-Dowmunt <Marcin.JunczysDowmunt@microsoft.com>
Date: Mon, 9 Sep 2019 17:05:07 -0700
Subject: [PATCH 596/838] enable compilation with compute_30 etc

---
 CMakeLists.txt             | 6 +++---
 src/functional/operators.h | 7 ++++---
 2 files changed, 7 insertions(+), 6 deletions(-)

diff --git a/CMakeLists.txt b/CMakeLists.txt
index 34d792d2c..3bb85947e 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -171,7 +171,7 @@ if(USE_STATIC_LIBS)
   endif()
 endif()
 
-find_package(CUDA "10.0")
+find_package(CUDA "8.0") # TODO: only enable FP16-related options for compute_70 and higher.
 if(CUDA_FOUND)
   # CUDA >= 10.0 requires CMake >= 3.12.2
   if((CUDA_VERSION VERSION_EQUAL "10.0" OR CUDA_VERSION VERSION_GREATER "10.0") AND (CMAKE_VERSION VERSION_LESS "3.12.2"))
@@ -225,9 +225,9 @@ endif(COMPILE_CUDA)
 
 # TODO: make compatible with older CUDA versions
 if(CMAKE_BUILD_TYPE STREQUAL "Debug")
-  list(APPEND CUDA_NVCC_FLAGS --default-stream per-thread; -O0; -g; --use_fast_math; -gencode=arch=compute_60,code=sm_60; -gencode=arch=compute_61,code=sm_61; -gencode=arch=compute_70,code=sm_70; -gencode=arch=compute_75,code=sm_75; -gencode=arch=compute_75,code=compute_75 ;)
+  list(APPEND CUDA_NVCC_FLAGS --default-stream per-thread; -O0; -g; --use_fast_math; -arch=sm_30; -gencode=arch=compute_30,code=sm_30; -gencode=arch=compute_50,code=sm_50; -gencode=arch=compute_52,code=sm_52; -gencode=arch=compute_60,code=sm_60; -gencode=arch=compute_61,code=sm_61; -gencode=arch=compute_70,code=sm_70; -gencode=arch=compute_75,code=sm_75; -gencode=arch=compute_75,code=compute_75 ;)
 else(CMAKE_BUILD_TYPE STREQUAL "Debug")
-  list(APPEND CUDA_NVCC_FLAGS --default-stream per-thread; -O3; -g; --use_fast_math; -gencode=arch=compute_60,code=sm_60; -gencode=arch=compute_61,code=sm_61; -gencode=arch=compute_70,code=sm_70; -gencode=arch=compute_75,code=sm_75; -gencode=arch=compute_75,code=compute_75 ;)
+  list(APPEND CUDA_NVCC_FLAGS --default-stream per-thread; -O3; -g; --use_fast_math; -arch=sm_30; -gencode=arch=compute_30,code=sm_30; -gencode=arch=compute_50,code=sm_50; -gencode=arch=compute_52,code=sm_52; -gencode=arch=compute_60,code=sm_60; -gencode=arch=compute_61,code=sm_61; -gencode=arch=compute_70,code=sm_70; -gencode=arch=compute_75,code=sm_75; -gencode=arch=compute_75,code=compute_75 ;)
 endif(CMAKE_BUILD_TYPE STREQUAL "Debug")
 if(NOT MSVC)
   # @TODO: add warnings here too
diff --git a/src/functional/operators.h b/src/functional/operators.h
index 4a210e134..bd6e1a4e8 100644
--- a/src/functional/operators.h
+++ b/src/functional/operators.h
@@ -3,7 +3,7 @@
 #include "common/types.h"
 #include <cmath>
 
-#ifdef __CUDA_ARCH__
+#if __CUDA_ARCH__ >= 700
 #include <cuda_fp16.h>
 #endif
 
@@ -426,7 +426,7 @@ struct Ops<float32x8> {
 
 #endif
 
-#ifdef __CUDA_ARCH__
+#if __CUDA_ARCH__ >= 700
 
 // Specialization for half
 template <>
@@ -437,9 +437,10 @@ struct Ops<half> {
   static DEVICE_INLINE half tan(const half& x)  { return hsin(x) / hcos(x); }
   static DEVICE_INLINE half log(const half& x)  { return hlog(x); }
   static DEVICE_INLINE half exp(const half& x)  { return hexp(x); }
-  static DEVICE_INLINE half abs(const half& x)  { return fabs((float)x); }// @TODO half has this information somewhere in the struct, right?
   static DEVICE_INLINE half sqrt(const half& x) { return hsqrt(x); }
   static DEVICE_INLINE half neg(const half& x)  { return -x; }
+  
+  static DEVICE_INLINE half abs(const half& x)  { return fabs((float)x); }// @TODO half has this information somewhere in the struct, right?
   static DEVICE_INLINE half sgn(const half& x)  { half zero = 0.f; return (zero < x) - (x < zero); } // @TODO half has this information somewhere in the struct, right?
 
   static DEVICE_INLINE half add(const half& x, const half& y)  { return x + y; }

From 46d9b99f26d01b2ac4cc78570a8f7f7d15bedcfe Mon Sep 17 00:00:00 2001
From: Marcin Junczys-Dowmunt <Marcin.JunczysDowmunt@microsoft.com>
Date: Mon, 9 Sep 2019 17:10:26 -0700
Subject: [PATCH 597/838] add comment on compute

---
 src/functional/operators.h | 1 +
 1 file changed, 1 insertion(+)

diff --git a/src/functional/operators.h b/src/functional/operators.h
index bd6e1a4e8..6043d0ad3 100644
--- a/src/functional/operators.h
+++ b/src/functional/operators.h
@@ -3,6 +3,7 @@
 #include "common/types.h"
 #include <cmath>
 
+// only compile with fp16 support for compute_70, i.e. VOLTA 100 and above.
 #if __CUDA_ARCH__ >= 700
 #include <cuda_fp16.h>
 #endif

From 356654c5acfaf25ac1f7de4166b0df3b0e0dc948 Mon Sep 17 00:00:00 2001
From: Marcin Junczys-Dowmunt <Marcin.JunczysDowmunt@microsoft.com>
Date: Mon, 9 Sep 2019 22:19:17 -0700
Subject: [PATCH 598/838] resolve a couple of comments

---
 src/tensors/cpu/element.h            | 21 ++++++++++++---------
 src/tensors/cpu/tensor_operators.cpp |  6 ++++--
 src/tensors/gpu/add.inc              |  4 +---
 src/tensors/gpu/element.inc          |  3 ---
 4 files changed, 17 insertions(+), 17 deletions(-)

diff --git a/src/tensors/cpu/element.h b/src/tensors/cpu/element.h
index 86f0be85a..c6270a133 100644
--- a/src/tensors/cpu/element.h
+++ b/src/tensors/cpu/element.h
@@ -18,15 +18,15 @@ namespace cpu {
 // computation.
 // Compiler optimizes this to single construct with nested(?) loops.
 
-namespace f = marian::functional;
+namespace F = marian::functional;
 
 template <size_t I = 0>
 struct E {
   template <size_t numArg, class Functor, typename ElementType>
   static inline void element(
       const Functor& functor,
-      f::Array<f::Tensor<ElementType>, numArg>& tensors,
-      f::Array<int, numArg> indices) {
+      F::Array<F::Tensor<ElementType>, numArg>& tensors,
+      F::Array<int, numArg> indices) {
     const auto& shape = tensors[0].shape();
 
     // loop over outer-most dimension
@@ -49,15 +49,15 @@ struct E {
 // specialization for inner-most single element (recursive stopping criterion)
 // using const reference for indices here to avoid copying. No loop.
 template <>
-struct E<f::Shape::size()> {
+struct E<F::Shape::size()> {
   template <size_t numArg, class Functor, typename ElementType>
   static inline void element(
       const Functor& functor,
-      f::Array<f::Tensor<ElementType>, numArg>& tensors,
-      const f::Array<int, numArg>& indices) {
+      F::Array<F::Tensor<ElementType>, numArg>& tensors,
+      const F::Array<int, numArg>& indices) {
     // just apply the function for all indexed elements across all tensors
     // @TODO: use converting operator[] on tensor
-    tensors[0].data()[indices[0]] = f::apply(functor, tensors, indices);
+    tensors[0].data()[indices[0]] = F::apply(functor, tensors, indices);
   }
 };
 
@@ -67,15 +67,18 @@ void element(const Functor& functor, marian::Tensor out, Tensors... tensors) {
   // Number of input tensors + 1 (output tensor)
   constexpr size_t argNum = sizeof...(tensors) + 1;
   // create and initialize indices to 0, one index per tensor
-  f::Array<int, argNum> indices;
+  F::Array<int, argNum> indices;
   indices.fill(0);
 
   // call elementwise operation going from outer-most dimension
   // to inner-most element.
-  f::Array<f::Tensor<ElementType>, argNum> gTensors = {out, tensors...};
+  F::Array<F::Tensor<ElementType>, argNum> gTensors = {out, tensors...};
   E<0>::element(functor, gTensors, indices);
 }
 
+// Dispatch elementwise functions with float element type based on number of 
+// elements. If dividable by 8 and AVX2 is available (TODO: check this?) use
+// AVX2 specific intrinsics. Similar for 4 and AVX. TODO: Add AVX512 support.
 template <class Functor, class... Tensors>
 void elementFloat(const Functor& functor, marian::Tensor out, Tensors... tensors) {
 #ifndef __CUDA_ARCH__
diff --git a/src/tensors/cpu/tensor_operators.cpp b/src/tensors/cpu/tensor_operators.cpp
index 300d46f7c..056f0e1d9 100644
--- a/src/tensors/cpu/tensor_operators.cpp
+++ b/src/tensors/cpu/tensor_operators.cpp
@@ -561,7 +561,9 @@ void PasteCols(Tensor out_,
   }
 }
 
-void Select2(Tensor out,
+// Optimized version of Select for axis=2
+// @TODO: make this generally fast without this special version
+void SelectAxis2(Tensor out,
              const Tensor in,
              const Tensor indices) {
 
@@ -606,7 +608,7 @@ void Select(Tensor out,
   int axisCPU = (int)(axis + functional::Shape::size() - out->shape().size());
 
   if(axisCPU == 2) // specialization for axis==2, assuming N=4
-    return Select2(out, in, indices);
+    return SelectAxis2(out, in, indices);
 
   auto odata = out->data();
   const auto idata = in->data();
diff --git a/src/tensors/gpu/add.inc b/src/tensors/gpu/add.inc
index df65805c6..1e11d011b 100755
--- a/src/tensors/gpu/add.inc
+++ b/src/tensors/gpu/add.inc
@@ -23,7 +23,6 @@ template void Add<BinaryFunctor<elem::Mult, BinaryFunctor<elem::Gt, Assignee<2>,
 template void Add<BinaryFunctor<elem::Mult, BinaryFunctor<elem::Leq, Assignee<2>, Assignee<3>>, Assignee<1>>, marian::Tensor, marian::Tensor, marian::Tensor>(BinaryFunctor<elem::Mult, BinaryFunctor<elem::Leq, Assignee<2>, Assignee<3>>, Assignee<1>>, float, marian::Tensor, marian::Tensor, marian::Tensor, marian::Tensor);
 template void Add<BinaryFunctor<elem::Mult, Assignee<1>, UnaryFunctor<elem::Sigmoid, BinaryFunctor<elem::Minus, Assignee<2>, Assignee<3>>>>, marian::Tensor, marian::Tensor, marian::Tensor>(BinaryFunctor<elem::Mult, Assignee<1>, UnaryFunctor<elem::Sigmoid, BinaryFunctor<elem::Minus, Assignee<2>, Assignee<3>>>>, float, marian::Tensor, marian::Tensor, marian::Tensor, marian::Tensor);
 template void Add<BinaryFunctor<elem::Div, Assignee<1>, Assignee<2> >, marian::Tensor, marian::Tensor >(BinaryFunctor<elem::Div, Assignee<1>, Assignee<2> >, float, marian::Tensor, marian::Tensor, marian::Tensor);
-
 template void marian::gpu::Add<marian::functional::BinaryFunctor<marian::functional::elem::Mult, marian::functional::Assignee<1>, marian::functional::Assignee<1> >, marian::Tensor >(marian::functional::BinaryFunctor<marian::functional::elem::Mult, marian::functional::Assignee<1>, marian::functional::Assignee<1> >, float, marian::Tensor, marian::Tensor);
 template void marian::gpu::Aggregate<marian::functional::Assignee<1>, marian::functional::BinaryFunctor<marian::functional::elem::Min, marian::functional::Assignee<1>, marian::functional::Assignee<2> >, marian::Tensor >(marian::functional::Assignee<1>, float, marian::functional::BinaryFunctor<marian::functional::elem::Min, marian::functional::Assignee<1>, marian::functional::Assignee<2> >, float, marian::Tensor, marian::Tensor);
 template void marian::gpu::Aggregate<marian::functional::Assignee<1>, marian::functional::BinaryFunctor<marian::functional::elem::Max, marian::functional::Assignee<1>, marian::functional::Assignee<2> >, marian::Tensor >(marian::functional::Assignee<1>, float, marian::functional::BinaryFunctor<marian::functional::elem::Max, marian::functional::Assignee<1>, marian::functional::Assignee<2> >, float, marian::Tensor, marian::Tensor);
@@ -33,5 +32,4 @@ template void marian::gpu::Add<marian::functional::BinaryFunctor<marian::functio
 template void marian::gpu::Add<marian::functional::BinaryFunctor<marian::functional::elem::Mult, marian::functional::Assignee<1>, marian::functional::UnaryFunctor<marian::functional::elem::Exp, marian::functional::BinaryFunctor<marian::functional::elem::Minus, marian::functional::Assignee<2>, marian::functional::Assignee<3> > > >, marian::Tensor, marian::Tensor, marian::Tensor >(marian::functional::BinaryFunctor<marian::functional::elem::Mult, marian::functional::Assignee<1>, marian::functional::UnaryFunctor<marian::functional::elem::Exp, marian::functional::BinaryFunctor<marian::functional::elem::Minus, marian::functional::Assignee<2>, marian::functional::Assignee<3> > > >, float, marian::Tensor, marian::Tensor, marian::Tensor, marian::Tensor);
 template void marian::gpu::Add<marian::functional::BinaryFunctor<marian::functional::elem::Div, marian::functional::BinaryFunctor<marian::functional::elem::Mult, marian::functional::Assignee<1>, marian::functional::Assignee<2> >, marian::functional::Assignee<3> >, marian::Tensor, marian::Tensor, marian::Tensor >(marian::functional::BinaryFunctor<marian::functional::elem::Div, marian::functional::BinaryFunctor<marian::functional::elem::Mult, marian::functional::Assignee<1>, marian::functional::Assignee<2> >, marian::functional::Assignee<3> >, float, marian::Tensor, marian::Tensor, marian::Tensor, marian::Tensor);
 template void marian::gpu::Add<marian::functional::BinaryFunctor<marian::functional::elem::Mult, marian::functional::BinaryFunctor<marian::functional::elem::Mult, marian::functional::Assignee<1>, marian::functional::Capture>, marian::functional::Assignee<2> >, marian::Tensor, marian::Tensor >(marian::functional::BinaryFunctor<marian::functional::elem::Mult, marian::functional::BinaryFunctor<marian::functional::elem::Mult, marian::functional::Assignee<1>, marian::functional::Capture>, marian::functional::Assignee<2> >, float, marian::Tensor, marian::Tensor, marian::Tensor);
-template void marian::gpu::Add<marian::functional::BinaryFunctor<marian::functional::elem::Mult, marian::functional::Assignee<1>, marian::functional::BinaryFunctor<marian::functional::elem::Plus, marian::functional::BinaryFunctor<marian::functional::elem::Mult, marian::functional::Capture, marian::functional::Assignee<3> >, marian::functional::BinaryFunctor<marian::functional::elem::Mult, marian::functional::UnaryFunctor<marian::functional::elem::Sigmoid, marian::functional::BinaryFunctor<marian::functional::elem::Mult, marian::functional::Capture, marian::functional::Assignee<2> > >, marian::functional::BinaryFunctor<marian::functional::elem::Minus, marian::functional::Capture, marian::functional::BinaryFunctor<marian::functional::elem::Mult, marian::functional::Capture, marian::functional::Assignee<3> > > > > >, marian::Tensor, marian::Tensor, marian::Tensor >(marian::functional::BinaryFunctor<marian::functional::elem::Mult, marian::functional::Assignee<1>, marian::functional::BinaryFunctor<marian::functional::elem::Plus, marian::functional::BinaryFunctor<marian::functional::elem::Mult, marian::functional::Capture, marian::functional::Assignee<3> >, marian::functional::BinaryFunctor<marian::functional::elem::Mult, marian::functional::UnaryFunctor<marian::functional::elem::Sigmoid, marian::functional::BinaryFunctor<marian::functional::elem::Mult, marian::functional::Capture, marian::functional::Assignee<2> > >, marian::functional::BinaryFunctor<marian::functional::elem::Minus, marian::functional::Capture, marian::functional::BinaryFunctor<marian::functional::elem::Mult, marian::functional::Capture, marian::functional::Assignee<3> > > > > >, float, marian::Tensor, marian::Tensor, marian::Tensor, marian::Tensor);
-
+template void marian::gpu::Add<marian::functional::BinaryFunctor<marian::functional::elem::Mult, marian::functional::Assignee<1>, marian::functional::BinaryFunctor<marian::functional::elem::Plus, marian::functional::BinaryFunctor<marian::functional::elem::Mult, marian::functional::Capture, marian::functional::Assignee<3> >, marian::functional::BinaryFunctor<marian::functional::elem::Mult, marian::functional::UnaryFunctor<marian::functional::elem::Sigmoid, marian::functional::BinaryFunctor<marian::functional::elem::Mult, marian::functional::Capture, marian::functional::Assignee<2> > >, marian::functional::BinaryFunctor<marian::functional::elem::Minus, marian::functional::Capture, marian::functional::BinaryFunctor<marian::functional::elem::Mult, marian::functional::Capture, marian::functional::Assignee<3> > > > > >, marian::Tensor, marian::Tensor, marian::Tensor >(marian::functional::BinaryFunctor<marian::functional::elem::Mult, marian::functional::Assignee<1>, marian::functional::BinaryFunctor<marian::functional::elem::Plus, marian::functional::BinaryFunctor<marian::functional::elem::Mult, marian::functional::Capture, marian::functional::Assignee<3> >, marian::functional::BinaryFunctor<marian::functional::elem::Mult, marian::functional::UnaryFunctor<marian::functional::elem::Sigmoid, marian::functional::BinaryFunctor<marian::functional::elem::Mult, marian::functional::Capture, marian::functional::Assignee<2> > >, marian::functional::BinaryFunctor<marian::functional::elem::Minus, marian::functional::Capture, marian::functional::BinaryFunctor<marian::functional::elem::Mult, marian::functional::Capture, marian::functional::Assignee<3> > > > > >, float, marian::Tensor, marian::Tensor, marian::Tensor, marian::Tensor);
\ No newline at end of file
diff --git a/src/tensors/gpu/element.inc b/src/tensors/gpu/element.inc
index d7ce5b79a..00aff3d91 100755
--- a/src/tensors/gpu/element.inc
+++ b/src/tensors/gpu/element.inc
@@ -54,13 +54,10 @@ template void Element<Assign<Var<1>, BinaryFunctor<elem::Minus, Assignee<1>, Bin
 template void Element<Assign<Var<1>, BinaryFunctor<elem::Minus, Assignee<1>, BinaryFunctor<elem::Mult, Capture, BinaryFunctor<elem::Plus, BinaryFunctor<elem::Div, BinaryFunctor<elem::Div, Assignee<2>, Capture>, BinaryFunctor<elem::Plus, UnaryFunctor<elem::Sqrt, BinaryFunctor<elem::Div, Assignee<3>, Capture>>, Capture>>, BinaryFunctor<elem::Mult, Capture, Assignee<1>>>>>>, marian::Tensor, marian::Tensor, marian::Tensor>(Assign<Var<1>, BinaryFunctor<elem::Minus, Assignee<1>, BinaryFunctor<elem::Mult, Capture, BinaryFunctor<elem::Plus, BinaryFunctor<elem::Div, BinaryFunctor<elem::Div, Assignee<2>, Capture>, BinaryFunctor<elem::Plus, UnaryFunctor<elem::Sqrt, BinaryFunctor<elem::Div, Assignee<3>, Capture>>, Capture>>, BinaryFunctor<elem::Mult, Capture, Assignee<1>>>>>>, marian::Tensor, marian::Tensor, marian::Tensor, marian::Tensor);
 template void Element<Assign<Var<1>, BinaryFunctor<elem::Minus, Assignee<1>, BinaryFunctor<elem::Mult, Capture, BinaryFunctor<elem::Plus, BinaryFunctor<elem::Div, BinaryFunctor<elem::Div, Assignee<2>, Capture>, BinaryFunctor<elem::Plus, UnaryFunctor<elem::Sqrt, BinaryFunctor<elem::Plus, BinaryFunctor<elem::Div, UnaryFunctor<elem::sReLU, BinaryFunctor<elem::Plus, BinaryFunctor<elem::Minus, Assignee<3>, BinaryFunctor<elem::Div, BinaryFunctor<elem::Mult, BinaryFunctor<elem::Mult, Capture, Assignee<5>>, Assignee<4>>, Capture>>, BinaryFunctor<elem::Div, BinaryFunctor<elem::Mult, BinaryFunctor<elem::Mult, Capture, Assignee<4>>, Assignee<4>>, Capture>>>, Capture>, BinaryFunctor<elem::Div, BinaryFunctor<elem::Mult, Assignee<4>, Assignee<4>>, Capture>>>, Capture>>, BinaryFunctor<elem::Mult, Capture, Assignee<1>>>>>>, marian::Tensor, marian::Tensor, marian::Tensor, marian::Tensor>(Assign<Var<1>, BinaryFunctor<elem::Minus, Assignee<1>, BinaryFunctor<elem::Mult, Capture, BinaryFunctor<elem::Plus, BinaryFunctor<elem::Div, BinaryFunctor<elem::Div, Assignee<2>, Capture>, BinaryFunctor<elem::Plus, UnaryFunctor<elem::Sqrt, BinaryFunctor<elem::Plus, BinaryFunctor<elem::Div, UnaryFunctor<elem::sReLU, BinaryFunctor<elem::Plus, BinaryFunctor<elem::Minus, Assignee<3>, BinaryFunctor<elem::Div, BinaryFunctor<elem::Mult, BinaryFunctor<elem::Mult, Capture, Assignee<5>>, Assignee<4>>, Capture>>, BinaryFunctor<elem::Div, BinaryFunctor<elem::Mult, BinaryFunctor<elem::Mult, Capture, Assignee<4>>, Assignee<4>>, Capture>>>, Capture>, BinaryFunctor<elem::Div, BinaryFunctor<elem::Mult, Assignee<4>, Assignee<4>>, Capture>>>, Capture>>, BinaryFunctor<elem::Mult, Capture, Assignee<1>>>>>>, marian::Tensor, marian::Tensor, marian::Tensor, marian::Tensor, marian::Tensor);
 template void Element<Assign<Var<1>, BinaryFunctor<elem::Minus, Assignee<1>, BinaryFunctor<elem::Mult, Capture, BinaryFunctor<elem::Plus, BinaryFunctor<elem::Div, BinaryFunctor<elem::Div, Assignee<2>, Capture>, BinaryFunctor<elem::Plus, UnaryFunctor<elem::Sqrt, BinaryFunctor<elem::Div, Assignee<3>, Capture>>, Capture>>, BinaryFunctor<elem::Mult, Capture, Assignee<1>>>>>>, marian::Tensor, marian::Tensor, marian::Tensor, marian::Tensor>(Assign<Var<1>, BinaryFunctor<elem::Minus, Assignee<1>, BinaryFunctor<elem::Mult, Capture, BinaryFunctor<elem::Plus, BinaryFunctor<elem::Div, BinaryFunctor<elem::Div, Assignee<2>, Capture>, BinaryFunctor<elem::Plus, UnaryFunctor<elem::Sqrt, BinaryFunctor<elem::Div, Assignee<3>, Capture>>, Capture>>, BinaryFunctor<elem::Mult, Capture, Assignee<1>>>>>>, marian::Tensor, marian::Tensor, marian::Tensor, marian::Tensor, marian::Tensor);
-
 template void marian::gpu::Element<marian::functional::Assign<marian::functional::Var<1>, marian::functional::BinaryFunctor<marian::functional::elem::NEq, marian::functional::BinaryFunctor<marian::functional::elem::Eq, marian::functional::BinaryFunctor<marian::functional::elem::Minus, marian::functional::BinaryFunctor<marian::functional::elem::Gt, marian::functional::Assignee<2>, marian::functional::Assignee<3> >, marian::functional::BinaryFunctor<marian::functional::elem::Lt, marian::functional::Assignee<2>, marian::functional::Assignee<3> > >, marian::functional::Capture>, marian::functional::Capture> >, marian::Tensor, marian::Tensor >(marian::functional::Assign<marian::functional::Var<1>, marian::functional::BinaryFunctor<marian::functional::elem::NEq, marian::functional::BinaryFunctor<marian::functional::elem::Eq, marian::functional::BinaryFunctor<marian::functional::elem::Minus, marian::functional::BinaryFunctor<marian::functional::elem::Gt, marian::functional::Assignee<2>, marian::functional::Assignee<3> >, marian::functional::BinaryFunctor<marian::functional::elem::Lt, marian::functional::Assignee<2>, marian::functional::Assignee<3> > >, marian::functional::Capture>, marian::functional::Capture> >, marian::Tensor, marian::Tensor, marian::Tensor);
 template void marian::gpu::Element<marian::functional::Assign<marian::functional::Var<1>, marian::functional::UnaryFunctor<marian::functional::elem::Sqrt, marian::functional::Assignee<1> > >>(marian::functional::Assign<marian::functional::Var<1>, marian::functional::UnaryFunctor<marian::functional::elem::Sqrt, marian::functional::Assignee<1> > >, marian::Tensor);
 template void marian::gpu::Element<marian::functional::Assign<marian::functional::Var<1>, marian::functional::BinaryFunctor<marian::functional::elem::Mult, marian::functional::Assignee<2>, marian::functional::UnaryFunctor<marian::functional::elem::Sigmoid, marian::functional::BinaryFunctor<marian::functional::elem::Mult, marian::functional::Capture, marian::functional::Assignee<2> > > > >, marian::Tensor >(marian::functional::Assign<marian::functional::Var<1>, marian::functional::BinaryFunctor<marian::functional::elem::Mult, marian::functional::Assignee<2>, marian::functional::UnaryFunctor<marian::functional::elem::Sigmoid, marian::functional::BinaryFunctor<marian::functional::elem::Mult, marian::functional::Capture, marian::functional::Assignee<2> > > > >, marian::Tensor, marian::Tensor);
-
 template void marian::gpu::Element<marian::functional::Assign<marian::functional::Var<1>, marian::functional::BinaryFunctor<marian::functional::elem::Plus, marian::functional::BinaryFunctor<marian::functional::elem::Mult, marian::functional::BinaryFunctor<marian::functional::elem::Lt, marian::functional::Assignee<1>, marian::functional::Capture>, marian::functional::Capture>, marian::functional::Capture> >>(marian::functional::Assign<marian::functional::Var<1>, marian::functional::BinaryFunctor<marian::functional::elem::Plus, marian::functional::BinaryFunctor<marian::functional::elem::Mult, marian::functional::BinaryFunctor<marian::functional::elem::Lt, marian::functional::Assignee<1>, marian::functional::Capture>, marian::functional::Capture>, marian::functional::Capture> >, marian::Tensor);
-
 template void marian::gpu::Element<marian::functional::Assign<marian::functional::Var<1>, marian::functional::BinaryFunctor<marian::functional::elem::Minus, marian::functional::Assignee<1>, marian::functional::BinaryFunctor<marian::functional::elem::Mult, marian::functional::Capture, marian::functional::BinaryFunctor<marian::functional::elem::Plus, marian::functional::BinaryFunctor<marian::functional::elem::Div, marian::functional::Assignee<2>, marian::functional::Capture>, marian::functional::BinaryFunctor<marian::functional::elem::Mult, marian::functional::Capture, marian::functional::Assignee<1> > > > > >, IntrusivePtr<marian::TensorBase> >(marian::functional::Assign<marian::functional::Var<1>, marian::functional::BinaryFunctor<marian::functional::elem::Minus, marian::functional::Assignee<1>, marian::functional::BinaryFunctor<marian::functional::elem::Mult, marian::functional::Capture, marian::functional::BinaryFunctor<marian::functional::elem::Plus, marian::functional::BinaryFunctor<marian::functional::elem::Div, marian::functional::Assignee<2>, marian::functional::Capture>, marian::functional::BinaryFunctor<marian::functional::elem::Mult, marian::functional::Capture, marian::functional::Assignee<1> > > > > >, marian::Tensor, marian::Tensor);
 
 // How to add new specializations:

From 86c43565f274daba1975326a92c04f1ddf6b85e3 Mon Sep 17 00:00:00 2001
From: Marcin Junczys-Dowmunt <Marcin.JunczysDowmunt@microsoft.com>
Date: Tue, 10 Sep 2019 07:25:05 -0700
Subject: [PATCH 599/838] update changelog and version

---
 CHANGELOG.md       | 1 +
 VERSION            | 2 +-
 regression-tests   | 2 +-
 src/3rd_party/nccl | 2 +-
 4 files changed, 4 insertions(+), 3 deletions(-)

diff --git a/CHANGELOG.md b/CHANGELOG.md
index 3b9255493..c92636c66 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -10,6 +10,7 @@ and this project adheres to [Semantic Versioning](http://semver.org/spec/v2.0.0.
 ### Fixed
 - Output empty line when input is empty line. Previous behavior might result in 
   hallucinated outputs.
+- Compilation with CUDA 10.1
 
 ## [1.8.0] - 2019-09-04
 
diff --git a/VERSION b/VERSION
index 2fb69e15c..57f52717b 100644
--- a/VERSION
+++ b/VERSION
@@ -1 +1 @@
-v1.8.1
+v1.8.2
diff --git a/regression-tests b/regression-tests
index 232e21d91..71b0506a5 160000
--- a/regression-tests
+++ b/regression-tests
@@ -1 +1 @@
-Subproject commit 232e21d912681e78969e93ed18030f6e7010f8d7
+Subproject commit 71b0506a50e1f181e269fe90073a8ae470676dd1
diff --git a/src/3rd_party/nccl b/src/3rd_party/nccl
index d6297d250..8e3a3f7c5 160000
--- a/src/3rd_party/nccl
+++ b/src/3rd_party/nccl
@@ -1 +1 @@
-Subproject commit d6297d250433715c283d17f1969cfcb50d2b6531
+Subproject commit 8e3a3f7c5b520babff49cec54a866fa3eda3a3b6

From 66e0f1ed8214df07cff6760d3c2d57b126b04192 Mon Sep 17 00:00:00 2001
From: Marcin Junczys-Dowmunt <Marcin.JunczysDowmunt@microsoft.com>
Date: Tue, 10 Sep 2019 08:03:15 -0700
Subject: [PATCH 600/838] more comments

---
 src/common/types.h     | 4 ++--
 src/graph/parameters.h | 6 ++++++
 2 files changed, 8 insertions(+), 2 deletions(-)

diff --git a/src/common/types.h b/src/common/types.h
index 991cd7807..84c4e10e9 100644
--- a/src/common/types.h
+++ b/src/common/types.h
@@ -74,13 +74,13 @@ struct float32x4 {
 public:
   float32x4() {}
   float32x4(const __m128& f) : f_(f) {}
-  float32x4(const float& f) : f_(_mm_set1_ps(f)) {}
+  float32x4(const float& f) : f_(_mm_set1_ps(f)) {} // __m128 _mm_set1_ps(float) copies value into all slots
 
   operator const __m128&() const { return f_; }
   operator __m128&() { return f_; }
 
   float operator[] (size_t i) const {
-    return *(((float*)&f_) + i);
+    return *(((float*)&f_) + i); 
   }
 
   friend std::ostream& operator<<(std::ostream& out, float32x4 f4) {
diff --git a/src/graph/parameters.h b/src/graph/parameters.h
index 9a9206cf7..bb2fc97be 100644
--- a/src/graph/parameters.h
+++ b/src/graph/parameters.h
@@ -10,6 +10,12 @@
 
 namespace marian {
 
+// @TODO: Currently an ExpressionGraph only supports one Parameters object and 
+// the type of parameters has to be the inside on Parameters object. This limits
+// parameter types to a single chosen type, e.g. only fp32 or only fp16. This should
+// be extended to allow multiple sets of parameters. 
+// The reason here is to be able to efficiently compute updates of whole parameter
+// sets of one type.
 class Parameters {
 protected:
   /** @brief List of all parameter nodes of this expression graph. */

From f0880b472b7a6fa303b83df6af0b060bbccf1a87 Mon Sep 17 00:00:00 2001
From: Marcin Junczys-Dowmunt <Marcin.JunczysDowmunt@microsoft.com>
Date: Tue, 10 Sep 2019 08:05:10 -0700
Subject: [PATCH 601/838] set correct regression test version

---
 regression-tests | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/regression-tests b/regression-tests
index 71b0506a5..232e21d91 160000
--- a/regression-tests
+++ b/regression-tests
@@ -1 +1 @@
-Subproject commit 71b0506a50e1f181e269fe90073a8ae470676dd1
+Subproject commit 232e21d912681e78969e93ed18030f6e7010f8d7

From 1eb44c0ea62bb3243af3f86e4369c26b4b1b74c6 Mon Sep 17 00:00:00 2001
From: Marcin Junczys-Dowmunt <Marcin.JunczysDowmunt@microsoft.com>
Date: Tue, 10 Sep 2019 08:44:18 -0700
Subject: [PATCH 602/838] resolve comments from code review

---
 src/common/definitions.h            |  3 +++
 src/common/types.h                  |  9 ++++++---
 src/functional/functional.h         |  2 ++
 src/functional/operators.h          |  2 ++
 src/functional/shape.h              | 11 ++++++++---
 src/functional/tensor.h             | 11 +++++++----
 src/functional/tmp.h                |  2 ++
 src/tensors/allocator.h             | 18 +++++++++---------
 src/tensors/gpu/algorithm.cu        |  2 ++
 src/tensors/gpu/tensor_operators.cu |  6 +++---
 src/tensors/tensor.cpp              |  2 ++
 src/tensors/tensor.h                |  2 ++
 src/tensors/tensor_allocator.h      |  2 +-
 13 files changed, 49 insertions(+), 23 deletions(-)

diff --git a/src/common/definitions.h b/src/common/definitions.h
index b7de4ad14..45f5d5638 100755
--- a/src/common/definitions.h
+++ b/src/common/definitions.h
@@ -30,12 +30,15 @@ namespace marian {
 // This minimizes bandwith at little cost.
 typedef uint32_t IndexType;
 
+// @TODO: come up with better short name. "I..." stands for interface now. Here it stands
+// for "intrusive". Not a good overlap.
 template <class T>
 using IPtr = IntrusivePtr<T>;
 
 template <class T>
 using UPtr = std::unique_ptr<T>;
 
+// @TODO: come up with better short name. "I..." stands for interface now.
 template <class T>
 using IWeak = T*;
 
diff --git a/src/common/types.h b/src/common/types.h
index 84c4e10e9..e41045195 100644
--- a/src/common/types.h
+++ b/src/common/types.h
@@ -67,6 +67,8 @@ do { \
 namespace marian {
 
 #ifndef __CUDA_ARCH__
+
+// @TODO: check what intrinsics are actually available.
 struct float32x4 {
 private:
   __m128 f_;
@@ -80,7 +82,7 @@ struct float32x4 {
   operator __m128&() { return f_; }
 
   float operator[] (size_t i) const {
-    return *(((float*)&f_) + i); 
+    return *(((float*)&f_) + i); // potentially undefined, but efficient. In practice __m128 is an array of floats
   }
 
   friend std::ostream& operator<<(std::ostream& out, float32x4 f4) {
@@ -93,6 +95,7 @@ struct float32x4 {
   }
 };
 
+// @TODO: consider how code can be shared via templating
 struct float32x8 {
 private:
   __m256 f_;
@@ -100,13 +103,13 @@ struct float32x8 {
 public:
   float32x8() {}
   float32x8(const __m256& f) : f_(f) {}
-  float32x8(const float& f) : f_(_mm256_set1_ps(f)) {}
+  float32x8(const float& f) : f_(_mm256_set1_ps(f)) {} // __m256 _mm_set1_ps(float) copies value into all slots
 
   operator const __m256&() const { return f_; }
   operator __m256&() { return f_; }
 
   float operator[] (size_t i) const {
-    return *(((float*)&f_) + i);
+    return *(((float*)&f_) + i); // potentially undefined, but efficient. In practice __m128 is an array of floats
   }
 
   friend std::ostream& operator<<(std::ostream& out, float32x8 f8) {
diff --git a/src/functional/functional.h b/src/functional/functional.h
index f42eff951..a3e57e4c8 100644
--- a/src/functional/functional.h
+++ b/src/functional/functional.h
@@ -1,5 +1,7 @@
 #pragma once
 
+// this header is meant to be included for all operations from the "functional" namespace.
+
 #include "functional/operands.h"
 #include "functional/predicates.h"
 #include "functional/operators.h"
diff --git a/src/functional/operators.h b/src/functional/operators.h
index 6043d0ad3..1493f75d0 100644
--- a/src/functional/operators.h
+++ b/src/functional/operators.h
@@ -191,6 +191,8 @@ struct Ops<double> {
 
 };
 
+// stay invisible to NVCC as it seems to have problems with intrinsics;
+// will still be compiled into the binary by cpu-side gcc/g++
 #ifndef __CUDA_ARCH__
 
 #include "3rd_party/sse_mathfun.h"
diff --git a/src/functional/shape.h b/src/functional/shape.h
index d3ec80938..8f361386e 100644
--- a/src/functional/shape.h
+++ b/src/functional/shape.h
@@ -13,8 +13,9 @@ namespace functional {
 
 #define CONST_SHAPE_DIMS 4
 
+// attempts at low-level slicing and proper views, not integrated yet
+#if 0 
 const int MAX_INT = std::numeric_limits<int>::max();
-
 struct Slice {
   static const int END{MAX_INT}; // fix
 
@@ -45,6 +46,7 @@ struct Slice {
 };
 
 const Slice All;
+#endif
 
 /**
  * @brief Represents the size of each dimension in a tensor.
@@ -86,8 +88,8 @@ struct ConstantShape {
   }
 
   HOST_DEVICE ConstantShape(const Array<int, N>& shape,
-                       const Array<int, N>& stride,
-                       size_t offset)
+                            const Array<int, N>& stride,
+                            size_t offset)
   : shape_(shape), stride_(stride), offset_(offset) {
     updateElements();
   }
@@ -232,6 +234,8 @@ struct ConstantShape {
     return strm.str();
   }
 
+// @TODO: attempts at proper slicing. Works but not integrated anywhere. To be revisited.
+#if 0
   // Performs numpy-like slicing on a given shape object. The number
   // of slices corresponds to the number of dimensions.
   HOST_DEVICE_INLINE ConstantShape<N> slice(const Array<Slice, N>& slices) {
@@ -283,6 +287,7 @@ struct ConstantShape {
 
 //     return ConstantShape<D>(other.shape_, stride, offset_);
 //   }
+#endif
 
   friend std::ostream& operator<<(std::ostream& strm, const ConstantShape<N>& shape) {
     strm << shape.toString();
diff --git a/src/functional/tensor.h b/src/functional/tensor.h
index 601550178..ba5775176 100644
--- a/src/functional/tensor.h
+++ b/src/functional/tensor.h
@@ -7,10 +7,10 @@
 namespace marian {
 namespace functional {
 
-// general case, do nothing. Usually the number of elements in a tensor is correctly mirrored in the
-// shape object. Only special multi-element types like float32x4 (4 floats), float32x8 (8 floats)
-// and half2 (2 half) require special handling. Similar for multi-element integer types to be added
-// later.
+// By default for single valued types like float do nothing. Usually the number of elements in a tensor 
+// is correctly mirrored in the shape object. Only special multi-element types like float32x4 (4 floats), 
+// float32x8 (8 floats) and half2 (2 half) require special handling done by specializations below.
+// Similar for multi-element integer types to be added later.
 template <typename T>
 inline marian::Shape adapt(const marian::Shape& shape) {
   return shape;
@@ -156,6 +156,8 @@ struct View {
   }
 };
 
+// @TODO: Attempts at correct slicing, not supported anywhere yet.
+#if 0
 template <typename T, const int D>
 HOST_DEVICE_INLINE View<T, D> slice(View<T, D> view, const Array<Slice, D>& slices) {
   const auto& slicedShape = view.shape().slice(slices);
@@ -203,6 +205,7 @@ HOST_DEVICE_INLINE View<T, 4> slice(View<T, 4>& view,
 //   auto reshaped = view.shape().reshape(shape);
 //   return View<T, D2>(view.data(), reshaped);
 // }
+#endif
 
 template <typename T>
 using Tensor = View<T, CONST_SHAPE_DIMS>;
diff --git a/src/functional/tmp.h b/src/functional/tmp.h
index 69be3391a..42dc1385f 100755
--- a/src/functional/tmp.h
+++ b/src/functional/tmp.h
@@ -1,3 +1,5 @@
+// TMP here stands for Template Meta-Programming
+
 #pragma once
 
 #include "functional/array.h"
diff --git a/src/tensors/allocator.h b/src/tensors/allocator.h
index 946138e58..8dde088d8 100644
--- a/src/tensors/allocator.h
+++ b/src/tensors/allocator.h
@@ -91,7 +91,7 @@ class Allocator {
   std::unordered_map<uint8_t*, MemoryPiece::PtrType> allocated_;
 
   void grow(size_t add) {
-    add = align(add);
+    add = alignedSize(add);
     uint8_t* oldData = device_->data();
     size_t oldSize = device_->size();
 
@@ -115,7 +115,7 @@ class Allocator {
   }
 
   Gap getGap(size_t size) {
-    size = align(size);
+    size = alignedSize(size);
     auto it = std::lower_bound(gaps_.begin(), gaps_.end(), Gap(nullptr, size));
 
     if(throw_ && it == gaps_.end()) {
@@ -175,14 +175,14 @@ class Allocator {
     reserve(bytes);
   }
 
-  size_t align(size_t size) {
-    return (size_t)(ceil(size / (float)alignment_) * alignment_);
+  size_t alignedSize(size_t size) {
+    return (size_t)(ceil(size / (double)alignment_) * alignment_);
   }
 
   void throwAtReallocation(bool throwRealloc) { throw_ = throwRealloc; }
 
   void reserve(size_t bytes) {
-    bytes = align(bytes);
+    bytes = alignedSize(bytes);
     if(bytes > 0)
       device_->reserve(bytes);
     clear();
@@ -190,10 +190,10 @@ class Allocator {
 
   template <typename T>
   size_t capacity(size_t num) {
-    return align(num * sizeof(T));
+    return alignedSize(num * sizeof(T));
   }
 
-  size_t capacity(size_t num, Type type) { return align(num * sizeOf(type)); }
+  size_t capacity(size_t num, Type type) { return alignedSize(num * sizeOf(type)); }
 
   MemoryPiece::PtrType alloc(size_t num, Type type) {
     return alloc(num * sizeOf(type));
@@ -205,7 +205,7 @@ class Allocator {
   }
 
   MemoryPiece::PtrType alloc(size_t bytes) {
-    bytes = align(bytes);
+    bytes = alignedSize(bytes);
     Gap gap = getGap(bytes);
 
     if(gap.size() > bytes) {
@@ -219,7 +219,7 @@ class Allocator {
   }
 
   bool free(uint8_t* ptr, size_t bytes) {
-    bytes = align(bytes);
+    bytes = alignedSize(bytes);
 
     ABORT_IF(ptr == 0, "Double free?");
 
diff --git a/src/tensors/gpu/algorithm.cu b/src/tensors/gpu/algorithm.cu
index c3c8418bc..6c4369251 100644
--- a/src/tensors/gpu/algorithm.cu
+++ b/src/tensors/gpu/algorithm.cu
@@ -7,6 +7,8 @@
 
 #include <cuda_fp16.h>
 
+// @TODO: look at typedefs and decide whether to use float16, half or __half.
+
 namespace marian {
 namespace gpu {
 
diff --git a/src/tensors/gpu/tensor_operators.cu b/src/tensors/gpu/tensor_operators.cu
index e68a0f3f2..72e38f93a 100755
--- a/src/tensors/gpu/tensor_operators.cu
+++ b/src/tensors/gpu/tensor_operators.cu
@@ -1351,9 +1351,9 @@ float L2Norm(Tensor in) {
   uint8_t* data;
   cudaMalloc(&data, blocks * sizeof(float));
   Tensor out(TensorBase::New(MemoryPiece::New(data, blocks * sizeof(float)),
-                            Shape({1, blocks}),
-                            Type::float32,
-                            in->getBackend()));
+                             Shape({1, blocks}),
+                             Type::float32,
+                             in->getBackend()));
 
   using namespace functional;
   ReduceAll(_1 * _1, out, in);
diff --git a/src/tensors/tensor.cpp b/src/tensors/tensor.cpp
index 76042d7a3..18d419c3c 100755
--- a/src/tensors/tensor.cpp
+++ b/src/tensors/tensor.cpp
@@ -113,6 +113,8 @@ template std::string TensorBase::debug<int16_t>(int, int);
 template std::string TensorBase::debug<int32_t>(int, int);
 template std::string TensorBase::debug<int64_t>(int, int);
 
+// fill an io::item with data from a Tensor, used for saving 
+// and other IO operations.
 void TensorBase::get(io::Item& item, const std::string& name) {
   item.name  = name;
   item.shape = shape_;
diff --git a/src/tensors/tensor.h b/src/tensors/tensor.h
index 904f36b60..bdaea9426 100644
--- a/src/tensors/tensor.h
+++ b/src/tensors/tensor.h
@@ -89,6 +89,8 @@ class TensorBase {
     return TensorBase::New(mem, Shape{1, (int)size}, type(), backend_);
   }
 
+  // @TODO: review if we can eliminate GPU-specific code here, 
+  // potentially by moving this to non-class members.
   template <typename T>
   T get(size_t i) {
     if(!matchType<T>(type_)) {
diff --git a/src/tensors/tensor_allocator.h b/src/tensors/tensor_allocator.h
index e908ec94b..d229bebaf 100644
--- a/src/tensors/tensor_allocator.h
+++ b/src/tensors/tensor_allocator.h
@@ -47,7 +47,7 @@ class TensorAllocator {
   void reserveExact(const std::vector<size_t>& bytes) {
     size_t total = 0;
     for(auto part : bytes)
-      total += allocator_->align(part);
+      total += allocator_->alignedSize(part);
     reserveExact(total);
   }  
 

From 2881f737f40bc654b6c795fcaa5485701809b432 Mon Sep 17 00:00:00 2001
From: Marcin Junczys-Dowmunt <Marcin.JunczysDowmunt@microsoft.com>
Date: Tue, 10 Sep 2019 13:27:14 -0700
Subject: [PATCH 603/838] add initializers and more pieces of the type system

---
 src/common/io_item.h                 |  61 ++++++
 src/examples/iris/iris.cpp           |   6 +-
 src/examples/mnist/model.h           |   6 +-
 src/graph/chainable.h                |   5 +
 src/graph/expression_graph.cpp       | 241 +++++++++++++++++++++++-
 src/graph/expression_graph.h         | 213 +++++++++------------
 src/graph/expression_operators.cpp   |  88 ++++-----
 src/graph/expression_operators.h     |   8 +-
 src/graph/node.cpp                   |  19 +-
 src/graph/node.h                     |  80 ++++++--
 src/graph/node_initializers.cpp      | 265 +++++++++++++++------------
 src/graph/node_initializers.h        | 143 ++++++++++++---
 src/graph/node_operators.cpp         |  29 ++-
 src/graph/node_operators.h           |  21 ++-
 src/graph/node_operators_unary.h     |  87 +++++++++
 src/layers/generic.cpp               |  32 ++--
 src/layers/generic.h                 |  28 +--
 src/layers/guided_alignment.h        |   2 +-
 src/layers/loss.h                    |  19 +-
 src/layers/weight.cpp                |   2 +-
 src/models/bert.h                    |   4 +-
 src/models/costs.h                   |   2 +-
 src/models/decoder.h                 |   2 +-
 src/models/s2s.h                     |   2 +-
 src/models/transformer.h             |  28 +--
 src/rnn/attention.h                  |  20 +-
 src/rnn/cells.h                      | 180 +++++++++---------
 src/tensors/cpu/tensor_operators.cpp |  37 +++-
 src/tensors/gpu/tensor_operators.cu  |  62 +++++--
 src/tensors/tensor_operators.h       |  11 +-
 src/tests/pooling.cpp                |   4 +-
 src/tests/prod.cpp                   |  28 +--
 src/tests/units/attention_tests.cpp  |   6 +-
 src/tests/units/graph_tests.cpp      |  12 +-
 src/tests/units/operator_tests.cpp   |  88 ++++-----
 src/tests/units/rnn_tests.cpp        |   6 +-
 src/translator/beam_search.h         |   4 +-
 37 files changed, 1225 insertions(+), 626 deletions(-)
 mode change 100755 => 100644 src/graph/expression_operators.cpp

diff --git a/src/common/io_item.h b/src/common/io_item.h
index 2ef967f98..60ea03c82 100644
--- a/src/common/io_item.h
+++ b/src/common/io_item.h
@@ -30,6 +30,67 @@ struct Item {
     else
       return bytes.size();
   }
+
+  void append(const Item& other) {
+    ABORT_IF(mapped, "Memory-mapped items cannot be appended");
+    ABORT_IF(type != other.type, "Only item of same type can be appended");
+
+    // abort if any of the shapes is not a flat array, i.e. the number of elements in the
+    // last dimension has to correspond to the number of bytes.
+    ABORT_IF(shape[-1] != shape.elements(), "1 - Only flat items can be appended : {}", shape);
+    ABORT_IF(other.shape[-1] != other.shape.elements(), "2 - Only flat items can be appended: {}", other.shape);
+
+    // cut to size (get rid of padding if any) to make append operation work correctly
+    size_t bytesWithoutPadding = shape.elements() * sizeOf(type);
+    bytes.resize(bytesWithoutPadding);
+
+    shape.set(-1, shape.elements() + other.shape.elements());
+
+    size_t addbytesWithoutPadding = other.shape.elements() * sizeOf(other.type); // ignore padding if any
+    bytes.insert(bytes.end(), other.bytes.begin(), other.bytes.begin() + addbytesWithoutPadding);
+
+    // grow to align to 256 bytes boundary (will be undone when more pieces are appended)
+    size_t multiplier = (size_t)ceil((float)bytes.size() / (float)256);
+    bytes.resize(multiplier * 256);
+  }
+
+  template <typename From, typename To>
+  void convertFromTo() {
+    size_t elements = size() / sizeof(From);
+    size_t newSize = elements * sizeof(To);
+    std::vector<char> newBytes(newSize);
+
+    From* in = (From*)bytes.data();
+    To* out = (To*)newBytes.data();
+    for(int i = 0; i < elements; ++i)
+      out[i] = (To)in[i];
+
+    bytes.swap(newBytes);
+  }
+
+  template <typename T>
+  void convertTo() {
+    if(type == Type::float32)
+      convertFromTo<float, T>();
+    else if(type == Type::float16)
+      convertFromTo<float16, T>();
+    else 
+      ABORT("convert from type {} not implemented", type);
+  }
+
+  void convert(Type toType) {
+    if(type == toType)
+      return;
+
+    if(toType == Type::float32)
+      convertTo<float>();
+    else if(toType == Type::float16)
+      convertTo<float16>();
+    else
+      ABORT("convert to type {} not implemented", toType);
+
+    type = toType;
+  }
 };
 
 }  // namespace io
diff --git a/src/examples/iris/iris.cpp b/src/examples/iris/iris.cpp
index 2d66282d9..9878a1ff0 100644
--- a/src/examples/iris/iris.cpp
+++ b/src/examples/iris/iris.cpp
@@ -22,16 +22,16 @@ Expr buildIrisClassifier(Ptr<ExpressionGraph> graph,
   graph->clear();
 
   // Define the input layer
-  auto x = graph->constant({N, NUM_FEATURES}, inits::from_vector(inputData));
+  auto x = graph->constant({N, NUM_FEATURES}, inits::fromVector(inputData));
 
   // Define the hidden layer
   auto W1 = graph->param("W1", {NUM_FEATURES, 5}, inits::uniform(-0.1f, 0.1f));
-  auto b1 = graph->param("b1", {1, 5}, inits::zeros);
+  auto b1 = graph->param("b1", {1, 5}, inits::zeros());
   auto h = tanh(affine(x, W1, b1));
 
   // Define the output layer
   auto W2 = graph->param("W2", {5, NUM_LABELS}, inits::uniform(-0.1f, 0.1f));
-  auto b2 = graph->param("b2", {1, NUM_LABELS}, inits::zeros);
+  auto b2 = graph->param("b2", {1, NUM_LABELS}, inits::zeros());
   auto o = affine(h, W2, b2);
 
   if(train) {
diff --git a/src/examples/mnist/model.h b/src/examples/mnist/model.h
index d03800f7b..24543ad1d 100755
--- a/src/examples/mnist/model.h
+++ b/src/examples/mnist/model.h
@@ -117,7 +117,7 @@ class MnistFeedForwardNet : public IModel {
     auto features
         = std::static_pointer_cast<data::DataBatch>(batch)->features();
     auto x = g->constant({(int)batch->size(), dims[0]},
-                         inits::from_vector(features));
+                         inits::fromVector(features));
 
     // Construct hidden layers
     std::vector<Expr> layers, weights, biases;
@@ -141,11 +141,11 @@ class MnistFeedForwardNet : public IModel {
 
       // Construct a weight node for the outgoing connections from layer i
       weights.emplace_back(
-          g->param("W" + std::to_string(i), {in, out}, inits::glorot_uniform));
+          g->param("W" + std::to_string(i), {in, out}, inits::glorotUniform()));
 
       // Construct a bias node. These weights are initialized to zero
       biases.emplace_back(
-          g->param("b" + std::to_string(i), {1, out}, inits::zeros));
+          g->param("b" + std::to_string(i), {1, out}, inits::zeros()));
     }
 
     // Perform matrix multiplication and addition for the last layer
diff --git a/src/graph/chainable.h b/src/graph/chainable.h
index f785c57c6..5481ba79f 100644
--- a/src/graph/chainable.h
+++ b/src/graph/chainable.h
@@ -106,5 +106,10 @@ class Chainable {
   virtual bool equal(Expr) = 0;
 
   virtual void record(Ptr<AutoTunerRecorder>, size_t, bool) = 0;
+
+  virtual void markCheckpoint() = 0;
+  virtual bool isCheckpoint() const = 0;
+  virtual void setSubtape(Ptr<std::list<Expr>>) = 0;
+  virtual Ptr<std::list<Expr>> getSubtape() = 0;
 };
 }  // namespace marian
diff --git a/src/graph/expression_graph.cpp b/src/graph/expression_graph.cpp
index 8d7500f70..bd876a631 100755
--- a/src/graph/expression_graph.cpp
+++ b/src/graph/expression_graph.cpp
@@ -5,8 +5,10 @@
 
 namespace marian {
 
-ExpressionGraph::ExpressionGraph(bool inference)
-    : inferenceOnly_(inference), backend_(nullptr) {}
+ExpressionGraph::ExpressionGraph(bool inference, bool optimized)
+  : inferenceOnly_(inference),
+    optimized_(optimized),
+    backend_(nullptr) {}
 
 void ExpressionGraph::setDevice(DeviceId deviceId, Ptr<Device> device) {
   if(!backend_) {
@@ -20,16 +22,239 @@ void ExpressionGraph::setDevice(DeviceId deviceId, Ptr<Device> device) {
   }
 }
 
+Expr ExpressionGraph::add(Expr node) {
+  auto found = tensors_->findOrRemember(node);
+  if(found) {
+    return found;
+  } else {
+    node->setId(count_++);
+
+    // record in foward graph
+    nodesForward_.push_back(node);
+
+    // record in backward graph if training, and keep track of roots
+    if(!inferenceOnly_ && node->trainable()) {
+      nodesBackward_.push_back(node);
+      topNodes_.insert(node); // opportunistically record all new nodes as roots (gets removed once consumed)
+    }
+
+    if(topNodes_.count(node)) // only erase children of nodes with are themselves in the topNodes list
+      for(auto child : node->children())
+        topNodes_.erase(child); // this child is consumed and therefore not a root
+
+    return node;
+  }
+}
+
+// Call on every checkpoint in backwards order
+void createSubtape(Expr node) {
+  //std::cerr << "Recursing: " << node->getId() << " " << node->type() << std::endl;
+  auto subtape = New<std::list<Expr>>();
+
+  for(auto child : node->children()) {
+    if(child->isCheckpoint()) {
+      //std::cerr << "cut" << std::endl;
+      /* do not descend */
+    } else {
+      if(child->getSubtape()) {
+        //std::cerr << "Already visited: " << node->getId() << " " << node->type() << std::endl;
+        /* already visited */
+      } else {
+        createSubtape(child);
+        subtape->splice(subtape->end(), *(child->getSubtape()));
+      }
+    }
+  }
+
+  if(!node->isCheckpoint())
+    subtape->push_back(node);
+
+  node->setSubtape(subtape);
+}
+
+
+void ExpressionGraph::forwardNext() {
+  // @TODO: check if allocation works properly
+  tensors_->clearShorttermMemory();
+
+  if(checkpointing_) {
+    for(auto top : topNodes_)
+      top->markCheckpoint();
+
+    auto it = nodesBackward_.rbegin();
+    while(it != nodesBackward_.rend()) {
+      auto v = *it;
+      if(v->isCheckpoint())
+        createSubtape(v);
+      it++;
+    }
+
+    // To avoid recomputation of range from last checkpoint to the top,
+    // turn all node on last subtape into checkpoints and clear subtape.
+    // @TODO: put this into special backprob function? Needs to know that we are done with adding nodes
+    for(auto top : topNodes_) {
+      if(top->getSubtape()) {
+        for(auto& node : *top->getSubtape())
+          node->markCheckpoint();
+        top->getSubtape()->clear();
+      }
+    }
+  }
+
+  // graphviz("dump.dot");
+
+  forward(nodesForward_, /*finalPass=*/!checkpointing_); // if checkPointing, this is not final
+}
+
+void ExpressionGraph::forward(std::list<Expr>& forwardTape, bool finalPass) {
+  while(!forwardTape.empty()) {
+    auto v = forwardTape.front();
+
+    //std::cerr << "Forward: " << v->getId() << " " << v->type() << " ";
+    //std::cerr << v->isCheckpoint() << std::endl;
+
+    v->allocate();
+    /*if(!isFake())*/ v->init();
+
+    for(auto& child : v->children())
+      ABORT_IF(!child->val(), "De-allocated child {} {} of {} {}", child->getId(), child->type(), v->getId(), v->type());
+
+    /*if(!isFake())*/ v->forward();
+
+    if(v->trainable() && throwNan_) {
+      bool isNan = false, isInf = false;
+      checkNan(v->val(), isNan, isInf);
+      if(isNan || isInf) {
+        LOG(critical, "Detected NaN ({}) or Inf ({}) in value (forward pass)", isNan, isInf);
+        LOG(critical, "\tType: {}, Shape: {}, Name: {}, Id: {}, Hash: {}",
+            v->type(), v->shape(), v->name(), v->getId(), v->hash());
+        LOG(critical, "Children: {}", v->children().size());
+        for(auto&& child : v->children()) {
+          LOG(critical, "\tType: {}, Shape: {}, Name: {}, Id: {}, Hash: {}",
+            child->type(), child->shape(), child->name(), child->getId(), child->hash());
+        }
+      }
+    }
+
+    if(v->marked_for_debug()) {
+      Logger log = spdlog::get("general");
+      if(log) {
+        LOG(info, "Debug: {} op={}", v->debug_message(), v->type());
+        LOG(info, v->val()->debug());
+      }
+      else {
+        std::cerr << "Debug: " << v->debug_message() << " op=" << v->type() << std::endl;
+        std::cerr << v->val()->debug() << std::endl;
+      }
+    }
+
+    if(inferenceOnly_)
+      v->children().clear();
+
+    if(checkpointing_ && !finalPass) {
+      auto subtape = v->getSubtape();
+      if(subtape) {
+        for(auto& node : *subtape) {
+          //std::cerr << "Freeing: " << node->getId() << " " << node->type() << " " << node.useCount() << std::endl;
+          node->free();
+        }
+      }
+    }
+
+    forwardTape.pop_front();
+  }
+}
+
+void ExpressionGraph::backward(bool zero, float clipValue) {
+  if(topNodes_.size() > 1) {
+    LOG(critical, "There are more ({}) than one top most nodes for backward pass:", topNodes_.size());
+    for(auto node : topNodes_) {
+      LOG(critical,
+          "\tType: {}, Shape: {}, Name: {}, Id: {}, Hash: {}",
+          node->type(),
+          node->shape(),
+          node->name(),
+          node->getId(),
+          node->hash());
+    }
+    ABORT("Aborting");
+  }
+
+  params_->allocateBackward();
+  if(zero /*&& !isFake()*/)
+    params_->set_zero_adjoint();
+
+  for(auto&& v : topNodes_)
+    v->init_dependent();
+
+  // named_.clear();
+  topNodes_.clear();
+
+  tensors_->clearShorttermMemory();
+
+  bool firstNan = true;
+  while(!nodesBackward_.empty()) {
+    auto v = nodesBackward_.back();
+    nodesBackward_.pop_back();
+
+    for(auto&& child : v->children())
+      if(child->trainable() && child->type() != "param")
+        child->set_zero_adjoint();
+
+    if(checkpointing_ && v->getSubtape()) {
+      //std::cerr << "Back: " << v->getId() << " " << v->type() << " " << v->getSubtape()->size() << std::endl;
+      forward(*v->getSubtape(), /*finalPass=*/true);
+    }
+
+    if(v->trainable() && v->marked_for_debug()) {
+      LOG(info, "Debug Grad: {} op={}", v->debug_message(), v->type());
+      LOG(info, v->grad()->debug());
+    }
+
+    if(v->trainable() && clipValue != 0) {
+      using namespace functional;
+      Element(_1 = clip(_1, clipValue), v->grad());
+    }
+
+    if(v->trainable()/* && !isFake()*/)
+      v->backward();
+
+    if(throwNan_ && firstNan) {
+      for(auto&& child : v->children()) {
+        if(child->trainable()) {
+          bool isNan = false, isInf = false;
+          checkNan(child->grad(), isNan, isInf);
+          if(isNan) {
+            LOG(critical, "Detected NaN ({}) or Inf ({}) in gradient (backward pass) of child node", isNan, isInf);
+            LOG(critical, "Child - Type: {}, Shape: {}, Name: {}, Id: {}, Hash: {}",
+                child->type(), child->shape(), child->name(), child->getId(), child->hash());
+            LOG(critical, "Parent - Type: {}, Shape: {}, Name: {}, Id: {}, Hash: {}",
+                v->type(), v->shape(), v->name(), v->getId(), v->hash());
+            firstNan = false;
+          }
+        }
+      }
+    }
+
+
+    v->children().clear();
+  }
+}
+
+Expr ExpressionGraph::dropoutMask(float prob, const Shape& shape, Type valueType) {
+  return constant(shape, inits::dropout(prob), valueType);
+}
+
 Expr ExpressionGraph::dropoutMask(float prob, const Shape& shape) {
-  return constant(shape, inits::dropout(prob));
+  return constant(shape, inits::dropout(prob), parameterType_);
 }
 
-void ExpressionGraph::checkNan(Tensor t) {
-  ABORT_IF(throwNaN_, "Not implemented"); t;
-  // ABORT_IF(throwNaN_ && IsNan(t), "Tensor has NaN");
+void ExpressionGraph::checkNan(Tensor t, bool& isNan, bool& isInf) {
+  IsNan(t, allocator(), isNan, isInf);
 }
 
 void ExpressionGraph::save(std::vector<io::Item>& ioItems) {
+  // sorted by name in std::map
   for(auto p : params()->getMap()) {
     std::string pName = p.first;
 
@@ -38,12 +263,10 @@ void ExpressionGraph::save(std::vector<io::Item>& ioItems) {
         pName = pName.substr(namespace_.size() + 2);
     }
 
-    ABORT_IF(p.second->val()->type() != Type::float32,
-             "Only float32 supported at the moment");
-
     Tensor val = p.second->val();
     io::Item item;
     val->get(item, pName);
+    item.convert(saveType_);
     ioItems.emplace_back(std::move(item));
   }
 }
diff --git a/src/graph/expression_graph.h b/src/graph/expression_graph.h
index 28140a4e8..3a58d454d 100755
--- a/src/graph/expression_graph.h
+++ b/src/graph/expression_graph.h
@@ -1,6 +1,7 @@
 #pragma once
 
 #include "common/config.h"
+#include "common/definitions.h"
 
 #include "tensors/backend.h"
 #include "tensors/tensor_allocator.h"
@@ -90,13 +91,14 @@ class Tensors {
     auto it = shortterm_->find(hash);
     if(it != shortterm_->end()) {
       for(auto foundWeak : it->second) {
-        auto found = foundWeak;
+        //auto found = foundWeak.lock();
+        Expr found = foundWeak; // DO NOT LEAVE THIS HERE!!!
         if(node->equal(found)) {
           return found;
         }
       }
     }
-    (*shortterm_)[hash].push_back(node.get());
+    (*shortterm_)[hash].push_back(node.get()); // weakPtr
     return nullptr;
   }
 
@@ -111,7 +113,6 @@ class Tensors {
 };
 
 class ExpressionGraph : public std::enable_shared_from_this<ExpressionGraph> {
-private:
   size_t count_{0};
 
   std::list<Expr> nodesForward_;
@@ -119,57 +120,73 @@ class ExpressionGraph : public std::enable_shared_from_this<ExpressionGraph> {
 
   std::unordered_set<Expr> topNodes_; // current set of roots. In the end, all but one must have been consumed.
 
-  // Holds memory and expressions that correspond to graph parameters
-  Ptr<Parameters> params_;
-
   // Holds memory and expressions that correspond to temporary expressions.
   // This gets cleared before a new graph is built.
   Ptr<Tensors> tensors_;
 
   std::unordered_map<size_t, std::vector<Expr>> memoized_;
 
+  Type parameterType_{Type::float32};
+  Type saveType_{Type::float32};
+
   bool inferenceOnly_{false};
-  Ptr<Backend> backend_;
+  bool optimized_{false};
+
+  bool checkpointing_{false};
+  bool isFake_{false};
 
   bool reloaded_{false};
   std::string namespace_;
 
-  bool throwNaN_{false};
+  bool throwNan_{false};
 
 protected:
   // Delete, copy and move constructors
   ExpressionGraph(const ExpressionGraph&) = delete;
   ExpressionGraph(ExpressionGraph&&) = delete;
 
+  // Holds memory and expressions that correspond to graph parameters
+  Ptr<Parameters> params_;
+  Ptr<Backend> backend_;
+
 public:
   /** @brief Constructs a new expression graph
    *
    * Constructor should be used as New<ExpressionGraph>()
    */
-  ExpressionGraph(bool inference = false);
-
-  void setInference(bool inference) { inferenceOnly_ = inference; }
-  bool isInference() { return inferenceOnly_; }
+  ExpressionGraph(bool inference = false, bool optimized = false);
 
-  ~ExpressionGraph() {
+  virtual ~ExpressionGraph() {
     clear();
     params_->clear();
   }
 
-  void setDevice(DeviceId deviceId = {0, DeviceType::gpu},
+  virtual void setDevice(DeviceId deviceId = {0, DeviceType::gpu},
                  Ptr<Device> device = nullptr);
 
   DeviceId getDeviceId() { return backend_->getDeviceId(); }
 
   Ptr<Backend> getBackend() { return backend_; }
 
+  void setInference(bool inference) { inferenceOnly_ = inference; }
+  bool isInference() { return inferenceOnly_; }
+
+  void setOptimized(bool optimized) { optimized_ = optimized; }
+  bool isOptimized() { return (optimized_ && inferenceOnly_); }
+
+  void setCheckpointing(bool checkpointing) { checkpointing_ = checkpointing; }
+  bool isCheckpointing() { return checkpointing_; }
+
+  void setFake(bool fake) { isFake_ = fake; }
+  bool isFake() { return isFake_; }
+
   void switchParams(const std::string& newNamespace) {
     namespace_ = newNamespace;
   }
 
-  void copyParams(Ptr<ExpressionGraph> graph) {
+  virtual void copyParams(Ptr<ExpressionGraph> graph) {
     for(auto p : *graph->params())
-      param(p->name(), p->shape(), inits::dummy);
+      param(p->name(), p->shape(), inits::dummy(), p->value_type());
     params()->allocateForward();
     params()->vals()->copyFrom(graph->params()->vals());
   }
@@ -207,86 +224,17 @@ class ExpressionGraph : public std::enable_shared_from_this<ExpressionGraph> {
     return true;
   }
 
+  void checkNan(Tensor t, bool& isNan, bool& isInf);
+
   void forward() {
     params_->allocateForward();
     forwardNext();
   }
 
-  void checkNan(Tensor t);
-
-  void forwardNext() {
-    // @TODO: check if allocation works properly
-    tensors_->clearShorttermMemory();
-
-    while(!nodesForward_.empty()) {
-      auto v = nodesForward_.front();
-      v->allocate();
-      v->init();
-      v->forward();
-
-      checkNan(v->val());
+  void forwardNext();
+  void forward(std::list<Expr>& forwardTape, bool finalPass);
 
-      if(v->marked_for_debug()) {
-        std::cerr << "Debug: " << v->debug_message() << " op=" << v->type()
-                  << std::endl;
-        std::cerr << v->val()->debug() << std::endl;
-      }
-
-      if(inferenceOnly_)
-        v->children().clear();
-      nodesForward_.pop_front();
-    }
-  }
-
-  void backward(bool zero = true) {
-    if(topNodes_.size() > 1) {
-      LOG(critical, "There are more ({}) than one top most node for backward step:", topNodes_.size());
-      for(auto node : topNodes_) {
-        LOG(critical,
-            "\tType: {}, Shape: {}, Name: {}, Id: {}, Hash: {}",
-            node->type(),
-            node->shape(),
-            node->name(),
-            node->getId(),
-            node->hash());
-      }
-      ABORT("Aborting");
-    }
-
-    params_->allocateBackward();
-    if(zero)
-      params_->set_zero_adjoint();
-
-    for(auto&& v : topNodes_)
-      v->init_dependent();
-
-    // named_.clear();
-    topNodes_.clear();
-
-    tensors_->clearShorttermMemory();
-
-    while(!nodesBackward_.empty()) {
-      auto v = nodesBackward_.back();
-      nodesBackward_.pop_back();
-
-      for(auto&& child : v->children()) {
-        if(child->trainable() && child->type() != "param")
-          child->set_zero_adjoint();
-      }
-
-      if(v->trainable())
-        v->backward();
-
-      checkNan(v->grad());
-
-      if(v->trainable() && v->marked_for_debug()) {
-        std::cerr << "Debug Grad: " << v->debug_message() << std::endl;
-        std::cerr << v->grad()->debug() << std::endl;
-      }
-
-      v->children().clear();
-    }
-  }
+  void backward(bool zero = true, float clipValue = 0.f);
 
   std::string graphviz() {
     std::stringstream ss;
@@ -313,7 +261,8 @@ class ExpressionGraph : public std::enable_shared_from_this<ExpressionGraph> {
 
   Expr param(const std::string& pname,
              const Shape& shape,
-             const NodeInitializer& init,
+             const Ptr<inits::NodeInitializer>& init,
+             const Type valueType,
              bool fixed = false) {
     std::string name = pname;
     if(!namespace_.empty())
@@ -344,7 +293,7 @@ class ExpressionGraph : public std::enable_shared_from_this<ExpressionGraph> {
     ABORT_IF(get(name), "Non-parameter with name '{}' already exists", name);
 
     // create parameter node (adds to tape)
-    p = Expression<ParamNode>(shared_from_this(), shape, init, fixed);
+    p = Expression<ParamNode>(shared_from_this(), shape, init, valueType, fixed);
 
     // set name and id and add to list of parameters
     p->set_name(name);
@@ -353,8 +302,22 @@ class ExpressionGraph : public std::enable_shared_from_this<ExpressionGraph> {
     return p;
   }
 
-  Expr constant(const Shape& shape, const NodeInitializer& init, Type value_type = Type::float32) {
-    return Expression<ConstantNode>(shared_from_this(), shape, init, value_type);
+  Expr param(const std::string& pname,
+             const Shape& shape,
+             const Ptr<inits::NodeInitializer>& init,
+             bool fixed = false) {
+    return param(pname, shape, init, parameterType_, fixed);
+  }
+
+  Expr constant(const Shape& shape,
+                const Ptr<inits::NodeInitializer>& init,
+                Type valueType) {
+    return Expression<ConstantNode>(shared_from_this(), shape, init, valueType);
+  }
+
+  Expr constant(const Shape& shape,
+                const Ptr<inits::NodeInitializer>& init) {
+    return Expression<ConstantNode>(shared_from_this(), shape, init, parameterType_);
   }
 
   // @TODO: add version with iterators
@@ -362,7 +325,7 @@ class ExpressionGraph : public std::enable_shared_from_this<ExpressionGraph> {
   // like rows or select
   Expr indices(const std::vector<IndexType>& indicesVector) {
     return constant({(int)indicesVector.size()},
-                    inits::from_vector(indicesVector),
+                    inits::fromVector(indicesVector),
                     Type::uint32);
   }
   // this version sets up the shape such that the indices are in a given axis
@@ -373,19 +336,26 @@ class ExpressionGraph : public std::enable_shared_from_this<ExpressionGraph> {
     shape.resize(indexee->shape().size());
     shape.set(axis, indicesVector.size());
     return constant(Shape(shape),
-                    inits::from_vector(indicesVector),
+                    inits::fromVector(indicesVector),
                     Type::uint32);
   }
 
+  Expr ones(const Shape& shape, Type valueType) {
+    return constant(shape, inits::ones(), valueType);
+  }
   Expr ones(const Shape& shape) {
-    return constant(shape, inits::ones);
+    return constant(shape, inits::ones(), parameterType_);
   }
 
+  Expr zeros(const Shape& shape, Type valueType) {
+    return constant(shape, inits::zeros(), valueType);
+  }
   Expr zeros(const Shape& shape) {
-    return constant(shape, inits::zeros);
+    return constant(shape, inits::zeros(), parameterType_);
   }
 
   // prob = dropProb, e.g. 0.1 means 90% of values are kept
+  Expr dropoutMask(float dropProb, const Shape& shape, Type valueType);
   Expr dropoutMask(float dropProb, const Shape& shape);
 
   Expr get(std::string name) {
@@ -400,28 +370,21 @@ class ExpressionGraph : public std::enable_shared_from_this<ExpressionGraph> {
 
   Ptr<Parameters>& params() { return params_; }
 
-  Expr add(Expr node) {
-    auto found = tensors_->findOrRemember(node);
-    if(found) {
-      return found;
-    } else {
-      node->setId(count_++);
-
-      // record in foward graph
-      nodesForward_.push_back(node);
+  Type getParameterType() {
+    return parameterType_;
+  }
 
-      // record in backward graph if training, and keep track of roots
-      if(!inferenceOnly_ && node->trainable()) {
-        nodesBackward_.push_back(node);
-        topNodes_.insert(node); // opportunistically record all new nodes as roots (gets removed once consumed)
-      }
-      for(auto child : node->children())
-        topNodes_.erase(child); // this child is consumed and therefore not a root
+  Type getSaveType() {
+    return parameterType_;
+  }
 
-      return node;
-    }
+  void setParameterType(Type parameterType, Type saveType = Type::float32) {
+    parameterType_ = parameterType;
+    saveType_ = saveType;
   }
 
+  Expr add(Expr node);
+
   void allocateForward(Expr node) {
     if(tensors_)
       tensors_->allocateForward(node);
@@ -455,18 +418,19 @@ class ExpressionGraph : public std::enable_shared_from_this<ExpressionGraph> {
 
   void setReloaded(bool reloaded) { reloaded_ = reloaded; }
 
-  void setThrowNaN(bool throwNaN) { throwNaN_ = throwNaN; }
+  void setThrowNan(bool throwNan) { throwNan_ = throwNan; }
+  bool getThrowNan() { return throwNan_; }
 
 public:
-  // convert all parameters into an array of IoItem elements, for loading
-  void load(const std::vector<io::Item>& ioItems, bool markReloaded = true) {
+  // loading from array of io::Items
+  void load(std::vector<io::Item>& ioItems, bool markReloaded = true) {
     setReloaded(false);
     for(auto& item : ioItems) {
       std::string pName = item.name;
       // skip over special parameters starting with "special:"
       if(pName.substr(0, 8) == "special:")
         continue;
-      param(pName, item.shape, inits::from_item(item));
+      param(pName, item.shape, inits::fromItem(item));
     }
     if(markReloaded)
       setReloaded(true);
@@ -474,12 +438,14 @@ class ExpressionGraph : public std::enable_shared_from_this<ExpressionGraph> {
 
   void load(const std::string& name, bool markReloaded = true) {
     LOG(info, "Loading model from {}", name);
-    load(io::loadItems(name), markReloaded);
+    auto items = io::loadItems(name);
+    load(items, markReloaded);
   }
 
   void load(const void* ptr, bool markReloaded = true) {
     LOG(info, "Loading model from buffer at {}", ptr);
-    load(io::loadItems(ptr), markReloaded);
+    auto items = io::loadItems(ptr);
+    load(items, markReloaded);
   }
 
   void mmap(const void* ptr, bool markReloaded = true) {
@@ -490,7 +456,8 @@ class ExpressionGraph : public std::enable_shared_from_this<ExpressionGraph> {
     params_->init(backend_);
 
     LOG(info, "Memory mapping model at {}", ptr);
-    load(io::mmapItems(ptr), markReloaded);
+    auto items = io::mmapItems(ptr);
+    load(items, markReloaded);
   }
 
 public:
diff --git a/src/graph/expression_operators.cpp b/src/graph/expression_operators.cpp
old mode 100755
new mode 100644
index b045c2588..156ab1329
--- a/src/graph/expression_operators.cpp
+++ b/src/graph/expression_operators.cpp
@@ -20,6 +20,11 @@ Expr debug(Expr a, const std::string& message) {
   return a;
 }
 
+Expr checkpoint(Expr a) {
+  a->markCheckpoint();
+  return a;
+}
+
 // logistic function. Note: scipy name is expit()
 Expr sigmoid(Expr a) {
   return Expression<SigmoidNodeOp>(a);
@@ -78,14 +83,15 @@ Expr softmax(Expr a, int axis /*=-1*/)
 }
 
 Expr softmax(Expr a, Expr zeroOneMask, int axis /*=-1*/) {
-  auto logMask = (1 - zeroOneMask) * -99999999.f;
+  // This will return the smallest value for the input type converted to float
+  // So for Type::Float16 that will be the smallest fp16 value expressed as float
+  float smallestFloat = -NumericLimits<float>(a->value_type()).max;
+  auto logMask = (1.f - zeroOneMask) * smallestFloat;
   return softmax(a + logMask, axis);
 }
 
+// @TODO: add mask
 Expr logsoftmax(Expr a) {
-  if (a->type() == "logsoftmax") // logsoftmax(logsoftmax(x)) == logsoftmax(x)
-    // @TODO: Remove this. First add an ABORT() for a while to catch who relies on this, and if noone, then delete.
-    return a;
   return Expression<LogSoftmaxNodeOp>(a);
 }
 
@@ -126,19 +132,19 @@ Expr ge(Expr a, Expr b) { return Expression<CmpNodeOp>(a, b, -1,  true); }
 Expr ne(Expr a, Expr b) { return Expression<CmpNodeOp>(a, b,  0,  true); }
 Expr le(Expr a, Expr b) { return Expression<CmpNodeOp>(a, b,  1,  true); }
 
-Expr lt(float a, Expr b) { return Expression<CmpNodeOp>(b->graph()->constant({}, inits::from_value(a), b->value_type()), b, -1, false); }
-Expr eq(float a, Expr b) { return Expression<CmpNodeOp>(b->graph()->constant({}, inits::from_value(a), b->value_type()), b,  0, false); }
-Expr gt(float a, Expr b) { return Expression<CmpNodeOp>(b->graph()->constant({}, inits::from_value(a), b->value_type()), b,  1, false); }
-Expr ge(float a, Expr b) { return Expression<CmpNodeOp>(b->graph()->constant({}, inits::from_value(a), b->value_type()), b, -1,  true); }
-Expr ne(float a, Expr b) { return Expression<CmpNodeOp>(b->graph()->constant({}, inits::from_value(a), b->value_type()), b,  0,  true); }
-Expr le(float a, Expr b) { return Expression<CmpNodeOp>(b->graph()->constant({}, inits::from_value(a), b->value_type()), b,  1,  true); }
+Expr lt(float a, Expr b) { return Expression<CmpNodeOp>(b->graph()->constant({}, inits::fromValue(a), b->value_type()), b, -1, false); }
+Expr eq(float a, Expr b) { return Expression<CmpNodeOp>(b->graph()->constant({}, inits::fromValue(a), b->value_type()), b,  0, false); }
+Expr gt(float a, Expr b) { return Expression<CmpNodeOp>(b->graph()->constant({}, inits::fromValue(a), b->value_type()), b,  1, false); }
+Expr ge(float a, Expr b) { return Expression<CmpNodeOp>(b->graph()->constant({}, inits::fromValue(a), b->value_type()), b, -1,  true); }
+Expr ne(float a, Expr b) { return Expression<CmpNodeOp>(b->graph()->constant({}, inits::fromValue(a), b->value_type()), b,  0,  true); }
+Expr le(float a, Expr b) { return Expression<CmpNodeOp>(b->graph()->constant({}, inits::fromValue(a), b->value_type()), b,  1,  true); }
 
-Expr lt(Expr a, float b) { return Expression<CmpNodeOp>(a, a->graph()->constant({}, inits::from_value(b), a->value_type()), -1, false); }
-Expr eq(Expr a, float b) { return Expression<CmpNodeOp>(a, a->graph()->constant({}, inits::from_value(b), a->value_type()),  0, false); }
-Expr gt(Expr a, float b) { return Expression<CmpNodeOp>(a, a->graph()->constant({}, inits::from_value(b), a->value_type()),  1, false); }
-Expr ge(Expr a, float b) { return Expression<CmpNodeOp>(a, a->graph()->constant({}, inits::from_value(b), a->value_type()), -1,  true); }
-Expr ne(Expr a, float b) { return Expression<CmpNodeOp>(a, a->graph()->constant({}, inits::from_value(b), a->value_type()),  0,  true); }
-Expr le(Expr a, float b) { return Expression<CmpNodeOp>(a, a->graph()->constant({}, inits::from_value(b), a->value_type()),  1,  true); }
+Expr lt(Expr a, float b) { return Expression<CmpNodeOp>(a, a->graph()->constant({}, inits::fromValue(b), a->value_type()), -1, false); }
+Expr eq(Expr a, float b) { return Expression<CmpNodeOp>(a, a->graph()->constant({}, inits::fromValue(b), a->value_type()),  0, false); }
+Expr gt(Expr a, float b) { return Expression<CmpNodeOp>(a, a->graph()->constant({}, inits::fromValue(b), a->value_type()),  1, false); }
+Expr ge(Expr a, float b) { return Expression<CmpNodeOp>(a, a->graph()->constant({}, inits::fromValue(b), a->value_type()), -1,  true); }
+Expr ne(Expr a, float b) { return Expression<CmpNodeOp>(a, a->graph()->constant({}, inits::fromValue(b), a->value_type()),  0,  true); }
+Expr le(Expr a, float b) { return Expression<CmpNodeOp>(a, a->graph()->constant({}, inits::fromValue(b), a->value_type()),  1,  true); }
 
 /*********************************************************/
 
@@ -185,12 +191,12 @@ Expr operator*(Expr a, float b) {
 }
 
 Expr operator/(Expr a, float b) {
-  return a * (1.f / b);
+  return Expression<ScalarMultNodeOp>(a, 1.f / b);
 }
 
 // TODO: efficient version of this without constant()
 Expr operator/(float a, Expr b) {
-  auto aExpr = b->graph()->constant({}, inits::from_value(a));
+  auto aExpr = b->graph()->constant({}, inits::fromValue(a));
   return aExpr / b;
 }
 
@@ -226,6 +232,11 @@ Expr reshape(Expr a, Shape shape) {
   return Expression<ReshapeNodeOp>(a, shape);
 }
 
+Expr clipGradient(Expr a, float clipValue) {
+  // don't create node if no clipping
+  return clipValue != 0.f ? Expression<ClipGradientNodeOp>(a, clipValue) : a;
+}
+
 Expr atleast_1d(Expr a) {
   return atleast_nd(a, 1);
 }
@@ -266,15 +277,14 @@ Expr flatten_2d(Expr a) {
 
 Expr stopGradient(Expr a) {
   // implemented as a dummy reshape that is not trainable
-  auto res = Expression<ReshapeNodeOp>(a, a->shape());
+  auto res = reshape(a, a->shape());
   res->setTrainable(false);
   return res;
 }
 
-Expr constant_like(Expr a, const NodeInitializer& init) {
-  const auto& shape = a->shape();
+Expr constant_like(Expr a, const Ptr<inits::NodeInitializer>& init) {
   auto graph = a->graph();
-  return graph->constant(shape, init);
+  return graph->constant(a->shape(), init, a->value_type());
 }
 
 // gather() -- gather arbitrary elements along an axis; batched or non-batched
@@ -441,7 +451,7 @@ Expr affine(Expr a, Expr b, Expr bias, bool transA, bool transB, float scale) {
       util::hash_combine(hash, transA);
       util::hash_combine(hash, transB);
 
-#if USE_FBGEMM
+      #if USE_FBGEMM
       // Use Packed GEMM only if the node b in the graph is memoized.
       // More specifically, packed GEMM is used only if the B matrix (weight) is constant.
       // In general, 'memoized' means that the node is a constant variable or
@@ -527,7 +537,7 @@ Expr affine(Expr a, Expr b, Expr bias, bool transA, bool transB, float scale) {
           bc = recCblas(bc);
 
         int rows = ac->shape().elements() / ac->shape()[-1];
-        Expr ones = ac->graph()->ones({rows, 1});
+        Expr ones = ac->graph()->ones({rows, 1}, bias->value_type());
         std::vector<Expr> nodes = {ac, bc, bias, ones};
         return recCblas(Expression<AffineNodeOp>(nodes, transA, transB, scale),
                         true);
@@ -565,7 +575,7 @@ Expr affine(Expr a, Expr b, Expr bias, bool transA, bool transB, float scale) {
               scale);
         } else {
           int rows = a->shape().elements() / a->shape()[-1];
-          Expr ones = a->graph()->ones({rows, 1});
+          Expr ones = a->graph()->ones({rows, 1}, bias->value_type());
           std::vector<Expr> nodes = {clip(a, clipValue), clip(b, clipValue), bias, ones};
           return Expression<AffineNodeOp>(nodes, transA, transB, scale);
         }
@@ -582,7 +592,7 @@ Expr affine(Expr a, Expr b, Expr bias, bool transA, bool transB, float scale) {
         // in the future when we explore better ways to handle this.
 
         int rows = a->shape().elements() / a->shape()[-1];
-        Expr ones = a->graph()->ones({rows, 1});
+        Expr ones = a->graph()->ones({rows, 1}, bias->value_type());
         std::vector<Expr> nodes
             = {clip(a, clipValue), clip(b, clipValue), bias, ones};
         return Expression<AffineNodeOp>(nodes, transA, transB, scale);
@@ -637,6 +647,7 @@ Expr transpose(Expr a, const std::vector<int>& axes) {
   return Expression<TransposeNodeOp>(a, axes);
 }
 
+
 Expr swapAxes(Expr x, int axis1, int axis2)
 {
   const auto& shape = x->shape();
@@ -666,6 +677,14 @@ Expr swapAxes(Expr x, int axis1, int axis2)
   return transpose(x, axes);
 }
 
+Expr cast(Expr a, Type type) {
+  if(a->value_type() == type) {
+    return a;
+  } else {
+    return Expression<CastNodeOp>(a, type);
+  }
+}
+
 Expr cross_entropy(Expr a, Expr indices) {
   return Expression<CrossEntropyNodeOp>(a, indices);
 }
@@ -718,6 +737,8 @@ Expr layerNorm(Expr x,
                Expr gamma,
                Expr beta /*= nullptr*/,
                float eps /*= 1e-9*/) {
+
+  // layerNorm accumulates in float, so small eps is fine
   std::vector<Expr> nodes = {x, gamma};
   if(beta)
     nodes.push_back(beta);
@@ -747,25 +768,10 @@ Expr highway(const std::string prefix, Expr x) {
   // clang-format on
 }
 
-// Expr batch_norm(Expr x, Expr gamma, Expr beta) {
-//  auto mju = mean(x, keywords::axis=0);
-//  auto xmmju = x - mju;
-//  auto std = sqrt(mean(square(xmmju), keywords::axis=0), 1e-9);
-//
-//  if(beta)
-//    return gamma * (xmmju / std) + beta;
-//  else
-//    return gamma * (xmmju / std);
-//}
-
 Expr shift(Expr a, Shape shift, float padValue) {
   return Expression<ShiftNodeOp>(a, shift, padValue);
 }
 
-// Expr lexical_bias(Expr logits, Expr att, float eps, Ptr<sparse::CSR> lf) {
-//  return Expression<LexicalProbNodeOp>(logits, att, eps, lf);
-//}
-
 #ifdef CUDA_FOUND
 #ifdef CUDNN
 
diff --git a/src/graph/expression_operators.h b/src/graph/expression_operators.h
index a9433d2a6..7ed231cd1 100755
--- a/src/graph/expression_operators.h
+++ b/src/graph/expression_operators.h
@@ -6,6 +6,8 @@ namespace marian {
 
 Expr debug(Expr a, const std::string& message = "");
 
+Expr checkpoint(Expr a);
+
 typedef Expr(ActivationFunction)(Expr);
 
 Expr plus(const std::vector<Expr>&);
@@ -123,11 +125,15 @@ Expr transpose(Expr a, const std::vector<int>& axes);
 
 Expr swapAxes(Expr x, int axis1, int axis2);
 
+Expr cast(Expr a, Type type = Type::float32);
+
 Expr concatenate(const std::vector<Expr>& concats, int ax = 0);
 Expr repeat(Expr a, size_t repeats, int ax = 0);
 
 Expr reshape(Expr a, Shape shape);
 
+Expr clipGradient(Expr a, float clipValue);
+
 Expr atleast_1d(Expr a);
 Expr atleast_2d(Expr a);
 Expr atleast_3d(Expr a);
@@ -135,7 +141,7 @@ Expr atleast_4d(Expr a);
 Expr atleast_nd(Expr a, size_t dims);
 
 // create a constant of shape a->shape() and initialize with init
-Expr constant_like(Expr a, const NodeInitializer& init);
+Expr constant_like(Expr a, const Ptr<inits::NodeInitializer>& init);
 
 Expr flatten(Expr a);
 Expr flatten_2d(Expr a);
diff --git a/src/graph/node.cpp b/src/graph/node.cpp
index abbdacfd4..c15c4eb6a 100644
--- a/src/graph/node.cpp
+++ b/src/graph/node.cpp
@@ -15,14 +15,17 @@ size_t Node::allocate() {
 }
 
 void Node::free() {
-  if(graph()) {
-    if(val_) {
-      graph()->free(val_);
-      val_ = nullptr;
-    }
-    if(adj_) {
-      graph()->free(adj_);
-      adj_ = nullptr;
+  if(destroy_) { // don't free views, @TODO: better naming
+    //std::cerr << "Freeing" << std::endl;
+    if(graph()) {
+      if(val_) {
+        graph()->free(val_);
+        val_ = nullptr;
+      }
+      if(adj_) {
+        graph()->free(adj_);
+        adj_ = nullptr;
+      }
     }
   }
 }
diff --git a/src/graph/node.h b/src/graph/node.h
index 1397e74b0..400333232 100644
--- a/src/graph/node.h
+++ b/src/graph/node.h
@@ -17,8 +17,7 @@ namespace marian {
  * implements most common functions demanded by Chainable.
  * Each operation in a computation graph is a node.
  */
-class Node : public Chainable<Tensor>,
-             public std::enable_shared_from_this<Node> {
+class Node : public Chainable<Tensor> {
 protected:
   size_t id_{0};
   size_t edges_{0};
@@ -27,10 +26,11 @@ class Node : public Chainable<Tensor>,
   bool memoize_{false};
 
   std::vector<Expr> children_;
+  Ptr<std::list<Expr>> subtape_;
 
   Weak<ExpressionGraph> graph_;
   Shape shape_{1, 1, 1, 1};
-  Type value_type_{Type::float32};
+  Type valueType_{Type::float32};
 
   std::string name_{"none"};
 
@@ -40,18 +40,18 @@ class Node : public Chainable<Tensor>,
   bool markedForDebug_{false};
   std::string debugMessage_;
 
+  bool isCheckpoint_{false};
+
   Ptr<AutoTunerRecorder> recorder_;
   size_t recorderHash_;
   bool recorderStop_;
 
 public:
-  Node(Ptr<ExpressionGraph> graph, Shape shape, Type value_type = Type::float32)
-      : graph_(graph), shape_(shape), value_type_(value_type) {}
+  Node(Ptr<ExpressionGraph> graph, const Shape& shape, const Type& valueType = Type::float32)
+    : graph_(graph), shape_(shape), valueType_(valueType) {}
 
   virtual ~Node() {
-    if(destroy_) {
-      free();
-    }
+    free();
   }
 
   virtual float scalar() override;
@@ -104,7 +104,7 @@ class Node : public Chainable<Tensor>,
 
   virtual void free() override;
 
-  virtual void init() override{};
+  virtual void init() override {};
 
   virtual void init_dependent() override;
 
@@ -115,7 +115,7 @@ class Node : public Chainable<Tensor>,
   virtual Tensor& grad() override { return adj_; };
 
   virtual const Shape& shape() override { return shape_; }
-  virtual const Type& value_type() override { return value_type_; }
+  virtual const Type& value_type() override { return valueType_; }
 
   void set_name(const std::string& name) override { name_ = name; }
 
@@ -138,10 +138,22 @@ class Node : public Chainable<Tensor>,
 
   virtual std::string graphviz() override {
     std::stringstream ss;
-    ss << "\"" << this << "\" [shape=\"" << form() << "\", label=" << label()
-       << ", style=\"filled\", fillcolor=\"" << color() << "\"]" << std::endl;
+    ss << "\"" << this << "\" [" 
+      << "shape=\"" << form() << "\", "
+      << "label="   << label() << ", "
+      << "style=\"filled\", "
+      << (isCheckpoint_ ? "penwidth=3, " : "penwidth=1, ")
+      << "fillcolor=\"" << color() << "\"];" << std::endl;
+
     for(auto&& child : children())
-      ss << "\"" << child << "\" -> \"" << this << "\"" << std::endl;
+      ss << "\"" << child << "\" -> \"" << this << "\";" << std::endl;
+    
+    if(subtape_) {
+      for(auto&& dep : *subtape_)
+        ss << "\"" << dep << "\" -> \"" << this << "\" [style=dotted];" << std::endl;
+
+    }
+
     ss << std::endl;
     return ss.str();
   }
@@ -153,15 +165,49 @@ class Node : public Chainable<Tensor>,
   Ptr<Backend> getBackend();
 
   void record(Ptr<AutoTunerRecorder>, size_t, bool) override;
+
+  virtual void markCheckpoint() override {
+    isCheckpoint_ = true;
+  }
+
+  virtual bool isCheckpoint() const override {
+    return (children_.empty() || isCheckpoint_); // this node is a checkPoint if it's a leaf or if it has been marked.
+  }
+
+  virtual void setSubtape(Ptr<std::list<Expr>> subtape) override {
+    subtape_ = subtape;
+  }
+
+  virtual Ptr<std::list<Expr>> getSubtape() override {
+    return subtape_;
+  };
 };
 
 struct NaryNodeOp : public Node {
   size_t hash_{0};
 
+  // deduce type automatically, but then all types must be the same
+  Type commonType(const std::vector<Expr>& nodes) {
+    ABORT_IF(nodes.size() == 0, "NaryNodeOp has no children");
+    Type type = nodes[0]->value_type();
+    for(int i = 1; i < nodes.size(); ++i)
+      ABORT_IF(nodes[i]->value_type() != type,
+               "Child {} has different type (first: {} != child: {})",
+               i, type, nodes[i]->value_type());
+    return type;
+  }
+
+  NaryNodeOp(const std::vector<Expr>& nodes)
+  : NaryNodeOp(nodes, nodes[0]->shape()) {}
+
+  NaryNodeOp(const std::vector<Expr>& nodes, Shape shape)
+  : NaryNodeOp(nodes, shape, commonType(nodes)) {}
+
   NaryNodeOp(const std::vector<Expr>& nodes,
              Shape shape,
-             Type value_type = Type::float32)
+             Type value_type)
       : Node(nodes.front()->graph(), shape, value_type) {
+
     children_.resize(nodes.size());
     for(size_t i = 0; i < nodes.size(); ++i)
       children_[i] = nodes[i];
@@ -174,9 +220,6 @@ struct NaryNodeOp : public Node {
         nodes.begin(), nodes.end(), [](Expr a) { return a->memoize(); }));
   }
 
-  NaryNodeOp(const std::vector<Expr>& nodes)
-      : NaryNodeOp(nodes, nodes[0]->shape()) {}
-
   virtual ~NaryNodeOp() {}
 
   std::vector<Expr>& children() override { return children_; }
@@ -185,6 +228,7 @@ struct NaryNodeOp : public Node {
     if(!hash_) {
       std::size_t seed = util::hash<std::string>()(name());
       util::hash_combine(seed, type());
+      util::hash_combine(seed, (size_t)value_type());
       for(size_t i = 0; i < children_.size(); ++i)
         util::hash_combine(seed, child(i)->hash());
       hash_ = seed;
@@ -197,6 +241,8 @@ struct NaryNodeOp : public Node {
       return false;
     if(name() != node->name())
       return false;
+    if(value_type() != node->value_type())
+      return false;
     if(children().size() != node->children().size())
       return false;
     for(size_t i = 0; i < children().size(); ++i)
diff --git a/src/graph/node_initializers.cpp b/src/graph/node_initializers.cpp
index 4b52ca0be..ce29d999a 100755
--- a/src/graph/node_initializers.cpp
+++ b/src/graph/node_initializers.cpp
@@ -11,151 +11,162 @@ namespace marian {
 
 namespace inits {
 
-void zeros(Tensor t) {
-  t->set(0.f);
+class LambdaInit : public NodeInitializer {
+  private:
+    std::function<void(Tensor)> lambda_;
+
+  public:
+    LambdaInit(std::function<void(Tensor)>&& lambda) : lambda_(std::move(lambda)) {}
+
+    void apply(Tensor tensor) override {
+      lambda_(tensor);
+    }
+};
+
+class LambdaInitConvert : public NodeInitializer {
+  private:
+    std::function<void(Tensor)> lambda_;
+    Type intermediateType_;
+
+  public:
+    LambdaInitConvert(std::function<void(Tensor)>&& lambda,
+                      Type intermediateType)
+      : lambda_(std::move(lambda)), intermediateType_(intermediateType) {}
+
+    void apply(Tensor tensor) override {
+      if(tensor->type() != intermediateType_) {
+        auto sharedAllocator = allocator_.lock();
+        ABORT_IF(!sharedAllocator, "Allocator in LambdaInitConvert has not been set or is expired");
+
+        auto memory = sharedAllocator->alloc(tensor->size(), intermediateType_);
+        auto temp = TensorBase::New(memory,
+                                    tensor->shape(),
+                                    intermediateType_,
+                                    tensor->getBackend());
+        lambda_(temp);
+        CopyCast(tensor, temp); // Casting from temp to tensor
+        sharedAllocator->free(memory);
+      }
+      else {
+        lambda_(tensor);
+      }
+    }
+};
+
+Ptr<NodeInitializer> lambda(std::function<void(Tensor)>&& func) {
+  return New<LambdaInit>(std::move(func));
+}
+
+Ptr<NodeInitializer> lambda(std::function<void(Tensor)>&& func, Type intermediateType) {
+  return New<LambdaInitConvert>(std::move(func), intermediateType);
 }
 
-void ones(Tensor t) {
-  t->set(1.0f);
+Ptr<NodeInitializer> fromValue(float v) {
+  return lambda([v](Tensor t){ t->set(v); });
 }
 
-NodeInitializer from_value(float v) {
-  return [v](Tensor t) { t->set(v); };
+Ptr<NodeInitializer> zeros() {
+  return fromValue(0.0f);
+}
+
+Ptr<NodeInitializer> ones() {
+  return fromValue(1.0f);
 }
 
 // diagonal matrix with value val along diagonal
-NodeInitializer eye(float val) {
-  return [val](Tensor t) {
+Ptr<NodeInitializer> eye(float val) {
+  auto eyeLambda = [val](Tensor t) {
     ABORT_IF(t->shape().size() != 2 || t->shape()[-1] != t->shape()[-2],
-             "eye(val) is defined only for quadratic tensors, shape is {}",
-             t->shape());
+              "eye(val) is defined only for quadratic tensors, shape is {}",
+              t->shape());
 
     // @TODO: implement efficient version on the GPU
     std::vector<float> vec(t->size(), 0);
     for(int i = 0; i < t->shape()[-1]; ++i)
       vec[i * t->shape()[0] + i] = val;
+
     t->set(vec);
   };
-}
 
-NodeInitializer uniform(float a, float b) {
-  return [a, b](Tensor tensor) {
-    tensor->getBackend()->getRandomGenerator()->uniform(tensor, a, b);
-  };
+  return lambda(eyeLambda, Type::float32);
 }
 
-NodeInitializer normal(float mean, float stddev) {
-  return [mean, stddev](Tensor tensor) {
-    tensor->getBackend()->getRandomGenerator()->normal(tensor, mean, stddev);
-  };
+Ptr<NodeInitializer> uniform(float a, float b) {
+  // only works for float, hence the conversion through intermedia type Type::float32
+  return lambda([a, b](Tensor t) { t->getBackend()->getRandomGenerator()->uniform(t, a, b); }, Type::float32);
 }
 
-// @TODO: replace with parametered version below
-void glorot_uniform(Tensor tensor) {
-  float scale = sqrtf(6.0f / (tensor->shape()[-2] + tensor->shape()[-1]));
-  uniform(-scale, scale)(tensor);
+Ptr<NodeInitializer> normal(float mean, float stddev) {
+  // only works for float, hence the conversion through intermedia type Type::float32
+  return lambda([mean, stddev](Tensor t) { t->getBackend()->getRandomGenerator()->normal(t, mean, stddev); }, Type::float32);
 }
 
-NodeInitializer glorot_uniform2(bool fanIn, bool fanOut) {
-  return [fanIn, fanOut](Tensor tensor) {
-    float scale = 1.f;
-    if(fanIn && fanOut)
-      scale = sqrtf(6.0f / (tensor->shape()[-2] + tensor->shape()[-1]));
-    else if(!fanIn && fanOut)
-      scale = sqrtf(3.0f / tensor->shape()[-1]); // results in columns of matrix to be ~unit length
-    else if(fanIn && !fanOut)
-      scale = sqrtf(3.0f / tensor->shape()[-2]);
-    else
-      ABORT("You need to set fanIn or fanOut or both to true");
+Ptr<NodeInitializer> glorotUniform(bool fanIn, bool fanOut, float scalingFactor) {
+  return lambda([fanIn, fanOut, scalingFactor](Tensor t) {
+    float scale = sqrtf(6.0f / (t->shape()[-2] + t->shape()[-1]));
+    if(fanIn && !fanOut)
+      scale = sqrtf(3.0f / t->shape()[-2]); // results in columns of matrix to be ~unit length
+    if(!fanIn && fanOut)
+      scale = sqrtf(3.0f / t->shape()[-1]);
 
-    uniform(-scale, scale)(tensor);
-  };
-}
+    scale *= scalingFactor;
 
-// @TODO: replace with parametered version below
-void glorot_normal(Tensor tensor) {
-  float scale = sqrtf(2.0f / (tensor->shape()[-2] + tensor->shape()[-1]));
-  normal(0.f, scale)(tensor);
+    t->getBackend()->getRandomGenerator()->uniform(t, -scale, scale);
+  }, Type::float32);
 }
 
-NodeInitializer glorot_normal2(bool fanIn, bool fanOut) {
-  return [fanIn, fanOut](Tensor tensor) {
-    float scale = 1.f;
-    if(fanIn && fanOut)
-      scale = sqrtf(2.0f / (tensor->shape()[-2] + tensor->shape()[-1]));
-    else if(!fanIn && fanOut)
-      scale = sqrtf(1.0f / tensor->shape()[-1]);
-    else if(fanIn && !fanOut)
-      scale = sqrtf(1.0f / tensor->shape()[-2]);
-    else
-      ABORT("You need to set fanIn or fanOut or both to true");
+Ptr<NodeInitializer> glorotNormal(bool fanIn, bool fanOut, float scalingFactor) {
+  return lambda([fanIn, fanOut, scalingFactor](Tensor t) {
+    float scale = sqrtf(2.0f / (t->shape()[-2] + t->shape()[-1]));
+    if(fanIn && !fanOut)
+      scale = sqrtf(1.0f / t->shape()[-2]);
+    if(!fanIn && fanOut)
+      scale = sqrtf(1.0f / t->shape()[-1]);
 
-    normal(0.f, scale)(tensor);
-  };
+    scale *= scalingFactor;
+
+    t->getBackend()->getRandomGenerator()->normal(t, 0.f, scale);
+  }, Type::float32);
 }
 
-NodeInitializer bernoulli(float prob, float scale) {
-  return [prob, scale](Tensor tensor) {
-    Bernoulli(tensor, prob, scale);
-  };
+Ptr<NodeInitializer> bernoulli(float prob, float scale, float shift) {
+  return lambda([prob, scale, shift](Tensor t) { Bernoulli(t, prob, scale, shift); }, Type::float32);
 }
 
-NodeInitializer dropout(float dropProb) {
-  return [dropProb](Tensor t) {
-    Dropout(t, dropProb);
-  };
+Ptr<NodeInitializer> dropout(float dropProb) {
+  return lambda([dropProb](Tensor t) { Dropout(t, dropProb); }, Type::float32);
 }
 
 // gumbel noise:
 // -log(-log(uniform(0.f + eps, 1.f - eps)));
-void gumbel(Tensor tensor) {
-  using namespace functional;
-  // @TODO: make eps a parameter? Seems to influence amplitude quite heavily
-  float eps = 1e-05f;
-  uniform(0.f + eps, 1.f - eps)(tensor);
-  Element(_1 = -log(-log(_1)), tensor);
-}
-
-NodeInitializer from_vector(const std::vector<float>& v) {
-  auto vPtr = New<std::vector<float>>(v.begin(), v.end());
-  return
-      [vPtr](Tensor t) { t->set(vPtr->data(), vPtr->data() + vPtr->size()); };
-}
-
-// @TODO: handle this better with proper type support, the NodeInitializer
-// should be able to inform the calling function about the tensor type it
-// is expecting. Probably needs to turn into struct with type information.
-NodeInitializer from_vector(const std::vector<IndexType>& v) {
-  auto vPtr = New<std::vector<IndexType>>(v.begin(), v.end());
-  return
-      [vPtr](Tensor t) { t->set(vPtr->data(), vPtr->data() + vPtr->size()); };
-}
-
-NodeInitializer from_sparse_vector(
-    std::pair<std::vector<size_t>, std::vector<float>>& v) {
-  return [v](Tensor t) {
-    t->set(1e-6);
-    t->setSparse(v.first, v.second);
-  };
+Ptr<NodeInitializer> gumbel(float eps) {
+  return lambda([eps](Tensor tensor) {
+    tensor->getBackend()->getRandomGenerator()->uniform(tensor, 0.f + eps, 1.f - eps);
+    using namespace functional;
+    Element(_1 = -log(-log(_1)), tensor);
+  }, Type::float32);
 }
 
-// NodeInitializer from_numpy(const cnpy::NpyArrayPtr& np) {
-//  return [np](Tensor t) {
-//    size_t size = 1;
-//    for(size_t dim : np->shape)
-//      size *= dim;
-//    t->set((float*)np->data(), (float*)np->data() + size);
-//  };
-//}
+template <typename T>
+Ptr<NodeInitializer> fromVector(const std::vector<T>& v) {
+  return lambda([v](Tensor t) { t->set(v.data(), v.data() + v.size()); }, typeId<T>());
+}
+
+template Ptr<NodeInitializer> fromVector<float16>(const std::vector<float16>& v);
+template Ptr<NodeInitializer> fromVector<float>(const std::vector<float>& v);
+template Ptr<NodeInitializer> fromVector<IndexType>(const std::vector<IndexType>& v);
+
+Ptr<NodeInitializer> fromSparseVector(std::pair<std::vector<size_t>, std::vector<float>>& v) {
+  return lambda([v](Tensor t) { t->set(1e-6); t->setSparse(v.first, v.second); });
+}
 
 // move this somewhere else
-NodeInitializer from_word2vec(const std::string& file,
+Ptr<NodeInitializer> fromWord2vec(const std::string& file,
                               int dimVoc,
                               int dimEmb,
                               bool normalize /*= false*/) {
-  return [file, dimVoc, dimEmb, normalize](Tensor t) {
+  return lambda([file, dimVoc, dimEmb, normalize](Tensor t) {
     auto embs = Word2VecReader().read(file, dimVoc, dimEmb);
-
     if(normalize) {
       float norm = 0;
       for(auto e : embs)
@@ -166,34 +177,44 @@ NodeInitializer from_word2vec(const std::string& file,
           e = e / norm;
     }
     t->set(embs);
-  };
+  });
 }
 
-NodeInitializer from_item(const io::Item& item) {
+Ptr<NodeInitializer> fromItem(const io::Item& item) {
   if(item.mapped) {
-    return [item](Tensor t) {
+    return lambda([item](Tensor tensor) {
       // @TODO: implement other types, for now croak loudly.
-      ABORT_IF(t->getBackend()->getDeviceId().type != DeviceType::cpu,
+      ABORT_IF(tensor->getBackend()->getDeviceId().type != DeviceType::cpu,
                "Memory mapping only works for CPU tensors");
-      ABORT_IF(!matchType<float>(t->type()),
-               "Tensor type and type for mapping do not match");
-      auto mp = MemoryPiece::New((uint8_t*)item.ptr, t->size() * sizeof(float));
-      t->reset(mp);
-    };
+      ABORT_IF(tensor->type() != item.type,
+               "Tensor type ({}) and type for mapping ({}) do not match",
+               tensor->type(),
+               item.type);
+      ABORT_IF(tensor->size() != item.size() / sizeOf(item.type),
+               "Tensor size ({}) and mapped size ({}) do not match",
+               tensor->size(),
+               item.size() / sizeOf(item.type));
+      auto mp = MemoryPiece::New((uint8_t*)item.ptr, tensor->size() * sizeOf(item.type));
+      tensor->reset(mp);
+    });
   } else {
-    return [item](Tensor t) {
-      // @TODO: implement other types, for now croak loudly.
-      ABORT_IF(!matchType<float>(t->type()),
-               "Tensor type and type for mapping do not match");
-      t->set((const float*)item.bytes.data(),
-             (const float*)item.bytes.data() + t->size());
-    };
+    return lambda(
+      [item](Tensor tensor) { tensor->set(item); },
+      item.type);
   }
 }
 
+Ptr<NodeInitializer> fromTensor(Tensor externalTensor) {
+  return lambda([externalTensor](Tensor t) { t->copyFrom(externalTensor); }, externalTensor->type());
+}
+
+Ptr<NodeInitializer> dummy() {
+  return lambda([](Tensor /*t*/) { });
+}
+
 // Computes Google's sinusoidal position embeddings
-NodeInitializer sinusoidalPositionEmbeddings(int start) {
-  return [start](Tensor t) {
+Ptr<NodeInitializer> sinusoidalPositionEmbeddings(int start) {
+  return lambda([start](Tensor t) {
     int dimEmb   = t->shape()[-1];
     int dimWords = (int)t->size() / dimEmb;
 
@@ -209,8 +230,8 @@ NodeInitializer sinusoidalPositionEmbeddings(int start) {
       }
     }
 
-    from_vector(vPos)(t);
-  };
+    t->set(vPos);
+  }, Type::float32);
 }
 
 }  // namespace inits
diff --git a/src/graph/node_initializers.h b/src/graph/node_initializers.h
index fbb073482..6c8070b04 100644
--- a/src/graph/node_initializers.h
+++ b/src/graph/node_initializers.h
@@ -3,56 +3,141 @@
 
 #include "common/config.h"
 #include "tensors/tensor.h"
+#include "tensors/tensor_operators.h"
 
 #include <functional>
 #include <random>
 
 namespace marian {
 
-typedef std::function<void(Tensor)> NodeInitializer;
+class ExpressionGraph; // Forward declaration
 
 namespace inits {
 
-void zeros(Tensor t);
-
-void ones(Tensor t);
-
-NodeInitializer from_value(float v);
-
-NodeInitializer eye(float val = 1.f);
+/**
+ * Base class for specialized NodeInitializers.
+ *
+ * A NodeInitializer is a functor that can be invoked on a tensor
+ * during node intialization. You need to override operator()(Tensor)
+ * with your own functionality.
+ *
+ * See node_initializers.cpp for examples.
+ */
+class NodeInitializer {
+protected:
+  Weak<Allocator> allocator_;
 
-NodeInitializer normal(float mean = 0.f, float stddev = 1.f);
+public:
+  virtual void apply(Tensor t) = 0;
+  void setAllocator(Ptr<Allocator> allocator) { allocator_ = allocator; }
+};
 
-NodeInitializer uniform(float a = 0.f, float b = 1.f);
+/**
+ * Use a lambda function of form [](Tensor t) { do something with t } to initalize tensor
+ */
+Ptr<NodeInitializer> lambda(std::function<void(Tensor)>&& func);
 
-void glorot_uniform(Tensor t);
-NodeInitializer glorot_uniform2(bool fanIn = true, bool fanOut = true);
+/**
+ * Use a lambda function of form [](Tensor t) { do something with t } to initalize tensor
+ * Create temporary tensor of Type intermediateType first, initialize and then copy/convert to actual Tensor
+ * Useful for functions that can only operator on a specific type of tensor
+ */
+Ptr<NodeInitializer> lambda(std::function<void(Tensor)>&& func, Type intermediateType);
 
-void glorot_normal(Tensor t);
-NodeInitializer glorot_normal2(bool fanIn = true, bool fanOut = true);
+/**
+ * Fill tensor with given value
+ *
+ * Creates a NodeInitializer that will fill the given tensor
+ * with `value`. Works with any underlying numeric tensor type.
+ *
+ * @return A NodeInitializer which can be called on any tensor
+ */
+Ptr<NodeInitializer> fromValue(float value);
 
-NodeInitializer bernoulli(float p, float scale = 1.f);
+/**
+ * Fill tensor with `0`
+ *
+ * Creates a NodeInitializer that will fill the given tensor
+ * with `0`. Works with any underlying numeric tensor type.
+ *
+ * @return A NodeInitializer which can be called on any tensor
+ */
+Ptr<NodeInitializer> zeros();
 
-NodeInitializer dropout(float dropProb);
+/**
+ * Fill tensor with `1`
+ *
+ * Creates a NodeInitializer that will fill the given tensor
+ * with `1`. Works with any underlying numeric tensor type.
+ *
+ * @return A NodeInitializer which can be called on any tensor
+ */
+Ptr<NodeInitializer> ones();
 
-void gumbel(Tensor t);
+/**
+ * Set diagonal of two dimensional quadratic tensor to `value`.
+ *
+ * Sets all values of the tensor to 0 and fills the diagonal with
+ * the given `value`. If no value is specified `1` is used by default.
+ *
+ * @return A NodeInitializer which can be called on any tensor
+ */
+Ptr<NodeInitializer> eye(float value = 1.f);
 
-static inline void dummy(Tensor) {}
+/**
+ * Fill tensor with normally distributed random numbers
+ *
+ * Be default this generates floating point numbers from the
+ * normal distribution N(0, 1) unless specified differently.
+ *
+ * If compiled with `CUDA`, `marian` will use the `cuRand` library
+ * for both, GPU and CPU computation. The random sequences generated
+ * are the same on both devices.
+ *
+ * If `marian` is compiled without `CUDA`, a random generator
+ * from the C++ standard library is used. These random generators
+ * do not have the same random sequences.
+ *
+ * @return A NodeInitializer which can be called on any tensor
+ */
+Ptr<NodeInitializer> normal(float mean = 0.f, float stddev = 1.f);
 
-NodeInitializer from_vector(const std::vector<float>& v);
-NodeInitializer from_vector(const std::vector<IndexType>& v);
+/**
+ * Fill tensor with uniformly distributed random numbers
+ *
+ * Be default this generates floating point numbers from the
+ * uniform distribution U(0, 1) unless specified differently.
+ *
+ * If compiled with `CUDA`, `marian` will use the `cuRand` library
+ * for both, GPU and CPU computation. The random sequences generated
+ * are the same on both devices.
+ *
+ * If `marian` is compiled without `CUDA`, a random generator
+ * from the C++ standard library is used. These random generators
+ * do not have the same random sequences.
+ *
+ * @return A NodeInitializer which can be called on any tensor
+ */
+Ptr<NodeInitializer> uniform(float a = 0.f, float b = 1.f);
 
-NodeInitializer from_item(const io::Item& item);
+Ptr<NodeInitializer> bernoulli(float p, float scale = 1.f, float shift = 0.f);
+Ptr<NodeInitializer> glorotUniform(bool fanIn = false, bool fanOut = false, float scale = 1.f);
+Ptr<NodeInitializer> glorotNormal(bool fanIn = false, bool fanOut = false, float scale = 1.f);
 
-NodeInitializer from_sparse_vector(
-    std::pair<std::vector<size_t>, std::vector<float>>& v);
+Ptr<NodeInitializer> dropout(float dropoutProbabilty);
+Ptr<NodeInitializer> gumbel(float eps = 1e-5f);
+Ptr<NodeInitializer> dummy();
 
-// NodeInitializer from_numpy(const cnpy::NpyArrayPtr& np);
+template <typename T>
+Ptr<NodeInitializer> fromVector(const std::vector<T>& v);
+Ptr<NodeInitializer> fromSparseVector(std::pair<std::vector<size_t>, std::vector<float>>& v);
+Ptr<NodeInitializer> fromItem(const io::Item& item);
+Ptr<NodeInitializer> fromTensor(Tensor tensor);
 
-NodeInitializer from_word2vec(const std::string& file,
-                              int dimVoc,
-                              int dimEmb,
-                              bool normalize = false);
+Ptr<NodeInitializer> fromWord2vec(const std::string& file,
+                                  int dimVoc,
+                                  int dimEmb,
+                                  bool normalize = false);
 
 /**
  * Computes Google's sinusoidal position embeddings
@@ -66,7 +151,7 @@ NodeInitializer from_word2vec(const std::string& file,
  * embeddings are the same for each batch entry and change
  * over time.
  */
-NodeInitializer sinusoidalPositionEmbeddings(int start);
+Ptr<NodeInitializer> sinusoidalPositionEmbeddings(int start);
 
 }  // namespace inits
 
diff --git a/src/graph/node_operators.cpp b/src/graph/node_operators.cpp
index b5a37b9ba..468f12ac5 100644
--- a/src/graph/node_operators.cpp
+++ b/src/graph/node_operators.cpp
@@ -5,6 +5,17 @@
 
 namespace marian {
 
+ConstantNode::ConstantNode(Ptr<ExpressionGraph> graph,
+                          const Shape& shape,
+                          const Ptr<inits::NodeInitializer>& init,
+                          Type value_type)
+    : Node(graph, shape, value_type),
+      init_(init),
+      initialized_(false) {
+  init_->setAllocator(graph->allocator());
+  setTrainable(false);
+}
+
 size_t ConstantNode::allocate() {
   size_t elements = 0;
   if(!val_) {
@@ -16,7 +27,7 @@ size_t ConstantNode::allocate() {
 
 void ConstantNode::init() {
   if(!initialized_) {
-    (*init_)(val_);
+    init_->apply(val_);
     initialized_ = true;
   }
   init_.reset();
@@ -24,18 +35,26 @@ void ConstantNode::init() {
 
 ParamNode::ParamNode(Ptr<ExpressionGraph> graph,
                      const Shape& shape,
-                     const NodeInitializer& init,
+                     const Ptr<inits::NodeInitializer>& init,
+                     bool fixed)
+    : ParamNode(graph, shape, init, Type::float32, fixed) {}
+
+ParamNode::ParamNode(Ptr<ExpressionGraph> graph,
+                     const Shape& shape,
+                     const Ptr<inits::NodeInitializer>& init,
+                     const Type& valueType,
                      bool fixed)
-    : Node(graph, shape),  // TODO: add value_type
-      init_(new NodeInitializer(init)),
+    : Node(graph, shape, valueType),
+      init_(init),
       initialized_(false) {
+  init_->setAllocator(graph->allocator());
   setTrainable(!fixed);
   setMemoize(graph->isInference());
 }
 
 void ParamNode::init() {
   if(!initialized_) {
-    (*init_)(val_);
+    init_->apply(val_);
     initialized_ = true;
   }
   init_.reset();
diff --git a/src/graph/node_operators.h b/src/graph/node_operators.h
index 01c7f5904..0ce7f1b36 100644
--- a/src/graph/node_operators.h
+++ b/src/graph/node_operators.h
@@ -9,13 +9,8 @@ namespace marian {
 struct ConstantNode : public Node {
   ConstantNode(Ptr<ExpressionGraph> graph,
                const Shape& shape,
-               const NodeInitializer& init,
-               Type value_type = Type::float32)
-      : Node(graph, shape, value_type),
-        init_(new NodeInitializer(init)),
-        initialized_(false) {
-    setTrainable(false);
-  }
+               const Ptr<inits::NodeInitializer>& init,
+               Type value_type = Type::float32);
 
   ~ConstantNode() {}
 
@@ -37,14 +32,20 @@ struct ConstantNode : public Node {
   virtual void record(Ptr<AutoTunerRecorder>, size_t, bool) override{};
 
 private:
-  UPtr<NodeInitializer> init_;
+  Ptr<inits::NodeInitializer> init_;
   bool initialized_;
 };
 
 struct ParamNode : public Node {
   ParamNode(Ptr<ExpressionGraph> graph,
             const Shape& shape,
-            const NodeInitializer& init,
+            const Ptr<inits::NodeInitializer>& init,
+            bool fixed = false);
+
+  ParamNode(Ptr<ExpressionGraph> graph,
+            const Shape& shape,
+            const Ptr<inits::NodeInitializer>& init,
+            const Type& valueType,
             bool fixed = false);
 
   ~ParamNode() {}
@@ -72,7 +73,7 @@ struct ParamNode : public Node {
   virtual void record(Ptr<AutoTunerRecorder>, size_t, bool) override{};
 
 private:
-  UPtr<NodeInitializer> init_;
+  Ptr<inits::NodeInitializer> init_;
   bool initialized_;
 };
 }  // namespace marian
diff --git a/src/graph/node_operators_unary.h b/src/graph/node_operators_unary.h
index 05a09df68..c774a8432 100755
--- a/src/graph/node_operators_unary.h
+++ b/src/graph/node_operators_unary.h
@@ -62,6 +62,24 @@ struct ScalarAddNodeOp : public UnaryNodeOp {
   }
 };
 
+// Cast a tensor to a different type
+struct CastNodeOp : public UnaryNodeOp {
+public:
+  CastNodeOp(Expr a, Type type) : UnaryNodeOp(a, type) {}
+
+  NodeOps forwardOps() override {
+    using namespace functional;
+    return { NodeOp(CopyCast(val_, child(0)->val())) };
+  }
+
+  NodeOps backwardOps() override {
+    using namespace functional;
+    return { NodeOp(CopyCast(child(0)->grad(), adj_)) };
+  }
+
+  const std::string type() override { return "cast"; }
+};
+
 struct ScalarMultNodeOp : public UnaryNodeOp {
 private:
   float scalar_{0};
@@ -777,6 +795,75 @@ class ReshapeNodeOp : public UnaryNodeOp {
   }
 };
 
+// @TODO: review if still required as this is an ugly hack anyway.
+// Memory less operator that clips gradients during backward step
+// Executes this as an additional operation on the gradient.
+class ClipGradientNodeOp : public UnaryNodeOp {
+private:
+  Expr clipee_;
+  float clipValue_{0};
+
+public:
+  ClipGradientNodeOp(Expr a, float clipValue)
+   : UnaryNodeOp(a), clipee_(a), clipValue_(clipValue) {
+    Node::destroy_ = false;
+  }
+
+  ~ClipGradientNodeOp() {}
+
+  size_t allocate() override { return 0; }
+  void free() override {}
+
+  void forward() override {}
+
+  void backward() override {
+    using namespace marian::functional;
+    Element(_1 = clip(_1, clipValue_), adj_);
+  }
+
+  void init_dependent() override { clipee_->init_dependent(); }
+
+  void set_zero_adjoint() override { clipee_->set_zero_adjoint(); }
+
+  Tensor& val() override {
+    auto childVal = clipee_->val();
+    auto temp = TensorBase::New(childVal->memory(), shape(), childVal->type(), childVal->getBackend());
+    val_.swap(temp);
+    return val_;
+  };
+
+  Tensor& grad() override {
+    auto childGrad = clipee_->grad();
+    auto temp = TensorBase::New(childGrad->memory(), shape(), childGrad->type(), childGrad->getBackend());
+    adj_.swap(temp);
+    return adj_;
+  };
+
+  const std::string type() override { return "clipGradient"; }
+
+  const std::string color() override { return "grey"; }
+
+  virtual size_t hash() override {
+    if(!hash_) {
+      size_t seed = NaryNodeOp::hash();
+      util::hash_combine(seed, clipValue_);
+      hash_ = seed;
+    }
+    return hash_;
+  }
+
+  virtual bool equal(Expr node) override {
+    if(!NaryNodeOp::equal(node))
+      return false;
+    auto cnode = std::dynamic_pointer_cast<ClipGradientNodeOp>(node);
+    if(!cnode)
+      return false;
+    if(clipValue_ != cnode->clipValue_)
+      return false;
+    return true;
+  }
+};
+
 // narrow an axis to [begin, end)
 // The resulting object must be consecutive in memory.
 class SliceViewNodeOp : public UnaryNodeOp {
diff --git a/src/layers/generic.cpp b/src/layers/generic.cpp
index 97106dabe..5c20ccb3e 100755
--- a/src/layers/generic.cpp
+++ b/src/layers/generic.cpp
@@ -120,14 +120,14 @@ namespace marian {
     y = dot_csr(
         y,  // [B x U]
         factorMatrix.shape,
-        graph->constant({(int)factorMatrix.weights.size()}, inits::from_vector(factorMatrix.weights), Type::float32),
-        graph->constant({(int)factorMatrix.indices.size()}, inits::from_vector(factorMatrix.indices), Type::uint32),
-        graph->constant({(int)factorMatrix.offsets.size()}, inits::from_vector(factorMatrix.offsets), Type::uint32),
+        graph->constant({(int)factorMatrix.weights.size()}, inits::fromVector(factorMatrix.weights), Type::float32),
+        graph->constant({(int)factorMatrix.indices.size()}, inits::fromVector(factorMatrix.indices), Type::uint32),
+        graph->constant({(int)factorMatrix.offsets.size()}, inits::fromVector(factorMatrix.offsets), Type::uint32),
         /*transB=*/ true); // -> [B x V]
 
     // mask out gaps
     auto gapLogMask = factoredVocab_->getGapLogMask(); // [V]
-    y = y + graph->constant({ (int)gapLogMask.size() }, inits::from_vector(gapLogMask), Type::float32);
+    y = y + graph->constant({ (int)gapLogMask.size() }, inits::fromVector(gapLogMask), Type::float32);
 
     return y;
 #else
@@ -226,14 +226,14 @@ namespace marian {
         Wt_ = tiedParam_;
       } else {
         if (graph_->get(name + "_W")) { // support of legacy models that did not transpose
-          Wt_ = graph_->param(name + "_W", {inputDim, numOutputClasses}, inits::glorot_uniform2(true, false));
+          Wt_ = graph_->param(name + "_W", {inputDim, numOutputClasses}, inits::glorotUniform(true, false));
           isLegacyUntransposedW = true;
         }
         else // this is the regular case:
-          Wt_ = graph_->param(name + "_Wt", {numOutputClasses, inputDim}, inits::glorot_uniform2(false, true));
+          Wt_ = graph_->param(name + "_Wt", {numOutputClasses, inputDim}, inits::glorotUniform(false, true));
       }
 
-      b_ = graph_->param(name + "_b", {1, numOutputClasses}, inits::zeros);
+      b_ = graph_->param(name + "_b", {1, numOutputClasses}, inits::zeros());
 
       /*const*/ int lemmaDimEmb = options_->get<int>("lemma-dim-emb", 0);
       if (lemmaDimEmb > 0) {
@@ -243,7 +243,7 @@ namespace marian {
 #endif
         auto range = factoredVocab_->getGroupRange(0);
         auto lemmaVocabDim = (int)(range.second - range.first);
-        auto initFunc = inits::glorot_uniform2(/*fanIn=*/true, /*fanOut=*/false); // -> embedding vectors have roughly unit length
+        auto initFunc = inits::glorotUniform(/*fanIn=*/true, /*fanOut=*/false); // -> embedding vectors have roughly unit length
         lemmaEt_ = graph_->param(name + "_lemmaEt", {lemmaDimEmb, lemmaVocabDim}, initFunc); // [L x U] L=lemmaDimEmb; transposed for speed
       }
     }
@@ -286,7 +286,7 @@ namespace marian {
             int lemmaVocabDim = Plemma->shape()[-1];
             int factorVocabDim = factorLogits->shape()[-1];
             auto name = options_->get<std::string>("prefix");
-            Expr lemmaBt = graph_->param(name + "_lemmaBt_" + std::to_string(g), {factorVocabDim, lemmaVocabDim}, inits::zeros); // [U x U0] U0=#lemmas one bias per class per lemma
+            Expr lemmaBt = graph_->param(name + "_lemmaBt_" + std::to_string(g), {factorVocabDim, lemmaVocabDim}, inits::zeros()); // [U x U0] U0=#lemmas one bias per class per lemma
             auto b = dot(Plemma, lemmaBt, false, true); // [B... x U]
             factorLogits = factorLogits + b;
           }
@@ -323,7 +323,7 @@ namespace marian {
             int inputDim = input1->shape()[-1];
             auto name = options_->get<std::string>("prefix");
             // note: if the lemmaEt[:,w] have unit length (var = 1/L), then lemmaWt @ lemmaEt is also length 1
-            Expr lemmaWt = inputDim == lemmaDimEmb ? nullptr : graph_->param(name + "_lemmaWt", { inputDim,  lemmaDimEmb }, inits::glorot_uniform); // [D x L] D=hidden-vector dimension
+            Expr lemmaWt = inputDim == lemmaDimEmb ? nullptr : graph_->param(name + "_lemmaWt", { inputDim,  lemmaDimEmb }, inits::glorotUniform()); // [D x L] D=hidden-vector dimension
             auto f = lemmaWt ? dot(e, lemmaWt, false, true) : e; // [B... x D]
             // augment the original hidden vector with this additional information
             input1 = input1 + f;
@@ -352,13 +352,13 @@ namespace marian {
     }
 
     // Embedding layer initialization should depend only on embedding size, hence fanIn=false
-    auto initFunc = inits::glorot_uniform2(/*fanIn=*/false, /*fanOut=*/true); // -> embedding vectors have roughly unit length
+    auto initFunc = inits::glorotUniform(/*fanIn=*/false, /*fanOut=*/true); // -> embedding vectors have roughly unit length
     
     if (options_->has("embFile")) {
       std::string file = opt<std::string>("embFile");
       if (!file.empty()) {
         bool norm = opt<bool>("normalization", false);
-        initFunc = inits::from_word2vec(file, dimVoc, dimEmb, norm);
+        initFunc = inits::fromWord2vec(file, dimVoc, dimEmb, norm);
       }
     }
 
@@ -374,9 +374,9 @@ namespace marian {
     // [row index = word position index] -> set of factor indices for word at this position
     ABORT_IF(factoredData.shape != Shape({(int)factoredData.offsets.size()-1/*=rows of CSR*/, E_->shape()[0]}), "shape mismatch??");
     // the CSR matrix is passed in pieces
-    auto weights = graph->constant({ (int)factoredData.weights.size() }, inits::from_vector(factoredData.weights), Type::float32);
-    auto indices = graph->constant({ (int)factoredData.indices.size() }, inits::from_vector(factoredData.indices), Type::uint32);
-    auto offsets = graph->constant({ (int)factoredData.offsets.size() }, inits::from_vector(factoredData.offsets), Type::uint32);
+    auto weights = graph->constant({ (int)factoredData.weights.size() }, inits::fromVector(factoredData.weights), Type::float32);
+    auto indices = graph->constant({ (int)factoredData.indices.size() }, inits::fromVector(factoredData.indices), Type::uint32);
+    auto offsets = graph->constant({ (int)factoredData.offsets.size() }, inits::fromVector(factoredData.offsets), Type::uint32);
     // apply dropout
     // We apply it to the weights, i.e. factors get dropped out separately, but always as entire vectors.
     weights = dropout(weights, dropProb);
@@ -420,7 +420,7 @@ namespace marian {
 
     auto batchEmbeddings = apply(subBatch->data(), {dimWords, dimBatch, dimEmb});
     auto batchMask = graph->constant({dimWords, dimBatch, 1},
-                                     inits::from_vector(subBatch->mask()));
+                                     inits::fromVector(subBatch->mask()));
     return std::make_tuple(batchEmbeddings, batchMask);
   }
 
diff --git a/src/layers/generic.h b/src/layers/generic.h
index 741ebc88d..ee9854013 100755
--- a/src/layers/generic.h
+++ b/src/layers/generic.h
@@ -134,8 +134,8 @@ class Logits {
 private:
     // helper functions
     Ptr<ExpressionGraph> graph() const;
-    Expr constant(const Shape& shape, const std::vector<float>&    data) const { return graph()->constant(shape, inits::from_vector(data), Type::float32); }
-    Expr constant(const Shape& shape, const std::vector<uint32_t>& data) const { return graph()->constant(shape, inits::from_vector(data), Type::uint32);  }
+    Expr constant(const Shape& shape, const std::vector<float>&    data) const { return graph()->constant(shape, inits::fromVector(data), Type::float32); }
+    Expr constant(const Shape& shape, const std::vector<uint32_t>& data) const { return graph()->constant(shape, inits::fromVector(data), Type::uint32);  }
     template<typename T> Expr constant(const std::vector<T>& data) const { return constant(Shape{(int)data.size()}, data); } // same as constant() but assuming vector
     Expr indices(const std::vector<uint32_t>& data) const { return graph()->indices(data); } // actually the same as constant(data) for this data type
     std::vector<float> getFactorMasks(size_t factorGroup, const std::vector<WordIndex>& indices) const;
@@ -188,20 +188,20 @@ class Dense : public LayerBase, public IUnaryLayer {
         num = std::to_string(i);
 
       Expr W = g->param(
-          name + "_W" + num, {in->shape()[-1], dim}, inits::glorot_uniform);
-      Expr b = g->param(name + "_b" + num, {1, dim}, inits::zeros);
+          name + "_W" + num, {in->shape()[-1], dim}, inits::glorotUniform());
+      Expr b = g->param(name + "_b" + num, {1, dim}, inits::zeros());
 
       if(useLayerNorm) {
         if(useNematusNorm) {
           auto ln_s = g->param(
-              name + "_ln_s" + num, {1, dim}, inits::from_value(1.f));
-          auto ln_b = g->param(name + "_ln_b" + num, {1, dim}, inits::zeros);
+              name + "_ln_s" + num, {1, dim}, inits::fromValue(1.f));
+          auto ln_b = g->param(name + "_ln_b" + num, {1, dim}, inits::zeros());
 
           outputs.push_back(
               layerNorm(affine(in, W, b), ln_s, ln_b, NEMATUS_LN_EPS));
         } else {
           auto gamma = g->param(
-              name + "_gamma" + num, {1, dim}, inits::from_value(1.0));
+              name + "_gamma" + num, {1, dim}, inits::fromValue(1.0));
 
           outputs.push_back(layerNorm(dot(in, W), gamma, b));
         }
@@ -312,25 +312,25 @@ class ULREmbedding : public LayerBase, public IEmbeddingLayer {
     bool fixed = opt<bool>("fixed", false);
 
     // Embedding layer initialization should depend only on embedding size, hence fanIn=false
-    NodeInitializer initFunc = inits::glorot_uniform2(/*fanIn=*/false, /*fanOut=*/true);
+    auto initFunc = inits::glorotUniform(/*fanIn=*/false, /*fanOut=*/true);
 
     std::string queryFile = opt<std::string>("ulrQueryFile");
     std::string keyFile = opt<std::string>("ulrKeysFile");
     bool trainTrans = opt<bool>("ulrTrainTransform", false);
     if (!queryFile.empty() && !keyFile.empty()) {
-      initFunc = inits::from_word2vec(queryFile, dimQueries, dimUlrEmb, false);
+      initFunc = inits::fromWord2vec(queryFile, dimQueries, dimUlrEmb, false);
       name = "ulr_query";
       fixed = true;
       auto query_embed = graph_->param(name, { dimQueries, dimUlrEmb }, initFunc, fixed);
       ulrEmbeddings_.push_back(query_embed);
       // keys embeds
-      initFunc = inits::from_word2vec(keyFile, dimKeys, dimUlrEmb, false);
+      initFunc = inits::fromWord2vec(keyFile, dimKeys, dimUlrEmb, false);
       name = "ulr_keys";
       fixed = true;
       auto key_embed = graph_->param(name, { dimKeys, dimUlrEmb }, initFunc, fixed);
       ulrEmbeddings_.push_back(key_embed);
       // actual  trainable embedding
-      initFunc = inits::glorot_uniform;
+      initFunc = inits::glorotUniform();
       name = "ulr_embed";
       fixed = false;
       auto ulr_embed = graph_->param(name, {dimKeys , dimEmb }, initFunc, fixed);  // note the reverse dim
@@ -342,7 +342,7 @@ class ULREmbedding : public LayerBase, public IEmbeddingLayer {
       // ulr transformation matrix
       //initFunc = inits::eye(1.f); // identity matrix  - is it ok to init wiht identity or shall we make this to the fixed case only
       if (trainTrans) {
-        initFunc = inits::glorot_uniform;
+        initFunc = inits::glorotUniform();
         fixed = false;
       }
       else
@@ -354,7 +354,7 @@ class ULREmbedding : public LayerBase, public IEmbeddingLayer {
       auto ulrTransform = graph_->param(name, { dimUlrEmb, dimUlrEmb }, initFunc, fixed);
       ulrEmbeddings_.push_back(ulrTransform);
 
-      initFunc = inits::from_value(1.f);  // TBD: we should read sharable flags here - 1 means all sharable - 0 means no universal embeddings - should be zero for top freq only
+      initFunc = inits::fromValue(1.f);  // TBD: we should read sharable flags here - 1 means all sharable - 0 means no universal embeddings - should be zero for top freq only
       fixed = true;
       name = "ulr_shared";
       auto share_embed = graph_->param(name, { dimQueries, 1 }, initFunc, fixed);
@@ -400,7 +400,7 @@ class ULREmbedding : public LayerBase, public IEmbeddingLayer {
     auto batchEmbeddings = reshape(chosenEmbeddings_mix, { dimWords, dimBatch, dimEmb });
     auto graph = ulrEmbeddings_.front()->graph();
     auto batchMask = graph->constant({ dimWords, dimBatch, 1 },
-                                     inits::from_vector(subBatch->mask()));
+                                     inits::fromVector(subBatch->mask()));
     batchEmbeddings = dropout(batchEmbeddings, options_->get<float>("dropout", 0.0f), {batchEmbeddings->shape()[-3], 1, 1});
     return std::make_tuple(batchEmbeddings, batchMask);
   }
diff --git a/src/layers/guided_alignment.h b/src/layers/guided_alignment.h
index 8c912ddfc..55ade3658 100644
--- a/src/layers/guided_alignment.h
+++ b/src/layers/guided_alignment.h
@@ -18,7 +18,7 @@ static inline RationalLoss guidedAlignmentCost(Ptr<ExpressionGraph> graph,
   float guidedScalar = options->get<float>("guided-alignment-weight");
   
   float epsilon = 1e-6f;
-  Expr alignment = constant_like(attention, inits::from_vector(batch->getGuidedAlignment()));
+  Expr alignment = constant_like(attention, inits::fromVector(batch->getGuidedAlignment()));
   Expr alignmentLoss; // sum up loss over all attention/alignment positions
   if(guidedLossType == "mse") {
     alignmentLoss = sum(flatten(square(attention - alignment))) / 2.f;
diff --git a/src/layers/loss.h b/src/layers/loss.h
index b64d14aa3..a64f0fcdb 100755
--- a/src/layers/loss.h
+++ b/src/layers/loss.h
@@ -33,7 +33,7 @@ class RationalLoss {
 
   RationalLoss(Expr loss, float count)
   : loss_(loss),
-    count_(constant_like(loss, inits::from_value(count))) {}
+    count_(constant_like(loss, inits::fromValue(count))) {}
 
   RationalLoss(const RationalLoss& other)
   : loss_(other.loss_), count_(other.count_) {}
@@ -249,7 +249,7 @@ class MeanMultiRationalLoss : public MultiRationalLoss {
     if(count_)
       return count_; // keep the existing '1'
     else
-      return current.count()->graph()->ones({1}); // just '1' as labels are factored into loss_
+      return current.count()->graph()->ones({1}, current.loss()->value_type()); // just '1' as labels are factored into loss_
   }
 
 public:
@@ -281,8 +281,8 @@ class LabelwiseLoss {
     ABORT_IF(!loss, "Loss has not been computed");
     ABORT_IF(!labels, "Labels have not been computed");
 
-    Expr lossSum   = loss;
-    Expr labelsSum = labels;
+    Expr lossSum   = cast(loss, Type::float32);   // accumulate in float32
+    Expr labelsSum = cast(labels, Type::float32); // accumulate in float32
     for(int i = 0; i < axes_.size(); ++i) {
       lossSum   = sum(lossSum, axes_[i]);
       labelsSum = sum(labelsSum, axes_[i]);
@@ -295,7 +295,7 @@ class LabelwiseLoss {
   RationalLoss reduce(Expr loss) {
     ABORT_IF(!loss, "Loss has not been computed");
 
-    Expr  lossSum    = loss;
+    Expr  lossSum    = cast(loss, Type::float32);
     for(int i = 0; i < axes_.size(); ++i)
       lossSum = sum(lossSum, axes_[i]);
 
@@ -343,7 +343,7 @@ class CrossEntropyLoss : public LabelwiseLoss {
       logits = atleast_3d(logits); // we always assuma a time and batch dimension exists.
       // for bert training or classification the time dimension is lot.
       // Here safeguard against 2d classifier output, adds 1 on the left, non-op.
-      Expr ce = cross_entropy(logits, indices);
+      Expr ce = cast(cross_entropy(logits, indices), Type::float32);
       if (inFactor) {
         LOG_ONCE("scaling factor losses with weight {}", factorWeight_);
         ce = ce * factorWeight_;
@@ -354,7 +354,8 @@ class CrossEntropyLoss : public LabelwiseLoss {
         // ce' = -sum_i ((1-labelSmoothing_) y^_i + labelSmoothing_/N) log y_i(h)
         //     = -(1-labelSmoothing_) sum_i y^_i log y_i(h) - labelSmoothing_ mean_i log y_i(h)
         //     = (1-labelSmoothing_) ce - labelSmoothing_ mean_i log y_i(h)
-        auto ceqNeg = mean(logits, /*axis=*/ -1) - logsumexp(logits, /*axis=*/ -1);
+        auto logits32 = cast(logits, Type::float32);
+        auto ceqNeg = mean(logits32, /*axis=*/ -1) - logsumexp(logits32, /*axis=*/ -1);
         ce = (1 - labelSmoothing_) * ce - labelSmoothing_ * ceqNeg;
         //ce = ce - labelSmoothing_ * (ce + ceqNeg); // writing it this way saves one op :)
         inFactor = true;
@@ -363,10 +364,10 @@ class CrossEntropyLoss : public LabelwiseLoss {
     });
 
     if(mask)
-      ce = ce * mask;
+      ce = ce * cast(mask, Type::float32);
 
     if(labelWeights)
-      ce = ce * labelWeights;
+      ce = ce * cast(labelWeights, Type::float32);
 
     return ce;
   }
diff --git a/src/layers/weight.cpp b/src/layers/weight.cpp
index 50d749a21..3370f3985 100644
--- a/src/layers/weight.cpp
+++ b/src/layers/weight.cpp
@@ -16,7 +16,7 @@ Expr DataWeighting::getWeights(Ptr<ExpressionGraph> graph,
   int dimBatch = (int)batch->size();
   int dimWords = sentenceWeighting ? 1 : (int)batch->back()->batchWidth();
   auto weights = graph->constant({1, dimWords, dimBatch, 1},
-                                 inits::from_vector(batch->getDataWeights()));
+                                 inits::fromVector(batch->getDataWeights()));
   return weights;
 }
 }  // namespace marian
diff --git a/src/models/bert.h b/src/models/bert.h
index 80e95df07..51a932c5e 100755
--- a/src/models/bert.h
+++ b/src/models/bert.h
@@ -349,8 +349,8 @@ class BertMaskedLM : public ClassifierBase {
     else
       ABORT("Activation function {} not supported in BERT masked LM", activationType);
 
-    auto gamma = graph->param(prefix_ + "_ff_ln_scale", {1, dimModel}, inits::ones);
-    auto beta  = graph->param(prefix_ + "_ff_ln_bias",  {1, dimModel}, inits::zeros);
+    auto gamma = graph->param(prefix_ + "_ff_ln_scale", {1, dimModel}, inits::ones());
+    auto beta  = graph->param(prefix_ + "_ff_ln_bias",  {1, dimModel}, inits::zeros());
     intermediate = layerNorm(intermediate, gamma, beta);
 
     auto layer2 = mlp::mlp()
diff --git a/src/models/costs.h b/src/models/costs.h
index 8b23df905..abb3dbfb1 100755
--- a/src/models/costs.h
+++ b/src/models/costs.h
@@ -227,7 +227,7 @@ class GumbelSoftmaxStep : public ILogProbStep {
   virtual Ptr<DecoderState> apply(Ptr<DecoderState> state) override {
     state->setLogProbs(state->getLogProbs().applyUnaryFunctions(
       [](Expr logits){ // lemma gets gumbelled
-        return logsoftmax(logits + constant_like(logits, inits::gumbel));
+        return logsoftmax(logits + constant_like(logits, inits::gumbel()));
       },
       logsoftmax)); // factors don't
     return state;
diff --git a/src/models/decoder.h b/src/models/decoder.h
index 786766f58..bbad32c30 100755
--- a/src/models/decoder.h
+++ b/src/models/decoder.h
@@ -67,7 +67,7 @@ class DecoderBase : public EncoderDecoderLayerBase {
     Expr selectedEmbs;
     int dimEmb = opt<int>("dim-emb");
     if(words.empty())
-      selectedEmbs = graph_->constant({1, 1, dimBatch, dimEmb}, inits::zeros);
+      selectedEmbs = graph_->constant({1, 1, dimBatch, dimEmb}, inits::zeros());
     else
       selectedEmbs = embeddingLayer->apply(words, {dimBeam, 1, dimBatch, dimEmb});
     state->setTargetHistoryEmbeddings(selectedEmbs);
diff --git a/src/models/s2s.h b/src/models/s2s.h
index f10c77bea..391bf1516 100755
--- a/src/models/s2s.h
+++ b/src/models/s2s.h
@@ -239,7 +239,7 @@ class DecoderS2S : public DecoderBase {
       int dimBatch = (int)batch->size();
       int dimRnn = opt<int>("dim-rnn");
 
-      start = graph->constant({dimBatch, dimRnn}, inits::zeros);
+      start = graph->constant({dimBatch, dimRnn}, inits::zeros());
     }
 
     rnn::States startStates(opt<size_t>("dec-depth"), {start, start});
diff --git a/src/models/transformer.h b/src/models/transformer.h
index 5eb64f24a..bf77c0952 100755
--- a/src/models/transformer.h
+++ b/src/models/transformer.h
@@ -95,7 +95,7 @@ class Transformer : public EncoderOrDecoderBase {
     for(int i = 0; i < length; ++i)
       for(int j = 0; j <= i; ++j)
         vMask[i * length + j] = 1.f;
-    return graph_->constant({1, length, length}, inits::from_vector(vMask));
+    return graph_->constant({1, length, length}, inits::fromVector(vMask));
   }
 
   // convert multiplicative 1/0 mask to additive 0/-inf log mask, and transpose to match result of bdot() op in Attention()
@@ -139,8 +139,8 @@ class Transformer : public EncoderOrDecoderBase {
   {
     auto graph = x->graph();
 
-    auto W = graph->param(prefix + "_W" + suffix, { x->shape()[-1], outDim }, inits::glorot_uniform);
-    auto b = graph->param(prefix + "_b" + suffix, { 1,              outDim }, inits::zeros);
+    auto W = graph->param(prefix + "_W" + suffix, { x->shape()[-1], outDim }, inits::glorotUniform());
+    auto b = graph->param(prefix + "_b" + suffix, { 1,              outDim }, inits::zeros());
 
     x = affine(x, W, b);
     if (actFn)
@@ -151,8 +151,8 @@ class Transformer : public EncoderOrDecoderBase {
 
   Expr layerNorm(Expr x, std::string prefix, std::string suffix = std::string()) const {
     int dimModel = x->shape()[-1];
-    auto scale = graph_->param(prefix + "_ln_scale" + suffix, { 1, dimModel }, inits::ones);
-    auto bias  = graph_->param(prefix + "_ln_bias"  + suffix, { 1, dimModel }, inits::zeros);
+    auto scale = graph_->param(prefix + "_ln_scale" + suffix, { 1, dimModel }, inits::ones());
+    auto bias  = graph_->param(prefix + "_ln_bias"  + suffix, { 1, dimModel }, inits::zeros());
     return marian::layerNorm(x, scale, bias, 1e-6f);
   }
 
@@ -263,8 +263,8 @@ class Transformer : public EncoderOrDecoderBase {
                  bool saveAttentionWeights = false) {
     int dimModel = q->shape()[-1];
     // @TODO: good opportunity to implement auto-batching here or do something manually?
-    auto Wq = graph_->param(prefix + "_Wq", {dimModel, dimModel}, inits::glorot_uniform);
-    auto bq = graph_->param(prefix + "_bq", {       1, dimModel}, inits::zeros);
+    auto Wq = graph_->param(prefix + "_Wq", {dimModel, dimModel}, inits::glorotUniform());
+    auto bq = graph_->param(prefix + "_bq", {       1, dimModel}, inits::zeros());
     auto qh = affine(q, Wq, bq);
     qh = SplitHeads(qh, dimHeads); // [-4: beam depth * batch size, -3: num heads, -2: max length, -1: split vector dim]
 
@@ -273,8 +273,8 @@ class Transformer : public EncoderOrDecoderBase {
     // @TODO: set this automatically by memoizing encoder context and
     // memoization propagation (short-term)
     if (!cache || (cache && cache_.count(prefix + "_keys") == 0)) {
-      auto Wk = graph_->param(prefix + "_Wk", {dimModel, dimModel}, inits::glorot_uniform);
-      auto bk = graph_->param(prefix + "_bk", {1,        dimModel}, inits::zeros);
+      auto Wk = graph_->param(prefix + "_Wk", {dimModel, dimModel}, inits::glorotUniform());
+      auto bk = graph_->param(prefix + "_bk", {1,        dimModel}, inits::zeros());
 
       kh = affine(keys, Wk, bk);     // [-4: beam depth, -3: batch size, -2: max length, -1: vector dim]
       kh = SplitHeads(kh, dimHeads); // [-4: batch size, -3: num heads, -2: max length, -1: split vector dim]
@@ -286,8 +286,8 @@ class Transformer : public EncoderOrDecoderBase {
 
     Expr vh;
     if (!cache || (cache && cache_.count(prefix + "_values") == 0)) {
-      auto Wv = graph_->param(prefix + "_Wv", {dimModel, dimModel}, inits::glorot_uniform);
-      auto bv = graph_->param(prefix + "_bv", {1,        dimModel}, inits::zeros);
+      auto Wv = graph_->param(prefix + "_Wv", {dimModel, dimModel}, inits::glorotUniform());
+      auto bv = graph_->param(prefix + "_bv", {1,        dimModel}, inits::zeros());
 
       vh = affine(values, Wv, bv); // [-4: batch size, -3: num heads, -2: max length, -1: split vector dim]
       vh = SplitHeads(vh, dimHeads);
@@ -309,8 +309,8 @@ class Transformer : public EncoderOrDecoderBase {
     bool project = !opt<bool>("transformer-no-projection");
     if(project || dimAtt != dimOut) {
       auto Wo
-        = graph_->param(prefix + "_Wo", {dimAtt, dimOut}, inits::glorot_uniform);
-      auto bo = graph_->param(prefix + "_bo", {1, dimOut}, inits::zeros);
+        = graph_->param(prefix + "_Wo", {dimAtt, dimOut}, inits::glorotUniform());
+      auto bo = graph_->param(prefix + "_bo", {1, dimOut}, inits::zeros());
       output = affine(output, Wo, bo);
     }
 
@@ -613,7 +613,7 @@ class DecoderTransformer : public Transformer<DecoderBase> {
       int dimBatch = (int)batch->size();
       int dim = opt<int>("dim-emb");
 
-      auto start = graph->constant({1, 1, dimBatch, dim}, inits::zeros);
+      auto start = graph->constant({1, 1, dimBatch, dim}, inits::zeros());
       rnn::States startStates(opt<size_t>("dec-depth"), {start, start});
 
       // don't use TransformerState for RNN layers
diff --git a/src/rnn/attention.h b/src/rnn/attention.h
index 154f63ef4..aa8aedafb 100755
--- a/src/rnn/attention.h
+++ b/src/rnn/attention.h
@@ -50,12 +50,12 @@ class GlobalAttention : public CellInput {
 
     Wa_ = graph->param(prefix + "_W_comb_att",
                        {dimDecState, dimEncState},
-                       inits::glorot_uniform);
+                       inits::glorotUniform());
     Ua_ = graph->param(
-        prefix + "_Wc_att", {dimEncState, dimEncState}, inits::glorot_uniform);
+        prefix + "_Wc_att", {dimEncState, dimEncState}, inits::glorotUniform());
     va_ = graph->param(
-        prefix + "_U_att", {dimEncState, 1}, inits::glorot_uniform);
-    ba_ = graph->param(prefix + "_b_att", {1, dimEncState}, inits::zeros);
+        prefix + "_U_att", {dimEncState, 1}, inits::glorotUniform());
+    ba_ = graph->param(prefix + "_b_att", {1, dimEncState}, inits::zeros());
 
     if(dropout_ > 0.0f) {
       dropMaskContext_ = graph->dropoutMask(dropout_, {1, dimEncState});
@@ -68,15 +68,15 @@ class GlobalAttention : public CellInput {
       if(nematusNorm_) {
         // instead of gammaContext_
         Wc_att_lns_ = graph->param(
-            prefix + "_Wc_att_lns", {1, dimEncState}, inits::from_value(1.f));
+            prefix + "_Wc_att_lns", {1, dimEncState}, inits::fromValue(1.f));
         Wc_att_lnb_ = graph->param(
-            prefix + "_Wc_att_lnb", {1, dimEncState}, inits::zeros);
+            prefix + "_Wc_att_lnb", {1, dimEncState}, inits::zeros());
         // instead of gammaState_
         W_comb_att_lns_ = graph->param(prefix + "_W_comb_att_lns",
                                        {1, dimEncState},
-                                       inits::from_value(1.f));
+                                       inits::fromValue(1.f));
         W_comb_att_lnb_ = graph->param(
-            prefix + "_W_comb_att_lnb", {1, dimEncState}, inits::zeros);
+            prefix + "_W_comb_att_lnb", {1, dimEncState}, inits::zeros());
 
         mappedContext_ = layerNorm(affine(contextDropped_, Ua_, ba_),
                                    Wc_att_lns_,
@@ -84,9 +84,9 @@ class GlobalAttention : public CellInput {
                                    NEMATUS_LN_EPS);
       } else {
         gammaContext_ = graph->param(
-            prefix + "_att_gamma1", {1, dimEncState}, inits::from_value(1.0));
+            prefix + "_att_gamma1", {1, dimEncState}, inits::fromValue(1.0));
         gammaState_ = graph->param(
-            prefix + "_att_gamma2", {1, dimEncState}, inits::from_value(1.0));
+            prefix + "_att_gamma2", {1, dimEncState}, inits::fromValue(1.0));
 
         mappedContext_
             = layerNorm(dot(contextDropped_, Ua_), gammaContext_, ba_);
diff --git a/src/rnn/cells.h b/src/rnn/cells.h
index 36c3e24d1..9fbc8852e 100755
--- a/src/rnn/cells.h
+++ b/src/rnn/cells.h
@@ -36,13 +36,13 @@ class Tanh : public Cell {
     dropout_ = options_->get<float>("dropout", 0);
 
     U_ = graph->param(
-        prefix + "_U", {dimState, dimState}, inits::glorot_uniform);
+        prefix + "_U", {dimState, dimState}, inits::glorotUniform());
 
     if(dimInput)
       W_ = graph->param(
-          prefix + "_W", {dimInput, dimState}, inits::glorot_uniform);
+          prefix + "_W", {dimInput, dimState}, inits::glorotUniform());
 
-    b_ = graph->param(prefix + "_b", {1, dimState}, inits::zeros);
+    b_ = graph->param(prefix + "_b", {1, dimState}, inits::zeros());
 
     if(dropout_ > 0.0f) {
       if(dimInput)
@@ -53,9 +53,9 @@ class Tanh : public Cell {
     if(layerNorm_) {
       if(dimInput)
         gamma1_ = graph->param(
-            prefix + "_gamma1", {1, 3 * dimState}, inits::from_value(1.f));
+            prefix + "_gamma1", {1, 3 * dimState}, inits::fromValue(1.f));
       gamma2_ = graph->param(
-          prefix + "_gamma2", {1, 3 * dimState}, inits::from_value(1.f));
+          prefix + "_gamma2", {1, 3 * dimState}, inits::fromValue(1.f));
     }
   }
 
@@ -131,9 +131,9 @@ class ReLU : public Cell {
 
     if(dimInput)
       W_ = graph->param(
-          prefix + "_W", {dimInput, dimState}, inits::glorot_uniform);
+          prefix + "_W", {dimInput, dimState}, inits::glorotUniform());
 
-    b_ = graph->param(prefix + "_b", {1, dimState}, inits::zeros);
+    b_ = graph->param(prefix + "_b", {1, dimState}, inits::zeros());
 
     if(dropout_ > 0.0f) {
       if(dimInput)
@@ -143,8 +143,8 @@ class ReLU : public Cell {
 
     if(layerNorm_) {
       if(dimInput)
-        gamma1_ = graph->param(prefix + "_gamma1", {1, dimState}, inits::ones);
-      gamma2_ = graph->param(prefix + "_gamma2", {1, dimState}, inits::ones);
+        gamma1_ = graph->param(prefix + "_gamma1", {1, dimState}, inits::ones());
+      gamma2_ = graph->param(prefix + "_gamma2", {1, dimState}, inits::ones());
     }
   }
 
@@ -225,30 +225,30 @@ class GRU : public Cell {
     final_ = opt<bool>("final", false);
 
     auto U = graph->param(
-        prefix + "_U", {dimState, 2 * dimState}, inits::glorot_uniform);
+        prefix + "_U", {dimState, 2 * dimState}, inits::glorotUniform());
     auto Ux = graph->param(
-        prefix + "_Ux", {dimState, dimState}, inits::glorot_uniform);
+        prefix + "_Ux", {dimState, dimState}, inits::glorotUniform());
     U_ = concatenate({U, Ux}, /*axis =*/ -1);
 
     if(dimInput > 0) {
       auto W = graph->param(
-          prefix + "_W", {dimInput, 2 * dimState}, inits::glorot_uniform);
+          prefix + "_W", {dimInput, 2 * dimState}, inits::glorotUniform());
       auto Wx = graph->param(
-          prefix + "_Wx", {dimInput, dimState}, inits::glorot_uniform);
+          prefix + "_Wx", {dimInput, dimState}, inits::glorotUniform());
       W_ = concatenate({W, Wx}, /*axis =*/ -1);
     }
 
-    auto b = graph->param(prefix + "_b", {1, 2 * dimState}, inits::zeros);
-    auto bx = graph->param(prefix + "_bx", {1, dimState}, inits::zeros);
+    auto b = graph->param(prefix + "_b", {1, 2 * dimState}, inits::zeros());
+    auto bx = graph->param(prefix + "_bx", {1, dimState}, inits::zeros());
     b_ = concatenate({b, bx}, /*axis =*/ -1);
 
     // @TODO use this and adjust Amun model type saving and loading
     // U_ = graph->param(prefix + "_U", {dimState, 3 * dimState},
-    //                  (Expr a) : UnaryNodeOp(a)inits::glorot_uniform);
+    //                  (Expr a) : UnaryNodeOp(a)inits::glorotUniform());
     // W_ = graph->param(prefix + "_W", {dimInput, 3 * dimState},
-    //                  (Expr a) : UnaryNodeOp(a)inits::glorot_uniform);
+    //                  (Expr a) : UnaryNodeOp(a)inits::glorotUniform());
     // b_ = graph->param(prefix + "_b", {1, 3 * dimState},
-    //                  (Expr a) : UnaryNodeOp(a)inits::zeros);
+    //                  (Expr a) : UnaryNodeOp(a)inits::zeros());
 
     if(dropout_ > 0.0f) {
       if(dimInput)
@@ -259,9 +259,9 @@ class GRU : public Cell {
     if(layerNorm_) {
       if(dimInput)
         gamma1_ = graph->param(
-            prefix + "_gamma1", {1, 3 * dimState}, inits::from_value(1.f));
+            prefix + "_gamma1", {1, 3 * dimState}, inits::fromValue(1.f));
       gamma2_ = graph->param(
-          prefix + "_gamma2", {1, 3 * dimState}, inits::from_value(1.f));
+          prefix + "_gamma2", {1, 3 * dimState}, inits::fromValue(1.f));
     }
   }
 
@@ -303,7 +303,7 @@ class GRU : public Cell {
     Expr xW;
     if(xWs.empty()) {
       if(!fakeInput_ || fakeInput_->shape() != sU->shape())
-        fakeInput_ = sU->graph()->constant(sU->shape(), inits::zeros);
+        fakeInput_ = sU->graph()->constant(sU->shape(), inits::zeros());
       xW = fakeInput_;
     } else {
       xW = xWs.front();
@@ -370,9 +370,9 @@ class GRUNematus : public Cell {
     final_ = opt<bool>("final", false);
 
     auto U = graph->param(
-        prefix + "_U", {dimState, 2 * dimState}, inits::glorot_uniform);
+        prefix + "_U", {dimState, 2 * dimState}, inits::glorotUniform());
     auto Ux = graph->param(
-        prefix + "_Ux", {dimState, dimState}, inits::glorot_uniform);
+        prefix + "_Ux", {dimState, dimState}, inits::glorotUniform());
 
     if(layerNorm_) {
       U_ = U;
@@ -383,9 +383,9 @@ class GRUNematus : public Cell {
 
     if(dimInput > 0) {
       auto W = graph->param(
-          prefix + "_W", {dimInput, 2 * dimState}, inits::glorot_uniform);
+          prefix + "_W", {dimInput, 2 * dimState}, inits::glorotUniform());
       auto Wx = graph->param(
-          prefix + "_Wx", {dimInput, dimState}, inits::glorot_uniform);
+          prefix + "_Wx", {dimInput, dimState}, inits::glorotUniform());
       if(layerNorm_) {
         W_ = W;
         Wx_ = Wx;
@@ -394,8 +394,8 @@ class GRUNematus : public Cell {
       }
     }
 
-    auto b = graph->param(prefix + "_b", {1, 2 * dimState}, inits::zeros);
-    auto bx = graph->param(prefix + "_bx", {1, dimState}, inits::zeros);
+    auto b = graph->param(prefix + "_b", {1, 2 * dimState}, inits::zeros());
+    auto bx = graph->param(prefix + "_bx", {1, dimState}, inits::zeros());
 
     if(layerNorm_) {
       b_ = b;
@@ -403,10 +403,10 @@ class GRUNematus : public Cell {
 
       // in specific cases we need to pass bx to the kernel
       if(encoder_ && transition_) {
-        auto b0 = graph->constant({1, 2 * dimState}, inits::zeros);
+        auto b0 = graph->constant({1, 2 * dimState}, inits::zeros());
         bbx_ = concatenate({b0, bx}, /*axis =*/ -1);
       } else {
-        bbx_ = graph->constant({1, 3 * dimState}, inits::zeros);
+        bbx_ = graph->constant({1, 3 * dimState}, inits::zeros());
       }
     } else {
       bbx_ = concatenate({b, bx}, /*axis =*/ -1);
@@ -421,21 +421,21 @@ class GRUNematus : public Cell {
     if(layerNorm_) {
       if(dimInput) {
         W_lns_ = graph->param(
-            prefix + "_W_lns", {1, 2 * dimState}, inits::from_value(1.f));
+            prefix + "_W_lns", {1, 2 * dimState}, inits::fromValue(1.f));
         W_lnb_
-            = graph->param(prefix + "_W_lnb", {1, 2 * dimState}, inits::zeros);
+            = graph->param(prefix + "_W_lnb", {1, 2 * dimState}, inits::zeros());
         Wx_lns_ = graph->param(
-            prefix + "_Wx_lns", {1, 1 * dimState}, inits::from_value(1.f));
+            prefix + "_Wx_lns", {1, 1 * dimState}, inits::fromValue(1.f));
         Wx_lnb_
-            = graph->param(prefix + "_Wx_lnb", {1, 1 * dimState}, inits::zeros);
+            = graph->param(prefix + "_Wx_lnb", {1, 1 * dimState}, inits::zeros());
       }
       U_lns_ = graph->param(
-          prefix + "_U_lns", {1, 2 * dimState}, inits::from_value(1.f));
-      U_lnb_ = graph->param(prefix + "_U_lnb", {1, 2 * dimState}, inits::zeros);
+          prefix + "_U_lns", {1, 2 * dimState}, inits::fromValue(1.f));
+      U_lnb_ = graph->param(prefix + "_U_lnb", {1, 2 * dimState}, inits::zeros());
       Ux_lns_ = graph->param(
-          prefix + "_Ux_lns", {1, 1 * dimState}, inits::from_value(1.f));
+          prefix + "_Ux_lns", {1, 1 * dimState}, inits::fromValue(1.f));
       Ux_lnb_
-          = graph->param(prefix + "_Ux_lnb", {1, 1 * dimState}, inits::zeros);
+          = graph->param(prefix + "_Ux_lnb", {1, 1 * dimState}, inits::zeros());
     }
   }
 
@@ -522,7 +522,7 @@ class GRUNematus : public Cell {
     Expr xW;
     if(transition_) {
       if(!fakeInput_ || fakeInput_->shape() != sU->shape())
-        fakeInput_ = sU->graph()->constant(sU->shape(), inits::zeros);
+        fakeInput_ = sU->graph()->constant(sU->shape(), inits::zeros());
       xW = fakeInput_;
     } else {
       xW = xWs.front();
@@ -567,12 +567,12 @@ class FastLSTM : public Cell {
     dropout_ = opt<float>("dropout", 0);
 
     U_ = graph->param(
-        prefix + "_U", {dimState, 4 * dimState}, inits::glorot_uniform);
+        prefix + "_U", {dimState, 4 * dimState}, inits::glorotUniform());
     if(dimInput)
       W_ = graph->param(
-          prefix + "_W", {dimInput, 4 * dimState}, inits::glorot_uniform);
+          prefix + "_W", {dimInput, 4 * dimState}, inits::glorotUniform());
 
-    b_ = graph->param(prefix + "_b", {1, 4 * dimState}, inits::zeros);
+    b_ = graph->param(prefix + "_b", {1, 4 * dimState}, inits::zeros());
 
     if(dropout_ > 0.0f) {
       if(dimInput)
@@ -583,9 +583,9 @@ class FastLSTM : public Cell {
     if(layerNorm_) {
       if(dimInput)
         gamma1_ = graph->param(
-            prefix + "_gamma1", {1, 4 * dimState}, inits::from_value(1.f));
+            prefix + "_gamma1", {1, 4 * dimState}, inits::fromValue(1.f));
       gamma2_ = graph->param(
-          prefix + "_gamma2", {1, 4 * dimState}, inits::from_value(1.f));
+          prefix + "_gamma2", {1, 4 * dimState}, inits::fromValue(1.f));
     }
   }
 
@@ -631,7 +631,7 @@ class FastLSTM : public Cell {
     Expr xW;
     if(xWs.empty()) {
       if(!fakeInput_ || fakeInput_->shape() != sU->shape())
-        fakeInput_ = sU->graph()->constant(sU->shape(), inits::zeros);
+        fakeInput_ = sU->graph()->constant(sU->shape(), inits::zeros());
       xW = fakeInput_;
     } else {
       xW = xWs.front();
@@ -667,17 +667,17 @@ class Multiplicative : public CellType {
     std::string prefix = options->get<std::string>("prefix");
 
     Um_ = graph->param(
-        prefix + "_Um", {dimState, dimState}, inits::glorot_uniform);
+        prefix + "_Um", {dimState, dimState}, inits::glorotUniform());
     Wm_ = graph->param(
-        prefix + "_Wm", {dimInput, dimState}, inits::glorot_uniform);
-    bm_ = graph->param(prefix + "_bm", {1, dimState}, inits::zeros);
-    bwm_ = graph->param(prefix + "_bwm", {1, dimState}, inits::zeros);
+        prefix + "_Wm", {dimInput, dimState}, inits::glorotUniform());
+    bm_ = graph->param(prefix + "_bm", {1, dimState}, inits::zeros());
+    bwm_ = graph->param(prefix + "_bwm", {1, dimState}, inits::zeros());
 
     if(CellType::layerNorm_) {
       gamma1m_ = graph->param(
-          prefix + "_gamma1m", {1, dimState}, inits::from_value(1.f));
+          prefix + "_gamma1m", {1, dimState}, inits::fromValue(1.f));
       gamma2m_ = graph->param(
-          prefix + "_gamma2m", {1, dimState}, inits::from_value(1.f));
+          prefix + "_gamma2m", {1, dimState}, inits::fromValue(1.f));
     }
   }
 
@@ -736,28 +736,28 @@ class SlowLSTM : public Cell {
     std::string prefix = options->get<std::string>("prefix");
 
     Uf_ = graph->param(
-        prefix + "_Uf", {dimState, dimState}, inits::glorot_uniform);
+        prefix + "_Uf", {dimState, dimState}, inits::glorotUniform());
     Wf_ = graph->param(
-        prefix + "_Wf", {dimInput, dimState}, inits::glorot_uniform);
-    bf_ = graph->param(prefix + "_bf", {1, dimState}, inits::zeros);
+        prefix + "_Wf", {dimInput, dimState}, inits::glorotUniform());
+    bf_ = graph->param(prefix + "_bf", {1, dimState}, inits::zeros());
 
     Ui_ = graph->param(
-        prefix + "_Ui", {dimState, dimState}, inits::glorot_uniform);
+        prefix + "_Ui", {dimState, dimState}, inits::glorotUniform());
     Wi_ = graph->param(
-        prefix + "_Wi", {dimInput, dimState}, inits::glorot_uniform);
-    bi_ = graph->param(prefix + "_bi", {1, dimState}, inits::zeros);
+        prefix + "_Wi", {dimInput, dimState}, inits::glorotUniform());
+    bi_ = graph->param(prefix + "_bi", {1, dimState}, inits::zeros());
 
     Uc_ = graph->param(
-        prefix + "_Uc", {dimState, dimState}, inits::glorot_uniform);
+        prefix + "_Uc", {dimState, dimState}, inits::glorotUniform());
     Wc_ = graph->param(
-        prefix + "_Wc", {dimInput, dimState}, inits::glorot_uniform);
-    bc_ = graph->param(prefix + "_bc", {1, dimState}, inits::zeros);
+        prefix + "_Wc", {dimInput, dimState}, inits::glorotUniform());
+    bc_ = graph->param(prefix + "_bc", {1, dimState}, inits::zeros());
 
     Uo_ = graph->param(
-        prefix + "_Uo", {dimState, dimState}, inits::glorot_uniform);
+        prefix + "_Uo", {dimState, dimState}, inits::glorotUniform());
     Wo_ = graph->param(
-        prefix + "_Wo", {dimInput, dimState}, inits::glorot_uniform);
-    bo_ = graph->param(prefix + "_bo", {1, dimState}, inits::zeros);
+        prefix + "_Wo", {dimInput, dimState}, inits::glorotUniform());
+    bo_ = graph->param(prefix + "_bo", {1, dimState}, inits::zeros());
   }
 
   State apply(std::vector<Expr> inputs, State state, Expr mask = nullptr) {
@@ -818,28 +818,28 @@ class TestLSTM : public Cell {
     std::string prefix = options->get<std::string>("prefix");
 
     auto Uf = graph->param(
-        prefix + "_Uf", {dimState, dimState}, inits::glorot_uniform);
+        prefix + "_Uf", {dimState, dimState}, inits::glorotUniform());
     auto Wf = graph->param(
-        prefix + "_Wf", {dimInput, dimState}, inits::glorot_uniform);
-    auto bf = graph->param(prefix + "_bf", {1, dimState}, inits::zeros);
+        prefix + "_Wf", {dimInput, dimState}, inits::glorotUniform());
+    auto bf = graph->param(prefix + "_bf", {1, dimState}, inits::zeros());
 
     auto Ui = graph->param(
-        prefix + "_Ui", {dimState, dimState}, inits::glorot_uniform);
+        prefix + "_Ui", {dimState, dimState}, inits::glorotUniform());
     auto Wi = graph->param(
-        prefix + "_Wi", {dimInput, dimState}, inits::glorot_uniform);
-    auto bi = graph->param(prefix + "_bi", {1, dimState}, inits::zeros);
+        prefix + "_Wi", {dimInput, dimState}, inits::glorotUniform());
+    auto bi = graph->param(prefix + "_bi", {1, dimState}, inits::zeros());
 
     auto Uc = graph->param(
-        prefix + "_Uc", {dimState, dimState}, inits::glorot_uniform);
+        prefix + "_Uc", {dimState, dimState}, inits::glorotUniform());
     auto Wc = graph->param(
-        prefix + "_Wc", {dimInput, dimState}, inits::glorot_uniform);
-    auto bc = graph->param(prefix + "_bc", {1, dimState}, inits::zeros);
+        prefix + "_Wc", {dimInput, dimState}, inits::glorotUniform());
+    auto bc = graph->param(prefix + "_bc", {1, dimState}, inits::zeros());
 
     auto Uo = graph->param(
-        prefix + "_Uo", {dimState, dimState}, inits::glorot_uniform);
+        prefix + "_Uo", {dimState, dimState}, inits::glorotUniform());
     auto Wo = graph->param(
-        prefix + "_Wo", {dimInput, dimState}, inits::glorot_uniform);
-    auto bo = graph->param(prefix + "_bo", {1, dimState}, inits::zeros);
+        prefix + "_Wo", {dimInput, dimState}, inits::glorotUniform());
+    auto bo = graph->param(prefix + "_bo", {1, dimState}, inits::zeros());
 
     U_ = concatenate({Uf, Ui, Uc, Uo}, /*axis =*/ -1);
     W_ = concatenate({Wf, Wi, Wc, Wo}, /*axis =*/ -1);
@@ -909,15 +909,15 @@ class SRU : public Cell {
     layerNorm_ = opt<bool>("layer-normalization", false);
 
     W_ = graph->param(
-        prefix + "_W", {dimInput, dimInput}, inits::glorot_uniform);
+        prefix + "_W", {dimInput, dimInput}, inits::glorotUniform());
 
     Wf_ = graph->param(
-        prefix + "_Wf", {dimInput, dimInput}, inits::glorot_uniform);
-    bf_ = graph->param(prefix + "_bf", {1, dimInput}, inits::zeros);
+        prefix + "_Wf", {dimInput, dimInput}, inits::glorotUniform());
+    bf_ = graph->param(prefix + "_bf", {1, dimInput}, inits::zeros());
 
     Wr_ = graph->param(
-        prefix + "_Wr", {dimInput, dimInput}, inits::glorot_uniform);
-    br_ = graph->param(prefix + "_br", {1, dimInput}, inits::zeros);
+        prefix + "_Wr", {dimInput, dimInput}, inits::glorotUniform());
+    br_ = graph->param(prefix + "_br", {1, dimInput}, inits::zeros());
 
     if(dropout_ > 0.0f) {
       dropMaskX_ = graph->dropoutMask(dropout_, {1, dimInput});
@@ -925,9 +925,9 @@ class SRU : public Cell {
 
     if(layerNorm_) {
       if(dimInput)
-        gamma_ = graph->param(prefix + "_gamma", {1, dimState}, inits::ones);
-      gammar_ = graph->param(prefix + "_gammar", {1, dimState}, inits::ones);
-      gammaf_ = graph->param(prefix + "_gammaf", {1, dimState}, inits::ones);
+        gamma_ = graph->param(prefix + "_gamma", {1, dimState}, inits::ones());
+      gammar_ = graph->param(prefix + "_gammar", {1, dimState}, inits::ones());
+      gammaf_ = graph->param(prefix + "_gammaf", {1, dimState}, inits::ones());
     }
   }
 
@@ -1004,11 +1004,11 @@ class SSRU : public Cell {
     layerNorm_ = opt<bool>("layer-normalization", false);
 
     W_ = graph->param(
-        prefix + "_W", {dimInput, dimInput}, inits::glorot_uniform);
+        prefix + "_W", {dimInput, dimInput}, inits::glorotUniform());
 
     Wf_ = graph->param(
-        prefix + "_Wf", {dimInput, dimInput}, inits::glorot_uniform);
-    bf_ = graph->param(prefix + "_bf", {1, dimInput}, inits::zeros);
+        prefix + "_Wf", {dimInput, dimInput}, inits::glorotUniform());
+    bf_ = graph->param(prefix + "_bf", {1, dimInput}, inits::zeros());
 
     if(dropout_ > 0.0f) {
       dropMaskX_ = graph->dropoutMask(dropout_, {1, dimInput});
@@ -1016,8 +1016,8 @@ class SSRU : public Cell {
 
     if(layerNorm_) {
       if(dimInput)
-        gamma_ = graph->param(prefix + "_gamma", {1, dimState}, inits::ones);
-      gammaf_ = graph->param(prefix + "_gammaf", {1, dimState}, inits::ones);
+        gamma_ = graph->param(prefix + "_gamma", {1, dimState}, inits::ones());
+      gammaf_ = graph->param(prefix + "_gammaf", {1, dimState}, inits::ones());
     }
   }
 
@@ -1085,13 +1085,13 @@ class SSRU : public Cell {
 
 //     W_ = graph->param(prefix + "_W",
 //                        {dimInput, dimInput},
-//                        inits::glorot_uniform);
+//                        inits::glorotUniform());
 
 //     Wf_ = graph->param(prefix + "_Wf",
 //                        {dimInput, dimInput},
-//                        inits::glorot_uniform);
+//                        inits::glorotUniform());
 //     bf_ = graph->param(
-//         prefix + "_bf", {1, dimInput}, inits::zeros);
+//         prefix + "_bf", {1, dimInput}, inits::zeros());
 
 //     if(dropout_ > 0.0f) {
 //       dropMaskX_ = graph->dropoutMask(dropout_, {1, dimInput});
diff --git a/src/tensors/cpu/tensor_operators.cpp b/src/tensors/cpu/tensor_operators.cpp
index 056f0e1d9..22ece2f19 100644
--- a/src/tensors/cpu/tensor_operators.cpp
+++ b/src/tensors/cpu/tensor_operators.cpp
@@ -18,11 +18,6 @@ namespace marian {
 
 namespace cpu {
 
-void IsNan(const Tensor in, Ptr<Allocator> allocator, bool& isNan, bool& isInf, bool zero) {
-  isNan; isInf; zero;
-  ABORT("Not implemented");
-}
-
 inline float stableSigmoid(float x) {
   if(x >= 0) {
     float z = expf(-x);
@@ -33,6 +28,38 @@ inline float stableSigmoid(float x) {
   }
 }
 
+void IsNan(const Tensor in, Ptr<Allocator> allocator, bool& isNan, bool& isInf) {
+  ABORT("Not implemented");
+}
+
+template <typename To, typename From>
+void CopyCastTo(To* out, const From* in, int length) {
+  for(int i = 0; i < length; ++i)
+    out[i] = in[i];
+}
+
+template <typename T>
+void CopyCastFrom(Tensor out, const T* in, int length) {
+  if(out->type() == Type::float32) {
+    CopyCastTo(out->data<float>(), in, length);
+  } else if(out->type() == Type::float16) {
+    CopyCastTo(out->data<float16>(), in, length);
+  } else {
+    ABORT("CopyCastTo to type {} not implemented", out->type());
+  }
+}
+
+// currently useless on the CPU until more types are added
+void CopyCast(Tensor out, const Tensor in) {
+  if(in->type() == Type::float32) {
+    CopyCastFrom(out, in->data<float>(), (int)in->size());
+  } else if(in->type() == Type::float16) {
+    CopyCastFrom(out, in->data<float16>(), (int)in->size());
+  } else {
+    ABORT("CopyCastFrom from type {} not implemented", in->type());
+  }
+}
+
 void ConcatCont(Tensor out, const std::vector<Tensor>& inputs, int axis) {
   int step = 1;
   for(int i = 0; i < axis; ++i)
diff --git a/src/tensors/gpu/tensor_operators.cu b/src/tensors/gpu/tensor_operators.cu
index 72e38f93a..bd54bd94c 100755
--- a/src/tensors/gpu/tensor_operators.cu
+++ b/src/tensors/gpu/tensor_operators.cu
@@ -28,23 +28,17 @@ __device__ inline float stableSigmoid(float x) {
 }
 
 template <typename T>
-__global__ void gIsNan(T* in, int length, bool* isNan, bool* isInf, bool zero) {
+__global__ void gIsNan(const T* in, int length, bool* isNan, bool* isInf) {
   for(int bid = 0; bid < length; bid += blockDim.x * gridDim.x) {
     int index = bid + blockDim.x * blockIdx.x + threadIdx.x;
     if(index < length) {
-      if(isnan((float)in[index])) {
-        if(zero) in[index] = (T)0.f;
-        *isNan = true;
-      }
-      else if(isinf((float)in[index])) {
-        if(zero) in[index] = (T)0.f;
-        *isInf = true;
-      }
+      if(isnan((float)in[index])) *isNan = true;
+      if(isinf((float)in[index])) *isInf = true;
     }
   }
 }
 
-void IsNan(Tensor in, Ptr<Allocator> allocator, bool& isNan, bool& isInf, bool zero) {
+void IsNan(const Tensor in, Ptr<Allocator> allocator, bool& isNan, bool& isInf) {
   cudaSetDevice(in->getDeviceId().no);
 
   int length = in->size();
@@ -58,7 +52,9 @@ void IsNan(Tensor in, Ptr<Allocator> allocator, bool& isNan, bool& isInf, bool z
   fill(in->getBackend(), dIsNan, dIsNan + 2, false);
 
   if(in->type() == Type::float32) {
-    gIsNan<<<blocks, threads>>>(in->data<float>(), length, dIsNan, dIsInf, zero);
+    gIsNan<<<blocks, threads>>>(in->data<float>(), length, dIsNan, dIsInf);
+  } else if(in->type() == Type::float16) {
+    gIsNan<<<blocks, threads>>>(in->data<half>(), length, dIsNan, dIsInf);
   } else {
     ABORT("IsNan for type {} not implemented", in->type());
   }
@@ -71,6 +67,50 @@ void IsNan(Tensor in, Ptr<Allocator> allocator, bool& isNan, bool& isInf, bool z
   cudaStreamSynchronize(0);
 }
 
+template <typename To, typename From>
+__global__ void gCopyCastTo(To* out, const From* in, int length) {
+  for(int bid = 0; bid < length; bid += blockDim.x * gridDim.x) {
+    int index = bid + blockDim.x * blockIdx.x + threadIdx.x;
+    if(index < length) {
+      out[index] = in[index];
+    }
+  }
+}
+
+template <typename To, typename From>
+void CopyCastTo(To* out, const From* in, int length) {
+  int threads = std::min(MAX_THREADS, length);
+  int blocks = std::min(MAX_BLOCKS, length / threads + (length % threads != 0));
+  gCopyCastTo<<<blocks, threads>>>(out, in, length);
+}
+
+template <typename T>
+void CopyCastFrom(Tensor out, const T* in, int length) {
+  if(out->type() == Type::float32) {
+    CopyCastTo(out->data<float>(), in, length);
+  } else if(out->type() == Type::float16) {
+    CopyCastTo(out->data<half>(), in, length);
+  } else if(out->type() == Type::float64) {
+    CopyCastTo(out->data<double>(), in, length);
+  } else {
+    ABORT("CopyCastTo to type {} not implemented", out->type());
+  }
+}
+
+void CopyCast(Tensor out, const Tensor in) {
+  cudaSetDevice(out->getDeviceId().no);
+
+  if(in->type() == Type::float32) {
+    CopyCastFrom(out, in->data<float>(), (int)in->size());
+  } else if(in->type() == Type::float16) {
+    CopyCastFrom(out, in->data<half>(), (int)in->size());
+  } else if(in->type() == Type::float64) {
+    CopyCastFrom(out, in->data<double>(), (int)in->size());
+  } else {
+    ABORT("CopyCastFrom from type {} not implemented", in->type());
+  }
+}
+
 void ConcatCont(Tensor out, const std::vector<Tensor>& inputs, int axis) {
   cudaSetDevice(out->getDeviceId().no);
   int step = 1;
diff --git a/src/tensors/tensor_operators.h b/src/tensors/tensor_operators.h
index 5ee4c3031..a706c5ee6 100644
--- a/src/tensors/tensor_operators.h
+++ b/src/tensors/tensor_operators.h
@@ -34,7 +34,8 @@ void copy(Ptr<Backend> backend, const InIt beg, const InIt end, OutIt it) {
     std::copy(beg, end, it);
 }
 
-DISPATCH5(IsNan, const Tensor, Ptr<Allocator>, bool&, bool&, bool);
+DISPATCH2(CopyCast, marian::Tensor, const marian::Tensor);
+DISPATCH4(IsNan, const Tensor, Ptr<Allocator>, bool&, bool&);
 
 template <class Functor, class... Tensors>
 void Element(Functor functor, marian::Tensor out, Tensors... tensors) {
@@ -118,19 +119,19 @@ DISPATCH3(Concatenate, marian::Tensor, const std::vector<marian::Tensor>&, int)
 
 // clang-format on
 
-static inline void Bernoulli(Tensor resultTensor, float keepProb, float scale = 1.f) {
+// Bernoulli(tensor, 0.5f, 2.f, -1.f) generates a tensor composed of 50% of 1 and 50% of -1. 
+static inline void Bernoulli(Tensor resultTensor, float keepProb, float scale = 1.f, float shift = 0.f) {
   // in-place uniform distribution
   auto rnd = resultTensor->getBackend()->getRandomGenerator();
   rnd->uniform(resultTensor, 0.f, 1.f); // temporarily mis-use this to hold the random numbers
   using namespace functional;
-  Element(_1 = (_1 < keepProb) * scale, resultTensor);
+  Element(_1 = (_1 < keepProb) * scale + shift, resultTensor);
 }
 
-
 static inline void Dropout(Tensor tensor, float dropProb) {
   float keepProb = 1.f - dropProb;
   float scale = 1.f / keepProb;
-  Bernoulli(tensor, keepProb, scale);
+  Bernoulli(tensor, keepProb, scale, 0.f);
 }
 
 #ifdef CUDA_FOUND
diff --git a/src/tests/pooling.cpp b/src/tests/pooling.cpp
index 8f77cc7ba..27f3b5da5 100644
--- a/src/tests/pooling.cpp
+++ b/src/tests/pooling.cpp
@@ -42,10 +42,10 @@ int main(int argc, char** argv) {
   }
 
   auto x = graph->param("x", {dimBatch, dimWord, batchLength},
-                        inits::from_vector(embData));
+                        inits::fromVector(embData));
 
   auto xMask = graph->constant({dimBatch, 1, batchLength},
-                               inits::from_vector(embMask));
+                               inits::fromVector(embMask));
 
   // auto pooling = MaxPooling("pooling")(x, xMask);
   // auto idx = graph->constant({elemNum, 1}, inits::zeros);
diff --git a/src/tests/prod.cpp b/src/tests/prod.cpp
index 49b683039..232916eff 100644
--- a/src/tests/prod.cpp
+++ b/src/tests/prod.cpp
@@ -14,22 +14,22 @@ int main(int argc, char** argv) {
         for(int i = 0; i < 100; ++i) {
             g->clear();
 
-            auto x = g->constant({1, 4, 8, 256}, inits::glorot_uniform);
+            auto x = g->constant({1, 4, 8, 256}, inits::glorotUniform());
 
-            auto W1 = g->param("W1", {256, 2048}, inits::glorot_uniform);
-            auto b1 = g->param("b1", {1, 2048}, inits::glorot_uniform);
+            auto W1 = g->param("W1", {256, 2048}, inits::glorotUniform());
+            auto b1 = g->param("b1", {1, 2048}, inits::glorotUniform());
 
             auto out = affine(x, W1, b1);
 
             for(int i = 2; i < 20; ++i) {
-                auto Wi = g->param("W" + std::to_string(i), {2048, 2048}, inits::glorot_uniform);
-                auto bi = g->param("b" + std::to_string(i), {1, 2048}, inits::glorot_uniform);
+                auto Wi = g->param("W" + std::to_string(i), {2048, 2048}, inits::glorotUniform());
+                auto bi = g->param("b" + std::to_string(i), {1, 2048}, inits::glorotUniform());
 
                 out = relu(affine(out, Wi, bi));
             }
 
-            auto Wn = g->param("Wn", {2048, 256}, inits::glorot_uniform);
-            auto bn = g->param("bn", {1, 256}, inits::glorot_uniform);
+            auto Wn = g->param("Wn", {2048, 256}, inits::glorotUniform());
+            auto bn = g->param("bn", {1, 256}, inits::glorotUniform());
 
             auto y = affine(out, Wn, bn);
 
@@ -48,22 +48,22 @@ int main(int argc, char** argv) {
         for(int i = 0; i < 100; ++i) {
             g->clear();
 
-            auto x = g->constant({1, 4, 8, 256}, inits::glorot_uniform);
+            auto x = g->constant({1, 4, 8, 256}, inits::glorotUniform());
 
-            auto W1 = g->param("W1", {256, 2048}, inits::glorot_uniform);
-            auto b1 = g->param("b1", {1, 2048}, inits::glorot_uniform);
+            auto W1 = g->param("W1", {256, 2048}, inits::glorotUniform());
+            auto b1 = g->param("b1", {1, 2048}, inits::glorotUniform());
 
             auto out = affine(x, W1, b1);
 
             for(int i = 2; i < 20; ++i) {
-                auto Wi = g->param("W" + std::to_string(i), {2048, 2048}, inits::glorot_uniform);
-                auto bi = g->param("b" + std::to_string(i), {1, 2048}, inits::glorot_uniform);
+                auto Wi = g->param("W" + std::to_string(i), {2048, 2048}, inits::glorotUniform());
+                auto bi = g->param("b" + std::to_string(i), {1, 2048}, inits::glorotUniform());
 
                 out = relu(affine(out, Wi, bi));
             }
 
-            auto Wn = g->param("Wn", {2048, 256}, inits::glorot_uniform);
-            auto bn = g->param("bn", {1, 256}, inits::glorot_uniform);
+            auto Wn = g->param("Wn", {2048, 256}, inits::glorotUniform());
+            auto bn = g->param("bn", {1, 256}, inits::glorotUniform());
 
             auto y = affine(out, Wn, bn);
 
diff --git a/src/tests/units/attention_tests.cpp b/src/tests/units/attention_tests.cpp
index 6bd31069d..652dd4d2f 100644
--- a/src/tests/units/attention_tests.cpp
+++ b/src/tests/units/attention_tests.cpp
@@ -47,11 +47,11 @@ void tests(DeviceType type) {
 
     auto emb = graph->param("Embeddings",
                             {128, dimEmb},
-                            inits::glorot_uniform);
+                            inits::glorotUniform());
 
     auto input = reshape(rows(emb, vWords), {dimTime, dimBatch, dimEmb});
     auto mask = graph->constant({dimTime, dimBatch, 1},
-                                inits::from_vector(vMask));
+                                inits::fromVector(vMask));
 
     auto rnn = rnn::rnn()         //
           ("prefix", "rnntest")        //
@@ -76,7 +76,7 @@ void tests(DeviceType type) {
                   [](){ static int n = -32; return n++ / 64.f; });
 
     rnn::State state({graph->constant({1, 1, 4, 16},
-                                     inits::from_vector(vState)),
+                                     inits::fromVector(vState)),
                       nullptr});
 
     auto aligned = att->apply(state);
diff --git a/src/tests/units/graph_tests.cpp b/src/tests/units/graph_tests.cpp
index 822cb2e18..121969d33 100644
--- a/src/tests/units/graph_tests.cpp
+++ b/src/tests/units/graph_tests.cpp
@@ -25,7 +25,7 @@ TEST_CASE("Expression graph can be initialized with constant values",
   SECTION("initializing with zeros") {
     graph->clear();
     values.clear();
-    auto zeros = graph->param("0s", {2, 5}, inits::zeros);
+    auto zeros = graph->param("0s", {2, 5}, inits::zeros());
     graph->forward();
 
     zeros->val()->get(values);
@@ -35,7 +35,7 @@ TEST_CASE("Expression graph can be initialized with constant values",
   SECTION("initializing with ones") {
     graph->clear();
     values.clear();
-    auto ones = graph->param("1s", {2, 5}, inits::ones);
+    auto ones = graph->param("1s", {2, 5}, inits::ones());
     graph->forward();
 
     ones->val()->get(values);
@@ -46,7 +46,7 @@ TEST_CASE("Expression graph can be initialized with constant values",
     graph->clear();
     values.clear();
     std::vector<float> v({1, 2, 3, 4, 5, 6});
-    auto vals = graph->param("vs", {2, 3}, inits::from_vector(v));
+    auto vals = graph->param("vs", {2, 3}, inits::fromVector(v));
     graph->forward();
 
     REQUIRE(values.empty());
@@ -76,7 +76,7 @@ TEST_CASE("Expression graph can be initialized with constant values (cpu)",
   SECTION("initializing with zero (cpu)") {
     graph->clear();
     values.clear();
-    auto zeros = graph->param("0s", {2, 5}, inits::zeros);
+    auto zeros = graph->param("0s", {2, 5}, inits::zeros());
     graph->forward();
 
     zeros->val()->get(values);
@@ -86,7 +86,7 @@ TEST_CASE("Expression graph can be initialized with constant values (cpu)",
   SECTION("initializing with ones (cpu)") {
     graph->clear();
     values.clear();
-    auto ones = graph->param("1s", {2, 5}, inits::ones);
+    auto ones = graph->param("1s", {2, 5}, inits::ones());
     graph->forward();
 
     ones->val()->get(values);
@@ -97,7 +97,7 @@ TEST_CASE("Expression graph can be initialized with constant values (cpu)",
     graph->clear();
     values.clear();
     std::vector<float> v({1, 2, 3, 4, 5, 6});
-    auto vals = graph->param("vs", {2, 3}, inits::from_vector(v));
+    auto vals = graph->param("vs", {2, 3}, inits::fromVector(v));
     graph->forward();
 
     REQUIRE(values.empty());
diff --git a/src/tests/units/operator_tests.cpp b/src/tests/units/operator_tests.cpp
index 360da61b3..444532316 100644
--- a/src/tests/units/operator_tests.cpp
+++ b/src/tests/units/operator_tests.cpp
@@ -22,7 +22,7 @@ void tests(DeviceType device) {
     values.clear();
     std::vector<float> vB({1, 2, 3, 4, 5, 6});
 
-    auto B = graph->param("B", {3, 2}, inits::from_vector(vB));
+    auto B = graph->param("B", {3, 2}, inits::fromVector(vB));
     auto B2 = B * 2.0f;
     graph->forward();
 
@@ -40,8 +40,8 @@ void tests(DeviceType device) {
     std::vector<float> vA({1, -2, 3, -4});
     std::vector<float> vB({0.5, 1.5});
 
-    auto a = graph->constant({2, 2, 1}, inits::from_vector(vA));
-    auto b = graph->constant({2, 1}, inits::from_vector(vB));
+    auto a = graph->constant({2, 2, 1}, inits::fromVector(vA));
+    auto b = graph->constant({2, 1}, inits::fromVector(vB));
 
     auto compare = [&](Expr res, std::function<float(float,float)> f, bool exactMatch) -> bool {
       if (res->shape() != Shape({ 2, 2, 1 }))
@@ -93,7 +93,7 @@ void tests(DeviceType device) {
     std::vector<float> vT4({1, 5, 3, 7, 2, 6, 4, 8});
     std::vector<float> vT5({1, 2, 5, 6, 3, 4, 7, 8});
 
-    auto a = graph->constant({2, 4}, inits::from_vector(vA));
+    auto a = graph->constant({2, 4}, inits::fromVector(vA));
 
     auto t1 = transpose(a);
     auto t2 = transpose(t1);
@@ -144,7 +144,7 @@ void tests(DeviceType device) {
     std::vector<float> lsmOut({ -0.6444f, -0.7444f, -1.10319f, -0.40319f,
                                 -111.45f, 0.0f, -100.05001f, 0.0f });
 
-    auto input = graph->constant({2, 2, 2}, inits::from_vector(in));
+    auto input = graph->constant({2, 2, 2}, inits::fromVector(in));
 
     auto sm  = softmax(input);
     auto lsm = logsoftmax(input);
@@ -183,10 +183,10 @@ void tests(DeviceType device) {
     });
 #endif
 
-    auto a = graph->constant({2, 2, 4}, inits::glorot_uniform);
+    auto a = graph->constant({2, 2, 4}, inits::glorotUniform());
 
-    auto gamma = graph->param("gamma", {1, 4}, inits::ones);
-    auto beta = graph->param("beta", {1, 4}, inits::zeros);
+    auto gamma = graph->param("gamma", {1, 4}, inits::ones());
+    auto beta = graph->param("beta", {1, 4}, inits::zeros());
 
     auto ln = layerNorm(a, gamma, beta);
 
@@ -218,7 +218,7 @@ void tests(DeviceType device) {
     std::vector<float> vL9({8.13364336f, 7.17551536f});  // np.log(np.sum(np.exp(a), axis=1))
     std::vector<float> vW({5.0f, 4.55555556f});          // np.mean(a*s1,axis=-1) / np.mean(s1,axis=-1)
 
-    auto a = graph->constant({2, 4}, inits::from_vector(vA));
+    auto a = graph->constant({2, 4}, inits::fromVector(vA));
 
     auto s1 = sum(a, /*axis=*/ 0);
     auto s2 = sum(a, /*axis=*/ 1);
@@ -292,10 +292,10 @@ void tests(DeviceType device) {
                             3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,
                             4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4});
 
-    auto in1 = graph->constant({1, 2, 2, 3}, inits::from_value(1));
-    auto in2 = graph->constant({1, 2, 2, 3}, inits::from_value(2));
-    auto in3 = graph->constant({1, 2, 2, 3}, inits::from_value(3));
-    auto in4 = graph->constant({1, 2, 2, 3}, inits::from_value(4));
+    auto in1 = graph->constant({1, 2, 2, 3}, inits::fromValue(1));
+    auto in2 = graph->constant({1, 2, 2, 3}, inits::fromValue(2));
+    auto in3 = graph->constant({1, 2, 2, 3}, inits::fromValue(3));
+    auto in4 = graph->constant({1, 2, 2, 3}, inits::fromValue(4));
 
     auto c1out1 = concatenate({in1, in2, in3, in4}, /*axis=*/ 2);
     auto c1out2 = concatenate({in1, in2, in3, in4}, /*axis=*/ -1);
@@ -338,8 +338,8 @@ void tests(DeviceType device) {
                            76, 100,
                            103, 136});
 
-    auto A = graph->param("A", {2, 2, 3}, inits::from_vector(vA));
-    auto B = graph->param("B", {3, 2}, inits::from_vector(vB));
+    auto A = graph->param("A", {2, 2, 3}, inits::fromVector(vA));
+    auto B = graph->param("B", {3, 2}, inits::fromVector(vB));
     auto C = dot(A, B);
 
     CHECK(C->shape() == Shape({2, 2, 2}));
@@ -361,9 +361,9 @@ void tests(DeviceType device) {
                             4, 5, 6, 2.3, 6.7,
                             7, 8, 9, 3.4, 7.8,
                             1, 1, 2, 4.5, 8.9});
-      auto S  = graph->param("S",  { 2, 4 }, inits::from_vector(vS));
-      auto D  = graph->param("D",  { 4, 5 }, inits::from_vector(vD));
-      auto DT = graph->param("DT", { 5, 4 }, inits::from_vector(vD)); // example matrix with transposed dimensions
+      auto S  = graph->param("S",  { 2, 4 }, inits::fromVector(vS));
+      auto D  = graph->param("D",  { 4, 5 }, inits::fromVector(vD));
+      auto DT = graph->param("DT", { 5, 4 }, inits::fromVector(vD)); // example matrix with transposed dimensions
       std::vector<float> SV;    // create CSR version of S
       std::vector<IndexType> SI, SO;
       SO.push_back((IndexType)SI.size());
@@ -382,15 +382,15 @@ void tests(DeviceType device) {
       auto STxSxDd = dot(S, SxDd, /*transA=*/true);
       auto SxDs = csr_dot( // sparse x dense
             S->shape(),
-            graph->constant({(int)SV.size()}, inits::from_vector(SV), Type::float32),
-            graph->constant({(int)SI.size()}, inits::from_vector(SI), Type::uint32),
-            graph->constant({(int)SO.size()}, inits::from_vector(SO), Type::uint32),
+            graph->constant({(int)SV.size()}, inits::fromVector(SV), Type::float32),
+            graph->constant({(int)SI.size()}, inits::fromVector(SI), Type::uint32),
+            graph->constant({(int)SO.size()}, inits::fromVector(SO), Type::uint32),
             D);
       auto STxSxDs = csr_dot(   // transpose(sparse) x dense; we use result of previous since dimensions match
             S->shape(),
-            graph->constant({(int)SV.size()}, inits::from_vector(SV), Type::float32),
-            graph->constant({(int)SI.size()}, inits::from_vector(SI), Type::uint32),
-            graph->constant({(int)SO.size()}, inits::from_vector(SO), Type::uint32),
+            graph->constant({(int)SV.size()}, inits::fromVector(SV), Type::float32),
+            graph->constant({(int)SI.size()}, inits::fromVector(SI), Type::uint32),
+            graph->constant({(int)SO.size()}, inits::fromVector(SO), Type::uint32),
             SxDd, /*transS=*/true);
 
       auto DTxSTd   = dot(DT,     S, /*transA=*/false, /*transB=*/true);
@@ -398,16 +398,16 @@ void tests(DeviceType device) {
       auto DTxSTs = dot_csr( // dense x sparse
             DT,
             S->shape(),
-            graph->constant({(int)SV.size()}, inits::from_vector(SV), Type::float32),
-            graph->constant({(int)SI.size()}, inits::from_vector(SI), Type::uint32),
-            graph->constant({(int)SO.size()}, inits::from_vector(SO), Type::uint32),
+            graph->constant({(int)SV.size()}, inits::fromVector(SV), Type::float32),
+            graph->constant({(int)SI.size()}, inits::fromVector(SI), Type::uint32),
+            graph->constant({(int)SO.size()}, inits::fromVector(SO), Type::uint32),
             /*transS=*/true);
       auto DTxSTxSs = dot_csr( // dense x transpose(sparse)
             DTxSTd,
             S->shape(),
-            graph->constant({(int)SV.size()}, inits::from_vector(SV), Type::float32),
-            graph->constant({(int)SI.size()}, inits::from_vector(SI), Type::uint32),
-            graph->constant({(int)SO.size()}, inits::from_vector(SO), Type::uint32));
+            graph->constant({(int)SV.size()}, inits::fromVector(SV), Type::float32),
+            graph->constant({(int)SI.size()}, inits::fromVector(SI), Type::uint32),
+            graph->constant({(int)SO.size()}, inits::fromVector(SO), Type::uint32));
 
       CHECK(SxDs->shape() == SxDd->shape());
       CHECK(STxSxDs->shape() == STxSxDd->shape());
@@ -432,9 +432,9 @@ void tests(DeviceType device) {
     std::vector<float> vB({1, 2, 3, 4, 5, 6});
     std::vector<float> vAff({24, 30, 51, 66, 78, 102, 105, 138});
 
-    auto A = graph->param("A", {4, 3}, inits::from_vector(vA));
-    auto B = graph->param("B", {3, 2}, inits::from_vector(vB));
-    auto C = graph->param("C", {4, 2}, inits::from_value(2));
+    auto A = graph->param("A", {4, 3}, inits::fromVector(vA));
+    auto B = graph->param("B", {3, 2}, inits::fromVector(vB));
+    auto C = graph->param("C", {4, 2}, inits::fromValue(2));
     auto aff1 = affine(A, B, C);
     auto aff2 = dot(A, B) + C;
     graph->forward();
@@ -457,7 +457,7 @@ void tests(DeviceType device) {
     std::vector<float> vB({1, 2, 3, 4, 5, 6, 1, 2, 3, 4, 5, 6});
     std::vector<float> vC({1, 2, 3, 1, 2, 3, 4, 5, 6, 4, 5, 6});
 
-    auto A = graph->param("A", {2,3}, inits::from_vector(vA));
+    auto A = graph->param("A", {2,3}, inits::fromVector(vA));
     auto I = repeat(A, 1, 0);
     auto B = repeat(A, 2, 0);
     auto C = repeat(A, 2, 1);
@@ -482,9 +482,9 @@ void tests(DeviceType device) {
 
     std::vector<float> vIn({1, 2, 3, 4, 5, 6, 7, 8});
 
-    auto A = graph->param("A", {2, 4}, inits::from_vector(vIn));
+    auto A = graph->param("A", {2, 4}, inits::fromVector(vIn));
     auto Af = flatten(A);
-    auto B = graph->param("B", {2, 2, 1, 2}, inits::from_vector(vIn));
+    auto B = graph->param("B", {2, 2, 1, 2}, inits::fromVector(vIn));
     auto Bf = flatten(B);
     graph->forward();
 
@@ -517,7 +517,7 @@ void tests(DeviceType device) {
     std::vector<float> vB4({4, 5, 6, 4, 5, 6});
     std::vector<float> vB6;
 
-    auto A = graph->param("A", {4, 3}, inits::from_vector(vA));
+    auto A = graph->param("A", {4, 3}, inits::fromVector(vA));
     auto B0 = rows(A, iB0);
     auto B1 = rows(A, iB1);
     auto B2 = rows(A, iB2);
@@ -577,7 +577,7 @@ void tests(DeviceType device) {
     std::vector<float> vB4({2, 2, 6, 6, 10, 10});
     std::vector<float> vB6;
 
-    auto A = graph->param("A", {3, 4}, inits::from_vector(vA));
+    auto A = graph->param("A", {3, 4}, inits::fromVector(vA));
     auto B0 = cols(A, iB0);
     auto B1 = cols(A, iB1);
     auto B2 = cols(A, iB2);
@@ -624,10 +624,10 @@ void tests(DeviceType device) {
     std::vector<float> vA({0, .3333, -.2, -.3, 0, 4.5, 5.2, -10, 101.45, -100.05, 0, 1.05e-5});
     std::vector<IndexType> idx({0, 1});
 
-    auto A1 = graph->param("4x3", {4,3}, inits::from_vector(vA));
+    auto A1 = graph->param("4x3", {4,3}, inits::fromVector(vA));
     auto B1 = rows(transpose(A1), idx);
     auto C1 = transpose(cols(A1, idx));
-    auto A2 = graph->param("6x2", {6,2}, inits::from_vector(vA));
+    auto A2 = graph->param("6x2", {6,2}, inits::fromVector(vA));
     auto B2 = cols(transpose(A2), idx);
     auto C2 = transpose(rows(A2, idx));
     graph->forward();
@@ -675,7 +675,7 @@ void tests(DeviceType device) {
     std::vector<float> vS2({-4, 5, -6, 7, -8, 9});
     std::vector<float> vS3({7, -8, 9, -10, 11, -12});
 
-    auto A = graph->param("4x3", {4,3}, inits::from_vector(vA));
+    auto A = graph->param("4x3", {4,3}, inits::fromVector(vA));
     auto B1a = index_select(A, 0, IndexVector({0})); // always uses gather()
     auto B1b = slice(A,  0, 0);                        // memory-consecutive view
     auto B2  = slice(A,  1, 0);                        // not memory-consecutive
@@ -690,7 +690,7 @@ void tests(DeviceType device) {
     CHECK(B4b->type() == "sliceView"); // must use view
     CHECK(B5.get() == A.get());        // must be no-op
 
-    auto C = graph->param("2x3x2", {2, 3, 2}, inits::from_vector(vC));
+    auto C = graph->param("2x3x2", {2, 3, 2}, inits::fromVector(vC));
     auto D1 = slice(C,  0, 0);
     auto D2 = slice(C, -2, 2);
     auto D3 = index_select(C, 1, IndexVector({0, 2})); // C[:,(0,2),:]
@@ -698,7 +698,7 @@ void tests(DeviceType device) {
     CHECK(D2->type() == "gather");
     // enable this once gather() supports batched indices:
     //auto D4 = gather(C, 1, graph->constant({2, 2, 1}, // [C[0,(2,1),:],C[1,(0,2),:]]
-    //                                       inits::from_vector(std::vector<IndexType>{
+    //                                       inits::fromVector(std::vector<IndexType>{
     //                                         2, 1,
     //                                         0, 2 }),
     //                                       Type::uint32));
@@ -734,7 +734,7 @@ void tests(DeviceType device) {
     std::vector<float> vA({0, .3333, -.2, -.3, 0, 4.5, 5.2, -10, 101.45, -100.05, 0, 1.05e-5});
     std::vector<IndexType> indices({0, 2});
 
-    auto A = graph->param("4x3", {4, 3}, inits::from_vector(vA));
+    auto A = graph->param("4x3", {4, 3}, inits::fromVector(vA));
     auto B1 = rows(A, indices);
     auto B2 = gather(A, 0, graph->indices(indices, A, 0));
     auto C1 = cols(A, indices);
diff --git a/src/tests/units/rnn_tests.cpp b/src/tests/units/rnn_tests.cpp
index 2b4fda0b9..8999d3d70 100644
--- a/src/tests/units/rnn_tests.cpp
+++ b/src/tests/units/rnn_tests.cpp
@@ -41,7 +41,7 @@ void tests(DeviceType type) {
     std::vector<float> values;
 
     auto input = graph->constant({4, 1, 4},
-                                 inits::glorot_uniform);
+                                 inits::glorotUniform());
 
     auto rnn = rnn::rnn()         //
           ("prefix", "rnntest")   //
@@ -200,11 +200,11 @@ void tests(DeviceType type) {
 
     auto emb = graph->param("Embeddings",
                             {128, dimEmb},
-                            inits::glorot_uniform);
+                            inits::glorotUniform());
 
     auto input = reshape(rows(emb, vWords), {dimTime, dimBatch, dimEmb});
     auto mask = graph->constant({dimTime, dimBatch, 1},
-                                inits::from_vector(vMask));
+                                inits::fromVector(vMask));
 
     int dimRnn = 32;
     auto context1 = buildRnn("enc1", input, mask, dimRnn);
diff --git a/src/translator/beam_search.h b/src/translator/beam_search.h
index e014bb08c..8e1bcf9cb 100755
--- a/src/translator/beam_search.h
+++ b/src/translator/beam_search.h
@@ -311,7 +311,7 @@ class BeamSearch {
         Expr prevPathScores;               // [localBeamSize, 1, dimBatch, 1], path score that a hyp ended in (last axis will broadcast into vocab size when adding expandedPathScores)
         bool anyCanExpand = false; // stays false if all hyps are invalid factor expansions
         if(t == 0 && factorGroup == 0) { // no scores yet
-          prevPathScores = graph->constant({1, 1, 1, 1}, inits::from_value(0));
+          prevPathScores = graph->constant({1, 1, 1, 1}, inits::fromValue(0));
           anyCanExpand = true;
         } else {
           std::vector<float> prevScores;
@@ -334,7 +334,7 @@ class BeamSearch {
               }
             }
           }
-          prevPathScores = graph->constant({(int)localBeamSize, 1, dimBatch, 1}, inits::from_vector(prevScores));
+          prevPathScores = graph->constant({(int)localBeamSize, 1, dimBatch, 1}, inits::fromVector(prevScores));
         }
         if (!anyCanExpand) // all words cannot expand this factor: skip
           continue;

From a71fc51304bbde07e6368a2781b0ceb888536b1b Mon Sep 17 00:00:00 2001
From: Marcin Junczys-Dowmunt <Marcin.JunczysDowmunt@microsoft.com>
Date: Tue, 10 Sep 2019 14:45:13 -0700
Subject: [PATCH 604/838] update some type checks

---
 src/graph/node_operators_binary.h   |  32 ++---
 src/tensors/gpu/tensor_operators.cu | 180 ++++++++++++++++++----------
 src/tensors/tensor_operators.h      |  47 +++++++-
 3 files changed, 182 insertions(+), 77 deletions(-)

diff --git a/src/graph/node_operators_binary.h b/src/graph/node_operators_binary.h
index 5dbf64c82..0f05dfd08 100644
--- a/src/graph/node_operators_binary.h
+++ b/src/graph/node_operators_binary.h
@@ -211,7 +211,6 @@ class AffineNodeOp : public NaryNodeOp {
                       scalar_)),
           NodeOp(Prod(
               child(2)->grad(), child(3)->val(), adj_, true, false, 0.f, 1.f))
-          // NodeOp(Add(_1, child(2)->grad(), adj_))
       };
 
     if(transA_ && !transB_)
@@ -232,7 +231,6 @@ class AffineNodeOp : public NaryNodeOp {
                       scalar_)),
           NodeOp(Prod(
               child(2)->grad(), child(3)->val(), adj_, true, false, 0.f, 1.f))
-          // NodeOp(Add(_1, child(2)->grad(), adj_))
       };
 
     if(transA_ && transB_)
@@ -253,7 +251,6 @@ class AffineNodeOp : public NaryNodeOp {
                       scalar_)),
           NodeOp(Prod(
               child(2)->grad(), child(3)->val(), adj_, true, false, 0.f, 1.f))
-          // NodeOp(Add(_1, child(2)->grad(), adj_))
       };
 
     return {
@@ -273,7 +270,6 @@ class AffineNodeOp : public NaryNodeOp {
                     scalar_)),
         NodeOp(Prod(
             child(2)->grad(), child(3)->val(), adj_, true, false, 0.f, 1.f))
-        // NodeOp(Add(_1, child(2)->grad(), adj_))
     };
   }
 
@@ -416,8 +412,8 @@ class CSRDotNodeOp : public NaryNodeOp {
   bool swapOperands_;
 public:
   CSRDotNodeOp(const Shape& S_shape, Expr S_values, Expr S_indices, Expr S_offsets, Expr D, bool transS, bool swapOperands)
-      : NaryNodeOp({ S_values, S_indices, S_offsets, D }, newShape(S_shape, S_values, S_indices, S_offsets, D, transS, swapOperands)),
-                   transS_(transS), swapOperands_(swapOperands){
+      : NaryNodeOp({ S_values, S_indices, S_offsets, D }, newShape(S_shape, S_values, S_indices, S_offsets, D, transS, swapOperands), commonType({S_values, D})),
+                   transS_(transS), swapOperands_(swapOperands) {
     matchOrAbort<IndexType>(S_indices->value_type());
     matchOrAbort<IndexType>(S_offsets->value_type());
   }
@@ -513,8 +509,8 @@ struct ScalarProductNodeOp : public NaryNodeOp {
 
 struct RowsNodeOp : public NaryNodeOp {
   RowsNodeOp(Expr a, Expr indices)
-      : NaryNodeOp({a, indices}, newShape(a, indices->shape().elements()), a->value_type()) {
-    matchOrAbort<IndexType>(indices->value_type());
+    : NaryNodeOp({a, indices}, newShape(a, indices), a->value_type()) {
+      matchOrAbort<IndexType>(indices->value_type());
   }
 
   NodeOps forwardOps() override {
@@ -526,11 +522,11 @@ struct RowsNodeOp : public NaryNodeOp {
     return {NodeOp(PasteRows(child(0)->grad(), adj_, child(1)->val()))};
   }
 
-  Shape newShape(Expr a, size_t num) {
+  Shape newShape(Expr a, Expr indices) {
     Shape shape = a->shape();
     ABORT_IF(shape.size() != 2,
              "rows operator can only be used with 2-dimensional tensors");
-    shape.set(0, num);
+    shape.set(0, (int)indices->shape().elements());
     return shape;
   }
 
@@ -641,7 +637,7 @@ struct GatherNodeOp : public NaryNodeOp {
 
 struct ColsNodeOp : public NaryNodeOp {
   ColsNodeOp(Expr a, Expr indices)
-    : NaryNodeOp({a, indices}, newShape(a, indices->shape().elements()), a->value_type()) {
+    : NaryNodeOp({a, indices}, newShape(a, indices), a->value_type()) {
     matchOrAbort<IndexType>(indices->value_type());
   }
 
@@ -653,9 +649,9 @@ struct ColsNodeOp : public NaryNodeOp {
     return {NodeOp(PasteCols(child(0)->grad(), adj_, child(1)->val()))};
   }
 
-  Shape newShape(Expr a, size_t num) {
+  Shape newShape(Expr a, Expr indices) {
     Shape shape = a->shape();
-    shape.set(1, num);
+    shape.set(1, (int)indices->shape().elements());
     return shape;
   }
 
@@ -666,7 +662,8 @@ struct ColsNodeOp : public NaryNodeOp {
 
 
 struct ElementBinaryNodeOp : public NaryNodeOp {
-  ElementBinaryNodeOp(Expr a, Expr b) : NaryNodeOp({a, b}, newShape(a, b)) {}
+  ElementBinaryNodeOp(Expr a, Expr b)
+   : NaryNodeOp({a, b}, newShape(a, b)) {}
 
   Shape newShape(Expr a, Expr b) { return Shape::broadcast({a, b}); }
 
@@ -900,7 +897,8 @@ struct CmpNodeOp : public ElementBinaryNodeOp {
 // that matches the label indexed by i (the picked element).
 // C = sum_{v in V}(-logsoftmax(A) * delta(v, i) = -logsoftmax(A)[i]
 struct CrossEntropyNodeOp : public NaryNodeOp {
-  CrossEntropyNodeOp(Expr a, Expr indices) : NaryNodeOp({a, indices}, newShape(a)) {
+  CrossEntropyNodeOp(Expr a, Expr indices)
+    : NaryNodeOp({a, indices}, newShape(a), a->value_type()) {
     matchOrAbort<IndexType>(indices->value_type());
     int rows   = a->shape().elements() / a->shape()[-1];
     int labels = indices->shape().elements();
@@ -1005,7 +1003,9 @@ struct LayerNormalizationOp : public NaryNodeOp {
   }
 
   NodeOps backwardOps() override {
-    return {NodeOp(LayerNormalizationGrad(
+    return {NodeOp(
+      LayerNormalizationGrad(
+        graph()->allocator(),
         child(0)->grad(),
         child(1)->grad(),
         (children_.size() == 3) ? child(2)->grad() : nullptr,
diff --git a/src/tensors/gpu/tensor_operators.cu b/src/tensors/gpu/tensor_operators.cu
index bd54bd94c..b336a84e0 100755
--- a/src/tensors/gpu/tensor_operators.cu
+++ b/src/tensors/gpu/tensor_operators.cu
@@ -1632,44 +1632,52 @@ void LayerNormalization(Tensor out,
                                                eps);
 }
 
-__global__ void gLayerNormalizationGrad(float* gradX,
-                                        float* gradGamma,
-                                        float* gradBeta,
-                                        float* adj,
-                                        float* y,
-                                        float* x,
-                                        float* gamma,
-                                        float* beta,
+template <typename T, typename AccType = float>
+__global__ void gLayerNormalizationGrad(T* gradX,
+                                        T* gradGamma,
+                                        T* adj,
+                                        T* y,
+                                        T* x,
+                                        T* gamma,
+                                        T* beta,
                                         int rows,
                                         int cols,
-                                        float eps = 1e-9) {
-  extern __shared__ float shared[];
+                                        AccType eps = 1e-9) {
+  extern __shared__ uint8_t sharedBytes[];
+  AccType* shared = (AccType*)sharedBytes;
+
+  AccType N = cols;
 
   for(int bid = 0; bid < rows; bid += gridDim.x) {
     int j = bid + blockIdx.x;
     if(j < rows) {
-      float* sum_adj = shared;
-      float* sum_adj_x = shared + blockDim.x;
-      float* sum_x = shared + 2 * blockDim.x;
-      float* sum_sqr = shared + 3 * blockDim.x;
+      AccType* sum_adj   = shared;
+      AccType* sum_adj_x = shared +     blockDim.x;
+      AccType* sum_x     = shared + 2 * blockDim.x;
+      AccType* sum_sqr   = shared + 3 * blockDim.x;
 
-      const float* xRow = x + j * cols;
-      const float* yRow = y + j * cols;
-      const float* adjRow = adj + j * cols;
-      float* gradXRow = gradX + j * cols;
+      const T* xRow   =   x + j * cols;
+      const T* yRow   =   y + j * cols;
+      const T* adjRow = adj + j * cols;
 
-      sum_x[threadIdx.x] = 0.0f;
-      sum_adj[threadIdx.x] = 0.0f;
-      sum_adj_x[threadIdx.x] = 0.0f;
-      sum_sqr[threadIdx.x] = 0.0f;
+      sum_x[threadIdx.x]     = (AccType)0.0f;
+      sum_adj[threadIdx.x]   = (AccType)0.0f;
+      sum_adj_x[threadIdx.x] = (AccType)0.0f;
+      sum_sqr[threadIdx.x]   = (AccType)0.0f;
 
       for(int tid = 0; tid < cols; tid += blockDim.x) {
         int id = tid + threadIdx.x;
         if(id < cols) {
-          sum_x[threadIdx.x] += xRow[id];
-          sum_adj_x[threadIdx.x]
-              += adjRow[id] * (yRow[id] - ((beta) ? beta[id] : 0)) / gamma[id];
-          sum_adj[threadIdx.x] += adjRow[id];
+          AccType xv     = xRow[id];
+          AccType yv     = yRow[id];
+          AccType betav  = beta ? (AccType)beta[id] : (AccType)0.f;
+          AccType gammav = (AccType)gamma[id];
+          AccType adjv   = adjRow[id];
+          AccType lv     = (yv - betav) / (gammav + eps); // go back to LN(x) from scaled and shifted version for accumulation
+
+          sum_x[threadIdx.x]     += xv;
+          sum_adj_x[threadIdx.x] += adjv * lv;
+          sum_adj[threadIdx.x]   += adjv;
         }
       }
       __syncthreads();
@@ -1678,20 +1686,21 @@ __global__ void gLayerNormalizationGrad(float* gradX,
         __syncthreads();
         int skip = (len + 1) >> 1;
         if(threadIdx.x < (len >> 1)) {
-          sum_x[threadIdx.x] += sum_x[threadIdx.x + skip];
-          sum_adj[threadIdx.x] += sum_adj[threadIdx.x + skip];
-          sum_adj_x[threadIdx.x] += sum_adj_x[threadIdx.x + skip];
+          sum_x[threadIdx.x]     += sum_x[threadIdx.x     + skip]; // Accumulates in AccType
+          sum_adj[threadIdx.x]   += sum_adj[threadIdx.x   + skip]; // Accumulates in AccType
+          sum_adj_x[threadIdx.x] += sum_adj_x[threadIdx.x + skip]; // Accumulates in AccType
         }
         len = (len + 1) >> 1;
       }
       __syncthreads();
-      float mean = sum_x[0] / cols;
+      AccType mean = sum_x[0] / N;
       __syncthreads();
 
       for(int tid = 0; tid < cols; tid += blockDim.x) {
         int id = tid + threadIdx.x;
         if(id < cols) {
-          float ex = xRow[id] - mean;
+          AccType xv = xRow[id];
+          AccType ex = xv - mean;
           sum_sqr[threadIdx.x] += ex * ex;
         }
       }
@@ -1702,32 +1711,45 @@ __global__ void gLayerNormalizationGrad(float* gradX,
         __syncthreads();
         int skip = (len + 1) >> 1;
         if(threadIdx.x < (len >> 1))
-          sum_sqr[threadIdx.x] += sum_sqr[threadIdx.x + skip];
+          sum_sqr[threadIdx.x] += sum_sqr[threadIdx.x + skip]; // Accumulates in AccType
         len = (len + 1) >> 1;
       }
       __syncthreads();
-      float sigma = sqrtf(eps + (sum_sqr[0] / cols));
+      AccType sigma = functional::Ops<AccType>::sqrt(sum_sqr[0] / N);
       __syncthreads();
 
+      // Jacobian of layer norm
+      // J = [ \frac{1}{N\sigma} (N\delta_{ij} - l_i l_j - 1) ]_{ij}
+      // J * a = dC/dx_i = ( N v_i - l_i \sum_j l_j a_j - \sum_j a_j ) / (N \sigma)
+
       for(int tid = 0; tid < cols; tid += blockDim.x) {
         int id = tid + threadIdx.x;
         if(id < cols) {
-          float grad_x = 0.0f;
-          float x_hat = (yRow[id] - ((beta) ? beta[id] : 0)) / gamma[id];
-          grad_x += cols * adjRow[id];
-          grad_x -= sum_adj[0];
-          grad_x -= sum_adj_x[0] * x_hat;
-          grad_x /= (cols * sigma);
-
-          float valX = gamma[id] * grad_x;
-          float sign = (0.f < valX) - (valX < 0.f);
-          valX = fabs(valX) > 1000 ? sign * 1000 : valX;
-
-          gradXRow[id] += valX;
-          atomicAdd(gradGamma + id, adjRow[id] * x_hat);
-          if(beta) {
-            atomicAdd(gradBeta + id, adjRow[id]);
-          }
+
+          AccType xv     = xRow[id];
+          //AccType yv     = yRow[id];
+          //AccType betav  = beta ? (AccType)beta[id] : (AccType)0.f;
+          AccType gammav = (AccType)gamma[id];
+          AccType adjv   = adjRow[id];
+          AccType lv     = (xv - mean) / (sigma + eps);
+
+          AccType gradLv = N * adjv - lv * sum_adj_x[0] - sum_adj[0];
+          gradLv        /= N * (sigma + eps); // eps has to be inside parentheses for correct gradient
+
+          AccType gradXv = gammav * gradLv;
+
+          // Keep LN gradient between [-10, 10]
+          // AccType sign = functional::Ops<AccType>::sgn(gradXv);
+          // AccType cutoff = (AccType)10.f;
+          // gradXv = functional::Ops<AccType>::abs(gradXv) > cutoff ? sign * cutoff : gradXv;
+
+          T* gradXRow      = gradX     + j * cols;
+          gradXRow[id]    += (T)(gradXv);
+
+          T* gradGammaRow  = gradGamma + j * cols;
+          // assignment is correct here as this gets summed up
+          // in the next kernel via matrix product
+          gradGammaRow[id] = (T)(adjv * lv);
         }
       }
     }
@@ -1735,7 +1757,8 @@ __global__ void gLayerNormalizationGrad(float* gradX,
   }
 }
 
-void LayerNormalizationGrad(Tensor gradX,
+void LayerNormalizationGrad(Ptr<Allocator> allocator,
+                            Tensor gradX,
                             Tensor gradGamma,
                             Tensor gradBeta,
                             Tensor adj,
@@ -1750,20 +1773,57 @@ void LayerNormalizationGrad(Tensor gradX,
 
   int threads = std::min(MAX_THREADS, cols);
   int blocks = std::min(MAX_BLOCKS, rows);
-  int shared = sizeof(float) * threads * 4;
 
-  gLayerNormalizationGrad<<<blocks, threads, shared>>>(
-      gradX->data(),
-      gradGamma->data(),
-      (gradBeta) ? gradBeta->data() : nullptr,
-      adj->data(),
-      y->data(),
-      x->data(),
-      gamma->data(),
-      (beta) ? beta->data() : nullptr,
+  IPtr<MemoryPiece> tempGradGammaMemory = allocator->alloc(adj->memory()->size(), adj->type());
+  Tensor tempGradGamma = TensorBase::New(tempGradGammaMemory, adj->shape(), adj->type(), adj->getBackend());
+  tempGradGamma->set(0.f);
+
+  IPtr<MemoryPiece> tempOnesMemory = allocator->alloc(rows * sizeOf(adj->type()), adj->type());
+  Tensor tempOnes = TensorBase::New(tempOnesMemory, Shape({1, rows}), adj->type(), adj->getBackend());
+  tempOnes->set(1.f);
+
+  if(gradX->type() == Type::float32) {
+    int shared = sizeof(float) * threads * 4;
+    gLayerNormalizationGrad<float, float><<<blocks, threads, shared>>>(
+      gradX->data<float>(),
+      tempGradGamma->data<float>(),
+      adj->data<float>(),
+      y->data<float>(),
+      x->data<float>(),
+      gamma->data<float>(),
+      (beta) ? beta->data<float>() : nullptr,
       rows,
       cols,
       eps);
+#if 0
+  } else if (gradX->type() == Type::float16) {
+    // accumulate in float
+    int shared = sizeof(float) * threads * 4;
+    gLayerNormalizationGrad<half, float><<<blocks, threads, shared>>>(
+      gradX->data<half>(),
+      tempGradGamma->data<half>(),
+      adj->data<half>(),
+      y->data<half>(),
+      x->data<half>(),
+      gamma->data<half>(),
+      (beta) ? beta->data<half>() : nullptr,
+      rows,
+      cols,
+      eps);
+#endif
+  } else {
+    ABORT("LayerNormalizationGrad not implemented for type {}", gradX->type());
+  }
+
+  // We use this go get rid of the atomicAdd and perform a reduce of the gradients afterwards.
+  // This is much faster for fp16 which seems to have a broken atomicAdd implementation
+  gpu::Prod(gradGamma, tempOnes, tempGradGamma, false, false, 1, 1); // beta set to one to add
+
+  if(gradBeta) // dC/dbeta = adj - inverse broadcasting (reduction)
+    gpu::Prod(gradBeta, tempOnes, adj, false, false, 1, 1); // beta set to one to add
+
+  allocator->free(tempGradGammaMemory);
+  allocator->free(tempOnesMemory);
 }
 
 template <bool add>
diff --git a/src/tensors/tensor_operators.h b/src/tensors/tensor_operators.h
index a706c5ee6..74597c2df 100644
--- a/src/tensors/tensor_operators.h
+++ b/src/tensors/tensor_operators.h
@@ -161,7 +161,52 @@ static inline void Deconcatenate(std::vector<marian::Tensor>& outputs,
 
 // clang-format off
 DISPATCH5(LayerNormalization, marian::Tensor, marian::Tensor, marian::Tensor, marian::Tensor, float)
-DISPATCH9(LayerNormalizationGrad, marian::Tensor, marian::Tensor, marian::Tensor, marian::Tensor, marian::Tensor, marian::Tensor, marian::Tensor, marian::Tensor, float)
+
+#ifdef CUDA_FOUND
+namespace gpu {
+void LayerNormalizationGrad(Ptr<Allocator> allocator,
+                            Tensor gradX,
+                            Tensor gradGamma,
+                            Tensor gradBeta,
+                            Tensor adj,
+                            Tensor y,
+                            Tensor x,
+                            Tensor gamma,
+                            Tensor beta,
+                            float eps);
+}
+#endif
+
+namespace cpu {
+void LayerNormalizationGrad(Tensor gradX,
+                            Tensor gradGamma,
+                            Tensor gradBeta,
+                            Tensor adj,
+                            Tensor y,
+                            Tensor x,
+                            Tensor gamma,
+                            Tensor beta,
+                            float eps);
+}
+
+static inline void LayerNormalizationGrad(
+                            Ptr<Allocator> allocator,
+                            Tensor gradX,
+                            Tensor gradGamma,
+                            Tensor gradBeta,
+                            Tensor adj,
+                            Tensor y,
+                            Tensor x,
+                            Tensor gamma,
+                            Tensor beta,
+                            float eps) {
+#ifdef CUDA_FOUND
+  if(gradX->getBackend()->getDeviceId().type == DeviceType::gpu)
+    gpu::LayerNormalizationGrad(allocator, gradX, gradGamma, gradBeta, adj, y, x, gamma, beta, eps);
+  else
+#endif
+    cpu::LayerNormalizationGrad(gradX, gradGamma, gradBeta, adj, y, x, gamma, beta, eps);
+}
 
 DISPATCH4(HighwayForward, marian::Tensor, const marian::Tensor, const marian::Tensor, const marian::Tensor)
 DISPATCH7(HighwayBackward, marian::Tensor, marian::Tensor, marian::Tensor, const marian::Tensor, const marian::Tensor, const marian::Tensor, const marian::Tensor)

From 9eda116238343278820d5d4c18816580ff0ab43a Mon Sep 17 00:00:00 2001
From: Marcin Junczys-Dowmunt <Marcin.JunczysDowmunt@microsoft.com>
Date: Tue, 10 Sep 2019 14:47:46 -0700
Subject: [PATCH 605/838] correct regression test versoin

---
 regression-tests | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/regression-tests b/regression-tests
index 71b0506a5..232e21d91 160000
--- a/regression-tests
+++ b/regression-tests
@@ -1 +1 @@
-Subproject commit 71b0506a50e1f181e269fe90073a8ae470676dd1
+Subproject commit 232e21d912681e78969e93ed18030f6e7010f8d7

From e7479d9c7c30a3bed5d98e9bf233b5ed50c51537 Mon Sep 17 00:00:00 2001
From: Marcin Junczys-Dowmunt <Marcin.JunczysDowmunt@microsoft.com>
Date: Tue, 10 Sep 2019 15:00:45 -0700
Subject: [PATCH 606/838] add more comments

---
 src/graph/node_initializers.h | 26 ++++++++++++++++++++++++++
 1 file changed, 26 insertions(+)

diff --git a/src/graph/node_initializers.h b/src/graph/node_initializers.h
index 6c8070b04..fe88c2c2d 100644
--- a/src/graph/node_initializers.h
+++ b/src/graph/node_initializers.h
@@ -120,20 +120,46 @@ Ptr<NodeInitializer> normal(float mean = 0.f, float stddev = 1.f);
  */
 Ptr<NodeInitializer> uniform(float a = 0.f, float b = 1.f);
 
+// @TODO: add documentation
 Ptr<NodeInitializer> bernoulli(float p, float scale = 1.f, float shift = 0.f);
+
+// @TODO: add documentation
 Ptr<NodeInitializer> glorotUniform(bool fanIn = false, bool fanOut = false, float scale = 1.f);
+
+// @TODO: add documentation
 Ptr<NodeInitializer> glorotNormal(bool fanIn = false, bool fanOut = false, float scale = 1.f);
 
+// @TODO: add documentation
 Ptr<NodeInitializer> dropout(float dropoutProbabilty);
+
+/**
+ * Fill with gumbel noise, i.e. -log(-log(u)) where u ~ U(0 + eps, 1 - eps)
+ * 
+ * @return A NodeInitializer which can be called on any tensor
+ */
 Ptr<NodeInitializer> gumbel(float eps = 1e-5f);
+
+/** 
+ * Don't do anything, used as a placeholder
+ * 
+ * @return A NodeInitializer that wraps an empty lambda
+ */
 Ptr<NodeInitializer> dummy();
 
+// @TODO: add documentation
 template <typename T>
 Ptr<NodeInitializer> fromVector(const std::vector<T>& v);
+
+// @TODO: add documentation
 Ptr<NodeInitializer> fromSparseVector(std::pair<std::vector<size_t>, std::vector<float>>& v);
+
+// @TODO: add documentation
 Ptr<NodeInitializer> fromItem(const io::Item& item);
+
+// @TODO: add documentation
 Ptr<NodeInitializer> fromTensor(Tensor tensor);
 
+// @TODO: add documentation
 Ptr<NodeInitializer> fromWord2vec(const std::string& file,
                                   int dimVoc,
                                   int dimEmb,

From e83fc5fd04689fe57b93e23689797a904fef3f9a Mon Sep 17 00:00:00 2001
From: Marcin Junczys-Dowmunt <Marcin.JunczysDowmunt@microsoft.com>
Date: Tue, 10 Sep 2019 16:12:00 -0700
Subject: [PATCH 607/838] remove dummy matrices from amun and nematus loaders

---
 src/graph/expression_graph.cpp     | 19 ++++--------------
 src/graph/expression_graph.h       | 14 ++++----------
 src/graph/expression_operators.cpp |  1 -
 src/graph/node_operators.cpp       |  1 +
 src/models/amun.h                  | 31 +++++++++++++++++-------------
 src/models/nematus.h               | 21 ++++++++++++--------
 6 files changed, 40 insertions(+), 47 deletions(-)

diff --git a/src/graph/expression_graph.cpp b/src/graph/expression_graph.cpp
index bd876a631..02a3f9669 100755
--- a/src/graph/expression_graph.cpp
+++ b/src/graph/expression_graph.cpp
@@ -48,16 +48,13 @@ Expr ExpressionGraph::add(Expr node) {
 
 // Call on every checkpoint in backwards order
 void createSubtape(Expr node) {
-  //std::cerr << "Recursing: " << node->getId() << " " << node->type() << std::endl;
   auto subtape = New<std::list<Expr>>();
 
   for(auto child : node->children()) {
     if(child->isCheckpoint()) {
-      //std::cerr << "cut" << std::endl;
       /* do not descend */
     } else {
       if(child->getSubtape()) {
-        //std::cerr << "Already visited: " << node->getId() << " " << node->type() << std::endl;
         /* already visited */
       } else {
         createSubtape(child);
@@ -101,8 +98,6 @@ void ExpressionGraph::forwardNext() {
     }
   }
 
-  // graphviz("dump.dot");
-
   forward(nodesForward_, /*finalPass=*/!checkpointing_); // if checkPointing, this is not final
 }
 
@@ -110,16 +105,13 @@ void ExpressionGraph::forward(std::list<Expr>& forwardTape, bool finalPass) {
   while(!forwardTape.empty()) {
     auto v = forwardTape.front();
 
-    //std::cerr << "Forward: " << v->getId() << " " << v->type() << " ";
-    //std::cerr << v->isCheckpoint() << std::endl;
-
     v->allocate();
-    /*if(!isFake())*/ v->init();
+    v->init();
 
     for(auto& child : v->children())
       ABORT_IF(!child->val(), "De-allocated child {} {} of {} {}", child->getId(), child->type(), v->getId(), v->type());
 
-    /*if(!isFake())*/ v->forward();
+    v->forward();
 
     if(v->trainable() && throwNan_) {
       bool isNan = false, isInf = false;
@@ -155,7 +147,6 @@ void ExpressionGraph::forward(std::list<Expr>& forwardTape, bool finalPass) {
       auto subtape = v->getSubtape();
       if(subtape) {
         for(auto& node : *subtape) {
-          //std::cerr << "Freeing: " << node->getId() << " " << node->type() << " " << node.useCount() << std::endl;
           node->free();
         }
       }
@@ -181,13 +172,12 @@ void ExpressionGraph::backward(bool zero, float clipValue) {
   }
 
   params_->allocateBackward();
-  if(zero /*&& !isFake()*/)
+  if(zero)
     params_->set_zero_adjoint();
 
   for(auto&& v : topNodes_)
     v->init_dependent();
 
-  // named_.clear();
   topNodes_.clear();
 
   tensors_->clearShorttermMemory();
@@ -202,7 +192,6 @@ void ExpressionGraph::backward(bool zero, float clipValue) {
         child->set_zero_adjoint();
 
     if(checkpointing_ && v->getSubtape()) {
-      //std::cerr << "Back: " << v->getId() << " " << v->type() << " " << v->getSubtape()->size() << std::endl;
       forward(*v->getSubtape(), /*finalPass=*/true);
     }
 
@@ -216,7 +205,7 @@ void ExpressionGraph::backward(bool zero, float clipValue) {
       Element(_1 = clip(_1, clipValue), v->grad());
     }
 
-    if(v->trainable()/* && !isFake()*/)
+    if(v->trainable())
       v->backward();
 
     if(throwNan_ && firstNan) {
diff --git a/src/graph/expression_graph.h b/src/graph/expression_graph.h
index 3a58d454d..62bd6fde8 100755
--- a/src/graph/expression_graph.h
+++ b/src/graph/expression_graph.h
@@ -90,9 +90,7 @@ class Tensors {
 
     auto it = shortterm_->find(hash);
     if(it != shortterm_->end()) {
-      for(auto foundWeak : it->second) {
-        //auto found = foundWeak.lock();
-        Expr found = foundWeak; // DO NOT LEAVE THIS HERE!!!
+      for(auto found : it->second) {
         if(node->equal(found)) {
           return found;
         }
@@ -126,14 +124,13 @@ class ExpressionGraph : public std::enable_shared_from_this<ExpressionGraph> {
 
   std::unordered_map<size_t, std::vector<Expr>> memoized_;
 
-  Type parameterType_{Type::float32};
-  Type saveType_{Type::float32};
+  Type parameterType_{Type::float32}; // Type used for storing parameters, currently all parameters have to have the same type
+  Type saveType_{Type::float32};      // Type used for saving to disk, can be different, e.g. double or float16.
 
   bool inferenceOnly_{false};
   bool optimized_{false};
 
-  bool checkpointing_{false};
-  bool isFake_{false};
+  bool checkpointing_{false}; // use gradient checkpointing if true
 
   bool reloaded_{false};
   std::string namespace_;
@@ -177,9 +174,6 @@ class ExpressionGraph : public std::enable_shared_from_this<ExpressionGraph> {
   void setCheckpointing(bool checkpointing) { checkpointing_ = checkpointing; }
   bool isCheckpointing() { return checkpointing_; }
 
-  void setFake(bool fake) { isFake_ = fake; }
-  bool isFake() { return isFake_; }
-
   void switchParams(const std::string& newNamespace) {
     namespace_ = newNamespace;
   }
diff --git a/src/graph/expression_operators.cpp b/src/graph/expression_operators.cpp
index 156ab1329..227594832 100644
--- a/src/graph/expression_operators.cpp
+++ b/src/graph/expression_operators.cpp
@@ -647,7 +647,6 @@ Expr transpose(Expr a, const std::vector<int>& axes) {
   return Expression<TransposeNodeOp>(a, axes);
 }
 
-
 Expr swapAxes(Expr x, int axis1, int axis2)
 {
   const auto& shape = x->shape();
diff --git a/src/graph/node_operators.cpp b/src/graph/node_operators.cpp
index 468f12ac5..bf3fe4ba1 100644
--- a/src/graph/node_operators.cpp
+++ b/src/graph/node_operators.cpp
@@ -53,6 +53,7 @@ ParamNode::ParamNode(Ptr<ExpressionGraph> graph,
 }
 
 void ParamNode::init() {
+  std::cerr << name() << " " << shape() << std::endl;
   if(!initialized_) {
     init_->apply(val_);
     initialized_ = true;
diff --git a/src/models/amun.h b/src/models/amun.h
index 1c371d4ba..811d130ee 100755
--- a/src/models/amun.h
+++ b/src/models/amun.h
@@ -10,28 +10,28 @@ class Amun : public EncoderDecoder {
 public:
   Amun(Ptr<ExpressionGraph> graph, Ptr<Options> options) : EncoderDecoder(graph, options) {
     ABORT_IF(opt<int>("enc-depth") > 1,
-             "--type amun does not currently support multiple encoder "
+             "--type amun does not support multiple encoder "
              "layers, use --type s2s");
     ABORT_IF(opt<int>("enc-cell-depth") > 1,
-             "--type amun does not currently support stacked encoder "
+             "--type amun does not support stacked encoder "
              "cells, use --type s2s");
     ABORT_IF(opt<bool>("skip"),
-             "--type amun does not currently support skip connections, "
+             "--type amun does not support skip connections, "
              "use --type s2s");
     ABORT_IF(opt<int>("dec-depth") > 1,
-             "--type amun does not currently support multiple decoder "
+             "--type amun does not support multiple decoder "
              "layers, use --type s2s");
     ABORT_IF(opt<int>("dec-cell-base-depth") != 2,
-             "--type amun does not currently support multiple decoder "
+             "--type amun does not support multiple decoder "
              "base cells, use --type s2s");
     ABORT_IF(opt<int>("dec-cell-high-depth") > 1,
-             "--type amun does not currently support multiple decoder "
+             "--type amun does not support multiple decoder "
              "high cells, use --type s2s");
     ABORT_IF(opt<std::string>("enc-cell") != "gru",
-             "--type amun does not currently support other rnn cells than gru, "
+             "--type amun does not support other rnn cells than gru, "
              "use --type s2s");
     ABORT_IF(opt<std::string>("dec-cell") != "gru",
-             "--type amun does not currently support other rnn cells than gru, "
+             "--type amun does not support other rnn cells than gru, "
              "use --type s2s");
   }
 
@@ -92,15 +92,20 @@ class Amun : public EncoderDecoder {
     LOG(info, "Loading model from {}", name);
     // load items from .npz file
     auto ioItems = io::loadItems(name);
-    // map names and remove a dummy matrix 'decoder_c_tt' from items to avoid creating isolated node
+    // map names and remove a dummy matrices
     for(auto it = ioItems.begin(); it != ioItems.end();) {
       if(it->name == "decoder_c_tt") {
         it = ioItems.erase(it);
+      } else if(it->name == "uidx") {
+        it = ioItems.erase(it);
+      } else if(it->name == "history_errs") {
+        it = ioItems.erase(it);
+      } else {
+        auto pair = nameMap.find(it->name);
+        if(pair != nameMap.end())
+          it->name = pair->second;
+        it++;
       }
-      auto pair = nameMap.find(it->name);
-      if(pair != nameMap.end())
-        it->name = pair->second;
-      ++it;
     }
     // load items into the graph
     graph->load(ioItems);
diff --git a/src/models/nematus.h b/src/models/nematus.h
index d8e094ead..ebe3faa3c 100755
--- a/src/models/nematus.h
+++ b/src/models/nematus.h
@@ -10,18 +10,18 @@ class Nematus : public EncoderDecoder {
 public:
   Nematus(Ptr<ExpressionGraph> graph, Ptr<Options> options) : EncoderDecoder(graph, options), nameMap_(createNameMap()) {
     ABORT_IF(options_->get<std::string>("enc-type") != "bidirectional",
-             "--type nematus does not currently support other encoder "
+             "--type nematus does not support other encoder "
              "type than bidirectional, use --type s2s");
 
     ABORT_IF(options_->get<std::string>("enc-cell") != "gru-nematus",
-             "--type nematus does not currently support other rnn cells "
+             "--type nematus does not support other rnn cells "
              "than gru-nematus, use --type s2s");
     ABORT_IF(options_->get<std::string>("dec-cell") != "gru-nematus",
-             "--type nematus does not currently support other rnn cells "
+             "--type nematus does not support other rnn cells "
              "than gru-nematus, use --type s2s");
 
     ABORT_IF(options_->get<int>("dec-cell-high-depth") > 1,
-             "--type nematus does not currently support "
+             "--type nematus does not support "
              "--dec-cell-high-depth > 1, use --type s2s");
   }
 
@@ -35,11 +35,16 @@ class Nematus : public EncoderDecoder {
     for(auto it = ioItems.begin(); it != ioItems.end();) {
       if(it->name == "decoder_c_tt") {
         it = ioItems.erase(it);
+      } else if(it->name == "uidx") {
+        it = ioItems.erase(it);
+      } else if(it->name == "history_errs") {
+        it = ioItems.erase(it);
+      } else {
+        auto pair = nameMap_.find(it->name);
+        if(pair != nameMap_.end())
+          it->name = pair->second;
+        it++;
       }
-      auto pair = nameMap_.find(it->name);
-      if(pair != nameMap_.end())
-        it->name = pair->second;
-      ++it;
     }
     // load items into the graph
     graph->load(ioItems);

From e450cd0187e7b470a94966ddf77961c7607cc3ea Mon Sep 17 00:00:00 2001
From: Marcin Junczys-Dowmunt <Marcin.JunczysDowmunt@microsoft.com>
Date: Tue, 10 Sep 2019 16:18:25 -0700
Subject: [PATCH 608/838] remove debug code

---
 src/graph/node_operators.cpp | 1 -
 1 file changed, 1 deletion(-)

diff --git a/src/graph/node_operators.cpp b/src/graph/node_operators.cpp
index bf3fe4ba1..468f12ac5 100644
--- a/src/graph/node_operators.cpp
+++ b/src/graph/node_operators.cpp
@@ -53,7 +53,6 @@ ParamNode::ParamNode(Ptr<ExpressionGraph> graph,
 }
 
 void ParamNode::init() {
-  std::cerr << name() << " " << shape() << std::endl;
   if(!initialized_) {
     init_->apply(val_);
     initialized_ = true;

From 3f4170c468a3f546612eb2ae767a4507856781a3 Mon Sep 17 00:00:00 2001
From: Marcin Junczys-Dowmunt <Marcin.JunczysDowmunt@microsoft.com>
Date: Tue, 10 Sep 2019 16:26:25 -0700
Subject: [PATCH 609/838] add missing output type for int16::affine

---
 src/tensors/cpu/int16.h | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/src/tensors/cpu/int16.h b/src/tensors/cpu/int16.h
index b63cc91d8..abb465b6d 100644
--- a/src/tensors/cpu/int16.h
+++ b/src/tensors/cpu/int16.h
@@ -31,7 +31,7 @@ class DotNodeOp : public NaryNodeOp {
 
 public:
   DotNodeOp(Expr a, Expr b, float scalar)
-      : NaryNodeOp({a, b}, newShape(a, b)), scalar_(scalar) {}
+      : NaryNodeOp({a, b}, newShape(a, b), Type::float32), scalar_(scalar) {}
 
   Shape newShape(Expr a, Expr b) {
     auto shapeA = a->shape();
@@ -66,7 +66,7 @@ class AffineNodeOp : public NaryNodeOp {
 
 public:
   AffineNodeOp(const std::vector<Expr>& nodes, float scalar)
-      : NaryNodeOp(nodes, newShape(nodes[0], nodes[1])), scalar_(scalar) {}
+      : NaryNodeOp(nodes, newShape(nodes[0], nodes[1]), Type::float32), scalar_(scalar) {}
 
   Shape newShape(Expr a, Expr b) {
     auto shapeA = a->shape();

From a67f8035fd79c92316f3585bc3bec7f6d4f2fc75 Mon Sep 17 00:00:00 2001
From: Marcin Junczys-Dowmunt <Marcin.JunczysDowmunt@microsoft.com>
Date: Tue, 10 Sep 2019 16:56:36 -0700
Subject: [PATCH 610/838] reshape required for stop gradient

---
 src/graph/expression_operators.cpp | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/graph/expression_operators.cpp b/src/graph/expression_operators.cpp
index 227594832..ec5bfc119 100644
--- a/src/graph/expression_operators.cpp
+++ b/src/graph/expression_operators.cpp
@@ -277,7 +277,7 @@ Expr flatten_2d(Expr a) {
 
 Expr stopGradient(Expr a) {
   // implemented as a dummy reshape that is not trainable
-  auto res = reshape(a, a->shape());
+  auto res = Expression<ReshapeNodeOp>(a, a->shape());
   res->setTrainable(false);
   return res;
 }

From 43086d2160748594c7ed4c5778b3b71fbb172930 Mon Sep 17 00:00:00 2001
From: Marcin Junczys-Dowmunt <Marcin.JunczysDowmunt@microsoft.com>
Date: Wed, 11 Sep 2019 14:11:07 -0700
Subject: [PATCH 611/838] address comments from code review

---
 src/common/io_item.h                 |  1 +
 src/graph/expression_graph.cpp       | 35 +++++++-------
 src/graph/expression_graph.h         | 18 ++++----
 src/graph/expression_operators.cpp   |  8 ++--
 src/graph/node.h                     | 29 +++++++-----
 src/graph/node_initializers.cpp      | 55 +++++++++-------------
 src/graph/node_initializers.h        | 68 +++++++++++++---------------
 src/graph/node_operators.cpp         |  6 +--
 src/graph/node_operators.h           |  4 +-
 src/rnn/attention.h                  |  2 +
 src/tensors/cpu/tensor_operators.cpp |  7 ++-
 src/tensors/gpu/tensor_operators.cu  | 22 ++++-----
 src/tensors/tensor_operators.h       |  4 +-
 13 files changed, 128 insertions(+), 131 deletions(-)

diff --git a/src/common/io_item.h b/src/common/io_item.h
index 60ea03c82..7cb522047 100644
--- a/src/common/io_item.h
+++ b/src/common/io_item.h
@@ -31,6 +31,7 @@ struct Item {
       return bytes.size();
   }
 
+  // Extend this item with data and shape from the input item, creating a flattened concatenation.
   void append(const Item& other) {
     ABORT_IF(mapped, "Memory-mapped items cannot be appended");
     ABORT_IF(type != other.type, "Only item of same type can be appended");
diff --git a/src/graph/expression_graph.cpp b/src/graph/expression_graph.cpp
index 02a3f9669..0b593ab4a 100755
--- a/src/graph/expression_graph.cpp
+++ b/src/graph/expression_graph.cpp
@@ -87,7 +87,7 @@ void ExpressionGraph::forwardNext() {
     }
 
     // To avoid recomputation of range from last checkpoint to the top,
-    // turn all node on last subtape into checkpoints and clear subtape.
+    // turn all nodes on last subtape into checkpoints and clear subtape.
     // @TODO: put this into special backprob function? Needs to know that we are done with adding nodes
     for(auto top : topNodes_) {
       if(top->getSubtape()) {
@@ -113,11 +113,11 @@ void ExpressionGraph::forward(std::list<Expr>& forwardTape, bool finalPass) {
 
     v->forward();
 
-    if(v->trainable() && throwNan_) {
-      bool isNan = false, isInf = false;
-      checkNan(v->val(), isNan, isInf);
-      if(isNan || isInf) {
-        LOG(critical, "Detected NaN ({}) or Inf ({}) in value (forward pass)", isNan, isInf);
+    if(v->trainable() && throwNaN_) {
+      bool isNaN = false, isInf = false;
+      checkNaN(v->val(), isNaN, isInf);
+      if(isNaN || isInf) {
+        LOG(critical, "Detected NaN ({}) or Inf ({}) in value (forward pass)", isNaN, isInf);
         LOG(critical, "\tType: {}, Shape: {}, Name: {}, Id: {}, Hash: {}",
             v->type(), v->shape(), v->name(), v->getId(), v->hash());
         LOG(critical, "Children: {}", v->children().size());
@@ -156,7 +156,7 @@ void ExpressionGraph::forward(std::list<Expr>& forwardTape, bool finalPass) {
   }
 }
 
-void ExpressionGraph::backward(bool zero, float clipValue) {
+void ExpressionGraph::backward(bool reset, float clipValue) {
   if(topNodes_.size() > 1) {
     LOG(critical, "There are more ({}) than one top most nodes for backward pass:", topNodes_.size());
     for(auto node : topNodes_) {
@@ -172,7 +172,7 @@ void ExpressionGraph::backward(bool zero, float clipValue) {
   }
 
   params_->allocateBackward();
-  if(zero)
+  if(reset)
     params_->set_zero_adjoint();
 
   for(auto&& v : topNodes_)
@@ -182,7 +182,7 @@ void ExpressionGraph::backward(bool zero, float clipValue) {
 
   tensors_->clearShorttermMemory();
 
-  bool firstNan = true;
+  bool firstNaN = true;
   while(!nodesBackward_.empty()) {
     auto v = nodesBackward_.back();
     nodesBackward_.pop_back();
@@ -208,24 +208,23 @@ void ExpressionGraph::backward(bool zero, float clipValue) {
     if(v->trainable())
       v->backward();
 
-    if(throwNan_ && firstNan) {
+    if(throwNaN_ && firstNaN) {
       for(auto&& child : v->children()) {
         if(child->trainable()) {
-          bool isNan = false, isInf = false;
-          checkNan(child->grad(), isNan, isInf);
-          if(isNan) {
-            LOG(critical, "Detected NaN ({}) or Inf ({}) in gradient (backward pass) of child node", isNan, isInf);
+          bool isNaN = false, isInf = false;
+          checkNaN(child->grad(), isNaN, isInf);
+          if(isNaN) {
+            LOG(critical, "Detected NaN ({}) or Inf ({}) in gradient (backward pass) of child node", isNaN, isInf);
             LOG(critical, "Child - Type: {}, Shape: {}, Name: {}, Id: {}, Hash: {}",
                 child->type(), child->shape(), child->name(), child->getId(), child->hash());
             LOG(critical, "Parent - Type: {}, Shape: {}, Name: {}, Id: {}, Hash: {}",
                 v->type(), v->shape(), v->name(), v->getId(), v->hash());
-            firstNan = false;
+            firstNaN = false;
           }
         }
       }
     }
 
-
     v->children().clear();
   }
 }
@@ -238,8 +237,8 @@ Expr ExpressionGraph::dropoutMask(float prob, const Shape& shape) {
   return constant(shape, inits::dropout(prob), parameterType_);
 }
 
-void ExpressionGraph::checkNan(Tensor t, bool& isNan, bool& isInf) {
-  IsNan(t, allocator(), isNan, isInf);
+void ExpressionGraph::checkNaN(Tensor t, bool& isNaN, bool& isInf) {
+  IsNaN(t, allocator(), isNaN, isInf);
 }
 
 void ExpressionGraph::save(std::vector<io::Item>& ioItems) {
diff --git a/src/graph/expression_graph.h b/src/graph/expression_graph.h
index 62bd6fde8..1b5ab4a76 100755
--- a/src/graph/expression_graph.h
+++ b/src/graph/expression_graph.h
@@ -128,14 +128,13 @@ class ExpressionGraph : public std::enable_shared_from_this<ExpressionGraph> {
   Type saveType_{Type::float32};      // Type used for saving to disk, can be different, e.g. double or float16.
 
   bool inferenceOnly_{false};
-  bool optimized_{false};
-
+  bool optimized_{false};     // during inference, use optimizations that might lead to precision loss, e.g. 8-bit MatMul.
   bool checkpointing_{false}; // use gradient checkpointing if true
 
   bool reloaded_{false};
   std::string namespace_;
 
-  bool throwNan_{false};
+  bool throwNaN_{false};
 
 protected:
   // Delete, copy and move constructors
@@ -180,9 +179,8 @@ class ExpressionGraph : public std::enable_shared_from_this<ExpressionGraph> {
 
   virtual void copyParams(Ptr<ExpressionGraph> graph) {
     for(auto p : *graph->params())
-      param(p->name(), p->shape(), inits::dummy(), p->value_type());
-    params()->allocateForward();
-    params()->vals()->copyFrom(graph->params()->vals());
+      param(p->name(), p->shape(), inits::fromTensor(p->val()), p->value_type());
+    forward(); // this will allocate parameters, execute the intializers and therefore copy parameter values
   }
 
   void reserveWorkspaceMB(size_t num) {
@@ -218,7 +216,7 @@ class ExpressionGraph : public std::enable_shared_from_this<ExpressionGraph> {
     return true;
   }
 
-  void checkNan(Tensor t, bool& isNan, bool& isInf);
+  void checkNaN(Tensor t, bool& isNaN, bool& isInf);
 
   void forward() {
     params_->allocateForward();
@@ -228,7 +226,7 @@ class ExpressionGraph : public std::enable_shared_from_this<ExpressionGraph> {
   void forwardNext();
   void forward(std::list<Expr>& forwardTape, bool finalPass);
 
-  void backward(bool zero = true, float clipValue = 0.f);
+  void backward(bool reset = true, float clipValue = 0.f);
 
   std::string graphviz() {
     std::stringstream ss;
@@ -412,8 +410,8 @@ class ExpressionGraph : public std::enable_shared_from_this<ExpressionGraph> {
 
   void setReloaded(bool reloaded) { reloaded_ = reloaded; }
 
-  void setThrowNan(bool throwNan) { throwNan_ = throwNan; }
-  bool getThrowNan() { return throwNan_; }
+  void setThrowNaN(bool throwNaN) { throwNaN_ = throwNaN; }
+  bool getThrowNaN() { return throwNaN_; }
 
 public:
   // loading from array of io::Items
diff --git a/src/graph/expression_operators.cpp b/src/graph/expression_operators.cpp
index ec5bfc119..6f3d3aecc 100644
--- a/src/graph/expression_operators.cpp
+++ b/src/graph/expression_operators.cpp
@@ -83,9 +83,10 @@ Expr softmax(Expr a, int axis /*=-1*/)
 }
 
 Expr softmax(Expr a, Expr zeroOneMask, int axis /*=-1*/) {
-  // This will return the smallest value for the input type converted to float
+  // This will return the smallest value / 2 for the input type converted to float
   // So for Type::Float16 that will be the smallest fp16 value expressed as float
-  float smallestFloat = -NumericLimits<float>(a->value_type()).max;
+  // We divide by 2 to allow for some tolerance and overflow protection.
+  float smallestFloat = -NumericLimits<float>(a->value_type()).max / 2.f;
   auto logMask = (1.f - zeroOneMask) * smallestFloat;
   return softmax(a + logMask, axis);
 }
@@ -191,7 +192,7 @@ Expr operator*(Expr a, float b) {
 }
 
 Expr operator/(Expr a, float b) {
-  return Expression<ScalarMultNodeOp>(a, 1.f / b);
+  return a * (1.f / b);
 }
 
 // TODO: efficient version of this without constant()
@@ -232,6 +233,7 @@ Expr reshape(Expr a, Shape shape) {
   return Expression<ReshapeNodeOp>(a, shape);
 }
 
+// @TODO: remove this if it turns out that we can train FP16 without that
 Expr clipGradient(Expr a, float clipValue) {
   // don't create node if no clipping
   return clipValue != 0.f ? Expression<ClipGradientNodeOp>(a, clipValue) : a;
diff --git a/src/graph/node.h b/src/graph/node.h
index 400333232..e4c3ea02d 100644
--- a/src/graph/node.h
+++ b/src/graph/node.h
@@ -26,7 +26,6 @@ class Node : public Chainable<Tensor> {
   bool memoize_{false};
 
   std::vector<Expr> children_;
-  Ptr<std::list<Expr>> subtape_;
 
   Weak<ExpressionGraph> graph_;
   Shape shape_{1, 1, 1, 1};
@@ -40,7 +39,8 @@ class Node : public Chainable<Tensor> {
   bool markedForDebug_{false};
   std::string debugMessage_;
 
-  bool isCheckpoint_{false};
+  Ptr<std::list<Expr>> subtape_; // a subtape is used to keep track of nodes that need to be freed and recomputed with gradient-checkpointing.
+  bool isCheckpoint_{false};     // true if this node has been selected to be a checkpoint, currently only done manually. 
 
   Ptr<AutoTunerRecorder> recorder_;
   size_t recorderHash_;
@@ -151,7 +151,6 @@ class Node : public Chainable<Tensor> {
     if(subtape_) {
       for(auto&& dep : *subtape_)
         ss << "\"" << dep << "\" -> \"" << this << "\" [style=dotted];" << std::endl;
-
     }
 
     ss << std::endl;
@@ -166,6 +165,7 @@ class Node : public Chainable<Tensor> {
 
   void record(Ptr<AutoTunerRecorder>, size_t, bool) override;
 
+  // this is currently only called manually by checkpoint(Expr). In the future we will figure out a general algorithm
   virtual void markCheckpoint() override {
     isCheckpoint_ = true;
   }
@@ -186,7 +186,10 @@ class Node : public Chainable<Tensor> {
 struct NaryNodeOp : public Node {
   size_t hash_{0};
 
-  // deduce type automatically, but then all types must be the same
+  // Deduce type automatically, but then all types must be the same
+  // this is called automatically when no output type is specified.
+  // If the input types are mixed, the output type needs to be specified 
+  // in the constructor.
   Type commonType(const std::vector<Expr>& nodes) {
     ABORT_IF(nodes.size() == 0, "NaryNodeOp has no children");
     Type type = nodes[0]->value_type();
@@ -200,9 +203,11 @@ struct NaryNodeOp : public Node {
   NaryNodeOp(const std::vector<Expr>& nodes)
   : NaryNodeOp(nodes, nodes[0]->shape()) {}
 
+  // this contructor will try to deduce the node type automatically
   NaryNodeOp(const std::vector<Expr>& nodes, Shape shape)
   : NaryNodeOp(nodes, shape, commonType(nodes)) {}
 
+  // this contructor will takes a node type
   NaryNodeOp(const std::vector<Expr>& nodes,
              Shape shape,
              Type value_type)
@@ -239,16 +244,18 @@ struct NaryNodeOp : public Node {
   virtual bool equal(Expr node) override {
     if(type() != node->type())
       return false;
-    if(name() != node->name())
+    else if(name() != node->name())
       return false;
-    if(value_type() != node->value_type())
+    else if(value_type() != node->value_type())
       return false;
-    if(children().size() != node->children().size())
+    else if(children().size() != node->children().size())
       return false;
-    for(size_t i = 0; i < children().size(); ++i)
-      if(children()[i]->getId() != node->children()[i]->getId())
-        return false;
-    return true;
+    else {
+      for(size_t i = 0; i < children().size(); ++i)
+        if(children()[i]->getId() != node->children()[i]->getId())
+          return false;
+      return true;
+    }
   }
 };
 }  // namespace marian
diff --git a/src/graph/node_initializers.cpp b/src/graph/node_initializers.cpp
index ce29d999a..9d6af92ff 100755
--- a/src/graph/node_initializers.cpp
+++ b/src/graph/node_initializers.cpp
@@ -26,7 +26,8 @@ class LambdaInit : public NodeInitializer {
 class LambdaInitConvert : public NodeInitializer {
   private:
     std::function<void(Tensor)> lambda_;
-    Type intermediateType_;
+    Type intermediateType_; // is used for the creation of a temporary intermedia tensor on which the lambda actually operates.
+                            // This tensor is then automatically cast and copied to the type of the actual tensor. 
 
   public:
     LambdaInitConvert(std::function<void(Tensor)>&& lambda,
@@ -36,7 +37,7 @@ class LambdaInitConvert : public NodeInitializer {
     void apply(Tensor tensor) override {
       if(tensor->type() != intermediateType_) {
         auto sharedAllocator = allocator_.lock();
-        ABORT_IF(!sharedAllocator, "Allocator in LambdaInitConvert has not been set or is expired");
+        ABORT_IF(!sharedAllocator, "Allocator in LambdaInitConvert has not been set or expired");
 
         auto memory = sharedAllocator->alloc(tensor->size(), intermediateType_);
         auto temp = TensorBase::New(memory,
@@ -44,7 +45,7 @@ class LambdaInitConvert : public NodeInitializer {
                                     intermediateType_,
                                     tensor->getBackend());
         lambda_(temp);
-        CopyCast(tensor, temp); // Casting from temp to tensor
+        CopyCast(tensor, temp); // Cast and copy from temp to tensor
         sharedAllocator->free(memory);
       }
       else {
@@ -53,24 +54,16 @@ class LambdaInitConvert : public NodeInitializer {
     }
 };
 
-Ptr<NodeInitializer> lambda(std::function<void(Tensor)>&& func) {
+Ptr<NodeInitializer> fromLambda(std::function<void(Tensor)>&& func) {
   return New<LambdaInit>(std::move(func));
 }
 
-Ptr<NodeInitializer> lambda(std::function<void(Tensor)>&& func, Type intermediateType) {
+Ptr<NodeInitializer> fromLambda(std::function<void(Tensor)>&& func, Type intermediateType) {
   return New<LambdaInitConvert>(std::move(func), intermediateType);
 }
 
 Ptr<NodeInitializer> fromValue(float v) {
-  return lambda([v](Tensor t){ t->set(v); });
-}
-
-Ptr<NodeInitializer> zeros() {
-  return fromValue(0.0f);
-}
-
-Ptr<NodeInitializer> ones() {
-  return fromValue(1.0f);
+  return fromLambda([v](Tensor t){ t->set(v); });
 }
 
 // diagonal matrix with value val along diagonal
@@ -88,21 +81,21 @@ Ptr<NodeInitializer> eye(float val) {
     t->set(vec);
   };
 
-  return lambda(eyeLambda, Type::float32);
+  return fromLambda(eyeLambda, Type::float32);
 }
 
 Ptr<NodeInitializer> uniform(float a, float b) {
   // only works for float, hence the conversion through intermedia type Type::float32
-  return lambda([a, b](Tensor t) { t->getBackend()->getRandomGenerator()->uniform(t, a, b); }, Type::float32);
+  return fromLambda([a, b](Tensor t) { t->getBackend()->getRandomGenerator()->uniform(t, a, b); }, Type::float32);
 }
 
 Ptr<NodeInitializer> normal(float mean, float stddev) {
   // only works for float, hence the conversion through intermedia type Type::float32
-  return lambda([mean, stddev](Tensor t) { t->getBackend()->getRandomGenerator()->normal(t, mean, stddev); }, Type::float32);
+  return fromLambda([mean, stddev](Tensor t) { t->getBackend()->getRandomGenerator()->normal(t, mean, stddev); }, Type::float32);
 }
 
 Ptr<NodeInitializer> glorotUniform(bool fanIn, bool fanOut, float scalingFactor) {
-  return lambda([fanIn, fanOut, scalingFactor](Tensor t) {
+  return fromLambda([fanIn, fanOut, scalingFactor](Tensor t) {
     float scale = sqrtf(6.0f / (t->shape()[-2] + t->shape()[-1]));
     if(fanIn && !fanOut)
       scale = sqrtf(3.0f / t->shape()[-2]); // results in columns of matrix to be ~unit length
@@ -116,7 +109,7 @@ Ptr<NodeInitializer> glorotUniform(bool fanIn, bool fanOut, float scalingFactor)
 }
 
 Ptr<NodeInitializer> glorotNormal(bool fanIn, bool fanOut, float scalingFactor) {
-  return lambda([fanIn, fanOut, scalingFactor](Tensor t) {
+  return fromLambda([fanIn, fanOut, scalingFactor](Tensor t) {
     float scale = sqrtf(2.0f / (t->shape()[-2] + t->shape()[-1]));
     if(fanIn && !fanOut)
       scale = sqrtf(1.0f / t->shape()[-2]);
@@ -130,17 +123,17 @@ Ptr<NodeInitializer> glorotNormal(bool fanIn, bool fanOut, float scalingFactor)
 }
 
 Ptr<NodeInitializer> bernoulli(float prob, float scale, float shift) {
-  return lambda([prob, scale, shift](Tensor t) { Bernoulli(t, prob, scale, shift); }, Type::float32);
+  return fromLambda([prob, scale, shift](Tensor t) { Bernoulli(t, prob, scale, shift); }, Type::float32);
 }
 
 Ptr<NodeInitializer> dropout(float dropProb) {
-  return lambda([dropProb](Tensor t) { Dropout(t, dropProb); }, Type::float32);
+  return fromLambda([dropProb](Tensor t) { Dropout(t, dropProb); }, Type::float32);
 }
 
 // gumbel noise:
 // -log(-log(uniform(0.f + eps, 1.f - eps)));
 Ptr<NodeInitializer> gumbel(float eps) {
-  return lambda([eps](Tensor tensor) {
+  return fromLambda([eps](Tensor tensor) {
     tensor->getBackend()->getRandomGenerator()->uniform(tensor, 0.f + eps, 1.f - eps);
     using namespace functional;
     Element(_1 = -log(-log(_1)), tensor);
@@ -149,7 +142,7 @@ Ptr<NodeInitializer> gumbel(float eps) {
 
 template <typename T>
 Ptr<NodeInitializer> fromVector(const std::vector<T>& v) {
-  return lambda([v](Tensor t) { t->set(v.data(), v.data() + v.size()); }, typeId<T>());
+  return fromLambda([v](Tensor t) { t->set(v.data(), v.data() + v.size()); }, typeId<T>());
 }
 
 template Ptr<NodeInitializer> fromVector<float16>(const std::vector<float16>& v);
@@ -157,7 +150,7 @@ template Ptr<NodeInitializer> fromVector<float>(const std::vector<float>& v);
 template Ptr<NodeInitializer> fromVector<IndexType>(const std::vector<IndexType>& v);
 
 Ptr<NodeInitializer> fromSparseVector(std::pair<std::vector<size_t>, std::vector<float>>& v) {
-  return lambda([v](Tensor t) { t->set(1e-6); t->setSparse(v.first, v.second); });
+  return fromLambda([v](Tensor t) { t->set(1e-6); t->setSparse(v.first, v.second); });
 }
 
 // move this somewhere else
@@ -165,7 +158,7 @@ Ptr<NodeInitializer> fromWord2vec(const std::string& file,
                               int dimVoc,
                               int dimEmb,
                               bool normalize /*= false*/) {
-  return lambda([file, dimVoc, dimEmb, normalize](Tensor t) {
+  return fromLambda([file, dimVoc, dimEmb, normalize](Tensor t) {
     auto embs = Word2VecReader().read(file, dimVoc, dimEmb);
     if(normalize) {
       float norm = 0;
@@ -182,7 +175,7 @@ Ptr<NodeInitializer> fromWord2vec(const std::string& file,
 
 Ptr<NodeInitializer> fromItem(const io::Item& item) {
   if(item.mapped) {
-    return lambda([item](Tensor tensor) {
+    return fromLambda([item](Tensor tensor) {
       // @TODO: implement other types, for now croak loudly.
       ABORT_IF(tensor->getBackend()->getDeviceId().type != DeviceType::cpu,
                "Memory mapping only works for CPU tensors");
@@ -198,23 +191,19 @@ Ptr<NodeInitializer> fromItem(const io::Item& item) {
       tensor->reset(mp);
     });
   } else {
-    return lambda(
+    return fromLambda(
       [item](Tensor tensor) { tensor->set(item); },
       item.type);
   }
 }
 
 Ptr<NodeInitializer> fromTensor(Tensor externalTensor) {
-  return lambda([externalTensor](Tensor t) { t->copyFrom(externalTensor); }, externalTensor->type());
-}
-
-Ptr<NodeInitializer> dummy() {
-  return lambda([](Tensor /*t*/) { });
+  return fromLambda([externalTensor](Tensor t) { t->copyFrom(externalTensor); }, externalTensor->type());
 }
 
 // Computes Google's sinusoidal position embeddings
 Ptr<NodeInitializer> sinusoidalPositionEmbeddings(int start) {
-  return lambda([start](Tensor t) {
+  return fromLambda([start](Tensor t) {
     int dimEmb   = t->shape()[-1];
     int dimWords = (int)t->size() / dimEmb;
 
diff --git a/src/graph/node_initializers.h b/src/graph/node_initializers.h
index fe88c2c2d..b8aa2f6ed 100644
--- a/src/graph/node_initializers.h
+++ b/src/graph/node_initializers.h
@@ -17,9 +17,10 @@ namespace inits {
 /**
  * Base class for specialized NodeInitializers.
  *
- * A NodeInitializer is a functor that can be invoked on a tensor
- * during node intialization. You need to override operator()(Tensor)
- * with your own functionality.
+ * A NodeInitializer is a functor that is associated with parameters 
+ * and constants, and is invoked on a tensor during node intialization. 
+ * You need to override NodeIntializer::apply(Tensor) with your own 
+ * functionality or use a fromLambda intializer.
  *
  * See node_initializers.cpp for examples.
  */
@@ -35,60 +36,60 @@ class NodeInitializer {
 /**
  * Use a lambda function of form [](Tensor t) { do something with t } to initalize tensor
  */
-Ptr<NodeInitializer> lambda(std::function<void(Tensor)>&& func);
+Ptr<NodeInitializer> fromLambda(std::function<void(Tensor)>&& func);
 
 /**
  * Use a lambda function of form [](Tensor t) { do something with t } to initalize tensor
  * Create temporary tensor of Type intermediateType first, initialize and then copy/convert to actual Tensor
- * Useful for functions that can only operator on a specific type of tensor
+ * Useful for functions that can only operate on a specific type of tensor
  */
-Ptr<NodeInitializer> lambda(std::function<void(Tensor)>&& func, Type intermediateType);
+Ptr<NodeInitializer> fromLambda(std::function<void(Tensor)>&& func, Type intermediateType);
 
 /**
- * Fill tensor with given value
+ * Initialize tensor with given value
  *
- * Creates a NodeInitializer that will fill the given tensor
+ * Creates a NodeInitializer that will intialize the given tensor
  * with `value`. Works with any underlying numeric tensor type.
  *
- * @return A NodeInitializer which can be called on any tensor
+ * @return A NodeInitializer
  */
 Ptr<NodeInitializer> fromValue(float value);
 
 /**
  * Fill tensor with `0`
  *
- * Creates a NodeInitializer that will fill the given tensor
+ * Creates a NodeInitializer that will intialize the given tensor
  * with `0`. Works with any underlying numeric tensor type.
  *
- * @return A NodeInitializer which can be called on any tensor
+ * @return A NodeInitializer
  */
-Ptr<NodeInitializer> zeros();
+static Ptr<NodeInitializer> zeros() { return fromValue(0.0f); }
 
 /**
  * Fill tensor with `1`
  *
- * Creates a NodeInitializer that will fill the given tensor
+ * Creates a NodeInitializer that will intialize the given tensor
  * with `1`. Works with any underlying numeric tensor type.
  *
- * @return A NodeInitializer which can be called on any tensor
+ * @return A NodeInitializer
  */
-Ptr<NodeInitializer> ones();
+static Ptr<NodeInitializer> ones() { return fromValue(1.0f); }
 
 /**
- * Set diagonal of two dimensional quadratic tensor to `value`.
+ * Set diagonal of two dimensional quadratic matrix to `value`.
  *
- * Sets all values of the tensor to 0 and fills the diagonal with
+ * Sets all values of the tensor to 0 and intializes the diagonal with
  * the given `value`. If no value is specified `1` is used by default.
  *
- * @return A NodeInitializer which can be called on any tensor
+ * @return A NodeInitializer
  */
 Ptr<NodeInitializer> eye(float value = 1.f);
 
 /**
- * Fill tensor with normally distributed random numbers
+ * Intialize tensor with normally distributed random numbers
  *
  * Be default this generates floating point numbers from the
- * normal distribution N(0, 1) unless specified differently.
+ * normal distribution Normal(0, 1) unless specified differently.
  *
  * If compiled with `CUDA`, `marian` will use the `cuRand` library
  * for both, GPU and CPU computation. The random sequences generated
@@ -98,15 +99,15 @@ Ptr<NodeInitializer> eye(float value = 1.f);
  * from the C++ standard library is used. These random generators
  * do not have the same random sequences.
  *
- * @return A NodeInitializer which can be called on any tensor
+ * @return A NodeInitializer
  */
 Ptr<NodeInitializer> normal(float mean = 0.f, float stddev = 1.f);
 
 /**
- * Fill tensor with uniformly distributed random numbers
+ * Intialize tensor with uniformly distributed random numbers
  *
  * Be default this generates floating point numbers from the
- * uniform distribution U(0, 1) unless specified differently.
+ * uniform distribution Uniform(0, 1) unless specified differently.
  *
  * If compiled with `CUDA`, `marian` will use the `cuRand` library
  * for both, GPU and CPU computation. The random sequences generated
@@ -116,7 +117,7 @@ Ptr<NodeInitializer> normal(float mean = 0.f, float stddev = 1.f);
  * from the C++ standard library is used. These random generators
  * do not have the same random sequences.
  *
- * @return A NodeInitializer which can be called on any tensor
+ * @return A NodeInitializer
  */
 Ptr<NodeInitializer> uniform(float a = 0.f, float b = 1.f);
 
@@ -133,19 +134,12 @@ Ptr<NodeInitializer> glorotNormal(bool fanIn = false, bool fanOut = false, float
 Ptr<NodeInitializer> dropout(float dropoutProbabilty);
 
 /**
- * Fill with gumbel noise, i.e. -log(-log(u)) where u ~ U(0 + eps, 1 - eps)
+ * Intialize with gumbel noise, i.e. -log(-log(u)) where u ~ Uniform(0 + eps, 1 - eps)
  * 
- * @return A NodeInitializer which can be called on any tensor
+ * @return A NodeInitializer
  */
 Ptr<NodeInitializer> gumbel(float eps = 1e-5f);
 
-/** 
- * Don't do anything, used as a placeholder
- * 
- * @return A NodeInitializer that wraps an empty lambda
- */
-Ptr<NodeInitializer> dummy();
-
 // @TODO: add documentation
 template <typename T>
 Ptr<NodeInitializer> fromVector(const std::vector<T>& v);
@@ -166,16 +160,16 @@ Ptr<NodeInitializer> fromWord2vec(const std::string& file,
                                   bool normalize = false);
 
 /**
- * Computes Google's sinusoidal position embeddings
- * starting from position 'start' taking into account
- * batch and time dimensions of the tensor.
+ * Computes Google's Transformer-style sinusoidal position embeddings
+ * starting from position 'start' taking into account batch and time 
+ * dimensions of the tensor.
  *
  * Expected tensor layout {-2: time, -1: model}
  *
  * Usually gets later reshaped to {time, 1, model} and
  * added with a broadcast to learned embeddings. Positional
  * embeddings are the same for each batch entry and change
- * over time.
+ * over time steps.
  */
 Ptr<NodeInitializer> sinusoidalPositionEmbeddings(int start);
 
diff --git a/src/graph/node_operators.cpp b/src/graph/node_operators.cpp
index 468f12ac5..932fee888 100644
--- a/src/graph/node_operators.cpp
+++ b/src/graph/node_operators.cpp
@@ -8,8 +8,8 @@ namespace marian {
 ConstantNode::ConstantNode(Ptr<ExpressionGraph> graph,
                           const Shape& shape,
                           const Ptr<inits::NodeInitializer>& init,
-                          Type value_type)
-    : Node(graph, shape, value_type),
+                          Type valueType)
+    : Node(graph, shape, valueType),
       init_(init),
       initialized_(false) {
   init_->setAllocator(graph->allocator());
@@ -42,7 +42,7 @@ ParamNode::ParamNode(Ptr<ExpressionGraph> graph,
 ParamNode::ParamNode(Ptr<ExpressionGraph> graph,
                      const Shape& shape,
                      const Ptr<inits::NodeInitializer>& init,
-                     const Type& valueType,
+                     Type valueType,
                      bool fixed)
     : Node(graph, shape, valueType),
       init_(init),
diff --git a/src/graph/node_operators.h b/src/graph/node_operators.h
index 0ce7f1b36..7479bd691 100644
--- a/src/graph/node_operators.h
+++ b/src/graph/node_operators.h
@@ -10,7 +10,7 @@ struct ConstantNode : public Node {
   ConstantNode(Ptr<ExpressionGraph> graph,
                const Shape& shape,
                const Ptr<inits::NodeInitializer>& init,
-               Type value_type = Type::float32);
+               Type valueType = Type::float32);
 
   ~ConstantNode() {}
 
@@ -45,7 +45,7 @@ struct ParamNode : public Node {
   ParamNode(Ptr<ExpressionGraph> graph,
             const Shape& shape,
             const Ptr<inits::NodeInitializer>& init,
-            const Type& valueType,
+            Type valueType,
             bool fixed = false);
 
   ~ParamNode() {}
diff --git a/src/rnn/attention.h b/src/rnn/attention.h
index aa8aedafb..6b30cb556 100755
--- a/src/rnn/attention.h
+++ b/src/rnn/attention.h
@@ -9,6 +9,8 @@ namespace rnn {
 
 Expr attOps(Expr va, Expr context, Expr state);
 
+// Attitive attention used in RNN cells.
+// @TODO: come up with common framework for attention in RNNs and Transformer.
 class GlobalAttention : public CellInput {
 private:
   Expr Wa_, ba_, Ua_, va_;
diff --git a/src/tensors/cpu/tensor_operators.cpp b/src/tensors/cpu/tensor_operators.cpp
index 22ece2f19..bbce9a94a 100644
--- a/src/tensors/cpu/tensor_operators.cpp
+++ b/src/tensors/cpu/tensor_operators.cpp
@@ -28,7 +28,7 @@ inline float stableSigmoid(float x) {
   }
 }
 
-void IsNan(const Tensor in, Ptr<Allocator> allocator, bool& isNan, bool& isInf) {
+void IsNaN(const Tensor in, Ptr<Allocator> allocator, bool& isNaN, bool& isInf) {
   ABORT("Not implemented");
 }
 
@@ -38,6 +38,11 @@ void CopyCastTo(To* out, const From* in, int length) {
     out[i] = in[i];
 }
 
+// Casting has been factored into two functions "CopyCastFrom" and
+// "CopyCastTo". This only serves the purpuse to autmatically create
+// the full Carthesian product of possible type cast via template magic.
+// Extending CopyCast and CopyCastFrom with a new branch in the "if" clause
+// adds all possible variants.
 template <typename T>
 void CopyCastFrom(Tensor out, const T* in, int length) {
   if(out->type() == Type::float32) {
diff --git a/src/tensors/gpu/tensor_operators.cu b/src/tensors/gpu/tensor_operators.cu
index b336a84e0..1bfd00ef8 100755
--- a/src/tensors/gpu/tensor_operators.cu
+++ b/src/tensors/gpu/tensor_operators.cu
@@ -28,17 +28,17 @@ __device__ inline float stableSigmoid(float x) {
 }
 
 template <typename T>
-__global__ void gIsNan(const T* in, int length, bool* isNan, bool* isInf) {
+__global__ void gIsNaN(const T* in, int length, bool* isNaN, bool* isInf) {
   for(int bid = 0; bid < length; bid += blockDim.x * gridDim.x) {
     int index = bid + blockDim.x * blockIdx.x + threadIdx.x;
     if(index < length) {
-      if(isnan((float)in[index])) *isNan = true;
+      if(isnan((float)in[index])) *isNaN = true;
       if(isinf((float)in[index])) *isInf = true;
     }
   }
 }
 
-void IsNan(const Tensor in, Ptr<Allocator> allocator, bool& isNan, bool& isInf) {
+void IsNaN(const Tensor in, Ptr<Allocator> allocator, bool& isNaN, bool& isInf) {
   cudaSetDevice(in->getDeviceId().no);
 
   int length = in->size();
@@ -47,19 +47,19 @@ void IsNan(const Tensor in, Ptr<Allocator> allocator, bool& isNan, bool& isInf)
   int blocks = std::min(MAX_BLOCKS, length / threads + (length % threads != 0));
 
   auto mem = allocator->alloc<bool>(2);
-  bool* dIsNan = &mem->data<bool>()[0];
+  bool* dIsNaN = &mem->data<bool>()[0];
   bool* dIsInf = &mem->data<bool>()[1];
-  fill(in->getBackend(), dIsNan, dIsNan + 2, false);
+  fill(in->getBackend(), dIsNaN, dIsNaN + 2, false);
 
   if(in->type() == Type::float32) {
-    gIsNan<<<blocks, threads>>>(in->data<float>(), length, dIsNan, dIsInf);
+    gIsNaN<<<blocks, threads>>>(in->data<float>(), length, dIsNaN, dIsInf);
   } else if(in->type() == Type::float16) {
-    gIsNan<<<blocks, threads>>>(in->data<half>(), length, dIsNan, dIsInf);
+    gIsNaN<<<blocks, threads>>>(in->data<half>(), length, dIsNaN, dIsInf);
   } else {
-    ABORT("IsNan for type {} not implemented", in->type());
+    ABORT("IsNaN for type {} not implemented", in->type());
   }
 
-  CudaCopy(dIsNan, dIsNan + 1, &isNan);
+  CudaCopy(dIsNaN, dIsNaN + 1, &isNaN);
   CudaCopy(dIsInf, dIsInf + 1, &isInf);
 
   allocator->free(mem);
@@ -1774,11 +1774,11 @@ void LayerNormalizationGrad(Ptr<Allocator> allocator,
   int threads = std::min(MAX_THREADS, cols);
   int blocks = std::min(MAX_BLOCKS, rows);
 
-  IPtr<MemoryPiece> tempGradGammaMemory = allocator->alloc(adj->memory()->size(), adj->type());
+  auto tempGradGammaMemory = allocator->alloc(adj->memory()->size(), adj->type());
   Tensor tempGradGamma = TensorBase::New(tempGradGammaMemory, adj->shape(), adj->type(), adj->getBackend());
   tempGradGamma->set(0.f);
 
-  IPtr<MemoryPiece> tempOnesMemory = allocator->alloc(rows * sizeOf(adj->type()), adj->type());
+  auto tempOnesMemory = allocator->alloc(rows * sizeOf(adj->type()), adj->type());
   Tensor tempOnes = TensorBase::New(tempOnesMemory, Shape({1, rows}), adj->type(), adj->getBackend());
   tempOnes->set(1.f);
 
diff --git a/src/tensors/tensor_operators.h b/src/tensors/tensor_operators.h
index 74597c2df..c87f58669 100644
--- a/src/tensors/tensor_operators.h
+++ b/src/tensors/tensor_operators.h
@@ -35,7 +35,7 @@ void copy(Ptr<Backend> backend, const InIt beg, const InIt end, OutIt it) {
 }
 
 DISPATCH2(CopyCast, marian::Tensor, const marian::Tensor);
-DISPATCH4(IsNan, const Tensor, Ptr<Allocator>, bool&, bool&);
+DISPATCH4(IsNaN, const Tensor, Ptr<Allocator>, bool&, bool&);
 
 template <class Functor, class... Tensors>
 void Element(Functor functor, marian::Tensor out, Tensors... tensors) {
@@ -131,7 +131,7 @@ static inline void Bernoulli(Tensor resultTensor, float keepProb, float scale =
 static inline void Dropout(Tensor tensor, float dropProb) {
   float keepProb = 1.f - dropProb;
   float scale = 1.f / keepProb;
-  Bernoulli(tensor, keepProb, scale, 0.f);
+  Bernoulli(tensor, keepProb, scale, /*shift=*/0.f);
 }
 
 #ifdef CUDA_FOUND

From c26114331f14b506667d7bbae83476525938fac8 Mon Sep 17 00:00:00 2001
From: Marcin Junczys-Dowmunt <Marcin.JunczysDowmunt@microsoft.com>
Date: Wed, 11 Sep 2019 14:26:09 -0700
Subject: [PATCH 612/838] fix compilation for examples and exotic headers

---
 src/examples/mnist/model_lenet.h | 6 +++---
 src/layers/convolution.cpp       | 4 ++--
 src/models/char_s2s.h            | 2 +-
 3 files changed, 6 insertions(+), 6 deletions(-)

diff --git a/src/examples/mnist/model_lenet.h b/src/examples/mnist/model_lenet.h
index 5bc3e6919..3abe7aa41 100644
--- a/src/examples/mnist/model_lenet.h
+++ b/src/examples/mnist/model_lenet.h
@@ -28,7 +28,7 @@ class MnistLeNet : public MnistFeedForwardNet {
     auto features
         = std::static_pointer_cast<data::DataBatch>(batch)->features();
     auto x = g->constant({(int)batch->size(), 1, 28, 28},
-                         inits::from_vector(features));
+                         inits::fromVector(features));
 
     // Construct hidden layers
 
@@ -80,7 +80,7 @@ class MnistLeNet : public MnistFeedForwardNet {
 
       // Construct a bias node. These weights are initialized to zero
       biases.emplace_back(
-          g->param("b" + std::to_string(i), {1, out}, inits::zeros));
+          g->param("b" + std::to_string(i), {1, out}, inits::zeros()));
     }
 
     // Perform matrix multiplication and addition for the last layer
@@ -91,7 +91,7 @@ class MnistLeNet : public MnistFeedForwardNet {
       // Create an output layer of shape batchSize x 1 and populate it with
       // labels
       auto labels = std::static_pointer_cast<data::DataBatch>(batch)->labels();
-      auto y = g->constant({(int)batch->size(), 1}, inits::from_vector(labels));
+      auto y = g->constant({(int)batch->size(), 1}, inits::fromVector(labels));
 
       // Define a top-level node for training
       return mean(cross_entropy(last, y), /*axis =*/ 0);
diff --git a/src/layers/convolution.cpp b/src/layers/convolution.cpp
index 9453d7f01..d46ede36a 100644
--- a/src/layers/convolution.cpp
+++ b/src/layers/convolution.cpp
@@ -19,10 +19,10 @@ Expr Convolution::apply(Expr x) {
   auto kernel
       = graph->param(prefix + "_conv_kernels",
                       {layerIn, kernelNum, kernelDims.first, kernelDims.second},
-                      inits::glorot_uniform);
+                      inits::glorotUniform());
 
   auto bias = graph->param(
-      prefix + "_conv_bias", {1, kernelNum, 1, 1}, inits::zeros);
+      prefix + "_conv_bias", {1, kernelNum, 1, 1}, inits::zeros());
 
   std::vector<Expr> nodes = {x, kernel, bias};
   return Expression<ConvolutionOp>(
diff --git a/src/models/char_s2s.h b/src/models/char_s2s.h
index bcd7af9b2..3b9bb2fae 100755
--- a/src/models/char_s2s.h
+++ b/src/models/char_s2s.h
@@ -58,7 +58,7 @@ class CharS2SEncoder : public EncoderS2S {
     }
     size_t dimWords = strided.size() / dimBatch;
     auto stridedMask
-        = graph->constant({(int)dimWords, (int)dimBatch, 1}, inits::from_vector(strided));
+        = graph->constant({(int)dimWords, (int)dimBatch, 1}, inits::fromVector(strided));
     return stridedMask;
   }
 };

From ca37cb067a882028d9503d8eeb82d04d178e10b3 Mon Sep 17 00:00:00 2001
From: Marcin Junczys-Dowmunt <Marcin.JunczysDowmunt@microsoft.com>
Date: Wed, 11 Sep 2019 14:28:14 -0700
Subject: [PATCH 613/838] update version and changelog

---
 CHANGELOG.md | 4 ++++
 VERSION      | 2 +-
 2 files changed, 5 insertions(+), 1 deletion(-)

diff --git a/CHANGELOG.md b/CHANGELOG.md
index 8ad2202a8..ab57fa811 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -13,6 +13,7 @@ and this project adheres to [Semantic Versioning](http://semver.org/spec/v2.0.0.
 - Replacing std::shared_ptr<T> with custom IntrusivePtr<T> for small objects like 
   Tensors, Hypotheses and Expressions.
 - First steps towards integrating FP16 support, currently no-ops.
+- Gradient-checkpointing
 
 ### Fixed
 - Sort parameters by lexicographical order during allocation to ensure consistent 
@@ -21,6 +22,9 @@ and this project adheres to [Semantic Versioning](http://semver.org/spec/v2.0.0.
   hallucinated outputs.
 - Compilation with CUDA 10.1
 
+### Changed
+- Unified node intializers, same function API.
+
 ## [1.8.0] - 2019-09-04
 
 ### Added
diff --git a/VERSION b/VERSION
index 57f52717b..1182b2ff7 100644
--- a/VERSION
+++ b/VERSION
@@ -1 +1 @@
-v1.8.2
+v1.8.3

From e40491f44af3439ef2b39f4a18863fb48e041042 Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Wed, 11 Sep 2019 14:38:22 -0700
Subject: [PATCH 614/838] sorted out AVX2 build problems on Windows

---
 regression-tests                  |  2 +-
 src/3rd_party/avx_mathfun.h       | 21 ++++++++++++--------
 src/3rd_party/half_float/umHalf.h |  8 ++++----
 src/3rd_party/nccl                |  2 +-
 vs/Marian.vcxproj                 | 16 ++++++++++++---
 vs/Marian.vcxproj.filters         | 33 +++++++++++++++++++++++++++++++
 6 files changed, 65 insertions(+), 17 deletions(-)
 mode change 100644 => 100755 src/3rd_party/avx_mathfun.h
 mode change 100644 => 100755 src/3rd_party/half_float/umHalf.h

diff --git a/regression-tests b/regression-tests
index 71b0506a5..658cf86e2 160000
--- a/regression-tests
+++ b/regression-tests
@@ -1 +1 @@
-Subproject commit 71b0506a50e1f181e269fe90073a8ae470676dd1
+Subproject commit 658cf86e2ab4aa76b4ab3edabd750b93d2416f1a
diff --git a/src/3rd_party/avx_mathfun.h b/src/3rd_party/avx_mathfun.h
old mode 100644
new mode 100755
index 26b3132a8..9d1403b4f
--- a/src/3rd_party/avx_mathfun.h
+++ b/src/3rd_party/avx_mathfun.h
@@ -32,8 +32,13 @@
 #include <immintrin.h>
 
 /* yes I know, the top of this file is quite ugly */
+#ifdef _MSC_VER
+# define ALIGN32_BEG __declspec(align(32))
+# define ALIGN32_END 
+#else /* gcc or icc */
 # define ALIGN32_BEG
 # define ALIGN32_END __attribute__((aligned(32)))
+#endif
 
 /* __m128 is ugly to write */
 typedef __m256  v8sf; // vector of 8 float (avx)
@@ -94,17 +99,17 @@ typedef union imm_xmm_union {
   v4si xmm[2];
 } imm_xmm_union;
 
-#define COPY_IMM_TO_XMM(imm_, xmm0_, xmm1_) {    \
-    imm_xmm_union u __attribute__((aligned(32)));  \
-    u.imm = imm_;				   \
-    xmm0_ = u.xmm[0];                            \
-    xmm1_ = u.xmm[1];                            \
+#define COPY_IMM_TO_XMM(imm_, xmm0_, xmm1_) { \
+    ALIGN32_BEG imm_xmm_union u ALIGN32_END;  \
+    u.imm = imm_;                             \
+    xmm0_ = u.xmm[0];                         \
+    xmm1_ = u.xmm[1];                         \
 }
 
-#define COPY_XMM_TO_IMM(xmm0_, xmm1_, imm_) {                       \
-    imm_xmm_union u __attribute__((aligned(32))); \
+#define COPY_XMM_TO_IMM(xmm0_, xmm1_, imm_) {     \
+    ALIGN32_BEG imm_xmm_union u ALIGN32_END;      \
     u.xmm[0]=xmm0_; u.xmm[1]=xmm1_; imm_ = u.imm; \
-  }
+}
 
 
 #define AVX2_BITOP_USING_SSE2(fn) \
diff --git a/src/3rd_party/half_float/umHalf.h b/src/3rd_party/half_float/umHalf.h
old mode 100644
new mode 100755
index 4c065c02d..361dbdaa8
--- a/src/3rd_party/half_float/umHalf.h
+++ b/src/3rd_party/half_float/umHalf.h
@@ -44,11 +44,11 @@ OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
 #include <limits>
 #include <algorithm>
 
-#ifdef _MSC_VER
-#include "stdint.h"
-#else
+//#ifdef _MSC_VER
+//#include "stdint.h"
+//#else
 #include <stdint.h>
-#endif
+//#endif
 
 #undef min
 #undef max
diff --git a/src/3rd_party/nccl b/src/3rd_party/nccl
index 8e3a3f7c5..d6297d250 160000
--- a/src/3rd_party/nccl
+++ b/src/3rd_party/nccl
@@ -1 +1 @@
-Subproject commit 8e3a3f7c5b520babff49cec54a866fa3eda3a3b6
+Subproject commit d6297d250433715c283d17f1969cfcb50d2b6531
diff --git a/vs/Marian.vcxproj b/vs/Marian.vcxproj
index 0de76f656..915901608 100755
--- a/vs/Marian.vcxproj
+++ b/vs/Marian.vcxproj
@@ -42,7 +42,7 @@
   <PropertyGroup Condition="'$(Configuration)|$(Platform)'=='Debug|x64'">
     <LinkIncremental>true</LinkIncremental>
     <IntDir>$(SolutionDir)$(Platform)\$(Configuration)\Marian\</IntDir>
-    <IncludePath>%CUDA_PATH%\include;..\src;..\src\3rd_party;%BOOST_INCLUDE_PATH%;%ZLIB_PATH%\include;%MKL_PATH%\include;$(VC_IncludePath);$(WindowsSDK_IncludePath);</IncludePath>
+    <IncludePath>..\src\3rd_party\fbgemm\third_party\cpuinfo\deps\clog\include;..\src\3rd_party\fbgemm\third_party\cpuinfo\src;..\src\3rd_party\fbgemm\third_party\cpuinfo\include;..\src\3rd_party\fbgemm\third_party\asmjit\src;%MKL_PATH%\include;..\src\3rd_party\fbgemm\include;%CUDA_PATH%\include;..\src;..\src\3rd_party;%BOOST_INCLUDE_PATH%;%ZLIB_PATH%\include;$(VC_IncludePath);$(WindowsSDK_IncludePath);</IncludePath>
     <LibraryPath>%CUDA_PATH%\lib\x64;%BOOST_LIB_PATH%;%ZLIB_PATH%\lib;%MKL_PATH%\lib\intel64;$(VC_LibraryPath_x64);$(WindowsSDK_LibraryPath_x64);$(NETFXKitsDir)Lib\um\x64</LibraryPath>
   </PropertyGroup>
   <PropertyGroup Condition="'$(Configuration)|$(Platform)'=='Release|x64'">
@@ -69,7 +69,7 @@
       </PrecompiledHeader>
       <WarningLevel>Level4</WarningLevel>
       <Optimization>Disabled</Optimization>
-      <PreprocessorDefinitions>FBGEMM_EXPORTS;USE_FBGEMM=1;ASMJIT_VARAPI;CUDA_FOUND=1; MKL_FOUND=1; MPI_FOUND=1; BLAS_FOUND=1; MKL_ILP64; WIN32;_DEBUG;_CONSOLE;_LIB;%(PreprocessorDefinitions)</PreprocessorDefinitions>
+      <PreprocessorDefinitions>FBGEMM_EXPORTS; USE_FBGEMM=1; ASMJIT_VARAPI; USE_SSE2=1; CUDA_FOUND=1; MKL_FOUND=1; MPI_FOUND=1; BLAS_FOUND=1; MKL_ILP64; WIN32;_DEBUG;_CONSOLE;_LIB;%(PreprocessorDefinitions)</PreprocessorDefinitions>
       <SDLCheck>false</SDLCheck>
       <TreatWarningAsError>true</TreatWarningAsError>
       <AdditionalOptions>/bigobj %(AdditionalOptions) /arch:AVX2</AdditionalOptions>
@@ -105,7 +105,7 @@
       <Optimization>MaxSpeed</Optimization>
       <FunctionLevelLinking>true</FunctionLevelLinking>
       <IntrinsicFunctions>true</IntrinsicFunctions>
-      <PreprocessorDefinitions>FBGEMM_EXPORTS;USE_FBGEMM=1;CUDA_FOUND=1; MKL_FOUND=1; MPI_FOUND=1; BLAS_FOUND=1; MKL_ILP64; WIN32;NDEBUG;_CONSOLE;_LIB;%(PreprocessorDefinitions)</PreprocessorDefinitions>
+      <PreprocessorDefinitions>FBGEMM_EXPORTS; USE_FBGEMM=1; USE_SSE2=1; CUDA_FOUND=1; MKL_FOUND=1; MPI_FOUND=1; BLAS_FOUND=1; MKL_ILP64; WIN32;NDEBUG;_CONSOLE;_LIB;%(PreprocessorDefinitions)</PreprocessorDefinitions>
       <SDLCheck>false</SDLCheck>
       <FavorSizeOrSpeed>Speed</FavorSizeOrSpeed>
       <AdditionalOptions>/d2Zi+ /bigobj %(AdditionalOptions) /arch:AVX2</AdditionalOptions>
@@ -426,6 +426,7 @@
       <TreatWarningAsError Condition="'$(Configuration)|$(Platform)'=='Release|x64'">false</TreatWarningAsError>
       <TreatWarningAsError Condition="'$(Configuration)|$(Platform)'=='Debug|x64'">false</TreatWarningAsError>
     </ClCompile>
+    <ClCompile Include="..\src\3rd_party\half_float\HalfPrecisionFloatTest.cpp" />
     <ClCompile Include="..\src\3rd_party\pathie-cpp\src\entry_iterator.cpp" />
     <ClCompile Include="..\src\3rd_party\pathie-cpp\src\errors.cpp" />
     <ClCompile Include="..\src\3rd_party\pathie-cpp\src\path.cpp" />
@@ -635,6 +636,8 @@
     </ClCompile>
     <ClCompile Include="..\src\3rd_party\yaml-cpp\binary_renamed.cpp" />
     <ClCompile Include="..\src\3rd_party\yaml-cpp\yaml-node.cpp" />
+    <ClInclude Include="..\src\3rd_party\any_type.h" />
+    <ClInclude Include="..\src\3rd_party\avx_mathfun.h" />
     <ClInclude Include="..\src\3rd_party\ExceptionWithCallStack.h" />
     <ClInclude Include="..\src\3rd_party\fbgemm\include\fbgemm\ConvUtils.h" />
     <ClInclude Include="..\src\3rd_party\fbgemm\include\fbgemm\Fbgemm.h" />
@@ -709,6 +712,8 @@
     <ClInclude Include="..\src\3rd_party\fbgemm\third_party\cpuinfo\src\x86\api.h" />
     <ClInclude Include="..\src\3rd_party\fbgemm\third_party\cpuinfo\src\x86\cpuid.h" />
     <ClInclude Include="..\src\3rd_party\fbgemm\third_party\cpuinfo\src\x86\windows\api.h" />
+    <ClInclude Include="..\src\3rd_party\half_float\stdint.h" />
+    <ClInclude Include="..\src\3rd_party\half_float\umHalf.h" />
     <ClInclude Include="..\src\3rd_party\nccl\src\collectives\collectives.h">
       <ExcludedFromBuild Condition="'$(Configuration)|$(Platform)'=='Release|x64'">true</ExcludedFromBuild>
     </ClInclude>
@@ -906,6 +911,7 @@
       <ExcludedFromBuild Condition="'$(Configuration)|$(Platform)'=='Debug|x64'">true</ExcludedFromBuild>
       <ExcludedFromBuild Condition="'$(Configuration)|$(Platform)'=='Release|x64'">true</ExcludedFromBuild>
     </ClInclude>
+    <ClInclude Include="..\src\3rd_party\sse_mathfun.h" />
     <ClInclude Include="..\src\command\marian_decoder.cpp">
       <ExcludedFromBuild Condition="'$(Configuration)|$(Platform)'=='Debug|x64'">true</ExcludedFromBuild>
       <ExcludedFromBuild Condition="'$(Configuration)|$(Platform)'=='Release|x64'">true</ExcludedFromBuild>
@@ -1100,6 +1106,8 @@
     <ClInclude Include="..\src\examples\mnist\model_lenet.h" />
     <ClInclude Include="..\src\examples\mnist\training.h" />
     <ClInclude Include="..\src\examples\mnist\validator.h" />
+    <ClInclude Include="..\src\functional\approx.h" />
+    <ClInclude Include="..\src\functional\operators.h" />
     <ClInclude Include="..\src\layers\loss.h" />
     <ClInclude Include="..\src\layers\weight.h" />
     <ClInclude Include="..\src\marian.h" />
@@ -1369,6 +1377,8 @@
     <ClInclude Include="..\src\training\communicator_nccl.h" />
   </ItemGroup>
   <ItemGroup>
+    <None Include="..\src\3rd_party\half_float\Readme.md" />
+    <None Include="..\src\3rd_party\half_float\umHalf.inl" />
     <None Include="..\src\3rd_party\nccl\src\bootstrap.cu">
       <ExcludedFromBuild Condition="'$(Configuration)|$(Platform)'=='Release|x64'">true</ExcludedFromBuild>
     </None>
diff --git a/vs/Marian.vcxproj.filters b/vs/Marian.vcxproj.filters
index f905636a7..5ef68d74d 100755
--- a/vs/Marian.vcxproj.filters
+++ b/vs/Marian.vcxproj.filters
@@ -712,6 +712,9 @@
     <ClCompile Include="..\src\common\aliases.cpp">
       <Filter>common</Filter>
     </ClCompile>
+    <ClCompile Include="..\src\3rd_party\half_float\HalfPrecisionFloatTest.cpp">
+      <Filter>3rd_party\half_float</Filter>
+    </ClCompile>
   </ItemGroup>
   <ItemGroup>
     <ClInclude Include="..\src\marian.h" />
@@ -2002,6 +2005,27 @@
     <ClInclude Include="..\src\3rd_party\fbgemm\third_party\cpuinfo\deps\clog\include\clog.h">
       <Filter>3rd_party\fbgemm\third_party\cpuinfo\deps\clog\include</Filter>
     </ClInclude>
+    <ClInclude Include="..\src\3rd_party\half_float\stdint.h">
+      <Filter>3rd_party\half_float</Filter>
+    </ClInclude>
+    <ClInclude Include="..\src\3rd_party\half_float\umHalf.h">
+      <Filter>3rd_party\half_float</Filter>
+    </ClInclude>
+    <ClInclude Include="..\src\3rd_party\any_type.h">
+      <Filter>3rd_party</Filter>
+    </ClInclude>
+    <ClInclude Include="..\src\3rd_party\avx_mathfun.h">
+      <Filter>3rd_party</Filter>
+    </ClInclude>
+    <ClInclude Include="..\src\3rd_party\sse_mathfun.h">
+      <Filter>3rd_party</Filter>
+    </ClInclude>
+    <ClInclude Include="..\src\functional\approx.h">
+      <Filter>functional</Filter>
+    </ClInclude>
+    <ClInclude Include="..\src\functional\operators.h">
+      <Filter>functional</Filter>
+    </ClInclude>
   </ItemGroup>
   <ItemGroup>
     <Filter Include="3rd_party">
@@ -2232,6 +2256,9 @@
     <Filter Include="3rd_party\fbgemm\third_party\cpuinfo\deps\clog\src">
       <UniqueIdentifier>{8fd74b1e-d3c1-4158-ad46-4a447222934e}</UniqueIdentifier>
     </Filter>
+    <Filter Include="3rd_party\half_float">
+      <UniqueIdentifier>{defd3aec-3c56-4d70-a4bb-90ba9003d98d}</UniqueIdentifier>
+    </Filter>
   </ItemGroup>
   <ItemGroup>
     <None Include="..\src\3rd_party\nccl\src\bootstrap.cu">
@@ -2354,6 +2381,12 @@
     <None Include="..\src\examples\README.md">
       <Filter>examples</Filter>
     </None>
+    <None Include="..\src\3rd_party\half_float\Readme.md">
+      <Filter>3rd_party\half_float</Filter>
+    </None>
+    <None Include="..\src\3rd_party\half_float\umHalf.inl">
+      <Filter>3rd_party\half_float</Filter>
+    </None>
   </ItemGroup>
   <ItemGroup>
     <Text Include="..\src\3rd_party\sentencepiece\src\CMakeLists.txt">

From 0e266b117e0dfa5f71fa1555afccf052e51ee42a Mon Sep 17 00:00:00 2001
From: Marcin Junczys-Dowmunt <Marcin.JunczysDowmunt@microsoft.com>
Date: Wed, 11 Sep 2019 15:27:48 -0700
Subject: [PATCH 615/838] make compile with gcc-7

---
 src/common/types.h | 7 +++++++
 1 file changed, 7 insertions(+)

diff --git a/src/common/types.h b/src/common/types.h
index e41045195..64ab28e2c 100644
--- a/src/common/types.h
+++ b/src/common/types.h
@@ -1,6 +1,13 @@
 #pragma once
 
+#if __GNUC__ >= 7
+#pragma GCC diagnostic push
+#pragma GCC diagnostic ignored "-Wint-in-bool-context"
+#endif
 #include "half_float/umHalf.h"
+#if __GNUC__ >= 7
+#pragma GCC diagnostic pop
+#endif
 
 #include <iostream>
 #include <string>

From 8ae0706fa17e1ce30c3f440f9f9bb16e276d8beb Mon Sep 17 00:00:00 2001
From: Marcin Junczys-Dowmunt <Marcin.JunczysDowmunt@microsoft.com>
Date: Wed, 11 Sep 2019 17:16:46 -0700
Subject: [PATCH 616/838] clean up fp16 macros, enable fp16 tests

---
 src/common/types.h                  |   6 +
 src/functional/operators.h          |  37 ++++--
 src/tensors/gpu/add.cu              |   4 +-
 src/tensors/gpu/algorithm.cu        |  13 +-
 src/tensors/gpu/cuda_helpers.h      |  11 +-
 src/tensors/gpu/element.cu          |   8 +-
 src/tests/units/attention_tests.cpp |  28 ++--
 src/tests/units/graph_tests.cpp     |  15 +++
 src/tests/units/operator_tests.cpp  | 196 +++++++++++++++-------------
 src/tests/units/rnn_tests.cpp       |  37 +++---
 10 files changed, 218 insertions(+), 137 deletions(-)
 mode change 100644 => 100755 src/tests/units/attention_tests.cpp
 mode change 100644 => 100755 src/tests/units/operator_tests.cpp
 mode change 100644 => 100755 src/tests/units/rnn_tests.cpp

diff --git a/src/common/types.h b/src/common/types.h
index 64ab28e2c..39d9fcd2c 100644
--- a/src/common/types.h
+++ b/src/common/types.h
@@ -17,6 +17,12 @@
 #include <immintrin.h>
 #endif
 
+#ifdef __CUDA_ARCH__
+#if __CUDA_ARCH__ >= 700 && CUDA_VERSION >= 10000
+#define __USE_FP16__
+#endif
+#endif
+
 #define DISPATCH_BY_TYPE0(type, func) \
 do { \
   switch(type) { \
diff --git a/src/functional/operators.h b/src/functional/operators.h
index 1493f75d0..691f2c393 100644
--- a/src/functional/operators.h
+++ b/src/functional/operators.h
@@ -3,11 +3,6 @@
 #include "common/types.h"
 #include <cmath>
 
-// only compile with fp16 support for compute_70, i.e. VOLTA 100 and above.
-#if __CUDA_ARCH__ >= 700
-#include <cuda_fp16.h>
-#endif
-
 namespace marian {
 namespace functional {
 
@@ -191,12 +186,18 @@ struct Ops<double> {
 
 };
 
+} // end namespace functional
+} // end namespace marian
+
 // stay invisible to NVCC as it seems to have problems with intrinsics;
 // will still be compiled into the binary by cpu-side gcc/g++
 #ifndef __CUDA_ARCH__
 
 #include "3rd_party/sse_mathfun.h"
 
+namespace marian {
+namespace functional {
+
 // Specialization for float32x8 (=__m128, CPU SSE intrisics)
 template <>
 struct Ops<float32x4> {
@@ -314,8 +315,14 @@ struct Ops<float32x4> {
 
 };
 
+} // end namespace functional
+} // end namespace marian
+
 #include "3rd_party/avx_mathfun.h"
 
+namespace marian {
+namespace functional {
+
 //*******************************************************************************************
 // Specialization for float32x8 (=__m256, CPU AVX intrisics)
 template <>
@@ -427,9 +434,16 @@ struct Ops<float32x8> {
   }
 };
 
+}}
+
 #endif
 
-#if __CUDA_ARCH__ >= 700
+#ifdef __USE_FP16__
+// only compile with fp16 support for compute_70, i.e. VOLTA 100 and above.
+#include <cuda_fp16.h>
+
+namespace marian {
+namespace functional {
 
 // Specialization for half
 template <>
@@ -525,6 +539,9 @@ struct Ops<half> {
 
 };
 
+} // end namespace functional
+} // end namespace marian
+
 #endif
 
 //*******************************************************************************************
@@ -532,6 +549,9 @@ struct Ops<half> {
 #include "functional/defs.h"
 #include "functional/predicates.h"
 
+namespace marian {
+namespace functional {
+
 UNARY(Tanh,    tanh,       Ops<ElementType>::tanh(x));
 UNARY(Sin,     sin,        Ops<ElementType>::sin(x));
 UNARY(Cos,     cos,        Ops<ElementType>::cos(x));
@@ -574,6 +594,5 @@ UNARY(sReLUBack,   ReLUback,  Ops<ElementType>::reluBack(x));
 BINARY(sPReLU,     PReLU,     Ops<ElementType>::prelu(x, y));
 BINARY(sPReLUBack, PReLUback, Ops<ElementType>::preluBack(x, y));
 
-
-}
-}
\ No newline at end of file
+} // end namespace functional
+} // end namespace marian
\ No newline at end of file
diff --git a/src/tensors/gpu/add.cu b/src/tensors/gpu/add.cu
index 92d364bf7..c08d91e5b 100755
--- a/src/tensors/gpu/add.cu
+++ b/src/tensors/gpu/add.cu
@@ -170,9 +170,11 @@ template <class Functor, class AggFunctor, class... Tensors>
 void Aggregate(Functor functor, float aggInit, AggFunctor aggFunctor, float scale, marian::Tensor out, Tensors... tensors) {
   if(out->type() == Type::float32) {
     AggregateTyped<float, float>(functor, aggInit, aggFunctor, scale, out, tensors...);
-#if 0
   } else if(out->type() == Type::float16) {
+#ifdef __USE_FP16__
     AggregateTyped<half,  float>(functor, aggInit, aggFunctor, scale, out, tensors...);
+#else
+    ABORT("FP16 not supported with current hardware or CUDA version");
 #endif
   } else {
     ABORT("Type {} not yet supported", out->type());
diff --git a/src/tensors/gpu/algorithm.cu b/src/tensors/gpu/algorithm.cu
index 6c4369251..13ad57151 100644
--- a/src/tensors/gpu/algorithm.cu
+++ b/src/tensors/gpu/algorithm.cu
@@ -5,10 +5,6 @@
 #include "tensors/gpu/cuda_helpers.h"
 // clang-format on
 
-#include <cuda_fp16.h>
-
-// @TODO: look at typedefs and decide whether to use float16, half or __half.
-
 namespace marian {
 namespace gpu {
 
@@ -32,6 +28,7 @@ template void copy<uint64_t>(Ptr<Backend>, const uint64_t*, const uint64_t*, uin
 
 template void copy<char>(Ptr<Backend>, const char*, const char*, char*);
 template void copy<float16>(Ptr<Backend>, const float16*, const float16*, float16*);
+
 template void copy<float>(Ptr<Backend>, const float*, const float*, float*);
 template void copy<double>(Ptr<Backend>, const double*, const double*, double*);
 // clang-format on
@@ -65,11 +62,15 @@ void fill<float16>(Ptr<Backend> backend, float16* begin, float16* end, float16 v
   int size = end - begin;
   if (size == 0)
     return;
+#ifdef __USE_FP16__
   CUDA_CHECK(cudaSetDevice(backend->getDeviceId().no));
   int threadsPerBlock = std::min(MAX_THREADS, size);
   int blocks = (size / threadsPerBlock) + (size % threadsPerBlock != 0); // @TODO: (size+threadsPerBlock-1)/threadsPerBlock or CeilDiv(a,b)
   gFill<<<blocks, threadsPerBlock>>>((__half*)begin, size, (__half)value);
   CUDA_CHECK(cudaStreamSynchronize(0));
+#else
+  ABORT("FP16 not supported with current hardware or CUDA version");
+#endif
 }
 
 template void fill<bool>(Ptr<Backend>, bool*, bool*, bool);
@@ -125,11 +126,15 @@ void swap_ranges<float16>(Ptr<Backend> backend, float16* begin, float16* end, fl
   if (size == 0)
     return;
 
+#ifdef __USE_FP16__
   CUDA_CHECK(cudaSetDevice(backend->getDeviceId().no));
   int threadsPerBlock = std::min(MAX_THREADS, size);
   int blocks = (size / threadsPerBlock) + (size % threadsPerBlock != 0); // @TODO: (size+threadsPerBlock-1)/threadsPerBlock or CeilDiv(a,b)
   gSwap<<<blocks, threadsPerBlock>>>((__half*)begin, (__half*)dest, size);
   CUDA_CHECK(cudaStreamSynchronize(0));
+#else
+  ABORT("FP16 not supported with current hardware or CUDA version");
+#endif
 }
 
 // clang-format off
diff --git a/src/tensors/gpu/cuda_helpers.h b/src/tensors/gpu/cuda_helpers.h
index 920cec8c4..593ae7e59 100644
--- a/src/tensors/gpu/cuda_helpers.h
+++ b/src/tensors/gpu/cuda_helpers.h
@@ -1,6 +1,15 @@
 #pragma once
 #include "common/logging.h"
-#include "cuda_runtime.h"
+#include "common/types.h"
+
+#include <cuda_runtime.h>
+
+#ifdef __USE_FP16__
+#include <cuda_fp16.h>
+#endif
+
+// template <> inline bool matchType<__half>(Type type)  { return type == Type::float16; }
+// template <> inline std::string request<__half>()  { return "float16"; }
 
 // fixes a missing constant in CUDA device code
 #define CUDA_FLT_MAX 1.70141e+38; // note: 'static __constant__' causes a warning on gcc; non-static fails CUDA, so #define instead
diff --git a/src/tensors/gpu/element.cu b/src/tensors/gpu/element.cu
index 456958474..ff1689d7b 100755
--- a/src/tensors/gpu/element.cu
+++ b/src/tensors/gpu/element.cu
@@ -62,12 +62,14 @@ void Element(Functor functor, Tensor out, Tensors... tensors) {
 
   if(out->type() == Type::float32) {
     ElementTyped<float>(functor, out, tensors...);
-#if 0
   } else if(out->type() == Type::float16) {
-    ElementTyped<half>(functor, out, tensors...);
+#ifdef __USE_FP16__
+    ElementTyped<__half>(functor, out, tensors...);
+#else
+    ABORT("FP16 not supported with chosen current hardware or CUDA version");
+#endif
   } else if(out->type() == Type::float64) {
     ElementTyped<double>(functor, out, tensors...);
-#endif
   } else {
     ABORT("Type {} not yet supported", out->type());
   }
diff --git a/src/tests/units/attention_tests.cpp b/src/tests/units/attention_tests.cpp
old mode 100644
new mode 100755
index 652dd4d2f..b56f56022
--- a/src/tests/units/attention_tests.cpp
+++ b/src/tests/units/attention_tests.cpp
@@ -7,12 +7,13 @@
 
 using namespace marian;
 
-void tests(DeviceType type) {
-  auto floatApprox = [](float x, float y) { return x == Approx(y).epsilon(0.01); };
+template <typename T>
+void tests(DeviceType type, Type floatType = Type::float32) {
+  auto floatApprox = [](T x, T y) { return x == Approx(y).epsilon(0.01); };
 
   Config::seed = 1234;
 
-  std::vector<WordIndex> vWords = {
+  std::vector<IndexType> vWords = {
     43, 2, 83, 78,
     6, 38, 80, 40,
     40, 70, 26, 60,
@@ -23,7 +24,7 @@ void tests(DeviceType type) {
     0, 0, 0, 0
   };
 
-  std::vector<float> vMask = {
+  std::vector<T> vMask = {
     1, 1, 1, 1,
     1, 1, 1, 1,
     1, 1, 1, 1,
@@ -36,10 +37,11 @@ void tests(DeviceType type) {
 
   SECTION("Attention over encoder context") {
     auto graph = New<ExpressionGraph>();
+    graph->setParameterType(floatType);
     graph->setDevice({0, type});
     graph->reserveWorkspaceMB(16);
 
-    std::vector<float> values;
+    std::vector<T> values;
 
     int dimEmb = 16;
     int dimBatch = 4;
@@ -71,7 +73,7 @@ void tests(DeviceType type) {
 
     auto att = New<rnn::Attention>(graph, options, encState);
 
-    std::vector<float> vState(64);
+    std::vector<T> vState(64);
     std::generate(vState.begin(), vState.end(),
                   [](){ static int n = -32; return n++ / 64.f; });
 
@@ -86,7 +88,7 @@ void tests(DeviceType type) {
     CHECK(aligned->shape() == Shape({1, 1, 4, 8}));
 
 #ifdef CUDA_FOUND
-    std::vector<float> vAligned({
+    std::vector<T> vAligned({
       0.0396688, -0.0124071, -0.0159668, -0.00080064,
       -0.0132853, 0.0240206, 0.0744701, -0.0248388,
       0.0258906, -0.00868394, -0.0374499, 0.0357639,
@@ -97,7 +99,7 @@ void tests(DeviceType type) {
       -0.0330807, 0.018745, 0.0341848, -0.0111661
     });
 #else
-    std::vector<float> vAligned({
+    std::vector<T> vAligned({
       -0.061056, 0.0262615, -0.0393096, 0.115902,
       0.0941305, 0.00475613, -0.0159573, 0.00293181,
       -0.0919751, -0.018913, 0.00927365, -0.000343846,
@@ -116,12 +118,18 @@ void tests(DeviceType type) {
 
 #ifdef CUDA_FOUND
 TEST_CASE("Model components, Attention (gpu)", "[attention]") {
-  tests(DeviceType::gpu);
+  tests<float>(DeviceType::gpu);
+}
+#endif
+
+#ifdef CUDA_FOUND
+TEST_CASE("Model components, Attention (gpu, fp16)", "[attention]") {
+  tests<float16>(DeviceType::gpu, Type::float16);
 }
 #endif
 
 #ifdef BLAS_FOUND
 TEST_CASE("Model components, Attention (cpu)", "[attention]") {
-  tests(DeviceType::cpu);
+  tests<float>(DeviceType::cpu);
 }
 #endif
diff --git a/src/tests/units/graph_tests.cpp b/src/tests/units/graph_tests.cpp
index 121969d33..28b0e5b5c 100644
--- a/src/tests/units/graph_tests.cpp
+++ b/src/tests/units/graph_tests.cpp
@@ -53,6 +53,21 @@ TEST_CASE("Expression graph can be initialized with constant values",
     vals->val()->get(values);
     REQUIRE(values == v);
   }
+
+  SECTION("initializing float16 node from vector") {
+    graph->clear();
+    std::vector<float16> values16;
+    std::vector<float16> v({1, 2, 3, 4, 5, 6});
+    auto vals1 = graph->param("vs1", {2, 3}, inits::fromVector(v), Type::float16);
+    auto vals2 = graph->param("vs2", {2, 3}, inits::fromValue(5), Type::float16);
+
+    debug(vals1 + vals2);
+    graph->forward();
+
+    REQUIRE(values.empty());
+    vals1->val()->get(values16);
+    REQUIRE(values16 == v);
+  }
 }
 #endif
 
diff --git a/src/tests/units/operator_tests.cpp b/src/tests/units/operator_tests.cpp
old mode 100644
new mode 100755
index 444532316..1b633762e
--- a/src/tests/units/operator_tests.cpp
+++ b/src/tests/units/operator_tests.cpp
@@ -5,22 +5,23 @@
 
 using namespace marian;
 
-void tests(DeviceType device) {
-  auto floatApprox = [](float x, float y) -> bool { return x == Approx(y); };
-  auto floatEqual  = [](float x, float y) -> bool { return x == y; };
+template <typename T>
+void tests(DeviceType device, Type floatType = Type::float32) {
+  auto floatApprox = [](T x, T y) -> bool { return x == Approx(y).epsilon(0.01); };
+  auto floatEqual  = [](T x, T y) -> bool { return x == y; };
 
   Config::seed = 1234;
-
   auto graph = New<ExpressionGraph>();
+  graph->setParameterType(floatType);
   graph->setDevice({0, device});
   graph->reserveWorkspaceMB(16);
 
-  std::vector<float> values, values2;
+  std::vector<T> values, values2;
 
   SECTION("scalar multiplication") {
     graph->clear();
     values.clear();
-    std::vector<float> vB({1, 2, 3, 4, 5, 6});
+    std::vector<T> vB({1, 2, 3, 4, 5, 6});
 
     auto B = graph->param("B", {3, 2}, inits::fromVector(vB));
     auto B2 = B * 2.0f;
@@ -29,7 +30,7 @@ void tests(DeviceType device) {
     CHECK(B2->shape() == Shape({3, 2}));
     B2->val()->get(values);
 
-    std::vector<float> vB2({2, 4, 6, 8, 10, 12});
+    std::vector<T> vB2({2, 4, 6, 8, 10, 12});
     CHECK(values == vB2);
   }
 
@@ -37,8 +38,8 @@ void tests(DeviceType device) {
     graph->clear();
     values.clear();
 
-    std::vector<float> vA({1, -2, 3, -4});
-    std::vector<float> vB({0.5, 1.5});
+    std::vector<T> vA({1, -2, 3, -4});
+    std::vector<T> vB({0.5, 1.5});
 
     auto a = graph->constant({2, 2, 1}, inits::fromVector(vA));
     auto b = graph->constant({2, 1}, inits::fromVector(vB));
@@ -86,12 +87,12 @@ void tests(DeviceType device) {
     graph->clear();
     values.clear();
 
-    std::vector<float> vA({1, 2, 3, 4, 5, 6, 7, 8});
+    std::vector<T> vA({1, 2, 3, 4, 5, 6, 7, 8});
 
-    std::vector<float> vT1({1, 5, 2, 6, 3, 7, 4, 8});
-    std::vector<float> vT3({1, 2, 5, 6, 3, 4, 7, 8});
-    std::vector<float> vT4({1, 5, 3, 7, 2, 6, 4, 8});
-    std::vector<float> vT5({1, 2, 5, 6, 3, 4, 7, 8});
+    std::vector<T> vT1({1, 5, 2, 6, 3, 7, 4, 8});
+    std::vector<T> vT3({1, 2, 5, 6, 3, 4, 7, 8});
+    std::vector<T> vT4({1, 5, 3, 7, 2, 6, 4, 8});
+    std::vector<T> vT5({1, 2, 5, 6, 3, 4, 7, 8});
 
     auto a = graph->constant({2, 4}, inits::fromVector(vA));
 
@@ -136,12 +137,12 @@ void tests(DeviceType device) {
   SECTION("softmax and logsoftmax") {
     graph->clear();
     values.clear();
-    std::vector<float> in({-.2, -.3, 4.5, 5.2, -10, 101.45, -100.05, 1.05e-5});
+    std::vector<T> in({-.2, -.3, 4.5, 5.2, -10, 101.45, -100.05, 1.05e-5});
 
-    std::vector<float> smOut({ 0.52498f, 0.47502f, 0.33181f, 0.66819f,
+    std::vector<T> smOut({ 0.52498f, 0.47502f, 0.33181f, 0.66819f,
                                0.0f, 1.0f, 0.0f, 1.0f });
 
-    std::vector<float> lsmOut({ -0.6444f, -0.7444f, -1.10319f, -0.40319f,
+    std::vector<T> lsmOut({ -0.6444f, -0.7444f, -1.10319f, -0.40319f,
                                 -111.45f, 0.0f, -100.05001f, 0.0f });
 
     auto input = graph->constant({2, 2, 2}, inits::fromVector(in));
@@ -157,12 +158,12 @@ void tests(DeviceType device) {
     sm->val()->get(values);
 
     CHECK( std::equal(values.begin(), values.end(),
-                        smOut.begin(), floatApprox) );
+                      smOut.begin(), floatApprox) );
 
     lsm->val()->get(values);
 
     CHECK( std::equal(values.begin(), values.end(),
-                        lsmOut.begin(), floatApprox) );
+                      lsmOut.begin(), floatApprox) );
   }
 
   SECTION("layer normalization") {
@@ -170,13 +171,13 @@ void tests(DeviceType device) {
     values.clear();
 
 #ifdef CUDA_FOUND
-    std::vector<float> vLn({
+    std::vector<T> vLn({
       -1.1962, 1.43061, 0.380288, -0.614697, 0.816638, 0.622649,
       -1.69679, 0.257504, -1.12563, -0.151387, 1.61181, -0.334796,
       1.07207, -0.622614, 0.862014, -1.31147
     });
 #else
-    std::vector<float> vLn({
+    std::vector<T> vLn({
       -1.49821, -0.152206, 0.394932, 1.25548, -1.51701, -0.28032,
       0.9483, 0.849025, 0.855183, 1.11657, -0.788354, -1.1834,
       -0.85939, -1.13109, 0.972076, 1.01841
@@ -184,10 +185,8 @@ void tests(DeviceType device) {
 #endif
 
     auto a = graph->constant({2, 2, 4}, inits::glorotUniform());
-
     auto gamma = graph->param("gamma", {1, 4}, inits::ones());
     auto beta = graph->param("beta", {1, 4}, inits::zeros());
-
     auto ln = layerNorm(a, gamma, beta);
 
     graph->forward();
@@ -204,19 +203,19 @@ void tests(DeviceType device) {
     graph->clear();
     values.clear();
 
-    std::vector<float> vA({1, 6, 3, 8,
-                           5, 2, 7, 4});
+    std::vector<T> vA({1, 6, 3, 8,
+                       5, 2, 7, 4});
     // import numpy as np
     // a = np.array([[1, 6, 3, 8], [5, 2, 7, 4]])
-    std::vector<float> vS1({6, 8, 10, 12});              // s1 = np.sum(a, axis=0)
-    std::vector<float> vS2({18, 18});                    // np.sum(a, axis = 1)
-    std::vector<float> vS4({2.6925824f, 1.80277564f});   // np.std(a, axis = 1)
-    std::vector<float> vV5({7.25, 3.25});                // np.var(a, axis = 1)
-    std::vector<float> vM6({8, 7});                      // np.max(a, axis = 1)
-    std::vector<float> vM7({1, 2});                      // np.min(a, axis = 1)
-    std::vector<float> vP8({144, 280});                  // np.prod(a, axis = 1)
-    std::vector<float> vL9({8.13364336f, 7.17551536f});  // np.log(np.sum(np.exp(a), axis=1))
-    std::vector<float> vW({5.0f, 4.55555556f});          // np.mean(a*s1,axis=-1) / np.mean(s1,axis=-1)
+    std::vector<T> vS1({6, 8, 10, 12});              // s1 = np.sum(a, axis=0)
+    std::vector<T> vS2({18, 18});                    // np.sum(a, axis = 1)
+    std::vector<T> vS4({2.6925824f, 1.80277564f});   // np.std(a, axis = 1)
+    std::vector<T> vV5({7.25, 3.25});                // np.var(a, axis = 1)
+    std::vector<T> vM6({8, 7});                      // np.max(a, axis = 1)
+    std::vector<T> vM7({1, 2});                      // np.min(a, axis = 1)
+    std::vector<T> vP8({144, 280});                  // np.prod(a, axis = 1)
+    std::vector<T> vL9({8.13364336f, 7.17551536f});  // np.log(np.sum(np.exp(a), axis=1))
+    std::vector<T> vW({5.0f, 4.55555556f});          // np.mean(a*s1,axis=-1) / np.mean(s1,axis=-1)
 
     auto a = graph->constant({2, 4}, inits::fromVector(vA));
 
@@ -272,22 +271,22 @@ void tests(DeviceType device) {
     graph->clear();
     values.clear();
 
-    std::vector<float> vO1({ 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2,
+    std::vector<T> vO1({ 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2,
                              3, 3, 3, 3, 3, 3, 4, 4, 4, 4, 4, 4,
                              1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2,
                              3, 3, 3, 3, 3, 3, 4, 4, 4, 4, 4, 4});
 
-    std::vector<float> vO2({1, 1, 1, 2, 2, 2, 3, 3, 3, 4, 4, 4,
+    std::vector<T> vO2({1, 1, 1, 2, 2, 2, 3, 3, 3, 4, 4, 4,
                             1, 1, 1, 2, 2, 2, 3, 3, 3, 4, 4, 4,
                             1, 1, 1, 2, 2, 2, 3, 3, 3, 4, 4, 4,
                             1, 1, 1, 2, 2, 2, 3, 3, 3, 4, 4, 4});
 
-    std::vector<float> vO3({1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
+    std::vector<T> vO3({1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
                             2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
                             3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,
                             4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4});
 
-    std::vector<float> vO4({1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
+    std::vector<T> vO4({1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
                             2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
                             3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,
                             4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4});
@@ -326,17 +325,17 @@ void tests(DeviceType device) {
     graph->clear();
     values.clear();
 
-    std::vector<float> vA({1, 2, 3,
-                           4, 5, 6,
-                           7, 8, 9,
-                           10, 11, 12});
-    std::vector<float> vB({1, 2,
-                           3, 4,
-                           5, 6});
-    std::vector<float> vC({22, 28,
-                           49, 64,
-                           76, 100,
-                           103, 136});
+    std::vector<T> vA({1, 2, 3,
+                       4, 5, 6,
+                       7, 8, 9,
+                       10, 11, 12});
+    std::vector<T> vB({1, 2,
+                       3, 4,
+                       5, 6});
+    std::vector<T> vC({22, 28,
+                       49, 64,
+                       76, 100,
+                        103, 136});
 
     auto A = graph->param("A", {2, 2, 3}, inits::fromVector(vA));
     auto B = graph->param("B", {3, 2}, inits::fromVector(vB));
@@ -350,7 +349,8 @@ void tests(DeviceType device) {
     CHECK(values == vC);
   }
 
-  if(device == DeviceType::gpu) {
+  // Currently no support for fp16 or CPU - TODO use MKL for CPU, convert to float32 on the fly for fp16 via cast(x, Type::float16) or internally
+  if(device == DeviceType::gpu && floatType == Type::float32) {
     SECTION("csr-dot product") {
       graph->clear();
       values.clear();
@@ -382,13 +382,13 @@ void tests(DeviceType device) {
       auto STxSxDd = dot(S, SxDd, /*transA=*/true);
       auto SxDs = csr_dot( // sparse x dense
             S->shape(),
-            graph->constant({(int)SV.size()}, inits::fromVector(SV), Type::float32),
+            graph->constant({(int)SV.size()}, inits::fromVector(SV), floatType),
             graph->constant({(int)SI.size()}, inits::fromVector(SI), Type::uint32),
             graph->constant({(int)SO.size()}, inits::fromVector(SO), Type::uint32),
             D);
       auto STxSxDs = csr_dot(   // transpose(sparse) x dense; we use result of previous since dimensions match
             S->shape(),
-            graph->constant({(int)SV.size()}, inits::fromVector(SV), Type::float32),
+            graph->constant({(int)SV.size()}, inits::fromVector(SV), floatType),
             graph->constant({(int)SI.size()}, inits::fromVector(SI), Type::uint32),
             graph->constant({(int)SO.size()}, inits::fromVector(SO), Type::uint32),
             SxDd, /*transS=*/true);
@@ -398,14 +398,14 @@ void tests(DeviceType device) {
       auto DTxSTs = dot_csr( // dense x sparse
             DT,
             S->shape(),
-            graph->constant({(int)SV.size()}, inits::fromVector(SV), Type::float32),
+            graph->constant({(int)SV.size()}, inits::fromVector(SV), floatType),
             graph->constant({(int)SI.size()}, inits::fromVector(SI), Type::uint32),
             graph->constant({(int)SO.size()}, inits::fromVector(SO), Type::uint32),
             /*transS=*/true);
       auto DTxSTxSs = dot_csr( // dense x transpose(sparse)
             DTxSTd,
             S->shape(),
-            graph->constant({(int)SV.size()}, inits::fromVector(SV), Type::float32),
+            graph->constant({(int)SV.size()}, inits::fromVector(SV), floatType),
             graph->constant({(int)SI.size()}, inits::fromVector(SI), Type::uint32),
             graph->constant({(int)SO.size()}, inits::fromVector(SO), Type::uint32));
 
@@ -428,22 +428,24 @@ void tests(DeviceType device) {
     graph->clear();
     values.clear();
 
-    std::vector<float> vA({1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12});
-    std::vector<float> vB({1, 2, 3, 4, 5, 6});
-    std::vector<float> vAff({24, 30, 51, 66, 78, 102, 105, 138});
+    std::vector<T> vA({1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12});
+    std::vector<T> vB({1, 2, 3, 4, 5, 6});
+    std::vector<T> vAff({24, 30, 51, 66, 78, 102, 105, 138});
 
     auto A = graph->param("A", {4, 3}, inits::fromVector(vA));
     auto B = graph->param("B", {3, 2}, inits::fromVector(vB));
     auto C = graph->param("C", {4, 2}, inits::fromValue(2));
+
     auto aff1 = affine(A, B, C);
     auto aff2 = dot(A, B) + C;
+
     graph->forward();
 
     CHECK(aff1->shape() == Shape({4, 2}));
     aff1->val()->get(values);
     CHECK(values == vAff);
 
-    std::vector<float> values2;
+    std::vector<T> values2;
     CHECK(aff2->shape() == aff1->shape());
     aff2->val()->get(values2);
     CHECK(values2 == values);
@@ -453,9 +455,9 @@ void tests(DeviceType device) {
     graph->clear();
     values.clear();
 
-    std::vector<float> vA({1, 2, 3, 4, 5, 6});
-    std::vector<float> vB({1, 2, 3, 4, 5, 6, 1, 2, 3, 4, 5, 6});
-    std::vector<float> vC({1, 2, 3, 1, 2, 3, 4, 5, 6, 4, 5, 6});
+    std::vector<T> vA({1, 2, 3, 4, 5, 6});
+    std::vector<T> vB({1, 2, 3, 4, 5, 6, 1, 2, 3, 4, 5, 6});
+    std::vector<T> vC({1, 2, 3, 1, 2, 3, 4, 5, 6, 4, 5, 6});
 
     auto A = graph->param("A", {2,3}, inits::fromVector(vA));
     auto I = repeat(A, 1, 0);
@@ -480,7 +482,7 @@ void tests(DeviceType device) {
     graph->clear();
     values.clear();
 
-    std::vector<float> vIn({1, 2, 3, 4, 5, 6, 7, 8});
+    std::vector<T> vIn({1, 2, 3, 4, 5, 6, 7, 8});
 
     auto A = graph->param("A", {2, 4}, inits::fromVector(vIn));
     auto Af = flatten(A);
@@ -501,7 +503,7 @@ void tests(DeviceType device) {
     graph->clear();
     values.clear();
 
-    std::vector<float> vA({1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12});
+    std::vector<T> vA({1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12});
 
     std::vector<IndexType> iB0({0});            // first row
     std::vector<IndexType> iB1({0, 1, 2});      // several consecutive rows
@@ -510,12 +512,12 @@ void tests(DeviceType device) {
     std::vector<IndexType> iB4({1, 1});         // repeated rows
     std::vector<IndexType> iB5({0, 1, 2, 3});   // identity
     std::vector<IndexType> iB6({});             // empty
-    std::vector<float> vB0({1, 2, 3});
-    std::vector<float> vB1({1, 2, 3, 4, 5, 6, 7, 8, 9});
-    std::vector<float> vB2({1, 2, 3, 7, 8, 9});
-    std::vector<float> vB3({7, 8, 9, 4, 5, 6});
-    std::vector<float> vB4({4, 5, 6, 4, 5, 6});
-    std::vector<float> vB6;
+    std::vector<T> vB0({1, 2, 3});
+    std::vector<T> vB1({1, 2, 3, 4, 5, 6, 7, 8, 9});
+    std::vector<T> vB2({1, 2, 3, 7, 8, 9});
+    std::vector<T> vB3({7, 8, 9, 4, 5, 6});
+    std::vector<T> vB4({4, 5, 6, 4, 5, 6});
+    std::vector<T> vB6;
 
     auto A = graph->param("A", {4, 3}, inits::fromVector(vA));
     auto B0 = rows(A, iB0);
@@ -560,7 +562,7 @@ void tests(DeviceType device) {
     graph->clear();
     values.clear();
 
-    std::vector<float> vA({1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12});
+    std::vector<T> vA({1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12});
 
     std::vector<IndexType> iB0({0});            // first column
     std::vector<IndexType> iB1({0, 1, 2});      // several consecutive columns
@@ -570,12 +572,12 @@ void tests(DeviceType device) {
     std::vector<IndexType> iB5({0, 1, 2, 3});   // identity
     std::vector<IndexType> iB6({});             // empty
 
-    std::vector<float> vB0({1, 5, 9});
-    std::vector<float> vB1({1, 2, 3, 5, 6, 7, 9, 10, 11});
-    std::vector<float> vB2({1, 3, 5, 7, 9, 11});
-    std::vector<float> vB3({3, 2, 7, 6, 11, 10});
-    std::vector<float> vB4({2, 2, 6, 6, 10, 10});
-    std::vector<float> vB6;
+    std::vector<T> vB0({1, 5, 9});
+    std::vector<T> vB1({1, 2, 3, 5, 6, 7, 9, 10, 11});
+    std::vector<T> vB2({1, 3, 5, 7, 9, 11});
+    std::vector<T> vB3({3, 2, 7, 6, 11, 10});
+    std::vector<T> vB4({2, 2, 6, 6, 10, 10});
+    std::vector<T> vB6;
 
     auto A = graph->param("A", {3, 4}, inits::fromVector(vA));
     auto B0 = cols(A, iB0);
@@ -619,9 +621,9 @@ void tests(DeviceType device) {
   SECTION("relation of rows and columns selection using transpose") {
     graph->clear();
     values.clear();
-    std::vector<float> values2;
+    std::vector<T> values2;
 
-    std::vector<float> vA({0, .3333, -.2, -.3, 0, 4.5, 5.2, -10, 101.45, -100.05, 0, 1.05e-5});
+    std::vector<T> vA({0, .3333, -.2, -.3, 0, 4.5, 5.2, -10, 101.45, -100.05, 0, 1.05e-5});
     std::vector<IndexType> idx({0, 1});
 
     auto A1 = graph->param("4x3", {4,3}, inits::fromVector(vA));
@@ -652,28 +654,28 @@ void tests(DeviceType device) {
     graph->clear();
     values.clear();
 
-    std::vector<float> vA({  1, -2,   3,
+    std::vector<T> vA({  1, -2,   3,
                             -4,  5,  -6,
                              7, -8,   9,
                            -10, 11, -12});
-    std::vector<float> vC({ 1,  -2, // C = np.array([1, -2, 3, -4, 5, -6, 7, -8, 9, -10, 11, -12]).reshape((2, 3, 2))
+    std::vector<T> vC({ 1,  -2, // C = np.array([1, -2, 3, -4, 5, -6, 7, -8, 9, -10, 11, -12]).reshape((2, 3, 2))
                             3,  -4,
                             5,  -6,
 
                             7,  -8,
                             9, -10,
                            11, -12 });
-    std::vector<float> vB1({1, -2, 3});
-    std::vector<float> vB2({1, -4, 7, -10});
-    std::vector<float> vB3({-2, 5, -8, 11});
-    std::vector<float> vB4({1, -2, 3, -4, 5, -6});
-    std::vector<float> vD1(vB4);
-    std::vector<float> vD2({5, -6, 11, -12});
-    std::vector<float> vD3({1, -2, 5, -6, 7, -8, 11, -12}); // C[:,(0,2),:]
+    std::vector<T> vB1({1, -2, 3});
+    std::vector<T> vB2({1, -4, 7, -10});
+    std::vector<T> vB3({-2, 5, -8, 11});
+    std::vector<T> vB4({1, -2, 3, -4, 5, -6});
+    std::vector<T> vD1(vB4);
+    std::vector<T> vD2({5, -6, 11, -12});
+    std::vector<T> vD3({1, -2, 5, -6, 7, -8, 11, -12}); // C[:,(0,2),:]
     //std::vector<float> vD4({5, -6, 3, -4, 7, -8, 11, -12}); // [C[0,(2,1),:],C[1,(0,2),:]]
-    std::vector<float> vS1({7, -8, 9});
-    std::vector<float> vS2({-4, 5, -6, 7, -8, 9});
-    std::vector<float> vS3({7, -8, 9, -10, 11, -12});
+    std::vector<T> vS1({7, -8, 9});
+    std::vector<T> vS2({-4, 5, -6, 7, -8, 9});
+    std::vector<T> vS3({7, -8, 9, -10, 11, -12});
 
     auto A = graph->param("4x3", {4,3}, inits::fromVector(vA));
     auto B1a = index_select(A, 0, IndexVector({0})); // always uses gather()
@@ -729,9 +731,10 @@ void tests(DeviceType device) {
   SECTION("rows/cols as gather operations") {
     graph->clear();
     values.clear();
-    std::vector<float> values2;
+    std::vector<T> values2;
 
-    std::vector<float> vA({0, .3333, -.2, -.3, 0, 4.5, 5.2, -10, 101.45, -100.05, 0, 1.05e-5});
+
+    std::vector<T> vA({0, .3333, -.2, -.3, 0, 4.5, 5.2, -10, 101.45, -100.05, 0, 1.05e-5});
     std::vector<IndexType> indices({0, 2});
 
     auto A = graph->param("4x3", {4, 3}, inits::fromVector(vA));
@@ -739,6 +742,7 @@ void tests(DeviceType device) {
     auto B2 = gather(A, 0, graph->indices(indices, A, 0));
     auto C1 = cols(A, indices);
     auto C2 = gather(A, 1, graph->indices(indices, A, 1));
+
     graph->forward();
 
     CHECK(B1->shape() == B2->shape());
@@ -755,12 +759,16 @@ void tests(DeviceType device) {
 
 #ifdef CUDA_FOUND
 TEST_CASE("Expression graph supports basic math operations (gpu)", "[operator]") {
-  tests(DeviceType::gpu);
+  tests<float>(DeviceType::gpu);
+}
+
+TEST_CASE("Expression graph supports basic math operations (gpu fp16)", "[operator]") {
+  tests<float16>(DeviceType::gpu, Type::float16);
 }
 #endif
 
 #ifdef BLAS_FOUND
 TEST_CASE("Expression graph supports basic math operations (cpu)", "[operator]") {
-  tests(DeviceType::cpu);
+  tests<float>(DeviceType::cpu);
 }
 #endif
diff --git a/src/tests/units/rnn_tests.cpp b/src/tests/units/rnn_tests.cpp
old mode 100644
new mode 100755
index 8999d3d70..02f6979c7
--- a/src/tests/units/rnn_tests.cpp
+++ b/src/tests/units/rnn_tests.cpp
@@ -6,10 +6,11 @@
 
 using namespace marian;
 
-void tests(DeviceType type) {
-  auto floatApprox = [](float x, float y) { return x == Approx(y).epsilon(0.01); };
+template <typename T>
+void tests(DeviceType type, Type floatType = Type::float32) {
+  auto floatApprox = [](T x, T y) { return x == Approx(y).epsilon(0.01); };
 
-  std::vector<WordIndex> vWords = {
+  std::vector<IndexType> vWords = {
     43, 2, 83, 78,
     6, 38, 80, 40,
     40, 70, 26, 60,
@@ -20,7 +21,7 @@ void tests(DeviceType type) {
     0, 0, 0, 0
   };
 
-  std::vector<float> vMask = {
+  std::vector<T> vMask = {
     1, 1, 1, 1,
     1, 1, 1, 1,
     1, 1, 1, 1,
@@ -35,10 +36,11 @@ void tests(DeviceType type) {
     Config::seed = 1234;
 
     auto graph = New<ExpressionGraph>();
+    graph->setParameterType(floatType);
     graph->setDevice({0, type});
     graph->reserveWorkspaceMB(16);
 
-    std::vector<float> values;
+    std::vector<T> values;
 
     auto input = graph->constant({4, 1, 4},
                                  inits::glorotUniform());
@@ -58,14 +60,14 @@ void tests(DeviceType type) {
     CHECK(output->shape() == Shape({4, 1, 4}));
 
 #ifdef CUDA_FOUND
-    std::vector<float> vOutput({
+    std::vector<T> vOutput({
       0.637288, 0.906478, 0.603604, 0.152291,
       -0.5333, -0.854558, 0.458454, -0.179582,
       0.736857, 0.964425, 0.43848, 0.0261131,
       -0.533659, -0.733491, -0.953666, -0.965717
     });
 #else
-    std::vector<float> vOutput({
+    std::vector<T> vOutput({
       -0.523228, 0.645143, 0.430939, 0.273439,
       -0.747293, 0.131912, 0.115222, 0.363874,
       0.367535, -0.819531, -0.313036, -0.387701,
@@ -82,10 +84,11 @@ void tests(DeviceType type) {
     Config::seed = 1234;
 
     auto graph = New<ExpressionGraph>();
+    graph->setParameterType(floatType);
     graph->setDevice({0, type});
     graph->reserveWorkspaceMB(16);
 
-    std::vector<float> values;
+    std::vector<T> values;
 
     auto buildRnn = [&graph] (std::string prefix,
                               Expr input, Expr mask,
@@ -226,7 +229,7 @@ void tests(DeviceType type) {
     CHECK(contextSum1->shape() == Shape({dimTime, dimBatch, 1}));
 
 #ifdef CUDA_FOUND
-    std::vector<float> vContextSum1({
+    std::vector<T> vContextSum1({
       -0.110829, -0.510232, 0.265193, 0.194025,
       -0.242112, 0.185029, 0.0530527, 0.359336,
       0.60218, 0.46511, -0.240092, 0.100453,
@@ -237,7 +240,7 @@ void tests(DeviceType type) {
       0.360119, 0.422752, 0.55825, 0.0469481
     });
 #else
-    std::vector<float> vContextSum1({
+    std::vector<T> vContextSum1({
       -0.0674548, 0.383986, -0.613574, 0.226154,
       -0.819571, 0.47317, -1.39324, -0.401005,
       -0.24099, 0.64791, -0.120434, -0.818529,
@@ -256,7 +259,7 @@ void tests(DeviceType type) {
     CHECK(contextSum2->shape() == Shape({dimTime, dimBatch, 1}));
 
 #ifdef CUDA_FOUND
-    std::vector<float> vContextSum2({
+    std::vector<T> vContextSum2({
       -0.0282316, 0.0219561, -0.012136, 0.0206684,
       -0.0755229, 0.00091961, 0.0206883, 0.0176061,
       -0.0272491, 0.0833994, 0.0279131, 0.0170246,
@@ -267,7 +270,7 @@ void tests(DeviceType type) {
       0.123207, 0.0774718, 0.0741554, 0.0548368
     });
 #else
-    std::vector<float> vContextSum2({
+    std::vector<T> vContextSum2({
       0.0193405, -0.0580973, -0.0213983, 0.0381918,
       -0.0135365, -0.0934286, -0.0171637, 0.0198686,
       -0.0102693, -0.0865369, -0.0160779, 0.0393178,
@@ -285,7 +288,7 @@ void tests(DeviceType type) {
     //CHECK(context3->shape() == Shape({dimBatch, 2 * dimRnn, dimTime}));
     //CHECK(contextSum3->shape() == Shape({dimBatch, 1, dimTime}));
     //
-    //std::vector<float> vContextSum3({
+    //std::vector<T> vContextSum3({
     //  1.135, 2.40939, 2.37631, 2.03765,
     //  0.0583942, -4.89241, 5.31731, -1.52973,
     //  3.52754, 1.02098, -4.05162, -1.11594,
@@ -312,12 +315,16 @@ void tests(DeviceType type) {
 
 #ifdef CUDA_FOUND
 TEST_CASE("Model components, RNN etc. (gpu)", "[model]") {
-  tests(DeviceType::gpu);
+  tests<float>(DeviceType::gpu);
+}
+
+TEST_CASE("Model components, RNN etc. (gpu, fp16)", "[model]") {
+  tests<float16>(DeviceType::gpu, Type::float16);
 }
 #endif
 
 #ifdef BLAS_FOUND
 TEST_CASE("Model components, RNN etc. (cpu)", "[model]") {
-  tests(DeviceType::cpu);
+  tests<float>(DeviceType::cpu);
 }
 #endif

From 4498591d4b2815a7c5c324747080840b888ac8d7 Mon Sep 17 00:00:00 2001
From: Marcin Junczys-Dowmunt <Marcin.JunczysDowmunt@microsoft.com>
Date: Wed, 11 Sep 2019 20:06:31 -0700
Subject: [PATCH 617/838] towards fp16 inference

---
 src/common/types.h               |  4 ++--
 src/functional/operators.h       |  5 +++--
 src/graph/node_operators_unary.h | 12 +++++++++---
 src/tensors/gpu/element.cu       |  1 +
 4 files changed, 15 insertions(+), 7 deletions(-)

diff --git a/src/common/types.h b/src/common/types.h
index 39d9fcd2c..1b43cdee0 100644
--- a/src/common/types.h
+++ b/src/common/types.h
@@ -18,9 +18,9 @@
 #endif
 
 #ifdef __CUDA_ARCH__
-#if __CUDA_ARCH__ >= 700 && CUDA_VERSION >= 10000
+//#if __CUDA_ARCH__ >= 600 && CUDA_VERSION >= 10000 // @TODO: set this from 600 to 700, currently only testing on Pascal
 #define __USE_FP16__
-#endif
+//#endif
 #endif
 
 #define DISPATCH_BY_TYPE0(type, func) \
diff --git a/src/functional/operators.h b/src/functional/operators.h
index 691f2c393..504ca99e1 100644
--- a/src/functional/operators.h
+++ b/src/functional/operators.h
@@ -434,9 +434,10 @@ struct Ops<float32x8> {
   }
 };
 
-}}
+} // end namespace functional
+} // end namespace marian
 
-#endif
+#endif // of "#ifndef __CUDA_ARCH__"
 
 #ifdef __USE_FP16__
 // only compile with fp16 support for compute_70, i.e. VOLTA 100 and above.
diff --git a/src/graph/node_operators_unary.h b/src/graph/node_operators_unary.h
index c774a8432..b3f67bd69 100755
--- a/src/graph/node_operators_unary.h
+++ b/src/graph/node_operators_unary.h
@@ -14,12 +14,18 @@
 namespace marian {
 
 struct UnaryNodeOp : public NaryNodeOp {
-  UnaryNodeOp(Expr a, Shape shape, Type value_type = Type::float32)
+  UnaryNodeOp(Expr a, Shape shape, Type value_type)
       : NaryNodeOp({a}, shape, value_type) {}
 
-  UnaryNodeOp(Expr a, Type value_type = Type::float32)
+  UnaryNodeOp(Expr a, Type value_type)
       : NaryNodeOp({a}, a->shape(), value_type) {}
 
+  UnaryNodeOp(Expr a, Shape shape)
+      : NaryNodeOp({a}, shape, a->value_type()) {}
+
+  UnaryNodeOp(Expr a)
+      : NaryNodeOp({a}, a->shape(), a->value_type()) {}
+
   const std::string color() override { return "yellow"; }
 };
 
@@ -737,7 +743,7 @@ class ReshapeNodeOp : public UnaryNodeOp {
   Expr reshapee_;
 
 public:
-  ReshapeNodeOp(Expr a, Shape shape) : UnaryNodeOp(a, shape, a->value_type()), reshapee_(a) {
+  ReshapeNodeOp(Expr a, Shape shape) : UnaryNodeOp(a, shape), reshapee_(a) {
     ABORT_IF(a->shape().elements() != shape.elements(),
              "Reshape must not change the number of elements (from {} to {})", a->shape().toString(), shape.toString());
     Node::destroy_ = false;
diff --git a/src/tensors/gpu/element.cu b/src/tensors/gpu/element.cu
index ff1689d7b..1055c736b 100755
--- a/src/tensors/gpu/element.cu
+++ b/src/tensors/gpu/element.cu
@@ -4,6 +4,7 @@
 #include "functional/functional.h"
 #include "functional/tensor.h"
 #include "functional/tmp.h"
+
 #include "tensors/gpu/cuda_helpers.h"
 
 namespace marian {

From bf69071b493b898c1ed1773b6556c12771b7c78c Mon Sep 17 00:00:00 2001
From: Marcin Junczys-Dowmunt <Marcin.JunczysDowmunt@microsoft.com>
Date: Wed, 11 Sep 2019 21:43:19 -0700
Subject: [PATCH 618/838] make elementwise operations etc work with fp16

---
 CMakeLists.txt                       |    6 +-
 src/3rd_party/reduce_all.h           |   78 +-
 src/common/types.h                   |    6 +-
 src/functional/operators.h           |    3 +-
 src/optimizers/clippers.cpp          |    2 +-
 src/tensors/cpu/tensor_operators.cpp |  148 ++-
 src/tensors/gpu/add.cu               |    8 +-
 src/tensors/gpu/algorithm.cu         |   16 +-
 src/tensors/gpu/cuda_helpers.h       |    2 -
 src/tensors/gpu/element.cu           |    8 +-
 src/tensors/gpu/tensor_operators.cu  | 1459 ++++++++++++++++----------
 src/tensors/tensor_operators.h       |   10 +-
 12 files changed, 1092 insertions(+), 654 deletions(-)
 mode change 100755 => 100644 src/tensors/gpu/tensor_operators.cu

diff --git a/CMakeLists.txt b/CMakeLists.txt
index 0bea2e5e5..ef949c8f1 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -232,9 +232,11 @@ endif(COMPILE_CUDA)
 
 # TODO: make compatible with older CUDA versions
 if(CMAKE_BUILD_TYPE STREQUAL "Debug")
-  list(APPEND CUDA_NVCC_FLAGS --default-stream per-thread; -O0; -g; --use_fast_math; -arch=sm_30; -gencode=arch=compute_30,code=sm_30; -gencode=arch=compute_50,code=sm_50; -gencode=arch=compute_52,code=sm_52; -gencode=arch=compute_60,code=sm_60; -gencode=arch=compute_61,code=sm_61; -gencode=arch=compute_70,code=sm_70; -gencode=arch=compute_75,code=sm_75; -gencode=arch=compute_75,code=compute_75 ;)
+  list(APPEND CUDA_NVCC_FLAGS --default-stream per-thread; -O0; -g; --use_fast_math; -arch=sm_30; -gencode=arch=compute_30,code=sm_30; -gencode=arch=compute_50,code=sm_50; -gencode=arch=compute_52,code=sm_52; -gencode=arch=compute_60,code=sm_60; -gencode=arch=compute_61,code=sm_61; -gencode=arch=compute_70,code=sm_70; -gencode=arch=compute_70,code=compute_70 ;)
+#  list(APPEND CUDA_NVCC_FLAGS --default-stream per-thread; -O0; -g; --use_fast_math; -arch=sm_30; -gencode=arch=compute_30,code=sm_30; -gencode=arch=compute_50,code=sm_50; -gencode=arch=compute_52,code=sm_52; -gencode=arch=compute_60,code=sm_60; -gencode=arch=compute_61,code=sm_61; -gencode=arch=compute_70,code=sm_70; -gencode=arch=compute_70,code=compute_70 ;)
 else(CMAKE_BUILD_TYPE STREQUAL "Debug")
-  list(APPEND CUDA_NVCC_FLAGS --default-stream per-thread; -O3; -g; --use_fast_math; -arch=sm_30; -gencode=arch=compute_30,code=sm_30; -gencode=arch=compute_50,code=sm_50; -gencode=arch=compute_52,code=sm_52; -gencode=arch=compute_60,code=sm_60; -gencode=arch=compute_61,code=sm_61; -gencode=arch=compute_70,code=sm_70; -gencode=arch=compute_75,code=sm_75; -gencode=arch=compute_75,code=compute_75 ;)
+  list(APPEND CUDA_NVCC_FLAGS --default-stream per-thread; -O3; -g; --use_fast_math; -gencode=arch=compute_60,code=sm_60; -gencode=arch=compute_61,code=sm_61; -gencode=arch=compute_70,code=sm_70; -gencode=arch=compute_70,code=compute_70 ;)
+#  list(APPEND CUDA_NVCC_FLAGS --default-stream per-thread; -O3; -g; --use_fast_math; -arch=sm_30; -gencode=arch=compute_30,code=sm_30; -gencode=arch=compute_50,code=sm_50; -gencode=arch=compute_52,code=sm_52; -gencode=arch=compute_60,code=sm_60; -gencode=arch=compute_61,code=sm_61; -gencode=arch=compute_70,code=sm_70; -gencode=arch=compute_70,code=compute_70 ;)
 endif(CMAKE_BUILD_TYPE STREQUAL "Debug")
 if(NOT MSVC)
   # @TODO: add warnings here too
diff --git a/src/3rd_party/reduce_all.h b/src/3rd_party/reduce_all.h
index 1869d122b..42960f517 100644
--- a/src/3rd_party/reduce_all.h
+++ b/src/3rd_party/reduce_all.h
@@ -17,9 +17,9 @@
 
 namespace marian {
 
-template <unsigned int blockSize>
+template <unsigned int blockSize, typename AccType>
 __device__ void
-reduceBlock(volatile float *sdata, float mySum, const unsigned int tid)
+reduceBlock(volatile AccType *sdata, AccType mySum, const unsigned int tid)
 {
     sdata[tid] = mySum;
     __syncthreads();
@@ -89,29 +89,29 @@ reduceBlock(volatile float *sdata, float mySum, const unsigned int tid)
     }
 }
 
-template <unsigned int blockSize, bool nIsPow2, class Functor>
+template <unsigned int blockSize, bool nIsPow2, typename T, typename AccType, class Functor>
 __device__ void
-reduceBlocks(Functor f, float *g_idata, float *g_odata, unsigned int n)
+reduceBlocks(Functor f, T *g_idata, AccType *g_odata, unsigned int n)
 {
-    extern __shared__ float sdata[];
+    extern __shared__ AccType sdata[];
 
     // perform first level of reduction,
     // reading from global memory, writing to shared memory
     unsigned int tid = threadIdx.x;
     unsigned int i = blockIdx.x*(blockSize*2) + threadIdx.x;
     unsigned int gridSize = blockSize*2*gridDim.x;
-    float mySum = 0;
+    AccType mySum = 0;
 
     // we reduce multiple elements per thread.  The number is determined by the
     // number of active thread blocks (via gridDim).  More blocks will result
     // in a larger gridSize and therefore fewer elements per thread
     while (i < n)
     {
-        mySum += f(g_idata[i]);
+        mySum += f((AccType)g_idata[i]);
 
         // ensure we don't read out of bounds -- this is optimized away for powerOf2 sized arrays
         if (nIsPow2 || i + blockSize < n)
-            mySum += f(g_idata[i+blockSize]);
+            mySum += f((AccType)g_idata[i+blockSize]);
 
         i += gridSize;
     }
@@ -147,15 +147,15 @@ cudaError_t setRetirementCount(int retCnt)
 // For more details on the reduction algorithm (notably the multi-pass approach), see
 // the "reduction" sample in the CUDA SDK.
 
-template <unsigned int blockSize, bool nIsPow2, class Functor>
-__global__ void reduceSinglePass(Functor f, float *g_idata, float *g_odata, unsigned int n)
+template <unsigned int blockSize, bool nIsPow2, typename T, typename AccType, class Functor>
+__global__ void reduceSinglePass(Functor f, T *g_idata, AccType *g_odata, unsigned int n)
 {
 
     //
     // PHASE 1: Process all inputs assigned to this block
     //
 
-    reduceBlocks<blockSize, nIsPow2>(f, g_idata, g_odata, n);
+    reduceBlocks<blockSize, nIsPow2, T, AccType>(f, g_idata, g_odata, n);
 
     //
     // PHASE 2: Last block finished will process all partial sums
@@ -165,7 +165,7 @@ __global__ void reduceSinglePass(Functor f, float *g_idata, float *g_odata, unsi
     {
         const unsigned int tid = threadIdx.x;
         __shared__ bool amLast;
-        extern float __shared__ smem[];
+        extern AccType __shared__ smem[];
 
         // wait until all outstanding memory instructions in this thread are finished
         __threadfence();
@@ -184,7 +184,7 @@ __global__ void reduceSinglePass(Functor f, float *g_idata, float *g_odata, unsi
         if (amLast)
         {
             int i = tid;
-            float mySum = 0;
+            AccType mySum = 0;
 
             while (i < gridDim.x)
             {
@@ -210,20 +210,20 @@ bool isPow2(unsigned int x)
     return ((x&(x-1))==0);
 }
 
-template <class Functor>
-void ReduceAll(Functor f, Tensor out, Tensor in)
+template <typename T, typename AccType, class Functor>
+void ReduceAll(Functor f, Tensor blockMem, Tensor in)
 {
-    cudaSetDevice(out->getDeviceId().no);
+    cudaSetDevice(in->getDeviceId().no);
     int size = in->shape().elements();
     int threads = std::min(MAX_THREADS, size);
     int blocks  = std::min(MAX_BLOCKS, size / threads  + (size % threads != 0));
 
     dim3 dimBlock(threads, 1, 1);
     dim3 dimGrid(blocks, 1, 1);
-    int smemSize = threads * sizeof(float);
+    int smemSize = threads * sizeof(AccType);
 
-    float* d_idata = in->data();
-    float* d_odata = out->data();
+    T* d_idata = in->data<T>();
+    AccType* d_odata = blockMem->data<AccType>();
 
     // choose which of the optimized versions of reduction to launch
     if (isPow2(size))
@@ -231,43 +231,43 @@ void ReduceAll(Functor f, Tensor out, Tensor in)
         switch (threads)
         {
             case 512:
-                reduceSinglePass<512, true><<< dimGrid, dimBlock, smemSize >>>(f, d_idata, d_odata, size);
+                reduceSinglePass<512, true, T, AccType><<< dimGrid, dimBlock, smemSize >>>(f, d_idata, d_odata, size);
                 break;
 
             case 256:
-                reduceSinglePass<256, true><<< dimGrid, dimBlock, smemSize >>>(f, d_idata, d_odata, size);
+                reduceSinglePass<256, true, T, AccType><<< dimGrid, dimBlock, smemSize >>>(f, d_idata, d_odata, size);
                 break;
 
             case 128:
-                reduceSinglePass<128, true><<< dimGrid, dimBlock, smemSize >>>(f, d_idata, d_odata, size);
+                reduceSinglePass<128, true, T, AccType><<< dimGrid, dimBlock, smemSize >>>(f, d_idata, d_odata, size);
                 break;
 
             case 64:
-                reduceSinglePass< 64, true><<< dimGrid, dimBlock, smemSize >>>(f, d_idata, d_odata, size);
+                reduceSinglePass< 64, true, T, AccType><<< dimGrid, dimBlock, smemSize >>>(f, d_idata, d_odata, size);
                 break;
 
             case 32:
-                reduceSinglePass< 32, true><<< dimGrid, dimBlock, smemSize >>>(f, d_idata, d_odata, size);
+                reduceSinglePass< 32, true, T, AccType><<< dimGrid, dimBlock, smemSize >>>(f, d_idata, d_odata, size);
                 break;
 
             case 16:
-                reduceSinglePass< 16, true><<< dimGrid, dimBlock, smemSize >>>(f, d_idata, d_odata, size);
+                reduceSinglePass< 16, true, T, AccType><<< dimGrid, dimBlock, smemSize >>>(f, d_idata, d_odata, size);
                 break;
 
             case  8:
-                reduceSinglePass<  8, true><<< dimGrid, dimBlock, smemSize >>>(f, d_idata, d_odata, size);
+                reduceSinglePass<  8, true, T, AccType><<< dimGrid, dimBlock, smemSize >>>(f, d_idata, d_odata, size);
                 break;
 
             case  4:
-                reduceSinglePass<  4, true><<< dimGrid, dimBlock, smemSize >>>(f, d_idata, d_odata, size);
+                reduceSinglePass<  4, true, T, AccType><<< dimGrid, dimBlock, smemSize >>>(f, d_idata, d_odata, size);
                 break;
 
             case  2:
-                reduceSinglePass<  2, true><<< dimGrid, dimBlock, smemSize >>>(f, d_idata, d_odata, size);
+                reduceSinglePass<  2, true, T, AccType><<< dimGrid, dimBlock, smemSize >>>(f, d_idata, d_odata, size);
                 break;
 
             case  1:
-                reduceSinglePass<  1, true><<< dimGrid, dimBlock, smemSize >>>(f, d_idata, d_odata, size);
+                reduceSinglePass<  1, true, T, AccType><<< dimGrid, dimBlock, smemSize >>>(f, d_idata, d_odata, size);
                 break;
         }
     }
@@ -276,43 +276,43 @@ void ReduceAll(Functor f, Tensor out, Tensor in)
         switch (threads)
         {
             case 512:
-                reduceSinglePass<512, false><<< dimGrid, dimBlock, smemSize >>>(f, d_idata, d_odata, size);
+                reduceSinglePass<512, false, T, AccType><<< dimGrid, dimBlock, smemSize >>>(f, d_idata, d_odata, size);
                 break;
 
             case 256:
-                reduceSinglePass<256, false><<< dimGrid, dimBlock, smemSize >>>(f, d_idata, d_odata, size);
+                reduceSinglePass<256, false, T, AccType><<< dimGrid, dimBlock, smemSize >>>(f, d_idata, d_odata, size);
                 break;
 
             case 128:
-                reduceSinglePass<128, false><<< dimGrid, dimBlock, smemSize >>>(f, d_idata, d_odata, size);
+                reduceSinglePass<128, false, T, AccType><<< dimGrid, dimBlock, smemSize >>>(f, d_idata, d_odata, size);
                 break;
 
             case 64:
-                reduceSinglePass< 64, false><<< dimGrid, dimBlock, smemSize >>>(f, d_idata, d_odata, size);
+                reduceSinglePass< 64, false, T, AccType><<< dimGrid, dimBlock, smemSize >>>(f, d_idata, d_odata, size);
                 break;
 
             case 32:
-                reduceSinglePass< 32, false><<< dimGrid, dimBlock, smemSize >>>(f, d_idata, d_odata, size);
+                reduceSinglePass< 32, false, T, AccType><<< dimGrid, dimBlock, smemSize >>>(f, d_idata, d_odata, size);
                 break;
 
             case 16:
-                reduceSinglePass< 16, false><<< dimGrid, dimBlock, smemSize >>>(f, d_idata, d_odata, size);
+                reduceSinglePass< 16, false, T, AccType><<< dimGrid, dimBlock, smemSize >>>(f, d_idata, d_odata, size);
                 break;
 
             case  8:
-                reduceSinglePass<  8, false><<< dimGrid, dimBlock, smemSize >>>(f, d_idata, d_odata, size);
+                reduceSinglePass<  8, false, T, AccType><<< dimGrid, dimBlock, smemSize >>>(f, d_idata, d_odata, size);
                 break;
 
             case  4:
-                reduceSinglePass<  4, false><<< dimGrid, dimBlock, smemSize >>>(f, d_idata, d_odata, size);
+                reduceSinglePass<  4, false, T, AccType><<< dimGrid, dimBlock, smemSize >>>(f, d_idata, d_odata, size);
                 break;
 
             case  2:
-                reduceSinglePass<  2, false><<< dimGrid, dimBlock, smemSize >>>(f, d_idata, d_odata, size);
+                reduceSinglePass<  2, false, T, AccType><<< dimGrid, dimBlock, smemSize >>>(f, d_idata, d_odata, size);
                 break;
 
             case  1:
-                reduceSinglePass<  1, false><<< dimGrid, dimBlock, smemSize >>>(f, d_idata, d_odata, size);
+                reduceSinglePass<  1, false, T, AccType><<< dimGrid, dimBlock, smemSize >>>(f, d_idata, d_odata, size);
                 break;
         }
     }
diff --git a/src/common/types.h b/src/common/types.h
index 1b43cdee0..e4b71b522 100644
--- a/src/common/types.h
+++ b/src/common/types.h
@@ -17,11 +17,11 @@
 #include <immintrin.h>
 #endif
 
-#ifdef __CUDA_ARCH__
+//#ifdef __CUDA_ARCH__
 //#if __CUDA_ARCH__ >= 600 && CUDA_VERSION >= 10000 // @TODO: set this from 600 to 700, currently only testing on Pascal
-#define __USE_FP16__
+//#define __USE_FP16__
+//#endif
 //#endif
-#endif
 
 #define DISPATCH_BY_TYPE0(type, func) \
 do { \
diff --git a/src/functional/operators.h b/src/functional/operators.h
index 504ca99e1..9c66da65b 100644
--- a/src/functional/operators.h
+++ b/src/functional/operators.h
@@ -439,7 +439,8 @@ struct Ops<float32x8> {
 
 #endif // of "#ifndef __CUDA_ARCH__"
 
-#ifdef __USE_FP16__
+#ifdef __CUDA_ARCH__
+//#ifdef __USE_FP16__
 // only compile with fp16 support for compute_70, i.e. VOLTA 100 and above.
 #include <cuda_fp16.h>
 
diff --git a/src/optimizers/clippers.cpp b/src/optimizers/clippers.cpp
index 2507ccc40..5da8cfe6a 100644
--- a/src/optimizers/clippers.cpp
+++ b/src/optimizers/clippers.cpp
@@ -11,7 +11,7 @@ void Elementwise::clip(Tensor t) {
 
 void Norm::clip(Tensor t) {
   using namespace functional;
-  float l2Norm = L2Norm(t);
+  float l2Norm = L2Norm(t, nullptr); // @TODO: this is wrong, but will be replaced with better version in a PR or two.
   if(l2Norm >= c_)
     Element(_1 = (c_ / l2Norm) * _1, t);
 }
diff --git a/src/tensors/cpu/tensor_operators.cpp b/src/tensors/cpu/tensor_operators.cpp
index bbce9a94a..d04518284 100644
--- a/src/tensors/cpu/tensor_operators.cpp
+++ b/src/tensors/cpu/tensor_operators.cpp
@@ -5,10 +5,12 @@
 
 #include "tensors/tensor_operators.h"
 #include "tensors/cpu/backend.h"
+#include "tensors/allocator.h"
 
 #include "functional/approx.h"
 #include "functional/functional.h"
 #include "functional/tensor.h"
+#include "functional/operators.h"
 
 #if MKL_FOUND
 #include <mkl.h>
@@ -18,16 +20,6 @@ namespace marian {
 
 namespace cpu {
 
-inline float stableSigmoid(float x) {
-  if(x >= 0) {
-    float z = expf(-x);
-    return 1.0f / (1.0f + z);
-  } else {
-    float z = expf(x);
-    return z / (1.0f + z);
-  }
-}
-
 void IsNaN(const Tensor in, Ptr<Allocator> allocator, bool& isNaN, bool& isInf) {
   ABORT("Not implemented");
 }
@@ -355,10 +347,15 @@ void TransposeGeneric(Tensor out, Tensor in, const std::vector<int>& vAxis) {
     gOut.shape().dims(index, oDims);
     for(size_t i = 0; i < N; ++i)
       pDims[permute[i]] = oDims[i];
+
+    // @TODO: where does this change come from?
+    int inIndex = gIn.shape().index(pDims);
+
+    // @TODO: use internal conversion instead of raw indices
     if(add)
-      gOut[index] += gIn[pDims];
+      gOut.data()[index] += gIn.data()[inIndex];
     else
-      gOut[index] = gIn[pDims];
+      gOut.data()[index] = gIn.data()[inIndex];
   }
 }
 
@@ -383,64 +380,112 @@ void TransposeNDGrad(Tensor out, Tensor in, const std::vector<int>& vAxis) {
     TransposeGeneric<true>(out, in, vAxis);
 }
 
+template <typename ElementType>
 void Softmax(Tensor out, Tensor in) {
-  float* pOut = out->data();
-  const float* pIn = in->data();
+  using namespace functional;
+  functional::Tensor<ElementType> fout = out;
+  const functional::Tensor<ElementType> fin = in;
 
-  int rows = out->shape().elements() / out->shape().back();
-  int cols = out->shape().back();
+  ElementType* pOut = fout.data();
+  const ElementType* pIn = fin.data();
+
+  int rows = fout.shape().elements() / fout.shape().back();
+  int cols = fout.shape().back();
 
   for(int j = 0; j < rows; ++j) {
-    float* so = pOut + j * cols;
-    const float* sp = pIn + j * cols;
+    ElementType* so = pOut + j * cols;
+    const ElementType* sp = pIn + j * cols;
 
-    float max = sp[0];
-    for(int i = 1; i < cols; ++i)
-      max = std::max(max, sp[i]);
+    ElementType max = sp[0];
+    for(int i = 1; i < cols; ++i) {
+      max = Ops<ElementType>::max(max, sp[i]);
+    }
 
-    float sum = 0.f;
+    // if ElementType is a complex type, e.g. float32x8, find the max of these 8 values
+    typename Ops<ElementType>::Single maxs = Ops<ElementType>::maxReduce(max);
+
+    ElementType sum = 0.f;
     for(int i = 0; i < cols; ++i) {
-      float ex = expf(sp[i] - max);
+      ElementType ex = Ops<ElementType>::exp(Ops<ElementType>::sub(sp[i], maxs));
+      sum = Ops<ElementType>::add(sum, ex);
       so[i] = ex;
-      sum += ex;
     }
 
+    // if ElementType is a complex type, e.g. float32x8, sum these 8 values
+    typename Ops<ElementType>::Single sums = Ops<ElementType>::sumReduce(sum);
+
     for(int i = 0; i < cols; ++i) {
-      so[i] /= sum;
+      so[i] = Ops<ElementType>::div(so[i], sums);
     }
   }
 }
 
+
+void Softmax(Tensor out, Tensor in) {
+  matchOrAbort<float>(out->type());
+  matchOrAbort<float>(in->type());
+
+  if(out->shape()[-1] % 8 == 0) {
+    Softmax<float32x8>(out, in);
+  } else if(out->shape()[-1] % 4 == 0) {
+    Softmax<float32x4>(out, in);
+  } else {
+    Softmax<float>(out, in);
+  }
+}
+
+
+template <typename ElementType>
 void LogSoftmax(Tensor out, Tensor in) {
-  float* pOut = out->data();
-  const float* pIn = in->data();
 
-  int rows = out->shape().elements() / out->shape().back();
-  int cols = out->shape().back();
+  using namespace functional;
+  functional::Tensor<ElementType> fout = out;
+  const functional::Tensor<ElementType> fin = in;
+
+  ElementType* pOut = fout.data();
+  const ElementType* pIn = fin.data();
+
+  int rows = fout.shape().elements() / fout.shape().back();
+  int cols = fout.shape().back();
 
   for(int j = 0; j < rows; ++j) {
-    float* so = pOut + j * cols;
-    const float* sp = pIn + j * cols;
+    ElementType* so = pOut + j * cols;
+    const ElementType* sp = pIn + j * cols;
 
-    float max = sp[0];
+    ElementType max = sp[0];
     for(int i = 1; i < cols; ++i) {
-      max = std::max(max, sp[i]);
+      max = Ops<ElementType>::max(max, sp[i]);
     }
+    typename Ops<ElementType>::Single maxs = Ops<ElementType>::maxReduce(max); // global maximum
 
-    float sum = 0.f;
+    ElementType sum = 0.f;
     for(int i = 0; i < cols; ++i) {
-      float sm = sp[i] - max;
-      float ex = expf(sm);
+      ElementType sm = Ops<ElementType>::sub(sp[i], maxs);
+      sum = Ops<ElementType>::add(sum, Ops<ElementType>::exp(sm));
       so[i] = sm;
-      sum += ex;
     }
+    typename Ops<ElementType>::Single sums = Ops<ElementType>::sumReduce(sum); // global sum
 
+    ElementType logSum = Ops<ElementType>::log(sums); // broadcasts Single to ElementType
     for(int i = 0; i < cols; ++i) {
-      so[i] -= logf(sum);
+      so[i] = Ops<ElementType>::sub(so[i], logSum);
     }
   }
 }
 
+void LogSoftmax(Tensor out, Tensor in) {
+  matchOrAbort<float>(out->type());
+  matchOrAbort<float>(in->type());
+
+  if(out->shape()[-1] % 8 == 0) {
+    LogSoftmax<float32x8>(out, in);
+  } else if(out->shape()[-1] % 4 == 0) {
+    LogSoftmax<float32x4>(out, in);
+  } else {
+    LogSoftmax<float>(out, in);
+  }
+}
+
 // @TODO: Remove remaining underscores in CPU kernels
 void SoftmaxGrad(Tensor grad_, Tensor adj_, Tensor val_) {
   int rows = grad_->shape().elements() / grad_->shape()[-1];
@@ -642,14 +687,11 @@ void Select(Tensor out,
   if(axisCPU == 2) // specialization for axis==2, assuming N=4
     return SelectAxis2(out, in, indices);
 
-  auto odata = out->data();
-  const auto idata = in->data();
-
   for(int index = 0; index < length; ++index) {
     outShape.dims(index, dims);
     dims[axisCPU] = (int)indices->data<IndexType>()[dims[axisCPU]];
     int inIndex = inShape.index(dims);
-    odata[index] = idata[inIndex];
+    out->data()[index] = in->data()[inIndex];
   }
 }
 
@@ -699,11 +741,11 @@ void GRUFastForward(Tensor out_, std::vector<Tensor> inputs, bool final) {
 
 #pragma omp simd
     for(int i = 0; i < cols; ++i) {
-      float r = stableSigmoid(xWrow[i] + sUrow[i] + b[i]);
+      float r = functional::Ops<float>::sigmoid(xWrow[i] + sUrow[i] + b[i]);
 
       int k = i + cols;
 
-      float z = stableSigmoid(xWrow[k] + sUrow[k] + b[k]);
+      float z = functional::Ops<float>::sigmoid(xWrow[k] + sUrow[k] + b[k]);
 
       int l = i + 2 * cols;
       float h;
@@ -755,8 +797,8 @@ void GRUFastBackward(std::vector<Tensor> outputs,
       int k = i + cols;
       int l = i + 2 * cols;
 
-      float r = stableSigmoid(rowXW[i] + rowSU[i] + b[i]);
-      float z = stableSigmoid(rowXW[k] + rowSU[k] + b[k]);
+      float r = functional::Ops<float>::sigmoid(rowXW[i] + rowSU[i] + b[i]);
+      float z = functional::Ops<float>::sigmoid(rowXW[k] + rowSU[k] + b[k]);
 
       float h;
       if(final)
@@ -875,7 +917,7 @@ void CrossEntropyPickBackward(Tensor out,
   }
 }
 
-float L2Norm(Tensor in) {
+float L2Norm(Tensor in, Ptr<Allocator> /*not used*/) {
   float sum = 0.f;
   size_t size = in->size();
   const float* data = in->data();
@@ -1193,10 +1235,10 @@ void LSTMCellForward(Tensor out_, std::vector<Tensor> inputs) {
     const float* sUrow = sU + j * cols * 4;
 
     for(int i = 0; i < cols; ++i) {
-      float gf = stableSigmoid(xWrow[i] + sUrow[i] + b[i]);
+      float gf = functional::Ops<float>::sigmoid(xWrow[i] + sUrow[i] + b[i]);
 
       int k = i + cols;
-      float gi = stableSigmoid(xWrow[k] + sUrow[k] + b[k]);
+      float gi = functional::Ops<float>::sigmoid(xWrow[k] + sUrow[k] + b[k]);
 
       int l = i + 2 * cols;
       float gc = std::tanh(xWrow[l] + sUrow[l] + b[l]);
@@ -1226,7 +1268,7 @@ void LSTMOutputForward(Tensor out_, std::vector<Tensor> inputs) {
 
     for(int i = 0; i < cols; ++i) {
       int k = i + 3 * cols;
-      float go = stableSigmoid(xWrow[k] + sUrow[k] + b[k]);
+      float go = functional::Ops<float>::sigmoid(xWrow[k] + sUrow[k] + b[k]);
 
       rowOut[i] = go * std::tanh(rowCell[i]);
     }
@@ -1266,10 +1308,10 @@ void LSTMCellBackward(std::vector<Tensor> outputs,
     const float* rowAdj = adj + j * cols;
 
     for(int i = 0; i < cols; ++i) {
-      float gf = stableSigmoid(xWrow[i] + sUrow[i] + b[i]);
+      float gf = functional::Ops<float>::sigmoid(xWrow[i] + sUrow[i] + b[i]);
 
       int k = i + cols;
-      float gi = stableSigmoid(xWrow[k] + sUrow[k] + b[k]);
+      float gi = functional::Ops<float>::sigmoid(xWrow[k] + sUrow[k] + b[k]);
 
       int l = i + 2 * cols;
       float gc = std::tanh(xWrow[l] + sUrow[l] + b[l]);
@@ -1351,7 +1393,7 @@ void LSTMOutputBackward(std::vector<Tensor> outputs,
 
     for(int i = 0; i < cols; ++i) {
       int k = i + 3 * cols;
-      float go = stableSigmoid(xWrow[k] + sUrow[k] + b[k]);
+      float go = functional::Ops<float>::sigmoid(xWrow[k] + sUrow[k] + b[k]);
 
       float t = std::tanh(rowCell[i]);
 
diff --git a/src/tensors/gpu/add.cu b/src/tensors/gpu/add.cu
index c08d91e5b..70bafeae4 100755
--- a/src/tensors/gpu/add.cu
+++ b/src/tensors/gpu/add.cu
@@ -171,11 +171,11 @@ void Aggregate(Functor functor, float aggInit, AggFunctor aggFunctor, float scal
   if(out->type() == Type::float32) {
     AggregateTyped<float, float>(functor, aggInit, aggFunctor, scale, out, tensors...);
   } else if(out->type() == Type::float16) {
-#ifdef __USE_FP16__
+// #ifdef __USE_FP16__
     AggregateTyped<half,  float>(functor, aggInit, aggFunctor, scale, out, tensors...);
-#else
-    ABORT("FP16 not supported with current hardware or CUDA version");
-#endif
+// #else
+//     ABORT("FP16 not supported with current hardware or CUDA version");
+// #endif
   } else {
     ABORT("Type {} not yet supported", out->type());
   }
diff --git a/src/tensors/gpu/algorithm.cu b/src/tensors/gpu/algorithm.cu
index 13ad57151..1638d6639 100644
--- a/src/tensors/gpu/algorithm.cu
+++ b/src/tensors/gpu/algorithm.cu
@@ -62,15 +62,15 @@ void fill<float16>(Ptr<Backend> backend, float16* begin, float16* end, float16 v
   int size = end - begin;
   if (size == 0)
     return;
-#ifdef __USE_FP16__
+// #ifdef __USE_FP16__
   CUDA_CHECK(cudaSetDevice(backend->getDeviceId().no));
   int threadsPerBlock = std::min(MAX_THREADS, size);
   int blocks = (size / threadsPerBlock) + (size % threadsPerBlock != 0); // @TODO: (size+threadsPerBlock-1)/threadsPerBlock or CeilDiv(a,b)
   gFill<<<blocks, threadsPerBlock>>>((__half*)begin, size, (__half)value);
   CUDA_CHECK(cudaStreamSynchronize(0));
-#else
-  ABORT("FP16 not supported with current hardware or CUDA version");
-#endif
+// #else
+//   ABORT("FP16 not supported with current hardware or CUDA version");
+// #endif
 }
 
 template void fill<bool>(Ptr<Backend>, bool*, bool*, bool);
@@ -126,15 +126,15 @@ void swap_ranges<float16>(Ptr<Backend> backend, float16* begin, float16* end, fl
   if (size == 0)
     return;
 
-#ifdef __USE_FP16__
+// #ifdef __USE_FP16__
   CUDA_CHECK(cudaSetDevice(backend->getDeviceId().no));
   int threadsPerBlock = std::min(MAX_THREADS, size);
   int blocks = (size / threadsPerBlock) + (size % threadsPerBlock != 0); // @TODO: (size+threadsPerBlock-1)/threadsPerBlock or CeilDiv(a,b)
   gSwap<<<blocks, threadsPerBlock>>>((__half*)begin, (__half*)dest, size);
   CUDA_CHECK(cudaStreamSynchronize(0));
-#else
-  ABORT("FP16 not supported with current hardware or CUDA version");
-#endif
+// #else
+//   ABORT("FP16 not supported with current hardware or CUDA version");
+// #endif
 }
 
 // clang-format off
diff --git a/src/tensors/gpu/cuda_helpers.h b/src/tensors/gpu/cuda_helpers.h
index 593ae7e59..0c0c8923c 100644
--- a/src/tensors/gpu/cuda_helpers.h
+++ b/src/tensors/gpu/cuda_helpers.h
@@ -4,9 +4,7 @@
 
 #include <cuda_runtime.h>
 
-#ifdef __USE_FP16__
 #include <cuda_fp16.h>
-#endif
 
 // template <> inline bool matchType<__half>(Type type)  { return type == Type::float16; }
 // template <> inline std::string request<__half>()  { return "float16"; }
diff --git a/src/tensors/gpu/element.cu b/src/tensors/gpu/element.cu
index 1055c736b..52a920e6d 100755
--- a/src/tensors/gpu/element.cu
+++ b/src/tensors/gpu/element.cu
@@ -64,11 +64,11 @@ void Element(Functor functor, Tensor out, Tensors... tensors) {
   if(out->type() == Type::float32) {
     ElementTyped<float>(functor, out, tensors...);
   } else if(out->type() == Type::float16) {
-#ifdef __USE_FP16__
+//#ifdef __USE_FP16__
     ElementTyped<__half>(functor, out, tensors...);
-#else
-    ABORT("FP16 not supported with chosen current hardware or CUDA version");
-#endif
+//#else
+//    ABORT("FP16 not supported with chosen current hardware or CUDA version");
+//#endif
   } else if(out->type() == Type::float64) {
     ElementTyped<double>(functor, out, tensors...);
   } else {
diff --git a/src/tensors/gpu/tensor_operators.cu b/src/tensors/gpu/tensor_operators.cu
old mode 100755
new mode 100644
index 1bfd00ef8..76ad4ce91
--- a/src/tensors/gpu/tensor_operators.cu
+++ b/src/tensors/gpu/tensor_operators.cu
@@ -4,6 +4,7 @@
 
 #include "functional/functional.h"
 #include "functional/tensor.h"
+#include "tensors/allocator.h"
 #include "tensors/gpu/backend.h"
 #include "tensors/gpu/cuda_helpers.h"
 
@@ -13,18 +14,43 @@ namespace marian {
 
 namespace gpu {
 
-struct isnan_test {
-  __host__ __device__ bool operator()(const float a) const { return isnan(a); }
-};
+namespace atomics {
+
+static inline  __device__ void atomicAdd(float *address, float val) {
+  //*address += val;
+  ::atomicAdd(address, val);
+}
+
+// @TODO: copied from CuTorch, adapt this better, give credit.
+static inline  __device__ void atomicAdd(half *address, half val) {
+  //*address += val;
+
+#if __CUDA_ARCH__ >= 700 && CUDA_VERSION >= 10000 // compute capability 70 and higher with CUDA 10
+  ::atomicAdd(address, val);
+#else // __CUDA_ARCH__ < 700
+  unsigned int * address_as_ui =
+      (unsigned int *) ((char *)address - ((size_t)address & 2));
+  unsigned int old = *address_as_ui;
+  unsigned int assumed;
+
+  do {
+    assumed = old;
+  #if CUDA_VERSION < 9000
+    half hsum;
+    hsum.x = (size_t)address & 2 ? (old >> 16) : (old & 0xffff);
+    hsum = hsum + val;
+  #else
+    __half_raw hsum;
+    hsum.x = (size_t)address & 2 ? (old >> 16) : (old & 0xffff);
+    half tmpres = hsum + val;
+    hsum = __half_raw(tmpres);
+  #endif
+    old = (size_t)address & 2 ? (old & 0xffff) | (hsum.x << 16) : (old & 0xffff0000) | hsum.x;
+    old = atomicCAS(address_as_ui, assumed, old);
+    } while (assumed != old);
+#endif // __CUDA_ARCH__
+}
 
-__device__ inline float stableSigmoid(float x) {
-  if(x >= 0) {
-    float z = expf(-x);
-    return 1.0 / (1.0 + z);
-  } else {
-    float z = expf(x);
-    return z / (1.0 + z);
-  }
 }
 
 template <typename T>
@@ -120,12 +146,12 @@ void ConcatCont(Tensor out, const std::vector<Tensor>& inputs, int axis) {
   size_t offset1 = 0;
   for(int i = 0; i < step; ++i) {
     for(auto in : inputs) {
-      size_t size = in->shape().elements() / step;
+      size_t size = (in->shape().elements() / step) * sizeOf(out->type());
       size_t offset2 = i * size;
 
-      cudaMemcpy(out->data() + offset1,
-                 in->data() + offset2,
-                 size * sizeof(float),
+      cudaMemcpy(out->data<uint8_t>() + offset1,
+                 in->data<uint8_t>() + offset2,
+                 size,
                  cudaMemcpyDeviceToDevice);
 
       offset1 += size;
@@ -134,9 +160,9 @@ void ConcatCont(Tensor out, const std::vector<Tensor>& inputs, int axis) {
   cudaStreamSynchronize(0);
 }
 
-template <bool add>
-__global__ void gInsertCols(float* out,
-                            const float* in,
+template <bool add, typename T>
+__global__ void gInsertCols(T* out,
+                            const T* in,
                             size_t rows,
                             size_t cols,
                             size_t cols_out,
@@ -146,8 +172,8 @@ __global__ void gInsertCols(float* out,
   for(int bid = 0; bid < rows; bid += gridDim.x) {
     int j = bid + blockIdx.x;
     if(j < rows) { // @TODO: change to if j == rows then break, as that's what it means. In 4 functions in here.
-      float* rowOut = out + j * cols_out + offset_out;
-      const float* rowIn = in + j * cols_in + offset_in;
+      T* rowOut = out + j * cols_out + offset_out;
+      const T* rowIn = in + j * cols_in + offset_in;
 
       for(int tid = 0; tid < cols; tid += blockDim.x) {
         int i = tid + threadIdx.x;
@@ -177,19 +203,27 @@ void Concatenate1(Tensor out, const std::vector<Tensor>& inputs) {
     int blocks = std::min(MAX_BLOCKS, rows);
     int threads = std::min(MAX_THREADS, cols_in);
 
-    gInsertCols<false><<<blocks, threads>>>(
-        out->data(), in->data(), rows, cols_in, cols_out, cols_in, offset, 0);
+    if(out->type() == Type::float32) {
+      gInsertCols<false><<<blocks, threads>>>(
+          out->data<float>(), in->data<float>(), rows, cols_in, cols_out, cols_in, offset, 0);
+    } else if(out->type() == Type::float16) {
+      gInsertCols<false><<<blocks, threads>>>(
+          out->data<half>(), in->data<half>(), rows, cols_in, cols_out, cols_in, offset, 0);
+    } else {
+      ABORT("Concatenate1 not implemented for type {}", out->type());
+    }
     offset += cols_in;
   }
   cudaStreamSynchronize(0);
 }
 
-__global__ void gJoin2(float* out,
+template <typename T>
+__global__ void gJoin2(T* out,
                        size_t rowBatch,
                        size_t cols,
-                       const float* in1,
+                       const T* in1,
                        size_t inStride1,
-                       const float* in2,
+                       const T* in2,
                        size_t inStride2) {
   int outStride = inStride1 + inStride2;
   int rows = rowBatch * outStride;
@@ -197,7 +231,7 @@ __global__ void gJoin2(float* out,
   for(int bid = 0; bid < rows; bid += gridDim.x) {
     int j = bid + blockIdx.x;
     if(j < rows) {
-      float* rowOut = out + j * cols;
+      T* rowOut = out + j * cols;
 
       int curBatch = j / outStride;
       int curPos = j % outStride;
@@ -205,8 +239,8 @@ __global__ void gJoin2(float* out,
       int jIn1 = (curBatch * inStride1) + curPos;
       int jIn2 = (curBatch * inStride2) + curPos - inStride1;
 
-      const float* rowIn1 = in1 + jIn1 * cols;
-      const float* rowIn2 = in2 + jIn2 * cols;
+      const T* rowIn1 = in1 + jIn1 * cols;
+      const T* rowIn2 = in2 + jIn2 * cols;
 
       for(int tid = 0; tid < cols; tid += blockDim.x) {
         int i = tid + threadIdx.x;
@@ -235,13 +269,25 @@ void Concatenate2(Tensor out, Tensor in1, Tensor in2) {
   int blocks = std::min(MAX_BLOCKS, (int)rows);
   int threads = std::min(MAX_THREADS, (int)cols);
 
-  gJoin2<<<blocks, threads>>>(out->data(),
-                              rowBatch,
-                              cols,
-                              in1->data(),
-                              rowStride1,
-                              in2->data(),
-                              rowStride2);
+  if(out->type() == Type::float32) {
+     gJoin2<<<blocks, threads>>>(out->data<float>(),
+                                 rowBatch,
+                                 cols,
+                                 in1->data<float>(),
+                                 rowStride1,
+                                 in2->data<float>(),
+                                 rowStride2);
+  } else if(out->type() == Type::float16) {
+     gJoin2<<<blocks, threads>>>(out->data<half>(),
+                                 rowBatch,
+                                 cols,
+                                 in1->data<half>(),
+                                 rowStride1,
+                                 in2->data<half>(),
+                                 rowStride2);
+  } else {
+    ABORT("Concatenate2 not implemented for type {}", out->type());
+  }
 
   cudaStreamSynchronize(0);
 }
@@ -269,8 +315,16 @@ void Split1(std::vector<Tensor>& outputs, const Tensor in) {
     int blocks = std::min(MAX_BLOCKS, rows);
     int threads = std::min(MAX_THREADS, cols_out);
 
-    gInsertCols<true><<<blocks, threads>>>(
-        out->data(), in->data(), rows, cols_out, cols_out, cols_in, 0, offset);
+    if(out->type() == Type::float32) {
+      gInsertCols<true><<<blocks, threads>>>(
+          out->data<float>(), in->data<float>(), rows, cols_out, cols_out, cols_in, 0, offset);
+    } else if(out->type() == Type::float16) {
+      gInsertCols<true><<<blocks, threads>>>(
+          out->data<half>(), in->data<half>(), rows, cols_out, cols_out, cols_in, 0, offset);
+    } else {
+      ABORT("Split1 not implemented for type {}", out->type());
+    }
+
     offset += cols_out;
   }
   cudaStreamSynchronize(0);
@@ -278,7 +332,8 @@ void Split1(std::vector<Tensor>& outputs, const Tensor in) {
 
 // @TODO: this function is just a temporary fix until I come up with
 // something better for the situation below.
-__global__ void gAddRow(float* out, const float* in, int length) {
+template <typename T>
+__global__ void gAddRow(T* out, const T* in, int length) {
   for(int bid = 0; bid < length; bid += blockDim.x * gridDim.x) {
     int index = bid + blockDim.x * blockIdx.x + threadIdx.x;
     if(index < length) {
@@ -310,8 +365,15 @@ void SplitCont(std::vector<Tensor>& outputs, const Tensor in, int axis) {
       int threads = std::min(MAX_THREADS, size);
       int blocks = std::min(MAX_BLOCKS, size / threads + (size % threads != 0));
 
-      gAddRow<<<blocks, threads>>>(
-          out->data() + offset2, in->data() + offset1, size);
+      if(out->type() == Type::float32) {
+        gAddRow<<<blocks, threads>>>(
+            out->data<float>() + offset2, in->data<float>() + offset1, size);
+      } else if(out->type() == Type::float16) {
+        gAddRow<<<blocks, threads>>>(
+            out->data<half>() + offset2, in->data<half>() + offset1, size);
+      } else {
+        ABORT("SplitCont not implemented for type {}", out->type());
+      }
       offset1 += size;
     }
   }
@@ -325,10 +387,10 @@ void Deconcatenate(std::vector<Tensor>& outputs, const Tensor in, int ax) {
     SplitCont(outputs, in, ax);
 }
 
-template <bool add>
+template <bool add, typename T>
 __global__ void gTransposeND(
-    functional::Tensor<float> out,
-    const functional::Tensor<float> in,
+    functional::Tensor<T> out,
+    const functional::Tensor<T> in,
     const functional::Array<int, functional::Shape::size()> permute) {
   constexpr size_t N = functional::Shape::size();
   functional::Array<int, N> oDims;
@@ -341,17 +403,22 @@ __global__ void gTransposeND(
       out.shape().dims(index, oDims);
       for(int i = 0; i < N; ++i)
         pDims[permute[i]] = oDims[i];
+
+      int inIndex = in.shape().index(pDims);
+
+      // TODO: operates on raw indices, change to
+      // converting Tensor::operator[]
       if(add)
-        out[index] += in[pDims];
+        out.data()[index] += in.data()[inIndex];
       else
-        out[index] = in[pDims];
+        out.data()[index] = in.data()[inIndex];
     }
   }
 }
 
-template <bool add>
-__global__ void gTranspose0213(float* out,
-                               const float* in,
+template <bool add, typename T>
+__global__ void gTranspose0213(T* out,
+                               const T* in,
                                int rows,
                                int cols,
                                int stride1,
@@ -360,14 +427,14 @@ __global__ void gTranspose0213(float* out,
   for(int bid = 0; bid < rows; bid += gridDim.x) {
     int j = bid + blockIdx.x;
     if(j < rows) {
-      float* rowOut = out + j * cols;
+      T* rowOut = out + j * cols;
 
       int z = j / stride;
       int y = (j % stride) / stride1;
       int x = (j % stride) % stride1;
       int j2 = z * stride + x * stride2 + y;
 
-      const float* rowIn = in + j2 * cols;
+      const T* rowIn = in + j2 * cols;
 
       for(int tid = 0; tid < cols; tid += blockDim.x) {
         int i = tid + threadIdx.x;
@@ -394,8 +461,13 @@ void TransposeND(Tensor out, Tensor in, const std::vector<int>& vAxis) {
     int stride1 = out->shape()[-2];
     int stride2 = out->shape()[-3];
 
-    gTranspose0213<false><<<blocks, threads>>>(
-        out->data(), in->data(), rows, cols, stride1, stride2);
+    if(in->type() == Type::float32) {
+      gTranspose0213<false><<<blocks, threads>>>(out->data<float>(), in->data<float>(), rows, cols, stride1, stride2);
+    } else if(in->type() == Type::float16) {
+      gTranspose0213<false><<<blocks, threads>>>(out->data<__half>(), in->data<__half>(), rows, cols, stride1, stride2);
+    } else {
+      ABORT("Transpose for type {} not implemented", in->type());
+    }
   } else {
     functional::Array<int, functional::Shape::size()> axes;
     int diff = functional::Shape::size() - vAxis.size();
@@ -410,10 +482,17 @@ void TransposeND(Tensor out, Tensor in, const std::vector<int>& vAxis) {
     int blocks
         = std::min(MAX_BLOCKS, length / threads + (length % threads != 0));
 
-    gTransposeND<false><<<blocks, threads>>>(out, in, axes);
+    if(in->type() == Type::float32) {
+      gTransposeND<false, float><<<blocks, threads>>>(out, in, axes);
+    } else if(in->type() == Type::float16) {
+      gTransposeND<false, __half><<<blocks, threads>>>(out, in, axes);
+    } else {
+      ABORT("Transpose for type {} not implemented", in->type());
+    }
   }
 }
 
+//@TODO: code duplication?
 void TransposeNDGrad(Tensor out, Tensor in, const std::vector<int>& vAxis) {
   cudaSetDevice(out->getDeviceId().no);
   if(vAxis == std::vector<int>({0, 2, 1, 3})) {
@@ -426,8 +505,13 @@ void TransposeNDGrad(Tensor out, Tensor in, const std::vector<int>& vAxis) {
     int stride1 = out->shape()[-2];
     int stride2 = out->shape()[-3];
 
-    gTranspose0213<true><<<blocks, threads>>>(
-        out->data(), in->data(), rows, cols, stride1, stride2);
+    if(in->type() == Type::float32) {
+      gTranspose0213<true><<<blocks, threads>>>(out->data<float>(), in->data<float>(), rows, cols, stride1, stride2);
+    } else if(in->type() == Type::float16) {
+      gTranspose0213<true><<<blocks, threads>>>(out->data<__half>(), in->data<__half>(), rows, cols, stride1, stride2);
+    } else {
+      ABORT("Transpose for type {} not implemented", in->type());
+    }
   } else {
     functional::Array<int, functional::Shape::size()> axes;
     int diff = functional::Shape::size() - vAxis.size();
@@ -439,10 +523,15 @@ void TransposeNDGrad(Tensor out, Tensor in, const std::vector<int>& vAxis) {
 
     int length = out->shape().elements();
     int threads = std::min(MAX_THREADS, length);
-    int blocks
-        = std::min(MAX_BLOCKS, length / threads + (length % threads != 0));
-
-    gTransposeND<true><<<blocks, threads>>>(out, in, axes);
+    int blocks  = std::min(MAX_BLOCKS, length / threads + (length % threads != 0));
+
+    if(in->type() == Type::float32) {
+      gTransposeND<true, float><<<blocks, threads>>>(out, in, axes);
+    } else if(in->type() == Type::float16) {
+      gTransposeND<true, __half><<<blocks, threads>>>(out, in, axes);
+    } else {
+      ABORT("Transpose for type {} not implemented", in->type());
+    }
   }
 }
 
@@ -453,25 +542,32 @@ void TransposeNDGrad(Tensor out, Tensor in, const std::vector<int>& vAxis) {
 // rows are time, batch or beam dimensions
 // number of threads is number of cols or MAX_THREADS
 // number of blocks is number of rows or MAX_BLOCKS
-__global__ void gSoftmax(float* out,
+// @TODO: handle __half2
+template <typename T, typename AccType = float>
+__global__ void gSoftmax(T* out,
                          functional::Shape outShape,
-                         const float* in) {
+                         const T* in) {
+  using namespace functional;
+
   int rows = outShape.elements() / outShape.back();
   int cols = outShape.back();
 
   for(int bid = 0; bid < rows; bid += gridDim.x) { // loop over blocks of rows
     int j = bid + blockIdx.x; // blockIdx.x - row index (within block of rows)
     if(j < rows) { // compute softmax over one row, row elements distributed over threads
-      float* so = out + j * cols; // pointer to row input data
-      const float* sp = in + j * cols;
+      T* so = out + j * cols; // pointer to row input data
+      const T* sp = in + j * cols;
 
-      extern __shared__ float _share[];
+      // CUDA complains if type or size of shared memory changes, keep size constant.
+      extern __shared__ uint8_t _sharedBytes[];
+      T* _share = (T*)_sharedBytes;
+      AccType* _shareAccType = (AccType*)_sharedBytes;
 
       // determine max (used below to improve numeric stability)
-      float* _max = _share;
-      _max[threadIdx.x] = -CUDA_FLT_MAX; // [threadIdx.x = relative column index within a block of columns]
+      T* _max = _share;
+      _max[threadIdx.x] = -CUDA_FLT_MAX;  // mask
       // find max over column indices that have the same relative column index (=threadIdx.x) across all blocks of columns
-      for(int tid = 0; tid < cols; tid += blockDim.x) { // loop over blocks of columns, blockDim.x = index of block of columns
+      for(int tid = 0; tid < cols; tid += blockDim.x) {
         // threadIdx.x = column index within block of columns; we reduce over columns within a block, then over blocks
         int i = tid + threadIdx.x;
         if(i < cols) {
@@ -493,19 +589,19 @@ __global__ void gSoftmax(float* out,
         len = (len + 1) >> 1;
       }
       __syncthreads();
-      float max = _max[0];
-      __syncthreads(); // @TODO: do we need this?
+      T max = _max[0];
+      __syncthreads();
 
       // compute denominator
-      float* _sum = _share;
+      AccType* _sum = _shareAccType; // accumulate into AccType
       _sum[threadIdx.x] = 0.0;
       for(int tid = 0; tid < cols; tid += blockDim.x) {
         int i = tid + threadIdx.x;
         if(i < cols) {
           // @TODO: is it faster to cache the result of expf() in GPU RAM, or would it be faster to recompute it below?
-          float ex = __expf(sp[i] - max);
-          so[i] = ex;
-          _sum[threadIdx.x] += ex;
+          T ex = Ops<T>::exp(sp[i] - max);
+          so[i] = (T)ex;
+          _sum[threadIdx.x] += (AccType)ex; // accumulate into AccType
         }
       }
       __syncthreads();
@@ -521,11 +617,11 @@ __global__ void gSoftmax(float* out,
       __syncthreads();
 
       // produce final output data
-      float sum = _sum[0];
+      AccType sum = _sum[0];
       for(int tid = 0; tid < cols; tid += blockDim.x) {
         int i = tid + threadIdx.x;
         if(i < cols) {
-          so[i] = so[i] / sum;
+          so[i] = (T)((AccType)so[i] / sum); // divide as AccType then convert
         }
       }
     }
@@ -541,27 +637,40 @@ void Softmax(Tensor out, Tensor in) {
 
   int blocks = std::min(MAX_BLOCKS, (int)m);
   int threads = std::min(MAX_THREADS, (int)k);
-  int shared = sizeof(float) * threads;
+  int shared = sizeof(float) * threads;  // accumulate into float
 
-  gSoftmax<<<blocks, threads, shared>>>(out->data(), out->shape(), in->data());
+  if(in->type() == Type::float32) {
+    gSoftmax<float, float><<<blocks, threads, shared>>>(out->data<float>(), out->shape(), in->data<float>());
+  } else if (in->type() == Type::float16) {
+    gSoftmax<half, float><<<blocks, threads, shared>>>(out->data<__half>(), out->shape(), in->data<__half>());
+  } else {
+    ABORT("Softmax not implemented for type {}", in->type());
+  }
 }
 
 // @TODO: refactor to reuse code from softmax, add comments
-__global__ void gLogSoftmax(float* out,
+template <typename T, typename AccType = float>
+__global__ void gLogSoftmax(T* out,
                             const functional::Shape outShape,
-                            const float* in) {
+                            const T* in) {
+
+  using namespace functional;
+
   int rows = outShape.elements() / outShape.back();
   int cols = outShape.back();
 
   for(int bid = 0; bid < rows; bid += gridDim.x) {
     int j = bid + blockIdx.x;
     if(j < rows) {
-      float* so = out + j * cols;
-      const float* sp = in + j * cols;
+      T* so = out + j * cols;
+      const T* sp = in + j * cols;
 
-      extern __shared__ float _share[];
+      // CUDA complains if type or size of shared memory changes, keep size constant.
+      extern __shared__ uint8_t _sharedBytes[];
+      T* _share = (T*)_sharedBytes;
+      AccType* _shareAccType = (AccType*)_sharedBytes;
 
-      float* _max = _share;
+      T* _max = _share; // 16-bit is ok for max if applicable
       _max[threadIdx.x] = sp[threadIdx.x];
       for(int tid = 0; tid < cols; tid += blockDim.x) {
         int id = tid + threadIdx.x;
@@ -583,19 +692,19 @@ __global__ void gLogSoftmax(float* out,
         len = (len + 1) >> 1;
       }
       __syncthreads();
-      float max = _max[0];
+      T max = _max[0];
       __syncthreads();
 
-      float* _sum = _share;
+      AccType* _sum = _shareAccType; // keep AccType for accumulation
 
       _sum[threadIdx.x] = 0.0;
       for(int tid = 0; tid < cols; tid += blockDim.x) {
         int id = tid + threadIdx.x;
         if(id < cols) {
-          float sm = sp[id] - max;
-          float ex = __expf(sm);
+          T sm = sp[id] - max;
+          AccType ex = Ops<AccType>::exp(sm); // sum with AccType
           so[id] = sm;
-          _sum[threadIdx.x] += ex;
+          _sum[threadIdx.x] += ex; // sum with AccType
         }
       }
       __syncthreads();
@@ -608,10 +717,11 @@ __global__ void gLogSoftmax(float* out,
         len = (len + 1) >> 1;
       }
       __syncthreads();
+      AccType sum = _sum[0];
       for(int tid = 0; tid < cols; tid += blockDim.x) {
         int id = tid + threadIdx.x;
         if(id < cols)
-          so[id] -= __logf(_sum[0]);
+          so[id] -= (T)Ops<AccType>::log(sum); // take log at the end and convert
       }
     }
     __syncthreads();
@@ -626,33 +736,40 @@ void LogSoftmax(Tensor out, Tensor in) {
 
   int blocks = std::min(MAX_BLOCKS, (int)m);
   int threads = std::min(MAX_THREADS, (int)k);
-  int shared = sizeof(float) * threads;
+  int shared = sizeof(float) * threads; // use float32 as accumulation type
 
-  gLogSoftmax<<<blocks, threads, shared>>>(
-      out->data(), out->shape(), in->data());
+  if(in->type() == Type::float32) {
+    gLogSoftmax<float, float><<<blocks, threads, shared>>>(out->data<float>(), out->shape(), in->data<float>());
+  } else if (in->type() == Type::float16) {
+    gLogSoftmax<half, float><<<blocks, threads, shared>>>(out->data<__half>(), out->shape(), in->data<__half>());
+  } else {
+    ABORT("LogSoftmax not implemented for type {}", in->type());
+  }
 }
 
 ///////////////////////////////////////////////////////
 
-__global__ void gSoftmaxGrad(float* grad,
-                             const float* adj,
-                             const float* val,
+template <typename T, typename AccType = float>
+__global__ void gSoftmaxGrad(T* grad,
+                             const T* adj,
+                             const T* val,
                              const int rows,
                              const int cols) {
   for(int bid = 0; bid < rows; bid += gridDim.x) {
     int j = bid + blockIdx.x;
     if(j < rows) {
-      extern __shared__ float _share[];
-      float* _sum = _share;
 
-      float* gradRow = grad + j * cols;
-      const float* adjRow = adj + j * cols;
-      const float* valRow = val + j * cols;
-      _sum[threadIdx.x] = 0.0;
+      extern __shared__ uint8_t _sharedBytes[];
+      AccType* _sum = (AccType*)_sharedBytes;
+
+      T* gradRow = grad + j * cols;
+      const T* adjRow = adj + j * cols;
+      const T* valRow = val + j * cols;
+      _sum[threadIdx.x] = (AccType)0.0f;
       for(int tid = 0; tid < cols; tid += blockDim.x) {
         int id = tid + threadIdx.x;
         if(id < cols) {
-          _sum[threadIdx.x] += valRow[id] * adjRow[id];
+          _sum[threadIdx.x] += (AccType)valRow[id] * (AccType)adjRow[id];
         }
       }
       __syncthreads();
@@ -661,16 +778,16 @@ __global__ void gSoftmaxGrad(float* grad,
         __syncthreads();
         int skip = (len + 1) >> 1;
         if(threadIdx.x < (len >> 1))
-          _sum[threadIdx.x] += _sum[threadIdx.x + skip];
+          _sum[threadIdx.x] += _sum[threadIdx.x + skip]; // accumulates in AccType
         len = (len + 1) >> 1;
       }
       __syncthreads();
       for(int tid = 0; tid < cols; tid += blockDim.x) {
         int id = tid + threadIdx.x;
         if(id < cols) {
-          float val = valRow[id] * (adjRow[id] - _sum[0]);
+          AccType val = (AccType)valRow[id] * ((AccType)adjRow[id] - _sum[0]);
           if(val)
-            gradRow[id] += val;
+            gradRow[id] += (T)val;
         }
       }
     }
@@ -691,29 +808,39 @@ void SoftmaxGrad(Tensor grad, Tensor adj, Tensor val) {
   int blocks = std::min(MAX_BLOCKS, m);
   int threads = std::min(MAX_THREADS, k);
   int shared = sizeof(float) * threads;
-  gSoftmaxGrad<<<blocks, threads, shared>>>(
-      grad->data(), adj->data(), val->data(), m, k);
+
+  if(grad->type() == Type::float32) {
+    gSoftmaxGrad<float, float><<<blocks, threads, shared>>>(
+      grad->data<float>(), adj->data<float>(), val->data<float>(), m, k);
+  } else if (grad->type() == Type::float16) {
+    // Accumulate into float
+    gSoftmaxGrad<half, float><<<blocks, threads, shared>>>(
+      grad->data<half>(), adj->data<half>(), val->data<half>(), m, k);
+  } else {
+    ABORT("SoftmaxGrad not implemented for type {}", grad->type());
+  }
 }
 
-__global__ void gLogSoftmaxGrad(float* grad,
-                                const float* adj,
-                                const float* val,
+template <typename T, typename AccType = float>
+__global__ void gLogSoftmaxGrad(T* grad,
+                                const T* adj,
+                                const T* val,
                                 const int rows,
                                 const int cols) {
   for(int bid = 0; bid < rows; bid += gridDim.x) {
     int j = bid + blockIdx.x;
     if(j < rows) {
-      extern __shared__ float _share[];
-      float* _sum = _share;
+      extern __shared__ uint8_t _sharedBytes[];
+      AccType* _sum = (AccType*)_sharedBytes;
 
-      float* gradRow = grad + j * cols;
-      const float* adjRow = adj + j * cols;
-      const float* valRow = val + j * cols;
+      T* gradRow = grad + j * cols;
+      const T* adjRow = adj + j * cols;
+      const T* valRow = val + j * cols;
       _sum[threadIdx.x] = 0.0;
       for(int tid = 0; tid < cols; tid += blockDim.x) {
         int id = tid + threadIdx.x;
         if(id < cols) {
-          _sum[threadIdx.x] += adjRow[id];
+          _sum[threadIdx.x] += (AccType)adjRow[id];
         }
       }
       __syncthreads();
@@ -722,14 +849,14 @@ __global__ void gLogSoftmaxGrad(float* grad,
         __syncthreads();
         int skip = (len + 1) >> 1;
         if(threadIdx.x < (len >> 1))
-          _sum[threadIdx.x] += _sum[threadIdx.x + skip];
+          _sum[threadIdx.x] += _sum[threadIdx.x + skip]; // AccType
         len = (len + 1) >> 1;
       }
       __syncthreads();
       for(int tid = 0; tid < cols; tid += blockDim.x) {
         int id = tid + threadIdx.x;
         if(id < cols)
-          gradRow[id] += adjRow[id] - (expf(valRow[id]) * _sum[0]);
+          gradRow[id] += (T)((AccType)adjRow[id] - (functional::Ops<AccType>::exp((AccType)valRow[id]) * _sum[0]));
       }
     }
     __syncthreads();
@@ -748,35 +875,25 @@ void LogSoftmaxGrad(Tensor grad, Tensor adj, Tensor val) {
 
   int blocks = std::min(MAX_BLOCKS, m);
   int threads = std::min(MAX_THREADS, k);
-  int shared = sizeof(float) * threads;
-  gLogSoftmaxGrad<<<blocks, threads, shared>>>(
-      grad->data(), adj->data(), val->data(), m, k);
-}
-
-///////////////////////////////////////////////////////
-__global__ void gArgmax(float* out,
-                        const float* data,
-                        size_t rows,
-                        size_t cols) {
-  size_t row = blockIdx.x;
-  size_t startInd = row * cols;
-  float maxScore = -99999;
-  size_t maxInd;
-  for(size_t col = 0; col < cols; ++col) {
-    size_t ind = startInd + col;
-    float score = data[ind];
-    if(score > maxScore) {
-      maxScore = score;
-      maxInd = col;
-    }
+  int shared = sizeof(float) * threads; // Use float32 as accumulation type
+
+  if(grad->type() == Type::float32) {
+    gLogSoftmaxGrad<float, float><<<blocks, threads, shared>>>(
+      grad->data<float>(), adj->data<float>(), val->data<float>(), m, k);
+  } else if (grad->type() == Type::float16) {
+    // accumulate into float
+    gLogSoftmaxGrad<half, float><<<blocks, threads, shared>>>(
+      grad->data<half>(), adj->data<half>(), val->data<half>(), m, k);
+  } else {
+    ABORT("LogSoftmaxGrad not implemented for type {}", grad->type());
   }
-  out[row] = maxInd;
 }
 
 ///////////////////////////////////////////////////////
 
-__global__ void gCopyRows(float* out,
-                          const float* in,
+template <typename T>
+__global__ void gCopyRows(T* out,
+                          const T* in,
                           size_t cols,
                           const IndexType* sourceRowIdx,
                           size_t rows) {
@@ -786,8 +903,8 @@ __global__ void gCopyRows(float* out,
       size_t dstId = j;
       size_t srcId = sourceRowIdx[j];
 
-      float* rowOut = out + dstId * cols;
-      const float* rowIn = in + srcId * cols;
+      T* rowOut = out + dstId * cols;
+      const T* rowIn = in + srcId * cols;
 
       for(int tid = 0; tid < cols; tid += blockDim.x) {
         int i = tid + threadIdx.x;
@@ -812,12 +929,20 @@ void CopyRows(Tensor out,
   int threads = std::min(MAX_THREADS, (int)cols);
   int blocks = std::min(MAX_BLOCKS, (int)rowsToCopy);
 
-  gCopyRows<<<blocks, threads>>>(
-      out->data(), in->data(), cols, indices->data<IndexType>(), rowsToCopy);
+  if(out->type() == Type::float32) {
+    gCopyRows<<<blocks, threads>>>(
+      out->data<float>(), in->data<float>(), cols, indices->data<IndexType>(), rowsToCopy);
+  } else if (out->type() == Type::float16) {
+    gCopyRows<<<blocks, threads>>>(
+      out->data<half>(), in->data<half>(), cols, indices->data<IndexType>(), rowsToCopy);
+  } else {
+    ABORT("CopyRows not implemented for type {}", out->type());
+  }
 }
 
-__global__ void gPasteRows(float* out,
-                           const float* in,
+template <typename T>
+__global__ void gPasteRows(T* out,
+                           const T* in,
                            size_t cols,
                            const IndexType* targetRowIdx,
                            size_t rows) {
@@ -827,13 +952,15 @@ __global__ void gPasteRows(float* out,
       size_t dstId = targetRowIdx[j];
       size_t srcId = j;
 
-      float* rowOut = out + dstId * cols;
-      const float* rowIn = in + srcId * cols;
+      T* rowOut = out + dstId * cols;
+      const T* rowIn = in + srcId * cols;
 
       for(int tid = 0; tid < cols; tid += blockDim.x) {
         int i = tid + threadIdx.x;
-        if(i < cols)
-          atomicAdd(rowOut + i, rowIn[i]);
+        if(i < cols) {
+          // @TODO: Do we need to get rid of this atomic add? It seems slow for fp16
+          atomics::atomicAdd(rowOut + i, rowIn[i]);
+        }
       }
     }
   }
@@ -853,14 +980,22 @@ void PasteRows(Tensor out,
   int threads = std::min(MAX_THREADS, (int)cols);
   int blocks = std::min(MAX_BLOCKS, (int)rowsToCopy);
 
-  gPasteRows<<<blocks, threads>>>(
-      out->data(), in->data(), cols, indices->data<IndexType>(), rowsToCopy);
+  if(out->type() == Type::float32) {
+    gPasteRows<<<blocks, threads>>>(
+      out->data<float>(), in->data<float>(), cols, indices->data<IndexType>(), rowsToCopy);
+  } else if (out->type() == Type::float16) {
+    gPasteRows<<<blocks, threads>>>(
+      out->data<half>(), in->data<half>(), cols, indices->data<IndexType>(), rowsToCopy);
+  } else {
+    ABORT("CopyRows not implemented for type {}", out->type());
+  }
 }
 
 /////////////
 
-__global__ void gCopyCols(float* out,
-                          const float* in,
+template <typename T>
+__global__ void gCopyCols(T* out,
+                          const T* in,
                           size_t rows,
                           size_t colsIn,
                           const IndexType* sourceColIdx,
@@ -868,8 +1003,8 @@ __global__ void gCopyCols(float* out,
   for(int bid = 0; bid < rows; bid += gridDim.x) {
     int j = bid + blockIdx.x;
     if(j < rows) {
-      const float* rowIn = in + j * colsIn;
-      float* rowOut = out + j * colsOut;
+      const T* rowIn = in + j * colsIn;
+      T* rowOut = out + j * colsOut;
 
       for(int tid = 0; tid < colsOut; tid += blockDim.x) {
         int i = tid + threadIdx.x;
@@ -893,12 +1028,20 @@ void CopyCols(Tensor out, const Tensor in, const Tensor indices) {
   int threads = std::min(MAX_THREADS, (int)colsToCopy);
   int blocks = std::min(MAX_BLOCKS, (int)rows);
 
-  gCopyCols<<<blocks, threads>>>(
-      out->data(), in->data(), rows, cols, indices->data<IndexType>(), colsToCopy);
+  if(out->type() == Type::float32) {
+    gCopyCols<<<blocks, threads>>>(
+      out->data<float>(), in->data<float>(), rows, cols, indices->data<IndexType>(), colsToCopy);
+  } else if (out->type() == Type::float16) {
+    gCopyCols<<<blocks, threads>>>(
+      out->data<half>(), in->data<half>(), rows, cols, indices->data<IndexType>(), colsToCopy);
+  } else {
+    ABORT("CopyCols not implemented for type {}", out->type());
+  }
 }
 
-__global__ void gPasteCols(float* out,
-                           const float* in,
+template <typename T>
+__global__ void gPasteCols(T* out,
+                           const T* in,
                            size_t rows,
                            size_t colsOut,
                            const IndexType* targetColIdx,
@@ -906,13 +1049,13 @@ __global__ void gPasteCols(float* out,
   for(int bid = 0; bid < rows; bid += gridDim.x) {
     int j = bid + blockIdx.x;
     if(j < rows) {
-      const float* rowIn = in + j * colsIn;
-      float* rowOut = out + j * colsOut;
+      const T* rowIn = in + j * colsIn;
+      T* rowOut = out + j * colsOut;
 
       for(int tid = 0; tid < colsIn; tid += blockDim.x) {
         int i = tid + threadIdx.x;
         if(i < colsIn)
-          rowOut[targetColIdx[i]] += rowIn[i];
+          rowOut[targetColIdx[i]] += rowIn[i]; // @TODO: atomicAdd?
       }
     }
   }
@@ -933,13 +1076,21 @@ void PasteCols(Tensor out,
   int threads = std::min(MAX_THREADS, (int)colsToCopy);
   int blocks = std::min(MAX_BLOCKS, (int)rows);
 
-  gPasteCols<<<blocks, threads>>>(
-      out->data(), in->data(), rows, cols, indices->data<IndexType>(), colsToCopy);
+  if(out->type() == Type::float32) {
+    gPasteCols<<<blocks, threads>>>(
+      out->data<float>(), in->data<float>(), rows, cols, indices->data<IndexType>(), colsToCopy);
+  } else if (out->type() == Type::float16) {
+    gPasteCols<<<blocks, threads>>>(
+      out->data<half>(), in->data<half>(), rows, cols, indices->data<IndexType>(), colsToCopy);
+  } else {
+    ABORT("PasteCols not implemented for type {}", out->type());
+  }
 }
 
-__global__ void gSelect(float* out,
+template <typename T>
+__global__ void gSelect(T* out,
                         functional::Shape outShape,
-                        const float* in,
+                        const T* in,
                         const functional::Shape inShape,
                         int axis,
                         IndexType* d_indices) {
@@ -957,9 +1108,10 @@ __global__ void gSelect(float* out,
   }
 }
 
-__global__ void gInsert(float* out,
+template <typename T>
+__global__ void gInsert(T* out,
                         functional::Shape outShape,
-                        const float* in,
+                        const T* in,
                         const functional::Shape inShape,
                         int axis,
                         IndexType* d_indices) {
@@ -972,7 +1124,7 @@ __global__ void gInsert(float* out,
       inShape.dims(index, dims);
       dims[axis] = d_indices[dims[axis]];
       int outIndex = outShape.index(dims);
-      out[outIndex] += in[index];
+      out[outIndex] += in[index]; // this is probably wrong, atomicAdd?
     }
   }
 }
@@ -991,12 +1143,24 @@ void Select(Tensor out,
   int blocks = std::min(MAX_BLOCKS, length / threads + (length % threads != 0));
 
   int axisGPU = axis + functional::Shape::size() - out->shape().size();
-  gSelect<<<blocks, threads>>>(out->data(),
-                               out->shape(),
-                               in->data(),
-                               in->shape(),
-                               axisGPU,
-                               indices->data<IndexType>());
+
+  if(out->type() == Type::float32) {
+    gSelect<<<blocks, threads>>>(out->data<float>(),
+                                out->shape(),
+                                in->data<float>(),
+                                in->shape(),
+                                axisGPU,
+                                indices->data<IndexType>());
+  } else if (out->type() == Type::float16) {
+    gSelect<<<blocks, threads>>>(out->data<half>(),
+                                out->shape(),
+                                in->data<half>(),
+                                in->shape(),
+                                axisGPU,
+                                indices->data<IndexType>());
+  } else {
+    ABORT("Select not implemented for type {}", out->type());
+  }
 }
 
 void Insert(Tensor out,
@@ -1012,51 +1176,64 @@ void Insert(Tensor out,
   int blocks = std::min(MAX_BLOCKS, length / threads + (length % threads != 0));
 
   int axisGPU = axis + functional::Shape::size() - out->shape().size();
-  gInsert<<<blocks, threads>>>(out->data(),
-                               out->shape(),
-                               in->data(),
-                               in->shape(),
-                               axisGPU,
-                               indices->data<IndexType>());
-}
-
-__global__ void gGRUFastForward(float* out,
-                                const float* state,
-                                const float* xW,
-                                const float* sU,
-                                const float* b,
-                                const float* mask,
+
+  if(out->type() == Type::float32) {
+    gInsert<<<blocks, threads>>>(out->data<float>(),
+                                out->shape(),
+                                in->data<float>(),
+                                in->shape(),
+                                axisGPU,
+                                indices->data<IndexType>());
+  } else if (out->type() == Type::float16) {
+    gInsert<<<blocks, threads>>>(out->data<half>(),
+                                out->shape(),
+                                in->data<half>(),
+                                in->shape(),
+                                axisGPU,
+                                indices->data<IndexType>());
+  } else {
+    ABORT("Insert not implemented for type {}", out->type());
+  }
+}
+
+template <typename T>
+__global__ void gGRUFastForward(T* out,
+                                const T* state,
+                                const T* xW,
+                                const T* sU,
+                                const T* b,
+                                const T* mask,
                                 size_t rows,
                                 size_t cols,
                                 bool final) {
   for(int bid = 0; bid < rows; bid += gridDim.x) {
     int j = bid + blockIdx.x;
     if(j < rows) {
-      float m = !mask || mask[j];
-      float* rowOut = out + j * cols;
-      const float* rowState = state + j * cols;
+      T m = !mask || mask[j];
+      T* rowOut = out + j * cols;
+      const T* rowState = state + j * cols;
 
-      const float* xWrow = xW + j * cols * 3;
-      const float* sUrow = sU + j * cols * 3;
+      const T* xWrow = xW + j * cols * 3;
+      const T* sUrow = sU + j * cols * 3;
 
       for(int tid = 0; tid < cols; tid += blockDim.x) {
         int i = tid + threadIdx.x;
         if(i < cols) {
-          float r = stableSigmoid(xWrow[i] + sUrow[i] + b[i]);
+          T r = functional::Ops<T>::sigmoid(xWrow[i] + sUrow[i] + b[i]);
 
           int k = i + cols;
 
-          float z = stableSigmoid(xWrow[k] + sUrow[k] + b[k]);
+          T z = functional::Ops<T>::sigmoid(xWrow[k] + sUrow[k] + b[k]);
 
           int l = i + 2 * cols;
-          float h;
+          T h;
           if(final)
-            h = tanhf(xWrow[l] + (sUrow[l] + b[l]) * r);
+            h = functional::Ops<T>::tanh(xWrow[l] + (sUrow[l] + b[l]) * r);
           else
-            h = tanhf(xWrow[l] + sUrow[l] * r + b[l]);
+            h = functional::Ops<T>::tanh(xWrow[l] + sUrow[l] * r + b[l]);
 
-          float out = (1.0f - z) * h + z * rowState[i];
-          rowOut[i] = m * out + (1 - m) * rowState[i];
+          T out = ((T)1.f - z) * h + z * rowState[i];
+          rowOut[i] = m * out + ((T)1.f - m) * rowState[i];
         }
       }
     }
@@ -1072,44 +1249,60 @@ void GRUFastForward(Tensor out, std::vector<Tensor> inputs, bool final) {
   int blocks = std::min(MAX_BLOCKS, rows);
   int threads = std::min(MAX_THREADS, cols);
 
-  gGRUFastForward<<<blocks, threads>>>(
-      out->data(),                                // output
-      inputs[0]->data(),                          // state
-      inputs[1]->data(),                          // xW
-      inputs[2]->data(),                          // sU
-      inputs[3]->data(),                          // b
-      inputs.size() > 4 ? inputs[4]->data() : 0,  // mask
-      rows,
-      cols,
-      final);
-}
-
-__global__ void gGRUFastBackward(float* outState,
-                                 float* outXW,
-                                 float* outSU,
-                                 float* outB,
-                                 const float* state,
-                                 const float* xW,
-                                 const float* sU,
-                                 const float* b,
-                                 const float* mask,
-                                 const float* adj,
+  if(out->type() == Type::float32) {
+    gGRUFastForward<<<blocks, threads>>>(
+        out->data<float>(),                                // output
+        inputs[0]->data<float>(),                          // state
+        inputs[1]->data<float>(),                          // xW
+        inputs[2]->data<float>(),                          // sU
+        inputs[3]->data<float>(),                          // b
+        inputs.size() > 4 ? inputs[4]->data<float>() : 0,  // mask
+        rows,
+        cols,
+        final);
+  } else if (out->type() == Type::float16) {
+    gGRUFastForward<<<blocks, threads>>>(
+        out->data<half>(),                                // output
+        inputs[0]->data<half>(),                          // state
+        inputs[1]->data<half>(),                          // xW
+        inputs[2]->data<half>(),                          // sU
+        inputs[3]->data<half>(),                          // b
+        inputs.size() > 4 ? inputs[4]->data<half>() : 0,  // mask
+        rows,
+        cols,
+        final);
+  } else {
+    ABORT("GRUFastForward not implemented for type {}", out->type());
+  }
+}
+
+template <typename T>
+__global__ void gGRUFastBackward(T* outState,
+                                 T* outXW,
+                                 T* outSU,
+                                 T* outB,
+                                 const T* state,
+                                 const T* xW,
+                                 const T* sU,
+                                 const T* b,
+                                 const T* mask,
+                                 const T* adj,
                                  size_t rows,
                                  size_t cols,
                                  bool final) {
   for(int bid = 0; bid < rows; bid += gridDim.x) {
     int j = bid + blockIdx.x;
     if(j < rows) {
-      float m = !mask || mask[j];
+      T m = !mask || mask[j];
 
-      float* rowOutState = outState + j * cols;
-      float* rowOutXW = outXW + j * cols * 3;
-      float* rowOutSU = outSU + j * cols * 3;
+      T* rowOutState = outState + j * cols;
+      T* rowOutXW = outXW + j * cols * 3;
+      T* rowOutSU = outSU + j * cols * 3;
 
-      const float* rowState = state + j * cols;
-      const float* rowXW = xW + j * cols * 3;
-      const float* rowSU = sU + j * cols * 3;
-      const float* rowAdj = adj + j * cols;
+      const T* rowState = state + j * cols;
+      const T* rowXW = xW + j * cols * 3;
+      const T* rowSU = sU + j * cols * 3;
+      const T* rowAdj = adj + j * cols;
 
       for(int tid = 0; tid < cols; tid += blockDim.x) {
         int i = tid + threadIdx.x;
@@ -1117,25 +1310,25 @@ __global__ void gGRUFastBackward(float* outState,
           int k = i + cols;
           int l = i + 2 * cols;
 
-          float r = stableSigmoid(rowXW[i] + rowSU[i] + b[i]);
-          float z = stableSigmoid(rowXW[k] + rowSU[k] + b[k]);
+          T r = functional::Ops<T>::sigmoid(rowXW[i] + rowSU[i] + b[i]);
+          T z = functional::Ops<T>::sigmoid(rowXW[k] + rowSU[k] + b[k]);
 
-          float h;
+          T h;
           if(final)
-            h = tanhf(rowXW[l] + (rowSU[l] + b[l]) * r);
+            h = functional::Ops<T>::tanh(rowXW[l] + (rowSU[l] + b[l]) * r);
           else
-            h = tanhf(rowXW[l] + rowSU[l] * r + b[l]);
+            h = functional::Ops<T>::tanh(rowXW[l] + rowSU[l] * r + b[l]);
 
-          float adj = rowAdj[i];
+          T adj = rowAdj[i];
 
-          float t = (1 - z) * (1 - h * h);
+          T t = ((T)1.f - z) * ((T)1.f - h * h);
 
           // df/ds
           if(outState)
-            rowOutState[i] += (m * z - m + 1) * adj;
+            rowOutState[i] += (m * z - m + (T)1.f) * adj;
 
           // df/d(xW_r) ...
-          float dfdxW_r = m * r * (1 - r) * t * adj;
+          T dfdxW_r = m * r * ((T)1.f - r) * t * adj;
           if(final)
             dfdxW_r *= rowSU[l] + b[l];
           else
@@ -1145,28 +1338,28 @@ __global__ void gGRUFastBackward(float* outState,
           if(outSU)
             rowOutSU[i] += dfdxW_r;
           if(outB)
-            atomicAdd(outB + i, dfdxW_r);
+            atomics::atomicAdd(outB + i, dfdxW_r); // @TODO: get rid of atomicAdd everywhere
 
           // df/d(xW_z) ...
-          float dfdxW_z = m * (1 - z) * z * (rowState[i] - h) * adj;
+          T dfdxW_z = m * ((T)1.f - z) * z * (rowState[i] - h) * adj;
           if(outXW)
             rowOutXW[k] += dfdxW_z;
           if(outSU)
             rowOutSU[k] += dfdxW_z;
           if(outB)
-            atomicAdd(outB + k, dfdxW_z);
+            atomics::atomicAdd(outB + k, dfdxW_z);
 
           // df/d(xW_x) ...
-          float dfdxW_x = m * t * adj;
+          T dfdxW_x = m * t * adj;
           if(outXW)
             rowOutXW[l] += dfdxW_x;
           if(outSU)
             rowOutSU[l] += dfdxW_x * r;
           if(outB)
             if(final)
-              atomicAdd(outB + l, dfdxW_x * r);
+              atomics::atomicAdd(outB + l, dfdxW_x * r);
             else
-              atomicAdd(outB + l, dfdxW_x);
+              atomics::atomicAdd(outB + l, dfdxW_x);
         }
       }
     }
@@ -1185,38 +1378,58 @@ void GRUFastBackward(std::vector<Tensor> outputs,
   int blocks = std::min(MAX_BLOCKS, rows);
   int threads = std::min(MAX_THREADS, cols);
 
-  gGRUFastBackward<<<blocks, threads>>>(
-      outputs[0] ? outputs[0]->data() : 0,        // state - adj
-      outputs[1] ? outputs[1]->data() : 0,        // xW - adj
-      outputs[2] ? outputs[2]->data() : 0,        // sU - adj
-      outputs[3] ? outputs[3]->data() : 0,        // b - adj
-      inputs[0]->data(),                          // state
-      inputs[1]->data(),                          // xW
-      inputs[2]->data(),                          // sU
-      inputs[3]->data(),                          // b
-      inputs.size() > 4 ? inputs[4]->data() : 0,  // mask
-      adj->data(),
-      rows,
-      cols,
-      final);
+  if(adj->type() == Type::float32) {
+    gGRUFastBackward<<<blocks, threads>>>(
+        outputs[0] ? outputs[0]->data<float>() : 0,        // state - adj
+        outputs[1] ? outputs[1]->data<float>() : 0,        // xW - adj
+        outputs[2] ? outputs[2]->data<float>() : 0,        // sU - adj
+        outputs[3] ? outputs[3]->data<float>() : 0,        // b - adj
+        inputs[0]->data<float>(),                          // state
+        inputs[1]->data<float>(),                          // xW
+        inputs[2]->data<float>(),                          // sU
+        inputs[3]->data<float>(),                          // b
+        inputs.size() > 4 ? inputs[4]->data<float>() : 0,  // mask
+        adj->data<float>(),
+        rows,
+        cols,
+        final);
+  } else if (adj->type() == Type::float16) {
+    gGRUFastBackward<<<blocks, threads>>>(
+        outputs[0] ? outputs[0]->data<half>() : 0,        // state - adj
+        outputs[1] ? outputs[1]->data<half>() : 0,        // xW - adj
+        outputs[2] ? outputs[2]->data<half>() : 0,        // sU - adj
+        outputs[3] ? outputs[3]->data<half>() : 0,        // b - adj
+        inputs[0]->data<half>(),                          // state
+        inputs[1]->data<half>(),                          // xW
+        inputs[2]->data<half>(),                          // sU
+        inputs[3]->data<half>(),                          // b
+        inputs.size() > 4 ? inputs[4]->data<half>() : 0,  // mask
+        adj->data<half>(),
+        rows,
+        cols,
+        final);
+  } else {
+    ABORT("gGRUFastBackward not implemented for type {}", adj->type());
+  }
 }
 
-__global__ void gCrossEntropyPick(float* out,
+template <typename T, typename AccType = float>
+__global__ void gCrossEntropyPick(T* out,
                                   const functional::Shape outShape,
-                                  const float* in,
+                                  const T* in,
                                   const functional::Shape inShape,
                                   const IndexType* pick) {
   int rows = inShape.elements() / inShape.back();
   int cols = inShape.back();
 
+  extern __shared__ uint8_t _sharedBytes[];
+
   for(int bid = 0; bid < rows; bid += gridDim.x) {
     int j = bid + blockIdx.x;
     if(j < rows) {
-      const float* sp = in + j * cols;
-
-      extern __shared__ float _share[];
-      float* _max = _share;
+      const T* sp = in + j * cols;
 
+      T* _max = (T*)_sharedBytes;
       _max[threadIdx.x] = sp[threadIdx.x];
       for(int tid = 1; tid < cols; tid += blockDim.x) {
         int id = tid + threadIdx.x;
@@ -1238,15 +1451,16 @@ __global__ void gCrossEntropyPick(float* out,
         len = (len + 1) >> 1;
       }
       __syncthreads();
-      float max = _max[0];
+      T max = _max[0];
       __syncthreads();
 
-      float* _sum = _share;
-      _sum[threadIdx.x] = 0.0;
+      AccType* _sum = (AccType*)_sharedBytes;
+      _sum[threadIdx.x] = (AccType)0.0f;
+
       for(int tid = 0; tid < cols; tid += blockDim.x) {
         int id = tid + threadIdx.x;
         if(id < cols) {
-          _sum[threadIdx.x] += __expf(sp[id] - max);
+          _sum[threadIdx.x] += functional::Ops<AccType>::exp(sp[id] - max);
         }
       }
       __syncthreads();
@@ -1264,7 +1478,7 @@ __global__ void gCrossEntropyPick(float* out,
       for(int tid = 0; tid < cols; tid += blockDim.x) {
         int id = tid + threadIdx.x;
         if(id == (int)pick[j]) {
-          out[j] = __logf(_sum[0]) - sp[id] + max;
+          out[j] = (T)functional::Ops<AccType>::log(_sum[0]) - sp[id] + max;
         }
       }
     }
@@ -1286,28 +1500,36 @@ void CrossEntropyPick(Tensor out, Tensor in, Tensor indices) {
 
   int blocks = std::min(MAX_BLOCKS, (int)rows);
   int threads = std::min(MAX_THREADS, (int)cols);
-  int shared = sizeof(float) * threads;
+  int shared = sizeof(float) * threads; // Use float32 as accumulation type
 
-  gCrossEntropyPick<<<blocks, threads, shared>>>(
-      out->data(), out->shape(), in->data(), in->shape(), indices->data<IndexType>());
+  if(out->type() == Type::float32) {
+    gCrossEntropyPick<float, float><<<blocks, threads, shared>>>(
+      out->data<float>(), out->shape(), in->data<float>(), in->shape(), indices->data<IndexType>());
+  } else if(out->type() == Type::float16) {
+    gCrossEntropyPick<half, float><<<blocks, threads, shared>>>(
+      out->data<half>(), out->shape(), in->data<half>(), in->shape(), indices->data<IndexType>());
+  } else {
+    ABORT("CrossEntropyPick not implemented for type {}", out->type());
+  }
 }
 
-__global__ void gCrossEntropyPickBackward(float* out,
+template <typename T, typename AccType = float>
+__global__ void gCrossEntropyPickBackward(T* out,
                                           const functional::Shape outShape,
-                                          const float* adj,
-                                          const float* in,
+                                          const T* adj,
+                                          const T* in,
                                           const IndexType* pick) {
   int rows = outShape.elements() / outShape.back();
   int cols = outShape.back();
+
+  extern __shared__ uint8_t _sharedBytes[];
+
   for(int bid = 0; bid < rows; bid += gridDim.x) {
     int j = bid + blockIdx.x;
     if(j < rows) {
-      const float* sp = in + j * cols;
-      float* so = out + j * cols;
-
-      extern __shared__ float _share[];
-      float* _max = _share;
-
+      const T* sp = in + j * cols;
+      T* so = out + j * cols;
+      T* _max = (T*)_sharedBytes;
       _max[threadIdx.x] = sp[threadIdx.x];
       for(int tid = 1; tid < cols; tid += blockDim.x) {
         int id = tid + threadIdx.x;
@@ -1329,15 +1551,15 @@ __global__ void gCrossEntropyPickBackward(float* out,
         len = (len + 1) >> 1;
       }
       __syncthreads();
-      float max = _max[0];
+      T max = _max[0];
       __syncthreads();
 
-      float* _sum = _share;
+      AccType* _sum = (AccType*)_sharedBytes;
       _sum[threadIdx.x] = 0.0;
       for(int tid = 0; tid < cols; tid += blockDim.x) {
         int id = tid + threadIdx.x;
         if(id < cols) {
-          float ex = __expf(sp[id] - max);
+          AccType ex = functional::Ops<AccType>::exp(sp[id] - max);
           _sum[threadIdx.x] += ex;
         }
       }
@@ -1356,8 +1578,8 @@ __global__ void gCrossEntropyPickBackward(float* out,
       for(int tid = 0; tid < cols; tid += blockDim.x) {
         int id = tid + threadIdx.x;
         if(id < cols) {
-          float sub = (float)(id == (int)pick[j]);
-          so[id] += adj[j] * (__expf(sp[id] - max) / _sum[0] - sub);
+          AccType sub = (AccType)(id == (int)pick[j]);
+          so[id] += (AccType)adj[j] * (functional::Ops<AccType>::exp(sp[id] - max) / _sum[0] - sub);
         }
       }
     }
@@ -1375,38 +1597,69 @@ void CrossEntropyPickBackward(Tensor out, Tensor adj, Tensor a, Tensor indices)
 
   int blocks = std::min(MAX_BLOCKS, (int)rows);
   int threads = std::min(MAX_THREADS, (int)cols);
-  int shared = sizeof(float) * threads;
+  int shared = sizeof(float) * threads; // use float as accumulation type
 
-  gCrossEntropyPickBackward<<<blocks, threads, shared>>>(
-      out->data(), out->shape(), adj->data(), a->data(), indices->data<IndexType>());
+  if(out->type() == Type::float32) {
+    gCrossEntropyPickBackward<float, float><<<blocks, threads, shared>>>(
+      out->data<float>(), out->shape(), adj->data<float>(), a->data<float>(), indices->data<IndexType>());
+  } else if(out->type() == Type::float16) {
+    gCrossEntropyPickBackward<half, float><<<blocks, threads, shared>>>(
+      out->data<half>(), out->shape(), adj->data<half>(), a->data<half>(), indices->data<IndexType>());
+  } else {
+    ABORT("CrossEntropyPick not implemented for type {}", out->type());
+  }
 }
 
-float L2Norm(Tensor in) {
+float L2Norm(Tensor in, Ptr<Allocator> allocator) {
   cudaSetDevice(in->getDeviceId().no);
 
   int size = in->shape().elements();
   int threads = std::min(MAX_THREADS, size);
   int blocks = std::min(MAX_BLOCKS, size / threads + (size % threads != 0));
 
-  uint8_t* data;
-  cudaMalloc(&data, blocks * sizeof(float));
-  Tensor out(TensorBase::New(MemoryPiece::New(data, blocks * sizeof(float)),
-                             Shape({1, blocks}),
-                             Type::float32,
-                             in->getBackend()));
-
-  using namespace functional;
-  ReduceAll(_1 * _1, out, in);
-  float dataCpu = sqrtf(out->get(0));
-  out.reset();
-  cudaFree(data);
-  return dataCpu;
+  if(allocator) {
+    auto memoryPiece = allocator->alloc<float>(blocks);
+    auto blockMem = TensorBase::New(memoryPiece, Shape({1, blocks}), Type::float32, in->getBackend());
+
+    using namespace functional;
+    if(in->type() == Type::float32) {
+      ReduceAll<float, float>(_1 * _1, blockMem, in);
+    } else if(in->type() == Type::float16) {
+      ReduceAll<half, float>(_1 * _1, blockMem, in);
+    } else {
+      ABORT("L2Norm not implemented for type {}", in->type());
+    }
+    float dataCpu = sqrtf(blockMem->get<float>(0));
+    allocator->free(memoryPiece);
+    return dataCpu;
+  } else { // @TODO: this branch is to be removed with next PR, old version
+    uint8_t* data;
+    cudaMalloc(&data, blocks * sizeof(float));
+    Tensor out(TensorBase::New(MemoryPiece::New(data, blocks * sizeof(float)),
+                               Shape({1, blocks}),
+                               Type::float32,
+                               in->getBackend()));
+ 
+    using namespace functional;
+    if(in->type() == Type::float32) {
+      ReduceAll<float, float>(_1 * _1, out, in);
+    } else if(in->type() == Type::float16) {
+      ReduceAll<half, float>(_1 * _1, out, in);
+    } else {
+      ABORT("L2Norm not implemented for type {}", in->type());
+    }
+    float dataCpu = sqrtf(out->get<float>(0));
+    out.reset();
+    cudaFree(data);
+    return dataCpu;
+  }
 }
 
-__global__ void gAtt(float* out,
-                     const float* va,
-                     const float* ctx,
-                     const float* state,
+template <typename T, typename AccType = float>
+__global__ void gAtt(T* out,
+                     const T* va,
+                     const T* ctx,
+                     const T* state,
                      int m,  // total rows (batch x time x beam)
                      int k,  // depth
                      int b,  // batch size
@@ -1418,19 +1671,19 @@ __global__ void gAtt(float* out,
   for(int bid = 0; bid < rows; bid += gridDim.x) {
     int j = bid + blockIdx.x;
     if(j < rows) {
-      const float* vaRow = va;
-      const float* ctxRow = ctx + (j % (b * t)) * cols;
-      const float* stateRow = state + ((j / (b * t)) * b + j % b) * cols;
+      const T* vaRow = va;
+      const T* ctxRow = ctx + (j % (b * t)) * cols;
+      const T* stateRow = state + ((j / (b * t)) * b + j % b) * cols;
 
-      extern __shared__ float _share[];
-      float* _sum = _share;
+      extern __shared__ AccType _share[];
+      AccType* _sum = _share;
 
-      _sum[threadIdx.x] = 0.0;
+      _sum[threadIdx.x] = 0.f;
       for(int tid = 0; tid < cols; tid += blockDim.x) {
         int id = tid + threadIdx.x;
         if(id < cols) {
-          float z = ctxRow[id] + stateRow[id];
-          float ex = tanhf(z) * vaRow[id];
+          AccType z = (AccType)ctxRow[id] + (AccType)stateRow[id];
+          AccType ex = functional::Ops<AccType>::tanh(z) * (AccType)vaRow[id];
           _sum[threadIdx.x] += ex;
         }
       }
@@ -1444,7 +1697,7 @@ __global__ void gAtt(float* out,
         len = (len + 1) >> 1;
       }
       __syncthreads();
-      out[j] = _sum[0];
+      out[j] = (T)_sum[0];
     }
     __syncthreads();
   }
@@ -1462,17 +1715,25 @@ void Att(Tensor out, Tensor va, Tensor context, Tensor state) {
   int threads = std::min(MAX_THREADS, (int)k);
   int shared = sizeof(float) * threads;
 
-  gAtt<<<blocks, threads, shared>>>(
-      out->data(), va->data(), context->data(), state->data(), m, k, b, t);
+  if(out->type() == Type::float32) {
+    gAtt<float, float><<<blocks, threads, shared>>>(
+      out->data<float>(), va->data<float>(), context->data<float>(), state->data<float>(), m, k, b, t);
+  } else if (out->type() == Type::float16) {
+    gAtt<half, float><<<blocks, threads, shared>>>(
+      out->data<half>(), va->data<half>(), context->data<half>(), state->data<half>(), m, k, b, t);
+  } else {
+    ABORT("gAtt not implemented for type {}", out->type());
+  }
 }
 
-__global__ void gAttBack(float* gVa,
-                         float* gContext,
-                         float* gState,
-                         const float* va,
-                         const float* context,
-                         const float* state,
-                         const float* adj,
+template <typename T>
+__global__ void gAttBack(T* gVa,
+                         T* gContext,
+                         T* gState,
+                         const T* va,
+                         const T* context,
+                         const T* state,
+                         const T* adj,
                          int m,  // rows
                          int k,  // cols
                          int n   // batch size
@@ -1482,23 +1743,23 @@ __global__ void gAttBack(float* gVa,
   for(int bid = 0; bid < m; bid += gridDim.x) {
     int j = bid + blockIdx.x;
     if(j < rows) {
-      float* gcRow = gContext + j * cols;
-      float* gsRow = gState + (j % n) * cols;
+      T* gcRow = gContext + j * cols;
+      T* gsRow = gState + (j % n) * cols;
 
-      const float* cRow = context + j * cols;
-      const float* sRow = state + (j % n) * cols;
+      const T* cRow = context + j * cols;
+      const T* sRow = state + (j % n) * cols;
 
       for(int tid = 0; tid < cols; tid += blockDim.x) {
         int id = tid + threadIdx.x;
         if(id < cols) {
-          float z = cRow[id] + sRow[id];
+          T z = cRow[id] + sRow[id];
 
-          float t = tanhf(z);
-          float r = va[id] * (1.f - t * t);
+          T t = functional::Ops<T>::tanh(z);
+          T r = va[id] * ((T)1.f - t * t);
 
-          gcRow[id] += r * adj[j];
+          gcRow[id] += r * adj[j]; // atomicAdd? reasons for instabilities?
           gsRow[id] += r * adj[j];
-          atomicAdd(gVa + id, t * adj[j]);
+          atomics::atomicAdd(gVa + id, t * adj[j]); // @TODO: get rid of atomicAdd via Matmul
         }
       }
     }
@@ -1521,41 +1782,58 @@ void AttBack(Tensor gVa,
   int blocks = std::min(MAX_BLOCKS, (int)n);
   int threads = std::min(MAX_THREADS, (int)k);
 
-  gAttBack<<<blocks, threads>>>(gVa->data(),
-                                gContext->data(),
-                                gState->data(),
-
-                                va->data(),
-                                context->data(),
-                                state->data(),
-
-                                adj->data(),
-                                m,
-                                k,
-                                n);
+  if(gVa->type() == Type::float32) {
+    gAttBack<<<blocks, threads>>>(gVa->data<float>(),
+                                  gContext->data<float>(),
+                                  gState->data<float>(),
+                                  va->data<float>(),
+                                  context->data<float>(),
+                                  state->data<float>(),
+                                  adj->data<float>(),
+                                  m,
+                                  k,
+                                  n);
+  } else if (gVa->type() == Type::float16) {
+    gAttBack<<<blocks, threads>>>(gVa->data<half>(),
+                                  gContext->data<half>(),
+                                  gState->data<half>(),
+                                  va->data<half>(),
+                                  context->data<half>(),
+                                  state->data<half>(),
+                                  adj->data<half>(),
+                                  m,
+                                  k,
+                                  n);
+  } else {
+    ABORT("gAttBack not implemented for type {}", gVa->type());
+  }
 }
 
-__global__ void gLNormalization(float* out,
-                                const float* in,
-                                const float* alpha,
-                                const float* beta,
+template <typename T, typename AccType = float>
+__global__ void gLNormalization(T* out,
+                                const T* in,
+                                const T* gamma,
+                                const T* beta,
                                 int rows,
                                 int cols,
-                                float eps = 1e-9) {
-  extern __shared__ float _share[];
+                                AccType eps = 1e-9) {
+  extern __shared__ uint8_t _sharedBytes[];
+  AccType* _shareAccType = (AccType*)_sharedBytes;
+
+  AccType N = cols;
 
   for(int bid = 0; bid < rows; bid += gridDim.x) {
     int j = bid + blockIdx.x;
     if(j < rows) {
-      float* so = out + j * cols;
-      const float* sp = in + j * cols;
+      T* yRow       = out + j * cols;
+      const T* xRow =  in + j * cols;
 
-      float* _sum = _share;
-      _sum[threadIdx.x] = 0.0f;
+      AccType* _sum = _shareAccType; // accumulate into floats
+      _sum[threadIdx.x] = (AccType)0.0f;
       for(int tid = 0; tid < cols; tid += blockDim.x) {
         int id = tid + threadIdx.x;
         if(id < cols) {
-          _sum[threadIdx.x] += sp[id];
+          _sum[threadIdx.x] += (AccType)xRow[id];
         }
       }
       __syncthreads();
@@ -1569,16 +1847,17 @@ __global__ void gLNormalization(float* out,
         len = (len + 1) >> 1;
       }
       __syncthreads();
-      float mean = _sum[0] / cols;
+      AccType mean = _sum[0] / N;
       __syncthreads();
 
-      float* _sqSum = _share;
+      AccType* _sqSum = _shareAccType;
 
-      _sqSum[threadIdx.x] = 0.0;
+      _sqSum[threadIdx.x] = (AccType)0.0f;
       for(int tid = 0; tid < cols; tid += blockDim.x) {
         int id = tid + threadIdx.x;
         if(id < cols) {
-          float ex = sp[id] - mean;
+          AccType xv = (AccType)xRow[id];
+          AccType ex = xv - mean;
           _sqSum[threadIdx.x] += ex * ex;
         }
       }
@@ -1592,16 +1871,18 @@ __global__ void gLNormalization(float* out,
         len = (len + 1) >> 1;
       }
       __syncthreads();
-      float sigma = sqrtf(eps + (_sqSum[0] / cols));
+      AccType sigma = functional::Ops<AccType>::sqrt(_sqSum[0] / N); // all AccType
       __syncthreads();
 
       for(int tid = 0; tid < cols; tid += blockDim.x) {
         int id = tid + threadIdx.x;
         if(id < cols) {
-          float t = alpha[id] * ((sp[id] - mean) / sigma);
-          if(beta != nullptr)
-            t += beta[id];
-          so[id] = t;
+          AccType gammav = (AccType)gamma[id];
+          AccType xv     = (AccType)xRow[id];
+          AccType betav  = beta ? (AccType)beta[id] : (AccType)0.f;
+          AccType lv     = (xv - mean) / (sigma + eps);
+          AccType y      = gammav * lv + betav;
+          yRow[id]       = (T)y;
         }
       }
     }
@@ -1623,13 +1904,26 @@ void LayerNormalization(Tensor out,
   int threads = std::min(MAX_THREADS, (int)cols);
   int shared = threads * sizeof(float);
 
-  gLNormalization<<<blocks, threads, shared>>>(out->data(),
-                                               in->data(),
-                                               gamma->data(),
-                                               beta ? beta->data() : nullptr,
-                                               rows,
-                                               cols,
-                                               eps);
+  if(out->type() == Type::float32) {
+    gLNormalization<float, float><<<blocks, threads, shared>>>(out->data<float>(),
+                                                 in->data<float>(),
+                                                 gamma->data<float>(),
+                                                 beta ? beta->data<float>() : nullptr,
+                                                 rows,
+                                                 cols,
+                                                 eps);
+  } else if (out->type() == Type::float16) {
+    gLNormalization<half, float><<<blocks, threads, shared>>>(out->data<half>(),
+                                                 in->data<half>(),
+                                                 gamma->data<half>(),
+                                                 beta ? beta->data<half>() : nullptr,
+                                                 rows,
+                                                 cols,
+                                                 eps);
+
+  } else {
+    ABORT("LayerNormalization not implemented for type {}", out->type());
+  }
 }
 
 template <typename T, typename AccType = float>
@@ -1795,7 +2089,6 @@ void LayerNormalizationGrad(Ptr<Allocator> allocator,
       rows,
       cols,
       eps);
-#if 0
   } else if (gradX->type() == Type::float16) {
     // accumulate in float
     int shared = sizeof(float) * threads * 4;
@@ -1810,7 +2103,6 @@ void LayerNormalizationGrad(Ptr<Allocator> allocator,
       rows,
       cols,
       eps);
-#endif
   } else {
     ABORT("LayerNormalizationGrad not implemented for type {}", gradX->type());
   }
@@ -1826,9 +2118,9 @@ void LayerNormalizationGrad(Ptr<Allocator> allocator,
   allocator->free(tempOnesMemory);
 }
 
-template <bool add>
-__global__ void gShift(float* out,
-                       const float* in,
+template <bool add, typename T>
+__global__ void gShift(T* out,
+                       const T* in,
                        int length,
                        int offset,
                        float padValue) {
@@ -1840,7 +2132,7 @@ __global__ void gShift(float* out,
           out[index] += in[index - offset];
       } else {
         if(index - offset < 0 || index - offset >= length)
-          out[index] = padValue;
+          out[index] = (T)padValue;
         else
           out[index] = in[index - offset];
       }
@@ -1871,8 +2163,15 @@ void Shift(Tensor out,
   int threads = std::min(MAX_THREADS, length);
   int blocks = std::min(MAX_BLOCKS, length / threads + (length % threads != 0));
 
-  gShift<false>
-      <<<blocks, threads>>>(out->data(), in->data(), length, offset, padValue);
+  if(out->type() == Type::float32) {
+    gShift<false>
+        <<<blocks, threads>>>(out->data<float>(), in->data<float>(), length, offset, padValue);
+  } else if(out->type() == Type::float16) {
+    gShift<false>
+        <<<blocks, threads>>>(out->data<half>(), in->data<half>(), length, offset, padValue);
+  } else {
+    ABORT("Shift not implemented for type {}", out->type());
+  }
 }
 
 void ShiftGrad(Tensor out, Tensor in, marian::Shape shift, bool invert) {
@@ -1894,8 +2193,15 @@ void ShiftGrad(Tensor out, Tensor in, marian::Shape shift, bool invert) {
   int threads = std::min(MAX_THREADS, length);
   int blocks = std::min(MAX_BLOCKS, length / threads + (length % threads != 0));
 
-  gShift<true>
-      <<<blocks, threads>>>(out->data(), in->data(), length, offset, 0.f);
+  if(out->type() == Type::float32) {
+    gShift<true>
+        <<<blocks, threads>>>(out->data<float>(), in->data<float>(), length, offset, 0.f); // @TODO: What about padValue?
+  } else if(out->type() == Type::float16) {
+    gShift<true>
+        <<<blocks, threads>>>(out->data<half>(), in->data<half>(), length, offset, 0.f);
+  } else {
+    ABORT("Shift not implemented for type {}", out->type());
+  }
 }
 
 __global__ void gSetSparse(float* out,
@@ -1938,38 +2244,39 @@ void SetSparse(float* out,
 
 /******************************************************************************/
 
-__global__ void gLSTMCellForward(float* out,
-                                 const float* cell,
-                                 const float* xW,
-                                 const float* sU,
-                                 const float* b,
-                                 const float* mask,
+template <typename T>
+__global__ void gLSTMCellForward(T* out,
+                                 const T* cell,
+                                 const T* xW,
+                                 const T* sU,
+                                 const T* b,
+                                 const T* mask,
                                  size_t rows,
                                  size_t cols) {
   for(int bid = 0; bid < rows; bid += gridDim.x) {
     int j = bid + blockIdx.x;
     if(j < rows) {
-      float m = !mask || mask[j];
+      T m = !mask || mask[j];
 
-      float* rowOut = out + j * cols;
-      const float* rowCell = cell + j * cols;
+      T* rowOut = out + j * cols;
+      const T* rowCell = cell + j * cols;
 
-      const float* xWrow = xW + j * cols * 4;
-      const float* sUrow = sU + j * cols * 4;
+      const T* xWrow = xW + j * cols * 4;
+      const T* sUrow = sU + j * cols * 4;
 
       for(int tid = 0; tid < cols; tid += blockDim.x) {
         int i = tid + threadIdx.x;
         if(i < cols) {
-          float gf = stableSigmoid(xWrow[i] + sUrow[i] + b[i]);
+          T gf = functional::Ops<T>::sigmoid(xWrow[i] + sUrow[i] + b[i]);
 
           int k = i + cols;
-          float gi = stableSigmoid(xWrow[k] + sUrow[k] + b[k]);
+          T gi = functional::Ops<T>::sigmoid(xWrow[k] + sUrow[k] + b[k]);
 
           int l = i + 2 * cols;
-          float gc = tanhf(xWrow[l] + sUrow[l] + b[l]);
+          T gc = functional::Ops<T>::tanh(xWrow[l] + sUrow[l] + b[l]);
 
-          float cout = gf * rowCell[i] + gi * gc;
-          rowOut[i] = m * cout + (1 - m) * rowCell[i];
+          T cout = gf * rowCell[i] + gi * gc;
+          rowOut[i] = m * cout + ((T)1.f - m) * rowCell[i];
         }
       }
     }
@@ -1985,40 +2292,54 @@ void LSTMCellForward(Tensor out, std::vector<Tensor> inputs) {
   int blocks = std::min(MAX_BLOCKS, rows);
   int threads = std::min(MAX_THREADS, cols);
 
-  gLSTMCellForward<<<blocks, threads>>>(
-      out->data(),                                // output
-      inputs[0]->data(),                          // cell state
-      inputs[1]->data(),                          // xW
-      inputs[2]->data(),                          // sU
-      inputs[3]->data(),                          // b
-      inputs.size() > 4 ? inputs[4]->data() : 0,  // mask
+  if(out->type() == Type::float32) {
+   gLSTMCellForward<<<blocks, threads>>>(
+      out->data<float>(),                                // output
+      inputs[0]->data<float>(),                          // cell state
+      inputs[1]->data<float>(),                          // xW
+      inputs[2]->data<float>(),                          // sU
+      inputs[3]->data<float>(),                          // b
+      inputs.size() > 4 ? inputs[4]->data<float>() : 0,  // mask
+      rows,
+      cols);
+  } else if (out->type() == Type::float16) {
+    gLSTMCellForward<<<blocks, threads>>>(
+      out->data<half>(),                                // output
+      inputs[0]->data<half>(),                          // cell state
+      inputs[1]->data<half>(),                          // xW
+      inputs[2]->data<half>(),                          // sU
+      inputs[3]->data<half>(),                          // b
+      inputs.size() > 4 ? inputs[4]->data<half>() : 0,  // mask
       rows,
       cols);
+  } else {
+    ABORT("LSTMCellForward not implemented for type {}", out->type());
+  }
 }
 
-__global__ void gLSTMOutputForward(float* out,
-                                   const float* cell,
-                                   const float* xW,
-                                   const float* sU,
-                                   const float* b,
+template <typename T>
+__global__ void gLSTMOutputForward(T* out,
+                                   const T* cell,
+                                   const T* xW,
+                                   const T* sU,
+                                   const T* b,
                                    size_t rows,
                                    size_t cols) {
   for(int bid = 0; bid < rows; bid += gridDim.x) {
     int j = bid + blockIdx.x;
     if(j < rows) {
-      float* rowOut = out + j * cols;
-      const float* rowCell = cell + j * cols;
+      T* rowOut = out + j * cols;
+      const T* rowCell = cell + j * cols;
 
-      const float* xWrow = xW + j * cols * 4;
-      const float* sUrow = sU + j * cols * 4;
+      const T* xWrow = xW + j * cols * 4;
+      const T* sUrow = sU + j * cols * 4;
 
       for(int tid = 0; tid < cols; tid += blockDim.x) {
         int i = tid + threadIdx.x;
         if(i < cols) {
           int k = i + 3 * cols;
-          float go = stableSigmoid(xWrow[k] + sUrow[k] + b[k]);
-
-          rowOut[i] = go * tanhf(rowCell[i]);
+          T go = functional::Ops<T>::sigmoid(xWrow[k] + sUrow[k] + b[k]);
+          rowOut[i] = go * functional::Ops<T>::tanh(rowCell[i]);
         }
       }
     }
@@ -2034,85 +2355,98 @@ void LSTMOutputForward(Tensor out, std::vector<Tensor> inputs) {
   int blocks = std::min(MAX_BLOCKS, rows);
   int threads = std::min(MAX_THREADS, cols);
 
-  gLSTMOutputForward<<<blocks, threads>>>(out->data(),        // output
-                                          inputs[0]->data(),  // cell state
-                                          inputs[1]->data(),  // xW
-                                          inputs[2]->data(),  // sU
-                                          inputs[3]->data(),  // b
-                                          rows,
-                                          cols);
-}
-
-__global__ void gLSTMCellBackward(float* outCell,
-                                  float* outXW,
-                                  float* outSU,
-                                  float* outB,
-                                  const float* cell,
-                                  const float* xW,
-                                  const float* sU,
-                                  const float* b,
-                                  const float* mask,
-                                  const float* adj,
+  if(out->type() == Type::float32) {
+    gLSTMOutputForward<<<blocks, threads>>>(out->data<float>(),        // output
+                                            inputs[0]->data<float>(),  // cell state
+                                            inputs[1]->data<float>(),  // xW
+                                            inputs[2]->data<float>(),  // sU
+                                            inputs[3]->data<float>(),  // b
+                                            rows,
+                                            cols);
+  } else if (out->type() == Type::float16) {
+    gLSTMOutputForward<<<blocks, threads>>>(out->data<half>(),        // output
+                                            inputs[0]->data<half>(),  // cell state
+                                            inputs[1]->data<half>(),  // xW
+                                            inputs[2]->data<half>(),  // sU
+                                            inputs[3]->data<half>(),  // b
+                                            rows,
+                                            cols);
+  } else {
+    ABORT("gLSTMOutputForward not implemented for type {}", out->type());
+  }
+}
+
+template <typename T>
+__global__ void gLSTMCellBackward(T* outCell,
+                                  T* outXW,
+                                  T* outSU,
+                                  T* outB,
+                                  const T* cell,
+                                  const T* xW,
+                                  const T* sU,
+                                  const T* b,
+                                  const T* mask,
+                                  const T* adj,
                                   size_t rows,
                                   size_t cols) {
   for(int bid = 0; bid < rows; bid += gridDim.x) {
     int j = bid + blockIdx.x;
     if(j < rows) {
-      float m = !mask || mask[j];
+      T m = !mask || mask[j];
 
-      float* rowOutCell = outCell + j * cols;
-      float* rowOutXW = outXW + j * cols * 4;
-      float* rowOutSU = outSU + j * cols * 4;
+      T* rowOutCell = outCell + j * cols;
+      T* rowOutXW = outXW + j * cols * 4;
+      T* rowOutSU = outSU + j * cols * 4;
 
-      const float* rowCell = cell + j * cols;
-      const float* xWrow = xW + j * cols * 4;
-      const float* sUrow = sU + j * cols * 4;
+      const T* rowCell = cell + j * cols;
+      const T* xWrow = xW + j * cols * 4;
+      const T* sUrow = sU + j * cols * 4;
 
-      const float* rowAdj = adj + j * cols;
+      const T* rowAdj = adj + j * cols;
 
       for(int tid = 0; tid < cols; tid += blockDim.x) {
         int i = tid + threadIdx.x;
         if(i < cols) {
-          float gf = stableSigmoid(xWrow[i] + sUrow[i] + b[i]);
+          T gf = functional::Ops<T>::sigmoid(xWrow[i] + sUrow[i] + b[i]);
 
           int k = i + cols;
-          float gi = stableSigmoid(xWrow[k] + sUrow[k] + b[k]);
+          T gi = functional::Ops<T>::sigmoid(xWrow[k] + sUrow[k] + b[k]);
 
           int l = i + 2 * cols;
-          float gc = tanhf(xWrow[l] + sUrow[l] + b[l]);
+          T gc = functional::Ops<T>::tanh(xWrow[l] + sUrow[l] + b[l]);
 
-          float adj = rowAdj[i];
+          T adj = rowAdj[i];
 
           // dc/dc_{t-1}
           if(outCell)
-            rowOutCell[i] += (m * gf - m + 1) * adj;
+            rowOutCell[i] += (m * gf - m + (T)1.f) * adj;
 
           // dc/d(b_f) = dc/d(xW_f) ...
-          float dcdxf = m * rowCell[i] * gf * (1 - gf) * adj;
+          T dcdxf = m * rowCell[i] * gf * ((T)1.f - gf) * adj;
           if(outXW)
             rowOutXW[i] += dcdxf;
           if(outSU)
             rowOutSU[i] += dcdxf;
           if(outB)
-            atomicAdd(outB + i, dcdxf);
+            atomics::atomicAdd(outB + i, dcdxf); // @TODO: get rid of atomicAdd everywhere!
 
           // dc/d(b_i) ...
-          float dcdb_i = m * gc * gi * (1 - gi) * adj;
+          T dcdb_i = m * gc * gi * ((T)1.f - gi) * adj;
           if(outXW)
             rowOutXW[k] += dcdb_i;
           if(outSU)
             rowOutSU[k] += dcdb_i;
           if(outB)
-            atomicAdd(outB + k, dcdb_i);
+            atomics::atomicAdd(outB + k, dcdb_i);
 
           // dc/d(b_c) ...
-          float dcdxc = m * gi * (1 - gc * gc) * adj;
+          T dcdxc = m * gi * ((T)1.f - gc * gc) * adj;
           if(outXW)
             rowOutXW[l] += dcdxc;
           if(outSU)
             rowOutSU[l] += dcdxc;
           if(outB)
-            atomicAdd(outB + l, dcdxc);
+            atomics::atomicAdd(outB + l, dcdxc);
         }
       }
     }
@@ -2130,67 +2464,87 @@ void LSTMCellBackward(std::vector<Tensor> outputs,
   int blocks = std::min(MAX_BLOCKS, rows);
   int threads = std::min(MAX_THREADS, cols);
 
-  gLSTMCellBackward<<<blocks, threads>>>(
-      outputs[0] ? outputs[0]->data() : 0,        // state - adj
-      outputs[1] ? outputs[1]->data() : 0,        // xW - adj
-      outputs[2] ? outputs[2]->data() : 0,        // sU - adj
-      outputs[3] ? outputs[3]->data() : 0,        // b - adj
-      inputs[0]->data(),                          // state
-      inputs[1]->data(),                          // xW
-      inputs[2]->data(),                          // sU
-      inputs[3]->data(),                          // b
-      inputs.size() > 4 ? inputs[4]->data() : 0,  // mask
-      adj->data(),
+  if(adj->type() == Type::float32) {
+    gLSTMCellBackward<<<blocks, threads>>>(
+      outputs[0] ? outputs[0]->data<float>() : 0,        // state - adj
+      outputs[1] ? outputs[1]->data<float>() : 0,        // xW - adj
+      outputs[2] ? outputs[2]->data<float>() : 0,        // sU - adj
+      outputs[3] ? outputs[3]->data<float>() : 0,        // b - adj
+      inputs[0]->data<float>(),                          // state
+      inputs[1]->data<float>(),                          // xW
+      inputs[2]->data<float>(),                          // sU
+      inputs[3]->data<float>(),                          // b
+      inputs.size() > 4 ? inputs[4]->data<float>() : 0,  // mask
+      adj->data<float>(),
+      rows,
+      cols);
+  } else if (adj->type() == Type::float16) {
+    gLSTMCellBackward<<<blocks, threads>>>(
+      outputs[0] ? outputs[0]->data<half>() : 0,        // state - adj
+      outputs[1] ? outputs[1]->data<half>() : 0,        // xW - adj
+      outputs[2] ? outputs[2]->data<half>() : 0,        // sU - adj
+      outputs[3] ? outputs[3]->data<half>() : 0,        // b - adj
+      inputs[0]->data<half>(),                          // state
+      inputs[1]->data<half>(),                          // xW
+      inputs[2]->data<half>(),                          // sU
+      inputs[3]->data<half>(),                          // b
+      inputs.size() > 4 ? inputs[4]->data<half>() : 0,  // mask
+      adj->data<half>(),
       rows,
       cols);
+  } else {
+    ABORT("gLSTMCellBackward not implemented for type {}", adj->type());
+  }
+
 }
 
-__global__ void gLSTMOutputBackward(float* outCell,
-                                    float* outXW,
-                                    float* outSU,
-                                    float* outB,
-                                    const float* cell,
-                                    const float* xW,
-                                    const float* sU,
-                                    const float* b,
-                                    const float* adj,
+template <typename T>
+__global__ void gLSTMOutputBackward(T* outCell,
+                                    T* outXW,
+                                    T* outSU,
+                                    T* outB,
+                                    const T* cell,
+                                    const T* xW,
+                                    const T* sU,
+                                    const T* b,
+                                    const T* adj,
                                     size_t rows,
                                     size_t cols) {
   for(int bid = 0; bid < rows; bid += gridDim.x) {
     int j = bid + blockIdx.x;
     if(j < rows) {
-      float* rowOutCell = outCell + j * cols;
-      float* rowOutXW = outXW + j * cols * 4;
-      float* rowOutSU = outSU + j * cols * 4;
+      T* rowOutCell = outCell + j * cols;
+      T* rowOutXW = outXW + j * cols * 4;
+      T* rowOutSU = outSU + j * cols * 4;
 
-      const float* rowCell = cell + j * cols;
-      const float* xWrow = xW + j * cols * 4;
-      const float* sUrow = sU + j * cols * 4;
+      const T* rowCell = cell + j * cols;
+      const T* xWrow = xW + j * cols * 4;
+      const T* sUrow = sU + j * cols * 4;
 
-      const float* rowAdj = adj + j * cols;
+      const T* rowAdj = adj + j * cols;
 
       for(int tid = 0; tid < cols; tid += blockDim.x) {
         int i = tid + threadIdx.x;
         if(i < cols) {
           int k = i + 3 * cols;
-          float go = stableSigmoid(xWrow[k] + sUrow[k] + b[k]);
+          T go = functional::Ops<T>::sigmoid(xWrow[k] + sUrow[k] + b[k]);
 
-          float t = tanhf(rowCell[i]);
+          T t = functional::Ops<T>::tanh(rowCell[i]);
 
-          float adj = rowAdj[i];
+          T adj = rowAdj[i];
 
           // dc/dc_{t-1}
           if(outCell)
-            rowOutCell[i] += go * (1 - t * t) * adj;
+            rowOutCell[i] += go * ((T)1.f - t * t) * adj;
 
           // dc/d(b_o) = dc/d(xW_f) ...
-          float dcdxo = t * go * (1 - go) * adj;
+          float dcdxo = t * go * ((T)1.f - go) * adj;
           if(outXW)
             rowOutXW[k] += dcdxo;
           if(outSU)
             rowOutSU[k] += dcdxo;
           if(outB)
-            atomicAdd(outB + k, dcdxo);
+            atomics::atomicAdd(outB + k, dcdxo); // @TODO: get rid of atomicAdd
         }
       }
     }
@@ -2208,30 +2562,48 @@ void LSTMOutputBackward(std::vector<Tensor> outputs,
   int blocks = std::min(MAX_BLOCKS, rows);
   int threads = std::min(MAX_THREADS, cols);
 
-  gLSTMOutputBackward<<<blocks, threads>>>(
-      outputs[0] ? outputs[0]->data() : 0,  // state - adj
-      outputs[1] ? outputs[1]->data() : 0,  // xW - adj
-      outputs[2] ? outputs[2]->data() : 0,  // sU - adj
-      outputs[3] ? outputs[3]->data() : 0,  // b - adj
-      inputs[0]->data(),                    // state
-      inputs[1]->data(),                    // xW
-      inputs[2]->data(),                    // sU
-      inputs[3]->data(),                    // b
-      adj->data(),
-      rows,
-      cols);
+  if(adj->type() == Type::float32) {
+    gLSTMOutputBackward<<<blocks, threads>>>(
+        outputs[0] ? outputs[0]->data<float>() : 0,  // state - adj
+        outputs[1] ? outputs[1]->data<float>() : 0,  // xW - adj
+        outputs[2] ? outputs[2]->data<float>() : 0,  // sU - adj
+        outputs[3] ? outputs[3]->data<float>() : 0,  // b - adj
+        inputs[0]->data<float>(),                    // state
+        inputs[1]->data<float>(),                    // xW
+        inputs[2]->data<float>(),                    // sU
+        inputs[3]->data<float>(),                    // b
+        adj->data<float>(),
+        rows,
+        cols);
+  } else if (adj->type() == Type::float16) {
+    gLSTMOutputBackward<<<blocks, threads>>>(
+        outputs[0] ? outputs[0]->data<half>() : 0,  // state - adj
+        outputs[1] ? outputs[1]->data<half>() : 0,  // xW - adj
+        outputs[2] ? outputs[2]->data<half>() : 0,  // sU - adj
+        outputs[3] ? outputs[3]->data<half>() : 0,  // b - adj
+        inputs[0]->data<half>(),                    // state
+        inputs[1]->data<half>(),                    // xW
+        inputs[2]->data<half>(),                    // sU
+        inputs[3]->data<half>(),                    // b
+        adj->data<half>(),
+        rows,
+        cols);
+  } else {
+    ABORT("gLSTMOutputBackward not implemented for type {}", adj->type());
+  }
 }
 
-__global__ void gHighwayForward(float* out,
-                                const float* in1,
-                                const float* in2,
-                                const float* t,
+template <typename T>
+__global__ void gHighwayForward(T* out,
+                                const T* in1,
+                                const T* in2,
+                                const T* t,
                                 size_t length) {
   for(int bid = 0; bid < length; bid += blockDim.x * gridDim.x) {
     int index = bid + blockDim.x * blockIdx.x + threadIdx.x;
     if(index < length) {
-      float sigma = stableSigmoid(t[index]);
-      out[index] = in1[index] * sigma + in2[index] * (1.f - sigma);
+      T sigma = functional::Ops<T>::sigmoid(t[index]);
+      out[index] = in1[index] * sigma + in2[index] * ((T)1.f - sigma);
     }
   }
 }
@@ -2247,26 +2619,34 @@ void HighwayForward(Tensor out,
   int threads = std::min(MAX_THREADS, length);
   int blocks = std::min(MAX_BLOCKS, length / threads + (length % threads != 0));
 
-  gHighwayForward<<<blocks, threads>>>(
-      out->data(), in1->data(), in2->data(), t->data(), length);
+  if(out->type() == Type::float32) {
+    gHighwayForward<<<blocks, threads>>>(
+        out->data<float>(), in1->data<float>(), in2->data<float>(), t->data<float>(), length);
+  } else if(out->type() == Type::float16) {
+    gHighwayForward<<<blocks, threads>>>(
+        out->data<half>(), in1->data<half>(), in2->data<half>(), t->data<half>(), length);
+  } else {
+    ABORT("HighwayForward not implemented for type {}", out->type());
+  }
 }
 
-__global__ void gHighwayBackward(float* out1,
-                                 float* out2,
-                                 float* outt,
-                                 const float* in1,
-                                 const float* in2,
-                                 const float* t,
-                                 const float* adj,
+template <typename T>
+__global__ void gHighwayBackward(T* out1,
+                                 T* out2,
+                                 T* outt,
+                                 const T* in1,
+                                 const T* in2,
+                                 const T* t,
+                                 const T* adj,
                                  size_t length) {
   for(int bid = 0; bid < length; bid += blockDim.x * gridDim.x) {
     int index = bid + blockDim.x * blockIdx.x + threadIdx.x;
     if(index < length) {
-      float sigma = stableSigmoid(t[index]);
+      T sigma = functional::Ops<T>::sigmoid(t[index]);
       out1[index] = sigma * adj[index];
-      out2[index] = (1.f - sigma) * adj[index];
+      out2[index] = ((T)1.f - sigma) * adj[index];
       outt[index]
-          = sigma * (1.f - sigma) * (in1[index] - in2[index]) * adj[index];
+          = sigma * ((T)1.f - sigma) * (in1[index] - in2[index]) * adj[index];
     }
   }
 }
@@ -2285,14 +2665,27 @@ void HighwayBackward(Tensor out1,
   int threads = std::min(MAX_THREADS, length);
   int blocks = std::min(MAX_BLOCKS, length / threads + (length % threads != 0));
 
-  gHighwayBackward<<<blocks, threads>>>(out1->data(),
-                                        out2->data(),
-                                        outt->data(),
-                                        in1->data(),
-                                        in2->data(),
-                                        t->data(),
-                                        adj->data(),
-                                        length);
+  if(out1->type() == Type::float32) {
+    gHighwayBackward<<<blocks, threads>>>(out1->data<float>(),
+                                          out2->data<float>(),
+                                          outt->data<float>(),
+                                          in1->data<float>(),
+                                          in2->data<float>(),
+                                          t->data<float>(),
+                                          adj->data<float>(),
+                                          length);
+  } else if(out1->type() == Type::float16) {
+    gHighwayBackward<<<blocks, threads>>>(out1->data<half>(),
+                                          out2->data<half>(),
+                                          outt->data<half>(),
+                                          in1->data<half>(),
+                                          in2->data<half>(),
+                                          t->data<half>(),
+                                          adj->data<half>(),
+                                          length);
+  } else {
+    ABORT("HighwayForward not implemented for type {}", out1->type());
+  }
 }
 
 __global__ void gMaxPoolingForward(float* out,
@@ -2336,6 +2729,7 @@ void PoolingWithMaskingForward(Tensor out,
                                Tensor mask,
                                int width,
                                bool isEven) {
+  matchOrAbort<float>(out->type());
   int n = out->shape().elements();
   int threads = std::min(n, MAX_THREADS);
   int blocks = n / threads + (n % threads != 0);
@@ -2408,6 +2802,7 @@ void PoolingWithMaskingBackward(Tensor adj,
                                 Tensor mask,
                                 int width,
                                 bool isEven) {
+  matchOrAbort<float>(adj->type());
   int n = adj->shape().elements();
   int threads = std::min(n, 512);
   int blocks = n / threads + (n % threads != 0);
diff --git a/src/tensors/tensor_operators.h b/src/tensors/tensor_operators.h
index c87f58669..420131b88 100644
--- a/src/tensors/tensor_operators.h
+++ b/src/tensors/tensor_operators.h
@@ -311,21 +311,21 @@ DISPATCH7(AttBack, marian::Tensor, marian::Tensor, marian::Tensor, marian::Tenso
 
 #ifdef CUDA_FOUND
 namespace gpu {
-float L2Norm(marian::Tensor in);
+float L2Norm(marian::Tensor in, Ptr<Allocator> allocator);
 }
 #endif
 
 namespace cpu {
-float L2Norm(marian::Tensor in);
+float L2Norm(marian::Tensor in, Ptr<Allocator> allocator);
 }
 
-static inline float L2Norm(marian::Tensor in) {
+static inline float L2Norm(marian::Tensor in, Ptr<Allocator> allocator) {
 #ifdef CUDA_FOUND
   if(in->getBackend()->getDeviceId().type == DeviceType::gpu)
-    return gpu::L2Norm(in);
+    return gpu::L2Norm(in, allocator);
   else
 #endif
-    return cpu::L2Norm(in);
+    return cpu::L2Norm(in, allocator);
 }
 
 // clang-format off

From 7675b9baef19085777e9d6e32d00c84e3e3e38ca Mon Sep 17 00:00:00 2001
From: Marcin Junczys-Dowmunt <Marcin.JunczysDowmunt@microsoft.com>
Date: Wed, 11 Sep 2019 22:26:44 -0700
Subject: [PATCH 619/838] add fp16 matmul

---
 src/tensors/gpu/prod.cpp | 216 ++++++++++++++++++++++++++++++---------
 src/tensors/gpu/prod.h   |  10 +-
 2 files changed, 166 insertions(+), 60 deletions(-)

diff --git a/src/tensors/gpu/prod.cpp b/src/tensors/gpu/prod.cpp
index f2db243d4..767584d92 100755
--- a/src/tensors/gpu/prod.cpp
+++ b/src/tensors/gpu/prod.cpp
@@ -52,15 +52,53 @@ static void unsetTensorMode(cublasHandle_t cublasHandle) {
 #endif
 }
 
-void Prod(marian::Tensor C,
+cublasStatus_t cublasGemmTyped(cublasHandle_t handle,
+                               cublasOperation_t transa, 
+                               cublasOperation_t transb,
+                               int m, int n, int k,
+                               const float* alpha,
+                               const float* A, int lda,
+                               const float* B, int ldb,
+                               const float* beta,
+                               float* C, int ldc) {
+  return cublasGemmEx(handle, transa, transb, 
+                     m, n, k, alpha, 
+                     A, CUDA_R_32F, lda, 
+                     B, CUDA_R_32F, ldb, beta, 
+                     C, CUDA_R_32F, ldc,
+                     CUDA_R_32F, CUBLAS_GEMM_DEFAULT_TENSOR_OP); // @TODO: review algorithm
+}
+
+cublasStatus_t cublasGemmTyped(cublasHandle_t handle,
+                               cublasOperation_t transa, 
+                               cublasOperation_t transb,
+                               int m, int n, int k,
+                               const half* alpha,
+                               const half* A, int lda,
+                               const half* B, int ldb,
+                               const half* beta,
+                               half* C, int ldc) {
+  //float alphaf = __half2float(*alpha); // has to match computeType
+  //float betaf = __half2float(*beta);   // has to match computeType
+
+  return cublasGemmEx(handle, transa, transb, 
+                     m, n, k, alpha, 
+                     A, CUDA_R_16F, lda, 
+                     B, CUDA_R_16F, ldb, beta, 
+                     C, CUDA_R_16F, ldc,
+                     CUDA_R_16F, CUBLAS_GEMM_DEFAULT_TENSOR_OP); // @TODO: review algorithm
+}
+
+template <typename T>
+void ProdTyped(marian::Tensor C,
           const marian::Tensor& A,
           const marian::Tensor& B,
           bool transA,
           bool transB,
-          float beta,
-          float scalar) {
+          T beta,
+          T scalar) {
   cudaSetDevice((int)C->getDeviceId().no);
-  float alpha = scalar;
+  T alpha = scalar;
 
   size_t m = A->shape().elements() / A->shape().back();
   size_t k = A->shape().back();
@@ -86,33 +124,91 @@ void Prod(marian::Tensor C,
                           ->getCublasHandle();
 
   setTensorMode(cublasHandle);
-  CUBLAS_CHECK(cublasSgemm(cublasHandle,
-              opB,
-              opA,
-              (int)n,
-              (int)m,
-              (int)k,
-              &alpha,
-              B->data(),
-              (int)ldb,
-              A->data(),
-              (int)lda,
-              &beta,
-              C->data(),
-              (int)ldc));
+  CUBLAS_CHECK(cublasGemmTyped(cublasHandle,
+                  opB,
+                  opA,
+                  n,
+                  m,
+                  k,
+                  &alpha,
+                  B->data<T>(),
+                  ldb,
+                  A->data<T>(),
+                  lda,
+                  &beta,
+                  C->data<T>(),
+                  ldc));
   unsetTensorMode(cublasHandle);
 }
 
-void ProdBatched(marian::Tensor C,
+void Prod(marian::Tensor C,
+          const marian::Tensor& A,
+          const marian::Tensor& B,
+          bool transA,
+          bool transB,
+          float beta,
+          float scalar) {
+  if(C->type() == Type::float32) {
+    ProdTyped<float>(C, A, B, transA, transB, beta, scalar);
+  } else if(C->type() == Type::float16) {
+    ProdTyped<half>(C, A, B, transA, transB, __float2half(beta), __float2half(scalar));
+  } else {
+    ABORT("Prod not implemented for type {}", C->type());
+  }
+}
+
+cublasStatus_t cublasGemmBatchedTyped(cublasHandle_t handle,
+                                      cublasOperation_t transa, 
+                                      cublasOperation_t transb,
+                                      int m, int n, int k,
+                                      const float *alpha,
+                                      const float *Aarray[], int lda,
+                                      const float *Barray[], int ldb,
+                                      const float *beta,
+                                      float *Carray[], int ldc, 
+                                      int batchCount) {
+  return
+  cublasGemmBatchedEx(handle, transa, transb, 
+                      m, n, k, alpha, 
+                      (void* const*)Aarray, CUDA_R_32F, lda, 
+                      (void* const*)Barray, CUDA_R_32F, ldb, beta,
+                      (void**)Carray, CUDA_R_32F, ldc, batchCount,
+                      CUDA_R_32F, CUBLAS_GEMM_DEFAULT_TENSOR_OP);
+}
+
+cublasStatus_t cublasGemmBatchedTyped(cublasHandle_t handle,
+                                      cublasOperation_t transa, 
+                                      cublasOperation_t transb,
+                                      int m, int n, int k,
+                                      const half *alpha,
+                                      const half *Aarray[], int lda,
+                                      const half *Barray[], int ldb,
+                                      const half *beta,
+                                      half *Carray[], int ldc, 
+                                      int batchCount) {
+  //float alphaf = __half2float(*alpha); // has to match computeType
+  //float betaf = __half2float(*beta);   // has to match computeType
+
+  return
+  cublasGemmBatchedEx(handle, transa, transb, 
+                      m, n, k, alpha, 
+                      (void* const*)Aarray, CUDA_R_16F, lda, 
+                      (void* const*)Barray, CUDA_R_16F, ldb, beta,
+                      (void**)Carray, CUDA_R_16F, ldc, batchCount,
+                      CUDA_R_16F, CUBLAS_GEMM_DEFAULT_TENSOR_OP); // @TODO: to 16, this is testing
+}
+
+template <typename T>
+void ProdBatchedTyped(marian::Tensor C,                 
                  Ptr<Allocator> allocator,
                  const marian::Tensor A,
                  const marian::Tensor B,
                  bool transA,
                  bool transB,
-                 float beta,
-                 float scalar) {
+                 T beta,
+                 T scalar) {
   cudaSetDevice((int)C->getDeviceId().no);
-  float alpha = scalar;
+  T alpha = scalar;
 
   size_t batchA = A->shape().elements() / (A->shape()[-1] * A->shape()[-2]);
   size_t batchB = B->shape().elements() / (B->shape()[-1] * B->shape()[-2]);
@@ -145,43 +241,42 @@ void ProdBatched(marian::Tensor C,
   auto strideC = n * m;
   auto batchC = std::max(batchA, batchB);
 
-  std::vector<const float*> aptr;
-  std::vector<const float*> bptr;
-  std::vector<float*> cptr;
+  std::vector<const T*> aptr;
+  std::vector<const T*> bptr;
+  std::vector<T*> cptr;
 
   for(int i = 0; i < batchC; i++) {
-    aptr.push_back(A->data() + (i % batchA) * strideA);
-    bptr.push_back(B->data() + (i % batchB) * strideB);
-    cptr.push_back(C->data() + i * strideC);
+    aptr.push_back(A->data<T>() + (i % batchA) * strideA);
+    bptr.push_back(B->data<T>() + (i % batchB) * strideB);
+    cptr.push_back(C->data<T>() + i * strideC);
   }
 
-  auto mp_aptr = allocator->alloc<const float*>(aptr.size());
-  CudaCopy(
-      aptr.data(), aptr.data() + aptr.size(), mp_aptr->data<const float*>());
+  // auto fails here from weird reason
+  IPtr<MemoryPiece> mp_aptr = allocator->alloc<const T*>(aptr.size());
+  CudaCopy(aptr.data(), aptr.data() + aptr.size(), mp_aptr->data<const T*>());
 
-  auto mp_bptr = allocator->alloc<const float*>(bptr.size());
-  CudaCopy(
-      bptr.data(), bptr.data() + bptr.size(), mp_bptr->data<const float*>());
+  IPtr<MemoryPiece> mp_bptr = allocator->alloc<const T*>(bptr.size());
+  CudaCopy(bptr.data(), bptr.data() + bptr.size(), mp_bptr->data<const T*>());
 
-  auto mp_cptr = allocator->alloc<float*>(cptr.size());
-  CudaCopy(cptr.data(), cptr.data() + cptr.size(), mp_cptr->data<float*>());
+  IPtr<MemoryPiece> mp_cptr = allocator->alloc<T*>(cptr.size());
+  CudaCopy(cptr.data(), cptr.data() + cptr.size(), mp_cptr->data<T*>());
 
   setTensorMode(cublasHandle);
-  CUBLAS_CHECK(cublasSgemmBatched(cublasHandle,
-                     opB,
-                     opA,
-                     (int)n,
-                     (int)m,
-                     (int)k,
-                     &alpha,
-                     mp_bptr->data<const float*>(),
-                     (int)ldb,
-                     mp_aptr->data<const float*>(),
-                     (int)lda,
-                     &beta,
-                     mp_cptr->data<float*>(),
-                     (int)ldc,
-                     (int)batchC));
+  CUBLAS_CHECK(cublasGemmBatchedTyped(cublasHandle,
+                         opB,
+                         opA,
+                         n,
+                         m,
+                         k,
+                         &alpha,
+                         mp_bptr->data<const T*>(),
+                         ldb,
+                         mp_aptr->data<const T*>(),
+                         lda,
+                         &beta,
+                         mp_cptr->data<T*>(),
+                         ldc,
+                         batchC));
   unsetTensorMode(cublasHandle);
 
   allocator->free(mp_aptr);
@@ -189,6 +284,23 @@ void ProdBatched(marian::Tensor C,
   allocator->free(mp_cptr);
 }
 
+void ProdBatched(marian::Tensor C,
+                 Ptr<Allocator> allocator,
+                 const marian::Tensor A,
+                 const marian::Tensor B,
+                 bool transA,
+                 bool transB,
+                 float beta,
+                 float scalar) {
+  if(C->type() == Type::float32) {
+    ProdBatchedTyped<float>(C, allocator, A, B, transA, transB, beta, scalar);
+  } else if(C->type() == Type::float16) {
+    ProdBatchedTyped<half>(C, allocator, A, B, transA, transB, __float2half(beta), __float2half(scalar));
+  } else {
+    ABORT("ProdBatched not implemented for type {}", C->type());
+  }
+}
+
 // bug in cuSparse: sparse matrix is limited to 65535 columns
 // This function is a drop-in replacement that handles it (by slicing).
 cusparseStatus_t
@@ -213,6 +325,8 @@ static cusparseSgemmiEx(cusparseHandle_t handle, int m,
   return CUSPARSE_STATUS_SUCCESS;
 }
 
+// @TODO: make this work with fp16
+
 // C = op(S) x D if not swapOperands else C = D x op(S)
 // op(S) = S if not transA else S^T
 void CSRProd(marian::Tensor C,
diff --git a/src/tensors/gpu/prod.h b/src/tensors/gpu/prod.h
index b7177649f..9dc1220c4 100644
--- a/src/tensors/gpu/prod.h
+++ b/src/tensors/gpu/prod.h
@@ -16,15 +16,6 @@ void Prod(marian::Tensor C,
           float beta = 0,
           float scalar = 1);
 
-void ProdWithBias(marian::Tensor C,
-                  const marian::Tensor& A,
-                  const marian::Tensor& B,
-                  const marian::Tensor& bias,
-                  bool transA,
-                  bool transB,
-                  float beta = 0,
-                  float scalar = 1);
-
 void ProdBatched(marian::Tensor C,
                  Ptr<Allocator> allocator,
                  const marian::Tensor A,
@@ -33,6 +24,7 @@ void ProdBatched(marian::Tensor C,
                  bool transB,
                  float beta = 0,
                  float scalar = 1);
+
 void CSRProd(marian::Tensor C,
              Ptr<Allocator> allocator,
              const marian::Tensor& A_values,

From 75694e08a8aef1b47164a4370102e9e35e3213a7 Mon Sep 17 00:00:00 2001
From: Marcin Junczys-Dowmunt <Marcin.JunczysDowmunt@microsoft.com>
Date: Wed, 11 Sep 2019 23:07:23 -0700
Subject: [PATCH 620/838] change shuffling

---
 src/data/batch_generator.h     | 26 ++++++++++++++------------
 src/graph/expression_graph.cpp |  3 +--
 src/graph/expression_graph.h   |  3 ++-
 src/translator/translator.h    | 20 +++++++++++++++-----
 4 files changed, 32 insertions(+), 20 deletions(-)

diff --git a/src/data/batch_generator.h b/src/data/batch_generator.h
index 950a71b1f..c0b70c88a 100644
--- a/src/data/batch_generator.h
+++ b/src/data/batch_generator.h
@@ -65,7 +65,8 @@ class BatchGenerator : public RNGEngine {
   Ptr<DataSet> data_;
   Ptr<Options> options_;
   bool restored_{false};
-  bool shuffle_;
+  bool shuffleData_{false};
+  bool shuffleBatches_{false};
 
 private:
   Ptr<BatchStats> stats_;
@@ -96,8 +97,8 @@ class BatchGenerator : public RNGEngine {
           a.rbegin(), a.rend(), b.rbegin(), b.rend(), itemCmp);
     };
 
-    auto cmpNone = [](const Sample& a, const Sample& b) { return a.getId() > b.getId(); }; // sort in order of original ids = original data order unless shuffling
-
+    auto cmpNone = [](const Sample& a, const Sample& b) { return a.getId() < b.getId(); }; // sort in order of original ids = original data order unless shuffling
+    
     typedef std::function<bool(const Sample&, const Sample&)> cmp_type;
     typedef std::priority_queue<Sample, Samples, cmp_type> sample_queue;
 
@@ -206,7 +207,7 @@ class BatchGenerator : public RNGEngine {
       tempBatches.push_back(data_->toBatch(batchVector));
 
     // Shuffle the batches
-    if(shuffle_) {
+    if(shuffleBatches_) {
       std::shuffle(tempBatches.begin(), tempBatches.end(), eng_);
     }
     double totalSent{}, totalLabels{};
@@ -252,7 +253,11 @@ class BatchGenerator : public RNGEngine {
   BatchGenerator(Ptr<DataSet> data,
                  Ptr<Options> options,
                  Ptr<BatchStats> stats = nullptr)
-      : data_(data), options_(options), stats_(stats), threadPool_(1) {}
+      : data_(data), options_(options), stats_(stats), threadPool_(1) {
+    auto shuffle = options_->get<std::string>("shuffle");
+    shuffleData_ = shuffle == "data";
+    shuffleBatches_ = shuffleData_ || shuffle == "batches";
+  }
 
   ~BatchGenerator() {
     if (futureBufferedBatches_.valid()) // bg thread holds a reference to 'this',
@@ -268,23 +273,20 @@ class BatchGenerator : public RNGEngine {
   }
 
   // @TODO: get rid of this function, begin() or constructor should figure this out
-  void prepare(bool shuffle = true) {
-    if(shuffle)
+  void prepare() {
+    if(shuffleData_)
       data_->shuffle();
     else
       data_->reset();
     newlyPrepared_ = true;
 
-    // @TODO: solve this better, maybe use options
-    shuffle_ = shuffle;
-
     // start the background pre-fetch operation
     fetchBatchesAsync();
   }
 
   // Used to restore the state of a BatchGenerator after
   // an interrupted and resumed training.
-  bool restore(Ptr<TrainingState> state, bool shuffle) {
+  bool restore(Ptr<TrainingState> state) {
     if(state->epochs == 1 && state->batchesEpoch == 0)
       return false;
 
@@ -298,7 +300,7 @@ class BatchGenerator : public RNGEngine {
       setRNGState(state->seedBatch);
     }
 
-    prepare(shuffle);
+    prepare();
     for(size_t i = 0; i < state->batchesEpoch; ++i)
       next();
 
diff --git a/src/graph/expression_graph.cpp b/src/graph/expression_graph.cpp
index 0b593ab4a..8c568949b 100755
--- a/src/graph/expression_graph.cpp
+++ b/src/graph/expression_graph.cpp
@@ -5,9 +5,8 @@
 
 namespace marian {
 
-ExpressionGraph::ExpressionGraph(bool inference, bool optimized)
+ExpressionGraph::ExpressionGraph(bool inference)
   : inferenceOnly_(inference),
-    optimized_(optimized),
     backend_(nullptr) {}
 
 void ExpressionGraph::setDevice(DeviceId deviceId, Ptr<Device> device) {
diff --git a/src/graph/expression_graph.h b/src/graph/expression_graph.h
index 1b5ab4a76..c26f98a7e 100755
--- a/src/graph/expression_graph.h
+++ b/src/graph/expression_graph.h
@@ -128,6 +128,7 @@ class ExpressionGraph : public std::enable_shared_from_this<ExpressionGraph> {
   Type saveType_{Type::float32};      // Type used for saving to disk, can be different, e.g. double or float16.
 
   bool inferenceOnly_{false};
+
   bool optimized_{false};     // during inference, use optimizations that might lead to precision loss, e.g. 8-bit MatMul.
   bool checkpointing_{false}; // use gradient checkpointing if true
 
@@ -150,7 +151,7 @@ class ExpressionGraph : public std::enable_shared_from_this<ExpressionGraph> {
    *
    * Constructor should be used as New<ExpressionGraph>()
    */
-  ExpressionGraph(bool inference = false, bool optimized = false);
+  ExpressionGraph(bool inference = false);
 
   virtual ~ExpressionGraph() {
     clear();
diff --git a/src/translator/translator.h b/src/translator/translator.h
index 43bb5ac3c..afb7eb2c4 100755
--- a/src/translator/translator.h
+++ b/src/translator/translator.h
@@ -33,6 +33,8 @@ class Translate : public ModelTask {
     // This is currently safe as the translator is either created stand-alone or
     // or config is created anew from Options in the validator
     options_->set("inference", true);
+    options_->set("shuffle", "none");
+    ABORT("Fix this");
 
     corpus_ = New<data::Corpus>(options_, true);
 
@@ -57,6 +59,9 @@ class Translate : public ModelTask {
       auto task = [&](DeviceId device, size_t id) {
         auto graph = New<ExpressionGraph>(true);
         graph->setDevice(device);
+        auto prec = options_->get<std::vector<std::string>>("precision", {"float32"});
+        graph->setParameterType(typeFromString(prec[0]));
+
         graph->getBackend()->setClip(options_->get<float>("clip-gemm"));
         if (device.type == DeviceType::cpu) {
           graph->getBackend()->setOptimized(options_->get<bool>("optimize"));
@@ -91,8 +96,9 @@ class Translate : public ModelTask {
     if(options_->get<bool>("quiet-translation"))
       collector->setPrintingStrategy(New<QuietPrinting>());
 
-    bg.prepare(false);
+    bg.prepare();
 
+    bool doNbest = options_->get<bool>("n-best");
     for(auto batch : bg) {
       auto task = [=](size_t id) {
         thread_local Ptr<ExpressionGraph> graph;
@@ -113,7 +119,7 @@ class Translate : public ModelTask {
           collector->Write((long)history->getLineNum(),
                            best1.str(),
                            bestn.str(),
-                           options_->get<bool>("n-best"));
+                           doNbest);
         }
 
 
@@ -123,8 +129,8 @@ class Translate : public ModelTask {
             && id % 1000 == 0)  // hard beat once every 1000 batches
         {
           auto progress = 0.f; //fake progress for now
-          fprintf(stdout, "PROGRESS: %.2f%%\n", progress);
-          fflush(stdout);
+          fprintf(stderr, "PROGRESS: %.2f%%\n", progress);
+          fflush(stderr);
         }
       };
 
@@ -152,6 +158,7 @@ class TranslateService : public ModelServiceTask {
   TranslateService(Ptr<Options> options) : options_(options) {
     // initialize vocabs
     options_->set("inference", true);
+    options_->set("shuffle", "none");
 
     auto vocabPaths = options_->get<std::vector<std::string>>("vocabs");
     std::vector<int> maxVocabs = options_->get<std::vector<int>>("dim-vocabs");
@@ -173,6 +180,9 @@ class TranslateService : public ModelServiceTask {
     for(auto device : devices) {
       auto graph = New<ExpressionGraph>(true);
       graph->setDevice(device);
+      auto prec = options_->get<std::vector<std::string>>("precision", {"float32"});
+
+      graph->setParameterType(typeFromString(prec[0]));
       graph->getBackend()->setClip(options_->get<float>("clip-gemm"));
       if (device.type == DeviceType::cpu) {
         graph->getBackend()->setOptimized(options_->get<bool>("optimize"));
@@ -196,7 +206,7 @@ class TranslateService : public ModelServiceTask {
     auto printer = New<OutputPrinter>(options_, trgVocab_);
     size_t batchId = 0;
 
-    batchGenerator.prepare(false);
+    batchGenerator.prepare();
 
     {
       ThreadPool threadPool_(numDevices_, numDevices_);

From eea9473786e703a402fcc32bfadd0b1e524d1e96 Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Thu, 12 Sep 2019 10:08:12 -0700
Subject: [PATCH 621/838] Visual Studio Project builds again

---
 src/3rd_party/avx_mathfun.h                   |   2 +-
 .../half_float/HalfPrecisionFloatTest.cpp     |  38 ++--
 src/3rd_party/half_float/umHalf.h             |   4 +-
 src/3rd_party/sse_mathfun.h                   |   2 +-
 src/common/filesystem.cpp                     |  12 +-
 src/common/types.h                            |   6 +-
 src/data/batch.h                              |   2 +-
 src/functional/operators.h                    |   2 +-
 src/functional/shape.h                        |   4 +-
 src/functional/tensor.h                       |   2 +-
 src/tensors/cpu/sharp/packed_gemm.cpp         |   9 +-
 src/tensors/tensor.cpp                        |   8 +-
 src/tensors/tensor.h                          |  13 +-
 vs/Marian.vcxproj                             | 179 ++++++++++++------
 vs/Marian.vcxproj.filters                     |   6 +
 15 files changed, 183 insertions(+), 106 deletions(-)
 mode change 100644 => 100755 src/3rd_party/half_float/HalfPrecisionFloatTest.cpp
 mode change 100644 => 100755 src/3rd_party/sse_mathfun.h
 mode change 100644 => 100755 src/common/filesystem.cpp
 mode change 100644 => 100755 src/common/types.h
 mode change 100644 => 100755 src/data/batch.h
 mode change 100644 => 100755 src/functional/operators.h
 mode change 100644 => 100755 src/functional/shape.h
 mode change 100644 => 100755 src/functional/tensor.h
 mode change 100644 => 100755 src/tensors/cpu/sharp/packed_gemm.cpp
 mode change 100644 => 100755 src/tensors/tensor.h

diff --git a/src/3rd_party/avx_mathfun.h b/src/3rd_party/avx_mathfun.h
index 9d1403b4f..6840478c5 100755
--- a/src/3rd_party/avx_mathfun.h
+++ b/src/3rd_party/avx_mathfun.h
@@ -56,7 +56,7 @@ _PI32AVX_CONST(4, 4);
 
 /* declare some AVX constants -- why can't I figure a better way to do that? */
 #define _PS256_CONST(Name, Val)                                            \
-  static const ALIGN32_BEG float _ps256_##Name[8] ALIGN32_END = { Val, Val, Val, Val, Val, Val, Val, Val }
+  static const ALIGN32_BEG float _ps256_##Name[8] ALIGN32_END = { (float)Val, (float)Val, (float)Val, (float)Val, (float)Val, (float)Val, (float)Val, (float)Val }
 #define _PI32_CONST256(Name, Val)                                            \
   static const ALIGN32_BEG int _pi32_256_##Name[8] ALIGN32_END = { Val, Val, Val, Val, Val, Val, Val, Val }
 #define _PS256_CONST_TYPE(Name, Type, Val)                                 \
diff --git a/src/3rd_party/half_float/HalfPrecisionFloatTest.cpp b/src/3rd_party/half_float/HalfPrecisionFloatTest.cpp
old mode 100644
new mode 100755
index 281016191..a5d8b8466
--- a/src/3rd_party/half_float/HalfPrecisionFloatTest.cpp
+++ b/src/3rd_party/half_float/HalfPrecisionFloatTest.cpp
@@ -7,7 +7,7 @@
 
 #define VALIDATE(x) if (!(x)){std::cout << "Failed: " <<  #x << std::endl;assert((x));}
 
-int main(int argc, char* argv[])
+int main(int, char*)
 {
    half h = 1.f, h2 = 2.f;
    --h2;
@@ -57,33 +57,33 @@ int main(int argc, char* argv[])
    // ****************************************************************************
 
    // identical exponents
-   for (float f = 0.f; f < 1000.f; ++f)
+   for (float v = 0.f; v < 1000.f; ++v)
    {
-	  half one = f;
-	  half two = f;
+	  half one = v;
+	  half two = v;
       half three = one + two;
       f2 = three;
-      VALIDATE(f*2.f == f2);
+      VALIDATE(v*2.f == f2);
    }
 
     // different exponents
-   for (float f = 0.f, fp = 1000.f; f < 500.f; ++f, --fp)
+   for (float v = 0.f, fp = 1000.f; v < 500.f; ++v, --fp)
    {
-	  half one = f;
+	  half one = v;
 	  half two = fp;
       half three = one + two;
       f2 = three;
-      VALIDATE(f+fp == f2);
+      VALIDATE(v+fp == f2);
    }
 
    // very small numbers - this is already beyond the accuracy of 16 bit floats.
-   for (float f = 0.003f; f < 1000.f; f += 0.0005f)
+   for (float v = 0.003f; v < 1000.f; v += 0.0005f)
    {
-	  half one = f;
-	  half two = f;
+	  half one = v;
+	  half two = v;
       half three = one + two;
       f2 = three;
-	  float m = f*2.f;
+	  float m = v*2.f;
       VALIDATE(f2 > (m-0.05*m) && f2 < (m+0.05*m));
    }
 
@@ -92,23 +92,23 @@ int main(int argc, char* argv[])
    // ****************************************************************************
 
    // identical exponents
-   for (float f = 0.f; f < 1000.f; ++f)
+   for (float v = 0.f; v < 1000.f; ++v)
    {
-	  half one = f;
-	  half two = f;
+	  half one = v;
+	  half two = v;
       half three = one - two;
       f2 = three;
       VALIDATE(0.f == f2);
    }
 
-    // different exponents
-   for (float f = 0.f, fp = 1000.f; f < 500.f; ++f, --fp)
+   // different exponents
+   for (float v = 0.f, fp = 1000.f; v < 500.f; ++v, --fp)
    {
-	  half one = f;
+	  half one = v;
 	  half two = fp;
       half three = one - two;
       f2 = three;
-      VALIDATE(f-fp == f2);
+      VALIDATE(v-fp == f2);
    }
    return 0;		
 }
diff --git a/src/3rd_party/half_float/umHalf.h b/src/3rd_party/half_float/umHalf.h
index 361dbdaa8..f6b65ef90 100755
--- a/src/3rd_party/half_float/umHalf.h
+++ b/src/3rd_party/half_float/umHalf.h
@@ -243,7 +243,7 @@ class numeric_limits<HalfFloat> {
     static HalfFloat min ()
 		{return HalfFloat(0,1,0);}
     static HalfFloat max ()
-		{return HalfFloat(~0,HalfFloat::MAX_EXPONENT_VALUE-1,0);}
+		{return HalfFloat((uint16_t)~0,HalfFloat::MAX_EXPONENT_VALUE-1,0);}
     static const int radix = 2;
     static const int digits = 10;   // conservative assumption
     static const int digits10 = 2;  // conservative assumption
@@ -255,7 +255,7 @@ class numeric_limits<HalfFloat> {
     static const bool is_bounded	= true;
 
 	static const HalfFloat lowest() {
-	  return HalfFloat(~0,HalfFloat::MAX_EXPONENT_VALUE-1,~0);
+	  return HalfFloat((uint16_t)~0,HalfFloat::MAX_EXPONENT_VALUE-1,(uint16_t)~0);
 	}
 
 	// Floating point specific.
diff --git a/src/3rd_party/sse_mathfun.h b/src/3rd_party/sse_mathfun.h
old mode 100644
new mode 100755
index 59f69414b..a39518057
--- a/src/3rd_party/sse_mathfun.h
+++ b/src/3rd_party/sse_mathfun.h
@@ -53,7 +53,7 @@ typedef __m64 v2si;   // vector of 2 int (mmx)
 
 /* declare some SSE constants -- why can't I figure a better way to do that? */
 #define _PS_CONST(Name, Val)                                            \
-  static const ALIGN16_BEG float _ps_##Name[4] ALIGN16_END = { Val, Val, Val, Val }
+  static const ALIGN16_BEG float _ps_##Name[4] ALIGN16_END = { (float)Val, (float)Val, (float)Val, (float)Val }
 #define _PI32_CONST(Name, Val)                                            \
   static const ALIGN16_BEG int _pi32_##Name[4] ALIGN16_END = { Val, Val, Val, Val }
 #define _PS_CONST_TYPE(Name, Type, Val)                                 \
diff --git a/src/common/filesystem.cpp b/src/common/filesystem.cpp
old mode 100644
new mode 100755
index e1789e087..1abaeae43
--- a/src/common/filesystem.cpp
+++ b/src/common/filesystem.cpp
@@ -10,18 +10,18 @@
 namespace marian {
 namespace filesystem {
 
-bool is_fifo(char const* path) {
 #ifdef _MSC_VER
-  // Pretend that Windows knows no named pipes. It does, by the way, but
-  // they seem to be different from pipes on Unix / Linux. See
-  // https://docs.microsoft.com/en-us/windows/win32/ipc/named-pipes
-  return false;
+// Pretend that Windows knows no named pipes. It does, by the way, but
+// they seem to be different from pipes on Unix / Linux. See
+// https://docs.microsoft.com/en-us/windows/win32/ipc/named-pipes
+bool is_fifo(char const*) { return false; }
 #else
+bool is_fifo(char const* path) {
   struct stat buf;
   stat(path, &buf);
   return S_ISFIFO(buf.st_mode);
-#endif
 }
+#endif
 
 bool is_fifo(std::string const& path) {
   return is_fifo(path.c_str());
diff --git a/src/common/types.h b/src/common/types.h
old mode 100644
new mode 100755
index e41045195..8c0f17a1e
--- a/src/common/types.h
+++ b/src/common/types.h
@@ -10,6 +10,8 @@
 #include <immintrin.h>
 #endif
 
+// @BUGBUG: Visual Studio somehow fails on template expansions for float16.
+//          To be able to build on Windows, we temporarily disable this, until the greater merge has happened.
 #define DISPATCH_BY_TYPE0(type, func) \
 do { \
   switch(type) { \
@@ -21,7 +23,7 @@ do { \
     case Type::uint16:  return func<uint16_t>(); \
     case Type::uint32:  return func<uint32_t>(); \
     case Type::uint64:  return func<uint64_t>(); \
-    case Type::float16: return func<float16 >(); \
+    case Type::float16: ABORT("Broken type {}", type);/*return func<float16 >();*/ \
     case Type::float32: return func<float   >(); \
     case Type::float64: return func<double  >(); \
     default: ABORT("Unknown type {}", type); \
@@ -39,7 +41,7 @@ do { \
     case Type::uint16:  return func<uint16_t>(arg1); \
     case Type::uint32:  return func<uint32_t>(arg1); \
     case Type::uint64:  return func<uint64_t>(arg1); \
-    case Type::float16: return func<float16 >(arg1); \
+    case Type::float16: ABORT("Broken type {}", type);/*return func<float16 >(arg1);*/ \
     case Type::float32: return func<float   >(arg1); \
     case Type::float64: return func<double  >(arg1); \
     default: ABORT("Unknown type {}", type); \
diff --git a/src/data/batch.h b/src/data/batch.h
old mode 100644
new mode 100755
index 7296ae135..b1b109817
--- a/src/data/batch.h
+++ b/src/data/batch.h
@@ -17,7 +17,7 @@ class Batch {
   virtual size_t wordsTrg() const { return 0; };
   virtual size_t widthTrg() const { return 0; };
 
-  virtual void debug(bool printIndices = false) {};
+  virtual void debug(bool /*printIndices*/ = false) {};
 
   virtual std::vector<Ptr<Batch>> split(size_t n, size_t sizeLimit = SIZE_MAX) = 0;
 
diff --git a/src/functional/operators.h b/src/functional/operators.h
old mode 100644
new mode 100755
index 1493f75d0..1edcdb9be
--- a/src/functional/operators.h
+++ b/src/functional/operators.h
@@ -77,7 +77,7 @@ struct Ops<float> {
   static HOST_DEVICE_INLINE float abs(const float& x)  { return fabs(x); }
   static HOST_DEVICE_INLINE float sqrt(const float& x) { return sqrtf(x); }
   static HOST_DEVICE_INLINE float neg(const float& x)  { return -x; }
-  static HOST_DEVICE_INLINE float sgn(const float& x)  { return (0 < x) - (x < 0); }
+  static HOST_DEVICE_INLINE float sgn(const float& x)  { return (float)((0 < x) - (x < 0)); }
 
   static HOST_DEVICE_INLINE float add(const float& x, const float& y)  { return x + y; }
   static HOST_DEVICE_INLINE float sub(const float& x, const float& y)  { return x - y; }
diff --git a/src/functional/shape.h b/src/functional/shape.h
old mode 100644
new mode 100755
index 8f361386e..8072615e9
--- a/src/functional/shape.h
+++ b/src/functional/shape.h
@@ -193,11 +193,11 @@ struct ConstantShape {
   };
 
   HOST_DEVICE_INLINE int index(const Array<int, N>& dims) const {
-    return offset_ + I<N-1, N>::index(dims, stride_);
+    return (int)offset_ + I<N-1, N>::index(dims, stride_);
   }
 
   HOST_DEVICE_INLINE int index(int si) const {
-    return offset_ + I<N-1, N>::index(si, shape_, stride_);
+    return (int)offset_ + I<N-1, N>::index(si, shape_, stride_);
   }
 
   HOST_DEVICE_INLINE void dims(int si, Array<int, N>& dims) const {
diff --git a/src/functional/tensor.h b/src/functional/tensor.h
old mode 100644
new mode 100755
index ba5775176..ff9a9d42d
--- a/src/functional/tensor.h
+++ b/src/functional/tensor.h
@@ -58,7 +58,7 @@ struct View {
   HOST View(marian::Tensor t) : data_(t->data<T>()), shape_(adapt<T>(t->shape())) {}
 
   HOST_DEVICE_INLINE T& operator[](size_t i) {
-     return data_[shape_.index(i)];
+     return data_[shape_.index((int)i)];
   }
 
   HOST_DEVICE_INLINE const T& operator[](size_t i) const {
diff --git a/src/tensors/cpu/sharp/packed_gemm.cpp b/src/tensors/cpu/sharp/packed_gemm.cpp
old mode 100644
new mode 100755
index 28f798d40..087975197
--- a/src/tensors/cpu/sharp/packed_gemm.cpp
+++ b/src/tensors/cpu/sharp/packed_gemm.cpp
@@ -11,11 +11,14 @@
 #include <unordered_map>
 //#include <chrono>
 
+#if USE_FBGEMM
 #ifdef _MSC_VER
-#pragma warning(disable: 4505) // warning C4505: 'fbgemmAlignedAlloc' in fbgemm.h: unreferenced local function has been removed (missing 'static inline')
+#pragma warning(disable: 4505) // 'fbgemmAlignedAlloc' in fbgemm.h: unreferenced local function has been removed (missing 'static inline')
+#pragma warning(disable: 4251) // 'fbgemm::CompressedSparseColumn::colptr_': class 'std::vector<int,std::allocator<_Ty>>' needs to have dll-interface to be used by clients of class 'fbgemm::CompressedSparseColumn'
+// the following does not work; need to manually disable them in Linker options
+//#pragma comment(linker, "/ignore:4049") // locally defined symbol ...asmjit... imported
+//#pragma comment(linker, "/ignore:4217") // locally defined symbol ...asmjit... imported
 #endif
-
-#if USE_FBGEMM
 #include "3rd_party/fbgemm/include/fbgemm/FbgemmFP16.h"
 #include "3rd_party/fbgemm/include/fbgemm/QuantUtils.h"
 #include "3rd_party/fbgemm/include/fbgemm/Fbgemm.h"
diff --git a/src/tensors/tensor.cpp b/src/tensors/tensor.cpp
index 18d419c3c..75b6644e3 100755
--- a/src/tensors/tensor.cpp
+++ b/src/tensors/tensor.cpp
@@ -99,16 +99,16 @@ std::string TensorBase::debug(int precision, int dispCols) {
   return strm.str();
 }
 
-template std::string TensorBase::debug<float>(int, int);
 template std::string TensorBase::debug<float16>(int, int);
-template std::string TensorBase::debug<double>(int, int);
+template std::string TensorBase::debug<float  >(int, int);
+template std::string TensorBase::debug<double >(int, int);
 
-template std::string TensorBase::debug<uint8_t>(int, int);
+template std::string TensorBase::debug<uint8_t >(int, int);
 template std::string TensorBase::debug<uint16_t>(int, int);
 template std::string TensorBase::debug<uint32_t>(int, int);
 template std::string TensorBase::debug<uint64_t>(int, int);
 
-template std::string TensorBase::debug<int8_t>(int, int);
+template std::string TensorBase::debug<int8_t >(int, int);
 template std::string TensorBase::debug<int16_t>(int, int);
 template std::string TensorBase::debug<int32_t>(int, int);
 template std::string TensorBase::debug<int64_t>(int, int);
diff --git a/src/tensors/tensor.h b/src/tensors/tensor.h
old mode 100644
new mode 100755
index bdaea9426..62ce51058
--- a/src/tensors/tensor.h
+++ b/src/tensors/tensor.h
@@ -19,7 +19,6 @@
 namespace marian {
 
 class TensorBase {
-private:
   MemoryPiece::PtrType memory_;
   Shape shape_;
   Type type_{Type::float32};
@@ -76,9 +75,9 @@ class TensorBase {
     return get<T>(0);
   }
 
-  // this version converts all numeric types to float
+  // this non-template version converts all numeric types to float
   virtual float scalar() {
-    DISPATCH_BY_TYPE0(type_, scalar);
+    DISPATCH_BY_TYPE0(type_, (float)scalar);
   }
 
   Ptr<Backend> getBackend() { return backend_; }
@@ -94,7 +93,7 @@ class TensorBase {
   template <typename T>
   T get(size_t i) {
     if(!matchType<T>(type_)) {
-      DISPATCH_BY_TYPE1(type_, get, i);
+      DISPATCH_BY_TYPE1(type_, (T)get, i);
     } else {
       T temp = 0;
       if(backend_->getDeviceId().type == DeviceType::cpu) {
@@ -203,7 +202,7 @@ class TensorBase {
   template <typename T>
   void set(T value) {
     if(!matchType<T>(type_)) {
-      DISPATCH_BY_TYPE1(type_, set, value);
+      DISPATCH_BY_TYPE1(type_, setAs, value);
     } else {
       if(backend_->getDeviceId().type == DeviceType::cpu) {
         std::fill(data<T>(), data<T>() + size(), value);
@@ -215,6 +214,10 @@ class TensorBase {
   #endif
     }
   }
+private: // subroutine for above: helper that accepts any type and casts it to <T>
+    template <typename Tas, typename Tval>
+    void setAs(Tval value) { set((Tas)value); }
+public:
 
   void setSparse(const std::vector<size_t>& k, const std::vector<float>& v) {
     ABORT_IF(!matchType<float>(type_),
diff --git a/vs/Marian.vcxproj b/vs/Marian.vcxproj
index 915901608..d086e7c1e 100755
--- a/vs/Marian.vcxproj
+++ b/vs/Marian.vcxproj
@@ -69,7 +69,7 @@
       </PrecompiledHeader>
       <WarningLevel>Level4</WarningLevel>
       <Optimization>Disabled</Optimization>
-      <PreprocessorDefinitions>FBGEMM_EXPORTS; USE_FBGEMM=1; ASMJIT_VARAPI; USE_SSE2=1; CUDA_FOUND=1; MKL_FOUND=1; MPI_FOUND=1; BLAS_FOUND=1; MKL_ILP64; WIN32;_DEBUG;_CONSOLE;_LIB;%(PreprocessorDefinitions)</PreprocessorDefinitions>
+      <PreprocessorDefinitions>FBGEMM_EXPORTS; USE_FBGEMM=1; USE_SSE2=1; CUDA_FOUND=1; MKL_FOUND=1; MPI_FOUND=1; BLAS_FOUND=1; MKL_ILP64; WIN32;_DEBUG;_CONSOLE;_LIB;%(PreprocessorDefinitions)</PreprocessorDefinitions>
       <SDLCheck>false</SDLCheck>
       <TreatWarningAsError>true</TreatWarningAsError>
       <AdditionalOptions>/bigobj %(AdditionalOptions) /arch:AVX2</AdditionalOptions>
@@ -85,6 +85,7 @@
       <AdditionalDependencies>cudart_static.lib;cublas.lib;cusparse.lib;curand.lib;zlib.lib;msmpi.lib;mkl_intel_ilp64.lib;mkl_sequential.lib;mkl_core.lib;kernel32.lib;user32.lib;gdi32.lib;winspool.lib;comdlg32.lib;advapi32.lib;shell32.lib;ole32.lib;oleaut32.lib;uuid.lib;odbc32.lib;odbccp32.lib;shlwapi.lib;%(AdditionalDependencies)</AdditionalDependencies>
       <StackReserveSize>100000000</StackReserveSize>
       <TreatLinkerWarningAsErrors>false</TreatLinkerWarningAsErrors>
+      <AdditionalOptions>/ignore:4049 /ignore:4217 %(AdditionalOptions)</AdditionalOptions>
     </Link>
     <CudaCompile>
       <Include>$(SolutionDir)..\src\;$(SolutionDir)..\src\3rd_party</Include>
@@ -126,6 +127,7 @@
       <AdditionalDependencies>cudart_static.lib;cublas.lib;cusparse.lib;curand.lib;zlib.lib;msmpi.lib;mkl_intel_ilp64.lib;mkl_sequential.lib;mkl_core.lib;kernel32.lib;user32.lib;gdi32.lib;winspool.lib;comdlg32.lib;advapi32.lib;shell32.lib;ole32.lib;oleaut32.lib;uuid.lib;odbc32.lib;odbccp32.lib;shlwapi.lib;%(AdditionalDependencies)</AdditionalDependencies>
       <StackReserveSize>100000000</StackReserveSize>
       <TreatLinkerWarningAsErrors>false</TreatLinkerWarningAsErrors>
+      <AdditionalOptions>/ignore:4049 /ignore:4217 %(AdditionalOptions)</AdditionalOptions>
     </Link>
     <CudaCompile>
       <Include>$(SolutionDir)..\src\;$(SolutionDir)..\src\3rd_party</Include>
@@ -139,116 +141,172 @@
   <ItemGroup>
     <ClCompile Include="..\src\3rd_party\ExceptionWithCallStack.cpp" />
     <ClCompile Include="..\src\3rd_party\fbgemm\src\ExecuteKernel.cc">
-      <TreatWarningAsError Condition="'$(Configuration)|$(Platform)'=='Release|x64'">false</TreatWarningAsError>
-      <TreatWarningAsError Condition="'$(Configuration)|$(Platform)'=='Debug|x64'">false</TreatWarningAsError>
+      <TreatWarningAsError Condition="'$(Configuration)|$(Platform)'=='Release|x64'">true</TreatWarningAsError>
+      <TreatWarningAsError Condition="'$(Configuration)|$(Platform)'=='Debug|x64'">true</TreatWarningAsError>
+      <WarningLevel Condition="'$(Configuration)|$(Platform)'=='Debug|x64'">TurnOffAllWarnings</WarningLevel>
+      <WarningLevel Condition="'$(Configuration)|$(Platform)'=='Release|x64'">TurnOffAllWarnings</WarningLevel>
     </ClCompile>
     <ClCompile Include="..\src\3rd_party\fbgemm\src\ExecuteKernelU8S8.cc">
-      <TreatWarningAsError Condition="'$(Configuration)|$(Platform)'=='Release|x64'">false</TreatWarningAsError>
-      <TreatWarningAsError Condition="'$(Configuration)|$(Platform)'=='Debug|x64'">false</TreatWarningAsError>
+      <TreatWarningAsError Condition="'$(Configuration)|$(Platform)'=='Release|x64'">true</TreatWarningAsError>
+      <TreatWarningAsError Condition="'$(Configuration)|$(Platform)'=='Debug|x64'">true</TreatWarningAsError>
+      <WarningLevel Condition="'$(Configuration)|$(Platform)'=='Debug|x64'">TurnOffAllWarnings</WarningLevel>
+      <WarningLevel Condition="'$(Configuration)|$(Platform)'=='Release|x64'">TurnOffAllWarnings</WarningLevel>
     </ClCompile>
     <ClCompile Include="..\src\3rd_party\fbgemm\src\Fbgemm.cc">
-      <TreatWarningAsError Condition="'$(Configuration)|$(Platform)'=='Release|x64'">false</TreatWarningAsError>
-      <TreatWarningAsError Condition="'$(Configuration)|$(Platform)'=='Debug|x64'">false</TreatWarningAsError>
+      <TreatWarningAsError Condition="'$(Configuration)|$(Platform)'=='Release|x64'">true</TreatWarningAsError>
+      <TreatWarningAsError Condition="'$(Configuration)|$(Platform)'=='Debug|x64'">true</TreatWarningAsError>
+      <WarningLevel Condition="'$(Configuration)|$(Platform)'=='Debug|x64'">TurnOffAllWarnings</WarningLevel>
+      <WarningLevel Condition="'$(Configuration)|$(Platform)'=='Release|x64'">TurnOffAllWarnings</WarningLevel>
     </ClCompile>
     <ClCompile Include="..\src\3rd_party\fbgemm\src\FbgemmConv.cc">
-      <TreatWarningAsError Condition="'$(Configuration)|$(Platform)'=='Release|x64'">false</TreatWarningAsError>
-      <TreatWarningAsError Condition="'$(Configuration)|$(Platform)'=='Debug|x64'">false</TreatWarningAsError>
+      <TreatWarningAsError Condition="'$(Configuration)|$(Platform)'=='Release|x64'">true</TreatWarningAsError>
+      <TreatWarningAsError Condition="'$(Configuration)|$(Platform)'=='Debug|x64'">true</TreatWarningAsError>
+      <WarningLevel Condition="'$(Configuration)|$(Platform)'=='Debug|x64'">TurnOffAllWarnings</WarningLevel>
+      <WarningLevel Condition="'$(Configuration)|$(Platform)'=='Release|x64'">TurnOffAllWarnings</WarningLevel>
     </ClCompile>
     <ClCompile Include="..\src\3rd_party\fbgemm\src\FbgemmFP16.cc">
-      <TreatWarningAsError Condition="'$(Configuration)|$(Platform)'=='Release|x64'">false</TreatWarningAsError>
-      <TreatWarningAsError Condition="'$(Configuration)|$(Platform)'=='Debug|x64'">false</TreatWarningAsError>
+      <TreatWarningAsError Condition="'$(Configuration)|$(Platform)'=='Release|x64'">true</TreatWarningAsError>
+      <TreatWarningAsError Condition="'$(Configuration)|$(Platform)'=='Debug|x64'">true</TreatWarningAsError>
+      <WarningLevel Condition="'$(Configuration)|$(Platform)'=='Debug|x64'">TurnOffAllWarnings</WarningLevel>
+      <WarningLevel Condition="'$(Configuration)|$(Platform)'=='Release|x64'">TurnOffAllWarnings</WarningLevel>
     </ClCompile>
     <ClCompile Include="..\src\3rd_party\fbgemm\src\FbgemmFP16UKernelsAvx2.cc">
-      <TreatWarningAsError Condition="'$(Configuration)|$(Platform)'=='Release|x64'">false</TreatWarningAsError>
-      <TreatWarningAsError Condition="'$(Configuration)|$(Platform)'=='Debug|x64'">false</TreatWarningAsError>
+      <TreatWarningAsError Condition="'$(Configuration)|$(Platform)'=='Release|x64'">true</TreatWarningAsError>
+      <TreatWarningAsError Condition="'$(Configuration)|$(Platform)'=='Debug|x64'">true</TreatWarningAsError>
+      <WarningLevel Condition="'$(Configuration)|$(Platform)'=='Debug|x64'">TurnOffAllWarnings</WarningLevel>
+      <WarningLevel Condition="'$(Configuration)|$(Platform)'=='Release|x64'">TurnOffAllWarnings</WarningLevel>
     </ClCompile>
     <ClCompile Include="..\src\3rd_party\fbgemm\src\FbgemmI8DepthwiseAvx2.cc">
-      <TreatWarningAsError Condition="'$(Configuration)|$(Platform)'=='Release|x64'">false</TreatWarningAsError>
-      <TreatWarningAsError Condition="'$(Configuration)|$(Platform)'=='Debug|x64'">false</TreatWarningAsError>
+      <TreatWarningAsError Condition="'$(Configuration)|$(Platform)'=='Release|x64'">true</TreatWarningAsError>
+      <TreatWarningAsError Condition="'$(Configuration)|$(Platform)'=='Debug|x64'">true</TreatWarningAsError>
+      <WarningLevel Condition="'$(Configuration)|$(Platform)'=='Debug|x64'">TurnOffAllWarnings</WarningLevel>
+      <WarningLevel Condition="'$(Configuration)|$(Platform)'=='Release|x64'">TurnOffAllWarnings</WarningLevel>
     </ClCompile>
     <ClCompile Include="..\src\3rd_party\fbgemm\src\FbgemmI8Spmdm.cc">
-      <TreatWarningAsError Condition="'$(Configuration)|$(Platform)'=='Release|x64'">false</TreatWarningAsError>
-      <TreatWarningAsError Condition="'$(Configuration)|$(Platform)'=='Debug|x64'">false</TreatWarningAsError>
+      <TreatWarningAsError Condition="'$(Configuration)|$(Platform)'=='Release|x64'">true</TreatWarningAsError>
+      <TreatWarningAsError Condition="'$(Configuration)|$(Platform)'=='Debug|x64'">true</TreatWarningAsError>
+      <WarningLevel Condition="'$(Configuration)|$(Platform)'=='Debug|x64'">TurnOffAllWarnings</WarningLevel>
+      <WarningLevel Condition="'$(Configuration)|$(Platform)'=='Release|x64'">TurnOffAllWarnings</WarningLevel>
     </ClCompile>
     <ClCompile Include="..\src\3rd_party\fbgemm\src\GenerateKernelU8S8S32ACC16.cc">
-      <TreatWarningAsError Condition="'$(Configuration)|$(Platform)'=='Release|x64'">false</TreatWarningAsError>
-      <TreatWarningAsError Condition="'$(Configuration)|$(Platform)'=='Debug|x64'">false</TreatWarningAsError>
+      <TreatWarningAsError Condition="'$(Configuration)|$(Platform)'=='Release|x64'">true</TreatWarningAsError>
+      <TreatWarningAsError Condition="'$(Configuration)|$(Platform)'=='Debug|x64'">true</TreatWarningAsError>
+      <WarningLevel Condition="'$(Configuration)|$(Platform)'=='Debug|x64'">TurnOffAllWarnings</WarningLevel>
+      <WarningLevel Condition="'$(Configuration)|$(Platform)'=='Release|x64'">TurnOffAllWarnings</WarningLevel>
     </ClCompile>
     <ClCompile Include="..\src\3rd_party\fbgemm\src\GenerateKernelU8S8S32ACC16Avx512.cc">
-      <TreatWarningAsError Condition="'$(Configuration)|$(Platform)'=='Release|x64'">false</TreatWarningAsError>
-      <TreatWarningAsError Condition="'$(Configuration)|$(Platform)'=='Debug|x64'">false</TreatWarningAsError>
+      <TreatWarningAsError Condition="'$(Configuration)|$(Platform)'=='Release|x64'">true</TreatWarningAsError>
+      <TreatWarningAsError Condition="'$(Configuration)|$(Platform)'=='Debug|x64'">true</TreatWarningAsError>
+      <WarningLevel Condition="'$(Configuration)|$(Platform)'=='Debug|x64'">TurnOffAllWarnings</WarningLevel>
+      <WarningLevel Condition="'$(Configuration)|$(Platform)'=='Release|x64'">TurnOffAllWarnings</WarningLevel>
     </ClCompile>
     <ClCompile Include="..\src\3rd_party\fbgemm\src\GenerateKernelU8S8S32ACC32.cc">
-      <TreatWarningAsError Condition="'$(Configuration)|$(Platform)'=='Release|x64'">false</TreatWarningAsError>
-      <TreatWarningAsError Condition="'$(Configuration)|$(Platform)'=='Debug|x64'">false</TreatWarningAsError>
+      <TreatWarningAsError Condition="'$(Configuration)|$(Platform)'=='Release|x64'">true</TreatWarningAsError>
+      <TreatWarningAsError Condition="'$(Configuration)|$(Platform)'=='Debug|x64'">true</TreatWarningAsError>
+      <WarningLevel Condition="'$(Configuration)|$(Platform)'=='Debug|x64'">TurnOffAllWarnings</WarningLevel>
+      <WarningLevel Condition="'$(Configuration)|$(Platform)'=='Release|x64'">TurnOffAllWarnings</WarningLevel>
     </ClCompile>
     <ClCompile Include="..\src\3rd_party\fbgemm\src\GenerateKernelU8S8S32ACC32Avx512.cc">
-      <TreatWarningAsError Condition="'$(Configuration)|$(Platform)'=='Release|x64'">false</TreatWarningAsError>
-      <TreatWarningAsError Condition="'$(Configuration)|$(Platform)'=='Debug|x64'">false</TreatWarningAsError>
+      <TreatWarningAsError Condition="'$(Configuration)|$(Platform)'=='Release|x64'">true</TreatWarningAsError>
+      <TreatWarningAsError Condition="'$(Configuration)|$(Platform)'=='Debug|x64'">true</TreatWarningAsError>
+      <WarningLevel Condition="'$(Configuration)|$(Platform)'=='Debug|x64'">TurnOffAllWarnings</WarningLevel>
+      <WarningLevel Condition="'$(Configuration)|$(Platform)'=='Release|x64'">TurnOffAllWarnings</WarningLevel>
     </ClCompile>
     <ClCompile Include="..\src\3rd_party\fbgemm\src\GroupwiseConvAcc32Avx2.cc">
-      <TreatWarningAsError Condition="'$(Configuration)|$(Platform)'=='Release|x64'">false</TreatWarningAsError>
-      <TreatWarningAsError Condition="'$(Configuration)|$(Platform)'=='Debug|x64'">false</TreatWarningAsError>
+      <TreatWarningAsError Condition="'$(Configuration)|$(Platform)'=='Release|x64'">true</TreatWarningAsError>
+      <TreatWarningAsError Condition="'$(Configuration)|$(Platform)'=='Debug|x64'">true</TreatWarningAsError>
+      <WarningLevel Condition="'$(Configuration)|$(Platform)'=='Debug|x64'">TurnOffAllWarnings</WarningLevel>
+      <WarningLevel Condition="'$(Configuration)|$(Platform)'=='Release|x64'">TurnOffAllWarnings</WarningLevel>
     </ClCompile>
     <ClCompile Include="..\src\3rd_party\fbgemm\src\OptimizedKernelsAvx2.cc">
-      <TreatWarningAsError Condition="'$(Configuration)|$(Platform)'=='Release|x64'">false</TreatWarningAsError>
-      <TreatWarningAsError Condition="'$(Configuration)|$(Platform)'=='Debug|x64'">false</TreatWarningAsError>
+      <TreatWarningAsError Condition="'$(Configuration)|$(Platform)'=='Release|x64'">true</TreatWarningAsError>
+      <TreatWarningAsError Condition="'$(Configuration)|$(Platform)'=='Debug|x64'">true</TreatWarningAsError>
+      <WarningLevel Condition="'$(Configuration)|$(Platform)'=='Debug|x64'">TurnOffAllWarnings</WarningLevel>
+      <WarningLevel Condition="'$(Configuration)|$(Platform)'=='Release|x64'">TurnOffAllWarnings</WarningLevel>
     </ClCompile>
     <ClCompile Include="..\src\3rd_party\fbgemm\src\PackAMatrix.cc">
-      <TreatWarningAsError Condition="'$(Configuration)|$(Platform)'=='Release|x64'">false</TreatWarningAsError>
-      <TreatWarningAsError Condition="'$(Configuration)|$(Platform)'=='Debug|x64'">false</TreatWarningAsError>
+      <TreatWarningAsError Condition="'$(Configuration)|$(Platform)'=='Release|x64'">true</TreatWarningAsError>
+      <TreatWarningAsError Condition="'$(Configuration)|$(Platform)'=='Debug|x64'">true</TreatWarningAsError>
+      <WarningLevel Condition="'$(Configuration)|$(Platform)'=='Debug|x64'">TurnOffAllWarnings</WarningLevel>
+      <WarningLevel Condition="'$(Configuration)|$(Platform)'=='Release|x64'">TurnOffAllWarnings</WarningLevel>
     </ClCompile>
     <ClCompile Include="..\src\3rd_party\fbgemm\src\PackAWithIm2Col.cc">
-      <TreatWarningAsError Condition="'$(Configuration)|$(Platform)'=='Release|x64'">false</TreatWarningAsError>
-      <TreatWarningAsError Condition="'$(Configuration)|$(Platform)'=='Debug|x64'">false</TreatWarningAsError>
+      <TreatWarningAsError Condition="'$(Configuration)|$(Platform)'=='Release|x64'">true</TreatWarningAsError>
+      <TreatWarningAsError Condition="'$(Configuration)|$(Platform)'=='Debug|x64'">true</TreatWarningAsError>
+      <WarningLevel Condition="'$(Configuration)|$(Platform)'=='Debug|x64'">TurnOffAllWarnings</WarningLevel>
+      <WarningLevel Condition="'$(Configuration)|$(Platform)'=='Release|x64'">TurnOffAllWarnings</WarningLevel>
     </ClCompile>
     <ClCompile Include="..\src\3rd_party\fbgemm\src\PackAWithQuantRowOffset.cc">
-      <TreatWarningAsError Condition="'$(Configuration)|$(Platform)'=='Release|x64'">false</TreatWarningAsError>
-      <TreatWarningAsError Condition="'$(Configuration)|$(Platform)'=='Debug|x64'">false</TreatWarningAsError>
+      <TreatWarningAsError Condition="'$(Configuration)|$(Platform)'=='Release|x64'">true</TreatWarningAsError>
+      <TreatWarningAsError Condition="'$(Configuration)|$(Platform)'=='Debug|x64'">true</TreatWarningAsError>
+      <WarningLevel Condition="'$(Configuration)|$(Platform)'=='Debug|x64'">TurnOffAllWarnings</WarningLevel>
+      <WarningLevel Condition="'$(Configuration)|$(Platform)'=='Release|x64'">TurnOffAllWarnings</WarningLevel>
     </ClCompile>
     <ClCompile Include="..\src\3rd_party\fbgemm\src\PackAWithRowOffset.cc">
-      <TreatWarningAsError Condition="'$(Configuration)|$(Platform)'=='Release|x64'">false</TreatWarningAsError>
-      <TreatWarningAsError Condition="'$(Configuration)|$(Platform)'=='Debug|x64'">false</TreatWarningAsError>
+      <TreatWarningAsError Condition="'$(Configuration)|$(Platform)'=='Release|x64'">true</TreatWarningAsError>
+      <TreatWarningAsError Condition="'$(Configuration)|$(Platform)'=='Debug|x64'">true</TreatWarningAsError>
+      <WarningLevel Condition="'$(Configuration)|$(Platform)'=='Debug|x64'">TurnOffAllWarnings</WarningLevel>
+      <WarningLevel Condition="'$(Configuration)|$(Platform)'=='Release|x64'">TurnOffAllWarnings</WarningLevel>
     </ClCompile>
     <ClCompile Include="..\src\3rd_party\fbgemm\src\PackBMatrix.cc">
-      <TreatWarningAsError Condition="'$(Configuration)|$(Platform)'=='Release|x64'">false</TreatWarningAsError>
-      <TreatWarningAsError Condition="'$(Configuration)|$(Platform)'=='Debug|x64'">false</TreatWarningAsError>
+      <TreatWarningAsError Condition="'$(Configuration)|$(Platform)'=='Release|x64'">true</TreatWarningAsError>
+      <TreatWarningAsError Condition="'$(Configuration)|$(Platform)'=='Debug|x64'">true</TreatWarningAsError>
+      <WarningLevel Condition="'$(Configuration)|$(Platform)'=='Debug|x64'">TurnOffAllWarnings</WarningLevel>
+      <WarningLevel Condition="'$(Configuration)|$(Platform)'=='Release|x64'">TurnOffAllWarnings</WarningLevel>
     </ClCompile>
     <ClCompile Include="..\src\3rd_party\fbgemm\src\PackMatrix.cc">
-      <TreatWarningAsError Condition="'$(Configuration)|$(Platform)'=='Release|x64'">false</TreatWarningAsError>
-      <TreatWarningAsError Condition="'$(Configuration)|$(Platform)'=='Debug|x64'">false</TreatWarningAsError>
+      <TreatWarningAsError Condition="'$(Configuration)|$(Platform)'=='Release|x64'">true</TreatWarningAsError>
+      <TreatWarningAsError Condition="'$(Configuration)|$(Platform)'=='Debug|x64'">true</TreatWarningAsError>
+      <WarningLevel Condition="'$(Configuration)|$(Platform)'=='Debug|x64'">TurnOffAllWarnings</WarningLevel>
+      <WarningLevel Condition="'$(Configuration)|$(Platform)'=='Release|x64'">TurnOffAllWarnings</WarningLevel>
     </ClCompile>
     <ClCompile Include="..\src\3rd_party\fbgemm\src\PackWeightMatrixForGConv.cc">
-      <TreatWarningAsError Condition="'$(Configuration)|$(Platform)'=='Release|x64'">false</TreatWarningAsError>
-      <TreatWarningAsError Condition="'$(Configuration)|$(Platform)'=='Debug|x64'">false</TreatWarningAsError>
+      <TreatWarningAsError Condition="'$(Configuration)|$(Platform)'=='Release|x64'">true</TreatWarningAsError>
+      <TreatWarningAsError Condition="'$(Configuration)|$(Platform)'=='Debug|x64'">true</TreatWarningAsError>
+      <WarningLevel Condition="'$(Configuration)|$(Platform)'=='Debug|x64'">TurnOffAllWarnings</WarningLevel>
+      <WarningLevel Condition="'$(Configuration)|$(Platform)'=='Release|x64'">TurnOffAllWarnings</WarningLevel>
     </ClCompile>
     <ClCompile Include="..\src\3rd_party\fbgemm\src\PackWeightsForConv.cc">
-      <TreatWarningAsError Condition="'$(Configuration)|$(Platform)'=='Release|x64'">false</TreatWarningAsError>
-      <TreatWarningAsError Condition="'$(Configuration)|$(Platform)'=='Debug|x64'">false</TreatWarningAsError>
+      <TreatWarningAsError Condition="'$(Configuration)|$(Platform)'=='Release|x64'">true</TreatWarningAsError>
+      <TreatWarningAsError Condition="'$(Configuration)|$(Platform)'=='Debug|x64'">true</TreatWarningAsError>
+      <WarningLevel Condition="'$(Configuration)|$(Platform)'=='Debug|x64'">TurnOffAllWarnings</WarningLevel>
+      <WarningLevel Condition="'$(Configuration)|$(Platform)'=='Release|x64'">TurnOffAllWarnings</WarningLevel>
     </ClCompile>
     <ClCompile Include="..\src\3rd_party\fbgemm\src\QuantUtils.cc">
-      <TreatWarningAsError Condition="'$(Configuration)|$(Platform)'=='Release|x64'">false</TreatWarningAsError>
-      <TreatWarningAsError Condition="'$(Configuration)|$(Platform)'=='Debug|x64'">false</TreatWarningAsError>
+      <TreatWarningAsError Condition="'$(Configuration)|$(Platform)'=='Release|x64'">true</TreatWarningAsError>
+      <TreatWarningAsError Condition="'$(Configuration)|$(Platform)'=='Debug|x64'">true</TreatWarningAsError>
+      <WarningLevel Condition="'$(Configuration)|$(Platform)'=='Debug|x64'">TurnOffAllWarnings</WarningLevel>
+      <WarningLevel Condition="'$(Configuration)|$(Platform)'=='Release|x64'">TurnOffAllWarnings</WarningLevel>
     </ClCompile>
     <ClCompile Include="..\src\3rd_party\fbgemm\src\QuantUtilsAvx2.cc">
-      <TreatWarningAsError Condition="'$(Configuration)|$(Platform)'=='Release|x64'">false</TreatWarningAsError>
-      <TreatWarningAsError Condition="'$(Configuration)|$(Platform)'=='Debug|x64'">false</TreatWarningAsError>
+      <TreatWarningAsError Condition="'$(Configuration)|$(Platform)'=='Release|x64'">true</TreatWarningAsError>
+      <TreatWarningAsError Condition="'$(Configuration)|$(Platform)'=='Debug|x64'">true</TreatWarningAsError>
+      <WarningLevel Condition="'$(Configuration)|$(Platform)'=='Debug|x64'">TurnOffAllWarnings</WarningLevel>
+      <WarningLevel Condition="'$(Configuration)|$(Platform)'=='Release|x64'">TurnOffAllWarnings</WarningLevel>
     </ClCompile>
     <ClCompile Include="..\src\3rd_party\fbgemm\src\RefImplementations.cc">
-      <TreatWarningAsError Condition="'$(Configuration)|$(Platform)'=='Release|x64'">false</TreatWarningAsError>
-      <TreatWarningAsError Condition="'$(Configuration)|$(Platform)'=='Debug|x64'">false</TreatWarningAsError>
+      <TreatWarningAsError Condition="'$(Configuration)|$(Platform)'=='Release|x64'">true</TreatWarningAsError>
+      <TreatWarningAsError Condition="'$(Configuration)|$(Platform)'=='Debug|x64'">true</TreatWarningAsError>
+      <WarningLevel Condition="'$(Configuration)|$(Platform)'=='Debug|x64'">TurnOffAllWarnings</WarningLevel>
+      <WarningLevel Condition="'$(Configuration)|$(Platform)'=='Release|x64'">TurnOffAllWarnings</WarningLevel>
     </ClCompile>
     <ClCompile Include="..\src\3rd_party\fbgemm\src\Utils.cc">
-      <TreatWarningAsError Condition="'$(Configuration)|$(Platform)'=='Release|x64'">false</TreatWarningAsError>
-      <TreatWarningAsError Condition="'$(Configuration)|$(Platform)'=='Debug|x64'">false</TreatWarningAsError>
+      <TreatWarningAsError Condition="'$(Configuration)|$(Platform)'=='Release|x64'">true</TreatWarningAsError>
+      <TreatWarningAsError Condition="'$(Configuration)|$(Platform)'=='Debug|x64'">true</TreatWarningAsError>
+      <WarningLevel Condition="'$(Configuration)|$(Platform)'=='Debug|x64'">TurnOffAllWarnings</WarningLevel>
+      <WarningLevel Condition="'$(Configuration)|$(Platform)'=='Release|x64'">TurnOffAllWarnings</WarningLevel>
     </ClCompile>
     <ClCompile Include="..\src\3rd_party\fbgemm\src\UtilsAvx2.cc">
-      <TreatWarningAsError Condition="'$(Configuration)|$(Platform)'=='Release|x64'">false</TreatWarningAsError>
-      <TreatWarningAsError Condition="'$(Configuration)|$(Platform)'=='Debug|x64'">false</TreatWarningAsError>
+      <TreatWarningAsError Condition="'$(Configuration)|$(Platform)'=='Release|x64'">true</TreatWarningAsError>
+      <TreatWarningAsError Condition="'$(Configuration)|$(Platform)'=='Debug|x64'">true</TreatWarningAsError>
+      <WarningLevel Condition="'$(Configuration)|$(Platform)'=='Debug|x64'">TurnOffAllWarnings</WarningLevel>
+      <WarningLevel Condition="'$(Configuration)|$(Platform)'=='Release|x64'">TurnOffAllWarnings</WarningLevel>
     </ClCompile>
     <ClCompile Include="..\src\3rd_party\fbgemm\src\UtilsAvx512.cc">
-      <TreatWarningAsError Condition="'$(Configuration)|$(Platform)'=='Release|x64'">false</TreatWarningAsError>
-      <TreatWarningAsError Condition="'$(Configuration)|$(Platform)'=='Debug|x64'">false</TreatWarningAsError>
+      <TreatWarningAsError Condition="'$(Configuration)|$(Platform)'=='Release|x64'">true</TreatWarningAsError>
+      <TreatWarningAsError Condition="'$(Configuration)|$(Platform)'=='Debug|x64'">true</TreatWarningAsError>
+      <WarningLevel Condition="'$(Configuration)|$(Platform)'=='Debug|x64'">TurnOffAllWarnings</WarningLevel>
+      <WarningLevel Condition="'$(Configuration)|$(Platform)'=='Release|x64'">TurnOffAllWarnings</WarningLevel>
     </ClCompile>
     <ClCompile Include="..\src\3rd_party\fbgemm\third_party\asmjit\src\asmjit\base\arch.cpp">
       <TreatWarningAsError Condition="'$(Configuration)|$(Platform)'=='Release|x64'">false</TreatWarningAsError>
@@ -426,7 +484,10 @@
       <TreatWarningAsError Condition="'$(Configuration)|$(Platform)'=='Release|x64'">false</TreatWarningAsError>
       <TreatWarningAsError Condition="'$(Configuration)|$(Platform)'=='Debug|x64'">false</TreatWarningAsError>
     </ClCompile>
-    <ClCompile Include="..\src\3rd_party\half_float\HalfPrecisionFloatTest.cpp" />
+    <ClCompile Include="..\src\3rd_party\half_float\HalfPrecisionFloatTest.cpp">
+      <ExcludedFromBuild Condition="'$(Configuration)|$(Platform)'=='Debug|x64'">true</ExcludedFromBuild>
+      <ExcludedFromBuild Condition="'$(Configuration)|$(Platform)'=='Release|x64'">true</ExcludedFromBuild>
+    </ClCompile>
     <ClCompile Include="..\src\3rd_party\pathie-cpp\src\entry_iterator.cpp" />
     <ClCompile Include="..\src\3rd_party\pathie-cpp\src\errors.cpp" />
     <ClCompile Include="..\src\3rd_party\pathie-cpp\src\path.cpp" />
@@ -941,6 +1002,7 @@
     <ClCompile Include="..\src\common\cli_helper.cpp" />
     <ClCompile Include="..\src\common\cli_wrapper.cpp" />
     <ClCompile Include="..\src\common\config_validator.cpp" />
+    <ClCompile Include="..\src\common\filesystem.cpp" />
     <ClCompile Include="..\src\common\io.cpp" />
     <ClCompile Include="..\src\common\utils.cpp" />
     <ClCompile Include="..\src\common\logging.cpp" />
@@ -1000,6 +1062,7 @@
     <ClCompile Include="..\src\models\model_factory.cpp" />
     <ClCompile Include="..\src\models\encoder_decoder.cpp" />
     <ClCompile Include="..\src\tensors\rand.cpp" />
+    <ClCompile Include="..\src\tensors\tensor.cpp" />
     <ClCompile Include="..\src\tests\attention_tests.cpp">
       <ExcludedFromBuild Condition="'$(Configuration)|$(Platform)'=='Debug|x64'">true</ExcludedFromBuild>
       <ExcludedFromBuild Condition="'$(Configuration)|$(Platform)'=='Release|x64'">true</ExcludedFromBuild>
diff --git a/vs/Marian.vcxproj.filters b/vs/Marian.vcxproj.filters
index 5ef68d74d..39bd200c0 100755
--- a/vs/Marian.vcxproj.filters
+++ b/vs/Marian.vcxproj.filters
@@ -715,6 +715,12 @@
     <ClCompile Include="..\src\3rd_party\half_float\HalfPrecisionFloatTest.cpp">
       <Filter>3rd_party\half_float</Filter>
     </ClCompile>
+    <ClCompile Include="..\src\tensors\tensor.cpp">
+      <Filter>tensors</Filter>
+    </ClCompile>
+    <ClCompile Include="..\src\common\filesystem.cpp">
+      <Filter>common</Filter>
+    </ClCompile>
   </ItemGroup>
   <ItemGroup>
     <ClInclude Include="..\src\marian.h" />

From c5eeca2490e1a10beb47d2f89f5f2347543e31bb Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Thu, 12 Sep 2019 10:09:59 -0700
Subject: [PATCH 622/838] removed custom stdint.h, as it is not necessary

---
 src/3rd_party/half_float/umHalf.h | 4 ----
 vs/Marian.vcxproj                 | 1 -
 vs/Marian.vcxproj.filters         | 3 ---
 3 files changed, 8 deletions(-)

diff --git a/src/3rd_party/half_float/umHalf.h b/src/3rd_party/half_float/umHalf.h
index f6b65ef90..c7ea5dcce 100755
--- a/src/3rd_party/half_float/umHalf.h
+++ b/src/3rd_party/half_float/umHalf.h
@@ -44,11 +44,7 @@ OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
 #include <limits>
 #include <algorithm>
 
-//#ifdef _MSC_VER
-//#include "stdint.h"
-//#else
 #include <stdint.h>
-//#endif
 
 #undef min
 #undef max
diff --git a/vs/Marian.vcxproj b/vs/Marian.vcxproj
index d086e7c1e..8c9e81ade 100755
--- a/vs/Marian.vcxproj
+++ b/vs/Marian.vcxproj
@@ -773,7 +773,6 @@
     <ClInclude Include="..\src\3rd_party\fbgemm\third_party\cpuinfo\src\x86\api.h" />
     <ClInclude Include="..\src\3rd_party\fbgemm\third_party\cpuinfo\src\x86\cpuid.h" />
     <ClInclude Include="..\src\3rd_party\fbgemm\third_party\cpuinfo\src\x86\windows\api.h" />
-    <ClInclude Include="..\src\3rd_party\half_float\stdint.h" />
     <ClInclude Include="..\src\3rd_party\half_float\umHalf.h" />
     <ClInclude Include="..\src\3rd_party\nccl\src\collectives\collectives.h">
       <ExcludedFromBuild Condition="'$(Configuration)|$(Platform)'=='Release|x64'">true</ExcludedFromBuild>
diff --git a/vs/Marian.vcxproj.filters b/vs/Marian.vcxproj.filters
index 39bd200c0..8fe63a0bf 100755
--- a/vs/Marian.vcxproj.filters
+++ b/vs/Marian.vcxproj.filters
@@ -2011,9 +2011,6 @@
     <ClInclude Include="..\src\3rd_party\fbgemm\third_party\cpuinfo\deps\clog\include\clog.h">
       <Filter>3rd_party\fbgemm\third_party\cpuinfo\deps\clog\include</Filter>
     </ClInclude>
-    <ClInclude Include="..\src\3rd_party\half_float\stdint.h">
-      <Filter>3rd_party\half_float</Filter>
-    </ClInclude>
     <ClInclude Include="..\src\3rd_party\half_float\umHalf.h">
       <Filter>3rd_party\half_float</Filter>
     </ClInclude>

From 14d4506ba0e41b0afc290c185f916a3030f2e5a5 Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Thu, 12 Sep 2019 10:28:01 -0700
Subject: [PATCH 623/838] fixed two submodule refs

---
 regression-tests   | 2 +-
 src/3rd_party/nccl | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/regression-tests b/regression-tests
index 658cf86e2..71b0506a5 160000
--- a/regression-tests
+++ b/regression-tests
@@ -1 +1 @@
-Subproject commit 658cf86e2ab4aa76b4ab3edabd750b93d2416f1a
+Subproject commit 71b0506a50e1f181e269fe90073a8ae470676dd1
diff --git a/src/3rd_party/nccl b/src/3rd_party/nccl
index d6297d250..8e3a3f7c5 160000
--- a/src/3rd_party/nccl
+++ b/src/3rd_party/nccl
@@ -1 +1 @@
-Subproject commit d6297d250433715c283d17f1969cfcb50d2b6531
+Subproject commit 8e3a3f7c5b520babff49cec54a866fa3eda3a3b6

From 724443b203b3dec8b066a7e788d3935900d20679 Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Thu, 12 Sep 2019 10:45:52 -0700
Subject: [PATCH 624/838] removed compute_75 flags, as we are not doing
 anything special for Turing presently

---
 CMakeLists.txt | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/CMakeLists.txt b/CMakeLists.txt
index 0bea2e5e5..e2ec423a0 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -232,9 +232,9 @@ endif(COMPILE_CUDA)
 
 # TODO: make compatible with older CUDA versions
 if(CMAKE_BUILD_TYPE STREQUAL "Debug")
-  list(APPEND CUDA_NVCC_FLAGS --default-stream per-thread; -O0; -g; --use_fast_math; -arch=sm_30; -gencode=arch=compute_30,code=sm_30; -gencode=arch=compute_50,code=sm_50; -gencode=arch=compute_52,code=sm_52; -gencode=arch=compute_60,code=sm_60; -gencode=arch=compute_61,code=sm_61; -gencode=arch=compute_70,code=sm_70; -gencode=arch=compute_75,code=sm_75; -gencode=arch=compute_75,code=compute_75 ;)
+  list(APPEND CUDA_NVCC_FLAGS --default-stream per-thread; -O0; -g; --use_fast_math; -arch=sm_30; -gencode=arch=compute_30,code=sm_30; -gencode=arch=compute_50,code=sm_50; -gencode=arch=compute_52,code=sm_52; -gencode=arch=compute_60,code=sm_60; -gencode=arch=compute_61,code=sm_61; -gencode=arch=compute_70,code=sm_70;)
 else(CMAKE_BUILD_TYPE STREQUAL "Debug")
-  list(APPEND CUDA_NVCC_FLAGS --default-stream per-thread; -O3; -g; --use_fast_math; -arch=sm_30; -gencode=arch=compute_30,code=sm_30; -gencode=arch=compute_50,code=sm_50; -gencode=arch=compute_52,code=sm_52; -gencode=arch=compute_60,code=sm_60; -gencode=arch=compute_61,code=sm_61; -gencode=arch=compute_70,code=sm_70; -gencode=arch=compute_75,code=sm_75; -gencode=arch=compute_75,code=compute_75 ;)
+  list(APPEND CUDA_NVCC_FLAGS --default-stream per-thread; -O3; -g; --use_fast_math; -arch=sm_30; -gencode=arch=compute_30,code=sm_30; -gencode=arch=compute_50,code=sm_50; -gencode=arch=compute_52,code=sm_52; -gencode=arch=compute_60,code=sm_60; -gencode=arch=compute_61,code=sm_61; -gencode=arch=compute_70,code=sm_70;)
 endif(CMAKE_BUILD_TYPE STREQUAL "Debug")
 if(NOT MSVC)
   # @TODO: add warnings here too

From 2e4f04e308b533e05766d592676a38a58debf012 Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Fri, 13 Sep 2019 17:26:35 -0700
Subject: [PATCH 625/838] downgraded to AVX build, since AVX2 builds do not run
 on older machines even if no AVX2 math is used

---
 src/3rd_party/fbgemm | 2 +-
 vs/Marian.vcxproj    | 8 ++++----
 2 files changed, 5 insertions(+), 5 deletions(-)

diff --git a/src/3rd_party/fbgemm b/src/3rd_party/fbgemm
index 49e8018ab..57dcf5507 160000
--- a/src/3rd_party/fbgemm
+++ b/src/3rd_party/fbgemm
@@ -1 +1 @@
-Subproject commit 49e8018ab2397c175354317b35c6be6dd68f8932
+Subproject commit 57dcf55075e7173fd21eb81d825133aa740c7255
diff --git a/vs/Marian.vcxproj b/vs/Marian.vcxproj
index 8c9e81ade..bdfce4683 100755
--- a/vs/Marian.vcxproj
+++ b/vs/Marian.vcxproj
@@ -69,10 +69,10 @@
       </PrecompiledHeader>
       <WarningLevel>Level4</WarningLevel>
       <Optimization>Disabled</Optimization>
-      <PreprocessorDefinitions>FBGEMM_EXPORTS; USE_FBGEMM=1; USE_SSE2=1; CUDA_FOUND=1; MKL_FOUND=1; MPI_FOUND=1; BLAS_FOUND=1; MKL_ILP64; WIN32;_DEBUG;_CONSOLE;_LIB;%(PreprocessorDefinitions)</PreprocessorDefinitions>
+      <PreprocessorDefinitions>BOOST_CONFIG_SUPPRESS_OUTDATED_MESSAGE; FBGEMM_EXPORTS; USE_FBGEMM=1; USE_SSE2=1; CUDA_FOUND=1; MKL_FOUND=1; MPI_FOUND=1; BLAS_FOUND=1; MKL_ILP64; WIN32;_DEBUG;_CONSOLE;_LIB;%(PreprocessorDefinitions)</PreprocessorDefinitions>
       <SDLCheck>false</SDLCheck>
       <TreatWarningAsError>true</TreatWarningAsError>
-      <AdditionalOptions>/bigobj %(AdditionalOptions) /arch:AVX2</AdditionalOptions>
+      <AdditionalOptions>/bigobj %(AdditionalOptions) /arch:AVX</AdditionalOptions>
       <RuntimeLibrary Condition="'$(Configuration)|$(Platform)'=='Debug|x64'">MultiThreadedDebugDLL</RuntimeLibrary>
       <DisableSpecificWarnings>4996; 4702</DisableSpecificWarnings>
       <MultiProcessorCompilation>true</MultiProcessorCompilation>
@@ -106,10 +106,10 @@
       <Optimization>MaxSpeed</Optimization>
       <FunctionLevelLinking>true</FunctionLevelLinking>
       <IntrinsicFunctions>true</IntrinsicFunctions>
-      <PreprocessorDefinitions>FBGEMM_EXPORTS; USE_FBGEMM=1; USE_SSE2=1; CUDA_FOUND=1; MKL_FOUND=1; MPI_FOUND=1; BLAS_FOUND=1; MKL_ILP64; WIN32;NDEBUG;_CONSOLE;_LIB;%(PreprocessorDefinitions)</PreprocessorDefinitions>
+      <PreprocessorDefinitions>BOOST_CONFIG_SUPPRESS_OUTDATED_MESSAGE; FBGEMM_EXPORTS; USE_FBGEMM=1; USE_SSE2=1; CUDA_FOUND=1; MKL_FOUND=1; MPI_FOUND=1; BLAS_FOUND=1; MKL_ILP64; WIN32;NDEBUG;_CONSOLE;_LIB;%(PreprocessorDefinitions)</PreprocessorDefinitions>
       <SDLCheck>false</SDLCheck>
       <FavorSizeOrSpeed>Speed</FavorSizeOrSpeed>
-      <AdditionalOptions>/d2Zi+ /bigobj %(AdditionalOptions) /arch:AVX2</AdditionalOptions>
+      <AdditionalOptions>/d2Zi+ /bigobj %(AdditionalOptions) /arch:AVX</AdditionalOptions>
       <TreatWarningAsError>true</TreatWarningAsError>
       <RuntimeLibrary Condition="'$(Configuration)|$(Platform)'=='Release|x64'">MultiThreadedDLL</RuntimeLibrary>
       <RuntimeLibrary Condition="'$(Configuration)|$(Platform)'=='Release_NoOpt|x64'">MultiThreaded</RuntimeLibrary>

From b9f118c47e5645c162076843fbc9d08748b0f336 Mon Sep 17 00:00:00 2001
From: Marcin Junczys-Dowmunt <Marcin.JunczysDowmunt@microsoft.com>
Date: Sun, 15 Sep 2019 14:10:45 -0700
Subject: [PATCH 626/838] adapt validators and options

---
 src/common/aliases.cpp        |  20 ++++-
 src/common/config_parser.cpp  |  28 +++++-
 src/examples/mnist/training.h |   2 +-
 src/rescorer/rescorer.h       |   2 +-
 src/training/training.h       |   6 +-
 src/training/validator.h      | 157 +++++++++++++++++-----------------
 6 files changed, 125 insertions(+), 90 deletions(-)

diff --git a/src/common/aliases.cpp b/src/common/aliases.cpp
index 50a6bc20a..1ca997270 100644
--- a/src/common/aliases.cpp
+++ b/src/common/aliases.cpp
@@ -26,6 +26,18 @@ namespace marian {
  * regardless of its order in the command line or config file.
  */
 void ConfigParser::addAliases(cli::CLIWrapper& cli) {
+  // for backwards-compatibility with older version, "--no-shuffle" maps to "--shuffle none"
+  cli.alias("no-shuffle", "true", [](YAML::Node& config) {
+    config["shuffle"] = "none";
+  });
+
+#if 0 // @TODO: not available yet
+  cli.alias("fp16", "true", [](YAML::Node& config) {
+    config["precision"] = std::vector<std::string>({"float16", "float32", "float32"});
+    config["cost-scaling"] = std::vector<std::string>({"7", "2000", "2", "0.05", "10", "1"});
+  });
+#endif
+
   // Options setting the BiDeep architecture proposed in http://www.aclweb.org/anthology/W17-4710
   cli.alias("best-deep", "true", [](YAML::Node& config) {
     config["layer-normalization"] = true;
@@ -63,8 +75,8 @@ void ConfigParser::addAliases(cli::CLIWrapper& cli) {
     config["tied-embeddings-all"] = true;
     config["transformer-dim-ffn"] = 2048;
     config["transformer-heads"] = 8;
-    config["transformer-postprocess"] = "an";
-    config["transformer-preprocess"] = "d";
+    config["transformer-postprocess"] = "dan";
+    config["transformer-preprocess"] = "";
     config["transformer-ffn-activation"] = "relu";
     config["transformer-dropout"] = 0.1;
 
@@ -101,8 +113,8 @@ void ConfigParser::addAliases(cli::CLIWrapper& cli) {
     config["tied-embeddings-all"] = true;
     config["transformer-dim-ffn"] = 4096;
     config["transformer-heads"] = 16;
-    config["transformer-postprocess"] = "an";
-    config["transformer-preprocess"] = "d";
+    config["transformer-postprocess"] = "dan";
+    config["transformer-preprocess"] = "";
     config["transformer-ffn-activation"] = "relu";
     config["transformer-dropout"] = 0.1;
 
diff --git a/src/common/config_parser.cpp b/src/common/config_parser.cpp
index e1518e814..7159092a5 100755
--- a/src/common/config_parser.cpp
+++ b/src/common/config_parser.cpp
@@ -213,6 +213,8 @@ void ConfigParser::addOptionsModel(cli::CLIWrapper& cli) {
       "dan");
   cli.add<bool>("--transformer-train-position-embeddings",
       "Train positional embeddings instead of using static sinusoidal embeddings");
+  cli.add<bool>("--transformer-depth-scaling",
+      "Scale down weight initialization in transformer layers by 1 / sqrt(depth)");
 
   cli.add<std::string>("--bert-mask-symbol", "Masking symbol for BERT masked-LM training", "[MASK]");
   cli.add<std::string>("--bert-sep-symbol", "Sentence separator symbol for BERT next sentence prediction training", "[SEP]");
@@ -310,8 +312,12 @@ void ConfigParser::addOptionsTraining(cli::CLIWrapper& cli) {
   addSuboptionsInputLength(cli);
 
   // data management options
+  cli.add<std::string>("--shuffle",
+      "How to shuffle input data (data: shuffles data and sorted batches; batches: "
+      "data is read in order into batches, but batches are shuffled; none: no shuffling). "
+      "Use with '--maxi-batch-sort none' in order to achieve exact reading order", "data");
   cli.add<bool>("--no-shuffle",
-      "Skip shuffling of training data before each epoch");
+      "Shortcut for backwards compatiblity, equivalent to --shuffle none (deprecated)");
   cli.add<bool>("--no-restore-corpus",
       "Skip restoring corpus state after training is restarted");
   cli.add<std::string>("--tempdir,-T",
@@ -416,6 +422,22 @@ void ConfigParser::addOptionsTraining(cli::CLIWrapper& cli) {
   cli.add<bool>("--embedding-fix-trg",
      "Fix target embeddings. Affects all decoders");
 
+#if 0 // not doing anything yet, next PR
+  // mixed precision training
+  cli.add<bool>("--fp16", 
+      "Shortcut for mixed precision training with float16 and cost-scaling, "
+      "corresponds to: --precision float16 float32 float32 --cost-scaling 7 2000 2 0.05 10 1");
+  cli.add<std::vector<std::string>>("--precision",
+      "Mixed precision training for forward/backward pass and optimizaton. "
+      "Defines types for: forward/backward, optimization, saving.",
+      {"float32", "float32", "float32"});
+  cli.add<std::vector<std::string>>("--cost-scaling",
+      "Dynamic cost scaling for mixed precision training: "
+      "power of 2, scaling window, scaling factor, tolerance")->implicit_val("7.f 2000 2.f 0.05f 10 1.f");
+  cli.add<bool>("--normalize-gradient", "Normalize gradient by multiplying with worldsize / total labels");
+#endif
+
+  // multi-node training
   cli.add<bool>("--multi-node",
      "Enable asynchronous multi-node training through MPI (and legacy sync if combined with --sync-sgd)");
   cli.add<bool>("--multi-node-overlap",
@@ -635,6 +657,10 @@ void ConfigParser::addSuboptionsBatching(cli::CLIWrapper& cli) {
     cli.add<size_t>("--mini-batch-fit-step",
       "Step size for mini-batch-fit statistics",
       10);
+#if 0 // @TODO: not available yet
+    cli.add<bool>("--gradient-checkpointing", 
+      "Use gradient checkpointing to minimize memory usage");
+#endif
   }
 
   cli.add<int>("--maxi-batch",
diff --git a/src/examples/mnist/training.h b/src/examples/mnist/training.h
index 9e10bdd7e..0b25f7718 100644
--- a/src/examples/mnist/training.h
+++ b/src/examples/mnist/training.h
@@ -40,7 +40,7 @@ class TrainMNIST : public ModelTask {
 
     // Run training
     while(scheduler->keepGoing()) {
-      batchGenerator->prepare(!options_->get<bool>("no-shuffle"));
+      batchGenerator->prepare();
       for(auto batch : *batchGenerator) {
         if(!scheduler->keepGoing())
            break;
diff --git a/src/rescorer/rescorer.h b/src/rescorer/rescorer.h
index f2b1a0fe3..7a22f97b1 100755
--- a/src/rescorer/rescorer.h
+++ b/src/rescorer/rescorer.h
@@ -95,7 +95,7 @@ class Rescore : public ModelTask {
     LOG(info, "Scoring");
 
     auto batchGenerator = New<BatchGenerator<CorpusBase>>(corpus_, options_);
-    batchGenerator->prepare(false);
+    batchGenerator->prepare();
 
     Ptr<ScoreCollector> output = options_->get<bool>("n-best")
                                      ? std::static_pointer_cast<ScoreCollector>(
diff --git a/src/training/training.h b/src/training/training.h
index 8863c165d..f7f2b404e 100644
--- a/src/training/training.h
+++ b/src/training/training.h
@@ -66,16 +66,14 @@ class Train : public ModelTask {
     model->setTypicalTrgBatchWords(batchGenerator->estimateTypicalTrgBatchWords()); // needed for dynamic MB scaling
     model->load();
 
-    // @TODO: shuffle_ as a private attribute in BG
-    auto shuffle = !options_->get<bool>("no-shuffle");
     bool restored = !options_->get<bool>("no-restore-corpus")
-                    && batchGenerator->restore(trainState, shuffle);
+                    && batchGenerator->restore(trainState);
 
     // -- main training loop
     scheduler->started();
     while(scheduler->keepGoing()) {
       if(!restored)
-        batchGenerator->prepare(shuffle);
+        batchGenerator->prepare();
       restored = false;
 
       // main training loop for one epoch
diff --git a/src/training/validator.h b/src/training/validator.h
index 330b889e4..f53ed903c 100755
--- a/src/training/validator.h
+++ b/src/training/validator.h
@@ -56,7 +56,7 @@ class ValidatorBase : public TrainingObserver {
   }
 };
 
-template <class DataSet, class BuilderType>
+template <class DataSet, class BuilderType> // @TODO: BuilderType doesn't really serve a purpose here? Review and remove.
 class Validator : public ValidatorBase {
 public:
   Validator(std::vector<Ptr<Vocab>> vocabs, Ptr<Options> options, bool lowerIsBetter = true)
@@ -66,8 +66,12 @@ class Validator : public ValidatorBase {
         options_(New<Options>(options->clone())) {
     // set options common for all validators
     options_->set("inference", true);
-    if(options_->has("valid-max-length"))
+    options_->set("shuffle", "none"); // don't shuffle validation sets
+
+    if(options_->has("valid-max-length")) {
       options_->set("max-length", options_->get<size_t>("valid-max-length"));
+      options_->set("max-length-crop", true); // @TODO: make this configureable
+    }
     if(options_->has("valid-mini-batch"))
       options_->set("mini-batch", options_->get<size_t>("valid-mini-batch"));
     options_->set("mini-batch-sort", "src");
@@ -75,32 +79,14 @@ class Validator : public ValidatorBase {
   }
 
 protected:
+  // Create the BatchGenerator. Note that ScriptValidator does not use batchGenerator_.
   void createBatchGenerator(bool isTranslating) {
-    // Create the BatchGenerator. Note that ScriptValidator does not use batchGenerator_.
-
-    // Update validation options
-    auto opts = New<Options>();
-    opts->merge(options_);
-    opts->set("inference", true);
-
-    if (isTranslating) { // TranslationValidator and BleuValidator
-      opts->set("max-length", 1000);
-      opts->set("mini-batch", options_->get<int>("valid-mini-batch"));
-      opts->set("maxi-batch", 10);
-    }
-    else { // CrossEntropyValidator
-      opts->set("max-length", options_->get<size_t>("valid-max-length"));
-      if(options_->has("valid-mini-batch"))
-        opts->set("mini-batch", options_->get<size_t>("valid-mini-batch"));
-      opts->set("mini-batch-sort", "src");
-    }
-
     // Create corpus
     auto validPaths = options_->get<std::vector<std::string>>("valid-sets");
     auto corpus = New<DataSet>(validPaths, vocabs_, options_);
 
     // Create batch generator
-    batchGenerator_ = New<data::BatchGenerator<DataSet>>(corpus, opts);
+    batchGenerator_ = New<data::BatchGenerator<DataSet>>(corpus, options_);
   }
 public:
 
@@ -108,7 +94,7 @@ class Validator : public ValidatorBase {
     for(auto graph : graphs)
       graph->setInference(true);
 
-    batchGenerator_->prepare(false);
+    batchGenerator_->prepare();
 
     // Validate on batches
     float val = validateBG(graphs);
@@ -123,7 +109,7 @@ class Validator : public ValidatorBase {
 protected:
   std::vector<Ptr<Vocab>> vocabs_;
   Ptr<Options> options_;
-  Ptr<BuilderType> builder_;
+  Ptr<BuilderType> builder_; // @TODO: remove, this is not guaranteed to be state-free, hence not thread-safe, but we are using validators with multi-threading.
   Ptr<data::BatchGenerator<DataSet>> batchGenerator_;
 
   virtual float validateBG(const std::vector<Ptr<ExpressionGraph>>&)
@@ -157,11 +143,9 @@ class CrossEntropyValidator : public Validator<data::Corpus, models::ICriterionF
       : Validator(vocabs, options) {
     createBatchGenerator(/*isTranslating=*/false);
 
-    // @TODO: check if this is required.
-    Ptr<Options> opts = New<Options>();
-    opts->merge(options);
-    opts->set("inference", true);
-    opts->set("cost-type", "ce-sum");
+    auto opts = options_->with("inference", true, // @TODO: check if required
+                               "cost-type", "ce-sum");
+    // @TODO: remove, only used for saving?
     builder_ = models::createCriterionFunctionFromOptions(opts, models::usage::scoring);
   }
 
@@ -170,7 +154,11 @@ class CrossEntropyValidator : public Validator<data::Corpus, models::ICriterionF
 protected:
   virtual float validateBG(const std::vector<Ptr<ExpressionGraph>>& graphs) override {
     auto ctype = options_->get<std::string>("cost-type");
-    options_->set("cost-type", "ce-sum"); // @TODO: check if still needed, most likely not.
+
+    // @TODO: use with(...) everywhere, this will help with creating immutable options. 
+    // Make options const everywhere and get rid of "set"?
+    auto opts = options_->with("inference", true, 
+                               "cost-type", "ce-sum");
 
     StaticLoss loss;
     size_t samples = 0;
@@ -182,13 +170,18 @@ class CrossEntropyValidator : public Validator<data::Corpus, models::ICriterionF
       TaskBarrier taskBarrier;
       for(auto batch : *batchGenerator_) {
         auto task = [=, &loss, &samples](size_t id) {
-          thread_local Ptr<ExpressionGraph> graph;
-          thread_local auto builder = models::createCriterionFunctionFromOptions(options_, models::usage::scoring);
-
-          if(!graph) {
-            graph = graphs[id % graphs.size()];
+          thread_local size_t workerNo = (size_t)-1; // set to something silly
+          {
+            std::unique_lock<std::mutex> lock(mutex_);
+            if(workerNo == (size_t)-1) // if silly, set to actual worker id
+              workerNo = id % graphs.size(); // @TODO: this is not really safe, only works because processing a
+                                             // batch takes a long time and id % graph.size() is unlikely to occur
+                                             // again before cycling through all threads.
           }
 
+          auto graph = graphs[workerNo];
+          auto builder = models::createCriterionFunctionFromOptions(options_, models::usage::scoring);
+
           builder->clear(graph);
           auto dynamicLoss = builder->build(graph, batch);
           graph->forward();
@@ -204,9 +197,6 @@ class CrossEntropyValidator : public Validator<data::Corpus, models::ICriterionF
       // ~TaskBarrier waits until all are done
     }
 
-    // get back to the original cost type
-    options_->set("cost-type", ctype); // @TODO: check if still needed, most likely not.
-
     if(ctype == "perplexity")
       return std::exp(loss.loss / loss.count);
     if(ctype == "ce-mean-words")
@@ -225,11 +215,8 @@ class AccuracyValidator : public Validator<data::Corpus, models::IModel> {
       : Validator(vocabs, options, /*lowerIsBetter=*/false) {
     createBatchGenerator(/*isTranslating=*/false);
 
-    // @TODO: check if this is required.
-    Ptr<Options> opts = New<Options>();
-    opts->merge(options);
-    opts->set("inference", true);
-    builder_ = models::createModelFromOptions(opts, models::usage::raw);
+    // @TODO: remove, only used for saving?
+    builder_ = models::createModelFromOptions(options_, models::usage::raw);
   }
 
   std::string type() override { return "accuracy"; }
@@ -247,13 +234,18 @@ class AccuracyValidator : public Validator<data::Corpus, models::IModel> {
       TaskBarrier taskBarrier;
       for(auto batch : *batchGenerator_) {
         auto task = [=, &correct, &totalLabels](size_t id) {
-          thread_local Ptr<ExpressionGraph> graph;
-          thread_local auto builder = models::createModelFromOptions(options_, models::usage::raw);
-
-          if(!graph) {
-            graph = graphs[id % graphs.size()];
+          thread_local size_t workerNo = (size_t)-1; // set to something silly
+          {
+            std::unique_lock<std::mutex> lock(mutex_);
+            if(workerNo == (size_t)-1) // if silly, set to actual worker id
+              workerNo = id % graphs.size(); // @TODO: this is not really safe, only works because processing a
+                                             // batch takes a long time and id % graph.size() is unlikely to occur
+                                             // again before cycling through all threads.
           }
 
+          auto graph = graphs[workerNo];
+          auto builder = models::createModelFromOptions(options_, models::usage::raw);
+
           // @TODO: requires argmax implementation and integer arithmetics
           // builder->clear(graph);
           // auto predicted = argmax(builder->build(graph, batch), /*axis*/-1);
@@ -317,12 +309,8 @@ class BertAccuracyValidator : public Validator<data::Corpus, models::IModel> {
       : Validator(vocabs, options, /*lowerIsBetter=*/false),
         evalMaskedLM_(evalMaskedLM) {
     createBatchGenerator(/*isTranslating=*/false);
-
-    // @TODO: check if this is required.
-    Ptr<Options> opts = New<Options>();
-    opts->merge(options);
-    opts->set("inference", true);
-    builder_ = models::createModelFromOptions(opts, models::usage::raw);
+    // @TODO: remove, only used for saving?
+    builder_ = models::createModelFromOptions(options_, models::usage::raw);
   }
 
   std::string type() override {
@@ -345,14 +333,19 @@ class BertAccuracyValidator : public Validator<data::Corpus, models::IModel> {
       TaskBarrier taskBarrier;
       for(auto batch : *batchGenerator_) {
         auto task = [=, &correct, &totalLabels](size_t id) {
-          thread_local Ptr<ExpressionGraph> graph;
-          thread_local auto builder = models::createModelFromOptions(options_, models::usage::raw);
-          thread_local std::unique_ptr<std::mt19937> engine;
-
-          if(!graph) {
-            graph = graphs[id % graphs.size()];
+          thread_local size_t workerNo = (size_t)-1; // set to something silly
+          {
+            std::unique_lock<std::mutex> lock(mutex_);
+            if(workerNo == (size_t)-1) // if silly, set to actual worker id
+              workerNo = id % graphs.size(); // @TODO: this is not really safe, only works because processing a
+                                             // batch takes a long time and id % graph.size() is unlikely to occur
+                                             // again before cycling through all threads.
           }
 
+          auto graph = graphs[workerNo];
+          auto builder = models::createModelFromOptions(options_, models::usage::raw);
+
+          thread_local std::unique_ptr<std::mt19937> engine;
           if(!engine)
             engine.reset(new std::mt19937((unsigned int)(Config::seed + id)));
 
@@ -422,6 +415,7 @@ class ScriptValidator : public Validator<data::Corpus, models::IModel> {
 public:
   ScriptValidator(std::vector<Ptr<Vocab>> vocabs, Ptr<Options> options)
       : Validator(vocabs, options, false) {
+    // @TODO: remove, only used for saving?
     builder_ = models::createModelFromOptions(options_, models::usage::raw);
 
     ABORT_IF(!options_->hasAndNotEmpty("valid-script-path"),
@@ -458,6 +452,7 @@ class TranslationValidator : public Validator<data::Corpus, models::IModel> {
   TranslationValidator(std::vector<Ptr<Vocab>> vocabs, Ptr<Options> options)
       : Validator(vocabs, options, false),
         quiet_(options_->get<bool>("quiet-translation")) {
+    // @TODO: remove, only used for saving?
     builder_ = models::createModelFromOptions(options_, models::usage::translation);
 
     if(!options_->hasAndNotEmpty("valid-script-path"))
@@ -470,16 +465,11 @@ class TranslationValidator : public Validator<data::Corpus, models::IModel> {
     using namespace data;
 
     // Generate batches
-    batchGenerator_->prepare(false);
+    batchGenerator_->prepare();
 
     // Create scorer
     auto model = options_->get<std::string>("model");
 
-    // Temporary options for translation
-    auto mopts = New<Options>();
-    mopts->merge(options_);
-    mopts->set("inference", true);
-
     std::vector<Ptr<Scorer>> scorers;
     for(auto graph : graphs) {
       auto builder = models::createModelFromOptions(options_, models::usage::translation);
@@ -524,14 +514,18 @@ class TranslationValidator : public Validator<data::Corpus, models::IModel> {
       TaskBarrier taskBarrier;
       for(auto batch : *batchGenerator_) {
         auto task = [=](size_t id) {
-          thread_local Ptr<ExpressionGraph> graph;
-          thread_local Ptr<Scorer> scorer;
-
-          if(!graph) {
-            graph = graphs[id % graphs.size()];
-            scorer = scorers[id % graphs.size()];
+          thread_local size_t workerNo = (size_t)-1; // set to something silly
+          {
+            std::unique_lock<std::mutex> lock(mutex_);
+            if(workerNo == (size_t)-1) // if silly, set to actual worker id
+              workerNo = id % graphs.size(); // @TODO: this is not really safe, only works because processing a
+                                             // batch takes a long time and id % graph.size() is unlikely to occur
+                                             // again before cycling through all threads.
           }
 
+          auto graph = graphs[workerNo];
+          auto scorer = scorers[workerNo];
+
           auto search = New<BeamSearch>(options_,
                                         std::vector<Ptr<Scorer>>{scorer},
                                         vocabs_.back());
@@ -594,6 +588,7 @@ class BleuValidator : public Validator<data::Corpus, models::IModel> {
       : Validator(vocabs, options, false),
         detok_(detok),
         quiet_(options_->get<bool>("quiet-translation")) {
+    // @TODO: remove, only used for saving?
     builder_ = models::createModelFromOptions(options_, models::usage::translation);
 
     // @TODO: replace bleu-detok by a separate parameter to enable (various forms of) detok
@@ -609,7 +604,7 @@ class BleuValidator : public Validator<data::Corpus, models::IModel> {
     using namespace data;
 
     // Generate batches
-    batchGenerator_->prepare(false);
+    batchGenerator_->prepare();
 
     // Create scorer
     auto model = options_->get<std::string>("model");
@@ -662,14 +657,18 @@ class BleuValidator : public Validator<data::Corpus, models::IModel> {
       TaskBarrier taskBarrier;
       for(auto batch : *batchGenerator_) {
         auto task = [=, &stats](size_t id) {
-          thread_local Ptr<ExpressionGraph> graph;
-          thread_local Ptr<Scorer> scorer;
-
-          if(!graph) {
-            graph = graphs[id % graphs.size()];
-            scorer = scorers[id % graphs.size()];
+          thread_local size_t workerNo = (size_t)-1; // set to something silly
+          {
+            std::unique_lock<std::mutex> lock(mutex_);
+            if(workerNo == (size_t)-1) // if silly, set to actual worker id
+              workerNo = id % graphs.size(); // @TODO: this is not really safe, only works because processing a
+                                             // batch takes a long time and id % graph.size() is unlikely to occur
+                                             // again before cycling through all threads.
           }
 
+          auto graph = graphs[workerNo];
+          auto scorer = scorers[workerNo];
+
           auto search = New<BeamSearch>(options_,
                                         std::vector<Ptr<Scorer>>{scorer},
                                         vocabs_.back());

From 62380b4907f0171ed1dbf70334637e932b31b06b Mon Sep 17 00:00:00 2001
From: Marcin Junczys-Dowmunt <Marcin.JunczysDowmunt@microsoft.com>
Date: Sun, 15 Sep 2019 14:19:28 -0700
Subject: [PATCH 627/838] remove debug code

---
 src/translator/translator.h | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/src/translator/translator.h b/src/translator/translator.h
index afb7eb2c4..c1416cf46 100755
--- a/src/translator/translator.h
+++ b/src/translator/translator.h
@@ -29,12 +29,12 @@ class Translate : public ModelTask {
   size_t numDevices_;
 
 public:
-  Translate(Ptr<Options> options) : options_(options) {
+  Translate(Ptr<Options> options) : options_(New<Options>(options->clone())) { // @TODO: clone should return Ptr<Options> same as "with"?
     // This is currently safe as the translator is either created stand-alone or
     // or config is created anew from Options in the validator
+
     options_->set("inference", true);
     options_->set("shuffle", "none");
-    ABORT("Fix this");
 
     corpus_ = New<data::Corpus>(options_, true);
 

From 62b69f702ac15bffce9a878255b1e78f8af8459e Mon Sep 17 00:00:00 2001
From: Marcin Junczys-Dowmunt <Marcin.JunczysDowmunt@microsoft.com>
Date: Sun, 15 Sep 2019 14:19:53 -0700
Subject: [PATCH 628/838] add custom merge driver for git

---
 contrib/merge-and-verify-driver.sh | 3 +++
 1 file changed, 3 insertions(+)
 create mode 100755 contrib/merge-and-verify-driver.sh

diff --git a/contrib/merge-and-verify-driver.sh b/contrib/merge-and-verify-driver.sh
new file mode 100755
index 000000000..1cdcd24b5
--- /dev/null
+++ b/contrib/merge-and-verify-driver.sh
@@ -0,0 +1,3 @@
+#!/bin/bash
+git merge-file "${1}" "${2}" "${3}"
+exit 1

From 4870264f6ff46a021624ded827fbc408ba6f84c0 Mon Sep 17 00:00:00 2001
From: Marcin Junczys-Dowmunt <Marcin.JunczysDowmunt@microsoft.com>
Date: Sun, 15 Sep 2019 15:22:36 -0700
Subject: [PATCH 629/838] clean-up thread-safety in validators

---
 src/data/dataset.h          |   2 +-
 src/rescorer/rescorer.h     |   1 +
 src/training/validator.h    | 454 +++++++++++++++++-------------------
 src/translator/translator.h |   6 +-
 4 files changed, 226 insertions(+), 237 deletions(-)

diff --git a/src/data/dataset.h b/src/data/dataset.h
index 830fcb3bc..881126a34 100644
--- a/src/data/dataset.h
+++ b/src/data/dataset.h
@@ -21,7 +21,7 @@ class DatasetBase {
 
 public:
   typedef Batch batch_type;
-  typedef Ptr<Batch> batch_ptr;
+  typedef Ptr<Batch> batch_ptr; // @TODO: rename to camel case
   typedef Iterator iterator;
   typedef SampleType Sample;
 
diff --git a/src/rescorer/rescorer.h b/src/rescorer/rescorer.h
index 7a22f97b1..82543f3f7 100755
--- a/src/rescorer/rescorer.h
+++ b/src/rescorer/rescorer.h
@@ -56,6 +56,7 @@ class Rescore : public ModelTask {
              "Normalization by length cannot be used with summary scores");
 
     options_->set("inference", true);
+    options_->set("shuffle", "none");
     options_->set("cost-type", "ce-rescore"); // indicates that to keep separate per-batch-item scoresForSummary
 
     if(options_->get<bool>("n-best"))
diff --git a/src/training/validator.h b/src/training/validator.h
index f53ed903c..c1f0d40fa 100755
--- a/src/training/validator.h
+++ b/src/training/validator.h
@@ -78,6 +78,8 @@ class Validator : public ValidatorBase {
     options_->set("maxi-batch", 10);
   }
 
+  typedef typename DataSet::batch_ptr BatchPtr;
+
 protected:
   // Create the BatchGenerator. Note that ScriptValidator does not use batchGenerator_.
   void createBatchGenerator(bool isTranslating) {
@@ -138,6 +140,8 @@ class Validator : public ValidatorBase {
 };
 
 class CrossEntropyValidator : public Validator<data::Corpus, models::ICriterionFunction> {
+  using Validator::BatchPtr;
+
 public:
   CrossEntropyValidator(std::vector<Ptr<Vocab>> vocabs, Ptr<Options> options)
       : Validator(vocabs, options) {
@@ -162,38 +166,34 @@ class CrossEntropyValidator : public Validator<data::Corpus, models::ICriterionF
 
     StaticLoss loss;
     size_t samples = 0;
-    size_t batchId = 0;
+    std::deque<Ptr<ExpressionGraph>> graphQueue(graphs.begin(), graphs.end());
+
+    auto task = [=, &loss, &samples, &graphQueue](BatchPtr batch) {
+      thread_local Ptr<ExpressionGraph> graph;
+      
+      if(!graph) {
+        std::unique_lock<std::mutex> lock(mutex_);
+        ABORT_IF(graphQueue.empty(), "Asking for graph, but none left on queue");
+        graph = graphQueue.front();
+        graphQueue.pop_front();
+      }
+    
+      auto builder = models::createCriterionFunctionFromOptions(options_, models::usage::scoring);
+
+      builder->clear(graph);
+      auto dynamicLoss = builder->build(graph, batch);
+      graph->forward();
+
+      std::unique_lock<std::mutex> lock(mutex_);
+      loss += *dynamicLoss;
+      samples += batch->size();
+    };
 
     {
       threadPool_.reserve(graphs.size());
-
       TaskBarrier taskBarrier;
-      for(auto batch : *batchGenerator_) {
-        auto task = [=, &loss, &samples](size_t id) {
-          thread_local size_t workerNo = (size_t)-1; // set to something silly
-          {
-            std::unique_lock<std::mutex> lock(mutex_);
-            if(workerNo == (size_t)-1) // if silly, set to actual worker id
-              workerNo = id % graphs.size(); // @TODO: this is not really safe, only works because processing a
-                                             // batch takes a long time and id % graph.size() is unlikely to occur
-                                             // again before cycling through all threads.
-          }
-
-          auto graph = graphs[workerNo];
-          auto builder = models::createCriterionFunctionFromOptions(options_, models::usage::scoring);
-
-          builder->clear(graph);
-          auto dynamicLoss = builder->build(graph, batch);
-          graph->forward();
-
-          std::unique_lock<std::mutex> lock(mutex_);
-          loss += *dynamicLoss;
-          samples += batch->size();
-        };
-
-        taskBarrier.push_back(threadPool_.enqueue(task, batchId));
-        batchId++;
-      }
+      for(auto batch : *batchGenerator_)
+        taskBarrier.push_back(threadPool_.enqueue(task, batch));
       // ~TaskBarrier waits until all are done
     }
 
@@ -226,73 +226,60 @@ class AccuracyValidator : public Validator<data::Corpus, models::IModel> {
 
     size_t correct     = 0;
     size_t totalLabels = 0;
-    size_t batchId     = 0;
-
-    {
-      threadPool_.reserve(graphs.size());
-
-      TaskBarrier taskBarrier;
-      for(auto batch : *batchGenerator_) {
-        auto task = [=, &correct, &totalLabels](size_t id) {
-          thread_local size_t workerNo = (size_t)-1; // set to something silly
-          {
-            std::unique_lock<std::mutex> lock(mutex_);
-            if(workerNo == (size_t)-1) // if silly, set to actual worker id
-              workerNo = id % graphs.size(); // @TODO: this is not really safe, only works because processing a
-                                             // batch takes a long time and id % graph.size() is unlikely to occur
-                                             // again before cycling through all threads.
-          }
-
-          auto graph = graphs[workerNo];
-          auto builder = models::createModelFromOptions(options_, models::usage::raw);
+    std::deque<Ptr<ExpressionGraph>> graphQueue(graphs.begin(), graphs.end());
+
+    auto task = [=, &correct, &totalLabels, &graphQueue](BatchPtr batch) {
+      thread_local Ptr<ExpressionGraph> graph;
+      
+      if(!graph) {
+        std::unique_lock<std::mutex> lock(mutex_);
+        ABORT_IF(graphQueue.empty(), "Asking for graph, but none left on queue");
+        graph = graphQueue.front();
+        graphQueue.pop_front();
+      }
+    
+      auto builder = models::createModelFromOptions(options_, models::usage::raw);
 
-          // @TODO: requires argmax implementation and integer arithmetics
-          // builder->clear(graph);
-          // auto predicted = argmax(builder->build(graph, batch), /*axis*/-1);
-          // auto labels    = graph->indices(batch->back()->data());
-          // auto correct   = sum(flatten(predicted) == labels);
-          // graph->forward();
+      builder->clear(graph);
+      Expr logits = builder->build(graph, batch).getLogits();
+      graph->forward();
 
-          // std::unique_lock<std::mutex> lock(mutex_);
-          // totalLabels += labels->shape().elements();
-          // correct     += correct->scalar<IndexType>();
+      std::vector<float> vLogits;
+      logits->val()->get(vLogits);
 
-          builder->clear(graph);
-          Expr logits = builder->build(graph, batch).getLogits();
-          graph->forward();
+      const auto& groundTruth = batch->back()->data();
 
-          std::vector<float> vLogits;
-          logits->val()->get(vLogits);
+      IndexType cols = logits->shape()[-1];
 
-          const auto& groundTruth = batch->back()->data();
+      size_t thisCorrect = 0;
+      size_t thisLabels  = groundTruth.size();
 
-          IndexType cols = logits->shape()[-1];
+      for(int i = 0; i < thisLabels; ++i) {
+        // CPU-side Argmax
+        Word bestWord = Word::NONE;
+        float bestValue = std::numeric_limits<float>::lowest();
+        for(IndexType j = 0; j < cols; ++j) {
+          float currValue = vLogits[i * cols + j];
+          if(currValue > bestValue) {
+            bestValue = currValue;
+            bestWord = Word::fromWordIndex(j);
+          }
+        }
+        thisCorrect += (size_t)(bestWord == groundTruth[i]);
+      }
 
-          size_t thisCorrect = 0;
-          size_t thisLabels  = groundTruth.size();
+      std::unique_lock<std::mutex> lock(mutex_);
+      totalLabels += thisLabels;
+      correct     += thisCorrect;
+    };
 
-          for(int i = 0; i < thisLabels; ++i) {
-            // CPU-side Argmax
-            Word bestWord = Word::NONE;
-            float bestValue = std::numeric_limits<float>::lowest();
-            for(IndexType j = 0; j < cols; ++j) {
-              float currValue = vLogits[i * cols + j];
-              if(currValue > bestValue) {
-                bestValue = currValue;
-                bestWord = Word::fromWordIndex(j);
-              }
-            }
-            thisCorrect += (size_t)(bestWord == groundTruth[i]);
-          }
+    {
+      threadPool_.reserve(graphs.size());
 
-          std::unique_lock<std::mutex> lock(mutex_);
-          totalLabels += thisLabels;
-          correct     += thisCorrect;
-        };
+      TaskBarrier taskBarrier;
+      for(auto batch : *batchGenerator_)
+        taskBarrier.push_back(threadPool_.enqueue(task, batch));
 
-        taskBarrier.push_back(threadPool_.enqueue(task, batchId));
-        batchId++;
-      }
       // ~TaskBarrier waits until all are done
     }
 
@@ -326,81 +313,80 @@ class BertAccuracyValidator : public Validator<data::Corpus, models::IModel> {
     size_t correct     = 0;
     size_t totalLabels = 0;
     size_t batchId     = 0;
+    std::deque<Ptr<ExpressionGraph>> graphQueue(graphs.begin(), graphs.end());
+
+    auto task = [=, &correct, &totalLabels, &graphQueue](BatchPtr batch, size_t batchId) {
+      thread_local Ptr<ExpressionGraph> graph;
+      
+      if(!graph) {
+        std::unique_lock<std::mutex> lock(mutex_);
+        ABORT_IF(graphQueue.empty(), "Asking for graph, but none left on queue");
+        graph = graphQueue.front();
+        graphQueue.pop_front();
+      }
+      
+      auto builder = models::createModelFromOptions(options_, models::usage::raw);
+
+      thread_local std::unique_ptr<std::mt19937> engine;
+      if(!engine)
+        engine.reset(new std::mt19937((unsigned int)(Config::seed + batchId)));
+
+      auto bertBatch = New<data::BertBatch>(batch,
+                                            *engine,
+                                            options_->get<float>("bert-masking-fraction"),
+                                            options_->get<std::string>("bert-mask-symbol"),
+                                            options_->get<std::string>("bert-sep-symbol"),
+                                            options_->get<std::string>("bert-class-symbol"),
+                                            options_->get<int>("bert-type-vocab-size"));
+
+      builder->clear(graph);
+      auto classifierStates = std::dynamic_pointer_cast<BertEncoderClassifier>(builder)->apply(graph, bertBatch, true);
+      graph->forward();
+
+      auto maskedLMLogits = classifierStates[0]->getLogProbs();
+      const auto& maskedLMLabels = bertBatch->bertMaskedWords();
+
+      auto sentenceLogits = classifierStates[1]->getLogProbs();
+      const auto& sentenceLabels = bertBatch->back()->data();
+
+      auto count = [=, &correct, &totalLabels](Expr logits, const Words& labels) {
+        IndexType cols = logits->shape()[-1];
+        size_t thisCorrect = 0;
+        size_t thisLabels  = labels.size();
+
+        std::vector<float> vLogits;
+        logits->val()->get(vLogits);
+
+        for(int i = 0; i < thisLabels; ++i) {
+          // CPU-side Argmax
+          IndexType bestIndex = 0;
+          float bestValue = std::numeric_limits<float>::lowest();
+          for(IndexType j = 0; j < cols; ++j) {
+            float currValue = vLogits[i * cols + j];
+            if(currValue > bestValue) {
+              bestValue = currValue;
+              bestIndex = j;
+            }
+          }
+          thisCorrect += (size_t)(bestIndex == labels[i].toWordIndex());
+        }
+
+        std::unique_lock<std::mutex> lock(mutex_);
+        totalLabels += thisLabels;
+        correct     += thisCorrect;
+      };
+
+      if(evalMaskedLM_)
+        count(maskedLMLogits, maskedLMLabels);
+      else
+        count(sentenceLogits, sentenceLabels);
+    };
 
     {
       threadPool_.reserve(graphs.size());
-
       TaskBarrier taskBarrier;
       for(auto batch : *batchGenerator_) {
-        auto task = [=, &correct, &totalLabels](size_t id) {
-          thread_local size_t workerNo = (size_t)-1; // set to something silly
-          {
-            std::unique_lock<std::mutex> lock(mutex_);
-            if(workerNo == (size_t)-1) // if silly, set to actual worker id
-              workerNo = id % graphs.size(); // @TODO: this is not really safe, only works because processing a
-                                             // batch takes a long time and id % graph.size() is unlikely to occur
-                                             // again before cycling through all threads.
-          }
-
-          auto graph = graphs[workerNo];
-          auto builder = models::createModelFromOptions(options_, models::usage::raw);
-
-          thread_local std::unique_ptr<std::mt19937> engine;
-          if(!engine)
-            engine.reset(new std::mt19937((unsigned int)(Config::seed + id)));
-
-          auto bertBatch = New<data::BertBatch>(batch,
-                                                *engine,
-                                                options_->get<float>("bert-masking-fraction"),
-                                                options_->get<std::string>("bert-mask-symbol"),
-                                                options_->get<std::string>("bert-sep-symbol"),
-                                                options_->get<std::string>("bert-class-symbol"),
-                                                options_->get<int>("bert-type-vocab-size"));
-
-          builder->clear(graph);
-          auto classifierStates = std::dynamic_pointer_cast<BertEncoderClassifier>(builder)->apply(graph, bertBatch, true);
-          graph->forward();
-
-          auto maskedLMLogits = classifierStates[0]->getLogProbs();
-          const auto& maskedLMLabels = bertBatch->bertMaskedWords();
-
-          auto sentenceLogits = classifierStates[1]->getLogProbs();
-          const auto& sentenceLabels = bertBatch->back()->data();
-
-          auto count = [=, &correct, &totalLabels](Expr logits, const Words& labels) {
-            IndexType cols = logits->shape()[-1];
-            size_t thisCorrect = 0;
-            size_t thisLabels  = labels.size();
-
-            std::vector<float> vLogits;
-            logits->val()->get(vLogits);
-
-            for(int i = 0; i < thisLabels; ++i) {
-              // CPU-side Argmax
-              IndexType bestIndex = 0;
-              float bestValue = std::numeric_limits<float>::lowest();
-              for(IndexType j = 0; j < cols; ++j) {
-                float currValue = vLogits[i * cols + j];
-                if(currValue > bestValue) {
-                  bestValue = currValue;
-                  bestIndex = j;
-                }
-              }
-              thisCorrect += (size_t)(bestIndex == labels[i].toWordIndex());
-            }
-
-            std::unique_lock<std::mutex> lock(mutex_);
-            totalLabels += thisLabels;
-            correct     += thisCorrect;
-          };
-
-          if(evalMaskedLM_)
-            count(maskedLMLogits, maskedLMLabels);
-          else
-            count(sentenceLogits, sentenceLabels);
-        };
-
-        taskBarrier.push_back(threadPool_.enqueue(task, batchId));
+        taskBarrier.push_back(threadPool_.enqueue(task, batch, batchId));
         batchId++;
       }
       // ~TaskBarrier waits until all are done
@@ -474,7 +460,7 @@ class TranslationValidator : public Validator<data::Corpus, models::IModel> {
     for(auto graph : graphs) {
       auto builder = models::createModelFromOptions(options_, models::usage::translation);
       Ptr<Scorer> scorer = New<ScorerWrapper>(builder, "", 1.0f, model);
-      scorers.push_back(scorer);
+      scorers.push_back(scorer); // @TODO: should this be done in the contructor?
     }
 
     // Set up output file
@@ -508,43 +494,43 @@ class TranslationValidator : public Validator<data::Corpus, models::IModel> {
       else
         collector->setPrintingStrategy(New<GeometricPrinting>());
 
-      threadPool_.reserve(graphs.size());
-
-      size_t sentenceId = 0;
-      TaskBarrier taskBarrier;
-      for(auto batch : *batchGenerator_) {
-        auto task = [=](size_t id) {
-          thread_local size_t workerNo = (size_t)-1; // set to something silly
-          {
-            std::unique_lock<std::mutex> lock(mutex_);
-            if(workerNo == (size_t)-1) // if silly, set to actual worker id
-              workerNo = id % graphs.size(); // @TODO: this is not really safe, only works because processing a
-                                             // batch takes a long time and id % graph.size() is unlikely to occur
-                                             // again before cycling through all threads.
-          }
-
-          auto graph = graphs[workerNo];
-          auto scorer = scorers[workerNo];
-
-          auto search = New<BeamSearch>(options_,
-                                        std::vector<Ptr<Scorer>>{scorer},
-                                        vocabs_.back());
-          auto histories = search->search(graph, batch);
-
-          for(auto history : histories) {
-            std::stringstream best1;
-            std::stringstream bestn;
-            printer->print(history, best1, bestn);
-            collector->Write((long)history->getLineNum(),
-                             best1.str(),
-                             bestn.str(),
-                             options_->get<bool>("n-best"));
-          }
-        };
+      std::deque<Ptr<ExpressionGraph>> graphQueue(graphs.begin(), graphs.end());
+      std::deque<Ptr<Scorer>> scorerQueue(scorers.begin(), scorers.end());
+      auto task = [=, &graphQueue, &scorerQueue](BatchPtr batch) {
+        thread_local Ptr<ExpressionGraph> graph;
+        thread_local Ptr<Scorer> scorer;
+        
+        if(!graph) {
+          std::unique_lock<std::mutex> lock(mutex_);
+          ABORT_IF(graphQueue.empty(), "Asking for graph, but none left on queue");
+          graph = graphQueue.front();
+          graphQueue.pop_front();
+
+          ABORT_IF(scorerQueue.empty(), "Asking for scorer, but none left on queue");
+          scorer = scorerQueue.front();
+          scorerQueue.pop_front();
+        }
+      
+        auto search = New<BeamSearch>(options_,
+                                      std::vector<Ptr<Scorer>>{scorer},
+                                      vocabs_.back());
+        auto histories = search->search(graph, batch);
+
+        for(auto history : histories) {
+          std::stringstream best1;
+          std::stringstream bestn;
+          printer->print(history, best1, bestn);
+          collector->Write((long)history->getLineNum(),
+                            best1.str(),
+                            bestn.str(),
+                            options_->get<bool>("n-best"));
+        }
+      };
 
-        taskBarrier.push_back(threadPool_.enqueue(task, sentenceId));
-        sentenceId++;
-      }
+      threadPool_.reserve(graphs.size());
+      TaskBarrier taskBarrier;      
+      for(auto batch : *batchGenerator_)
+        taskBarrier.push_back(threadPool_.enqueue(task, batch));
       // ~TaskBarrier waits until all are done
     }
 
@@ -651,50 +637,50 @@ class BleuValidator : public Validator<data::Corpus, models::IModel> {
       else
         collector->setPrintingStrategy(New<GeometricPrinting>());
 
-      threadPool_.reserve(graphs.size());
+      std::deque<Ptr<ExpressionGraph>> graphQueue(graphs.begin(), graphs.end());
+      std::deque<Ptr<Scorer>> scorerQueue(scorers.begin(), scorers.end());
+      auto task = [=, &stats, &graphQueue, &scorerQueue](BatchPtr batch) {
+        thread_local Ptr<ExpressionGraph> graph;
+        thread_local Ptr<Scorer> scorer;
+        
+        if(!graph) {
+          std::unique_lock<std::mutex> lock(mutex_);
+          ABORT_IF(graphQueue.empty(), "Asking for graph, but none left on queue");
+          graph = graphQueue.front();
+          graphQueue.pop_front();
+
+          ABORT_IF(scorerQueue.empty(), "Asking for scorer, but none left on queue");
+          scorer = scorerQueue.front();
+          scorerQueue.pop_front();
+        }
+      
+        auto search = New<BeamSearch>(options_,
+                                      std::vector<Ptr<Scorer>>{scorer},
+                                      vocabs_.back());
+        auto histories = search->search(graph, batch);
+
+        size_t no = 0;
+        std::lock_guard<std::mutex> statsLock(mutex_);
+        for(auto history : histories) {
+          auto result = history->top();
+          const auto& words = std::get<0>(result);
+          updateStats(stats, words, batch, no, vocabs_.back()->getEosId());
+
+          std::stringstream best1;
+          std::stringstream bestn;
+          printer->print(history, best1, bestn);
+          collector->Write((long)history->getLineNum(),
+                            best1.str(),
+                            bestn.str(),
+                            /*nbest=*/ false);
+          no++;
+        }
+      };
 
-      size_t sentenceId = 0;
+      threadPool_.reserve(graphs.size());
       TaskBarrier taskBarrier;
-      for(auto batch : *batchGenerator_) {
-        auto task = [=, &stats](size_t id) {
-          thread_local size_t workerNo = (size_t)-1; // set to something silly
-          {
-            std::unique_lock<std::mutex> lock(mutex_);
-            if(workerNo == (size_t)-1) // if silly, set to actual worker id
-              workerNo = id % graphs.size(); // @TODO: this is not really safe, only works because processing a
-                                             // batch takes a long time and id % graph.size() is unlikely to occur
-                                             // again before cycling through all threads.
-          }
-
-          auto graph = graphs[workerNo];
-          auto scorer = scorers[workerNo];
-
-          auto search = New<BeamSearch>(options_,
-                                        std::vector<Ptr<Scorer>>{scorer},
-                                        vocabs_.back());
-          auto histories = search->search(graph, batch);
-
-          size_t no = 0;
-          std::lock_guard<std::mutex> statsLock(mutex_);
-          for(auto history : histories) {
-            auto result = history->top();
-            const auto& words = std::get<0>(result);
-            updateStats(stats, words, batch, no, vocabs_.back()->getEosId());
-
-            std::stringstream best1;
-            std::stringstream bestn;
-            printer->print(history, best1, bestn);
-            collector->Write((long)history->getLineNum(),
-                             best1.str(),
-                             bestn.str(),
-                             /*nbest=*/ false);
-            no++;
-          }
-        };
-
-        taskBarrier.push_back(threadPool_.enqueue(task, sentenceId));
-        sentenceId++;
-      }
+      for(auto batch : *batchGenerator_)
+        taskBarrier.push_back(threadPool_.enqueue(task, batch));
       // ~TaskBarrier waits until all are done
     }
 
diff --git a/src/translator/translator.h b/src/translator/translator.h
index c1416cf46..13ae74cdb 100755
--- a/src/translator/translator.h
+++ b/src/translator/translator.h
@@ -29,7 +29,8 @@ class Translate : public ModelTask {
   size_t numDevices_;
 
 public:
-  Translate(Ptr<Options> options) : options_(New<Options>(options->clone())) { // @TODO: clone should return Ptr<Options> same as "with"?
+  Translate(Ptr<Options> options) 
+    : options_(New<Options>(options->clone())) { // @TODO: clone should return Ptr<Options> same as "with"?
     // This is currently safe as the translator is either created stand-alone or
     // or config is created anew from Options in the validator
 
@@ -155,7 +156,8 @@ class TranslateService : public ModelServiceTask {
 public:
   virtual ~TranslateService() {}
 
-  TranslateService(Ptr<Options> options) : options_(options) {
+  TranslateService(Ptr<Options> options) 
+    : options_(New<Options>(options->clone())) {
     // initialize vocabs
     options_->set("inference", true);
     options_->set("shuffle", "none");

From b5d1a152d637e701de4b0039f5b5fc6cdcebbb29 Mon Sep 17 00:00:00 2001
From: Marcin Junczys-Dowmunt <Marcin.JunczysDowmunt@microsoft.com>
Date: Sun, 15 Sep 2019 16:17:35 -0700
Subject: [PATCH 630/838] enable fp16 options

---
 src/common/aliases.cpp       |  5 ++---
 src/common/config_parser.cpp | 17 +++++++++++++++--
 src/rescorer/rescorer.h      |  6 ++++++
 src/translator/translator.h  |  5 +++--
 4 files changed, 26 insertions(+), 7 deletions(-)

diff --git a/src/common/aliases.cpp b/src/common/aliases.cpp
index 1ca997270..f2c0a514d 100644
--- a/src/common/aliases.cpp
+++ b/src/common/aliases.cpp
@@ -31,12 +31,11 @@ void ConfigParser::addAliases(cli::CLIWrapper& cli) {
     config["shuffle"] = "none";
   });
 
-#if 0 // @TODO: not available yet
   cli.alias("fp16", "true", [](YAML::Node& config) {
     config["precision"] = std::vector<std::string>({"float16", "float32", "float32"});
-    config["cost-scaling"] = std::vector<std::string>({"7", "2000", "2", "0.05", "10", "1"});
+    if(config["cost-scaling"]) // set by parser hence override @TODO: this is a bit hacky, should we rather see modes like "training" or "tanslating"?
+      config["cost-scaling"] = std::vector<std::string>({"7", "2000", "2", "0.05", "10", "1"});
   });
-#endif
 
   // Options setting the BiDeep architecture proposed in http://www.aclweb.org/anthology/W17-4710
   cli.alias("best-deep", "true", [](YAML::Node& config) {
diff --git a/src/common/config_parser.cpp b/src/common/config_parser.cpp
index 7159092a5..fa7750ac5 100755
--- a/src/common/config_parser.cpp
+++ b/src/common/config_parser.cpp
@@ -422,7 +422,6 @@ void ConfigParser::addOptionsTraining(cli::CLIWrapper& cli) {
   cli.add<bool>("--embedding-fix-trg",
      "Fix target embeddings. Affects all decoders");
 
-#if 0 // not doing anything yet, next PR
   // mixed precision training
   cli.add<bool>("--fp16", 
       "Shortcut for mixed precision training with float16 and cost-scaling, "
@@ -435,7 +434,6 @@ void ConfigParser::addOptionsTraining(cli::CLIWrapper& cli) {
       "Dynamic cost scaling for mixed precision training: "
       "power of 2, scaling window, scaling factor, tolerance")->implicit_val("7.f 2000 2.f 0.05f 10 1.f");
   cli.add<bool>("--normalize-gradient", "Normalize gradient by multiplying with worldsize / total labels");
-#endif
 
   // multi-node training
   cli.add<bool>("--multi-node",
@@ -556,6 +554,12 @@ void ConfigParser::addOptionsTranslation(cli::CLIWrapper& cli) {
   cli.add<std::string>("--gemm-type",
       "Select GEMM options: auto, mklfp32, intrinint16, fp16packed, int8packed",
       "auto");
+  
+  cli.add<bool>("--fp16", 
+      "Shortcut for mixed precision inference with float16, corresponds to: --precision float16");
+  cli.add<std::vector<std::string>>("--precision",
+      "Mixed precision training for forward/backward pass and optimizaton",
+      {"float32"});
 
   cli.add<std::vector<std::string>>("--shortlist",
      "Use softmax shortlist: path first best prune");
@@ -606,6 +610,15 @@ void ConfigParser::addOptionsScoring(cli::CLIWrapper& cli) {
 
   cli.add<bool>("--optimize",
       "Optimize speed aggressively sacrificing memory or precision");
+
+  //@TODO: gemm-type missing? fix this
+
+  cli.add<bool>("--fp16", 
+      "Shortcut for mixed precision inference with float16, corresponds to: --precision float16");
+  cli.add<std::vector<std::string>>("--precision",
+      "Mixed precision training for forward/backward pass and optimizaton",
+      {"float32"});
+  
   // clang-format on
 }
 
diff --git a/src/rescorer/rescorer.h b/src/rescorer/rescorer.h
index 82543f3f7..4a71140b1 100755
--- a/src/rescorer/rescorer.h
+++ b/src/rescorer/rescorer.h
@@ -70,10 +70,16 @@ class Rescore : public ModelTask {
     for(auto device : devices) {
       auto graph = New<ExpressionGraph>(true);
       graph->setDevice(device);
+
+      auto precison = options_->get<std::vector<std::string>>("precision", {"float32"});
+      graph->setParameterType(typeFromString(precison[0])); // only use first type, used for parameter type in graph
+
+      graph->getBackend()->setClip(options_->get<float>("clip-gemm"));
       if (device.type == DeviceType::cpu) {
         graph->getBackend()->setOptimized(options_->get<bool>("optimize"));
         graph->getBackend()->setGemmType(options_->get<std::string>("gemm-type"));
       }
+
       graph->reserveWorkspaceMB(options_->get<size_t>("workspace"));
       graphs_.push_back(graph);
     }
diff --git a/src/translator/translator.h b/src/translator/translator.h
index 13ae74cdb..5d02a692b 100755
--- a/src/translator/translator.h
+++ b/src/translator/translator.h
@@ -182,9 +182,10 @@ class TranslateService : public ModelServiceTask {
     for(auto device : devices) {
       auto graph = New<ExpressionGraph>(true);
       graph->setDevice(device);
-      auto prec = options_->get<std::vector<std::string>>("precision", {"float32"});
+      
+      auto precison = options_->get<std::vector<std::string>>("precision", {"float32"});
+      graph->setParameterType(typeFromString(precison[0])); // only use first type, used for parameter type in graph
 
-      graph->setParameterType(typeFromString(prec[0]));
       graph->getBackend()->setClip(options_->get<float>("clip-gemm"));
       if (device.type == DeviceType::cpu) {
         graph->getBackend()->setOptimized(options_->get<bool>("optimize"));

From 20d8ac7cdd90963b2df312955944e5e0cf76f77c Mon Sep 17 00:00:00 2001
From: Marcin Junczys-Dowmunt <Marcin.JunczysDowmunt@microsoft.com>
Date: Sun, 15 Sep 2019 16:55:52 -0700
Subject: [PATCH 631/838] add aliases to other modes

---
 src/common/config_parser.cpp | 8 +++++---
 1 file changed, 5 insertions(+), 3 deletions(-)

diff --git a/src/common/config_parser.cpp b/src/common/config_parser.cpp
index fa7750ac5..046dbe4c2 100755
--- a/src/common/config_parser.cpp
+++ b/src/common/config_parser.cpp
@@ -558,7 +558,7 @@ void ConfigParser::addOptionsTranslation(cli::CLIWrapper& cli) {
   cli.add<bool>("--fp16", 
       "Shortcut for mixed precision inference with float16, corresponds to: --precision float16");
   cli.add<std::vector<std::string>>("--precision",
-      "Mixed precision training for forward/backward pass and optimizaton",
+      "Mixed precision for inference, set parameter type in expression graph",
       {"float32"});
 
   cli.add<std::vector<std::string>>("--shortlist",
@@ -616,7 +616,7 @@ void ConfigParser::addOptionsScoring(cli::CLIWrapper& cli) {
   cli.add<bool>("--fp16", 
       "Shortcut for mixed precision inference with float16, corresponds to: --precision float16");
   cli.add<std::vector<std::string>>("--precision",
-      "Mixed precision training for forward/backward pass and optimizaton",
+      "Mixed precision for inference, set parameter type in expression graph",
       {"float32"});
   
   // clang-format on
@@ -766,13 +766,15 @@ void ConfigParser::parseOptions(int argc, char** argv, bool doValidate) {
     case cli::mode::training:
       addOptionsTraining(cli);
       addOptionsValidation(cli);
-      addAliases(cli);
+      addAliases(cli); // @TODO: add the mode here?
       break;
     case cli::mode::translation:
       addOptionsTranslation(cli);
+      addAliases(cli); // @TODO: add the mode here?
       break;
     case cli::mode::scoring:
       addOptionsScoring(cli);
+      addAliases(cli); // @TODO: add the mode here?
       break;
     default:
       ABORT("wrong CLI mode");

From fbe6b5ae85ffabc6083fecc3c074977332cfa83e Mon Sep 17 00:00:00 2001
From: Marcin Junczys-Dowmunt <marcinjd@microsoft.com>
Date: Mon, 16 Sep 2019 00:14:08 +0000
Subject: [PATCH 632/838] make aliases work with translation and rescoring

---
 regression-tests             |  2 +-
 src/3rd_party/nccl           |  2 +-
 src/common/aliases.cpp       | 23 +++++++++++++++--------
 src/common/config_parser.cpp |  5 ++---
 4 files changed, 19 insertions(+), 13 deletions(-)

diff --git a/regression-tests b/regression-tests
index 71b0506a5..906f7731a 160000
--- a/regression-tests
+++ b/regression-tests
@@ -1 +1 @@
-Subproject commit 71b0506a50e1f181e269fe90073a8ae470676dd1
+Subproject commit 906f7731a463dae5d4d3e8e3ff5623f2ed19222b
diff --git a/src/3rd_party/nccl b/src/3rd_party/nccl
index 8e3a3f7c5..f93fe9bfd 160000
--- a/src/3rd_party/nccl
+++ b/src/3rd_party/nccl
@@ -1 +1 @@
-Subproject commit 8e3a3f7c5b520babff49cec54a866fa3eda3a3b6
+Subproject commit f93fe9bfd94884cec2ba711897222e0df5569a53
diff --git a/src/common/aliases.cpp b/src/common/aliases.cpp
index f2c0a514d..6a7d5a431 100644
--- a/src/common/aliases.cpp
+++ b/src/common/aliases.cpp
@@ -26,17 +26,23 @@ namespace marian {
  * regardless of its order in the command line or config file.
  */
 void ConfigParser::addAliases(cli::CLIWrapper& cli) {
-  // for backwards-compatibility with older version, "--no-shuffle" maps to "--shuffle none"
-  cli.alias("no-shuffle", "true", [](YAML::Node& config) {
-    config["shuffle"] = "none";
+  cli.alias("fp16", "true", [&](YAML::Node& config) {
+    if(mode_ == cli::mode::training) {
+      config["precision"] = std::vector<std::string>({"float16", "float32", "float32"});
+      if(config["cost-scaling"]) // set by parser hence override @TODO: this is a bit hacky, should we rather see modes like "training" or "tanslating"?
+        config["cost-scaling"] = std::vector<std::string>({"7", "2000", "2", "0.05", "10", "1"});
+    } else {
+      config["precision"] = std::vector<std::string>({"float16"}); // for inference we do not need the other types
+    }
   });
 
-  cli.alias("fp16", "true", [](YAML::Node& config) {
-    config["precision"] = std::vector<std::string>({"float16", "float32", "float32"});
-    if(config["cost-scaling"]) // set by parser hence override @TODO: this is a bit hacky, should we rather see modes like "training" or "tanslating"?
-      config["cost-scaling"] = std::vector<std::string>({"7", "2000", "2", "0.05", "10", "1"});
-  });
+  if(mode_ == cli::mode::training) {
+    // for backwards-compatibility with older version, "--no-shuffle" maps to "--shuffle none"
+    cli.alias("no-shuffle", "true", [](YAML::Node& config) {
+      config["shuffle"] = "none";
+    });
 
+  // @TODO: indent
   // Options setting the BiDeep architecture proposed in http://www.aclweb.org/anthology/W17-4710
   cli.alias("best-deep", "true", [](YAML::Node& config) {
     config["layer-normalization"] = true;
@@ -138,6 +144,7 @@ void ConfigParser::addAliases(cli::CLIWrapper& cli) {
     config["valid-mini-batch"] = 8;
     config["normalize"] = 1.0;
   });
+  }
 }
 
 }  // namespace marian
diff --git a/src/common/config_parser.cpp b/src/common/config_parser.cpp
index 046dbe4c2..515e2d44b 100755
--- a/src/common/config_parser.cpp
+++ b/src/common/config_parser.cpp
@@ -766,15 +766,12 @@ void ConfigParser::parseOptions(int argc, char** argv, bool doValidate) {
     case cli::mode::training:
       addOptionsTraining(cli);
       addOptionsValidation(cli);
-      addAliases(cli); // @TODO: add the mode here?
       break;
     case cli::mode::translation:
       addOptionsTranslation(cli);
-      addAliases(cli); // @TODO: add the mode here?
       break;
     case cli::mode::scoring:
       addOptionsScoring(cli);
-      addAliases(cli); // @TODO: add the mode here?
       break;
     default:
       ABORT("wrong CLI mode");
@@ -782,6 +779,8 @@ void ConfigParser::parseOptions(int argc, char** argv, bool doValidate) {
   }
   // clang-format on
 
+  addAliases(cli);
+
   // parse command-line options and fill wrapped YAML config
   cli.parse(argc, argv);
 

From 48f872723509b5430858809141f64e4cf643cf23 Mon Sep 17 00:00:00 2001
From: Marcin Junczys-Dowmunt <Marcin.JunczysDowmunt@microsoft.com>
Date: Sun, 15 Sep 2019 17:44:25 -0700
Subject: [PATCH 633/838] change old error message back

---
 regression-tests     | 2 +-
 src/3rd_party/nccl   | 2 +-
 src/models/nematus.h | 2 +-
 3 files changed, 3 insertions(+), 3 deletions(-)

diff --git a/regression-tests b/regression-tests
index 906f7731a..08c1485c2 160000
--- a/regression-tests
+++ b/regression-tests
@@ -1 +1 @@
-Subproject commit 906f7731a463dae5d4d3e8e3ff5623f2ed19222b
+Subproject commit 08c1485c2c925e4e1ed8a3e5df686ed5c7dc496e
diff --git a/src/3rd_party/nccl b/src/3rd_party/nccl
index f93fe9bfd..8e3a3f7c5 160000
--- a/src/3rd_party/nccl
+++ b/src/3rd_party/nccl
@@ -1 +1 @@
-Subproject commit f93fe9bfd94884cec2ba711897222e0df5569a53
+Subproject commit 8e3a3f7c5b520babff49cec54a866fa3eda3a3b6
diff --git a/src/models/nematus.h b/src/models/nematus.h
index ebe3faa3c..3c0a549e9 100755
--- a/src/models/nematus.h
+++ b/src/models/nematus.h
@@ -21,7 +21,7 @@ class Nematus : public EncoderDecoder {
              "than gru-nematus, use --type s2s");
 
     ABORT_IF(options_->get<int>("dec-cell-high-depth") > 1,
-             "--type nematus does not support "
+             "--type nematus does not currently support "
              "--dec-cell-high-depth > 1, use --type s2s");
   }
 

From 406931ef529026882a0a5e28d7e33a39f6deb0f1 Mon Sep 17 00:00:00 2001
From: Marcin Junczys-Dowmunt <Marcin.JunczysDowmunt@microsoft.com>
Date: Sun, 15 Sep 2019 22:03:19 -0700
Subject: [PATCH 634/838] corrected mask in transformert

---
 src/models/transformer.h        |  11 ++--
 src/tests/units/graph_tests.cpp | 110 +++++++++++++++++---------------
 2 files changed, 63 insertions(+), 58 deletions(-)

diff --git a/src/models/transformer.h b/src/models/transformer.h
index bf77c0952..126ee04f7 100755
--- a/src/models/transformer.h
+++ b/src/models/transformer.h
@@ -101,7 +101,8 @@ class Transformer : public EncoderOrDecoderBase {
   // convert multiplicative 1/0 mask to additive 0/-inf log mask, and transpose to match result of bdot() op in Attention()
   static Expr transposedLogMask(Expr mask) { // mask: [-4: beam depth=1, -3: batch size, -2: vector dim=1, -1: max length]
     auto ms = mask->shape();
-    mask = (1 - mask) * -99999999.f;
+    float maskFactor = std::min(NumericLimits<float>(mask->value_type()).max / 2.f, 99999.f); // to make sure we do not overflow for fp16
+    mask = (1 - mask) * -maskFactor;
     return reshape(mask, {ms[-3], 1, ms[-2], ms[-1]}); // [-4: batch size, -3: num heads broadcast=1, -2: max length broadcast=1, -1: max length]
   }
 
@@ -240,7 +241,7 @@ class Transformer : public EncoderOrDecoderBase {
 
     // take softmax along src sequence axis (-1)
     auto weights = softmax(z); // [-4: beam depth * batch size, -3: num heads, -2: max tgt length, -1: max src length]
-
+    
     if(saveAttentionWeights)
       collectOneHead(weights, dimBeam);
 
@@ -249,6 +250,7 @@ class Transformer : public EncoderOrDecoderBase {
 
     // apply attention weights to values
     auto output = bdot(weights, v);   // [-4: beam depth * batch size, -3: num heads, -2: max tgt length, -1: split vector dim]
+
     return output;
   }
 
@@ -334,7 +336,7 @@ class Transformer : public EncoderOrDecoderBase {
 
     // multi-head self-attention over previous input
     output = MultiHead(prefix, dimModel, heads, output, keys, values, mask, cache, saveAttentionWeights);
-
+    
     auto opsPost = opt<std::string>("transformer-postprocess");
     output = postProcess(prefix + "_Wo", opsPost, output, input, dropProb);
 
@@ -512,7 +514,6 @@ class EncoderTransformer : public Transformer<EncoderBase> {
 
     auto embeddingLayer = getEmbeddingLayer(options_->has("ulr") && options_->get<bool>("ulr"));
     std::tie(batchEmbeddings, batchMask) = embeddingLayer->apply((*batch)[batchIndex_]);
-
     batchEmbeddings = addSpecialEmbeddings(batchEmbeddings, /*start=*/0, batch);
 
     // reorganize batch and timestep
@@ -774,7 +775,7 @@ class DecoderTransformer : public Transformer<DecoderBase> {
     if(shortlist_)
       output_->setShortlist(shortlist_);
     auto logits = output_->applyAsLogits(decoderContext); // [-4: beam depth=1, -3: max length, -2: batch size, -1: vocab or shortlist dim]
-
+    
     // return unormalized(!) probabilities
     Ptr<DecoderState> nextState;
     if (opt<std::string>("transformer-decoder-autoreg", "self-attention") == "rnn") {
diff --git a/src/tests/units/graph_tests.cpp b/src/tests/units/graph_tests.cpp
index 28b0e5b5c..ad35e0c38 100644
--- a/src/tests/units/graph_tests.cpp
+++ b/src/tests/units/graph_tests.cpp
@@ -6,67 +6,71 @@ using namespace marian;
 
 #ifdef CUDA_FOUND
 TEST_CASE("Graph device is set", "[graph]") {
-  auto graph = New<ExpressionGraph>();
-
+  auto graph = New<ExpressionGraph>();    
   graph->setDevice({0, DeviceType::gpu});
-
+  
   DeviceId testId{0, DeviceType::gpu};
   REQUIRE(graph->getDeviceId() == testId);
 }
 
 TEST_CASE("Expression graph can be initialized with constant values",
           "[graph]") {
-  auto graph = New<ExpressionGraph>();
-  graph->setDevice({0, DeviceType::gpu});
-  graph->reserveWorkspaceMB(4);
-
-  std::vector<float> values;
-
-  SECTION("initializing with zeros") {
-    graph->clear();
-    values.clear();
-    auto zeros = graph->param("0s", {2, 5}, inits::zeros());
-    graph->forward();
-
-    zeros->val()->get(values);
-    REQUIRE(values == std::vector<float>(10, 0.0f));
-  }
-
-  SECTION("initializing with ones") {
-    graph->clear();
-    values.clear();
-    auto ones = graph->param("1s", {2, 5}, inits::ones());
-    graph->forward();
-
-    ones->val()->get(values);
-    REQUIRE(values == std::vector<float>(10, 1.0f));
-  }
-
-  SECTION("initializing from vector") {
-    graph->clear();
-    values.clear();
-    std::vector<float> v({1, 2, 3, 4, 5, 6});
-    auto vals = graph->param("vs", {2, 3}, inits::fromVector(v));
-    graph->forward();
 
-    REQUIRE(values.empty());
-    vals->val()->get(values);
-    REQUIRE(values == v);
-  }
-
-  SECTION("initializing float16 node from vector") {
-    graph->clear();
-    std::vector<float16> values16;
-    std::vector<float16> v({1, 2, 3, 4, 5, 6});
-    auto vals1 = graph->param("vs1", {2, 3}, inits::fromVector(v), Type::float16);
-    auto vals2 = graph->param("vs2", {2, 3}, inits::fromValue(5), Type::float16);
-
-    debug(vals1 + vals2);
-    graph->forward();
-
-    REQUIRE(values.empty());
-    vals1->val()->get(values16);
-    REQUIRE(values16 == v);
+  for(auto type : std::vector<Type>({Type::float32, Type::float16})) {
+    auto graph = New<ExpressionGraph>();
+    graph->setDevice({0, DeviceType::gpu});
+    graph->setParameterType(type);
+    graph->reserveWorkspaceMB(4);
+
+    std::vector<float> values;
+
+    SECTION("initializing with zeros") {
+      graph->clear();
+      values.clear();
+      auto zeros = graph->param("0s", {2, 5}, inits::zeros());
+      graph->forward();
+
+      zeros->val()->get(values);
+      REQUIRE(values == std::vector<float>(10, 0.0f));
+    }
+
+    SECTION("initializing with ones") {
+      graph->clear();
+      values.clear();
+      auto ones = graph->param("1s", {2, 5}, inits::ones());
+      graph->forward();
+
+      ones->val()->get(values);
+      REQUIRE(values == std::vector<float>(10, 1.0f));
+    }
+
+    SECTION("initializing from vector") {
+      graph->clear();
+      values.clear();
+      std::vector<float> v({1, 2, 3, 4, 5, 6});
+      auto vals = graph->param("vs", {2, 3}, inits::fromVector(v));
+      graph->forward();
+
+      REQUIRE(values.empty());
+      vals->val()->get(values);
+      REQUIRE(values == v);
+    }
+
+    SECTION("initializing float16 node from vector") {
+      // This does not test fp16 computation, only float16 to float32 conversion
+      graph->clear();
+      std::vector<float16> values16;
+      std::vector<float16> v({1, 2, 3, 4, 5, 6});
+      auto vals1 = graph->param("vs1", {2, 3}, inits::fromVector(v), Type::float16);
+      auto vals2 = graph->param("vs2", {2, 3}, inits::fromValue(5), Type::float16);
+
+      debug(vals1 + vals2);
+      graph->forward();
+
+      REQUIRE(values.empty());
+      vals1->val()->get(values16);
+      REQUIRE(values16 == v);
+    }
   }
 }
 #endif

From 391543b43412ce2294a2d7e92fb42548ad93a92a Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Mon, 16 Sep 2019 09:33:42 -0700
Subject: [PATCH 635/838] added Marcin's implementation of HighwayBackward

---
 src/data/factored_vocab.cpp          |  9 +++++----
 src/layers/generic.cpp               |  1 +
 src/tensors/cpu/tensor_operators.cpp | 19 +++++++++++--------
 3 files changed, 17 insertions(+), 12 deletions(-)
 mode change 100644 => 100755 src/tensors/cpu/tensor_operators.cpp

diff --git a/src/data/factored_vocab.cpp b/src/data/factored_vocab.cpp
index 3b12d9d39..32bea9cae 100755
--- a/src/data/factored_vocab.cpp
+++ b/src/data/factored_vocab.cpp
@@ -617,10 +617,11 @@ std::string FactoredVocab::surfaceForm(const Words& sentence) const /*override f
     unescapeHexEscapes(lemma); // unescape \x.. and \u....
     if (utils::beginsWith(lemma, "\xE2\x96\x81"))  // remove leading _ (\u2581, for DistinguishInitialAndInternalPieces mode)
         lemma = lemma.substr(3);
-    if      (has("ci")) lemma = utils::utf8Capitalized(lemma);
-    else if (has("ca")) lemma = utils::utf8ToUpper    (lemma);
-    else if (has("cn")) lemma = utils::utf8ToLower    (lemma);
-    else                lemma =                        lemma ;
+    if      (has("ci"))  lemma = utils::utf8Capitalized(lemma);
+    else if (has("ca"))  lemma = utils::utf8ToUpper    (lemma);
+    else if (has("cn"))  lemma = utils::utf8ToLower    (lemma);
+    else if (has("scu")) lemma = utils::utf8ToUpper    (lemma);
+    else if (has("scl")) lemma = utils::utf8ToLower    (lemma);
     res.append(lemma);
   }
   //std::cerr << "\n" << res << "\n";
diff --git a/src/layers/generic.cpp b/src/layers/generic.cpp
index 97106dabe..8356fbcf1 100755
--- a/src/layers/generic.cpp
+++ b/src/layers/generic.cpp
@@ -236,6 +236,7 @@ namespace marian {
       b_ = graph_->param(name + "_b", {1, numOutputClasses}, inits::zeros);
 
       /*const*/ int lemmaDimEmb = options_->get<int>("lemma-dim-emb", 0);
+      ABORT_IF(lemmaDimEmb && !factoredVocab_, "--lemma-dim-emb requires a factored vocabulary");
       if (lemmaDimEmb > 0) {
 #define HARDMAX_HACK
 #ifdef HARDMAX_HACK
diff --git a/src/tensors/cpu/tensor_operators.cpp b/src/tensors/cpu/tensor_operators.cpp
old mode 100644
new mode 100755
index 056f0e1d9..a27136d09
--- a/src/tensors/cpu/tensor_operators.cpp
+++ b/src/tensors/cpu/tensor_operators.cpp
@@ -1354,14 +1354,17 @@ void HighwayForward(Tensor out,
   cpu::Element(_1 = _1 * _2 + (1.f - _1) * _3, out, in1, in2);
 }
 
-void HighwayBackward(Tensor /*out1*/,
-                     Tensor /*out2*/,
-                     Tensor /*outt*/,
-                     const Tensor /*in1*/,
-                     const Tensor /*in2*/,
-                     const Tensor /*t*/,
-                     const Tensor /*adj*/) {
-  ABORT("Not implemented!");
+void HighwayBackward(Tensor out1,
+                     Tensor out2,
+                     Tensor outt,
+                     const Tensor in1,
+                     const Tensor in2,
+                     const Tensor t,
+                     const Tensor adj) {
+  using namespace functional;
+  cpu::Element(_1 +=        sigmoid(_2)  * _3, out1, t, adj);
+  cpu::Element(_1 += (1.f - sigmoid(_2)) * _3, out2, t, adj);
+  cpu::Element(_1 += sigmoid(_2) * (1.f - sigmoid(_2)) * (_3 - _4) * _5, outt, t, in1, in2, adj);
 }
 
 void PoolingWithMaskingForward(Tensor /*out*/,

From de808ef42978b90c06c6a3a43ca74e79d3b8fa2b Mon Sep 17 00:00:00 2001
From: Marcin Junczys-Dowmunt <Marcin.JunczysDowmunt@microsoft.com>
Date: Mon, 16 Sep 2019 10:20:58 -0700
Subject: [PATCH 636/838] adapt nth_element to work with fp16

---
 src/translator/nth_element.cu | 91 ++++++++++++++++++-----------------
 1 file changed, 47 insertions(+), 44 deletions(-)

diff --git a/src/translator/nth_element.cu b/src/translator/nth_element.cu
index b89285ec9..323efb855 100644
--- a/src/translator/nth_element.cu
+++ b/src/translator/nth_element.cu
@@ -20,11 +20,14 @@ namespace marian {
     }                                    \
   }
 
+template <typename T>
 __global__ void gMaxElement(float* d_out,
                             int* d_ind,
-                            float* d_in,
+                            T* d_in, // this is the probs array, only one with type float or half
                             int numBatches,
-                            int* batchFirstElementIdxs) {
+                            int* batchFirstElementIdxs,
+                            float minimal) // minimal is used to blank out found values, type-depdendent
+{
   extern __shared__ float sdata[];
   __shared__ int indices[512];
 
@@ -36,16 +39,16 @@ __global__ void gMaxElement(float* d_out,
 
     int i = begin + blockIdx.x * (blockDim.x * 2) + tid;
 
-    sdata[tid] = -3.40282e+38f;
+    sdata[tid] = minimal;
 
     if(i < end) {
-      sdata[tid] = d_in[i];
+      sdata[tid] = (float)d_in[i];
       indices[tid] = i;
     }
 
     if(i + blockDim.x < end) {
-      float a = d_in[i];
-      float b = d_in[i + blockDim.x];
+      float a = (float)d_in[i];
+      float b = (float)d_in[i + blockDim.x];
       if(a > b) {
         sdata[tid] = a;
         indices[tid] = i;
@@ -58,14 +61,14 @@ __global__ void gMaxElement(float* d_out,
     while(i + 2 * gridDim.x * blockDim.x < end) {
       i += 2 * gridDim.x * blockDim.x;
 
-      float a = d_in[i];
+      float a = (float)d_in[i];
       if(a > sdata[tid]) {
         sdata[tid] = a;
         indices[tid] = i;
       }
 
       if(i + blockDim.x < end) {
-        float b = d_in[i + blockDim.x];
+        float b = (float)d_in[i + blockDim.x];
         if(b > sdata[tid]) {
           sdata[tid] = b;
           indices[tid] = i + blockDim.x;
@@ -100,14 +103,16 @@ __global__ void gMaxElement(float* d_out,
   }
 }
 
+template <typename T>
 __global__ void gMaxElementUpdate(float* binCosts,
                                   int* binIdxs,
-                                  float* probs,
+                                  T* probs, // should work well enough with half, uses float everywhere else
                                   int* batchFirstElements,
                                   float* outCosts,
                                   int* outIdxs,
-                                  int* cummulatedBeamSizes,
-                                  int NUM_BLOCKS) {
+                                  int* cumulativeBeamSizes,
+                                  int NUM_BLOCKS,
+                                  float minimal) {
   extern __shared__ float sdata[];
   __shared__ int indices[512];
   __shared__ float bestBinCost;
@@ -121,12 +126,12 @@ __global__ void gMaxElementUpdate(float* binCosts,
     num_bins = 500;
   }
 
-  for(int pos = cummulatedBeamSizes[batchIdx];
-      pos < cummulatedBeamSizes[batchIdx + 1];
+  for(int pos = cumulativeBeamSizes[batchIdx];
+      pos < cumulativeBeamSizes[batchIdx + 1];
       ++pos) {
     int i = tid;
 
-    sdata[tid] = -3.40282e+38f;
+    sdata[tid] = minimal;
 
     if(i < num_bins) {
       sdata[tid] = binCosts[batchIdx * NUM_BLOCKS + i];
@@ -186,7 +191,7 @@ __global__ void gMaxElementUpdate(float* binCosts,
       bestBinCost = sdata[0];
       bestBinCostIdx = batchIdx * NUM_BLOCKS + indices[0];
 
-      probs[binIdxs[bestBinCostIdx]] = -3.40282e+38f;
+      probs[binIdxs[bestBinCostIdx]] = minimal;
 
       outIdxs[pos] = binIdxs[bestBinCostIdx];
       outCosts[pos] = bestBinCost;
@@ -198,16 +203,16 @@ __global__ void gMaxElementUpdate(float* binCosts,
         + (bestBinCostIdx - batchIdx * NUM_BLOCKS) * (blockDim.x * 2) + tid;
     const int dist = num_bins * 2 * blockDim.x;
 
-    sdata[tid] = -3.40282e+38f;
+    sdata[tid] = minimal;
 
     if(i < batchFirstElements[batchIdx + 1]) {
-      sdata[tid] = probs[i];
+      sdata[tid] = (float)probs[i];
       indices[tid] = i;
     }
 
     if(i + blockDim.x < batchFirstElements[batchIdx + 1]) {
-      float a = probs[i];
-      float b = probs[i + blockDim.x];
+      float a = (float)probs[i];
+      float b = (float)probs[i + blockDim.x];
       if(a > b) {
         sdata[tid] = a;
         indices[tid] = i;
@@ -220,14 +225,14 @@ __global__ void gMaxElementUpdate(float* binCosts,
     while(i + dist < batchFirstElements[batchIdx + 1]) {
       i += dist;
 
-      float a = probs[i];
+      float a = (float)probs[i];
       if(a > sdata[tid]) {
         sdata[tid] = a;
         indices[tid] = i;
       }
 
       if(i + blockDim.x < batchFirstElements[batchIdx + 1]) {
-        float b = probs[i + blockDim.x];
+        float b = (float)probs[i + blockDim.x];
         if(b > sdata[tid]) {
           sdata[tid] = b;
           indices[tid] = i + blockDim.x;
@@ -317,9 +322,12 @@ public:
   }
 
 private:
-  void selectNBest(float* probs,
+  template <typename T>
+  void selectNBest(T* probs,
                    const std::vector<int>& batchFirstElementIdxs,
-                   const std::vector<int>& cumulativeBeamSizes) {
+                   const std::vector<int>& cumulativeBeamSizes,
+                   float minimal) {
+
     cudaSetDevice(deviceId_.no);
     CUDA_CHECK(cudaMemcpyAsync(d_batchPosition,
                                batchFirstElementIdxs.data(),
@@ -336,13 +344,13 @@ private:
 
     gMaxElement<<<NUM_BLOCKS,
                   BLOCK_SIZE,
-                  BLOCK_SIZE * sizeof(float),
+                  BLOCK_SIZE * sizeof(float), // shared memory size
                   /* stream_ */ 0>>>(
-        d_out, d_ind, probs, numBatches, d_batchPosition);
+        d_out, d_ind, probs, numBatches, d_batchPosition, minimal);
 
     gMaxElementUpdate<<<numBatches,
                         BLOCK_SIZE,
-                        BLOCK_SIZE * sizeof(float),
+                        BLOCK_SIZE * sizeof(float),  // shared memory size
                         /* stream_ */ 0>>>(d_out,
                                            d_ind,
                                            probs,
@@ -350,7 +358,8 @@ private:
                                            d_res,
                                            d_res_idx,
                                            d_cumBeamSizes,
-                                           NUM_BLOCKS);
+                                           NUM_BLOCKS,
+                                           minimal);
   }
 
 public:
@@ -370,10 +379,10 @@ public:
     ABORT_IF(std::max(N, (size_t)inputN) > maxBeamSize_, "GetNBestList(): actual beam size {} exceeds initialization parameter {}", N, maxBeamSize_);
 
     const std::vector<size_t> beamSizes(dimBatch, N);
-    std::vector<int> cumulativeBeamSizes(beamSizes.size() + 1, 0);
+    std::vector<int> cumulativeBeamSizes(beamSizes.size() + 1, 0);    
     std::vector<int> batchFirstElementIdxs(beamSizes.size() + 1, 0);
 
-    for(size_t batchIdx = 0; batchIdx < beamSizes.size(); ++batchIdx) {
+     for(size_t batchIdx = 0; batchIdx < beamSizes.size(); ++batchIdx) {
 #if 1
       cumulativeBeamSizes[batchIdx + 1] = (batchIdx + 1) * (int)N;
       batchFirstElementIdxs[batchIdx + 1] += (batchIdx + 1) * inputN * vocabSize;
@@ -388,7 +397,15 @@ public:
 #endif
     }
 
-    selectNBest(scores->data(), batchFirstElementIdxs, cumulativeBeamSizes);
+    if(scores->type() == Type::float32) {
+      float minimal = std::numeric_limits<float>::lowest();
+      selectNBest(scores->data<float>(), batchFirstElementIdxs, cumulativeBeamSizes, minimal);
+    } else if(scores->type() == Type::float16) {
+      float minimal = std::numeric_limits<float16>::lowest();
+      selectNBest(scores->data<half>(), batchFirstElementIdxs, cumulativeBeamSizes, minimal);
+    } else {
+      ABORT("getNBestList not implemented for type {}", scores->type());
+    }
     getPairs(dimBatch * N, outKeys, outCosts);
     ABORT_IF(cumulativeBeamSizes.back() != dimBatch * N, "cumulativeBeamSizes.back() wrong??");
   }
@@ -418,20 +435,6 @@ private:
     //lastN = number;
   }
 
-  //void getValueByKey(std::vector<float>& out, float* d_in) {
-  //  cudaSetDevice(deviceId_.no);
-  //
-  //  gGetValueByKey<<<1, lastN, 0, /* stream_ */ 0>>>(
-  //      d_in, d_breakdown, h_res_idx, lastN);
-  //
-  //  CUDA_CHECK(cudaMemcpyAsync(out.data(),
-  //                             d_breakdown,
-  //                             lastN * sizeof(float),
-  //                             cudaMemcpyDeviceToHost,
-  //                             /* stream_ */ 0));
-  //  CUDA_CHECK(cudaStreamSynchronize(/* stream_ */ 0));
-  //}
-
   DeviceId deviceId_;
 
   const int MAX_VOCAB_SIZE = 500000;

From 29e276254136a8a98a1219b5e956a630c9c3efb5 Mon Sep 17 00:00:00 2001
From: Marcin Junczys-Dowmunt <Marcin.JunczysDowmunt@microsoft.com>
Date: Mon, 16 Sep 2019 13:54:27 -0700
Subject: [PATCH 637/838] working compilatoin with different compute
 capabilities

---
 CMakeLists.txt                      |  3 +-
 src/common/types.h                  | 15 ++++--
 src/functional/operators.h          |  3 +-
 src/models/transformer.h            |  2 +-
 src/tensors/gpu/add.cu              |  8 +--
 src/tensors/gpu/algorithm.cu        | 16 +++---
 src/tensors/gpu/cuda_helpers.h      |  2 +
 src/tensors/gpu/element.cu          |  8 +--
 src/tensors/gpu/tensor_operators.cu | 84 ++++++++++++++++++++++++++++-
 src/translator/nth_element.cu       |  2 +
 10 files changed, 116 insertions(+), 27 deletions(-)

diff --git a/CMakeLists.txt b/CMakeLists.txt
index b5113545a..876d1b424 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -234,8 +234,7 @@ endif(COMPILE_CUDA)
 if(CMAKE_BUILD_TYPE STREQUAL "Debug")
   list(APPEND CUDA_NVCC_FLAGS --default-stream per-thread; -O0; -g; --use_fast_math; -arch=sm_30; -gencode=arch=compute_30,code=sm_30; -gencode=arch=compute_50,code=sm_50; -gencode=arch=compute_52,code=sm_52; -gencode=arch=compute_60,code=sm_60; -gencode=arch=compute_61,code=sm_61; -gencode=arch=compute_70,code=sm_70; -gencode=arch=compute_70,code=compute_70 ;)
 else(CMAKE_BUILD_TYPE STREQUAL "Debug")
-  list(APPEND CUDA_NVCC_FLAGS --default-stream per-thread; -O3; -g; --use_fast_math; -gencode=arch=compute_60,code=sm_60; -gencode=arch=compute_61,code=sm_61; -gencode=arch=compute_70,code=sm_70; -gencode=arch=compute_70,code=compute_70 ;)
-  #list(APPEND CUDA_NVCC_FLAGS --default-stream per-thread; -O3; -g; --use_fast_math; -arch=sm_30; -gencode=arch=compute_30,code=sm_30; -gencode=arch=compute_50,code=sm_50; -gencode=arch=compute_52,code=sm_52; -gencode=arch=compute_60,code=sm_60; -gencode=arch=compute_61,code=sm_61; -gencode=arch=compute_70,code=sm_70; -gencode=arch=compute_70,code=compute_70 ;)
+  list(APPEND CUDA_NVCC_FLAGS --default-stream per-thread; -O3; -g; --use_fast_math; -arch=sm_30; -gencode=arch=compute_30,code=sm_30; -gencode=arch=compute_50,code=sm_50; -gencode=arch=compute_52,code=sm_52; -gencode=arch=compute_60,code=sm_60; -gencode=arch=compute_61,code=sm_61; -gencode=arch=compute_70,code=sm_70; -gencode=arch=compute_70,code=compute_70 ;)
 endif(CMAKE_BUILD_TYPE STREQUAL "Debug")
 if(NOT MSVC)
   # @TODO: add warnings here too
diff --git a/src/common/types.h b/src/common/types.h
index 732d41af9..2ba2cb941 100755
--- a/src/common/types.h
+++ b/src/common/types.h
@@ -1,4 +1,5 @@
 #pragma once
+#include "common/logging.h" // for ABORT and ABORT_IF
 
 #if __GNUC__ >= 7
 #pragma GCC diagnostic push
@@ -17,11 +18,15 @@
 #include <immintrin.h>
 #endif
 
-//#ifdef __CUDA_ARCH__
-//#if __CUDA_ARCH__ >= 600 && CUDA_VERSION >= 10000 // @TODO: set this from 600 to 700, currently only testing on Pascal
-//#define __USE_FP16__
-//#endif
-//#endif
+#ifdef __CUDACC__ // nvcc is compiling this code
+  #if (__CUDA_ARCH__ >= 600 || !defined(__CUDA_ARCH__))
+    #define __USE_FP16__ 1 // we are in GPU code and we know what to do with FP16 code
+  #else
+    #define __USE_FP16__ 0 // we are in GPU code, but compute capability is too low to use FP16
+  #endif
+#else // other compiler, likely host code
+  #define __USE_FP16__ 0
+#endif
 
 #ifdef _MSC_VER
 // @BUGBUG: Visual Studio somehow fails on template expansions for float16.
diff --git a/src/functional/operators.h b/src/functional/operators.h
index 5aef76c9b..d2a3b4654 100755
--- a/src/functional/operators.h
+++ b/src/functional/operators.h
@@ -440,7 +440,7 @@ struct Ops<float32x8> {
 #endif // of "#ifndef __CUDA_ARCH__"
 
 #ifdef __CUDA_ARCH__
-//#ifdef __USE_FP16__
+#if __USE_FP16__
 // only compile with fp16 support for compute_70, i.e. VOLTA 100 and above.
 #include <cuda_fp16.h>
 
@@ -544,6 +544,7 @@ struct Ops<half> {
 } // end namespace functional
 } // end namespace marian
 
+#endif
 #endif
 
 //*******************************************************************************************
diff --git a/src/models/transformer.h b/src/models/transformer.h
index 126ee04f7..d56516e52 100755
--- a/src/models/transformer.h
+++ b/src/models/transformer.h
@@ -515,7 +515,7 @@ class EncoderTransformer : public Transformer<EncoderBase> {
     auto embeddingLayer = getEmbeddingLayer(options_->has("ulr") && options_->get<bool>("ulr"));
     std::tie(batchEmbeddings, batchMask) = embeddingLayer->apply((*batch)[batchIndex_]);
     batchEmbeddings = addSpecialEmbeddings(batchEmbeddings, /*start=*/0, batch);
-
+    
     // reorganize batch and timestep
     batchEmbeddings = atleast_nd(batchEmbeddings, 4);
     batchMask = atleast_nd(batchMask, 4);
diff --git a/src/tensors/gpu/add.cu b/src/tensors/gpu/add.cu
index 70bafeae4..e4591f954 100755
--- a/src/tensors/gpu/add.cu
+++ b/src/tensors/gpu/add.cu
@@ -171,11 +171,11 @@ void Aggregate(Functor functor, float aggInit, AggFunctor aggFunctor, float scal
   if(out->type() == Type::float32) {
     AggregateTyped<float, float>(functor, aggInit, aggFunctor, scale, out, tensors...);
   } else if(out->type() == Type::float16) {
-// #ifdef __USE_FP16__
+#if __USE_FP16__
     AggregateTyped<half,  float>(functor, aggInit, aggFunctor, scale, out, tensors...);
-// #else
-//     ABORT("FP16 not supported with current hardware or CUDA version");
-// #endif
+#else
+     ABORT("FP16 not supported with current hardware or CUDA version");
+#endif
   } else {
     ABORT("Type {} not yet supported", out->type());
   }
diff --git a/src/tensors/gpu/algorithm.cu b/src/tensors/gpu/algorithm.cu
index 1638d6639..0ce6b3655 100644
--- a/src/tensors/gpu/algorithm.cu
+++ b/src/tensors/gpu/algorithm.cu
@@ -62,15 +62,15 @@ void fill<float16>(Ptr<Backend> backend, float16* begin, float16* end, float16 v
   int size = end - begin;
   if (size == 0)
     return;
-// #ifdef __USE_FP16__
+#if __USE_FP16__
   CUDA_CHECK(cudaSetDevice(backend->getDeviceId().no));
   int threadsPerBlock = std::min(MAX_THREADS, size);
   int blocks = (size / threadsPerBlock) + (size % threadsPerBlock != 0); // @TODO: (size+threadsPerBlock-1)/threadsPerBlock or CeilDiv(a,b)
   gFill<<<blocks, threadsPerBlock>>>((__half*)begin, size, (__half)value);
   CUDA_CHECK(cudaStreamSynchronize(0));
-// #else
-//   ABORT("FP16 not supported with current hardware or CUDA version");
-// #endif
+#else
+   ABORT("FP16 not supported with current hardware or CUDA version");
+#endif
 }
 
 template void fill<bool>(Ptr<Backend>, bool*, bool*, bool);
@@ -126,15 +126,15 @@ void swap_ranges<float16>(Ptr<Backend> backend, float16* begin, float16* end, fl
   if (size == 0)
     return;
 
-// #ifdef __USE_FP16__
+#if __USE_FP16__
   CUDA_CHECK(cudaSetDevice(backend->getDeviceId().no));
   int threadsPerBlock = std::min(MAX_THREADS, size);
   int blocks = (size / threadsPerBlock) + (size % threadsPerBlock != 0); // @TODO: (size+threadsPerBlock-1)/threadsPerBlock or CeilDiv(a,b)
   gSwap<<<blocks, threadsPerBlock>>>((__half*)begin, (__half*)dest, size);
   CUDA_CHECK(cudaStreamSynchronize(0));
-// #else
-//   ABORT("FP16 not supported with current hardware or CUDA version");
-// #endif
+#else
+  ABORT("FP16 not supported with current hardware or CUDA version");
+#endif
 }
 
 // clang-format off
diff --git a/src/tensors/gpu/cuda_helpers.h b/src/tensors/gpu/cuda_helpers.h
index 0c0c8923c..9163784b1 100644
--- a/src/tensors/gpu/cuda_helpers.h
+++ b/src/tensors/gpu/cuda_helpers.h
@@ -4,7 +4,9 @@
 
 #include <cuda_runtime.h>
 
+#if __USE_FP16__
 #include <cuda_fp16.h>
+#endif
 
 // template <> inline bool matchType<__half>(Type type)  { return type == Type::float16; }
 // template <> inline std::string request<__half>()  { return "float16"; }
diff --git a/src/tensors/gpu/element.cu b/src/tensors/gpu/element.cu
index 52a920e6d..5425625f3 100755
--- a/src/tensors/gpu/element.cu
+++ b/src/tensors/gpu/element.cu
@@ -64,11 +64,11 @@ void Element(Functor functor, Tensor out, Tensors... tensors) {
   if(out->type() == Type::float32) {
     ElementTyped<float>(functor, out, tensors...);
   } else if(out->type() == Type::float16) {
-//#ifdef __USE_FP16__
+#if __USE_FP16__
     ElementTyped<__half>(functor, out, tensors...);
-//#else
-//    ABORT("FP16 not supported with chosen current hardware or CUDA version");
-//#endif
+#else
+    ABORT("FP16 not supported with chosen current hardware or CUDA version");
+#endif
   } else if(out->type() == Type::float64) {
     ElementTyped<double>(functor, out, tensors...);
   } else {
diff --git a/src/tensors/gpu/tensor_operators.cu b/src/tensors/gpu/tensor_operators.cu
index 76ad4ce91..4299a57e3 100644
--- a/src/tensors/gpu/tensor_operators.cu
+++ b/src/tensors/gpu/tensor_operators.cu
@@ -1,5 +1,6 @@
 //#include <thrust/transform_reduce.h>
 
+#include "common/types.h"
 #include "tensors/tensor_operators.h"
 
 #include "functional/functional.h"
@@ -21,6 +22,7 @@ static inline  __device__ void atomicAdd(float *address, float val) {
   ::atomicAdd(address, val);
 }
 
+#if __USE_FP16__
 // @TODO: copied from CuTorch, adapt this better, give credit.
 static inline  __device__ void atomicAdd(half *address, half val) {
   //*address += val;
@@ -50,6 +52,7 @@ static inline  __device__ void atomicAdd(half *address, half val) {
     } while (assumed != old);
 #endif // __CUDA_ARCH__
 }
+#endif
 
 }
 
@@ -79,8 +82,10 @@ void IsNaN(const Tensor in, Ptr<Allocator> allocator, bool& isNaN, bool& isInf)
 
   if(in->type() == Type::float32) {
     gIsNaN<<<blocks, threads>>>(in->data<float>(), length, dIsNaN, dIsInf);
+#if __USE_FP16__
   } else if(in->type() == Type::float16) {
     gIsNaN<<<blocks, threads>>>(in->data<half>(), length, dIsNaN, dIsInf);
+#endif
   } else {
     ABORT("IsNaN for type {} not implemented", in->type());
   }
@@ -114,8 +119,10 @@ template <typename T>
 void CopyCastFrom(Tensor out, const T* in, int length) {
   if(out->type() == Type::float32) {
     CopyCastTo(out->data<float>(), in, length);
+#if __USE_FP16__
   } else if(out->type() == Type::float16) {
-    CopyCastTo(out->data<half>(), in, length);
+    CopyCastTo(out->data<__half>(), in, length);
+#endif
   } else if(out->type() == Type::float64) {
     CopyCastTo(out->data<double>(), in, length);
   } else {
@@ -128,8 +135,10 @@ void CopyCast(Tensor out, const Tensor in) {
 
   if(in->type() == Type::float32) {
     CopyCastFrom(out, in->data<float>(), (int)in->size());
+#if __USE_FP16__
   } else if(in->type() == Type::float16) {
     CopyCastFrom(out, in->data<half>(), (int)in->size());
+#endif
   } else if(in->type() == Type::float64) {
     CopyCastFrom(out, in->data<double>(), (int)in->size());
   } else {
@@ -206,9 +215,11 @@ void Concatenate1(Tensor out, const std::vector<Tensor>& inputs) {
     if(out->type() == Type::float32) {
       gInsertCols<false><<<blocks, threads>>>(
           out->data<float>(), in->data<float>(), rows, cols_in, cols_out, cols_in, offset, 0);
+#if __USE_FP16__
     } else if(out->type() == Type::float16) {
       gInsertCols<false><<<blocks, threads>>>(
           out->data<half>(), in->data<half>(), rows, cols_in, cols_out, cols_in, offset, 0);
+#endif
     } else {
       ABORT("Concatenate1 not implemented for type {}", out->type());
     }
@@ -277,6 +288,7 @@ void Concatenate2(Tensor out, Tensor in1, Tensor in2) {
                                  rowStride1,
                                  in2->data<float>(),
                                  rowStride2);
+#if __USE_FP16__
   } else if(out->type() == Type::float16) {
      gJoin2<<<blocks, threads>>>(out->data<half>(),
                                  rowBatch,
@@ -285,6 +297,7 @@ void Concatenate2(Tensor out, Tensor in1, Tensor in2) {
                                  rowStride1,
                                  in2->data<half>(),
                                  rowStride2);
+#endif
   } else {
     ABORT("Concatenate2 not implemented for type {}", out->type());
   }
@@ -318,9 +331,11 @@ void Split1(std::vector<Tensor>& outputs, const Tensor in) {
     if(out->type() == Type::float32) {
       gInsertCols<true><<<blocks, threads>>>(
           out->data<float>(), in->data<float>(), rows, cols_out, cols_out, cols_in, 0, offset);
+#if __USE_FP16__
     } else if(out->type() == Type::float16) {
       gInsertCols<true><<<blocks, threads>>>(
           out->data<half>(), in->data<half>(), rows, cols_out, cols_out, cols_in, 0, offset);
+#endif
     } else {
       ABORT("Split1 not implemented for type {}", out->type());
     }
@@ -368,9 +383,11 @@ void SplitCont(std::vector<Tensor>& outputs, const Tensor in, int axis) {
       if(out->type() == Type::float32) {
         gAddRow<<<blocks, threads>>>(
             out->data<float>() + offset2, in->data<float>() + offset1, size);
+#if __USE_FP16__
       } else if(out->type() == Type::float16) {
         gAddRow<<<blocks, threads>>>(
             out->data<half>() + offset2, in->data<half>() + offset1, size);
+#endif
       } else {
         ABORT("SplitCont not implemented for type {}", out->type());
       }
@@ -463,8 +480,10 @@ void TransposeND(Tensor out, Tensor in, const std::vector<int>& vAxis) {
 
     if(in->type() == Type::float32) {
       gTranspose0213<false><<<blocks, threads>>>(out->data<float>(), in->data<float>(), rows, cols, stride1, stride2);
+#if __USE_FP16__
     } else if(in->type() == Type::float16) {
       gTranspose0213<false><<<blocks, threads>>>(out->data<__half>(), in->data<__half>(), rows, cols, stride1, stride2);
+#endif
     } else {
       ABORT("Transpose for type {} not implemented", in->type());
     }
@@ -484,8 +503,10 @@ void TransposeND(Tensor out, Tensor in, const std::vector<int>& vAxis) {
 
     if(in->type() == Type::float32) {
       gTransposeND<false, float><<<blocks, threads>>>(out, in, axes);
+#if __USE_FP16__
     } else if(in->type() == Type::float16) {
       gTransposeND<false, __half><<<blocks, threads>>>(out, in, axes);
+#endif
     } else {
       ABORT("Transpose for type {} not implemented", in->type());
     }
@@ -507,8 +528,10 @@ void TransposeNDGrad(Tensor out, Tensor in, const std::vector<int>& vAxis) {
 
     if(in->type() == Type::float32) {
       gTranspose0213<true><<<blocks, threads>>>(out->data<float>(), in->data<float>(), rows, cols, stride1, stride2);
+#if __USE_FP16__
     } else if(in->type() == Type::float16) {
       gTranspose0213<true><<<blocks, threads>>>(out->data<__half>(), in->data<__half>(), rows, cols, stride1, stride2);
+#endif
     } else {
       ABORT("Transpose for type {} not implemented", in->type());
     }
@@ -527,8 +550,10 @@ void TransposeNDGrad(Tensor out, Tensor in, const std::vector<int>& vAxis) {
 
     if(in->type() == Type::float32) {
       gTransposeND<true, float><<<blocks, threads>>>(out, in, axes);
+#if __USE_FP16__
     } else if(in->type() == Type::float16) {
       gTransposeND<true, __half><<<blocks, threads>>>(out, in, axes);
+#endif
     } else {
       ABORT("Transpose for type {} not implemented", in->type());
     }
@@ -641,8 +666,10 @@ void Softmax(Tensor out, Tensor in) {
 
   if(in->type() == Type::float32) {
     gSoftmax<float, float><<<blocks, threads, shared>>>(out->data<float>(), out->shape(), in->data<float>());
+#if __USE_FP16__
   } else if (in->type() == Type::float16) {
     gSoftmax<half, float><<<blocks, threads, shared>>>(out->data<__half>(), out->shape(), in->data<__half>());
+#endif
   } else {
     ABORT("Softmax not implemented for type {}", in->type());
   }
@@ -740,8 +767,10 @@ void LogSoftmax(Tensor out, Tensor in) {
 
   if(in->type() == Type::float32) {
     gLogSoftmax<float, float><<<blocks, threads, shared>>>(out->data<float>(), out->shape(), in->data<float>());
+#if __USE_FP16__
   } else if (in->type() == Type::float16) {
     gLogSoftmax<half, float><<<blocks, threads, shared>>>(out->data<__half>(), out->shape(), in->data<__half>());
+#endif
   } else {
     ABORT("LogSoftmax not implemented for type {}", in->type());
   }
@@ -812,10 +841,12 @@ void SoftmaxGrad(Tensor grad, Tensor adj, Tensor val) {
   if(grad->type() == Type::float32) {
     gSoftmaxGrad<float, float><<<blocks, threads, shared>>>(
       grad->data<float>(), adj->data<float>(), val->data<float>(), m, k);
+#if __USE_FP16__
   } else if (grad->type() == Type::float16) {
     // Accumulate into float
     gSoftmaxGrad<half, float><<<blocks, threads, shared>>>(
       grad->data<half>(), adj->data<half>(), val->data<half>(), m, k);
+#endif
   } else {
     ABORT("SoftmaxGrad not implemented for type {}", grad->type());
   }
@@ -880,10 +911,12 @@ void LogSoftmaxGrad(Tensor grad, Tensor adj, Tensor val) {
   if(grad->type() == Type::float32) {
     gLogSoftmaxGrad<float, float><<<blocks, threads, shared>>>(
       grad->data<float>(), adj->data<float>(), val->data<float>(), m, k);
+#if __USE_FP16__
   } else if (grad->type() == Type::float16) {
     // accumulate into float
     gLogSoftmaxGrad<half, float><<<blocks, threads, shared>>>(
       grad->data<half>(), adj->data<half>(), val->data<half>(), m, k);
+#endif
   } else {
     ABORT("LogSoftmaxGrad not implemented for type {}", grad->type());
   }
@@ -932,9 +965,11 @@ void CopyRows(Tensor out,
   if(out->type() == Type::float32) {
     gCopyRows<<<blocks, threads>>>(
       out->data<float>(), in->data<float>(), cols, indices->data<IndexType>(), rowsToCopy);
+#if __USE_FP16__
   } else if (out->type() == Type::float16) {
     gCopyRows<<<blocks, threads>>>(
       out->data<half>(), in->data<half>(), cols, indices->data<IndexType>(), rowsToCopy);
+#endif
   } else {
     ABORT("CopyRows not implemented for type {}", out->type());
   }
@@ -983,9 +1018,11 @@ void PasteRows(Tensor out,
   if(out->type() == Type::float32) {
     gPasteRows<<<blocks, threads>>>(
       out->data<float>(), in->data<float>(), cols, indices->data<IndexType>(), rowsToCopy);
+#if __USE_FP16__
   } else if (out->type() == Type::float16) {
     gPasteRows<<<blocks, threads>>>(
       out->data<half>(), in->data<half>(), cols, indices->data<IndexType>(), rowsToCopy);
+#endif
   } else {
     ABORT("CopyRows not implemented for type {}", out->type());
   }
@@ -1031,9 +1068,11 @@ void CopyCols(Tensor out, const Tensor in, const Tensor indices) {
   if(out->type() == Type::float32) {
     gCopyCols<<<blocks, threads>>>(
       out->data<float>(), in->data<float>(), rows, cols, indices->data<IndexType>(), colsToCopy);
+#if __USE_FP16__
   } else if (out->type() == Type::float16) {
     gCopyCols<<<blocks, threads>>>(
       out->data<half>(), in->data<half>(), rows, cols, indices->data<IndexType>(), colsToCopy);
+#endif
   } else {
     ABORT("CopyCols not implemented for type {}", out->type());
   }
@@ -1079,9 +1118,11 @@ void PasteCols(Tensor out,
   if(out->type() == Type::float32) {
     gPasteCols<<<blocks, threads>>>(
       out->data<float>(), in->data<float>(), rows, cols, indices->data<IndexType>(), colsToCopy);
+#if __USE_FP16__
   } else if (out->type() == Type::float16) {
     gPasteCols<<<blocks, threads>>>(
       out->data<half>(), in->data<half>(), rows, cols, indices->data<IndexType>(), colsToCopy);
+#endif
   } else {
     ABORT("PasteCols not implemented for type {}", out->type());
   }
@@ -1151,6 +1192,7 @@ void Select(Tensor out,
                                 in->shape(),
                                 axisGPU,
                                 indices->data<IndexType>());
+#if __USE_FP16__
   } else if (out->type() == Type::float16) {
     gSelect<<<blocks, threads>>>(out->data<half>(),
                                 out->shape(),
@@ -1158,6 +1200,7 @@ void Select(Tensor out,
                                 in->shape(),
                                 axisGPU,
                                 indices->data<IndexType>());
+#endif
   } else {
     ABORT("Select not implemented for type {}", out->type());
   }
@@ -1184,6 +1227,7 @@ void Insert(Tensor out,
                                 in->shape(),
                                 axisGPU,
                                 indices->data<IndexType>());
+#if __USE_FP16__
   } else if (out->type() == Type::float16) {
     gInsert<<<blocks, threads>>>(out->data<half>(),
                                 out->shape(),
@@ -1191,6 +1235,7 @@ void Insert(Tensor out,
                                 in->shape(),
                                 axisGPU,
                                 indices->data<IndexType>());
+#endif
   } else {
     ABORT("Insert not implemented for type {}", out->type());
   }
@@ -1260,6 +1305,7 @@ void GRUFastForward(Tensor out, std::vector<Tensor> inputs, bool final) {
         rows,
         cols,
         final);
+#if __USE_FP16__
   } else if (out->type() == Type::float16) {
     gGRUFastForward<<<blocks, threads>>>(
         out->data<half>(),                                // output
@@ -1271,6 +1317,7 @@ void GRUFastForward(Tensor out, std::vector<Tensor> inputs, bool final) {
         rows,
         cols,
         final);
+#endif
   } else {
     ABORT("GRUFastForward not implemented for type {}", out->type());
   }
@@ -1393,6 +1440,7 @@ void GRUFastBackward(std::vector<Tensor> outputs,
         rows,
         cols,
         final);
+#if __USE_FP16__
   } else if (adj->type() == Type::float16) {
     gGRUFastBackward<<<blocks, threads>>>(
         outputs[0] ? outputs[0]->data<half>() : 0,        // state - adj
@@ -1408,6 +1456,7 @@ void GRUFastBackward(std::vector<Tensor> outputs,
         rows,
         cols,
         final);
+#endif
   } else {
     ABORT("gGRUFastBackward not implemented for type {}", adj->type());
   }
@@ -1505,9 +1554,11 @@ void CrossEntropyPick(Tensor out, Tensor in, Tensor indices) {
   if(out->type() == Type::float32) {
     gCrossEntropyPick<float, float><<<blocks, threads, shared>>>(
       out->data<float>(), out->shape(), in->data<float>(), in->shape(), indices->data<IndexType>());
+#if __USE_FP16__
   } else if(out->type() == Type::float16) {
     gCrossEntropyPick<half, float><<<blocks, threads, shared>>>(
       out->data<half>(), out->shape(), in->data<half>(), in->shape(), indices->data<IndexType>());
+#endif
   } else {
     ABORT("CrossEntropyPick not implemented for type {}", out->type());
   }
@@ -1602,9 +1653,11 @@ void CrossEntropyPickBackward(Tensor out, Tensor adj, Tensor a, Tensor indices)
   if(out->type() == Type::float32) {
     gCrossEntropyPickBackward<float, float><<<blocks, threads, shared>>>(
       out->data<float>(), out->shape(), adj->data<float>(), a->data<float>(), indices->data<IndexType>());
+#if __USE_FP16__
   } else if(out->type() == Type::float16) {
     gCrossEntropyPickBackward<half, float><<<blocks, threads, shared>>>(
       out->data<half>(), out->shape(), adj->data<half>(), a->data<half>(), indices->data<IndexType>());
+#endif
   } else {
     ABORT("CrossEntropyPick not implemented for type {}", out->type());
   }
@@ -1624,8 +1677,10 @@ float L2Norm(Tensor in, Ptr<Allocator> allocator) {
     using namespace functional;
     if(in->type() == Type::float32) {
       ReduceAll<float, float>(_1 * _1, blockMem, in);
+#if __USE_FP16__
     } else if(in->type() == Type::float16) {
       ReduceAll<half, float>(_1 * _1, blockMem, in);
+#endif
     } else {
       ABORT("L2Norm not implemented for type {}", in->type());
     }
@@ -1643,8 +1698,10 @@ float L2Norm(Tensor in, Ptr<Allocator> allocator) {
     using namespace functional;
     if(in->type() == Type::float32) {
       ReduceAll<float, float>(_1 * _1, out, in);
+#if __USE_FP16__
     } else if(in->type() == Type::float16) {
       ReduceAll<half, float>(_1 * _1, out, in);
+#endif
     } else {
       ABORT("L2Norm not implemented for type {}", in->type());
     }
@@ -1718,9 +1775,11 @@ void Att(Tensor out, Tensor va, Tensor context, Tensor state) {
   if(out->type() == Type::float32) {
     gAtt<float, float><<<blocks, threads, shared>>>(
       out->data<float>(), va->data<float>(), context->data<float>(), state->data<float>(), m, k, b, t);
+#if __USE_FP16__
   } else if (out->type() == Type::float16) {
     gAtt<half, float><<<blocks, threads, shared>>>(
       out->data<half>(), va->data<half>(), context->data<half>(), state->data<half>(), m, k, b, t);
+#endif
   } else {
     ABORT("gAtt not implemented for type {}", out->type());
   }
@@ -1793,6 +1852,7 @@ void AttBack(Tensor gVa,
                                   m,
                                   k,
                                   n);
+#if __USE_FP16__
   } else if (gVa->type() == Type::float16) {
     gAttBack<<<blocks, threads>>>(gVa->data<half>(),
                                   gContext->data<half>(),
@@ -1804,6 +1864,7 @@ void AttBack(Tensor gVa,
                                   m,
                                   k,
                                   n);
+#endif
   } else {
     ABORT("gAttBack not implemented for type {}", gVa->type());
   }
@@ -1912,6 +1973,7 @@ void LayerNormalization(Tensor out,
                                                  rows,
                                                  cols,
                                                  eps);
+#if __USE_FP16__
   } else if (out->type() == Type::float16) {
     gLNormalization<half, float><<<blocks, threads, shared>>>(out->data<half>(),
                                                  in->data<half>(),
@@ -1920,7 +1982,7 @@ void LayerNormalization(Tensor out,
                                                  rows,
                                                  cols,
                                                  eps);
-
+#endif
   } else {
     ABORT("LayerNormalization not implemented for type {}", out->type());
   }
@@ -2089,6 +2151,7 @@ void LayerNormalizationGrad(Ptr<Allocator> allocator,
       rows,
       cols,
       eps);
+#if __USE_FP16__
   } else if (gradX->type() == Type::float16) {
     // accumulate in float
     int shared = sizeof(float) * threads * 4;
@@ -2103,6 +2166,7 @@ void LayerNormalizationGrad(Ptr<Allocator> allocator,
       rows,
       cols,
       eps);
+#endif
   } else {
     ABORT("LayerNormalizationGrad not implemented for type {}", gradX->type());
   }
@@ -2166,9 +2230,11 @@ void Shift(Tensor out,
   if(out->type() == Type::float32) {
     gShift<false>
         <<<blocks, threads>>>(out->data<float>(), in->data<float>(), length, offset, padValue);
+#if __USE_FP16__
   } else if(out->type() == Type::float16) {
     gShift<false>
         <<<blocks, threads>>>(out->data<half>(), in->data<half>(), length, offset, padValue);
+#endif
   } else {
     ABORT("Shift not implemented for type {}", out->type());
   }
@@ -2196,9 +2262,11 @@ void ShiftGrad(Tensor out, Tensor in, marian::Shape shift, bool invert) {
   if(out->type() == Type::float32) {
     gShift<true>
         <<<blocks, threads>>>(out->data<float>(), in->data<float>(), length, offset, 0.f); // @TODO: What about padValue?
+#if __USE_FP16__
   } else if(out->type() == Type::float16) {
     gShift<true>
         <<<blocks, threads>>>(out->data<half>(), in->data<half>(), length, offset, 0.f);
+#endif
   } else {
     ABORT("Shift not implemented for type {}", out->type());
   }
@@ -2302,6 +2370,7 @@ void LSTMCellForward(Tensor out, std::vector<Tensor> inputs) {
       inputs.size() > 4 ? inputs[4]->data<float>() : 0,  // mask
       rows,
       cols);
+#if __USE_FP16__
   } else if (out->type() == Type::float16) {
     gLSTMCellForward<<<blocks, threads>>>(
       out->data<half>(),                                // output
@@ -2312,6 +2381,7 @@ void LSTMCellForward(Tensor out, std::vector<Tensor> inputs) {
       inputs.size() > 4 ? inputs[4]->data<half>() : 0,  // mask
       rows,
       cols);
+#endif
   } else {
     ABORT("LSTMCellForward not implemented for type {}", out->type());
   }
@@ -2363,6 +2433,7 @@ void LSTMOutputForward(Tensor out, std::vector<Tensor> inputs) {
                                             inputs[3]->data<float>(),  // b
                                             rows,
                                             cols);
+#if __USE_FP16__
   } else if (out->type() == Type::float16) {
     gLSTMOutputForward<<<blocks, threads>>>(out->data<half>(),        // output
                                             inputs[0]->data<half>(),  // cell state
@@ -2371,6 +2442,7 @@ void LSTMOutputForward(Tensor out, std::vector<Tensor> inputs) {
                                             inputs[3]->data<half>(),  // b
                                             rows,
                                             cols);
+#endif
   } else {
     ABORT("gLSTMOutputForward not implemented for type {}", out->type());
   }
@@ -2478,6 +2550,7 @@ void LSTMCellBackward(std::vector<Tensor> outputs,
       adj->data<float>(),
       rows,
       cols);
+#if __USE_FP16__
   } else if (adj->type() == Type::float16) {
     gLSTMCellBackward<<<blocks, threads>>>(
       outputs[0] ? outputs[0]->data<half>() : 0,        // state - adj
@@ -2492,6 +2565,7 @@ void LSTMCellBackward(std::vector<Tensor> outputs,
       adj->data<half>(),
       rows,
       cols);
+#endif
   } else {
     ABORT("gLSTMCellBackward not implemented for type {}", adj->type());
   }
@@ -2575,6 +2649,7 @@ void LSTMOutputBackward(std::vector<Tensor> outputs,
         adj->data<float>(),
         rows,
         cols);
+#if __USE_FP16__
   } else if (adj->type() == Type::float16) {
     gLSTMOutputBackward<<<blocks, threads>>>(
         outputs[0] ? outputs[0]->data<half>() : 0,  // state - adj
@@ -2588,6 +2663,7 @@ void LSTMOutputBackward(std::vector<Tensor> outputs,
         adj->data<half>(),
         rows,
         cols);
+#endif
   } else {
     ABORT("gLSTMOutputBackward not implemented for type {}", adj->type());
   }
@@ -2622,9 +2698,11 @@ void HighwayForward(Tensor out,
   if(out->type() == Type::float32) {
     gHighwayForward<<<blocks, threads>>>(
         out->data<float>(), in1->data<float>(), in2->data<float>(), t->data<float>(), length);
+#if __USE_FP16__
   } else if(out->type() == Type::float16) {
     gHighwayForward<<<blocks, threads>>>(
         out->data<half>(), in1->data<half>(), in2->data<half>(), t->data<half>(), length);
+#endif
   } else {
     ABORT("HighwayForward not implemented for type {}", out->type());
   }
@@ -2674,6 +2752,7 @@ void HighwayBackward(Tensor out1,
                                           t->data<float>(),
                                           adj->data<float>(),
                                           length);
+#if __USE_FP16__
   } else if(out1->type() == Type::float16) {
     gHighwayBackward<<<blocks, threads>>>(out1->data<half>(),
                                           out2->data<half>(),
@@ -2683,6 +2762,7 @@ void HighwayBackward(Tensor out1,
                                           t->data<half>(),
                                           adj->data<half>(),
                                           length);
+#endif
   } else {
     ABORT("HighwayForward not implemented for type {}", out1->type());
   }
diff --git a/src/translator/nth_element.cu b/src/translator/nth_element.cu
index 323efb855..b899ab5bd 100644
--- a/src/translator/nth_element.cu
+++ b/src/translator/nth_element.cu
@@ -400,9 +400,11 @@ public:
     if(scores->type() == Type::float32) {
       float minimal = std::numeric_limits<float>::lowest();
       selectNBest(scores->data<float>(), batchFirstElementIdxs, cumulativeBeamSizes, minimal);
+#if __USE_FP16__
     } else if(scores->type() == Type::float16) {
       float minimal = std::numeric_limits<float16>::lowest();
       selectNBest(scores->data<half>(), batchFirstElementIdxs, cumulativeBeamSizes, minimal);
+#endif
     } else {
       ABORT("getNBestList not implemented for type {}", scores->type());
     }

From 9c8b635ff38723069fd183b7af1ead9425a5132f Mon Sep 17 00:00:00 2001
From: Marcin Junczys-Dowmunt <Marcin.JunczysDowmunt@microsoft.com>
Date: Mon, 16 Sep 2019 14:13:23 -0700
Subject: [PATCH 638/838] add warning about type

---
 src/common/types.h        | 4 ++--
 src/tensors/cpu/element.h | 2 +-
 src/tensors/gpu/prod.cpp  | 2 +-
 3 files changed, 4 insertions(+), 4 deletions(-)

diff --git a/src/common/types.h b/src/common/types.h
index 2ba2cb941..61ce4c5b2 100755
--- a/src/common/types.h
+++ b/src/common/types.h
@@ -24,8 +24,8 @@
   #else
     #define __USE_FP16__ 0 // we are in GPU code, but compute capability is too low to use FP16
   #endif
-#else // other compiler, likely host code
-  #define __USE_FP16__ 0
+#else // other compiler, likely host code. Sould be fine with seeing the correct includes with host code
+  #define __USE_FP16__ 1
 #endif
 
 #ifdef _MSC_VER
diff --git a/src/tensors/cpu/element.h b/src/tensors/cpu/element.h
index c6270a133..3a936b31a 100644
--- a/src/tensors/cpu/element.h
+++ b/src/tensors/cpu/element.h
@@ -119,7 +119,7 @@ void Element(const Functor& functor, marian::Tensor out, Tensors... tensors) {
   switch(out->type()) {
     case Type::float32: elementFloat(functor, out, tensors...); break;
     //case Type::uint32:  element<uint32_t>(functor, out, tensors...); break;
-    default: ABORT("Unsupported type for element-wise operation"); break;
+    default: ABORT("Unsupported type for element-wise operation: {}", out->type()); break;
   }
 }
 
diff --git a/src/tensors/gpu/prod.cpp b/src/tensors/gpu/prod.cpp
index 767584d92..193095677 100755
--- a/src/tensors/gpu/prod.cpp
+++ b/src/tensors/gpu/prod.cpp
@@ -294,7 +294,7 @@ void ProdBatched(marian::Tensor C,
                  float scalar) {
   if(C->type() == Type::float32) {
     ProdBatchedTyped<float>(C, allocator, A, B, transA, transB, beta, scalar);
-  } else if(C->type() == Type::float16) {
+  } else if(C->type() == Type::float16) { // not a *.cu file
     ProdBatchedTyped<half>(C, allocator, A, B, transA, transB, __float2half(beta), __float2half(scalar));
   } else {
     ABORT("ProdBatched not implemented for type {}", C->type());

From 6168cf20d24ea12c3ba3a283af855d55d73a35ac Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Mon, 16 Sep 2019 15:20:53 -0700
Subject: [PATCH 639/838] minor build fix for VS

---
 src/tensors/cpu/tensor_operators.cpp | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/tensors/cpu/tensor_operators.cpp b/src/tensors/cpu/tensor_operators.cpp
index 07d56acdb..9e1fe7fb8 100755
--- a/src/tensors/cpu/tensor_operators.cpp
+++ b/src/tensors/cpu/tensor_operators.cpp
@@ -28,7 +28,7 @@ inline float stableSigmoid(float x) {
   }
 }
 
-void IsNaN(const Tensor in, Ptr<Allocator> allocator, bool& isNaN, bool& isInf) {
+void IsNaN(const Tensor in, Ptr<Allocator> allocator, bool& /*isNaN*/, bool& /*isInf*/) {
   ABORT("Not implemented");
 }
 

From b948cd63db808de7dc360581d3964bd809e61cc5 Mon Sep 17 00:00:00 2001
From: Marcin Junczys-Dowmunt <Marcin.JunczysDowmunt@microsoft.com>
Date: Tue, 17 Sep 2019 09:53:45 -0700
Subject: [PATCH 640/838] missing part for fp16 inference, some clean-up for
 types

---
 src/common/types.h                 | 38 +++++++++++++++++++++++-------
 src/graph/expression_operators.cpp |  2 +-
 src/models/transformer.h           |  4 ++--
 src/translator/helpers.cu          | 25 ++++++++++++++------
 4 files changed, 51 insertions(+), 18 deletions(-)

diff --git a/src/common/types.h b/src/common/types.h
index 61ce4c5b2..f41ff05fc 100755
--- a/src/common/types.h
+++ b/src/common/types.h
@@ -13,6 +13,7 @@
 #include <iostream>
 #include <string>
 #include <functional>
+#include <type_traits>
 
 #ifndef __CUDA_ARCH__
 #include <immintrin.h>
@@ -344,14 +345,35 @@ void matchOrAbort(Type type) {
            type);
 }
 
-template <typename T>
+namespace typeMagic { // just in case, let's not pollute the namespace
+  template <typename RequestType, typename ReturnType>
+  constexpr bool fitsIntoMax() { return std::numeric_limits<RequestType>::max() <= std::numeric_limits<ReturnType>::max(); } // for built-in types everything is constexpr
+
+  // add specializations here when needed
+  template <> constexpr bool fitsIntoMax<float16, float>() { return true; };  // for float16 conversion to float is not constexpr, hence specializations
+  template <> constexpr bool fitsIntoMax<float, float16>() { return false; }; // for float16 conversion to float is not constexpr, hence specializations
+}
+
+template <typename ReturnType>
 class NumericLimits {
 private:
-  template <typename L>
+  
+  template <typename MaxType> void setLimitsMax() {
+    max    = std::numeric_limits<MaxType>::max();
+    min    = std::numeric_limits<MaxType>::min();
+    lowest = std::numeric_limits<MaxType>::lowest();
+  }
+
+  template <typename RequestType>
   void setLimits() {
-    max = std::numeric_limits<L>::max();
-    min = std::numeric_limits<L>::min();
-    lowest = std::numeric_limits<L>::lowest();
+    // check if the maximum of type RequestType fits into ReturnType
+    constexpr bool fits = typeMagic::fitsIntoMax<RequestType, ReturnType>();
+    // and then use the smaller of each types to determine max, min, lowest.
+    using MaxType = typename std::conditional<fits, RequestType, ReturnType>::type;
+    setLimitsMax<MaxType>();
+
+    // @TODO: should we rather abort if the RequestType does not fit into ReturnType instead of clipping to smaller type? 
+    // ABORT_IF(!fits, "Type {} is too small to contain max of type {}", typeId<ReturnType>(), typeId<RequestType>());
   }
 
   void setLimits(Type type) {
@@ -359,9 +381,9 @@ class NumericLimits {
   }
 
 public:
-  T max;
-  T min;
-  T lowest;
+  ReturnType max;
+  ReturnType min;
+  ReturnType lowest;
 
   NumericLimits(Type type) {
     setLimits(type);
diff --git a/src/graph/expression_operators.cpp b/src/graph/expression_operators.cpp
index 6f3d3aecc..a627c2ba1 100644
--- a/src/graph/expression_operators.cpp
+++ b/src/graph/expression_operators.cpp
@@ -86,7 +86,7 @@ Expr softmax(Expr a, Expr zeroOneMask, int axis /*=-1*/) {
   // This will return the smallest value / 2 for the input type converted to float
   // So for Type::Float16 that will be the smallest fp16 value expressed as float
   // We divide by 2 to allow for some tolerance and overflow protection.
-  float smallestFloat = -NumericLimits<float>(a->value_type()).max / 2.f;
+  float smallestFloat = NumericLimits<float>(a->value_type()).lowest / 2.f;
   auto logMask = (1.f - zeroOneMask) * smallestFloat;
   return softmax(a + logMask, axis);
 }
diff --git a/src/models/transformer.h b/src/models/transformer.h
index d56516e52..103ca0ba4 100755
--- a/src/models/transformer.h
+++ b/src/models/transformer.h
@@ -101,8 +101,8 @@ class Transformer : public EncoderOrDecoderBase {
   // convert multiplicative 1/0 mask to additive 0/-inf log mask, and transpose to match result of bdot() op in Attention()
   static Expr transposedLogMask(Expr mask) { // mask: [-4: beam depth=1, -3: batch size, -2: vector dim=1, -1: max length]
     auto ms = mask->shape();
-    float maskFactor = std::min(NumericLimits<float>(mask->value_type()).max / 2.f, 99999.f); // to make sure we do not overflow for fp16
-    mask = (1 - mask) * -maskFactor;
+    float maskFactor = std::max(NumericLimits<float>(mask->value_type()).lowest / 2.f, -99999.f); // to make sure we do not overflow for fp16
+    mask = (1 - mask) * maskFactor;
     return reshape(mask, {ms[-3], 1, ms[-2], ms[-1]}); // [-4: batch size, -3: num heads broadcast=1, -2: max length broadcast=1, -1: max length]
   }
 
diff --git a/src/translator/helpers.cu b/src/translator/helpers.cu
index a5dfd2e89..dda369fcb 100644
--- a/src/translator/helpers.cu
+++ b/src/translator/helpers.cu
@@ -10,15 +10,18 @@
 #include "tensors/tensor.h"
 #include "translator/helpers.h"
 
+#include "tensors/gpu/cuda_helpers.h"
+
 namespace marian {
 
 namespace gpu {
 
-__global__ void gSetColumn(float* d_in,
+template <typename T>
+__global__ void gSetColumn(T* d_in,
                            size_t n_columns,
                            size_t n_rows,
                            size_t noColumn,
-                           float value) {
+                           T value) {
   size_t rowNumber = threadIdx.x + blockDim.x * blockIdx.x;
   size_t index = noColumn + rowNumber * n_columns;
 
@@ -27,18 +30,26 @@ __global__ void gSetColumn(float* d_in,
   }
 }
 
-void SetColumn(Tensor in_, size_t col, float value) {
-  int nRows = in_->shape().elements() / in_->shape()[-1];
-  int nColumns = in_->shape()[-1];
+void SetColumn(Tensor in, size_t col, float value) {
+  int nRows = in->shape().elements() / in->shape()[-1];
+  int nColumns = in->shape()[-1];
 
   int nBlocks = nRows / 512 + ((nRows % 512 == 0) ? 0 : 1);
   int nThreads = std::min(512, nRows);
 
-  gSetColumn<<<nBlocks, nThreads>>>(in_->data(), nColumns, nRows, col, value);
+ if(in->type() == Type::float32) {
+   gSetColumn<<<nBlocks, nThreads>>>(in->data<float>(), nColumns, nRows, col, value);
+#if __USE_FP16__
+ } else if(in->type() == Type::float16) {
+   gSetColumn<<<nBlocks, nThreads>>>(in->data<half>(), nColumns, nRows, col, (half)value);
+#endif
+ } else {
+   ABORT("suppressWord not implemented for type {}", in->type());
+ }
 }
 
 void suppressWord(Expr probs, WordIndex wordIndex) {
-  SetColumn(probs->val(), wordIndex, std::numeric_limits<float>::lowest());
+  SetColumn(probs->val(), wordIndex, NumericLimits<float>(probs->value_type()).lowest);
 }
 }  // namespace gpu
 }  // namespace marian

From 16e6fb9faeb140e09e195d608d69433365c1155c Mon Sep 17 00:00:00 2001
From: Marcin Junczys-Dowmunt <Marcin.JunczysDowmunt@microsoft.com>
Date: Tue, 17 Sep 2019 11:30:46 -0700
Subject: [PATCH 641/838] small clean up, move fbgemm pointer

---
 CMakeLists.txt               | 20 ++++++++++----------
 src/3rd_party/fbgemm         |  2 +-
 src/common/aliases.cpp       |  5 ++---
 src/common/config_parser.cpp |  2 --
 4 files changed, 13 insertions(+), 16 deletions(-)

diff --git a/CMakeLists.txt b/CMakeLists.txt
index 876d1b424..0c0df36ca 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -348,14 +348,14 @@ add_subdirectory(src)
 
 
 # Add a target to generate API documentation with Doxygen
-find_package(Doxygen)
-if(DOXYGEN_FOUND)
-  configure_file(${CMAKE_CURRENT_SOURCE_DIR}/Doxyfile.in
-           ${CMAKE_CURRENT_BINARY_DIR}/Doxyfile @ONLY)
-  add_custom_target(doc
-    ${DOXYGEN_EXECUTABLE} ${CMAKE_CURRENT_BINARY_DIR}/Doxyfile
-    WORKING_DIRECTORY ${CMAKE_CURRENT_BINARY_DIR}
-    COMMENT "Generating API documentation with Doxygen" VERBATIM
-  )
-endif(DOXYGEN_FOUND)
+#find_package(Doxygen)
+#if(DOXYGEN_FOUND)
+#  configure_file(${CMAKE_CURRENT_SOURCE_DIR}/Doxyfile.in
+#           ${CMAKE_CURRENT_BINARY_DIR}/Doxyfile @ONLY)
+#  add_custom_target(doc
+#    ${DOXYGEN_EXECUTABLE} ${CMAKE_CURRENT_BINARY_DIR}/Doxyfile
+#    WORKING_DIRECTORY ${CMAKE_CURRENT_BINARY_DIR}
+#    COMMENT "Generating API documentation with Doxygen" VERBATIM
+#  )
+#endif(DOXYGEN_FOUND)
 
diff --git a/src/3rd_party/fbgemm b/src/3rd_party/fbgemm
index 49e8018ab..f0b354327 160000
--- a/src/3rd_party/fbgemm
+++ b/src/3rd_party/fbgemm
@@ -1 +1 @@
-Subproject commit 49e8018ab2397c175354317b35c6be6dd68f8932
+Subproject commit f0b354327aaf2330c65340725b1981040c8bec9e
diff --git a/src/common/aliases.cpp b/src/common/aliases.cpp
index 6a7d5a431..616ad1f8d 100644
--- a/src/common/aliases.cpp
+++ b/src/common/aliases.cpp
@@ -28,9 +28,8 @@ namespace marian {
 void ConfigParser::addAliases(cli::CLIWrapper& cli) {
   cli.alias("fp16", "true", [&](YAML::Node& config) {
     if(mode_ == cli::mode::training) {
-      config["precision"] = std::vector<std::string>({"float16", "float32", "float32"});
-      if(config["cost-scaling"]) // set by parser hence override @TODO: this is a bit hacky, should we rather see modes like "training" or "tanslating"?
-        config["cost-scaling"] = std::vector<std::string>({"7", "2000", "2", "0.05", "10", "1"});
+      config["precision"] = std::vector<std::string>({"float16", "float32", "float32"}); // inference type, optimization type, save type
+      config["cost-scaling"] = std::vector<std::string>({"7", "2000", "2", "0.05", "10", "1"});
     } else {
       config["precision"] = std::vector<std::string>({"float16"}); // for inference we do not need the other types
     }
diff --git a/src/common/config_parser.cpp b/src/common/config_parser.cpp
index 515e2d44b..048b86363 100755
--- a/src/common/config_parser.cpp
+++ b/src/common/config_parser.cpp
@@ -670,10 +670,8 @@ void ConfigParser::addSuboptionsBatching(cli::CLIWrapper& cli) {
     cli.add<size_t>("--mini-batch-fit-step",
       "Step size for mini-batch-fit statistics",
       10);
-#if 0 // @TODO: not available yet
     cli.add<bool>("--gradient-checkpointing", 
       "Use gradient checkpointing to minimize memory usage");
-#endif
   }
 
   cli.add<int>("--maxi-batch",

From 4d10ae9212d4c4fc3b996f89c5fd550bc120e3bf Mon Sep 17 00:00:00 2001
From: Marcin Junczys-Dowmunt <Marcin.JunczysDowmunt@microsoft.com>
Date: Tue, 17 Sep 2019 13:46:52 -0700
Subject: [PATCH 642/838] small changes to cmake

---
 CMakeLists.txt               | 24 ++++++++++++------------
 src/tensors/gpu/algorithm.cu |  3 ---
 2 files changed, 12 insertions(+), 15 deletions(-)

diff --git a/CMakeLists.txt b/CMakeLists.txt
index 0c0df36ca..dfbca9a4c 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -232,9 +232,9 @@ endif(COMPILE_CUDA)
 
 # TODO: make compatible with older CUDA versions
 if(CMAKE_BUILD_TYPE STREQUAL "Debug")
-  list(APPEND CUDA_NVCC_FLAGS --default-stream per-thread; -O0; -g; --use_fast_math; -arch=sm_30; -gencode=arch=compute_30,code=sm_30; -gencode=arch=compute_50,code=sm_50; -gencode=arch=compute_52,code=sm_52; -gencode=arch=compute_60,code=sm_60; -gencode=arch=compute_61,code=sm_61; -gencode=arch=compute_70,code=sm_70; -gencode=arch=compute_70,code=compute_70 ;)
+  list(APPEND CUDA_NVCC_FLAGS --default-stream per-thread; -O0; -g; --use_fast_math; -arch=sm_35; -gencode=arch=compute_35,code=sm_35; -gencode=arch=compute_50,code=sm_50; -gencode=arch=compute_52,code=sm_52; -gencode=arch=compute_60,code=sm_60; -gencode=arch=compute_61,code=sm_61; -gencode=arch=compute_70,code=sm_70; -gencode=arch=compute_70,code=compute_70 ;)
 else(CMAKE_BUILD_TYPE STREQUAL "Debug")
-  list(APPEND CUDA_NVCC_FLAGS --default-stream per-thread; -O3; -g; --use_fast_math; -arch=sm_30; -gencode=arch=compute_30,code=sm_30; -gencode=arch=compute_50,code=sm_50; -gencode=arch=compute_52,code=sm_52; -gencode=arch=compute_60,code=sm_60; -gencode=arch=compute_61,code=sm_61; -gencode=arch=compute_70,code=sm_70; -gencode=arch=compute_70,code=compute_70 ;)
+  list(APPEND CUDA_NVCC_FLAGS --default-stream per-thread; -O3; -g; --use_fast_math; -arch=sm_35; -gencode=arch=compute_35,code=sm_35; -gencode=arch=compute_50,code=sm_50; -gencode=arch=compute_52,code=sm_52; -gencode=arch=compute_60,code=sm_60; -gencode=arch=compute_61,code=sm_61; -gencode=arch=compute_70,code=sm_70; -gencode=arch=compute_70,code=compute_70 ;)
 endif(CMAKE_BUILD_TYPE STREQUAL "Debug")
 if(NOT MSVC)
   # @TODO: add warnings here too
@@ -348,14 +348,14 @@ add_subdirectory(src)
 
 
 # Add a target to generate API documentation with Doxygen
-#find_package(Doxygen)
-#if(DOXYGEN_FOUND)
-#  configure_file(${CMAKE_CURRENT_SOURCE_DIR}/Doxyfile.in
-#           ${CMAKE_CURRENT_BINARY_DIR}/Doxyfile @ONLY)
-#  add_custom_target(doc
-#    ${DOXYGEN_EXECUTABLE} ${CMAKE_CURRENT_BINARY_DIR}/Doxyfile
-#    WORKING_DIRECTORY ${CMAKE_CURRENT_BINARY_DIR}
-#    COMMENT "Generating API documentation with Doxygen" VERBATIM
-#  )
-#endif(DOXYGEN_FOUND)
+find_package(Doxygen)
+if(DOXYGEN_FOUND)
+  configure_file(${CMAKE_CURRENT_SOURCE_DIR}/Doxyfile.in
+           ${CMAKE_CURRENT_BINARY_DIR}/Doxyfile @ONLY)
+  add_custom_target(doc
+    ${DOXYGEN_EXECUTABLE} ${CMAKE_CURRENT_BINARY_DIR}/Doxyfile
+    WORKING_DIRECTORY ${CMAKE_CURRENT_BINARY_DIR}
+   COMMENT "Generating API documentation with Doxygen" VERBATIM
+  )
+endif(DOXYGEN_FOUND)
 
diff --git a/src/tensors/gpu/algorithm.cu b/src/tensors/gpu/algorithm.cu
index 0ce6b3655..9a8ae8b17 100644
--- a/src/tensors/gpu/algorithm.cu
+++ b/src/tensors/gpu/algorithm.cu
@@ -20,15 +20,12 @@ template void copy<int8_t>(Ptr<Backend>, const int8_t*, const int8_t*, int8_t*);
 template void copy<int16_t>(Ptr<Backend>, const int16_t*, const int16_t*, int16_t*);
 template void copy<int32_t>(Ptr<Backend>, const int32_t*, const int32_t*, int32_t*);
 template void copy<int64_t>(Ptr<Backend>, const int64_t*, const int64_t*, int64_t*);
-
 template void copy<uint8_t>(Ptr<Backend>, const uint8_t*, const uint8_t*, uint8_t*);
 template void copy<uint16_t>(Ptr<Backend>, const uint16_t*, const uint16_t*, uint16_t*);
 template void copy<uint32_t>(Ptr<Backend>, const uint32_t*, const uint32_t*, uint32_t*);
 template void copy<uint64_t>(Ptr<Backend>, const uint64_t*, const uint64_t*, uint64_t*);
-
 template void copy<char>(Ptr<Backend>, const char*, const char*, char*);
 template void copy<float16>(Ptr<Backend>, const float16*, const float16*, float16*);
-
 template void copy<float>(Ptr<Backend>, const float*, const float*, float*);
 template void copy<double>(Ptr<Backend>, const double*, const double*, double*);
 // clang-format on

From efed4edf3d34ec7007575d6aee2b5113a2677e23 Mon Sep 17 00:00:00 2001
From: Marcin Junczys-Dowmunt <Marcin.JunczysDowmunt@microsoft.com>
Date: Tue, 17 Sep 2019 14:15:33 -0700
Subject: [PATCH 643/838] some more clean-up

---
 src/common/aliases.cpp        | 189 +++++++++++++++++-----------------
 src/translator/nth_element.cu |   4 +-
 2 files changed, 96 insertions(+), 97 deletions(-)

diff --git a/src/common/aliases.cpp b/src/common/aliases.cpp
index 616ad1f8d..49c60bdd5 100644
--- a/src/common/aliases.cpp
+++ b/src/common/aliases.cpp
@@ -41,108 +41,107 @@ void ConfigParser::addAliases(cli::CLIWrapper& cli) {
       config["shuffle"] = "none";
     });
 
-  // @TODO: indent
-  // Options setting the BiDeep architecture proposed in http://www.aclweb.org/anthology/W17-4710
-  cli.alias("best-deep", "true", [](YAML::Node& config) {
-    config["layer-normalization"] = true;
-    config["tied-embeddings"] = true;
-    config["enc-type"] = "alternating";
-    config["enc-cell-depth"] = 2;
-    config["enc-depth"] = 4;
-    config["dec-cell-base-depth"] = 4;
-    config["dec-cell-high-depth"] = 2;
-    config["dec-depth"] = 4;
-    config["skip"] = true;
+    // Options setting the BiDeep architecture proposed in http://www.aclweb.org/anthology/W17-4710
+    cli.alias("best-deep", "true", [](YAML::Node& config) {
+      config["layer-normalization"] = true;
+      config["tied-embeddings"] = true;
+      config["enc-type"] = "alternating";
+      config["enc-cell-depth"] = 2;
+      config["enc-depth"] = 4;
+      config["dec-cell-base-depth"] = 4;
+      config["dec-cell-high-depth"] = 2;
+      config["dec-depth"] = 4;
+      config["skip"] = true;
 
-    // Training specific options
-    config["learn-rate"] = 0.0003;
-    config["cost-type"] = "ce-mean-words";
-    config["lr-decay-inv-sqrt"] = 16000;
-    config["label-smoothing"] = 0.1;
-    config["clip-norm"] = 5;
-    config["sync-sgd"] = true;
-    config["exponential-smoothing"] = 1e-4;
-    config["mini-batch-fit"] = true;
-    config["mini-batch"] = 1000;
-    config["maxi-batch"] = 1000;
-    // config["workspace"] = 6500;
-  });
+      // Training specific options
+      config["learn-rate"] = 0.0003;
+      config["cost-type"] = "ce-mean-words";
+      config["lr-decay-inv-sqrt"] = 16000;
+      config["label-smoothing"] = 0.1;
+      config["clip-norm"] = 5;
+      config["sync-sgd"] = true;
+      config["exponential-smoothing"] = 1e-4;
+      config["mini-batch-fit"] = true;
+      config["mini-batch"] = 1000;
+      config["maxi-batch"] = 1000;
+      // config["workspace"] = 6500;
+    });
 
-  // Architecture and proposed training settings for a Transformer "base" model introduced in
-  // https://papers.nips.cc/paper/7181-attention-is-all-you-need.pdf
-  cli.alias("task", "transformer-base", [](YAML::Node& config) {
-    // Model options
-    config["type"] = "transformer";
-    config["enc-depth"] = 6;
-    config["dec-depth"] = 6;
-    config["dim-emb"] = 512;
-    config["tied-embeddings-all"] = true;
-    config["transformer-dim-ffn"] = 2048;
-    config["transformer-heads"] = 8;
-    config["transformer-postprocess"] = "dan";
-    config["transformer-preprocess"] = "";
-    config["transformer-ffn-activation"] = "relu";
-    config["transformer-dropout"] = 0.1;
+    // Architecture and proposed training settings for a Transformer "base" model introduced in
+    // https://papers.nips.cc/paper/7181-attention-is-all-you-need.pdf
+    cli.alias("task", "transformer-base", [](YAML::Node& config) {
+      // Model options
+      config["type"] = "transformer";
+      config["enc-depth"] = 6;
+      config["dec-depth"] = 6;
+      config["dim-emb"] = 512;
+      config["tied-embeddings-all"] = true;
+      config["transformer-dim-ffn"] = 2048;
+      config["transformer-heads"] = 8;
+      config["transformer-postprocess"] = "dan";
+      config["transformer-preprocess"] = "";
+      config["transformer-ffn-activation"] = "relu";
+      config["transformer-dropout"] = 0.1;
 
-    // Training specific options
-    config["learn-rate"] = 0.0003;
-    config["cost-type"] = "ce-mean-words";
-    config["lr-warmup"] = 16000;
-    config["lr-decay-inv-sqrt"] = 16000;
-    config["label-smoothing"] = 0.1;
-    config["clip-norm"] = 5;
-    config["sync-sgd"] = true;
-    config["exponential-smoothing"] = 1e-4;
-    config["max-length"] = 100;
-    config["mini-batch-fit"] = true;
-    config["mini-batch"] = 1000;
-    config["maxi-batch"] = 1000;
-    config["workspace"] = 9500;
-    config["optimizer-params"] = std::vector<float>({0.9f, 0.98f, 1e-09f});
+      // Training specific options
+      config["learn-rate"] = 0.0003;
+      config["cost-type"] = "ce-mean-words";
+      config["lr-warmup"] = 16000;
+      config["lr-decay-inv-sqrt"] = 16000;
+      config["label-smoothing"] = 0.1;
+      config["clip-norm"] = 5;
+      config["sync-sgd"] = true;
+      config["exponential-smoothing"] = 1e-4;
+      config["max-length"] = 100;
+      config["mini-batch-fit"] = true;
+      config["mini-batch"] = 1000;
+      config["maxi-batch"] = 1000;
+      config["workspace"] = 9500;
+      config["optimizer-params"] = std::vector<float>({0.9f, 0.98f, 1e-09f});
 
-    // Validation specific options
-    config["beam-size"] = 8;
-    config["valid-mini-batch"] = 16;
-    config["normalize"] = 1.0;
-  });
+      // Validation specific options
+      config["beam-size"] = 8;
+      config["valid-mini-batch"] = 16;
+      config["normalize"] = 1.0;
+    });
 
-  // Architecture and proposed training settings for a Transformer "big" model introduced in
-  // https://papers.nips.cc/paper/7181-attention-is-all-you-need.pdf
-  cli.alias("task", "transformer-big", [](YAML::Node& config) {
-    // Model options
-    config["type"] = "transformer";
-    config["enc-depth"] = 6;
-    config["dec-depth"] = 6;
-    config["dim-emb"] = 1024;
-    config["tied-embeddings-all"] = true;
-    config["transformer-dim-ffn"] = 4096;
-    config["transformer-heads"] = 16;
-    config["transformer-postprocess"] = "dan";
-    config["transformer-preprocess"] = "";
-    config["transformer-ffn-activation"] = "relu";
-    config["transformer-dropout"] = 0.1;
+    // Architecture and proposed training settings for a Transformer "big" model introduced in
+    // https://papers.nips.cc/paper/7181-attention-is-all-you-need.pdf
+    cli.alias("task", "transformer-big", [](YAML::Node& config) {
+      // Model options
+      config["type"] = "transformer";
+      config["enc-depth"] = 6;
+      config["dec-depth"] = 6;
+      config["dim-emb"] = 1024;
+      config["tied-embeddings-all"] = true;
+      config["transformer-dim-ffn"] = 4096;
+      config["transformer-heads"] = 16;
+      config["transformer-postprocess"] = "dan";
+      config["transformer-preprocess"] = "";
+      config["transformer-ffn-activation"] = "relu";
+      config["transformer-dropout"] = 0.1;
 
-    // Training specific options
-    config["learn-rate"] = 0.0002;
-    config["cost-type"] = "ce-mean-words";
-    config["lr-warmup"] = 8000;
-    config["lr-decay-inv-sqrt"] = 8000;
-    config["label-smoothing"] = 0.1;
-    config["clip-norm"] = 5;
-    config["sync-sgd"] = true;
-    config["exponential-smoothing"] = 1e-4;
-    config["max-length"] = 100;
-    config["mini-batch-fit"] = true;
-    config["mini-batch"] = 1000;
-    config["maxi-batch"] = 1000;
-    config["workspace"] = 13000;
-    config["optimizer-params"] = std::vector<float>({0.9f, 0.998f, 1e-09f});
+      // Training specific options
+      config["learn-rate"] = 0.0002;
+      config["cost-type"] = "ce-mean-words";
+      config["lr-warmup"] = 8000;
+      config["lr-decay-inv-sqrt"] = 8000;
+      config["label-smoothing"] = 0.1;
+      config["clip-norm"] = 5;
+      config["sync-sgd"] = true;
+      config["exponential-smoothing"] = 1e-4;
+      config["max-length"] = 100;
+      config["mini-batch-fit"] = true;
+      config["mini-batch"] = 1000;
+      config["maxi-batch"] = 1000;
+      config["workspace"] = 13000;
+      config["optimizer-params"] = std::vector<float>({0.9f, 0.998f, 1e-09f});
 
-    // Validation specific options
-    config["beam-size"] = 8;
-    config["valid-mini-batch"] = 8;
-    config["normalize"] = 1.0;
-  });
+      // Validation specific options
+      config["beam-size"] = 8;
+      config["valid-mini-batch"] = 8;
+      config["normalize"] = 1.0;
+    });
   }
 }
 
diff --git a/src/translator/nth_element.cu b/src/translator/nth_element.cu
index b899ab5bd..6398c5a5c 100644
--- a/src/translator/nth_element.cu
+++ b/src/translator/nth_element.cu
@@ -398,11 +398,11 @@ public:
     }
 
     if(scores->type() == Type::float32) {
-      float minimal = std::numeric_limits<float>::lowest();
+      float minimal = NumericLimits<float>(scores->type()).lowest;
       selectNBest(scores->data<float>(), batchFirstElementIdxs, cumulativeBeamSizes, minimal);
 #if __USE_FP16__
     } else if(scores->type() == Type::float16) {
-      float minimal = std::numeric_limits<float16>::lowest();
+      float minimal = NumericLimits<float>(scores->type()).lowest;
       selectNBest(scores->data<half>(), batchFirstElementIdxs, cumulativeBeamSizes, minimal);
 #endif
     } else {

From bc146302f39ce5b5dff1b900fab9a26afb67ced1 Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Wed, 18 Sep 2019 13:30:14 -0700
Subject: [PATCH 644/838] bug fix: workaround for a bug fix in
 FactoredSegmenter that caused a backcompat issue

---
 src/common/io_item.h        | 2 +-
 src/common/types.h          | 6 +++---
 src/data/factored_vocab.cpp | 6 ++++++
 3 files changed, 10 insertions(+), 4 deletions(-)
 mode change 100644 => 100755 src/common/io_item.h

diff --git a/src/common/io_item.h b/src/common/io_item.h
old mode 100644
new mode 100755
index 7cb522047..e10930238
--- a/src/common/io_item.h
+++ b/src/common/io_item.h
@@ -74,7 +74,7 @@ struct Item {
     if(type == Type::float32)
       convertFromTo<float, T>();
     else if(type == Type::float16)
-      convertFromTo<float16, T>();
+      convertFromTo<HalfFloat, T>();
     else 
       ABORT("convert from type {} not implemented", type);
   }
diff --git a/src/common/types.h b/src/common/types.h
index 8c0f17a1e..4339d7239 100755
--- a/src/common/types.h
+++ b/src/common/types.h
@@ -293,9 +293,9 @@ class NumericLimits {
 private:
   template <typename L>
   void setLimits() {
-    max = std::numeric_limits<L>::max();
-    min = std::numeric_limits<L>::min();
-    lowest = std::numeric_limits<L>::lowest();
+    max    = (T)std::numeric_limits<L>::max();
+    min    = (T)std::numeric_limits<L>::min();
+    lowest = (T)std::numeric_limits<L>::lowest();
   }
 
   void setLimits(Type type) {
diff --git a/src/data/factored_vocab.cpp b/src/data/factored_vocab.cpp
index 32bea9cae..144e87128 100755
--- a/src/data/factored_vocab.cpp
+++ b/src/data/factored_vocab.cpp
@@ -47,6 +47,12 @@ namespace marian {
     std::map<std::string,std::set<std::string>> factorTypeMap; // [type name] -> {factor-type names}
     std::vector<std::string> deferredFactorVocab; // factor surface forms are presently expected to be at the end of factorVocab_, so collect them here first
     while(io::getline(in, line)) {
+#if 1 // workaround for a bug fix in FactoredSegmenter that made old .fsv files incompatible
+      if (line      == "\xef\xb8\x8f : _lemma _has_wb")         // old vocabs have a wrong factor in here
+        line         = "\xef\xb8\x8f : _lemma _has_gl _has_gr"; // patch it to the correct one
+      else if (line == "\xef\xb8\x8e : _lemma _has_wb")
+        line         = "\xef\xb8\x8e : _lemma _has_gl _has_gr";
+#endif
       utils::splitAny(line, tokBuf, " \t");
       if (tokBuf.empty() || tokBuf[0][0] == '#') // skip comments and blank lines
         continue;

From 834905411e302659c1c7f877ba7f7bfbed28b88a Mon Sep 17 00:00:00 2001
From: Marcin Junczys-Dowmunt <Marcin.JunczysDowmunt@microsoft.com>
Date: Mon, 7 Oct 2019 15:49:46 -0700
Subject: [PATCH 645/838] address code review comments

---
 CMakeLists.txt                      |   2 +-
 contrib/merge-and-verify-driver.sh  |   3 -
 src/3rd_party/reduce_all.h          |  28 ++++++-
 src/common/aliases.cpp              |   5 +-
 src/common/config_parser.cpp        |   4 +-
 src/common/types.h                  |  14 ++--
 src/data/batch_generator.h          |   9 ++-
 src/functional/operators.h          |   2 +-
 src/models/transformer.h            |   2 +-
 src/optimizers/clippers.cpp         |   2 +-
 src/tensors/gpu/add.cu              |   2 +-
 src/tensors/gpu/algorithm.cu        |   4 +-
 src/tensors/gpu/cuda_helpers.h      |   2 +-
 src/tensors/gpu/element.cu          |   2 +-
 src/tensors/gpu/prod.cpp            | 120 ++++++++++++++--------------
 src/tensors/gpu/tensor_operators.cu | 104 ++++++++++++------------
 src/translator/helpers.cu           |   2 +-
 src/translator/nth_element.cu       |  28 +++----
 18 files changed, 184 insertions(+), 151 deletions(-)
 delete mode 100755 contrib/merge-and-verify-driver.sh

diff --git a/CMakeLists.txt b/CMakeLists.txt
index dfbca9a4c..f5125ef8d 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -348,7 +348,7 @@ add_subdirectory(src)
 
 
 # Add a target to generate API documentation with Doxygen
-find_package(Doxygen)
+#find_package(Doxygen)
 if(DOXYGEN_FOUND)
   configure_file(${CMAKE_CURRENT_SOURCE_DIR}/Doxyfile.in
            ${CMAKE_CURRENT_BINARY_DIR}/Doxyfile @ONLY)
diff --git a/contrib/merge-and-verify-driver.sh b/contrib/merge-and-verify-driver.sh
deleted file mode 100755
index 1cdcd24b5..000000000
--- a/contrib/merge-and-verify-driver.sh
+++ /dev/null
@@ -1,3 +0,0 @@
-#!/bin/bash
-git merge-file "${1}" "${2}" "${3}"
-exit 1
diff --git a/src/3rd_party/reduce_all.h b/src/3rd_party/reduce_all.h
index 42960f517..c5285bbfa 100644
--- a/src/3rd_party/reduce_all.h
+++ b/src/3rd_party/reduce_all.h
@@ -1,3 +1,5 @@
+// This software contains source code provided by NVIDIA Corporation.
+
 /*
  * Copyright 1993-2015 NVIDIA Corporation.  All rights reserved.
  *
@@ -6,9 +8,33 @@
  * this software. Any use, reproduction, disclosure, or distribution of
  * this software and related documentation outside the terms of the EULA
  * is strictly prohibited.
- *
  */
 
+/*
+MJD: Relevant text from the NVIDIA EULA:
+
+2.1 Sample Source Code Modification, Ownership and Distribution
+
+Subject to the terms of the SLA and this Supplement, NVIDIA hereby grants you a non-
+exclusive, non-transferable license, without the right to sublicense, during the applicable
+license term unless earlier terminated pursuant to the SLA, to have Authorized Users
+modify and create derivative works of CUDA Licensed Software that constitutes sample
+source code, when provided to you by NVIDIA in source code form. You hold all rights,
+title and interest in and to your modifications and derivative works of the sample source
+code software that you create as permitted hereunder (collective, Derivatives”), subject
+to NVIDIA’s underlying Intellectual Property Rights in and to the CUDA Licensed
+Software; provided, however that you grant NVIDIA and its Affiliates an irrevocable,
+perpetual, nonexclusive, worldwide, royalty-free paid-up license to make, have made,
+use, have used, reproduce, license, distribute, sublicense, transfer and otherwise
+commercialize Derivatives including (without limitation) with the CUDA Licensed
+Software or other NVIDIA products, technologies or materials. You may distribute the
+CUDA Supplement to Software License Agreement End User License Agreements (EULA) 
+DR-06739-001_v01_v9.0 | 14 sample source code as delivered by NVIDIA and/or your Derivatives, 
+provided that all NVIDIA copyright notices and trademarks are maintained and used properly 
+and the sample source code includes the following notice: “This software contains source code
+provided by NVIDIA Corporation.”
+*/
+
 #pragma once
 
 #include "tensors/tensor.h"
diff --git a/src/common/aliases.cpp b/src/common/aliases.cpp
index 49c60bdd5..14679efc8 100644
--- a/src/common/aliases.cpp
+++ b/src/common/aliases.cpp
@@ -29,7 +29,10 @@ void ConfigParser::addAliases(cli::CLIWrapper& cli) {
   cli.alias("fp16", "true", [&](YAML::Node& config) {
     if(mode_ == cli::mode::training) {
       config["precision"] = std::vector<std::string>({"float16", "float32", "float32"}); // inference type, optimization type, save type
-      config["cost-scaling"] = std::vector<std::string>({"7", "2000", "2", "0.05", "10", "1"});
+      // @TODO: review this
+      // scaling factor (power of 2), frequency, multiplier at increase, tolerance, range, minium factor
+      config["cost-scaling"] = std::vector<std::string>({"7", "2000", "2", "0.05", "10", "1"}); 
+      ABORT("fp16 training currently not yet supported.");
     } else {
       config["precision"] = std::vector<std::string>({"float16"}); // for inference we do not need the other types
     }
diff --git a/src/common/config_parser.cpp b/src/common/config_parser.cpp
index 048b86363..af30d97e0 100755
--- a/src/common/config_parser.cpp
+++ b/src/common/config_parser.cpp
@@ -432,7 +432,7 @@ void ConfigParser::addOptionsTraining(cli::CLIWrapper& cli) {
       {"float32", "float32", "float32"});
   cli.add<std::vector<std::string>>("--cost-scaling",
       "Dynamic cost scaling for mixed precision training: "
-      "power of 2, scaling window, scaling factor, tolerance")->implicit_val("7.f 2000 2.f 0.05f 10 1.f");
+      "power of 2, scaling window, scaling factor, tolerance, range, minimum factor")->implicit_val("7.f 2000 2.f 0.05f 10 1.f");
   cli.add<bool>("--normalize-gradient", "Normalize gradient by multiplying with worldsize / total labels");
 
   // multi-node training
@@ -671,7 +671,7 @@ void ConfigParser::addSuboptionsBatching(cli::CLIWrapper& cli) {
       "Step size for mini-batch-fit statistics",
       10);
     cli.add<bool>("--gradient-checkpointing", 
-      "Use gradient checkpointing to minimize memory usage");
+      "Enable gradient-checkpointing to minimize memory usage");
   }
 
   cli.add<int>("--maxi-batch",
diff --git a/src/common/types.h b/src/common/types.h
index f41ff05fc..3db52f995 100755
--- a/src/common/types.h
+++ b/src/common/types.h
@@ -21,12 +21,12 @@
 
 #ifdef __CUDACC__ // nvcc is compiling this code
   #if (__CUDA_ARCH__ >= 600 || !defined(__CUDA_ARCH__))
-    #define __USE_FP16__ 1 // we are in GPU code and we know what to do with FP16 code
+    #define COMPILE_FP16 1 // we are in GPU code and we know what to do with FP16 code
   #else
-    #define __USE_FP16__ 0 // we are in GPU code, but compute capability is too low to use FP16
+    #define COMPILE_FP16 0 // we are in GPU code, but compute capability is too low to use FP16
   #endif
 #else // other compiler, likely host code. Sould be fine with seeing the correct includes with host code
-  #define __USE_FP16__ 1
+  #define COMPILE_FP16 1
 #endif
 
 #ifdef _MSC_VER
@@ -345,7 +345,9 @@ void matchOrAbort(Type type) {
            type);
 }
 
-namespace typeMagic { // just in case, let's not pollute the namespace
+namespace typeFitting { // own namespace instead of in class, otherwise we get error "explicit specialization in non-namespace scope"
+
+  // compares max for different types as constexpr, so can be used at compile-time to determine if RequestType type max fits into ReturnType max, see std::conditional below.
   template <typename RequestType, typename ReturnType>
   constexpr bool fitsIntoMax() { return std::numeric_limits<RequestType>::max() <= std::numeric_limits<ReturnType>::max(); } // for built-in types everything is constexpr
 
@@ -360,14 +362,13 @@ class NumericLimits {
   
   template <typename MaxType> void setLimitsMax() {
     max    = std::numeric_limits<MaxType>::max();
-    min    = std::numeric_limits<MaxType>::min();
     lowest = std::numeric_limits<MaxType>::lowest();
   }
 
   template <typename RequestType>
   void setLimits() {
     // check if the maximum of type RequestType fits into ReturnType
-    constexpr bool fits = typeMagic::fitsIntoMax<RequestType, ReturnType>();
+    constexpr bool fits = typeFitting::fitsIntoMax<RequestType, ReturnType>();
     // and then use the smaller of each types to determine max, min, lowest.
     using MaxType = typename std::conditional<fits, RequestType, ReturnType>::type;
     setLimitsMax<MaxType>();
@@ -382,7 +383,6 @@ class NumericLimits {
 
 public:
   ReturnType max;
-  ReturnType min;
   ReturnType lowest;
 
   NumericLimits(Type type) {
diff --git a/src/data/batch_generator.h b/src/data/batch_generator.h
index c0b70c88a..b44660865 100644
--- a/src/data/batch_generator.h
+++ b/src/data/batch_generator.h
@@ -65,8 +65,13 @@ class BatchGenerator : public RNGEngine {
   Ptr<DataSet> data_;
   Ptr<Options> options_;
   bool restored_{false};
-  bool shuffleData_{false};
-  bool shuffleBatches_{false};
+
+  // replacing old shuffle_ with two variants that determine more fine-grained shuffling behavior. 
+  // Both set to false is equivalent to old shuffle_ == false. 
+  // Now we can not shuffle the data, but shuffle batches. Useful for linear reading of very large data sets with pre-reading.
+  // Parameters like maxi-batch determine how much data is pre-read and sorted by length or other criteria. 
+  bool shuffleData_{false};    // determine if full data should be shuffled before reading and batching.
+  bool shuffleBatches_{false}; // determine if batches should be shuffled after batching.
 
 private:
   Ptr<BatchStats> stats_;
diff --git a/src/functional/operators.h b/src/functional/operators.h
index d2a3b4654..a41961501 100755
--- a/src/functional/operators.h
+++ b/src/functional/operators.h
@@ -440,7 +440,7 @@ struct Ops<float32x8> {
 #endif // of "#ifndef __CUDA_ARCH__"
 
 #ifdef __CUDA_ARCH__
-#if __USE_FP16__
+#if COMPILE_FP16
 // only compile with fp16 support for compute_70, i.e. VOLTA 100 and above.
 #include <cuda_fp16.h>
 
diff --git a/src/models/transformer.h b/src/models/transformer.h
index 103ca0ba4..65e9b607f 100755
--- a/src/models/transformer.h
+++ b/src/models/transformer.h
@@ -101,7 +101,7 @@ class Transformer : public EncoderOrDecoderBase {
   // convert multiplicative 1/0 mask to additive 0/-inf log mask, and transpose to match result of bdot() op in Attention()
   static Expr transposedLogMask(Expr mask) { // mask: [-4: beam depth=1, -3: batch size, -2: vector dim=1, -1: max length]
     auto ms = mask->shape();
-    float maskFactor = std::max(NumericLimits<float>(mask->value_type()).lowest / 2.f, -99999.f); // to make sure we do not overflow for fp16
+    float maskFactor = std::max(NumericLimits<float>(mask->value_type()).lowest / 2.f, -99999999.f); // to make sure we do not overflow for fp16
     mask = (1 - mask) * maskFactor;
     return reshape(mask, {ms[-3], 1, ms[-2], ms[-1]}); // [-4: batch size, -3: num heads broadcast=1, -2: max length broadcast=1, -1: max length]
   }
diff --git a/src/optimizers/clippers.cpp b/src/optimizers/clippers.cpp
index 5da8cfe6a..7f3b81196 100644
--- a/src/optimizers/clippers.cpp
+++ b/src/optimizers/clippers.cpp
@@ -11,7 +11,7 @@ void Elementwise::clip(Tensor t) {
 
 void Norm::clip(Tensor t) {
   using namespace functional;
-  float l2Norm = L2Norm(t, nullptr); // @TODO: this is wrong, but will be replaced with better version in a PR or two.
+  float l2Norm = L2Norm(t, nullptr); // @TODO: this is a placeholder for a memory allocator, will be replaced with better version in a PR or two.
   if(l2Norm >= c_)
     Element(_1 = (c_ / l2Norm) * _1, t);
 }
diff --git a/src/tensors/gpu/add.cu b/src/tensors/gpu/add.cu
index e4591f954..0a3f959a7 100755
--- a/src/tensors/gpu/add.cu
+++ b/src/tensors/gpu/add.cu
@@ -171,7 +171,7 @@ void Aggregate(Functor functor, float aggInit, AggFunctor aggFunctor, float scal
   if(out->type() == Type::float32) {
     AggregateTyped<float, float>(functor, aggInit, aggFunctor, scale, out, tensors...);
   } else if(out->type() == Type::float16) {
-#if __USE_FP16__
+#if COMPILE_FP16
     AggregateTyped<half,  float>(functor, aggInit, aggFunctor, scale, out, tensors...);
 #else
      ABORT("FP16 not supported with current hardware or CUDA version");
diff --git a/src/tensors/gpu/algorithm.cu b/src/tensors/gpu/algorithm.cu
index 9a8ae8b17..926e9deb5 100644
--- a/src/tensors/gpu/algorithm.cu
+++ b/src/tensors/gpu/algorithm.cu
@@ -59,7 +59,7 @@ void fill<float16>(Ptr<Backend> backend, float16* begin, float16* end, float16 v
   int size = end - begin;
   if (size == 0)
     return;
-#if __USE_FP16__
+#if COMPILE_FP16
   CUDA_CHECK(cudaSetDevice(backend->getDeviceId().no));
   int threadsPerBlock = std::min(MAX_THREADS, size);
   int blocks = (size / threadsPerBlock) + (size % threadsPerBlock != 0); // @TODO: (size+threadsPerBlock-1)/threadsPerBlock or CeilDiv(a,b)
@@ -123,7 +123,7 @@ void swap_ranges<float16>(Ptr<Backend> backend, float16* begin, float16* end, fl
   if (size == 0)
     return;
 
-#if __USE_FP16__
+#if COMPILE_FP16
   CUDA_CHECK(cudaSetDevice(backend->getDeviceId().no));
   int threadsPerBlock = std::min(MAX_THREADS, size);
   int blocks = (size / threadsPerBlock) + (size % threadsPerBlock != 0); // @TODO: (size+threadsPerBlock-1)/threadsPerBlock or CeilDiv(a,b)
diff --git a/src/tensors/gpu/cuda_helpers.h b/src/tensors/gpu/cuda_helpers.h
index 9163784b1..41c2d0c55 100644
--- a/src/tensors/gpu/cuda_helpers.h
+++ b/src/tensors/gpu/cuda_helpers.h
@@ -4,7 +4,7 @@
 
 #include <cuda_runtime.h>
 
-#if __USE_FP16__
+#if COMPILE_FP16
 #include <cuda_fp16.h>
 #endif
 
diff --git a/src/tensors/gpu/element.cu b/src/tensors/gpu/element.cu
index 5425625f3..8525b71b3 100755
--- a/src/tensors/gpu/element.cu
+++ b/src/tensors/gpu/element.cu
@@ -64,7 +64,7 @@ void Element(Functor functor, Tensor out, Tensors... tensors) {
   if(out->type() == Type::float32) {
     ElementTyped<float>(functor, out, tensors...);
   } else if(out->type() == Type::float16) {
-#if __USE_FP16__
+#if COMPILE_FP16
     ElementTyped<__half>(functor, out, tensors...);
 #else
     ABORT("FP16 not supported with chosen current hardware or CUDA version");
diff --git a/src/tensors/gpu/prod.cpp b/src/tensors/gpu/prod.cpp
index 193095677..970709b6c 100755
--- a/src/tensors/gpu/prod.cpp
+++ b/src/tensors/gpu/prod.cpp
@@ -52,6 +52,7 @@ static void unsetTensorMode(cublasHandle_t cublasHandle) {
 #endif
 }
 
+// overload for float, contains configuratio settings for float32
 cublasStatus_t cublasGemmTyped(cublasHandle_t handle,
                                cublasOperation_t transa, 
                                cublasOperation_t transb,
@@ -62,13 +63,14 @@ cublasStatus_t cublasGemmTyped(cublasHandle_t handle,
                                const float* beta,
                                float* C, int ldc) {
   return cublasGemmEx(handle, transa, transb, 
-                     m, n, k, alpha, 
-                     A, CUDA_R_32F, lda, 
-                     B, CUDA_R_32F, ldb, beta, 
-                     C, CUDA_R_32F, ldc,
-                     CUDA_R_32F, CUBLAS_GEMM_DEFAULT_TENSOR_OP); // @TODO: review algorithm
+                      m, n, k, alpha, 
+                      A, CUDA_R_32F, lda, 
+                      B, CUDA_R_32F, ldb, beta, 
+                      C, CUDA_R_32F, ldc,
+                      CUDA_R_32F, CUBLAS_GEMM_DEFAULT_TENSOR_OP); // @TODO: review algorithm
 }
 
+// overload for half, contains configuratio settings for float16
 cublasStatus_t cublasGemmTyped(cublasHandle_t handle,
                                cublasOperation_t transa, 
                                cublasOperation_t transb,
@@ -82,37 +84,37 @@ cublasStatus_t cublasGemmTyped(cublasHandle_t handle,
   //float betaf = __half2float(*beta);   // has to match computeType
 
   return cublasGemmEx(handle, transa, transb, 
-                     m, n, k, alpha, 
-                     A, CUDA_R_16F, lda, 
-                     B, CUDA_R_16F, ldb, beta, 
-                     C, CUDA_R_16F, ldc,
-                     CUDA_R_16F, CUBLAS_GEMM_DEFAULT_TENSOR_OP); // @TODO: review algorithm
+                      m, n, k, alpha, 
+                      A, CUDA_R_16F, lda, 
+                      B, CUDA_R_16F, ldb, beta, 
+                      C, CUDA_R_16F, ldc,
+                      CUDA_R_16F, CUBLAS_GEMM_DEFAULT_TENSOR_OP); // @TODO: review algorithm
 }
 
 template <typename T>
 void ProdTyped(marian::Tensor C,
-          const marian::Tensor& A,
-          const marian::Tensor& B,
-          bool transA,
-          bool transB,
-          T beta,
-          T scalar) {
+               const marian::Tensor& A,
+               const marian::Tensor& B,
+               bool transA,
+               bool transB,
+               T beta,
+               T scalar) {
   cudaSetDevice((int)C->getDeviceId().no);
   T alpha = scalar;
 
-  size_t m = A->shape().elements() / A->shape().back();
-  size_t k = A->shape().back();
+  int m = A->shape().elements() / A->shape().back();
+  int k = A->shape().back();
   if(transA)
     std::swap(m, k);
 
-  size_t l = B->shape().elements() / B->shape().back();
-  size_t n = B->shape().back();
+  int l = B->shape().elements() / B->shape().back();
+  int n = B->shape().back();
   if(transB)
     std::swap(l, n);
 
-  size_t lda = A->shape().back();
-  size_t ldb = B->shape().back();
-  size_t ldc = B->shape().back();
+  int lda = A->shape().back();
+  int ldb = B->shape().back();
+  int ldc = B->shape().back();
 
   if(transB)
     ldc = B->shape().elements() / B->shape().back();
@@ -125,19 +127,19 @@ void ProdTyped(marian::Tensor C,
 
   setTensorMode(cublasHandle);
   CUBLAS_CHECK(cublasGemmTyped(cublasHandle,
-                  opB,
-                  opA,
-                  n,
-                  m,
-                  k,
-                  &alpha,
-                  B->data<T>(),
-                  ldb,
-                  A->data<T>(),
-                  lda,
-                  &beta,
-                  C->data<T>(),
-                  ldc));
+                               opB,
+                               opA,
+                               n,
+                               m,
+                               k,
+                               &alpha,
+                               B->data<T>(),
+                               ldb,
+                               A->data<T>(),
+                               lda,
+                               &beta,
+                               C->data<T>(),
+                               ldc));
   unsetTensorMode(cublasHandle);
 }
 
@@ -210,22 +212,22 @@ void ProdBatchedTyped(marian::Tensor C,
   cudaSetDevice((int)C->getDeviceId().no);
   T alpha = scalar;
 
-  size_t batchA = A->shape().elements() / (A->shape()[-1] * A->shape()[-2]);
-  size_t batchB = B->shape().elements() / (B->shape()[-1] * B->shape()[-2]);
+  int batchA = A->shape().elements() / (A->shape()[-1] * A->shape()[-2]);
+  int batchB = B->shape().elements() / (B->shape()[-1] * B->shape()[-2]);
 
-  size_t m = A->shape()[-2];
-  size_t k = A->shape()[-1];
+  int m = A->shape()[-2];
+  int k = A->shape()[-1];
   if(transA)
     std::swap(m, k);
 
-  size_t l = B->shape()[-2];
-  size_t n = B->shape()[-1];
+  int l = B->shape()[-2];
+  int n = B->shape()[-1];
   if(transB)
     std::swap(l, n);
 
-  size_t lda = A->shape()[-1];
-  size_t ldb = B->shape()[-1];
-  size_t ldc = B->shape()[-1];
+  int lda = A->shape()[-1];
+  int ldb = B->shape()[-1];
+  int ldc = B->shape()[-1];
 
   if(transB)
     ldc = B->shape()[-2];
@@ -263,20 +265,20 @@ void ProdBatchedTyped(marian::Tensor C,
 
   setTensorMode(cublasHandle);
   CUBLAS_CHECK(cublasGemmBatchedTyped(cublasHandle,
-                         opB,
-                         opA,
-                         n,
-                         m,
-                         k,
-                         &alpha,
-                         mp_bptr->data<const T*>(),
-                         ldb,
-                         mp_aptr->data<const T*>(),
-                         lda,
-                         &beta,
-                         mp_cptr->data<T*>(),
-                         ldc,
-                         batchC));
+                                      opB,
+                                      opA,
+                                      n,
+                                      m,
+                                      k,
+                                      &alpha,
+                                      mp_bptr->data<const T*>(),
+                                      ldb,
+                                      mp_aptr->data<const T*>(),
+                                      lda,
+                                      &beta,
+                                      mp_cptr->data<T*>(),
+                                      ldc,
+                                      batchC));
   unsetTensorMode(cublasHandle);
 
   allocator->free(mp_aptr);
diff --git a/src/tensors/gpu/tensor_operators.cu b/src/tensors/gpu/tensor_operators.cu
index 4299a57e3..af7c974fb 100644
--- a/src/tensors/gpu/tensor_operators.cu
+++ b/src/tensors/gpu/tensor_operators.cu
@@ -22,7 +22,7 @@ static inline  __device__ void atomicAdd(float *address, float val) {
   ::atomicAdd(address, val);
 }
 
-#if __USE_FP16__
+#if COMPILE_FP16
 // @TODO: copied from CuTorch, adapt this better, give credit.
 static inline  __device__ void atomicAdd(half *address, half val) {
   //*address += val;
@@ -82,7 +82,7 @@ void IsNaN(const Tensor in, Ptr<Allocator> allocator, bool& isNaN, bool& isInf)
 
   if(in->type() == Type::float32) {
     gIsNaN<<<blocks, threads>>>(in->data<float>(), length, dIsNaN, dIsInf);
-#if __USE_FP16__
+#if COMPILE_FP16
   } else if(in->type() == Type::float16) {
     gIsNaN<<<blocks, threads>>>(in->data<half>(), length, dIsNaN, dIsInf);
 #endif
@@ -119,9 +119,9 @@ template <typename T>
 void CopyCastFrom(Tensor out, const T* in, int length) {
   if(out->type() == Type::float32) {
     CopyCastTo(out->data<float>(), in, length);
-#if __USE_FP16__
+#if COMPILE_FP16
   } else if(out->type() == Type::float16) {
-    CopyCastTo(out->data<__half>(), in, length);
+    CopyCastTo(out->data<half>(), in, length);
 #endif
   } else if(out->type() == Type::float64) {
     CopyCastTo(out->data<double>(), in, length);
@@ -135,7 +135,7 @@ void CopyCast(Tensor out, const Tensor in) {
 
   if(in->type() == Type::float32) {
     CopyCastFrom(out, in->data<float>(), (int)in->size());
-#if __USE_FP16__
+#if COMPILE_FP16
   } else if(in->type() == Type::float16) {
     CopyCastFrom(out, in->data<half>(), (int)in->size());
 #endif
@@ -196,6 +196,7 @@ __global__ void gInsertCols(T* out,
   }
 }
 
+// Special version for axis = -1 @TODO: write common better version
 void Concatenate1(Tensor out, const std::vector<Tensor>& inputs) {
   cudaSetDevice(out->getDeviceId().no);
 
@@ -215,7 +216,7 @@ void Concatenate1(Tensor out, const std::vector<Tensor>& inputs) {
     if(out->type() == Type::float32) {
       gInsertCols<false><<<blocks, threads>>>(
           out->data<float>(), in->data<float>(), rows, cols_in, cols_out, cols_in, offset, 0);
-#if __USE_FP16__
+#if COMPILE_FP16
     } else if(out->type() == Type::float16) {
       gInsertCols<false><<<blocks, threads>>>(
           out->data<half>(), in->data<half>(), rows, cols_in, cols_out, cols_in, offset, 0);
@@ -266,6 +267,7 @@ __global__ void gJoin2(T* out,
   }
 }
 
+// Special version for axis = -2 @TODO: write common better version
 void Concatenate2(Tensor out, Tensor in1, Tensor in2) {
   cudaSetDevice(out->getDeviceId().no);
 
@@ -288,7 +290,7 @@ void Concatenate2(Tensor out, Tensor in1, Tensor in2) {
                                  rowStride1,
                                  in2->data<float>(),
                                  rowStride2);
-#if __USE_FP16__
+#if COMPILE_FP16
   } else if(out->type() == Type::float16) {
      gJoin2<<<blocks, threads>>>(out->data<half>(),
                                  rowBatch,
@@ -331,7 +333,7 @@ void Split1(std::vector<Tensor>& outputs, const Tensor in) {
     if(out->type() == Type::float32) {
       gInsertCols<true><<<blocks, threads>>>(
           out->data<float>(), in->data<float>(), rows, cols_out, cols_out, cols_in, 0, offset);
-#if __USE_FP16__
+#if COMPILE_FP16
     } else if(out->type() == Type::float16) {
       gInsertCols<true><<<blocks, threads>>>(
           out->data<half>(), in->data<half>(), rows, cols_out, cols_out, cols_in, 0, offset);
@@ -381,12 +383,10 @@ void SplitCont(std::vector<Tensor>& outputs, const Tensor in, int axis) {
       int blocks = std::min(MAX_BLOCKS, size / threads + (size % threads != 0));
 
       if(out->type() == Type::float32) {
-        gAddRow<<<blocks, threads>>>(
-            out->data<float>() + offset2, in->data<float>() + offset1, size);
-#if __USE_FP16__
+        gAddRow<<<blocks, threads>>>(out->data<float>() + offset2, in->data<float>() + offset1, size);
+#if COMPILE_FP16
       } else if(out->type() == Type::float16) {
-        gAddRow<<<blocks, threads>>>(
-            out->data<half>() + offset2, in->data<half>() + offset1, size);
+        gAddRow<<<blocks, threads>>>(out->data<half>() + offset2, in->data<half>() + offset1, size);
 #endif
       } else {
         ABORT("SplitCont not implemented for type {}", out->type());
@@ -480,9 +480,9 @@ void TransposeND(Tensor out, Tensor in, const std::vector<int>& vAxis) {
 
     if(in->type() == Type::float32) {
       gTranspose0213<false><<<blocks, threads>>>(out->data<float>(), in->data<float>(), rows, cols, stride1, stride2);
-#if __USE_FP16__
+#if COMPILE_FP16
     } else if(in->type() == Type::float16) {
-      gTranspose0213<false><<<blocks, threads>>>(out->data<__half>(), in->data<__half>(), rows, cols, stride1, stride2);
+      gTranspose0213<false><<<blocks, threads>>>(out->data<half>(), in->data<half>(), rows, cols, stride1, stride2);
 #endif
     } else {
       ABORT("Transpose for type {} not implemented", in->type());
@@ -503,9 +503,9 @@ void TransposeND(Tensor out, Tensor in, const std::vector<int>& vAxis) {
 
     if(in->type() == Type::float32) {
       gTransposeND<false, float><<<blocks, threads>>>(out, in, axes);
-#if __USE_FP16__
+#if COMPILE_FP16
     } else if(in->type() == Type::float16) {
-      gTransposeND<false, __half><<<blocks, threads>>>(out, in, axes);
+      gTransposeND<false, half><<<blocks, threads>>>(out, in, axes);
 #endif
     } else {
       ABORT("Transpose for type {} not implemented", in->type());
@@ -528,9 +528,9 @@ void TransposeNDGrad(Tensor out, Tensor in, const std::vector<int>& vAxis) {
 
     if(in->type() == Type::float32) {
       gTranspose0213<true><<<blocks, threads>>>(out->data<float>(), in->data<float>(), rows, cols, stride1, stride2);
-#if __USE_FP16__
+#if COMPILE_FP16
     } else if(in->type() == Type::float16) {
-      gTranspose0213<true><<<blocks, threads>>>(out->data<__half>(), in->data<__half>(), rows, cols, stride1, stride2);
+      gTranspose0213<true><<<blocks, threads>>>(out->data<half>(), in->data<half>(), rows, cols, stride1, stride2);
 #endif
     } else {
       ABORT("Transpose for type {} not implemented", in->type());
@@ -550,9 +550,9 @@ void TransposeNDGrad(Tensor out, Tensor in, const std::vector<int>& vAxis) {
 
     if(in->type() == Type::float32) {
       gTransposeND<true, float><<<blocks, threads>>>(out, in, axes);
-#if __USE_FP16__
+#if COMPILE_FP16
     } else if(in->type() == Type::float16) {
-      gTransposeND<true, __half><<<blocks, threads>>>(out, in, axes);
+      gTransposeND<true, half><<<blocks, threads>>>(out, in, axes);
 #endif
     } else {
       ABORT("Transpose for type {} not implemented", in->type());
@@ -567,7 +567,7 @@ void TransposeNDGrad(Tensor out, Tensor in, const std::vector<int>& vAxis) {
 // rows are time, batch or beam dimensions
 // number of threads is number of cols or MAX_THREADS
 // number of blocks is number of rows or MAX_BLOCKS
-// @TODO: handle __half2
+// @TODO: handle half2
 template <typename T, typename AccType = float>
 __global__ void gSoftmax(T* out,
                          functional::Shape outShape,
@@ -666,9 +666,9 @@ void Softmax(Tensor out, Tensor in) {
 
   if(in->type() == Type::float32) {
     gSoftmax<float, float><<<blocks, threads, shared>>>(out->data<float>(), out->shape(), in->data<float>());
-#if __USE_FP16__
+#if COMPILE_FP16
   } else if (in->type() == Type::float16) {
-    gSoftmax<half, float><<<blocks, threads, shared>>>(out->data<__half>(), out->shape(), in->data<__half>());
+    gSoftmax<half, float><<<blocks, threads, shared>>>(out->data<half>(), out->shape(), in->data<half>());
 #endif
   } else {
     ABORT("Softmax not implemented for type {}", in->type());
@@ -767,9 +767,9 @@ void LogSoftmax(Tensor out, Tensor in) {
 
   if(in->type() == Type::float32) {
     gLogSoftmax<float, float><<<blocks, threads, shared>>>(out->data<float>(), out->shape(), in->data<float>());
-#if __USE_FP16__
+#if COMPILE_FP16
   } else if (in->type() == Type::float16) {
-    gLogSoftmax<half, float><<<blocks, threads, shared>>>(out->data<__half>(), out->shape(), in->data<__half>());
+    gLogSoftmax<half, float><<<blocks, threads, shared>>>(out->data<half>(), out->shape(), in->data<half>());
 #endif
   } else {
     ABORT("LogSoftmax not implemented for type {}", in->type());
@@ -841,7 +841,7 @@ void SoftmaxGrad(Tensor grad, Tensor adj, Tensor val) {
   if(grad->type() == Type::float32) {
     gSoftmaxGrad<float, float><<<blocks, threads, shared>>>(
       grad->data<float>(), adj->data<float>(), val->data<float>(), m, k);
-#if __USE_FP16__
+#if COMPILE_FP16
   } else if (grad->type() == Type::float16) {
     // Accumulate into float
     gSoftmaxGrad<half, float><<<blocks, threads, shared>>>(
@@ -911,7 +911,7 @@ void LogSoftmaxGrad(Tensor grad, Tensor adj, Tensor val) {
   if(grad->type() == Type::float32) {
     gLogSoftmaxGrad<float, float><<<blocks, threads, shared>>>(
       grad->data<float>(), adj->data<float>(), val->data<float>(), m, k);
-#if __USE_FP16__
+#if COMPILE_FP16
   } else if (grad->type() == Type::float16) {
     // accumulate into float
     gLogSoftmaxGrad<half, float><<<blocks, threads, shared>>>(
@@ -965,7 +965,7 @@ void CopyRows(Tensor out,
   if(out->type() == Type::float32) {
     gCopyRows<<<blocks, threads>>>(
       out->data<float>(), in->data<float>(), cols, indices->data<IndexType>(), rowsToCopy);
-#if __USE_FP16__
+#if COMPILE_FP16
   } else if (out->type() == Type::float16) {
     gCopyRows<<<blocks, threads>>>(
       out->data<half>(), in->data<half>(), cols, indices->data<IndexType>(), rowsToCopy);
@@ -1018,7 +1018,7 @@ void PasteRows(Tensor out,
   if(out->type() == Type::float32) {
     gPasteRows<<<blocks, threads>>>(
       out->data<float>(), in->data<float>(), cols, indices->data<IndexType>(), rowsToCopy);
-#if __USE_FP16__
+#if COMPILE_FP16
   } else if (out->type() == Type::float16) {
     gPasteRows<<<blocks, threads>>>(
       out->data<half>(), in->data<half>(), cols, indices->data<IndexType>(), rowsToCopy);
@@ -1068,7 +1068,7 @@ void CopyCols(Tensor out, const Tensor in, const Tensor indices) {
   if(out->type() == Type::float32) {
     gCopyCols<<<blocks, threads>>>(
       out->data<float>(), in->data<float>(), rows, cols, indices->data<IndexType>(), colsToCopy);
-#if __USE_FP16__
+#if COMPILE_FP16
   } else if (out->type() == Type::float16) {
     gCopyCols<<<blocks, threads>>>(
       out->data<half>(), in->data<half>(), rows, cols, indices->data<IndexType>(), colsToCopy);
@@ -1118,7 +1118,7 @@ void PasteCols(Tensor out,
   if(out->type() == Type::float32) {
     gPasteCols<<<blocks, threads>>>(
       out->data<float>(), in->data<float>(), rows, cols, indices->data<IndexType>(), colsToCopy);
-#if __USE_FP16__
+#if COMPILE_FP16
   } else if (out->type() == Type::float16) {
     gPasteCols<<<blocks, threads>>>(
       out->data<half>(), in->data<half>(), rows, cols, indices->data<IndexType>(), colsToCopy);
@@ -1192,7 +1192,7 @@ void Select(Tensor out,
                                 in->shape(),
                                 axisGPU,
                                 indices->data<IndexType>());
-#if __USE_FP16__
+#if COMPILE_FP16
   } else if (out->type() == Type::float16) {
     gSelect<<<blocks, threads>>>(out->data<half>(),
                                 out->shape(),
@@ -1227,7 +1227,7 @@ void Insert(Tensor out,
                                 in->shape(),
                                 axisGPU,
                                 indices->data<IndexType>());
-#if __USE_FP16__
+#if COMPILE_FP16
   } else if (out->type() == Type::float16) {
     gInsert<<<blocks, threads>>>(out->data<half>(),
                                 out->shape(),
@@ -1305,7 +1305,7 @@ void GRUFastForward(Tensor out, std::vector<Tensor> inputs, bool final) {
         rows,
         cols,
         final);
-#if __USE_FP16__
+#if COMPILE_FP16
   } else if (out->type() == Type::float16) {
     gGRUFastForward<<<blocks, threads>>>(
         out->data<half>(),                                // output
@@ -1440,7 +1440,7 @@ void GRUFastBackward(std::vector<Tensor> outputs,
         rows,
         cols,
         final);
-#if __USE_FP16__
+#if COMPILE_FP16
   } else if (adj->type() == Type::float16) {
     gGRUFastBackward<<<blocks, threads>>>(
         outputs[0] ? outputs[0]->data<half>() : 0,        // state - adj
@@ -1554,7 +1554,7 @@ void CrossEntropyPick(Tensor out, Tensor in, Tensor indices) {
   if(out->type() == Type::float32) {
     gCrossEntropyPick<float, float><<<blocks, threads, shared>>>(
       out->data<float>(), out->shape(), in->data<float>(), in->shape(), indices->data<IndexType>());
-#if __USE_FP16__
+#if COMPILE_FP16
   } else if(out->type() == Type::float16) {
     gCrossEntropyPick<half, float><<<blocks, threads, shared>>>(
       out->data<half>(), out->shape(), in->data<half>(), in->shape(), indices->data<IndexType>());
@@ -1653,7 +1653,7 @@ void CrossEntropyPickBackward(Tensor out, Tensor adj, Tensor a, Tensor indices)
   if(out->type() == Type::float32) {
     gCrossEntropyPickBackward<float, float><<<blocks, threads, shared>>>(
       out->data<float>(), out->shape(), adj->data<float>(), a->data<float>(), indices->data<IndexType>());
-#if __USE_FP16__
+#if COMPILE_FP16
   } else if(out->type() == Type::float16) {
     gCrossEntropyPickBackward<half, float><<<blocks, threads, shared>>>(
       out->data<half>(), out->shape(), adj->data<half>(), a->data<half>(), indices->data<IndexType>());
@@ -1677,7 +1677,7 @@ float L2Norm(Tensor in, Ptr<Allocator> allocator) {
     using namespace functional;
     if(in->type() == Type::float32) {
       ReduceAll<float, float>(_1 * _1, blockMem, in);
-#if __USE_FP16__
+#if COMPILE_FP16
     } else if(in->type() == Type::float16) {
       ReduceAll<half, float>(_1 * _1, blockMem, in);
 #endif
@@ -1698,7 +1698,7 @@ float L2Norm(Tensor in, Ptr<Allocator> allocator) {
     using namespace functional;
     if(in->type() == Type::float32) {
       ReduceAll<float, float>(_1 * _1, out, in);
-#if __USE_FP16__
+#if COMPILE_FP16
     } else if(in->type() == Type::float16) {
       ReduceAll<half, float>(_1 * _1, out, in);
 #endif
@@ -1775,7 +1775,7 @@ void Att(Tensor out, Tensor va, Tensor context, Tensor state) {
   if(out->type() == Type::float32) {
     gAtt<float, float><<<blocks, threads, shared>>>(
       out->data<float>(), va->data<float>(), context->data<float>(), state->data<float>(), m, k, b, t);
-#if __USE_FP16__
+#if COMPILE_FP16
   } else if (out->type() == Type::float16) {
     gAtt<half, float><<<blocks, threads, shared>>>(
       out->data<half>(), va->data<half>(), context->data<half>(), state->data<half>(), m, k, b, t);
@@ -1852,7 +1852,7 @@ void AttBack(Tensor gVa,
                                   m,
                                   k,
                                   n);
-#if __USE_FP16__
+#if COMPILE_FP16
   } else if (gVa->type() == Type::float16) {
     gAttBack<<<blocks, threads>>>(gVa->data<half>(),
                                   gContext->data<half>(),
@@ -1973,7 +1973,7 @@ void LayerNormalization(Tensor out,
                                                  rows,
                                                  cols,
                                                  eps);
-#if __USE_FP16__
+#if COMPILE_FP16
   } else if (out->type() == Type::float16) {
     gLNormalization<half, float><<<blocks, threads, shared>>>(out->data<half>(),
                                                  in->data<half>(),
@@ -2151,7 +2151,7 @@ void LayerNormalizationGrad(Ptr<Allocator> allocator,
       rows,
       cols,
       eps);
-#if __USE_FP16__
+#if COMPILE_FP16
   } else if (gradX->type() == Type::float16) {
     // accumulate in float
     int shared = sizeof(float) * threads * 4;
@@ -2230,7 +2230,7 @@ void Shift(Tensor out,
   if(out->type() == Type::float32) {
     gShift<false>
         <<<blocks, threads>>>(out->data<float>(), in->data<float>(), length, offset, padValue);
-#if __USE_FP16__
+#if COMPILE_FP16
   } else if(out->type() == Type::float16) {
     gShift<false>
         <<<blocks, threads>>>(out->data<half>(), in->data<half>(), length, offset, padValue);
@@ -2262,7 +2262,7 @@ void ShiftGrad(Tensor out, Tensor in, marian::Shape shift, bool invert) {
   if(out->type() == Type::float32) {
     gShift<true>
         <<<blocks, threads>>>(out->data<float>(), in->data<float>(), length, offset, 0.f); // @TODO: What about padValue?
-#if __USE_FP16__
+#if COMPILE_FP16
   } else if(out->type() == Type::float16) {
     gShift<true>
         <<<blocks, threads>>>(out->data<half>(), in->data<half>(), length, offset, 0.f);
@@ -2370,7 +2370,7 @@ void LSTMCellForward(Tensor out, std::vector<Tensor> inputs) {
       inputs.size() > 4 ? inputs[4]->data<float>() : 0,  // mask
       rows,
       cols);
-#if __USE_FP16__
+#if COMPILE_FP16
   } else if (out->type() == Type::float16) {
     gLSTMCellForward<<<blocks, threads>>>(
       out->data<half>(),                                // output
@@ -2433,7 +2433,7 @@ void LSTMOutputForward(Tensor out, std::vector<Tensor> inputs) {
                                             inputs[3]->data<float>(),  // b
                                             rows,
                                             cols);
-#if __USE_FP16__
+#if COMPILE_FP16
   } else if (out->type() == Type::float16) {
     gLSTMOutputForward<<<blocks, threads>>>(out->data<half>(),        // output
                                             inputs[0]->data<half>(),  // cell state
@@ -2550,7 +2550,7 @@ void LSTMCellBackward(std::vector<Tensor> outputs,
       adj->data<float>(),
       rows,
       cols);
-#if __USE_FP16__
+#if COMPILE_FP16
   } else if (adj->type() == Type::float16) {
     gLSTMCellBackward<<<blocks, threads>>>(
       outputs[0] ? outputs[0]->data<half>() : 0,        // state - adj
@@ -2649,7 +2649,7 @@ void LSTMOutputBackward(std::vector<Tensor> outputs,
         adj->data<float>(),
         rows,
         cols);
-#if __USE_FP16__
+#if COMPILE_FP16
   } else if (adj->type() == Type::float16) {
     gLSTMOutputBackward<<<blocks, threads>>>(
         outputs[0] ? outputs[0]->data<half>() : 0,  // state - adj
@@ -2698,7 +2698,7 @@ void HighwayForward(Tensor out,
   if(out->type() == Type::float32) {
     gHighwayForward<<<blocks, threads>>>(
         out->data<float>(), in1->data<float>(), in2->data<float>(), t->data<float>(), length);
-#if __USE_FP16__
+#if COMPILE_FP16
   } else if(out->type() == Type::float16) {
     gHighwayForward<<<blocks, threads>>>(
         out->data<half>(), in1->data<half>(), in2->data<half>(), t->data<half>(), length);
@@ -2752,7 +2752,7 @@ void HighwayBackward(Tensor out1,
                                           t->data<float>(),
                                           adj->data<float>(),
                                           length);
-#if __USE_FP16__
+#if COMPILE_FP16
   } else if(out1->type() == Type::float16) {
     gHighwayBackward<<<blocks, threads>>>(out1->data<half>(),
                                           out2->data<half>(),
diff --git a/src/translator/helpers.cu b/src/translator/helpers.cu
index dda369fcb..48a079fc0 100644
--- a/src/translator/helpers.cu
+++ b/src/translator/helpers.cu
@@ -39,7 +39,7 @@ void SetColumn(Tensor in, size_t col, float value) {
 
  if(in->type() == Type::float32) {
    gSetColumn<<<nBlocks, nThreads>>>(in->data<float>(), nColumns, nRows, col, value);
-#if __USE_FP16__
+#if COMPILE_FP16
  } else if(in->type() == Type::float16) {
    gSetColumn<<<nBlocks, nThreads>>>(in->data<half>(), nColumns, nRows, col, (half)value);
 #endif
diff --git a/src/translator/nth_element.cu b/src/translator/nth_element.cu
index 6398c5a5c..753a02056 100644
--- a/src/translator/nth_element.cu
+++ b/src/translator/nth_element.cu
@@ -26,7 +26,7 @@ __global__ void gMaxElement(float* d_out,
                             T* d_in, // this is the probs array, only one with type float or half
                             int numBatches,
                             int* batchFirstElementIdxs,
-                            float minimal) // minimal is used to blank out found values, type-depdendent
+                            float disabledPathScore) // disabledPathScore is used to blank out found values, type-dependent
 {
   extern __shared__ float sdata[];
   __shared__ int indices[512];
@@ -39,7 +39,7 @@ __global__ void gMaxElement(float* d_out,
 
     int i = begin + blockIdx.x * (blockDim.x * 2) + tid;
 
-    sdata[tid] = minimal;
+    sdata[tid] = disabledPathScore;
 
     if(i < end) {
       sdata[tid] = (float)d_in[i];
@@ -112,7 +112,7 @@ __global__ void gMaxElementUpdate(float* binCosts,
                                   int* outIdxs,
                                   int* cumulativeBeamSizes,
                                   int NUM_BLOCKS,
-                                  float minimal) {
+                                  float disabledPathScore) {
   extern __shared__ float sdata[];
   __shared__ int indices[512];
   __shared__ float bestBinCost;
@@ -131,7 +131,7 @@ __global__ void gMaxElementUpdate(float* binCosts,
       ++pos) {
     int i = tid;
 
-    sdata[tid] = minimal;
+    sdata[tid] = disabledPathScore;
 
     if(i < num_bins) {
       sdata[tid] = binCosts[batchIdx * NUM_BLOCKS + i];
@@ -191,7 +191,7 @@ __global__ void gMaxElementUpdate(float* binCosts,
       bestBinCost = sdata[0];
       bestBinCostIdx = batchIdx * NUM_BLOCKS + indices[0];
 
-      probs[binIdxs[bestBinCostIdx]] = minimal;
+      probs[binIdxs[bestBinCostIdx]] = disabledPathScore;
 
       outIdxs[pos] = binIdxs[bestBinCostIdx];
       outCosts[pos] = bestBinCost;
@@ -203,7 +203,7 @@ __global__ void gMaxElementUpdate(float* binCosts,
         + (bestBinCostIdx - batchIdx * NUM_BLOCKS) * (blockDim.x * 2) + tid;
     const int dist = num_bins * 2 * blockDim.x;
 
-    sdata[tid] = minimal;
+    sdata[tid] = disabledPathScore;
 
     if(i < batchFirstElements[batchIdx + 1]) {
       sdata[tid] = (float)probs[i];
@@ -326,7 +326,7 @@ private:
   void selectNBest(T* probs,
                    const std::vector<int>& batchFirstElementIdxs,
                    const std::vector<int>& cumulativeBeamSizes,
-                   float minimal) {
+                   float disabledPathScore) {
 
     cudaSetDevice(deviceId_.no);
     CUDA_CHECK(cudaMemcpyAsync(d_batchPosition,
@@ -346,7 +346,7 @@ private:
                   BLOCK_SIZE,
                   BLOCK_SIZE * sizeof(float), // shared memory size
                   /* stream_ */ 0>>>(
-        d_out, d_ind, probs, numBatches, d_batchPosition, minimal);
+        d_out, d_ind, probs, numBatches, d_batchPosition, disabledPathScore);
 
     gMaxElementUpdate<<<numBatches,
                         BLOCK_SIZE,
@@ -359,7 +359,7 @@ private:
                                            d_res_idx,
                                            d_cumBeamSizes,
                                            NUM_BLOCKS,
-                                           minimal);
+                                           disabledPathScore);
   }
 
 public:
@@ -398,12 +398,12 @@ public:
     }
 
     if(scores->type() == Type::float32) {
-      float minimal = NumericLimits<float>(scores->type()).lowest;
-      selectNBest(scores->data<float>(), batchFirstElementIdxs, cumulativeBeamSizes, minimal);
-#if __USE_FP16__
+      float disabledPathScore = NumericLimits<float>(scores->type()).lowest;
+      selectNBest(scores->data<float>(), batchFirstElementIdxs, cumulativeBeamSizes, disabledPathScore);
+#if COMPILE_FP16
     } else if(scores->type() == Type::float16) {
-      float minimal = NumericLimits<float>(scores->type()).lowest;
-      selectNBest(scores->data<half>(), batchFirstElementIdxs, cumulativeBeamSizes, minimal);
+      float disabledPathScore = NumericLimits<float>(scores->type()).lowest;
+      selectNBest(scores->data<half>(), batchFirstElementIdxs, cumulativeBeamSizes, disabledPathScore);
 #endif
     } else {
       ABORT("getNBestList not implemented for type {}", scores->type());

From dfbf4f403a0ab50af028fd3ad7d5fd36c581a2bd Mon Sep 17 00:00:00 2001
From: Marcin Junczys-Dowmunt <Marcin.JunczysDowmunt@microsoft.com>
Date: Mon, 7 Oct 2019 15:52:24 -0700
Subject: [PATCH 646/838] remove wrong abort

---
 src/common/aliases.cpp | 1 -
 1 file changed, 1 deletion(-)

diff --git a/src/common/aliases.cpp b/src/common/aliases.cpp
index 14679efc8..a59280648 100644
--- a/src/common/aliases.cpp
+++ b/src/common/aliases.cpp
@@ -32,7 +32,6 @@ void ConfigParser::addAliases(cli::CLIWrapper& cli) {
       // @TODO: review this
       // scaling factor (power of 2), frequency, multiplier at increase, tolerance, range, minium factor
       config["cost-scaling"] = std::vector<std::string>({"7", "2000", "2", "0.05", "10", "1"}); 
-      ABORT("fp16 training currently not yet supported.");
     } else {
       config["precision"] = std::vector<std::string>({"float16"}); // for inference we do not need the other types
     }

From cd18ee0396754aba222798abd0e022bf6722866f Mon Sep 17 00:00:00 2001
From: Marcin Junczys-Dowmunt <Marcin.JunczysDowmunt@microsoft.com>
Date: Tue, 8 Oct 2019 15:54:59 -0700
Subject: [PATCH 647/838] safeguard cmake against rogue doxygen

---
 CMakeLists.txt | 7 ++++---
 1 file changed, 4 insertions(+), 3 deletions(-)

diff --git a/CMakeLists.txt b/CMakeLists.txt
index f5125ef8d..8bde2f456 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -22,6 +22,7 @@ option(COMPILE_EXAMPLES "Compile examples" OFF)
 option(COMPILE_TESTS "Compile tests" OFF)
 option(COMPILE_SERVER "Compile marian-server" ON)
 option(USE_FBGEMM "Use FBGEMM" ON)
+option(USE_DOXYGEN "Build documentation with Doxygen" ON)
 
 # Project versioning
 find_package(Git QUIET)
@@ -346,9 +347,9 @@ configure_file(${CMAKE_CURRENT_SOURCE_DIR}/src/common/project_version.h.in
 include_directories(${marian_SOURCE_DIR}/src)
 add_subdirectory(src)
 
-
+if(USE_DOXYGEN)
 # Add a target to generate API documentation with Doxygen
-#find_package(Doxygen)
+find_package(Doxygen)
 if(DOXYGEN_FOUND)
   configure_file(${CMAKE_CURRENT_SOURCE_DIR}/Doxyfile.in
            ${CMAKE_CURRENT_BINARY_DIR}/Doxyfile @ONLY)
@@ -358,4 +359,4 @@ if(DOXYGEN_FOUND)
    COMMENT "Generating API documentation with Doxygen" VERBATIM
   )
 endif(DOXYGEN_FOUND)
-
+endif(USE_DOXYGEN)

From e897f913b648680924875fc407546d15eded0ebe Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Tue, 8 Oct 2019 17:27:00 -0700
Subject: [PATCH 648/838] bug fix: reading and writing .gz files on Windows
 should explicitly specify binary mode

---
 src/common/file_stream.h | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)
 mode change 100644 => 100755 src/common/file_stream.h

diff --git a/src/common/file_stream.h b/src/common/file_stream.h
old mode 100644
new mode 100755
index 4ce2d6ca0..46b038b28
--- a/src/common/file_stream.h
+++ b/src/common/file_stream.h
@@ -156,7 +156,7 @@ class InputFileStream {
     ABORT_IF(!marian::filesystem::exists(file_), "File '{}' does not exist", file);
 
     if(file_.extension() == marian::filesystem::Path(".gz"))
-      istream_ = std::make_unique<zstr::ifstream>(file_.string());
+      istream_ = std::make_unique<zstr::ifstream>(file_.string(), std::ios_base::in | std::ios_base::binary);
     else
       istream_ = std::make_unique<std::ifstream>(file_.string());
     ABORT_IF(fail(), "Error {} ({}) opening file '{}'", errno, strerror(errno), path());
@@ -253,7 +253,7 @@ class OutputFileStream {
 public:
   OutputFileStream(const std::string& file) : file_(file) {
     if(file_.extension() == marian::filesystem::Path(".gz"))
-      ostream_ = std::make_unique<zstr::ofstream>(file_.string());
+      ostream_ = std::make_unique<zstr::ofstream>(file_.string(), std::ios_base::out | std::ios_base::binary);
     else
       ostream_ = std::make_unique<std::ofstream>(file_.string());
     ABORT_IF(!marian::filesystem::exists(file_), "File '{}' could not be opened", file);

From 0bad3f9a159dc497562fb54f1c8e2d973c2b3a04 Mon Sep 17 00:00:00 2001
From: Hieu Hoang <hihoan@microsoft.com>
Date: Tue, 8 Oct 2019 17:36:57 -0700
Subject: [PATCH 649/838] test commit

---
 src/common/file_stream.h | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/src/common/file_stream.h b/src/common/file_stream.h
index 4ce2d6ca0..ecabae916 100644
--- a/src/common/file_stream.h
+++ b/src/common/file_stream.h
@@ -166,6 +166,8 @@ class InputFileStream {
       : fds_(tempfile.getFileDescriptor(), boost::iostreams::never_close_handle) {
     lseek(tempfile.getFileDescriptor(), 0, SEEK_SET);
 
+    std::cerr << "HH1" << std::endl;
+    
     namespace bio = boost::iostreams;
     fdsBuffer_ = std::make_unique<bio::stream_buffer<bio::file_descriptor_source>>(fds_);
     istream_ = std::make_unique<std::istream>(fdsBuffer_.get());

From f8061fc2986d5727a328e3d233aad0f27c09a6a4 Mon Sep 17 00:00:00 2001
From: Hieu Hoang <hihoan@microsoft.com>
Date: Tue, 8 Oct 2019 18:23:10 -0700
Subject: [PATCH 650/838] file stream without boost /Ken

---
 src/CMakeLists.txt         |   3 +-
 src/common/file_stream.cpp | 170 +++++++++++++++++++++++++++++++++++++
 src/common/file_stream.h   |  97 +++++++++++++++------
 3 files changed, 244 insertions(+), 26 deletions(-)
 create mode 100644 src/common/file_stream.cpp

diff --git a/src/CMakeLists.txt b/src/CMakeLists.txt
index a684bd6a2..afbf3bf07 100644
--- a/src/CMakeLists.txt
+++ b/src/CMakeLists.txt
@@ -20,7 +20,8 @@ add_library(marian STATIC
   common/binary.cpp
   common/io.cpp
   common/filesystem.cpp
-
+  common/file_stream.cpp
+  
   data/alignment.cpp
   data/vocab.cpp
   data/default_vocab.cpp
diff --git a/src/common/file_stream.cpp b/src/common/file_stream.cpp
new file mode 100644
index 000000000..e02f63d01
--- /dev/null
+++ b/src/common/file_stream.cpp
@@ -0,0 +1,170 @@
+#include "common/file_stream.h"
+
+#include <streambuf>
+#include <string>
+#include <vector>
+#ifdef _MSC_VER
+#include <windows.h>
+#include <io.h>
+#else
+#include <sys/types.h>
+#include <unistd.h>
+#endif
+
+namespace marian {
+namespace io {
+
+// Get error strings out of errno.
+namespace {
+#ifdef __GNUC__
+const char *HandleStrerror(int ret, const char *buf) __attribute__ ((unused));
+const char *HandleStrerror(const char *ret, const char * /*buf*/) __attribute__ ((unused));
+#endif
+// At least one of these functions will not be called.
+#ifdef __clang__
+#pragma clang diagnostic push
+#pragma clang diagnostic ignored "-Wunused-function"
+#endif
+// The XOPEN version.
+const char *HandleStrerror(int ret, const char *buf) {
+  if (!ret) return buf;
+  return NULL;
+}
+
+// The GNU version.
+const char *HandleStrerror(const char *ret, const char * /*buf*/) {
+  return ret;
+}
+#ifdef __clang__
+#pragma clang diagnostic pop
+#endif
+
+std::string StrError() {
+  char buf[200];
+  buf[0] = 0;
+#if defined(sun) || defined(_WIN32) || defined(_WIN64)
+  return strerror(errno);
+#else
+  return HandleStrerror(strerror_r(errno, buf, 200), buf);
+#endif
+}
+} // namespace
+
+void RewindFile(int fd) {
+  ABORT_IF((off_t)-1 == lseek(fd, 0, SEEK_SET), "lseek to 0 failed in fd {}", fd);
+}
+
+ReadFDBuf::ReadFDBuf(int fd, std::size_t buffer_size)
+: fd_(fd), mem_(buffer_size) {
+  setg(End(), End(), End());
+}
+
+ReadFDBuf::int_type ReadFDBuf::underflow() {
+  if (gptr() == egptr()) {
+    // Reached end, refill
+    ssize_t got = Read();
+    if (!got) {
+      // End of file
+      return traits_type::eof();
+    }
+    setg(Begin(), Begin(), Begin() + got);
+  }
+  return traits_type::to_int_type(*gptr());
+}
+
+// If the putback goes below the buffer, try to seek backwards.
+ReadFDBuf::int_type ReadFDBuf::pbackfail(int c) {
+  /* "It is unspecified whether the content of the controlled input
+   * sequence is modified if the function succeeds and c does not match the
+   * character at that position."
+   * -- http://www.cplusplus.com/reference/streambuf/streambuf/pbackfail/
+   */
+  if (gptr() > Begin()) {
+    setg(Begin(), gptr() - 1, End());
+  } else {
+    if (lseek(fd_, -1, SEEK_CUR) == -1) {
+      return EOF;
+    }
+    ssize_t got = Read();
+    if (!got) {
+      // This happens if the file was truncated underneath us.
+      return traits_type::eof();
+    }
+    setg(Begin(), Begin(), Begin() + got);
+  }
+  return traits_type::to_int_type(*gptr());
+}
+
+// Read some amount into [Begin(), End()), returning the amount read.
+ssize_t ReadFDBuf::Read() {
+  ssize_t got;
+  // Loop to keep reading if EINTR happens.
+  // This way the program is robust to Ctrl+Z then backgrounding.
+  do {
+    errno = 0;
+    got =
+#ifdef _MSC_VER
+      _read
+#else
+      read
+#endif
+      (fd_, Begin(), End() - Begin());
+  } while (got == -1 && errno == EINTR);
+  ABORT_IF(got < 0, "Error reading fd {}: {}", fd_, StrError());
+  return got;
+}
+
+WriteFDBuf::WriteFDBuf(int fd, std::size_t buffer_size)
+: fd_(fd), mem_(buffer_size) {
+  setp(End(), End());
+}
+
+WriteFDBuf::~WriteFDBuf() { sync(); }
+
+WriteFDBuf::int_type WriteFDBuf::overflow(int c) {
+  if (c == EOF) {
+    // Apparently overflow(EOF) means sync().
+    sync();
+    return c;
+  }
+  if (pptr() == epptr()) {
+    // Out of buffer.  Write and reset.
+    sync();
+    setp(Begin(), End());
+  }
+  // Put character on the end.
+  *pptr() = traits_type::to_char_type(c);
+  pbump(1);
+  return c;
+}
+
+// Write everything in the buffer to the file.
+int WriteFDBuf::sync() {
+  const char *from = pbase();
+  const char *to = pptr();
+  while (from != to) {
+    from += WriteSome(from, to);
+  }
+  // We die on all failures.
+  return 0;
+}
+
+// Write part of the buffer, returning the amount written.
+ssize_t WriteFDBuf::WriteSome(const char *from, const char *to) {
+  ssize_t put = 0;
+  do {
+    errno = 0;
+    put =
+#ifdef _MSC_VER
+      _write
+#else
+      write
+#endif
+      (fd_, from, to - from);
+  } while (put == -1 && errno == EINTR);
+  ABORT_IF(put < 0, "Error writing to fd {}: {}", fd_, StrError());
+  return put;
+}
+
+} // namespace io
+} // namespace marian
diff --git a/src/common/file_stream.h b/src/common/file_stream.h
index c16d1c293..a35b2b10f 100755
--- a/src/common/file_stream.h
+++ b/src/common/file_stream.h
@@ -13,8 +13,6 @@
 #pragma warning(disable : 4101)
 #endif
 #include "3rd_party/zstr/zstr.hpp"
-#include <boost/iostreams/device/file_descriptor.hpp>
-#include <boost/iostreams/stream_buffer.hpp>
 #ifdef __GNUC__
 #pragma GCC diagnostic pop
 #endif
@@ -25,6 +23,7 @@
 
 #include <iostream>
 #include <memory>
+#include <vector>
 
 #ifdef __GNUC__ // not supported; maybe we just need to increment a standard flag in gcc/cmake?
 namespace std {
@@ -149,31 +148,83 @@ class TemporaryFile {
   std::string getFileName() { return name_; }
 };
 
+// A streambuf to read from a file descriptor.
+class ReadFDBuf : public std::streambuf {
+  public:
+    // Does not take ownership of file.
+    explicit ReadFDBuf(int fd, std::size_t buffer_size = 4096);
+
+  private:
+    int_type underflow() override;
+
+    // If the putback goes below the buffer, try to seek backwards.
+    int_type pbackfail(int c = EOF) override;
+
+    // TODO: override setbuf.
+
+    // Read some amount into [Begin(), End()), returning the amount read.
+    ssize_t Read();
+
+    char *Begin() { return &mem_.front(); }
+    char *End() { return &mem_.back() + 1; }
+
+    int fd_;
+    std::vector<char> mem_;
+
+    ReadFDBuf(const ReadFDBuf &) = delete;
+    ReadFDBuf &operator=(const ReadFDBuf &) = delete;
+};
+
+// A streambuf to Write to a file descriptor.
+class WriteFDBuf : public std::streambuf {
+  public:
+    explicit WriteFDBuf(int fd, std::size_t buffer_size = 4096);
+
+    ~WriteFDBuf();
+
+  private:
+    int_type overflow(int c = EOF) override;
+
+    // Write everything in the buffer to the file.
+    int sync() override;
+
+    // Write part of the buffer, returning the amount written.
+    ssize_t WriteSome(const char *from, const char *to);
+
+    char *Begin() { return &mem_.front(); }
+    char *End() { return &mem_.back() + 1; }
+
+    int fd_;
+    std::vector<char> mem_;
+
+    WriteFDBuf(const WriteFDBuf &) = delete;
+    WriteFDBuf &operator=(const WriteFDBuf &) = delete;
+};
+
+// lseek(fd, 0, SEEK_SET) but with error checking.
+void RewindFile(int fd);
+
 class InputFileStream {
 public:
-  InputFileStream(const std::string& file)
+  explicit InputFileStream(const std::string& file)
   : file_(file) {
     ABORT_IF(!marian::filesystem::exists(file_), "File '{}' does not exist", file);
 
     if(file_.extension() == marian::filesystem::Path(".gz"))
-      istream_ = std::make_unique<zstr::ifstream>(file_.string(), std::ios_base::in | std::ios_base::binary);
+      istream_ = std::make_unique<zstr::ifstream>(file_.string());
     else
       istream_ = std::make_unique<std::ifstream>(file_.string());
     ABORT_IF(fail(), "Error {} ({}) opening file '{}'", errno, strerror(errno), path());
   }
 
-  InputFileStream(TemporaryFile& tempfile)
-      : fds_(tempfile.getFileDescriptor(), boost::iostreams::never_close_handle) {
-    lseek(tempfile.getFileDescriptor(), 0, SEEK_SET);
-
+  explicit InputFileStream(TemporaryFile& tempfile) {
+    RewindFile(tempfile.getFileDescriptor());
+    temporary_reader_.reset(new ReadFDBuf(tempfile.getFileDescriptor()));
+    istream_.reset(new std::istream(temporary_reader_.get()));
     std::cerr << "HH1" << std::endl;
-    
-    namespace bio = boost::iostreams;
-    fdsBuffer_ = std::make_unique<bio::stream_buffer<bio::file_descriptor_source>>(fds_);
-    istream_ = std::make_unique<std::istream>(fdsBuffer_.get());
   }
 
-  InputFileStream(std::istream& strm)
+  explicit InputFileStream(std::istream& strm)
   : istream_(new std::istream(strm.rdbuf())) {}
 
   operator std::istream&() { return *istream_; }
@@ -222,9 +273,8 @@ class InputFileStream {
   marian::filesystem::Path file_;
   std::unique_ptr<std::istream> istream_;
 
-  boost::iostreams::file_descriptor_source fds_;
   mutable std::vector<char> readBuf_; // for setbuf()
-  std::unique_ptr<boost::iostreams::stream_buffer<boost::iostreams::file_descriptor_source>> fdsBuffer_;
+  std::unique_ptr<ReadFDBuf> temporary_reader_;
 };
 
 // wrapper around std::getline() that handles Windows input files with extra CR
@@ -241,6 +291,7 @@ static inline InputFileStream& getline(InputFileStream& in, std::string& line) {
 
 // wrapper around std::getline() that handles Windows input files with extra CR
 // chars at the line end
+// To be pedantic, shouldn't this require delim == '\n' to consume a '\r'?
 static inline InputFileStream& getline(InputFileStream& in, std::string& line, char delim) {
   std::getline((std::istream&)in, line, delim);
   // bad() seems to be correct here. Should not abort on EOF.
@@ -255,19 +306,16 @@ class OutputFileStream {
 public:
   OutputFileStream(const std::string& file) : file_(file) {
     if(file_.extension() == marian::filesystem::Path(".gz"))
-      ostream_ = std::make_unique<zstr::ofstream>(file_.string(), std::ios_base::out | std::ios_base::binary);
+      ostream_ = std::make_unique<zstr::ofstream>(file_.string());
     else
       ostream_ = std::make_unique<std::ofstream>(file_.string());
     ABORT_IF(!marian::filesystem::exists(file_), "File '{}' could not be opened", file);
   }
 
-  OutputFileStream(TemporaryFile& tempfile)
-      : fds_(tempfile.getFileDescriptor(), boost::iostreams::never_close_handle) {
-    lseek(tempfile.getFileDescriptor(), 0, SEEK_SET);
-
-    namespace bio = boost::iostreams;
-    fdsBuffer_ = std::make_unique<bio::stream_buffer<bio::file_descriptor_sink>>(fds_);
-    ostream_ = std::make_unique<std::ostream>(fdsBuffer_.get());
+  OutputFileStream(TemporaryFile& tempfile) {
+    RewindFile(tempfile.getFileDescriptor());
+    temporary_writer_.reset(new WriteFDBuf(tempfile.getFileDescriptor()));
+    ostream_.reset(new std::ostream(temporary_writer_.get()));
   }
 
   OutputFileStream(std::ostream& strm) {
@@ -313,8 +361,7 @@ class OutputFileStream {
   std::unique_ptr<std::ostream> ostream_;
 
 
-  boost::iostreams::file_descriptor_sink fds_;
-  std::unique_ptr<boost::iostreams::stream_buffer<boost::iostreams::file_descriptor_sink>> fdsBuffer_;
+  std::unique_ptr<WriteFDBuf> temporary_writer_;
 };
 
 }

From fef5c301ca3cf251637e6cc7b6b167133bf7d67e Mon Sep 17 00:00:00 2001
From: Hieu Hoang <hihoan@microsoft.com>
Date: Mon, 21 Oct 2019 10:48:14 -0700
Subject: [PATCH 651/838] figure out where temp file class is used

---
 src/common/file_stream.h | 10 +++++-----
 src/data/corpus.cpp      |  2 +-
 src/data/corpus.h        |  2 +-
 src/data/corpus_nbest.h  |  2 +-
 src/training/validator.h |  4 ++--
 vs/Marian.vcxproj        |  2 +-
 6 files changed, 11 insertions(+), 11 deletions(-)

diff --git a/src/common/file_stream.h b/src/common/file_stream.h
index a35b2b10f..ab6baf618 100755
--- a/src/common/file_stream.h
+++ b/src/common/file_stream.h
@@ -43,7 +43,7 @@ namespace std {
 namespace marian {
 namespace io {
 
-class TemporaryFile {
+class TemporaryFile2 {
 private:
   int fd_{-1};
   bool unlink_;
@@ -112,14 +112,14 @@ class TemporaryFile {
   }
 
 public:
-  TemporaryFile(const std::string base = "/tmp/", bool earlyUnlink = true)
+  TemporaryFile2(const std::string base = "/tmp/", bool earlyUnlink = true)
       : unlink_(earlyUnlink) {
     std::string baseTemp(base);
     NormalizeTempPrefix(baseTemp);
     fd_ = MakeTemp(baseTemp);
   }
 
-  ~TemporaryFile() {
+  ~TemporaryFile2() {
 #ifdef _MSC_VER
     if (fd_ == -1)
       return;
@@ -217,7 +217,7 @@ class InputFileStream {
     ABORT_IF(fail(), "Error {} ({}) opening file '{}'", errno, strerror(errno), path());
   }
 
-  explicit InputFileStream(TemporaryFile& tempfile) {
+  explicit InputFileStream(TemporaryFile2& tempfile) {
     RewindFile(tempfile.getFileDescriptor());
     temporary_reader_.reset(new ReadFDBuf(tempfile.getFileDescriptor()));
     istream_.reset(new std::istream(temporary_reader_.get()));
@@ -312,7 +312,7 @@ class OutputFileStream {
     ABORT_IF(!marian::filesystem::exists(file_), "File '{}' could not be opened", file);
   }
 
-  OutputFileStream(TemporaryFile& tempfile) {
+  OutputFileStream(TemporaryFile2& tempfile) {
     RewindFile(tempfile.getFileDescriptor());
     temporary_writer_.reset(new WriteFDBuf(tempfile.getFileDescriptor()));
     ostream_.reset(new std::ostream(temporary_writer_.get()));
diff --git a/src/data/corpus.cpp b/src/data/corpus.cpp
index 26abdd0c5..20afac34f 100644
--- a/src/data/corpus.cpp
+++ b/src/data/corpus.cpp
@@ -192,7 +192,7 @@ void Corpus::shuffleData(const std::vector<std::string>& paths) {
     // create temp files that contain the data in randomized order
     tempFiles_.resize(numStreams);
     for(size_t i = 0; i < numStreams; ++i) {
-      tempFiles_[i].reset(new io::TemporaryFile(options_->get<std::string>("tempdir")));
+      tempFiles_[i].reset(new io::TemporaryFile2(options_->get<std::string>("tempdir")));
       io::OutputFileStream out(*tempFiles_[i]);
       const auto& corpusStream = corpus[i];
       for(auto id : ids_) {
diff --git a/src/data/corpus.h b/src/data/corpus.h
index 41272a811..41e26421a 100644
--- a/src/data/corpus.h
+++ b/src/data/corpus.h
@@ -18,7 +18,7 @@ namespace data {
 
 class Corpus : public CorpusBase {
 private:
-  std::vector<UPtr<io::TemporaryFile>> tempFiles_;
+  std::vector<UPtr<io::TemporaryFile2>> tempFiles_;
   std::vector<size_t> ids_;
 
   // for shuffle-in-ram
diff --git a/src/data/corpus_nbest.h b/src/data/corpus_nbest.h
index 2c7958b19..3e06f639b 100644
--- a/src/data/corpus_nbest.h
+++ b/src/data/corpus_nbest.h
@@ -18,7 +18,7 @@ namespace data {
 
 class CorpusNBest : public CorpusBase {
 private:
-  std::vector<UPtr<io::TemporaryFile>> tempFiles_;
+  std::vector<UPtr<io::TemporaryFile2>> tempFiles_;
   std::vector<size_t> ids_;
   int lastNum_{-1};
   std::vector<std::string> lastLines_;
diff --git a/src/training/validator.h b/src/training/validator.h
index c1f0d40fa..a096dab91 100755
--- a/src/training/validator.h
+++ b/src/training/validator.h
@@ -465,12 +465,12 @@ class TranslationValidator : public Validator<data::Corpus, models::IModel> {
 
     // Set up output file
     std::string fileName;
-    Ptr<io::TemporaryFile> tempFile;
+    Ptr<io::TemporaryFile2> tempFile;
 
     if(options_->hasAndNotEmpty("valid-translation-output")) {
       fileName = options_->get<std::string>("valid-translation-output");
     } else {
-      tempFile.reset(new io::TemporaryFile(options_->get<std::string>("tempdir"), false));
+      tempFile.reset(new io::TemporaryFile2(options_->get<std::string>("tempdir"), false));
       fileName = tempFile->getFileName();
     }
 
diff --git a/vs/Marian.vcxproj b/vs/Marian.vcxproj
index bdfce4683..532f206b4 100755
--- a/vs/Marian.vcxproj
+++ b/vs/Marian.vcxproj
@@ -42,7 +42,7 @@
   <PropertyGroup Condition="'$(Configuration)|$(Platform)'=='Debug|x64'">
     <LinkIncremental>true</LinkIncremental>
     <IntDir>$(SolutionDir)$(Platform)\$(Configuration)\Marian\</IntDir>
-    <IncludePath>..\src\3rd_party\fbgemm\third_party\cpuinfo\deps\clog\include;..\src\3rd_party\fbgemm\third_party\cpuinfo\src;..\src\3rd_party\fbgemm\third_party\cpuinfo\include;..\src\3rd_party\fbgemm\third_party\asmjit\src;%MKL_PATH%\include;..\src\3rd_party\fbgemm\include;%CUDA_PATH%\include;..\src;..\src\3rd_party;%BOOST_INCLUDE_PATH%;%ZLIB_PATH%\include;$(VC_IncludePath);$(WindowsSDK_IncludePath);</IncludePath>
+    <IncludePath>..\src\3rd_party\fbgemm\third_party\cpuinfo\deps\clog\include;..\src\3rd_party\fbgemm\third_party\cpuinfo\src;..\src\3rd_party\fbgemm\third_party\cpuinfo\include;..\src\3rd_party\fbgemm\third_party\asmjit\src;%MKL_PATH%\include;..\src\3rd_party\fbgemm\include;%CUDA_PATH%\include;..\src;..\src\3rd_party;%BOOST_INCLUDE_PATH%;%ZLIB_PATH%\include;$(VC_IncludePath);$(WindowsSDK_IncludePath)</IncludePath>
     <LibraryPath>%CUDA_PATH%\lib\x64;%BOOST_LIB_PATH%;%ZLIB_PATH%\lib;%MKL_PATH%\lib\intel64;$(VC_LibraryPath_x64);$(WindowsSDK_LibraryPath_x64);$(NETFXKitsDir)Lib\um\x64</LibraryPath>
   </PropertyGroup>
   <PropertyGroup Condition="'$(Configuration)|$(Platform)'=='Release|x64'">

From 14ae130a6bf3f3db0fc1d916f89af340db8250aa Mon Sep 17 00:00:00 2001
From: Hieu Hoang <hihoan@microsoft.com>
Date: Mon, 21 Oct 2019 11:26:50 -0700
Subject: [PATCH 652/838] move code .h -> .cpp

---
 src/common/file_stream.cpp | 128 +++++++++++++++++++
 src/common/file_stream.h   | 246 ++++++++++---------------------------
 2 files changed, 194 insertions(+), 180 deletions(-)

diff --git a/src/common/file_stream.cpp b/src/common/file_stream.cpp
index e02f63d01..e0fea2e77 100644
--- a/src/common/file_stream.cpp
+++ b/src/common/file_stream.cpp
@@ -166,5 +166,133 @@ ssize_t WriteFDBuf::WriteSome(const char *from, const char *to) {
   return put;
 }
 
+///////////////////////////////////////////////////////////////////////////////////////////////
+#ifndef _MSC_VER
+int TemporaryFile2::mkstemp_and_unlink(char *tmpl) {
+  int ret = mkstemp(tmpl);
+  if(unlink_ && ret != -1) {
+    ABORT_IF(unlink(tmpl), "Error while deleting '{}'", tmpl);
+  }
+  return ret;
+}
+#endif
+
+int TemporaryFile2::MakeTemp(const std::string &base) {
+#ifdef _MSC_VER
+  char *name = tempnam(base.c_str(), "marian.");
+  ABORT_IF(name == NULL, "Error while making a temporary based on '{}'", base);
+
+  int oflag = _O_RDWR | _O_CREAT | _O_EXCL;
+  if(unlink_)
+    oflag |= _O_TEMPORARY;
+
+  int ret = open(name, oflag, _S_IREAD | _S_IWRITE);
+  ABORT_IF(ret == -1, "Error while making a temporary based on '{}'", base);
+
+  name_ = name;
+  free(name);
+
+  return ret;
+#else
+  std::string name(base);
+  name += "marian.XXXXXX";
+  name.push_back(0);
+  int ret;
+  ABORT_IF(-1 == (ret = mkstemp_and_unlink(&name[0])),
+           "Error while making a temporary based on '{}'",
+           base);
+  name_ = name;
+  return ret;
+#endif
+}
+
+void TemporaryFile2::NormalizeTempPrefix(std::string &base) {
+  if(base.empty())
+    return;
+
+#ifdef _MSC_VER
+  if(base.substr(0, 4) == "/tmp")
+    base = getenv("TMP");
+#else
+  if(base[base.size() - 1] == '/')
+    return;
+  struct stat sb;
+  // It's fine for it to not exist.
+  if(stat(base.c_str(), &sb) == -1)
+    return;
+  if(S_ISDIR(sb.st_mode))
+    base += '/';
+#endif
+}
+
+TemporaryFile2::TemporaryFile2(const std::string base, bool earlyUnlink) : unlink_(earlyUnlink) {
+  std::string baseTemp(base);
+  NormalizeTempPrefix(baseTemp);
+  fd_ = MakeTemp(baseTemp);
+}
+
+TemporaryFile2::~TemporaryFile2() {
+#ifdef _MSC_VER
+  if(fd_ == -1)
+    return;
+
+  if(close(fd_)) {
+    std::cerr << "Could not close file " << fd_ << std::endl;
+    std::abort();
+  }
+
+  if(!unlink_) {
+    ABORT_IF(remove(name_.c_str()), "Error while deleting '{}'", name_);
+  }
+#else
+  if(fd_ != -1 && !unlink_) {
+    ABORT_IF(unlink(name_.c_str()), "Error while deleting '{}'", name_);
+  }
+  if(fd_ != -1 && close(fd_)) {
+    std::cerr << "Could not close file " << fd_ << std::endl;
+    std::abort();
+  }
+#endif
+}
+
+///////////////////////////////////////////////////////////////////////////////////////////////
+InputFileStream::InputFileStream(const std::string &file) : file_(file) {
+  ABORT_IF(!marian::filesystem::exists(file_), "File '{}' does not exist", file);
+
+  if(file_.extension() == marian::filesystem::Path(".gz"))
+    istream_ = std::make_unique<zstr::ifstream>(file_.string());
+  else
+    istream_ = std::make_unique<std::ifstream>(file_.string());
+  ABORT_IF(fail(), "Error {} ({}) opening file '{}'", errno, strerror(errno), path());
+}
+
+InputFileStream::InputFileStream(TemporaryFile2 &tempfile) {
+  RewindFile(tempfile.getFileDescriptor());
+  temporary_reader_.reset(new ReadFDBuf(tempfile.getFileDescriptor()));
+  istream_.reset(new std::istream(temporary_reader_.get()));
+  std::cerr << "HH1" << std::endl;
+}
+
+void InputFileStream::setbufsize(size_t size) const {
+  istream_->rdbuf()->pubsetbuf(0, 0);
+  readBuf_.resize(size);
+  istream_->rdbuf()->pubsetbuf(readBuf_.data(), readBuf_.size());
+}
+
+///////////////////////////////////////////////////////////////////////////////////////////////
+OutputFileStream::OutputFileStream(const std::string &file) : file_(file) {
+  if(file_.extension() == marian::filesystem::Path(".gz"))
+    ostream_ = std::make_unique<zstr::ofstream>(file_.string());
+  else
+    ostream_ = std::make_unique<std::ofstream>(file_.string());
+  ABORT_IF(!marian::filesystem::exists(file_), "File '{}' could not be opened", file);
+}
+
+OutputFileStream::OutputFileStream(TemporaryFile2 &tempfile) {
+  RewindFile(tempfile.getFileDescriptor());
+  temporary_writer_.reset(new WriteFDBuf(tempfile.getFileDescriptor()));
+  ostream_.reset(new std::ostream(temporary_writer_.get()));
+}
+
 } // namespace io
 } // namespace marian
diff --git a/src/common/file_stream.h b/src/common/file_stream.h
index ab6baf618..55f687e25 100755
--- a/src/common/file_stream.h
+++ b/src/common/file_stream.h
@@ -1,8 +1,8 @@
 #pragma once
 
+#include "common/definitions.h"
 #include "common/filesystem.h"
 #include "common/logging.h"
-#include "common/definitions.h"
 
 #ifdef __GNUC__
 #pragma GCC diagnostic push
@@ -20,18 +20,17 @@
 #pragma warning(pop)
 #endif
 
-
 #include <iostream>
 #include <memory>
 #include <vector>
 
-#ifdef __GNUC__ // not supported; maybe we just need to increment a standard flag in gcc/cmake?
+#ifdef __GNUC__  // not supported; maybe we just need to increment a standard flag in gcc/cmake?
 namespace std {
-  template<typename T, typename... Args>
-  std::unique_ptr<T> make_unique(Args&&... args) {
-    return std::unique_ptr<T>(new T(std::forward<Args>(args)...));
-  }
+template <typename T, typename... Args>
+std::unique_ptr<T> make_unique(Args&&... args) {
+  return std::unique_ptr<T>(new T(std::forward<Args>(args)...));
 }
+}  // namespace std
 #endif
 
 #ifdef _MSC_VER
@@ -49,99 +48,18 @@ class TemporaryFile2 {
   bool unlink_;
   std::string name_;
 
-#ifndef _MSC_VER
-  int mkstemp_and_unlink(char* tmpl) {
-    int ret = mkstemp(tmpl);
-    if(unlink_ && ret != -1) {
-      ABORT_IF(unlink(tmpl), "Error while deleting '{}'", tmpl);
-    }
-    return ret;
-  }
-#endif
+  #ifndef _MSC_VER
+  int mkstemp_and_unlink(char* tmpl);
+  #endif
 
+  int MakeTemp(const std::string& base);
 
-  int MakeTemp(const std::string& base) {
-#ifdef _MSC_VER
-    char* name = tempnam(base.c_str(), "marian.");
-    ABORT_IF(name == NULL,
-      "Error while making a temporary based on '{}'",
-      base);
-
-    int oflag = _O_RDWR | _O_CREAT | _O_EXCL;
-    if (unlink_) oflag |= _O_TEMPORARY;
-
-    int ret = open(name, oflag, _S_IREAD | _S_IWRITE);
-    ABORT_IF(ret == -1,
-      "Error while making a temporary based on '{}'",
-      base);
-
-    name_ = name;
-    free(name);
-
-    return ret;
-#else
-    std::string name(base);
-    name += "marian.XXXXXX";
-    name.push_back(0);
-    int ret;
-    ABORT_IF(-1 == (ret = mkstemp_and_unlink(&name[0])),
-      "Error while making a temporary based on '{}'",
-      base);
-    name_ = name;
-    return ret;
-#endif
-  }
-
-  void NormalizeTempPrefix(std::string& base) {
-    if(base.empty())
-      return;
-
-#ifdef _MSC_VER
-    if(base.substr(0,4) == "/tmp")
-      base = getenv("TMP");
-#else
-    if(base[base.size() - 1] == '/')
-      return;
-    struct stat sb;
-    // It's fine for it to not exist.
-    if(stat(base.c_str(), &sb) == - 1)
-      return;
-    if(S_ISDIR(sb.st_mode))
-      base += '/';
-#endif
-  }
+  void NormalizeTempPrefix(std::string& base);
 
 public:
-  TemporaryFile2(const std::string base = "/tmp/", bool earlyUnlink = true)
-      : unlink_(earlyUnlink) {
-    std::string baseTemp(base);
-    NormalizeTempPrefix(baseTemp);
-    fd_ = MakeTemp(baseTemp);
-  }
+  TemporaryFile2(const std::string base = "/tmp/", bool earlyUnlink = true);
 
-  ~TemporaryFile2() {
-#ifdef _MSC_VER
-    if (fd_ == -1)
-      return;
-
-    if(close(fd_)) {
-      std::cerr << "Could not close file " << fd_ << std::endl;
-      std::abort();
-    }
-
-    if(!unlink_) {
-      ABORT_IF(remove(name_.c_str()), "Error while deleting '{}'", name_);
-    }
-#else
-    if(fd_ != -1 && !unlink_) {
-      ABORT_IF(unlink(name_.c_str()), "Error while deleting '{}'", name_);
-    }
-    if(fd_ != -1 && close(fd_)) {
-      std::cerr << "Could not close file " << fd_ << std::endl;
-      std::abort();
-    }
-#endif
-  }
+  ~TemporaryFile2();
 
   int getFileDescriptor() { return fd_; }
 
@@ -150,55 +68,55 @@ class TemporaryFile2 {
 
 // A streambuf to read from a file descriptor.
 class ReadFDBuf : public std::streambuf {
-  public:
-    // Does not take ownership of file.
-    explicit ReadFDBuf(int fd, std::size_t buffer_size = 4096);
+public:
+  // Does not take ownership of file.
+  explicit ReadFDBuf(int fd, std::size_t buffer_size = 4096);
 
-  private:
-    int_type underflow() override;
+private:
+  int_type underflow() override;
 
-    // If the putback goes below the buffer, try to seek backwards.
-    int_type pbackfail(int c = EOF) override;
+  // If the putback goes below the buffer, try to seek backwards.
+  int_type pbackfail(int c = EOF) override;
 
-    // TODO: override setbuf.
+  // TODO: override setbuf.
 
-    // Read some amount into [Begin(), End()), returning the amount read.
-    ssize_t Read();
+  // Read some amount into [Begin(), End()), returning the amount read.
+  ssize_t Read();
 
-    char *Begin() { return &mem_.front(); }
-    char *End() { return &mem_.back() + 1; }
+  char* Begin() { return &mem_.front(); }
+  char* End() { return &mem_.back() + 1; }
 
-    int fd_;
-    std::vector<char> mem_;
+  int fd_;
+  std::vector<char> mem_;
 
-    ReadFDBuf(const ReadFDBuf &) = delete;
-    ReadFDBuf &operator=(const ReadFDBuf &) = delete;
+  ReadFDBuf(const ReadFDBuf&) = delete;
+  ReadFDBuf& operator=(const ReadFDBuf&) = delete;
 };
 
 // A streambuf to Write to a file descriptor.
 class WriteFDBuf : public std::streambuf {
-  public:
-    explicit WriteFDBuf(int fd, std::size_t buffer_size = 4096);
+public:
+  explicit WriteFDBuf(int fd, std::size_t buffer_size = 4096);
 
-    ~WriteFDBuf();
+  ~WriteFDBuf();
 
-  private:
-    int_type overflow(int c = EOF) override;
+private:
+  int_type overflow(int c = EOF) override;
 
-    // Write everything in the buffer to the file.
-    int sync() override;
+  // Write everything in the buffer to the file.
+  int sync() override;
 
-    // Write part of the buffer, returning the amount written.
-    ssize_t WriteSome(const char *from, const char *to);
+  // Write part of the buffer, returning the amount written.
+  ssize_t WriteSome(const char* from, const char* to);
 
-    char *Begin() { return &mem_.front(); }
-    char *End() { return &mem_.back() + 1; }
+  char* Begin() { return &mem_.front(); }
+  char* End() { return &mem_.back() + 1; }
 
-    int fd_;
-    std::vector<char> mem_;
+  int fd_;
+  std::vector<char> mem_;
 
-    WriteFDBuf(const WriteFDBuf &) = delete;
-    WriteFDBuf &operator=(const WriteFDBuf &) = delete;
+  WriteFDBuf(const WriteFDBuf&) = delete;
+  WriteFDBuf& operator=(const WriteFDBuf&) = delete;
 };
 
 // lseek(fd, 0, SEEK_SET) but with error checking.
@@ -206,58 +124,37 @@ void RewindFile(int fd);
 
 class InputFileStream {
 public:
-  explicit InputFileStream(const std::string& file)
-  : file_(file) {
-    ABORT_IF(!marian::filesystem::exists(file_), "File '{}' does not exist", file);
-
-    if(file_.extension() == marian::filesystem::Path(".gz"))
-      istream_ = std::make_unique<zstr::ifstream>(file_.string());
-    else
-      istream_ = std::make_unique<std::ifstream>(file_.string());
-    ABORT_IF(fail(), "Error {} ({}) opening file '{}'", errno, strerror(errno), path());
-  }
+  explicit InputFileStream(const std::string& file);
 
-  explicit InputFileStream(TemporaryFile2& tempfile) {
-    RewindFile(tempfile.getFileDescriptor());
-    temporary_reader_.reset(new ReadFDBuf(tempfile.getFileDescriptor()));
-    istream_.reset(new std::istream(temporary_reader_.get()));
-    std::cerr << "HH1" << std::endl;
-  }
+  explicit InputFileStream(TemporaryFile2& tempfile);
 
-  explicit InputFileStream(std::istream& strm)
-  : istream_(new std::istream(strm.rdbuf())) {}
+  explicit InputFileStream(std::istream& strm) : istream_(new std::istream(strm.rdbuf())) {}
 
   operator std::istream&() { return *istream_; }
 
   operator bool() { return (bool)*istream_; }
 
-  bool bad() const {
-    return istream_->bad();
-  }
+  bool bad() const { return istream_->bad(); }
 
-  bool fail() const {
-    return istream_->fail();
-  }
+  bool fail() const { return istream_->fail(); }
 
-  char widen(char c) {
-    return istream_->widen(c);
-  }
+  char widen(char c) { return istream_->widen(c); }
 
   std::string path() { return file_.string(); }
 
   bool empty() { return istream_->peek() == std::ifstream::traits_type::eof(); }
 
-  void setbufsize(size_t size) const {
-    istream_->rdbuf()->pubsetbuf(0, 0);
-    readBuf_.resize(size);
-    istream_->rdbuf()->pubsetbuf(readBuf_.data(), readBuf_.size());
-  }
+  void setbufsize(size_t size) const;
 
   template <typename T>
   friend InputFileStream& operator>>(InputFileStream& stream, T& t) {
     *stream.istream_ >> t;
     // bad() seems to be correct here. Should not abort on EOF.
-    ABORT_IF(stream.bad(), "Error {} ({}) reading from file '{}'", errno, strerror(errno), stream.path());
+    ABORT_IF(stream.bad(),
+             "Error {} ({}) reading from file '{}'",
+             errno,
+             strerror(errno),
+             stream.path());
     return stream;
   }
 
@@ -273,13 +170,14 @@ class InputFileStream {
   marian::filesystem::Path file_;
   std::unique_ptr<std::istream> istream_;
 
-  mutable std::vector<char> readBuf_; // for setbuf()
+  mutable std::vector<char> readBuf_;  // for setbuf()
   std::unique_ptr<ReadFDBuf> temporary_reader_;
 };
 
 // wrapper around std::getline() that handles Windows input files with extra CR
 // chars at the line end
-static inline InputFileStream& getline(InputFileStream& in, std::string& line) {
+static inline InputFileStream& getline(InputFileStream& in, std::string& line)
+{
   std::getline((std::istream&)in, line);
   // bad() seems to be correct here. Should not abort on EOF.
   ABORT_IF(in.bad(), "Error reading from file '{}'", in.path());
@@ -304,23 +202,11 @@ static inline InputFileStream& getline(InputFileStream& in, std::string& line, c
 
 class OutputFileStream {
 public:
-  OutputFileStream(const std::string& file) : file_(file) {
-    if(file_.extension() == marian::filesystem::Path(".gz"))
-      ostream_ = std::make_unique<zstr::ofstream>(file_.string());
-    else
-      ostream_ = std::make_unique<std::ofstream>(file_.string());
-    ABORT_IF(!marian::filesystem::exists(file_), "File '{}' could not be opened", file);
-  }
+  OutputFileStream(const std::string& file);
 
-  OutputFileStream(TemporaryFile2& tempfile) {
-    RewindFile(tempfile.getFileDescriptor());
-    temporary_writer_.reset(new WriteFDBuf(tempfile.getFileDescriptor()));
-    ostream_.reset(new std::ostream(temporary_writer_.get()));
-  }
+  OutputFileStream(TemporaryFile2& tempfile);
 
-  OutputFileStream(std::ostream& strm) {
-    ostream_ = std::make_unique<std::ostream>(strm.rdbuf());
-  }
+  OutputFileStream(std::ostream& strm) { ostream_ = std::make_unique<std::ostream>(strm.rdbuf()); }
 
   operator std::ostream&() { return *ostream_; }
 
@@ -339,7 +225,8 @@ class OutputFileStream {
   }
 
   // handle things like std::endl which is actually a function not a value
-  friend OutputFileStream& operator<<(OutputFileStream& stream, std::ostream& (*var)(std::ostream&)) {
+  friend OutputFileStream& operator<<(OutputFileStream& stream,
+                                      std::ostream& (*var)(std::ostream&)) {
     *stream.ostream_ << var;
     // fail() seems to be correct here. Failure to write should abort.
     ABORT_IF(stream.fail(), "Error writing to file '{}'", stream.path());
@@ -360,9 +247,8 @@ class OutputFileStream {
   marian::filesystem::Path file_;
   std::unique_ptr<std::ostream> ostream_;
 
-
   std::unique_ptr<WriteFDBuf> temporary_writer_;
 };
 
-}
-}
+}  // namespace io
+}  // namespace marian

From c33074ce5441d2a62db1f19eee2f07725d8d1122 Mon Sep 17 00:00:00 2001
From: Hieu Hoang <hihoan@microsoft.com>
Date: Mon, 21 Oct 2019 12:17:35 -0700
Subject: [PATCH 653/838] move code .h -> .cpp

---
 src/training/validator.cpp | 584 +++++++++++++++++++++++++++++++++++++
 src/training/validator.h   | 578 ++----------------------------------
 vs/Marian.vcxproj          |   1 +
 vs/Marian.vcxproj.filters  |   3 +
 4 files changed, 605 insertions(+), 561 deletions(-)

diff --git a/src/training/validator.cpp b/src/training/validator.cpp
index f6b1c86a1..24f523273 100644
--- a/src/training/validator.cpp
+++ b/src/training/validator.cpp
@@ -47,4 +47,588 @@ std::vector<Ptr<ValidatorBase/*<data::Corpus>*/>> Validators(
 
   return validators;
 }
+
+
+///////////////////////////////////////////////////////////////////////////////////////
+float ValidatorBase::initScore() {
+  return lowerIsBetter_ ? std::numeric_limits<float>::max() : std::numeric_limits<float>::lowest();
+}
+
+void ValidatorBase::actAfterLoaded(TrainingState& state) {
+  if(state.validators[type()]) {
+    lastBest_ = state.validators[type()]["last-best"].as<float>();
+    stalled_ = state.validators[type()]["stalled"].as<size_t>();
+  }
+}
+
+///////////////////////////////////////////////////////////////////////////////////////
+CrossEntropyValidator::CrossEntropyValidator(std::vector<Ptr<Vocab>> vocabs, Ptr<Options> options)
+    : Validator(vocabs, options) {
+  createBatchGenerator(/*isTranslating=*/false);
+
+  auto opts = options_->with("inference",
+                             true,  // @TODO: check if required
+                             "cost-type",
+                             "ce-sum");
+  // @TODO: remove, only used for saving?
+  builder_ = models::createCriterionFunctionFromOptions(opts, models::usage::scoring);
+}
+
+float CrossEntropyValidator::validateBG(const std::vector<Ptr<ExpressionGraph>>& graphs) {
+  auto ctype = options_->get<std::string>("cost-type");
+
+  // @TODO: use with(...) everywhere, this will help with creating immutable options.
+  // Make options const everywhere and get rid of "set"?
+  auto opts = options_->with("inference", true, "cost-type", "ce-sum");
+
+  StaticLoss loss;
+  size_t samples = 0;
+  std::deque<Ptr<ExpressionGraph>> graphQueue(graphs.begin(), graphs.end());
+
+  auto task = [=, &loss, &samples, &graphQueue](BatchPtr batch) {
+    thread_local Ptr<ExpressionGraph> graph;
+
+    if(!graph) {
+      std::unique_lock<std::mutex> lock(mutex_);
+      ABORT_IF(graphQueue.empty(), "Asking for graph, but none left on queue");
+      graph = graphQueue.front();
+      graphQueue.pop_front();
+    }
+
+    auto builder = models::createCriterionFunctionFromOptions(options_, models::usage::scoring);
+
+    builder->clear(graph);
+    auto dynamicLoss = builder->build(graph, batch);
+    graph->forward();
+
+    std::unique_lock<std::mutex> lock(mutex_);
+    loss += *dynamicLoss;
+    samples += batch->size();
+  };
+
+  {
+    threadPool_.reserve(graphs.size());
+    TaskBarrier taskBarrier;
+    for(auto batch : *batchGenerator_)
+      taskBarrier.push_back(threadPool_.enqueue(task, batch));
+    // ~TaskBarrier waits until all are done
+  }
+
+  if(ctype == "perplexity")
+    return std::exp(loss.loss / loss.count);
+  if(ctype == "ce-mean-words")
+    return loss.loss / loss.count;
+  if(ctype == "ce-sum")
+    return loss.loss;
+  else
+    return loss.loss / samples;  // @TODO: back-compat, to be removed
+}
+
+///////////////////////////////////////////////////////////////////////////////////////
+AccuracyValidator::AccuracyValidator(std::vector<Ptr<Vocab>> vocabs, Ptr<Options> options)
+    : Validator(vocabs, options, /*lowerIsBetter=*/false) {
+  createBatchGenerator(/*isTranslating=*/false);
+
+  // @TODO: remove, only used for saving?
+  builder_ = models::createModelFromOptions(options_, models::usage::raw);
+}
+
+float AccuracyValidator::validateBG(const std::vector<Ptr<ExpressionGraph>>& graphs) {
+  size_t correct = 0;
+  size_t totalLabels = 0;
+  std::deque<Ptr<ExpressionGraph>> graphQueue(graphs.begin(), graphs.end());
+
+  auto task = [=, &correct, &totalLabels, &graphQueue](BatchPtr batch) {
+    thread_local Ptr<ExpressionGraph> graph;
+
+    if(!graph) {
+      std::unique_lock<std::mutex> lock(mutex_);
+      ABORT_IF(graphQueue.empty(), "Asking for graph, but none left on queue");
+      graph = graphQueue.front();
+      graphQueue.pop_front();
+    }
+
+    auto builder = models::createModelFromOptions(options_, models::usage::raw);
+
+    builder->clear(graph);
+    Expr logits = builder->build(graph, batch).getLogits();
+    graph->forward();
+
+    std::vector<float> vLogits;
+    logits->val()->get(vLogits);
+
+    const auto& groundTruth = batch->back()->data();
+
+    IndexType cols = logits->shape()[-1];
+
+    size_t thisCorrect = 0;
+    size_t thisLabels = groundTruth.size();
+
+    for(int i = 0; i < thisLabels; ++i) {
+      // CPU-side Argmax
+      Word bestWord = Word::NONE;
+      float bestValue = std::numeric_limits<float>::lowest();
+      for(IndexType j = 0; j < cols; ++j) {
+        float currValue = vLogits[i * cols + j];
+        if(currValue > bestValue) {
+          bestValue = currValue;
+          bestWord = Word::fromWordIndex(j);
+        }
+      }
+      thisCorrect += (size_t)(bestWord == groundTruth[i]);
+    }
+
+    std::unique_lock<std::mutex> lock(mutex_);
+    totalLabels += thisLabels;
+    correct += thisCorrect;
+  };
+
+  {
+    threadPool_.reserve(graphs.size());
+
+    TaskBarrier taskBarrier;
+    for(auto batch : *batchGenerator_)
+      taskBarrier.push_back(threadPool_.enqueue(task, batch));
+
+    // ~TaskBarrier waits until all are done
+  }
+
+  return (float)correct / (float)totalLabels;
+}
+
+///////////////////////////////////////////////////////////////////////////////////////
+BertAccuracyValidator::BertAccuracyValidator(std::vector<Ptr<Vocab>> vocabs,
+                                           Ptr<Options> options,
+                                           bool evalMaskedLM)
+    : Validator(vocabs, options, /*lowerIsBetter=*/false), evalMaskedLM_(evalMaskedLM) {
+  createBatchGenerator(/*isTranslating=*/false);
+  // @TODO: remove, only used for saving?
+  builder_ = models::createModelFromOptions(options_, models::usage::raw);
+}
+
+float BertAccuracyValidator::validateBG(const std::vector<Ptr<ExpressionGraph>>& graphs) {
+  size_t correct = 0;
+  size_t totalLabels = 0;
+  size_t batchId = 0;
+  std::deque<Ptr<ExpressionGraph>> graphQueue(graphs.begin(), graphs.end());
+
+  auto task = [=, &correct, &totalLabels, &graphQueue](BatchPtr batch, size_t batchId) {
+    thread_local Ptr<ExpressionGraph> graph;
+
+    if(!graph) {
+      std::unique_lock<std::mutex> lock(mutex_);
+      ABORT_IF(graphQueue.empty(), "Asking for graph, but none left on queue");
+      graph = graphQueue.front();
+      graphQueue.pop_front();
+    }
+
+    auto builder = models::createModelFromOptions(options_, models::usage::raw);
+
+    thread_local std::unique_ptr<std::mt19937> engine;
+    if(!engine)
+      engine.reset(new std::mt19937((unsigned int)(Config::seed + batchId)));
+
+    auto bertBatch = New<data::BertBatch>(batch,
+                                          *engine,
+                                          options_->get<float>("bert-masking-fraction"),
+                                          options_->get<std::string>("bert-mask-symbol"),
+                                          options_->get<std::string>("bert-sep-symbol"),
+                                          options_->get<std::string>("bert-class-symbol"),
+                                          options_->get<int>("bert-type-vocab-size"));
+
+    builder->clear(graph);
+    auto classifierStates
+        = std::dynamic_pointer_cast<BertEncoderClassifier>(builder)->apply(graph, bertBatch, true);
+    graph->forward();
+
+    auto maskedLMLogits = classifierStates[0]->getLogProbs();
+    const auto& maskedLMLabels = bertBatch->bertMaskedWords();
+
+    auto sentenceLogits = classifierStates[1]->getLogProbs();
+    const auto& sentenceLabels = bertBatch->back()->data();
+
+    auto count = [=, &correct, &totalLabels](Expr logits, const Words& labels) {
+      IndexType cols = logits->shape()[-1];
+      size_t thisCorrect = 0;
+      size_t thisLabels = labels.size();
+
+      std::vector<float> vLogits;
+      logits->val()->get(vLogits);
+
+      for(int i = 0; i < thisLabels; ++i) {
+        // CPU-side Argmax
+        IndexType bestIndex = 0;
+        float bestValue = std::numeric_limits<float>::lowest();
+        for(IndexType j = 0; j < cols; ++j) {
+          float currValue = vLogits[i * cols + j];
+          if(currValue > bestValue) {
+            bestValue = currValue;
+            bestIndex = j;
+          }
+        }
+        thisCorrect += (size_t)(bestIndex == labels[i].toWordIndex());
+      }
+
+      std::unique_lock<std::mutex> lock(mutex_);
+      totalLabels += thisLabels;
+      correct += thisCorrect;
+    };
+
+    if(evalMaskedLM_)
+      count(maskedLMLogits, maskedLMLabels);
+    else
+      count(sentenceLogits, sentenceLabels);
+  };
+
+  {
+    threadPool_.reserve(graphs.size());
+    TaskBarrier taskBarrier;
+    for(auto batch : *batchGenerator_) {
+      taskBarrier.push_back(threadPool_.enqueue(task, batch, batchId));
+      batchId++;
+    }
+    // ~TaskBarrier waits until all are done
+  }
+
+  return (float)correct / (float)totalLabels;
+}
+
+///////////////////////////////////////////////////////////////////////////////////////
+ScriptValidator::ScriptValidator(std::vector<Ptr<Vocab>> vocabs, Ptr<Options> options)
+    : Validator(vocabs, options, false) {
+  // @TODO: remove, only used for saving?
+  builder_ = models::createModelFromOptions(options_, models::usage::raw);
+
+  ABORT_IF(!options_->hasAndNotEmpty("valid-script-path"),
+           "valid-script metric but no script given");
+}
+
+float ScriptValidator::validate(const std::vector<Ptr<ExpressionGraph>>& graphs) {
+  using namespace data;
+  auto model = options_->get<std::string>("model");
+  std::string suffix = model.substr(model.size() - 4);
+  ABORT_IF(suffix != ".npz" && suffix != ".bin", "Unknown model suffix {}", suffix);
+
+  builder_->save(graphs[0], model + ".dev" + suffix, true);
+
+  auto valStr = utils::exec(options_->get<std::string>("valid-script-path"),
+                            options_->get<std::vector<std::string>>("valid-script-args"));
+  float val = (float)std::atof(valStr.c_str());
+  updateStalled(graphs, val);
+
+  return val;
+}
+
+///////////////////////////////////////////////////////////////////////////////////////
+TranslationValidator::TranslationValidator(std::vector<Ptr<Vocab>> vocabs, Ptr<Options> options)
+    : Validator(vocabs, options, false), quiet_(options_->get<bool>("quiet-translation")) {
+  // @TODO: remove, only used for saving?
+  builder_ = models::createModelFromOptions(options_, models::usage::translation);
+
+  if(!options_->hasAndNotEmpty("valid-script-path"))
+    LOG_VALID(warn, "No post-processing script given for validating translator");
+
+  createBatchGenerator(/*isTranslating=*/true);
+}
+
+float TranslationValidator::validate(const std::vector<Ptr<ExpressionGraph>>& graphs) {
+  using namespace data;
+
+  // Generate batches
+  batchGenerator_->prepare();
+
+  // Create scorer
+  auto model = options_->get<std::string>("model");
+
+  std::vector<Ptr<Scorer>> scorers;
+  for(auto graph : graphs) {
+    auto builder = models::createModelFromOptions(options_, models::usage::translation);
+    Ptr<Scorer> scorer = New<ScorerWrapper>(builder, "", 1.0f, model);
+    scorers.push_back(scorer);  // @TODO: should this be done in the contructor?
+  }
+
+  // Set up output file
+  std::string fileName;
+  Ptr<io::TemporaryFile2> tempFile;
+
+  if(options_->hasAndNotEmpty("valid-translation-output")) {
+    fileName = options_->get<std::string>("valid-translation-output");
+  } else {
+    tempFile.reset(new io::TemporaryFile2(options_->get<std::string>("tempdir"), false));
+    fileName = tempFile->getFileName();
+  }
+
+  for(auto graph : graphs)
+    graph->setInference(true);
+
+  if(!quiet_)
+    LOG(info, "Translating validation set...");
+
+  timer::Timer timer;
+  {
+    auto printer = New<OutputPrinter>(options_, vocabs_.back());
+    // @TODO: This can be simplified. If there is no "valid-translation-output", fileName already
+    // contains the name of temporary file that should be used?
+    auto collector = options_->hasAndNotEmpty("valid-translation-output")
+                         ? New<OutputCollector>(fileName)
+                         : New<OutputCollector>(*tempFile);
+
+    if(quiet_)
+      collector->setPrintingStrategy(New<QuietPrinting>());
+    else
+      collector->setPrintingStrategy(New<GeometricPrinting>());
+
+    std::deque<Ptr<ExpressionGraph>> graphQueue(graphs.begin(), graphs.end());
+    std::deque<Ptr<Scorer>> scorerQueue(scorers.begin(), scorers.end());
+    auto task = [=, &graphQueue, &scorerQueue](BatchPtr batch) {
+      thread_local Ptr<ExpressionGraph> graph;
+      thread_local Ptr<Scorer> scorer;
+
+      if(!graph) {
+        std::unique_lock<std::mutex> lock(mutex_);
+        ABORT_IF(graphQueue.empty(), "Asking for graph, but none left on queue");
+        graph = graphQueue.front();
+        graphQueue.pop_front();
+
+        ABORT_IF(scorerQueue.empty(), "Asking for scorer, but none left on queue");
+        scorer = scorerQueue.front();
+        scorerQueue.pop_front();
+      }
+
+      auto search = New<BeamSearch>(options_, std::vector<Ptr<Scorer>>{scorer}, vocabs_.back());
+      auto histories = search->search(graph, batch);
+
+      for(auto history : histories) {
+        std::stringstream best1;
+        std::stringstream bestn;
+        printer->print(history, best1, bestn);
+        collector->Write(
+            (long)history->getLineNum(), best1.str(), bestn.str(), options_->get<bool>("n-best"));
+      }
+    };
+
+    threadPool_.reserve(graphs.size());
+    TaskBarrier taskBarrier;
+    for(auto batch : *batchGenerator_)
+      taskBarrier.push_back(threadPool_.enqueue(task, batch));
+    // ~TaskBarrier waits until all are done
+  }
+
+  if(!quiet_)
+    LOG(info, "Total translation time: {:.5f}s", timer.elapsed());
+
+  for(auto graph : graphs)
+    graph->setInference(false);
+
+  float val = 0.0f;
+
+  // Run post-processing script if given
+  if(options_->hasAndNotEmpty("valid-script-path")) {
+    // auto command = options_->get<std::string>("valid-script-path") + " " + fileName;
+    // auto valStr = utils::exec(command);
+    auto valStr = utils::exec(options_->get<std::string>("valid-script-path"),
+                              options_->get<std::vector<std::string>>("valid-script-args"),
+                              fileName);
+    val = (float)std::atof(valStr.c_str());
+    updateStalled(graphs, val);
+  }
+
+  return val;
+};
+
+///////////////////////////////////////////////////////////////////////////////////////
+BleuValidator::BleuValidator(std::vector<Ptr<Vocab>> vocabs, Ptr<Options> options, bool detok)
+    : Validator(vocabs, options, false),
+      detok_(detok),
+      quiet_(options_->get<bool>("quiet-translation")) {
+  // @TODO: remove, only used for saving?
+  builder_ = models::createModelFromOptions(options_, models::usage::translation);
+
+  // @TODO: replace bleu-detok by a separate parameter to enable (various forms of) detok
+  auto vocab = vocabs_.back();
+  ABORT_IF(detok_ && vocab->type() != "SentencePieceVocab" && vocab->type() != "FactoredVocab",
+           "Detokenizing BLEU validator expects the target vocabulary to be SentencePieceVocab or "
+           "FactoredVocab. "
+           "Current vocabulary type is {}",
+           vocab->type());
+
+  createBatchGenerator(/*isTranslating=*/true);
+}
+
+float BleuValidator::validate(const std::vector<Ptr<ExpressionGraph>>& graphs) {
+  using namespace data;
+
+  // Generate batches
+  batchGenerator_->prepare();
+
+  // Create scorer
+  auto model = options_->get<std::string>("model");
+
+  // @TODO: check if required - Temporary options for translation
+  auto mopts = New<Options>();
+  mopts->merge(options_);
+  mopts->set("inference", true);
+
+  std::vector<Ptr<Scorer>> scorers;
+  for(auto graph : graphs) {
+    auto builder = models::createModelFromOptions(options_, models::usage::translation);
+    Ptr<Scorer> scorer = New<ScorerWrapper>(builder, "", 1.0f, model);
+    scorers.push_back(scorer);
+  }
+
+  for(auto graph : graphs)
+    graph->setInference(true);
+
+  if(!quiet_)
+    LOG(info, "Translating validation set...");
+
+  // 0: 1-grams matched, 1: 1-grams total,
+  // ...,
+  // 6: 4-grams matched, 7: 4-grams total,
+  // 8: reference length
+  std::vector<float> stats(9, 0.f);
+
+  timer::Timer timer;
+  {
+    auto printer = New<OutputPrinter>(options_, vocabs_.back());
+
+    Ptr<OutputCollector> collector;
+    if(options_->hasAndNotEmpty("valid-translation-output")) {
+      auto fileName = options_->get<std::string>("valid-translation-output");
+      collector = New<OutputCollector>(fileName);  // for debugging
+    } else {
+      collector = New<OutputCollector>(/* null */);  // don't print, but log
+    }
+
+    if(quiet_)
+      collector->setPrintingStrategy(New<QuietPrinting>());
+    else
+      collector->setPrintingStrategy(New<GeometricPrinting>());
+
+    std::deque<Ptr<ExpressionGraph>> graphQueue(graphs.begin(), graphs.end());
+    std::deque<Ptr<Scorer>> scorerQueue(scorers.begin(), scorers.end());
+    auto task = [=, &stats, &graphQueue, &scorerQueue](BatchPtr batch) {
+      thread_local Ptr<ExpressionGraph> graph;
+      thread_local Ptr<Scorer> scorer;
+
+      if(!graph) {
+        std::unique_lock<std::mutex> lock(mutex_);
+        ABORT_IF(graphQueue.empty(), "Asking for graph, but none left on queue");
+        graph = graphQueue.front();
+        graphQueue.pop_front();
+
+        ABORT_IF(scorerQueue.empty(), "Asking for scorer, but none left on queue");
+        scorer = scorerQueue.front();
+        scorerQueue.pop_front();
+      }
+
+      auto search = New<BeamSearch>(options_, std::vector<Ptr<Scorer>>{scorer}, vocabs_.back());
+      auto histories = search->search(graph, batch);
+
+      size_t no = 0;
+      std::lock_guard<std::mutex> statsLock(mutex_);
+      for(auto history : histories) {
+        auto result = history->top();
+        const auto& words = std::get<0>(result);
+        updateStats(stats, words, batch, no, vocabs_.back()->getEosId());
+
+        std::stringstream best1;
+        std::stringstream bestn;
+        printer->print(history, best1, bestn);
+        collector->Write((long)history->getLineNum(),
+                         best1.str(),
+                         bestn.str(),
+                         /*nbest=*/false);
+        no++;
+      }
+    };
+
+    threadPool_.reserve(graphs.size());
+    TaskBarrier taskBarrier;
+    for(auto batch : *batchGenerator_)
+      taskBarrier.push_back(threadPool_.enqueue(task, batch));
+    // ~TaskBarrier waits until all are done
+  }
+
+  if(!quiet_)
+    LOG(info, "Total translation time: {:.5f}s", timer.elapsed());
+
+  for(auto graph : graphs)
+    graph->setInference(false);
+
+  float val = calcBLEU(stats);
+  updateStalled(graphs, val);
+
+  return val;
+}
+
+std::vector<std::string> BleuValidator::decode(const Words& words, bool addEOS) {
+  auto vocab = vocabs_.back();
+  auto tokenString = vocab->surfaceForm(words);  // detokenize to surface form
+  tokenString = tokenize(tokenString);           // tokenize according to SacreBLEU rules
+  tokenString
+      = tokenizeContinuousScript(tokenString);  // CJT scripts only: further break into characters
+  auto tokens = utils::splitAny(tokenString, " ");
+  if(addEOS)
+    tokens.push_back("</s>");
+  return tokens;
+}
+
+void BleuValidator::updateStats(std::vector<float>& stats,
+                 const Words& cand,
+                 const Ptr<data::Batch> batch,
+                 size_t no,
+                 Word eos) {
+  auto corpusBatch = std::static_pointer_cast<data::CorpusBatch>(batch);
+  auto subBatch = corpusBatch->back();
+
+  size_t size = subBatch->batchSize();
+  size_t width = subBatch->batchWidth();
+
+  Words ref;  // fill ref
+  for(size_t i = 0; i < width; ++i) {
+    Word w = subBatch->data()[i * size + no];
+    if(w == eos)
+      break;
+    ref.push_back(w);
+  }
+
+  bool detok = detok_;
+#if 1  // hack for now, to get this feature when running under Flo
+  // Problem is that Flo pieces that pass 'bleu' do not know whether vocab is factored,
+  // hence cannot select 'bleu-detok'.
+  // @TODO: We agreed that we will replace bleu-detok by bleu with an additional
+  // parameter to select the detokenization method, which will default to detok for
+  // FactoredSegmenter, and no-op for base vocab.
+  if(vocabs_.back()->type() == "FactoredVocab") {
+    if(!quiet_)
+      LOG_ONCE(info, "[valid] FactoredVocab implies using detokenized BLEU");
+    detok = true;  // always use bleu-detok
+  }
+#endif
+  if(detok) {  // log the first detokenized string
+    LOG_ONCE(info, "[valid] First sentence's tokens after detokenization, as scored:");
+    LOG_ONCE(info, "[valid]  Hyp: {}", utils::join(decode(cand, /*addEOS=*/true)));
+    LOG_ONCE(info, "[valid]  Ref: {}", utils::join(decode(ref)));
+  }
+  if(detok)
+    updateStats(stats, decode(cand, /*addEOS=*/true), decode(ref));
+  else
+    updateStats(stats, cand, ref);
+}
+
+float BleuValidator::calcBLEU(const std::vector<float>& stats) {
+  float logbleu = 0;
+  for(int i = 0; i < 8; i += 2) {
+    if(stats[i] == 0.f)
+      return 0.f;
+    logbleu += std::log(stats[i] / stats[i + 1]);
+  }
+
+  logbleu /= 4.f;
+
+  float brev_penalty = 1.f - std::max(stats[8] / stats[1], 1.f);
+  return std::exp(logbleu + brev_penalty) * 100;
+}
+
 }  // namespace marian
diff --git a/src/training/validator.h b/src/training/validator.h
index a096dab91..ee633f555 100755
--- a/src/training/validator.h
+++ b/src/training/validator.h
@@ -43,17 +43,8 @@ class ValidatorBase : public TrainingObserver {
   float lastBest() { return lastBest_; }
   size_t stalled() { return stalled_; }
 
-  virtual float initScore() {
-    return lowerIsBetter_ ? std::numeric_limits<float>::max()
-                          : std::numeric_limits<float>::lowest();
-  }
-
-  virtual void actAfterLoaded(TrainingState& state) override {
-    if(state.validators[type()]) {
-      lastBest_ = state.validators[type()]["last-best"].as<float>();
-      stalled_ = state.validators[type()]["stalled"].as<size_t>();
-    }
-  }
+  virtual float initScore();
+  virtual void actAfterLoaded(TrainingState& state) override;
 };
 
 template <class DataSet, class BuilderType> // @TODO: BuilderType doesn't really serve a purpose here? Review and remove.
@@ -143,148 +134,23 @@ class CrossEntropyValidator : public Validator<data::Corpus, models::ICriterionF
   using Validator::BatchPtr;
 
 public:
-  CrossEntropyValidator(std::vector<Ptr<Vocab>> vocabs, Ptr<Options> options)
-      : Validator(vocabs, options) {
-    createBatchGenerator(/*isTranslating=*/false);
-
-    auto opts = options_->with("inference", true, // @TODO: check if required
-                               "cost-type", "ce-sum");
-    // @TODO: remove, only used for saving?
-    builder_ = models::createCriterionFunctionFromOptions(opts, models::usage::scoring);
-  }
+  CrossEntropyValidator(std::vector<Ptr<Vocab>> vocabs, Ptr<Options> options);
 
   std::string type() override { return options_->get<std::string>("cost-type"); }
 
 protected:
-  virtual float validateBG(const std::vector<Ptr<ExpressionGraph>>& graphs) override {
-    auto ctype = options_->get<std::string>("cost-type");
-
-    // @TODO: use with(...) everywhere, this will help with creating immutable options. 
-    // Make options const everywhere and get rid of "set"?
-    auto opts = options_->with("inference", true, 
-                               "cost-type", "ce-sum");
-
-    StaticLoss loss;
-    size_t samples = 0;
-    std::deque<Ptr<ExpressionGraph>> graphQueue(graphs.begin(), graphs.end());
-
-    auto task = [=, &loss, &samples, &graphQueue](BatchPtr batch) {
-      thread_local Ptr<ExpressionGraph> graph;
-      
-      if(!graph) {
-        std::unique_lock<std::mutex> lock(mutex_);
-        ABORT_IF(graphQueue.empty(), "Asking for graph, but none left on queue");
-        graph = graphQueue.front();
-        graphQueue.pop_front();
-      }
-    
-      auto builder = models::createCriterionFunctionFromOptions(options_, models::usage::scoring);
-
-      builder->clear(graph);
-      auto dynamicLoss = builder->build(graph, batch);
-      graph->forward();
-
-      std::unique_lock<std::mutex> lock(mutex_);
-      loss += *dynamicLoss;
-      samples += batch->size();
-    };
-
-    {
-      threadPool_.reserve(graphs.size());
-      TaskBarrier taskBarrier;
-      for(auto batch : *batchGenerator_)
-        taskBarrier.push_back(threadPool_.enqueue(task, batch));
-      // ~TaskBarrier waits until all are done
-    }
-
-    if(ctype == "perplexity")
-      return std::exp(loss.loss / loss.count);
-    if(ctype == "ce-mean-words")
-      return loss.loss / loss.count;
-    if(ctype == "ce-sum")
-      return loss.loss;
-    else
-      return loss.loss / samples; // @TODO: back-compat, to be removed
-  }
+  virtual float validateBG(const std::vector<Ptr<ExpressionGraph>>& graphs) override;
 };
 
 // Used for validating with classifiers. Compute prediction accuracy versus ground truth for a set of classes
 class AccuracyValidator : public Validator<data::Corpus, models::IModel> {
 public:
-  AccuracyValidator(std::vector<Ptr<Vocab>> vocabs, Ptr<Options> options)
-      : Validator(vocabs, options, /*lowerIsBetter=*/false) {
-    createBatchGenerator(/*isTranslating=*/false);
-
-    // @TODO: remove, only used for saving?
-    builder_ = models::createModelFromOptions(options_, models::usage::raw);
-  }
+  AccuracyValidator(std::vector<Ptr<Vocab>> vocabs, Ptr<Options> options);
 
   std::string type() override { return "accuracy"; }
 
 protected:
-  virtual float validateBG(const std::vector<Ptr<ExpressionGraph>>& graphs) override {
-
-    size_t correct     = 0;
-    size_t totalLabels = 0;
-    std::deque<Ptr<ExpressionGraph>> graphQueue(graphs.begin(), graphs.end());
-
-    auto task = [=, &correct, &totalLabels, &graphQueue](BatchPtr batch) {
-      thread_local Ptr<ExpressionGraph> graph;
-      
-      if(!graph) {
-        std::unique_lock<std::mutex> lock(mutex_);
-        ABORT_IF(graphQueue.empty(), "Asking for graph, but none left on queue");
-        graph = graphQueue.front();
-        graphQueue.pop_front();
-      }
-    
-      auto builder = models::createModelFromOptions(options_, models::usage::raw);
-
-      builder->clear(graph);
-      Expr logits = builder->build(graph, batch).getLogits();
-      graph->forward();
-
-      std::vector<float> vLogits;
-      logits->val()->get(vLogits);
-
-      const auto& groundTruth = batch->back()->data();
-
-      IndexType cols = logits->shape()[-1];
-
-      size_t thisCorrect = 0;
-      size_t thisLabels  = groundTruth.size();
-
-      for(int i = 0; i < thisLabels; ++i) {
-        // CPU-side Argmax
-        Word bestWord = Word::NONE;
-        float bestValue = std::numeric_limits<float>::lowest();
-        for(IndexType j = 0; j < cols; ++j) {
-          float currValue = vLogits[i * cols + j];
-          if(currValue > bestValue) {
-            bestValue = currValue;
-            bestWord = Word::fromWordIndex(j);
-          }
-        }
-        thisCorrect += (size_t)(bestWord == groundTruth[i]);
-      }
-
-      std::unique_lock<std::mutex> lock(mutex_);
-      totalLabels += thisLabels;
-      correct     += thisCorrect;
-    };
-
-    {
-      threadPool_.reserve(graphs.size());
-
-      TaskBarrier taskBarrier;
-      for(auto batch : *batchGenerator_)
-        taskBarrier.push_back(threadPool_.enqueue(task, batch));
-
-      // ~TaskBarrier waits until all are done
-    }
-
-    return (float)correct / (float)totalLabels;
-  }
+  virtual float validateBG(const std::vector<Ptr<ExpressionGraph>>& graphs) override;
 };
 
 class BertAccuracyValidator : public Validator<data::Corpus, models::IModel> {
@@ -292,13 +158,7 @@ class BertAccuracyValidator : public Validator<data::Corpus, models::IModel> {
   bool evalMaskedLM_{true};
 
 public:
-  BertAccuracyValidator(std::vector<Ptr<Vocab>> vocabs, Ptr<Options> options, bool evalMaskedLM)
-      : Validator(vocabs, options, /*lowerIsBetter=*/false),
-        evalMaskedLM_(evalMaskedLM) {
-    createBatchGenerator(/*isTranslating=*/false);
-    // @TODO: remove, only used for saving?
-    builder_ = models::createModelFromOptions(options_, models::usage::raw);
-  }
+  BertAccuracyValidator(std::vector<Ptr<Vocab>> vocabs, Ptr<Options> options, bool evalMaskedLM);
 
   std::string type() override {
     if(evalMaskedLM_)
@@ -308,121 +168,15 @@ class BertAccuracyValidator : public Validator<data::Corpus, models::IModel> {
   }
 
 protected:
-  virtual float validateBG(const std::vector<Ptr<ExpressionGraph>>& graphs) override {
-
-    size_t correct     = 0;
-    size_t totalLabels = 0;
-    size_t batchId     = 0;
-    std::deque<Ptr<ExpressionGraph>> graphQueue(graphs.begin(), graphs.end());
-
-    auto task = [=, &correct, &totalLabels, &graphQueue](BatchPtr batch, size_t batchId) {
-      thread_local Ptr<ExpressionGraph> graph;
-      
-      if(!graph) {
-        std::unique_lock<std::mutex> lock(mutex_);
-        ABORT_IF(graphQueue.empty(), "Asking for graph, but none left on queue");
-        graph = graphQueue.front();
-        graphQueue.pop_front();
-      }
-      
-      auto builder = models::createModelFromOptions(options_, models::usage::raw);
-
-      thread_local std::unique_ptr<std::mt19937> engine;
-      if(!engine)
-        engine.reset(new std::mt19937((unsigned int)(Config::seed + batchId)));
-
-      auto bertBatch = New<data::BertBatch>(batch,
-                                            *engine,
-                                            options_->get<float>("bert-masking-fraction"),
-                                            options_->get<std::string>("bert-mask-symbol"),
-                                            options_->get<std::string>("bert-sep-symbol"),
-                                            options_->get<std::string>("bert-class-symbol"),
-                                            options_->get<int>("bert-type-vocab-size"));
-
-      builder->clear(graph);
-      auto classifierStates = std::dynamic_pointer_cast<BertEncoderClassifier>(builder)->apply(graph, bertBatch, true);
-      graph->forward();
-
-      auto maskedLMLogits = classifierStates[0]->getLogProbs();
-      const auto& maskedLMLabels = bertBatch->bertMaskedWords();
-
-      auto sentenceLogits = classifierStates[1]->getLogProbs();
-      const auto& sentenceLabels = bertBatch->back()->data();
-
-      auto count = [=, &correct, &totalLabels](Expr logits, const Words& labels) {
-        IndexType cols = logits->shape()[-1];
-        size_t thisCorrect = 0;
-        size_t thisLabels  = labels.size();
-
-        std::vector<float> vLogits;
-        logits->val()->get(vLogits);
-
-        for(int i = 0; i < thisLabels; ++i) {
-          // CPU-side Argmax
-          IndexType bestIndex = 0;
-          float bestValue = std::numeric_limits<float>::lowest();
-          for(IndexType j = 0; j < cols; ++j) {
-            float currValue = vLogits[i * cols + j];
-            if(currValue > bestValue) {
-              bestValue = currValue;
-              bestIndex = j;
-            }
-          }
-          thisCorrect += (size_t)(bestIndex == labels[i].toWordIndex());
-        }
-
-        std::unique_lock<std::mutex> lock(mutex_);
-        totalLabels += thisLabels;
-        correct     += thisCorrect;
-      };
-
-      if(evalMaskedLM_)
-        count(maskedLMLogits, maskedLMLabels);
-      else
-        count(sentenceLogits, sentenceLabels);
-    };
-
-    {
-      threadPool_.reserve(graphs.size());
-      TaskBarrier taskBarrier;
-      for(auto batch : *batchGenerator_) {
-        taskBarrier.push_back(threadPool_.enqueue(task, batch, batchId));
-        batchId++;
-      }
-      // ~TaskBarrier waits until all are done
-    }
-
-    return (float)correct / (float)totalLabels;
-  }
+  virtual float validateBG(const std::vector<Ptr<ExpressionGraph>>& graphs) override;
 };
 
 
 class ScriptValidator : public Validator<data::Corpus, models::IModel> {
 public:
-  ScriptValidator(std::vector<Ptr<Vocab>> vocabs, Ptr<Options> options)
-      : Validator(vocabs, options, false) {
-    // @TODO: remove, only used for saving?
-    builder_ = models::createModelFromOptions(options_, models::usage::raw);
+  ScriptValidator(std::vector<Ptr<Vocab>> vocabs, Ptr<Options> options);
 
-    ABORT_IF(!options_->hasAndNotEmpty("valid-script-path"),
-             "valid-script metric but no script given");
-  }
-
-  virtual float validate(const std::vector<Ptr<ExpressionGraph>>& graphs) override {
-    using namespace data;
-    auto model = options_->get<std::string>("model");
-    std::string suffix = model.substr(model.size() - 4);
-    ABORT_IF(suffix != ".npz" && suffix != ".bin", "Unknown model suffix {}", suffix);
-
-    builder_->save(graphs[0], model + ".dev" + suffix, true);
-
-    auto valStr = utils::exec(options_->get<std::string>("valid-script-path"),
-                              options_->get<std::vector<std::string>>("valid-script-args"));
-    float val = (float)std::atof(valStr.c_str());
-    updateStalled(graphs, val);
-
-    return val;
-  };
+  virtual float validate(const std::vector<Ptr<ExpressionGraph>>& graphs) override;
 
   std::string type() override { return "valid-script"; }
 
@@ -435,126 +189,9 @@ class ScriptValidator : public Validator<data::Corpus, models::IModel> {
 // validator that translates and computes BLEU (or any metric) with an external script
 class TranslationValidator : public Validator<data::Corpus, models::IModel> {
 public:
-  TranslationValidator(std::vector<Ptr<Vocab>> vocabs, Ptr<Options> options)
-      : Validator(vocabs, options, false),
-        quiet_(options_->get<bool>("quiet-translation")) {
-    // @TODO: remove, only used for saving?
-    builder_ = models::createModelFromOptions(options_, models::usage::translation);
-
-    if(!options_->hasAndNotEmpty("valid-script-path"))
-      LOG_VALID(warn, "No post-processing script given for validating translator");
-
-    createBatchGenerator(/*isTranslating=*/true);
-  }
+  TranslationValidator(std::vector<Ptr<Vocab>> vocabs, Ptr<Options> options);
 
-  virtual float validate(const std::vector<Ptr<ExpressionGraph>>& graphs) override {
-    using namespace data;
-
-    // Generate batches
-    batchGenerator_->prepare();
-
-    // Create scorer
-    auto model = options_->get<std::string>("model");
-
-    std::vector<Ptr<Scorer>> scorers;
-    for(auto graph : graphs) {
-      auto builder = models::createModelFromOptions(options_, models::usage::translation);
-      Ptr<Scorer> scorer = New<ScorerWrapper>(builder, "", 1.0f, model);
-      scorers.push_back(scorer); // @TODO: should this be done in the contructor?
-    }
-
-    // Set up output file
-    std::string fileName;
-    Ptr<io::TemporaryFile2> tempFile;
-
-    if(options_->hasAndNotEmpty("valid-translation-output")) {
-      fileName = options_->get<std::string>("valid-translation-output");
-    } else {
-      tempFile.reset(new io::TemporaryFile2(options_->get<std::string>("tempdir"), false));
-      fileName = tempFile->getFileName();
-    }
-
-    for(auto graph : graphs)
-      graph->setInference(true);
-
-    if(!quiet_)
-      LOG(info, "Translating validation set...");
-
-    timer::Timer timer;
-    {
-      auto printer = New<OutputPrinter>(options_, vocabs_.back());
-      // @TODO: This can be simplified. If there is no "valid-translation-output", fileName already
-      // contains the name of temporary file that should be used?
-      auto collector = options_->hasAndNotEmpty("valid-translation-output")
-                           ? New<OutputCollector>(fileName)
-                           : New<OutputCollector>(*tempFile);
-
-      if(quiet_)
-        collector->setPrintingStrategy(New<QuietPrinting>());
-      else
-        collector->setPrintingStrategy(New<GeometricPrinting>());
-
-      std::deque<Ptr<ExpressionGraph>> graphQueue(graphs.begin(), graphs.end());
-      std::deque<Ptr<Scorer>> scorerQueue(scorers.begin(), scorers.end());
-      auto task = [=, &graphQueue, &scorerQueue](BatchPtr batch) {
-        thread_local Ptr<ExpressionGraph> graph;
-        thread_local Ptr<Scorer> scorer;
-        
-        if(!graph) {
-          std::unique_lock<std::mutex> lock(mutex_);
-          ABORT_IF(graphQueue.empty(), "Asking for graph, but none left on queue");
-          graph = graphQueue.front();
-          graphQueue.pop_front();
-
-          ABORT_IF(scorerQueue.empty(), "Asking for scorer, but none left on queue");
-          scorer = scorerQueue.front();
-          scorerQueue.pop_front();
-        }
-      
-        auto search = New<BeamSearch>(options_,
-                                      std::vector<Ptr<Scorer>>{scorer},
-                                      vocabs_.back());
-        auto histories = search->search(graph, batch);
-
-        for(auto history : histories) {
-          std::stringstream best1;
-          std::stringstream bestn;
-          printer->print(history, best1, bestn);
-          collector->Write((long)history->getLineNum(),
-                            best1.str(),
-                            bestn.str(),
-                            options_->get<bool>("n-best"));
-        }
-      };
-
-      threadPool_.reserve(graphs.size());
-      TaskBarrier taskBarrier;      
-      for(auto batch : *batchGenerator_)
-        taskBarrier.push_back(threadPool_.enqueue(task, batch));
-      // ~TaskBarrier waits until all are done
-    }
-
-    if(!quiet_)
-      LOG(info, "Total translation time: {:.5f}s", timer.elapsed());
-
-    for(auto graph : graphs)
-      graph->setInference(false);
-
-    float val = 0.0f;
-
-    // Run post-processing script if given
-    if(options_->hasAndNotEmpty("valid-script-path")) {
-      //auto command = options_->get<std::string>("valid-script-path") + " " + fileName;
-      //auto valStr = utils::exec(command);
-      auto valStr = utils::exec(options_->get<std::string>("valid-script-path"),
-                                options_->get<std::vector<std::string>>("valid-script-args"),
-                                fileName);
-      val = (float)std::atof(valStr.c_str());
-      updateStalled(graphs, val);
-    }
-
-    return val;
-  };
+  virtual float validate(const std::vector<Ptr<ExpressionGraph>>& graphs) override;
 
   std::string type() override { return "translation"; }
 
@@ -570,131 +207,9 @@ class TranslationValidator : public Validator<data::Corpus, models::IModel> {
 // @TODO: combine with TranslationValidator (above) to avoid code duplication
 class BleuValidator : public Validator<data::Corpus, models::IModel> {
 public:
-  BleuValidator(std::vector<Ptr<Vocab>> vocabs, Ptr<Options> options, bool detok = false)
-      : Validator(vocabs, options, false),
-        detok_(detok),
-        quiet_(options_->get<bool>("quiet-translation")) {
-    // @TODO: remove, only used for saving?
-    builder_ = models::createModelFromOptions(options_, models::usage::translation);
-
-    // @TODO: replace bleu-detok by a separate parameter to enable (various forms of) detok
-    auto vocab = vocabs_.back();
-    ABORT_IF(detok_ && vocab->type() != "SentencePieceVocab" && vocab->type() != "FactoredVocab",
-             "Detokenizing BLEU validator expects the target vocabulary to be SentencePieceVocab or FactoredVocab. "
-             "Current vocabulary type is {}", vocab->type());
-
-    createBatchGenerator(/*isTranslating=*/true);
-  }
-
-  virtual float validate(const std::vector<Ptr<ExpressionGraph>>& graphs) override {
-    using namespace data;
-
-    // Generate batches
-    batchGenerator_->prepare();
-
-    // Create scorer
-    auto model = options_->get<std::string>("model");
-
-    // @TODO: check if required - Temporary options for translation
-    auto mopts = New<Options>();
-    mopts->merge(options_);
-    mopts->set("inference", true);
-
-    std::vector<Ptr<Scorer>> scorers;
-    for(auto graph : graphs) {
-      auto builder = models::createModelFromOptions(options_, models::usage::translation);
-      Ptr<Scorer> scorer = New<ScorerWrapper>(builder, "", 1.0f, model);
-      scorers.push_back(scorer);
-    }
-
-    for(auto graph : graphs)
-      graph->setInference(true);
-
-    if(!quiet_)
-      LOG(info, "Translating validation set...");
-
-    // 0: 1-grams matched, 1: 1-grams total,
-    // ...,
-    // 6: 4-grams matched, 7: 4-grams total,
-    // 8: reference length
-    std::vector<float> stats(9, 0.f);
-
-    timer::Timer timer;
-    {
-      auto printer = New<OutputPrinter>(options_, vocabs_.back());
+  BleuValidator(std::vector<Ptr<Vocab>> vocabs, Ptr<Options> options, bool detok = false);
 
-      Ptr<OutputCollector> collector;
-      if(options_->hasAndNotEmpty("valid-translation-output")) {
-        auto fileName = options_->get<std::string>("valid-translation-output");
-        collector = New<OutputCollector>(fileName); // for debugging
-      }
-      else {
-        collector = New<OutputCollector>(/* null */); // don't print, but log
-      }
-
-      if(quiet_)
-        collector->setPrintingStrategy(New<QuietPrinting>());
-      else
-        collector->setPrintingStrategy(New<GeometricPrinting>());
-
-      std::deque<Ptr<ExpressionGraph>> graphQueue(graphs.begin(), graphs.end());
-      std::deque<Ptr<Scorer>> scorerQueue(scorers.begin(), scorers.end());
-      auto task = [=, &stats, &graphQueue, &scorerQueue](BatchPtr batch) {
-        thread_local Ptr<ExpressionGraph> graph;
-        thread_local Ptr<Scorer> scorer;
-        
-        if(!graph) {
-          std::unique_lock<std::mutex> lock(mutex_);
-          ABORT_IF(graphQueue.empty(), "Asking for graph, but none left on queue");
-          graph = graphQueue.front();
-          graphQueue.pop_front();
-
-          ABORT_IF(scorerQueue.empty(), "Asking for scorer, but none left on queue");
-          scorer = scorerQueue.front();
-          scorerQueue.pop_front();
-        }
-      
-        auto search = New<BeamSearch>(options_,
-                                      std::vector<Ptr<Scorer>>{scorer},
-                                      vocabs_.back());
-        auto histories = search->search(graph, batch);
-
-        size_t no = 0;
-        std::lock_guard<std::mutex> statsLock(mutex_);
-        for(auto history : histories) {
-          auto result = history->top();
-          const auto& words = std::get<0>(result);
-          updateStats(stats, words, batch, no, vocabs_.back()->getEosId());
-
-          std::stringstream best1;
-          std::stringstream bestn;
-          printer->print(history, best1, bestn);
-          collector->Write((long)history->getLineNum(),
-                            best1.str(),
-                            bestn.str(),
-                            /*nbest=*/ false);
-          no++;
-        }
-      };
-
-      threadPool_.reserve(graphs.size());
-      TaskBarrier taskBarrier;
-      for(auto batch : *batchGenerator_)
-        taskBarrier.push_back(threadPool_.enqueue(task, batch));
-      // ~TaskBarrier waits until all are done
-    }
-
-    if(!quiet_)
-      LOG(info, "Total translation time: {:.5f}s", timer.elapsed());
-
-    for(auto graph : graphs)
-      graph->setInference(false);
-
-    float val = calcBLEU(stats);
-    updateStalled(graphs, val);
-
-    return val;
-  };
+  virtual float validate(const std::vector<Ptr<ExpressionGraph>>& graphs) override;
 
   // @TODO: why do we return this string, but not pass it to the constructor?
   std::string type() override { return detok_ ? "bleu-detok" : "bleu"; }
@@ -748,16 +263,7 @@ class BleuValidator : public Validator<data::Corpus, models::IModel> {
     return utils::utf8FromUnicodeString(out);
   }
 
-  std::vector<std::string> decode(const Words& words, bool addEOS = false) {
-    auto vocab = vocabs_.back();
-    auto tokenString = vocab->surfaceForm(words);        // detokenize to surface form
-    tokenString = tokenize(tokenString);                 // tokenize according to SacreBLEU rules
-    tokenString = tokenizeContinuousScript(tokenString); // CJT scripts only: further break into characters
-    auto tokens = utils::splitAny(tokenString, " ");
-    if(addEOS)
-      tokens.push_back("</s>");
-    return tokens;
-  }
+  std::vector<std::string> decode(const Words& words, bool addEOS = false);
 
   // Update document-wide sufficient statistics for BLEU with single sentence n-gram stats.
   template <typename T>
@@ -802,59 +308,9 @@ class BleuValidator : public Validator<data::Corpus, models::IModel> {
                    const Words& cand,
                    const Ptr<data::Batch> batch,
                    size_t no,
-                   Word eos) {
+                   Word eos);
 
-    auto corpusBatch = std::static_pointer_cast<data::CorpusBatch>(batch);
-    auto subBatch = corpusBatch->back();
-
-    size_t size = subBatch->batchSize();
-    size_t width = subBatch->batchWidth();
-
-    Words ref;  // fill ref
-    for(size_t i = 0; i < width; ++i) {
-      Word w = subBatch->data()[i * size + no];
-      if(w == eos)
-        break;
-      ref.push_back(w);
-    }
-
-    bool detok = detok_;
-#if 1 // hack for now, to get this feature when running under Flo
-    // Problem is that Flo pieces that pass 'bleu' do not know whether vocab is factored,
-    // hence cannot select 'bleu-detok'.
-    // @TODO: We agreed that we will replace bleu-detok by bleu with an additional
-    // parameter to select the detokenization method, which will default to detok for FactoredSegmenter,
-    // and no-op for base vocab.
-    if (vocabs_.back()->type() == "FactoredVocab") {
-      if (!quiet_)
-        LOG_ONCE(info, "[valid] FactoredVocab implies using detokenized BLEU");
-      detok = true; // always use bleu-detok
-    }
-#endif
-    if(detok) { // log the first detokenized string
-      LOG_ONCE(info, "[valid] First sentence's tokens after detokenization, as scored:");
-      LOG_ONCE(info, "[valid]  Hyp: {}", utils::join(decode(cand, /*addEOS=*/ true)));
-      LOG_ONCE(info, "[valid]  Ref: {}", utils::join(decode(ref)));
-    }
-    if(detok)
-      updateStats(stats, decode(cand, /*addEOS=*/ true), decode(ref));
-    else
-      updateStats(stats, cand, ref);
-  }
-
-  float calcBLEU(const std::vector<float>& stats) {
-    float logbleu = 0;
-    for(int i = 0; i < 8; i += 2) {
-      if(stats[i] == 0.f)
-        return 0.f;
-      logbleu += std::log(stats[i] / stats[i + 1]);
-    }
-
-    logbleu /= 4.f;
-
-    float brev_penalty = 1.f - std::max(stats[8] / stats[1], 1.f);
-    return std::exp(logbleu + brev_penalty) * 100;
-  }
+  float calcBLEU(const std::vector<float>& stats);
 
   virtual float validateBG(const std::vector<Ptr<ExpressionGraph>>& /*graphs*/) override {
     return 0;
diff --git a/vs/Marian.vcxproj b/vs/Marian.vcxproj
index 532f206b4..dadfaf96e 100755
--- a/vs/Marian.vcxproj
+++ b/vs/Marian.vcxproj
@@ -1002,6 +1002,7 @@
     <ClCompile Include="..\src\common\cli_wrapper.cpp" />
     <ClCompile Include="..\src\common\config_validator.cpp" />
     <ClCompile Include="..\src\common\filesystem.cpp" />
+    <ClCompile Include="..\src\common\file_stream.cpp" />
     <ClCompile Include="..\src\common\io.cpp" />
     <ClCompile Include="..\src\common\utils.cpp" />
     <ClCompile Include="..\src\common\logging.cpp" />
diff --git a/vs/Marian.vcxproj.filters b/vs/Marian.vcxproj.filters
index 8fe63a0bf..4341e49d5 100755
--- a/vs/Marian.vcxproj.filters
+++ b/vs/Marian.vcxproj.filters
@@ -721,6 +721,9 @@
     <ClCompile Include="..\src\common\filesystem.cpp">
       <Filter>common</Filter>
     </ClCompile>
+    <ClCompile Include="..\src\common\file_stream.cpp">
+      <Filter>common</Filter>
+    </ClCompile>
   </ItemGroup>
   <ItemGroup>
     <ClInclude Include="..\src\marian.h" />

From 7a838161c649443075d5f2cb112a728c4f48aaa4 Mon Sep 17 00:00:00 2001
From: Hieu Hoang <hihoan@microsoft.com>
Date: Mon, 21 Oct 2019 12:24:08 -0700
Subject: [PATCH 654/838] move code .h -> .cpp

---
 src/data/corpus.cpp | 47 +++++++++++++++++++++++++++++++++++++++++++++
 src/data/corpus.h   | 46 +-------------------------------------------
 2 files changed, 48 insertions(+), 45 deletions(-)

diff --git a/src/data/corpus.cpp b/src/data/corpus.cpp
index 20afac34f..129c282f4 100644
--- a/src/data/corpus.cpp
+++ b/src/data/corpus.cpp
@@ -210,5 +210,52 @@ void Corpus::shuffleData(const std::vector<std::string>& paths) {
   }
   pos_ = 0;
 }
+
+CorpusBase::batch_ptr Corpus::toBatch(const std::vector<Sample>& batchVector) {
+  size_t batchSize = batchVector.size();
+
+  std::vector<size_t> sentenceIds;
+
+  std::vector<int> maxDims;      // @TODO: What's this? widths? maxLengths?
+  for(auto& ex : batchVector) {  // @TODO: rename 'ex' to 'sample' or 'sentenceTuple'
+    if(maxDims.size() < ex.size())
+      maxDims.resize(ex.size(), 0);
+    for(size_t i = 0; i < ex.size(); ++i) {
+      if(ex[i].size() > (size_t)maxDims[i])
+        maxDims[i] = (int)ex[i].size();
+    }
+    sentenceIds.push_back(ex.getId());
+  }
+
+  std::vector<Ptr<SubBatch>> subBatches;
+  for(size_t j = 0; j < maxDims.size(); ++j) {
+    subBatches.emplace_back(New<SubBatch>(batchSize, maxDims[j], vocabs_[j]));
+  }
+
+  std::vector<size_t> words(maxDims.size(), 0);
+  for(size_t i = 0; i < batchSize; ++i) {
+    for(size_t j = 0; j < maxDims.size(); ++j) {
+      for(size_t k = 0; k < batchVector[i][j].size(); ++k) {
+        subBatches[j]->data()[k * batchSize + i] = batchVector[i][j][k];
+        subBatches[j]->mask()[k * batchSize + i] = 1.f;
+        words[j]++;
+      }
+    }
+  }
+
+  for(size_t j = 0; j < maxDims.size(); ++j)
+    subBatches[j]->setWords(words[j]);
+
+  auto batch = batch_ptr(new batch_type(subBatches));
+  batch->setSentenceIds(sentenceIds);
+
+  if(options_->get("guided-alignment", std::string("none")) != "none" && alignFileIdx_)
+    addAlignmentsToBatch(batch, batchVector);
+  if(options_->hasAndNotEmpty("data-weighting") && weightFileIdx_)
+    addWeightsToBatch(batch, batchVector);
+
+  return batch;
+}
+
 }  // namespace data
 }  // namespace marian
diff --git a/src/data/corpus.h b/src/data/corpus.h
index 41e26421a..9bf00b071 100644
--- a/src/data/corpus.h
+++ b/src/data/corpus.h
@@ -63,51 +63,7 @@ class Corpus : public CorpusBase {
 
   std::vector<Ptr<Vocab>>& getVocabs() override { return vocabs_; }
 
-  batch_ptr toBatch(const std::vector<Sample>& batchVector) override {
-    size_t batchSize = batchVector.size();
-
-    std::vector<size_t> sentenceIds;
-
-    std::vector<int> maxDims;       // @TODO: What's this? widths? maxLengths?
-    for(auto& ex : batchVector) {   // @TODO: rename 'ex' to 'sample' or 'sentenceTuple'
-      if(maxDims.size() < ex.size())
-        maxDims.resize(ex.size(), 0);
-      for(size_t i = 0; i < ex.size(); ++i) {
-        if(ex[i].size() > (size_t)maxDims[i])
-          maxDims[i] = (int)ex[i].size();
-      }
-      sentenceIds.push_back(ex.getId());
-    }
-
-    std::vector<Ptr<SubBatch>> subBatches;
-    for(size_t j = 0; j < maxDims.size(); ++j) {
-      subBatches.emplace_back(New<SubBatch>(batchSize, maxDims[j], vocabs_[j]));
-    }
-
-    std::vector<size_t> words(maxDims.size(), 0);
-    for(size_t i = 0; i < batchSize; ++i) {
-      for(size_t j = 0; j < maxDims.size(); ++j) {
-        for(size_t k = 0; k < batchVector[i][j].size(); ++k) {
-          subBatches[j]->data()[k * batchSize + i] = batchVector[i][j][k];
-          subBatches[j]->mask()[k * batchSize + i] = 1.f;
-          words[j]++;
-        }
-      }
-    }
-
-    for(size_t j = 0; j < maxDims.size(); ++j)
-      subBatches[j]->setWords(words[j]);
-
-    auto batch = batch_ptr(new batch_type(subBatches));
-    batch->setSentenceIds(sentenceIds);
-
-    if(options_->get("guided-alignment", std::string("none")) != "none" && alignFileIdx_)
-      addAlignmentsToBatch(batch, batchVector);
-    if(options_->hasAndNotEmpty("data-weighting") && weightFileIdx_)
-      addWeightsToBatch(batch, batchVector);
-
-    return batch;
-  }
+  batch_ptr toBatch(const std::vector<Sample>& batchVector) override;
 };
 }  // namespace data
 }  // namespace marian

From 6e27d021f427ac738c87b3bcdbb3f95ec9aa45f3 Mon Sep 17 00:00:00 2001
From: Hieu Hoang <hihoan@microsoft.com>
Date: Mon, 21 Oct 2019 14:05:48 -0700
Subject: [PATCH 655/838] add new TemporaryFile class

---
 src/common/file_stream.cpp | 40 ++++++++++++++++++++++++++++++++++++++
 src/common/file_stream.h   | 28 ++++++++++++++++++++++----
 2 files changed, 64 insertions(+), 4 deletions(-)

diff --git a/src/common/file_stream.cpp b/src/common/file_stream.cpp
index e0fea2e77..22d273dca 100644
--- a/src/common/file_stream.cpp
+++ b/src/common/file_stream.cpp
@@ -166,6 +166,46 @@ ssize_t WriteFDBuf::WriteSome(const char *from, const char *to) {
   return put;
 }
 
+///////////////////////////////////////////////////////////////////////////////////////////////
+TemporaryFile::TemporaryFile() {
+}
+
+int TemporaryFile::MakeTemp(const std::string &base) {
+#ifdef _MSC_VER
+  char *name = tempnam(base.c_str(), "marian.");
+  ABORT_IF(name == NULL, "Error while making a temporary based on '{}'", base);
+
+  int oflag = _O_RDWR | _O_CREAT | _O_EXCL | _O_TEMPORARY;
+  int ret = std::fstream::open(name, oflag, _S_IREAD | _S_IWRITE);
+  ABORT_IF(ret == -1, "Error while making a temporary based on '{}'", base);
+
+  name_ = name;
+  free(name);
+
+  return ret;
+#else
+  std::string name(base);
+  name += "marian.XXXXXX";
+  name.push_back(0);
+  int ret;
+  ABORT_IF(-1 == (ret = mkstemp_and_unlink(&name[0])),
+           "Error while making a temporary based on '{}'",
+           base);
+  name_ = name;
+  return ret;
+#endif
+}
+
+#ifndef _MSC_VER
+int TemporaryFile::mkstemp_and_unlink(char *tmpl) {
+  int ret = mkstemp(tmpl);
+  if(ret != -1) {
+    ABORT_IF(unlink(tmpl), "Error while deleting '{}'", tmpl);
+  }
+  return ret;
+}
+#endif
+
 ///////////////////////////////////////////////////////////////////////////////////////////////
 #ifndef _MSC_VER
 int TemporaryFile2::mkstemp_and_unlink(char *tmpl) {
diff --git a/src/common/file_stream.h b/src/common/file_stream.h
index 55f687e25..4b1f100d9 100755
--- a/src/common/file_stream.h
+++ b/src/common/file_stream.h
@@ -1,5 +1,6 @@
 #pragma once
 
+#include <fstream>
 #include "common/definitions.h"
 #include "common/filesystem.h"
 #include "common/logging.h"
@@ -42,15 +43,31 @@ std::unique_ptr<T> make_unique(Args&&... args) {
 namespace marian {
 namespace io {
 
+///////////////////////////////////////////////////////////////////////////////////////////////
+class TemporaryFile : public std::fstream {
+public:
+  TemporaryFile();
+
+protected:
+  std::string name_;
+
+  int MakeTemp(const std::string& base);
+
+#ifndef _MSC_VER
+  int mkstemp_and_unlink(char* tmpl);
+#endif
+};
+
+///////////////////////////////////////////////////////////////////////////////////////////////
 class TemporaryFile2 {
 private:
   int fd_{-1};
   bool unlink_;
   std::string name_;
 
-  #ifndef _MSC_VER
+#ifndef _MSC_VER
   int mkstemp_and_unlink(char* tmpl);
-  #endif
+#endif
 
   int MakeTemp(const std::string& base);
 
@@ -66,6 +83,7 @@ class TemporaryFile2 {
   std::string getFileName() { return name_; }
 };
 
+///////////////////////////////////////////////////////////////////////////////////////////////
 // A streambuf to read from a file descriptor.
 class ReadFDBuf : public std::streambuf {
 public:
@@ -93,6 +111,7 @@ class ReadFDBuf : public std::streambuf {
   ReadFDBuf& operator=(const ReadFDBuf&) = delete;
 };
 
+///////////////////////////////////////////////////////////////////////////////////////////////
 // A streambuf to Write to a file descriptor.
 class WriteFDBuf : public std::streambuf {
 public:
@@ -119,6 +138,7 @@ class WriteFDBuf : public std::streambuf {
   WriteFDBuf& operator=(const WriteFDBuf&) = delete;
 };
 
+///////////////////////////////////////////////////////////////////////////////////////////////
 // lseek(fd, 0, SEEK_SET) but with error checking.
 void RewindFile(int fd);
 
@@ -176,8 +196,7 @@ class InputFileStream {
 
 // wrapper around std::getline() that handles Windows input files with extra CR
 // chars at the line end
-static inline InputFileStream& getline(InputFileStream& in, std::string& line)
-{
+static inline InputFileStream& getline(InputFileStream& in, std::string& line) {
   std::getline((std::istream&)in, line);
   // bad() seems to be correct here. Should not abort on EOF.
   ABORT_IF(in.bad(), "Error reading from file '{}'", in.path());
@@ -200,6 +219,7 @@ static inline InputFileStream& getline(InputFileStream& in, std::string& line, c
   return in;
 }
 
+///////////////////////////////////////////////////////////////////////////////////////////////
 class OutputFileStream {
 public:
   OutputFileStream(const std::string& file);

From 532c0d177141e58541b12124384f91fbcc166687 Mon Sep 17 00:00:00 2001
From: Hieu Hoang <hihoan@microsoft.com>
Date: Mon, 21 Oct 2019 14:15:09 -0700
Subject: [PATCH 656/838] base arg

---
 src/common/file_stream.cpp | 9 ++++-----
 src/common/file_stream.h   | 2 +-
 2 files changed, 5 insertions(+), 6 deletions(-)

diff --git a/src/common/file_stream.cpp b/src/common/file_stream.cpp
index 22d273dca..c96c5c41c 100644
--- a/src/common/file_stream.cpp
+++ b/src/common/file_stream.cpp
@@ -167,7 +167,8 @@ ssize_t WriteFDBuf::WriteSome(const char *from, const char *to) {
 }
 
 ///////////////////////////////////////////////////////////////////////////////////////////////
-TemporaryFile::TemporaryFile() {
+TemporaryFile::TemporaryFile(const std::string &base) {
+  MakeTemp(base);
 }
 
 int TemporaryFile::MakeTemp(const std::string &base) {
@@ -176,13 +177,11 @@ int TemporaryFile::MakeTemp(const std::string &base) {
   ABORT_IF(name == NULL, "Error while making a temporary based on '{}'", base);
 
   int oflag = _O_RDWR | _O_CREAT | _O_EXCL | _O_TEMPORARY;
-  int ret = std::fstream::open(name, oflag, _S_IREAD | _S_IWRITE);
-  ABORT_IF(ret == -1, "Error while making a temporary based on '{}'", base);
+  std::fstream::open(name, oflag, _S_IREAD | _S_IWRITE);
+  //ABORT_IF(ret == -1, "Error while making a temporary based on '{}'", base);
 
   name_ = name;
   free(name);
-
-  return ret;
 #else
   std::string name(base);
   name += "marian.XXXXXX";
diff --git a/src/common/file_stream.h b/src/common/file_stream.h
index 4b1f100d9..7e9a090f5 100755
--- a/src/common/file_stream.h
+++ b/src/common/file_stream.h
@@ -46,7 +46,7 @@ namespace io {
 ///////////////////////////////////////////////////////////////////////////////////////////////
 class TemporaryFile : public std::fstream {
 public:
-  TemporaryFile();
+  TemporaryFile(const std::string &base = "/tmp/");
 
 protected:
   std::string name_;

From 01da99fc528f92fe4fa4a1cfd636f0fc204db2a6 Mon Sep 17 00:00:00 2001
From: Hieu Hoang <hihoan@microsoft.com>
Date: Mon, 21 Oct 2019 14:57:08 -0700
Subject: [PATCH 657/838] add InputFileStreamNew class

---
 src/common/file_stream.cpp | 84 ++++++++++++++++++++------------------
 src/common/file_stream.h   | 40 +++++++++++-------
 src/data/corpus.cpp        |  1 +
 3 files changed, 71 insertions(+), 54 deletions(-)

diff --git a/src/common/file_stream.cpp b/src/common/file_stream.cpp
index c96c5c41c..8bff792fd 100644
--- a/src/common/file_stream.cpp
+++ b/src/common/file_stream.cpp
@@ -166,45 +166,6 @@ ssize_t WriteFDBuf::WriteSome(const char *from, const char *to) {
   return put;
 }
 
-///////////////////////////////////////////////////////////////////////////////////////////////
-TemporaryFile::TemporaryFile(const std::string &base) {
-  MakeTemp(base);
-}
-
-int TemporaryFile::MakeTemp(const std::string &base) {
-#ifdef _MSC_VER
-  char *name = tempnam(base.c_str(), "marian.");
-  ABORT_IF(name == NULL, "Error while making a temporary based on '{}'", base);
-
-  int oflag = _O_RDWR | _O_CREAT | _O_EXCL | _O_TEMPORARY;
-  std::fstream::open(name, oflag, _S_IREAD | _S_IWRITE);
-  //ABORT_IF(ret == -1, "Error while making a temporary based on '{}'", base);
-
-  name_ = name;
-  free(name);
-#else
-  std::string name(base);
-  name += "marian.XXXXXX";
-  name.push_back(0);
-  int ret;
-  ABORT_IF(-1 == (ret = mkstemp_and_unlink(&name[0])),
-           "Error while making a temporary based on '{}'",
-           base);
-  name_ = name;
-  return ret;
-#endif
-}
-
-#ifndef _MSC_VER
-int TemporaryFile::mkstemp_and_unlink(char *tmpl) {
-  int ret = mkstemp(tmpl);
-  if(ret != -1) {
-    ABORT_IF(unlink(tmpl), "Error while deleting '{}'", tmpl);
-  }
-  return ret;
-}
-#endif
-
 ///////////////////////////////////////////////////////////////////////////////////////////////
 #ifndef _MSC_VER
 int TemporaryFile2::mkstemp_and_unlink(char *tmpl) {
@@ -333,5 +294,50 @@ OutputFileStream::OutputFileStream(TemporaryFile2 &tempfile) {
   ostream_.reset(new std::ostream(temporary_writer_.get()));
 }
 
+///////////////////////////////////////////////////////////////////////////////////////////////
+TemporaryFileNew::TemporaryFileNew(const std::string &base) {
+  MakeTemp(base);
+}
+
+int TemporaryFileNew::MakeTemp(const std::string &base) {
+#ifdef _MSC_VER
+  char *name = tempnam(base.c_str(), "marian.");
+  ABORT_IF(name == NULL, "Error while making a temporary based on '{}'", base);
+
+  int oflag = _O_RDWR | _O_CREAT | _O_EXCL | _O_TEMPORARY;
+  std::fstream::open(name, oflag, _S_IREAD | _S_IWRITE);
+  // ABORT_IF(ret == -1, "Error while making a temporary based on '{}'", base);
+
+  name_ = name;
+  free(name);
+#else
+  std::string name(base);
+  name += "marian.XXXXXX";
+  name.push_back(0);
+  int ret;
+  ABORT_IF(-1 == (ret = mkstemp_and_unlink(&name[0])),
+           "Error while making a temporary based on '{}'",
+           base);
+  name_ = name;
+  return ret;
+#endif
+}
+
+#ifndef _MSC_VER
+int TemporaryFileNew::mkstemp_and_unlink(char *tmpl) {
+  int ret = mkstemp(tmpl);
+  if(ret != -1) {
+    ABORT_IF(unlink(tmpl), "Error while deleting '{}'", tmpl);
+  }
+  return ret;
+}
+#endif
+
+///////////////////////////////////////////////////////////////////////////////////////////////
+InputFileStreamNew::InputFileStreamNew(const std::string &file)
+    : zstr::ifstream(file), file_(file) {
+  ABORT_IF(!marian::filesystem::exists(file_), "File '{}' does not exist", file);
+}
+
 } // namespace io
 } // namespace marian
diff --git a/src/common/file_stream.h b/src/common/file_stream.h
index 7e9a090f5..2e081bb36 100755
--- a/src/common/file_stream.h
+++ b/src/common/file_stream.h
@@ -43,21 +43,6 @@ std::unique_ptr<T> make_unique(Args&&... args) {
 namespace marian {
 namespace io {
 
-///////////////////////////////////////////////////////////////////////////////////////////////
-class TemporaryFile : public std::fstream {
-public:
-  TemporaryFile(const std::string &base = "/tmp/");
-
-protected:
-  std::string name_;
-
-  int MakeTemp(const std::string& base);
-
-#ifndef _MSC_VER
-  int mkstemp_and_unlink(char* tmpl);
-#endif
-};
-
 ///////////////////////////////////////////////////////////////////////////////////////////////
 class TemporaryFile2 {
 private:
@@ -270,5 +255,30 @@ class OutputFileStream {
   std::unique_ptr<WriteFDBuf> temporary_writer_;
 };
 
+///////////////////////////////////////////////////////////////////////////////////////////////
+class TemporaryFileNew : public std::fstream {
+public:
+  TemporaryFileNew(const std::string& base = "/tmp/");
+
+protected:
+  std::string name_;
+
+  int MakeTemp(const std::string& base);
+
+#ifndef _MSC_VER
+  int mkstemp_and_unlink(char* tmpl);
+#endif
+};
+
+//////////////////////////////////////////////////////////////////////////////////////////////
+class InputFileStreamNew : public zstr::ifstream {
+public:
+  explicit InputFileStreamNew(const std::string& file);
+
+protected:
+  std::string file_;
+};
+
+
 }  // namespace io
 }  // namespace marian
diff --git a/src/data/corpus.cpp b/src/data/corpus.cpp
index 129c282f4..107af670b 100644
--- a/src/data/corpus.cpp
+++ b/src/data/corpus.cpp
@@ -189,6 +189,7 @@ void Corpus::shuffleData(const std::vector<std::string>& paths) {
     LOG(info, "[data] Done shuffling {} sentences (cached in RAM)", numSentences);
   }
   else {
+    std::cerr << "Corpus::shuffleData" << std::endl;
     // create temp files that contain the data in randomized order
     tempFiles_.resize(numStreams);
     for(size_t i = 0; i < numStreams; ++i) {

From 0e8117600bafbbc2c52e46f849ebc1b602b11848 Mon Sep 17 00:00:00 2001
From: Hieu Hoang <hihoan@microsoft.com>
Date: Mon, 21 Oct 2019 15:02:51 -0700
Subject: [PATCH 658/838] add OutputFileStreamNew class

---
 src/common/file_stream.cpp | 6 ++++++
 src/common/file_stream.h   | 8 ++++++++
 2 files changed, 14 insertions(+)

diff --git a/src/common/file_stream.cpp b/src/common/file_stream.cpp
index 8bff792fd..bf2445f2b 100644
--- a/src/common/file_stream.cpp
+++ b/src/common/file_stream.cpp
@@ -339,5 +339,11 @@ InputFileStreamNew::InputFileStreamNew(const std::string &file)
   ABORT_IF(!marian::filesystem::exists(file_), "File '{}' does not exist", file);
 }
 
+///////////////////////////////////////////////////////////////////////////////////////////////
+OutputFileStreamNew::OutputFileStreamNew(const std::string &file)
+    : zstr::ofstream(file), file_(file) {
+  ABORT_IF(!marian::filesystem::exists(file_), "File '{}' does not exist", file);
+}
+
 } // namespace io
 } // namespace marian
diff --git a/src/common/file_stream.h b/src/common/file_stream.h
index 2e081bb36..f7d724d69 100755
--- a/src/common/file_stream.h
+++ b/src/common/file_stream.h
@@ -279,6 +279,14 @@ class InputFileStreamNew : public zstr::ifstream {
   std::string file_;
 };
 
+//////////////////////////////////////////////////////////////////////////////////////////////
+class OutputFileStreamNew : public zstr::ofstream {
+public:
+  explicit OutputFileStreamNew(const std::string& file);
+
+protected:
+  std::string file_;
+};
 
 }  // namespace io
 }  // namespace marian

From 5896a41fbce48e81063c8358f674282e37529b67 Mon Sep 17 00:00:00 2001
From: Hieu Hoang <hihoan@microsoft.com>
Date: Mon, 21 Oct 2019 18:14:59 -0700
Subject: [PATCH 659/838] debug

---
 src/common/config_parser.cpp | 3 ++-
 src/common/file_stream.cpp   | 5 ++++-
 2 files changed, 6 insertions(+), 2 deletions(-)

diff --git a/src/common/config_parser.cpp b/src/common/config_parser.cpp
index af30d97e0..46aa10628 100755
--- a/src/common/config_parser.cpp
+++ b/src/common/config_parser.cpp
@@ -849,7 +849,8 @@ YAML::Node ConfigParser::loadConfigFiles(const std::vector<std::string>& paths)
 
   for(auto& path : paths) {
     // load single config file
-    YAML::Node config = YAML::Load(io::InputFileStream(path));
+    io::InputFileStreamNew strm(path);
+    YAML::Node config = YAML::Load(strm);
 
     // expand relative paths if requested
     if(config["relative-paths"] && config["relative-paths"].as<bool>()) {
diff --git a/src/common/file_stream.cpp b/src/common/file_stream.cpp
index bf2445f2b..2d63c8c1b 100644
--- a/src/common/file_stream.cpp
+++ b/src/common/file_stream.cpp
@@ -264,13 +264,15 @@ InputFileStream::InputFileStream(const std::string &file) : file_(file) {
   else
     istream_ = std::make_unique<std::ifstream>(file_.string());
   ABORT_IF(fail(), "Error {} ({}) opening file '{}'", errno, strerror(errno), path());
+
+  std::cerr << "InputFileStreamOld1 created" << std::endl;
 }
 
 InputFileStream::InputFileStream(TemporaryFile2 &tempfile) {
   RewindFile(tempfile.getFileDescriptor());
   temporary_reader_.reset(new ReadFDBuf(tempfile.getFileDescriptor()));
   istream_.reset(new std::istream(temporary_reader_.get()));
-  std::cerr << "HH1" << std::endl;
+  std::cerr << "InputFileStreamOld2 created" << std::endl;
 }
 
 void InputFileStream::setbufsize(size_t size) const {
@@ -337,6 +339,7 @@ int TemporaryFileNew::mkstemp_and_unlink(char *tmpl) {
 InputFileStreamNew::InputFileStreamNew(const std::string &file)
     : zstr::ifstream(file), file_(file) {
   ABORT_IF(!marian::filesystem::exists(file_), "File '{}' does not exist", file);
+  std::cerr << "InputFileStreamNew created" << std::endl;
 }
 
 ///////////////////////////////////////////////////////////////////////////////////////////////

From 0baf0aeeb3264d09567da7c2d6d58a50082c5cbe Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Tue, 22 Oct 2019 17:28:05 +0000
Subject: [PATCH 660/838] Merged PR 9998: new build option NO_BOOST to disable
 inclusion of Boost and all features that

This PR is to allow Marian to be built on Windows without Boost, since Boost 1.66 causes build errors under Flo.

With this PR, if the preprocessor symbol `NO_BOOST` is defined, Boost will not be included anywhere, and configurations that rely on that cannot be used.

The long-term goal is to remove Boost completely. This `#define` allows to easily locate all relevant locations.

Boost is currently used for two things:

* the timer in the AutoTuner
* when shuffling the corpus to a temp file (not --shuffle-in-ram)

With `NO_BOOST`, attempting to shuffle to file will terminate the program, and auto-tuner will select the first algorithm.

Not tested with Linux.
---
 src/common/file_stream.h           | 23 ++++++++++++++++++++---
 src/common/timer.h                 |  6 ++++++
 src/data/corpus.cpp                |  4 ++++
 src/graph/auto_tuner.h             | 11 +++++++++++
 src/graph/expression_operators.cpp |  3 +--
 src/graph/node.cpp                 |  0
 src/tensors/gpu/cuda_helpers.h     |  7 +++++++
 src/tensors/rand.cpp               |  1 -
 src/training/validator.h           |  2 +-
 9 files changed, 50 insertions(+), 7 deletions(-)
 mode change 100644 => 100755 src/graph/auto_tuner.h
 mode change 100644 => 100755 src/graph/expression_operators.cpp
 mode change 100644 => 100755 src/graph/node.cpp
 mode change 100644 => 100755 src/tensors/gpu/cuda_helpers.h
 mode change 100644 => 100755 src/tensors/rand.cpp

diff --git a/src/common/file_stream.h b/src/common/file_stream.h
index 46b038b28..8e050bfea 100755
--- a/src/common/file_stream.h
+++ b/src/common/file_stream.h
@@ -13,14 +13,16 @@
 #pragma warning(disable : 4101)
 #endif
 #include "3rd_party/zstr/zstr.hpp"
+#ifndef NO_BOOST
 #include <boost/iostreams/device/file_descriptor.hpp>
 #include <boost/iostreams/stream_buffer.hpp>
-#ifdef __GNUC__
-#pragma GCC diagnostic pop
 #endif
 #ifdef _MSC_VER
 #pragma warning(pop)
 #endif
+#ifdef __GNUC__
+#pragma GCC diagnostic pop
+#endif
 
 
 #include <iostream>
@@ -162,14 +164,16 @@ class InputFileStream {
     ABORT_IF(fail(), "Error {} ({}) opening file '{}'", errno, strerror(errno), path());
   }
 
+#ifndef NO_BOOST
   InputFileStream(TemporaryFile& tempfile)
       : fds_(tempfile.getFileDescriptor(), boost::iostreams::never_close_handle) {
     lseek(tempfile.getFileDescriptor(), 0, SEEK_SET);
-
+  
     namespace bio = boost::iostreams;
     fdsBuffer_ = std::make_unique<bio::stream_buffer<bio::file_descriptor_source>>(fds_);
     istream_ = std::make_unique<std::istream>(fdsBuffer_.get());
   }
+#endif
 
   InputFileStream(std::istream& strm)
   : istream_(new std::istream(strm.rdbuf())) {}
@@ -220,9 +224,13 @@ class InputFileStream {
   marian::filesystem::Path file_;
   std::unique_ptr<std::istream> istream_;
 
+#ifndef NO_BOOST
   boost::iostreams::file_descriptor_source fds_;
+#endif
   mutable std::vector<char> readBuf_; // for setbuf()
+#ifndef NO_BOOST
   std::unique_ptr<boost::iostreams::stream_buffer<boost::iostreams::file_descriptor_source>> fdsBuffer_;
+#endif
 };
 
 // wrapper around std::getline() that handles Windows input files with extra CR
@@ -259,6 +267,7 @@ class OutputFileStream {
     ABORT_IF(!marian::filesystem::exists(file_), "File '{}' could not be opened", file);
   }
 
+#ifndef NO_BOOST
   OutputFileStream(TemporaryFile& tempfile)
       : fds_(tempfile.getFileDescriptor(), boost::iostreams::never_close_handle) {
     lseek(tempfile.getFileDescriptor(), 0, SEEK_SET);
@@ -267,6 +276,12 @@ class OutputFileStream {
     fdsBuffer_ = std::make_unique<bio::stream_buffer<bio::file_descriptor_sink>>(fds_);
     ostream_ = std::make_unique<std::ostream>(fdsBuffer_.get());
   }
+#else
+  OutputFileStream(TemporaryFile& tempfile) {
+    tempfile;
+    ABORT("OutputFileStream(tempFile) not supported when compiling with NO_BOOST");
+  }
+#endif
 
   OutputFileStream(std::ostream& strm) {
     ostream_ = std::make_unique<std::ostream>(strm.rdbuf());
@@ -311,8 +326,10 @@ class OutputFileStream {
   std::unique_ptr<std::ostream> ostream_;
 
 
+#ifndef NO_BOOST
   boost::iostreams::file_descriptor_sink fds_;
   std::unique_ptr<boost::iostreams::stream_buffer<boost::iostreams::file_descriptor_sink>> fdsBuffer_;
+#endif
 };
 
 }
diff --git a/src/common/timer.h b/src/common/timer.h
index fd9307dfc..649bba9d5 100644
--- a/src/common/timer.h
+++ b/src/common/timer.h
@@ -4,15 +4,19 @@
 #pragma GCC diagnostic push
 #pragma GCC diagnostic ignored "-Wsuggest-override"
 #endif
+#ifndef NO_BOOST
 #include <boost/timer/timer.hpp>
+#endif
 #ifdef __GNUC__
 #pragma GCC diagnostic pop
 #endif
 
 #ifdef _MSC_VER
 // (needed on Windows only to resolve a link error, but causes a warning on Linux)
+#ifndef NO_BOOST
 #include <boost/chrono.hpp>
 #endif
+#endif
 
 #include <chrono>
 #include <sstream>
@@ -94,7 +98,9 @@ class AutoTimer : public Timer {
 // Check get_clocktime on Linux: https://linux.die.net/man/3/clock_gettime
 // Check GetThreadTimes on Windows:
 // https://docs.microsoft.com/en-gb/windows/desktop/api/processthreadsapi/nf-processthreadsapi-getthreadtimes
+#ifndef NO_BOOST
 using CPUTimer = boost::timer::cpu_timer;
+#endif
 
 }  // namespace timer
 }  // namespace marian
diff --git a/src/data/corpus.cpp b/src/data/corpus.cpp
index 26abdd0c5..4aa8ff1e8 100644
--- a/src/data/corpus.cpp
+++ b/src/data/corpus.cpp
@@ -189,6 +189,7 @@ void Corpus::shuffleData(const std::vector<std::string>& paths) {
     LOG(info, "[data] Done shuffling {} sentences (cached in RAM)", numSentences);
   }
   else {
+#ifndef NO_BOOST
     // create temp files that contain the data in randomized order
     tempFiles_.resize(numStreams);
     for(size_t i = 0; i < numStreams; ++i) {
@@ -207,6 +208,9 @@ void Corpus::shuffleData(const std::vector<std::string>& paths) {
       files_[i]->setbufsize(10000000);
     }
     LOG(info, "[data] Done shuffling {} sentences to temp files", numSentences);
+#else
+    ABORT("Shuffling to temp file is not available when compiling with NO_BOOST option. Use --shuffle-in-ram.");
+#endif
   }
   pos_ = 0;
 }
diff --git a/src/graph/auto_tuner.h b/src/graph/auto_tuner.h
old mode 100644
new mode 100755
index 6a0543320..6e0fe5eb3
--- a/src/graph/auto_tuner.h
+++ b/src/graph/auto_tuner.h
@@ -25,7 +25,9 @@ class AutoTuner : public AutoTunerRecorder {
   // collects the statistics.
   const size_t collectStatMax = 50;
 
+#ifndef NO_BOOST
   UPtr<timer::CPUTimer> timer_;
+#endif
 
   // This structure holds a hash key an algorithm function (e.g. int16, packed gemm, mkl gemm)
   // for a specific operation size
@@ -91,11 +93,17 @@ class AutoTuner : public AutoTunerRecorder {
   Return run(Args... args) { return algorithms_[choose()].algorithm(args...); }
 
   void start(size_t hash) override {
+#ifndef NO_BOOST
     if(!timer_ && done_.count(hash) == 0)
       timer_.reset(new timer::CPUTimer());
+#else
+    hash;
+    LOG_ONCE(warn, "Auto-tuner not available when compiling with NO_BOOST option. Will use the first option.");
+#endif
   }
 
   void stop(size_t hash, bool stop) override {
+#ifndef NO_BOOST
     if(stop && done_.count(hash) == 0) {
       timer_->stop();
 
@@ -114,6 +122,9 @@ class AutoTuner : public AutoTunerRecorder {
 
       timer_.reset(nullptr);
     }
+#else
+    hash; stop;
+#endif
   }
 };
 
diff --git a/src/graph/expression_operators.cpp b/src/graph/expression_operators.cpp
old mode 100644
new mode 100755
index a627c2ba1..f27942fcd
--- a/src/graph/expression_operators.cpp
+++ b/src/graph/expression_operators.cpp
@@ -453,7 +453,7 @@ Expr affine(Expr a, Expr b, Expr bias, bool transA, bool transB, float scale) {
       util::hash_combine(hash, transA);
       util::hash_combine(hash, transB);
 
-      #if USE_FBGEMM
+#if USE_FBGEMM
       // Use Packed GEMM only if the node b in the graph is memoized.
       // More specifically, packed GEMM is used only if the B matrix (weight) is constant.
       // In general, 'memoized' means that the node is a constant variable or
@@ -548,7 +548,6 @@ Expr affine(Expr a, Expr b, Expr bias, bool transA, bool transB, float scale) {
 
       // execute algorithm with autotuning
       return tuner->run();
-
     } else {
       if(gemmType == GemmType::IntrinInt16) {
         // cpu int16 version
diff --git a/src/graph/node.cpp b/src/graph/node.cpp
old mode 100644
new mode 100755
diff --git a/src/tensors/gpu/cuda_helpers.h b/src/tensors/gpu/cuda_helpers.h
old mode 100644
new mode 100755
index 41c2d0c55..d51b257d4
--- a/src/tensors/gpu/cuda_helpers.h
+++ b/src/tensors/gpu/cuda_helpers.h
@@ -5,7 +5,14 @@
 #include <cuda_runtime.h>
 
 #if COMPILE_FP16
+#ifdef _MSC_VER
+#pragma warning(push)
+#pragma warning(disable : 4505) // unreferenced local function has been removed
+#endif
 #include <cuda_fp16.h>
+#ifdef _MSC_VER
+#pragma warning(pop)
+#endif
 #endif
 
 // template <> inline bool matchType<__half>(Type type)  { return type == Type::float16; }
diff --git a/src/tensors/rand.cpp b/src/tensors/rand.cpp
old mode 100644
new mode 100755
index 3c7a519bf..bd3b4730d
--- a/src/tensors/rand.cpp
+++ b/src/tensors/rand.cpp
@@ -4,7 +4,6 @@
 
 #ifdef CUDA_FOUND
 #include "gpu/cuda_helpers.h"
-
 #include <curand.h>
 #endif
 
diff --git a/src/training/validator.h b/src/training/validator.h
index c1f0d40fa..15630eec0 100755
--- a/src/training/validator.h
+++ b/src/training/validator.h
@@ -82,7 +82,7 @@ class Validator : public ValidatorBase {
 
 protected:
   // Create the BatchGenerator. Note that ScriptValidator does not use batchGenerator_.
-  void createBatchGenerator(bool isTranslating) {
+  void createBatchGenerator(bool /*isTranslating*/) {
     // Create corpus
     auto validPaths = options_->get<std::vector<std::string>>("valid-sets");
     auto corpus = New<DataSet>(validPaths, vocabs_, options_);

From 8e8aae3cd9b5728f01081cc4e28d4a5b6dcf1113 Mon Sep 17 00:00:00 2001
From: Hieu Hoang <hihoan@microsoft.com>
Date: Tue, 22 Oct 2019 11:34:53 -0700
Subject: [PATCH 661/838] use std::filebuf and InputFileStream derives from
 istream rather than zstr::ifstream. Optional gzip filter

---
 src/common/file_stream.cpp | 15 ++++++++++++++-
 src/common/file_stream.h   |  5 +++--
 src/data/default_vocab.cpp |  3 ++-
 3 files changed, 19 insertions(+), 4 deletions(-)

diff --git a/src/common/file_stream.cpp b/src/common/file_stream.cpp
index 2d63c8c1b..897ca28ea 100644
--- a/src/common/file_stream.cpp
+++ b/src/common/file_stream.cpp
@@ -337,8 +337,21 @@ int TemporaryFileNew::mkstemp_and_unlink(char *tmpl) {
 
 ///////////////////////////////////////////////////////////////////////////////////////////////
 InputFileStreamNew::InputFileStreamNew(const std::string &file)
-    : zstr::ifstream(file), file_(file) {
+    : std::istream(NULL), file_(file), streamBuf_(NULL) {
   ABORT_IF(!marian::filesystem::exists(file_), "File '{}' does not exist", file);
+
+  std::filebuf *fileBuf = new std::filebuf();
+  streamBuf_ = fileBuf->open(file.c_str(), std::ios::in);
+  if(!streamBuf_) {
+    ABORT("File can't be read", file);
+  }
+
+  if(file_.extension() == marian::filesystem::Path(".gz")) {
+    streamBuf_ = new zstr::istreambuf(streamBuf_);
+  }
+
+  this->init(streamBuf_);
+
   std::cerr << "InputFileStreamNew created" << std::endl;
 }
 
diff --git a/src/common/file_stream.h b/src/common/file_stream.h
index f7d724d69..e8259c947 100755
--- a/src/common/file_stream.h
+++ b/src/common/file_stream.h
@@ -271,12 +271,13 @@ class TemporaryFileNew : public std::fstream {
 };
 
 //////////////////////////////////////////////////////////////////////////////////////////////
-class InputFileStreamNew : public zstr::ifstream {
+class InputFileStreamNew : public std::istream {
 public:
   explicit InputFileStreamNew(const std::string& file);
 
 protected:
-  std::string file_;
+  marian::filesystem::Path file_;
+  std::streambuf* streamBuf_;
 };
 
 //////////////////////////////////////////////////////////////////////////////////////////////
diff --git a/src/data/default_vocab.cpp b/src/data/default_vocab.cpp
index 1f40ca3ab..40a4fd6b0 100644
--- a/src/data/default_vocab.cpp
+++ b/src/data/default_vocab.cpp
@@ -99,7 +99,8 @@ class DefaultVocab : public IVocab {
     std::map<std::string, Word> vocab;
     // read from JSON (or Yaml) file
     if(isJson) {
-      YAML::Node vocabNode = YAML::Load(io::InputFileStream(vocabPath));
+      io::InputFileStreamNew strm(vocabPath);
+      YAML::Node vocabNode = YAML::Load(strm);
       for(auto&& pair : vocabNode)
         vocab.insert({pair.first.as<std::string>(), Word::fromWordIndex(pair.second.as<IndexType>())});
     }

From 61d7ba4d4d2e1131a522663fd612b9754a78d30e Mon Sep 17 00:00:00 2001
From: Hieu Hoang <hihoan@microsoft.com>
Date: Tue, 22 Oct 2019 12:27:39 -0700
Subject: [PATCH 662/838] delete file buffer in destructor

---
 src/common/binary.cpp      | 2 +-
 src/common/file_stream.cpp | 8 ++++++++
 src/common/file_stream.h   | 3 ++-
 src/data/shortlist.h       | 2 +-
 4 files changed, 12 insertions(+), 3 deletions(-)

diff --git a/src/common/binary.cpp b/src/common/binary.cpp
index d4f443985..3e1ff1f7b 100644
--- a/src/common/binary.cpp
+++ b/src/common/binary.cpp
@@ -76,7 +76,7 @@ void loadItems(const std::string& fileName, std::vector<io::Item>& items) {
   ABORT_IF(rc != buf.size(), "Error {} ('{}') reading file '{}'", errno, strerror(errno), fileName);
   fclose(f);
 #else
-  io::InputFileStream in(fileName);
+  io::InputFileStreamNew in(fileName);
   in.read(buf.data(), buf.size());
 #endif
 
diff --git a/src/common/file_stream.cpp b/src/common/file_stream.cpp
index 897ca28ea..b96f2883c 100644
--- a/src/common/file_stream.cpp
+++ b/src/common/file_stream.cpp
@@ -275,6 +275,10 @@ InputFileStream::InputFileStream(TemporaryFile2 &tempfile) {
   std::cerr << "InputFileStreamOld2 created" << std::endl;
 }
 
+InputFileStream::InputFileStream(std::istream &strm) : istream_(new std::istream(strm.rdbuf())) {
+  std::cerr << "InputFileStreamOld3 created" << std::endl;
+}
+
 void InputFileStream::setbufsize(size_t size) const {
   istream_->rdbuf()->pubsetbuf(0, 0);
   readBuf_.resize(size);
@@ -355,6 +359,10 @@ InputFileStreamNew::InputFileStreamNew(const std::string &file)
   std::cerr << "InputFileStreamNew created" << std::endl;
 }
 
+InputFileStreamNew::~InputFileStreamNew() {
+  delete streamBuf_;
+}
+
 ///////////////////////////////////////////////////////////////////////////////////////////////
 OutputFileStreamNew::OutputFileStreamNew(const std::string &file)
     : zstr::ofstream(file), file_(file) {
diff --git a/src/common/file_stream.h b/src/common/file_stream.h
index e8259c947..5ec592702 100755
--- a/src/common/file_stream.h
+++ b/src/common/file_stream.h
@@ -133,7 +133,7 @@ class InputFileStream {
 
   explicit InputFileStream(TemporaryFile2& tempfile);
 
-  explicit InputFileStream(std::istream& strm) : istream_(new std::istream(strm.rdbuf())) {}
+  explicit InputFileStream(std::istream& strm);
 
   operator std::istream&() { return *istream_; }
 
@@ -274,6 +274,7 @@ class TemporaryFileNew : public std::fstream {
 class InputFileStreamNew : public std::istream {
 public:
   explicit InputFileStreamNew(const std::string& file);
+  virtual ~InputFileStreamNew();
 
 protected:
   marian::filesystem::Path file_;
diff --git a/src/data/shortlist.h b/src/data/shortlist.h
index a44268a90..798794be1 100644
--- a/src/data/shortlist.h
+++ b/src/data/shortlist.h
@@ -136,7 +136,7 @@ class LexicalShortlistGenerator : public ShortlistGenerator {
   std::vector<std::unordered_map<WordIndex, float>> data_; // [WordIndex src] -> [WordIndex tgt] -> P_trans(tgt|src) --@TODO: rename data_ accordingly
 
   void load(const std::string& fname) {
-    io::InputFileStream in(fname);
+    io::InputFileStreamNew in(fname);
 
     std::string src, trg;
     float prob;

From 84ce4cd64d372838a12230b7f8812a561b83df7f Mon Sep 17 00:00:00 2001
From: Hieu Hoang <hihoan@microsoft.com>
Date: Tue, 22 Oct 2019 14:35:39 -0700
Subject: [PATCH 663/838] replace InputFileStream with InputFileStreamNew in
 some files

---
 src/data/default_vocab.cpp       | 10 +++++-----
 src/data/factored_vocab.cpp      | 12 ++++++------
 src/data/sentencepiece_vocab.cpp |  6 +++---
 src/data/text_input.cpp          |  4 ++--
 src/layers/word2vec_reader.h     |  6 +++---
 src/rescorer/score_collector.cpp |  4 ++--
 src/rescorer/score_collector.h   |  2 +-
 src/tests/sqlite.cpp             |  8 ++++----
 8 files changed, 26 insertions(+), 26 deletions(-)

diff --git a/src/data/default_vocab.cpp b/src/data/default_vocab.cpp
index 40a4fd6b0..7eac5fa96 100644
--- a/src/data/default_vocab.cpp
+++ b/src/data/default_vocab.cpp
@@ -106,9 +106,9 @@ class DefaultVocab : public IVocab {
     }
     // read from flat text file
     else {
-      io::InputFileStream in(vocabPath);
+      io::InputFileStreamNew in(vocabPath);
       std::string line;
-      while(io::getline(in, line)) {
+      while(std::getline(in, line)) {
         ABORT_IF(line.empty(),
                 "DefaultVocabulary file {} must not contain empty lines",
                 vocabPath);
@@ -208,9 +208,9 @@ class DefaultVocab : public IVocab {
 
   void addCounts(std::unordered_map<std::string, size_t>& counter,
                  const std::string& trainPath) {
-    std::unique_ptr<io::InputFileStream> trainStrm(
-      trainPath == "stdin" ? new io::InputFileStream(std::cin)
-                           : new io::InputFileStream(trainPath)
+    std::unique_ptr<std::istream> trainStrm(
+      trainPath == "stdin" ? new std::istream(std::cin.rdbuf())
+                           : new io::InputFileStreamNew(trainPath)
     );
 
     std::string line;
diff --git a/src/data/factored_vocab.cpp b/src/data/factored_vocab.cpp
index 144e87128..2d8e958f9 100755
--- a/src/data/factored_vocab.cpp
+++ b/src/data/factored_vocab.cpp
@@ -42,11 +42,11 @@ namespace marian {
     //     - _lemma is special
     // The current version of the code just converts it internally to the legacy form.
     // @TODO: Once the legacy form is no longer needed, simplify this.
-    io::InputFileStream in(modelPath);
+    io::InputFileStreamNew in(modelPath);
     WordIndex v = 0;
     std::map<std::string,std::set<std::string>> factorTypeMap; // [type name] -> {factor-type names}
     std::vector<std::string> deferredFactorVocab; // factor surface forms are presently expected to be at the end of factorVocab_, so collect them here first
-    while(io::getline(in, line)) {
+    while(std::getline(in, line)) {
 #if 1 // workaround for a bug fix in FactoredSegmenter that made old .fsv files incompatible
       if (line      == "\xef\xb8\x8f : _lemma _has_wb")         // old vocabs have a wrong factor in here
         line         = "\xef\xb8\x8f : _lemma _has_gl _has_gr"; // patch it to the correct one
@@ -129,8 +129,8 @@ namespace marian {
     groupPrefixes_ = { "(lemma)", "@C", "@GL", "@GR", "@WB"/*, "@WE"*/, "@CB"/*, "@CE"*/ }; // @TODO: hard-coded for these initial experiments
     // @TODO: add checks for empty factor groups until it stops crashing (training already works; decoder still crashes)
 
-    io::InputFileStream in(modelPath);
-    for (WordIndex v = 0; io::getline(in, line); v++) {
+    io::InputFileStreamNew in(modelPath);
+    for (WordIndex v = 0; std::getline(in, line); v++) {
       utils::splitAny(line, tokBuf, " \t");
       factorMapTokenized.push_back(tokBuf);
     }
@@ -714,8 +714,8 @@ bool FactoredVocab::WordLUT::tryFind(const std::string& word, WordIndex& index)
 }
 size_t FactoredVocab::WordLUT::load(const std::string& path) {
   std::string line;
-  io::InputFileStream in(path);
-  for (WordIndex v = 0; io::getline(in, line); v++)
+  io::InputFileStreamNew in(path);
+  for (WordIndex v = 0; std::getline(in, line); v++)
     add(line, v);
   return size();
 }
diff --git a/src/data/sentencepiece_vocab.cpp b/src/data/sentencepiece_vocab.cpp
index 607385b38..d60ef38cc 100644
--- a/src/data/sentencepiece_vocab.cpp
+++ b/src/data/sentencepiece_vocab.cpp
@@ -43,8 +43,8 @@ class SentencePieceVocab : public IVocab {
 
     ABORT_IF(maxLines == 0, "Sample needs to be larger 0");
 
-    std::unique_ptr<io::InputFileStream> trainStrm(
-      trainPath == "stdin" ? new io::InputFileStream(std::cin)
+    std::unique_ptr<std::istream> trainStrm(
+      trainPath == "stdin" ? &std::cin
                            : new io::InputFileStream(trainPath)
     );
 
@@ -96,7 +96,7 @@ class SentencePieceVocab : public IVocab {
     std::string line;
     io::OutputFileStream out(temp);
     for(const auto& trainPath : trainPaths) {
-      io::InputFileStream in(trainPath);
+      io::InputFileStreamNew in(trainPath);
       while(getline(in, line)) {
         if(line.size() > 0 && line.size() < maxBytes) {
           out << line << std::endl;
diff --git a/src/data/text_input.cpp b/src/data/text_input.cpp
index 5c81bc1fb..af709faad 100644
--- a/src/data/text_input.cpp
+++ b/src/data/text_input.cpp
@@ -39,9 +39,9 @@ SentenceTuple TextInput::next() {
   // fill up the sentence tuple with source and/or target sentences
   SentenceTuple tup(curId);
   for(size_t i = 0; i < files_.size(); ++i) {
-    io::InputFileStream dummyStream(*files_[i]);
+    std::istream &dummyStream = *files_[i];
     std::string line;
-    if(io::getline(dummyStream, line)) {
+    if(std::getline(dummyStream, line)) {
       Words words = vocabs_[i]->encode(line, /*addEOS =*/ true, /*inference =*/ inference_);
       if(words.empty())
         words.push_back(Word::ZERO); // @TODO: What is this for? @BUGBUG: addEOS=true, so this can never happen, right?
diff --git a/src/layers/word2vec_reader.h b/src/layers/word2vec_reader.h
index 4bfc67091..af10b6a95 100644
--- a/src/layers/word2vec_reader.h
+++ b/src/layers/word2vec_reader.h
@@ -17,7 +17,7 @@ class Word2VecReader {
   std::vector<float> read(const std::string& fileName, int dimVoc, int dimEmb) {
     LOG(info, "[data] Loading embedding vectors from {}", fileName);
 
-    io::InputFileStream embFile(fileName);
+    io::InputFileStreamNew embFile(fileName);
 
     std::string line;
     std::vector<std::string> values;
@@ -25,7 +25,7 @@ class Word2VecReader {
 
     // The first line contains two values: the number of words in the
     // vocabulary and the length of embedding vectors
-    io::getline(embFile, line);
+    std::getline(embFile, line);
     utils::split(line, values);
     ABORT_IF(values.size() != 2,
              "Unexpected format of the first line of the embedding file");
@@ -34,7 +34,7 @@ class Word2VecReader {
 
     // Read embedding vectors into a map
     std::unordered_map<WordIndex, std::vector<float>> word2vec;
-    while(io::getline(embFile, line)) {
+    while(std::getline(embFile, line)) {
       values.clear();
       utils::split(line, values);
 
diff --git a/src/rescorer/score_collector.cpp b/src/rescorer/score_collector.cpp
index 28d582609..5cf8a5ed6 100644
--- a/src/rescorer/score_collector.cpp
+++ b/src/rescorer/score_collector.cpp
@@ -81,7 +81,7 @@ ScoreCollectorNBest::ScoreCollectorNBest(const Ptr<Options>& options)
     : ScoreCollector(options),
       nBestList_(options->get<std::vector<std::string>>("train-sets").back()),
       fname_(options->get<std::string>("n-best-feature")) {
-  file_.reset(new io::InputFileStream(nBestList_));
+  file_.reset(new io::InputFileStreamNew(nBestList_));
 }
 
 void ScoreCollectorNBest::Write(long id,
@@ -96,7 +96,7 @@ void ScoreCollectorNBest::Write(long id,
                "Entry {} < {} already read but not in buffer",
                id,
                lastRead_);
-      while(lastRead_ < id && io::getline(*file_, line)) {
+      while(lastRead_ < id && std::getline(*file_, line)) {
         lastRead_++;
         iter = buffer_.emplace(lastRead_, line).first;
       }
diff --git a/src/rescorer/score_collector.h b/src/rescorer/score_collector.h
index e09681ca1..a11261748 100644
--- a/src/rescorer/score_collector.h
+++ b/src/rescorer/score_collector.h
@@ -56,7 +56,7 @@ class ScoreCollectorNBest : public ScoreCollector {
   std::string nBestList_;
   std::string fname_;
   long lastRead_{-1};
-  UPtr<io::InputFileStream> file_;
+  UPtr<io::InputFileStreamNew> file_;
   std::map<long, std::string> buffer_;
 
   std::string addToNBest(const std::string nbest,
diff --git a/src/tests/sqlite.cpp b/src/tests/sqlite.cpp
index 217485149..35908b532 100644
--- a/src/tests/sqlite.cpp
+++ b/src/tests/sqlite.cpp
@@ -26,12 +26,12 @@ int main(int argc, char** argv) {
 
     std::cerr << "Reading from " << argv[1] << " and " << argv[2] << std::endl;
 
-    marian::io::InputFileStream file0(argv[1]);
-    marian::io::InputFileStream file1(argv[2]);
+    marian::io::InputFileStreamNew file0(argv[1]);
+    marian::io::InputFileStreamNew file1(argv[2]);
 
     db.exec("begin;");
-    while(marian::io::getline(file0, line0)
-          && marian::io::getline(file1, line1)) {
+    while(std::getline(file0, line0)
+          && std::getline(file1, line1)) {
       ps.bind(1, (int)lines);
       ps.bind(2, line0);
       ps.bind(3, line1);

From 750d204e3781f46b6436b660e6e4d402ea53e5c1 Mon Sep 17 00:00:00 2001
From: Hieu Hoang <hihoan@microsoft.com>
Date: Tue, 22 Oct 2019 15:11:57 -0700
Subject: [PATCH 664/838] replace InputFileStream with InputFileStreamNew in
 everything except Corpus classes

---
 src/data/sentencepiece_vocab.cpp | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/src/data/sentencepiece_vocab.cpp b/src/data/sentencepiece_vocab.cpp
index d60ef38cc..705d19519 100644
--- a/src/data/sentencepiece_vocab.cpp
+++ b/src/data/sentencepiece_vocab.cpp
@@ -44,8 +44,8 @@ class SentencePieceVocab : public IVocab {
     ABORT_IF(maxLines == 0, "Sample needs to be larger 0");
 
     std::unique_ptr<std::istream> trainStrm(
-      trainPath == "stdin" ? &std::cin
-                           : new io::InputFileStream(trainPath)
+      trainPath == "stdin" ? new std::istream(&std::cin)
+                           : new io::InputFileStreamNew(trainPath)
     );
 
     std::string line;

From d342e4481834fee5d1602df9e026b35e70301cd5 Mon Sep 17 00:00:00 2001
From: Hieu Hoang <hihoan@microsoft.com>
Date: Tue, 22 Oct 2019 15:38:28 -0700
Subject: [PATCH 665/838] replace all InputFileStream with InputFileStreamNew

---
 src/common/file_stream.cpp |  8 ++++++++
 src/common/file_stream.h   |  3 +++
 src/data/corpus.cpp        | 18 ++++++++++--------
 src/data/corpus_base.cpp   | 22 +++++++++++++---------
 src/data/corpus_base.h     |  2 +-
 src/data/corpus_nbest.cpp  |  8 ++++----
 src/data/corpus_sqlite.cpp |  2 +-
 7 files changed, 40 insertions(+), 23 deletions(-)

diff --git a/src/common/file_stream.cpp b/src/common/file_stream.cpp
index b96f2883c..1f71fd1a1 100644
--- a/src/common/file_stream.cpp
+++ b/src/common/file_stream.cpp
@@ -363,6 +363,14 @@ InputFileStreamNew::~InputFileStreamNew() {
   delete streamBuf_;
 }
 
+bool InputFileStreamNew::empty() {
+  return this->peek() == std::ifstream::traits_type::eof();
+}
+
+void InputFileStreamNew::setbufsize(size_t size) const {
+  // do nothing. Is this needed?
+}
+
 ///////////////////////////////////////////////////////////////////////////////////////////////
 OutputFileStreamNew::OutputFileStreamNew(const std::string &file)
     : zstr::ofstream(file), file_(file) {
diff --git a/src/common/file_stream.h b/src/common/file_stream.h
index 5ec592702..1d5589753 100755
--- a/src/common/file_stream.h
+++ b/src/common/file_stream.h
@@ -276,6 +276,9 @@ class InputFileStreamNew : public std::istream {
   explicit InputFileStreamNew(const std::string& file);
   virtual ~InputFileStreamNew();
 
+  bool empty();
+  void setbufsize(size_t size) const;
+
 protected:
   marian::filesystem::Path file_;
   std::streambuf* streamBuf_;
diff --git a/src/data/corpus.cpp b/src/data/corpus.cpp
index 107af670b..1193336f7 100644
--- a/src/data/corpus.cpp
+++ b/src/data/corpus.cpp
@@ -71,7 +71,7 @@ SentenceTuple Corpus::next() {
         }
       }
       else {
-        bool gotLine = io::getline(*files_[i], line);
+        std::istream &gotLine = std::getline(*files_[i], line);
         if(!gotLine) {
           eofsHit++;
           continue;
@@ -121,7 +121,7 @@ void Corpus::reset() {
   pos_ = 0;
   for (size_t i = 0; i < paths_.size(); ++i) {
       if(paths_[i] == "stdin") {
-        files_[i].reset(new io::InputFileStream(std::cin));
+        files_[i].reset(new std::istream(std::cin.rdbuf()));
         // Probably not necessary, unless there are some buffers
         // that we want flushed.
       }
@@ -131,7 +131,7 @@ void Corpus::reset() {
         // Do NOT reset named pipes; that closes them and triggers a SIGPIPE
         // (lost pipe) at the writing end, which may do whatever it wants
         // in this situation.
-        files_[i].reset(new io::InputFileStream(paths_[i]));
+        files_[i].reset(new io::InputFileStreamNew(paths_[i]));
       }
     }
 }
@@ -154,8 +154,9 @@ void Corpus::shuffleData(const std::vector<std::string>& paths) {
   else {
     files_.resize(numStreams);
     for(size_t i = 0; i < numStreams; ++i) {
-      files_[i].reset(new io::InputFileStream(paths[i]));
-      files_[i]->setbufsize(10000000); // huge read-ahead buffer to avoid network round-trips
+      io::InputFileStreamNew *strm = new io::InputFileStreamNew(paths[i]);
+      files_[i].reset(strm);
+      strm->setbufsize(10000000); // huge read-ahead buffer to avoid network round-trips
     }
 
     // read entire corpus into RAM
@@ -163,7 +164,7 @@ void Corpus::shuffleData(const std::vector<std::string>& paths) {
     for (;;) {
       size_t eofsHit = 0;
       for(size_t i = 0; i < numStreams; ++i) {
-        bool gotLine = io::getline(*files_[i], lineBuf);
+        std::istream &gotLine = std::getline(*files_[i], lineBuf);
         if (gotLine)
           corpus[i].push_back(lineBuf);
         else
@@ -204,8 +205,9 @@ void Corpus::shuffleData(const std::vector<std::string>& paths) {
     // replace files_[] by the tempfiles we just created
     files_.resize(numStreams);
     for(size_t i = 0; i < numStreams; ++i) {
-      files_[i].reset(new io::InputFileStream(*tempFiles_[i]));
-      files_[i]->setbufsize(10000000);
+      io::InputFileStreamNew* strm = new io::InputFileStreamNew(tempFiles_[i]->getFileName());
+      files_[i].reset(strm);
+      strm->setbufsize(10000000);
     }
     LOG(info, "[data] Done shuffling {} sentences to temp files", numSentences);
   }
diff --git a/src/data/corpus_base.cpp b/src/data/corpus_base.cpp
index 03633e828..da708141a 100755
--- a/src/data/corpus_base.cpp
+++ b/src/data/corpus_base.cpp
@@ -40,8 +40,9 @@ CorpusBase::CorpusBase(const std::vector<std::string>& paths,
            "Number of corpus files and vocab files does not agree");
 
   for(auto path : paths_) {
-    files_.emplace_back(new io::InputFileStream(path));
-    ABORT_IF(files_.back()->empty(), "File '{}' is empty", path);
+    io::InputFileStreamNew *strm = new io::InputFileStreamNew(path);
+    ABORT_IF(strm->empty(), "File '{}' is empty", path);
+    files_.emplace_back(strm);
   }
 
   initEOS(/*training=*/true);
@@ -153,10 +154,11 @@ CorpusBase::CorpusBase(Ptr<Options> options, bool translate)
 
   for(auto path : paths_) {
     if(path == "stdin")
-      files_.emplace_back(new io::InputFileStream(std::cin));
+      files_.emplace_back(new std::istream(std::cin.rdbuf()));
     else {
-      files_.emplace_back(new io::InputFileStream(path));
-      ABORT_IF(files_.back()->empty(), "File '{}' is empty", path);
+      io::InputFileStreamNew *strm = new io::InputFileStreamNew(path);
+      ABORT_IF(strm->empty(), "File '{}' is empty", path);
+      files_.emplace_back(strm);
     }
   }
 
@@ -174,8 +176,9 @@ CorpusBase::CorpusBase(Ptr<Options> options, bool translate)
 
     alignFileIdx_ = paths_.size();
     paths_.emplace_back(path);
-    files_.emplace_back(new io::InputFileStream(path));
-    ABORT_IF(files_.back()->empty(), "File with alignments '{}' is empty", path);
+    io::InputFileStreamNew* strm = new io::InputFileStreamNew(path);
+    ABORT_IF(strm->empty(), "File with alignments '{}' is empty", path);
+    files_.emplace_back(strm);
   }
 
   if(training && options_->hasAndNotEmpty("data-weighting")) {
@@ -186,8 +189,9 @@ CorpusBase::CorpusBase(Ptr<Options> options, bool translate)
 
     weightFileIdx_ = paths_.size();
     paths_.emplace_back(path);
-    files_.emplace_back(new io::InputFileStream(path));
-    ABORT_IF(files_.back()->empty(), "File with weights '{}' is empty", path);
+    io::InputFileStreamNew* strm = new io::InputFileStreamNew(path);
+    ABORT_IF(strm->empty(), "File with weights '{}' is empty", path);
+    files_.emplace_back(strm);
   }
 }
 
diff --git a/src/data/corpus_base.h b/src/data/corpus_base.h
index 62bdb18dd..8a8d46b87 100644
--- a/src/data/corpus_base.h
+++ b/src/data/corpus_base.h
@@ -516,7 +516,7 @@ class CorpusBase
   virtual std::vector<Ptr<Vocab>>& getVocabs() = 0;
 
 protected:
-  std::vector<UPtr<io::InputFileStream>> files_;
+  std::vector<UPtr<std::istream>> files_;
   std::vector<Ptr<Vocab>> vocabs_;
 
   /**
diff --git a/src/data/corpus_nbest.cpp b/src/data/corpus_nbest.cpp
index c5a85a231..dc0de1194 100644
--- a/src/data/corpus_nbest.cpp
+++ b/src/data/corpus_nbest.cpp
@@ -49,13 +49,13 @@ SentenceTuple CorpusNBest::next() {
     lastLines_.resize(files_.size() - 1);
     size_t last = files_.size() - 1;
 
-    if(io::getline(*files_[last], line)) {
+    if(std::getline(*files_[last], line)) {
       int curr_num = numFromNbest(line);
       std::string curr_text = lineFromNbest(line);
 
       for(size_t i = 0; i < last; ++i) {
         if(curr_num > lastNum_) {
-          ABORT_IF(!io::getline(*files_[i], lastLines_[i]),
+          ABORT_IF(!std::getline(*files_[i], lastLines_[i]),
                    "Too few lines in input {}",
                    i);
         }
@@ -86,9 +86,9 @@ void CorpusNBest::reset() {
   lastNum_ = -1;
   for(auto& path : paths_) {
     if(path == "stdin")
-      files_.emplace_back(new io::InputFileStream(std::cin));
+      files_.emplace_back(new std::istream(std::cin.rdbuf()));
     else
-      files_.emplace_back(new io::InputFileStream(path));
+      files_.emplace_back(new io::InputFileStreamNew(path));
   }
 }
 }  // namespace data
diff --git a/src/data/corpus_sqlite.cpp b/src/data/corpus_sqlite.cpp
index 714a70f01..74ffe1935 100644
--- a/src/data/corpus_sqlite.cpp
+++ b/src/data/corpus_sqlite.cpp
@@ -78,7 +78,7 @@ void CorpusSQLite::fillSQLite() {
 
       std::string line;
       for(size_t i = 0; i < files_.size(); ++i) {
-        cont = cont && io::getline(*files_[i], line);
+        cont = cont && std::getline(*files_[i], line);
         if(cont)
           ps.bind((int)(i + 2), line);
       }

From b8e2f06be37c85726a2d358a538d326a2bd9327e Mon Sep 17 00:00:00 2001
From: Hieu Hoang <hihoan@microsoft.com>
Date: Tue, 22 Oct 2019 15:44:13 -0700
Subject: [PATCH 666/838] delete InputFileStream

---
 src/common/file_stream.cpp | 28 --------------
 src/common/file_stream.h   | 77 --------------------------------------
 2 files changed, 105 deletions(-)

diff --git a/src/common/file_stream.cpp b/src/common/file_stream.cpp
index 1f71fd1a1..467ddc402 100644
--- a/src/common/file_stream.cpp
+++ b/src/common/file_stream.cpp
@@ -256,34 +256,6 @@ TemporaryFile2::~TemporaryFile2() {
 }
 
 ///////////////////////////////////////////////////////////////////////////////////////////////
-InputFileStream::InputFileStream(const std::string &file) : file_(file) {
-  ABORT_IF(!marian::filesystem::exists(file_), "File '{}' does not exist", file);
-
-  if(file_.extension() == marian::filesystem::Path(".gz"))
-    istream_ = std::make_unique<zstr::ifstream>(file_.string());
-  else
-    istream_ = std::make_unique<std::ifstream>(file_.string());
-  ABORT_IF(fail(), "Error {} ({}) opening file '{}'", errno, strerror(errno), path());
-
-  std::cerr << "InputFileStreamOld1 created" << std::endl;
-}
-
-InputFileStream::InputFileStream(TemporaryFile2 &tempfile) {
-  RewindFile(tempfile.getFileDescriptor());
-  temporary_reader_.reset(new ReadFDBuf(tempfile.getFileDescriptor()));
-  istream_.reset(new std::istream(temporary_reader_.get()));
-  std::cerr << "InputFileStreamOld2 created" << std::endl;
-}
-
-InputFileStream::InputFileStream(std::istream &strm) : istream_(new std::istream(strm.rdbuf())) {
-  std::cerr << "InputFileStreamOld3 created" << std::endl;
-}
-
-void InputFileStream::setbufsize(size_t size) const {
-  istream_->rdbuf()->pubsetbuf(0, 0);
-  readBuf_.resize(size);
-  istream_->rdbuf()->pubsetbuf(readBuf_.data(), readBuf_.size());
-}
 
 ///////////////////////////////////////////////////////////////////////////////////////////////
 OutputFileStream::OutputFileStream(const std::string &file) : file_(file) {
diff --git a/src/common/file_stream.h b/src/common/file_stream.h
index 1d5589753..43e2c3cb5 100755
--- a/src/common/file_stream.h
+++ b/src/common/file_stream.h
@@ -127,83 +127,6 @@ class WriteFDBuf : public std::streambuf {
 // lseek(fd, 0, SEEK_SET) but with error checking.
 void RewindFile(int fd);
 
-class InputFileStream {
-public:
-  explicit InputFileStream(const std::string& file);
-
-  explicit InputFileStream(TemporaryFile2& tempfile);
-
-  explicit InputFileStream(std::istream& strm);
-
-  operator std::istream&() { return *istream_; }
-
-  operator bool() { return (bool)*istream_; }
-
-  bool bad() const { return istream_->bad(); }
-
-  bool fail() const { return istream_->fail(); }
-
-  char widen(char c) { return istream_->widen(c); }
-
-  std::string path() { return file_.string(); }
-
-  bool empty() { return istream_->peek() == std::ifstream::traits_type::eof(); }
-
-  void setbufsize(size_t size) const;
-
-  template <typename T>
-  friend InputFileStream& operator>>(InputFileStream& stream, T& t) {
-    *stream.istream_ >> t;
-    // bad() seems to be correct here. Should not abort on EOF.
-    ABORT_IF(stream.bad(),
-             "Error {} ({}) reading from file '{}'",
-             errno,
-             strerror(errno),
-             stream.path());
-    return stream;
-  }
-
-  template <typename T>
-  size_t read(T* ptr, size_t num = 1) {
-    istream_->read((char*)ptr, num * sizeof(T));
-    // fail() seems to be correct here. Failure to read should abort.
-    ABORT_IF(fail(), "Error {} ({}) reading from file '{}'", errno, strerror(errno), path());
-    return num * sizeof(T);
-  }
-
-private:
-  marian::filesystem::Path file_;
-  std::unique_ptr<std::istream> istream_;
-
-  mutable std::vector<char> readBuf_;  // for setbuf()
-  std::unique_ptr<ReadFDBuf> temporary_reader_;
-};
-
-// wrapper around std::getline() that handles Windows input files with extra CR
-// chars at the line end
-static inline InputFileStream& getline(InputFileStream& in, std::string& line) {
-  std::getline((std::istream&)in, line);
-  // bad() seems to be correct here. Should not abort on EOF.
-  ABORT_IF(in.bad(), "Error reading from file '{}'", in.path());
-  // strip terminal CR if present
-  if(in && !line.empty() && line.back() == in.widen('\r'))
-    line.pop_back();
-  return in;
-}
-
-// wrapper around std::getline() that handles Windows input files with extra CR
-// chars at the line end
-// To be pedantic, shouldn't this require delim == '\n' to consume a '\r'?
-static inline InputFileStream& getline(InputFileStream& in, std::string& line, char delim) {
-  std::getline((std::istream&)in, line, delim);
-  // bad() seems to be correct here. Should not abort on EOF.
-  ABORT_IF(in.bad(), "Error reading from file '{}'", in.path());
-  // strip terminal CR if present
-  if(in && !line.empty() && line.back() == in.widen('\r'))
-    line.pop_back();
-  return in;
-}
-
 ///////////////////////////////////////////////////////////////////////////////////////////////
 class OutputFileStream {
 public:

From a10f5fbe6e3f436ac4285f4478ec2c3777b5c04b Mon Sep 17 00:00:00 2001
From: Hieu Hoang <hihoan@microsoft.com>
Date: Tue, 22 Oct 2019 16:03:06 -0700
Subject: [PATCH 667/838] start OutputFileStream

---
 src/common/file_stream.cpp | 25 ++++++++++++++++++++++++-
 src/common/file_stream.h   |  7 ++++---
 2 files changed, 28 insertions(+), 4 deletions(-)

diff --git a/src/common/file_stream.cpp b/src/common/file_stream.cpp
index 467ddc402..ed78e40d2 100644
--- a/src/common/file_stream.cpp
+++ b/src/common/file_stream.cpp
@@ -264,12 +264,21 @@ OutputFileStream::OutputFileStream(const std::string &file) : file_(file) {
   else
     ostream_ = std::make_unique<std::ofstream>(file_.string());
   ABORT_IF(!marian::filesystem::exists(file_), "File '{}' could not be opened", file);
+
+std::cerr << "OutputFileStreamOld1 created" << std::endl;
 }
 
 OutputFileStream::OutputFileStream(TemporaryFile2 &tempfile) {
   RewindFile(tempfile.getFileDescriptor());
   temporary_writer_.reset(new WriteFDBuf(tempfile.getFileDescriptor()));
   ostream_.reset(new std::ostream(temporary_writer_.get()));
+
+  std::cerr << "OutputFileStreamOld2 created" << std::endl;
+}
+
+OutputFileStream::OutputFileStream(std::ostream &strm) {
+  ostream_ = std::make_unique<std::ostream>(strm.rdbuf());
+  std::cerr << "OutputFileStreamOld3 created" << std::endl;
 }
 
 ///////////////////////////////////////////////////////////////////////////////////////////////
@@ -345,8 +354,22 @@ void InputFileStreamNew::setbufsize(size_t size) const {
 
 ///////////////////////////////////////////////////////////////////////////////////////////////
 OutputFileStreamNew::OutputFileStreamNew(const std::string &file)
-    : zstr::ofstream(file), file_(file) {
+    : std::ostream(NULL), file_(file), streamBuf_(NULL) {
   ABORT_IF(!marian::filesystem::exists(file_), "File '{}' does not exist", file);
+
+  std::filebuf *fileBuf = new std::filebuf();
+  streamBuf_ = fileBuf->open(file.c_str(), std::ios::out);
+  if(!streamBuf_) {
+    ABORT("File can't be read", file);
+  }
+
+  if(file_.extension() == marian::filesystem::Path(".gz")) {
+    streamBuf_ = new zstr::istreambuf(streamBuf_);
+  }
+
+  this->init(streamBuf_);
+
+  std::cerr << "OutputFileStreamNew created" << std::endl;
 }
 
 } // namespace io
diff --git a/src/common/file_stream.h b/src/common/file_stream.h
index 43e2c3cb5..b2da96cc8 100755
--- a/src/common/file_stream.h
+++ b/src/common/file_stream.h
@@ -134,7 +134,7 @@ class OutputFileStream {
 
   OutputFileStream(TemporaryFile2& tempfile);
 
-  OutputFileStream(std::ostream& strm) { ostream_ = std::make_unique<std::ostream>(strm.rdbuf()); }
+  OutputFileStream(std::ostream& strm);
 
   operator std::ostream&() { return *ostream_; }
 
@@ -208,12 +208,13 @@ class InputFileStreamNew : public std::istream {
 };
 
 //////////////////////////////////////////////////////////////////////////////////////////////
-class OutputFileStreamNew : public zstr::ofstream {
+class OutputFileStreamNew : public std::ostream {
 public:
   explicit OutputFileStreamNew(const std::string& file);
 
 protected:
-  std::string file_;
+  marian::filesystem::Path file_;
+  std::streambuf* streamBuf_;
 };
 
 }  // namespace io

From 908dcad06f5cdb8efc6f8871cd1de6d0702e1727 Mon Sep 17 00:00:00 2001
From: Hieu Hoang <hihoan@microsoft.com>
Date: Tue, 22 Oct 2019 16:18:31 -0700
Subject: [PATCH 668/838] try using OutputFileStream

---
 src/common/file_stream.cpp          | 4 ++--
 src/training/validator.cpp          | 2 +-
 src/translator/output_collector.cpp | 4 ++--
 src/translator/output_collector.h   | 4 ++--
 4 files changed, 7 insertions(+), 7 deletions(-)

diff --git a/src/common/file_stream.cpp b/src/common/file_stream.cpp
index ed78e40d2..1b6bb7216 100644
--- a/src/common/file_stream.cpp
+++ b/src/common/file_stream.cpp
@@ -360,11 +360,11 @@ OutputFileStreamNew::OutputFileStreamNew(const std::string &file)
   std::filebuf *fileBuf = new std::filebuf();
   streamBuf_ = fileBuf->open(file.c_str(), std::ios::out);
   if(!streamBuf_) {
-    ABORT("File can't be read", file);
+    ABORT("File can't be opened", file);
   }
 
   if(file_.extension() == marian::filesystem::Path(".gz")) {
-    streamBuf_ = new zstr::istreambuf(streamBuf_);
+    streamBuf_ = new zstr::ostreambuf(streamBuf_);
   }
 
   this->init(streamBuf_);
diff --git a/src/training/validator.cpp b/src/training/validator.cpp
index 24f523273..7d58858ec 100644
--- a/src/training/validator.cpp
+++ b/src/training/validator.cpp
@@ -371,7 +371,7 @@ float TranslationValidator::validate(const std::vector<Ptr<ExpressionGraph>>& gr
     // contains the name of temporary file that should be used?
     auto collector = options_->hasAndNotEmpty("valid-translation-output")
                          ? New<OutputCollector>(fileName)
-                         : New<OutputCollector>(*tempFile);
+                         : New<OutputCollector>(tempFile->getFileName());
 
     if(quiet_)
       collector->setPrintingStrategy(New<QuietPrinting>());
diff --git a/src/translator/output_collector.cpp b/src/translator/output_collector.cpp
index 58fba69b0..5a97982e2 100644
--- a/src/translator/output_collector.cpp
+++ b/src/translator/output_collector.cpp
@@ -12,10 +12,10 @@ OutputCollector::OutputCollector()
 
 OutputCollector::OutputCollector(std::string outFile)
   : nextId_(0),
-    outStrm_(new io::OutputFileStream(std::cout)),
+    outStrm_(new std::ostream(std::cout.rdbuf())),
     printing_(new DefaultPrinting()) {
   if (outFile != "stdout")
-    outStrm_.reset(new io::OutputFileStream(outFile));
+    outStrm_.reset(new io::OutputFileStreamNew(outFile));
 }
 
 void OutputCollector::Write(long sourceId,
diff --git a/src/translator/output_collector.h b/src/translator/output_collector.h
index c616f03ff..9e4934092 100644
--- a/src/translator/output_collector.h
+++ b/src/translator/output_collector.h
@@ -49,7 +49,7 @@ class OutputCollector {
   OutputCollector(std::string outFile);
 
   template <class T>
-  OutputCollector(T&& arg) : nextId_(0), outStrm_(new io::OutputFileStream(arg)) {}
+  OutputCollector(T&& arg) : nextId_(0), outStrm_(new io::OutputFileStreamNew(arg)) {}
 
   OutputCollector(const OutputCollector&) = delete;
 
@@ -66,7 +66,7 @@ class OutputCollector {
   typedef std::map<long, std::pair<std::string, std::string>> Outputs;
   Outputs outputs_;
   long nextId_;
-  UPtr<io::OutputFileStream> outStrm_;
+  UPtr<std::ostream> outStrm_;
   Ptr<PrintingStrategy> printing_;
   std::mutex mutex_;
 };

From e4182e544654195bea7c42a35ee101b4d14f5a8b Mon Sep 17 00:00:00 2001
From: Hieu Hoang <hihoan@microsoft.com>
Date: Tue, 22 Oct 2019 16:38:49 -0700
Subject: [PATCH 669/838] flush stream to make sure everything comes out.
 Doens't work

---
 src/common/file_stream.cpp | 6 +++++-
 src/common/file_stream.h   | 3 ++-
 2 files changed, 7 insertions(+), 2 deletions(-)

diff --git a/src/common/file_stream.cpp b/src/common/file_stream.cpp
index 1b6bb7216..f00d2cc0a 100644
--- a/src/common/file_stream.cpp
+++ b/src/common/file_stream.cpp
@@ -355,7 +355,6 @@ void InputFileStreamNew::setbufsize(size_t size) const {
 ///////////////////////////////////////////////////////////////////////////////////////////////
 OutputFileStreamNew::OutputFileStreamNew(const std::string &file)
     : std::ostream(NULL), file_(file), streamBuf_(NULL) {
-  ABORT_IF(!marian::filesystem::exists(file_), "File '{}' does not exist", file);
 
   std::filebuf *fileBuf = new std::filebuf();
   streamBuf_ = fileBuf->open(file.c_str(), std::ios::out);
@@ -372,5 +371,10 @@ OutputFileStreamNew::OutputFileStreamNew(const std::string &file)
   std::cerr << "OutputFileStreamNew created" << std::endl;
 }
 
+OutputFileStreamNew::~OutputFileStreamNew() {
+  this->flush();
+  delete streamBuf_;
+}
+  
 } // namespace io
 } // namespace marian
diff --git a/src/common/file_stream.h b/src/common/file_stream.h
index b2da96cc8..48e1f163b 100755
--- a/src/common/file_stream.h
+++ b/src/common/file_stream.h
@@ -211,7 +211,8 @@ class InputFileStreamNew : public std::istream {
 class OutputFileStreamNew : public std::ostream {
 public:
   explicit OutputFileStreamNew(const std::string& file);
-
+  virtual ~OutputFileStreamNew();
+  
 protected:
   marian::filesystem::Path file_;
   std::streambuf* streamBuf_;

From 1f53deaefd17210674dfbde15e50ccb1d20a63ad Mon Sep 17 00:00:00 2001
From: Hieu Hoang <hihoan@microsoft.com>
Date: Tue, 22 Oct 2019 17:22:23 -0700
Subject: [PATCH 670/838] flush both filters

---
 src/common/file_stream.cpp | 21 +++++++++++----------
 src/common/file_stream.h   |  3 ++-
 2 files changed, 13 insertions(+), 11 deletions(-)

diff --git a/src/common/file_stream.cpp b/src/common/file_stream.cpp
index f00d2cc0a..a9b534658 100644
--- a/src/common/file_stream.cpp
+++ b/src/common/file_stream.cpp
@@ -354,27 +354,28 @@ void InputFileStreamNew::setbufsize(size_t size) const {
 
 ///////////////////////////////////////////////////////////////////////////////////////////////
 OutputFileStreamNew::OutputFileStreamNew(const std::string &file)
-    : std::ostream(NULL), file_(file), streamBuf_(NULL) {
-
+    : std::ostream(NULL), file_(file), streamBuf1_(NULL), streamBuf2_(NULL) {
   std::filebuf *fileBuf = new std::filebuf();
-  streamBuf_ = fileBuf->open(file.c_str(), std::ios::out);
-  if(!streamBuf_) {
-    ABORT("File can't be opened", file);
+  streamBuf1_ = fileBuf->open(file.c_str(), std::ios::out | std::ios_base::binary);
+  if(!streamBuf1_) {
+    std::cerr << "File can't be opened" << file << std::endl;
   }
 
   if(file_.extension() == marian::filesystem::Path(".gz")) {
-    streamBuf_ = new zstr::ostreambuf(streamBuf_);
+    streamBuf2_ = new zstr::ostreambuf(streamBuf1_);
+    this->init(streamBuf2_);
+  } else {
+    this->init(streamBuf1_);
   }
 
-  this->init(streamBuf_);
-
   std::cerr << "OutputFileStreamNew created" << std::endl;
 }
 
 OutputFileStreamNew::~OutputFileStreamNew() {
   this->flush();
-  delete streamBuf_;
+  delete streamBuf2_;
+  delete streamBuf1_;
 }
-  
+
 } // namespace io
 } // namespace marian
diff --git a/src/common/file_stream.h b/src/common/file_stream.h
index 48e1f163b..d4b434ff7 100755
--- a/src/common/file_stream.h
+++ b/src/common/file_stream.h
@@ -215,7 +215,8 @@ class OutputFileStreamNew : public std::ostream {
   
 protected:
   marian::filesystem::Path file_;
-  std::streambuf* streamBuf_;
+  std::streambuf* streamBuf1_;
+  std::streambuf* streamBuf2_;
 };
 
 }  // namespace io

From c5c1a4c4499c480dcfb21ce888c97912a10a7744 Mon Sep 17 00:00:00 2001
From: Hieu Hoang <hihoan@microsoft.com>
Date: Tue, 22 Oct 2019 17:50:32 -0700
Subject: [PATCH 671/838] replace all OutputFileStream with OutputFileStreamNew

---
 src/common/binary.cpp            |  2 +-
 src/common/config.cpp            |  2 +-
 src/common/file_stream.h         |  8 ++++++++
 src/data/corpus.cpp              |  2 +-
 src/data/default_vocab.cpp       | 12 ++++++------
 src/data/factored_vocab.cpp      |  2 +-
 src/data/sentencepiece_vocab.cpp |  4 ++--
 src/data/shortlist.h             |  4 ++--
 src/models/amun.h                |  2 +-
 src/models/encoder_decoder.cpp   |  2 +-
 src/models/nematus.h             |  2 +-
 src/rescorer/score_collector.cpp |  4 ++--
 src/rescorer/score_collector.h   |  2 +-
 vs/Marian.vcxproj                |  2 ++
 vs/Marian.vcxproj.filters        |  6 ++++++
 15 files changed, 36 insertions(+), 20 deletions(-)

diff --git a/src/common/binary.cpp b/src/common/binary.cpp
index 3e1ff1f7b..a42e4c0b4 100644
--- a/src/common/binary.cpp
+++ b/src/common/binary.cpp
@@ -108,7 +108,7 @@ io::Item getItem(const std::string& fileName, const std::string& varName) {
 
 void saveItems(const std::string& fileName,
                const std::vector<io::Item>& items) {
-  io::OutputFileStream out(fileName);
+  io::OutputFileStreamNew out(fileName);
   size_t pos = 0;
 
   size_t binaryFileVersion = BINARY_FILE_VERSION;
diff --git a/src/common/config.cpp b/src/common/config.cpp
index ddfb5199e..23a0f56f7 100644
--- a/src/common/config.cpp
+++ b/src/common/config.cpp
@@ -127,7 +127,7 @@ YAML::Node& Config::get() {
 }
 
 void Config::save(const std::string& name) {
-  io::OutputFileStream out(name);
+  io::OutputFileStreamNew out(name);
   out << *this;
 }
 
diff --git a/src/common/file_stream.h b/src/common/file_stream.h
index d4b434ff7..f58d0d21e 100755
--- a/src/common/file_stream.h
+++ b/src/common/file_stream.h
@@ -213,6 +213,14 @@ class OutputFileStreamNew : public std::ostream {
   explicit OutputFileStreamNew(const std::string& file);
   virtual ~OutputFileStreamNew();
   
+  template <typename T>
+  size_t write(const T* ptr, size_t num = 1) {
+    this->write((char*)ptr, num * sizeof(T));
+    // fail() seems to be correct here. Failure to write should abort.
+    ABORT_IF(fail(), "Error writing to file '{}'", file_.string());
+    return num * sizeof(T);
+  }
+
 protected:
   marian::filesystem::Path file_;
   std::streambuf* streamBuf1_;
diff --git a/src/data/corpus.cpp b/src/data/corpus.cpp
index 1193336f7..94ed9644c 100644
--- a/src/data/corpus.cpp
+++ b/src/data/corpus.cpp
@@ -195,7 +195,7 @@ void Corpus::shuffleData(const std::vector<std::string>& paths) {
     tempFiles_.resize(numStreams);
     for(size_t i = 0; i < numStreams; ++i) {
       tempFiles_[i].reset(new io::TemporaryFile2(options_->get<std::string>("tempdir")));
-      io::OutputFileStream out(*tempFiles_[i]);
+      io::OutputFileStreamNew out(tempFiles_[i]->getFileName());
       const auto& corpusStream = corpus[i];
       for(auto id : ids_) {
         out << corpusStream[id] << std::endl;
diff --git a/src/data/default_vocab.cpp b/src/data/default_vocab.cpp
index 7eac5fa96..82bef25aa 100644
--- a/src/data/default_vocab.cpp
+++ b/src/data/default_vocab.cpp
@@ -249,9 +249,9 @@ class DefaultVocab : public IVocab {
     for(size_t i = 0; i < vocabSize; ++i)
       vocabYaml.force_insert(vocabVec[i], i + maxSpec + 1);
 
-    std::unique_ptr<io::OutputFileStream> vocabStrm(
-      vocabPath == "stdout" ? new io::OutputFileStream(std::cout)
-                            : new io::OutputFileStream(vocabPath)
+    std::unique_ptr<std::ostream> vocabStrm(
+      vocabPath == "stdout" ? new std::ostream(std::cout.rdbuf())
+                            : new io::OutputFileStreamNew(vocabPath)
     );
     *vocabStrm << vocabYaml;
   }
@@ -315,9 +315,9 @@ class ClassVocab : public DefaultVocab {
     for(size_t i = 0; i < vocabVec.size(); ++i)
       vocabYaml.force_insert(vocabVec[i], i);
 
-    std::unique_ptr<io::OutputFileStream> vocabStrm(
-      vocabPath == "stdout" ? new io::OutputFileStream(std::cout)
-                            : new io::OutputFileStream(vocabPath)
+    std::unique_ptr<std::ostream> vocabStrm(
+      vocabPath == "stdout" ? new std::ostream(std::cout.rdbuf())
+                            : new io::OutputFileStreamNew(vocabPath)
     );
     *vocabStrm << vocabYaml;
   }
diff --git a/src/data/factored_vocab.cpp b/src/data/factored_vocab.cpp
index 2d8e958f9..408687df9 100755
--- a/src/data/factored_vocab.cpp
+++ b/src/data/factored_vocab.cpp
@@ -721,7 +721,7 @@ size_t FactoredVocab::WordLUT::load(const std::string& path) {
 }
 
 void FactoredVocab::WordLUT::dumpToFile(const std::string& path) {
-  io::OutputFileStream out(path);
+  io::OutputFileStreamNew out(path);
   for (auto kvp : index2str_)
     out << kvp.second << "\t" << utils::withCommas(kvp.first) << "\n";
 }
diff --git a/src/data/sentencepiece_vocab.cpp b/src/data/sentencepiece_vocab.cpp
index 705d19519..6744a0ad8 100644
--- a/src/data/sentencepiece_vocab.cpp
+++ b/src/data/sentencepiece_vocab.cpp
@@ -78,7 +78,7 @@ class SentencePieceVocab : public IVocab {
       reservoirSampling(sample, seenLines, trainPath, maxLines, maxBytes);
     std::shuffle(sample.begin(), sample.end(), generator_);
 
-    io::OutputFileStream out(temp);
+    io::OutputFileStreamNew out(temp);
     for(const auto& line : sample)
         out << line << std::endl;
 
@@ -94,7 +94,7 @@ class SentencePieceVocab : public IVocab {
 
     size_t seenLines = 0;
     std::string line;
-    io::OutputFileStream out(temp);
+    io::OutputFileStreamNew out(temp);
     for(const auto& trainPath : trainPaths) {
       io::InputFileStreamNew in(trainPath);
       while(getline(in, line)) {
diff --git a/src/data/shortlist.h b/src/data/shortlist.h
index 798794be1..05d6c3bce 100644
--- a/src/data/shortlist.h
+++ b/src/data/shortlist.h
@@ -216,12 +216,12 @@ class LexicalShortlistGenerator : public ShortlistGenerator {
   virtual void dump(const std::string& prefix) override {
     // Dump top most frequent words from target vocabulary
     LOG(info, "[data] Saving shortlist dump to {}", prefix + ".{top,dic}");
-    io::OutputFileStream outTop(prefix + ".top");
+    io::OutputFileStreamNew outTop(prefix + ".top");
     for(WordIndex i = 0; i < firstNum_ && i < trgVocab_->size(); ++i)
       outTop << (*trgVocab_)[Word::fromWordIndex(i)] << std::endl;
 
     // Dump translation pairs from dictionary
-    io::OutputFileStream outDic(prefix + ".dic");
+    io::OutputFileStreamNew outDic(prefix + ".dic");
     for(WordIndex srcId = 0; srcId < data_.size(); srcId++) {
       for(auto& it : data_[srcId]) {
         auto trgId = it.first;
diff --git a/src/models/amun.h b/src/models/amun.h
index 811d130ee..334a79965 100755
--- a/src/models/amun.h
+++ b/src/models/amun.h
@@ -209,7 +209,7 @@ class Amun : public EncoderDecoder {
     amun["normalize"] = opt<float>("normalize") > 0;
     amun["beam-size"] = opt<size_t>("beam-size");
 
-    io::OutputFileStream out(name + ".amun.yml");
+    io::OutputFileStreamNew out(name + ".amun.yml");
     out << amun;
   }
 };
diff --git a/src/models/encoder_decoder.cpp b/src/models/encoder_decoder.cpp
index 2f29c045b..7b202a763 100755
--- a/src/models/encoder_decoder.cpp
+++ b/src/models/encoder_decoder.cpp
@@ -114,7 +114,7 @@ void EncoderDecoder::createDecoderConfig(const std::string& name) {
   decoder["maxi-batch"] = opt<size_t>("valid-mini-batch") > 1 ? 100 : 1;
   decoder["maxi-batch-sort"] = opt<size_t>("valid-mini-batch") > 1 ? "src" : "none";
 
-  io::OutputFileStream out(name + ".decoder.yml");
+  io::OutputFileStreamNew out(name + ".decoder.yml");
   out << decoder;
 }
 
diff --git a/src/models/nematus.h b/src/models/nematus.h
index 3c0a549e9..2730732b2 100755
--- a/src/models/nematus.h
+++ b/src/models/nematus.h
@@ -186,7 +186,7 @@ class Nematus : public EncoderDecoder {
     amun["scorers"]["F0"]["type"] = "nematus2";
     amun["weights"]["F0"] = 1.0f;
 
-    io::OutputFileStream out(name + ".amun.yml");
+    io::OutputFileStreamNew out(name + ".amun.yml");
     out << amun;
   }
 };
diff --git a/src/rescorer/score_collector.cpp b/src/rescorer/score_collector.cpp
index 5cf8a5ed6..2013f62f0 100644
--- a/src/rescorer/score_collector.cpp
+++ b/src/rescorer/score_collector.cpp
@@ -13,9 +13,9 @@ ScoreCollector::ScoreCollector(const Ptr<Options>& options)
       alignmentThreshold_(getAlignmentThreshold(alignment_)) {
 
     if(options->get<std::string>("output") == "stdout")
-      outStrm_.reset(new io::OutputFileStream(std::cout));
+      outStrm_.reset(new std::ostream(std::cout.rdbuf()));
     else
-      outStrm_.reset(new io::OutputFileStream(options->get<std::string>("output")));
+      outStrm_.reset(new io::OutputFileStreamNew(options->get<std::string>("output")));
   }
 
 void ScoreCollector::Write(long id, const std::string& message) {
diff --git a/src/rescorer/score_collector.h b/src/rescorer/score_collector.h
index a11261748..f66642fdb 100644
--- a/src/rescorer/score_collector.h
+++ b/src/rescorer/score_collector.h
@@ -21,7 +21,7 @@ class ScoreCollector {
 
 protected:
   long nextId_{0};
-  UPtr<io::OutputFileStream> outStrm_;
+  UPtr<std::ostream> outStrm_;
   std::mutex mutex_;
 
   typedef std::map<long, std::string> Outputs;
diff --git a/vs/Marian.vcxproj b/vs/Marian.vcxproj
index dadfaf96e..b8e9c7f02 100755
--- a/vs/Marian.vcxproj
+++ b/vs/Marian.vcxproj
@@ -972,6 +972,8 @@
       <ExcludedFromBuild Condition="'$(Configuration)|$(Platform)'=='Release|x64'">true</ExcludedFromBuild>
     </ClInclude>
     <ClInclude Include="..\src\3rd_party\sse_mathfun.h" />
+    <ClInclude Include="..\src\3rd_party\zstr\strict_fstream.hpp" />
+    <ClInclude Include="..\src\3rd_party\zstr\zstr.hpp" />
     <ClInclude Include="..\src\command\marian_decoder.cpp">
       <ExcludedFromBuild Condition="'$(Configuration)|$(Platform)'=='Debug|x64'">true</ExcludedFromBuild>
       <ExcludedFromBuild Condition="'$(Configuration)|$(Platform)'=='Release|x64'">true</ExcludedFromBuild>
diff --git a/vs/Marian.vcxproj.filters b/vs/Marian.vcxproj.filters
index 4341e49d5..acc84990a 100755
--- a/vs/Marian.vcxproj.filters
+++ b/vs/Marian.vcxproj.filters
@@ -2032,6 +2032,12 @@
     <ClInclude Include="..\src\functional\operators.h">
       <Filter>functional</Filter>
     </ClInclude>
+    <ClInclude Include="..\src\3rd_party\zstr\strict_fstream.hpp">
+      <Filter>3rd_party</Filter>
+    </ClInclude>
+    <ClInclude Include="..\src\3rd_party\zstr\zstr.hpp">
+      <Filter>3rd_party</Filter>
+    </ClInclude>
   </ItemGroup>
   <ItemGroup>
     <Filter Include="3rd_party">

From 33f60022cf7f54746be476fc1d800ab99655518d Mon Sep 17 00:00:00 2001
From: Hieu Hoang <hihoan@microsoft.com>
Date: Tue, 22 Oct 2019 17:56:09 -0700
Subject: [PATCH 672/838] delete OutputFileStream and other crappy classes

---
 src/common/file_stream.cpp | 142 -------------------------------------
 src/common/file_stream.h   | 110 ----------------------------
 2 files changed, 252 deletions(-)

diff --git a/src/common/file_stream.cpp b/src/common/file_stream.cpp
index a9b534658..3bcb3370c 100644
--- a/src/common/file_stream.cpp
+++ b/src/common/file_stream.cpp
@@ -50,122 +50,6 @@ std::string StrError() {
 }
 } // namespace
 
-void RewindFile(int fd) {
-  ABORT_IF((off_t)-1 == lseek(fd, 0, SEEK_SET), "lseek to 0 failed in fd {}", fd);
-}
-
-ReadFDBuf::ReadFDBuf(int fd, std::size_t buffer_size)
-: fd_(fd), mem_(buffer_size) {
-  setg(End(), End(), End());
-}
-
-ReadFDBuf::int_type ReadFDBuf::underflow() {
-  if (gptr() == egptr()) {
-    // Reached end, refill
-    ssize_t got = Read();
-    if (!got) {
-      // End of file
-      return traits_type::eof();
-    }
-    setg(Begin(), Begin(), Begin() + got);
-  }
-  return traits_type::to_int_type(*gptr());
-}
-
-// If the putback goes below the buffer, try to seek backwards.
-ReadFDBuf::int_type ReadFDBuf::pbackfail(int c) {
-  /* "It is unspecified whether the content of the controlled input
-   * sequence is modified if the function succeeds and c does not match the
-   * character at that position."
-   * -- http://www.cplusplus.com/reference/streambuf/streambuf/pbackfail/
-   */
-  if (gptr() > Begin()) {
-    setg(Begin(), gptr() - 1, End());
-  } else {
-    if (lseek(fd_, -1, SEEK_CUR) == -1) {
-      return EOF;
-    }
-    ssize_t got = Read();
-    if (!got) {
-      // This happens if the file was truncated underneath us.
-      return traits_type::eof();
-    }
-    setg(Begin(), Begin(), Begin() + got);
-  }
-  return traits_type::to_int_type(*gptr());
-}
-
-// Read some amount into [Begin(), End()), returning the amount read.
-ssize_t ReadFDBuf::Read() {
-  ssize_t got;
-  // Loop to keep reading if EINTR happens.
-  // This way the program is robust to Ctrl+Z then backgrounding.
-  do {
-    errno = 0;
-    got =
-#ifdef _MSC_VER
-      _read
-#else
-      read
-#endif
-      (fd_, Begin(), End() - Begin());
-  } while (got == -1 && errno == EINTR);
-  ABORT_IF(got < 0, "Error reading fd {}: {}", fd_, StrError());
-  return got;
-}
-
-WriteFDBuf::WriteFDBuf(int fd, std::size_t buffer_size)
-: fd_(fd), mem_(buffer_size) {
-  setp(End(), End());
-}
-
-WriteFDBuf::~WriteFDBuf() { sync(); }
-
-WriteFDBuf::int_type WriteFDBuf::overflow(int c) {
-  if (c == EOF) {
-    // Apparently overflow(EOF) means sync().
-    sync();
-    return c;
-  }
-  if (pptr() == epptr()) {
-    // Out of buffer.  Write and reset.
-    sync();
-    setp(Begin(), End());
-  }
-  // Put character on the end.
-  *pptr() = traits_type::to_char_type(c);
-  pbump(1);
-  return c;
-}
-
-// Write everything in the buffer to the file.
-int WriteFDBuf::sync() {
-  const char *from = pbase();
-  const char *to = pptr();
-  while (from != to) {
-    from += WriteSome(from, to);
-  }
-  // We die on all failures.
-  return 0;
-}
-
-// Write part of the buffer, returning the amount written.
-ssize_t WriteFDBuf::WriteSome(const char *from, const char *to) {
-  ssize_t put = 0;
-  do {
-    errno = 0;
-    put =
-#ifdef _MSC_VER
-      _write
-#else
-      write
-#endif
-      (fd_, from, to - from);
-  } while (put == -1 && errno == EINTR);
-  ABORT_IF(put < 0, "Error writing to fd {}: {}", fd_, StrError());
-  return put;
-}
-
 ///////////////////////////////////////////////////////////////////////////////////////////////
 #ifndef _MSC_VER
 int TemporaryFile2::mkstemp_and_unlink(char *tmpl) {
@@ -255,32 +139,6 @@ TemporaryFile2::~TemporaryFile2() {
 #endif
 }
 
-///////////////////////////////////////////////////////////////////////////////////////////////
-
-///////////////////////////////////////////////////////////////////////////////////////////////
-OutputFileStream::OutputFileStream(const std::string &file) : file_(file) {
-  if(file_.extension() == marian::filesystem::Path(".gz"))
-    ostream_ = std::make_unique<zstr::ofstream>(file_.string());
-  else
-    ostream_ = std::make_unique<std::ofstream>(file_.string());
-  ABORT_IF(!marian::filesystem::exists(file_), "File '{}' could not be opened", file);
-
-std::cerr << "OutputFileStreamOld1 created" << std::endl;
-}
-
-OutputFileStream::OutputFileStream(TemporaryFile2 &tempfile) {
-  RewindFile(tempfile.getFileDescriptor());
-  temporary_writer_.reset(new WriteFDBuf(tempfile.getFileDescriptor()));
-  ostream_.reset(new std::ostream(temporary_writer_.get()));
-
-  std::cerr << "OutputFileStreamOld2 created" << std::endl;
-}
-
-OutputFileStream::OutputFileStream(std::ostream &strm) {
-  ostream_ = std::make_unique<std::ostream>(strm.rdbuf());
-  std::cerr << "OutputFileStreamOld3 created" << std::endl;
-}
-
 ///////////////////////////////////////////////////////////////////////////////////////////////
 TemporaryFileNew::TemporaryFileNew(const std::string &base) {
   MakeTemp(base);
diff --git a/src/common/file_stream.h b/src/common/file_stream.h
index f58d0d21e..0fe596d1c 100755
--- a/src/common/file_stream.h
+++ b/src/common/file_stream.h
@@ -68,116 +68,6 @@ class TemporaryFile2 {
   std::string getFileName() { return name_; }
 };
 
-///////////////////////////////////////////////////////////////////////////////////////////////
-// A streambuf to read from a file descriptor.
-class ReadFDBuf : public std::streambuf {
-public:
-  // Does not take ownership of file.
-  explicit ReadFDBuf(int fd, std::size_t buffer_size = 4096);
-
-private:
-  int_type underflow() override;
-
-  // If the putback goes below the buffer, try to seek backwards.
-  int_type pbackfail(int c = EOF) override;
-
-  // TODO: override setbuf.
-
-  // Read some amount into [Begin(), End()), returning the amount read.
-  ssize_t Read();
-
-  char* Begin() { return &mem_.front(); }
-  char* End() { return &mem_.back() + 1; }
-
-  int fd_;
-  std::vector<char> mem_;
-
-  ReadFDBuf(const ReadFDBuf&) = delete;
-  ReadFDBuf& operator=(const ReadFDBuf&) = delete;
-};
-
-///////////////////////////////////////////////////////////////////////////////////////////////
-// A streambuf to Write to a file descriptor.
-class WriteFDBuf : public std::streambuf {
-public:
-  explicit WriteFDBuf(int fd, std::size_t buffer_size = 4096);
-
-  ~WriteFDBuf();
-
-private:
-  int_type overflow(int c = EOF) override;
-
-  // Write everything in the buffer to the file.
-  int sync() override;
-
-  // Write part of the buffer, returning the amount written.
-  ssize_t WriteSome(const char* from, const char* to);
-
-  char* Begin() { return &mem_.front(); }
-  char* End() { return &mem_.back() + 1; }
-
-  int fd_;
-  std::vector<char> mem_;
-
-  WriteFDBuf(const WriteFDBuf&) = delete;
-  WriteFDBuf& operator=(const WriteFDBuf&) = delete;
-};
-
-///////////////////////////////////////////////////////////////////////////////////////////////
-// lseek(fd, 0, SEEK_SET) but with error checking.
-void RewindFile(int fd);
-
-///////////////////////////////////////////////////////////////////////////////////////////////
-class OutputFileStream {
-public:
-  OutputFileStream(const std::string& file);
-
-  OutputFileStream(TemporaryFile2& tempfile);
-
-  OutputFileStream(std::ostream& strm);
-
-  operator std::ostream&() { return *ostream_; }
-
-  operator bool() { return (bool)*ostream_; }
-
-  bool bad() const { return ostream_->bad(); }
-
-  bool fail() const { return ostream_->fail(); }
-
-  template <typename T>
-  friend OutputFileStream& operator<<(OutputFileStream& stream, const T& t) {
-    *stream.ostream_ << t;
-    // fail() seems to be correct here. Failure to write should abort.
-    ABORT_IF(stream.fail(), "Error writing to file '{}'", stream.path());
-    return stream;
-  }
-
-  // handle things like std::endl which is actually a function not a value
-  friend OutputFileStream& operator<<(OutputFileStream& stream,
-                                      std::ostream& (*var)(std::ostream&)) {
-    *stream.ostream_ << var;
-    // fail() seems to be correct here. Failure to write should abort.
-    ABORT_IF(stream.fail(), "Error writing to file '{}'", stream.path());
-    return stream;
-  }
-
-  template <typename T>
-  size_t write(const T* ptr, size_t num = 1) {
-    ostream_->write((char*)ptr, num * sizeof(T));
-    // fail() seems to be correct here. Failure to write should abort.
-    ABORT_IF(fail(), "Error writing to file '{}'", path());
-    return num * sizeof(T);
-  }
-
-  std::string path() { return file_.string(); }
-
-private:
-  marian::filesystem::Path file_;
-  std::unique_ptr<std::ostream> ostream_;
-
-  std::unique_ptr<WriteFDBuf> temporary_writer_;
-};
-
 ///////////////////////////////////////////////////////////////////////////////////////////////
 class TemporaryFileNew : public std::fstream {
 public:

From cdf9b5281f74f85d49a7cbd61274843a1e30bb9e Mon Sep 17 00:00:00 2001
From: Hieu Hoang <hihoan@microsoft.com>
Date: Wed, 23 Oct 2019 09:51:12 -0700
Subject: [PATCH 673/838] debuggging message

---
 src/common/file_stream.cpp | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/src/common/file_stream.cpp b/src/common/file_stream.cpp
index 3bcb3370c..c6935187f 100644
--- a/src/common/file_stream.cpp
+++ b/src/common/file_stream.cpp
@@ -113,6 +113,8 @@ TemporaryFile2::TemporaryFile2(const std::string base, bool earlyUnlink) : unlin
   std::string baseTemp(base);
   NormalizeTempPrefix(baseTemp);
   fd_ = MakeTemp(baseTemp);
+
+  std::cerr << "TemporaryFile2 created" << std::endl;
 }
 
 TemporaryFile2::~TemporaryFile2() {

From 999b8fa0bb883d662102cfa42c665cd17c369bf1 Mon Sep 17 00:00:00 2001
From: Hieu Hoang <hihoan@microsoft.com>
Date: Wed, 23 Oct 2019 09:51:37 -0700
Subject: [PATCH 674/838] start temp file class

---
 src/data/corpus.cpp | 6 +++---
 src/data/corpus.h   | 2 +-
 2 files changed, 4 insertions(+), 4 deletions(-)

diff --git a/src/data/corpus.cpp b/src/data/corpus.cpp
index 94ed9644c..35882d2d6 100644
--- a/src/data/corpus.cpp
+++ b/src/data/corpus.cpp
@@ -194,11 +194,11 @@ void Corpus::shuffleData(const std::vector<std::string>& paths) {
     // create temp files that contain the data in randomized order
     tempFiles_.resize(numStreams);
     for(size_t i = 0; i < numStreams; ++i) {
-      tempFiles_[i].reset(new io::TemporaryFile2(options_->get<std::string>("tempdir")));
-      io::OutputFileStreamNew out(tempFiles_[i]->getFileName());
+      io::TemporaryFileNew *out = new io::TemporaryFileNew(options_->get<std::string>("tempdir"));
+	  tempFiles_[i].reset(out);
       const auto& corpusStream = corpus[i];
       for(auto id : ids_) {
-        out << corpusStream[id] << std::endl;
+        (*out) << corpusStream[id] << std::endl;
       }
     }
 
diff --git a/src/data/corpus.h b/src/data/corpus.h
index 9bf00b071..0d75cd690 100644
--- a/src/data/corpus.h
+++ b/src/data/corpus.h
@@ -18,7 +18,7 @@ namespace data {
 
 class Corpus : public CorpusBase {
 private:
-  std::vector<UPtr<io::TemporaryFile2>> tempFiles_;
+  std::vector<UPtr<io::TemporaryFileNew>> tempFiles_;
   std::vector<size_t> ids_;
 
   // for shuffle-in-ram

From f4056af240e7cd72cacbd7743245d896d8e51cc2 Mon Sep 17 00:00:00 2001
From: Hieu Hoang <hihoan@microsoft.com>
Date: Wed, 23 Oct 2019 10:51:02 -0700
Subject: [PATCH 675/838] use new temp file class in corpus

---
 src/common/file_stream.cpp | 6 +++++-
 src/common/file_stream.h   | 4 +++-
 src/data/corpus.cpp        | 3 +--
 src/data/corpus_nbest.h    | 2 +-
 4 files changed, 10 insertions(+), 5 deletions(-)

diff --git a/src/common/file_stream.cpp b/src/common/file_stream.cpp
index c6935187f..31adfaec7 100644
--- a/src/common/file_stream.cpp
+++ b/src/common/file_stream.cpp
@@ -142,7 +142,7 @@ TemporaryFile2::~TemporaryFile2() {
 }
 
 ///////////////////////////////////////////////////////////////////////////////////////////////
-TemporaryFileNew::TemporaryFileNew(const std::string &base) {
+TemporaryFileNew::TemporaryFileNew(const std::string &base, bool earlyUnlink) {
   MakeTemp(base);
 }
 
@@ -180,6 +180,10 @@ int TemporaryFileNew::mkstemp_and_unlink(char *tmpl) {
 }
 #endif
 
+std::string TemporaryFileNew::getFileName() {
+  return name_;
+}
+
 ///////////////////////////////////////////////////////////////////////////////////////////////
 InputFileStreamNew::InputFileStreamNew(const std::string &file)
     : std::istream(NULL), file_(file), streamBuf_(NULL) {
diff --git a/src/common/file_stream.h b/src/common/file_stream.h
index 0fe596d1c..df83efcad 100755
--- a/src/common/file_stream.h
+++ b/src/common/file_stream.h
@@ -71,11 +71,13 @@ class TemporaryFile2 {
 ///////////////////////////////////////////////////////////////////////////////////////////////
 class TemporaryFileNew : public std::fstream {
 public:
-  TemporaryFileNew(const std::string& base = "/tmp/");
+  TemporaryFileNew(const std::string& base = "/tmp/", bool earlyUnlink = true);
 
 protected:
   std::string name_;
 
+  std::string getFileName();
+
   int MakeTemp(const std::string& base);
 
 #ifndef _MSC_VER
diff --git a/src/data/corpus.cpp b/src/data/corpus.cpp
index 35882d2d6..71d09957a 100644
--- a/src/data/corpus.cpp
+++ b/src/data/corpus.cpp
@@ -205,9 +205,8 @@ void Corpus::shuffleData(const std::vector<std::string>& paths) {
     // replace files_[] by the tempfiles we just created
     files_.resize(numStreams);
     for(size_t i = 0; i < numStreams; ++i) {
-      io::InputFileStreamNew* strm = new io::InputFileStreamNew(tempFiles_[i]->getFileName());
+      io::TemporaryFileNew* strm = tempFiles_[i].get();
       files_[i].reset(strm);
-      strm->setbufsize(10000000);
     }
     LOG(info, "[data] Done shuffling {} sentences to temp files", numSentences);
   }
diff --git a/src/data/corpus_nbest.h b/src/data/corpus_nbest.h
index 3e06f639b..6dfaebc68 100644
--- a/src/data/corpus_nbest.h
+++ b/src/data/corpus_nbest.h
@@ -18,7 +18,7 @@ namespace data {
 
 class CorpusNBest : public CorpusBase {
 private:
-  std::vector<UPtr<io::TemporaryFile2>> tempFiles_;
+  //std::vector<UPtr<io::TemporaryFileNew>> tempFiles_;
   std::vector<size_t> ids_;
   int lastNum_{-1};
   std::vector<std::string> lastLines_;

From d1ed2c2506b58a1d00da3747fc0638d3d5ba22a3 Mon Sep 17 00:00:00 2001
From: Ulrich Germann <ulrich.germann@gmail.com>
Date: Wed, 23 Oct 2019 18:54:34 +0100
Subject: [PATCH 676/838] More helpful error message when CUDA libraries cannot
 be found.

---
 CMakeLists.txt | 9 ++++++++-
 1 file changed, 8 insertions(+), 1 deletion(-)

diff --git a/CMakeLists.txt b/CMakeLists.txt
index 73b8652b8..5b4b0aa19 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -216,7 +216,14 @@ if(CUDA_FOUND)
   endif()
 
 else(CUDA_FOUND)
-  message(FATAL_ERROR "CUDA has not been found, set -DCOMPILE_CUDA=off to avoid this check and to compile the CPU version only")
+  message("
+Cannot find suitable CUDA libraries. Specify the path explicitly with
+  -DCUDA_TOOLKIT_ROOT_DIR=/path/to/appropriate/cuda/installation
+   (hint: try /usr/local/$(readlink /usr/local/cuda))
+OR compile the CPU-only version of Marian with
+  -DCOMPILE_CUDA=off
+")
+  message(FATAL_ERROR "FATAL ERROR: No suitable CUDA library found.")
 endif(CUDA_FOUND)
 
 else(COMPILE_CUDA)

From 8f591f67a2257f11e90206ac59ad3167549ac79c Mon Sep 17 00:00:00 2001
From: Hieu Hoang <hihoan@microsoft.com>
Date: Wed, 23 Oct 2019 11:21:06 -0700
Subject: [PATCH 677/838] use new temp file class everywhere

---
 src/common/file_stream.cpp | 64 ++++++++++++++++++++++++--------------
 src/common/file_stream.h   | 12 +++----
 src/training/validator.cpp |  4 +--
 3 files changed, 47 insertions(+), 33 deletions(-)

diff --git a/src/common/file_stream.cpp b/src/common/file_stream.cpp
index 31adfaec7..787d3836f 100644
--- a/src/common/file_stream.cpp
+++ b/src/common/file_stream.cpp
@@ -142,43 +142,59 @@ TemporaryFile2::~TemporaryFile2() {
 }
 
 ///////////////////////////////////////////////////////////////////////////////////////////////
-TemporaryFileNew::TemporaryFileNew(const std::string &base, bool earlyUnlink) {
-  MakeTemp(base);
+TemporaryFileNew::TemporaryFileNew(const std::string &base, bool earlyUnlink)
+    : unlink_(earlyUnlink) {
+  std::string baseTemp(base);
+  NormalizeTempPrefix(baseTemp);
+  MakeTemp(baseTemp);
+
+  std::cerr << "TemporaryFileNew created" << std::endl;
+}
+
+TemporaryFileNew::~TemporaryFileNew() {
+  if(!unlink_) {
+    ABORT_IF(remove(name_.c_str()), "Error while deleting '{}'", name_);
+  }
 }
 
-int TemporaryFileNew::MakeTemp(const std::string &base) {
+void TemporaryFileNew::NormalizeTempPrefix(std::string &base) {
+  if(base.empty())
+    return;
+
 #ifdef _MSC_VER
+  if(base.substr(0, 4) == "/tmp")
+    base = getenv("TMP");
+#else
+  if(base[base.size() - 1] == '/')
+    return;
+  struct stat sb;
+  // It's fine for it to not exist.
+  if(stat(base.c_str(), &sb) == -1)
+    return;
+  if(S_ISDIR(sb.st_mode))
+    base += '/';
+#endif
+}
+
+void TemporaryFileNew::MakeTemp(const std::string &base) {
   char *name = tempnam(base.c_str(), "marian.");
   ABORT_IF(name == NULL, "Error while making a temporary based on '{}'", base);
 
+#ifdef _MSC_VER
   int oflag = _O_RDWR | _O_CREAT | _O_EXCL | _O_TEMPORARY;
   std::fstream::open(name, oflag, _S_IREAD | _S_IWRITE);
-  // ABORT_IF(ret == -1, "Error while making a temporary based on '{}'", base);
+  ABORT_IF(errno, "Error while making a temporary based on '{}'", base);
+#else
+  std::fstream::open(name, std::fstream::in | std::fstream::out);
+  ABORT_IF(errno, "Error while making a temporary based on '{}'", base);
+
+  ABORT_IF(remove(name), "Error while deleting '{}'", name);
+#endif
 
   name_ = name;
   free(name);
-#else
-  std::string name(base);
-  name += "marian.XXXXXX";
-  name.push_back(0);
-  int ret;
-  ABORT_IF(-1 == (ret = mkstemp_and_unlink(&name[0])),
-           "Error while making a temporary based on '{}'",
-           base);
-  name_ = name;
-  return ret;
-#endif
 }
 
-#ifndef _MSC_VER
-int TemporaryFileNew::mkstemp_and_unlink(char *tmpl) {
-  int ret = mkstemp(tmpl);
-  if(ret != -1) {
-    ABORT_IF(unlink(tmpl), "Error while deleting '{}'", tmpl);
-  }
-  return ret;
-}
-#endif
 
 std::string TemporaryFileNew::getFileName() {
   return name_;
diff --git a/src/common/file_stream.h b/src/common/file_stream.h
index df83efcad..b7900da32 100755
--- a/src/common/file_stream.h
+++ b/src/common/file_stream.h
@@ -72,17 +72,15 @@ class TemporaryFile2 {
 class TemporaryFileNew : public std::fstream {
 public:
   TemporaryFileNew(const std::string& base = "/tmp/", bool earlyUnlink = true);
+  std::string getFileName();
+  virtual ~TemporaryFileNew();
 
 protected:
   std::string name_;
+  bool unlink_;
 
-  std::string getFileName();
-
-  int MakeTemp(const std::string& base);
-
-#ifndef _MSC_VER
-  int mkstemp_and_unlink(char* tmpl);
-#endif
+  void NormalizeTempPrefix(std::string& base);
+  void MakeTemp(const std::string& base);
 };
 
 //////////////////////////////////////////////////////////////////////////////////////////////
diff --git a/src/training/validator.cpp b/src/training/validator.cpp
index 7d58858ec..780bb3cce 100644
--- a/src/training/validator.cpp
+++ b/src/training/validator.cpp
@@ -349,12 +349,12 @@ float TranslationValidator::validate(const std::vector<Ptr<ExpressionGraph>>& gr
 
   // Set up output file
   std::string fileName;
-  Ptr<io::TemporaryFile2> tempFile;
+  Ptr<io::TemporaryFileNew> tempFile;
 
   if(options_->hasAndNotEmpty("valid-translation-output")) {
     fileName = options_->get<std::string>("valid-translation-output");
   } else {
-    tempFile.reset(new io::TemporaryFile2(options_->get<std::string>("tempdir"), false));
+    tempFile.reset(new io::TemporaryFileNew(options_->get<std::string>("tempdir"), false));
     fileName = tempFile->getFileName();
   }
 

From 46b8ebae24f2feaf0e8cbcd6afe15e892fdf6b6d Mon Sep 17 00:00:00 2001
From: Hieu Hoang <hihoan@microsoft.com>
Date: Wed, 23 Oct 2019 11:25:53 -0700
Subject: [PATCH 678/838] delete old temp file class

---
 src/common/file_stream.cpp | 91 --------------------------------------
 src/common/file_stream.h   | 25 -----------
 2 files changed, 116 deletions(-)

diff --git a/src/common/file_stream.cpp b/src/common/file_stream.cpp
index 787d3836f..276d6ff32 100644
--- a/src/common/file_stream.cpp
+++ b/src/common/file_stream.cpp
@@ -50,97 +50,6 @@ std::string StrError() {
 }
 } // namespace
 
-///////////////////////////////////////////////////////////////////////////////////////////////
-#ifndef _MSC_VER
-int TemporaryFile2::mkstemp_and_unlink(char *tmpl) {
-  int ret = mkstemp(tmpl);
-  if(unlink_ && ret != -1) {
-    ABORT_IF(unlink(tmpl), "Error while deleting '{}'", tmpl);
-  }
-  return ret;
-}
-#endif
-
-int TemporaryFile2::MakeTemp(const std::string &base) {
-#ifdef _MSC_VER
-  char *name = tempnam(base.c_str(), "marian.");
-  ABORT_IF(name == NULL, "Error while making a temporary based on '{}'", base);
-
-  int oflag = _O_RDWR | _O_CREAT | _O_EXCL;
-  if(unlink_)
-    oflag |= _O_TEMPORARY;
-
-  int ret = open(name, oflag, _S_IREAD | _S_IWRITE);
-  ABORT_IF(ret == -1, "Error while making a temporary based on '{}'", base);
-
-  name_ = name;
-  free(name);
-
-  return ret;
-#else
-  std::string name(base);
-  name += "marian.XXXXXX";
-  name.push_back(0);
-  int ret;
-  ABORT_IF(-1 == (ret = mkstemp_and_unlink(&name[0])),
-           "Error while making a temporary based on '{}'",
-           base);
-  name_ = name;
-  return ret;
-#endif
-}
-
-void TemporaryFile2::NormalizeTempPrefix(std::string &base) {
-  if(base.empty())
-    return;
-
-#ifdef _MSC_VER
-  if(base.substr(0, 4) == "/tmp")
-    base = getenv("TMP");
-#else
-  if(base[base.size() - 1] == '/')
-    return;
-  struct stat sb;
-  // It's fine for it to not exist.
-  if(stat(base.c_str(), &sb) == -1)
-    return;
-  if(S_ISDIR(sb.st_mode))
-    base += '/';
-#endif
-}
-
-TemporaryFile2::TemporaryFile2(const std::string base, bool earlyUnlink) : unlink_(earlyUnlink) {
-  std::string baseTemp(base);
-  NormalizeTempPrefix(baseTemp);
-  fd_ = MakeTemp(baseTemp);
-
-  std::cerr << "TemporaryFile2 created" << std::endl;
-}
-
-TemporaryFile2::~TemporaryFile2() {
-#ifdef _MSC_VER
-  if(fd_ == -1)
-    return;
-
-  if(close(fd_)) {
-    std::cerr << "Could not close file " << fd_ << std::endl;
-    std::abort();
-  }
-
-  if(!unlink_) {
-    ABORT_IF(remove(name_.c_str()), "Error while deleting '{}'", name_);
-  }
-#else
-  if(fd_ != -1 && !unlink_) {
-    ABORT_IF(unlink(name_.c_str()), "Error while deleting '{}'", name_);
-  }
-  if(fd_ != -1 && close(fd_)) {
-    std::cerr << "Could not close file " << fd_ << std::endl;
-    std::abort();
-  }
-#endif
-}
-
 ///////////////////////////////////////////////////////////////////////////////////////////////
 TemporaryFileNew::TemporaryFileNew(const std::string &base, bool earlyUnlink)
     : unlink_(earlyUnlink) {
diff --git a/src/common/file_stream.h b/src/common/file_stream.h
index b7900da32..c264b43f0 100755
--- a/src/common/file_stream.h
+++ b/src/common/file_stream.h
@@ -43,31 +43,6 @@ std::unique_ptr<T> make_unique(Args&&... args) {
 namespace marian {
 namespace io {
 
-///////////////////////////////////////////////////////////////////////////////////////////////
-class TemporaryFile2 {
-private:
-  int fd_{-1};
-  bool unlink_;
-  std::string name_;
-
-#ifndef _MSC_VER
-  int mkstemp_and_unlink(char* tmpl);
-#endif
-
-  int MakeTemp(const std::string& base);
-
-  void NormalizeTempPrefix(std::string& base);
-
-public:
-  TemporaryFile2(const std::string base = "/tmp/", bool earlyUnlink = true);
-
-  ~TemporaryFile2();
-
-  int getFileDescriptor() { return fd_; }
-
-  std::string getFileName() { return name_; }
-};
-
 ///////////////////////////////////////////////////////////////////////////////////////////////
 class TemporaryFileNew : public std::fstream {
 public:

From 9a89c25664c4874583e73ca2ee1ae28eb8d4f956 Mon Sep 17 00:00:00 2001
From: Ulrich Germann <ulrich.germann@gmail.com>
Date: Wed, 23 Oct 2019 20:57:18 +0100
Subject: [PATCH 679/838] Roll-back of all changes not directly related to
 graceful shutdown.

---
 CMakeLists.txt                    | 10 +---
 src/data/batch_generator.h        |  9 +---
 src/data/corpus.cpp               | 19 --------
 src/data/corpus.h                 |  2 -
 src/training/communicator.h       | 10 ++++
 src/training/graph_group_sync.cpp |  4 --
 src/training/scheduler.cpp        | 12 -----
 src/training/scheduler.h          | 41 ++++------------
 src/training/training.h           | 80 +++++++++++++++++--------------
 9 files changed, 65 insertions(+), 122 deletions(-)

diff --git a/CMakeLists.txt b/CMakeLists.txt
index 8d91b2c8b..73b8652b8 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -216,15 +216,7 @@ if(CUDA_FOUND)
   endif()
 
 else(CUDA_FOUND)
-  message("
-Cannot find suitable CUDA libraries. Specify the path explicitly with
-  -DCUDA_TOOLKIT_ROOT_DIR=/path/to/appropriate/cuda/installation
-   (hint: try /usr/local/$(readlink /usr/local/cuda))
-OR compile the CPU-only version of Marian with
-  -DCOMPILE_CUDA=off
-")
-  message(FATAL_ERROR
-    "FATAL ERROR: No suitable CUDA library found.")
+  message(FATAL_ERROR "CUDA has not been found, set -DCOMPILE_CUDA=off to avoid this check and to compile the CPU version only")
 endif(CUDA_FOUND)
 
 else(COMPILE_CUDA)
diff --git a/src/data/batch_generator.h b/src/data/batch_generator.h
index a3115be56..950a71b1f 100644
--- a/src/data/batch_generator.h
+++ b/src/data/batch_generator.h
@@ -268,11 +268,7 @@ class BatchGenerator : public RNGEngine {
   }
 
   // @TODO: get rid of this function, begin() or constructor should figure this out
-  void prepare(bool shuffle) {
-    if(restored_) { // state was just restored, restore() calls prepare()
-      restored_ = false;
-      return;
-    }
+  void prepare(bool shuffle = true) {
     if(shuffle)
       data_->shuffle();
     else
@@ -291,8 +287,6 @@ class BatchGenerator : public RNGEngine {
   bool restore(Ptr<TrainingState> state, bool shuffle) {
     if(state->epochs == 1 && state->batchesEpoch == 0)
       return false;
-    if (options_->get<bool>("no-restore-corpus"))
-      return false;
 
     LOG(info,
         "[data] Restoring the corpus state to epoch {}, batch {}",
@@ -308,7 +302,6 @@ class BatchGenerator : public RNGEngine {
     for(size_t i = 0; i < state->batchesEpoch; ++i)
       next();
 
-    restored_ = true;
     return true;
   }
 
diff --git a/src/data/corpus.cpp b/src/data/corpus.cpp
index 4f9ead7c2..26abdd0c5 100644
--- a/src/data/corpus.cpp
+++ b/src/data/corpus.cpp
@@ -7,7 +7,6 @@
 #include "common/filesystem.h"
 
 #include "data/corpus.h"
-#include "data/corpus_sqlite.h"
 
 namespace marian {
 namespace data {
@@ -211,23 +210,5 @@ void Corpus::shuffleData(const std::vector<std::string>& paths) {
   }
   pos_ = 0;
 }
-
-Ptr<CorpusBase> prepareTrainingData(Ptr<Options> options) {
-  // factory function to set up the training corpus for training
-  // code moved here from Train::run() in training.h
-  Ptr<CorpusBase> dataset;
-  if(!options->get<std::string>("sqlite").empty())
-#ifdef _MSC_VER // @TODO: include SqLite in Visual Studio project
-    ABORT("SqLite presently not supported on Windows");
-#else
-    dataset = New<CorpusSQLite>(options);
-#endif
-  else
-    dataset = New<Corpus>(options);
-  dataset->prepare();
-  return dataset;
-}
-
-
 }  // namespace data
 }  // namespace marian
diff --git a/src/data/corpus.h b/src/data/corpus.h
index d0c698a46..41272a811 100644
--- a/src/data/corpus.h
+++ b/src/data/corpus.h
@@ -16,8 +16,6 @@
 namespace marian {
 namespace data {
 
-Ptr<CorpusBase> prepareTrainingData(Ptr<Options> options);
-
 class Corpus : public CorpusBase {
 private:
   std::vector<UPtr<io::TemporaryFile>> tempFiles_;
diff --git a/src/training/communicator.h b/src/training/communicator.h
index 4190401a8..6ccce204f 100644
--- a/src/training/communicator.h
+++ b/src/training/communicator.h
@@ -175,7 +175,17 @@ class DefaultCommunicator : public ICommunicator {
       }
     };
 
+    // reset gradients outside current shard
+    auto reset = [this, shardSize](size_t idx, size_t begin, size_t end) {
+      auto grad = graphs_[idx]->params()->grads();
+      if (begin > 0)
+        grad->subtensor(0, begin)->set(0);
+      if (end < grad->size())
+        grad->subtensor(end, grad->size()-end)->set(0);
+    };
+
     foreach(scatter);
+    foreach(reset);
   }
 
   void allGatherParams() const override {
diff --git a/src/training/graph_group_sync.cpp b/src/training/graph_group_sync.cpp
index f3e72ab87..6c6da17bb 100755
--- a/src/training/graph_group_sync.cpp
+++ b/src/training/graph_group_sync.cpp
@@ -538,10 +538,6 @@ void SyncGraphGroup::save(bool final) /*override*/ {
 
 void SyncGraphGroup::finalize() /*override*/ {
   validate();
-  // Note to the uninitiated: validate() has nothing to do with
-  // validation on the validation set. It's just a sanity check. [UG]
-  // @TODO: rename validate() to avoid confusion with model
-  //        validation on withheld data. [UG]
   Base::finalize();
 }
 
diff --git a/src/training/scheduler.cpp b/src/training/scheduler.cpp
index 19d19f67e..ea918e780 100644
--- a/src/training/scheduler.cpp
+++ b/src/training/scheduler.cpp
@@ -4,8 +4,6 @@
 
 namespace marian {
 bool Scheduler::sigterm_{false};
-bool Scheduler::sigusr1_{false};
-bool Scheduler::sigusr2_{false};
 
 void
 Scheduler::
@@ -18,14 +16,6 @@ signalHandler_(int sig) {
     LOG(info, "[training] Scheduler received signal SIGTERM");
     Scheduler::sigterm_ = true;
     break;
-  case SIGUSR1: // currently has no effect
-    LOG(info, "[training] Scheduler received signal SIGUSR1");
-    Scheduler::sigusr1_ = true;
-    break;
-  case SIGUSR2: // currently has no effect
-    LOG(info, "[training] Scheduler received signal SIGUSR2");
-    Scheduler::sigusr2_ = true;
-    break;
   default:
     ABORT("No action defined for signal {}", sig);
   }
@@ -37,8 +27,6 @@ Scheduler::
 installSignalHandlers_() {
   // TODO: use sigaction instead of signal
   signal(SIGTERM, Scheduler::signalHandler_);
-  signal(SIGUSR1, Scheduler::signalHandler_);
-  signal(SIGUSR2, Scheduler::signalHandler_);
 }
 
 }
diff --git a/src/training/scheduler.h b/src/training/scheduler.h
index b888f35b0..2c7f18b4d 100644
--- a/src/training/scheduler.h
+++ b/src/training/scheduler.h
@@ -4,7 +4,6 @@
 #include "training/training_state.h"
 #include "training/validator.h"
 #include "training/communicator.h"
-#include "data/vocab.h"
 #include "layers/loss.h"
 
 namespace marian {
@@ -20,10 +19,7 @@ class Scheduler : public TrainingObserver {
   timer::Timer timer_;
   timer::Timer heartBeatTimer_;
 
-  std::string validFreq_;
-  std::string saveFreq_;
-
-  static bool sigterm_, sigusr1_, sigusr2_;
+  static bool sigterm_;
   void installSignalHandlers_();
   static void signalHandler_(int sig);
 
@@ -113,10 +109,6 @@ class Scheduler : public TrainingObserver {
   }
 
 public:
-  bool gotSigTerm() const { return sigterm_; }
-  // bool gotSigUsr1() const { return sigusr1_; } // currently has no effect
-  // bool gotSigUsr2() const { return sigusr2_; } // currently has no effect
-
   // test if any parameters specify dynamic MB scaling
   bool isDynamicMBSizeScaling() const {
     auto mbWarmup = SchedulingParameter::parse(options_->get<std::string>("mini-batch-warmup"));
@@ -156,8 +148,6 @@ class Scheduler : public TrainingObserver {
 
   Scheduler(Ptr<Options> options, Ptr<TrainingState> state)
       : options_(options), state_(state) {
-    validFreq_ = options_->get<std::string>("valid-freq");
-    saveFreq_ = options_->get<std::string>("save-freq");
     ABORT_IF(state_->factor != 1, "state.factor unexpectedly not 1 at this point??");
     updateLearningRate(*state);
     installSignalHandlers_();
@@ -201,18 +191,6 @@ class Scheduler : public TrainingObserver {
       LOG(info, "Training finished");
   }
 
-  void setupValidators(std::vector<Ptr<Vocab>>& vocabs) {
-    // setup validators if specified
-    if (!SchedulingParameter::parse(options_->get<std::string>("valid-freq")))
-      return; // no validation interval given
-
-    if(!options_->hasAndNotEmpty("valid-sets") &&
-       !options_->hasAndNotEmpty("valid-script-path"))
-      return; // no validators specified
-
-    for(auto validator : Validators(vocabs, options_))
-      addValidator(validator);
-  }
 
   void addValidator(Ptr<ValidatorBase> validator) {
     validators_.push_back(validator);
@@ -229,22 +207,21 @@ class Scheduler : public TrainingObserver {
 
   bool validating() {
     return (!validators_.empty()
-            && state_->enteredNewPeriodOf(validFreq_)
+            && state_->enteredNewPeriodOf(options_->get<std::string>("valid-freq"))
             && keepGoing());
   }
 
   bool saving() {
-    return state_->enteredNewPeriodOf(saveFreq_);
+    return state_->enteredNewPeriodOf(options_->get<std::string>("save-freq"));
   }
 
   void validate(const std::vector<Ptr<ExpressionGraph>>& graphs,
                 bool final = false) {
-    // SKIP VALIDATION IF
-    // - it's not time to validate anyway
-    // - we received SIGTERM
-    // - the current state has been validated (e.g., model was just loaded)
-    if((!state_->enteredNewPeriodOf(validFreq_) && !final)
-       || state_->validated || sigterm_)
+    // Do not validate if already validated (for instance, after the model is
+    // loaded) or if validation is scheduled for another update
+    if(sigterm_ 
+       || state_->validated
+       || (!state_->enteredNewPeriodOf(options_->get<std::string>("valid-freq")) && !final))
       return;
 
     bool firstValidator = true;
@@ -275,7 +252,7 @@ class Scheduler : public TrainingObserver {
       }
 
       state_->validators[validator->type()]["last-best"]
-        = validator->lastBest();
+          = validator->lastBest();
       state_->validators[validator->type()]["stalled"] = validator->stalled();
 
       // notify training observers if the first validator did not improve
diff --git a/src/training/training.h b/src/training/training.h
index fb674ba52..8863c165d 100644
--- a/src/training/training.h
+++ b/src/training/training.h
@@ -11,27 +11,6 @@
 
 namespace marian {
 
-template<class ModelWrapper>
-Ptr<data::BatchStats>
-miniBatchFit(Ptr<Options> options, Ptr<data::CorpusBase> data,
-             Ptr<Scheduler> scheduler, Ptr<IMPIWrapper> mpi) {
-  // collect stats for fitting minibatches to available memory
-  Ptr<data::BatchStats> stats;
-  if(options->get<bool>("mini-batch-fit")) {
-    LOG(info,
-        "[batching] Collecting statistics for batch fitting with step size {}",
-        options->get<size_t>("mini-batch-fit-step"));
-    // @TODO this should receive a function object that can generate a fake batch;
-    // that way vocabs would not be exposed.
-    auto model = New<ModelWrapper>(options, mpi);
-    model->setScheduler(scheduler); // collectStats() needs to know about dynamic MB scaling
-    stats = model->collectStats(data->getVocabs());
-    LOG(info, "[batching] Done. Typical MB size is {} target words",
-        stats->estimateTypicalTrgWords());
-  }
-  return stats;
-}
-
 template <class ModelWrapper>
 class Train : public ModelTask {
 private:
@@ -43,35 +22,64 @@ class Train : public ModelTask {
   void run() override {
     using namespace data;
 
-    // TRAINING RUN SETUP
-    Ptr<CorpusBase> dataset = prepareTrainingData(options_);
+    Ptr<CorpusBase> dataset;
+    if(!options_->get<std::string>("sqlite").empty())
+#ifndef _MSC_VER // @TODO: include SqLite in Visual Studio project
+      dataset = New<CorpusSQLite>(options_);
+#else
+      ABORT("SqLite presently not supported on Windows");
+#endif
+    else
+      dataset = New<Corpus>(options_);
+
+    dataset->prepare();
+
     auto trainState = New<TrainingState>(options_->get<float>("learn-rate"));
     auto scheduler = New<Scheduler>(options_, trainState);
-    auto mpi = initMPI(/*multiThreaded=*/!options_->get<bool>("sync-sgd"));
-    // @TODO: do we need the multiThreaded distinction in initMPI at all?
+    auto mpi = initMPI(/*multiThreaded=*/!options_->get<bool>("sync-sgd")); // @TODO: do we need the multiThreaded distinction at all?
+
+    Ptr<BatchStats> stats;
+    if(options_->get<bool>("mini-batch-fit")) {
+      LOG(info,
+          "[batching] Collecting statistics for batch fitting with step size {}",
+          options_->get<size_t>("mini-batch-fit-step"));
+      // @TODO this should receive a function object that can generate a fake batch;
+      // that way vocabs would not be exposed.
+      auto model = New<ModelWrapper>(options_, mpi);
+      model->setScheduler(scheduler); // collectStats() needs to know about dynamic MB scaling
+      stats = model->collectStats(dataset->getVocabs());
+      LOG(info, "[batching] Done. Typical MB size is {} target words", stats->estimateTypicalTrgWords());
+    }
+
+    if((options_->hasAndNotEmpty("valid-sets") || options_->hasAndNotEmpty("valid-script-path"))
+       && SchedulingParameter::parse(options_->get<std::string>("valid-freq"))) {
+      for(auto validator : Validators(dataset->getVocabs(), options_))
+        scheduler->addValidator(validator);
+    }
 
-    Ptr<BatchStats> stats
-      = miniBatchFit<ModelWrapper>(options_, dataset, scheduler, mpi);
     auto batchGenerator = New<CorpusBatchGenerator>(dataset, options_, stats);
-    scheduler->setupValidators(dataset->getVocabs());
+
     scheduler->registerTrainingObserver(batchGenerator);
 
     auto model = New<ModelWrapper>(options_, mpi);
     model->setScheduler(scheduler);
-    model->setTypicalTrgBatchWords(batchGenerator->estimateTypicalTrgBatchWords());
-    // typical no. of trg wrds in batch is needed for dynamic MiniBatch scaling
-    model->load(); //!! ALSO CHANGES scheduler AND trainState ON A RESUMED RUN!
+    model->setTypicalTrgBatchWords(batchGenerator->estimateTypicalTrgBatchWords()); // needed for dynamic MB scaling
+    model->load();
+
+    // @TODO: shuffle_ as a private attribute in BG
+    auto shuffle = !options_->get<bool>("no-shuffle");
+    bool restored = !options_->get<bool>("no-restore-corpus")
+                    && batchGenerator->restore(trainState, shuffle);
 
     // -- main training loop
     scheduler->started();
-    bool shuffle = !options_->get<bool>("no-shuffle");
-    batchGenerator->restore(trainState,shuffle);
     while(scheduler->keepGoing()) {
-      batchGenerator->prepare(shuffle);
+      if(!restored)
+        batchGenerator->prepare(shuffle);
+      restored = false;
 
       // main training loop for one epoch
-      // @TODO: try to use for(auto ...)
-      for(auto batchIt = std::begin(*batchGenerator);
+      for(auto batchIt = std::begin(*batchGenerator); // @TODO: try to use for(auto ...)
           batchIt != std::end(*batchGenerator);
           batchIt++) {
         if (!scheduler->keepGoing())

From 7159f45ab73df5e30c45e02a349a126ffeb44d31 Mon Sep 17 00:00:00 2001
From: Hieu Hoang <hihoan@microsoft.com>
Date: Wed, 23 Oct 2019 13:28:08 -0700
Subject: [PATCH 680/838] make sure file open even if it doesn't exist

---
 src/common/file_stream.cpp | 10 +++++-----
 1 file changed, 5 insertions(+), 5 deletions(-)

diff --git a/src/common/file_stream.cpp b/src/common/file_stream.cpp
index 276d6ff32..79b7458b2 100644
--- a/src/common/file_stream.cpp
+++ b/src/common/file_stream.cpp
@@ -57,7 +57,7 @@ TemporaryFileNew::TemporaryFileNew(const std::string &base, bool earlyUnlink)
   NormalizeTempPrefix(baseTemp);
   MakeTemp(baseTemp);
 
-  std::cerr << "TemporaryFileNew created" << std::endl;
+  std::cerr << "TemporaryFileNew created" << name_ << std::endl;
 }
 
 TemporaryFileNew::~TemporaryFileNew() {
@@ -92,12 +92,12 @@ void TemporaryFileNew::MakeTemp(const std::string &base) {
 #ifdef _MSC_VER
   int oflag = _O_RDWR | _O_CREAT | _O_EXCL | _O_TEMPORARY;
   std::fstream::open(name, oflag, _S_IREAD | _S_IWRITE);
-  ABORT_IF(errno, "Error while making a temporary based on '{}'", base);
+  ABORT_IF(errno, "Error creating file {}, errno {} {}", name, errno, StrError());                                                                                    
 #else
-  std::fstream::open(name, std::fstream::in | std::fstream::out);
-  ABORT_IF(errno, "Error while making a temporary based on '{}'", base);
+  std::fstream::open(name, std::fstream::in | std::fstream::out | std::fstream::trunc);
+  ABORT_IF(errno, "Error creating file {}, errno {} {}", name, errno, StrError());
 
-  ABORT_IF(remove(name), "Error while deleting '{}'", name);
+  ABORT_IF(remove(name), "Error while deleting {}", name);
 #endif
 
   name_ = name;

From 7c7fc5472c76da7758143dacdcd3832c52de9d6d Mon Sep 17 00:00:00 2001
From: Hieu Hoang <hihoan@microsoft.com>
Date: Wed, 23 Oct 2019 14:24:27 -0700
Subject: [PATCH 681/838] CreateFileName()

---
 src/common/file_stream.cpp | 22 +++++++++++++---------
 src/common/file_stream.h   |  2 ++
 2 files changed, 15 insertions(+), 9 deletions(-)

diff --git a/src/common/file_stream.cpp b/src/common/file_stream.cpp
index 79b7458b2..c4159e6da 100644
--- a/src/common/file_stream.cpp
+++ b/src/common/file_stream.cpp
@@ -85,23 +85,27 @@ void TemporaryFileNew::NormalizeTempPrefix(std::string &base) {
 #endif
 }
 
-void TemporaryFileNew::MakeTemp(const std::string &base) {
+std::string TemporaryFileNew::CreateFileName(const std::string &base) const {
   char *name = tempnam(base.c_str(), "marian.");
   ABORT_IF(name == NULL, "Error while making a temporary based on '{}'", base);
+  std::string ret = name;
+  free(name);
+  return ret;
+}
+
+void TemporaryFileNew::MakeTemp(const std::string &base) {
+  name_ = CreateFileName(base);
 
 #ifdef _MSC_VER
   int oflag = _O_RDWR | _O_CREAT | _O_EXCL | _O_TEMPORARY;
-  std::fstream::open(name, oflag, _S_IREAD | _S_IWRITE);
-  ABORT_IF(errno, "Error creating file {}, errno {} {}", name, errno, StrError());                                                                                    
+  std::fstream::open(name_, oflag, _S_IREAD | _S_IWRITE);
+  ABORT_IF(errno, "Error creating file {}, errno {} {}", name_, errno, StrError());
 #else
-  std::fstream::open(name, std::fstream::in | std::fstream::out | std::fstream::trunc);
-  ABORT_IF(errno, "Error creating file {}, errno {} {}", name, errno, StrError());
+  std::fstream::open(name_, std::fstream::in | std::fstream::out | std::fstream::trunc);
+  ABORT_IF(errno, "Error creating file {}, errno {} {}", name_, errno, StrError());
 
-  ABORT_IF(remove(name), "Error while deleting {}", name);
+  ABORT_IF(remove(name_.c_str()), "Error while deleting {}", name_);
 #endif
-
-  name_ = name;
-  free(name);
 }
 
 
diff --git a/src/common/file_stream.h b/src/common/file_stream.h
index c264b43f0..44cff7239 100755
--- a/src/common/file_stream.h
+++ b/src/common/file_stream.h
@@ -54,6 +54,8 @@ class TemporaryFileNew : public std::fstream {
   std::string name_;
   bool unlink_;
 
+  std::string CreateFileName(const std::string& base) const;
+
   void NormalizeTempPrefix(std::string& base);
   void MakeTemp(const std::string& base);
 };

From 79d83078c527744df601f51b6aff49a07dfa2b9a Mon Sep 17 00:00:00 2001
From: Hieu Hoang <hihoan@microsoft.com>
Date: Wed, 23 Oct 2019 14:42:45 -0700
Subject: [PATCH 682/838] merge NormalizeTempPrefix into CreateFileName()

---
 src/common/file_stream.cpp | 47 +++++++++++++++++++-------------------
 src/common/file_stream.h   |  1 -
 2 files changed, 23 insertions(+), 25 deletions(-)

diff --git a/src/common/file_stream.cpp b/src/common/file_stream.cpp
index c4159e6da..3e2ced63d 100644
--- a/src/common/file_stream.cpp
+++ b/src/common/file_stream.cpp
@@ -53,9 +53,7 @@ std::string StrError() {
 ///////////////////////////////////////////////////////////////////////////////////////////////
 TemporaryFileNew::TemporaryFileNew(const std::string &base, bool earlyUnlink)
     : unlink_(earlyUnlink) {
-  std::string baseTemp(base);
-  NormalizeTempPrefix(baseTemp);
-  MakeTemp(baseTemp);
+  MakeTemp(base);
 
   std::cerr << "TemporaryFileNew created" << name_ << std::endl;
 }
@@ -66,29 +64,30 @@ TemporaryFileNew::~TemporaryFileNew() {
   }
 }
 
-void TemporaryFileNew::NormalizeTempPrefix(std::string &base) {
-  if(base.empty())
-    return;
-
-#ifdef _MSC_VER
-  if(base.substr(0, 4) == "/tmp")
-    base = getenv("TMP");
-#else
-  if(base[base.size() - 1] == '/')
-    return;
-  struct stat sb;
-  // It's fine for it to not exist.
-  if(stat(base.c_str(), &sb) == -1)
-    return;
-  if(S_ISDIR(sb.st_mode))
-    base += '/';
-#endif
-}
-
 std::string TemporaryFileNew::CreateFileName(const std::string &base) const {
-  char *name = tempnam(base.c_str(), "marian.");
+  //NormalizeTempPrefix
+  std::string ret = base;
+  if(!base.empty()) {
+	#ifdef _MSC_VER
+      if(ret.substr(0, 4) == "/tmp") {
+        ret = getenv("TMP");
+      }
+	#else
+	  if(ret[ret.size() - 1] != '/') {
+		  struct stat sb;
+		  // It's fine for it to not exist.
+		  if(stat(ret.c_str(), &sb) != -1) {
+            if(S_ISDIR(sb.st_mode)) {
+              ret += '/';
+            }
+		  }
+      }
+	#endif
+  }
+
+  char *name = tempnam(ret.c_str(), "marian.");
   ABORT_IF(name == NULL, "Error while making a temporary based on '{}'", base);
-  std::string ret = name;
+  ret = name;
   free(name);
   return ret;
 }
diff --git a/src/common/file_stream.h b/src/common/file_stream.h
index 44cff7239..35ae3396d 100755
--- a/src/common/file_stream.h
+++ b/src/common/file_stream.h
@@ -56,7 +56,6 @@ class TemporaryFileNew : public std::fstream {
 
   std::string CreateFileName(const std::string& base) const;
 
-  void NormalizeTempPrefix(std::string& base);
   void MakeTemp(const std::string& base);
 };
 

From 16d4ce64ece8f665bf1dfc1ac46daf5a0be19918 Mon Sep 17 00:00:00 2001
From: Hieu Hoang <hihoan@microsoft.com>
Date: Wed, 23 Oct 2019 15:01:12 -0700
Subject: [PATCH 683/838] rename name_ -> file_

---
 src/common/file_stream.cpp | 123 ++++++++++++++++++-------------------
 src/common/file_stream.h   |  32 +++++-----
 2 files changed, 77 insertions(+), 78 deletions(-)

diff --git a/src/common/file_stream.cpp b/src/common/file_stream.cpp
index 3e2ced63d..f9cad8438 100644
--- a/src/common/file_stream.cpp
+++ b/src/common/file_stream.cpp
@@ -50,68 +50,6 @@ std::string StrError() {
 }
 } // namespace
 
-///////////////////////////////////////////////////////////////////////////////////////////////
-TemporaryFileNew::TemporaryFileNew(const std::string &base, bool earlyUnlink)
-    : unlink_(earlyUnlink) {
-  MakeTemp(base);
-
-  std::cerr << "TemporaryFileNew created" << name_ << std::endl;
-}
-
-TemporaryFileNew::~TemporaryFileNew() {
-  if(!unlink_) {
-    ABORT_IF(remove(name_.c_str()), "Error while deleting '{}'", name_);
-  }
-}
-
-std::string TemporaryFileNew::CreateFileName(const std::string &base) const {
-  //NormalizeTempPrefix
-  std::string ret = base;
-  if(!base.empty()) {
-	#ifdef _MSC_VER
-      if(ret.substr(0, 4) == "/tmp") {
-        ret = getenv("TMP");
-      }
-	#else
-	  if(ret[ret.size() - 1] != '/') {
-		  struct stat sb;
-		  // It's fine for it to not exist.
-		  if(stat(ret.c_str(), &sb) != -1) {
-            if(S_ISDIR(sb.st_mode)) {
-              ret += '/';
-            }
-		  }
-      }
-	#endif
-  }
-
-  char *name = tempnam(ret.c_str(), "marian.");
-  ABORT_IF(name == NULL, "Error while making a temporary based on '{}'", base);
-  ret = name;
-  free(name);
-  return ret;
-}
-
-void TemporaryFileNew::MakeTemp(const std::string &base) {
-  name_ = CreateFileName(base);
-
-#ifdef _MSC_VER
-  int oflag = _O_RDWR | _O_CREAT | _O_EXCL | _O_TEMPORARY;
-  std::fstream::open(name_, oflag, _S_IREAD | _S_IWRITE);
-  ABORT_IF(errno, "Error creating file {}, errno {} {}", name_, errno, StrError());
-#else
-  std::fstream::open(name_, std::fstream::in | std::fstream::out | std::fstream::trunc);
-  ABORT_IF(errno, "Error creating file {}, errno {} {}", name_, errno, StrError());
-
-  ABORT_IF(remove(name_.c_str()), "Error while deleting {}", name_);
-#endif
-}
-
-
-std::string TemporaryFileNew::getFileName() {
-  return name_;
-}
-
 ///////////////////////////////////////////////////////////////////////////////////////////////
 InputFileStreamNew::InputFileStreamNew(const std::string &file)
     : std::istream(NULL), file_(file), streamBuf_(NULL) {
@@ -169,5 +107,66 @@ OutputFileStreamNew::~OutputFileStreamNew() {
   delete streamBuf1_;
 }
 
+///////////////////////////////////////////////////////////////////////////////////////////////
+TemporaryFileNew::TemporaryFileNew(const std::string &base, bool earlyUnlink)
+    : unlink_(earlyUnlink) {
+  MakeTemp(base);
+
+  std::cerr << "TemporaryFileNew created" << file_ << std::endl;
+}
+
+TemporaryFileNew::~TemporaryFileNew() {
+  if(!unlink_) {
+    ABORT_IF(remove(file_.c_str()), "Error while deleting '{}'", file_);
+  }
+}
+
+std::string TemporaryFileNew::CreateFileName(const std::string &base) const {
+  // NormalizeTempPrefix
+  std::string ret = base;
+  if(!base.empty()) {
+#ifdef _MSC_VER
+    if(ret.substr(0, 4) == "/tmp") {
+      ret = getenv("TMP");
+    }
+#else
+    if(ret[ret.size() - 1] != '/') {
+      struct stat sb;
+      // It's fine for it to not exist.
+      if(stat(ret.c_str(), &sb) != -1) {
+        if(S_ISDIR(sb.st_mode)) {
+          ret += '/';
+        }
+      }
+    }
+#endif
+  }
+
+  char *name = tempnam(ret.c_str(), "marian.");
+  ABORT_IF(name == NULL, "Error while making a temporary based on '{}'", base);
+  ret = name;
+  free(name);
+  return ret;
+}
+
+void TemporaryFileNew::MakeTemp(const std::string &base) {
+  file_ = CreateFileName(base);
+
+#ifdef _MSC_VER
+  int oflag = _O_RDWR | _O_CREAT | _O_EXCL | _O_TEMPORARY;
+  std::fstream::open(file_, oflag, _S_IREAD | _S_IWRITE);
+  ABORT_IF(errno, "Error creating file {}, errno {} {}", file_, errno, StrError());
+#else
+  std::fstream::open(file_, std::fstream::in | std::fstream::out | std::fstream::trunc);
+  ABORT_IF(errno, "Error creating file {}, errno {} {}", file_, errno, StrError());
+
+  ABORT_IF(remove(file_.c_str()), "Error while deleting {}", file_);
+#endif
+}
+
+std::string TemporaryFileNew::getFileName() {
+  return file_;
+}
+
 } // namespace io
 } // namespace marian
diff --git a/src/common/file_stream.h b/src/common/file_stream.h
index 35ae3396d..8f2349843 100755
--- a/src/common/file_stream.h
+++ b/src/common/file_stream.h
@@ -43,22 +43,6 @@ std::unique_ptr<T> make_unique(Args&&... args) {
 namespace marian {
 namespace io {
 
-///////////////////////////////////////////////////////////////////////////////////////////////
-class TemporaryFileNew : public std::fstream {
-public:
-  TemporaryFileNew(const std::string& base = "/tmp/", bool earlyUnlink = true);
-  std::string getFileName();
-  virtual ~TemporaryFileNew();
-
-protected:
-  std::string name_;
-  bool unlink_;
-
-  std::string CreateFileName(const std::string& base) const;
-
-  void MakeTemp(const std::string& base);
-};
-
 //////////////////////////////////////////////////////////////////////////////////////////////
 class InputFileStreamNew : public std::istream {
 public:
@@ -93,5 +77,21 @@ class OutputFileStreamNew : public std::ostream {
   std::streambuf* streamBuf2_;
 };
 
+///////////////////////////////////////////////////////////////////////////////////////////////
+class TemporaryFileNew : public std::fstream {
+public:
+  TemporaryFileNew(const std::string& base = "/tmp/", bool earlyUnlink = true);
+  std::string getFileName();
+  virtual ~TemporaryFileNew();
+
+protected:
+  std::string file_;
+  bool unlink_;
+
+  std::string CreateFileName(const std::string& base) const;
+
+  void MakeTemp(const std::string& base);
+};
+
 }  // namespace io
 }  // namespace marian

From 95fc443f7441e6c8bbe142dad3e342c28c9ea171 Mon Sep 17 00:00:00 2001
From: Hieu Hoang <hihoan@microsoft.com>
Date: Wed, 23 Oct 2019 15:06:33 -0700
Subject: [PATCH 684/838] string -> path

---
 src/common/file_stream.cpp | 14 +++++++-------
 src/common/file_stream.h   |  2 +-
 2 files changed, 8 insertions(+), 8 deletions(-)

diff --git a/src/common/file_stream.cpp b/src/common/file_stream.cpp
index f9cad8438..2b780bc31 100644
--- a/src/common/file_stream.cpp
+++ b/src/common/file_stream.cpp
@@ -112,12 +112,12 @@ TemporaryFileNew::TemporaryFileNew(const std::string &base, bool earlyUnlink)
     : unlink_(earlyUnlink) {
   MakeTemp(base);
 
-  std::cerr << "TemporaryFileNew created" << file_ << std::endl;
+  std::cerr << "TemporaryFileNew created" << file_.string() << std::endl;
 }
 
 TemporaryFileNew::~TemporaryFileNew() {
   if(!unlink_) {
-    ABORT_IF(remove(file_.c_str()), "Error while deleting '{}'", file_);
+    ABORT_IF(remove(file_.string().c_str()), "Error while deleting '{}'", file_.string());
   }
 }
 
@@ -154,18 +154,18 @@ void TemporaryFileNew::MakeTemp(const std::string &base) {
 
 #ifdef _MSC_VER
   int oflag = _O_RDWR | _O_CREAT | _O_EXCL | _O_TEMPORARY;
-  std::fstream::open(file_, oflag, _S_IREAD | _S_IWRITE);
-  ABORT_IF(errno, "Error creating file {}, errno {} {}", file_, errno, StrError());
+  std::fstream::open(file_.string(), oflag, _S_IREAD | _S_IWRITE);
+  ABORT_IF(errno, "Error creating file {}, errno {} {}", file_.string(), errno, StrError());
 #else
   std::fstream::open(file_, std::fstream::in | std::fstream::out | std::fstream::trunc);
-  ABORT_IF(errno, "Error creating file {}, errno {} {}", file_, errno, StrError());
+  ABORT_IF(errno, "Error creating file {}, errno {} {}", file_.string(), errno, StrError());
 
-  ABORT_IF(remove(file_.c_str()), "Error while deleting {}", file_);
+  ABORT_IF(remove(file_.string().c_str()), "Error while deleting {}", file_.string());
 #endif
 }
 
 std::string TemporaryFileNew::getFileName() {
-  return file_;
+  return file_.string();
 }
 
 } // namespace io
diff --git a/src/common/file_stream.h b/src/common/file_stream.h
index 8f2349843..94744c54f 100755
--- a/src/common/file_stream.h
+++ b/src/common/file_stream.h
@@ -85,7 +85,7 @@ class TemporaryFileNew : public std::fstream {
   virtual ~TemporaryFileNew();
 
 protected:
-  std::string file_;
+  marian::filesystem::Path file_;
   bool unlink_;
 
   std::string CreateFileName(const std::string& base) const;

From 9963beade4417ba8902fc1733514382c6d7837f3 Mon Sep 17 00:00:00 2001
From: Hieu Hoang <hihoan@microsoft.com>
Date: Wed, 23 Oct 2019 15:41:07 -0700
Subject: [PATCH 685/838] inherite from OutputFileStreamNew instead of
 std::fstream

---
 src/common/file_stream.cpp | 22 ++++++----------------
 src/common/file_stream.h   |  9 +++++----
 src/data/corpus.cpp        |  4 ++--
 3 files changed, 13 insertions(+), 22 deletions(-)

diff --git a/src/common/file_stream.cpp b/src/common/file_stream.cpp
index 2b780bc31..579b2c892 100644
--- a/src/common/file_stream.cpp
+++ b/src/common/file_stream.cpp
@@ -109,13 +109,14 @@ OutputFileStreamNew::~OutputFileStreamNew() {
 
 ///////////////////////////////////////////////////////////////////////////////////////////////
 TemporaryFileNew::TemporaryFileNew(const std::string &base, bool earlyUnlink)
-    : unlink_(earlyUnlink) {
-  MakeTemp(base);
+    : OutputFileStreamNew(CreateFileName(base)), unlink_(earlyUnlink), inSteam_(NULL) {
+  inSteam_ = new InputFileStreamNew(file_.string());
 
   std::cerr << "TemporaryFileNew created" << file_.string() << std::endl;
 }
 
 TemporaryFileNew::~TemporaryFileNew() {
+  delete inSteam_;
   if(!unlink_) {
     ABORT_IF(remove(file_.string().c_str()), "Error while deleting '{}'", file_.string());
   }
@@ -149,22 +150,11 @@ std::string TemporaryFileNew::CreateFileName(const std::string &base) const {
   return ret;
 }
 
-void TemporaryFileNew::MakeTemp(const std::string &base) {
-  file_ = CreateFileName(base);
-
-#ifdef _MSC_VER
-  int oflag = _O_RDWR | _O_CREAT | _O_EXCL | _O_TEMPORARY;
-  std::fstream::open(file_.string(), oflag, _S_IREAD | _S_IWRITE);
-  ABORT_IF(errno, "Error creating file {}, errno {} {}", file_.string(), errno, StrError());
-#else
-  std::fstream::open(file_, std::fstream::in | std::fstream::out | std::fstream::trunc);
-  ABORT_IF(errno, "Error creating file {}, errno {} {}", file_.string(), errno, StrError());
-
-  ABORT_IF(remove(file_.string().c_str()), "Error while deleting {}", file_.string());
-#endif
+InputFileStreamNew &TemporaryFileNew::getInputStream() {
+  return *inSteam_;
 }
 
-std::string TemporaryFileNew::getFileName() {
+std::string TemporaryFileNew::getFileName() const {
   return file_.string();
 }
 
diff --git a/src/common/file_stream.h b/src/common/file_stream.h
index 94744c54f..f3a2c8374 100755
--- a/src/common/file_stream.h
+++ b/src/common/file_stream.h
@@ -78,19 +78,20 @@ class OutputFileStreamNew : public std::ostream {
 };
 
 ///////////////////////////////////////////////////////////////////////////////////////////////
-class TemporaryFileNew : public std::fstream {
+class TemporaryFileNew : public OutputFileStreamNew {
 public:
   TemporaryFileNew(const std::string& base = "/tmp/", bool earlyUnlink = true);
-  std::string getFileName();
   virtual ~TemporaryFileNew();
 
+  InputFileStreamNew& getInputStream();
+  std::string getFileName() const;
+
 protected:
-  marian::filesystem::Path file_;
   bool unlink_;
+  InputFileStreamNew* inSteam_;
 
   std::string CreateFileName(const std::string& base) const;
 
-  void MakeTemp(const std::string& base);
 };
 
 }  // namespace io
diff --git a/src/data/corpus.cpp b/src/data/corpus.cpp
index 71d09957a..c33b53df2 100644
--- a/src/data/corpus.cpp
+++ b/src/data/corpus.cpp
@@ -205,8 +205,8 @@ void Corpus::shuffleData(const std::vector<std::string>& paths) {
     // replace files_[] by the tempfiles we just created
     files_.resize(numStreams);
     for(size_t i = 0; i < numStreams; ++i) {
-      io::TemporaryFileNew* strm = tempFiles_[i].get();
-      files_[i].reset(strm);
+      io::TemporaryFileNew* tempFile = tempFiles_[i].get();
+      files_[i].reset(&tempFile->getInputStream());
     }
     LOG(info, "[data] Done shuffling {} sentences to temp files", numSentences);
   }

From 6915f9cb435f81621105bd3492f0ddb2b2ed5965 Mon Sep 17 00:00:00 2001
From: Hieu Hoang <hihoan@microsoft.com>
Date: Wed, 23 Oct 2019 16:09:20 -0700
Subject: [PATCH 686/838] don't delete inStream_. Passing to a unique ptr

---
 src/common/file_stream.cpp | 10 ++++++----
 src/common/file_stream.h   |  4 ++--
 src/data/corpus.cpp        |  2 +-
 3 files changed, 9 insertions(+), 7 deletions(-)

diff --git a/src/common/file_stream.cpp b/src/common/file_stream.cpp
index 579b2c892..9b9b82c56 100644
--- a/src/common/file_stream.cpp
+++ b/src/common/file_stream.cpp
@@ -111,12 +111,14 @@ OutputFileStreamNew::~OutputFileStreamNew() {
 TemporaryFileNew::TemporaryFileNew(const std::string &base, bool earlyUnlink)
     : OutputFileStreamNew(CreateFileName(base)), unlink_(earlyUnlink), inSteam_(NULL) {
   inSteam_ = new InputFileStreamNew(file_.string());
-
+  if (unlink_) {
+    ABORT_IF(remove(file_.string().c_str()), "Error while deleting '{}'", file_.string());
+  }
+  
   std::cerr << "TemporaryFileNew created" << file_.string() << std::endl;
 }
 
 TemporaryFileNew::~TemporaryFileNew() {
-  delete inSteam_;
   if(!unlink_) {
     ABORT_IF(remove(file_.string().c_str()), "Error while deleting '{}'", file_.string());
   }
@@ -150,8 +152,8 @@ std::string TemporaryFileNew::CreateFileName(const std::string &base) const {
   return ret;
 }
 
-InputFileStreamNew &TemporaryFileNew::getInputStream() {
-  return *inSteam_;
+InputFileStreamNew *TemporaryFileNew::getInputStream() {
+  return inSteam_;
 }
 
 std::string TemporaryFileNew::getFileName() const {
diff --git a/src/common/file_stream.h b/src/common/file_stream.h
index f3a2c8374..b0f4aff77 100755
--- a/src/common/file_stream.h
+++ b/src/common/file_stream.h
@@ -83,12 +83,12 @@ class TemporaryFileNew : public OutputFileStreamNew {
   TemporaryFileNew(const std::string& base = "/tmp/", bool earlyUnlink = true);
   virtual ~TemporaryFileNew();
 
-  InputFileStreamNew& getInputStream();
+  InputFileStreamNew *getInputStream();
   std::string getFileName() const;
 
 protected:
   bool unlink_;
-  InputFileStreamNew* inSteam_;
+  InputFileStreamNew *inSteam_;
 
   std::string CreateFileName(const std::string& base) const;
 
diff --git a/src/data/corpus.cpp b/src/data/corpus.cpp
index c33b53df2..efe701d8f 100644
--- a/src/data/corpus.cpp
+++ b/src/data/corpus.cpp
@@ -206,7 +206,7 @@ void Corpus::shuffleData(const std::vector<std::string>& paths) {
     files_.resize(numStreams);
     for(size_t i = 0; i < numStreams; ++i) {
       io::TemporaryFileNew* tempFile = tempFiles_[i].get();
-      files_[i].reset(&tempFile->getInputStream());
+      files_[i].reset(tempFile->getInputStream());
     }
     LOG(info, "[data] Done shuffling {} sentences to temp files", numSentences);
   }

From b26ce693417859d632049eec27395a7b8abafa34 Mon Sep 17 00:00:00 2001
From: Hieu Hoang <hihoan@microsoft.com>
Date: Wed, 23 Oct 2019 16:22:09 -0700
Subject: [PATCH 687/838] store iStream_ in unique pointer and move to new
 location when requested

---
 src/common/file_stream.cpp | 8 ++++----
 src/common/file_stream.h   | 4 ++--
 src/data/corpus.cpp        | 2 +-
 3 files changed, 7 insertions(+), 7 deletions(-)

diff --git a/src/common/file_stream.cpp b/src/common/file_stream.cpp
index 9b9b82c56..806d34d60 100644
--- a/src/common/file_stream.cpp
+++ b/src/common/file_stream.cpp
@@ -109,8 +109,8 @@ OutputFileStreamNew::~OutputFileStreamNew() {
 
 ///////////////////////////////////////////////////////////////////////////////////////////////
 TemporaryFileNew::TemporaryFileNew(const std::string &base, bool earlyUnlink)
-    : OutputFileStreamNew(CreateFileName(base)), unlink_(earlyUnlink), inSteam_(NULL) {
-  inSteam_ = new InputFileStreamNew(file_.string());
+    : OutputFileStreamNew(CreateFileName(base)), unlink_(earlyUnlink) {
+  inSteam_.reset(new InputFileStreamNew(file_.string()));
   if (unlink_) {
     ABORT_IF(remove(file_.string().c_str()), "Error while deleting '{}'", file_.string());
   }
@@ -152,8 +152,8 @@ std::string TemporaryFileNew::CreateFileName(const std::string &base) const {
   return ret;
 }
 
-InputFileStreamNew *TemporaryFileNew::getInputStream() {
-  return inSteam_;
+UPtr<InputFileStreamNew> TemporaryFileNew::getInputStream() {
+  return std::move(inSteam_);
 }
 
 std::string TemporaryFileNew::getFileName() const {
diff --git a/src/common/file_stream.h b/src/common/file_stream.h
index b0f4aff77..cdb29358f 100755
--- a/src/common/file_stream.h
+++ b/src/common/file_stream.h
@@ -83,12 +83,12 @@ class TemporaryFileNew : public OutputFileStreamNew {
   TemporaryFileNew(const std::string& base = "/tmp/", bool earlyUnlink = true);
   virtual ~TemporaryFileNew();
 
-  InputFileStreamNew *getInputStream();
+  UPtr<InputFileStreamNew> getInputStream();
   std::string getFileName() const;
 
 protected:
   bool unlink_;
-  InputFileStreamNew *inSteam_;
+  UPtr<InputFileStreamNew> inSteam_;
 
   std::string CreateFileName(const std::string& base) const;
 
diff --git a/src/data/corpus.cpp b/src/data/corpus.cpp
index efe701d8f..a5d800a30 100644
--- a/src/data/corpus.cpp
+++ b/src/data/corpus.cpp
@@ -206,7 +206,7 @@ void Corpus::shuffleData(const std::vector<std::string>& paths) {
     files_.resize(numStreams);
     for(size_t i = 0; i < numStreams; ++i) {
       io::TemporaryFileNew* tempFile = tempFiles_[i].get();
-      files_[i].reset(tempFile->getInputStream());
+      files_[i] = tempFile->getInputStream();
     }
     LOG(info, "[data] Done shuffling {} sentences to temp files", numSentences);
   }

From 41d247edb95d433ed7c744c7b2c7bf8a25c1e2fc Mon Sep 17 00:00:00 2001
From: Hieu Hoang <hihoan@microsoft.com>
Date: Wed, 23 Oct 2019 16:36:58 -0700
Subject: [PATCH 688/838] rename new classes. Delete debugging messages

---
 src/common/binary.cpp               |  4 ++--
 src/common/config.cpp               |  2 +-
 src/common/config_parser.cpp        |  2 +-
 src/common/file_stream.cpp          | 36 +++++++++++------------------
 src/common/file_stream.h            | 22 +++++++++---------
 src/data/corpus.cpp                 |  9 ++++----
 src/data/corpus.h                   |  2 +-
 src/data/corpus_base.cpp            |  8 +++----
 src/data/corpus_nbest.cpp           |  2 +-
 src/data/corpus_nbest.h             |  2 +-
 src/data/default_vocab.cpp          | 10 ++++----
 src/data/factored_vocab.cpp         |  8 +++----
 src/data/sentencepiece_vocab.cpp    |  8 +++----
 src/data/shortlist.h                |  6 ++---
 src/layers/word2vec_reader.h        |  2 +-
 src/models/amun.h                   |  2 +-
 src/models/encoder_decoder.cpp      |  2 +-
 src/models/nematus.h                |  2 +-
 src/rescorer/score_collector.cpp    |  4 ++--
 src/rescorer/score_collector.h      |  2 +-
 src/training/validator.cpp          |  4 ++--
 src/translator/output_collector.cpp |  2 +-
 src/translator/output_collector.h   |  2 +-
 23 files changed, 67 insertions(+), 76 deletions(-)

diff --git a/src/common/binary.cpp b/src/common/binary.cpp
index a42e4c0b4..d4f443985 100644
--- a/src/common/binary.cpp
+++ b/src/common/binary.cpp
@@ -76,7 +76,7 @@ void loadItems(const std::string& fileName, std::vector<io::Item>& items) {
   ABORT_IF(rc != buf.size(), "Error {} ('{}') reading file '{}'", errno, strerror(errno), fileName);
   fclose(f);
 #else
-  io::InputFileStreamNew in(fileName);
+  io::InputFileStream in(fileName);
   in.read(buf.data(), buf.size());
 #endif
 
@@ -108,7 +108,7 @@ io::Item getItem(const std::string& fileName, const std::string& varName) {
 
 void saveItems(const std::string& fileName,
                const std::vector<io::Item>& items) {
-  io::OutputFileStreamNew out(fileName);
+  io::OutputFileStream out(fileName);
   size_t pos = 0;
 
   size_t binaryFileVersion = BINARY_FILE_VERSION;
diff --git a/src/common/config.cpp b/src/common/config.cpp
index 23a0f56f7..ddfb5199e 100644
--- a/src/common/config.cpp
+++ b/src/common/config.cpp
@@ -127,7 +127,7 @@ YAML::Node& Config::get() {
 }
 
 void Config::save(const std::string& name) {
-  io::OutputFileStreamNew out(name);
+  io::OutputFileStream out(name);
   out << *this;
 }
 
diff --git a/src/common/config_parser.cpp b/src/common/config_parser.cpp
index 46aa10628..9c056f5e3 100755
--- a/src/common/config_parser.cpp
+++ b/src/common/config_parser.cpp
@@ -849,7 +849,7 @@ YAML::Node ConfigParser::loadConfigFiles(const std::vector<std::string>& paths)
 
   for(auto& path : paths) {
     // load single config file
-    io::InputFileStreamNew strm(path);
+    io::InputFileStream strm(path);
     YAML::Node config = YAML::Load(strm);
 
     // expand relative paths if requested
diff --git a/src/common/file_stream.cpp b/src/common/file_stream.cpp
index 806d34d60..dd87d1d72 100644
--- a/src/common/file_stream.cpp
+++ b/src/common/file_stream.cpp
@@ -51,7 +51,7 @@ std::string StrError() {
 } // namespace
 
 ///////////////////////////////////////////////////////////////////////////////////////////////
-InputFileStreamNew::InputFileStreamNew(const std::string &file)
+InputFileStream::InputFileStream(const std::string &file)
     : std::istream(NULL), file_(file), streamBuf_(NULL) {
   ABORT_IF(!marian::filesystem::exists(file_), "File '{}' does not exist", file);
 
@@ -66,30 +66,26 @@ InputFileStreamNew::InputFileStreamNew(const std::string &file)
   }
 
   this->init(streamBuf_);
-
-  std::cerr << "InputFileStreamNew created" << std::endl;
 }
 
-InputFileStreamNew::~InputFileStreamNew() {
+InputFileStream::~InputFileStream() {
   delete streamBuf_;
 }
 
-bool InputFileStreamNew::empty() {
+bool InputFileStream::empty() {
   return this->peek() == std::ifstream::traits_type::eof();
 }
 
-void InputFileStreamNew::setbufsize(size_t size) const {
+void InputFileStream::setbufsize(size_t size) const {
   // do nothing. Is this needed?
 }
 
 ///////////////////////////////////////////////////////////////////////////////////////////////
-OutputFileStreamNew::OutputFileStreamNew(const std::string &file)
+OutputFileStream::OutputFileStream(const std::string &file)
     : std::ostream(NULL), file_(file), streamBuf1_(NULL), streamBuf2_(NULL) {
   std::filebuf *fileBuf = new std::filebuf();
   streamBuf1_ = fileBuf->open(file.c_str(), std::ios::out | std::ios_base::binary);
-  if(!streamBuf1_) {
-    std::cerr << "File can't be opened" << file << std::endl;
-  }
+  ABORT_IF(!streamBuf1_, "File can't be opened", file);
 
   if(file_.extension() == marian::filesystem::Path(".gz")) {
     streamBuf2_ = new zstr::ostreambuf(streamBuf1_);
@@ -97,34 +93,30 @@ OutputFileStreamNew::OutputFileStreamNew(const std::string &file)
   } else {
     this->init(streamBuf1_);
   }
-
-  std::cerr << "OutputFileStreamNew created" << std::endl;
 }
 
-OutputFileStreamNew::~OutputFileStreamNew() {
+OutputFileStream::~OutputFileStream() {
   this->flush();
   delete streamBuf2_;
   delete streamBuf1_;
 }
 
 ///////////////////////////////////////////////////////////////////////////////////////////////
-TemporaryFileNew::TemporaryFileNew(const std::string &base, bool earlyUnlink)
-    : OutputFileStreamNew(CreateFileName(base)), unlink_(earlyUnlink) {
-  inSteam_.reset(new InputFileStreamNew(file_.string()));
+TemporaryFile::TemporaryFile(const std::string &base, bool earlyUnlink)
+    : OutputFileStream(CreateFileName(base)), unlink_(earlyUnlink) {
+  inSteam_.reset(new InputFileStream(file_.string()));
   if (unlink_) {
     ABORT_IF(remove(file_.string().c_str()), "Error while deleting '{}'", file_.string());
   }
-  
-  std::cerr << "TemporaryFileNew created" << file_.string() << std::endl;
 }
 
-TemporaryFileNew::~TemporaryFileNew() {
+TemporaryFile::~TemporaryFile() {
   if(!unlink_) {
     ABORT_IF(remove(file_.string().c_str()), "Error while deleting '{}'", file_.string());
   }
 }
 
-std::string TemporaryFileNew::CreateFileName(const std::string &base) const {
+std::string TemporaryFile::CreateFileName(const std::string &base) const {
   // NormalizeTempPrefix
   std::string ret = base;
   if(!base.empty()) {
@@ -152,11 +144,11 @@ std::string TemporaryFileNew::CreateFileName(const std::string &base) const {
   return ret;
 }
 
-UPtr<InputFileStreamNew> TemporaryFileNew::getInputStream() {
+UPtr<InputFileStream> TemporaryFile::getInputStream() {
   return std::move(inSteam_);
 }
 
-std::string TemporaryFileNew::getFileName() const {
+std::string TemporaryFile::getFileName() const {
   return file_.string();
 }
 
diff --git a/src/common/file_stream.h b/src/common/file_stream.h
index cdb29358f..eb968b5a9 100755
--- a/src/common/file_stream.h
+++ b/src/common/file_stream.h
@@ -44,10 +44,10 @@ namespace marian {
 namespace io {
 
 //////////////////////////////////////////////////////////////////////////////////////////////
-class InputFileStreamNew : public std::istream {
+class InputFileStream : public std::istream {
 public:
-  explicit InputFileStreamNew(const std::string& file);
-  virtual ~InputFileStreamNew();
+  explicit InputFileStream(const std::string& file);
+  virtual ~InputFileStream();
 
   bool empty();
   void setbufsize(size_t size) const;
@@ -58,10 +58,10 @@ class InputFileStreamNew : public std::istream {
 };
 
 //////////////////////////////////////////////////////////////////////////////////////////////
-class OutputFileStreamNew : public std::ostream {
+class OutputFileStream : public std::ostream {
 public:
-  explicit OutputFileStreamNew(const std::string& file);
-  virtual ~OutputFileStreamNew();
+  explicit OutputFileStream(const std::string& file);
+  virtual ~OutputFileStream();
   
   template <typename T>
   size_t write(const T* ptr, size_t num = 1) {
@@ -78,17 +78,17 @@ class OutputFileStreamNew : public std::ostream {
 };
 
 ///////////////////////////////////////////////////////////////////////////////////////////////
-class TemporaryFileNew : public OutputFileStreamNew {
+class TemporaryFile : public OutputFileStream {
 public:
-  TemporaryFileNew(const std::string& base = "/tmp/", bool earlyUnlink = true);
-  virtual ~TemporaryFileNew();
+  TemporaryFile(const std::string& base = "/tmp/", bool earlyUnlink = true);
+  virtual ~TemporaryFile();
 
-  UPtr<InputFileStreamNew> getInputStream();
+  UPtr<InputFileStream> getInputStream();
   std::string getFileName() const;
 
 protected:
   bool unlink_;
-  UPtr<InputFileStreamNew> inSteam_;
+  UPtr<InputFileStream> inSteam_;
 
   std::string CreateFileName(const std::string& base) const;
 
diff --git a/src/data/corpus.cpp b/src/data/corpus.cpp
index a5d800a30..7b6550ca1 100644
--- a/src/data/corpus.cpp
+++ b/src/data/corpus.cpp
@@ -131,7 +131,7 @@ void Corpus::reset() {
         // Do NOT reset named pipes; that closes them and triggers a SIGPIPE
         // (lost pipe) at the writing end, which may do whatever it wants
         // in this situation.
-        files_[i].reset(new io::InputFileStreamNew(paths_[i]));
+        files_[i].reset(new io::InputFileStream(paths_[i]));
       }
     }
 }
@@ -154,7 +154,7 @@ void Corpus::shuffleData(const std::vector<std::string>& paths) {
   else {
     files_.resize(numStreams);
     for(size_t i = 0; i < numStreams; ++i) {
-      io::InputFileStreamNew *strm = new io::InputFileStreamNew(paths[i]);
+      io::InputFileStream *strm = new io::InputFileStream(paths[i]);
       files_[i].reset(strm);
       strm->setbufsize(10000000); // huge read-ahead buffer to avoid network round-trips
     }
@@ -190,11 +190,10 @@ void Corpus::shuffleData(const std::vector<std::string>& paths) {
     LOG(info, "[data] Done shuffling {} sentences (cached in RAM)", numSentences);
   }
   else {
-    std::cerr << "Corpus::shuffleData" << std::endl;
     // create temp files that contain the data in randomized order
     tempFiles_.resize(numStreams);
     for(size_t i = 0; i < numStreams; ++i) {
-      io::TemporaryFileNew *out = new io::TemporaryFileNew(options_->get<std::string>("tempdir"));
+      io::TemporaryFile *out = new io::TemporaryFile(options_->get<std::string>("tempdir"));
 	  tempFiles_[i].reset(out);
       const auto& corpusStream = corpus[i];
       for(auto id : ids_) {
@@ -205,7 +204,7 @@ void Corpus::shuffleData(const std::vector<std::string>& paths) {
     // replace files_[] by the tempfiles we just created
     files_.resize(numStreams);
     for(size_t i = 0; i < numStreams; ++i) {
-      io::TemporaryFileNew* tempFile = tempFiles_[i].get();
+      io::TemporaryFile* tempFile = tempFiles_[i].get();
       files_[i] = tempFile->getInputStream();
     }
     LOG(info, "[data] Done shuffling {} sentences to temp files", numSentences);
diff --git a/src/data/corpus.h b/src/data/corpus.h
index 0d75cd690..4ac437248 100644
--- a/src/data/corpus.h
+++ b/src/data/corpus.h
@@ -18,7 +18,7 @@ namespace data {
 
 class Corpus : public CorpusBase {
 private:
-  std::vector<UPtr<io::TemporaryFileNew>> tempFiles_;
+  std::vector<UPtr<io::TemporaryFile>> tempFiles_;
   std::vector<size_t> ids_;
 
   // for shuffle-in-ram
diff --git a/src/data/corpus_base.cpp b/src/data/corpus_base.cpp
index da708141a..d6c9fa391 100755
--- a/src/data/corpus_base.cpp
+++ b/src/data/corpus_base.cpp
@@ -40,7 +40,7 @@ CorpusBase::CorpusBase(const std::vector<std::string>& paths,
            "Number of corpus files and vocab files does not agree");
 
   for(auto path : paths_) {
-    io::InputFileStreamNew *strm = new io::InputFileStreamNew(path);
+    io::InputFileStream *strm = new io::InputFileStream(path);
     ABORT_IF(strm->empty(), "File '{}' is empty", path);
     files_.emplace_back(strm);
   }
@@ -156,7 +156,7 @@ CorpusBase::CorpusBase(Ptr<Options> options, bool translate)
     if(path == "stdin")
       files_.emplace_back(new std::istream(std::cin.rdbuf()));
     else {
-      io::InputFileStreamNew *strm = new io::InputFileStreamNew(path);
+      io::InputFileStream *strm = new io::InputFileStream(path);
       ABORT_IF(strm->empty(), "File '{}' is empty", path);
       files_.emplace_back(strm);
     }
@@ -176,7 +176,7 @@ CorpusBase::CorpusBase(Ptr<Options> options, bool translate)
 
     alignFileIdx_ = paths_.size();
     paths_.emplace_back(path);
-    io::InputFileStreamNew* strm = new io::InputFileStreamNew(path);
+    io::InputFileStream* strm = new io::InputFileStream(path);
     ABORT_IF(strm->empty(), "File with alignments '{}' is empty", path);
     files_.emplace_back(strm);
   }
@@ -189,7 +189,7 @@ CorpusBase::CorpusBase(Ptr<Options> options, bool translate)
 
     weightFileIdx_ = paths_.size();
     paths_.emplace_back(path);
-    io::InputFileStreamNew* strm = new io::InputFileStreamNew(path);
+    io::InputFileStream* strm = new io::InputFileStream(path);
     ABORT_IF(strm->empty(), "File with weights '{}' is empty", path);
     files_.emplace_back(strm);
   }
diff --git a/src/data/corpus_nbest.cpp b/src/data/corpus_nbest.cpp
index dc0de1194..b21ed61b0 100644
--- a/src/data/corpus_nbest.cpp
+++ b/src/data/corpus_nbest.cpp
@@ -88,7 +88,7 @@ void CorpusNBest::reset() {
     if(path == "stdin")
       files_.emplace_back(new std::istream(std::cin.rdbuf()));
     else
-      files_.emplace_back(new io::InputFileStreamNew(path));
+      files_.emplace_back(new io::InputFileStream(path));
   }
 }
 }  // namespace data
diff --git a/src/data/corpus_nbest.h b/src/data/corpus_nbest.h
index 6dfaebc68..e17eb2000 100644
--- a/src/data/corpus_nbest.h
+++ b/src/data/corpus_nbest.h
@@ -18,7 +18,7 @@ namespace data {
 
 class CorpusNBest : public CorpusBase {
 private:
-  //std::vector<UPtr<io::TemporaryFileNew>> tempFiles_;
+  //std::vector<UPtr<io::TemporaryFile>> tempFiles_;
   std::vector<size_t> ids_;
   int lastNum_{-1};
   std::vector<std::string> lastLines_;
diff --git a/src/data/default_vocab.cpp b/src/data/default_vocab.cpp
index 82bef25aa..69b73d031 100644
--- a/src/data/default_vocab.cpp
+++ b/src/data/default_vocab.cpp
@@ -99,14 +99,14 @@ class DefaultVocab : public IVocab {
     std::map<std::string, Word> vocab;
     // read from JSON (or Yaml) file
     if(isJson) {
-      io::InputFileStreamNew strm(vocabPath);
+      io::InputFileStream strm(vocabPath);
       YAML::Node vocabNode = YAML::Load(strm);
       for(auto&& pair : vocabNode)
         vocab.insert({pair.first.as<std::string>(), Word::fromWordIndex(pair.second.as<IndexType>())});
     }
     // read from flat text file
     else {
-      io::InputFileStreamNew in(vocabPath);
+      io::InputFileStream in(vocabPath);
       std::string line;
       while(std::getline(in, line)) {
         ABORT_IF(line.empty(),
@@ -210,7 +210,7 @@ class DefaultVocab : public IVocab {
                  const std::string& trainPath) {
     std::unique_ptr<std::istream> trainStrm(
       trainPath == "stdin" ? new std::istream(std::cin.rdbuf())
-                           : new io::InputFileStreamNew(trainPath)
+                           : new io::InputFileStream(trainPath)
     );
 
     std::string line;
@@ -251,7 +251,7 @@ class DefaultVocab : public IVocab {
 
     std::unique_ptr<std::ostream> vocabStrm(
       vocabPath == "stdout" ? new std::ostream(std::cout.rdbuf())
-                            : new io::OutputFileStreamNew(vocabPath)
+                            : new io::OutputFileStream(vocabPath)
     );
     *vocabStrm << vocabYaml;
   }
@@ -317,7 +317,7 @@ class ClassVocab : public DefaultVocab {
 
     std::unique_ptr<std::ostream> vocabStrm(
       vocabPath == "stdout" ? new std::ostream(std::cout.rdbuf())
-                            : new io::OutputFileStreamNew(vocabPath)
+                            : new io::OutputFileStream(vocabPath)
     );
     *vocabStrm << vocabYaml;
   }
diff --git a/src/data/factored_vocab.cpp b/src/data/factored_vocab.cpp
index 408687df9..24b75941e 100755
--- a/src/data/factored_vocab.cpp
+++ b/src/data/factored_vocab.cpp
@@ -42,7 +42,7 @@ namespace marian {
     //     - _lemma is special
     // The current version of the code just converts it internally to the legacy form.
     // @TODO: Once the legacy form is no longer needed, simplify this.
-    io::InputFileStreamNew in(modelPath);
+    io::InputFileStream in(modelPath);
     WordIndex v = 0;
     std::map<std::string,std::set<std::string>> factorTypeMap; // [type name] -> {factor-type names}
     std::vector<std::string> deferredFactorVocab; // factor surface forms are presently expected to be at the end of factorVocab_, so collect them here first
@@ -129,7 +129,7 @@ namespace marian {
     groupPrefixes_ = { "(lemma)", "@C", "@GL", "@GR", "@WB"/*, "@WE"*/, "@CB"/*, "@CE"*/ }; // @TODO: hard-coded for these initial experiments
     // @TODO: add checks for empty factor groups until it stops crashing (training already works; decoder still crashes)
 
-    io::InputFileStreamNew in(modelPath);
+    io::InputFileStream in(modelPath);
     for (WordIndex v = 0; std::getline(in, line); v++) {
       utils::splitAny(line, tokBuf, " \t");
       factorMapTokenized.push_back(tokBuf);
@@ -714,14 +714,14 @@ bool FactoredVocab::WordLUT::tryFind(const std::string& word, WordIndex& index)
 }
 size_t FactoredVocab::WordLUT::load(const std::string& path) {
   std::string line;
-  io::InputFileStreamNew in(path);
+  io::InputFileStream in(path);
   for (WordIndex v = 0; std::getline(in, line); v++)
     add(line, v);
   return size();
 }
 
 void FactoredVocab::WordLUT::dumpToFile(const std::string& path) {
-  io::OutputFileStreamNew out(path);
+  io::OutputFileStream out(path);
   for (auto kvp : index2str_)
     out << kvp.second << "\t" << utils::withCommas(kvp.first) << "\n";
 }
diff --git a/src/data/sentencepiece_vocab.cpp b/src/data/sentencepiece_vocab.cpp
index 6744a0ad8..0c370c1de 100644
--- a/src/data/sentencepiece_vocab.cpp
+++ b/src/data/sentencepiece_vocab.cpp
@@ -45,7 +45,7 @@ class SentencePieceVocab : public IVocab {
 
     std::unique_ptr<std::istream> trainStrm(
       trainPath == "stdin" ? new std::istream(&std::cin)
-                           : new io::InputFileStreamNew(trainPath)
+                           : new io::InputFileStream(trainPath)
     );
 
     std::string line;
@@ -78,7 +78,7 @@ class SentencePieceVocab : public IVocab {
       reservoirSampling(sample, seenLines, trainPath, maxLines, maxBytes);
     std::shuffle(sample.begin(), sample.end(), generator_);
 
-    io::OutputFileStreamNew out(temp);
+    io::OutputFileStream out(temp);
     for(const auto& line : sample)
         out << line << std::endl;
 
@@ -94,9 +94,9 @@ class SentencePieceVocab : public IVocab {
 
     size_t seenLines = 0;
     std::string line;
-    io::OutputFileStreamNew out(temp);
+    io::OutputFileStream out(temp);
     for(const auto& trainPath : trainPaths) {
-      io::InputFileStreamNew in(trainPath);
+      io::InputFileStream in(trainPath);
       while(getline(in, line)) {
         if(line.size() > 0 && line.size() < maxBytes) {
           out << line << std::endl;
diff --git a/src/data/shortlist.h b/src/data/shortlist.h
index 05d6c3bce..a44268a90 100644
--- a/src/data/shortlist.h
+++ b/src/data/shortlist.h
@@ -136,7 +136,7 @@ class LexicalShortlistGenerator : public ShortlistGenerator {
   std::vector<std::unordered_map<WordIndex, float>> data_; // [WordIndex src] -> [WordIndex tgt] -> P_trans(tgt|src) --@TODO: rename data_ accordingly
 
   void load(const std::string& fname) {
-    io::InputFileStreamNew in(fname);
+    io::InputFileStream in(fname);
 
     std::string src, trg;
     float prob;
@@ -216,12 +216,12 @@ class LexicalShortlistGenerator : public ShortlistGenerator {
   virtual void dump(const std::string& prefix) override {
     // Dump top most frequent words from target vocabulary
     LOG(info, "[data] Saving shortlist dump to {}", prefix + ".{top,dic}");
-    io::OutputFileStreamNew outTop(prefix + ".top");
+    io::OutputFileStream outTop(prefix + ".top");
     for(WordIndex i = 0; i < firstNum_ && i < trgVocab_->size(); ++i)
       outTop << (*trgVocab_)[Word::fromWordIndex(i)] << std::endl;
 
     // Dump translation pairs from dictionary
-    io::OutputFileStreamNew outDic(prefix + ".dic");
+    io::OutputFileStream outDic(prefix + ".dic");
     for(WordIndex srcId = 0; srcId < data_.size(); srcId++) {
       for(auto& it : data_[srcId]) {
         auto trgId = it.first;
diff --git a/src/layers/word2vec_reader.h b/src/layers/word2vec_reader.h
index af10b6a95..cc3ab99fd 100644
--- a/src/layers/word2vec_reader.h
+++ b/src/layers/word2vec_reader.h
@@ -17,7 +17,7 @@ class Word2VecReader {
   std::vector<float> read(const std::string& fileName, int dimVoc, int dimEmb) {
     LOG(info, "[data] Loading embedding vectors from {}", fileName);
 
-    io::InputFileStreamNew embFile(fileName);
+    io::InputFileStream embFile(fileName);
 
     std::string line;
     std::vector<std::string> values;
diff --git a/src/models/amun.h b/src/models/amun.h
index 334a79965..811d130ee 100755
--- a/src/models/amun.h
+++ b/src/models/amun.h
@@ -209,7 +209,7 @@ class Amun : public EncoderDecoder {
     amun["normalize"] = opt<float>("normalize") > 0;
     amun["beam-size"] = opt<size_t>("beam-size");
 
-    io::OutputFileStreamNew out(name + ".amun.yml");
+    io::OutputFileStream out(name + ".amun.yml");
     out << amun;
   }
 };
diff --git a/src/models/encoder_decoder.cpp b/src/models/encoder_decoder.cpp
index 7b202a763..2f29c045b 100755
--- a/src/models/encoder_decoder.cpp
+++ b/src/models/encoder_decoder.cpp
@@ -114,7 +114,7 @@ void EncoderDecoder::createDecoderConfig(const std::string& name) {
   decoder["maxi-batch"] = opt<size_t>("valid-mini-batch") > 1 ? 100 : 1;
   decoder["maxi-batch-sort"] = opt<size_t>("valid-mini-batch") > 1 ? "src" : "none";
 
-  io::OutputFileStreamNew out(name + ".decoder.yml");
+  io::OutputFileStream out(name + ".decoder.yml");
   out << decoder;
 }
 
diff --git a/src/models/nematus.h b/src/models/nematus.h
index 2730732b2..3c0a549e9 100755
--- a/src/models/nematus.h
+++ b/src/models/nematus.h
@@ -186,7 +186,7 @@ class Nematus : public EncoderDecoder {
     amun["scorers"]["F0"]["type"] = "nematus2";
     amun["weights"]["F0"] = 1.0f;
 
-    io::OutputFileStreamNew out(name + ".amun.yml");
+    io::OutputFileStream out(name + ".amun.yml");
     out << amun;
   }
 };
diff --git a/src/rescorer/score_collector.cpp b/src/rescorer/score_collector.cpp
index 2013f62f0..22bc99dd8 100644
--- a/src/rescorer/score_collector.cpp
+++ b/src/rescorer/score_collector.cpp
@@ -15,7 +15,7 @@ ScoreCollector::ScoreCollector(const Ptr<Options>& options)
     if(options->get<std::string>("output") == "stdout")
       outStrm_.reset(new std::ostream(std::cout.rdbuf()));
     else
-      outStrm_.reset(new io::OutputFileStreamNew(options->get<std::string>("output")));
+      outStrm_.reset(new io::OutputFileStream(options->get<std::string>("output")));
   }
 
 void ScoreCollector::Write(long id, const std::string& message) {
@@ -81,7 +81,7 @@ ScoreCollectorNBest::ScoreCollectorNBest(const Ptr<Options>& options)
     : ScoreCollector(options),
       nBestList_(options->get<std::vector<std::string>>("train-sets").back()),
       fname_(options->get<std::string>("n-best-feature")) {
-  file_.reset(new io::InputFileStreamNew(nBestList_));
+  file_.reset(new io::InputFileStream(nBestList_));
 }
 
 void ScoreCollectorNBest::Write(long id,
diff --git a/src/rescorer/score_collector.h b/src/rescorer/score_collector.h
index f66642fdb..ba4884366 100644
--- a/src/rescorer/score_collector.h
+++ b/src/rescorer/score_collector.h
@@ -56,7 +56,7 @@ class ScoreCollectorNBest : public ScoreCollector {
   std::string nBestList_;
   std::string fname_;
   long lastRead_{-1};
-  UPtr<io::InputFileStreamNew> file_;
+  UPtr<io::InputFileStream> file_;
   std::map<long, std::string> buffer_;
 
   std::string addToNBest(const std::string nbest,
diff --git a/src/training/validator.cpp b/src/training/validator.cpp
index 780bb3cce..3a38e7a93 100644
--- a/src/training/validator.cpp
+++ b/src/training/validator.cpp
@@ -349,12 +349,12 @@ float TranslationValidator::validate(const std::vector<Ptr<ExpressionGraph>>& gr
 
   // Set up output file
   std::string fileName;
-  Ptr<io::TemporaryFileNew> tempFile;
+  Ptr<io::TemporaryFile> tempFile;
 
   if(options_->hasAndNotEmpty("valid-translation-output")) {
     fileName = options_->get<std::string>("valid-translation-output");
   } else {
-    tempFile.reset(new io::TemporaryFileNew(options_->get<std::string>("tempdir"), false));
+    tempFile.reset(new io::TemporaryFile(options_->get<std::string>("tempdir"), false));
     fileName = tempFile->getFileName();
   }
 
diff --git a/src/translator/output_collector.cpp b/src/translator/output_collector.cpp
index 5a97982e2..76bc4cbbc 100644
--- a/src/translator/output_collector.cpp
+++ b/src/translator/output_collector.cpp
@@ -15,7 +15,7 @@ OutputCollector::OutputCollector(std::string outFile)
     outStrm_(new std::ostream(std::cout.rdbuf())),
     printing_(new DefaultPrinting()) {
   if (outFile != "stdout")
-    outStrm_.reset(new io::OutputFileStreamNew(outFile));
+    outStrm_.reset(new io::OutputFileStream(outFile));
 }
 
 void OutputCollector::Write(long sourceId,
diff --git a/src/translator/output_collector.h b/src/translator/output_collector.h
index 9e4934092..9e0e12b53 100644
--- a/src/translator/output_collector.h
+++ b/src/translator/output_collector.h
@@ -49,7 +49,7 @@ class OutputCollector {
   OutputCollector(std::string outFile);
 
   template <class T>
-  OutputCollector(T&& arg) : nextId_(0), outStrm_(new io::OutputFileStreamNew(arg)) {}
+  OutputCollector(T&& arg) : nextId_(0), outStrm_(new io::OutputFileStream(arg)) {}
 
   OutputCollector(const OutputCollector&) = delete;
 

From ff50a04c538a1c0af78150c10b2803e0bcfbeb61 Mon Sep 17 00:00:00 2001
From: Hieu Hoang <hihoan@microsoft.com>
Date: Wed, 23 Oct 2019 17:32:38 -0700
Subject: [PATCH 689/838] delete commented out code

---
 src/data/corpus_nbest.h | 1 -
 1 file changed, 1 deletion(-)

diff --git a/src/data/corpus_nbest.h b/src/data/corpus_nbest.h
index e17eb2000..2da55ccb5 100644
--- a/src/data/corpus_nbest.h
+++ b/src/data/corpus_nbest.h
@@ -18,7 +18,6 @@ namespace data {
 
 class CorpusNBest : public CorpusBase {
 private:
-  //std::vector<UPtr<io::TemporaryFile>> tempFiles_;
   std::vector<size_t> ids_;
   int lastNum_{-1};
   std::vector<std::string> lastLines_;

From 0c29e7f44fc4f6f98f24251dcda4559f385c925b Mon Sep 17 00:00:00 2001
From: Hieu Hoang <hihoan@microsoft.com>
Date: Wed, 23 Oct 2019 18:16:40 -0700
Subject: [PATCH 690/838] add getline back in

---
 src/common/file_stream.cpp | 15 +++++++++++++++
 src/common/file_stream.h   |  5 ++++-
 src/data/corpus_nbest.cpp  |  2 +-
 src/data/default_vocab.cpp |  2 +-
 4 files changed, 21 insertions(+), 3 deletions(-)

diff --git a/src/common/file_stream.cpp b/src/common/file_stream.cpp
index dd87d1d72..7984a7c86 100644
--- a/src/common/file_stream.cpp
+++ b/src/common/file_stream.cpp
@@ -80,6 +80,21 @@ void InputFileStream::setbufsize(size_t size) const {
   // do nothing. Is this needed?
 }
 
+std::string InputFileStream::getFileName() const {
+  return file_.string();
+}
+
+// wrapper around std::getline() that handles Windows input files with extra CR
+// chars at the line end
+std::istream &getline(std::istream &in, std::string &line) {
+  std::getline(in, line);
+  // bad() seems to be correct here. Should not abort on EOF.
+  ABORT_IF(in.bad(), "Error reading from stream");
+  // strip terminal CR if present
+  if(in && !line.empty() && line.back() == in.widen('\r'))
+    line.pop_back();
+  return in;
+}
 ///////////////////////////////////////////////////////////////////////////////////////////////
 OutputFileStream::OutputFileStream(const std::string &file)
     : std::ostream(NULL), file_(file), streamBuf1_(NULL), streamBuf2_(NULL) {
diff --git a/src/common/file_stream.h b/src/common/file_stream.h
index eb968b5a9..3f0384c21 100755
--- a/src/common/file_stream.h
+++ b/src/common/file_stream.h
@@ -51,13 +51,16 @@ class InputFileStream : public std::istream {
 
   bool empty();
   void setbufsize(size_t size) const;
+  std::string getFileName() const;
 
 protected:
   marian::filesystem::Path file_;
   std::streambuf* streamBuf_;
 };
 
-//////////////////////////////////////////////////////////////////////////////////////////////
+std::istream& getline(std::istream& in, std::string& line);
+
+  //////////////////////////////////////////////////////////////////////////////////////////////
 class OutputFileStream : public std::ostream {
 public:
   explicit OutputFileStream(const std::string& file);
diff --git a/src/data/corpus_nbest.cpp b/src/data/corpus_nbest.cpp
index b21ed61b0..d5a48d8df 100644
--- a/src/data/corpus_nbest.cpp
+++ b/src/data/corpus_nbest.cpp
@@ -49,7 +49,7 @@ SentenceTuple CorpusNBest::next() {
     lastLines_.resize(files_.size() - 1);
     size_t last = files_.size() - 1;
 
-    if(std::getline(*files_[last], line)) {
+    if(io::getline(*files_[last], line)) {
       int curr_num = numFromNbest(line);
       std::string curr_text = lineFromNbest(line);
 
diff --git a/src/data/default_vocab.cpp b/src/data/default_vocab.cpp
index 69b73d031..53f90a5d7 100644
--- a/src/data/default_vocab.cpp
+++ b/src/data/default_vocab.cpp
@@ -108,7 +108,7 @@ class DefaultVocab : public IVocab {
     else {
       io::InputFileStream in(vocabPath);
       std::string line;
-      while(std::getline(in, line)) {
+      while(io::getline(in, line)) {
         ABORT_IF(line.empty(),
                 "DefaultVocabulary file {} must not contain empty lines",
                 vocabPath);

From 8972fd9da91d05fa66b0a5408e958120d6484787 Mon Sep 17 00:00:00 2001
From: Hieu Hoang <hihoan@microsoft.com>
Date: Thu, 24 Oct 2019 09:49:39 -0700
Subject: [PATCH 691/838] fix code in files that didn't compile with default
 cmake command

---
 src/data/sentencepiece_vocab.cpp | 8 +++-----
 src/tests/sqlite.cpp             | 4 ++--
 2 files changed, 5 insertions(+), 7 deletions(-)

diff --git a/src/data/sentencepiece_vocab.cpp b/src/data/sentencepiece_vocab.cpp
index 0c370c1de..2e9462b60 100644
--- a/src/data/sentencepiece_vocab.cpp
+++ b/src/data/sentencepiece_vocab.cpp
@@ -44,7 +44,7 @@ class SentencePieceVocab : public IVocab {
     ABORT_IF(maxLines == 0, "Sample needs to be larger 0");
 
     std::unique_ptr<std::istream> trainStrm(
-      trainPath == "stdin" ? new std::istream(&std::cin)
+			    trainPath == "stdin" ? new std::istream(std::cin.rdbuf())
                            : new io::InputFileStream(trainPath)
     );
 
@@ -78,9 +78,8 @@ class SentencePieceVocab : public IVocab {
       reservoirSampling(sample, seenLines, trainPath, maxLines, maxBytes);
     std::shuffle(sample.begin(), sample.end(), generator_);
 
-    io::OutputFileStream out(temp);
     for(const auto& line : sample)
-        out << line << std::endl;
+        temp << line << std::endl;
 
     LOG(info, "[SentencePiece] Selected {} lines", sample.size());
     return sample.size();
@@ -94,12 +93,11 @@ class SentencePieceVocab : public IVocab {
 
     size_t seenLines = 0;
     std::string line;
-    io::OutputFileStream out(temp);
     for(const auto& trainPath : trainPaths) {
       io::InputFileStream in(trainPath);
       while(getline(in, line)) {
         if(line.size() > 0 && line.size() < maxBytes) {
-          out << line << std::endl;
+          temp << line << std::endl;
           seenLines++;
         }
       }
diff --git a/src/tests/sqlite.cpp b/src/tests/sqlite.cpp
index 35908b532..ac37f6f53 100644
--- a/src/tests/sqlite.cpp
+++ b/src/tests/sqlite.cpp
@@ -26,8 +26,8 @@ int main(int argc, char** argv) {
 
     std::cerr << "Reading from " << argv[1] << " and " << argv[2] << std::endl;
 
-    marian::io::InputFileStreamNew file0(argv[1]);
-    marian::io::InputFileStreamNew file1(argv[2]);
+    marian::io::InputFileStream file0(argv[1]);
+    marian::io::InputFileStream file1(argv[2]);
 
     db.exec("begin;");
     while(std::getline(file0, line0)

From 12c07c02662e4cb3bedc2d2ca22b8bfbe049bff6 Mon Sep 17 00:00:00 2001
From: Hieu Hoang <hihoan@microsoft.com>
Date: Thu, 24 Oct 2019 10:14:43 -0700
Subject: [PATCH 692/838] change std::getline -> io::getline

---
 src/data/corpus.cpp              | 4 ++--
 src/data/corpus_sqlite.cpp       | 2 +-
 src/data/factored_vocab.cpp      | 2 +-
 src/data/text_input.cpp          | 2 +-
 src/layers/word2vec_reader.h     | 4 ++--
 src/rescorer/score_collector.cpp | 2 +-
 src/tests/sqlite.cpp             | 4 ++--
 7 files changed, 10 insertions(+), 10 deletions(-)

diff --git a/src/data/corpus.cpp b/src/data/corpus.cpp
index 8606c3cdf..4369d3c36 100644
--- a/src/data/corpus.cpp
+++ b/src/data/corpus.cpp
@@ -71,7 +71,7 @@ SentenceTuple Corpus::next() {
         }
       }
       else {
-        std::istream &gotLine = std::getline(*files_[i], line);
+        std::istream &gotLine = io::getline(*files_[i], line);
         if(!gotLine) {
           eofsHit++;
           continue;
@@ -164,7 +164,7 @@ void Corpus::shuffleData(const std::vector<std::string>& paths) {
     for (;;) {
       size_t eofsHit = 0;
       for(size_t i = 0; i < numStreams; ++i) {
-        std::istream &gotLine = std::getline(*files_[i], lineBuf);
+        std::istream &gotLine = io::getline(*files_[i], lineBuf);
         if (gotLine)
           corpus[i].push_back(lineBuf);
         else
diff --git a/src/data/corpus_sqlite.cpp b/src/data/corpus_sqlite.cpp
index 74ffe1935..714a70f01 100644
--- a/src/data/corpus_sqlite.cpp
+++ b/src/data/corpus_sqlite.cpp
@@ -78,7 +78,7 @@ void CorpusSQLite::fillSQLite() {
 
       std::string line;
       for(size_t i = 0; i < files_.size(); ++i) {
-        cont = cont && std::getline(*files_[i], line);
+        cont = cont && io::getline(*files_[i], line);
         if(cont)
           ps.bind((int)(i + 2), line);
       }
diff --git a/src/data/factored_vocab.cpp b/src/data/factored_vocab.cpp
index 24b75941e..8e291eebd 100755
--- a/src/data/factored_vocab.cpp
+++ b/src/data/factored_vocab.cpp
@@ -46,7 +46,7 @@ namespace marian {
     WordIndex v = 0;
     std::map<std::string,std::set<std::string>> factorTypeMap; // [type name] -> {factor-type names}
     std::vector<std::string> deferredFactorVocab; // factor surface forms are presently expected to be at the end of factorVocab_, so collect them here first
-    while(std::getline(in, line)) {
+    while(io::getline(in, line)) {
 #if 1 // workaround for a bug fix in FactoredSegmenter that made old .fsv files incompatible
       if (line      == "\xef\xb8\x8f : _lemma _has_wb")         // old vocabs have a wrong factor in here
         line         = "\xef\xb8\x8f : _lemma _has_gl _has_gr"; // patch it to the correct one
diff --git a/src/data/text_input.cpp b/src/data/text_input.cpp
index af709faad..66a68c205 100644
--- a/src/data/text_input.cpp
+++ b/src/data/text_input.cpp
@@ -41,7 +41,7 @@ SentenceTuple TextInput::next() {
   for(size_t i = 0; i < files_.size(); ++i) {
     std::istream &dummyStream = *files_[i];
     std::string line;
-    if(std::getline(dummyStream, line)) {
+    if(io::getline(dummyStream, line)) {
       Words words = vocabs_[i]->encode(line, /*addEOS =*/ true, /*inference =*/ inference_);
       if(words.empty())
         words.push_back(Word::ZERO); // @TODO: What is this for? @BUGBUG: addEOS=true, so this can never happen, right?
diff --git a/src/layers/word2vec_reader.h b/src/layers/word2vec_reader.h
index cc3ab99fd..4bfc67091 100644
--- a/src/layers/word2vec_reader.h
+++ b/src/layers/word2vec_reader.h
@@ -25,7 +25,7 @@ class Word2VecReader {
 
     // The first line contains two values: the number of words in the
     // vocabulary and the length of embedding vectors
-    std::getline(embFile, line);
+    io::getline(embFile, line);
     utils::split(line, values);
     ABORT_IF(values.size() != 2,
              "Unexpected format of the first line of the embedding file");
@@ -34,7 +34,7 @@ class Word2VecReader {
 
     // Read embedding vectors into a map
     std::unordered_map<WordIndex, std::vector<float>> word2vec;
-    while(std::getline(embFile, line)) {
+    while(io::getline(embFile, line)) {
       values.clear();
       utils::split(line, values);
 
diff --git a/src/rescorer/score_collector.cpp b/src/rescorer/score_collector.cpp
index 22bc99dd8..1577feba7 100644
--- a/src/rescorer/score_collector.cpp
+++ b/src/rescorer/score_collector.cpp
@@ -96,7 +96,7 @@ void ScoreCollectorNBest::Write(long id,
                "Entry {} < {} already read but not in buffer",
                id,
                lastRead_);
-      while(lastRead_ < id && std::getline(*file_, line)) {
+      while(lastRead_ < id && io::getline(*file_, line)) {
         lastRead_++;
         iter = buffer_.emplace(lastRead_, line).first;
       }
diff --git a/src/tests/sqlite.cpp b/src/tests/sqlite.cpp
index ac37f6f53..217485149 100644
--- a/src/tests/sqlite.cpp
+++ b/src/tests/sqlite.cpp
@@ -30,8 +30,8 @@ int main(int argc, char** argv) {
     marian::io::InputFileStream file1(argv[2]);
 
     db.exec("begin;");
-    while(std::getline(file0, line0)
-          && std::getline(file1, line1)) {
+    while(marian::io::getline(file0, line0)
+          && marian::io::getline(file1, line1)) {
       ps.bind(1, (int)lines);
       ps.bind(2, line0);
       ps.bind(3, line1);

From 15c1fdfd94c379158735fcb1b808b211a37679de Mon Sep 17 00:00:00 2001
From: Hieu Hoang <hihoan@microsoft.com>
Date: Thu, 24 Oct 2019 10:15:54 -0700
Subject: [PATCH 693/838] sheath naked pointer

---
 src/data/corpus.cpp | 5 +++--
 1 file changed, 3 insertions(+), 2 deletions(-)

diff --git a/src/data/corpus.cpp b/src/data/corpus.cpp
index 8606c3cdf..b0c36e348 100644
--- a/src/data/corpus.cpp
+++ b/src/data/corpus.cpp
@@ -205,8 +205,9 @@ void Corpus::shuffleData(const std::vector<std::string>& paths) {
     // replace files_[] by the tempfiles we just created
     files_.resize(numStreams);
     for(size_t i = 0; i < numStreams; ++i) {
-      io::TemporaryFile* tempFile = tempFiles_[i].get();
-      files_[i] = tempFile->getInputStream();
+      UPtr<io::InputFileStream> inputStream = tempFiles_[i]->getInputStream();
+      inputStream->setbufsize(10000000);
+      files_[i] = std::move(inputStream);
     }
     LOG(info, "[data] Done shuffling {} sentences to temp files", numSentences);
 #else

From 8cfa33d2f5ccc18005b546ea18465cee8582239a Mon Sep 17 00:00:00 2001
From: Hieu Hoang <hihoan@microsoft.com>
Date: Thu, 24 Oct 2019 12:16:50 -0700
Subject: [PATCH 694/838] changes for code review

---
 src/common/file_stream.cpp |  2 +-
 src/data/corpus.cpp        | 12 ++++++------
 src/data/corpus_base.cpp   |  4 ++--
 src/data/text_input.cpp    |  2 +-
 4 files changed, 10 insertions(+), 10 deletions(-)

diff --git a/src/common/file_stream.cpp b/src/common/file_stream.cpp
index 7984a7c86..cb2def8b8 100644
--- a/src/common/file_stream.cpp
+++ b/src/common/file_stream.cpp
@@ -119,7 +119,7 @@ OutputFileStream::~OutputFileStream() {
 ///////////////////////////////////////////////////////////////////////////////////////////////
 TemporaryFile::TemporaryFile(const std::string &base, bool earlyUnlink)
     : OutputFileStream(CreateFileName(base)), unlink_(earlyUnlink) {
-  inSteam_.reset(new InputFileStream(file_.string()));
+  inSteam_ = std::make_unique<io::InputFileStream>(file_.string());
   if (unlink_) {
     ABORT_IF(remove(file_.string().c_str()), "Error while deleting '{}'", file_.string());
   }
diff --git a/src/data/corpus.cpp b/src/data/corpus.cpp
index fcf00373b..567d7f38c 100644
--- a/src/data/corpus.cpp
+++ b/src/data/corpus.cpp
@@ -154,9 +154,9 @@ void Corpus::shuffleData(const std::vector<std::string>& paths) {
   else {
     files_.resize(numStreams);
     for(size_t i = 0; i < numStreams; ++i) {
-      io::InputFileStream *strm = new io::InputFileStream(paths[i]);
-      files_[i].reset(strm);
-      strm->setbufsize(10000000); // huge read-ahead buffer to avoid network round-trips
+      UPtr<io::InputFileStream> strm = std::make_unique<io::InputFileStream>(paths[i]);
+      strm->setbufsize(10000000);  // huge read-ahead buffer to avoid network round-trips
+      files_[i] = std::move(strm);
     }
 
     // read entire corpus into RAM
@@ -194,11 +194,11 @@ void Corpus::shuffleData(const std::vector<std::string>& paths) {
     // create temp files that contain the data in randomized order
     tempFiles_.resize(numStreams);
     for(size_t i = 0; i < numStreams; ++i) {
-      io::TemporaryFile *out = new io::TemporaryFile(options_->get<std::string>("tempdir"));
-	  tempFiles_[i].reset(out);
+      tempFiles_[i].reset(new io::TemporaryFile(options_->get<std::string>("tempdir")));
+      io::TemporaryFile &out = *tempFiles_[i];
       const auto& corpusStream = corpus[i];
       for(auto id : ids_) {
-        (*out) << corpusStream[id] << std::endl;
+        out << corpusStream[id] << std::endl;
       }
     }
 
diff --git a/src/data/corpus_base.cpp b/src/data/corpus_base.cpp
index d6c9fa391..1494fe164 100755
--- a/src/data/corpus_base.cpp
+++ b/src/data/corpus_base.cpp
@@ -40,9 +40,9 @@ CorpusBase::CorpusBase(const std::vector<std::string>& paths,
            "Number of corpus files and vocab files does not agree");
 
   for(auto path : paths_) {
-    io::InputFileStream *strm = new io::InputFileStream(path);
+    UPtr<io::InputFileStream> strm = std::make_unique<io::InputFileStream>(path);
     ABORT_IF(strm->empty(), "File '{}' is empty", path);
-    files_.emplace_back(strm);
+    files_.emplace_back(std::move(strm));
   }
 
   initEOS(/*training=*/true);
diff --git a/src/data/text_input.cpp b/src/data/text_input.cpp
index 66a68c205..7a8b65f6e 100644
--- a/src/data/text_input.cpp
+++ b/src/data/text_input.cpp
@@ -39,7 +39,7 @@ SentenceTuple TextInput::next() {
   // fill up the sentence tuple with source and/or target sentences
   SentenceTuple tup(curId);
   for(size_t i = 0; i < files_.size(); ++i) {
-    std::istream &dummyStream = *files_[i];
+    std::istringstream& dummyStream = *files_[i];
     std::string line;
     if(io::getline(dummyStream, line)) {
       Words words = vocabs_[i]->encode(line, /*addEOS =*/ true, /*inference =*/ inference_);

From 05695ff8e9b4f08199f96ce69d171aaecf6ef631 Mon Sep 17 00:00:00 2001
From: Ulrich Germann <ugermann@inf.ed.ac.uk>
Date: Thu, 24 Oct 2019 20:38:47 +0100
Subject: [PATCH 695/838] Fix whitespace and end of line.

---
 src/training/scheduler.h | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/training/scheduler.h b/src/training/scheduler.h
index 2c7f18b4d..f06e10ade 100644
--- a/src/training/scheduler.h
+++ b/src/training/scheduler.h
@@ -219,7 +219,7 @@ class Scheduler : public TrainingObserver {
                 bool final = false) {
     // Do not validate if already validated (for instance, after the model is
     // loaded) or if validation is scheduled for another update
-    if(sigterm_ 
+    if(sigterm_
        || state_->validated
        || (!state_->enteredNewPeriodOf(options_->get<std::string>("valid-freq")) && !final))
       return;

From 216608dc1df24f4f35b6cfbffceadbc44b7e1ae9 Mon Sep 17 00:00:00 2001
From: Hieu Hoang <hihoan@microsoft.com>
Date: Thu, 24 Oct 2019 13:09:27 -0700
Subject: [PATCH 696/838] manually roll back 'figure out where temp file class
 is used'

---
 src/common/file_stream.cpp | 14 +++++++-------
 src/common/file_stream.h   | 10 +++++-----
 src/data/corpus.cpp        |  2 +-
 src/data/corpus.h          |  2 +-
 src/data/corpus_nbest.h    |  2 +-
 src/training/validator.cpp |  4 ++--
 6 files changed, 17 insertions(+), 17 deletions(-)

diff --git a/src/common/file_stream.cpp b/src/common/file_stream.cpp
index e0fea2e77..84101d944 100644
--- a/src/common/file_stream.cpp
+++ b/src/common/file_stream.cpp
@@ -168,7 +168,7 @@ ssize_t WriteFDBuf::WriteSome(const char *from, const char *to) {
 
 ///////////////////////////////////////////////////////////////////////////////////////////////
 #ifndef _MSC_VER
-int TemporaryFile2::mkstemp_and_unlink(char *tmpl) {
+int TemporaryFile::mkstemp_and_unlink(char *tmpl) {
   int ret = mkstemp(tmpl);
   if(unlink_ && ret != -1) {
     ABORT_IF(unlink(tmpl), "Error while deleting '{}'", tmpl);
@@ -177,7 +177,7 @@ int TemporaryFile2::mkstemp_and_unlink(char *tmpl) {
 }
 #endif
 
-int TemporaryFile2::MakeTemp(const std::string &base) {
+int TemporaryFile::MakeTemp(const std::string &base) {
 #ifdef _MSC_VER
   char *name = tempnam(base.c_str(), "marian.");
   ABORT_IF(name == NULL, "Error while making a temporary based on '{}'", base);
@@ -206,7 +206,7 @@ int TemporaryFile2::MakeTemp(const std::string &base) {
 #endif
 }
 
-void TemporaryFile2::NormalizeTempPrefix(std::string &base) {
+void TemporaryFile::NormalizeTempPrefix(std::string &base) {
   if(base.empty())
     return;
 
@@ -225,13 +225,13 @@ void TemporaryFile2::NormalizeTempPrefix(std::string &base) {
 #endif
 }
 
-TemporaryFile2::TemporaryFile2(const std::string base, bool earlyUnlink) : unlink_(earlyUnlink) {
+TemporaryFile::TemporaryFile(const std::string base, bool earlyUnlink) : unlink_(earlyUnlink) {
   std::string baseTemp(base);
   NormalizeTempPrefix(baseTemp);
   fd_ = MakeTemp(baseTemp);
 }
 
-TemporaryFile2::~TemporaryFile2() {
+TemporaryFile::~TemporaryFile() {
 #ifdef _MSC_VER
   if(fd_ == -1)
     return;
@@ -266,7 +266,7 @@ InputFileStream::InputFileStream(const std::string &file) : file_(file) {
   ABORT_IF(fail(), "Error {} ({}) opening file '{}'", errno, strerror(errno), path());
 }
 
-InputFileStream::InputFileStream(TemporaryFile2 &tempfile) {
+InputFileStream::InputFileStream(TemporaryFile &tempfile) {
   RewindFile(tempfile.getFileDescriptor());
   temporary_reader_.reset(new ReadFDBuf(tempfile.getFileDescriptor()));
   istream_.reset(new std::istream(temporary_reader_.get()));
@@ -288,7 +288,7 @@ OutputFileStream::OutputFileStream(const std::string &file) : file_(file) {
   ABORT_IF(!marian::filesystem::exists(file_), "File '{}' could not be opened", file);
 }
 
-OutputFileStream::OutputFileStream(TemporaryFile2 &tempfile) {
+OutputFileStream::OutputFileStream(TemporaryFile &tempfile) {
   RewindFile(tempfile.getFileDescriptor());
   temporary_writer_.reset(new WriteFDBuf(tempfile.getFileDescriptor()));
   ostream_.reset(new std::ostream(temporary_writer_.get()));
diff --git a/src/common/file_stream.h b/src/common/file_stream.h
index 55f687e25..5b72982d0 100755
--- a/src/common/file_stream.h
+++ b/src/common/file_stream.h
@@ -42,7 +42,7 @@ std::unique_ptr<T> make_unique(Args&&... args) {
 namespace marian {
 namespace io {
 
-class TemporaryFile2 {
+class TemporaryFile {
 private:
   int fd_{-1};
   bool unlink_;
@@ -57,9 +57,9 @@ class TemporaryFile2 {
   void NormalizeTempPrefix(std::string& base);
 
 public:
-  TemporaryFile2(const std::string base = "/tmp/", bool earlyUnlink = true);
+  TemporaryFile(const std::string base = "/tmp/", bool earlyUnlink = true);
 
-  ~TemporaryFile2();
+  ~TemporaryFile();
 
   int getFileDescriptor() { return fd_; }
 
@@ -126,7 +126,7 @@ class InputFileStream {
 public:
   explicit InputFileStream(const std::string& file);
 
-  explicit InputFileStream(TemporaryFile2& tempfile);
+  explicit InputFileStream(TemporaryFile& tempfile);
 
   explicit InputFileStream(std::istream& strm) : istream_(new std::istream(strm.rdbuf())) {}
 
@@ -204,7 +204,7 @@ class OutputFileStream {
 public:
   OutputFileStream(const std::string& file);
 
-  OutputFileStream(TemporaryFile2& tempfile);
+  OutputFileStream(TemporaryFile& tempfile);
 
   OutputFileStream(std::ostream& strm) { ostream_ = std::make_unique<std::ostream>(strm.rdbuf()); }
 
diff --git a/src/data/corpus.cpp b/src/data/corpus.cpp
index 129c282f4..f0c144433 100644
--- a/src/data/corpus.cpp
+++ b/src/data/corpus.cpp
@@ -192,7 +192,7 @@ void Corpus::shuffleData(const std::vector<std::string>& paths) {
     // create temp files that contain the data in randomized order
     tempFiles_.resize(numStreams);
     for(size_t i = 0; i < numStreams; ++i) {
-      tempFiles_[i].reset(new io::TemporaryFile2(options_->get<std::string>("tempdir")));
+      tempFiles_[i].reset(new io::TemporaryFile(options_->get<std::string>("tempdir")));
       io::OutputFileStream out(*tempFiles_[i]);
       const auto& corpusStream = corpus[i];
       for(auto id : ids_) {
diff --git a/src/data/corpus.h b/src/data/corpus.h
index 9bf00b071..4ac437248 100644
--- a/src/data/corpus.h
+++ b/src/data/corpus.h
@@ -18,7 +18,7 @@ namespace data {
 
 class Corpus : public CorpusBase {
 private:
-  std::vector<UPtr<io::TemporaryFile2>> tempFiles_;
+  std::vector<UPtr<io::TemporaryFile>> tempFiles_;
   std::vector<size_t> ids_;
 
   // for shuffle-in-ram
diff --git a/src/data/corpus_nbest.h b/src/data/corpus_nbest.h
index 3e06f639b..2c7958b19 100644
--- a/src/data/corpus_nbest.h
+++ b/src/data/corpus_nbest.h
@@ -18,7 +18,7 @@ namespace data {
 
 class CorpusNBest : public CorpusBase {
 private:
-  std::vector<UPtr<io::TemporaryFile2>> tempFiles_;
+  std::vector<UPtr<io::TemporaryFile>> tempFiles_;
   std::vector<size_t> ids_;
   int lastNum_{-1};
   std::vector<std::string> lastLines_;
diff --git a/src/training/validator.cpp b/src/training/validator.cpp
index 24f523273..5e8660291 100644
--- a/src/training/validator.cpp
+++ b/src/training/validator.cpp
@@ -349,12 +349,12 @@ float TranslationValidator::validate(const std::vector<Ptr<ExpressionGraph>>& gr
 
   // Set up output file
   std::string fileName;
-  Ptr<io::TemporaryFile2> tempFile;
+  Ptr<io::TemporaryFile> tempFile;
 
   if(options_->hasAndNotEmpty("valid-translation-output")) {
     fileName = options_->get<std::string>("valid-translation-output");
   } else {
-    tempFile.reset(new io::TemporaryFile2(options_->get<std::string>("tempdir"), false));
+    tempFile.reset(new io::TemporaryFile(options_->get<std::string>("tempdir"), false));
     fileName = tempFile->getFileName();
   }
 

From 723b7fcad12e647eb39314233dab53c5ff0550c6 Mon Sep 17 00:00:00 2001
From: Hieu Hoang <hihoan@microsoft.com>
Date: Thu, 24 Oct 2019 13:16:10 -0700
Subject: [PATCH 697/838] roll back ken's non-boost code

---
 src/common/file_stream.cpp | 285 -------------------------------------
 src/common/file_stream.h   | 228 +++++++++++++++++++----------
 2 files changed, 151 insertions(+), 362 deletions(-)

diff --git a/src/common/file_stream.cpp b/src/common/file_stream.cpp
index 84101d944..5d48c6a87 100644
--- a/src/common/file_stream.cpp
+++ b/src/common/file_stream.cpp
@@ -11,288 +11,3 @@
 #include <unistd.h>
 #endif
 
-namespace marian {
-namespace io {
-
-// Get error strings out of errno.
-namespace {
-#ifdef __GNUC__
-const char *HandleStrerror(int ret, const char *buf) __attribute__ ((unused));
-const char *HandleStrerror(const char *ret, const char * /*buf*/) __attribute__ ((unused));
-#endif
-// At least one of these functions will not be called.
-#ifdef __clang__
-#pragma clang diagnostic push
-#pragma clang diagnostic ignored "-Wunused-function"
-#endif
-// The XOPEN version.
-const char *HandleStrerror(int ret, const char *buf) {
-  if (!ret) return buf;
-  return NULL;
-}
-
-// The GNU version.
-const char *HandleStrerror(const char *ret, const char * /*buf*/) {
-  return ret;
-}
-#ifdef __clang__
-#pragma clang diagnostic pop
-#endif
-
-std::string StrError() {
-  char buf[200];
-  buf[0] = 0;
-#if defined(sun) || defined(_WIN32) || defined(_WIN64)
-  return strerror(errno);
-#else
-  return HandleStrerror(strerror_r(errno, buf, 200), buf);
-#endif
-}
-} // namespace
-
-void RewindFile(int fd) {
-  ABORT_IF((off_t)-1 == lseek(fd, 0, SEEK_SET), "lseek to 0 failed in fd {}", fd);
-}
-
-ReadFDBuf::ReadFDBuf(int fd, std::size_t buffer_size)
-: fd_(fd), mem_(buffer_size) {
-  setg(End(), End(), End());
-}
-
-ReadFDBuf::int_type ReadFDBuf::underflow() {
-  if (gptr() == egptr()) {
-    // Reached end, refill
-    ssize_t got = Read();
-    if (!got) {
-      // End of file
-      return traits_type::eof();
-    }
-    setg(Begin(), Begin(), Begin() + got);
-  }
-  return traits_type::to_int_type(*gptr());
-}
-
-// If the putback goes below the buffer, try to seek backwards.
-ReadFDBuf::int_type ReadFDBuf::pbackfail(int c) {
-  /* "It is unspecified whether the content of the controlled input
-   * sequence is modified if the function succeeds and c does not match the
-   * character at that position."
-   * -- http://www.cplusplus.com/reference/streambuf/streambuf/pbackfail/
-   */
-  if (gptr() > Begin()) {
-    setg(Begin(), gptr() - 1, End());
-  } else {
-    if (lseek(fd_, -1, SEEK_CUR) == -1) {
-      return EOF;
-    }
-    ssize_t got = Read();
-    if (!got) {
-      // This happens if the file was truncated underneath us.
-      return traits_type::eof();
-    }
-    setg(Begin(), Begin(), Begin() + got);
-  }
-  return traits_type::to_int_type(*gptr());
-}
-
-// Read some amount into [Begin(), End()), returning the amount read.
-ssize_t ReadFDBuf::Read() {
-  ssize_t got;
-  // Loop to keep reading if EINTR happens.
-  // This way the program is robust to Ctrl+Z then backgrounding.
-  do {
-    errno = 0;
-    got =
-#ifdef _MSC_VER
-      _read
-#else
-      read
-#endif
-      (fd_, Begin(), End() - Begin());
-  } while (got == -1 && errno == EINTR);
-  ABORT_IF(got < 0, "Error reading fd {}: {}", fd_, StrError());
-  return got;
-}
-
-WriteFDBuf::WriteFDBuf(int fd, std::size_t buffer_size)
-: fd_(fd), mem_(buffer_size) {
-  setp(End(), End());
-}
-
-WriteFDBuf::~WriteFDBuf() { sync(); }
-
-WriteFDBuf::int_type WriteFDBuf::overflow(int c) {
-  if (c == EOF) {
-    // Apparently overflow(EOF) means sync().
-    sync();
-    return c;
-  }
-  if (pptr() == epptr()) {
-    // Out of buffer.  Write and reset.
-    sync();
-    setp(Begin(), End());
-  }
-  // Put character on the end.
-  *pptr() = traits_type::to_char_type(c);
-  pbump(1);
-  return c;
-}
-
-// Write everything in the buffer to the file.
-int WriteFDBuf::sync() {
-  const char *from = pbase();
-  const char *to = pptr();
-  while (from != to) {
-    from += WriteSome(from, to);
-  }
-  // We die on all failures.
-  return 0;
-}
-
-// Write part of the buffer, returning the amount written.
-ssize_t WriteFDBuf::WriteSome(const char *from, const char *to) {
-  ssize_t put = 0;
-  do {
-    errno = 0;
-    put =
-#ifdef _MSC_VER
-      _write
-#else
-      write
-#endif
-      (fd_, from, to - from);
-  } while (put == -1 && errno == EINTR);
-  ABORT_IF(put < 0, "Error writing to fd {}: {}", fd_, StrError());
-  return put;
-}
-
-///////////////////////////////////////////////////////////////////////////////////////////////
-#ifndef _MSC_VER
-int TemporaryFile::mkstemp_and_unlink(char *tmpl) {
-  int ret = mkstemp(tmpl);
-  if(unlink_ && ret != -1) {
-    ABORT_IF(unlink(tmpl), "Error while deleting '{}'", tmpl);
-  }
-  return ret;
-}
-#endif
-
-int TemporaryFile::MakeTemp(const std::string &base) {
-#ifdef _MSC_VER
-  char *name = tempnam(base.c_str(), "marian.");
-  ABORT_IF(name == NULL, "Error while making a temporary based on '{}'", base);
-
-  int oflag = _O_RDWR | _O_CREAT | _O_EXCL;
-  if(unlink_)
-    oflag |= _O_TEMPORARY;
-
-  int ret = open(name, oflag, _S_IREAD | _S_IWRITE);
-  ABORT_IF(ret == -1, "Error while making a temporary based on '{}'", base);
-
-  name_ = name;
-  free(name);
-
-  return ret;
-#else
-  std::string name(base);
-  name += "marian.XXXXXX";
-  name.push_back(0);
-  int ret;
-  ABORT_IF(-1 == (ret = mkstemp_and_unlink(&name[0])),
-           "Error while making a temporary based on '{}'",
-           base);
-  name_ = name;
-  return ret;
-#endif
-}
-
-void TemporaryFile::NormalizeTempPrefix(std::string &base) {
-  if(base.empty())
-    return;
-
-#ifdef _MSC_VER
-  if(base.substr(0, 4) == "/tmp")
-    base = getenv("TMP");
-#else
-  if(base[base.size() - 1] == '/')
-    return;
-  struct stat sb;
-  // It's fine for it to not exist.
-  if(stat(base.c_str(), &sb) == -1)
-    return;
-  if(S_ISDIR(sb.st_mode))
-    base += '/';
-#endif
-}
-
-TemporaryFile::TemporaryFile(const std::string base, bool earlyUnlink) : unlink_(earlyUnlink) {
-  std::string baseTemp(base);
-  NormalizeTempPrefix(baseTemp);
-  fd_ = MakeTemp(baseTemp);
-}
-
-TemporaryFile::~TemporaryFile() {
-#ifdef _MSC_VER
-  if(fd_ == -1)
-    return;
-
-  if(close(fd_)) {
-    std::cerr << "Could not close file " << fd_ << std::endl;
-    std::abort();
-  }
-
-  if(!unlink_) {
-    ABORT_IF(remove(name_.c_str()), "Error while deleting '{}'", name_);
-  }
-#else
-  if(fd_ != -1 && !unlink_) {
-    ABORT_IF(unlink(name_.c_str()), "Error while deleting '{}'", name_);
-  }
-  if(fd_ != -1 && close(fd_)) {
-    std::cerr << "Could not close file " << fd_ << std::endl;
-    std::abort();
-  }
-#endif
-}
-
-///////////////////////////////////////////////////////////////////////////////////////////////
-InputFileStream::InputFileStream(const std::string &file) : file_(file) {
-  ABORT_IF(!marian::filesystem::exists(file_), "File '{}' does not exist", file);
-
-  if(file_.extension() == marian::filesystem::Path(".gz"))
-    istream_ = std::make_unique<zstr::ifstream>(file_.string());
-  else
-    istream_ = std::make_unique<std::ifstream>(file_.string());
-  ABORT_IF(fail(), "Error {} ({}) opening file '{}'", errno, strerror(errno), path());
-}
-
-InputFileStream::InputFileStream(TemporaryFile &tempfile) {
-  RewindFile(tempfile.getFileDescriptor());
-  temporary_reader_.reset(new ReadFDBuf(tempfile.getFileDescriptor()));
-  istream_.reset(new std::istream(temporary_reader_.get()));
-  std::cerr << "HH1" << std::endl;
-}
-
-void InputFileStream::setbufsize(size_t size) const {
-  istream_->rdbuf()->pubsetbuf(0, 0);
-  readBuf_.resize(size);
-  istream_->rdbuf()->pubsetbuf(readBuf_.data(), readBuf_.size());
-}
-
-///////////////////////////////////////////////////////////////////////////////////////////////
-OutputFileStream::OutputFileStream(const std::string &file) : file_(file) {
-  if(file_.extension() == marian::filesystem::Path(".gz"))
-    ostream_ = std::make_unique<zstr::ofstream>(file_.string());
-  else
-    ostream_ = std::make_unique<std::ofstream>(file_.string());
-  ABORT_IF(!marian::filesystem::exists(file_), "File '{}' could not be opened", file);
-}
-
-OutputFileStream::OutputFileStream(TemporaryFile &tempfile) {
-  RewindFile(tempfile.getFileDescriptor());
-  temporary_writer_.reset(new WriteFDBuf(tempfile.getFileDescriptor()));
-  ostream_.reset(new std::ostream(temporary_writer_.get()));
-}
-
-} // namespace io
-} // namespace marian
diff --git a/src/common/file_stream.h b/src/common/file_stream.h
index 5b72982d0..fa0eb42e8 100755
--- a/src/common/file_stream.h
+++ b/src/common/file_stream.h
@@ -13,16 +13,19 @@
 #pragma warning(disable : 4101)
 #endif
 #include "3rd_party/zstr/zstr.hpp"
-#ifdef __GNUC__
-#pragma GCC diagnostic pop
+#ifndef NO_BOOST
+#include <boost/iostreams/device/file_descriptor.hpp>
+#include <boost/iostreams/stream_buffer.hpp>
 #endif
 #ifdef _MSC_VER
 #pragma warning(pop)
 #endif
+#ifdef __GNUC__
+#pragma GCC diagnostic pop
+#endif
 
 #include <iostream>
 #include <memory>
-#include <vector>
 
 #ifdef __GNUC__  // not supported; maybe we just need to increment a standard flag in gcc/cmake?
 namespace std {
@@ -48,87 +51,125 @@ class TemporaryFile {
   bool unlink_;
   std::string name_;
 
-  #ifndef _MSC_VER
-  int mkstemp_and_unlink(char* tmpl);
-  #endif
+#ifndef _MSC_VER
+  int mkstemp_and_unlink(char* tmpl) {
+    int ret = mkstemp(tmpl);
+    if(unlink_ && ret != -1) {
+      ABORT_IF(unlink(tmpl), "Error while deleting '{}'", tmpl);
+    }
+    return ret;
+  }
+#endif
+
+  int MakeTemp(const std::string& base) {
+#ifdef _MSC_VER
+    char* name = tempnam(base.c_str(), "marian.");
+    ABORT_IF(name == NULL, "Error while making a temporary based on '{}'", base);
+
+    int oflag = _O_RDWR | _O_CREAT | _O_EXCL;
+    if(unlink_)
+      oflag |= _O_TEMPORARY;
+
+    int ret = open(name, oflag, _S_IREAD | _S_IWRITE);
+    ABORT_IF(ret == -1, "Error while making a temporary based on '{}'", base);
+
+    name_ = name;
+    free(name);
+
+    return ret;
+#else
+    std::string name(base);
+    name += "marian.XXXXXX";
+    name.push_back(0);
+    int ret;
+    ABORT_IF(-1 == (ret = mkstemp_and_unlink(&name[0])),
+             "Error while making a temporary based on '{}'",
+             base);
+    name_ = name;
+    return ret;
+#endif
+  }
 
-  int MakeTemp(const std::string& base);
+  void NormalizeTempPrefix(std::string& base) {
+    if(base.empty())
+      return;
 
-  void NormalizeTempPrefix(std::string& base);
+#ifdef _MSC_VER
+    if(base.substr(0, 4) == "/tmp")
+      base = getenv("TMP");
+#else
+    if(base[base.size() - 1] == '/')
+      return;
+    struct stat sb;
+    // It's fine for it to not exist.
+    if(stat(base.c_str(), &sb) == -1)
+      return;
+    if(S_ISDIR(sb.st_mode))
+      base += '/';
+#endif
+  }
 
 public:
-  TemporaryFile(const std::string base = "/tmp/", bool earlyUnlink = true);
+  TemporaryFile(const std::string base = "/tmp/", bool earlyUnlink = true) : unlink_(earlyUnlink) {
+    std::string baseTemp(base);
+    NormalizeTempPrefix(baseTemp);
+    fd_ = MakeTemp(baseTemp);
+  }
 
-  ~TemporaryFile();
+  ~TemporaryFile() {
+#ifdef _MSC_VER
+    if(fd_ == -1)
+      return;
+
+    if(close(fd_)) {
+      std::cerr << "Could not close file " << fd_ << std::endl;
+      std::abort();
+    }
+
+    if(!unlink_) {
+      ABORT_IF(remove(name_.c_str()), "Error while deleting '{}'", name_);
+    }
+#else
+    if(fd_ != -1 && !unlink_) {
+      ABORT_IF(unlink(name_.c_str()), "Error while deleting '{}'", name_);
+    }
+    if(fd_ != -1 && close(fd_)) {
+      std::cerr << "Could not close file " << fd_ << std::endl;
+      std::abort();
+    }
+#endif
+  }
 
   int getFileDescriptor() { return fd_; }
 
   std::string getFileName() { return name_; }
 };
 
-// A streambuf to read from a file descriptor.
-class ReadFDBuf : public std::streambuf {
-public:
-  // Does not take ownership of file.
-  explicit ReadFDBuf(int fd, std::size_t buffer_size = 4096);
-
-private:
-  int_type underflow() override;
-
-  // If the putback goes below the buffer, try to seek backwards.
-  int_type pbackfail(int c = EOF) override;
-
-  // TODO: override setbuf.
-
-  // Read some amount into [Begin(), End()), returning the amount read.
-  ssize_t Read();
-
-  char* Begin() { return &mem_.front(); }
-  char* End() { return &mem_.back() + 1; }
-
-  int fd_;
-  std::vector<char> mem_;
-
-  ReadFDBuf(const ReadFDBuf&) = delete;
-  ReadFDBuf& operator=(const ReadFDBuf&) = delete;
-};
-
-// A streambuf to Write to a file descriptor.
-class WriteFDBuf : public std::streambuf {
-public:
-  explicit WriteFDBuf(int fd, std::size_t buffer_size = 4096);
-
-  ~WriteFDBuf();
-
-private:
-  int_type overflow(int c = EOF) override;
-
-  // Write everything in the buffer to the file.
-  int sync() override;
-
-  // Write part of the buffer, returning the amount written.
-  ssize_t WriteSome(const char* from, const char* to);
-
-  char* Begin() { return &mem_.front(); }
-  char* End() { return &mem_.back() + 1; }
-
-  int fd_;
-  std::vector<char> mem_;
-
-  WriteFDBuf(const WriteFDBuf&) = delete;
-  WriteFDBuf& operator=(const WriteFDBuf&) = delete;
-};
-
-// lseek(fd, 0, SEEK_SET) but with error checking.
-void RewindFile(int fd);
-
 class InputFileStream {
 public:
-  explicit InputFileStream(const std::string& file);
+  InputFileStream(const std::string& file) : file_(file) {
+    ABORT_IF(!marian::filesystem::exists(file_), "File '{}' does not exist", file);
+
+    if(file_.extension() == marian::filesystem::Path(".gz"))
+      istream_ = std::make_unique<zstr::ifstream>(file_.string(),
+                                                  std::ios_base::in | std::ios_base::binary);
+    else
+      istream_ = std::make_unique<std::ifstream>(file_.string());
+    ABORT_IF(fail(), "Error {} ({}) opening file '{}'", errno, strerror(errno), path());
+  }
+
+#ifndef NO_BOOST
+  InputFileStream(TemporaryFile& tempfile)
+      : fds_(tempfile.getFileDescriptor(), boost::iostreams::never_close_handle) {
+    lseek(tempfile.getFileDescriptor(), 0, SEEK_SET);
 
-  explicit InputFileStream(TemporaryFile& tempfile);
+    namespace bio = boost::iostreams;
+    fdsBuffer_ = std::make_unique<bio::stream_buffer<bio::file_descriptor_source>>(fds_);
+    istream_ = std::make_unique<std::istream>(fdsBuffer_.get());
+  }
+#endif
 
-  explicit InputFileStream(std::istream& strm) : istream_(new std::istream(strm.rdbuf())) {}
+  InputFileStream(std::istream& strm) : istream_(new std::istream(strm.rdbuf())) {}
 
   operator std::istream&() { return *istream_; }
 
@@ -144,7 +185,11 @@ class InputFileStream {
 
   bool empty() { return istream_->peek() == std::ifstream::traits_type::eof(); }
 
-  void setbufsize(size_t size) const;
+  void setbufsize(size_t size) const {
+    istream_->rdbuf()->pubsetbuf(0, 0);
+    readBuf_.resize(size);
+    istream_->rdbuf()->pubsetbuf(readBuf_.data(), readBuf_.size());
+  }
 
   template <typename T>
   friend InputFileStream& operator>>(InputFileStream& stream, T& t) {
@@ -170,14 +215,19 @@ class InputFileStream {
   marian::filesystem::Path file_;
   std::unique_ptr<std::istream> istream_;
 
+#ifndef NO_BOOST
+  boost::iostreams::file_descriptor_source fds_;
+#endif
   mutable std::vector<char> readBuf_;  // for setbuf()
-  std::unique_ptr<ReadFDBuf> temporary_reader_;
+#ifndef NO_BOOST
+  std::unique_ptr<boost::iostreams::stream_buffer<boost::iostreams::file_descriptor_source>>
+      fdsBuffer_;
+#endif
 };
 
 // wrapper around std::getline() that handles Windows input files with extra CR
 // chars at the line end
-static inline InputFileStream& getline(InputFileStream& in, std::string& line)
-{
+static inline InputFileStream& getline(InputFileStream& in, std::string& line) {
   std::getline((std::istream&)in, line);
   // bad() seems to be correct here. Should not abort on EOF.
   ABORT_IF(in.bad(), "Error reading from file '{}'", in.path());
@@ -189,7 +239,6 @@ static inline InputFileStream& getline(InputFileStream& in, std::string& line)
 
 // wrapper around std::getline() that handles Windows input files with extra CR
 // chars at the line end
-// To be pedantic, shouldn't this require delim == '\n' to consume a '\r'?
 static inline InputFileStream& getline(InputFileStream& in, std::string& line, char delim) {
   std::getline((std::istream&)in, line, delim);
   // bad() seems to be correct here. Should not abort on EOF.
@@ -202,9 +251,30 @@ static inline InputFileStream& getline(InputFileStream& in, std::string& line, c
 
 class OutputFileStream {
 public:
-  OutputFileStream(const std::string& file);
+  OutputFileStream(const std::string& file) : file_(file) {
+    if(file_.extension() == marian::filesystem::Path(".gz"))
+      ostream_ = std::make_unique<zstr::ofstream>(file_.string(),
+                                                  std::ios_base::out | std::ios_base::binary);
+    else
+      ostream_ = std::make_unique<std::ofstream>(file_.string());
+    ABORT_IF(!marian::filesystem::exists(file_), "File '{}' could not be opened", file);
+  }
+
+#ifndef NO_BOOST
+  OutputFileStream(TemporaryFile& tempfile)
+      : fds_(tempfile.getFileDescriptor(), boost::iostreams::never_close_handle) {
+    lseek(tempfile.getFileDescriptor(), 0, SEEK_SET);
 
-  OutputFileStream(TemporaryFile& tempfile);
+    namespace bio = boost::iostreams;
+    fdsBuffer_ = std::make_unique<bio::stream_buffer<bio::file_descriptor_sink>>(fds_);
+    ostream_ = std::make_unique<std::ostream>(fdsBuffer_.get());
+  }
+#else
+  OutputFileStream(TemporaryFile& tempfile) {
+    tempfile;
+    ABORT("OutputFileStream(tempFile) not supported when compiling with NO_BOOST");
+  }
+#endif
 
   OutputFileStream(std::ostream& strm) { ostream_ = std::make_unique<std::ostream>(strm.rdbuf()); }
 
@@ -247,7 +317,11 @@ class OutputFileStream {
   marian::filesystem::Path file_;
   std::unique_ptr<std::ostream> ostream_;
 
-  std::unique_ptr<WriteFDBuf> temporary_writer_;
+#ifndef NO_BOOST
+  boost::iostreams::file_descriptor_sink fds_;
+  std::unique_ptr<boost::iostreams::stream_buffer<boost::iostreams::file_descriptor_sink>>
+      fdsBuffer_;
+#endif
 };
 
 }  // namespace io

From cf283d03366b5a5b9a5dd8873173b089a40e6ccf Mon Sep 17 00:00:00 2001
From: Hieu Hoang <hihoan@microsoft.com>
Date: Thu, 24 Oct 2019 13:24:12 -0700
Subject: [PATCH 698/838] delete unused includes

---
 src/common/file_stream.cpp | 12 ------------
 1 file changed, 12 deletions(-)

diff --git a/src/common/file_stream.cpp b/src/common/file_stream.cpp
index 5d48c6a87..ec2a2a31b 100644
--- a/src/common/file_stream.cpp
+++ b/src/common/file_stream.cpp
@@ -1,13 +1 @@
 #include "common/file_stream.h"
-
-#include <streambuf>
-#include <string>
-#include <vector>
-#ifdef _MSC_VER
-#include <windows.h>
-#include <io.h>
-#else
-#include <sys/types.h>
-#include <unistd.h>
-#endif
-

From 47000c59da63a40be8636b11c627e2b2122abd35 Mon Sep 17 00:00:00 2001
From: Hieu Hoang <hihoan@microsoft.com>
Date: Thu, 24 Oct 2019 13:28:02 -0700
Subject: [PATCH 699/838] roll back changes to vs project files

---
 vs/Marian.vcxproj         | 3 +--
 vs/Marian.vcxproj.filters | 3 ---
 2 files changed, 1 insertion(+), 5 deletions(-)

diff --git a/vs/Marian.vcxproj b/vs/Marian.vcxproj
index dadfaf96e..bdfce4683 100755
--- a/vs/Marian.vcxproj
+++ b/vs/Marian.vcxproj
@@ -42,7 +42,7 @@
   <PropertyGroup Condition="'$(Configuration)|$(Platform)'=='Debug|x64'">
     <LinkIncremental>true</LinkIncremental>
     <IntDir>$(SolutionDir)$(Platform)\$(Configuration)\Marian\</IntDir>
-    <IncludePath>..\src\3rd_party\fbgemm\third_party\cpuinfo\deps\clog\include;..\src\3rd_party\fbgemm\third_party\cpuinfo\src;..\src\3rd_party\fbgemm\third_party\cpuinfo\include;..\src\3rd_party\fbgemm\third_party\asmjit\src;%MKL_PATH%\include;..\src\3rd_party\fbgemm\include;%CUDA_PATH%\include;..\src;..\src\3rd_party;%BOOST_INCLUDE_PATH%;%ZLIB_PATH%\include;$(VC_IncludePath);$(WindowsSDK_IncludePath)</IncludePath>
+    <IncludePath>..\src\3rd_party\fbgemm\third_party\cpuinfo\deps\clog\include;..\src\3rd_party\fbgemm\third_party\cpuinfo\src;..\src\3rd_party\fbgemm\third_party\cpuinfo\include;..\src\3rd_party\fbgemm\third_party\asmjit\src;%MKL_PATH%\include;..\src\3rd_party\fbgemm\include;%CUDA_PATH%\include;..\src;..\src\3rd_party;%BOOST_INCLUDE_PATH%;%ZLIB_PATH%\include;$(VC_IncludePath);$(WindowsSDK_IncludePath);</IncludePath>
     <LibraryPath>%CUDA_PATH%\lib\x64;%BOOST_LIB_PATH%;%ZLIB_PATH%\lib;%MKL_PATH%\lib\intel64;$(VC_LibraryPath_x64);$(WindowsSDK_LibraryPath_x64);$(NETFXKitsDir)Lib\um\x64</LibraryPath>
   </PropertyGroup>
   <PropertyGroup Condition="'$(Configuration)|$(Platform)'=='Release|x64'">
@@ -1002,7 +1002,6 @@
     <ClCompile Include="..\src\common\cli_wrapper.cpp" />
     <ClCompile Include="..\src\common\config_validator.cpp" />
     <ClCompile Include="..\src\common\filesystem.cpp" />
-    <ClCompile Include="..\src\common\file_stream.cpp" />
     <ClCompile Include="..\src\common\io.cpp" />
     <ClCompile Include="..\src\common\utils.cpp" />
     <ClCompile Include="..\src\common\logging.cpp" />
diff --git a/vs/Marian.vcxproj.filters b/vs/Marian.vcxproj.filters
index 4341e49d5..8fe63a0bf 100755
--- a/vs/Marian.vcxproj.filters
+++ b/vs/Marian.vcxproj.filters
@@ -721,9 +721,6 @@
     <ClCompile Include="..\src\common\filesystem.cpp">
       <Filter>common</Filter>
     </ClCompile>
-    <ClCompile Include="..\src\common\file_stream.cpp">
-      <Filter>common</Filter>
-    </ClCompile>
   </ItemGroup>
   <ItemGroup>
     <ClInclude Include="..\src\marian.h" />

From 5bafca434a90747d9020ee12ebe9020c52673d0a Mon Sep 17 00:00:00 2001
From: Hieu Hoang <hihoan@microsoft.com>
Date: Thu, 24 Oct 2019 16:05:22 -0700
Subject: [PATCH 700/838] more getline reversion

---
 src/data/factored_vocab.cpp | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/src/data/factored_vocab.cpp b/src/data/factored_vocab.cpp
index 8e291eebd..144e87128 100755
--- a/src/data/factored_vocab.cpp
+++ b/src/data/factored_vocab.cpp
@@ -130,7 +130,7 @@ namespace marian {
     // @TODO: add checks for empty factor groups until it stops crashing (training already works; decoder still crashes)
 
     io::InputFileStream in(modelPath);
-    for (WordIndex v = 0; std::getline(in, line); v++) {
+    for (WordIndex v = 0; io::getline(in, line); v++) {
       utils::splitAny(line, tokBuf, " \t");
       factorMapTokenized.push_back(tokBuf);
     }
@@ -715,7 +715,7 @@ bool FactoredVocab::WordLUT::tryFind(const std::string& word, WordIndex& index)
 size_t FactoredVocab::WordLUT::load(const std::string& path) {
   std::string line;
   io::InputFileStream in(path);
-  for (WordIndex v = 0; std::getline(in, line); v++)
+  for (WordIndex v = 0; io::getline(in, line); v++)
     add(line, v);
   return size();
 }

From a73a5e404ccdd5a17f09cd357530c7e35d75d9b4 Mon Sep 17 00:00:00 2001
From: Hieu Hoang <hihoan@microsoft.com>
Date: Thu, 24 Oct 2019 16:18:26 -0700
Subject: [PATCH 701/838] don't use std::make_unique. Not C++11 compatible
 apparently

---
 src/common/file_stream.cpp | 2 +-
 src/common/file_stream.h   | 9 ---------
 src/data/corpus.cpp        | 2 +-
 src/data/corpus_base.cpp   | 2 +-
 4 files changed, 3 insertions(+), 12 deletions(-)

diff --git a/src/common/file_stream.cpp b/src/common/file_stream.cpp
index cb2def8b8..a1c59bc77 100644
--- a/src/common/file_stream.cpp
+++ b/src/common/file_stream.cpp
@@ -119,7 +119,7 @@ OutputFileStream::~OutputFileStream() {
 ///////////////////////////////////////////////////////////////////////////////////////////////
 TemporaryFile::TemporaryFile(const std::string &base, bool earlyUnlink)
     : OutputFileStream(CreateFileName(base)), unlink_(earlyUnlink) {
-  inSteam_ = std::make_unique<io::InputFileStream>(file_.string());
+  inSteam_ = UPtr<io::InputFileStream>(new io::InputFileStream(file_.string()));
   if (unlink_) {
     ABORT_IF(remove(file_.string().c_str()), "Error while deleting '{}'", file_.string());
   }
diff --git a/src/common/file_stream.h b/src/common/file_stream.h
index 3f0384c21..ef28cd2fe 100755
--- a/src/common/file_stream.h
+++ b/src/common/file_stream.h
@@ -25,15 +25,6 @@
 #include <memory>
 #include <vector>
 
-#ifdef __GNUC__  // not supported; maybe we just need to increment a standard flag in gcc/cmake?
-namespace std {
-template <typename T, typename... Args>
-std::unique_ptr<T> make_unique(Args&&... args) {
-  return std::unique_ptr<T>(new T(std::forward<Args>(args)...));
-}
-}  // namespace std
-#endif
-
 #ifdef _MSC_VER
 #include <fcntl.h>
 #include <io.h>
diff --git a/src/data/corpus.cpp b/src/data/corpus.cpp
index 567d7f38c..a8d965cf9 100644
--- a/src/data/corpus.cpp
+++ b/src/data/corpus.cpp
@@ -154,7 +154,7 @@ void Corpus::shuffleData(const std::vector<std::string>& paths) {
   else {
     files_.resize(numStreams);
     for(size_t i = 0; i < numStreams; ++i) {
-      UPtr<io::InputFileStream> strm = std::make_unique<io::InputFileStream>(paths[i]);
+      UPtr<io::InputFileStream> strm(new io::InputFileStream(paths[i]));
       strm->setbufsize(10000000);  // huge read-ahead buffer to avoid network round-trips
       files_[i] = std::move(strm);
     }
diff --git a/src/data/corpus_base.cpp b/src/data/corpus_base.cpp
index 1494fe164..857c21d02 100755
--- a/src/data/corpus_base.cpp
+++ b/src/data/corpus_base.cpp
@@ -40,7 +40,7 @@ CorpusBase::CorpusBase(const std::vector<std::string>& paths,
            "Number of corpus files and vocab files does not agree");
 
   for(auto path : paths_) {
-    UPtr<io::InputFileStream> strm = std::make_unique<io::InputFileStream>(path);
+    UPtr<io::InputFileStream> strm(new io::InputFileStream(path));
     ABORT_IF(strm->empty(), "File '{}' is empty", path);
     files_.emplace_back(std::move(strm));
   }

From ae849231a5a9eb975d318a619e12d1f0e682f703 Mon Sep 17 00:00:00 2001
From: Hieu Hoang <hihoan@microsoft.com>
Date: Thu, 24 Oct 2019 16:23:39 -0700
Subject: [PATCH 702/838] put back header include and #ifdef

---
 src/common/file_stream.h | 16 ++++------------
 1 file changed, 4 insertions(+), 12 deletions(-)

diff --git a/src/common/file_stream.h b/src/common/file_stream.h
index ef28cd2fe..732a1dfb4 100755
--- a/src/common/file_stream.h
+++ b/src/common/file_stream.h
@@ -1,5 +1,7 @@
 #pragma once
 
+#include <iostream>
+#include <memory>
 #include <fstream>
 #include "common/definitions.h"
 #include "common/filesystem.h"
@@ -14,21 +16,11 @@
 #pragma warning(disable : 4101)
 #endif
 #include "3rd_party/zstr/zstr.hpp"
-#ifdef __GNUC__
-#pragma GCC diagnostic pop
-#endif
 #ifdef _MSC_VER
 #pragma warning(pop)
 #endif
-
-#include <iostream>
-#include <memory>
-#include <vector>
-
-#ifdef _MSC_VER
-#include <fcntl.h>
-#include <io.h>
-#include <stdlib.h>
+#ifdef __GNUC__
+#pragma GCC diagnostic pop
 #endif
 
 namespace marian {

From 583a9277d544d943100b5c8e38edc7bb19dea213 Mon Sep 17 00:00:00 2001
From: Hieu Hoang <hihoan@microsoft.com>
Date: Thu, 24 Oct 2019 17:46:25 -0700
Subject: [PATCH 703/838] use original mkstemp to create temp file on linux

---
 src/common/file_stream.cpp | 96 ++++++++++++++++++++++++++------------
 src/common/file_stream.h   | 13 ++++--
 2 files changed, 74 insertions(+), 35 deletions(-)

diff --git a/src/common/file_stream.cpp b/src/common/file_stream.cpp
index a1c59bc77..fd5ab2b5c 100644
--- a/src/common/file_stream.cpp
+++ b/src/common/file_stream.cpp
@@ -4,8 +4,8 @@
 #include <string>
 #include <vector>
 #ifdef _MSC_VER
-#include <windows.h>
 #include <io.h>
+#include <windows.h>
 #else
 #include <sys/types.h>
 #include <unistd.h>
@@ -17,8 +17,8 @@ namespace io {
 // Get error strings out of errno.
 namespace {
 #ifdef __GNUC__
-const char *HandleStrerror(int ret, const char *buf) __attribute__ ((unused));
-const char *HandleStrerror(const char *ret, const char * /*buf*/) __attribute__ ((unused));
+const char *HandleStrerror(int ret, const char *buf) __attribute__((unused));
+const char *HandleStrerror(const char *ret, const char * /*buf*/) __attribute__((unused));
 #endif
 // At least one of these functions will not be called.
 #ifdef __clang__
@@ -27,7 +27,8 @@ const char *HandleStrerror(const char *ret, const char * /*buf*/) __attribute__
 #endif
 // The XOPEN version.
 const char *HandleStrerror(int ret, const char *buf) {
-  if (!ret) return buf;
+  if(!ret)
+    return buf;
   return NULL;
 }
 
@@ -48,7 +49,7 @@ std::string StrError() {
   return HandleStrerror(strerror_r(errno, buf, 200), buf);
 #endif
 }
-} // namespace
+}  // namespace
 
 ///////////////////////////////////////////////////////////////////////////////////////////////
 InputFileStream::InputFileStream(const std::string &file)
@@ -110,6 +111,9 @@ OutputFileStream::OutputFileStream(const std::string &file)
   }
 }
 
+OutputFileStream::OutputFileStream()
+    : std::ostream(NULL), streamBuf1_(NULL), streamBuf2_(NULL) {}
+
 OutputFileStream::~OutputFileStream() {
   this->flush();
   delete streamBuf2_;
@@ -118,9 +122,13 @@ OutputFileStream::~OutputFileStream() {
 
 ///////////////////////////////////////////////////////////////////////////////////////////////
 TemporaryFile::TemporaryFile(const std::string &base, bool earlyUnlink)
-    : OutputFileStream(CreateFileName(base)), unlink_(earlyUnlink) {
+    : OutputFileStream(), unlink_(earlyUnlink) {
+  std::string baseTemp(base);
+  NormalizeTempPrefix(baseTemp);
+  MakeTemp(baseTemp);
+
   inSteam_ = UPtr<io::InputFileStream>(new io::InputFileStream(file_.string()));
-  if (unlink_) {
+  if(unlink_) {
     ABORT_IF(remove(file_.string().c_str()), "Error while deleting '{}'", file_.string());
   }
 }
@@ -131,32 +139,60 @@ TemporaryFile::~TemporaryFile() {
   }
 }
 
-std::string TemporaryFile::CreateFileName(const std::string &base) const {
-  // NormalizeTempPrefix
-  std::string ret = base;
-  if(!base.empty()) {
+void TemporaryFile::NormalizeTempPrefix(std::string &base) {
+  if(base.empty())
+    return;
+
 #ifdef _MSC_VER
-    if(ret.substr(0, 4) == "/tmp") {
-      ret = getenv("TMP");
-    }
+  if(base.substr(0, 4) == "/tmp")
+    base = getenv("TMP");
 #else
-    if(ret[ret.size() - 1] != '/') {
-      struct stat sb;
-      // It's fine for it to not exist.
-      if(stat(ret.c_str(), &sb) != -1) {
-        if(S_ISDIR(sb.st_mode)) {
-          ret += '/';
-        }
-      }
-    }
+  if(base[base.size() - 1] == '/')
+    return;
+  struct stat sb;
+  // It's fine for it to not exist.
+  if(stat(base.c_str(), &sb) == -1)
+    return;
+  if(S_ISDIR(sb.st_mode))
+    base += '/';
 #endif
-  }
-
-  char *name = tempnam(ret.c_str(), "marian.");
+}
+void TemporaryFile::MakeTemp(const std::string &base) {
+#ifdef _MSC_VER
+  char *name = tempnam(base.c_str(), "marian.");
   ABORT_IF(name == NULL, "Error while making a temporary based on '{}'", base);
-  ret = name;
+
+  int oflag = _O_RDWR | _O_CREAT | _O_EXCL;
+  if(unlink_)
+    oflag |= _O_TEMPORARY;
+
+  int fd = open(name, oflag, _S_IREAD | _S_IWRITE);
+  ABORT_IF(fd == -1, "Error while making a temporary based on '{}'", base);
+
+#else
+  // create temp file
+  std::string name(base);
+  name += "marian.XXXXXX";
+  name.push_back(0);
+  int fd = mkstemp(&name[0]);
+  ABORT_IF(fd == -1, "Error creating temp file {}", name);
+
+  file_ = name;
+#endif
+
+  // open again with c++
+  std::filebuf *fileBuf = new std::filebuf();
+  streamBuf1_ = fileBuf->open(name, std::ios::out | std::ios_base::binary);
+  ABORT_IF(!streamBuf1_, "File can't be temp opened", name);
+
+  this->init(streamBuf1_);
+
+  // close original file descriptor
+  ABORT_IF(close(fd), "Can't close file descriptor", name);
+
+#ifdef _MSC_VER
   free(name);
-  return ret;
+#endif
 }
 
 UPtr<InputFileStream> TemporaryFile::getInputStream() {
@@ -167,5 +203,5 @@ std::string TemporaryFile::getFileName() const {
   return file_.string();
 }
 
-} // namespace io
-} // namespace marian
+}  // namespace io
+}  // namespace marian
diff --git a/src/common/file_stream.h b/src/common/file_stream.h
index 732a1dfb4..03df8e396 100755
--- a/src/common/file_stream.h
+++ b/src/common/file_stream.h
@@ -1,8 +1,8 @@
 #pragma once
 
+#include <fstream>
 #include <iostream>
 #include <memory>
-#include <fstream>
 #include "common/definitions.h"
 #include "common/filesystem.h"
 #include "common/logging.h"
@@ -43,12 +43,12 @@ class InputFileStream : public std::istream {
 
 std::istream& getline(std::istream& in, std::string& line);
 
-  //////////////////////////////////////////////////////////////////////////////////////////////
+//////////////////////////////////////////////////////////////////////////////////////////////
 class OutputFileStream : public std::ostream {
 public:
   explicit OutputFileStream(const std::string& file);
   virtual ~OutputFileStream();
-  
+
   template <typename T>
   size_t write(const T* ptr, size_t num = 1) {
     this->write((char*)ptr, num * sizeof(T));
@@ -58,6 +58,8 @@ class OutputFileStream : public std::ostream {
   }
 
 protected:
+  explicit OutputFileStream();  // for temp file
+
   marian::filesystem::Path file_;
   std::streambuf* streamBuf1_;
   std::streambuf* streamBuf2_;
@@ -76,8 +78,9 @@ class TemporaryFile : public OutputFileStream {
   bool unlink_;
   UPtr<InputFileStream> inSteam_;
 
-  std::string CreateFileName(const std::string& base) const;
-
+  void NormalizeTempPrefix(std::string& base);
+  void MakeTemp(const std::string& base);
+ 
 };
 
 }  // namespace io

From 6745abdafab3185620a8d43de33336b389d82894 Mon Sep 17 00:00:00 2001
From: Hieu Hoang <hihoan@microsoft.com>
Date: Thu, 24 Oct 2019 17:47:46 -0700
Subject: [PATCH 704/838] delete ken's error handling gunk

---
 src/common/file_stream.cpp | 37 -------------------------------------
 1 file changed, 37 deletions(-)

diff --git a/src/common/file_stream.cpp b/src/common/file_stream.cpp
index fd5ab2b5c..8c333fae8 100644
--- a/src/common/file_stream.cpp
+++ b/src/common/file_stream.cpp
@@ -14,43 +14,6 @@
 namespace marian {
 namespace io {
 
-// Get error strings out of errno.
-namespace {
-#ifdef __GNUC__
-const char *HandleStrerror(int ret, const char *buf) __attribute__((unused));
-const char *HandleStrerror(const char *ret, const char * /*buf*/) __attribute__((unused));
-#endif
-// At least one of these functions will not be called.
-#ifdef __clang__
-#pragma clang diagnostic push
-#pragma clang diagnostic ignored "-Wunused-function"
-#endif
-// The XOPEN version.
-const char *HandleStrerror(int ret, const char *buf) {
-  if(!ret)
-    return buf;
-  return NULL;
-}
-
-// The GNU version.
-const char *HandleStrerror(const char *ret, const char * /*buf*/) {
-  return ret;
-}
-#ifdef __clang__
-#pragma clang diagnostic pop
-#endif
-
-std::string StrError() {
-  char buf[200];
-  buf[0] = 0;
-#if defined(sun) || defined(_WIN32) || defined(_WIN64)
-  return strerror(errno);
-#else
-  return HandleStrerror(strerror_r(errno, buf, 200), buf);
-#endif
-}
-}  // namespace
-
 ///////////////////////////////////////////////////////////////////////////////////////////////
 InputFileStream::InputFileStream(const std::string &file)
     : std::istream(NULL), file_(file), streamBuf_(NULL) {

From bd0118cc660e42101487a32d2bfa6a02ec70572b Mon Sep 17 00:00:00 2001
From: Hieu Hoang <hihoan@microsoft.com>
Date: Fri, 25 Oct 2019 07:30:49 -0700
Subject: [PATCH 705/838] const NormalizeTempPrefix(). Retain all streamBuf so
 we can delete them afterwards

---
 src/common/file_stream.cpp | 22 ++++++++++++----------
 src/common/file_stream.h   |  5 +++--
 vs/Marian.vcxproj          |  1 +
 vs/Marian.vcxproj.filters  |  3 +++
 4 files changed, 19 insertions(+), 12 deletions(-)

diff --git a/src/common/file_stream.cpp b/src/common/file_stream.cpp
index 8c333fae8..1f5a36f15 100644
--- a/src/common/file_stream.cpp
+++ b/src/common/file_stream.cpp
@@ -16,24 +16,25 @@ namespace io {
 
 ///////////////////////////////////////////////////////////////////////////////////////////////
 InputFileStream::InputFileStream(const std::string &file)
-    : std::istream(NULL), file_(file), streamBuf_(NULL) {
+    : std::istream(NULL), file_(file), streamBuf1_(NULL), streamBuf2_(NULL) {
   ABORT_IF(!marian::filesystem::exists(file_), "File '{}' does not exist", file);
 
   std::filebuf *fileBuf = new std::filebuf();
-  streamBuf_ = fileBuf->open(file.c_str(), std::ios::in);
-  if(!streamBuf_) {
-    ABORT("File can't be read", file);
-  }
+  streamBuf1_ = fileBuf->open(file.c_str(), std::ios::in);
+  ABORT_IF(!streamBuf1_, "File can't be opened", file);
+  assert(fileBuf == streamBuf1_);
 
   if(file_.extension() == marian::filesystem::Path(".gz")) {
-    streamBuf_ = new zstr::istreambuf(streamBuf_);
+    streamBuf2_ = new zstr::ostreambuf(streamBuf1_);
+    this->init(streamBuf2_);
+  } else {
+    this->init(streamBuf1_);
   }
-
-  this->init(streamBuf_);
 }
 
 InputFileStream::~InputFileStream() {
-  delete streamBuf_;
+  delete streamBuf2_;
+  delete streamBuf1_;
 }
 
 bool InputFileStream::empty() {
@@ -65,6 +66,7 @@ OutputFileStream::OutputFileStream(const std::string &file)
   std::filebuf *fileBuf = new std::filebuf();
   streamBuf1_ = fileBuf->open(file.c_str(), std::ios::out | std::ios_base::binary);
   ABORT_IF(!streamBuf1_, "File can't be opened", file);
+  assert(fileBuf == streamBuf1_);
 
   if(file_.extension() == marian::filesystem::Path(".gz")) {
     streamBuf2_ = new zstr::ostreambuf(streamBuf1_);
@@ -102,7 +104,7 @@ TemporaryFile::~TemporaryFile() {
   }
 }
 
-void TemporaryFile::NormalizeTempPrefix(std::string &base) {
+void TemporaryFile::NormalizeTempPrefix(std::string &base) const {
   if(base.empty())
     return;
 
diff --git a/src/common/file_stream.h b/src/common/file_stream.h
index 03df8e396..c1916da31 100755
--- a/src/common/file_stream.h
+++ b/src/common/file_stream.h
@@ -38,7 +38,8 @@ class InputFileStream : public std::istream {
 
 protected:
   marian::filesystem::Path file_;
-  std::streambuf* streamBuf_;
+  std::streambuf* streamBuf1_;
+  std::streambuf* streamBuf2_;
 };
 
 std::istream& getline(std::istream& in, std::string& line);
@@ -78,7 +79,7 @@ class TemporaryFile : public OutputFileStream {
   bool unlink_;
   UPtr<InputFileStream> inSteam_;
 
-  void NormalizeTempPrefix(std::string& base);
+  void NormalizeTempPrefix(std::string& base) const;
   void MakeTemp(const std::string& base);
  
 };
diff --git a/vs/Marian.vcxproj b/vs/Marian.vcxproj
index 27126109f..12faec2fc 100755
--- a/vs/Marian.vcxproj
+++ b/vs/Marian.vcxproj
@@ -1004,6 +1004,7 @@
     <ClCompile Include="..\src\common\cli_wrapper.cpp" />
     <ClCompile Include="..\src\common\config_validator.cpp" />
     <ClCompile Include="..\src\common\filesystem.cpp" />
+    <ClCompile Include="..\src\common\file_stream.cpp" />
     <ClCompile Include="..\src\common\io.cpp" />
     <ClCompile Include="..\src\common\utils.cpp" />
     <ClCompile Include="..\src\common\logging.cpp" />
diff --git a/vs/Marian.vcxproj.filters b/vs/Marian.vcxproj.filters
index f39995e44..acc84990a 100755
--- a/vs/Marian.vcxproj.filters
+++ b/vs/Marian.vcxproj.filters
@@ -721,6 +721,9 @@
     <ClCompile Include="..\src\common\filesystem.cpp">
       <Filter>common</Filter>
     </ClCompile>
+    <ClCompile Include="..\src\common\file_stream.cpp">
+      <Filter>common</Filter>
+    </ClCompile>
   </ItemGroup>
   <ItemGroup>
     <ClInclude Include="..\src\marian.h" />

From 49e8792d68acd18b1427a42e6e98861265fd8a9b Mon Sep 17 00:00:00 2001
From: Hieu Hoang <hihoan@microsoft.com>
Date: Fri, 25 Oct 2019 07:49:47 -0700
Subject: [PATCH 706/838] istreambuf for istream class

---
 src/common/file_stream.cpp | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/common/file_stream.cpp b/src/common/file_stream.cpp
index 1f5a36f15..2ebdaadc0 100644
--- a/src/common/file_stream.cpp
+++ b/src/common/file_stream.cpp
@@ -25,7 +25,7 @@ InputFileStream::InputFileStream(const std::string &file)
   assert(fileBuf == streamBuf1_);
 
   if(file_.extension() == marian::filesystem::Path(".gz")) {
-    streamBuf2_ = new zstr::ostreambuf(streamBuf1_);
+    streamBuf2_ = new zstr::istreambuf(streamBuf1_);
     this->init(streamBuf2_);
   } else {
     this->init(streamBuf1_);

From e94fd1701ae69c5383a1d5a3daf6710b2858e27e Mon Sep 17 00:00:00 2001
From: Hieu Hoang <hihoan@microsoft.com>
Date: Fri, 25 Oct 2019 08:56:10 -0700
Subject: [PATCH 707/838] Use auto /Frank

---
 src/data/corpus.cpp | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/data/corpus.cpp b/src/data/corpus.cpp
index a8d965cf9..ea548807e 100644
--- a/src/data/corpus.cpp
+++ b/src/data/corpus.cpp
@@ -205,7 +205,7 @@ void Corpus::shuffleData(const std::vector<std::string>& paths) {
     // replace files_[] by the tempfiles we just created
     files_.resize(numStreams);
     for(size_t i = 0; i < numStreams; ++i) {
-      UPtr<io::InputFileStream> inputStream = tempFiles_[i]->getInputStream();
+      auto inputStream = tempFiles_[i]->getInputStream();
       inputStream->setbufsize(10000000);
       files_[i] = std::move(inputStream);
     }

From bbbc9a336c1c6360d4710b96f2c194cff74ecb66 Mon Sep 17 00:00:00 2001
From: Hieu Hoang <hihoan@microsoft.com>
Date: Fri, 25 Oct 2019 09:13:25 -0700
Subject: [PATCH 708/838] resolve remaining issues

---
 src/data/corpus.cpp     | 4 ++--
 src/data/text_input.cpp | 3 +--
 2 files changed, 3 insertions(+), 4 deletions(-)

diff --git a/src/data/corpus.cpp b/src/data/corpus.cpp
index ea548807e..227efde12 100644
--- a/src/data/corpus.cpp
+++ b/src/data/corpus.cpp
@@ -71,7 +71,7 @@ SentenceTuple Corpus::next() {
         }
       }
       else {
-        std::istream &gotLine = io::getline(*files_[i], line);
+        bool gotLine = io::getline(*files_[i], line).good();
         if(!gotLine) {
           eofsHit++;
           continue;
@@ -164,7 +164,7 @@ void Corpus::shuffleData(const std::vector<std::string>& paths) {
     for (;;) {
       size_t eofsHit = 0;
       for(size_t i = 0; i < numStreams; ++i) {
-        std::istream &gotLine = io::getline(*files_[i], lineBuf);
+        bool gotLine = io::getline(*files_[i], lineBuf).good();
         if (gotLine)
           corpus[i].push_back(lineBuf);
         else
diff --git a/src/data/text_input.cpp b/src/data/text_input.cpp
index 7a8b65f6e..5d68d9fb4 100644
--- a/src/data/text_input.cpp
+++ b/src/data/text_input.cpp
@@ -39,9 +39,8 @@ SentenceTuple TextInput::next() {
   // fill up the sentence tuple with source and/or target sentences
   SentenceTuple tup(curId);
   for(size_t i = 0; i < files_.size(); ++i) {
-    std::istringstream& dummyStream = *files_[i];
     std::string line;
-    if(io::getline(dummyStream, line)) {
+    if(io::getline(*files_[i], line)) {
       Words words = vocabs_[i]->encode(line, /*addEOS =*/ true, /*inference =*/ inference_);
       if(words.empty())
         words.push_back(Word::ZERO); // @TODO: What is this for? @BUGBUG: addEOS=true, so this can never happen, right?

From 1469e456cd62659105c1c56b8bfc2784ae9846d9 Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Fri, 25 Oct 2019 10:50:22 -0700
Subject: [PATCH 709/838] updated reference to sentencepiece repo to version
 without softlink

---
 src/3rd_party/sentencepiece | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/3rd_party/sentencepiece b/src/3rd_party/sentencepiece
index 61ae30fa8..1d33bb67c 160000
--- a/src/3rd_party/sentencepiece
+++ b/src/3rd_party/sentencepiece
@@ -1 +1 @@
-Subproject commit 61ae30fa8313c72ea36c41f1ea0402c7ed8c2fe0
+Subproject commit 1d33bb67c3b6b2a51d3c9ffd55f37725801da39d

From ebec8221111669c68c1396e03ad2a5454aa45c9c Mon Sep 17 00:00:00 2001
From: Hieu Hoang <hihoan@microsoft.com>
Date: Fri, 25 Oct 2019 11:42:18 -0700
Subject: [PATCH 710/838] extra assert

---
 src/common/file_stream.cpp | 1 +
 1 file changed, 1 insertion(+)

diff --git a/src/common/file_stream.cpp b/src/common/file_stream.cpp
index 2ebdaadc0..d0de3da51 100644
--- a/src/common/file_stream.cpp
+++ b/src/common/file_stream.cpp
@@ -149,6 +149,7 @@ void TemporaryFile::MakeTemp(const std::string &base) {
   std::filebuf *fileBuf = new std::filebuf();
   streamBuf1_ = fileBuf->open(name, std::ios::out | std::ios_base::binary);
   ABORT_IF(!streamBuf1_, "File can't be temp opened", name);
+  assert(fileBuf == streamBuf1_);
 
   this->init(streamBuf1_);
 

From deb9b6a7510058b4a6043c1ddef698bfd1c3ba0b Mon Sep 17 00:00:00 2001
From: Hieu Hoang <hihoan@microsoft.com>
Date: Fri, 25 Oct 2019 14:18:49 -0700
Subject: [PATCH 711/838] implement setbufsize

---
 src/common/file_stream.cpp | 6 ++++--
 src/common/file_stream.h   | 3 ++-
 2 files changed, 6 insertions(+), 3 deletions(-)

diff --git a/src/common/file_stream.cpp b/src/common/file_stream.cpp
index d0de3da51..074e36ebf 100644
--- a/src/common/file_stream.cpp
+++ b/src/common/file_stream.cpp
@@ -41,8 +41,10 @@ bool InputFileStream::empty() {
   return this->peek() == std::ifstream::traits_type::eof();
 }
 
-void InputFileStream::setbufsize(size_t size) const {
-  // do nothing. Is this needed?
+void InputFileStream::setbufsize(size_t size) {
+  rdbuf()->pubsetbuf(0, 0);
+  readBuf_.resize(size);
+  rdbuf()->pubsetbuf(readBuf_.data(), readBuf_.size());
 }
 
 std::string InputFileStream::getFileName() const {
diff --git a/src/common/file_stream.h b/src/common/file_stream.h
index c1916da31..ce495a71f 100755
--- a/src/common/file_stream.h
+++ b/src/common/file_stream.h
@@ -33,13 +33,14 @@ class InputFileStream : public std::istream {
   virtual ~InputFileStream();
 
   bool empty();
-  void setbufsize(size_t size) const;
+  void setbufsize(size_t size);
   std::string getFileName() const;
 
 protected:
   marian::filesystem::Path file_;
   std::streambuf* streamBuf1_;
   std::streambuf* streamBuf2_;
+  std::vector<char> readBuf_;
 };
 
 std::istream& getline(std::istream& in, std::string& line);

From fe72735e3173d8439b8f232c934008fdf77d7f22 Mon Sep 17 00:00:00 2001
From: Ulrich Germann <ugermann@inf.ed.ac.uk>
Date: Fri, 25 Oct 2019 22:25:00 +0100
Subject: [PATCH 712/838] Update scheduler.cpp

As requested by @emjotde
---
 src/training/scheduler.cpp | 20 ++++++++------------
 1 file changed, 8 insertions(+), 12 deletions(-)

diff --git a/src/training/scheduler.cpp b/src/training/scheduler.cpp
index ea918e780..56b97f7d0 100644
--- a/src/training/scheduler.cpp
+++ b/src/training/scheduler.cpp
@@ -5,26 +5,22 @@
 namespace marian {
 bool Scheduler::sigterm_{false};
 
-void
-Scheduler::
-signalHandler_(int sig) {
+void Scheduler::signalHandler(int sig) {
   // Note: sys_siglist[sig] or stdsignal() describe the effect (e.g.,
   // 'Terminated' rather than provide the signal name (which are #define(s)
   // in signal.h), so we have to do custom log messages here.
   switch (sig) {
-  case SIGTERM: // save models and exit
-    LOG(info, "[training] Scheduler received signal SIGTERM");
-    Scheduler::sigterm_ = true;
-    break;
-  default:
-    ABORT("No action defined for signal {}", sig);
+    case SIGTERM: // save models and exit
+      LOG(info, "[training] Scheduler received signal SIGTERM");
+      Scheduler::sigterm_ = true;
+      break;
+    default:
+      ABORT("No action defined for signal {}", sig);
   }
 }
 
 
-void
-Scheduler::
-installSignalHandlers_() {
+void Scheduler::installSignalHandlers() {
   // TODO: use sigaction instead of signal
   signal(SIGTERM, Scheduler::signalHandler_);
 }

From d60cae079db100734e77ce0f1be879b2aa8f9ecb Mon Sep 17 00:00:00 2001
From: Ulrich Germann <ugermann@inf.ed.ac.uk>
Date: Fri, 25 Oct 2019 22:43:31 +0100
Subject: [PATCH 713/838] Update scheduler.h

As requested my @emjotde.
---
 src/training/scheduler.h | 22 +++++++++++++++-------
 1 file changed, 15 insertions(+), 7 deletions(-)

diff --git a/src/training/scheduler.h b/src/training/scheduler.h
index f06e10ade..2422b5985 100644
--- a/src/training/scheduler.h
+++ b/src/training/scheduler.h
@@ -19,9 +19,17 @@ class Scheduler : public TrainingObserver {
   timer::Timer timer_;
   timer::Timer heartBeatTimer_;
 
-  static bool sigterm_;
-  void installSignalHandlers_();
-  static void signalHandler_(int sig);
+  // SIGNAL HANDLING
+  // Currently, only the following is handled by a custom signal handler: 
+  // SIGTERM: When SIGTERM is received, the global (static member) flag sigterm_ (false by default) is set to true 
+  //     by signalHandler(). When sigterm_ is true, keepGoing() returns false, and the current state of training models 
+  //     is saved prior to exiting.
+  //        This functionality is helpful when training on clusters with time limits on compute slots, e.g., on s
+  //     clusters managed by slurm. Slurm can be asked to sending a (custom) warning signal to a process at a given 
+  //     point in time prior to the hard "time's up".
+  static bool sigterm_; // flag signalling that SIGTERM has been received false by default, set to true by signalHandler(SIGTERM)
+  void installSignalHandlers(); // installs signalHandler() for select signals (currently only SIGTERM) 
+  static void signalHandler(int sig); // handler for select signals (currently SIGTERM only)`` 
 
   // determine scheduled LR decay factor (--lr-decay-inv-sqrt option)
   float getScheduledLRDecayFactor(const TrainingState& state) const {
@@ -218,10 +226,10 @@ class Scheduler : public TrainingObserver {
   void validate(const std::vector<Ptr<ExpressionGraph>>& graphs,
                 bool final = false) {
     // Do not validate if already validated (for instance, after the model is
-    // loaded) or if validation is scheduled for another update
-    if(sigterm_
-       || state_->validated
-       || (!state_->enteredNewPeriodOf(options_->get<std::string>("valid-freq")) && !final))
+    // loaded) or if validation is scheduled for another update, or when signal SIGTERM was received
+    if(sigterm_ // SIGTERM was received
+       || state_->validated // already valudated (in resumed training, for example)
+       || (!state_->enteredNewPeriodOf(options_->get<std::string>("valid-freq")) && !final)) // not now
       return;
 
     bool firstValidator = true;

From 3dfd32cfd88085c86e564e7ff22e8596c42c8874 Mon Sep 17 00:00:00 2001
From: Ulrich Germann <ugermann@inf.ed.ac.uk>
Date: Fri, 25 Oct 2019 22:50:05 +0100
Subject: [PATCH 714/838] Update scheduler.h

As requested by @emjotde.
---
 src/training/scheduler.h | 5 +++--
 1 file changed, 3 insertions(+), 2 deletions(-)

diff --git a/src/training/scheduler.h b/src/training/scheduler.h
index 2422b5985..a7ee866f6 100644
--- a/src/training/scheduler.h
+++ b/src/training/scheduler.h
@@ -29,7 +29,8 @@ class Scheduler : public TrainingObserver {
   //     point in time prior to the hard "time's up".
   static bool sigterm_; // flag signalling that SIGTERM has been received false by default, set to true by signalHandler(SIGTERM)
   void installSignalHandlers(); // installs signalHandler() for select signals (currently only SIGTERM) 
-  static void signalHandler(int sig); // handler for select signals (currently SIGTERM only)`` 
+  static void signalHandler(int sig); // handler for select signals (currently SIGTERM only)
+  // END OF SIGNAL HANDLING CODE
 
   // determine scheduled LR decay factor (--lr-decay-inv-sqrt option)
   float getScheduledLRDecayFactor(const TrainingState& state) const {
@@ -193,7 +194,7 @@ class Scheduler : public TrainingObserver {
 
   void started() { LOG(info, "Training started"); }
   void finished() {
-    if (keepGoing(false)) // false means: ignore sigterm flag
+    if (keepGoing(/*checkForSigTERM=*/false)) // i.e., ignore sigterm flag
       LOG(info, "Training interrupted (SIGTERM).");
     else
       LOG(info, "Training finished");

From 369148c9c645a72f45895bcfb7b6c9c8070d7d7a Mon Sep 17 00:00:00 2001
From: Ulrich Germann <ugermann@inf.ed.ac.uk>
Date: Fri, 25 Oct 2019 22:54:17 +0100
Subject: [PATCH 715/838] Update scheduler.h

Fix typo in comment.
---
 src/training/scheduler.h | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/training/scheduler.h b/src/training/scheduler.h
index a7ee866f6..fd08215d1 100644
--- a/src/training/scheduler.h
+++ b/src/training/scheduler.h
@@ -229,7 +229,7 @@ class Scheduler : public TrainingObserver {
     // Do not validate if already validated (for instance, after the model is
     // loaded) or if validation is scheduled for another update, or when signal SIGTERM was received
     if(sigterm_ // SIGTERM was received
-       || state_->validated // already valudated (in resumed training, for example)
+       || state_->validated // already validated (in resumed training, for example)
        || (!state_->enteredNewPeriodOf(options_->get<std::string>("valid-freq")) && !final)) // not now
       return;
 

From ed5f5866c25a1fd79bb1e5cfbdef266137defdd0 Mon Sep 17 00:00:00 2001
From: Marcin Junczys-Dowmunt <Marcin.JunczysDowmunt@microsoft.com>
Date: Fri, 25 Oct 2019 17:02:16 -0700
Subject: [PATCH 716/838] bye bye boost

---
 CMakeLists.txt         | 23 +++++++++++++----------
 src/common/timer.h     | 33 +++++----------------------------
 src/data/corpus.cpp    |  4 ----
 src/graph/auto_tuner.h | 18 +++---------------
 4 files changed, 21 insertions(+), 57 deletions(-)

diff --git a/CMakeLists.txt b/CMakeLists.txt
index 8bde2f456..c8d0d3526 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -20,7 +20,7 @@ option(USE_NCCL "Use NCCL library" ON)
 option(USE_MPI "Use MPI library" OFF)
 option(COMPILE_EXAMPLES "Compile examples" OFF)
 option(COMPILE_TESTS "Compile tests" OFF)
-option(COMPILE_SERVER "Compile marian-server" ON)
+option(COMPILE_SERVER "Compile marian-server" OFF)
 option(USE_FBGEMM "Use FBGEMM" ON)
 option(USE_DOXYGEN "Build documentation with Doxygen" ON)
 
@@ -293,7 +293,7 @@ if(COMPILE_CPU)
   endif(MKL_FOUND)
 endif(COMPILE_CPU)
 
-set(BOOST_COMPONENTS timer iostreams filesystem system chrono)
+set(BOOST_COMPONENTS "")
 if("${CMAKE_CXX_COMPILER_ID}" STREQUAL "GNU" AND CMAKE_CXX_COMPILER_VERSION VERSION_LESS 4.9)
   add_definitions(-DUSE_BOOST_REGEX=1)
   set(BOOST_COMPONENTS ${BOOST_COMPONENTS} regex)
@@ -308,6 +308,7 @@ if(COMPILE_SERVER)
     message(STATUS "Found OpenSSL")
     include_directories(${OPENSSL_INCLUDE_DIR})
     set(EXT_LIBS ${EXT_LIBS} ${OPENSSL_CRYPTO_LIBRARY})
+    set(BOOST_COMPONENTS ${BOOST_COMPONENTS} system)
   else(OpenSSL_FOUND)
     message(WARNING "Cannot find OpenSSL library. Not compiling server.")
     set(COMPILE_SERVER "off")
@@ -322,14 +323,16 @@ if(USE_STATIC_LIBS)
   set(Boost_USE_STATIC_LIBS ON)
 endif()
 
-find_package(Boost COMPONENTS ${BOOST_COMPONENTS})
-if(Boost_FOUND)
-  include_directories(${Boost_INCLUDE_DIRS})
-  set(EXT_LIBS ${EXT_LIBS} ${Boost_LIBRARIES})
-  set(EXT_LIBS ${EXT_LIBS} ${ZLIB_LIBRARIES}) # hack for static compilation
-else(Boost_FOUND)
-  message(SEND_ERROR "Cannot find Boost libraries. Terminating.")
-endif(Boost_FOUND)
+if(BOOST_COMPONENTS)
+  find_package(Boost COMPONENTS ${BOOST_COMPONENTS})
+  if(Boost_FOUND)
+    include_directories(${Boost_INCLUDE_DIRS})
+    set(EXT_LIBS ${EXT_LIBS} ${Boost_LIBRARIES})
+    set(EXT_LIBS ${EXT_LIBS} ${ZLIB_LIBRARIES}) # hack for static compilation
+  else(Boost_FOUND)
+    message(SEND_ERROR "Cannot find Boost libraries. Terminating.")
+  endif(Boost_FOUND)
+endif(BOOST_COMPONENTS)
 
 if(COMPILE_TESTS)
   enable_testing()
diff --git a/src/common/timer.h b/src/common/timer.h
index 649bba9d5..ac83b3634 100644
--- a/src/common/timer.h
+++ b/src/common/timer.h
@@ -1,25 +1,8 @@
 #pragma once
 
-#ifdef __GNUC__
-#pragma GCC diagnostic push
-#pragma GCC diagnostic ignored "-Wsuggest-override"
-#endif
-#ifndef NO_BOOST
-#include <boost/timer/timer.hpp>
-#endif
-#ifdef __GNUC__
-#pragma GCC diagnostic pop
-#endif
-
-#ifdef _MSC_VER
-// (needed on Windows only to resolve a link error, but causes a warning on Linux)
-#ifndef NO_BOOST
-#include <boost/chrono.hpp>
-#endif
-#endif
-
-#include <chrono>
+#include <iostream>
 #include <sstream>
+#include <chrono>
 
 namespace marian {
 namespace timer {
@@ -33,7 +16,6 @@ static inline std::string currentDate() {
 }
 
 // Timer measures elapsed time.
-// This is a wrapper around std::chrono providing wall time only
 class Timer {
 protected:
   using clock = std::chrono::steady_clock;
@@ -93,14 +75,9 @@ class AutoTimer : public Timer {
   }
 };
 
-// @TODO: replace with a timer providing CPU/thread time on both Linux and Windows. This is required
-// for auto-tuner.
-// Check get_clocktime on Linux: https://linux.die.net/man/3/clock_gettime
-// Check GetThreadTimes on Windows:
-// https://docs.microsoft.com/en-gb/windows/desktop/api/processthreadsapi/nf-processthreadsapi-getthreadtimes
-#ifndef NO_BOOST
-using CPUTimer = boost::timer::cpu_timer;
-#endif
+// std::chrono::steady_clock seems to be the right choice here.
+using CPUTimer = Timer;
+
 
 }  // namespace timer
 }  // namespace marian
diff --git a/src/data/corpus.cpp b/src/data/corpus.cpp
index 227efde12..84d5f9f42 100644
--- a/src/data/corpus.cpp
+++ b/src/data/corpus.cpp
@@ -190,7 +190,6 @@ void Corpus::shuffleData(const std::vector<std::string>& paths) {
     LOG(info, "[data] Done shuffling {} sentences (cached in RAM)", numSentences);
   }
   else {
-#ifndef NO_BOOST
     // create temp files that contain the data in randomized order
     tempFiles_.resize(numStreams);
     for(size_t i = 0; i < numStreams; ++i) {
@@ -210,9 +209,6 @@ void Corpus::shuffleData(const std::vector<std::string>& paths) {
       files_[i] = std::move(inputStream);
     }
     LOG(info, "[data] Done shuffling {} sentences to temp files", numSentences);
-#else
-    ABORT("Shuffling to temp file is not available when compiling with NO_BOOST option. Use --shuffle-in-ram.");
-#endif
   }
   pos_ = 0;
 }
diff --git a/src/graph/auto_tuner.h b/src/graph/auto_tuner.h
index 6e0fe5eb3..7a248bf96 100755
--- a/src/graph/auto_tuner.h
+++ b/src/graph/auto_tuner.h
@@ -25,9 +25,7 @@ class AutoTuner : public AutoTunerRecorder {
   // collects the statistics.
   const size_t collectStatMax = 50;
 
-#ifndef NO_BOOST
   UPtr<timer::CPUTimer> timer_;
-#endif
 
   // This structure holds a hash key an algorithm function (e.g. int16, packed gemm, mkl gemm)
   // for a specific operation size
@@ -93,38 +91,28 @@ class AutoTuner : public AutoTunerRecorder {
   Return run(Args... args) { return algorithms_[choose()].algorithm(args...); }
 
   void start(size_t hash) override {
-#ifndef NO_BOOST
     if(!timer_ && done_.count(hash) == 0)
       timer_.reset(new timer::CPUTimer());
-#else
-    hash;
-    LOG_ONCE(warn, "Auto-tuner not available when compiling with NO_BOOST option. Will use the first option.");
-#endif
   }
 
   void stop(size_t hash, bool stop) override {
-#ifndef NO_BOOST
     if(stop && done_.count(hash) == 0) {
       timer_->stop();
 
-      typedef std::chrono::duration<double> sec;
-      sec seconds = std::chrono::nanoseconds(timer_->elapsed().user);
+      auto seconds = timer_->elapsed();
 
       auto it = stats_.find(hash);
       if(it != stats_.end()) {
         if(it->second.runs < collectStatMax) {
-          it->second.time += seconds.count();
+          it->second.time += seconds;
           it->second.runs += 1;
         }
       } else {
-        stats_.emplace(hash, Stat({seconds.count(), 1}));
+        stats_.emplace(hash, Stat({seconds, 1}));
       }
 
       timer_.reset(nullptr);
     }
-#else
-    hash; stop;
-#endif
   }
 };
 

From 5406641fff1761be04c4787b9533d4d0196f5ca4 Mon Sep 17 00:00:00 2001
From: Marcin Junczys-Dowmunt <Marcin.JunczysDowmunt@microsoft.com>
Date: Fri, 25 Oct 2019 17:11:25 -0700
Subject: [PATCH 717/838] handle warnings from FBGEMM when compiled statically

---
 src/tensors/cpu/expanded_gemm.h       | 11 +++++++++++
 src/tensors/cpu/sharp/packed_gemm.cpp | 11 +++++++++++
 2 files changed, 22 insertions(+)

diff --git a/src/tensors/cpu/expanded_gemm.h b/src/tensors/cpu/expanded_gemm.h
index 0c68ccfd7..7bdf4a411 100644
--- a/src/tensors/cpu/expanded_gemm.h
+++ b/src/tensors/cpu/expanded_gemm.h
@@ -4,7 +4,18 @@
 #include "tensors/cpu/sharp/packed_gemm.h"
 
 #if USE_FBGEMM
+
+#ifdef __GNUC__
+#pragma GCC diagnostic push
+#pragma GCC diagnostic ignored "-Wunused-variable"
+#endif
+
 #include "3rd_party/fbgemm/include/fbgemm/FbgemmFP16.h"
+
+#ifdef __GNUC__
+#pragma GCC diagnostic pop
+#endif
+
 using namespace fbgemm;
 #endif  // USE_FBGEMM
 
diff --git a/src/tensors/cpu/sharp/packed_gemm.cpp b/src/tensors/cpu/sharp/packed_gemm.cpp
index 087975197..bae58288d 100755
--- a/src/tensors/cpu/sharp/packed_gemm.cpp
+++ b/src/tensors/cpu/sharp/packed_gemm.cpp
@@ -19,10 +19,21 @@
 //#pragma comment(linker, "/ignore:4049") // locally defined symbol ...asmjit... imported
 //#pragma comment(linker, "/ignore:4217") // locally defined symbol ...asmjit... imported
 #endif
+
+
+#ifdef __GNUC__
+#pragma GCC diagnostic push
+#pragma GCC diagnostic ignored "-Wunused-variable"
+#endif
+
 #include "3rd_party/fbgemm/include/fbgemm/FbgemmFP16.h"
 #include "3rd_party/fbgemm/include/fbgemm/QuantUtils.h"
 #include "3rd_party/fbgemm/include/fbgemm/Fbgemm.h"
 
+#ifdef __GNUC__
+#pragma GCC diagnostic pop
+#endif
+
 #ifdef _OPENMP
 #include <omp.h>
 #endif

From 4f4989b22154d45de4baab0aae4b7afc2bbb4734 Mon Sep 17 00:00:00 2001
From: Ulrich Germann <ulrich.germann@gmail.com>
Date: Sat, 26 Oct 2019 02:48:15 +0100
Subject: [PATCH 718/838] Bug fix: installSignalHandlers_() ->
 installSignalHandlers() in src/training/scheduler.h

---
 src/training/scheduler.h | 12 ++++++------
 1 file changed, 6 insertions(+), 6 deletions(-)

diff --git a/src/training/scheduler.h b/src/training/scheduler.h
index fd08215d1..8aa8f408d 100644
--- a/src/training/scheduler.h
+++ b/src/training/scheduler.h
@@ -20,15 +20,15 @@ class Scheduler : public TrainingObserver {
   timer::Timer heartBeatTimer_;
 
   // SIGNAL HANDLING
-  // Currently, only the following is handled by a custom signal handler: 
-  // SIGTERM: When SIGTERM is received, the global (static member) flag sigterm_ (false by default) is set to true 
-  //     by signalHandler(). When sigterm_ is true, keepGoing() returns false, and the current state of training models 
+  // Currently, only the following is handled by a custom signal handler:
+  // SIGTERM: When SIGTERM is received, the global (static member) flag sigterm_ (false by default) is set to true
+  //     by signalHandler(). When sigterm_ is true, keepGoing() returns false, and the current state of training models
   //     is saved prior to exiting.
   //        This functionality is helpful when training on clusters with time limits on compute slots, e.g., on s
-  //     clusters managed by slurm. Slurm can be asked to sending a (custom) warning signal to a process at a given 
+  //     clusters managed by slurm. Slurm can be asked to sending a (custom) warning signal to a process at a given
   //     point in time prior to the hard "time's up".
   static bool sigterm_; // flag signalling that SIGTERM has been received false by default, set to true by signalHandler(SIGTERM)
-  void installSignalHandlers(); // installs signalHandler() for select signals (currently only SIGTERM) 
+  void installSignalHandlers(); // installs signalHandler() for select signals (currently only SIGTERM)
   static void signalHandler(int sig); // handler for select signals (currently SIGTERM only)
   // END OF SIGNAL HANDLING CODE
 
@@ -159,7 +159,7 @@ class Scheduler : public TrainingObserver {
       : options_(options), state_(state) {
     ABORT_IF(state_->factor != 1, "state.factor unexpectedly not 1 at this point??");
     updateLearningRate(*state);
-    installSignalHandlers_();
+    installSignalHandlers();
   }
 
   bool keepGoing(bool checkForSigTerm=true) {

From 0f7e40eff0fbe0506ec0e2d1a5147c227d1cef0e Mon Sep 17 00:00:00 2001
From: Ulrich Germann <ulrich.germann@gmail.com>
Date: Sat, 26 Oct 2019 03:07:39 +0100
Subject: [PATCH 719/838] Bug fix: signalHandler_ -> signalHandler in
 src/training/scheduler.cpp

---
 src/training/scheduler.cpp | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/training/scheduler.cpp b/src/training/scheduler.cpp
index 56b97f7d0..7fbcc8d6e 100644
--- a/src/training/scheduler.cpp
+++ b/src/training/scheduler.cpp
@@ -22,7 +22,7 @@ void Scheduler::signalHandler(int sig) {
 
 void Scheduler::installSignalHandlers() {
   // TODO: use sigaction instead of signal
-  signal(SIGTERM, Scheduler::signalHandler_);
+  signal(SIGTERM, Scheduler::signalHandler);
 }
 
 }

From b22d3162b8b1a4576be90c8ec4cb880244808646 Mon Sep 17 00:00:00 2001
From: Marcin Junczys-Dowmunt <Marcin.JunczysDowmunt@microsoft.com>
Date: Fri, 25 Oct 2019 22:01:39 -0700
Subject: [PATCH 720/838] small clean-up

---
 src/CMakeLists.txt | 7 -------
 1 file changed, 7 deletions(-)

diff --git a/src/CMakeLists.txt b/src/CMakeLists.txt
index afbf3bf07..b4435151f 100644
--- a/src/CMakeLists.txt
+++ b/src/CMakeLists.txt
@@ -216,13 +216,6 @@ foreach(exec ${EXECUTABLES})
   set_target_properties(${exec} PROPERTIES RUNTIME_OUTPUT_DIRECTORY "${CMAKE_BINARY_DIR}")
 endforeach(exec)
 
-#add_executable(
-#  align2steps
-#  tools/align2steps.cpp
-#)
-
-#set_target_properties(align2steps PROPERTIES RUNTIME_OUTPUT_DIRECTORY "${CMAKE_BINARY_DIR}")
-
 if(COMPILE_TESTS)
   set(CATCH_INCLUDE_DIR ${CMAKE_CURRENT_SOURCE_DIR}/3rd_party)
   add_library(Catch INTERFACE)

From 31bac7dc27f240ac6191f397dfc65172d81c0268 Mon Sep 17 00:00:00 2001
From: Marcin Junczys-Dowmunt <Marcin.JunczysDowmunt@microsoft.com>
Date: Fri, 25 Oct 2019 23:39:45 -0700
Subject: [PATCH 721/838] switch off gradient clipping

---
 src/common/aliases.cpp       | 6 +++---
 src/common/config_parser.cpp | 4 ++--
 2 files changed, 5 insertions(+), 5 deletions(-)

diff --git a/src/common/aliases.cpp b/src/common/aliases.cpp
index a59280648..03cb60cf6 100644
--- a/src/common/aliases.cpp
+++ b/src/common/aliases.cpp
@@ -60,7 +60,7 @@ void ConfigParser::addAliases(cli::CLIWrapper& cli) {
       config["cost-type"] = "ce-mean-words";
       config["lr-decay-inv-sqrt"] = 16000;
       config["label-smoothing"] = 0.1;
-      config["clip-norm"] = 5;
+      config["clip-norm"] = 0;
       config["sync-sgd"] = true;
       config["exponential-smoothing"] = 1e-4;
       config["mini-batch-fit"] = true;
@@ -91,7 +91,7 @@ void ConfigParser::addAliases(cli::CLIWrapper& cli) {
       config["lr-warmup"] = 16000;
       config["lr-decay-inv-sqrt"] = 16000;
       config["label-smoothing"] = 0.1;
-      config["clip-norm"] = 5;
+      config["clip-norm"] = 0;
       config["sync-sgd"] = true;
       config["exponential-smoothing"] = 1e-4;
       config["max-length"] = 100;
@@ -129,7 +129,7 @@ void ConfigParser::addAliases(cli::CLIWrapper& cli) {
       config["lr-warmup"] = 8000;
       config["lr-decay-inv-sqrt"] = 8000;
       config["label-smoothing"] = 0.1;
-      config["clip-norm"] = 5;
+      config["clip-norm"] = 0;
       config["sync-sgd"] = true;
       config["exponential-smoothing"] = 1e-4;
       config["max-length"] = 100;
diff --git a/src/common/config_parser.cpp b/src/common/config_parser.cpp
index 3a56caaca..4f4003db5 100755
--- a/src/common/config_parser.cpp
+++ b/src/common/config_parser.cpp
@@ -446,7 +446,7 @@ void ConfigParser::addOptionsTraining(cli::CLIWrapper& cli) {
      "Weight for loss function for factors (factored vocab only) (1 to disable)", 1.0f);
   cli.add<float>("--clip-norm",
      "Clip gradient norm to  arg  (0 to disable)",
-     1.f);
+     0.f);
   cli.add<float>("--exponential-smoothing",
      "Maintain smoothed version of parameters for validation and saving with smoothing factor. 0 to disable. "
       "Auto-adjusted to --mini-batch-words-ref if given.",
@@ -487,7 +487,7 @@ void ConfigParser::addOptionsTraining(cli::CLIWrapper& cli) {
   cli.add<std::vector<std::string>>("--cost-scaling",
       "Dynamic cost scaling for mixed precision training: "
       "power of 2, scaling window, scaling factor, tolerance, range, minimum factor")->implicit_val("7.f 2000 2.f 0.05f 10 1.f");
-  cli.add<bool>("--normalize-gradient", "Normalize gradient by multiplying with worldsize / total labels");
+  cli.add<bool>("--normalize-gradient", "Normalize gradient by multiplying with no. devices / total labels");
 
   // multi-node training
   cli.add<bool>("--multi-node",

From e20de4924e37eab2b70ba4f5cbfa638374a49af4 Mon Sep 17 00:00:00 2001
From: Marcin Junczys-Dowmunt <Marcin.JunczysDowmunt@microsoft.com>
Date: Fri, 25 Oct 2019 23:42:54 -0700
Subject: [PATCH 722/838] move pointer to fitting regression tests

---
 regression-tests | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/regression-tests b/regression-tests
index 95b66e74d..1497bb340 160000
--- a/regression-tests
+++ b/regression-tests
@@ -1 +1 @@
-Subproject commit 95b66e74d47107a2e3abad5a4c5338904129a25a
+Subproject commit 1497bb34001afb40ab4003c5003f4f54f491249c

From 27ce11826e44a9536a88149dffd034076adfadcd Mon Sep 17 00:00:00 2001
From: Marcin Junczys-Dowmunt <Marcin.JunczysDowmunt@microsoft.com>
Date: Sat, 26 Oct 2019 08:15:59 -0700
Subject: [PATCH 723/838] add comments about gradient clipping bug

---
 src/common/config_parser.cpp  | 2 +-
 src/optimizers/optimizers.cpp | 2 +-
 src/optimizers/optimizers.h   | 2 +-
 3 files changed, 3 insertions(+), 3 deletions(-)

diff --git a/src/common/config_parser.cpp b/src/common/config_parser.cpp
index 4f4003db5..86174e383 100755
--- a/src/common/config_parser.cpp
+++ b/src/common/config_parser.cpp
@@ -446,7 +446,7 @@ void ConfigParser::addOptionsTraining(cli::CLIWrapper& cli) {
      "Weight for loss function for factors (factored vocab only) (1 to disable)", 1.0f);
   cli.add<float>("--clip-norm",
      "Clip gradient norm to  arg  (0 to disable)",
-     0.f);
+     1.f); // @TODO: this is currently wrong with ce-sum and should rather be disabled or fixed by multiplying with labels
   cli.add<float>("--exponential-smoothing",
      "Maintain smoothed version of parameters for validation and saving with smoothing factor. 0 to disable. "
       "Auto-adjusted to --mini-batch-words-ref if given.",
diff --git a/src/optimizers/optimizers.cpp b/src/optimizers/optimizers.cpp
index 9d963b647..083f94c01 100644
--- a/src/optimizers/optimizers.cpp
+++ b/src/optimizers/optimizers.cpp
@@ -313,7 +313,7 @@ Ptr<OptimizerBase> Optimizer(Ptr<Options> options) {
   Ptr<ClipperBase> clipper = nullptr;
   float clipNorm = options->get<float>("clip-norm");
   if(clipNorm > 0)
-    clipper = Clipper<Norm>(clipNorm);
+    clipper = Clipper<Norm>(clipNorm); // @BUGBUG: this is not scaling by number of labels?
 
   auto opt = options->get<std::string>("optimizer");
 
diff --git a/src/optimizers/optimizers.h b/src/optimizers/optimizers.h
index 44d603120..2b70ba848 100644
--- a/src/optimizers/optimizers.h
+++ b/src/optimizers/optimizers.h
@@ -40,7 +40,7 @@ class OptimizerBase : public TrainingObserver {
 
   void update(Tensor params, Tensor grads, size_t mbSize = mbSizeNotProvided) {
     if(clipper_)
-      clipper_->clip(grads);
+      clipper_->clip(grads); //@BUGBUG: take into account actual mini-batch size since gradients are not normalized
 
     size_t refMBWords = refMBWordsParam_;
     if (refMBWords == 0) { // optimizer not configured to use hyper-parameter auto-adjustment

From abf95d0cddebb182688a5a3ba86cc230ab79b6a7 Mon Sep 17 00:00:00 2001
From: Marcin Junczys-Dowmunt <Marcin.JunczysDowmunt@microsoft.com>
Date: Sat, 26 Oct 2019 08:41:27 -0700
Subject: [PATCH 724/838] move regtest pointer

---
 regression-tests | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/regression-tests b/regression-tests
index 1497bb340..66c6f498d 160000
--- a/regression-tests
+++ b/regression-tests
@@ -1 +1 @@
-Subproject commit 1497bb34001afb40ab4003c5003f4f54f491249c
+Subproject commit 66c6f498d55fbe7a3d524f5697a185000f2d928d

From e375905404123dfa8a226d7362a447f98ef97724 Mon Sep 17 00:00:00 2001
From: Marcin Junczys-Dowmunt <Marcin.JunczysDowmunt@microsoft.com>
Date: Sat, 26 Oct 2019 08:53:57 -0700
Subject: [PATCH 725/838] update changelog and version

---
 CHANGELOG.md | 4 +++-
 VERSION      | 2 +-
 2 files changed, 4 insertions(+), 2 deletions(-)

diff --git a/CHANGELOG.md b/CHANGELOG.md
index ab57fa811..c6fb8b85f 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -8,11 +8,13 @@ and this project adheres to [Semantic Versioning](http://semver.org/spec/v2.0.0.
 ## [Unreleased]
 
 ### Added
+- Boost is now optional, still required for marian_server and GCC-4.9 due to lack
+  of support for built-in std::regex.
 - Automatic vectorization of elementwise operations on CPU for tensors dims that 
   are divible by 4 (AVX) and 8 (AVX2)
 - Replacing std::shared_ptr<T> with custom IntrusivePtr<T> for small objects like 
   Tensors, Hypotheses and Expressions.
-- First steps towards integrating FP16 support, currently no-ops.
+- Fp16 inference working for translation
 - Gradient-checkpointing
 
 ### Fixed
diff --git a/VERSION b/VERSION
index 1182b2ff7..bb667f9c3 100644
--- a/VERSION
+++ b/VERSION
@@ -1 +1 @@
-v1.8.3
+v1.8.5

From 68f9d90bfafb5b7248076786028cd04977d57d4a Mon Sep 17 00:00:00 2001
From: Marcin Junczys-Dowmunt <Marcin.JunczysDowmunt@microsoft.com>
Date: Sat, 26 Oct 2019 09:04:18 -0700
Subject: [PATCH 726/838] fix changelog

---
 CHANGELOG.md | 3 +--
 1 file changed, 1 insertion(+), 2 deletions(-)

diff --git a/CHANGELOG.md b/CHANGELOG.md
index c6fb8b85f..5cb854aa9 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -8,8 +8,6 @@ and this project adheres to [Semantic Versioning](http://semver.org/spec/v2.0.0.
 ## [Unreleased]
 
 ### Added
-- Boost is now optional, still required for marian_server and GCC-4.9 due to lack
-  of support for built-in std::regex.
 - Automatic vectorization of elementwise operations on CPU for tensors dims that 
   are divible by 4 (AVX) and 8 (AVX2)
 - Replacing std::shared_ptr<T> with custom IntrusivePtr<T> for small objects like 
@@ -25,6 +23,7 @@ and this project adheres to [Semantic Versioning](http://semver.org/spec/v2.0.0.
 - Compilation with CUDA 10.1
 
 ### Changed
+- Simplified file stream and temporary file handling
 - Unified node intializers, same function API.
 
 ## [1.8.0] - 2019-09-04

From 55e3bcfd74510b87d9b9e0d479228a8b73a033cd Mon Sep 17 00:00:00 2001
From: Marcin Junczys-Dowmunt <Marcin.JunczysDowmunt@microsoft.com>
Date: Sat, 26 Oct 2019 15:19:01 -0700
Subject: [PATCH 727/838] bump patch and changelog

---
 CHANGELOG.md     | 2 ++
 VERSION          | 2 +-
 regression-tests | 2 +-
 3 files changed, 4 insertions(+), 2 deletions(-)

diff --git a/CHANGELOG.md b/CHANGELOG.md
index 5cb854aa9..65f3e8550 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -23,6 +23,8 @@ and this project adheres to [Semantic Versioning](http://semver.org/spec/v2.0.0.
 - Compilation with CUDA 10.1
 
 ### Changed
+- Boost depdendency is now optional and only required for marian_server 
+  or for boost::regex when compiling with g++-4.9
 - Simplified file stream and temporary file handling
 - Unified node intializers, same function API.
 
diff --git a/VERSION b/VERSION
index bb667f9c3..4e0d75544 100644
--- a/VERSION
+++ b/VERSION
@@ -1 +1 @@
-v1.8.5
+v1.8.6
diff --git a/regression-tests b/regression-tests
index 66c6f498d..08c1485c2 160000
--- a/regression-tests
+++ b/regression-tests
@@ -1 +1 @@
-Subproject commit 66c6f498d55fbe7a3d524f5697a185000f2d928d
+Subproject commit 08c1485c2c925e4e1ed8a3e5df686ed5c7dc496e

From 8fc0772facf10c3d3e85d5220476410a462cd443 Mon Sep 17 00:00:00 2001
From: Marcin Junczys-Dowmunt <Marcin.JunczysDowmunt@microsoft.com>
Date: Sat, 26 Oct 2019 16:56:57 -0700
Subject: [PATCH 728/838] move regression test pointer

---
 regression-tests | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/regression-tests b/regression-tests
index 08c1485c2..66c6f498d 160000
--- a/regression-tests
+++ b/regression-tests
@@ -1 +1 @@
-Subproject commit 08c1485c2c925e4e1ed8a3e5df686ed5c7dc496e
+Subproject commit 66c6f498d55fbe7a3d524f5697a185000f2d928d

From 86533709d49625fd4a3bf19236aef708e7b1632a Mon Sep 17 00:00:00 2001
From: Hieu Hoang <hihoan@microsoft.com>
Date: Sun, 27 Oct 2019 10:52:02 -0700
Subject: [PATCH 729/838] tweaks for windows compile

---
 src/common/file_stream.cpp | 2 ++
 src/common/file_stream.h   | 2 +-
 2 files changed, 3 insertions(+), 1 deletion(-)

diff --git a/src/common/file_stream.cpp b/src/common/file_stream.cpp
index 074e36ebf..8bc513006 100644
--- a/src/common/file_stream.cpp
+++ b/src/common/file_stream.cpp
@@ -6,6 +6,8 @@
 #ifdef _MSC_VER
 #include <io.h>
 #include <windows.h>
+#include <fcntl.h>
+#include <stdlib.h>
 #else
 #include <sys/types.h>
 #include <unistd.h>
diff --git a/src/common/file_stream.h b/src/common/file_stream.h
index ce495a71f..08326d5ea 100755
--- a/src/common/file_stream.h
+++ b/src/common/file_stream.h
@@ -53,7 +53,7 @@ class OutputFileStream : public std::ostream {
 
   template <typename T>
   size_t write(const T* ptr, size_t num = 1) {
-    this->write((char*)ptr, num * sizeof(T));
+    std::ostream::write((char*)ptr, num * sizeof(T));
     // fail() seems to be correct here. Failure to write should abort.
     ABORT_IF(fail(), "Error writing to file '{}'", file_.string());
     return num * sizeof(T);

From aff86fd1e56b0a367084f7404187045397d87b6d Mon Sep 17 00:00:00 2001
From: Ulrich Germann <ugermann@inf.ed.ac.uk>
Date: Sun, 27 Oct 2019 18:26:24 +0000
Subject: [PATCH 730/838] Update scheduler.cpp

Added link to explanation as to why sigaction is deemed better than signal.
---
 src/training/scheduler.cpp | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/src/training/scheduler.cpp b/src/training/scheduler.cpp
index 7fbcc8d6e..55003a172 100644
--- a/src/training/scheduler.cpp
+++ b/src/training/scheduler.cpp
@@ -21,7 +21,8 @@ void Scheduler::signalHandler(int sig) {
 
 
 void Scheduler::installSignalHandlers() {
-  // TODO: use sigaction instead of signal
+  // TODO: use sigaction instead of signal, 
+  // cf. https://stackoverflow.com/questions/231912/what-is-the-difference-between-sigaction-and-signal
   signal(SIGTERM, Scheduler::signalHandler);
 }
 

From ad6f7ff5a0639234ac81f6a9c4778ffc7baa47f9 Mon Sep 17 00:00:00 2001
From: Ulrich Germann <ulrich.germann@gmail.com>
Date: Sun, 27 Oct 2019 21:37:07 +0000
Subject: [PATCH 731/838] Changes as requested by @snukky.

---
 src/training/scheduler.h | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/src/training/scheduler.h b/src/training/scheduler.h
index 8aa8f408d..ff43ed99e 100644
--- a/src/training/scheduler.h
+++ b/src/training/scheduler.h
@@ -162,9 +162,9 @@ class Scheduler : public TrainingObserver {
     installSignalHandlers();
   }
 
-  bool keepGoing(bool checkForSigTerm=true) {
+  bool keepGoing() {
 
-    if (checkForSigTerm && sigterm_) // received signam SIGERM => exit gracefully
+    if (sigterm_) // received signal SIGERM => exit gracefully
       return false;
 
     // stop if it reached the maximum number of epochs
@@ -194,7 +194,7 @@ class Scheduler : public TrainingObserver {
 
   void started() { LOG(info, "Training started"); }
   void finished() {
-    if (keepGoing(/*checkForSigTERM=*/false)) // i.e., ignore sigterm flag
+    if (sigterm_)
       LOG(info, "Training interrupted (SIGTERM).");
     else
       LOG(info, "Training finished");

From 398ed0c8d27d19bd51575f3ec6f4b026a9b5f0e2 Mon Sep 17 00:00:00 2001
From: Marcin Junczys-Dowmunt <Marcin.JunczysDowmunt@microsoft.com>
Date: Sun, 27 Oct 2019 15:02:18 -0700
Subject: [PATCH 732/838] update version and changelog

---
 CHANGELOG.md | 1 +
 VERSION      | 2 +-
 2 files changed, 2 insertions(+), 1 deletion(-)

diff --git a/CHANGELOG.md b/CHANGELOG.md
index 65f3e8550..9377b2a6c 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -8,6 +8,7 @@ and this project adheres to [Semantic Versioning](http://semver.org/spec/v2.0.0.
 ## [Unreleased]
 
 ### Added
+- Safe handling of sigterm signal
 - Automatic vectorization of elementwise operations on CPU for tensors dims that 
   are divible by 4 (AVX) and 8 (AVX2)
 - Replacing std::shared_ptr<T> with custom IntrusivePtr<T> for small objects like 
diff --git a/VERSION b/VERSION
index 4e0d75544..d72a3cbc1 100644
--- a/VERSION
+++ b/VERSION
@@ -1 +1 @@
-v1.8.6
+v1.8.7

From 321fab9511418a72a1d051d5ae3290160387ad3c Mon Sep 17 00:00:00 2001
From: Marcin Junczys-Dowmunt <Marcin.JunczysDowmunt@microsoft.com>
Date: Sun, 27 Oct 2019 19:12:45 -0700
Subject: [PATCH 733/838] move sigterm handling out of class Scheduler

---
 src/training/scheduler.cpp | 28 +++++++++++++++++++++-------
 src/training/scheduler.h   | 22 ++++++----------------
 2 files changed, 27 insertions(+), 23 deletions(-)

diff --git a/src/training/scheduler.cpp b/src/training/scheduler.cpp
index 55003a172..4c30cb04e 100644
--- a/src/training/scheduler.cpp
+++ b/src/training/scheduler.cpp
@@ -3,27 +3,41 @@
 #include <cassert>
 
 namespace marian {
-bool Scheduler::sigterm_{false};
 
-void Scheduler::signalHandler(int sig) {
+// SIGNAL HANDLING, see scheduler.cpp for definitions
+// Currently, only the following is handled by a custom signal handler:
+// SIGTERM: When SIGTERM is received, the global (static member) flag sigterm_ (false by default) is set to true
+//     by signalHandler(). When sigterm_ is true, keepGoing() returns false, and the current state of training models
+//     is saved prior to exiting.
+//        This functionality is helpful when training on clusters with time limits on compute slots, e.g., on s
+//     clusters managed by slurm. Slurm can be asked to sending a (custom) warning signal to a process at a given
+//     point in time prior to the hard "time's up".
+
+bool sigterm_{false}; // flag signalling that SIGTERM has been received false by default, set to true by signalHandler(SIGTERM)
+
+void signalHandler(int sig) {
   // Note: sys_siglist[sig] or stdsignal() describe the effect (e.g.,
   // 'Terminated' rather than provide the signal name (which are #define(s)
   // in signal.h), so we have to do custom log messages here.
   switch (sig) {
     case SIGTERM: // save models and exit
-      LOG(info, "[training] Scheduler received signal SIGTERM");
-      Scheduler::sigterm_ = true;
+      LOG(info, "[training] Scheduler received signal SIGTERM"); // @TODO: figure out if this is safe. The logs are global and thread-safe, so should be OK?
+      sigterm_ = true;
       break;
     default:
       ABORT("No action defined for signal {}", sig);
   }
 }
 
-
-void Scheduler::installSignalHandlers() {
+// installs signalHandler() for select signals (currently only SIGTERM)
+void installSignalHandlers() {
   // TODO: use sigaction instead of signal, 
   // cf. https://stackoverflow.com/questions/231912/what-is-the-difference-between-sigaction-and-signal
-  signal(SIGTERM, Scheduler::signalHandler);
+  signal(SIGTERM, signalHandler);
+}
+
+bool getSigtermFlag() {
+  return sigterm_;
 }
 
 }
diff --git a/src/training/scheduler.h b/src/training/scheduler.h
index ff43ed99e..c9130d922 100644
--- a/src/training/scheduler.h
+++ b/src/training/scheduler.h
@@ -8,6 +8,9 @@
 
 namespace marian {
 
+bool getSigtermFlag();
+void installSignalHandlers(); 
+
 class Scheduler : public TrainingObserver {
 private:
   Ptr<Options> options_;
@@ -19,19 +22,6 @@ class Scheduler : public TrainingObserver {
   timer::Timer timer_;
   timer::Timer heartBeatTimer_;
 
-  // SIGNAL HANDLING
-  // Currently, only the following is handled by a custom signal handler:
-  // SIGTERM: When SIGTERM is received, the global (static member) flag sigterm_ (false by default) is set to true
-  //     by signalHandler(). When sigterm_ is true, keepGoing() returns false, and the current state of training models
-  //     is saved prior to exiting.
-  //        This functionality is helpful when training on clusters with time limits on compute slots, e.g., on s
-  //     clusters managed by slurm. Slurm can be asked to sending a (custom) warning signal to a process at a given
-  //     point in time prior to the hard "time's up".
-  static bool sigterm_; // flag signalling that SIGTERM has been received false by default, set to true by signalHandler(SIGTERM)
-  void installSignalHandlers(); // installs signalHandler() for select signals (currently only SIGTERM)
-  static void signalHandler(int sig); // handler for select signals (currently SIGTERM only)
-  // END OF SIGNAL HANDLING CODE
-
   // determine scheduled LR decay factor (--lr-decay-inv-sqrt option)
   float getScheduledLRDecayFactor(const TrainingState& state) const {
     auto args = options_->get<std::vector<std::string>>("lr-decay-inv-sqrt");
@@ -164,7 +154,7 @@ class Scheduler : public TrainingObserver {
 
   bool keepGoing() {
 
-    if (sigterm_) // received signal SIGERM => exit gracefully
+    if(getSigtermFlag()) // received signal SIGERM => exit gracefully
       return false;
 
     // stop if it reached the maximum number of epochs
@@ -194,7 +184,7 @@ class Scheduler : public TrainingObserver {
 
   void started() { LOG(info, "Training started"); }
   void finished() {
-    if (sigterm_)
+    if (getSigtermFlag())
       LOG(info, "Training interrupted (SIGTERM).");
     else
       LOG(info, "Training finished");
@@ -228,7 +218,7 @@ class Scheduler : public TrainingObserver {
                 bool final = false) {
     // Do not validate if already validated (for instance, after the model is
     // loaded) or if validation is scheduled for another update, or when signal SIGTERM was received
-    if(sigterm_ // SIGTERM was received
+    if(getSigtermFlag() // SIGTERM was received
        || state_->validated // already validated (in resumed training, for example)
        || (!state_->enteredNewPeriodOf(options_->get<std::string>("valid-freq")) && !final)) // not now
       return;

From 0a89e8f168a04ffa86dfb6251ed5fd0dfae56fbf Mon Sep 17 00:00:00 2001
From: Marcin Junczys-Dowmunt <Marcin.JunczysDowmunt@microsoft.com>
Date: Mon, 28 Oct 2019 13:18:07 -0700
Subject: [PATCH 734/838] fix compilation for various gcc and cuda combinations

---
 CMakeLists.txt                     | 10 ++++++++--
 src/common/types.h                 |  9 +++++----
 src/functional/defs.h              |  2 +-
 src/functional/operators.h         |  8 +++++---
 src/functional/tensor.h            |  2 +-
 src/tensors/cpu/element.h          |  2 +-
 src/tests/units/operator_tests.cpp |  6 +++---
 7 files changed, 24 insertions(+), 15 deletions(-)

diff --git a/CMakeLists.txt b/CMakeLists.txt
index 1b366c0ce..dabe95c41 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -159,6 +159,8 @@ set(EXT_LIBS ${EXT_LIBS} ${CMAKE_DL_LIBS})
 
 if(COMPILE_CUDA)
 
+LIST(APPEND COMPUTE -arch=sm_35; -gencode=arch=compute_35,code=sm_35; -gencode=arch=compute_50,code=sm_50; -gencode=arch=compute_52,code=sm_52; -gencode=arch=compute_60,code=sm_60; -gencode=arch=compute_61,code=sm_61;)
+
 if(USE_STATIC_LIBS)
   # link statically to stdlib libraries
   set(CMAKE_EXE_LINKER_FLAGS "-static-libgcc -static-libstdc++")
@@ -179,6 +181,10 @@ if(CUDA_FOUND)
       message(WARNING "On some Unix systems CUDA 10.0+ requires CMake 3.12.2+; you use CMake ${CMAKE_VERSION}")
   endif()
 
+  if(CUDA_VERSION VERSION_GREATER "8.0")
+    LIST(APPEND COMPUTE -gencode=arch=compute_70,code=sm_70; -gencode=arch=compute_70,code=compute_70)
+  endif()
+
   if(USE_STATIC_LIBS)
     find_library(CUDA_culibos_LIBRARY NAMES culibos PATHS ${CUDA_TOOLKIT_ROOT_DIR}/lib64)
     set(EXT_LIBS ${EXT_LIBS} ${CUDA_curand_LIBRARY} ${CUDA_cusparse_LIBRARY} ${CUDA_culibos_LIBRARY} ${CUDA_CUBLAS_LIBRARIES})
@@ -240,9 +246,9 @@ endif(COMPILE_CUDA)
 
 # TODO: make compatible with older CUDA versions
 if(CMAKE_BUILD_TYPE STREQUAL "Debug")
-  list(APPEND CUDA_NVCC_FLAGS --default-stream per-thread; -O0; -g; --use_fast_math; -arch=sm_35; -gencode=arch=compute_35,code=sm_35; -gencode=arch=compute_50,code=sm_50; -gencode=arch=compute_52,code=sm_52; -gencode=arch=compute_60,code=sm_60; -gencode=arch=compute_61,code=sm_61; -gencode=arch=compute_70,code=sm_70; -gencode=arch=compute_70,code=compute_70 ;)
+  list(APPEND CUDA_NVCC_FLAGS --default-stream per-thread; -O0; -g; --use_fast_math; ${COMPUTE})
 else(CMAKE_BUILD_TYPE STREQUAL "Debug")
-  list(APPEND CUDA_NVCC_FLAGS --default-stream per-thread; -O3; -g; --use_fast_math; -arch=sm_35; -gencode=arch=compute_35,code=sm_35; -gencode=arch=compute_50,code=sm_50; -gencode=arch=compute_52,code=sm_52; -gencode=arch=compute_60,code=sm_60; -gencode=arch=compute_61,code=sm_61; -gencode=arch=compute_70,code=sm_70; -gencode=arch=compute_70,code=compute_70 ;)
+  list(APPEND CUDA_NVCC_FLAGS --default-stream per-thread; -O3; -g; --use_fast_math; ${COMPUTE})
 endif(CMAKE_BUILD_TYPE STREQUAL "Debug")
 if(NOT MSVC)
   # @TODO: add warnings here too
diff --git a/src/common/types.h b/src/common/types.h
index 38462be40..fb0e04d2f 100755
--- a/src/common/types.h
+++ b/src/common/types.h
@@ -15,17 +15,18 @@
 #include <functional>
 #include <type_traits>
 
-#ifndef __CUDA_ARCH__
+#ifndef __CUDACC__
 #include <immintrin.h>
 #endif
 
 #ifdef __CUDACC__ // nvcc is compiling this code
-  #if (__CUDA_ARCH__ >= 600 || !defined(__CUDA_ARCH__))
+  #include <cuda.h> // required to see CUDA_VERSION
+  #if (CUDA_VERSION > 8000 && (__CUDA_ARCH__ >= 600 || !defined(__CUDA_ARCH__)))
     #define COMPILE_FP16 1 // we are in GPU code and we know what to do with FP16 code
   #else
     #define COMPILE_FP16 0 // we are in GPU code, but compute capability is too low to use FP16
   #endif
-#else // other compiler, likely host code. Sould be fine with seeing the correct includes with host code
+#else // other compiler, likely host code. Should be fine with seeing the correct includes with host code
   #define COMPILE_FP16 1
 #endif
 
@@ -125,7 +126,7 @@ do { \
 
 namespace marian {
 
-#ifndef __CUDA_ARCH__
+#ifndef __CUDACC__
 
 // @TODO: check what intrinsics are actually available.
 struct float32x4 {
diff --git a/src/functional/defs.h b/src/functional/defs.h
index e423dc2a1..a6562ed53 100644
--- a/src/functional/defs.h
+++ b/src/functional/defs.h
@@ -1,6 +1,6 @@
 #pragma once
 
-#ifdef __CUDA_ARCH__
+#ifdef __CUDACC__
 
 #include <cuda.h>
 #define HOST __host__
diff --git a/src/functional/operators.h b/src/functional/operators.h
index a41961501..66914bf75 100755
--- a/src/functional/operators.h
+++ b/src/functional/operators.h
@@ -191,7 +191,9 @@ struct Ops<double> {
 
 // stay invisible to NVCC as it seems to have problems with intrinsics;
 // will still be compiled into the binary by cpu-side gcc/g++
-#ifndef __CUDA_ARCH__
+// __CUDACC__ is defined when compiling with NVCC regardless of device type
+// __CUDA_ARCH__ is defined when compiling devicde (GPU) code
+#ifndef __CUDACC__
 
 #include "3rd_party/sse_mathfun.h"
 
@@ -437,9 +439,9 @@ struct Ops<float32x8> {
 } // end namespace functional
 } // end namespace marian
 
-#endif // of "#ifndef __CUDA_ARCH__"
+#endif // of "#ifndef __CUDACC__"
 
-#ifdef __CUDA_ARCH__
+#ifdef __CUDACC__
 #if COMPILE_FP16
 // only compile with fp16 support for compute_70, i.e. VOLTA 100 and above.
 #include <cuda_fp16.h>
diff --git a/src/functional/tensor.h b/src/functional/tensor.h
index ff9a9d42d..aecc25015 100755
--- a/src/functional/tensor.h
+++ b/src/functional/tensor.h
@@ -19,7 +19,7 @@ inline marian::Shape adapt(const marian::Shape& shape) {
 // modify last shape dimension to automatically map to a larger stride. We are moving now by 4 floats
 // at once and need to stop earlier. This is a shallow typecast to bascially an array of 4 floats.
 
-#ifndef __CUDA_ARCH__
+#ifndef __CUDACC__
 
 template <>
 inline marian::Shape adapt<float32x4>(const marian::Shape& shape) {
diff --git a/src/tensors/cpu/element.h b/src/tensors/cpu/element.h
index 3a936b31a..c145ae825 100644
--- a/src/tensors/cpu/element.h
+++ b/src/tensors/cpu/element.h
@@ -81,7 +81,7 @@ void element(const Functor& functor, marian::Tensor out, Tensors... tensors) {
 // AVX2 specific intrinsics. Similar for 4 and AVX. TODO: Add AVX512 support.
 template <class Functor, class... Tensors>
 void elementFloat(const Functor& functor, marian::Tensor out, Tensors... tensors) {
-#ifndef __CUDA_ARCH__
+#ifndef __CUDACC__
   std::vector<marian::Tensor> ts({tensors...});
   bool div8 = true;
   bool div4 = true;
diff --git a/src/tests/units/operator_tests.cpp b/src/tests/units/operator_tests.cpp
index 1b633762e..722f191dc 100755
--- a/src/tests/units/operator_tests.cpp
+++ b/src/tests/units/operator_tests.cpp
@@ -14,7 +14,7 @@ void tests(DeviceType device, Type floatType = Type::float32) {
   auto graph = New<ExpressionGraph>();
   graph->setParameterType(floatType);
   graph->setDevice({0, device});
-  graph->reserveWorkspaceMB(16);
+  graph->reserveWorkspaceMB(32);
 
   std::vector<T> values, values2;
 
@@ -71,8 +71,8 @@ void tests(DeviceType device, Type floatType = Type::float32) {
     CHECK(compare(rplus,  [](float a, float b) {return a + b;}, true));
     CHECK(compare(rminus, [](float a, float b) {return a - b;}, true));
     CHECK(compare(rmult,  [](float a, float b) {return a * b;}, true));
-    CHECK(compare(rdiv,   [](float a, float b) {return a / b;}, /*exactMatch=*/false));
-    CHECK(compare(rlae,   [](float a, float b) {return logf(expf(a) + expf(b));}, /*exactMatch=*/false));
+    CHECK(compare(rdiv,   [](float a, float b) {return a / b;}, false));
+    CHECK(compare(rlae,   [](float a, float b) {return logf(expf(a) + expf(b));}, false));
     CHECK(compare(rmax,   [](float a, float b) {return std::max(a, b);}, true));
     CHECK(compare(rmin,   [](float a, float b) {return std::min(a, b);}, true));
     CHECK(compare(rlt,    [](float a, float b) {return a <  b;}, true));

From a0e472294ac842ced98db67d0e19c66317599ec2 Mon Sep 17 00:00:00 2001
From: Marcin Junczys-Dowmunt <Marcin.JunczysDowmunt@microsoft.com>
Date: Mon, 28 Oct 2019 13:57:43 -0700
Subject: [PATCH 735/838] remove compatibility check for gcc 4.9 and below, as
 we cannot compile on 4.9 anyway

---
 CMakeLists.txt     | 13 +++++--------
 src/common/regex.h |  6 +-----
 2 files changed, 6 insertions(+), 13 deletions(-)

diff --git a/CMakeLists.txt b/CMakeLists.txt
index dabe95c41..19be22398 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -67,6 +67,11 @@ if(MSVC)
   set(EXT_LIBS ${EXT_LIBS} SHLWAPI)
 else(MSVC)
 
+  # Check we are using at least g++ 5.0
+  if(CMAKE_CXX_COMPILER_ID STREQUAL "GNU" AND CMAKE_CXX_COMPILER_VERSION VERSION_LESS 5.0)
+    message(FATAL_ERROR "FATAL ERROR: Compiling Marian requires at least g++ 5.0, your version is ${CMAKE_CXX_COMPILER_VERSION}")
+  endif()
+
   # Detect support CPU instrinsics for the current platform. This will
   # only by used with BUILD_ARCH=native. For overridden BUILD_ARCH we
   # minimally use -msse4.1. This seems to work with MKL.
@@ -307,14 +312,6 @@ if(COMPILE_CPU)
 endif(COMPILE_CPU)
 
 set(BOOST_COMPONENTS "")
-if("${CMAKE_CXX_COMPILER_ID}" STREQUAL "GNU" AND CMAKE_CXX_COMPILER_VERSION VERSION_LESS 4.9)
-  add_definitions(-DUSE_BOOST_REGEX=1)
-  set(BOOST_COMPONENTS ${BOOST_COMPONENTS} regex)
-  message(STATUS "Using boost::regex")
-else()
-  message(STATUS "Using std::regex")
-endif()
-
 if(COMPILE_SERVER)
   find_package(OpenSSL)
   if(OpenSSL_FOUND)
diff --git a/src/common/regex.h b/src/common/regex.h
index eb7482a8f..f2ef95c1a 100644
--- a/src/common/regex.h
+++ b/src/common/regex.h
@@ -1,8 +1,4 @@
+#pragma once
 
-#ifdef USE_BOOST_REGEX
-#include <boost/regex.hpp>
-namespace regex = boost;
-#else
 #include <regex>
 namespace regex = std;
-#endif

From 47eb65625f0bbcf197ffba239be65827bff20f60 Mon Sep 17 00:00:00 2001
From: Marcin Junczys-Dowmunt <Marcin.JunczysDowmunt@microsoft.com>
Date: Mon, 28 Oct 2019 17:28:37 -0700
Subject: [PATCH 736/838] back-compat with CUDA 8.0

---
 src/common/types.h                  | 13 +++++--
 src/tensors/gpu/prod.cpp            | 60 ++++++++++++++++++-----------
 src/tests/units/attention_tests.cpp |  4 +-
 src/tests/units/operator_tests.cpp  |  2 +
 src/tests/units/rnn_tests.cpp       |  2 +
 5 files changed, 54 insertions(+), 27 deletions(-)

diff --git a/src/common/types.h b/src/common/types.h
index fb0e04d2f..5c8eb128d 100755
--- a/src/common/types.h
+++ b/src/common/types.h
@@ -21,13 +21,20 @@
 
 #ifdef __CUDACC__ // nvcc is compiling this code
   #include <cuda.h> // required to see CUDA_VERSION
-  #if (CUDA_VERSION > 8000 && (__CUDA_ARCH__ >= 600 || !defined(__CUDA_ARCH__)))
+  #if (CUDA_VERSION > 9000 && (__CUDA_ARCH__ >= 600 || !defined(__CUDA_ARCH__)))
     #define COMPILE_FP16 1 // we are in GPU code and we know what to do with FP16 code
   #else
     #define COMPILE_FP16 0 // we are in GPU code, but compute capability is too low to use FP16
   #endif
-#else // other compiler, likely host code. Should be fine with seeing the correct includes with host code
-  #define COMPILE_FP16 1
+#elif CUDA_FOUND // other compiler, likely host code. Should be fine with seeing the correct includes with host code
+  #include <cuda.h> // required to see CUDA_VERSION
+  #if (CUDA_VERSION > 9000)
+    #define COMPILE_FP16 1
+  #else 
+    #define COMPILE_FP16 0
+  #endif
+#else
+  #define COMPILE_FP16 0
 #endif
 
 #ifdef _MSC_VER
diff --git a/src/tensors/gpu/prod.cpp b/src/tensors/gpu/prod.cpp
index 970709b6c..74d1e4718 100755
--- a/src/tensors/gpu/prod.cpp
+++ b/src/tensors/gpu/prod.cpp
@@ -52,7 +52,7 @@ static void unsetTensorMode(cublasHandle_t cublasHandle) {
 #endif
 }
 
-// overload for float, contains configuratio settings for float32
+// overload for float, contains configuration settings for float32
 cublasStatus_t cublasGemmTyped(cublasHandle_t handle,
                                cublasOperation_t transa, 
                                cublasOperation_t transb,
@@ -62,15 +62,24 @@ cublasStatus_t cublasGemmTyped(cublasHandle_t handle,
                                const float* B, int ldb,
                                const float* beta,
                                float* C, int ldc) {
+#if CUDA_VERSION > 9000
   return cublasGemmEx(handle, transa, transb, 
                       m, n, k, alpha, 
                       A, CUDA_R_32F, lda, 
                       B, CUDA_R_32F, ldb, beta, 
                       C, CUDA_R_32F, ldc,
                       CUDA_R_32F, CUBLAS_GEMM_DEFAULT_TENSOR_OP); // @TODO: review algorithm
+#else
+  return cublasSgemm(handle, transa, transb, 
+                     m, n, k, alpha, 
+                     A, lda, 
+                     B, ldb, beta, 
+                     C, ldc);
+#endif
 }
 
-// overload for half, contains configuratio settings for float16
+#if COMPILE_FP16
+// overload for half, contains configuration settings for float16
 cublasStatus_t cublasGemmTyped(cublasHandle_t handle,
                                cublasOperation_t transa, 
                                cublasOperation_t transb,
@@ -80,9 +89,6 @@ cublasStatus_t cublasGemmTyped(cublasHandle_t handle,
                                const half* B, int ldb,
                                const half* beta,
                                half* C, int ldc) {
-  //float alphaf = __half2float(*alpha); // has to match computeType
-  //float betaf = __half2float(*beta);   // has to match computeType
-
   return cublasGemmEx(handle, transa, transb, 
                       m, n, k, alpha, 
                       A, CUDA_R_16F, lda, 
@@ -90,6 +96,7 @@ cublasStatus_t cublasGemmTyped(cublasHandle_t handle,
                       C, CUDA_R_16F, ldc,
                       CUDA_R_16F, CUBLAS_GEMM_DEFAULT_TENSOR_OP); // @TODO: review algorithm
 }
+#endif
 
 template <typename T>
 void ProdTyped(marian::Tensor C,
@@ -152,8 +159,10 @@ void Prod(marian::Tensor C,
           float scalar) {
   if(C->type() == Type::float32) {
     ProdTyped<float>(C, A, B, transA, transB, beta, scalar);
+#if COMPILE_FP16
   } else if(C->type() == Type::float16) {
     ProdTyped<half>(C, A, B, transA, transB, __float2half(beta), __float2half(scalar));
+#endif
   } else {
     ABORT("Prod not implemented for type {}", C->type());
   }
@@ -169,15 +178,23 @@ cublasStatus_t cublasGemmBatchedTyped(cublasHandle_t handle,
                                       const float *beta,
                                       float *Carray[], int ldc, 
                                       int batchCount) {
-  return
-  cublasGemmBatchedEx(handle, transa, transb, 
-                      m, n, k, alpha, 
-                      (void* const*)Aarray, CUDA_R_32F, lda, 
-                      (void* const*)Barray, CUDA_R_32F, ldb, beta,
-                      (void**)Carray, CUDA_R_32F, ldc, batchCount,
-                      CUDA_R_32F, CUBLAS_GEMM_DEFAULT_TENSOR_OP);
+#if CUDA_VERSION > 9000
+  return cublasGemmBatchedEx(handle, transa, transb, 
+                             m, n, k, alpha, 
+                             (void* const*)Aarray, CUDA_R_32F, lda, 
+                             (void* const*)Barray, CUDA_R_32F, ldb, beta,
+                             (void**)Carray, CUDA_R_32F, ldc, batchCount,
+                             CUDA_R_32F, CUBLAS_GEMM_DEFAULT_TENSOR_OP);
+#else
+  return cublasSgemmBatched(handle, transa, transb, 
+                            m, n, k, alpha, 
+                            Aarray, lda, 
+                            Barray, ldb, beta,
+                            Carray, ldc, batchCount);
+#endif
 }
 
+#if COMPILE_FP16
 cublasStatus_t cublasGemmBatchedTyped(cublasHandle_t handle,
                                       cublasOperation_t transa, 
                                       cublasOperation_t transb,
@@ -188,17 +205,14 @@ cublasStatus_t cublasGemmBatchedTyped(cublasHandle_t handle,
                                       const half *beta,
                                       half *Carray[], int ldc, 
                                       int batchCount) {
-  //float alphaf = __half2float(*alpha); // has to match computeType
-  //float betaf = __half2float(*beta);   // has to match computeType
-
-  return
-  cublasGemmBatchedEx(handle, transa, transb, 
-                      m, n, k, alpha, 
-                      (void* const*)Aarray, CUDA_R_16F, lda, 
-                      (void* const*)Barray, CUDA_R_16F, ldb, beta,
-                      (void**)Carray, CUDA_R_16F, ldc, batchCount,
-                      CUDA_R_16F, CUBLAS_GEMM_DEFAULT_TENSOR_OP); // @TODO: to 16, this is testing
+  return cublasGemmBatchedEx(handle, transa, transb, 
+                             m, n, k, alpha, 
+                             (void* const*)Aarray, CUDA_R_16F, lda, 
+                             (void* const*)Barray, CUDA_R_16F, ldb, beta,
+                             (void**)Carray, CUDA_R_16F, ldc, batchCount,
+                             CUDA_R_16F, CUBLAS_GEMM_DEFAULT_TENSOR_OP); // @TODO: to 16, this is testing
 }
+#endif
 
 template <typename T>
 void ProdBatchedTyped(marian::Tensor C,                 
@@ -296,8 +310,10 @@ void ProdBatched(marian::Tensor C,
                  float scalar) {
   if(C->type() == Type::float32) {
     ProdBatchedTyped<float>(C, allocator, A, B, transA, transB, beta, scalar);
+#if COMPILE_FP16
   } else if(C->type() == Type::float16) { // not a *.cu file
     ProdBatchedTyped<half>(C, allocator, A, B, transA, transB, __float2half(beta), __float2half(scalar));
+#endif
   } else {
     ABORT("ProdBatched not implemented for type {}", C->type());
   }
diff --git a/src/tests/units/attention_tests.cpp b/src/tests/units/attention_tests.cpp
index b56f56022..7c7d4b36d 100755
--- a/src/tests/units/attention_tests.cpp
+++ b/src/tests/units/attention_tests.cpp
@@ -120,13 +120,13 @@ void tests(DeviceType type, Type floatType = Type::float32) {
 TEST_CASE("Model components, Attention (gpu)", "[attention]") {
   tests<float>(DeviceType::gpu);
 }
-#endif
 
-#ifdef CUDA_FOUND
+#if COMPILE_FP16
 TEST_CASE("Model components, Attention (gpu, fp16)", "[attention]") {
   tests<float16>(DeviceType::gpu, Type::float16);
 }
 #endif
+#endif
 
 #ifdef BLAS_FOUND
 TEST_CASE("Model components, Attention (cpu)", "[attention]") {
diff --git a/src/tests/units/operator_tests.cpp b/src/tests/units/operator_tests.cpp
index 722f191dc..9b91cfd82 100755
--- a/src/tests/units/operator_tests.cpp
+++ b/src/tests/units/operator_tests.cpp
@@ -762,10 +762,12 @@ TEST_CASE("Expression graph supports basic math operations (gpu)", "[operator]")
   tests<float>(DeviceType::gpu);
 }
 
+#if COMPILE_FP16
 TEST_CASE("Expression graph supports basic math operations (gpu fp16)", "[operator]") {
   tests<float16>(DeviceType::gpu, Type::float16);
 }
 #endif
+#endif
 
 #ifdef BLAS_FOUND
 TEST_CASE("Expression graph supports basic math operations (cpu)", "[operator]") {
diff --git a/src/tests/units/rnn_tests.cpp b/src/tests/units/rnn_tests.cpp
index 02f6979c7..23f98cc05 100755
--- a/src/tests/units/rnn_tests.cpp
+++ b/src/tests/units/rnn_tests.cpp
@@ -318,10 +318,12 @@ TEST_CASE("Model components, RNN etc. (gpu)", "[model]") {
   tests<float>(DeviceType::gpu);
 }
 
+#if COMPILE_FP16
 TEST_CASE("Model components, RNN etc. (gpu, fp16)", "[model]") {
   tests<float16>(DeviceType::gpu, Type::float16);
 }
 #endif
+#endif
 
 #ifdef BLAS_FOUND
 TEST_CASE("Model components, RNN etc. (cpu)", "[model]") {

From bf10d36d1e9c52f774a28788cc351b4e532587e4 Mon Sep 17 00:00:00 2001
From: Marcin Junczys-Dowmunt <Marcin.JunczysDowmunt@microsoft.com>
Date: Tue, 29 Oct 2019 07:29:20 -0700
Subject: [PATCH 737/838] disable FBGEMM by default

---
 CMakeLists.txt | 12 +++++++-----
 1 file changed, 7 insertions(+), 5 deletions(-)

diff --git a/CMakeLists.txt b/CMakeLists.txt
index 19be22398..a914b7017 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -11,17 +11,19 @@ set(CMAKE_CXX_STANDARD_REQUIRED ON)
 set(BUILD_ARCH native CACHE STRING "Compile for this CPU architecture.")
 
 # Custom CMake options
-option(COMPILE_CPU "Compile CPU version" ON)
-option(COMPILE_CUDA "Compile GPU version" ON)
 option(USE_SENTENCEPIECE "Download and compile SentencePiece" OFF)
-option(USE_STATIC_LIBS "Link statically against non-system libs" OFF)
-option(USE_CUDNN "Use CUDNN library" OFF)
+option(USE_FBGEMM "Use FBGEMM" OFF)
 option(USE_NCCL "Use NCCL library" ON)
 option(USE_MPI "Use MPI library" OFF)
+option(USE_CUDNN "Use CUDNN library" OFF)
+
+option(COMPILE_CPU "Compile CPU version" ON)
+option(COMPILE_CUDA "Compile GPU version" ON)
+option(USE_STATIC_LIBS "Link statically against non-system libs" OFF)
 option(COMPILE_EXAMPLES "Compile examples" OFF)
 option(COMPILE_TESTS "Compile tests" OFF)
 option(COMPILE_SERVER "Compile marian-server" OFF)
-option(USE_FBGEMM "Use FBGEMM" ON)
+
 option(USE_DOXYGEN "Build documentation with Doxygen" ON)
 
 # Project versioning

From 42250be8aa86ef7bb23104caa10da967df147de4 Mon Sep 17 00:00:00 2001
From: Roman Grundkiewicz <rgrundki@exseed.ed.ac.uk>
Date: Tue, 29 Oct 2019 15:20:44 +0000
Subject: [PATCH 738/838] Add --authors and --cite flags

---
 src/common/authors.h         | 62 ++++++++++++++++++++++++++++++++++++
 src/common/config_parser.cpp | 27 ++++++++++++----
 2 files changed, 83 insertions(+), 6 deletions(-)
 create mode 100644 src/common/authors.h

diff --git a/src/common/authors.h b/src/common/authors.h
new file mode 100644
index 000000000..9d3f0f1ee
--- /dev/null
+++ b/src/common/authors.h
@@ -0,0 +1,62 @@
+#pragma once
+
+#include <string>
+
+namespace marian {
+
+std::string citation() {
+  return "Marian: Fast Neural Machine Translation in C++\n"
+         "\n"
+         "Please cite the following paper if you use Marian (formerly AmuNMT):\n"
+         "\n"
+         "@InProceedings{mariannmt,\n"
+         "  title     = {Marian: Fast Neural Machine Translation in {C++}},\n"
+         "  author    = {Junczys-Dowmunt, Marcin and Grundkiewicz, Roman and\n"
+         "               Dwojak, Tomasz and Hoang, Hieu and Heafield, Kenneth and\n"
+         "               Neckermann, Tom and Seide, Frank and Germann, Ulrich and\n"
+         "               Fikri Aji, Alham and Bogoychev, Nikolay and\n"
+         "               Martins, Andr\\'{e} F. T. and Birch, Alexandra},\n"
+         "  booktitle = {Proceedings of ACL 2018, System Demonstrations},\n"
+         "  pages     = {116--121},\n"
+         "  publisher = {Association for Computational Linguistics},\n"
+         "  year      = {2018},\n"
+         "  month     = {July},\n"
+         "  address   = {Melbourne, Australia},\n"
+         "  url       = {http://www.aclweb.org/anthology/P18-4020}\n"
+         "}\n";
+}
+
+std::string authors() {
+  return "Marian: Fast Neural Machine Translation in C++\n"
+         "\n"
+         "An inevitably non-exhaustive list of contributors:\n"
+         "\n"
+         "Marcin Junczys-Dowmunt <marcinjd@microsoft.com>\n"
+         "Roman Grundkiewicz <rgrundki@inf.ed.ac.uk>\n"
+         "Frank Seide <fseide@microsoft.com>\n"
+         "Hieu Hoang <hieuhoang@gmail.com>\n"
+         "Tomasz Dwojak <t.dwojak@amu.edu.pl>\n"
+         "Ulrich Germann <ugermann@inf.ed.ac.uk>\n"
+         "Alham Fikri Aji <afaji321@gmail.com>\n"
+         "Cédric Rousseau <cedrou@gmail.com>\n"
+         "Young Jin Kim <youki@microsoft.com>\n"
+         "Lane Schwartz <dowobeha@gmail.com>\n"
+         "Andre Martins <andre.t.martins@gmail.com>\n"
+         "Nikolay Bogoychev <n.bogoych@ed.ac.uk>\n"
+         "Kenneth Heafield <kheafiel@ed.ac.uk>\n"
+         "Maximiliana Behnke <mbehnke@inf.ed.ac.uk>\n"
+         "Tom Neckermann <tomneckermann@gmail.com>\n"
+         "Hany Hassan Awadalla <hanyh@microsoft.com>\n"
+         "Jim Geovedi <jim@geovedi.com>\n"
+         "Catarina Silva <catarina.cruz.csilva@gmail.com>\n"
+         "Jon Clark <jonathac@microsoft.com>\n"
+         "Rihards Krišlauks <rihards.krislauks@gmail.com>\n"
+         "Vishal Chowdhary <vishalc@microsoft.com>\n"
+         "Barry Haddow <bhaddow@inf.ed.ac.uk>\n"
+         "Dominik Stańczak <stanczakdominik@gmail.com>\n"
+         "Michael Hutt <Michael.Hutt@gmail.com>\n"
+         "Richard Wei <rxwei@users.noreply.github.com>\n";
+         "Wenyong Huang <weyo.huang@gmail.com>\n"
+         "alancucki <alancucki+github@gmail.com>\n"
+}
+}  // namespace marian
diff --git a/src/common/config_parser.cpp b/src/common/config_parser.cpp
index 86174e383..e8b2e4c40 100755
--- a/src/common/config_parser.cpp
+++ b/src/common/config_parser.cpp
@@ -1,3 +1,4 @@
+#include "common/authors.h"
 #include "common/cli_helper.h"
 #include "common/config.h"
 #include "common/config_parser.h"
@@ -106,6 +107,10 @@ void ConfigParser::addOptionsGeneral(cli::CLIWrapper& cli) {
   cli.switchGroup("General options");
 
   // clang-format off
+  cli.add<bool>("--authors",
+    "Print list of authors and exit");
+  cli.add<bool>("--cite",
+    "Print citation and exit");
   cli.add<std::vector<std::string>>("--config,-c",
     "Configuration file(s). If multiple, later overrides earlier");
   cli.add<size_t>("--workspace,-w",
@@ -477,7 +482,7 @@ void ConfigParser::addOptionsTraining(cli::CLIWrapper& cli) {
      "Fix target embeddings. Affects all decoders");
 
   // mixed precision training
-  cli.add<bool>("--fp16", 
+  cli.add<bool>("--fp16",
       "Shortcut for mixed precision training with float16 and cost-scaling, "
       "corresponds to: --precision float16 float32 float32 --cost-scaling 7 2000 2 0.05 10 1");
   cli.add<std::vector<std::string>>("--precision",
@@ -610,8 +615,8 @@ void ConfigParser::addOptionsTranslation(cli::CLIWrapper& cli) {
   cli.add<std::string>("--gemm-type",
       "Select GEMM options: auto, mklfp32, intrinint16, fp16packed, int8packed",
       "auto");
-  
-  cli.add<bool>("--fp16", 
+
+  cli.add<bool>("--fp16",
       "Shortcut for mixed precision inference with float16, corresponds to: --precision float16");
   cli.add<std::vector<std::string>>("--precision",
       "Mixed precision for inference, set parameter type in expression graph",
@@ -667,12 +672,12 @@ void ConfigParser::addOptionsScoring(cli::CLIWrapper& cli) {
 
   cli.add<bool>("--optimize",
       "Optimize speed aggressively sacrificing memory or precision");
-  cli.add<bool>("--fp16", 
+  cli.add<bool>("--fp16",
       "Shortcut for mixed precision inference with float16, corresponds to: --precision float16");
   cli.add<std::vector<std::string>>("--precision",
       "Mixed precision for inference, set parameter type in expression graph",
       {"float32"});
-  
+
   cli.switchGroup(previous_group);
   // clang-format on
 }
@@ -725,7 +730,7 @@ void ConfigParser::addSuboptionsBatching(cli::CLIWrapper& cli) {
     cli.add<size_t>("--mini-batch-fit-step",
       "Step size for mini-batch-fit statistics",
       10);
-    cli.add<bool>("--gradient-checkpointing", 
+    cli.add<bool>("--gradient-checkpointing",
       "Enable gradient-checkpointing to minimize memory usage");
   }
 
@@ -811,6 +816,16 @@ Ptr<Options> ConfigParser::parseOptions(int argc, char** argv, bool doValidate){
   // parse command-line options and fill wrapped YAML config
   cli_.parse(argc, argv);
 
+  if(get<bool>("authors")) {
+    std::cerr << authors() << std::endl;
+    exit(0);
+  }
+
+  if(get<bool>("cite")) {
+    std::cerr << citation() << std::endl;
+    exit(0);
+  }
+
   // get paths to extra config files
   auto configPaths = findConfigPaths();
   if(!configPaths.empty()) {

From 5476f623b8218985d179ba4ac0cf9b0683fd3b45 Mon Sep 17 00:00:00 2001
From: Marcin Junczys-Dowmunt <Marcin.JunczysDowmunt@microsoft.com>
Date: Tue, 29 Oct 2019 08:39:25 -0700
Subject: [PATCH 739/838] add checking for GPU compute capability

---
 src/tensors/backend.h              | 11 ++++++-----
 src/tensors/gpu/backend.h          | 17 +++++++++++++++++
 src/tests/units/operator_tests.cpp | 17 ++++++++++++++++-
 3 files changed, 39 insertions(+), 6 deletions(-)

diff --git a/src/tensors/backend.h b/src/tensors/backend.h
index 1ac60bc79..ff6da92f0 100644
--- a/src/tensors/backend.h
+++ b/src/tensors/backend.h
@@ -6,11 +6,12 @@
 namespace marian {
 
 // GEMM type enum
-typedef enum { Auto = 0,            // auto tuning between available GEMMs
-               MklFp32 = 1,         // MKL based GEMM, fp32
-               IntrinInt16 = 2,     // Intrinsic implementation of Int 16 GEMM
-               FbFp16Packed = 10,   // FBGEMM based fp16 GEMM with packing
-               FbInt8Packed = 11    // FBGEMM based int8 GEMM with packing
+typedef enum { 
+  Auto = 0,            // auto tuning between available GEMMs
+  MklFp32 = 1,         // MKL based GEMM, fp32
+  IntrinInt16 = 2,     // Intrinsic implementation of Int 16 GEMM
+  FbFp16Packed = 10,   // FBGEMM based fp16 GEMM with packing
+  FbInt8Packed = 11    // FBGEMM based int8 GEMM with packing
 } GemmType;
 
 class Backend {
diff --git a/src/tensors/gpu/backend.h b/src/tensors/gpu/backend.h
index 2a7672555..cc42c43b7 100644
--- a/src/tensors/gpu/backend.h
+++ b/src/tensors/gpu/backend.h
@@ -13,12 +13,24 @@
 namespace marian {
 namespace gpu {
 
+struct CudaCompute {
+  int major;
+  int minor;
+};
+
 class Backend : public marian::Backend {
+private:
+  void setCudaComputeCapability() {
+    cudaDeviceGetAttribute(&compute_.major, cudaDevAttrComputeCapabilityMajor, (int)deviceId_.no);
+    cudaDeviceGetAttribute(&compute_.minor, cudaDevAttrComputeCapabilityMinor, (int)deviceId_.no);
+  }
+
 public:
   Backend(DeviceId deviceId, size_t seed) : marian::Backend(deviceId, seed) {
     setDevice();
     cublasCreate(&cublasHandle_);
     cusparseCreate(&cusparseHandle_);
+    setCudaComputeCapability();
   }
 
   ~Backend() {
@@ -34,14 +46,18 @@ class Backend : public marian::Backend {
   cublasHandle_t getCublasHandle() { return cublasHandle_; }
   cusparseHandle_t getCusparseHandle() { return cusparseHandle_; }
 
+  CudaCompute getCudaComputeCapability() { return compute_; }
+
   // for CPU, sets to use optimized code for inference.
   // for GPU, this is invalid. for gpu, isOptimized() function always returns false.
   void setOptimized(bool optimize) override {
     LOG_ONCE(info, "setOptimized() not supported for GPU_{}", optimize);
   }
+  
   bool isOptimized() override {
     return false;
   }
+
   // for CPU, selects different GEMM types for the inference.
   // for GPU, there's no gemm type. so, it does nothing.
   void setGemmType(std::string gemmType) override {
@@ -55,6 +71,7 @@ class Backend : public marian::Backend {
 private:
   cublasHandle_t cublasHandle_;
   cusparseHandle_t cusparseHandle_;
+  CudaCompute compute_;
 };
 }  // namespace gpu
 }  // namespace marian
diff --git a/src/tests/units/operator_tests.cpp b/src/tests/units/operator_tests.cpp
index 9b91cfd82..f21b4dcc6 100755
--- a/src/tests/units/operator_tests.cpp
+++ b/src/tests/units/operator_tests.cpp
@@ -1,6 +1,11 @@
 #include "catch.hpp"
 #include "graph/expression_graph.h"
 #include "graph/expression_operators.h"
+
+#ifdef CUDA_FOUND
+#include "tensors/gpu/backend.h"
+#endif
+
 #include <cmath>
 
 using namespace marian;
@@ -14,7 +19,17 @@ void tests(DeviceType device, Type floatType = Type::float32) {
   auto graph = New<ExpressionGraph>();
   graph->setParameterType(floatType);
   graph->setDevice({0, device});
-  graph->reserveWorkspaceMB(32);
+  graph->reserveWorkspaceMB(16);
+
+// Checking for FP16 support and skipping if not supported.
+#ifdef CUDA_FOUND
+  auto gpuBackend = std::dynamic_pointer_cast<gpu::Backend>(graph->getBackend());
+  if(gpuBackend) {
+    auto cudaCompute = gpuBackend->getCudaComputeCapability();
+    if(cudaCompute.major < 6 && floatType == Type::float16)
+      return;
+  }
+#endif
 
   std::vector<T> values, values2;
 

From 6b6444dcc13c7c998296ad927c11c61bab01b94f Mon Sep 17 00:00:00 2001
From: Marcin Junczys-Dowmunt <Marcin.JunczysDowmunt@microsoft.com>
Date: Tue, 29 Oct 2019 08:52:55 -0700
Subject: [PATCH 740/838] fix tests for GPUs with lower compute capability

---
 src/tensors/gpu/backend.h           |  1 +
 src/tests/units/attention_tests.cpp | 14 ++++++++++++++
 src/tests/units/graph_tests.cpp     | 13 +++++++++++++
 src/tests/units/operator_tests.cpp  | 21 +++++++++++----------
 src/tests/units/rnn_tests.cpp       | 14 ++++++++++++++
 5 files changed, 53 insertions(+), 10 deletions(-)

diff --git a/src/tensors/gpu/backend.h b/src/tensors/gpu/backend.h
index cc42c43b7..90d06e921 100644
--- a/src/tensors/gpu/backend.h
+++ b/src/tensors/gpu/backend.h
@@ -13,6 +13,7 @@
 namespace marian {
 namespace gpu {
 
+// @TODO: in the future this should pobably become a fully fledged CudaInfo class with many attributes
 struct CudaCompute {
   int major;
   int minor;
diff --git a/src/tests/units/attention_tests.cpp b/src/tests/units/attention_tests.cpp
index 7c7d4b36d..7e95c55c3 100755
--- a/src/tests/units/attention_tests.cpp
+++ b/src/tests/units/attention_tests.cpp
@@ -1,6 +1,10 @@
 #include "catch.hpp"
 #include "marian.h"
 
+#ifdef CUDA_FOUND
+#include "tensors/gpu/backend.h"
+#endif
+
 #include "rnn/rnn.h"
 #include "rnn/constructors.h"
 #include "rnn/attention.h"
@@ -9,6 +13,16 @@ using namespace marian;
 
 template <typename T>
 void tests(DeviceType type, Type floatType = Type::float32) {
+
+// Checking for FP16 support and skipping if not supported.
+#ifdef CUDA_FOUND
+  if(type == DeviceType::gpu && floatType == Type::float16) {
+    auto gpuBackend = New<gpu::Backend>(DeviceId({0, type}), /*seed=*/1234);
+    auto cudaCompute = gpuBackend->getCudaComputeCapability();
+    if(cudaCompute.major < 6) return;
+  }
+#endif
+
   auto floatApprox = [](T x, T y) { return x == Approx(y).epsilon(0.01); };
 
   Config::seed = 1234;
diff --git a/src/tests/units/graph_tests.cpp b/src/tests/units/graph_tests.cpp
index ad35e0c38..afb656e0e 100644
--- a/src/tests/units/graph_tests.cpp
+++ b/src/tests/units/graph_tests.cpp
@@ -2,6 +2,10 @@
 #include "graph/expression_graph.h"
 #include "graph/expression_operators.h"
 
+#ifdef CUDA_FOUND
+#include "tensors/gpu/backend.h"
+#endif
+
 using namespace marian;
 
 #ifdef CUDA_FOUND
@@ -17,11 +21,20 @@ TEST_CASE("Expression graph can be initialized with constant values",
           "[graph]") {
 
   for(auto type : std::vector<Type>({Type::float32, Type::float16})) {
+
     auto graph = New<ExpressionGraph>();
     graph->setDevice({0, DeviceType::gpu});
     graph->setParameterType(type);
     graph->reserveWorkspaceMB(4);
 
+    if(type == Type::float16) {
+      auto gpuBackend = std::dynamic_pointer_cast<gpu::Backend>(graph->getBackend());
+      if(gpuBackend) {
+        auto cudaCompute = gpuBackend->getCudaComputeCapability();
+        if(cudaCompute.major < 6) continue;
+      }
+    }
+
     std::vector<float> values;
 
     SECTION("initializing with zeros") {
diff --git a/src/tests/units/operator_tests.cpp b/src/tests/units/operator_tests.cpp
index f21b4dcc6..127ae679f 100755
--- a/src/tests/units/operator_tests.cpp
+++ b/src/tests/units/operator_tests.cpp
@@ -12,6 +12,17 @@ using namespace marian;
 
 template <typename T>
 void tests(DeviceType device, Type floatType = Type::float32) {
+
+// Checking for FP16 support and skipping if not supported.
+#ifdef CUDA_FOUND
+  if(device == DeviceType::gpu && floatType == Type::float16) {
+    auto gpuBackend = New<gpu::Backend>(DeviceId({0, device}), /*seed=*/1234);
+    auto cudaCompute = gpuBackend->getCudaComputeCapability();
+    if(cudaCompute.major < 6) return;
+  }
+#endif
+
+
   auto floatApprox = [](T x, T y) -> bool { return x == Approx(y).epsilon(0.01); };
   auto floatEqual  = [](T x, T y) -> bool { return x == y; };
 
@@ -21,16 +32,6 @@ void tests(DeviceType device, Type floatType = Type::float32) {
   graph->setDevice({0, device});
   graph->reserveWorkspaceMB(16);
 
-// Checking for FP16 support and skipping if not supported.
-#ifdef CUDA_FOUND
-  auto gpuBackend = std::dynamic_pointer_cast<gpu::Backend>(graph->getBackend());
-  if(gpuBackend) {
-    auto cudaCompute = gpuBackend->getCudaComputeCapability();
-    if(cudaCompute.major < 6 && floatType == Type::float16)
-      return;
-  }
-#endif
-
   std::vector<T> values, values2;
 
   SECTION("scalar multiplication") {
diff --git a/src/tests/units/rnn_tests.cpp b/src/tests/units/rnn_tests.cpp
index 23f98cc05..66cacc5ce 100755
--- a/src/tests/units/rnn_tests.cpp
+++ b/src/tests/units/rnn_tests.cpp
@@ -1,6 +1,10 @@
 #include "catch.hpp"
 #include "marian.h"
 
+#ifdef CUDA_FOUND
+#include "tensors/gpu/backend.h"
+#endif
+
 #include "rnn/rnn.h"
 #include "rnn/constructors.h"
 
@@ -8,6 +12,16 @@ using namespace marian;
 
 template <typename T>
 void tests(DeviceType type, Type floatType = Type::float32) {
+
+// Checking for FP16 support and skipping if not supported.
+#ifdef CUDA_FOUND
+  if(type == DeviceType::gpu && floatType == Type::float16) {
+    auto gpuBackend = New<gpu::Backend>(DeviceId({0, type}), /*seed=*/1234);
+    auto cudaCompute = gpuBackend->getCudaComputeCapability();
+    if(cudaCompute.major < 6) return;
+  }
+#endif
+
   auto floatApprox = [](T x, T y) { return x == Approx(y).epsilon(0.01); };
 
   std::vector<IndexType> vWords = {

From 3a16eb624fbfb83929c5dc7650288d29f5d1c1e8 Mon Sep 17 00:00:00 2001
From: Marcin Junczys-Dowmunt <Marcin.JunczysDowmunt@microsoft.com>
Date: Tue, 29 Oct 2019 08:58:29 -0700
Subject: [PATCH 741/838] remove left-over debug info

---
 src/tests/units/graph_tests.cpp | 1 -
 1 file changed, 1 deletion(-)

diff --git a/src/tests/units/graph_tests.cpp b/src/tests/units/graph_tests.cpp
index afb656e0e..ac4c7e47b 100644
--- a/src/tests/units/graph_tests.cpp
+++ b/src/tests/units/graph_tests.cpp
@@ -77,7 +77,6 @@ TEST_CASE("Expression graph can be initialized with constant values",
       auto vals1 = graph->param("vs1", {2, 3}, inits::fromVector(v), Type::float16);
       auto vals2 = graph->param("vs2", {2, 3}, inits::fromValue(5), Type::float16);
 
-      debug(vals1 + vals2);
       graph->forward();
 
       REQUIRE(values.empty());

From 880cc5dca8a70cf99b2bc094d2ca88d585bcbc84 Mon Sep 17 00:00:00 2001
From: Marcin Junczys-Dowmunt <Marcin.JunczysDowmunt@microsoft.com>
Date: Tue, 29 Oct 2019 11:37:27 -0700
Subject: [PATCH 742/838] make sure that old GPUs do not run cublasGemmEx

---
 src/tensors/gpu/backend.h       |  8 ++--
 src/tensors/gpu/prod.cpp        | 72 +++++++++++++++++++--------------
 src/tests/units/graph_tests.cpp | 15 -------
 3 files changed, 46 insertions(+), 49 deletions(-)

diff --git a/src/tensors/gpu/backend.h b/src/tensors/gpu/backend.h
index 90d06e921..19f7dd22d 100644
--- a/src/tensors/gpu/backend.h
+++ b/src/tensors/gpu/backend.h
@@ -22,8 +22,8 @@ struct CudaCompute {
 class Backend : public marian::Backend {
 private:
   void setCudaComputeCapability() {
-    cudaDeviceGetAttribute(&compute_.major, cudaDevAttrComputeCapabilityMajor, (int)deviceId_.no);
-    cudaDeviceGetAttribute(&compute_.minor, cudaDevAttrComputeCapabilityMinor, (int)deviceId_.no);
+    CUDA_CHECK(cudaDeviceGetAttribute(&compute_.major, cudaDevAttrComputeCapabilityMajor, (int)deviceId_.no));
+    CUDA_CHECK(cudaDeviceGetAttribute(&compute_.minor, cudaDevAttrComputeCapabilityMinor, (int)deviceId_.no));
   }
 
 public:
@@ -40,9 +40,9 @@ class Backend : public marian::Backend {
     cublasDestroy(cublasHandle_);
   }
 
-  void setDevice() override { cudaSetDevice((int)deviceId_.no); }
+  void setDevice() override { CUDA_CHECK(cudaSetDevice((int)deviceId_.no)); }
 
-  void synchronize() override { cudaStreamSynchronize(0); }
+  void synchronize() override { CUDA_CHECK(cudaStreamSynchronize(0)); }
 
   cublasHandle_t getCublasHandle() { return cublasHandle_; }
   cusparseHandle_t getCusparseHandle() { return cusparseHandle_; }
diff --git a/src/tensors/gpu/prod.cpp b/src/tensors/gpu/prod.cpp
index 74d1e4718..894df3d80 100755
--- a/src/tensors/gpu/prod.cpp
+++ b/src/tensors/gpu/prod.cpp
@@ -54,6 +54,7 @@ static void unsetTensorMode(cublasHandle_t cublasHandle) {
 
 // overload for float, contains configuration settings for float32
 cublasStatus_t cublasGemmTyped(cublasHandle_t handle,
+                               int compute,
                                cublasOperation_t transa, 
                                cublasOperation_t transb,
                                int m, int n, int k,
@@ -62,25 +63,25 @@ cublasStatus_t cublasGemmTyped(cublasHandle_t handle,
                                const float* B, int ldb,
                                const float* beta,
                                float* C, int ldc) {
-#if CUDA_VERSION > 9000
-  return cublasGemmEx(handle, transa, transb, 
-                      m, n, k, alpha, 
-                      A, CUDA_R_32F, lda, 
-                      B, CUDA_R_32F, ldb, beta, 
-                      C, CUDA_R_32F, ldc,
-                      CUDA_R_32F, CUBLAS_GEMM_DEFAULT_TENSOR_OP); // @TODO: review algorithm
-#else
-  return cublasSgemm(handle, transa, transb, 
-                     m, n, k, alpha, 
-                     A, lda, 
-                     B, ldb, beta, 
-                     C, ldc);
-#endif
+  if(compute >= 5 && CUDA_VERSION > 9000)
+    return cublasGemmEx(handle, transa, transb, 
+                        m, n, k, alpha, 
+                        A, CUDA_R_32F, lda, 
+                        B, CUDA_R_32F, ldb, beta, 
+                        C, CUDA_R_32F, ldc,
+                        CUDA_R_32F, CUBLAS_GEMM_DEFAULT_TENSOR_OP); // @TODO: review algorithm
+  else 
+    return cublasSgemm(handle, transa, transb, 
+                        m, n, k, alpha, 
+                        A, lda, 
+                        B, ldb, beta, 
+                        C, ldc);
 }
 
 #if COMPILE_FP16
 // overload for half, contains configuration settings for float16
 cublasStatus_t cublasGemmTyped(cublasHandle_t handle,
+                               int compute,
                                cublasOperation_t transa, 
                                cublasOperation_t transb,
                                int m, int n, int k,
@@ -89,6 +90,7 @@ cublasStatus_t cublasGemmTyped(cublasHandle_t handle,
                                const half* B, int ldb,
                                const half* beta,
                                half* C, int ldc) {
+  ABORT_IF(compute < 6, "Compute capability {} below 6 should not happen for FP16", compute);
   return cublasGemmEx(handle, transa, transb, 
                       m, n, k, alpha, 
                       A, CUDA_R_16F, lda, 
@@ -106,7 +108,7 @@ void ProdTyped(marian::Tensor C,
                bool transB,
                T beta,
                T scalar) {
-  cudaSetDevice((int)C->getDeviceId().no);
+  CUDA_CHECK(cudaSetDevice((int)C->getDeviceId().no));
   T alpha = scalar;
 
   int m = A->shape().elements() / A->shape().back();
@@ -132,8 +134,12 @@ void ProdTyped(marian::Tensor C,
   auto cublasHandle = std::static_pointer_cast<gpu::Backend>(C->getBackend())
                           ->getCublasHandle();
 
+  int compute;
+  CUDA_CHECK(cudaDeviceGetAttribute(&compute, cudaDevAttrComputeCapabilityMajor, (int)C->getDeviceId().no));
+
   setTensorMode(cublasHandle);
   CUBLAS_CHECK(cublasGemmTyped(cublasHandle,
+                               compute,
                                opB,
                                opA,
                                n,
@@ -169,6 +175,7 @@ void Prod(marian::Tensor C,
 }
 
 cublasStatus_t cublasGemmBatchedTyped(cublasHandle_t handle,
+                                      int compute,
                                       cublasOperation_t transa, 
                                       cublasOperation_t transb,
                                       int m, int n, int k,
@@ -178,24 +185,24 @@ cublasStatus_t cublasGemmBatchedTyped(cublasHandle_t handle,
                                       const float *beta,
                                       float *Carray[], int ldc, 
                                       int batchCount) {
-#if CUDA_VERSION > 9000
-  return cublasGemmBatchedEx(handle, transa, transb, 
-                             m, n, k, alpha, 
-                             (void* const*)Aarray, CUDA_R_32F, lda, 
-                             (void* const*)Barray, CUDA_R_32F, ldb, beta,
-                             (void**)Carray, CUDA_R_32F, ldc, batchCount,
-                             CUDA_R_32F, CUBLAS_GEMM_DEFAULT_TENSOR_OP);
-#else
-  return cublasSgemmBatched(handle, transa, transb, 
-                            m, n, k, alpha, 
-                            Aarray, lda, 
-                            Barray, ldb, beta,
-                            Carray, ldc, batchCount);
-#endif
+  if(compute >= 5 && CUDA_VERSION > 9000)
+    return cublasGemmBatchedEx(handle, transa, transb, 
+                              m, n, k, alpha, 
+                              (void* const*)Aarray, CUDA_R_32F, lda, 
+                              (void* const*)Barray, CUDA_R_32F, ldb, beta,
+                              (void**)Carray, CUDA_R_32F, ldc, batchCount,
+                              CUDA_R_32F, CUBLAS_GEMM_DEFAULT_TENSOR_OP);
+  else
+    return cublasSgemmBatched(handle, transa, transb, 
+                              m, n, k, alpha, 
+                              Aarray, lda, 
+                              Barray, ldb, beta,
+                              Carray, ldc, batchCount);
 }
 
 #if COMPILE_FP16
 cublasStatus_t cublasGemmBatchedTyped(cublasHandle_t handle,
+                                      int compute,
                                       cublasOperation_t transa, 
                                       cublasOperation_t transb,
                                       int m, int n, int k,
@@ -205,6 +212,7 @@ cublasStatus_t cublasGemmBatchedTyped(cublasHandle_t handle,
                                       const half *beta,
                                       half *Carray[], int ldc, 
                                       int batchCount) {
+  ABORT_IF(compute < 6, "Compute capability {} below 6 should not happen for FP16", compute);
   return cublasGemmBatchedEx(handle, transa, transb, 
                              m, n, k, alpha, 
                              (void* const*)Aarray, CUDA_R_16F, lda, 
@@ -223,7 +231,7 @@ void ProdBatchedTyped(marian::Tensor C,
                  bool transB,
                  T beta,
                  T scalar) {
-  cudaSetDevice((int)C->getDeviceId().no);
+  CUDA_CHECK(cudaSetDevice((int)C->getDeviceId().no));
   T alpha = scalar;
 
   int batchA = A->shape().elements() / (A->shape()[-1] * A->shape()[-2]);
@@ -277,8 +285,12 @@ void ProdBatchedTyped(marian::Tensor C,
   IPtr<MemoryPiece> mp_cptr = allocator->alloc<T*>(cptr.size());
   CudaCopy(cptr.data(), cptr.data() + cptr.size(), mp_cptr->data<T*>());
 
+  int compute;
+  CUDA_CHECK(cudaDeviceGetAttribute(&compute, cudaDevAttrComputeCapabilityMajor, (int)C->getDeviceId().no));
+
   setTensorMode(cublasHandle);
   CUBLAS_CHECK(cublasGemmBatchedTyped(cublasHandle,
+                                      compute,
                                       opB,
                                       opA,
                                       n,
diff --git a/src/tests/units/graph_tests.cpp b/src/tests/units/graph_tests.cpp
index ac4c7e47b..1c03c389b 100644
--- a/src/tests/units/graph_tests.cpp
+++ b/src/tests/units/graph_tests.cpp
@@ -68,21 +68,6 @@ TEST_CASE("Expression graph can be initialized with constant values",
       vals->val()->get(values);
       REQUIRE(values == v);
     }
-
-    SECTION("initializing float16 node from vector") {
-      // This does not test fp16 computation, only float16 to float32 conversion
-      graph->clear();
-      std::vector<float16> values16;
-      std::vector<float16> v({1, 2, 3, 4, 5, 6});
-      auto vals1 = graph->param("vs1", {2, 3}, inits::fromVector(v), Type::float16);
-      auto vals2 = graph->param("vs2", {2, 3}, inits::fromValue(5), Type::float16);
-
-      graph->forward();
-
-      REQUIRE(values.empty());
-      vals1->val()->get(values16);
-      REQUIRE(values16 == v);
-    }
   }
 }
 #endif

From 9d8777b60e48c5759ed80dd57f87a6ec72e3d5bd Mon Sep 17 00:00:00 2001
From: Marcin Junczys-Dowmunt <Marcin.JunczysDowmunt@microsoft.com>
Date: Tue, 29 Oct 2019 13:05:06 -0700
Subject: [PATCH 743/838] fix CUDA 8.0 compilation

---
 src/tensors/gpu/prod.cpp | 32 +++++++++++++++++++++++++++-----
 1 file changed, 27 insertions(+), 5 deletions(-)

diff --git a/src/tensors/gpu/prod.cpp b/src/tensors/gpu/prod.cpp
index 894df3d80..83d1d5155 100755
--- a/src/tensors/gpu/prod.cpp
+++ b/src/tensors/gpu/prod.cpp
@@ -63,19 +63,30 @@ cublasStatus_t cublasGemmTyped(cublasHandle_t handle,
                                const float* B, int ldb,
                                const float* beta,
                                float* C, int ldc) {
-  if(compute >= 5 && CUDA_VERSION > 9000)
+// double #if and if unfortunately required to safeguard against compilation error 
+// with CUDA 8.0 and runtime error with CUDA >9.0 on GPUs with compute capabilite under 5
+#if CUDA_VERSION > 9000
+  if(compute >= 5) {
     return cublasGemmEx(handle, transa, transb, 
                         m, n, k, alpha, 
                         A, CUDA_R_32F, lda, 
                         B, CUDA_R_32F, ldb, beta, 
                         C, CUDA_R_32F, ldc,
                         CUDA_R_32F, CUBLAS_GEMM_DEFAULT_TENSOR_OP); // @TODO: review algorithm
-  else 
+  } else {
     return cublasSgemm(handle, transa, transb, 
                         m, n, k, alpha, 
                         A, lda, 
                         B, ldb, beta, 
                         C, ldc);
+  }
+#else
+  return cublasSgemm(handle, transa, transb, 
+                      m, n, k, alpha, 
+                      A, lda, 
+                      B, ldb, beta, 
+                      C, ldc);
+#endif
 }
 
 #if COMPILE_FP16
@@ -185,22 +196,33 @@ cublasStatus_t cublasGemmBatchedTyped(cublasHandle_t handle,
                                       const float *beta,
                                       float *Carray[], int ldc, 
                                       int batchCount) {
-  if(compute >= 5 && CUDA_VERSION > 9000)
+// double #if and if unfortunately required to safeguard against compilation error 
+// with CUDA 8.0 and runtime error with CUDA >9.0 on GPUs with compute capabilite under 5
+#if CUDA_VERSION > 9000
+  if(compute >= 5) {
     return cublasGemmBatchedEx(handle, transa, transb, 
                               m, n, k, alpha, 
                               (void* const*)Aarray, CUDA_R_32F, lda, 
                               (void* const*)Barray, CUDA_R_32F, ldb, beta,
                               (void**)Carray, CUDA_R_32F, ldc, batchCount,
                               CUDA_R_32F, CUBLAS_GEMM_DEFAULT_TENSOR_OP);
-  else
+  } else {
     return cublasSgemmBatched(handle, transa, transb, 
                               m, n, k, alpha, 
                               Aarray, lda, 
                               Barray, ldb, beta,
                               Carray, ldc, batchCount);
+  }
+#else
+  return cublasSgemmBatched(handle, transa, transb, 
+                            m, n, k, alpha, 
+                            Aarray, lda, 
+                            Barray, ldb, beta,
+                            Carray, ldc, batchCount);
+#endif
 }
 
-#if COMPILE_FP16
+#if COMPILE_FP16 // should not be visible for CUDA 9.0 and below
 cublasStatus_t cublasGemmBatchedTyped(cublasHandle_t handle,
                                       int compute,
                                       cublasOperation_t transa, 

From 939625ff6ce14e3c67e6b6dfaffe5a5752880a3c Mon Sep 17 00:00:00 2001
From: Marcin Junczys-Dowmunt <Marcin.JunczysDowmunt@microsoft.com>
Date: Tue, 29 Oct 2019 13:35:22 -0700
Subject: [PATCH 744/838] simplify code for compute capability of matrix
 products

---
 src/tensors/gpu/prod.cpp | 24 ++++++++++--------------
 1 file changed, 10 insertions(+), 14 deletions(-)

diff --git a/src/tensors/gpu/prod.cpp b/src/tensors/gpu/prod.cpp
index 83d1d5155..9e21878f5 100755
--- a/src/tensors/gpu/prod.cpp
+++ b/src/tensors/gpu/prod.cpp
@@ -64,7 +64,7 @@ cublasStatus_t cublasGemmTyped(cublasHandle_t handle,
                                const float* beta,
                                float* C, int ldc) {
 // double #if and if unfortunately required to safeguard against compilation error 
-// with CUDA 8.0 and runtime error with CUDA >9.0 on GPUs with compute capabilite under 5
+// with CUDA 8.0 and runtime error with CUDA >9.0 on GPUs with compute capability under 5
 #if CUDA_VERSION > 9000
   if(compute >= 5) {
     return cublasGemmEx(handle, transa, transb, 
@@ -142,15 +142,13 @@ void ProdTyped(marian::Tensor C,
   cublasOperation_t opA = transA ? CUBLAS_OP_T : CUBLAS_OP_N;
   cublasOperation_t opB = transB ? CUBLAS_OP_T : CUBLAS_OP_N;
 
-  auto cublasHandle = std::static_pointer_cast<gpu::Backend>(C->getBackend())
-                          ->getCublasHandle();
-
-  int compute;
-  CUDA_CHECK(cudaDeviceGetAttribute(&compute, cudaDevAttrComputeCapabilityMajor, (int)C->getDeviceId().no));
+  auto backend = std::static_pointer_cast<gpu::Backend>(C->getBackend());
+  auto cublasHandle = backend->getCublasHandle();
+  auto compute = backend->getCudaComputeCapability();
 
   setTensorMode(cublasHandle);
   CUBLAS_CHECK(cublasGemmTyped(cublasHandle,
-                               compute,
+                               compute.major,
                                opB,
                                opA,
                                n,
@@ -197,7 +195,7 @@ cublasStatus_t cublasGemmBatchedTyped(cublasHandle_t handle,
                                       float *Carray[], int ldc, 
                                       int batchCount) {
 // double #if and if unfortunately required to safeguard against compilation error 
-// with CUDA 8.0 and runtime error with CUDA >9.0 on GPUs with compute capabilite under 5
+// with CUDA 8.0 and runtime error with CUDA >9.0 on GPUs with compute capability under 5
 #if CUDA_VERSION > 9000
   if(compute >= 5) {
     return cublasGemmBatchedEx(handle, transa, transb, 
@@ -279,8 +277,9 @@ void ProdBatchedTyped(marian::Tensor C,
   cublasOperation_t opA = transA ? CUBLAS_OP_T : CUBLAS_OP_N;
   cublasOperation_t opB = transB ? CUBLAS_OP_T : CUBLAS_OP_N;
 
-  auto cublasHandle = std::static_pointer_cast<gpu::Backend>(C->getBackend())
-                          ->getCublasHandle();
+  auto backend = std::static_pointer_cast<gpu::Backend>(C->getBackend());
+  auto cublasHandle = backend->getCublasHandle();
+  auto compute = backend->getCudaComputeCapability();
 
   auto strideA = batchA == 1 ? 0 : m * k;
   auto strideB = batchB == 1 ? 0 : n * k;
@@ -307,12 +306,9 @@ void ProdBatchedTyped(marian::Tensor C,
   IPtr<MemoryPiece> mp_cptr = allocator->alloc<T*>(cptr.size());
   CudaCopy(cptr.data(), cptr.data() + cptr.size(), mp_cptr->data<T*>());
 
-  int compute;
-  CUDA_CHECK(cudaDeviceGetAttribute(&compute, cudaDevAttrComputeCapabilityMajor, (int)C->getDeviceId().no));
-
   setTensorMode(cublasHandle);
   CUBLAS_CHECK(cublasGemmBatchedTyped(cublasHandle,
-                                      compute,
+                                      compute.major,
                                       opB,
                                       opA,
                                       n,

From af7df9d7b75c30433937a49b456da82bec70cc49 Mon Sep 17 00:00:00 2001
From: Marcin Junczys-Dowmunt <Marcin.JunczysDowmunt@microsoft.com>
Date: Tue, 29 Oct 2019 14:31:51 -0700
Subject: [PATCH 745/838] Address comments from review

---
 src/common/types.h                 | 32 +++++------
 src/functional/defs.h              |  4 +-
 src/functional/operators.h         |  2 +-
 src/functional/tensor.h            |  2 +-
 src/tensors/gpu/prod.cpp           | 88 ++++++++++++------------------
 src/tests/units/operator_tests.cpp |  1 -
 6 files changed, 56 insertions(+), 73 deletions(-)

diff --git a/src/common/types.h b/src/common/types.h
index 5c8eb128d..0593a0a03 100755
--- a/src/common/types.h
+++ b/src/common/types.h
@@ -3,7 +3,7 @@
 
 #if __GNUC__ >= 7
 #pragma GCC diagnostic push
-#pragma GCC diagnostic ignored "-Wint-in-bool-context"
+#pragma GCC diagnostic ignored "-Wint-in-bool-context" // gcc-7 introduces this warning, triggered in 3rd-party code
 #endif
 #include "half_float/umHalf.h"
 #if __GNUC__ >= 7
@@ -15,26 +15,26 @@
 #include <functional>
 #include <type_traits>
 
-#ifndef __CUDACC__
+#ifndef __CUDACC__ // NVCC is very unreliable when it comes to CPU intrinsics, we hide them completely from NVCC-compiled code
 #include <immintrin.h>
 #endif
 
 #ifdef __CUDACC__ // nvcc is compiling this code
-  #include <cuda.h> // required to see CUDA_VERSION
-  #if (CUDA_VERSION > 9000 && (__CUDA_ARCH__ >= 600 || !defined(__CUDA_ARCH__)))
-    #define COMPILE_FP16 1 // we are in GPU code and we know what to do with FP16 code
-  #else
-    #define COMPILE_FP16 0 // we are in GPU code, but compute capability is too low to use FP16
-  #endif
+#include <cuda.h> // required to see CUDA_VERSION
+#if (CUDA_VERSION > 9000 && (__CUDA_ARCH__ >= 600 || !defined(__CUDA_ARCH__)))
+#define COMPILE_FP16 1 // we are in GPU code and we know what to do with FP16 code
+#else
+#define COMPILE_FP16 0 // we are in GPU code, but compute capability is too low to use FP16
+#endif
 #elif CUDA_FOUND // other compiler, likely host code. Should be fine with seeing the correct includes with host code
-  #include <cuda.h> // required to see CUDA_VERSION
-  #if (CUDA_VERSION > 9000)
-    #define COMPILE_FP16 1
-  #else 
-    #define COMPILE_FP16 0
-  #endif
+#include <cuda.h> // required to see CUDA_VERSION
+#if (CUDA_VERSION > 9000)
+#define COMPILE_FP16 1
+#else 
+#define COMPILE_FP16 0
+#endif
 #else
-  #define COMPILE_FP16 0
+#define COMPILE_FP16 0
 #endif
 
 #ifdef _MSC_VER
@@ -133,7 +133,7 @@ do { \
 
 namespace marian {
 
-#ifndef __CUDACC__
+#ifndef __CUDACC__ // vectorized types not available from .cu files
 
 // @TODO: check what intrinsics are actually available.
 struct float32x4 {
diff --git a/src/functional/defs.h b/src/functional/defs.h
index a6562ed53..6873a453d 100644
--- a/src/functional/defs.h
+++ b/src/functional/defs.h
@@ -1,6 +1,6 @@
 #pragma once
 
-#ifdef __CUDACC__
+#ifdef __CUDACC__  // Compiling with NVCC, host or device code
 
 #include <cuda.h>
 #define HOST __host__
@@ -10,7 +10,7 @@
 #define HOST_DEVICE __host__ __device__
 #define HOST_DEVICE_INLINE __host__ __device__ inline
 
-#else
+#else // Compiling with GCC or other host compiler
 
 #define HOST
 #define DEVICE
diff --git a/src/functional/operators.h b/src/functional/operators.h
index 66914bf75..08d8af38c 100755
--- a/src/functional/operators.h
+++ b/src/functional/operators.h
@@ -192,7 +192,7 @@ struct Ops<double> {
 // stay invisible to NVCC as it seems to have problems with intrinsics;
 // will still be compiled into the binary by cpu-side gcc/g++
 // __CUDACC__ is defined when compiling with NVCC regardless of device type
-// __CUDA_ARCH__ is defined when compiling devicde (GPU) code
+// __CUDA_ARCH__ is defined when compiling device (GPU) code
 #ifndef __CUDACC__
 
 #include "3rd_party/sse_mathfun.h"
diff --git a/src/functional/tensor.h b/src/functional/tensor.h
index aecc25015..3948e8e9e 100755
--- a/src/functional/tensor.h
+++ b/src/functional/tensor.h
@@ -19,7 +19,7 @@ inline marian::Shape adapt(const marian::Shape& shape) {
 // modify last shape dimension to automatically map to a larger stride. We are moving now by 4 floats
 // at once and need to stop earlier. This is a shallow typecast to bascially an array of 4 floats.
 
-#ifndef __CUDACC__
+#ifndef __CUDACC__ // vectorized types not available from .cu files
 
 template <>
 inline marian::Shape adapt<float32x4>(const marian::Shape& shape) {
diff --git a/src/tensors/gpu/prod.cpp b/src/tensors/gpu/prod.cpp
index 9e21878f5..4189c01d3 100755
--- a/src/tensors/gpu/prod.cpp
+++ b/src/tensors/gpu/prod.cpp
@@ -53,55 +53,47 @@ static void unsetTensorMode(cublasHandle_t cublasHandle) {
 }
 
 // overload for float, contains configuration settings for float32
-cublasStatus_t cublasGemmTyped(cublasHandle_t handle,
-                               int compute,
-                               cublasOperation_t transa, 
-                               cublasOperation_t transb,
-                               int m, int n, int k,
-                               const float* alpha,
-                               const float* A, int lda,
-                               const float* B, int ldb,
-                               const float* beta,
-                               float* C, int ldc) {
+static cublasStatus_t cublasGemmTyped(cublasHandle_t handle,
+                                      CudaCompute computeCapability,
+                                      cublasOperation_t transa, 
+                                      cublasOperation_t transb,
+                                      int m, int n, int k,
+                                      const float* alpha,
+                                      const float* A, int lda,
+                                      const float* B, int ldb,
+                                      const float* beta,
+                                      float* C, int ldc) {
 // double #if and if unfortunately required to safeguard against compilation error 
 // with CUDA 8.0 and runtime error with CUDA >9.0 on GPUs with compute capability under 5
 #if CUDA_VERSION > 9000
-  if(compute >= 5) {
+  if(computeCapability.major >= 5)
     return cublasGemmEx(handle, transa, transb, 
                         m, n, k, alpha, 
                         A, CUDA_R_32F, lda, 
                         B, CUDA_R_32F, ldb, beta, 
                         C, CUDA_R_32F, ldc,
                         CUDA_R_32F, CUBLAS_GEMM_DEFAULT_TENSOR_OP); // @TODO: review algorithm
-  } else {
-    return cublasSgemm(handle, transa, transb, 
-                        m, n, k, alpha, 
-                        A, lda, 
-                        B, ldb, beta, 
-                        C, ldc);
-  }
-#else
+#endif
   return cublasSgemm(handle, transa, transb, 
                       m, n, k, alpha, 
                       A, lda, 
                       B, ldb, beta, 
                       C, ldc);
-#endif
 }
 
 #if COMPILE_FP16
 // overload for half, contains configuration settings for float16
-cublasStatus_t cublasGemmTyped(cublasHandle_t handle,
-                               int compute,
-                               cublasOperation_t transa, 
-                               cublasOperation_t transb,
-                               int m, int n, int k,
-                               const half* alpha,
-                               const half* A, int lda,
-                               const half* B, int ldb,
-                               const half* beta,
-                               half* C, int ldc) {
-  ABORT_IF(compute < 6, "Compute capability {} below 6 should not happen for FP16", compute);
+static cublasStatus_t cublasGemmTyped(cublasHandle_t handle,
+                                      CudaCompute computeCapability,
+                                      cublasOperation_t transa, 
+                                      cublasOperation_t transb,
+                                      int m, int n, int k,
+                                      const half* alpha,
+                                      const half* A, int lda,
+                                      const half* B, int ldb,
+                                      const half* beta,
+                                      half* C, int ldc) {
+  ABORT_IF(computeCapability.major < 6, "Compute capability {} below 6 should not happen for FP16", compute);
   return cublasGemmEx(handle, transa, transb, 
                       m, n, k, alpha, 
                       A, CUDA_R_16F, lda, 
@@ -144,11 +136,11 @@ void ProdTyped(marian::Tensor C,
 
   auto backend = std::static_pointer_cast<gpu::Backend>(C->getBackend());
   auto cublasHandle = backend->getCublasHandle();
-  auto compute = backend->getCudaComputeCapability();
+  auto computeCapability = backend->getCudaComputeCapability();
 
   setTensorMode(cublasHandle);
   CUBLAS_CHECK(cublasGemmTyped(cublasHandle,
-                               compute.major,
+                               computeCapability,
                                opB,
                                opA,
                                n,
@@ -184,7 +176,7 @@ void Prod(marian::Tensor C,
 }
 
 cublasStatus_t cublasGemmBatchedTyped(cublasHandle_t handle,
-                                      int compute,
+                                      CudaCompute computeCapability,
                                       cublasOperation_t transa, 
                                       cublasOperation_t transb,
                                       int m, int n, int k,
@@ -197,32 +189,24 @@ cublasStatus_t cublasGemmBatchedTyped(cublasHandle_t handle,
 // double #if and if unfortunately required to safeguard against compilation error 
 // with CUDA 8.0 and runtime error with CUDA >9.0 on GPUs with compute capability under 5
 #if CUDA_VERSION > 9000
-  if(compute >= 5) {
+  if(computeCapability.major >= 5)
     return cublasGemmBatchedEx(handle, transa, transb, 
-                              m, n, k, alpha, 
-                              (void* const*)Aarray, CUDA_R_32F, lda, 
-                              (void* const*)Barray, CUDA_R_32F, ldb, beta,
-                              (void**)Carray, CUDA_R_32F, ldc, batchCount,
-                              CUDA_R_32F, CUBLAS_GEMM_DEFAULT_TENSOR_OP);
-  } else {
-    return cublasSgemmBatched(handle, transa, transb, 
-                              m, n, k, alpha, 
-                              Aarray, lda, 
-                              Barray, ldb, beta,
-                              Carray, ldc, batchCount);
-  }
-#else
+                               m, n, k, alpha, 
+                               (void* const*)Aarray, CUDA_R_32F, lda, 
+                               (void* const*)Barray, CUDA_R_32F, ldb, beta,
+                               (void**)Carray, CUDA_R_32F, ldc, batchCount,
+                               CUDA_R_32F, CUBLAS_GEMM_DEFAULT_TENSOR_OP);
+#endif
   return cublasSgemmBatched(handle, transa, transb, 
                             m, n, k, alpha, 
                             Aarray, lda, 
                             Barray, ldb, beta,
                             Carray, ldc, batchCount);
-#endif
 }
 
 #if COMPILE_FP16 // should not be visible for CUDA 9.0 and below
 cublasStatus_t cublasGemmBatchedTyped(cublasHandle_t handle,
-                                      int compute,
+                                      CudaCompute computeCapability,
                                       cublasOperation_t transa, 
                                       cublasOperation_t transb,
                                       int m, int n, int k,
@@ -232,7 +216,7 @@ cublasStatus_t cublasGemmBatchedTyped(cublasHandle_t handle,
                                       const half *beta,
                                       half *Carray[], int ldc, 
                                       int batchCount) {
-  ABORT_IF(compute < 6, "Compute capability {} below 6 should not happen for FP16", compute);
+  ABORT_IF(computeCapability.major < 6, "Compute capability {} below 6 should not happen for FP16", compute);
   return cublasGemmBatchedEx(handle, transa, transb, 
                              m, n, k, alpha, 
                              (void* const*)Aarray, CUDA_R_16F, lda, 
@@ -308,7 +292,7 @@ void ProdBatchedTyped(marian::Tensor C,
 
   setTensorMode(cublasHandle);
   CUBLAS_CHECK(cublasGemmBatchedTyped(cublasHandle,
-                                      compute.major,
+                                      compute,
                                       opB,
                                       opA,
                                       n,
diff --git a/src/tests/units/operator_tests.cpp b/src/tests/units/operator_tests.cpp
index 127ae679f..2d3fa167b 100755
--- a/src/tests/units/operator_tests.cpp
+++ b/src/tests/units/operator_tests.cpp
@@ -22,7 +22,6 @@ void tests(DeviceType device, Type floatType = Type::float32) {
   }
 #endif
 
-
   auto floatApprox = [](T x, T y) -> bool { return x == Approx(y).epsilon(0.01); };
   auto floatEqual  = [](T x, T y) -> bool { return x == y; };
 

From 81c14cd1bbb6214e636e895e96684e68396548a0 Mon Sep 17 00:00:00 2001
From: Marcin Junczys-Dowmunt <Marcin.JunczysDowmunt@microsoft.com>
Date: Tue, 29 Oct 2019 14:44:10 -0700
Subject: [PATCH 746/838] fix compilation error

---
 src/tensors/gpu/prod.cpp | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/src/tensors/gpu/prod.cpp b/src/tensors/gpu/prod.cpp
index 4189c01d3..f60fae9ab 100755
--- a/src/tensors/gpu/prod.cpp
+++ b/src/tensors/gpu/prod.cpp
@@ -93,7 +93,7 @@ static cublasStatus_t cublasGemmTyped(cublasHandle_t handle,
                                       const half* B, int ldb,
                                       const half* beta,
                                       half* C, int ldc) {
-  ABORT_IF(computeCapability.major < 6, "Compute capability {} below 6 should not happen for FP16", compute);
+  ABORT_IF(computeCapability.major < 6, "Compute capability {} below 6 should not happen for FP16", computeCapability.major);
   return cublasGemmEx(handle, transa, transb, 
                       m, n, k, alpha, 
                       A, CUDA_R_16F, lda, 
@@ -216,7 +216,7 @@ cublasStatus_t cublasGemmBatchedTyped(cublasHandle_t handle,
                                       const half *beta,
                                       half *Carray[], int ldc, 
                                       int batchCount) {
-  ABORT_IF(computeCapability.major < 6, "Compute capability {} below 6 should not happen for FP16", compute);
+  ABORT_IF(computeCapability.major < 6, "Compute capability {} below 6 should not happen for FP16", computeCapability.major);
   return cublasGemmBatchedEx(handle, transa, transb, 
                              m, n, k, alpha, 
                              (void* const*)Aarray, CUDA_R_16F, lda, 

From 011664664a4a9334d6d45415755c7a00166939e9 Mon Sep 17 00:00:00 2001
From: Roman Grundkiewicz <rgrundki@exseed.ed.ac.uk>
Date: Wed, 30 Oct 2019 13:54:59 +0000
Subject: [PATCH 747/838] Update badges

---
 README.md | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/README.md b/README.md
index d96f7c898..150f2030d 100644
--- a/README.md
+++ b/README.md
@@ -1,9 +1,9 @@
 Marian
 ======
 
-[![Build Status CUDA 8.0](https://img.shields.io/jenkins/s/http/vali.inf.ed.ac.uk/jenkins/view/marian/job/marian-dev-cuda-8.0.svg?label=CUDA%208.0)](http://vali.inf.ed.ac.uk/jenkins/job/marian-dev-cuda-8.0/)
-[![Build Status CUDA 9.2](https://img.shields.io/jenkins/s/http/vali.inf.ed.ac.uk/jenkins/view/marian/job/marian-dev.svg?label=CUDA%209.2)](http://vali.inf.ed.ac.uk/jenkins/job/marian-dev/)
-[![Build Status CUDA 10.0](https://img.shields.io/jenkins/s/http/vali.inf.ed.ac.uk/jenkins/view/marian/job/marian-dev-cuda-10.0.svg?label=CUDA%2010.0)](http://vali.inf.ed.ac.uk/jenkins/job/marian-dev-cuda-10.0/)
+[![Build Status CUDA 8](https://img.shields.io/jenkins/s/http/vali.inf.ed.ac.uk/jenkins/view/marian/job/marian-dev-cuda-8.0.svg?label=CUDA%208)](http://vali.inf.ed.ac.uk/jenkins/job/marian-dev-cuda-8.0/)
+[![Build Status CUDA 9](https://img.shields.io/jenkins/s/http/vali.inf.ed.ac.uk/jenkins/view/marian/job/marian-dev-cuda-9.2.svg?label=CUDA%209)](http://vali.inf.ed.ac.uk/jenkins/job/marian-dev-cuda-9.2/)
+[![Build Status CUDA 10](https://img.shields.io/jenkins/s/http/vali.inf.ed.ac.uk/jenkins/view/marian/job/marian-dev-cuda-10.1.svg?label=CUDA%2010)](http://vali.inf.ed.ac.uk/jenkins/job/marian-dev-cuda-10.1/)
 [![Build Status CPU](https://img.shields.io/jenkins/s/http/vali.inf.ed.ac.uk/jenkins/view/marian/job/marian-dev-cpu.svg?label=CPU)](http://vali.inf.ed.ac.uk/jenkins/job/marian-dev-cpu/)
 [![Tests Status](https://img.shields.io/jenkins/s/http/vali.inf.ed.ac.uk/jenkins/view/marian/job/marian-regression-tests.svg?label=tests)](http://vali.inf.ed.ac.uk/jenkins/job/marian-regression-tests/)
 [![Latest release](https://img.shields.io/github/release/marian-nmt/marian.svg?label=release)](https://github.com/marian-nmt/marian/releases)

From 9e8c7721ea759109c01a3b34ffd975ba4029f243 Mon Sep 17 00:00:00 2001
From: Roman Grundkiewicz <rgrundki@exseed.ed.ac.uk>
Date: Wed, 30 Oct 2019 14:10:46 +0000
Subject: [PATCH 748/838] Add a comment to authors()

---
 src/common/authors.h | 5 ++++-
 1 file changed, 4 insertions(+), 1 deletion(-)

diff --git a/src/common/authors.h b/src/common/authors.h
index 9d3f0f1ee..c99241c47 100644
--- a/src/common/authors.h
+++ b/src/common/authors.h
@@ -7,7 +7,7 @@ namespace marian {
 std::string citation() {
   return "Marian: Fast Neural Machine Translation in C++\n"
          "\n"
-         "Please cite the following paper if you use Marian (formerly AmuNMT):\n"
+         "Please cite the following paper if you use Marian:\n"
          "\n"
          "@InProceedings{mariannmt,\n"
          "  title     = {Marian: Fast Neural Machine Translation in {C++}},\n"
@@ -26,6 +26,9 @@ std::string citation() {
          "}\n";
 }
 
+// The list of contributors has been compiled semi-automatically from the
+// GitHub contributor list in default order. That list can be printed out with
+// `git shortlog -s -n`.
 std::string authors() {
   return "Marian: Fast Neural Machine Translation in C++\n"
          "\n"

From b930762c7a8e6144c8080dfa6e2d3cd5a8f019d0 Mon Sep 17 00:00:00 2001
From: Roman Grundkiewicz <rgrundki@exseed.ed.ac.uk>
Date: Wed, 30 Oct 2019 15:25:18 +0000
Subject: [PATCH 749/838] Update regression-tests submodule

---
 regression-tests | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/regression-tests b/regression-tests
index 66c6f498d..5df112b64 160000
--- a/regression-tests
+++ b/regression-tests
@@ -1 +1 @@
-Subproject commit 66c6f498d55fbe7a3d524f5697a185000f2d928d
+Subproject commit 5df112b64f48c46643109e1ec37eb0560dba0feb

From e53a46ae214a244e2d11e5b93bec17315a8943c8 Mon Sep 17 00:00:00 2001
From: Roman Grundkiewicz <rgrundki@exseed.ed.ac.uk>
Date: Wed, 30 Oct 2019 16:51:13 +0000
Subject: [PATCH 750/838] Fix multi-line string

---
 src/common/authors.h | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/src/common/authors.h b/src/common/authors.h
index c99241c47..5dcdaf283 100644
--- a/src/common/authors.h
+++ b/src/common/authors.h
@@ -58,8 +58,8 @@ std::string authors() {
          "Barry Haddow <bhaddow@inf.ed.ac.uk>\n"
          "Dominik Stańczak <stanczakdominik@gmail.com>\n"
          "Michael Hutt <Michael.Hutt@gmail.com>\n"
-         "Richard Wei <rxwei@users.noreply.github.com>\n";
+         "Richard Wei <rxwei@users.noreply.github.com>\n"
          "Wenyong Huang <weyo.huang@gmail.com>\n"
-         "alancucki <alancucki+github@gmail.com>\n"
+         "alancucki <alancucki+github@gmail.com>\n";
 }
 }  // namespace marian

From 7b36b329ef7bc2eed195f3bb9ebdeff11ad6dc1b Mon Sep 17 00:00:00 2001
From: Ulrich Germann <ugermann@inf.ed.ac.uk>
Date: Wed, 30 Oct 2019 17:29:27 +0000
Subject: [PATCH 751/838] Use ccache for faster compilation if available.
 (#525)

* Use ccache only when requested via cmake -DUSE_CCACHE=on
* Add link to https://ccache.dev in comment about using ccache.
* Issue success / missing ccache message when ccache is requested during the CCACHE run
* Issue cmake warning instead of cmake status message when use of ccache is requested but ccache cannot be found.
---
 CMakeLists.txt | 12 ++++++++++++
 1 file changed, 12 insertions(+)

diff --git a/CMakeLists.txt b/CMakeLists.txt
index a914b7017..d07616656 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -25,6 +25,18 @@ option(COMPILE_TESTS "Compile tests" OFF)
 option(COMPILE_SERVER "Compile marian-server" OFF)
 
 option(USE_DOXYGEN "Build documentation with Doxygen" ON)
+option(USE_CCACHE "Use ccache compiler cache (https://ccache.dev)" OFF)
+
+# use ccache (https://ccache.dev) for faster compilation if requested and available
+if(USE_CCACHE)
+find_program(CCACHE_PROGRAM ccache)
+if(CCACHE_PROGRAM)
+  message(STATUS "Found and will be using ccache for faster repeat compilation (use cmake -DUSE_CCACHE=off to disable).")
+  set_property(GLOBAL PROPERTY RULE_LAUNCH_COMPILE "${CCACHE_PROGRAM}")
+else(CCACHE_PROGRAM)
+  message(WARNING "Compilation with ccache requested but no ccache found.")
+endif(CCACHE_PROGRAM)
+endif(USE_CCACHE)
 
 # Project versioning
 find_package(Git QUIET)

From 7efde49c739446b82372c55517215418047f6f35 Mon Sep 17 00:00:00 2001
From: Martin Junczys-Dowmunt <Marcin.JunczysDowmunt@microsoft.com>
Date: Wed, 30 Oct 2019 21:27:34 +0000
Subject: [PATCH 752/838] Merged PR 10205: towards multiple separated parameter
 types

This is a helper PR for making Young's packed models cleaner. Parameter objects in Marian are meant to be of one type (say float32) and cannot be correctly mixed for various reasons. This introduces a type -> parameter object map that separates parameters with different types into different spaces. Currently this only works reliably for inference (I hope) and will abort for training if more than one parameter type are specified. With this we will be able to have a parameter object for packed16 and one for float32.

It should also be possible in the future to have the same name for a parameter but differentiated by type, which might solve the tied-embedding problem we discussed a while ago.
---
 src/common/types.h                  |   9 ++
 src/graph/expression_graph.cpp      |  45 +++++-----
 src/graph/expression_graph.h        | 126 +++++++++++++++++-----------
 src/graph/parameters.h              |  22 ++---
 src/rescorer/rescorer.h             |   2 +-
 src/tests/units/attention_tests.cpp |   2 +-
 src/tests/units/graph_tests.cpp     |   2 +-
 src/tests/units/operator_tests.cpp  |   2 +-
 src/tests/units/rnn_tests.cpp       |   4 +-
 src/translator/translator.h         |  10 +--
 10 files changed, 135 insertions(+), 89 deletions(-)

diff --git a/src/common/types.h b/src/common/types.h
index 0593a0a03..86bb108fe 100755
--- a/src/common/types.h
+++ b/src/common/types.h
@@ -398,3 +398,12 @@ class NumericLimits {
 };
 
 }  // namespace marian
+
+// custom specialization of std::hash can be injected in namespace std
+namespace std {
+  template<> struct hash<::marian::Type> {
+    size_t operator()(const ::marian::Type& type) const noexcept {
+      return (size_t)type; // type is already a unique value of type size_t
+    }
+  };
+}
diff --git a/src/graph/expression_graph.cpp b/src/graph/expression_graph.cpp
index 8c568949b..369990a28 100755
--- a/src/graph/expression_graph.cpp
+++ b/src/graph/expression_graph.cpp
@@ -12,8 +12,10 @@ ExpressionGraph::ExpressionGraph(bool inference)
 void ExpressionGraph::setDevice(DeviceId deviceId, Ptr<Device> device) {
   if(!backend_) {
     backend_ = BackendByDeviceId(deviceId, Config::seed);
-    params_ = New<Parameters>();
-    params_->init(backend_);
+    auto params = New<Parameters>(defaultElementType_);
+    params->init(backend_);
+    paramsByElementType_[defaultElementType_] = params;
+    
     if(device)
       tensors_ = New<Tensors>(backend_, device);
     else
@@ -170,9 +172,11 @@ void ExpressionGraph::backward(bool reset, float clipValue) {
     ABORT("Aborting");
   }
 
-  params_->allocateBackward();
-  if(reset)
-    params_->set_zero_adjoint();
+  for(auto kvParams : paramsByElementType_) {
+    kvParams.second->allocateBackward();
+    if(reset)
+      kvParams.second->set_zero_adjoint();
+  }
 
   for(auto&& v : topNodes_)
     v->init_dependent();
@@ -233,28 +237,31 @@ Expr ExpressionGraph::dropoutMask(float prob, const Shape& shape, Type valueType
 }
 
 Expr ExpressionGraph::dropoutMask(float prob, const Shape& shape) {
-  return constant(shape, inits::dropout(prob), parameterType_);
+  return constant(shape, inits::dropout(prob), defaultElementType_);
 }
 
 void ExpressionGraph::checkNaN(Tensor t, bool& isNaN, bool& isInf) {
   IsNaN(t, allocator(), isNaN, isInf);
 }
 
-void ExpressionGraph::save(std::vector<io::Item>& ioItems) {
-  // sorted by name in std::map
-  for(auto p : params()->getMap()) {
-    std::string pName = p.first;
+void ExpressionGraph::save(std::vector<io::Item>& ioItems, Type saveElementType) {
+  // sorted by type in std::map
+  for(auto kvParams : paramsByElementType_) {
+    // sorted by name in std::map
+    for(auto p : kvParams.second->getMap()) {
+      std::string pName = p.first;
 
-    if(!namespace_.empty()) {
-      if(pName.substr(0, namespace_.size() + 2) == namespace_ + "::")
-        pName = pName.substr(namespace_.size() + 2);
-    }
+      if(!namespace_.empty()) {
+        if(pName.substr(0, namespace_.size() + 2) == namespace_ + "::")
+          pName = pName.substr(namespace_.size() + 2);
+      }
 
-    Tensor val = p.second->val();
-    io::Item item;
-    val->get(item, pName);
-    item.convert(saveType_);
-    ioItems.emplace_back(std::move(item));
+      Tensor val = p.second->val();
+      io::Item item;
+      val->get(item, pName);
+      item.convert(saveElementType);
+      ioItems.emplace_back(std::move(item));
+    }
   }
 }
 
diff --git a/src/graph/expression_graph.h b/src/graph/expression_graph.h
index c26f98a7e..02a14e022 100755
--- a/src/graph/expression_graph.h
+++ b/src/graph/expression_graph.h
@@ -110,6 +110,8 @@ class Tensors {
   void clearLongtermMemory() { longterm_->clear(); }
 };
 
+typedef std::map<Type, Ptr<Parameters>> ElementTypeParamsMap; // keep it sorted, hence map not unordered map
+
 class ExpressionGraph : public std::enable_shared_from_this<ExpressionGraph> {
   size_t count_{0};
 
@@ -124,8 +126,7 @@ class ExpressionGraph : public std::enable_shared_from_this<ExpressionGraph> {
 
   std::unordered_map<size_t, std::vector<Expr>> memoized_;
 
-  Type parameterType_{Type::float32}; // Type used for storing parameters, currently all parameters have to have the same type
-  Type saveType_{Type::float32};      // Type used for saving to disk, can be different, e.g. double or float16.
+  Type defaultElementType_{Type::float32}; // Type used for storing parameters, currently all parameters have to have the same type
 
   bool inferenceOnly_{false};
 
@@ -143,7 +144,12 @@ class ExpressionGraph : public std::enable_shared_from_this<ExpressionGraph> {
   ExpressionGraph(ExpressionGraph&&) = delete;
 
   // Holds memory and expressions that correspond to graph parameters
-  Ptr<Parameters> params_;
+  // Now we can have multiple types of parameters in a separate parameters object per value type. 
+  // This is currently only accessible through private functions during loading, will abort during training
+  // when params() is called (e.g. optimizer) and there is more or other types than the default parameter type.
+  // Currently the only usecase is inference. Trying to access params() for non-default parameter type is going
+  // to abort. Inference does not need to access a whole set of parameters.
+  ElementTypeParamsMap paramsByElementType_;
   Ptr<Backend> backend_;
 
 public:
@@ -155,11 +161,12 @@ class ExpressionGraph : public std::enable_shared_from_this<ExpressionGraph> {
 
   virtual ~ExpressionGraph() {
     clear();
-    params_->clear();
+    for(auto kvParams : paramsByElementType_)
+      kvParams.second->clear();
   }
 
   virtual void setDevice(DeviceId deviceId = {0, DeviceType::gpu},
-                 Ptr<Device> device = nullptr);
+                         Ptr<Device> device = nullptr);
 
   DeviceId getDeviceId() { return backend_->getDeviceId(); }
 
@@ -220,7 +227,8 @@ class ExpressionGraph : public std::enable_shared_from_this<ExpressionGraph> {
   void checkNaN(Tensor t, bool& isNaN, bool& isInf);
 
   void forward() {
-    params_->allocateForward();
+    for(auto kvParams : paramsByElementType_)
+      kvParams.second->allocateForward();
     forwardNext();
   }
 
@@ -262,19 +270,27 @@ class ExpressionGraph : public std::enable_shared_from_this<ExpressionGraph> {
       name = namespace_ + "::" + name;
 
     // check first if parameter already exists
-    auto p = params_->get(name);
-    if(p) {
-      // if yes add to tape and return
-      ABORT_IF(shape != p->shape(),
-               "Requested shape {} for existing parameter '{}' does not match "
-               "original shape {}",
-               shape,
-               name,
-               p->shape());
-
-      p->setTrainable(!fixed);
-      add(p);
-      return p;
+    auto it = paramsByElementType_.find(valueType);
+    Ptr<Parameters> params = it != paramsByElementType_.end() ? it->second : nullptr;
+    if(!params) { 
+      params = New<Parameters>(valueType);
+      params->init(backend_);
+      paramsByElementType_.insert({valueType, params});
+    } else {
+      Expr p = params->get(name);
+      if(p) {
+        // if yes add to tape and return
+        ABORT_IF(shape != p->shape(),
+                "Requested shape {} for existing parameter '{}' does not match "
+                "original shape {}",
+                shape,
+                name,
+                p->shape());
+
+        p->setTrainable(!fixed);
+        add(p);
+        return p;
+      }
     }
 
     // if graph was reloaded do not allow creation of new parameters
@@ -283,14 +299,16 @@ class ExpressionGraph : public std::enable_shared_from_this<ExpressionGraph> {
              name);
 
     // if not check if name is not taken by other node
-    ABORT_IF(get(name), "Non-parameter with name '{}' already exists", name);
+    auto other = get(name);
+    ABORT_IF(other, "Parameter with name '{}' already exists and has type {}", name, other->value_type());
 
     // create parameter node (adds to tape)
-    p = Expression<ParamNode>(shared_from_this(), shape, init, valueType, fixed);
+    Expr p = Expression<ParamNode>(shared_from_this(), shape, init, valueType, fixed);
+    LOG(debug, "Created parameter {} with shape {} and type {}", pname, shape, valueType);
 
     // set name and id and add to list of parameters
     p->set_name(name);
-    params_->add(p, name);
+    params->add(p, name);
 
     return p;
   }
@@ -299,7 +317,7 @@ class ExpressionGraph : public std::enable_shared_from_this<ExpressionGraph> {
              const Shape& shape,
              const Ptr<inits::NodeInitializer>& init,
              bool fixed = false) {
-    return param(pname, shape, init, parameterType_, fixed);
+    return param(pname, shape, init, defaultElementType_, fixed);
   }
 
   Expr constant(const Shape& shape,
@@ -310,7 +328,7 @@ class ExpressionGraph : public std::enable_shared_from_this<ExpressionGraph> {
 
   Expr constant(const Shape& shape,
                 const Ptr<inits::NodeInitializer>& init) {
-    return Expression<ConstantNode>(shared_from_this(), shape, init, parameterType_);
+    return Expression<ConstantNode>(shared_from_this(), shape, init, defaultElementType_);
   }
 
   // @TODO: add version with iterators
@@ -337,14 +355,14 @@ class ExpressionGraph : public std::enable_shared_from_this<ExpressionGraph> {
     return constant(shape, inits::ones(), valueType);
   }
   Expr ones(const Shape& shape) {
-    return constant(shape, inits::ones(), parameterType_);
+    return constant(shape, inits::ones(), defaultElementType_);
   }
 
   Expr zeros(const Shape& shape, Type valueType) {
     return constant(shape, inits::zeros(), valueType);
   }
   Expr zeros(const Shape& shape) {
-    return constant(shape, inits::zeros(), parameterType_);
+    return constant(shape, inits::zeros(), defaultElementType_);
   }
 
   // prob = dropProb, e.g. 0.1 means 90% of values are kept
@@ -355,25 +373,31 @@ class ExpressionGraph : public std::enable_shared_from_this<ExpressionGraph> {
     if(!namespace_.empty())
       name = namespace_ + "::" + name;
 
-    auto e = params_->get(name);
-    if(e)
-      return e;
-    return Expr();  // @TODO: how is this different from just returning 'e'?
-  }
+    for(auto kvParams : paramsByElementType_)
+      return kvParams.second->get(name);
 
-  Ptr<Parameters>& params() { return params_; }
-
-  Type getParameterType() {
-    return parameterType_;
+    return Expr();
   }
 
-  Type getSaveType() {
-    return parameterType_;
+  Ptr<Parameters>& params() { 
+    // There are no parameter objects, that's weird.
+    ABORT_IF(paramsByElementType_.empty(), "No parameter object has been created");
+    
+    // Safeguard against accessing parameters from the outside with multiple parameter types, not yet supported
+    ABORT_IF(paramsByElementType_.size() > 1, "Calling of params() is currently not supported with multiple ({}) parameters", paramsByElementType_.size());
+    
+    // Safeguard against accessing parameters from the outside with other than default parameter type, not yet supported
+    auto it = paramsByElementType_.find(defaultElementType_);
+    ABORT_IF(it == paramsByElementType_.end(), "Parameter object for type {} does not exist", defaultElementType_);
+
+    return it->second;
   }
 
-  void setParameterType(Type parameterType, Type saveType = Type::float32) {
-    parameterType_ = parameterType;
-    saveType_ = saveType;
+  void setDefaultElementType(Type defaultElementType) {
+    ABORT_IF(!paramsByElementType_.empty() && defaultElementType != defaultElementType_, 
+             "Parameter objects already exist, cannot change default type from {} to {}", 
+             defaultElementType_, defaultElementType);
+    defaultElementType_ = defaultElementType;
   }
 
   Expr add(Expr node);
@@ -407,8 +431,6 @@ class ExpressionGraph : public std::enable_shared_from_this<ExpressionGraph> {
     tensors_->clear();
   }
 
-  void clearParameters() { params_->clear(); }
-
   void setReloaded(bool reloaded) { reloaded_ = reloaded; }
 
   void setThrowNaN(bool throwNaN) { throwNaN_ = throwNaN; }
@@ -445,23 +467,31 @@ class ExpressionGraph : public std::enable_shared_from_this<ExpressionGraph> {
     ABORT_IF(backend_->getDeviceId().type != DeviceType::cpu || !inferenceOnly_,
              "Memory mapping only supported for CPU inference mode");
 
-    params_ = New<MappedParameters>();
-    params_->init(backend_);
-
     LOG(info, "Memory mapping model at {}", ptr);
     auto items = io::mmapItems(ptr);
+    
+    // pre-populate parameters by type
+    for(auto& item : items) {
+      auto it = paramsByElementType_.find(item.type);
+      if(it == paramsByElementType_.end()) {
+        auto params = New<MappedParameters>(item.type);
+        params->init(backend_);
+        paramsByElementType_.insert({item.type, params});
+      }
+    }
+
     load(items, markReloaded);
   }
 
 public:
   // convert all parameters into an array of io::Item elements, for saving
-  void save(std::vector<io::Item>& ioItems);
+  void save(std::vector<io::Item>& ioItems, Type saveElementType = Type::float32);
 
-  void save(const std::string& name, const std::string& meta = "") {
+  void save(const std::string& name, const std::string& meta = "", Type saveElementType = Type::float32) {
     // LOG(info, "Saving model to {}", name);
 
     std::vector<io::Item> ioItems;
-    save(ioItems);
+    save(ioItems, saveElementType);
     if(!meta.empty())
       io::addMetaToItems(meta, "special:model.yml", ioItems);
     io::saveItems(name, ioItems);
diff --git a/src/graph/parameters.h b/src/graph/parameters.h
index bb2fc97be..2f460a313 100644
--- a/src/graph/parameters.h
+++ b/src/graph/parameters.h
@@ -18,6 +18,8 @@ namespace marian {
 // sets of one type.
 class Parameters {
 protected:
+  Type acceptedElementType_; // this parameter object only takes paramters of this type
+
   /** @brief List of all parameter nodes of this expression graph. */
   std::vector<Expr> params_;
   std::map<std::string, Expr> named_;
@@ -25,10 +27,6 @@ class Parameters {
   Ptr<TensorAllocator> vals_;
   Ptr<TensorAllocator> grads_;
 
-  // A set of parameters should be of the same type
-  Type parameterType_{Type::float32};
-  bool parameterTypeSet_{false};
-
   size_t totalCapacity(Ptr<TensorAllocator> alloc) {
     size_t sum = 0;
     for(auto p : params_) {
@@ -38,6 +36,10 @@ class Parameters {
   }
 
 public:
+  Parameters(Type acceptedType) : acceptedElementType_(acceptedType) {
+    LOG(debug, "Created parameter object of type {}", acceptedElementType_);
+  }
+
   auto begin() -> decltype(params_.begin()) { return params_.begin(); }
 
   auto end() -> decltype(params_.begin()) { return params_.end(); }
@@ -57,13 +59,11 @@ class Parameters {
 
   void add(Expr p, const std::string& name) {
     ABORT_IF(named_.count(name), "Parameter '{}' already exists", name);
-    ABORT_IF(parameterTypeSet_ && p->value_type() != parameterType_,
+    ABORT_IF(p->value_type() != acceptedElementType_,
              "Requested parameter type ({}) is different from chosen parameter type ({})",
-             p->value_type(), parameterType_);
+             p->value_type(), acceptedElementType_);
     params_.push_back(p);
     named_[name] = p;
-    parameterType_ = p->value_type();
-    parameterTypeSet_ = true;
   }
 
   virtual void init(Ptr<Backend> backend) {
@@ -106,9 +106,9 @@ class Parameters {
 
   virtual void set_zero_adjoint() { grads()->set(0.f); }
 
-  virtual Tensor vals() { return vals_->asTensor(parameterType_); }
+  virtual Tensor vals() { return vals_->asTensor(acceptedElementType_); }
 
-  virtual Tensor grads() { return grads_->asTensor(parameterType_); }
+  virtual Tensor grads() { return grads_->asTensor(acceptedElementType_); }
 
   virtual void clear() {
     params_.clear();
@@ -124,6 +124,8 @@ class MappedParameters : public Parameters {
   Ptr<Backend> backend_;
 
 public:
+  MappedParameters(Type acceptedType) : Parameters(acceptedType) {}
+
   virtual void init(Ptr<Backend> backend) override { backend_ = backend; }
   virtual void init(Ptr<Backend> backend, Ptr<Device>) override { init(backend); }
 
diff --git a/src/rescorer/rescorer.h b/src/rescorer/rescorer.h
index 4a71140b1..e81b3721a 100755
--- a/src/rescorer/rescorer.h
+++ b/src/rescorer/rescorer.h
@@ -72,7 +72,7 @@ class Rescore : public ModelTask {
       graph->setDevice(device);
 
       auto precison = options_->get<std::vector<std::string>>("precision", {"float32"});
-      graph->setParameterType(typeFromString(precison[0])); // only use first type, used for parameter type in graph
+      graph->setDefaultElementType(typeFromString(precison[0])); // only use first type, used for parameter type in graph
 
       graph->getBackend()->setClip(options_->get<float>("clip-gemm"));
       if (device.type == DeviceType::cpu) {
diff --git a/src/tests/units/attention_tests.cpp b/src/tests/units/attention_tests.cpp
index 7e95c55c3..e13e7943d 100755
--- a/src/tests/units/attention_tests.cpp
+++ b/src/tests/units/attention_tests.cpp
@@ -51,7 +51,7 @@ void tests(DeviceType type, Type floatType = Type::float32) {
 
   SECTION("Attention over encoder context") {
     auto graph = New<ExpressionGraph>();
-    graph->setParameterType(floatType);
+    graph->setDefaultElementType(floatType);
     graph->setDevice({0, type});
     graph->reserveWorkspaceMB(16);
 
diff --git a/src/tests/units/graph_tests.cpp b/src/tests/units/graph_tests.cpp
index 1c03c389b..fd4d3006a 100644
--- a/src/tests/units/graph_tests.cpp
+++ b/src/tests/units/graph_tests.cpp
@@ -24,7 +24,7 @@ TEST_CASE("Expression graph can be initialized with constant values",
 
     auto graph = New<ExpressionGraph>();
     graph->setDevice({0, DeviceType::gpu});
-    graph->setParameterType(type);
+    graph->setDefaultElementType(type);
     graph->reserveWorkspaceMB(4);
 
     if(type == Type::float16) {
diff --git a/src/tests/units/operator_tests.cpp b/src/tests/units/operator_tests.cpp
index 2d3fa167b..f65c59445 100755
--- a/src/tests/units/operator_tests.cpp
+++ b/src/tests/units/operator_tests.cpp
@@ -27,7 +27,7 @@ void tests(DeviceType device, Type floatType = Type::float32) {
 
   Config::seed = 1234;
   auto graph = New<ExpressionGraph>();
-  graph->setParameterType(floatType);
+  graph->setDefaultElementType(floatType);
   graph->setDevice({0, device});
   graph->reserveWorkspaceMB(16);
 
diff --git a/src/tests/units/rnn_tests.cpp b/src/tests/units/rnn_tests.cpp
index 66cacc5ce..56a2d1fdd 100755
--- a/src/tests/units/rnn_tests.cpp
+++ b/src/tests/units/rnn_tests.cpp
@@ -50,7 +50,7 @@ void tests(DeviceType type, Type floatType = Type::float32) {
     Config::seed = 1234;
 
     auto graph = New<ExpressionGraph>();
-    graph->setParameterType(floatType);
+    graph->setDefaultElementType(floatType);
     graph->setDevice({0, type});
     graph->reserveWorkspaceMB(16);
 
@@ -98,7 +98,7 @@ void tests(DeviceType type, Type floatType = Type::float32) {
     Config::seed = 1234;
 
     auto graph = New<ExpressionGraph>();
-    graph->setParameterType(floatType);
+    graph->setDefaultElementType(floatType);
     graph->setDevice({0, type});
     graph->reserveWorkspaceMB(16);
 
diff --git a/src/translator/translator.h b/src/translator/translator.h
index 5d02a692b..b02320b99 100755
--- a/src/translator/translator.h
+++ b/src/translator/translator.h
@@ -59,10 +59,9 @@ class Translate : public ModelTask {
     for(auto device : devices) {
       auto task = [&](DeviceId device, size_t id) {
         auto graph = New<ExpressionGraph>(true);
-        graph->setDevice(device);
         auto prec = options_->get<std::vector<std::string>>("precision", {"float32"});
-        graph->setParameterType(typeFromString(prec[0]));
-
+        graph->setDefaultElementType(typeFromString(prec[0]));
+        graph->setDevice(device);
         graph->getBackend()->setClip(options_->get<float>("clip-gemm"));
         if (device.type == DeviceType::cpu) {
           graph->getBackend()->setOptimized(options_->get<bool>("optimize"));
@@ -181,11 +180,10 @@ class TranslateService : public ModelServiceTask {
     // initialize scorers
     for(auto device : devices) {
       auto graph = New<ExpressionGraph>(true);
-      graph->setDevice(device);
       
       auto precison = options_->get<std::vector<std::string>>("precision", {"float32"});
-      graph->setParameterType(typeFromString(precison[0])); // only use first type, used for parameter type in graph
-
+      graph->setDefaultElementType(typeFromString(precison[0])); // only use first type, used for parameter type in graph
+      graph->setDevice(device);
       graph->getBackend()->setClip(options_->get<float>("clip-gemm"));
       if (device.type == DeviceType::cpu) {
         graph->getBackend()->setOptimized(options_->get<bool>("optimize"));

From 8c3cb069444eb1a57e61d753e294ac7967a13b4b Mon Sep 17 00:00:00 2001
From: Marcin Junczys-Dowmunt <Marcin.JunczysDowmunt@microsoft.com>
Date: Wed, 30 Oct 2019 15:05:16 -0700
Subject: [PATCH 753/838] move regression tests

---
 regression-tests | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/regression-tests b/regression-tests
index 66c6f498d..2a5833e41 160000
--- a/regression-tests
+++ b/regression-tests
@@ -1 +1 @@
-Subproject commit 66c6f498d55fbe7a3d524f5697a185000f2d928d
+Subproject commit 2a5833e41110c19f0bbe9f3cf2aa92caad96cf42

From 5ed441f843140d583d7caf7684a5e496aff2f576 Mon Sep 17 00:00:00 2001
From: Young Jin Kim <youki@microsoft.com>
Date: Fri, 1 Nov 2019 17:24:49 +0000
Subject: [PATCH 754/838] Merged PR 9284: Packed model support in production

This PR enables loading and serving of offline packed models.
* Enable support for FBGEMM offline-packed models
* Added different parameters sets for different parameter element types (currently inference only)
* New types (packed*) are used for the packed memory format.
* Added CPU instruction query into the quicksand API.

Related work items: #102572
---
 src/CMakeLists.txt                    |   1 +
 src/command/marian_conv.cpp           |  13 +-
 src/common/binary.cpp                 |  31 ++--
 src/common/binary.h                   |   6 +-
 src/common/config_parser.cpp          |   3 -
 src/common/file_stream.h              |   2 +-
 src/common/filesystem.cpp             |   0
 src/common/io.cpp                     |   3 +-
 src/common/io_item.h                  |   7 +-
 src/common/options.cpp                |   2 +-
 src/common/types.cpp                  |  20 +++
 src/common/types.h                    |  92 ++++++----
 src/graph/auto_tuner.h                |   1 -
 src/graph/chainable.h                 |   1 +
 src/graph/expression_graph.cpp        |   5 +-
 src/graph/expression_graph.h          | 120 +++++++++----
 src/graph/expression_graph_packable.h | 103 ++++++++++++
 src/graph/expression_operators.cpp    | 232 ++++++--------------------
 src/graph/node.h                      |   2 +-
 src/graph/node_initializers.cpp       |  12 +-
 src/graph/parameters.h                |   6 +
 src/layers/generic.cpp                |   2 +-
 src/microsoft/quicksand.cpp           |  68 +++++++-
 src/microsoft/quicksand.h             |  12 ++
 src/rescorer/rescorer.h               |   1 -
 src/tensors/allocator.h               |   6 -
 src/tensors/backend.h                 |   4 -
 src/tensors/cpu/backend.h             |  12 --
 src/tensors/cpu/expanded_gemm.h       |  25 ++-
 src/tensors/cpu/sharp/packed_gemm.cpp |  88 ++++++++--
 src/tensors/cpu/sharp/packed_gemm.h   |  22 ++-
 src/tensors/gpu/backend.h             |  10 --
 src/tensors/gpu/tensor_operators.cu   |  10 +-
 src/tensors/tensor.cpp                |  16 +-
 src/tensors/tensor.h                  |  41 ++---
 src/tensors/tensor_allocator.h        |   9 +-
 src/tests/prod.cpp                    |   1 -
 src/training/communicator.cpp         |   2 +-
 src/translator/translator.h           |   2 -
 vs/Marian.vcxproj                     |  21 ++-
 vs/Marian.vcxproj.filters             |  12 ++
 41 files changed, 615 insertions(+), 411 deletions(-)
 mode change 100755 => 100644 src/common/file_stream.h
 mode change 100755 => 100644 src/common/filesystem.cpp
 create mode 100644 src/common/types.cpp
 mode change 100755 => 100644 src/common/types.h
 create mode 100644 src/graph/expression_graph_packable.h
 mode change 100755 => 100644 src/tensors/cpu/sharp/packed_gemm.cpp

diff --git a/src/CMakeLists.txt b/src/CMakeLists.txt
index 8fcdda496..4562a0ec2 100644
--- a/src/CMakeLists.txt
+++ b/src/CMakeLists.txt
@@ -22,6 +22,7 @@ add_library(marian STATIC
   common/io.cpp
   common/filesystem.cpp
   common/file_stream.cpp
+  common/types.cpp
   
   data/alignment.cpp
   data/vocab.cpp
diff --git a/src/command/marian_conv.cpp b/src/command/marian_conv.cpp
index aeda1e3ca..304a6bd6a 100644
--- a/src/command/marian_conv.cpp
+++ b/src/command/marian_conv.cpp
@@ -4,6 +4,8 @@
 
 #include <sstream>
 
+#include "graph/expression_graph_packable.h"
+
 int main(int argc, char** argv) {
   using namespace marian;
 
@@ -13,16 +15,18 @@ int main(int argc, char** argv) {
   {
     auto cli = New<cli::CLIWrapper>(
         options,
-        "Convert a model in the .npz format to a mmap-able binary model",
+        "Convert a model in the .npz format and normal memory layout to a mmap-able binary model which could be in normal memory layout or packed memory layout",
         "Allowed options",
         "Examples:\n"
-        "  ./marian-conv -f model.npz -t model.bin");
+        "  ./marian-conv -f model.npz -t model.bin --gemm-type fp16packed");
     cli->add<std::string>("--from,-f", "Input model", "model.npz");
     cli->add<std::string>("--to,-t", "Output model", "model.bin");
+    cli->add<std::string>("--gemm-type,-g", "GEMM Type to be used with this weights", "mklfp32");
     cli->parse(argc, argv);
   }
   auto modelFrom = options->get<std::string>("from");
   auto modelTo = options->get<std::string>("to");
+  auto saveGemmType = options->get<std::string>("gemm-type");
 
   LOG(info, "Outputting {}", modelTo);
 
@@ -31,13 +35,14 @@ int main(int argc, char** argv) {
   marian::io::getYamlFromModel(config, "special:model.yml", modelFrom);
   configStr << config;
 
-  auto graph = New<ExpressionGraph>(true);
+  auto graph = New<ExpressionGraphPackable>();
   graph->setDevice(CPU0);
   graph->getBackend()->setOptimized(false);
 
   graph->load(modelFrom);
   graph->forward();
-  graph->save(modelTo, configStr.str());
+  // added a flag if the weights needs to be packed or not
+  graph->packAndSave(modelTo, configStr.str(), /* --gemm-type */ saveGemmType, Type::float32);
 
   // graph->saveBinary(vm["bin"].as<std::string>());
 
diff --git a/src/common/binary.cpp b/src/common/binary.cpp
index d4f443985..654b35162 100644
--- a/src/common/binary.cpp
+++ b/src/common/binary.cpp
@@ -18,6 +18,7 @@ struct Header {
   size_t dataLength;
 };
 
+// cast current void pointer to T pointer and move forward by num elements 
 template <typename T>
 const T* get(const void*& current, size_t num = 1) {
   const T* ptr = (const T*)current;
@@ -32,9 +33,10 @@ void loadItems(const void* current, std::vector<io::Item>& items, bool mapped) {
            binaryFileVersion,
            BINARY_FILE_VERSION);
 
-  size_t numHeaders = *get<size_t>(current);
-  const Header* headers = get<Header>(current, numHeaders);
+  size_t numHeaders = *get<size_t>(current); // number of item headers that follow
+  const Header* headers = get<Header>(current, numHeaders); // read that many headers
 
+  // prepopulate items with meta data from headers
   items.resize(numHeaders);
   for(int i = 0; i < numHeaders; ++i) {
     items[i].type = (Type)headers[i].type;
@@ -42,21 +44,22 @@ void loadItems(const void* current, std::vector<io::Item>& items, bool mapped) {
     items[i].mapped = mapped;
   }
 
+  // read in actual shape and data
   for(int i = 0; i < numHeaders; ++i) {
     size_t len = headers[i].shapeLength;
-    items[i].shape.resize(len);
-    const int* arr = get<int>(current, len);
-    std::copy(arr, arr + len, items[i].shape.begin());
+    items[i].shape.resize(len); 
+    const int* arr = get<int>(current, len); // read shape
+    std::copy(arr, arr + len, items[i].shape.begin()); // copy to Item::shape 
   }
 
-  // move by offset bytes
+  // move by offset bytes, aligned to 256-bytes boundary
   size_t offset = *get<size_t>(current);
   get<char>(current, offset);
 
   for(int i = 0; i < numHeaders; ++i) {
-    if(items[i].mapped) {
+    if(items[i].mapped) { // memory-mapped, hence only set pointer
       items[i].ptr = get<char>(current, headers[i].dataLength);
-    } else {
+    } else { // reading into item data
       size_t len = headers[i].dataLength;
       items[i].bytes.resize(len);
       const char* ptr = get<char>(current, len);
@@ -69,6 +72,7 @@ void loadItems(const std::string& fileName, std::vector<io::Item>& items) {
   // Read file into buffer
   size_t fileSize = filesystem::fileSize(fileName);
   std::vector<char> buf(fileSize);
+// @TODO: check this again:
 #if 1 // for some reason, the #else branch fails with "file not found" in the *read* operation (open succeeds)
   FILE *f = fopen(fileName.c_str(), "rb");
   ABORT_IF(f == nullptr, "Error {} ('{}') opening file '{}'", errno, strerror(errno), fileName);
@@ -119,7 +123,8 @@ void saveItems(const std::string& fileName,
     headers.push_back(Header{item.name.size() + 1,
                              (size_t)item.type,
                              item.shape.size(),
-                             item.size()});
+                             item.size()}); // item size without padding 
+                                            // @TODO: should this be done with padding as asked below?
   }
 
   size_t headerSize = headers.size();
@@ -146,9 +151,11 @@ void saveItems(const std::string& fileName,
   }
 
   // Write out all values
-  for(const auto& item : items) {
-    pos += out.write(item.data(), item.size());
-  }
+  for(const auto& item : items)
+    pos += out.write(item.data(), item.size()); // writes out data without padding, not aligned, @BUGBUG?
+
+    // @TODO: find out if padding should be enforced for memory-mapped storage like this:
+    // pos += out.write(item.data(), item.bytes.size()); // writes out data with padding
 }
 
 }  // namespace binary
diff --git a/src/common/binary.h b/src/common/binary.h
index a15033483..7d73834b5 100644
--- a/src/common/binary.h
+++ b/src/common/binary.h
@@ -5,10 +5,10 @@
 #include <string>
 #include <vector>
 
-// Increase this if binary format changes
-#define BINARY_FILE_VERSION 1
-
 namespace marian {
+
+const static int BINARY_FILE_VERSION = 1;
+
 namespace io {
 namespace binary {
 
diff --git a/src/common/config_parser.cpp b/src/common/config_parser.cpp
index 86174e383..03e08a9e8 100755
--- a/src/common/config_parser.cpp
+++ b/src/common/config_parser.cpp
@@ -607,9 +607,6 @@ void ConfigParser::addOptionsTranslation(cli::CLIWrapper& cli) {
       "Optimize speed aggressively sacrificing memory or precision");
   cli.add<bool>("--skip-cost",
       "Ignore model cost during translation, not recommended for beam-size > 1");
-  cli.add<std::string>("--gemm-type",
-      "Select GEMM options: auto, mklfp32, intrinint16, fp16packed, int8packed",
-      "auto");
   
   cli.add<bool>("--fp16", 
       "Shortcut for mixed precision inference with float16, corresponds to: --precision float16");
diff --git a/src/common/file_stream.h b/src/common/file_stream.h
old mode 100755
new mode 100644
index 08326d5ea..7f360bb04
--- a/src/common/file_stream.h
+++ b/src/common/file_stream.h
@@ -12,7 +12,7 @@
 #pragma GCC diagnostic ignored "-Wsuggest-override"
 #endif
 #ifdef _MSC_VER
-#pragma warning(push)
+#pragma warning(push) // 4101: 'identifier' : unreferenced local variable. One parameter variable in zstr.hpp is not used.
 #pragma warning(disable : 4101)
 #endif
 #include "3rd_party/zstr/zstr.hpp"
diff --git a/src/common/filesystem.cpp b/src/common/filesystem.cpp
old mode 100755
new mode 100644
diff --git a/src/common/io.cpp b/src/common/io.cpp
index ee19105f4..decc4acac 100644
--- a/src/common/io.cpp
+++ b/src/common/io.cpp
@@ -139,8 +139,7 @@ void saveItemsNpz(const std::string& fileName, const std::vector<Item>& items) {
     else
       ABORT("Other types not supported yet");
 
-    npzItems.emplace_back(
-        item.name, item.bytes, shape, type, sizeOf(item.type));
+    npzItems.emplace_back(item.name, item.bytes, shape, type, sizeOf(item.type));
   }
   cnpy::npz_save(fileName, npzItems);
 }
diff --git a/src/common/io_item.h b/src/common/io_item.h
index e10930238..d86c01acc 100755
--- a/src/common/io_item.h
+++ b/src/common/io_item.h
@@ -24,11 +24,8 @@ struct Item {
       return bytes.data();
   }
 
-  size_t size() const {
-    if(mapped)
-      return shape.elements() * sizeOf(type);
-    else
-      return bytes.size();
+  size_t size() const { // @TODO: review this again for 256-bytes boundary alignment
+    return requiredBytes(shape, type);
   }
 
   // Extend this item with data and shape from the input item, creating a flattened concatenation.
diff --git a/src/common/options.cpp b/src/common/options.cpp
index 37bfd5251..0e8ad8a81 100644
--- a/src/common/options.cpp
+++ b/src/common/options.cpp
@@ -49,7 +49,7 @@ namespace marian {
     }
     try {
       return !options_[key].as<std::string>().empty();
-    } catch(const YAML::BadConversion& e) {
+    } catch(const YAML::BadConversion& /* e */) {
       ABORT("Option '{}' is neither a sequence nor a text");
     }
     return false;
diff --git a/src/common/types.cpp b/src/common/types.cpp
new file mode 100644
index 000000000..d17c58f6c
--- /dev/null
+++ b/src/common/types.cpp
@@ -0,0 +1,20 @@
+#include "common/types.h"
+#include "tensors/cpu/sharp/packed_gemm.h"
+
+namespace marian {
+
+// this function calculates the amount of bytes needed to contain a tensor of given shape and type. 
+// For most situation that is trivial (just number of elements time size of single element).
+// But for instance, for intransparent types like packed tensors, it cannot easily be inferred by
+// multiplying. All cases are handed here and can later be passed to allocators etc. 
+size_t requiredBytes(const Shape& shape, Type type) {
+  if(isPacked(type)) {
+    uint64_t packsize;
+    cpu::variant::PackInfoFp32(shape, false, packsize);
+    return (size_t)packsize;
+  } else {
+    return shape.elements() * sizeOf(type);
+  }
+}
+
+}
\ No newline at end of file
diff --git a/src/common/types.h b/src/common/types.h
old mode 100755
new mode 100644
index 86bb108fe..28f0378dd
--- a/src/common/types.h
+++ b/src/common/types.h
@@ -1,5 +1,6 @@
 #pragma once
 #include "common/logging.h" // for ABORT and ABORT_IF
+#include "common/shape.h"
 
 #if __GNUC__ >= 7
 #pragma GCC diagnostic push
@@ -133,6 +134,16 @@ do { \
 
 namespace marian {
 
+// small struct to enable templating based on types use for packing
+struct packed8 {
+  uint8_t x;
+};
+
+// small struct to enable templating based on types use for packing
+struct packed16 {
+  uint16_t x;
+};
+
 #ifndef __CUDACC__ // vectorized types not available from .cu files
 
 // @TODO: check what intrinsics are actually available.
@@ -190,31 +201,38 @@ struct float32x8 {
 };
 #endif
 
+// Internal to types.h, don't use. Use test functions below.
 enum class TypeClass : size_t {
-  signed_type = 0x100,
+  signed_type   = 0x100,
   unsigned_type = 0x200,
-  float_type = 0x400,
-  size_mask = 0x0FF
+  float_type    = 0x400,
+  packed_type   = 0x800, // special packed (CPU cache friendly) type class, used in FBGEMM, not meant to be used anywhere else
+            
+  size_mask     = 0x0FF
 };
 
 constexpr inline size_t operator+(TypeClass typeClass, size_t val) {
   return (size_t)typeClass + val;
 }
 
+// @TODO: rename to ElementType when things become stable, so it's easier to review
 enum class Type : size_t {
-  int8  = TypeClass::signed_type + 1u,
-  int16 = TypeClass::signed_type + 2u,
-  int32 = TypeClass::signed_type + 4u,
-  int64 = TypeClass::signed_type + 8u,
-
-  uint8  = TypeClass::unsigned_type + 1u,
-  uint16 = TypeClass::unsigned_type + 2u,
-  uint32 = TypeClass::unsigned_type + 4u,
-  uint64 = TypeClass::unsigned_type + 8u,
-
-  float16 = TypeClass::float_type + 2u,
-  float32 = TypeClass::float_type + 4u,
-  float64 = TypeClass::float_type + 8u
+  int8     = TypeClass::signed_type + 1u,
+  int16    = TypeClass::signed_type + 2u,
+  int32    = TypeClass::signed_type + 4u,
+  int64    = TypeClass::signed_type + 8u,
+
+  uint8    = TypeClass::unsigned_type + 1u,
+  uint16   = TypeClass::unsigned_type + 2u,
+  uint32   = TypeClass::unsigned_type + 4u,
+  uint64   = TypeClass::unsigned_type + 8u,
+
+  float16  = TypeClass::float_type + 2u,
+  float32  = TypeClass::float_type + 4u,
+  float64  = TypeClass::float_type + 8u,
+
+  packed8  = TypeClass::packed_type + 1u, // special type for FBGEMM, not meant to be used anywhere else, not meant to be accessed invidually. Internal actual type (uint8) is meaningless.
+  packed16 = TypeClass::packed_type + 2u  // special type for FBGEMM, not meant to be used anywhere else, not meant to be accessed invidually. Internal actual type (uint16) is meaningless.
 };
 
 static inline size_t operator&(TypeClass typeClass, Type type) {
@@ -241,23 +259,33 @@ static inline bool isFloat(Type type) {
   return (TypeClass::float_type & type) != 0;
 }
 
+static inline bool isPacked(Type type) {
+  return (TypeClass::packed_type & type) != 0;
+}
+
+size_t requiredBytes(const Shape& shape, Type type); // towards Frank's vision of joint Shape/Type
+
 template <typename T>
 inline bool matchType(Type type);
 
 // clang-format off
-template <> inline bool matchType<int8_t>(Type type)   { return type == Type::int8; }
-template <> inline bool matchType<int16_t>(Type type)  { return type == Type::int16; }
-template <> inline bool matchType<int32_t>(Type type)  { return type == Type::int32; }
-template <> inline bool matchType<int64_t>(Type type)  { return type == Type::int64; }
-
-template <> inline bool matchType<uint8_t>(Type type)  { return type == Type::uint8; }
-template <> inline bool matchType<uint16_t>(Type type) { return type == Type::uint16; }
-template <> inline bool matchType<uint32_t>(Type type) { return type == Type::uint32; }
-template <> inline bool matchType<uint64_t>(Type type) { return type == Type::uint64; }
-
-template <> inline bool matchType<float16>(Type type)  { return type == Type::float16; }
-template <> inline bool matchType<float>(Type type)    { return type == Type::float32; }
-template <> inline bool matchType<double>(Type type)   { return type == Type::float64; }
+template <> inline bool matchType<int8_t>(Type type)   { return type == Type::int8;     }
+template <> inline bool matchType<int16_t>(Type type)  { return type == Type::int16;    }
+template <> inline bool matchType<int32_t>(Type type)  { return type == Type::int32;    }
+template <> inline bool matchType<int64_t>(Type type)  { return type == Type::int64;    }
+
+// In case of packed type, it uses uint8 as underlying memory type
+template <> inline bool matchType<uint8_t>(Type type)  { return type == Type::uint8;    }
+template <> inline bool matchType<uint16_t>(Type type) { return type == Type::uint16;   }
+template <> inline bool matchType<uint32_t>(Type type) { return type == Type::uint32;   }
+template <> inline bool matchType<uint64_t>(Type type) { return type == Type::uint64;   }
+
+template <> inline bool matchType<float16>(Type type)  { return type == Type::float16;  }
+template <> inline bool matchType<float>(Type type)    { return type == Type::float32;  }
+template <> inline bool matchType<double>(Type type)   { return type == Type::float64;  }
+
+template <> inline bool matchType<packed8>(Type type)  { return type == Type::packed8;  }
+template <> inline bool matchType<packed16>(Type type) { return type == Type::packed16; }
 // clang-format on
 
 static inline std::ostream& operator<<(std::ostream& out, Type type) {
@@ -275,6 +303,9 @@ static inline std::ostream& operator<<(std::ostream& out, Type type) {
     case Type::float16 : out << "float16"; break;
     case Type::float32 : out << "float32"; break;
     case Type::float64 : out << "float64"; break;
+
+    case Type::packed8 : out << "packed8"; break;
+    case Type::packed16: out << "packed16"; break;
   }
   return out;
 }
@@ -296,6 +327,9 @@ template <> inline std::string request<uint64_t>() { return "uint64"; }
 template <> inline std::string request<float16>()  { return "float16"; }
 template <> inline std::string request<float>()    { return "float32"; }
 template <> inline std::string request<double>()   { return "float64"; }
+
+template <> inline std::string request<packed8>()  { return "packed8"; }
+template <> inline std::string request<packed16>() { return "packed16"; }
 // clang-format on
 
 static Type inline typeFromString(const std::string& str) {
diff --git a/src/graph/auto_tuner.h b/src/graph/auto_tuner.h
index 7a248bf96..01f330853 100755
--- a/src/graph/auto_tuner.h
+++ b/src/graph/auto_tuner.h
@@ -24,7 +24,6 @@ class AutoTuner : public AutoTunerRecorder {
   // the autotuner runs each algorithm at least this 'collectStatMax' number of times and
   // collects the statistics.
   const size_t collectStatMax = 50;
-
   UPtr<timer::CPUTimer> timer_;
 
   // This structure holds a hash key an algorithm function (e.g. int16, packed gemm, mkl gemm)
diff --git a/src/graph/chainable.h b/src/graph/chainable.h
index 5481ba79f..b78eb485a 100644
--- a/src/graph/chainable.h
+++ b/src/graph/chainable.h
@@ -4,6 +4,7 @@
 
 #include <memory>
 #include <vector>
+#include <list>
 
 namespace marian {
 
diff --git a/src/graph/expression_graph.cpp b/src/graph/expression_graph.cpp
index 369990a28..ed8fef6d7 100755
--- a/src/graph/expression_graph.cpp
+++ b/src/graph/expression_graph.cpp
@@ -1,8 +1,8 @@
 #include "graph/expression_graph.h"
-#include <sstream>
-
 #include "tensors/tensor_operators.h"
 
+#include <sstream>
+
 namespace marian {
 
 ExpressionGraph::ExpressionGraph(bool inference)
@@ -70,7 +70,6 @@ void createSubtape(Expr node) {
   node->setSubtape(subtape);
 }
 
-
 void ExpressionGraph::forwardNext() {
   // @TODO: check if allocation works properly
   tensors_->clearShorttermMemory();
diff --git a/src/graph/expression_graph.h b/src/graph/expression_graph.h
index 02a14e022..d75d069d6 100755
--- a/src/graph/expression_graph.h
+++ b/src/graph/expression_graph.h
@@ -130,11 +130,12 @@ class ExpressionGraph : public std::enable_shared_from_this<ExpressionGraph> {
 
   bool inferenceOnly_{false};
 
-  bool optimized_{false};     // during inference, use optimizations that might lead to precision loss, e.g. 8-bit MatMul.
+  // during inference, use optimizations that might lead to precision loss, e.g. 8-bit MatMul.
+  // At this moment, this is used for int16 qunatized Matmul - 11/1/2019
+  bool optimized_{false};
   bool checkpointing_{false}; // use gradient checkpointing if true
 
   bool reloaded_{false};
-  std::string namespace_;
 
   bool throwNaN_{false};
 
@@ -152,6 +153,8 @@ class ExpressionGraph : public std::enable_shared_from_this<ExpressionGraph> {
   ElementTypeParamsMap paramsByElementType_;
   Ptr<Backend> backend_;
 
+  std::string namespace_;
+
 public:
   /** @brief Constructs a new expression graph
    *
@@ -260,32 +263,64 @@ class ExpressionGraph : public std::enable_shared_from_this<ExpressionGraph> {
     dot.close();
   }
 
+private:
+
+  // Find the named parameter and its typed parent parameter object (params) and return both.
+  // If the parameter is not found return the parent parameter object that the parameter should be added to.
+  // Return [nullptr, nullptr] if no matching parent parameter object exists. 
+  std::tuple<Expr, Ptr<Parameters>> findParams(const std::string& name, 
+                                               Type elementType, 
+                                               bool typeSpecified) const {
+    Expr p; Ptr<Parameters> params;
+    if(typeSpecified) { // type has been specified, so we are only allowed to look for a parameter with that type
+      auto it = paramsByElementType_.find(elementType);
+      if(it != paramsByElementType_.end()) {
+        params = it->second;
+        p = params->get(name);
+      }
+    } else { // type has not been specified, so we take any type as long as the name matches
+      for(auto kvParams : paramsByElementType_) {
+        p = kvParams.second->get(name);
+        
+        if(p) { // p has been found, return with matching params object
+          params = kvParams.second;
+          break;
+        }
+        
+        if(kvParams.first == elementType) // even if p has not been found, set the params object to be returned
+          params = kvParams.second;
+      }
+    }
+
+    return std::make_tuple(p, params);
+  }
+
   Expr param(const std::string& pname,
              const Shape& shape,
              const Ptr<inits::NodeInitializer>& init,
-             const Type valueType,
-             bool fixed = false) {
+             const Type elementType,
+             bool fixed,
+             bool typeSpecified) {
     std::string name = pname;
     if(!namespace_.empty())
       name = namespace_ + "::" + name;
 
-    // check first if parameter already exists
-    auto it = paramsByElementType_.find(valueType);
-    Ptr<Parameters> params = it != paramsByElementType_.end() ? it->second : nullptr;
+    Expr p; Ptr<Parameters> params; std::tie
+    (p, params) = findParams(name, elementType, typeSpecified);
+    
     if(!params) { 
-      params = New<Parameters>(valueType);
+      params = New<Parameters>(elementType);
       params->init(backend_);
-      paramsByElementType_.insert({valueType, params});
+      paramsByElementType_.insert({elementType, params});
     } else {
-      Expr p = params->get(name);
       if(p) {
         // if yes add to tape and return
         ABORT_IF(shape != p->shape(),
-                "Requested shape {} for existing parameter '{}' does not match "
-                "original shape {}",
-                shape,
-                name,
-                p->shape());
+                 "Requested shape {} for existing parameter '{}' does not match "
+                 "original shape {}",
+                 shape,
+                 name,
+                 p->shape());
 
         p->setTrainable(!fixed);
         add(p);
@@ -295,16 +330,16 @@ class ExpressionGraph : public std::enable_shared_from_this<ExpressionGraph> {
 
     // if graph was reloaded do not allow creation of new parameters
     ABORT_IF(reloaded_,
-             "Graph was reloaded and parameter '{}' is newly created",
-             name);
+             "Graph was reloaded and parameter '{}' with type {} (specified: {}) is newly created",
+             name, elementType, typeSpecified);
 
     // if not check if name is not taken by other node
     auto other = get(name);
     ABORT_IF(other, "Parameter with name '{}' already exists and has type {}", name, other->value_type());
 
     // create parameter node (adds to tape)
-    Expr p = Expression<ParamNode>(shared_from_this(), shape, init, valueType, fixed);
-    LOG(debug, "Created parameter {} with shape {} and type {}", pname, shape, valueType);
+    p = Expression<ParamNode>(shared_from_this(), shape, init, elementType, fixed);
+    LOG(debug, "Created parameter {} with shape {} and type {}", name, shape, elementType);
 
     // set name and id and add to list of parameters
     p->set_name(name);
@@ -313,17 +348,28 @@ class ExpressionGraph : public std::enable_shared_from_this<ExpressionGraph> {
     return p;
   }
 
+public:
+  Expr param(const std::string& pname,
+             const Shape& shape,
+             const Ptr<inits::NodeInitializer>& init,
+             const Type elementType,
+             bool fixed = false) {
+    // since this param is called with out a specified type, we assume defaultElementType but allow to check for a different type
+    return param(pname, shape, init, elementType, fixed, /*typeSpecified=*/true);
+  }
+
   Expr param(const std::string& pname,
              const Shape& shape,
              const Ptr<inits::NodeInitializer>& init,
              bool fixed = false) {
-    return param(pname, shape, init, defaultElementType_, fixed);
+    // since this param is called with out a specified type, we assume defaultElementType but allow to check for a different type
+    return param(pname, shape, init, defaultElementType_, fixed, /*typeSpecified=*/false);
   }
 
   Expr constant(const Shape& shape,
                 const Ptr<inits::NodeInitializer>& init,
-                Type valueType) {
-    return Expression<ConstantNode>(shared_from_this(), shape, init, valueType);
+                Type elementType) {
+    return Expression<ConstantNode>(shared_from_this(), shape, init, elementType);
   }
 
   Expr constant(const Shape& shape,
@@ -351,32 +397,38 @@ class ExpressionGraph : public std::enable_shared_from_this<ExpressionGraph> {
                     Type::uint32);
   }
 
-  Expr ones(const Shape& shape, Type valueType) {
-    return constant(shape, inits::ones(), valueType);
+  Expr ones(const Shape& shape, Type elementType) {
+    return constant(shape, inits::ones(), elementType);
   }
   Expr ones(const Shape& shape) {
     return constant(shape, inits::ones(), defaultElementType_);
   }
 
-  Expr zeros(const Shape& shape, Type valueType) {
-    return constant(shape, inits::zeros(), valueType);
+  Expr zeros(const Shape& shape, Type elementType) {
+    return constant(shape, inits::zeros(), elementType);
   }
   Expr zeros(const Shape& shape) {
     return constant(shape, inits::zeros(), defaultElementType_);
   }
 
   // prob = dropProb, e.g. 0.1 means 90% of values are kept
-  Expr dropoutMask(float dropProb, const Shape& shape, Type valueType);
+  Expr dropoutMask(float dropProb, const Shape& shape, Type elementType);
   Expr dropoutMask(float dropProb, const Shape& shape);
 
   Expr get(std::string name) {
     if(!namespace_.empty())
       name = namespace_ + "::" + name;
+    Expr p; Ptr<Parameters> params; std::tie
+    (p, params) = findParams(name, defaultElementType_, /*specifiedType=*/false);
+    return p;
+  }
 
-    for(auto kvParams : paramsByElementType_)
-      return kvParams.second->get(name);
-
-    return Expr();
+  Expr get(std::string name, Type specifiedElementType) {
+    if(!namespace_.empty())
+      name = namespace_ + "::" + name;
+    Expr p; Ptr<Parameters> params; std::tie
+    (p, params) = findParams(name, specifiedElementType, /*specifiedType=*/true);
+    return p;
   }
 
   Ptr<Parameters>& params() { 
@@ -445,7 +497,7 @@ class ExpressionGraph : public std::enable_shared_from_this<ExpressionGraph> {
       // skip over special parameters starting with "special:"
       if(pName.substr(0, 8) == "special:")
         continue;
-      param(pName, item.shape, inits::fromItem(item));
+      param(pName, item.shape, inits::fromItem(item), item.type, /*fixed=*/false);
     }
     if(markReloaded)
       setReloaded(true);
@@ -488,15 +540,11 @@ class ExpressionGraph : public std::enable_shared_from_this<ExpressionGraph> {
   void save(std::vector<io::Item>& ioItems, Type saveElementType = Type::float32);
 
   void save(const std::string& name, const std::string& meta = "", Type saveElementType = Type::float32) {
-    // LOG(info, "Saving model to {}", name);
-
     std::vector<io::Item> ioItems;
     save(ioItems, saveElementType);
     if(!meta.empty())
       io::addMetaToItems(meta, "special:model.yml", ioItems);
     io::saveItems(name, ioItems);
-
-    // LOG(info, "Saved {} items.", ioItems.size());
   }
 };
 
diff --git a/src/graph/expression_graph_packable.h b/src/graph/expression_graph_packable.h
new file mode 100644
index 000000000..62569cfa6
--- /dev/null
+++ b/src/graph/expression_graph_packable.h
@@ -0,0 +1,103 @@
+#pragma once
+
+#include "graph/expression_graph.h"
+#include "tensors/cpu/sharp/packed_gemm.h"
+
+namespace marian {
+
+// When FBGEMM based packed GEMM is used, some weight matrices need to be packed offline.
+// The decision which weights can be packed or not should be done walking through the graph.
+// This requires some more changes, but we temporarily do this just by name ("_W") of the weights.
+// And, this introduces a low level packed_gemm.h apis interact with high level graph class.
+// So, we make a subclass of ExpressionGraph and put those immature codes in this class.
+// We will improve this in the near future. 
+class ExpressionGraphPackable : public ExpressionGraph {
+public:
+  ExpressionGraphPackable()
+    : ExpressionGraph( /* inference =  */ true) {} // Packable expression graph only supports inference
+
+  virtual ~ExpressionGraphPackable() {}
+
+  // Convert model weights into packed format and save to IO items.
+  // @TODO: review this
+  void packAndSave(const std::string& name, const std::string& meta, std::string& saveGemmType, Type saveElementType = Type::float32) {
+    std::vector<io::Item> ioItems;
+
+    // sorted by name in std::map
+    for (auto p : params()->getMap()) {
+      std::string pName = p.first;
+
+      if (!namespace_.empty()) {
+        if (pName.substr(0, namespace_.size() + 2) == namespace_ + "::")
+          pName = pName.substr(namespace_.size() + 2);
+      }
+
+      Tensor val = p.second->val();
+
+      // save as packed format
+      // @TODO Hardcoded to find packable weights - all the weights used for affine op
+      if (saveGemmType == "fp16packed" && pName.find("_W") == pName.length() - 3) {
+        using namespace marian::cpu::variant;
+
+        // packing information
+        int nrow, ncol, kernel_ncol_blocks, brow, bcol, last_brow, nbrow, nbcol;
+        uint64_t packsize;
+
+        PackInfoFp32(val->shape(),
+          false,
+          nrow,
+          ncol,
+          kernel_ncol_blocks,
+          brow,
+          bcol,
+          last_brow,
+          nbrow,
+          nbcol,
+          packsize);
+
+        auto allocator = New<TensorAllocator>(getBackend());
+
+        Tensor packedTensor;
+        allocator->allocate(packedTensor, { 1, (int32_t)packsize }, Type::uint8);
+
+        // PackFp32
+        PackFp32(packedTensor,
+          val->data(),
+          false,
+          nrow,
+          ncol,
+          kernel_ncol_blocks,
+          brow,
+          bcol,
+          last_brow,
+          nbrow,
+          nbcol,
+          packsize);
+        io::Item item;
+        item.name = pName;
+        item.shape = val->shape();
+        item.type = Type::packed16;
+
+        // Use the actual memory as this will be aligned and padded.
+        // When memory mapping this is required. Shape keeps track of
+        // tensor size. Saving to *.npz will cut to size.
+        auto mem = packedTensor->memory();
+        item.bytes.resize(mem->size());
+        copy(backend_, mem->data<char>(), mem->data<char>() + mem->size(), item.bytes.data());
+
+        ioItems.emplace_back(std::move(item));
+      } else {
+        io::Item item;
+        val->get(item, pName);
+        item.convert(saveElementType);
+        ioItems.emplace_back(std::move(item));
+      }
+    }
+
+    if (!meta.empty())
+      io::addMetaToItems(meta, "special:model.yml", ioItems);
+    io::saveItems(name, ioItems);
+  }
+};
+
+}  // namespace marian
\ No newline at end of file
diff --git a/src/graph/expression_operators.cpp b/src/graph/expression_operators.cpp
index f27942fcd..a527f09da 100755
--- a/src/graph/expression_operators.cpp
+++ b/src/graph/expression_operators.cpp
@@ -403,8 +403,7 @@ Expr dot(Expr a, Expr b, bool transA, bool transB, float scale) {
 
   // Currently only true when command line options
   // --optimize --cpu-thread=N with N > 0 are set.
-  if(device == DeviceType::cpu && a->graph()->getBackend()->isOptimized()
-     && a->graph()->getBackend()->getGemmType() == GemmType::IntrinInt16) {
+  if(device == DeviceType::cpu && a->graph()->getBackend()->isOptimized()) {
     // dotInt16 computes A * B.T, hence the transpose for B to get A * B
     // if transA = false and transB = false.
 
@@ -422,198 +421,73 @@ Expr bdot(Expr a, Expr b, bool transA, bool transB, float scale) {
   return Expression<DotBatchedNodeOp>(a, b, transA, transB, scale);
 }
 
+static Expr affineDefault(Expr a, Expr b, Expr bias, bool transA, bool transB, float scale) {
+  // general version, MKL, CBlas or CUDA
+
+  // if clipValue > 0, the inputs will be clipped to range [-clipValue,
+  // clipValue] This is meant to keep values at the same range as used during
+  // training when optimizing for 8-bit integer products. Likely to be removed
+  // in the future when we explore better ways to handle this.
+  float clipValue = a->graph()->getBackend()->getClip();
+
+  int rows = a->shape().elements() / a->shape()[-1];
+  Expr ones = a->graph()->ones({ rows, 1 });
+  std::vector<Expr> nodes
+    = { clip(a, clipValue), clip(b, clipValue), bias, ones };
+  return Expression<AffineNodeOp>(nodes, transA, transB, scale);
+}
+
+// This operation used to implement auto-tuning. We have removed it for now due to complexity, but plan to revisit it in the future. 
+// The last branch with auto-tuner is: 
+// youki/packed-model-pr-backup1031
+// https://machinetranslation.visualstudio.com/Marian/_git/marian-dev?version=GByouki%2Fpacked-model-pr-backup1031
+// SHA: 3456a7ed1d1608cfad74cd2c414e7e8fe141aa52
 Expr affine(Expr a, Expr b, Expr bias, bool transA, bool transB, float scale) {
   auto device = a->graph()->getDeviceId().type;
 
   float clipValue = a->graph()->getBackend()->getClip();
+  Type aElementType = a->value_type();
+  Type bElementType = b->value_type();
 
-  if(device == DeviceType::cpu && a->graph()->getBackend()->isOptimized()) {
-    GemmType gemmType = a->graph()->getBackend()->getGemmType();
-    // When gemmType is set to 'auto', an autotuner decides the best algorithm available.
-    // A new autotuner is created, then different kinds of algorithms are added to the autotuner.
-    // For each GEMM size, there is a unique hash key.
-    // (e.g. m, n, k, transpose A, transpose B, bias size for GEMM)
-    if(gemmType == GemmType::Auto) {
-      thread_local Ptr<AutoTuner<Expr>> tuner = New<AutoTuner<Expr>>();
-
-      // start with new set of algorithms
-      tuner->clear();
-
-      // lower precicion for shapes, reduces data sparsity
-      auto sh = [](Shape sh) {
-        for(size_t i = 0; i < sh.size(); ++i)
-          sh.set(i, sh[i] / 4);
-        return sh;
-      };
-
-      // create context for current call as hash
-      std::size_t hash = sh(a->shape()).hash();
-      util::hash_combine(hash, sh(b->shape()).hash());
-      util::hash_combine(hash, sh(bias->shape()).hash());
-      util::hash_combine(hash, transA);
-      util::hash_combine(hash, transB);
-
+  if(device == DeviceType::cpu) {
+    if(isFloat(aElementType) && isFloat(bElementType)) {
+      if(a->graph()->getBackend()->isOptimized()) {
+        // cpu int16 version
+        return cpu::int16::affine(
+          cpu::int16::quantize(transA ? transpose(a) : a, clipValue),
+          cpu::int16::quantize(transB ? b : transpose(b), clipValue),
+          bias,
+          scale);
+      } else {
+        return affineDefault(a, b, bias, transA, transB, scale);
+      }
+    } else if(isFloat(aElementType) && isPacked(bElementType)) {
 #if USE_FBGEMM
-      // Use Packed GEMM only if the node b in the graph is memoized.
-      // More specifically, packed GEMM is used only if the B matrix (weight) is constant.
-      // In general, 'memoized' means that the node is a constant variable or
-      // a combination of contant nodes which is also a constant variable
-      // when it's computed once.
-      // Those memoized nodes are cached to avoid duplicated computations.
       // 07/10/2019 - Use packed GEMM only if the cpu architecture supports AVX2
       // one of the fbgemm's sub modules, cpuinfo (https://github.com/pytorch/cpuinfo).
-      // It looks at the cpu register 
+      // It looks at the cpu register
       // (https://github.com/pytorch/cpuinfo/blob/master/src/x86/isa.c#L391),
       // and this cpu lookup is executed only once and the state is kept in FBGEMM.
-      if(fbgemm::fbgemmHasAvx2Support() && b->memoize()) {
-        // add packed GEMM algorithm variant (Packed GEMM) to the autotuner
-        // Once an algorithm is added to the autotuner,
-        // autotuner runs all the added algorithms for a designated times.
-        // One algorithm is run per one this operation call
-        // and the stat for that algorithm is collected.
-        // When all the algorithms reach the maximum stat collection count,
-        // the autotuner decide the best algorithm, and keep using it afterward.
-        size_t hashPack = hash;
-        util::hash_combine(hashPack, 1);
-        auto recPack = [=](Expr e, bool stop = false) {
-          e->record(tuner, hashPack, stop);
-          return e;
-        };
-
-        auto algPack = [=]() {
-          auto packed = cpu::variant::pack(b, cpu::variant::PackMatrix::B, transB, clipValue);
-
-          return recPack(
-              cpu::variant::affine(
-                  clip(a, clipValue),
-                  packed,
-                  b->shape(),
-                  bias,
-                  transA,
-                  transB,
-                  scale),
-              true);
-        };
-        tuner->insert({hashPack, algPack});
+      if(fbgemm::fbgemmHasAvx2Support()) {
+        return cpu::variant::affine(clip(a, clipValue),
+                                    b,
+                                    b->shape(),
+                                    bias,
+                                    transA,
+                                    transB,
+                                    scale);
+      } else {
+        ABORT("No on-the-fly packing at the moment");
       }
-#endif // USE_FBGEMM
-
-      // add second algorithm variant (Int16) to the autotuner
-      size_t hashInt16 = hash;
-      util::hash_combine(hashInt16, 2);
-      auto recInt16 = [=](Expr e, bool stop = false) {
-        e->record(tuner, hashInt16, stop);
-        return e;
-      };
-      auto algInt16 = [=]() {
-        return recInt16(
-            cpu::int16::affine(
-                recInt16(
-                    cpu::int16::quantize(
-                        transA ? recInt16(transpose(a)) : a,
-                        clipValue)),
-                cpu::int16::quantize(
-                    transB ? b : transpose(b),
-                    clipValue),
-                bias,
-                scale),
-            true);
-      };
-      tuner->insert({hashInt16, algInt16});
-
-      // add third algorithm variant (CBlas) to the autotuner
-      size_t hashCblas = hash;
-      util::hash_combine(hashCblas, 3);
-      auto recCblas = [=](Expr e, bool stop = false) {
-        e->record(tuner, hashCblas, stop);
-        return e;
-      };
-
-      auto algCblas = [=]() {
-        auto ac = clip(a, clipValue);
-        if(ac != a)
-          ac = recCblas(ac);
-
-        auto bc = clip(b, clipValue);
-        if(bc != b)
-          bc = recCblas(bc);
-
-        int rows = ac->shape().elements() / ac->shape()[-1];
-        Expr ones = ac->graph()->ones({rows, 1}, bias->value_type());
-        std::vector<Expr> nodes = {ac, bc, bias, ones};
-        return recCblas(Expression<AffineNodeOp>(nodes, transA, transB, scale),
-                        true);
-      };
-      tuner->insert({hashCblas, algCblas});
-
-      // execute algorithm with autotuning
-      return tuner->run();
-    } else {
-      if(gemmType == GemmType::IntrinInt16) {
-        // cpu int16 version
-        return cpu::int16::affine(
-            cpu::int16::quantize(transA ? transpose(a) : a, clipValue),
-            cpu::int16::quantize(transB ? b : transpose(b), clipValue),
-            bias,
-            scale);
-      } else if(gemmType == GemmType::FbFp16Packed) {
-#if USE_FBGEMM
-        // 07/10/2019 - Use packed GEMM only if the cpu architecture supports AVX2
-        // one of the fbgemm's sub modules, cpuinfo (https://github.com/pytorch/cpuinfo).
-        // It looks at the cpu register
-        // (https://github.com/pytorch/cpuinfo/blob/master/src/x86/isa.c#L391),
-        // and this cpu lookup is executed only once and the state is kept in FBGEMM.
-        if(fbgemm::fbgemmHasAvx2Support() && b->memoize()) {
-          auto packed = cpu::variant::pack(b, cpu::variant::PackMatrix::B, transB, clipValue);
-
-          return cpu::variant::affine(
-              clip(a, clipValue),
-              packed,
-              b->shape(),
-              bias,
-              transA,
-              transB,
-              scale);
-        } else {
-          int rows = a->shape().elements() / a->shape()[-1];
-          Expr ones = a->graph()->ones({rows, 1}, bias->value_type());
-          std::vector<Expr> nodes = {clip(a, clipValue), clip(b, clipValue), bias, ones};
-          return Expression<AffineNodeOp>(nodes, transA, transB, scale);
-        }
 #else
-        ABORT("Packed GEMM is not available in this build");
+      ABORT("Packed GEMM is not available in this build");
 #endif  // USE_FBGEMM
-
-      } else if(gemmType == GemmType::MklFp32) {
-        // general version, MKL, CBlas or CUDA
-
-        // if clipValue > 0, the inputs will be clipped to range [-clipValue,
-        // clipValue] This is meant to keep values at the same range as used during
-        // training when optimizing for 8-bit integer products. Likely to be removed
-        // in the future when we explore better ways to handle this.
-
-        int rows = a->shape().elements() / a->shape()[-1];
-        Expr ones = a->graph()->ones({rows, 1}, bias->value_type());
-        std::vector<Expr> nodes
-            = {clip(a, clipValue), clip(b, clipValue), bias, ones};
-        return Expression<AffineNodeOp>(nodes, transA, transB, scale);
-      } else {
-        ABORT("GemmType..{} not available by affine()", gemmType);
-      }
+    } else {
+      ABORT("Combination of types A: {} B: {} not supported", aElementType, bElementType);
     }
   } else {
-    // general version, MKL, CBlas or CUDA
-
-    // if clipValue > 0, the inputs will be clipped to range [-clipValue,
-    // clipValue] This is meant to keep values at the same range as used during
-    // training when optimizing for 8-bit integer products. Likely to be removed
-    // in the future when we explore better ways to handle this.
-
-    int rows = a->shape().elements() / a->shape()[-1];
-    Expr ones = a->graph()->ones({rows, 1});
-    std::vector<Expr> nodes
-        = {clip(a, clipValue), clip(b, clipValue), bias, ones};
-    return Expression<AffineNodeOp>(nodes, transA, transB, scale);
+    // Default GEMM
+    return affineDefault(a, b, bias, transA, transB, scale);
   }
 }
 
diff --git a/src/graph/node.h b/src/graph/node.h
index e4c3ea02d..4d591674f 100644
--- a/src/graph/node.h
+++ b/src/graph/node.h
@@ -85,7 +85,7 @@ class Node : public Chainable<Tensor> {
   virtual void setId(size_t id) override { id_ = id; }
 
   virtual size_t getId() override { return id_; }
-
+
   virtual void increaseEdges(size_t edges = 1) { edges_ += edges; };
   virtual void decreaseEdges(size_t edges = 1) { edges_ -= edges; };
   virtual size_t edges() { return edges_; };
diff --git a/src/graph/node_initializers.cpp b/src/graph/node_initializers.cpp
index 9d6af92ff..c2332906f 100755
--- a/src/graph/node_initializers.cpp
+++ b/src/graph/node_initializers.cpp
@@ -39,7 +39,7 @@ class LambdaInitConvert : public NodeInitializer {
         auto sharedAllocator = allocator_.lock();
         ABORT_IF(!sharedAllocator, "Allocator in LambdaInitConvert has not been set or expired");
 
-        auto memory = sharedAllocator->alloc(tensor->size(), intermediateType_);
+        auto memory = sharedAllocator->alloc(requiredBytes(tensor->shape(), intermediateType_));
         auto temp = TensorBase::New(memory,
                                     tensor->shape(),
                                     intermediateType_,
@@ -183,11 +183,11 @@ Ptr<NodeInitializer> fromItem(const io::Item& item) {
                "Tensor type ({}) and type for mapping ({}) do not match",
                tensor->type(),
                item.type);
-      ABORT_IF(tensor->size() != item.size() / sizeOf(item.type),
-               "Tensor size ({}) and mapped size ({}) do not match",
-               tensor->size(),
-               item.size() / sizeOf(item.type));
-      auto mp = MemoryPiece::New((uint8_t*)item.ptr, tensor->size() * sizeOf(item.type));
+      ABORT_IF(tensor->shape() != item.shape,
+               "Tensor shape ({}) and shape of mapped item ({}) do not match",
+               tensor->shape(),
+               item.shape);
+      auto mp = MemoryPiece::New((uint8_t*)item.ptr, item.size()); // @TODO: this is not properly aligned now
       tensor->reset(mp);
     });
   } else {
diff --git a/src/graph/parameters.h b/src/graph/parameters.h
index 2f460a313..ca3905490 100644
--- a/src/graph/parameters.h
+++ b/src/graph/parameters.h
@@ -40,6 +40,10 @@ class Parameters {
     LOG(debug, "Created parameter object of type {}", acceptedElementType_);
   }
 
+  ~Parameters() {
+    LOG(debug, "Destroyed parameter object of type {}", acceptedElementType_);
+  }
+
   auto begin() -> decltype(params_.begin()) { return params_.begin(); }
 
   auto end() -> decltype(params_.begin()) { return params_.end(); }
@@ -58,6 +62,8 @@ class Parameters {
   size_t size() { return params_.size(); }
 
   void add(Expr p, const std::string& name) {
+    LOG(debug, "Adding parameter {} to parameter object of type {}", name, acceptedElementType_);
+
     ABORT_IF(named_.count(name), "Parameter '{}' already exists", name);
     ABORT_IF(p->value_type() != acceptedElementType_,
              "Requested parameter type ({}) is different from chosen parameter type ({})",
diff --git a/src/layers/generic.cpp b/src/layers/generic.cpp
index d8df32544..09cd27b62 100755
--- a/src/layers/generic.cpp
+++ b/src/layers/generic.cpp
@@ -281,7 +281,7 @@ namespace marian {
             factorB  = slice(b_,                              -1, Slice((int)range.first, (int)range.second));
           }
           // @TODO: b_ should be a vector, not a matrix; but shotlists use cols() in, which requires a matrix
-          auto factorLogits = affine(input1, factorWt, factorB, false, /*transB=*/isLegacyUntransposedW ? false : true); // [B... x U] factor logits
+          auto factorLogits = affine(input1, factorWt, factorB, false, /*transB=*/isLegacyUntransposedW ? false : true, /*scale=*/1.0f); // [B... x U] factor logits
           // optionally add lemma-dependent bias
           if (Plemma) { // [B... x U0]
             int lemmaVocabDim = Plemma->shape()[-1];
diff --git a/src/microsoft/quicksand.cpp b/src/microsoft/quicksand.cpp
index 4a9a032e6..20cb1bcc0 100755
--- a/src/microsoft/quicksand.cpp
+++ b/src/microsoft/quicksand.cpp
@@ -10,6 +10,11 @@
 #include "translator/scorers.h"
 #include "data/alignment.h"
 #include "data/vocab_base.h"
+#include "graph/expression_graph_packable.h"
+
+#if USE_FBGEMM
+#include "fbgemm/Utils.h"
+#endif
 
 namespace marian {
 
@@ -69,10 +74,6 @@ class BeamSearchDecoder : public IBeamSearchDecoder {
     device_ = New<cpu::WrappedDevice>(deviceId);
     graph_->setDevice(deviceId, device_);
 
-    // Use packed GEMM for the production
-    graph_->getBackend()->setOptimized(true);
-    graph_->getBackend()->setGemmType("fp16packed");
-
 #if MKL_FOUND
     mkl_set_num_threads(options->get<int>("mkl-threads", 1));
 #endif
@@ -211,5 +212,64 @@ std::vector<Ptr<IVocabWrapper>> loadVocabs(const std::vector<std::string>& vocab
   return res;
 }
 
+// query CPU AVX version
+DecoderCpuAvxVersion getCpuAvxVersion() {
+#if USE_FBGEMM
+  // Default value is AVX
+  DecoderCpuAvxVersion cpuAvxVer = DecoderCpuAvxVersion::AVX;
+  if (fbgemm::fbgemmHasAvx512Support())
+    cpuAvxVer = DecoderCpuAvxVersion::AVX512;
+  else if (fbgemm::fbgemmHasAvx2Support())
+    cpuAvxVer = DecoderCpuAvxVersion::AVX2;
+
+  return cpuAvxVer;
+#else
+  // Default value is AVX
+  return DecoderCpuAvxVersion::AVX;
+#endif
+}
+
+DecoderCpuAvxVersion parseCpuAvxVersion(std::string name) {
+  if (name == "avx") {
+    return DecoderCpuAvxVersion::AVX;
+  } else if (name == "avx2") {
+    return DecoderCpuAvxVersion::AVX2;
+  } else if (name == "avx512") {
+    return DecoderCpuAvxVersion::AVX512;
+  } else {
+    ABORT("Unknown CPU Instruction Set: {}", name);
+    return DecoderCpuAvxVersion::AVX;
+  }
+}
+
+// @TODO: clean-up this code and unify with marian-conv. The targetPrec parameter is not clear enought etc. 
+bool convertModel(std::string inputFile, std::string outputFile, int32_t targetPrec) {
+  std::cout << "Converting from: " << inputFile << ", to: " << outputFile << std::endl;
+
+  YAML::Node config;
+  std::stringstream configStr;
+  marian::io::getYamlFromModel(config, "special:model.yml", inputFile);
+  configStr << config;
+
+  auto graph = New<ExpressionGraphPackable>();
+  graph->setDevice(CPU0);
+  graph->getBackend()->setOptimized(false);
+
+  graph->load(inputFile);
+  graph->forward();
+  std::string saveGemmType = "fp32default";
+  if (targetPrec == 16)
+    saveGemmType = "fp16packed";
+  else if (targetPrec == 8)
+    saveGemmType = "int8packed";
+
+  // added a flag if the weights needs to be packed or not
+  graph->packAndSave(outputFile, configStr.str(), saveGemmType); // @TODO: this should just be type-based
+
+  std::cout << "Conversion Finished." << std::endl;
+
+  return true;
+}
+
 }  // namespace quicksand
 }  // namespace marian
diff --git a/src/microsoft/quicksand.h b/src/microsoft/quicksand.h
index ffab6c0d4..1089be148 100755
--- a/src/microsoft/quicksand.h
+++ b/src/microsoft/quicksand.h
@@ -25,6 +25,12 @@ typedef std::tuple<WordIndices, AlignmentSets, float> QSSentenceWithProb;
 typedef std::vector<QSSentenceWithProb> QSNBest;
 typedef std::vector<QSNBest> QSNBestBatch;
 
+enum class DecoderCpuAvxVersion {
+  AVX,
+  AVX2,
+  AVX512
+};
+
 Ptr<Options> newOptions();
 
 template <class T>
@@ -64,5 +70,11 @@ Ptr<IBeamSearchDecoder> newDecoder(Ptr<Options> options,
 // load src and tgt vocabs
 std::vector<Ptr<IVocabWrapper>> loadVocabs(const std::vector<std::string>& vocabPaths);
 
+// query CPU AVX version
+DecoderCpuAvxVersion getCpuAvxVersion();
+DecoderCpuAvxVersion parseCpuAvxVersion(std::string name);
+
+bool convertModel(std::string inputFile, std::string outputFile, int32_t targetPrec);
+
 }  // namespace quicksand
 }  // namespace marian
diff --git a/src/rescorer/rescorer.h b/src/rescorer/rescorer.h
index e81b3721a..77e63bfc7 100755
--- a/src/rescorer/rescorer.h
+++ b/src/rescorer/rescorer.h
@@ -77,7 +77,6 @@ class Rescore : public ModelTask {
       graph->getBackend()->setClip(options_->get<float>("clip-gemm"));
       if (device.type == DeviceType::cpu) {
         graph->getBackend()->setOptimized(options_->get<bool>("optimize"));
-        graph->getBackend()->setGemmType(options_->get<std::string>("gemm-type"));
       }
 
       graph->reserveWorkspaceMB(options_->get<size_t>("workspace"));
diff --git a/src/tensors/allocator.h b/src/tensors/allocator.h
index 8dde088d8..9dc44f58e 100644
--- a/src/tensors/allocator.h
+++ b/src/tensors/allocator.h
@@ -193,12 +193,6 @@ class Allocator {
     return alignedSize(num * sizeof(T));
   }
 
-  size_t capacity(size_t num, Type type) { return alignedSize(num * sizeOf(type)); }
-
-  MemoryPiece::PtrType alloc(size_t num, Type type) {
-    return alloc(num * sizeOf(type));
-  }
-
   template <typename T>
   MemoryPiece::PtrType alloc(size_t num) {
     return alloc(capacity<T>(num));
diff --git a/src/tensors/backend.h b/src/tensors/backend.h
index ff6da92f0..8a9b2f906 100644
--- a/src/tensors/backend.h
+++ b/src/tensors/backend.h
@@ -41,10 +41,6 @@ class Backend {
   // for GPU, this is invalid. for gpu, isOptimized() function always returns false.
   virtual void setOptimized(bool optimize) = 0;
   virtual bool isOptimized() = 0;
-  // for CPU, selects different GEMM types for the inference.
-  // for GPU, there's no gemm type. so, it does nothing.
-  virtual void setGemmType(std::string gemmType) = 0;
-  virtual GemmType getGemmType() = 0;
 };
 
 Ptr<Backend> BackendByDeviceId(DeviceId deviceId, size_t seed);
diff --git a/src/tensors/cpu/backend.h b/src/tensors/cpu/backend.h
index dd496843a..9414fbd2a 100644
--- a/src/tensors/cpu/backend.h
+++ b/src/tensors/cpu/backend.h
@@ -22,18 +22,6 @@ class Backend : public marian::Backend {
   // for CPU & inference only, sets to use optimized code for inference. Does nothing for GPU.
   void setOptimized(bool optimize) override { optimized_ = optimize; }
   bool isOptimized() override { return optimized_; }
-  // for CPU only, selects different GEMM types for the inference. Does nothing for GPU.
-  void setGemmType(std::string gemmType) override {
-    if      (gemmType == "auto")        gemmType_ = GemmType::Auto;
-    else if (gemmType == "mklfp32")     gemmType_ = GemmType::MklFp32;
-    else if (gemmType == "intrinint16") gemmType_ = GemmType::IntrinInt16;
-#if USE_FBGEMM
-    else if (gemmType == "fp16packed")  gemmType_ = GemmType::FbFp16Packed;
-    else if (gemmType == "int8packed")  gemmType_ = GemmType::FbInt8Packed;
-#endif // USE_FBGEMM
-    else ABORT("Unknown GEMM type - '{}'", gemmType);
-  }
-  GemmType getGemmType() override { return gemmType_; }
 };
 }  // namespace cpu
 }  // namespace marian
diff --git a/src/tensors/cpu/expanded_gemm.h b/src/tensors/cpu/expanded_gemm.h
index 7bdf4a411..c9a119c37 100644
--- a/src/tensors/cpu/expanded_gemm.h
+++ b/src/tensors/cpu/expanded_gemm.h
@@ -4,7 +4,6 @@
 #include "tensors/cpu/sharp/packed_gemm.h"
 
 #if USE_FBGEMM
-
 #ifdef __GNUC__
 #pragma GCC diagnostic push
 #pragma GCC diagnostic ignored "-Wunused-variable"
@@ -70,7 +69,7 @@ struct PackNodeOp : public UnaryNodeOp {
 
   NodeOps forwardOps() override {
     return {NodeOp(PackFp32(val_,
-                            child(0)->val(),
+                            child(0)->val()->data(),
                             transpose_,
                             nrow_,
                             ncol_,
@@ -97,17 +96,17 @@ struct PackNodeOp : public UnaryNodeOp {
     // Should be 2D - weight matrix
     ABORT_IF(shapeMat.size() != 2,
              "Weight Matrix should be 2D");
-    nrow_ = transpose ? shapeMat[1] : shapeMat[0];
-    ncol_ = transpose ? shapeMat[0] : shapeMat[1];
-    kernel_ncol_blocks_ = 2;
-    brow_ = 512;
-    bcol_ = 8 * kernel_ncol_blocks_;
-    last_brow_ = nrow_ % brow_ == 0 ? brow_ : nrow_ % brow_;
-    nbrow_ = nrow_ % brow_ == 0 ? nrow_ / brow_ : (nrow_ + brow_) / brow_;
-    nbcol_ = ncol_ % bcol_ == 0 ? ncol_ / bcol_ : (ncol_ + bcol_) / bcol_;
-    const int padding = 1024;  // required by sw pipelined kernels
-    const int specialMem = 256;
-    packsize_ = ((nbrow_ * brow_) * (nbcol_ * bcol_)) * sizeof(fbgemm::float16) + padding + specialMem;
+    PackInfoFp32(shapeMat,
+                 transpose,
+                 nrow_,
+                 ncol_,
+                 kernel_ncol_blocks_,
+                 brow_,
+                 bcol_,
+                 last_brow_,
+                 nbrow_,
+                 nbcol_,
+                 packsize_);
 
     Shape outShape({(int)packsize_});
 
diff --git a/src/tensors/cpu/sharp/packed_gemm.cpp b/src/tensors/cpu/sharp/packed_gemm.cpp
old mode 100755
new mode 100644
index bae58288d..e4d296a22
--- a/src/tensors/cpu/sharp/packed_gemm.cpp
+++ b/src/tensors/cpu/sharp/packed_gemm.cpp
@@ -20,16 +20,13 @@
 //#pragma comment(linker, "/ignore:4217") // locally defined symbol ...asmjit... imported
 #endif
 
-
 #ifdef __GNUC__
 #pragma GCC diagnostic push
 #pragma GCC diagnostic ignored "-Wunused-variable"
 #endif
-
 #include "3rd_party/fbgemm/include/fbgemm/FbgemmFP16.h"
 #include "3rd_party/fbgemm/include/fbgemm/QuantUtils.h"
 #include "3rd_party/fbgemm/include/fbgemm/Fbgemm.h"
-
 #ifdef __GNUC__
 #pragma GCC diagnostic pop
 #endif
@@ -68,6 +65,14 @@ namespace variant { // Variants of GEMM implementations
 // different marian instances should not share this variable.
 static thread_local PackedGemmMatrixFP16 packedPlaceholder(1, 1, 1, 1, 1, 1, 1, 1);
 
+// Copied code from fbgemm. It's padding required from some kernel in FBGEMM
+// Verbatim - 'required by sw pipelined kernels'
+// https://github.com/marian-nmt/FBGEMM/blob/master/include/fbgemm/FbgemmFP16.h#L109
+const int PACK16_PADDING = 1024;  
+
+// This is a memory space to store auxiliary variables for FBGEMM (e.g. block row, block column, kernel_ncol_blocks and etc.)
+const int PACK16_SPECIALMEM = 256;
+
 // This is copied from FBGEMM code
 // A better way?
 // will be removed, when FBGEMM api is changed
@@ -106,8 +111,39 @@ inline uint64_t addr(const int r_,
   return index;
 }
 
+void PackInfoFp32(const marian::Shape& shape,
+                  const bool transpose,
+                  uint64_t& packsize) {
+  int nrow, ncol, kernel_ncol_blocks, brow = 512, bcol, last_brow, nbrow, nbcol;
+  PackInfoFp32(shape, transpose, nrow, ncol, kernel_ncol_blocks, brow, bcol, last_brow, nbrow, nbcol, packsize);
+}
+
+void PackInfoFp32(const marian::Shape& shape,
+                  const bool transpose,
+                  int& nrow,
+                  int& ncol,
+                  int& kernel_ncol_blocks,
+                  int& brow,
+                  int& bcol,
+                  int& last_brow,
+                  int& nbrow,
+                  int& nbcol,
+                  uint64_t& packsize) {
+  nrow = transpose ? shape[1] : shape[0];
+  ncol = transpose ? shape[0] : shape[1];
+  kernel_ncol_blocks = 2;
+  brow = 512;
+  bcol = 8 * kernel_ncol_blocks;
+  last_brow = nrow % brow == 0 ? brow : nrow % brow;
+  nbrow = nrow % brow == 0 ? nrow / brow : (nrow + brow) / brow;
+  nbcol = ncol % bcol == 0 ? ncol / bcol : (ncol + bcol) / bcol;
+  ABORT_IF(ncol % bcol != 0, "ncol (number of columns) should be multiple of 16. {}", ncol);
+  packsize = ((nbrow * brow) * (nbcol * bcol)) * sizeof(fbgemm::float16) + PACK16_PADDING
+             + PACK16_SPECIALMEM;
+}
+
 void PackFp32(marian::Tensor out,
-              const marian::Tensor in,
+              const float* inData, // Packing is only available for 2D weight matrix in Marian. Otherwise, it's aborted in expanded_gemm.h.
               const bool transpose,
               const int nrow,
               const int ncol,
@@ -141,11 +177,10 @@ void PackFp32(marian::Tensor out,
   fbgemm::float16* outmem = (fbgemm::float16*)(outmemorg + 256);
   fbgemm::float16* dummy = new fbgemm::float16;
   // pack the matrix
-  float* inmem = in->data();
   for(int i = 0; i < nrow; i++) {
     for(int j = 0; j < ncol; j++) {
       outmem[addr(i, j, brow, bcol, nbrow, nbcol, last_brow)]
-          = tconv(!transpose ? inmem[i * ncol + j] : inmem[i + nrow * j], *dummy);
+          = tconv(!transpose ? inData[i * ncol + j] : inData[i + nrow * j], *dummy);
     }
   }
   delete dummy;
@@ -186,15 +221,17 @@ void GemmPackFp32(marian::Tensor C,
   // packed matrix
   packedPlaceholder.pmat_ = (fbgemm::float16*)(B->data<uint8_t>() + 256);
 
+  if(bias != nullptr) {
 #if MKL_FOUND
-  for(int i = 0; i < m; ++i) {
-    mkl_somatcopy('R', 'N', 1, n, 1, bias->data(), n, C->data() + n * i, n);
-  }
+    for(int i = 0; i < m; ++i) {
+      mkl_somatcopy('R', 'N', 1, n, 1, bias->data(), n, C->data() + n * i, n);
+    }
 #else
-  for(int i = 0; i < m; ++i) {
-    std::copy(bias->data(), bias->data() + n, C->data() + n * i);
-  }
+    for(int i = 0; i < m; ++i) {
+      std::copy(bias->data(), bias->data() + n, C->data() + n * i);
+    }
 #endif
+  }
 
 #ifdef _OPENMP
 #pragma omp parallel
@@ -211,7 +248,7 @@ void GemmPackFp32(marian::Tensor C,
                       (int)m,
                       A->data(),
                       packedPlaceholder,
-                      1,
+                      bias != nullptr ? 1.0 : 0.0,
                       C->data(),
                       tid,
                       num_threads);
@@ -221,8 +258,31 @@ void GemmPackFp32(marian::Tensor C,
   packedPlaceholder.pmat_ = pmat;
 }
 #else // USE_FBGEMM
+
+void PackInfoFp32(const marian::Shape& shape,
+                  const bool transpose,
+                  uint64_t& packsize) {
+  // does nothing. supports only FBGEMM based packed gemm at this moment.
+  ABORT("FBGEMM is needed to use packed GEMM.");
+}
+
+void PackInfoFp32(const marian::Shape& shape,
+                  const bool transpose,
+                  int& nrow,
+                  int& ncol,
+                  int& kernel_ncol_blocks,
+                  int& brow,
+                  int& bcol,
+                  int& last_brow,
+                  int& nbrow,
+                  int& nbcol,
+                  uint64_t& packsize) {
+  // does nothing. supports only FBGEMM based packed gemm at this moment.
+  ABORT("FBGEMM is needed to use packed GEMM.");
+}
+
 void PackFp32(marian::Tensor out,
-              const marian::Tensor in,
+              const float* inData,
               const bool transpose,
               const int nrow,
               const int ncol,
diff --git a/src/tensors/cpu/sharp/packed_gemm.h b/src/tensors/cpu/sharp/packed_gemm.h
index 0946e551f..70b093939 100644
--- a/src/tensors/cpu/sharp/packed_gemm.h
+++ b/src/tensors/cpu/sharp/packed_gemm.h
@@ -6,9 +6,25 @@ namespace marian {
 namespace cpu {
 namespace variant { // Variants of GEMM implementations
 
+void PackInfoFp32(const marian::Shape& shape,
+                  const bool transpose,
+                  /*out*/uint64_t& packsize);
+
+void PackInfoFp32(const marian::Shape& shape,
+                  const bool transpose,
+                  int& nrow,
+                  int& ncol,
+                  int& kernel_ncol_blocks,
+                  int& brow,
+                  int& bcol,
+                  int& last_brow,
+                  int& nbrow,
+                  int& nbcol,
+                  /*out*/uint64_t& packsize); // @TODO: change to size_t where appropriate
+
 // Pack a matrix into cache utilization efficient way (block format)
 // out: output tensor - packed format
-// in: input tensor - normal format
+// inData: input tensor data - pointer of float data
 // transpose: the matrix is transposed
 // nrow: the number of rows
 // ncol: the number of columns
@@ -21,7 +37,7 @@ namespace variant { // Variants of GEMM implementations
 // packsize: the size of the packed matrix
 //          (the number of fp16 elements + padding (1024) + extra temporary memory (256))
 void PackFp32(marian::Tensor out,
-              const marian::Tensor in,
+              const float* inData,
               const bool transpose,
               const int nrow,
               const int ncol,
@@ -31,7 +47,7 @@ void PackFp32(marian::Tensor out,
               const int last_brow,
               const int nbrow,
               const int nbcol,
-              const uint64_t packsize);
+              const uint64_t packsize); // @TODO: change to size_t where appropriate
 
 // GEMM operation on the packed B matrix
 // C: output matrix
diff --git a/src/tensors/gpu/backend.h b/src/tensors/gpu/backend.h
index 19f7dd22d..044c344f5 100644
--- a/src/tensors/gpu/backend.h
+++ b/src/tensors/gpu/backend.h
@@ -59,16 +59,6 @@ class Backend : public marian::Backend {
     return false;
   }
 
-  // for CPU, selects different GEMM types for the inference.
-  // for GPU, there's no gemm type. so, it does nothing.
-  void setGemmType(std::string gemmType) override {
-    LOG_ONCE(info, "setGemmType() not supported for GPU_{}", gemmType);
-  }
-  GemmType getGemmType() override {
-    LOG_ONCE(info, "getGemmType() not supported for GPU");
-    return GemmType::Auto;
-  }
-
 private:
   cublasHandle_t cublasHandle_;
   cusparseHandle_t cusparseHandle_;
diff --git a/src/tensors/gpu/tensor_operators.cu b/src/tensors/gpu/tensor_operators.cu
index af7c974fb..a2940e32f 100644
--- a/src/tensors/gpu/tensor_operators.cu
+++ b/src/tensors/gpu/tensor_operators.cu
@@ -214,12 +214,10 @@ void Concatenate1(Tensor out, const std::vector<Tensor>& inputs) {
     int threads = std::min(MAX_THREADS, cols_in);
 
     if(out->type() == Type::float32) {
-      gInsertCols<false><<<blocks, threads>>>(
-          out->data<float>(), in->data<float>(), rows, cols_in, cols_out, cols_in, offset, 0);
+      gInsertCols<false><<<blocks, threads>>>(out->data<float>(), in->data<float>(), rows, cols_in, cols_out, cols_in, offset, 0);
 #if COMPILE_FP16
     } else if(out->type() == Type::float16) {
-      gInsertCols<false><<<blocks, threads>>>(
-          out->data<half>(), in->data<half>(), rows, cols_in, cols_out, cols_in, offset, 0);
+      gInsertCols<false><<<blocks, threads>>>(out->data<half>(), in->data<half>(), rows, cols_in, cols_out, cols_in, offset, 0);
 #endif
     } else {
       ABORT("Concatenate1 not implemented for type {}", out->type());
@@ -2130,11 +2128,11 @@ void LayerNormalizationGrad(Ptr<Allocator> allocator,
   int threads = std::min(MAX_THREADS, cols);
   int blocks = std::min(MAX_BLOCKS, rows);
 
-  auto tempGradGammaMemory = allocator->alloc(adj->memory()->size(), adj->type());
+  auto tempGradGammaMemory = allocator->alloc(adj->memory()->size());
   Tensor tempGradGamma = TensorBase::New(tempGradGammaMemory, adj->shape(), adj->type(), adj->getBackend());
   tempGradGamma->set(0.f);
 
-  auto tempOnesMemory = allocator->alloc(rows * sizeOf(adj->type()), adj->type());
+  auto tempOnesMemory = allocator->alloc(rows * sizeOf(adj->type()));
   Tensor tempOnes = TensorBase::New(tempOnesMemory, Shape({1, rows}), adj->type(), adj->getBackend());
   tempOnes->set(1.f);
 
diff --git a/src/tensors/tensor.cpp b/src/tensors/tensor.cpp
index 75b6644e3..4a0c90b2b 100755
--- a/src/tensors/tensor.cpp
+++ b/src/tensors/tensor.cpp
@@ -1,5 +1,6 @@
 #include "tensors/tensor.h"
 #include "tensors/tensor_operators.h"
+#include "common/io.h"
 
 namespace marian {
 
@@ -120,13 +121,22 @@ void TensorBase::get(io::Item& item, const std::string& name) {
   item.shape = shape_;
   item.type  = type_;
 
-  size_t bytesWithoutPadding = shape_.elements() * sizeOf(type_);
-  item.bytes.resize(bytesWithoutPadding);
+  item.bytes.resize(memory_->size());
   copy(backend_,
        memory_->data<char>(),
-       memory_->data<char>() + bytesWithoutPadding,
+       memory_->data<char>() + memory_->size(),
        item.bytes.data());
 }
 
+void TensorBase::set(const io::Item& item) {
+  ABORT_IF(item.type != type_, "Tensor type {} and item type {} do not match", type_, item.type);
+  ABORT_IF(item.shape != shape_, "Tensor shape {} and item shape {} do not match", shape_, item.shape);
+  ABORT_IF(item.bytes.size() > memory_->size(), "Item data size {} too large for memory {}", item.bytes.size(), memory_->size());
+  copy(backend_,
+       item.bytes.data(),
+       item.bytes.data() + item.bytes.size(),
+       memory_->data<char>());
+}
+
 }  // namespace marian
 
diff --git a/src/tensors/tensor.h b/src/tensors/tensor.h
index 62ce51058..6f4c202ab 100755
--- a/src/tensors/tensor.h
+++ b/src/tensors/tensor.h
@@ -3,7 +3,6 @@
 #include "common/definitions.h"
 #include "common/shape.h"
 #include "common/types.h"
-#include "common/io.h"
 #include "tensors/backend.h"
 #include "tensors/memory_piece.h"
 #ifdef CUDA_FOUND
@@ -18,6 +17,10 @@
 
 namespace marian {
 
+namespace io {
+  struct Item;
+}
+
 class TensorBase {
   MemoryPiece::PtrType memory_;
   Shape shape_;
@@ -150,6 +153,11 @@ class TensorBase {
 
   template <typename T>
   void set(const T* begin, const T* end) {
+    ABORT_IF(end - begin != shape_.elements(),
+             "Vector size ({}) and underlying shape ({}, {}) do not match",
+             end - begin,
+             std::string(shape_),
+             memory_->size());
     matchOrAbort<T>(type_);
 
     if(backend_->getDeviceId().type == DeviceType::cpu) {
@@ -167,36 +175,7 @@ class TensorBase {
     set(v.data(), v.data() + v.size());
   }
 
-  // a binary copy with type checking
-  void set(const char* begin, const char* end, Type type) {
-    ABORT_IF(type_ != type,
-             "Tensor type ({}) and data type ({}) do not match",
-             type_,
-             type);
-
-    size_t dataSize = (end - begin) / sizeOf(type);
-    ABORT_IF(size() != dataSize,
-             "Tensor size ({}) and mapped size ({}) do not match",
-             size(),
-             dataSize);
-
-    if(backend_->getDeviceId().type == DeviceType::cpu) {
-      std::copy(begin, end, data<char>());
-    }
-#ifdef CUDA_FOUND
-    else {
-      gpu::copy(backend_, begin, end, data<char>());
-    }
-#endif
-  }
-
-  void set(const std::vector<char>& v, Type type) {
-    set(v.data(), v.data() + v.size(), type);
-  }
-
-  void set(const io::Item& item) {
-    set(item.bytes.data(), item.bytes.data() + item.bytes.size(), item.type);
-  }
+  void set(const io::Item& item);
 
   // For single values enable conversion to other numeric formats if possible
   template <typename T>
diff --git a/src/tensors/tensor_allocator.h b/src/tensors/tensor_allocator.h
index d229bebaf..e3bc79f96 100644
--- a/src/tensors/tensor_allocator.h
+++ b/src/tensors/tensor_allocator.h
@@ -70,14 +70,13 @@ class TensorAllocator {
   void clear() { allocator_->clear(); }
 
   size_t capacity(Shape shape, Type type = Type::float32) {
-    return allocator_->capacity(shape.elements(), type);
+    return allocator_->capacity<char>(requiredBytes(shape, type));
   }
 
-  void allocate(Tensor& t, Shape shape, Type type = Type::float32) {
+  void allocate(/*out*/ Tensor& t, Shape shape, Type type = Type::float32) {
     if(!t || t->shape() != shape) {
-      int size = shape.elements();
-      auto mem = allocator_->alloc(size, type);
-      t = TensorBase::New(mem, shape, type, backend_);
+      auto mem = allocator_->alloc(requiredBytes(shape, type));
+      t = Tensor(TensorBase::New(mem, shape, type, backend_));
     }
   }
 
diff --git a/src/tests/prod.cpp b/src/tests/prod.cpp
index 232916eff..37809bacd 100644
--- a/src/tests/prod.cpp
+++ b/src/tests/prod.cpp
@@ -41,7 +41,6 @@ int main(int argc, char** argv) {
         auto g = New<ExpressionGraph>(true);
         g->setDevice({0, DeviceType::cpu});
         g->getBackend()->setOptimized(true);
-        g->getBackend()->setGemmType("auto");
         g->reserveWorkspaceMB(2512);
 
         timer::AutoTimer timer;
diff --git a/src/training/communicator.cpp b/src/training/communicator.cpp
index 6307cc8b1..be9b23be5 100644
--- a/src/training/communicator.cpp
+++ b/src/training/communicator.cpp
@@ -169,7 +169,7 @@ class FakeMPIWrapper : public IMPIWrapper
     //        to only accept one parameter, and remove this error check can be removed.
     ABORT_IF(sendbuf != recvbuf, "FakeMPIWrapper::allReduce() only implemented for in-place operation"); // otherwise it's not a no-op, we must copy data
   }
-#pragma warning(push)
+#pragma warning(pop)
   virtual void finalize() override { }
 };
 
diff --git a/src/translator/translator.h b/src/translator/translator.h
index b02320b99..a888ec5f8 100755
--- a/src/translator/translator.h
+++ b/src/translator/translator.h
@@ -65,7 +65,6 @@ class Translate : public ModelTask {
         graph->getBackend()->setClip(options_->get<float>("clip-gemm"));
         if (device.type == DeviceType::cpu) {
           graph->getBackend()->setOptimized(options_->get<bool>("optimize"));
-          graph->getBackend()->setGemmType(options_->get<std::string>("gemm-type"));
         }
         graph->reserveWorkspaceMB(options_->get<size_t>("workspace"));
         graphs_[id] = graph;
@@ -187,7 +186,6 @@ class TranslateService : public ModelServiceTask {
       graph->getBackend()->setClip(options_->get<float>("clip-gemm"));
       if (device.type == DeviceType::cpu) {
         graph->getBackend()->setOptimized(options_->get<bool>("optimize"));
-        graph->getBackend()->setGemmType(options_->get<std::string>("gemm-type"));
       }
       graph->reserveWorkspaceMB(options_->get<size_t>("workspace"));
       graphs_.push_back(graph);
diff --git a/vs/Marian.vcxproj b/vs/Marian.vcxproj
index 12faec2fc..c47318a03 100755
--- a/vs/Marian.vcxproj
+++ b/vs/Marian.vcxproj
@@ -16,6 +16,7 @@
     <RootNamespace>Marian</RootNamespace>
     <ProjectName>Marian</ProjectName>
     <WindowsTargetPlatformVersion>10.0.17763.0</WindowsTargetPlatformVersion>
+    <CudaToolkitDir Condition="'$(CudaToolkitDir)' == ''">$(CUDA_PATH)</CudaToolkitDir>
   </PropertyGroup>
   <Import Project="$(VCTargetsPath)\Microsoft.Cpp.Default.props" />
   <PropertyGroup Condition="'$(Configuration)|$(Platform)'=='Debug|x64'" Label="Configuration">
@@ -33,7 +34,7 @@
   </PropertyGroup>
   <Import Project="$(VCTargetsPath)\Microsoft.Cpp.props" />
   <ImportGroup Label="ExtensionSettings">
-    <Import Project="$(VCTargetsPath)\BuildCustomizations\CUDA 10.1.props" />
+    <Import Project="$(CudaToolkitDir)\extras\visual_studio_integration\MSBuildExtensions\CUDA 10.1.props" />
   </ImportGroup>
   <ImportGroup Label="PropertySheets">
     <Import Project="$(UserRootDir)\Microsoft.Cpp.$(Platform).user.props" Condition="exists('$(UserRootDir)\Microsoft.Cpp.$(Platform).user.props')" Label="LocalAppDataPlatform" />
@@ -42,15 +43,15 @@
   <PropertyGroup Condition="'$(Configuration)|$(Platform)'=='Debug|x64'">
     <LinkIncremental>true</LinkIncremental>
     <IntDir>$(SolutionDir)$(Platform)\$(Configuration)\Marian\</IntDir>
-    <IncludePath>..\src\3rd_party\fbgemm\third_party\cpuinfo\deps\clog\include;..\src\3rd_party\fbgemm\third_party\cpuinfo\src;..\src\3rd_party\fbgemm\third_party\cpuinfo\include;..\src\3rd_party\fbgemm\third_party\asmjit\src;%MKL_PATH%\include;..\src\3rd_party\fbgemm\include;%CUDA_PATH%\include;..\src;..\src\3rd_party;%BOOST_INCLUDE_PATH%;%ZLIB_PATH%\include;$(VC_IncludePath);$(WindowsSDK_IncludePath);</IncludePath>
-    <LibraryPath>%CUDA_PATH%\lib\x64;%BOOST_LIB_PATH%;%ZLIB_PATH%\lib;%MKL_PATH%\lib\intel64;$(VC_LibraryPath_x64);$(WindowsSDK_LibraryPath_x64);$(NETFXKitsDir)Lib\um\x64</LibraryPath>
+    <IncludePath>$(CudaToolkitIncludeDir);..\src\3rd_party\fbgemm\third_party\cpuinfo\deps\clog\include;..\src\3rd_party\fbgemm\third_party\cpuinfo\src;..\src\3rd_party\fbgemm\third_party\cpuinfo\include;..\src\3rd_party\fbgemm\third_party\asmjit\src;%MKL_PATH%\include;..\src\3rd_party\fbgemm\include;..\src;..\src\3rd_party;%BOOST_INCLUDE_PATH%;%ZLIB_PATH%\include;$(VC_IncludePath);$(WindowsSDK_IncludePath)</IncludePath>
+    <LibraryPath>$(CudaToolkitLibDir);%BOOST_LIB_PATH%;%ZLIB_PATH%\lib;%MKL_PATH%\lib\intel64;$(VC_LibraryPath_x64);$(WindowsSDK_LibraryPath_x64);$(NETFXKitsDir)Lib\um\x64</LibraryPath>
   </PropertyGroup>
   <PropertyGroup Condition="'$(Configuration)|$(Platform)'=='Release|x64'">
     <LinkIncremental>false</LinkIncremental>
     <ExecutablePath>$(ExecutablePath)</ExecutablePath>
     <IntDir>$(SolutionDir)$(Platform)\$(Configuration)\Marian\</IntDir>
-    <IncludePath>..\src\3rd_party\fbgemm\third_party\cpuinfo\deps\clog\include;..\src\3rd_party\fbgemm\third_party\cpuinfo\src;..\src\3rd_party\fbgemm\third_party\cpuinfo\include;..\src\3rd_party\fbgemm\third_party\asmjit\src;%MKL_PATH%\include;..\src\3rd_party\fbgemm\include;%CUDA_PATH%\include;..\src;..\src\3rd_party;%BOOST_INCLUDE_PATH%;%ZLIB_PATH%\include;$(VC_IncludePath);$(WindowsSDK_IncludePath);</IncludePath>
-    <LibraryPath>%CUDA_PATH%\lib\x64;%BOOST_LIB_PATH%;%ZLIB_PATH%\lib;%MKL_PATH%\lib\intel64;$(VC_LibraryPath_x64);$(WindowsSDK_LibraryPath_x64);$(NETFXKitsDir)Lib\um\x64</LibraryPath>
+    <IncludePath>$(CudaToolkitIncludeDir);..\src\3rd_party\fbgemm\third_party\cpuinfo\deps\clog\include;..\src\3rd_party\fbgemm\third_party\cpuinfo\src;..\src\3rd_party\fbgemm\third_party\cpuinfo\include;..\src\3rd_party\fbgemm\third_party\asmjit\src;%MKL_PATH%\include;..\src\3rd_party\fbgemm\include;..\src;..\src\3rd_party;%BOOST_INCLUDE_PATH%;%ZLIB_PATH%\include;$(VC_IncludePath);$(WindowsSDK_IncludePath)</IncludePath>
+    <LibraryPath>$(CudaToolkitLibDir);%BOOST_LIB_PATH%;%ZLIB_PATH%\lib;%MKL_PATH%\lib\intel64;$(VC_LibraryPath_x64);$(WindowsSDK_LibraryPath_x64);$(NETFXKitsDir)Lib\um\x64</LibraryPath>
   </PropertyGroup>
   <ItemDefinitionGroup>
     <ClCompile>
@@ -72,7 +73,7 @@
       <PreprocessorDefinitions>BOOST_CONFIG_SUPPRESS_OUTDATED_MESSAGE; FBGEMM_EXPORTS; USE_FBGEMM=1; USE_SSE2=1; CUDA_FOUND=1; MKL_FOUND=1; MPI_FOUND=1; BLAS_FOUND=1; MKL_ILP64; WIN32;_DEBUG;_CONSOLE;_LIB;%(PreprocessorDefinitions)</PreprocessorDefinitions>
       <SDLCheck>false</SDLCheck>
       <TreatWarningAsError>true</TreatWarningAsError>
-      <AdditionalOptions>/bigobj %(AdditionalOptions) /arch:AVX</AdditionalOptions>
+      <AdditionalOptions>/bigobj /arch:AVX %(AdditionalOptions)</AdditionalOptions>
       <RuntimeLibrary Condition="'$(Configuration)|$(Platform)'=='Debug|x64'">MultiThreadedDebugDLL</RuntimeLibrary>
       <DisableSpecificWarnings>4996; 4702</DisableSpecificWarnings>
       <MultiProcessorCompilation>true</MultiProcessorCompilation>
@@ -109,7 +110,7 @@
       <PreprocessorDefinitions>BOOST_CONFIG_SUPPRESS_OUTDATED_MESSAGE; FBGEMM_EXPORTS; USE_FBGEMM=1; USE_SSE2=1; CUDA_FOUND=1; MKL_FOUND=1; MPI_FOUND=1; BLAS_FOUND=1; MKL_ILP64; WIN32;NDEBUG;_CONSOLE;_LIB;%(PreprocessorDefinitions)</PreprocessorDefinitions>
       <SDLCheck>false</SDLCheck>
       <FavorSizeOrSpeed>Speed</FavorSizeOrSpeed>
-      <AdditionalOptions>/d2Zi+ /bigobj %(AdditionalOptions) /arch:AVX</AdditionalOptions>
+      <AdditionalOptions>/d2Zi+ /bigobj /arch:AVX %(AdditionalOptions)</AdditionalOptions>
       <TreatWarningAsError>true</TreatWarningAsError>
       <RuntimeLibrary Condition="'$(Configuration)|$(Platform)'=='Release|x64'">MultiThreadedDLL</RuntimeLibrary>
       <RuntimeLibrary Condition="'$(Configuration)|$(Platform)'=='Release_NoOpt|x64'">MultiThreaded</RuntimeLibrary>
@@ -1006,6 +1007,8 @@
     <ClCompile Include="..\src\common\filesystem.cpp" />
     <ClCompile Include="..\src\common\file_stream.cpp" />
     <ClCompile Include="..\src\common\io.cpp" />
+    <ClCompile Include="..\src\common\options.cpp" />
+    <ClCompile Include="..\src\common\types.cpp" />
     <ClCompile Include="..\src\common\utils.cpp" />
     <ClCompile Include="..\src\common\logging.cpp" />
     <ClCompile Include="..\src\common\config.cpp" />
@@ -1111,6 +1114,7 @@
     </ClCompile>
     <ClCompile Include="..\src\training\communicator.cpp" />
     <ClCompile Include="..\src\training\graph_group_multinode_sync.cpp" />
+    <ClCompile Include="..\src\training\scheduler.cpp" />
     <ClCompile Include="..\src\translator\history.cpp" />
     <ClCompile Include="..\src\translator\output_collector.cpp" />
     <ClCompile Include="..\src\translator\nth_element.cpp" />
@@ -1173,6 +1177,7 @@
     <ClInclude Include="..\src\examples\mnist\validator.h" />
     <ClInclude Include="..\src\functional\approx.h" />
     <ClInclude Include="..\src\functional\operators.h" />
+    <ClInclude Include="..\src\graph\expression_graph_packable.h" />
     <ClInclude Include="..\src\layers\loss.h" />
     <ClInclude Include="..\src\layers\weight.h" />
     <ClInclude Include="..\src\marian.h" />
@@ -1607,6 +1612,6 @@
   </ItemGroup>
   <Import Project="$(VCTargetsPath)\Microsoft.Cpp.targets" />
   <ImportGroup Label="ExtensionTargets">
-    <Import Project="$(VCTargetsPath)\BuildCustomizations\CUDA 10.1.targets" />
+    <Import Project="$(CudaToolkitDir)\extras\visual_studio_integration\MSBuildExtensions\CUDA 10.1.targets" />
   </ImportGroup>
 </Project>
\ No newline at end of file
diff --git a/vs/Marian.vcxproj.filters b/vs/Marian.vcxproj.filters
index acc84990a..87ba99453 100755
--- a/vs/Marian.vcxproj.filters
+++ b/vs/Marian.vcxproj.filters
@@ -724,6 +724,15 @@
     <ClCompile Include="..\src\common\file_stream.cpp">
       <Filter>common</Filter>
     </ClCompile>
+    <ClCompile Include="..\src\training\scheduler.cpp">
+      <Filter>training</Filter>
+    </ClCompile>
+    <ClCompile Include="..\src\common\options.cpp">
+      <Filter>common</Filter>
+    </ClCompile>
+    <ClCompile Include="..\src\common\types.cpp">
+      <Filter>common</Filter>
+    </ClCompile>
   </ItemGroup>
   <ItemGroup>
     <ClInclude Include="..\src\marian.h" />
@@ -2038,6 +2047,9 @@
     <ClInclude Include="..\src\3rd_party\zstr\zstr.hpp">
       <Filter>3rd_party</Filter>
     </ClInclude>
+    <ClInclude Include="..\src\graph\expression_graph_packable.h">
+      <Filter>graph</Filter>
+    </ClInclude>
   </ItemGroup>
   <ItemGroup>
     <Filter Include="3rd_party">

From 5050aad8db06873ac45e4712d9faf1cd2884870a Mon Sep 17 00:00:00 2001
From: Young Jin Kim <youki@microsoft.com>
Date: Fri, 1 Nov 2019 11:03:56 -0700
Subject: [PATCH 755/838] Fix vs project warnings

---
 src/tensors/cpu/sharp/packed_gemm.cpp |  2 +-
 vs/Marian.vcxproj                     | 90 +++++++++++++++++++++++++++
 2 files changed, 91 insertions(+), 1 deletion(-)

diff --git a/src/tensors/cpu/sharp/packed_gemm.cpp b/src/tensors/cpu/sharp/packed_gemm.cpp
index e4d296a22..a94335eef 100644
--- a/src/tensors/cpu/sharp/packed_gemm.cpp
+++ b/src/tensors/cpu/sharp/packed_gemm.cpp
@@ -248,7 +248,7 @@ void GemmPackFp32(marian::Tensor C,
                       (int)m,
                       A->data(),
                       packedPlaceholder,
-                      bias != nullptr ? 1.0 : 0.0,
+                      bias != nullptr ? 1.0f : 0.0f,
                       C->data(),
                       tid,
                       num_threads);
diff --git a/vs/Marian.vcxproj b/vs/Marian.vcxproj
index c47318a03..6cd8f3416 100755
--- a/vs/Marian.vcxproj
+++ b/vs/Marian.vcxproj
@@ -312,182 +312,272 @@
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       <TreatWarningAsError Condition="'$(Configuration)|$(Platform)'=='Release|x64'">false</TreatWarningAsError>
       <TreatWarningAsError Condition="'$(Configuration)|$(Platform)'=='Debug|x64'">false</TreatWarningAsError>
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       <TreatWarningAsError Condition="'$(Configuration)|$(Platform)'=='Debug|x64'">false</TreatWarningAsError>
+      <WarningLevel Condition="'$(Configuration)|$(Platform)'=='Debug|x64'">TurnOffAllWarnings</WarningLevel>
+      <WarningLevel Condition="'$(Configuration)|$(Platform)'=='Release|x64'">TurnOffAllWarnings</WarningLevel>
     </ClCompile>
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+      <WarningLevel Condition="'$(Configuration)|$(Platform)'=='Release|x64'">TurnOffAllWarnings</WarningLevel>
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+      <WarningLevel Condition="'$(Configuration)|$(Platform)'=='Release|x64'">TurnOffAllWarnings</WarningLevel>
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+      <WarningLevel Condition="'$(Configuration)|$(Platform)'=='Release|x64'">TurnOffAllWarnings</WarningLevel>
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+      <WarningLevel Condition="'$(Configuration)|$(Platform)'=='Release|x64'">TurnOffAllWarnings</WarningLevel>
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       <TreatWarningAsError Condition="'$(Configuration)|$(Platform)'=='Debug|x64'">false</TreatWarningAsError>
+      <WarningLevel Condition="'$(Configuration)|$(Platform)'=='Debug|x64'">TurnOffAllWarnings</WarningLevel>
+      <WarningLevel Condition="'$(Configuration)|$(Platform)'=='Release|x64'">TurnOffAllWarnings</WarningLevel>
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+      <WarningLevel Condition="'$(Configuration)|$(Platform)'=='Release|x64'">TurnOffAllWarnings</WarningLevel>
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+      <WarningLevel Condition="'$(Configuration)|$(Platform)'=='Release|x64'">TurnOffAllWarnings</WarningLevel>
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+      <WarningLevel Condition="'$(Configuration)|$(Platform)'=='Release|x64'">TurnOffAllWarnings</WarningLevel>
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+      <WarningLevel Condition="'$(Configuration)|$(Platform)'=='Release|x64'">TurnOffAllWarnings</WarningLevel>
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+      <WarningLevel Condition="'$(Configuration)|$(Platform)'=='Release|x64'">TurnOffAllWarnings</WarningLevel>
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       <TreatWarningAsError Condition="'$(Configuration)|$(Platform)'=='Debug|x64'">false</TreatWarningAsError>
+      <WarningLevel Condition="'$(Configuration)|$(Platform)'=='Debug|x64'">TurnOffAllWarnings</WarningLevel>
+      <WarningLevel Condition="'$(Configuration)|$(Platform)'=='Release|x64'">TurnOffAllWarnings</WarningLevel>
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+      <WarningLevel Condition="'$(Configuration)|$(Platform)'=='Release|x64'">TurnOffAllWarnings</WarningLevel>
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     <ClCompile Include="..\src\3rd_party\fbgemm\third_party\asmjit\src\asmjit\x86\x86assembler.cpp">
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       <TreatWarningAsError Condition="'$(Configuration)|$(Platform)'=='Debug|x64'">false</TreatWarningAsError>
+      <WarningLevel Condition="'$(Configuration)|$(Platform)'=='Debug|x64'">TurnOffAllWarnings</WarningLevel>
+      <WarningLevel Condition="'$(Configuration)|$(Platform)'=='Release|x64'">TurnOffAllWarnings</WarningLevel>
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+      <WarningLevel Condition="'$(Configuration)|$(Platform)'=='Debug|x64'">TurnOffAllWarnings</WarningLevel>
+      <WarningLevel Condition="'$(Configuration)|$(Platform)'=='Release|x64'">TurnOffAllWarnings</WarningLevel>
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       <TreatWarningAsError Condition="'$(Configuration)|$(Platform)'=='Debug|x64'">false</TreatWarningAsError>
+      <WarningLevel Condition="'$(Configuration)|$(Platform)'=='Debug|x64'">TurnOffAllWarnings</WarningLevel>
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       <TreatWarningAsError Condition="'$(Configuration)|$(Platform)'=='Release|x64'">false</TreatWarningAsError>
       <TreatWarningAsError Condition="'$(Configuration)|$(Platform)'=='Debug|x64'">false</TreatWarningAsError>
+      <WarningLevel Condition="'$(Configuration)|$(Platform)'=='Debug|x64'">TurnOffAllWarnings</WarningLevel>
+      <WarningLevel Condition="'$(Configuration)|$(Platform)'=='Release|x64'">TurnOffAllWarnings</WarningLevel>
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     <ClCompile Include="..\src\3rd_party\fbgemm\third_party\asmjit\src\asmjit\x86\x86instimpl.cpp">
       <TreatWarningAsError Condition="'$(Configuration)|$(Platform)'=='Release|x64'">false</TreatWarningAsError>
       <TreatWarningAsError Condition="'$(Configuration)|$(Platform)'=='Debug|x64'">false</TreatWarningAsError>
+      <WarningLevel Condition="'$(Configuration)|$(Platform)'=='Debug|x64'">TurnOffAllWarnings</WarningLevel>
+      <WarningLevel Condition="'$(Configuration)|$(Platform)'=='Release|x64'">TurnOffAllWarnings</WarningLevel>
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       <TreatWarningAsError Condition="'$(Configuration)|$(Platform)'=='Release|x64'">false</TreatWarningAsError>
       <TreatWarningAsError Condition="'$(Configuration)|$(Platform)'=='Debug|x64'">false</TreatWarningAsError>
+      <WarningLevel Condition="'$(Configuration)|$(Platform)'=='Debug|x64'">TurnOffAllWarnings</WarningLevel>
+      <WarningLevel Condition="'$(Configuration)|$(Platform)'=='Release|x64'">TurnOffAllWarnings</WarningLevel>
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     <ClCompile Include="..\src\3rd_party\fbgemm\third_party\asmjit\src\asmjit\x86\x86logging.cpp">
       <TreatWarningAsError Condition="'$(Configuration)|$(Platform)'=='Release|x64'">false</TreatWarningAsError>
       <TreatWarningAsError Condition="'$(Configuration)|$(Platform)'=='Debug|x64'">false</TreatWarningAsError>
+      <WarningLevel Condition="'$(Configuration)|$(Platform)'=='Debug|x64'">TurnOffAllWarnings</WarningLevel>
+      <WarningLevel Condition="'$(Configuration)|$(Platform)'=='Release|x64'">TurnOffAllWarnings</WarningLevel>
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     <ClCompile Include="..\src\3rd_party\fbgemm\third_party\asmjit\src\asmjit\x86\x86operand.cpp">
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       <TreatWarningAsError Condition="'$(Configuration)|$(Platform)'=='Debug|x64'">false</TreatWarningAsError>
+      <WarningLevel Condition="'$(Configuration)|$(Platform)'=='Debug|x64'">TurnOffAllWarnings</WarningLevel>
+      <WarningLevel Condition="'$(Configuration)|$(Platform)'=='Release|x64'">TurnOffAllWarnings</WarningLevel>
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     <ClCompile Include="..\src\3rd_party\fbgemm\third_party\asmjit\src\asmjit\x86\x86operand_regs.cpp">
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       <TreatWarningAsError Condition="'$(Configuration)|$(Platform)'=='Debug|x64'">false</TreatWarningAsError>
+      <WarningLevel Condition="'$(Configuration)|$(Platform)'=='Debug|x64'">TurnOffAllWarnings</WarningLevel>
+      <WarningLevel Condition="'$(Configuration)|$(Platform)'=='Release|x64'">TurnOffAllWarnings</WarningLevel>
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       <TreatWarningAsError Condition="'$(Configuration)|$(Platform)'=='Debug|x64'">false</TreatWarningAsError>
+      <WarningLevel Condition="'$(Configuration)|$(Platform)'=='Debug|x64'">TurnOffAllWarnings</WarningLevel>
+      <WarningLevel Condition="'$(Configuration)|$(Platform)'=='Release|x64'">TurnOffAllWarnings</WarningLevel>
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       <TreatWarningAsError Condition="'$(Configuration)|$(Platform)'=='Debug|x64'">false</TreatWarningAsError>
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+      <WarningLevel Condition="'$(Configuration)|$(Platform)'=='Release|x64'">TurnOffAllWarnings</WarningLevel>
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+      <WarningLevel Condition="'$(Configuration)|$(Platform)'=='Release|x64'">TurnOffAllWarnings</WarningLevel>
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+      <WarningLevel Condition="'$(Configuration)|$(Platform)'=='Release|x64'">TurnOffAllWarnings</WarningLevel>
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       <TreatWarningAsError Condition="'$(Configuration)|$(Platform)'=='Release|x64'">false</TreatWarningAsError>
       <TreatWarningAsError Condition="'$(Configuration)|$(Platform)'=='Debug|x64'">false</TreatWarningAsError>
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+      <WarningLevel Condition="'$(Configuration)|$(Platform)'=='Release|x64'">TurnOffAllWarnings</WarningLevel>
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       <TreatWarningAsError Condition="'$(Configuration)|$(Platform)'=='Release|x64'">false</TreatWarningAsError>
       <TreatWarningAsError Condition="'$(Configuration)|$(Platform)'=='Debug|x64'">false</TreatWarningAsError>
+      <WarningLevel Condition="'$(Configuration)|$(Platform)'=='Debug|x64'">TurnOffAllWarnings</WarningLevel>
+      <WarningLevel Condition="'$(Configuration)|$(Platform)'=='Release|x64'">TurnOffAllWarnings</WarningLevel>
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       <TreatWarningAsError Condition="'$(Configuration)|$(Platform)'=='Release|x64'">false</TreatWarningAsError>
       <TreatWarningAsError Condition="'$(Configuration)|$(Platform)'=='Debug|x64'">false</TreatWarningAsError>
+      <WarningLevel Condition="'$(Configuration)|$(Platform)'=='Debug|x64'">TurnOffAllWarnings</WarningLevel>
+      <WarningLevel Condition="'$(Configuration)|$(Platform)'=='Release|x64'">TurnOffAllWarnings</WarningLevel>
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     <ClCompile Include="..\src\3rd_party\fbgemm\third_party\cpuinfo\src\x86\name.c">
       <TreatWarningAsError Condition="'$(Configuration)|$(Platform)'=='Release|x64'">false</TreatWarningAsError>
       <TreatWarningAsError Condition="'$(Configuration)|$(Platform)'=='Debug|x64'">false</TreatWarningAsError>
+      <WarningLevel Condition="'$(Configuration)|$(Platform)'=='Debug|x64'">TurnOffAllWarnings</WarningLevel>
+      <WarningLevel Condition="'$(Configuration)|$(Platform)'=='Release|x64'">TurnOffAllWarnings</WarningLevel>
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     <ClCompile Include="..\src\3rd_party\fbgemm\third_party\cpuinfo\src\x86\topology.c">
       <TreatWarningAsError Condition="'$(Configuration)|$(Platform)'=='Release|x64'">false</TreatWarningAsError>
       <TreatWarningAsError Condition="'$(Configuration)|$(Platform)'=='Debug|x64'">false</TreatWarningAsError>
+      <WarningLevel Condition="'$(Configuration)|$(Platform)'=='Debug|x64'">TurnOffAllWarnings</WarningLevel>
+      <WarningLevel Condition="'$(Configuration)|$(Platform)'=='Release|x64'">TurnOffAllWarnings</WarningLevel>
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     <ClCompile Include="..\src\3rd_party\fbgemm\third_party\cpuinfo\src\x86\uarch.c">
       <TreatWarningAsError Condition="'$(Configuration)|$(Platform)'=='Release|x64'">false</TreatWarningAsError>
       <TreatWarningAsError Condition="'$(Configuration)|$(Platform)'=='Debug|x64'">false</TreatWarningAsError>
+      <WarningLevel Condition="'$(Configuration)|$(Platform)'=='Debug|x64'">TurnOffAllWarnings</WarningLevel>
+      <WarningLevel Condition="'$(Configuration)|$(Platform)'=='Release|x64'">TurnOffAllWarnings</WarningLevel>
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     <ClCompile Include="..\src\3rd_party\fbgemm\third_party\cpuinfo\src\x86\vendor.c">
       <TreatWarningAsError Condition="'$(Configuration)|$(Platform)'=='Release|x64'">false</TreatWarningAsError>
       <TreatWarningAsError Condition="'$(Configuration)|$(Platform)'=='Debug|x64'">false</TreatWarningAsError>
+      <WarningLevel Condition="'$(Configuration)|$(Platform)'=='Debug|x64'">TurnOffAllWarnings</WarningLevel>
+      <WarningLevel Condition="'$(Configuration)|$(Platform)'=='Release|x64'">TurnOffAllWarnings</WarningLevel>
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     <ClCompile Include="..\src\3rd_party\fbgemm\third_party\cpuinfo\src\x86\windows\init.c">
       <TreatWarningAsError Condition="'$(Configuration)|$(Platform)'=='Release|x64'">false</TreatWarningAsError>
       <TreatWarningAsError Condition="'$(Configuration)|$(Platform)'=='Debug|x64'">false</TreatWarningAsError>
+      <WarningLevel Condition="'$(Configuration)|$(Platform)'=='Debug|x64'">TurnOffAllWarnings</WarningLevel>
+      <WarningLevel Condition="'$(Configuration)|$(Platform)'=='Release|x64'">TurnOffAllWarnings</WarningLevel>
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     <ClCompile Include="..\src\3rd_party\half_float\HalfPrecisionFloatTest.cpp">
       <ExcludedFromBuild Condition="'$(Configuration)|$(Platform)'=='Debug|x64'">true</ExcludedFromBuild>
       <ExcludedFromBuild Condition="'$(Configuration)|$(Platform)'=='Release|x64'">true</ExcludedFromBuild>
+      <WarningLevel Condition="'$(Configuration)|$(Platform)'=='Debug|x64'">TurnOffAllWarnings</WarningLevel>
+      <WarningLevel Condition="'$(Configuration)|$(Platform)'=='Release|x64'">TurnOffAllWarnings</WarningLevel>
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     <ClCompile Include="..\src\3rd_party\pathie-cpp\src\entry_iterator.cpp" />
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From 52c761898b71d69a69f1aaab38bd401afe8ec1b2 Mon Sep 17 00:00:00 2001
From: Martin Junczys-Dowmunt <Marcin.JunczysDowmunt@microsoft.com>
Date: Fri, 1 Nov 2019 21:22:38 +0000
Subject: [PATCH 756/838] Merged PR 10259: switch to change abort to exception

Switch to change abort to exception, first attempt.
---
 src/3rd_party/threadpool.h     |  1 +
 src/command/marian_decoder.cpp |  1 -
 src/common/logging.cpp         |  6 ++++
 src/common/logging.h           | 58 ++++++++++++++++++++++++++--------
 src/microsoft/quicksand.cpp    |  1 +
 5 files changed, 53 insertions(+), 14 deletions(-)

diff --git a/src/3rd_party/threadpool.h b/src/3rd_party/threadpool.h
index c97c8848f..d77ce43cb 100644
--- a/src/3rd_party/threadpool.h
+++ b/src/3rd_party/threadpool.h
@@ -107,6 +107,7 @@ class ThreadPool {
 // the constructor just launches some amount of workers
 inline ThreadPool::ThreadPool(size_t threads, size_t in_bound)
   : bound(in_bound), stop(false) {
+    ABORT_IF(getThrowExceptionOnAbort(), "Throwing of MarianRuntimeException not presently supported in threads");
     reserve(threads);
 }
 
diff --git a/src/command/marian_decoder.cpp b/src/command/marian_decoder.cpp
index 98efe49dd..1621a7f39 100644
--- a/src/command/marian_decoder.cpp
+++ b/src/command/marian_decoder.cpp
@@ -8,7 +8,6 @@
 
 int main(int argc, char** argv) {
   using namespace marian;
-
   auto options = parseOptions(argc, argv, cli::mode::translation);
   auto task = New<Translate<BeamSearch>>(options);
 
diff --git a/src/common/logging.cpp b/src/common/logging.cpp
index 57b55de96..93bfcbd89 100644
--- a/src/common/logging.cpp
+++ b/src/common/logging.cpp
@@ -14,6 +14,12 @@
 #define noinline __attribute__((noinline))
 #endif
 
+namespace marian {
+  static bool throwExceptionOnAbort = false;
+  bool getThrowExceptionOnAbort() { return throwExceptionOnAbort; }
+  void setThrowExceptionOnAbort(bool doThrowExceptionOnAbort) { throwExceptionOnAbort = doThrowExceptionOnAbort; };
+}
+
 std::shared_ptr<spdlog::logger> createStderrLogger(const std::string& name,
                                                    const std::string& pattern,
                                                    const std::vector<std::string>& files,
diff --git a/src/common/logging.h b/src/common/logging.h
index 2889d6c7f..6f292f619 100644
--- a/src/common/logging.h
+++ b/src/common/logging.h
@@ -4,9 +4,37 @@
 
 #include "spdlog/spdlog.h"
 
+
 namespace marian {
   void logCallStack(size_t skipLevels);
   std::string getCallStack(size_t skipLevels);
+
+  // Marian gives a basic exception guarantee. If you catch a
+  // MarianRuntimeError you must assume that the object can be 
+  // safely destructed, but cannot be used otherwise.
+
+  // Internal multi-threading in exception-throwing mode is not 
+  // allowed; and constructing a thread-pool will cause an exception.
+  
+  class MarianRuntimeException : public std::runtime_error {
+  private:
+    std::string callStack_;
+
+  public:
+    MarianRuntimeException(const std::string& message, const std::string& callStack) 
+    : std::runtime_error(message), 
+      callStack_(callStack) {}
+
+    const char* getCallStack() const throw() {
+      return callStack_.c_str();
+    }
+  };
+
+  // Get the state of throwExceptionOnAbort (see logging.cpp), by default false
+  bool getThrowExceptionOnAbort();
+
+  // Set the state of throwExceptionOnAbort (see logging.cpp)
+  void setThrowExceptionOnAbort(bool);
 }
 
 /**
@@ -57,19 +85,23 @@ namespace marian {
  *
  * @param ... Message text and variables
  */
-#define ABORT(...)                                                             \
-  do {                                                                         \
-    auto logger = spdlog::get("general");                                      \
-    if(logger == nullptr)                                                      \
-      logger = createStderrLogger("general", "[%Y-%m-%d %T] Error: %v");       \
-    else                                                                       \
-      logger->set_pattern("[%Y-%m-%d %T] Error: %v");                          \
-    checkedLog("general", "critical", __VA_ARGS__);                            \
-    checkedLog("general", "critical", "Aborted from {} in {}:{}",              \
-               FUNCTION_NAME, __FILE__, __LINE__);                             \
-    logger->set_pattern("%v");                                                 \
-    checkedLog("general", "critical", marian::getCallStack(/*skipLevels=*/0)); \
-    std::abort();                                                              \
+#define ABORT(...)                                                               \
+  do {                                                                           \
+    auto logger = spdlog::get("general");                                        \
+    if(logger == nullptr)                                                        \
+      logger = createStderrLogger("general", "[%Y-%m-%d %T] Error: %v");         \
+    else                                                                         \
+      logger->set_pattern("[%Y-%m-%d %T] Error: %v");                            \
+    checkedLog("general", "critical", __VA_ARGS__);                              \
+    checkedLog("general", "critical", "Aborted from {} in {}:{}",                \
+               FUNCTION_NAME, __FILE__, __LINE__);                               \
+    logger->set_pattern("%v");                                                   \
+    auto callStack = marian::getCallStack(/*skipLevels=*/0);                     \
+    checkedLog("general", "critical", callStack);                                \
+    if(marian::getThrowExceptionOnAbort())                                       \
+      throw marian::MarianRuntimeException(fmt::format(__VA_ARGS__), callStack); \
+    else                                                                         \
+      std::abort();                                                              \
   } while(0)
 
 /**
diff --git a/src/microsoft/quicksand.cpp b/src/microsoft/quicksand.cpp
index 20cb1bcc0..04993e706 100755
--- a/src/microsoft/quicksand.cpp
+++ b/src/microsoft/quicksand.cpp
@@ -190,6 +190,7 @@ Ptr<IBeamSearchDecoder> newDecoder(Ptr<Options> options,
                                    const std::vector<const void*>& ptrs,
                                    const std::vector<Ptr<IVocabWrapper>>& vocabs,
                                    WordIndex eosDummy) { // @TODO: remove this parameter
+  marian::setThrowExceptionOnAbort(true); // globally defined to throw now
   ABORT_IF(marian::Word::fromWordIndex(eosDummy) != std::dynamic_pointer_cast<VocabWrapper>(vocabs[1])->getVocab()->getEosId(), "Inconsistent eos vs. vocabs_[1]");
 
   return New<BeamSearchDecoder>(options, ptrs, vocabs/*, eos*/);

From 54fba7868e2a78d070f17808e62f5d28fab08b95 Mon Sep 17 00:00:00 2001
From: Marcin Junczys-Dowmunt <Marcin.JunczysDowmunt@microsoft.com>
Date: Fri, 1 Nov 2019 16:33:29 -0700
Subject: [PATCH 757/838] update changelog and version

---
 CHANGELOG.md | 4 ++++
 VERSION      | 2 +-
 2 files changed, 5 insertions(+), 1 deletion(-)

diff --git a/CHANGELOG.md b/CHANGELOG.md
index 9377b2a6c..03344bd76 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -8,6 +8,9 @@ and this project adheres to [Semantic Versioning](http://semver.org/spec/v2.0.0.
 ## [Unreleased]
 
 ### Added
+- Switch to change abort to exception, only to be used in library mode
+- Support for 16-bit packed models with FBGEMM
+- Multiple separated parameter types in ExpressionGraph, currently inference-only
 - Safe handling of sigterm signal
 - Automatic vectorization of elementwise operations on CPU for tensors dims that 
   are divible by 4 (AVX) and 8 (AVX2)
@@ -24,6 +27,7 @@ and this project adheres to [Semantic Versioning](http://semver.org/spec/v2.0.0.
 - Compilation with CUDA 10.1
 
 ### Changed
+- Removed autotuner for now, will be switched back on later
 - Boost depdendency is now optional and only required for marian_server 
   or for boost::regex when compiling with g++-4.9
 - Simplified file stream and temporary file handling
diff --git a/VERSION b/VERSION
index d72a3cbc1..ceec92322 100644
--- a/VERSION
+++ b/VERSION
@@ -1 +1 @@
-v1.8.7
+v1.8.10

From 1abd12520d8eae89c19d7dde4eec3ce44edcd946 Mon Sep 17 00:00:00 2001
From: Marcin Junczys-Dowmunt <Marcin.JunczysDowmunt@microsoft.com>
Date: Fri, 1 Nov 2019 16:37:12 -0700
Subject: [PATCH 758/838] bump version once more

---
 CHANGELOG.md | 2 ++
 VERSION      | 2 +-
 2 files changed, 3 insertions(+), 1 deletion(-)

diff --git a/CHANGELOG.md b/CHANGELOG.md
index 03344bd76..e4b29debd 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -8,6 +8,8 @@ and this project adheres to [Semantic Versioning](http://semver.org/spec/v2.0.0.
 ## [Unreleased]
 
 ### Added
+- Added --cite and --authors flag
+- Added optional support for ccache
 - Switch to change abort to exception, only to be used in library mode
 - Support for 16-bit packed models with FBGEMM
 - Multiple separated parameter types in ExpressionGraph, currently inference-only
diff --git a/VERSION b/VERSION
index ceec92322..6deeb5e3e 100644
--- a/VERSION
+++ b/VERSION
@@ -1 +1 @@
-v1.8.10
+v1.8.12

From 7886867576d96d32ca58ed53efb1c95dbb0a5c07 Mon Sep 17 00:00:00 2001
From: Marcin Junczys-Dowmunt <Marcin.JunczysDowmunt@microsoft.com>
Date: Fri, 1 Nov 2019 16:58:19 -0700
Subject: [PATCH 759/838] fix unit test

---
 src/tests/units/graph_tests.cpp | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/tests/units/graph_tests.cpp b/src/tests/units/graph_tests.cpp
index fd4d3006a..403b06e90 100644
--- a/src/tests/units/graph_tests.cpp
+++ b/src/tests/units/graph_tests.cpp
@@ -23,8 +23,8 @@ TEST_CASE("Expression graph can be initialized with constant values",
   for(auto type : std::vector<Type>({Type::float32, Type::float16})) {
 
     auto graph = New<ExpressionGraph>();
-    graph->setDevice({0, DeviceType::gpu});
     graph->setDefaultElementType(type);
+    graph->setDevice({0, DeviceType::gpu});
     graph->reserveWorkspaceMB(4);
 
     if(type == Type::float16) {

From baf4d299e96520821c2a2a24bbe08521f3885c0a Mon Sep 17 00:00:00 2001
From: Marcin Junczys-Dowmunt <Marcin.JunczysDowmunt@microsoft.com>
Date: Sun, 3 Nov 2019 15:36:13 -0800
Subject: [PATCH 760/838] make compile for GCC >= 7

---
 src/common/file_stream.cpp    |  8 ++++++++
 src/tensors/gpu/device.cu     |  9 ++++++++-
 src/tensors/rand.cpp          | 10 +++++++++-
 src/translator/nth_element.cu | 10 ++++++++--
 4 files changed, 33 insertions(+), 4 deletions(-)

diff --git a/src/common/file_stream.cpp b/src/common/file_stream.cpp
index 8bc513006..97d376b95 100644
--- a/src/common/file_stream.cpp
+++ b/src/common/file_stream.cpp
@@ -104,7 +104,15 @@ TemporaryFile::TemporaryFile(const std::string &base, bool earlyUnlink)
 
 TemporaryFile::~TemporaryFile() {
   if(!unlink_) {
+// suppress 'throw will always call terminate() [-Wterminate]'
+#if __GNUC__ >= 7
+#pragma GCC diagnostic push
+#pragma GCC diagnostic ignored "-Wterminate"
+#endif
     ABORT_IF(remove(file_.string().c_str()), "Error while deleting '{}'", file_.string());
+#if __GNUC__ >= 7
+#pragma GCC diagnostic pop
+#endif
   }
 }
 
diff --git a/src/tensors/gpu/device.cu b/src/tensors/gpu/device.cu
index a82b387ef..4e8fb0f72 100644
--- a/src/tensors/gpu/device.cu
+++ b/src/tensors/gpu/device.cu
@@ -8,12 +8,19 @@ namespace marian {
 namespace gpu {
 
 Device::~Device() {
-  // Note: The CUDA_CHECKs here are not throwing, but will terminate the program.
+// suppress 'throw will always call terminate() [-Wterminate]'
+#if __GNUC__ >= 7
+#pragma GCC diagnostic push
+#pragma GCC diagnostic ignored "-Wterminate"
+#endif
   CUDA_CHECK(cudaSetDevice(deviceId_.no));
   if(data_) {
     CUDA_CHECK(cudaFree(data_));
   }
   CUDA_CHECK(cudaDeviceSynchronize());
+#if __GNUC__ >= 7
+#pragma GCC diagnostic pop
+#endif
 }
 
 void Device::reserve(size_t size) {
diff --git a/src/tensors/rand.cpp b/src/tensors/rand.cpp
index bd3b4730d..ea5da23d9 100755
--- a/src/tensors/rand.cpp
+++ b/src/tensors/rand.cpp
@@ -82,7 +82,15 @@ CurandRandomGenerator::CurandRandomGenerator(size_t seed, DeviceId deviceId)
 CurandRandomGenerator::~CurandRandomGenerator() {
     if(deviceId_.type == DeviceType::gpu)
       cudaSetDevice((int)deviceId_.no);
-    CURAND_CHECK(curandDestroyGenerator(generator_));
+// suppress 'throw will always call terminate() [-Wterminate]'
+#if __GNUC__ >= 7
+#pragma GCC diagnostic push
+#pragma GCC diagnostic ignored "-Wterminate"
+#endif
+    CURAND_CHECK(curandDestroyGenerator(generator_)); // this might throw
+#if __GNUC__ >= 7
+#pragma GCC diagnostic pop
+#endif
 }
 
 void CurandRandomGenerator::uniform(Tensor tensor, float a, float b) {
diff --git a/src/translator/nth_element.cu b/src/translator/nth_element.cu
index 753a02056..d159b50e4 100644
--- a/src/translator/nth_element.cu
+++ b/src/translator/nth_element.cu
@@ -308,8 +308,11 @@ public:
   }
 
   ~NthElementGPU() {
-    cudaSetDevice(deviceId_.no);
-
+#if __GNUC__ >= 7
+#pragma GCC diagnostic push
+#pragma GCC diagnostic ignored "-Wterminate"
+#endif
+    CUDA_CHECK(cudaSetDevice(deviceId_.no));
     CUDA_CHECK(cudaFree(d_cumBeamSizes));
     CUDA_CHECK(cudaFree(d_batchPosition));
     CUDA_CHECK(cudaFree(d_breakdown));
@@ -319,6 +322,9 @@ public:
     CUDA_CHECK(cudaFree(d_res_idx));
     CUDA_CHECK(cudaFree(d_out));
     CUDA_CHECK(cudaFree(d_ind));
+#if __GNUC__ >= 7
+#pragma GCC diagnostic pop
+#endif
   }
 
 private:

From f042eae53fc9c844aa628b138998defe2ae49b5d Mon Sep 17 00:00:00 2001
From: Martin Junczys-Dowmunt <Marcin.JunczysDowmunt@microsoft.com>
Date: Tue, 5 Nov 2019 16:07:00 +0000
Subject: [PATCH 761/838] Merged PR 10268: Make compile for GCC >= 7

Removes error checking from destructors that might throw.
---
 src/common/file_stream.cpp    |  6 +++---
 src/tensors/gpu/device.cu     |  8 ++++----
 src/tensors/rand.cpp          |  7 ++++---
 src/translator/nth_element.cu | 20 ++++++++++----------
 4 files changed, 21 insertions(+), 20 deletions(-)

diff --git a/src/common/file_stream.cpp b/src/common/file_stream.cpp
index 8bc513006..15871254e 100644
--- a/src/common/file_stream.cpp
+++ b/src/common/file_stream.cpp
@@ -103,9 +103,9 @@ TemporaryFile::TemporaryFile(const std::string &base, bool earlyUnlink)
 }
 
 TemporaryFile::~TemporaryFile() {
-  if(!unlink_) {
-    ABORT_IF(remove(file_.string().c_str()), "Error while deleting '{}'", file_.string());
-  }
+  if(!unlink_)
+    // We do not check for errors here as this is the destructor and we cannot really fix an error anyway.
+    remove(file_.string().c_str()), "Error while deleting '{}'", file_.string();
 }
 
 void TemporaryFile::NormalizeTempPrefix(std::string &base) const {
diff --git a/src/tensors/gpu/device.cu b/src/tensors/gpu/device.cu
index a82b387ef..25997c750 100644
--- a/src/tensors/gpu/device.cu
+++ b/src/tensors/gpu/device.cu
@@ -8,12 +8,12 @@ namespace marian {
 namespace gpu {
 
 Device::~Device() {
-  // Note: The CUDA_CHECKs here are not throwing, but will terminate the program.
-  CUDA_CHECK(cudaSetDevice(deviceId_.no));
+  // No CUDA error checking as this is a destructor and we cannot do anything about errors anyway.
+  cudaSetDevice(deviceId_.no);
   if(data_) {
-    CUDA_CHECK(cudaFree(data_));
+    cudaFree(data_);
   }
-  CUDA_CHECK(cudaDeviceSynchronize());
+  cudaDeviceSynchronize();
 }
 
 void Device::reserve(size_t size) {
diff --git a/src/tensors/rand.cpp b/src/tensors/rand.cpp
index bd3b4730d..e6dbc46ed 100755
--- a/src/tensors/rand.cpp
+++ b/src/tensors/rand.cpp
@@ -80,9 +80,10 @@ CurandRandomGenerator::CurandRandomGenerator(size_t seed, DeviceId deviceId)
 }
 
 CurandRandomGenerator::~CurandRandomGenerator() {
-    if(deviceId_.type == DeviceType::gpu)
-      cudaSetDevice((int)deviceId_.no);
-    CURAND_CHECK(curandDestroyGenerator(generator_));
+  // No CUDA error checking as this is a destructor and we cannot do anything about errors anyway.
+  if(deviceId_.type == DeviceType::gpu)
+    cudaSetDevice((int)deviceId_.no);
+  curandDestroyGenerator(generator_);
 }
 
 void CurandRandomGenerator::uniform(Tensor tensor, float a, float b) {
diff --git a/src/translator/nth_element.cu b/src/translator/nth_element.cu
index 753a02056..e8786ee79 100644
--- a/src/translator/nth_element.cu
+++ b/src/translator/nth_element.cu
@@ -308,17 +308,17 @@ public:
   }
 
   ~NthElementGPU() {
+    // No CUDA error checking as this is a destructor and we cannot do anything about errors anyway.
     cudaSetDevice(deviceId_.no);
-
-    CUDA_CHECK(cudaFree(d_cumBeamSizes));
-    CUDA_CHECK(cudaFree(d_batchPosition));
-    CUDA_CHECK(cudaFree(d_breakdown));
-    CUDA_CHECK(cudaFreeHost(h_res_idx));
-    CUDA_CHECK(cudaFreeHost(h_res));
-    CUDA_CHECK(cudaFree(d_res));
-    CUDA_CHECK(cudaFree(d_res_idx));
-    CUDA_CHECK(cudaFree(d_out));
-    CUDA_CHECK(cudaFree(d_ind));
+    cudaFree(d_cumBeamSizes);
+    cudaFree(d_batchPosition);
+    cudaFree(d_breakdown);
+    cudaFreeHost(h_res_idx);
+    cudaFreeHost(h_res);
+    cudaFree(d_res);
+    cudaFree(d_res_idx);
+    cudaFree(d_out);
+    cudaFree(d_ind);
   }
 
 private:

From 78f671c39f453c31a53cab58cda44751eca0e178 Mon Sep 17 00:00:00 2001
From: Martin Junczys-Dowmunt <Marcin.JunczysDowmunt@microsoft.com>
Date: Tue, 5 Nov 2019 19:28:23 +0000
Subject: [PATCH 762/838] Merged PR 10297: Fast option look-up with lazy option
 contruction

Thie PR adds the FastOpt object and extends the Options object by
* Using YAML only as a background option container that is used for lazily updating the FastOpt object.
* The FastOpt object gets rebuilt whenever an access is about to occur and the doLazyRebuild_ flag is set.
* This will be the case whenever the underlying YAML object has been modified through the public interface.
* The YAML object itself it not accessible by the outside anymore (or only through cloning) to avoid untrackable modifications.

This seems to result in a >20% speed-up for decoding.
---
 src/3rd_party/catch.hpp             | 23602 ++++++++++++++++----------
 src/3rd_party/phf/LICENSE           |    19 +
 src/3rd_party/phf/README.md         |   182 +
 src/3rd_party/phf/phf.cc            |  1478 ++
 src/3rd_party/phf/phf.h             |   299 +
 src/CMakeLists.txt                  |     5 +-
 src/command/marian_conv.cpp         |     4 +-
 src/command/marian_vocab.cpp        |     6 +-
 src/common/cli_wrapper.cpp          |    11 +-
 src/common/cli_wrapper.h            |    13 +-
 src/common/config.cpp               |     2 +-
 src/common/fastopt.cpp              |    89 +
 src/common/fastopt.h                |   364 +
 src/common/options.cpp              |    55 +-
 src/common/options.h                |    55 +-
 src/data/corpus_base.cpp            |    51 +-
 src/graph/expression_operators.cpp  |     4 +-
 src/layers/factory.h                |    12 +-
 src/layers/generic.h                |     4 +-
 src/microsoft/quicksand.cpp         |     2 +-
 src/models/bert.h                   |     2 +-
 src/models/encoder_classifier.h     |     5 +-
 src/models/encoder_decoder.cpp      |     5 +-
 src/models/s2s.h                    |     3 +-
 src/models/transformer.h            |    48 +-
 src/tests/cli.cpp                   |     6 +-
 src/tests/units/CMakeLists.txt      |     1 +
 src/tests/units/attention_tests.cpp |     0
 src/tests/units/fastopt_tests.cpp   |    76 +
 src/tests/units/operator_tests.cpp  |     0
 src/tests/units/rnn_tests.cpp       |     0
 src/training/scheduler.h            |     3 +-
 src/translator/translator.h         |     4 +-
 33 files changed, 17470 insertions(+), 8940 deletions(-)
 create mode 100644 src/3rd_party/phf/LICENSE
 create mode 100644 src/3rd_party/phf/README.md
 create mode 100644 src/3rd_party/phf/phf.cc
 create mode 100644 src/3rd_party/phf/phf.h
 create mode 100644 src/common/fastopt.cpp
 create mode 100644 src/common/fastopt.h
 mode change 100755 => 100644 src/tests/units/attention_tests.cpp
 create mode 100644 src/tests/units/fastopt_tests.cpp
 mode change 100755 => 100644 src/tests/units/operator_tests.cpp
 mode change 100755 => 100644 src/tests/units/rnn_tests.cpp

diff --git a/src/3rd_party/catch.hpp b/src/3rd_party/catch.hpp
index 621f6e667..5d104bc46 100644
--- a/src/3rd_party/catch.hpp
+++ b/src/3rd_party/catch.hpp
@@ -1,17 +1,21 @@
 /*
- *  Catch v1.9.4
- *  Generated: 2017-05-16 13:51:55.506519
+ *  Catch v2.10.1
+ *  Generated: 2019-10-20 20:52:21.372334
  *  ----------------------------------------------------------
  *  This file has been merged from multiple headers. Please don't edit it directly
- *  Copyright (c) 2012 Two Blue Cubes Ltd. All rights reserved.
+ *  Copyright (c) 2019 Two Blue Cubes Ltd. All rights reserved.
  *
  *  Distributed under the Boost Software License, Version 1.0. (See accompanying
  *  file LICENSE_1_0.txt or copy at http://www.boost.org/LICENSE_1_0.txt)
  */
 #ifndef TWOBLUECUBES_SINGLE_INCLUDE_CATCH_HPP_INCLUDED
 #define TWOBLUECUBES_SINGLE_INCLUDE_CATCH_HPP_INCLUDED
+// start catch.hpp
 
-#define TWOBLUECUBES_CATCH_HPP_INCLUDED
+
+#define CATCH_VERSION_MAJOR 2
+#define CATCH_VERSION_MINOR 10
+#define CATCH_VERSION_PATCH 1
 
 #ifdef __clang__
 #    pragma clang system_header
@@ -19,36 +23,66 @@
 #    pragma GCC system_header
 #endif
 
-// #included from: internal/catch_suppress_warnings.h
+// start catch_suppress_warnings.h
 
 #ifdef __clang__
 #   ifdef __ICC // icpc defines the __clang__ macro
 #       pragma warning(push)
 #       pragma warning(disable: 161 1682)
 #   else // __ICC
-#       pragma clang diagnostic ignored "-Wglobal-constructors"
-#       pragma clang diagnostic ignored "-Wvariadic-macros"
-#       pragma clang diagnostic ignored "-Wc99-extensions"
-#       pragma clang diagnostic ignored "-Wunused-variable"
 #       pragma clang diagnostic push
 #       pragma clang diagnostic ignored "-Wpadded"
-#       pragma clang diagnostic ignored "-Wc++98-compat"
-#       pragma clang diagnostic ignored "-Wc++98-compat-pedantic"
 #       pragma clang diagnostic ignored "-Wswitch-enum"
 #       pragma clang diagnostic ignored "-Wcovered-switch-default"
 #    endif
 #elif defined __GNUC__
-#    pragma GCC diagnostic ignored "-Wvariadic-macros"
-#    pragma GCC diagnostic ignored "-Wunused-variable"
-#    pragma GCC diagnostic ignored "-Wparentheses"
+     // Because REQUIREs trigger GCC's -Wparentheses, and because still
+     // supported version of g++ have only buggy support for _Pragmas,
+     // Wparentheses have to be suppressed globally.
+#    pragma GCC diagnostic ignored "-Wparentheses" // See #674 for details
 
 #    pragma GCC diagnostic push
+#    pragma GCC diagnostic ignored "-Wunused-variable"
 #    pragma GCC diagnostic ignored "-Wpadded"
 #endif
+// end catch_suppress_warnings.h
 #if defined(CATCH_CONFIG_MAIN) || defined(CATCH_CONFIG_RUNNER)
 #  define CATCH_IMPL
+#  define CATCH_CONFIG_ALL_PARTS
+#endif
+
+// In the impl file, we want to have access to all parts of the headers
+// Can also be used to sanely support PCHs
+#if defined(CATCH_CONFIG_ALL_PARTS)
+#  define CATCH_CONFIG_EXTERNAL_INTERFACES
+#  if defined(CATCH_CONFIG_DISABLE_MATCHERS)
+#    undef CATCH_CONFIG_DISABLE_MATCHERS
+#  endif
+#  if !defined(CATCH_CONFIG_ENABLE_CHRONO_STRINGMAKER)
+#    define CATCH_CONFIG_ENABLE_CHRONO_STRINGMAKER
+#  endif
+#endif
+
+#if !defined(CATCH_CONFIG_IMPL_ONLY)
+// start catch_platform.h
+
+#ifdef __APPLE__
+# include <TargetConditionals.h>
+# if TARGET_OS_OSX == 1
+#  define CATCH_PLATFORM_MAC
+# elif TARGET_OS_IPHONE == 1
+#  define CATCH_PLATFORM_IPHONE
+# endif
+
+#elif defined(linux) || defined(__linux) || defined(__linux__)
+#  define CATCH_PLATFORM_LINUX
+
+#elif defined(WIN32) || defined(__WIN32__) || defined(_WIN32) || defined(_MSC_VER) || defined(__MINGW32__)
+#  define CATCH_PLATFORM_WINDOWS
 #endif
 
+// end catch_platform.h
+
 #ifdef CATCH_IMPL
 #  ifndef CLARA_CONFIG_MAIN
 #    define CLARA_CONFIG_MAIN_NOT_DEFINED
@@ -56,74 +90,59 @@
 #  endif
 #endif
 
-// #included from: internal/catch_notimplemented_exception.h
-#define TWOBLUECUBES_CATCH_NOTIMPLEMENTED_EXCEPTION_H_INCLUDED
+// start catch_user_interfaces.h
+
+namespace Catch {
+    unsigned int rngSeed();
+}
+
+// end catch_user_interfaces.h
+// start catch_tag_alias_autoregistrar.h
 
-// #included from: catch_common.h
-#define TWOBLUECUBES_CATCH_COMMON_H_INCLUDED
+// start catch_common.h
 
-// #included from: catch_compiler_capabilities.h
-#define TWOBLUECUBES_CATCH_COMPILER_CAPABILITIES_HPP_INCLUDED
+// start catch_compiler_capabilities.h
 
-// Detect a number of compiler features - mostly C++11/14 conformance - by compiler
+// Detect a number of compiler features - by compiler
 // The following features are defined:
 //
-// CATCH_CONFIG_CPP11_NULLPTR : is nullptr supported?
-// CATCH_CONFIG_CPP11_NOEXCEPT : is noexcept supported?
-// CATCH_CONFIG_CPP11_GENERATED_METHODS : The delete and default keywords for compiler generated methods
-// CATCH_CONFIG_CPP11_IS_ENUM : std::is_enum is supported?
-// CATCH_CONFIG_CPP11_TUPLE : std::tuple is supported
-// CATCH_CONFIG_CPP11_LONG_LONG : is long long supported?
-// CATCH_CONFIG_CPP11_OVERRIDE : is override supported?
-// CATCH_CONFIG_CPP11_UNIQUE_PTR : is unique_ptr supported (otherwise use auto_ptr)
-// CATCH_CONFIG_CPP11_SHUFFLE : is std::shuffle supported?
-// CATCH_CONFIG_CPP11_TYPE_TRAITS : are type_traits and enable_if supported?
-
-// CATCH_CONFIG_CPP11_OR_GREATER : Is C++11 supported?
-
-// CATCH_CONFIG_VARIADIC_MACROS : are variadic macros supported?
 // CATCH_CONFIG_COUNTER : is the __COUNTER__ macro supported?
 // CATCH_CONFIG_WINDOWS_SEH : is Windows SEH supported?
 // CATCH_CONFIG_POSIX_SIGNALS : are POSIX signals supported?
+// CATCH_CONFIG_DISABLE_EXCEPTIONS : Are exceptions enabled?
 // ****************
 // Note to maintainers: if new toggles are added please document them
 // in configuration.md, too
 // ****************
 
 // In general each macro has a _NO_<feature name> form
-// (e.g. CATCH_CONFIG_CPP11_NO_NULLPTR) which disables the feature.
+// (e.g. CATCH_CONFIG_NO_POSIX_SIGNALS) which disables the feature.
 // Many features, at point of detection, define an _INTERNAL_ macro, so they
 // can be combined, en-mass, with the _NO_ forms later.
 
-// All the C++11 features can be disabled with CATCH_CONFIG_NO_CPP11
-
 #ifdef __cplusplus
 
-#  if __cplusplus >= 201103L
-#    define CATCH_CPP11_OR_GREATER
+#  if (__cplusplus >= 201402L) || (defined(_MSVC_LANG) && _MSVC_LANG >= 201402L)
+#    define CATCH_CPP14_OR_GREATER
 #  endif
 
-#  if __cplusplus >= 201402L
-#    define CATCH_CPP14_OR_GREATER
+#  if (__cplusplus >= 201703L) || (defined(_MSVC_LANG) && _MSVC_LANG >= 201703L)
+#    define CATCH_CPP17_OR_GREATER
 #  endif
 
 #endif
 
-#ifdef __clang__
-
-#  if __has_feature(cxx_nullptr)
-#    define CATCH_INTERNAL_CONFIG_CPP11_NULLPTR
-#  endif
+#if defined(CATCH_CPP17_OR_GREATER)
+#  define CATCH_INTERNAL_CONFIG_CPP17_UNCAUGHT_EXCEPTIONS
+#endif
 
-#  if __has_feature(cxx_noexcept)
-#    define CATCH_INTERNAL_CONFIG_CPP11_NOEXCEPT
-#  endif
+#ifdef __clang__
 
-#   if defined(CATCH_CPP11_OR_GREATER)
-#       define CATCH_INTERNAL_SUPPRESS_ETD_WARNINGS \
+#       define CATCH_INTERNAL_SUPPRESS_GLOBALS_WARNINGS \
             _Pragma( "clang diagnostic push" ) \
-            _Pragma( "clang diagnostic ignored \"-Wexit-time-destructors\"" )
-#       define CATCH_INTERNAL_UNSUPPRESS_ETD_WARNINGS \
+            _Pragma( "clang diagnostic ignored \"-Wexit-time-destructors\"" ) \
+            _Pragma( "clang diagnostic ignored \"-Wglobal-constructors\"")
+#       define CATCH_INTERNAL_UNSUPPRESS_GLOBALS_WARNINGS \
             _Pragma( "clang diagnostic pop" )
 
 #       define CATCH_INTERNAL_SUPPRESS_PARENTHESES_WARNINGS \
@@ -131,238 +150,298 @@
             _Pragma( "clang diagnostic ignored \"-Wparentheses\"" )
 #       define CATCH_INTERNAL_UNSUPPRESS_PARENTHESES_WARNINGS \
             _Pragma( "clang diagnostic pop" )
-#   endif
 
-#endif // __clang__
+#       define CATCH_INTERNAL_SUPPRESS_UNUSED_WARNINGS \
+            _Pragma( "clang diagnostic push" ) \
+            _Pragma( "clang diagnostic ignored \"-Wunused-variable\"" )
+#       define CATCH_INTERNAL_UNSUPPRESS_UNUSED_WARNINGS \
+            _Pragma( "clang diagnostic pop" )
 
-////////////////////////////////////////////////////////////////////////////////
-// We know some environments not to support full POSIX signals
-#if defined(__CYGWIN__) || defined(__QNX__)
+#       define CATCH_INTERNAL_SUPPRESS_ZERO_VARIADIC_WARNINGS \
+            _Pragma( "clang diagnostic push" ) \
+            _Pragma( "clang diagnostic ignored \"-Wgnu-zero-variadic-macro-arguments\"" )
+#       define CATCH_INTERNAL_UNSUPPRESS_ZERO_VARIADIC_WARNINGS \
+            _Pragma( "clang diagnostic pop" )
 
-#   if !defined(CATCH_CONFIG_POSIX_SIGNALS)
-#       define CATCH_INTERNAL_CONFIG_NO_POSIX_SIGNALS
-#   endif
+#       define CATCH_INTERNAL_SUPPRESS_UNUSED_TEMPLATE_WARNINGS \
+            _Pragma( "clang diagnostic push" ) \
+            _Pragma( "clang diagnostic ignored \"-Wunused-template\"" )
+#       define CATCH_INTERNAL_UNSUPPRESS_UNUSED_TEMPLATE_WARNINGS \
+            _Pragma( "clang diagnostic pop" )
+#endif // __clang__
 
+////////////////////////////////////////////////////////////////////////////////
+// Assume that non-Windows platforms support posix signals by default
+#if !defined(CATCH_PLATFORM_WINDOWS)
+    #define CATCH_INTERNAL_CONFIG_POSIX_SIGNALS
 #endif
 
 ////////////////////////////////////////////////////////////////////////////////
-// Cygwin
-#ifdef __CYGWIN__
-
-// Required for some versions of Cygwin to declare gettimeofday
-// see: http://stackoverflow.com/questions/36901803/gettimeofday-not-declared-in-this-scope-cygwin
-#   define _BSD_SOURCE
+// We know some environments not to support full POSIX signals
+#if defined(__CYGWIN__) || defined(__QNX__) || defined(__EMSCRIPTEN__) || defined(__DJGPP__)
+    #define CATCH_INTERNAL_CONFIG_NO_POSIX_SIGNALS
+#endif
 
-#endif // __CYGWIN__
+#ifdef __OS400__
+#       define CATCH_INTERNAL_CONFIG_NO_POSIX_SIGNALS
+#       define CATCH_CONFIG_COLOUR_NONE
+#endif
 
 ////////////////////////////////////////////////////////////////////////////////
-// Borland
-#ifdef __BORLANDC__
-
-#endif // __BORLANDC__
+// Android somehow still does not support std::to_string
+#if defined(__ANDROID__)
+#    define CATCH_INTERNAL_CONFIG_NO_CPP11_TO_STRING
+#    define CATCH_INTERNAL_CONFIG_ANDROID_LOGWRITE
+#endif
 
 ////////////////////////////////////////////////////////////////////////////////
-// EDG
-#ifdef __EDG_VERSION__
-
-#endif // __EDG_VERSION__
+// Not all Windows environments support SEH properly
+#if defined(__MINGW32__)
+#    define CATCH_INTERNAL_CONFIG_NO_WINDOWS_SEH
+#endif
 
 ////////////////////////////////////////////////////////////////////////////////
-// Digital Mars
-#ifdef __DMC__
-
-#endif // __DMC__
+// PS4
+#if defined(__ORBIS__)
+#    define CATCH_INTERNAL_CONFIG_NO_NEW_CAPTURE
+#endif
 
 ////////////////////////////////////////////////////////////////////////////////
-// GCC
-#ifdef __GNUC__
+// Cygwin
+#ifdef __CYGWIN__
 
-#   if __GNUC__ == 4 && __GNUC_MINOR__ >= 6 && defined(__GXX_EXPERIMENTAL_CXX0X__)
-#       define CATCH_INTERNAL_CONFIG_CPP11_NULLPTR
-#   endif
+// Required for some versions of Cygwin to declare gettimeofday
+// see: http://stackoverflow.com/questions/36901803/gettimeofday-not-declared-in-this-scope-cygwin
+#   define _BSD_SOURCE
+// some versions of cygwin (most) do not support std::to_string. Use the libstd check.
+// https://gcc.gnu.org/onlinedocs/gcc-4.8.2/libstdc++/api/a01053_source.html line 2812-2813
+# if !((__cplusplus >= 201103L) && defined(_GLIBCXX_USE_C99) \
+           && !defined(_GLIBCXX_HAVE_BROKEN_VSWPRINTF))
 
-// - otherwise more recent versions define __cplusplus >= 201103L
-// and will get picked up below
+#    define CATCH_INTERNAL_CONFIG_NO_CPP11_TO_STRING
 
-#endif // __GNUC__
+# endif
+#endif // __CYGWIN__
 
 ////////////////////////////////////////////////////////////////////////////////
 // Visual C++
 #ifdef _MSC_VER
 
-#define CATCH_INTERNAL_CONFIG_WINDOWS_SEH
+#  if _MSC_VER >= 1900 // Visual Studio 2015 or newer
+#    define CATCH_INTERNAL_CONFIG_CPP17_UNCAUGHT_EXCEPTIONS
+#  endif
 
-#if (_MSC_VER >= 1600)
-#   define CATCH_INTERNAL_CONFIG_CPP11_NULLPTR
-#   define CATCH_INTERNAL_CONFIG_CPP11_UNIQUE_PTR
-#endif
+// Universal Windows platform does not support SEH
+// Or console colours (or console at all...)
+#  if defined(WINAPI_FAMILY) && (WINAPI_FAMILY == WINAPI_FAMILY_APP)
+#    define CATCH_CONFIG_COLOUR_NONE
+#  else
+#    define CATCH_INTERNAL_CONFIG_WINDOWS_SEH
+#  endif
 
-#if (_MSC_VER >= 1900 ) // (VC++ 13 (VS2015))
-#define CATCH_INTERNAL_CONFIG_CPP11_NOEXCEPT
-#define CATCH_INTERNAL_CONFIG_CPP11_GENERATED_METHODS
-#define CATCH_INTERNAL_CONFIG_CPP11_SHUFFLE
-#define CATCH_INTERNAL_CONFIG_CPP11_TYPE_TRAITS
-#endif
+// MSVC traditional preprocessor needs some workaround for __VA_ARGS__
+// _MSVC_TRADITIONAL == 0 means new conformant preprocessor
+// _MSVC_TRADITIONAL == 1 means old traditional non-conformant preprocessor
+#  if !defined(_MSVC_TRADITIONAL) || (defined(_MSVC_TRADITIONAL) && _MSVC_TRADITIONAL)
+#    define CATCH_INTERNAL_CONFIG_TRADITIONAL_MSVC_PREPROCESSOR
+#  endif
+#endif // _MSC_VER
 
+#if defined(_REENTRANT) || defined(_MSC_VER)
+// Enable async processing, as -pthread is specified or no additional linking is required
+# define CATCH_INTERNAL_CONFIG_USE_ASYNC
 #endif // _MSC_VER
 
 ////////////////////////////////////////////////////////////////////////////////
-
-// Use variadic macros if the compiler supports them
-#if ( defined _MSC_VER && _MSC_VER > 1400 && !defined __EDGE__) || \
-    ( defined __WAVE__ && __WAVE_HAS_VARIADICS ) || \
-    ( defined __GNUC__ && __GNUC__ >= 3 ) || \
-    ( !defined __cplusplus && __STDC_VERSION__ >= 199901L || __cplusplus >= 201103L )
-
-#define CATCH_INTERNAL_CONFIG_VARIADIC_MACROS
-
+// Check if we are compiled with -fno-exceptions or equivalent
+#if defined(__EXCEPTIONS) || defined(__cpp_exceptions) || defined(_CPPUNWIND)
+#  define CATCH_INTERNAL_CONFIG_EXCEPTIONS_ENABLED
 #endif
 
-// Use __COUNTER__ if the compiler supports it
-#if ( defined _MSC_VER && _MSC_VER >= 1300 ) || \
-    ( defined __GNUC__  && __GNUC__ >= 4 && __GNUC_MINOR__ >= 3 ) || \
-    ( defined __clang__ && __clang_major__ >= 3 )
-
-#define CATCH_INTERNAL_CONFIG_COUNTER
+////////////////////////////////////////////////////////////////////////////////
+// DJGPP
+#ifdef __DJGPP__
+#  define CATCH_INTERNAL_CONFIG_NO_WCHAR
+#endif // __DJGPP__
 
+////////////////////////////////////////////////////////////////////////////////
+// Embarcadero C++Build
+#if defined(__BORLANDC__)
+    #define CATCH_INTERNAL_CONFIG_POLYFILL_ISNAN
 #endif
 
 ////////////////////////////////////////////////////////////////////////////////
-// C++ language feature support
-
-// catch all support for C++11
-#if defined(CATCH_CPP11_OR_GREATER)
-
-#  if !defined(CATCH_INTERNAL_CONFIG_CPP11_NULLPTR)
-#    define CATCH_INTERNAL_CONFIG_CPP11_NULLPTR
-#  endif
 
-#  ifndef CATCH_INTERNAL_CONFIG_CPP11_NOEXCEPT
-#    define CATCH_INTERNAL_CONFIG_CPP11_NOEXCEPT
-#  endif
-
-#  ifndef CATCH_INTERNAL_CONFIG_CPP11_GENERATED_METHODS
-#    define CATCH_INTERNAL_CONFIG_CPP11_GENERATED_METHODS
-#  endif
-
-#  ifndef CATCH_INTERNAL_CONFIG_CPP11_IS_ENUM
-#    define CATCH_INTERNAL_CONFIG_CPP11_IS_ENUM
-#  endif
-
-#  ifndef CATCH_INTERNAL_CONFIG_CPP11_TUPLE
-#    define CATCH_INTERNAL_CONFIG_CPP11_TUPLE
-#  endif
+// Use of __COUNTER__ is suppressed during code analysis in
+// CLion/AppCode 2017.2.x and former, because __COUNTER__ is not properly
+// handled by it.
+// Otherwise all supported compilers support COUNTER macro,
+// but user still might want to turn it off
+#if ( !defined(__JETBRAINS_IDE__) || __JETBRAINS_IDE__ >= 20170300L )
+    #define CATCH_INTERNAL_CONFIG_COUNTER
+#endif
 
-#  ifndef CATCH_INTERNAL_CONFIG_VARIADIC_MACROS
-#    define CATCH_INTERNAL_CONFIG_VARIADIC_MACROS
-#  endif
+////////////////////////////////////////////////////////////////////////////////
 
-#  if !defined(CATCH_INTERNAL_CONFIG_CPP11_LONG_LONG)
-#    define CATCH_INTERNAL_CONFIG_CPP11_LONG_LONG
-#  endif
+// RTX is a special version of Windows that is real time.
+// This means that it is detected as Windows, but does not provide
+// the same set of capabilities as real Windows does.
+#if defined(UNDER_RTSS) || defined(RTX64_BUILD)
+    #define CATCH_INTERNAL_CONFIG_NO_WINDOWS_SEH
+    #define CATCH_INTERNAL_CONFIG_NO_ASYNC
+    #define CATCH_CONFIG_COLOUR_NONE
+#endif
 
-#  if !defined(CATCH_INTERNAL_CONFIG_CPP11_OVERRIDE)
-#    define CATCH_INTERNAL_CONFIG_CPP11_OVERRIDE
-#  endif
-#  if !defined(CATCH_INTERNAL_CONFIG_CPP11_UNIQUE_PTR)
-#    define CATCH_INTERNAL_CONFIG_CPP11_UNIQUE_PTR
-#  endif
-# if !defined(CATCH_INTERNAL_CONFIG_CPP11_SHUFFLE)
-#   define CATCH_INTERNAL_CONFIG_CPP11_SHUFFLE
-#  endif
-# if !defined(CATCH_INTERNAL_CONFIG_CPP11_TYPE_TRAITS)
-#  define CATCH_INTERNAL_CONFIG_CPP11_TYPE_TRAITS
-# endif
+#if defined(__UCLIBC__)
+#define CATCH_INTERNAL_CONFIG_GLOBAL_NEXTAFTER
+#endif
 
-#endif // __cplusplus >= 201103L
+// Various stdlib support checks that require __has_include
+#if defined(__has_include)
+  // Check if string_view is available and usable
+  #if __has_include(<string_view>) && defined(CATCH_CPP17_OR_GREATER)
+  #    define CATCH_INTERNAL_CONFIG_CPP17_STRING_VIEW
+  #endif
+
+  // Check if optional is available and usable
+  #  if __has_include(<optional>) && defined(CATCH_CPP17_OR_GREATER)
+  #    define CATCH_INTERNAL_CONFIG_CPP17_OPTIONAL
+  #  endif // __has_include(<optional>) && defined(CATCH_CPP17_OR_GREATER)
+
+  // Check if byte is available and usable
+  #  if __has_include(<cstddef>) && defined(CATCH_CPP17_OR_GREATER)
+  #    define CATCH_INTERNAL_CONFIG_CPP17_BYTE
+  #  endif // __has_include(<cstddef>) && defined(CATCH_CPP17_OR_GREATER)
+
+  // Check if variant is available and usable
+  #  if __has_include(<variant>) && defined(CATCH_CPP17_OR_GREATER)
+  #    if defined(__clang__) && (__clang_major__ < 8)
+         // work around clang bug with libstdc++ https://bugs.llvm.org/show_bug.cgi?id=31852
+         // fix should be in clang 8, workaround in libstdc++ 8.2
+  #      include <ciso646>
+  #      if defined(__GLIBCXX__) && defined(_GLIBCXX_RELEASE) && (_GLIBCXX_RELEASE < 9)
+  #        define CATCH_CONFIG_NO_CPP17_VARIANT
+  #      else
+  #        define CATCH_INTERNAL_CONFIG_CPP17_VARIANT
+  #      endif // defined(__GLIBCXX__) && defined(_GLIBCXX_RELEASE) && (_GLIBCXX_RELEASE < 9)
+  #    else
+  #      define CATCH_INTERNAL_CONFIG_CPP17_VARIANT
+  #    endif // defined(__clang__) && (__clang_major__ < 8)
+  #  endif // __has_include(<variant>) && defined(CATCH_CPP17_OR_GREATER)
+#endif // defined(__has_include)
+
+#if defined(CATCH_INTERNAL_CONFIG_COUNTER) && !defined(CATCH_CONFIG_NO_COUNTER) && !defined(CATCH_CONFIG_COUNTER)
+#   define CATCH_CONFIG_COUNTER
+#endif
+#if defined(CATCH_INTERNAL_CONFIG_WINDOWS_SEH) && !defined(CATCH_CONFIG_NO_WINDOWS_SEH) && !defined(CATCH_CONFIG_WINDOWS_SEH) && !defined(CATCH_INTERNAL_CONFIG_NO_WINDOWS_SEH)
+#   define CATCH_CONFIG_WINDOWS_SEH
+#endif
+// This is set by default, because we assume that unix compilers are posix-signal-compatible by default.
+#if defined(CATCH_INTERNAL_CONFIG_POSIX_SIGNALS) && !defined(CATCH_INTERNAL_CONFIG_NO_POSIX_SIGNALS) && !defined(CATCH_CONFIG_NO_POSIX_SIGNALS) && !defined(CATCH_CONFIG_POSIX_SIGNALS)
+#   define CATCH_CONFIG_POSIX_SIGNALS
+#endif
+// This is set by default, because we assume that compilers with no wchar_t support are just rare exceptions.
+#if !defined(CATCH_INTERNAL_CONFIG_NO_WCHAR) && !defined(CATCH_CONFIG_NO_WCHAR) && !defined(CATCH_CONFIG_WCHAR)
+#   define CATCH_CONFIG_WCHAR
+#endif
 
-// Now set the actual defines based on the above + anything the user has configured
-#if defined(CATCH_INTERNAL_CONFIG_CPP11_NULLPTR) && !defined(CATCH_CONFIG_CPP11_NO_NULLPTR) && !defined(CATCH_CONFIG_CPP11_NULLPTR) && !defined(CATCH_CONFIG_NO_CPP11)
-#   define CATCH_CONFIG_CPP11_NULLPTR
+#if !defined(CATCH_INTERNAL_CONFIG_NO_CPP11_TO_STRING) && !defined(CATCH_CONFIG_NO_CPP11_TO_STRING) && !defined(CATCH_CONFIG_CPP11_TO_STRING)
+#    define CATCH_CONFIG_CPP11_TO_STRING
 #endif
-#if defined(CATCH_INTERNAL_CONFIG_CPP11_NOEXCEPT) && !defined(CATCH_CONFIG_CPP11_NO_NOEXCEPT) && !defined(CATCH_CONFIG_CPP11_NOEXCEPT) && !defined(CATCH_CONFIG_NO_CPP11)
-#   define CATCH_CONFIG_CPP11_NOEXCEPT
+
+#if defined(CATCH_INTERNAL_CONFIG_CPP17_OPTIONAL) && !defined(CATCH_CONFIG_NO_CPP17_OPTIONAL) && !defined(CATCH_CONFIG_CPP17_OPTIONAL)
+#  define CATCH_CONFIG_CPP17_OPTIONAL
 #endif
-#if defined(CATCH_INTERNAL_CONFIG_CPP11_GENERATED_METHODS) && !defined(CATCH_CONFIG_CPP11_NO_GENERATED_METHODS) && !defined(CATCH_CONFIG_CPP11_GENERATED_METHODS) && !defined(CATCH_CONFIG_NO_CPP11)
-#   define CATCH_CONFIG_CPP11_GENERATED_METHODS
+
+#if defined(CATCH_INTERNAL_CONFIG_CPP17_UNCAUGHT_EXCEPTIONS) && !defined(CATCH_CONFIG_NO_CPP17_UNCAUGHT_EXCEPTIONS) && !defined(CATCH_CONFIG_CPP17_UNCAUGHT_EXCEPTIONS)
+#  define CATCH_CONFIG_CPP17_UNCAUGHT_EXCEPTIONS
 #endif
-#if defined(CATCH_INTERNAL_CONFIG_CPP11_IS_ENUM) && !defined(CATCH_CONFIG_CPP11_NO_IS_ENUM) && !defined(CATCH_CONFIG_CPP11_IS_ENUM) && !defined(CATCH_CONFIG_NO_CPP11)
-#   define CATCH_CONFIG_CPP11_IS_ENUM
+
+#if defined(CATCH_INTERNAL_CONFIG_CPP17_STRING_VIEW) && !defined(CATCH_CONFIG_NO_CPP17_STRING_VIEW) && !defined(CATCH_CONFIG_CPP17_STRING_VIEW)
+#  define CATCH_CONFIG_CPP17_STRING_VIEW
 #endif
-#if defined(CATCH_INTERNAL_CONFIG_CPP11_TUPLE) && !defined(CATCH_CONFIG_CPP11_NO_TUPLE) && !defined(CATCH_CONFIG_CPP11_TUPLE) && !defined(CATCH_CONFIG_NO_CPP11)
-#   define CATCH_CONFIG_CPP11_TUPLE
+
+#if defined(CATCH_INTERNAL_CONFIG_CPP17_VARIANT) && !defined(CATCH_CONFIG_NO_CPP17_VARIANT) && !defined(CATCH_CONFIG_CPP17_VARIANT)
+#  define CATCH_CONFIG_CPP17_VARIANT
 #endif
-#if defined(CATCH_INTERNAL_CONFIG_VARIADIC_MACROS) && !defined(CATCH_CONFIG_NO_VARIADIC_MACROS) && !defined(CATCH_CONFIG_VARIADIC_MACROS)
-#   define CATCH_CONFIG_VARIADIC_MACROS
+
+#if defined(CATCH_INTERNAL_CONFIG_CPP17_BYTE) && !defined(CATCH_CONFIG_NO_CPP17_BYTE) && !defined(CATCH_CONFIG_CPP17_BYTE)
+#  define CATCH_CONFIG_CPP17_BYTE
 #endif
-#if defined(CATCH_INTERNAL_CONFIG_CPP11_LONG_LONG) && !defined(CATCH_CONFIG_CPP11_NO_LONG_LONG) && !defined(CATCH_CONFIG_CPP11_LONG_LONG) && !defined(CATCH_CONFIG_NO_CPP11)
-#   define CATCH_CONFIG_CPP11_LONG_LONG
+
+#if defined(CATCH_CONFIG_EXPERIMENTAL_REDIRECT)
+#  define CATCH_INTERNAL_CONFIG_NEW_CAPTURE
 #endif
-#if defined(CATCH_INTERNAL_CONFIG_CPP11_OVERRIDE) && !defined(CATCH_CONFIG_CPP11_NO_OVERRIDE) && !defined(CATCH_CONFIG_CPP11_OVERRIDE) && !defined(CATCH_CONFIG_NO_CPP11)
-#   define CATCH_CONFIG_CPP11_OVERRIDE
+
+#if defined(CATCH_INTERNAL_CONFIG_NEW_CAPTURE) && !defined(CATCH_INTERNAL_CONFIG_NO_NEW_CAPTURE) && !defined(CATCH_CONFIG_NO_NEW_CAPTURE) && !defined(CATCH_CONFIG_NEW_CAPTURE)
+#  define CATCH_CONFIG_NEW_CAPTURE
 #endif
-#if defined(CATCH_INTERNAL_CONFIG_CPP11_UNIQUE_PTR) && !defined(CATCH_CONFIG_CPP11_NO_UNIQUE_PTR) && !defined(CATCH_CONFIG_CPP11_UNIQUE_PTR) && !defined(CATCH_CONFIG_NO_CPP11)
-#   define CATCH_CONFIG_CPP11_UNIQUE_PTR
+
+#if !defined(CATCH_INTERNAL_CONFIG_EXCEPTIONS_ENABLED) && !defined(CATCH_CONFIG_DISABLE_EXCEPTIONS)
+#  define CATCH_CONFIG_DISABLE_EXCEPTIONS
 #endif
-// Use of __COUNTER__ is suppressed if __JETBRAINS_IDE__ is #defined (meaning we're being parsed by a JetBrains IDE for
-// analytics) because, at time of writing, __COUNTER__ is not properly handled by it.
-// This does not affect compilation
-#if defined(CATCH_INTERNAL_CONFIG_COUNTER) && !defined(CATCH_CONFIG_NO_COUNTER) && !defined(CATCH_CONFIG_COUNTER) && !defined(__JETBRAINS_IDE__)
-#   define CATCH_CONFIG_COUNTER
+
+#if defined(CATCH_INTERNAL_CONFIG_POLYFILL_ISNAN) && !defined(CATCH_CONFIG_NO_POLYFILL_ISNAN) && !defined(CATCH_CONFIG_POLYFILL_ISNAN)
+#  define CATCH_CONFIG_POLYFILL_ISNAN
 #endif
-#if defined(CATCH_INTERNAL_CONFIG_CPP11_SHUFFLE) && !defined(CATCH_CONFIG_CPP11_NO_SHUFFLE) && !defined(CATCH_CONFIG_CPP11_SHUFFLE) && !defined(CATCH_CONFIG_NO_CPP11)
-#   define CATCH_CONFIG_CPP11_SHUFFLE
+
+#if defined(CATCH_INTERNAL_CONFIG_USE_ASYNC)  && !defined(CATCH_INTERNAL_CONFIG_NO_ASYNC) && !defined(CATCH_CONFIG_NO_USE_ASYNC) && !defined(CATCH_CONFIG_USE_ASYNC)
+#  define CATCH_CONFIG_USE_ASYNC
 #endif
-# if defined(CATCH_INTERNAL_CONFIG_CPP11_TYPE_TRAITS) && !defined(CATCH_CONFIG_CPP11_NO_TYPE_TRAITS) && !defined(CATCH_CONFIG_CPP11_TYPE_TRAITS) && !defined(CATCH_CONFIG_NO_CPP11)
-#  define CATCH_CONFIG_CPP11_TYPE_TRAITS
-# endif
-#if defined(CATCH_INTERNAL_CONFIG_WINDOWS_SEH) && !defined(CATCH_CONFIG_NO_WINDOWS_SEH) && !defined(CATCH_CONFIG_WINDOWS_SEH)
-#   define CATCH_CONFIG_WINDOWS_SEH
+
+#if defined(CATCH_INTERNAL_CONFIG_ANDROID_LOGWRITE) && !defined(CATCH_CONFIG_NO_ANDROID_LOGWRITE) && !defined(CATCH_CONFIG_ANDROID_LOGWRITE)
+#  define CATCH_CONFIG_ANDROID_LOGWRITE
 #endif
-// This is set by default, because we assume that unix compilers are posix-signal-compatible by default.
-#if !defined(CATCH_INTERNAL_CONFIG_NO_POSIX_SIGNALS) && !defined(CATCH_CONFIG_NO_POSIX_SIGNALS) && !defined(CATCH_CONFIG_POSIX_SIGNALS)
-#   define CATCH_CONFIG_POSIX_SIGNALS
+
+#if defined(CATCH_INTERNAL_CONFIG_GLOBAL_NEXTAFTER) && !defined(CATCH_CONFIG_NO_GLOBAL_NEXTAFTER) && !defined(CATCH_CONFIG_GLOBAL_NEXTAFTER)
+#  define CATCH_CONFIG_GLOBAL_NEXTAFTER
 #endif
 
 #if !defined(CATCH_INTERNAL_SUPPRESS_PARENTHESES_WARNINGS)
 #   define CATCH_INTERNAL_SUPPRESS_PARENTHESES_WARNINGS
 #   define CATCH_INTERNAL_UNSUPPRESS_PARENTHESES_WARNINGS
 #endif
-#if !defined(CATCH_INTERNAL_SUPPRESS_ETD_WARNINGS)
-#   define CATCH_INTERNAL_SUPPRESS_ETD_WARNINGS
-#   define CATCH_INTERNAL_UNSUPPRESS_ETD_WARNINGS
+#if !defined(CATCH_INTERNAL_SUPPRESS_GLOBALS_WARNINGS)
+#   define CATCH_INTERNAL_SUPPRESS_GLOBALS_WARNINGS
+#   define CATCH_INTERNAL_UNSUPPRESS_GLOBALS_WARNINGS
+#endif
+#if !defined(CATCH_INTERNAL_SUPPRESS_UNUSED_WARNINGS)
+#   define CATCH_INTERNAL_SUPPRESS_UNUSED_WARNINGS
+#   define CATCH_INTERNAL_UNSUPPRESS_UNUSED_WARNINGS
+#endif
+#if !defined(CATCH_INTERNAL_SUPPRESS_ZERO_VARIADIC_WARNINGS)
+#   define CATCH_INTERNAL_SUPPRESS_ZERO_VARIADIC_WARNINGS
+#   define CATCH_INTERNAL_UNSUPPRESS_ZERO_VARIADIC_WARNINGS
 #endif
 
-// noexcept support:
-#if defined(CATCH_CONFIG_CPP11_NOEXCEPT) && !defined(CATCH_NOEXCEPT)
-#  define CATCH_NOEXCEPT noexcept
-#  define CATCH_NOEXCEPT_IS(x) noexcept(x)
-#else
-#  define CATCH_NOEXCEPT throw()
-#  define CATCH_NOEXCEPT_IS(x)
+#if defined(__APPLE__) && defined(__apple_build_version__) && (__clang_major__ < 10)
+#   undef CATCH_INTERNAL_SUPPRESS_UNUSED_TEMPLATE_WARNINGS
+#   undef CATCH_INTERNAL_UNSUPPRESS_UNUSED_TEMPLATE_WARNINGS
+#elif defined(__clang__) && (__clang_major__ < 5)
+#   undef CATCH_INTERNAL_SUPPRESS_UNUSED_TEMPLATE_WARNINGS
+#   undef CATCH_INTERNAL_UNSUPPRESS_UNUSED_TEMPLATE_WARNINGS
 #endif
 
-// nullptr support
-#ifdef CATCH_CONFIG_CPP11_NULLPTR
-#   define CATCH_NULL nullptr
-#else
-#   define CATCH_NULL NULL
+#if !defined(CATCH_INTERNAL_SUPPRESS_UNUSED_TEMPLATE_WARNINGS)
+#   define CATCH_INTERNAL_SUPPRESS_UNUSED_TEMPLATE_WARNINGS
+#   define CATCH_INTERNAL_UNSUPPRESS_UNUSED_TEMPLATE_WARNINGS
 #endif
 
-// override support
-#ifdef CATCH_CONFIG_CPP11_OVERRIDE
-#   define CATCH_OVERRIDE override
+#if defined(CATCH_CONFIG_DISABLE_EXCEPTIONS)
+#define CATCH_TRY if ((true))
+#define CATCH_CATCH_ALL if ((false))
+#define CATCH_CATCH_ANON(type) if ((false))
 #else
-#   define CATCH_OVERRIDE
+#define CATCH_TRY try
+#define CATCH_CATCH_ALL catch (...)
+#define CATCH_CATCH_ANON(type) catch (type)
 #endif
 
-// unique_ptr support
-#ifdef CATCH_CONFIG_CPP11_UNIQUE_PTR
-#   define CATCH_AUTO_PTR( T ) std::unique_ptr<T>
-#else
-#   define CATCH_AUTO_PTR( T ) std::auto_ptr<T>
+#if defined(CATCH_INTERNAL_CONFIG_TRADITIONAL_MSVC_PREPROCESSOR) && !defined(CATCH_CONFIG_NO_TRADITIONAL_MSVC_PREPROCESSOR) && !defined(CATCH_CONFIG_TRADITIONAL_MSVC_PREPROCESSOR)
+#define CATCH_CONFIG_TRADITIONAL_MSVC_PREPROCESSOR
 #endif
 
+// end catch_compiler_capabilities.h
 #define INTERNAL_CATCH_UNIQUE_NAME_LINE2( name, line ) name##line
 #define INTERNAL_CATCH_UNIQUE_NAME_LINE( name, line ) INTERNAL_CATCH_UNIQUE_NAME_LINE2( name, line )
 #ifdef CATCH_CONFIG_COUNTER
@@ -371,95 +450,48 @@
 #  define INTERNAL_CATCH_UNIQUE_NAME( name ) INTERNAL_CATCH_UNIQUE_NAME_LINE( name, __LINE__ )
 #endif
 
-#define INTERNAL_CATCH_STRINGIFY2( expr ) #expr
-#define INTERNAL_CATCH_STRINGIFY( expr ) INTERNAL_CATCH_STRINGIFY2( expr )
+#include <iosfwd>
+#include <string>
+#include <cstdint>
 
-#include <sstream>
-#include <algorithm>
+// We need a dummy global operator<< so we can bring it into Catch namespace later
+struct Catch_global_namespace_dummy {};
+std::ostream& operator<<(std::ostream&, Catch_global_namespace_dummy);
 
 namespace Catch {
 
-    struct IConfig;
-
     struct CaseSensitive { enum Choice {
         Yes,
         No
     }; };
 
     class NonCopyable {
-#ifdef CATCH_CONFIG_CPP11_GENERATED_METHODS
         NonCopyable( NonCopyable const& )              = delete;
         NonCopyable( NonCopyable && )                  = delete;
         NonCopyable& operator = ( NonCopyable const& ) = delete;
         NonCopyable& operator = ( NonCopyable && )     = delete;
-#else
-        NonCopyable( NonCopyable const& info );
-        NonCopyable& operator = ( NonCopyable const& );
-#endif
 
     protected:
-        NonCopyable() {}
+        NonCopyable();
         virtual ~NonCopyable();
     };
 
-    class SafeBool {
-    public:
-        typedef void (SafeBool::*type)() const;
-
-        static type makeSafe( bool value ) {
-            return value ? &SafeBool::trueValue : 0;
-        }
-    private:
-        void trueValue() const {}
-    };
-
-    template<typename ContainerT>
-    inline void deleteAll( ContainerT& container ) {
-        typename ContainerT::const_iterator it = container.begin();
-        typename ContainerT::const_iterator itEnd = container.end();
-        for(; it != itEnd; ++it )
-            delete *it;
-    }
-    template<typename AssociativeContainerT>
-    inline void deleteAllValues( AssociativeContainerT& container ) {
-        typename AssociativeContainerT::const_iterator it = container.begin();
-        typename AssociativeContainerT::const_iterator itEnd = container.end();
-        for(; it != itEnd; ++it )
-            delete it->second;
-    }
-
-    bool startsWith( std::string const& s, std::string const& prefix );
-    bool startsWith( std::string const& s, char prefix );
-    bool endsWith( std::string const& s, std::string const& suffix );
-    bool endsWith( std::string const& s, char suffix );
-    bool contains( std::string const& s, std::string const& infix );
-    void toLowerInPlace( std::string& s );
-    std::string toLower( std::string const& s );
-    std::string trim( std::string const& str );
-    bool replaceInPlace( std::string& str, std::string const& replaceThis, std::string const& withThis );
-
-    struct pluralise {
-        pluralise( std::size_t count, std::string const& label );
-
-        friend std::ostream& operator << ( std::ostream& os, pluralise const& pluraliser );
+    struct SourceLineInfo {
 
-        std::size_t m_count;
-        std::string m_label;
-    };
+        SourceLineInfo() = delete;
+        SourceLineInfo( char const* _file, std::size_t _line ) noexcept
+        :   file( _file ),
+            line( _line )
+        {}
 
-    struct SourceLineInfo {
+        SourceLineInfo( SourceLineInfo const& other )            = default;
+        SourceLineInfo& operator = ( SourceLineInfo const& )     = default;
+        SourceLineInfo( SourceLineInfo&& )              noexcept = default;
+        SourceLineInfo& operator = ( SourceLineInfo&& ) noexcept = default;
 
-        SourceLineInfo();
-        SourceLineInfo( char const* _file, std::size_t _line );
-#  ifdef CATCH_CONFIG_CPP11_GENERATED_METHODS
-        SourceLineInfo(SourceLineInfo const& other)          = default;
-        SourceLineInfo( SourceLineInfo && )                  = default;
-        SourceLineInfo& operator = ( SourceLineInfo const& ) = default;
-        SourceLineInfo& operator = ( SourceLineInfo && )     = default;
-#  endif
-        bool empty() const;
-        bool operator == ( SourceLineInfo const& other ) const;
-        bool operator < ( SourceLineInfo const& other ) const;
+        bool empty() const noexcept { return file[0] == '\0'; }
+        bool operator == ( SourceLineInfo const& other ) const noexcept;
+        bool operator < ( SourceLineInfo const& other ) const noexcept;
 
         char const* file;
         std::size_t line;
@@ -467,24 +499,17 @@ namespace Catch {
 
     std::ostream& operator << ( std::ostream& os, SourceLineInfo const& info );
 
-    // This is just here to avoid compiler warnings with macro constants and boolean literals
-    inline bool isTrue( bool value ){ return value; }
-    inline bool alwaysTrue() { return true; }
-    inline bool alwaysFalse() { return false; }
-
-    void throwLogicError( std::string const& message, SourceLineInfo const& locationInfo );
-
-    void seedRng( IConfig const& config );
-    unsigned int rngSeed();
+    // Bring in operator<< from global namespace into Catch namespace
+    // This is necessary because the overload of operator<< above makes
+    // lookup stop at namespace Catch
+    using ::operator<<;
 
     // Use this in variadic streaming macros to allow
     //    >> +StreamEndStop
     // as well as
     //    >> stuff +StreamEndStop
     struct StreamEndStop {
-        std::string operator+() {
-            return std::string();
-        }
+        std::string operator+() const;
     };
     template<typename T>
     T const& operator + ( T const& value, StreamEndStop ) {
@@ -492,364 +517,834 @@ namespace Catch {
     }
 }
 
-#define CATCH_INTERNAL_LINEINFO ::Catch::SourceLineInfo( __FILE__, static_cast<std::size_t>( __LINE__ ) )
-#define CATCH_INTERNAL_ERROR( msg ) ::Catch::throwLogicError( msg, CATCH_INTERNAL_LINEINFO );
+#define CATCH_INTERNAL_LINEINFO \
+    ::Catch::SourceLineInfo( __FILE__, static_cast<std::size_t>( __LINE__ ) )
 
+// end catch_common.h
 namespace Catch {
 
-    class NotImplementedException : public std::exception
-    {
-    public:
-        NotImplementedException( SourceLineInfo const& lineInfo );
-        NotImplementedException( NotImplementedException const& ) {}
-
-        virtual ~NotImplementedException() CATCH_NOEXCEPT {}
-
-        virtual const char* what() const CATCH_NOEXCEPT;
-
-    private:
-        std::string m_what;
-        SourceLineInfo m_lineInfo;
+    struct RegistrarForTagAliases {
+        RegistrarForTagAliases( char const* alias, char const* tag, SourceLineInfo const& lineInfo );
     };
 
 } // end namespace Catch
 
-///////////////////////////////////////////////////////////////////////////////
-#define CATCH_NOT_IMPLEMENTED throw Catch::NotImplementedException( CATCH_INTERNAL_LINEINFO )
+#define CATCH_REGISTER_TAG_ALIAS( alias, spec ) \
+    CATCH_INTERNAL_SUPPRESS_GLOBALS_WARNINGS \
+    namespace{ Catch::RegistrarForTagAliases INTERNAL_CATCH_UNIQUE_NAME( AutoRegisterTagAlias )( alias, spec, CATCH_INTERNAL_LINEINFO ); } \
+    CATCH_INTERNAL_UNSUPPRESS_GLOBALS_WARNINGS
 
-// #included from: internal/catch_context.h
-#define TWOBLUECUBES_CATCH_CONTEXT_H_INCLUDED
+// end catch_tag_alias_autoregistrar.h
+// start catch_test_registry.h
 
-// #included from: catch_interfaces_generators.h
-#define TWOBLUECUBES_CATCH_INTERFACES_GENERATORS_H_INCLUDED
+// start catch_interfaces_testcase.h
 
-#include <string>
+#include <vector>
 
 namespace Catch {
 
-    struct IGeneratorInfo {
-        virtual ~IGeneratorInfo();
-        virtual bool moveNext() = 0;
-        virtual std::size_t getCurrentIndex() const = 0;
+    class TestSpec;
+
+    struct ITestInvoker {
+        virtual void invoke () const = 0;
+        virtual ~ITestInvoker();
     };
 
-    struct IGeneratorsForTest {
-        virtual ~IGeneratorsForTest();
+    class TestCase;
+    struct IConfig;
 
-        virtual IGeneratorInfo& getGeneratorInfo( std::string const& fileInfo, std::size_t size ) = 0;
-        virtual bool moveNext() = 0;
+    struct ITestCaseRegistry {
+        virtual ~ITestCaseRegistry();
+        virtual std::vector<TestCase> const& getAllTests() const = 0;
+        virtual std::vector<TestCase> const& getAllTestsSorted( IConfig const& config ) const = 0;
     };
 
-    IGeneratorsForTest* createGeneratorsForTest();
+    bool isThrowSafe( TestCase const& testCase, IConfig const& config );
+    bool matchTest( TestCase const& testCase, TestSpec const& testSpec, IConfig const& config );
+    std::vector<TestCase> filterTests( std::vector<TestCase> const& testCases, TestSpec const& testSpec, IConfig const& config );
+    std::vector<TestCase> const& getAllTestCasesSorted( IConfig const& config );
 
-} // end namespace Catch
+}
 
-// #included from: catch_ptr.hpp
-#define TWOBLUECUBES_CATCH_PTR_HPP_INCLUDED
+// end catch_interfaces_testcase.h
+// start catch_stringref.h
 
-#ifdef __clang__
-#pragma clang diagnostic push
-#pragma clang diagnostic ignored "-Wpadded"
-#endif
+#include <cstddef>
+#include <string>
+#include <iosfwd>
+#include <cassert>
 
 namespace Catch {
 
-    // An intrusive reference counting smart pointer.
-    // T must implement addRef() and release() methods
-    // typically implementing the IShared interface
-    template<typename T>
-    class Ptr {
+    /// A non-owning string class (similar to the forthcoming std::string_view)
+    /// Note that, because a StringRef may be a substring of another string,
+    /// it may not be null terminated. c_str() must return a null terminated
+    /// string, however, and so the StringRef will internally take ownership
+    /// (taking a copy), if necessary. In theory this ownership is not externally
+    /// visible - but it does mean (substring) StringRefs should not be shared between
+    /// threads.
+    class StringRef {
     public:
-        Ptr() : m_p( CATCH_NULL ){}
-        Ptr( T* p ) : m_p( p ){
-            if( m_p )
-                m_p->addRef();
-        }
-        Ptr( Ptr const& other ) : m_p( other.m_p ){
-            if( m_p )
-                m_p->addRef();
-        }
-        ~Ptr(){
-            if( m_p )
-                m_p->release();
-        }
-        void reset() {
-            if( m_p )
-                m_p->release();
-            m_p = CATCH_NULL;
+        using size_type = std::size_t;
+        using const_iterator = const char*;
+
+    private:
+        friend struct StringRefTestAccess;
+
+        char const* m_start;
+        size_type m_size;
+
+        char* m_data = nullptr;
+
+        void takeOwnership();
+
+        static constexpr char const* const s_empty = "";
+
+    public: // construction/ assignment
+        StringRef() noexcept
+        :   StringRef( s_empty, 0 )
+        {}
+
+        StringRef( StringRef const& other ) noexcept
+        :   m_start( other.m_start ),
+            m_size( other.m_size )
+        {}
+
+        StringRef( StringRef&& other ) noexcept
+        :   m_start( other.m_start ),
+            m_size( other.m_size ),
+            m_data( other.m_data )
+        {
+            other.m_data = nullptr;
         }
-        Ptr& operator = ( T* p ){
-            Ptr temp( p );
-            swap( temp );
-            return *this;
+
+        StringRef( char const* rawChars ) noexcept;
+
+        StringRef( char const* rawChars, size_type size ) noexcept
+        :   m_start( rawChars ),
+            m_size( size )
+        {}
+
+        StringRef( std::string const& stdString ) noexcept
+        :   m_start( stdString.c_str() ),
+            m_size( stdString.size() )
+        {}
+
+        ~StringRef() noexcept {
+            delete[] m_data;
         }
-        Ptr& operator = ( Ptr const& other ){
-            Ptr temp( other );
-            swap( temp );
+
+        auto operator = ( StringRef const &other ) noexcept -> StringRef& {
+            delete[] m_data;
+            m_data = nullptr;
+            m_start = other.m_start;
+            m_size = other.m_size;
             return *this;
         }
-        void swap( Ptr& other ) { std::swap( m_p, other.m_p ); }
-        T* get() const{ return m_p; }
-        T& operator*() const { return *m_p; }
-        T* operator->() const { return m_p; }
-        bool operator !() const { return m_p == CATCH_NULL; }
-        operator SafeBool::type() const { return SafeBool::makeSafe( m_p != CATCH_NULL ); }
 
-    private:
-        T* m_p;
-    };
+        explicit operator std::string() const {
+            return std::string(m_start, m_size);
+        }
 
-    struct IShared : NonCopyable {
-        virtual ~IShared();
-        virtual void addRef() const = 0;
-        virtual void release() const = 0;
-    };
+        void swap( StringRef& other ) noexcept;
 
-    template<typename T = IShared>
-    struct SharedImpl : T {
+    public: // operators
+        auto operator == ( StringRef const& other ) const noexcept -> bool;
+        auto operator != ( StringRef const& other ) const noexcept -> bool;
 
-        SharedImpl() : m_rc( 0 ){}
+        auto operator[] ( size_type index ) const noexcept -> char {
+            assert(index < m_size);
+            return m_start[index];
+        }
 
-        virtual void addRef() const {
-            ++m_rc;
+    public: // named queries
+        auto empty() const noexcept -> bool {
+            return m_size == 0;
         }
-        virtual void release() const {
-            if( --m_rc == 0 )
-                delete this;
+        auto size() const noexcept -> size_type {
+            return m_size;
         }
 
-        mutable unsigned int m_rc;
-    };
+        auto c_str() const -> char const*;
 
-} // end namespace Catch
+    public: // substrings and searches
+        auto substr( size_type start, size_type size ) const noexcept -> StringRef;
 
-#ifdef __clang__
-#pragma clang diagnostic pop
-#endif
+        // Returns the current start pointer.
+        // Note that the pointer can change when if the StringRef is a substring
+        auto currentData() const noexcept -> char const*;
 
-namespace Catch {
+    public: // iterators
+        const_iterator begin() const { return m_start; }
+        const_iterator end() const { return m_start + m_size; }
 
-    class TestCase;
-    class Stream;
-    struct IResultCapture;
-    struct IRunner;
-    struct IGeneratorsForTest;
-    struct IConfig;
+    private: // ownership queries - may not be consistent between calls
+        auto isOwned() const noexcept -> bool;
+        auto isSubstring() const noexcept -> bool;
+    };
 
-    struct IContext
-    {
-        virtual ~IContext();
+    auto operator += ( std::string& lhs, StringRef const& sr ) -> std::string&;
+    auto operator << ( std::ostream& os, StringRef const& sr ) -> std::ostream&;
 
-        virtual IResultCapture* getResultCapture() = 0;
-        virtual IRunner* getRunner() = 0;
-        virtual size_t getGeneratorIndex( std::string const& fileInfo, size_t totalSize ) = 0;
-        virtual bool advanceGeneratorsForCurrentTest() = 0;
-        virtual Ptr<IConfig const> getConfig() const = 0;
-    };
-
-    struct IMutableContext : IContext
-    {
-        virtual ~IMutableContext();
-        virtual void setResultCapture( IResultCapture* resultCapture ) = 0;
-        virtual void setRunner( IRunner* runner ) = 0;
-        virtual void setConfig( Ptr<IConfig const> const& config ) = 0;
-    };
+    inline auto operator "" _sr( char const* rawChars, std::size_t size ) noexcept -> StringRef {
+        return StringRef( rawChars, size );
+    }
 
-    IContext& getCurrentContext();
-    IMutableContext& getCurrentMutableContext();
-    void cleanUpContext();
-    Stream createStream( std::string const& streamName );
+} // namespace Catch
 
+inline auto operator "" _catch_sr( char const* rawChars, std::size_t size ) noexcept -> Catch::StringRef {
+    return Catch::StringRef( rawChars, size );
 }
 
-// #included from: internal/catch_test_registry.hpp
-#define TWOBLUECUBES_CATCH_TEST_REGISTRY_HPP_INCLUDED
+// end catch_stringref.h
+// start catch_preprocessor.hpp
 
-// #included from: catch_interfaces_testcase.h
-#define TWOBLUECUBES_CATCH_INTERFACES_TESTCASE_H_INCLUDED
 
-#include <vector>
+#define CATCH_RECURSION_LEVEL0(...) __VA_ARGS__
+#define CATCH_RECURSION_LEVEL1(...) CATCH_RECURSION_LEVEL0(CATCH_RECURSION_LEVEL0(CATCH_RECURSION_LEVEL0(__VA_ARGS__)))
+#define CATCH_RECURSION_LEVEL2(...) CATCH_RECURSION_LEVEL1(CATCH_RECURSION_LEVEL1(CATCH_RECURSION_LEVEL1(__VA_ARGS__)))
+#define CATCH_RECURSION_LEVEL3(...) CATCH_RECURSION_LEVEL2(CATCH_RECURSION_LEVEL2(CATCH_RECURSION_LEVEL2(__VA_ARGS__)))
+#define CATCH_RECURSION_LEVEL4(...) CATCH_RECURSION_LEVEL3(CATCH_RECURSION_LEVEL3(CATCH_RECURSION_LEVEL3(__VA_ARGS__)))
+#define CATCH_RECURSION_LEVEL5(...) CATCH_RECURSION_LEVEL4(CATCH_RECURSION_LEVEL4(CATCH_RECURSION_LEVEL4(__VA_ARGS__)))
 
-namespace Catch {
+#ifdef CATCH_CONFIG_TRADITIONAL_MSVC_PREPROCESSOR
+#define INTERNAL_CATCH_EXPAND_VARGS(...) __VA_ARGS__
+// MSVC needs more evaluations
+#define CATCH_RECURSION_LEVEL6(...) CATCH_RECURSION_LEVEL5(CATCH_RECURSION_LEVEL5(CATCH_RECURSION_LEVEL5(__VA_ARGS__)))
+#define CATCH_RECURSE(...)  CATCH_RECURSION_LEVEL6(CATCH_RECURSION_LEVEL6(__VA_ARGS__))
+#else
+#define CATCH_RECURSE(...)  CATCH_RECURSION_LEVEL5(__VA_ARGS__)
+#endif
 
-    class TestSpec;
+#define CATCH_REC_END(...)
+#define CATCH_REC_OUT
+
+#define CATCH_EMPTY()
+#define CATCH_DEFER(id) id CATCH_EMPTY()
+
+#define CATCH_REC_GET_END2() 0, CATCH_REC_END
+#define CATCH_REC_GET_END1(...) CATCH_REC_GET_END2
+#define CATCH_REC_GET_END(...) CATCH_REC_GET_END1
+#define CATCH_REC_NEXT0(test, next, ...) next CATCH_REC_OUT
+#define CATCH_REC_NEXT1(test, next) CATCH_DEFER ( CATCH_REC_NEXT0 ) ( test, next, 0)
+#define CATCH_REC_NEXT(test, next)  CATCH_REC_NEXT1(CATCH_REC_GET_END test, next)
+
+#define CATCH_REC_LIST0(f, x, peek, ...) , f(x) CATCH_DEFER ( CATCH_REC_NEXT(peek, CATCH_REC_LIST1) ) ( f, peek, __VA_ARGS__ )
+#define CATCH_REC_LIST1(f, x, peek, ...) , f(x) CATCH_DEFER ( CATCH_REC_NEXT(peek, CATCH_REC_LIST0) ) ( f, peek, __VA_ARGS__ )
+#define CATCH_REC_LIST2(f, x, peek, ...)   f(x) CATCH_DEFER ( CATCH_REC_NEXT(peek, CATCH_REC_LIST1) ) ( f, peek, __VA_ARGS__ )
+
+#define CATCH_REC_LIST0_UD(f, userdata, x, peek, ...) , f(userdata, x) CATCH_DEFER ( CATCH_REC_NEXT(peek, CATCH_REC_LIST1_UD) ) ( f, userdata, peek, __VA_ARGS__ )
+#define CATCH_REC_LIST1_UD(f, userdata, x, peek, ...) , f(userdata, x) CATCH_DEFER ( CATCH_REC_NEXT(peek, CATCH_REC_LIST0_UD) ) ( f, userdata, peek, __VA_ARGS__ )
+#define CATCH_REC_LIST2_UD(f, userdata, x, peek, ...)   f(userdata, x) CATCH_DEFER ( CATCH_REC_NEXT(peek, CATCH_REC_LIST1_UD) ) ( f, userdata, peek, __VA_ARGS__ )
+
+// Applies the function macro `f` to each of the remaining parameters, inserts commas between the results,
+// and passes userdata as the first parameter to each invocation,
+// e.g. CATCH_REC_LIST_UD(f, x, a, b, c) evaluates to f(x, a), f(x, b), f(x, c)
+#define CATCH_REC_LIST_UD(f, userdata, ...) CATCH_RECURSE(CATCH_REC_LIST2_UD(f, userdata, __VA_ARGS__, ()()(), ()()(), ()()(), 0))
+
+#define CATCH_REC_LIST(f, ...) CATCH_RECURSE(CATCH_REC_LIST2(f, __VA_ARGS__, ()()(), ()()(), ()()(), 0))
+
+#define INTERNAL_CATCH_EXPAND1(param) INTERNAL_CATCH_EXPAND2(param)
+#define INTERNAL_CATCH_EXPAND2(...) INTERNAL_CATCH_NO## __VA_ARGS__
+#define INTERNAL_CATCH_DEF(...) INTERNAL_CATCH_DEF __VA_ARGS__
+#define INTERNAL_CATCH_NOINTERNAL_CATCH_DEF
+#define INTERNAL_CATCH_STRINGIZE(...) INTERNAL_CATCH_STRINGIZE2(__VA_ARGS__)
+#ifndef CATCH_CONFIG_TRADITIONAL_MSVC_PREPROCESSOR
+#define INTERNAL_CATCH_STRINGIZE2(...) #__VA_ARGS__
+#define INTERNAL_CATCH_STRINGIZE_WITHOUT_PARENS(param) INTERNAL_CATCH_STRINGIZE(INTERNAL_CATCH_REMOVE_PARENS(param))
+#else
+// MSVC is adding extra space and needs another indirection to expand INTERNAL_CATCH_NOINTERNAL_CATCH_DEF
+#define INTERNAL_CATCH_STRINGIZE2(...) INTERNAL_CATCH_STRINGIZE3(__VA_ARGS__)
+#define INTERNAL_CATCH_STRINGIZE3(...) #__VA_ARGS__
+#define INTERNAL_CATCH_STRINGIZE_WITHOUT_PARENS(param) (INTERNAL_CATCH_STRINGIZE(INTERNAL_CATCH_REMOVE_PARENS(param)) + 1)
+#endif
 
-    struct ITestCase : IShared {
-        virtual void invoke () const = 0;
-    protected:
-        virtual ~ITestCase();
-    };
+#define INTERNAL_CATCH_MAKE_NAMESPACE2(...) ns_##__VA_ARGS__
+#define INTERNAL_CATCH_MAKE_NAMESPACE(name) INTERNAL_CATCH_MAKE_NAMESPACE2(name)
 
-    class TestCase;
-    struct IConfig;
+#define INTERNAL_CATCH_REMOVE_PARENS(...) INTERNAL_CATCH_EXPAND1(INTERNAL_CATCH_DEF __VA_ARGS__)
 
-    struct ITestCaseRegistry {
-        virtual ~ITestCaseRegistry();
-        virtual std::vector<TestCase> const& getAllTests() const = 0;
-        virtual std::vector<TestCase> const& getAllTestsSorted( IConfig const& config ) const = 0;
-    };
+#ifndef CATCH_CONFIG_TRADITIONAL_MSVC_PREPROCESSOR
+#define INTERNAL_CATCH_MAKE_TYPE_LIST2(...) decltype(get_wrapper<INTERNAL_CATCH_REMOVE_PARENS_GEN(__VA_ARGS__)>())
+#define INTERNAL_CATCH_MAKE_TYPE_LIST(...) INTERNAL_CATCH_MAKE_TYPE_LIST2(INTERNAL_CATCH_REMOVE_PARENS(__VA_ARGS__))
+#else
+#define INTERNAL_CATCH_MAKE_TYPE_LIST2(...) INTERNAL_CATCH_EXPAND_VARGS(decltype(get_wrapper<INTERNAL_CATCH_REMOVE_PARENS_GEN(__VA_ARGS__)>()))
+#define INTERNAL_CATCH_MAKE_TYPE_LIST(...) INTERNAL_CATCH_EXPAND_VARGS(INTERNAL_CATCH_MAKE_TYPE_LIST2(INTERNAL_CATCH_REMOVE_PARENS(__VA_ARGS__)))
+#endif
 
-    bool matchTest( TestCase const& testCase, TestSpec const& testSpec, IConfig const& config );
-    std::vector<TestCase> filterTests( std::vector<TestCase> const& testCases, TestSpec const& testSpec, IConfig const& config );
-    std::vector<TestCase> const& getAllTestCasesSorted( IConfig const& config );
+#define INTERNAL_CATCH_MAKE_TYPE_LISTS_FROM_TYPES(...)\
+    CATCH_REC_LIST(INTERNAL_CATCH_MAKE_TYPE_LIST,__VA_ARGS__)
+
+#define INTERNAL_CATCH_REMOVE_PARENS_1_ARG(_0) INTERNAL_CATCH_REMOVE_PARENS(_0)
+#define INTERNAL_CATCH_REMOVE_PARENS_2_ARG(_0, _1) INTERNAL_CATCH_REMOVE_PARENS(_0), INTERNAL_CATCH_REMOVE_PARENS_1_ARG(_1)
+#define INTERNAL_CATCH_REMOVE_PARENS_3_ARG(_0, _1, _2) INTERNAL_CATCH_REMOVE_PARENS(_0), INTERNAL_CATCH_REMOVE_PARENS_2_ARG(_1, _2)
+#define INTERNAL_CATCH_REMOVE_PARENS_4_ARG(_0, _1, _2, _3) INTERNAL_CATCH_REMOVE_PARENS(_0), INTERNAL_CATCH_REMOVE_PARENS_3_ARG(_1, _2, _3)
+#define INTERNAL_CATCH_REMOVE_PARENS_5_ARG(_0, _1, _2, _3, _4) INTERNAL_CATCH_REMOVE_PARENS(_0), INTERNAL_CATCH_REMOVE_PARENS_4_ARG(_1, _2, _3, _4)
+#define INTERNAL_CATCH_REMOVE_PARENS_6_ARG(_0, _1, _2, _3, _4, _5) INTERNAL_CATCH_REMOVE_PARENS(_0), INTERNAL_CATCH_REMOVE_PARENS_5_ARG(_1, _2, _3, _4, _5)
+#define INTERNAL_CATCH_REMOVE_PARENS_7_ARG(_0, _1, _2, _3, _4, _5, _6) INTERNAL_CATCH_REMOVE_PARENS(_0), INTERNAL_CATCH_REMOVE_PARENS_6_ARG(_1, _2, _4, _5, _6)
+#define INTERNAL_CATCH_REMOVE_PARENS_8_ARG(_0, _1, _2, _3, _4, _5, _6, _7) INTERNAL_CATCH_REMOVE_PARENS(_0), INTERNAL_CATCH_REMOVE_PARENS_7_ARG(_1, _2, _3, _4, _5, _6, _7)
+#define INTERNAL_CATCH_REMOVE_PARENS_9_ARG(_0, _1, _2, _3, _4, _5, _6, _7, _8) INTERNAL_CATCH_REMOVE_PARENS(_0), INTERNAL_CATCH_REMOVE_PARENS_8_ARG(_1, _2, _3, _4, _5, _6, _7, _8)
+#define INTERNAL_CATCH_REMOVE_PARENS_10_ARG(_0, _1, _2, _3, _4, _5, _6, _7, _8, _9) INTERNAL_CATCH_REMOVE_PARENS(_0), INTERNAL_CATCH_REMOVE_PARENS_9_ARG(_1, _2, _3, _4, _5, _6, _7, _8, _9)
+#define INTERNAL_CATCH_REMOVE_PARENS_11_ARG(_0, _1, _2, _3, _4, _5, _6, _7, _8, _9, _10) INTERNAL_CATCH_REMOVE_PARENS(_0), INTERNAL_CATCH_REMOVE_PARENS_10_ARG(_1, _2, _3, _4, _5, _6, _7, _8, _9, _10)
+
+#define INTERNAL_CATCH_VA_NARGS_IMPL(_0, _1, _2, _3, _4, _5, _6, _7, _8, _9, _10, N, ...) N
+
+#define INTERNAL_CATCH_TYPE_GEN\
+    template<typename...> struct TypeList {};\
+    template<typename...Ts>\
+    constexpr auto get_wrapper() noexcept -> TypeList<Ts...> { return {}; }\
+    template<template<typename...> class...> struct TemplateTypeList{};\
+    template<template<typename...> class...Cs>\
+    constexpr auto get_wrapper() noexcept -> TemplateTypeList<Cs...> { return {}; }\
+    template<typename...>\
+    struct append;\
+    template<typename...>\
+    struct rewrap;\
+    template<template<typename...> class, typename...>\
+    struct create;\
+    template<template<typename...> class, typename>\
+    struct convert;\
+    \
+    template<typename T> \
+    struct append<T> { using type = T; };\
+    template< template<typename...> class L1, typename...E1, template<typename...> class L2, typename...E2, typename...Rest>\
+    struct append<L1<E1...>, L2<E2...>, Rest...> { using type = typename append<L1<E1...,E2...>, Rest...>::type; };\
+    template< template<typename...> class L1, typename...E1, typename...Rest>\
+    struct append<L1<E1...>, TypeList<mpl_::na>, Rest...> { using type = L1<E1...>; };\
+    \
+    template< template<typename...> class Container, template<typename...> class List, typename...elems>\
+    struct rewrap<TemplateTypeList<Container>, List<elems...>> { using type = TypeList<Container<elems...>>; };\
+    template< template<typename...> class Container, template<typename...> class List, class...Elems, typename...Elements>\
+    struct rewrap<TemplateTypeList<Container>, List<Elems...>, Elements...> { using type = typename append<TypeList<Container<Elems...>>, typename rewrap<TemplateTypeList<Container>, Elements...>::type>::type; };\
+    \
+    template<template <typename...> class Final, template< typename...> class...Containers, typename...Types>\
+    struct create<Final, TemplateTypeList<Containers...>, TypeList<Types...>> { using type = typename append<Final<>, typename rewrap<TemplateTypeList<Containers>, Types...>::type...>::type; };\
+    template<template <typename...> class Final, template <typename...> class List, typename...Ts>\
+    struct convert<Final, List<Ts...>> { using type = typename append<Final<>,TypeList<Ts>...>::type; };
+
+#define INTERNAL_CATCH_NTTP_1(signature, ...)\
+    template<INTERNAL_CATCH_REMOVE_PARENS(signature)> struct Nttp{};\
+    template<INTERNAL_CATCH_REMOVE_PARENS(signature)>\
+    constexpr auto get_wrapper() noexcept -> Nttp<__VA_ARGS__> { return {}; } \
+    template<template<INTERNAL_CATCH_REMOVE_PARENS(signature)> class...> struct NttpTemplateTypeList{};\
+    template<template<INTERNAL_CATCH_REMOVE_PARENS(signature)> class...Cs>\
+    constexpr auto get_wrapper() noexcept -> NttpTemplateTypeList<Cs...> { return {}; } \
+    \
+    template< template<INTERNAL_CATCH_REMOVE_PARENS(signature)> class Container, template<INTERNAL_CATCH_REMOVE_PARENS(signature)> class List, INTERNAL_CATCH_REMOVE_PARENS(signature)>\
+    struct rewrap<NttpTemplateTypeList<Container>, List<__VA_ARGS__>> { using type = TypeList<Container<__VA_ARGS__>>; };\
+    template< template<INTERNAL_CATCH_REMOVE_PARENS(signature)> class Container, template<INTERNAL_CATCH_REMOVE_PARENS(signature)> class List, INTERNAL_CATCH_REMOVE_PARENS(signature), typename...Elements>\
+    struct rewrap<NttpTemplateTypeList<Container>, List<__VA_ARGS__>, Elements...> { using type = typename append<TypeList<Container<__VA_ARGS__>>, typename rewrap<NttpTemplateTypeList<Container>, Elements...>::type>::type; };\
+    template<template <typename...> class Final, template<INTERNAL_CATCH_REMOVE_PARENS(signature)> class...Containers, typename...Types>\
+    struct create<Final, NttpTemplateTypeList<Containers...>, TypeList<Types...>> { using type = typename append<Final<>, typename rewrap<NttpTemplateTypeList<Containers>, Types...>::type...>::type; };
+
+#define INTERNAL_CATCH_DECLARE_SIG_TEST0(TestName)
+#define INTERNAL_CATCH_DECLARE_SIG_TEST1(TestName, signature)\
+    template<INTERNAL_CATCH_REMOVE_PARENS(signature)>\
+    static void TestName()
+#define INTERNAL_CATCH_DECLARE_SIG_TEST_X(TestName, signature, ...)\
+    template<INTERNAL_CATCH_REMOVE_PARENS(signature)>\
+    static void TestName()
+
+#define INTERNAL_CATCH_DEFINE_SIG_TEST0(TestName)
+#define INTERNAL_CATCH_DEFINE_SIG_TEST1(TestName, signature)\
+    template<INTERNAL_CATCH_REMOVE_PARENS(signature)>\
+    static void TestName()
+#define INTERNAL_CATCH_DEFINE_SIG_TEST_X(TestName, signature,...)\
+    template<INTERNAL_CATCH_REMOVE_PARENS(signature)>\
+    static void TestName()
+
+#define INTERNAL_CATCH_NTTP_REGISTER0(TestFunc, signature)\
+    template<typename Type>\
+    void reg_test(TypeList<Type>, Catch::NameAndTags nameAndTags)\
+    {\
+        Catch::AutoReg( Catch::makeTestInvoker(&TestFunc<Type>), CATCH_INTERNAL_LINEINFO, Catch::StringRef(), nameAndTags);\
+    }
+
+#define INTERNAL_CATCH_NTTP_REGISTER(TestFunc, signature, ...)\
+    template<INTERNAL_CATCH_REMOVE_PARENS(signature)>\
+    void reg_test(Nttp<__VA_ARGS__>, Catch::NameAndTags nameAndTags)\
+    {\
+        Catch::AutoReg( Catch::makeTestInvoker(&TestFunc<__VA_ARGS__>), CATCH_INTERNAL_LINEINFO, Catch::StringRef(), nameAndTags);\
+    }
+
+#define INTERNAL_CATCH_NTTP_REGISTER_METHOD0(TestName, signature, ...)\
+    template<typename Type>\
+    void reg_test(TypeList<Type>, Catch::StringRef className, Catch::NameAndTags nameAndTags)\
+    {\
+        Catch::AutoReg( Catch::makeTestInvoker(&TestName<Type>::test), CATCH_INTERNAL_LINEINFO, className, nameAndTags);\
+    }
+
+#define INTERNAL_CATCH_NTTP_REGISTER_METHOD(TestName, signature, ...)\
+    template<INTERNAL_CATCH_REMOVE_PARENS(signature)>\
+    void reg_test(Nttp<__VA_ARGS__>, Catch::StringRef className, Catch::NameAndTags nameAndTags)\
+    {\
+        Catch::AutoReg( Catch::makeTestInvoker(&TestName<__VA_ARGS__>::test), CATCH_INTERNAL_LINEINFO, className, nameAndTags);\
+    }
+
+#define INTERNAL_CATCH_DECLARE_SIG_TEST_METHOD0(TestName, ClassName)
+#define INTERNAL_CATCH_DECLARE_SIG_TEST_METHOD1(TestName, ClassName, signature)\
+    template<typename TestType> \
+    struct TestName : INTERNAL_CATCH_REMOVE_PARENS(ClassName)<TestType> { \
+        void test();\
+    }
+
+#define INTERNAL_CATCH_DECLARE_SIG_TEST_METHOD_X(TestName, ClassName, signature, ...)\
+    template<INTERNAL_CATCH_REMOVE_PARENS(signature)> \
+    struct TestName : INTERNAL_CATCH_REMOVE_PARENS(ClassName)<__VA_ARGS__> { \
+        void test();\
+    }
+
+#define INTERNAL_CATCH_DEFINE_SIG_TEST_METHOD0(TestName)
+#define INTERNAL_CATCH_DEFINE_SIG_TEST_METHOD1(TestName, signature)\
+    template<typename TestType> \
+    void INTERNAL_CATCH_MAKE_NAMESPACE(TestName)::TestName<TestType>::test()
+#define INTERNAL_CATCH_DEFINE_SIG_TEST_METHOD_X(TestName, signature, ...)\
+    template<INTERNAL_CATCH_REMOVE_PARENS(signature)> \
+    void INTERNAL_CATCH_MAKE_NAMESPACE(TestName)::TestName<__VA_ARGS__>::test()
+
+#ifndef CATCH_CONFIG_TRADITIONAL_MSVC_PREPROCESSOR
+#define INTERNAL_CATCH_NTTP_0
+#define INTERNAL_CATCH_NTTP_GEN(...) INTERNAL_CATCH_VA_NARGS_IMPL(__VA_ARGS__, INTERNAL_CATCH_NTTP_1(__VA_ARGS__), INTERNAL_CATCH_NTTP_1(__VA_ARGS__), INTERNAL_CATCH_NTTP_1(__VA_ARGS__), INTERNAL_CATCH_NTTP_1(__VA_ARGS__), INTERNAL_CATCH_NTTP_1(__VA_ARGS__), INTERNAL_CATCH_NTTP_1( __VA_ARGS__), INTERNAL_CATCH_NTTP_1( __VA_ARGS__), INTERNAL_CATCH_NTTP_1( __VA_ARGS__), INTERNAL_CATCH_NTTP_1( __VA_ARGS__),INTERNAL_CATCH_NTTP_1( __VA_ARGS__), INTERNAL_CATCH_NTTP_0)
+#define INTERNAL_CATCH_DEFINE_SIG_TEST_METHOD(TestName, ...) INTERNAL_CATCH_VA_NARGS_IMPL( "dummy", __VA_ARGS__, INTERNAL_CATCH_DEFINE_SIG_TEST_METHOD_X,INTERNAL_CATCH_DEFINE_SIG_TEST_METHOD_X, INTERNAL_CATCH_DEFINE_SIG_TEST_METHOD_X, INTERNAL_CATCH_DEFINE_SIG_TEST_METHOD_X, INTERNAL_CATCH_DEFINE_SIG_TEST_METHOD_X, INTERNAL_CATCH_DEFINE_SIG_TEST_METHOD_X, INTERNAL_CATCH_DEFINE_SIG_TEST_METHOD_X,INTERNAL_CATCH_DEFINE_SIG_TEST_METHOD_X,INTERNAL_CATCH_DEFINE_SIG_TEST_METHOD_X, INTERNAL_CATCH_DEFINE_SIG_TEST_METHOD1, INTERNAL_CATCH_DEFINE_SIG_TEST_METHOD0)(TestName, __VA_ARGS__)
+#define INTERNAL_CATCH_DECLARE_SIG_TEST_METHOD(TestName, ClassName, ...) INTERNAL_CATCH_VA_NARGS_IMPL( "dummy", __VA_ARGS__, INTERNAL_CATCH_DECLARE_SIG_TEST_METHOD_X,INTERNAL_CATCH_DECLARE_SIG_TEST_METHOD_X, INTERNAL_CATCH_DECLARE_SIG_TEST_METHOD_X, INTERNAL_CATCH_DECLARE_SIG_TEST_METHOD_X, INTERNAL_CATCH_DECLARE_SIG_TEST_METHOD_X, INTERNAL_CATCH_DECLARE_SIG_TEST_METHOD_X, INTERNAL_CATCH_DECLARE_SIG_TEST_METHOD_X,INTERNAL_CATCH_DECLARE_SIG_TEST_METHOD_X,INTERNAL_CATCH_DECLARE_SIG_TEST_METHOD_X, INTERNAL_CATCH_DECLARE_SIG_TEST_METHOD1, INTERNAL_CATCH_DECLARE_SIG_TEST_METHOD0)(TestName, ClassName, __VA_ARGS__)
+#define INTERNAL_CATCH_NTTP_REG_METHOD_GEN(TestName, ...) INTERNAL_CATCH_VA_NARGS_IMPL( "dummy", __VA_ARGS__, INTERNAL_CATCH_NTTP_REGISTER_METHOD, INTERNAL_CATCH_NTTP_REGISTER_METHOD, INTERNAL_CATCH_NTTP_REGISTER_METHOD, INTERNAL_CATCH_NTTP_REGISTER_METHOD, INTERNAL_CATCH_NTTP_REGISTER_METHOD, INTERNAL_CATCH_NTTP_REGISTER_METHOD, INTERNAL_CATCH_NTTP_REGISTER_METHOD, INTERNAL_CATCH_NTTP_REGISTER_METHOD, INTERNAL_CATCH_NTTP_REGISTER_METHOD, INTERNAL_CATCH_NTTP_REGISTER_METHOD0, INTERNAL_CATCH_NTTP_REGISTER_METHOD0)(TestName, __VA_ARGS__)
+#define INTERNAL_CATCH_NTTP_REG_GEN(TestFunc, ...) INTERNAL_CATCH_VA_NARGS_IMPL( "dummy", __VA_ARGS__, INTERNAL_CATCH_NTTP_REGISTER, INTERNAL_CATCH_NTTP_REGISTER, INTERNAL_CATCH_NTTP_REGISTER, INTERNAL_CATCH_NTTP_REGISTER, INTERNAL_CATCH_NTTP_REGISTER, INTERNAL_CATCH_NTTP_REGISTER, INTERNAL_CATCH_NTTP_REGISTER, INTERNAL_CATCH_NTTP_REGISTER, INTERNAL_CATCH_NTTP_REGISTER, INTERNAL_CATCH_NTTP_REGISTER0, INTERNAL_CATCH_NTTP_REGISTER0)(TestFunc, __VA_ARGS__)
+#define INTERNAL_CATCH_DEFINE_SIG_TEST(TestName, ...) INTERNAL_CATCH_VA_NARGS_IMPL( "dummy", __VA_ARGS__, INTERNAL_CATCH_DEFINE_SIG_TEST_X, INTERNAL_CATCH_DEFINE_SIG_TEST_X, INTERNAL_CATCH_DEFINE_SIG_TEST_X, INTERNAL_CATCH_DEFINE_SIG_TEST_X, INTERNAL_CATCH_DEFINE_SIG_TEST_X, INTERNAL_CATCH_DEFINE_SIG_TEST_X, INTERNAL_CATCH_DEFINE_SIG_TEST_X, INTERNAL_CATCH_DEFINE_SIG_TEST_X,INTERNAL_CATCH_DEFINE_SIG_TEST_X,INTERNAL_CATCH_DEFINE_SIG_TEST1, INTERNAL_CATCH_DEFINE_SIG_TEST0)(TestName, __VA_ARGS__)
+#define INTERNAL_CATCH_DECLARE_SIG_TEST(TestName, ...) INTERNAL_CATCH_VA_NARGS_IMPL( "dummy", __VA_ARGS__, INTERNAL_CATCH_DECLARE_SIG_TEST_X,INTERNAL_CATCH_DECLARE_SIG_TEST_X, INTERNAL_CATCH_DECLARE_SIG_TEST_X, INTERNAL_CATCH_DECLARE_SIG_TEST_X, INTERNAL_CATCH_DECLARE_SIG_TEST_X, INTERNAL_CATCH_DECLARE_SIG_TEST_X, INTERNAL_CATCH_DEFINE_SIG_TEST_X,INTERNAL_CATCH_DECLARE_SIG_TEST_X,INTERNAL_CATCH_DECLARE_SIG_TEST_X, INTERNAL_CATCH_DECLARE_SIG_TEST1, INTERNAL_CATCH_DECLARE_SIG_TEST0)(TestName, __VA_ARGS__)
+#define INTERNAL_CATCH_REMOVE_PARENS_GEN(...) INTERNAL_CATCH_VA_NARGS_IMPL(__VA_ARGS__, INTERNAL_CATCH_REMOVE_PARENS_11_ARG,INTERNAL_CATCH_REMOVE_PARENS_10_ARG,INTERNAL_CATCH_REMOVE_PARENS_9_ARG,INTERNAL_CATCH_REMOVE_PARENS_8_ARG,INTERNAL_CATCH_REMOVE_PARENS_7_ARG,INTERNAL_CATCH_REMOVE_PARENS_6_ARG,INTERNAL_CATCH_REMOVE_PARENS_5_ARG,INTERNAL_CATCH_REMOVE_PARENS_4_ARG,INTERNAL_CATCH_REMOVE_PARENS_3_ARG,INTERNAL_CATCH_REMOVE_PARENS_2_ARG,INTERNAL_CATCH_REMOVE_PARENS_1_ARG)(__VA_ARGS__)
+#else
+#define INTERNAL_CATCH_NTTP_0(signature)
+#define INTERNAL_CATCH_NTTP_GEN(...) INTERNAL_CATCH_EXPAND_VARGS(INTERNAL_CATCH_VA_NARGS_IMPL(__VA_ARGS__, INTERNAL_CATCH_NTTP_1, INTERNAL_CATCH_NTTP_1, INTERNAL_CATCH_NTTP_1, INTERNAL_CATCH_NTTP_1, INTERNAL_CATCH_NTTP_1, INTERNAL_CATCH_NTTP_1, INTERNAL_CATCH_NTTP_1, INTERNAL_CATCH_NTTP_1, INTERNAL_CATCH_NTTP_1,INTERNAL_CATCH_NTTP_1, INTERNAL_CATCH_NTTP_0)( __VA_ARGS__))
+#define INTERNAL_CATCH_DEFINE_SIG_TEST_METHOD(TestName, ...) INTERNAL_CATCH_EXPAND_VARGS(INTERNAL_CATCH_VA_NARGS_IMPL( "dummy", __VA_ARGS__, INTERNAL_CATCH_DEFINE_SIG_TEST_METHOD_X,INTERNAL_CATCH_DEFINE_SIG_TEST_METHOD_X, INTERNAL_CATCH_DEFINE_SIG_TEST_METHOD_X, INTERNAL_CATCH_DEFINE_SIG_TEST_METHOD_X, INTERNAL_CATCH_DEFINE_SIG_TEST_METHOD_X, INTERNAL_CATCH_DEFINE_SIG_TEST_METHOD_X, INTERNAL_CATCH_DEFINE_SIG_TEST_METHOD_X,INTERNAL_CATCH_DEFINE_SIG_TEST_METHOD_X,INTERNAL_CATCH_DEFINE_SIG_TEST_METHOD_X, INTERNAL_CATCH_DEFINE_SIG_TEST_METHOD1, INTERNAL_CATCH_DEFINE_SIG_TEST_METHOD0)(TestName, __VA_ARGS__))
+#define INTERNAL_CATCH_DECLARE_SIG_TEST_METHOD(TestName, ClassName, ...) INTERNAL_CATCH_EXPAND_VARGS(INTERNAL_CATCH_VA_NARGS_IMPL( "dummy", __VA_ARGS__, INTERNAL_CATCH_DECLARE_SIG_TEST_METHOD_X,INTERNAL_CATCH_DECLARE_SIG_TEST_METHOD_X, INTERNAL_CATCH_DECLARE_SIG_TEST_METHOD_X, INTERNAL_CATCH_DECLARE_SIG_TEST_METHOD_X, INTERNAL_CATCH_DECLARE_SIG_TEST_METHOD_X, INTERNAL_CATCH_DECLARE_SIG_TEST_METHOD_X, INTERNAL_CATCH_DECLARE_SIG_TEST_METHOD_X,INTERNAL_CATCH_DECLARE_SIG_TEST_METHOD_X,INTERNAL_CATCH_DECLARE_SIG_TEST_METHOD_X, INTERNAL_CATCH_DECLARE_SIG_TEST_METHOD1, INTERNAL_CATCH_DECLARE_SIG_TEST_METHOD0)(TestName, ClassName, __VA_ARGS__))
+#define INTERNAL_CATCH_NTTP_REG_METHOD_GEN(TestName, ...) INTERNAL_CATCH_EXPAND_VARGS(INTERNAL_CATCH_VA_NARGS_IMPL( "dummy", __VA_ARGS__, INTERNAL_CATCH_NTTP_REGISTER_METHOD, INTERNAL_CATCH_NTTP_REGISTER_METHOD, INTERNAL_CATCH_NTTP_REGISTER_METHOD, INTERNAL_CATCH_NTTP_REGISTER_METHOD, INTERNAL_CATCH_NTTP_REGISTER_METHOD, INTERNAL_CATCH_NTTP_REGISTER_METHOD, INTERNAL_CATCH_NTTP_REGISTER_METHOD, INTERNAL_CATCH_NTTP_REGISTER_METHOD, INTERNAL_CATCH_NTTP_REGISTER_METHOD, INTERNAL_CATCH_NTTP_REGISTER_METHOD0, INTERNAL_CATCH_NTTP_REGISTER_METHOD0)(TestName, __VA_ARGS__))
+#define INTERNAL_CATCH_NTTP_REG_GEN(TestFunc, ...) INTERNAL_CATCH_EXPAND_VARGS(INTERNAL_CATCH_VA_NARGS_IMPL( "dummy", __VA_ARGS__, INTERNAL_CATCH_NTTP_REGISTER, INTERNAL_CATCH_NTTP_REGISTER, INTERNAL_CATCH_NTTP_REGISTER, INTERNAL_CATCH_NTTP_REGISTER, INTERNAL_CATCH_NTTP_REGISTER, INTERNAL_CATCH_NTTP_REGISTER, INTERNAL_CATCH_NTTP_REGISTER, INTERNAL_CATCH_NTTP_REGISTER, INTERNAL_CATCH_NTTP_REGISTER, INTERNAL_CATCH_NTTP_REGISTER0, INTERNAL_CATCH_NTTP_REGISTER0)(TestFunc, __VA_ARGS__))
+#define INTERNAL_CATCH_DEFINE_SIG_TEST(TestName, ...) INTERNAL_CATCH_EXPAND_VARGS(INTERNAL_CATCH_VA_NARGS_IMPL( "dummy", __VA_ARGS__, INTERNAL_CATCH_DEFINE_SIG_TEST_X, INTERNAL_CATCH_DEFINE_SIG_TEST_X, INTERNAL_CATCH_DEFINE_SIG_TEST_X, INTERNAL_CATCH_DEFINE_SIG_TEST_X, INTERNAL_CATCH_DEFINE_SIG_TEST_X, INTERNAL_CATCH_DEFINE_SIG_TEST_X, INTERNAL_CATCH_DEFINE_SIG_TEST_X, INTERNAL_CATCH_DEFINE_SIG_TEST_X,INTERNAL_CATCH_DEFINE_SIG_TEST_X,INTERNAL_CATCH_DEFINE_SIG_TEST1, INTERNAL_CATCH_DEFINE_SIG_TEST0)(TestName, __VA_ARGS__))
+#define INTERNAL_CATCH_DECLARE_SIG_TEST(TestName, ...) INTERNAL_CATCH_EXPAND_VARGS(INTERNAL_CATCH_VA_NARGS_IMPL( "dummy", __VA_ARGS__, INTERNAL_CATCH_DECLARE_SIG_TEST_X,INTERNAL_CATCH_DECLARE_SIG_TEST_X, INTERNAL_CATCH_DECLARE_SIG_TEST_X, INTERNAL_CATCH_DECLARE_SIG_TEST_X, INTERNAL_CATCH_DECLARE_SIG_TEST_X, INTERNAL_CATCH_DECLARE_SIG_TEST_X, INTERNAL_CATCH_DEFINE_SIG_TEST_X,INTERNAL_CATCH_DECLARE_SIG_TEST_X,INTERNAL_CATCH_DECLARE_SIG_TEST_X, INTERNAL_CATCH_DECLARE_SIG_TEST1, INTERNAL_CATCH_DECLARE_SIG_TEST0)(TestName, __VA_ARGS__))
+#define INTERNAL_CATCH_REMOVE_PARENS_GEN(...) INTERNAL_CATCH_EXPAND_VARGS(INTERNAL_CATCH_VA_NARGS_IMPL(__VA_ARGS__, INTERNAL_CATCH_REMOVE_PARENS_11_ARG,INTERNAL_CATCH_REMOVE_PARENS_10_ARG,INTERNAL_CATCH_REMOVE_PARENS_9_ARG,INTERNAL_CATCH_REMOVE_PARENS_8_ARG,INTERNAL_CATCH_REMOVE_PARENS_7_ARG,INTERNAL_CATCH_REMOVE_PARENS_6_ARG,INTERNAL_CATCH_REMOVE_PARENS_5_ARG,INTERNAL_CATCH_REMOVE_PARENS_4_ARG,INTERNAL_CATCH_REMOVE_PARENS_3_ARG,INTERNAL_CATCH_REMOVE_PARENS_2_ARG,INTERNAL_CATCH_REMOVE_PARENS_1_ARG)(__VA_ARGS__))
+#endif
+
+// end catch_preprocessor.hpp
+// start catch_meta.hpp
+
+
+#include <type_traits>
+
+namespace Catch {
+template<typename T>
+struct always_false : std::false_type {};
+
+template <typename> struct true_given : std::true_type {};
+struct is_callable_tester {
+    template <typename Fun, typename... Args>
+    true_given<decltype(std::declval<Fun>()(std::declval<Args>()...))> static test(int);
+    template <typename...>
+    std::false_type static test(...);
+};
+
+template <typename T>
+struct is_callable;
 
+template <typename Fun, typename... Args>
+struct is_callable<Fun(Args...)> : decltype(is_callable_tester::test<Fun, Args...>(0)) {};
+
+} // namespace Catch
+
+namespace mpl_{
+    struct na;
 }
 
+// end catch_meta.hpp
 namespace Catch {
 
 template<typename C>
-class MethodTestCase : public SharedImpl<ITestCase> {
-
+class TestInvokerAsMethod : public ITestInvoker {
+    void (C::*m_testAsMethod)();
 public:
-    MethodTestCase( void (C::*method)() ) : m_method( method ) {}
+    TestInvokerAsMethod( void (C::*testAsMethod)() ) noexcept : m_testAsMethod( testAsMethod ) {}
 
-    virtual void invoke() const {
+    void invoke() const override {
         C obj;
-        (obj.*m_method)();
+        (obj.*m_testAsMethod)();
     }
-
-private:
-    virtual ~MethodTestCase() {}
-
-    void (C::*m_method)();
 };
 
-typedef void(*TestFunction)();
+auto makeTestInvoker( void(*testAsFunction)() ) noexcept -> ITestInvoker*;
 
-struct NameAndDesc {
-    NameAndDesc( const char* _name = "", const char* _description= "" )
-    : name( _name ), description( _description )
-    {}
+template<typename C>
+auto makeTestInvoker( void (C::*testAsMethod)() ) noexcept -> ITestInvoker* {
+    return new(std::nothrow) TestInvokerAsMethod<C>( testAsMethod );
+}
 
-    const char* name;
-    const char* description;
+struct NameAndTags {
+    NameAndTags( StringRef const& name_ = StringRef(), StringRef const& tags_ = StringRef() ) noexcept;
+    StringRef name;
+    StringRef tags;
 };
 
-void registerTestCase
-    (   ITestCase* testCase,
-        char const* className,
-        NameAndDesc const& nameAndDesc,
-        SourceLineInfo const& lineInfo );
-
-struct AutoReg {
-
-    AutoReg
-        (   TestFunction function,
-            SourceLineInfo const& lineInfo,
-            NameAndDesc const& nameAndDesc );
-
-    template<typename C>
-    AutoReg
-        (   void (C::*method)(),
-            char const* className,
-            NameAndDesc const& nameAndDesc,
-            SourceLineInfo const& lineInfo ) {
+struct AutoReg : NonCopyable {
+    AutoReg( ITestInvoker* invoker, SourceLineInfo const& lineInfo, StringRef const& classOrMethod, NameAndTags const& nameAndTags ) noexcept;
+    ~AutoReg();
+};
 
-        registerTestCase
-            (   new MethodTestCase<C>( method ),
-                className,
-                nameAndDesc,
-                lineInfo );
-    }
+} // end namespace Catch
 
-    ~AutoReg();
+#if defined(CATCH_CONFIG_DISABLE)
+    #define INTERNAL_CATCH_TESTCASE_NO_REGISTRATION( TestName, ... ) \
+        static void TestName()
+    #define INTERNAL_CATCH_TESTCASE_METHOD_NO_REGISTRATION( TestName, ClassName, ... ) \
+        namespace{                        \
+            struct TestName : INTERNAL_CATCH_REMOVE_PARENS(ClassName) { \
+                void test();              \
+            };                            \
+        }                                 \
+        void TestName::test()
+    #define INTERNAL_CATCH_TEMPLATE_TEST_CASE_NO_REGISTRATION_2( TestName, TestFunc, Name, Tags, Signature, ... )  \
+        INTERNAL_CATCH_DEFINE_SIG_TEST(TestFunc, INTERNAL_CATCH_REMOVE_PARENS(Signature))
+    #define INTERNAL_CATCH_TEMPLATE_TEST_CASE_METHOD_NO_REGISTRATION_2( TestNameClass, TestName, ClassName, Name, Tags, Signature, ... )    \
+        namespace{                                                                                  \
+            namespace INTERNAL_CATCH_MAKE_NAMESPACE(TestName) {                                      \
+            INTERNAL_CATCH_DECLARE_SIG_TEST_METHOD(TestName, ClassName, INTERNAL_CATCH_REMOVE_PARENS(Signature));\
+        }                                                                                           \
+        }                                                                                           \
+        INTERNAL_CATCH_DEFINE_SIG_TEST_METHOD(TestName, INTERNAL_CATCH_REMOVE_PARENS(Signature))
+
+    #ifndef CATCH_CONFIG_TRADITIONAL_MSVC_PREPROCESSOR
+        #define INTERNAL_CATCH_TEMPLATE_TEST_CASE_NO_REGISTRATION(Name, Tags, ...) \
+            INTERNAL_CATCH_TEMPLATE_TEST_CASE_NO_REGISTRATION_2( INTERNAL_CATCH_UNIQUE_NAME( ____C_A_T_C_H____T_E_M_P_L_A_T_E____T_E_S_T____ ), INTERNAL_CATCH_UNIQUE_NAME( ____C_A_T_C_H____T_E_M_P_L_A_T_E____T_E_S_T____F_U_N_C____ ), Name, Tags, typename TestType, __VA_ARGS__ )
+    #else
+        #define INTERNAL_CATCH_TEMPLATE_TEST_CASE_NO_REGISTRATION(Name, Tags, ...) \
+            INTERNAL_CATCH_EXPAND_VARGS( INTERNAL_CATCH_TEMPLATE_TEST_CASE_NO_REGISTRATION_2( INTERNAL_CATCH_UNIQUE_NAME( ____C_A_T_C_H____T_E_M_P_L_A_T_E____T_E_S_T____ ), INTERNAL_CATCH_UNIQUE_NAME( ____C_A_T_C_H____T_E_M_P_L_A_T_E____T_E_S_T____F_U_N_C____ ), Name, Tags, typename TestType, __VA_ARGS__ ) )
+    #endif
 
-private:
-    AutoReg( AutoReg const& );
-    void operator= ( AutoReg const& );
-};
+    #ifndef CATCH_CONFIG_TRADITIONAL_MSVC_PREPROCESSOR
+        #define INTERNAL_CATCH_TEMPLATE_TEST_CASE_SIG_NO_REGISTRATION(Name, Tags, Signature, ...) \
+            INTERNAL_CATCH_TEMPLATE_TEST_CASE_NO_REGISTRATION_2( INTERNAL_CATCH_UNIQUE_NAME( ____C_A_T_C_H____T_E_M_P_L_A_T_E____T_E_S_T____ ), INTERNAL_CATCH_UNIQUE_NAME( ____C_A_T_C_H____T_E_M_P_L_A_T_E____T_E_S_T____F_U_N_C____ ), Name, Tags, Signature, __VA_ARGS__ )
+    #else
+        #define INTERNAL_CATCH_TEMPLATE_TEST_CASE_SIG_NO_REGISTRATION(Name, Tags, Signature, ...) \
+            INTERNAL_CATCH_EXPAND_VARGS( INTERNAL_CATCH_TEMPLATE_TEST_CASE_NO_REGISTRATION_2( INTERNAL_CATCH_UNIQUE_NAME( ____C_A_T_C_H____T_E_M_P_L_A_T_E____T_E_S_T____ ), INTERNAL_CATCH_UNIQUE_NAME( ____C_A_T_C_H____T_E_M_P_L_A_T_E____T_E_S_T____F_U_N_C____ ), Name, Tags, Signature, __VA_ARGS__ ) )
+    #endif
 
-void registerTestCaseFunction
-    (   TestFunction function,
-        SourceLineInfo const& lineInfo,
-        NameAndDesc const& nameAndDesc );
+    #ifndef CATCH_CONFIG_TRADITIONAL_MSVC_PREPROCESSOR
+        #define INTERNAL_CATCH_TEMPLATE_TEST_CASE_METHOD_NO_REGISTRATION( ClassName, Name, Tags,... ) \
+            INTERNAL_CATCH_TEMPLATE_TEST_CASE_METHOD_NO_REGISTRATION_2( INTERNAL_CATCH_UNIQUE_NAME( ____C_A_T_C_H____T_E_M_P_L_A_T_E____T_E_S_T____C_L_A_S_S____ ), INTERNAL_CATCH_UNIQUE_NAME( ____C_A_T_C_H____T_E_M_P_L_A_T_E____T_E_S_T____ ) , ClassName, Name, Tags, typename T, __VA_ARGS__ )
+    #else
+        #define INTERNAL_CATCH_TEMPLATE_TEST_CASE_METHOD_NO_REGISTRATION( ClassName, Name, Tags,... ) \
+            INTERNAL_CATCH_EXPAND_VARGS( INTERNAL_CATCH_TEMPLATE_TEST_CASE_METHOD_NO_REGISTRATION_2( INTERNAL_CATCH_UNIQUE_NAME( ____C_A_T_C_H____T_E_M_P_L_A_T_E____T_E_S_T____C_L_A_S_S____ ), INTERNAL_CATCH_UNIQUE_NAME( ____C_A_T_C_H____T_E_M_P_L_A_T_E____T_E_S_T____ ) , ClassName, Name, Tags, typename T, __VA_ARGS__ ) )
+    #endif
 
-} // end namespace Catch
+    #ifndef CATCH_CONFIG_TRADITIONAL_MSVC_PREPROCESSOR
+        #define INTERNAL_CATCH_TEMPLATE_TEST_CASE_METHOD_SIG_NO_REGISTRATION( ClassName, Name, Tags, Signature, ... ) \
+            INTERNAL_CATCH_TEMPLATE_TEST_CASE_METHOD_NO_REGISTRATION_2( INTERNAL_CATCH_UNIQUE_NAME( ____C_A_T_C_H____T_E_M_P_L_A_T_E____T_E_S_T____C_L_A_S_S____ ), INTERNAL_CATCH_UNIQUE_NAME( ____C_A_T_C_H____T_E_M_P_L_A_T_E____T_E_S_T____ ) , ClassName, Name, Tags, Signature, __VA_ARGS__ )
+    #else
+        #define INTERNAL_CATCH_TEMPLATE_TEST_CASE_METHOD_SIG_NO_REGISTRATION( ClassName, Name, Tags, Signature, ... ) \
+            INTERNAL_CATCH_EXPAND_VARGS( INTERNAL_CATCH_TEMPLATE_TEST_CASE_METHOD_NO_REGISTRATION_2( INTERNAL_CATCH_UNIQUE_NAME( ____C_A_T_C_H____T_E_M_P_L_A_T_E____T_E_S_T____C_L_A_S_S____ ), INTERNAL_CATCH_UNIQUE_NAME( ____C_A_T_C_H____T_E_M_P_L_A_T_E____T_E_S_T____ ) , ClassName, Name, Tags, Signature, __VA_ARGS__ ) )
+    #endif
+#endif
 
-#ifdef CATCH_CONFIG_VARIADIC_MACROS
     ///////////////////////////////////////////////////////////////////////////////
     #define INTERNAL_CATCH_TESTCASE2( TestName, ... ) \
         static void TestName(); \
-        CATCH_INTERNAL_SUPPRESS_ETD_WARNINGS \
-        namespace{ Catch::AutoReg INTERNAL_CATCH_UNIQUE_NAME( autoRegistrar )( &TestName, CATCH_INTERNAL_LINEINFO, Catch::NameAndDesc( __VA_ARGS__ ) ); } \
-        CATCH_INTERNAL_UNSUPPRESS_ETD_WARNINGS \
+        CATCH_INTERNAL_SUPPRESS_GLOBALS_WARNINGS \
+        namespace{ Catch::AutoReg INTERNAL_CATCH_UNIQUE_NAME( autoRegistrar )( Catch::makeTestInvoker( &TestName ), CATCH_INTERNAL_LINEINFO, Catch::StringRef(), Catch::NameAndTags{ __VA_ARGS__ } ); } /* NOLINT */ \
+        CATCH_INTERNAL_UNSUPPRESS_GLOBALS_WARNINGS \
         static void TestName()
     #define INTERNAL_CATCH_TESTCASE( ... ) \
         INTERNAL_CATCH_TESTCASE2( INTERNAL_CATCH_UNIQUE_NAME( ____C_A_T_C_H____T_E_S_T____ ), __VA_ARGS__ )
 
     ///////////////////////////////////////////////////////////////////////////////
     #define INTERNAL_CATCH_METHOD_AS_TEST_CASE( QualifiedMethod, ... ) \
-        CATCH_INTERNAL_SUPPRESS_ETD_WARNINGS \
-        namespace{ Catch::AutoReg INTERNAL_CATCH_UNIQUE_NAME( autoRegistrar )( &QualifiedMethod, "&" #QualifiedMethod, Catch::NameAndDesc( __VA_ARGS__ ), CATCH_INTERNAL_LINEINFO ); } \
-        CATCH_INTERNAL_UNSUPPRESS_ETD_WARNINGS
+        CATCH_INTERNAL_SUPPRESS_GLOBALS_WARNINGS \
+        namespace{ Catch::AutoReg INTERNAL_CATCH_UNIQUE_NAME( autoRegistrar )( Catch::makeTestInvoker( &QualifiedMethod ), CATCH_INTERNAL_LINEINFO, "&" #QualifiedMethod, Catch::NameAndTags{ __VA_ARGS__ } ); } /* NOLINT */ \
+        CATCH_INTERNAL_UNSUPPRESS_GLOBALS_WARNINGS
 
     ///////////////////////////////////////////////////////////////////////////////
     #define INTERNAL_CATCH_TEST_CASE_METHOD2( TestName, ClassName, ... )\
-        CATCH_INTERNAL_SUPPRESS_ETD_WARNINGS \
+        CATCH_INTERNAL_SUPPRESS_GLOBALS_WARNINGS \
         namespace{ \
-            struct TestName : ClassName{ \
+            struct TestName : INTERNAL_CATCH_REMOVE_PARENS(ClassName) { \
                 void test(); \
             }; \
-            Catch::AutoReg INTERNAL_CATCH_UNIQUE_NAME( autoRegistrar ) ( &TestName::test, #ClassName, Catch::NameAndDesc( __VA_ARGS__ ), CATCH_INTERNAL_LINEINFO ); \
+            Catch::AutoReg INTERNAL_CATCH_UNIQUE_NAME( autoRegistrar ) ( Catch::makeTestInvoker( &TestName::test ), CATCH_INTERNAL_LINEINFO, #ClassName, Catch::NameAndTags{ __VA_ARGS__ } ); /* NOLINT */ \
         } \
-        CATCH_INTERNAL_UNSUPPRESS_ETD_WARNINGS \
+        CATCH_INTERNAL_UNSUPPRESS_GLOBALS_WARNINGS \
         void TestName::test()
     #define INTERNAL_CATCH_TEST_CASE_METHOD( ClassName, ... ) \
         INTERNAL_CATCH_TEST_CASE_METHOD2( INTERNAL_CATCH_UNIQUE_NAME( ____C_A_T_C_H____T_E_S_T____ ), ClassName, __VA_ARGS__ )
 
     ///////////////////////////////////////////////////////////////////////////////
     #define INTERNAL_CATCH_REGISTER_TESTCASE( Function, ... ) \
-        CATCH_INTERNAL_SUPPRESS_ETD_WARNINGS \
-        Catch::AutoReg( Function, CATCH_INTERNAL_LINEINFO, Catch::NameAndDesc( __VA_ARGS__ ) ); \
-        CATCH_INTERNAL_UNSUPPRESS_ETD_WARNINGS
+        CATCH_INTERNAL_SUPPRESS_GLOBALS_WARNINGS \
+        Catch::AutoReg INTERNAL_CATCH_UNIQUE_NAME( autoRegistrar )( Catch::makeTestInvoker( Function ), CATCH_INTERNAL_LINEINFO, Catch::StringRef(), Catch::NameAndTags{ __VA_ARGS__ } ); /* NOLINT */ \
+        CATCH_INTERNAL_UNSUPPRESS_GLOBALS_WARNINGS
 
-#else
     ///////////////////////////////////////////////////////////////////////////////
-    #define INTERNAL_CATCH_TESTCASE2( TestName, Name, Desc ) \
-        static void TestName(); \
-        CATCH_INTERNAL_SUPPRESS_ETD_WARNINGS \
-        namespace{ Catch::AutoReg INTERNAL_CATCH_UNIQUE_NAME( autoRegistrar )( &TestName, CATCH_INTERNAL_LINEINFO, Catch::NameAndDesc( Name, Desc ) ); }\
-        CATCH_INTERNAL_UNSUPPRESS_ETD_WARNINGS \
-        static void TestName()
-    #define INTERNAL_CATCH_TESTCASE( Name, Desc ) \
-        INTERNAL_CATCH_TESTCASE2( INTERNAL_CATCH_UNIQUE_NAME( ____C_A_T_C_H____T_E_S_T____ ), Name, Desc )
+    #define INTERNAL_CATCH_TEMPLATE_TEST_CASE_2(TestName, TestFunc, Name, Tags, Signature, ... )\
+        CATCH_INTERNAL_SUPPRESS_GLOBALS_WARNINGS \
+        CATCH_INTERNAL_SUPPRESS_ZERO_VARIADIC_WARNINGS \
+        CATCH_INTERNAL_SUPPRESS_UNUSED_TEMPLATE_WARNINGS \
+        INTERNAL_CATCH_DECLARE_SIG_TEST(TestFunc, INTERNAL_CATCH_REMOVE_PARENS(Signature));\
+        namespace {\
+        namespace INTERNAL_CATCH_MAKE_NAMESPACE(TestName){\
+            INTERNAL_CATCH_TYPE_GEN\
+            INTERNAL_CATCH_NTTP_GEN(INTERNAL_CATCH_REMOVE_PARENS(Signature))\
+            INTERNAL_CATCH_NTTP_REG_GEN(TestFunc,INTERNAL_CATCH_REMOVE_PARENS(Signature))\
+            template<typename...Types> \
+            struct TestName{\
+                TestName(){\
+                    int index = 0;                                    \
+                    constexpr char const* tmpl_types[] = {CATCH_REC_LIST(INTERNAL_CATCH_STRINGIZE_WITHOUT_PARENS, __VA_ARGS__)};\
+                    using expander = int[];\
+                    (void)expander{(reg_test(Types{}, Catch::NameAndTags{ Name " - " + std::string(tmpl_types[index]), Tags } ), index++, 0)... };/* NOLINT */ \
+                }\
+            };\
+            static int INTERNAL_CATCH_UNIQUE_NAME( globalRegistrar ) = [](){\
+            TestName<INTERNAL_CATCH_MAKE_TYPE_LISTS_FROM_TYPES(__VA_ARGS__)>();\
+            return 0;\
+        }();\
+        }\
+        }\
+        CATCH_INTERNAL_UNSUPPRESS_GLOBALS_WARNINGS \
+        CATCH_INTERNAL_UNSUPPRESS_ZERO_VARIADIC_WARNINGS \
+        CATCH_INTERNAL_UNSUPPRESS_UNUSED_TEMPLATE_WARNINGS \
+        INTERNAL_CATCH_DEFINE_SIG_TEST(TestFunc,INTERNAL_CATCH_REMOVE_PARENS(Signature))
+
+#ifndef CATCH_CONFIG_TRADITIONAL_MSVC_PREPROCESSOR
+    #define INTERNAL_CATCH_TEMPLATE_TEST_CASE(Name, Tags, ...) \
+        INTERNAL_CATCH_TEMPLATE_TEST_CASE_2( INTERNAL_CATCH_UNIQUE_NAME( ____C_A_T_C_H____T_E_M_P_L_A_T_E____T_E_S_T____ ), INTERNAL_CATCH_UNIQUE_NAME( ____C_A_T_C_H____T_E_M_P_L_A_T_E____T_E_S_T____F_U_N_C____ ), Name, Tags, typename TestType, __VA_ARGS__ )
+#else
+    #define INTERNAL_CATCH_TEMPLATE_TEST_CASE(Name, Tags, ...) \
+        INTERNAL_CATCH_EXPAND_VARGS( INTERNAL_CATCH_TEMPLATE_TEST_CASE_2( INTERNAL_CATCH_UNIQUE_NAME( ____C_A_T_C_H____T_E_M_P_L_A_T_E____T_E_S_T____ ), INTERNAL_CATCH_UNIQUE_NAME( ____C_A_T_C_H____T_E_M_P_L_A_T_E____T_E_S_T____F_U_N_C____ ), Name, Tags, typename TestType, __VA_ARGS__ ) )
+#endif
 
-    ///////////////////////////////////////////////////////////////////////////////
-    #define INTERNAL_CATCH_METHOD_AS_TEST_CASE( QualifiedMethod, Name, Desc ) \
-        CATCH_INTERNAL_SUPPRESS_ETD_WARNINGS \
-        namespace{ Catch::AutoReg INTERNAL_CATCH_UNIQUE_NAME( autoRegistrar )( &QualifiedMethod, "&" #QualifiedMethod, Catch::NameAndDesc( Name, Desc ), CATCH_INTERNAL_LINEINFO ); } \
-        CATCH_INTERNAL_UNSUPPRESS_ETD_WARNINGS
+#ifndef CATCH_CONFIG_TRADITIONAL_MSVC_PREPROCESSOR
+    #define INTERNAL_CATCH_TEMPLATE_TEST_CASE_SIG(Name, Tags, Signature, ...) \
+        INTERNAL_CATCH_TEMPLATE_TEST_CASE_2( INTERNAL_CATCH_UNIQUE_NAME( ____C_A_T_C_H____T_E_M_P_L_A_T_E____T_E_S_T____ ), INTERNAL_CATCH_UNIQUE_NAME( ____C_A_T_C_H____T_E_M_P_L_A_T_E____T_E_S_T____F_U_N_C____ ), Name, Tags, Signature, __VA_ARGS__ )
+#else
+    #define INTERNAL_CATCH_TEMPLATE_TEST_CASE_SIG(Name, Tags, Signature, ...) \
+        INTERNAL_CATCH_EXPAND_VARGS( INTERNAL_CATCH_TEMPLATE_TEST_CASE_2( INTERNAL_CATCH_UNIQUE_NAME( ____C_A_T_C_H____T_E_M_P_L_A_T_E____T_E_S_T____ ), INTERNAL_CATCH_UNIQUE_NAME( ____C_A_T_C_H____T_E_M_P_L_A_T_E____T_E_S_T____F_U_N_C____ ), Name, Tags, Signature, __VA_ARGS__ ) )
+#endif
 
-    ///////////////////////////////////////////////////////////////////////////////
-    #define INTERNAL_CATCH_TEST_CASE_METHOD2( TestCaseName, ClassName, TestName, Desc )\
-        CATCH_INTERNAL_SUPPRESS_ETD_WARNINGS \
-        namespace{ \
-            struct TestCaseName : ClassName{ \
-                void test(); \
-            }; \
-            Catch::AutoReg INTERNAL_CATCH_UNIQUE_NAME( autoRegistrar ) ( &TestCaseName::test, #ClassName, Catch::NameAndDesc( TestName, Desc ), CATCH_INTERNAL_LINEINFO ); \
-        } \
-        CATCH_INTERNAL_UNSUPPRESS_ETD_WARNINGS \
-        void TestCaseName::test()
-    #define INTERNAL_CATCH_TEST_CASE_METHOD( ClassName, TestName, Desc )\
-        INTERNAL_CATCH_TEST_CASE_METHOD2( INTERNAL_CATCH_UNIQUE_NAME( ____C_A_T_C_H____T_E_S_T____ ), ClassName, TestName, Desc )
+    #define INTERNAL_CATCH_TEMPLATE_PRODUCT_TEST_CASE2(TestName, TestFuncName, Name, Tags, Signature, TmplTypes, TypesList) \
+        CATCH_INTERNAL_SUPPRESS_GLOBALS_WARNINGS                      \
+        CATCH_INTERNAL_SUPPRESS_ZERO_VARIADIC_WARNINGS                \
+        CATCH_INTERNAL_SUPPRESS_UNUSED_TEMPLATE_WARNINGS                       \
+        template<typename TestType> static void TestFuncName();       \
+        namespace {\
+        namespace INTERNAL_CATCH_MAKE_NAMESPACE(TestName) {                                     \
+            INTERNAL_CATCH_TYPE_GEN                                                  \
+            INTERNAL_CATCH_NTTP_GEN(INTERNAL_CATCH_REMOVE_PARENS(Signature))         \
+            template<typename... Types>                               \
+            struct TestName {                                         \
+                void reg_tests() {                                          \
+                    int index = 0;                                    \
+                    using expander = int[];                           \
+                    constexpr char const* tmpl_types[] = {CATCH_REC_LIST(INTERNAL_CATCH_STRINGIZE_WITHOUT_PARENS, INTERNAL_CATCH_REMOVE_PARENS(TmplTypes))};\
+                    constexpr char const* types_list[] = {CATCH_REC_LIST(INTERNAL_CATCH_STRINGIZE_WITHOUT_PARENS, INTERNAL_CATCH_REMOVE_PARENS(TypesList))};\
+                    constexpr auto num_types = sizeof(types_list) / sizeof(types_list[0]);\
+                    (void)expander{(Catch::AutoReg( Catch::makeTestInvoker( &TestFuncName<Types> ), CATCH_INTERNAL_LINEINFO, Catch::StringRef(), Catch::NameAndTags{ Name " - " + std::string(tmpl_types[index / num_types]) + "<" + std::string(types_list[index % num_types]) + ">", Tags } ), index++, 0)... };/* NOLINT */\
+                }                                                     \
+            };                                                        \
+            static int INTERNAL_CATCH_UNIQUE_NAME( globalRegistrar ) = [](){ \
+                using TestInit = typename create<TestName, decltype(get_wrapper<INTERNAL_CATCH_REMOVE_PARENS(TmplTypes)>()), TypeList<INTERNAL_CATCH_MAKE_TYPE_LISTS_FROM_TYPES(INTERNAL_CATCH_REMOVE_PARENS(TypesList))>>::type; \
+                TestInit t;                                           \
+                t.reg_tests();                                        \
+                return 0;                                             \
+            }();                                                      \
+        }                                                             \
+        }                                                             \
+        CATCH_INTERNAL_UNSUPPRESS_GLOBALS_WARNINGS                    \
+        CATCH_INTERNAL_UNSUPPRESS_ZERO_VARIADIC_WARNINGS              \
+        CATCH_INTERNAL_UNSUPPRESS_UNUSED_TEMPLATE_WARNINGS                     \
+        template<typename TestType>                                   \
+        static void TestFuncName()
+
+#ifndef CATCH_CONFIG_TRADITIONAL_MSVC_PREPROCESSOR
+    #define INTERNAL_CATCH_TEMPLATE_PRODUCT_TEST_CASE(Name, Tags, ...)\
+        INTERNAL_CATCH_TEMPLATE_PRODUCT_TEST_CASE2(INTERNAL_CATCH_UNIQUE_NAME( ____C_A_T_C_H____T_E_M_P_L_A_T_E____T_E_S_T____ ), INTERNAL_CATCH_UNIQUE_NAME( ____C_A_T_C_H____T_E_M_P_L_A_T_E____T_E_S_T____F_U_N_C____ ), Name, Tags, typename T,__VA_ARGS__)
+#else
+    #define INTERNAL_CATCH_TEMPLATE_PRODUCT_TEST_CASE(Name, Tags, ...)\
+        INTERNAL_CATCH_EXPAND_VARGS( INTERNAL_CATCH_TEMPLATE_PRODUCT_TEST_CASE2( INTERNAL_CATCH_UNIQUE_NAME( ____C_A_T_C_H____T_E_M_P_L_A_T_E____T_E_S_T____ ), INTERNAL_CATCH_UNIQUE_NAME( ____C_A_T_C_H____T_E_M_P_L_A_T_E____T_E_S_T____F_U_N_C____ ), Name, Tags, typename T, __VA_ARGS__ ) )
+#endif
 
-    ///////////////////////////////////////////////////////////////////////////////
-    #define INTERNAL_CATCH_REGISTER_TESTCASE( Function, Name, Desc ) \
-        CATCH_INTERNAL_SUPPRESS_ETD_WARNINGS \
-        Catch::AutoReg( Function, CATCH_INTERNAL_LINEINFO, Catch::NameAndDesc( Name, Desc ) ); \
-        CATCH_INTERNAL_UNSUPPRESS_ETD_WARNINGS
+#ifndef CATCH_CONFIG_TRADITIONAL_MSVC_PREPROCESSOR
+    #define INTERNAL_CATCH_TEMPLATE_PRODUCT_TEST_CASE_SIG(Name, Tags, Signature, ...)\
+        INTERNAL_CATCH_TEMPLATE_PRODUCT_TEST_CASE2(INTERNAL_CATCH_UNIQUE_NAME( ____C_A_T_C_H____T_E_M_P_L_A_T_E____T_E_S_T____ ), INTERNAL_CATCH_UNIQUE_NAME( ____C_A_T_C_H____T_E_M_P_L_A_T_E____T_E_S_T____F_U_N_C____ ), Name, Tags, Signature, __VA_ARGS__)
+#else
+    #define INTERNAL_CATCH_TEMPLATE_PRODUCT_TEST_CASE_SIG(Name, Tags, Signature, ...)\
+        INTERNAL_CATCH_EXPAND_VARGS( INTERNAL_CATCH_TEMPLATE_PRODUCT_TEST_CASE2( INTERNAL_CATCH_UNIQUE_NAME( ____C_A_T_C_H____T_E_M_P_L_A_T_E____T_E_S_T____ ), INTERNAL_CATCH_UNIQUE_NAME( ____C_A_T_C_H____T_E_M_P_L_A_T_E____T_E_S_T____F_U_N_C____ ), Name, Tags, Signature, __VA_ARGS__ ) )
+#endif
+
+    #define INTERNAL_CATCH_TEMPLATE_LIST_TEST_CASE_2(TestName, TestFunc, Name, Tags, TmplList)\
+        CATCH_INTERNAL_SUPPRESS_GLOBALS_WARNINGS \
+        CATCH_INTERNAL_SUPPRESS_UNUSED_TEMPLATE_WARNINGS \
+        template<typename TestType> static void TestFunc();       \
+        namespace {\
+        namespace INTERNAL_CATCH_MAKE_NAMESPACE(TestName){\
+        INTERNAL_CATCH_TYPE_GEN\
+        template<typename... Types>                               \
+        struct TestName {                                         \
+            void reg_tests() {                                          \
+                int index = 0;                                    \
+                using expander = int[];                           \
+                (void)expander{(Catch::AutoReg( Catch::makeTestInvoker( &TestFunc<Types> ), CATCH_INTERNAL_LINEINFO, Catch::StringRef(), Catch::NameAndTags{ Name " - " + std::string(INTERNAL_CATCH_STRINGIZE(TmplList)) + " - " + std::to_string(index), Tags } ), index++, 0)... };/* NOLINT */\
+            }                                                     \
+        };\
+        static int INTERNAL_CATCH_UNIQUE_NAME( globalRegistrar ) = [](){ \
+                using TestInit = typename convert<TestName, TmplList>::type; \
+                TestInit t;                                           \
+                t.reg_tests();                                        \
+                return 0;                                             \
+            }();                                                        \
+        }}\
+        CATCH_INTERNAL_UNSUPPRESS_GLOBALS_WARNINGS                    \
+        CATCH_INTERNAL_UNSUPPRESS_UNUSED_TEMPLATE_WARNINGS \
+        template<typename TestType>                                   \
+        static void TestFunc()
+
+    #define INTERNAL_CATCH_TEMPLATE_LIST_TEST_CASE(Name, Tags, TmplList) \
+        INTERNAL_CATCH_TEMPLATE_LIST_TEST_CASE_2( INTERNAL_CATCH_UNIQUE_NAME( ____C_A_T_C_H____T_E_M_P_L_A_T_E____T_E_S_T____ ), INTERNAL_CATCH_UNIQUE_NAME( ____C_A_T_C_H____T_E_M_P_L_A_T_E____T_E_S_T____F_U_N_C____ ), Name, Tags, TmplList )
+
+    #define INTERNAL_CATCH_TEMPLATE_TEST_CASE_METHOD_2( TestNameClass, TestName, ClassName, Name, Tags, Signature, ... ) \
+        CATCH_INTERNAL_SUPPRESS_GLOBALS_WARNINGS \
+        CATCH_INTERNAL_SUPPRESS_ZERO_VARIADIC_WARNINGS \
+        CATCH_INTERNAL_SUPPRESS_UNUSED_TEMPLATE_WARNINGS \
+        namespace {\
+        namespace INTERNAL_CATCH_MAKE_NAMESPACE(TestName){ \
+            INTERNAL_CATCH_TYPE_GEN\
+            INTERNAL_CATCH_NTTP_GEN(INTERNAL_CATCH_REMOVE_PARENS(Signature))\
+            INTERNAL_CATCH_DECLARE_SIG_TEST_METHOD(TestName, ClassName, INTERNAL_CATCH_REMOVE_PARENS(Signature));\
+            INTERNAL_CATCH_NTTP_REG_METHOD_GEN(TestName, INTERNAL_CATCH_REMOVE_PARENS(Signature))\
+            template<typename...Types> \
+            struct TestNameClass{\
+                TestNameClass(){\
+                    int index = 0;                                    \
+                    constexpr char const* tmpl_types[] = {CATCH_REC_LIST(INTERNAL_CATCH_STRINGIZE_WITHOUT_PARENS, __VA_ARGS__)};\
+                    using expander = int[];\
+                    (void)expander{(reg_test(Types{}, #ClassName, Catch::NameAndTags{ Name " - " + std::string(tmpl_types[index]), Tags } ), index++, 0)... };/* NOLINT */ \
+                }\
+            };\
+            static int INTERNAL_CATCH_UNIQUE_NAME( globalRegistrar ) = [](){\
+                TestNameClass<INTERNAL_CATCH_MAKE_TYPE_LISTS_FROM_TYPES(__VA_ARGS__)>();\
+                return 0;\
+        }();\
+        }\
+        }\
+        CATCH_INTERNAL_UNSUPPRESS_GLOBALS_WARNINGS\
+        CATCH_INTERNAL_UNSUPPRESS_ZERO_VARIADIC_WARNINGS\
+        CATCH_INTERNAL_UNSUPPRESS_UNUSED_TEMPLATE_WARNINGS\
+        INTERNAL_CATCH_DEFINE_SIG_TEST_METHOD(TestName, INTERNAL_CATCH_REMOVE_PARENS(Signature))
+
+#ifndef CATCH_CONFIG_TRADITIONAL_MSVC_PREPROCESSOR
+    #define INTERNAL_CATCH_TEMPLATE_TEST_CASE_METHOD( ClassName, Name, Tags,... ) \
+        INTERNAL_CATCH_TEMPLATE_TEST_CASE_METHOD_2( INTERNAL_CATCH_UNIQUE_NAME( ____C_A_T_C_H____T_E_M_P_L_A_T_E____T_E_S_T____C_L_A_S_S____ ), INTERNAL_CATCH_UNIQUE_NAME( ____C_A_T_C_H____T_E_M_P_L_A_T_E____T_E_S_T____ ) , ClassName, Name, Tags, typename T, __VA_ARGS__ )
+#else
+    #define INTERNAL_CATCH_TEMPLATE_TEST_CASE_METHOD( ClassName, Name, Tags,... ) \
+        INTERNAL_CATCH_EXPAND_VARGS( INTERNAL_CATCH_TEMPLATE_TEST_CASE_METHOD_2( INTERNAL_CATCH_UNIQUE_NAME( ____C_A_T_C_H____T_E_M_P_L_A_T_E____T_E_S_T____C_L_A_S_S____ ), INTERNAL_CATCH_UNIQUE_NAME( ____C_A_T_C_H____T_E_M_P_L_A_T_E____T_E_S_T____ ) , ClassName, Name, Tags, typename T, __VA_ARGS__ ) )
+#endif
 
+#ifndef CATCH_CONFIG_TRADITIONAL_MSVC_PREPROCESSOR
+    #define INTERNAL_CATCH_TEMPLATE_TEST_CASE_METHOD_SIG( ClassName, Name, Tags, Signature, ... ) \
+        INTERNAL_CATCH_TEMPLATE_TEST_CASE_METHOD_2( INTERNAL_CATCH_UNIQUE_NAME( ____C_A_T_C_H____T_E_M_P_L_A_T_E____T_E_S_T____C_L_A_S_S____ ), INTERNAL_CATCH_UNIQUE_NAME( ____C_A_T_C_H____T_E_M_P_L_A_T_E____T_E_S_T____ ) , ClassName, Name, Tags, Signature, __VA_ARGS__ )
+#else
+    #define INTERNAL_CATCH_TEMPLATE_TEST_CASE_METHOD_SIG( ClassName, Name, Tags, Signature, ... ) \
+        INTERNAL_CATCH_EXPAND_VARGS( INTERNAL_CATCH_TEMPLATE_TEST_CASE_METHOD_2( INTERNAL_CATCH_UNIQUE_NAME( ____C_A_T_C_H____T_E_M_P_L_A_T_E____T_E_S_T____C_L_A_S_S____ ), INTERNAL_CATCH_UNIQUE_NAME( ____C_A_T_C_H____T_E_M_P_L_A_T_E____T_E_S_T____ ) , ClassName, Name, Tags, Signature, __VA_ARGS__ ) )
 #endif
 
-// #included from: internal/catch_capture.hpp
-#define TWOBLUECUBES_CATCH_CAPTURE_HPP_INCLUDED
+    #define INTERNAL_CATCH_TEMPLATE_PRODUCT_TEST_CASE_METHOD_2(TestNameClass, TestName, ClassName, Name, Tags, Signature, TmplTypes, TypesList)\
+        CATCH_INTERNAL_SUPPRESS_GLOBALS_WARNINGS \
+        CATCH_INTERNAL_SUPPRESS_ZERO_VARIADIC_WARNINGS \
+        CATCH_INTERNAL_SUPPRESS_UNUSED_TEMPLATE_WARNINGS \
+        template<typename TestType> \
+            struct TestName : INTERNAL_CATCH_REMOVE_PARENS(ClassName <TestType>) { \
+                void test();\
+            };\
+        namespace {\
+        namespace INTERNAL_CATCH_MAKE_NAMESPACE(TestNameClass) {\
+            INTERNAL_CATCH_TYPE_GEN                  \
+            INTERNAL_CATCH_NTTP_GEN(INTERNAL_CATCH_REMOVE_PARENS(Signature))\
+            template<typename...Types>\
+            struct TestNameClass{\
+                void reg_tests(){\
+                    int index = 0;\
+                    using expander = int[];\
+                    constexpr char const* tmpl_types[] = {CATCH_REC_LIST(INTERNAL_CATCH_STRINGIZE_WITHOUT_PARENS, INTERNAL_CATCH_REMOVE_PARENS(TmplTypes))};\
+                    constexpr char const* types_list[] = {CATCH_REC_LIST(INTERNAL_CATCH_STRINGIZE_WITHOUT_PARENS, INTERNAL_CATCH_REMOVE_PARENS(TypesList))};\
+                    constexpr auto num_types = sizeof(types_list) / sizeof(types_list[0]);\
+                    (void)expander{(Catch::AutoReg( Catch::makeTestInvoker( &TestName<Types>::test ), CATCH_INTERNAL_LINEINFO, #ClassName, Catch::NameAndTags{ Name " - " + std::string(tmpl_types[index / num_types]) + "<" + std::string(types_list[index % num_types]) + ">", Tags } ), index++, 0)... };/* NOLINT */ \
+                }\
+            };\
+            static int INTERNAL_CATCH_UNIQUE_NAME( globalRegistrar ) = [](){\
+                using TestInit = typename create<TestNameClass, decltype(get_wrapper<INTERNAL_CATCH_REMOVE_PARENS(TmplTypes)>()), TypeList<INTERNAL_CATCH_MAKE_TYPE_LISTS_FROM_TYPES(INTERNAL_CATCH_REMOVE_PARENS(TypesList))>>::type;\
+                TestInit t;\
+                t.reg_tests();\
+                return 0;\
+            }(); \
+        }\
+        }\
+        CATCH_INTERNAL_UNSUPPRESS_GLOBALS_WARNINGS \
+        CATCH_INTERNAL_UNSUPPRESS_ZERO_VARIADIC_WARNINGS \
+        CATCH_INTERNAL_UNSUPPRESS_UNUSED_TEMPLATE_WARNINGS \
+        template<typename TestType> \
+        void TestName<TestType>::test()
+
+#ifndef CATCH_CONFIG_TRADITIONAL_MSVC_PREPROCESSOR
+    #define INTERNAL_CATCH_TEMPLATE_PRODUCT_TEST_CASE_METHOD( ClassName, Name, Tags, ... )\
+        INTERNAL_CATCH_TEMPLATE_PRODUCT_TEST_CASE_METHOD_2( INTERNAL_CATCH_UNIQUE_NAME( ____C_A_T_C_H____T_E_M_P_L_A_T_E____T_E_S_T____ ), INTERNAL_CATCH_UNIQUE_NAME( ____C_A_T_C_H____T_E_M_P_L_A_T_E____T_E_S_T____F_U_N_C____ ), ClassName, Name, Tags, typename T, __VA_ARGS__ )
+#else
+    #define INTERNAL_CATCH_TEMPLATE_PRODUCT_TEST_CASE_METHOD( ClassName, Name, Tags, ... )\
+        INTERNAL_CATCH_EXPAND_VARGS( INTERNAL_CATCH_TEMPLATE_PRODUCT_TEST_CASE_METHOD_2( INTERNAL_CATCH_UNIQUE_NAME( ____C_A_T_C_H____T_E_M_P_L_A_T_E____T_E_S_T____ ), INTERNAL_CATCH_UNIQUE_NAME( ____C_A_T_C_H____T_E_M_P_L_A_T_E____T_E_S_T____F_U_N_C____ ), ClassName, Name, Tags, typename T,__VA_ARGS__ ) )
+#endif
 
-// #included from: catch_result_builder.h
-#define TWOBLUECUBES_CATCH_RESULT_BUILDER_H_INCLUDED
+#ifndef CATCH_CONFIG_TRADITIONAL_MSVC_PREPROCESSOR
+    #define INTERNAL_CATCH_TEMPLATE_PRODUCT_TEST_CASE_METHOD_SIG( ClassName, Name, Tags, Signature, ... )\
+        INTERNAL_CATCH_TEMPLATE_PRODUCT_TEST_CASE_METHOD_2( INTERNAL_CATCH_UNIQUE_NAME( ____C_A_T_C_H____T_E_M_P_L_A_T_E____T_E_S_T____ ), INTERNAL_CATCH_UNIQUE_NAME( ____C_A_T_C_H____T_E_M_P_L_A_T_E____T_E_S_T____F_U_N_C____ ), ClassName, Name, Tags, Signature, __VA_ARGS__ )
+#else
+    #define INTERNAL_CATCH_TEMPLATE_PRODUCT_TEST_CASE_METHOD_SIG( ClassName, Name, Tags, Signature, ... )\
+        INTERNAL_CATCH_EXPAND_VARGS( INTERNAL_CATCH_TEMPLATE_PRODUCT_TEST_CASE_METHOD_2( INTERNAL_CATCH_UNIQUE_NAME( ____C_A_T_C_H____T_E_M_P_L_A_T_E____T_E_S_T____ ), INTERNAL_CATCH_UNIQUE_NAME( ____C_A_T_C_H____T_E_M_P_L_A_T_E____T_E_S_T____F_U_N_C____ ), ClassName, Name, Tags, Signature,__VA_ARGS__ ) )
+#endif
 
-// #included from: catch_result_type.h
-#define TWOBLUECUBES_CATCH_RESULT_TYPE_H_INCLUDED
+    #define INTERNAL_CATCH_TEMPLATE_LIST_TEST_CASE_METHOD_2( TestNameClass, TestName, ClassName, Name, Tags, TmplList) \
+        CATCH_INTERNAL_SUPPRESS_GLOBALS_WARNINGS \
+        CATCH_INTERNAL_SUPPRESS_UNUSED_TEMPLATE_WARNINGS \
+        template<typename TestType> \
+        struct TestName : INTERNAL_CATCH_REMOVE_PARENS(ClassName <TestType>) { \
+            void test();\
+        };\
+        namespace {\
+        namespace INTERNAL_CATCH_MAKE_NAMESPACE(TestName){ \
+            INTERNAL_CATCH_TYPE_GEN\
+            template<typename...Types>\
+            struct TestNameClass{\
+                void reg_tests(){\
+                    int index = 0;\
+                    using expander = int[];\
+                    (void)expander{(Catch::AutoReg( Catch::makeTestInvoker( &TestName<Types>::test ), CATCH_INTERNAL_LINEINFO, #ClassName, Catch::NameAndTags{ Name " - " + std::string(INTERNAL_CATCH_STRINGIZE(TmplList)) + " - " + std::to_string(index), Tags } ), index++, 0)... };/* NOLINT */ \
+                }\
+            };\
+            static int INTERNAL_CATCH_UNIQUE_NAME( globalRegistrar ) = [](){\
+                using TestInit = typename convert<TestNameClass, TmplList>::type;\
+                TestInit t;\
+                t.reg_tests();\
+                return 0;\
+            }(); \
+        }}\
+        CATCH_INTERNAL_UNSUPPRESS_GLOBALS_WARNINGS \
+        CATCH_INTERNAL_UNSUPPRESS_UNUSED_TEMPLATE_WARNINGS \
+        template<typename TestType> \
+        void TestName<TestType>::test()
+
+#define INTERNAL_CATCH_TEMPLATE_LIST_TEST_CASE_METHOD(ClassName, Name, Tags, TmplList) \
+        INTERNAL_CATCH_TEMPLATE_LIST_TEST_CASE_METHOD_2( INTERNAL_CATCH_UNIQUE_NAME( ____C_A_T_C_H____T_E_M_P_L_A_T_E____T_E_S_T____ ), INTERNAL_CATCH_UNIQUE_NAME( ____C_A_T_C_H____T_E_M_P_L_A_T_E____T_E_S_T____F_U_N_C____ ), ClassName, Name, Tags, TmplList )
+
+// end catch_test_registry.h
+// start catch_capture.hpp
+
+// start catch_assertionhandler.h
+
+// start catch_assertioninfo.h
+
+// start catch_result_type.h
 
 namespace Catch {
 
@@ -874,12 +1369,8 @@ namespace Catch {
 
     }; };
 
-    inline bool isOk( ResultWas::OfType resultType ) {
-        return ( resultType & ResultWas::FailureBit ) == 0;
-    }
-    inline bool isJustInfo( int flags ) {
-        return flags == ResultWas::Info;
-    }
+    bool isOk( ResultWas::OfType resultType );
+    bool isJustInfo( int flags );
 
     // ResultDisposition::Flags enum
     struct ResultDisposition { enum Flags {
@@ -890,601 +1381,123 @@ namespace Catch {
         SuppressFail = 0x08         // Failures are reported but do not fail the test
     }; };
 
-    inline ResultDisposition::Flags operator | ( ResultDisposition::Flags lhs, ResultDisposition::Flags rhs ) {
-        return static_cast<ResultDisposition::Flags>( static_cast<int>( lhs ) | static_cast<int>( rhs ) );
-    }
+    ResultDisposition::Flags operator | ( ResultDisposition::Flags lhs, ResultDisposition::Flags rhs );
 
-    inline bool shouldContinueOnFailure( int flags )    { return ( flags & ResultDisposition::ContinueOnFailure ) != 0; }
-    inline bool isFalseTest( int flags )                { return ( flags & ResultDisposition::FalseTest ) != 0; }
-    inline bool shouldSuppressFailure( int flags )      { return ( flags & ResultDisposition::SuppressFail ) != 0; }
+    bool shouldContinueOnFailure( int flags );
+    inline bool isFalseTest( int flags ) { return ( flags & ResultDisposition::FalseTest ) != 0; }
+    bool shouldSuppressFailure( int flags );
 
 } // end namespace Catch
 
-// #included from: catch_assertionresult.h
-#define TWOBLUECUBES_CATCH_ASSERTIONRESULT_H_INCLUDED
-
-#include <string>
-
+// end catch_result_type.h
 namespace Catch {
 
-    struct STATIC_ASSERT_Expression_Too_Complex_Please_Rewrite_As_Binary_Comparison;
-
-    struct DecomposedExpression
-    {
-        virtual ~DecomposedExpression() {}
-        virtual bool isBinaryExpression() const {
-            return false;
-        }
-        virtual void reconstructExpression( std::string& dest ) const = 0;
-
-        // Only simple binary comparisons can be decomposed.
-        // If more complex check is required then wrap sub-expressions in parentheses.
-        template<typename T> STATIC_ASSERT_Expression_Too_Complex_Please_Rewrite_As_Binary_Comparison& operator + ( T const& );
-        template<typename T> STATIC_ASSERT_Expression_Too_Complex_Please_Rewrite_As_Binary_Comparison& operator - ( T const& );
-        template<typename T> STATIC_ASSERT_Expression_Too_Complex_Please_Rewrite_As_Binary_Comparison& operator * ( T const& );
-        template<typename T> STATIC_ASSERT_Expression_Too_Complex_Please_Rewrite_As_Binary_Comparison& operator / ( T const& );
-        template<typename T> STATIC_ASSERT_Expression_Too_Complex_Please_Rewrite_As_Binary_Comparison& operator % ( T const& );
-        template<typename T> STATIC_ASSERT_Expression_Too_Complex_Please_Rewrite_As_Binary_Comparison& operator && ( T const& );
-        template<typename T> STATIC_ASSERT_Expression_Too_Complex_Please_Rewrite_As_Binary_Comparison& operator || ( T const& );
-
-    private:
-        DecomposedExpression& operator = (DecomposedExpression const&);
-    };
-
     struct AssertionInfo
     {
-        AssertionInfo() {}
-        AssertionInfo(  std::string const& _macroName,
-                        SourceLineInfo const& _lineInfo,
-                        std::string const& _capturedExpression,
-                        ResultDisposition::Flags _resultDisposition );
-
-        std::string macroName;
+        StringRef macroName;
         SourceLineInfo lineInfo;
-        std::string capturedExpression;
+        StringRef capturedExpression;
         ResultDisposition::Flags resultDisposition;
-    };
-
-    struct AssertionResultData
-    {
-        AssertionResultData() : decomposedExpression( CATCH_NULL )
-                              , resultType( ResultWas::Unknown )
-                              , negated( false )
-                              , parenthesized( false ) {}
-
-        void negate( bool parenthesize ) {
-            negated = !negated;
-            parenthesized = parenthesize;
-            if( resultType == ResultWas::Ok )
-                resultType = ResultWas::ExpressionFailed;
-            else if( resultType == ResultWas::ExpressionFailed )
-                resultType = ResultWas::Ok;
-        }
-
-        std::string const& reconstructExpression() const {
-            if( decomposedExpression != CATCH_NULL ) {
-                decomposedExpression->reconstructExpression( reconstructedExpression );
-                if( parenthesized ) {
-                    reconstructedExpression.insert( 0, 1, '(' );
-                    reconstructedExpression.append( 1, ')' );
-                }
-                if( negated ) {
-                    reconstructedExpression.insert( 0, 1, '!' );
-                }
-                decomposedExpression = CATCH_NULL;
-            }
-            return reconstructedExpression;
-        }
 
-        mutable DecomposedExpression const* decomposedExpression;
-        mutable std::string reconstructedExpression;
-        std::string message;
-        ResultWas::OfType resultType;
-        bool negated;
-        bool parenthesized;
+        // We want to delete this constructor but a compiler bug in 4.8 means
+        // the struct is then treated as non-aggregate
+        //AssertionInfo() = delete;
     };
 
-    class AssertionResult {
-    public:
-        AssertionResult();
-        AssertionResult( AssertionInfo const& info, AssertionResultData const& data );
-        ~AssertionResult();
-#  ifdef CATCH_CONFIG_CPP11_GENERATED_METHODS
-         AssertionResult( AssertionResult const& )              = default;
-         AssertionResult( AssertionResult && )                  = default;
-         AssertionResult& operator = ( AssertionResult const& ) = default;
-         AssertionResult& operator = ( AssertionResult && )     = default;
-#  endif
+} // end namespace Catch
 
-        bool isOk() const;
-        bool succeeded() const;
-        ResultWas::OfType getResultType() const;
-        bool hasExpression() const;
-        bool hasMessage() const;
-        std::string getExpression() const;
-        std::string getExpressionInMacro() const;
-        bool hasExpandedExpression() const;
-        std::string getExpandedExpression() const;
-        std::string getMessage() const;
-        SourceLineInfo getSourceInfo() const;
-        std::string getTestMacroName() const;
-        void discardDecomposedExpression() const;
-        void expandDecomposedExpression() const;
+// end catch_assertioninfo.h
+// start catch_decomposer.h
 
-    protected:
-        AssertionInfo m_info;
-        AssertionResultData m_resultData;
-    };
+// start catch_tostring.h
 
-} // end namespace Catch
+#include <vector>
+#include <cstddef>
+#include <type_traits>
+#include <string>
+// start catch_stream.h
 
-// #included from: catch_matchers.hpp
-#define TWOBLUECUBES_CATCH_MATCHERS_HPP_INCLUDED
+#include <iosfwd>
+#include <cstddef>
+#include <ostream>
 
 namespace Catch {
-namespace Matchers {
-    namespace Impl {
 
-        template<typename ArgT> struct MatchAllOf;
-        template<typename ArgT> struct MatchAnyOf;
-        template<typename ArgT> struct MatchNotOf;
+    std::ostream& cout();
+    std::ostream& cerr();
+    std::ostream& clog();
 
-        class MatcherUntypedBase {
-        public:
-            std::string toString() const {
-                if( m_cachedToString.empty() )
-                    m_cachedToString = describe();
-                return m_cachedToString;
-            }
+    class StringRef;
 
-        protected:
-            virtual ~MatcherUntypedBase();
-            virtual std::string describe() const = 0;
-            mutable std::string m_cachedToString;
-        private:
-            MatcherUntypedBase& operator = ( MatcherUntypedBase const& );
-        };
+    struct IStream {
+        virtual ~IStream();
+        virtual std::ostream& stream() const = 0;
+    };
 
-        template<typename ObjectT>
-        struct MatcherMethod {
-            virtual bool match( ObjectT const& arg ) const = 0;
-        };
-        template<typename PtrT>
-        struct MatcherMethod<PtrT*> {
-            virtual bool match( PtrT* arg ) const = 0;
-        };
+    auto makeStream( StringRef const &filename ) -> IStream const*;
 
-        template<typename ObjectT, typename ComparatorT = ObjectT>
-        struct MatcherBase : MatcherUntypedBase, MatcherMethod<ObjectT> {
+    class ReusableStringStream {
+        std::size_t m_index;
+        std::ostream* m_oss;
+    public:
+        ReusableStringStream();
+        ~ReusableStringStream();
 
-            MatchAllOf<ComparatorT> operator && ( MatcherBase const& other ) const;
-            MatchAnyOf<ComparatorT> operator || ( MatcherBase const& other ) const;
-            MatchNotOf<ComparatorT> operator ! () const;
-        };
+        auto str() const -> std::string;
 
-        template<typename ArgT>
-        struct MatchAllOf : MatcherBase<ArgT> {
-            virtual bool match( ArgT const& arg ) const CATCH_OVERRIDE {
-                for( std::size_t i = 0; i < m_matchers.size(); ++i ) {
-                    if (!m_matchers[i]->match(arg))
-                        return false;
-                }
-                return true;
-            }
-            virtual std::string describe() const CATCH_OVERRIDE {
-                std::string description;
-                description.reserve( 4 + m_matchers.size()*32 );
-                description += "( ";
-                for( std::size_t i = 0; i < m_matchers.size(); ++i ) {
-                    if( i != 0 )
-                        description += " and ";
-                    description += m_matchers[i]->toString();
-                }
-                description += " )";
-                return description;
-            }
+        template<typename T>
+        auto operator << ( T const& value ) -> ReusableStringStream& {
+            *m_oss << value;
+            return *this;
+        }
+        auto get() -> std::ostream& { return *m_oss; }
+    };
+}
 
-            MatchAllOf<ArgT>& operator && ( MatcherBase<ArgT> const& other ) {
-                m_matchers.push_back( &other );
-                return *this;
-            }
+// end catch_stream.h
+// start catch_interfaces_enum_values_registry.h
 
-            std::vector<MatcherBase<ArgT> const*> m_matchers;
-        };
-        template<typename ArgT>
-        struct MatchAnyOf : MatcherBase<ArgT> {
+#include <vector>
 
-            virtual bool match( ArgT const& arg ) const CATCH_OVERRIDE {
-                for( std::size_t i = 0; i < m_matchers.size(); ++i ) {
-                    if (m_matchers[i]->match(arg))
-                        return true;
-                }
-                return false;
-            }
-            virtual std::string describe() const CATCH_OVERRIDE {
-                std::string description;
-                description.reserve( 4 + m_matchers.size()*32 );
-                description += "( ";
-                for( std::size_t i = 0; i < m_matchers.size(); ++i ) {
-                    if( i != 0 )
-                        description += " or ";
-                    description += m_matchers[i]->toString();
-                }
-                description += " )";
-                return description;
-            }
+namespace Catch {
 
-            MatchAnyOf<ArgT>& operator || ( MatcherBase<ArgT> const& other ) {
-                m_matchers.push_back( &other );
-                return *this;
-            }
+    namespace Detail {
+        struct EnumInfo {
+            StringRef m_name;
+            std::vector<std::pair<int, StringRef>> m_values;
 
-            std::vector<MatcherBase<ArgT> const*> m_matchers;
+            ~EnumInfo();
+
+            StringRef lookup( int value ) const;
         };
+    } // namespace Detail
 
-        template<typename ArgT>
-        struct MatchNotOf : MatcherBase<ArgT> {
+    struct IMutableEnumValuesRegistry {
+        virtual ~IMutableEnumValuesRegistry();
 
-            MatchNotOf( MatcherBase<ArgT> const& underlyingMatcher ) : m_underlyingMatcher( underlyingMatcher ) {}
+        virtual Detail::EnumInfo const& registerEnum( StringRef enumName, StringRef allEnums, std::vector<int> const& values ) = 0;
 
-            virtual bool match( ArgT const& arg ) const CATCH_OVERRIDE {
-                return !m_underlyingMatcher.match( arg );
-            }
+        template<typename E>
+        Detail::EnumInfo const& registerEnum( StringRef enumName, StringRef allEnums, std::initializer_list<E> values ) {
+            static_assert(sizeof(int) >= sizeof(E), "Cannot serialize enum to int");
+            std::vector<int> intValues;
+            intValues.reserve( values.size() );
+            for( auto enumValue : values )
+                intValues.push_back( static_cast<int>( enumValue ) );
+            return registerEnum( enumName, allEnums, intValues );
+        }
+    };
 
-            virtual std::string describe() const CATCH_OVERRIDE {
-                return "not " + m_underlyingMatcher.toString();
-            }
-            MatcherBase<ArgT> const& m_underlyingMatcher;
-        };
+} // Catch
 
-        template<typename ObjectT, typename ComparatorT>
-        MatchAllOf<ComparatorT> MatcherBase<ObjectT, ComparatorT>::operator && ( MatcherBase const& other ) const {
-            return MatchAllOf<ComparatorT>() && *this && other;
-        }
-        template<typename ObjectT, typename ComparatorT>
-        MatchAnyOf<ComparatorT> MatcherBase<ObjectT, ComparatorT>::operator || ( MatcherBase const& other ) const {
-            return MatchAnyOf<ComparatorT>() || *this || other;
-        }
-        template<typename ObjectT, typename ComparatorT>
-        MatchNotOf<ComparatorT> MatcherBase<ObjectT, ComparatorT>::operator ! () const {
-            return MatchNotOf<ComparatorT>( *this );
-        }
+// end catch_interfaces_enum_values_registry.h
 
-    } // namespace Impl
-
-    // The following functions create the actual matcher objects.
-    // This allows the types to be inferred
-    // - deprecated: prefer ||, && and !
-    template<typename T>
-    inline Impl::MatchNotOf<T> Not( Impl::MatcherBase<T> const& underlyingMatcher ) {
-        return Impl::MatchNotOf<T>( underlyingMatcher );
-    }
-    template<typename T>
-    inline Impl::MatchAllOf<T> AllOf( Impl::MatcherBase<T> const& m1, Impl::MatcherBase<T> const& m2 ) {
-        return Impl::MatchAllOf<T>() && m1 && m2;
-    }
-    template<typename T>
-    inline Impl::MatchAllOf<T> AllOf( Impl::MatcherBase<T> const& m1, Impl::MatcherBase<T> const& m2, Impl::MatcherBase<T> const& m3 ) {
-        return Impl::MatchAllOf<T>() && m1 && m2 && m3;
-    }
-    template<typename T>
-    inline Impl::MatchAnyOf<T> AnyOf( Impl::MatcherBase<T> const& m1, Impl::MatcherBase<T> const& m2 ) {
-        return Impl::MatchAnyOf<T>() || m1 || m2;
-    }
-    template<typename T>
-    inline Impl::MatchAnyOf<T> AnyOf( Impl::MatcherBase<T> const& m1, Impl::MatcherBase<T> const& m2, Impl::MatcherBase<T> const& m3 ) {
-        return Impl::MatchAnyOf<T>() || m1 || m2 || m3;
-    }
-
-} // namespace Matchers
-
-using namespace Matchers;
-using Matchers::Impl::MatcherBase;
-
-} // namespace Catch
-
-namespace Catch {
-
-    struct TestFailureException{};
-
-    template<typename T> class ExpressionLhs;
-
-    struct CopyableStream {
-        CopyableStream() {}
-        CopyableStream( CopyableStream const& other ) {
-            oss << other.oss.str();
-        }
-        CopyableStream& operator=( CopyableStream const& other ) {
-            oss.str(std::string());
-            oss << other.oss.str();
-            return *this;
-        }
-        std::ostringstream oss;
-    };
-
-    class ResultBuilder : public DecomposedExpression {
-    public:
-        ResultBuilder(  char const* macroName,
-                        SourceLineInfo const& lineInfo,
-                        char const* capturedExpression,
-                        ResultDisposition::Flags resultDisposition,
-                        char const* secondArg = "" );
-        ~ResultBuilder();
-
-        template<typename T>
-        ExpressionLhs<T const&> operator <= ( T const& operand );
-        ExpressionLhs<bool> operator <= ( bool value );
-
-        template<typename T>
-        ResultBuilder& operator << ( T const& value ) {
-            m_stream.oss << value;
-            return *this;
-        }
-
-        ResultBuilder& setResultType( ResultWas::OfType result );
-        ResultBuilder& setResultType( bool result );
-
-        void endExpression( DecomposedExpression const& expr );
-
-        virtual void reconstructExpression( std::string& dest ) const CATCH_OVERRIDE;
-
-        AssertionResult build() const;
-        AssertionResult build( DecomposedExpression const& expr ) const;
-
-        void useActiveException( ResultDisposition::Flags resultDisposition = ResultDisposition::Normal );
-        void captureResult( ResultWas::OfType resultType );
-        void captureExpression();
-        void captureExpectedException( std::string const& expectedMessage );
-        void captureExpectedException( Matchers::Impl::MatcherBase<std::string> const& matcher );
-        void handleResult( AssertionResult const& result );
-        void react();
-        bool shouldDebugBreak() const;
-        bool allowThrows() const;
-
-        template<typename ArgT, typename MatcherT>
-        void captureMatch( ArgT const& arg, MatcherT const& matcher, char const* matcherString );
-
-        void setExceptionGuard();
-        void unsetExceptionGuard();
-
-    private:
-        AssertionInfo m_assertionInfo;
-        AssertionResultData m_data;
-        CopyableStream m_stream;
-
-        bool m_shouldDebugBreak;
-        bool m_shouldThrow;
-        bool m_guardException;
-    };
-
-} // namespace Catch
-
-// Include after due to circular dependency:
-// #included from: catch_expression_lhs.hpp
-#define TWOBLUECUBES_CATCH_EXPRESSION_LHS_HPP_INCLUDED
-
-// #included from: catch_evaluate.hpp
-#define TWOBLUECUBES_CATCH_EVALUATE_HPP_INCLUDED
-
-#ifdef _MSC_VER
-#pragma warning(push)
-#pragma warning(disable:4389) // '==' : signed/unsigned mismatch
-#pragma warning(disable:4312) // Converting int to T* using reinterpret_cast (issue on x64 platform)
-#endif
-
-#include <cstddef>
-
-namespace Catch {
-namespace Internal {
-
-    enum Operator {
-        IsEqualTo,
-        IsNotEqualTo,
-        IsLessThan,
-        IsGreaterThan,
-        IsLessThanOrEqualTo,
-        IsGreaterThanOrEqualTo
-    };
-
-    template<Operator Op> struct OperatorTraits             { static const char* getName(){ return "*error*"; } };
-    template<> struct OperatorTraits<IsEqualTo>             { static const char* getName(){ return "=="; } };
-    template<> struct OperatorTraits<IsNotEqualTo>          { static const char* getName(){ return "!="; } };
-    template<> struct OperatorTraits<IsLessThan>            { static const char* getName(){ return "<"; } };
-    template<> struct OperatorTraits<IsGreaterThan>         { static const char* getName(){ return ">"; } };
-    template<> struct OperatorTraits<IsLessThanOrEqualTo>   { static const char* getName(){ return "<="; } };
-    template<> struct OperatorTraits<IsGreaterThanOrEqualTo>{ static const char* getName(){ return ">="; } };
-
-    template<typename T>
-    inline T& opCast(T const& t) { return const_cast<T&>(t); }
-
-// nullptr_t support based on pull request #154 from Konstantin Baumann
-#ifdef CATCH_CONFIG_CPP11_NULLPTR
-    inline std::nullptr_t opCast(std::nullptr_t) { return nullptr; }
-#endif // CATCH_CONFIG_CPP11_NULLPTR
-
-    // So the compare overloads can be operator agnostic we convey the operator as a template
-    // enum, which is used to specialise an Evaluator for doing the comparison.
-    template<typename T1, typename T2, Operator Op>
-    class Evaluator{};
-
-    template<typename T1, typename T2>
-    struct Evaluator<T1, T2, IsEqualTo> {
-        static bool evaluate( T1 const& lhs, T2 const& rhs) {
-            return bool( opCast( lhs ) ==  opCast( rhs ) );
-        }
-    };
-    template<typename T1, typename T2>
-    struct Evaluator<T1, T2, IsNotEqualTo> {
-        static bool evaluate( T1 const& lhs, T2 const& rhs ) {
-            return bool( opCast( lhs ) != opCast( rhs ) );
-        }
-    };
-    template<typename T1, typename T2>
-    struct Evaluator<T1, T2, IsLessThan> {
-        static bool evaluate( T1 const& lhs, T2 const& rhs ) {
-            return bool( opCast( lhs ) < opCast( rhs ) );
-        }
-    };
-    template<typename T1, typename T2>
-    struct Evaluator<T1, T2, IsGreaterThan> {
-        static bool evaluate( T1 const& lhs, T2 const& rhs ) {
-            return bool( opCast( lhs ) > opCast( rhs ) );
-        }
-    };
-    template<typename T1, typename T2>
-    struct Evaluator<T1, T2, IsGreaterThanOrEqualTo> {
-        static bool evaluate( T1 const& lhs, T2 const& rhs ) {
-            return bool( opCast( lhs ) >= opCast( rhs ) );
-        }
-    };
-    template<typename T1, typename T2>
-    struct Evaluator<T1, T2, IsLessThanOrEqualTo> {
-        static bool evaluate( T1 const& lhs, T2 const& rhs ) {
-            return bool( opCast( lhs ) <= opCast( rhs ) );
-        }
-    };
-
-    template<Operator Op, typename T1, typename T2>
-    bool applyEvaluator( T1 const& lhs, T2 const& rhs ) {
-        return Evaluator<T1, T2, Op>::evaluate( lhs, rhs );
-    }
-
-    // This level of indirection allows us to specialise for integer types
-    // to avoid signed/ unsigned warnings
-
-    // "base" overload
-    template<Operator Op, typename T1, typename T2>
-    bool compare( T1 const& lhs, T2 const& rhs ) {
-        return Evaluator<T1, T2, Op>::evaluate( lhs, rhs );
-    }
-
-    // unsigned X to int
-    template<Operator Op> bool compare( unsigned int lhs, int rhs ) {
-        return applyEvaluator<Op>( lhs, static_cast<unsigned int>( rhs ) );
-    }
-    template<Operator Op> bool compare( unsigned long lhs, int rhs ) {
-        return applyEvaluator<Op>( lhs, static_cast<unsigned int>( rhs ) );
-    }
-    template<Operator Op> bool compare( unsigned char lhs, int rhs ) {
-        return applyEvaluator<Op>( lhs, static_cast<unsigned int>( rhs ) );
-    }
-
-    // unsigned X to long
-    template<Operator Op> bool compare( unsigned int lhs, long rhs ) {
-        return applyEvaluator<Op>( lhs, static_cast<unsigned long>( rhs ) );
-    }
-    template<Operator Op> bool compare( unsigned long lhs, long rhs ) {
-        return applyEvaluator<Op>( lhs, static_cast<unsigned long>( rhs ) );
-    }
-    template<Operator Op> bool compare( unsigned char lhs, long rhs ) {
-        return applyEvaluator<Op>( lhs, static_cast<unsigned long>( rhs ) );
-    }
-
-    // int to unsigned X
-    template<Operator Op> bool compare( int lhs, unsigned int rhs ) {
-        return applyEvaluator<Op>( static_cast<unsigned int>( lhs ), rhs );
-    }
-    template<Operator Op> bool compare( int lhs, unsigned long rhs ) {
-        return applyEvaluator<Op>( static_cast<unsigned int>( lhs ), rhs );
-    }
-    template<Operator Op> bool compare( int lhs, unsigned char rhs ) {
-        return applyEvaluator<Op>( static_cast<unsigned int>( lhs ), rhs );
-    }
-
-    // long to unsigned X
-    template<Operator Op> bool compare( long lhs, unsigned int rhs ) {
-        return applyEvaluator<Op>( static_cast<unsigned long>( lhs ), rhs );
-    }
-    template<Operator Op> bool compare( long lhs, unsigned long rhs ) {
-        return applyEvaluator<Op>( static_cast<unsigned long>( lhs ), rhs );
-    }
-    template<Operator Op> bool compare( long lhs, unsigned char rhs ) {
-        return applyEvaluator<Op>( static_cast<unsigned long>( lhs ), rhs );
-    }
-
-    // pointer to long (when comparing against NULL)
-    template<Operator Op, typename T> bool compare( long lhs, T* rhs ) {
-        return Evaluator<T*, T*, Op>::evaluate( reinterpret_cast<T*>( lhs ), rhs );
-    }
-    template<Operator Op, typename T> bool compare( T* lhs, long rhs ) {
-        return Evaluator<T*, T*, Op>::evaluate( lhs, reinterpret_cast<T*>( rhs ) );
-    }
-
-    // pointer to int (when comparing against NULL)
-    template<Operator Op, typename T> bool compare( int lhs, T* rhs ) {
-        return Evaluator<T*, T*, Op>::evaluate( reinterpret_cast<T*>( lhs ), rhs );
-    }
-    template<Operator Op, typename T> bool compare( T* lhs, int rhs ) {
-        return Evaluator<T*, T*, Op>::evaluate( lhs, reinterpret_cast<T*>( rhs ) );
-    }
-
-#ifdef CATCH_CONFIG_CPP11_LONG_LONG
-    // long long to unsigned X
-    template<Operator Op> bool compare( long long lhs, unsigned int rhs ) {
-        return applyEvaluator<Op>( static_cast<unsigned long>( lhs ), rhs );
-    }
-    template<Operator Op> bool compare( long long lhs, unsigned long rhs ) {
-        return applyEvaluator<Op>( static_cast<unsigned long>( lhs ), rhs );
-    }
-    template<Operator Op> bool compare( long long lhs, unsigned long long rhs ) {
-        return applyEvaluator<Op>( static_cast<unsigned long>( lhs ), rhs );
-    }
-    template<Operator Op> bool compare( long long lhs, unsigned char rhs ) {
-        return applyEvaluator<Op>( static_cast<unsigned long>( lhs ), rhs );
-    }
-
-    // unsigned long long to X
-    template<Operator Op> bool compare( unsigned long long lhs, int rhs ) {
-        return applyEvaluator<Op>( static_cast<long>( lhs ), rhs );
-    }
-    template<Operator Op> bool compare( unsigned long long lhs, long rhs ) {
-        return applyEvaluator<Op>( static_cast<long>( lhs ), rhs );
-    }
-    template<Operator Op> bool compare( unsigned long long lhs, long long rhs ) {
-        return applyEvaluator<Op>( static_cast<long>( lhs ), rhs );
-    }
-    template<Operator Op> bool compare( unsigned long long lhs, char rhs ) {
-        return applyEvaluator<Op>( static_cast<long>( lhs ), rhs );
-    }
-
-    // pointer to long long (when comparing against NULL)
-    template<Operator Op, typename T> bool compare( long long lhs, T* rhs ) {
-        return Evaluator<T*, T*, Op>::evaluate( reinterpret_cast<T*>( lhs ), rhs );
-    }
-    template<Operator Op, typename T> bool compare( T* lhs, long long rhs ) {
-        return Evaluator<T*, T*, Op>::evaluate( lhs, reinterpret_cast<T*>( rhs ) );
-    }
-#endif // CATCH_CONFIG_CPP11_LONG_LONG
-
-#ifdef CATCH_CONFIG_CPP11_NULLPTR
-    // pointer to nullptr_t (when comparing against nullptr)
-    template<Operator Op, typename T> bool compare( std::nullptr_t, T* rhs ) {
-        return Evaluator<T*, T*, Op>::evaluate( nullptr, rhs );
-    }
-    template<Operator Op, typename T> bool compare( T* lhs, std::nullptr_t ) {
-        return Evaluator<T*, T*, Op>::evaluate( lhs, nullptr );
-    }
-#endif // CATCH_CONFIG_CPP11_NULLPTR
-
-} // end of namespace Internal
-} // end of namespace Catch
-
-#ifdef _MSC_VER
-#pragma warning(pop)
-#endif
-
-// #included from: catch_tostring.h
-#define TWOBLUECUBES_CATCH_TOSTRING_H_INCLUDED
-
-#include <sstream>
-#include <iomanip>
-#include <limits>
-#include <vector>
-#include <cstddef>
+#ifdef CATCH_CONFIG_CPP17_STRING_VIEW
+#include <string_view>
+#endif
 
 #ifdef __OBJC__
-// #included from: catch_objc_arc.hpp
-#define TWOBLUECUBES_CATCH_OBJC_ARC_HPP_INCLUDED
+// start catch_objc_arc.hpp
 
 #import <Foundation/Foundation.h>
 
@@ -1526,873 +1539,1320 @@ inline id performOptionalSelector( id obj, SEL sel ) {
 #define CATCH_ARC_STRONG __strong
 #endif
 
+// end catch_objc_arc.hpp
 #endif
 
-#ifdef CATCH_CONFIG_CPP11_TUPLE
-#include <tuple>
-#endif
-
-#ifdef CATCH_CONFIG_CPP11_IS_ENUM
-#include <type_traits>
+#ifdef _MSC_VER
+#pragma warning(push)
+#pragma warning(disable:4180) // We attempt to stream a function (address) by const&, which MSVC complains about but is harmless
 #endif
 
 namespace Catch {
+    namespace Detail {
 
-// Why we're here.
-template<typename T>
-std::string toString( T const& value );
+        extern const std::string unprintableString;
+
+        std::string rawMemoryToString( const void *object, std::size_t size );
+
+        template<typename T>
+        std::string rawMemoryToString( const T& object ) {
+          return rawMemoryToString( &object, sizeof(object) );
+        }
+
+        template<typename T>
+        class IsStreamInsertable {
+            template<typename Stream, typename U>
+            static auto test(int)
+                -> decltype(std::declval<Stream&>() << std::declval<U>(), std::true_type());
+
+            template<typename, typename>
+            static auto test(...)->std::false_type;
+
+        public:
+            static const bool value = decltype(test<std::ostream, const T&>(0))::value;
+        };
+
+        template<typename E>
+        std::string convertUnknownEnumToString( E e );
 
-// Built in overloads
+        template<typename T>
+        typename std::enable_if<
+            !std::is_enum<T>::value && !std::is_base_of<std::exception, T>::value,
+        std::string>::type convertUnstreamable( T const& ) {
+            return Detail::unprintableString;
+        }
+        template<typename T>
+        typename std::enable_if<
+            !std::is_enum<T>::value && std::is_base_of<std::exception, T>::value,
+         std::string>::type convertUnstreamable(T const& ex) {
+            return ex.what();
+        }
 
-std::string toString( std::string const& value );
-std::string toString( std::wstring const& value );
-std::string toString( const char* const value );
-std::string toString( char* const value );
-std::string toString( const wchar_t* const value );
-std::string toString( wchar_t* const value );
-std::string toString( int value );
-std::string toString( unsigned long value );
-std::string toString( unsigned int value );
-std::string toString( const double value );
-std::string toString( const float value );
-std::string toString( bool value );
-std::string toString( char value );
-std::string toString( signed char value );
-std::string toString( unsigned char value );
+        template<typename T>
+        typename std::enable_if<
+            std::is_enum<T>::value
+        , std::string>::type convertUnstreamable( T const& value ) {
+            return convertUnknownEnumToString( value );
+        }
 
-#ifdef CATCH_CONFIG_CPP11_LONG_LONG
-std::string toString( long long value );
-std::string toString( unsigned long long value );
+#if defined(_MANAGED)
+        //! Convert a CLR string to a utf8 std::string
+        template<typename T>
+        std::string clrReferenceToString( T^ ref ) {
+            if (ref == nullptr)
+                return std::string("null");
+            auto bytes = System::Text::Encoding::UTF8->GetBytes(ref->ToString());
+            cli::pin_ptr<System::Byte> p = &bytes[0];
+            return std::string(reinterpret_cast<char const *>(p), bytes->Length);
+        }
 #endif
 
-#ifdef CATCH_CONFIG_CPP11_NULLPTR
-std::string toString( std::nullptr_t );
+    } // namespace Detail
+
+    // If we decide for C++14, change these to enable_if_ts
+    template <typename T, typename = void>
+    struct StringMaker {
+        template <typename Fake = T>
+        static
+        typename std::enable_if<::Catch::Detail::IsStreamInsertable<Fake>::value, std::string>::type
+            convert(const Fake& value) {
+                ReusableStringStream rss;
+                // NB: call using the function-like syntax to avoid ambiguity with
+                // user-defined templated operator<< under clang.
+                rss.operator<<(value);
+                return rss.str();
+        }
+
+        template <typename Fake = T>
+        static
+        typename std::enable_if<!::Catch::Detail::IsStreamInsertable<Fake>::value, std::string>::type
+            convert( const Fake& value ) {
+#if !defined(CATCH_CONFIG_FALLBACK_STRINGIFIER)
+            return Detail::convertUnstreamable(value);
+#else
+            return CATCH_CONFIG_FALLBACK_STRINGIFIER(value);
 #endif
+        }
+    };
 
-#ifdef __OBJC__
-    std::string toString( NSString const * const& nsstring );
-    std::string toString( NSString * CATCH_ARC_STRONG const& nsstring );
-    std::string toString( NSObject* const& nsObject );
+    namespace Detail {
+
+        // This function dispatches all stringification requests inside of Catch.
+        // Should be preferably called fully qualified, like ::Catch::Detail::stringify
+        template <typename T>
+        std::string stringify(const T& e) {
+            return ::Catch::StringMaker<typename std::remove_cv<typename std::remove_reference<T>::type>::type>::convert(e);
+        }
+
+        template<typename E>
+        std::string convertUnknownEnumToString( E e ) {
+            return ::Catch::Detail::stringify(static_cast<typename std::underlying_type<E>::type>(e));
+        }
+
+#if defined(_MANAGED)
+        template <typename T>
+        std::string stringify( T^ e ) {
+            return ::Catch::StringMaker<T^>::convert(e);
+        }
 #endif
 
-namespace Detail {
+    } // namespace Detail
 
-    extern const std::string unprintableString;
+    // Some predefined specializations
 
- #if !defined(CATCH_CONFIG_CPP11_STREAM_INSERTABLE_CHECK)
-    struct BorgType {
-        template<typename T> BorgType( T const& );
+    template<>
+    struct StringMaker<std::string> {
+        static std::string convert(const std::string& str);
     };
 
-    struct TrueType { char sizer[1]; };
-    struct FalseType { char sizer[2]; };
-
-    TrueType& testStreamable( std::ostream& );
-    FalseType testStreamable( FalseType );
+#ifdef CATCH_CONFIG_CPP17_STRING_VIEW
+    template<>
+    struct StringMaker<std::string_view> {
+        static std::string convert(std::string_view str);
+    };
+#endif
 
-    FalseType operator<<( std::ostream const&, BorgType const& );
+    template<>
+    struct StringMaker<char const *> {
+        static std::string convert(char const * str);
+    };
+    template<>
+    struct StringMaker<char *> {
+        static std::string convert(char * str);
+    };
 
-    template<typename T>
-    struct IsStreamInsertable {
-        static std::ostream &s;
-        static T  const&t;
-        enum { value = sizeof( testStreamable(s << t) ) == sizeof( TrueType ) };
+#ifdef CATCH_CONFIG_WCHAR
+    template<>
+    struct StringMaker<std::wstring> {
+        static std::string convert(const std::wstring& wstr);
     };
-#else
-    template<typename T>
-    class IsStreamInsertable {
-        template<typename SS, typename TT>
-        static auto test(int)
-        -> decltype( std::declval<SS&>() << std::declval<TT>(), std::true_type() );
 
-        template<typename, typename>
-        static auto test(...) -> std::false_type;
+# ifdef CATCH_CONFIG_CPP17_STRING_VIEW
+    template<>
+    struct StringMaker<std::wstring_view> {
+        static std::string convert(std::wstring_view str);
+    };
+# endif
 
-    public:
-        static const bool value = decltype(test<std::ostream,const T&>(0))::value;
+    template<>
+    struct StringMaker<wchar_t const *> {
+        static std::string convert(wchar_t const * str);
+    };
+    template<>
+    struct StringMaker<wchar_t *> {
+        static std::string convert(wchar_t * str);
     };
 #endif
 
-#if defined(CATCH_CONFIG_CPP11_IS_ENUM)
-    template<typename T,
-             bool IsEnum = std::is_enum<T>::value
-             >
-    struct EnumStringMaker
-    {
-        static std::string convert( T const& ) { return unprintableString; }
+    // TBD: Should we use `strnlen` to ensure that we don't go out of the buffer,
+    //      while keeping string semantics?
+    template<int SZ>
+    struct StringMaker<char[SZ]> {
+        static std::string convert(char const* str) {
+            return ::Catch::Detail::stringify(std::string{ str });
+        }
     };
-
-    template<typename T>
-    struct EnumStringMaker<T,true>
-    {
-        static std::string convert( T const& v )
-        {
-            return ::Catch::toString(
-                static_cast<typename std::underlying_type<T>::type>(v)
-                );
+    template<int SZ>
+    struct StringMaker<signed char[SZ]> {
+        static std::string convert(signed char const* str) {
+            return ::Catch::Detail::stringify(std::string{ reinterpret_cast<char const *>(str) });
         }
     };
-#endif
-    template<bool C>
-    struct StringMakerBase {
-#if defined(CATCH_CONFIG_CPP11_IS_ENUM)
-        template<typename T>
-        static std::string convert( T const& v )
-        {
-            return EnumStringMaker<T>::convert( v );
+    template<int SZ>
+    struct StringMaker<unsigned char[SZ]> {
+        static std::string convert(unsigned char const* str) {
+            return ::Catch::Detail::stringify(std::string{ reinterpret_cast<char const *>(str) });
         }
-#else
-        template<typename T>
-        static std::string convert( T const& ) { return unprintableString; }
-#endif
     };
 
+#if defined(CATCH_CONFIG_CPP17_BYTE)
     template<>
-    struct StringMakerBase<true> {
-        template<typename T>
-        static std::string convert( T const& _value ) {
-            std::ostringstream oss;
-            oss << _value;
-            return oss.str();
-        }
+    struct StringMaker<std::byte> {
+        static std::string convert(std::byte value);
+    };
+#endif // defined(CATCH_CONFIG_CPP17_BYTE)
+    template<>
+    struct StringMaker<int> {
+        static std::string convert(int value);
+    };
+    template<>
+    struct StringMaker<long> {
+        static std::string convert(long value);
+    };
+    template<>
+    struct StringMaker<long long> {
+        static std::string convert(long long value);
+    };
+    template<>
+    struct StringMaker<unsigned int> {
+        static std::string convert(unsigned int value);
+    };
+    template<>
+    struct StringMaker<unsigned long> {
+        static std::string convert(unsigned long value);
+    };
+    template<>
+    struct StringMaker<unsigned long long> {
+        static std::string convert(unsigned long long value);
     };
 
-    std::string rawMemoryToString( const void *object, std::size_t size );
+    template<>
+    struct StringMaker<bool> {
+        static std::string convert(bool b);
+    };
 
-    template<typename T>
-    inline std::string rawMemoryToString( const T& object ) {
-      return rawMemoryToString( &object, sizeof(object) );
-    }
+    template<>
+    struct StringMaker<char> {
+        static std::string convert(char c);
+    };
+    template<>
+    struct StringMaker<signed char> {
+        static std::string convert(signed char c);
+    };
+    template<>
+    struct StringMaker<unsigned char> {
+        static std::string convert(unsigned char c);
+    };
 
-} // end namespace Detail
+    template<>
+    struct StringMaker<std::nullptr_t> {
+        static std::string convert(std::nullptr_t);
+    };
 
-template<typename T>
-struct StringMaker :
-    Detail::StringMakerBase<Detail::IsStreamInsertable<T>::value> {};
+    template<>
+    struct StringMaker<float> {
+        static std::string convert(float value);
+        static int precision;
+    };
 
-template<typename T>
-struct StringMaker<T*> {
-    template<typename U>
-    static std::string convert( U* p ) {
-        if( !p )
-            return "NULL";
-        else
-            return Detail::rawMemoryToString( p );
-    }
-};
+    template<>
+    struct StringMaker<double> {
+        static std::string convert(double value);
+        static int precision;
+    };
 
-template<typename R, typename C>
-struct StringMaker<R C::*> {
-    static std::string convert( R C::* p ) {
-        if( !p )
-            return "NULL";
-        else
-            return Detail::rawMemoryToString( p );
+    template <typename T>
+    struct StringMaker<T*> {
+        template <typename U>
+        static std::string convert(U* p) {
+            if (p) {
+                return ::Catch::Detail::rawMemoryToString(p);
+            } else {
+                return "nullptr";
+            }
+        }
+    };
+
+    template <typename R, typename C>
+    struct StringMaker<R C::*> {
+        static std::string convert(R C::* p) {
+            if (p) {
+                return ::Catch::Detail::rawMemoryToString(p);
+            } else {
+                return "nullptr";
+            }
+        }
+    };
+
+#if defined(_MANAGED)
+    template <typename T>
+    struct StringMaker<T^> {
+        static std::string convert( T^ ref ) {
+            return ::Catch::Detail::clrReferenceToString(ref);
+        }
+    };
+#endif
+
+    namespace Detail {
+        template<typename InputIterator>
+        std::string rangeToString(InputIterator first, InputIterator last) {
+            ReusableStringStream rss;
+            rss << "{ ";
+            if (first != last) {
+                rss << ::Catch::Detail::stringify(*first);
+                for (++first; first != last; ++first)
+                    rss << ", " << ::Catch::Detail::stringify(*first);
+            }
+            rss << " }";
+            return rss.str();
+        }
     }
-};
 
-namespace Detail {
-    template<typename InputIterator>
-    std::string rangeToString( InputIterator first, InputIterator last );
-}
-
-//template<typename T, typename Allocator>
-//struct StringMaker<std::vector<T, Allocator> > {
-//    static std::string convert( std::vector<T,Allocator> const& v ) {
-//        return Detail::rangeToString( v.begin(), v.end() );
-//    }
-//};
-
-template<typename T, typename Allocator>
-std::string toString( std::vector<T,Allocator> const& v ) {
-    return Detail::rangeToString( v.begin(), v.end() );
-}
-
-#ifdef CATCH_CONFIG_CPP11_TUPLE
-
-// toString for tuples
-namespace TupleDetail {
-  template<
-      typename Tuple,
-      std::size_t N = 0,
-      bool = (N < std::tuple_size<Tuple>::value)
-      >
-  struct ElementPrinter {
-      static void print( const Tuple& tuple, std::ostream& os )
-      {
-          os << ( N ? ", " : " " )
-             << Catch::toString(std::get<N>(tuple));
-          ElementPrinter<Tuple,N+1>::print(tuple,os);
-      }
-  };
+#ifdef __OBJC__
+    template<>
+    struct StringMaker<NSString*> {
+        static std::string convert(NSString * nsstring) {
+            if (!nsstring)
+                return "nil";
+            return std::string("@") + [nsstring UTF8String];
+        }
+    };
+    template<>
+    struct StringMaker<NSObject*> {
+        static std::string convert(NSObject* nsObject) {
+            return ::Catch::Detail::stringify([nsObject description]);
+        }
 
-  template<
-      typename Tuple,
-      std::size_t N
-      >
-  struct ElementPrinter<Tuple,N,false> {
-      static void print( const Tuple&, std::ostream& ) {}
-  };
+    };
+    namespace Detail {
+        inline std::string stringify( NSString* nsstring ) {
+            return StringMaker<NSString*>::convert( nsstring );
+        }
 
-}
+    } // namespace Detail
+#endif // __OBJC__
 
-template<typename ...Types>
-struct StringMaker<std::tuple<Types...>> {
+} // namespace Catch
 
-    static std::string convert( const std::tuple<Types...>& tuple )
-    {
-        std::ostringstream os;
-        os << '{';
-        TupleDetail::ElementPrinter<std::tuple<Types...>>::print( tuple, os );
-        os << " }";
-        return os.str();
-    }
-};
-#endif // CATCH_CONFIG_CPP11_TUPLE
+//////////////////////////////////////////////////////
+// Separate std-lib types stringification, so it can be selectively enabled
+// This means that we do not bring in
 
-namespace Detail {
-    template<typename T>
-    std::string makeString( T const& value ) {
-        return StringMaker<T>::convert( value );
-    }
-} // end namespace Detail
+#if defined(CATCH_CONFIG_ENABLE_ALL_STRINGMAKERS)
+#  define CATCH_CONFIG_ENABLE_PAIR_STRINGMAKER
+#  define CATCH_CONFIG_ENABLE_TUPLE_STRINGMAKER
+#  define CATCH_CONFIG_ENABLE_VARIANT_STRINGMAKER
+#  define CATCH_CONFIG_ENABLE_CHRONO_STRINGMAKER
+#  define CATCH_CONFIG_ENABLE_OPTIONAL_STRINGMAKER
+#endif
 
-/// \brief converts any type to a string
-///
-/// The default template forwards on to ostringstream - except when an
-/// ostringstream overload does not exist - in which case it attempts to detect
-/// that and writes {?}.
-/// Overload (not specialise) this template for custom typs that you don't want
-/// to provide an ostream overload for.
-template<typename T>
-std::string toString( T const& value ) {
-    return StringMaker<T>::convert( value );
+// Separate std::pair specialization
+#if defined(CATCH_CONFIG_ENABLE_PAIR_STRINGMAKER)
+#include <utility>
+namespace Catch {
+    template<typename T1, typename T2>
+    struct StringMaker<std::pair<T1, T2> > {
+        static std::string convert(const std::pair<T1, T2>& pair) {
+            ReusableStringStream rss;
+            rss << "{ "
+                << ::Catch::Detail::stringify(pair.first)
+                << ", "
+                << ::Catch::Detail::stringify(pair.second)
+                << " }";
+            return rss.str();
+        }
+    };
+}
+#endif // CATCH_CONFIG_ENABLE_PAIR_STRINGMAKER
+
+#if defined(CATCH_CONFIG_ENABLE_OPTIONAL_STRINGMAKER) && defined(CATCH_CONFIG_CPP17_OPTIONAL)
+#include <optional>
+namespace Catch {
+    template<typename T>
+    struct StringMaker<std::optional<T> > {
+        static std::string convert(const std::optional<T>& optional) {
+            ReusableStringStream rss;
+            if (optional.has_value()) {
+                rss << ::Catch::Detail::stringify(*optional);
+            } else {
+                rss << "{ }";
+            }
+            return rss.str();
+        }
+    };
 }
+#endif // CATCH_CONFIG_ENABLE_OPTIONAL_STRINGMAKER
 
+// Separate std::tuple specialization
+#if defined(CATCH_CONFIG_ENABLE_TUPLE_STRINGMAKER)
+#include <tuple>
+namespace Catch {
     namespace Detail {
-    template<typename InputIterator>
-    std::string rangeToString( InputIterator first, InputIterator last ) {
-        std::ostringstream oss;
-        oss << "{ ";
-        if( first != last ) {
-            oss << Catch::toString( *first );
-            for( ++first ; first != last ; ++first )
-                oss << ", " << Catch::toString( *first );
-        }
-        oss << " }";
-        return oss.str();
+        template<
+            typename Tuple,
+            std::size_t N = 0,
+            bool = (N < std::tuple_size<Tuple>::value)
+            >
+            struct TupleElementPrinter {
+            static void print(const Tuple& tuple, std::ostream& os) {
+                os << (N ? ", " : " ")
+                    << ::Catch::Detail::stringify(std::get<N>(tuple));
+                TupleElementPrinter<Tuple, N + 1>::print(tuple, os);
+            }
+        };
+
+        template<
+            typename Tuple,
+            std::size_t N
+        >
+            struct TupleElementPrinter<Tuple, N, false> {
+            static void print(const Tuple&, std::ostream&) {}
+        };
+
     }
-}
 
-} // end namespace Catch
+    template<typename ...Types>
+    struct StringMaker<std::tuple<Types...>> {
+        static std::string convert(const std::tuple<Types...>& tuple) {
+            ReusableStringStream rss;
+            rss << '{';
+            Detail::TupleElementPrinter<std::tuple<Types...>>::print(tuple, rss.get());
+            rss << " }";
+            return rss.str();
+        }
+    };
+}
+#endif // CATCH_CONFIG_ENABLE_TUPLE_STRINGMAKER
 
+#if defined(CATCH_CONFIG_ENABLE_VARIANT_STRINGMAKER) && defined(CATCH_CONFIG_CPP17_VARIANT)
+#include <variant>
 namespace Catch {
+    template<>
+    struct StringMaker<std::monostate> {
+        static std::string convert(const std::monostate&) {
+            return "{ }";
+        }
+    };
 
-template<typename LhsT, Internal::Operator Op, typename RhsT>
-class BinaryExpression;
+    template<typename... Elements>
+    struct StringMaker<std::variant<Elements...>> {
+        static std::string convert(const std::variant<Elements...>& variant) {
+            if (variant.valueless_by_exception()) {
+                return "{valueless variant}";
+            } else {
+                return std::visit(
+                    [](const auto& value) {
+                        return ::Catch::Detail::stringify(value);
+                    },
+                    variant
+                );
+            }
+        }
+    };
+}
+#endif // CATCH_CONFIG_ENABLE_VARIANT_STRINGMAKER
 
-template<typename ArgT, typename MatcherT>
-class MatchExpression;
+namespace Catch {
+    struct not_this_one {}; // Tag type for detecting which begin/ end are being selected
 
-// Wraps the LHS of an expression and overloads comparison operators
-// for also capturing those and RHS (if any)
-template<typename T>
-class ExpressionLhs : public DecomposedExpression {
-public:
-    ExpressionLhs( ResultBuilder& rb, T lhs ) : m_rb( rb ), m_lhs( lhs ), m_truthy(false) {}
+    // Import begin/ end from std here so they are considered alongside the fallback (...) overloads in this namespace
+    using std::begin;
+    using std::end;
 
-    ExpressionLhs& operator = ( const ExpressionLhs& );
+    not_this_one begin( ... );
+    not_this_one end( ... );
 
-    template<typename RhsT>
-    BinaryExpression<T, Internal::IsEqualTo, RhsT const&>
-    operator == ( RhsT const& rhs ) {
-        return captureExpression<Internal::IsEqualTo>( rhs );
-    }
+    template <typename T>
+    struct is_range {
+        static const bool value =
+            !std::is_same<decltype(begin(std::declval<T>())), not_this_one>::value &&
+            !std::is_same<decltype(end(std::declval<T>())), not_this_one>::value;
+    };
 
-    template<typename RhsT>
-    BinaryExpression<T, Internal::IsNotEqualTo, RhsT const&>
-    operator != ( RhsT const& rhs ) {
-        return captureExpression<Internal::IsNotEqualTo>( rhs );
-    }
+#if defined(_MANAGED) // Managed types are never ranges
+    template <typename T>
+    struct is_range<T^> {
+        static const bool value = false;
+    };
+#endif
 
-    template<typename RhsT>
-    BinaryExpression<T, Internal::IsLessThan, RhsT const&>
-    operator < ( RhsT const& rhs ) {
-        return captureExpression<Internal::IsLessThan>( rhs );
+    template<typename Range>
+    std::string rangeToString( Range const& range ) {
+        return ::Catch::Detail::rangeToString( begin( range ), end( range ) );
     }
 
-    template<typename RhsT>
-    BinaryExpression<T, Internal::IsGreaterThan, RhsT const&>
-    operator > ( RhsT const& rhs ) {
-        return captureExpression<Internal::IsGreaterThan>( rhs );
+    // Handle vector<bool> specially
+    template<typename Allocator>
+    std::string rangeToString( std::vector<bool, Allocator> const& v ) {
+        ReusableStringStream rss;
+        rss << "{ ";
+        bool first = true;
+        for( bool b : v ) {
+            if( first )
+                first = false;
+            else
+                rss << ", ";
+            rss << ::Catch::Detail::stringify( b );
+        }
+        rss << " }";
+        return rss.str();
     }
 
-    template<typename RhsT>
-    BinaryExpression<T, Internal::IsLessThanOrEqualTo, RhsT const&>
-    operator <= ( RhsT const& rhs ) {
-        return captureExpression<Internal::IsLessThanOrEqualTo>( rhs );
-    }
+    template<typename R>
+    struct StringMaker<R, typename std::enable_if<is_range<R>::value && !::Catch::Detail::IsStreamInsertable<R>::value>::type> {
+        static std::string convert( R const& range ) {
+            return rangeToString( range );
+        }
+    };
 
-    template<typename RhsT>
-    BinaryExpression<T, Internal::IsGreaterThanOrEqualTo, RhsT const&>
-    operator >= ( RhsT const& rhs ) {
-        return captureExpression<Internal::IsGreaterThanOrEqualTo>( rhs );
-    }
+    template <typename T, int SZ>
+    struct StringMaker<T[SZ]> {
+        static std::string convert(T const(&arr)[SZ]) {
+            return rangeToString(arr);
+        }
+    };
 
-    BinaryExpression<T, Internal::IsEqualTo, bool> operator == ( bool rhs ) {
-        return captureExpression<Internal::IsEqualTo>( rhs );
-    }
+} // namespace Catch
 
-    BinaryExpression<T, Internal::IsNotEqualTo, bool> operator != ( bool rhs ) {
-        return captureExpression<Internal::IsNotEqualTo>( rhs );
-    }
+// Separate std::chrono::duration specialization
+#if defined(CATCH_CONFIG_ENABLE_CHRONO_STRINGMAKER)
+#include <ctime>
+#include <ratio>
+#include <chrono>
 
-    void endExpression() {
-        m_truthy = m_lhs ? true : false;
-        m_rb
-            .setResultType( m_truthy )
-            .endExpression( *this );
-    }
+namespace Catch {
 
-    virtual void reconstructExpression( std::string& dest ) const CATCH_OVERRIDE {
-        dest = Catch::toString( m_truthy );
-    }
+template <class Ratio>
+struct ratio_string {
+    static std::string symbol();
+};
 
-private:
-    template<Internal::Operator Op, typename RhsT>
-    BinaryExpression<T, Op, RhsT&> captureExpression( RhsT& rhs ) const {
-        return BinaryExpression<T, Op, RhsT&>( m_rb, m_lhs, rhs );
-    }
+template <class Ratio>
+std::string ratio_string<Ratio>::symbol() {
+    Catch::ReusableStringStream rss;
+    rss << '[' << Ratio::num << '/'
+        << Ratio::den << ']';
+    return rss.str();
+}
+template <>
+struct ratio_string<std::atto> {
+    static std::string symbol();
+};
+template <>
+struct ratio_string<std::femto> {
+    static std::string symbol();
+};
+template <>
+struct ratio_string<std::pico> {
+    static std::string symbol();
+};
+template <>
+struct ratio_string<std::nano> {
+    static std::string symbol();
+};
+template <>
+struct ratio_string<std::micro> {
+    static std::string symbol();
+};
+template <>
+struct ratio_string<std::milli> {
+    static std::string symbol();
+};
 
-    template<Internal::Operator Op>
-    BinaryExpression<T, Op, bool> captureExpression( bool rhs ) const {
-        return BinaryExpression<T, Op, bool>( m_rb, m_lhs, rhs );
-    }
+    ////////////
+    // std::chrono::duration specializations
+    template<typename Value, typename Ratio>
+    struct StringMaker<std::chrono::duration<Value, Ratio>> {
+        static std::string convert(std::chrono::duration<Value, Ratio> const& duration) {
+            ReusableStringStream rss;
+            rss << duration.count() << ' ' << ratio_string<Ratio>::symbol() << 's';
+            return rss.str();
+        }
+    };
+    template<typename Value>
+    struct StringMaker<std::chrono::duration<Value, std::ratio<1>>> {
+        static std::string convert(std::chrono::duration<Value, std::ratio<1>> const& duration) {
+            ReusableStringStream rss;
+            rss << duration.count() << " s";
+            return rss.str();
+        }
+    };
+    template<typename Value>
+    struct StringMaker<std::chrono::duration<Value, std::ratio<60>>> {
+        static std::string convert(std::chrono::duration<Value, std::ratio<60>> const& duration) {
+            ReusableStringStream rss;
+            rss << duration.count() << " m";
+            return rss.str();
+        }
+    };
+    template<typename Value>
+    struct StringMaker<std::chrono::duration<Value, std::ratio<3600>>> {
+        static std::string convert(std::chrono::duration<Value, std::ratio<3600>> const& duration) {
+            ReusableStringStream rss;
+            rss << duration.count() << " h";
+            return rss.str();
+        }
+    };
 
-private:
-    ResultBuilder& m_rb;
-    T m_lhs;
-    bool m_truthy;
-};
+    ////////////
+    // std::chrono::time_point specialization
+    // Generic time_point cannot be specialized, only std::chrono::time_point<system_clock>
+    template<typename Clock, typename Duration>
+    struct StringMaker<std::chrono::time_point<Clock, Duration>> {
+        static std::string convert(std::chrono::time_point<Clock, Duration> const& time_point) {
+            return ::Catch::Detail::stringify(time_point.time_since_epoch()) + " since epoch";
+        }
+    };
+    // std::chrono::time_point<system_clock> specialization
+    template<typename Duration>
+    struct StringMaker<std::chrono::time_point<std::chrono::system_clock, Duration>> {
+        static std::string convert(std::chrono::time_point<std::chrono::system_clock, Duration> const& time_point) {
+            auto converted = std::chrono::system_clock::to_time_t(time_point);
 
-template<typename LhsT, Internal::Operator Op, typename RhsT>
-class BinaryExpression : public DecomposedExpression {
-public:
-    BinaryExpression( ResultBuilder& rb, LhsT lhs, RhsT rhs )
-        : m_rb( rb ), m_lhs( lhs ), m_rhs( rhs ) {}
+#ifdef _MSC_VER
+            std::tm timeInfo = {};
+            gmtime_s(&timeInfo, &converted);
+#else
+            std::tm* timeInfo = std::gmtime(&converted);
+#endif
 
-    BinaryExpression& operator = ( BinaryExpression& );
+            auto const timeStampSize = sizeof("2017-01-16T17:06:45Z");
+            char timeStamp[timeStampSize];
+            const char * const fmt = "%Y-%m-%dT%H:%M:%SZ";
 
-    void endExpression() const {
-        m_rb
-            .setResultType( Internal::compare<Op>( m_lhs, m_rhs ) )
-            .endExpression( *this );
-    }
+#ifdef _MSC_VER
+            std::strftime(timeStamp, timeStampSize, fmt, &timeInfo);
+#else
+            std::strftime(timeStamp, timeStampSize, fmt, timeInfo);
+#endif
+            return std::string(timeStamp);
+        }
+    };
+}
+#endif // CATCH_CONFIG_ENABLE_CHRONO_STRINGMAKER
+
+#define INTERNAL_CATCH_REGISTER_ENUM( enumName, ... ) \
+namespace Catch { \
+    template<> struct StringMaker<enumName> { \
+        static std::string convert( enumName value ) { \
+            static const auto& enumInfo = ::Catch::getMutableRegistryHub().getMutableEnumValuesRegistry().registerEnum( #enumName, #__VA_ARGS__, { __VA_ARGS__ } ); \
+            return static_cast<std::string>(enumInfo.lookup( static_cast<int>( value ) )); \
+        } \
+    }; \
+}
 
-    virtual bool isBinaryExpression() const CATCH_OVERRIDE {
-        return true;
-    }
+#define CATCH_REGISTER_ENUM( enumName, ... ) INTERNAL_CATCH_REGISTER_ENUM( enumName, __VA_ARGS__ )
 
-    virtual void reconstructExpression( std::string& dest ) const CATCH_OVERRIDE {
-        std::string lhs = Catch::toString( m_lhs );
-        std::string rhs = Catch::toString( m_rhs );
-        char delim = lhs.size() + rhs.size() < 40 &&
-                     lhs.find('\n') == std::string::npos &&
-                     rhs.find('\n') == std::string::npos ? ' ' : '\n';
-        dest.reserve( 7 + lhs.size() + rhs.size() );
-                   // 2 for spaces around operator
-                   // 2 for operator
-                   // 2 for parentheses (conditionally added later)
-                   // 1 for negation (conditionally added later)
-        dest = lhs;
-        dest += delim;
-        dest += Internal::OperatorTraits<Op>::getName();
-        dest += delim;
-        dest += rhs;
-    }
+#ifdef _MSC_VER
+#pragma warning(pop)
+#endif
 
-private:
-    ResultBuilder& m_rb;
-    LhsT m_lhs;
-    RhsT m_rhs;
-};
+// end catch_tostring.h
+#include <iosfwd>
 
-template<typename ArgT, typename MatcherT>
-class MatchExpression : public DecomposedExpression {
-public:
-    MatchExpression( ArgT arg, MatcherT matcher, char const* matcherString )
-        : m_arg( arg ), m_matcher( matcher ), m_matcherString( matcherString ) {}
+#ifdef _MSC_VER
+#pragma warning(push)
+#pragma warning(disable:4389) // '==' : signed/unsigned mismatch
+#pragma warning(disable:4018) // more "signed/unsigned mismatch"
+#pragma warning(disable:4312) // Converting int to T* using reinterpret_cast (issue on x64 platform)
+#pragma warning(disable:4180) // qualifier applied to function type has no meaning
+#pragma warning(disable:4800) // Forcing result to true or false
+#endif
 
-    virtual bool isBinaryExpression() const CATCH_OVERRIDE {
-        return true;
-    }
+namespace Catch {
 
-    virtual void reconstructExpression( std::string& dest ) const CATCH_OVERRIDE {
-        std::string matcherAsString = m_matcher.toString();
-        dest = Catch::toString( m_arg );
-        dest += ' ';
-        if( matcherAsString == Detail::unprintableString )
-            dest += m_matcherString;
-        else
-            dest += matcherAsString;
-    }
+    struct ITransientExpression {
+        auto isBinaryExpression() const -> bool { return m_isBinaryExpression; }
+        auto getResult() const -> bool { return m_result; }
+        virtual void streamReconstructedExpression( std::ostream &os ) const = 0;
 
-private:
-    ArgT m_arg;
-    MatcherT m_matcher;
-    char const* m_matcherString;
-};
+        ITransientExpression( bool isBinaryExpression, bool result )
+        :   m_isBinaryExpression( isBinaryExpression ),
+            m_result( result )
+        {}
 
-} // end namespace Catch
+        // We don't actually need a virtual destructor, but many static analysers
+        // complain if it's not here :-(
+        virtual ~ITransientExpression();
 
+        bool m_isBinaryExpression;
+        bool m_result;
 
-namespace Catch {
+    };
 
-    template<typename T>
-    inline ExpressionLhs<T const&> ResultBuilder::operator <= ( T const& operand ) {
-        return ExpressionLhs<T const&>( *this, operand );
-    }
+    void formatReconstructedExpression( std::ostream &os, std::string const& lhs, StringRef op, std::string const& rhs );
 
-    inline ExpressionLhs<bool> ResultBuilder::operator <= ( bool value ) {
-        return ExpressionLhs<bool>( *this, value );
-    }
+    template<typename LhsT, typename RhsT>
+    class BinaryExpr  : public ITransientExpression {
+        LhsT m_lhs;
+        StringRef m_op;
+        RhsT m_rhs;
 
-    template<typename ArgT, typename MatcherT>
-    inline void ResultBuilder::captureMatch( ArgT const& arg, MatcherT const& matcher,
-                                             char const* matcherString ) {
-        MatchExpression<ArgT const&, MatcherT const&> expr( arg, matcher, matcherString );
-        setResultType( matcher.match( arg ) );
-        endExpression( expr );
-    }
+        void streamReconstructedExpression( std::ostream &os ) const override {
+            formatReconstructedExpression
+                    ( os, Catch::Detail::stringify( m_lhs ), m_op, Catch::Detail::stringify( m_rhs ) );
+        }
 
-} // namespace Catch
+    public:
+        BinaryExpr( bool comparisonResult, LhsT lhs, StringRef op, RhsT rhs )
+        :   ITransientExpression{ true, comparisonResult },
+            m_lhs( lhs ),
+            m_op( op ),
+            m_rhs( rhs )
+        {}
 
-// #included from: catch_message.h
-#define TWOBLUECUBES_CATCH_MESSAGE_H_INCLUDED
+        template<typename T>
+        auto operator && ( T ) const -> BinaryExpr<LhsT, RhsT const&> const {
+            static_assert(always_false<T>::value,
+            "chained comparisons are not supported inside assertions, "
+            "wrap the expression inside parentheses, or decompose it");
+        }
 
-#include <string>
+        template<typename T>
+        auto operator || ( T ) const -> BinaryExpr<LhsT, RhsT const&> const {
+            static_assert(always_false<T>::value,
+            "chained comparisons are not supported inside assertions, "
+            "wrap the expression inside parentheses, or decompose it");
+        }
 
-namespace Catch {
+        template<typename T>
+        auto operator == ( T ) const -> BinaryExpr<LhsT, RhsT const&> const {
+            static_assert(always_false<T>::value,
+            "chained comparisons are not supported inside assertions, "
+            "wrap the expression inside parentheses, or decompose it");
+        }
 
-    struct MessageInfo {
-        MessageInfo(    std::string const& _macroName,
-                        SourceLineInfo const& _lineInfo,
-                        ResultWas::OfType _type );
+        template<typename T>
+        auto operator != ( T ) const -> BinaryExpr<LhsT, RhsT const&> const {
+            static_assert(always_false<T>::value,
+            "chained comparisons are not supported inside assertions, "
+            "wrap the expression inside parentheses, or decompose it");
+        }
 
-        std::string macroName;
-        SourceLineInfo lineInfo;
-        ResultWas::OfType type;
-        std::string message;
-        unsigned int sequence;
+        template<typename T>
+        auto operator > ( T ) const -> BinaryExpr<LhsT, RhsT const&> const {
+            static_assert(always_false<T>::value,
+            "chained comparisons are not supported inside assertions, "
+            "wrap the expression inside parentheses, or decompose it");
+        }
+
+        template<typename T>
+        auto operator < ( T ) const -> BinaryExpr<LhsT, RhsT const&> const {
+            static_assert(always_false<T>::value,
+            "chained comparisons are not supported inside assertions, "
+            "wrap the expression inside parentheses, or decompose it");
+        }
 
-        bool operator == ( MessageInfo const& other ) const {
-            return sequence == other.sequence;
+        template<typename T>
+        auto operator >= ( T ) const -> BinaryExpr<LhsT, RhsT const&> const {
+            static_assert(always_false<T>::value,
+            "chained comparisons are not supported inside assertions, "
+            "wrap the expression inside parentheses, or decompose it");
         }
-        bool operator < ( MessageInfo const& other ) const {
-            return sequence < other.sequence;
+
+        template<typename T>
+        auto operator <= ( T ) const -> BinaryExpr<LhsT, RhsT const&> const {
+            static_assert(always_false<T>::value,
+            "chained comparisons are not supported inside assertions, "
+            "wrap the expression inside parentheses, or decompose it");
         }
-    private:
-        static unsigned int globalCount;
     };
 
-    struct MessageBuilder {
-        MessageBuilder( std::string const& macroName,
-                        SourceLineInfo const& lineInfo,
-                        ResultWas::OfType type )
-        : m_info( macroName, lineInfo, type )
-        {}
+    template<typename LhsT>
+    class UnaryExpr : public ITransientExpression {
+        LhsT m_lhs;
 
-        template<typename T>
-        MessageBuilder& operator << ( T const& value ) {
-            m_stream << value;
-            return *this;
+        void streamReconstructedExpression( std::ostream &os ) const override {
+            os << Catch::Detail::stringify( m_lhs );
         }
 
-        MessageInfo m_info;
-        std::ostringstream m_stream;
+    public:
+        explicit UnaryExpr( LhsT lhs )
+        :   ITransientExpression{ false, static_cast<bool>(lhs) },
+            m_lhs( lhs )
+        {}
     };
 
-    class ScopedMessage {
+    // Specialised comparison functions to handle equality comparisons between ints and pointers (NULL deduces as an int)
+    template<typename LhsT, typename RhsT>
+    auto compareEqual( LhsT const& lhs, RhsT const& rhs ) -> bool { return static_cast<bool>(lhs == rhs); }
+    template<typename T>
+    auto compareEqual( T* const& lhs, int rhs ) -> bool { return lhs == reinterpret_cast<void const*>( rhs ); }
+    template<typename T>
+    auto compareEqual( T* const& lhs, long rhs ) -> bool { return lhs == reinterpret_cast<void const*>( rhs ); }
+    template<typename T>
+    auto compareEqual( int lhs, T* const& rhs ) -> bool { return reinterpret_cast<void const*>( lhs ) == rhs; }
+    template<typename T>
+    auto compareEqual( long lhs, T* const& rhs ) -> bool { return reinterpret_cast<void const*>( lhs ) == rhs; }
+
+    template<typename LhsT, typename RhsT>
+    auto compareNotEqual( LhsT const& lhs, RhsT&& rhs ) -> bool { return static_cast<bool>(lhs != rhs); }
+    template<typename T>
+    auto compareNotEqual( T* const& lhs, int rhs ) -> bool { return lhs != reinterpret_cast<void const*>( rhs ); }
+    template<typename T>
+    auto compareNotEqual( T* const& lhs, long rhs ) -> bool { return lhs != reinterpret_cast<void const*>( rhs ); }
+    template<typename T>
+    auto compareNotEqual( int lhs, T* const& rhs ) -> bool { return reinterpret_cast<void const*>( lhs ) != rhs; }
+    template<typename T>
+    auto compareNotEqual( long lhs, T* const& rhs ) -> bool { return reinterpret_cast<void const*>( lhs ) != rhs; }
+
+    template<typename LhsT>
+    class ExprLhs {
+        LhsT m_lhs;
     public:
-        ScopedMessage( MessageBuilder const& builder );
-        ScopedMessage( ScopedMessage const& other );
-        ~ScopedMessage();
+        explicit ExprLhs( LhsT lhs ) : m_lhs( lhs ) {}
 
-        MessageInfo m_info;
+        template<typename RhsT>
+        auto operator == ( RhsT const& rhs ) -> BinaryExpr<LhsT, RhsT const&> const {
+            return { compareEqual( m_lhs, rhs ), m_lhs, "==", rhs };
+        }
+        auto operator == ( bool rhs ) -> BinaryExpr<LhsT, bool> const {
+            return { m_lhs == rhs, m_lhs, "==", rhs };
+        }
+
+        template<typename RhsT>
+        auto operator != ( RhsT const& rhs ) -> BinaryExpr<LhsT, RhsT const&> const {
+            return { compareNotEqual( m_lhs, rhs ), m_lhs, "!=", rhs };
+        }
+        auto operator != ( bool rhs ) -> BinaryExpr<LhsT, bool> const {
+            return { m_lhs != rhs, m_lhs, "!=", rhs };
+        }
+
+        template<typename RhsT>
+        auto operator > ( RhsT const& rhs ) -> BinaryExpr<LhsT, RhsT const&> const {
+            return { static_cast<bool>(m_lhs > rhs), m_lhs, ">", rhs };
+        }
+        template<typename RhsT>
+        auto operator < ( RhsT const& rhs ) -> BinaryExpr<LhsT, RhsT const&> const {
+            return { static_cast<bool>(m_lhs < rhs), m_lhs, "<", rhs };
+        }
+        template<typename RhsT>
+        auto operator >= ( RhsT const& rhs ) -> BinaryExpr<LhsT, RhsT const&> const {
+            return { static_cast<bool>(m_lhs >= rhs), m_lhs, ">=", rhs };
+        }
+        template<typename RhsT>
+        auto operator <= ( RhsT const& rhs ) -> BinaryExpr<LhsT, RhsT const&> const {
+            return { static_cast<bool>(m_lhs <= rhs), m_lhs, "<=", rhs };
+        }
+
+        template<typename RhsT>
+        auto operator && ( RhsT const& ) -> BinaryExpr<LhsT, RhsT const&> const {
+            static_assert(always_false<RhsT>::value,
+            "operator&& is not supported inside assertions, "
+            "wrap the expression inside parentheses, or decompose it");
+        }
+
+        template<typename RhsT>
+        auto operator || ( RhsT const& ) -> BinaryExpr<LhsT, RhsT const&> const {
+            static_assert(always_false<RhsT>::value,
+            "operator|| is not supported inside assertions, "
+            "wrap the expression inside parentheses, or decompose it");
+        }
+
+        auto makeUnaryExpr() const -> UnaryExpr<LhsT> {
+            return UnaryExpr<LhsT>{ m_lhs };
+        }
+    };
+
+    void handleExpression( ITransientExpression const& expr );
+
+    template<typename T>
+    void handleExpression( ExprLhs<T> const& expr ) {
+        handleExpression( expr.makeUnaryExpr() );
+    }
+
+    struct Decomposer {
+        template<typename T>
+        auto operator <= ( T const& lhs ) -> ExprLhs<T const&> {
+            return ExprLhs<T const&>{ lhs };
+        }
+
+        auto operator <=( bool value ) -> ExprLhs<bool> {
+            return ExprLhs<bool>{ value };
+        }
     };
 
 } // end namespace Catch
 
-// #included from: catch_interfaces_capture.h
-#define TWOBLUECUBES_CATCH_INTERFACES_CAPTURE_H_INCLUDED
+#ifdef _MSC_VER
+#pragma warning(pop)
+#endif
+
+// end catch_decomposer.h
+// start catch_interfaces_capture.h
 
 #include <string>
+#include <chrono>
 
 namespace Catch {
 
-    class TestCase;
     class AssertionResult;
     struct AssertionInfo;
     struct SectionInfo;
     struct SectionEndInfo;
     struct MessageInfo;
-    class ScopedMessageBuilder;
+    struct MessageBuilder;
     struct Counts;
+    struct AssertionReaction;
+    struct SourceLineInfo;
+
+    struct ITransientExpression;
+    struct IGeneratorTracker;
+
+#if defined(CATCH_CONFIG_ENABLE_BENCHMARKING)
+    struct BenchmarkInfo;
+    template <typename Duration = std::chrono::duration<double, std::nano>>
+    struct BenchmarkStats;
+#endif // CATCH_CONFIG_ENABLE_BENCHMARKING
 
     struct IResultCapture {
 
         virtual ~IResultCapture();
 
-        virtual void assertionEnded( AssertionResult const& result ) = 0;
         virtual bool sectionStarted(    SectionInfo const& sectionInfo,
                                         Counts& assertions ) = 0;
         virtual void sectionEnded( SectionEndInfo const& endInfo ) = 0;
         virtual void sectionEndedEarly( SectionEndInfo const& endInfo ) = 0;
-        virtual void pushScopedMessage( MessageInfo const& message ) = 0;
-        virtual void popScopedMessage( MessageInfo const& message ) = 0;
 
+        virtual auto acquireGeneratorTracker( SourceLineInfo const& lineInfo ) -> IGeneratorTracker& = 0;
+
+#if defined(CATCH_CONFIG_ENABLE_BENCHMARKING)
+        virtual void benchmarkPreparing( std::string const& name ) = 0;
+        virtual void benchmarkStarting( BenchmarkInfo const& info ) = 0;
+        virtual void benchmarkEnded( BenchmarkStats<> const& stats ) = 0;
+        virtual void benchmarkFailed( std::string const& error ) = 0;
+#endif // CATCH_CONFIG_ENABLE_BENCHMARKING
+
+        virtual void pushScopedMessage( MessageInfo const& message ) = 0;
+        virtual void popScopedMessage( MessageInfo const& message ) = 0;
+
+        virtual void emplaceUnscopedMessage( MessageBuilder const& builder ) = 0;
+
+        virtual void handleFatalErrorCondition( StringRef message ) = 0;
+
+        virtual void handleExpr
+                (   AssertionInfo const& info,
+                    ITransientExpression const& expr,
+                    AssertionReaction& reaction ) = 0;
+        virtual void handleMessage
+                (   AssertionInfo const& info,
+                    ResultWas::OfType resultType,
+                    StringRef const& message,
+                    AssertionReaction& reaction ) = 0;
+        virtual void handleUnexpectedExceptionNotThrown
+                (   AssertionInfo const& info,
+                    AssertionReaction& reaction ) = 0;
+        virtual void handleUnexpectedInflightException
+                (   AssertionInfo const& info,
+                    std::string const& message,
+                    AssertionReaction& reaction ) = 0;
+        virtual void handleIncomplete
+                (   AssertionInfo const& info ) = 0;
+        virtual void handleNonExpr
+                (   AssertionInfo const &info,
+                    ResultWas::OfType resultType,
+                    AssertionReaction &reaction ) = 0;
+
+        virtual bool lastAssertionPassed() = 0;
+        virtual void assertionPassed() = 0;
+
+        // Deprecated, do not use:
         virtual std::string getCurrentTestName() const = 0;
         virtual const AssertionResult* getLastResult() const = 0;
-
         virtual void exceptionEarlyReported() = 0;
-
-        virtual void handleFatalErrorCondition( std::string const& message ) = 0;
     };
 
     IResultCapture& getResultCapture();
 }
 
-// #included from: catch_debugger.h
-#define TWOBLUECUBES_CATCH_DEBUGGER_H_INCLUDED
+// end catch_interfaces_capture.h
+namespace Catch {
 
-// #included from: catch_platform.h
-#define TWOBLUECUBES_CATCH_PLATFORM_H_INCLUDED
+    struct TestFailureException{};
+    struct AssertionResultData;
+    struct IResultCapture;
+    class RunContext;
 
-#if defined(__MAC_OS_X_VERSION_MIN_REQUIRED)
-#  define CATCH_PLATFORM_MAC
-#elif  defined(__IPHONE_OS_VERSION_MIN_REQUIRED)
-#  define CATCH_PLATFORM_IPHONE
-#elif defined(linux) || defined(__linux) || defined(__linux__)
-#  define CATCH_PLATFORM_LINUX
-#elif defined(WIN32) || defined(__WIN32__) || defined(_WIN32) || defined(_MSC_VER)
-#  define CATCH_PLATFORM_WINDOWS
-#  if !defined(NOMINMAX) && !defined(CATCH_CONFIG_NO_NOMINMAX)
-#    define CATCH_DEFINES_NOMINMAX
-#  endif
-#  if !defined(WIN32_LEAN_AND_MEAN) && !defined(CATCH_CONFIG_NO_WIN32_LEAN_AND_MEAN)
-#    define CATCH_DEFINES_WIN32_LEAN_AND_MEAN
-#  endif
-#endif
+    class LazyExpression {
+        friend class AssertionHandler;
+        friend struct AssertionStats;
+        friend class RunContext;
 
-#include <string>
+        ITransientExpression const* m_transientExpression = nullptr;
+        bool m_isNegated;
+    public:
+        LazyExpression( bool isNegated );
+        LazyExpression( LazyExpression const& other );
+        LazyExpression& operator = ( LazyExpression const& ) = delete;
 
-namespace Catch{
+        explicit operator bool() const;
 
-    bool isDebuggerActive();
-    void writeToDebugConsole( std::string const& text );
-}
+        friend auto operator << ( std::ostream& os, LazyExpression const& lazyExpr ) -> std::ostream&;
+    };
 
-#ifdef CATCH_PLATFORM_MAC
+    struct AssertionReaction {
+        bool shouldDebugBreak = false;
+        bool shouldThrow = false;
+    };
 
-    // The following code snippet based on:
-    // http://cocoawithlove.com/2008/03/break-into-debugger.html
-    #if defined(__ppc64__) || defined(__ppc__)
-        #define CATCH_TRAP() \
-                __asm__("li r0, 20\nsc\nnop\nli r0, 37\nli r4, 2\nsc\nnop\n" \
-                : : : "memory","r0","r3","r4" )
-    #else
-        #define CATCH_TRAP() __asm__("int $3\n" : : )
-    #endif
+    class AssertionHandler {
+        AssertionInfo m_assertionInfo;
+        AssertionReaction m_reaction;
+        bool m_completed = false;
+        IResultCapture& m_resultCapture;
 
-#elif defined(CATCH_PLATFORM_LINUX)
-    // If we can use inline assembler, do it because this allows us to break
-    // directly at the location of the failing check instead of breaking inside
-    // raise() called from it, i.e. one stack frame below.
-    #if defined(__GNUC__) && (defined(__i386) || defined(__x86_64))
-        #define CATCH_TRAP() asm volatile ("int $3")
-    #else // Fall back to the generic way.
-        #include <signal.h>
+    public:
+        AssertionHandler
+            (   StringRef const& macroName,
+                SourceLineInfo const& lineInfo,
+                StringRef capturedExpression,
+                ResultDisposition::Flags resultDisposition );
+        ~AssertionHandler() {
+            if ( !m_completed ) {
+                m_resultCapture.handleIncomplete( m_assertionInfo );
+            }
+        }
 
-        #define CATCH_TRAP() raise(SIGTRAP)
-    #endif
-#elif defined(_MSC_VER)
-    #define CATCH_TRAP() __debugbreak()
-#elif defined(__MINGW32__)
-    extern "C" __declspec(dllimport) void __stdcall DebugBreak();
-    #define CATCH_TRAP() DebugBreak()
-#endif
+        template<typename T>
+        void handleExpr( ExprLhs<T> const& expr ) {
+            handleExpr( expr.makeUnaryExpr() );
+        }
+        void handleExpr( ITransientExpression const& expr );
 
-#ifdef CATCH_TRAP
-    #define CATCH_BREAK_INTO_DEBUGGER() if( Catch::isDebuggerActive() ) { CATCH_TRAP(); }
-#else
-    #define CATCH_BREAK_INTO_DEBUGGER() Catch::alwaysTrue();
-#endif
+        void handleMessage(ResultWas::OfType resultType, StringRef const& message);
+
+        void handleExceptionThrownAsExpected();
+        void handleUnexpectedExceptionNotThrown();
+        void handleExceptionNotThrownAsExpected();
+        void handleThrowingCallSkipped();
+        void handleUnexpectedInflightException();
+
+        void complete();
+        void setCompleted();
+
+        // query
+        auto allowThrows() const -> bool;
+    };
 
-// #included from: catch_interfaces_runner.h
-#define TWOBLUECUBES_CATCH_INTERFACES_RUNNER_H_INCLUDED
+    void handleExceptionMatchExpr( AssertionHandler& handler, std::string const& str, StringRef const& matcherString );
+
+} // namespace Catch
+
+// end catch_assertionhandler.h
+// start catch_message.h
+
+#include <string>
+#include <vector>
 
 namespace Catch {
-    class TestCase;
 
-    struct IRunner {
-        virtual ~IRunner();
-        virtual bool aborting() const = 0;
+    struct MessageInfo {
+        MessageInfo(    StringRef const& _macroName,
+                        SourceLineInfo const& _lineInfo,
+                        ResultWas::OfType _type );
+
+        StringRef macroName;
+        std::string message;
+        SourceLineInfo lineInfo;
+        ResultWas::OfType type;
+        unsigned int sequence;
+
+        bool operator == ( MessageInfo const& other ) const;
+        bool operator < ( MessageInfo const& other ) const;
+    private:
+        static unsigned int globalCount;
     };
-}
 
-#if defined(CATCH_CONFIG_FAST_COMPILE)
-///////////////////////////////////////////////////////////////////////////////
-// We can speedup compilation significantly by breaking into debugger lower in
-// the callstack, because then we don't have to expand CATCH_BREAK_INTO_DEBUGGER
-// macro in each assertion
-#define INTERNAL_CATCH_REACT( resultBuilder ) \
-    resultBuilder.react();
+    struct MessageStream {
+
+        template<typename T>
+        MessageStream& operator << ( T const& value ) {
+            m_stream << value;
+            return *this;
+        }
+
+        ReusableStringStream m_stream;
+    };
+
+    struct MessageBuilder : MessageStream {
+        MessageBuilder( StringRef const& macroName,
+                        SourceLineInfo const& lineInfo,
+                        ResultWas::OfType type );
+
+        template<typename T>
+        MessageBuilder& operator << ( T const& value ) {
+            m_stream << value;
+            return *this;
+        }
+
+        MessageInfo m_info;
+    };
+
+    class ScopedMessage {
+    public:
+        explicit ScopedMessage( MessageBuilder const& builder );
+        ScopedMessage( ScopedMessage& duplicate ) = delete;
+        ScopedMessage( ScopedMessage&& old );
+        ~ScopedMessage();
+
+        MessageInfo m_info;
+        bool m_moved;
+    };
+
+    class Capturer {
+        std::vector<MessageInfo> m_messages;
+        IResultCapture& m_resultCapture = getResultCapture();
+        size_t m_captured = 0;
+    public:
+        Capturer( StringRef macroName, SourceLineInfo const& lineInfo, ResultWas::OfType resultType, StringRef names );
+        ~Capturer();
+
+        void captureValue( size_t index, std::string const& value );
+
+        template<typename T>
+        void captureValues( size_t index, T const& value ) {
+            captureValue( index, Catch::Detail::stringify( value ) );
+        }
+
+        template<typename T, typename... Ts>
+        void captureValues( size_t index, T const& value, Ts const&... values ) {
+            captureValue( index, Catch::Detail::stringify(value) );
+            captureValues( index+1, values... );
+        }
+    };
+
+} // end namespace Catch
+
+// end catch_message.h
+#if !defined(CATCH_CONFIG_DISABLE)
+
+#if !defined(CATCH_CONFIG_DISABLE_STRINGIFICATION)
+  #define CATCH_INTERNAL_STRINGIFY(...) #__VA_ARGS__
+#else
+  #define CATCH_INTERNAL_STRINGIFY(...) "Disabled by CATCH_CONFIG_DISABLE_STRINGIFICATION"
+#endif
+
+#if defined(CATCH_CONFIG_FAST_COMPILE) || defined(CATCH_CONFIG_DISABLE_EXCEPTIONS)
 
 ///////////////////////////////////////////////////////////////////////////////
 // Another way to speed-up compilation is to omit local try-catch for REQUIRE*
 // macros.
-// This can potentially cause false negative, if the test code catches
-// the exception before it propagates back up to the runner.
-#define INTERNAL_CATCH_TEST_NO_TRY( macroName, resultDisposition, expr ) \
-    do { \
-        Catch::ResultBuilder __catchResult( macroName, CATCH_INTERNAL_LINEINFO, #expr, resultDisposition ); \
-        __catchResult.setExceptionGuard(); \
-        CATCH_INTERNAL_SUPPRESS_PARENTHESES_WARNINGS \
-        ( __catchResult <= expr ).endExpression(); \
-        CATCH_INTERNAL_UNSUPPRESS_PARENTHESES_WARNINGS \
-        __catchResult.unsetExceptionGuard(); \
-        INTERNAL_CATCH_REACT( __catchResult ) \
-    } while( Catch::isTrue( false && static_cast<bool>( !!(expr) ) ) ) // expr here is never evaluated at runtime but it forces the compiler to give it a look
-// The double negation silences MSVC's C4800 warning, the static_cast forces short-circuit evaluation if the type has overloaded &&.
-
-#define INTERNAL_CHECK_THAT_NO_TRY( macroName, matcher, resultDisposition, arg ) \
-    do { \
-        Catch::ResultBuilder __catchResult( macroName, CATCH_INTERNAL_LINEINFO, #arg ", " #matcher, resultDisposition ); \
-        __catchResult.setExceptionGuard(); \
-        __catchResult.captureMatch( arg, matcher, #matcher ); \
-        __catchResult.unsetExceptionGuard(); \
-        INTERNAL_CATCH_REACT( __catchResult ) \
-    } while( Catch::alwaysFalse() )
+#define INTERNAL_CATCH_TRY
+#define INTERNAL_CATCH_CATCH( capturer )
+
+#else // CATCH_CONFIG_FAST_COMPILE
+
+#define INTERNAL_CATCH_TRY try
+#define INTERNAL_CATCH_CATCH( handler ) catch(...) { handler.handleUnexpectedInflightException(); }
 
-#else
-///////////////////////////////////////////////////////////////////////////////
-// In the event of a failure works out if the debugger needs to be invoked
-// and/or an exception thrown and takes appropriate action.
-// This needs to be done as a macro so the debugger will stop in the user
-// source code rather than in Catch library code
-#define INTERNAL_CATCH_REACT( resultBuilder ) \
-    if( resultBuilder.shouldDebugBreak() ) CATCH_BREAK_INTO_DEBUGGER(); \
-    resultBuilder.react();
 #endif
 
+#define INTERNAL_CATCH_REACT( handler ) handler.complete();
+
 ///////////////////////////////////////////////////////////////////////////////
-#define INTERNAL_CATCH_TEST( macroName, resultDisposition, expr ) \
+#define INTERNAL_CATCH_TEST( macroName, resultDisposition, ... ) \
     do { \
-        Catch::ResultBuilder __catchResult( macroName, CATCH_INTERNAL_LINEINFO, #expr, resultDisposition ); \
-        try { \
+        Catch::AssertionHandler catchAssertionHandler( macroName##_catch_sr, CATCH_INTERNAL_LINEINFO, CATCH_INTERNAL_STRINGIFY(__VA_ARGS__), resultDisposition ); \
+        INTERNAL_CATCH_TRY { \
             CATCH_INTERNAL_SUPPRESS_PARENTHESES_WARNINGS \
-            ( __catchResult <= expr ).endExpression(); \
+            catchAssertionHandler.handleExpr( Catch::Decomposer() <= __VA_ARGS__ ); \
             CATCH_INTERNAL_UNSUPPRESS_PARENTHESES_WARNINGS \
-        } \
-        catch( ... ) { \
-            __catchResult.useActiveException( resultDisposition ); \
-        } \
-        INTERNAL_CATCH_REACT( __catchResult ) \
-    } while( Catch::isTrue( false && static_cast<bool>( !!(expr) ) ) ) // expr here is never evaluated at runtime but it forces the compiler to give it a look
+        } INTERNAL_CATCH_CATCH( catchAssertionHandler ) \
+        INTERNAL_CATCH_REACT( catchAssertionHandler ) \
+    } while( (void)0, (false) && static_cast<bool>( !!(__VA_ARGS__) ) ) // the expression here is never evaluated at runtime but it forces the compiler to give it a look
     // The double negation silences MSVC's C4800 warning, the static_cast forces short-circuit evaluation if the type has overloaded &&.
 
 ///////////////////////////////////////////////////////////////////////////////
-#define INTERNAL_CATCH_IF( macroName, resultDisposition, expr ) \
-    INTERNAL_CATCH_TEST( macroName, resultDisposition, expr ); \
-    if( Catch::getResultCapture().getLastResult()->succeeded() )
+#define INTERNAL_CATCH_IF( macroName, resultDisposition, ... ) \
+    INTERNAL_CATCH_TEST( macroName, resultDisposition, __VA_ARGS__ ); \
+    if( Catch::getResultCapture().lastAssertionPassed() )
 
 ///////////////////////////////////////////////////////////////////////////////
-#define INTERNAL_CATCH_ELSE( macroName, resultDisposition, expr ) \
-    INTERNAL_CATCH_TEST( macroName, resultDisposition, expr ); \
-    if( !Catch::getResultCapture().getLastResult()->succeeded() )
+#define INTERNAL_CATCH_ELSE( macroName, resultDisposition, ... ) \
+    INTERNAL_CATCH_TEST( macroName, resultDisposition, __VA_ARGS__ ); \
+    if( !Catch::getResultCapture().lastAssertionPassed() )
 
 ///////////////////////////////////////////////////////////////////////////////
-#define INTERNAL_CATCH_NO_THROW( macroName, resultDisposition, expr ) \
+#define INTERNAL_CATCH_NO_THROW( macroName, resultDisposition, ... ) \
     do { \
-        Catch::ResultBuilder __catchResult( macroName, CATCH_INTERNAL_LINEINFO, #expr, resultDisposition ); \
+        Catch::AssertionHandler catchAssertionHandler( macroName##_catch_sr, CATCH_INTERNAL_LINEINFO, CATCH_INTERNAL_STRINGIFY(__VA_ARGS__), resultDisposition ); \
         try { \
-            static_cast<void>(expr); \
-            __catchResult.captureResult( Catch::ResultWas::Ok ); \
+            static_cast<void>(__VA_ARGS__); \
+            catchAssertionHandler.handleExceptionNotThrownAsExpected(); \
         } \
         catch( ... ) { \
-            __catchResult.useActiveException( resultDisposition ); \
+            catchAssertionHandler.handleUnexpectedInflightException(); \
         } \
-        INTERNAL_CATCH_REACT( __catchResult ) \
-    } while( Catch::alwaysFalse() )
+        INTERNAL_CATCH_REACT( catchAssertionHandler ) \
+    } while( false )
 
 ///////////////////////////////////////////////////////////////////////////////
-#define INTERNAL_CATCH_THROWS( macroName, resultDisposition, matcher, expr ) \
+#define INTERNAL_CATCH_THROWS( macroName, resultDisposition, ... ) \
     do { \
-        Catch::ResultBuilder __catchResult( macroName, CATCH_INTERNAL_LINEINFO, #expr, resultDisposition, #matcher ); \
-        if( __catchResult.allowThrows() ) \
+        Catch::AssertionHandler catchAssertionHandler( macroName##_catch_sr, CATCH_INTERNAL_LINEINFO, CATCH_INTERNAL_STRINGIFY(__VA_ARGS__), resultDisposition); \
+        if( catchAssertionHandler.allowThrows() ) \
             try { \
-                static_cast<void>(expr); \
-                __catchResult.captureResult( Catch::ResultWas::DidntThrowException ); \
+                static_cast<void>(__VA_ARGS__); \
+                catchAssertionHandler.handleUnexpectedExceptionNotThrown(); \
             } \
             catch( ... ) { \
-                __catchResult.captureExpectedException( matcher ); \
+                catchAssertionHandler.handleExceptionThrownAsExpected(); \
             } \
         else \
-            __catchResult.captureResult( Catch::ResultWas::Ok ); \
-        INTERNAL_CATCH_REACT( __catchResult ) \
-    } while( Catch::alwaysFalse() )
+            catchAssertionHandler.handleThrowingCallSkipped(); \
+        INTERNAL_CATCH_REACT( catchAssertionHandler ) \
+    } while( false )
 
 ///////////////////////////////////////////////////////////////////////////////
 #define INTERNAL_CATCH_THROWS_AS( macroName, exceptionType, resultDisposition, expr ) \
     do { \
-        Catch::ResultBuilder __catchResult( macroName, CATCH_INTERNAL_LINEINFO, #expr ", " #exceptionType, resultDisposition ); \
-        if( __catchResult.allowThrows() ) \
+        Catch::AssertionHandler catchAssertionHandler( macroName##_catch_sr, CATCH_INTERNAL_LINEINFO, CATCH_INTERNAL_STRINGIFY(expr) ", " CATCH_INTERNAL_STRINGIFY(exceptionType), resultDisposition ); \
+        if( catchAssertionHandler.allowThrows() ) \
             try { \
                 static_cast<void>(expr); \
-                __catchResult.captureResult( Catch::ResultWas::DidntThrowException ); \
+                catchAssertionHandler.handleUnexpectedExceptionNotThrown(); \
             } \
-            catch( exceptionType ) { \
-                __catchResult.captureResult( Catch::ResultWas::Ok ); \
+            catch( exceptionType const& ) { \
+                catchAssertionHandler.handleExceptionThrownAsExpected(); \
             } \
             catch( ... ) { \
-                __catchResult.useActiveException( resultDisposition ); \
+                catchAssertionHandler.handleUnexpectedInflightException(); \
             } \
         else \
-            __catchResult.captureResult( Catch::ResultWas::Ok ); \
-        INTERNAL_CATCH_REACT( __catchResult ) \
-    } while( Catch::alwaysFalse() )
+            catchAssertionHandler.handleThrowingCallSkipped(); \
+        INTERNAL_CATCH_REACT( catchAssertionHandler ) \
+    } while( false )
 
 ///////////////////////////////////////////////////////////////////////////////
-#ifdef CATCH_CONFIG_VARIADIC_MACROS
-    #define INTERNAL_CATCH_MSG( macroName, messageType, resultDisposition, ... ) \
-        do { \
-            Catch::ResultBuilder __catchResult( macroName, CATCH_INTERNAL_LINEINFO, "", resultDisposition ); \
-            __catchResult << __VA_ARGS__ + ::Catch::StreamEndStop(); \
-            __catchResult.captureResult( messageType ); \
-            INTERNAL_CATCH_REACT( __catchResult ) \
-        } while( Catch::alwaysFalse() )
-#else
-    #define INTERNAL_CATCH_MSG( macroName, messageType, resultDisposition, log ) \
-        do { \
-            Catch::ResultBuilder __catchResult( macroName, CATCH_INTERNAL_LINEINFO, "", resultDisposition ); \
-            __catchResult << log + ::Catch::StreamEndStop(); \
-            __catchResult.captureResult( messageType ); \
-            INTERNAL_CATCH_REACT( __catchResult ) \
-        } while( Catch::alwaysFalse() )
-#endif
+#define INTERNAL_CATCH_MSG( macroName, messageType, resultDisposition, ... ) \
+    do { \
+        Catch::AssertionHandler catchAssertionHandler( macroName##_catch_sr, CATCH_INTERNAL_LINEINFO, Catch::StringRef(), resultDisposition ); \
+        catchAssertionHandler.handleMessage( messageType, ( Catch::MessageStream() << __VA_ARGS__ + ::Catch::StreamEndStop() ).m_stream.str() ); \
+        INTERNAL_CATCH_REACT( catchAssertionHandler ) \
+    } while( false )
+
+///////////////////////////////////////////////////////////////////////////////
+#define INTERNAL_CATCH_CAPTURE( varName, macroName, ... ) \
+    auto varName = Catch::Capturer( macroName, CATCH_INTERNAL_LINEINFO, Catch::ResultWas::Info, #__VA_ARGS__ ); \
+    varName.captureValues( 0, __VA_ARGS__ )
 
 ///////////////////////////////////////////////////////////////////////////////
 #define INTERNAL_CATCH_INFO( macroName, log ) \
-    Catch::ScopedMessage INTERNAL_CATCH_UNIQUE_NAME( scopedMessage ) = Catch::MessageBuilder( macroName, CATCH_INTERNAL_LINEINFO, Catch::ResultWas::Info ) << log;
+    Catch::ScopedMessage INTERNAL_CATCH_UNIQUE_NAME( scopedMessage )( Catch::MessageBuilder( macroName##_catch_sr, CATCH_INTERNAL_LINEINFO, Catch::ResultWas::Info ) << log );
 
 ///////////////////////////////////////////////////////////////////////////////
-#define INTERNAL_CHECK_THAT( macroName, matcher, resultDisposition, arg ) \
+#define INTERNAL_CATCH_UNSCOPED_INFO( macroName, log ) \
+    Catch::getResultCapture().emplaceUnscopedMessage( Catch::MessageBuilder( macroName##_catch_sr, CATCH_INTERNAL_LINEINFO, Catch::ResultWas::Info ) << log )
+
+///////////////////////////////////////////////////////////////////////////////
+// Although this is matcher-based, it can be used with just a string
+#define INTERNAL_CATCH_THROWS_STR_MATCHES( macroName, resultDisposition, matcher, ... ) \
     do { \
-        Catch::ResultBuilder __catchResult( macroName, CATCH_INTERNAL_LINEINFO, #arg ", " #matcher, resultDisposition ); \
-        try { \
-            __catchResult.captureMatch( arg, matcher, #matcher ); \
-        } catch( ... ) { \
-            __catchResult.useActiveException( resultDisposition | Catch::ResultDisposition::ContinueOnFailure ); \
-        } \
-        INTERNAL_CATCH_REACT( __catchResult ) \
-    } while( Catch::alwaysFalse() )
+        Catch::AssertionHandler catchAssertionHandler( macroName##_catch_sr, CATCH_INTERNAL_LINEINFO, CATCH_INTERNAL_STRINGIFY(__VA_ARGS__) ", " CATCH_INTERNAL_STRINGIFY(matcher), resultDisposition ); \
+        if( catchAssertionHandler.allowThrows() ) \
+            try { \
+                static_cast<void>(__VA_ARGS__); \
+                catchAssertionHandler.handleUnexpectedExceptionNotThrown(); \
+            } \
+            catch( ... ) { \
+                Catch::handleExceptionMatchExpr( catchAssertionHandler, matcher, #matcher##_catch_sr ); \
+            } \
+        else \
+            catchAssertionHandler.handleThrowingCallSkipped(); \
+        INTERNAL_CATCH_REACT( catchAssertionHandler ) \
+    } while( false )
+
+#endif // CATCH_CONFIG_DISABLE
 
-// #included from: internal/catch_section.h
-#define TWOBLUECUBES_CATCH_SECTION_H_INCLUDED
+// end catch_capture.hpp
+// start catch_section.h
 
-// #included from: catch_section_info.h
-#define TWOBLUECUBES_CATCH_SECTION_INFO_H_INCLUDED
+// start catch_section_info.h
 
-// #included from: catch_totals.hpp
-#define TWOBLUECUBES_CATCH_TOTALS_HPP_INCLUDED
+// start catch_totals.h
 
 #include <cstddef>
 
 namespace Catch {
 
     struct Counts {
-        Counts() : passed( 0 ), failed( 0 ), failedButOk( 0 ) {}
-
-        Counts operator - ( Counts const& other ) const {
-            Counts diff;
-            diff.passed = passed - other.passed;
-            diff.failed = failed - other.failed;
-            diff.failedButOk = failedButOk - other.failedButOk;
-            return diff;
-        }
-        Counts& operator += ( Counts const& other ) {
-            passed += other.passed;
-            failed += other.failed;
-            failedButOk += other.failedButOk;
-            return *this;
-        }
+        Counts operator - ( Counts const& other ) const;
+        Counts& operator += ( Counts const& other );
 
-        std::size_t total() const {
-            return passed + failed + failedButOk;
-        }
-        bool allPassed() const {
-            return failed == 0 && failedButOk == 0;
-        }
-        bool allOk() const {
-            return failed == 0;
-        }
+        std::size_t total() const;
+        bool allPassed() const;
+        bool allOk() const;
 
-        std::size_t passed;
-        std::size_t failed;
-        std::size_t failedButOk;
+        std::size_t passed = 0;
+        std::size_t failed = 0;
+        std::size_t failedButOk = 0;
     };
 
     struct Totals {
 
-        Totals operator - ( Totals const& other ) const {
-            Totals diff;
-            diff.assertions = assertions - other.assertions;
-            diff.testCases = testCases - other.testCases;
-            return diff;
-        }
-
-        Totals delta( Totals const& prevTotals ) const {
-            Totals diff = *this - prevTotals;
-            if( diff.assertions.failed > 0 )
-                ++diff.testCases.failed;
-            else if( diff.assertions.failedButOk > 0 )
-                ++diff.testCases.failedButOk;
-            else
-                ++diff.testCases.passed;
-            return diff;
-        }
+        Totals operator - ( Totals const& other ) const;
+        Totals& operator += ( Totals const& other );
 
-        Totals& operator += ( Totals const& other ) {
-            assertions += other.assertions;
-            testCases += other.testCases;
-            return *this;
-        }
+        Totals delta( Totals const& prevTotals ) const;
 
+        int error = 0;
         Counts assertions;
         Counts testCases;
     };
 }
 
+// end catch_totals.h
 #include <string>
 
 namespace Catch {
 
     struct SectionInfo {
+        SectionInfo
+            (   SourceLineInfo const& _lineInfo,
+                std::string const& _name );
+
+        // Deprecated
         SectionInfo
             (   SourceLineInfo const& _lineInfo,
                 std::string const& _name,
-                std::string const& _description = std::string() );
+                std::string const& ) : SectionInfo( _lineInfo, _name ) {}
 
         std::string name;
-        std::string description;
+        std::string description; // !Deprecated: this will always be empty
         SourceLineInfo lineInfo;
     };
 
     struct SectionEndInfo {
-        SectionEndInfo( SectionInfo const& _sectionInfo, Counts const& _prevAssertions, double _durationInSeconds )
-        : sectionInfo( _sectionInfo ), prevAssertions( _prevAssertions ), durationInSeconds( _durationInSeconds )
-        {}
-
         SectionInfo sectionInfo;
         Counts prevAssertions;
         double durationInSeconds;
@@ -2400,36 +2860,29 @@ namespace Catch {
 
 } // end namespace Catch
 
-// #included from: catch_timer.h
-#define TWOBLUECUBES_CATCH_TIMER_H_INCLUDED
+// end catch_section_info.h
+// start catch_timer.h
 
-#ifdef _MSC_VER
+#include <cstdint>
 
 namespace Catch {
-    typedef unsigned long long UInt64;
-}
-#else
-#include <stdint.h>
-namespace Catch {
-    typedef uint64_t UInt64;
-}
-#endif
 
-namespace Catch {
+    auto getCurrentNanosecondsSinceEpoch() -> uint64_t;
+    auto getEstimatedClockResolution() -> uint64_t;
+
     class Timer {
+        uint64_t m_nanoseconds = 0;
     public:
-        Timer() : m_ticks( 0 ) {}
         void start();
-        unsigned int getElapsedMicroseconds() const;
-        unsigned int getElapsedMilliseconds() const;
-        double getElapsedSeconds() const;
-
-    private:
-        UInt64 m_ticks;
+        auto getElapsedNanoseconds() const -> uint64_t;
+        auto getElapsedMicroseconds() const -> uint64_t;
+        auto getElapsedMilliseconds() const -> unsigned int;
+        auto getElapsedSeconds() const -> double;
     };
 
 } // namespace Catch
 
+// end catch_timer.h
 #include <string>
 
 namespace Catch {
@@ -2440,7 +2893,7 @@ namespace Catch {
         ~Section();
 
         // This indicates whether the section should be executed or not
-        operator bool() const;
+        explicit operator bool() const;
 
     private:
         SectionInfo m_info;
@@ -2453,271 +2906,108 @@ namespace Catch {
 
 } // end namespace Catch
 
-#ifdef CATCH_CONFIG_VARIADIC_MACROS
-    #define INTERNAL_CATCH_SECTION( ... ) \
-        if( Catch::Section const& INTERNAL_CATCH_UNIQUE_NAME( catch_internal_Section ) = Catch::SectionInfo( CATCH_INTERNAL_LINEINFO, __VA_ARGS__ ) )
-#else
-    #define INTERNAL_CATCH_SECTION( name, desc ) \
-        if( Catch::Section const& INTERNAL_CATCH_UNIQUE_NAME( catch_internal_Section ) = Catch::SectionInfo( CATCH_INTERNAL_LINEINFO, name, desc ) )
-#endif
+#define INTERNAL_CATCH_SECTION( ... ) \
+    CATCH_INTERNAL_SUPPRESS_UNUSED_WARNINGS \
+    if( Catch::Section const& INTERNAL_CATCH_UNIQUE_NAME( catch_internal_Section ) = Catch::SectionInfo( CATCH_INTERNAL_LINEINFO, __VA_ARGS__ ) ) \
+    CATCH_INTERNAL_UNSUPPRESS_UNUSED_WARNINGS
 
-// #included from: internal/catch_generators.hpp
-#define TWOBLUECUBES_CATCH_GENERATORS_HPP_INCLUDED
+#define INTERNAL_CATCH_DYNAMIC_SECTION( ... ) \
+    CATCH_INTERNAL_SUPPRESS_UNUSED_WARNINGS \
+    if( Catch::Section const& INTERNAL_CATCH_UNIQUE_NAME( catch_internal_Section ) = Catch::SectionInfo( CATCH_INTERNAL_LINEINFO, (Catch::ReusableStringStream() << __VA_ARGS__).str() ) ) \
+    CATCH_INTERNAL_UNSUPPRESS_UNUSED_WARNINGS
+
+// end catch_section.h
+// start catch_interfaces_exception.h
+
+// start catch_interfaces_registry_hub.h
 
-#include <vector>
 #include <string>
-#include <stdlib.h>
+#include <memory>
 
 namespace Catch {
 
-template<typename T>
-struct IGenerator {
-    virtual ~IGenerator() {}
-    virtual T getValue( std::size_t index ) const = 0;
-    virtual std::size_t size () const = 0;
-};
+    class TestCase;
+    struct ITestCaseRegistry;
+    struct IExceptionTranslatorRegistry;
+    struct IExceptionTranslator;
+    struct IReporterRegistry;
+    struct IReporterFactory;
+    struct ITagAliasRegistry;
+    struct IMutableEnumValuesRegistry;
 
-template<typename T>
-class BetweenGenerator : public IGenerator<T> {
-public:
-    BetweenGenerator( T from, T to ) : m_from( from ), m_to( to ){}
+    class StartupExceptionRegistry;
 
-    virtual T getValue( std::size_t index ) const {
-        return m_from+static_cast<int>( index );
-    }
+    using IReporterFactoryPtr = std::shared_ptr<IReporterFactory>;
 
-    virtual std::size_t size() const {
-        return static_cast<std::size_t>( 1+m_to-m_from );
-    }
+    struct IRegistryHub {
+        virtual ~IRegistryHub();
 
-private:
+        virtual IReporterRegistry const& getReporterRegistry() const = 0;
+        virtual ITestCaseRegistry const& getTestCaseRegistry() const = 0;
+        virtual ITagAliasRegistry const& getTagAliasRegistry() const = 0;
+        virtual IExceptionTranslatorRegistry const& getExceptionTranslatorRegistry() const = 0;
 
-    T m_from;
-    T m_to;
-};
+        virtual StartupExceptionRegistry const& getStartupExceptionRegistry() const = 0;
+    };
 
-template<typename T>
-class ValuesGenerator : public IGenerator<T> {
-public:
-    ValuesGenerator(){}
+    struct IMutableRegistryHub {
+        virtual ~IMutableRegistryHub();
+        virtual void registerReporter( std::string const& name, IReporterFactoryPtr const& factory ) = 0;
+        virtual void registerListener( IReporterFactoryPtr const& factory ) = 0;
+        virtual void registerTest( TestCase const& testInfo ) = 0;
+        virtual void registerTranslator( const IExceptionTranslator* translator ) = 0;
+        virtual void registerTagAlias( std::string const& alias, std::string const& tag, SourceLineInfo const& lineInfo ) = 0;
+        virtual void registerStartupException() noexcept = 0;
+        virtual IMutableEnumValuesRegistry& getMutableEnumValuesRegistry() = 0;
+    };
 
-    void add( T value ) {
-        m_values.push_back( value );
-    }
+    IRegistryHub const& getRegistryHub();
+    IMutableRegistryHub& getMutableRegistryHub();
+    void cleanUp();
+    std::string translateActiveException();
 
-    virtual T getValue( std::size_t index ) const {
-        return m_values[index];
-    }
+}
 
-    virtual std::size_t size() const {
-        return m_values.size();
-    }
+// end catch_interfaces_registry_hub.h
+#if defined(CATCH_CONFIG_DISABLE)
+    #define INTERNAL_CATCH_TRANSLATE_EXCEPTION_NO_REG( translatorName, signature) \
+        static std::string translatorName( signature )
+#endif
 
-private:
-    std::vector<T> m_values;
-};
+#include <exception>
+#include <string>
+#include <vector>
 
-template<typename T>
-class CompositeGenerator {
-public:
-    CompositeGenerator() : m_totalSize( 0 ) {}
+namespace Catch {
+    using exceptionTranslateFunction = std::string(*)();
 
-    // *** Move semantics, similar to auto_ptr ***
-    CompositeGenerator( CompositeGenerator& other )
-    :   m_fileInfo( other.m_fileInfo ),
-        m_totalSize( 0 )
-    {
-        move( other );
-    }
+    struct IExceptionTranslator;
+    using ExceptionTranslators = std::vector<std::unique_ptr<IExceptionTranslator const>>;
 
-    CompositeGenerator& setFileInfo( const char* fileInfo ) {
-        m_fileInfo = fileInfo;
-        return *this;
-    }
+    struct IExceptionTranslator {
+        virtual ~IExceptionTranslator();
+        virtual std::string translate( ExceptionTranslators::const_iterator it, ExceptionTranslators::const_iterator itEnd ) const = 0;
+    };
 
-    ~CompositeGenerator() {
-        deleteAll( m_composed );
-    }
+    struct IExceptionTranslatorRegistry {
+        virtual ~IExceptionTranslatorRegistry();
 
-    operator T () const {
-        size_t overallIndex = getCurrentContext().getGeneratorIndex( m_fileInfo, m_totalSize );
+        virtual std::string translateActiveException() const = 0;
+    };
 
-        typename std::vector<const IGenerator<T>*>::const_iterator it = m_composed.begin();
-        typename std::vector<const IGenerator<T>*>::const_iterator itEnd = m_composed.end();
-        for( size_t index = 0; it != itEnd; ++it )
-        {
-            const IGenerator<T>* generator = *it;
-            if( overallIndex >= index && overallIndex < index + generator->size() )
-            {
-                return generator->getValue( overallIndex-index );
-            }
-            index += generator->size();
-        }
-        CATCH_INTERNAL_ERROR( "Indexed past end of generated range" );
-        return T(); // Suppress spurious "not all control paths return a value" warning in Visual Studio - if you know how to fix this please do so
-    }
+    class ExceptionTranslatorRegistrar {
+        template<typename T>
+        class ExceptionTranslator : public IExceptionTranslator {
+        public:
 
-    void add( const IGenerator<T>* generator ) {
-        m_totalSize += generator->size();
-        m_composed.push_back( generator );
-    }
+            ExceptionTranslator( std::string(*translateFunction)( T& ) )
+            : m_translateFunction( translateFunction )
+            {}
 
-    CompositeGenerator& then( CompositeGenerator& other ) {
-        move( other );
-        return *this;
-    }
-
-    CompositeGenerator& then( T value ) {
-        ValuesGenerator<T>* valuesGen = new ValuesGenerator<T>();
-        valuesGen->add( value );
-        add( valuesGen );
-        return *this;
-    }
-
-private:
-
-    void move( CompositeGenerator& other ) {
-        m_composed.insert( m_composed.end(), other.m_composed.begin(), other.m_composed.end() );
-        m_totalSize += other.m_totalSize;
-        other.m_composed.clear();
-    }
-
-    std::vector<const IGenerator<T>*> m_composed;
-    std::string m_fileInfo;
-    size_t m_totalSize;
-};
-
-namespace Generators
-{
-    template<typename T>
-    CompositeGenerator<T> between( T from, T to ) {
-        CompositeGenerator<T> generators;
-        generators.add( new BetweenGenerator<T>( from, to ) );
-        return generators;
-    }
-
-    template<typename T>
-    CompositeGenerator<T> values( T val1, T val2 ) {
-        CompositeGenerator<T> generators;
-        ValuesGenerator<T>* valuesGen = new ValuesGenerator<T>();
-        valuesGen->add( val1 );
-        valuesGen->add( val2 );
-        generators.add( valuesGen );
-        return generators;
-    }
-
-    template<typename T>
-    CompositeGenerator<T> values( T val1, T val2, T val3 ){
-        CompositeGenerator<T> generators;
-        ValuesGenerator<T>* valuesGen = new ValuesGenerator<T>();
-        valuesGen->add( val1 );
-        valuesGen->add( val2 );
-        valuesGen->add( val3 );
-        generators.add( valuesGen );
-        return generators;
-    }
-
-    template<typename T>
-    CompositeGenerator<T> values( T val1, T val2, T val3, T val4 ) {
-        CompositeGenerator<T> generators;
-        ValuesGenerator<T>* valuesGen = new ValuesGenerator<T>();
-        valuesGen->add( val1 );
-        valuesGen->add( val2 );
-        valuesGen->add( val3 );
-        valuesGen->add( val4 );
-        generators.add( valuesGen );
-        return generators;
-    }
-
-} // end namespace Generators
-
-using namespace Generators;
-
-} // end namespace Catch
-
-#define INTERNAL_CATCH_LINESTR2( line ) #line
-#define INTERNAL_CATCH_LINESTR( line ) INTERNAL_CATCH_LINESTR2( line )
-
-#define INTERNAL_CATCH_GENERATE( expr ) expr.setFileInfo( __FILE__ "(" INTERNAL_CATCH_LINESTR( __LINE__ ) ")" )
-
-// #included from: internal/catch_interfaces_exception.h
-#define TWOBLUECUBES_CATCH_INTERFACES_EXCEPTION_H_INCLUDED
-
-#include <string>
-#include <vector>
-
-// #included from: catch_interfaces_registry_hub.h
-#define TWOBLUECUBES_CATCH_INTERFACES_REGISTRY_HUB_H_INCLUDED
-
-#include <string>
-
-namespace Catch {
-
-    class TestCase;
-    struct ITestCaseRegistry;
-    struct IExceptionTranslatorRegistry;
-    struct IExceptionTranslator;
-    struct IReporterRegistry;
-    struct IReporterFactory;
-    struct ITagAliasRegistry;
-
-    struct IRegistryHub {
-        virtual ~IRegistryHub();
-
-        virtual IReporterRegistry const& getReporterRegistry() const = 0;
-        virtual ITestCaseRegistry const& getTestCaseRegistry() const = 0;
-        virtual ITagAliasRegistry const& getTagAliasRegistry() const = 0;
-
-        virtual IExceptionTranslatorRegistry& getExceptionTranslatorRegistry() = 0;
-    };
-
-    struct IMutableRegistryHub {
-        virtual ~IMutableRegistryHub();
-        virtual void registerReporter( std::string const& name, Ptr<IReporterFactory> const& factory ) = 0;
-        virtual void registerListener( Ptr<IReporterFactory> const& factory ) = 0;
-        virtual void registerTest( TestCase const& testInfo ) = 0;
-        virtual void registerTranslator( const IExceptionTranslator* translator ) = 0;
-        virtual void registerTagAlias( std::string const& alias, std::string const& tag, SourceLineInfo const& lineInfo ) = 0;
-    };
-
-    IRegistryHub& getRegistryHub();
-    IMutableRegistryHub& getMutableRegistryHub();
-    void cleanUp();
-    std::string translateActiveException();
-
-}
-
-namespace Catch {
-
-    typedef std::string(*exceptionTranslateFunction)();
-
-    struct IExceptionTranslator;
-    typedef std::vector<const IExceptionTranslator*> ExceptionTranslators;
-
-    struct IExceptionTranslator {
-        virtual ~IExceptionTranslator();
-        virtual std::string translate( ExceptionTranslators::const_iterator it, ExceptionTranslators::const_iterator itEnd ) const = 0;
-    };
-
-    struct IExceptionTranslatorRegistry {
-        virtual ~IExceptionTranslatorRegistry();
-
-        virtual std::string translateActiveException() const = 0;
-    };
-
-    class ExceptionTranslatorRegistrar {
-        template<typename T>
-        class ExceptionTranslator : public IExceptionTranslator {
-        public:
-
-            ExceptionTranslator( std::string(*translateFunction)( T& ) )
-            : m_translateFunction( translateFunction )
-            {}
-
-            virtual std::string translate( ExceptionTranslators::const_iterator it, ExceptionTranslators::const_iterator itEnd ) const CATCH_OVERRIDE {
+            std::string translate( ExceptionTranslators::const_iterator it, ExceptionTranslators::const_iterator itEnd ) const override {
                 try {
                     if( it == itEnd )
-                        throw;
+                        std::rethrow_exception(std::current_exception());
                     else
                         return (*it)->translate( it+1, itEnd );
                 }
@@ -2742,68 +3032,55 @@ namespace Catch {
 ///////////////////////////////////////////////////////////////////////////////
 #define INTERNAL_CATCH_TRANSLATE_EXCEPTION2( translatorName, signature ) \
     static std::string translatorName( signature ); \
-    namespace{ Catch::ExceptionTranslatorRegistrar INTERNAL_CATCH_UNIQUE_NAME( catch_internal_ExceptionRegistrar )( &translatorName ); }\
+    CATCH_INTERNAL_SUPPRESS_GLOBALS_WARNINGS \
+    namespace{ Catch::ExceptionTranslatorRegistrar INTERNAL_CATCH_UNIQUE_NAME( catch_internal_ExceptionRegistrar )( &translatorName ); } \
+    CATCH_INTERNAL_UNSUPPRESS_GLOBALS_WARNINGS \
     static std::string translatorName( signature )
 
 #define INTERNAL_CATCH_TRANSLATE_EXCEPTION( signature ) INTERNAL_CATCH_TRANSLATE_EXCEPTION2( INTERNAL_CATCH_UNIQUE_NAME( catch_internal_ExceptionTranslator ), signature )
 
-// #included from: internal/catch_approx.hpp
-#define TWOBLUECUBES_CATCH_APPROX_HPP_INCLUDED
-
-#include <cmath>
-#include <limits>
+// end catch_interfaces_exception.h
+// start catch_approx.h
 
-#if defined(CATCH_CONFIG_CPP11_TYPE_TRAITS)
 #include <type_traits>
-#endif
 
 namespace Catch {
 namespace Detail {
 
     class Approx {
-    public:
-        explicit Approx ( double value )
-        :   m_epsilon( std::numeric_limits<float>::epsilon()*100 ),
-            m_margin( 0.0 ),
-            m_scale( 1.0 ),
-            m_value( value )
-        {}
+    private:
+        bool equalityComparisonImpl(double other) const;
+        // Validates the new margin (margin >= 0)
+        // out-of-line to avoid including stdexcept in the header
+        void setMargin(double margin);
+        // Validates the new epsilon (0 < epsilon < 1)
+        // out-of-line to avoid including stdexcept in the header
+        void setEpsilon(double epsilon);
 
-        Approx( Approx const& other )
-        :   m_epsilon( other.m_epsilon ),
-            m_margin( other.m_margin ),
-            m_scale( other.m_scale ),
-            m_value( other.m_value )
-        {}
+    public:
+        explicit Approx ( double value );
 
-        static Approx custom() {
-            return Approx( 0 );
-        }
+        static Approx custom();
 
-#if defined(CATCH_CONFIG_CPP11_TYPE_TRAITS)
+        Approx operator-() const;
 
         template <typename T, typename = typename std::enable_if<std::is_constructible<double, T>::value>::type>
-        Approx operator()( T value ) {
+        Approx operator()( T const& value ) {
             Approx approx( static_cast<double>(value) );
-            approx.epsilon( m_epsilon );
-            approx.margin( m_margin );
-            approx.scale( m_scale );
+            approx.m_epsilon = m_epsilon;
+            approx.m_margin = m_margin;
+            approx.m_scale = m_scale;
             return approx;
         }
 
         template <typename T, typename = typename std::enable_if<std::is_constructible<double, T>::value>::type>
-        explicit Approx( T value ): Approx(static_cast<double>(value))
+        explicit Approx( T const& value ): Approx(static_cast<double>(value))
         {}
 
         template <typename T, typename = typename std::enable_if<std::is_constructible<double, T>::value>::type>
         friend bool operator == ( const T& lhs, Approx const& rhs ) {
-            // Thanks to Richard Harris for his help refining this formula
-            auto lhs_v = double(lhs);
-            bool relativeOK = std::fabs(lhs_v - rhs.m_value) < rhs.m_epsilon * (rhs.m_scale + (std::max)(std::fabs(lhs_v), std::fabs(rhs.m_value)));
-            if (relativeOK) {
-                return true;
-            }
-            return std::fabs(lhs_v - rhs.m_value) < rhs.m_margin;
+            auto lhs_v = static_cast<double>(lhs);
+            return rhs.equalityComparisonImpl(lhs_v);
         }
 
         template <typename T, typename = typename std::enable_if<std::is_constructible<double, T>::value>::type>
@@ -2812,121 +3089,56 @@ namespace Detail {
         }
 
         template <typename T, typename = typename std::enable_if<std::is_constructible<double, T>::value>::type>
-        friend bool operator != ( T lhs, Approx const& rhs ) {
+        friend bool operator != ( T const& lhs, Approx const& rhs ) {
             return !operator==( lhs, rhs );
         }
 
         template <typename T, typename = typename std::enable_if<std::is_constructible<double, T>::value>::type>
-        friend bool operator != ( Approx const& lhs, T rhs ) {
+        friend bool operator != ( Approx const& lhs, T const& rhs ) {
             return !operator==( rhs, lhs );
         }
 
         template <typename T, typename = typename std::enable_if<std::is_constructible<double, T>::value>::type>
-        friend bool operator <= ( T lhs, Approx const& rhs ) {
-            return double(lhs) < rhs.m_value || lhs == rhs;
+        friend bool operator <= ( T const& lhs, Approx const& rhs ) {
+            return static_cast<double>(lhs) < rhs.m_value || lhs == rhs;
         }
 
         template <typename T, typename = typename std::enable_if<std::is_constructible<double, T>::value>::type>
-        friend bool operator <= ( Approx const& lhs, T rhs ) {
-            return lhs.m_value < double(rhs) || lhs == rhs;
+        friend bool operator <= ( Approx const& lhs, T const& rhs ) {
+            return lhs.m_value < static_cast<double>(rhs) || lhs == rhs;
         }
 
         template <typename T, typename = typename std::enable_if<std::is_constructible<double, T>::value>::type>
-        friend bool operator >= ( T lhs, Approx const& rhs ) {
-            return double(lhs) > rhs.m_value || lhs == rhs;
+        friend bool operator >= ( T const& lhs, Approx const& rhs ) {
+            return static_cast<double>(lhs) > rhs.m_value || lhs == rhs;
         }
 
         template <typename T, typename = typename std::enable_if<std::is_constructible<double, T>::value>::type>
-        friend bool operator >= ( Approx const& lhs, T rhs ) {
-            return lhs.m_value > double(rhs) || lhs == rhs;
+        friend bool operator >= ( Approx const& lhs, T const& rhs ) {
+            return lhs.m_value > static_cast<double>(rhs) || lhs == rhs;
         }
 
         template <typename T, typename = typename std::enable_if<std::is_constructible<double, T>::value>::type>
-        Approx& epsilon( T newEpsilon ) {
-            m_epsilon = double(newEpsilon);
+        Approx& epsilon( T const& newEpsilon ) {
+            double epsilonAsDouble = static_cast<double>(newEpsilon);
+            setEpsilon(epsilonAsDouble);
             return *this;
         }
 
         template <typename T, typename = typename std::enable_if<std::is_constructible<double, T>::value>::type>
-        Approx& margin( T newMargin ) {
-            m_margin = double(newMargin);
+        Approx& margin( T const& newMargin ) {
+            double marginAsDouble = static_cast<double>(newMargin);
+            setMargin(marginAsDouble);
             return *this;
         }
 
         template <typename T, typename = typename std::enable_if<std::is_constructible<double, T>::value>::type>
-        Approx& scale( T newScale ) {
-            m_scale = double(newScale);
-            return *this;
-        }
-
-#else
-
-        Approx operator()( double value ) {
-            Approx approx( value );
-            approx.epsilon( m_epsilon );
-            approx.margin( m_margin );
-            approx.scale( m_scale );
-            return approx;
-        }
-
-        friend bool operator == ( double lhs, Approx const& rhs ) {
-            // Thanks to Richard Harris for his help refining this formula
-            bool relativeOK = std::fabs( lhs - rhs.m_value ) < rhs.m_epsilon * (rhs.m_scale + (std::max)( std::fabs(lhs), std::fabs(rhs.m_value) ) );
-            if (relativeOK) {
-                return true;
-            }
-            return std::fabs(lhs - rhs.m_value) < rhs.m_margin;
-        }
-
-        friend bool operator == ( Approx const& lhs, double rhs ) {
-            return operator==( rhs, lhs );
-        }
-
-        friend bool operator != ( double lhs, Approx const& rhs ) {
-            return !operator==( lhs, rhs );
-        }
-
-        friend bool operator != ( Approx const& lhs, double rhs ) {
-            return !operator==( rhs, lhs );
-        }
-
-        friend bool operator <= ( double lhs, Approx const& rhs ) {
-            return lhs < rhs.m_value || lhs == rhs;
-        }
-
-        friend bool operator <= ( Approx const& lhs, double rhs ) {
-            return lhs.m_value < rhs || lhs == rhs;
-        }
-
-        friend bool operator >= ( double lhs, Approx const& rhs ) {
-            return lhs > rhs.m_value || lhs == rhs;
-        }
-
-        friend bool operator >= ( Approx const& lhs, double rhs ) {
-            return lhs.m_value > rhs || lhs == rhs;
-        }
-
-        Approx& epsilon( double newEpsilon ) {
-            m_epsilon = newEpsilon;
-            return *this;
-        }
-
-        Approx& margin( double newMargin ) {
-            m_margin = newMargin;
-            return *this;
-        }
-
-        Approx& scale( double newScale ) {
-            m_scale = newScale;
+        Approx& scale( T const& newScale ) {
+            m_scale = static_cast<double>(newScale);
             return *this;
         }
-#endif
 
-        std::string toString() const {
-            std::ostringstream oss;
-            oss << "Approx( " << Catch::toString( m_value ) << " )";
-            return oss.str();
-        }
+        std::string toString() const;
 
     private:
         double m_epsilon;
@@ -2934,137 +3146,560 @@ namespace Detail {
         double m_scale;
         double m_value;
     };
-}
+} // end namespace Detail
+
+namespace literals {
+    Detail::Approx operator "" _a(long double val);
+    Detail::Approx operator "" _a(unsigned long long val);
+} // end namespace literals
 
 template<>
-inline std::string toString<Detail::Approx>( Detail::Approx const& value ) {
-    return value.toString();
-}
+struct StringMaker<Catch::Detail::Approx> {
+    static std::string convert(Catch::Detail::Approx const& value);
+};
 
 } // end namespace Catch
 
-// #included from: internal/catch_matchers_string.h
-#define TWOBLUECUBES_CATCH_MATCHERS_STRING_H_INCLUDED
-
-namespace Catch {
-namespace Matchers {
-
-    namespace StdString {
+// end catch_approx.h
+// start catch_string_manip.h
 
-        struct CasedString
-        {
-            CasedString( std::string const& str, CaseSensitive::Choice caseSensitivity );
-            std::string adjustString( std::string const& str ) const;
-            std::string caseSensitivitySuffix() const;
+#include <string>
+#include <iosfwd>
+#include <vector>
 
-            CaseSensitive::Choice m_caseSensitivity;
-            std::string m_str;
-        };
+namespace Catch {
 
-        struct StringMatcherBase : MatcherBase<std::string> {
-            StringMatcherBase( std::string const& operation, CasedString const& comparator );
-            virtual std::string describe() const CATCH_OVERRIDE;
+    bool startsWith( std::string const& s, std::string const& prefix );
+    bool startsWith( std::string const& s, char prefix );
+    bool endsWith( std::string const& s, std::string const& suffix );
+    bool endsWith( std::string const& s, char suffix );
+    bool contains( std::string const& s, std::string const& infix );
+    void toLowerInPlace( std::string& s );
+    std::string toLower( std::string const& s );
+    //! Returns a new string without whitespace at the start/end
+    std::string trim( std::string const& str );
+    //! Returns a substring of the original ref without whitespace. Beware lifetimes!
+    StringRef trim(StringRef ref);
 
-            CasedString m_comparator;
-            std::string m_operation;
-        };
+    // !!! Be aware, returns refs into original string - make sure original string outlives them
+    std::vector<StringRef> splitStringRef( StringRef str, char delimiter );
+    bool replaceInPlace( std::string& str, std::string const& replaceThis, std::string const& withThis );
 
-        struct EqualsMatcher : StringMatcherBase {
-            EqualsMatcher( CasedString const& comparator );
-            virtual bool match( std::string const& source ) const CATCH_OVERRIDE;
-        };
-        struct ContainsMatcher : StringMatcherBase {
-            ContainsMatcher( CasedString const& comparator );
-            virtual bool match( std::string const& source ) const CATCH_OVERRIDE;
-        };
-        struct StartsWithMatcher : StringMatcherBase {
-            StartsWithMatcher( CasedString const& comparator );
-            virtual bool match( std::string const& source ) const CATCH_OVERRIDE;
-        };
-        struct EndsWithMatcher : StringMatcherBase {
-            EndsWithMatcher( CasedString const& comparator );
-            virtual bool match( std::string const& source ) const CATCH_OVERRIDE;
-        };
+    struct pluralise {
+        pluralise( std::size_t count, std::string const& label );
 
-    } // namespace StdString
+        friend std::ostream& operator << ( std::ostream& os, pluralise const& pluraliser );
 
-    // The following functions create the actual matcher objects.
-    // This allows the types to be inferred
+        std::size_t m_count;
+        std::string m_label;
+    };
+}
 
-    StdString::EqualsMatcher Equals( std::string const& str, CaseSensitive::Choice caseSensitivity = CaseSensitive::Yes );
-    StdString::ContainsMatcher Contains( std::string const& str, CaseSensitive::Choice caseSensitivity = CaseSensitive::Yes );
-    StdString::EndsWithMatcher EndsWith( std::string const& str, CaseSensitive::Choice caseSensitivity = CaseSensitive::Yes );
-    StdString::StartsWithMatcher StartsWith( std::string const& str, CaseSensitive::Choice caseSensitivity = CaseSensitive::Yes );
+// end catch_string_manip.h
+#ifndef CATCH_CONFIG_DISABLE_MATCHERS
+// start catch_capture_matchers.h
 
-} // namespace Matchers
-} // namespace Catch
+// start catch_matchers.h
 
-// #included from: internal/catch_matchers_vector.h
-#define TWOBLUECUBES_CATCH_MATCHERS_VECTOR_H_INCLUDED
+#include <string>
+#include <vector>
 
 namespace Catch {
 namespace Matchers {
+    namespace Impl {
 
-    namespace Vector {
+        template<typename ArgT> struct MatchAllOf;
+        template<typename ArgT> struct MatchAnyOf;
+        template<typename ArgT> struct MatchNotOf;
 
-        template<typename T>
-        struct ContainsElementMatcher : MatcherBase<std::vector<T>, T> {
+        class MatcherUntypedBase {
+        public:
+            MatcherUntypedBase() = default;
+            MatcherUntypedBase ( MatcherUntypedBase const& ) = default;
+            MatcherUntypedBase& operator = ( MatcherUntypedBase const& ) = delete;
+            std::string toString() const;
 
-            ContainsElementMatcher(T const &comparator) : m_comparator( comparator) {}
+        protected:
+            virtual ~MatcherUntypedBase();
+            virtual std::string describe() const = 0;
+            mutable std::string m_cachedToString;
+        };
 
-            bool match(std::vector<T> const &v) const CATCH_OVERRIDE {
-                return std::find(v.begin(), v.end(), m_comparator) != v.end();
-            }
+#ifdef __clang__
+#    pragma clang diagnostic push
+#    pragma clang diagnostic ignored "-Wnon-virtual-dtor"
+#endif
 
-            virtual std::string describe() const CATCH_OVERRIDE {
-                return "Contains: " + Catch::toString( m_comparator );
-            }
+        template<typename ObjectT>
+        struct MatcherMethod {
+            virtual bool match( ObjectT const& arg ) const = 0;
+        };
 
-            T const& m_comparator;
+#if defined(__OBJC__)
+        // Hack to fix Catch GH issue #1661. Could use id for generic Object support.
+        // use of const for Object pointers is very uncommon and under ARC it causes some kind of signature mismatch that breaks compilation
+        template<>
+        struct MatcherMethod<NSString*> {
+            virtual bool match( NSString* arg ) const = 0;
         };
+#endif
+
+#ifdef __clang__
+#    pragma clang diagnostic pop
+#endif
 
         template<typename T>
-        struct ContainsMatcher : MatcherBase<std::vector<T>, std::vector<T> > {
+        struct MatcherBase : MatcherUntypedBase, MatcherMethod<T> {
 
-            ContainsMatcher(std::vector<T> const &comparator) : m_comparator( comparator ) {}
+            MatchAllOf<T> operator && ( MatcherBase const& other ) const;
+            MatchAnyOf<T> operator || ( MatcherBase const& other ) const;
+            MatchNotOf<T> operator ! () const;
+        };
 
-            bool match(std::vector<T> const &v) const CATCH_OVERRIDE {
-                // !TBD: see note in EqualsMatcher
-                if (m_comparator.size() > v.size())
-                    return false;
-                for (size_t i = 0; i < m_comparator.size(); ++i)
-                    if (std::find(v.begin(), v.end(), m_comparator[i]) == v.end())
+        template<typename ArgT>
+        struct MatchAllOf : MatcherBase<ArgT> {
+            bool match( ArgT const& arg ) const override {
+                for( auto matcher : m_matchers ) {
+                    if (!matcher->match(arg))
                         return false;
+                }
                 return true;
             }
-            virtual std::string describe() const CATCH_OVERRIDE {
-                return "Contains: " + Catch::toString( m_comparator );
+            std::string describe() const override {
+                std::string description;
+                description.reserve( 4 + m_matchers.size()*32 );
+                description += "( ";
+                bool first = true;
+                for( auto matcher : m_matchers ) {
+                    if( first )
+                        first = false;
+                    else
+                        description += " and ";
+                    description += matcher->toString();
+                }
+                description += " )";
+                return description;
+            }
+
+            MatchAllOf<ArgT>& operator && ( MatcherBase<ArgT> const& other ) {
+                m_matchers.push_back( &other );
+                return *this;
+            }
+
+            std::vector<MatcherBase<ArgT> const*> m_matchers;
+        };
+        template<typename ArgT>
+        struct MatchAnyOf : MatcherBase<ArgT> {
+
+            bool match( ArgT const& arg ) const override {
+                for( auto matcher : m_matchers ) {
+                    if (matcher->match(arg))
+                        return true;
+                }
+                return false;
+            }
+            std::string describe() const override {
+                std::string description;
+                description.reserve( 4 + m_matchers.size()*32 );
+                description += "( ";
+                bool first = true;
+                for( auto matcher : m_matchers ) {
+                    if( first )
+                        first = false;
+                    else
+                        description += " or ";
+                    description += matcher->toString();
+                }
+                description += " )";
+                return description;
+            }
+
+            MatchAnyOf<ArgT>& operator || ( MatcherBase<ArgT> const& other ) {
+                m_matchers.push_back( &other );
+                return *this;
+            }
+
+            std::vector<MatcherBase<ArgT> const*> m_matchers;
+        };
+
+        template<typename ArgT>
+        struct MatchNotOf : MatcherBase<ArgT> {
+
+            MatchNotOf( MatcherBase<ArgT> const& underlyingMatcher ) : m_underlyingMatcher( underlyingMatcher ) {}
+
+            bool match( ArgT const& arg ) const override {
+                return !m_underlyingMatcher.match( arg );
+            }
+
+            std::string describe() const override {
+                return "not " + m_underlyingMatcher.toString();
+            }
+            MatcherBase<ArgT> const& m_underlyingMatcher;
+        };
+
+        template<typename T>
+        MatchAllOf<T> MatcherBase<T>::operator && ( MatcherBase const& other ) const {
+            return MatchAllOf<T>() && *this && other;
+        }
+        template<typename T>
+        MatchAnyOf<T> MatcherBase<T>::operator || ( MatcherBase const& other ) const {
+            return MatchAnyOf<T>() || *this || other;
+        }
+        template<typename T>
+        MatchNotOf<T> MatcherBase<T>::operator ! () const {
+            return MatchNotOf<T>( *this );
+        }
+
+    } // namespace Impl
+
+} // namespace Matchers
+
+using namespace Matchers;
+using Matchers::Impl::MatcherBase;
+
+} // namespace Catch
+
+// end catch_matchers.h
+// start catch_matchers_exception.hpp
+
+namespace Catch {
+namespace Matchers {
+namespace Exception {
+
+class ExceptionMessageMatcher : public MatcherBase<std::exception> {
+    std::string m_message;
+public:
+
+    ExceptionMessageMatcher(std::string const& message):
+        m_message(message)
+    {}
+
+    bool match(std::exception const& ex) const override;
+
+    std::string describe() const override;
+};
+
+} // namespace Exception
+
+Exception::ExceptionMessageMatcher Message(std::string const& message);
+
+} // namespace Matchers
+} // namespace Catch
+
+// end catch_matchers_exception.hpp
+// start catch_matchers_floating.h
+
+namespace Catch {
+namespace Matchers {
+
+    namespace Floating {
+
+        enum class FloatingPointKind : uint8_t;
+
+        struct WithinAbsMatcher : MatcherBase<double> {
+            WithinAbsMatcher(double target, double margin);
+            bool match(double const& matchee) const override;
+            std::string describe() const override;
+        private:
+            double m_target;
+            double m_margin;
+        };
+
+        struct WithinUlpsMatcher : MatcherBase<double> {
+            WithinUlpsMatcher(double target, uint64_t ulps, FloatingPointKind baseType);
+            bool match(double const& matchee) const override;
+            std::string describe() const override;
+        private:
+            double m_target;
+            uint64_t m_ulps;
+            FloatingPointKind m_type;
+        };
+
+        // Given IEEE-754 format for floats and doubles, we can assume
+        // that float -> double promotion is lossless. Given this, we can
+        // assume that if we do the standard relative comparison of
+        // |lhs - rhs| <= epsilon * max(fabs(lhs), fabs(rhs)), then we get
+        // the same result if we do this for floats, as if we do this for
+        // doubles that were promoted from floats.
+        struct WithinRelMatcher : MatcherBase<double> {
+            WithinRelMatcher(double target, double epsilon);
+            bool match(double const& matchee) const override;
+            std::string describe() const override;
+        private:
+            double m_target;
+            double m_epsilon;
+        };
+
+    } // namespace Floating
+
+    // The following functions create the actual matcher objects.
+    // This allows the types to be inferred
+    Floating::WithinUlpsMatcher WithinULP(double target, uint64_t maxUlpDiff);
+    Floating::WithinUlpsMatcher WithinULP(float target, uint64_t maxUlpDiff);
+    Floating::WithinAbsMatcher WithinAbs(double target, double margin);
+    Floating::WithinRelMatcher WithinRel(double target, double eps);
+    // defaults epsilon to 100*numeric_limits<double>::epsilon()
+    Floating::WithinRelMatcher WithinRel(double target);
+    Floating::WithinRelMatcher WithinRel(float target, float eps);
+    // defaults epsilon to 100*numeric_limits<float>::epsilon()
+    Floating::WithinRelMatcher WithinRel(float target);
+
+} // namespace Matchers
+} // namespace Catch
+
+// end catch_matchers_floating.h
+// start catch_matchers_generic.hpp
+
+#include <functional>
+#include <string>
+
+namespace Catch {
+namespace Matchers {
+namespace Generic {
+
+namespace Detail {
+    std::string finalizeDescription(const std::string& desc);
+}
+
+template <typename T>
+class PredicateMatcher : public MatcherBase<T> {
+    std::function<bool(T const&)> m_predicate;
+    std::string m_description;
+public:
+
+    PredicateMatcher(std::function<bool(T const&)> const& elem, std::string const& descr)
+        :m_predicate(std::move(elem)),
+        m_description(Detail::finalizeDescription(descr))
+    {}
+
+    bool match( T const& item ) const override {
+        return m_predicate(item);
+    }
+
+    std::string describe() const override {
+        return m_description;
+    }
+};
+
+} // namespace Generic
+
+    // The following functions create the actual matcher objects.
+    // The user has to explicitly specify type to the function, because
+    // inferring std::function<bool(T const&)> is hard (but possible) and
+    // requires a lot of TMP.
+    template<typename T>
+    Generic::PredicateMatcher<T> Predicate(std::function<bool(T const&)> const& predicate, std::string const& description = "") {
+        return Generic::PredicateMatcher<T>(predicate, description);
+    }
+
+} // namespace Matchers
+} // namespace Catch
+
+// end catch_matchers_generic.hpp
+// start catch_matchers_string.h
+
+#include <string>
+
+namespace Catch {
+namespace Matchers {
+
+    namespace StdString {
+
+        struct CasedString
+        {
+            CasedString( std::string const& str, CaseSensitive::Choice caseSensitivity );
+            std::string adjustString( std::string const& str ) const;
+            std::string caseSensitivitySuffix() const;
+
+            CaseSensitive::Choice m_caseSensitivity;
+            std::string m_str;
+        };
+
+        struct StringMatcherBase : MatcherBase<std::string> {
+            StringMatcherBase( std::string const& operation, CasedString const& comparator );
+            std::string describe() const override;
+
+            CasedString m_comparator;
+            std::string m_operation;
+        };
+
+        struct EqualsMatcher : StringMatcherBase {
+            EqualsMatcher( CasedString const& comparator );
+            bool match( std::string const& source ) const override;
+        };
+        struct ContainsMatcher : StringMatcherBase {
+            ContainsMatcher( CasedString const& comparator );
+            bool match( std::string const& source ) const override;
+        };
+        struct StartsWithMatcher : StringMatcherBase {
+            StartsWithMatcher( CasedString const& comparator );
+            bool match( std::string const& source ) const override;
+        };
+        struct EndsWithMatcher : StringMatcherBase {
+            EndsWithMatcher( CasedString const& comparator );
+            bool match( std::string const& source ) const override;
+        };
+
+        struct RegexMatcher : MatcherBase<std::string> {
+            RegexMatcher( std::string regex, CaseSensitive::Choice caseSensitivity );
+            bool match( std::string const& matchee ) const override;
+            std::string describe() const override;
+
+        private:
+            std::string m_regex;
+            CaseSensitive::Choice m_caseSensitivity;
+        };
+
+    } // namespace StdString
+
+    // The following functions create the actual matcher objects.
+    // This allows the types to be inferred
+
+    StdString::EqualsMatcher Equals( std::string const& str, CaseSensitive::Choice caseSensitivity = CaseSensitive::Yes );
+    StdString::ContainsMatcher Contains( std::string const& str, CaseSensitive::Choice caseSensitivity = CaseSensitive::Yes );
+    StdString::EndsWithMatcher EndsWith( std::string const& str, CaseSensitive::Choice caseSensitivity = CaseSensitive::Yes );
+    StdString::StartsWithMatcher StartsWith( std::string const& str, CaseSensitive::Choice caseSensitivity = CaseSensitive::Yes );
+    StdString::RegexMatcher Matches( std::string const& regex, CaseSensitive::Choice caseSensitivity = CaseSensitive::Yes );
+
+} // namespace Matchers
+} // namespace Catch
+
+// end catch_matchers_string.h
+// start catch_matchers_vector.h
+
+#include <algorithm>
+
+namespace Catch {
+namespace Matchers {
+
+    namespace Vector {
+        template<typename T>
+        struct ContainsElementMatcher : MatcherBase<std::vector<T>> {
+
+            ContainsElementMatcher(T const &comparator) : m_comparator( comparator) {}
+
+            bool match(std::vector<T> const &v) const override {
+                for (auto const& el : v) {
+                    if (el == m_comparator) {
+                        return true;
+                    }
+                }
+                return false;
+            }
+
+            std::string describe() const override {
+                return "Contains: " + ::Catch::Detail::stringify( m_comparator );
+            }
+
+            T const& m_comparator;
+        };
+
+        template<typename T>
+        struct ContainsMatcher : MatcherBase<std::vector<T>> {
+
+            ContainsMatcher(std::vector<T> const &comparator) : m_comparator( comparator ) {}
+
+            bool match(std::vector<T> const &v) const override {
+                // !TBD: see note in EqualsMatcher
+                if (m_comparator.size() > v.size())
+                    return false;
+                for (auto const& comparator : m_comparator) {
+                    auto present = false;
+                    for (const auto& el : v) {
+                        if (el == comparator) {
+                            present = true;
+                            break;
+                        }
+                    }
+                    if (!present) {
+                        return false;
+                    }
+                }
+                return true;
+            }
+            std::string describe() const override {
+                return "Contains: " + ::Catch::Detail::stringify( m_comparator );
             }
 
             std::vector<T> const& m_comparator;
         };
 
         template<typename T>
-        struct EqualsMatcher : MatcherBase<std::vector<T>, std::vector<T> > {
+        struct EqualsMatcher : MatcherBase<std::vector<T>> {
 
             EqualsMatcher(std::vector<T> const &comparator) : m_comparator( comparator ) {}
 
-            bool match(std::vector<T> const &v) const CATCH_OVERRIDE {
+            bool match(std::vector<T> const &v) const override {
                 // !TBD: This currently works if all elements can be compared using !=
                 // - a more general approach would be via a compare template that defaults
                 // to using !=. but could be specialised for, e.g. std::vector<T> etc
                 // - then just call that directly
                 if (m_comparator.size() != v.size())
                     return false;
-                for (size_t i = 0; i < v.size(); ++i)
+                for (std::size_t i = 0; i < v.size(); ++i)
                     if (m_comparator[i] != v[i])
                         return false;
                 return true;
             }
-            virtual std::string describe() const CATCH_OVERRIDE {
-                return "Equals: " + Catch::toString( m_comparator );
+            std::string describe() const override {
+                return "Equals: " + ::Catch::Detail::stringify( m_comparator );
+            }
+            std::vector<T> const& m_comparator;
+        };
+
+        template<typename T>
+        struct ApproxMatcher : MatcherBase<std::vector<T>> {
+
+            ApproxMatcher(std::vector<T> const& comparator) : m_comparator( comparator ) {}
+
+            bool match(std::vector<T> const &v) const override {
+                if (m_comparator.size() != v.size())
+                    return false;
+                for (std::size_t i = 0; i < v.size(); ++i)
+                    if (m_comparator[i] != approx(v[i]))
+                        return false;
+                return true;
+            }
+            std::string describe() const override {
+                return "is approx: " + ::Catch::Detail::stringify( m_comparator );
+            }
+            template <typename = typename std::enable_if<std::is_constructible<double, T>::value>::type>
+            ApproxMatcher& epsilon( T const& newEpsilon ) {
+                approx.epsilon(newEpsilon);
+                return *this;
+            }
+            template <typename = typename std::enable_if<std::is_constructible<double, T>::value>::type>
+            ApproxMatcher& margin( T const& newMargin ) {
+                approx.margin(newMargin);
+                return *this;
+            }
+            template <typename = typename std::enable_if<std::is_constructible<double, T>::value>::type>
+            ApproxMatcher& scale( T const& newScale ) {
+                approx.scale(newScale);
+                return *this;
             }
+
             std::vector<T> const& m_comparator;
+            mutable Catch::Detail::Approx approx = Catch::Detail::Approx::custom();
+        };
+
+        template<typename T>
+        struct UnorderedEqualsMatcher : MatcherBase<std::vector<T>> {
+            UnorderedEqualsMatcher(std::vector<T> const& target) : m_target(target) {}
+            bool match(std::vector<T> const& vec) const override {
+                // Note: This is a reimplementation of std::is_permutation,
+                //       because I don't want to include <algorithm> inside the common path
+                if (m_target.size() != vec.size()) {
+                    return false;
+                }
+                return std::is_permutation(m_target.begin(), m_target.end(), vec.begin());
+            }
+
+            std::string describe() const override {
+                return "UnorderedEquals: " + ::Catch::Detail::stringify(m_target);
+            }
+        private:
+            std::vector<T> const& m_target;
         };
 
     } // namespace Vector
@@ -3087,4245 +3722,8416 @@ namespace Matchers {
         return Vector::EqualsMatcher<T>( comparator );
     }
 
-} // namespace Matchers
-} // namespace Catch
-
-// #included from: internal/catch_interfaces_tag_alias_registry.h
-#define TWOBLUECUBES_CATCH_INTERFACES_TAG_ALIAS_REGISTRY_H_INCLUDED
+    template<typename T>
+    Vector::ApproxMatcher<T> Approx( std::vector<T> const& comparator ) {
+        return Vector::ApproxMatcher<T>( comparator );
+    }
 
-// #included from: catch_tag_alias.h
-#define TWOBLUECUBES_CATCH_TAG_ALIAS_H_INCLUDED
+    template<typename T>
+    Vector::UnorderedEqualsMatcher<T> UnorderedEquals(std::vector<T> const& target) {
+        return Vector::UnorderedEqualsMatcher<T>(target);
+    }
+
+} // namespace Matchers
+} // namespace Catch
+
+// end catch_matchers_vector.h
+namespace Catch {
+
+    template<typename ArgT, typename MatcherT>
+    class MatchExpr : public ITransientExpression {
+        ArgT const& m_arg;
+        MatcherT m_matcher;
+        StringRef m_matcherString;
+    public:
+        MatchExpr( ArgT const& arg, MatcherT const& matcher, StringRef const& matcherString )
+        :   ITransientExpression{ true, matcher.match( arg ) },
+            m_arg( arg ),
+            m_matcher( matcher ),
+            m_matcherString( matcherString )
+        {}
+
+        void streamReconstructedExpression( std::ostream &os ) const override {
+            auto matcherAsString = m_matcher.toString();
+            os << Catch::Detail::stringify( m_arg ) << ' ';
+            if( matcherAsString == Detail::unprintableString )
+                os << m_matcherString;
+            else
+                os << matcherAsString;
+        }
+    };
+
+    using StringMatcher = Matchers::Impl::MatcherBase<std::string>;
+
+    void handleExceptionMatchExpr( AssertionHandler& handler, StringMatcher const& matcher, StringRef const& matcherString  );
+
+    template<typename ArgT, typename MatcherT>
+    auto makeMatchExpr( ArgT const& arg, MatcherT const& matcher, StringRef const& matcherString  ) -> MatchExpr<ArgT, MatcherT> {
+        return MatchExpr<ArgT, MatcherT>( arg, matcher, matcherString );
+    }
+
+} // namespace Catch
+
+///////////////////////////////////////////////////////////////////////////////
+#define INTERNAL_CHECK_THAT( macroName, matcher, resultDisposition, arg ) \
+    do { \
+        Catch::AssertionHandler catchAssertionHandler( macroName##_catch_sr, CATCH_INTERNAL_LINEINFO, CATCH_INTERNAL_STRINGIFY(arg) ", " CATCH_INTERNAL_STRINGIFY(matcher), resultDisposition ); \
+        INTERNAL_CATCH_TRY { \
+            catchAssertionHandler.handleExpr( Catch::makeMatchExpr( arg, matcher, #matcher##_catch_sr ) ); \
+        } INTERNAL_CATCH_CATCH( catchAssertionHandler ) \
+        INTERNAL_CATCH_REACT( catchAssertionHandler ) \
+    } while( false )
+
+///////////////////////////////////////////////////////////////////////////////
+#define INTERNAL_CATCH_THROWS_MATCHES( macroName, exceptionType, resultDisposition, matcher, ... ) \
+    do { \
+        Catch::AssertionHandler catchAssertionHandler( macroName##_catch_sr, CATCH_INTERNAL_LINEINFO, CATCH_INTERNAL_STRINGIFY(__VA_ARGS__) ", " CATCH_INTERNAL_STRINGIFY(exceptionType) ", " CATCH_INTERNAL_STRINGIFY(matcher), resultDisposition ); \
+        if( catchAssertionHandler.allowThrows() ) \
+            try { \
+                static_cast<void>(__VA_ARGS__ ); \
+                catchAssertionHandler.handleUnexpectedExceptionNotThrown(); \
+            } \
+            catch( exceptionType const& ex ) { \
+                catchAssertionHandler.handleExpr( Catch::makeMatchExpr( ex, matcher, #matcher##_catch_sr ) ); \
+            } \
+            catch( ... ) { \
+                catchAssertionHandler.handleUnexpectedInflightException(); \
+            } \
+        else \
+            catchAssertionHandler.handleThrowingCallSkipped(); \
+        INTERNAL_CATCH_REACT( catchAssertionHandler ) \
+    } while( false )
+
+// end catch_capture_matchers.h
+#endif
+// start catch_generators.hpp
+
+// start catch_interfaces_generatortracker.h
+
+
+#include <memory>
+
+namespace Catch {
+
+    namespace Generators {
+        class GeneratorUntypedBase {
+        public:
+            GeneratorUntypedBase() = default;
+            virtual ~GeneratorUntypedBase();
+            // Attempts to move the generator to the next element
+             //
+             // Returns true iff the move succeeded (and a valid element
+             // can be retrieved).
+            virtual bool next() = 0;
+        };
+        using GeneratorBasePtr = std::unique_ptr<GeneratorUntypedBase>;
+
+    } // namespace Generators
+
+    struct IGeneratorTracker {
+        virtual ~IGeneratorTracker();
+        virtual auto hasGenerator() const -> bool = 0;
+        virtual auto getGenerator() const -> Generators::GeneratorBasePtr const& = 0;
+        virtual void setGenerator( Generators::GeneratorBasePtr&& generator ) = 0;
+    };
+
+} // namespace Catch
+
+// end catch_interfaces_generatortracker.h
+// start catch_enforce.h
+
+#include <exception>
+
+namespace Catch {
+#if !defined(CATCH_CONFIG_DISABLE_EXCEPTIONS)
+    template <typename Ex>
+    [[noreturn]]
+    void throw_exception(Ex const& e) {
+        throw e;
+    }
+#else // ^^ Exceptions are enabled //  Exceptions are disabled vv
+    [[noreturn]]
+    void throw_exception(std::exception const& e);
+#endif
+
+    [[noreturn]]
+    void throw_logic_error(std::string const& msg);
+    [[noreturn]]
+    void throw_domain_error(std::string const& msg);
+    [[noreturn]]
+    void throw_runtime_error(std::string const& msg);
+
+} // namespace Catch;
+
+#define CATCH_MAKE_MSG(...) \
+    (Catch::ReusableStringStream() << __VA_ARGS__).str()
+
+#define CATCH_INTERNAL_ERROR(...) \
+    Catch::throw_logic_error(CATCH_MAKE_MSG( CATCH_INTERNAL_LINEINFO << ": Internal Catch2 error: " << __VA_ARGS__))
+
+#define CATCH_ERROR(...) \
+    Catch::throw_domain_error(CATCH_MAKE_MSG( __VA_ARGS__ ))
+
+#define CATCH_RUNTIME_ERROR(...) \
+    Catch::throw_runtime_error(CATCH_MAKE_MSG( __VA_ARGS__ ))
+
+#define CATCH_ENFORCE( condition, ... ) \
+    do{ if( !(condition) ) CATCH_ERROR( __VA_ARGS__ ); } while(false)
+
+// end catch_enforce.h
+#include <memory>
+#include <vector>
+#include <cassert>
+
+#include <utility>
+#include <exception>
+
+namespace Catch {
+
+class GeneratorException : public std::exception {
+    const char* const m_msg = "";
+
+public:
+    GeneratorException(const char* msg):
+        m_msg(msg)
+    {}
+
+    const char* what() const noexcept override final;
+};
+
+namespace Generators {
+
+    // !TBD move this into its own location?
+    namespace pf{
+        template<typename T, typename... Args>
+        std::unique_ptr<T> make_unique( Args&&... args ) {
+            return std::unique_ptr<T>(new T(std::forward<Args>(args)...));
+        }
+    }
+
+    template<typename T>
+    struct IGenerator : GeneratorUntypedBase {
+        virtual ~IGenerator() = default;
+
+        // Returns the current element of the generator
+        //
+        // \Precondition The generator is either freshly constructed,
+        // or the last call to `next()` returned true
+        virtual T const& get() const = 0;
+        using type = T;
+    };
+
+    template<typename T>
+    class SingleValueGenerator final : public IGenerator<T> {
+        T m_value;
+    public:
+        SingleValueGenerator(T const& value) : m_value( value ) {}
+        SingleValueGenerator(T&& value) : m_value(std::move(value)) {}
+
+        T const& get() const override {
+            return m_value;
+        }
+        bool next() override {
+            return false;
+        }
+    };
+
+    template<typename T>
+    class FixedValuesGenerator final : public IGenerator<T> {
+        static_assert(!std::is_same<T, bool>::value,
+            "FixedValuesGenerator does not support bools because of std::vector<bool>"
+            "specialization, use SingleValue Generator instead.");
+        std::vector<T> m_values;
+        size_t m_idx = 0;
+    public:
+        FixedValuesGenerator( std::initializer_list<T> values ) : m_values( values ) {}
+
+        T const& get() const override {
+            return m_values[m_idx];
+        }
+        bool next() override {
+            ++m_idx;
+            return m_idx < m_values.size();
+        }
+    };
+
+    template <typename T>
+    class GeneratorWrapper final {
+        std::unique_ptr<IGenerator<T>> m_generator;
+    public:
+        GeneratorWrapper(std::unique_ptr<IGenerator<T>> generator):
+            m_generator(std::move(generator))
+        {}
+        T const& get() const {
+            return m_generator->get();
+        }
+        bool next() {
+            return m_generator->next();
+        }
+    };
+
+    template <typename T>
+    GeneratorWrapper<T> value(T&& value) {
+        return GeneratorWrapper<T>(pf::make_unique<SingleValueGenerator<T>>(std::forward<T>(value)));
+    }
+    template <typename T>
+    GeneratorWrapper<T> values(std::initializer_list<T> values) {
+        return GeneratorWrapper<T>(pf::make_unique<FixedValuesGenerator<T>>(values));
+    }
+
+    template<typename T>
+    class Generators : public IGenerator<T> {
+        std::vector<GeneratorWrapper<T>> m_generators;
+        size_t m_current = 0;
+
+        void populate(GeneratorWrapper<T>&& generator) {
+            m_generators.emplace_back(std::move(generator));
+        }
+        void populate(T&& val) {
+            m_generators.emplace_back(value(std::move(val)));
+        }
+        template<typename U>
+        void populate(U&& val) {
+            populate(T(std::move(val)));
+        }
+        template<typename U, typename... Gs>
+        void populate(U&& valueOrGenerator, Gs... moreGenerators) {
+            populate(std::forward<U>(valueOrGenerator));
+            populate(std::forward<Gs>(moreGenerators)...);
+        }
+
+    public:
+        template <typename... Gs>
+        Generators(Gs... moreGenerators) {
+            m_generators.reserve(sizeof...(Gs));
+            populate(std::forward<Gs>(moreGenerators)...);
+        }
+
+        T const& get() const override {
+            return m_generators[m_current].get();
+        }
+
+        bool next() override {
+            if (m_current >= m_generators.size()) {
+                return false;
+            }
+            const bool current_status = m_generators[m_current].next();
+            if (!current_status) {
+                ++m_current;
+            }
+            return m_current < m_generators.size();
+        }
+    };
+
+    template<typename... Ts>
+    GeneratorWrapper<std::tuple<Ts...>> table( std::initializer_list<std::tuple<typename std::decay<Ts>::type...>> tuples ) {
+        return values<std::tuple<Ts...>>( tuples );
+    }
+
+    // Tag type to signal that a generator sequence should convert arguments to a specific type
+    template <typename T>
+    struct as {};
+
+    template<typename T, typename... Gs>
+    auto makeGenerators( GeneratorWrapper<T>&& generator, Gs... moreGenerators ) -> Generators<T> {
+        return Generators<T>(std::move(generator), std::forward<Gs>(moreGenerators)...);
+    }
+    template<typename T>
+    auto makeGenerators( GeneratorWrapper<T>&& generator ) -> Generators<T> {
+        return Generators<T>(std::move(generator));
+    }
+    template<typename T, typename... Gs>
+    auto makeGenerators( T&& val, Gs... moreGenerators ) -> Generators<T> {
+        return makeGenerators( value( std::forward<T>( val ) ), std::forward<Gs>( moreGenerators )... );
+    }
+    template<typename T, typename U, typename... Gs>
+    auto makeGenerators( as<T>, U&& val, Gs... moreGenerators ) -> Generators<T> {
+        return makeGenerators( value( T( std::forward<U>( val ) ) ), std::forward<Gs>( moreGenerators )... );
+    }
+
+    auto acquireGeneratorTracker( SourceLineInfo const& lineInfo ) -> IGeneratorTracker&;
+
+    template<typename L>
+    // Note: The type after -> is weird, because VS2015 cannot parse
+    //       the expression used in the typedef inside, when it is in
+    //       return type. Yeah.
+    auto generate( SourceLineInfo const& lineInfo, L const& generatorExpression ) -> decltype(std::declval<decltype(generatorExpression())>().get()) {
+        using UnderlyingType = typename decltype(generatorExpression())::type;
+
+        IGeneratorTracker& tracker = acquireGeneratorTracker( lineInfo );
+        if (!tracker.hasGenerator()) {
+            tracker.setGenerator(pf::make_unique<Generators<UnderlyingType>>(generatorExpression()));
+        }
+
+        auto const& generator = static_cast<IGenerator<UnderlyingType> const&>( *tracker.getGenerator() );
+        return generator.get();
+    }
+
+} // namespace Generators
+} // namespace Catch
+
+#define GENERATE( ... ) \
+    Catch::Generators::generate( CATCH_INTERNAL_LINEINFO, [ ]{ using namespace Catch::Generators; return makeGenerators( __VA_ARGS__ ); } )
+#define GENERATE_COPY( ... ) \
+    Catch::Generators::generate( CATCH_INTERNAL_LINEINFO, [=]{ using namespace Catch::Generators; return makeGenerators( __VA_ARGS__ ); } )
+#define GENERATE_REF( ... ) \
+    Catch::Generators::generate( CATCH_INTERNAL_LINEINFO, [&]{ using namespace Catch::Generators; return makeGenerators( __VA_ARGS__ ); } )
+
+// end catch_generators.hpp
+// start catch_generators_generic.hpp
+
+namespace Catch {
+namespace Generators {
+
+    template <typename T>
+    class TakeGenerator : public IGenerator<T> {
+        GeneratorWrapper<T> m_generator;
+        size_t m_returned = 0;
+        size_t m_target;
+    public:
+        TakeGenerator(size_t target, GeneratorWrapper<T>&& generator):
+            m_generator(std::move(generator)),
+            m_target(target)
+        {
+            assert(target != 0 && "Empty generators are not allowed");
+        }
+        T const& get() const override {
+            return m_generator.get();
+        }
+        bool next() override {
+            ++m_returned;
+            if (m_returned >= m_target) {
+                return false;
+            }
+
+            const auto success = m_generator.next();
+            // If the underlying generator does not contain enough values
+            // then we cut short as well
+            if (!success) {
+                m_returned = m_target;
+            }
+            return success;
+        }
+    };
+
+    template <typename T>
+    GeneratorWrapper<T> take(size_t target, GeneratorWrapper<T>&& generator) {
+        return GeneratorWrapper<T>(pf::make_unique<TakeGenerator<T>>(target, std::move(generator)));
+    }
+
+    template <typename T, typename Predicate>
+    class FilterGenerator : public IGenerator<T> {
+        GeneratorWrapper<T> m_generator;
+        Predicate m_predicate;
+    public:
+        template <typename P = Predicate>
+        FilterGenerator(P&& pred, GeneratorWrapper<T>&& generator):
+            m_generator(std::move(generator)),
+            m_predicate(std::forward<P>(pred))
+        {
+            if (!m_predicate(m_generator.get())) {
+                // It might happen that there are no values that pass the
+                // filter. In that case we throw an exception.
+                auto has_initial_value = next();
+                if (!has_initial_value) {
+                    Catch::throw_exception(GeneratorException("No valid value found in filtered generator"));
+                }
+            }
+        }
+
+        T const& get() const override {
+            return m_generator.get();
+        }
+
+        bool next() override {
+            bool success = m_generator.next();
+            if (!success) {
+                return false;
+            }
+            while (!m_predicate(m_generator.get()) && (success = m_generator.next()) == true);
+            return success;
+        }
+    };
+
+    template <typename T, typename Predicate>
+    GeneratorWrapper<T> filter(Predicate&& pred, GeneratorWrapper<T>&& generator) {
+        return GeneratorWrapper<T>(std::unique_ptr<IGenerator<T>>(pf::make_unique<FilterGenerator<T, Predicate>>(std::forward<Predicate>(pred), std::move(generator))));
+    }
+
+    template <typename T>
+    class RepeatGenerator : public IGenerator<T> {
+        static_assert(!std::is_same<T, bool>::value,
+            "RepeatGenerator currently does not support bools"
+            "because of std::vector<bool> specialization");
+        GeneratorWrapper<T> m_generator;
+        mutable std::vector<T> m_returned;
+        size_t m_target_repeats;
+        size_t m_current_repeat = 0;
+        size_t m_repeat_index = 0;
+    public:
+        RepeatGenerator(size_t repeats, GeneratorWrapper<T>&& generator):
+            m_generator(std::move(generator)),
+            m_target_repeats(repeats)
+        {
+            assert(m_target_repeats > 0 && "Repeat generator must repeat at least once");
+        }
+
+        T const& get() const override {
+            if (m_current_repeat == 0) {
+                m_returned.push_back(m_generator.get());
+                return m_returned.back();
+            }
+            return m_returned[m_repeat_index];
+        }
+
+        bool next() override {
+            // There are 2 basic cases:
+            // 1) We are still reading the generator
+            // 2) We are reading our own cache
+
+            // In the first case, we need to poke the underlying generator.
+            // If it happily moves, we are left in that state, otherwise it is time to start reading from our cache
+            if (m_current_repeat == 0) {
+                const auto success = m_generator.next();
+                if (!success) {
+                    ++m_current_repeat;
+                }
+                return m_current_repeat < m_target_repeats;
+            }
+
+            // In the second case, we need to move indices forward and check that we haven't run up against the end
+            ++m_repeat_index;
+            if (m_repeat_index == m_returned.size()) {
+                m_repeat_index = 0;
+                ++m_current_repeat;
+            }
+            return m_current_repeat < m_target_repeats;
+        }
+    };
+
+    template <typename T>
+    GeneratorWrapper<T> repeat(size_t repeats, GeneratorWrapper<T>&& generator) {
+        return GeneratorWrapper<T>(pf::make_unique<RepeatGenerator<T>>(repeats, std::move(generator)));
+    }
+
+    template <typename T, typename U, typename Func>
+    class MapGenerator : public IGenerator<T> {
+        // TBD: provide static assert for mapping function, for friendly error message
+        GeneratorWrapper<U> m_generator;
+        Func m_function;
+        // To avoid returning dangling reference, we have to save the values
+        T m_cache;
+    public:
+        template <typename F2 = Func>
+        MapGenerator(F2&& function, GeneratorWrapper<U>&& generator) :
+            m_generator(std::move(generator)),
+            m_function(std::forward<F2>(function)),
+            m_cache(m_function(m_generator.get()))
+        {}
+
+        T const& get() const override {
+            return m_cache;
+        }
+        bool next() override {
+            const auto success = m_generator.next();
+            if (success) {
+                m_cache = m_function(m_generator.get());
+            }
+            return success;
+        }
+    };
+
+#if defined(__cpp_lib_is_invocable) && __cpp_lib_is_invocable >= 201703
+    // std::result_of is deprecated in C++17 and removed in C++20. Hence, it is
+    // replaced with std::invoke_result here. Also *_t format is preferred over
+    // typename *::type format.
+    template <typename Func, typename U>
+    using MapFunctionReturnType = std::remove_reference_t<std::remove_cv_t<std::invoke_result_t<Func, U>>>;
+#else
+    template <typename Func, typename U>
+    using MapFunctionReturnType = typename std::remove_reference<typename std::remove_cv<typename std::result_of<Func(U)>::type>::type>::type;
+#endif
+
+    template <typename Func, typename U, typename T = MapFunctionReturnType<Func, U>>
+    GeneratorWrapper<T> map(Func&& function, GeneratorWrapper<U>&& generator) {
+        return GeneratorWrapper<T>(
+            pf::make_unique<MapGenerator<T, U, Func>>(std::forward<Func>(function), std::move(generator))
+        );
+    }
+
+    template <typename T, typename U, typename Func>
+    GeneratorWrapper<T> map(Func&& function, GeneratorWrapper<U>&& generator) {
+        return GeneratorWrapper<T>(
+            pf::make_unique<MapGenerator<T, U, Func>>(std::forward<Func>(function), std::move(generator))
+        );
+    }
+
+    template <typename T>
+    class ChunkGenerator final : public IGenerator<std::vector<T>> {
+        std::vector<T> m_chunk;
+        size_t m_chunk_size;
+        GeneratorWrapper<T> m_generator;
+        bool m_used_up = false;
+    public:
+        ChunkGenerator(size_t size, GeneratorWrapper<T> generator) :
+            m_chunk_size(size), m_generator(std::move(generator))
+        {
+            m_chunk.reserve(m_chunk_size);
+            if (m_chunk_size != 0) {
+                m_chunk.push_back(m_generator.get());
+                for (size_t i = 1; i < m_chunk_size; ++i) {
+                    if (!m_generator.next()) {
+                        Catch::throw_exception(GeneratorException("Not enough values to initialize the first chunk"));
+                    }
+                    m_chunk.push_back(m_generator.get());
+                }
+            }
+        }
+        std::vector<T> const& get() const override {
+            return m_chunk;
+        }
+        bool next() override {
+            m_chunk.clear();
+            for (size_t idx = 0; idx < m_chunk_size; ++idx) {
+                if (!m_generator.next()) {
+                    return false;
+                }
+                m_chunk.push_back(m_generator.get());
+            }
+            return true;
+        }
+    };
+
+    template <typename T>
+    GeneratorWrapper<std::vector<T>> chunk(size_t size, GeneratorWrapper<T>&& generator) {
+        return GeneratorWrapper<std::vector<T>>(
+            pf::make_unique<ChunkGenerator<T>>(size, std::move(generator))
+        );
+    }
+
+} // namespace Generators
+} // namespace Catch
+
+// end catch_generators_generic.hpp
+// start catch_generators_specific.hpp
+
+// start catch_context.h
+
+#include <memory>
+
+namespace Catch {
+
+    struct IResultCapture;
+    struct IRunner;
+    struct IConfig;
+    struct IMutableContext;
+
+    using IConfigPtr = std::shared_ptr<IConfig const>;
+
+    struct IContext
+    {
+        virtual ~IContext();
+
+        virtual IResultCapture* getResultCapture() = 0;
+        virtual IRunner* getRunner() = 0;
+        virtual IConfigPtr const& getConfig() const = 0;
+    };
+
+    struct IMutableContext : IContext
+    {
+        virtual ~IMutableContext();
+        virtual void setResultCapture( IResultCapture* resultCapture ) = 0;
+        virtual void setRunner( IRunner* runner ) = 0;
+        virtual void setConfig( IConfigPtr const& config ) = 0;
+
+    private:
+        static IMutableContext *currentContext;
+        friend IMutableContext& getCurrentMutableContext();
+        friend void cleanUpContext();
+        static void createContext();
+    };
+
+    inline IMutableContext& getCurrentMutableContext()
+    {
+        if( !IMutableContext::currentContext )
+            IMutableContext::createContext();
+        // NOLINTNEXTLINE(clang-analyzer-core.uninitialized.UndefReturn)
+        return *IMutableContext::currentContext;
+    }
+
+    inline IContext& getCurrentContext()
+    {
+        return getCurrentMutableContext();
+    }
+
+    void cleanUpContext();
+
+    class SimplePcg32;
+    SimplePcg32& rng();
+}
+
+// end catch_context.h
+// start catch_interfaces_config.h
+
+// start catch_option.hpp
+
+namespace Catch {
+
+    // An optional type
+    template<typename T>
+    class Option {
+    public:
+        Option() : nullableValue( nullptr ) {}
+        Option( T const& _value )
+        : nullableValue( new( storage ) T( _value ) )
+        {}
+        Option( Option const& _other )
+        : nullableValue( _other ? new( storage ) T( *_other ) : nullptr )
+        {}
+
+        ~Option() {
+            reset();
+        }
+
+        Option& operator= ( Option const& _other ) {
+            if( &_other != this ) {
+                reset();
+                if( _other )
+                    nullableValue = new( storage ) T( *_other );
+            }
+            return *this;
+        }
+        Option& operator = ( T const& _value ) {
+            reset();
+            nullableValue = new( storage ) T( _value );
+            return *this;
+        }
+
+        void reset() {
+            if( nullableValue )
+                nullableValue->~T();
+            nullableValue = nullptr;
+        }
+
+        T& operator*() { return *nullableValue; }
+        T const& operator*() const { return *nullableValue; }
+        T* operator->() { return nullableValue; }
+        const T* operator->() const { return nullableValue; }
+
+        T valueOr( T const& defaultValue ) const {
+            return nullableValue ? *nullableValue : defaultValue;
+        }
+
+        bool some() const { return nullableValue != nullptr; }
+        bool none() const { return nullableValue == nullptr; }
+
+        bool operator !() const { return nullableValue == nullptr; }
+        explicit operator bool() const {
+            return some();
+        }
+
+    private:
+        T *nullableValue;
+        alignas(alignof(T)) char storage[sizeof(T)];
+    };
+
+} // end namespace Catch
+
+// end catch_option.hpp
+#include <iosfwd>
+#include <string>
+#include <vector>
+#include <memory>
+
+namespace Catch {
+
+    enum class Verbosity {
+        Quiet = 0,
+        Normal,
+        High
+    };
+
+    struct WarnAbout { enum What {
+        Nothing = 0x00,
+        NoAssertions = 0x01,
+        NoTests = 0x02
+    }; };
+
+    struct ShowDurations { enum OrNot {
+        DefaultForReporter,
+        Always,
+        Never
+    }; };
+    struct RunTests { enum InWhatOrder {
+        InDeclarationOrder,
+        InLexicographicalOrder,
+        InRandomOrder
+    }; };
+    struct UseColour { enum YesOrNo {
+        Auto,
+        Yes,
+        No
+    }; };
+    struct WaitForKeypress { enum When {
+        Never,
+        BeforeStart = 1,
+        BeforeExit = 2,
+        BeforeStartAndExit = BeforeStart | BeforeExit
+    }; };
+
+    class TestSpec;
+
+    struct IConfig : NonCopyable {
+
+        virtual ~IConfig();
+
+        virtual bool allowThrows() const = 0;
+        virtual std::ostream& stream() const = 0;
+        virtual std::string name() const = 0;
+        virtual bool includeSuccessfulResults() const = 0;
+        virtual bool shouldDebugBreak() const = 0;
+        virtual bool warnAboutMissingAssertions() const = 0;
+        virtual bool warnAboutNoTests() const = 0;
+        virtual int abortAfter() const = 0;
+        virtual bool showInvisibles() const = 0;
+        virtual ShowDurations::OrNot showDurations() const = 0;
+        virtual TestSpec const& testSpec() const = 0;
+        virtual bool hasTestFilters() const = 0;
+        virtual std::vector<std::string> const& getTestsOrTags() const = 0;
+        virtual RunTests::InWhatOrder runOrder() const = 0;
+        virtual unsigned int rngSeed() const = 0;
+        virtual UseColour::YesOrNo useColour() const = 0;
+        virtual std::vector<std::string> const& getSectionsToRun() const = 0;
+        virtual Verbosity verbosity() const = 0;
+
+        virtual bool benchmarkNoAnalysis() const = 0;
+        virtual int benchmarkSamples() const = 0;
+        virtual double benchmarkConfidenceInterval() const = 0;
+        virtual unsigned int benchmarkResamples() const = 0;
+    };
+
+    using IConfigPtr = std::shared_ptr<IConfig const>;
+}
+
+// end catch_interfaces_config.h
+// start catch_random_number_generator.h
+
+#include <cstdint>
+
+namespace Catch {
+
+    // This is a simple implementation of C++11 Uniform Random Number
+    // Generator. It does not provide all operators, because Catch2
+    // does not use it, but it should behave as expected inside stdlib's
+    // distributions.
+    // The implementation is based on the PCG family (http://pcg-random.org)
+    class SimplePcg32 {
+        using state_type = std::uint64_t;
+    public:
+        using result_type = std::uint32_t;
+        static constexpr result_type (min)() {
+            return 0;
+        }
+        static constexpr result_type (max)() {
+            return static_cast<result_type>(-1);
+        }
+
+        // Provide some default initial state for the default constructor
+        SimplePcg32():SimplePcg32(0xed743cc4U) {}
+
+        explicit SimplePcg32(result_type seed_);
+
+        void seed(result_type seed_);
+        void discard(uint64_t skip);
+
+        result_type operator()();
+
+    private:
+        friend bool operator==(SimplePcg32 const& lhs, SimplePcg32 const& rhs);
+        friend bool operator!=(SimplePcg32 const& lhs, SimplePcg32 const& rhs);
+
+        // In theory we also need operator<< and operator>>
+        // In practice we do not use them, so we will skip them for now
+
+        std::uint64_t m_state;
+        // This part of the state determines which "stream" of the numbers
+        // is chosen -- we take it as a constant for Catch2, so we only
+        // need to deal with seeding the main state.
+        // Picked by reading 8 bytes from `/dev/random` :-)
+        static const std::uint64_t s_inc = (0x13ed0cc53f939476ULL << 1ULL) | 1ULL;
+    };
+
+} // end namespace Catch
+
+// end catch_random_number_generator.h
+#include <random>
+
+namespace Catch {
+namespace Generators {
+
+template <typename Float>
+class RandomFloatingGenerator final : public IGenerator<Float> {
+    Catch::SimplePcg32& m_rng;
+    std::uniform_real_distribution<Float> m_dist;
+    Float m_current_number;
+public:
+
+    RandomFloatingGenerator(Float a, Float b):
+        m_rng(rng()),
+        m_dist(a, b) {
+        static_cast<void>(next());
+    }
+
+    Float const& get() const override {
+        return m_current_number;
+    }
+    bool next() override {
+        m_current_number = m_dist(m_rng);
+        return true;
+    }
+};
+
+template <typename Integer>
+class RandomIntegerGenerator final : public IGenerator<Integer> {
+    Catch::SimplePcg32& m_rng;
+    std::uniform_int_distribution<Integer> m_dist;
+    Integer m_current_number;
+public:
+
+    RandomIntegerGenerator(Integer a, Integer b):
+        m_rng(rng()),
+        m_dist(a, b) {
+        static_cast<void>(next());
+    }
+
+    Integer const& get() const override {
+        return m_current_number;
+    }
+    bool next() override {
+        m_current_number = m_dist(m_rng);
+        return true;
+    }
+};
+
+// TODO: Ideally this would be also constrained against the various char types,
+//       but I don't expect users to run into that in practice.
+template <typename T>
+typename std::enable_if<std::is_integral<T>::value && !std::is_same<T, bool>::value,
+GeneratorWrapper<T>>::type
+random(T a, T b) {
+    return GeneratorWrapper<T>(
+        pf::make_unique<RandomIntegerGenerator<T>>(a, b)
+    );
+}
+
+template <typename T>
+typename std::enable_if<std::is_floating_point<T>::value,
+GeneratorWrapper<T>>::type
+random(T a, T b) {
+    return GeneratorWrapper<T>(
+        pf::make_unique<RandomFloatingGenerator<T>>(a, b)
+    );
+}
+
+template <typename T>
+class RangeGenerator final : public IGenerator<T> {
+    T m_current;
+    T m_end;
+    T m_step;
+    bool m_positive;
+
+public:
+    RangeGenerator(T const& start, T const& end, T const& step):
+        m_current(start),
+        m_end(end),
+        m_step(step),
+        m_positive(m_step > T(0))
+    {
+        assert(m_current != m_end && "Range start and end cannot be equal");
+        assert(m_step != T(0) && "Step size cannot be zero");
+        assert(((m_positive && m_current <= m_end) || (!m_positive && m_current >= m_end)) && "Step moves away from end");
+    }
+
+    RangeGenerator(T const& start, T const& end):
+        RangeGenerator(start, end, (start < end) ? T(1) : T(-1))
+    {}
+
+    T const& get() const override {
+        return m_current;
+    }
+
+    bool next() override {
+        m_current += m_step;
+        return (m_positive) ? (m_current < m_end) : (m_current > m_end);
+    }
+};
+
+template <typename T>
+GeneratorWrapper<T> range(T const& start, T const& end, T const& step) {
+    static_assert(std::is_integral<T>::value && !std::is_same<T, bool>::value, "Type must be an integer");
+    return GeneratorWrapper<T>(pf::make_unique<RangeGenerator<T>>(start, end, step));
+}
+
+template <typename T>
+GeneratorWrapper<T> range(T const& start, T const& end) {
+    static_assert(std::is_integral<T>::value && !std::is_same<T, bool>::value, "Type must be an integer");
+    return GeneratorWrapper<T>(pf::make_unique<RangeGenerator<T>>(start, end));
+}
+
+template <typename T>
+class IteratorGenerator final : public IGenerator<T> {
+    static_assert(!std::is_same<T, bool>::value,
+        "IteratorGenerator currently does not support bools"
+        "because of std::vector<bool> specialization");
+
+    std::vector<T> m_elems;
+    size_t m_current = 0;
+public:
+    template <typename InputIterator, typename InputSentinel>
+    IteratorGenerator(InputIterator first, InputSentinel last):m_elems(first, last) {
+        if (m_elems.empty()) {
+            Catch::throw_exception(GeneratorException("IteratorGenerator received no valid values"));
+        }
+    }
+
+    T const& get() const override {
+        return m_elems[m_current];
+    }
+
+    bool next() override {
+        ++m_current;
+        return m_current != m_elems.size();
+    }
+};
+
+template <typename InputIterator,
+          typename InputSentinel,
+          typename ResultType = typename std::iterator_traits<InputIterator>::value_type>
+GeneratorWrapper<ResultType> from_range(InputIterator from, InputSentinel to) {
+    return GeneratorWrapper<ResultType>(pf::make_unique<IteratorGenerator<ResultType>>(from, to));
+}
+
+template <typename Container,
+          typename ResultType = typename Container::value_type>
+GeneratorWrapper<ResultType> from_range(Container const& cnt) {
+    return GeneratorWrapper<ResultType>(pf::make_unique<IteratorGenerator<ResultType>>(cnt.begin(), cnt.end()));
+}
+
+} // namespace Generators
+} // namespace Catch
+
+// end catch_generators_specific.hpp
+
+// These files are included here so the single_include script doesn't put them
+// in the conditionally compiled sections
+// start catch_test_case_info.h
+
+#include <string>
+#include <vector>
+#include <memory>
+
+#ifdef __clang__
+#pragma clang diagnostic push
+#pragma clang diagnostic ignored "-Wpadded"
+#endif
+
+namespace Catch {
+
+    struct ITestInvoker;
+
+    struct TestCaseInfo {
+        enum SpecialProperties{
+            None = 0,
+            IsHidden = 1 << 1,
+            ShouldFail = 1 << 2,
+            MayFail = 1 << 3,
+            Throws = 1 << 4,
+            NonPortable = 1 << 5,
+            Benchmark = 1 << 6
+        };
+
+        TestCaseInfo(   std::string const& _name,
+                        std::string const& _className,
+                        std::string const& _description,
+                        std::vector<std::string> const& _tags,
+                        SourceLineInfo const& _lineInfo );
+
+        friend void setTags( TestCaseInfo& testCaseInfo, std::vector<std::string> tags );
+
+        bool isHidden() const;
+        bool throws() const;
+        bool okToFail() const;
+        bool expectedToFail() const;
+
+        std::string tagsAsString() const;
+
+        std::string name;
+        std::string className;
+        std::string description;
+        std::vector<std::string> tags;
+        std::vector<std::string> lcaseTags;
+        SourceLineInfo lineInfo;
+        SpecialProperties properties;
+    };
+
+    class TestCase : public TestCaseInfo {
+    public:
+
+        TestCase( ITestInvoker* testCase, TestCaseInfo&& info );
+
+        TestCase withName( std::string const& _newName ) const;
+
+        void invoke() const;
+
+        TestCaseInfo const& getTestCaseInfo() const;
+
+        bool operator == ( TestCase const& other ) const;
+        bool operator < ( TestCase const& other ) const;
+
+    private:
+        std::shared_ptr<ITestInvoker> test;
+    };
+
+    TestCase makeTestCase(  ITestInvoker* testCase,
+                            std::string const& className,
+                            NameAndTags const& nameAndTags,
+                            SourceLineInfo const& lineInfo );
+}
+
+#ifdef __clang__
+#pragma clang diagnostic pop
+#endif
+
+// end catch_test_case_info.h
+// start catch_interfaces_runner.h
+
+namespace Catch {
+
+    struct IRunner {
+        virtual ~IRunner();
+        virtual bool aborting() const = 0;
+    };
+}
+
+// end catch_interfaces_runner.h
+
+#ifdef __OBJC__
+// start catch_objc.hpp
+
+#import <objc/runtime.h>
+
+#include <string>
+
+// NB. Any general catch headers included here must be included
+// in catch.hpp first to make sure they are included by the single
+// header for non obj-usage
+
+///////////////////////////////////////////////////////////////////////////////
+// This protocol is really only here for (self) documenting purposes, since
+// all its methods are optional.
+@protocol OcFixture
+
+@optional
+
+-(void) setUp;
+-(void) tearDown;
+
+@end
+
+namespace Catch {
+
+    class OcMethod : public ITestInvoker {
+
+    public:
+        OcMethod( Class cls, SEL sel ) : m_cls( cls ), m_sel( sel ) {}
+
+        virtual void invoke() const {
+            id obj = [[m_cls alloc] init];
+
+            performOptionalSelector( obj, @selector(setUp)  );
+            performOptionalSelector( obj, m_sel );
+            performOptionalSelector( obj, @selector(tearDown)  );
+
+            arcSafeRelease( obj );
+        }
+    private:
+        virtual ~OcMethod() {}
+
+        Class m_cls;
+        SEL m_sel;
+    };
+
+    namespace Detail{
+
+        inline std::string getAnnotation(   Class cls,
+                                            std::string const& annotationName,
+                                            std::string const& testCaseName ) {
+            NSString* selStr = [[NSString alloc] initWithFormat:@"Catch_%s_%s", annotationName.c_str(), testCaseName.c_str()];
+            SEL sel = NSSelectorFromString( selStr );
+            arcSafeRelease( selStr );
+            id value = performOptionalSelector( cls, sel );
+            if( value )
+                return [(NSString*)value UTF8String];
+            return "";
+        }
+    }
+
+    inline std::size_t registerTestMethods() {
+        std::size_t noTestMethods = 0;
+        int noClasses = objc_getClassList( nullptr, 0 );
+
+        Class* classes = (CATCH_UNSAFE_UNRETAINED Class *)malloc( sizeof(Class) * noClasses);
+        objc_getClassList( classes, noClasses );
+
+        for( int c = 0; c < noClasses; c++ ) {
+            Class cls = classes[c];
+            {
+                u_int count;
+                Method* methods = class_copyMethodList( cls, &count );
+                for( u_int m = 0; m < count ; m++ ) {
+                    SEL selector = method_getName(methods[m]);
+                    std::string methodName = sel_getName(selector);
+                    if( startsWith( methodName, "Catch_TestCase_" ) ) {
+                        std::string testCaseName = methodName.substr( 15 );
+                        std::string name = Detail::getAnnotation( cls, "Name", testCaseName );
+                        std::string desc = Detail::getAnnotation( cls, "Description", testCaseName );
+                        const char* className = class_getName( cls );
+
+                        getMutableRegistryHub().registerTest( makeTestCase( new OcMethod( cls, selector ), className, NameAndTags( name.c_str(), desc.c_str() ), SourceLineInfo("",0) ) );
+                        noTestMethods++;
+                    }
+                }
+                free(methods);
+            }
+        }
+        return noTestMethods;
+    }
+
+#if !defined(CATCH_CONFIG_DISABLE_MATCHERS)
+
+    namespace Matchers {
+        namespace Impl {
+        namespace NSStringMatchers {
+
+            struct StringHolder : MatcherBase<NSString*>{
+                StringHolder( NSString* substr ) : m_substr( [substr copy] ){}
+                StringHolder( StringHolder const& other ) : m_substr( [other.m_substr copy] ){}
+                StringHolder() {
+                    arcSafeRelease( m_substr );
+                }
+
+                bool match( NSString* str ) const override {
+                    return false;
+                }
+
+                NSString* CATCH_ARC_STRONG m_substr;
+            };
+
+            struct Equals : StringHolder {
+                Equals( NSString* substr ) : StringHolder( substr ){}
+
+                bool match( NSString* str ) const override {
+                    return  (str != nil || m_substr == nil ) &&
+                            [str isEqualToString:m_substr];
+                }
+
+                std::string describe() const override {
+                    return "equals string: " + Catch::Detail::stringify( m_substr );
+                }
+            };
+
+            struct Contains : StringHolder {
+                Contains( NSString* substr ) : StringHolder( substr ){}
+
+                bool match( NSString* str ) const override {
+                    return  (str != nil || m_substr == nil ) &&
+                            [str rangeOfString:m_substr].location != NSNotFound;
+                }
+
+                std::string describe() const override {
+                    return "contains string: " + Catch::Detail::stringify( m_substr );
+                }
+            };
+
+            struct StartsWith : StringHolder {
+                StartsWith( NSString* substr ) : StringHolder( substr ){}
+
+                bool match( NSString* str ) const override {
+                    return  (str != nil || m_substr == nil ) &&
+                            [str rangeOfString:m_substr].location == 0;
+                }
+
+                std::string describe() const override {
+                    return "starts with: " + Catch::Detail::stringify( m_substr );
+                }
+            };
+            struct EndsWith : StringHolder {
+                EndsWith( NSString* substr ) : StringHolder( substr ){}
+
+                bool match( NSString* str ) const override {
+                    return  (str != nil || m_substr == nil ) &&
+                            [str rangeOfString:m_substr].location == [str length] - [m_substr length];
+                }
+
+                std::string describe() const override {
+                    return "ends with: " + Catch::Detail::stringify( m_substr );
+                }
+            };
+
+        } // namespace NSStringMatchers
+        } // namespace Impl
+
+        inline Impl::NSStringMatchers::Equals
+            Equals( NSString* substr ){ return Impl::NSStringMatchers::Equals( substr ); }
+
+        inline Impl::NSStringMatchers::Contains
+            Contains( NSString* substr ){ return Impl::NSStringMatchers::Contains( substr ); }
+
+        inline Impl::NSStringMatchers::StartsWith
+            StartsWith( NSString* substr ){ return Impl::NSStringMatchers::StartsWith( substr ); }
+
+        inline Impl::NSStringMatchers::EndsWith
+            EndsWith( NSString* substr ){ return Impl::NSStringMatchers::EndsWith( substr ); }
+
+    } // namespace Matchers
+
+    using namespace Matchers;
+
+#endif // CATCH_CONFIG_DISABLE_MATCHERS
+
+} // namespace Catch
+
+///////////////////////////////////////////////////////////////////////////////
+#define OC_MAKE_UNIQUE_NAME( root, uniqueSuffix ) root##uniqueSuffix
+#define OC_TEST_CASE2( name, desc, uniqueSuffix ) \
++(NSString*) OC_MAKE_UNIQUE_NAME( Catch_Name_test_, uniqueSuffix ) \
+{ \
+return @ name; \
+} \
++(NSString*) OC_MAKE_UNIQUE_NAME( Catch_Description_test_, uniqueSuffix ) \
+{ \
+return @ desc; \
+} \
+-(void) OC_MAKE_UNIQUE_NAME( Catch_TestCase_test_, uniqueSuffix )
+
+#define OC_TEST_CASE( name, desc ) OC_TEST_CASE2( name, desc, __LINE__ )
+
+// end catch_objc.hpp
+#endif
+
+// Benchmarking needs the externally-facing parts of reporters to work
+#if defined(CATCH_CONFIG_EXTERNAL_INTERFACES) || defined(CATCH_CONFIG_ENABLE_BENCHMARKING)
+// start catch_external_interfaces.h
+
+// start catch_reporter_bases.hpp
+
+// start catch_interfaces_reporter.h
+
+// start catch_config.hpp
+
+// start catch_test_spec_parser.h
+
+#ifdef __clang__
+#pragma clang diagnostic push
+#pragma clang diagnostic ignored "-Wpadded"
+#endif
+
+// start catch_test_spec.h
+
+#ifdef __clang__
+#pragma clang diagnostic push
+#pragma clang diagnostic ignored "-Wpadded"
+#endif
+
+// start catch_wildcard_pattern.h
+
+namespace Catch
+{
+    class WildcardPattern {
+        enum WildcardPosition {
+            NoWildcard = 0,
+            WildcardAtStart = 1,
+            WildcardAtEnd = 2,
+            WildcardAtBothEnds = WildcardAtStart | WildcardAtEnd
+        };
+
+    public:
+
+        WildcardPattern( std::string const& pattern, CaseSensitive::Choice caseSensitivity );
+        virtual ~WildcardPattern() = default;
+        virtual bool matches( std::string const& str ) const;
+
+    private:
+        std::string normaliseString( std::string const& str ) const;
+        CaseSensitive::Choice m_caseSensitivity;
+        WildcardPosition m_wildcard = NoWildcard;
+        std::string m_pattern;
+    };
+}
+
+// end catch_wildcard_pattern.h
+#include <string>
+#include <vector>
+#include <memory>
+
+namespace Catch {
+
+    struct IConfig;
+
+    class TestSpec {
+        class Pattern {
+        public:
+            explicit Pattern( std::string const& name );
+            virtual ~Pattern();
+            virtual bool matches( TestCaseInfo const& testCase ) const = 0;
+            std::string const& name() const;
+        private:
+            std::string const m_name;
+        };
+        using PatternPtr = std::shared_ptr<Pattern>;
+
+        class NamePattern : public Pattern {
+        public:
+            explicit NamePattern( std::string const& name, std::string const& filterString );
+            bool matches( TestCaseInfo const& testCase ) const override;
+        private:
+            WildcardPattern m_wildcardPattern;
+        };
+
+        class TagPattern : public Pattern {
+        public:
+            explicit TagPattern( std::string const& tag, std::string const& filterString );
+            bool matches( TestCaseInfo const& testCase ) const override;
+        private:
+            std::string m_tag;
+        };
+
+        class ExcludedPattern : public Pattern {
+        public:
+            explicit ExcludedPattern( PatternPtr const& underlyingPattern );
+            bool matches( TestCaseInfo const& testCase ) const override;
+        private:
+            PatternPtr m_underlyingPattern;
+        };
+
+        struct Filter {
+            std::vector<PatternPtr> m_patterns;
+
+            bool matches( TestCaseInfo const& testCase ) const;
+            std::string name() const;
+        };
+
+    public:
+        struct FilterMatch {
+            std::string name;
+            std::vector<TestCase const*> tests;
+        };
+        using Matches = std::vector<FilterMatch>;
+        using vectorStrings = std::vector<std::string>;
+
+        bool hasFilters() const;
+        bool matches( TestCaseInfo const& testCase ) const;
+        Matches matchesByFilter( std::vector<TestCase> const& testCases, IConfig const& config ) const;
+        const vectorStrings & getInvalidArgs() const;
+
+    private:
+        std::vector<Filter> m_filters;
+        std::vector<std::string> m_invalidArgs;
+        friend class TestSpecParser;
+    };
+}
+
+#ifdef __clang__
+#pragma clang diagnostic pop
+#endif
+
+// end catch_test_spec.h
+// start catch_interfaces_tag_alias_registry.h
+
+#include <string>
+
+namespace Catch {
+
+    struct TagAlias;
+
+    struct ITagAliasRegistry {
+        virtual ~ITagAliasRegistry();
+        // Nullptr if not present
+        virtual TagAlias const* find( std::string const& alias ) const = 0;
+        virtual std::string expandAliases( std::string const& unexpandedTestSpec ) const = 0;
+
+        static ITagAliasRegistry const& get();
+    };
+
+} // end namespace Catch
+
+// end catch_interfaces_tag_alias_registry.h
+namespace Catch {
+
+    class TestSpecParser {
+        enum Mode{ None, Name, QuotedName, Tag, EscapedName };
+        Mode m_mode = None;
+        Mode lastMode = None;
+        bool m_exclusion = false;
+        std::size_t m_pos = 0;
+        std::size_t m_realPatternPos = 0;
+        std::string m_arg;
+        std::string m_substring;
+        std::string m_patternName;
+        std::vector<std::size_t> m_escapeChars;
+        TestSpec::Filter m_currentFilter;
+        TestSpec m_testSpec;
+        ITagAliasRegistry const* m_tagAliases = nullptr;
+
+    public:
+        TestSpecParser( ITagAliasRegistry const& tagAliases );
+
+        TestSpecParser& parse( std::string const& arg );
+        TestSpec testSpec();
+
+    private:
+        bool visitChar( char c );
+        void startNewMode( Mode mode );
+        bool processNoneChar( char c );
+        void processNameChar( char c );
+        bool processOtherChar( char c );
+        void endMode();
+        void escape();
+        bool isControlChar( char c ) const;
+        void saveLastMode();
+        void revertBackToLastMode();
+        void addFilter();
+        bool separate();
+
+        template<typename T>
+        void addPattern() {
+            std::string token = m_patternName;
+            for( std::size_t i = 0; i < m_escapeChars.size(); ++i )
+                token = token.substr( 0, m_escapeChars[i] - i ) + token.substr( m_escapeChars[i] -i +1 );
+            m_escapeChars.clear();
+            if( startsWith( token, "exclude:" ) ) {
+                m_exclusion = true;
+                token = token.substr( 8 );
+            }
+            if( !token.empty() ) {
+                TestSpec::PatternPtr pattern = std::make_shared<T>( token, m_substring );
+                if( m_exclusion )
+                    pattern = std::make_shared<TestSpec::ExcludedPattern>( pattern );
+                m_currentFilter.m_patterns.push_back( pattern );
+            }
+            m_substring.clear();
+            m_patternName.clear();
+            m_exclusion = false;
+            m_mode = None;
+        }
+
+        inline void addCharToPattern(char c) {
+            m_substring += c;
+            m_patternName += c;
+            m_realPatternPos++;
+        }
+
+    };
+    TestSpec parseTestSpec( std::string const& arg );
+
+} // namespace Catch
+
+#ifdef __clang__
+#pragma clang diagnostic pop
+#endif
+
+// end catch_test_spec_parser.h
+// Libstdc++ doesn't like incomplete classes for unique_ptr
+
+#include <memory>
+#include <vector>
+#include <string>
+
+#ifndef CATCH_CONFIG_CONSOLE_WIDTH
+#define CATCH_CONFIG_CONSOLE_WIDTH 80
+#endif
+
+namespace Catch {
+
+    struct IStream;
+
+    struct ConfigData {
+        bool listTests = false;
+        bool listTags = false;
+        bool listReporters = false;
+        bool listTestNamesOnly = false;
+
+        bool showSuccessfulTests = false;
+        bool shouldDebugBreak = false;
+        bool noThrow = false;
+        bool showHelp = false;
+        bool showInvisibles = false;
+        bool filenamesAsTags = false;
+        bool libIdentify = false;
+
+        int abortAfter = -1;
+        unsigned int rngSeed = 0;
+
+        bool benchmarkNoAnalysis = false;
+        unsigned int benchmarkSamples = 100;
+        double benchmarkConfidenceInterval = 0.95;
+        unsigned int benchmarkResamples = 100000;
+
+        Verbosity verbosity = Verbosity::Normal;
+        WarnAbout::What warnings = WarnAbout::Nothing;
+        ShowDurations::OrNot showDurations = ShowDurations::DefaultForReporter;
+        RunTests::InWhatOrder runOrder = RunTests::InDeclarationOrder;
+        UseColour::YesOrNo useColour = UseColour::Auto;
+        WaitForKeypress::When waitForKeypress = WaitForKeypress::Never;
+
+        std::string outputFilename;
+        std::string name;
+        std::string processName;
+#ifndef CATCH_CONFIG_DEFAULT_REPORTER
+#define CATCH_CONFIG_DEFAULT_REPORTER "console"
+#endif
+        std::string reporterName = CATCH_CONFIG_DEFAULT_REPORTER;
+#undef CATCH_CONFIG_DEFAULT_REPORTER
+
+        std::vector<std::string> testsOrTags;
+        std::vector<std::string> sectionsToRun;
+    };
+
+    class Config : public IConfig {
+    public:
+
+        Config() = default;
+        Config( ConfigData const& data );
+        virtual ~Config() = default;
+
+        std::string const& getFilename() const;
+
+        bool listTests() const;
+        bool listTestNamesOnly() const;
+        bool listTags() const;
+        bool listReporters() const;
+
+        std::string getProcessName() const;
+        std::string const& getReporterName() const;
+
+        std::vector<std::string> const& getTestsOrTags() const override;
+        std::vector<std::string> const& getSectionsToRun() const override;
+
+        TestSpec const& testSpec() const override;
+        bool hasTestFilters() const override;
+
+        bool showHelp() const;
+
+        // IConfig interface
+        bool allowThrows() const override;
+        std::ostream& stream() const override;
+        std::string name() const override;
+        bool includeSuccessfulResults() const override;
+        bool warnAboutMissingAssertions() const override;
+        bool warnAboutNoTests() const override;
+        ShowDurations::OrNot showDurations() const override;
+        RunTests::InWhatOrder runOrder() const override;
+        unsigned int rngSeed() const override;
+        UseColour::YesOrNo useColour() const override;
+        bool shouldDebugBreak() const override;
+        int abortAfter() const override;
+        bool showInvisibles() const override;
+        Verbosity verbosity() const override;
+        bool benchmarkNoAnalysis() const override;
+        int benchmarkSamples() const override;
+        double benchmarkConfidenceInterval() const override;
+        unsigned int benchmarkResamples() const override;
+
+    private:
+
+        IStream const* openStream();
+        ConfigData m_data;
+
+        std::unique_ptr<IStream const> m_stream;
+        TestSpec m_testSpec;
+        bool m_hasTestFilters = false;
+    };
+
+} // end namespace Catch
+
+// end catch_config.hpp
+// start catch_assertionresult.h
+
+#include <string>
+
+namespace Catch {
+
+    struct AssertionResultData
+    {
+        AssertionResultData() = delete;
+
+        AssertionResultData( ResultWas::OfType _resultType, LazyExpression const& _lazyExpression );
+
+        std::string message;
+        mutable std::string reconstructedExpression;
+        LazyExpression lazyExpression;
+        ResultWas::OfType resultType;
+
+        std::string reconstructExpression() const;
+    };
+
+    class AssertionResult {
+    public:
+        AssertionResult() = delete;
+        AssertionResult( AssertionInfo const& info, AssertionResultData const& data );
+
+        bool isOk() const;
+        bool succeeded() const;
+        ResultWas::OfType getResultType() const;
+        bool hasExpression() const;
+        bool hasMessage() const;
+        std::string getExpression() const;
+        std::string getExpressionInMacro() const;
+        bool hasExpandedExpression() const;
+        std::string getExpandedExpression() const;
+        std::string getMessage() const;
+        SourceLineInfo getSourceInfo() const;
+        StringRef getTestMacroName() const;
+
+    //protected:
+        AssertionInfo m_info;
+        AssertionResultData m_resultData;
+    };
+
+} // end namespace Catch
+
+// end catch_assertionresult.h
+#if defined(CATCH_CONFIG_ENABLE_BENCHMARKING)
+// start catch_estimate.hpp
+
+ // Statistics estimates
+
+
+namespace Catch {
+    namespace Benchmark {
+        template <typename Duration>
+        struct Estimate {
+            Duration point;
+            Duration lower_bound;
+            Duration upper_bound;
+            double confidence_interval;
+
+            template <typename Duration2>
+            operator Estimate<Duration2>() const {
+                return { point, lower_bound, upper_bound, confidence_interval };
+            }
+        };
+    } // namespace Benchmark
+} // namespace Catch
+
+// end catch_estimate.hpp
+// start catch_outlier_classification.hpp
+
+// Outlier information
+
+namespace Catch {
+    namespace Benchmark {
+        struct OutlierClassification {
+            int samples_seen = 0;
+            int low_severe = 0;     // more than 3 times IQR below Q1
+            int low_mild = 0;       // 1.5 to 3 times IQR below Q1
+            int high_mild = 0;      // 1.5 to 3 times IQR above Q3
+            int high_severe = 0;    // more than 3 times IQR above Q3
+
+            int total() const {
+                return low_severe + low_mild + high_mild + high_severe;
+            }
+        };
+    } // namespace Benchmark
+} // namespace Catch
+
+// end catch_outlier_classification.hpp
+#endif // CATCH_CONFIG_ENABLE_BENCHMARKING
+
+#include <string>
+#include <iosfwd>
+#include <map>
+#include <set>
+#include <memory>
+#include <algorithm>
+
+namespace Catch {
+
+    struct ReporterConfig {
+        explicit ReporterConfig( IConfigPtr const& _fullConfig );
+
+        ReporterConfig( IConfigPtr const& _fullConfig, std::ostream& _stream );
+
+        std::ostream& stream() const;
+        IConfigPtr fullConfig() const;
+
+    private:
+        std::ostream* m_stream;
+        IConfigPtr m_fullConfig;
+    };
+
+    struct ReporterPreferences {
+        bool shouldRedirectStdOut = false;
+        bool shouldReportAllAssertions = false;
+    };
+
+    template<typename T>
+    struct LazyStat : Option<T> {
+        LazyStat& operator=( T const& _value ) {
+            Option<T>::operator=( _value );
+            used = false;
+            return *this;
+        }
+        void reset() {
+            Option<T>::reset();
+            used = false;
+        }
+        bool used = false;
+    };
+
+    struct TestRunInfo {
+        TestRunInfo( std::string const& _name );
+        std::string name;
+    };
+    struct GroupInfo {
+        GroupInfo(  std::string const& _name,
+                    std::size_t _groupIndex,
+                    std::size_t _groupsCount );
+
+        std::string name;
+        std::size_t groupIndex;
+        std::size_t groupsCounts;
+    };
+
+    struct AssertionStats {
+        AssertionStats( AssertionResult const& _assertionResult,
+                        std::vector<MessageInfo> const& _infoMessages,
+                        Totals const& _totals );
+
+        AssertionStats( AssertionStats const& )              = default;
+        AssertionStats( AssertionStats && )                  = default;
+        AssertionStats& operator = ( AssertionStats const& ) = delete;
+        AssertionStats& operator = ( AssertionStats && )     = delete;
+        virtual ~AssertionStats();
+
+        AssertionResult assertionResult;
+        std::vector<MessageInfo> infoMessages;
+        Totals totals;
+    };
+
+    struct SectionStats {
+        SectionStats(   SectionInfo const& _sectionInfo,
+                        Counts const& _assertions,
+                        double _durationInSeconds,
+                        bool _missingAssertions );
+        SectionStats( SectionStats const& )              = default;
+        SectionStats( SectionStats && )                  = default;
+        SectionStats& operator = ( SectionStats const& ) = default;
+        SectionStats& operator = ( SectionStats && )     = default;
+        virtual ~SectionStats();
+
+        SectionInfo sectionInfo;
+        Counts assertions;
+        double durationInSeconds;
+        bool missingAssertions;
+    };
+
+    struct TestCaseStats {
+        TestCaseStats(  TestCaseInfo const& _testInfo,
+                        Totals const& _totals,
+                        std::string const& _stdOut,
+                        std::string const& _stdErr,
+                        bool _aborting );
+
+        TestCaseStats( TestCaseStats const& )              = default;
+        TestCaseStats( TestCaseStats && )                  = default;
+        TestCaseStats& operator = ( TestCaseStats const& ) = default;
+        TestCaseStats& operator = ( TestCaseStats && )     = default;
+        virtual ~TestCaseStats();
+
+        TestCaseInfo testInfo;
+        Totals totals;
+        std::string stdOut;
+        std::string stdErr;
+        bool aborting;
+    };
+
+    struct TestGroupStats {
+        TestGroupStats( GroupInfo const& _groupInfo,
+                        Totals const& _totals,
+                        bool _aborting );
+        TestGroupStats( GroupInfo const& _groupInfo );
+
+        TestGroupStats( TestGroupStats const& )              = default;
+        TestGroupStats( TestGroupStats && )                  = default;
+        TestGroupStats& operator = ( TestGroupStats const& ) = default;
+        TestGroupStats& operator = ( TestGroupStats && )     = default;
+        virtual ~TestGroupStats();
+
+        GroupInfo groupInfo;
+        Totals totals;
+        bool aborting;
+    };
+
+    struct TestRunStats {
+        TestRunStats(   TestRunInfo const& _runInfo,
+                        Totals const& _totals,
+                        bool _aborting );
+
+        TestRunStats( TestRunStats const& )              = default;
+        TestRunStats( TestRunStats && )                  = default;
+        TestRunStats& operator = ( TestRunStats const& ) = default;
+        TestRunStats& operator = ( TestRunStats && )     = default;
+        virtual ~TestRunStats();
+
+        TestRunInfo runInfo;
+        Totals totals;
+        bool aborting;
+    };
+
+#if defined(CATCH_CONFIG_ENABLE_BENCHMARKING)
+    struct BenchmarkInfo {
+        std::string name;
+        double estimatedDuration;
+        int iterations;
+        int samples;
+        unsigned int resamples;
+        double clockResolution;
+        double clockCost;
+    };
+
+    template <class Duration>
+    struct BenchmarkStats {
+        BenchmarkInfo info;
+
+        std::vector<Duration> samples;
+        Benchmark::Estimate<Duration> mean;
+        Benchmark::Estimate<Duration> standardDeviation;
+        Benchmark::OutlierClassification outliers;
+        double outlierVariance;
+
+        template <typename Duration2>
+        operator BenchmarkStats<Duration2>() const {
+            std::vector<Duration2> samples2;
+            samples2.reserve(samples.size());
+            std::transform(samples.begin(), samples.end(), std::back_inserter(samples2), [](Duration d) { return Duration2(d); });
+            return {
+                info,
+                std::move(samples2),
+                mean,
+                standardDeviation,
+                outliers,
+                outlierVariance,
+            };
+        }
+    };
+#endif // CATCH_CONFIG_ENABLE_BENCHMARKING
+
+    struct IStreamingReporter {
+        virtual ~IStreamingReporter() = default;
+
+        // Implementing class must also provide the following static methods:
+        // static std::string getDescription();
+        // static std::set<Verbosity> getSupportedVerbosities()
+
+        virtual ReporterPreferences getPreferences() const = 0;
+
+        virtual void noMatchingTestCases( std::string const& spec ) = 0;
+
+        virtual void reportInvalidArguments(std::string const&) {}
+
+        virtual void testRunStarting( TestRunInfo const& testRunInfo ) = 0;
+        virtual void testGroupStarting( GroupInfo const& groupInfo ) = 0;
+
+        virtual void testCaseStarting( TestCaseInfo const& testInfo ) = 0;
+        virtual void sectionStarting( SectionInfo const& sectionInfo ) = 0;
+
+#if defined(CATCH_CONFIG_ENABLE_BENCHMARKING)
+        virtual void benchmarkPreparing( std::string const& ) {}
+        virtual void benchmarkStarting( BenchmarkInfo const& ) {}
+        virtual void benchmarkEnded( BenchmarkStats<> const& ) {}
+        virtual void benchmarkFailed( std::string const& ) {}
+#endif // CATCH_CONFIG_ENABLE_BENCHMARKING
+
+        virtual void assertionStarting( AssertionInfo const& assertionInfo ) = 0;
+
+        // The return value indicates if the messages buffer should be cleared:
+        virtual bool assertionEnded( AssertionStats const& assertionStats ) = 0;
+
+        virtual void sectionEnded( SectionStats const& sectionStats ) = 0;
+        virtual void testCaseEnded( TestCaseStats const& testCaseStats ) = 0;
+        virtual void testGroupEnded( TestGroupStats const& testGroupStats ) = 0;
+        virtual void testRunEnded( TestRunStats const& testRunStats ) = 0;
+
+        virtual void skipTest( TestCaseInfo const& testInfo ) = 0;
+
+        // Default empty implementation provided
+        virtual void fatalErrorEncountered( StringRef name );
+
+        virtual bool isMulti() const;
+    };
+    using IStreamingReporterPtr = std::unique_ptr<IStreamingReporter>;
+
+    struct IReporterFactory {
+        virtual ~IReporterFactory();
+        virtual IStreamingReporterPtr create( ReporterConfig const& config ) const = 0;
+        virtual std::string getDescription() const = 0;
+    };
+    using IReporterFactoryPtr = std::shared_ptr<IReporterFactory>;
+
+    struct IReporterRegistry {
+        using FactoryMap = std::map<std::string, IReporterFactoryPtr>;
+        using Listeners = std::vector<IReporterFactoryPtr>;
+
+        virtual ~IReporterRegistry();
+        virtual IStreamingReporterPtr create( std::string const& name, IConfigPtr const& config ) const = 0;
+        virtual FactoryMap const& getFactories() const = 0;
+        virtual Listeners const& getListeners() const = 0;
+    };
+
+} // end namespace Catch
+
+// end catch_interfaces_reporter.h
+#include <algorithm>
+#include <cstring>
+#include <cfloat>
+#include <cstdio>
+#include <cassert>
+#include <memory>
+#include <ostream>
+
+namespace Catch {
+    void prepareExpandedExpression(AssertionResult& result);
+
+    // Returns double formatted as %.3f (format expected on output)
+    std::string getFormattedDuration( double duration );
+
+    std::string serializeFilters( std::vector<std::string> const& container );
+
+    template<typename DerivedT>
+    struct StreamingReporterBase : IStreamingReporter {
+
+        StreamingReporterBase( ReporterConfig const& _config )
+        :   m_config( _config.fullConfig() ),
+            stream( _config.stream() )
+        {
+            m_reporterPrefs.shouldRedirectStdOut = false;
+            if( !DerivedT::getSupportedVerbosities().count( m_config->verbosity() ) )
+                CATCH_ERROR( "Verbosity level not supported by this reporter" );
+        }
+
+        ReporterPreferences getPreferences() const override {
+            return m_reporterPrefs;
+        }
+
+        static std::set<Verbosity> getSupportedVerbosities() {
+            return { Verbosity::Normal };
+        }
+
+        ~StreamingReporterBase() override = default;
+
+        void noMatchingTestCases(std::string const&) override {}
+
+        void reportInvalidArguments(std::string const&) override {}
+
+        void testRunStarting(TestRunInfo const& _testRunInfo) override {
+            currentTestRunInfo = _testRunInfo;
+        }
+
+        void testGroupStarting(GroupInfo const& _groupInfo) override {
+            currentGroupInfo = _groupInfo;
+        }
+
+        void testCaseStarting(TestCaseInfo const& _testInfo) override  {
+            currentTestCaseInfo = _testInfo;
+        }
+        void sectionStarting(SectionInfo const& _sectionInfo) override {
+            m_sectionStack.push_back(_sectionInfo);
+        }
+
+        void sectionEnded(SectionStats const& /* _sectionStats */) override {
+            m_sectionStack.pop_back();
+        }
+        void testCaseEnded(TestCaseStats const& /* _testCaseStats */) override {
+            currentTestCaseInfo.reset();
+        }
+        void testGroupEnded(TestGroupStats const& /* _testGroupStats */) override {
+            currentGroupInfo.reset();
+        }
+        void testRunEnded(TestRunStats const& /* _testRunStats */) override {
+            currentTestCaseInfo.reset();
+            currentGroupInfo.reset();
+            currentTestRunInfo.reset();
+        }
+
+        void skipTest(TestCaseInfo const&) override {
+            // Don't do anything with this by default.
+            // It can optionally be overridden in the derived class.
+        }
+
+        IConfigPtr m_config;
+        std::ostream& stream;
+
+        LazyStat<TestRunInfo> currentTestRunInfo;
+        LazyStat<GroupInfo> currentGroupInfo;
+        LazyStat<TestCaseInfo> currentTestCaseInfo;
+
+        std::vector<SectionInfo> m_sectionStack;
+        ReporterPreferences m_reporterPrefs;
+    };
+
+    template<typename DerivedT>
+    struct CumulativeReporterBase : IStreamingReporter {
+        template<typename T, typename ChildNodeT>
+        struct Node {
+            explicit Node( T const& _value ) : value( _value ) {}
+            virtual ~Node() {}
+
+            using ChildNodes = std::vector<std::shared_ptr<ChildNodeT>>;
+            T value;
+            ChildNodes children;
+        };
+        struct SectionNode {
+            explicit SectionNode(SectionStats const& _stats) : stats(_stats) {}
+            virtual ~SectionNode() = default;
+
+            bool operator == (SectionNode const& other) const {
+                return stats.sectionInfo.lineInfo == other.stats.sectionInfo.lineInfo;
+            }
+            bool operator == (std::shared_ptr<SectionNode> const& other) const {
+                return operator==(*other);
+            }
+
+            SectionStats stats;
+            using ChildSections = std::vector<std::shared_ptr<SectionNode>>;
+            using Assertions = std::vector<AssertionStats>;
+            ChildSections childSections;
+            Assertions assertions;
+            std::string stdOut;
+            std::string stdErr;
+        };
+
+        struct BySectionInfo {
+            BySectionInfo( SectionInfo const& other ) : m_other( other ) {}
+            BySectionInfo( BySectionInfo const& other ) : m_other( other.m_other ) {}
+            bool operator() (std::shared_ptr<SectionNode> const& node) const {
+                return ((node->stats.sectionInfo.name == m_other.name) &&
+                        (node->stats.sectionInfo.lineInfo == m_other.lineInfo));
+            }
+            void operator=(BySectionInfo const&) = delete;
+
+        private:
+            SectionInfo const& m_other;
+        };
+
+        using TestCaseNode = Node<TestCaseStats, SectionNode>;
+        using TestGroupNode = Node<TestGroupStats, TestCaseNode>;
+        using TestRunNode = Node<TestRunStats, TestGroupNode>;
+
+        CumulativeReporterBase( ReporterConfig const& _config )
+        :   m_config( _config.fullConfig() ),
+            stream( _config.stream() )
+        {
+            m_reporterPrefs.shouldRedirectStdOut = false;
+            if( !DerivedT::getSupportedVerbosities().count( m_config->verbosity() ) )
+                CATCH_ERROR( "Verbosity level not supported by this reporter" );
+        }
+        ~CumulativeReporterBase() override = default;
+
+        ReporterPreferences getPreferences() const override {
+            return m_reporterPrefs;
+        }
+
+        static std::set<Verbosity> getSupportedVerbosities() {
+            return { Verbosity::Normal };
+        }
+
+        void testRunStarting( TestRunInfo const& ) override {}
+        void testGroupStarting( GroupInfo const& ) override {}
+
+        void testCaseStarting( TestCaseInfo const& ) override {}
+
+        void sectionStarting( SectionInfo const& sectionInfo ) override {
+            SectionStats incompleteStats( sectionInfo, Counts(), 0, false );
+            std::shared_ptr<SectionNode> node;
+            if( m_sectionStack.empty() ) {
+                if( !m_rootSection )
+                    m_rootSection = std::make_shared<SectionNode>( incompleteStats );
+                node = m_rootSection;
+            }
+            else {
+                SectionNode& parentNode = *m_sectionStack.back();
+                auto it =
+                    std::find_if(   parentNode.childSections.begin(),
+                                    parentNode.childSections.end(),
+                                    BySectionInfo( sectionInfo ) );
+                if( it == parentNode.childSections.end() ) {
+                    node = std::make_shared<SectionNode>( incompleteStats );
+                    parentNode.childSections.push_back( node );
+                }
+                else
+                    node = *it;
+            }
+            m_sectionStack.push_back( node );
+            m_deepestSection = std::move(node);
+        }
+
+        void assertionStarting(AssertionInfo const&) override {}
+
+        bool assertionEnded(AssertionStats const& assertionStats) override {
+            assert(!m_sectionStack.empty());
+            // AssertionResult holds a pointer to a temporary DecomposedExpression,
+            // which getExpandedExpression() calls to build the expression string.
+            // Our section stack copy of the assertionResult will likely outlive the
+            // temporary, so it must be expanded or discarded now to avoid calling
+            // a destroyed object later.
+            prepareExpandedExpression(const_cast<AssertionResult&>( assertionStats.assertionResult ) );
+            SectionNode& sectionNode = *m_sectionStack.back();
+            sectionNode.assertions.push_back(assertionStats);
+            return true;
+        }
+        void sectionEnded(SectionStats const& sectionStats) override {
+            assert(!m_sectionStack.empty());
+            SectionNode& node = *m_sectionStack.back();
+            node.stats = sectionStats;
+            m_sectionStack.pop_back();
+        }
+        void testCaseEnded(TestCaseStats const& testCaseStats) override {
+            auto node = std::make_shared<TestCaseNode>(testCaseStats);
+            assert(m_sectionStack.size() == 0);
+            node->children.push_back(m_rootSection);
+            m_testCases.push_back(node);
+            m_rootSection.reset();
+
+            assert(m_deepestSection);
+            m_deepestSection->stdOut = testCaseStats.stdOut;
+            m_deepestSection->stdErr = testCaseStats.stdErr;
+        }
+        void testGroupEnded(TestGroupStats const& testGroupStats) override {
+            auto node = std::make_shared<TestGroupNode>(testGroupStats);
+            node->children.swap(m_testCases);
+            m_testGroups.push_back(node);
+        }
+        void testRunEnded(TestRunStats const& testRunStats) override {
+            auto node = std::make_shared<TestRunNode>(testRunStats);
+            node->children.swap(m_testGroups);
+            m_testRuns.push_back(node);
+            testRunEndedCumulative();
+        }
+        virtual void testRunEndedCumulative() = 0;
+
+        void skipTest(TestCaseInfo const&) override {}
+
+        IConfigPtr m_config;
+        std::ostream& stream;
+        std::vector<AssertionStats> m_assertions;
+        std::vector<std::vector<std::shared_ptr<SectionNode>>> m_sections;
+        std::vector<std::shared_ptr<TestCaseNode>> m_testCases;
+        std::vector<std::shared_ptr<TestGroupNode>> m_testGroups;
+
+        std::vector<std::shared_ptr<TestRunNode>> m_testRuns;
+
+        std::shared_ptr<SectionNode> m_rootSection;
+        std::shared_ptr<SectionNode> m_deepestSection;
+        std::vector<std::shared_ptr<SectionNode>> m_sectionStack;
+        ReporterPreferences m_reporterPrefs;
+    };
+
+    template<char C>
+    char const* getLineOfChars() {
+        static char line[CATCH_CONFIG_CONSOLE_WIDTH] = {0};
+        if( !*line ) {
+            std::memset( line, C, CATCH_CONFIG_CONSOLE_WIDTH-1 );
+            line[CATCH_CONFIG_CONSOLE_WIDTH-1] = 0;
+        }
+        return line;
+    }
+
+    struct TestEventListenerBase : StreamingReporterBase<TestEventListenerBase> {
+        TestEventListenerBase( ReporterConfig const& _config );
+
+        static std::set<Verbosity> getSupportedVerbosities();
+
+        void assertionStarting(AssertionInfo const&) override;
+        bool assertionEnded(AssertionStats const&) override;
+    };
+
+} // end namespace Catch
+
+// end catch_reporter_bases.hpp
+// start catch_console_colour.h
+
+namespace Catch {
+
+    struct Colour {
+        enum Code {
+            None = 0,
+
+            White,
+            Red,
+            Green,
+            Blue,
+            Cyan,
+            Yellow,
+            Grey,
+
+            Bright = 0x10,
+
+            BrightRed = Bright | Red,
+            BrightGreen = Bright | Green,
+            LightGrey = Bright | Grey,
+            BrightWhite = Bright | White,
+            BrightYellow = Bright | Yellow,
+
+            // By intention
+            FileName = LightGrey,
+            Warning = BrightYellow,
+            ResultError = BrightRed,
+            ResultSuccess = BrightGreen,
+            ResultExpectedFailure = Warning,
+
+            Error = BrightRed,
+            Success = Green,
+
+            OriginalExpression = Cyan,
+            ReconstructedExpression = BrightYellow,
+
+            SecondaryText = LightGrey,
+            Headers = White
+        };
+
+        // Use constructed object for RAII guard
+        Colour( Code _colourCode );
+        Colour( Colour&& other ) noexcept;
+        Colour& operator=( Colour&& other ) noexcept;
+        ~Colour();
+
+        // Use static method for one-shot changes
+        static void use( Code _colourCode );
+
+    private:
+        bool m_moved = false;
+    };
+
+    std::ostream& operator << ( std::ostream& os, Colour const& );
+
+} // end namespace Catch
+
+// end catch_console_colour.h
+// start catch_reporter_registrars.hpp
+
+
+namespace Catch {
+
+    template<typename T>
+    class ReporterRegistrar {
+
+        class ReporterFactory : public IReporterFactory {
+
+            IStreamingReporterPtr create( ReporterConfig const& config ) const override {
+                return std::unique_ptr<T>( new T( config ) );
+            }
+
+            std::string getDescription() const override {
+                return T::getDescription();
+            }
+        };
+
+    public:
+
+        explicit ReporterRegistrar( std::string const& name ) {
+            getMutableRegistryHub().registerReporter( name, std::make_shared<ReporterFactory>() );
+        }
+    };
+
+    template<typename T>
+    class ListenerRegistrar {
+
+        class ListenerFactory : public IReporterFactory {
+
+            IStreamingReporterPtr create( ReporterConfig const& config ) const override {
+                return std::unique_ptr<T>( new T( config ) );
+            }
+            std::string getDescription() const override {
+                return std::string();
+            }
+        };
+
+    public:
+
+        ListenerRegistrar() {
+            getMutableRegistryHub().registerListener( std::make_shared<ListenerFactory>() );
+        }
+    };
+}
+
+#if !defined(CATCH_CONFIG_DISABLE)
+
+#define CATCH_REGISTER_REPORTER( name, reporterType ) \
+    CATCH_INTERNAL_SUPPRESS_GLOBALS_WARNINGS          \
+    namespace{ Catch::ReporterRegistrar<reporterType> catch_internal_RegistrarFor##reporterType( name ); } \
+    CATCH_INTERNAL_UNSUPPRESS_GLOBALS_WARNINGS
+
+#define CATCH_REGISTER_LISTENER( listenerType ) \
+     CATCH_INTERNAL_SUPPRESS_GLOBALS_WARNINGS   \
+     namespace{ Catch::ListenerRegistrar<listenerType> catch_internal_RegistrarFor##listenerType; } \
+     CATCH_INTERNAL_SUPPRESS_GLOBALS_WARNINGS
+#else // CATCH_CONFIG_DISABLE
+
+#define CATCH_REGISTER_REPORTER(name, reporterType)
+#define CATCH_REGISTER_LISTENER(listenerType)
+
+#endif // CATCH_CONFIG_DISABLE
+
+// end catch_reporter_registrars.hpp
+// Allow users to base their work off existing reporters
+// start catch_reporter_compact.h
+
+namespace Catch {
+
+    struct CompactReporter : StreamingReporterBase<CompactReporter> {
+
+        using StreamingReporterBase::StreamingReporterBase;
+
+        ~CompactReporter() override;
+
+        static std::string getDescription();
+
+        ReporterPreferences getPreferences() const override;
+
+        void noMatchingTestCases(std::string const& spec) override;
+
+        void assertionStarting(AssertionInfo const&) override;
+
+        bool assertionEnded(AssertionStats const& _assertionStats) override;
+
+        void sectionEnded(SectionStats const& _sectionStats) override;
+
+        void testRunEnded(TestRunStats const& _testRunStats) override;
+
+    };
+
+} // end namespace Catch
+
+// end catch_reporter_compact.h
+// start catch_reporter_console.h
+
+#if defined(_MSC_VER)
+#pragma warning(push)
+#pragma warning(disable:4061) // Not all labels are EXPLICITLY handled in switch
+                              // Note that 4062 (not all labels are handled
+                              // and default is missing) is enabled
+#endif
+
+namespace Catch {
+    // Fwd decls
+    struct SummaryColumn;
+    class TablePrinter;
+
+    struct ConsoleReporter : StreamingReporterBase<ConsoleReporter> {
+        std::unique_ptr<TablePrinter> m_tablePrinter;
+
+        ConsoleReporter(ReporterConfig const& config);
+        ~ConsoleReporter() override;
+        static std::string getDescription();
+
+        void noMatchingTestCases(std::string const& spec) override;
+
+        void reportInvalidArguments(std::string const&arg) override;
+
+        void assertionStarting(AssertionInfo const&) override;
+
+        bool assertionEnded(AssertionStats const& _assertionStats) override;
+
+        void sectionStarting(SectionInfo const& _sectionInfo) override;
+        void sectionEnded(SectionStats const& _sectionStats) override;
+
+#if defined(CATCH_CONFIG_ENABLE_BENCHMARKING)
+        void benchmarkPreparing(std::string const& name) override;
+        void benchmarkStarting(BenchmarkInfo const& info) override;
+        void benchmarkEnded(BenchmarkStats<> const& stats) override;
+        void benchmarkFailed(std::string const& error) override;
+#endif // CATCH_CONFIG_ENABLE_BENCHMARKING
+
+        void testCaseEnded(TestCaseStats const& _testCaseStats) override;
+        void testGroupEnded(TestGroupStats const& _testGroupStats) override;
+        void testRunEnded(TestRunStats const& _testRunStats) override;
+        void testRunStarting(TestRunInfo const& _testRunInfo) override;
+    private:
+
+        void lazyPrint();
+
+        void lazyPrintWithoutClosingBenchmarkTable();
+        void lazyPrintRunInfo();
+        void lazyPrintGroupInfo();
+        void printTestCaseAndSectionHeader();
+
+        void printClosedHeader(std::string const& _name);
+        void printOpenHeader(std::string const& _name);
+
+        // if string has a : in first line will set indent to follow it on
+        // subsequent lines
+        void printHeaderString(std::string const& _string, std::size_t indent = 0);
+
+        void printTotals(Totals const& totals);
+        void printSummaryRow(std::string const& label, std::vector<SummaryColumn> const& cols, std::size_t row);
+
+        void printTotalsDivider(Totals const& totals);
+        void printSummaryDivider();
+        void printTestFilters();
+
+    private:
+        bool m_headerPrinted = false;
+    };
+
+} // end namespace Catch
+
+#if defined(_MSC_VER)
+#pragma warning(pop)
+#endif
+
+// end catch_reporter_console.h
+// start catch_reporter_junit.h
+
+// start catch_xmlwriter.h
+
+#include <vector>
+
+namespace Catch {
+
+    class XmlEncode {
+    public:
+        enum ForWhat { ForTextNodes, ForAttributes };
+
+        XmlEncode( std::string const& str, ForWhat forWhat = ForTextNodes );
+
+        void encodeTo( std::ostream& os ) const;
+
+        friend std::ostream& operator << ( std::ostream& os, XmlEncode const& xmlEncode );
+
+    private:
+        std::string m_str;
+        ForWhat m_forWhat;
+    };
+
+    class XmlWriter {
+    public:
+
+        class ScopedElement {
+        public:
+            ScopedElement( XmlWriter* writer );
+
+            ScopedElement( ScopedElement&& other ) noexcept;
+            ScopedElement& operator=( ScopedElement&& other ) noexcept;
+
+            ~ScopedElement();
+
+            ScopedElement& writeText( std::string const& text, bool indent = true );
+
+            template<typename T>
+            ScopedElement& writeAttribute( std::string const& name, T const& attribute ) {
+                m_writer->writeAttribute( name, attribute );
+                return *this;
+            }
+
+        private:
+            mutable XmlWriter* m_writer = nullptr;
+        };
+
+        XmlWriter( std::ostream& os = Catch::cout() );
+        ~XmlWriter();
+
+        XmlWriter( XmlWriter const& ) = delete;
+        XmlWriter& operator=( XmlWriter const& ) = delete;
+
+        XmlWriter& startElement( std::string const& name );
+
+        ScopedElement scopedElement( std::string const& name );
+
+        XmlWriter& endElement();
+
+        XmlWriter& writeAttribute( std::string const& name, std::string const& attribute );
+
+        XmlWriter& writeAttribute( std::string const& name, bool attribute );
+
+        template<typename T>
+        XmlWriter& writeAttribute( std::string const& name, T const& attribute ) {
+            ReusableStringStream rss;
+            rss << attribute;
+            return writeAttribute( name, rss.str() );
+        }
+
+        XmlWriter& writeText( std::string const& text, bool indent = true );
+
+        XmlWriter& writeComment( std::string const& text );
+
+        void writeStylesheetRef( std::string const& url );
+
+        XmlWriter& writeBlankLine();
+
+        void ensureTagClosed();
+
+    private:
+
+        void writeDeclaration();
+
+        void newlineIfNecessary();
+
+        bool m_tagIsOpen = false;
+        bool m_needsNewline = false;
+        std::vector<std::string> m_tags;
+        std::string m_indent;
+        std::ostream& m_os;
+    };
+
+}
+
+// end catch_xmlwriter.h
+namespace Catch {
+
+    class JunitReporter : public CumulativeReporterBase<JunitReporter> {
+    public:
+        JunitReporter(ReporterConfig const& _config);
+
+        ~JunitReporter() override;
+
+        static std::string getDescription();
+
+        void noMatchingTestCases(std::string const& /*spec*/) override;
+
+        void testRunStarting(TestRunInfo const& runInfo) override;
+
+        void testGroupStarting(GroupInfo const& groupInfo) override;
+
+        void testCaseStarting(TestCaseInfo const& testCaseInfo) override;
+        bool assertionEnded(AssertionStats const& assertionStats) override;
+
+        void testCaseEnded(TestCaseStats const& testCaseStats) override;
+
+        void testGroupEnded(TestGroupStats const& testGroupStats) override;
+
+        void testRunEndedCumulative() override;
+
+        void writeGroup(TestGroupNode const& groupNode, double suiteTime);
+
+        void writeTestCase(TestCaseNode const& testCaseNode);
+
+        void writeSection(std::string const& className,
+                          std::string const& rootName,
+                          SectionNode const& sectionNode);
+
+        void writeAssertions(SectionNode const& sectionNode);
+        void writeAssertion(AssertionStats const& stats);
+
+        XmlWriter xml;
+        Timer suiteTimer;
+        std::string stdOutForSuite;
+        std::string stdErrForSuite;
+        unsigned int unexpectedExceptions = 0;
+        bool m_okToFail = false;
+    };
+
+} // end namespace Catch
+
+// end catch_reporter_junit.h
+// start catch_reporter_xml.h
+
+namespace Catch {
+    class XmlReporter : public StreamingReporterBase<XmlReporter> {
+    public:
+        XmlReporter(ReporterConfig const& _config);
+
+        ~XmlReporter() override;
+
+        static std::string getDescription();
+
+        virtual std::string getStylesheetRef() const;
+
+        void writeSourceInfo(SourceLineInfo const& sourceInfo);
+
+    public: // StreamingReporterBase
+
+        void noMatchingTestCases(std::string const& s) override;
+
+        void testRunStarting(TestRunInfo const& testInfo) override;
+
+        void testGroupStarting(GroupInfo const& groupInfo) override;
+
+        void testCaseStarting(TestCaseInfo const& testInfo) override;
+
+        void sectionStarting(SectionInfo const& sectionInfo) override;
+
+        void assertionStarting(AssertionInfo const&) override;
+
+        bool assertionEnded(AssertionStats const& assertionStats) override;
+
+        void sectionEnded(SectionStats const& sectionStats) override;
+
+        void testCaseEnded(TestCaseStats const& testCaseStats) override;
+
+        void testGroupEnded(TestGroupStats const& testGroupStats) override;
+
+        void testRunEnded(TestRunStats const& testRunStats) override;
+
+#if defined(CATCH_CONFIG_ENABLE_BENCHMARKING)
+        void benchmarkPreparing(std::string const& name) override;
+        void benchmarkStarting(BenchmarkInfo const&) override;
+        void benchmarkEnded(BenchmarkStats<> const&) override;
+        void benchmarkFailed(std::string const&) override;
+#endif // CATCH_CONFIG_ENABLE_BENCHMARKING
+
+    private:
+        Timer m_testCaseTimer;
+        XmlWriter m_xml;
+        int m_sectionDepth = 0;
+    };
+
+} // end namespace Catch
+
+// end catch_reporter_xml.h
+
+// end catch_external_interfaces.h
+#endif
+
+#if defined(CATCH_CONFIG_ENABLE_BENCHMARKING)
+// start catch_benchmark.hpp
+
+ // Benchmark
+
+// start catch_chronometer.hpp
+
+// User-facing chronometer
+
+
+// start catch_clock.hpp
+
+// Clocks
+
+
+#include <chrono>
+#include <ratio>
+
+namespace Catch {
+    namespace Benchmark {
+        template <typename Clock>
+        using ClockDuration = typename Clock::duration;
+        template <typename Clock>
+        using FloatDuration = std::chrono::duration<double, typename Clock::period>;
+
+        template <typename Clock>
+        using TimePoint = typename Clock::time_point;
+
+        using default_clock = std::chrono::steady_clock;
+
+        template <typename Clock>
+        struct now {
+            TimePoint<Clock> operator()() const {
+                return Clock::now();
+            }
+        };
+
+        using fp_seconds = std::chrono::duration<double, std::ratio<1>>;
+    } // namespace Benchmark
+} // namespace Catch
+
+// end catch_clock.hpp
+// start catch_optimizer.hpp
+
+ // Hinting the optimizer
+
+
+#if defined(_MSC_VER)
+#   include <atomic> // atomic_thread_fence
+#endif
+
+namespace Catch {
+    namespace Benchmark {
+#if defined(__GNUC__) || defined(__clang__)
+        template <typename T>
+        inline void keep_memory(T* p) {
+            asm volatile("" : : "g"(p) : "memory");
+        }
+        inline void keep_memory() {
+            asm volatile("" : : : "memory");
+        }
+
+        namespace Detail {
+            inline void optimizer_barrier() { keep_memory(); }
+        } // namespace Detail
+#elif defined(_MSC_VER)
+
+#pragma optimize("", off)
+        template <typename T>
+        inline void keep_memory(T* p) {
+            // thanks @milleniumbug
+            *reinterpret_cast<char volatile*>(p) = *reinterpret_cast<char const volatile*>(p);
+        }
+        // TODO equivalent keep_memory()
+#pragma optimize("", on)
+
+        namespace Detail {
+            inline void optimizer_barrier() {
+                std::atomic_thread_fence(std::memory_order_seq_cst);
+            }
+        } // namespace Detail
+
+#endif
+
+        template <typename T>
+        inline void deoptimize_value(T&& x) {
+            keep_memory(&x);
+        }
+
+        template <typename Fn, typename... Args>
+        inline auto invoke_deoptimized(Fn&& fn, Args&&... args) -> typename std::enable_if<!std::is_same<void, decltype(fn(args...))>::value>::type {
+            deoptimize_value(std::forward<Fn>(fn) (std::forward<Args...>(args...)));
+        }
+
+        template <typename Fn, typename... Args>
+        inline auto invoke_deoptimized(Fn&& fn, Args&&... args) -> typename std::enable_if<std::is_same<void, decltype(fn(args...))>::value>::type {
+            std::forward<Fn>(fn) (std::forward<Args...>(args...));
+        }
+    } // namespace Benchmark
+} // namespace Catch
+
+// end catch_optimizer.hpp
+// start catch_complete_invoke.hpp
+
+// Invoke with a special case for void
+
+
+#include <type_traits>
+#include <utility>
+
+namespace Catch {
+    namespace Benchmark {
+        namespace Detail {
+            template <typename T>
+            struct CompleteType { using type = T; };
+            template <>
+            struct CompleteType<void> { struct type {}; };
+
+            template <typename T>
+            using CompleteType_t = typename CompleteType<T>::type;
+
+            template <typename Result>
+            struct CompleteInvoker {
+                template <typename Fun, typename... Args>
+                static Result invoke(Fun&& fun, Args&&... args) {
+                    return std::forward<Fun>(fun)(std::forward<Args>(args)...);
+                }
+            };
+            template <>
+            struct CompleteInvoker<void> {
+                template <typename Fun, typename... Args>
+                static CompleteType_t<void> invoke(Fun&& fun, Args&&... args) {
+                    std::forward<Fun>(fun)(std::forward<Args>(args)...);
+                    return {};
+                }
+            };
+            template <typename Sig>
+            using ResultOf_t = typename std::result_of<Sig>::type;
+
+            // invoke and not return void :(
+            template <typename Fun, typename... Args>
+            CompleteType_t<ResultOf_t<Fun(Args...)>> complete_invoke(Fun&& fun, Args&&... args) {
+                return CompleteInvoker<ResultOf_t<Fun(Args...)>>::invoke(std::forward<Fun>(fun), std::forward<Args>(args)...);
+            }
+
+            const std::string benchmarkErrorMsg = "a benchmark failed to run successfully";
+        } // namespace Detail
+
+        template <typename Fun>
+        Detail::CompleteType_t<Detail::ResultOf_t<Fun()>> user_code(Fun&& fun) {
+            CATCH_TRY{
+                return Detail::complete_invoke(std::forward<Fun>(fun));
+            } CATCH_CATCH_ALL{
+                getResultCapture().benchmarkFailed(translateActiveException());
+                CATCH_RUNTIME_ERROR(Detail::benchmarkErrorMsg);
+            }
+        }
+    } // namespace Benchmark
+} // namespace Catch
+
+// end catch_complete_invoke.hpp
+namespace Catch {
+    namespace Benchmark {
+        namespace Detail {
+            struct ChronometerConcept {
+                virtual void start() = 0;
+                virtual void finish() = 0;
+                virtual ~ChronometerConcept() = default;
+            };
+            template <typename Clock>
+            struct ChronometerModel final : public ChronometerConcept {
+                void start() override { started = Clock::now(); }
+                void finish() override { finished = Clock::now(); }
+
+                ClockDuration<Clock> elapsed() const { return finished - started; }
+
+                TimePoint<Clock> started;
+                TimePoint<Clock> finished;
+            };
+        } // namespace Detail
+
+        struct Chronometer {
+        public:
+            template <typename Fun>
+            void measure(Fun&& fun) { measure(std::forward<Fun>(fun), is_callable<Fun(int)>()); }
+
+            int runs() const { return k; }
+
+            Chronometer(Detail::ChronometerConcept& meter, int k)
+                : impl(&meter)
+                , k(k) {}
+
+        private:
+            template <typename Fun>
+            void measure(Fun&& fun, std::false_type) {
+                measure([&fun](int) { return fun(); }, std::true_type());
+            }
+
+            template <typename Fun>
+            void measure(Fun&& fun, std::true_type) {
+                Detail::optimizer_barrier();
+                impl->start();
+                for (int i = 0; i < k; ++i) invoke_deoptimized(fun, i);
+                impl->finish();
+                Detail::optimizer_barrier();
+            }
+
+            Detail::ChronometerConcept* impl;
+            int k;
+        };
+    } // namespace Benchmark
+} // namespace Catch
+
+// end catch_chronometer.hpp
+// start catch_environment.hpp
+
+// Environment information
+
+
+namespace Catch {
+    namespace Benchmark {
+        template <typename Duration>
+        struct EnvironmentEstimate {
+            Duration mean;
+            OutlierClassification outliers;
+
+            template <typename Duration2>
+            operator EnvironmentEstimate<Duration2>() const {
+                return { mean, outliers };
+            }
+        };
+        template <typename Clock>
+        struct Environment {
+            using clock_type = Clock;
+            EnvironmentEstimate<FloatDuration<Clock>> clock_resolution;
+            EnvironmentEstimate<FloatDuration<Clock>> clock_cost;
+        };
+    } // namespace Benchmark
+} // namespace Catch
+
+// end catch_environment.hpp
+// start catch_execution_plan.hpp
+
+ // Execution plan
+
+
+// start catch_benchmark_function.hpp
+
+ // Dumb std::function implementation for consistent call overhead
+
+
+#include <cassert>
+#include <type_traits>
+#include <utility>
+#include <memory>
+
+namespace Catch {
+    namespace Benchmark {
+        namespace Detail {
+            template <typename T>
+            using Decay = typename std::decay<T>::type;
+            template <typename T, typename U>
+            struct is_related
+                : std::is_same<Decay<T>, Decay<U>> {};
+
+            /// We need to reinvent std::function because every piece of code that might add overhead
+            /// in a measurement context needs to have consistent performance characteristics so that we
+            /// can account for it in the measurement.
+            /// Implementations of std::function with optimizations that aren't always applicable, like
+            /// small buffer optimizations, are not uncommon.
+            /// This is effectively an implementation of std::function without any such optimizations;
+            /// it may be slow, but it is consistently slow.
+            struct BenchmarkFunction {
+            private:
+                struct callable {
+                    virtual void call(Chronometer meter) const = 0;
+                    virtual callable* clone() const = 0;
+                    virtual ~callable() = default;
+                };
+                template <typename Fun>
+                struct model : public callable {
+                    model(Fun&& fun) : fun(std::move(fun)) {}
+                    model(Fun const& fun) : fun(fun) {}
+
+                    model<Fun>* clone() const override { return new model<Fun>(*this); }
+
+                    void call(Chronometer meter) const override {
+                        call(meter, is_callable<Fun(Chronometer)>());
+                    }
+                    void call(Chronometer meter, std::true_type) const {
+                        fun(meter);
+                    }
+                    void call(Chronometer meter, std::false_type) const {
+                        meter.measure(fun);
+                    }
+
+                    Fun fun;
+                };
+
+                struct do_nothing { void operator()() const {} };
+
+                template <typename T>
+                BenchmarkFunction(model<T>* c) : f(c) {}
+
+            public:
+                BenchmarkFunction()
+                    : f(new model<do_nothing>{ {} }) {}
+
+                template <typename Fun,
+                    typename std::enable_if<!is_related<Fun, BenchmarkFunction>::value, int>::type = 0>
+                    BenchmarkFunction(Fun&& fun)
+                    : f(new model<typename std::decay<Fun>::type>(std::forward<Fun>(fun))) {}
+
+                BenchmarkFunction(BenchmarkFunction&& that)
+                    : f(std::move(that.f)) {}
+
+                BenchmarkFunction(BenchmarkFunction const& that)
+                    : f(that.f->clone()) {}
+
+                BenchmarkFunction& operator=(BenchmarkFunction&& that) {
+                    f = std::move(that.f);
+                    return *this;
+                }
+
+                BenchmarkFunction& operator=(BenchmarkFunction const& that) {
+                    f.reset(that.f->clone());
+                    return *this;
+                }
+
+                void operator()(Chronometer meter) const { f->call(meter); }
+
+            private:
+                std::unique_ptr<callable> f;
+            };
+        } // namespace Detail
+    } // namespace Benchmark
+} // namespace Catch
+
+// end catch_benchmark_function.hpp
+// start catch_repeat.hpp
+
+// repeat algorithm
+
+
+#include <type_traits>
+#include <utility>
+
+namespace Catch {
+    namespace Benchmark {
+        namespace Detail {
+            template <typename Fun>
+            struct repeater {
+                void operator()(int k) const {
+                    for (int i = 0; i < k; ++i) {
+                        fun();
+                    }
+                }
+                Fun fun;
+            };
+            template <typename Fun>
+            repeater<typename std::decay<Fun>::type> repeat(Fun&& fun) {
+                return { std::forward<Fun>(fun) };
+            }
+        } // namespace Detail
+    } // namespace Benchmark
+} // namespace Catch
+
+// end catch_repeat.hpp
+// start catch_run_for_at_least.hpp
+
+// Run a function for a minimum amount of time
+
+
+// start catch_measure.hpp
+
+// Measure
+
+
+// start catch_timing.hpp
+
+// Timing
+
+
+#include <tuple>
+#include <type_traits>
+
+namespace Catch {
+    namespace Benchmark {
+        template <typename Duration, typename Result>
+        struct Timing {
+            Duration elapsed;
+            Result result;
+            int iterations;
+        };
+        template <typename Clock, typename Sig>
+        using TimingOf = Timing<ClockDuration<Clock>, Detail::CompleteType_t<Detail::ResultOf_t<Sig>>>;
+    } // namespace Benchmark
+} // namespace Catch
+
+// end catch_timing.hpp
+#include <utility>
+
+namespace Catch {
+    namespace Benchmark {
+        namespace Detail {
+            template <typename Clock, typename Fun, typename... Args>
+            TimingOf<Clock, Fun(Args...)> measure(Fun&& fun, Args&&... args) {
+                auto start = Clock::now();
+                auto&& r = Detail::complete_invoke(fun, std::forward<Args>(args)...);
+                auto end = Clock::now();
+                auto delta = end - start;
+                return { delta, std::forward<decltype(r)>(r), 1 };
+            }
+        } // namespace Detail
+    } // namespace Benchmark
+} // namespace Catch
+
+// end catch_measure.hpp
+#include <utility>
+#include <type_traits>
+
+namespace Catch {
+    namespace Benchmark {
+        namespace Detail {
+            template <typename Clock, typename Fun>
+            TimingOf<Clock, Fun(int)> measure_one(Fun&& fun, int iters, std::false_type) {
+                return Detail::measure<Clock>(fun, iters);
+            }
+            template <typename Clock, typename Fun>
+            TimingOf<Clock, Fun(Chronometer)> measure_one(Fun&& fun, int iters, std::true_type) {
+                Detail::ChronometerModel<Clock> meter;
+                auto&& result = Detail::complete_invoke(fun, Chronometer(meter, iters));
+
+                return { meter.elapsed(), std::move(result), iters };
+            }
+
+            template <typename Clock, typename Fun>
+            using run_for_at_least_argument_t = typename std::conditional<is_callable<Fun(Chronometer)>::value, Chronometer, int>::type;
+
+            struct optimized_away_error : std::exception {
+                const char* what() const noexcept override {
+                    return "could not measure benchmark, maybe it was optimized away";
+                }
+            };
+
+            template <typename Clock, typename Fun>
+            TimingOf<Clock, Fun(run_for_at_least_argument_t<Clock, Fun>)> run_for_at_least(ClockDuration<Clock> how_long, int seed, Fun&& fun) {
+                auto iters = seed;
+                while (iters < (1 << 30)) {
+                    auto&& Timing = measure_one<Clock>(fun, iters, is_callable<Fun(Chronometer)>());
+
+                    if (Timing.elapsed >= how_long) {
+                        return { Timing.elapsed, std::move(Timing.result), iters };
+                    }
+                    iters *= 2;
+                }
+                throw optimized_away_error{};
+            }
+        } // namespace Detail
+    } // namespace Benchmark
+} // namespace Catch
+
+// end catch_run_for_at_least.hpp
+#include <algorithm>
+
+namespace Catch {
+    namespace Benchmark {
+        template <typename Duration>
+        struct ExecutionPlan {
+            int iterations_per_sample;
+            Duration estimated_duration;
+            Detail::BenchmarkFunction benchmark;
+            Duration warmup_time;
+            int warmup_iterations;
+
+            template <typename Duration2>
+            operator ExecutionPlan<Duration2>() const {
+                return { iterations_per_sample, estimated_duration, benchmark, warmup_time, warmup_iterations };
+            }
+
+            template <typename Clock>
+            std::vector<FloatDuration<Clock>> run(const IConfig &cfg, Environment<FloatDuration<Clock>> env) const {
+                // warmup a bit
+                Detail::run_for_at_least<Clock>(std::chrono::duration_cast<ClockDuration<Clock>>(warmup_time), warmup_iterations, Detail::repeat(now<Clock>{}));
+
+                std::vector<FloatDuration<Clock>> times;
+                times.reserve(cfg.benchmarkSamples());
+                std::generate_n(std::back_inserter(times), cfg.benchmarkSamples(), [this, env] {
+                    Detail::ChronometerModel<Clock> model;
+                    this->benchmark(Chronometer(model, iterations_per_sample));
+                    auto sample_time = model.elapsed() - env.clock_cost.mean;
+                    if (sample_time < FloatDuration<Clock>::zero()) sample_time = FloatDuration<Clock>::zero();
+                    return sample_time / iterations_per_sample;
+                });
+                return times;
+            }
+        };
+    } // namespace Benchmark
+} // namespace Catch
+
+// end catch_execution_plan.hpp
+// start catch_estimate_clock.hpp
+
+ // Environment measurement
+
+
+// start catch_stats.hpp
+
+// Statistical analysis tools
+
+
+#include <algorithm>
+#include <functional>
+#include <vector>
+#include <numeric>
+#include <tuple>
+#include <cmath>
+#include <utility>
+#include <cstddef>
+
+namespace Catch {
+    namespace Benchmark {
+        namespace Detail {
+            using sample = std::vector<double>;
+
+            double weighted_average_quantile(int k, int q, std::vector<double>::iterator first, std::vector<double>::iterator last);
+
+            template <typename Iterator>
+            OutlierClassification classify_outliers(Iterator first, Iterator last) {
+                std::vector<double> copy(first, last);
+
+                auto q1 = weighted_average_quantile(1, 4, copy.begin(), copy.end());
+                auto q3 = weighted_average_quantile(3, 4, copy.begin(), copy.end());
+                auto iqr = q3 - q1;
+                auto los = q1 - (iqr * 3.);
+                auto lom = q1 - (iqr * 1.5);
+                auto him = q3 + (iqr * 1.5);
+                auto his = q3 + (iqr * 3.);
+
+                OutlierClassification o;
+                for (; first != last; ++first) {
+                    auto&& t = *first;
+                    if (t < los) ++o.low_severe;
+                    else if (t < lom) ++o.low_mild;
+                    else if (t > his) ++o.high_severe;
+                    else if (t > him) ++o.high_mild;
+                    ++o.samples_seen;
+                }
+                return o;
+            }
+
+            template <typename Iterator>
+            double mean(Iterator first, Iterator last) {
+                auto count = last - first;
+                double sum = std::accumulate(first, last, 0.);
+                return sum / count;
+            }
+
+            template <typename URng, typename Iterator, typename Estimator>
+            sample resample(URng& rng, int resamples, Iterator first, Iterator last, Estimator& estimator) {
+                auto n = last - first;
+                std::uniform_int_distribution<decltype(n)> dist(0, n - 1);
+
+                sample out;
+                out.reserve(resamples);
+                std::generate_n(std::back_inserter(out), resamples, [n, first, &estimator, &dist, &rng] {
+                    std::vector<double> resampled;
+                    resampled.reserve(n);
+                    std::generate_n(std::back_inserter(resampled), n, [first, &dist, &rng] { return first[dist(rng)]; });
+                    return estimator(resampled.begin(), resampled.end());
+                });
+                std::sort(out.begin(), out.end());
+                return out;
+            }
+
+            template <typename Estimator, typename Iterator>
+            sample jackknife(Estimator&& estimator, Iterator first, Iterator last) {
+                auto n = last - first;
+                auto second = std::next(first);
+                sample results;
+                results.reserve(n);
+
+                for (auto it = first; it != last; ++it) {
+                    std::iter_swap(it, first);
+                    results.push_back(estimator(second, last));
+                }
+
+                return results;
+            }
+
+            inline double normal_cdf(double x) {
+                return std::erfc(-x / std::sqrt(2.0)) / 2.0;
+            }
+
+            double erfc_inv(double x);
+
+            double normal_quantile(double p);
+
+            template <typename Iterator, typename Estimator>
+            Estimate<double> bootstrap(double confidence_level, Iterator first, Iterator last, sample const& resample, Estimator&& estimator) {
+                auto n_samples = last - first;
+
+                double point = estimator(first, last);
+                // Degenerate case with a single sample
+                if (n_samples == 1) return { point, point, point, confidence_level };
+
+                sample jack = jackknife(estimator, first, last);
+                double jack_mean = mean(jack.begin(), jack.end());
+                double sum_squares, sum_cubes;
+                std::tie(sum_squares, sum_cubes) = std::accumulate(jack.begin(), jack.end(), std::make_pair(0., 0.), [jack_mean](std::pair<double, double> sqcb, double x) -> std::pair<double, double> {
+                    auto d = jack_mean - x;
+                    auto d2 = d * d;
+                    auto d3 = d2 * d;
+                    return { sqcb.first + d2, sqcb.second + d3 };
+                });
+
+                double accel = sum_cubes / (6 * std::pow(sum_squares, 1.5));
+                int n = static_cast<int>(resample.size());
+                double prob_n = std::count_if(resample.begin(), resample.end(), [point](double x) { return x < point; }) / (double)n;
+                // degenerate case with uniform samples
+                if (prob_n == 0) return { point, point, point, confidence_level };
+
+                double bias = normal_quantile(prob_n);
+                double z1 = normal_quantile((1. - confidence_level) / 2.);
+
+                auto cumn = [n](double x) -> int {
+                    return std::lround(normal_cdf(x) * n); };
+                auto a = [bias, accel](double b) { return bias + b / (1. - accel * b); };
+                double b1 = bias + z1;
+                double b2 = bias - z1;
+                double a1 = a(b1);
+                double a2 = a(b2);
+                auto lo = std::max(cumn(a1), 0);
+                auto hi = std::min(cumn(a2), n - 1);
+
+                return { point, resample[lo], resample[hi], confidence_level };
+            }
+
+            double outlier_variance(Estimate<double> mean, Estimate<double> stddev, int n);
+
+            struct bootstrap_analysis {
+                Estimate<double> mean;
+                Estimate<double> standard_deviation;
+                double outlier_variance;
+            };
+
+            bootstrap_analysis analyse_samples(double confidence_level, int n_resamples, std::vector<double>::iterator first, std::vector<double>::iterator last);
+        } // namespace Detail
+    } // namespace Benchmark
+} // namespace Catch
+
+// end catch_stats.hpp
+#include <algorithm>
+#include <iterator>
+#include <tuple>
+#include <vector>
+#include <cmath>
+
+namespace Catch {
+    namespace Benchmark {
+        namespace Detail {
+            template <typename Clock>
+            std::vector<double> resolution(int k) {
+                std::vector<TimePoint<Clock>> times;
+                times.reserve(k + 1);
+                std::generate_n(std::back_inserter(times), k + 1, now<Clock>{});
+
+                std::vector<double> deltas;
+                deltas.reserve(k);
+                std::transform(std::next(times.begin()), times.end(), times.begin(),
+                    std::back_inserter(deltas),
+                    [](TimePoint<Clock> a, TimePoint<Clock> b) { return static_cast<double>((a - b).count()); });
+
+                return deltas;
+            }
+
+            const auto warmup_iterations = 10000;
+            const auto warmup_time = std::chrono::milliseconds(100);
+            const auto minimum_ticks = 1000;
+            const auto warmup_seed = 10000;
+            const auto clock_resolution_estimation_time = std::chrono::milliseconds(500);
+            const auto clock_cost_estimation_time_limit = std::chrono::seconds(1);
+            const auto clock_cost_estimation_tick_limit = 100000;
+            const auto clock_cost_estimation_time = std::chrono::milliseconds(10);
+            const auto clock_cost_estimation_iterations = 10000;
+
+            template <typename Clock>
+            int warmup() {
+                return run_for_at_least<Clock>(std::chrono::duration_cast<ClockDuration<Clock>>(warmup_time), warmup_seed, &resolution<Clock>)
+                    .iterations;
+            }
+            template <typename Clock>
+            EnvironmentEstimate<FloatDuration<Clock>> estimate_clock_resolution(int iterations) {
+                auto r = run_for_at_least<Clock>(std::chrono::duration_cast<ClockDuration<Clock>>(clock_resolution_estimation_time), iterations, &resolution<Clock>)
+                    .result;
+                return {
+                    FloatDuration<Clock>(mean(r.begin(), r.end())),
+                    classify_outliers(r.begin(), r.end()),
+                };
+            }
+            template <typename Clock>
+            EnvironmentEstimate<FloatDuration<Clock>> estimate_clock_cost(FloatDuration<Clock> resolution) {
+                auto time_limit = std::min(resolution * clock_cost_estimation_tick_limit, FloatDuration<Clock>(clock_cost_estimation_time_limit));
+                auto time_clock = [](int k) {
+                    return Detail::measure<Clock>([k] {
+                        for (int i = 0; i < k; ++i) {
+                            volatile auto ignored = Clock::now();
+                            (void)ignored;
+                        }
+                    }).elapsed;
+                };
+                time_clock(1);
+                int iters = clock_cost_estimation_iterations;
+                auto&& r = run_for_at_least<Clock>(std::chrono::duration_cast<ClockDuration<Clock>>(clock_cost_estimation_time), iters, time_clock);
+                std::vector<double> times;
+                int nsamples = static_cast<int>(std::ceil(time_limit / r.elapsed));
+                times.reserve(nsamples);
+                std::generate_n(std::back_inserter(times), nsamples, [time_clock, &r] {
+                    return static_cast<double>((time_clock(r.iterations) / r.iterations).count());
+                });
+                return {
+                    FloatDuration<Clock>(mean(times.begin(), times.end())),
+                    classify_outliers(times.begin(), times.end()),
+                };
+            }
+
+            template <typename Clock>
+            Environment<FloatDuration<Clock>> measure_environment() {
+                static Environment<FloatDuration<Clock>>* env = nullptr;
+                if (env) {
+                    return *env;
+                }
+
+                auto iters = Detail::warmup<Clock>();
+                auto resolution = Detail::estimate_clock_resolution<Clock>(iters);
+                auto cost = Detail::estimate_clock_cost<Clock>(resolution.mean);
+
+                env = new Environment<FloatDuration<Clock>>{ resolution, cost };
+                return *env;
+            }
+        } // namespace Detail
+    } // namespace Benchmark
+} // namespace Catch
+
+// end catch_estimate_clock.hpp
+// start catch_analyse.hpp
+
+ // Run and analyse one benchmark
+
+
+// start catch_sample_analysis.hpp
+
+// Benchmark results
+
+
+#include <algorithm>
+#include <vector>
+#include <string>
+#include <iterator>
+
+namespace Catch {
+    namespace Benchmark {
+        template <typename Duration>
+        struct SampleAnalysis {
+            std::vector<Duration> samples;
+            Estimate<Duration> mean;
+            Estimate<Duration> standard_deviation;
+            OutlierClassification outliers;
+            double outlier_variance;
+
+            template <typename Duration2>
+            operator SampleAnalysis<Duration2>() const {
+                std::vector<Duration2> samples2;
+                samples2.reserve(samples.size());
+                std::transform(samples.begin(), samples.end(), std::back_inserter(samples2), [](Duration d) { return Duration2(d); });
+                return {
+                    std::move(samples2),
+                    mean,
+                    standard_deviation,
+                    outliers,
+                    outlier_variance,
+                };
+            }
+        };
+    } // namespace Benchmark
+} // namespace Catch
+
+// end catch_sample_analysis.hpp
+#include <algorithm>
+#include <iterator>
+#include <vector>
+
+namespace Catch {
+    namespace Benchmark {
+        namespace Detail {
+            template <typename Duration, typename Iterator>
+            SampleAnalysis<Duration> analyse(const IConfig &cfg, Environment<Duration>, Iterator first, Iterator last) {
+                if (!cfg.benchmarkNoAnalysis()) {
+                    std::vector<double> samples;
+                    samples.reserve(last - first);
+                    std::transform(first, last, std::back_inserter(samples), [](Duration d) { return d.count(); });
+
+                    auto analysis = Catch::Benchmark::Detail::analyse_samples(cfg.benchmarkConfidenceInterval(), cfg.benchmarkResamples(), samples.begin(), samples.end());
+                    auto outliers = Catch::Benchmark::Detail::classify_outliers(samples.begin(), samples.end());
+
+                    auto wrap_estimate = [](Estimate<double> e) {
+                        return Estimate<Duration> {
+                            Duration(e.point),
+                                Duration(e.lower_bound),
+                                Duration(e.upper_bound),
+                                e.confidence_interval,
+                        };
+                    };
+                    std::vector<Duration> samples2;
+                    samples2.reserve(samples.size());
+                    std::transform(samples.begin(), samples.end(), std::back_inserter(samples2), [](double d) { return Duration(d); });
+                    return {
+                        std::move(samples2),
+                        wrap_estimate(analysis.mean),
+                        wrap_estimate(analysis.standard_deviation),
+                        outliers,
+                        analysis.outlier_variance,
+                    };
+                } else {
+                    std::vector<Duration> samples;
+                    samples.reserve(last - first);
+
+                    Duration mean = Duration(0);
+                    int i = 0;
+                    for (auto it = first; it < last; ++it, ++i) {
+                        samples.push_back(Duration(*it));
+                        mean += Duration(*it);
+                    }
+                    mean /= i;
+
+                    return {
+                        std::move(samples),
+                        Estimate<Duration>{mean, mean, mean, 0.0},
+                        Estimate<Duration>{Duration(0), Duration(0), Duration(0), 0.0},
+                        OutlierClassification{},
+                        0.0
+                    };
+                }
+            }
+        } // namespace Detail
+    } // namespace Benchmark
+} // namespace Catch
+
+// end catch_analyse.hpp
+#include <algorithm>
+#include <functional>
+#include <string>
+#include <vector>
+#include <cmath>
+
+namespace Catch {
+    namespace Benchmark {
+        struct Benchmark {
+            Benchmark(std::string &&name)
+                : name(std::move(name)) {}
+
+            template <class FUN>
+            Benchmark(std::string &&name, FUN &&func)
+                : fun(std::move(func)), name(std::move(name)) {}
+
+            template <typename Clock>
+            ExecutionPlan<FloatDuration<Clock>> prepare(const IConfig &cfg, Environment<FloatDuration<Clock>> env) const {
+                auto min_time = env.clock_resolution.mean * Detail::minimum_ticks;
+                auto run_time = std::max(min_time, std::chrono::duration_cast<decltype(min_time)>(Detail::warmup_time));
+                auto&& test = Detail::run_for_at_least<Clock>(std::chrono::duration_cast<ClockDuration<Clock>>(run_time), 1, fun);
+                int new_iters = static_cast<int>(std::ceil(min_time * test.iterations / test.elapsed));
+                return { new_iters, test.elapsed / test.iterations * new_iters * cfg.benchmarkSamples(), fun, std::chrono::duration_cast<FloatDuration<Clock>>(Detail::warmup_time), Detail::warmup_iterations };
+            }
+
+            template <typename Clock = default_clock>
+            void run() {
+                IConfigPtr cfg = getCurrentContext().getConfig();
+
+                auto env = Detail::measure_environment<Clock>();
+
+                getResultCapture().benchmarkPreparing(name);
+                CATCH_TRY{
+                    auto plan = user_code([&] {
+                        return prepare<Clock>(*cfg, env);
+                    });
+
+                    BenchmarkInfo info {
+                        name,
+                        plan.estimated_duration.count(),
+                        plan.iterations_per_sample,
+                        cfg->benchmarkSamples(),
+                        cfg->benchmarkResamples(),
+                        env.clock_resolution.mean.count(),
+                        env.clock_cost.mean.count()
+                    };
+
+                    getResultCapture().benchmarkStarting(info);
+
+                    auto samples = user_code([&] {
+                        return plan.template run<Clock>(*cfg, env);
+                    });
+
+                    auto analysis = Detail::analyse(*cfg, env, samples.begin(), samples.end());
+                    BenchmarkStats<std::chrono::duration<double, std::nano>> stats{ info, analysis.samples, analysis.mean, analysis.standard_deviation, analysis.outliers, analysis.outlier_variance };
+                    getResultCapture().benchmarkEnded(stats);
+
+                } CATCH_CATCH_ALL{
+                    if (translateActiveException() != Detail::benchmarkErrorMsg) // benchmark errors have been reported, otherwise rethrow.
+                        std::rethrow_exception(std::current_exception());
+                }
+            }
+
+            // sets lambda to be used in fun *and* executes benchmark!
+            template <typename Fun,
+                typename std::enable_if<!Detail::is_related<Fun, Benchmark>::value, int>::type = 0>
+                Benchmark & operator=(Fun func) {
+                fun = Detail::BenchmarkFunction(func);
+                run();
+                return *this;
+            }
+
+            explicit operator bool() {
+                return true;
+            }
+
+        private:
+            Detail::BenchmarkFunction fun;
+            std::string name;
+        };
+    }
+} // namespace Catch
+
+#define INTERNAL_CATCH_GET_1_ARG(arg1, arg2, ...) arg1
+#define INTERNAL_CATCH_GET_2_ARG(arg1, arg2, ...) arg2
+
+#define INTERNAL_CATCH_BENCHMARK(BenchmarkName, name, benchmarkIndex)\
+    if( Catch::Benchmark::Benchmark BenchmarkName{name} ) \
+        BenchmarkName = [&](int benchmarkIndex)
+
+#define INTERNAL_CATCH_BENCHMARK_ADVANCED(BenchmarkName, name)\
+    if( Catch::Benchmark::Benchmark BenchmarkName{name} ) \
+        BenchmarkName = [&]
+
+// end catch_benchmark.hpp
+#endif
+
+#endif // ! CATCH_CONFIG_IMPL_ONLY
+
+#ifdef CATCH_IMPL
+// start catch_impl.hpp
+
+#ifdef __clang__
+#pragma clang diagnostic push
+#pragma clang diagnostic ignored "-Wweak-vtables"
+#endif
+
+// Keep these here for external reporters
+// start catch_test_case_tracker.h
+
+#include <string>
+#include <vector>
+#include <memory>
+
+namespace Catch {
+namespace TestCaseTracking {
+
+    struct NameAndLocation {
+        std::string name;
+        SourceLineInfo location;
+
+        NameAndLocation( std::string const& _name, SourceLineInfo const& _location );
+    };
+
+    struct ITracker;
 
-#include <string>
+    using ITrackerPtr = std::shared_ptr<ITracker>;
+
+    struct ITracker {
+        virtual ~ITracker();
+
+        // static queries
+        virtual NameAndLocation const& nameAndLocation() const = 0;
+
+        // dynamic queries
+        virtual bool isComplete() const = 0; // Successfully completed or failed
+        virtual bool isSuccessfullyCompleted() const = 0;
+        virtual bool isOpen() const = 0; // Started but not complete
+        virtual bool hasChildren() const = 0;
+
+        virtual ITracker& parent() = 0;
+
+        // actions
+        virtual void close() = 0; // Successfully complete
+        virtual void fail() = 0;
+        virtual void markAsNeedingAnotherRun() = 0;
+
+        virtual void addChild( ITrackerPtr const& child ) = 0;
+        virtual ITrackerPtr findChild( NameAndLocation const& nameAndLocation ) = 0;
+        virtual void openChild() = 0;
+
+        // Debug/ checking
+        virtual bool isSectionTracker() const = 0;
+        virtual bool isGeneratorTracker() const = 0;
+    };
+
+    class TrackerContext {
+
+        enum RunState {
+            NotStarted,
+            Executing,
+            CompletedCycle
+        };
+
+        ITrackerPtr m_rootTracker;
+        ITracker* m_currentTracker = nullptr;
+        RunState m_runState = NotStarted;
+
+    public:
+
+        ITracker& startRun();
+        void endRun();
+
+        void startCycle();
+        void completeCycle();
+
+        bool completedCycle() const;
+        ITracker& currentTracker();
+        void setCurrentTracker( ITracker* tracker );
+    };
+
+    class TrackerBase : public ITracker {
+    protected:
+        enum CycleState {
+            NotStarted,
+            Executing,
+            ExecutingChildren,
+            NeedsAnotherRun,
+            CompletedSuccessfully,
+            Failed
+        };
+
+        using Children = std::vector<ITrackerPtr>;
+        NameAndLocation m_nameAndLocation;
+        TrackerContext& m_ctx;
+        ITracker* m_parent;
+        Children m_children;
+        CycleState m_runState = NotStarted;
+
+    public:
+        TrackerBase( NameAndLocation const& nameAndLocation, TrackerContext& ctx, ITracker* parent );
+
+        NameAndLocation const& nameAndLocation() const override;
+        bool isComplete() const override;
+        bool isSuccessfullyCompleted() const override;
+        bool isOpen() const override;
+        bool hasChildren() const override;
+
+        void addChild( ITrackerPtr const& child ) override;
+
+        ITrackerPtr findChild( NameAndLocation const& nameAndLocation ) override;
+        ITracker& parent() override;
+
+        void openChild() override;
+
+        bool isSectionTracker() const override;
+        bool isGeneratorTracker() const override;
+
+        void open();
+
+        void close() override;
+        void fail() override;
+        void markAsNeedingAnotherRun() override;
+
+    private:
+        void moveToParent();
+        void moveToThis();
+    };
+
+    class SectionTracker : public TrackerBase {
+        std::vector<std::string> m_filters;
+        std::string m_trimmed_name;
+    public:
+        SectionTracker( NameAndLocation const& nameAndLocation, TrackerContext& ctx, ITracker* parent );
+
+        bool isSectionTracker() const override;
+
+        bool isComplete() const override;
+
+        static SectionTracker& acquire( TrackerContext& ctx, NameAndLocation const& nameAndLocation );
+
+        void tryOpen();
+
+        void addInitialFilters( std::vector<std::string> const& filters );
+        void addNextFilters( std::vector<std::string> const& filters );
+    };
+
+} // namespace TestCaseTracking
+
+using TestCaseTracking::ITracker;
+using TestCaseTracking::TrackerContext;
+using TestCaseTracking::SectionTracker;
+
+} // namespace Catch
+
+// end catch_test_case_tracker.h
+
+// start catch_leak_detector.h
 
 namespace Catch {
 
-    struct TagAlias {
-        TagAlias( std::string const& _tag, SourceLineInfo _lineInfo ) : tag( _tag ), lineInfo( _lineInfo ) {}
+    struct LeakDetector {
+        LeakDetector();
+        ~LeakDetector();
+    };
 
-        std::string tag;
-        SourceLineInfo lineInfo;
+}
+// end catch_leak_detector.h
+// Cpp files will be included in the single-header file here
+// start catch_stats.cpp
+
+// Statistical analysis tools
+
+#if defined(CATCH_CONFIG_ENABLE_BENCHMARKING)
+
+#include <cassert>
+#include <random>
+
+#if defined(CATCH_CONFIG_USE_ASYNC)
+#include <future>
+#endif
+
+namespace {
+    double erf_inv(double x) {
+        // Code accompanying the article "Approximating the erfinv function" in GPU Computing Gems, Volume 2
+        double w, p;
+
+        w = -log((1.0 - x) * (1.0 + x));
+
+        if (w < 6.250000) {
+            w = w - 3.125000;
+            p = -3.6444120640178196996e-21;
+            p = -1.685059138182016589e-19 + p * w;
+            p = 1.2858480715256400167e-18 + p * w;
+            p = 1.115787767802518096e-17 + p * w;
+            p = -1.333171662854620906e-16 + p * w;
+            p = 2.0972767875968561637e-17 + p * w;
+            p = 6.6376381343583238325e-15 + p * w;
+            p = -4.0545662729752068639e-14 + p * w;
+            p = -8.1519341976054721522e-14 + p * w;
+            p = 2.6335093153082322977e-12 + p * w;
+            p = -1.2975133253453532498e-11 + p * w;
+            p = -5.4154120542946279317e-11 + p * w;
+            p = 1.051212273321532285e-09 + p * w;
+            p = -4.1126339803469836976e-09 + p * w;
+            p = -2.9070369957882005086e-08 + p * w;
+            p = 4.2347877827932403518e-07 + p * w;
+            p = -1.3654692000834678645e-06 + p * w;
+            p = -1.3882523362786468719e-05 + p * w;
+            p = 0.0001867342080340571352 + p * w;
+            p = -0.00074070253416626697512 + p * w;
+            p = -0.0060336708714301490533 + p * w;
+            p = 0.24015818242558961693 + p * w;
+            p = 1.6536545626831027356 + p * w;
+        } else if (w < 16.000000) {
+            w = sqrt(w) - 3.250000;
+            p = 2.2137376921775787049e-09;
+            p = 9.0756561938885390979e-08 + p * w;
+            p = -2.7517406297064545428e-07 + p * w;
+            p = 1.8239629214389227755e-08 + p * w;
+            p = 1.5027403968909827627e-06 + p * w;
+            p = -4.013867526981545969e-06 + p * w;
+            p = 2.9234449089955446044e-06 + p * w;
+            p = 1.2475304481671778723e-05 + p * w;
+            p = -4.7318229009055733981e-05 + p * w;
+            p = 6.8284851459573175448e-05 + p * w;
+            p = 2.4031110387097893999e-05 + p * w;
+            p = -0.0003550375203628474796 + p * w;
+            p = 0.00095328937973738049703 + p * w;
+            p = -0.0016882755560235047313 + p * w;
+            p = 0.0024914420961078508066 + p * w;
+            p = -0.0037512085075692412107 + p * w;
+            p = 0.005370914553590063617 + p * w;
+            p = 1.0052589676941592334 + p * w;
+            p = 3.0838856104922207635 + p * w;
+        } else {
+            w = sqrt(w) - 5.000000;
+            p = -2.7109920616438573243e-11;
+            p = -2.5556418169965252055e-10 + p * w;
+            p = 1.5076572693500548083e-09 + p * w;
+            p = -3.7894654401267369937e-09 + p * w;
+            p = 7.6157012080783393804e-09 + p * w;
+            p = -1.4960026627149240478e-08 + p * w;
+            p = 2.9147953450901080826e-08 + p * w;
+            p = -6.7711997758452339498e-08 + p * w;
+            p = 2.2900482228026654717e-07 + p * w;
+            p = -9.9298272942317002539e-07 + p * w;
+            p = 4.5260625972231537039e-06 + p * w;
+            p = -1.9681778105531670567e-05 + p * w;
+            p = 7.5995277030017761139e-05 + p * w;
+            p = -0.00021503011930044477347 + p * w;
+            p = -0.00013871931833623122026 + p * w;
+            p = 1.0103004648645343977 + p * w;
+            p = 4.8499064014085844221 + p * w;
+        }
+        return p * x;
+    }
+
+    double standard_deviation(std::vector<double>::iterator first, std::vector<double>::iterator last) {
+        auto m = Catch::Benchmark::Detail::mean(first, last);
+        double variance = std::accumulate(first, last, 0., [m](double a, double b) {
+            double diff = b - m;
+            return a + diff * diff;
+            }) / (last - first);
+            return std::sqrt(variance);
+    }
+
+}
+
+namespace Catch {
+    namespace Benchmark {
+        namespace Detail {
+
+            double weighted_average_quantile(int k, int q, std::vector<double>::iterator first, std::vector<double>::iterator last) {
+                auto count = last - first;
+                double idx = (count - 1) * k / static_cast<double>(q);
+                int j = static_cast<int>(idx);
+                double g = idx - j;
+                std::nth_element(first, first + j, last);
+                auto xj = first[j];
+                if (g == 0) return xj;
+
+                auto xj1 = *std::min_element(first + (j + 1), last);
+                return xj + g * (xj1 - xj);
+            }
+
+            double erfc_inv(double x) {
+                return erf_inv(1.0 - x);
+            }
+
+            double normal_quantile(double p) {
+                static const double ROOT_TWO = std::sqrt(2.0);
+
+                double result = 0.0;
+                assert(p >= 0 && p <= 1);
+                if (p < 0 || p > 1) {
+                    return result;
+                }
+
+                result = -erfc_inv(2.0 * p);
+                // result *= normal distribution standard deviation (1.0) * sqrt(2)
+                result *= /*sd * */ ROOT_TWO;
+                // result += normal disttribution mean (0)
+                return result;
+            }
+
+            double outlier_variance(Estimate<double> mean, Estimate<double> stddev, int n) {
+                double sb = stddev.point;
+                double mn = mean.point / n;
+                double mg_min = mn / 2.;
+                double sg = std::min(mg_min / 4., sb / std::sqrt(n));
+                double sg2 = sg * sg;
+                double sb2 = sb * sb;
+
+                auto c_max = [n, mn, sb2, sg2](double x) -> double {
+                    double k = mn - x;
+                    double d = k * k;
+                    double nd = n * d;
+                    double k0 = -n * nd;
+                    double k1 = sb2 - n * sg2 + nd;
+                    double det = k1 * k1 - 4 * sg2 * k0;
+                    return (int)(-2. * k0 / (k1 + std::sqrt(det)));
+                };
+
+                auto var_out = [n, sb2, sg2](double c) {
+                    double nc = n - c;
+                    return (nc / n) * (sb2 - nc * sg2);
+                };
+
+                return std::min(var_out(1), var_out(std::min(c_max(0.), c_max(mg_min)))) / sb2;
+            }
+
+            bootstrap_analysis analyse_samples(double confidence_level, int n_resamples, std::vector<double>::iterator first, std::vector<double>::iterator last) {
+                CATCH_INTERNAL_SUPPRESS_GLOBALS_WARNINGS
+                static std::random_device entropy;
+                CATCH_INTERNAL_UNSUPPRESS_GLOBALS_WARNINGS
+
+                auto n = static_cast<int>(last - first); // seriously, one can't use integral types without hell in C++
+
+                auto mean = &Detail::mean<std::vector<double>::iterator>;
+                auto stddev = &standard_deviation;
+
+#if defined(CATCH_CONFIG_USE_ASYNC)
+                auto Estimate = [=](double(*f)(std::vector<double>::iterator, std::vector<double>::iterator)) {
+                    auto seed = entropy();
+                    return std::async(std::launch::async, [=] {
+                        std::mt19937 rng(seed);
+                        auto resampled = resample(rng, n_resamples, first, last, f);
+                        return bootstrap(confidence_level, first, last, resampled, f);
+                    });
+                };
+
+                auto mean_future = Estimate(mean);
+                auto stddev_future = Estimate(stddev);
+
+                auto mean_estimate = mean_future.get();
+                auto stddev_estimate = stddev_future.get();
+#else
+                auto Estimate = [=](double(*f)(std::vector<double>::iterator, std::vector<double>::iterator)) {
+                    auto seed = entropy();
+                    std::mt19937 rng(seed);
+                    auto resampled = resample(rng, n_resamples, first, last, f);
+                    return bootstrap(confidence_level, first, last, resampled, f);
+                };
+
+                auto mean_estimate = Estimate(mean);
+                auto stddev_estimate = Estimate(stddev);
+#endif // CATCH_USE_ASYNC
+
+                double outlier_variance = Detail::outlier_variance(mean_estimate, stddev_estimate, n);
+
+                return { mean_estimate, stddev_estimate, outlier_variance };
+            }
+        } // namespace Detail
+    } // namespace Benchmark
+} // namespace Catch
+
+#endif // CATCH_CONFIG_ENABLE_BENCHMARKING
+// end catch_stats.cpp
+// start catch_approx.cpp
+
+#include <cmath>
+#include <limits>
+
+namespace {
+
+// Performs equivalent check of std::fabs(lhs - rhs) <= margin
+// But without the subtraction to allow for INFINITY in comparison
+bool marginComparison(double lhs, double rhs, double margin) {
+    return (lhs + margin >= rhs) && (rhs + margin >= lhs);
+}
+
+}
+
+namespace Catch {
+namespace Detail {
+
+    Approx::Approx ( double value )
+    :   m_epsilon( std::numeric_limits<float>::epsilon()*100 ),
+        m_margin( 0.0 ),
+        m_scale( 0.0 ),
+        m_value( value )
+    {}
+
+    Approx Approx::custom() {
+        return Approx( 0 );
+    }
+
+    Approx Approx::operator-() const {
+        auto temp(*this);
+        temp.m_value = -temp.m_value;
+        return temp;
+    }
+
+    std::string Approx::toString() const {
+        ReusableStringStream rss;
+        rss << "Approx( " << ::Catch::Detail::stringify( m_value ) << " )";
+        return rss.str();
+    }
+
+    bool Approx::equalityComparisonImpl(const double other) const {
+        // First try with fixed margin, then compute margin based on epsilon, scale and Approx's value
+        // Thanks to Richard Harris for his help refining the scaled margin value
+        return marginComparison(m_value, other, m_margin)
+            || marginComparison(m_value, other, m_epsilon * (m_scale + std::fabs(std::isinf(m_value)? 0 : m_value)));
+    }
+
+    void Approx::setMargin(double newMargin) {
+        CATCH_ENFORCE(newMargin >= 0,
+            "Invalid Approx::margin: " << newMargin << '.'
+            << " Approx::Margin has to be non-negative.");
+        m_margin = newMargin;
+    }
+
+    void Approx::setEpsilon(double newEpsilon) {
+        CATCH_ENFORCE(newEpsilon >= 0 && newEpsilon <= 1.0,
+            "Invalid Approx::epsilon: " << newEpsilon << '.'
+            << " Approx::epsilon has to be in [0, 1]");
+        m_epsilon = newEpsilon;
+    }
+
+} // end namespace Detail
+
+namespace literals {
+    Detail::Approx operator "" _a(long double val) {
+        return Detail::Approx(val);
+    }
+    Detail::Approx operator "" _a(unsigned long long val) {
+        return Detail::Approx(val);
+    }
+} // end namespace literals
+
+std::string StringMaker<Catch::Detail::Approx>::convert(Catch::Detail::Approx const& value) {
+    return value.toString();
+}
+
+} // end namespace Catch
+// end catch_approx.cpp
+// start catch_assertionhandler.cpp
+
+// start catch_debugger.h
+
+namespace Catch {
+    bool isDebuggerActive();
+}
+
+#ifdef CATCH_PLATFORM_MAC
+
+    #define CATCH_TRAP() __asm__("int $3\n" : : ) /* NOLINT */
+
+#elif defined(CATCH_PLATFORM_LINUX)
+    // If we can use inline assembler, do it because this allows us to break
+    // directly at the location of the failing check instead of breaking inside
+    // raise() called from it, i.e. one stack frame below.
+    #if defined(__GNUC__) && (defined(__i386) || defined(__x86_64))
+        #define CATCH_TRAP() asm volatile ("int $3") /* NOLINT */
+    #else // Fall back to the generic way.
+        #include <signal.h>
+
+        #define CATCH_TRAP() raise(SIGTRAP)
+    #endif
+#elif defined(_MSC_VER)
+    #define CATCH_TRAP() __debugbreak()
+#elif defined(__MINGW32__)
+    extern "C" __declspec(dllimport) void __stdcall DebugBreak();
+    #define CATCH_TRAP() DebugBreak()
+#endif
+
+#ifdef CATCH_TRAP
+    #define CATCH_BREAK_INTO_DEBUGGER() []{ if( Catch::isDebuggerActive() ) { CATCH_TRAP(); } }()
+#else
+    #define CATCH_BREAK_INTO_DEBUGGER() []{}()
+#endif
+
+// end catch_debugger.h
+// start catch_run_context.h
+
+// start catch_fatal_condition.h
+
+// start catch_windows_h_proxy.h
+
+
+#if defined(CATCH_PLATFORM_WINDOWS)
+
+#if !defined(NOMINMAX) && !defined(CATCH_CONFIG_NO_NOMINMAX)
+#  define CATCH_DEFINED_NOMINMAX
+#  define NOMINMAX
+#endif
+#if !defined(WIN32_LEAN_AND_MEAN) && !defined(CATCH_CONFIG_NO_WIN32_LEAN_AND_MEAN)
+#  define CATCH_DEFINED_WIN32_LEAN_AND_MEAN
+#  define WIN32_LEAN_AND_MEAN
+#endif
+
+#ifdef __AFXDLL
+#include <AfxWin.h>
+#else
+#include <windows.h>
+#endif
+
+#ifdef CATCH_DEFINED_NOMINMAX
+#  undef NOMINMAX
+#endif
+#ifdef CATCH_DEFINED_WIN32_LEAN_AND_MEAN
+#  undef WIN32_LEAN_AND_MEAN
+#endif
+
+#endif // defined(CATCH_PLATFORM_WINDOWS)
+
+// end catch_windows_h_proxy.h
+#if defined( CATCH_CONFIG_WINDOWS_SEH )
+
+namespace Catch {
+
+    struct FatalConditionHandler {
+
+        static LONG CALLBACK handleVectoredException(PEXCEPTION_POINTERS ExceptionInfo);
+        FatalConditionHandler();
+        static void reset();
+        ~FatalConditionHandler();
+
+    private:
+        static bool isSet;
+        static ULONG guaranteeSize;
+        static PVOID exceptionHandlerHandle;
     };
 
-    struct RegistrarForTagAliases {
-        RegistrarForTagAliases( char const* alias, char const* tag, SourceLineInfo const& lineInfo );
+} // namespace Catch
+
+#elif defined ( CATCH_CONFIG_POSIX_SIGNALS )
+
+#include <signal.h>
+
+namespace Catch {
+
+    struct FatalConditionHandler {
+
+        static bool isSet;
+        static struct sigaction oldSigActions[];
+        static stack_t oldSigStack;
+        static char altStackMem[];
+
+        static void handleSignal( int sig );
+
+        FatalConditionHandler();
+        ~FatalConditionHandler();
+        static void reset();
+    };
+
+} // namespace Catch
+
+#else
+
+namespace Catch {
+    struct FatalConditionHandler {
+        void reset();
     };
+}
+
+#endif
+
+// end catch_fatal_condition.h
+#include <string>
+
+namespace Catch {
 
-} // end namespace Catch
+    struct IMutableContext;
 
-#define CATCH_REGISTER_TAG_ALIAS( alias, spec ) namespace{ Catch::RegistrarForTagAliases INTERNAL_CATCH_UNIQUE_NAME( AutoRegisterTagAlias )( alias, spec, CATCH_INTERNAL_LINEINFO ); }
-// #included from: catch_option.hpp
-#define TWOBLUECUBES_CATCH_OPTION_HPP_INCLUDED
+    ///////////////////////////////////////////////////////////////////////////
 
-namespace Catch {
+    class RunContext : public IResultCapture, public IRunner {
 
-    // An optional type
-    template<typename T>
-    class Option {
     public:
-        Option() : nullableValue( CATCH_NULL ) {}
-        Option( T const& _value )
-        : nullableValue( new( storage ) T( _value ) )
-        {}
-        Option( Option const& _other )
-        : nullableValue( _other ? new( storage ) T( *_other ) : CATCH_NULL )
-        {}
+        RunContext( RunContext const& ) = delete;
+        RunContext& operator =( RunContext const& ) = delete;
 
-        ~Option() {
-            reset();
-        }
+        explicit RunContext( IConfigPtr const& _config, IStreamingReporterPtr&& reporter );
 
-        Option& operator= ( Option const& _other ) {
-            if( &_other != this ) {
-                reset();
-                if( _other )
-                    nullableValue = new( storage ) T( *_other );
-            }
-            return *this;
-        }
-        Option& operator = ( T const& _value ) {
-            reset();
-            nullableValue = new( storage ) T( _value );
-            return *this;
-        }
+        ~RunContext() override;
 
-        void reset() {
-            if( nullableValue )
-                nullableValue->~T();
-            nullableValue = CATCH_NULL;
-        }
+        void testGroupStarting( std::string const& testSpec, std::size_t groupIndex, std::size_t groupsCount );
+        void testGroupEnded( std::string const& testSpec, Totals const& totals, std::size_t groupIndex, std::size_t groupsCount );
 
-        T& operator*() { return *nullableValue; }
-        T const& operator*() const { return *nullableValue; }
-        T* operator->() { return nullableValue; }
-        const T* operator->() const { return nullableValue; }
+        Totals runTest(TestCase const& testCase);
 
-        T valueOr( T const& defaultValue ) const {
-            return nullableValue ? *nullableValue : defaultValue;
-        }
+        IConfigPtr config() const;
+        IStreamingReporter& reporter() const;
 
-        bool some() const { return nullableValue != CATCH_NULL; }
-        bool none() const { return nullableValue == CATCH_NULL; }
+    public: // IResultCapture
 
-        bool operator !() const { return nullableValue == CATCH_NULL; }
-        operator SafeBool::type() const {
-            return SafeBool::makeSafe( some() );
-        }
+        // Assertion handlers
+        void handleExpr
+                (   AssertionInfo const& info,
+                    ITransientExpression const& expr,
+                    AssertionReaction& reaction ) override;
+        void handleMessage
+                (   AssertionInfo const& info,
+                    ResultWas::OfType resultType,
+                    StringRef const& message,
+                    AssertionReaction& reaction ) override;
+        void handleUnexpectedExceptionNotThrown
+                (   AssertionInfo const& info,
+                    AssertionReaction& reaction ) override;
+        void handleUnexpectedInflightException
+                (   AssertionInfo const& info,
+                    std::string const& message,
+                    AssertionReaction& reaction ) override;
+        void handleIncomplete
+                (   AssertionInfo const& info ) override;
+        void handleNonExpr
+                (   AssertionInfo const &info,
+                    ResultWas::OfType resultType,
+                    AssertionReaction &reaction ) override;
 
-    private:
-        T *nullableValue;
-        union {
-            char storage[sizeof(T)];
+        bool sectionStarted( SectionInfo const& sectionInfo, Counts& assertions ) override;
 
-            // These are here to force alignment for the storage
-            long double dummy1;
-            void (*dummy2)();
-            long double dummy3;
-#ifdef CATCH_CONFIG_CPP11_LONG_LONG
-            long long dummy4;
-#endif
-        };
-    };
+        void sectionEnded( SectionEndInfo const& endInfo ) override;
+        void sectionEndedEarly( SectionEndInfo const& endInfo ) override;
 
-} // end namespace Catch
+        auto acquireGeneratorTracker( SourceLineInfo const& lineInfo ) -> IGeneratorTracker& override;
 
-namespace Catch {
+#if defined(CATCH_CONFIG_ENABLE_BENCHMARKING)
+        void benchmarkPreparing( std::string const& name ) override;
+        void benchmarkStarting( BenchmarkInfo const& info ) override;
+        void benchmarkEnded( BenchmarkStats<> const& stats ) override;
+        void benchmarkFailed( std::string const& error ) override;
+#endif // CATCH_CONFIG_ENABLE_BENCHMARKING
 
-    struct ITagAliasRegistry {
-        virtual ~ITagAliasRegistry();
-        virtual Option<TagAlias> find( std::string const& alias ) const = 0;
-        virtual std::string expandAliases( std::string const& unexpandedTestSpec ) const = 0;
+        void pushScopedMessage( MessageInfo const& message ) override;
+        void popScopedMessage( MessageInfo const& message ) override;
 
-        static ITagAliasRegistry const& get();
-    };
+        void emplaceUnscopedMessage( MessageBuilder const& builder ) override;
 
-} // end namespace Catch
+        std::string getCurrentTestName() const override;
 
-// These files are included here so the single_include script doesn't put them
-// in the conditionally compiled sections
-// #included from: internal/catch_test_case_info.h
-#define TWOBLUECUBES_CATCH_TEST_CASE_INFO_H_INCLUDED
+        const AssertionResult* getLastResult() const override;
 
-#include <string>
-#include <set>
+        void exceptionEarlyReported() override;
 
-#ifdef __clang__
-#pragma clang diagnostic push
-#pragma clang diagnostic ignored "-Wpadded"
-#endif
+        void handleFatalErrorCondition( StringRef message ) override;
 
-namespace Catch {
+        bool lastAssertionPassed() override;
 
-    struct ITestCase;
+        void assertionPassed() override;
 
-    struct TestCaseInfo {
-        enum SpecialProperties{
-            None = 0,
-            IsHidden = 1 << 1,
-            ShouldFail = 1 << 2,
-            MayFail = 1 << 3,
-            Throws = 1 << 4,
-            NonPortable = 1 << 5
-        };
+    public:
+        // !TBD We need to do this another way!
+        bool aborting() const final;
 
-        TestCaseInfo(   std::string const& _name,
-                        std::string const& _className,
-                        std::string const& _description,
-                        std::set<std::string> const& _tags,
-                        SourceLineInfo const& _lineInfo );
+    private:
 
-        TestCaseInfo( TestCaseInfo const& other );
+        void runCurrentTest( std::string& redirectedCout, std::string& redirectedCerr );
+        void invokeActiveTestCase();
 
-        friend void setTags( TestCaseInfo& testCaseInfo, std::set<std::string> const& tags );
+        void resetAssertionInfo();
+        bool testForMissingAssertions( Counts& assertions );
 
-        bool isHidden() const;
-        bool throws() const;
-        bool okToFail() const;
-        bool expectedToFail() const;
+        void assertionEnded( AssertionResult const& result );
+        void reportExpr
+                (   AssertionInfo const &info,
+                    ResultWas::OfType resultType,
+                    ITransientExpression const *expr,
+                    bool negated );
 
-        std::string name;
-        std::string className;
-        std::string description;
-        std::set<std::string> tags;
-        std::set<std::string> lcaseTags;
-        std::string tagsAsString;
-        SourceLineInfo lineInfo;
-        SpecialProperties properties;
+        void populateReaction( AssertionReaction& reaction );
+
+    private:
+
+        void handleUnfinishedSections();
+
+        TestRunInfo m_runInfo;
+        IMutableContext& m_context;
+        TestCase const* m_activeTestCase = nullptr;
+        ITracker* m_testCaseTracker = nullptr;
+        Option<AssertionResult> m_lastResult;
+
+        IConfigPtr m_config;
+        Totals m_totals;
+        IStreamingReporterPtr m_reporter;
+        std::vector<MessageInfo> m_messages;
+        std::vector<ScopedMessage> m_messageScopes; /* Keeps owners of so-called unscoped messages. */
+        AssertionInfo m_lastAssertionInfo;
+        std::vector<SectionEndInfo> m_unfinishedSections;
+        std::vector<ITracker*> m_activeSections;
+        TrackerContext m_trackerContext;
+        bool m_lastAssertionPassed = false;
+        bool m_shouldReportUnexpected = true;
+        bool m_includeSuccessfulResults;
     };
 
-    class TestCase : public TestCaseInfo {
-    public:
+    void seedRng(IConfig const& config);
+    unsigned int rngSeed();
+} // end namespace Catch
 
-        TestCase( ITestCase* testCase, TestCaseInfo const& info );
-        TestCase( TestCase const& other );
+// end catch_run_context.h
+namespace Catch {
 
-        TestCase withName( std::string const& _newName ) const;
+    namespace {
+        auto operator <<( std::ostream& os, ITransientExpression const& expr ) -> std::ostream& {
+            expr.streamReconstructedExpression( os );
+            return os;
+        }
+    }
 
-        void invoke() const;
+    LazyExpression::LazyExpression( bool isNegated )
+    :   m_isNegated( isNegated )
+    {}
 
-        TestCaseInfo const& getTestCaseInfo() const;
+    LazyExpression::LazyExpression( LazyExpression const& other ) : m_isNegated( other.m_isNegated ) {}
 
-        void swap( TestCase& other );
-        bool operator == ( TestCase const& other ) const;
-        bool operator < ( TestCase const& other ) const;
-        TestCase& operator = ( TestCase const& other );
+    LazyExpression::operator bool() const {
+        return m_transientExpression != nullptr;
+    }
 
-    private:
-        Ptr<ITestCase> test;
-    };
+    auto operator << ( std::ostream& os, LazyExpression const& lazyExpr ) -> std::ostream& {
+        if( lazyExpr.m_isNegated )
+            os << "!";
 
-    TestCase makeTestCase(  ITestCase* testCase,
-                            std::string const& className,
-                            std::string const& name,
-                            std::string const& description,
-                            SourceLineInfo const& lineInfo );
-}
+        if( lazyExpr ) {
+            if( lazyExpr.m_isNegated && lazyExpr.m_transientExpression->isBinaryExpression() )
+                os << "(" << *lazyExpr.m_transientExpression << ")";
+            else
+                os << *lazyExpr.m_transientExpression;
+        }
+        else {
+            os << "{** error - unchecked empty expression requested **}";
+        }
+        return os;
+    }
 
-#ifdef __clang__
-#pragma clang diagnostic pop
-#endif
+    AssertionHandler::AssertionHandler
+        (   StringRef const& macroName,
+            SourceLineInfo const& lineInfo,
+            StringRef capturedExpression,
+            ResultDisposition::Flags resultDisposition )
+    :   m_assertionInfo{ macroName, lineInfo, capturedExpression, resultDisposition },
+        m_resultCapture( getResultCapture() )
+    {}
 
+    void AssertionHandler::handleExpr( ITransientExpression const& expr ) {
+        m_resultCapture.handleExpr( m_assertionInfo, expr, m_reaction );
+    }
+    void AssertionHandler::handleMessage(ResultWas::OfType resultType, StringRef const& message) {
+        m_resultCapture.handleMessage( m_assertionInfo, resultType, message, m_reaction );
+    }
 
-#ifdef __OBJC__
-// #included from: internal/catch_objc.hpp
-#define TWOBLUECUBES_CATCH_OBJC_HPP_INCLUDED
+    auto AssertionHandler::allowThrows() const -> bool {
+        return getCurrentContext().getConfig()->allowThrows();
+    }
 
-#import <objc/runtime.h>
+    void AssertionHandler::complete() {
+        setCompleted();
+        if( m_reaction.shouldDebugBreak ) {
 
-#include <string>
+            // If you find your debugger stopping you here then go one level up on the
+            // call-stack for the code that caused it (typically a failed assertion)
 
-// NB. Any general catch headers included here must be included
-// in catch.hpp first to make sure they are included by the single
-// header for non obj-usage
+            // (To go back to the test and change execution, jump over the throw, next)
+            CATCH_BREAK_INTO_DEBUGGER();
+        }
+        if (m_reaction.shouldThrow) {
+#if !defined(CATCH_CONFIG_DISABLE_EXCEPTIONS)
+            throw Catch::TestFailureException();
+#else
+            CATCH_ERROR( "Test failure requires aborting test!" );
+#endif
+        }
+    }
+    void AssertionHandler::setCompleted() {
+        m_completed = true;
+    }
 
-///////////////////////////////////////////////////////////////////////////////
-// This protocol is really only here for (self) documenting purposes, since
-// all its methods are optional.
-@protocol OcFixture
+    void AssertionHandler::handleUnexpectedInflightException() {
+        m_resultCapture.handleUnexpectedInflightException( m_assertionInfo, Catch::translateActiveException(), m_reaction );
+    }
 
-@optional
+    void AssertionHandler::handleExceptionThrownAsExpected() {
+        m_resultCapture.handleNonExpr(m_assertionInfo, ResultWas::Ok, m_reaction);
+    }
+    void AssertionHandler::handleExceptionNotThrownAsExpected() {
+        m_resultCapture.handleNonExpr(m_assertionInfo, ResultWas::Ok, m_reaction);
+    }
 
--(void) setUp;
--(void) tearDown;
+    void AssertionHandler::handleUnexpectedExceptionNotThrown() {
+        m_resultCapture.handleUnexpectedExceptionNotThrown( m_assertionInfo, m_reaction );
+    }
 
-@end
+    void AssertionHandler::handleThrowingCallSkipped() {
+        m_resultCapture.handleNonExpr(m_assertionInfo, ResultWas::Ok, m_reaction);
+    }
+
+    // This is the overload that takes a string and infers the Equals matcher from it
+    // The more general overload, that takes any string matcher, is in catch_capture_matchers.cpp
+    void handleExceptionMatchExpr( AssertionHandler& handler, std::string const& str, StringRef const& matcherString  ) {
+        handleExceptionMatchExpr( handler, Matchers::Equals( str ), matcherString );
+    }
+
+} // namespace Catch
+// end catch_assertionhandler.cpp
+// start catch_assertionresult.cpp
 
 namespace Catch {
+    AssertionResultData::AssertionResultData(ResultWas::OfType _resultType, LazyExpression const & _lazyExpression):
+        lazyExpression(_lazyExpression),
+        resultType(_resultType) {}
 
-    class OcMethod : public SharedImpl<ITestCase> {
+    std::string AssertionResultData::reconstructExpression() const {
 
-    public:
-        OcMethod( Class cls, SEL sel ) : m_cls( cls ), m_sel( sel ) {}
+        if( reconstructedExpression.empty() ) {
+            if( lazyExpression ) {
+                ReusableStringStream rss;
+                rss << lazyExpression;
+                reconstructedExpression = rss.str();
+            }
+        }
+        return reconstructedExpression;
+    }
 
-        virtual void invoke() const {
-            id obj = [[m_cls alloc] init];
+    AssertionResult::AssertionResult( AssertionInfo const& info, AssertionResultData const& data )
+    :   m_info( info ),
+        m_resultData( data )
+    {}
 
-            performOptionalSelector( obj, @selector(setUp)  );
-            performOptionalSelector( obj, m_sel );
-            performOptionalSelector( obj, @selector(tearDown)  );
+    // Result was a success
+    bool AssertionResult::succeeded() const {
+        return Catch::isOk( m_resultData.resultType );
+    }
 
-            arcSafeRelease( obj );
-        }
-    private:
-        virtual ~OcMethod() {}
+    // Result was a success, or failure is suppressed
+    bool AssertionResult::isOk() const {
+        return Catch::isOk( m_resultData.resultType ) || shouldSuppressFailure( m_info.resultDisposition );
+    }
 
-        Class m_cls;
-        SEL m_sel;
-    };
+    ResultWas::OfType AssertionResult::getResultType() const {
+        return m_resultData.resultType;
+    }
 
-    namespace Detail{
+    bool AssertionResult::hasExpression() const {
+        return !m_info.capturedExpression.empty();
+    }
 
-        inline std::string getAnnotation(   Class cls,
-                                            std::string const& annotationName,
-                                            std::string const& testCaseName ) {
-            NSString* selStr = [[NSString alloc] initWithFormat:@"Catch_%s_%s", annotationName.c_str(), testCaseName.c_str()];
-            SEL sel = NSSelectorFromString( selStr );
-            arcSafeRelease( selStr );
-            id value = performOptionalSelector( cls, sel );
-            if( value )
-                return [(NSString*)value UTF8String];
-            return "";
+    bool AssertionResult::hasMessage() const {
+        return !m_resultData.message.empty();
+    }
+
+    std::string AssertionResult::getExpression() const {
+        // Possibly overallocating by 3 characters should be basically free
+        std::string expr; expr.reserve(m_info.capturedExpression.size() + 3);
+        if (isFalseTest(m_info.resultDisposition)) {
+            expr += "!(";
+        }
+        expr += m_info.capturedExpression;
+        if (isFalseTest(m_info.resultDisposition)) {
+            expr += ')';
         }
+        return expr;
     }
 
-    inline size_t registerTestMethods() {
-        size_t noTestMethods = 0;
-        int noClasses = objc_getClassList( CATCH_NULL, 0 );
+    std::string AssertionResult::getExpressionInMacro() const {
+        std::string expr;
+        if( m_info.macroName.empty() )
+            expr = static_cast<std::string>(m_info.capturedExpression);
+        else {
+            expr.reserve( m_info.macroName.size() + m_info.capturedExpression.size() + 4 );
+            expr += m_info.macroName;
+            expr += "( ";
+            expr += m_info.capturedExpression;
+            expr += " )";
+        }
+        return expr;
+    }
 
-        Class* classes = (CATCH_UNSAFE_UNRETAINED Class *)malloc( sizeof(Class) * noClasses);
-        objc_getClassList( classes, noClasses );
+    bool AssertionResult::hasExpandedExpression() const {
+        return hasExpression() && getExpandedExpression() != getExpression();
+    }
 
-        for( int c = 0; c < noClasses; c++ ) {
-            Class cls = classes[c];
-            {
-                u_int count;
-                Method* methods = class_copyMethodList( cls, &count );
-                for( u_int m = 0; m < count ; m++ ) {
-                    SEL selector = method_getName(methods[m]);
-                    std::string methodName = sel_getName(selector);
-                    if( startsWith( methodName, "Catch_TestCase_" ) ) {
-                        std::string testCaseName = methodName.substr( 15 );
-                        std::string name = Detail::getAnnotation( cls, "Name", testCaseName );
-                        std::string desc = Detail::getAnnotation( cls, "Description", testCaseName );
-                        const char* className = class_getName( cls );
+    std::string AssertionResult::getExpandedExpression() const {
+        std::string expr = m_resultData.reconstructExpression();
+        return expr.empty()
+                ? getExpression()
+                : expr;
+    }
 
-                        getMutableRegistryHub().registerTest( makeTestCase( new OcMethod( cls, selector ), className, name.c_str(), desc.c_str(), SourceLineInfo() ) );
-                        noTestMethods++;
-                    }
-                }
-                free(methods);
-            }
-        }
-        return noTestMethods;
+    std::string AssertionResult::getMessage() const {
+        return m_resultData.message;
+    }
+    SourceLineInfo AssertionResult::getSourceInfo() const {
+        return m_info.lineInfo;
+    }
+
+    StringRef AssertionResult::getTestMacroName() const {
+        return m_info.macroName;
     }
 
-    namespace Matchers {
-        namespace Impl {
-        namespace NSStringMatchers {
+} // end namespace Catch
+// end catch_assertionresult.cpp
+// start catch_capture_matchers.cpp
 
-            struct StringHolder : MatcherBase<NSString*>{
-                StringHolder( NSString* substr ) : m_substr( [substr copy] ){}
-                StringHolder( StringHolder const& other ) : m_substr( [other.m_substr copy] ){}
-                StringHolder() {
-                    arcSafeRelease( m_substr );
-                }
+namespace Catch {
 
-                virtual bool match( NSString* arg ) const CATCH_OVERRIDE {
-                    return false;
-                }
+    using StringMatcher = Matchers::Impl::MatcherBase<std::string>;
 
-                NSString* m_substr;
-            };
+    // This is the general overload that takes a any string matcher
+    // There is another overload, in catch_assertionhandler.h/.cpp, that only takes a string and infers
+    // the Equals matcher (so the header does not mention matchers)
+    void handleExceptionMatchExpr( AssertionHandler& handler, StringMatcher const& matcher, StringRef const& matcherString  ) {
+        std::string exceptionMessage = Catch::translateActiveException();
+        MatchExpr<std::string, StringMatcher const&> expr( exceptionMessage, matcher, matcherString );
+        handler.handleExpr( expr );
+    }
 
-            struct Equals : StringHolder {
-                Equals( NSString* substr ) : StringHolder( substr ){}
+} // namespace Catch
+// end catch_capture_matchers.cpp
+// start catch_commandline.cpp
 
-                virtual bool match( NSString* str ) const CATCH_OVERRIDE {
-                    return  (str != nil || m_substr == nil ) &&
-                            [str isEqualToString:m_substr];
-                }
+// start catch_commandline.h
 
-                virtual std::string describe() const CATCH_OVERRIDE {
-                    return "equals string: " + Catch::toString( m_substr );
-                }
-            };
+// start catch_clara.h
 
-            struct Contains : StringHolder {
-                Contains( NSString* substr ) : StringHolder( substr ){}
+// Use Catch's value for console width (store Clara's off to the side, if present)
+#ifdef CLARA_CONFIG_CONSOLE_WIDTH
+#define CATCH_TEMP_CLARA_CONFIG_CONSOLE_WIDTH CATCH_CLARA_TEXTFLOW_CONFIG_CONSOLE_WIDTH
+#undef CATCH_CLARA_TEXTFLOW_CONFIG_CONSOLE_WIDTH
+#endif
+#define CATCH_CLARA_TEXTFLOW_CONFIG_CONSOLE_WIDTH CATCH_CONFIG_CONSOLE_WIDTH-1
 
-                virtual bool match( NSString* str ) const {
-                    return  (str != nil || m_substr == nil ) &&
-                            [str rangeOfString:m_substr].location != NSNotFound;
-                }
+#ifdef __clang__
+#pragma clang diagnostic push
+#pragma clang diagnostic ignored "-Wweak-vtables"
+#pragma clang diagnostic ignored "-Wexit-time-destructors"
+#pragma clang diagnostic ignored "-Wshadow"
+#endif
 
-                virtual std::string describe() const CATCH_OVERRIDE {
-                    return "contains string: " + Catch::toString( m_substr );
-                }
-            };
+// start clara.hpp
+// Copyright 2017 Two Blue Cubes Ltd. All rights reserved.
+//
+// Distributed under the Boost Software License, Version 1.0. (See accompanying
+// file LICENSE_1_0.txt or copy at http://www.boost.org/LICENSE_1_0.txt)
+//
+// See https://github.com/philsquared/Clara for more details
 
-            struct StartsWith : StringHolder {
-                StartsWith( NSString* substr ) : StringHolder( substr ){}
+// Clara v1.1.5
 
-                virtual bool match( NSString* str ) const {
-                    return  (str != nil || m_substr == nil ) &&
-                            [str rangeOfString:m_substr].location == 0;
-                }
 
-                virtual std::string describe() const CATCH_OVERRIDE {
-                    return "starts with: " + Catch::toString( m_substr );
-                }
-            };
-            struct EndsWith : StringHolder {
-                EndsWith( NSString* substr ) : StringHolder( substr ){}
+#ifndef CATCH_CLARA_CONFIG_CONSOLE_WIDTH
+#define CATCH_CLARA_CONFIG_CONSOLE_WIDTH 80
+#endif
 
-                virtual bool match( NSString* str ) const {
-                    return  (str != nil || m_substr == nil ) &&
-                            [str rangeOfString:m_substr].location == [str length] - [m_substr length];
-                }
+#ifndef CATCH_CLARA_TEXTFLOW_CONFIG_CONSOLE_WIDTH
+#define CATCH_CLARA_TEXTFLOW_CONFIG_CONSOLE_WIDTH CATCH_CLARA_CONFIG_CONSOLE_WIDTH
+#endif
 
-                virtual std::string describe() const CATCH_OVERRIDE {
-                    return "ends with: " + Catch::toString( m_substr );
-                }
-            };
+#ifndef CLARA_CONFIG_OPTIONAL_TYPE
+#ifdef __has_include
+#if __has_include(<optional>) && __cplusplus >= 201703L
+#include <optional>
+#define CLARA_CONFIG_OPTIONAL_TYPE std::optional
+#endif
+#endif
+#endif
 
-        } // namespace NSStringMatchers
-        } // namespace Impl
+// ----------- #included from clara_textflow.hpp -----------
 
-        inline Impl::NSStringMatchers::Equals
-            Equals( NSString* substr ){ return Impl::NSStringMatchers::Equals( substr ); }
+// TextFlowCpp
+//
+// A single-header library for wrapping and laying out basic text, by Phil Nash
+//
+// Distributed under the Boost Software License, Version 1.0. (See accompanying
+// file LICENSE.txt or copy at http://www.boost.org/LICENSE_1_0.txt)
+//
+// This project is hosted at https://github.com/philsquared/textflowcpp
 
-        inline Impl::NSStringMatchers::Contains
-            Contains( NSString* substr ){ return Impl::NSStringMatchers::Contains( substr ); }
 
-        inline Impl::NSStringMatchers::StartsWith
-            StartsWith( NSString* substr ){ return Impl::NSStringMatchers::StartsWith( substr ); }
+#include <cassert>
+#include <ostream>
+#include <sstream>
+#include <vector>
 
-        inline Impl::NSStringMatchers::EndsWith
-            EndsWith( NSString* substr ){ return Impl::NSStringMatchers::EndsWith( substr ); }
+#ifndef CATCH_CLARA_TEXTFLOW_CONFIG_CONSOLE_WIDTH
+#define CATCH_CLARA_TEXTFLOW_CONFIG_CONSOLE_WIDTH 80
+#endif
 
-    } // namespace Matchers
+namespace Catch {
+namespace clara {
+namespace TextFlow {
 
-    using namespace Matchers;
+inline auto isWhitespace(char c) -> bool {
+	static std::string chars = " \t\n\r";
+	return chars.find(c) != std::string::npos;
+}
+inline auto isBreakableBefore(char c) -> bool {
+	static std::string chars = "[({<|";
+	return chars.find(c) != std::string::npos;
+}
+inline auto isBreakableAfter(char c) -> bool {
+	static std::string chars = "])}>.,:;*+-=&/\\";
+	return chars.find(c) != std::string::npos;
+}
 
-} // namespace Catch
+class Columns;
 
-///////////////////////////////////////////////////////////////////////////////
-#define OC_TEST_CASE( name, desc )\
-+(NSString*) INTERNAL_CATCH_UNIQUE_NAME( Catch_Name_test ) \
-{\
-return @ name; \
-}\
-+(NSString*) INTERNAL_CATCH_UNIQUE_NAME( Catch_Description_test ) \
-{ \
-return @ desc; \
-} \
--(void) INTERNAL_CATCH_UNIQUE_NAME( Catch_TestCase_test )
+class Column {
+	std::vector<std::string> m_strings;
+	size_t m_width = CATCH_CLARA_TEXTFLOW_CONFIG_CONSOLE_WIDTH;
+	size_t m_indent = 0;
+	size_t m_initialIndent = std::string::npos;
 
-#endif
+public:
+	class iterator {
+		friend Column;
+
+		Column const& m_column;
+		size_t m_stringIndex = 0;
+		size_t m_pos = 0;
+
+		size_t m_len = 0;
+		size_t m_end = 0;
+		bool m_suffix = false;
+
+		iterator(Column const& column, size_t stringIndex)
+			: m_column(column),
+			m_stringIndex(stringIndex) {}
+
+		auto line() const -> std::string const& { return m_column.m_strings[m_stringIndex]; }
+
+		auto isBoundary(size_t at) const -> bool {
+			assert(at > 0);
+			assert(at <= line().size());
+
+			return at == line().size() ||
+				(isWhitespace(line()[at]) && !isWhitespace(line()[at - 1])) ||
+				isBreakableBefore(line()[at]) ||
+				isBreakableAfter(line()[at - 1]);
+		}
+
+		void calcLength() {
+			assert(m_stringIndex < m_column.m_strings.size());
+
+			m_suffix = false;
+			auto width = m_column.m_width - indent();
+			m_end = m_pos;
+			if (line()[m_pos] == '\n') {
+				++m_end;
+			}
+			while (m_end < line().size() && line()[m_end] != '\n')
+				++m_end;
+
+			if (m_end < m_pos + width) {
+				m_len = m_end - m_pos;
+			} else {
+				size_t len = width;
+				while (len > 0 && !isBoundary(m_pos + len))
+					--len;
+				while (len > 0 && isWhitespace(line()[m_pos + len - 1]))
+					--len;
+
+				if (len > 0) {
+					m_len = len;
+				} else {
+					m_suffix = true;
+					m_len = width - 1;
+				}
+			}
+		}
+
+		auto indent() const -> size_t {
+			auto initial = m_pos == 0 && m_stringIndex == 0 ? m_column.m_initialIndent : std::string::npos;
+			return initial == std::string::npos ? m_column.m_indent : initial;
+		}
+
+		auto addIndentAndSuffix(std::string const &plain) const -> std::string {
+			return std::string(indent(), ' ') + (m_suffix ? plain + "-" : plain);
+		}
+
+	public:
+		using difference_type = std::ptrdiff_t;
+		using value_type = std::string;
+		using pointer = value_type * ;
+		using reference = value_type & ;
+		using iterator_category = std::forward_iterator_tag;
+
+		explicit iterator(Column const& column) : m_column(column) {
+			assert(m_column.m_width > m_column.m_indent);
+			assert(m_column.m_initialIndent == std::string::npos || m_column.m_width > m_column.m_initialIndent);
+			calcLength();
+			if (m_len == 0)
+				m_stringIndex++; // Empty string
+		}
+
+		auto operator *() const -> std::string {
+			assert(m_stringIndex < m_column.m_strings.size());
+			assert(m_pos <= m_end);
+			return addIndentAndSuffix(line().substr(m_pos, m_len));
+		}
+
+		auto operator ++() -> iterator& {
+			m_pos += m_len;
+			if (m_pos < line().size() && line()[m_pos] == '\n')
+				m_pos += 1;
+			else
+				while (m_pos < line().size() && isWhitespace(line()[m_pos]))
+					++m_pos;
+
+			if (m_pos == line().size()) {
+				m_pos = 0;
+				++m_stringIndex;
+			}
+			if (m_stringIndex < m_column.m_strings.size())
+				calcLength();
+			return *this;
+		}
+		auto operator ++(int) -> iterator {
+			iterator prev(*this);
+			operator++();
+			return prev;
+		}
+
+		auto operator ==(iterator const& other) const -> bool {
+			return
+				m_pos == other.m_pos &&
+				m_stringIndex == other.m_stringIndex &&
+				&m_column == &other.m_column;
+		}
+		auto operator !=(iterator const& other) const -> bool {
+			return !operator==(other);
+		}
+	};
+	using const_iterator = iterator;
+
+	explicit Column(std::string const& text) { m_strings.push_back(text); }
+
+	auto width(size_t newWidth) -> Column& {
+		assert(newWidth > 0);
+		m_width = newWidth;
+		return *this;
+	}
+	auto indent(size_t newIndent) -> Column& {
+		m_indent = newIndent;
+		return *this;
+	}
+	auto initialIndent(size_t newIndent) -> Column& {
+		m_initialIndent = newIndent;
+		return *this;
+	}
+
+	auto width() const -> size_t { return m_width; }
+	auto begin() const -> iterator { return iterator(*this); }
+	auto end() const -> iterator { return { *this, m_strings.size() }; }
+
+	inline friend std::ostream& operator << (std::ostream& os, Column const& col) {
+		bool first = true;
+		for (auto line : col) {
+			if (first)
+				first = false;
+			else
+				os << "\n";
+			os << line;
+		}
+		return os;
+	}
+
+	auto operator + (Column const& other)->Columns;
+
+	auto toString() const -> std::string {
+		std::ostringstream oss;
+		oss << *this;
+		return oss.str();
+	}
+};
 
-#ifdef CATCH_IMPL
+class Spacer : public Column {
 
-// !TBD: Move the leak detector code into a separate header
-#ifdef CATCH_CONFIG_WINDOWS_CRTDBG
-#include <crtdbg.h>
-class LeakDetector {
 public:
-    LeakDetector() {
-        int flag = _CrtSetDbgFlag(_CRTDBG_REPORT_FLAG);
-        flag |= _CRTDBG_LEAK_CHECK_DF;
-        flag |= _CRTDBG_ALLOC_MEM_DF;
-        _CrtSetDbgFlag(flag);
-        _CrtSetReportMode(_CRT_WARN, _CRTDBG_MODE_FILE | _CRTDBG_MODE_DEBUG);
-        _CrtSetReportFile(_CRT_WARN, _CRTDBG_FILE_STDERR);
-        // Change this to leaking allocation's number to break there
-        _CrtSetBreakAlloc(-1);
-    }
+	explicit Spacer(size_t spaceWidth) : Column("") {
+		width(spaceWidth);
+	}
 };
-#else
-class LeakDetector {};
-#endif
 
-LeakDetector leakDetector;
+class Columns {
+	std::vector<Column> m_columns;
 
-// #included from: internal/catch_impl.hpp
-#define TWOBLUECUBES_CATCH_IMPL_HPP_INCLUDED
-
-// Collect all the implementation files together here
-// These are the equivalent of what would usually be cpp files
+public:
 
-#ifdef __clang__
-#pragma clang diagnostic push
-#pragma clang diagnostic ignored "-Wweak-vtables"
-#endif
+	class iterator {
+		friend Columns;
+		struct EndTag {};
+
+		std::vector<Column> const& m_columns;
+		std::vector<Column::iterator> m_iterators;
+		size_t m_activeIterators;
+
+		iterator(Columns const& columns, EndTag)
+			: m_columns(columns.m_columns),
+			m_activeIterators(0) {
+			m_iterators.reserve(m_columns.size());
+
+			for (auto const& col : m_columns)
+				m_iterators.push_back(col.end());
+		}
+
+	public:
+		using difference_type = std::ptrdiff_t;
+		using value_type = std::string;
+		using pointer = value_type * ;
+		using reference = value_type & ;
+		using iterator_category = std::forward_iterator_tag;
+
+		explicit iterator(Columns const& columns)
+			: m_columns(columns.m_columns),
+			m_activeIterators(m_columns.size()) {
+			m_iterators.reserve(m_columns.size());
+
+			for (auto const& col : m_columns)
+				m_iterators.push_back(col.begin());
+		}
+
+		auto operator ==(iterator const& other) const -> bool {
+			return m_iterators == other.m_iterators;
+		}
+		auto operator !=(iterator const& other) const -> bool {
+			return m_iterators != other.m_iterators;
+		}
+		auto operator *() const -> std::string {
+			std::string row, padding;
+
+			for (size_t i = 0; i < m_columns.size(); ++i) {
+				auto width = m_columns[i].width();
+				if (m_iterators[i] != m_columns[i].end()) {
+					std::string col = *m_iterators[i];
+					row += padding + col;
+					if (col.size() < width)
+						padding = std::string(width - col.size(), ' ');
+					else
+						padding = "";
+				} else {
+					padding += std::string(width, ' ');
+				}
+			}
+			return row;
+		}
+		auto operator ++() -> iterator& {
+			for (size_t i = 0; i < m_columns.size(); ++i) {
+				if (m_iterators[i] != m_columns[i].end())
+					++m_iterators[i];
+			}
+			return *this;
+		}
+		auto operator ++(int) -> iterator {
+			iterator prev(*this);
+			operator++();
+			return prev;
+		}
+	};
+	using const_iterator = iterator;
+
+	auto begin() const -> iterator { return iterator(*this); }
+	auto end() const -> iterator { return { *this, iterator::EndTag() }; }
+
+	auto operator += (Column const& col) -> Columns& {
+		m_columns.push_back(col);
+		return *this;
+	}
+	auto operator + (Column const& col) -> Columns {
+		Columns combined = *this;
+		combined += col;
+		return combined;
+	}
+
+	inline friend std::ostream& operator << (std::ostream& os, Columns const& cols) {
+
+		bool first = true;
+		for (auto line : cols) {
+			if (first)
+				first = false;
+			else
+				os << "\n";
+			os << line;
+		}
+		return os;
+	}
+
+	auto toString() const -> std::string {
+		std::ostringstream oss;
+		oss << *this;
+		return oss.str();
+	}
+};
 
-// #included from: ../catch_session.hpp
-#define TWOBLUECUBES_CATCH_RUNNER_HPP_INCLUDED
+inline auto Column::operator + (Column const& other) -> Columns {
+	Columns cols;
+	cols += *this;
+	cols += other;
+	return cols;
+}
+}
 
-// #included from: internal/catch_commandline.hpp
-#define TWOBLUECUBES_CATCH_COMMANDLINE_HPP_INCLUDED
+}
+}
 
-// #included from: catch_config.hpp
-#define TWOBLUECUBES_CATCH_CONFIG_HPP_INCLUDED
+// ----------- end of #include from clara_textflow.hpp -----------
+// ........... back in clara.hpp
 
-// #included from: catch_test_spec_parser.hpp
-#define TWOBLUECUBES_CATCH_TEST_SPEC_PARSER_HPP_INCLUDED
+#include <cctype>
+#include <string>
+#include <memory>
+#include <set>
+#include <algorithm>
 
-#ifdef __clang__
-#pragma clang diagnostic push
-#pragma clang diagnostic ignored "-Wpadded"
+#if !defined(CATCH_PLATFORM_WINDOWS) && ( defined(WIN32) || defined(__WIN32__) || defined(_WIN32) || defined(_MSC_VER) )
+#define CATCH_PLATFORM_WINDOWS
 #endif
 
-// #included from: catch_test_spec.hpp
-#define TWOBLUECUBES_CATCH_TEST_SPEC_HPP_INCLUDED
+namespace Catch { namespace clara {
+namespace detail {
 
-#ifdef __clang__
-#pragma clang diagnostic push
-#pragma clang diagnostic ignored "-Wpadded"
-#endif
+    // Traits for extracting arg and return type of lambdas (for single argument lambdas)
+    template<typename L>
+    struct UnaryLambdaTraits : UnaryLambdaTraits<decltype( &L::operator() )> {};
 
-// #included from: catch_wildcard_pattern.hpp
-#define TWOBLUECUBES_CATCH_WILDCARD_PATTERN_HPP_INCLUDED
+    template<typename ClassT, typename ReturnT, typename... Args>
+    struct UnaryLambdaTraits<ReturnT( ClassT::* )( Args... ) const> {
+        static const bool isValid = false;
+    };
 
-#include <stdexcept>
+    template<typename ClassT, typename ReturnT, typename ArgT>
+    struct UnaryLambdaTraits<ReturnT( ClassT::* )( ArgT ) const> {
+        static const bool isValid = true;
+        using ArgType = typename std::remove_const<typename std::remove_reference<ArgT>::type>::type;
+        using ReturnType = ReturnT;
+    };
 
-namespace Catch
-{
-    class WildcardPattern {
-        enum WildcardPosition {
-            NoWildcard = 0,
-            WildcardAtStart = 1,
-            WildcardAtEnd = 2,
-            WildcardAtBothEnds = WildcardAtStart | WildcardAtEnd
-        };
+    class TokenStream;
+
+    // Transport for raw args (copied from main args, or supplied via init list for testing)
+    class Args {
+        friend TokenStream;
+        std::string m_exeName;
+        std::vector<std::string> m_args;
 
     public:
+        Args( int argc, char const* const* argv )
+            : m_exeName(argv[0]),
+              m_args(argv + 1, argv + argc) {}
 
-        WildcardPattern( std::string const& pattern, CaseSensitive::Choice caseSensitivity )
-        :   m_caseSensitivity( caseSensitivity ),
-            m_wildcard( NoWildcard ),
-            m_pattern( adjustCase( pattern ) )
-        {
-            if( startsWith( m_pattern, '*' ) ) {
-                m_pattern = m_pattern.substr( 1 );
-                m_wildcard = WildcardAtStart;
-            }
-            if( endsWith( m_pattern, '*' ) ) {
-                m_pattern = m_pattern.substr( 0, m_pattern.size()-1 );
-                m_wildcard = static_cast<WildcardPosition>( m_wildcard | WildcardAtEnd );
-            }
+        Args( std::initializer_list<std::string> args )
+        :   m_exeName( *args.begin() ),
+            m_args( args.begin()+1, args.end() )
+        {}
+
+        auto exeName() const -> std::string {
+            return m_exeName;
         }
-        virtual ~WildcardPattern();
-        virtual bool matches( std::string const& str ) const {
-            switch( m_wildcard ) {
-                case NoWildcard:
-                    return m_pattern == adjustCase( str );
-                case WildcardAtStart:
-                    return endsWith( adjustCase( str ), m_pattern );
-                case WildcardAtEnd:
-                    return startsWith( adjustCase( str ), m_pattern );
-                case WildcardAtBothEnds:
-                    return contains( adjustCase( str ), m_pattern );
-            }
+    };
 
-#ifdef __clang__
-#pragma clang diagnostic push
-#pragma clang diagnostic ignored "-Wunreachable-code"
-#endif
-            throw std::logic_error( "Unknown enum" );
-#ifdef __clang__
-#pragma clang diagnostic pop
+    // Wraps a token coming from a token stream. These may not directly correspond to strings as a single string
+    // may encode an option + its argument if the : or = form is used
+    enum class TokenType {
+        Option, Argument
+    };
+    struct Token {
+        TokenType type;
+        std::string token;
+    };
+
+    inline auto isOptPrefix( char c ) -> bool {
+        return c == '-'
+#ifdef CATCH_PLATFORM_WINDOWS
+            || c == '/'
 #endif
+        ;
+    }
+
+    // Abstracts iterators into args as a stream of tokens, with option arguments uniformly handled
+    class TokenStream {
+        using Iterator = std::vector<std::string>::const_iterator;
+        Iterator it;
+        Iterator itEnd;
+        std::vector<Token> m_tokenBuffer;
+
+        void loadBuffer() {
+            m_tokenBuffer.resize( 0 );
+
+            // Skip any empty strings
+            while( it != itEnd && it->empty() )
+                ++it;
+
+            if( it != itEnd ) {
+                auto const &next = *it;
+                if( isOptPrefix( next[0] ) ) {
+                    auto delimiterPos = next.find_first_of( " :=" );
+                    if( delimiterPos != std::string::npos ) {
+                        m_tokenBuffer.push_back( { TokenType::Option, next.substr( 0, delimiterPos ) } );
+                        m_tokenBuffer.push_back( { TokenType::Argument, next.substr( delimiterPos + 1 ) } );
+                    } else {
+                        if( next[1] != '-' && next.size() > 2 ) {
+                            std::string opt = "- ";
+                            for( size_t i = 1; i < next.size(); ++i ) {
+                                opt[1] = next[i];
+                                m_tokenBuffer.push_back( { TokenType::Option, opt } );
+                            }
+                        } else {
+                            m_tokenBuffer.push_back( { TokenType::Option, next } );
+                        }
+                    }
+                } else {
+                    m_tokenBuffer.push_back( { TokenType::Argument, next } );
+                }
+            }
         }
-    private:
-        std::string adjustCase( std::string const& str ) const {
-            return m_caseSensitivity == CaseSensitive::No ? toLower( str ) : str;
+
+    public:
+        explicit TokenStream( Args const &args ) : TokenStream( args.m_args.begin(), args.m_args.end() ) {}
+
+        TokenStream( Iterator it, Iterator itEnd ) : it( it ), itEnd( itEnd ) {
+            loadBuffer();
         }
-        CaseSensitive::Choice m_caseSensitivity;
-        WildcardPosition m_wildcard;
-        std::string m_pattern;
-    };
-}
 
-#include <string>
-#include <vector>
+        explicit operator bool() const {
+            return !m_tokenBuffer.empty() || it != itEnd;
+        }
 
-namespace Catch {
+        auto count() const -> size_t { return m_tokenBuffer.size() + (itEnd - it); }
 
-    class TestSpec {
-        struct Pattern : SharedImpl<> {
-            virtual ~Pattern();
-            virtual bool matches( TestCaseInfo const& testCase ) const = 0;
-        };
-        class NamePattern : public Pattern {
-        public:
-            NamePattern( std::string const& name )
-            : m_wildcardPattern( toLower( name ), CaseSensitive::No )
-            {}
-            virtual ~NamePattern();
-            virtual bool matches( TestCaseInfo const& testCase ) const {
-                return m_wildcardPattern.matches( toLower( testCase.name ) );
-            }
-        private:
-            WildcardPattern m_wildcardPattern;
-        };
+        auto operator*() const -> Token {
+            assert( !m_tokenBuffer.empty() );
+            return m_tokenBuffer.front();
+        }
 
-        class TagPattern : public Pattern {
-        public:
-            TagPattern( std::string const& tag ) : m_tag( toLower( tag ) ) {}
-            virtual ~TagPattern();
-            virtual bool matches( TestCaseInfo const& testCase ) const {
-                return testCase.lcaseTags.find( m_tag ) != testCase.lcaseTags.end();
+        auto operator->() const -> Token const * {
+            assert( !m_tokenBuffer.empty() );
+            return &m_tokenBuffer.front();
+        }
+
+        auto operator++() -> TokenStream & {
+            if( m_tokenBuffer.size() >= 2 ) {
+                m_tokenBuffer.erase( m_tokenBuffer.begin() );
+            } else {
+                if( it != itEnd )
+                    ++it;
+                loadBuffer();
             }
-        private:
-            std::string m_tag;
-        };
+            return *this;
+        }
+    };
 
-        class ExcludedPattern : public Pattern {
-        public:
-            ExcludedPattern( Ptr<Pattern> const& underlyingPattern ) : m_underlyingPattern( underlyingPattern ) {}
-            virtual ~ExcludedPattern();
-            virtual bool matches( TestCaseInfo const& testCase ) const { return !m_underlyingPattern->matches( testCase ); }
-        private:
-            Ptr<Pattern> m_underlyingPattern;
+    class ResultBase {
+    public:
+        enum Type {
+            Ok, LogicError, RuntimeError
         };
 
-        struct Filter {
-            std::vector<Ptr<Pattern> > m_patterns;
+    protected:
+        ResultBase( Type type ) : m_type( type ) {}
+        virtual ~ResultBase() = default;
 
-            bool matches( TestCaseInfo const& testCase ) const {
-                // All patterns in a filter must match for the filter to be a match
-                for( std::vector<Ptr<Pattern> >::const_iterator it = m_patterns.begin(), itEnd = m_patterns.end(); it != itEnd; ++it ) {
-                    if( !(*it)->matches( testCase ) )
-                        return false;
-                }
-                return true;
-            }
-        };
+        virtual void enforceOk() const = 0;
+
+        Type m_type;
+    };
 
+    template<typename T>
+    class ResultValueBase : public ResultBase {
     public:
-        bool hasFilters() const {
-            return !m_filters.empty();
+        auto value() const -> T const & {
+            enforceOk();
+            return m_value;
         }
-        bool matches( TestCaseInfo const& testCase ) const {
-            // A TestSpec matches if any filter matches
-            for( std::vector<Filter>::const_iterator it = m_filters.begin(), itEnd = m_filters.end(); it != itEnd; ++it )
-                if( it->matches( testCase ) )
-                    return true;
-            return false;
+
+    protected:
+        ResultValueBase( Type type ) : ResultBase( type ) {}
+
+        ResultValueBase( ResultValueBase const &other ) : ResultBase( other ) {
+            if( m_type == ResultBase::Ok )
+                new( &m_value ) T( other.m_value );
         }
 
-    private:
-        std::vector<Filter> m_filters;
+        ResultValueBase( Type, T const &value ) : ResultBase( Ok ) {
+            new( &m_value ) T( value );
+        }
 
-        friend class TestSpecParser;
+        auto operator=( ResultValueBase const &other ) -> ResultValueBase & {
+            if( m_type == ResultBase::Ok )
+                m_value.~T();
+            ResultBase::operator=(other);
+            if( m_type == ResultBase::Ok )
+                new( &m_value ) T( other.m_value );
+            return *this;
+        }
+
+        ~ResultValueBase() override {
+            if( m_type == Ok )
+                m_value.~T();
+        }
+
+        union {
+            T m_value;
+        };
     };
-}
 
-#ifdef __clang__
-#pragma clang diagnostic pop
-#endif
+    template<>
+    class ResultValueBase<void> : public ResultBase {
+    protected:
+        using ResultBase::ResultBase;
+    };
 
-namespace Catch {
+    template<typename T = void>
+    class BasicResult : public ResultValueBase<T> {
+    public:
+        template<typename U>
+        explicit BasicResult( BasicResult<U> const &other )
+        :   ResultValueBase<T>( other.type() ),
+            m_errorMessage( other.errorMessage() )
+        {
+            assert( type() != ResultBase::Ok );
+        }
 
-    class TestSpecParser {
-        enum Mode{ None, Name, QuotedName, Tag, EscapedName };
-        Mode m_mode;
-        bool m_exclusion;
-        std::size_t m_start, m_pos;
-        std::string m_arg;
-        std::vector<std::size_t> m_escapeChars;
-        TestSpec::Filter m_currentFilter;
-        TestSpec m_testSpec;
-        ITagAliasRegistry const* m_tagAliases;
+        template<typename U>
+        static auto ok( U const &value ) -> BasicResult { return { ResultBase::Ok, value }; }
+        static auto ok() -> BasicResult { return { ResultBase::Ok }; }
+        static auto logicError( std::string const &message ) -> BasicResult { return { ResultBase::LogicError, message }; }
+        static auto runtimeError( std::string const &message ) -> BasicResult { return { ResultBase::RuntimeError, message }; }
+
+        explicit operator bool() const { return m_type == ResultBase::Ok; }
+        auto type() const -> ResultBase::Type { return m_type; }
+        auto errorMessage() const -> std::string { return m_errorMessage; }
+
+    protected:
+        void enforceOk() const override {
+
+            // Errors shouldn't reach this point, but if they do
+            // the actual error message will be in m_errorMessage
+            assert( m_type != ResultBase::LogicError );
+            assert( m_type != ResultBase::RuntimeError );
+            if( m_type != ResultBase::Ok )
+                std::abort();
+        }
+
+        std::string m_errorMessage; // Only populated if resultType is an error
+
+        BasicResult( ResultBase::Type type, std::string const &message )
+        :   ResultValueBase<T>(type),
+            m_errorMessage(message)
+        {
+            assert( m_type != ResultBase::Ok );
+        }
+
+        using ResultValueBase<T>::ResultValueBase;
+        using ResultBase::m_type;
+    };
+
+    enum class ParseResultType {
+        Matched, NoMatch, ShortCircuitAll, ShortCircuitSame
+    };
 
+    class ParseState {
     public:
-        TestSpecParser( ITagAliasRegistry const& tagAliases ) : m_tagAliases( &tagAliases ) {}
 
-        TestSpecParser& parse( std::string const& arg ) {
-            m_mode = None;
-            m_exclusion = false;
-            m_start = std::string::npos;
-            m_arg = m_tagAliases->expandAliases( arg );
-            m_escapeChars.clear();
-            for( m_pos = 0; m_pos < m_arg.size(); ++m_pos )
-                visitChar( m_arg[m_pos] );
-            if( m_mode == Name )
-                addPattern<TestSpec::NamePattern>();
-            return *this;
-        }
-        TestSpec testSpec() {
-            addFilter();
-            return m_testSpec;
-        }
+        ParseState( ParseResultType type, TokenStream const &remainingTokens )
+        : m_type(type),
+          m_remainingTokens( remainingTokens )
+        {}
+
+        auto type() const -> ParseResultType { return m_type; }
+        auto remainingTokens() const -> TokenStream { return m_remainingTokens; }
+
     private:
-        void visitChar( char c ) {
-            if( m_mode == None ) {
-                switch( c ) {
-                case ' ': return;
-                case '~': m_exclusion = true; return;
-                case '[': return startNewMode( Tag, ++m_pos );
-                case '"': return startNewMode( QuotedName, ++m_pos );
-                case '\\': return escape();
-                default: startNewMode( Name, m_pos ); break;
-                }
-            }
-            if( m_mode == Name ) {
-                if( c == ',' ) {
-                    addPattern<TestSpec::NamePattern>();
-                    addFilter();
-                }
-                else if( c == '[' ) {
-                    if( subString() == "exclude:" )
-                        m_exclusion = true;
-                    else
-                        addPattern<TestSpec::NamePattern>();
-                    startNewMode( Tag, ++m_pos );
-                }
-                else if( c == '\\' )
-                    escape();
-            }
-            else if( m_mode == EscapedName )
-                m_mode = Name;
-            else if( m_mode == QuotedName && c == '"' )
-                addPattern<TestSpec::NamePattern>();
-            else if( m_mode == Tag && c == ']' )
-                addPattern<TestSpec::TagPattern>();
-        }
-        void startNewMode( Mode mode, std::size_t start ) {
-            m_mode = mode;
-            m_start = start;
-        }
-        void escape() {
-            if( m_mode == None )
-                m_start = m_pos;
-            m_mode = EscapedName;
-            m_escapeChars.push_back( m_pos );
-        }
-        std::string subString() const { return m_arg.substr( m_start, m_pos - m_start ); }
-        template<typename T>
-        void addPattern() {
-            std::string token = subString();
-            for( size_t i = 0; i < m_escapeChars.size(); ++i )
-                token = token.substr( 0, m_escapeChars[i]-m_start-i ) + token.substr( m_escapeChars[i]-m_start-i+1 );
-            m_escapeChars.clear();
-            if( startsWith( token, "exclude:" ) ) {
-                m_exclusion = true;
-                token = token.substr( 8 );
-            }
-            if( !token.empty() ) {
-                Ptr<TestSpec::Pattern> pattern = new T( token );
-                if( m_exclusion )
-                    pattern = new TestSpec::ExcludedPattern( pattern );
-                m_currentFilter.m_patterns.push_back( pattern );
-            }
-            m_exclusion = false;
-            m_mode = None;
-        }
-        void addFilter() {
-            if( !m_currentFilter.m_patterns.empty() ) {
-                m_testSpec.m_filters.push_back( m_currentFilter );
-                m_currentFilter = TestSpec::Filter();
-            }
-        }
+        ParseResultType m_type;
+        TokenStream m_remainingTokens;
     };
-    inline TestSpec parseTestSpec( std::string const& arg ) {
-        return TestSpecParser( ITagAliasRegistry::get() ).parse( arg ).testSpec();
+
+    using Result = BasicResult<void>;
+    using ParserResult = BasicResult<ParseResultType>;
+    using InternalParseResult = BasicResult<ParseState>;
+
+    struct HelpColumns {
+        std::string left;
+        std::string right;
+    };
+
+    template<typename T>
+    inline auto convertInto( std::string const &source, T& target ) -> ParserResult {
+        std::stringstream ss;
+        ss << source;
+        ss >> target;
+        if( ss.fail() )
+            return ParserResult::runtimeError( "Unable to convert '" + source + "' to destination type" );
+        else
+            return ParserResult::ok( ParseResultType::Matched );
+    }
+    inline auto convertInto( std::string const &source, std::string& target ) -> ParserResult {
+        target = source;
+        return ParserResult::ok( ParseResultType::Matched );
+    }
+    inline auto convertInto( std::string const &source, bool &target ) -> ParserResult {
+        std::string srcLC = source;
+        std::transform( srcLC.begin(), srcLC.end(), srcLC.begin(), []( char c ) { return static_cast<char>( std::tolower(c) ); } );
+        if (srcLC == "y" || srcLC == "1" || srcLC == "true" || srcLC == "yes" || srcLC == "on")
+            target = true;
+        else if (srcLC == "n" || srcLC == "0" || srcLC == "false" || srcLC == "no" || srcLC == "off")
+            target = false;
+        else
+            return ParserResult::runtimeError( "Expected a boolean value but did not recognise: '" + source + "'" );
+        return ParserResult::ok( ParseResultType::Matched );
     }
+#ifdef CLARA_CONFIG_OPTIONAL_TYPE
+    template<typename T>
+    inline auto convertInto( std::string const &source, CLARA_CONFIG_OPTIONAL_TYPE<T>& target ) -> ParserResult {
+        T temp;
+        auto result = convertInto( source, temp );
+        if( result )
+            target = std::move(temp);
+        return result;
+    }
+#endif // CLARA_CONFIG_OPTIONAL_TYPE
+
+    struct NonCopyable {
+        NonCopyable() = default;
+        NonCopyable( NonCopyable const & ) = delete;
+        NonCopyable( NonCopyable && ) = delete;
+        NonCopyable &operator=( NonCopyable const & ) = delete;
+        NonCopyable &operator=( NonCopyable && ) = delete;
+    };
 
-} // namespace Catch
+    struct BoundRef : NonCopyable {
+        virtual ~BoundRef() = default;
+        virtual auto isContainer() const -> bool { return false; }
+        virtual auto isFlag() const -> bool { return false; }
+    };
+    struct BoundValueRefBase : BoundRef {
+        virtual auto setValue( std::string const &arg ) -> ParserResult = 0;
+    };
+    struct BoundFlagRefBase : BoundRef {
+        virtual auto setFlag( bool flag ) -> ParserResult = 0;
+        virtual auto isFlag() const -> bool { return true; }
+    };
 
-#ifdef __clang__
-#pragma clang diagnostic pop
-#endif
+    template<typename T>
+    struct BoundValueRef : BoundValueRefBase {
+        T &m_ref;
 
-// #included from: catch_interfaces_config.h
-#define TWOBLUECUBES_CATCH_INTERFACES_CONFIG_H_INCLUDED
+        explicit BoundValueRef( T &ref ) : m_ref( ref ) {}
 
-#include <iosfwd>
-#include <string>
-#include <vector>
+        auto setValue( std::string const &arg ) -> ParserResult override {
+            return convertInto( arg, m_ref );
+        }
+    };
 
-namespace Catch {
+    template<typename T>
+    struct BoundValueRef<std::vector<T>> : BoundValueRefBase {
+        std::vector<T> &m_ref;
 
-    struct Verbosity { enum Level {
-        NoOutput = 0,
-        Quiet,
-        Normal
-    }; };
+        explicit BoundValueRef( std::vector<T> &ref ) : m_ref( ref ) {}
 
-    struct WarnAbout { enum What {
-        Nothing = 0x00,
-        NoAssertions = 0x01
-    }; };
+        auto isContainer() const -> bool override { return true; }
 
-    struct ShowDurations { enum OrNot {
-        DefaultForReporter,
-        Always,
-        Never
-    }; };
-    struct RunTests { enum InWhatOrder {
-        InDeclarationOrder,
-        InLexicographicalOrder,
-        InRandomOrder
-    }; };
-    struct UseColour { enum YesOrNo {
-        Auto,
-        Yes,
-        No
-    }; };
+        auto setValue( std::string const &arg ) -> ParserResult override {
+            T temp;
+            auto result = convertInto( arg, temp );
+            if( result )
+                m_ref.push_back( temp );
+            return result;
+        }
+    };
 
-    class TestSpec;
+    struct BoundFlagRef : BoundFlagRefBase {
+        bool &m_ref;
 
-    struct IConfig : IShared {
+        explicit BoundFlagRef( bool &ref ) : m_ref( ref ) {}
 
-        virtual ~IConfig();
+        auto setFlag( bool flag ) -> ParserResult override {
+            m_ref = flag;
+            return ParserResult::ok( ParseResultType::Matched );
+        }
+    };
 
-        virtual bool allowThrows() const = 0;
-        virtual std::ostream& stream() const = 0;
-        virtual std::string name() const = 0;
-        virtual bool includeSuccessfulResults() const = 0;
-        virtual bool shouldDebugBreak() const = 0;
-        virtual bool warnAboutMissingAssertions() const = 0;
-        virtual int abortAfter() const = 0;
-        virtual bool showInvisibles() const = 0;
-        virtual ShowDurations::OrNot showDurations() const = 0;
-        virtual TestSpec const& testSpec() const = 0;
-        virtual RunTests::InWhatOrder runOrder() const = 0;
-        virtual unsigned int rngSeed() const = 0;
-        virtual UseColour::YesOrNo useColour() const = 0;
-        virtual std::vector<std::string> const& getSectionsToRun() const = 0;
+    template<typename ReturnType>
+    struct LambdaInvoker {
+        static_assert( std::is_same<ReturnType, ParserResult>::value, "Lambda must return void or clara::ParserResult" );
 
+        template<typename L, typename ArgType>
+        static auto invoke( L const &lambda, ArgType const &arg ) -> ParserResult {
+            return lambda( arg );
+        }
     };
-}
 
-// #included from: catch_stream.h
-#define TWOBLUECUBES_CATCH_STREAM_H_INCLUDED
+    template<>
+    struct LambdaInvoker<void> {
+        template<typename L, typename ArgType>
+        static auto invoke( L const &lambda, ArgType const &arg ) -> ParserResult {
+            lambda( arg );
+            return ParserResult::ok( ParseResultType::Matched );
+        }
+    };
 
-// #included from: catch_streambuf.h
-#define TWOBLUECUBES_CATCH_STREAMBUF_H_INCLUDED
+    template<typename ArgType, typename L>
+    inline auto invokeLambda( L const &lambda, std::string const &arg ) -> ParserResult {
+        ArgType temp{};
+        auto result = convertInto( arg, temp );
+        return !result
+           ? result
+           : LambdaInvoker<typename UnaryLambdaTraits<L>::ReturnType>::invoke( lambda, temp );
+    }
 
-#include <streambuf>
+    template<typename L>
+    struct BoundLambda : BoundValueRefBase {
+        L m_lambda;
 
-namespace Catch {
+        static_assert( UnaryLambdaTraits<L>::isValid, "Supplied lambda must take exactly one argument" );
+        explicit BoundLambda( L const &lambda ) : m_lambda( lambda ) {}
 
-    class StreamBufBase : public std::streambuf {
-    public:
-        virtual ~StreamBufBase() CATCH_NOEXCEPT;
+        auto setValue( std::string const &arg ) -> ParserResult override {
+            return invokeLambda<typename UnaryLambdaTraits<L>::ArgType>( m_lambda, arg );
+        }
     };
-}
 
-#include <streambuf>
-#include <ostream>
-#include <fstream>
-#include <memory>
+    template<typename L>
+    struct BoundFlagLambda : BoundFlagRefBase {
+        L m_lambda;
 
-namespace Catch {
+        static_assert( UnaryLambdaTraits<L>::isValid, "Supplied lambda must take exactly one argument" );
+        static_assert( std::is_same<typename UnaryLambdaTraits<L>::ArgType, bool>::value, "flags must be boolean" );
 
-    std::ostream& cout();
-    std::ostream& cerr();
+        explicit BoundFlagLambda( L const &lambda ) : m_lambda( lambda ) {}
 
-    struct IStream {
-        virtual ~IStream() CATCH_NOEXCEPT;
-        virtual std::ostream& stream() const = 0;
+        auto setFlag( bool flag ) -> ParserResult override {
+            return LambdaInvoker<typename UnaryLambdaTraits<L>::ReturnType>::invoke( m_lambda, flag );
+        }
     };
 
-    class FileStream : public IStream {
-        mutable std::ofstream m_ofs;
-    public:
-        FileStream( std::string const& filename );
-        virtual ~FileStream() CATCH_NOEXCEPT;
-    public: // IStream
-        virtual std::ostream& stream() const CATCH_OVERRIDE;
-    };
+    enum class Optionality { Optional, Required };
 
-    class CoutStream : public IStream {
-        mutable std::ostream m_os;
+    struct Parser;
+
+    class ParserBase {
     public:
-        CoutStream();
-        virtual ~CoutStream() CATCH_NOEXCEPT;
+        virtual ~ParserBase() = default;
+        virtual auto validate() const -> Result { return Result::ok(); }
+        virtual auto parse( std::string const& exeName, TokenStream const &tokens) const -> InternalParseResult  = 0;
+        virtual auto cardinality() const -> size_t { return 1; }
 
-    public: // IStream
-        virtual std::ostream& stream() const CATCH_OVERRIDE;
+        auto parse( Args const &args ) const -> InternalParseResult {
+            return parse( args.exeName(), TokenStream( args ) );
+        }
     };
 
-    class DebugOutStream : public IStream {
-        CATCH_AUTO_PTR( StreamBufBase ) m_streamBuf;
-        mutable std::ostream m_os;
+    template<typename DerivedT>
+    class ComposableParserImpl : public ParserBase {
     public:
-        DebugOutStream();
-        virtual ~DebugOutStream() CATCH_NOEXCEPT;
+        template<typename T>
+        auto operator|( T const &other ) const -> Parser;
 
-    public: // IStream
-        virtual std::ostream& stream() const CATCH_OVERRIDE;
+		template<typename T>
+        auto operator+( T const &other ) const -> Parser;
     };
-}
-
-#include <memory>
-#include <vector>
-#include <string>
-#include <stdexcept>
 
-#ifndef CATCH_CONFIG_CONSOLE_WIDTH
-#define CATCH_CONFIG_CONSOLE_WIDTH 80
-#endif
+    // Common code and state for Args and Opts
+    template<typename DerivedT>
+    class ParserRefImpl : public ComposableParserImpl<DerivedT> {
+    protected:
+        Optionality m_optionality = Optionality::Optional;
+        std::shared_ptr<BoundRef> m_ref;
+        std::string m_hint;
+        std::string m_description;
 
-namespace Catch {
+        explicit ParserRefImpl( std::shared_ptr<BoundRef> const &ref ) : m_ref( ref ) {}
 
-    struct ConfigData {
+    public:
+        template<typename T>
+        ParserRefImpl( T &ref, std::string const &hint )
+        :   m_ref( std::make_shared<BoundValueRef<T>>( ref ) ),
+            m_hint( hint )
+        {}
 
-        ConfigData()
-        :   listTests( false ),
-            listTags( false ),
-            listReporters( false ),
-            listTestNamesOnly( false ),
-            showSuccessfulTests( false ),
-            shouldDebugBreak( false ),
-            noThrow( false ),
-            showHelp( false ),
-            showInvisibles( false ),
-            filenamesAsTags( false ),
-            abortAfter( -1 ),
-            rngSeed( 0 ),
-            verbosity( Verbosity::Normal ),
-            warnings( WarnAbout::Nothing ),
-            showDurations( ShowDurations::DefaultForReporter ),
-            runOrder( RunTests::InDeclarationOrder ),
-            useColour( UseColour::Auto )
+        template<typename LambdaT>
+        ParserRefImpl( LambdaT const &ref, std::string const &hint )
+        :   m_ref( std::make_shared<BoundLambda<LambdaT>>( ref ) ),
+            m_hint(hint)
         {}
 
-        bool listTests;
-        bool listTags;
-        bool listReporters;
-        bool listTestNamesOnly;
+        auto operator()( std::string const &description ) -> DerivedT & {
+            m_description = description;
+            return static_cast<DerivedT &>( *this );
+        }
 
-        bool showSuccessfulTests;
-        bool shouldDebugBreak;
-        bool noThrow;
-        bool showHelp;
-        bool showInvisibles;
-        bool filenamesAsTags;
+        auto optional() -> DerivedT & {
+            m_optionality = Optionality::Optional;
+            return static_cast<DerivedT &>( *this );
+        };
 
-        int abortAfter;
-        unsigned int rngSeed;
+        auto required() -> DerivedT & {
+            m_optionality = Optionality::Required;
+            return static_cast<DerivedT &>( *this );
+        };
 
-        Verbosity::Level verbosity;
-        WarnAbout::What warnings;
-        ShowDurations::OrNot showDurations;
-        RunTests::InWhatOrder runOrder;
-        UseColour::YesOrNo useColour;
+        auto isOptional() const -> bool {
+            return m_optionality == Optionality::Optional;
+        }
 
-        std::string outputFilename;
-        std::string name;
-        std::string processName;
+        auto cardinality() const -> size_t override {
+            if( m_ref->isContainer() )
+                return 0;
+            else
+                return 1;
+        }
 
-        std::vector<std::string> reporterNames;
-        std::vector<std::string> testsOrTags;
-        std::vector<std::string> sectionsToRun;
+        auto hint() const -> std::string { return m_hint; }
     };
 
-    class Config : public SharedImpl<IConfig> {
-    private:
-        Config( Config const& other );
-        Config& operator = ( Config const& other );
-        virtual void dummy();
-    public:
+    class ExeName : public ComposableParserImpl<ExeName> {
+        std::shared_ptr<std::string> m_name;
+        std::shared_ptr<BoundValueRefBase> m_ref;
 
-        Config()
-        {}
+        template<typename LambdaT>
+        static auto makeRef(LambdaT const &lambda) -> std::shared_ptr<BoundValueRefBase> {
+            return std::make_shared<BoundLambda<LambdaT>>( lambda) ;
+        }
 
-        Config( ConfigData const& data )
-        :   m_data( data ),
-            m_stream( openStream() )
-        {
-            if( !data.testsOrTags.empty() ) {
-                TestSpecParser parser( ITagAliasRegistry::get() );
-                for( std::size_t i = 0; i < data.testsOrTags.size(); ++i )
-                    parser.parse( data.testsOrTags[i] );
-                m_testSpec = parser.testSpec();
-            }
+    public:
+        ExeName() : m_name( std::make_shared<std::string>( "<executable>" ) ) {}
+
+        explicit ExeName( std::string &ref ) : ExeName() {
+            m_ref = std::make_shared<BoundValueRef<std::string>>( ref );
         }
 
-        virtual ~Config() {}
+        template<typename LambdaT>
+        explicit ExeName( LambdaT const& lambda ) : ExeName() {
+            m_ref = std::make_shared<BoundLambda<LambdaT>>( lambda );
+        }
 
-        std::string const& getFilename() const {
-            return m_data.outputFilename ;
+        // The exe name is not parsed out of the normal tokens, but is handled specially
+        auto parse( std::string const&, TokenStream const &tokens ) const -> InternalParseResult override {
+            return InternalParseResult::ok( ParseState( ParseResultType::NoMatch, tokens ) );
         }
 
-        bool listTests() const { return m_data.listTests; }
-        bool listTestNamesOnly() const { return m_data.listTestNamesOnly; }
-        bool listTags() const { return m_data.listTags; }
-        bool listReporters() const { return m_data.listReporters; }
+        auto name() const -> std::string { return *m_name; }
+        auto set( std::string const& newName ) -> ParserResult {
 
-        std::string getProcessName() const { return m_data.processName; }
+            auto lastSlash = newName.find_last_of( "\\/" );
+            auto filename = ( lastSlash == std::string::npos )
+                    ? newName
+                    : newName.substr( lastSlash+1 );
 
-        std::vector<std::string> const& getReporterNames() const { return m_data.reporterNames; }
-        std::vector<std::string> const& getSectionsToRun() const CATCH_OVERRIDE { return m_data.sectionsToRun; }
+            *m_name = filename;
+            if( m_ref )
+                return m_ref->setValue( filename );
+            else
+                return ParserResult::ok( ParseResultType::Matched );
+        }
+    };
 
-        virtual TestSpec const& testSpec() const CATCH_OVERRIDE { return m_testSpec; }
+    class Arg : public ParserRefImpl<Arg> {
+    public:
+        using ParserRefImpl::ParserRefImpl;
 
-        bool showHelp() const { return m_data.showHelp; }
+        auto parse( std::string const &, TokenStream const &tokens ) const -> InternalParseResult override {
+            auto validationResult = validate();
+            if( !validationResult )
+                return InternalParseResult( validationResult );
 
-        // IConfig interface
-        virtual bool allowThrows() const CATCH_OVERRIDE                 { return !m_data.noThrow; }
-        virtual std::ostream& stream() const CATCH_OVERRIDE             { return m_stream->stream(); }
-        virtual std::string name() const CATCH_OVERRIDE                 { return m_data.name.empty() ? m_data.processName : m_data.name; }
-        virtual bool includeSuccessfulResults() const CATCH_OVERRIDE    { return m_data.showSuccessfulTests; }
-        virtual bool warnAboutMissingAssertions() const CATCH_OVERRIDE  { return m_data.warnings & WarnAbout::NoAssertions; }
-        virtual ShowDurations::OrNot showDurations() const CATCH_OVERRIDE { return m_data.showDurations; }
-        virtual RunTests::InWhatOrder runOrder() const CATCH_OVERRIDE   { return m_data.runOrder; }
-        virtual unsigned int rngSeed() const CATCH_OVERRIDE             { return m_data.rngSeed; }
-        virtual UseColour::YesOrNo useColour() const CATCH_OVERRIDE     { return m_data.useColour; }
-        virtual bool shouldDebugBreak() const CATCH_OVERRIDE { return m_data.shouldDebugBreak; }
-        virtual int abortAfter() const CATCH_OVERRIDE { return m_data.abortAfter; }
-        virtual bool showInvisibles() const CATCH_OVERRIDE { return m_data.showInvisibles; }
+            auto remainingTokens = tokens;
+            auto const &token = *remainingTokens;
+            if( token.type != TokenType::Argument )
+                return InternalParseResult::ok( ParseState( ParseResultType::NoMatch, remainingTokens ) );
 
-    private:
+            assert( !m_ref->isFlag() );
+            auto valueRef = static_cast<detail::BoundValueRefBase*>( m_ref.get() );
 
-        IStream const* openStream() {
-            if( m_data.outputFilename.empty() )
-                return new CoutStream();
-            else if( m_data.outputFilename[0] == '%' ) {
-                if( m_data.outputFilename == "%debug" )
-                    return new DebugOutStream();
-                else
-                    throw std::domain_error( "Unrecognised stream: " + m_data.outputFilename );
-            }
+            auto result = valueRef->setValue( remainingTokens->token );
+            if( !result )
+                return InternalParseResult( result );
             else
-                return new FileStream( m_data.outputFilename );
+                return InternalParseResult::ok( ParseState( ParseResultType::Matched, ++remainingTokens ) );
         }
-        ConfigData m_data;
-
-        CATCH_AUTO_PTR( IStream const ) m_stream;
-        TestSpec m_testSpec;
     };
 
-} // end namespace Catch
+    inline auto normaliseOpt( std::string const &optName ) -> std::string {
+#ifdef CATCH_PLATFORM_WINDOWS
+        if( optName[0] == '/' )
+            return "-" + optName.substr( 1 );
+        else
+#endif
+            return optName;
+    }
 
-// #included from: catch_clara.h
-#define TWOBLUECUBES_CATCH_CLARA_H_INCLUDED
+    class Opt : public ParserRefImpl<Opt> {
+    protected:
+        std::vector<std::string> m_optNames;
 
-// Use Catch's value for console width (store Clara's off to the side, if present)
-#ifdef CLARA_CONFIG_CONSOLE_WIDTH
-#define CATCH_TEMP_CLARA_CONFIG_CONSOLE_WIDTH CLARA_CONFIG_CONSOLE_WIDTH
-#undef CLARA_CONFIG_CONSOLE_WIDTH
-#endif
-#define CLARA_CONFIG_CONSOLE_WIDTH CATCH_CONFIG_CONSOLE_WIDTH
+    public:
+        template<typename LambdaT>
+        explicit Opt( LambdaT const &ref ) : ParserRefImpl( std::make_shared<BoundFlagLambda<LambdaT>>( ref ) ) {}
 
-// Declare Clara inside the Catch namespace
-#define STITCH_CLARA_OPEN_NAMESPACE namespace Catch {
-// #included from: ../external/clara.h
+        explicit Opt( bool &ref ) : ParserRefImpl( std::make_shared<BoundFlagRef>( ref ) ) {}
 
-// Version 0.0.2.4
+        template<typename LambdaT>
+        Opt( LambdaT const &ref, std::string const &hint ) : ParserRefImpl( ref, hint ) {}
 
-// Only use header guard if we are not using an outer namespace
-#if !defined(TWOBLUECUBES_CLARA_H_INCLUDED) || defined(STITCH_CLARA_OPEN_NAMESPACE)
+        template<typename T>
+        Opt( T &ref, std::string const &hint ) : ParserRefImpl( ref, hint ) {}
 
-#ifndef STITCH_CLARA_OPEN_NAMESPACE
-#define TWOBLUECUBES_CLARA_H_INCLUDED
-#define STITCH_CLARA_OPEN_NAMESPACE
-#define STITCH_CLARA_CLOSE_NAMESPACE
-#else
-#define STITCH_CLARA_CLOSE_NAMESPACE }
-#endif
+        auto operator[]( std::string const &optName ) -> Opt & {
+            m_optNames.push_back( optName );
+            return *this;
+        }
+
+        auto getHelpColumns() const -> std::vector<HelpColumns> {
+            std::ostringstream oss;
+            bool first = true;
+            for( auto const &opt : m_optNames ) {
+                if (first)
+                    first = false;
+                else
+                    oss << ", ";
+                oss << opt;
+            }
+            if( !m_hint.empty() )
+                oss << " <" << m_hint << ">";
+            return { { oss.str(), m_description } };
+        }
 
-#define STITCH_TBC_TEXT_FORMAT_OPEN_NAMESPACE STITCH_CLARA_OPEN_NAMESPACE
+        auto isMatch( std::string const &optToken ) const -> bool {
+            auto normalisedToken = normaliseOpt( optToken );
+            for( auto const &name : m_optNames ) {
+                if( normaliseOpt( name ) == normalisedToken )
+                    return true;
+            }
+            return false;
+        }
 
-// ----------- #included from tbc_text_format.h -----------
+        using ParserBase::parse;
+
+        auto parse( std::string const&, TokenStream const &tokens ) const -> InternalParseResult override {
+            auto validationResult = validate();
+            if( !validationResult )
+                return InternalParseResult( validationResult );
+
+            auto remainingTokens = tokens;
+            if( remainingTokens && remainingTokens->type == TokenType::Option ) {
+                auto const &token = *remainingTokens;
+                if( isMatch(token.token ) ) {
+                    if( m_ref->isFlag() ) {
+                        auto flagRef = static_cast<detail::BoundFlagRefBase*>( m_ref.get() );
+                        auto result = flagRef->setFlag( true );
+                        if( !result )
+                            return InternalParseResult( result );
+                        if( result.value() == ParseResultType::ShortCircuitAll )
+                            return InternalParseResult::ok( ParseState( result.value(), remainingTokens ) );
+                    } else {
+                        auto valueRef = static_cast<detail::BoundValueRefBase*>( m_ref.get() );
+                        ++remainingTokens;
+                        if( !remainingTokens )
+                            return InternalParseResult::runtimeError( "Expected argument following " + token.token );
+                        auto const &argToken = *remainingTokens;
+                        if( argToken.type != TokenType::Argument )
+                            return InternalParseResult::runtimeError( "Expected argument following " + token.token );
+                        auto result = valueRef->setValue( argToken.token );
+                        if( !result )
+                            return InternalParseResult( result );
+                        if( result.value() == ParseResultType::ShortCircuitAll )
+                            return InternalParseResult::ok( ParseState( result.value(), remainingTokens ) );
+                    }
+                    return InternalParseResult::ok( ParseState( ParseResultType::Matched, ++remainingTokens ) );
+                }
+            }
+            return InternalParseResult::ok( ParseState( ParseResultType::NoMatch, remainingTokens ) );
+        }
 
-// Only use header guard if we are not using an outer namespace
-#if !defined(TBC_TEXT_FORMAT_H_INCLUDED) || defined(STITCH_TBC_TEXT_FORMAT_OUTER_NAMESPACE)
-#ifndef STITCH_TBC_TEXT_FORMAT_OUTER_NAMESPACE
-#define TBC_TEXT_FORMAT_H_INCLUDED
+        auto validate() const -> Result override {
+            if( m_optNames.empty() )
+                return Result::logicError( "No options supplied to Opt" );
+            for( auto const &name : m_optNames ) {
+                if( name.empty() )
+                    return Result::logicError( "Option name cannot be empty" );
+#ifdef CATCH_PLATFORM_WINDOWS
+                if( name[0] != '-' && name[0] != '/' )
+                    return Result::logicError( "Option name must begin with '-' or '/'" );
+#else
+                if( name[0] != '-' )
+                    return Result::logicError( "Option name must begin with '-'" );
 #endif
+            }
+            return ParserRefImpl::validate();
+        }
+    };
 
-#include <string>
-#include <vector>
-#include <sstream>
-#include <algorithm>
-#include <cctype>
+    struct Help : Opt {
+        Help( bool &showHelpFlag )
+        :   Opt([&]( bool flag ) {
+                showHelpFlag = flag;
+                return ParserResult::ok( ParseResultType::ShortCircuitAll );
+            })
+        {
+            static_cast<Opt &>( *this )
+                    ("display usage information")
+                    ["-?"]["-h"]["--help"]
+                    .optional();
+        }
+    };
 
-// Use optional outer namespace
-#ifdef STITCH_TBC_TEXT_FORMAT_OUTER_NAMESPACE
-namespace STITCH_TBC_TEXT_FORMAT_OUTER_NAMESPACE {
-#endif
+    struct Parser : ParserBase {
 
-namespace Tbc {
+        mutable ExeName m_exeName;
+        std::vector<Opt> m_options;
+        std::vector<Arg> m_args;
 
-#ifdef TBC_TEXT_FORMAT_CONSOLE_WIDTH
-    const unsigned int consoleWidth = TBC_TEXT_FORMAT_CONSOLE_WIDTH;
-#else
-    const unsigned int consoleWidth = 80;
-#endif
+        auto operator|=( ExeName const &exeName ) -> Parser & {
+            m_exeName = exeName;
+            return *this;
+        }
 
-    struct TextAttributes {
-        TextAttributes()
-        :   initialIndent( std::string::npos ),
-            indent( 0 ),
-            width( consoleWidth-1 ),
-            tabChar( '\t' )
-        {}
+        auto operator|=( Arg const &arg ) -> Parser & {
+            m_args.push_back(arg);
+            return *this;
+        }
 
-        TextAttributes& setInitialIndent( std::size_t _value )  { initialIndent = _value; return *this; }
-        TextAttributes& setIndent( std::size_t _value )         { indent = _value; return *this; }
-        TextAttributes& setWidth( std::size_t _value )          { width = _value; return *this; }
-        TextAttributes& setTabChar( char _value )               { tabChar = _value; return *this; }
+        auto operator|=( Opt const &opt ) -> Parser & {
+            m_options.push_back(opt);
+            return *this;
+        }
 
-        std::size_t initialIndent;  // indent of first line, or npos
-        std::size_t indent;         // indent of subsequent lines, or all if initialIndent is npos
-        std::size_t width;          // maximum width of text, including indent. Longer text will wrap
-        char tabChar;               // If this char is seen the indent is changed to current pos
-    };
+        auto operator|=( Parser const &other ) -> Parser & {
+            m_options.insert(m_options.end(), other.m_options.begin(), other.m_options.end());
+            m_args.insert(m_args.end(), other.m_args.begin(), other.m_args.end());
+            return *this;
+        }
 
-    class Text {
-    public:
-        Text( std::string const& _str, TextAttributes const& _attr = TextAttributes() )
-        : attr( _attr )
-        {
-            std::string wrappableChars = " [({.,/|\\-";
-            std::size_t indent = _attr.initialIndent != std::string::npos
-                ? _attr.initialIndent
-                : _attr.indent;
-            std::string remainder = _str;
-
-            while( !remainder.empty() ) {
-                if( lines.size() >= 1000 ) {
-                    lines.push_back( "... message truncated due to excessive size" );
-                    return;
-                }
-                std::size_t tabPos = std::string::npos;
-                std::size_t width = (std::min)( remainder.size(), _attr.width - indent );
-                std::size_t pos = remainder.find_first_of( '\n' );
-                if( pos <= width ) {
-                    width = pos;
-                }
-                pos = remainder.find_last_of( _attr.tabChar, width );
-                if( pos != std::string::npos ) {
-                    tabPos = pos;
-                    if( remainder[width] == '\n' )
-                        width--;
-                    remainder = remainder.substr( 0, tabPos ) + remainder.substr( tabPos+1 );
-                }
+        template<typename T>
+        auto operator|( T const &other ) const -> Parser {
+            return Parser( *this ) |= other;
+        }
 
-                if( width == remainder.size() ) {
-                    spliceLine( indent, remainder, width );
-                }
-                else if( remainder[width] == '\n' ) {
-                    spliceLine( indent, remainder, width );
-                    if( width <= 1 || remainder.size() != 1 )
-                        remainder = remainder.substr( 1 );
-                    indent = _attr.indent;
-                }
-                else {
-                    pos = remainder.find_last_of( wrappableChars, width );
-                    if( pos != std::string::npos && pos > 0 ) {
-                        spliceLine( indent, remainder, pos );
-                        if( remainder[0] == ' ' )
-                            remainder = remainder.substr( 1 );
-                    }
-                    else {
-                        spliceLine( indent, remainder, width-1 );
-                        lines.back() += "-";
+        // Forward deprecated interface with '+' instead of '|'
+        template<typename T>
+        auto operator+=( T const &other ) -> Parser & { return operator|=( other ); }
+        template<typename T>
+        auto operator+( T const &other ) const -> Parser { return operator|( other ); }
+
+        auto getHelpColumns() const -> std::vector<HelpColumns> {
+            std::vector<HelpColumns> cols;
+            for (auto const &o : m_options) {
+                auto childCols = o.getHelpColumns();
+                cols.insert( cols.end(), childCols.begin(), childCols.end() );
+            }
+            return cols;
+        }
+
+        void writeToStream( std::ostream &os ) const {
+            if (!m_exeName.name().empty()) {
+                os << "usage:\n" << "  " << m_exeName.name() << " ";
+                bool required = true, first = true;
+                for( auto const &arg : m_args ) {
+                    if (first)
+                        first = false;
+                    else
+                        os << " ";
+                    if( arg.isOptional() && required ) {
+                        os << "[";
+                        required = false;
                     }
-                    if( lines.size() == 1 )
-                        indent = _attr.indent;
-                    if( tabPos != std::string::npos )
-                        indent += tabPos;
+                    os << "<" << arg.hint() << ">";
+                    if( arg.cardinality() == 0 )
+                        os << " ... ";
                 }
+                if( !required )
+                    os << "]";
+                if( !m_options.empty() )
+                    os << " options";
+                os << "\n\nwhere options are:" << std::endl;
+            }
+
+            auto rows = getHelpColumns();
+            size_t consoleWidth = CATCH_CLARA_CONFIG_CONSOLE_WIDTH;
+            size_t optWidth = 0;
+            for( auto const &cols : rows )
+                optWidth = (std::max)(optWidth, cols.left.size() + 2);
+
+            optWidth = (std::min)(optWidth, consoleWidth/2);
+
+            for( auto const &cols : rows ) {
+                auto row =
+                        TextFlow::Column( cols.left ).width( optWidth ).indent( 2 ) +
+                        TextFlow::Spacer(4) +
+                        TextFlow::Column( cols.right ).width( consoleWidth - 7 - optWidth );
+                os << row << std::endl;
             }
         }
 
-        void spliceLine( std::size_t _indent, std::string& _remainder, std::size_t _pos ) {
-            lines.push_back( std::string( _indent, ' ' ) + _remainder.substr( 0, _pos ) );
-            _remainder = _remainder.substr( _pos );
+        friend auto operator<<( std::ostream &os, Parser const &parser ) -> std::ostream& {
+            parser.writeToStream( os );
+            return os;
+        }
+
+        auto validate() const -> Result override {
+            for( auto const &opt : m_options ) {
+                auto result = opt.validate();
+                if( !result )
+                    return result;
+            }
+            for( auto const &arg : m_args ) {
+                auto result = arg.validate();
+                if( !result )
+                    return result;
+            }
+            return Result::ok();
         }
 
-        typedef std::vector<std::string>::const_iterator const_iterator;
+        using ParserBase::parse;
+
+        auto parse( std::string const& exeName, TokenStream const &tokens ) const -> InternalParseResult override {
+
+            struct ParserInfo {
+                ParserBase const* parser = nullptr;
+                size_t count = 0;
+            };
+            const size_t totalParsers = m_options.size() + m_args.size();
+            assert( totalParsers < 512 );
+            // ParserInfo parseInfos[totalParsers]; // <-- this is what we really want to do
+            ParserInfo parseInfos[512];
+
+            {
+                size_t i = 0;
+                for (auto const &opt : m_options) parseInfos[i++].parser = &opt;
+                for (auto const &arg : m_args) parseInfos[i++].parser = &arg;
+            }
 
-        const_iterator begin() const { return lines.begin(); }
-        const_iterator end() const { return lines.end(); }
-        std::string const& last() const { return lines.back(); }
-        std::size_t size() const { return lines.size(); }
-        std::string const& operator[]( std::size_t _index ) const { return lines[_index]; }
-        std::string toString() const {
-            std::ostringstream oss;
-            oss << *this;
-            return oss.str();
-        }
+            m_exeName.set( exeName );
+
+            auto result = InternalParseResult::ok( ParseState( ParseResultType::NoMatch, tokens ) );
+            while( result.value().remainingTokens() ) {
+                bool tokenParsed = false;
+
+                for( size_t i = 0; i < totalParsers; ++i ) {
+                    auto&  parseInfo = parseInfos[i];
+                    if( parseInfo.parser->cardinality() == 0 || parseInfo.count < parseInfo.parser->cardinality() ) {
+                        result = parseInfo.parser->parse(exeName, result.value().remainingTokens());
+                        if (!result)
+                            return result;
+                        if (result.value().type() != ParseResultType::NoMatch) {
+                            tokenParsed = true;
+                            ++parseInfo.count;
+                            break;
+                        }
+                    }
+                }
 
-        inline friend std::ostream& operator << ( std::ostream& _stream, Text const& _text ) {
-            for( Text::const_iterator it = _text.begin(), itEnd = _text.end();
-                it != itEnd; ++it ) {
-                if( it != _text.begin() )
-                    _stream << "\n";
-                _stream << *it;
+                if( result.value().type() == ParseResultType::ShortCircuitAll )
+                    return result;
+                if( !tokenParsed )
+                    return InternalParseResult::runtimeError( "Unrecognised token: " + result.value().remainingTokens()->token );
             }
-            return _stream;
+            // !TBD Check missing required options
+            return result;
         }
-
-    private:
-        std::string str;
-        TextAttributes attr;
-        std::vector<std::string> lines;
     };
 
-} // end namespace Tbc
-
-#ifdef STITCH_TBC_TEXT_FORMAT_OUTER_NAMESPACE
-} // end outer namespace
-#endif
-
-#endif // TBC_TEXT_FORMAT_H_INCLUDED
+    template<typename DerivedT>
+    template<typename T>
+    auto ComposableParserImpl<DerivedT>::operator|( T const &other ) const -> Parser {
+        return Parser() | static_cast<DerivedT const &>( *this ) | other;
+    }
+} // namespace detail
 
-// ----------- end of #include from tbc_text_format.h -----------
-// ........... back in clara.h
+// A Combined parser
+using detail::Parser;
 
-#undef STITCH_TBC_TEXT_FORMAT_OPEN_NAMESPACE
+// A parser for options
+using detail::Opt;
 
-// ----------- #included from clara_compilers.h -----------
+// A parser for arguments
+using detail::Arg;
 
-#ifndef TWOBLUECUBES_CLARA_COMPILERS_H_INCLUDED
-#define TWOBLUECUBES_CLARA_COMPILERS_H_INCLUDED
+// Wrapper for argc, argv from main()
+using detail::Args;
 
-// Detect a number of compiler features - mostly C++11/14 conformance - by compiler
-// The following features are defined:
-//
-// CLARA_CONFIG_CPP11_NULLPTR : is nullptr supported?
-// CLARA_CONFIG_CPP11_NOEXCEPT : is noexcept supported?
-// CLARA_CONFIG_CPP11_GENERATED_METHODS : The delete and default keywords for compiler generated methods
-// CLARA_CONFIG_CPP11_OVERRIDE : is override supported?
-// CLARA_CONFIG_CPP11_UNIQUE_PTR : is unique_ptr supported (otherwise use auto_ptr)
+// Specifies the name of the executable
+using detail::ExeName;
 
-// CLARA_CONFIG_CPP11_OR_GREATER : Is C++11 supported?
+// Convenience wrapper for option parser that specifies the help option
+using detail::Help;
 
-// CLARA_CONFIG_VARIADIC_MACROS : are variadic macros supported?
+// enum of result types from a parse
+using detail::ParseResultType;
 
-// In general each macro has a _NO_<feature name> form
-// (e.g. CLARA_CONFIG_CPP11_NO_NULLPTR) which disables the feature.
-// Many features, at point of detection, define an _INTERNAL_ macro, so they
-// can be combined, en-mass, with the _NO_ forms later.
+// Result type for parser operation
+using detail::ParserResult;
 
-// All the C++11 features can be disabled with CLARA_CONFIG_NO_CPP11
+}} // namespace Catch::clara
 
+// end clara.hpp
 #ifdef __clang__
-
-#if __has_feature(cxx_nullptr)
-#define CLARA_INTERNAL_CONFIG_CPP11_NULLPTR
+#pragma clang diagnostic pop
 #endif
 
-#if __has_feature(cxx_noexcept)
-#define CLARA_INTERNAL_CONFIG_CPP11_NOEXCEPT
+// Restore Clara's value for console width, if present
+#ifdef CATCH_TEMP_CLARA_CONFIG_CONSOLE_WIDTH
+#define CATCH_CLARA_TEXTFLOW_CONFIG_CONSOLE_WIDTH CATCH_TEMP_CLARA_CONFIG_CONSOLE_WIDTH
+#undef CATCH_TEMP_CLARA_CONFIG_CONSOLE_WIDTH
 #endif
 
-#endif // __clang__
+// end catch_clara.h
+namespace Catch {
 
-////////////////////////////////////////////////////////////////////////////////
-// GCC
-#ifdef __GNUC__
+    clara::Parser makeCommandLineParser( ConfigData& config );
 
-#if __GNUC__ == 4 && __GNUC_MINOR__ >= 6 && defined(__GXX_EXPERIMENTAL_CXX0X__)
-#define CLARA_INTERNAL_CONFIG_CPP11_NULLPTR
-#endif
+} // end namespace Catch
 
-// - otherwise more recent versions define __cplusplus >= 201103L
-// and will get picked up below
+// end catch_commandline.h
+#include <fstream>
+#include <ctime>
 
-#endif // __GNUC__
+namespace Catch {
 
-////////////////////////////////////////////////////////////////////////////////
-// Visual C++
-#ifdef _MSC_VER
+    clara::Parser makeCommandLineParser( ConfigData& config ) {
 
-#if (_MSC_VER >= 1600)
-#define CLARA_INTERNAL_CONFIG_CPP11_NULLPTR
-#define CLARA_INTERNAL_CONFIG_CPP11_UNIQUE_PTR
-#endif
+        using namespace clara;
 
-#if (_MSC_VER >= 1900 ) // (VC++ 13 (VS2015))
-#define CLARA_INTERNAL_CONFIG_CPP11_NOEXCEPT
-#define CLARA_INTERNAL_CONFIG_CPP11_GENERATED_METHODS
-#endif
+        auto const setWarning = [&]( std::string const& warning ) {
+                auto warningSet = [&]() {
+                    if( warning == "NoAssertions" )
+                        return WarnAbout::NoAssertions;
 
-#endif // _MSC_VER
+                    if ( warning == "NoTests" )
+                        return WarnAbout::NoTests;
 
-////////////////////////////////////////////////////////////////////////////////
-// C++ language feature support
+                    return WarnAbout::Nothing;
+                }();
 
-// catch all support for C++11
-#if defined(__cplusplus) && __cplusplus >= 201103L
+                if (warningSet == WarnAbout::Nothing)
+                    return ParserResult::runtimeError( "Unrecognised warning: '" + warning + "'" );
+                config.warnings = static_cast<WarnAbout::What>( config.warnings | warningSet );
+                return ParserResult::ok( ParseResultType::Matched );
+            };
+        auto const loadTestNamesFromFile = [&]( std::string const& filename ) {
+                std::ifstream f( filename.c_str() );
+                if( !f.is_open() )
+                    return ParserResult::runtimeError( "Unable to load input file: '" + filename + "'" );
+
+                std::string line;
+                while( std::getline( f, line ) ) {
+                    line = trim(line);
+                    if( !line.empty() && !startsWith( line, '#' ) ) {
+                        if( !startsWith( line, '"' ) )
+                            line = '"' + line + '"';
+                        config.testsOrTags.push_back( line );
+                        config.testsOrTags.push_back( "," );
 
-#define CLARA_CPP11_OR_GREATER
+                    }
+                }
+                //Remove comma in the end
+                if(!config.testsOrTags.empty())
+                    config.testsOrTags.erase( config.testsOrTags.end()-1 );
 
-#if !defined(CLARA_INTERNAL_CONFIG_CPP11_NULLPTR)
-#define CLARA_INTERNAL_CONFIG_CPP11_NULLPTR
-#endif
+                return ParserResult::ok( ParseResultType::Matched );
+            };
+        auto const setTestOrder = [&]( std::string const& order ) {
+                if( startsWith( "declared", order ) )
+                    config.runOrder = RunTests::InDeclarationOrder;
+                else if( startsWith( "lexical", order ) )
+                    config.runOrder = RunTests::InLexicographicalOrder;
+                else if( startsWith( "random", order ) )
+                    config.runOrder = RunTests::InRandomOrder;
+                else
+                    return clara::ParserResult::runtimeError( "Unrecognised ordering: '" + order + "'" );
+                return ParserResult::ok( ParseResultType::Matched );
+            };
+        auto const setRngSeed = [&]( std::string const& seed ) {
+                if( seed != "time" )
+                    return clara::detail::convertInto( seed, config.rngSeed );
+                config.rngSeed = static_cast<unsigned int>( std::time(nullptr) );
+                return ParserResult::ok( ParseResultType::Matched );
+            };
+        auto const setColourUsage = [&]( std::string const& useColour ) {
+                    auto mode = toLower( useColour );
+
+                    if( mode == "yes" )
+                        config.useColour = UseColour::Yes;
+                    else if( mode == "no" )
+                        config.useColour = UseColour::No;
+                    else if( mode == "auto" )
+                        config.useColour = UseColour::Auto;
+                    else
+                        return ParserResult::runtimeError( "colour mode must be one of: auto, yes or no. '" + useColour + "' not recognised" );
+                return ParserResult::ok( ParseResultType::Matched );
+            };
+        auto const setWaitForKeypress = [&]( std::string const& keypress ) {
+                auto keypressLc = toLower( keypress );
+                if( keypressLc == "start" )
+                    config.waitForKeypress = WaitForKeypress::BeforeStart;
+                else if( keypressLc == "exit" )
+                    config.waitForKeypress = WaitForKeypress::BeforeExit;
+                else if( keypressLc == "both" )
+                    config.waitForKeypress = WaitForKeypress::BeforeStartAndExit;
+                else
+                    return ParserResult::runtimeError( "keypress argument must be one of: start, exit or both. '" + keypress + "' not recognised" );
+            return ParserResult::ok( ParseResultType::Matched );
+            };
+        auto const setVerbosity = [&]( std::string const& verbosity ) {
+            auto lcVerbosity = toLower( verbosity );
+            if( lcVerbosity == "quiet" )
+                config.verbosity = Verbosity::Quiet;
+            else if( lcVerbosity == "normal" )
+                config.verbosity = Verbosity::Normal;
+            else if( lcVerbosity == "high" )
+                config.verbosity = Verbosity::High;
+            else
+                return ParserResult::runtimeError( "Unrecognised verbosity, '" + verbosity + "'" );
+            return ParserResult::ok( ParseResultType::Matched );
+        };
+        auto const setReporter = [&]( std::string const& reporter ) {
+            IReporterRegistry::FactoryMap const& factories = getRegistryHub().getReporterRegistry().getFactories();
 
-#ifndef CLARA_INTERNAL_CONFIG_CPP11_NOEXCEPT
-#define CLARA_INTERNAL_CONFIG_CPP11_NOEXCEPT
-#endif
+            auto lcReporter = toLower( reporter );
+            auto result = factories.find( lcReporter );
 
-#ifndef CLARA_INTERNAL_CONFIG_CPP11_GENERATED_METHODS
-#define CLARA_INTERNAL_CONFIG_CPP11_GENERATED_METHODS
-#endif
+            if( factories.end() != result )
+                config.reporterName = lcReporter;
+            else
+                return ParserResult::runtimeError( "Unrecognized reporter, '" + reporter + "'. Check available with --list-reporters" );
+            return ParserResult::ok( ParseResultType::Matched );
+        };
 
-#if !defined(CLARA_INTERNAL_CONFIG_CPP11_OVERRIDE)
-#define CLARA_INTERNAL_CONFIG_CPP11_OVERRIDE
-#endif
-#if !defined(CLARA_INTERNAL_CONFIG_CPP11_UNIQUE_PTR)
-#define CLARA_INTERNAL_CONFIG_CPP11_UNIQUE_PTR
-#endif
+        auto cli
+            = ExeName( config.processName )
+            | Help( config.showHelp )
+            | Opt( config.listTests )
+                ["-l"]["--list-tests"]
+                ( "list all/matching test cases" )
+            | Opt( config.listTags )
+                ["-t"]["--list-tags"]
+                ( "list all/matching tags" )
+            | Opt( config.showSuccessfulTests )
+                ["-s"]["--success"]
+                ( "include successful tests in output" )
+            | Opt( config.shouldDebugBreak )
+                ["-b"]["--break"]
+                ( "break into debugger on failure" )
+            | Opt( config.noThrow )
+                ["-e"]["--nothrow"]
+                ( "skip exception tests" )
+            | Opt( config.showInvisibles )
+                ["-i"]["--invisibles"]
+                ( "show invisibles (tabs, newlines)" )
+            | Opt( config.outputFilename, "filename" )
+                ["-o"]["--out"]
+                ( "output filename" )
+            | Opt( setReporter, "name" )
+                ["-r"]["--reporter"]
+                ( "reporter to use (defaults to console)" )
+            | Opt( config.name, "name" )
+                ["-n"]["--name"]
+                ( "suite name" )
+            | Opt( [&]( bool ){ config.abortAfter = 1; } )
+                ["-a"]["--abort"]
+                ( "abort at first failure" )
+            | Opt( [&]( int x ){ config.abortAfter = x; }, "no. failures" )
+                ["-x"]["--abortx"]
+                ( "abort after x failures" )
+            | Opt( setWarning, "warning name" )
+                ["-w"]["--warn"]
+                ( "enable warnings" )
+            | Opt( [&]( bool flag ) { config.showDurations = flag ? ShowDurations::Always : ShowDurations::Never; }, "yes|no" )
+                ["-d"]["--durations"]
+                ( "show test durations" )
+            | Opt( loadTestNamesFromFile, "filename" )
+                ["-f"]["--input-file"]
+                ( "load test names to run from a file" )
+            | Opt( config.filenamesAsTags )
+                ["-#"]["--filenames-as-tags"]
+                ( "adds a tag for the filename" )
+            | Opt( config.sectionsToRun, "section name" )
+                ["-c"]["--section"]
+                ( "specify section to run" )
+            | Opt( setVerbosity, "quiet|normal|high" )
+                ["-v"]["--verbosity"]
+                ( "set output verbosity" )
+            | Opt( config.listTestNamesOnly )
+                ["--list-test-names-only"]
+                ( "list all/matching test cases names only" )
+            | Opt( config.listReporters )
+                ["--list-reporters"]
+                ( "list all reporters" )
+            | Opt( setTestOrder, "decl|lex|rand" )
+                ["--order"]
+                ( "test case order (defaults to decl)" )
+            | Opt( setRngSeed, "'time'|number" )
+                ["--rng-seed"]
+                ( "set a specific seed for random numbers" )
+            | Opt( setColourUsage, "yes|no" )
+                ["--use-colour"]
+                ( "should output be colourised" )
+            | Opt( config.libIdentify )
+                ["--libidentify"]
+                ( "report name and version according to libidentify standard" )
+            | Opt( setWaitForKeypress, "start|exit|both" )
+                ["--wait-for-keypress"]
+                ( "waits for a keypress before exiting" )
+            | Opt( config.benchmarkSamples, "samples" )
+                ["--benchmark-samples"]
+                ( "number of samples to collect (default: 100)" )
+            | Opt( config.benchmarkResamples, "resamples" )
+                ["--benchmark-resamples"]
+                ( "number of resamples for the bootstrap (default: 100000)" )
+            | Opt( config.benchmarkConfidenceInterval, "confidence interval" )
+                ["--benchmark-confidence-interval"]
+                ( "confidence interval for the bootstrap (between 0 and 1, default: 0.95)" )
+            | Opt( config.benchmarkNoAnalysis )
+                ["--benchmark-no-analysis"]
+                ( "perform only measurements; do not perform any analysis" )
+			| Arg( config.testsOrTags, "test name|pattern|tags" )
+                ( "which test or tests to use" );
 
-#endif // __cplusplus >= 201103L
+        return cli;
+    }
 
-// Now set the actual defines based on the above + anything the user has configured
-#if defined(CLARA_INTERNAL_CONFIG_CPP11_NULLPTR) && !defined(CLARA_CONFIG_CPP11_NO_NULLPTR) && !defined(CLARA_CONFIG_CPP11_NULLPTR) && !defined(CLARA_CONFIG_NO_CPP11)
-#define CLARA_CONFIG_CPP11_NULLPTR
-#endif
-#if defined(CLARA_INTERNAL_CONFIG_CPP11_NOEXCEPT) && !defined(CLARA_CONFIG_CPP11_NO_NOEXCEPT) && !defined(CLARA_CONFIG_CPP11_NOEXCEPT) && !defined(CLARA_CONFIG_NO_CPP11)
-#define CLARA_CONFIG_CPP11_NOEXCEPT
-#endif
-#if defined(CLARA_INTERNAL_CONFIG_CPP11_GENERATED_METHODS) && !defined(CLARA_CONFIG_CPP11_NO_GENERATED_METHODS) && !defined(CLARA_CONFIG_CPP11_GENERATED_METHODS) && !defined(CLARA_CONFIG_NO_CPP11)
-#define CLARA_CONFIG_CPP11_GENERATED_METHODS
-#endif
-#if defined(CLARA_INTERNAL_CONFIG_CPP11_OVERRIDE) && !defined(CLARA_CONFIG_NO_OVERRIDE) && !defined(CLARA_CONFIG_CPP11_OVERRIDE) && !defined(CLARA_CONFIG_NO_CPP11)
-#define CLARA_CONFIG_CPP11_OVERRIDE
-#endif
-#if defined(CLARA_INTERNAL_CONFIG_CPP11_UNIQUE_PTR) && !defined(CLARA_CONFIG_NO_UNIQUE_PTR) && !defined(CLARA_CONFIG_CPP11_UNIQUE_PTR) && !defined(CLARA_CONFIG_NO_CPP11)
-#define CLARA_CONFIG_CPP11_UNIQUE_PTR
-#endif
+} // end namespace Catch
+// end catch_commandline.cpp
+// start catch_common.cpp
 
-// noexcept support:
-#if defined(CLARA_CONFIG_CPP11_NOEXCEPT) && !defined(CLARA_NOEXCEPT)
-#define CLARA_NOEXCEPT noexcept
-#  define CLARA_NOEXCEPT_IS(x) noexcept(x)
-#else
-#define CLARA_NOEXCEPT throw()
-#  define CLARA_NOEXCEPT_IS(x)
-#endif
+#include <cstring>
+#include <ostream>
 
-// nullptr support
-#ifdef CLARA_CONFIG_CPP11_NULLPTR
-#define CLARA_NULL nullptr
-#else
-#define CLARA_NULL NULL
-#endif
+namespace Catch {
 
-// override support
-#ifdef CLARA_CONFIG_CPP11_OVERRIDE
-#define CLARA_OVERRIDE override
-#else
-#define CLARA_OVERRIDE
-#endif
+    bool SourceLineInfo::operator == ( SourceLineInfo const& other ) const noexcept {
+        return line == other.line && (file == other.file || std::strcmp(file, other.file) == 0);
+    }
+    bool SourceLineInfo::operator < ( SourceLineInfo const& other ) const noexcept {
+        // We can assume that the same file will usually have the same pointer.
+        // Thus, if the pointers are the same, there is no point in calling the strcmp
+        return line < other.line || ( line == other.line && file != other.file && (std::strcmp(file, other.file) < 0));
+    }
 
-// unique_ptr support
-#ifdef CLARA_CONFIG_CPP11_UNIQUE_PTR
-#   define CLARA_AUTO_PTR( T ) std::unique_ptr<T>
+    std::ostream& operator << ( std::ostream& os, SourceLineInfo const& info ) {
+#ifndef __GNUG__
+        os << info.file << '(' << info.line << ')';
 #else
-#   define CLARA_AUTO_PTR( T ) std::auto_ptr<T>
+        os << info.file << ':' << info.line;
 #endif
+        return os;
+    }
 
-#endif // TWOBLUECUBES_CLARA_COMPILERS_H_INCLUDED
+    std::string StreamEndStop::operator+() const {
+        return std::string();
+    }
 
-// ----------- end of #include from clara_compilers.h -----------
-// ........... back in clara.h
+    NonCopyable::NonCopyable() = default;
+    NonCopyable::~NonCopyable() = default;
 
-#include <map>
-#include <stdexcept>
-#include <memory>
+}
+// end catch_common.cpp
+// start catch_config.cpp
 
-#if defined(WIN32) || defined(__WIN32__) || defined(_WIN32) || defined(_MSC_VER)
-#define CLARA_PLATFORM_WINDOWS
-#endif
+namespace Catch {
 
-// Use optional outer namespace
-#ifdef STITCH_CLARA_OPEN_NAMESPACE
-STITCH_CLARA_OPEN_NAMESPACE
-#endif
+    Config::Config( ConfigData const& data )
+    :   m_data( data ),
+        m_stream( openStream() )
+    {
+        // We need to trim filter specs to avoid trouble with superfluous
+        // whitespace (esp. important for bdd macros, as those are manually
+        // aligned with whitespace).
+
+        for (auto& elem : m_data.testsOrTags) {
+            elem = trim(elem);
+        }
+        for (auto& elem : m_data.sectionsToRun) {
+            elem = trim(elem);
+        }
 
-namespace Clara {
+        TestSpecParser parser(ITagAliasRegistry::get());
+        if (!m_data.testsOrTags.empty()) {
+            m_hasTestFilters = true;
+            for (auto const& testOrTags : m_data.testsOrTags) {
+                parser.parse(testOrTags);
+            }
+        }
+        m_testSpec = parser.testSpec();
+    }
 
-    struct UnpositionalTag {};
+    std::string const& Config::getFilename() const {
+        return m_data.outputFilename ;
+    }
 
-    extern UnpositionalTag _;
+    bool Config::listTests() const          { return m_data.listTests; }
+    bool Config::listTestNamesOnly() const  { return m_data.listTestNamesOnly; }
+    bool Config::listTags() const           { return m_data.listTags; }
+    bool Config::listReporters() const      { return m_data.listReporters; }
 
-#ifdef CLARA_CONFIG_MAIN
-    UnpositionalTag _;
-#endif
+    std::string Config::getProcessName() const { return m_data.processName; }
+    std::string const& Config::getReporterName() const { return m_data.reporterName; }
 
-    namespace Detail {
+    std::vector<std::string> const& Config::getTestsOrTags() const { return m_data.testsOrTags; }
+    std::vector<std::string> const& Config::getSectionsToRun() const { return m_data.sectionsToRun; }
 
-#ifdef CLARA_CONSOLE_WIDTH
-    const unsigned int consoleWidth = CLARA_CONFIG_CONSOLE_WIDTH;
-#else
-    const unsigned int consoleWidth = 80;
-#endif
+    TestSpec const& Config::testSpec() const { return m_testSpec; }
+    bool Config::hasTestFilters() const { return m_hasTestFilters; }
 
-        using namespace Tbc;
+    bool Config::showHelp() const { return m_data.showHelp; }
 
-        inline bool startsWith( std::string const& str, std::string const& prefix ) {
-            return str.size() >= prefix.size() && str.substr( 0, prefix.size() ) == prefix;
-        }
+    // IConfig interface
+    bool Config::allowThrows() const                   { return !m_data.noThrow; }
+    std::ostream& Config::stream() const               { return m_stream->stream(); }
+    std::string Config::name() const                   { return m_data.name.empty() ? m_data.processName : m_data.name; }
+    bool Config::includeSuccessfulResults() const      { return m_data.showSuccessfulTests; }
+    bool Config::warnAboutMissingAssertions() const    { return !!(m_data.warnings & WarnAbout::NoAssertions); }
+    bool Config::warnAboutNoTests() const              { return !!(m_data.warnings & WarnAbout::NoTests); }
+    ShowDurations::OrNot Config::showDurations() const { return m_data.showDurations; }
+    RunTests::InWhatOrder Config::runOrder() const     { return m_data.runOrder; }
+    unsigned int Config::rngSeed() const               { return m_data.rngSeed; }
+    UseColour::YesOrNo Config::useColour() const       { return m_data.useColour; }
+    bool Config::shouldDebugBreak() const              { return m_data.shouldDebugBreak; }
+    int Config::abortAfter() const                     { return m_data.abortAfter; }
+    bool Config::showInvisibles() const                { return m_data.showInvisibles; }
+    Verbosity Config::verbosity() const                { return m_data.verbosity; }
 
-        template<typename T> struct RemoveConstRef{ typedef T type; };
-        template<typename T> struct RemoveConstRef<T&>{ typedef T type; };
-        template<typename T> struct RemoveConstRef<T const&>{ typedef T type; };
-        template<typename T> struct RemoveConstRef<T const>{ typedef T type; };
+    bool Config::benchmarkNoAnalysis() const           { return m_data.benchmarkNoAnalysis; }
+    int Config::benchmarkSamples() const               { return m_data.benchmarkSamples; }
+    double Config::benchmarkConfidenceInterval() const { return m_data.benchmarkConfidenceInterval; }
+    unsigned int Config::benchmarkResamples() const    { return m_data.benchmarkResamples; }
 
-        template<typename T>    struct IsBool       { static const bool value = false; };
-        template<>              struct IsBool<bool> { static const bool value = true; };
+    IStream const* Config::openStream() {
+        return Catch::makeStream(m_data.outputFilename);
+    }
 
-        template<typename T>
-        void convertInto( std::string const& _source, T& _dest ) {
-            std::stringstream ss;
-            ss << _source;
-            ss >> _dest;
-            if( ss.fail() )
-                throw std::runtime_error( "Unable to convert " + _source + " to destination type" );
-        }
-        inline void convertInto( std::string const& _source, std::string& _dest ) {
-            _dest = _source;
-        }
-        char toLowerCh(char c) {
-            return static_cast<char>( std::tolower( c ) );
-        }
-        inline void convertInto( std::string const& _source, bool& _dest ) {
-            std::string sourceLC = _source;
-            std::transform( sourceLC.begin(), sourceLC.end(), sourceLC.begin(), toLowerCh );
-            if( sourceLC == "y" || sourceLC == "1" || sourceLC == "true" || sourceLC == "yes" || sourceLC == "on" )
-                _dest = true;
-            else if( sourceLC == "n" || sourceLC == "0" || sourceLC == "false" || sourceLC == "no" || sourceLC == "off" )
-                _dest = false;
-            else
-                throw std::runtime_error( "Expected a boolean value but did not recognise:\n  '" + _source + "'" );
-        }
+} // end namespace Catch
+// end catch_config.cpp
+// start catch_console_colour.cpp
 
-        template<typename ConfigT>
-        struct IArgFunction {
-            virtual ~IArgFunction() {}
-#ifdef CLARA_CONFIG_CPP11_GENERATED_METHODS
-            IArgFunction()                      = default;
-            IArgFunction( IArgFunction const& ) = default;
+#if defined(__clang__)
+#    pragma clang diagnostic push
+#    pragma clang diagnostic ignored "-Wexit-time-destructors"
 #endif
-            virtual void set( ConfigT& config, std::string const& value ) const = 0;
-            virtual bool takesArg() const = 0;
-            virtual IArgFunction* clone() const = 0;
-        };
 
-        template<typename ConfigT>
-        class BoundArgFunction {
-        public:
-            BoundArgFunction() : functionObj( CLARA_NULL ) {}
-            BoundArgFunction( IArgFunction<ConfigT>* _functionObj ) : functionObj( _functionObj ) {}
-            BoundArgFunction( BoundArgFunction const& other ) : functionObj( other.functionObj ? other.functionObj->clone() : CLARA_NULL ) {}
-            BoundArgFunction& operator = ( BoundArgFunction const& other ) {
-                IArgFunction<ConfigT>* newFunctionObj = other.functionObj ? other.functionObj->clone() : CLARA_NULL;
-                delete functionObj;
-                functionObj = newFunctionObj;
-                return *this;
-            }
-            ~BoundArgFunction() { delete functionObj; }
+// start catch_errno_guard.h
 
-            void set( ConfigT& config, std::string const& value ) const {
-                functionObj->set( config, value );
-            }
-            bool takesArg() const { return functionObj->takesArg(); }
+namespace Catch {
 
-            bool isSet() const {
-                return functionObj != CLARA_NULL;
-            }
-        private:
-            IArgFunction<ConfigT>* functionObj;
-        };
+    class ErrnoGuard {
+    public:
+        ErrnoGuard();
+        ~ErrnoGuard();
+    private:
+        int m_oldErrno;
+    };
 
-        template<typename C>
-        struct NullBinder : IArgFunction<C>{
-            virtual void set( C&, std::string const& ) const {}
-            virtual bool takesArg() const { return true; }
-            virtual IArgFunction<C>* clone() const { return new NullBinder( *this ); }
-        };
+}
 
-        template<typename C, typename M>
-        struct BoundDataMember : IArgFunction<C>{
-            BoundDataMember( M C::* _member ) : member( _member ) {}
-            virtual void set( C& p, std::string const& stringValue ) const {
-                convertInto( stringValue, p.*member );
-            }
-            virtual bool takesArg() const { return !IsBool<M>::value; }
-            virtual IArgFunction<C>* clone() const { return new BoundDataMember( *this ); }
-            M C::* member;
-        };
-        template<typename C, typename M>
-        struct BoundUnaryMethod : IArgFunction<C>{
-            BoundUnaryMethod( void (C::*_member)( M ) ) : member( _member ) {}
-            virtual void set( C& p, std::string const& stringValue ) const {
-                typename RemoveConstRef<M>::type value;
-                convertInto( stringValue, value );
-                (p.*member)( value );
-            }
-            virtual bool takesArg() const { return !IsBool<M>::value; }
-            virtual IArgFunction<C>* clone() const { return new BoundUnaryMethod( *this ); }
-            void (C::*member)( M );
-        };
-        template<typename C>
-        struct BoundNullaryMethod : IArgFunction<C>{
-            BoundNullaryMethod( void (C::*_member)() ) : member( _member ) {}
-            virtual void set( C& p, std::string const& stringValue ) const {
-                bool value;
-                convertInto( stringValue, value );
-                if( value )
-                    (p.*member)();
-            }
-            virtual bool takesArg() const { return false; }
-            virtual IArgFunction<C>* clone() const { return new BoundNullaryMethod( *this ); }
-            void (C::*member)();
-        };
+// end catch_errno_guard.h
+#include <sstream>
 
-        template<typename C>
-        struct BoundUnaryFunction : IArgFunction<C>{
-            BoundUnaryFunction( void (*_function)( C& ) ) : function( _function ) {}
-            virtual void set( C& obj, std::string const& stringValue ) const {
-                bool value;
-                convertInto( stringValue, value );
-                if( value )
-                    function( obj );
-            }
-            virtual bool takesArg() const { return false; }
-            virtual IArgFunction<C>* clone() const { return new BoundUnaryFunction( *this ); }
-            void (*function)( C& );
-        };
+namespace Catch {
+    namespace {
 
-        template<typename C, typename T>
-        struct BoundBinaryFunction : IArgFunction<C>{
-            BoundBinaryFunction( void (*_function)( C&, T ) ) : function( _function ) {}
-            virtual void set( C& obj, std::string const& stringValue ) const {
-                typename RemoveConstRef<T>::type value;
-                convertInto( stringValue, value );
-                function( obj, value );
-            }
-            virtual bool takesArg() const { return !IsBool<T>::value; }
-            virtual IArgFunction<C>* clone() const { return new BoundBinaryFunction( *this ); }
-            void (*function)( C&, T );
+        struct IColourImpl {
+            virtual ~IColourImpl() = default;
+            virtual void use( Colour::Code _colourCode ) = 0;
         };
 
-    } // namespace Detail
-
-    inline std::vector<std::string> argsToVector( int argc, char const* const* const argv ) {
-        std::vector<std::string> args( static_cast<std::size_t>( argc ) );
-        for( std::size_t i = 0; i < static_cast<std::size_t>( argc ); ++i )
-            args[i] = argv[i];
-
-        return args;
-    }
-
-    class Parser {
-        enum Mode { None, MaybeShortOpt, SlashOpt, ShortOpt, LongOpt, Positional };
-        Mode mode;
-        std::size_t from;
-        bool inQuotes;
-    public:
+        struct NoColourImpl : IColourImpl {
+            void use( Colour::Code ) {}
 
-        struct Token {
-            enum Type { Positional, ShortOpt, LongOpt };
-            Token( Type _type, std::string const& _data ) : type( _type ), data( _data ) {}
-            Type type;
-            std::string data;
+            static IColourImpl* instance() {
+                static NoColourImpl s_instance;
+                return &s_instance;
+            }
         };
 
-        Parser() : mode( None ), from( 0 ), inQuotes( false ){}
-
-        void parseIntoTokens( std::vector<std::string> const& args, std::vector<Token>& tokens ) {
-            const std::string doubleDash = "--";
-            for( std::size_t i = 1; i < args.size() && args[i] != doubleDash; ++i )
-                parseIntoTokens( args[i], tokens);
-        }
-
-        void parseIntoTokens( std::string const& arg, std::vector<Token>& tokens ) {
-            for( std::size_t i = 0; i < arg.size(); ++i ) {
-                char c = arg[i];
-                if( c == '"' )
-                    inQuotes = !inQuotes;
-                mode = handleMode( i, c, arg, tokens );
-            }
-            mode = handleMode( arg.size(), '\0', arg, tokens );
-        }
-        Mode handleMode( std::size_t i, char c, std::string const& arg, std::vector<Token>& tokens ) {
-            switch( mode ) {
-                case None: return handleNone( i, c );
-                case MaybeShortOpt: return handleMaybeShortOpt( i, c );
-                case ShortOpt:
-                case LongOpt:
-                case SlashOpt: return handleOpt( i, c, arg, tokens );
-                case Positional: return handlePositional( i, c, arg, tokens );
-                default: throw std::logic_error( "Unknown mode" );
-            }
-        }
+    } // anon namespace
+} // namespace Catch
 
-        Mode handleNone( std::size_t i, char c ) {
-            if( inQuotes ) {
-                from = i;
-                return Positional;
-            }
-            switch( c ) {
-                case '-': return MaybeShortOpt;
-#ifdef CLARA_PLATFORM_WINDOWS
-                case '/': from = i+1; return SlashOpt;
+#if !defined( CATCH_CONFIG_COLOUR_NONE ) && !defined( CATCH_CONFIG_COLOUR_WINDOWS ) && !defined( CATCH_CONFIG_COLOUR_ANSI )
+#   ifdef CATCH_PLATFORM_WINDOWS
+#       define CATCH_CONFIG_COLOUR_WINDOWS
+#   else
+#       define CATCH_CONFIG_COLOUR_ANSI
+#   endif
 #endif
-                default: from = i; return Positional;
-            }
-        }
-        Mode handleMaybeShortOpt( std::size_t i, char c ) {
-            switch( c ) {
-                case '-': from = i+1; return LongOpt;
-                default: from = i; return ShortOpt;
-            }
-        }
 
-        Mode handleOpt( std::size_t i, char c, std::string const& arg, std::vector<Token>& tokens ) {
-            if( std::string( ":=\0", 3 ).find( c ) == std::string::npos )
-                return mode;
+#if defined ( CATCH_CONFIG_COLOUR_WINDOWS ) /////////////////////////////////////////
 
-            std::string optName = arg.substr( from, i-from );
-            if( mode == ShortOpt )
-                for( std::size_t j = 0; j < optName.size(); ++j )
-                    tokens.push_back( Token( Token::ShortOpt, optName.substr( j, 1 ) ) );
-            else if( mode == SlashOpt && optName.size() == 1 )
-                tokens.push_back( Token( Token::ShortOpt, optName ) );
-            else
-                tokens.push_back( Token( Token::LongOpt, optName ) );
-            return None;
-        }
-        Mode handlePositional( std::size_t i, char c, std::string const& arg, std::vector<Token>& tokens ) {
-            if( inQuotes || std::string( "\0", 1 ).find( c ) == std::string::npos )
-                return mode;
+namespace Catch {
+namespace {
 
-            std::string data = arg.substr( from, i-from );
-            tokens.push_back( Token( Token::Positional, data ) );
-            return None;
+    class Win32ColourImpl : public IColourImpl {
+    public:
+        Win32ColourImpl() : stdoutHandle( GetStdHandle(STD_OUTPUT_HANDLE) )
+        {
+            CONSOLE_SCREEN_BUFFER_INFO csbiInfo;
+            GetConsoleScreenBufferInfo( stdoutHandle, &csbiInfo );
+            originalForegroundAttributes = csbiInfo.wAttributes & ~( BACKGROUND_GREEN | BACKGROUND_RED | BACKGROUND_BLUE | BACKGROUND_INTENSITY );
+            originalBackgroundAttributes = csbiInfo.wAttributes & ~( FOREGROUND_GREEN | FOREGROUND_RED | FOREGROUND_BLUE | FOREGROUND_INTENSITY );
         }
-    };
 
-    template<typename ConfigT>
-    struct CommonArgProperties {
-        CommonArgProperties() {}
-        CommonArgProperties( Detail::BoundArgFunction<ConfigT> const& _boundField ) : boundField( _boundField ) {}
+        void use( Colour::Code _colourCode ) override {
+            switch( _colourCode ) {
+                case Colour::None:      return setTextAttribute( originalForegroundAttributes );
+                case Colour::White:     return setTextAttribute( FOREGROUND_GREEN | FOREGROUND_RED | FOREGROUND_BLUE );
+                case Colour::Red:       return setTextAttribute( FOREGROUND_RED );
+                case Colour::Green:     return setTextAttribute( FOREGROUND_GREEN );
+                case Colour::Blue:      return setTextAttribute( FOREGROUND_BLUE );
+                case Colour::Cyan:      return setTextAttribute( FOREGROUND_BLUE | FOREGROUND_GREEN );
+                case Colour::Yellow:    return setTextAttribute( FOREGROUND_RED | FOREGROUND_GREEN );
+                case Colour::Grey:      return setTextAttribute( 0 );
 
-        Detail::BoundArgFunction<ConfigT> boundField;
-        std::string description;
-        std::string detail;
-        std::string placeholder; // Only value if boundField takes an arg
+                case Colour::LightGrey:     return setTextAttribute( FOREGROUND_INTENSITY );
+                case Colour::BrightRed:     return setTextAttribute( FOREGROUND_INTENSITY | FOREGROUND_RED );
+                case Colour::BrightGreen:   return setTextAttribute( FOREGROUND_INTENSITY | FOREGROUND_GREEN );
+                case Colour::BrightWhite:   return setTextAttribute( FOREGROUND_INTENSITY | FOREGROUND_GREEN | FOREGROUND_RED | FOREGROUND_BLUE );
+                case Colour::BrightYellow:  return setTextAttribute( FOREGROUND_INTENSITY | FOREGROUND_RED | FOREGROUND_GREEN );
 
-        bool takesArg() const {
-            return !placeholder.empty();
-        }
-        void validate() const {
-            if( !boundField.isSet() )
-                throw std::logic_error( "option not bound" );
-        }
-    };
-    struct OptionArgProperties {
-        std::vector<std::string> shortNames;
-        std::string longName;
+                case Colour::Bright: CATCH_INTERNAL_ERROR( "not a colour" );
 
-        bool hasShortName( std::string const& shortName ) const {
-            return std::find( shortNames.begin(), shortNames.end(), shortName ) != shortNames.end();
-        }
-        bool hasLongName( std::string const& _longName ) const {
-            return _longName == longName;
+                default:
+                    CATCH_ERROR( "Unknown colour requested" );
+            }
         }
-    };
-    struct PositionalArgProperties {
-        PositionalArgProperties() : position( -1 ) {}
-        int position; // -1 means non-positional (floating)
 
-        bool isFixedPositional() const {
-            return position != -1;
+    private:
+        void setTextAttribute( WORD _textAttribute ) {
+            SetConsoleTextAttribute( stdoutHandle, _textAttribute | originalBackgroundAttributes );
         }
+        HANDLE stdoutHandle;
+        WORD originalForegroundAttributes;
+        WORD originalBackgroundAttributes;
     };
 
-    template<typename ConfigT>
-    class CommandLine {
+    IColourImpl* platformColourInstance() {
+        static Win32ColourImpl s_instance;
 
-        struct Arg : CommonArgProperties<ConfigT>, OptionArgProperties, PositionalArgProperties {
-            Arg() {}
-            Arg( Detail::BoundArgFunction<ConfigT> const& _boundField ) : CommonArgProperties<ConfigT>( _boundField ) {}
+        IConfigPtr config = getCurrentContext().getConfig();
+        UseColour::YesOrNo colourMode = config
+            ? config->useColour()
+            : UseColour::Auto;
+        if( colourMode == UseColour::Auto )
+            colourMode = UseColour::Yes;
+        return colourMode == UseColour::Yes
+            ? &s_instance
+            : NoColourImpl::instance();
+    }
 
-            using CommonArgProperties<ConfigT>::placeholder; // !TBD
+} // end anon namespace
+} // end namespace Catch
 
-            std::string dbgName() const {
-                if( !longName.empty() )
-                    return "--" + longName;
-                if( !shortNames.empty() )
-                    return "-" + shortNames[0];
-                return "positional args";
-            }
-            std::string commands() const {
-                std::ostringstream oss;
-                bool first = true;
-                std::vector<std::string>::const_iterator it = shortNames.begin(), itEnd = shortNames.end();
-                for(; it != itEnd; ++it ) {
-                    if( first )
-                        first = false;
-                    else
-                        oss << ", ";
-                    oss << "-" << *it;
-                }
-                if( !longName.empty() ) {
-                    if( !first )
-                        oss << ", ";
-                    oss << "--" << longName;
-                }
-                if( !placeholder.empty() )
-                    oss << " <" << placeholder << ">";
-                return oss.str();
-            }
-        };
+#elif defined( CATCH_CONFIG_COLOUR_ANSI ) //////////////////////////////////////
 
-        typedef CLARA_AUTO_PTR( Arg ) ArgAutoPtr;
+#include <unistd.h>
 
-        friend void addOptName( Arg& arg, std::string const& optName )
-        {
-            if( optName.empty() )
-                return;
-            if( Detail::startsWith( optName, "--" ) ) {
-                if( !arg.longName.empty() )
-                    throw std::logic_error( "Only one long opt may be specified. '"
-                        + arg.longName
-                        + "' already specified, now attempting to add '"
-                        + optName + "'" );
-                arg.longName = optName.substr( 2 );
-            }
-            else if( Detail::startsWith( optName, "-" ) )
-                arg.shortNames.push_back( optName.substr( 1 ) );
-            else
-                throw std::logic_error( "option must begin with - or --. Option was: '" + optName + "'" );
-        }
-        friend void setPositionalArg( Arg& arg, int position )
-        {
-            arg.position = position;
-        }
+namespace Catch {
+namespace {
 
-        class ArgBuilder {
-        public:
-            ArgBuilder( Arg* arg ) : m_arg( arg ) {}
+    // use POSIX/ ANSI console terminal codes
+    // Thanks to Adam Strzelecki for original contribution
+    // (http://github.com/nanoant)
+    // https://github.com/philsquared/Catch/pull/131
+    class PosixColourImpl : public IColourImpl {
+    public:
+        void use( Colour::Code _colourCode ) override {
+            switch( _colourCode ) {
+                case Colour::None:
+                case Colour::White:     return setColour( "[0m" );
+                case Colour::Red:       return setColour( "[0;31m" );
+                case Colour::Green:     return setColour( "[0;32m" );
+                case Colour::Blue:      return setColour( "[0;34m" );
+                case Colour::Cyan:      return setColour( "[0;36m" );
+                case Colour::Yellow:    return setColour( "[0;33m" );
+                case Colour::Grey:      return setColour( "[1;30m" );
 
-            // Bind a non-boolean data member (requires placeholder string)
-            template<typename C, typename M>
-            void bind( M C::* field, std::string const& placeholder ) {
-                m_arg->boundField = new Detail::BoundDataMember<C,M>( field );
-                m_arg->placeholder = placeholder;
-            }
-            // Bind a boolean data member (no placeholder required)
-            template<typename C>
-            void bind( bool C::* field ) {
-                m_arg->boundField = new Detail::BoundDataMember<C,bool>( field );
-            }
+                case Colour::LightGrey:     return setColour( "[0;37m" );
+                case Colour::BrightRed:     return setColour( "[1;31m" );
+                case Colour::BrightGreen:   return setColour( "[1;32m" );
+                case Colour::BrightWhite:   return setColour( "[1;37m" );
+                case Colour::BrightYellow:  return setColour( "[1;33m" );
 
-            // Bind a method taking a single, non-boolean argument (requires a placeholder string)
-            template<typename C, typename M>
-            void bind( void (C::* unaryMethod)( M ), std::string const& placeholder ) {
-                m_arg->boundField = new Detail::BoundUnaryMethod<C,M>( unaryMethod );
-                m_arg->placeholder = placeholder;
+                case Colour::Bright: CATCH_INTERNAL_ERROR( "not a colour" );
+                default: CATCH_INTERNAL_ERROR( "Unknown colour requested" );
             }
+        }
+        static IColourImpl* instance() {
+            static PosixColourImpl s_instance;
+            return &s_instance;
+        }
 
-            // Bind a method taking a single, boolean argument (no placeholder string required)
-            template<typename C>
-            void bind( void (C::* unaryMethod)( bool ) ) {
-                m_arg->boundField = new Detail::BoundUnaryMethod<C,bool>( unaryMethod );
-            }
+    private:
+        void setColour( const char* _escapeCode ) {
+            getCurrentContext().getConfig()->stream()
+                << '\033' << _escapeCode;
+        }
+    };
 
-            // Bind a method that takes no arguments (will be called if opt is present)
-            template<typename C>
-            void bind( void (C::* nullaryMethod)() ) {
-                m_arg->boundField = new Detail::BoundNullaryMethod<C>( nullaryMethod );
-            }
+    bool useColourOnPlatform() {
+        return
+#ifdef CATCH_PLATFORM_MAC
+            !isDebuggerActive() &&
+#endif
+#if !(defined(__DJGPP__) && defined(__STRICT_ANSI__))
+            isatty(STDOUT_FILENO)
+#else
+            false
+#endif
+            ;
+    }
+    IColourImpl* platformColourInstance() {
+        ErrnoGuard guard;
+        IConfigPtr config = getCurrentContext().getConfig();
+        UseColour::YesOrNo colourMode = config
+            ? config->useColour()
+            : UseColour::Auto;
+        if( colourMode == UseColour::Auto )
+            colourMode = useColourOnPlatform()
+                ? UseColour::Yes
+                : UseColour::No;
+        return colourMode == UseColour::Yes
+            ? PosixColourImpl::instance()
+            : NoColourImpl::instance();
+    }
 
-            // Bind a free function taking a single argument - the object to operate on (no placeholder string required)
-            template<typename C>
-            void bind( void (* unaryFunction)( C& ) ) {
-                m_arg->boundField = new Detail::BoundUnaryFunction<C>( unaryFunction );
-            }
+} // end anon namespace
+} // end namespace Catch
 
-            // Bind a free function taking a single argument - the object to operate on (requires a placeholder string)
-            template<typename C, typename T>
-            void bind( void (* binaryFunction)( C&, T ), std::string const& placeholder ) {
-                m_arg->boundField = new Detail::BoundBinaryFunction<C, T>( binaryFunction );
-                m_arg->placeholder = placeholder;
-            }
+#else  // not Windows or ANSI ///////////////////////////////////////////////
 
-            ArgBuilder& describe( std::string const& description ) {
-                m_arg->description = description;
-                return *this;
-            }
-            ArgBuilder& detail( std::string const& detail ) {
-                m_arg->detail = detail;
-                return *this;
-            }
+namespace Catch {
 
-        protected:
-            Arg* m_arg;
-        };
+    static IColourImpl* platformColourInstance() { return NoColourImpl::instance(); }
 
-        class OptBuilder : public ArgBuilder {
-        public:
-            OptBuilder( Arg* arg ) : ArgBuilder( arg ) {}
-            OptBuilder( OptBuilder& other ) : ArgBuilder( other ) {}
+} // end namespace Catch
 
-            OptBuilder& operator[]( std::string const& optName ) {
-                addOptName( *ArgBuilder::m_arg, optName );
-                return *this;
-            }
-        };
+#endif // Windows/ ANSI/ None
 
-    public:
+namespace Catch {
 
-        CommandLine()
-        :   m_boundProcessName( new Detail::NullBinder<ConfigT>() ),
-            m_highestSpecifiedArgPosition( 0 ),
-            m_throwOnUnrecognisedTokens( false )
-        {}
-        CommandLine( CommandLine const& other )
-        :   m_boundProcessName( other.m_boundProcessName ),
-            m_options ( other.m_options ),
-            m_positionalArgs( other.m_positionalArgs ),
-            m_highestSpecifiedArgPosition( other.m_highestSpecifiedArgPosition ),
-            m_throwOnUnrecognisedTokens( other.m_throwOnUnrecognisedTokens )
-        {
-            if( other.m_floatingArg.get() )
-                m_floatingArg.reset( new Arg( *other.m_floatingArg ) );
-        }
+    Colour::Colour( Code _colourCode ) { use( _colourCode ); }
+    Colour::Colour( Colour&& rhs ) noexcept {
+        m_moved = rhs.m_moved;
+        rhs.m_moved = true;
+    }
+    Colour& Colour::operator=( Colour&& rhs ) noexcept {
+        m_moved = rhs.m_moved;
+        rhs.m_moved  = true;
+        return *this;
+    }
 
-        CommandLine& setThrowOnUnrecognisedTokens( bool shouldThrow = true ) {
-            m_throwOnUnrecognisedTokens = shouldThrow;
-            return *this;
-        }
+    Colour::~Colour(){ if( !m_moved ) use( None ); }
 
-        OptBuilder operator[]( std::string const& optName ) {
-            m_options.push_back( Arg() );
-            addOptName( m_options.back(), optName );
-            OptBuilder builder( &m_options.back() );
-            return builder;
+    void Colour::use( Code _colourCode ) {
+        static IColourImpl* impl = platformColourInstance();
+        // Strictly speaking, this cannot possibly happen.
+        // However, under some conditions it does happen (see #1626),
+        // and this change is small enough that we can let practicality
+        // triumph over purity in this case.
+        if (impl != NULL) {
+            impl->use( _colourCode );
         }
+    }
 
-        ArgBuilder operator[]( int position ) {
-            m_positionalArgs.insert( std::make_pair( position, Arg() ) );
-            if( position > m_highestSpecifiedArgPosition )
-                m_highestSpecifiedArgPosition = position;
-            setPositionalArg( m_positionalArgs[position], position );
-            ArgBuilder builder( &m_positionalArgs[position] );
-            return builder;
-        }
+    std::ostream& operator << ( std::ostream& os, Colour const& ) {
+        return os;
+    }
 
-        // Invoke this with the _ instance
-        ArgBuilder operator[]( UnpositionalTag ) {
-            if( m_floatingArg.get() )
-                throw std::logic_error( "Only one unpositional argument can be added" );
-            m_floatingArg.reset( new Arg() );
-            ArgBuilder builder( m_floatingArg.get() );
-            return builder;
-        }
+} // end namespace Catch
 
-        template<typename C, typename M>
-        void bindProcessName( M C::* field ) {
-            m_boundProcessName = new Detail::BoundDataMember<C,M>( field );
-        }
-        template<typename C, typename M>
-        void bindProcessName( void (C::*_unaryMethod)( M ) ) {
-            m_boundProcessName = new Detail::BoundUnaryMethod<C,M>( _unaryMethod );
-        }
+#if defined(__clang__)
+#    pragma clang diagnostic pop
+#endif
 
-        void optUsage( std::ostream& os, std::size_t indent = 0, std::size_t width = Detail::consoleWidth ) const {
-            typename std::vector<Arg>::const_iterator itBegin = m_options.begin(), itEnd = m_options.end(), it;
-            std::size_t maxWidth = 0;
-            for( it = itBegin; it != itEnd; ++it )
-                maxWidth = (std::max)( maxWidth, it->commands().size() );
+// end catch_console_colour.cpp
+// start catch_context.cpp
 
-            for( it = itBegin; it != itEnd; ++it ) {
-                Detail::Text usage( it->commands(), Detail::TextAttributes()
-                                                        .setWidth( maxWidth+indent )
-                                                        .setIndent( indent ) );
-                Detail::Text desc( it->description, Detail::TextAttributes()
-                                                        .setWidth( width - maxWidth - 3 ) );
+namespace Catch {
 
-                for( std::size_t i = 0; i < (std::max)( usage.size(), desc.size() ); ++i ) {
-                    std::string usageCol = i < usage.size() ? usage[i] : "";
-                    os << usageCol;
+    class Context : public IMutableContext, NonCopyable {
 
-                    if( i < desc.size() && !desc[i].empty() )
-                        os  << std::string( indent + 2 + maxWidth - usageCol.size(), ' ' )
-                            << desc[i];
-                    os << "\n";
-                }
-            }
-        }
-        std::string optUsage() const {
-            std::ostringstream oss;
-            optUsage( oss );
-            return oss.str();
-        }
-
-        void argSynopsis( std::ostream& os ) const {
-            for( int i = 1; i <= m_highestSpecifiedArgPosition; ++i ) {
-                if( i > 1 )
-                    os << " ";
-                typename std::map<int, Arg>::const_iterator it = m_positionalArgs.find( i );
-                if( it != m_positionalArgs.end() )
-                    os << "<" << it->second.placeholder << ">";
-                else if( m_floatingArg.get() )
-                    os << "<" << m_floatingArg->placeholder << ">";
-                else
-                    throw std::logic_error( "non consecutive positional arguments with no floating args" );
-            }
-            // !TBD No indication of mandatory args
-            if( m_floatingArg.get() ) {
-                if( m_highestSpecifiedArgPosition > 1 )
-                    os << " ";
-                os << "[<" << m_floatingArg->placeholder << "> ...]";
-            }
+    public: // IContext
+        IResultCapture* getResultCapture() override {
+            return m_resultCapture;
         }
-        std::string argSynopsis() const {
-            std::ostringstream oss;
-            argSynopsis( oss );
-            return oss.str();
+        IRunner* getRunner() override {
+            return m_runner;
         }
 
-        void usage( std::ostream& os, std::string const& procName ) const {
-            validate();
-            os << "usage:\n  " << procName << " ";
-            argSynopsis( os );
-            if( !m_options.empty() ) {
-                os << " [options]\n\nwhere options are: \n";
-                optUsage( os, 2 );
-            }
-            os << "\n";
-        }
-        std::string usage( std::string const& procName ) const {
-            std::ostringstream oss;
-            usage( oss, procName );
-            return oss.str();
-        }
-
-        ConfigT parse( std::vector<std::string> const& args ) const {
-            ConfigT config;
-            parseInto( args, config );
-            return config;
-        }
-
-        std::vector<Parser::Token> parseInto( std::vector<std::string> const& args, ConfigT& config ) const {
-            std::string processName = args.empty() ? std::string() : args[0];
-            std::size_t lastSlash = processName.find_last_of( "/\\" );
-            if( lastSlash != std::string::npos )
-                processName = processName.substr( lastSlash+1 );
-            m_boundProcessName.set( config, processName );
-            std::vector<Parser::Token> tokens;
-            Parser parser;
-            parser.parseIntoTokens( args, tokens );
-            return populate( tokens, config );
-        }
-
-        std::vector<Parser::Token> populate( std::vector<Parser::Token> const& tokens, ConfigT& config ) const {
-            validate();
-            std::vector<Parser::Token> unusedTokens = populateOptions( tokens, config );
-            unusedTokens = populateFixedArgs( unusedTokens, config );
-            unusedTokens = populateFloatingArgs( unusedTokens, config );
-            return unusedTokens;
-        }
-
-        std::vector<Parser::Token> populateOptions( std::vector<Parser::Token> const& tokens, ConfigT& config ) const {
-            std::vector<Parser::Token> unusedTokens;
-            std::vector<std::string> errors;
-            for( std::size_t i = 0; i < tokens.size(); ++i ) {
-                Parser::Token const& token = tokens[i];
-                typename std::vector<Arg>::const_iterator it = m_options.begin(), itEnd = m_options.end();
-                for(; it != itEnd; ++it ) {
-                    Arg const& arg = *it;
-
-                    try {
-                        if( ( token.type == Parser::Token::ShortOpt && arg.hasShortName( token.data ) ) ||
-                            ( token.type == Parser::Token::LongOpt && arg.hasLongName( token.data ) ) ) {
-                            if( arg.takesArg() ) {
-                                if( i == tokens.size()-1 || tokens[i+1].type != Parser::Token::Positional )
-                                    errors.push_back( "Expected argument to option: " + token.data );
-                                else
-                                    arg.boundField.set( config, tokens[++i].data );
-                            }
-                            else {
-                                arg.boundField.set( config, "true" );
-                            }
-                            break;
-                        }
-                    }
-                    catch( std::exception& ex ) {
-                        errors.push_back( std::string( ex.what() ) + "\n- while parsing: (" + arg.commands() + ")" );
-                    }
-                }
-                if( it == itEnd ) {
-                    if( token.type == Parser::Token::Positional || !m_throwOnUnrecognisedTokens )
-                        unusedTokens.push_back( token );
-                    else if( errors.empty() && m_throwOnUnrecognisedTokens )
-                        errors.push_back( "unrecognised option: " + token.data );
-                }
-            }
-            if( !errors.empty() ) {
-                std::ostringstream oss;
-                for( std::vector<std::string>::const_iterator it = errors.begin(), itEnd = errors.end();
-                        it != itEnd;
-                        ++it ) {
-                    if( it != errors.begin() )
-                        oss << "\n";
-                    oss << *it;
-                }
-                throw std::runtime_error( oss.str() );
-            }
-            return unusedTokens;
-        }
-        std::vector<Parser::Token> populateFixedArgs( std::vector<Parser::Token> const& tokens, ConfigT& config ) const {
-            std::vector<Parser::Token> unusedTokens;
-            int position = 1;
-            for( std::size_t i = 0; i < tokens.size(); ++i ) {
-                Parser::Token const& token = tokens[i];
-                typename std::map<int, Arg>::const_iterator it = m_positionalArgs.find( position );
-                if( it != m_positionalArgs.end() )
-                    it->second.boundField.set( config, token.data );
-                else
-                    unusedTokens.push_back( token );
-                if( token.type == Parser::Token::Positional )
-                    position++;
-            }
-            return unusedTokens;
-        }
-        std::vector<Parser::Token> populateFloatingArgs( std::vector<Parser::Token> const& tokens, ConfigT& config ) const {
-            if( !m_floatingArg.get() )
-                return tokens;
-            std::vector<Parser::Token> unusedTokens;
-            for( std::size_t i = 0; i < tokens.size(); ++i ) {
-                Parser::Token const& token = tokens[i];
-                if( token.type == Parser::Token::Positional )
-                    m_floatingArg->boundField.set( config, token.data );
-                else
-                    unusedTokens.push_back( token );
-            }
-            return unusedTokens;
+        IConfigPtr const& getConfig() const override {
+            return m_config;
         }
 
-        void validate() const
-        {
-            if( m_options.empty() && m_positionalArgs.empty() && !m_floatingArg.get() )
-                throw std::logic_error( "No options or arguments specified" );
+        ~Context() override;
 
-            for( typename std::vector<Arg>::const_iterator  it = m_options.begin(),
-                                                            itEnd = m_options.end();
-                    it != itEnd; ++it )
-                it->validate();
+    public: // IMutableContext
+        void setResultCapture( IResultCapture* resultCapture ) override {
+            m_resultCapture = resultCapture;
+        }
+        void setRunner( IRunner* runner ) override {
+            m_runner = runner;
+        }
+        void setConfig( IConfigPtr const& config ) override {
+            m_config = config;
         }
 
+        friend IMutableContext& getCurrentMutableContext();
+
     private:
-        Detail::BoundArgFunction<ConfigT> m_boundProcessName;
-        std::vector<Arg> m_options;
-        std::map<int, Arg> m_positionalArgs;
-        ArgAutoPtr m_floatingArg;
-        int m_highestSpecifiedArgPosition;
-        bool m_throwOnUnrecognisedTokens;
+        IConfigPtr m_config;
+        IRunner* m_runner = nullptr;
+        IResultCapture* m_resultCapture = nullptr;
     };
 
-} // end namespace Clara
+    IMutableContext *IMutableContext::currentContext = nullptr;
 
-STITCH_CLARA_CLOSE_NAMESPACE
-#undef STITCH_CLARA_OPEN_NAMESPACE
-#undef STITCH_CLARA_CLOSE_NAMESPACE
+    void IMutableContext::createContext()
+    {
+        currentContext = new Context();
+    }
 
-#endif // TWOBLUECUBES_CLARA_H_INCLUDED
-#undef STITCH_CLARA_OPEN_NAMESPACE
+    void cleanUpContext() {
+        delete IMutableContext::currentContext;
+        IMutableContext::currentContext = nullptr;
+    }
+    IContext::~IContext() = default;
+    IMutableContext::~IMutableContext() = default;
+    Context::~Context() = default;
 
-// Restore Clara's value for console width, if present
-#ifdef CATCH_TEMP_CLARA_CONFIG_CONSOLE_WIDTH
-#define CLARA_CONFIG_CONSOLE_WIDTH CATCH_TEMP_CLARA_CONFIG_CONSOLE_WIDTH
-#undef CATCH_TEMP_CLARA_CONFIG_CONSOLE_WIDTH
-#endif
+    SimplePcg32& rng() {
+        static SimplePcg32 s_rng;
+        return s_rng;
+    }
 
-#include <fstream>
-#include <ctime>
+}
+// end catch_context.cpp
+// start catch_debug_console.cpp
+
+// start catch_debug_console.h
+
+#include <string>
 
 namespace Catch {
+    void writeToDebugConsole( std::string const& text );
+}
 
-    inline void abortAfterFirst( ConfigData& config ) { config.abortAfter = 1; }
-    inline void abortAfterX( ConfigData& config, int x ) {
-        if( x < 1 )
-            throw std::runtime_error( "Value after -x or --abortAfter must be greater than zero" );
-        config.abortAfter = x;
-    }
-    inline void addTestOrTags( ConfigData& config, std::string const& _testSpec ) { config.testsOrTags.push_back( _testSpec ); }
-    inline void addSectionToRun( ConfigData& config, std::string const& sectionName ) { config.sectionsToRun.push_back( sectionName ); }
-    inline void addReporterName( ConfigData& config, std::string const& _reporterName ) { config.reporterNames.push_back( _reporterName ); }
+// end catch_debug_console.h
+#if defined(CATCH_CONFIG_ANDROID_LOGWRITE)
+#include <android/log.h>
 
-    inline void addWarning( ConfigData& config, std::string const& _warning ) {
-        if( _warning == "NoAssertions" )
-            config.warnings = static_cast<WarnAbout::What>( config.warnings | WarnAbout::NoAssertions );
-        else
-            throw std::runtime_error( "Unrecognised warning: '" + _warning + '\'' );
-    }
-    inline void setOrder( ConfigData& config, std::string const& order ) {
-        if( startsWith( "declared", order ) )
-            config.runOrder = RunTests::InDeclarationOrder;
-        else if( startsWith( "lexical", order ) )
-            config.runOrder = RunTests::InLexicographicalOrder;
-        else if( startsWith( "random", order ) )
-            config.runOrder = RunTests::InRandomOrder;
-        else
-            throw std::runtime_error( "Unrecognised ordering: '" + order + '\'' );
-    }
-    inline void setRngSeed( ConfigData& config, std::string const& seed ) {
-        if( seed == "time" ) {
-            config.rngSeed = static_cast<unsigned int>( std::time(0) );
+    namespace Catch {
+        void writeToDebugConsole( std::string const& text ) {
+            __android_log_write( ANDROID_LOG_DEBUG, "Catch", text.c_str() );
         }
-        else {
-            std::stringstream ss;
-            ss << seed;
-            ss >> config.rngSeed;
-            if( ss.fail() )
-                throw std::runtime_error( "Argument to --rng-seed should be the word 'time' or a number" );
-        }
-    }
-    inline void setVerbosity( ConfigData& config, int level ) {
-        // !TBD: accept strings?
-        config.verbosity = static_cast<Verbosity::Level>( level );
-    }
-    inline void setShowDurations( ConfigData& config, bool _showDurations ) {
-        config.showDurations = _showDurations
-            ? ShowDurations::Always
-            : ShowDurations::Never;
-    }
-    inline void setUseColour( ConfigData& config, std::string const& value ) {
-        std::string mode = toLower( value );
-
-        if( mode == "yes" )
-            config.useColour = UseColour::Yes;
-        else if( mode == "no" )
-            config.useColour = UseColour::No;
-        else if( mode == "auto" )
-            config.useColour = UseColour::Auto;
-        else
-            throw std::runtime_error( "colour mode must be one of: auto, yes or no" );
     }
-    inline void forceColour( ConfigData& config ) {
-        config.useColour = UseColour::Yes;
-    }
-    inline void loadTestNamesFromFile( ConfigData& config, std::string const& _filename ) {
-        std::ifstream f( _filename.c_str() );
-        if( !f.is_open() )
-            throw std::domain_error( "Unable to load input file: " + _filename );
 
-        std::string line;
-        while( std::getline( f, line ) ) {
-            line = trim(line);
-            if( !line.empty() && !startsWith( line, '#' ) ) {
-                if( !startsWith( line, '"' ) )
-                    line = '"' + line + '"';
-                addTestOrTags( config, line + ',' );
-            }
+#elif defined(CATCH_PLATFORM_WINDOWS)
+
+    namespace Catch {
+        void writeToDebugConsole( std::string const& text ) {
+            ::OutputDebugStringA( text.c_str() );
         }
     }
 
-    inline Clara::CommandLine<ConfigData> makeCommandLineParser() {
-
-        using namespace Clara;
-        CommandLine<ConfigData> cli;
-
-        cli.bindProcessName( &ConfigData::processName );
+#else
 
-        cli["-?"]["-h"]["--help"]
-            .describe( "display usage information" )
-            .bind( &ConfigData::showHelp );
+    namespace Catch {
+        void writeToDebugConsole( std::string const& text ) {
+            // !TBD: Need a version for Mac/ XCode and other IDEs
+            Catch::cout() << text;
+        }
+    }
 
-        cli["-l"]["--list-tests"]
-            .describe( "list all/matching test cases" )
-            .bind( &ConfigData::listTests );
+#endif // Platform
+// end catch_debug_console.cpp
+// start catch_debugger.cpp
 
-        cli["-t"]["--list-tags"]
-            .describe( "list all/matching tags" )
-            .bind( &ConfigData::listTags );
+#ifdef CATCH_PLATFORM_MAC
 
-        cli["-s"]["--success"]
-            .describe( "include successful tests in output" )
-            .bind( &ConfigData::showSuccessfulTests );
+#  include <assert.h>
+#  include <stdbool.h>
+#  include <sys/types.h>
+#  include <unistd.h>
+#  include <cstddef>
+#  include <ostream>
+
+#ifdef __apple_build_version__
+    // These headers will only compile with AppleClang (XCode)
+    // For other compilers (Clang, GCC, ... ) we need to exclude them
+#  include <sys/sysctl.h>
+#endif
 
-        cli["-b"]["--break"]
-            .describe( "break into debugger on failure" )
-            .bind( &ConfigData::shouldDebugBreak );
+    namespace Catch {
+        #ifdef __apple_build_version__
+        // The following function is taken directly from the following technical note:
+        // https://developer.apple.com/library/archive/qa/qa1361/_index.html
 
-        cli["-e"]["--nothrow"]
-            .describe( "skip exception tests" )
-            .bind( &ConfigData::noThrow );
+        // Returns true if the current process is being debugged (either
+        // running under the debugger or has a debugger attached post facto).
+        bool isDebuggerActive(){
+            int                 mib[4];
+            struct kinfo_proc   info;
+            std::size_t         size;
 
-        cli["-i"]["--invisibles"]
-            .describe( "show invisibles (tabs, newlines)" )
-            .bind( &ConfigData::showInvisibles );
+            // Initialize the flags so that, if sysctl fails for some bizarre
+            // reason, we get a predictable result.
 
-        cli["-o"]["--out"]
-            .describe( "output filename" )
-            .bind( &ConfigData::outputFilename, "filename" );
+            info.kp_proc.p_flag = 0;
 
-        cli["-r"]["--reporter"]
-//            .placeholder( "name[:filename]" )
-            .describe( "reporter to use (defaults to console)" )
-            .bind( &addReporterName, "name" );
+            // Initialize mib, which tells sysctl the info we want, in this case
+            // we're looking for information about a specific process ID.
 
-        cli["-n"]["--name"]
-            .describe( "suite name" )
-            .bind( &ConfigData::name, "name" );
+            mib[0] = CTL_KERN;
+            mib[1] = KERN_PROC;
+            mib[2] = KERN_PROC_PID;
+            mib[3] = getpid();
 
-        cli["-a"]["--abort"]
-            .describe( "abort at first failure" )
-            .bind( &abortAfterFirst );
+            // Call sysctl.
 
-        cli["-x"]["--abortx"]
-            .describe( "abort after x failures" )
-            .bind( &abortAfterX, "no. failures" );
+            size = sizeof(info);
+            if( sysctl(mib, sizeof(mib) / sizeof(*mib), &info, &size, nullptr, 0) != 0 ) {
+                Catch::cerr() << "\n** Call to sysctl failed - unable to determine if debugger is active **\n" << std::endl;
+                return false;
+            }
 
-        cli["-w"]["--warn"]
-            .describe( "enable warnings" )
-            .bind( &addWarning, "warning name" );
+            // We're being debugged if the P_TRACED flag is set.
 
-// - needs updating if reinstated
-//        cli.into( &setVerbosity )
-//            .describe( "level of verbosity (0=no output)" )
-//            .shortOpt( "v")
-//            .longOpt( "verbosity" )
-//            .placeholder( "level" );
+            return ( (info.kp_proc.p_flag & P_TRACED) != 0 );
+        }
+        #else
+        bool isDebuggerActive() {
+            // We need to find another way to determine this for non-appleclang compilers on macOS
+            return false;
+        }
+        #endif
+    } // namespace Catch
 
-        cli[_]
-            .describe( "which test or tests to use" )
-            .bind( &addTestOrTags, "test name, pattern or tags" );
+#elif defined(CATCH_PLATFORM_LINUX)
+    #include <fstream>
+    #include <string>
 
-        cli["-d"]["--durations"]
-            .describe( "show test durations" )
-            .bind( &setShowDurations, "yes|no" );
+    namespace Catch{
+        // The standard POSIX way of detecting a debugger is to attempt to
+        // ptrace() the process, but this needs to be done from a child and not
+        // this process itself to still allow attaching to this process later
+        // if wanted, so is rather heavy. Under Linux we have the PID of the
+        // "debugger" (which doesn't need to be gdb, of course, it could also
+        // be strace, for example) in /proc/$PID/status, so just get it from
+        // there instead.
+        bool isDebuggerActive(){
+            // Libstdc++ has a bug, where std::ifstream sets errno to 0
+            // This way our users can properly assert over errno values
+            ErrnoGuard guard;
+            std::ifstream in("/proc/self/status");
+            for( std::string line; std::getline(in, line); ) {
+                static const int PREFIX_LEN = 11;
+                if( line.compare(0, PREFIX_LEN, "TracerPid:\t") == 0 ) {
+                    // We're traced if the PID is not 0 and no other PID starts
+                    // with 0 digit, so it's enough to check for just a single
+                    // character.
+                    return line.length() > PREFIX_LEN && line[PREFIX_LEN] != '0';
+                }
+            }
 
-        cli["-f"]["--input-file"]
-            .describe( "load test names to run from a file" )
-            .bind( &loadTestNamesFromFile, "filename" );
+            return false;
+        }
+    } // namespace Catch
+#elif defined(_MSC_VER)
+    extern "C" __declspec(dllimport) int __stdcall IsDebuggerPresent();
+    namespace Catch {
+        bool isDebuggerActive() {
+            return IsDebuggerPresent() != 0;
+        }
+    }
+#elif defined(__MINGW32__)
+    extern "C" __declspec(dllimport) int __stdcall IsDebuggerPresent();
+    namespace Catch {
+        bool isDebuggerActive() {
+            return IsDebuggerPresent() != 0;
+        }
+    }
+#else
+    namespace Catch {
+       bool isDebuggerActive() { return false; }
+    }
+#endif // Platform
+// end catch_debugger.cpp
+// start catch_decomposer.cpp
 
-        cli["-#"]["--filenames-as-tags"]
-            .describe( "adds a tag for the filename" )
-            .bind( &ConfigData::filenamesAsTags );
+namespace Catch {
 
-        cli["-c"]["--section"]
-                .describe( "specify section to run" )
-                .bind( &addSectionToRun, "section name" );
+    ITransientExpression::~ITransientExpression() = default;
 
-        // Less common commands which don't have a short form
-        cli["--list-test-names-only"]
-            .describe( "list all/matching test cases names only" )
-            .bind( &ConfigData::listTestNamesOnly );
+    void formatReconstructedExpression( std::ostream &os, std::string const& lhs, StringRef op, std::string const& rhs ) {
+        if( lhs.size() + rhs.size() < 40 &&
+                lhs.find('\n') == std::string::npos &&
+                rhs.find('\n') == std::string::npos )
+            os << lhs << " " << op << " " << rhs;
+        else
+            os << lhs << "\n" << op << "\n" << rhs;
+    }
+}
+// end catch_decomposer.cpp
+// start catch_enforce.cpp
 
-        cli["--list-reporters"]
-            .describe( "list all reporters" )
-            .bind( &ConfigData::listReporters );
+#include <stdexcept>
 
-        cli["--order"]
-            .describe( "test case order (defaults to decl)" )
-            .bind( &setOrder, "decl|lex|rand" );
+namespace Catch {
+#if defined(CATCH_CONFIG_DISABLE_EXCEPTIONS) && !defined(CATCH_CONFIG_DISABLE_EXCEPTIONS_CUSTOM_HANDLER)
+    [[noreturn]]
+    void throw_exception(std::exception const& e) {
+        Catch::cerr() << "Catch will terminate because it needed to throw an exception.\n"
+                      << "The message was: " << e.what() << '\n';
+        std::terminate();
+    }
+#endif
 
-        cli["--rng-seed"]
-            .describe( "set a specific seed for random numbers" )
-            .bind( &setRngSeed, "'time'|number" );
+    [[noreturn]]
+    void throw_logic_error(std::string const& msg) {
+        throw_exception(std::logic_error(msg));
+    }
 
-        cli["--force-colour"]
-            .describe( "force colourised output (deprecated)" )
-            .bind( &forceColour );
-
-        cli["--use-colour"]
-            .describe( "should output be colourised" )
-            .bind( &setUseColour, "yes|no" );
+    [[noreturn]]
+    void throw_domain_error(std::string const& msg) {
+        throw_exception(std::domain_error(msg));
+    }
 
-        return cli;
+    [[noreturn]]
+    void throw_runtime_error(std::string const& msg) {
+        throw_exception(std::runtime_error(msg));
     }
 
-} // end namespace Catch
+} // namespace Catch;
+// end catch_enforce.cpp
+// start catch_enum_values_registry.cpp
+// start catch_enum_values_registry.h
 
-// #included from: internal/catch_list.hpp
-#define TWOBLUECUBES_CATCH_LIST_HPP_INCLUDED
+#include <vector>
+#include <memory>
 
-// #included from: catch_text.h
-#define TWOBLUECUBES_CATCH_TEXT_H_INCLUDED
+namespace Catch {
 
-#define TBC_TEXT_FORMAT_CONSOLE_WIDTH CATCH_CONFIG_CONSOLE_WIDTH
+    namespace Detail {
 
-#define CLICHE_TBC_TEXT_FORMAT_OUTER_NAMESPACE Catch
-// #included from: ../external/tbc_text_format.h
-// Only use header guard if we are not using an outer namespace
-#ifndef CLICHE_TBC_TEXT_FORMAT_OUTER_NAMESPACE
-# ifdef TWOBLUECUBES_TEXT_FORMAT_H_INCLUDED
-#  ifndef TWOBLUECUBES_TEXT_FORMAT_H_ALREADY_INCLUDED
-#   define TWOBLUECUBES_TEXT_FORMAT_H_ALREADY_INCLUDED
-#  endif
-# else
-#  define TWOBLUECUBES_TEXT_FORMAT_H_INCLUDED
-# endif
-#endif
-#ifndef TWOBLUECUBES_TEXT_FORMAT_H_ALREADY_INCLUDED
-#include <string>
-#include <vector>
-#include <sstream>
+        std::unique_ptr<EnumInfo> makeEnumInfo( StringRef enumName, StringRef allValueNames, std::vector<int> const& values );
 
-// Use optional outer namespace
-#ifdef CLICHE_TBC_TEXT_FORMAT_OUTER_NAMESPACE
-namespace CLICHE_TBC_TEXT_FORMAT_OUTER_NAMESPACE {
-#endif
+        class EnumValuesRegistry : public IMutableEnumValuesRegistry {
 
-namespace Tbc {
+            std::vector<std::unique_ptr<EnumInfo>> m_enumInfos;
 
-#ifdef TBC_TEXT_FORMAT_CONSOLE_WIDTH
-    const unsigned int consoleWidth = TBC_TEXT_FORMAT_CONSOLE_WIDTH;
-#else
-    const unsigned int consoleWidth = 80;
-#endif
+            EnumInfo const& registerEnum( StringRef enumName, StringRef allEnums, std::vector<int> const& values) override;
+        };
 
-    struct TextAttributes {
-        TextAttributes()
-        :   initialIndent( std::string::npos ),
-            indent( 0 ),
-            width( consoleWidth-1 )
-        {}
+        std::vector<StringRef> parseEnums( StringRef enums );
 
-        TextAttributes& setInitialIndent( std::size_t _value )  { initialIndent = _value; return *this; }
-        TextAttributes& setIndent( std::size_t _value )         { indent = _value; return *this; }
-        TextAttributes& setWidth( std::size_t _value )          { width = _value; return *this; }
+    } // Detail
 
-        std::size_t initialIndent;  // indent of first line, or npos
-        std::size_t indent;         // indent of subsequent lines, or all if initialIndent is npos
-        std::size_t width;          // maximum width of text, including indent. Longer text will wrap
-    };
+} // Catch
 
-    class Text {
-    public:
-        Text( std::string const& _str, TextAttributes const& _attr = TextAttributes() )
-        : attr( _attr )
-        {
-            const std::string wrappableBeforeChars = "[({<\t";
-            const std::string wrappableAfterChars = "])}>-,./|\\";
-            const std::string wrappableInsteadOfChars = " \n\r";
-            std::string indent = _attr.initialIndent != std::string::npos
-                ? std::string( _attr.initialIndent, ' ' )
-                : std::string( _attr.indent, ' ' );
-
-            typedef std::string::const_iterator iterator;
-            iterator it = _str.begin();
-            const iterator strEnd = _str.end();
-
-            while( it != strEnd ) {
-
-                if( lines.size() >= 1000 ) {
-                    lines.push_back( "... message truncated due to excessive size" );
-                    return;
-                }
+// end catch_enum_values_registry.h
 
-                std::string suffix;
-                std::size_t width = (std::min)( static_cast<size_t>( strEnd-it ), _attr.width-static_cast<size_t>( indent.size() ) );
-                iterator itEnd = it+width;
-                iterator itNext = _str.end();
-
-                iterator itNewLine = std::find( it, itEnd, '\n' );
-                if( itNewLine != itEnd )
-                    itEnd = itNewLine;
-
-                if( itEnd != strEnd  ) {
-                    bool foundWrapPoint = false;
-                    iterator findIt = itEnd;
-                    do {
-                        if( wrappableAfterChars.find( *findIt ) != std::string::npos && findIt != itEnd ) {
-                            itEnd = findIt+1;
-                            itNext = findIt+1;
-                            foundWrapPoint = true;
-                        }
-                        else if( findIt > it && wrappableBeforeChars.find( *findIt ) != std::string::npos ) {
-                            itEnd = findIt;
-                            itNext = findIt;
-                            foundWrapPoint = true;
-                        }
-                        else if( wrappableInsteadOfChars.find( *findIt ) != std::string::npos ) {
-                            itNext = findIt+1;
-                            itEnd = findIt;
-                            foundWrapPoint = true;
-                        }
-                        if( findIt == it )
-                            break;
-                        else
-                            --findIt;
-                    }
-                    while( !foundWrapPoint );
+#include <map>
+#include <cassert>
 
-                    if( !foundWrapPoint ) {
-                        // No good wrap char, so we'll break mid word and add a hyphen
-                        --itEnd;
-                        itNext = itEnd;
-                        suffix = "-";
-                    }
-                    else {
-                        while( itEnd > it && wrappableInsteadOfChars.find( *(itEnd-1) ) != std::string::npos )
-                            --itEnd;
-                    }
-                }
-                lines.push_back( indent + std::string( it, itEnd ) + suffix );
+namespace Catch {
+
+    IMutableEnumValuesRegistry::~IMutableEnumValuesRegistry() {}
 
-                if( indent.size() != _attr.indent )
-                    indent = std::string( _attr.indent, ' ' );
-                it = itNext;
+    namespace Detail {
+
+        namespace {
+            // Extracts the actual name part of an enum instance
+            // In other words, it returns the Blue part of Bikeshed::Colour::Blue
+            StringRef extractInstanceName(StringRef enumInstance) {
+                // Find last occurence of ":"
+                size_t name_start = enumInstance.size();
+                while (name_start > 0 && enumInstance[name_start - 1] != ':') {
+                    --name_start;
+                }
+                return enumInstance.substr(name_start, enumInstance.size() - name_start);
             }
         }
 
-        typedef std::vector<std::string>::const_iterator const_iterator;
-
-        const_iterator begin() const { return lines.begin(); }
-        const_iterator end() const { return lines.end(); }
-        std::string const& last() const { return lines.back(); }
-        std::size_t size() const { return lines.size(); }
-        std::string const& operator[]( std::size_t _index ) const { return lines[_index]; }
-        std::string toString() const {
-            std::ostringstream oss;
-            oss << *this;
-            return oss.str();
+        std::vector<StringRef> parseEnums( StringRef enums ) {
+            auto enumValues = splitStringRef( enums, ',' );
+            std::vector<StringRef> parsed;
+            parsed.reserve( enumValues.size() );
+            for( auto const& enumValue : enumValues ) {
+                parsed.push_back(trim(extractInstanceName(enumValue)));
+            }
+            return parsed;
         }
 
-        inline friend std::ostream& operator << ( std::ostream& _stream, Text const& _text ) {
-            for( Text::const_iterator it = _text.begin(), itEnd = _text.end();
-                it != itEnd; ++it ) {
-                if( it != _text.begin() )
-                    _stream << "\n";
-                _stream << *it;
+        EnumInfo::~EnumInfo() {}
+
+        StringRef EnumInfo::lookup( int value ) const {
+            for( auto const& valueToName : m_values ) {
+                if( valueToName.first == value )
+                    return valueToName.second;
             }
-            return _stream;
+            return "{** unexpected enum value **}"_sr;
         }
 
-    private:
-        std::string str;
-        TextAttributes attr;
-        std::vector<std::string> lines;
-    };
+        std::unique_ptr<EnumInfo> makeEnumInfo( StringRef enumName, StringRef allValueNames, std::vector<int> const& values ) {
+            std::unique_ptr<EnumInfo> enumInfo( new EnumInfo );
+            enumInfo->m_name = enumName;
+            enumInfo->m_values.reserve( values.size() );
 
-} // end namespace Tbc
+            const auto valueNames = Catch::Detail::parseEnums( allValueNames );
+            assert( valueNames.size() == values.size() );
+            std::size_t i = 0;
+            for( auto value : values )
+                enumInfo->m_values.push_back({ value, valueNames[i++] });
 
-#ifdef CLICHE_TBC_TEXT_FORMAT_OUTER_NAMESPACE
-} // end outer namespace
-#endif
+            return enumInfo;
+        }
+
+        EnumInfo const& EnumValuesRegistry::registerEnum( StringRef enumName, StringRef allValueNames, std::vector<int> const& values ) {
+            m_enumInfos.push_back(makeEnumInfo(enumName, allValueNames, values));
+            return *m_enumInfos.back();
+        }
 
-#endif // TWOBLUECUBES_TEXT_FORMAT_H_ALREADY_INCLUDED
-#undef CLICHE_TBC_TEXT_FORMAT_OUTER_NAMESPACE
+    } // Detail
+} // Catch
 
-namespace Catch {
-    using Tbc::Text;
-    using Tbc::TextAttributes;
-}
+// end catch_enum_values_registry.cpp
+// start catch_errno_guard.cpp
 
-// #included from: catch_console_colour.hpp
-#define TWOBLUECUBES_CATCH_CONSOLE_COLOUR_HPP_INCLUDED
+#include <cerrno>
 
 namespace Catch {
+        ErrnoGuard::ErrnoGuard():m_oldErrno(errno){}
+        ErrnoGuard::~ErrnoGuard() { errno = m_oldErrno; }
+}
+// end catch_errno_guard.cpp
+// start catch_exception_translator_registry.cpp
 
-    struct Colour {
-        enum Code {
-            None = 0,
+// start catch_exception_translator_registry.h
 
-            White,
-            Red,
-            Green,
-            Blue,
-            Cyan,
-            Yellow,
-            Grey,
+#include <vector>
+#include <string>
+#include <memory>
 
-            Bright = 0x10,
+namespace Catch {
 
-            BrightRed = Bright | Red,
-            BrightGreen = Bright | Green,
-            LightGrey = Bright | Grey,
-            BrightWhite = Bright | White,
+    class ExceptionTranslatorRegistry : public IExceptionTranslatorRegistry {
+    public:
+        ~ExceptionTranslatorRegistry();
+        virtual void registerTranslator( const IExceptionTranslator* translator );
+        std::string translateActiveException() const override;
+        std::string tryTranslators() const;
 
-            // By intention
-            FileName = LightGrey,
-            Warning = Yellow,
-            ResultError = BrightRed,
-            ResultSuccess = BrightGreen,
-            ResultExpectedFailure = Warning,
+    private:
+        std::vector<std::unique_ptr<IExceptionTranslator const>> m_translators;
+    };
+}
 
-            Error = BrightRed,
-            Success = Green,
+// end catch_exception_translator_registry.h
+#ifdef __OBJC__
+#import "Foundation/Foundation.h"
+#endif
 
-            OriginalExpression = Cyan,
-            ReconstructedExpression = Yellow,
+namespace Catch {
 
-            SecondaryText = LightGrey,
-            Headers = White
-        };
+    ExceptionTranslatorRegistry::~ExceptionTranslatorRegistry() {
+    }
 
-        // Use constructed object for RAII guard
-        Colour( Code _colourCode );
-        Colour( Colour const& other );
-        ~Colour();
+    void ExceptionTranslatorRegistry::registerTranslator( const IExceptionTranslator* translator ) {
+        m_translators.push_back( std::unique_ptr<const IExceptionTranslator>( translator ) );
+    }
 
-        // Use static method for one-shot changes
-        static void use( Code _colourCode );
+#if !defined(CATCH_CONFIG_DISABLE_EXCEPTIONS)
+    std::string ExceptionTranslatorRegistry::translateActiveException() const {
+        try {
+#ifdef __OBJC__
+            // In Objective-C try objective-c exceptions first
+            @try {
+                return tryTranslators();
+            }
+            @catch (NSException *exception) {
+                return Catch::Detail::stringify( [exception description] );
+            }
+#else
+            // Compiling a mixed mode project with MSVC means that CLR
+            // exceptions will be caught in (...) as well. However, these
+            // do not fill-in std::current_exception and thus lead to crash
+            // when attempting rethrow.
+            // /EHa switch also causes structured exceptions to be caught
+            // here, but they fill-in current_exception properly, so
+            // at worst the output should be a little weird, instead of
+            // causing a crash.
+            if (std::current_exception() == nullptr) {
+                return "Non C++ exception. Possibly a CLR exception.";
+            }
+            return tryTranslators();
+#endif
+        }
+        catch( TestFailureException& ) {
+            std::rethrow_exception(std::current_exception());
+        }
+        catch( std::exception& ex ) {
+            return ex.what();
+        }
+        catch( std::string& msg ) {
+            return msg;
+        }
+        catch( const char* msg ) {
+            return msg;
+        }
+        catch(...) {
+            return "Unknown exception";
+        }
+    }
 
-    private:
-        bool m_moved;
-    };
+    std::string ExceptionTranslatorRegistry::tryTranslators() const {
+        if (m_translators.empty()) {
+            std::rethrow_exception(std::current_exception());
+        } else {
+            return m_translators[0]->translate(m_translators.begin() + 1, m_translators.end());
+        }
+    }
 
-    inline std::ostream& operator << ( std::ostream& os, Colour const& ) { return os; }
+#else // ^^ Exceptions are enabled // Exceptions are disabled vv
+    std::string ExceptionTranslatorRegistry::translateActiveException() const {
+        CATCH_INTERNAL_ERROR("Attempted to translate active exception under CATCH_CONFIG_DISABLE_EXCEPTIONS!");
+    }
 
-} // end namespace Catch
+    std::string ExceptionTranslatorRegistry::tryTranslators() const {
+        CATCH_INTERNAL_ERROR("Attempted to use exception translators under CATCH_CONFIG_DISABLE_EXCEPTIONS!");
+    }
+#endif
 
-// #included from: catch_interfaces_reporter.h
-#define TWOBLUECUBES_CATCH_INTERFACES_REPORTER_H_INCLUDED
+}
+// end catch_exception_translator_registry.cpp
+// start catch_fatal_condition.cpp
 
-#include <string>
-#include <ostream>
-#include <map>
+#if defined(__GNUC__)
+#    pragma GCC diagnostic push
+#    pragma GCC diagnostic ignored "-Wmissing-field-initializers"
+#endif
 
-namespace Catch
-{
-    struct ReporterConfig {
-        explicit ReporterConfig( Ptr<IConfig const> const& _fullConfig )
-        :   m_stream( &_fullConfig->stream() ), m_fullConfig( _fullConfig ) {}
+#if defined( CATCH_CONFIG_WINDOWS_SEH ) || defined( CATCH_CONFIG_POSIX_SIGNALS )
 
-        ReporterConfig( Ptr<IConfig const> const& _fullConfig, std::ostream& _stream )
-        :   m_stream( &_stream ), m_fullConfig( _fullConfig ) {}
+namespace {
+    // Report the error condition
+    void reportFatal( char const * const message ) {
+        Catch::getCurrentContext().getResultCapture()->handleFatalErrorCondition( message );
+    }
+}
 
-        std::ostream& stream() const    { return *m_stream; }
-        Ptr<IConfig const> fullConfig() const { return m_fullConfig; }
+#endif // signals/SEH handling
 
-    private:
-        std::ostream* m_stream;
-        Ptr<IConfig const> m_fullConfig;
-    };
+#if defined( CATCH_CONFIG_WINDOWS_SEH )
 
-    struct ReporterPreferences {
-        ReporterPreferences()
-        : shouldRedirectStdOut( false )
-        {}
+namespace Catch {
+    struct SignalDefs { DWORD id; const char* name; };
 
-        bool shouldRedirectStdOut;
+    // There is no 1-1 mapping between signals and windows exceptions.
+    // Windows can easily distinguish between SO and SigSegV,
+    // but SigInt, SigTerm, etc are handled differently.
+    static SignalDefs signalDefs[] = {
+        { static_cast<DWORD>(EXCEPTION_ILLEGAL_INSTRUCTION),  "SIGILL - Illegal instruction signal" },
+        { static_cast<DWORD>(EXCEPTION_STACK_OVERFLOW), "SIGSEGV - Stack overflow" },
+        { static_cast<DWORD>(EXCEPTION_ACCESS_VIOLATION), "SIGSEGV - Segmentation violation signal" },
+        { static_cast<DWORD>(EXCEPTION_INT_DIVIDE_BY_ZERO), "Divide by zero error" },
     };
 
-    template<typename T>
-    struct LazyStat : Option<T> {
-        LazyStat() : used( false ) {}
-        LazyStat& operator=( T const& _value ) {
-            Option<T>::operator=( _value );
-            used = false;
-            return *this;
-        }
-        void reset() {
-            Option<T>::reset();
-            used = false;
+    LONG CALLBACK FatalConditionHandler::handleVectoredException(PEXCEPTION_POINTERS ExceptionInfo) {
+        for (auto const& def : signalDefs) {
+            if (ExceptionInfo->ExceptionRecord->ExceptionCode == def.id) {
+                reportFatal(def.name);
+            }
         }
-        bool used;
-    };
-
-    struct TestRunInfo {
-        TestRunInfo( std::string const& _name ) : name( _name ) {}
-        std::string name;
-    };
-    struct GroupInfo {
-        GroupInfo(  std::string const& _name,
-                    std::size_t _groupIndex,
-                    std::size_t _groupsCount )
-        :   name( _name ),
-            groupIndex( _groupIndex ),
-            groupsCounts( _groupsCount )
-        {}
-
-        std::string name;
-        std::size_t groupIndex;
-        std::size_t groupsCounts;
-    };
+        // If its not an exception we care about, pass it along.
+        // This stops us from eating debugger breaks etc.
+        return EXCEPTION_CONTINUE_SEARCH;
+    }
 
-    struct AssertionStats {
-        AssertionStats( AssertionResult const& _assertionResult,
-                        std::vector<MessageInfo> const& _infoMessages,
-                        Totals const& _totals )
-        :   assertionResult( _assertionResult ),
-            infoMessages( _infoMessages ),
-            totals( _totals )
-        {
-            if( assertionResult.hasMessage() ) {
-                // Copy message into messages list.
-                // !TBD This should have been done earlier, somewhere
-                MessageBuilder builder( assertionResult.getTestMacroName(), assertionResult.getSourceInfo(), assertionResult.getResultType() );
-                builder << assertionResult.getMessage();
-                builder.m_info.message = builder.m_stream.str();
+    FatalConditionHandler::FatalConditionHandler() {
+        isSet = true;
+        // 32k seems enough for Catch to handle stack overflow,
+        // but the value was found experimentally, so there is no strong guarantee
+        guaranteeSize = 32 * 1024;
+        exceptionHandlerHandle = nullptr;
+        // Register as first handler in current chain
+        exceptionHandlerHandle = AddVectoredExceptionHandler(1, handleVectoredException);
+        // Pass in guarantee size to be filled
+        SetThreadStackGuarantee(&guaranteeSize);
+    }
 
-                infoMessages.push_back( builder.m_info );
-            }
+    void FatalConditionHandler::reset() {
+        if (isSet) {
+            RemoveVectoredExceptionHandler(exceptionHandlerHandle);
+            SetThreadStackGuarantee(&guaranteeSize);
+            exceptionHandlerHandle = nullptr;
+            isSet = false;
         }
-        virtual ~AssertionStats();
+    }
 
-#  ifdef CATCH_CONFIG_CPP11_GENERATED_METHODS
-        AssertionStats( AssertionStats const& )              = default;
-        AssertionStats( AssertionStats && )                  = default;
-        AssertionStats& operator = ( AssertionStats const& ) = default;
-        AssertionStats& operator = ( AssertionStats && )     = default;
-#  endif
+    FatalConditionHandler::~FatalConditionHandler() {
+        reset();
+    }
 
-        AssertionResult assertionResult;
-        std::vector<MessageInfo> infoMessages;
-        Totals totals;
-    };
+bool FatalConditionHandler::isSet = false;
+ULONG FatalConditionHandler::guaranteeSize = 0;
+PVOID FatalConditionHandler::exceptionHandlerHandle = nullptr;
 
-    struct SectionStats {
-        SectionStats(   SectionInfo const& _sectionInfo,
-                        Counts const& _assertions,
-                        double _durationInSeconds,
-                        bool _missingAssertions )
-        :   sectionInfo( _sectionInfo ),
-            assertions( _assertions ),
-            durationInSeconds( _durationInSeconds ),
-            missingAssertions( _missingAssertions )
-        {}
-        virtual ~SectionStats();
-#  ifdef CATCH_CONFIG_CPP11_GENERATED_METHODS
-        SectionStats( SectionStats const& )              = default;
-        SectionStats( SectionStats && )                  = default;
-        SectionStats& operator = ( SectionStats const& ) = default;
-        SectionStats& operator = ( SectionStats && )     = default;
-#  endif
+} // namespace Catch
 
-        SectionInfo sectionInfo;
-        Counts assertions;
-        double durationInSeconds;
-        bool missingAssertions;
-    };
+#elif defined( CATCH_CONFIG_POSIX_SIGNALS )
 
-    struct TestCaseStats {
-        TestCaseStats(  TestCaseInfo const& _testInfo,
-                        Totals const& _totals,
-                        std::string const& _stdOut,
-                        std::string const& _stdErr,
-                        bool _aborting )
-        : testInfo( _testInfo ),
-            totals( _totals ),
-            stdOut( _stdOut ),
-            stdErr( _stdErr ),
-            aborting( _aborting )
-        {}
-        virtual ~TestCaseStats();
+namespace Catch {
 
-#  ifdef CATCH_CONFIG_CPP11_GENERATED_METHODS
-        TestCaseStats( TestCaseStats const& )              = default;
-        TestCaseStats( TestCaseStats && )                  = default;
-        TestCaseStats& operator = ( TestCaseStats const& ) = default;
-        TestCaseStats& operator = ( TestCaseStats && )     = default;
-#  endif
+    struct SignalDefs {
+        int id;
+        const char* name;
+    };
 
-        TestCaseInfo testInfo;
-        Totals totals;
-        std::string stdOut;
-        std::string stdErr;
-        bool aborting;
+    // 32kb for the alternate stack seems to be sufficient. However, this value
+    // is experimentally determined, so that's not guaranteed.
+    static constexpr std::size_t sigStackSize = 32768 >= MINSIGSTKSZ ? 32768 : MINSIGSTKSZ;
+
+    static SignalDefs signalDefs[] = {
+        { SIGINT,  "SIGINT - Terminal interrupt signal" },
+        { SIGILL,  "SIGILL - Illegal instruction signal" },
+        { SIGFPE,  "SIGFPE - Floating point error signal" },
+        { SIGSEGV, "SIGSEGV - Segmentation violation signal" },
+        { SIGTERM, "SIGTERM - Termination request signal" },
+        { SIGABRT, "SIGABRT - Abort (abnormal termination) signal" }
     };
 
-    struct TestGroupStats {
-        TestGroupStats( GroupInfo const& _groupInfo,
-                        Totals const& _totals,
-                        bool _aborting )
-        :   groupInfo( _groupInfo ),
-            totals( _totals ),
-            aborting( _aborting )
-        {}
-        TestGroupStats( GroupInfo const& _groupInfo )
-        :   groupInfo( _groupInfo ),
-            aborting( false )
-        {}
-        virtual ~TestGroupStats();
+    void FatalConditionHandler::handleSignal( int sig ) {
+        char const * name = "<unknown signal>";
+        for (auto const& def : signalDefs) {
+            if (sig == def.id) {
+                name = def.name;
+                break;
+            }
+        }
+        reset();
+        reportFatal(name);
+        raise( sig );
+    }
 
-#  ifdef CATCH_CONFIG_CPP11_GENERATED_METHODS
-        TestGroupStats( TestGroupStats const& )              = default;
-        TestGroupStats( TestGroupStats && )                  = default;
-        TestGroupStats& operator = ( TestGroupStats const& ) = default;
-        TestGroupStats& operator = ( TestGroupStats && )     = default;
-#  endif
+    FatalConditionHandler::FatalConditionHandler() {
+        isSet = true;
+        stack_t sigStack;
+        sigStack.ss_sp = altStackMem;
+        sigStack.ss_size = sigStackSize;
+        sigStack.ss_flags = 0;
+        sigaltstack(&sigStack, &oldSigStack);
+        struct sigaction sa = { };
 
-        GroupInfo groupInfo;
-        Totals totals;
-        bool aborting;
-    };
+        sa.sa_handler = handleSignal;
+        sa.sa_flags = SA_ONSTACK;
+        for (std::size_t i = 0; i < sizeof(signalDefs)/sizeof(SignalDefs); ++i) {
+            sigaction(signalDefs[i].id, &sa, &oldSigActions[i]);
+        }
+    }
 
-    struct TestRunStats {
-        TestRunStats(   TestRunInfo const& _runInfo,
-                        Totals const& _totals,
-                        bool _aborting )
-        :   runInfo( _runInfo ),
-            totals( _totals ),
-            aborting( _aborting )
-        {}
-        virtual ~TestRunStats();
+    FatalConditionHandler::~FatalConditionHandler() {
+        reset();
+    }
 
-#  ifndef CATCH_CONFIG_CPP11_GENERATED_METHODS
-        TestRunStats( TestRunStats const& _other )
-        :   runInfo( _other.runInfo ),
-            totals( _other.totals ),
-            aborting( _other.aborting )
-        {}
-#  else
-        TestRunStats( TestRunStats const& )              = default;
-        TestRunStats( TestRunStats && )                  = default;
-        TestRunStats& operator = ( TestRunStats const& ) = default;
-        TestRunStats& operator = ( TestRunStats && )     = default;
-#  endif
+    void FatalConditionHandler::reset() {
+        if( isSet ) {
+            // Set signals back to previous values -- hopefully nobody overwrote them in the meantime
+            for( std::size_t i = 0; i < sizeof(signalDefs)/sizeof(SignalDefs); ++i ) {
+                sigaction(signalDefs[i].id, &oldSigActions[i], nullptr);
+            }
+            // Return the old stack
+            sigaltstack(&oldSigStack, nullptr);
+            isSet = false;
+        }
+    }
 
-        TestRunInfo runInfo;
-        Totals totals;
-        bool aborting;
-    };
+    bool FatalConditionHandler::isSet = false;
+    struct sigaction FatalConditionHandler::oldSigActions[sizeof(signalDefs)/sizeof(SignalDefs)] = {};
+    stack_t FatalConditionHandler::oldSigStack = {};
+    char FatalConditionHandler::altStackMem[sigStackSize] = {};
 
-    class MultipleReporters;
+} // namespace Catch
 
-    struct IStreamingReporter : IShared {
-        virtual ~IStreamingReporter();
+#else
 
-        // Implementing class must also provide the following static method:
-        // static std::string getDescription();
+namespace Catch {
+    void FatalConditionHandler::reset() {}
+}
 
-        virtual ReporterPreferences getPreferences() const = 0;
+#endif // signals/SEH handling
 
-        virtual void noMatchingTestCases( std::string const& spec ) = 0;
+#if defined(__GNUC__)
+#    pragma GCC diagnostic pop
+#endif
+// end catch_fatal_condition.cpp
+// start catch_generators.cpp
 
-        virtual void testRunStarting( TestRunInfo const& testRunInfo ) = 0;
-        virtual void testGroupStarting( GroupInfo const& groupInfo ) = 0;
+#include <limits>
+#include <set>
 
-        virtual void testCaseStarting( TestCaseInfo const& testInfo ) = 0;
-        virtual void sectionStarting( SectionInfo const& sectionInfo ) = 0;
+namespace Catch {
 
-        virtual void assertionStarting( AssertionInfo const& assertionInfo ) = 0;
+IGeneratorTracker::~IGeneratorTracker() {}
 
-        // The return value indicates if the messages buffer should be cleared:
-        virtual bool assertionEnded( AssertionStats const& assertionStats ) = 0;
+const char* GeneratorException::what() const noexcept {
+    return m_msg;
+}
 
-        virtual void sectionEnded( SectionStats const& sectionStats ) = 0;
-        virtual void testCaseEnded( TestCaseStats const& testCaseStats ) = 0;
-        virtual void testGroupEnded( TestGroupStats const& testGroupStats ) = 0;
-        virtual void testRunEnded( TestRunStats const& testRunStats ) = 0;
+namespace Generators {
 
-        virtual void skipTest( TestCaseInfo const& testInfo ) = 0;
+    GeneratorUntypedBase::~GeneratorUntypedBase() {}
 
-        virtual MultipleReporters* tryAsMulti() { return CATCH_NULL; }
-    };
+    auto acquireGeneratorTracker( SourceLineInfo const& lineInfo ) -> IGeneratorTracker& {
+        return getResultCapture().acquireGeneratorTracker( lineInfo );
+    }
 
-    struct IReporterFactory : IShared {
-        virtual ~IReporterFactory();
-        virtual IStreamingReporter* create( ReporterConfig const& config ) const = 0;
-        virtual std::string getDescription() const = 0;
-    };
+} // namespace Generators
+} // namespace Catch
+// end catch_generators.cpp
+// start catch_interfaces_capture.cpp
 
-    struct IReporterRegistry {
-        typedef std::map<std::string, Ptr<IReporterFactory> > FactoryMap;
-        typedef std::vector<Ptr<IReporterFactory> > Listeners;
+namespace Catch {
+    IResultCapture::~IResultCapture() = default;
+}
+// end catch_interfaces_capture.cpp
+// start catch_interfaces_config.cpp
 
-        virtual ~IReporterRegistry();
-        virtual IStreamingReporter* create( std::string const& name, Ptr<IConfig const> const& config ) const = 0;
-        virtual FactoryMap const& getFactories() const = 0;
-        virtual Listeners const& getListeners() const = 0;
-    };
+namespace Catch {
+    IConfig::~IConfig() = default;
+}
+// end catch_interfaces_config.cpp
+// start catch_interfaces_exception.cpp
 
-    Ptr<IStreamingReporter> addReporter( Ptr<IStreamingReporter> const& existingReporter, Ptr<IStreamingReporter> const& additionalReporter );
+namespace Catch {
+    IExceptionTranslator::~IExceptionTranslator() = default;
+    IExceptionTranslatorRegistry::~IExceptionTranslatorRegistry() = default;
+}
+// end catch_interfaces_exception.cpp
+// start catch_interfaces_registry_hub.cpp
 
+namespace Catch {
+    IRegistryHub::~IRegistryHub() = default;
+    IMutableRegistryHub::~IMutableRegistryHub() = default;
 }
+// end catch_interfaces_registry_hub.cpp
+// start catch_interfaces_reporter.cpp
 
-#include <limits>
-#include <algorithm>
+// start catch_reporter_listening.h
 
 namespace Catch {
 
-    inline std::size_t listTests( Config const& config ) {
-
-        TestSpec testSpec = config.testSpec();
-        if( config.testSpec().hasFilters() )
-            Catch::cout() << "Matching test cases:\n";
-        else {
-            Catch::cout() << "All available test cases:\n";
-            testSpec = TestSpecParser( ITagAliasRegistry::get() ).parse( "*" ).testSpec();
-        }
+    class ListeningReporter : public IStreamingReporter {
+        using Reporters = std::vector<IStreamingReporterPtr>;
+        Reporters m_listeners;
+        IStreamingReporterPtr m_reporter = nullptr;
+        ReporterPreferences m_preferences;
 
-        std::size_t matchedTests = 0;
-        TextAttributes nameAttr, tagsAttr;
-        nameAttr.setInitialIndent( 2 ).setIndent( 4 );
-        tagsAttr.setIndent( 6 );
+    public:
+        ListeningReporter();
 
-        std::vector<TestCase> matchedTestCases = filterTests( getAllTestCasesSorted( config ), testSpec, config );
-        for( std::vector<TestCase>::const_iterator it = matchedTestCases.begin(), itEnd = matchedTestCases.end();
-                it != itEnd;
-                ++it ) {
-            matchedTests++;
-            TestCaseInfo const& testCaseInfo = it->getTestCaseInfo();
-            Colour::Code colour = testCaseInfo.isHidden()
-                ? Colour::SecondaryText
-                : Colour::None;
-            Colour colourGuard( colour );
+        void addListener( IStreamingReporterPtr&& listener );
+        void addReporter( IStreamingReporterPtr&& reporter );
 
-            Catch::cout() << Text( testCaseInfo.name, nameAttr ) << std::endl;
-            if( !testCaseInfo.tags.empty() )
-                Catch::cout() << Text( testCaseInfo.tagsAsString, tagsAttr ) << std::endl;
-        }
+    public: // IStreamingReporter
 
-        if( !config.testSpec().hasFilters() )
-            Catch::cout() << pluralise( matchedTests, "test case" ) << '\n' << std::endl;
-        else
-            Catch::cout() << pluralise( matchedTests, "matching test case" ) << '\n' << std::endl;
-        return matchedTests;
-    }
+        ReporterPreferences getPreferences() const override;
 
-    inline std::size_t listTestsNamesOnly( Config const& config ) {
-        TestSpec testSpec = config.testSpec();
-        if( !config.testSpec().hasFilters() )
-            testSpec = TestSpecParser( ITagAliasRegistry::get() ).parse( "*" ).testSpec();
-        std::size_t matchedTests = 0;
-        std::vector<TestCase> matchedTestCases = filterTests( getAllTestCasesSorted( config ), testSpec, config );
-        for( std::vector<TestCase>::const_iterator it = matchedTestCases.begin(), itEnd = matchedTestCases.end();
-                it != itEnd;
-                ++it ) {
-            matchedTests++;
-            TestCaseInfo const& testCaseInfo = it->getTestCaseInfo();
-            if( startsWith( testCaseInfo.name, '#' ) )
-               Catch::cout() << '"' << testCaseInfo.name << '"' << std::endl;
-            else
-               Catch::cout() << testCaseInfo.name << std::endl;
-        }
-        return matchedTests;
-    }
+        void noMatchingTestCases( std::string const& spec ) override;
 
-    struct TagInfo {
-        TagInfo() : count ( 0 ) {}
-        void add( std::string const& spelling ) {
-            ++count;
-            spellings.insert( spelling );
-        }
-        std::string all() const {
-            std::string out;
-            for( std::set<std::string>::const_iterator it = spellings.begin(), itEnd = spellings.end();
-                        it != itEnd;
-                        ++it )
-                out += "[" + *it + "]";
-            return out;
-        }
-        std::set<std::string> spellings;
-        std::size_t count;
-    };
+        void reportInvalidArguments(std::string const&arg) override;
 
-    inline std::size_t listTags( Config const& config ) {
-        TestSpec testSpec = config.testSpec();
-        if( config.testSpec().hasFilters() )
-            Catch::cout() << "Tags for matching test cases:\n";
-        else {
-            Catch::cout() << "All available tags:\n";
-            testSpec = TestSpecParser( ITagAliasRegistry::get() ).parse( "*" ).testSpec();
-        }
+        static std::set<Verbosity> getSupportedVerbosities();
 
-        std::map<std::string, TagInfo> tagCounts;
+#if defined(CATCH_CONFIG_ENABLE_BENCHMARKING)
+        void benchmarkPreparing(std::string const& name) override;
+        void benchmarkStarting( BenchmarkInfo const& benchmarkInfo ) override;
+        void benchmarkEnded( BenchmarkStats<> const& benchmarkStats ) override;
+        void benchmarkFailed(std::string const&) override;
+#endif // CATCH_CONFIG_ENABLE_BENCHMARKING
 
-        std::vector<TestCase> matchedTestCases = filterTests( getAllTestCasesSorted( config ), testSpec, config );
-        for( std::vector<TestCase>::const_iterator it = matchedTestCases.begin(), itEnd = matchedTestCases.end();
-                it != itEnd;
-                ++it ) {
-            for( std::set<std::string>::const_iterator  tagIt = it->getTestCaseInfo().tags.begin(),
-                                                        tagItEnd = it->getTestCaseInfo().tags.end();
-                    tagIt != tagItEnd;
-                    ++tagIt ) {
-                std::string tagName = *tagIt;
-                std::string lcaseTagName = toLower( tagName );
-                std::map<std::string, TagInfo>::iterator countIt = tagCounts.find( lcaseTagName );
-                if( countIt == tagCounts.end() )
-                    countIt = tagCounts.insert( std::make_pair( lcaseTagName, TagInfo() ) ).first;
-                countIt->second.add( tagName );
-            }
-        }
+        void testRunStarting( TestRunInfo const& testRunInfo ) override;
+        void testGroupStarting( GroupInfo const& groupInfo ) override;
+        void testCaseStarting( TestCaseInfo const& testInfo ) override;
+        void sectionStarting( SectionInfo const& sectionInfo ) override;
+        void assertionStarting( AssertionInfo const& assertionInfo ) override;
 
-        for( std::map<std::string, TagInfo>::const_iterator countIt = tagCounts.begin(),
-                                                            countItEnd = tagCounts.end();
-                countIt != countItEnd;
-                ++countIt ) {
-            std::ostringstream oss;
-            oss << "  " << std::setw(2) << countIt->second.count << "  ";
-            Text wrapper( countIt->second.all(), TextAttributes()
-                                                    .setInitialIndent( 0 )
-                                                    .setIndent( oss.str().size() )
-                                                    .setWidth( CATCH_CONFIG_CONSOLE_WIDTH-10 ) );
-            Catch::cout() << oss.str() << wrapper << '\n';
-        }
-        Catch::cout() << pluralise( tagCounts.size(), "tag" ) << '\n' << std::endl;
-        return tagCounts.size();
-    }
+        // The return value indicates if the messages buffer should be cleared:
+        bool assertionEnded( AssertionStats const& assertionStats ) override;
+        void sectionEnded( SectionStats const& sectionStats ) override;
+        void testCaseEnded( TestCaseStats const& testCaseStats ) override;
+        void testGroupEnded( TestGroupStats const& testGroupStats ) override;
+        void testRunEnded( TestRunStats const& testRunStats ) override;
 
-    inline std::size_t listReporters( Config const& /*config*/ ) {
-        Catch::cout() << "Available reporters:\n";
-        IReporterRegistry::FactoryMap const& factories = getRegistryHub().getReporterRegistry().getFactories();
-        IReporterRegistry::FactoryMap::const_iterator itBegin = factories.begin(), itEnd = factories.end(), it;
-        std::size_t maxNameLen = 0;
-        for(it = itBegin; it != itEnd; ++it )
-            maxNameLen = (std::max)( maxNameLen, it->first.size() );
-
-        for(it = itBegin; it != itEnd; ++it ) {
-            Text wrapper( it->second->getDescription(), TextAttributes()
-                                                        .setInitialIndent( 0 )
-                                                        .setIndent( 7+maxNameLen )
-                                                        .setWidth( CATCH_CONFIG_CONSOLE_WIDTH - maxNameLen-8 ) );
-            Catch::cout() << "  "
-                    << it->first
-                    << ':'
-                    << std::string( maxNameLen - it->first.size() + 2, ' ' )
-                    << wrapper << '\n';
-        }
-        Catch::cout() << std::endl;
-        return factories.size();
-    }
+        void skipTest( TestCaseInfo const& testInfo ) override;
+        bool isMulti() const override;
 
-    inline Option<std::size_t> list( Config const& config ) {
-        Option<std::size_t> listedCount;
-        if( config.listTests() )
-            listedCount = listedCount.valueOr(0) + listTests( config );
-        if( config.listTestNamesOnly() )
-            listedCount = listedCount.valueOr(0) + listTestsNamesOnly( config );
-        if( config.listTags() )
-            listedCount = listedCount.valueOr(0) + listTags( config );
-        if( config.listReporters() )
-            listedCount = listedCount.valueOr(0) + listReporters( config );
-        return listedCount;
-    }
+    };
 
 } // end namespace Catch
 
-// #included from: internal/catch_run_context.hpp
-#define TWOBLUECUBES_CATCH_RUNNER_IMPL_HPP_INCLUDED
+// end catch_reporter_listening.h
+namespace Catch {
 
-// #included from: catch_test_case_tracker.hpp
-#define TWOBLUECUBES_CATCH_TEST_CASE_TRACKER_HPP_INCLUDED
+    ReporterConfig::ReporterConfig( IConfigPtr const& _fullConfig )
+    :   m_stream( &_fullConfig->stream() ), m_fullConfig( _fullConfig ) {}
 
-#include <algorithm>
-#include <string>
-#include <assert.h>
-#include <vector>
-#include <stdexcept>
+    ReporterConfig::ReporterConfig( IConfigPtr const& _fullConfig, std::ostream& _stream )
+    :   m_stream( &_stream ), m_fullConfig( _fullConfig ) {}
 
-CATCH_INTERNAL_SUPPRESS_ETD_WARNINGS
+    std::ostream& ReporterConfig::stream() const { return *m_stream; }
+    IConfigPtr ReporterConfig::fullConfig() const { return m_fullConfig; }
 
-namespace Catch {
-namespace TestCaseTracking {
+    TestRunInfo::TestRunInfo( std::string const& _name ) : name( _name ) {}
 
-    struct NameAndLocation {
-        std::string name;
-        SourceLineInfo location;
+    GroupInfo::GroupInfo(  std::string const& _name,
+                           std::size_t _groupIndex,
+                           std::size_t _groupsCount )
+    :   name( _name ),
+        groupIndex( _groupIndex ),
+        groupsCounts( _groupsCount )
+    {}
 
-        NameAndLocation( std::string const& _name, SourceLineInfo const& _location )
-        :   name( _name ),
-            location( _location )
-        {}
-    };
+     AssertionStats::AssertionStats( AssertionResult const& _assertionResult,
+                                     std::vector<MessageInfo> const& _infoMessages,
+                                     Totals const& _totals )
+    :   assertionResult( _assertionResult ),
+        infoMessages( _infoMessages ),
+        totals( _totals )
+    {
+        assertionResult.m_resultData.lazyExpression.m_transientExpression = _assertionResult.m_resultData.lazyExpression.m_transientExpression;
 
-    struct ITracker : SharedImpl<> {
-        virtual ~ITracker();
+        if( assertionResult.hasMessage() ) {
+            // Copy message into messages list.
+            // !TBD This should have been done earlier, somewhere
+            MessageBuilder builder( assertionResult.getTestMacroName(), assertionResult.getSourceInfo(), assertionResult.getResultType() );
+            builder << assertionResult.getMessage();
+            builder.m_info.message = builder.m_stream.str();
 
-        // static queries
-        virtual NameAndLocation const& nameAndLocation() const = 0;
+            infoMessages.push_back( builder.m_info );
+        }
+    }
 
-        // dynamic queries
-        virtual bool isComplete() const = 0; // Successfully completed or failed
-        virtual bool isSuccessfullyCompleted() const = 0;
-        virtual bool isOpen() const = 0; // Started but not complete
-        virtual bool hasChildren() const = 0;
+     AssertionStats::~AssertionStats() = default;
 
-        virtual ITracker& parent() = 0;
+    SectionStats::SectionStats(  SectionInfo const& _sectionInfo,
+                                 Counts const& _assertions,
+                                 double _durationInSeconds,
+                                 bool _missingAssertions )
+    :   sectionInfo( _sectionInfo ),
+        assertions( _assertions ),
+        durationInSeconds( _durationInSeconds ),
+        missingAssertions( _missingAssertions )
+    {}
 
-        // actions
-        virtual void close() = 0; // Successfully complete
-        virtual void fail() = 0;
-        virtual void markAsNeedingAnotherRun() = 0;
+    SectionStats::~SectionStats() = default;
+
+    TestCaseStats::TestCaseStats(  TestCaseInfo const& _testInfo,
+                                   Totals const& _totals,
+                                   std::string const& _stdOut,
+                                   std::string const& _stdErr,
+                                   bool _aborting )
+    : testInfo( _testInfo ),
+        totals( _totals ),
+        stdOut( _stdOut ),
+        stdErr( _stdErr ),
+        aborting( _aborting )
+    {}
 
-        virtual void addChild( Ptr<ITracker> const& child ) = 0;
-        virtual ITracker* findChild( NameAndLocation const& nameAndLocation ) = 0;
-        virtual void openChild() = 0;
+    TestCaseStats::~TestCaseStats() = default;
 
-        // Debug/ checking
-        virtual bool isSectionTracker() const = 0;
-        virtual bool isIndexTracker() const = 0;
-    };
+    TestGroupStats::TestGroupStats( GroupInfo const& _groupInfo,
+                                    Totals const& _totals,
+                                    bool _aborting )
+    :   groupInfo( _groupInfo ),
+        totals( _totals ),
+        aborting( _aborting )
+    {}
 
-    class  TrackerContext {
+    TestGroupStats::TestGroupStats( GroupInfo const& _groupInfo )
+    :   groupInfo( _groupInfo ),
+        aborting( false )
+    {}
 
-        enum RunState {
-            NotStarted,
-            Executing,
-            CompletedCycle
-        };
+    TestGroupStats::~TestGroupStats() = default;
 
-        Ptr<ITracker> m_rootTracker;
-        ITracker* m_currentTracker;
-        RunState m_runState;
+    TestRunStats::TestRunStats(   TestRunInfo const& _runInfo,
+                    Totals const& _totals,
+                    bool _aborting )
+    :   runInfo( _runInfo ),
+        totals( _totals ),
+        aborting( _aborting )
+    {}
 
-    public:
+    TestRunStats::~TestRunStats() = default;
 
-        static TrackerContext& instance() {
-            static TrackerContext s_instance;
-            return s_instance;
-        }
+    void IStreamingReporter::fatalErrorEncountered( StringRef ) {}
+    bool IStreamingReporter::isMulti() const { return false; }
 
-        TrackerContext()
-        :   m_currentTracker( CATCH_NULL ),
-            m_runState( NotStarted )
-        {}
+    IReporterFactory::~IReporterFactory() = default;
+    IReporterRegistry::~IReporterRegistry() = default;
 
-        ITracker& startRun();
+} // end namespace Catch
+// end catch_interfaces_reporter.cpp
+// start catch_interfaces_runner.cpp
 
-        void endRun() {
-            m_rootTracker.reset();
-            m_currentTracker = CATCH_NULL;
-            m_runState = NotStarted;
-        }
+namespace Catch {
+    IRunner::~IRunner() = default;
+}
+// end catch_interfaces_runner.cpp
+// start catch_interfaces_testcase.cpp
 
-        void startCycle() {
-            m_currentTracker = m_rootTracker.get();
-            m_runState = Executing;
-        }
-        void completeCycle() {
-            m_runState = CompletedCycle;
-        }
+namespace Catch {
+    ITestInvoker::~ITestInvoker() = default;
+    ITestCaseRegistry::~ITestCaseRegistry() = default;
+}
+// end catch_interfaces_testcase.cpp
+// start catch_leak_detector.cpp
 
-        bool completedCycle() const {
-            return m_runState == CompletedCycle;
-        }
-        ITracker& currentTracker() {
-            return *m_currentTracker;
-        }
-        void setCurrentTracker( ITracker* tracker ) {
-            m_currentTracker = tracker;
-        }
-    };
+#ifdef CATCH_CONFIG_WINDOWS_CRTDBG
+#include <crtdbg.h>
 
-    class TrackerBase : public ITracker {
-    protected:
-        enum CycleState {
-            NotStarted,
-            Executing,
-            ExecutingChildren,
-            NeedsAnotherRun,
-            CompletedSuccessfully,
-            Failed
-        };
-        class TrackerHasName {
-            NameAndLocation m_nameAndLocation;
-        public:
-            TrackerHasName( NameAndLocation const& nameAndLocation ) : m_nameAndLocation( nameAndLocation ) {}
-            bool operator ()( Ptr<ITracker> const& tracker ) {
-                return
-                    tracker->nameAndLocation().name == m_nameAndLocation.name &&
-                    tracker->nameAndLocation().location == m_nameAndLocation.location;
-            }
-        };
-        typedef std::vector<Ptr<ITracker> > Children;
-        NameAndLocation m_nameAndLocation;
-        TrackerContext& m_ctx;
-        ITracker* m_parent;
-        Children m_children;
-        CycleState m_runState;
-    public:
-        TrackerBase( NameAndLocation const& nameAndLocation, TrackerContext& ctx, ITracker* parent )
-        :   m_nameAndLocation( nameAndLocation ),
-            m_ctx( ctx ),
-            m_parent( parent ),
-            m_runState( NotStarted )
-        {}
-        virtual ~TrackerBase();
+namespace Catch {
 
-        virtual NameAndLocation const& nameAndLocation() const CATCH_OVERRIDE {
-            return m_nameAndLocation;
-        }
-        virtual bool isComplete() const CATCH_OVERRIDE {
-            return m_runState == CompletedSuccessfully || m_runState == Failed;
-        }
-        virtual bool isSuccessfullyCompleted() const CATCH_OVERRIDE {
-            return m_runState == CompletedSuccessfully;
-        }
-        virtual bool isOpen() const CATCH_OVERRIDE {
-            return m_runState != NotStarted && !isComplete();
-        }
-        virtual bool hasChildren() const CATCH_OVERRIDE {
-            return !m_children.empty();
-        }
+    LeakDetector::LeakDetector() {
+        int flag = _CrtSetDbgFlag(_CRTDBG_REPORT_FLAG);
+        flag |= _CRTDBG_LEAK_CHECK_DF;
+        flag |= _CRTDBG_ALLOC_MEM_DF;
+        _CrtSetDbgFlag(flag);
+        _CrtSetReportMode(_CRT_WARN, _CRTDBG_MODE_FILE | _CRTDBG_MODE_DEBUG);
+        _CrtSetReportFile(_CRT_WARN, _CRTDBG_FILE_STDERR);
+        // Change this to leaking allocation's number to break there
+        _CrtSetBreakAlloc(-1);
+    }
+}
 
-        virtual void addChild( Ptr<ITracker> const& child ) CATCH_OVERRIDE {
-            m_children.push_back( child );
-        }
+#else
 
-        virtual ITracker* findChild( NameAndLocation const& nameAndLocation ) CATCH_OVERRIDE {
-            Children::const_iterator it = std::find_if( m_children.begin(), m_children.end(), TrackerHasName( nameAndLocation ) );
-            return( it != m_children.end() )
-                ? it->get()
-                : CATCH_NULL;
-        }
-        virtual ITracker& parent() CATCH_OVERRIDE {
-            assert( m_parent ); // Should always be non-null except for root
-            return *m_parent;
-        }
+    Catch::LeakDetector::LeakDetector() {}
 
-        virtual void openChild() CATCH_OVERRIDE {
-            if( m_runState != ExecutingChildren ) {
-                m_runState = ExecutingChildren;
-                if( m_parent )
-                    m_parent->openChild();
-            }
-        }
+#endif
 
-        virtual bool isSectionTracker() const CATCH_OVERRIDE { return false; }
-        virtual bool isIndexTracker() const CATCH_OVERRIDE { return false; }
+Catch::LeakDetector::~LeakDetector() {
+    Catch::cleanUp();
+}
+// end catch_leak_detector.cpp
+// start catch_list.cpp
 
-        void open() {
-            m_runState = Executing;
-            moveToThis();
-            if( m_parent )
-                m_parent->openChild();
-        }
+// start catch_list.h
 
-        virtual void close() CATCH_OVERRIDE {
+#include <set>
 
-            // Close any still open children (e.g. generators)
-            while( &m_ctx.currentTracker() != this )
-                m_ctx.currentTracker().close();
+namespace Catch {
 
-            switch( m_runState ) {
-                case NotStarted:
-                case CompletedSuccessfully:
-                case Failed:
-                    throw std::logic_error( "Illogical state" );
+    std::size_t listTests( Config const& config );
 
-                case NeedsAnotherRun:
-                    break;;
+    std::size_t listTestsNamesOnly( Config const& config );
 
-                case Executing:
-                    m_runState = CompletedSuccessfully;
-                    break;
-                case ExecutingChildren:
-                    if( m_children.empty() || m_children.back()->isComplete() )
-                        m_runState = CompletedSuccessfully;
-                    break;
+    struct TagInfo {
+        void add( std::string const& spelling );
+        std::string all() const;
 
-                default:
-                    throw std::logic_error( "Unexpected state" );
-            }
-            moveToParent();
-            m_ctx.completeCycle();
-        }
-        virtual void fail() CATCH_OVERRIDE {
-            m_runState = Failed;
-            if( m_parent )
-                m_parent->markAsNeedingAnotherRun();
-            moveToParent();
-            m_ctx.completeCycle();
-        }
-        virtual void markAsNeedingAnotherRun() CATCH_OVERRIDE {
-            m_runState = NeedsAnotherRun;
-        }
-    private:
-        void moveToParent() {
-            assert( m_parent );
-            m_ctx.setCurrentTracker( m_parent );
-        }
-        void moveToThis() {
-            m_ctx.setCurrentTracker( this );
-        }
+        std::set<std::string> spellings;
+        std::size_t count = 0;
     };
 
-    class SectionTracker : public TrackerBase {
-        std::vector<std::string> m_filters;
-    public:
-        SectionTracker( NameAndLocation const& nameAndLocation, TrackerContext& ctx, ITracker* parent )
-        :   TrackerBase( nameAndLocation, ctx, parent )
-        {
-            if( parent ) {
-                while( !parent->isSectionTracker() )
-                    parent = &parent->parent();
+    std::size_t listTags( Config const& config );
 
-                SectionTracker& parentSection = static_cast<SectionTracker&>( *parent );
-                addNextFilters( parentSection.m_filters );
-            }
-        }
-        virtual ~SectionTracker();
+    std::size_t listReporters();
 
-        virtual bool isSectionTracker() const CATCH_OVERRIDE { return true; }
+    Option<std::size_t> list( std::shared_ptr<Config> const& config );
 
-        static SectionTracker& acquire( TrackerContext& ctx, NameAndLocation const& nameAndLocation ) {
-            SectionTracker* section = CATCH_NULL;
+} // end namespace Catch
 
-            ITracker& currentTracker = ctx.currentTracker();
-            if( ITracker* childTracker = currentTracker.findChild( nameAndLocation ) ) {
-                assert( childTracker );
-                assert( childTracker->isSectionTracker() );
-                section = static_cast<SectionTracker*>( childTracker );
-            }
-            else {
-                section = new SectionTracker( nameAndLocation, ctx, &currentTracker );
-                currentTracker.addChild( section );
-            }
-            if( !ctx.completedCycle() )
-                section->tryOpen();
-            return *section;
+// end catch_list.h
+// start catch_text.h
+
+namespace Catch {
+    using namespace clara::TextFlow;
+}
+
+// end catch_text.h
+#include <limits>
+#include <algorithm>
+#include <iomanip>
+
+namespace Catch {
+
+    std::size_t listTests( Config const& config ) {
+        TestSpec testSpec = config.testSpec();
+        if( config.hasTestFilters() )
+            Catch::cout() << "Matching test cases:\n";
+        else {
+            Catch::cout() << "All available test cases:\n";
         }
 
-        void tryOpen() {
-            if( !isComplete() && (m_filters.empty() || m_filters[0].empty() ||  m_filters[0] == m_nameAndLocation.name ) )
-                open();
-        }
+        auto matchedTestCases = filterTests( getAllTestCasesSorted( config ), testSpec, config );
+        for( auto const& testCaseInfo : matchedTestCases ) {
+            Colour::Code colour = testCaseInfo.isHidden()
+                ? Colour::SecondaryText
+                : Colour::None;
+            Colour colourGuard( colour );
 
-        void addInitialFilters( std::vector<std::string> const& filters ) {
-            if( !filters.empty() ) {
-                m_filters.push_back(""); // Root - should never be consulted
-                m_filters.push_back(""); // Test Case - not a section filter
-                m_filters.insert( m_filters.end(), filters.begin(), filters.end() );
+            Catch::cout() << Column( testCaseInfo.name ).initialIndent( 2 ).indent( 4 ) << "\n";
+            if( config.verbosity() >= Verbosity::High ) {
+                Catch::cout() << Column( Catch::Detail::stringify( testCaseInfo.lineInfo ) ).indent(4) << std::endl;
+                std::string description = testCaseInfo.description;
+                if( description.empty() )
+                    description = "(NO DESCRIPTION)";
+                Catch::cout() << Column( description ).indent(4) << std::endl;
             }
+            if( !testCaseInfo.tags.empty() )
+                Catch::cout() << Column( testCaseInfo.tagsAsString() ).indent( 6 ) << "\n";
         }
-        void addNextFilters( std::vector<std::string> const& filters ) {
-            if( filters.size() > 1 )
-                m_filters.insert( m_filters.end(), ++filters.begin(), filters.end() );
-        }
-    };
 
-    class IndexTracker : public TrackerBase {
-        int m_size;
-        int m_index;
-    public:
-        IndexTracker( NameAndLocation const& nameAndLocation, TrackerContext& ctx, ITracker* parent, int size )
-        :   TrackerBase( nameAndLocation, ctx, parent ),
-            m_size( size ),
-            m_index( -1 )
-        {}
-        virtual ~IndexTracker();
+        if( !config.hasTestFilters() )
+            Catch::cout() << pluralise( matchedTestCases.size(), "test case" ) << '\n' << std::endl;
+        else
+            Catch::cout() << pluralise( matchedTestCases.size(), "matching test case" ) << '\n' << std::endl;
+        return matchedTestCases.size();
+    }
 
-        virtual bool isIndexTracker() const CATCH_OVERRIDE { return true; }
+    std::size_t listTestsNamesOnly( Config const& config ) {
+        TestSpec testSpec = config.testSpec();
+        std::size_t matchedTests = 0;
+        std::vector<TestCase> matchedTestCases = filterTests( getAllTestCasesSorted( config ), testSpec, config );
+        for( auto const& testCaseInfo : matchedTestCases ) {
+            matchedTests++;
+            if( startsWith( testCaseInfo.name, '#' ) )
+               Catch::cout() << '"' << testCaseInfo.name << '"';
+            else
+               Catch::cout() << testCaseInfo.name;
+            if ( config.verbosity() >= Verbosity::High )
+                Catch::cout() << "\t@" << testCaseInfo.lineInfo;
+            Catch::cout() << std::endl;
+        }
+        return matchedTests;
+    }
 
-        static IndexTracker& acquire( TrackerContext& ctx, NameAndLocation const& nameAndLocation, int size ) {
-            IndexTracker* tracker = CATCH_NULL;
+    void TagInfo::add( std::string const& spelling ) {
+        ++count;
+        spellings.insert( spelling );
+    }
 
-            ITracker& currentTracker = ctx.currentTracker();
-            if( ITracker* childTracker = currentTracker.findChild( nameAndLocation ) ) {
-                assert( childTracker );
-                assert( childTracker->isIndexTracker() );
-                tracker = static_cast<IndexTracker*>( childTracker );
-            }
-            else {
-                tracker = new IndexTracker( nameAndLocation, ctx, &currentTracker, size );
-                currentTracker.addChild( tracker );
-            }
+    std::string TagInfo::all() const {
+        size_t size = 0;
+        for (auto const& spelling : spellings) {
+            // Add 2 for the brackes
+            size += spelling.size() + 2;
+        }
 
-            if( !ctx.completedCycle() && !tracker->isComplete() ) {
-                if( tracker->m_runState != ExecutingChildren && tracker->m_runState != NeedsAnotherRun )
-                    tracker->moveNext();
-                tracker->open();
-            }
+        std::string out; out.reserve(size);
+        for (auto const& spelling : spellings) {
+            out += '[';
+            out += spelling;
+            out += ']';
+        }
+        return out;
+    }
 
-            return *tracker;
+    std::size_t listTags( Config const& config ) {
+        TestSpec testSpec = config.testSpec();
+        if( config.hasTestFilters() )
+            Catch::cout() << "Tags for matching test cases:\n";
+        else {
+            Catch::cout() << "All available tags:\n";
         }
 
-        int index() const { return m_index; }
+        std::map<std::string, TagInfo> tagCounts;
 
-        void moveNext() {
-            m_index++;
-            m_children.clear();
+        std::vector<TestCase> matchedTestCases = filterTests( getAllTestCasesSorted( config ), testSpec, config );
+        for( auto const& testCase : matchedTestCases ) {
+            for( auto const& tagName : testCase.getTestCaseInfo().tags ) {
+                std::string lcaseTagName = toLower( tagName );
+                auto countIt = tagCounts.find( lcaseTagName );
+                if( countIt == tagCounts.end() )
+                    countIt = tagCounts.insert( std::make_pair( lcaseTagName, TagInfo() ) ).first;
+                countIt->second.add( tagName );
+            }
         }
 
-        virtual void close() CATCH_OVERRIDE {
-            TrackerBase::close();
-            if( m_runState == CompletedSuccessfully && m_index < m_size-1 )
-                m_runState = Executing;
+        for( auto const& tagCount : tagCounts ) {
+            ReusableStringStream rss;
+            rss << "  " << std::setw(2) << tagCount.second.count << "  ";
+            auto str = rss.str();
+            auto wrapper = Column( tagCount.second.all() )
+                                                    .initialIndent( 0 )
+                                                    .indent( str.size() )
+                                                    .width( CATCH_CONFIG_CONSOLE_WIDTH-10 );
+            Catch::cout() << str << wrapper << '\n';
         }
-    };
+        Catch::cout() << pluralise( tagCounts.size(), "tag" ) << '\n' << std::endl;
+        return tagCounts.size();
+    }
 
-    inline ITracker& TrackerContext::startRun() {
-        m_rootTracker = new SectionTracker( NameAndLocation( "{root}", CATCH_INTERNAL_LINEINFO ), *this, CATCH_NULL );
-        m_currentTracker = CATCH_NULL;
-        m_runState = Executing;
-        return *m_rootTracker;
+    std::size_t listReporters() {
+        Catch::cout() << "Available reporters:\n";
+        IReporterRegistry::FactoryMap const& factories = getRegistryHub().getReporterRegistry().getFactories();
+        std::size_t maxNameLen = 0;
+        for( auto const& factoryKvp : factories )
+            maxNameLen = (std::max)( maxNameLen, factoryKvp.first.size() );
+
+        for( auto const& factoryKvp : factories ) {
+            Catch::cout()
+                    << Column( factoryKvp.first + ":" )
+                            .indent(2)
+                            .width( 5+maxNameLen )
+                    +  Column( factoryKvp.second->getDescription() )
+                            .initialIndent(0)
+                            .indent(2)
+                            .width( CATCH_CONFIG_CONSOLE_WIDTH - maxNameLen-8 )
+                    << "\n";
+        }
+        Catch::cout() << std::endl;
+        return factories.size();
     }
 
-} // namespace TestCaseTracking
+    Option<std::size_t> list( std::shared_ptr<Config> const& config ) {
+        Option<std::size_t> listedCount;
+        getCurrentMutableContext().setConfig( config );
+        if( config->listTests() )
+            listedCount = listedCount.valueOr(0) + listTests( *config );
+        if( config->listTestNamesOnly() )
+            listedCount = listedCount.valueOr(0) + listTestsNamesOnly( *config );
+        if( config->listTags() )
+            listedCount = listedCount.valueOr(0) + listTags( *config );
+        if( config->listReporters() )
+            listedCount = listedCount.valueOr(0) + listReporters();
+        return listedCount;
+    }
 
-using TestCaseTracking::ITracker;
-using TestCaseTracking::TrackerContext;
-using TestCaseTracking::SectionTracker;
-using TestCaseTracking::IndexTracker;
+} // end namespace Catch
+// end catch_list.cpp
+// start catch_matchers.cpp
 
-} // namespace Catch
+namespace Catch {
+namespace Matchers {
+    namespace Impl {
 
-CATCH_INTERNAL_UNSUPPRESS_ETD_WARNINGS
+        std::string MatcherUntypedBase::toString() const {
+            if( m_cachedToString.empty() )
+                m_cachedToString = describe();
+            return m_cachedToString;
+        }
 
-// #included from: catch_fatal_condition.hpp
-#define TWOBLUECUBES_CATCH_FATAL_CONDITION_H_INCLUDED
+        MatcherUntypedBase::~MatcherUntypedBase() = default;
 
-namespace Catch {
+    } // namespace Impl
+} // namespace Matchers
 
-    // Report the error condition
-    inline void reportFatal( std::string const& message ) {
-        IContext& context = Catch::getCurrentContext();
-        IResultCapture* resultCapture = context.getResultCapture();
-        resultCapture->handleFatalErrorCondition( message );
-    }
+using namespace Matchers;
+using Matchers::Impl::MatcherBase;
 
 } // namespace Catch
+// end catch_matchers.cpp
+// start catch_matchers_exception.cpp
 
-#if defined ( CATCH_PLATFORM_WINDOWS ) /////////////////////////////////////////
-// #included from: catch_windows_h_proxy.h
-
-#define TWOBLUECUBES_CATCH_WINDOWS_H_PROXY_H_INCLUDED
+namespace Catch {
+namespace Matchers {
+namespace Exception {
 
-#ifdef CATCH_DEFINES_NOMINMAX
-#  define NOMINMAX
-#endif
-#ifdef CATCH_DEFINES_WIN32_LEAN_AND_MEAN
-#  define WIN32_LEAN_AND_MEAN
-#endif
+bool ExceptionMessageMatcher::match(std::exception const& ex) const {
+    return ex.what() == m_message;
+}
 
-#ifdef __AFXDLL
-#include <AfxWin.h>
-#else
-#include <windows.h>
-#endif
+std::string ExceptionMessageMatcher::describe() const {
+    return "exception message matches \"" + m_message + "\"";
+}
 
-#ifdef CATCH_DEFINES_NOMINMAX
-#  undef NOMINMAX
-#endif
-#ifdef CATCH_DEFINES_WIN32_LEAN_AND_MEAN
-#  undef WIN32_LEAN_AND_MEAN
-#endif
+}
+Exception::ExceptionMessageMatcher Message(std::string const& message) {
+    return Exception::ExceptionMessageMatcher(message);
+}
 
+// namespace Exception
+} // namespace Matchers
+} // namespace Catch
+// end catch_matchers_exception.cpp
+// start catch_matchers_floating.cpp
 
-#  if !defined ( CATCH_CONFIG_WINDOWS_SEH )
+// start catch_polyfills.hpp
 
 namespace Catch {
-    struct FatalConditionHandler {
-        void reset() {}
-    };
+    bool isnan(float f);
+    bool isnan(double d);
 }
 
-#  else // CATCH_CONFIG_WINDOWS_SEH is defined
+// end catch_polyfills.hpp
+// start catch_to_string.hpp
+
+#include <string>
 
 namespace Catch {
+    template <typename T>
+    std::string to_string(T const& t) {
+#if defined(CATCH_CONFIG_CPP11_TO_STRING)
+        return std::to_string(t);
+#else
+        ReusableStringStream rss;
+        rss << t;
+        return rss.str();
+#endif
+    }
+} // end namespace Catch
 
-    struct SignalDefs { DWORD id; const char* name; };
-    extern SignalDefs signalDefs[];
-    // There is no 1-1 mapping between signals and windows exceptions.
-    // Windows can easily distinguish between SO and SigSegV,
-    // but SigInt, SigTerm, etc are handled differently.
-    SignalDefs signalDefs[] = {
-        { EXCEPTION_ILLEGAL_INSTRUCTION,  "SIGILL - Illegal instruction signal" },
-        { EXCEPTION_STACK_OVERFLOW, "SIGSEGV - Stack overflow" },
-        { EXCEPTION_ACCESS_VIOLATION, "SIGSEGV - Segmentation violation signal" },
-        { EXCEPTION_INT_DIVIDE_BY_ZERO, "Divide by zero error" },
-    };
+// end catch_to_string.hpp
+#include <algorithm>
+#include <cmath>
+#include <cstdlib>
+#include <cstdint>
+#include <cstring>
+#include <sstream>
+#include <type_traits>
+#include <iomanip>
+#include <limits>
 
-    struct FatalConditionHandler {
+namespace Catch {
+namespace {
 
-        static LONG CALLBACK handleVectoredException(PEXCEPTION_POINTERS ExceptionInfo) {
-            for (int i = 0; i < sizeof(signalDefs) / sizeof(SignalDefs); ++i) {
-                if (ExceptionInfo->ExceptionRecord->ExceptionCode == signalDefs[i].id) {
-                    reportFatal(signalDefs[i].name);
-                }
-            }
-            // If its not an exception we care about, pass it along.
-            // This stops us from eating debugger breaks etc.
-            return EXCEPTION_CONTINUE_SEARCH;
-        }
+    int32_t convert(float f) {
+        static_assert(sizeof(float) == sizeof(int32_t), "Important ULP matcher assumption violated");
+        int32_t i;
+        std::memcpy(&i, &f, sizeof(f));
+        return i;
+    }
 
-        FatalConditionHandler() {
-            isSet = true;
-            // 32k seems enough for Catch to handle stack overflow,
-            // but the value was found experimentally, so there is no strong guarantee
-            guaranteeSize = 32 * 1024;
-            exceptionHandlerHandle = CATCH_NULL;
-            // Register as first handler in current chain
-            exceptionHandlerHandle = AddVectoredExceptionHandler(1, handleVectoredException);
-            // Pass in guarantee size to be filled
-            SetThreadStackGuarantee(&guaranteeSize);
-        }
+    int64_t convert(double d) {
+        static_assert(sizeof(double) == sizeof(int64_t), "Important ULP matcher assumption violated");
+        int64_t i;
+        std::memcpy(&i, &d, sizeof(d));
+        return i;
+    }
 
-        static void reset() {
-            if (isSet) {
-                // Unregister handler and restore the old guarantee
-                RemoveVectoredExceptionHandler(exceptionHandlerHandle);
-                SetThreadStackGuarantee(&guaranteeSize);
-                exceptionHandlerHandle = CATCH_NULL;
-                isSet = false;
-            }
+    template <typename FP>
+    bool almostEqualUlps(FP lhs, FP rhs, uint64_t maxUlpDiff) {
+        // Comparison with NaN should always be false.
+        // This way we can rule it out before getting into the ugly details
+        if (Catch::isnan(lhs) || Catch::isnan(rhs)) {
+            return false;
         }
 
-        ~FatalConditionHandler() {
-            reset();
+        auto lc = convert(lhs);
+        auto rc = convert(rhs);
+
+        if ((lc < 0) != (rc < 0)) {
+            // Potentially we can have +0 and -0
+            return lhs == rhs;
         }
-    private:
-        static bool isSet;
-        static ULONG guaranteeSize;
-        static PVOID exceptionHandlerHandle;
-    };
 
-    bool FatalConditionHandler::isSet = false;
-    ULONG FatalConditionHandler::guaranteeSize = 0;
-    PVOID FatalConditionHandler::exceptionHandlerHandle = CATCH_NULL;
+        auto ulpDiff = std::abs(lc - rc);
+        return static_cast<uint64_t>(ulpDiff) <= maxUlpDiff;
+    }
 
-} // namespace Catch
+} //end anonymous namespace
 
-#  endif // CATCH_CONFIG_WINDOWS_SEH
+#if defined(CATCH_CONFIG_GLOBAL_NEXTAFTER)
 
-#else // Not Windows - assumed to be POSIX compatible //////////////////////////
+#if defined(__clang__)
+#pragma clang diagnostic push
+// The long double overload is currently unused
+#pragma clang diagnostic ignored "-Wunused-function"
+#endif
 
-#  if !defined(CATCH_CONFIG_POSIX_SIGNALS)
+    float nextafter(float x, float y) {
+        return ::nextafterf(x, y);
+    }
 
-namespace Catch {
-    struct FatalConditionHandler {
-        void reset() {}
-    };
-}
+    double nextafter(double x, double y) {
+        return ::nextafter(x, y);
+    }
 
-#  else // CATCH_CONFIG_POSIX_SIGNALS is defined
+    long double nextafter(long double x, long double y) {
+        return ::nextafterl(x, y);
+    }
 
-#include <signal.h>
+#if defined(__clang__)
+#pragma clang diagnostic pop
+#endif
 
-namespace Catch {
+#endif // ^^^ CATCH_CONFIG_GLOBAL_NEXTAFTER ^^^
 
-    struct SignalDefs {
-        int id;
-        const char* name;
-    };
-    extern SignalDefs signalDefs[];
-    SignalDefs signalDefs[] = {
-            { SIGINT,  "SIGINT - Terminal interrupt signal" },
-            { SIGILL,  "SIGILL - Illegal instruction signal" },
-            { SIGFPE,  "SIGFPE - Floating point error signal" },
-            { SIGSEGV, "SIGSEGV - Segmentation violation signal" },
-            { SIGTERM, "SIGTERM - Termination request signal" },
-            { SIGABRT, "SIGABRT - Abort (abnormal termination) signal" }
-    };
+namespace {
 
-    struct FatalConditionHandler {
+template <typename FP>
+FP step(FP start, FP direction, uint64_t steps) {
+    for (uint64_t i = 0; i < steps; ++i) {
+#if defined(CATCH_CONFIG_GLOBAL_NEXTAFTER)
+        start = Catch::nextafter(start, direction);
+#else
+        start = std::nextafter(start, direction);
+#endif
+    }
+    return start;
+}
 
-        static bool isSet;
-        static struct sigaction oldSigActions [sizeof(signalDefs)/sizeof(SignalDefs)];
-        static stack_t oldSigStack;
-        static char altStackMem[SIGSTKSZ];
-
-        static void handleSignal( int sig ) {
-            std::string name = "<unknown signal>";
-            for (std::size_t i = 0; i < sizeof(signalDefs) / sizeof(SignalDefs); ++i) {
-                SignalDefs &def = signalDefs[i];
-                if (sig == def.id) {
-                    name = def.name;
-                    break;
-                }
-            }
-            reset();
-            reportFatal(name);
-            raise( sig );
-        }
+// Performs equivalent check of std::fabs(lhs - rhs) <= margin
+// But without the subtraction to allow for INFINITY in comparison
+bool marginComparison(double lhs, double rhs, double margin) {
+    return (lhs + margin >= rhs) && (rhs + margin >= lhs);
+}
 
-        FatalConditionHandler() {
-            isSet = true;
-            stack_t sigStack;
-            sigStack.ss_sp = altStackMem;
-            sigStack.ss_size = SIGSTKSZ;
-            sigStack.ss_flags = 0;
-            sigaltstack(&sigStack, &oldSigStack);
-            struct sigaction sa = { 0 };
+template <typename FloatingPoint>
+void write(std::ostream& out, FloatingPoint num) {
+    out << std::scientific
+        << std::setprecision(std::numeric_limits<FloatingPoint>::max_digits10 - 1)
+        << num;
+}
 
-            sa.sa_handler = handleSignal;
-            sa.sa_flags = SA_ONSTACK;
-            for (std::size_t i = 0; i < sizeof(signalDefs)/sizeof(SignalDefs); ++i) {
-                sigaction(signalDefs[i].id, &sa, &oldSigActions[i]);
-            }
-        }
+} // end anonymous namespace
 
-        ~FatalConditionHandler() {
-            reset();
-        }
-        static void reset() {
-            if( isSet ) {
-                // Set signals back to previous values -- hopefully nobody overwrote them in the meantime
-                for( std::size_t i = 0; i < sizeof(signalDefs)/sizeof(SignalDefs); ++i ) {
-                    sigaction(signalDefs[i].id, &oldSigActions[i], CATCH_NULL);
-                }
-                // Return the old stack
-                sigaltstack(&oldSigStack, CATCH_NULL);
-                isSet = false;
-            }
-        }
+namespace Matchers {
+namespace Floating {
+
+    enum class FloatingPointKind : uint8_t {
+        Float,
+        Double
     };
 
-    bool FatalConditionHandler::isSet = false;
-    struct sigaction FatalConditionHandler::oldSigActions[sizeof(signalDefs)/sizeof(SignalDefs)] = {};
-    stack_t FatalConditionHandler::oldSigStack = {};
-    char FatalConditionHandler::altStackMem[SIGSTKSZ] = {};
+    WithinAbsMatcher::WithinAbsMatcher(double target, double margin)
+        :m_target{ target }, m_margin{ margin } {
+        CATCH_ENFORCE(margin >= 0, "Invalid margin: " << margin << '.'
+            << " Margin has to be non-negative.");
+    }
 
-} // namespace Catch
+    // Performs equivalent check of std::fabs(lhs - rhs) <= margin
+    // But without the subtraction to allow for INFINITY in comparison
+    bool WithinAbsMatcher::match(double const& matchee) const {
+        return (matchee + m_margin >= m_target) && (m_target + m_margin >= matchee);
+    }
 
-#  endif // CATCH_CONFIG_POSIX_SIGNALS
+    std::string WithinAbsMatcher::describe() const {
+        return "is within " + ::Catch::Detail::stringify(m_margin) + " of " + ::Catch::Detail::stringify(m_target);
+    }
 
-#endif // not Windows
+    WithinUlpsMatcher::WithinUlpsMatcher(double target, uint64_t ulps, FloatingPointKind baseType)
+        :m_target{ target }, m_ulps{ ulps }, m_type{ baseType } {
+        CATCH_ENFORCE(m_type == FloatingPointKind::Double
+                   || m_ulps < (std::numeric_limits<uint32_t>::max)(),
+            "Provided ULP is impossibly large for a float comparison.");
+    }
 
-#include <set>
-#include <string>
+#if defined(__clang__)
+#pragma clang diagnostic push
+// Clang <3.5 reports on the default branch in the switch below
+#pragma clang diagnostic ignored "-Wunreachable-code"
+#endif
 
-namespace Catch {
+    bool WithinUlpsMatcher::match(double const& matchee) const {
+        switch (m_type) {
+        case FloatingPointKind::Float:
+            return almostEqualUlps<float>(static_cast<float>(matchee), static_cast<float>(m_target), m_ulps);
+        case FloatingPointKind::Double:
+            return almostEqualUlps<double>(matchee, m_target, m_ulps);
+        default:
+            CATCH_INTERNAL_ERROR( "Unknown FloatingPointKind value" );
+        }
+    }
+
+#if defined(__clang__)
+#pragma clang diagnostic pop
+#endif
 
-    class StreamRedirect {
+    std::string WithinUlpsMatcher::describe() const {
+        std::stringstream ret;
 
-    public:
-        StreamRedirect( std::ostream& stream, std::string& targetString )
-        :   m_stream( stream ),
-            m_prevBuf( stream.rdbuf() ),
-            m_targetString( targetString )
-        {
-            stream.rdbuf( m_oss.rdbuf() );
+        ret << "is within " << m_ulps << " ULPs of ";
+
+        if (m_type == FloatingPointKind::Float) {
+            write(ret, static_cast<float>(m_target));
+            ret << 'f';
+        } else {
+            write(ret, m_target);
         }
 
-        ~StreamRedirect() {
-            m_targetString += m_oss.str();
-            m_stream.rdbuf( m_prevBuf );
+        ret << " ([";
+        if (m_type == FloatingPointKind::Double) {
+            write(ret, step(m_target, static_cast<double>(-INFINITY), m_ulps));
+            ret << ", ";
+            write(ret, step(m_target, static_cast<double>( INFINITY), m_ulps));
+        } else {
+            write(ret, step(static_cast<float>(m_target), -INFINITY, m_ulps));
+            ret << ", ";
+            write(ret, step(static_cast<float>(m_target),  INFINITY, m_ulps));
         }
+        ret << "])";
 
-    private:
-        std::ostream& m_stream;
-        std::streambuf* m_prevBuf;
-        std::ostringstream m_oss;
-        std::string& m_targetString;
-    };
+        return ret.str();
+    }
 
-    ///////////////////////////////////////////////////////////////////////////
+    WithinRelMatcher::WithinRelMatcher(double target, double epsilon):
+        m_target(target),
+        m_epsilon(epsilon){
+        CATCH_ENFORCE(m_epsilon >= 0., "Relative comparison with epsilon <  0 does not make sense.");
+        CATCH_ENFORCE(m_epsilon  < 1., "Relative comparison with epsilon >= 1 does not make sense.");
+    }
 
-    class RunContext : public IResultCapture, public IRunner {
+    bool WithinRelMatcher::match(double const& matchee) const {
+        const auto relMargin = m_epsilon * (std::max)(std::fabs(matchee), std::fabs(m_target));
+        return marginComparison(matchee, m_target,
+                                std::isinf(relMargin)? 0 : relMargin);
+    }
 
-        RunContext( RunContext const& );
-        void operator =( RunContext const& );
+    std::string WithinRelMatcher::describe() const {
+        Catch::ReusableStringStream sstr;
+        sstr << "and " << m_target << " are within " << m_epsilon * 100. << "% of each other";
+        return sstr.str();
+    }
 
-    public:
+}// namespace Floating
 
-        explicit RunContext( Ptr<IConfig const> const& _config, Ptr<IStreamingReporter> const& reporter )
-        :   m_runInfo( _config->name() ),
-            m_context( getCurrentMutableContext() ),
-            m_activeTestCase( CATCH_NULL ),
-            m_config( _config ),
-            m_reporter( reporter ),
-            m_shouldReportUnexpected ( true )
-        {
-            m_context.setRunner( this );
-            m_context.setConfig( m_config );
-            m_context.setResultCapture( this );
-            m_reporter->testRunStarting( m_runInfo );
-        }
+Floating::WithinUlpsMatcher WithinULP(double target, uint64_t maxUlpDiff) {
+    return Floating::WithinUlpsMatcher(target, maxUlpDiff, Floating::FloatingPointKind::Double);
+}
 
-        virtual ~RunContext() {
-            m_reporter->testRunEnded( TestRunStats( m_runInfo, m_totals, aborting() ) );
-        }
+Floating::WithinUlpsMatcher WithinULP(float target, uint64_t maxUlpDiff) {
+    return Floating::WithinUlpsMatcher(target, maxUlpDiff, Floating::FloatingPointKind::Float);
+}
 
-        void testGroupStarting( std::string const& testSpec, std::size_t groupIndex, std::size_t groupsCount ) {
-            m_reporter->testGroupStarting( GroupInfo( testSpec, groupIndex, groupsCount ) );
-        }
-        void testGroupEnded( std::string const& testSpec, Totals const& totals, std::size_t groupIndex, std::size_t groupsCount ) {
-            m_reporter->testGroupEnded( TestGroupStats( GroupInfo( testSpec, groupIndex, groupsCount ), totals, aborting() ) );
-        }
+Floating::WithinAbsMatcher WithinAbs(double target, double margin) {
+    return Floating::WithinAbsMatcher(target, margin);
+}
+
+Floating::WithinRelMatcher WithinRel(double target, double eps) {
+    return Floating::WithinRelMatcher(target, eps);
+}
+
+Floating::WithinRelMatcher WithinRel(double target) {
+    return Floating::WithinRelMatcher(target, std::numeric_limits<double>::epsilon() * 100);
+}
+
+Floating::WithinRelMatcher WithinRel(float target, float eps) {
+    return Floating::WithinRelMatcher(target, eps);
+}
 
-        Totals runTest( TestCase const& testCase ) {
-            Totals prevTotals = m_totals;
+Floating::WithinRelMatcher WithinRel(float target) {
+    return Floating::WithinRelMatcher(target, std::numeric_limits<float>::epsilon() * 100);
+}
 
-            std::string redirectedCout;
-            std::string redirectedCerr;
+} // namespace Matchers
+} // namespace Catch
 
-            TestCaseInfo testInfo = testCase.getTestCaseInfo();
+// end catch_matchers_floating.cpp
+// start catch_matchers_generic.cpp
 
-            m_reporter->testCaseStarting( testInfo );
+std::string Catch::Matchers::Generic::Detail::finalizeDescription(const std::string& desc) {
+    if (desc.empty()) {
+        return "matches undescribed predicate";
+    } else {
+        return "matches predicate: \"" + desc + '"';
+    }
+}
+// end catch_matchers_generic.cpp
+// start catch_matchers_string.cpp
 
-            m_activeTestCase = &testCase;
+#include <regex>
 
-            do {
-                ITracker& rootTracker = m_trackerContext.startRun();
-                assert( rootTracker.isSectionTracker() );
-                static_cast<SectionTracker&>( rootTracker ).addInitialFilters( m_config->getSectionsToRun() );
-                do {
-                    m_trackerContext.startCycle();
-                    m_testCaseTracker = &SectionTracker::acquire( m_trackerContext, TestCaseTracking::NameAndLocation( testInfo.name, testInfo.lineInfo ) );
-                    runCurrentTest( redirectedCout, redirectedCerr );
-                }
-                while( !m_testCaseTracker->isSuccessfullyCompleted() && !aborting() );
-            }
-            // !TBD: deprecated - this will be replaced by indexed trackers
-            while( getCurrentContext().advanceGeneratorsForCurrentTest() && !aborting() );
+namespace Catch {
+namespace Matchers {
 
-            Totals deltaTotals = m_totals.delta( prevTotals );
-            if( testInfo.expectedToFail() && deltaTotals.testCases.passed > 0 ) {
-                deltaTotals.assertions.failed++;
-                deltaTotals.testCases.passed--;
-                deltaTotals.testCases.failed++;
-            }
-            m_totals.testCases += deltaTotals.testCases;
-            m_reporter->testCaseEnded( TestCaseStats(   testInfo,
-                                                        deltaTotals,
-                                                        redirectedCout,
-                                                        redirectedCerr,
-                                                        aborting() ) );
+    namespace StdString {
 
-            m_activeTestCase = CATCH_NULL;
-            m_testCaseTracker = CATCH_NULL;
+        CasedString::CasedString( std::string const& str, CaseSensitive::Choice caseSensitivity )
+        :   m_caseSensitivity( caseSensitivity ),
+            m_str( adjustString( str ) )
+        {}
+        std::string CasedString::adjustString( std::string const& str ) const {
+            return m_caseSensitivity == CaseSensitive::No
+                   ? toLower( str )
+                   : str;
+        }
+        std::string CasedString::caseSensitivitySuffix() const {
+            return m_caseSensitivity == CaseSensitive::No
+                   ? " (case insensitive)"
+                   : std::string();
+        }
 
-            return deltaTotals;
+        StringMatcherBase::StringMatcherBase( std::string const& operation, CasedString const& comparator )
+        : m_comparator( comparator ),
+          m_operation( operation ) {
         }
 
-        Ptr<IConfig const> config() const {
-            return m_config;
+        std::string StringMatcherBase::describe() const {
+            std::string description;
+            description.reserve(5 + m_operation.size() + m_comparator.m_str.size() +
+                                        m_comparator.caseSensitivitySuffix().size());
+            description += m_operation;
+            description += ": \"";
+            description += m_comparator.m_str;
+            description += "\"";
+            description += m_comparator.caseSensitivitySuffix();
+            return description;
         }
 
-    private: // IResultCapture
+        EqualsMatcher::EqualsMatcher( CasedString const& comparator ) : StringMatcherBase( "equals", comparator ) {}
 
-        virtual void assertionEnded( AssertionResult const& result ) {
-            if( result.getResultType() == ResultWas::Ok ) {
-                m_totals.assertions.passed++;
-            }
-            else if( !result.isOk() ) {
-                m_totals.assertions.failed++;
-            }
+        bool EqualsMatcher::match( std::string const& source ) const {
+            return m_comparator.adjustString( source ) == m_comparator.m_str;
+        }
 
-            // We have no use for the return value (whether messages should be cleared), because messages were made scoped
-            // and should be let to clear themselves out.
-            static_cast<void>(m_reporter->assertionEnded(AssertionStats(result, m_messages, m_totals)));
+        ContainsMatcher::ContainsMatcher( CasedString const& comparator ) : StringMatcherBase( "contains", comparator ) {}
 
-            // Reset working state
-            m_lastAssertionInfo = AssertionInfo( std::string(), m_lastAssertionInfo.lineInfo, "{Unknown expression after the reported line}" , m_lastAssertionInfo.resultDisposition );
-            m_lastResult = result;
+        bool ContainsMatcher::match( std::string const& source ) const {
+            return contains( m_comparator.adjustString( source ), m_comparator.m_str );
         }
 
-        virtual bool sectionStarted (
-            SectionInfo const& sectionInfo,
-            Counts& assertions
-        )
-        {
-            ITracker& sectionTracker = SectionTracker::acquire( m_trackerContext, TestCaseTracking::NameAndLocation( sectionInfo.name, sectionInfo.lineInfo ) );
-            if( !sectionTracker.isOpen() )
-                return false;
-            m_activeSections.push_back( &sectionTracker );
-
-            m_lastAssertionInfo.lineInfo = sectionInfo.lineInfo;
+        StartsWithMatcher::StartsWithMatcher( CasedString const& comparator ) : StringMatcherBase( "starts with", comparator ) {}
 
-            m_reporter->sectionStarting( sectionInfo );
+        bool StartsWithMatcher::match( std::string const& source ) const {
+            return startsWith( m_comparator.adjustString( source ), m_comparator.m_str );
+        }
 
-            assertions = m_totals.assertions;
+        EndsWithMatcher::EndsWithMatcher( CasedString const& comparator ) : StringMatcherBase( "ends with", comparator ) {}
 
-            return true;
-        }
-        bool testForMissingAssertions( Counts& assertions ) {
-            if( assertions.total() != 0 )
-                return false;
-            if( !m_config->warnAboutMissingAssertions() )
-                return false;
-            if( m_trackerContext.currentTracker().hasChildren() )
-                return false;
-            m_totals.assertions.failed++;
-            assertions.failed++;
-            return true;
+        bool EndsWithMatcher::match( std::string const& source ) const {
+            return endsWith( m_comparator.adjustString( source ), m_comparator.m_str );
         }
 
-        virtual void sectionEnded( SectionEndInfo const& endInfo ) {
-            Counts assertions = m_totals.assertions - endInfo.prevAssertions;
-            bool missingAssertions = testForMissingAssertions( assertions );
+        RegexMatcher::RegexMatcher(std::string regex, CaseSensitive::Choice caseSensitivity): m_regex(std::move(regex)), m_caseSensitivity(caseSensitivity) {}
 
-            if( !m_activeSections.empty() ) {
-                m_activeSections.back()->close();
-                m_activeSections.pop_back();
+        bool RegexMatcher::match(std::string const& matchee) const {
+            auto flags = std::regex::ECMAScript; // ECMAScript is the default syntax option anyway
+            if (m_caseSensitivity == CaseSensitive::Choice::No) {
+                flags |= std::regex::icase;
             }
+            auto reg = std::regex(m_regex, flags);
+            return std::regex_match(matchee, reg);
+        }
 
-            m_reporter->sectionEnded( SectionStats( endInfo.sectionInfo, assertions, endInfo.durationInSeconds, missingAssertions ) );
-            m_messages.clear();
+        std::string RegexMatcher::describe() const {
+            return "matches " + ::Catch::Detail::stringify(m_regex) + ((m_caseSensitivity == CaseSensitive::Choice::Yes)? " case sensitively" : " case insensitively");
         }
 
-        virtual void sectionEndedEarly( SectionEndInfo const& endInfo ) {
-            if( m_unfinishedSections.empty() )
-                m_activeSections.back()->fail();
-            else
-                m_activeSections.back()->close();
-            m_activeSections.pop_back();
+    } // namespace StdString
 
-            m_unfinishedSections.push_back( endInfo );
-        }
+    StdString::EqualsMatcher Equals( std::string const& str, CaseSensitive::Choice caseSensitivity ) {
+        return StdString::EqualsMatcher( StdString::CasedString( str, caseSensitivity) );
+    }
+    StdString::ContainsMatcher Contains( std::string const& str, CaseSensitive::Choice caseSensitivity ) {
+        return StdString::ContainsMatcher( StdString::CasedString( str, caseSensitivity) );
+    }
+    StdString::EndsWithMatcher EndsWith( std::string const& str, CaseSensitive::Choice caseSensitivity ) {
+        return StdString::EndsWithMatcher( StdString::CasedString( str, caseSensitivity) );
+    }
+    StdString::StartsWithMatcher StartsWith( std::string const& str, CaseSensitive::Choice caseSensitivity ) {
+        return StdString::StartsWithMatcher( StdString::CasedString( str, caseSensitivity) );
+    }
 
-        virtual void pushScopedMessage( MessageInfo const& message ) {
-            m_messages.push_back( message );
-        }
+    StdString::RegexMatcher Matches(std::string const& regex, CaseSensitive::Choice caseSensitivity) {
+        return StdString::RegexMatcher(regex, caseSensitivity);
+    }
 
-        virtual void popScopedMessage( MessageInfo const& message ) {
-            m_messages.erase( std::remove( m_messages.begin(), m_messages.end(), message ), m_messages.end() );
-        }
+} // namespace Matchers
+} // namespace Catch
+// end catch_matchers_string.cpp
+// start catch_message.cpp
 
-        virtual std::string getCurrentTestName() const {
-            return m_activeTestCase
-                ? m_activeTestCase->getTestCaseInfo().name
-                : std::string();
-        }
+// start catch_uncaught_exceptions.h
 
-        virtual const AssertionResult* getLastResult() const {
-            return &m_lastResult;
-        }
+namespace Catch {
+    bool uncaught_exceptions();
+} // end namespace Catch
 
-        virtual void exceptionEarlyReported() {
-            m_shouldReportUnexpected = false;
-        }
+// end catch_uncaught_exceptions.h
+#include <cassert>
+#include <stack>
 
-        virtual void handleFatalErrorCondition( std::string const& message ) {
-            // Don't rebuild the result -- the stringification itself can cause more fatal errors
-            // Instead, fake a result data.
-            AssertionResultData tempResult;
-            tempResult.resultType = ResultWas::FatalErrorCondition;
-            tempResult.message = message;
-            AssertionResult result(m_lastAssertionInfo, tempResult);
+namespace Catch {
 
-            getResultCapture().assertionEnded(result);
+    MessageInfo::MessageInfo(   StringRef const& _macroName,
+                                SourceLineInfo const& _lineInfo,
+                                ResultWas::OfType _type )
+    :   macroName( _macroName ),
+        lineInfo( _lineInfo ),
+        type( _type ),
+        sequence( ++globalCount )
+    {}
 
-            handleUnfinishedSections();
+    bool MessageInfo::operator==( MessageInfo const& other ) const {
+        return sequence == other.sequence;
+    }
 
-            // Recreate section for test case (as we will lose the one that was in scope)
-            TestCaseInfo const& testCaseInfo = m_activeTestCase->getTestCaseInfo();
-            SectionInfo testCaseSection( testCaseInfo.lineInfo, testCaseInfo.name, testCaseInfo.description );
+    bool MessageInfo::operator<( MessageInfo const& other ) const {
+        return sequence < other.sequence;
+    }
 
-            Counts assertions;
-            assertions.failed = 1;
-            SectionStats testCaseSectionStats( testCaseSection, assertions, 0, false );
-            m_reporter->sectionEnded( testCaseSectionStats );
+    // This may need protecting if threading support is added
+    unsigned int MessageInfo::globalCount = 0;
 
-            TestCaseInfo testInfo = m_activeTestCase->getTestCaseInfo();
+    ////////////////////////////////////////////////////////////////////////////
 
-            Totals deltaTotals;
-            deltaTotals.testCases.failed = 1;
-            m_reporter->testCaseEnded( TestCaseStats(   testInfo,
-                                                        deltaTotals,
-                                                        std::string(),
-                                                        std::string(),
-                                                        false ) );
-            m_totals.testCases.failed++;
-            testGroupEnded( std::string(), m_totals, 1, 1 );
-            m_reporter->testRunEnded( TestRunStats( m_runInfo, m_totals, false ) );
-        }
+    Catch::MessageBuilder::MessageBuilder( StringRef const& macroName,
+                                           SourceLineInfo const& lineInfo,
+                                           ResultWas::OfType type )
+        :m_info(macroName, lineInfo, type) {}
 
-    public:
-        // !TBD We need to do this another way!
-        bool aborting() const {
-            return m_totals.assertions.failed == static_cast<std::size_t>( m_config->abortAfter() );
-        }
+    ////////////////////////////////////////////////////////////////////////////
 
-    private:
+    ScopedMessage::ScopedMessage( MessageBuilder const& builder )
+    : m_info( builder.m_info ), m_moved()
+    {
+        m_info.message = builder.m_stream.str();
+        getResultCapture().pushScopedMessage( m_info );
+    }
 
-        void runCurrentTest( std::string& redirectedCout, std::string& redirectedCerr ) {
-            TestCaseInfo const& testCaseInfo = m_activeTestCase->getTestCaseInfo();
-            SectionInfo testCaseSection( testCaseInfo.lineInfo, testCaseInfo.name, testCaseInfo.description );
-            m_reporter->sectionStarting( testCaseSection );
-            Counts prevAssertions = m_totals.assertions;
-            double duration = 0;
-            m_shouldReportUnexpected = true;
-            try {
-                m_lastAssertionInfo = AssertionInfo( "TEST_CASE", testCaseInfo.lineInfo, std::string(), ResultDisposition::Normal );
+    ScopedMessage::ScopedMessage( ScopedMessage&& old )
+    : m_info( old.m_info ), m_moved()
+    {
+        old.m_moved = true;
+    }
 
-                seedRng( *m_config );
+    ScopedMessage::~ScopedMessage() {
+        if ( !uncaught_exceptions() && !m_moved ){
+            getResultCapture().popScopedMessage(m_info);
+        }
+    }
 
-                Timer timer;
-                timer.start();
-                if( m_reporter->getPreferences().shouldRedirectStdOut ) {
-                    StreamRedirect coutRedir( Catch::cout(), redirectedCout );
-                    StreamRedirect cerrRedir( Catch::cerr(), redirectedCerr );
-                    invokeActiveTestCase();
-                }
-                else {
-                    invokeActiveTestCase();
-                }
-                duration = timer.getElapsedSeconds();
+    Capturer::Capturer( StringRef macroName, SourceLineInfo const& lineInfo, ResultWas::OfType resultType, StringRef names ) {
+        auto trimmed = [&] (size_t start, size_t end) {
+            while (names[start] == ',' || isspace(names[start])) {
+                ++start;
             }
-            catch( TestFailureException& ) {
-                // This just means the test was aborted due to failure
+            while (names[end] == ',' || isspace(names[end])) {
+                --end;
             }
-            catch(...) {
-                // Under CATCH_CONFIG_FAST_COMPILE, unexpected exceptions under REQUIRE assertions
-                // are reported without translation at the point of origin.
-                if (m_shouldReportUnexpected) {
-                    makeUnexpectedResultBuilder().useActiveException();
-                }
+            return names.substr(start, end - start + 1);
+        };
+        auto skipq = [&] (size_t start, char quote) {
+            for (auto i = start + 1; i < names.size() ; ++i) {
+                if (names[i] == quote)
+                    return i;
+                if (names[i] == '\\')
+                    ++i;
             }
-            m_testCaseTracker->close();
-            handleUnfinishedSections();
-            m_messages.clear();
-
-            Counts assertions = m_totals.assertions - prevAssertions;
-            bool missingAssertions = testForMissingAssertions( assertions );
+            CATCH_INTERNAL_ERROR("CAPTURE parsing encountered unmatched quote");
+        };
 
-            if( testCaseInfo.okToFail() ) {
-                std::swap( assertions.failedButOk, assertions.failed );
-                m_totals.assertions.failed -= assertions.failedButOk;
-                m_totals.assertions.failedButOk += assertions.failedButOk;
+        size_t start = 0;
+        std::stack<char> openings;
+        for (size_t pos = 0; pos < names.size(); ++pos) {
+            char c = names[pos];
+            switch (c) {
+            case '[':
+            case '{':
+            case '(':
+            // It is basically impossible to disambiguate between
+            // comparison and start of template args in this context
+//            case '<':
+                openings.push(c);
+                break;
+            case ']':
+            case '}':
+            case ')':
+//           case '>':
+                openings.pop();
+                break;
+            case '"':
+            case '\'':
+                pos = skipq(pos, c);
+                break;
+            case ',':
+                if (start != pos && openings.size() == 0) {
+                    m_messages.emplace_back(macroName, lineInfo, resultType);
+                    m_messages.back().message = static_cast<std::string>(trimmed(start, pos));
+                    m_messages.back().message += " := ";
+                    start = pos;
+                }
             }
-
-            SectionStats testCaseSectionStats( testCaseSection, assertions, duration, missingAssertions );
-            m_reporter->sectionEnded( testCaseSectionStats );
         }
-
-        void invokeActiveTestCase() {
-            FatalConditionHandler fatalConditionHandler; // Handle signals
-            m_activeTestCase->invoke();
-            fatalConditionHandler.reset();
-        }
-
-    private:
-
-        ResultBuilder makeUnexpectedResultBuilder() const {
-            return ResultBuilder(   m_lastAssertionInfo.macroName.c_str(),
-                                    m_lastAssertionInfo.lineInfo,
-                                    m_lastAssertionInfo.capturedExpression.c_str(),
-                                    m_lastAssertionInfo.resultDisposition );
-        }
-
-        void handleUnfinishedSections() {
-            // If sections ended prematurely due to an exception we stored their
-            // infos here so we can tear them down outside the unwind process.
-            for( std::vector<SectionEndInfo>::const_reverse_iterator it = m_unfinishedSections.rbegin(),
-                        itEnd = m_unfinishedSections.rend();
-                    it != itEnd;
-                    ++it )
-                sectionEnded( *it );
-            m_unfinishedSections.clear();
+        assert(openings.size() == 0 && "Mismatched openings");
+        m_messages.emplace_back(macroName, lineInfo, resultType);
+        m_messages.back().message = static_cast<std::string>(trimmed(start, names.size() - 1));
+        m_messages.back().message += " := ";
+    }
+    Capturer::~Capturer() {
+        if ( !uncaught_exceptions() ){
+            assert( m_captured == m_messages.size() );
+            for( size_t i = 0; i < m_captured; ++i  )
+                m_resultCapture.popScopedMessage( m_messages[i] );
         }
+    }
 
-        TestRunInfo m_runInfo;
-        IMutableContext& m_context;
-        TestCase const* m_activeTestCase;
-        ITracker* m_testCaseTracker;
-        ITracker* m_currentSectionTracker;
-        AssertionResult m_lastResult;
-
-        Ptr<IConfig const> m_config;
-        Totals m_totals;
-        Ptr<IStreamingReporter> m_reporter;
-        std::vector<MessageInfo> m_messages;
-        AssertionInfo m_lastAssertionInfo;
-        std::vector<SectionEndInfo> m_unfinishedSections;
-        std::vector<ITracker*> m_activeSections;
-        TrackerContext m_trackerContext;
-        bool m_shouldReportUnexpected;
-    };
-
-    IResultCapture& getResultCapture() {
-        if( IResultCapture* capture = getCurrentContext().getResultCapture() )
-            return *capture;
-        else
-            throw std::logic_error( "No result capture instance" );
+    void Capturer::captureValue( size_t index, std::string const& value ) {
+        assert( index < m_messages.size() );
+        m_messages[index].message += value;
+        m_resultCapture.pushScopedMessage( m_messages[index] );
+        m_captured++;
     }
 
 } // end namespace Catch
+// end catch_message.cpp
+// start catch_output_redirect.cpp
+
+// start catch_output_redirect.h
+#ifndef TWOBLUECUBES_CATCH_OUTPUT_REDIRECT_H
+#define TWOBLUECUBES_CATCH_OUTPUT_REDIRECT_H
 
-// #included from: internal/catch_version.h
-#define TWOBLUECUBES_CATCH_VERSION_H_INCLUDED
+#include <cstdio>
+#include <iosfwd>
+#include <string>
 
 namespace Catch {
 
-    // Versioning information
-    struct Version {
-        Version(    unsigned int _majorVersion,
-                    unsigned int _minorVersion,
-                    unsigned int _patchNumber,
-                    char const * const _branchName,
-                    unsigned int _buildNumber );
+    class RedirectedStream {
+        std::ostream& m_originalStream;
+        std::ostream& m_redirectionStream;
+        std::streambuf* m_prevBuf;
 
-        unsigned int const majorVersion;
-        unsigned int const minorVersion;
-        unsigned int const patchNumber;
+    public:
+        RedirectedStream( std::ostream& originalStream, std::ostream& redirectionStream );
+        ~RedirectedStream();
+    };
 
-        // buildNumber is only used if branchName is not null
-        char const * const branchName;
-        unsigned int const buildNumber;
+    class RedirectedStdOut {
+        ReusableStringStream m_rss;
+        RedirectedStream m_cout;
+    public:
+        RedirectedStdOut();
+        auto str() const -> std::string;
+    };
 
-        friend std::ostream& operator << ( std::ostream& os, Version const& version );
+    // StdErr has two constituent streams in C++, std::cerr and std::clog
+    // This means that we need to redirect 2 streams into 1 to keep proper
+    // order of writes
+    class RedirectedStdErr {
+        ReusableStringStream m_rss;
+        RedirectedStream m_cerr;
+        RedirectedStream m_clog;
+    public:
+        RedirectedStdErr();
+        auto str() const -> std::string;
+    };
 
+    class RedirectedStreams {
+    public:
+        RedirectedStreams(RedirectedStreams const&) = delete;
+        RedirectedStreams& operator=(RedirectedStreams const&) = delete;
+        RedirectedStreams(RedirectedStreams&&) = delete;
+        RedirectedStreams& operator=(RedirectedStreams&&) = delete;
+
+        RedirectedStreams(std::string& redirectedCout, std::string& redirectedCerr);
+        ~RedirectedStreams();
     private:
-        void operator=( Version const& );
+        std::string& m_redirectedCout;
+        std::string& m_redirectedCerr;
+        RedirectedStdOut m_redirectedStdOut;
+        RedirectedStdErr m_redirectedStdErr;
     };
 
-    inline Version libraryVersion();
-}
+#if defined(CATCH_CONFIG_NEW_CAPTURE)
 
-#include <fstream>
-#include <stdlib.h>
-#include <limits>
+    // Windows's implementation of std::tmpfile is terrible (it tries
+    // to create a file inside system folder, thus requiring elevated
+    // privileges for the binary), so we have to use tmpnam(_s) and
+    // create the file ourselves there.
+    class TempFile {
+    public:
+        TempFile(TempFile const&) = delete;
+        TempFile& operator=(TempFile const&) = delete;
+        TempFile(TempFile&&) = delete;
+        TempFile& operator=(TempFile&&) = delete;
 
-namespace Catch {
+        TempFile();
+        ~TempFile();
 
-    Ptr<IStreamingReporter> createReporter( std::string const& reporterName, Ptr<Config> const& config ) {
-        Ptr<IStreamingReporter> reporter = getRegistryHub().getReporterRegistry().create( reporterName, config.get() );
-        if( !reporter ) {
-            std::ostringstream oss;
-            oss << "No reporter registered with name: '" << reporterName << "'";
-            throw std::domain_error( oss.str() );
-        }
-        return reporter;
-    }
+        std::FILE* getFile();
+        std::string getContents();
 
-    Ptr<IStreamingReporter> makeReporter( Ptr<Config> const& config ) {
-        std::vector<std::string> reporters = config->getReporterNames();
-        if( reporters.empty() )
-            reporters.push_back( "console" );
+    private:
+        std::FILE* m_file = nullptr;
+    #if defined(_MSC_VER)
+        char m_buffer[L_tmpnam] = { 0 };
+    #endif
+    };
 
-        Ptr<IStreamingReporter> reporter;
-        for( std::vector<std::string>::const_iterator it = reporters.begin(), itEnd = reporters.end();
-                it != itEnd;
-                ++it )
-            reporter = addReporter( reporter, createReporter( *it, config ) );
-        return reporter;
-    }
-    Ptr<IStreamingReporter> addListeners( Ptr<IConfig const> const& config, Ptr<IStreamingReporter> reporters ) {
-        IReporterRegistry::Listeners listeners = getRegistryHub().getReporterRegistry().getListeners();
-        for( IReporterRegistry::Listeners::const_iterator it = listeners.begin(), itEnd = listeners.end();
-                it != itEnd;
-                ++it )
-            reporters = addReporter(reporters, (*it)->create( ReporterConfig( config ) ) );
-        return reporters;
-    }
+    class OutputRedirect {
+    public:
+        OutputRedirect(OutputRedirect const&) = delete;
+        OutputRedirect& operator=(OutputRedirect const&) = delete;
+        OutputRedirect(OutputRedirect&&) = delete;
+        OutputRedirect& operator=(OutputRedirect&&) = delete;
 
-    Totals runTests( Ptr<Config> const& config ) {
+        OutputRedirect(std::string& stdout_dest, std::string& stderr_dest);
+        ~OutputRedirect();
 
-        Ptr<IConfig const> iconfig = config.get();
+    private:
+        int m_originalStdout = -1;
+        int m_originalStderr = -1;
+        TempFile m_stdoutFile;
+        TempFile m_stderrFile;
+        std::string& m_stdoutDest;
+        std::string& m_stderrDest;
+    };
 
-        Ptr<IStreamingReporter> reporter = makeReporter( config );
-        reporter = addListeners( iconfig, reporter );
+#endif
 
-        RunContext context( iconfig, reporter );
+} // end namespace Catch
 
-        Totals totals;
+#endif // TWOBLUECUBES_CATCH_OUTPUT_REDIRECT_H
+// end catch_output_redirect.h
+#include <cstdio>
+#include <cstring>
+#include <fstream>
+#include <sstream>
+#include <stdexcept>
 
-        context.testGroupStarting( config->name(), 1, 1 );
+#if defined(CATCH_CONFIG_NEW_CAPTURE)
+    #if defined(_MSC_VER)
+    #include <io.h>      //_dup and _dup2
+    #define dup _dup
+    #define dup2 _dup2
+    #define fileno _fileno
+    #else
+    #include <unistd.h>  // dup and dup2
+    #endif
+#endif
 
-        TestSpec testSpec = config->testSpec();
-        if( !testSpec.hasFilters() )
-            testSpec = TestSpecParser( ITagAliasRegistry::get() ).parse( "~[.]" ).testSpec(); // All not hidden tests
+namespace Catch {
 
-        std::vector<TestCase> const& allTestCases = getAllTestCasesSorted( *iconfig );
-        for( std::vector<TestCase>::const_iterator it = allTestCases.begin(), itEnd = allTestCases.end();
-                it != itEnd;
-                ++it ) {
-            if( !context.aborting() && matchTest( *it, testSpec, *iconfig ) )
-                totals += context.runTest( *it );
-            else
-                reporter->skipTest( *it );
-        }
+    RedirectedStream::RedirectedStream( std::ostream& originalStream, std::ostream& redirectionStream )
+    :   m_originalStream( originalStream ),
+        m_redirectionStream( redirectionStream ),
+        m_prevBuf( m_originalStream.rdbuf() )
+    {
+        m_originalStream.rdbuf( m_redirectionStream.rdbuf() );
+    }
 
-        context.testGroupEnded( iconfig->name(), totals, 1, 1 );
-        return totals;
+    RedirectedStream::~RedirectedStream() {
+        m_originalStream.rdbuf( m_prevBuf );
     }
 
-    void applyFilenamesAsTags( IConfig const& config ) {
-        std::vector<TestCase> const& tests = getAllTestCasesSorted( config );
-        for(std::size_t i = 0; i < tests.size(); ++i ) {
-            TestCase& test = const_cast<TestCase&>( tests[i] );
-            std::set<std::string> tags = test.tags;
+    RedirectedStdOut::RedirectedStdOut() : m_cout( Catch::cout(), m_rss.get() ) {}
+    auto RedirectedStdOut::str() const -> std::string { return m_rss.str(); }
 
-            std::string filename = test.lineInfo.file;
-            std::string::size_type lastSlash = filename.find_last_of( "\\/" );
-            if( lastSlash != std::string::npos )
-                filename = filename.substr( lastSlash+1 );
+    RedirectedStdErr::RedirectedStdErr()
+    :   m_cerr( Catch::cerr(), m_rss.get() ),
+        m_clog( Catch::clog(), m_rss.get() )
+    {}
+    auto RedirectedStdErr::str() const -> std::string { return m_rss.str(); }
 
-            std::string::size_type lastDot = filename.find_last_of( "." );
-            if( lastDot != std::string::npos )
-                filename = filename.substr( 0, lastDot );
+    RedirectedStreams::RedirectedStreams(std::string& redirectedCout, std::string& redirectedCerr)
+    :   m_redirectedCout(redirectedCout),
+        m_redirectedCerr(redirectedCerr)
+    {}
 
-            tags.insert( "#" + filename );
-            setTags( test, tags );
-        }
+    RedirectedStreams::~RedirectedStreams() {
+        m_redirectedCout += m_redirectedStdOut.str();
+        m_redirectedCerr += m_redirectedStdErr.str();
     }
 
-    class Session : NonCopyable {
-        static bool alreadyInstantiated;
-
-    public:
-
-        struct OnUnusedOptions { enum DoWhat { Ignore, Fail }; };
+#if defined(CATCH_CONFIG_NEW_CAPTURE)
 
-        Session()
-        : m_cli( makeCommandLineParser() ) {
-            if( alreadyInstantiated ) {
-                std::string msg = "Only one instance of Catch::Session can ever be used";
-                Catch::cerr() << msg << std::endl;
-                throw std::logic_error( msg );
+#if defined(_MSC_VER)
+    TempFile::TempFile() {
+        if (tmpnam_s(m_buffer)) {
+            CATCH_RUNTIME_ERROR("Could not get a temp filename");
+        }
+        if (fopen_s(&m_file, m_buffer, "w")) {
+            char buffer[100];
+            if (strerror_s(buffer, errno)) {
+                CATCH_RUNTIME_ERROR("Could not translate errno to a string");
             }
-            alreadyInstantiated = true;
+            CATCH_RUNTIME_ERROR("Could not open the temp file: '" << m_buffer << "' because: " << buffer);
         }
-        ~Session() {
-            Catch::cleanUp();
+    }
+#else
+    TempFile::TempFile() {
+        m_file = std::tmpfile();
+        if (!m_file) {
+            CATCH_RUNTIME_ERROR("Could not create a temp file.");
         }
+    }
 
-        void showHelp( std::string const& processName ) {
-            Catch::cout() << "\nCatch v" << libraryVersion() << "\n";
+#endif
 
-            m_cli.usage( Catch::cout(), processName );
-            Catch::cout() << "For more detail usage please see the project docs\n" << std::endl;
-        }
+    TempFile::~TempFile() {
+         // TBD: What to do about errors here?
+         std::fclose(m_file);
+         // We manually create the file on Windows only, on Linux
+         // it will be autodeleted
+#if defined(_MSC_VER)
+         std::remove(m_buffer);
+#endif
+    }
 
-        int applyCommandLine( int argc, char const* const* const argv, OnUnusedOptions::DoWhat unusedOptionBehaviour = OnUnusedOptions::Fail ) {
-            try {
-                m_cli.setThrowOnUnrecognisedTokens( unusedOptionBehaviour == OnUnusedOptions::Fail );
-                m_unusedTokens = m_cli.parseInto( Clara::argsToVector( argc, argv ), m_configData );
-                if( m_configData.showHelp )
-                    showHelp( m_configData.processName );
-                m_config.reset();
-            }
-            catch( std::exception& ex ) {
-                {
-                    Colour colourGuard( Colour::Red );
-                    Catch::cerr()
-                        << "\nError(s) in input:\n"
-                        << Text( ex.what(), TextAttributes().setIndent(2) )
-                        << "\n\n";
-                }
-                m_cli.usage( Catch::cout(), m_configData.processName );
-                return (std::numeric_limits<int>::max)();
-            }
-            return 0;
-        }
+    FILE* TempFile::getFile() {
+        return m_file;
+    }
 
-        void useConfigData( ConfigData const& _configData ) {
-            m_configData = _configData;
-            m_config.reset();
+    std::string TempFile::getContents() {
+        std::stringstream sstr;
+        char buffer[100] = {};
+        std::rewind(m_file);
+        while (std::fgets(buffer, sizeof(buffer), m_file)) {
+            sstr << buffer;
         }
+        return sstr.str();
+    }
 
-        int run( int argc, char const* const* const argv ) {
+    OutputRedirect::OutputRedirect(std::string& stdout_dest, std::string& stderr_dest) :
+        m_originalStdout(dup(1)),
+        m_originalStderr(dup(2)),
+        m_stdoutDest(stdout_dest),
+        m_stderrDest(stderr_dest) {
+        dup2(fileno(m_stdoutFile.getFile()), 1);
+        dup2(fileno(m_stderrFile.getFile()), 2);
+    }
 
-            int returnCode = applyCommandLine( argc, argv );
-            if( returnCode == 0 )
-                returnCode = run();
-            return returnCode;
-        }
+    OutputRedirect::~OutputRedirect() {
+        Catch::cout() << std::flush;
+        fflush(stdout);
+        // Since we support overriding these streams, we flush cerr
+        // even though std::cerr is unbuffered
+        Catch::cerr() << std::flush;
+        Catch::clog() << std::flush;
+        fflush(stderr);
 
-    #if defined(WIN32) && defined(UNICODE)
-        int run( int argc, wchar_t const* const* const argv ) {
+        dup2(m_originalStdout, 1);
+        dup2(m_originalStderr, 2);
 
-            char **utf8Argv = new char *[ argc ];
+        m_stdoutDest += m_stdoutFile.getContents();
+        m_stderrDest += m_stderrFile.getContents();
+    }
 
-            for ( int i = 0; i < argc; ++i ) {
-                int bufSize = WideCharToMultiByte( CP_UTF8, 0, argv[i], -1, NULL, 0, NULL, NULL );
+#endif // CATCH_CONFIG_NEW_CAPTURE
 
-                utf8Argv[ i ] = new char[ bufSize ];
+} // namespace Catch
 
-                WideCharToMultiByte( CP_UTF8, 0, argv[i], -1, utf8Argv[i], bufSize, NULL, NULL );
-            }
+#if defined(CATCH_CONFIG_NEW_CAPTURE)
+    #if defined(_MSC_VER)
+    #undef dup
+    #undef dup2
+    #undef fileno
+    #endif
+#endif
+// end catch_output_redirect.cpp
+// start catch_polyfills.cpp
 
-            int returnCode = applyCommandLine( argc, utf8Argv );
-            if( returnCode == 0 )
-                returnCode = run();
+#include <cmath>
+
+namespace Catch {
+
+#if !defined(CATCH_CONFIG_POLYFILL_ISNAN)
+    bool isnan(float f) {
+        return std::isnan(f);
+    }
+    bool isnan(double d) {
+        return std::isnan(d);
+    }
+#else
+    // For now we only use this for embarcadero
+    bool isnan(float f) {
+        return std::_isnan(f);
+    }
+    bool isnan(double d) {
+        return std::_isnan(d);
+    }
+#endif
 
-            for ( int i = 0; i < argc; ++i )
-                delete [] utf8Argv[ i ];
+} // end namespace Catch
+// end catch_polyfills.cpp
+// start catch_random_number_generator.cpp
 
-            delete [] utf8Argv;
+namespace Catch {
 
-            return returnCode;
-        }
-    #endif
+namespace {
 
-        int run() {
-            if( m_configData.showHelp )
-                return 0;
+#if defined(_MSC_VER)
+#pragma warning(push)
+#pragma warning(disable:4146) // we negate uint32 during the rotate
+#endif
+        // Safe rotr implementation thanks to John Regehr
+        uint32_t rotate_right(uint32_t val, uint32_t count) {
+            const uint32_t mask = 31;
+            count &= mask;
+            return (val >> count) | (val << (-count & mask));
+        }
 
-            try
-            {
-                config(); // Force config to be constructed
+#if defined(_MSC_VER)
+#pragma warning(pop)
+#endif
 
-                seedRng( *m_config );
+}
 
-                if( m_configData.filenamesAsTags )
-                    applyFilenamesAsTags( *m_config );
+    SimplePcg32::SimplePcg32(result_type seed_) {
+        seed(seed_);
+    }
 
-                // Handle list request
-                if( Option<std::size_t> listed = list( config() ) )
-                    return static_cast<int>( *listed );
+    void SimplePcg32::seed(result_type seed_) {
+        m_state = 0;
+        (*this)();
+        m_state += seed_;
+        (*this)();
+    }
 
-                return static_cast<int>( runTests( m_config ).assertions.failed );
-            }
-            catch( std::exception& ex ) {
-                Catch::cerr() << ex.what() << std::endl;
-                return (std::numeric_limits<int>::max)();
-            }
+    void SimplePcg32::discard(uint64_t skip) {
+        // We could implement this to run in O(log n) steps, but this
+        // should suffice for our use case.
+        for (uint64_t s = 0; s < skip; ++s) {
+            static_cast<void>((*this)());
         }
+    }
 
-        Clara::CommandLine<ConfigData> const& cli() const {
-            return m_cli;
-        }
-        std::vector<Clara::Parser::Token> const& unusedTokens() const {
-            return m_unusedTokens;
-        }
-        ConfigData& configData() {
-            return m_configData;
-        }
-        Config& config() {
-            if( !m_config )
-                m_config = new Config( m_configData );
-            return *m_config;
-        }
-    private:
-        Clara::CommandLine<ConfigData> m_cli;
-        std::vector<Clara::Parser::Token> m_unusedTokens;
-        ConfigData m_configData;
-        Ptr<Config> m_config;
-    };
+    SimplePcg32::result_type SimplePcg32::operator()() {
+        // prepare the output value
+        const uint32_t xorshifted = static_cast<uint32_t>(((m_state >> 18u) ^ m_state) >> 27u);
+        const auto output = rotate_right(xorshifted, m_state >> 59u);
 
-    bool Session::alreadyInstantiated = false;
+        // advance state
+        m_state = m_state * 6364136223846793005ULL + s_inc;
+
+        return output;
+    }
 
-} // end namespace Catch
+    bool operator==(SimplePcg32 const& lhs, SimplePcg32 const& rhs) {
+        return lhs.m_state == rhs.m_state;
+    }
 
-// #included from: catch_registry_hub.hpp
-#define TWOBLUECUBES_CATCH_REGISTRY_HUB_HPP_INCLUDED
+    bool operator!=(SimplePcg32 const& lhs, SimplePcg32 const& rhs) {
+        return lhs.m_state != rhs.m_state;
+    }
+}
+// end catch_random_number_generator.cpp
+// start catch_registry_hub.cpp
 
-// #included from: catch_test_case_registry_impl.hpp
-#define TWOBLUECUBES_CATCH_TEST_CASE_REGISTRY_IMPL_HPP_INCLUDED
+// start catch_test_case_registry_impl.h
 
 #include <vector>
 #include <set>
-#include <sstream>
 #include <algorithm>
+#include <ios>
 
 namespace Catch {
 
-    struct RandomNumberGenerator {
-        typedef std::ptrdiff_t result_type;
-
-        result_type operator()( result_type n ) const { return std::rand() % n; }
-
-#ifdef CATCH_CONFIG_CPP11_SHUFFLE
-        static constexpr result_type min() { return 0; }
-        static constexpr result_type max() { return 1000000; }
-        result_type operator()() const { return std::rand() % max(); }
-#endif
-        template<typename V>
-        static void shuffle( V& vector ) {
-            RandomNumberGenerator rng;
-#ifdef CATCH_CONFIG_CPP11_SHUFFLE
-            std::shuffle( vector.begin(), vector.end(), rng );
-#else
-            std::random_shuffle( vector.begin(), vector.end(), rng );
-#endif
-        }
-    };
-
-    inline std::vector<TestCase> sortTests( IConfig const& config, std::vector<TestCase> const& unsortedTestCases ) {
-
-        std::vector<TestCase> sorted = unsortedTestCases;
-
-        switch( config.runOrder() ) {
-            case RunTests::InLexicographicalOrder:
-                std::sort( sorted.begin(), sorted.end() );
-                break;
-            case RunTests::InRandomOrder:
-                {
-                    seedRng( config );
-                    RandomNumberGenerator::shuffle( sorted );
-                }
-                break;
-            case RunTests::InDeclarationOrder:
-                // already in declaration order
-                break;
-        }
-        return sorted;
-    }
-    bool matchTest( TestCase const& testCase, TestSpec const& testSpec, IConfig const& config ) {
-        return testSpec.matches( testCase ) && ( config.allowThrows() || !testCase.throws() );
-    }
+    class TestCase;
+    struct IConfig;
 
-    void enforceNoDuplicateTestCases( std::vector<TestCase> const& functions ) {
-        std::set<TestCase> seenFunctions;
-        for( std::vector<TestCase>::const_iterator it = functions.begin(), itEnd = functions.end();
-            it != itEnd;
-            ++it ) {
-            std::pair<std::set<TestCase>::const_iterator, bool> prev = seenFunctions.insert( *it );
-            if( !prev.second ) {
-                std::ostringstream ss;
+    std::vector<TestCase> sortTests( IConfig const& config, std::vector<TestCase> const& unsortedTestCases );
 
-                ss  << Colour( Colour::Red )
-                    << "error: TEST_CASE( \"" << it->name << "\" ) already defined.\n"
-                    << "\tFirst seen at " << prev.first->getTestCaseInfo().lineInfo << '\n'
-                    << "\tRedefined at " << it->getTestCaseInfo().lineInfo << std::endl;
+    bool isThrowSafe( TestCase const& testCase, IConfig const& config );
+    bool matchTest( TestCase const& testCase, TestSpec const& testSpec, IConfig const& config );
 
-                throw std::runtime_error(ss.str());
-            }
-        }
-    }
+    void enforceNoDuplicateTestCases( std::vector<TestCase> const& functions );
 
-    std::vector<TestCase> filterTests( std::vector<TestCase> const& testCases, TestSpec const& testSpec, IConfig const& config ) {
-        std::vector<TestCase> filtered;
-        filtered.reserve( testCases.size() );
-        for( std::vector<TestCase>::const_iterator it = testCases.begin(), itEnd = testCases.end();
-                it != itEnd;
-                ++it )
-            if( matchTest( *it, testSpec, config ) )
-                filtered.push_back( *it );
-        return filtered;
-    }
-    std::vector<TestCase> const& getAllTestCasesSorted( IConfig const& config ) {
-        return getRegistryHub().getTestCaseRegistry().getAllTestsSorted( config );
-    }
+    std::vector<TestCase> filterTests( std::vector<TestCase> const& testCases, TestSpec const& testSpec, IConfig const& config );
+    std::vector<TestCase> const& getAllTestCasesSorted( IConfig const& config );
 
     class TestRegistry : public ITestCaseRegistry {
     public:
-        TestRegistry()
-        :   m_currentSortOrder( RunTests::InDeclarationOrder ),
-            m_unnamedCount( 0 )
-        {}
-        virtual ~TestRegistry();
-
-        virtual void registerTest( TestCase const& testCase ) {
-            std::string name = testCase.getTestCaseInfo().name;
-            if( name.empty() ) {
-                std::ostringstream oss;
-                oss << "Anonymous test case " << ++m_unnamedCount;
-                return registerTest( testCase.withName( oss.str() ) );
-            }
-            m_functions.push_back( testCase );
-        }
+        virtual ~TestRegistry() = default;
 
-        virtual std::vector<TestCase> const& getAllTests() const {
-            return m_functions;
-        }
-        virtual std::vector<TestCase> const& getAllTestsSorted( IConfig const& config ) const {
-            if( m_sortedFunctions.empty() )
-                enforceNoDuplicateTestCases( m_functions );
+        virtual void registerTest( TestCase const& testCase );
 
-            if(  m_currentSortOrder != config.runOrder() || m_sortedFunctions.empty() ) {
-                m_sortedFunctions = sortTests( config, m_functions );
-                m_currentSortOrder = config.runOrder();
-            }
-            return m_sortedFunctions;
-        }
+        std::vector<TestCase> const& getAllTests() const override;
+        std::vector<TestCase> const& getAllTestsSorted( IConfig const& config ) const override;
 
     private:
         std::vector<TestCase> m_functions;
-        mutable RunTests::InWhatOrder m_currentSortOrder;
+        mutable RunTests::InWhatOrder m_currentSortOrder = RunTests::InDeclarationOrder;
         mutable std::vector<TestCase> m_sortedFunctions;
-        size_t m_unnamedCount;
+        std::size_t m_unnamedCount = 0;
         std::ios_base::Init m_ostreamInit; // Forces cout/ cerr to be initialised
     };
 
     ///////////////////////////////////////////////////////////////////////////
 
-    class FreeFunctionTestCase : public SharedImpl<ITestCase> {
+    class TestInvokerAsFunction : public ITestInvoker {
+        void(*m_testAsFunction)();
     public:
+        TestInvokerAsFunction( void(*testAsFunction)() ) noexcept;
 
-        FreeFunctionTestCase( TestFunction fun ) : m_fun( fun ) {}
-
-        virtual void invoke() const {
-            m_fun();
-        }
-
-    private:
-        virtual ~FreeFunctionTestCase();
-
-        TestFunction m_fun;
+        void invoke() const override;
     };
 
-    inline std::string extractClassName( std::string const& classOrQualifiedMethodName ) {
-        std::string className = classOrQualifiedMethodName;
-        if( startsWith( className, '&' ) )
-        {
-            std::size_t lastColons = className.rfind( "::" );
-            std::size_t penultimateColons = className.rfind( "::", lastColons-1 );
-            if( penultimateColons == std::string::npos )
-                penultimateColons = 1;
-            className = className.substr( penultimateColons, lastColons-penultimateColons );
-        }
-        return className;
-    }
-
-    void registerTestCase
-        (   ITestCase* testCase,
-            char const* classOrQualifiedMethodName,
-            NameAndDesc const& nameAndDesc,
-            SourceLineInfo const& lineInfo ) {
-
-        getMutableRegistryHub().registerTest
-            ( makeTestCase
-                (   testCase,
-                    extractClassName( classOrQualifiedMethodName ),
-                    nameAndDesc.name,
-                    nameAndDesc.description,
-                    lineInfo ) );
-    }
-    void registerTestCaseFunction
-        (   TestFunction function,
-            SourceLineInfo const& lineInfo,
-            NameAndDesc const& nameAndDesc ) {
-        registerTestCase( new FreeFunctionTestCase( function ), "", nameAndDesc, lineInfo );
-    }
+    std::string extractClassName( StringRef const& classOrQualifiedMethodName );
 
     ///////////////////////////////////////////////////////////////////////////
 
-    AutoReg::AutoReg
-        (   TestFunction function,
-            SourceLineInfo const& lineInfo,
-            NameAndDesc const& nameAndDesc ) {
-        registerTestCaseFunction( function, lineInfo, nameAndDesc );
-    }
-
-    AutoReg::~AutoReg() {}
-
 } // end namespace Catch
 
-// #included from: catch_reporter_registry.hpp
-#define TWOBLUECUBES_CATCH_REPORTER_REGISTRY_HPP_INCLUDED
+// end catch_test_case_registry_impl.h
+// start catch_reporter_registry.h
 
 #include <map>
 
@@ -7335,28 +12141,15 @@ namespace Catch {
 
     public:
 
-        virtual ~ReporterRegistry() CATCH_OVERRIDE {}
+        ~ReporterRegistry() override;
 
-        virtual IStreamingReporter* create( std::string const& name, Ptr<IConfig const> const& config ) const CATCH_OVERRIDE {
-            FactoryMap::const_iterator it =  m_factories.find( name );
-            if( it == m_factories.end() )
-                return CATCH_NULL;
-            return it->second->create( ReporterConfig( config ) );
-        }
+        IStreamingReporterPtr create( std::string const& name, IConfigPtr const& config ) const override;
 
-        void registerReporter( std::string const& name, Ptr<IReporterFactory> const& factory ) {
-            m_factories.insert( std::make_pair( name, factory ) );
-        }
-        void registerListener( Ptr<IReporterFactory> const& factory ) {
-            m_listeners.push_back( factory );
-        }
+        void registerReporter( std::string const& name, IReporterFactoryPtr const& factory );
+        void registerListener( IReporterFactoryPtr const& factory );
 
-        virtual FactoryMap const& getFactories() const CATCH_OVERRIDE {
-            return m_factories;
-        }
-        virtual Listeners const& getListeners() const CATCH_OVERRIDE {
-            return m_listeners;
-        }
+        FactoryMap const& getFactories() const override;
+        Listeners const& getListeners() const override;
 
     private:
         FactoryMap m_factories;
@@ -7364,80 +12157,34 @@ namespace Catch {
     };
 }
 
-// #included from: catch_exception_translator_registry.hpp
-#define TWOBLUECUBES_CATCH_EXCEPTION_TRANSLATOR_REGISTRY_HPP_INCLUDED
-
-#ifdef __OBJC__
-#import "Foundation/Foundation.h"
-#endif
-
-namespace Catch {
+// end catch_reporter_registry.h
+// start catch_tag_alias_registry.h
 
-    class ExceptionTranslatorRegistry : public IExceptionTranslatorRegistry {
-    public:
-        ~ExceptionTranslatorRegistry() {
-            deleteAll( m_translators );
-        }
+// start catch_tag_alias.h
 
-        virtual void registerTranslator( const IExceptionTranslator* translator ) {
-            m_translators.push_back( translator );
-        }
+#include <string>
 
-        virtual std::string translateActiveException() const {
-            try {
-#ifdef __OBJC__
-                // In Objective-C try objective-c exceptions first
-                @try {
-                    return tryTranslators();
-                }
-                @catch (NSException *exception) {
-                    return Catch::toString( [exception description] );
-                }
-#else
-                return tryTranslators();
-#endif
-            }
-            catch( TestFailureException& ) {
-                throw;
-            }
-            catch( std::exception& ex ) {
-                return ex.what();
-            }
-            catch( std::string& msg ) {
-                return msg;
-            }
-            catch( const char* msg ) {
-                return msg;
-            }
-            catch(...) {
-                return "Unknown exception";
-            }
-        }
+namespace Catch {
 
-        std::string tryTranslators() const {
-            if( m_translators.empty() )
-                throw;
-            else
-                return m_translators[0]->translate( m_translators.begin()+1, m_translators.end() );
-        }
+    struct TagAlias {
+        TagAlias(std::string const& _tag, SourceLineInfo _lineInfo);
 
-    private:
-        std::vector<const IExceptionTranslator*> m_translators;
+        std::string tag;
+        SourceLineInfo lineInfo;
     };
-}
 
-// #included from: catch_tag_alias_registry.h
-#define TWOBLUECUBES_CATCH_TAG_ALIAS_REGISTRY_H_INCLUDED
+} // end namespace Catch
 
+// end catch_tag_alias.h
 #include <map>
 
 namespace Catch {
 
     class TagAliasRegistry : public ITagAliasRegistry {
     public:
-        virtual ~TagAliasRegistry();
-        virtual Option<TagAlias> find( std::string const& alias ) const;
-        virtual std::string expandAliases( std::string const& unexpandedTestSpec ) const;
+        ~TagAliasRegistry() override;
+        TagAlias const* find( std::string const& alias ) const override;
+        std::string expandAliases( std::string const& unexpandedTestSpec ) const override;
         void add( std::string const& alias, std::string const& tag, SourceLineInfo const& lineInfo );
 
     private:
@@ -7446,73 +12193,128 @@ namespace Catch {
 
 } // end namespace Catch
 
+// end catch_tag_alias_registry.h
+// start catch_startup_exception_registry.h
+
+#include <vector>
+#include <exception>
+
 namespace Catch {
 
-    namespace {
+    class StartupExceptionRegistry {
+    public:
+        void add(std::exception_ptr const& exception) noexcept;
+        std::vector<std::exception_ptr> const& getExceptions() const noexcept;
+    private:
+        std::vector<std::exception_ptr> m_exceptions;
+    };
 
-        class RegistryHub : public IRegistryHub, public IMutableRegistryHub {
+} // end namespace Catch
 
-            RegistryHub( RegistryHub const& );
-            void operator=( RegistryHub const& );
+// end catch_startup_exception_registry.h
+// start catch_singletons.hpp
 
-        public: // IRegistryHub
-            RegistryHub() {
+namespace Catch {
+
+    struct ISingleton {
+        virtual ~ISingleton();
+    };
+
+    void addSingleton( ISingleton* singleton );
+    void cleanupSingletons();
+
+    template<typename SingletonImplT, typename InterfaceT = SingletonImplT, typename MutableInterfaceT = InterfaceT>
+    class Singleton : SingletonImplT, public ISingleton {
+
+        static auto getInternal() -> Singleton* {
+            static Singleton* s_instance = nullptr;
+            if( !s_instance ) {
+                s_instance = new Singleton;
+                addSingleton( s_instance );
             }
-            virtual IReporterRegistry const& getReporterRegistry() const CATCH_OVERRIDE {
+            return s_instance;
+        }
+
+    public:
+        static auto get() -> InterfaceT const& {
+            return *getInternal();
+        }
+        static auto getMutable() -> MutableInterfaceT& {
+            return *getInternal();
+        }
+    };
+
+} // namespace Catch
+
+// end catch_singletons.hpp
+namespace Catch {
+
+    namespace {
+
+        class RegistryHub : public IRegistryHub, public IMutableRegistryHub,
+                            private NonCopyable {
+
+        public: // IRegistryHub
+            RegistryHub() = default;
+            IReporterRegistry const& getReporterRegistry() const override {
                 return m_reporterRegistry;
             }
-            virtual ITestCaseRegistry const& getTestCaseRegistry() const CATCH_OVERRIDE {
+            ITestCaseRegistry const& getTestCaseRegistry() const override {
                 return m_testCaseRegistry;
             }
-            virtual IExceptionTranslatorRegistry& getExceptionTranslatorRegistry() CATCH_OVERRIDE {
+            IExceptionTranslatorRegistry const& getExceptionTranslatorRegistry() const override {
                 return m_exceptionTranslatorRegistry;
             }
-            virtual ITagAliasRegistry const& getTagAliasRegistry() const CATCH_OVERRIDE {
+            ITagAliasRegistry const& getTagAliasRegistry() const override {
                 return m_tagAliasRegistry;
             }
+            StartupExceptionRegistry const& getStartupExceptionRegistry() const override {
+                return m_exceptionRegistry;
+            }
 
         public: // IMutableRegistryHub
-            virtual void registerReporter( std::string const& name, Ptr<IReporterFactory> const& factory ) CATCH_OVERRIDE {
+            void registerReporter( std::string const& name, IReporterFactoryPtr const& factory ) override {
                 m_reporterRegistry.registerReporter( name, factory );
             }
-            virtual void registerListener( Ptr<IReporterFactory> const& factory ) CATCH_OVERRIDE {
+            void registerListener( IReporterFactoryPtr const& factory ) override {
                 m_reporterRegistry.registerListener( factory );
             }
-            virtual void registerTest( TestCase const& testInfo ) CATCH_OVERRIDE {
+            void registerTest( TestCase const& testInfo ) override {
                 m_testCaseRegistry.registerTest( testInfo );
             }
-            virtual void registerTranslator( const IExceptionTranslator* translator ) CATCH_OVERRIDE {
+            void registerTranslator( const IExceptionTranslator* translator ) override {
                 m_exceptionTranslatorRegistry.registerTranslator( translator );
             }
-            virtual void registerTagAlias( std::string const& alias, std::string const& tag, SourceLineInfo const& lineInfo ) CATCH_OVERRIDE {
+            void registerTagAlias( std::string const& alias, std::string const& tag, SourceLineInfo const& lineInfo ) override {
                 m_tagAliasRegistry.add( alias, tag, lineInfo );
             }
+            void registerStartupException() noexcept override {
+                m_exceptionRegistry.add(std::current_exception());
+            }
+            IMutableEnumValuesRegistry& getMutableEnumValuesRegistry() override {
+                return m_enumValuesRegistry;
+            }
 
         private:
             TestRegistry m_testCaseRegistry;
             ReporterRegistry m_reporterRegistry;
             ExceptionTranslatorRegistry m_exceptionTranslatorRegistry;
             TagAliasRegistry m_tagAliasRegistry;
+            StartupExceptionRegistry m_exceptionRegistry;
+            Detail::EnumValuesRegistry m_enumValuesRegistry;
         };
-
-        // Single, global, instance
-        inline RegistryHub*& getTheRegistryHub() {
-            static RegistryHub* theRegistryHub = CATCH_NULL;
-            if( !theRegistryHub )
-                theRegistryHub = new RegistryHub();
-            return theRegistryHub;
-        }
     }
 
-    IRegistryHub& getRegistryHub() {
-        return *getTheRegistryHub();
+    using RegistryHubSingleton = Singleton<RegistryHub, IRegistryHub, IMutableRegistryHub>;
+
+    IRegistryHub const& getRegistryHub() {
+        return RegistryHubSingleton::get();
     }
     IMutableRegistryHub& getMutableRegistryHub() {
-        return *getTheRegistryHub();
+        return RegistryHubSingleton::getMutable();
     }
     void cleanUp() {
-        delete getTheRegistryHub();
-        getTheRegistryHub() = CATCH_NULL;
+        cleanupSingletons();
         cleanUpContext();
     }
     std::string translateActiveException() {
@@ -7520,1026 +12322,1218 @@ namespace Catch {
     }
 
 } // end namespace Catch
+// end catch_registry_hub.cpp
+// start catch_reporter_registry.cpp
 
-// #included from: catch_notimplemented_exception.hpp
-#define TWOBLUECUBES_CATCH_NOTIMPLEMENTED_EXCEPTION_HPP_INCLUDED
+namespace Catch {
 
-#include <sstream>
+    ReporterRegistry::~ReporterRegistry() = default;
+
+    IStreamingReporterPtr ReporterRegistry::create( std::string const& name, IConfigPtr const& config ) const {
+        auto it =  m_factories.find( name );
+        if( it == m_factories.end() )
+            return nullptr;
+        return it->second->create( ReporterConfig( config ) );
+    }
+
+    void ReporterRegistry::registerReporter( std::string const& name, IReporterFactoryPtr const& factory ) {
+        m_factories.emplace(name, factory);
+    }
+    void ReporterRegistry::registerListener( IReporterFactoryPtr const& factory ) {
+        m_listeners.push_back( factory );
+    }
+
+    IReporterRegistry::FactoryMap const& ReporterRegistry::getFactories() const {
+        return m_factories;
+    }
+    IReporterRegistry::Listeners const& ReporterRegistry::getListeners() const {
+        return m_listeners;
+    }
+
+}
+// end catch_reporter_registry.cpp
+// start catch_result_type.cpp
 
 namespace Catch {
 
-    NotImplementedException::NotImplementedException( SourceLineInfo const& lineInfo )
-    :   m_lineInfo( lineInfo ) {
-        std::ostringstream oss;
-        oss << lineInfo << ": function ";
-        oss << "not implemented";
-        m_what = oss.str();
+    bool isOk( ResultWas::OfType resultType ) {
+        return ( resultType & ResultWas::FailureBit ) == 0;
+    }
+    bool isJustInfo( int flags ) {
+        return flags == ResultWas::Info;
     }
 
-    const char* NotImplementedException::what() const CATCH_NOEXCEPT {
-        return m_what.c_str();
+    ResultDisposition::Flags operator | ( ResultDisposition::Flags lhs, ResultDisposition::Flags rhs ) {
+        return static_cast<ResultDisposition::Flags>( static_cast<int>( lhs ) | static_cast<int>( rhs ) );
     }
 
+    bool shouldContinueOnFailure( int flags )    { return ( flags & ResultDisposition::ContinueOnFailure ) != 0; }
+    bool shouldSuppressFailure( int flags )      { return ( flags & ResultDisposition::SuppressFail ) != 0; }
+
 } // end namespace Catch
+// end catch_result_type.cpp
+// start catch_run_context.cpp
 
-// #included from: catch_context_impl.hpp
-#define TWOBLUECUBES_CATCH_CONTEXT_IMPL_HPP_INCLUDED
+#include <cassert>
+#include <algorithm>
+#include <sstream>
 
-// #included from: catch_stream.hpp
-#define TWOBLUECUBES_CATCH_STREAM_HPP_INCLUDED
+namespace Catch {
 
-#include <stdexcept>
-#include <cstdio>
-#include <iostream>
+    namespace Generators {
+        struct GeneratorTracker : TestCaseTracking::TrackerBase, IGeneratorTracker {
+            GeneratorBasePtr m_generator;
 
-namespace Catch {
+            GeneratorTracker( TestCaseTracking::NameAndLocation const& nameAndLocation, TrackerContext& ctx, ITracker* parent )
+            :   TrackerBase( nameAndLocation, ctx, parent )
+            {}
+            ~GeneratorTracker();
 
-    template<typename WriterF, size_t bufferSize=256>
-    class StreamBufImpl : public StreamBufBase {
-        char data[bufferSize];
-        WriterF m_writer;
+            static GeneratorTracker& acquire( TrackerContext& ctx, TestCaseTracking::NameAndLocation const& nameAndLocation ) {
+                std::shared_ptr<GeneratorTracker> tracker;
 
-    public:
-        StreamBufImpl() {
-            setp( data, data + sizeof(data) );
-        }
+                ITracker& currentTracker = ctx.currentTracker();
+                if( TestCaseTracking::ITrackerPtr childTracker = currentTracker.findChild( nameAndLocation ) ) {
+                    assert( childTracker );
+                    assert( childTracker->isGeneratorTracker() );
+                    tracker = std::static_pointer_cast<GeneratorTracker>( childTracker );
+                }
+                else {
+                    tracker = std::make_shared<GeneratorTracker>( nameAndLocation, ctx, &currentTracker );
+                    currentTracker.addChild( tracker );
+                }
 
-        ~StreamBufImpl() CATCH_NOEXCEPT {
-            sync();
-        }
+                if( !ctx.completedCycle() && !tracker->isComplete() ) {
+                    tracker->open();
+                }
 
-    private:
-        int overflow( int c ) {
-            sync();
+                return *tracker;
+            }
 
-            if( c != EOF ) {
-                if( pbase() == epptr() )
-                    m_writer( std::string( 1, static_cast<char>( c ) ) );
-                else
-                    sputc( static_cast<char>( c ) );
+            // TrackerBase interface
+            bool isGeneratorTracker() const override { return true; }
+            auto hasGenerator() const -> bool override {
+                return !!m_generator;
+            }
+            void close() override {
+                TrackerBase::close();
+                // Generator interface only finds out if it has another item on atual move
+                if (m_runState == CompletedSuccessfully && m_generator->next()) {
+                    m_children.clear();
+                    m_runState = Executing;
+                }
             }
-            return 0;
-        }
 
-        int sync() {
-            if( pbase() != pptr() ) {
-                m_writer( std::string( pbase(), static_cast<std::string::size_type>( pptr() - pbase() ) ) );
-                setp( pbase(), epptr() );
+            // IGeneratorTracker interface
+            auto getGenerator() const -> GeneratorBasePtr const& override {
+                return m_generator;
             }
-            return 0;
-        }
-    };
+            void setGenerator( GeneratorBasePtr&& generator ) override {
+                m_generator = std::move( generator );
+            }
+        };
+        GeneratorTracker::~GeneratorTracker() {}
+    }
 
-    ///////////////////////////////////////////////////////////////////////////
+    RunContext::RunContext(IConfigPtr const& _config, IStreamingReporterPtr&& reporter)
+    :   m_runInfo(_config->name()),
+        m_context(getCurrentMutableContext()),
+        m_config(_config),
+        m_reporter(std::move(reporter)),
+        m_lastAssertionInfo{ StringRef(), SourceLineInfo("",0), StringRef(), ResultDisposition::Normal },
+        m_includeSuccessfulResults( m_config->includeSuccessfulResults() || m_reporter->getPreferences().shouldReportAllAssertions )
+    {
+        m_context.setRunner(this);
+        m_context.setConfig(m_config);
+        m_context.setResultCapture(this);
+        m_reporter->testRunStarting(m_runInfo);
+    }
 
-    FileStream::FileStream( std::string const& filename ) {
-        m_ofs.open( filename.c_str() );
-        if( m_ofs.fail() ) {
-            std::ostringstream oss;
-            oss << "Unable to open file: '" << filename << '\'';
-            throw std::domain_error( oss.str() );
-        }
+    RunContext::~RunContext() {
+        m_reporter->testRunEnded(TestRunStats(m_runInfo, m_totals, aborting()));
+    }
+
+    void RunContext::testGroupStarting(std::string const& testSpec, std::size_t groupIndex, std::size_t groupsCount) {
+        m_reporter->testGroupStarting(GroupInfo(testSpec, groupIndex, groupsCount));
     }
 
-    std::ostream& FileStream::stream() const {
-        return m_ofs;
+    void RunContext::testGroupEnded(std::string const& testSpec, Totals const& totals, std::size_t groupIndex, std::size_t groupsCount) {
+        m_reporter->testGroupEnded(TestGroupStats(GroupInfo(testSpec, groupIndex, groupsCount), totals, aborting()));
     }
 
-    struct OutputDebugWriter {
+    Totals RunContext::runTest(TestCase const& testCase) {
+        Totals prevTotals = m_totals;
 
-        void operator()( std::string const&str ) {
-            writeToDebugConsole( str );
+        std::string redirectedCout;
+        std::string redirectedCerr;
+
+        auto const& testInfo = testCase.getTestCaseInfo();
+
+        m_reporter->testCaseStarting(testInfo);
+
+        m_activeTestCase = &testCase;
+
+        ITracker& rootTracker = m_trackerContext.startRun();
+        assert(rootTracker.isSectionTracker());
+        static_cast<SectionTracker&>(rootTracker).addInitialFilters(m_config->getSectionsToRun());
+        do {
+            m_trackerContext.startCycle();
+            m_testCaseTracker = &SectionTracker::acquire(m_trackerContext, TestCaseTracking::NameAndLocation(testInfo.name, testInfo.lineInfo));
+            runCurrentTest(redirectedCout, redirectedCerr);
+        } while (!m_testCaseTracker->isSuccessfullyCompleted() && !aborting());
+
+        Totals deltaTotals = m_totals.delta(prevTotals);
+        if (testInfo.expectedToFail() && deltaTotals.testCases.passed > 0) {
+            deltaTotals.assertions.failed++;
+            deltaTotals.testCases.passed--;
+            deltaTotals.testCases.failed++;
         }
-    };
+        m_totals.testCases += deltaTotals.testCases;
+        m_reporter->testCaseEnded(TestCaseStats(testInfo,
+                                  deltaTotals,
+                                  redirectedCout,
+                                  redirectedCerr,
+                                  aborting()));
 
-    DebugOutStream::DebugOutStream()
-    :   m_streamBuf( new StreamBufImpl<OutputDebugWriter>() ),
-        m_os( m_streamBuf.get() )
-    {}
+        m_activeTestCase = nullptr;
+        m_testCaseTracker = nullptr;
 
-    std::ostream& DebugOutStream::stream() const {
-        return m_os;
+        return deltaTotals;
     }
 
-    // Store the streambuf from cout up-front because
-    // cout may get redirected when running tests
-    CoutStream::CoutStream()
-    :   m_os( Catch::cout().rdbuf() )
-    {}
+    IConfigPtr RunContext::config() const {
+        return m_config;
+    }
 
-    std::ostream& CoutStream::stream() const {
-        return m_os;
+    IStreamingReporter& RunContext::reporter() const {
+        return *m_reporter;
     }
 
-#ifndef CATCH_CONFIG_NOSTDOUT // If you #define this you must implement these functions
-    std::ostream& cout() {
-        return std::cout;
+    void RunContext::assertionEnded(AssertionResult const & result) {
+        if (result.getResultType() == ResultWas::Ok) {
+            m_totals.assertions.passed++;
+            m_lastAssertionPassed = true;
+        } else if (!result.isOk()) {
+            m_lastAssertionPassed = false;
+            if( m_activeTestCase->getTestCaseInfo().okToFail() )
+                m_totals.assertions.failedButOk++;
+            else
+                m_totals.assertions.failed++;
+        }
+        else {
+            m_lastAssertionPassed = true;
+        }
+
+        // We have no use for the return value (whether messages should be cleared), because messages were made scoped
+        // and should be let to clear themselves out.
+        static_cast<void>(m_reporter->assertionEnded(AssertionStats(result, m_messages, m_totals)));
+
+        if (result.getResultType() != ResultWas::Warning)
+            m_messageScopes.clear();
+
+        // Reset working state
+        resetAssertionInfo();
+        m_lastResult = result;
     }
-    std::ostream& cerr() {
-        return std::cerr;
+    void RunContext::resetAssertionInfo() {
+        m_lastAssertionInfo.macroName = StringRef();
+        m_lastAssertionInfo.capturedExpression = "{Unknown expression after the reported line}"_sr;
     }
-#endif
-}
 
-namespace Catch {
+    bool RunContext::sectionStarted(SectionInfo const & sectionInfo, Counts & assertions) {
+        ITracker& sectionTracker = SectionTracker::acquire(m_trackerContext, TestCaseTracking::NameAndLocation(sectionInfo.name, sectionInfo.lineInfo));
+        if (!sectionTracker.isOpen())
+            return false;
+        m_activeSections.push_back(&sectionTracker);
 
-    class Context : public IMutableContext {
+        m_lastAssertionInfo.lineInfo = sectionInfo.lineInfo;
 
-        Context() : m_config( CATCH_NULL ), m_runner( CATCH_NULL ), m_resultCapture( CATCH_NULL ) {}
-        Context( Context const& );
-        void operator=( Context const& );
+        m_reporter->sectionStarting(sectionInfo);
 
-    public:
-        virtual ~Context() {
-            deleteAllValues( m_generatorsByTestName );
-        }
+        assertions = m_totals.assertions;
 
-    public: // IContext
-        virtual IResultCapture* getResultCapture() {
-            return m_resultCapture;
-        }
-        virtual IRunner* getRunner() {
-            return m_runner;
-        }
-        virtual size_t getGeneratorIndex( std::string const& fileInfo, size_t totalSize ) {
-            return getGeneratorsForCurrentTest()
-            .getGeneratorInfo( fileInfo, totalSize )
-            .getCurrentIndex();
-        }
-        virtual bool advanceGeneratorsForCurrentTest() {
-            IGeneratorsForTest* generators = findGeneratorsForCurrentTest();
-            return generators && generators->moveNext();
-        }
+        return true;
+    }
+    auto RunContext::acquireGeneratorTracker( SourceLineInfo const& lineInfo ) -> IGeneratorTracker& {
+        using namespace Generators;
+        GeneratorTracker& tracker = GeneratorTracker::acquire( m_trackerContext, TestCaseTracking::NameAndLocation( "generator", lineInfo ) );
+        assert( tracker.isOpen() );
+        m_lastAssertionInfo.lineInfo = lineInfo;
+        return tracker;
+    }
 
-        virtual Ptr<IConfig const> getConfig() const {
-            return m_config;
-        }
+    bool RunContext::testForMissingAssertions(Counts& assertions) {
+        if (assertions.total() != 0)
+            return false;
+        if (!m_config->warnAboutMissingAssertions())
+            return false;
+        if (m_trackerContext.currentTracker().hasChildren())
+            return false;
+        m_totals.assertions.failed++;
+        assertions.failed++;
+        return true;
+    }
 
-    public: // IMutableContext
-        virtual void setResultCapture( IResultCapture* resultCapture ) {
-            m_resultCapture = resultCapture;
-        }
-        virtual void setRunner( IRunner* runner ) {
-            m_runner = runner;
-        }
-        virtual void setConfig( Ptr<IConfig const> const& config ) {
-            m_config = config;
+    void RunContext::sectionEnded(SectionEndInfo const & endInfo) {
+        Counts assertions = m_totals.assertions - endInfo.prevAssertions;
+        bool missingAssertions = testForMissingAssertions(assertions);
+
+        if (!m_activeSections.empty()) {
+            m_activeSections.back()->close();
+            m_activeSections.pop_back();
         }
 
-        friend IMutableContext& getCurrentMutableContext();
+        m_reporter->sectionEnded(SectionStats(endInfo.sectionInfo, assertions, endInfo.durationInSeconds, missingAssertions));
+        m_messages.clear();
+        m_messageScopes.clear();
+    }
 
-    private:
-        IGeneratorsForTest* findGeneratorsForCurrentTest() {
-            std::string testName = getResultCapture()->getCurrentTestName();
+    void RunContext::sectionEndedEarly(SectionEndInfo const & endInfo) {
+        if (m_unfinishedSections.empty())
+            m_activeSections.back()->fail();
+        else
+            m_activeSections.back()->close();
+        m_activeSections.pop_back();
 
-            std::map<std::string, IGeneratorsForTest*>::const_iterator it =
-                m_generatorsByTestName.find( testName );
-            return it != m_generatorsByTestName.end()
-                ? it->second
-                : CATCH_NULL;
-        }
+        m_unfinishedSections.push_back(endInfo);
+    }
 
-        IGeneratorsForTest& getGeneratorsForCurrentTest() {
-            IGeneratorsForTest* generators = findGeneratorsForCurrentTest();
-            if( !generators ) {
-                std::string testName = getResultCapture()->getCurrentTestName();
-                generators = createGeneratorsForTest();
-                m_generatorsByTestName.insert( std::make_pair( testName, generators ) );
-            }
-            return *generators;
-        }
+#if defined(CATCH_CONFIG_ENABLE_BENCHMARKING)
+    void RunContext::benchmarkPreparing(std::string const& name) {
+		m_reporter->benchmarkPreparing(name);
+	}
+    void RunContext::benchmarkStarting( BenchmarkInfo const& info ) {
+        m_reporter->benchmarkStarting( info );
+    }
+    void RunContext::benchmarkEnded( BenchmarkStats<> const& stats ) {
+        m_reporter->benchmarkEnded( stats );
+    }
+	void RunContext::benchmarkFailed(std::string const & error) {
+		m_reporter->benchmarkFailed(error);
+	}
+#endif // CATCH_CONFIG_ENABLE_BENCHMARKING
 
-    private:
-        Ptr<IConfig const> m_config;
-        IRunner* m_runner;
-        IResultCapture* m_resultCapture;
-        std::map<std::string, IGeneratorsForTest*> m_generatorsByTestName;
-    };
+    void RunContext::pushScopedMessage(MessageInfo const & message) {
+        m_messages.push_back(message);
+    }
 
-    namespace {
-        Context* currentContext = CATCH_NULL;
+    void RunContext::popScopedMessage(MessageInfo const & message) {
+        m_messages.erase(std::remove(m_messages.begin(), m_messages.end(), message), m_messages.end());
     }
-    IMutableContext& getCurrentMutableContext() {
-        if( !currentContext )
-            currentContext = new Context();
-        return *currentContext;
+
+    void RunContext::emplaceUnscopedMessage( MessageBuilder const& builder ) {
+        m_messageScopes.emplace_back( builder );
     }
-    IContext& getCurrentContext() {
-        return getCurrentMutableContext();
+
+    std::string RunContext::getCurrentTestName() const {
+        return m_activeTestCase
+            ? m_activeTestCase->getTestCaseInfo().name
+            : std::string();
     }
 
-    void cleanUpContext() {
-        delete currentContext;
-        currentContext = CATCH_NULL;
+    const AssertionResult * RunContext::getLastResult() const {
+        return &(*m_lastResult);
     }
-}
 
-// #included from: catch_console_colour_impl.hpp
-#define TWOBLUECUBES_CATCH_CONSOLE_COLOUR_IMPL_HPP_INCLUDED
+    void RunContext::exceptionEarlyReported() {
+        m_shouldReportUnexpected = false;
+    }
 
-// #included from: catch_errno_guard.hpp
-#define TWOBLUECUBES_CATCH_ERRNO_GUARD_HPP_INCLUDED
+    void RunContext::handleFatalErrorCondition( StringRef message ) {
+        // First notify reporter that bad things happened
+        m_reporter->fatalErrorEncountered(message);
 
-#include <cerrno>
+        // Don't rebuild the result -- the stringification itself can cause more fatal errors
+        // Instead, fake a result data.
+        AssertionResultData tempResult( ResultWas::FatalErrorCondition, { false } );
+        tempResult.message = static_cast<std::string>(message);
+        AssertionResult result(m_lastAssertionInfo, tempResult);
 
-namespace Catch {
+        assertionEnded(result);
 
-    class ErrnoGuard {
-    public:
-        ErrnoGuard():m_oldErrno(errno){}
-        ~ErrnoGuard() { errno = m_oldErrno; }
-    private:
-        int m_oldErrno;
-    };
+        handleUnfinishedSections();
 
-}
+        // Recreate section for test case (as we will lose the one that was in scope)
+        auto const& testCaseInfo = m_activeTestCase->getTestCaseInfo();
+        SectionInfo testCaseSection(testCaseInfo.lineInfo, testCaseInfo.name);
 
-namespace Catch {
-    namespace {
+        Counts assertions;
+        assertions.failed = 1;
+        SectionStats testCaseSectionStats(testCaseSection, assertions, 0, false);
+        m_reporter->sectionEnded(testCaseSectionStats);
 
-        struct IColourImpl {
-            virtual ~IColourImpl() {}
-            virtual void use( Colour::Code _colourCode ) = 0;
-        };
+        auto const& testInfo = m_activeTestCase->getTestCaseInfo();
 
-        struct NoColourImpl : IColourImpl {
-            void use( Colour::Code ) {}
+        Totals deltaTotals;
+        deltaTotals.testCases.failed = 1;
+        deltaTotals.assertions.failed = 1;
+        m_reporter->testCaseEnded(TestCaseStats(testInfo,
+                                  deltaTotals,
+                                  std::string(),
+                                  std::string(),
+                                  false));
+        m_totals.testCases.failed++;
+        testGroupEnded(std::string(), m_totals, 1, 1);
+        m_reporter->testRunEnded(TestRunStats(m_runInfo, m_totals, false));
+    }
 
-            static IColourImpl* instance() {
-                static NoColourImpl s_instance;
-                return &s_instance;
-            }
-        };
+    bool RunContext::lastAssertionPassed() {
+         return m_lastAssertionPassed;
+    }
 
-    } // anon namespace
-} // namespace Catch
+    void RunContext::assertionPassed() {
+        m_lastAssertionPassed = true;
+        ++m_totals.assertions.passed;
+        resetAssertionInfo();
+        m_messageScopes.clear();
+    }
 
-#if !defined( CATCH_CONFIG_COLOUR_NONE ) && !defined( CATCH_CONFIG_COLOUR_WINDOWS ) && !defined( CATCH_CONFIG_COLOUR_ANSI )
-#   ifdef CATCH_PLATFORM_WINDOWS
-#       define CATCH_CONFIG_COLOUR_WINDOWS
-#   else
-#       define CATCH_CONFIG_COLOUR_ANSI
-#   endif
-#endif
+    bool RunContext::aborting() const {
+        return m_totals.assertions.failed >= static_cast<std::size_t>(m_config->abortAfter());
+    }
 
-#if defined ( CATCH_CONFIG_COLOUR_WINDOWS ) /////////////////////////////////////////
+    void RunContext::runCurrentTest(std::string & redirectedCout, std::string & redirectedCerr) {
+        auto const& testCaseInfo = m_activeTestCase->getTestCaseInfo();
+        SectionInfo testCaseSection(testCaseInfo.lineInfo, testCaseInfo.name);
+        m_reporter->sectionStarting(testCaseSection);
+        Counts prevAssertions = m_totals.assertions;
+        double duration = 0;
+        m_shouldReportUnexpected = true;
+        m_lastAssertionInfo = { "TEST_CASE"_sr, testCaseInfo.lineInfo, StringRef(), ResultDisposition::Normal };
 
-namespace Catch {
-namespace {
+        seedRng(*m_config);
 
-    class Win32ColourImpl : public IColourImpl {
-    public:
-        Win32ColourImpl() : stdoutHandle( GetStdHandle(STD_OUTPUT_HANDLE) )
-        {
-            CONSOLE_SCREEN_BUFFER_INFO csbiInfo;
-            GetConsoleScreenBufferInfo( stdoutHandle, &csbiInfo );
-            originalForegroundAttributes = csbiInfo.wAttributes & ~( BACKGROUND_GREEN | BACKGROUND_RED | BACKGROUND_BLUE | BACKGROUND_INTENSITY );
-            originalBackgroundAttributes = csbiInfo.wAttributes & ~( FOREGROUND_GREEN | FOREGROUND_RED | FOREGROUND_BLUE | FOREGROUND_INTENSITY );
+        Timer timer;
+        CATCH_TRY {
+            if (m_reporter->getPreferences().shouldRedirectStdOut) {
+#if !defined(CATCH_CONFIG_EXPERIMENTAL_REDIRECT)
+                RedirectedStreams redirectedStreams(redirectedCout, redirectedCerr);
+
+                timer.start();
+                invokeActiveTestCase();
+#else
+                OutputRedirect r(redirectedCout, redirectedCerr);
+                timer.start();
+                invokeActiveTestCase();
+#endif
+            } else {
+                timer.start();
+                invokeActiveTestCase();
+            }
+            duration = timer.getElapsedSeconds();
+        } CATCH_CATCH_ANON (TestFailureException&) {
+            // This just means the test was aborted due to failure
+        } CATCH_CATCH_ALL {
+            // Under CATCH_CONFIG_FAST_COMPILE, unexpected exceptions under REQUIRE assertions
+            // are reported without translation at the point of origin.
+            if( m_shouldReportUnexpected ) {
+                AssertionReaction dummyReaction;
+                handleUnexpectedInflightException( m_lastAssertionInfo, translateActiveException(), dummyReaction );
+            }
         }
+        Counts assertions = m_totals.assertions - prevAssertions;
+        bool missingAssertions = testForMissingAssertions(assertions);
 
-        virtual void use( Colour::Code _colourCode ) {
-            switch( _colourCode ) {
-                case Colour::None:      return setTextAttribute( originalForegroundAttributes );
-                case Colour::White:     return setTextAttribute( FOREGROUND_GREEN | FOREGROUND_RED | FOREGROUND_BLUE );
-                case Colour::Red:       return setTextAttribute( FOREGROUND_RED );
-                case Colour::Green:     return setTextAttribute( FOREGROUND_GREEN );
-                case Colour::Blue:      return setTextAttribute( FOREGROUND_BLUE );
-                case Colour::Cyan:      return setTextAttribute( FOREGROUND_BLUE | FOREGROUND_GREEN );
-                case Colour::Yellow:    return setTextAttribute( FOREGROUND_RED | FOREGROUND_GREEN );
-                case Colour::Grey:      return setTextAttribute( 0 );
+        m_testCaseTracker->close();
+        handleUnfinishedSections();
+        m_messages.clear();
+        m_messageScopes.clear();
 
-                case Colour::LightGrey:     return setTextAttribute( FOREGROUND_INTENSITY );
-                case Colour::BrightRed:     return setTextAttribute( FOREGROUND_INTENSITY | FOREGROUND_RED );
-                case Colour::BrightGreen:   return setTextAttribute( FOREGROUND_INTENSITY | FOREGROUND_GREEN );
-                case Colour::BrightWhite:   return setTextAttribute( FOREGROUND_INTENSITY | FOREGROUND_GREEN | FOREGROUND_RED | FOREGROUND_BLUE );
+        SectionStats testCaseSectionStats(testCaseSection, assertions, duration, missingAssertions);
+        m_reporter->sectionEnded(testCaseSectionStats);
+    }
 
-                case Colour::Bright: throw std::logic_error( "not a colour" );
+    void RunContext::invokeActiveTestCase() {
+        FatalConditionHandler fatalConditionHandler; // Handle signals
+        m_activeTestCase->invoke();
+        fatalConditionHandler.reset();
+    }
+
+    void RunContext::handleUnfinishedSections() {
+        // If sections ended prematurely due to an exception we stored their
+        // infos here so we can tear them down outside the unwind process.
+        for (auto it = m_unfinishedSections.rbegin(),
+             itEnd = m_unfinishedSections.rend();
+             it != itEnd;
+             ++it)
+            sectionEnded(*it);
+        m_unfinishedSections.clear();
+    }
+
+    void RunContext::handleExpr(
+        AssertionInfo const& info,
+        ITransientExpression const& expr,
+        AssertionReaction& reaction
+    ) {
+        m_reporter->assertionStarting( info );
+
+        bool negated = isFalseTest( info.resultDisposition );
+        bool result = expr.getResult() != negated;
+
+        if( result ) {
+            if (!m_includeSuccessfulResults) {
+                assertionPassed();
+            }
+            else {
+                reportExpr(info, ResultWas::Ok, &expr, negated);
             }
         }
-
-    private:
-        void setTextAttribute( WORD _textAttribute ) {
-            SetConsoleTextAttribute( stdoutHandle, _textAttribute | originalBackgroundAttributes );
+        else {
+            reportExpr(info, ResultWas::ExpressionFailed, &expr, negated );
+            populateReaction( reaction );
         }
-        HANDLE stdoutHandle;
-        WORD originalForegroundAttributes;
-        WORD originalBackgroundAttributes;
-    };
+    }
+    void RunContext::reportExpr(
+            AssertionInfo const &info,
+            ResultWas::OfType resultType,
+            ITransientExpression const *expr,
+            bool negated ) {
 
-    IColourImpl* platformColourInstance() {
-        static Win32ColourImpl s_instance;
+        m_lastAssertionInfo = info;
+        AssertionResultData data( resultType, LazyExpression( negated ) );
 
-        Ptr<IConfig const> config = getCurrentContext().getConfig();
-        UseColour::YesOrNo colourMode = config
-            ? config->useColour()
-            : UseColour::Auto;
-        if( colourMode == UseColour::Auto )
-            colourMode = !isDebuggerActive()
-                ? UseColour::Yes
-                : UseColour::No;
-        return colourMode == UseColour::Yes
-            ? &s_instance
-            : NoColourImpl::instance();
+        AssertionResult assertionResult{ info, data };
+        assertionResult.m_resultData.lazyExpression.m_transientExpression = expr;
+
+        assertionEnded( assertionResult );
     }
 
-} // end anon namespace
-} // end namespace Catch
+    void RunContext::handleMessage(
+            AssertionInfo const& info,
+            ResultWas::OfType resultType,
+            StringRef const& message,
+            AssertionReaction& reaction
+    ) {
+        m_reporter->assertionStarting( info );
 
-#elif defined( CATCH_CONFIG_COLOUR_ANSI ) //////////////////////////////////////
+        m_lastAssertionInfo = info;
 
-#include <unistd.h>
+        AssertionResultData data( resultType, LazyExpression( false ) );
+        data.message = static_cast<std::string>(message);
+        AssertionResult assertionResult{ m_lastAssertionInfo, data };
+        assertionEnded( assertionResult );
+        if( !assertionResult.isOk() )
+            populateReaction( reaction );
+    }
+    void RunContext::handleUnexpectedExceptionNotThrown(
+            AssertionInfo const& info,
+            AssertionReaction& reaction
+    ) {
+        handleNonExpr(info, Catch::ResultWas::DidntThrowException, reaction);
+    }
 
-namespace Catch {
-namespace {
+    void RunContext::handleUnexpectedInflightException(
+            AssertionInfo const& info,
+            std::string const& message,
+            AssertionReaction& reaction
+    ) {
+        m_lastAssertionInfo = info;
 
-    // use POSIX/ ANSI console terminal codes
-    // Thanks to Adam Strzelecki for original contribution
-    // (http://github.com/nanoant)
-    // https://github.com/philsquared/Catch/pull/131
-    class PosixColourImpl : public IColourImpl {
-    public:
-        virtual void use( Colour::Code _colourCode ) {
-            switch( _colourCode ) {
-                case Colour::None:
-                case Colour::White:     return setColour( "[0m" );
-                case Colour::Red:       return setColour( "[0;31m" );
-                case Colour::Green:     return setColour( "[0;32m" );
-                case Colour::Blue:      return setColour( "[0;34m" );
-                case Colour::Cyan:      return setColour( "[0;36m" );
-                case Colour::Yellow:    return setColour( "[0;33m" );
-                case Colour::Grey:      return setColour( "[1;30m" );
+        AssertionResultData data( ResultWas::ThrewException, LazyExpression( false ) );
+        data.message = message;
+        AssertionResult assertionResult{ info, data };
+        assertionEnded( assertionResult );
+        populateReaction( reaction );
+    }
 
-                case Colour::LightGrey:     return setColour( "[0;37m" );
-                case Colour::BrightRed:     return setColour( "[1;31m" );
-                case Colour::BrightGreen:   return setColour( "[1;32m" );
-                case Colour::BrightWhite:   return setColour( "[1;37m" );
+    void RunContext::populateReaction( AssertionReaction& reaction ) {
+        reaction.shouldDebugBreak = m_config->shouldDebugBreak();
+        reaction.shouldThrow = aborting() || (m_lastAssertionInfo.resultDisposition & ResultDisposition::Normal);
+    }
 
-                case Colour::Bright: throw std::logic_error( "not a colour" );
-            }
-        }
-        static IColourImpl* instance() {
-            static PosixColourImpl s_instance;
-            return &s_instance;
+    void RunContext::handleIncomplete(
+            AssertionInfo const& info
+    ) {
+        m_lastAssertionInfo = info;
+
+        AssertionResultData data( ResultWas::ThrewException, LazyExpression( false ) );
+        data.message = "Exception translation was disabled by CATCH_CONFIG_FAST_COMPILE";
+        AssertionResult assertionResult{ info, data };
+        assertionEnded( assertionResult );
+    }
+    void RunContext::handleNonExpr(
+            AssertionInfo const &info,
+            ResultWas::OfType resultType,
+            AssertionReaction &reaction
+    ) {
+        m_lastAssertionInfo = info;
+
+        AssertionResultData data( resultType, LazyExpression( false ) );
+        AssertionResult assertionResult{ info, data };
+        assertionEnded( assertionResult );
+
+        if( !assertionResult.isOk() )
+            populateReaction( reaction );
+    }
+
+    IResultCapture& getResultCapture() {
+        if (auto* capture = getCurrentContext().getResultCapture())
+            return *capture;
+        else
+            CATCH_INTERNAL_ERROR("No result capture instance");
+    }
+
+    void seedRng(IConfig const& config) {
+        if (config.rngSeed() != 0) {
+            std::srand(config.rngSeed());
+            rng().seed(config.rngSeed());
         }
+    }
 
-    private:
-        void setColour( const char* _escapeCode ) {
-            Catch::cout() << '\033' << _escapeCode;
+    unsigned int rngSeed() {
+        return getCurrentContext().getConfig()->rngSeed();
+    }
+
+}
+// end catch_run_context.cpp
+// start catch_section.cpp
+
+namespace Catch {
+
+    Section::Section( SectionInfo const& info )
+    :   m_info( info ),
+        m_sectionIncluded( getResultCapture().sectionStarted( m_info, m_assertions ) )
+    {
+        m_timer.start();
+    }
+
+    Section::~Section() {
+        if( m_sectionIncluded ) {
+            SectionEndInfo endInfo{ m_info, m_assertions, m_timer.getElapsedSeconds() };
+            if( uncaught_exceptions() )
+                getResultCapture().sectionEndedEarly( endInfo );
+            else
+                getResultCapture().sectionEnded( endInfo );
         }
-    };
+    }
 
-    IColourImpl* platformColourInstance() {
-        ErrnoGuard guard;
-        Ptr<IConfig const> config = getCurrentContext().getConfig();
-        UseColour::YesOrNo colourMode = config
-            ? config->useColour()
-            : UseColour::Auto;
-        if( colourMode == UseColour::Auto )
-            colourMode = (!isDebuggerActive() && isatty(STDOUT_FILENO) )
-                ? UseColour::Yes
-                : UseColour::No;
-        return colourMode == UseColour::Yes
-            ? PosixColourImpl::instance()
-            : NoColourImpl::instance();
+    // This indicates whether the section should be executed or not
+    Section::operator bool() const {
+        return m_sectionIncluded;
     }
 
-} // end anon namespace
 } // end namespace Catch
-
-#else  // not Windows or ANSI ///////////////////////////////////////////////
+// end catch_section.cpp
+// start catch_section_info.cpp
 
 namespace Catch {
 
-    static IColourImpl* platformColourInstance() { return NoColourImpl::instance(); }
+    SectionInfo::SectionInfo
+        (   SourceLineInfo const& _lineInfo,
+            std::string const& _name )
+    :   name( _name ),
+        lineInfo( _lineInfo )
+    {}
 
 } // end namespace Catch
+// end catch_section_info.cpp
+// start catch_session.cpp
 
-#endif // Windows/ ANSI/ None
+// start catch_session.h
+
+#include <memory>
 
 namespace Catch {
 
-    Colour::Colour( Code _colourCode ) : m_moved( false ) { use( _colourCode ); }
-    Colour::Colour( Colour const& _other ) : m_moved( false ) { const_cast<Colour&>( _other ).m_moved = true; }
-    Colour::~Colour(){ if( !m_moved ) use( None ); }
+    class Session : NonCopyable {
+    public:
 
-    void Colour::use( Code _colourCode ) {
-        static IColourImpl* impl = platformColourInstance();
-        impl->use( _colourCode );
-    }
+        Session();
+        ~Session() override;
+
+        void showHelp() const;
+        void libIdentify();
+
+        int applyCommandLine( int argc, char const * const * argv );
+    #if defined(CATCH_CONFIG_WCHAR) && defined(_WIN32) && defined(UNICODE)
+        int applyCommandLine( int argc, wchar_t const * const * argv );
+    #endif
+
+        void useConfigData( ConfigData const& configData );
+
+        template<typename CharT>
+        int run(int argc, CharT const * const argv[]) {
+            if (m_startupExceptions)
+                return 1;
+            int returnCode = applyCommandLine(argc, argv);
+            if (returnCode == 0)
+                returnCode = run();
+            return returnCode;
+        }
+
+        int run();
+
+        clara::Parser const& cli() const;
+        void cli( clara::Parser const& newParser );
+        ConfigData& configData();
+        Config& config();
+    private:
+        int runInternal();
+
+        clara::Parser m_cli;
+        ConfigData m_configData;
+        std::shared_ptr<Config> m_config;
+        bool m_startupExceptions = false;
+    };
 
 } // end namespace Catch
 
-// #included from: catch_generators_impl.hpp
-#define TWOBLUECUBES_CATCH_GENERATORS_IMPL_HPP_INCLUDED
+// end catch_session.h
+// start catch_version.h
 
-#include <vector>
-#include <string>
-#include <map>
+#include <iosfwd>
 
 namespace Catch {
 
-    struct GeneratorInfo : IGeneratorInfo {
+    // Versioning information
+    struct Version {
+        Version( Version const& ) = delete;
+        Version& operator=( Version const& ) = delete;
+        Version(    unsigned int _majorVersion,
+                    unsigned int _minorVersion,
+                    unsigned int _patchNumber,
+                    char const * const _branchName,
+                    unsigned int _buildNumber );
+
+        unsigned int const majorVersion;
+        unsigned int const minorVersion;
+        unsigned int const patchNumber;
+
+        // buildNumber is only used if branchName is not null
+        char const * const branchName;
+        unsigned int const buildNumber;
 
-        GeneratorInfo( std::size_t size )
-        :   m_size( size ),
-            m_currentIndex( 0 )
-        {}
+        friend std::ostream& operator << ( std::ostream& os, Version const& version );
+    };
 
-        bool moveNext() {
-            if( ++m_currentIndex == m_size ) {
-                m_currentIndex = 0;
-                return false;
-            }
-            return true;
-        }
+    Version const& libraryVersion();
+}
 
-        std::size_t getCurrentIndex() const {
-            return m_currentIndex;
-        }
+// end catch_version.h
+#include <cstdlib>
+#include <iomanip>
+#include <set>
+#include <iterator>
 
-        std::size_t m_size;
-        std::size_t m_currentIndex;
-    };
+namespace Catch {
 
-    ///////////////////////////////////////////////////////////////////////////
+    namespace {
+        const int MaxExitCode = 255;
 
-    class GeneratorsForTest : public IGeneratorsForTest {
+        IStreamingReporterPtr createReporter(std::string const& reporterName, IConfigPtr const& config) {
+            auto reporter = Catch::getRegistryHub().getReporterRegistry().create(reporterName, config);
+            CATCH_ENFORCE(reporter, "No reporter registered with name: '" << reporterName << "'");
 
-    public:
-        ~GeneratorsForTest() {
-            deleteAll( m_generatorsInOrder );
+            return reporter;
         }
 
-        IGeneratorInfo& getGeneratorInfo( std::string const& fileInfo, std::size_t size ) {
-            std::map<std::string, IGeneratorInfo*>::const_iterator it = m_generatorsByName.find( fileInfo );
-            if( it == m_generatorsByName.end() ) {
-                IGeneratorInfo* info = new GeneratorInfo( size );
-                m_generatorsByName.insert( std::make_pair( fileInfo, info ) );
-                m_generatorsInOrder.push_back( info );
-                return *info;
+        IStreamingReporterPtr makeReporter(std::shared_ptr<Config> const& config) {
+            if (Catch::getRegistryHub().getReporterRegistry().getListeners().empty()) {
+                return createReporter(config->getReporterName(), config);
             }
-            return *it->second;
-        }
 
-        bool moveNext() {
-            std::vector<IGeneratorInfo*>::const_iterator it = m_generatorsInOrder.begin();
-            std::vector<IGeneratorInfo*>::const_iterator itEnd = m_generatorsInOrder.end();
-            for(; it != itEnd; ++it ) {
-                if( (*it)->moveNext() )
-                    return true;
+            // On older platforms, returning std::unique_ptr<ListeningReporter>
+            // when the return type is std::unique_ptr<IStreamingReporter>
+            // doesn't compile without a std::move call. However, this causes
+            // a warning on newer platforms. Thus, we have to work around
+            // it a bit and downcast the pointer manually.
+            auto ret = std::unique_ptr<IStreamingReporter>(new ListeningReporter);
+            auto& multi = static_cast<ListeningReporter&>(*ret);
+            auto const& listeners = Catch::getRegistryHub().getReporterRegistry().getListeners();
+            for (auto const& listener : listeners) {
+                multi.addListener(listener->create(Catch::ReporterConfig(config)));
             }
-            return false;
+            multi.addReporter(createReporter(config->getReporterName(), config));
+            return ret;
         }
 
-    private:
-        std::map<std::string, IGeneratorInfo*> m_generatorsByName;
-        std::vector<IGeneratorInfo*> m_generatorsInOrder;
-    };
-
-    IGeneratorsForTest* createGeneratorsForTest()
-    {
-        return new GeneratorsForTest();
-    }
-
-} // end namespace Catch
+        class TestGroup {
+        public:
+            explicit TestGroup(std::shared_ptr<Config> const& config)
+            : m_config{config}
+            , m_context{config, makeReporter(config)}
+            {
+                auto const& allTestCases = getAllTestCasesSorted(*m_config);
+                m_matches = m_config->testSpec().matchesByFilter(allTestCases, *m_config);
+                auto const& invalidArgs = m_config->testSpec().getInvalidArgs();
+
+                if (m_matches.empty() && invalidArgs.empty()) {
+                    for (auto const& test : allTestCases)
+                        if (!test.isHidden())
+                            m_tests.emplace(&test);
+                } else {
+                    for (auto const& match : m_matches)
+                        m_tests.insert(match.tests.begin(), match.tests.end());
+                }
+            }
 
-// #included from: catch_assertionresult.hpp
-#define TWOBLUECUBES_CATCH_ASSERTIONRESULT_HPP_INCLUDED
+            Totals execute() {
+                auto const& invalidArgs = m_config->testSpec().getInvalidArgs();
+                Totals totals;
+                m_context.testGroupStarting(m_config->name(), 1, 1);
+                for (auto const& testCase : m_tests) {
+                    if (!m_context.aborting())
+                        totals += m_context.runTest(*testCase);
+                    else
+                        m_context.reporter().skipTest(*testCase);
+                }
 
-namespace Catch {
+                for (auto const& match : m_matches) {
+                    if (match.tests.empty()) {
+                        m_context.reporter().noMatchingTestCases(match.name);
+                        totals.error = -1;
+                    }
+                }
 
-    AssertionInfo::AssertionInfo(   std::string const& _macroName,
-                                    SourceLineInfo const& _lineInfo,
-                                    std::string const& _capturedExpression,
-                                    ResultDisposition::Flags _resultDisposition )
-    :   macroName( _macroName ),
-        lineInfo( _lineInfo ),
-        capturedExpression( _capturedExpression ),
-        resultDisposition( _resultDisposition )
-    {}
+                if (!invalidArgs.empty()) {
+                    for (auto const& invalidArg: invalidArgs)
+                         m_context.reporter().reportInvalidArguments(invalidArg);
+                }
 
-    AssertionResult::AssertionResult() {}
+                m_context.testGroupEnded(m_config->name(), totals, 1, 1);
+                return totals;
+            }
 
-    AssertionResult::AssertionResult( AssertionInfo const& info, AssertionResultData const& data )
-    :   m_info( info ),
-        m_resultData( data )
-    {}
+        private:
+            using Tests = std::set<TestCase const*>;
 
-    AssertionResult::~AssertionResult() {}
+            std::shared_ptr<Config> m_config;
+            RunContext m_context;
+            Tests m_tests;
+            TestSpec::Matches m_matches;
+        };
 
-    // Result was a success
-    bool AssertionResult::succeeded() const {
-        return Catch::isOk( m_resultData.resultType );
-    }
+        void applyFilenamesAsTags(Catch::IConfig const& config) {
+            auto& tests = const_cast<std::vector<TestCase>&>(getAllTestCasesSorted(config));
+            for (auto& testCase : tests) {
+                auto tags = testCase.tags;
 
-    // Result was a success, or failure is suppressed
-    bool AssertionResult::isOk() const {
-        return Catch::isOk( m_resultData.resultType ) || shouldSuppressFailure( m_info.resultDisposition );
-    }
+                std::string filename = testCase.lineInfo.file;
+                auto lastSlash = filename.find_last_of("\\/");
+                if (lastSlash != std::string::npos) {
+                    filename.erase(0, lastSlash);
+                    filename[0] = '#';
+                }
 
-    ResultWas::OfType AssertionResult::getResultType() const {
-        return m_resultData.resultType;
-    }
+                auto lastDot = filename.find_last_of('.');
+                if (lastDot != std::string::npos) {
+                    filename.erase(lastDot);
+                }
 
-    bool AssertionResult::hasExpression() const {
-        return !m_info.capturedExpression.empty();
-    }
+                tags.push_back(std::move(filename));
+                setTags(testCase, tags);
+            }
+        }
 
-    bool AssertionResult::hasMessage() const {
-        return !m_resultData.message.empty();
-    }
+    } // anon namespace
 
-    std::string AssertionResult::getExpression() const {
-        if( isFalseTest( m_info.resultDisposition ) )
-            return '!' + m_info.capturedExpression;
-        else
-            return m_info.capturedExpression;
-    }
-    std::string AssertionResult::getExpressionInMacro() const {
-        if( m_info.macroName.empty() )
-            return m_info.capturedExpression;
-        else
-            return m_info.macroName + "( " + m_info.capturedExpression + " )";
-    }
+    Session::Session() {
+        static bool alreadyInstantiated = false;
+        if( alreadyInstantiated ) {
+            CATCH_TRY { CATCH_INTERNAL_ERROR( "Only one instance of Catch::Session can ever be used" ); }
+            CATCH_CATCH_ALL { getMutableRegistryHub().registerStartupException(); }
+        }
+
+        // There cannot be exceptions at startup in no-exception mode.
+#if !defined(CATCH_CONFIG_DISABLE_EXCEPTIONS)
+        const auto& exceptions = getRegistryHub().getStartupExceptionRegistry().getExceptions();
+        if ( !exceptions.empty() ) {
+            config();
+            getCurrentMutableContext().setConfig(m_config);
+
+            m_startupExceptions = true;
+            Colour colourGuard( Colour::Red );
+            Catch::cerr() << "Errors occurred during startup!" << '\n';
+            // iterate over all exceptions and notify user
+            for ( const auto& ex_ptr : exceptions ) {
+                try {
+                    std::rethrow_exception(ex_ptr);
+                } catch ( std::exception const& ex ) {
+                    Catch::cerr() << Column( ex.what() ).indent(2) << '\n';
+                }
+            }
+        }
+#endif
 
-    bool AssertionResult::hasExpandedExpression() const {
-        return hasExpression() && getExpandedExpression() != getExpression();
+        alreadyInstantiated = true;
+        m_cli = makeCommandLineParser( m_configData );
     }
-
-    std::string AssertionResult::getExpandedExpression() const {
-        return m_resultData.reconstructExpression();
+    Session::~Session() {
+        Catch::cleanUp();
     }
 
-    std::string AssertionResult::getMessage() const {
-        return m_resultData.message;
+    void Session::showHelp() const {
+        Catch::cout()
+                << "\nCatch v" << libraryVersion() << "\n"
+                << m_cli << std::endl
+                << "For more detailed usage please see the project docs\n" << std::endl;
     }
-    SourceLineInfo AssertionResult::getSourceInfo() const {
-        return m_info.lineInfo;
+    void Session::libIdentify() {
+        Catch::cout()
+                << std::left << std::setw(16) << "description: " << "A Catch2 test executable\n"
+                << std::left << std::setw(16) << "category: " << "testframework\n"
+                << std::left << std::setw(16) << "framework: " << "Catch Test\n"
+                << std::left << std::setw(16) << "version: " << libraryVersion() << std::endl;
     }
 
-    std::string AssertionResult::getTestMacroName() const {
-        return m_info.macroName;
-    }
+    int Session::applyCommandLine( int argc, char const * const * argv ) {
+        if( m_startupExceptions )
+            return 1;
 
-    void AssertionResult::discardDecomposedExpression() const {
-        m_resultData.decomposedExpression = CATCH_NULL;
-    }
+        auto result = m_cli.parse( clara::Args( argc, argv ) );
+        if( !result ) {
+            config();
+            getCurrentMutableContext().setConfig(m_config);
+            Catch::cerr()
+                << Colour( Colour::Red )
+                << "\nError(s) in input:\n"
+                << Column( result.errorMessage() ).indent( 2 )
+                << "\n\n";
+            Catch::cerr() << "Run with -? for usage\n" << std::endl;
+            return MaxExitCode;
+        }
 
-    void AssertionResult::expandDecomposedExpression() const {
-        m_resultData.reconstructExpression();
+        if( m_configData.showHelp )
+            showHelp();
+        if( m_configData.libIdentify )
+            libIdentify();
+        m_config.reset();
+        return 0;
     }
 
-} // end namespace Catch
+#if defined(CATCH_CONFIG_WCHAR) && defined(_WIN32) && defined(UNICODE)
+    int Session::applyCommandLine( int argc, wchar_t const * const * argv ) {
 
-// #included from: catch_test_case_info.hpp
-#define TWOBLUECUBES_CATCH_TEST_CASE_INFO_HPP_INCLUDED
+        char **utf8Argv = new char *[ argc ];
 
-#include <cctype>
+        for ( int i = 0; i < argc; ++i ) {
+            int bufSize = WideCharToMultiByte( CP_UTF8, 0, argv[i], -1, NULL, 0, NULL, NULL );
 
-namespace Catch {
+            utf8Argv[ i ] = new char[ bufSize ];
 
-    inline TestCaseInfo::SpecialProperties parseSpecialTag( std::string const& tag ) {
-        if( startsWith( tag, '.' ) ||
-            tag == "hide" ||
-            tag == "!hide" )
-            return TestCaseInfo::IsHidden;
-        else if( tag == "!throws" )
-            return TestCaseInfo::Throws;
-        else if( tag == "!shouldfail" )
-            return TestCaseInfo::ShouldFail;
-        else if( tag == "!mayfail" )
-            return TestCaseInfo::MayFail;
-        else if( tag == "!nonportable" )
-            return TestCaseInfo::NonPortable;
-        else
-            return TestCaseInfo::None;
-    }
-    inline bool isReservedTag( std::string const& tag ) {
-        return parseSpecialTag( tag ) == TestCaseInfo::None && tag.size() > 0 && !std::isalnum( tag[0] );
-    }
-    inline void enforceNotReservedTag( std::string const& tag, SourceLineInfo const& _lineInfo ) {
-        if( isReservedTag( tag ) ) {
-            std::ostringstream ss;
-            ss << Colour(Colour::Red)
-               << "Tag name [" << tag << "] not allowed.\n"
-               << "Tag names starting with non alpha-numeric characters are reserved\n"
-               << Colour(Colour::FileName)
-               << _lineInfo << '\n';
-            throw std::runtime_error(ss.str());
+            WideCharToMultiByte( CP_UTF8, 0, argv[i], -1, utf8Argv[i], bufSize, NULL, NULL );
         }
-    }
 
-    TestCase makeTestCase(  ITestCase* _testCase,
-                            std::string const& _className,
-                            std::string const& _name,
-                            std::string const& _descOrTags,
-                            SourceLineInfo const& _lineInfo )
-    {
-        bool isHidden( startsWith( _name, "./" ) ); // Legacy support
+        int returnCode = applyCommandLine( argc, utf8Argv );
 
-        // Parse out tags
-        std::set<std::string> tags;
-        std::string desc, tag;
-        bool inTag = false;
-        for( std::size_t i = 0; i < _descOrTags.size(); ++i ) {
-            char c = _descOrTags[i];
-            if( !inTag ) {
-                if( c == '[' )
-                    inTag = true;
-                else
-                    desc += c;
-            }
-            else {
-                if( c == ']' ) {
-                    TestCaseInfo::SpecialProperties prop = parseSpecialTag( tag );
-                    if( prop == TestCaseInfo::IsHidden )
-                        isHidden = true;
-                    else if( prop == TestCaseInfo::None )
-                        enforceNotReservedTag( tag, _lineInfo );
+        for ( int i = 0; i < argc; ++i )
+            delete [] utf8Argv[ i ];
 
-                    tags.insert( tag );
-                    tag.clear();
-                    inTag = false;
-                }
-                else
-                    tag += c;
-            }
-        }
-        if( isHidden ) {
-            tags.insert( "hide" );
-            tags.insert( "." );
-        }
+        delete [] utf8Argv;
 
-        TestCaseInfo info( _name, _className, desc, tags, _lineInfo );
-        return TestCase( _testCase, info );
+        return returnCode;
     }
+#endif
 
-    void setTags( TestCaseInfo& testCaseInfo, std::set<std::string> const& tags )
-    {
-        testCaseInfo.tags = tags;
-        testCaseInfo.lcaseTags.clear();
-
-        std::ostringstream oss;
-        for( std::set<std::string>::const_iterator it = tags.begin(), itEnd = tags.end(); it != itEnd; ++it ) {
-            oss << '[' << *it << ']';
-            std::string lcaseTag = toLower( *it );
-            testCaseInfo.properties = static_cast<TestCaseInfo::SpecialProperties>( testCaseInfo.properties | parseSpecialTag( lcaseTag ) );
-            testCaseInfo.lcaseTags.insert( lcaseTag );
-        }
-        testCaseInfo.tagsAsString = oss.str();
+    void Session::useConfigData( ConfigData const& configData ) {
+        m_configData = configData;
+        m_config.reset();
     }
 
-    TestCaseInfo::TestCaseInfo( std::string const& _name,
-                                std::string const& _className,
-                                std::string const& _description,
-                                std::set<std::string> const& _tags,
-                                SourceLineInfo const& _lineInfo )
-    :   name( _name ),
-        className( _className ),
-        description( _description ),
-        lineInfo( _lineInfo ),
-        properties( None )
-    {
-        setTags( *this, _tags );
+    int Session::run() {
+        if( ( m_configData.waitForKeypress & WaitForKeypress::BeforeStart ) != 0 ) {
+            Catch::cout() << "...waiting for enter/ return before starting" << std::endl;
+            static_cast<void>(std::getchar());
+        }
+        int exitCode = runInternal();
+        if( ( m_configData.waitForKeypress & WaitForKeypress::BeforeExit ) != 0 ) {
+            Catch::cout() << "...waiting for enter/ return before exiting, with code: " << exitCode << std::endl;
+            static_cast<void>(std::getchar());
+        }
+        return exitCode;
     }
 
-    TestCaseInfo::TestCaseInfo( TestCaseInfo const& other )
-    :   name( other.name ),
-        className( other.className ),
-        description( other.description ),
-        tags( other.tags ),
-        lcaseTags( other.lcaseTags ),
-        tagsAsString( other.tagsAsString ),
-        lineInfo( other.lineInfo ),
-        properties( other.properties )
-    {}
-
-    bool TestCaseInfo::isHidden() const {
-        return ( properties & IsHidden ) != 0;
+    clara::Parser const& Session::cli() const {
+        return m_cli;
     }
-    bool TestCaseInfo::throws() const {
-        return ( properties & Throws ) != 0;
+    void Session::cli( clara::Parser const& newParser ) {
+        m_cli = newParser;
     }
-    bool TestCaseInfo::okToFail() const {
-        return ( properties & (ShouldFail | MayFail ) ) != 0;
+    ConfigData& Session::configData() {
+        return m_configData;
     }
-    bool TestCaseInfo::expectedToFail() const {
-        return ( properties & (ShouldFail ) ) != 0;
+    Config& Session::config() {
+        if( !m_config )
+            m_config = std::make_shared<Config>( m_configData );
+        return *m_config;
     }
 
-    TestCase::TestCase( ITestCase* testCase, TestCaseInfo const& info ) : TestCaseInfo( info ), test( testCase ) {}
+    int Session::runInternal() {
+        if( m_startupExceptions )
+            return 1;
 
-    TestCase::TestCase( TestCase const& other )
-    :   TestCaseInfo( other ),
-        test( other.test )
-    {}
+        if (m_configData.showHelp || m_configData.libIdentify) {
+            return 0;
+        }
 
-    TestCase TestCase::withName( std::string const& _newName ) const {
-        TestCase other( *this );
-        other.name = _newName;
-        return other;
-    }
+        CATCH_TRY {
+            config(); // Force config to be constructed
 
-    void TestCase::swap( TestCase& other ) {
-        test.swap( other.test );
-        name.swap( other.name );
-        className.swap( other.className );
-        description.swap( other.description );
-        tags.swap( other.tags );
-        lcaseTags.swap( other.lcaseTags );
-        tagsAsString.swap( other.tagsAsString );
-        std::swap( TestCaseInfo::properties, static_cast<TestCaseInfo&>( other ).properties );
-        std::swap( lineInfo, other.lineInfo );
-    }
+            seedRng( *m_config );
 
-    void TestCase::invoke() const {
-        test->invoke();
-    }
+            if( m_configData.filenamesAsTags )
+                applyFilenamesAsTags( *m_config );
 
-    bool TestCase::operator == ( TestCase const& other ) const {
-        return  test.get() == other.test.get() &&
-                name == other.name &&
-                className == other.className;
-    }
+            // Handle list request
+            if( Option<std::size_t> listed = list( m_config ) )
+                return static_cast<int>( *listed );
 
-    bool TestCase::operator < ( TestCase const& other ) const {
-        return name < other.name;
-    }
-    TestCase& TestCase::operator = ( TestCase const& other ) {
-        TestCase temp( other );
-        swap( temp );
-        return *this;
-    }
+            TestGroup tests { m_config };
+            auto const totals = tests.execute();
 
-    TestCaseInfo const& TestCase::getTestCaseInfo() const
-    {
-        return *this;
+            if( m_config->warnAboutNoTests() && totals.error == -1 )
+                return 2;
+
+            // Note that on unices only the lower 8 bits are usually used, clamping
+            // the return value to 255 prevents false negative when some multiple
+            // of 256 tests has failed
+            return (std::min) (MaxExitCode, (std::max) (totals.error, static_cast<int>(totals.assertions.failed)));
+        }
+#if !defined(CATCH_CONFIG_DISABLE_EXCEPTIONS)
+        catch( std::exception& ex ) {
+            Catch::cerr() << ex.what() << std::endl;
+            return MaxExitCode;
+        }
+#endif
     }
 
 } // end namespace Catch
+// end catch_session.cpp
+// start catch_singletons.cpp
 
-// #included from: catch_version.hpp
-#define TWOBLUECUBES_CATCH_VERSION_HPP_INCLUDED
+#include <vector>
 
 namespace Catch {
 
-    Version::Version
-        (   unsigned int _majorVersion,
-            unsigned int _minorVersion,
-            unsigned int _patchNumber,
-            char const * const _branchName,
-            unsigned int _buildNumber )
-    :   majorVersion( _majorVersion ),
-        minorVersion( _minorVersion ),
-        patchNumber( _patchNumber ),
-        branchName( _branchName ),
-        buildNumber( _buildNumber )
-    {}
+    namespace {
+        static auto getSingletons() -> std::vector<ISingleton*>*& {
+            static std::vector<ISingleton*>* g_singletons = nullptr;
+            if( !g_singletons )
+                g_singletons = new std::vector<ISingleton*>();
+            return g_singletons;
+        }
+    }
 
-    std::ostream& operator << ( std::ostream& os, Version const& version ) {
-        os  << version.majorVersion << '.'
-            << version.minorVersion << '.'
-            << version.patchNumber;
-        // branchName is never null -> 0th char is \0 if it is empty
-        if (version.branchName[0]) {
-            os << '-' << version.branchName
-               << '.' << version.buildNumber;
+    ISingleton::~ISingleton() {}
+
+    void addSingleton(ISingleton* singleton ) {
+        getSingletons()->push_back( singleton );
+    }
+    void cleanupSingletons() {
+        auto& singletons = getSingletons();
+        for( auto singleton : *singletons )
+            delete singleton;
+        delete singletons;
+        singletons = nullptr;
+    }
+
+} // namespace Catch
+// end catch_singletons.cpp
+// start catch_startup_exception_registry.cpp
+
+namespace Catch {
+void StartupExceptionRegistry::add( std::exception_ptr const& exception ) noexcept {
+        CATCH_TRY {
+            m_exceptions.push_back(exception);
+        } CATCH_CATCH_ALL {
+            // If we run out of memory during start-up there's really not a lot more we can do about it
+            std::terminate();
         }
-        return os;
     }
 
-    inline Version libraryVersion() {
-        static Version version( 1, 9, 4, "", 0 );
-        return version;
+    std::vector<std::exception_ptr> const& StartupExceptionRegistry::getExceptions() const noexcept {
+        return m_exceptions;
     }
 
-}
+} // end namespace Catch
+// end catch_startup_exception_registry.cpp
+// start catch_stream.cpp
 
-// #included from: catch_message.hpp
-#define TWOBLUECUBES_CATCH_MESSAGE_HPP_INCLUDED
+#include <cstdio>
+#include <iostream>
+#include <fstream>
+#include <sstream>
+#include <vector>
+#include <memory>
 
 namespace Catch {
 
-    MessageInfo::MessageInfo(   std::string const& _macroName,
-                                SourceLineInfo const& _lineInfo,
-                                ResultWas::OfType _type )
-    :   macroName( _macroName ),
-        lineInfo( _lineInfo ),
-        type( _type ),
-        sequence( ++globalCount )
-    {}
+    Catch::IStream::~IStream() = default;
 
-    // This may need protecting if threading support is added
-    unsigned int MessageInfo::globalCount = 0;
+    namespace Detail { namespace {
+        template<typename WriterF, std::size_t bufferSize=256>
+        class StreamBufImpl : public std::streambuf {
+            char data[bufferSize];
+            WriterF m_writer;
 
-    ////////////////////////////////////////////////////////////////////////////
+        public:
+            StreamBufImpl() {
+                setp( data, data + sizeof(data) );
+            }
 
-    ScopedMessage::ScopedMessage( MessageBuilder const& builder )
-    : m_info( builder.m_info )
-    {
-        m_info.message = builder.m_stream.str();
-        getResultCapture().pushScopedMessage( m_info );
-    }
-    ScopedMessage::ScopedMessage( ScopedMessage const& other )
-    : m_info( other.m_info )
-    {}
+            ~StreamBufImpl() noexcept {
+                StreamBufImpl::sync();
+            }
 
-    ScopedMessage::~ScopedMessage() {
-        if ( !std::uncaught_exception() ){
-            getResultCapture().popScopedMessage(m_info);
-        }
-    }
+        private:
+            int overflow( int c ) override {
+                sync();
+
+                if( c != EOF ) {
+                    if( pbase() == epptr() )
+                        m_writer( std::string( 1, static_cast<char>( c ) ) );
+                    else
+                        sputc( static_cast<char>( c ) );
+                }
+                return 0;
+            }
+
+            int sync() override {
+                if( pbase() != pptr() ) {
+                    m_writer( std::string( pbase(), static_cast<std::string::size_type>( pptr() - pbase() ) ) );
+                    setp( pbase(), epptr() );
+                }
+                return 0;
+            }
+        };
 
-} // end namespace Catch
+        ///////////////////////////////////////////////////////////////////////////
 
-// #included from: catch_legacy_reporter_adapter.hpp
-#define TWOBLUECUBES_CATCH_LEGACY_REPORTER_ADAPTER_HPP_INCLUDED
+        struct OutputDebugWriter {
 
-// #included from: catch_legacy_reporter_adapter.h
-#define TWOBLUECUBES_CATCH_LEGACY_REPORTER_ADAPTER_H_INCLUDED
+            void operator()( std::string const&str ) {
+                writeToDebugConsole( str );
+            }
+        };
 
-namespace Catch
-{
-    // Deprecated
-    struct IReporter : IShared {
-        virtual ~IReporter();
-
-        virtual bool shouldRedirectStdout() const = 0;
-
-        virtual void StartTesting() = 0;
-        virtual void EndTesting( Totals const& totals ) = 0;
-        virtual void StartGroup( std::string const& groupName ) = 0;
-        virtual void EndGroup( std::string const& groupName, Totals const& totals ) = 0;
-        virtual void StartTestCase( TestCaseInfo const& testInfo ) = 0;
-        virtual void EndTestCase( TestCaseInfo const& testInfo, Totals const& totals, std::string const& stdOut, std::string const& stdErr ) = 0;
-        virtual void StartSection( std::string const& sectionName, std::string const& description ) = 0;
-        virtual void EndSection( std::string const& sectionName, Counts const& assertions ) = 0;
-        virtual void NoAssertionsInSection( std::string const& sectionName ) = 0;
-        virtual void NoAssertionsInTestCase( std::string const& testName ) = 0;
-        virtual void Aborted() = 0;
-        virtual void Result( AssertionResult const& result ) = 0;
-    };
-
-    class LegacyReporterAdapter : public SharedImpl<IStreamingReporter>
-    {
-    public:
-        LegacyReporterAdapter( Ptr<IReporter> const& legacyReporter );
-        virtual ~LegacyReporterAdapter();
-
-        virtual ReporterPreferences getPreferences() const;
-        virtual void noMatchingTestCases( std::string const& );
-        virtual void testRunStarting( TestRunInfo const& );
-        virtual void testGroupStarting( GroupInfo const& groupInfo );
-        virtual void testCaseStarting( TestCaseInfo const& testInfo );
-        virtual void sectionStarting( SectionInfo const& sectionInfo );
-        virtual void assertionStarting( AssertionInfo const& );
-        virtual bool assertionEnded( AssertionStats const& assertionStats );
-        virtual void sectionEnded( SectionStats const& sectionStats );
-        virtual void testCaseEnded( TestCaseStats const& testCaseStats );
-        virtual void testGroupEnded( TestGroupStats const& testGroupStats );
-        virtual void testRunEnded( TestRunStats const& testRunStats );
-        virtual void skipTest( TestCaseInfo const& );
+        ///////////////////////////////////////////////////////////////////////////
 
-    private:
-        Ptr<IReporter> m_legacyReporter;
-    };
-}
+        class FileStream : public IStream {
+            mutable std::ofstream m_ofs;
+        public:
+            FileStream( StringRef filename ) {
+                m_ofs.open( filename.c_str() );
+                CATCH_ENFORCE( !m_ofs.fail(), "Unable to open file: '" << filename << "'" );
+            }
+            ~FileStream() override = default;
+        public: // IStream
+            std::ostream& stream() const override {
+                return m_ofs;
+            }
+        };
 
-namespace Catch
-{
-    LegacyReporterAdapter::LegacyReporterAdapter( Ptr<IReporter> const& legacyReporter )
-    :   m_legacyReporter( legacyReporter )
-    {}
-    LegacyReporterAdapter::~LegacyReporterAdapter() {}
+        ///////////////////////////////////////////////////////////////////////////
 
-    ReporterPreferences LegacyReporterAdapter::getPreferences() const {
-        ReporterPreferences prefs;
-        prefs.shouldRedirectStdOut = m_legacyReporter->shouldRedirectStdout();
-        return prefs;
-    }
+        class CoutStream : public IStream {
+            mutable std::ostream m_os;
+        public:
+            // Store the streambuf from cout up-front because
+            // cout may get redirected when running tests
+            CoutStream() : m_os( Catch::cout().rdbuf() ) {}
+            ~CoutStream() override = default;
 
-    void LegacyReporterAdapter::noMatchingTestCases( std::string const& ) {}
-    void LegacyReporterAdapter::testRunStarting( TestRunInfo const& ) {
-        m_legacyReporter->StartTesting();
-    }
-    void LegacyReporterAdapter::testGroupStarting( GroupInfo const& groupInfo ) {
-        m_legacyReporter->StartGroup( groupInfo.name );
-    }
-    void LegacyReporterAdapter::testCaseStarting( TestCaseInfo const& testInfo ) {
-        m_legacyReporter->StartTestCase( testInfo );
-    }
-    void LegacyReporterAdapter::sectionStarting( SectionInfo const& sectionInfo ) {
-        m_legacyReporter->StartSection( sectionInfo.name, sectionInfo.description );
-    }
-    void LegacyReporterAdapter::assertionStarting( AssertionInfo const& ) {
-        // Not on legacy interface
-    }
+        public: // IStream
+            std::ostream& stream() const override { return m_os; }
+        };
 
-    bool LegacyReporterAdapter::assertionEnded( AssertionStats const& assertionStats ) {
-        if( assertionStats.assertionResult.getResultType() != ResultWas::Ok ) {
-            for( std::vector<MessageInfo>::const_iterator it = assertionStats.infoMessages.begin(), itEnd = assertionStats.infoMessages.end();
-                    it != itEnd;
-                    ++it ) {
-                if( it->type == ResultWas::Info ) {
-                    ResultBuilder rb( it->macroName.c_str(), it->lineInfo, "", ResultDisposition::Normal );
-                    rb << it->message;
-                    rb.setResultType( ResultWas::Info );
-                    AssertionResult result = rb.build();
-                    m_legacyReporter->Result( result );
-                }
-            }
-        }
-        m_legacyReporter->Result( assertionStats.assertionResult );
-        return true;
-    }
-    void LegacyReporterAdapter::sectionEnded( SectionStats const& sectionStats ) {
-        if( sectionStats.missingAssertions )
-            m_legacyReporter->NoAssertionsInSection( sectionStats.sectionInfo.name );
-        m_legacyReporter->EndSection( sectionStats.sectionInfo.name, sectionStats.assertions );
-    }
-    void LegacyReporterAdapter::testCaseEnded( TestCaseStats const& testCaseStats ) {
-        m_legacyReporter->EndTestCase
-            (   testCaseStats.testInfo,
-                testCaseStats.totals,
-                testCaseStats.stdOut,
-                testCaseStats.stdErr );
-    }
-    void LegacyReporterAdapter::testGroupEnded( TestGroupStats const& testGroupStats ) {
-        if( testGroupStats.aborting )
-            m_legacyReporter->Aborted();
-        m_legacyReporter->EndGroup( testGroupStats.groupInfo.name, testGroupStats.totals );
-    }
-    void LegacyReporterAdapter::testRunEnded( TestRunStats const& testRunStats ) {
-        m_legacyReporter->EndTesting( testRunStats.totals );
-    }
-    void LegacyReporterAdapter::skipTest( TestCaseInfo const& ) {
-    }
-}
+        ///////////////////////////////////////////////////////////////////////////
 
-// #included from: catch_timer.hpp
+        class DebugOutStream : public IStream {
+            std::unique_ptr<StreamBufImpl<OutputDebugWriter>> m_streamBuf;
+            mutable std::ostream m_os;
+        public:
+            DebugOutStream()
+            :   m_streamBuf( new StreamBufImpl<OutputDebugWriter>() ),
+                m_os( m_streamBuf.get() )
+            {}
 
-#ifdef __clang__
-#pragma clang diagnostic push
-#pragma clang diagnostic ignored "-Wc++11-long-long"
-#endif
+            ~DebugOutStream() override = default;
 
-#ifdef CATCH_PLATFORM_WINDOWS
+        public: // IStream
+            std::ostream& stream() const override { return m_os; }
+        };
 
-#else
+    }} // namespace anon::detail
 
-#include <sys/time.h>
+    ///////////////////////////////////////////////////////////////////////////
 
-#endif
+    auto makeStream( StringRef const &filename ) -> IStream const* {
+        if( filename.empty() )
+            return new Detail::CoutStream();
+        else if( filename[0] == '%' ) {
+            if( filename == "%debug" )
+                return new Detail::DebugOutStream();
+            else
+                CATCH_ERROR( "Unrecognised stream: '" << filename << "'" );
+        }
+        else
+            return new Detail::FileStream( filename );
+    }
 
-namespace Catch {
+    // This class encapsulates the idea of a pool of ostringstreams that can be reused.
+    struct StringStreams {
+        std::vector<std::unique_ptr<std::ostringstream>> m_streams;
+        std::vector<std::size_t> m_unused;
+        std::ostringstream m_referenceStream; // Used for copy state/ flags from
 
-    namespace {
-#ifdef CATCH_PLATFORM_WINDOWS
-        UInt64 getCurrentTicks() {
-            static UInt64 hz=0, hzo=0;
-            if (!hz) {
-                QueryPerformanceFrequency( reinterpret_cast<LARGE_INTEGER*>( &hz ) );
-                QueryPerformanceCounter( reinterpret_cast<LARGE_INTEGER*>( &hzo ) );
+        auto add() -> std::size_t {
+            if( m_unused.empty() ) {
+                m_streams.push_back( std::unique_ptr<std::ostringstream>( new std::ostringstream ) );
+                return m_streams.size()-1;
+            }
+            else {
+                auto index = m_unused.back();
+                m_unused.pop_back();
+                return index;
             }
-            UInt64 t;
-            QueryPerformanceCounter( reinterpret_cast<LARGE_INTEGER*>( &t ) );
-            return ((t-hzo)*1000000)/hz;
         }
-#else
-        UInt64 getCurrentTicks() {
-            timeval t;
-            gettimeofday(&t,CATCH_NULL);
-            return static_cast<UInt64>( t.tv_sec ) * 1000000ull + static_cast<UInt64>( t.tv_usec );
+
+        void release( std::size_t index ) {
+            m_streams[index]->copyfmt( m_referenceStream ); // Restore initial flags and other state
+            m_unused.push_back(index);
         }
-#endif
-    }
+    };
 
-    void Timer::start() {
-        m_ticks = getCurrentTicks();
-    }
-    unsigned int Timer::getElapsedMicroseconds() const {
-        return static_cast<unsigned int>(getCurrentTicks() - m_ticks);
-    }
-    unsigned int Timer::getElapsedMilliseconds() const {
-        return static_cast<unsigned int>(getElapsedMicroseconds()/1000);
+    ReusableStringStream::ReusableStringStream()
+    :   m_index( Singleton<StringStreams>::getMutable().add() ),
+        m_oss( Singleton<StringStreams>::getMutable().m_streams[m_index].get() )
+    {}
+
+    ReusableStringStream::~ReusableStringStream() {
+        static_cast<std::ostringstream*>( m_oss )->str("");
+        m_oss->clear();
+        Singleton<StringStreams>::getMutable().release( m_index );
     }
-    double Timer::getElapsedSeconds() const {
-        return getElapsedMicroseconds()/1000000.0;
+
+    auto ReusableStringStream::str() const -> std::string {
+        return static_cast<std::ostringstream*>( m_oss )->str();
     }
 
-} // namespace Catch
+    ///////////////////////////////////////////////////////////////////////////
 
-#ifdef __clang__
-#pragma clang diagnostic pop
+#ifndef CATCH_CONFIG_NOSTDOUT // If you #define this you must implement these functions
+    std::ostream& cout() { return std::cout; }
+    std::ostream& cerr() { return std::cerr; }
+    std::ostream& clog() { return std::clog; }
 #endif
-// #included from: catch_common.hpp
-#define TWOBLUECUBES_CATCH_COMMON_HPP_INCLUDED
+}
+// end catch_stream.cpp
+// start catch_string_manip.cpp
 
+#include <algorithm>
+#include <ostream>
 #include <cstring>
 #include <cctype>
+#include <vector>
 
 namespace Catch {
 
+    namespace {
+        char toLowerCh(char c) {
+            return static_cast<char>( std::tolower( c ) );
+        }
+    }
+
     bool startsWith( std::string const& s, std::string const& prefix ) {
         return s.size() >= prefix.size() && std::equal(prefix.begin(), prefix.end(), s.begin());
     }
@@ -8555,9 +13549,6 @@ namespace Catch {
     bool contains( std::string const& s, std::string const& infix ) {
         return s.find( infix ) != std::string::npos;
     }
-    char toLowerCh(char c) {
-        return static_cast<char>( std::tolower( c ) );
-    }
     void toLowerInPlace( std::string& s ) {
         std::transform( s.begin(), s.end(), s.begin(), toLowerCh );
     }
@@ -8574,6 +13565,18 @@ namespace Catch {
         return start != std::string::npos ? str.substr( start, 1+end-start ) : std::string();
     }
 
+    StringRef trim(StringRef ref) {
+        const auto is_ws = [](char c) {
+            return c == ' ' || c == '\t' || c == '\n' || c == '\r';
+        };
+        size_t real_begin = 0;
+        while (real_begin < ref.size() && is_ws(ref[real_begin])) { ++real_begin; }
+        size_t real_end = ref.size();
+        while (real_end > real_begin && is_ws(ref[real_end - 1])) { --real_end; }
+
+        return ref.substr(real_begin, real_end - real_begin);
+    }
+
     bool replaceInPlace( std::string& str, std::string const& replaceThis, std::string const& withThis ) {
         bool replaced = false;
         std::size_t i = str.find( replaceThis );
@@ -8588,6 +13591,21 @@ namespace Catch {
         return replaced;
     }
 
+    std::vector<StringRef> splitStringRef( StringRef str, char delimiter ) {
+        std::vector<StringRef> subStrings;
+        std::size_t start = 0;
+        for(std::size_t pos = 0; pos < str.size(); ++pos ) {
+            if( str[pos] == delimiter ) {
+                if( pos - start > 1 )
+                    subStrings.push_back( str.substr( start, pos-start ) );
+                start = pos+1;
+            }
+        }
+        if( start < str.size() )
+            subStrings.push_back( str.substr( start, str.size()-start ) );
+        return subStrings;
+    }
+
     pluralise::pluralise( std::size_t count, std::string const& label )
     :   m_count( count ),
         m_label( label )
@@ -8600,1665 +13618,2734 @@ namespace Catch {
         return os;
     }
 
-    SourceLineInfo::SourceLineInfo() : file(""), line( 0 ){}
-    SourceLineInfo::SourceLineInfo( char const* _file, std::size_t _line )
-    :   file( _file ),
-        line( _line )
-    {}
-    bool SourceLineInfo::empty() const {
-        return file[0] == '\0';
-    }
-    bool SourceLineInfo::operator == ( SourceLineInfo const& other ) const {
-        return line == other.line && (file == other.file || std::strcmp(file, other.file) == 0);
-    }
-    bool SourceLineInfo::operator < ( SourceLineInfo const& other ) const {
-        return line < other.line || ( line == other.line && (std::strcmp(file, other.file) < 0));
+}
+// end catch_string_manip.cpp
+// start catch_stringref.cpp
+
+#if defined(__clang__)
+#    pragma clang diagnostic push
+#    pragma clang diagnostic ignored "-Wexit-time-destructors"
+#endif
+
+#include <ostream>
+#include <cstring>
+#include <cstdint>
+
+namespace Catch {
+    StringRef::StringRef( char const* rawChars ) noexcept
+    : StringRef( rawChars, static_cast<StringRef::size_type>(std::strlen(rawChars) ) )
+    {}
+
+    void StringRef::swap( StringRef& other ) noexcept {
+        std::swap( m_start, other.m_start );
+        std::swap( m_size, other.m_size );
+        std::swap( m_data, other.m_data );
+    }
+
+    auto StringRef::c_str() const -> char const* {
+        if( !isSubstring() )
+            return m_start;
+
+        const_cast<StringRef *>( this )->takeOwnership();
+        return m_data;
+    }
+    auto StringRef::currentData() const noexcept -> char const* {
+        return m_start;
+    }
+
+    auto StringRef::isOwned() const noexcept -> bool {
+        return m_data != nullptr;
+    }
+    auto StringRef::isSubstring() const noexcept -> bool {
+        return m_start[m_size] != '\0';
+    }
+
+    void StringRef::takeOwnership() {
+        if( !isOwned() ) {
+            m_data = new char[m_size+1];
+            memcpy( m_data, m_start, m_size );
+            m_data[m_size] = '\0';
+        }
+    }
+    auto StringRef::substr( size_type start, size_type size ) const noexcept -> StringRef {
+        if( start < m_size )
+            return StringRef( m_start+start, size );
+        else
+            return StringRef();
+    }
+    auto StringRef::operator == ( StringRef const& other ) const noexcept -> bool {
+        return
+            size() == other.size() &&
+            (std::strncmp( m_start, other.m_start, size() ) == 0);
+    }
+    auto StringRef::operator != ( StringRef const& other ) const noexcept -> bool {
+        return !operator==( other );
+    }
+
+    auto operator << ( std::ostream& os, StringRef const& str ) -> std::ostream& {
+        return os.write(str.currentData(), str.size());
+    }
+
+    auto operator+=( std::string& lhs, StringRef const& rhs ) -> std::string& {
+        lhs.append(rhs.currentData(), rhs.size());
+        return lhs;
+    }
+
+} // namespace Catch
+
+#if defined(__clang__)
+#    pragma clang diagnostic pop
+#endif
+// end catch_stringref.cpp
+// start catch_tag_alias.cpp
+
+namespace Catch {
+    TagAlias::TagAlias(std::string const & _tag, SourceLineInfo _lineInfo): tag(_tag), lineInfo(_lineInfo) {}
+}
+// end catch_tag_alias.cpp
+// start catch_tag_alias_autoregistrar.cpp
+
+namespace Catch {
+
+    RegistrarForTagAliases::RegistrarForTagAliases(char const* alias, char const* tag, SourceLineInfo const& lineInfo) {
+        CATCH_TRY {
+            getMutableRegistryHub().registerTagAlias(alias, tag, lineInfo);
+        } CATCH_CATCH_ALL {
+            // Do not throw when constructing global objects, instead register the exception to be processed later
+            getMutableRegistryHub().registerStartupException();
+        }
+    }
+
+}
+// end catch_tag_alias_autoregistrar.cpp
+// start catch_tag_alias_registry.cpp
+
+#include <sstream>
+
+namespace Catch {
+
+    TagAliasRegistry::~TagAliasRegistry() {}
+
+    TagAlias const* TagAliasRegistry::find( std::string const& alias ) const {
+        auto it = m_registry.find( alias );
+        if( it != m_registry.end() )
+            return &(it->second);
+        else
+            return nullptr;
+    }
+
+    std::string TagAliasRegistry::expandAliases( std::string const& unexpandedTestSpec ) const {
+        std::string expandedTestSpec = unexpandedTestSpec;
+        for( auto const& registryKvp : m_registry ) {
+            std::size_t pos = expandedTestSpec.find( registryKvp.first );
+            if( pos != std::string::npos ) {
+                expandedTestSpec =  expandedTestSpec.substr( 0, pos ) +
+                                    registryKvp.second.tag +
+                                    expandedTestSpec.substr( pos + registryKvp.first.size() );
+            }
+        }
+        return expandedTestSpec;
+    }
+
+    void TagAliasRegistry::add( std::string const& alias, std::string const& tag, SourceLineInfo const& lineInfo ) {
+        CATCH_ENFORCE( startsWith(alias, "[@") && endsWith(alias, ']'),
+                      "error: tag alias, '" << alias << "' is not of the form [@alias name].\n" << lineInfo );
+
+        CATCH_ENFORCE( m_registry.insert(std::make_pair(alias, TagAlias(tag, lineInfo))).second,
+                      "error: tag alias, '" << alias << "' already registered.\n"
+                      << "\tFirst seen at: " << find(alias)->lineInfo << "\n"
+                      << "\tRedefined at: " << lineInfo );
+    }
+
+    ITagAliasRegistry::~ITagAliasRegistry() {}
+
+    ITagAliasRegistry const& ITagAliasRegistry::get() {
+        return getRegistryHub().getTagAliasRegistry();
+    }
+
+} // end namespace Catch
+// end catch_tag_alias_registry.cpp
+// start catch_test_case_info.cpp
+
+#include <cctype>
+#include <exception>
+#include <algorithm>
+#include <sstream>
+
+namespace Catch {
+
+    namespace {
+        TestCaseInfo::SpecialProperties parseSpecialTag( std::string const& tag ) {
+            if( startsWith( tag, '.' ) ||
+                tag == "!hide" )
+                return TestCaseInfo::IsHidden;
+            else if( tag == "!throws" )
+                return TestCaseInfo::Throws;
+            else if( tag == "!shouldfail" )
+                return TestCaseInfo::ShouldFail;
+            else if( tag == "!mayfail" )
+                return TestCaseInfo::MayFail;
+            else if( tag == "!nonportable" )
+                return TestCaseInfo::NonPortable;
+            else if( tag == "!benchmark" )
+                return static_cast<TestCaseInfo::SpecialProperties>( TestCaseInfo::Benchmark | TestCaseInfo::IsHidden );
+            else
+                return TestCaseInfo::None;
+        }
+        bool isReservedTag( std::string const& tag ) {
+            return parseSpecialTag( tag ) == TestCaseInfo::None && tag.size() > 0 && !std::isalnum( static_cast<unsigned char>(tag[0]) );
+        }
+        void enforceNotReservedTag( std::string const& tag, SourceLineInfo const& _lineInfo ) {
+            CATCH_ENFORCE( !isReservedTag(tag),
+                          "Tag name: [" << tag << "] is not allowed.\n"
+                          << "Tag names starting with non alphanumeric characters are reserved\n"
+                          << _lineInfo );
+        }
+    }
+
+    TestCase makeTestCase(  ITestInvoker* _testCase,
+                            std::string const& _className,
+                            NameAndTags const& nameAndTags,
+                            SourceLineInfo const& _lineInfo )
+    {
+        bool isHidden = false;
+
+        // Parse out tags
+        std::vector<std::string> tags;
+        std::string desc, tag;
+        bool inTag = false;
+        for (char c : nameAndTags.tags) {
+            if( !inTag ) {
+                if( c == '[' )
+                    inTag = true;
+                else
+                    desc += c;
+            }
+            else {
+                if( c == ']' ) {
+                    TestCaseInfo::SpecialProperties prop = parseSpecialTag( tag );
+                    if( ( prop & TestCaseInfo::IsHidden ) != 0 )
+                        isHidden = true;
+                    else if( prop == TestCaseInfo::None )
+                        enforceNotReservedTag( tag, _lineInfo );
+
+                    // Merged hide tags like `[.approvals]` should be added as
+                    // `[.][approvals]`. The `[.]` is added at later point, so
+                    // we only strip the prefix
+                    if (startsWith(tag, '.') && tag.size() > 1) {
+                        tag.erase(0, 1);
+                    }
+                    tags.push_back( tag );
+                    tag.clear();
+                    inTag = false;
+                }
+                else
+                    tag += c;
+            }
+        }
+        if( isHidden ) {
+            tags.push_back( "." );
+        }
+
+        TestCaseInfo info( static_cast<std::string>(nameAndTags.name), _className, desc, tags, _lineInfo );
+        return TestCase( _testCase, std::move(info) );
+    }
+
+    void setTags( TestCaseInfo& testCaseInfo, std::vector<std::string> tags ) {
+        std::sort(begin(tags), end(tags));
+        tags.erase(std::unique(begin(tags), end(tags)), end(tags));
+        testCaseInfo.lcaseTags.clear();
+
+        for( auto const& tag : tags ) {
+            std::string lcaseTag = toLower( tag );
+            testCaseInfo.properties = static_cast<TestCaseInfo::SpecialProperties>( testCaseInfo.properties | parseSpecialTag( lcaseTag ) );
+            testCaseInfo.lcaseTags.push_back( lcaseTag );
+        }
+        testCaseInfo.tags = std::move(tags);
+    }
+
+    TestCaseInfo::TestCaseInfo( std::string const& _name,
+                                std::string const& _className,
+                                std::string const& _description,
+                                std::vector<std::string> const& _tags,
+                                SourceLineInfo const& _lineInfo )
+    :   name( _name ),
+        className( _className ),
+        description( _description ),
+        lineInfo( _lineInfo ),
+        properties( None )
+    {
+        setTags( *this, _tags );
+    }
+
+    bool TestCaseInfo::isHidden() const {
+        return ( properties & IsHidden ) != 0;
+    }
+    bool TestCaseInfo::throws() const {
+        return ( properties & Throws ) != 0;
+    }
+    bool TestCaseInfo::okToFail() const {
+        return ( properties & (ShouldFail | MayFail ) ) != 0;
+    }
+    bool TestCaseInfo::expectedToFail() const {
+        return ( properties & (ShouldFail ) ) != 0;
+    }
+
+    std::string TestCaseInfo::tagsAsString() const {
+        std::string ret;
+        // '[' and ']' per tag
+        std::size_t full_size = 2 * tags.size();
+        for (const auto& tag : tags) {
+            full_size += tag.size();
+        }
+        ret.reserve(full_size);
+        for (const auto& tag : tags) {
+            ret.push_back('[');
+            ret.append(tag);
+            ret.push_back(']');
+        }
+
+        return ret;
+    }
+
+    TestCase::TestCase( ITestInvoker* testCase, TestCaseInfo&& info ) : TestCaseInfo( std::move(info) ), test( testCase ) {}
+
+    TestCase TestCase::withName( std::string const& _newName ) const {
+        TestCase other( *this );
+        other.name = _newName;
+        return other;
+    }
+
+    void TestCase::invoke() const {
+        test->invoke();
     }
 
-    void seedRng( IConfig const& config ) {
-        if( config.rngSeed() != 0 )
-            std::srand( config.rngSeed() );
-    }
-    unsigned int rngSeed() {
-        return getCurrentContext().getConfig()->rngSeed();
+    bool TestCase::operator == ( TestCase const& other ) const {
+        return  test.get() == other.test.get() &&
+                name == other.name &&
+                className == other.className;
     }
 
-    std::ostream& operator << ( std::ostream& os, SourceLineInfo const& info ) {
-#ifndef __GNUG__
-        os << info.file << '(' << info.line << ')';
-#else
-        os << info.file << ':' << info.line;
-#endif
-        return os;
+    bool TestCase::operator < ( TestCase const& other ) const {
+        return name < other.name;
     }
 
-    void throwLogicError( std::string const& message, SourceLineInfo const& locationInfo ) {
-        std::ostringstream oss;
-        oss << locationInfo << ": Internal Catch error: '" << message << '\'';
-        if( alwaysTrue() )
-            throw std::logic_error( oss.str() );
+    TestCaseInfo const& TestCase::getTestCaseInfo() const
+    {
+        return *this;
     }
-}
 
-// #included from: catch_section.hpp
-#define TWOBLUECUBES_CATCH_SECTION_HPP_INCLUDED
+} // end namespace Catch
+// end catch_test_case_info.cpp
+// start catch_test_case_registry_impl.cpp
+
+#include <sstream>
 
 namespace Catch {
 
-    SectionInfo::SectionInfo
-        (   SourceLineInfo const& _lineInfo,
-            std::string const& _name,
-            std::string const& _description )
-    :   name( _name ),
-        description( _description ),
-        lineInfo( _lineInfo )
-    {}
+    std::vector<TestCase> sortTests( IConfig const& config, std::vector<TestCase> const& unsortedTestCases ) {
 
-    Section::Section( SectionInfo const& info )
-    :   m_info( info ),
-        m_sectionIncluded( getResultCapture().sectionStarted( m_info, m_assertions ) )
-    {
-        m_timer.start();
-    }
+        std::vector<TestCase> sorted = unsortedTestCases;
 
-    Section::~Section() {
-        if( m_sectionIncluded ) {
-            SectionEndInfo endInfo( m_info, m_assertions, m_timer.getElapsedSeconds() );
-            if( std::uncaught_exception() )
-                getResultCapture().sectionEndedEarly( endInfo );
-            else
-                getResultCapture().sectionEnded( endInfo );
+        switch( config.runOrder() ) {
+            case RunTests::InLexicographicalOrder:
+                std::sort( sorted.begin(), sorted.end() );
+                break;
+            case RunTests::InRandomOrder:
+                seedRng( config );
+                std::shuffle( sorted.begin(), sorted.end(), rng() );
+                break;
+            case RunTests::InDeclarationOrder:
+                // already in declaration order
+                break;
         }
+        return sorted;
     }
 
-    // This indicates whether the section should be executed or not
-    Section::operator bool() const {
-        return m_sectionIncluded;
+    bool isThrowSafe( TestCase const& testCase, IConfig const& config ) {
+        return !testCase.throws() || config.allowThrows();
     }
 
-} // end namespace Catch
+    bool matchTest( TestCase const& testCase, TestSpec const& testSpec, IConfig const& config ) {
+        return testSpec.matches( testCase ) && isThrowSafe( testCase, config );
+    }
 
-// #included from: catch_debugger.hpp
-#define TWOBLUECUBES_CATCH_DEBUGGER_HPP_INCLUDED
+    void enforceNoDuplicateTestCases( std::vector<TestCase> const& functions ) {
+        std::set<TestCase> seenFunctions;
+        for( auto const& function : functions ) {
+            auto prev = seenFunctions.insert( function );
+            CATCH_ENFORCE( prev.second,
+                    "error: TEST_CASE( \"" << function.name << "\" ) already defined.\n"
+                    << "\tFirst seen at " << prev.first->getTestCaseInfo().lineInfo << "\n"
+                    << "\tRedefined at " << function.getTestCaseInfo().lineInfo );
+        }
+    }
 
-#ifdef CATCH_PLATFORM_MAC
+    std::vector<TestCase> filterTests( std::vector<TestCase> const& testCases, TestSpec const& testSpec, IConfig const& config ) {
+        std::vector<TestCase> filtered;
+        filtered.reserve( testCases.size() );
+        for (auto const& testCase : testCases) {
+            if ((!testSpec.hasFilters() && !testCase.isHidden()) ||
+                (testSpec.hasFilters() && matchTest(testCase, testSpec, config))) {
+                filtered.push_back(testCase);
+            }
+        }
+        return filtered;
+    }
+    std::vector<TestCase> const& getAllTestCasesSorted( IConfig const& config ) {
+        return getRegistryHub().getTestCaseRegistry().getAllTestsSorted( config );
+    }
 
-    #include <assert.h>
-    #include <stdbool.h>
-    #include <sys/types.h>
-    #include <unistd.h>
-    #include <sys/sysctl.h>
+    void TestRegistry::registerTest( TestCase const& testCase ) {
+        std::string name = testCase.getTestCaseInfo().name;
+        if( name.empty() ) {
+            ReusableStringStream rss;
+            rss << "Anonymous test case " << ++m_unnamedCount;
+            return registerTest( testCase.withName( rss.str() ) );
+        }
+        m_functions.push_back( testCase );
+    }
 
-    namespace Catch{
+    std::vector<TestCase> const& TestRegistry::getAllTests() const {
+        return m_functions;
+    }
+    std::vector<TestCase> const& TestRegistry::getAllTestsSorted( IConfig const& config ) const {
+        if( m_sortedFunctions.empty() )
+            enforceNoDuplicateTestCases( m_functions );
 
-        // The following function is taken directly from the following technical note:
-        // http://developer.apple.com/library/mac/#qa/qa2004/qa1361.html
+        if(  m_currentSortOrder != config.runOrder() || m_sortedFunctions.empty() ) {
+            m_sortedFunctions = sortTests( config, m_functions );
+            m_currentSortOrder = config.runOrder();
+        }
+        return m_sortedFunctions;
+    }
 
-        // Returns true if the current process is being debugged (either
-        // running under the debugger or has a debugger attached post facto).
-        bool isDebuggerActive(){
+    ///////////////////////////////////////////////////////////////////////////
+    TestInvokerAsFunction::TestInvokerAsFunction( void(*testAsFunction)() ) noexcept : m_testAsFunction( testAsFunction ) {}
 
-            int                 mib[4];
-            struct kinfo_proc   info;
-            size_t              size;
+    void TestInvokerAsFunction::invoke() const {
+        m_testAsFunction();
+    }
 
-            // Initialize the flags so that, if sysctl fails for some bizarre
-            // reason, we get a predictable result.
+    std::string extractClassName( StringRef const& classOrQualifiedMethodName ) {
+        std::string className(classOrQualifiedMethodName);
+        if( startsWith( className, '&' ) )
+        {
+            std::size_t lastColons = className.rfind( "::" );
+            std::size_t penultimateColons = className.rfind( "::", lastColons-1 );
+            if( penultimateColons == std::string::npos )
+                penultimateColons = 1;
+            className = className.substr( penultimateColons, lastColons-penultimateColons );
+        }
+        return className;
+    }
 
-            info.kp_proc.p_flag = 0;
+} // end namespace Catch
+// end catch_test_case_registry_impl.cpp
+// start catch_test_case_tracker.cpp
 
-            // Initialize mib, which tells sysctl the info we want, in this case
-            // we're looking for information about a specific process ID.
+#include <algorithm>
+#include <cassert>
+#include <stdexcept>
+#include <memory>
+#include <sstream>
 
-            mib[0] = CTL_KERN;
-            mib[1] = KERN_PROC;
-            mib[2] = KERN_PROC_PID;
-            mib[3] = getpid();
+#if defined(__clang__)
+#    pragma clang diagnostic push
+#    pragma clang diagnostic ignored "-Wexit-time-destructors"
+#endif
 
-            // Call sysctl.
+namespace Catch {
+namespace TestCaseTracking {
 
-            size = sizeof(info);
-            if( sysctl(mib, sizeof(mib) / sizeof(*mib), &info, &size, CATCH_NULL, 0) != 0 ) {
-                Catch::cerr() << "\n** Call to sysctl failed - unable to determine if debugger is active **\n" << std::endl;
-                return false;
-            }
+    NameAndLocation::NameAndLocation( std::string const& _name, SourceLineInfo const& _location )
+    :   name( _name ),
+        location( _location )
+    {}
 
-            // We're being debugged if the P_TRACED flag is set.
+    ITracker::~ITracker() = default;
 
-            return ( (info.kp_proc.p_flag & P_TRACED) != 0 );
-        }
-    } // namespace Catch
+    ITracker& TrackerContext::startRun() {
+        m_rootTracker = std::make_shared<SectionTracker>( NameAndLocation( "{root}", CATCH_INTERNAL_LINEINFO ), *this, nullptr );
+        m_currentTracker = nullptr;
+        m_runState = Executing;
+        return *m_rootTracker;
+    }
 
-#elif defined(CATCH_PLATFORM_LINUX)
-    #include <fstream>
-    #include <string>
+    void TrackerContext::endRun() {
+        m_rootTracker.reset();
+        m_currentTracker = nullptr;
+        m_runState = NotStarted;
+    }
 
-    namespace Catch{
-        // The standard POSIX way of detecting a debugger is to attempt to
-        // ptrace() the process, but this needs to be done from a child and not
-        // this process itself to still allow attaching to this process later
-        // if wanted, so is rather heavy. Under Linux we have the PID of the
-        // "debugger" (which doesn't need to be gdb, of course, it could also
-        // be strace, for example) in /proc/$PID/status, so just get it from
-        // there instead.
-        bool isDebuggerActive(){
-            // Libstdc++ has a bug, where std::ifstream sets errno to 0
-            // This way our users can properly assert over errno values
-            ErrnoGuard guard;
-            std::ifstream in("/proc/self/status");
-            for( std::string line; std::getline(in, line); ) {
-                static const int PREFIX_LEN = 11;
-                if( line.compare(0, PREFIX_LEN, "TracerPid:\t") == 0 ) {
-                    // We're traced if the PID is not 0 and no other PID starts
-                    // with 0 digit, so it's enough to check for just a single
-                    // character.
-                    return line.length() > PREFIX_LEN && line[PREFIX_LEN] != '0';
-                }
-            }
+    void TrackerContext::startCycle() {
+        m_currentTracker = m_rootTracker.get();
+        m_runState = Executing;
+    }
+    void TrackerContext::completeCycle() {
+        m_runState = CompletedCycle;
+    }
 
-            return false;
-        }
-    } // namespace Catch
-#elif defined(_MSC_VER)
-    extern "C" __declspec(dllimport) int __stdcall IsDebuggerPresent();
-    namespace Catch {
-        bool isDebuggerActive() {
-            return IsDebuggerPresent() != 0;
-        }
+    bool TrackerContext::completedCycle() const {
+        return m_runState == CompletedCycle;
     }
-#elif defined(__MINGW32__)
-    extern "C" __declspec(dllimport) int __stdcall IsDebuggerPresent();
-    namespace Catch {
-        bool isDebuggerActive() {
-            return IsDebuggerPresent() != 0;
-        }
+    ITracker& TrackerContext::currentTracker() {
+        return *m_currentTracker;
     }
-#else
-    namespace Catch {
-       inline bool isDebuggerActive() { return false; }
+    void TrackerContext::setCurrentTracker( ITracker* tracker ) {
+        m_currentTracker = tracker;
     }
-#endif // Platform
 
-#ifdef CATCH_PLATFORM_WINDOWS
+    TrackerBase::TrackerBase( NameAndLocation const& nameAndLocation, TrackerContext& ctx, ITracker* parent )
+    :   m_nameAndLocation( nameAndLocation ),
+        m_ctx( ctx ),
+        m_parent( parent )
+    {}
 
-    namespace Catch {
-        void writeToDebugConsole( std::string const& text ) {
-            ::OutputDebugStringA( text.c_str() );
-        }
+    NameAndLocation const& TrackerBase::nameAndLocation() const {
+        return m_nameAndLocation;
     }
-#else
-    namespace Catch {
-        void writeToDebugConsole( std::string const& text ) {
-            // !TBD: Need a version for Mac/ XCode and other IDEs
-            Catch::cout() << text;
+    bool TrackerBase::isComplete() const {
+        return m_runState == CompletedSuccessfully || m_runState == Failed;
+    }
+    bool TrackerBase::isSuccessfullyCompleted() const {
+        return m_runState == CompletedSuccessfully;
+    }
+    bool TrackerBase::isOpen() const {
+        return m_runState != NotStarted && !isComplete();
+    }
+    bool TrackerBase::hasChildren() const {
+        return !m_children.empty();
+    }
+
+    void TrackerBase::addChild( ITrackerPtr const& child ) {
+        m_children.push_back( child );
+    }
+
+    ITrackerPtr TrackerBase::findChild( NameAndLocation const& nameAndLocation ) {
+        auto it = std::find_if( m_children.begin(), m_children.end(),
+            [&nameAndLocation]( ITrackerPtr const& tracker ){
+                return
+                    tracker->nameAndLocation().location == nameAndLocation.location &&
+                    tracker->nameAndLocation().name == nameAndLocation.name;
+            } );
+        return( it != m_children.end() )
+            ? *it
+            : nullptr;
+    }
+    ITracker& TrackerBase::parent() {
+        assert( m_parent ); // Should always be non-null except for root
+        return *m_parent;
+    }
+
+    void TrackerBase::openChild() {
+        if( m_runState != ExecutingChildren ) {
+            m_runState = ExecutingChildren;
+            if( m_parent )
+                m_parent->openChild();
         }
     }
-#endif // Platform
 
-// #included from: catch_tostring.hpp
-#define TWOBLUECUBES_CATCH_TOSTRING_HPP_INCLUDED
+    bool TrackerBase::isSectionTracker() const { return false; }
+    bool TrackerBase::isGeneratorTracker() const { return false; }
 
-namespace Catch {
+    void TrackerBase::open() {
+        m_runState = Executing;
+        moveToThis();
+        if( m_parent )
+            m_parent->openChild();
+    }
 
-namespace Detail {
+    void TrackerBase::close() {
 
-    const std::string unprintableString = "{?}";
+        // Close any still open children (e.g. generators)
+        while( &m_ctx.currentTracker() != this )
+            m_ctx.currentTracker().close();
 
-    namespace {
-        const int hexThreshold = 255;
+        switch( m_runState ) {
+            case NeedsAnotherRun:
+                break;
 
-        struct Endianness {
-            enum Arch { Big, Little };
+            case Executing:
+                m_runState = CompletedSuccessfully;
+                break;
+            case ExecutingChildren:
+                if( std::all_of(m_children.begin(), m_children.end(), [](ITrackerPtr const& t){ return t->isComplete(); }) )
+                    m_runState = CompletedSuccessfully;
+                break;
 
-            static Arch which() {
-                union _{
-                    int asInt;
-                    char asChar[sizeof (int)];
-                } u;
+            case NotStarted:
+            case CompletedSuccessfully:
+            case Failed:
+                CATCH_INTERNAL_ERROR( "Illogical state: " << m_runState );
 
-                u.asInt = 1;
-                return ( u.asChar[sizeof(int)-1] == 1 ) ? Big : Little;
-            }
-        };
+            default:
+                CATCH_INTERNAL_ERROR( "Unknown state: " << m_runState );
+        }
+        moveToParent();
+        m_ctx.completeCycle();
+    }
+    void TrackerBase::fail() {
+        m_runState = Failed;
+        if( m_parent )
+            m_parent->markAsNeedingAnotherRun();
+        moveToParent();
+        m_ctx.completeCycle();
+    }
+    void TrackerBase::markAsNeedingAnotherRun() {
+        m_runState = NeedsAnotherRun;
+    }
+
+    void TrackerBase::moveToParent() {
+        assert( m_parent );
+        m_ctx.setCurrentTracker( m_parent );
+    }
+    void TrackerBase::moveToThis() {
+        m_ctx.setCurrentTracker( this );
     }
 
-    std::string rawMemoryToString( const void *object, std::size_t size )
+    SectionTracker::SectionTracker( NameAndLocation const& nameAndLocation, TrackerContext& ctx, ITracker* parent )
+    :   TrackerBase( nameAndLocation, ctx, parent ),
+        m_trimmed_name(trim(nameAndLocation.name))
     {
-        // Reverse order for little endian architectures
-        int i = 0, end = static_cast<int>( size ), inc = 1;
-        if( Endianness::which() == Endianness::Little ) {
-            i = end-1;
-            end = inc = -1;
-        }
+        if( parent ) {
+            while( !parent->isSectionTracker() )
+                parent = &parent->parent();
 
-        unsigned char const *bytes = static_cast<unsigned char const *>(object);
-        std::ostringstream os;
-        os << "0x" << std::setfill('0') << std::hex;
-        for( ; i != end; i += inc )
-             os << std::setw(2) << static_cast<unsigned>(bytes[i]);
-       return os.str();
+            SectionTracker& parentSection = static_cast<SectionTracker&>( *parent );
+            addNextFilters( parentSection.m_filters );
+        }
     }
-}
 
-std::string toString( std::string const& value ) {
-    std::string s = value;
-    if( getCurrentContext().getConfig()->showInvisibles() ) {
-        for(size_t i = 0; i < s.size(); ++i ) {
-            std::string subs;
-            switch( s[i] ) {
-            case '\n': subs = "\\n"; break;
-            case '\t': subs = "\\t"; break;
-            default: break;
-            }
-            if( !subs.empty() ) {
-                s = s.substr( 0, i ) + subs + s.substr( i+1 );
-                ++i;
-            }
+    bool SectionTracker::isComplete() const {
+        bool complete = true;
+
+        if ((m_filters.empty() || m_filters[0] == "")
+            || std::find(m_filters.begin(), m_filters.end(), m_trimmed_name) != m_filters.end()) {
+            complete = TrackerBase::isComplete();
         }
+        return complete;
     }
-    return '"' + s + '"';
-}
-std::string toString( std::wstring const& value ) {
 
-    std::string s;
-    s.reserve( value.size() );
-    for(size_t i = 0; i < value.size(); ++i )
-        s += value[i] <= 0xff ? static_cast<char>( value[i] ) : '?';
-    return Catch::toString( s );
-}
+    bool SectionTracker::isSectionTracker() const { return true; }
+
+    SectionTracker& SectionTracker::acquire( TrackerContext& ctx, NameAndLocation const& nameAndLocation ) {
+        std::shared_ptr<SectionTracker> section;
+
+        ITracker& currentTracker = ctx.currentTracker();
+        if( ITrackerPtr childTracker = currentTracker.findChild( nameAndLocation ) ) {
+            assert( childTracker );
+            assert( childTracker->isSectionTracker() );
+            section = std::static_pointer_cast<SectionTracker>( childTracker );
+        }
+        else {
+            section = std::make_shared<SectionTracker>( nameAndLocation, ctx, &currentTracker );
+            currentTracker.addChild( section );
+        }
+        if( !ctx.completedCycle() )
+            section->tryOpen();
+        return *section;
+    }
 
-std::string toString( const char* const value ) {
-    return value ? Catch::toString( std::string( value ) ) : std::string( "{null string}" );
-}
+    void SectionTracker::tryOpen() {
+        if( !isComplete() )
+            open();
+    }
 
-std::string toString( char* const value ) {
-    return Catch::toString( static_cast<const char*>( value ) );
-}
+    void SectionTracker::addInitialFilters( std::vector<std::string> const& filters ) {
+        if( !filters.empty() ) {
+            m_filters.reserve( m_filters.size() + filters.size() + 2 );
+            m_filters.push_back(""); // Root - should never be consulted
+            m_filters.push_back(""); // Test Case - not a section filter
+            m_filters.insert( m_filters.end(), filters.begin(), filters.end() );
+        }
+    }
+    void SectionTracker::addNextFilters( std::vector<std::string> const& filters ) {
+        if( filters.size() > 1 )
+            m_filters.insert( m_filters.end(), filters.begin()+1, filters.end() );
+    }
 
-std::string toString( const wchar_t* const value )
-{
-    return value ? Catch::toString( std::wstring(value) ) : std::string( "{null string}" );
-}
+} // namespace TestCaseTracking
 
-std::string toString( wchar_t* const value )
-{
-    return Catch::toString( static_cast<const wchar_t*>( value ) );
-}
+using TestCaseTracking::ITracker;
+using TestCaseTracking::TrackerContext;
+using TestCaseTracking::SectionTracker;
 
-std::string toString( int value ) {
-    std::ostringstream oss;
-    oss << value;
-    if( value > Detail::hexThreshold )
-        oss << " (0x" << std::hex << value << ')';
-    return oss.str();
-}
+} // namespace Catch
 
-std::string toString( unsigned long value ) {
-    std::ostringstream oss;
-    oss << value;
-    if( value > Detail::hexThreshold )
-        oss << " (0x" << std::hex << value << ')';
-    return oss.str();
-}
+#if defined(__clang__)
+#    pragma clang diagnostic pop
+#endif
+// end catch_test_case_tracker.cpp
+// start catch_test_registry.cpp
 
-std::string toString( unsigned int value ) {
-    return Catch::toString( static_cast<unsigned long>( value ) );
-}
+namespace Catch {
 
-template<typename T>
-std::string fpToString( T value, int precision ) {
-    std::ostringstream oss;
-    oss << std::setprecision( precision )
-        << std::fixed
-        << value;
-    std::string d = oss.str();
-    std::size_t i = d.find_last_not_of( '0' );
-    if( i != std::string::npos && i != d.size()-1 ) {
-        if( d[i] == '.' )
-            i++;
-        d = d.substr( 0, i+1 );
+    auto makeTestInvoker( void(*testAsFunction)() ) noexcept -> ITestInvoker* {
+        return new(std::nothrow) TestInvokerAsFunction( testAsFunction );
     }
-    return d;
-}
 
-std::string toString( const double value ) {
-    return fpToString( value, 10 );
-}
-std::string toString( const float value ) {
-    return fpToString( value, 5 ) + 'f';
-}
+    NameAndTags::NameAndTags( StringRef const& name_ , StringRef const& tags_ ) noexcept : name( name_ ), tags( tags_ ) {}
 
-std::string toString( bool value ) {
-    return value ? "true" : "false";
-}
+    AutoReg::AutoReg( ITestInvoker* invoker, SourceLineInfo const& lineInfo, StringRef const& classOrMethod, NameAndTags const& nameAndTags ) noexcept {
+        CATCH_TRY {
+            getMutableRegistryHub()
+                    .registerTest(
+                        makeTestCase(
+                            invoker,
+                            extractClassName( classOrMethod ),
+                            nameAndTags,
+                            lineInfo));
+        } CATCH_CATCH_ALL {
+            // Do not throw when constructing global objects, instead register the exception to be processed later
+            getMutableRegistryHub().registerStartupException();
+        }
+    }
 
-std::string toString( char value ) {
-    if ( value == '\r' )
-        return "'\\r'";
-    if ( value == '\f' )
-        return "'\\f'";
-    if ( value == '\n' )
-        return "'\\n'";
-    if ( value == '\t' )
-        return "'\\t'";
-    if ( '\0' <= value && value < ' ' )
-        return toString( static_cast<unsigned int>( value ) );
-    char chstr[] = "' '";
-    chstr[1] = value;
-    return chstr;
+    AutoReg::~AutoReg() = default;
 }
+// end catch_test_registry.cpp
+// start catch_test_spec.cpp
 
-std::string toString( signed char value ) {
-    return toString( static_cast<char>( value ) );
-}
+#include <algorithm>
+#include <string>
+#include <vector>
+#include <memory>
 
-std::string toString( unsigned char value ) {
-    return toString( static_cast<char>( value ) );
-}
+namespace Catch {
 
-#ifdef CATCH_CONFIG_CPP11_LONG_LONG
-std::string toString( long long value ) {
-    std::ostringstream oss;
-    oss << value;
-    if( value > Detail::hexThreshold )
-        oss << " (0x" << std::hex << value << ')';
-    return oss.str();
-}
-std::string toString( unsigned long long value ) {
-    std::ostringstream oss;
-    oss << value;
-    if( value > Detail::hexThreshold )
-        oss << " (0x" << std::hex << value << ')';
-    return oss.str();
-}
-#endif
+    TestSpec::Pattern::Pattern( std::string const& name )
+    : m_name( name )
+    {}
 
-#ifdef CATCH_CONFIG_CPP11_NULLPTR
-std::string toString( std::nullptr_t ) {
-    return "nullptr";
-}
-#endif
+    TestSpec::Pattern::~Pattern() = default;
 
-#ifdef __OBJC__
-    std::string toString( NSString const * const& nsstring ) {
-        if( !nsstring )
-            return "nil";
-        return "@" + toString([nsstring UTF8String]);
-    }
-    std::string toString( NSString * CATCH_ARC_STRONG const& nsstring ) {
-        if( !nsstring )
-            return "nil";
-        return "@" + toString([nsstring UTF8String]);
+    std::string const& TestSpec::Pattern::name() const {
+        return m_name;
     }
-    std::string toString( NSObject* const& nsObject ) {
-        return toString( [nsObject description] );
-    }
-#endif
 
-} // end namespace Catch
+    TestSpec::NamePattern::NamePattern( std::string const& name, std::string const& filterString )
+    : Pattern( filterString )
+    , m_wildcardPattern( toLower( name ), CaseSensitive::No )
+    {}
 
-// #included from: catch_result_builder.hpp
-#define TWOBLUECUBES_CATCH_RESULT_BUILDER_HPP_INCLUDED
+    bool TestSpec::NamePattern::matches( TestCaseInfo const& testCase ) const {
+        return m_wildcardPattern.matches( testCase.name );
+    }
 
-namespace Catch {
+    TestSpec::TagPattern::TagPattern( std::string const& tag, std::string const& filterString )
+    : Pattern( filterString )
+    , m_tag( toLower( tag ) )
+    {}
 
-    std::string capturedExpressionWithSecondArgument( std::string const& capturedExpression, std::string const& secondArg ) {
-        return secondArg.empty() || secondArg == "\"\""
-            ? capturedExpression
-            : capturedExpression + ", " + secondArg;
+    bool TestSpec::TagPattern::matches( TestCaseInfo const& testCase ) const {
+        return std::find(begin(testCase.lcaseTags),
+                         end(testCase.lcaseTags),
+                         m_tag) != end(testCase.lcaseTags);
     }
-    ResultBuilder::ResultBuilder(   char const* macroName,
-                                    SourceLineInfo const& lineInfo,
-                                    char const* capturedExpression,
-                                    ResultDisposition::Flags resultDisposition,
-                                    char const* secondArg )
-    :   m_assertionInfo( macroName, lineInfo, capturedExpressionWithSecondArgument( capturedExpression, secondArg ), resultDisposition ),
-        m_shouldDebugBreak( false ),
-        m_shouldThrow( false ),
-        m_guardException( false )
+
+    TestSpec::ExcludedPattern::ExcludedPattern( PatternPtr const& underlyingPattern )
+    : Pattern( underlyingPattern->name() )
+    , m_underlyingPattern( underlyingPattern )
     {}
 
-    ResultBuilder::~ResultBuilder() {
-#if defined(CATCH_CONFIG_FAST_COMPILE)
-        if ( m_guardException ) {
-            m_stream.oss << "Exception translation was disabled by CATCH_CONFIG_FAST_COMPILE";
-            captureResult( ResultWas::ThrewException );
-            getCurrentContext().getResultCapture()->exceptionEarlyReported();
-        }
-#endif
+    bool TestSpec::ExcludedPattern::matches( TestCaseInfo const& testCase ) const {
+        return !m_underlyingPattern->matches( testCase );
     }
 
-    ResultBuilder& ResultBuilder::setResultType( ResultWas::OfType result ) {
-        m_data.resultType = result;
-        return *this;
+    bool TestSpec::Filter::matches( TestCaseInfo const& testCase ) const {
+        return std::all_of( m_patterns.begin(), m_patterns.end(), [&]( PatternPtr const& p ){ return p->matches( testCase ); } );
     }
-    ResultBuilder& ResultBuilder::setResultType( bool result ) {
-        m_data.resultType = result ? ResultWas::Ok : ResultWas::ExpressionFailed;
-        return *this;
+
+    std::string TestSpec::Filter::name() const {
+        std::string name;
+        for( auto const& p : m_patterns )
+            name += p->name();
+        return name;
     }
 
-    void ResultBuilder::endExpression( DecomposedExpression const& expr ) {
-        AssertionResult result = build( expr );
-        handleResult( result );
+    bool TestSpec::hasFilters() const {
+        return !m_filters.empty();
     }
 
-    void ResultBuilder::useActiveException( ResultDisposition::Flags resultDisposition ) {
-        m_assertionInfo.resultDisposition = resultDisposition;
-        m_stream.oss << Catch::translateActiveException();
-        captureResult( ResultWas::ThrewException );
+    bool TestSpec::matches( TestCaseInfo const& testCase ) const {
+        return std::any_of( m_filters.begin(), m_filters.end(), [&]( Filter const& f ){ return f.matches( testCase ); } );
     }
 
-    void ResultBuilder::captureResult( ResultWas::OfType resultType ) {
-        setResultType( resultType );
-        captureExpression();
+    TestSpec::Matches TestSpec::matchesByFilter( std::vector<TestCase> const& testCases, IConfig const& config ) const
+    {
+        Matches matches( m_filters.size() );
+        std::transform( m_filters.begin(), m_filters.end(), matches.begin(), [&]( Filter const& filter ){
+            std::vector<TestCase const*> currentMatches;
+            for( auto const& test : testCases )
+                if( isThrowSafe( test, config ) && filter.matches( test ) )
+                    currentMatches.emplace_back( &test );
+            return FilterMatch{ filter.name(), currentMatches };
+        } );
+        return matches;
     }
 
-    void ResultBuilder::captureExpectedException( std::string const& expectedMessage ) {
-        if( expectedMessage.empty() )
-            captureExpectedException( Matchers::Impl::MatchAllOf<std::string>() );
-        else
-            captureExpectedException( Matchers::Equals( expectedMessage ) );
+    const TestSpec::vectorStrings& TestSpec::getInvalidArgs() const{
+        return  (m_invalidArgs);
     }
 
-    void ResultBuilder::captureExpectedException( Matchers::Impl::MatcherBase<std::string> const& matcher ) {
+}
+// end catch_test_spec.cpp
+// start catch_test_spec_parser.cpp
 
-        assert( !isFalseTest( m_assertionInfo.resultDisposition ) );
-        AssertionResultData data = m_data;
-        data.resultType = ResultWas::Ok;
-        data.reconstructedExpression = m_assertionInfo.capturedExpression;
+namespace Catch {
 
-        std::string actualMessage = Catch::translateActiveException();
-        if( !matcher.match( actualMessage ) ) {
-            data.resultType = ResultWas::ExpressionFailed;
-            data.reconstructedExpression = actualMessage;
-        }
-        AssertionResult result( m_assertionInfo, data );
-        handleResult( result );
+    TestSpecParser::TestSpecParser( ITagAliasRegistry const& tagAliases ) : m_tagAliases( &tagAliases ) {}
+
+    TestSpecParser& TestSpecParser::parse( std::string const& arg ) {
+        m_mode = None;
+        m_exclusion = false;
+        m_arg = m_tagAliases->expandAliases( arg );
+        m_escapeChars.clear();
+        m_substring.reserve(m_arg.size());
+        m_patternName.reserve(m_arg.size());
+        m_realPatternPos = 0;
+
+        for( m_pos = 0; m_pos < m_arg.size(); ++m_pos )
+          //if visitChar fails
+           if( !visitChar( m_arg[m_pos] ) ){
+               m_testSpec.m_invalidArgs.push_back(arg);
+               break;
+           }
+        endMode();
+        return *this;
     }
-
-    void ResultBuilder::captureExpression() {
-        AssertionResult result = build();
-        handleResult( result );
+    TestSpec TestSpecParser::testSpec() {
+        addFilter();
+        return m_testSpec;
     }
+    bool TestSpecParser::visitChar( char c ) {
+        if( (m_mode != EscapedName) && (c == '\\') ) {
+            escape();
+            addCharToPattern(c);
+            return true;
+        }else if((m_mode != EscapedName) && (c == ',') )  {
+            return separate();
+        }
 
-    void ResultBuilder::handleResult( AssertionResult const& result )
-    {
-        getResultCapture().assertionEnded( result );
+        switch( m_mode ) {
+        case None:
+            if( processNoneChar( c ) )
+                return true;
+            break;
+        case Name:
+            processNameChar( c );
+            break;
+        case EscapedName:
+            endMode();
+            addCharToPattern(c);
+            return true;
+        default:
+        case Tag:
+        case QuotedName:
+            if( processOtherChar( c ) )
+                return true;
+            break;
+        }
 
-        if( !result.isOk() ) {
-            if( getCurrentContext().getConfig()->shouldDebugBreak() )
-                m_shouldDebugBreak = true;
-            if( getCurrentContext().getRunner()->aborting() || (m_assertionInfo.resultDisposition & ResultDisposition::Normal) )
-                m_shouldThrow = true;
+        m_substring += c;
+        if( !isControlChar( c ) ) {
+            m_patternName += c;
+            m_realPatternPos++;
         }
+        return true;
     }
-
-    void ResultBuilder::react() {
-#if defined(CATCH_CONFIG_FAST_COMPILE)
-        if (m_shouldDebugBreak) {
-            ///////////////////////////////////////////////////////////////////
-            // To inspect the state during test, you need to go one level up the callstack
-            // To go back to the test and change execution, jump over the throw statement
-            ///////////////////////////////////////////////////////////////////
-            CATCH_BREAK_INTO_DEBUGGER();
+    // Two of the processing methods return true to signal the caller to return
+    // without adding the given character to the current pattern strings
+    bool TestSpecParser::processNoneChar( char c ) {
+        switch( c ) {
+        case ' ':
+            return true;
+        case '~':
+            m_exclusion = true;
+            return false;
+        case '[':
+            startNewMode( Tag );
+            return false;
+        case '"':
+            startNewMode( QuotedName );
+            return false;
+        default:
+            startNewMode( Name );
+            return false;
         }
-#endif
-        if( m_shouldThrow )
-            throw Catch::TestFailureException();
     }
-
-    bool ResultBuilder::shouldDebugBreak() const { return m_shouldDebugBreak; }
-    bool ResultBuilder::allowThrows() const { return getCurrentContext().getConfig()->allowThrows(); }
-
-    AssertionResult ResultBuilder::build() const
-    {
-        return build( *this );
+    void TestSpecParser::processNameChar( char c ) {
+        if( c == '[' ) {
+            if( m_substring == "exclude:" )
+                m_exclusion = true;
+            else
+                endMode();
+            startNewMode( Tag );
+        }
+    }
+    bool TestSpecParser::processOtherChar( char c ) {
+        if( !isControlChar( c ) )
+            return false;
+        m_substring += c;
+        endMode();
+        return true;
+    }
+    void TestSpecParser::startNewMode( Mode mode ) {
+        m_mode = mode;
+    }
+    void TestSpecParser::endMode() {
+        switch( m_mode ) {
+        case Name:
+        case QuotedName:
+            return addPattern<TestSpec::NamePattern>();
+        case Tag:
+            return addPattern<TestSpec::TagPattern>();
+        case EscapedName:
+            revertBackToLastMode();
+            return;
+        case None:
+        default:
+            return startNewMode( None );
+        }
+    }
+    void TestSpecParser::escape() {
+        saveLastMode();
+        m_mode = EscapedName;
+        m_escapeChars.push_back(m_realPatternPos);
+    }
+    bool TestSpecParser::isControlChar( char c ) const {
+        switch( m_mode ) {
+            default:
+                return false;
+            case None:
+                return c == '~';
+            case Name:
+                return c == '[';
+            case EscapedName:
+                return true;
+            case QuotedName:
+                return c == '"';
+            case Tag:
+                return c == '[' || c == ']';
+        }
     }
 
-    // CAVEAT: The returned AssertionResult stores a pointer to the argument expr,
-    //         a temporary DecomposedExpression, which in turn holds references to
-    //         operands, possibly temporary as well.
-    //         It should immediately be passed to handleResult; if the expression
-    //         needs to be reported, its string expansion must be composed before
-    //         the temporaries are destroyed.
-    AssertionResult ResultBuilder::build( DecomposedExpression const& expr ) const
-    {
-        assert( m_data.resultType != ResultWas::Unknown );
-        AssertionResultData data = m_data;
-
-        // Flip bool results if FalseTest flag is set
-        if( isFalseTest( m_assertionInfo.resultDisposition ) ) {
-            data.negate( expr.isBinaryExpression() );
+    void TestSpecParser::addFilter() {
+        if( !m_currentFilter.m_patterns.empty() ) {
+            m_testSpec.m_filters.push_back( m_currentFilter );
+            m_currentFilter = TestSpec::Filter();
         }
+    }
 
-        data.message = m_stream.oss.str();
-        data.decomposedExpression = &expr; // for lazy reconstruction
-        return AssertionResult( m_assertionInfo, data );
+    void TestSpecParser::saveLastMode() {
+      lastMode = m_mode;
     }
 
-    void ResultBuilder::reconstructExpression( std::string& dest ) const {
-        dest = m_assertionInfo.capturedExpression;
+    void TestSpecParser::revertBackToLastMode() {
+      m_mode = lastMode;
     }
 
-    void ResultBuilder::setExceptionGuard() {
-        m_guardException = true;
+    bool TestSpecParser::separate() {
+      if( (m_mode==QuotedName) || (m_mode==Tag) ){
+         //invalid argument, signal failure to previous scope.
+         m_mode = None;
+         m_pos = m_arg.size();
+         m_substring.clear();
+         m_patternName.clear();
+         return false;
+      }
+      endMode();
+      addFilter();
+      return true; //success
     }
-    void ResultBuilder::unsetExceptionGuard() {
-        m_guardException = false;
+
+    TestSpec parseTestSpec( std::string const& arg ) {
+        return TestSpecParser( ITagAliasRegistry::get() ).parse( arg ).testSpec();
     }
 
-} // end namespace Catch
+} // namespace Catch
+// end catch_test_spec_parser.cpp
+// start catch_timer.cpp
 
-// #included from: catch_tag_alias_registry.hpp
-#define TWOBLUECUBES_CATCH_TAG_ALIAS_REGISTRY_HPP_INCLUDED
+#include <chrono>
 
-namespace Catch {
+static const uint64_t nanosecondsInSecond = 1000000000;
 
-    TagAliasRegistry::~TagAliasRegistry() {}
+namespace Catch {
 
-    Option<TagAlias> TagAliasRegistry::find( std::string const& alias ) const {
-        std::map<std::string, TagAlias>::const_iterator it = m_registry.find( alias );
-        if( it != m_registry.end() )
-            return it->second;
-        else
-            return Option<TagAlias>();
+    auto getCurrentNanosecondsSinceEpoch() -> uint64_t {
+        return std::chrono::duration_cast<std::chrono::nanoseconds>( std::chrono::high_resolution_clock::now().time_since_epoch() ).count();
     }
 
-    std::string TagAliasRegistry::expandAliases( std::string const& unexpandedTestSpec ) const {
-        std::string expandedTestSpec = unexpandedTestSpec;
-        for( std::map<std::string, TagAlias>::const_iterator it = m_registry.begin(), itEnd = m_registry.end();
-                it != itEnd;
-                ++it ) {
-            std::size_t pos = expandedTestSpec.find( it->first );
-            if( pos != std::string::npos ) {
-                expandedTestSpec =  expandedTestSpec.substr( 0, pos ) +
-                                    it->second.tag +
-                                    expandedTestSpec.substr( pos + it->first.size() );
-            }
-        }
-        return expandedTestSpec;
-    }
+    namespace {
+        auto estimateClockResolution() -> uint64_t {
+            uint64_t sum = 0;
+            static const uint64_t iterations = 1000000;
 
-    void TagAliasRegistry::add( std::string const& alias, std::string const& tag, SourceLineInfo const& lineInfo ) {
+            auto startTime = getCurrentNanosecondsSinceEpoch();
 
-        if( !startsWith( alias, "[@" ) || !endsWith( alias, ']' ) ) {
-            std::ostringstream oss;
-            oss << Colour( Colour::Red )
-                << "error: tag alias, \"" << alias << "\" is not of the form [@alias name].\n"
-                << Colour( Colour::FileName )
-                << lineInfo << '\n';
-            throw std::domain_error( oss.str().c_str() );
-        }
-        if( !m_registry.insert( std::make_pair( alias, TagAlias( tag, lineInfo ) ) ).second ) {
-            std::ostringstream oss;
-            oss << Colour( Colour::Red )
-                << "error: tag alias, \"" << alias << "\" already registered.\n"
-                << "\tFirst seen at "
-                << Colour( Colour::Red ) << find(alias)->lineInfo << '\n'
-                << Colour( Colour::Red ) << "\tRedefined at "
-                << Colour( Colour::FileName) << lineInfo << '\n';
-            throw std::domain_error( oss.str().c_str() );
-        }
-    }
+            for( std::size_t i = 0; i < iterations; ++i ) {
 
-    ITagAliasRegistry::~ITagAliasRegistry() {}
+                uint64_t ticks;
+                uint64_t baseTicks = getCurrentNanosecondsSinceEpoch();
+                do {
+                    ticks = getCurrentNanosecondsSinceEpoch();
+                } while( ticks == baseTicks );
 
-    ITagAliasRegistry const& ITagAliasRegistry::get() {
-        return getRegistryHub().getTagAliasRegistry();
+                auto delta = ticks - baseTicks;
+                sum += delta;
+
+                // If we have been calibrating for over 3 seconds -- the clock
+                // is terrible and we should move on.
+                // TBD: How to signal that the measured resolution is probably wrong?
+                if (ticks > startTime + 3 * nanosecondsInSecond) {
+                    return sum / ( i + 1u );
+                }
+            }
+
+            // We're just taking the mean, here. To do better we could take the std. dev and exclude outliers
+            // - and potentially do more iterations if there's a high variance.
+            return sum/iterations;
+        }
+    }
+    auto getEstimatedClockResolution() -> uint64_t {
+        static auto s_resolution = estimateClockResolution();
+        return s_resolution;
     }
 
-    RegistrarForTagAliases::RegistrarForTagAliases( char const* alias, char const* tag, SourceLineInfo const& lineInfo ) {
-        getMutableRegistryHub().registerTagAlias( alias, tag, lineInfo );
+    void Timer::start() {
+       m_nanoseconds = getCurrentNanosecondsSinceEpoch();
+    }
+    auto Timer::getElapsedNanoseconds() const -> uint64_t {
+        return getCurrentNanosecondsSinceEpoch() - m_nanoseconds;
+    }
+    auto Timer::getElapsedMicroseconds() const -> uint64_t {
+        return getElapsedNanoseconds()/1000;
+    }
+    auto Timer::getElapsedMilliseconds() const -> unsigned int {
+        return static_cast<unsigned int>(getElapsedMicroseconds()/1000);
+    }
+    auto Timer::getElapsedSeconds() const -> double {
+        return getElapsedMicroseconds()/1000000.0;
     }
 
-} // end namespace Catch
+} // namespace Catch
+// end catch_timer.cpp
+// start catch_tostring.cpp
+
+#if defined(__clang__)
+#    pragma clang diagnostic push
+#    pragma clang diagnostic ignored "-Wexit-time-destructors"
+#    pragma clang diagnostic ignored "-Wglobal-constructors"
+#endif
+
+// Enable specific decls locally
+#if !defined(CATCH_CONFIG_ENABLE_CHRONO_STRINGMAKER)
+#define CATCH_CONFIG_ENABLE_CHRONO_STRINGMAKER
+#endif
 
-// #included from: catch_matchers_string.hpp
+#include <cmath>
+#include <iomanip>
 
 namespace Catch {
-namespace Matchers {
 
-    namespace StdString {
+namespace Detail {
 
-        CasedString::CasedString( std::string const& str, CaseSensitive::Choice caseSensitivity )
-        :   m_caseSensitivity( caseSensitivity ),
-            m_str( adjustString( str ) )
-        {}
-        std::string CasedString::adjustString( std::string const& str ) const {
-            return m_caseSensitivity == CaseSensitive::No
-                   ? toLower( str )
-                   : str;
-        }
-        std::string CasedString::caseSensitivitySuffix() const {
-            return m_caseSensitivity == CaseSensitive::No
-                   ? " (case insensitive)"
-                   : std::string();
-        }
+    const std::string unprintableString = "{?}";
 
-        StringMatcherBase::StringMatcherBase( std::string const& operation, CasedString const& comparator )
-        : m_comparator( comparator ),
-          m_operation( operation ) {
-        }
+    namespace {
+        const int hexThreshold = 255;
 
-        std::string StringMatcherBase::describe() const {
-            std::string description;
-            description.reserve(5 + m_operation.size() + m_comparator.m_str.size() +
-                                        m_comparator.caseSensitivitySuffix().size());
-            description += m_operation;
-            description += ": \"";
-            description += m_comparator.m_str;
-            description += "\"";
-            description += m_comparator.caseSensitivitySuffix();
-            return description;
-        }
+        struct Endianness {
+            enum Arch { Big, Little };
 
-        EqualsMatcher::EqualsMatcher( CasedString const& comparator ) : StringMatcherBase( "equals", comparator ) {}
+            static Arch which() {
+                union _{
+                    int asInt;
+                    char asChar[sizeof (int)];
+                } u;
 
-        bool EqualsMatcher::match( std::string const& source ) const {
-            return m_comparator.adjustString( source ) == m_comparator.m_str;
+                u.asInt = 1;
+                return ( u.asChar[sizeof(int)-1] == 1 ) ? Big : Little;
+            }
+        };
+    }
+
+    std::string rawMemoryToString( const void *object, std::size_t size ) {
+        // Reverse order for little endian architectures
+        int i = 0, end = static_cast<int>( size ), inc = 1;
+        if( Endianness::which() == Endianness::Little ) {
+            i = end-1;
+            end = inc = -1;
         }
 
-        ContainsMatcher::ContainsMatcher( CasedString const& comparator ) : StringMatcherBase( "contains", comparator ) {}
+        unsigned char const *bytes = static_cast<unsigned char const *>(object);
+        ReusableStringStream rss;
+        rss << "0x" << std::setfill('0') << std::hex;
+        for( ; i != end; i += inc )
+             rss << std::setw(2) << static_cast<unsigned>(bytes[i]);
+       return rss.str();
+    }
+}
 
-        bool ContainsMatcher::match( std::string const& source ) const {
-            return contains( m_comparator.adjustString( source ), m_comparator.m_str );
-        }
+template<typename T>
+std::string fpToString( T value, int precision ) {
+    if (Catch::isnan(value)) {
+        return "nan";
+    }
 
-        StartsWithMatcher::StartsWithMatcher( CasedString const& comparator ) : StringMatcherBase( "starts with", comparator ) {}
+    ReusableStringStream rss;
+    rss << std::setprecision( precision )
+        << std::fixed
+        << value;
+    std::string d = rss.str();
+    std::size_t i = d.find_last_not_of( '0' );
+    if( i != std::string::npos && i != d.size()-1 ) {
+        if( d[i] == '.' )
+            i++;
+        d = d.substr( 0, i+1 );
+    }
+    return d;
+}
 
-        bool StartsWithMatcher::match( std::string const& source ) const {
-            return startsWith( m_comparator.adjustString( source ), m_comparator.m_str );
+//// ======================================================= ////
+//
+//   Out-of-line defs for full specialization of StringMaker
+//
+//// ======================================================= ////
+
+std::string StringMaker<std::string>::convert(const std::string& str) {
+    if (!getCurrentContext().getConfig()->showInvisibles()) {
+        return '"' + str + '"';
+    }
+
+    std::string s("\"");
+    for (char c : str) {
+        switch (c) {
+        case '\n':
+            s.append("\\n");
+            break;
+        case '\t':
+            s.append("\\t");
+            break;
+        default:
+            s.push_back(c);
+            break;
         }
+    }
+    s.append("\"");
+    return s;
+}
 
-        EndsWithMatcher::EndsWithMatcher( CasedString const& comparator ) : StringMatcherBase( "ends with", comparator ) {}
+#ifdef CATCH_CONFIG_CPP17_STRING_VIEW
+std::string StringMaker<std::string_view>::convert(std::string_view str) {
+    return ::Catch::Detail::stringify(std::string{ str });
+}
+#endif
+
+std::string StringMaker<char const*>::convert(char const* str) {
+    if (str) {
+        return ::Catch::Detail::stringify(std::string{ str });
+    } else {
+        return{ "{null string}" };
+    }
+}
+std::string StringMaker<char*>::convert(char* str) {
+    if (str) {
+        return ::Catch::Detail::stringify(std::string{ str });
+    } else {
+        return{ "{null string}" };
+    }
+}
 
-        bool EndsWithMatcher::match( std::string const& source ) const {
-            return endsWith( m_comparator.adjustString( source ), m_comparator.m_str );
-        }
+#ifdef CATCH_CONFIG_WCHAR
+std::string StringMaker<std::wstring>::convert(const std::wstring& wstr) {
+    std::string s;
+    s.reserve(wstr.size());
+    for (auto c : wstr) {
+        s += (c <= 0xff) ? static_cast<char>(c) : '?';
+    }
+    return ::Catch::Detail::stringify(s);
+}
 
-    } // namespace StdString
+# ifdef CATCH_CONFIG_CPP17_STRING_VIEW
+std::string StringMaker<std::wstring_view>::convert(std::wstring_view str) {
+    return StringMaker<std::wstring>::convert(std::wstring(str));
+}
+# endif
 
-    StdString::EqualsMatcher Equals( std::string const& str, CaseSensitive::Choice caseSensitivity ) {
-        return StdString::EqualsMatcher( StdString::CasedString( str, caseSensitivity) );
+std::string StringMaker<wchar_t const*>::convert(wchar_t const * str) {
+    if (str) {
+        return ::Catch::Detail::stringify(std::wstring{ str });
+    } else {
+        return{ "{null string}" };
     }
-    StdString::ContainsMatcher Contains( std::string const& str, CaseSensitive::Choice caseSensitivity ) {
-        return StdString::ContainsMatcher( StdString::CasedString( str, caseSensitivity) );
+}
+std::string StringMaker<wchar_t *>::convert(wchar_t * str) {
+    if (str) {
+        return ::Catch::Detail::stringify(std::wstring{ str });
+    } else {
+        return{ "{null string}" };
     }
-    StdString::EndsWithMatcher EndsWith( std::string const& str, CaseSensitive::Choice caseSensitivity ) {
-        return StdString::EndsWithMatcher( StdString::CasedString( str, caseSensitivity) );
+}
+#endif
+
+#if defined(CATCH_CONFIG_CPP17_BYTE)
+#include <cstddef>
+std::string StringMaker<std::byte>::convert(std::byte value) {
+    return ::Catch::Detail::stringify(std::to_integer<unsigned long long>(value));
+}
+#endif // defined(CATCH_CONFIG_CPP17_BYTE)
+
+std::string StringMaker<int>::convert(int value) {
+    return ::Catch::Detail::stringify(static_cast<long long>(value));
+}
+std::string StringMaker<long>::convert(long value) {
+    return ::Catch::Detail::stringify(static_cast<long long>(value));
+}
+std::string StringMaker<long long>::convert(long long value) {
+    ReusableStringStream rss;
+    rss << value;
+    if (value > Detail::hexThreshold) {
+        rss << " (0x" << std::hex << value << ')';
     }
-    StdString::StartsWithMatcher StartsWith( std::string const& str, CaseSensitive::Choice caseSensitivity ) {
-        return StdString::StartsWithMatcher( StdString::CasedString( str, caseSensitivity) );
+    return rss.str();
+}
+
+std::string StringMaker<unsigned int>::convert(unsigned int value) {
+    return ::Catch::Detail::stringify(static_cast<unsigned long long>(value));
+}
+std::string StringMaker<unsigned long>::convert(unsigned long value) {
+    return ::Catch::Detail::stringify(static_cast<unsigned long long>(value));
+}
+std::string StringMaker<unsigned long long>::convert(unsigned long long value) {
+    ReusableStringStream rss;
+    rss << value;
+    if (value > Detail::hexThreshold) {
+        rss << " (0x" << std::hex << value << ')';
     }
+    return rss.str();
+}
 
-} // namespace Matchers
-} // namespace Catch
-// #included from: ../reporters/catch_reporter_multi.hpp
-#define TWOBLUECUBES_CATCH_REPORTER_MULTI_HPP_INCLUDED
+std::string StringMaker<bool>::convert(bool b) {
+    return b ? "true" : "false";
+}
 
-namespace Catch {
+std::string StringMaker<signed char>::convert(signed char value) {
+    if (value == '\r') {
+        return "'\\r'";
+    } else if (value == '\f') {
+        return "'\\f'";
+    } else if (value == '\n') {
+        return "'\\n'";
+    } else if (value == '\t') {
+        return "'\\t'";
+    } else if ('\0' <= value && value < ' ') {
+        return ::Catch::Detail::stringify(static_cast<unsigned int>(value));
+    } else {
+        char chstr[] = "' '";
+        chstr[1] = value;
+        return chstr;
+    }
+}
+std::string StringMaker<char>::convert(char c) {
+    return ::Catch::Detail::stringify(static_cast<signed char>(c));
+}
+std::string StringMaker<unsigned char>::convert(unsigned char c) {
+    return ::Catch::Detail::stringify(static_cast<char>(c));
+}
 
-class MultipleReporters : public SharedImpl<IStreamingReporter> {
-    typedef std::vector<Ptr<IStreamingReporter> > Reporters;
-    Reporters m_reporters;
+std::string StringMaker<std::nullptr_t>::convert(std::nullptr_t) {
+    return "nullptr";
+}
 
-public:
-    void add( Ptr<IStreamingReporter> const& reporter ) {
-        m_reporters.push_back( reporter );
-    }
+int StringMaker<float>::precision = 5;
 
-public: // IStreamingReporter
+std::string StringMaker<float>::convert(float value) {
+    return fpToString(value, precision) + 'f';
+}
 
-    virtual ReporterPreferences getPreferences() const CATCH_OVERRIDE {
-        return m_reporters[0]->getPreferences();
-    }
+int StringMaker<double>::precision = 10;
 
-    virtual void noMatchingTestCases( std::string const& spec ) CATCH_OVERRIDE {
-        for( Reporters::const_iterator it = m_reporters.begin(), itEnd = m_reporters.end();
-                it != itEnd;
-                ++it )
-            (*it)->noMatchingTestCases( spec );
-    }
+std::string StringMaker<double>::convert(double value) {
+    return fpToString(value, precision);
+}
 
-    virtual void testRunStarting( TestRunInfo const& testRunInfo ) CATCH_OVERRIDE {
-        for( Reporters::const_iterator it = m_reporters.begin(), itEnd = m_reporters.end();
-                it != itEnd;
-                ++it )
-            (*it)->testRunStarting( testRunInfo );
-    }
+std::string ratio_string<std::atto>::symbol() { return "a"; }
+std::string ratio_string<std::femto>::symbol() { return "f"; }
+std::string ratio_string<std::pico>::symbol() { return "p"; }
+std::string ratio_string<std::nano>::symbol() { return "n"; }
+std::string ratio_string<std::micro>::symbol() { return "u"; }
+std::string ratio_string<std::milli>::symbol() { return "m"; }
 
-    virtual void testGroupStarting( GroupInfo const& groupInfo ) CATCH_OVERRIDE {
-        for( Reporters::const_iterator it = m_reporters.begin(), itEnd = m_reporters.end();
-                it != itEnd;
-                ++it )
-            (*it)->testGroupStarting( groupInfo );
-    }
+} // end namespace Catch
 
-    virtual void testCaseStarting( TestCaseInfo const& testInfo ) CATCH_OVERRIDE {
-        for( Reporters::const_iterator it = m_reporters.begin(), itEnd = m_reporters.end();
-                it != itEnd;
-                ++it )
-            (*it)->testCaseStarting( testInfo );
-    }
+#if defined(__clang__)
+#    pragma clang diagnostic pop
+#endif
 
-    virtual void sectionStarting( SectionInfo const& sectionInfo ) CATCH_OVERRIDE {
-        for( Reporters::const_iterator it = m_reporters.begin(), itEnd = m_reporters.end();
-                it != itEnd;
-                ++it )
-            (*it)->sectionStarting( sectionInfo );
-    }
+// end catch_tostring.cpp
+// start catch_totals.cpp
 
-    virtual void assertionStarting( AssertionInfo const& assertionInfo ) CATCH_OVERRIDE {
-        for( Reporters::const_iterator it = m_reporters.begin(), itEnd = m_reporters.end();
-                it != itEnd;
-                ++it )
-            (*it)->assertionStarting( assertionInfo );
-    }
+namespace Catch {
 
-    // The return value indicates if the messages buffer should be cleared:
-    virtual bool assertionEnded( AssertionStats const& assertionStats ) CATCH_OVERRIDE {
-        bool clearBuffer = false;
-        for( Reporters::const_iterator it = m_reporters.begin(), itEnd = m_reporters.end();
-                it != itEnd;
-                ++it )
-            clearBuffer |= (*it)->assertionEnded( assertionStats );
-        return clearBuffer;
+    Counts Counts::operator - ( Counts const& other ) const {
+        Counts diff;
+        diff.passed = passed - other.passed;
+        diff.failed = failed - other.failed;
+        diff.failedButOk = failedButOk - other.failedButOk;
+        return diff;
     }
 
-    virtual void sectionEnded( SectionStats const& sectionStats ) CATCH_OVERRIDE {
-        for( Reporters::const_iterator it = m_reporters.begin(), itEnd = m_reporters.end();
-                it != itEnd;
-                ++it )
-            (*it)->sectionEnded( sectionStats );
+    Counts& Counts::operator += ( Counts const& other ) {
+        passed += other.passed;
+        failed += other.failed;
+        failedButOk += other.failedButOk;
+        return *this;
     }
 
-    virtual void testCaseEnded( TestCaseStats const& testCaseStats ) CATCH_OVERRIDE {
-        for( Reporters::const_iterator it = m_reporters.begin(), itEnd = m_reporters.end();
-                it != itEnd;
-                ++it )
-            (*it)->testCaseEnded( testCaseStats );
+    std::size_t Counts::total() const {
+        return passed + failed + failedButOk;
     }
-
-    virtual void testGroupEnded( TestGroupStats const& testGroupStats ) CATCH_OVERRIDE {
-        for( Reporters::const_iterator it = m_reporters.begin(), itEnd = m_reporters.end();
-                it != itEnd;
-                ++it )
-            (*it)->testGroupEnded( testGroupStats );
+    bool Counts::allPassed() const {
+        return failed == 0 && failedButOk == 0;
     }
-
-    virtual void testRunEnded( TestRunStats const& testRunStats ) CATCH_OVERRIDE {
-        for( Reporters::const_iterator it = m_reporters.begin(), itEnd = m_reporters.end();
-                it != itEnd;
-                ++it )
-            (*it)->testRunEnded( testRunStats );
+    bool Counts::allOk() const {
+        return failed == 0;
     }
 
-    virtual void skipTest( TestCaseInfo const& testInfo ) CATCH_OVERRIDE {
-        for( Reporters::const_iterator it = m_reporters.begin(), itEnd = m_reporters.end();
-                it != itEnd;
-                ++it )
-            (*it)->skipTest( testInfo );
+    Totals Totals::operator - ( Totals const& other ) const {
+        Totals diff;
+        diff.assertions = assertions - other.assertions;
+        diff.testCases = testCases - other.testCases;
+        return diff;
     }
 
-    virtual MultipleReporters* tryAsMulti() CATCH_OVERRIDE {
-        return this;
+    Totals& Totals::operator += ( Totals const& other ) {
+        assertions += other.assertions;
+        testCases += other.testCases;
+        return *this;
     }
 
-};
-
-Ptr<IStreamingReporter> addReporter( Ptr<IStreamingReporter> const& existingReporter, Ptr<IStreamingReporter> const& additionalReporter ) {
-    Ptr<IStreamingReporter> resultingReporter;
-
-    if( existingReporter ) {
-        MultipleReporters* multi = existingReporter->tryAsMulti();
-        if( !multi ) {
-            multi = new MultipleReporters;
-            resultingReporter = Ptr<IStreamingReporter>( multi );
-            if( existingReporter )
-                multi->add( existingReporter );
-        }
+    Totals Totals::delta( Totals const& prevTotals ) const {
+        Totals diff = *this - prevTotals;
+        if( diff.assertions.failed > 0 )
+            ++diff.testCases.failed;
+        else if( diff.assertions.failedButOk > 0 )
+            ++diff.testCases.failedButOk;
         else
-            resultingReporter = existingReporter;
-        multi->add( additionalReporter );
+            ++diff.testCases.passed;
+        return diff;
     }
-    else
-        resultingReporter = additionalReporter;
 
-    return resultingReporter;
 }
+// end catch_totals.cpp
+// start catch_uncaught_exceptions.cpp
 
-} // end namespace Catch
-
-// #included from: ../reporters/catch_reporter_xml.hpp
-#define TWOBLUECUBES_CATCH_REPORTER_XML_HPP_INCLUDED
+#include <exception>
 
-// #included from: catch_reporter_bases.hpp
-#define TWOBLUECUBES_CATCH_REPORTER_BASES_HPP_INCLUDED
+namespace Catch {
+    bool uncaught_exceptions() {
+#if defined(CATCH_CONFIG_CPP17_UNCAUGHT_EXCEPTIONS)
+        return std::uncaught_exceptions() > 0;
+#else
+        return std::uncaught_exception();
+#endif
+  }
+} // end namespace Catch
+// end catch_uncaught_exceptions.cpp
+// start catch_version.cpp
 
-#include <cstring>
-#include <cfloat>
-#include <cstdio>
-#include <assert.h>
+#include <ostream>
 
 namespace Catch {
 
-    namespace {
-        // Because formatting using c++ streams is stateful, drop down to C is required
-        // Alternatively we could use stringstream, but its performance is... not good.
-        std::string getFormattedDuration( double duration ) {
-            // Max exponent + 1 is required to represent the whole part
-            // + 1 for decimal point
-            // + 3 for the 3 decimal places
-            // + 1 for null terminator
-            const size_t maxDoubleSize = DBL_MAX_10_EXP + 1 + 1 + 3 + 1;
-            char buffer[maxDoubleSize];
-
-            // Save previous errno, to prevent sprintf from overwriting it
-            ErrnoGuard guard;
-#ifdef _MSC_VER
-            sprintf_s(buffer, "%.3f", duration);
-#else
-            sprintf(buffer, "%.3f", duration);
-#endif
-            return std::string(buffer);
+    Version::Version
+        (   unsigned int _majorVersion,
+            unsigned int _minorVersion,
+            unsigned int _patchNumber,
+            char const * const _branchName,
+            unsigned int _buildNumber )
+    :   majorVersion( _majorVersion ),
+        minorVersion( _minorVersion ),
+        patchNumber( _patchNumber ),
+        branchName( _branchName ),
+        buildNumber( _buildNumber )
+    {}
+
+    std::ostream& operator << ( std::ostream& os, Version const& version ) {
+        os  << version.majorVersion << '.'
+            << version.minorVersion << '.'
+            << version.patchNumber;
+        // branchName is never null -> 0th char is \0 if it is empty
+        if (version.branchName[0]) {
+            os << '-' << version.branchName
+               << '.' << version.buildNumber;
         }
+        return os;
     }
 
-    struct StreamingReporterBase : SharedImpl<IStreamingReporter> {
+    Version const& libraryVersion() {
+        static Version version( 2, 10, 1, "", 0 );
+        return version;
+    }
 
-        StreamingReporterBase( ReporterConfig const& _config )
-        :   m_config( _config.fullConfig() ),
-            stream( _config.stream() )
-        {
-            m_reporterPrefs.shouldRedirectStdOut = false;
+}
+// end catch_version.cpp
+// start catch_wildcard_pattern.cpp
+
+namespace Catch {
+
+    WildcardPattern::WildcardPattern( std::string const& pattern,
+                                      CaseSensitive::Choice caseSensitivity )
+    :   m_caseSensitivity( caseSensitivity ),
+        m_pattern( normaliseString( pattern ) )
+    {
+        if( startsWith( m_pattern, '*' ) ) {
+            m_pattern = m_pattern.substr( 1 );
+            m_wildcard = WildcardAtStart;
         }
+        if( endsWith( m_pattern, '*' ) ) {
+            m_pattern = m_pattern.substr( 0, m_pattern.size()-1 );
+            m_wildcard = static_cast<WildcardPosition>( m_wildcard | WildcardAtEnd );
+        }
+    }
 
-        virtual ReporterPreferences getPreferences() const CATCH_OVERRIDE {
-            return m_reporterPrefs;
+    bool WildcardPattern::matches( std::string const& str ) const {
+        switch( m_wildcard ) {
+            case NoWildcard:
+                return m_pattern == normaliseString( str );
+            case WildcardAtStart:
+                return endsWith( normaliseString( str ), m_pattern );
+            case WildcardAtEnd:
+                return startsWith( normaliseString( str ), m_pattern );
+            case WildcardAtBothEnds:
+                return contains( normaliseString( str ), m_pattern );
+            default:
+                CATCH_INTERNAL_ERROR( "Unknown enum" );
         }
+    }
 
-        virtual ~StreamingReporterBase() CATCH_OVERRIDE;
+    std::string WildcardPattern::normaliseString( std::string const& str ) const {
+        return trim( m_caseSensitivity == CaseSensitive::No ? toLower( str ) : str );
+    }
+}
+// end catch_wildcard_pattern.cpp
+// start catch_xmlwriter.cpp
 
-        virtual void noMatchingTestCases( std::string const& ) CATCH_OVERRIDE {}
+#include <iomanip>
 
-        virtual void testRunStarting( TestRunInfo const& _testRunInfo ) CATCH_OVERRIDE {
-            currentTestRunInfo = _testRunInfo;
-        }
-        virtual void testGroupStarting( GroupInfo const& _groupInfo ) CATCH_OVERRIDE {
-            currentGroupInfo = _groupInfo;
-        }
+using uchar = unsigned char;
 
-        virtual void testCaseStarting( TestCaseInfo const& _testInfo ) CATCH_OVERRIDE {
-            currentTestCaseInfo = _testInfo;
-        }
-        virtual void sectionStarting( SectionInfo const& _sectionInfo ) CATCH_OVERRIDE {
-            m_sectionStack.push_back( _sectionInfo );
-        }
+namespace Catch {
 
-        virtual void sectionEnded( SectionStats const& /* _sectionStats */ ) CATCH_OVERRIDE {
-            m_sectionStack.pop_back();
-        }
-        virtual void testCaseEnded( TestCaseStats const& /* _testCaseStats */ ) CATCH_OVERRIDE {
-            currentTestCaseInfo.reset();
+namespace {
+
+    size_t trailingBytes(unsigned char c) {
+        if ((c & 0xE0) == 0xC0) {
+            return 2;
         }
-        virtual void testGroupEnded( TestGroupStats const& /* _testGroupStats */ ) CATCH_OVERRIDE {
-            currentGroupInfo.reset();
+        if ((c & 0xF0) == 0xE0) {
+            return 3;
         }
-        virtual void testRunEnded( TestRunStats const& /* _testRunStats */ ) CATCH_OVERRIDE {
-            currentTestCaseInfo.reset();
-            currentGroupInfo.reset();
-            currentTestRunInfo.reset();
+        if ((c & 0xF8) == 0xF0) {
+            return 4;
         }
+        CATCH_INTERNAL_ERROR("Invalid multibyte utf-8 start byte encountered");
+    }
 
-        virtual void skipTest( TestCaseInfo const& ) CATCH_OVERRIDE {
-            // Don't do anything with this by default.
-            // It can optionally be overridden in the derived class.
+    uint32_t headerValue(unsigned char c) {
+        if ((c & 0xE0) == 0xC0) {
+            return c & 0x1F;
+        }
+        if ((c & 0xF0) == 0xE0) {
+            return c & 0x0F;
         }
+        if ((c & 0xF8) == 0xF0) {
+            return c & 0x07;
+        }
+        CATCH_INTERNAL_ERROR("Invalid multibyte utf-8 start byte encountered");
+    }
 
-        Ptr<IConfig const> m_config;
-        std::ostream& stream;
+    void hexEscapeChar(std::ostream& os, unsigned char c) {
+        std::ios_base::fmtflags f(os.flags());
+        os << "\\x"
+            << std::uppercase << std::hex << std::setfill('0') << std::setw(2)
+            << static_cast<int>(c);
+        os.flags(f);
+    }
 
-        LazyStat<TestRunInfo> currentTestRunInfo;
-        LazyStat<GroupInfo> currentGroupInfo;
-        LazyStat<TestCaseInfo> currentTestCaseInfo;
+} // anonymous namespace
 
-        std::vector<SectionInfo> m_sectionStack;
-        ReporterPreferences m_reporterPrefs;
-    };
+    XmlEncode::XmlEncode( std::string const& str, ForWhat forWhat )
+    :   m_str( str ),
+        m_forWhat( forWhat )
+    {}
 
-    struct CumulativeReporterBase : SharedImpl<IStreamingReporter> {
-        template<typename T, typename ChildNodeT>
-        struct Node : SharedImpl<> {
-            explicit Node( T const& _value ) : value( _value ) {}
-            virtual ~Node() {}
+    void XmlEncode::encodeTo( std::ostream& os ) const {
+        // Apostrophe escaping not necessary if we always use " to write attributes
+        // (see: http://www.w3.org/TR/xml/#syntax)
 
-            typedef std::vector<Ptr<ChildNodeT> > ChildNodes;
-            T value;
-            ChildNodes children;
-        };
-        struct SectionNode : SharedImpl<> {
-            explicit SectionNode( SectionStats const& _stats ) : stats( _stats ) {}
-            virtual ~SectionNode();
+        for( std::size_t idx = 0; idx < m_str.size(); ++ idx ) {
+            uchar c = m_str[idx];
+            switch (c) {
+            case '<':   os << "&lt;"; break;
+            case '&':   os << "&amp;"; break;
 
-            bool operator == ( SectionNode const& other ) const {
-                return stats.sectionInfo.lineInfo == other.stats.sectionInfo.lineInfo;
-            }
-            bool operator == ( Ptr<SectionNode> const& other ) const {
-                return operator==( *other );
-            }
+            case '>':
+                // See: http://www.w3.org/TR/xml/#syntax
+                if (idx > 2 && m_str[idx - 1] == ']' && m_str[idx - 2] == ']')
+                    os << "&gt;";
+                else
+                    os << c;
+                break;
 
-            SectionStats stats;
-            typedef std::vector<Ptr<SectionNode> > ChildSections;
-            typedef std::vector<AssertionStats> Assertions;
-            ChildSections childSections;
-            Assertions assertions;
-            std::string stdOut;
-            std::string stdErr;
-        };
+            case '\"':
+                if (m_forWhat == ForAttributes)
+                    os << "&quot;";
+                else
+                    os << c;
+                break;
 
-        struct BySectionInfo {
-            BySectionInfo( SectionInfo const& other ) : m_other( other ) {}
-            BySectionInfo( BySectionInfo const& other ) : m_other( other.m_other ) {}
-            bool operator() ( Ptr<SectionNode> const& node ) const {
-                return node->stats.sectionInfo.lineInfo == m_other.lineInfo;
-            }
-        private:
-            void operator=( BySectionInfo const& );
-            SectionInfo const& m_other;
-        };
+            default:
+                // Check for control characters and invalid utf-8
 
-        typedef Node<TestCaseStats, SectionNode> TestCaseNode;
-        typedef Node<TestGroupStats, TestCaseNode> TestGroupNode;
-        typedef Node<TestRunStats, TestGroupNode> TestRunNode;
+                // Escape control characters in standard ascii
+                // see http://stackoverflow.com/questions/404107/why-are-control-characters-illegal-in-xml-1-0
+                if (c < 0x09 || (c > 0x0D && c < 0x20) || c == 0x7F) {
+                    hexEscapeChar(os, c);
+                    break;
+                }
 
-        CumulativeReporterBase( ReporterConfig const& _config )
-        :   m_config( _config.fullConfig() ),
-            stream( _config.stream() )
-        {
-            m_reporterPrefs.shouldRedirectStdOut = false;
-        }
-        ~CumulativeReporterBase();
+                // Plain ASCII: Write it to stream
+                if (c < 0x7F) {
+                    os << c;
+                    break;
+                }
 
-        virtual ReporterPreferences getPreferences() const CATCH_OVERRIDE {
-            return m_reporterPrefs;
-        }
+                // UTF-8 territory
+                // Check if the encoding is valid and if it is not, hex escape bytes.
+                // Important: We do not check the exact decoded values for validity, only the encoding format
+                // First check that this bytes is a valid lead byte:
+                // This means that it is not encoded as 1111 1XXX
+                // Or as 10XX XXXX
+                if (c <  0xC0 ||
+                    c >= 0xF8) {
+                    hexEscapeChar(os, c);
+                    break;
+                }
 
-        virtual void testRunStarting( TestRunInfo const& ) CATCH_OVERRIDE {}
-        virtual void testGroupStarting( GroupInfo const& ) CATCH_OVERRIDE {}
+                auto encBytes = trailingBytes(c);
+                // Are there enough bytes left to avoid accessing out-of-bounds memory?
+                if (idx + encBytes - 1 >= m_str.size()) {
+                    hexEscapeChar(os, c);
+                    break;
+                }
+                // The header is valid, check data
+                // The next encBytes bytes must together be a valid utf-8
+                // This means: bitpattern 10XX XXXX and the extracted value is sane (ish)
+                bool valid = true;
+                uint32_t value = headerValue(c);
+                for (std::size_t n = 1; n < encBytes; ++n) {
+                    uchar nc = m_str[idx + n];
+                    valid &= ((nc & 0xC0) == 0x80);
+                    value = (value << 6) | (nc & 0x3F);
+                }
 
-        virtual void testCaseStarting( TestCaseInfo const& ) CATCH_OVERRIDE {}
+                if (
+                    // Wrong bit pattern of following bytes
+                    (!valid) ||
+                    // Overlong encodings
+                    (value < 0x80) ||
+                    (0x80 <= value && value < 0x800   && encBytes > 2) ||
+                    (0x800 < value && value < 0x10000 && encBytes > 3) ||
+                    // Encoded value out of range
+                    (value >= 0x110000)
+                    ) {
+                    hexEscapeChar(os, c);
+                    break;
+                }
 
-        virtual void sectionStarting( SectionInfo const& sectionInfo ) CATCH_OVERRIDE {
-            SectionStats incompleteStats( sectionInfo, Counts(), 0, false );
-            Ptr<SectionNode> node;
-            if( m_sectionStack.empty() ) {
-                if( !m_rootSection )
-                    m_rootSection = new SectionNode( incompleteStats );
-                node = m_rootSection;
-            }
-            else {
-                SectionNode& parentNode = *m_sectionStack.back();
-                SectionNode::ChildSections::const_iterator it =
-                    std::find_if(   parentNode.childSections.begin(),
-                                    parentNode.childSections.end(),
-                                    BySectionInfo( sectionInfo ) );
-                if( it == parentNode.childSections.end() ) {
-                    node = new SectionNode( incompleteStats );
-                    parentNode.childSections.push_back( node );
+                // If we got here, this is in fact a valid(ish) utf-8 sequence
+                for (std::size_t n = 0; n < encBytes; ++n) {
+                    os << m_str[idx + n];
                 }
-                else
-                    node = *it;
+                idx += encBytes - 1;
+                break;
             }
-            m_sectionStack.push_back( node );
-            m_deepestSection = node;
         }
+    }
 
-        virtual void assertionStarting( AssertionInfo const& ) CATCH_OVERRIDE {}
+    std::ostream& operator << ( std::ostream& os, XmlEncode const& xmlEncode ) {
+        xmlEncode.encodeTo( os );
+        return os;
+    }
 
-        virtual bool assertionEnded( AssertionStats const& assertionStats ) CATCH_OVERRIDE {
-            assert( !m_sectionStack.empty() );
-            SectionNode& sectionNode = *m_sectionStack.back();
-            sectionNode.assertions.push_back( assertionStats );
-            // AssertionResult holds a pointer to a temporary DecomposedExpression,
-            // which getExpandedExpression() calls to build the expression string.
-            // Our section stack copy of the assertionResult will likely outlive the
-            // temporary, so it must be expanded or discarded now to avoid calling
-            // a destroyed object later.
-            prepareExpandedExpression( sectionNode.assertions.back().assertionResult );
-            return true;
-        }
-        virtual void sectionEnded( SectionStats const& sectionStats ) CATCH_OVERRIDE {
-            assert( !m_sectionStack.empty() );
-            SectionNode& node = *m_sectionStack.back();
-            node.stats = sectionStats;
-            m_sectionStack.pop_back();
-        }
-        virtual void testCaseEnded( TestCaseStats const& testCaseStats ) CATCH_OVERRIDE {
-            Ptr<TestCaseNode> node = new TestCaseNode( testCaseStats );
-            assert( m_sectionStack.size() == 0 );
-            node->children.push_back( m_rootSection );
-            m_testCases.push_back( node );
-            m_rootSection.reset();
+    XmlWriter::ScopedElement::ScopedElement( XmlWriter* writer )
+    :   m_writer( writer )
+    {}
 
-            assert( m_deepestSection );
-            m_deepestSection->stdOut = testCaseStats.stdOut;
-            m_deepestSection->stdErr = testCaseStats.stdErr;
+    XmlWriter::ScopedElement::ScopedElement( ScopedElement&& other ) noexcept
+    :   m_writer( other.m_writer ){
+        other.m_writer = nullptr;
+    }
+    XmlWriter::ScopedElement& XmlWriter::ScopedElement::operator=( ScopedElement&& other ) noexcept {
+        if ( m_writer ) {
+            m_writer->endElement();
         }
-        virtual void testGroupEnded( TestGroupStats const& testGroupStats ) CATCH_OVERRIDE {
-            Ptr<TestGroupNode> node = new TestGroupNode( testGroupStats );
-            node->children.swap( m_testCases );
-            m_testGroups.push_back( node );
+        m_writer = other.m_writer;
+        other.m_writer = nullptr;
+        return *this;
+    }
+
+    XmlWriter::ScopedElement::~ScopedElement() {
+        if( m_writer )
+            m_writer->endElement();
+    }
+
+    XmlWriter::ScopedElement& XmlWriter::ScopedElement::writeText( std::string const& text, bool indent ) {
+        m_writer->writeText( text, indent );
+        return *this;
+    }
+
+    XmlWriter::XmlWriter( std::ostream& os ) : m_os( os )
+    {
+        writeDeclaration();
+    }
+
+    XmlWriter::~XmlWriter() {
+        while( !m_tags.empty() )
+            endElement();
+    }
+
+    XmlWriter& XmlWriter::startElement( std::string const& name ) {
+        ensureTagClosed();
+        newlineIfNecessary();
+        m_os << m_indent << '<' << name;
+        m_tags.push_back( name );
+        m_indent += "  ";
+        m_tagIsOpen = true;
+        return *this;
+    }
+
+    XmlWriter::ScopedElement XmlWriter::scopedElement( std::string const& name ) {
+        ScopedElement scoped( this );
+        startElement( name );
+        return scoped;
+    }
+
+    XmlWriter& XmlWriter::endElement() {
+        newlineIfNecessary();
+        m_indent = m_indent.substr( 0, m_indent.size()-2 );
+        if( m_tagIsOpen ) {
+            m_os << "/>";
+            m_tagIsOpen = false;
         }
-        virtual void testRunEnded( TestRunStats const& testRunStats ) CATCH_OVERRIDE {
-            Ptr<TestRunNode> node = new TestRunNode( testRunStats );
-            node->children.swap( m_testGroups );
-            m_testRuns.push_back( node );
-            testRunEndedCumulative();
+        else {
+            m_os << m_indent << "</" << m_tags.back() << ">";
         }
-        virtual void testRunEndedCumulative() = 0;
+        m_os << std::endl;
+        m_tags.pop_back();
+        return *this;
+    }
 
-        virtual void skipTest( TestCaseInfo const& ) CATCH_OVERRIDE {}
+    XmlWriter& XmlWriter::writeAttribute( std::string const& name, std::string const& attribute ) {
+        if( !name.empty() && !attribute.empty() )
+            m_os << ' ' << name << "=\"" << XmlEncode( attribute, XmlEncode::ForAttributes ) << '"';
+        return *this;
+    }
 
-        virtual void prepareExpandedExpression( AssertionResult& result ) const {
-            if( result.isOk() )
-                result.discardDecomposedExpression();
-            else
-                result.expandDecomposedExpression();
-        }
+    XmlWriter& XmlWriter::writeAttribute( std::string const& name, bool attribute ) {
+        m_os << ' ' << name << "=\"" << ( attribute ? "true" : "false" ) << '"';
+        return *this;
+    }
 
-        Ptr<IConfig const> m_config;
-        std::ostream& stream;
-        std::vector<AssertionStats> m_assertions;
-        std::vector<std::vector<Ptr<SectionNode> > > m_sections;
-        std::vector<Ptr<TestCaseNode> > m_testCases;
-        std::vector<Ptr<TestGroupNode> > m_testGroups;
+    XmlWriter& XmlWriter::writeText( std::string const& text, bool indent ) {
+        if( !text.empty() ){
+            bool tagWasOpen = m_tagIsOpen;
+            ensureTagClosed();
+            if( tagWasOpen && indent )
+                m_os << m_indent;
+            m_os << XmlEncode( text );
+            m_needsNewline = true;
+        }
+        return *this;
+    }
 
-        std::vector<Ptr<TestRunNode> > m_testRuns;
+    XmlWriter& XmlWriter::writeComment( std::string const& text ) {
+        ensureTagClosed();
+        m_os << m_indent << "<!--" << text << "-->";
+        m_needsNewline = true;
+        return *this;
+    }
 
-        Ptr<SectionNode> m_rootSection;
-        Ptr<SectionNode> m_deepestSection;
-        std::vector<Ptr<SectionNode> > m_sectionStack;
-        ReporterPreferences m_reporterPrefs;
+    void XmlWriter::writeStylesheetRef( std::string const& url ) {
+        m_os << "<?xml-stylesheet type=\"text/xsl\" href=\"" << url << "\"?>\n";
+    }
 
-    };
+    XmlWriter& XmlWriter::writeBlankLine() {
+        ensureTagClosed();
+        m_os << '\n';
+        return *this;
+    }
 
-    template<char C>
-    char const* getLineOfChars() {
-        static char line[CATCH_CONFIG_CONSOLE_WIDTH] = {0};
-        if( !*line ) {
-            std::memset( line, C, CATCH_CONFIG_CONSOLE_WIDTH-1 );
-            line[CATCH_CONFIG_CONSOLE_WIDTH-1] = 0;
+    void XmlWriter::ensureTagClosed() {
+        if( m_tagIsOpen ) {
+            m_os << ">" << std::endl;
+            m_tagIsOpen = false;
         }
-        return line;
     }
 
-    struct TestEventListenerBase : StreamingReporterBase {
-        TestEventListenerBase( ReporterConfig const& _config )
-        :   StreamingReporterBase( _config )
-        {}
+    void XmlWriter::writeDeclaration() {
+        m_os << "<?xml version=\"1.0\" encoding=\"UTF-8\"?>\n";
+    }
 
-        virtual void assertionStarting( AssertionInfo const& ) CATCH_OVERRIDE {}
-        virtual bool assertionEnded( AssertionStats const& ) CATCH_OVERRIDE {
-            return false;
+    void XmlWriter::newlineIfNecessary() {
+        if( m_needsNewline ) {
+            m_os << std::endl;
+            m_needsNewline = false;
         }
-    };
-
-} // end namespace Catch
+    }
+}
+// end catch_xmlwriter.cpp
+// start catch_reporter_bases.cpp
 
-// #included from: ../internal/catch_reporter_registrars.hpp
-#define TWOBLUECUBES_CATCH_REPORTER_REGISTRARS_HPP_INCLUDED
+#include <cstring>
+#include <cfloat>
+#include <cstdio>
+#include <cassert>
+#include <memory>
 
 namespace Catch {
+    void prepareExpandedExpression(AssertionResult& result) {
+        result.getExpandedExpression();
+    }
 
-    template<typename T>
-    class LegacyReporterRegistrar {
-
-        class ReporterFactory : public IReporterFactory {
-            virtual IStreamingReporter* create( ReporterConfig const& config ) const {
-                return new LegacyReporterAdapter( new T( config ) );
-            }
+    // Because formatting using c++ streams is stateful, drop down to C is required
+    // Alternatively we could use stringstream, but its performance is... not good.
+    std::string getFormattedDuration( double duration ) {
+        // Max exponent + 1 is required to represent the whole part
+        // + 1 for decimal point
+        // + 3 for the 3 decimal places
+        // + 1 for null terminator
+        const std::size_t maxDoubleSize = DBL_MAX_10_EXP + 1 + 1 + 3 + 1;
+        char buffer[maxDoubleSize];
 
-            virtual std::string getDescription() const {
-                return T::getDescription();
-            }
-        };
+        // Save previous errno, to prevent sprintf from overwriting it
+        ErrnoGuard guard;
+#ifdef _MSC_VER
+        sprintf_s(buffer, "%.3f", duration);
+#else
+        std::sprintf(buffer, "%.3f", duration);
+#endif
+        return std::string(buffer);
+    }
 
-    public:
+    std::string serializeFilters( std::vector<std::string> const& container ) {
+        ReusableStringStream oss;
+        bool first = true;
+        for (auto&& filter : container)
+        {
+            if (!first)
+                oss << ' ';
+            else
+                first = false;
 
-        LegacyReporterRegistrar( std::string const& name ) {
-            getMutableRegistryHub().registerReporter( name, new ReporterFactory() );
+            oss << filter;
         }
-    };
+        return oss.str();
+    }
 
-    template<typename T>
-    class ReporterRegistrar {
+    TestEventListenerBase::TestEventListenerBase(ReporterConfig const & _config)
+        :StreamingReporterBase(_config) {}
 
-        class ReporterFactory : public SharedImpl<IReporterFactory> {
+    std::set<Verbosity> TestEventListenerBase::getSupportedVerbosities() {
+        return { Verbosity::Quiet, Verbosity::Normal, Verbosity::High };
+    }
 
-            // *** Please Note ***:
-            // - If you end up here looking at a compiler error because it's trying to register
-            // your custom reporter class be aware that the native reporter interface has changed
-            // to IStreamingReporter. The "legacy" interface, IReporter, is still supported via
-            // an adapter. Just use REGISTER_LEGACY_REPORTER to take advantage of the adapter.
-            // However please consider updating to the new interface as the old one is now
-            // deprecated and will probably be removed quite soon!
-            // Please contact me via github if you have any questions at all about this.
-            // In fact, ideally, please contact me anyway to let me know you've hit this - as I have
-            // no idea who is actually using custom reporters at all (possibly no-one!).
-            // The new interface is designed to minimise exposure to interface changes in the future.
-            virtual IStreamingReporter* create( ReporterConfig const& config ) const {
-                return new T( config );
-            }
+    void TestEventListenerBase::assertionStarting(AssertionInfo const &) {}
 
-            virtual std::string getDescription() const {
-                return T::getDescription();
-            }
-        };
+    bool TestEventListenerBase::assertionEnded(AssertionStats const &) {
+        return false;
+    }
 
-    public:
+} // end namespace Catch
+// end catch_reporter_bases.cpp
+// start catch_reporter_compact.cpp
 
-        ReporterRegistrar( std::string const& name ) {
-            getMutableRegistryHub().registerReporter( name, new ReporterFactory() );
-        }
-    };
+namespace {
 
-    template<typename T>
-    class ListenerRegistrar {
+#ifdef CATCH_PLATFORM_MAC
+    const char* failedString() { return "FAILED"; }
+    const char* passedString() { return "PASSED"; }
+#else
+    const char* failedString() { return "failed"; }
+    const char* passedString() { return "passed"; }
+#endif
 
-        class ListenerFactory : public SharedImpl<IReporterFactory> {
+    // Colour::LightGrey
+    Catch::Colour::Code dimColour() { return Catch::Colour::FileName; }
 
-            virtual IStreamingReporter* create( ReporterConfig const& config ) const {
-                return new T( config );
-            }
-            virtual std::string getDescription() const {
-                return std::string();
-            }
-        };
+    std::string bothOrAll( std::size_t count ) {
+        return count == 1 ? std::string() :
+               count == 2 ? "both " : "all " ;
+    }
 
-    public:
+} // anon namespace
 
-        ListenerRegistrar() {
-            getMutableRegistryHub().registerListener( new ListenerFactory() );
-        }
-    };
+namespace Catch {
+namespace {
+// Colour, message variants:
+// - white: No tests ran.
+// -   red: Failed [both/all] N test cases, failed [both/all] M assertions.
+// - white: Passed [both/all] N test cases (no assertions).
+// -   red: Failed N tests cases, failed M assertions.
+// - green: Passed [both/all] N tests cases with M assertions.
+void printTotals(std::ostream& out, const Totals& totals) {
+    if (totals.testCases.total() == 0) {
+        out << "No tests ran.";
+    } else if (totals.testCases.failed == totals.testCases.total()) {
+        Colour colour(Colour::ResultError);
+        const std::string qualify_assertions_failed =
+            totals.assertions.failed == totals.assertions.total() ?
+            bothOrAll(totals.assertions.failed) : std::string();
+        out <<
+            "Failed " << bothOrAll(totals.testCases.failed)
+            << pluralise(totals.testCases.failed, "test case") << ", "
+            "failed " << qualify_assertions_failed <<
+            pluralise(totals.assertions.failed, "assertion") << '.';
+    } else if (totals.assertions.total() == 0) {
+        out <<
+            "Passed " << bothOrAll(totals.testCases.total())
+            << pluralise(totals.testCases.total(), "test case")
+            << " (no assertions).";
+    } else if (totals.assertions.failed) {
+        Colour colour(Colour::ResultError);
+        out <<
+            "Failed " << pluralise(totals.testCases.failed, "test case") << ", "
+            "failed " << pluralise(totals.assertions.failed, "assertion") << '.';
+    } else {
+        Colour colour(Colour::ResultSuccess);
+        out <<
+            "Passed " << bothOrAll(totals.testCases.passed)
+            << pluralise(totals.testCases.passed, "test case") <<
+            " with " << pluralise(totals.assertions.passed, "assertion") << '.';
+    }
 }
 
-#define INTERNAL_CATCH_REGISTER_LEGACY_REPORTER( name, reporterType ) \
-    namespace{ Catch::LegacyReporterRegistrar<reporterType> catch_internal_RegistrarFor##reporterType( name ); }
+// Implementation of CompactReporter formatting
+class AssertionPrinter {
+public:
+    AssertionPrinter& operator= (AssertionPrinter const&) = delete;
+    AssertionPrinter(AssertionPrinter const&) = delete;
+    AssertionPrinter(std::ostream& _stream, AssertionStats const& _stats, bool _printInfoMessages)
+        : stream(_stream)
+        , result(_stats.assertionResult)
+        , messages(_stats.infoMessages)
+        , itMessage(_stats.infoMessages.begin())
+        , printInfoMessages(_printInfoMessages) {}
+
+    void print() {
+        printSourceInfo();
+
+        itMessage = messages.begin();
+
+        switch (result.getResultType()) {
+        case ResultWas::Ok:
+            printResultType(Colour::ResultSuccess, passedString());
+            printOriginalExpression();
+            printReconstructedExpression();
+            if (!result.hasExpression())
+                printRemainingMessages(Colour::None);
+            else
+                printRemainingMessages();
+            break;
+        case ResultWas::ExpressionFailed:
+            if (result.isOk())
+                printResultType(Colour::ResultSuccess, failedString() + std::string(" - but was ok"));
+            else
+                printResultType(Colour::Error, failedString());
+            printOriginalExpression();
+            printReconstructedExpression();
+            printRemainingMessages();
+            break;
+        case ResultWas::ThrewException:
+            printResultType(Colour::Error, failedString());
+            printIssue("unexpected exception with message:");
+            printMessage();
+            printExpressionWas();
+            printRemainingMessages();
+            break;
+        case ResultWas::FatalErrorCondition:
+            printResultType(Colour::Error, failedString());
+            printIssue("fatal error condition with message:");
+            printMessage();
+            printExpressionWas();
+            printRemainingMessages();
+            break;
+        case ResultWas::DidntThrowException:
+            printResultType(Colour::Error, failedString());
+            printIssue("expected exception, got none");
+            printExpressionWas();
+            printRemainingMessages();
+            break;
+        case ResultWas::Info:
+            printResultType(Colour::None, "info");
+            printMessage();
+            printRemainingMessages();
+            break;
+        case ResultWas::Warning:
+            printResultType(Colour::None, "warning");
+            printMessage();
+            printRemainingMessages();
+            break;
+        case ResultWas::ExplicitFailure:
+            printResultType(Colour::Error, failedString());
+            printIssue("explicitly");
+            printRemainingMessages(Colour::None);
+            break;
+            // These cases are here to prevent compiler warnings
+        case ResultWas::Unknown:
+        case ResultWas::FailureBit:
+        case ResultWas::Exception:
+            printResultType(Colour::Error, "** internal error **");
+            break;
+        }
+    }
 
-#define INTERNAL_CATCH_REGISTER_REPORTER( name, reporterType ) \
-    namespace{ Catch::ReporterRegistrar<reporterType> catch_internal_RegistrarFor##reporterType( name ); }
+private:
+    void printSourceInfo() const {
+        Colour colourGuard(Colour::FileName);
+        stream << result.getSourceInfo() << ':';
+    }
 
-// Deprecated - use the form without INTERNAL_
-#define INTERNAL_CATCH_REGISTER_LISTENER( listenerType ) \
-    namespace{ Catch::ListenerRegistrar<listenerType> catch_internal_RegistrarFor##listenerType; }
+    void printResultType(Colour::Code colour, std::string const& passOrFail) const {
+        if (!passOrFail.empty()) {
+            {
+                Colour colourGuard(colour);
+                stream << ' ' << passOrFail;
+            }
+            stream << ':';
+        }
+    }
 
-#define CATCH_REGISTER_LISTENER( listenerType ) \
-    namespace{ Catch::ListenerRegistrar<listenerType> catch_internal_RegistrarFor##listenerType; }
+    void printIssue(std::string const& issue) const {
+        stream << ' ' << issue;
+    }
 
-// #included from: ../internal/catch_xmlwriter.hpp
-#define TWOBLUECUBES_CATCH_XMLWRITER_HPP_INCLUDED
+    void printExpressionWas() {
+        if (result.hasExpression()) {
+            stream << ';';
+            {
+                Colour colour(dimColour());
+                stream << " expression was:";
+            }
+            printOriginalExpression();
+        }
+    }
 
-#include <sstream>
-#include <string>
-#include <vector>
-#include <iomanip>
+    void printOriginalExpression() const {
+        if (result.hasExpression()) {
+            stream << ' ' << result.getExpression();
+        }
+    }
 
-namespace Catch {
+    void printReconstructedExpression() const {
+        if (result.hasExpandedExpression()) {
+            {
+                Colour colour(dimColour());
+                stream << " for: ";
+            }
+            stream << result.getExpandedExpression();
+        }
+    }
 
-    class XmlEncode {
-    public:
-        enum ForWhat { ForTextNodes, ForAttributes };
+    void printMessage() {
+        if (itMessage != messages.end()) {
+            stream << " '" << itMessage->message << '\'';
+            ++itMessage;
+        }
+    }
 
-        XmlEncode( std::string const& str, ForWhat forWhat = ForTextNodes )
-        :   m_str( str ),
-            m_forWhat( forWhat )
-        {}
+    void printRemainingMessages(Colour::Code colour = dimColour()) {
+        if (itMessage == messages.end())
+            return;
 
-        void encodeTo( std::ostream& os ) const {
-
-            // Apostrophe escaping not necessary if we always use " to write attributes
-            // (see: http://www.w3.org/TR/xml/#syntax)
-
-            for( std::size_t i = 0; i < m_str.size(); ++ i ) {
-                char c = m_str[i];
-                switch( c ) {
-                    case '<':   os << "&lt;"; break;
-                    case '&':   os << "&amp;"; break;
-
-                    case '>':
-                        // See: http://www.w3.org/TR/xml/#syntax
-                        if( i > 2 && m_str[i-1] == ']' && m_str[i-2] == ']' )
-                            os << "&gt;";
-                        else
-                            os << c;
-                        break;
-
-                    case '\"':
-                        if( m_forWhat == ForAttributes )
-                            os << "&quot;";
-                        else
-                            os << c;
-                        break;
-
-                    default:
-                        // Escape control chars - based on contribution by @espenalb in PR #465 and
-                        // by @mrpi PR #588
-                        if ( ( c >= 0 && c < '\x09' ) || ( c > '\x0D' && c < '\x20') || c=='\x7F' ) {
-                            // see http://stackoverflow.com/questions/404107/why-are-control-characters-illegal-in-xml-1-0
-                            os << "\\x" << std::uppercase << std::hex << std::setfill('0') << std::setw(2)
-                               << static_cast<int>( c );
-                        }
-                        else
-                            os << c;
+        const auto itEnd = messages.cend();
+        const auto N = static_cast<std::size_t>(std::distance(itMessage, itEnd));
+
+        {
+            Colour colourGuard(colour);
+            stream << " with " << pluralise(N, "message") << ':';
+        }
+
+        while (itMessage != itEnd) {
+            // If this assertion is a warning ignore any INFO messages
+            if (printInfoMessages || itMessage->type != ResultWas::Info) {
+                printMessage();
+                if (itMessage != itEnd) {
+                    Colour colourGuard(dimColour());
+                    stream << " and";
                 }
+                continue;
             }
+            ++itMessage;
         }
+    }
 
-        friend std::ostream& operator << ( std::ostream& os, XmlEncode const& xmlEncode ) {
-            xmlEncode.encodeTo( os );
-            return os;
-        }
+private:
+    std::ostream& stream;
+    AssertionResult const& result;
+    std::vector<MessageInfo> messages;
+    std::vector<MessageInfo>::const_iterator itMessage;
+    bool printInfoMessages;
+};
 
-    private:
-        std::string m_str;
-        ForWhat m_forWhat;
-    };
+} // anon namespace
 
-    class XmlWriter {
-    public:
+        std::string CompactReporter::getDescription() {
+            return "Reports test results on a single line, suitable for IDEs";
+        }
 
-        class ScopedElement {
-        public:
-            ScopedElement( XmlWriter* writer )
-            :   m_writer( writer )
-            {}
+        ReporterPreferences CompactReporter::getPreferences() const {
+            return m_reporterPrefs;
+        }
 
-            ScopedElement( ScopedElement const& other )
-            :   m_writer( other.m_writer ){
-                other.m_writer = CATCH_NULL;
-            }
+        void CompactReporter::noMatchingTestCases( std::string const& spec ) {
+            stream << "No test cases matched '" << spec << '\'' << std::endl;
+        }
 
-            ~ScopedElement() {
-                if( m_writer )
-                    m_writer->endElement();
-            }
+        void CompactReporter::assertionStarting( AssertionInfo const& ) {}
 
-            ScopedElement& writeText( std::string const& text, bool indent = true ) {
-                m_writer->writeText( text, indent );
-                return *this;
-            }
+        bool CompactReporter::assertionEnded( AssertionStats const& _assertionStats ) {
+            AssertionResult const& result = _assertionStats.assertionResult;
 
-            template<typename T>
-            ScopedElement& writeAttribute( std::string const& name, T const& attribute ) {
-                m_writer->writeAttribute( name, attribute );
-                return *this;
+            bool printInfoMessages = true;
+
+            // Drop out if result was successful and we're not printing those
+            if( !m_config->includeSuccessfulResults() && result.isOk() ) {
+                if( result.getResultType() != ResultWas::Warning )
+                    return false;
+                printInfoMessages = false;
             }
 
-        private:
-            mutable XmlWriter* m_writer;
-        };
+            AssertionPrinter printer( stream, _assertionStats, printInfoMessages );
+            printer.print();
 
-        XmlWriter()
-        :   m_tagIsOpen( false ),
-            m_needsNewline( false ),
-            m_os( Catch::cout() )
-        {
-            writeDeclaration();
+            stream << std::endl;
+            return true;
         }
 
-        XmlWriter( std::ostream& os )
-        :   m_tagIsOpen( false ),
-            m_needsNewline( false ),
-            m_os( os )
-        {
-            writeDeclaration();
+        void CompactReporter::sectionEnded(SectionStats const& _sectionStats) {
+            if (m_config->showDurations() == ShowDurations::Always) {
+                stream << getFormattedDuration(_sectionStats.durationInSeconds) << " s: " << _sectionStats.sectionInfo.name << std::endl;
+            }
         }
 
-        ~XmlWriter() {
-            while( !m_tags.empty() )
-                endElement();
+        void CompactReporter::testRunEnded( TestRunStats const& _testRunStats ) {
+            printTotals( stream, _testRunStats.totals );
+            stream << '\n' << std::endl;
+            StreamingReporterBase::testRunEnded( _testRunStats );
         }
 
-        XmlWriter& startElement( std::string const& name ) {
-            ensureTagClosed();
-            newlineIfNecessary();
-            m_os << m_indent << '<' << name;
-            m_tags.push_back( name );
-            m_indent += "  ";
-            m_tagIsOpen = true;
-            return *this;
-        }
+        CompactReporter::~CompactReporter() {}
 
-        ScopedElement scopedElement( std::string const& name ) {
-            ScopedElement scoped( this );
-            startElement( name );
-            return scoped;
-        }
+    CATCH_REGISTER_REPORTER( "compact", CompactReporter )
 
-        XmlWriter& endElement() {
-            newlineIfNecessary();
-            m_indent = m_indent.substr( 0, m_indent.size()-2 );
-            if( m_tagIsOpen ) {
-                m_os << "/>";
-                m_tagIsOpen = false;
-            }
-            else {
-                m_os << m_indent << "</" << m_tags.back() << ">";
-            }
-            m_os << std::endl;
-            m_tags.pop_back();
-            return *this;
-        }
+} // end namespace Catch
+// end catch_reporter_compact.cpp
+// start catch_reporter_console.cpp
 
-        XmlWriter& writeAttribute( std::string const& name, std::string const& attribute ) {
-            if( !name.empty() && !attribute.empty() )
-                m_os << ' ' << name << "=\"" << XmlEncode( attribute, XmlEncode::ForAttributes ) << '"';
-            return *this;
-        }
+#include <cfloat>
+#include <cstdio>
 
-        XmlWriter& writeAttribute( std::string const& name, bool attribute ) {
-            m_os << ' ' << name << "=\"" << ( attribute ? "true" : "false" ) << '"';
-            return *this;
-        }
+#if defined(_MSC_VER)
+#pragma warning(push)
+#pragma warning(disable:4061) // Not all labels are EXPLICITLY handled in switch
+ // Note that 4062 (not all labels are handled and default is missing) is enabled
+#endif
 
-        template<typename T>
-        XmlWriter& writeAttribute( std::string const& name, T const& attribute ) {
-            std::ostringstream oss;
-            oss << attribute;
-            return writeAttribute( name, oss.str() );
-        }
+#if defined(__clang__)
+#  pragma clang diagnostic push
+// For simplicity, benchmarking-only helpers are always enabled
+#  pragma clang diagnostic ignored "-Wunused-function"
+#endif
+
+namespace Catch {
+
+namespace {
 
-        XmlWriter& writeText( std::string const& text, bool indent = true ) {
-            if( !text.empty() ){
-                bool tagWasOpen = m_tagIsOpen;
-                ensureTagClosed();
-                if( tagWasOpen && indent )
-                    m_os << m_indent;
-                m_os << XmlEncode( text );
-                m_needsNewline = true;
+// Formatter impl for ConsoleReporter
+class ConsoleAssertionPrinter {
+public:
+    ConsoleAssertionPrinter& operator= (ConsoleAssertionPrinter const&) = delete;
+    ConsoleAssertionPrinter(ConsoleAssertionPrinter const&) = delete;
+    ConsoleAssertionPrinter(std::ostream& _stream, AssertionStats const& _stats, bool _printInfoMessages)
+        : stream(_stream),
+        stats(_stats),
+        result(_stats.assertionResult),
+        colour(Colour::None),
+        message(result.getMessage()),
+        messages(_stats.infoMessages),
+        printInfoMessages(_printInfoMessages) {
+        switch (result.getResultType()) {
+        case ResultWas::Ok:
+            colour = Colour::Success;
+            passOrFail = "PASSED";
+            //if( result.hasMessage() )
+            if (_stats.infoMessages.size() == 1)
+                messageLabel = "with message";
+            if (_stats.infoMessages.size() > 1)
+                messageLabel = "with messages";
+            break;
+        case ResultWas::ExpressionFailed:
+            if (result.isOk()) {
+                colour = Colour::Success;
+                passOrFail = "FAILED - but was ok";
+            } else {
+                colour = Colour::Error;
+                passOrFail = "FAILED";
             }
-            return *this;
+            if (_stats.infoMessages.size() == 1)
+                messageLabel = "with message";
+            if (_stats.infoMessages.size() > 1)
+                messageLabel = "with messages";
+            break;
+        case ResultWas::ThrewException:
+            colour = Colour::Error;
+            passOrFail = "FAILED";
+            messageLabel = "due to unexpected exception with ";
+            if (_stats.infoMessages.size() == 1)
+                messageLabel += "message";
+            if (_stats.infoMessages.size() > 1)
+                messageLabel += "messages";
+            break;
+        case ResultWas::FatalErrorCondition:
+            colour = Colour::Error;
+            passOrFail = "FAILED";
+            messageLabel = "due to a fatal error condition";
+            break;
+        case ResultWas::DidntThrowException:
+            colour = Colour::Error;
+            passOrFail = "FAILED";
+            messageLabel = "because no exception was thrown where one was expected";
+            break;
+        case ResultWas::Info:
+            messageLabel = "info";
+            break;
+        case ResultWas::Warning:
+            messageLabel = "warning";
+            break;
+        case ResultWas::ExplicitFailure:
+            passOrFail = "FAILED";
+            colour = Colour::Error;
+            if (_stats.infoMessages.size() == 1)
+                messageLabel = "explicitly with message";
+            if (_stats.infoMessages.size() > 1)
+                messageLabel = "explicitly with messages";
+            break;
+            // These cases are here to prevent compiler warnings
+        case ResultWas::Unknown:
+        case ResultWas::FailureBit:
+        case ResultWas::Exception:
+            passOrFail = "** internal error **";
+            colour = Colour::Error;
+            break;
+        }
+    }
+
+    void print() const {
+        printSourceInfo();
+        if (stats.totals.assertions.total() > 0) {
+            printResultType();
+            printOriginalExpression();
+            printReconstructedExpression();
+        } else {
+            stream << '\n';
         }
+        printMessage();
+    }
 
-        XmlWriter& writeComment( std::string const& text ) {
-            ensureTagClosed();
-            m_os << m_indent << "<!--" << text << "-->";
-            m_needsNewline = true;
-            return *this;
+private:
+    void printResultType() const {
+        if (!passOrFail.empty()) {
+            Colour colourGuard(colour);
+            stream << passOrFail << ":\n";
         }
-
-        void writeStylesheetRef( std::string const& url ) {
-            m_os << "<?xml-stylesheet type=\"text/xsl\" href=\"" << url << "\"?>\n";
+    }
+    void printOriginalExpression() const {
+        if (result.hasExpression()) {
+            Colour colourGuard(Colour::OriginalExpression);
+            stream << "  ";
+            stream << result.getExpressionInMacro();
+            stream << '\n';
         }
-
-        XmlWriter& writeBlankLine() {
-            ensureTagClosed();
-            m_os << '\n';
-            return *this;
+    }
+    void printReconstructedExpression() const {
+        if (result.hasExpandedExpression()) {
+            stream << "with expansion:\n";
+            Colour colourGuard(Colour::ReconstructedExpression);
+            stream << Column(result.getExpandedExpression()).indent(2) << '\n';
         }
-
-        void ensureTagClosed() {
-            if( m_tagIsOpen ) {
-                m_os << ">" << std::endl;
-                m_tagIsOpen = false;
-            }
+    }
+    void printMessage() const {
+        if (!messageLabel.empty())
+            stream << messageLabel << ':' << '\n';
+        for (auto const& msg : messages) {
+            // If this assertion is a warning ignore any INFO messages
+            if (printInfoMessages || msg.type != ResultWas::Info)
+                stream << Column(msg.message).indent(2) << '\n';
         }
+    }
+    void printSourceInfo() const {
+        Colour colourGuard(Colour::FileName);
+        stream << result.getSourceInfo() << ": ";
+    }
 
-    private:
-        XmlWriter( XmlWriter const& );
-        void operator=( XmlWriter const& );
+    std::ostream& stream;
+    AssertionStats const& stats;
+    AssertionResult const& result;
+    Colour::Code colour;
+    std::string passOrFail;
+    std::string messageLabel;
+    std::string message;
+    std::vector<MessageInfo> messages;
+    bool printInfoMessages;
+};
+
+std::size_t makeRatio(std::size_t number, std::size_t total) {
+    std::size_t ratio = total > 0 ? CATCH_CONFIG_CONSOLE_WIDTH * number / total : 0;
+    return (ratio == 0 && number > 0) ? 1 : ratio;
+}
+
+std::size_t& findMax(std::size_t& i, std::size_t& j, std::size_t& k) {
+    if (i > j && i > k)
+        return i;
+    else if (j > k)
+        return j;
+    else
+        return k;
+}
+
+struct ColumnInfo {
+    enum Justification { Left, Right };
+    std::string name;
+    int width;
+    Justification justification;
+};
+struct ColumnBreak {};
+struct RowBreak {};
 
-        void writeDeclaration() {
-            m_os << "<?xml version=\"1.0\" encoding=\"UTF-8\"?>\n";
+class Duration {
+    enum class Unit {
+        Auto,
+        Nanoseconds,
+        Microseconds,
+        Milliseconds,
+        Seconds,
+        Minutes
+    };
+    static const uint64_t s_nanosecondsInAMicrosecond = 1000;
+    static const uint64_t s_nanosecondsInAMillisecond = 1000 * s_nanosecondsInAMicrosecond;
+    static const uint64_t s_nanosecondsInASecond = 1000 * s_nanosecondsInAMillisecond;
+    static const uint64_t s_nanosecondsInAMinute = 60 * s_nanosecondsInASecond;
+
+    uint64_t m_inNanoseconds;
+    Unit m_units;
+
+public:
+	explicit Duration(double inNanoseconds, Unit units = Unit::Auto)
+        : Duration(static_cast<uint64_t>(inNanoseconds), units) {
+    }
+
+    explicit Duration(uint64_t inNanoseconds, Unit units = Unit::Auto)
+        : m_inNanoseconds(inNanoseconds),
+        m_units(units) {
+        if (m_units == Unit::Auto) {
+            if (m_inNanoseconds < s_nanosecondsInAMicrosecond)
+                m_units = Unit::Nanoseconds;
+            else if (m_inNanoseconds < s_nanosecondsInAMillisecond)
+                m_units = Unit::Microseconds;
+            else if (m_inNanoseconds < s_nanosecondsInASecond)
+                m_units = Unit::Milliseconds;
+            else if (m_inNanoseconds < s_nanosecondsInAMinute)
+                m_units = Unit::Seconds;
+            else
+                m_units = Unit::Minutes;
         }
 
-        void newlineIfNecessary() {
-            if( m_needsNewline ) {
-                m_os << std::endl;
-                m_needsNewline = false;
-            }
+    }
+
+    auto value() const -> double {
+        switch (m_units) {
+        case Unit::Microseconds:
+            return m_inNanoseconds / static_cast<double>(s_nanosecondsInAMicrosecond);
+        case Unit::Milliseconds:
+            return m_inNanoseconds / static_cast<double>(s_nanosecondsInAMillisecond);
+        case Unit::Seconds:
+            return m_inNanoseconds / static_cast<double>(s_nanosecondsInASecond);
+        case Unit::Minutes:
+            return m_inNanoseconds / static_cast<double>(s_nanosecondsInAMinute);
+        default:
+            return static_cast<double>(m_inNanoseconds);
+        }
+    }
+    auto unitsAsString() const -> std::string {
+        switch (m_units) {
+        case Unit::Nanoseconds:
+            return "ns";
+        case Unit::Microseconds:
+            return "us";
+        case Unit::Milliseconds:
+            return "ms";
+        case Unit::Seconds:
+            return "s";
+        case Unit::Minutes:
+            return "m";
+        default:
+            return "** internal error **";
         }
 
-        bool m_tagIsOpen;
-        bool m_needsNewline;
-        std::vector<std::string> m_tags;
-        std::string m_indent;
-        std::ostream& m_os;
-    };
+    }
+    friend auto operator << (std::ostream& os, Duration const& duration) -> std::ostream& {
+        return os << duration.value() << ' ' << duration.unitsAsString();
+    }
+};
+} // end anon namespace
+
+class TablePrinter {
+    std::ostream& m_os;
+    std::vector<ColumnInfo> m_columnInfos;
+    std::ostringstream m_oss;
+    int m_currentColumn = -1;
+    bool m_isOpen = false;
 
-}
-// #included from: catch_reenable_warnings.h
+public:
+    TablePrinter( std::ostream& os, std::vector<ColumnInfo> columnInfos )
+    :   m_os( os ),
+        m_columnInfos( std::move( columnInfos ) ) {}
 
-#define TWOBLUECUBES_CATCH_REENABLE_WARNINGS_H_INCLUDED
+    auto columnInfos() const -> std::vector<ColumnInfo> const& {
+        return m_columnInfos;
+    }
 
-#ifdef __clang__
-#    ifdef __ICC // icpc defines the __clang__ macro
-#        pragma warning(pop)
-#    else
-#        pragma clang diagnostic pop
-#    endif
-#elif defined __GNUC__
-#    pragma GCC diagnostic pop
-#endif
+    void open() {
+        if (!m_isOpen) {
+            m_isOpen = true;
+            *this << RowBreak();
 
+			Columns headerCols;
+			Spacer spacer(2);
+			for (auto const& info : m_columnInfos) {
+				headerCols += Column(info.name).width(static_cast<std::size_t>(info.width - 2));
+				headerCols += spacer;
+			}
+			m_os << headerCols << '\n';
 
-namespace Catch {
-    class XmlReporter : public StreamingReporterBase {
-    public:
-        XmlReporter( ReporterConfig const& _config )
-        :   StreamingReporterBase( _config ),
-            m_xml(_config.stream()),
-            m_sectionDepth( 0 )
-        {
-            m_reporterPrefs.shouldRedirectStdOut = true;
+            m_os << Catch::getLineOfChars<'-'>() << '\n';
         }
+    }
+    void close() {
+        if (m_isOpen) {
+            *this << RowBreak();
+            m_os << std::endl;
+            m_isOpen = false;
+        }
+    }
 
-        virtual ~XmlReporter() CATCH_OVERRIDE;
+    template<typename T>
+    friend TablePrinter& operator << (TablePrinter& tp, T const& value) {
+        tp.m_oss << value;
+        return tp;
+    }
+
+    friend TablePrinter& operator << (TablePrinter& tp, ColumnBreak) {
+        auto colStr = tp.m_oss.str();
+        const auto strSize = colStr.size();
+        tp.m_oss.str("");
+        tp.open();
+        if (tp.m_currentColumn == static_cast<int>(tp.m_columnInfos.size() - 1)) {
+            tp.m_currentColumn = -1;
+            tp.m_os << '\n';
+        }
+        tp.m_currentColumn++;
+
+        auto colInfo = tp.m_columnInfos[tp.m_currentColumn];
+        auto padding = (strSize + 1 < static_cast<std::size_t>(colInfo.width))
+            ? std::string(colInfo.width - (strSize + 1), ' ')
+            : std::string();
+        if (colInfo.justification == ColumnInfo::Left)
+            tp.m_os << colStr << padding << ' ';
+        else
+            tp.m_os << padding << colStr << ' ';
+        return tp;
+    }
 
-        static std::string getDescription() {
-            return "Reports test results as an XML document";
+    friend TablePrinter& operator << (TablePrinter& tp, RowBreak) {
+        if (tp.m_currentColumn > 0) {
+            tp.m_os << '\n';
+            tp.m_currentColumn = -1;
         }
+        return tp;
+    }
+};
 
-        virtual std::string getStylesheetRef() const {
-            return std::string();
+ConsoleReporter::ConsoleReporter(ReporterConfig const& config)
+    : StreamingReporterBase(config),
+    m_tablePrinter(new TablePrinter(config.stream(),
+        [&config]() -> std::vector<ColumnInfo> {
+        if (config.fullConfig()->benchmarkNoAnalysis())
+        {
+            return{
+                { "benchmark name", CATCH_CONFIG_CONSOLE_WIDTH - 43, ColumnInfo::Left },
+                { "     samples", 14, ColumnInfo::Right },
+                { "  iterations", 14, ColumnInfo::Right },
+                { "        mean", 14, ColumnInfo::Right }
+            };
         }
-
-        void writeSourceInfo( SourceLineInfo const& sourceInfo ) {
-            m_xml
-                .writeAttribute( "filename", sourceInfo.file )
-                .writeAttribute( "line", sourceInfo.line );
+        else
+        {
+            return{
+                { "benchmark name", CATCH_CONFIG_CONSOLE_WIDTH - 32, ColumnInfo::Left },
+                { "samples      mean       std dev", 14, ColumnInfo::Right },
+                { "iterations   low mean   low std dev", 14, ColumnInfo::Right },
+                { "estimated    high mean  high std dev", 14, ColumnInfo::Right }
+            };
         }
+    }())) {}
+ConsoleReporter::~ConsoleReporter() = default;
 
-    public: // StreamingReporterBase
+std::string ConsoleReporter::getDescription() {
+    return "Reports test results as plain lines of text";
+}
 
-        virtual void noMatchingTestCases( std::string const& s ) CATCH_OVERRIDE {
-            StreamingReporterBase::noMatchingTestCases( s );
-        }
+void ConsoleReporter::noMatchingTestCases(std::string const& spec) {
+    stream << "No test cases matched '" << spec << '\'' << std::endl;
+}
 
-        virtual void testRunStarting( TestRunInfo const& testInfo ) CATCH_OVERRIDE {
-            StreamingReporterBase::testRunStarting( testInfo );
-            std::string stylesheetRef = getStylesheetRef();
-            if( !stylesheetRef.empty() )
-                m_xml.writeStylesheetRef( stylesheetRef );
-            m_xml.startElement( "Catch" );
-            if( !m_config->name().empty() )
-                m_xml.writeAttribute( "name", m_config->name() );
-        }
+void ConsoleReporter::reportInvalidArguments(std::string const&arg){
+    stream << "Invalid Filter: " << arg << std::endl;
+}
 
-        virtual void testGroupStarting( GroupInfo const& groupInfo ) CATCH_OVERRIDE {
-            StreamingReporterBase::testGroupStarting( groupInfo );
-            m_xml.startElement( "Group" )
-                .writeAttribute( "name", groupInfo.name );
-        }
+void ConsoleReporter::assertionStarting(AssertionInfo const&) {}
 
-        virtual void testCaseStarting( TestCaseInfo const& testInfo ) CATCH_OVERRIDE {
-            StreamingReporterBase::testCaseStarting(testInfo);
-            m_xml.startElement( "TestCase" )
-                .writeAttribute( "name", trim( testInfo.name ) )
-                .writeAttribute( "description", testInfo.description )
-                .writeAttribute( "tags", testInfo.tagsAsString );
+bool ConsoleReporter::assertionEnded(AssertionStats const& _assertionStats) {
+    AssertionResult const& result = _assertionStats.assertionResult;
 
-            writeSourceInfo( testInfo.lineInfo );
+    bool includeResults = m_config->includeSuccessfulResults() || !result.isOk();
 
-            if ( m_config->showDurations() == ShowDurations::Always )
-                m_testCaseTimer.start();
-            m_xml.ensureTagClosed();
-        }
+    // Drop out if result was successful but we're not printing them.
+    if (!includeResults && result.getResultType() != ResultWas::Warning)
+        return false;
 
-        virtual void sectionStarting( SectionInfo const& sectionInfo ) CATCH_OVERRIDE {
-            StreamingReporterBase::sectionStarting( sectionInfo );
-            if( m_sectionDepth++ > 0 ) {
-                m_xml.startElement( "Section" )
-                    .writeAttribute( "name", trim( sectionInfo.name ) )
-                    .writeAttribute( "description", sectionInfo.description );
-                writeSourceInfo( sectionInfo.lineInfo );
-                m_xml.ensureTagClosed();
-            }
-        }
+    lazyPrint();
 
-        virtual void assertionStarting( AssertionInfo const& ) CATCH_OVERRIDE { }
+    ConsoleAssertionPrinter printer(stream, _assertionStats, includeResults);
+    printer.print();
+    stream << std::endl;
+    return true;
+}
 
-        virtual bool assertionEnded( AssertionStats const& assertionStats ) CATCH_OVERRIDE {
+void ConsoleReporter::sectionStarting(SectionInfo const& _sectionInfo) {
+    m_tablePrinter->close();
+    m_headerPrinted = false;
+    StreamingReporterBase::sectionStarting(_sectionInfo);
+}
+void ConsoleReporter::sectionEnded(SectionStats const& _sectionStats) {
+    m_tablePrinter->close();
+    if (_sectionStats.missingAssertions) {
+        lazyPrint();
+        Colour colour(Colour::ResultError);
+        if (m_sectionStack.size() > 1)
+            stream << "\nNo assertions in section";
+        else
+            stream << "\nNo assertions in test case";
+        stream << " '" << _sectionStats.sectionInfo.name << "'\n" << std::endl;
+    }
+    if (m_config->showDurations() == ShowDurations::Always) {
+        stream << getFormattedDuration(_sectionStats.durationInSeconds) << " s: " << _sectionStats.sectionInfo.name << std::endl;
+    }
+    if (m_headerPrinted) {
+        m_headerPrinted = false;
+    }
+    StreamingReporterBase::sectionEnded(_sectionStats);
+}
 
-            AssertionResult const& result = assertionStats.assertionResult;
+#if defined(CATCH_CONFIG_ENABLE_BENCHMARKING)
+void ConsoleReporter::benchmarkPreparing(std::string const& name) {
+	lazyPrintWithoutClosingBenchmarkTable();
 
-            bool includeResults = m_config->includeSuccessfulResults() || !result.isOk();
+	auto nameCol = Column(name).width(static_cast<std::size_t>(m_tablePrinter->columnInfos()[0].width - 2));
 
-            if( includeResults ) {
-                // Print any info messages in <Info> tags.
-                for( std::vector<MessageInfo>::const_iterator it = assertionStats.infoMessages.begin(), itEnd = assertionStats.infoMessages.end();
-                     it != itEnd;
-                     ++it ) {
-                    if( it->type == ResultWas::Info ) {
-                        m_xml.scopedElement( "Info" )
-                                .writeText( it->message );
-                    } else if ( it->type == ResultWas::Warning ) {
-                        m_xml.scopedElement( "Warning" )
-                                .writeText( it->message );
-                    }
-                }
-            }
+	bool firstLine = true;
+	for (auto line : nameCol) {
+		if (!firstLine)
+			(*m_tablePrinter) << ColumnBreak() << ColumnBreak() << ColumnBreak();
+		else
+			firstLine = false;
 
-            // Drop out if result was successful but we're not printing them.
-            if( !includeResults && result.getResultType() != ResultWas::Warning )
-                return true;
+		(*m_tablePrinter) << line << ColumnBreak();
+	}
+}
+
+void ConsoleReporter::benchmarkStarting(BenchmarkInfo const& info) {
+    (*m_tablePrinter) << info.samples << ColumnBreak()
+        << info.iterations << ColumnBreak();
+    if (!m_config->benchmarkNoAnalysis())
+        (*m_tablePrinter) << Duration(info.estimatedDuration) << ColumnBreak();
+}
+void ConsoleReporter::benchmarkEnded(BenchmarkStats<> const& stats) {
+    if (m_config->benchmarkNoAnalysis())
+    {
+        (*m_tablePrinter) << Duration(stats.mean.point.count()) << ColumnBreak();
+    }
+    else
+    {
+        (*m_tablePrinter) << ColumnBreak()
+            << Duration(stats.mean.point.count()) << ColumnBreak()
+            << Duration(stats.mean.lower_bound.count()) << ColumnBreak()
+            << Duration(stats.mean.upper_bound.count()) << ColumnBreak() << ColumnBreak()
+            << Duration(stats.standardDeviation.point.count()) << ColumnBreak()
+            << Duration(stats.standardDeviation.lower_bound.count()) << ColumnBreak()
+            << Duration(stats.standardDeviation.upper_bound.count()) << ColumnBreak() << ColumnBreak() << ColumnBreak() << ColumnBreak() << ColumnBreak();
+    }
+}
 
-            // Print the expression if there is one.
-            if( result.hasExpression() ) {
-                m_xml.startElement( "Expression" )
-                    .writeAttribute( "success", result.succeeded() )
-                    .writeAttribute( "type", result.getTestMacroName() );
+void ConsoleReporter::benchmarkFailed(std::string const& error) {
+	Colour colour(Colour::Red);
+    (*m_tablePrinter)
+        << "Benchmark failed (" << error << ')'
+        << ColumnBreak() << RowBreak();
+}
+#endif // CATCH_CONFIG_ENABLE_BENCHMARKING
 
-                writeSourceInfo( result.getSourceInfo() );
+void ConsoleReporter::testCaseEnded(TestCaseStats const& _testCaseStats) {
+    m_tablePrinter->close();
+    StreamingReporterBase::testCaseEnded(_testCaseStats);
+    m_headerPrinted = false;
+}
+void ConsoleReporter::testGroupEnded(TestGroupStats const& _testGroupStats) {
+    if (currentGroupInfo.used) {
+        printSummaryDivider();
+        stream << "Summary for group '" << _testGroupStats.groupInfo.name << "':\n";
+        printTotals(_testGroupStats.totals);
+        stream << '\n' << std::endl;
+    }
+    StreamingReporterBase::testGroupEnded(_testGroupStats);
+}
+void ConsoleReporter::testRunEnded(TestRunStats const& _testRunStats) {
+    printTotalsDivider(_testRunStats.totals);
+    printTotals(_testRunStats.totals);
+    stream << std::endl;
+    StreamingReporterBase::testRunEnded(_testRunStats);
+}
+void ConsoleReporter::testRunStarting(TestRunInfo const& _testInfo) {
+    StreamingReporterBase::testRunStarting(_testInfo);
+    printTestFilters();
+}
 
-                m_xml.scopedElement( "Original" )
-                    .writeText( result.getExpression() );
-                m_xml.scopedElement( "Expanded" )
-                    .writeText( result.getExpandedExpression() );
-            }
+void ConsoleReporter::lazyPrint() {
 
-            // And... Print a result applicable to each result type.
-            switch( result.getResultType() ) {
-                case ResultWas::ThrewException:
-                    m_xml.startElement( "Exception" );
-                    writeSourceInfo( result.getSourceInfo() );
-                    m_xml.writeText( result.getMessage() );
-                    m_xml.endElement();
-                    break;
-                case ResultWas::FatalErrorCondition:
-                    m_xml.startElement( "FatalErrorCondition" );
-                    writeSourceInfo( result.getSourceInfo() );
-                    m_xml.writeText( result.getMessage() );
-                    m_xml.endElement();
-                    break;
-                case ResultWas::Info:
-                    m_xml.scopedElement( "Info" )
-                        .writeText( result.getMessage() );
-                    break;
-                case ResultWas::Warning:
-                    // Warning will already have been written
-                    break;
-                case ResultWas::ExplicitFailure:
-                    m_xml.startElement( "Failure" );
-                    writeSourceInfo( result.getSourceInfo() );
-                    m_xml.writeText( result.getMessage() );
-                    m_xml.endElement();
-                    break;
-                default:
-                    break;
-            }
+    m_tablePrinter->close();
+    lazyPrintWithoutClosingBenchmarkTable();
+}
 
-            if( result.hasExpression() )
-                m_xml.endElement();
+void ConsoleReporter::lazyPrintWithoutClosingBenchmarkTable() {
 
-            return true;
-        }
+    if (!currentTestRunInfo.used)
+        lazyPrintRunInfo();
+    if (!currentGroupInfo.used)
+        lazyPrintGroupInfo();
+
+    if (!m_headerPrinted) {
+        printTestCaseAndSectionHeader();
+        m_headerPrinted = true;
+    }
+}
+void ConsoleReporter::lazyPrintRunInfo() {
+    stream << '\n' << getLineOfChars<'~'>() << '\n';
+    Colour colour(Colour::SecondaryText);
+    stream << currentTestRunInfo->name
+        << " is a Catch v" << libraryVersion() << " host application.\n"
+        << "Run with -? for options\n\n";
 
-        virtual void sectionEnded( SectionStats const& sectionStats ) CATCH_OVERRIDE {
-            StreamingReporterBase::sectionEnded( sectionStats );
-            if( --m_sectionDepth > 0 ) {
-                XmlWriter::ScopedElement e = m_xml.scopedElement( "OverallResults" );
-                e.writeAttribute( "successes", sectionStats.assertions.passed );
-                e.writeAttribute( "failures", sectionStats.assertions.failed );
-                e.writeAttribute( "expectedFailures", sectionStats.assertions.failedButOk );
+    if (m_config->rngSeed() != 0)
+        stream << "Randomness seeded to: " << m_config->rngSeed() << "\n\n";
 
-                if ( m_config->showDurations() == ShowDurations::Always )
-                    e.writeAttribute( "durationInSeconds", sectionStats.durationInSeconds );
+    currentTestRunInfo.used = true;
+}
+void ConsoleReporter::lazyPrintGroupInfo() {
+    if (!currentGroupInfo->name.empty() && currentGroupInfo->groupsCounts > 1) {
+        printClosedHeader("Group: " + currentGroupInfo->name);
+        currentGroupInfo.used = true;
+    }
+}
+void ConsoleReporter::printTestCaseAndSectionHeader() {
+    assert(!m_sectionStack.empty());
+    printOpenHeader(currentTestCaseInfo->name);
 
-                m_xml.endElement();
-            }
-        }
+    if (m_sectionStack.size() > 1) {
+        Colour colourGuard(Colour::Headers);
 
-        virtual void testCaseEnded( TestCaseStats const& testCaseStats ) CATCH_OVERRIDE {
-            StreamingReporterBase::testCaseEnded( testCaseStats );
-            XmlWriter::ScopedElement e = m_xml.scopedElement( "OverallResult" );
-            e.writeAttribute( "success", testCaseStats.totals.assertions.allOk() );
+        auto
+            it = m_sectionStack.begin() + 1, // Skip first section (test case)
+            itEnd = m_sectionStack.end();
+        for (; it != itEnd; ++it)
+            printHeaderString(it->name, 2);
+    }
 
-            if ( m_config->showDurations() == ShowDurations::Always )
-                e.writeAttribute( "durationInSeconds", m_testCaseTimer.getElapsedSeconds() );
+    SourceLineInfo lineInfo = m_sectionStack.back().lineInfo;
 
-            if( !testCaseStats.stdOut.empty() )
-                m_xml.scopedElement( "StdOut" ).writeText( trim( testCaseStats.stdOut ), false );
-            if( !testCaseStats.stdErr.empty() )
-                m_xml.scopedElement( "StdErr" ).writeText( trim( testCaseStats.stdErr ), false );
+    stream << getLineOfChars<'-'>() << '\n';
+    Colour colourGuard(Colour::FileName);
+    stream << lineInfo << '\n';
+    stream << getLineOfChars<'.'>() << '\n' << std::endl;
+}
 
-            m_xml.endElement();
-        }
+void ConsoleReporter::printClosedHeader(std::string const& _name) {
+    printOpenHeader(_name);
+    stream << getLineOfChars<'.'>() << '\n';
+}
+void ConsoleReporter::printOpenHeader(std::string const& _name) {
+    stream << getLineOfChars<'-'>() << '\n';
+    {
+        Colour colourGuard(Colour::Headers);
+        printHeaderString(_name);
+    }
+}
 
-        virtual void testGroupEnded( TestGroupStats const& testGroupStats ) CATCH_OVERRIDE {
-            StreamingReporterBase::testGroupEnded( testGroupStats );
-            // TODO: Check testGroupStats.aborting and act accordingly.
-            m_xml.scopedElement( "OverallResults" )
-                .writeAttribute( "successes", testGroupStats.totals.assertions.passed )
-                .writeAttribute( "failures", testGroupStats.totals.assertions.failed )
-                .writeAttribute( "expectedFailures", testGroupStats.totals.assertions.failedButOk );
-            m_xml.endElement();
-        }
+// if string has a : in first line will set indent to follow it on
+// subsequent lines
+void ConsoleReporter::printHeaderString(std::string const& _string, std::size_t indent) {
+    std::size_t i = _string.find(": ");
+    if (i != std::string::npos)
+        i += 2;
+    else
+        i = 0;
+    stream << Column(_string).indent(indent + i).initialIndent(indent) << '\n';
+}
 
-        virtual void testRunEnded( TestRunStats const& testRunStats ) CATCH_OVERRIDE {
-            StreamingReporterBase::testRunEnded( testRunStats );
-            m_xml.scopedElement( "OverallResults" )
-                .writeAttribute( "successes", testRunStats.totals.assertions.passed )
-                .writeAttribute( "failures", testRunStats.totals.assertions.failed )
-                .writeAttribute( "expectedFailures", testRunStats.totals.assertions.failedButOk );
-            m_xml.endElement();
+struct SummaryColumn {
+
+    SummaryColumn( std::string _label, Colour::Code _colour )
+    :   label( std::move( _label ) ),
+        colour( _colour ) {}
+    SummaryColumn addRow( std::size_t count ) {
+        ReusableStringStream rss;
+        rss << count;
+        std::string row = rss.str();
+        for (auto& oldRow : rows) {
+            while (oldRow.size() < row.size())
+                oldRow = ' ' + oldRow;
+            while (oldRow.size() > row.size())
+                row = ' ' + row;
+        }
+        rows.push_back(row);
+        return *this;
+    }
+
+    std::string label;
+    Colour::Code colour;
+    std::vector<std::string> rows;
+
+};
+
+void ConsoleReporter::printTotals( Totals const& totals ) {
+    if (totals.testCases.total() == 0) {
+        stream << Colour(Colour::Warning) << "No tests ran\n";
+    } else if (totals.assertions.total() > 0 && totals.testCases.allPassed()) {
+        stream << Colour(Colour::ResultSuccess) << "All tests passed";
+        stream << " ("
+            << pluralise(totals.assertions.passed, "assertion") << " in "
+            << pluralise(totals.testCases.passed, "test case") << ')'
+            << '\n';
+    } else {
+
+        std::vector<SummaryColumn> columns;
+        columns.push_back(SummaryColumn("", Colour::None)
+                          .addRow(totals.testCases.total())
+                          .addRow(totals.assertions.total()));
+        columns.push_back(SummaryColumn("passed", Colour::Success)
+                          .addRow(totals.testCases.passed)
+                          .addRow(totals.assertions.passed));
+        columns.push_back(SummaryColumn("failed", Colour::ResultError)
+                          .addRow(totals.testCases.failed)
+                          .addRow(totals.assertions.failed));
+        columns.push_back(SummaryColumn("failed as expected", Colour::ResultExpectedFailure)
+                          .addRow(totals.testCases.failedButOk)
+                          .addRow(totals.assertions.failedButOk));
+
+        printSummaryRow("test cases", columns, 0);
+        printSummaryRow("assertions", columns, 1);
+    }
+}
+void ConsoleReporter::printSummaryRow(std::string const& label, std::vector<SummaryColumn> const& cols, std::size_t row) {
+    for (auto col : cols) {
+        std::string value = col.rows[row];
+        if (col.label.empty()) {
+            stream << label << ": ";
+            if (value != "0")
+                stream << value;
+            else
+                stream << Colour(Colour::Warning) << "- none -";
+        } else if (value != "0") {
+            stream << Colour(Colour::LightGrey) << " | ";
+            stream << Colour(col.colour)
+                << value << ' ' << col.label;
         }
+    }
+    stream << '\n';
+}
 
-    private:
-        Timer m_testCaseTimer;
-        XmlWriter m_xml;
-        int m_sectionDepth;
-    };
+void ConsoleReporter::printTotalsDivider(Totals const& totals) {
+    if (totals.testCases.total() > 0) {
+        std::size_t failedRatio = makeRatio(totals.testCases.failed, totals.testCases.total());
+        std::size_t failedButOkRatio = makeRatio(totals.testCases.failedButOk, totals.testCases.total());
+        std::size_t passedRatio = makeRatio(totals.testCases.passed, totals.testCases.total());
+        while (failedRatio + failedButOkRatio + passedRatio < CATCH_CONFIG_CONSOLE_WIDTH - 1)
+            findMax(failedRatio, failedButOkRatio, passedRatio)++;
+        while (failedRatio + failedButOkRatio + passedRatio > CATCH_CONFIG_CONSOLE_WIDTH - 1)
+            findMax(failedRatio, failedButOkRatio, passedRatio)--;
+
+        stream << Colour(Colour::Error) << std::string(failedRatio, '=');
+        stream << Colour(Colour::ResultExpectedFailure) << std::string(failedButOkRatio, '=');
+        if (totals.testCases.allPassed())
+            stream << Colour(Colour::ResultSuccess) << std::string(passedRatio, '=');
+        else
+            stream << Colour(Colour::Success) << std::string(passedRatio, '=');
+    } else {
+        stream << Colour(Colour::Warning) << std::string(CATCH_CONFIG_CONSOLE_WIDTH - 1, '=');
+    }
+    stream << '\n';
+}
+void ConsoleReporter::printSummaryDivider() {
+    stream << getLineOfChars<'-'>() << '\n';
+}
 
-     INTERNAL_CATCH_REGISTER_REPORTER( "xml", XmlReporter )
+void ConsoleReporter::printTestFilters() {
+    if (m_config->testSpec().hasFilters())
+        stream << Colour(Colour::BrightYellow) << "Filters: " << serializeFilters( m_config->getTestsOrTags() ) << '\n';
+}
+
+CATCH_REGISTER_REPORTER("console", ConsoleReporter)
 
 } // end namespace Catch
 
-// #included from: ../reporters/catch_reporter_junit.hpp
-#define TWOBLUECUBES_CATCH_REPORTER_JUNIT_HPP_INCLUDED
+#if defined(_MSC_VER)
+#pragma warning(pop)
+#endif
+
+#if defined(__clang__)
+#  pragma clang diagnostic pop
+#endif
+// end catch_reporter_console.cpp
+// start catch_reporter_junit.cpp
 
-#include <assert.h>
+#include <cassert>
+#include <sstream>
+#include <ctime>
+#include <algorithm>
 
 namespace Catch {
 
@@ -10268,7 +16355,7 @@ namespace Catch {
             // Also, UTC only, again because of backward compatibility (%z is C++11)
             time_t rawtime;
             std::time(&rawtime);
-            const size_t timeStampSize = sizeof("2017-01-16T17:06:45Z");
+            auto const timeStampSize = sizeof("2017-01-16T17:06:45Z");
 
 #ifdef _MSC_VER
             std::tm timeInfo = {};
@@ -10289,1013 +16376,657 @@ namespace Catch {
             return std::string(timeStamp);
         }
 
-    }
+        std::string fileNameTag(const std::vector<std::string> &tags) {
+            auto it = std::find_if(begin(tags),
+                                   end(tags),
+                                   [] (std::string const& tag) {return tag.front() == '#'; });
+            if (it != tags.end())
+                return it->substr(1);
+            return std::string();
+        }
+    } // anonymous namespace
 
-    class JunitReporter : public CumulativeReporterBase {
-    public:
-        JunitReporter( ReporterConfig const& _config )
+    JunitReporter::JunitReporter( ReporterConfig const& _config )
         :   CumulativeReporterBase( _config ),
-            xml( _config.stream() ),
-            m_okToFail( false )
+            xml( _config.stream() )
         {
             m_reporterPrefs.shouldRedirectStdOut = true;
+            m_reporterPrefs.shouldReportAllAssertions = true;
         }
 
-        virtual ~JunitReporter() CATCH_OVERRIDE;
-
-        static std::string getDescription() {
-            return "Reports test results in an XML format that looks like Ant's junitreport target";
-        }
-
-        virtual void noMatchingTestCases( std::string const& /*spec*/ ) CATCH_OVERRIDE {}
-
-        virtual void testRunStarting( TestRunInfo const& runInfo ) CATCH_OVERRIDE {
-            CumulativeReporterBase::testRunStarting( runInfo );
-            xml.startElement( "testsuites" );
-        }
-
-        virtual void testGroupStarting( GroupInfo const& groupInfo ) CATCH_OVERRIDE {
-            suiteTimer.start();
-            stdOutForSuite.str("");
-            stdErrForSuite.str("");
-            unexpectedExceptions = 0;
-            CumulativeReporterBase::testGroupStarting( groupInfo );
-        }
-
-        virtual void testCaseStarting( TestCaseInfo const& testCaseInfo ) CATCH_OVERRIDE {
-            m_okToFail = testCaseInfo.okToFail();
-        }
-        virtual bool assertionEnded( AssertionStats const& assertionStats ) CATCH_OVERRIDE {
-            if( assertionStats.assertionResult.getResultType() == ResultWas::ThrewException && !m_okToFail )
-                unexpectedExceptions++;
-            return CumulativeReporterBase::assertionEnded( assertionStats );
-        }
-
-        virtual void testCaseEnded( TestCaseStats const& testCaseStats ) CATCH_OVERRIDE {
-            stdOutForSuite << testCaseStats.stdOut;
-            stdErrForSuite << testCaseStats.stdErr;
-            CumulativeReporterBase::testCaseEnded( testCaseStats );
-        }
-
-        virtual void testGroupEnded( TestGroupStats const& testGroupStats ) CATCH_OVERRIDE {
-            double suiteTime = suiteTimer.getElapsedSeconds();
-            CumulativeReporterBase::testGroupEnded( testGroupStats );
-            writeGroup( *m_testGroups.back(), suiteTime );
-        }
-
-        virtual void testRunEndedCumulative() CATCH_OVERRIDE {
-            xml.endElement();
-        }
+    JunitReporter::~JunitReporter() {}
 
-        void writeGroup( TestGroupNode const& groupNode, double suiteTime ) {
-            XmlWriter::ScopedElement e = xml.scopedElement( "testsuite" );
-            TestGroupStats const& stats = groupNode.value;
-            xml.writeAttribute( "name", stats.groupInfo.name );
-            xml.writeAttribute( "errors", unexpectedExceptions );
-            xml.writeAttribute( "failures", stats.totals.assertions.failed-unexpectedExceptions );
-            xml.writeAttribute( "tests", stats.totals.assertions.total() );
-            xml.writeAttribute( "hostname", "tbd" ); // !TBD
-            if( m_config->showDurations() == ShowDurations::Never )
-                xml.writeAttribute( "time", "" );
-            else
-                xml.writeAttribute( "time", suiteTime );
-            xml.writeAttribute( "timestamp", getCurrentTimestamp() );
+    std::string JunitReporter::getDescription() {
+        return "Reports test results in an XML format that looks like Ant's junitreport target";
+    }
 
-            // Write test cases
-            for( TestGroupNode::ChildNodes::const_iterator
-                    it = groupNode.children.begin(), itEnd = groupNode.children.end();
-                    it != itEnd;
-                    ++it )
-                writeTestCase( **it );
+    void JunitReporter::noMatchingTestCases( std::string const& /*spec*/ ) {}
 
-            xml.scopedElement( "system-out" ).writeText( trim( stdOutForSuite.str() ), false );
-            xml.scopedElement( "system-err" ).writeText( trim( stdErrForSuite.str() ), false );
-        }
+    void JunitReporter::testRunStarting( TestRunInfo const& runInfo )  {
+        CumulativeReporterBase::testRunStarting( runInfo );
+        xml.startElement( "testsuites" );
+    }
 
-        void writeTestCase( TestCaseNode const& testCaseNode ) {
-            TestCaseStats const& stats = testCaseNode.value;
+    void JunitReporter::testGroupStarting( GroupInfo const& groupInfo ) {
+        suiteTimer.start();
+        stdOutForSuite.clear();
+        stdErrForSuite.clear();
+        unexpectedExceptions = 0;
+        CumulativeReporterBase::testGroupStarting( groupInfo );
+    }
 
-            // All test cases have exactly one section - which represents the
-            // test case itself. That section may have 0-n nested sections
-            assert( testCaseNode.children.size() == 1 );
-            SectionNode const& rootSection = *testCaseNode.children.front();
+    void JunitReporter::testCaseStarting( TestCaseInfo const& testCaseInfo ) {
+        m_okToFail = testCaseInfo.okToFail();
+    }
 
-            std::string className = stats.testInfo.className;
+    bool JunitReporter::assertionEnded( AssertionStats const& assertionStats ) {
+        if( assertionStats.assertionResult.getResultType() == ResultWas::ThrewException && !m_okToFail )
+            unexpectedExceptions++;
+        return CumulativeReporterBase::assertionEnded( assertionStats );
+    }
 
-            if( className.empty() ) {
-                if( rootSection.childSections.empty() )
-                    className = "global";
-            }
-            writeSection( className, "", rootSection );
-        }
-
-        void writeSection(  std::string const& className,
-                            std::string const& rootName,
-                            SectionNode const& sectionNode ) {
-            std::string name = trim( sectionNode.stats.sectionInfo.name );
-            if( !rootName.empty() )
-                name = rootName + '/' + name;
-
-            if( !sectionNode.assertions.empty() ||
-                !sectionNode.stdOut.empty() ||
-                !sectionNode.stdErr.empty() ) {
-                XmlWriter::ScopedElement e = xml.scopedElement( "testcase" );
-                if( className.empty() ) {
-                    xml.writeAttribute( "classname", name );
-                    xml.writeAttribute( "name", "root" );
-                }
-                else {
-                    xml.writeAttribute( "classname", className );
-                    xml.writeAttribute( "name", name );
-                }
-                xml.writeAttribute( "time", Catch::toString( sectionNode.stats.durationInSeconds ) );
-
-                writeAssertions( sectionNode );
-
-                if( !sectionNode.stdOut.empty() )
-                    xml.scopedElement( "system-out" ).writeText( trim( sectionNode.stdOut ), false );
-                if( !sectionNode.stdErr.empty() )
-                    xml.scopedElement( "system-err" ).writeText( trim( sectionNode.stdErr ), false );
-            }
-            for( SectionNode::ChildSections::const_iterator
-                    it = sectionNode.childSections.begin(),
-                    itEnd = sectionNode.childSections.end();
-                    it != itEnd;
-                    ++it )
-                if( className.empty() )
-                    writeSection( name, "", **it );
-                else
-                    writeSection( className, name, **it );
-        }
-
-        void writeAssertions( SectionNode const& sectionNode ) {
-            for( SectionNode::Assertions::const_iterator
-                    it = sectionNode.assertions.begin(), itEnd = sectionNode.assertions.end();
-                    it != itEnd;
-                    ++it )
-                writeAssertion( *it );
-        }
-        void writeAssertion( AssertionStats const& stats ) {
-            AssertionResult const& result = stats.assertionResult;
-            if( !result.isOk() ) {
-                std::string elementName;
-                switch( result.getResultType() ) {
-                    case ResultWas::ThrewException:
-                    case ResultWas::FatalErrorCondition:
-                        elementName = "error";
-                        break;
-                    case ResultWas::ExplicitFailure:
-                        elementName = "failure";
-                        break;
-                    case ResultWas::ExpressionFailed:
-                        elementName = "failure";
-                        break;
-                    case ResultWas::DidntThrowException:
-                        elementName = "failure";
-                        break;
-
-                    // We should never see these here:
-                    case ResultWas::Info:
-                    case ResultWas::Warning:
-                    case ResultWas::Ok:
-                    case ResultWas::Unknown:
-                    case ResultWas::FailureBit:
-                    case ResultWas::Exception:
-                        elementName = "internalError";
-                        break;
-                }
+    void JunitReporter::testCaseEnded( TestCaseStats const& testCaseStats ) {
+        stdOutForSuite += testCaseStats.stdOut;
+        stdErrForSuite += testCaseStats.stdErr;
+        CumulativeReporterBase::testCaseEnded( testCaseStats );
+    }
 
-                XmlWriter::ScopedElement e = xml.scopedElement( elementName );
+    void JunitReporter::testGroupEnded( TestGroupStats const& testGroupStats ) {
+        double suiteTime = suiteTimer.getElapsedSeconds();
+        CumulativeReporterBase::testGroupEnded( testGroupStats );
+        writeGroup( *m_testGroups.back(), suiteTime );
+    }
 
-                xml.writeAttribute( "message", result.getExpandedExpression() );
-                xml.writeAttribute( "type", result.getTestMacroName() );
+    void JunitReporter::testRunEndedCumulative() {
+        xml.endElement();
+    }
 
-                std::ostringstream oss;
-                if( !result.getMessage().empty() )
-                    oss << result.getMessage() << '\n';
-                for( std::vector<MessageInfo>::const_iterator
-                        it = stats.infoMessages.begin(),
-                        itEnd = stats.infoMessages.end();
-                            it != itEnd;
-                            ++it )
-                    if( it->type == ResultWas::Info )
-                        oss << it->message << '\n';
+    void JunitReporter::writeGroup( TestGroupNode const& groupNode, double suiteTime ) {
+        XmlWriter::ScopedElement e = xml.scopedElement( "testsuite" );
 
-                oss << "at " << result.getSourceInfo();
-                xml.writeText( oss.str(), false );
+        TestGroupStats const& stats = groupNode.value;
+        xml.writeAttribute( "name", stats.groupInfo.name );
+        xml.writeAttribute( "errors", unexpectedExceptions );
+        xml.writeAttribute( "failures", stats.totals.assertions.failed-unexpectedExceptions );
+        xml.writeAttribute( "tests", stats.totals.assertions.total() );
+        xml.writeAttribute( "hostname", "tbd" ); // !TBD
+        if( m_config->showDurations() == ShowDurations::Never )
+            xml.writeAttribute( "time", "" );
+        else
+            xml.writeAttribute( "time", suiteTime );
+        xml.writeAttribute( "timestamp", getCurrentTimestamp() );
+
+        // Write properties if there are any
+        if (m_config->hasTestFilters() || m_config->rngSeed() != 0) {
+            auto properties = xml.scopedElement("properties");
+            if (m_config->hasTestFilters()) {
+                xml.scopedElement("property")
+                    .writeAttribute("name", "filters")
+                    .writeAttribute("value", serializeFilters(m_config->getTestsOrTags()));
+            }
+            if (m_config->rngSeed() != 0) {
+                xml.scopedElement("property")
+                    .writeAttribute("name", "random-seed")
+                    .writeAttribute("value", m_config->rngSeed());
             }
         }
 
-        XmlWriter xml;
-        Timer suiteTimer;
-        std::ostringstream stdOutForSuite;
-        std::ostringstream stdErrForSuite;
-        unsigned int unexpectedExceptions;
-        bool m_okToFail;
-    };
-
-    INTERNAL_CATCH_REGISTER_REPORTER( "junit", JunitReporter )
-
-} // end namespace Catch
-
-// #included from: ../reporters/catch_reporter_console.hpp
-#define TWOBLUECUBES_CATCH_REPORTER_CONSOLE_HPP_INCLUDED
-
-#include <cfloat>
-#include <cstdio>
+        // Write test cases
+        for( auto const& child : groupNode.children )
+            writeTestCase( *child );
 
-namespace Catch {
+        xml.scopedElement( "system-out" ).writeText( trim( stdOutForSuite ), false );
+        xml.scopedElement( "system-err" ).writeText( trim( stdErrForSuite ), false );
+    }
 
-    struct ConsoleReporter : StreamingReporterBase {
-        ConsoleReporter( ReporterConfig const& _config )
-        :   StreamingReporterBase( _config ),
-            m_headerPrinted( false )
-        {}
+    void JunitReporter::writeTestCase( TestCaseNode const& testCaseNode ) {
+        TestCaseStats const& stats = testCaseNode.value;
 
-        virtual ~ConsoleReporter() CATCH_OVERRIDE;
-        static std::string getDescription() {
-            return "Reports test results as plain lines of text";
-        }
+        // All test cases have exactly one section - which represents the
+        // test case itself. That section may have 0-n nested sections
+        assert( testCaseNode.children.size() == 1 );
+        SectionNode const& rootSection = *testCaseNode.children.front();
 
-        virtual void noMatchingTestCases( std::string const& spec ) CATCH_OVERRIDE {
-            stream << "No test cases matched '" << spec << '\'' << std::endl;
-        }
+        std::string className = stats.testInfo.className;
 
-        virtual void assertionStarting( AssertionInfo const& ) CATCH_OVERRIDE {
+        if( className.empty() ) {
+            className = fileNameTag(stats.testInfo.tags);
+            if ( className.empty() )
+                className = "global";
         }
 
-        virtual bool assertionEnded( AssertionStats const& _assertionStats ) CATCH_OVERRIDE {
-            AssertionResult const& result = _assertionStats.assertionResult;
-
-            bool includeResults = m_config->includeSuccessfulResults() || !result.isOk();
-
-            // Drop out if result was successful but we're not printing them.
-            if( !includeResults && result.getResultType() != ResultWas::Warning )
-                return false;
+        if ( !m_config->name().empty() )
+            className = m_config->name() + "." + className;
 
-            lazyPrint();
+        writeSection( className, "", rootSection );
+    }
 
-            AssertionPrinter printer( stream, _assertionStats, includeResults );
-            printer.print();
-            stream << std::endl;
-            return true;
-        }
+    void JunitReporter::writeSection(  std::string const& className,
+                        std::string const& rootName,
+                        SectionNode const& sectionNode ) {
+        std::string name = trim( sectionNode.stats.sectionInfo.name );
+        if( !rootName.empty() )
+            name = rootName + '/' + name;
 
-        virtual void sectionStarting( SectionInfo const& _sectionInfo ) CATCH_OVERRIDE {
-            m_headerPrinted = false;
-            StreamingReporterBase::sectionStarting( _sectionInfo );
-        }
-        virtual void sectionEnded( SectionStats const& _sectionStats ) CATCH_OVERRIDE {
-            if( _sectionStats.missingAssertions ) {
-                lazyPrint();
-                Colour colour( Colour::ResultError );
-                if( m_sectionStack.size() > 1 )
-                    stream << "\nNo assertions in section";
-                else
-                    stream << "\nNo assertions in test case";
-                stream << " '" << _sectionStats.sectionInfo.name << "'\n" << std::endl;
-            }
-            if( m_config->showDurations() == ShowDurations::Always ) {
-                stream << getFormattedDuration(_sectionStats.durationInSeconds) << " s: " << _sectionStats.sectionInfo.name << std::endl;
+        if( !sectionNode.assertions.empty() ||
+            !sectionNode.stdOut.empty() ||
+            !sectionNode.stdErr.empty() ) {
+            XmlWriter::ScopedElement e = xml.scopedElement( "testcase" );
+            if( className.empty() ) {
+                xml.writeAttribute( "classname", name );
+                xml.writeAttribute( "name", "root" );
             }
-            if( m_headerPrinted ) {
-                m_headerPrinted = false;
+            else {
+                xml.writeAttribute( "classname", className );
+                xml.writeAttribute( "name", name );
             }
-            StreamingReporterBase::sectionEnded( _sectionStats );
-        }
+            xml.writeAttribute( "time", ::Catch::Detail::stringify( sectionNode.stats.durationInSeconds ) );
 
-        virtual void testCaseEnded( TestCaseStats const& _testCaseStats ) CATCH_OVERRIDE {
-            StreamingReporterBase::testCaseEnded( _testCaseStats );
-            m_headerPrinted = false;
-        }
-        virtual void testGroupEnded( TestGroupStats const& _testGroupStats ) CATCH_OVERRIDE {
-            if( currentGroupInfo.used ) {
-                printSummaryDivider();
-                stream << "Summary for group '" << _testGroupStats.groupInfo.name << "':\n";
-                printTotals( _testGroupStats.totals );
-                stream << '\n' << std::endl;
-            }
-            StreamingReporterBase::testGroupEnded( _testGroupStats );
-        }
-        virtual void testRunEnded( TestRunStats const& _testRunStats ) CATCH_OVERRIDE {
-            printTotalsDivider( _testRunStats.totals );
-            printTotals( _testRunStats.totals );
-            stream << std::endl;
-            StreamingReporterBase::testRunEnded( _testRunStats );
-        }
+            writeAssertions( sectionNode );
 
-    private:
+            if( !sectionNode.stdOut.empty() )
+                xml.scopedElement( "system-out" ).writeText( trim( sectionNode.stdOut ), false );
+            if( !sectionNode.stdErr.empty() )
+                xml.scopedElement( "system-err" ).writeText( trim( sectionNode.stdErr ), false );
+        }
+        for( auto const& childNode : sectionNode.childSections )
+            if( className.empty() )
+                writeSection( name, "", *childNode );
+            else
+                writeSection( className, name, *childNode );
+    }
 
-        class AssertionPrinter {
-            void operator= ( AssertionPrinter const& );
-        public:
-            AssertionPrinter( std::ostream& _stream, AssertionStats const& _stats, bool _printInfoMessages )
-            :   stream( _stream ),
-                stats( _stats ),
-                result( _stats.assertionResult ),
-                colour( Colour::None ),
-                message( result.getMessage() ),
-                messages( _stats.infoMessages ),
-                printInfoMessages( _printInfoMessages )
-            {
-                switch( result.getResultType() ) {
-                    case ResultWas::Ok:
-                        colour = Colour::Success;
-                        passOrFail = "PASSED";
-                        //if( result.hasMessage() )
-                        if( _stats.infoMessages.size() == 1 )
-                            messageLabel = "with message";
-                        if( _stats.infoMessages.size() > 1 )
-                            messageLabel = "with messages";
-                        break;
-                    case ResultWas::ExpressionFailed:
-                        if( result.isOk() ) {
-                            colour = Colour::Success;
-                            passOrFail = "FAILED - but was ok";
-                        }
-                        else {
-                            colour = Colour::Error;
-                            passOrFail = "FAILED";
-                        }
-                        if( _stats.infoMessages.size() == 1 )
-                            messageLabel = "with message";
-                        if( _stats.infoMessages.size() > 1 )
-                            messageLabel = "with messages";
-                        break;
-                    case ResultWas::ThrewException:
-                        colour = Colour::Error;
-                        passOrFail = "FAILED";
-                        messageLabel = "due to unexpected exception with ";
-                        if (_stats.infoMessages.size() == 1)
-                            messageLabel += "message";
-                        if (_stats.infoMessages.size() > 1)
-                            messageLabel += "messages";
-                        break;
-                    case ResultWas::FatalErrorCondition:
-                        colour = Colour::Error;
-                        passOrFail = "FAILED";
-                        messageLabel = "due to a fatal error condition";
-                        break;
-                    case ResultWas::DidntThrowException:
-                        colour = Colour::Error;
-                        passOrFail = "FAILED";
-                        messageLabel = "because no exception was thrown where one was expected";
-                        break;
-                    case ResultWas::Info:
-                        messageLabel = "info";
-                        break;
-                    case ResultWas::Warning:
-                        messageLabel = "warning";
-                        break;
-                    case ResultWas::ExplicitFailure:
-                        passOrFail = "FAILED";
-                        colour = Colour::Error;
-                        if( _stats.infoMessages.size() == 1 )
-                            messageLabel = "explicitly with message";
-                        if( _stats.infoMessages.size() > 1 )
-                            messageLabel = "explicitly with messages";
-                        break;
-                    // These cases are here to prevent compiler warnings
-                    case ResultWas::Unknown:
-                    case ResultWas::FailureBit:
-                    case ResultWas::Exception:
-                        passOrFail = "** internal error **";
-                        colour = Colour::Error;
-                        break;
-                }
-            }
+    void JunitReporter::writeAssertions( SectionNode const& sectionNode ) {
+        for( auto const& assertion : sectionNode.assertions )
+            writeAssertion( assertion );
+    }
 
-            void print() const {
-                printSourceInfo();
-                if( stats.totals.assertions.total() > 0 ) {
-                    if( result.isOk() )
-                        stream << '\n';
-                    printResultType();
-                    printOriginalExpression();
-                    printReconstructedExpression();
-                }
-                else {
-                    stream << '\n';
-                }
-                printMessage();
-            }
+    void JunitReporter::writeAssertion( AssertionStats const& stats ) {
+        AssertionResult const& result = stats.assertionResult;
+        if( !result.isOk() ) {
+            std::string elementName;
+            switch( result.getResultType() ) {
+                case ResultWas::ThrewException:
+                case ResultWas::FatalErrorCondition:
+                    elementName = "error";
+                    break;
+                case ResultWas::ExplicitFailure:
+                    elementName = "failure";
+                    break;
+                case ResultWas::ExpressionFailed:
+                    elementName = "failure";
+                    break;
+                case ResultWas::DidntThrowException:
+                    elementName = "failure";
+                    break;
 
-        private:
-            void printResultType() const {
-                if( !passOrFail.empty() ) {
-                    Colour colourGuard( colour );
-                    stream << passOrFail << ":\n";
-                }
-            }
-            void printOriginalExpression() const {
-                if( result.hasExpression() ) {
-                    Colour colourGuard( Colour::OriginalExpression );
-                    stream  << "  ";
-                    stream << result.getExpressionInMacro();
-                    stream << '\n';
-                }
-            }
-            void printReconstructedExpression() const {
-                if( result.hasExpandedExpression() ) {
-                    stream << "with expansion:\n";
-                    Colour colourGuard( Colour::ReconstructedExpression );
-                    stream << Text( result.getExpandedExpression(), TextAttributes().setIndent(2) ) << '\n';
-                }
-            }
-            void printMessage() const {
-                if( !messageLabel.empty() )
-                    stream << messageLabel << ':' << '\n';
-                for( std::vector<MessageInfo>::const_iterator it = messages.begin(), itEnd = messages.end();
-                        it != itEnd;
-                        ++it ) {
-                    // If this assertion is a warning ignore any INFO messages
-                    if( printInfoMessages || it->type != ResultWas::Info )
-                        stream << Text( it->message, TextAttributes().setIndent(2) ) << '\n';
-                }
-            }
-            void printSourceInfo() const {
-                Colour colourGuard( Colour::FileName );
-                stream << result.getSourceInfo() << ": ";
+                // We should never see these here:
+                case ResultWas::Info:
+                case ResultWas::Warning:
+                case ResultWas::Ok:
+                case ResultWas::Unknown:
+                case ResultWas::FailureBit:
+                case ResultWas::Exception:
+                    elementName = "internalError";
+                    break;
             }
 
-            std::ostream& stream;
-            AssertionStats const& stats;
-            AssertionResult const& result;
-            Colour::Code colour;
-            std::string passOrFail;
-            std::string messageLabel;
-            std::string message;
-            std::vector<MessageInfo> messages;
-            bool printInfoMessages;
-        };
+            XmlWriter::ScopedElement e = xml.scopedElement( elementName );
 
-        void lazyPrint() {
+            xml.writeAttribute( "message", result.getExpandedExpression() );
+            xml.writeAttribute( "type", result.getTestMacroName() );
 
-            if( !currentTestRunInfo.used )
-                lazyPrintRunInfo();
-            if( !currentGroupInfo.used )
-                lazyPrintGroupInfo();
+            ReusableStringStream rss;
+            if( !result.getMessage().empty() )
+                rss << result.getMessage() << '\n';
+            for( auto const& msg : stats.infoMessages )
+                if( msg.type == ResultWas::Info )
+                    rss << msg.message << '\n';
 
-            if( !m_headerPrinted ) {
-                printTestCaseAndSectionHeader();
-                m_headerPrinted = true;
-            }
+            rss << "at " << result.getSourceInfo();
+            xml.writeText( rss.str(), false );
         }
-        void lazyPrintRunInfo() {
-            stream  << '\n' << getLineOfChars<'~'>() << '\n';
-            Colour colour( Colour::SecondaryText );
-            stream  << currentTestRunInfo->name
-                    << " is a Catch v"  << libraryVersion() << " host application.\n"
-                    << "Run with -? for options\n\n";
+    }
 
-            if( m_config->rngSeed() != 0 )
-                stream << "Randomness seeded to: " << m_config->rngSeed() << "\n\n";
+    CATCH_REGISTER_REPORTER( "junit", JunitReporter )
 
-            currentTestRunInfo.used = true;
-        }
-        void lazyPrintGroupInfo() {
-            if( !currentGroupInfo->name.empty() && currentGroupInfo->groupsCounts > 1 ) {
-                printClosedHeader( "Group: " + currentGroupInfo->name );
-                currentGroupInfo.used = true;
-            }
-        }
-        void printTestCaseAndSectionHeader() {
-            assert( !m_sectionStack.empty() );
-            printOpenHeader( currentTestCaseInfo->name );
+} // end namespace Catch
+// end catch_reporter_junit.cpp
+// start catch_reporter_listening.cpp
 
-            if( m_sectionStack.size() > 1 ) {
-                Colour colourGuard( Colour::Headers );
+#include <cassert>
 
-                std::vector<SectionInfo>::const_iterator
-                    it = m_sectionStack.begin()+1, // Skip first section (test case)
-                    itEnd = m_sectionStack.end();
-                for( ; it != itEnd; ++it )
-                    printHeaderString( it->name, 2 );
-            }
+namespace Catch {
 
-            SourceLineInfo lineInfo = m_sectionStack.back().lineInfo;
+    ListeningReporter::ListeningReporter() {
+        // We will assume that listeners will always want all assertions
+        m_preferences.shouldReportAllAssertions = true;
+    }
 
-            if( !lineInfo.empty() ){
-                stream << getLineOfChars<'-'>() << '\n';
-                Colour colourGuard( Colour::FileName );
-                stream << lineInfo << '\n';
-            }
-            stream << getLineOfChars<'.'>() << '\n' << std::endl;
+    void ListeningReporter::addListener( IStreamingReporterPtr&& listener ) {
+        m_listeners.push_back( std::move( listener ) );
+    }
+
+    void ListeningReporter::addReporter(IStreamingReporterPtr&& reporter) {
+        assert(!m_reporter && "Listening reporter can wrap only 1 real reporter");
+        m_reporter = std::move( reporter );
+        m_preferences.shouldRedirectStdOut = m_reporter->getPreferences().shouldRedirectStdOut;
+    }
+
+    ReporterPreferences ListeningReporter::getPreferences() const {
+        return m_preferences;
+    }
+
+    std::set<Verbosity> ListeningReporter::getSupportedVerbosities() {
+        return std::set<Verbosity>{ };
+    }
+
+    void ListeningReporter::noMatchingTestCases( std::string const& spec ) {
+        for ( auto const& listener : m_listeners ) {
+            listener->noMatchingTestCases( spec );
         }
+        m_reporter->noMatchingTestCases( spec );
+    }
 
-        void printClosedHeader( std::string const& _name ) {
-            printOpenHeader( _name );
-            stream << getLineOfChars<'.'>() << '\n';
+    void ListeningReporter::reportInvalidArguments(std::string const&arg){
+        for ( auto const& listener : m_listeners ) {
+            listener->reportInvalidArguments( arg );
         }
-        void printOpenHeader( std::string const& _name ) {
-            stream  << getLineOfChars<'-'>() << '\n';
-            {
-                Colour colourGuard( Colour::Headers );
-                printHeaderString( _name );
-            }
+        m_reporter->reportInvalidArguments( arg );
+    }
+
+#if defined(CATCH_CONFIG_ENABLE_BENCHMARKING)
+    void ListeningReporter::benchmarkPreparing( std::string const& name ) {
+		for (auto const& listener : m_listeners) {
+			listener->benchmarkPreparing(name);
+		}
+		m_reporter->benchmarkPreparing(name);
+	}
+    void ListeningReporter::benchmarkStarting( BenchmarkInfo const& benchmarkInfo ) {
+        for ( auto const& listener : m_listeners ) {
+            listener->benchmarkStarting( benchmarkInfo );
+        }
+        m_reporter->benchmarkStarting( benchmarkInfo );
+    }
+    void ListeningReporter::benchmarkEnded( BenchmarkStats<> const& benchmarkStats ) {
+        for ( auto const& listener : m_listeners ) {
+            listener->benchmarkEnded( benchmarkStats );
         }
+        m_reporter->benchmarkEnded( benchmarkStats );
+    }
 
-        // if string has a : in first line will set indent to follow it on
-        // subsequent lines
-        void printHeaderString( std::string const& _string, std::size_t indent = 0 ) {
-            std::size_t i = _string.find( ": " );
-            if( i != std::string::npos )
-                i+=2;
-            else
-                i = 0;
-            stream << Text( _string, TextAttributes()
-                                        .setIndent( indent+i)
-                                        .setInitialIndent( indent ) ) << '\n';
+	void ListeningReporter::benchmarkFailed( std::string const& error ) {
+		for (auto const& listener : m_listeners) {
+			listener->benchmarkFailed(error);
+		}
+		m_reporter->benchmarkFailed(error);
+	}
+#endif // CATCH_CONFIG_ENABLE_BENCHMARKING
+
+    void ListeningReporter::testRunStarting( TestRunInfo const& testRunInfo ) {
+        for ( auto const& listener : m_listeners ) {
+            listener->testRunStarting( testRunInfo );
         }
+        m_reporter->testRunStarting( testRunInfo );
+    }
 
-        struct SummaryColumn {
+    void ListeningReporter::testGroupStarting( GroupInfo const& groupInfo ) {
+        for ( auto const& listener : m_listeners ) {
+            listener->testGroupStarting( groupInfo );
+        }
+        m_reporter->testGroupStarting( groupInfo );
+    }
 
-            SummaryColumn( std::string const& _label, Colour::Code _colour )
-            :   label( _label ),
-                colour( _colour )
-            {}
-            SummaryColumn addRow( std::size_t count ) {
-                std::ostringstream oss;
-                oss << count;
-                std::string row = oss.str();
-                for( std::vector<std::string>::iterator it = rows.begin(); it != rows.end(); ++it ) {
-                    while( it->size() < row.size() )
-                        *it = ' ' + *it;
-                    while( it->size() > row.size() )
-                        row = ' ' + row;
-                }
-                rows.push_back( row );
-                return *this;
-            }
+    void ListeningReporter::testCaseStarting( TestCaseInfo const& testInfo ) {
+        for ( auto const& listener : m_listeners ) {
+            listener->testCaseStarting( testInfo );
+        }
+        m_reporter->testCaseStarting( testInfo );
+    }
 
-            std::string label;
-            Colour::Code colour;
-            std::vector<std::string> rows;
+    void ListeningReporter::sectionStarting( SectionInfo const& sectionInfo ) {
+        for ( auto const& listener : m_listeners ) {
+            listener->sectionStarting( sectionInfo );
+        }
+        m_reporter->sectionStarting( sectionInfo );
+    }
 
-        };
+    void ListeningReporter::assertionStarting( AssertionInfo const& assertionInfo ) {
+        for ( auto const& listener : m_listeners ) {
+            listener->assertionStarting( assertionInfo );
+        }
+        m_reporter->assertionStarting( assertionInfo );
+    }
 
-        void printTotals( Totals const& totals ) {
-            if( totals.testCases.total() == 0 ) {
-                stream << Colour( Colour::Warning ) << "No tests ran\n";
-            }
-            else if( totals.assertions.total() > 0 && totals.testCases.allPassed() ) {
-                stream << Colour( Colour::ResultSuccess ) << "All tests passed";
-                stream << " ("
-                        << pluralise( totals.assertions.passed, "assertion" ) << " in "
-                        << pluralise( totals.testCases.passed, "test case" ) << ')'
-                        << '\n';
-            }
-            else {
+    // The return value indicates if the messages buffer should be cleared:
+    bool ListeningReporter::assertionEnded( AssertionStats const& assertionStats ) {
+        for( auto const& listener : m_listeners ) {
+            static_cast<void>( listener->assertionEnded( assertionStats ) );
+        }
+        return m_reporter->assertionEnded( assertionStats );
+    }
 
-                std::vector<SummaryColumn> columns;
-                columns.push_back( SummaryColumn( "", Colour::None )
-                                        .addRow( totals.testCases.total() )
-                                        .addRow( totals.assertions.total() ) );
-                columns.push_back( SummaryColumn( "passed", Colour::Success )
-                                        .addRow( totals.testCases.passed )
-                                        .addRow( totals.assertions.passed ) );
-                columns.push_back( SummaryColumn( "failed", Colour::ResultError )
-                                        .addRow( totals.testCases.failed )
-                                        .addRow( totals.assertions.failed ) );
-                columns.push_back( SummaryColumn( "failed as expected", Colour::ResultExpectedFailure )
-                                        .addRow( totals.testCases.failedButOk )
-                                        .addRow( totals.assertions.failedButOk ) );
-
-                printSummaryRow( "test cases", columns, 0 );
-                printSummaryRow( "assertions", columns, 1 );
-            }
-        }
-        void printSummaryRow( std::string const& label, std::vector<SummaryColumn> const& cols, std::size_t row ) {
-            for( std::vector<SummaryColumn>::const_iterator it = cols.begin(); it != cols.end(); ++it ) {
-                std::string value = it->rows[row];
-                if( it->label.empty() ) {
-                    stream << label << ": ";
-                    if( value != "0" )
-                        stream << value;
-                    else
-                        stream << Colour( Colour::Warning ) << "- none -";
-                }
-                else if( value != "0" ) {
-                    stream  << Colour( Colour::LightGrey ) << " | ";
-                    stream  << Colour( it->colour )
-                            << value << ' ' << it->label;
-                }
-            }
-            stream << '\n';
+    void ListeningReporter::sectionEnded( SectionStats const& sectionStats ) {
+        for ( auto const& listener : m_listeners ) {
+            listener->sectionEnded( sectionStats );
         }
+        m_reporter->sectionEnded( sectionStats );
+    }
 
-        static std::size_t makeRatio( std::size_t number, std::size_t total ) {
-            std::size_t ratio = total > 0 ? CATCH_CONFIG_CONSOLE_WIDTH * number/ total : 0;
-            return ( ratio == 0 && number > 0 ) ? 1 : ratio;
+    void ListeningReporter::testCaseEnded( TestCaseStats const& testCaseStats ) {
+        for ( auto const& listener : m_listeners ) {
+            listener->testCaseEnded( testCaseStats );
         }
-        static std::size_t& findMax( std::size_t& i, std::size_t& j, std::size_t& k ) {
-            if( i > j && i > k )
-                return i;
-            else if( j > k )
-                return j;
-            else
-                return k;
-        }
-
-        void printTotalsDivider( Totals const& totals ) {
-            if( totals.testCases.total() > 0 ) {
-                std::size_t failedRatio = makeRatio( totals.testCases.failed, totals.testCases.total() );
-                std::size_t failedButOkRatio = makeRatio( totals.testCases.failedButOk, totals.testCases.total() );
-                std::size_t passedRatio = makeRatio( totals.testCases.passed, totals.testCases.total() );
-                while( failedRatio + failedButOkRatio + passedRatio < CATCH_CONFIG_CONSOLE_WIDTH-1 )
-                    findMax( failedRatio, failedButOkRatio, passedRatio )++;
-                while( failedRatio + failedButOkRatio + passedRatio > CATCH_CONFIG_CONSOLE_WIDTH-1 )
-                    findMax( failedRatio, failedButOkRatio, passedRatio )--;
-
-                stream << Colour( Colour::Error ) << std::string( failedRatio, '=' );
-                stream << Colour( Colour::ResultExpectedFailure ) << std::string( failedButOkRatio, '=' );
-                if( totals.testCases.allPassed() )
-                    stream << Colour( Colour::ResultSuccess ) << std::string( passedRatio, '=' );
-                else
-                    stream << Colour( Colour::Success ) << std::string( passedRatio, '=' );
-            }
-            else {
-                stream << Colour( Colour::Warning ) << std::string( CATCH_CONFIG_CONSOLE_WIDTH-1, '=' );
-            }
-            stream << '\n';
+        m_reporter->testCaseEnded( testCaseStats );
+    }
+
+    void ListeningReporter::testGroupEnded( TestGroupStats const& testGroupStats ) {
+        for ( auto const& listener : m_listeners ) {
+            listener->testGroupEnded( testGroupStats );
         }
-        void printSummaryDivider() {
-            stream << getLineOfChars<'-'>() << '\n';
+        m_reporter->testGroupEnded( testGroupStats );
+    }
+
+    void ListeningReporter::testRunEnded( TestRunStats const& testRunStats ) {
+        for ( auto const& listener : m_listeners ) {
+            listener->testRunEnded( testRunStats );
         }
+        m_reporter->testRunEnded( testRunStats );
+    }
 
-    private:
-        bool m_headerPrinted;
-    };
+    void ListeningReporter::skipTest( TestCaseInfo const& testInfo ) {
+        for ( auto const& listener : m_listeners ) {
+            listener->skipTest( testInfo );
+        }
+        m_reporter->skipTest( testInfo );
+    }
 
-    INTERNAL_CATCH_REGISTER_REPORTER( "console", ConsoleReporter )
+    bool ListeningReporter::isMulti() const {
+        return true;
+    }
 
 } // end namespace Catch
+// end catch_reporter_listening.cpp
+// start catch_reporter_xml.cpp
 
-// #included from: ../reporters/catch_reporter_compact.hpp
-#define TWOBLUECUBES_CATCH_REPORTER_COMPACT_HPP_INCLUDED
+#if defined(_MSC_VER)
+#pragma warning(push)
+#pragma warning(disable:4061) // Not all labels are EXPLICITLY handled in switch
+                              // Note that 4062 (not all labels are handled
+                              // and default is missing) is enabled
+#endif
 
 namespace Catch {
+    XmlReporter::XmlReporter( ReporterConfig const& _config )
+    :   StreamingReporterBase( _config ),
+        m_xml(_config.stream())
+    {
+        m_reporterPrefs.shouldRedirectStdOut = true;
+        m_reporterPrefs.shouldReportAllAssertions = true;
+    }
 
-    struct CompactReporter : StreamingReporterBase {
-
-        CompactReporter( ReporterConfig const& _config )
-        : StreamingReporterBase( _config )
-        {}
+    XmlReporter::~XmlReporter() = default;
 
-        virtual ~CompactReporter();
+    std::string XmlReporter::getDescription() {
+        return "Reports test results as an XML document";
+    }
 
-        static std::string getDescription() {
-            return "Reports test results on a single line, suitable for IDEs";
-        }
+    std::string XmlReporter::getStylesheetRef() const {
+        return std::string();
+    }
 
-        virtual ReporterPreferences getPreferences() const {
-            ReporterPreferences prefs;
-            prefs.shouldRedirectStdOut = false;
-            return prefs;
-        }
+    void XmlReporter::writeSourceInfo( SourceLineInfo const& sourceInfo ) {
+        m_xml
+            .writeAttribute( "filename", sourceInfo.file )
+            .writeAttribute( "line", sourceInfo.line );
+    }
 
-        virtual void noMatchingTestCases( std::string const& spec ) {
-            stream << "No test cases matched '" << spec << '\'' << std::endl;
-        }
+    void XmlReporter::noMatchingTestCases( std::string const& s ) {
+        StreamingReporterBase::noMatchingTestCases( s );
+    }
 
-        virtual void assertionStarting( AssertionInfo const& ) {}
+    void XmlReporter::testRunStarting( TestRunInfo const& testInfo ) {
+        StreamingReporterBase::testRunStarting( testInfo );
+        std::string stylesheetRef = getStylesheetRef();
+        if( !stylesheetRef.empty() )
+            m_xml.writeStylesheetRef( stylesheetRef );
+        m_xml.startElement( "Catch" );
+        if( !m_config->name().empty() )
+            m_xml.writeAttribute( "name", m_config->name() );
+        if (m_config->testSpec().hasFilters())
+            m_xml.writeAttribute( "filters", serializeFilters( m_config->getTestsOrTags() ) );
+        if( m_config->rngSeed() != 0 )
+            m_xml.scopedElement( "Randomness" )
+                .writeAttribute( "seed", m_config->rngSeed() );
+    }
 
-        virtual bool assertionEnded( AssertionStats const& _assertionStats ) {
-            AssertionResult const& result = _assertionStats.assertionResult;
+    void XmlReporter::testGroupStarting( GroupInfo const& groupInfo ) {
+        StreamingReporterBase::testGroupStarting( groupInfo );
+        m_xml.startElement( "Group" )
+            .writeAttribute( "name", groupInfo.name );
+    }
 
-            bool printInfoMessages = true;
+    void XmlReporter::testCaseStarting( TestCaseInfo const& testInfo ) {
+        StreamingReporterBase::testCaseStarting(testInfo);
+        m_xml.startElement( "TestCase" )
+            .writeAttribute( "name", trim( testInfo.name ) )
+            .writeAttribute( "description", testInfo.description )
+            .writeAttribute( "tags", testInfo.tagsAsString() );
 
-            // Drop out if result was successful and we're not printing those
-            if( !m_config->includeSuccessfulResults() && result.isOk() ) {
-                if( result.getResultType() != ResultWas::Warning )
-                    return false;
-                printInfoMessages = false;
-            }
+        writeSourceInfo( testInfo.lineInfo );
 
-            AssertionPrinter printer( stream, _assertionStats, printInfoMessages );
-            printer.print();
+        if ( m_config->showDurations() == ShowDurations::Always )
+            m_testCaseTimer.start();
+        m_xml.ensureTagClosed();
+    }
 
-            stream << std::endl;
-            return true;
+    void XmlReporter::sectionStarting( SectionInfo const& sectionInfo ) {
+        StreamingReporterBase::sectionStarting( sectionInfo );
+        if( m_sectionDepth++ > 0 ) {
+            m_xml.startElement( "Section" )
+                .writeAttribute( "name", trim( sectionInfo.name ) );
+            writeSourceInfo( sectionInfo.lineInfo );
+            m_xml.ensureTagClosed();
         }
+    }
 
-        virtual void sectionEnded(SectionStats const& _sectionStats) CATCH_OVERRIDE {
-            if (m_config->showDurations() == ShowDurations::Always) {
-                stream << getFormattedDuration(_sectionStats.durationInSeconds) << " s: " << _sectionStats.sectionInfo.name << std::endl;
-            }
-        }
+    void XmlReporter::assertionStarting( AssertionInfo const& ) { }
 
-        virtual void testRunEnded( TestRunStats const& _testRunStats ) {
-            printTotals( _testRunStats.totals );
-            stream << '\n' << std::endl;
-            StreamingReporterBase::testRunEnded( _testRunStats );
-        }
+    bool XmlReporter::assertionEnded( AssertionStats const& assertionStats ) {
 
-    private:
-        class AssertionPrinter {
-            void operator= ( AssertionPrinter const& );
-        public:
-            AssertionPrinter( std::ostream& _stream, AssertionStats const& _stats, bool _printInfoMessages )
-            : stream( _stream )
-            , stats( _stats )
-            , result( _stats.assertionResult )
-            , messages( _stats.infoMessages )
-            , itMessage( _stats.infoMessages.begin() )
-            , printInfoMessages( _printInfoMessages )
-            {}
+        AssertionResult const& result = assertionStats.assertionResult;
+
+        bool includeResults = m_config->includeSuccessfulResults() || !result.isOk();
 
-            void print() {
-                printSourceInfo();
-
-                itMessage = messages.begin();
-
-                switch( result.getResultType() ) {
-                    case ResultWas::Ok:
-                        printResultType( Colour::ResultSuccess, passedString() );
-                        printOriginalExpression();
-                        printReconstructedExpression();
-                        if ( ! result.hasExpression() )
-                            printRemainingMessages( Colour::None );
-                        else
-                            printRemainingMessages();
-                        break;
-                    case ResultWas::ExpressionFailed:
-                        if( result.isOk() )
-                            printResultType( Colour::ResultSuccess, failedString() + std::string( " - but was ok" ) );
-                        else
-                            printResultType( Colour::Error, failedString() );
-                        printOriginalExpression();
-                        printReconstructedExpression();
-                        printRemainingMessages();
-                        break;
-                    case ResultWas::ThrewException:
-                        printResultType( Colour::Error, failedString() );
-                        printIssue( "unexpected exception with message:" );
-                        printMessage();
-                        printExpressionWas();
-                        printRemainingMessages();
-                        break;
-                    case ResultWas::FatalErrorCondition:
-                        printResultType( Colour::Error, failedString() );
-                        printIssue( "fatal error condition with message:" );
-                        printMessage();
-                        printExpressionWas();
-                        printRemainingMessages();
-                        break;
-                    case ResultWas::DidntThrowException:
-                        printResultType( Colour::Error, failedString() );
-                        printIssue( "expected exception, got none" );
-                        printExpressionWas();
-                        printRemainingMessages();
-                        break;
-                    case ResultWas::Info:
-                        printResultType( Colour::None, "info" );
-                        printMessage();
-                        printRemainingMessages();
-                        break;
-                    case ResultWas::Warning:
-                        printResultType( Colour::None, "warning" );
-                        printMessage();
-                        printRemainingMessages();
-                        break;
-                    case ResultWas::ExplicitFailure:
-                        printResultType( Colour::Error, failedString() );
-                        printIssue( "explicitly" );
-                        printRemainingMessages( Colour::None );
-                        break;
-                    // These cases are here to prevent compiler warnings
-                    case ResultWas::Unknown:
-                    case ResultWas::FailureBit:
-                    case ResultWas::Exception:
-                        printResultType( Colour::Error, "** internal error **" );
-                        break;
+        if( includeResults || result.getResultType() == ResultWas::Warning ) {
+            // Print any info messages in <Info> tags.
+            for( auto const& msg : assertionStats.infoMessages ) {
+                if( msg.type == ResultWas::Info && includeResults ) {
+                    m_xml.scopedElement( "Info" )
+                            .writeText( msg.message );
+                } else if ( msg.type == ResultWas::Warning ) {
+                    m_xml.scopedElement( "Warning" )
+                            .writeText( msg.message );
                 }
             }
+        }
 
-        private:
-            // Colour::LightGrey
-
-            static Colour::Code dimColour() { return Colour::FileName; }
+        // Drop out if result was successful but we're not printing them.
+        if( !includeResults && result.getResultType() != ResultWas::Warning )
+            return true;
 
-#ifdef CATCH_PLATFORM_MAC
-            static const char* failedString() { return "FAILED"; }
-            static const char* passedString() { return "PASSED"; }
-#else
-            static const char* failedString() { return "failed"; }
-            static const char* passedString() { return "passed"; }
-#endif
+        // Print the expression if there is one.
+        if( result.hasExpression() ) {
+            m_xml.startElement( "Expression" )
+                .writeAttribute( "success", result.succeeded() )
+                .writeAttribute( "type", result.getTestMacroName() );
 
-            void printSourceInfo() const {
-                Colour colourGuard( Colour::FileName );
-                stream << result.getSourceInfo() << ':';
-            }
+            writeSourceInfo( result.getSourceInfo() );
 
-            void printResultType( Colour::Code colour, std::string const& passOrFail ) const {
-                if( !passOrFail.empty() ) {
-                    {
-                        Colour colourGuard( colour );
-                        stream << ' ' << passOrFail;
-                    }
-                    stream << ':';
-                }
-            }
+            m_xml.scopedElement( "Original" )
+                .writeText( result.getExpression() );
+            m_xml.scopedElement( "Expanded" )
+                .writeText( result.getExpandedExpression() );
+        }
 
-            void printIssue( std::string const& issue ) const {
-                stream << ' ' << issue;
-            }
+        // And... Print a result applicable to each result type.
+        switch( result.getResultType() ) {
+            case ResultWas::ThrewException:
+                m_xml.startElement( "Exception" );
+                writeSourceInfo( result.getSourceInfo() );
+                m_xml.writeText( result.getMessage() );
+                m_xml.endElement();
+                break;
+            case ResultWas::FatalErrorCondition:
+                m_xml.startElement( "FatalErrorCondition" );
+                writeSourceInfo( result.getSourceInfo() );
+                m_xml.writeText( result.getMessage() );
+                m_xml.endElement();
+                break;
+            case ResultWas::Info:
+                m_xml.scopedElement( "Info" )
+                    .writeText( result.getMessage() );
+                break;
+            case ResultWas::Warning:
+                // Warning will already have been written
+                break;
+            case ResultWas::ExplicitFailure:
+                m_xml.startElement( "Failure" );
+                writeSourceInfo( result.getSourceInfo() );
+                m_xml.writeText( result.getMessage() );
+                m_xml.endElement();
+                break;
+            default:
+                break;
+        }
 
-            void printExpressionWas() {
-                if( result.hasExpression() ) {
-                    stream << ';';
-                    {
-                        Colour colour( dimColour() );
-                        stream << " expression was:";
-                    }
-                    printOriginalExpression();
-                }
-            }
+        if( result.hasExpression() )
+            m_xml.endElement();
 
-            void printOriginalExpression() const {
-                if( result.hasExpression() ) {
-                    stream << ' ' << result.getExpression();
-                }
-            }
+        return true;
+    }
 
-            void printReconstructedExpression() const {
-                if( result.hasExpandedExpression() ) {
-                    {
-                        Colour colour( dimColour() );
-                        stream << " for: ";
-                    }
-                    stream << result.getExpandedExpression();
-                }
-            }
+    void XmlReporter::sectionEnded( SectionStats const& sectionStats ) {
+        StreamingReporterBase::sectionEnded( sectionStats );
+        if( --m_sectionDepth > 0 ) {
+            XmlWriter::ScopedElement e = m_xml.scopedElement( "OverallResults" );
+            e.writeAttribute( "successes", sectionStats.assertions.passed );
+            e.writeAttribute( "failures", sectionStats.assertions.failed );
+            e.writeAttribute( "expectedFailures", sectionStats.assertions.failedButOk );
 
-            void printMessage() {
-                if ( itMessage != messages.end() ) {
-                    stream << " '" << itMessage->message << '\'';
-                    ++itMessage;
-                }
-            }
+            if ( m_config->showDurations() == ShowDurations::Always )
+                e.writeAttribute( "durationInSeconds", sectionStats.durationInSeconds );
 
-            void printRemainingMessages( Colour::Code colour = dimColour() ) {
-                if ( itMessage == messages.end() )
-                    return;
+            m_xml.endElement();
+        }
+    }
 
-                // using messages.end() directly yields compilation error:
-                std::vector<MessageInfo>::const_iterator itEnd = messages.end();
-                const std::size_t N = static_cast<std::size_t>( std::distance( itMessage, itEnd ) );
+    void XmlReporter::testCaseEnded( TestCaseStats const& testCaseStats ) {
+        StreamingReporterBase::testCaseEnded( testCaseStats );
+        XmlWriter::ScopedElement e = m_xml.scopedElement( "OverallResult" );
+        e.writeAttribute( "success", testCaseStats.totals.assertions.allOk() );
 
-                {
-                    Colour colourGuard( colour );
-                    stream << " with " << pluralise( N, "message" ) << ':';
-                }
+        if ( m_config->showDurations() == ShowDurations::Always )
+            e.writeAttribute( "durationInSeconds", m_testCaseTimer.getElapsedSeconds() );
 
-                for(; itMessage != itEnd; ) {
-                    // If this assertion is a warning ignore any INFO messages
-                    if( printInfoMessages || itMessage->type != ResultWas::Info ) {
-                        stream << " '" << itMessage->message << '\'';
-                        if ( ++itMessage != itEnd ) {
-                            Colour colourGuard( dimColour() );
-                            stream << " and";
-                        }
-                    }
-                }
-            }
+        if( !testCaseStats.stdOut.empty() )
+            m_xml.scopedElement( "StdOut" ).writeText( trim( testCaseStats.stdOut ), false );
+        if( !testCaseStats.stdErr.empty() )
+            m_xml.scopedElement( "StdErr" ).writeText( trim( testCaseStats.stdErr ), false );
 
-        private:
-            std::ostream& stream;
-            AssertionStats const& stats;
-            AssertionResult const& result;
-            std::vector<MessageInfo> messages;
-            std::vector<MessageInfo>::const_iterator itMessage;
-            bool printInfoMessages;
-        };
+        m_xml.endElement();
+    }
 
-        // Colour, message variants:
-        // - white: No tests ran.
-        // -   red: Failed [both/all] N test cases, failed [both/all] M assertions.
-        // - white: Passed [both/all] N test cases (no assertions).
-        // -   red: Failed N tests cases, failed M assertions.
-        // - green: Passed [both/all] N tests cases with M assertions.
-
-        std::string bothOrAll( std::size_t count ) const {
-            return count == 1 ? std::string() : count == 2 ? "both " : "all " ;
-        }
-
-        void printTotals( const Totals& totals ) const {
-            if( totals.testCases.total() == 0 ) {
-                stream << "No tests ran.";
-            }
-            else if( totals.testCases.failed == totals.testCases.total() ) {
-                Colour colour( Colour::ResultError );
-                const std::string qualify_assertions_failed =
-                    totals.assertions.failed == totals.assertions.total() ?
-                        bothOrAll( totals.assertions.failed ) : std::string();
-                stream <<
-                    "Failed " << bothOrAll( totals.testCases.failed )
-                              << pluralise( totals.testCases.failed, "test case"  ) << ", "
-                    "failed " << qualify_assertions_failed <<
-                                 pluralise( totals.assertions.failed, "assertion" ) << '.';
-            }
-            else if( totals.assertions.total() == 0 ) {
-                stream <<
-                    "Passed " << bothOrAll( totals.testCases.total() )
-                              << pluralise( totals.testCases.total(), "test case" )
-                              << " (no assertions).";
-            }
-            else if( totals.assertions.failed ) {
-                Colour colour( Colour::ResultError );
-                stream <<
-                    "Failed " << pluralise( totals.testCases.failed, "test case"  ) << ", "
-                    "failed " << pluralise( totals.assertions.failed, "assertion" ) << '.';
-            }
-            else {
-                Colour colour( Colour::ResultSuccess );
-                stream <<
-                    "Passed " << bothOrAll( totals.testCases.passed )
-                              << pluralise( totals.testCases.passed, "test case"  ) <<
-                    " with "  << pluralise( totals.assertions.passed, "assertion" ) << '.';
-            }
-        }
-    };
+    void XmlReporter::testGroupEnded( TestGroupStats const& testGroupStats ) {
+        StreamingReporterBase::testGroupEnded( testGroupStats );
+        // TODO: Check testGroupStats.aborting and act accordingly.
+        m_xml.scopedElement( "OverallResults" )
+            .writeAttribute( "successes", testGroupStats.totals.assertions.passed )
+            .writeAttribute( "failures", testGroupStats.totals.assertions.failed )
+            .writeAttribute( "expectedFailures", testGroupStats.totals.assertions.failedButOk );
+        m_xml.endElement();
+    }
 
-    INTERNAL_CATCH_REGISTER_REPORTER( "compact", CompactReporter )
+    void XmlReporter::testRunEnded( TestRunStats const& testRunStats ) {
+        StreamingReporterBase::testRunEnded( testRunStats );
+        m_xml.scopedElement( "OverallResults" )
+            .writeAttribute( "successes", testRunStats.totals.assertions.passed )
+            .writeAttribute( "failures", testRunStats.totals.assertions.failed )
+            .writeAttribute( "expectedFailures", testRunStats.totals.assertions.failedButOk );
+        m_xml.endElement();
+    }
 
-} // end namespace Catch
+#if defined(CATCH_CONFIG_ENABLE_BENCHMARKING)
+    void XmlReporter::benchmarkPreparing(std::string const& name) {
+        m_xml.startElement("BenchmarkResults")
+            .writeAttribute("name", name);
+    }
 
-namespace Catch {
-    // These are all here to avoid warnings about not having any out of line
-    // virtual methods
-    NonCopyable::~NonCopyable() {}
-    IShared::~IShared() {}
-    IStream::~IStream() CATCH_NOEXCEPT {}
-    FileStream::~FileStream() CATCH_NOEXCEPT {}
-    CoutStream::~CoutStream() CATCH_NOEXCEPT {}
-    DebugOutStream::~DebugOutStream() CATCH_NOEXCEPT {}
-    StreamBufBase::~StreamBufBase() CATCH_NOEXCEPT {}
-    IContext::~IContext() {}
-    IResultCapture::~IResultCapture() {}
-    ITestCase::~ITestCase() {}
-    ITestCaseRegistry::~ITestCaseRegistry() {}
-    IRegistryHub::~IRegistryHub() {}
-    IMutableRegistryHub::~IMutableRegistryHub() {}
-    IExceptionTranslator::~IExceptionTranslator() {}
-    IExceptionTranslatorRegistry::~IExceptionTranslatorRegistry() {}
-    IReporter::~IReporter() {}
-    IReporterFactory::~IReporterFactory() {}
-    IReporterRegistry::~IReporterRegistry() {}
-    IStreamingReporter::~IStreamingReporter() {}
-    AssertionStats::~AssertionStats() {}
-    SectionStats::~SectionStats() {}
-    TestCaseStats::~TestCaseStats() {}
-    TestGroupStats::~TestGroupStats() {}
-    TestRunStats::~TestRunStats() {}
-    CumulativeReporterBase::SectionNode::~SectionNode() {}
-    CumulativeReporterBase::~CumulativeReporterBase() {}
-
-    StreamingReporterBase::~StreamingReporterBase() {}
-    ConsoleReporter::~ConsoleReporter() {}
-    CompactReporter::~CompactReporter() {}
-    IRunner::~IRunner() {}
-    IMutableContext::~IMutableContext() {}
-    IConfig::~IConfig() {}
-    XmlReporter::~XmlReporter() {}
-    JunitReporter::~JunitReporter() {}
-    TestRegistry::~TestRegistry() {}
-    FreeFunctionTestCase::~FreeFunctionTestCase() {}
-    IGeneratorInfo::~IGeneratorInfo() {}
-    IGeneratorsForTest::~IGeneratorsForTest() {}
-    WildcardPattern::~WildcardPattern() {}
-    TestSpec::Pattern::~Pattern() {}
-    TestSpec::NamePattern::~NamePattern() {}
-    TestSpec::TagPattern::~TagPattern() {}
-    TestSpec::ExcludedPattern::~ExcludedPattern() {}
-    Matchers::Impl::MatcherUntypedBase::~MatcherUntypedBase() {}
+    void XmlReporter::benchmarkStarting(BenchmarkInfo const &info) {
+        m_xml.writeAttribute("samples", info.samples)
+            .writeAttribute("resamples", info.resamples)
+            .writeAttribute("iterations", info.iterations)
+            .writeAttribute("clockResolution", static_cast<uint64_t>(info.clockResolution))
+            .writeAttribute("estimatedDuration", static_cast<uint64_t>(info.estimatedDuration))
+            .writeComment("All values in nano seconds");
+    }
 
-    void Config::dummy() {}
+    void XmlReporter::benchmarkEnded(BenchmarkStats<> const& benchmarkStats) {
+        m_xml.startElement("mean")
+            .writeAttribute("value", static_cast<uint64_t>(benchmarkStats.mean.point.count()))
+            .writeAttribute("lowerBound", static_cast<uint64_t>(benchmarkStats.mean.lower_bound.count()))
+            .writeAttribute("upperBound", static_cast<uint64_t>(benchmarkStats.mean.upper_bound.count()))
+            .writeAttribute("ci", benchmarkStats.mean.confidence_interval);
+        m_xml.endElement();
+        m_xml.startElement("standardDeviation")
+            .writeAttribute("value", benchmarkStats.standardDeviation.point.count())
+            .writeAttribute("lowerBound", benchmarkStats.standardDeviation.lower_bound.count())
+            .writeAttribute("upperBound", benchmarkStats.standardDeviation.upper_bound.count())
+            .writeAttribute("ci", benchmarkStats.standardDeviation.confidence_interval);
+        m_xml.endElement();
+        m_xml.startElement("outliers")
+            .writeAttribute("variance", benchmarkStats.outlierVariance)
+            .writeAttribute("lowMild", benchmarkStats.outliers.low_mild)
+            .writeAttribute("lowSevere", benchmarkStats.outliers.low_severe)
+            .writeAttribute("highMild", benchmarkStats.outliers.high_mild)
+            .writeAttribute("highSevere", benchmarkStats.outliers.high_severe);
+        m_xml.endElement();
+        m_xml.endElement();
+    }
 
-    namespace TestCaseTracking {
-        ITracker::~ITracker() {}
-        TrackerBase::~TrackerBase() {}
-        SectionTracker::~SectionTracker() {}
-        IndexTracker::~IndexTracker() {}
+    void XmlReporter::benchmarkFailed(std::string const &error) {
+        m_xml.scopedElement("failed").
+            writeAttribute("message", error);
+        m_xml.endElement();
     }
+#endif // CATCH_CONFIG_ENABLE_BENCHMARKING
+
+    CATCH_REGISTER_REPORTER( "xml", XmlReporter )
+
+} // end namespace Catch
+
+#if defined(_MSC_VER)
+#pragma warning(pop)
+#endif
+// end catch_reporter_xml.cpp
+
+namespace Catch {
+    LeakDetector leakDetector;
 }
 
 #ifdef __clang__
 #pragma clang diagnostic pop
 #endif
 
+// end catch_impl.hpp
 #endif
 
 #ifdef CATCH_CONFIG_MAIN
-// #included from: internal/catch_default_main.hpp
-#define TWOBLUECUBES_CATCH_DEFAULT_MAIN_HPP_INCLUDED
+// start catch_default_main.hpp
 
 #ifndef __OBJC__
 
-#if defined(WIN32) && defined(_UNICODE) && !defined(DO_NOT_USE_WMAIN)
+#if defined(CATCH_CONFIG_WCHAR) && defined(WIN32) && defined(_UNICODE) && !defined(DO_NOT_USE_WMAIN)
 // Standard C/C++ Win32 Unicode wmain entry point
 extern "C" int wmain (int argc, wchar_t * argv[], wchar_t * []) {
 #else
@@ -11303,8 +17034,7 @@ extern "C" int wmain (int argc, wchar_t * argv[], wchar_t * []) {
 int main (int argc, char * argv[]) {
 #endif
 
-    int result = Catch::Session().run(argc, argv);
-    return ( result < 0xff ? result : 0xff );
+    return Catch::Session().run( argc, argv );
 }
 
 #else // __OBJC__
@@ -11316,193 +17046,425 @@ int main (int argc, char * const argv[]) {
 #endif
 
     Catch::registerTestMethods();
-    int result = Catch::Session().run( argc, (char* const*)argv );
+    int result = Catch::Session().run( argc, (char**)argv );
 
 #if !CATCH_ARC_ENABLED
     [pool drain];
 #endif
 
-    return ( result < 0xff ? result : 0xff );
+    return result;
 }
 
 #endif // __OBJC__
 
+// end catch_default_main.hpp
 #endif
 
+#if !defined(CATCH_CONFIG_IMPL_ONLY)
+
 #ifdef CLARA_CONFIG_MAIN_NOT_DEFINED
 #  undef CLARA_CONFIG_MAIN
 #endif
 
+#if !defined(CATCH_CONFIG_DISABLE)
 //////
-
 // If this config identifier is defined then all CATCH macros are prefixed with CATCH_
 #ifdef CATCH_CONFIG_PREFIX_ALL
 
-#if defined(CATCH_CONFIG_FAST_COMPILE)
-#define CATCH_REQUIRE( expr ) INTERNAL_CATCH_TEST_NO_TRY( "CATCH_REQUIRE", Catch::ResultDisposition::Normal, expr )
-#define CATCH_REQUIRE_FALSE( expr ) INTERNAL_CATCH_TEST_NO_TRY( "CATCH_REQUIRE_FALSE", Catch::ResultDisposition::Normal | Catch::ResultDisposition::FalseTest, expr )
-#else
-#define CATCH_REQUIRE( expr ) INTERNAL_CATCH_TEST( "CATCH_REQUIRE", Catch::ResultDisposition::Normal, expr )
-#define CATCH_REQUIRE_FALSE( expr ) INTERNAL_CATCH_TEST( "CATCH_REQUIRE_FALSE", Catch::ResultDisposition::Normal | Catch::ResultDisposition::FalseTest, expr  )
-#endif
+#define CATCH_REQUIRE( ... ) INTERNAL_CATCH_TEST( "CATCH_REQUIRE", Catch::ResultDisposition::Normal, __VA_ARGS__ )
+#define CATCH_REQUIRE_FALSE( ... ) INTERNAL_CATCH_TEST( "CATCH_REQUIRE_FALSE", Catch::ResultDisposition::Normal | Catch::ResultDisposition::FalseTest, __VA_ARGS__ )
 
-#define CATCH_REQUIRE_THROWS( expr ) INTERNAL_CATCH_THROWS( "CATCH_REQUIRE_THROWS", Catch::ResultDisposition::Normal, "", expr )
+#define CATCH_REQUIRE_THROWS( ... ) INTERNAL_CATCH_THROWS( "CATCH_REQUIRE_THROWS", Catch::ResultDisposition::Normal, __VA_ARGS__ )
 #define CATCH_REQUIRE_THROWS_AS( expr, exceptionType ) INTERNAL_CATCH_THROWS_AS( "CATCH_REQUIRE_THROWS_AS", exceptionType, Catch::ResultDisposition::Normal, expr )
-#define CATCH_REQUIRE_THROWS_WITH( expr, matcher ) INTERNAL_CATCH_THROWS( "CATCH_REQUIRE_THROWS_WITH", Catch::ResultDisposition::Normal, matcher, expr )
-#define CATCH_REQUIRE_NOTHROW( expr ) INTERNAL_CATCH_NO_THROW( "CATCH_REQUIRE_NOTHROW", Catch::ResultDisposition::Normal, expr )
-
-#define CATCH_CHECK( expr ) INTERNAL_CATCH_TEST( "CATCH_CHECK", Catch::ResultDisposition::ContinueOnFailure, expr )
-#define CATCH_CHECK_FALSE( expr ) INTERNAL_CATCH_TEST( "CATCH_CHECK_FALSE", Catch::ResultDisposition::ContinueOnFailure | Catch::ResultDisposition::FalseTest, expr )
-#define CATCH_CHECKED_IF( expr ) INTERNAL_CATCH_IF( "CATCH_CHECKED_IF", Catch::ResultDisposition::ContinueOnFailure, expr )
-#define CATCH_CHECKED_ELSE( expr ) INTERNAL_CATCH_ELSE( "CATCH_CHECKED_ELSE", Catch::ResultDisposition::ContinueOnFailure, expr )
-#define CATCH_CHECK_NOFAIL( expr ) INTERNAL_CATCH_TEST( "CATCH_CHECK_NOFAIL", Catch::ResultDisposition::ContinueOnFailure | Catch::ResultDisposition::SuppressFail, expr )
-
-#define CATCH_CHECK_THROWS( expr )  INTERNAL_CATCH_THROWS( "CATCH_CHECK_THROWS", Catch::ResultDisposition::ContinueOnFailure, "", expr )
+#define CATCH_REQUIRE_THROWS_WITH( expr, matcher ) INTERNAL_CATCH_THROWS_STR_MATCHES( "CATCH_REQUIRE_THROWS_WITH", Catch::ResultDisposition::Normal, matcher, expr )
+#if !defined(CATCH_CONFIG_DISABLE_MATCHERS)
+#define CATCH_REQUIRE_THROWS_MATCHES( expr, exceptionType, matcher ) INTERNAL_CATCH_THROWS_MATCHES( "CATCH_REQUIRE_THROWS_MATCHES", exceptionType, Catch::ResultDisposition::Normal, matcher, expr )
+#endif// CATCH_CONFIG_DISABLE_MATCHERS
+#define CATCH_REQUIRE_NOTHROW( ... ) INTERNAL_CATCH_NO_THROW( "CATCH_REQUIRE_NOTHROW", Catch::ResultDisposition::Normal, __VA_ARGS__ )
+
+#define CATCH_CHECK( ... ) INTERNAL_CATCH_TEST( "CATCH_CHECK", Catch::ResultDisposition::ContinueOnFailure, __VA_ARGS__ )
+#define CATCH_CHECK_FALSE( ... ) INTERNAL_CATCH_TEST( "CATCH_CHECK_FALSE", Catch::ResultDisposition::ContinueOnFailure | Catch::ResultDisposition::FalseTest, __VA_ARGS__ )
+#define CATCH_CHECKED_IF( ... ) INTERNAL_CATCH_IF( "CATCH_CHECKED_IF", Catch::ResultDisposition::ContinueOnFailure, __VA_ARGS__ )
+#define CATCH_CHECKED_ELSE( ... ) INTERNAL_CATCH_ELSE( "CATCH_CHECKED_ELSE", Catch::ResultDisposition::ContinueOnFailure, __VA_ARGS__ )
+#define CATCH_CHECK_NOFAIL( ... ) INTERNAL_CATCH_TEST( "CATCH_CHECK_NOFAIL", Catch::ResultDisposition::ContinueOnFailure | Catch::ResultDisposition::SuppressFail, __VA_ARGS__ )
+
+#define CATCH_CHECK_THROWS( ... )  INTERNAL_CATCH_THROWS( "CATCH_CHECK_THROWS", Catch::ResultDisposition::ContinueOnFailure, __VA_ARGS__ )
 #define CATCH_CHECK_THROWS_AS( expr, exceptionType ) INTERNAL_CATCH_THROWS_AS( "CATCH_CHECK_THROWS_AS", exceptionType, Catch::ResultDisposition::ContinueOnFailure, expr )
-#define CATCH_CHECK_THROWS_WITH( expr, matcher ) INTERNAL_CATCH_THROWS( "CATCH_CHECK_THROWS_WITH", Catch::ResultDisposition::ContinueOnFailure, matcher, expr )
-#define CATCH_CHECK_NOTHROW( expr ) INTERNAL_CATCH_NO_THROW( "CATCH_CHECK_NOTHROW", Catch::ResultDisposition::ContinueOnFailure, expr )
+#define CATCH_CHECK_THROWS_WITH( expr, matcher ) INTERNAL_CATCH_THROWS_STR_MATCHES( "CATCH_CHECK_THROWS_WITH", Catch::ResultDisposition::ContinueOnFailure, matcher, expr )
+#if !defined(CATCH_CONFIG_DISABLE_MATCHERS)
+#define CATCH_CHECK_THROWS_MATCHES( expr, exceptionType, matcher ) INTERNAL_CATCH_THROWS_MATCHES( "CATCH_CHECK_THROWS_MATCHES", exceptionType, Catch::ResultDisposition::ContinueOnFailure, matcher, expr )
+#endif // CATCH_CONFIG_DISABLE_MATCHERS
+#define CATCH_CHECK_NOTHROW( ... ) INTERNAL_CATCH_NO_THROW( "CATCH_CHECK_NOTHROW", Catch::ResultDisposition::ContinueOnFailure, __VA_ARGS__ )
 
+#if !defined(CATCH_CONFIG_DISABLE_MATCHERS)
 #define CATCH_CHECK_THAT( arg, matcher ) INTERNAL_CHECK_THAT( "CATCH_CHECK_THAT", matcher, Catch::ResultDisposition::ContinueOnFailure, arg )
 
-#if defined(CATCH_CONFIG_FAST_COMPILE)
-#define CATCH_REQUIRE_THAT( arg, matcher ) INTERNAL_CHECK_THAT_NO_TRY( "CATCH_REQUIRE_THAT", matcher, Catch::ResultDisposition::Normal, arg )
-#else
 #define CATCH_REQUIRE_THAT( arg, matcher ) INTERNAL_CHECK_THAT( "CATCH_REQUIRE_THAT", matcher, Catch::ResultDisposition::Normal, arg )
-#endif
+#endif // CATCH_CONFIG_DISABLE_MATCHERS
 
 #define CATCH_INFO( msg ) INTERNAL_CATCH_INFO( "CATCH_INFO", msg )
+#define CATCH_UNSCOPED_INFO( msg ) INTERNAL_CATCH_UNSCOPED_INFO( "CATCH_UNSCOPED_INFO", msg )
 #define CATCH_WARN( msg ) INTERNAL_CATCH_MSG( "CATCH_WARN", Catch::ResultWas::Warning, Catch::ResultDisposition::ContinueOnFailure, msg )
-#define CATCH_SCOPED_INFO( msg ) INTERNAL_CATCH_INFO( "CATCH_INFO", msg )
-#define CATCH_CAPTURE( msg ) INTERNAL_CATCH_INFO( "CATCH_CAPTURE", #msg " := " << Catch::toString(msg) )
-#define CATCH_SCOPED_CAPTURE( msg ) INTERNAL_CATCH_INFO( "CATCH_CAPTURE", #msg " := " << Catch::toString(msg) )
-
-#ifdef CATCH_CONFIG_VARIADIC_MACROS
-    #define CATCH_TEST_CASE( ... ) INTERNAL_CATCH_TESTCASE( __VA_ARGS__ )
-    #define CATCH_TEST_CASE_METHOD( className, ... ) INTERNAL_CATCH_TEST_CASE_METHOD( className, __VA_ARGS__ )
-    #define CATCH_METHOD_AS_TEST_CASE( method, ... ) INTERNAL_CATCH_METHOD_AS_TEST_CASE( method, __VA_ARGS__ )
-    #define CATCH_REGISTER_TEST_CASE( Function, ... ) INTERNAL_CATCH_REGISTER_TESTCASE( Function, __VA_ARGS__ )
-    #define CATCH_SECTION( ... ) INTERNAL_CATCH_SECTION( __VA_ARGS__ )
-    #define CATCH_FAIL( ... ) INTERNAL_CATCH_MSG( "CATCH_FAIL", Catch::ResultWas::ExplicitFailure, Catch::ResultDisposition::Normal, __VA_ARGS__ )
-    #define CATCH_FAIL_CHECK( ... ) INTERNAL_CATCH_MSG( "CATCH_FAIL_CHECK", Catch::ResultWas::ExplicitFailure, Catch::ResultDisposition::ContinueOnFailure, __VA_ARGS__ )
-    #define CATCH_SUCCEED( ... ) INTERNAL_CATCH_MSG( "CATCH_SUCCEED", Catch::ResultWas::Ok, Catch::ResultDisposition::ContinueOnFailure, __VA_ARGS__ )
+#define CATCH_CAPTURE( ... ) INTERNAL_CATCH_CAPTURE( INTERNAL_CATCH_UNIQUE_NAME(capturer), "CATCH_CAPTURE",__VA_ARGS__ )
+
+#define CATCH_TEST_CASE( ... ) INTERNAL_CATCH_TESTCASE( __VA_ARGS__ )
+#define CATCH_TEST_CASE_METHOD( className, ... ) INTERNAL_CATCH_TEST_CASE_METHOD( className, __VA_ARGS__ )
+#define CATCH_METHOD_AS_TEST_CASE( method, ... ) INTERNAL_CATCH_METHOD_AS_TEST_CASE( method, __VA_ARGS__ )
+#define CATCH_REGISTER_TEST_CASE( Function, ... ) INTERNAL_CATCH_REGISTER_TESTCASE( Function, __VA_ARGS__ )
+#define CATCH_SECTION( ... ) INTERNAL_CATCH_SECTION( __VA_ARGS__ )
+#define CATCH_DYNAMIC_SECTION( ... ) INTERNAL_CATCH_DYNAMIC_SECTION( __VA_ARGS__ )
+#define CATCH_FAIL( ... ) INTERNAL_CATCH_MSG( "CATCH_FAIL", Catch::ResultWas::ExplicitFailure, Catch::ResultDisposition::Normal, __VA_ARGS__ )
+#define CATCH_FAIL_CHECK( ... ) INTERNAL_CATCH_MSG( "CATCH_FAIL_CHECK", Catch::ResultWas::ExplicitFailure, Catch::ResultDisposition::ContinueOnFailure, __VA_ARGS__ )
+#define CATCH_SUCCEED( ... ) INTERNAL_CATCH_MSG( "CATCH_SUCCEED", Catch::ResultWas::Ok, Catch::ResultDisposition::ContinueOnFailure, __VA_ARGS__ )
+
+#define CATCH_ANON_TEST_CASE() INTERNAL_CATCH_TESTCASE()
+
+#ifndef CATCH_CONFIG_TRADITIONAL_MSVC_PREPROCESSOR
+#define CATCH_TEMPLATE_TEST_CASE( ... ) INTERNAL_CATCH_TEMPLATE_TEST_CASE( __VA_ARGS__ )
+#define CATCH_TEMPLATE_TEST_CASE_SIG( ... ) INTERNAL_CATCH_TEMPLATE_TEST_CASE_SIG( __VA_ARGS__ )
+#define CATCH_TEMPLATE_TEST_CASE_METHOD( className, ... ) INTERNAL_CATCH_TEMPLATE_TEST_CASE_METHOD( className, __VA_ARGS__ )
+#define CATCH_TEMPLATE_TEST_CASE_METHOD_SIG( className, ... ) INTERNAL_CATCH_TEMPLATE_TEST_CASE_METHOD_SIG( className, __VA_ARGS__ )
+#define CATCH_TEMPLATE_PRODUCT_TEST_CASE( ... ) INTERNAL_CATCH_TEMPLATE_PRODUCT_TEST_CASE( __VA_ARGS__ )
+#define CATCH_TEMPLATE_PRODUCT_TEST_CASE_SIG( ... ) INTERNAL_CATCH_TEMPLATE_PRODUCT_TEST_CASE_SIG( __VA_ARGS__ )
+#define CATCH_TEMPLATE_PRODUCT_TEST_CASE_METHOD( className, ... ) INTERNAL_CATCH_TEMPLATE_PRODUCT_TEST_CASE_METHOD( className, __VA_ARGS__ )
+#define CATCH_TEMPLATE_PRODUCT_TEST_CASE_METHOD_SIG( className, ... ) INTERNAL_CATCH_TEMPLATE_PRODUCT_TEST_CASE_METHOD_SIG( className, __VA_ARGS__ )
 #else
-    #define CATCH_TEST_CASE( name, description ) INTERNAL_CATCH_TESTCASE( name, description )
-    #define CATCH_TEST_CASE_METHOD( className, name, description ) INTERNAL_CATCH_TEST_CASE_METHOD( className, name, description )
-    #define CATCH_METHOD_AS_TEST_CASE( method, name, description ) INTERNAL_CATCH_METHOD_AS_TEST_CASE( method, name, description )
-    #define CATCH_REGISTER_TEST_CASE( function, name, description ) INTERNAL_CATCH_REGISTER_TESTCASE( function, name, description )
-    #define CATCH_SECTION( name, description ) INTERNAL_CATCH_SECTION( name, description )
-    #define CATCH_FAIL( msg ) INTERNAL_CATCH_MSG( "CATCH_FAIL", Catch::ResultWas::ExplicitFailure, Catch::ResultDisposition::Normal, msg )
-    #define CATCH_FAIL_CHECK( msg ) INTERNAL_CATCH_MSG( "CATCH_FAIL_CHECK", Catch::ResultWas::ExplicitFailure, Catch::ResultDisposition::ContinueOnFailure, msg )
-    #define CATCH_SUCCEED( msg ) INTERNAL_CATCH_MSG( "CATCH_SUCCEED", Catch::ResultWas::Ok, Catch::ResultDisposition::ContinueOnFailure, msg )
+#define CATCH_TEMPLATE_TEST_CASE( ... ) INTERNAL_CATCH_EXPAND_VARGS( INTERNAL_CATCH_TEMPLATE_TEST_CASE( __VA_ARGS__ ) )
+#define CATCH_TEMPLATE_TEST_CASE_SIG( ... ) INTERNAL_CATCH_EXPAND_VARGS( INTERNAL_CATCH_TEMPLATE_TEST_CASE_SIG( __VA_ARGS__ ) )
+#define CATCH_TEMPLATE_TEST_CASE_METHOD( className, ... ) INTERNAL_CATCH_EXPAND_VARGS( INTERNAL_CATCH_TEMPLATE_TEST_CASE_METHOD( className, __VA_ARGS__ ) )
+#define CATCH_TEMPLATE_TEST_CASE_METHOD_SIG( className, ... ) INTERNAL_CATCH_EXPAND_VARGS( INTERNAL_CATCH_TEMPLATE_TEST_CASE_METHOD_SIG( className, __VA_ARGS__ ) )
+#define CATCH_TEMPLATE_PRODUCT_TEST_CASE( ... ) INTERNAL_CATCH_EXPAND_VARGS( INTERNAL_CATCH_TEMPLATE_PRODUCT_TEST_CASE( __VA_ARGS__ ) )
+#define CATCH_TEMPLATE_PRODUCT_TEST_CASE_SIG( ... ) INTERNAL_CATCH_EXPAND_VARGS( INTERNAL_CATCH_TEMPLATE_PRODUCT_TEST_CASE_SIG( __VA_ARGS__ ) )
+#define CATCH_TEMPLATE_PRODUCT_TEST_CASE_METHOD( className, ... ) INTERNAL_CATCH_EXPAND_VARGS( INTERNAL_CATCH_TEMPLATE_PRODUCT_TEST_CASE_METHOD( className, __VA_ARGS__ ) )
+#define CATCH_TEMPLATE_PRODUCT_TEST_CASE_METHOD_SIG( className, ... ) INTERNAL_CATCH_EXPAND_VARGS( INTERNAL_CATCH_TEMPLATE_PRODUCT_TEST_CASE_METHOD_SIG( className, __VA_ARGS__ ) )
 #endif
-#define CATCH_ANON_TEST_CASE() INTERNAL_CATCH_TESTCASE( "", "" )
-
-#define CATCH_REGISTER_REPORTER( name, reporterType ) INTERNAL_CATCH_REGISTER_REPORTER( name, reporterType )
-#define CATCH_REGISTER_LEGACY_REPORTER( name, reporterType ) INTERNAL_CATCH_REGISTER_LEGACY_REPORTER( name, reporterType )
 
-#define CATCH_GENERATE( expr) INTERNAL_CATCH_GENERATE( expr )
+#if !defined(CATCH_CONFIG_RUNTIME_STATIC_REQUIRE)
+#define CATCH_STATIC_REQUIRE( ... )       static_assert(   __VA_ARGS__ ,      #__VA_ARGS__ );     CATCH_SUCCEED( #__VA_ARGS__ )
+#define CATCH_STATIC_REQUIRE_FALSE( ... ) static_assert( !(__VA_ARGS__), "!(" #__VA_ARGS__ ")" ); CATCH_SUCCEED( #__VA_ARGS__ )
+#else
+#define CATCH_STATIC_REQUIRE( ... )       CATCH_REQUIRE( __VA_ARGS__ )
+#define CATCH_STATIC_REQUIRE_FALSE( ... ) CATCH_REQUIRE_FALSE( __VA_ARGS__ )
+#endif
 
 // "BDD-style" convenience wrappers
-#ifdef CATCH_CONFIG_VARIADIC_MACROS
 #define CATCH_SCENARIO( ... ) CATCH_TEST_CASE( "Scenario: " __VA_ARGS__ )
 #define CATCH_SCENARIO_METHOD( className, ... ) INTERNAL_CATCH_TEST_CASE_METHOD( className, "Scenario: " __VA_ARGS__ )
-#else
-#define CATCH_SCENARIO( name, tags ) CATCH_TEST_CASE( "Scenario: " name, tags )
-#define CATCH_SCENARIO_METHOD( className, name, tags ) INTERNAL_CATCH_TEST_CASE_METHOD( className, "Scenario: " name, tags )
-#endif
-#define CATCH_GIVEN( desc )    CATCH_SECTION( std::string( "Given: ") + desc, "" )
-#define CATCH_WHEN( desc )     CATCH_SECTION( std::string( " When: ") + desc, "" )
-#define CATCH_AND_WHEN( desc ) CATCH_SECTION( std::string( "  And: ") + desc, "" )
-#define CATCH_THEN( desc )     CATCH_SECTION( std::string( " Then: ") + desc, "" )
-#define CATCH_AND_THEN( desc ) CATCH_SECTION( std::string( "  And: ") + desc, "" )
+#define CATCH_GIVEN( desc )     INTERNAL_CATCH_DYNAMIC_SECTION( "    Given: " << desc )
+#define CATCH_AND_GIVEN( desc ) INTERNAL_CATCH_DYNAMIC_SECTION( "And given: " << desc )
+#define CATCH_WHEN( desc )      INTERNAL_CATCH_DYNAMIC_SECTION( "     When: " << desc )
+#define CATCH_AND_WHEN( desc )  INTERNAL_CATCH_DYNAMIC_SECTION( " And when: " << desc )
+#define CATCH_THEN( desc )      INTERNAL_CATCH_DYNAMIC_SECTION( "     Then: " << desc )
+#define CATCH_AND_THEN( desc )  INTERNAL_CATCH_DYNAMIC_SECTION( "      And: " << desc )
+
+#if defined(CATCH_CONFIG_ENABLE_BENCHMARKING)
+#define CATCH_BENCHMARK(...) \
+    INTERNAL_CATCH_BENCHMARK(INTERNAL_CATCH_UNIQUE_NAME(____C_A_T_C_H____B_E_N_C_H____), INTERNAL_CATCH_GET_1_ARG(__VA_ARGS__,,), INTERNAL_CATCH_GET_2_ARG(__VA_ARGS__,,))
+#define CATCH_BENCHMARK_ADVANCED(name) \
+    INTERNAL_CATCH_BENCHMARK_ADVANCED(INTERNAL_CATCH_UNIQUE_NAME(____C_A_T_C_H____B_E_N_C_H____), name)
+#endif // CATCH_CONFIG_ENABLE_BENCHMARKING
 
 // If CATCH_CONFIG_PREFIX_ALL is not defined then the CATCH_ prefix is not required
 #else
 
-#if defined(CATCH_CONFIG_FAST_COMPILE)
-#define REQUIRE( expr ) INTERNAL_CATCH_TEST_NO_TRY( "REQUIRE", Catch::ResultDisposition::Normal, expr )
-#define REQUIRE_FALSE( expr ) INTERNAL_CATCH_TEST_NO_TRY( "REQUIRE_FALSE", Catch::ResultDisposition::Normal | Catch::ResultDisposition::FalseTest, expr )
-
-#else
-#define REQUIRE( expr ) INTERNAL_CATCH_TEST( "REQUIRE", Catch::ResultDisposition::Normal, expr  )
-#define REQUIRE_FALSE( expr ) INTERNAL_CATCH_TEST( "REQUIRE_FALSE", Catch::ResultDisposition::Normal | Catch::ResultDisposition::FalseTest, expr )
-#endif
+#define REQUIRE( ... ) INTERNAL_CATCH_TEST( "REQUIRE", Catch::ResultDisposition::Normal, __VA_ARGS__  )
+#define REQUIRE_FALSE( ... ) INTERNAL_CATCH_TEST( "REQUIRE_FALSE", Catch::ResultDisposition::Normal | Catch::ResultDisposition::FalseTest, __VA_ARGS__ )
 
-#define REQUIRE_THROWS( expr ) INTERNAL_CATCH_THROWS( "REQUIRE_THROWS", Catch::ResultDisposition::Normal, "", expr )
+#define REQUIRE_THROWS( ... ) INTERNAL_CATCH_THROWS( "REQUIRE_THROWS", Catch::ResultDisposition::Normal, __VA_ARGS__ )
 #define REQUIRE_THROWS_AS( expr, exceptionType ) INTERNAL_CATCH_THROWS_AS( "REQUIRE_THROWS_AS", exceptionType, Catch::ResultDisposition::Normal, expr )
-#define REQUIRE_THROWS_WITH( expr, matcher ) INTERNAL_CATCH_THROWS( "REQUIRE_THROWS_WITH", Catch::ResultDisposition::Normal, matcher, expr )
-#define REQUIRE_NOTHROW( expr ) INTERNAL_CATCH_NO_THROW( "REQUIRE_NOTHROW", Catch::ResultDisposition::Normal, expr )
-
-#define CHECK( expr ) INTERNAL_CATCH_TEST( "CHECK", Catch::ResultDisposition::ContinueOnFailure, expr )
-#define CHECK_FALSE( expr ) INTERNAL_CATCH_TEST( "CHECK_FALSE", Catch::ResultDisposition::ContinueOnFailure | Catch::ResultDisposition::FalseTest, expr )
-#define CHECKED_IF( expr ) INTERNAL_CATCH_IF( "CHECKED_IF", Catch::ResultDisposition::ContinueOnFailure, expr )
-#define CHECKED_ELSE( expr ) INTERNAL_CATCH_ELSE( "CHECKED_ELSE", Catch::ResultDisposition::ContinueOnFailure, expr )
-#define CHECK_NOFAIL( expr ) INTERNAL_CATCH_TEST( "CHECK_NOFAIL", Catch::ResultDisposition::ContinueOnFailure | Catch::ResultDisposition::SuppressFail, expr )
-
-#define CHECK_THROWS( expr )  INTERNAL_CATCH_THROWS( "CHECK_THROWS", Catch::ResultDisposition::ContinueOnFailure, "", expr )
+#define REQUIRE_THROWS_WITH( expr, matcher ) INTERNAL_CATCH_THROWS_STR_MATCHES( "REQUIRE_THROWS_WITH", Catch::ResultDisposition::Normal, matcher, expr )
+#if !defined(CATCH_CONFIG_DISABLE_MATCHERS)
+#define REQUIRE_THROWS_MATCHES( expr, exceptionType, matcher ) INTERNAL_CATCH_THROWS_MATCHES( "REQUIRE_THROWS_MATCHES", exceptionType, Catch::ResultDisposition::Normal, matcher, expr )
+#endif // CATCH_CONFIG_DISABLE_MATCHERS
+#define REQUIRE_NOTHROW( ... ) INTERNAL_CATCH_NO_THROW( "REQUIRE_NOTHROW", Catch::ResultDisposition::Normal, __VA_ARGS__ )
+
+#define CHECK( ... ) INTERNAL_CATCH_TEST( "CHECK", Catch::ResultDisposition::ContinueOnFailure, __VA_ARGS__ )
+#define CHECK_FALSE( ... ) INTERNAL_CATCH_TEST( "CHECK_FALSE", Catch::ResultDisposition::ContinueOnFailure | Catch::ResultDisposition::FalseTest, __VA_ARGS__ )
+#define CHECKED_IF( ... ) INTERNAL_CATCH_IF( "CHECKED_IF", Catch::ResultDisposition::ContinueOnFailure, __VA_ARGS__ )
+#define CHECKED_ELSE( ... ) INTERNAL_CATCH_ELSE( "CHECKED_ELSE", Catch::ResultDisposition::ContinueOnFailure, __VA_ARGS__ )
+#define CHECK_NOFAIL( ... ) INTERNAL_CATCH_TEST( "CHECK_NOFAIL", Catch::ResultDisposition::ContinueOnFailure | Catch::ResultDisposition::SuppressFail, __VA_ARGS__ )
+
+#define CHECK_THROWS( ... )  INTERNAL_CATCH_THROWS( "CHECK_THROWS", Catch::ResultDisposition::ContinueOnFailure, __VA_ARGS__ )
 #define CHECK_THROWS_AS( expr, exceptionType ) INTERNAL_CATCH_THROWS_AS( "CHECK_THROWS_AS", exceptionType, Catch::ResultDisposition::ContinueOnFailure, expr )
-#define CHECK_THROWS_WITH( expr, matcher ) INTERNAL_CATCH_THROWS( "CHECK_THROWS_WITH", Catch::ResultDisposition::ContinueOnFailure, matcher, expr )
-#define CHECK_NOTHROW( expr ) INTERNAL_CATCH_NO_THROW( "CHECK_NOTHROW", Catch::ResultDisposition::ContinueOnFailure, expr )
+#define CHECK_THROWS_WITH( expr, matcher ) INTERNAL_CATCH_THROWS_STR_MATCHES( "CHECK_THROWS_WITH", Catch::ResultDisposition::ContinueOnFailure, matcher, expr )
+#if !defined(CATCH_CONFIG_DISABLE_MATCHERS)
+#define CHECK_THROWS_MATCHES( expr, exceptionType, matcher ) INTERNAL_CATCH_THROWS_MATCHES( "CHECK_THROWS_MATCHES", exceptionType, Catch::ResultDisposition::ContinueOnFailure, matcher, expr )
+#endif // CATCH_CONFIG_DISABLE_MATCHERS
+#define CHECK_NOTHROW( ... ) INTERNAL_CATCH_NO_THROW( "CHECK_NOTHROW", Catch::ResultDisposition::ContinueOnFailure, __VA_ARGS__ )
 
+#if !defined(CATCH_CONFIG_DISABLE_MATCHERS)
 #define CHECK_THAT( arg, matcher ) INTERNAL_CHECK_THAT( "CHECK_THAT", matcher, Catch::ResultDisposition::ContinueOnFailure, arg )
 
-#if defined(CATCH_CONFIG_FAST_COMPILE)
-#define REQUIRE_THAT( arg, matcher ) INTERNAL_CHECK_THAT_NO_TRY( "REQUIRE_THAT", matcher, Catch::ResultDisposition::Normal, arg )
-#else
 #define REQUIRE_THAT( arg, matcher ) INTERNAL_CHECK_THAT( "REQUIRE_THAT", matcher, Catch::ResultDisposition::Normal, arg )
-#endif
+#endif // CATCH_CONFIG_DISABLE_MATCHERS
 
 #define INFO( msg ) INTERNAL_CATCH_INFO( "INFO", msg )
+#define UNSCOPED_INFO( msg ) INTERNAL_CATCH_UNSCOPED_INFO( "UNSCOPED_INFO", msg )
 #define WARN( msg ) INTERNAL_CATCH_MSG( "WARN", Catch::ResultWas::Warning, Catch::ResultDisposition::ContinueOnFailure, msg )
-#define SCOPED_INFO( msg ) INTERNAL_CATCH_INFO( "INFO", msg )
-#define CAPTURE( msg ) INTERNAL_CATCH_INFO( "CAPTURE", #msg " := " << Catch::toString(msg) )
-#define SCOPED_CAPTURE( msg ) INTERNAL_CATCH_INFO( "CAPTURE", #msg " := " << Catch::toString(msg) )
+#define CAPTURE( ... ) INTERNAL_CATCH_CAPTURE( INTERNAL_CATCH_UNIQUE_NAME(capturer), "CAPTURE",__VA_ARGS__ )
 
-#ifdef CATCH_CONFIG_VARIADIC_MACROS
 #define TEST_CASE( ... ) INTERNAL_CATCH_TESTCASE( __VA_ARGS__ )
 #define TEST_CASE_METHOD( className, ... ) INTERNAL_CATCH_TEST_CASE_METHOD( className, __VA_ARGS__ )
 #define METHOD_AS_TEST_CASE( method, ... ) INTERNAL_CATCH_METHOD_AS_TEST_CASE( method, __VA_ARGS__ )
 #define REGISTER_TEST_CASE( Function, ... ) INTERNAL_CATCH_REGISTER_TESTCASE( Function, __VA_ARGS__ )
 #define SECTION( ... ) INTERNAL_CATCH_SECTION( __VA_ARGS__ )
+#define DYNAMIC_SECTION( ... ) INTERNAL_CATCH_DYNAMIC_SECTION( __VA_ARGS__ )
 #define FAIL( ... ) INTERNAL_CATCH_MSG( "FAIL", Catch::ResultWas::ExplicitFailure, Catch::ResultDisposition::Normal, __VA_ARGS__ )
 #define FAIL_CHECK( ... ) INTERNAL_CATCH_MSG( "FAIL_CHECK", Catch::ResultWas::ExplicitFailure, Catch::ResultDisposition::ContinueOnFailure, __VA_ARGS__ )
 #define SUCCEED( ... ) INTERNAL_CATCH_MSG( "SUCCEED", Catch::ResultWas::Ok, Catch::ResultDisposition::ContinueOnFailure, __VA_ARGS__ )
+#define ANON_TEST_CASE() INTERNAL_CATCH_TESTCASE()
+
+#ifndef CATCH_CONFIG_TRADITIONAL_MSVC_PREPROCESSOR
+#define TEMPLATE_TEST_CASE( ... ) INTERNAL_CATCH_TEMPLATE_TEST_CASE( __VA_ARGS__ )
+#define TEMPLATE_TEST_CASE_SIG( ... ) INTERNAL_CATCH_TEMPLATE_TEST_CASE_SIG( __VA_ARGS__ )
+#define TEMPLATE_TEST_CASE_METHOD( className, ... ) INTERNAL_CATCH_TEMPLATE_TEST_CASE_METHOD( className, __VA_ARGS__ )
+#define TEMPLATE_TEST_CASE_METHOD_SIG( className, ... ) INTERNAL_CATCH_TEMPLATE_TEST_CASE_METHOD_SIG( className, __VA_ARGS__ )
+#define TEMPLATE_PRODUCT_TEST_CASE( ... ) INTERNAL_CATCH_TEMPLATE_PRODUCT_TEST_CASE( __VA_ARGS__ )
+#define TEMPLATE_PRODUCT_TEST_CASE_SIG( ... ) INTERNAL_CATCH_TEMPLATE_PRODUCT_TEST_CASE_SIG( __VA_ARGS__ )
+#define TEMPLATE_PRODUCT_TEST_CASE_METHOD( className, ... ) INTERNAL_CATCH_TEMPLATE_PRODUCT_TEST_CASE_METHOD( className, __VA_ARGS__ )
+#define TEMPLATE_PRODUCT_TEST_CASE_METHOD_SIG( className, ... ) INTERNAL_CATCH_TEMPLATE_PRODUCT_TEST_CASE_METHOD_SIG( className, __VA_ARGS__ )
+#define TEMPLATE_LIST_TEST_CASE( ... ) INTERNAL_CATCH_TEMPLATE_LIST_TEST_CASE(__VA_ARGS__)
+#define TEMPLATE_LIST_TEST_CASE_METHOD( className, ... ) INTERNAL_CATCH_TEMPLATE_LIST_TEST_CASE_METHOD( className, __VA_ARGS__ )
 #else
-#define TEST_CASE( name, description ) INTERNAL_CATCH_TESTCASE( name, description )
-    #define TEST_CASE_METHOD( className, name, description ) INTERNAL_CATCH_TEST_CASE_METHOD( className, name, description )
-    #define METHOD_AS_TEST_CASE( method, name, description ) INTERNAL_CATCH_METHOD_AS_TEST_CASE( method, name, description )
-    #define REGISTER_TEST_CASE( method, name, description ) INTERNAL_CATCH_REGISTER_TESTCASE( method, name, description )
-    #define SECTION( name, description ) INTERNAL_CATCH_SECTION( name, description )
-    #define FAIL( msg ) INTERNAL_CATCH_MSG( "FAIL", Catch::ResultWas::ExplicitFailure, Catch::ResultDisposition::Normal, msg )
-    #define FAIL_CHECK( msg ) INTERNAL_CATCH_MSG( "FAIL_CHECK", Catch::ResultWas::ExplicitFailure, Catch::ResultDisposition::ContinueOnFailure, msg )
-    #define SUCCEED( msg ) INTERNAL_CATCH_MSG( "SUCCEED", Catch::ResultWas::Ok, Catch::ResultDisposition::ContinueOnFailure, msg )
+#define TEMPLATE_TEST_CASE( ... ) INTERNAL_CATCH_EXPAND_VARGS( INTERNAL_CATCH_TEMPLATE_TEST_CASE( __VA_ARGS__ ) )
+#define TEMPLATE_TEST_CASE_SIG( ... ) INTERNAL_CATCH_EXPAND_VARGS( INTERNAL_CATCH_TEMPLATE_TEST_CASE_SIG( __VA_ARGS__ ) )
+#define TEMPLATE_TEST_CASE_METHOD( className, ... ) INTERNAL_CATCH_EXPAND_VARGS( INTERNAL_CATCH_TEMPLATE_TEST_CASE_METHOD( className, __VA_ARGS__ ) )
+#define TEMPLATE_TEST_CASE_METHOD_SIG( className, ... ) INTERNAL_CATCH_EXPAND_VARGS( INTERNAL_CATCH_TEMPLATE_TEST_CASE_METHOD_SIG( className, __VA_ARGS__ ) )
+#define TEMPLATE_PRODUCT_TEST_CASE( ... ) INTERNAL_CATCH_EXPAND_VARGS( INTERNAL_CATCH_TEMPLATE_PRODUCT_TEST_CASE( __VA_ARGS__ ) )
+#define TEMPLATE_PRODUCT_TEST_CASE_SIG( ... ) INTERNAL_CATCH_EXPAND_VARGS( INTERNAL_CATCH_TEMPLATE_PRODUCT_TEST_CASE_SIG( __VA_ARGS__ ) )
+#define TEMPLATE_PRODUCT_TEST_CASE_METHOD( className, ... ) INTERNAL_CATCH_EXPAND_VARGS( INTERNAL_CATCH_TEMPLATE_PRODUCT_TEST_CASE_METHOD( className, __VA_ARGS__ ) )
+#define TEMPLATE_PRODUCT_TEST_CASE_METHOD_SIG( className, ... ) INTERNAL_CATCH_EXPAND_VARGS( INTERNAL_CATCH_TEMPLATE_PRODUCT_TEST_CASE_METHOD_SIG( className, __VA_ARGS__ ) )
+#define TEMPLATE_LIST_TEST_CASE( ... ) INTERNAL_CATCH_EXPAND_VARGS( INTERNAL_CATCH_TEMPLATE_LIST_TEST_CASE( __VA_ARGS__ ) )
+#define TEMPLATE_LIST_TEST_CASE_METHOD( className, ... ) INTERNAL_CATCH_EXPAND_VARGS( INTERNAL_CATCH_TEMPLATE_LIST_TEST_CASE_METHOD( className, __VA_ARGS__ ) )
 #endif
-#define ANON_TEST_CASE() INTERNAL_CATCH_TESTCASE( "", "" )
-
-#define REGISTER_REPORTER( name, reporterType ) INTERNAL_CATCH_REGISTER_REPORTER( name, reporterType )
-#define REGISTER_LEGACY_REPORTER( name, reporterType ) INTERNAL_CATCH_REGISTER_LEGACY_REPORTER( name, reporterType )
 
-#define GENERATE( expr) INTERNAL_CATCH_GENERATE( expr )
+#if !defined(CATCH_CONFIG_RUNTIME_STATIC_REQUIRE)
+#define STATIC_REQUIRE( ... )       static_assert(   __VA_ARGS__,  #__VA_ARGS__ ); SUCCEED( #__VA_ARGS__ )
+#define STATIC_REQUIRE_FALSE( ... ) static_assert( !(__VA_ARGS__), "!(" #__VA_ARGS__ ")" ); SUCCEED( "!(" #__VA_ARGS__ ")" )
+#else
+#define STATIC_REQUIRE( ... )       REQUIRE( __VA_ARGS__ )
+#define STATIC_REQUIRE_FALSE( ... ) REQUIRE_FALSE( __VA_ARGS__ )
+#endif
 
 #endif
 
 #define CATCH_TRANSLATE_EXCEPTION( signature ) INTERNAL_CATCH_TRANSLATE_EXCEPTION( signature )
 
 // "BDD-style" convenience wrappers
-#ifdef CATCH_CONFIG_VARIADIC_MACROS
 #define SCENARIO( ... ) TEST_CASE( "Scenario: " __VA_ARGS__ )
 #define SCENARIO_METHOD( className, ... ) INTERNAL_CATCH_TEST_CASE_METHOD( className, "Scenario: " __VA_ARGS__ )
+
+#define GIVEN( desc )     INTERNAL_CATCH_DYNAMIC_SECTION( "    Given: " << desc )
+#define AND_GIVEN( desc ) INTERNAL_CATCH_DYNAMIC_SECTION( "And given: " << desc )
+#define WHEN( desc )      INTERNAL_CATCH_DYNAMIC_SECTION( "     When: " << desc )
+#define AND_WHEN( desc )  INTERNAL_CATCH_DYNAMIC_SECTION( " And when: " << desc )
+#define THEN( desc )      INTERNAL_CATCH_DYNAMIC_SECTION( "     Then: " << desc )
+#define AND_THEN( desc )  INTERNAL_CATCH_DYNAMIC_SECTION( "      And: " << desc )
+
+#if defined(CATCH_CONFIG_ENABLE_BENCHMARKING)
+#define BENCHMARK(...) \
+    INTERNAL_CATCH_BENCHMARK(INTERNAL_CATCH_UNIQUE_NAME(____C_A_T_C_H____B_E_N_C_H____), INTERNAL_CATCH_GET_1_ARG(__VA_ARGS__,,), INTERNAL_CATCH_GET_2_ARG(__VA_ARGS__,,))
+#define BENCHMARK_ADVANCED(name) \
+    INTERNAL_CATCH_BENCHMARK_ADVANCED(INTERNAL_CATCH_UNIQUE_NAME(____C_A_T_C_H____B_E_N_C_H____), name)
+#endif // CATCH_CONFIG_ENABLE_BENCHMARKING
+
+using Catch::Detail::Approx;
+
+#else // CATCH_CONFIG_DISABLE
+
+//////
+// If this config identifier is defined then all CATCH macros are prefixed with CATCH_
+#ifdef CATCH_CONFIG_PREFIX_ALL
+
+#define CATCH_REQUIRE( ... )        (void)(0)
+#define CATCH_REQUIRE_FALSE( ... )  (void)(0)
+
+#define CATCH_REQUIRE_THROWS( ... ) (void)(0)
+#define CATCH_REQUIRE_THROWS_AS( expr, exceptionType ) (void)(0)
+#define CATCH_REQUIRE_THROWS_WITH( expr, matcher )     (void)(0)
+#if !defined(CATCH_CONFIG_DISABLE_MATCHERS)
+#define CATCH_REQUIRE_THROWS_MATCHES( expr, exceptionType, matcher ) (void)(0)
+#endif// CATCH_CONFIG_DISABLE_MATCHERS
+#define CATCH_REQUIRE_NOTHROW( ... ) (void)(0)
+
+#define CATCH_CHECK( ... )         (void)(0)
+#define CATCH_CHECK_FALSE( ... )   (void)(0)
+#define CATCH_CHECKED_IF( ... )    if (__VA_ARGS__)
+#define CATCH_CHECKED_ELSE( ... )  if (!(__VA_ARGS__))
+#define CATCH_CHECK_NOFAIL( ... )  (void)(0)
+
+#define CATCH_CHECK_THROWS( ... )  (void)(0)
+#define CATCH_CHECK_THROWS_AS( expr, exceptionType ) (void)(0)
+#define CATCH_CHECK_THROWS_WITH( expr, matcher )     (void)(0)
+#if !defined(CATCH_CONFIG_DISABLE_MATCHERS)
+#define CATCH_CHECK_THROWS_MATCHES( expr, exceptionType, matcher ) (void)(0)
+#endif // CATCH_CONFIG_DISABLE_MATCHERS
+#define CATCH_CHECK_NOTHROW( ... ) (void)(0)
+
+#if !defined(CATCH_CONFIG_DISABLE_MATCHERS)
+#define CATCH_CHECK_THAT( arg, matcher )   (void)(0)
+
+#define CATCH_REQUIRE_THAT( arg, matcher ) (void)(0)
+#endif // CATCH_CONFIG_DISABLE_MATCHERS
+
+#define CATCH_INFO( msg )          (void)(0)
+#define CATCH_UNSCOPED_INFO( msg ) (void)(0)
+#define CATCH_WARN( msg )          (void)(0)
+#define CATCH_CAPTURE( msg )       (void)(0)
+
+#define CATCH_TEST_CASE( ... ) INTERNAL_CATCH_TESTCASE_NO_REGISTRATION(INTERNAL_CATCH_UNIQUE_NAME( ____C_A_T_C_H____T_E_S_T____ ))
+#define CATCH_TEST_CASE_METHOD( className, ... ) INTERNAL_CATCH_TESTCASE_NO_REGISTRATION(INTERNAL_CATCH_UNIQUE_NAME( ____C_A_T_C_H____T_E_S_T____ ))
+#define CATCH_METHOD_AS_TEST_CASE( method, ... )
+#define CATCH_REGISTER_TEST_CASE( Function, ... ) (void)(0)
+#define CATCH_SECTION( ... )
+#define CATCH_DYNAMIC_SECTION( ... )
+#define CATCH_FAIL( ... ) (void)(0)
+#define CATCH_FAIL_CHECK( ... ) (void)(0)
+#define CATCH_SUCCEED( ... ) (void)(0)
+
+#define CATCH_ANON_TEST_CASE() INTERNAL_CATCH_TESTCASE_NO_REGISTRATION(INTERNAL_CATCH_UNIQUE_NAME( ____C_A_T_C_H____T_E_S_T____ ))
+
+#ifndef CATCH_CONFIG_TRADITIONAL_MSVC_PREPROCESSOR
+#define CATCH_TEMPLATE_TEST_CASE( ... ) INTERNAL_CATCH_TEMPLATE_TEST_CASE_NO_REGISTRATION(__VA_ARGS__)
+#define CATCH_TEMPLATE_TEST_CASE_SIG( ... ) INTERNAL_CATCH_TEMPLATE_TEST_CASE_SIG_NO_REGISTRATION(__VA_ARGS__)
+#define CATCH_TEMPLATE_TEST_CASE_METHOD( className, ... ) INTERNAL_CATCH_TEMPLATE_TEST_CASE_METHOD_NO_REGISTRATION(className, __VA_ARGS__)
+#define CATCH_TEMPLATE_TEST_CASE_METHOD_SIG( className, ... ) INTERNAL_CATCH_TEMPLATE_TEST_CASE_METHOD_SIG_NO_REGISTRATION(className, __VA_ARGS__ )
+#define CATCH_TEMPLATE_PRODUCT_TEST_CASE( ... ) CATCH_TEMPLATE_TEST_CASE( __VA_ARGS__ )
+#define CATCH_TEMPLATE_PRODUCT_TEST_CASE_SIG( ... ) CATCH_TEMPLATE_TEST_CASE( __VA_ARGS__ )
+#define CATCH_TEMPLATE_PRODUCT_TEST_CASE_METHOD( className, ... ) CATCH_TEMPLATE_TEST_CASE_METHOD( className, __VA_ARGS__ )
+#define CATCH_TEMPLATE_PRODUCT_TEST_CASE_METHOD_SIG( className, ... ) CATCH_TEMPLATE_TEST_CASE_METHOD( className, __VA_ARGS__ )
+#else
+#define CATCH_TEMPLATE_TEST_CASE( ... ) INTERNAL_CATCH_EXPAND_VARGS( INTERNAL_CATCH_TEMPLATE_TEST_CASE_NO_REGISTRATION(__VA_ARGS__) )
+#define CATCH_TEMPLATE_TEST_CASE_SIG( ... ) INTERNAL_CATCH_EXPAND_VARGS( INTERNAL_CATCH_TEMPLATE_TEST_CASE_SIG_NO_REGISTRATION(__VA_ARGS__) )
+#define CATCH_TEMPLATE_TEST_CASE_METHOD( className, ... ) INTERNAL_CATCH_EXPAND_VARGS( INTERNAL_CATCH_TEMPLATE_TEST_CASE_METHOD_NO_REGISTRATION(className, __VA_ARGS__ ) )
+#define CATCH_TEMPLATE_TEST_CASE_METHOD_SIG( className, ... ) INTERNAL_CATCH_EXPAND_VARGS( INTERNAL_CATCH_TEMPLATE_TEST_CASE_METHOD_SIG_NO_REGISTRATION(className, __VA_ARGS__ ) )
+#define CATCH_TEMPLATE_PRODUCT_TEST_CASE( ... ) CATCH_TEMPLATE_TEST_CASE( __VA_ARGS__ )
+#define CATCH_TEMPLATE_PRODUCT_TEST_CASE_SIG( ... ) CATCH_TEMPLATE_TEST_CASE( __VA_ARGS__ )
+#define CATCH_TEMPLATE_PRODUCT_TEST_CASE_METHOD( className, ... ) CATCH_TEMPLATE_TEST_CASE_METHOD( className, __VA_ARGS__ )
+#define CATCH_TEMPLATE_PRODUCT_TEST_CASE_METHOD_SIG( className, ... ) CATCH_TEMPLATE_TEST_CASE_METHOD( className, __VA_ARGS__ )
+#endif
+
+// "BDD-style" convenience wrappers
+#define CATCH_SCENARIO( ... ) INTERNAL_CATCH_TESTCASE_NO_REGISTRATION(INTERNAL_CATCH_UNIQUE_NAME( ____C_A_T_C_H____T_E_S_T____ ))
+#define CATCH_SCENARIO_METHOD( className, ... ) INTERNAL_CATCH_TESTCASE_METHOD_NO_REGISTRATION(INTERNAL_CATCH_UNIQUE_NAME( ____C_A_T_C_H____T_E_S_T____ ), className )
+#define CATCH_GIVEN( desc )
+#define CATCH_AND_GIVEN( desc )
+#define CATCH_WHEN( desc )
+#define CATCH_AND_WHEN( desc )
+#define CATCH_THEN( desc )
+#define CATCH_AND_THEN( desc )
+
+#define CATCH_STATIC_REQUIRE( ... )       (void)(0)
+#define CATCH_STATIC_REQUIRE_FALSE( ... ) (void)(0)
+
+// If CATCH_CONFIG_PREFIX_ALL is not defined then the CATCH_ prefix is not required
 #else
-#define SCENARIO( name, tags ) TEST_CASE( "Scenario: " name, tags )
-#define SCENARIO_METHOD( className, name, tags ) INTERNAL_CATCH_TEST_CASE_METHOD( className, "Scenario: " name, tags )
+
+#define REQUIRE( ... )       (void)(0)
+#define REQUIRE_FALSE( ... ) (void)(0)
+
+#define REQUIRE_THROWS( ... ) (void)(0)
+#define REQUIRE_THROWS_AS( expr, exceptionType ) (void)(0)
+#define REQUIRE_THROWS_WITH( expr, matcher ) (void)(0)
+#if !defined(CATCH_CONFIG_DISABLE_MATCHERS)
+#define REQUIRE_THROWS_MATCHES( expr, exceptionType, matcher ) (void)(0)
+#endif // CATCH_CONFIG_DISABLE_MATCHERS
+#define REQUIRE_NOTHROW( ... ) (void)(0)
+
+#define CHECK( ... ) (void)(0)
+#define CHECK_FALSE( ... ) (void)(0)
+#define CHECKED_IF( ... ) if (__VA_ARGS__)
+#define CHECKED_ELSE( ... ) if (!(__VA_ARGS__))
+#define CHECK_NOFAIL( ... ) (void)(0)
+
+#define CHECK_THROWS( ... )  (void)(0)
+#define CHECK_THROWS_AS( expr, exceptionType ) (void)(0)
+#define CHECK_THROWS_WITH( expr, matcher ) (void)(0)
+#if !defined(CATCH_CONFIG_DISABLE_MATCHERS)
+#define CHECK_THROWS_MATCHES( expr, exceptionType, matcher ) (void)(0)
+#endif // CATCH_CONFIG_DISABLE_MATCHERS
+#define CHECK_NOTHROW( ... ) (void)(0)
+
+#if !defined(CATCH_CONFIG_DISABLE_MATCHERS)
+#define CHECK_THAT( arg, matcher ) (void)(0)
+
+#define REQUIRE_THAT( arg, matcher ) (void)(0)
+#endif // CATCH_CONFIG_DISABLE_MATCHERS
+
+#define INFO( msg ) (void)(0)
+#define UNSCOPED_INFO( msg ) (void)(0)
+#define WARN( msg ) (void)(0)
+#define CAPTURE( msg ) (void)(0)
+
+#define TEST_CASE( ... )  INTERNAL_CATCH_TESTCASE_NO_REGISTRATION(INTERNAL_CATCH_UNIQUE_NAME( ____C_A_T_C_H____T_E_S_T____ ))
+#define TEST_CASE_METHOD( className, ... ) INTERNAL_CATCH_TESTCASE_NO_REGISTRATION(INTERNAL_CATCH_UNIQUE_NAME( ____C_A_T_C_H____T_E_S_T____ ))
+#define METHOD_AS_TEST_CASE( method, ... )
+#define REGISTER_TEST_CASE( Function, ... ) (void)(0)
+#define SECTION( ... )
+#define DYNAMIC_SECTION( ... )
+#define FAIL( ... ) (void)(0)
+#define FAIL_CHECK( ... ) (void)(0)
+#define SUCCEED( ... ) (void)(0)
+#define ANON_TEST_CASE() INTERNAL_CATCH_TESTCASE_NO_REGISTRATION(INTERNAL_CATCH_UNIQUE_NAME( ____C_A_T_C_H____T_E_S_T____ ))
+
+#ifndef CATCH_CONFIG_TRADITIONAL_MSVC_PREPROCESSOR
+#define TEMPLATE_TEST_CASE( ... ) INTERNAL_CATCH_TEMPLATE_TEST_CASE_NO_REGISTRATION(__VA_ARGS__)
+#define TEMPLATE_TEST_CASE_SIG( ... ) INTERNAL_CATCH_TEMPLATE_TEST_CASE_SIG_NO_REGISTRATION(__VA_ARGS__)
+#define TEMPLATE_TEST_CASE_METHOD( className, ... ) INTERNAL_CATCH_TEMPLATE_TEST_CASE_METHOD_NO_REGISTRATION(className, __VA_ARGS__)
+#define TEMPLATE_TEST_CASE_METHOD_SIG( className, ... ) INTERNAL_CATCH_TEMPLATE_TEST_CASE_METHOD_SIG_NO_REGISTRATION(className, __VA_ARGS__ )
+#define TEMPLATE_PRODUCT_TEST_CASE( ... ) TEMPLATE_TEST_CASE( __VA_ARGS__ )
+#define TEMPLATE_PRODUCT_TEST_CASE_SIG( ... ) TEMPLATE_TEST_CASE( __VA_ARGS__ )
+#define TEMPLATE_PRODUCT_TEST_CASE_METHOD( className, ... ) TEMPLATE_TEST_CASE_METHOD( className, __VA_ARGS__ )
+#define TEMPLATE_PRODUCT_TEST_CASE_METHOD_SIG( className, ... ) TEMPLATE_TEST_CASE_METHOD( className, __VA_ARGS__ )
+#else
+#define TEMPLATE_TEST_CASE( ... ) INTERNAL_CATCH_EXPAND_VARGS( INTERNAL_CATCH_TEMPLATE_TEST_CASE_NO_REGISTRATION(__VA_ARGS__) )
+#define TEMPLATE_TEST_CASE_SIG( ... ) INTERNAL_CATCH_EXPAND_VARGS( INTERNAL_CATCH_TEMPLATE_TEST_CASE_SIG_NO_REGISTRATION(__VA_ARGS__) )
+#define TEMPLATE_TEST_CASE_METHOD( className, ... ) INTERNAL_CATCH_EXPAND_VARGS( INTERNAL_CATCH_TEMPLATE_TEST_CASE_METHOD_NO_REGISTRATION(className, __VA_ARGS__ ) )
+#define TEMPLATE_TEST_CASE_METHOD_SIG( className, ... ) INTERNAL_CATCH_EXPAND_VARGS( INTERNAL_CATCH_TEMPLATE_TEST_CASE_METHOD_SIG_NO_REGISTRATION(className, __VA_ARGS__ ) )
+#define TEMPLATE_PRODUCT_TEST_CASE( ... ) TEMPLATE_TEST_CASE( __VA_ARGS__ )
+#define TEMPLATE_PRODUCT_TEST_CASE_SIG( ... ) TEMPLATE_TEST_CASE( __VA_ARGS__ )
+#define TEMPLATE_PRODUCT_TEST_CASE_METHOD( className, ... ) TEMPLATE_TEST_CASE_METHOD( className, __VA_ARGS__ )
+#define TEMPLATE_PRODUCT_TEST_CASE_METHOD_SIG( className, ... ) TEMPLATE_TEST_CASE_METHOD( className, __VA_ARGS__ )
+#endif
+
+#define STATIC_REQUIRE( ... )       (void)(0)
+#define STATIC_REQUIRE_FALSE( ... ) (void)(0)
+
 #endif
-#define GIVEN( desc )    SECTION( std::string("   Given: ") + desc, "" )
-#define WHEN( desc )     SECTION( std::string("    When: ") + desc, "" )
-#define AND_WHEN( desc ) SECTION( std::string("And when: ") + desc, "" )
-#define THEN( desc )     SECTION( std::string("    Then: ") + desc, "" )
-#define AND_THEN( desc ) SECTION( std::string("     And: ") + desc, "" )
+
+#define CATCH_TRANSLATE_EXCEPTION( signature ) INTERNAL_CATCH_TRANSLATE_EXCEPTION_NO_REG( INTERNAL_CATCH_UNIQUE_NAME( catch_internal_ExceptionTranslator ), signature )
+
+// "BDD-style" convenience wrappers
+#define SCENARIO( ... ) INTERNAL_CATCH_TESTCASE_NO_REGISTRATION(INTERNAL_CATCH_UNIQUE_NAME( ____C_A_T_C_H____T_E_S_T____ ) )
+#define SCENARIO_METHOD( className, ... ) INTERNAL_CATCH_TESTCASE_METHOD_NO_REGISTRATION(INTERNAL_CATCH_UNIQUE_NAME( ____C_A_T_C_H____T_E_S_T____ ), className )
+
+#define GIVEN( desc )
+#define AND_GIVEN( desc )
+#define WHEN( desc )
+#define AND_WHEN( desc )
+#define THEN( desc )
+#define AND_THEN( desc )
 
 using Catch::Detail::Approx;
 
+#endif
+
+#endif // ! CATCH_CONFIG_IMPL_ONLY
+
+// start catch_reenable_warnings.h
+
+
+#ifdef __clang__
+#    ifdef __ICC // icpc defines the __clang__ macro
+#        pragma warning(pop)
+#    else
+#        pragma clang diagnostic pop
+#    endif
+#elif defined __GNUC__
+#    pragma GCC diagnostic pop
+#endif
+
+// end catch_reenable_warnings.h
+// end catch.hpp
 #endif // TWOBLUECUBES_SINGLE_INCLUDE_CATCH_HPP_INCLUDED
 
diff --git a/src/3rd_party/phf/LICENSE b/src/3rd_party/phf/LICENSE
new file mode 100644
index 000000000..039208c40
--- /dev/null
+++ b/src/3rd_party/phf/LICENSE
@@ -0,0 +1,19 @@
+Copyright (c) 2014-2015  William Ahern
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to
+deal in the Software without restriction, including without limitation the
+rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
+sell copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in
+all copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
+FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS
+IN THE SOFTWARE.
diff --git a/src/3rd_party/phf/README.md b/src/3rd_party/phf/README.md
new file mode 100644
index 000000000..f3fd6287e
--- /dev/null
+++ b/src/3rd_party/phf/README.md
@@ -0,0 +1,182 @@
+# Introduction #
+
+This is a simple implementation of the CHD perfect hash algorithm. CHD can
+generate perfect hash functions for very large key sets--on the order of
+millions of keys--in a very short time. On my circa 2012 desktop and using
+the default parameters (hash load factor of 80% and average displacement map
+bucket load of 4.0 keys) this implementation can generate a hash function
+for 1,000 keys in less than 1/100th of a second, and 1,000,000 keys in less
+than a second.
+
+For more information about the algorithm, see
+http://cmph.sourceforge.net/chd.html.
+
+# Dependencies #
+
+* No runtime dependencies.
+* Requires a modern C++ compiler to build.
+* The included build requires GNU Make.
+
+# Building #
+
+## Make Macros ##
+
+The typical GNU macros can be used control the build.
+
+### Compilation ###
+
+Note that the modules for Lua 5.1, 5.2, and 5.3 can be built simultaneously.
+
+* CXX: C++ compiler path.
+* CXXFLAGS: C++ compiler flags.
+* CPPFLAGS: C preprocessor flags. Necessary if Lua API cannot be discovered
+  automatically. You can specify multiple include paths if building more than
+  one Lua module.
+* LDFLAGS: Linker flags. Not normally needed.
+* SOFLAGS: Flags needed to build dynamic library.
+* LOFLAGS: Flags needed to build loadable module. Normally should be the
+  same as SOFLAGS, except on OS X.
+* LIBS: Library dependencies. Normally empty, but see the section Avoiding
+  C++ Dependencies.
+
+#### Avoiding C++ Dependencies
+
+Defining the preprocessor macro PHF_NO_LIBCXX to 1 will prevent usage of C++
+interfaces such as std::string that would require a dependency on libc++ or
+libstdc++. This allows using platform-dependent flags in CXXFLAGS, LDFLAGS,
+and SOFLAGS to prevent a dependency on the system C++ library.
+
+For example, on OS X you can do:
+```sh
+$ make CPPFLAGS="-DPHF_NO_LIBCXX" \
+CXXFLAGS="-std=c++11 -fno-rtti -fno-exceptions -O3 -march=native" \
+LDFLAGS="-nostdlib" \
+LIBS="-lSystem"
+```
+
+### Installation ####
+* prefix
+* includedir
+* libdir
+* luacpath: Lua C module install path. Can be used for one-shot installation
+  of a particular Lua version module.
+* lua51cpath: Lua 5.1 C module install path.
+* lua52cpath: Same as above, for 5.2.
+* lua53cpath: Same as above, for 5.3.
+
+## Make Targets ##
+
+* phf: Builds command-line utility (development)
+* libphf.so: Builds dynamic library for non-OS X
+* libphf.dylib: Builds dynamic library for OS X
+* lua5.1: Builds Lua 5.1 module at 5.1/phf.so. Lua 5.1 headers should be
+  specified using CPPFLAGS if not in normal locations.
+* lua5.2: Same as above, for Lua 5.2.
+* lua5.3: Same as above, for Lua 5.3.
+
+# Usage #
+
+## Lua ##
+
+## API ###
+
+### phf.new(keys[, lambda][, alpha][, seed][, nodiv]) ###
+
+* keys: array of keys in order from 1..#keys. They should be all
+  numbers or all strings.
+
+* lambda: number of keys per bucket when generating the g() function mapping.
+
+* alpha: output hash space loading factor as percentage from
+  1..100. 100% generates a *minimal* perfect hash function. But note that
+  the implementation does *not* implement the necessary optimizations to
+  ensure timely generation of minimal perfect hash functions. Normally you
+  want a loading factor of 80% to 90% for large key sets.
+
+* seed: random integer seed.
+
+* nodiv: if true rounds r and m to powers of 2, and performs modular
+  reduction using bitwise AND. Otherwise, r and m are rounded up to the
+  nearest primes and modulo division used when indexing tables. Note that
+  the rounding occurs after calculation of the intermediate and output hash
+  table loading.
+
+  This is more important when building small hash tables with the C
+  interface. The optimization is substantial when the compiler can inline
+  the code, but isn't substantial from Lua.
+
+Returns a callable object.
+
+### phf:hash(key)
+
+* Returns an integer hash in the range 1..phf:m(). The returned integer will
+  be unique for all keys in the original set. Otherwise the result is
+  unspecified.
+
+### Example ###
+
+```Lua
+local phf = require"phf"
+
+local lambda = 4 -- how many keys per intermediate bucket
+local alpha = 80 -- output hash space loading in percentage.
+
+local keys = { "apple", "banana", "cherry", "date", "eggplant", "fig",
+               "guava", "honeydew", "jackfruit", "kiwi", "lemon", "mango" }
+
+local F = phf.new(keys, lambda, alpha)
+
+for i=1,#keys do
+	print(keys[i], F(keys[i]))
+end
+
+```
+
+## C++ ##
+
+## API ##
+
+### PHF::uniq<T>(T k[], size_t n); ###
+
+Similar to the shell command `sort | uniq`. Sorts, deduplicates, and shifts
+down the keys in the array k. Returns the number of unique keys, which will
+have been moved to the beginning of the array. If necessary do this before
+calling PHF::init, as PHF::init does not tolerate duplicate keys.
+
+### int PHF::init<T, nodiv>(struct phf *f, const T k[], size_t n, size_t l, size_t a, phf_seed_t s);
+
+Generate a perfect hash function for the n keys in array k and store the
+results in f. Returns a system error number on failure, or 0 on success. f
+is unmodified on failure.
+
+### void PHF::destroy(struct phf *);
+
+Deallocates internal tables, but not the struct object itself.
+
+### void PHF::compact<T, nodiv>(struct phf *);
+
+By default the displacement map is an array of uint32_t integers. This
+function will select the smallest type necessary to hold the largest
+displacement value and update the internal state accordingly. For a loading
+factor of 80% (0.8) in the output hash space, and displacement map loading
+factor of 4 (400%), the smallest primitive type will often be uint8_t.
+
+### phf_hash_t PHF::hash<T>(struct phf *f, T k);
+
+Returns an integer hash value, h, where 0 <= h < f->m. h will be unique for
+each unique key provided when generating the function. f->m will be larger
+than the number of unique keys and is based on the specified loading factor
+(alpha), rounded up to the nearest prime or nearest power of 2, depending on
+the mode of modular reduction selected. For example, for a loading factor of
+80% m will be 127: 100 is 80% of 125, and 127 is the closest prime greater
+than or equal to 125. With the nodiv option, m would be 128: 100 is 80% of
+125, and 128 is the closest power of 2 greater than or equal to 125.
+
+## C ##
+
+The C API is nearly identical to the C++ API, except the prefix is phf_
+instead of PHF::. phf_uniq, phf_init, and phf_hash are macros which utilize
+C11's _Generic or GCC's __builtin_types_compatible_p interfaces to overload
+the interfaces by key type. The explicit suffixes _uint32, _uint64, and
+_string may be used directly.
+
diff --git a/src/3rd_party/phf/phf.cc b/src/3rd_party/phf/phf.cc
new file mode 100644
index 000000000..d00d5c5e2
--- /dev/null
+++ b/src/3rd_party/phf/phf.cc
@@ -0,0 +1,1478 @@
+/* ==========================================================================
+ * phf.cc - Tiny perfect hash function library.
+ * --------------------------------------------------------------------------
+ * Copyright (c) 2014-2015  William Ahern
+ *
+ * Permission is hereby granted, free of charge, to any person obtaining a
+ * copy of this software and associated documentation files (the
+ * "Software"), to deal in the Software without restriction, including
+ * without limitation the rights to use, copy, modify, merge, publish,
+ * distribute, sublicense, and/or sell copies of the Software, and to permit
+ * persons to whom the Software is furnished to do so, subject to the
+ * following conditions:
+ *
+ * The above copyright notice and this permission notice shall be included
+ * in all copies or substantial portions of the Software.
+ *
+ * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS
+ * OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
+ * MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN
+ * NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM,
+ * DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR
+ * OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE
+ * USE OR OTHER DEALINGS IN THE SOFTWARE.
+ * ==========================================================================
+ */
+#include <limits.h>   /* CHAR_BIT SIZE_MAX */
+#include <inttypes.h> /* PRIu32 PRIu64 PRIx64 */
+#include <stdint.h>   /* UINT32_C UINT64_C uint32_t uint64_t */
+#include <stdlib.h>   /* abort(3) calloc(3) free(3) qsort(3) */
+#include <string.h>   /* memset(3) */
+#include <errno.h>    /* errno */
+#include <assert.h>   /* assert(3) */
+#if !PHF_NO_LIBCXX
+#include <string>     /* std::string */
+#endif
+
+
+#include "phf.h"
+
+
+#ifdef __clang__
+#pragma clang diagnostic ignored "-Wunused-function"
+#if __cplusplus < 201103L
+#pragma clang diagnostic ignored "-Wc++11-long-long"
+#endif
+#elif PHF_GNUC_PREREQ(4, 6)
+#pragma GCC diagnostic ignored "-Wunused-function"
+#if __cplusplus < 201103L
+#pragma GCC diagnostic ignored "-Wlong-long"
+#pragma GCC diagnostic ignored "-Wformat" // %zu
+#endif
+#endif
+
+
+
+/*
+ * M A C R O  R O U T I N E S
+ *
+ * Mostly copies of <sys/param.h>
+ *
+ * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * */
+
+#define PHF_BITS(T) (sizeof (T) * CHAR_BIT)
+#define PHF_HOWMANY(x, y) (((x) + ((y) - 1)) / (y))
+#define PHF_MIN(a, b) (((a) < (b))? (a) : (b))
+#define PHF_MAX(a, b) (((a) > (b))? (a) : (b))
+#define PHF_ROTL(x, y) (((x) << (y)) | ((x) >> (PHF_BITS(x) - (y))))
+#define PHF_COUNTOF(a) (sizeof (a) / sizeof *(a))
+
+
+/*
+ * M O D U L A R  A R I T H M E T I C  R O U T I N E S
+ *
+ * Two modular reduction schemes are supported: bitwise AND and naive
+ * modular division. For bitwise AND we must round up the values r and m to
+ * a power of 2.
+ *
+ * TODO: Implement and test Barrett reduction as alternative to naive
+ * modular division.
+ *
+ * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * */
+
+/* round up to nearest power of 2 */
+static inline size_t phf_powerup(size_t i) {
+#if defined SIZE_MAX
+	i--;
+	i |= i >> 1;
+	i |= i >> 2;
+	i |= i >> 4;
+	i |= i >> 8;
+	i |= i >> 16;
+#if SIZE_MAX != 0xffffffffu
+	i |= i >> 32;
+#endif
+	return ++i;
+#else
+#error No SIZE_MAX defined
+#endif
+} /* phf_powerup() */
+
+static inline uint64_t phf_a_s_mod_n(uint64_t a, uint64_t s, uint64_t n) {
+	uint64_t v;
+
+	assert(n <= UINT32_MAX);
+
+	v = 1;
+	a %= n;
+
+	while (s > 0) {
+		if (s % 2 == 1)
+			v = (v * a) % n;
+		a = (a * a) % n;
+		s /= 2;
+	}
+
+	return v;
+} /* phf_a_s_mod_n() */
+
+/*
+ * Rabin-Miller primality test adapted from Niels Ferguson and Bruce
+ * Schneier, "Practical Cryptography" (Wiley, 2003), 201-204.
+ */
+static inline bool phf_witness(uint64_t n, uint64_t a, uint64_t s, uint64_t t) {
+	uint64_t v, i;
+
+	assert(a > 0 && a < n);
+	assert(n <= UINT32_MAX);
+
+	if (1 == (v = phf_a_s_mod_n(a, s, n)))
+		return 1;
+
+	for (i = 0; v != n - 1; i++) {
+		if (i == t - 1)
+			return 0;
+		v = (v * v) % n;
+	}
+
+	return 1;
+} /* phf_witness() */
+
+static inline bool phf_rabinmiller(uint64_t n) {
+	/*
+	 * Witness 2 is deterministic for all n < 2047. Witnesses 2, 7, 61
+	 * are deterministic for all n < 4,759,123,141.
+	 */
+	static const int witness[] = { 2, 7, 61 };
+	uint64_t s, t, i;
+
+	assert(n <= UINT32_MAX);
+
+	if (n < 3 || n % 2 == 0)
+		return 0;
+
+	/* derive 2^t * s = n - 1 where s is odd */
+	s = n - 1;
+	t = 0;
+	while (s % 2 == 0) {
+		s /= 2;
+		t++;
+	}
+
+	/* NB: witness a must be 1 <= a < n */
+	if (n < 2027)
+		return phf_witness(n, 2, s, t);
+
+	for (i = 0; i < PHF_COUNTOF(witness); i++) {
+		if (!phf_witness(n, witness[i], s, t))
+			return 0;
+	}
+
+	return 1;
+} /* phf_rabinmiller() */
+
+static inline bool phf_isprime(size_t n) {
+	static const char map[] = { 0, 1, 2, 3, 0, 5, 0, 7 };
+	size_t i;
+
+	if (n < PHF_COUNTOF(map))
+		return map[n];
+
+	for (i = 2; i < PHF_COUNTOF(map); i++) {
+		if (map[i] && (n % map[i] == 0))
+			return 0;
+	}
+
+	return phf_rabinmiller(n);
+} /* phf_isprime() */
+
+static inline size_t phf_primeup(size_t n) {
+	/* NB: 4294967291 is largest 32-bit prime */
+	if (n > 4294967291)
+		return 0;
+
+	while (n < SIZE_MAX && !phf_isprime(n))
+		n++;
+
+	return n;
+} /* phf_primeup() */
+
+
+/*
+ * B I T M A P  R O U T I N E S
+ *
+ * We use a bitmap to track output hash occupancy when searching for
+ * displacement values.
+ *
+ * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * */
+
+typedef unsigned long phf_bits_t;
+
+static inline bool phf_isset(phf_bits_t *set, size_t i) {
+	return set[i / PHF_BITS(*set)] & ((size_t)1 << (i % PHF_BITS(*set)));
+} /* phf_isset() */
+
+static inline void phf_setbit(phf_bits_t *set, size_t i) {
+	set[i / PHF_BITS(*set)] |= ((size_t)1 << (i % PHF_BITS(*set)));
+} /* phf_setbit() */
+
+static inline void phf_clrbit(phf_bits_t *set, size_t i) {
+	set[i / PHF_BITS(*set)] &= ~((size_t)1 << (i % PHF_BITS(*set)));
+} /* phf_clrbit() */
+
+static inline void phf_clrall(phf_bits_t *set, size_t n) {
+	memset(set, '\0', PHF_HOWMANY(n, PHF_BITS(*set)) * sizeof *set);
+} /* phf_clrall() */
+
+
+/*
+ * K E Y  D E D U P L I C A T I O N
+ *
+ * Auxiliary routine to ensure uniqueness of each key in array.
+ *
+ * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * */
+
+namespace PHF {
+	namespace Uniq {
+		static bool operator!=(const phf_string_t &a, const phf_string_t &b) {
+			return a.n != b.n || 0 != memcmp(a.p, b.p, a.n);
+		}
+
+		template<typename T>
+		static int cmp(const T *a, const T *b) {
+			if (*a > *b)
+				return -1;
+			if (*a < *b)
+				return 1;
+			return 0;
+		} /* cmp() */
+
+		template<>
+		int cmp(const phf_string_t *a, const phf_string_t *b) {
+			int cmp;
+			if ((cmp = memcmp(a->p, b->p, PHF_MIN(a->n, b->n))))
+				return cmp;
+			if (a->n > b->n)
+				return -1;
+			if (a->n < b->n)
+				return 1;
+			return 0;
+		} /* cmp<phf_string_t>() */
+	} /* Uniq:: */
+} /* PHF:: */
+
+template<typename key_t>
+PHF_PUBLIC size_t PHF::uniq(key_t k[], const size_t n) {
+	using namespace PHF::Uniq;
+	size_t i, j;
+
+	qsort(k, n, sizeof *k, reinterpret_cast<int(*)(const void *, const void *)>(&cmp<key_t>));
+
+	for (i = 1, j = 0; i < n; i++) {
+		if (k[i] != k[j])
+			k[++j] = k[i];
+	}
+
+	return (n > 0)? j + 1 : 0;
+} /* PHF::uniq() */
+
+template size_t PHF::uniq<uint32_t>(uint32_t[], const size_t);
+template size_t PHF::uniq<uint64_t>(uint64_t[], const size_t);
+template size_t PHF::uniq<phf_string_t>(phf_string_t[], const size_t);
+#if !PHF_NO_LIBCXX
+template size_t PHF::uniq<std::string>(std::string[], const size_t);
+#endif
+
+PHF_PUBLIC size_t phf_uniq_uint32(uint32_t k[], const size_t n) {
+	return PHF::uniq(k, n);
+} /* phf_uniq_uint32() */
+
+PHF_PUBLIC size_t phf_uniq_uint64(uint64_t k[], const size_t n) {
+	return PHF::uniq(k, n);
+} /* phf_uniq_uint64() */
+
+PHF_PUBLIC size_t phf_uniq_string(phf_string_t k[], const size_t n) {
+	return PHF::uniq(k, n);
+} /* phf_uniq_string() */
+
+
+/*
+ * H A S H  P R I M I T I V E S
+ *
+ * Universal hash based on MurmurHash3_x86_32. Variants for 32- and 64-bit
+ * integer keys, and string keys.
+ *
+ * We use a random seed to address the non-cryptographic-strength collision
+ * resistance of MurmurHash3. A stronger hash like SipHash is just too slow
+ * and unnecessary for my particular needs. For some environments a
+ * cryptographically stronger hash may be prudent.
+ *
+ * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * */
+
+static inline uint32_t phf_round32(uint32_t k1, uint32_t h1) {
+	k1 *= UINT32_C(0xcc9e2d51);
+	k1 = PHF_ROTL(k1, 15);
+	k1 *= UINT32_C(0x1b873593);
+
+	h1 ^= k1;
+	h1 = PHF_ROTL(h1, 13);
+	h1 = h1 * 5 + UINT32_C(0xe6546b64);
+
+	return h1;
+} /* phf_round32() */
+
+static inline uint32_t phf_round32(const unsigned char *p, size_t n, uint32_t h1) {
+	uint32_t k1;
+
+	while (n >= 4) {
+		k1 = (p[0] << 24)
+		   | (p[1] << 16)
+		   | (p[2] << 8)
+		   | (p[3] << 0);
+
+		h1 = phf_round32(k1, h1);
+
+		p += 4;
+		n -= 4;
+	}
+
+	k1 = 0;
+
+	switch (n & 3) {
+	case 3:
+		k1 |= p[2] << 8;
+		/* FALLTHRU */
+	case 2:
+		k1 |= p[1] << 16;
+		/* FALLTHRU */
+	case 1:
+		k1 |= p[0] << 24;
+		h1 = phf_round32(k1, h1);
+	}
+
+	return h1;
+} /* phf_round32() */
+
+static inline uint32_t phf_round32(phf_string_t k, uint32_t h1) {
+	return phf_round32(reinterpret_cast<const unsigned char *>(k.p), k.n, h1);
+} /* phf_round32() */
+
+#if !PHF_NO_LIBCXX
+static inline uint32_t phf_round32(std::string k, uint32_t h1) {
+	return phf_round32(reinterpret_cast<const unsigned char *>(k.c_str()), k.length(), h1);
+} /* phf_round32() */
+#endif
+
+static inline uint32_t phf_mix32(uint32_t h1) {
+	h1 ^= h1 >> 16;
+	h1 *= UINT32_C(0x85ebca6b);
+	h1 ^= h1 >> 13;
+	h1 *= UINT32_C(0xc2b2ae35);
+	h1 ^= h1 >> 16;
+
+	return h1;
+} /* phf_mix32() */
+
+
+/*
+ * g(k) & f(d, k)  S P E C I A L I Z A T I O N S
+ *
+ * For every key we first calculate g(k). Then for every group of collisions
+ * from g(k) we search for a displacement value d such that f(d, k) places
+ * each key into a unique hash slot.
+ *
+ * g() and f() are specialized for 32-bit, 64-bit, and string keys.
+ *
+ * g_mod_r() and f_mod_n() are specialized for the method of modular
+ * reduction--modular division or bitwise AND. bitwise AND is substantially
+ * faster than modular division, and more than makes up for any space
+ * inefficiency, particularly for small hash tables.
+ *
+ * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * */
+
+/* 32-bit, phf_string_t, and std::string keys */
+template<typename T>
+static inline uint32_t phf_g(T k, uint32_t seed) {
+	uint32_t h1 = seed;
+
+	h1 = phf_round32(k, h1);
+
+	return phf_mix32(h1);
+} /* phf_g() */
+
+template<typename T>
+static inline uint32_t phf_f(uint32_t d, T k, uint32_t seed) {
+	uint32_t h1 = seed;
+
+	h1 = phf_round32(d, h1);
+	h1 = phf_round32(k, h1);
+
+	return phf_mix32(h1);
+} /* phf_f() */
+
+
+/* 64-bit keys */
+static inline uint32_t phf_g(uint64_t k, uint32_t seed) {
+	uint32_t h1 = seed;
+
+	h1 = phf_round32(k, h1);
+	h1 = phf_round32(k >> 32, h1);
+
+	return phf_mix32(h1);
+} /* phf_g() */
+
+static inline uint32_t phf_f(uint32_t d, uint64_t k, uint32_t seed) {
+	uint32_t h1 = seed;
+
+	h1 = phf_round32(d, h1);
+	h1 = phf_round32(static_cast<uint32_t>(k), h1);
+	h1 = phf_round32(static_cast<uint32_t>(k >> 32), h1);
+
+	return phf_mix32(h1);
+} /* phf_f() */
+
+
+/* g() and f() which parameterize modular reduction */
+template<bool nodiv, typename T>
+static inline uint32_t phf_g_mod_r(T k, uint32_t seed, size_t r) {
+	return (nodiv)? (phf_g(k, seed) & (r - 1)) : (phf_g(k, seed) % r);
+} /* phf_g_mod_r() */
+
+template<bool nodiv, typename T>
+static inline uint32_t phf_f_mod_m(uint32_t d, T k, uint32_t seed, size_t m) {
+	return (nodiv)? (phf_f(d, k, seed) & (m - 1)) : (phf_f(d, k, seed) % m);
+} /* phf_f_mod_m() */
+
+
+/*
+ * B U C K E T  S O R T I N G  I N T E R F A C E S
+ *
+ * For every key [0..n) we calculate g(k) % r, where 0 < r <= n, and
+ * associate it with a bucket [0..r). We then sort the buckets in decreasing
+ * order according to the number of keys. The sorting is required for both
+ * optimal time complexity when calculating f(d, k) (less contention) and
+ * optimal space complexity (smaller d).
+ *
+ * The actual sorting is done in the core routine. The buckets are organized
+ * and sorted as a 1-dimensional array to minimize run-time memory (less
+ * data structure overhead) and improve data locality (less pointer
+ * indirection). The following section merely implements a templated
+ * bucket-key structure and the comparison routine passed to qsort(3).
+ *
+ * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * */
+
+static bool operator==(const phf_string_t &a, const phf_string_t &b) {
+	return a.n == b.n && 0 == memcmp(a.p, b.p, a.n);
+}
+
+template<typename T>
+struct phf_key {
+	T k;
+	phf_hash_t g; /* result of g(k) % r */
+	size_t *n;  /* number of keys in bucket g */
+}; /* struct phf_key */
+
+template<typename T>
+static int phf_keycmp(const phf_key<T> *a, const phf_key<T> *b) {
+	if (*(a->n) > *(b->n))
+		return -1;
+	if (*(a->n) < *(b->n))
+		return 1;
+	if (a->g > b->g)
+		return -1;
+	if (a->g < b->g)
+		return 1;
+
+	/* duplicate key? */
+	if (a->k == b->k && a != b) {
+		assert(!(a->k == b->k));
+		abort(); /* if NDEBUG defined */
+	}
+
+	return 0;
+} /* phf_keycmp() */
+
+
+/*
+ * C O R E  F U N C T I O N  G E N E R A T O R
+ *
+ * The entire algorithm is contained in PHF:init. Everything else in this
+ * source file is either a simple utility routine used by PHF:init, or an
+ * interface to PHF:init or the generated function state.
+ *
+ * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * */
+
+template<typename key_t, bool nodiv>
+PHF_PUBLIC int PHF::init(struct phf *phf, const key_t k[], const size_t n, const size_t l, const size_t a, const phf_seed_t seed) {
+	size_t n1 = PHF_MAX(n, 1); /* for computations that require n > 0 */
+	size_t l1 = PHF_MAX(l, 1);
+	size_t a1 = PHF_MAX(PHF_MIN(a, 100), 1);
+	size_t r; /* number of buckets */
+	size_t m; /* size of output array */
+	phf_key<key_t> *B_k = NULL; /* linear bucket-slot array */
+	size_t *B_z = NULL;         /* number of slots per bucket */
+	phf_key<key_t> *B_p, *B_pe;
+	phf_bits_t *T = NULL; /* bitmap to track index occupancy */
+	phf_bits_t *T_b;      /* per-bucket working bitmap */
+	size_t T_n;
+	uint32_t *g = NULL; /* displacement map */
+	uint32_t d_max = 0; /* maximum displacement value */
+	int error;
+
+	if ((phf->nodiv = nodiv)) {
+		/* round to power-of-2 so we can use bit masks instead of modulo division */
+		r = phf_powerup(n1 / PHF_MIN(l1, n1));
+		m = phf_powerup((n1 * 100) / a1);
+	} else {
+		r = phf_primeup(PHF_HOWMANY(n1, l1));
+		/* XXX: should we bother rounding m to prime number for small n? */
+		m = phf_primeup((n1 * 100) / a1);
+	}
+
+	if (r == 0 || m == 0)
+		return ERANGE;
+
+	if (!(B_k = static_cast<phf_key<key_t> *>(calloc(n1, sizeof *B_k))))
+		goto syerr;
+	if (!(B_z = static_cast<size_t *>(calloc(r, sizeof *B_z))))
+		goto syerr;
+
+	for (size_t i = 0; i < n; i++) {
+		phf_hash_t g = phf_g_mod_r<nodiv>(k[i], seed, r);
+
+		B_k[i].k = k[i];
+		B_k[i].g = g;
+		B_k[i].n = &B_z[g];
+		++*B_k[i].n;
+	}
+
+	qsort(B_k, n1, sizeof *B_k, reinterpret_cast<int(*)(const void *, const void *)>(&phf_keycmp<key_t>));
+
+	T_n = PHF_HOWMANY(m, PHF_BITS(*T));
+	if (!(T = static_cast<phf_bits_t *>(calloc(T_n * 2, sizeof *T))))
+		goto syerr;
+	T_b = &T[T_n]; /* share single allocation */
+
+	/*
+	 * FIXME: T_b[] is unnecessary. We could clear T[] the same way we
+	 * clear T_b[]. In fact, at the end of generation T_b[] is identical
+	 * to T[] because we don't clear T_b[] on success.
+	 *
+	 * We just need to tweak the current reset logic to stop before the
+	 * key that failed, and then we can elide the commit to T[] at the
+	 * end of the outer loop.
+	 */
+
+	if (!(g = static_cast<uint32_t *>(calloc(r, sizeof *g))))
+		goto syerr;
+
+	B_p = B_k;
+	B_pe = &B_k[n];
+
+	for (; B_p < B_pe && *B_p->n > 0; B_p += *B_p->n) {
+		phf_key<key_t> *Bi_p, *Bi_pe;
+		size_t d = 0;
+		uint32_t f;
+retry:
+		d++;
+		Bi_p = B_p;
+		Bi_pe = B_p + *B_p->n;
+
+		for (; Bi_p < Bi_pe; Bi_p++) {
+			f = phf_f_mod_m<nodiv>(d, Bi_p->k, seed, m);
+
+			if (phf_isset(T, f) || phf_isset(T_b, f)) {
+				/* reset T_b[] */
+				for (Bi_p = B_p; Bi_p < Bi_pe; Bi_p++) {
+					f = phf_f_mod_m<nodiv>(d, Bi_p->k, seed, m);
+					phf_clrbit(T_b, f);
+				}
+
+				goto retry;
+			} else {
+				phf_setbit(T_b, f);
+			}
+		}
+
+		/* commit to T[] */
+		for (Bi_p = B_p; Bi_p < Bi_pe; Bi_p++) {
+			f = phf_f_mod_m<nodiv>(d, Bi_p->k, seed, m);
+			phf_setbit(T, f);
+		}
+
+		/* commit to g[] */
+		g[B_p->g] = d;
+		d_max = PHF_MAX(d, d_max);
+	}
+
+	phf->seed = seed;
+	phf->r = r;
+	phf->m = m;
+
+	phf->g = g;
+	g = NULL;
+
+	phf->d_max = d_max;
+	phf->g_op = (nodiv)? phf::PHF_G_UINT32_BAND_R : phf::PHF_G_UINT32_MOD_R;
+	phf->g_jmp = NULL;
+
+	error = 0;
+
+	goto clean;
+syerr:
+	error = errno;
+clean:
+	free(g);
+	free(T);
+	free(B_z);
+	free(B_k);
+
+	return error;
+} /* PHF::init() */
+
+
+/*
+ * D I S P L A C E M E N T  M A P  C O M P A C T I O N
+ *
+ * By default the displacement map is an array of uint32_t. This routine
+ * compacts the map by using the smallest primitive type that will fit the
+ * largest displacement value.
+ *
+ * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * */
+
+template<typename dst_t, typename src_t>
+static inline void phf_memmove(dst_t *dst, src_t *src, size_t n) {
+	for (size_t i = 0; i < n; i++) {
+		dst_t tmp = src[i];
+		dst[i] = tmp;
+	}
+} /* phf_memmove() */
+
+PHF_PUBLIC void PHF::compact(struct phf *phf) {
+	size_t size = 0;
+	void *tmp;
+
+	switch (phf->g_op) {
+	case phf::PHF_G_UINT32_MOD_R:
+	case phf::PHF_G_UINT32_BAND_R:
+		break;
+	default:
+		return; /* already compacted */
+	}
+
+	if (phf->d_max <= 255) {
+		phf_memmove(reinterpret_cast<uint8_t *>(phf->g), reinterpret_cast<uint32_t *>(phf->g), phf->r);
+		phf->g_op = (phf->nodiv)? phf::PHF_G_UINT8_BAND_R : phf::PHF_G_UINT8_MOD_R;
+		size = sizeof (uint8_t);
+	} else if (phf->d_max <= 65535) {
+		phf_memmove(reinterpret_cast<uint16_t *>(phf->g), reinterpret_cast<uint32_t *>(phf->g), phf->r);
+		phf->g_op = (phf->nodiv)? phf::PHF_G_UINT16_BAND_R : phf::PHF_G_UINT16_MOD_R;
+		size = sizeof (uint16_t);
+	} else {
+		return; /* nothing to compact */
+	}
+
+	/* simply keep old array if realloc fails */
+	if ((tmp = realloc(phf->g, phf->r * size)))
+		phf->g = static_cast<uint32_t *>(tmp);
+} /* PHF::compact() */
+
+
+/*
+ * F U N C T I O N  G E N E R A T O R  &  S T A T E  I N T E R F A C E S
+ *
+ * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * */
+
+template int PHF::init<uint32_t, true>(struct phf *, const uint32_t[], const size_t, const size_t, const size_t, const phf_seed_t);
+template int PHF::init<uint64_t, true>(struct phf *, const uint64_t[], const size_t, const size_t, const size_t, const phf_seed_t);
+template int PHF::init<phf_string_t, true>(struct phf *, const phf_string_t[], const size_t, const size_t, const size_t, const phf_seed_t);
+#if !PHF_NO_LIBCXX
+template int PHF::init<std::string, true>(struct phf *, const std::string[], const size_t, const size_t, const size_t, const phf_seed_t);
+#endif
+
+template int PHF::init<uint32_t, false>(struct phf *, const uint32_t[], const size_t, const size_t, const size_t, const phf_seed_t);
+template int PHF::init<uint64_t, false>(struct phf *, const uint64_t[], const size_t, const size_t, const size_t, const phf_seed_t);
+template int PHF::init<phf_string_t, false>(struct phf *, const phf_string_t[], const size_t, const size_t, const size_t, const phf_seed_t);
+#if !PHF_NO_LIBCXX
+template int PHF::init<std::string, false>(struct phf *, const std::string[], const size_t, const size_t, const size_t, const phf_seed_t);
+#endif
+
+template<bool nodiv, typename map_t, typename key_t>
+static inline phf_hash_t phf_hash_(map_t *g, key_t k, uint32_t seed, size_t r, size_t m) {
+	if (nodiv) {
+		uint32_t d = g[phf_g(k, seed) & (r - 1)];
+
+		return phf_f(d, k, seed) & (m - 1);
+	} else {
+		uint32_t d = g[phf_g(k, seed) % r];
+
+		return phf_f(d, k, seed) % m;
+	}
+} /* phf_hash_() */
+
+template<typename T>
+PHF_PUBLIC phf_hash_t PHF::hash(struct phf *phf, T k) {
+#if PHF_HAVE_COMPUTED_GOTOS && !PHF_NO_COMPUTED_GOTOS
+	static const void *const jmp[] = {
+		NULL,
+		&&uint8_mod_r, &&uint8_band_r,
+		&&uint16_mod_r, &&uint16_band_r,
+		&&uint32_mod_r, &&uint32_band_r,
+	};
+
+	goto *((phf->g_jmp)? phf->g_jmp : (phf->g_jmp = jmp[phf->g_op]));
+
+	uint8_mod_r:
+		return phf_hash_<false>(reinterpret_cast<uint8_t *>(phf->g), k, phf->seed, phf->r, phf->m);
+	uint8_band_r:
+		return phf_hash_<true>(reinterpret_cast<uint8_t *>(phf->g), k, phf->seed, phf->r, phf->m);
+	uint16_mod_r:
+		return phf_hash_<false>(reinterpret_cast<uint16_t *>(phf->g), k, phf->seed, phf->r, phf->m);
+	uint16_band_r:
+		return phf_hash_<true>(reinterpret_cast<uint16_t *>(phf->g), k, phf->seed, phf->r, phf->m);
+	uint32_mod_r:
+		return phf_hash_<false>(reinterpret_cast<uint32_t *>(phf->g), k, phf->seed, phf->r, phf->m);
+	uint32_band_r:
+		return phf_hash_<true>(reinterpret_cast<uint32_t *>(phf->g), k, phf->seed, phf->r, phf->m);
+#else
+	switch (phf->g_op) {
+	case phf::PHF_G_UINT8_MOD_R:
+		return phf_hash_<false>(reinterpret_cast<uint8_t *>(phf->g), k, phf->seed, phf->r, phf->m);
+	case phf::PHF_G_UINT8_BAND_R:
+		return phf_hash_<true>(reinterpret_cast<uint8_t *>(phf->g), k, phf->seed, phf->r, phf->m);
+	case phf::PHF_G_UINT16_MOD_R:
+		return phf_hash_<false>(reinterpret_cast<uint16_t *>(phf->g), k, phf->seed, phf->r, phf->m);
+	case phf::PHF_G_UINT16_BAND_R:
+		return phf_hash_<true>(reinterpret_cast<uint16_t *>(phf->g), k, phf->seed, phf->r, phf->m);
+	case phf::PHF_G_UINT32_MOD_R:
+		return phf_hash_<false>(reinterpret_cast<uint32_t *>(phf->g), k, phf->seed, phf->r, phf->m);
+	case phf::PHF_G_UINT32_BAND_R:
+		return phf_hash_<true>(reinterpret_cast<uint32_t *>(phf->g), k, phf->seed, phf->r, phf->m);
+	default:
+		abort();
+		return 0;
+	}
+#endif
+} /* PHF::hash() */
+
+template phf_hash_t PHF::hash<uint32_t>(struct phf *, uint32_t);
+template phf_hash_t PHF::hash<uint64_t>(struct phf *, uint64_t);
+template phf_hash_t PHF::hash<phf_string_t>(struct phf *, phf_string_t);
+#if !PHF_NO_LIBCXX
+template phf_hash_t PHF::hash<std::string>(struct phf *, std::string);
+#endif
+
+PHF_PUBLIC void PHF::destroy(struct phf *phf) {
+	free(phf->g);
+	phf->g = NULL;
+} /* PHF::destroy() */
+
+PHF_PUBLIC int phf_init_uint32(struct phf *phf, const uint32_t *k, const size_t n, const size_t lambda, const size_t alpha, const phf_seed_t seed, const bool nodiv) {
+	if (nodiv)
+		return PHF::init<uint32_t, true>(phf, k, n, lambda, alpha, seed);
+	else
+		return PHF::init<uint32_t, false>(phf, k, n, lambda, alpha, seed);
+} /* phf_init_uint32() */
+
+PHF_PUBLIC int phf_init_uint64(struct phf *phf, const uint64_t *k, const size_t n, const size_t lambda, const size_t alpha, const phf_seed_t seed, const bool nodiv) {
+	if (nodiv)
+		return PHF::init<uint64_t, true>(phf, k, n, lambda, alpha, seed);
+	else
+		return PHF::init<uint64_t, false>(phf, k, n, lambda, alpha, seed);
+} /* phf_init_uint64() */
+
+PHF_PUBLIC int phf_init_string(struct phf *phf, const phf_string_t *k, const size_t n, const size_t lambda, const size_t alpha, const phf_seed_t seed, const bool nodiv) {
+	if (nodiv)
+		return PHF::init<phf_string_t, true>(phf, k, n, lambda, alpha, seed);
+	else
+		return PHF::init<phf_string_t, false>(phf, k, n, lambda, alpha, seed);
+} /* phf_init_string() */
+
+PHF_PUBLIC void phf_compact(struct phf *phf) {
+	PHF::compact(phf);
+} /* phf_compact() */
+
+PHF_PUBLIC phf_hash_t phf_hash_uint32(struct phf *phf, const uint32_t k) {
+	return PHF::hash(phf, k);
+} /* phf_hash_uint32() */
+
+PHF_PUBLIC phf_hash_t phf_hash_uint64(struct phf *phf, const uint64_t k) {
+	return PHF::hash(phf, k);
+} /* phf_hash_uint64() */
+
+PHF_PUBLIC phf_hash_t phf_hash_string(struct phf *phf, const phf_string_t k) {
+	return PHF::hash(phf, k);
+} /* phf_hash_string() */
+
+PHF_PUBLIC void phf_destroy(struct phf *phf) {
+	PHF::destroy(phf);
+} /* phf_destroy() */
+
+
+#if PHF_LUALIB
+#include <time.h> /* time(2) */
+
+#include <lua.hpp>
+
+
+#if LUA_VERSION_NUM < 502
+static int lua_absindex(lua_State *L, int idx) {
+	return (idx > 0 || idx <= LUA_REGISTRYINDEX)? idx : lua_gettop(L) + idx + 1;
+} /* lua_absindex() */
+
+#define lua_rawlen(t, index) lua_objlen(t, index)
+#endif
+
+
+struct phfctx {
+	int (*hash)(struct phf *, lua_State *, int index);
+	struct phf ctx;
+}; /* struct phfctx */
+
+
+static int phf_hash_uint32(struct phf *phf, lua_State *L, int index) {
+	uint32_t k = static_cast<uint32_t>(luaL_checkinteger(L, index));
+
+	lua_pushinteger(L, static_cast<lua_Integer>(PHF::hash(phf, k) + 1));
+
+	return 1;
+} /* phf_hash_uint32() */
+
+static int phf_hash_uint64(struct phf *phf, lua_State *L, int index) {
+	uint64_t k = static_cast<uint64_t>(luaL_checkinteger(L, index));
+
+	lua_pushinteger(L, static_cast<lua_Integer>(PHF::hash(phf, k) + 1));
+
+	return 1;
+} /* phf_hash_uint64() */
+
+static int phf_hash_string(struct phf *phf, lua_State *L, int index) {
+	phf_string_t k;
+
+	k.p = const_cast<char *>(luaL_checklstring(L, index, &k.n));
+
+	lua_pushinteger(L, static_cast<lua_Integer>(PHF::hash(phf, k) + 1));
+
+	return 1;
+} /* phf_hash_string() */
+
+static phf_seed_t phf_seed(lua_State *L) {
+	return phf_g(static_cast<uint32_t>(reinterpret_cast<intptr_t>(L)), static_cast<uint32_t>(time(NULL)));
+} /* phf_seed() */
+
+template<typename T>
+static phf_error_t phf_reallocarray(T **p, size_t count) {
+	T *tmp;
+
+	if (SIZE_MAX / sizeof **p < count)
+		return ENOMEM;
+
+	if (!(tmp = static_cast<T*>(realloc(*p, count * sizeof **p))))
+		return errno;
+
+	*p = tmp;
+
+	return 0;
+} /* phf_reallocarray() */
+
+static phf_error_t phf_tokey(lua_State *L, int index, uint32_t *k) {
+	if (LUA_TNUMBER != lua_type(L, index))
+		return EINVAL;
+
+#if LUA_VERSION_NUM > 502
+	lua_Integer v;
+
+	v = static_cast<lua_Integer>(lua_tointeger(L, index));
+
+	if (v > UINT32_MAX)
+		return ERANGE;
+#else
+	lua_Number v;
+
+	v = static_cast<lua_Number>(lua_tonumber(L, index));
+
+	if (v > UINT32_MAX)
+		return ERANGE;
+#endif
+	*k = static_cast<uint32_t>(v);
+
+	return 0;
+} /* phf_tokey() */
+
+static phf_error_t phf_tokey(lua_State *L, int index, uint64_t *k) {
+	if (LUA_TNUMBER != lua_type(L, index))
+		return EINVAL;
+
+#if LUA_VERSION_NUM > 502
+	lua_Integer v;
+
+	v = static_cast<lua_Integer>(lua_tointeger(L, index));
+#else
+	lua_Number v;
+
+	v = static_cast<lua_Number>(lua_tonumber(L, index));
+#endif
+	*k = static_cast<uint64_t>(v);
+
+	return 0;
+} /* phf_tokey() */
+
+static phf_error_t phf_tokey(lua_State *L, int index, phf_string_t *k) {
+	if (LUA_TSTRING != lua_type(L, index))
+		return EINVAL;
+
+	k->p = const_cast<char *>(lua_tolstring(L, index, &k->n));
+
+	return 0;
+} /* phf_tokey() */
+
+template<typename T>
+static phf_error_t phf_addkeys(lua_State *L, int index, T **keys, int *n) {
+	int i, error = 0;
+	T *p;
+
+	*n = lua_rawlen(L, index);
+
+	if ((error = phf_reallocarray(keys, *n)))
+		return error;
+
+	p = *keys;
+
+	for (i = 1; i <= *n; i++) {
+		lua_rawgeti(L, index, i);
+
+		error = phf_tokey(L, -1, p++);
+
+		lua_pop(L, 1);
+
+		if (error)
+			return error;
+
+	}
+
+	*n = PHF::uniq(*keys, *n);
+
+	return 0;
+} /* phf_addkeys() */
+
+static int phf_new(lua_State *L) {
+	size_t l = static_cast<size_t>(luaL_optinteger(L, 2, 4));
+	size_t a = static_cast<size_t>(luaL_optinteger(L, 3, 80));
+	phf_seed_t seed = (lua_isnoneornil(L, 4))? phf_seed(L) : static_cast<phf_seed_t>(luaL_checkinteger(L, 4));
+	bool nodiv = static_cast<bool>(lua_toboolean(L, 5));
+	void *keys = NULL;
+	struct phfctx *phf;
+	int n, error;
+
+	lua_settop(L, 5);
+	luaL_checktype(L, 1, LUA_TTABLE);
+
+	phf = static_cast<struct phfctx *>(lua_newuserdata(L, sizeof *phf));
+	memset(phf, 0, sizeof *phf);
+
+	luaL_getmetatable(L, "PHF*");
+	lua_setmetatable(L, -2);
+
+	switch ((error = phf_addkeys(L, 1, reinterpret_cast<uint32_t **>(&keys), &n))) {
+	case 0:
+		break;
+	case ERANGE:
+		goto uint64;
+	case EINVAL:
+		goto string;
+	default:
+		goto error;
+	}
+
+	if (n == 0)
+		goto empty;
+
+	if ((error = phf_init_uint32(&phf->ctx, reinterpret_cast<uint32_t *>(keys), n, l, a, seed, nodiv)))
+		goto error;
+
+	phf->hash = &phf_hash_uint32;
+
+	goto done;
+uint64:
+	switch ((error = phf_addkeys(L, 1, reinterpret_cast<uint64_t **>(&keys), &n))) {
+	case 0:
+		break;
+	case EINVAL:
+		goto string;
+	default:
+		goto error;
+	}
+
+	if (n == 0)
+		goto empty;
+
+	if ((error = phf_init_uint64(&phf->ctx, reinterpret_cast<uint64_t *>(keys), n, l, a, seed, nodiv)))
+		goto error;
+
+	phf->hash = &phf_hash_uint64;
+
+	goto done;
+string:
+	if ((error = phf_addkeys(L, 1, reinterpret_cast<phf_string_t **>(&keys), &n)))
+		goto error;
+
+	if (n == 0)
+		goto empty;
+
+	if ((error = phf_init_string(&phf->ctx, reinterpret_cast<phf_string_t *>(keys), n, l, a, seed, nodiv)))
+		goto error;
+
+	phf->hash = &phf_hash_string;
+
+	goto done;
+done:
+	free(keys);
+
+	PHF::compact(&phf->ctx);
+
+	return 1;
+empty:
+	free(keys);
+
+	lua_pushstring(L, "empty key set");
+
+	return lua_error(L);
+error:
+	free(keys);
+
+	lua_pushstring(L, strerror(error));
+
+	return lua_error(L);
+} /* phf_new() */
+
+static int phf_r(lua_State *L) {
+	struct phfctx *phf = static_cast<struct phfctx *>(luaL_checkudata(L, 1, "PHF*"));
+
+	lua_pushinteger(L, static_cast<lua_Integer>(phf->ctx.r));
+
+	return 1;
+} /* phf_r() */
+
+static int phf_m(lua_State *L) {
+	struct phfctx *phf = static_cast<struct phfctx *>(luaL_checkudata(L, 1, "PHF*"));
+
+	lua_pushinteger(L, static_cast<lua_Integer>(phf->ctx.m));
+
+	return 1;
+} /* phf_m() */
+
+static int (phf_hash)(lua_State *L) {
+	struct phfctx *phf = static_cast<struct phfctx *>(luaL_checkudata(L, 1, "PHF*"));
+
+	return phf->hash(&phf->ctx, L, 2);
+} /* phf_hash() */
+
+static int phf__gc(lua_State *L) {
+	struct phfctx *phf = (struct phfctx *)luaL_checkudata(L, 1, "PHF*");
+
+	phf_destroy(&phf->ctx);
+
+	return 0;
+} /* phf__gc() */
+
+static const luaL_Reg phf_methods[] = {
+	{ "hash", &(phf_hash) },
+	{ "r",    &phf_r },
+	{ "m",    &phf_m },
+	{ NULL,   NULL },
+}; /* phf_methods[] */
+
+static const luaL_Reg phf_metatable[] = {
+	{ "__call", &phf_hash },
+	{ "__gc",   &phf__gc },
+	{ NULL,     NULL },
+}; /* phf_metatable[] */
+
+static const luaL_Reg phf_globals[] = {
+	{ "new", &phf_new },
+	{ NULL,  NULL },
+}; /* phf_globals[] */
+
+static void phf_register(lua_State *L, const luaL_Reg *l) {
+#if LUA_VERSION_NUM >= 502
+	luaL_setfuncs(L, l, 0);
+#else
+	luaL_register(L, NULL, l);
+#endif
+} /* phf_register() */
+
+extern "C" int luaopen_phf(lua_State *L) {
+	if (luaL_newmetatable(L, "PHF*")) {
+		phf_register(L, phf_metatable);
+		lua_newtable(L);
+		phf_register(L, phf_methods);
+		lua_setfield(L, -2, "__index");
+	}
+
+	lua_pop(L, 1);
+
+	lua_newtable(L);
+	phf_register(L, phf_globals);
+
+	return 1;
+} /* luaopen_phf() */
+
+#endif /* PHF_LUALIB */
+
+
+#if PHF_MAIN
+
+#include <stdlib.h>    /* arc4random(3) free(3) realloc(3) */
+#include <stdio.h>     /* fclose(3) fopen(3) fprintf(3) fread(3) freopen(3) printf(3) */
+#include <time.h>      /* CLOCKS_PER_SEC clock(3) */
+#include <string.h>    /* strcmp(3) */
+#include <sys/param.h> /* BSD */
+#include <unistd.h>    /* getopt(3) */
+#include <strings.h>   /* ffsl(3) */
+#include <err.h>       /* err(3) errx(3) warnx(3) */
+
+
+static uint32_t randomseed(void) {
+#if defined BSD /* catchall for modern BSDs, which all have arc4random */
+	return arc4random();
+#else
+	FILE *fp;
+	uint32_t seed;
+
+	if (!(fp = fopen("/dev/urandom", "r")))
+		err(1, "/dev/urandom");
+
+	if (1 != fread(&seed, sizeof seed, 1, fp))
+		err(1, "/dev/urandom");
+
+	fclose(fp);
+
+	return seed;
+#endif
+} /* randomseed() */
+
+
+template<typename T>
+static void pushkey(T **k, size_t *n, size_t *z, T kn) {
+	if (!(*n < *z)) {
+		size_t z1 = PHF_MAX(*z, 1) * 2;
+		T *p;
+
+		if (z1 < *z || (SIZE_MAX / sizeof **k) < z1)
+			errx(1, "addkey: %s", strerror(ERANGE));
+
+		if (!(p = (T *)realloc(*k, z1 * sizeof **k)))
+			err(1, "realloc");
+
+		*k = p;
+		*z = z1;
+	}
+
+	(*k)[(*n)++] = kn;
+} /* pushkey() */
+
+
+template<typename T>
+static void addkey(T **k, size_t *n, size_t *z, const char *src) {
+	pushkey(k, n, z, static_cast<T>(strtoull(src, NULL, 0)));
+} /* addkey() */
+
+static void addkey(phf_string_t **k, size_t *n, size_t *z, char *src, size_t len) {
+	phf_string_t kn = { (void *)src, len };
+	pushkey(k, n, z, kn);
+} /* addkey() */
+
+static void addkey(phf_string_t **k, size_t *n, size_t *z, char *src) {
+	addkey(k, n, z, src, strlen(src));
+} /* addkey() */
+
+#if !PHF_NO_LIBCXX
+static void addkey(std::string **k, size_t *n, size_t *z, char *src, size_t len) {
+	pushkey(k, n, z, std::string(src, len));
+} /* addkey() */
+
+static void addkey(std::string **k, size_t *n, size_t *z, char *src) {
+	addkey(k, n, z, src, strlen(src));
+} /* addkey() */
+#endif
+
+template<typename T>
+static void addkeys(T **k, size_t *n, size_t *z, char **src, int count) {
+	for (int i = 0; i < count; i++)
+		addkey(k, n, z, src[i]);
+} /* addkey() */
+
+template<typename T>
+static void addkeys(T **k, size_t *n, size_t *z, FILE *fp, char **data) {
+	char *ln = NULL;
+	size_t lz = 0;
+	ssize_t len;
+
+	(void)data;
+
+	while ((len = getline(&ln, &lz, fp)) > 0) {
+		if (--len > 0) {
+			if (ln[len] == '\n')
+				ln[len] = '\0';
+			addkey(k, n, z, ln);
+		}
+	}
+	
+	free(ln);
+} /* addkeys() */
+
+/* slurp file into a single string and take pointers */
+static void addkeys(phf_string_t **k, size_t *n, size_t *z, FILE *fp, char **data) {
+	size_t p = 0, pe = 0, tp;
+	char buf[BUFSIZ], *tmp;
+	size_t buflen;
+
+	while ((buflen = fread(buf, 1, sizeof buf, fp))) {
+		if (buflen > (pe - p)) {
+			if (~buflen < pe || 0 == (pe = phf_powerup(buflen + pe)))
+				errx(1, "realloc: %s", strerror(ERANGE));
+			if (!(tmp = (char *)realloc(*data, pe)))
+				err(1, "realloc");
+			*data = tmp;
+		}
+
+		memcpy(*data + p, buf, buflen);
+		p += buflen;
+	}
+
+	for (p = 0; p < pe; ) {
+		while (p < pe && (*data)[p] == '\n')
+			p++;
+
+		tp = p;
+
+		while (p < pe && (*data)[p] != '\n')
+			p++;
+				
+		if (p > tp)
+			addkey(k, n, z, &(*data)[tp], (size_t)(p - tp));
+	}
+} /* addkeys() */
+
+
+static inline void printkey(phf_string_t &k, phf_hash_t hash) {
+	printf("%-32.*s : %" PHF_PRIuHASH "\n", (int)k.n, (char *)k.p, hash);
+} /* printkey() */
+
+#if !PHF_NO_LIBCXX
+static inline void printkey(std::string &k, phf_hash_t hash) {
+	printf("%-32s : %" PHF_PRIuHASH "\n", k.c_str(), hash);
+} /* printkey() */
+#endif
+
+template<typename T>
+static inline void printkey(T k, phf_hash_t hash) {
+	printf("%llu : %" PHF_PRIuHASH "\n", (unsigned long long)k, hash);
+} /* printkey() */
+
+template<typename T, bool nodiv>
+static inline void exec(int argc, char **argv, size_t lambda, size_t alpha, size_t seed, bool verbose, bool noprint) {
+	T *k = NULL;
+	size_t n = 0, z = 0;
+	char *data = NULL;
+	struct phf phf;
+	clock_t begin, end;
+
+	addkeys(&k, &n, &z, argv, argc);
+	addkeys(&k, &n, &z, stdin, &data);
+
+	size_t m = PHF::uniq(k, n);
+	if (verbose)
+		warnx("loaded %zu keys (%zu duplicates)", m, (n - m));
+	n = m;
+
+	begin = clock();
+	PHF::init<T, nodiv>(&phf, k, n, lambda, alpha, seed);
+	end = clock();
+
+
+	if (verbose) {
+		warnx("found perfect hash for %zu keys in %fs", n, (double)(end - begin) / CLOCKS_PER_SEC);
+
+		begin = clock();
+		PHF::compact(&phf);
+		end = clock();
+		warnx("compacted displacement map in %fs", (double)(end - begin) / CLOCKS_PER_SEC);
+
+		int d_bits = ffsl((long)phf_powerup(phf.d_max));
+		double k_bits = ((double)phf.r * d_bits) / n;
+		double g_load = (double)n / phf.r;
+		warnx("r:%zu m:%zu d_max:%zu d_bits:%d k_bits:%.2f g_load:%.2f", phf.r, phf.m, phf.d_max, d_bits, k_bits, g_load);
+
+		size_t x = 0;
+		begin = clock();
+		for (size_t i = 0; i < n; i++) {
+			x += PHF::hash(&phf, k[i]);
+		}
+		end = clock();
+		warnx("hashed %zu keys in %fs (x:%zu)", n, (double)(end - begin) / CLOCKS_PER_SEC, x);
+	}
+
+	if (!noprint) {
+		for (size_t i = 0; i < n; i++) {
+			printkey(k[i], PHF::hash(&phf, k[i]));
+		}
+	}
+
+	phf_destroy(&phf);
+	free(data);
+	free(k);
+} /* exec() */
+
+static void printprimes(int argc, char **argv) {
+	intmax_t n = 0, m = UINT32_MAX;
+	char *end;
+
+	if (argc > 0) {
+		n = strtoimax(argv[0], &end, 0);
+		if (end == argv[0] || *end != '\0' || n < 0 || n > UINT32_MAX)
+			errx(1, "%s: invalid number", argv[0]);
+		n = PHF_MAX(n, 2);
+	}
+
+	if (argc > 1) {
+		m = strtoimax(argv[1], &end, 0);
+		if (end == argv[1] || *end != '\0' || m < n || m > UINT32_MAX)
+			errx(1, "%s: invalid number", argv[1]);
+	}
+
+	for (; n <= m; n++) {
+		if (phf_isprime(n))
+			printf("%" PRIdMAX "\n", n);
+	}
+} /* printprimes() */
+
+int main(int argc, char **argv) {
+	const char *path = "/dev/null";
+	size_t lambda = 4;
+	size_t alpha = 80;
+	uint32_t seed = randomseed();
+	bool verbose = 0;
+	bool noprint = 0;
+	bool nodiv = 0;
+	enum {
+		PHF_UINT32,
+		PHF_UINT64,
+		PHF_STRING,
+#if !PHF_NO_LIBCXX
+		PHF_STD_STRING
+#endif
+	} type = PHF_UINT32;
+	bool primes = 0;
+	extern char *optarg;
+	extern int optind;
+	int optc;
+
+	while (-1 != (optc = getopt(argc, argv, "f:l:a:s:2t:nvph"))) {
+		switch (optc) {
+		case 'f':
+			path = optarg;
+			break;
+		case 'l':
+			lambda = strtoul(optarg, NULL, 0);
+			break;
+		case 'a':
+			alpha = strtoul(optarg, NULL, 0);
+			break;
+		case 's':
+			seed = strtoul(optarg, NULL, 0);
+			break;
+		case '2':
+			nodiv = 1;
+			break;
+		case 't':
+			if (!strcmp(optarg, "uint32")) {
+				type = PHF_UINT32;
+			} else if (!strcmp(optarg, "uint64")) {
+				type = PHF_UINT64;
+			} else if (!strcmp(optarg, "string")) {
+				type = PHF_STRING;
+#if !PHF_NO_LIBCXX
+			} else if (!strcmp(optarg, "std::string")) {
+				type = PHF_STD_STRING;
+#endif
+			} else {
+				errx(1, "%s: invalid key type", optarg);
+			}
+
+			break;
+		case 'n':
+			noprint = 1;
+			break;
+		case 'v':
+			verbose = 1;
+			break;
+		case 'p':
+			primes = 1;
+			break;
+		case 'h':
+			/* FALL THROUGH */
+		default:
+			fprintf(optc == 'h'? stdout : stderr,
+				"%s [-f:l:a:s:t:2nvph] [key [...]]\n"
+				"  -f PATH  read keys from PATH (- for stdin)\n"
+				"  -l NUM   number of keys per displacement map bucket (reported as g_load)\n"
+				"  -a PCT   hash table load factor (1%% - 100%%)\n"
+				"  -s SEED  random seed\n"
+				"  -t TYPE  parse and hash keys as uint32, uint64, or string\n"
+				"  -2       avoid modular division by rounding r and m to power of 2\n"
+				"  -n       do not print key-hash pairs\n"
+				"  -v       report hashing status\n"
+				"  -p       operate like primes(3) utility\n"
+				"  -h       print usage message\n"
+				"\n"
+				"Report bugs to <william@25thandClement.com>\n",
+				argv[0]
+			);
+
+			return optc == 'h'? 0 : 1;
+		}
+	}
+
+	argc -= optind;
+	argv += optind;
+
+	if (primes)
+		return printprimes(argc, argv), 0;
+
+	if (strcmp(path, "-") && !freopen(path, "r", stdin))
+		err(1, "%s", path);
+
+	switch (type) {
+	case PHF_UINT32:
+		if (nodiv)
+			exec<uint32_t, true>(argc, argv, lambda, alpha, seed, verbose, noprint);
+		else
+			exec<uint32_t, false>(argc, argv, lambda, alpha, seed, verbose, noprint);
+		break;
+	case PHF_UINT64:
+		if (nodiv)
+			exec<uint64_t, true>(argc, argv, lambda, alpha, seed, verbose, noprint);
+		else
+			exec<uint64_t, false>(argc, argv, lambda, alpha, seed, verbose, noprint);
+		break;
+	case PHF_STRING:
+		if (nodiv)
+			exec<phf_string_t, true>(argc, argv, lambda, alpha, seed, verbose, noprint);
+		else
+			exec<phf_string_t, false>(argc, argv, lambda, alpha, seed, verbose, noprint);
+		break;
+#if !PHF_NO_LIBCXX
+	case PHF_STD_STRING:
+		if (nodiv)
+			exec<std::string, true>(argc, argv, lambda, alpha, seed, verbose, noprint);
+		else
+			exec<std::string, false>(argc, argv, lambda, alpha, seed, verbose, noprint);
+		break;
+#endif
+	}
+
+	return 0;
+} /* main() */
+
+#endif /* PHF_MAIN */
diff --git a/src/3rd_party/phf/phf.h b/src/3rd_party/phf/phf.h
new file mode 100644
index 000000000..f821dfa47
--- /dev/null
+++ b/src/3rd_party/phf/phf.h
@@ -0,0 +1,299 @@
+/* ==========================================================================
+ * phf.h - Tiny perfect hash function library.
+ * --------------------------------------------------------------------------
+ * Copyright (c) 2014-2015  William Ahern
+ *
+ * Permission is hereby granted, free of charge, to any person obtaining a
+ * copy of this software and associated documentation files (the
+ * "Software"), to deal in the Software without restriction, including
+ * without limitation the rights to use, copy, modify, merge, publish,
+ * distribute, sublicense, and/or sell copies of the Software, and to permit
+ * persons to whom the Software is furnished to do so, subject to the
+ * following conditions:
+ *
+ * The above copyright notice and this permission notice shall be included
+ * in all copies or substantial portions of the Software.
+ *
+ * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS
+ * OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
+ * MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN
+ * NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM,
+ * DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR
+ * OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE
+ * USE OR OTHER DEALINGS IN THE SOFTWARE.
+ * ==========================================================================
+ */
+#ifndef PHF_H
+#define PHF_H
+
+#include <stddef.h>   /* size_t */
+#include <stdint.h>   /* UINT32_MAX uint32_t uint64_t */
+#include <stdbool.h>  /* bool */
+#include <inttypes.h> /* PRIu32 PRIx32 */
+
+
+/*
+ * C O M P I L E R  F E A T U R E S  &  D I A G N O S T I C S
+ *
+ * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * */
+
+#define PHF_GNUC_PREREQ(M, m) (__GNUC__ > (M) || (__GNUC__ == (M) && __GNUC_MINOR__ >= (m)))
+
+#ifdef __clang__
+#define phf_has_extension(x) __has_extension(x)
+#define phf_has_attribute(x) __has_attribute(x)
+#else
+#define phf_has_extension(x) 0
+#define phf_has_attribute(x) 0
+#endif
+
+#ifndef PHF_HAVE_NOEXCEPT
+#define PHF_HAVE_NOEXCEPT \
+	(__cplusplus >= 201103L || \
+	 phf_has_extension(cxx_noexcept) || \
+	 PHF_GNUC_PREREQ(4, 6))
+#endif
+
+#ifndef PHF_HAVE_GENERIC
+#define PHF_HAVE_GENERIC \
+	(__STDC_VERSION__ >= 201112L || \
+	 phf_has_extension(c_generic_selections) || \
+	 PHF_GNUC_PREREQ(4, 9))
+#endif
+
+#ifndef PHF_HAVE_BUILTIN_TYPES_COMPATIBLE_P
+#define PHF_HAVE_BUILTIN_TYPES_COMPATIBLE_P (defined __GNUC__)
+#endif
+
+#ifndef PHF_HAVE_BUILTIN_CHOOSE_EXPR
+#define PHF_HAVE_BUILTIN_CHOOSE_EXPR (defined __GNUC__)
+#endif
+
+#ifndef PHF_HAVE_ATTRIBUTE_VISIBILITY
+#define PHF_HAVE_ATTRIBUTE_VISIBILITY \
+	(phf_has_attribute(visibility) || PHF_GNUC_PREREQ(4, 0))
+#endif
+
+#ifndef PHF_HAVE_COMPUTED_GOTOS
+#ifdef __GNUC__
+#define PHF_HAVE_COMPUTED_GOTOS 1
+#else
+#define PHF_HAVE_COMPUTED_GOTOS 0
+#endif
+#endif
+
+#ifdef __clang__
+#pragma clang diagnostic push
+#if __cplusplus < 201103L
+#pragma clang diagnostic ignored "-Wc++11-extensions"
+#pragma clang diagnostic ignored "-Wvariadic-macros"
+#endif
+#elif PHF_GNUC_PREREQ(4, 6)
+#pragma GCC diagnostic push
+#if __cplusplus < 201103L
+#pragma GCC diagnostic ignored "-Wpedantic"
+#pragma GCC diagnostic ignored "-Wvariadic-macros"
+#endif
+#endif
+
+
+/*
+ * C / C + +  V I S I B I L I T Y
+ *
+ * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * */
+
+#ifndef PHF_PUBLIC
+#define PHF_PUBLIC
+#endif
+
+#ifndef PHF_LOCAL
+#if PHF_HAVE_ATTRIBUTE_VISIBILITY
+#define PHF_LOCAL __attribute__((visibility("hidden")))
+#else
+#define PHF_LOCAL
+#endif
+#endif
+
+
+/*
+ * C / C + +  S H A R E D  T Y P E S
+ *
+ * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * */
+
+#define phf_error_t int /* for documentation purposes */
+
+#define PHF_HASH_MAX UINT32_MAX
+#define PHF_PRIuHASH PRIu32
+#define PHF_PRIxHASH PRIx32
+
+typedef uint32_t phf_hash_t;
+typedef uint32_t phf_seed_t;
+
+typedef struct phf_string {
+	void *p;
+	size_t n;
+} phf_string_t;
+
+struct phf {
+	bool nodiv;
+
+	phf_seed_t seed;
+
+	size_t r; /* number of elements in g */
+	size_t m; /* number of elements in perfect hash */
+	uint32_t *g; /* displacement map indexed by g(k) % r */
+
+	size_t d_max; /* maximum displacement value in g */
+
+	enum {
+		PHF_G_UINT8_MOD_R = 1,
+		PHF_G_UINT8_BAND_R,
+		PHF_G_UINT16_MOD_R,
+		PHF_G_UINT16_BAND_R,
+		PHF_G_UINT32_MOD_R,
+		PHF_G_UINT32_BAND_R,
+	} g_op;
+
+	const void *g_jmp;
+}; /* struct phf */
+
+
+/*
+ * C + +  I N T E R F A C E S
+ *
+ * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * */
+#ifdef __cplusplus
+
+#if !PHF_NO_LIBCXX
+#include <string> /* std::string */
+#endif
+
+namespace PHF {
+	template<typename key_t>
+	PHF_PUBLIC size_t uniq(key_t[], const size_t);
+
+	template<typename key_t, bool nodiv>
+	PHF_PUBLIC phf_error_t init(struct phf *, const key_t[], const size_t, const size_t, const size_t, const phf_seed_t);
+
+	PHF_PUBLIC void compact(struct phf *);
+
+	template<typename key_t>
+	PHF_PUBLIC phf_hash_t hash(struct phf *, key_t);
+
+	PHF_PUBLIC void destroy(struct phf *);
+}
+
+extern template size_t PHF::uniq<uint32_t>(uint32_t[], const size_t);
+extern template size_t PHF::uniq<uint64_t>(uint64_t[], const size_t);
+extern template size_t PHF::uniq<phf_string_t>(phf_string_t[], const size_t);
+#if !PHF_NO_LIBCXX
+extern template size_t PHF::uniq<std::string>(std::string[], const size_t);
+#endif
+
+extern template phf_error_t PHF::init<uint32_t, true>(struct phf *, const uint32_t[], const size_t, const size_t, const size_t, const phf_seed_t);
+extern template phf_error_t PHF::init<uint64_t, true>(struct phf *, const uint64_t[], const size_t, const size_t, const size_t, const phf_seed_t);
+extern template phf_error_t PHF::init<phf_string_t, true>(struct phf *, const phf_string_t[], const size_t, const size_t, const size_t, const phf_seed_t);
+#if !PHF_NO_LIBCXX
+extern template phf_error_t PHF::init<std::string, true>(struct phf *, const std::string[], const size_t, const size_t, const size_t, const phf_seed_t);
+#endif
+
+extern template phf_error_t PHF::init<uint32_t, false>(struct phf *, const uint32_t[], const size_t, const size_t, const size_t, const phf_seed_t);
+extern template phf_error_t PHF::init<uint64_t, false>(struct phf *, const uint64_t[], const size_t, const size_t, const size_t, const phf_seed_t);
+extern template phf_error_t PHF::init<phf_string_t, false>(struct phf *, const phf_string_t[], const size_t, const size_t, const size_t, const phf_seed_t);
+#if !PHF_NO_LIBCXX
+extern template phf_error_t PHF::init<std::string, false>(struct phf *, const std::string[], const size_t, const size_t, const size_t, const phf_seed_t);
+#endif
+
+extern template phf_hash_t PHF::hash<uint32_t>(struct phf *, uint32_t);
+extern template phf_hash_t PHF::hash<uint64_t>(struct phf *, uint64_t);
+extern template phf_hash_t PHF::hash<phf_string_t>(struct phf *, phf_string_t);
+#if !PHF_NO_LIBCXX
+extern template phf_hash_t PHF::hash<std::string>(struct phf *, std::string);
+#endif
+
+#endif /* __cplusplus */
+
+
+/*
+ * C 8 9  I N T E R F A C E S
+ *
+ * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * */
+#ifdef __cplusplus
+extern "C" {
+#endif
+
+PHF_PUBLIC size_t phf_uniq_uint32(uint32_t *, const size_t);
+PHF_PUBLIC size_t phf_uniq_uint64(uint64_t *, const size_t);
+PHF_PUBLIC size_t phf_uniq_string(phf_string_t *, const size_t);
+
+PHF_PUBLIC phf_error_t phf_init_uint32(struct phf *, const uint32_t *, const size_t, const size_t, const size_t, const phf_seed_t, const bool nodiv);
+PHF_PUBLIC phf_error_t phf_init_uint64(struct phf *, const uint64_t *, const size_t, const size_t, const size_t, const phf_seed_t, const bool nodiv);
+PHF_PUBLIC phf_error_t phf_init_string(struct phf *, const phf_string_t *, const size_t, const size_t, const size_t, const phf_seed_t, const bool nodiv);
+
+PHF_PUBLIC void phf_compact(struct phf *);
+
+PHF_PUBLIC phf_hash_t phf_hash_uint32(struct phf *, const uint32_t);
+PHF_PUBLIC phf_hash_t phf_hash_uint64(struct phf *, const uint64_t);
+PHF_PUBLIC phf_hash_t phf_hash_string(struct phf *, const phf_string_t);
+
+PHF_PUBLIC void phf_destroy(struct phf *);
+
+#ifdef __cplusplus
+}
+#endif
+
+
+/*
+ * C 1 1 / G N U  I N T E R F A C E S
+ *
+ * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * */
+#if PHF_HAVE_GENERIC
+
+#define phf_uniq(k, n) _Generic(*(k), \
+	uint32_t: phf_uniq_uint32, \
+	uint64_t: phf_uniq_uint64, \
+	phf_string_t: phf_uniq_string)((k), (n))
+
+#define phf_init(f, k, ...) _Generic(*(k), \
+	uint32_t: phf_init_uint32, \
+	uint64_t: phf_init_uint64, \
+	phf_string_t: phf_init_string)((f), (k), __VA_ARGS__)
+
+#define phf_hash(f, k) _Generic((k), \
+	uint32_t: phf_hash_uint32, \
+	uint64_t: phf_hash_uint64, \
+	phf_string_t: phf_hash_string)((f), (k))
+
+#elif PHF_HAVE_BUILTIN_TYPES_COMPATIBLE_P && PHF_HAVE_BUILTIN_CHOOSE_EXPR
+
+#define phf_choose(cond, a, b) __builtin_choose_expr(cond, a, b)
+#define phf_istype(E, T) __builtin_types_compatible_p(__typeof__(E), T)
+
+#define phf_uniq(k, n) \
+	phf_choose(phf_istype(*(k), uint32_t), phf_uniq_uint32((uint32_t *)(k), (n)), \
+	phf_choose(phf_istype(*(k), uint64_t), phf_uniq_uint64((uint64_t *)(k), (n)), \
+	phf_choose(phf_istype(*(k), phf_string_t), phf_uniq_string((phf_string_t *)(k), (n)), \
+	(void)0)))
+
+#define phf_init(f, k, ...) \
+	phf_choose(phf_istype(*(k), uint32_t), phf_init_uint32((f), (const uint32_t *)(k), __VA_ARGS__), \
+	phf_choose(phf_istype(*(k), uint64_t), phf_init_uint64((f), (const uint64_t *)(k), __VA_ARGS__), \
+	phf_choose(phf_istype(*(k), phf_string_t), phf_init_string((f), (const phf_string_t *)(k), __VA_ARGS__), \
+	(void)0)))
+
+#define phf_hash(f, k) ((*(phf_hash_t (*)()) \
+	phf_choose(phf_istype((k), uint32_t), &phf_hash_uint32, \
+	phf_choose(phf_istype((k), uint64_t), &phf_hash_uint64, \
+	phf_choose(phf_istype((k), phf_string_t), &phf_hash_string, \
+	(void)0))))((f), (k)))
+
+#endif
+
+
+#ifdef __clang__
+#pragma clang diagnostic pop
+#elif PHF_GNUC_PREREQ(4, 6)
+#pragma GCC diagnostic pop
+#endif
+
+#endif /* PHF_H */
diff --git a/src/CMakeLists.txt b/src/CMakeLists.txt
index 4562a0ec2..c8529d178 100644
--- a/src/CMakeLists.txt
+++ b/src/CMakeLists.txt
@@ -8,6 +8,8 @@ include_directories(3rd_party/fbgemm/include)
 include_directories(${CMAKE_BINARY_DIR}/local/include)
 
 add_library(marian STATIC
+  common/aliases.cpp
+  common/fastopt.cpp
   common/version.cpp
   common/utils.cpp
   common/logging.cpp
@@ -15,7 +17,6 @@ add_library(marian STATIC
   common/cli_wrapper.cpp
   common/config.cpp
   common/config_parser.cpp
-  common/aliases.cpp
   common/config_validator.cpp
   common/options.cpp
   common/binary.cpp
@@ -38,6 +39,8 @@ add_library(marian STATIC
   3rd_party/cnpy/cnpy.cpp
   3rd_party/ExceptionWithCallStack.cpp
 
+  3rd_party/phf/phf.cc
+
   tensors/backend.cpp
   tensors/rand.cpp
   tensors/tensor.cpp
diff --git a/src/command/marian_conv.cpp b/src/command/marian_conv.cpp
index 304a6bd6a..5d41fc288 100644
--- a/src/command/marian_conv.cpp
+++ b/src/command/marian_conv.cpp
@@ -13,8 +13,9 @@ int main(int argc, char** argv) {
 
   auto options = New<Options>();
   {
+    YAML::Node config; // @TODO: get rid of YAML::Node here entirely to avoid the pattern. Currently not fixing as it requires more changes to the Options object.
     auto cli = New<cli::CLIWrapper>(
-        options,
+        config,
         "Convert a model in the .npz format and normal memory layout to a mmap-able binary model which could be in normal memory layout or packed memory layout",
         "Allowed options",
         "Examples:\n"
@@ -23,6 +24,7 @@ int main(int argc, char** argv) {
     cli->add<std::string>("--to,-t", "Output model", "model.bin");
     cli->add<std::string>("--gemm-type,-g", "GEMM Type to be used with this weights", "mklfp32");
     cli->parse(argc, argv);
+    options->merge(config);
   }
   auto modelFrom = options->get<std::string>("from");
   auto modelTo = options->get<std::string>("to");
diff --git a/src/command/marian_vocab.cpp b/src/command/marian_vocab.cpp
index de8ef3c7c..d9c83b5a7 100644
--- a/src/command/marian_vocab.cpp
+++ b/src/command/marian_vocab.cpp
@@ -9,10 +9,11 @@ int main(int argc, char** argv) {
 
   createLoggers();
 
-  auto options = New<Options>();
+  Ptr<Options> options = New<Options>();
   {
+    YAML::Node config; // @TODO: get rid of YAML::Node here entirely to avoid the pattern. Currently not fixing as it requires more changes to the Options object.
     auto cli = New<cli::CLIWrapper>(
-        options,
+        config,
         "Create a vocabulary from text corpora given on STDIN",
         "Allowed options",
         "Examples:\n"
@@ -20,6 +21,7 @@ int main(int argc, char** argv) {
         "  cat text.src text.trg | ./marian-vocab > vocab.yml");
     cli->add<size_t>("--max-size,-m", "Generate only UINT most common vocabulary items", 0);
     cli->parse(argc, argv);
+    options->merge(config);
   }
 
   LOG(info, "Creating vocabulary...");
diff --git a/src/common/cli_wrapper.cpp b/src/common/cli_wrapper.cpp
index 4eab96c44..52a248873 100644
--- a/src/common/cli_wrapper.cpp
+++ b/src/common/cli_wrapper.cpp
@@ -100,14 +100,6 @@ CLIWrapper::CLIWrapper(YAML::Node &config,
   optVersion_->group(defaultGroup_);
 }
 
-CLIWrapper::CLIWrapper(Ptr<marian::Options> options,
-                       const std::string &description,
-                       const std::string &header,
-                       const std::string &footer,
-                       size_t columnWidth,
-                       size_t screenWidth)
-    : CLIWrapper(options->getYaml(), description, header, footer, columnWidth, screenWidth) {}
-
 CLIWrapper::~CLIWrapper() {}
 
 // set current group to name, return previous group
@@ -202,8 +194,7 @@ void CLIWrapper::updateConfig(const YAML::Node &config, cli::OptionPriority prio
       } else {
         // Default value is a sequence and incoming node is a scalar, hence we can upcast to
         // single element sequence
-        if(config_[key].Type() == YAML::NodeType::Sequence
-           && it.second.Type() == YAML::NodeType::Scalar) {
+        if(config_[key].IsSequence() && it.second.IsScalar()) {
           // create single element sequence
           YAML::Node sequence;
           sequence.push_back(YAML::Clone(it.second));
diff --git a/src/common/cli_wrapper.h b/src/common/cli_wrapper.h
index 291917dfd..349d353ba 100644
--- a/src/common/cli_wrapper.h
+++ b/src/common/cli_wrapper.h
@@ -100,6 +100,7 @@ class CLIWrapper {
   static std::string failureMessage(const CLI::App *app, const CLI::Error &e);
 
 public:
+
   /**
    * @brief Create an instance of the command-line argument parser
    *
@@ -119,18 +120,6 @@ class CLIWrapper {
              size_t columnWidth = 40,
              size_t screenWidth = 0);
 
-  /**
-   * @brief Create an instance of the command-line argument parser, short-cut for Options object.
-   *
-   * @see Other constructor
-   */
-  CLIWrapper(Ptr<Options> options,
-             const std::string &description = "",
-             const std::string &header = "General options",
-             const std::string &footer = "",
-             size_t columnWidth = 30,
-             size_t screenWidth = 0);
-
   virtual ~CLIWrapper();
 
   /**
diff --git a/src/common/config.cpp b/src/common/config.cpp
index ace01a208..a6ce44c4d 100644
--- a/src/common/config.cpp
+++ b/src/common/config.cpp
@@ -25,7 +25,7 @@ Config::Config(int argc, char** argv, cli::mode mode, bool validate /*= true*/)
   : Config(ConfigParser(argc, argv, mode, validate)) {}
 
 Config::Config(const Config& other) : config_(YAML::Clone(other.config_)) {}
-Config::Config(const Options& options) : config_(YAML::Clone(options.getYaml())) {}
+Config::Config(const Options& options) : config_(options.cloneToYamlNode()) {}
 
 void Config::initialize(ConfigParser const& cp) {
   config_ = YAML::Clone(cp.getConfig());
diff --git a/src/common/fastopt.cpp b/src/common/fastopt.cpp
new file mode 100644
index 000000000..509543ecc
--- /dev/null
+++ b/src/common/fastopt.cpp
@@ -0,0 +1,89 @@
+#include "common/fastopt.h"
+
+namespace marian {
+
+// see fastopt.h for comments
+namespace fastopt_helpers {
+
+// helper structs for dynamic type conversion and specializations
+// for different conversion scenarios.
+
+// general template, mostly for numerical and logical types
+template <typename To, typename From>
+struct Convert { 
+  static inline To apply(const From& from) { 
+    return (To)from; 
+  } 
+};
+
+// specialization for translating from string, @TODO check if this is required at all, mostly for compilation now.
+template <typename To>
+struct Convert<To, std::string> { 
+  static inline To apply(const std::string& from) { 
+    ABORT("Not implemented");
+  }
+};
+
+// convert anything to string, checked at compile-time
+template <typename From>
+struct Convert<std::string, From> { 
+  static inline std::string apply(const From& from) { 
+    return std::to_string(from);
+  }
+};
+
+// do nothing conversion for std::string
+template <>
+struct Convert<std::string, std::string> { 
+  static inline std::string apply(const std::string& from) { 
+    return from;
+  }
+};
+
+// helper class for FastOpt::as<T>() used for specializations
+template <typename T>
+T As<T>::apply(const FastOpt& node) {
+  ABORT_IF(!node.isScalar(), "Node is not a scalar node");
+
+  if(node.isBool())
+    return Convert<T, bool>::apply(node.value_->as<bool>());
+  else if(node.isInt())
+    return Convert<T, int64_t>::apply(node.value_->as<int64_t>());
+  else if(node.isFloat())
+    return Convert<T, double>::apply(node.value_->as<double>());
+  else if(node.isString())
+    return Convert<T, std::string>::apply(node.value_->as<std::string>());      
+  else {
+    ABORT("Casting of value failed");
+  }
+}
+
+// specialization of above class for std::vector<T>
+template <typename T>
+std::vector<T> As<std::vector<T>>::apply(const FastOpt& node) {
+  ABORT_IF(!node.isSequence(), "Node is not a sequence node");
+
+  std::vector<T> seq;
+  for(const auto& elem : node.array_)
+    seq.push_back(elem->as<T>());
+  return seq;
+}
+
+// specializations for simple types
+template struct As<bool>;
+template struct As<int>;
+template struct As<unsigned long>;
+template struct As<float>;
+template struct As<double>;
+template struct As<std::string>;
+
+// specializations for simple vector types
+template struct As<std::vector<bool>>;
+template struct As<std::vector<int>>;
+template struct As<std::vector<unsigned long>>;
+template struct As<std::vector<float>>;
+template struct As<std::vector<double>>;
+template struct As<std::vector<std::string>>;
+
+}
+}
\ No newline at end of file
diff --git a/src/common/fastopt.h b/src/common/fastopt.h
new file mode 100644
index 000000000..1f1c6e6bf
--- /dev/null
+++ b/src/common/fastopt.h
@@ -0,0 +1,364 @@
+#pragma once
+
+#include "common/intrusive_ptr.h"
+#include "3rd_party/any_type.h"
+#include "3rd_party/phf/phf.h"
+#include "3rd_party/yaml-cpp/yaml.h"
+
+// This file contains code to create a fast access option class, 
+// meant as a replacment/supplement to YAML::Node.
+
+namespace marian {
+
+namespace crc {
+// has to stay in header due to constexpr
+
+// This code comes from https://notes.underscorediscovery.com/constexpr-fnv1a/
+// and is distributed as public domain as stated by the author under that link
+
+// constants for hash computations
+constexpr uint64_t val_64_const = 0xcbf29ce484222325;
+constexpr uint64_t prime_64_const = 0x100000001b3;
+
+// recursive compile-time hash, looking for stack-overflow source
+inline constexpr uint64_t
+hash_64_fnv1a_const(const char* const str,
+                    const uint64_t value = val_64_const) noexcept {
+  return (str[0] == '\0') ? value :
+      hash_64_fnv1a_const(&str[1], (value ^ uint64_t(str[0])) * prime_64_const);
+}
+
+// Compile time string hashing. Should work particularly well for option look up with explicitly used keys like options->get("dim-input");
+inline constexpr uint64_t crc(const char* const str) noexcept {
+  return hash_64_fnv1a_const(str);
+}
+
+}
+
+/*****************************************************************************/
+
+// PerfectHash constructs a perfect hash for a set K of n numeric keys. The size of 
+// the hash is m > n (not much larger) and n << max(K) (much smaller). If I am not wrong m 
+// is the next power of 2 larger than n? We then build an array of size m with n fields defined.
+// m - n fields stay undefined (a bit of waste).
+class PerfectHash {
+private:
+  ENABLE_INTRUSIVE_PTR(PerfectHash)
+
+  phf phf_;
+
+  PerfectHash(const uint64_t keys[], size_t num) {
+    int error = PHF::init<uint64_t, true>(&phf_, keys, num,
+      /* bucket size */ 4,
+      /* loading factor */ 90,
+      /* seed */ 123456);
+    ABORT_IF(error != 0, "PHF error {}", error);
+  }
+
+public:
+
+  PerfectHash(const std::vector<uint64_t>& v)
+   : PerfectHash(v.data(), v.size()) { }
+
+  ~PerfectHash() {
+    PHF::destroy(&phf_);
+  }
+
+  uint32_t operator[](const uint64_t& key) const {
+    return PHF::hash<uint64_t>(const_cast<phf*>(&phf_), key);
+  }
+
+  uint32_t operator[](const char* const keyStr) const {
+    return (*this)[crc::crc(keyStr)];
+  }
+
+  size_t size() const {
+    return phf_.m;
+  }
+};
+
+/*****************************************************************************/
+
+class FastOpt;
+
+// helper class for conversion, see fastopt.cpp
+namespace fastopt_helpers {
+  template <typename T> 
+  struct As {
+    static T apply(const FastOpt&);
+  };
+
+  template <typename T> 
+  struct As<std::vector<T>> {
+    static std::vector<T> apply(const FastOpt&);
+  };
+}
+
+// Fast access option class, meant as a replacment/supplement to YAML::Node.
+// Relatively expensive to construct, fast to access (not visible in profiler)
+// via std::vector or perfect hash. The perfect hash only requires a few look-ups
+// and arithmentic operations, still O(1).
+// Still requires YAML::Node support for parsing and modification via rebuilding.
+class FastOpt {
+private:
+  ENABLE_INTRUSIVE_PTR(FastOpt)
+
+  template <typename T>
+  friend struct fastopt_helpers::As;
+
+public:
+  // Node types for FastOpt, seem to be enough to cover YAML:NodeType
+  enum struct NodeType {
+    Null, Bool, Int64, Float64, String, Sequence, Map
+  };
+
+private:
+  struct ElementType {
+    ENABLE_INTRUSIVE_PTR(ElementType)
+
+    any_type value{0};
+
+    template <typename T>
+    const T& as() const { return value.as<T>(); }
+  };
+
+  IntrusivePtr<ElementType> value_;
+  IntrusivePtr<PerfectHash> ph_;
+  std::vector<IntrusivePtr<FastOpt>> array_;
+  NodeType type_{NodeType::Null};
+
+  uint64_t fingerprint_{0}; // When node is used as a value in a map, used to check if the perfect hash 
+                            // returned the right value (they can produce false positives)
+  size_t elements_{0};      // Number of elements if isMap or isSequence is true, 0 otherwise.
+
+  // Used to find elements if isSequence() is true.
+  inline const IntrusivePtr<FastOpt> arrayLookup(size_t keyId) const {
+    if(keyId < array_.size())
+      return array_[keyId];
+    else
+      return nullptr;
+  }
+
+  // Used to find elements if isMap() is true.
+  inline const IntrusivePtr<FastOpt> phLookup(size_t keyId) const {
+    if(ph_)
+      return array_[(*ph_)[keyId]];
+    else
+      return nullptr;
+  }
+
+  // Build Null node.
+  void makeNull() {
+    value_.reset(nullptr);
+    elements_ = 0;
+    type_ = NodeType::Null;
+  }
+
+  // Build Scalar node via controlled failure to convert from a YAML::Node object.
+  void makeScalar(const YAML::Node& v) {
+    elements_ = 0;
+    value_.reset(new ElementType());
+    
+    try {
+      value_->value = v.as<bool>();
+      type_ = NodeType::Bool;
+    } catch(const YAML::BadConversion& /*e*/) {
+      try {
+        value_->value = v.as<int64_t>();
+        type_ = NodeType::Int64;
+      } catch(const YAML::BadConversion& /*e*/) {
+        try {
+          value_->value = v.as<double>();
+          type_ = NodeType::Float64;
+        } catch(const YAML::BadConversion& /*e*/) {
+          try { 
+            value_->value = v.as<std::string>();
+            type_ = NodeType::String;
+          } catch (const YAML::BadConversion& /*e*/) {
+            ABORT("Cannot convert YAML node {}", v);
+          }
+        }
+      }
+    }
+  }
+
+  // Build a Sequence node, can by converted to std::vector<T> if elements can be converted to T.
+  void makeSequence(const std::vector<YAML::Node>& v) {
+    array_.resize(v.size());
+    elements_ = v.size();
+
+    for(size_t pos = 0; pos < v.size(); ++pos) {
+      array_[pos].reset(new FastOpt(v[pos]));
+      array_[pos]->fingerprint_ = pos;
+    }
+
+    type_ = NodeType::Sequence;
+  }
+
+  // Build a Map node.
+  void makeMap(const std::map<uint64_t, YAML::Node>& m) {
+    std::vector<uint64_t> keys;
+    for(const auto& it : m)
+      keys.push_back(it.first);
+
+    ph_.reset(new PerfectHash(keys));
+
+    array_.resize(ph_->size());
+    elements_ = keys.size();
+
+    for(const auto& it : m) {
+      uint64_t key = it.first;
+      size_t pos = (*ph_)[key];
+      array_[pos].reset(new FastOpt(it.second));
+      array_[pos]->fingerprint_ = key;
+    }
+
+    type_ = NodeType::Map;
+  }
+
+  // Build a Map node, uses std::string as key, which gets hashed to size_t and used in the function above.
+  void makeMap(const std::map<std::string, YAML::Node>& m) {
+    std::map<uint64_t, YAML::Node> mi;
+    for(const auto& it : m) {
+      auto key = it.first.c_str();
+      mi[crc::crc(key)] = it.second;
+    }
+
+    makeMap(mi);
+  }
+
+  // Only build from YAML::Node
+  FastOpt(const FastOpt&) = delete;
+  FastOpt() = delete;
+
+public:
+  // Constructor to recursively create a FastOpt object from a YAML::Node following the yaml structure.
+  FastOpt(const YAML::Node& node) {
+    switch(node.Type()) {
+      case YAML::NodeType::Scalar:
+        makeScalar(node);
+        break;
+      case YAML::NodeType::Sequence: {
+        std::vector<YAML::Node> nodesVec;
+        for(auto&& n : node)
+          nodesVec.push_back(n);
+        makeSequence(nodesVec);
+      } break;
+      case YAML::NodeType::Map: {
+        std::map<std::string, YAML::Node> nodesMap;
+        for(auto& n : node) {
+          auto key = n.first.as<std::string>();
+          nodesMap[key] = n.second;
+        }
+        makeMap(nodesMap);
+      } break;
+      case YAML::NodeType::Undefined:
+      case YAML::NodeType::Null:
+        makeNull();
+    }
+  }
+
+  bool isSequence() const {
+    return type_ == NodeType::Sequence;
+  }
+
+  bool isMap() const {
+    return type_ == NodeType::Map;
+  }
+
+  bool isScalar() const {
+    return type_ == NodeType::Bool 
+      || type_ == NodeType::Float64 
+      || type_ == NodeType::Int64 
+      || type_ == NodeType::String;
+  }
+
+  bool isNull() const {
+    return type_ == NodeType::Null;
+  }  
+
+  bool isInt() const {
+    return type_ == NodeType::Int64;
+  }  
+  
+  bool isBool() const {
+    return type_ == NodeType::Bool;
+  }
+
+  bool isFloat() const {
+    return type_ == NodeType::Float64;
+  }
+
+  bool isString() const {
+    return type_ == NodeType::String;
+  }
+
+  // actual number of elements in a sequence or map, 0 (not 1) for scalar nodes.
+  // 0 here means rather "not applicable".
+  size_t size() const {
+    return elements_;
+  }
+
+  // replace current node with an externally built FastOpt object
+  void swap(FastOpt& other) {
+    value_.swap(other.value_);
+    ph_.swap(other.ph_);
+    array_.swap(other.array_);
+    std::swap(type_, other.type_);
+    std::swap(elements_, other.elements_);
+    // leave fingerprint alone as it needed by parent node. 
+  }
+
+  // Is the hashed key in a map? 
+  bool has(size_t keyId) const {
+    if(isMap() && elements_ > 0) {
+      const auto ptr = phLookup(keyId);
+      return ptr ? ptr->fingerprint_ == keyId : false;
+    } else {
+      return false;
+    }
+  }
+
+  bool has(const char* const key) const {
+    return has(crc::crc(key));
+  }
+
+  bool has(const std::string& key) const {
+    return has(key.c_str());
+  }
+
+  // convert to requested type
+  template <typename T>
+  inline T as() const {
+    return fastopt_helpers::As<T>::apply(*this);
+  }
+
+  // access sequence or map element
+  const FastOpt& operator[](size_t keyId) const {
+    if(isSequence()) {
+      return *arrayLookup(keyId);
+    } else if(isMap()) {
+      const auto ptr = phLookup(keyId);
+      ABORT_IF(!ptr || ptr->fingerprint_ != keyId, 
+               "Unseen key {}" , 
+               keyId);
+      return *ptr; 
+    } else {
+      ABORT("Not a sequence or map node");
+    }
+  }
+
+  const FastOpt& operator[](int key) const {
+    return operator[]((size_t)key);
+  }
+
+  const FastOpt& operator[](const char* const key) const {
+    return operator[](crc::crc(key));
+  }
+
+  const FastOpt& operator[](const std::string& key) const {
+    return operator[](key.c_str());
+  }
+};
+
+}
\ No newline at end of file
diff --git a/src/common/options.cpp b/src/common/options.cpp
index 0e8ad8a81..2e4487ac9 100644
--- a/src/common/options.cpp
+++ b/src/common/options.cpp
@@ -1,61 +1,70 @@
 #include "options.h"
 
 namespace marian {
-  Options::Options() {}
+  Options::Options() 
+  : fastOptions_(options_) {}
 
   Options::Options(const Options& other)
-    : options_(YAML::Clone(other.options_)) {}
+    : options_(YAML::Clone(other.options_)),
+      fastOptions_(options_) {}
 
   Options Options::clone() const {
-    return Options(*this);
+    return Options(*this); // fastOptions_ get set in constructor above
   }
 
-  YAML::Node& Options::getYaml() {
-    return options_;
-  }
-
-  const YAML::Node& Options::getYaml() const {
-    return options_;
+  YAML::Node Options::cloneToYamlNode() const {
+    return YAML::Clone(options_); // Do not give access to internal YAML object
   }
 
   void Options::parse(const std::string& yaml) {
     auto node = YAML::Load(yaml);
     for(auto it : node)
       options_[it.first.as<std::string>()] = YAML::Clone(it.second);
+    setLazyRebuild();
   }
 
   void Options::merge(const YAML::Node& node, bool overwrite) {
     for(auto it : node)
       if(overwrite || !options_[it.first.as<std::string>()])
         options_[it.first.as<std::string>()] = YAML::Clone(it.second);
+    setLazyRebuild();
   }
 
   void Options::merge(Ptr<Options> options) {
-    merge(options->getYaml());
+    merge(options->options_);
   }
 
-  std::string Options::str() {
+  std::string Options::asYamlString() {
     std::stringstream ss;
     ss << options_;
     return ss.str();
   }
 
-  bool Options::hasAndNotEmpty(const std::string& key) const {
-    if(!has(key)) {
+  bool Options::hasAndNotEmpty(const char* const key) const {
+    lazyRebuild();
+    if(!fastOptions_.has(key)) {
       return false;
+    } else {
+      auto& node = fastOptions_[key];
+      if(node.isSequence())
+        return node.size() != 0;
+      else if(node.isScalar()) // numerical values count as non-empty
+        return !node.as<std::string>().empty();
+      else
+        ABORT("Wrong node type");
     }
-    if(options_[key].IsSequence()) {
-      return options_[key].size() != 0;
-    }
-    try {
-      return !options_[key].as<std::string>().empty();
-    } catch(const YAML::BadConversion& /* e */) {
-      ABORT("Option '{}' is neither a sequence nor a text");
-    }
-    return false;
+  }
+
+  bool Options::hasAndNotEmpty(const std::string& key) const {
+    return hasAndNotEmpty(key.c_str());
+  }
+
+  bool Options::has(const char* const key) const {
+    lazyRebuild();
+    return fastOptions_.has(key);
   }
 
   bool Options::has(const std::string& key) const {
-    return options_[key];
+    return has(key.c_str());
   }
 }
diff --git a/src/common/options.h b/src/common/options.h
index 8e4a528f8..7a18e1f2b 100755
--- a/src/common/options.h
+++ b/src/common/options.h
@@ -3,7 +3,7 @@
 #include <sstream>
 #include <string>
 #include "common/definitions.h"
-
+#include "common/fastopt.h"
 #include "3rd_party/yaml-cpp/yaml.h"
 
 #define YAML_REGISTER_TYPE(registered, type)                \
@@ -29,7 +29,25 @@ namespace marian {
  */
 class Options {
 protected:
-  YAML::Node options_;
+  YAML::Node options_;  // YAML options use for parsing, modification and printing
+  
+  // Only to be modified in lazyRebuild and setLazyRebuild
+  mutable FastOpt fastOptions_; // FastOpt used for fast lookup, lazily rebuilt from YYAML whenever required
+  mutable bool lazyRebuildPending_{false}; // flag if need to lazily rebuild
+
+  // set flag that a rebuild is required
+  void setLazyRebuild() const {
+    lazyRebuildPending_ = true;
+  }
+
+  // check if rebuild is required, rebuild, unset flag.
+  void lazyRebuild() const {
+    if(lazyRebuildPending_) {
+      FastOpt temp(options_);
+      fastOptions_.swap(temp);
+      lazyRebuildPending_ = false;
+    }
+  }
 
 public:
   Options();
@@ -56,9 +74,10 @@ class Options {
    */
   Options clone() const;
 
-  YAML::Node& getYaml();
-
-  const YAML::Node& getYaml() const;
+  // Do not allow access to internal YAML object as changes on the outside are difficult to track
+  // and mess with the rebuilding of the fast options lookup. Hence only return a clone which guarentees
+  // full encapsulation.
+  YAML::Node cloneToYamlNode() const;
 
   void parse(const std::string& yaml);
 
@@ -74,11 +93,12 @@ class Options {
   void merge(const YAML::Node& node, bool overwrite = false);
   void merge(Ptr<Options> options);
 
-  std::string str();
+  std::string asYamlString();
 
   template <typename T>
   void set(const std::string& key, T value) {
     options_[key] = value;
+    setLazyRebuild();
   }
 
   // set multiple
@@ -87,22 +107,35 @@ class Options {
   void set(const std::string& key, T value, Args&&... moreArgs) {
     set(key, value);
     set(std::forward<Args>(moreArgs)...);
+    setLazyRebuild();
   }
 
   template <typename T>
-  T get(const std::string& key) const {
+  T get(const char* const key) const {
+    lazyRebuild();
     ABORT_IF(!has(key), "Required option '{}' has not been set", key);
-    return options_[key].as<T>();
+    return fastOptions_[key].as<T>();
   }
 
   template <typename T>
-  T get(const std::string& key, T defaultValue) const {
+  T get(const std::string& key) const {
+    return get<T>(key.c_str());
+  }
+
+  template <typename T>
+  T get(const char* const key, T defaultValue) const {
+    lazyRebuild();
     if(has(key))
-      return options_[key].as<T>();
+      return fastOptions_[key].as<T>();
     else
       return defaultValue;
   }
 
+  template <typename T>
+  T get(const std::string& key, T defaultValue) const {
+    return get<T>(key.c_str(), defaultValue);
+  }
+
   /**
    * @brief Check if a sequence or string option is defined and nonempty
    *
@@ -113,8 +146,10 @@ class Options {
    *
    * @return true if the option is defined and is a nonempty sequence or string
    */
+  bool hasAndNotEmpty(const char* const key) const;
   bool hasAndNotEmpty(const std::string& key) const;
 
+  bool has(const char* const key) const;
   bool has(const std::string& key) const;
 };
 
diff --git a/src/data/corpus_base.cpp b/src/data/corpus_base.cpp
index 857c21d02..d398c81a7 100755
--- a/src/data/corpus_base.cpp
+++ b/src/data/corpus_base.cpp
@@ -83,22 +83,24 @@ CorpusBase::CorpusBase(Ptr<Options> options, bool translate)
       LOG(info, "No vocabulary files given, trying to find or build based on training data. "
                 "Vocabularies will be built separately for each file.");
 
+      std::vector<int> vocabDims(paths_.size(), 0);
+      std::vector<std::string> vocabPaths(paths_.size());
       // Create vocabs if not provided
       for(size_t i = 0; i < paths_.size(); ++i) {
         Ptr<Vocab> vocab = New<Vocab>(options_, i);
         std::vector<std::string> trainPaths = { paths_[i] };
-        size_t vocSize = vocab->loadOrCreate("", trainPaths, maxVocabs[i]);
-        // TODO: this is not nice as it modifies the option object and needs to expose the changes
-        // outside the corpus as models need to know about the vocabulary size; extract the vocab
-        // creation functionality from the class.
-        options_->getYaml()["dim-vocabs"][i] = vocSize;
-
-        options_->getYaml()["vocabs"].push_back(paths_[i] + ".yml");
+        vocabDims[i] = vocab->loadOrCreate("", trainPaths, maxVocabs[i]);
+        vocabPaths[i] = paths_[i] + ".yml";
         vocabs_.emplace_back(vocab);
       }
+      // TODO: this is not nice as it modifies the option object and needs to expose the changes
+      // outside the corpus as models need to know about the vocabulary size; extract the vocab
+      // creation functionality from the class.
+      options_->set("dim-vocabs", vocabDims, "vocabs", vocabPaths);
     } else {
       // Load all vocabs
-      if(maxVocabs.size() < vocabPaths.size())
+      size_t numVocs = vocabPaths.size();
+      if(maxVocabs.size() < numVocs)
         maxVocabs.resize(paths_.size(), 0);
 
       // Helper object to for grouping training data based on vocabulary file name
@@ -111,28 +113,28 @@ CorpusBase::CorpusBase(Ptr<Options> options, bool translate)
       // vocab path corresponds to different training files, this means
       // that a single vocab should combine tokens from all files.
       std::map<std::string, PathsAndSize> groupVocab;
-      for(size_t i = 0; i < vocabPaths.size(); ++i) {
+      for(size_t i = 0; i < numVocs; ++i) {
         groupVocab[vocabPaths[i]].paths.insert(paths_[i]);
         if(groupVocab[vocabPaths[i]].size < maxVocabs[i])
           groupVocab[vocabPaths[i]].size = maxVocabs[i];
       }
 
-      for(size_t i = 0; i < vocabPaths.size(); ++i) {
+      auto vocabDims = options_->get<std::vector<int>>("dim-vocabs");
+      vocabDims.resize(numVocs, 0);
+      for(size_t i = 0; i < numVocs; ++i) {
         Ptr<Vocab> vocab = New<Vocab>(options_, i);
 
         // Get the set of files that corresponds to the vocab. If the next file is the same vocab,
         // it wild not be created again, but just correctly loaded.
         auto pathsAndSize = groupVocab[vocabPaths[i]];
         std::vector<std::string> groupedPaths(pathsAndSize.paths.begin(), pathsAndSize.paths.end());
-        size_t vocSize = vocab->loadOrCreate(vocabPaths[i], groupedPaths, pathsAndSize.size);
-
-        // TODO: this is not nice as it modifies the option object and needs to expose the changes
-        // outside the corpus, because models need to know about the vocabulary size; extract the
-        // vocab creation functionality from the class.
-        options_->getYaml()["dim-vocabs"][i] = vocSize;
-
+        vocabDims[i] = vocab->loadOrCreate(vocabPaths[i], groupedPaths, pathsAndSize.size);
         vocabs_.emplace_back(vocab);
       }
+      // TODO: this is not nice as it modifies the option object and needs to expose the changes
+      // outside the corpus as models need to know about the vocabulary size; extract the vocab
+      // creation functionality from the class.
+      options_->set("dim-vocabs", vocabDims);
     }
   }
 
@@ -140,16 +142,21 @@ CorpusBase::CorpusBase(Ptr<Options> options, bool translate)
     ABORT_IF(vocabPaths.empty(),
              "Translating, but vocabularies are not given!");
 
-    if(maxVocabs.size() < vocabPaths.size())
+    size_t numVocs = vocabPaths.size();
+    if(maxVocabs.size() < numVocs)
       maxVocabs.resize(paths_.size(), 0);
 
-    for(size_t i = 0; i + 1 < vocabPaths.size(); ++i) {
+    auto vocabDims = options_->get<std::vector<int>>("dim-vocabs");
+    vocabDims.resize(numVocs, 0);
+    for(size_t i = 0; i + 1 < numVocs; ++i) {
       Ptr<Vocab> vocab = New<Vocab>(options_, i);
-      size_t vocSize = vocab->load(vocabPaths[i], maxVocabs[i]);
-      options_->getYaml()["dim-vocabs"][i] = vocSize;
-
+      vocabDims[i] = vocab->load(vocabPaths[i], maxVocabs[i]);
       vocabs_.emplace_back(vocab);
     }
+    // TODO: As above, this is not nice as it modifies the option object and needs to expose the changes
+    // outside the corpus as models need to know about the vocabulary size; extract the vocab
+    // creation functionality from the class.
+    options_->set("dim-vocabs", vocabDims);
   }
 
   for(auto path : paths_) {
diff --git a/src/graph/expression_operators.cpp b/src/graph/expression_operators.cpp
index a527f09da..3956e02cd 100755
--- a/src/graph/expression_operators.cpp
+++ b/src/graph/expression_operators.cpp
@@ -631,12 +631,12 @@ Expr highway(const std::string prefix, Expr x) {
   auto g = mlp::dense()
       ("prefix", prefix + "_highway_d1")
       ("dim", outDim)
-      ("activation", mlp::act::sigmoid)
+      ("activation", (int)mlp::act::sigmoid)
       .construct(graph)->apply(x);
   auto relued = mlp::dense()
       ("prefix", prefix + "_highway_d2")
       ("dim", outDim)
-      ("activation", mlp::act::ReLU)
+      ("activation", (int)mlp::act::ReLU)
       .construct(graph)->apply(x);
   return (g * relued) + ((1 - g) * x);
   // clang-format on
diff --git a/src/layers/factory.h b/src/layers/factory.h
index bcb1db0c4..f9e4ddf92 100755
--- a/src/layers/factory.h
+++ b/src/layers/factory.h
@@ -31,15 +31,21 @@ class Factory : public std::enable_shared_from_this<Factory> {
 
   virtual ~Factory() {}
 
-  std::string str() { return options_->str(); }
+  std::string asYamlString() { return options_->asYamlString(); }
 
   // retrieve an option
   // auto val = opt<T>("var");
   template <typename T>
-  T opt(const std::string& key) { return options_->get<T>(key); }
+  T opt(const char* const key) { return options_->get<T>(key); }
 
   template <typename T>
-  T opt(const std::string& key, T defaultValue) { return options_->get<T>(key, defaultValue); }
+  T opt(const char* const key, T defaultValue) { return options_->get<T>(key, defaultValue); }
+
+  template <typename T>
+  T opt(const std::string& key) { return options_->get<T>(key.c_str()); }
+
+  template <typename T>
+  T opt(const std::string& key, T defaultValue) { return options_->get<T>(key.c_str(), defaultValue); }
 
   // set a single option
   // setOpt("var", val);
diff --git a/src/layers/generic.h b/src/layers/generic.h
index ee9854013..88a458acd 100755
--- a/src/layers/generic.h
+++ b/src/layers/generic.h
@@ -12,8 +12,6 @@ namespace marian { namespace mlp {
   enum struct act : int { linear, tanh, sigmoid, ReLU, LeakyReLU, PReLU, swish };
 }}
 
-YAML_REGISTER_TYPE(marian::mlp::act, int)
-
 namespace marian {
 
 // Each layer consists of LayerBase and IXXXLayer which defines one or more apply()
@@ -175,7 +173,7 @@ class Dense : public LayerBase, public IUnaryLayer {
 
     auto useLayerNorm = opt<bool>("layer-normalization", false);
     auto useNematusNorm = opt<bool>("nematus-normalization", false);
-    auto activation = opt<act>("activation", act::linear);
+    auto activation = (act)opt<int>("activation", (int)act::linear);
 
     auto g = graph_;
 
diff --git a/src/microsoft/quicksand.cpp b/src/microsoft/quicksand.cpp
index 04993e706..6b30089c1 100755
--- a/src/microsoft/quicksand.cpp
+++ b/src/microsoft/quicksand.cpp
@@ -93,7 +93,7 @@ class BeamSearchDecoder : public IBeamSearchDecoder {
       modelOpts->merge(options_);
       modelOpts->merge(config);
 
-      std::cerr << modelOpts->str() << std::flush;
+      std::cerr << modelOpts->asYamlString() << std::flush; // @TODO: take a look at why this is even here.
 
       auto encdec = models::createModelFromOptions(modelOpts, models::usage::translation);
 
diff --git a/src/models/bert.h b/src/models/bert.h
index 51a932c5e..514274572 100755
--- a/src/models/bert.h
+++ b/src/models/bert.h
@@ -283,7 +283,7 @@ class BertClassifier : public ClassifierBase {
                     .push_back(mlp::dense()                           //
                                  ("prefix", prefix_ + "_ff_logit_l1") //
                                  ("dim", dimModel)                    //
-                                 ("activation", mlp::act::tanh))      // @TODO: do we actually need this?
+                                 ("activation", (int)mlp::act::tanh))      // @TODO: do we actually need this?
                     .push_back(mlp::output()                          //
                                  ("dim", dimTrgCls))                  //
                                  ("prefix", prefix_ + "_ff_logit_l2") //
diff --git a/src/models/encoder_classifier.h b/src/models/encoder_classifier.h
index 1b3b223a1..b551205d0 100644
--- a/src/models/encoder_classifier.h
+++ b/src/models/encoder_classifier.h
@@ -67,11 +67,12 @@ class EncoderClassifier : public EncoderClassifierBase {
 
   Config::YamlNode getModelParameters() {
     Config::YamlNode modelParams;
+    auto clone = options_->cloneToYamlNode();
     for(auto& key : modelFeatures_)
-        modelParams[key] = options_->getYaml()[key];
+      modelParams[key] = clone[key];
 
     if(options_->has("original-type"))
-        modelParams["type"] = options_->getYaml()["original-type"];
+      modelParams["type"] = clone["original-type"];
 
     modelParams["version"] = buildVersion();
     return modelParams;
diff --git a/src/models/encoder_decoder.cpp b/src/models/encoder_decoder.cpp
index 2f29c045b..e968a33ab 100755
--- a/src/models/encoder_decoder.cpp
+++ b/src/models/encoder_decoder.cpp
@@ -120,11 +120,12 @@ void EncoderDecoder::createDecoderConfig(const std::string& name) {
 
 Config::YamlNode EncoderDecoder::getModelParameters() {
   Config::YamlNode modelParams;
+  auto clone = options_->cloneToYamlNode();
   for(auto& key : modelFeatures_)
-    modelParams[key] = options_->getYaml()[key];
+    modelParams[key] = clone[key];
 
   if(options_->has("original-type"))
-    modelParams["type"] = options_->getYaml()["original-type"];
+    modelParams["type"] = clone["original-type"];
 
   modelParams["version"] = buildVersion();
   return modelParams;
diff --git a/src/models/s2s.h b/src/models/s2s.h
index 391bf1516..b340b5a5b 100755
--- a/src/models/s2s.h
+++ b/src/models/s2s.h
@@ -280,10 +280,11 @@ class DecoderS2S : public DecoderBase {
 
     if(!output_) {
       // construct deep output multi-layer network layer-wise
+
       auto hidden = mlp::dense()                                     //
           ("prefix", prefix_ + "_ff_logit_l1")                       //
           ("dim", opt<int>("dim-emb"))                               //
-          ("activation", mlp::act::tanh)                             //
+          ("activation", (int)mlp::act::tanh)                        //
           ("layer-normalization", opt<bool>("layer-normalization"))  //
           ("nematus-normalization",
            options_->has("original-type")
diff --git a/src/models/transformer.h b/src/models/transformer.h
index 65e9b607f..660c179ae 100755
--- a/src/models/transformer.h
+++ b/src/models/transformer.h
@@ -33,10 +33,18 @@ class Transformer : public EncoderOrDecoderBase {
   // It can be accessed by getAlignments(). @TODO: move into a state or return-value object
   std::vector<Expr> alignments_; // [max tgt len or 1][beam depth, max src length, batch size, 1]
 
-  template <typename T> T opt(const std::string& key) const { Ptr<Options> options = options_; return options->get<T>(key); }  // need to duplicate, since somehow using Base::opt is not working
-  // FIXME: that separate options assignment is weird
+  // @TODO: make this go away
+  template <typename T> 
+  T opt(const char* const key) const { Ptr<Options> options = options_; return options->get<T>(key); }  
 
-  template <typename T> T opt(const std::string& key, const T& def) const { Ptr<Options> options = options_; if (options->has(key)) return options->get<T>(key); else return def; }
+  template <typename T> 
+  T opt(const std::string& key) const { return opt<T>(key.c_str()); }  
+
+  template <typename T> 
+  T opt(const char* const key, const T& def) const { Ptr<Options> options = options_; return options->get<T>(key, def);  }
+
+  template <typename T> 
+  T opt(const std::string& key, const T& def) const { opt<T>(key.c_str(), def); }
 
 public:
   static Expr transposeTimeBatch(Expr input) { return transpose(input, {0, 2, 1, 3}); }
@@ -461,7 +469,8 @@ class Transformer : public EncoderOrDecoderBase {
     return LayerAAN(prefix, input, output);
   }
 
-  Expr DecoderLayerRNN(rnn::State& decoderState,
+  Expr DecoderLayerRNN(std::unordered_map<std::string, Ptr<rnn::RNN>>& perLayerRnn, // @TODO: rewrite this whole organically grown mess
+                       rnn::State& decoderState,
                        const rnn::State& prevDecoderState,
                        std::string prefix,
                        Expr input,
@@ -469,15 +478,18 @@ class Transformer : public EncoderOrDecoderBase {
                        int /*startPos*/) const {
     float dropoutRnn = inference_ ? 0.f : opt<float>("dropout-rnn");
 
-    auto rnn = rnn::rnn(
-         "type", opt<std::string>("dec-cell"),
-         "prefix", prefix,
-         "dimInput", opt<int>("dim-emb"),
-         "dimState", opt<int>("dim-emb"),
-         "dropout", dropoutRnn,
-         "layer-normalization", opt<bool>("layer-normalization"))
-        .push_back(rnn::cell())
-        .construct(graph_);
+    if(!perLayerRnn[prefix]) // lazily created and cache RNNs in the docoder to avoid costly recreation @TODO: turn this into class members
+      perLayerRnn[prefix] = rnn::rnn(
+          "type", opt<std::string>("dec-cell"),
+          "prefix", prefix,
+          "dimInput", opt<int>("dim-emb"),
+          "dimState", opt<int>("dim-emb"),
+          "dropout", dropoutRnn,
+          "layer-normalization", opt<bool>("layer-normalization"))
+          .push_back(rnn::cell())
+          .construct(graph_);
+
+    auto rnn = perLayerRnn[prefix];
 
     float dropProb = inference_ ? 0 : opt<float>("transformer-dropout");
     auto opsPre = opt<std::string>("transformer-preprocess");
@@ -512,7 +524,7 @@ class EncoderTransformer : public Transformer<EncoderBase> {
     // embed the source words in the batch
     Expr batchEmbeddings, batchMask;
 
-    auto embeddingLayer = getEmbeddingLayer(options_->has("ulr") && options_->get<bool>("ulr"));
+    auto embeddingLayer = getEmbeddingLayer(opt<bool>("ulr", false));
     std::tie(batchEmbeddings, batchMask) = embeddingLayer->apply((*batch)[batchIndex_]);
     batchEmbeddings = addSpecialEmbeddings(batchEmbeddings, /*start=*/0, batch);
     
@@ -579,6 +591,10 @@ class DecoderTransformer : public Transformer<DecoderBase> {
 private:
   Ptr<mlp::Output> output_;
 
+  // This caches RNN objects to avoid reconstruction between batches or deocoding steps.
+  // To be removed after refactoring of transformer.h
+  std::unordered_map<std::string, Ptr<rnn::RNN>> perLayerRnn_;
+
 private:
   // @TODO: move this out for sharing with other models
   void lazyCreateOutputLayer()
@@ -601,8 +617,6 @@ class DecoderTransformer : public Transformer<DecoderBase> {
   }
 
 public:
-  //DecoderTransformer(Ptr<ExpressionGraph> graph, Ptr<Options> options) : Transformer(graph, options) {}
-
   virtual Ptr<DecoderState> startState(
       Ptr<ExpressionGraph> graph,
       Ptr<data::CorpusBatch> batch,
@@ -720,7 +734,7 @@ class DecoderTransformer : public Transformer<DecoderBase> {
       else if(layerType == "average-attention")
         query = DecoderLayerAAN(decoderState, prevDecoderState, prefix_ + "_l" + layerNo + "_aan", query, selfMask, startPos);
       else if(layerType == "rnn")
-        query = DecoderLayerRNN(decoderState, prevDecoderState, prefix_ + "_l" + layerNo + "_rnn", query, selfMask, startPos);
+        query = DecoderLayerRNN(perLayerRnn_, decoderState, prevDecoderState, prefix_ + "_l" + layerNo + "_rnn", query, selfMask, startPos);
       else
         ABORT("Unknown auto-regressive layer type in transformer decoder {}",
               layerType);
diff --git a/src/tests/cli.cpp b/src/tests/cli.cpp
index 9c7c9a51c..67c9bdd72 100644
--- a/src/tests/cli.cpp
+++ b/src/tests/cli.cpp
@@ -39,7 +39,8 @@ int main(int argc, char** argv) {
 
   auto options = New<Options>();
   {
-    auto w = New<CLIWrapper>(options);
+    YAML::Node config;
+    auto w = New<CLIWrapper>(config);
     w->add<int>("-i,--int", "help message")->implicit_val("555")->default_val("123");
     w->add<std::string>("-s,--str", "help message");
     w->add<std::vector<float>>("-v,--vec", "help message")->expected(-2);
@@ -53,6 +54,7 @@ int main(int argc, char** argv) {
     //w.add<color>("-e,--enum", "help message for enum");
 
     w->parse(argc, argv);
+    options->merge(config);
   }
 
   options->get<int>("int");
@@ -65,7 +67,7 @@ int main(int argc, char** argv) {
   //w.get<color>("enum");
 
   YAML::Emitter emit;
-  OutputYaml(options->getYaml(), emit);
+  OutputYaml(options->cloneToYamlNode(), emit);
   std::cout << emit.c_str() << std::endl;
 
   std::cout << "===" << std::endl;
diff --git a/src/tests/units/CMakeLists.txt b/src/tests/units/CMakeLists.txt
index e9e89d09f..3814b4810 100644
--- a/src/tests/units/CMakeLists.txt
+++ b/src/tests/units/CMakeLists.txt
@@ -4,6 +4,7 @@ set(UNIT_TESTS
     operator_tests
     rnn_tests
     attention_tests
+    fastopt_tests
 )
 
 foreach(test ${UNIT_TESTS})
diff --git a/src/tests/units/attention_tests.cpp b/src/tests/units/attention_tests.cpp
old mode 100755
new mode 100644
diff --git a/src/tests/units/fastopt_tests.cpp b/src/tests/units/fastopt_tests.cpp
new file mode 100644
index 000000000..e846ab87a
--- /dev/null
+++ b/src/tests/units/fastopt_tests.cpp
@@ -0,0 +1,76 @@
+#include "catch.hpp"
+#include "common/fastopt.h"
+#include "3rd_party/yaml-cpp/yaml.h"
+
+using namespace marian;
+
+TEST_CASE("FastOpt can be constructed from a YAML node", "[fastopt]") {
+  YAML::Node node;
+  
+  SECTION("from a simple node") {
+    YAML::Node node = YAML::Load("{foo: bar}");
+    const FastOpt o(node);
+
+    CHECK( o.has("foo") );
+    CHECK_FALSE( o.has("bar") );
+    CHECK_FALSE( o.has("baz") );
+  }
+
+  SECTION("from a sequence node") {
+    YAML::Node node = YAML::Load("{foo: [bar, baz]}");
+    const FastOpt o(node);
+    CHECK( o.has("foo") );
+  }
+
+  SECTION("from nested nodes") {
+    YAML::Node node = YAML::Load("{foo: {bar: 123, baz}}");
+    const FastOpt o(node);
+    CHECK( o.has("foo") );
+    CHECK( o["foo"].has("bar") );
+    CHECK( o["foo"].has("baz") );    
+    CHECK( o["foo"]["bar"].as<int>() == 123 );
+    CHECK( o["foo"]["baz"].isNull() );
+  }
+}
+
+TEST_CASE("Options can be accessed", "[fastopt]") {
+  YAML::Node node = YAML::Load("{"
+      "foo: bar,"
+      "seq: [1, 2, 3],"
+      "subnode: {"
+      "  baz: [ 111.5, False ],"
+      "  qux: 222,"
+      "  }"
+      "}");
+
+  const FastOpt o(node);
+
+  SECTION("using operator[]") {
+    auto& oo = o["subnode"];
+    CHECK( oo.has("baz") );
+    CHECK( oo.has("qux") );
+    CHECK_NOTHROW( o["subnode"]["baz"] );
+  }
+
+  SECTION("using as<T>()") {
+    CHECK( o["foo"].as<std::string>() == "bar" );
+    CHECK( o["subnode"]["baz"][0].as<float>() == 111.5f );
+    CHECK( o["subnode"]["baz"][1].as<bool>() == false );
+    CHECK( o["subnode"]["baz"][0].as<int>() == 111 );
+  }
+
+  node["foo"] = "baz";
+  if(o.has("foo")) {
+    FastOpt temp(node["foo"]);
+    const_cast<FastOpt&>(o["foo"]).swap(temp);
+  }
+
+  CHECK( o["foo"].as<std::string>() == "baz" );
+
+  // for(auto k : o[subnode].keys())
+  //   o[subnode][k].type()
+
+  SECTION("using as<std::vector<T>>()") {
+    CHECK( o["seq"].as<std::vector<double>>() == std::vector<double>({1, 2, 3}) );
+  } 
+}
diff --git a/src/tests/units/operator_tests.cpp b/src/tests/units/operator_tests.cpp
old mode 100755
new mode 100644
diff --git a/src/tests/units/rnn_tests.cpp b/src/tests/units/rnn_tests.cpp
old mode 100755
new mode 100644
diff --git a/src/training/scheduler.h b/src/training/scheduler.h
index c9130d922..c8e90c95e 100644
--- a/src/training/scheduler.h
+++ b/src/training/scheduler.h
@@ -385,9 +385,8 @@ class Scheduler : public TrainingObserver {
 
   void save(const std::string& name) {
     // Save config options
-    YAML::Node yaml = options_->getYaml();
     std::ofstream fout(name + ".yml");
-    fout << yaml;
+    fout << options_->asYamlString();
     // Save training progress
     state_->save(name + ".progress.yml");
   }
diff --git a/src/translator/translator.h b/src/translator/translator.h
index a888ec5f8..bd17ec178 100755
--- a/src/translator/translator.h
+++ b/src/translator/translator.h
@@ -34,8 +34,8 @@ class Translate : public ModelTask {
     // This is currently safe as the translator is either created stand-alone or
     // or config is created anew from Options in the validator
 
-    options_->set("inference", true);
-    options_->set("shuffle", "none");
+    options_->set("inference", true, 
+                  "shuffle", "none");
 
     corpus_ = New<data::Corpus>(options_, true);
 

From 7ba804b20b303df797669306ad315124e4329cea Mon Sep 17 00:00:00 2001
From: Marcin Junczys-Dowmunt <Marcin.JunczysDowmunt@microsoft.com>
Date: Tue, 5 Nov 2019 11:46:43 -0800
Subject: [PATCH 763/838] bump patch version, update changelog

---
 CHANGELOG.md | 2 ++
 VERSION      | 2 +-
 2 files changed, 3 insertions(+), 1 deletion(-)

diff --git a/CHANGELOG.md b/CHANGELOG.md
index e4b29debd..d8b2e678f 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -22,6 +22,7 @@ and this project adheres to [Semantic Versioning](http://semver.org/spec/v2.0.0.
 - Gradient-checkpointing
 
 ### Fixed
+- Compilation for GCC >= 7 due to exception thrown in destrutor
 - Sort parameters by lexicographical order during allocation to ensure consistent 
   memory-layout during allocation, loading, saving.
 - Output empty line when input is empty line. Previous behavior might result in 
@@ -29,6 +30,7 @@ and this project adheres to [Semantic Versioning](http://semver.org/spec/v2.0.0.
 - Compilation with CUDA 10.1
 
 ### Changed
+- Faster option look-up, up to 20-30% faster translation
 - Removed autotuner for now, will be switched back on later
 - Boost depdendency is now optional and only required for marian_server 
   or for boost::regex when compiling with g++-4.9
diff --git a/VERSION b/VERSION
index 6deeb5e3e..6b1c62644 100644
--- a/VERSION
+++ b/VERSION
@@ -1 +1 @@
-v1.8.12
+v1.8.14

From 233281cc2667e9ebee72a81cb781726f831b54a1 Mon Sep 17 00:00:00 2001
From: Martin Junczys-Dowmunt <Marcin.JunczysDowmunt@microsoft.com>
Date: Tue, 5 Nov 2019 23:18:45 +0000
Subject: [PATCH 764/838] Merged PR 10304: Remove naked pointers, add binary
 read mode

* Remove naked pointers in file_stream.{h,cpp}
* Add binary read mode
---
 CHANGELOG.md               |  3 ++-
 VERSION                    |  2 +-
 src/common/file_stream.cpp | 51 +++++++++++++++++---------------------
 src/common/file_stream.h   |  8 +++---
 4 files changed, 30 insertions(+), 34 deletions(-)

diff --git a/CHANGELOG.md b/CHANGELOG.md
index d8b2e678f..5f892f36d 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -22,7 +22,8 @@ and this project adheres to [Semantic Versioning](http://semver.org/spec/v2.0.0.
 - Gradient-checkpointing
 
 ### Fixed
-- Compilation for GCC >= 7 due to exception thrown in destrutor
+- Removed naked pointer and potential memory-leak from file_stream.{cpp,h}
+- Compilation for GCC >= 7 due to exception thrown in destructor
 - Sort parameters by lexicographical order during allocation to ensure consistent 
   memory-layout during allocation, loading, saving.
 - Output empty line when input is empty line. Previous behavior might result in 
diff --git a/VERSION b/VERSION
index 6b1c62644..9cf3faad6 100644
--- a/VERSION
+++ b/VERSION
@@ -1 +1 @@
-v1.8.14
+v1.8.15
diff --git a/src/common/file_stream.cpp b/src/common/file_stream.cpp
index 15871254e..815048304 100644
--- a/src/common/file_stream.cpp
+++ b/src/common/file_stream.cpp
@@ -18,26 +18,23 @@ namespace io {
 
 ///////////////////////////////////////////////////////////////////////////////////////////////
 InputFileStream::InputFileStream(const std::string &file)
-    : std::istream(NULL), file_(file), streamBuf1_(NULL), streamBuf2_(NULL) {
+    : std::istream(NULL), file_(file) {
   ABORT_IF(!marian::filesystem::exists(file_), "File '{}' does not exist", file);
 
-  std::filebuf *fileBuf = new std::filebuf();
-  streamBuf1_ = fileBuf->open(file.c_str(), std::ios::in);
-  ABORT_IF(!streamBuf1_, "File can't be opened", file);
-  assert(fileBuf == streamBuf1_);
+  streamBuf1_.reset(new std::filebuf());
+  auto ret = static_cast<std::filebuf*>(streamBuf1_.get())->open(file.c_str(), std::ios::in | std::ios::binary); 
+  ABORT_IF(!ret, "File cannot be opened", file);
+  ABORT_IF(ret != streamBuf1_.get(), "Return value is not equal to streambuf pointer, that is weird");
 
   if(file_.extension() == marian::filesystem::Path(".gz")) {
-    streamBuf2_ = new zstr::istreambuf(streamBuf1_);
-    this->init(streamBuf2_);
+    streamBuf2_.reset(new zstr::istreambuf(streamBuf1_.get()));
+    this->init(streamBuf2_.get());
   } else {
-    this->init(streamBuf1_);
+    this->init(streamBuf1_.get());
   }
 }
 
-InputFileStream::~InputFileStream() {
-  delete streamBuf2_;
-  delete streamBuf1_;
-}
+InputFileStream::~InputFileStream() {}
 
 bool InputFileStream::empty() {
   return this->peek() == std::ifstream::traits_type::eof();
@@ -66,27 +63,25 @@ std::istream &getline(std::istream &in, std::string &line) {
 }
 ///////////////////////////////////////////////////////////////////////////////////////////////
 OutputFileStream::OutputFileStream(const std::string &file)
-    : std::ostream(NULL), file_(file), streamBuf1_(NULL), streamBuf2_(NULL) {
-  std::filebuf *fileBuf = new std::filebuf();
-  streamBuf1_ = fileBuf->open(file.c_str(), std::ios::out | std::ios_base::binary);
-  ABORT_IF(!streamBuf1_, "File can't be opened", file);
-  assert(fileBuf == streamBuf1_);
+    : std::ostream(NULL), file_(file) {
+  streamBuf1_.reset(new std::filebuf());
+  auto ret = static_cast<std::filebuf*>(streamBuf1_.get())->open(file.c_str(), std::ios::out | std::ios_base::binary);
+  ABORT_IF(!ret, "File cannot be opened", file);
+  ABORT_IF(ret != streamBuf1_.get(), "Return value is not equal to streambuf pointer, that is weird");
 
   if(file_.extension() == marian::filesystem::Path(".gz")) {
-    streamBuf2_ = new zstr::ostreambuf(streamBuf1_);
-    this->init(streamBuf2_);
+    streamBuf2_.reset(new zstr::ostreambuf(streamBuf1_.get()));
+    this->init(streamBuf2_.get());
   } else {
-    this->init(streamBuf1_);
+    this->init(streamBuf1_.get());
   }
 }
 
 OutputFileStream::OutputFileStream()
-    : std::ostream(NULL), streamBuf1_(NULL), streamBuf2_(NULL) {}
+    : std::ostream(NULL) {}
 
 OutputFileStream::~OutputFileStream() {
   this->flush();
-  delete streamBuf2_;
-  delete streamBuf1_;
 }
 
 ///////////////////////////////////////////////////////////////////////////////////////////////
@@ -150,12 +145,12 @@ void TemporaryFile::MakeTemp(const std::string &base) {
 #endif
 
   // open again with c++
-  std::filebuf *fileBuf = new std::filebuf();
-  streamBuf1_ = fileBuf->open(name, std::ios::out | std::ios_base::binary);
-  ABORT_IF(!streamBuf1_, "File can't be temp opened", name);
-  assert(fileBuf == streamBuf1_);
+  streamBuf1_.reset(new std::filebuf());
+  auto ret = static_cast<std::filebuf*>(streamBuf1_.get())->open(name, std::ios::out | std::ios_base::binary);
+  ABORT_IF(!streamBuf1_, "File cannot be temp opened", name);
+  ABORT_IF(ret != streamBuf1_.get(), "Return value is not equal to streambuf pointer, that is weird");
 
-  this->init(streamBuf1_);
+  this->init(streamBuf1_.get());
 
   // close original file descriptor
   ABORT_IF(close(fd), "Can't close file descriptor", name);
diff --git a/src/common/file_stream.h b/src/common/file_stream.h
index 7f360bb04..c81512079 100644
--- a/src/common/file_stream.h
+++ b/src/common/file_stream.h
@@ -38,8 +38,8 @@ class InputFileStream : public std::istream {
 
 protected:
   marian::filesystem::Path file_;
-  std::streambuf* streamBuf1_;
-  std::streambuf* streamBuf2_;
+  std::unique_ptr<std::streambuf> streamBuf1_;
+  std::unique_ptr<std::streambuf> streamBuf2_;
   std::vector<char> readBuf_;
 };
 
@@ -63,8 +63,8 @@ class OutputFileStream : public std::ostream {
   explicit OutputFileStream();  // for temp file
 
   marian::filesystem::Path file_;
-  std::streambuf* streamBuf1_;
-  std::streambuf* streamBuf2_;
+  std::unique_ptr<std::streambuf> streamBuf1_;
+  std::unique_ptr<std::streambuf> streamBuf2_;
 };
 
 ///////////////////////////////////////////////////////////////////////////////////////////////

From a8826c5d12bd0eeeb104a374b25e08de9918f79e Mon Sep 17 00:00:00 2001
From: Marcin Junczys-Dowmunt <Marcin.JunczysDowmunt@microsoft.com>
Date: Tue, 5 Nov 2019 23:53:40 -0800
Subject: [PATCH 765/838] make compile with cudnn

---
 src/common/fastopt.cpp | 28 ++++++++++++++++++++--------
 src/common/fastopt.h   |  5 +++++
 2 files changed, 25 insertions(+), 8 deletions(-)

diff --git a/src/common/fastopt.cpp b/src/common/fastopt.cpp
index 509543ecc..7cf7908b7 100644
--- a/src/common/fastopt.cpp
+++ b/src/common/fastopt.cpp
@@ -1,5 +1,7 @@
 #include "common/fastopt.h"
 
+#include <utility>
+
 namespace marian {
 
 // see fastopt.h for comments
@@ -58,6 +60,14 @@ T As<T>::apply(const FastOpt& node) {
   }
 }
 
+// specializations for simple types
+template struct As<bool>;
+template struct As<int>;
+template struct As<unsigned long>;
+template struct As<float>;
+template struct As<double>;
+template struct As<std::string>;
+
 // specialization of above class for std::vector<T>
 template <typename T>
 std::vector<T> As<std::vector<T>>::apply(const FastOpt& node) {
@@ -69,14 +79,6 @@ std::vector<T> As<std::vector<T>>::apply(const FastOpt& node) {
   return seq;
 }
 
-// specializations for simple types
-template struct As<bool>;
-template struct As<int>;
-template struct As<unsigned long>;
-template struct As<float>;
-template struct As<double>;
-template struct As<std::string>;
-
 // specializations for simple vector types
 template struct As<std::vector<bool>>;
 template struct As<std::vector<int>>;
@@ -85,5 +87,15 @@ template struct As<std::vector<float>>;
 template struct As<std::vector<double>>;
 template struct As<std::vector<std::string>>;
 
+// specialization of above class for std::pair<T>
+template <typename T1, typename T2>
+std::pair<T1, T2> As<std::pair<T1, T2>>::apply(const FastOpt& node) {
+  ABORT_IF(!node.isSequence(), "Node is not a sequence node");
+  ABORT_IF(node.size() != 2, "Sequence must contain two elements in order to convert to pair");
+  return std::make_pair(node[0].as<T1>(), node[1].as<T2>());
+}
+
+template struct As<std::pair<int, int>>;
+
 }
 }
\ No newline at end of file
diff --git a/src/common/fastopt.h b/src/common/fastopt.h
index 1f1c6e6bf..1d1c56688 100644
--- a/src/common/fastopt.h
+++ b/src/common/fastopt.h
@@ -92,6 +92,11 @@ namespace fastopt_helpers {
   struct As<std::vector<T>> {
     static std::vector<T> apply(const FastOpt&);
   };
+
+  template <typename T1, typename T2> 
+  struct As<std::pair<T1, T2>> {
+    static std::pair<T1, T2> apply(const FastOpt&);
+  };
 }
 
 // Fast access option class, meant as a replacment/supplement to YAML::Node.

From 59011c8129e44cface873fb3b09043cc9f5ed6b8 Mon Sep 17 00:00:00 2001
From: alvations <alvations@gmail.com>
Date: Thu, 7 Nov 2019 07:07:12 +0800
Subject: [PATCH 766/838] added ssse4.2 support

---
 CMakeLists.txt      |  5 +++++
 cmake/FindSSE.cmake | 14 +++++++++++---
 2 files changed, 16 insertions(+), 3 deletions(-)

diff --git a/CMakeLists.txt b/CMakeLists.txt
index d07616656..f5bef7412 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -110,6 +110,11 @@ else(MSVC)
       set(INTRINSICS "${INTRINSICS} -msse4.1")
       list(APPEND INTRINSICS_NVCC -Xcompiler\ -msse4.1)
     endif(SSE4_1_FOUND)
+    if(SSE4_2_FOUND)
+      message(STATUS "SSE4.2 support found")
+      set(INTRINSICS "${INTRINSICS} -msse4.2")
+      list(APPEND INTRINSICS_NVCC -Xcompiler\ -msse4.2)
+    endif(SSE4_2_FOUND)
     if(AVX_FOUND)
       message(STATUS "AVX support found")
       set(INTRINSICS "${INTRINSICS} -mavx")
diff --git a/cmake/FindSSE.cmake b/cmake/FindSSE.cmake
index e6d3e9caf..82ee7f3e0 100644
--- a/cmake/FindSSE.cmake
+++ b/cmake/FindSSE.cmake
@@ -41,6 +41,14 @@ IF(CMAKE_SYSTEM_NAME MATCHES "Linux")
       set(SSE4_1_FOUND false CACHE BOOL "SSE4.1 available on host")
    ENDIF (SSE41_TRUE)
 
+   STRING(REGEX REPLACE "^.*(sse4_2).*$" "\\1" SSE_THERE ${CPUINFO})
+   STRING(COMPARE EQUAL "sse4_2" "${SSE_THERE}" SSE42_TRUE)
+   IF (SSE42_TRUE)
+      set(SSE4_2_FOUND true CACHE BOOL "SSE4.2 available on host")
+   ELSE (SSE42_TRUE)
+      set(SSE4_2_FOUND false CACHE BOOL "SSE4.2 available on host")
+   ENDIF (SSE42_TRUE)
+
    STRING(REGEX REPLACE "^.*(avx).*$" "\\1" SSE_THERE ${CPUINFO})
    STRING(COMPARE EQUAL "avx" "${SSE_THERE}" AVX_TRUE)
    IF (AVX_TRUE)
@@ -48,7 +56,7 @@ IF(CMAKE_SYSTEM_NAME MATCHES "Linux")
    ELSE (AVX_TRUE)
       set(AVX_FOUND false CACHE BOOL "AVX available on host")
    ENDIF (AVX_TRUE)
- 
+
    STRING(REGEX REPLACE "^.*(avx2).*$" "\\1" SSE_THERE ${CPUINFO})
    STRING(COMPARE EQUAL "avx2" "${SSE_THERE}" AVX2_TRUE)
    IF (AVX2_TRUE)
@@ -56,7 +64,7 @@ IF(CMAKE_SYSTEM_NAME MATCHES "Linux")
    ELSE (AVX2_TRUE)
       set(AVX2_FOUND false CACHE BOOL "AVX2 available on host")
    ENDIF (AVX2_TRUE)
- 
+
    STRING(REGEX REPLACE "^.*(avx512).*$" "\\1" SSE_THERE ${CPUINFO})
    STRING(COMPARE EQUAL "avx512" "${SSE_THERE}" AVX512_TRUE)
    IF (AVX512_TRUE)
@@ -116,7 +124,7 @@ ELSEIF(CMAKE_SYSTEM_NAME MATCHES "Darwin")
    ELSE (AVX2_TRUE)
       set(AVX2_FOUND false CACHE BOOL "AVX2 available on host")
    ENDIF (AVX2_TRUE)
- 
+
    STRING(REGEX REPLACE "^.*(avx512).*$" "\\1" SSE_THERE ${CPUINFO})
    STRING(COMPARE EQUAL "avx512" "${SSE_THERE}" AVX512_TRUE)
    IF (AVX512_TRUE)

From ca610330d55567ae58db9f1dc89c4c68a7250a72 Mon Sep 17 00:00:00 2001
From: Marcin Junczys-Dowmunt <Marcin.JunczysDowmunt@microsoft.com>
Date: Sat, 9 Nov 2019 00:30:24 -0800
Subject: [PATCH 767/838] remove reference counting from fastopt

---
 src/common/fastopt.cpp |   8 +--
 src/common/fastopt.h   |  95 ++++++++++++++--------------
 src/common/options.cpp | 137 +++++++++++++++++++++++++----------------
 src/common/options.h   |  30 +++++++--
 4 files changed, 164 insertions(+), 106 deletions(-)

diff --git a/src/common/fastopt.cpp b/src/common/fastopt.cpp
index 7cf7908b7..e5446681c 100644
--- a/src/common/fastopt.cpp
+++ b/src/common/fastopt.cpp
@@ -48,13 +48,13 @@ T As<T>::apply(const FastOpt& node) {
   ABORT_IF(!node.isScalar(), "Node is not a scalar node");
 
   if(node.isBool())
-    return Convert<T, bool>::apply(node.value_->as<bool>());
+    return Convert<T, bool>::apply(node.value_.as<bool>());
   else if(node.isInt())
-    return Convert<T, int64_t>::apply(node.value_->as<int64_t>());
+    return Convert<T, int64_t>::apply(node.value_.as<int64_t>());
   else if(node.isFloat())
-    return Convert<T, double>::apply(node.value_->as<double>());
+    return Convert<T, double>::apply(node.value_.as<double>());
   else if(node.isString())
-    return Convert<T, std::string>::apply(node.value_->as<std::string>());      
+    return Convert<T, std::string>::apply(node.value_.as<std::string>());      
   else {
     ABORT("Casting of value failed");
   }
diff --git a/src/common/fastopt.h b/src/common/fastopt.h
index 1d1c56688..b56819660 100644
--- a/src/common/fastopt.h
+++ b/src/common/fastopt.h
@@ -1,6 +1,6 @@
 #pragma once
 
-#include "common/intrusive_ptr.h"
+#include "common/definitions.h"
 #include "3rd_party/any_type.h"
 #include "3rd_party/phf/phf.h"
 #include "3rd_party/yaml-cpp/yaml.h"
@@ -43,8 +43,6 @@ inline constexpr uint64_t crc(const char* const str) noexcept {
 // m - n fields stay undefined (a bit of waste).
 class PerfectHash {
 private:
-  ENABLE_INTRUSIVE_PTR(PerfectHash)
-
   phf phf_;
 
   PerfectHash(const uint64_t keys[], size_t num) {
@@ -106,8 +104,6 @@ namespace fastopt_helpers {
 // Still requires YAML::Node support for parsing and modification via rebuilding.
 class FastOpt {
 private:
-  ENABLE_INTRUSIVE_PTR(FastOpt)
-
   template <typename T>
   friend struct fastopt_helpers::As;
 
@@ -118,18 +114,9 @@ class FastOpt {
   };
 
 private:
-  struct ElementType {
-    ENABLE_INTRUSIVE_PTR(ElementType)
-
-    any_type value{0};
-
-    template <typename T>
-    const T& as() const { return value.as<T>(); }
-  };
-
-  IntrusivePtr<ElementType> value_;
-  IntrusivePtr<PerfectHash> ph_;
-  std::vector<IntrusivePtr<FastOpt>> array_;
+  any_type value_;
+  const PerfectHash* ph_{nullptr};
+  std::vector<const FastOpt*> array_;
   NodeType type_{NodeType::Null};
 
   uint64_t fingerprint_{0}; // When node is used as a value in a map, used to check if the perfect hash 
@@ -137,7 +124,7 @@ class FastOpt {
   size_t elements_{0};      // Number of elements if isMap or isSequence is true, 0 otherwise.
 
   // Used to find elements if isSequence() is true.
-  inline const IntrusivePtr<FastOpt> arrayLookup(size_t keyId) const {
+  inline const FastOpt* arrayLookup(size_t keyId) const {
     if(keyId < array_.size())
       return array_[keyId];
     else
@@ -145,7 +132,7 @@ class FastOpt {
   }
 
   // Used to find elements if isMap() is true.
-  inline const IntrusivePtr<FastOpt> phLookup(size_t keyId) const {
+  inline const FastOpt* phLookup(size_t keyId) const {
     if(ph_)
       return array_[(*ph_)[keyId]];
     else
@@ -154,30 +141,30 @@ class FastOpt {
 
   // Build Null node.
   void makeNull() {
-    value_.reset(nullptr);
     elements_ = 0;
     type_ = NodeType::Null;
+
+    ABORT_IF(ph_, "ph_ should be undefined");
+    ABORT_IF(!array_.empty(), "array_ should be empty");
   }
 
   // Build Scalar node via controlled failure to convert from a YAML::Node object.
   void makeScalar(const YAML::Node& v) {
     elements_ = 0;
-    value_.reset(new ElementType());
-    
     try {
-      value_->value = v.as<bool>();
+      value_ = v.as<bool>();
       type_ = NodeType::Bool;
     } catch(const YAML::BadConversion& /*e*/) {
       try {
-        value_->value = v.as<int64_t>();
+        value_ = v.as<int64_t>();
         type_ = NodeType::Int64;
       } catch(const YAML::BadConversion& /*e*/) {
         try {
-          value_->value = v.as<double>();
+          value_ = v.as<double>();
           type_ = NodeType::Float64;
         } catch(const YAML::BadConversion& /*e*/) {
           try { 
-            value_->value = v.as<std::string>();
+            value_ = v.as<std::string>();
             type_ = NodeType::String;
           } catch (const YAML::BadConversion& /*e*/) {
             ABORT("Cannot convert YAML node {}", v);
@@ -185,19 +172,21 @@ class FastOpt {
         }
       }
     }
+    
+    ABORT_IF(ph_, "ph_ should be undefined");
+    ABORT_IF(!array_.empty(), "array_ should be empty");
   }
 
   // Build a Sequence node, can by converted to std::vector<T> if elements can be converted to T.
   void makeSequence(const std::vector<YAML::Node>& v) {
-    array_.resize(v.size());
     elements_ = v.size();
-
+    ABORT_IF(!array_.empty(), "array_ is not empty??");
     for(size_t pos = 0; pos < v.size(); ++pos) {
-      array_[pos].reset(new FastOpt(v[pos]));
-      array_[pos]->fingerprint_ = pos;
+      array_.emplace_back(new FastOpt(v[pos], pos));
     }
-
     type_ = NodeType::Sequence;
+
+    ABORT_IF(ph_, "ph_ should be undefined");
   }
 
   // Build a Map node.
@@ -206,16 +195,17 @@ class FastOpt {
     for(const auto& it : m)
       keys.push_back(it.first);
 
-    ph_.reset(new PerfectHash(keys));
+    ABORT_IF(ph_, "ph_ is already defined??");
+    ph_ = new PerfectHash(keys);
 
-    array_.resize(ph_->size());
+    ABORT_IF(!array_.empty(), "array_ is not empty??");
+    array_.resize(ph_->size(), nullptr);
     elements_ = keys.size();
 
     for(const auto& it : m) {
       uint64_t key = it.first;
       size_t pos = (*ph_)[key];
-      array_[pos].reset(new FastOpt(it.second));
-      array_[pos]->fingerprint_ = key;
+      array_[pos] = new FastOpt(it.second, key);
     }
 
     type_ = NodeType::Map;
@@ -236,9 +226,7 @@ class FastOpt {
   FastOpt(const FastOpt&) = delete;
   FastOpt() = delete;
 
-public:
-  // Constructor to recursively create a FastOpt object from a YAML::Node following the yaml structure.
-  FastOpt(const YAML::Node& node) {
+  void construct(const YAML::Node& node) {
     switch(node.Type()) {
       case YAML::NodeType::Scalar:
         makeScalar(node);
@@ -263,6 +251,23 @@ class FastOpt {
     }
   }
 
+public:
+  // Constructor to recursively create a FastOpt object from a YAML::Node following the yaml structure.
+  FastOpt(const YAML::Node& node) 
+  { construct(node); }
+
+  FastOpt(const YAML::Node& node, uint64_t fingerprint) 
+     : fingerprint_{fingerprint}
+  { construct(node); }
+
+  ~FastOpt() {
+    if(ph_)
+      delete ph_;
+    for(auto ptr : array_)
+      if(ptr)
+        delete ptr;
+  }
+
   bool isSequence() const {
     return type_ == NodeType::Sequence;
   }
@@ -306,9 +311,9 @@ class FastOpt {
 
   // replace current node with an externally built FastOpt object
   void swap(FastOpt& other) {
-    value_.swap(other.value_);
-    ph_.swap(other.ph_);
-    array_.swap(other.array_);
+    std::swap(value_, other.value_);
+    std::swap(ph_, other.ph_);
+    std::swap(array_, other.array_);
     std::swap(type_, other.type_);
     std::swap(elements_, other.elements_);
     // leave fingerprint alone as it needed by parent node. 
@@ -341,12 +346,12 @@ class FastOpt {
   // access sequence or map element
   const FastOpt& operator[](size_t keyId) const {
     if(isSequence()) {
-      return *arrayLookup(keyId);
+      const auto ptr = arrayLookup(keyId);
+      ABORT_IF(!ptr, "Unseen key {}" , keyId);
+      return *ptr;
     } else if(isMap()) {
       const auto ptr = phLookup(keyId);
-      ABORT_IF(!ptr || ptr->fingerprint_ != keyId, 
-               "Unseen key {}" , 
-               keyId);
+      ABORT_IF(!ptr || ptr->fingerprint_ != keyId, "Unseen key {}", keyId);
       return *ptr; 
     } else {
       ABORT("Not a sequence or map node");
diff --git a/src/common/options.cpp b/src/common/options.cpp
index 2e4487ac9..e39b55222 100644
--- a/src/common/options.cpp
+++ b/src/common/options.cpp
@@ -1,70 +1,101 @@
 #include "options.h"
 
 namespace marian {
-  Options::Options() 
-  : fastOptions_(options_) {}
 
-  Options::Options(const Options& other)
-    : options_(YAML::Clone(other.options_)),
-      fastOptions_(options_) {}
+Options::Options() 
+#if FASTOPT
+  : fastOptions_(options_) 
+#endif
+{}
 
-  Options Options::clone() const {
-    return Options(*this); // fastOptions_ get set in constructor above
-  }
+Options::Options(const Options& other)
+#if FASTOPT
+  : options_(YAML::Clone(other.options_)),
+    fastOptions_(options_) {}
+#else
+  : options_(YAML::Clone(other.options_)) {}
+#endif
 
-  YAML::Node Options::cloneToYamlNode() const {
-    return YAML::Clone(options_); // Do not give access to internal YAML object
-  }
+Options Options::clone() const {
+  return Options(*this); // fastOptions_ get set in constructor above
+}
 
-  void Options::parse(const std::string& yaml) {
-    auto node = YAML::Load(yaml);
-    for(auto it : node)
+YAML::Node Options::cloneToYamlNode() const {
+  return YAML::Clone(options_); // Do not give access to internal YAML object
+}
+
+void Options::parse(const std::string& yaml) {
+  auto node = YAML::Load(yaml);
+  for(auto it : node)
+    options_[it.first.as<std::string>()] = YAML::Clone(it.second);
+#if FASTOPT
+  setLazyRebuild();
+#endif
+}
+
+void Options::merge(const YAML::Node& node, bool overwrite) {
+  for(auto it : node)
+    if(overwrite || !options_[it.first.as<std::string>()])
       options_[it.first.as<std::string>()] = YAML::Clone(it.second);
-    setLazyRebuild();
-  }
+#if FASTOPT
+  setLazyRebuild();
+#endif
+}
 
-  void Options::merge(const YAML::Node& node, bool overwrite) {
-    for(auto it : node)
-      if(overwrite || !options_[it.first.as<std::string>()])
-        options_[it.first.as<std::string>()] = YAML::Clone(it.second);
-    setLazyRebuild();
-  }
+void Options::merge(Ptr<Options> options) {
+  merge(options->options_);
+}
 
-  void Options::merge(Ptr<Options> options) {
-    merge(options->options_);
-  }
+std::string Options::asYamlString() {
+  std::stringstream ss;
+  ss << options_;
+  return ss.str();
+}
 
-  std::string Options::asYamlString() {
-    std::stringstream ss;
-    ss << options_;
-    return ss.str();
+bool Options::hasAndNotEmpty(const char* const key) const {
+#if FASTOPT
+  lazyRebuild();
+  if(!fastOptions_.has(key)) {
+    return false;
+  } else {
+    auto& node = fastOptions_[key];
+    if(node.isSequence())
+      return node.size() != 0;
+    else if(node.isScalar()) // numerical values count as non-empty
+      return !node.as<std::string>().empty();
+    else
+      ABORT("Wrong node type");
   }
-
-  bool Options::hasAndNotEmpty(const char* const key) const {
-    lazyRebuild();
-    if(!fastOptions_.has(key)) {
-      return false;
-    } else {
-      auto& node = fastOptions_[key];
-      if(node.isSequence())
-        return node.size() != 0;
-      else if(node.isScalar()) // numerical values count as non-empty
-        return !node.as<std::string>().empty();
-      else
-        ABORT("Wrong node type");
-    }
+#else
+  if(!options_[key]) {
+    return false;
+  } else {
+    auto& node = options_[key];
+    if(node.IsSequence())
+      return node.size() != 0;
+    else if(node.IsScalar()) // numerical values count as non-empty
+      return !node.as<std::string>().empty();
+    else
+      ABORT("Wrong node type");
   }
+#endif
+}
 
-  bool Options::hasAndNotEmpty(const std::string& key) const {
-    return hasAndNotEmpty(key.c_str());
-  }
+bool Options::hasAndNotEmpty(const std::string& key) const {
+  return hasAndNotEmpty(key.c_str());
+}
 
-  bool Options::has(const char* const key) const {
-    lazyRebuild();
-    return fastOptions_.has(key);
-  }
+bool Options::has(const char* const key) const {
+#if FASTOPT
+  lazyRebuild();
+  return fastOptions_.has(key);
+#else
+  return options_[key];
+#endif
+}
+
+bool Options::has(const std::string& key) const {
+  return has(key.c_str());
+}
 
-  bool Options::has(const std::string& key) const {
-    return has(key.c_str());
-  }
 }
diff --git a/src/common/options.h b/src/common/options.h
index 7a18e1f2b..2940e0d12 100755
--- a/src/common/options.h
+++ b/src/common/options.h
@@ -1,13 +1,19 @@
 #pragma once
 
+// @TODO: to be removed when sure it works
+#define FASTOPT 1 // for diagnostics, 0 reverts to old behavior
+
 #include <sstream>
 #include <string>
 #include "common/definitions.h"
-#include "common/fastopt.h"
 #include "3rd_party/yaml-cpp/yaml.h"
 
+#ifdef FASTOPT
+#include "common/fastopt.h"
+#endif
+
 #define YAML_REGISTER_TYPE(registered, type)                \
-  namespace YAML {                                          \
+namespace YAML {                                            \
   template <>                                               \
   struct convert<registered> {                              \
     static Node encode(const registered& rhs) {             \
@@ -20,7 +26,7 @@
       return true;                                          \
     }                                                       \
   };                                                        \
-  }
+}
 
 namespace marian {
 
@@ -31,6 +37,7 @@ class Options {
 protected:
   YAML::Node options_;  // YAML options use for parsing, modification and printing
   
+#if FASTOPT
   // Only to be modified in lazyRebuild and setLazyRebuild
   mutable FastOpt fastOptions_; // FastOpt used for fast lookup, lazily rebuilt from YYAML whenever required
   mutable bool lazyRebuildPending_{false}; // flag if need to lazily rebuild
@@ -48,6 +55,7 @@ class Options {
       lazyRebuildPending_ = false;
     }
   }
+#endif
 
 public:
   Options();
@@ -59,7 +67,7 @@ class Options {
   Options(const std::string& key, T value, Args&&... moreArgs) : Options() {
     set(key, value, std::forward<Args>(moreArgs)...);
   }
-
+  
   // constructor that clones and zero or more updates
   // options->with("var1", val1, "var2", val2, ...)
   template <typename... Args>
@@ -98,7 +106,9 @@ class Options {
   template <typename T>
   void set(const std::string& key, T value) {
     options_[key] = value;
+#if FASTOPT
     setLazyRebuild();
+#endif
   }
 
   // set multiple
@@ -107,14 +117,21 @@ class Options {
   void set(const std::string& key, T value, Args&&... moreArgs) {
     set(key, value);
     set(std::forward<Args>(moreArgs)...);
+#if FASTOPT
     setLazyRebuild();
+#endif
   }
 
   template <typename T>
   T get(const char* const key) const {
+#if FASTOPT
     lazyRebuild();
     ABORT_IF(!has(key), "Required option '{}' has not been set", key);
     return fastOptions_[key].as<T>();
+#else
+    ABORT_IF(!has(key), "Required option '{}' has not been set", key);
+    return options_[key].as<T>();
+#endif
   }
 
   template <typename T>
@@ -124,9 +141,14 @@ class Options {
 
   template <typename T>
   T get(const char* const key, T defaultValue) const {
+#if FASTOPT
     lazyRebuild();
     if(has(key))
       return fastOptions_[key].as<T>();
+#else
+    if(has(key))
+      return options_[key].as<T>();
+#endif
     else
       return defaultValue;
   }

From 13e61820fd5457a722b07da152565d1b762c84f7 Mon Sep 17 00:00:00 2001
From: Marcin Junczys-Dowmunt <Marcin.JunczysDowmunt@microsoft.com>
Date: Sat, 9 Nov 2019 01:00:24 -0800
Subject: [PATCH 768/838] bump version and changelog

---
 CHANGELOG.md | 1 +
 VERSION      | 2 +-
 2 files changed, 2 insertions(+), 1 deletion(-)

diff --git a/CHANGELOG.md b/CHANGELOG.md
index 5f892f36d..57459c3dc 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -22,6 +22,7 @@ and this project adheres to [Semantic Versioning](http://semver.org/spec/v2.0.0.
 - Gradient-checkpointing
 
 ### Fixed
+- Remove thread-unsafe reference counting from FastOpt, use normal pointers
 - Removed naked pointer and potential memory-leak from file_stream.{cpp,h}
 - Compilation for GCC >= 7 due to exception thrown in destructor
 - Sort parameters by lexicographical order during allocation to ensure consistent 
diff --git a/VERSION b/VERSION
index 9cf3faad6..4799d244a 100644
--- a/VERSION
+++ b/VERSION
@@ -1 +1 @@
-v1.8.15
+v1.8.16

From 66b95a5afa581a6ded6dae680cfe8b6f133d88af Mon Sep 17 00:00:00 2001
From: Ulrich Germann <ugermann@inf.ed.ac.uk>
Date: Mon, 11 Nov 2019 09:33:13 +0000
Subject: [PATCH 769/838] Sort custom cmake options alphabetically (#547)

---
 CMakeLists.txt | 18 ++++++++----------
 1 file changed, 8 insertions(+), 10 deletions(-)

diff --git a/CMakeLists.txt b/CMakeLists.txt
index d07616656..09246d469 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -11,21 +11,19 @@ set(CMAKE_CXX_STANDARD_REQUIRED ON)
 set(BUILD_ARCH native CACHE STRING "Compile for this CPU architecture.")
 
 # Custom CMake options
-option(USE_SENTENCEPIECE "Download and compile SentencePiece" OFF)
-option(USE_FBGEMM "Use FBGEMM" OFF)
-option(USE_NCCL "Use NCCL library" ON)
-option(USE_MPI "Use MPI library" OFF)
-option(USE_CUDNN "Use CUDNN library" OFF)
-
 option(COMPILE_CPU "Compile CPU version" ON)
 option(COMPILE_CUDA "Compile GPU version" ON)
-option(USE_STATIC_LIBS "Link statically against non-system libs" OFF)
 option(COMPILE_EXAMPLES "Compile examples" OFF)
-option(COMPILE_TESTS "Compile tests" OFF)
 option(COMPILE_SERVER "Compile marian-server" OFF)
-
-option(USE_DOXYGEN "Build documentation with Doxygen" ON)
+option(COMPILE_TESTS "Compile tests" OFF)
 option(USE_CCACHE "Use ccache compiler cache (https://ccache.dev)" OFF)
+option(USE_CUDNN "Use CUDNN library" OFF)
+option(USE_DOXYGEN "Build documentation with Doxygen" ON)
+option(USE_FBGEMM "Use FBGEMM" OFF)
+option(USE_MPI "Use MPI library" OFF)
+option(USE_NCCL "Use NCCL library" ON)
+option(USE_SENTENCEPIECE "Download and compile SentencePiece" OFF)
+option(USE_STATIC_LIBS "Link statically against non-system libs" OFF)
 
 # use ccache (https://ccache.dev) for faster compilation if requested and available
 if(USE_CCACHE)

From fd3404deab29e5a9448b5971363100a0b9081b07 Mon Sep 17 00:00:00 2001
From: Martin Junczys-Dowmunt <Marcin.JunczysDowmunt@microsoft.com>
Date: Mon, 11 Nov 2019 17:19:04 +0000
Subject: [PATCH 770/838] Merged PR 10382: Fix cublas math mode querying

While moving to the explicit matmul functions like `cublasGemmEx` the specific algorithm needs to be set. This seems to override math mode from `cublasSetMathMode`.  Now we query explicitly and choose the algorithm accordingly.
---
 src/tensors/gpu/prod.cpp | 35 ++++++++++++++++++++++++++++-------
 1 file changed, 28 insertions(+), 7 deletions(-)

diff --git a/src/tensors/gpu/prod.cpp b/src/tensors/gpu/prod.cpp
index f60fae9ab..b6852a39d 100755
--- a/src/tensors/gpu/prod.cpp
+++ b/src/tensors/gpu/prod.cpp
@@ -16,6 +16,19 @@ namespace marian {
 
 namespace gpu {
 
+// The explicit version of matmult like cublasGemmEx choose their math mode based on the algorithm that
+// has been passed into the function call and seem to ignore setMathMode. Here we query the used math mode
+// to choose the algorithm.
+static bool tensorOpsEnabled(cublasHandle_t cublasHandle) {
+#if CUDA_VERSION >= 9000
+  cublasMath_t actual = CUBLAS_DEFAULT_MATH;
+  cublasGetMathMode(cublasHandle, &actual);
+  return actual == CUBLAS_TENSOR_OP_MATH;
+#else
+  return false;
+#endif
+}
+
 static void setTensorMode(cublasHandle_t cublasHandle) {
   cublasHandle; // fool warnings
 #if CUDA_VERSION >= 9000
@@ -25,9 +38,9 @@ static void setTensorMode(cublasHandle_t cublasHandle) {
     if (!var)
       var = "1";
     switch(var[0]) {
-    case '0': mode = -1; break;
-    case '1': mode =  1; break;
-    default: ABORT("Invalid ENABLE_CUBLAS_TENSOR_OP_MATH_FP32={}", var);
+      case '0': mode = -1; break;
+      case '1': mode =  1; break;
+      default: ABORT("Invalid ENABLE_CUBLAS_TENSOR_OP_MATH_FP32={}", var);
     }
     if (mode > 0) { // try whether it can be set   --@TODO: check whether this actually works
       CUBLAS_CHECK(cublasSetMathMode(cublasHandle, CUBLAS_TENSOR_OP_MATH));
@@ -66,13 +79,15 @@ static cublasStatus_t cublasGemmTyped(cublasHandle_t handle,
 // double #if and if unfortunately required to safeguard against compilation error 
 // with CUDA 8.0 and runtime error with CUDA >9.0 on GPUs with compute capability under 5
 #if CUDA_VERSION > 9000
+  // query math mode and set algorithm accordingly
+  auto algorithm = tensorOpsEnabled(handle) ? CUBLAS_GEMM_DEFAULT_TENSOR_OP : CUBLAS_GEMM_DEFAULT;
   if(computeCapability.major >= 5)
     return cublasGemmEx(handle, transa, transb, 
                         m, n, k, alpha, 
                         A, CUDA_R_32F, lda, 
                         B, CUDA_R_32F, ldb, beta, 
                         C, CUDA_R_32F, ldc,
-                        CUDA_R_32F, CUBLAS_GEMM_DEFAULT_TENSOR_OP); // @TODO: review algorithm
+                        CUDA_R_32F, algorithm); // @TODO: review algorithm
 #endif
   return cublasSgemm(handle, transa, transb, 
                       m, n, k, alpha, 
@@ -94,12 +109,14 @@ static cublasStatus_t cublasGemmTyped(cublasHandle_t handle,
                                       const half* beta,
                                       half* C, int ldc) {
   ABORT_IF(computeCapability.major < 6, "Compute capability {} below 6 should not happen for FP16", computeCapability.major);
+  // query math mode and set algorithm accordingly
+  auto algorithm = tensorOpsEnabled(handle) ? CUBLAS_GEMM_DEFAULT_TENSOR_OP : CUBLAS_GEMM_DEFAULT;
   return cublasGemmEx(handle, transa, transb, 
                       m, n, k, alpha, 
                       A, CUDA_R_16F, lda, 
                       B, CUDA_R_16F, ldb, beta, 
                       C, CUDA_R_16F, ldc,
-                      CUDA_R_16F, CUBLAS_GEMM_DEFAULT_TENSOR_OP); // @TODO: review algorithm
+                      CUDA_R_16F, algorithm); // @TODO: review algorithm
 }
 #endif
 
@@ -189,13 +206,15 @@ cublasStatus_t cublasGemmBatchedTyped(cublasHandle_t handle,
 // double #if and if unfortunately required to safeguard against compilation error 
 // with CUDA 8.0 and runtime error with CUDA >9.0 on GPUs with compute capability under 5
 #if CUDA_VERSION > 9000
+  // query math mode and set algorithm accordingly
+  auto algorithm = tensorOpsEnabled(handle) ? CUBLAS_GEMM_DEFAULT_TENSOR_OP : CUBLAS_GEMM_DEFAULT;
   if(computeCapability.major >= 5)
     return cublasGemmBatchedEx(handle, transa, transb, 
                                m, n, k, alpha, 
                                (void* const*)Aarray, CUDA_R_32F, lda, 
                                (void* const*)Barray, CUDA_R_32F, ldb, beta,
                                (void**)Carray, CUDA_R_32F, ldc, batchCount,
-                               CUDA_R_32F, CUBLAS_GEMM_DEFAULT_TENSOR_OP);
+                               CUDA_R_32F, algorithm);
 #endif
   return cublasSgemmBatched(handle, transa, transb, 
                             m, n, k, alpha, 
@@ -217,12 +236,14 @@ cublasStatus_t cublasGemmBatchedTyped(cublasHandle_t handle,
                                       half *Carray[], int ldc, 
                                       int batchCount) {
   ABORT_IF(computeCapability.major < 6, "Compute capability {} below 6 should not happen for FP16", computeCapability.major);
+  // query math mode and set algorithm accordingly
+  auto algorithm = tensorOpsEnabled(handle) ? CUBLAS_GEMM_DEFAULT_TENSOR_OP : CUBLAS_GEMM_DEFAULT;
   return cublasGemmBatchedEx(handle, transa, transb, 
                              m, n, k, alpha, 
                              (void* const*)Aarray, CUDA_R_16F, lda, 
                              (void* const*)Barray, CUDA_R_16F, ldb, beta,
                              (void**)Carray, CUDA_R_16F, ldc, batchCount,
-                             CUDA_R_16F, CUBLAS_GEMM_DEFAULT_TENSOR_OP); // @TODO: to 16, this is testing
+                             CUDA_R_16F, algorithm); // @TODO: to 16, this is testing
 }
 #endif
 

From c96d709d58f44cbb8b740b84c0da49744a49db14 Mon Sep 17 00:00:00 2001
From: Martin Junczys-Dowmunt <Marcin.JunczysDowmunt@microsoft.com>
Date: Mon, 11 Nov 2019 17:30:54 +0000
Subject: [PATCH 771/838] Merged PR 10376: Fix memory-mapping bug for default
 parameter-object - [Let's not merge before discussion of comment.]

This fixes the memory increase while doing memory-mapping (we need regression tests for that in Marian, although that particular error would be hard to monitor. Maybe the current way by observing runtime behavior was just correct).

**This PR does:**
* Fixes memory-mapping for default parameter type. The buggy code was not replacing the in-memory parameter set with mmapping for the new default parameter. Now the parameter object is destroyed and replaced with a mmapable version if it is not mmapable.
* Add small cross-system library (mio) for memory-mapping, currently only to enable easy diagnostics of mmap on Linux. to be extended to a proper feature.

**Comments:**
* It seems we need to re-binarize the models for correct mmap behavior. Since we have identified a smaller bug in the binary file (incorrect byte-boundary alignment) this might be the right moment to fix this issue. This might help to stabilize the format for the future.
* The incorrect alignment needs to be addressed anyway, and currently is causing small losses in speed (about 3%).
---
 src/3rd_party/mio/LICENSE    |   21 +
 src/3rd_party/mio/README.md  |  337 +++++++
 src/3rd_party/mio/mio.hpp    | 1748 ++++++++++++++++++++++++++++++++++
 src/graph/expression_graph.h |   14 +
 src/graph/parameters.h       |    4 +-
 src/translator/scorers.cpp   |    9 +
 src/translator/scorers.h     |    2 +
 src/translator/translator.h  |   27 +
 8 files changed, 2161 insertions(+), 1 deletion(-)
 create mode 100644 src/3rd_party/mio/LICENSE
 create mode 100644 src/3rd_party/mio/README.md
 create mode 100644 src/3rd_party/mio/mio.hpp

diff --git a/src/3rd_party/mio/LICENSE b/src/3rd_party/mio/LICENSE
new file mode 100644
index 000000000..361770744
--- /dev/null
+++ b/src/3rd_party/mio/LICENSE
@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) 2018 https://github.com/mandreyel/
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.
diff --git a/src/3rd_party/mio/README.md b/src/3rd_party/mio/README.md
new file mode 100644
index 000000000..e50b23ffc
--- /dev/null
+++ b/src/3rd_party/mio/README.md
@@ -0,0 +1,337 @@
+# mio
+An easy to use header-only cross-platform C++11 memory mapping library with an MIT license.
+
+mio has been created with the goal to be easily includable (i.e. no dependencies) in any C++ project that needs memory mapped file IO without the need to pull in Boost.
+
+Please feel free to open an issue, I'll try to address any concerns as best I can.
+
+### Why?
+Because memory mapping is the best thing since sliced bread!
+
+More seriously, the primary motivation for writing this library instead of using Boost.Iostreams, was the lack of support for establishing a memory mapping with an already open file handle/descriptor. This is possible with mio.
+
+Furthermore, Boost.Iostreams' solution requires that the user pick offsets exactly at page boundaries, which is cumbersome and error prone. mio, on the other hand, manages this internally, accepting any offset and finding the nearest page boundary.
+
+Albeit a minor nitpick, Boost.Iostreams implements memory mapped file IO with a `std::shared_ptr` to provide shared semantics, even if not needed, and the overhead of the heap allocation may be unnecessary and/or unwanted.
+In mio, there are two classes to cover the two use-cases: one that is move-only (basically a zero-cost abstraction over the system specific mmapping functions), and the other that acts just like its Boost.Iostreams counterpart, with shared semantics.
+
+### How to create a mapping
+NOTE: the file must exist before creating a mapping.
+
+There are three ways to map a file into memory:
+
+- Using the constructor, which throws a `std::system_error` on failure:
+```c++
+mio::mmap_source mmap(path, offset, size_to_map);
+```
+or you can omit the `offset` and `size_to_map` arguments, in which case the
+entire file is mapped:
+```c++
+mio::mmap_source mmap(path);
+```
+
+- Using the factory function:
+```c++
+std::error_code error;
+mio::mmap_source mmap = mio::make_mmap_source(path, offset, size_to_map, error);
+```
+or:
+```c++
+mio::mmap_source mmap = mio::make_mmap_source(path, error);
+```
+
+- Using the `map` member function:
+```c++
+std::error_code error;
+mio::mmap_source mmap;
+mmap.map(path, offset, size_to_map, error);
+```
+or:
+```c++
+mmap.map(path, error);
+```
+**NOTE:** The constructors **require** exceptions to be enabled. If you prefer
+to build your projects with `-fno-exceptions`, you can still use the other ways.
+
+Moreover, in each case, you can provide either some string type for the file's path, or you can use an existing, valid file handle.
+```c++
+#include <sys/types.h>
+#include <sys/stat.h>
+#include <fcntl.h>
+#include <mio/mmap.hpp>
+#include <algorithm>
+
+int main()
+{
+    // NOTE: error handling omitted for brevity.
+    const int fd = open("file.txt", O_RDONLY);
+    mio::mmap_source mmap(fd, 0, mio::map_entire_file);
+    // ...
+}
+```
+However, mio does not check whether the provided file descriptor has the same access permissions as the desired mapping, so the mapping may fail. Such errors are reported via the `std::error_code` out parameter that is passed to the mapping function.
+
+**WINDOWS USERS**: This library *does* support the use of wide character types
+for functions where character strings are expected (e.g. path parameters).
+
+### Example
+
+```c++
+#include <mio/mmap.hpp>
+#include <system_error> // for std::error_code
+#include <cstdio> // for std::printf
+#include <cassert>
+#include <algorithm>
+#include <fstream>
+
+int handle_error(const std::error_code& error);
+void allocate_file(const std::string& path, const int size);
+
+int main()
+{
+    const auto path = "file.txt";
+
+    // NOTE: mio does *not* create the file for you if it doesn't exist! You
+    // must ensure that the file exists before establishing a mapping. It
+    // must also be non-empty. So for illustrative purposes the file is
+    // created now.
+    allocate_file(path, 155);
+
+    // Read-write memory map the whole file by using `map_entire_file` where the
+    // length of the mapping is otherwise expected, with the factory method.
+    std::error_code error;
+    mio::mmap_sink rw_mmap = mio::make_mmap_sink(
+            path, 0, mio::map_entire_file, error);
+    if (error) { return handle_error(error); }
+
+    // You can use any iterator based function.
+    std::fill(rw_mmap.begin(), rw_mmap.end(), 'a');
+
+    // Or manually iterate through the mapped region just as if it were any other 
+    // container, and change each byte's value (since this is a read-write mapping).
+    for (auto& b : rw_mmap) {
+        b += 10;
+    }
+
+    // Or just change one value with the subscript operator.
+    const int answer_index = rw_mmap.size() / 2;
+    rw_mmap[answer_index] = 42;
+
+    // Don't forget to flush changes to disk before unmapping. However, if
+    // `rw_mmap` were to go out of scope at this point, the destructor would also
+    // automatically invoke `sync` before `unmap`.
+    rw_mmap.sync(error);
+    if (error) { return handle_error(error); }
+
+    // We can then remove the mapping, after which rw_mmap will be in a default
+    // constructed state, i.e. this and the above call to `sync` have the same
+    // effect as if the destructor had been invoked.
+    rw_mmap.unmap();
+
+    // Now create the same mapping, but in read-only mode. Note that calling the
+    // overload without the offset and file length parameters maps the entire
+    // file.
+    mio::mmap_source ro_mmap;
+    ro_mmap.map(path, error);
+    if (error) { return handle_error(error); }
+
+    const int the_answer_to_everything = ro_mmap[answer_index];
+    assert(the_answer_to_everything == 42);
+}
+
+int handle_error(const std::error_code& error)
+{
+    const auto& errmsg = error.message();
+    std::printf("error mapping file: %s, exiting...\n", errmsg.c_str());
+    return error.value();
+}
+
+void allocate_file(const std::string& path, const int size)
+{
+    std::ofstream file(path);
+    std::string s(size, '0');
+    file << s;
+}
+```
+
+`mio::basic_mmap` is move-only, but if multiple copies to the same mapping are needed, use `mio::basic_shared_mmap` which has `std::shared_ptr` semantics and has the same interface as `mio::basic_mmap`.
+```c++
+#include <mio/shared_mmap.hpp>
+
+mio::shared_mmap_source shared_mmap1("path", offset, size_to_map);
+mio::shared_mmap_source shared_mmap2(std::move(mmap1)); // or use operator=
+mio::shared_mmap_source shared_mmap3(std::make_shared<mio::mmap_source>(mmap1)); // or use operator=
+mio::shared_mmap_source shared_mmap4;
+shared_mmap4.map("path", offset, size_to_map, error);
+```
+
+It's possible to define the type of a byte (which has to be the same width as `char`), though aliases for the most common ones are provided by default:
+```c++
+using mmap_source = basic_mmap_source<char>;
+using ummap_source = basic_mmap_source<unsigned char>;
+
+using mmap_sink = basic_mmap_sink<char>;
+using ummap_sink = basic_mmap_sink<unsigned char>;
+```
+But it may be useful to define your own types, say when using the new `std::byte` type in C++17:
+```c++
+using mmap_source = mio::basic_mmap_source<std::byte>;
+using mmap_sink = mio::basic_mmap_sink<std::byte>;
+```
+
+Though generally not needed, since mio maps users requested offsets to page boundaries, you can query the underlying system's page allocation granularity by invoking `mio::page_size()`, which is located in `mio/page.hpp`.
+
+### Single Header File 
+Mio can be added to your project as a single header file simply by including `\single_include\mio\mio.hpp`. Single header files can be regenerated at any time by running the `amalgamate.py` script within `\third_party`.  
+```
+python amalgamate.py -c config.json -s ../include
+```
+
+## CMake
+As a header-only library, mio has no compiled components. Nevertheless, a [CMake](https://cmake.org/overview/) build system is provided to allow easy testing, installation, and subproject composition on many platforms and operating systems.
+
+### Testing
+Mio is distributed with a small suite of tests and examples.
+When mio is configured as the highest level CMake project, this suite of executables is built by default.
+Mio's test executables are integrated with the CMake test driver program, [CTest](https://cmake.org/cmake/help/latest/manual/ctest.1.html).
+
+CMake supports a number of backends for compilation and linking.
+
+To use a static configuration build tool, such as GNU Make or Ninja:
+
+```sh
+cd <mio source directory>
+mkdir build
+cd build
+
+# Configure the build
+cmake -D CMAKE_BUILD_TYPE=<Debug | Release> \
+      -G <"Unix Makefiles" | "Ninja"> ..
+
+# build the tests
+< make | ninja | cmake --build . >
+
+# run the tests
+< make test | ninja test | cmake --build . --target test | ctest >
+```
+
+To use a dynamic configuration build tool, such as Visual Studio or Xcode:
+
+```sh
+cd <mio source directory>
+mkdir build
+cd build
+
+# Configure the build
+cmake -G <"Visual Studio 14 2015 Win64" | "Xcode"> ..
+
+# build the tests
+cmake --build . --config <Debug | Release>
+
+# run the tests via ctest...
+ctest --build-config <Debug | Release>
+
+# ... or via CMake build tool mode...
+cmake --build . --config <Debug | Release> --target test
+```
+
+Of course the **build** and **test** steps can also be executed via the **all** and **test** targets, respectively, from within the IDE after opening the project file generated during the configuration step.
+
+Mio's testing is also configured to operate as a client to the [CDash](https://www.cdash.org/) software quality dashboard application. Please see the [Kitware documentation](https://cmake.org/cmake/help/latest/manual/ctest.1.html#dashboard-client) for more information on this mode of operation.
+
+### Installation
+
+Mio's build system provides an installation target and support for downstream consumption via CMake's [`find_package`](https://cmake.org/cmake/help/v3.0/command/find_package.html) intrinsic function.
+CMake allows installation to an arbitrary location, which may be specified by defining `CMAKE_INSTALL_PREFIX` at configure time.
+In the absense of a user specification, CMake will install mio to conventional location based on the platform operating system.
+
+To use a static configuration build tool, such as GNU Make or Ninja:
+
+```sh
+cd <mio source directory>
+mkdir build
+cd build
+
+# Configure the build
+cmake [-D CMAKE_INSTALL_PREFIX="path/to/installation"] \
+      [-D BUILD_TESTING=False]                         \
+      -D CMAKE_BUILD_TYPE=Release                      \
+      -G <"Unix Makefiles" | "Ninja"> ..
+
+# install mio
+<make install | ninja install | cmake --build . --target install>
+```
+
+To use a dynamic configuration build tool, such as Visual Studio or Xcode:
+
+```sh
+cd <mio source directory>
+mkdir build
+cd build
+
+# Configure the project
+cmake [-D CMAKE_INSTALL_PREFIX="path/to/installation"] \
+      [-D BUILD_TESTING=False]                         \
+      -G <"Visual Studio 14 2015 Win64" | "Xcode"> ..
+
+# install mio
+cmake --build . --config Release --target install
+```
+
+Note that the last command of the installation sequence may require administrator privileges (e.g. `sudo`) if the installation root directory lies outside your home directory.
+
+This installation
++ copies the mio header files to the `include/mio` subdirectory of the installation root
++ generates and copies several CMake configuration files to the `share/cmake/mio` subdirectory of the installation root
+
+This latter step allows downstream CMake projects to consume mio via `find_package`, e.g.
+
+```cmake
+find_package( mio REQUIRED )
+target_link_libraries( MyTarget PUBLIC mio::mio )
+```
+
+**WINDOWS USERS**: The `mio::mio` target `#define`s `WIN32_LEAN_AND_MEAN` and `NOMINMAX`. The former ensures the imported surface area of the Win API is minimal, and the latter disables Windows' `min` and `max` macros so they don't intefere with `std::min` and `std::max`. Because *mio* is a header only library, these defintions will leak into downstream CMake builds. If their presence is causing problems with your build then you can use the alternative `mio::mio_full_winapi` target, which adds none of these defintions.
+
+If mio was installed to a non-conventional location, it may be necessary for downstream projects to specify the mio installation root directory via either
+
++ the `CMAKE_PREFIX_PATH` configuration option,
++ the `CMAKE_PREFIX_PATH` environment variable, or
++ `mio_DIR` environment variable.
+
+Please see the [Kitware documentation](https://cmake.org/cmake/help/v3.0/command/find_package.html) for more information.
+
+In addition, mio supports packaged relocatable installations via [CPack](https://cmake.org/cmake/help/latest/manual/cpack.1.html).
+Following configuration, from the build directory, invoke cpack as follows to generate a packaged installation:
+
+```sh
+cpack -G <generator name> -C Release
+```
+
+The list of supported generators varies from platform to platform. See the output of `cpack --help` for a complete list of supported generators on your platform.
+
+### Subproject Composition
+To use mio as a subproject, copy the mio repository to your project's dependencies/externals folder.
+If your project is version controlled using git, a git submodule or git subtree can be used to syncronize with the updstream repository.
+The [use](https://services.github.com/on-demand/downloads/submodule-vs-subtree-cheat-sheet/) and [relative advantages](https://andrey.nering.com.br/2016/git-submodules-vs-subtrees/) of these git facilities is beyond the scope of this document, but in brief, each may be established as follows:
+
+```sh
+# via git submodule
+cd <my project's dependencies directory>
+git submodule add -b master https://github.com/mandreyel/mio.git
+
+# via git subtree
+cd <my project's root directory>
+git subtree add --prefix <path/to/dependencies>/mio       \
+    https://github.com/mandreyel/mio.git master --squash
+```
+
+Given a mio subdirectory in a project, simply add the following lines to your project's to add mio include directories to your target's include path.
+
+```cmake
+add_subdirectory( path/to/mio/ )
+target_link_libraries( MyTarget PUBLIC <mio::mio | mio> )
+```
+
+Note that, as a subproject, mio's tests and examples will not be built and CPack integration is deferred to the host project.
+
diff --git a/src/3rd_party/mio/mio.hpp b/src/3rd_party/mio/mio.hpp
new file mode 100644
index 000000000..b4b8cd5e9
--- /dev/null
+++ b/src/3rd_party/mio/mio.hpp
@@ -0,0 +1,1748 @@
+/* Copyright 2017 https://github.com/mandreyel
+ *
+ * Permission is hereby granted, free of charge, to any person obtaining a copy of this
+ * software and associated documentation files (the "Software"), to deal in the Software
+ * without restriction, including without limitation the rights to use, copy, modify,
+ * merge, publish, distribute, sublicense, and/or sell copies of the Software, and to
+ * permit persons to whom the Software is furnished to do so, subject to the following
+ * conditions:
+ *
+ * The above copyright notice and this permission notice shall be included in all copies
+ * or substantial portions of the Software.
+ *
+ * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED,
+ * INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A
+ * PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT
+ * HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF
+ * CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE
+ * OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
+ */
+
+#ifndef MIO_MMAP_HEADER
+#define MIO_MMAP_HEADER
+
+// #include "mio/page.hpp"
+/* Copyright 2017 https://github.com/mandreyel
+ *
+ * Permission is hereby granted, free of charge, to any person obtaining a copy of this
+ * software and associated documentation files (the "Software"), to deal in the Software
+ * without restriction, including without limitation the rights to use, copy, modify,
+ * merge, publish, distribute, sublicense, and/or sell copies of the Software, and to
+ * permit persons to whom the Software is furnished to do so, subject to the following
+ * conditions:
+ *
+ * The above copyright notice and this permission notice shall be included in all copies
+ * or substantial portions of the Software.
+ *
+ * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED,
+ * INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A
+ * PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT
+ * HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF
+ * CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE
+ * OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
+ */
+
+#ifndef MIO_PAGE_HEADER
+#define MIO_PAGE_HEADER
+
+#ifdef _WIN32
+# include <windows.h>
+#else
+# include <unistd.h>
+#endif
+
+namespace mio {
+
+/**
+ * This is used by `basic_mmap` to determine whether to create a read-only or
+ * a read-write memory mapping.
+ */
+enum class access_mode
+{
+    read,
+    write
+};
+
+/**
+ * Determines the operating system's page allocation granularity.
+ *
+ * On the first call to this function, it invokes the operating system specific syscall
+ * to determine the page size, caches the value, and returns it. Any subsequent call to
+ * this function serves the cached value, so no further syscalls are made.
+ */
+inline size_t page_size()
+{
+    static const size_t page_size = []
+    {
+#ifdef _WIN32
+        SYSTEM_INFO SystemInfo;
+        GetSystemInfo(&SystemInfo);
+        return SystemInfo.dwAllocationGranularity;
+#else
+        return sysconf(_SC_PAGE_SIZE);
+#endif
+    }();
+    return page_size;
+}
+
+/**
+ * Alligns `offset` to the operating's system page size such that it subtracts the
+ * difference until the nearest page boundary before `offset`, or does nothing if
+ * `offset` is already page aligned.
+ */
+inline size_t make_offset_page_aligned(size_t offset) noexcept
+{
+    const size_t page_size_ = page_size();
+    // Use integer division to round down to the nearest page alignment.
+    return offset / page_size_ * page_size_;
+}
+
+} // namespace mio
+
+#endif // MIO_PAGE_HEADER
+
+
+#include <iterator>
+#include <string>
+#include <system_error>
+#include <cstdint>
+
+#ifdef _WIN32
+# ifndef WIN32_LEAN_AND_MEAN
+#  define WIN32_LEAN_AND_MEAN
+# endif // WIN32_LEAN_AND_MEAN
+# include <windows.h>
+#else // ifdef _WIN32
+# define INVALID_HANDLE_VALUE -1
+#endif // ifdef _WIN32
+
+namespace mio {
+
+// This value may be provided as the `length` parameter to the constructor or
+// `map`, in which case a memory mapping of the entire file is created.
+enum { map_entire_file = 0 };
+
+#ifdef _WIN32
+using file_handle_type = HANDLE;
+#else
+using file_handle_type = int;
+#endif
+
+// This value represents an invalid file handle type. This can be used to
+// determine whether `basic_mmap::file_handle` is valid, for example.
+const static file_handle_type invalid_handle = INVALID_HANDLE_VALUE;
+
+template<access_mode AccessMode, typename ByteT>
+struct basic_mmap
+{
+    using value_type = ByteT;
+    using size_type = size_t;
+    using reference = value_type&;
+    using const_reference = const value_type&;
+    using pointer = value_type*;
+    using const_pointer = const value_type*;
+    using difference_type = std::ptrdiff_t;
+    using iterator = pointer;
+    using const_iterator = const_pointer;
+    using reverse_iterator = std::reverse_iterator<iterator>;
+    using const_reverse_iterator = std::reverse_iterator<const_iterator>;
+    using iterator_category = std::random_access_iterator_tag;
+    using handle_type = file_handle_type;
+
+    static_assert(sizeof(ByteT) == sizeof(char), "ByteT must be the same size as char.");
+
+private:
+    // Points to the first requested byte, and not to the actual start of the mapping.
+    pointer data_ = nullptr;
+
+    // Length--in bytes--requested by user (which may not be the length of the
+    // full mapping) and the length of the full mapping.
+    size_type length_ = 0;
+    size_type mapped_length_ = 0;
+
+    // Letting user map a file using both an existing file handle and a path
+    // introcudes some complexity (see `is_handle_internal_`).
+    // On POSIX, we only need a file handle to create a mapping, while on
+    // Windows systems the file handle is necessary to retrieve a file mapping
+    // handle, but any subsequent operations on the mapped region must be done
+    // through the latter.
+    handle_type file_handle_ = INVALID_HANDLE_VALUE;
+#ifdef _WIN32
+    handle_type file_mapping_handle_ = INVALID_HANDLE_VALUE;
+#endif
+
+    // Letting user map a file using both an existing file handle and a path
+    // introcudes some complexity in that we must not close the file handle if
+    // user provided it, but we must close it if we obtained it using the
+    // provided path. For this reason, this flag is used to determine when to
+    // close `file_handle_`.
+    bool is_handle_internal_;
+
+public:
+    /**
+     * The default constructed mmap object is in a non-mapped state, that is,
+     * any operation that attempts to access nonexistent underlying data will
+     * result in undefined behaviour/segmentation faults.
+     */
+    basic_mmap() = default;
+
+#ifdef __cpp_exceptions
+    /**
+     * The same as invoking the `map` function, except any error that may occur
+     * while establishing the mapping is wrapped in a `std::system_error` and is
+     * thrown.
+     */
+    template<typename String>
+    basic_mmap(const String& path, const size_type offset = 0, const size_type length = map_entire_file)
+    {
+        std::error_code error;
+        map(path, offset, length, error);
+        if(error) { throw std::system_error(error); }
+    }
+
+    /**
+     * The same as invoking the `map` function, except any error that may occur
+     * while establishing the mapping is wrapped in a `std::system_error` and is
+     * thrown.
+     */
+    basic_mmap(const handle_type handle, const size_type offset = 0, const size_type length = map_entire_file)
+    {
+        std::error_code error;
+        map(handle, offset, length, error);
+        if(error) { throw std::system_error(error); }
+    }
+#endif // __cpp_exceptions
+
+    /**
+     * `basic_mmap` has single-ownership semantics, so transferring ownership
+     * may only be accomplished by moving the object.
+     */
+    basic_mmap(const basic_mmap&) = delete;
+    basic_mmap(basic_mmap&&);
+    basic_mmap& operator=(const basic_mmap&) = delete;
+    basic_mmap& operator=(basic_mmap&&);
+
+    /**
+     * If this is a read-write mapping, the destructor invokes sync. Regardless
+     * of the access mode, unmap is invoked as a final step.
+     */
+    ~basic_mmap();
+
+    /**
+     * On UNIX systems 'file_handle' and 'mapping_handle' are the same. On Windows,
+     * however, a mapped region of a file gets its own handle, which is returned by
+     * 'mapping_handle'.
+     */
+    handle_type file_handle() const noexcept { return file_handle_; }
+    handle_type mapping_handle() const noexcept;
+
+    /** Returns whether a valid memory mapping has been created. */
+    bool is_open() const noexcept { return file_handle_ != invalid_handle; }
+
+    /**
+     * Returns true if no mapping was established, that is, conceptually the
+     * same as though the length that was mapped was 0. This function is
+     * provided so that this class has Container semantics.
+     */
+    bool empty() const noexcept { return length() == 0; }
+
+    /** Returns true if a mapping was established. */
+    bool is_mapped() const noexcept;
+
+    /**
+     * `size` and `length` both return the logical length, i.e. the number of bytes
+     * user requested to be mapped, while `mapped_length` returns the actual number of
+     * bytes that were mapped which is a multiple of the underlying operating system's
+     * page allocation granularity.
+     */
+    size_type size() const noexcept { return length(); }
+    size_type length() const noexcept { return length_; }
+    size_type mapped_length() const noexcept { return mapped_length_; }
+
+    /** Returns the offset relative to the start of the mapping. */
+    size_type mapping_offset() const noexcept
+    {
+        return mapped_length_ - length_;
+    }
+
+    /**
+     * Returns a pointer to the first requested byte, or `nullptr` if no memory mapping
+     * exists.
+     */
+    template<
+        access_mode A = AccessMode,
+        typename = typename std::enable_if<A == access_mode::write>::type
+    > pointer data() noexcept { return data_; }
+    const_pointer data() const noexcept { return data_; }
+
+    /**
+     * Returns an iterator to the first requested byte, if a valid memory mapping
+     * exists, otherwise this function call is undefined behaviour.
+     */
+    template<
+        access_mode A = AccessMode,
+        typename = typename std::enable_if<A == access_mode::write>::type
+    > iterator begin() noexcept { return data(); }
+    const_iterator begin() const noexcept { return data(); }
+    const_iterator cbegin() const noexcept { return data(); }
+
+    /**
+     * Returns an iterator one past the last requested byte, if a valid memory mapping
+     * exists, otherwise this function call is undefined behaviour.
+     */
+    template<
+        access_mode A = AccessMode,
+        typename = typename std::enable_if<A == access_mode::write>::type
+    > iterator end() noexcept { return data() + length(); }
+    const_iterator end() const noexcept { return data() + length(); }
+    const_iterator cend() const noexcept { return data() + length(); }
+
+    /**
+     * Returns a reverse iterator to the last memory mapped byte, if a valid
+     * memory mapping exists, otherwise this function call is undefined
+     * behaviour.
+     */
+    template<
+        access_mode A = AccessMode,
+        typename = typename std::enable_if<A == access_mode::write>::type
+    > reverse_iterator rbegin() noexcept { return reverse_iterator(end()); }
+    const_reverse_iterator rbegin() const noexcept
+    { return const_reverse_iterator(end()); }
+    const_reverse_iterator crbegin() const noexcept
+    { return const_reverse_iterator(end()); }
+
+    /**
+     * Returns a reverse iterator past the first mapped byte, if a valid memory
+     * mapping exists, otherwise this function call is undefined behaviour.
+     */
+    template<
+        access_mode A = AccessMode,
+        typename = typename std::enable_if<A == access_mode::write>::type
+    > reverse_iterator rend() noexcept { return reverse_iterator(begin()); }
+    const_reverse_iterator rend() const noexcept
+    { return const_reverse_iterator(begin()); }
+    const_reverse_iterator crend() const noexcept
+    { return const_reverse_iterator(begin()); }
+
+    /**
+     * Returns a reference to the `i`th byte from the first requested byte (as returned
+     * by `data`). If this is invoked when no valid memory mapping has been created
+     * prior to this call, undefined behaviour ensues.
+     */
+    reference operator[](const size_type i) noexcept { return data_[i]; }
+    const_reference operator[](const size_type i) const noexcept { return data_[i]; }
+
+    /**
+     * Establishes a memory mapping with AccessMode. If the mapping is unsuccesful, the
+     * reason is reported via `error` and the object remains in a state as if this
+     * function hadn't been called.
+     *
+     * `path`, which must be a path to an existing file, is used to retrieve a file
+     * handle (which is closed when the object destructs or `unmap` is called), which is
+     * then used to memory map the requested region. Upon failure, `error` is set to
+     * indicate the reason and the object remains in an unmapped state.
+     *
+     * `offset` is the number of bytes, relative to the start of the file, where the
+     * mapping should begin. When specifying it, there is no need to worry about
+     * providing a value that is aligned with the operating system's page allocation
+     * granularity. This is adjusted by the implementation such that the first requested
+     * byte (as returned by `data` or `begin`), so long as `offset` is valid, will be at
+     * `offset` from the start of the file.
+     *
+     * `length` is the number of bytes to map. It may be `map_entire_file`, in which
+     * case a mapping of the entire file is created.
+     */
+    template<typename String>
+    void map(const String& path, const size_type offset,
+            const size_type length, std::error_code& error);
+
+    /**
+     * Establishes a memory mapping with AccessMode. If the mapping is unsuccesful, the
+     * reason is reported via `error` and the object remains in a state as if this
+     * function hadn't been called.
+     *
+     * `path`, which must be a path to an existing file, is used to retrieve a file
+     * handle (which is closed when the object destructs or `unmap` is called), which is
+     * then used to memory map the requested region. Upon failure, `error` is set to
+     * indicate the reason and the object remains in an unmapped state.
+     * 
+     * The entire file is mapped.
+     */
+    template<typename String>
+    void map(const String& path, std::error_code& error)
+    {
+        map(path, 0, map_entire_file, error);
+    }
+
+    /**
+     * Establishes a memory mapping with AccessMode. If the mapping is
+     * unsuccesful, the reason is reported via `error` and the object remains in
+     * a state as if this function hadn't been called.
+     *
+     * `handle`, which must be a valid file handle, which is used to memory map the
+     * requested region. Upon failure, `error` is set to indicate the reason and the
+     * object remains in an unmapped state.
+     *
+     * `offset` is the number of bytes, relative to the start of the file, where the
+     * mapping should begin. When specifying it, there is no need to worry about
+     * providing a value that is aligned with the operating system's page allocation
+     * granularity. This is adjusted by the implementation such that the first requested
+     * byte (as returned by `data` or `begin`), so long as `offset` is valid, will be at
+     * `offset` from the start of the file.
+     *
+     * `length` is the number of bytes to map. It may be `map_entire_file`, in which
+     * case a mapping of the entire file is created.
+     */
+    void map(const handle_type handle, const size_type offset,
+            const size_type length, std::error_code& error);
+
+    /**
+     * Establishes a memory mapping with AccessMode. If the mapping is
+     * unsuccesful, the reason is reported via `error` and the object remains in
+     * a state as if this function hadn't been called.
+     *
+     * `handle`, which must be a valid file handle, which is used to memory map the
+     * requested region. Upon failure, `error` is set to indicate the reason and the
+     * object remains in an unmapped state.
+     * 
+     * The entire file is mapped.
+     */
+    void map(const handle_type handle, std::error_code& error)
+    {
+        map(handle, 0, map_entire_file, error);
+    }
+
+    /**
+     * If a valid memory mapping has been created prior to this call, this call
+     * instructs the kernel to unmap the memory region and disassociate this object
+     * from the file.
+     *
+     * The file handle associated with the file that is mapped is only closed if the
+     * mapping was created using a file path. If, on the other hand, an existing
+     * file handle was used to create the mapping, the file handle is not closed.
+     */
+    void unmap();
+
+    void swap(basic_mmap& other);
+
+    /** Flushes the memory mapped page to disk. Errors are reported via `error`. */
+    template<access_mode A = AccessMode>
+    typename std::enable_if<A == access_mode::write, void>::type
+    sync(std::error_code& error);
+
+    /**
+     * All operators compare the address of the first byte and size of the two mapped
+     * regions.
+     */
+
+private:
+    template<
+        access_mode A = AccessMode,
+        typename = typename std::enable_if<A == access_mode::write>::type
+    > pointer get_mapping_start() noexcept
+    {
+        return !data() ? nullptr : data() - mapping_offset();
+    }
+
+    const_pointer get_mapping_start() const noexcept
+    {
+        return !data() ? nullptr : data() - mapping_offset();
+    }
+
+    /**
+     * The destructor syncs changes to disk if `AccessMode` is `write`, but not
+     * if it's `read`, but since the destructor cannot be templated, we need to
+     * do SFINAE in a dedicated function, where one syncs and the other is a noop.
+     */
+    template<access_mode A = AccessMode>
+    typename std::enable_if<A == access_mode::write, void>::type
+    conditional_sync();
+    template<access_mode A = AccessMode>
+    typename std::enable_if<A == access_mode::read, void>::type conditional_sync();
+};
+
+template<access_mode AccessMode, typename ByteT>
+bool operator==(const basic_mmap<AccessMode, ByteT>& a,
+        const basic_mmap<AccessMode, ByteT>& b);
+
+template<access_mode AccessMode, typename ByteT>
+bool operator!=(const basic_mmap<AccessMode, ByteT>& a,
+        const basic_mmap<AccessMode, ByteT>& b);
+
+template<access_mode AccessMode, typename ByteT>
+bool operator<(const basic_mmap<AccessMode, ByteT>& a,
+        const basic_mmap<AccessMode, ByteT>& b);
+
+template<access_mode AccessMode, typename ByteT>
+bool operator<=(const basic_mmap<AccessMode, ByteT>& a,
+        const basic_mmap<AccessMode, ByteT>& b);
+
+template<access_mode AccessMode, typename ByteT>
+bool operator>(const basic_mmap<AccessMode, ByteT>& a,
+        const basic_mmap<AccessMode, ByteT>& b);
+
+template<access_mode AccessMode, typename ByteT>
+bool operator>=(const basic_mmap<AccessMode, ByteT>& a,
+        const basic_mmap<AccessMode, ByteT>& b);
+
+/**
+ * This is the basis for all read-only mmap objects and should be preferred over
+ * directly using `basic_mmap`.
+ */
+template<typename ByteT>
+using basic_mmap_source = basic_mmap<access_mode::read, ByteT>;
+
+/**
+ * This is the basis for all read-write mmap objects and should be preferred over
+ * directly using `basic_mmap`.
+ */
+template<typename ByteT>
+using basic_mmap_sink = basic_mmap<access_mode::write, ByteT>;
+
+/**
+ * These aliases cover the most common use cases, both representing a raw byte stream
+ * (either with a char or an unsigned char/uint8_t).
+ */
+using mmap_source = basic_mmap_source<char>;
+using ummap_source = basic_mmap_source<unsigned char>;
+
+using mmap_sink = basic_mmap_sink<char>;
+using ummap_sink = basic_mmap_sink<unsigned char>;
+
+/**
+ * Convenience factory method that constructs a mapping for any `basic_mmap` or
+ * `basic_mmap` type.
+ */
+template<
+    typename MMap,
+    typename MappingToken
+> MMap make_mmap(const MappingToken& token,
+        int64_t offset, int64_t length, std::error_code& error)
+{
+    MMap mmap;
+    mmap.map(token, offset, length, error);
+    return mmap;
+}
+
+/**
+ * Convenience factory method.
+ *
+ * MappingToken may be a String (`std::string`, `std::string_view`, `const char*`,
+ * `std::filesystem::path`, `std::vector<char>`, or similar), or a
+ * `mmap_source::handle_type`.
+ */
+template<typename MappingToken>
+mmap_source make_mmap_source(const MappingToken& token, mmap_source::size_type offset,
+        mmap_source::size_type length, std::error_code& error)
+{
+    return make_mmap<mmap_source>(token, offset, length, error);
+}
+
+template<typename MappingToken>
+mmap_source make_mmap_source(const MappingToken& token, std::error_code& error)
+{
+    return make_mmap_source(token, 0, map_entire_file, error);
+}
+
+/**
+ * Convenience factory method.
+ *
+ * MappingToken may be a String (`std::string`, `std::string_view`, `const char*`,
+ * `std::filesystem::path`, `std::vector<char>`, or similar), or a
+ * `mmap_sink::handle_type`.
+ */
+template<typename MappingToken>
+mmap_sink make_mmap_sink(const MappingToken& token, mmap_sink::size_type offset,
+        mmap_sink::size_type length, std::error_code& error)
+{
+    return make_mmap<mmap_sink>(token, offset, length, error);
+}
+
+template<typename MappingToken>
+mmap_sink make_mmap_sink(const MappingToken& token, std::error_code& error)
+{
+    return make_mmap_sink(token, 0, map_entire_file, error);
+}
+
+} // namespace mio
+
+// #include "detail/mmap.ipp"
+/* Copyright 2017 https://github.com/mandreyel
+ *
+ * Permission is hereby granted, free of charge, to any person obtaining a copy of this
+ * software and associated documentation files (the "Software"), to deal in the Software
+ * without restriction, including without limitation the rights to use, copy, modify,
+ * merge, publish, distribute, sublicense, and/or sell copies of the Software, and to
+ * permit persons to whom the Software is furnished to do so, subject to the following
+ * conditions:
+ *
+ * The above copyright notice and this permission notice shall be included in all copies
+ * or substantial portions of the Software.
+ *
+ * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED,
+ * INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A
+ * PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT
+ * HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF
+ * CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE
+ * OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
+ */
+
+#ifndef MIO_BASIC_MMAP_IMPL
+#define MIO_BASIC_MMAP_IMPL
+
+// #include "mio/mmap.hpp"
+
+// #include "mio/page.hpp"
+
+// #include "mio/detail/string_util.hpp"
+/* Copyright 2017 https://github.com/mandreyel
+ *
+ * Permission is hereby granted, free of charge, to any person obtaining a copy of this
+ * software and associated documentation files (the "Software"), to deal in the Software
+ * without restriction, including without limitation the rights to use, copy, modify,
+ * merge, publish, distribute, sublicense, and/or sell copies of the Software, and to
+ * permit persons to whom the Software is furnished to do so, subject to the following
+ * conditions:
+ *
+ * The above copyright notice and this permission notice shall be included in all copies
+ * or substantial portions of the Software.
+ *
+ * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED,
+ * INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A
+ * PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT
+ * HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF
+ * CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE
+ * OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
+ */
+
+#ifndef MIO_STRING_UTIL_HEADER
+#define MIO_STRING_UTIL_HEADER
+
+#include <type_traits>
+
+namespace mio {
+namespace detail {
+
+template<
+    typename S,
+    typename C = typename std::decay<S>::type,
+    typename = decltype(std::declval<C>().data()),
+    typename = typename std::enable_if<
+        std::is_same<typename C::value_type, char>::value
+#ifdef _WIN32
+        || std::is_same<typename C::value_type, wchar_t>::value
+#endif
+    >::type
+> struct char_type_helper {
+    using type = typename C::value_type;
+};
+
+template<class T>
+struct char_type {
+    using type = typename char_type_helper<T>::type;
+};
+
+// TODO: can we avoid this brute force approach?
+template<>
+struct char_type<char*> {
+    using type = char;
+};
+
+template<>
+struct char_type<const char*> {
+    using type = char;
+};
+
+template<size_t N>
+struct char_type<char[N]> {
+    using type = char;
+};
+
+template<size_t N>
+struct char_type<const char[N]> {
+    using type = char;
+};
+
+#ifdef _WIN32
+template<>
+struct char_type<wchar_t*> {
+    using type = wchar_t;
+};
+
+template<>
+struct char_type<const wchar_t*> {
+    using type = wchar_t;
+};
+
+template<size_t N>
+struct char_type<wchar_t[N]> {
+    using type = wchar_t;
+};
+
+template<size_t N>
+struct char_type<const wchar_t[N]> {
+    using type = wchar_t;
+};
+#endif // _WIN32
+
+template<typename CharT, typename S>
+struct is_c_str_helper
+{
+    static constexpr bool value = std::is_same<
+        CharT*,
+        // TODO: I'm so sorry for this... Can this be made cleaner?
+        typename std::add_pointer<
+            typename std::remove_cv<
+                typename std::remove_pointer<
+                    typename std::decay<
+                        S
+                    >::type
+                >::type
+            >::type
+        >::type
+    >::value;
+};
+
+template<typename S>
+struct is_c_str
+{
+    static constexpr bool value = is_c_str_helper<char, S>::value;
+};
+
+#ifdef _WIN32
+template<typename S>
+struct is_c_wstr
+{
+    static constexpr bool value = is_c_str_helper<wchar_t, S>::value;
+};
+#endif // _WIN32
+
+template<typename S>
+struct is_c_str_or_c_wstr
+{
+    static constexpr bool value = is_c_str<S>::value
+#ifdef _WIN32
+        || is_c_wstr<S>::value
+#endif
+        ;
+};
+
+template<
+    typename String,
+    typename = decltype(std::declval<String>().data()),
+    typename = typename std::enable_if<!is_c_str_or_c_wstr<String>::value>::type
+> const typename char_type<String>::type* c_str(const String& path)
+{
+    return path.data();
+}
+
+template<
+    typename String,
+    typename = decltype(std::declval<String>().empty()),
+    typename = typename std::enable_if<!is_c_str_or_c_wstr<String>::value>::type
+> bool empty(const String& path)
+{
+    return path.empty();
+}
+
+template<
+    typename String,
+    typename = typename std::enable_if<is_c_str_or_c_wstr<String>::value>::type
+> const typename char_type<String>::type* c_str(String path)
+{
+    return path;
+}
+
+template<
+    typename String,
+    typename = typename std::enable_if<is_c_str_or_c_wstr<String>::value>::type
+> bool empty(String path)
+{
+    return !path || (*path == 0);
+}
+
+} // namespace detail
+} // namespace mio
+
+#endif // MIO_STRING_UTIL_HEADER
+
+
+#include <algorithm>
+
+#ifndef _WIN32
+# include <unistd.h>
+# include <fcntl.h>
+# include <sys/mman.h>
+# include <sys/stat.h>
+#endif
+
+namespace mio {
+namespace detail {
+
+#ifdef _WIN32
+namespace win {
+
+/** Returns the 4 upper bytes of an 8-byte integer. */
+inline DWORD int64_high(int64_t n) noexcept
+{
+    return n >> 32;
+}
+
+/** Returns the 4 lower bytes of an 8-byte integer. */
+inline DWORD int64_low(int64_t n) noexcept
+{
+    return n & 0xffffffff;
+}
+
+template<
+    typename String,
+    typename = typename std::enable_if<
+        std::is_same<typename char_type<String>::type, char>::value
+    >::type
+> file_handle_type open_file_helper(const String& path, const access_mode mode)
+{
+    return ::CreateFileA(c_str(path),
+            mode == access_mode::read ? GENERIC_READ : GENERIC_READ | GENERIC_WRITE,
+            FILE_SHARE_READ | FILE_SHARE_WRITE,
+            0,
+            OPEN_EXISTING,
+            FILE_ATTRIBUTE_NORMAL,
+            0);
+}
+
+template<typename String>
+typename std::enable_if<
+    std::is_same<typename char_type<String>::type, wchar_t>::value,
+    file_handle_type
+>::type open_file_helper(const String& path, const access_mode mode)
+{
+    return ::CreateFileW(c_str(path),
+            mode == access_mode::read ? GENERIC_READ : GENERIC_READ | GENERIC_WRITE,
+            FILE_SHARE_READ | FILE_SHARE_WRITE,
+            0,
+            OPEN_EXISTING,
+            FILE_ATTRIBUTE_NORMAL,
+            0);
+}
+
+} // win
+#endif // _WIN32
+
+/**
+ * Returns the last platform specific system error (errno on POSIX and
+ * GetLastError on Win) as a `std::error_code`.
+ */
+inline std::error_code last_error() noexcept
+{
+    std::error_code error;
+#ifdef _WIN32
+    error.assign(GetLastError(), std::system_category());
+#else
+    error.assign(errno, std::system_category());
+#endif
+    return error;
+}
+
+template<typename String>
+file_handle_type open_file(const String& path, const access_mode mode,
+        std::error_code& error)
+{
+    error.clear();
+    if(detail::empty(path))
+    {
+        error = std::make_error_code(std::errc::invalid_argument);
+        return invalid_handle;
+    }
+#ifdef _WIN32
+    const auto handle = win::open_file_helper(path, mode);
+#else // POSIX
+    const auto handle = ::open(c_str(path),
+            mode == access_mode::read ? O_RDONLY : O_RDWR);
+#endif
+    if(handle == invalid_handle)
+    {
+        error = detail::last_error();
+    }
+    return handle;
+}
+
+inline size_t query_file_size(file_handle_type handle, std::error_code& error)
+{
+    error.clear();
+#ifdef _WIN32
+    LARGE_INTEGER file_size;
+    if(::GetFileSizeEx(handle, &file_size) == 0)
+    {
+        error = detail::last_error();
+        return 0;
+    }
+	return static_cast<int64_t>(file_size.QuadPart);
+#else // POSIX
+    struct stat sbuf;
+    if(::fstat(handle, &sbuf) == -1)
+    {
+        error = detail::last_error();
+        return 0;
+    }
+    return sbuf.st_size;
+#endif
+}
+
+struct mmap_context
+{
+    char* data;
+    int64_t length;
+    int64_t mapped_length;
+#ifdef _WIN32
+    file_handle_type file_mapping_handle;
+#endif
+};
+
+inline mmap_context memory_map(const file_handle_type file_handle, const int64_t offset,
+    const int64_t length, const access_mode mode, std::error_code& error)
+{
+    const int64_t aligned_offset = make_offset_page_aligned(offset);
+    const int64_t length_to_map = offset - aligned_offset + length;
+#ifdef _WIN32
+    const int64_t max_file_size = offset + length;
+    const auto file_mapping_handle = ::CreateFileMapping(
+            file_handle,
+            0,
+            mode == access_mode::read ? PAGE_READONLY : PAGE_READWRITE,
+            win::int64_high(max_file_size),
+            win::int64_low(max_file_size),
+            0);
+    if(file_mapping_handle == invalid_handle)
+    {
+        error = detail::last_error();
+        return {};
+    }
+    char* mapping_start = static_cast<char*>(::MapViewOfFile(
+            file_mapping_handle,
+            mode == access_mode::read ? FILE_MAP_READ : FILE_MAP_WRITE,
+            win::int64_high(aligned_offset),
+            win::int64_low(aligned_offset),
+            length_to_map));
+    if(mapping_start == nullptr)
+    {
+        error = detail::last_error();
+        return {};
+    }
+#else // POSIX
+    char* mapping_start = static_cast<char*>(::mmap(
+            0, // Don't give hint as to where to map.
+            length_to_map,
+            mode == access_mode::read ? PROT_READ : PROT_WRITE,
+            MAP_SHARED,
+            file_handle,
+            aligned_offset));
+    if(mapping_start == MAP_FAILED)
+    {
+        error = detail::last_error();
+        return {};
+    }
+#endif
+    mmap_context ctx;
+    ctx.data = mapping_start + offset - aligned_offset;
+    ctx.length = length;
+    ctx.mapped_length = length_to_map;
+#ifdef _WIN32
+    ctx.file_mapping_handle = file_mapping_handle;
+#endif
+    return ctx;
+}
+
+} // namespace detail
+
+// -- basic_mmap --
+
+template<access_mode AccessMode, typename ByteT>
+basic_mmap<AccessMode, ByteT>::~basic_mmap()
+{
+    conditional_sync();
+    unmap();
+}
+
+template<access_mode AccessMode, typename ByteT>
+basic_mmap<AccessMode, ByteT>::basic_mmap(basic_mmap&& other)
+    : data_(std::move(other.data_))
+    , length_(std::move(other.length_))
+    , mapped_length_(std::move(other.mapped_length_))
+    , file_handle_(std::move(other.file_handle_))
+#ifdef _WIN32
+    , file_mapping_handle_(std::move(other.file_mapping_handle_))
+#endif
+    , is_handle_internal_(std::move(other.is_handle_internal_))
+{
+    other.data_ = nullptr;
+    other.length_ = other.mapped_length_ = 0;
+    other.file_handle_ = invalid_handle;
+#ifdef _WIN32
+    other.file_mapping_handle_ = invalid_handle;
+#endif
+}
+
+template<access_mode AccessMode, typename ByteT>
+basic_mmap<AccessMode, ByteT>&
+basic_mmap<AccessMode, ByteT>::operator=(basic_mmap&& other)
+{
+    if(this != &other)
+    {
+        // First the existing mapping needs to be removed.
+        unmap();
+        data_ = std::move(other.data_);
+        length_ = std::move(other.length_);
+        mapped_length_ = std::move(other.mapped_length_);
+        file_handle_ = std::move(other.file_handle_);
+#ifdef _WIN32
+        file_mapping_handle_ = std::move(other.file_mapping_handle_);
+#endif
+        is_handle_internal_ = std::move(other.is_handle_internal_);
+
+        // The moved from basic_mmap's fields need to be reset, because
+        // otherwise other's destructor will unmap the same mapping that was
+        // just moved into this.
+        other.data_ = nullptr;
+        other.length_ = other.mapped_length_ = 0;
+        other.file_handle_ = invalid_handle;
+#ifdef _WIN32
+        other.file_mapping_handle_ = invalid_handle;
+#endif
+        other.is_handle_internal_ = false;
+    }
+    return *this;
+}
+
+template<access_mode AccessMode, typename ByteT>
+typename basic_mmap<AccessMode, ByteT>::handle_type
+basic_mmap<AccessMode, ByteT>::mapping_handle() const noexcept
+{
+#ifdef _WIN32
+    return file_mapping_handle_;
+#else
+    return file_handle_;
+#endif
+}
+
+template<access_mode AccessMode, typename ByteT>
+template<typename String>
+void basic_mmap<AccessMode, ByteT>::map(const String& path, const size_type offset,
+        const size_type length, std::error_code& error)
+{
+    error.clear();
+    if(detail::empty(path))
+    {
+        error = std::make_error_code(std::errc::invalid_argument);
+        return;
+    }
+    const auto handle = detail::open_file(path, AccessMode, error);
+    if(error)
+    {
+        return;
+    }
+
+    map(handle, offset, length, error);
+    // This MUST be after the call to map, as that sets this to true.
+    if(!error)
+    {
+        is_handle_internal_ = true;
+    }
+}
+
+template<access_mode AccessMode, typename ByteT>
+void basic_mmap<AccessMode, ByteT>::map(const handle_type handle,
+        const size_type offset, const size_type length, std::error_code& error)
+{
+    error.clear();
+    if(handle == invalid_handle)
+    {
+        error = std::make_error_code(std::errc::bad_file_descriptor);
+        return;
+    }
+
+    const auto file_size = detail::query_file_size(handle, error);
+    if(error)
+    {
+        return;
+    }
+
+    if(offset + length > file_size)
+    {
+        error = std::make_error_code(std::errc::invalid_argument);
+        return;
+    }
+
+    const auto ctx = detail::memory_map(handle, offset,
+            length == map_entire_file ? (file_size - offset) : length,
+            AccessMode, error);
+    if(!error)
+    {
+        // We must unmap the previous mapping that may have existed prior to this call.
+        // Note that this must only be invoked after a new mapping has been created in
+        // order to provide the strong guarantee that, should the new mapping fail, the
+        // `map` function leaves this instance in a state as though the function had
+        // never been invoked.
+        unmap();
+        file_handle_ = handle;
+        is_handle_internal_ = false;
+        data_ = reinterpret_cast<pointer>(ctx.data);
+        length_ = ctx.length;
+        mapped_length_ = ctx.mapped_length;
+#ifdef _WIN32
+        file_mapping_handle_ = ctx.file_mapping_handle;
+#endif
+    }
+}
+
+template<access_mode AccessMode, typename ByteT>
+template<access_mode A>
+typename std::enable_if<A == access_mode::write, void>::type
+basic_mmap<AccessMode, ByteT>::sync(std::error_code& error)
+{
+    error.clear();
+    if(!is_open())
+    {
+        error = std::make_error_code(std::errc::bad_file_descriptor);
+        return;
+    }
+
+    if(data())
+    {
+#ifdef _WIN32
+        if(::FlushViewOfFile(get_mapping_start(), mapped_length_) == 0
+           || ::FlushFileBuffers(file_handle_) == 0)
+#else // POSIX
+        if(::msync(get_mapping_start(), mapped_length_, MS_SYNC) != 0)
+#endif
+        {
+            error = detail::last_error();
+            return;
+        }
+    }
+#ifdef _WIN32
+    if(::FlushFileBuffers(file_handle_) == 0)
+    {
+        error = detail::last_error();
+    }
+#endif
+}
+
+template<access_mode AccessMode, typename ByteT>
+void basic_mmap<AccessMode, ByteT>::unmap()
+{
+    if(!is_open()) { return; }
+    // TODO do we care about errors here?
+#ifdef _WIN32
+    if(is_mapped())
+    {
+        ::UnmapViewOfFile(get_mapping_start());
+        ::CloseHandle(file_mapping_handle_);
+    }
+#else // POSIX
+    if(data_) { ::munmap(const_cast<pointer>(get_mapping_start()), mapped_length_); }
+#endif
+
+    // If `file_handle_` was obtained by our opening it (when map is called with
+    // a path, rather than an existing file handle), we need to close it,
+    // otherwise it must not be closed as it may still be used outside this
+    // instance.
+    if(is_handle_internal_)
+    {
+#ifdef _WIN32
+        ::CloseHandle(file_handle_);
+#else // POSIX
+        ::close(file_handle_);
+#endif
+    }
+
+    // Reset fields to their default values.
+    data_ = nullptr;
+    length_ = mapped_length_ = 0;
+    file_handle_ = invalid_handle;
+#ifdef _WIN32
+    file_mapping_handle_ = invalid_handle;
+#endif
+}
+
+template<access_mode AccessMode, typename ByteT>
+bool basic_mmap<AccessMode, ByteT>::is_mapped() const noexcept
+{
+#ifdef _WIN32
+    return file_mapping_handle_ != invalid_handle;
+#else // POSIX
+    return is_open();
+#endif
+}
+
+template<access_mode AccessMode, typename ByteT>
+void basic_mmap<AccessMode, ByteT>::swap(basic_mmap& other)
+{
+    if(this != &other)
+    {
+        using std::swap;
+        swap(data_, other.data_); 
+        swap(file_handle_, other.file_handle_); 
+#ifdef _WIN32
+        swap(file_mapping_handle_, other.file_mapping_handle_); 
+#endif
+        swap(length_, other.length_); 
+        swap(mapped_length_, other.mapped_length_); 
+        swap(is_handle_internal_, other.is_handle_internal_); 
+    }
+}
+
+template<access_mode AccessMode, typename ByteT>
+template<access_mode A>
+typename std::enable_if<A == access_mode::write, void>::type
+basic_mmap<AccessMode, ByteT>::conditional_sync()
+{
+    // This is invoked from the destructor, so not much we can do about
+    // failures here.
+    std::error_code ec;
+    sync(ec);
+}
+
+template<access_mode AccessMode, typename ByteT>
+template<access_mode A>
+typename std::enable_if<A == access_mode::read, void>::type
+basic_mmap<AccessMode, ByteT>::conditional_sync()
+{
+    // noop
+}
+
+template<access_mode AccessMode, typename ByteT>
+bool operator==(const basic_mmap<AccessMode, ByteT>& a,
+        const basic_mmap<AccessMode, ByteT>& b)
+{
+    return a.data() == b.data()
+        && a.size() == b.size();
+}
+
+template<access_mode AccessMode, typename ByteT>
+bool operator!=(const basic_mmap<AccessMode, ByteT>& a,
+        const basic_mmap<AccessMode, ByteT>& b)
+{
+    return !(a == b);
+}
+
+template<access_mode AccessMode, typename ByteT>
+bool operator<(const basic_mmap<AccessMode, ByteT>& a,
+        const basic_mmap<AccessMode, ByteT>& b)
+{
+    if(a.data() == b.data()) { return a.size() < b.size(); }
+    return a.data() < b.data();
+}
+
+template<access_mode AccessMode, typename ByteT>
+bool operator<=(const basic_mmap<AccessMode, ByteT>& a,
+        const basic_mmap<AccessMode, ByteT>& b)
+{
+    return !(a > b);
+}
+
+template<access_mode AccessMode, typename ByteT>
+bool operator>(const basic_mmap<AccessMode, ByteT>& a,
+        const basic_mmap<AccessMode, ByteT>& b)
+{
+    if(a.data() == b.data()) { return a.size() > b.size(); }
+    return a.data() > b.data();
+}
+
+template<access_mode AccessMode, typename ByteT>
+bool operator>=(const basic_mmap<AccessMode, ByteT>& a,
+        const basic_mmap<AccessMode, ByteT>& b)
+{
+    return !(a < b);
+}
+
+} // namespace mio
+
+#endif // MIO_BASIC_MMAP_IMPL
+
+
+#endif // MIO_MMAP_HEADER
+/* Copyright 2017 https://github.com/mandreyel
+ *
+ * Permission is hereby granted, free of charge, to any person obtaining a copy of this
+ * software and associated documentation files (the "Software"), to deal in the Software
+ * without restriction, including without limitation the rights to use, copy, modify,
+ * merge, publish, distribute, sublicense, and/or sell copies of the Software, and to
+ * permit persons to whom the Software is furnished to do so, subject to the following
+ * conditions:
+ *
+ * The above copyright notice and this permission notice shall be included in all copies
+ * or substantial portions of the Software.
+ *
+ * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED,
+ * INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A
+ * PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT
+ * HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF
+ * CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE
+ * OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
+ */
+
+#ifndef MIO_PAGE_HEADER
+#define MIO_PAGE_HEADER
+
+#ifdef _WIN32
+# include <windows.h>
+#else
+# include <unistd.h>
+#endif
+
+namespace mio {
+
+/**
+ * This is used by `basic_mmap` to determine whether to create a read-only or
+ * a read-write memory mapping.
+ */
+enum class access_mode
+{
+    read,
+    write
+};
+
+/**
+ * Determines the operating system's page allocation granularity.
+ *
+ * On the first call to this function, it invokes the operating system specific syscall
+ * to determine the page size, caches the value, and returns it. Any subsequent call to
+ * this function serves the cached value, so no further syscalls are made.
+ */
+inline size_t page_size()
+{
+    static const size_t page_size = []
+    {
+#ifdef _WIN32
+        SYSTEM_INFO SystemInfo;
+        GetSystemInfo(&SystemInfo);
+        return SystemInfo.dwAllocationGranularity;
+#else
+        return sysconf(_SC_PAGE_SIZE);
+#endif
+    }();
+    return page_size;
+}
+
+/**
+ * Alligns `offset` to the operating's system page size such that it subtracts the
+ * difference until the nearest page boundary before `offset`, or does nothing if
+ * `offset` is already page aligned.
+ */
+inline size_t make_offset_page_aligned(size_t offset) noexcept
+{
+    const size_t page_size_ = page_size();
+    // Use integer division to round down to the nearest page alignment.
+    return offset / page_size_ * page_size_;
+}
+
+} // namespace mio
+
+#endif // MIO_PAGE_HEADER
+/* Copyright 2017 https://github.com/mandreyel
+ *
+ * Permission is hereby granted, free of charge, to any person obtaining a copy of this
+ * software and associated documentation files (the "Software"), to deal in the Software
+ * without restriction, including without limitation the rights to use, copy, modify,
+ * merge, publish, distribute, sublicense, and/or sell copies of the Software, and to
+ * permit persons to whom the Software is furnished to do so, subject to the following
+ * conditions:
+ *
+ * The above copyright notice and this permission notice shall be included in all copies
+ * or substantial portions of the Software.
+ *
+ * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED,
+ * INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A
+ * PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT
+ * HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF
+ * CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE
+ * OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
+ */
+
+#ifndef MIO_SHARED_MMAP_HEADER
+#define MIO_SHARED_MMAP_HEADER
+
+// #include "mio/mmap.hpp"
+
+
+#include <system_error> // std::error_code
+#include <memory> // std::shared_ptr
+
+namespace mio {
+
+/**
+ * Exposes (nearly) the same interface as `basic_mmap`, but endowes it with
+ * `std::shared_ptr` semantics.
+ *
+ * This is not the default behaviour of `basic_mmap` to avoid allocating on the heap if
+ * shared semantics are not required.
+ */
+template<
+    access_mode AccessMode,
+    typename ByteT
+> class basic_shared_mmap
+{
+    using impl_type = basic_mmap<AccessMode, ByteT>;
+    std::shared_ptr<impl_type> pimpl_;
+
+public:
+    using value_type = typename impl_type::value_type;
+    using size_type = typename impl_type::size_type;
+    using reference = typename impl_type::reference;
+    using const_reference = typename impl_type::const_reference;
+    using pointer = typename impl_type::pointer;
+    using const_pointer = typename impl_type::const_pointer;
+    using difference_type = typename impl_type::difference_type;
+    using iterator = typename impl_type::iterator;
+    using const_iterator = typename impl_type::const_iterator;
+    using reverse_iterator = typename impl_type::reverse_iterator;
+    using const_reverse_iterator = typename impl_type::const_reverse_iterator;
+    using iterator_category = typename impl_type::iterator_category;
+    using handle_type = typename impl_type::handle_type;
+    using mmap_type = impl_type;
+
+    basic_shared_mmap() = default;
+    basic_shared_mmap(const basic_shared_mmap&) = default;
+    basic_shared_mmap& operator=(const basic_shared_mmap&) = default;
+    basic_shared_mmap(basic_shared_mmap&&) = default;
+    basic_shared_mmap& operator=(basic_shared_mmap&&) = default;
+
+    /** Takes ownership of an existing mmap object. */
+    basic_shared_mmap(mmap_type&& mmap)
+        : pimpl_(std::make_shared<mmap_type>(std::move(mmap)))
+    {}
+
+    /** Takes ownership of an existing mmap object. */
+    basic_shared_mmap& operator=(mmap_type&& mmap)
+    {
+        pimpl_ = std::make_shared<mmap_type>(std::move(mmap));
+        return *this;
+    }
+
+    /** Initializes this object with an already established shared mmap. */
+    basic_shared_mmap(std::shared_ptr<mmap_type> mmap) : pimpl_(std::move(mmap)) {}
+
+    /** Initializes this object with an already established shared mmap. */
+    basic_shared_mmap& operator=(std::shared_ptr<mmap_type> mmap)
+    {
+        pimpl_ = std::move(mmap);
+        return *this;
+    }
+
+#ifdef __cpp_exceptions
+    /**
+     * The same as invoking the `map` function, except any error that may occur
+     * while establishing the mapping is wrapped in a `std::system_error` and is
+     * thrown.
+     */
+    template<typename String>
+    basic_shared_mmap(const String& path, const size_type offset = 0, const size_type length = map_entire_file)
+    {
+        std::error_code error;
+        map(path, offset, length, error);
+        if(error) { throw std::system_error(error); }
+    }
+
+    /**
+     * The same as invoking the `map` function, except any error that may occur
+     * while establishing the mapping is wrapped in a `std::system_error` and is
+     * thrown.
+     */
+    basic_shared_mmap(const handle_type handle, const size_type offset = 0, const size_type length = map_entire_file)
+    {
+        std::error_code error;
+        map(handle, offset, length, error);
+        if(error) { throw std::system_error(error); }
+    }
+#endif // __cpp_exceptions
+
+    /**
+     * If this is a read-write mapping and the last reference to the mapping,
+     * the destructor invokes sync. Regardless of the access mode, unmap is
+     * invoked as a final step.
+     */
+    ~basic_shared_mmap() = default;
+
+    /** Returns the underlying `std::shared_ptr` instance that holds the mmap. */
+    std::shared_ptr<mmap_type> get_shared_ptr() { return pimpl_; }
+
+    /**
+     * On UNIX systems 'file_handle' and 'mapping_handle' are the same. On Windows,
+     * however, a mapped region of a file gets its own handle, which is returned by
+     * 'mapping_handle'.
+     */
+    handle_type file_handle() const noexcept
+    {
+        return pimpl_ ? pimpl_->file_handle() : invalid_handle;
+    }
+
+    handle_type mapping_handle() const noexcept
+    {
+        return pimpl_ ? pimpl_->mapping_handle() : invalid_handle;
+    }
+
+    /** Returns whether a valid memory mapping has been created. */
+    bool is_open() const noexcept { return pimpl_ && pimpl_->is_open(); }
+
+    /**
+     * Returns true if no mapping was established, that is, conceptually the
+     * same as though the length that was mapped was 0. This function is
+     * provided so that this class has Container semantics.
+     */
+    bool empty() const noexcept { return !pimpl_ || pimpl_->empty(); }
+
+    /**
+     * `size` and `length` both return the logical length, i.e. the number of bytes
+     * user requested to be mapped, while `mapped_length` returns the actual number of
+     * bytes that were mapped which is a multiple of the underlying operating system's
+     * page allocation granularity.
+     */
+    size_type size() const noexcept { return pimpl_ ? pimpl_->length() : 0; }
+    size_type length() const noexcept { return pimpl_ ? pimpl_->length() : 0; }
+    size_type mapped_length() const noexcept
+    {
+        return pimpl_ ? pimpl_->mapped_length() : 0;
+    }
+
+    /**
+     * Returns a pointer to the first requested byte, or `nullptr` if no memory mapping
+     * exists.
+     */
+    template<
+        access_mode A = AccessMode,
+        typename = typename std::enable_if<A == access_mode::write>::type
+    > pointer data() noexcept { return pimpl_->data(); }
+    const_pointer data() const noexcept { return pimpl_ ? pimpl_->data() : nullptr; }
+
+    /**
+     * Returns an iterator to the first requested byte, if a valid memory mapping
+     * exists, otherwise this function call is undefined behaviour.
+     */
+    iterator begin() noexcept { return pimpl_->begin(); }
+    const_iterator begin() const noexcept { return pimpl_->begin(); }
+    const_iterator cbegin() const noexcept { return pimpl_->cbegin(); }
+
+    /**
+     * Returns an iterator one past the last requested byte, if a valid memory mapping
+     * exists, otherwise this function call is undefined behaviour.
+     */
+    template<
+        access_mode A = AccessMode,
+        typename = typename std::enable_if<A == access_mode::write>::type
+    > iterator end() noexcept { return pimpl_->end(); }
+    const_iterator end() const noexcept { return pimpl_->end(); }
+    const_iterator cend() const noexcept { return pimpl_->cend(); }
+
+    /**
+     * Returns a reverse iterator to the last memory mapped byte, if a valid
+     * memory mapping exists, otherwise this function call is undefined
+     * behaviour.
+     */
+    template<
+        access_mode A = AccessMode,
+        typename = typename std::enable_if<A == access_mode::write>::type
+    > reverse_iterator rbegin() noexcept { return pimpl_->rbegin(); }
+    const_reverse_iterator rbegin() const noexcept { return pimpl_->rbegin(); }
+    const_reverse_iterator crbegin() const noexcept { return pimpl_->crbegin(); }
+
+    /**
+     * Returns a reverse iterator past the first mapped byte, if a valid memory
+     * mapping exists, otherwise this function call is undefined behaviour.
+     */
+    template<
+        access_mode A = AccessMode,
+        typename = typename std::enable_if<A == access_mode::write>::type
+    > reverse_iterator rend() noexcept { return pimpl_->rend(); }
+    const_reverse_iterator rend() const noexcept { return pimpl_->rend(); }
+    const_reverse_iterator crend() const noexcept { return pimpl_->crend(); }
+
+    /**
+     * Returns a reference to the `i`th byte from the first requested byte (as returned
+     * by `data`). If this is invoked when no valid memory mapping has been created
+     * prior to this call, undefined behaviour ensues.
+     */
+    reference operator[](const size_type i) noexcept { return (*pimpl_)[i]; }
+    const_reference operator[](const size_type i) const noexcept { return (*pimpl_)[i]; }
+
+    /**
+     * Establishes a memory mapping with AccessMode. If the mapping is unsuccesful, the
+     * reason is reported via `error` and the object remains in a state as if this
+     * function hadn't been called.
+     *
+     * `path`, which must be a path to an existing file, is used to retrieve a file
+     * handle (which is closed when the object destructs or `unmap` is called), which is
+     * then used to memory map the requested region. Upon failure, `error` is set to
+     * indicate the reason and the object remains in an unmapped state.
+     *
+     * `offset` is the number of bytes, relative to the start of the file, where the
+     * mapping should begin. When specifying it, there is no need to worry about
+     * providing a value that is aligned with the operating system's page allocation
+     * granularity. This is adjusted by the implementation such that the first requested
+     * byte (as returned by `data` or `begin`), so long as `offset` is valid, will be at
+     * `offset` from the start of the file.
+     *
+     * `length` is the number of bytes to map. It may be `map_entire_file`, in which
+     * case a mapping of the entire file is created.
+     */
+    template<typename String>
+    void map(const String& path, const size_type offset,
+        const size_type length, std::error_code& error)
+    {
+        map_impl(path, offset, length, error);
+    }
+
+    /**
+     * Establishes a memory mapping with AccessMode. If the mapping is unsuccesful, the
+     * reason is reported via `error` and the object remains in a state as if this
+     * function hadn't been called.
+     *
+     * `path`, which must be a path to an existing file, is used to retrieve a file
+     * handle (which is closed when the object destructs or `unmap` is called), which is
+     * then used to memory map the requested region. Upon failure, `error` is set to
+     * indicate the reason and the object remains in an unmapped state.
+     *
+     * The entire file is mapped.
+     */
+    template<typename String>
+    void map(const String& path, std::error_code& error)
+    {
+        map_impl(path, 0, map_entire_file, error);
+    }
+
+    /**
+     * Establishes a memory mapping with AccessMode. If the mapping is unsuccesful, the
+     * reason is reported via `error` and the object remains in a state as if this
+     * function hadn't been called.
+     *
+     * `handle`, which must be a valid file handle, which is used to memory map the
+     * requested region. Upon failure, `error` is set to indicate the reason and the
+     * object remains in an unmapped state.
+     *
+     * `offset` is the number of bytes, relative to the start of the file, where the
+     * mapping should begin. When specifying it, there is no need to worry about
+     * providing a value that is aligned with the operating system's page allocation
+     * granularity. This is adjusted by the implementation such that the first requested
+     * byte (as returned by `data` or `begin`), so long as `offset` is valid, will be at
+     * `offset` from the start of the file.
+     *
+     * `length` is the number of bytes to map. It may be `map_entire_file`, in which
+     * case a mapping of the entire file is created.
+     */
+    void map(const handle_type handle, const size_type offset,
+        const size_type length, std::error_code& error)
+    {
+        map_impl(handle, offset, length, error);
+    }
+
+    /**
+     * Establishes a memory mapping with AccessMode. If the mapping is unsuccesful, the
+     * reason is reported via `error` and the object remains in a state as if this
+     * function hadn't been called.
+     *
+     * `handle`, which must be a valid file handle, which is used to memory map the
+     * requested region. Upon failure, `error` is set to indicate the reason and the
+     * object remains in an unmapped state.
+     *
+     * The entire file is mapped.
+     */
+    void map(const handle_type handle, std::error_code& error)
+    {
+        map_impl(handle, 0, map_entire_file, error);
+    }
+
+    /**
+     * If a valid memory mapping has been created prior to this call, this call
+     * instructs the kernel to unmap the memory region and disassociate this object
+     * from the file.
+     *
+     * The file handle associated with the file that is mapped is only closed if the
+     * mapping was created using a file path. If, on the other hand, an existing
+     * file handle was used to create the mapping, the file handle is not closed.
+     */
+    void unmap() { if(pimpl_) pimpl_->unmap(); }
+
+    void swap(basic_shared_mmap& other) { pimpl_.swap(other.pimpl_); }
+
+    /** Flushes the memory mapped page to disk. Errors are reported via `error`. */
+    template<
+        access_mode A = AccessMode,
+        typename = typename std::enable_if<A == access_mode::write>::type
+    > void sync(std::error_code& error) { if(pimpl_) pimpl_->sync(error); }
+
+    /** All operators compare the underlying `basic_mmap`'s addresses. */
+
+    friend bool operator==(const basic_shared_mmap& a, const basic_shared_mmap& b)
+    {
+        return a.pimpl_ == b.pimpl_;
+    }
+
+    friend bool operator!=(const basic_shared_mmap& a, const basic_shared_mmap& b)
+    {
+        return !(a == b);
+    }
+
+    friend bool operator<(const basic_shared_mmap& a, const basic_shared_mmap& b)
+    {
+        return a.pimpl_ < b.pimpl_;
+    }
+
+    friend bool operator<=(const basic_shared_mmap& a, const basic_shared_mmap& b)
+    {
+        return a.pimpl_ <= b.pimpl_;
+    }
+
+    friend bool operator>(const basic_shared_mmap& a, const basic_shared_mmap& b)
+    {
+        return a.pimpl_ > b.pimpl_;
+    }
+
+    friend bool operator>=(const basic_shared_mmap& a, const basic_shared_mmap& b)
+    {
+        return a.pimpl_ >= b.pimpl_;
+    }
+
+private:
+    template<typename MappingToken>
+    void map_impl(const MappingToken& token, const size_type offset,
+        const size_type length, std::error_code& error)
+    {
+        if(!pimpl_)
+        {
+            mmap_type mmap = make_mmap<mmap_type>(token, offset, length, error);
+            if(error) { return; }
+            pimpl_ = std::make_shared<mmap_type>(std::move(mmap));
+        }
+        else
+        {
+            pimpl_->map(token, offset, length, error);
+        }
+    }
+};
+
+/**
+ * This is the basis for all read-only mmap objects and should be preferred over
+ * directly using basic_shared_mmap.
+ */
+template<typename ByteT>
+using basic_shared_mmap_source = basic_shared_mmap<access_mode::read, ByteT>;
+
+/**
+ * This is the basis for all read-write mmap objects and should be preferred over
+ * directly using basic_shared_mmap.
+ */
+template<typename ByteT>
+using basic_shared_mmap_sink = basic_shared_mmap<access_mode::write, ByteT>;
+
+/**
+ * These aliases cover the most common use cases, both representing a raw byte stream
+ * (either with a char or an unsigned char/uint8_t).
+ */
+using shared_mmap_source = basic_shared_mmap_source<char>;
+using shared_ummap_source = basic_shared_mmap_source<unsigned char>;
+
+using shared_mmap_sink = basic_shared_mmap_sink<char>;
+using shared_ummap_sink = basic_shared_mmap_sink<unsigned char>;
+
+} // namespace mio
+
+#endif // MIO_SHARED_MMAP_HEADER
diff --git a/src/graph/expression_graph.h b/src/graph/expression_graph.h
index d75d069d6..2c6120d91 100755
--- a/src/graph/expression_graph.h
+++ b/src/graph/expression_graph.h
@@ -522,6 +522,20 @@ class ExpressionGraph : public std::enable_shared_from_this<ExpressionGraph> {
     LOG(info, "Memory mapping model at {}", ptr);
     auto items = io::mmapItems(ptr);
     
+    // deal with default parameter object that might not be a mapped object
+    auto it = paramsByElementType_.find(defaultElementType_);
+    if(it != paramsByElementType_.end()) {
+      // there is parameter object for that type
+      auto defaultParams = std::dynamic_pointer_cast<MappedParameters>(it->second);
+      if(!defaultParams) {
+        // but it's not mapped, so delete it and replace it with a mapped version
+        auto defaultParams = New<MappedParameters>(defaultElementType_);
+        defaultParams->init(backend_);
+        paramsByElementType_[defaultElementType_] = defaultParams;
+      }
+    }
+
+
     // pre-populate parameters by type
     for(auto& item : items) {
       auto it = paramsByElementType_.find(item.type);
diff --git a/src/graph/parameters.h b/src/graph/parameters.h
index ca3905490..e07aff072 100644
--- a/src/graph/parameters.h
+++ b/src/graph/parameters.h
@@ -130,7 +130,9 @@ class MappedParameters : public Parameters {
   Ptr<Backend> backend_;
 
 public:
-  MappedParameters(Type acceptedType) : Parameters(acceptedType) {}
+  MappedParameters(Type acceptedElementType_) : Parameters(acceptedElementType_) {
+    LOG(debug, "Created mapped parameter object of type {}", acceptedElementType_);
+  }
 
   virtual void init(Ptr<Backend> backend) override { backend_ = backend; }
   virtual void init(Ptr<Backend> backend, Ptr<Device>) override { init(backend); }
diff --git a/src/translator/scorers.cpp b/src/translator/scorers.cpp
index 4c4d908cc..d1c8b1602 100644
--- a/src/translator/scorers.cpp
+++ b/src/translator/scorers.cpp
@@ -122,4 +122,13 @@ std::vector<Ptr<Scorer>> createScorers(Ptr<Options> options, const std::vector<c
   return scorers;
 }
 
+std::vector<Ptr<Scorer>> createScorers(Ptr<Options> options, const std::vector<mio::mmap_source>& mmaps) {
+  std::vector<const void*> ptrs;
+  for(const auto& mmap : mmaps) {
+    ABORT_IF(!mmap.is_mapped(), "Memory mapping did not succeed");
+    ptrs.push_back(mmap.data());
+  }
+  return createScorers(options, ptrs);
+}
+
 }  // namespace marian
diff --git a/src/translator/scorers.h b/src/translator/scorers.h
index dc5ee615d..d3fc4f3b3 100755
--- a/src/translator/scorers.h
+++ b/src/translator/scorers.h
@@ -4,6 +4,7 @@
 
 #include "data/shortlist.h"
 #include "models/model_factory.h"
+#include "3rd_party/mio/mio.hpp"
 
 namespace marian {
 
@@ -147,5 +148,6 @@ Ptr<Scorer> scorerByType(const std::string& fname,
                          Ptr<Options> config);
 
 std::vector<Ptr<Scorer>> createScorers(Ptr<Options> options, const std::vector<const void*>& ptrs);
+std::vector<Ptr<Scorer>> createScorers(Ptr<Options> options, const std::vector<mio::mmap_source>& mmaps);
 
 }  // namespace marian
diff --git a/src/translator/translator.h b/src/translator/translator.h
index bd17ec178..95d7f2def 100755
--- a/src/translator/translator.h
+++ b/src/translator/translator.h
@@ -6,6 +6,7 @@
 #include "data/text_input.h"
 
 #include "3rd_party/threadpool.h"
+
 #include "translator/history.h"
 #include "translator/output_collector.h"
 #include "translator/output_printer.h"
@@ -13,6 +14,14 @@
 #include "models/model_task.h"
 #include "translator/scorers.h"
 
+// currently for diagnostics only, will try to mmap files ending in *.bin suffix when enabled.
+// @TODO: add this as an actual feature.
+#define MMAP 0
+
+#if MMAP
+#include "3rd_party/mio/mio.hpp"
+#endif
+
 namespace marian {
 
 template <class Search>
@@ -28,6 +37,10 @@ class Translate : public ModelTask {
 
   size_t numDevices_;
 
+#if MMAP
+  std::vector<mio::mmap_source> mmaps_;
+#endif
+
 public:
   Translate(Ptr<Options> options) 
     : options_(New<Options>(options->clone())) { // @TODO: clone should return Ptr<Options> same as "with"?
@@ -55,6 +68,16 @@ class Translate : public ModelTask {
     scorers_.resize(numDevices_);
     graphs_.resize(numDevices_);
 
+#if MMAP
+    auto models = options->get<std::vector<std::string>>("models");
+    for(auto model : models) {
+      marian::filesystem::Path modelPath(model);
+      ABORT_IF(modelPath.extension() != marian::filesystem::Path(".bin"), 
+              "Non-binarized models cannot be mapped");
+      mmaps_.push_back(std::move(mio::mmap_source(model)));
+    }
+#endif
+
     size_t id = 0;
     for(auto device : devices) {
       auto task = [&](DeviceId device, size_t id) {
@@ -69,7 +92,11 @@ class Translate : public ModelTask {
         graph->reserveWorkspaceMB(options_->get<size_t>("workspace"));
         graphs_[id] = graph;
 
+#if MMAP
+        auto scorers = createScorers(options_, mmaps_);
+#else 
         auto scorers = createScorers(options_);
+#endif
         for(auto scorer : scorers) {
           scorer->init(graph);
           if(shortlistGenerator_)

From 5dfd8a702618c3c435b11cd28f20b113d3c80d29 Mon Sep 17 00:00:00 2001
From: Martin Junczys-Dowmunt <Marcin.JunczysDowmunt@microsoft.com>
Date: Mon, 11 Nov 2019 18:56:12 +0000
Subject: [PATCH 772/838] Merged PR 10383: Align items while saving at 256-byte
 boundary

Align items while saving at 256-byte boundary. Maintains binary-compatibility.
---
 CHANGELOG.md                 |  3 +++
 VERSION                      |  2 +-
 src/common/binary.cpp        | 11 +++++------
 src/graph/expression_graph.h |  4 +++-
 src/translator/translator.h  |  2 +-
 5 files changed, 13 insertions(+), 9 deletions(-)

diff --git a/CHANGELOG.md b/CHANGELOG.md
index 5f892f36d..e4872422d 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -22,6 +22,9 @@ and this project adheres to [Semantic Versioning](http://semver.org/spec/v2.0.0.
 - Gradient-checkpointing
 
 ### Fixed
+- Make sure that items are 256-byte aligned during saving
+- Make explicit matmul functions respect setting of cublasMathMode
+- Fix memory mapping for mixed paramter models
 - Removed naked pointer and potential memory-leak from file_stream.{cpp,h}
 - Compilation for GCC >= 7 due to exception thrown in destructor
 - Sort parameters by lexicographical order during allocation to ensure consistent 
diff --git a/VERSION b/VERSION
index 9cf3faad6..cdc4b60a7 100644
--- a/VERSION
+++ b/VERSION
@@ -1 +1 @@
-v1.8.15
+v1.8.18
diff --git a/src/common/binary.cpp b/src/common/binary.cpp
index 654b35162..1531fed6c 100644
--- a/src/common/binary.cpp
+++ b/src/common/binary.cpp
@@ -123,8 +123,7 @@ void saveItems(const std::string& fileName,
     headers.push_back(Header{item.name.size() + 1,
                              (size_t)item.type,
                              item.shape.size(),
-                             item.size()}); // item size without padding 
-                                            // @TODO: should this be done with padding as asked below?
+                             item.bytes.size()}); // binary item size with padding, will be 256-byte-aligned
   }
 
   size_t headerSize = headers.size();
@@ -152,10 +151,10 @@ void saveItems(const std::string& fileName,
 
   // Write out all values
   for(const auto& item : items)
-    pos += out.write(item.data(), item.size()); // writes out data without padding, not aligned, @BUGBUG?
-
-    // @TODO: find out if padding should be enforced for memory-mapped storage like this:
-    // pos += out.write(item.data(), item.bytes.size()); // writes out data with padding
+    pos += out.write(item.data(), item.bytes.size()); // writes out data with padding, keeps 256-byte boundary. 
+                                                      // Amazingly this is binary-compatible with V1 and aligned and 
+                                                      // non-aligned models can be read with the same procedure.
+                                                      // No version-bump required. Gets 5-8% of speed back when mmapped.
 }
 
 }  // namespace binary
diff --git a/src/graph/expression_graph.h b/src/graph/expression_graph.h
index 2c6120d91..4fe422517 100755
--- a/src/graph/expression_graph.h
+++ b/src/graph/expression_graph.h
@@ -522,7 +522,9 @@ class ExpressionGraph : public std::enable_shared_from_this<ExpressionGraph> {
     LOG(info, "Memory mapping model at {}", ptr);
     auto items = io::mmapItems(ptr);
     
-    // deal with default parameter object that might not be a mapped object
+    // Deal with default parameter set object that might not be a mapped object.
+    // This gets assigned during ExpressionGraph::setDevice(...) and by default 
+    // would contain allocated tensors. Here we replace it with a mmapped version.
     auto it = paramsByElementType_.find(defaultElementType_);
     if(it != paramsByElementType_.end()) {
       // there is parameter object for that type
diff --git a/src/translator/translator.h b/src/translator/translator.h
index 95d7f2def..5dd87f949 100755
--- a/src/translator/translator.h
+++ b/src/translator/translator.h
@@ -73,7 +73,7 @@ class Translate : public ModelTask {
     for(auto model : models) {
       marian::filesystem::Path modelPath(model);
       ABORT_IF(modelPath.extension() != marian::filesystem::Path(".bin"), 
-              "Non-binarized models cannot be mapped");
+              "Non-binarized models cannot be mmapped");
       mmaps_.push_back(std::move(mio::mmap_source(model)));
     }
 #endif

From 9353f065f8b3e0e1858084313fe41a71693d7fd5 Mon Sep 17 00:00:00 2001
From: Martin Junczys-Dowmunt <Marcin.JunczysDowmunt@microsoft.com>
Date: Mon, 11 Nov 2019 22:04:19 +0000
Subject: [PATCH 773/838] Merged PR 10373: Replace IntrusivePtr with
 std::uniq_ptr in FastOpt

In FastOpt we do not want to use locking during access, but that makes reference counting not thread-safe. We now use std::unique_ptr to const objects or const references everywhere. This fixes random segfaults with multi-GPU training. @TODO: clean-up option merging to make option generally immutable.
---
 CHANGELOG.md           |   2 +
 VERSION                |   2 +-
 src/common/fastopt.cpp |  10 +--
 src/common/fastopt.h   |  98 ++++++++++++++---------------
 src/common/options.cpp | 137 +++++++++++++++++++++++++----------------
 src/common/options.h   |  31 ++++++++--
 6 files changed, 170 insertions(+), 110 deletions(-)

diff --git a/CHANGELOG.md b/CHANGELOG.md
index e4872422d..43c8e58b6 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -22,6 +22,8 @@ and this project adheres to [Semantic Versioning](http://semver.org/spec/v2.0.0.
 - Gradient-checkpointing
 
 ### Fixed
+- Replace IntrusivePtr with std::uniq_ptr in FastOpt, fixes random segfaults 
+  due to thread-non-safty of reference counting.
 - Make sure that items are 256-byte aligned during saving
 - Make explicit matmul functions respect setting of cublasMathMode
 - Fix memory mapping for mixed paramter models
diff --git a/VERSION b/VERSION
index cdc4b60a7..30e349845 100644
--- a/VERSION
+++ b/VERSION
@@ -1 +1 @@
-v1.8.18
+v1.8.19
diff --git a/src/common/fastopt.cpp b/src/common/fastopt.cpp
index 7cf7908b7..779a1b532 100644
--- a/src/common/fastopt.cpp
+++ b/src/common/fastopt.cpp
@@ -4,6 +4,8 @@
 
 namespace marian {
 
+const std::unique_ptr<const FastOpt> FastOpt::uniqueNullPtr{nullptr};
+
 // see fastopt.h for comments
 namespace fastopt_helpers {
 
@@ -48,13 +50,13 @@ T As<T>::apply(const FastOpt& node) {
   ABORT_IF(!node.isScalar(), "Node is not a scalar node");
 
   if(node.isBool())
-    return Convert<T, bool>::apply(node.value_->as<bool>());
+    return Convert<T, bool>::apply(node.value_.as<bool>());
   else if(node.isInt())
-    return Convert<T, int64_t>::apply(node.value_->as<int64_t>());
+    return Convert<T, int64_t>::apply(node.value_.as<int64_t>());
   else if(node.isFloat())
-    return Convert<T, double>::apply(node.value_->as<double>());
+    return Convert<T, double>::apply(node.value_.as<double>());
   else if(node.isString())
-    return Convert<T, std::string>::apply(node.value_->as<std::string>());      
+    return Convert<T, std::string>::apply(node.value_.as<std::string>());      
   else {
     ABORT("Casting of value failed");
   }
diff --git a/src/common/fastopt.h b/src/common/fastopt.h
index 1d1c56688..36bc7814b 100644
--- a/src/common/fastopt.h
+++ b/src/common/fastopt.h
@@ -1,6 +1,6 @@
 #pragma once
 
-#include "common/intrusive_ptr.h"
+#include "common/definitions.h"
 #include "3rd_party/any_type.h"
 #include "3rd_party/phf/phf.h"
 #include "3rd_party/yaml-cpp/yaml.h"
@@ -43,8 +43,6 @@ inline constexpr uint64_t crc(const char* const str) noexcept {
 // m - n fields stay undefined (a bit of waste).
 class PerfectHash {
 private:
-  ENABLE_INTRUSIVE_PTR(PerfectHash)
-
   phf phf_;
 
   PerfectHash(const uint64_t keys[], size_t num) {
@@ -106,8 +104,6 @@ namespace fastopt_helpers {
 // Still requires YAML::Node support for parsing and modification via rebuilding.
 class FastOpt {
 private:
-  ENABLE_INTRUSIVE_PTR(FastOpt)
-
   template <typename T>
   friend struct fastopt_helpers::As;
 
@@ -118,66 +114,59 @@ class FastOpt {
   };
 
 private:
-  struct ElementType {
-    ENABLE_INTRUSIVE_PTR(ElementType)
-
-    any_type value{0};
-
-    template <typename T>
-    const T& as() const { return value.as<T>(); }
-  };
-
-  IntrusivePtr<ElementType> value_;
-  IntrusivePtr<PerfectHash> ph_;
-  std::vector<IntrusivePtr<FastOpt>> array_;
+  any_type value_;
+  std::unique_ptr<const PerfectHash> ph_;
+  std::vector<std::unique_ptr<const FastOpt>> array_;
   NodeType type_{NodeType::Null};
 
+  static const std::unique_ptr<const FastOpt> uniqueNullPtr; // return this unique_ptr if key not found, equivalent to nullptr
+
   uint64_t fingerprint_{0}; // When node is used as a value in a map, used to check if the perfect hash 
                             // returned the right value (they can produce false positives)
   size_t elements_{0};      // Number of elements if isMap or isSequence is true, 0 otherwise.
 
   // Used to find elements if isSequence() is true.
-  inline const IntrusivePtr<FastOpt> arrayLookup(size_t keyId) const {
+  inline const std::unique_ptr<const FastOpt>& arrayLookup(size_t keyId) const {
     if(keyId < array_.size())
       return array_[keyId];
     else
-      return nullptr;
+      return uniqueNullPtr;
   }
 
   // Used to find elements if isMap() is true.
-  inline const IntrusivePtr<FastOpt> phLookup(size_t keyId) const {
+  inline const std::unique_ptr<const FastOpt>& phLookup(size_t keyId) const {
     if(ph_)
       return array_[(*ph_)[keyId]];
     else
-      return nullptr;
+      return uniqueNullPtr;
   }
 
   // Build Null node.
   void makeNull() {
-    value_.reset(nullptr);
     elements_ = 0;
     type_ = NodeType::Null;
+
+    ABORT_IF(ph_, "ph_ should be undefined");
+    ABORT_IF(!array_.empty(), "array_ should be empty");
   }
 
   // Build Scalar node via controlled failure to convert from a YAML::Node object.
   void makeScalar(const YAML::Node& v) {
     elements_ = 0;
-    value_.reset(new ElementType());
-    
     try {
-      value_->value = v.as<bool>();
+      value_ = v.as<bool>();
       type_ = NodeType::Bool;
     } catch(const YAML::BadConversion& /*e*/) {
       try {
-        value_->value = v.as<int64_t>();
+        value_ = v.as<int64_t>();
         type_ = NodeType::Int64;
       } catch(const YAML::BadConversion& /*e*/) {
         try {
-          value_->value = v.as<double>();
+          value_ = v.as<double>();
           type_ = NodeType::Float64;
         } catch(const YAML::BadConversion& /*e*/) {
           try { 
-            value_->value = v.as<std::string>();
+            value_ = v.as<std::string>();
             type_ = NodeType::String;
           } catch (const YAML::BadConversion& /*e*/) {
             ABORT("Cannot convert YAML node {}", v);
@@ -185,19 +174,21 @@ class FastOpt {
         }
       }
     }
+    
+    ABORT_IF(ph_, "ph_ should be undefined");
+    ABORT_IF(!array_.empty(), "array_ should be empty");
   }
 
   // Build a Sequence node, can by converted to std::vector<T> if elements can be converted to T.
   void makeSequence(const std::vector<YAML::Node>& v) {
-    array_.resize(v.size());
     elements_ = v.size();
-
+    ABORT_IF(!array_.empty(), "array_ is not empty??");
     for(size_t pos = 0; pos < v.size(); ++pos) {
-      array_[pos].reset(new FastOpt(v[pos]));
-      array_[pos]->fingerprint_ = pos;
+      array_.emplace_back(new FastOpt(v[pos], pos));
     }
-
     type_ = NodeType::Sequence;
+
+    ABORT_IF(ph_, "ph_ should be undefined");
   }
 
   // Build a Map node.
@@ -206,16 +197,20 @@ class FastOpt {
     for(const auto& it : m)
       keys.push_back(it.first);
 
+    ABORT_IF(ph_, "ph_ is already defined??");
     ph_.reset(new PerfectHash(keys));
 
-    array_.resize(ph_->size());
+    ABORT_IF(!array_.empty(), "array_ is not empty??");
+
+    // for lack of resize_emplace
+    for(int i = 0; i < ph_->size(); ++i)
+      array_.emplace_back(nullptr);
     elements_ = keys.size();
 
     for(const auto& it : m) {
       uint64_t key = it.first;
       size_t pos = (*ph_)[key];
-      array_[pos].reset(new FastOpt(it.second));
-      array_[pos]->fingerprint_ = key;
+      array_[pos].reset(new FastOpt(it.second, key));
     }
 
     type_ = NodeType::Map;
@@ -236,9 +231,7 @@ class FastOpt {
   FastOpt(const FastOpt&) = delete;
   FastOpt() = delete;
 
-public:
-  // Constructor to recursively create a FastOpt object from a YAML::Node following the yaml structure.
-  FastOpt(const YAML::Node& node) {
+  void construct(const YAML::Node& node) {
     switch(node.Type()) {
       case YAML::NodeType::Scalar:
         makeScalar(node);
@@ -263,6 +256,15 @@ class FastOpt {
     }
   }
 
+public:
+  // Constructor to recursively create a FastOpt object from a YAML::Node following the yaml structure.
+  FastOpt(const YAML::Node& node) 
+  { construct(node); }
+
+  FastOpt(const YAML::Node& node, uint64_t fingerprint) 
+     : fingerprint_{fingerprint}
+  { construct(node); }
+
   bool isSequence() const {
     return type_ == NodeType::Sequence;
   }
@@ -306,9 +308,9 @@ class FastOpt {
 
   // replace current node with an externally built FastOpt object
   void swap(FastOpt& other) {
-    value_.swap(other.value_);
-    ph_.swap(other.ph_);
-    array_.swap(other.array_);
+    std::swap(value_, other.value_);
+    std::swap(ph_, other.ph_);
+    std::swap(array_, other.array_);
     std::swap(type_, other.type_);
     std::swap(elements_, other.elements_);
     // leave fingerprint alone as it needed by parent node. 
@@ -317,7 +319,7 @@ class FastOpt {
   // Is the hashed key in a map? 
   bool has(size_t keyId) const {
     if(isMap() && elements_ > 0) {
-      const auto ptr = phLookup(keyId);
+      const auto& ptr = phLookup(keyId);
       return ptr ? ptr->fingerprint_ == keyId : false;
     } else {
       return false;
@@ -341,12 +343,12 @@ class FastOpt {
   // access sequence or map element
   const FastOpt& operator[](size_t keyId) const {
     if(isSequence()) {
-      return *arrayLookup(keyId);
+      const auto& ptr = arrayLookup(keyId);
+      ABORT_IF(!ptr, "Unseen key {}" , keyId);
+      return *ptr;
     } else if(isMap()) {
-      const auto ptr = phLookup(keyId);
-      ABORT_IF(!ptr || ptr->fingerprint_ != keyId, 
-               "Unseen key {}" , 
-               keyId);
+      const auto& ptr = phLookup(keyId);
+      ABORT_IF(!ptr || ptr->fingerprint_ != keyId, "Unseen key {}", keyId);
       return *ptr; 
     } else {
       ABORT("Not a sequence or map node");
diff --git a/src/common/options.cpp b/src/common/options.cpp
index 2e4487ac9..e39b55222 100644
--- a/src/common/options.cpp
+++ b/src/common/options.cpp
@@ -1,70 +1,101 @@
 #include "options.h"
 
 namespace marian {
-  Options::Options() 
-  : fastOptions_(options_) {}
 
-  Options::Options(const Options& other)
-    : options_(YAML::Clone(other.options_)),
-      fastOptions_(options_) {}
+Options::Options() 
+#if FASTOPT
+  : fastOptions_(options_) 
+#endif
+{}
 
-  Options Options::clone() const {
-    return Options(*this); // fastOptions_ get set in constructor above
-  }
+Options::Options(const Options& other)
+#if FASTOPT
+  : options_(YAML::Clone(other.options_)),
+    fastOptions_(options_) {}
+#else
+  : options_(YAML::Clone(other.options_)) {}
+#endif
 
-  YAML::Node Options::cloneToYamlNode() const {
-    return YAML::Clone(options_); // Do not give access to internal YAML object
-  }
+Options Options::clone() const {
+  return Options(*this); // fastOptions_ get set in constructor above
+}
 
-  void Options::parse(const std::string& yaml) {
-    auto node = YAML::Load(yaml);
-    for(auto it : node)
+YAML::Node Options::cloneToYamlNode() const {
+  return YAML::Clone(options_); // Do not give access to internal YAML object
+}
+
+void Options::parse(const std::string& yaml) {
+  auto node = YAML::Load(yaml);
+  for(auto it : node)
+    options_[it.first.as<std::string>()] = YAML::Clone(it.second);
+#if FASTOPT
+  setLazyRebuild();
+#endif
+}
+
+void Options::merge(const YAML::Node& node, bool overwrite) {
+  for(auto it : node)
+    if(overwrite || !options_[it.first.as<std::string>()])
       options_[it.first.as<std::string>()] = YAML::Clone(it.second);
-    setLazyRebuild();
-  }
+#if FASTOPT
+  setLazyRebuild();
+#endif
+}
 
-  void Options::merge(const YAML::Node& node, bool overwrite) {
-    for(auto it : node)
-      if(overwrite || !options_[it.first.as<std::string>()])
-        options_[it.first.as<std::string>()] = YAML::Clone(it.second);
-    setLazyRebuild();
-  }
+void Options::merge(Ptr<Options> options) {
+  merge(options->options_);
+}
 
-  void Options::merge(Ptr<Options> options) {
-    merge(options->options_);
-  }
+std::string Options::asYamlString() {
+  std::stringstream ss;
+  ss << options_;
+  return ss.str();
+}
 
-  std::string Options::asYamlString() {
-    std::stringstream ss;
-    ss << options_;
-    return ss.str();
+bool Options::hasAndNotEmpty(const char* const key) const {
+#if FASTOPT
+  lazyRebuild();
+  if(!fastOptions_.has(key)) {
+    return false;
+  } else {
+    auto& node = fastOptions_[key];
+    if(node.isSequence())
+      return node.size() != 0;
+    else if(node.isScalar()) // numerical values count as non-empty
+      return !node.as<std::string>().empty();
+    else
+      ABORT("Wrong node type");
   }
-
-  bool Options::hasAndNotEmpty(const char* const key) const {
-    lazyRebuild();
-    if(!fastOptions_.has(key)) {
-      return false;
-    } else {
-      auto& node = fastOptions_[key];
-      if(node.isSequence())
-        return node.size() != 0;
-      else if(node.isScalar()) // numerical values count as non-empty
-        return !node.as<std::string>().empty();
-      else
-        ABORT("Wrong node type");
-    }
+#else
+  if(!options_[key]) {
+    return false;
+  } else {
+    auto& node = options_[key];
+    if(node.IsSequence())
+      return node.size() != 0;
+    else if(node.IsScalar()) // numerical values count as non-empty
+      return !node.as<std::string>().empty();
+    else
+      ABORT("Wrong node type");
   }
+#endif
+}
 
-  bool Options::hasAndNotEmpty(const std::string& key) const {
-    return hasAndNotEmpty(key.c_str());
-  }
+bool Options::hasAndNotEmpty(const std::string& key) const {
+  return hasAndNotEmpty(key.c_str());
+}
 
-  bool Options::has(const char* const key) const {
-    lazyRebuild();
-    return fastOptions_.has(key);
-  }
+bool Options::has(const char* const key) const {
+#if FASTOPT
+  lazyRebuild();
+  return fastOptions_.has(key);
+#else
+  return options_[key];
+#endif
+}
+
+bool Options::has(const std::string& key) const {
+  return has(key.c_str());
+}
 
-  bool Options::has(const std::string& key) const {
-    return has(key.c_str());
-  }
 }
diff --git a/src/common/options.h b/src/common/options.h
index 7a18e1f2b..d288f0a3d 100755
--- a/src/common/options.h
+++ b/src/common/options.h
@@ -1,13 +1,19 @@
 #pragma once
 
+// @TODO: to be removed when sure it works
+#define FASTOPT 1 // for diagnostics, 0 reverts to old behavior
+
 #include <sstream>
 #include <string>
 #include "common/definitions.h"
-#include "common/fastopt.h"
 #include "3rd_party/yaml-cpp/yaml.h"
 
+#ifdef FASTOPT
+#include "common/fastopt.h"
+#endif
+
 #define YAML_REGISTER_TYPE(registered, type)                \
-  namespace YAML {                                          \
+namespace YAML {                                            \
   template <>                                               \
   struct convert<registered> {                              \
     static Node encode(const registered& rhs) {             \
@@ -20,17 +26,19 @@
       return true;                                          \
     }                                                       \
   };                                                        \
-  }
+}
 
 namespace marian {
 
 /**
  * Container for options stored as key-value pairs. Keys are unique strings.
+ * This is not thread-safe and locking is the responsibility of the caller.
  */
 class Options {
 protected:
   YAML::Node options_;  // YAML options use for parsing, modification and printing
   
+#if FASTOPT
   // Only to be modified in lazyRebuild and setLazyRebuild
   mutable FastOpt fastOptions_; // FastOpt used for fast lookup, lazily rebuilt from YYAML whenever required
   mutable bool lazyRebuildPending_{false}; // flag if need to lazily rebuild
@@ -48,6 +56,7 @@ class Options {
       lazyRebuildPending_ = false;
     }
   }
+#endif
 
 public:
   Options();
@@ -59,7 +68,7 @@ class Options {
   Options(const std::string& key, T value, Args&&... moreArgs) : Options() {
     set(key, value, std::forward<Args>(moreArgs)...);
   }
-
+  
   // constructor that clones and zero or more updates
   // options->with("var1", val1, "var2", val2, ...)
   template <typename... Args>
@@ -98,7 +107,9 @@ class Options {
   template <typename T>
   void set(const std::string& key, T value) {
     options_[key] = value;
+#if FASTOPT
     setLazyRebuild();
+#endif
   }
 
   // set multiple
@@ -107,14 +118,21 @@ class Options {
   void set(const std::string& key, T value, Args&&... moreArgs) {
     set(key, value);
     set(std::forward<Args>(moreArgs)...);
+#if FASTOPT
     setLazyRebuild();
+#endif
   }
 
   template <typename T>
   T get(const char* const key) const {
+#if FASTOPT
     lazyRebuild();
     ABORT_IF(!has(key), "Required option '{}' has not been set", key);
     return fastOptions_[key].as<T>();
+#else
+    ABORT_IF(!has(key), "Required option '{}' has not been set", key);
+    return options_[key].as<T>();
+#endif
   }
 
   template <typename T>
@@ -124,9 +142,14 @@ class Options {
 
   template <typename T>
   T get(const char* const key, T defaultValue) const {
+#if FASTOPT
     lazyRebuild();
     if(has(key))
       return fastOptions_[key].as<T>();
+#else
+    if(has(key))
+      return options_[key].as<T>();
+#endif
     else
       return defaultValue;
   }

From 189d89e1dd468a8849538262fbfadb1c3e4077ab Mon Sep 17 00:00:00 2001
From: Martin Junczys-Dowmunt <Marcin.JunczysDowmunt@microsoft.com>
Date: Tue, 12 Nov 2019 05:13:15 +0000
Subject: [PATCH 774/838] Merged PR 10333: Batch-pruning in beam search

This PR introduces batch-purging in Marian, i.e. whenever a virtual beam becomes inactive (empty) the entire batch entry that corresponds to that beam can be removed from the encoder and decoder neural states. The CPU-side beam search keeps tracks of the hypotheses as before, but needs to perform mappings between original and shifted batch indices.
---
 CHANGELOG.md                       |   7 +-
 VERSION                            |   2 +-
 src/graph/expression_operators.cpp |   3 +
 src/layers/generic.cpp             |   6 +-
 src/models/costs.h                 |   9 +-
 src/models/encoder_decoder.cpp     |  12 +-
 src/models/encoder_decoder.h       |   8 +-
 src/models/s2s.h                   |  17 +-
 src/models/states.h                |  21 ++-
 src/models/transformer.h           |  39 ++--
 src/translator/beam_search.h       | 277 ++++++++++++++++++-----------
 src/translator/history.h           |  27 ++-
 src/translator/scorers.h           |   6 +-
 13 files changed, 275 insertions(+), 159 deletions(-)

diff --git a/CHANGELOG.md b/CHANGELOG.md
index 43c8e58b6..cb5a86a8f 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -8,6 +8,8 @@ and this project adheres to [Semantic Versioning](http://semver.org/spec/v2.0.0.
 ## [Unreleased]
 
 ### Added
+- Purging of finished hypotheses during beam-search. A lot faster for large batches.
+- Faster option look-up, up to 20-30% faster translation
 - Added --cite and --authors flag
 - Added optional support for ccache
 - Switch to change abort to exception, only to be used in library mode
@@ -15,7 +17,7 @@ and this project adheres to [Semantic Versioning](http://semver.org/spec/v2.0.0.
 - Multiple separated parameter types in ExpressionGraph, currently inference-only
 - Safe handling of sigterm signal
 - Automatic vectorization of elementwise operations on CPU for tensors dims that 
-  are divible by 4 (AVX) and 8 (AVX2)
+  are divisible by 4 (AVX) and 8 (AVX2)
 - Replacing std::shared_ptr<T> with custom IntrusivePtr<T> for small objects like 
   Tensors, Hypotheses and Expressions.
 - Fp16 inference working for translation
@@ -36,10 +38,9 @@ and this project adheres to [Semantic Versioning](http://semver.org/spec/v2.0.0.
 - Compilation with CUDA 10.1
 
 ### Changed
-- Faster option look-up, up to 20-30% faster translation
 - Removed autotuner for now, will be switched back on later
 - Boost depdendency is now optional and only required for marian_server 
-  or for boost::regex when compiling with g++-4.9
+- Dropped support for g++-4.9
 - Simplified file stream and temporary file handling
 - Unified node intializers, same function API.
 
diff --git a/VERSION b/VERSION
index 30e349845..06e497ffd 100644
--- a/VERSION
+++ b/VERSION
@@ -1 +1 @@
-v1.8.19
+v1.8.20
diff --git a/src/graph/expression_operators.cpp b/src/graph/expression_operators.cpp
index 3956e02cd..f0c06b67b 100755
--- a/src/graph/expression_operators.cpp
+++ b/src/graph/expression_operators.cpp
@@ -487,6 +487,9 @@ Expr affine(Expr a, Expr b, Expr bias, bool transA, bool transB, float scale) {
     }
   } else {
     // Default GEMM
+    ABORT_IF(!isFloat(aElementType) || !isFloat(bElementType), 
+             "GPU-based GEMM only supports float types, you have A: {} and B: {}", 
+             aElementType, bElementType);
     return affineDefault(a, b, bias, transA, transB, scale);
   }
 }
diff --git a/src/layers/generic.cpp b/src/layers/generic.cpp
index 09cd27b62..7dc828d9e 100755
--- a/src/layers/generic.cpp
+++ b/src/layers/generic.cpp
@@ -64,7 +64,7 @@ namespace marian {
 
   // get logits for one factor group
   // For groupIndex == 0, the function also requires the shortlist if there is one.
-  Expr Logits::getFactoredLogits(size_t groupIndex, Ptr<data::Shortlist> shortlist /*= nullptr*/, const std::vector<IndexType>& selIdx /*= {}*/, size_t beamSize /*= 0*/) const {
+  Expr Logits::getFactoredLogits(size_t groupIndex, Ptr<data::Shortlist> shortlist /*= nullptr*/, const std::vector<IndexType>& hypIndices /*= {}*/, size_t beamSize /*= 0*/) const {
     ABORT_IF(empty(), "Attempted to read out logits on empty Logits object");
     auto sel = logits_[groupIndex]->loss(); // [localBeamSize, 1, dimBatch, dimFactorVocab]
 
@@ -84,8 +84,8 @@ namespace marian {
     }
 
     // if selIdx are given, then we must reshuffle accordingly
-    if (!selIdx.empty()) // use the same function that shuffles decoder state
-      sel = rnn::State::select(sel, selIdx, (int)beamSize, /*isBatchMajor=*/false);
+    if (!hypIndices.empty()) // use the same function that shuffles decoder state
+      sel = rnn::State::select(sel, hypIndices, (int)beamSize, /*isBatchMajor=*/false);
     return sel;
   }
 
diff --git a/src/models/costs.h b/src/models/costs.h
index abb3dbfb1..776ca0999 100755
--- a/src/models/costs.h
+++ b/src/models/costs.h
@@ -281,12 +281,11 @@ class Stepwise : public IEncoderDecoder {
 
   virtual Ptr<DecoderState> step(Ptr<ExpressionGraph> graph,
                                  Ptr<DecoderState> state,
-                                 const std::vector<IndexType>& hypIndices,
-                                 const Words& words,
-                                 int dimBatch,
+                                 const std::vector<IndexType>& hypIndices,   // [beamIndex * activeBatchSize + batchIndex]
+                                 const Words& words,                         // [beamIndex * activeBatchSize + batchIndex]
+                                 const std::vector<IndexType>& batchIndices, // [batchIndex]
                                  int beamSize) override {
-    auto nextState = encdec_->step(
-        graph, state, hypIndices, words, dimBatch, beamSize);
+    auto nextState = encdec_->step(graph, state, hypIndices, words, batchIndices, beamSize);
     return cost_->apply(nextState);
   }
 
diff --git a/src/models/encoder_decoder.cpp b/src/models/encoder_decoder.cpp
index e968a33ab..6a82f0ea9 100755
--- a/src/models/encoder_decoder.cpp
+++ b/src/models/encoder_decoder.cpp
@@ -188,17 +188,17 @@ Ptr<DecoderState> EncoderDecoder::startState(Ptr<ExpressionGraph> graph,
 
 Ptr<DecoderState> EncoderDecoder::step(Ptr<ExpressionGraph> graph,
                                        Ptr<DecoderState> state,
-                                       const std::vector<IndexType>& hypIndices, // [beamIndex * activeBatchSize + batchIndex]
-                                       const Words& words,                       // [beamIndex * activeBatchSize + batchIndex]
-                                       int dimBatch,
+                                       const std::vector<IndexType>& hypIndices,   // [beamIndex * activeBatchSize + batchIndex]
+                                       const Words& words,                         // [beamIndex * activeBatchSize + batchIndex]
+                                       const std::vector<IndexType>& batchIndices, // [batchIndex]
                                        int beamSize) {
   // create updated state that reflects reordering and dropping of hypotheses
-  state = hypIndices.empty() ? state : state->select(hypIndices, beamSize);
+  state = hypIndices.empty() ? state : state->select(hypIndices, batchIndices, beamSize);
 
   // Fill state with embeddings based on last prediction
-  decoders_[0]->embeddingsFromPrediction(graph, state, words, dimBatch, beamSize);
+  decoders_[0]->embeddingsFromPrediction(graph, state, words, batchIndices.size(), beamSize);
   auto nextState = decoders_[0]->step(graph, state);
-
+  
   return nextState;
 }
 
diff --git a/src/models/encoder_decoder.h b/src/models/encoder_decoder.h
index 60e607f6e..89ee68f6d 100755
--- a/src/models/encoder_decoder.h
+++ b/src/models/encoder_decoder.h
@@ -41,9 +41,9 @@ class IEncoderDecoder : public models::IModel {
 
   virtual Ptr<DecoderState> step(Ptr<ExpressionGraph> graph,
                                  Ptr<DecoderState> state,
-                                 const std::vector<IndexType>& hypIndices,
-                                 const Words& words,
-                                 int dimBatch,
+                                 const std::vector<IndexType>& hypIndices,   // [beamIndex * activeBatchSize + batchIndex]
+                                 const Words& words,                         // [beamIndex * activeBatchSize + batchIndex]
+                                 const std::vector<IndexType>& batchIndices, // [batchIndex]
                                  int beamSize)
       = 0;
 
@@ -148,7 +148,7 @@ class EncoderDecoder : public IEncoderDecoder, public LayerBase {
                                  Ptr<DecoderState> state,
                                  const std::vector<IndexType>& hypIndices,
                                  const Words& words,
-                                 int dimBatch,
+                                 const std::vector<IndexType>& batchIndices,
                                  int beamSize) override;
 
   virtual Ptr<DecoderState> stepAll(Ptr<ExpressionGraph> graph,
diff --git a/src/models/s2s.h b/src/models/s2s.h
index b340b5a5b..4c0fd0194 100755
--- a/src/models/s2s.h
+++ b/src/models/s2s.h
@@ -143,6 +143,8 @@ class DecoderS2S : public DecoderBase {
 private:
   Ptr<rnn::RNN> rnn_;
   Ptr<mlp::MLP> output_;
+  int lastDimBatch_{-1}; // monitor dimBatch to take into account batch-pruning during decoding
+                         // may require to lazily rebuild decoder RNN
 
   Ptr<rnn::RNN> constructDecoderRNN(Ptr<ExpressionGraph> graph,
                                     Ptr<DecoderState> state) {
@@ -251,8 +253,19 @@ class DecoderS2S : public DecoderBase {
 
     auto embeddings = state->getTargetHistoryEmbeddings();
 
-    if(!rnn_)
-      rnn_ = constructDecoderRNN(graph, state);
+    // The batch dimension of the inputs can change due to batch-pruning, in that case
+    // cached elements need to be rebuilt, in this case the mapped encoder context in the 
+    // attention mechanism of the decoder RNN.
+    int currDimBatch = embeddings->shape()[-2];
+    if(!rnn_ || lastDimBatch_ != currDimBatch)  // if currDimBatch is different, rebuild the cached RNN
+      rnn_ = constructDecoderRNN(graph, state); // @TODO: add a member to encoder/decoder state `bool batchDimChanged()`
+    lastDimBatch_ = currDimBatch;
+    // Also @TODO: maybe implement a Cached(build, updateIf) that runs a check and rebuild if required
+    // at dereferecing :
+    // rnn_ = Cached<decltype(constructDecoderRNN(graph, state))>(
+    //          /*build=*/[]{ return constructDecoderRNN(graph, state); }, 
+    //          /*updateIf=*/[]{ return state->batchDimChanged() });
+    // rnn_->transduce(...);
 
     // apply RNN to embeddings, initialized with encoder context mapped into
     // decoder space
diff --git a/src/models/states.h b/src/models/states.h
index a2e609c91..e0a070847 100644
--- a/src/models/states.h
+++ b/src/models/states.h
@@ -25,6 +25,12 @@ class EncoderState {
   virtual const Words& getSourceWords() {
     return batch_->front()->data();
   }
+
+  // Sub-select active batch entries from encoder context and context mask
+  Ptr<EncoderState> select(const std::vector<IndexType>& batchIndices) { // [batchIndex] indices of active batch entries
+    // Dimension -2 is OK for both, RNN and Transformer models as the encoder context in Transformer gets transposed to the same dimension layout
+    return New<EncoderState>(index_select(context_, -2, batchIndices), index_select(mask_, -2, batchIndices), batch_);
+  }
 };
 
 class DecoderState {
@@ -57,13 +63,20 @@ class DecoderState {
   virtual void setLogProbs(Logits logProbs) { logProbs_ = logProbs; }
 
   // @TODO: should this be a constructor? Then derived classes can call this without the New<> in the loop
-  virtual Ptr<DecoderState> select(const std::vector<IndexType>& selIdx,
+  virtual Ptr<DecoderState> select(const std::vector<IndexType>& hypIndices,   // [beamIndex * activeBatchSize + batchIndex]
+                                   const std::vector<IndexType>& batchIndices, // [batchIndex]
                                    int beamSize) const {
+
+    std::vector<Ptr<EncoderState>> newEncStates;
+    for(auto& es : encStates_) 
+      // If the size of the batch dimension of the encoder state context changed, subselect the correct batch entries
+      newEncStates.push_back(es->getContext()->shape()[-2] == batchIndices.size() ? es : es->select(batchIndices));
+      
+    // hypindices matches batchIndices in terms of batch dimension, so we only need hypIndices
     auto selectedState = New<DecoderState>(
-        states_.select(selIdx, beamSize, /*isBatchMajor=*/false), logProbs_, encStates_, batch_);
+        states_.select(hypIndices, beamSize, /*isBatchMajor=*/false), logProbs_, newEncStates, batch_);
 
-    // Set positon of new state based on the target token position of current
-    // state
+    // Set positon of new state based on the target token position of current state
     selectedState->setPosition(getPosition());
     return selectedState;
   }
diff --git a/src/models/transformer.h b/src/models/transformer.h
index 660c179ae..991693541 100755
--- a/src/models/transformer.h
+++ b/src/models/transformer.h
@@ -282,7 +282,12 @@ class Transformer : public EncoderOrDecoderBase {
     // Caching transformation of the encoder that should not be created again.
     // @TODO: set this automatically by memoizing encoder context and
     // memoization propagation (short-term)
-    if (!cache || (cache && cache_.count(prefix + "_keys") == 0)) {
+    if (cache                                                                          // if caching
+        && cache_.count(prefix + "_keys") > 0                                          // and the keys expression has been seen
+        && cache_[prefix + "_keys"]->shape().elements() == keys->shape().elements()) { // and the underlying element size did not change
+      kh = cache_[prefix + "_keys"];                                                   // then return cached tensor
+    }
+    else {
       auto Wk = graph_->param(prefix + "_Wk", {dimModel, dimModel}, inits::glorotUniform());
       auto bk = graph_->param(prefix + "_bk", {1,        dimModel}, inits::zeros());
 
@@ -290,20 +295,19 @@ class Transformer : public EncoderOrDecoderBase {
       kh = SplitHeads(kh, dimHeads); // [-4: batch size, -3: num heads, -2: max length, -1: split vector dim]
       cache_[prefix + "_keys"] = kh;
     }
-    else {
-      kh = cache_[prefix + "_keys"];
-    }
 
     Expr vh;
-    if (!cache || (cache && cache_.count(prefix + "_values") == 0)) {
+    if (cache 
+        && cache_.count(prefix + "_values") > 0 
+        && cache_[prefix + "_values"]->shape().elements() == values->shape().elements()) {
+      vh = cache_[prefix + "_values"];
+    } else {
       auto Wv = graph_->param(prefix + "_Wv", {dimModel, dimModel}, inits::glorotUniform());
       auto bv = graph_->param(prefix + "_bv", {1,        dimModel}, inits::zeros());
 
       vh = affine(values, Wv, bv); // [-4: batch size, -3: num heads, -2: max length, -1: split vector dim]
       vh = SplitHeads(vh, dimHeads);
       cache_[prefix + "_values"] = vh;
-    } else {
-      vh = cache_[prefix + "_values"];
     }
 
     int dimBeam = q->shape()[-4];
@@ -573,10 +577,19 @@ class TransformerState : public DecoderState {
                    Ptr<data::CorpusBatch> batch)
       : DecoderState(states, logProbs, encStates, batch) {}
 
-  virtual Ptr<DecoderState> select(const std::vector<IndexType>& selIdx,
+  virtual Ptr<DecoderState> select(const std::vector<IndexType>& hypIndices,   // [beamIndex * activeBatchSize + batchIndex]
+                                   const std::vector<IndexType>& batchIndices, // [batchIndex]
                                    int beamSize) const override {
+
+    // @TODO: code duplication with DecoderState only because of isBatchMajor=true, should rather be a contructor argument of DecoderState?
+    
+    std::vector<Ptr<EncoderState>> newEncStates;
+    for(auto& es : encStates_) 
+      // If the size of the batch dimension of the encoder state context changed, subselect the correct batch entries    
+      newEncStates.push_back(es->getContext()->shape()[-2] == batchIndices.size() ? es : es->select(batchIndices));
+
     // Create hypothesis-selected state based on current state and hyp indices
-    auto selectedState = New<TransformerState>(states_.select(selIdx, beamSize, /*isBatchMajor=*/true), logProbs_, encStates_, batch_);
+    auto selectedState = New<TransformerState>(states_.select(hypIndices, beamSize, /*isBatchMajor=*/true), logProbs_, newEncStates, batch_); 
 
     // Set the same target token position as the current state
     // @TODO: This is the same as in base function.
@@ -691,10 +704,14 @@ class DecoderTransformer : public Transformer<DecoderBase> {
       auto encoderMask = encoderState->getMask();
 
       encoderContext = transposeTimeBatch(encoderContext); // [-4: beam depth=1, -3: batch size, -2: max length, -1: vector dim]
-
       int dimSrcWords = encoderContext->shape()[-2];
 
-      //int dims = encoderMask->shape().size();
+      // This would happen if something goes wrong during batch pruning.
+      ABORT_IF(encoderContext->shape()[-3] != dimBatch,
+               "Context and query batch dimension do not match {} != {}", 
+               encoderContext->shape()[-3], 
+               dimBatch);
+
       encoderMask = atleast_nd(encoderMask, 4);
       encoderMask = reshape(transposeTimeBatch(encoderMask),
                             {1, dimBatch, 1, dimSrcWords});
diff --git a/src/translator/beam_search.h b/src/translator/beam_search.h
index 8e1bcf9cb..d273de534 100755
--- a/src/translator/beam_search.h
+++ b/src/translator/beam_search.h
@@ -19,6 +19,7 @@ class BeamSearch {
   Ptr<Vocab> trgVocab_;
 
   static constexpr auto INVALID_PATH_SCORE = -9999; // (@TODO: change to -9999.0 once C++ allows that)
+  static constexpr auto PURGE_BATCH = true; // @TODO: diagnostic, to-be-removed once confirmed there are no issues.
 
 public:
   BeamSearch(Ptr<Options> options,
@@ -26,28 +27,41 @@ class BeamSearch {
              Ptr<Vocab> trgVocab)
       : options_(options),
         scorers_(scorers),
-        beamSize_(options_->has("beam-size")
-                      ? options_->get<size_t>("beam-size")
-                      : 3),
+        beamSize_(options_->get<size_t>("beam-size")),
         trgVocab_(trgVocab) {}
 
   // combine new expandedPathScores and previous beams into new set of beams
-  Beams toHyps(const std::vector<unsigned int>& nBestKeys, // [dimBatch, beamSize] flattened -> ((batchIdx, beamHypIdx) flattened, word idx) flattened
-               const std::vector<float>& nBestPathScores,  // [dimBatch, beamSize] flattened
+  Beams toHyps(const std::vector<unsigned int>& nBestKeys, // [currentDimBatch, beamSize] flattened -> ((batchIdx, beamHypIdx) flattened, word idx) flattened
+               const std::vector<float>& nBestPathScores,  // [currentDimBatch, beamSize] flattened
                const size_t nBestBeamSize, // for interpretation of nBestKeys
                const size_t vocabSize,     // ditto.
                const Beams& beams,
                const std::vector<Ptr<ScorerState /*const*/>>& states,
                Ptr<data::CorpusBatch /*const*/> batch, // for alignments only
-               Ptr<FactoredVocab/*const*/> factoredVocab, size_t factorGroup) const {
-    std::vector<float> align;
-    if(options_->hasAndNotEmpty("alignment") && factorGroup == 0)
-      align = scorers_[0]->getAlignment(); // [beam depth * max src length * batch size] -> P(s|t); use alignments from the first scorer, even if ensemble
-
-    const auto dimBatch = beams.size();
-    Beams newBeams(dimBatch);   // return value of this function goes here
+               Ptr<FactoredVocab/*const*/> factoredVocab, size_t factorGroup,
+               const std::vector<IndexType>& batchIdxMap) const { // [origBatchIdx -> currentBatchIdx]
+    std::vector<float> align; // collects alignment information from the last executed time step
+    if(options_->hasAndNotEmpty("alignment") && factorGroup == 0) 
+      align = scorers_[0]->getAlignment(); // [beam depth * max src length * current batch size] -> P(s|t); use alignments from the first scorer, even if ensemble, 
+
+    const auto origDimBatch = beams.size(); // see function search for definition of origDimBatch and currentDimBatch etc.
+    Beams newBeams(origDimBatch);           // return value of this function goes here. There are always origDimBatch beams.
+
+    // create a reverse batchMap to obtain original batchIdx in the starting batch size
+    // and calculate the current batch size based on non-empty beams
+    std::vector<IndexType> reverseBatchIdxMap(batchIdxMap.size());
+    size_t currentDimBatch = beams.size();
+    if(PURGE_BATCH) {
+      currentDimBatch = 0;
+      for(int i = 0; i < batchIdxMap.size(); ++i) {
+        reverseBatchIdxMap[batchIdxMap[i]] = i; // reverse batch index mapping, multiple occurences get overwritten with the last one, 
+                                                // which is expected due to down-shifting
+        if(!beams[i].empty())
+          currentDimBatch++;
+      }
+    }
 
-    for(size_t i = 0; i < nBestKeys.size(); ++i) { // [dimBatch, beamSize] flattened
+    for(size_t i = 0; i < nBestKeys.size(); ++i) { // [currentDimBatch, beamSize] flattened
       // Keys encode batchIdx, beamHypIdx, and word index in the entire beam.
       // They can be between 0 and (vocabSize * nBestBeamSize * batchSize)-1.
       // (beamHypIdx refers to the GPU tensors, *not* the beams[] array; they are not the same in case of purging)
@@ -55,12 +69,16 @@ class BeamSearch {
       const float pathScore = nBestPathScores[i]; // expanded path score for (batchIdx, beamHypIdx, word)
 
       // decompose key into individual indices (batchIdx, beamHypIdx, wordIdx)
-      const auto wordIdx    = (WordIndex)(key % vocabSize);
-      const auto beamHypIdx =            (key / vocabSize) % nBestBeamSize;
-      const auto batchIdx   =            (key / vocabSize) / nBestBeamSize;
+      const auto wordIdx         = (WordIndex)(key % vocabSize);
+      const auto beamHypIdx      =            (key / vocabSize) % nBestBeamSize;
+      const auto currentBatchIdx =            (key / vocabSize) / nBestBeamSize;
+
+      auto origBatchIdx          = currentBatchIdx;
+      if(PURGE_BATCH)
+        origBatchIdx = reverseBatchIdxMap[currentBatchIdx]; // map currentBatchIdx back into original position within starting maximal batch size, required to find correct beam
 
-      const auto& beam = beams[batchIdx];
-      auto& newBeam = newBeams[batchIdx];
+      const auto& beam = beams[origBatchIdx];
+      auto& newBeam = newBeams[origBatchIdx]; // extended hypotheses are going to be placed in this new beam
 
       if (newBeam.size() >= beam.size()) // getNBestList() generates N for all batch entries incl. those that already have a narrower beam
         continue;
@@ -109,12 +127,15 @@ class BeamSearch {
                  "A word without this factor snuck through to here??");
         breakDown.resize(states.size(), 0); // at start, this is empty, so this will set the initial score to 0
         for(size_t j = 0; j < states.size(); ++j) {
-          auto lval = states[j]->getLogProbs().getFactoredLogitsTensor(factorGroup); // [localBeamSize, 1, dimBatch, dimFactorVocab]
-          size_t flattenedLogitIndex = (beamHypIdx * dimBatch + batchIdx) * vocabSize + wordIdx;  // (beam idx, batch idx, word idx); note: beam and batch are transposed, compared to 'key'
+          auto lval = states[j]->getLogProbs().getFactoredLogitsTensor(factorGroup); // [maxBeamSize, 1, currentDimBatch, dimFactorVocab]
+          // The flatting happens based on actual (current) batch size and batch index computed with batch-pruning as we are looking into the pruned tensor
+          size_t flattenedLogitIndex = (beamHypIdx * currentDimBatch + currentBatchIdx) * vocabSize + wordIdx;  // (beam idx, batch idx, word idx); note: beam and batch are transposed, compared to 'key'
+          
           // @TODO: use a function on shape() to index, or new method val->at({i1, i2, i3, i4}) with broadcasting
-          ABORT_IF(lval->shape() != Shape({(int)nBestBeamSize, 1, (int)dimBatch, (int)vocabSize}) &&
-                   (beamHypIdx == 0 && lval->shape() != Shape({1, 1, (int)dimBatch, (int)vocabSize})),
-                   "Unexpected shape of logits?? {} != {}", lval->shape(), Shape({(int)nBestBeamSize, 1, (int)dimBatch, (int)vocabSize}));
+          ABORT_IF(lval->shape() != Shape({(int)nBestBeamSize, 1, (int)currentDimBatch, (int)vocabSize}) &&
+                   (beamHypIdx == 0 && lval->shape() != Shape({1, 1, (int)currentDimBatch, (int)vocabSize})),
+                   "Unexpected shape of logits?? {} != {}", lval->shape(), Shape({(int)nBestBeamSize, 1, (int)currentDimBatch, (int)vocabSize}));
+          
           breakDown[j] += lval->get(flattenedLogitIndex);
         }
         hyp->setScoreBreakdown(breakDown);
@@ -122,13 +143,13 @@ class BeamSearch {
 
       // Set alignments
       if(!align.empty())
-        hyp->setAlignment(getAlignmentsForHypothesis(align, batch, (int)beamHypIdx, (int)batchIdx));
+        hyp->setAlignment(getAlignmentsForHypothesis(align, batch, (int)beamHypIdx, (int)currentBatchIdx, (int)origBatchIdx, (int)currentDimBatch));
       else // not first factor: just copy
         hyp->setAlignment(beam[beamHypIdx]->getAlignment());
 
       newBeam.push_back(hyp);
     }
-
+    
     // if factored vocab and this is not the first factor, we need to
     // also propagate factored hypotheses that do not get expanded in this step because they don't have this factor
     if (factorGroup > 0) {
@@ -157,10 +178,12 @@ class BeamSearch {
   }
 
   std::vector<float> getAlignmentsForHypothesis( // -> P(s|t) for current t and given beam and batch dim
-      const std::vector<float> alignAll, // [beam depth, max src length, batch size, 1], flattened
+      const std::vector<float> alignAll, // [beam depth, max src length, batch size, 1], flattened vector of all attention probablities
       Ptr<data::CorpusBatch> batch,
       int beamHypIdx,
-      int batchIdx) const {
+      int currentBatchIdx,
+      int origBatchIdx,
+      int currentDimBatch) const {
     // Let's B be the beam size, N be the number of batched sentences,
     // and L the number of words in the longest sentence in the batch.
     // The alignment vector:
@@ -178,33 +201,46 @@ class BeamSearch {
     // in a single beam, i.e.:
     //   * [word1-batch1, word1-batch2, ..., word2-batch1, ...]
     //
-    size_t batchSize  = batch->size();               // number of sentences in batch
-    size_t batchWidth = batch->width();              // max src length
-    size_t batchWidthXSize = batchWidth * batchSize; // total number of words in the batch incl. padding = product of last 3 tensor dimensions
+    
+    size_t origDimBatch = batch->size();  // number of sentences in batch
+    size_t batchWidth   = batch->width(); // max src length
 
-    // loop over words of batch entry 'batchIdx' and beam entry 'beamHypIdx'
+    // loop over words of batch entry 'currentBatchIdx' and beam entry 'beamHypIdx'
     std::vector<float> align;
     for(size_t srcPos = 0; srcPos < batchWidth; ++srcPos) { // loop over source positions
-      size_t a = ((batchWidthXSize * beamHypIdx) + batchIdx) + (batchSize * srcPos); // = flatten [beam index, s, batch index, 0]
-      size_t m = a % batchWidthXSize; // == batchIdx + (batchSize * srcPos) = flatten [0, s, batch index, 0]
-      if(batch->front()->mask()[m] != 0)
-        align.emplace_back(alignAll[a]);
+      // We are looking into the probabilites from an actual tensor, hence we need to use currentDimBatch and currentBatchIdx.
+      size_t currentAttIdx = (batchWidth * beamHypIdx + srcPos) * currentDimBatch + currentBatchIdx; // = flatten [beam index, s, batch index, 0]
+
+      // We are looking into the mask from the orginal batch, hence we need to use origDmBatch and origBatchIdx.
+      size_t origAttIdx  = (batchWidth * beamHypIdx + srcPos) * origDimBatch + origBatchIdx;; // = flatten [beam index, s, batch index, 0]
+      size_t origMaskIdx = origAttIdx % (batchWidth * origDimBatch); // == batchIdx + (batchSize * srcPos) = flatten [0, s, batch index, 0]
+
+      // If the original position is not masked out used the corresponding current attention score.
+      if(batch->front()->mask()[origMaskIdx] != 0)
+        align.emplace_back(alignAll[currentAttIdx]);
     }
     return align;
   }
 
   // remove all beam entries that have reached EOS
-  Beams purgeBeams(const Beams& beams) {
+  Beams purgeBeams(const Beams& beams, /*in/out=*/std::vector<IndexType>& batchIdxMap) {
     const auto trgEosId = trgVocab_->getEosId();
     Beams newBeams;
+    size_t beamIdx = 0; // beam index
     for(auto beam : beams) {
-      Beam newBeam;
-      for(auto hyp : beam) {
-        if(hyp->getWord() != trgEosId) {
-          newBeam.push_back(hyp);
-        }
+      Beam newBeam; // a beam of surviving hyps
+      for(auto hyp : beam)
+        if(hyp->getWord() != trgEosId) // if this hyp is not finished, 
+          newBeam.push_back(hyp);      // move over to beam of surviving hyps
+
+      if(PURGE_BATCH)
+        if(newBeam.empty() && !beam.empty()) {      // previous beam had hyps, but all were finished in this step, newBeam will now stay empty
+          for(int i = beamIdx + 1; i < beams.size(); ++i) // for all entries above this beam
+            batchIdxMap[i] = batchIdxMap[i] - 1;  // make them look at one batch index below, as the current entry will be removed from the batch.
       }
+
       newBeams.push_back(newBeam);
+      beamIdx++; // move to next beam index
     }
     return newBeams;
   }
@@ -220,18 +256,20 @@ class BeamSearch {
     if (numFactorGroups == 1) // if no factors then we didn't need this object in the first place
       factoredVocab.reset();
 
-    const int dimBatch = (int)batch->size();
+    // We will use the prefix "origBatch..." whenever we refer to batch dimensions of the original batch. These do not change during search.
+    // We will use the prefix "currentBatch.." whenever we refer to batch dimension that can change due to batch-pruning.
+    const int origDimBatch = (int)batch->size();
     const auto trgEosId = trgVocab_->getEosId();
     const auto trgUnkId = trgVocab_->getUnkId();
 
-    auto getNBestList = createGetNBestListFn(beamSize_, dimBatch, graph->getDeviceId());
+    auto getNBestList = createGetNBestListFn(beamSize_, origDimBatch, graph->getDeviceId());
 
     for(auto scorer : scorers_) {
       scorer->clear(graph);
     }
 
-    Histories histories(dimBatch);
-    for(int i = 0; i < dimBatch; ++i) {
+    Histories histories(origDimBatch);
+    for(int i = 0; i < origDimBatch; ++i) {
       size_t sentId = batch->getSentenceIds()[i];
       histories[i] = New<History>(sentId,
                                   options_->get<float>("normalize"),
@@ -244,24 +282,30 @@ class BeamSearch {
       states.push_back(scorer->startState(graph, batch));
     }
 
-    // create one beam per batch entry with sentence-start hypothesis
-    Beams beams(dimBatch, Beam(beamSize_, Hypothesis::New())); // array [dimBatch] of array [localBeamSize] of Hypothesis
-
     const auto srcEosId = batch->front()->vocab()->getEosId();
-    for(int batchIdx = 0; batchIdx < dimBatch; ++batchIdx) {
-      auto& beam = beams[batchIdx];
-      histories[batchIdx]->add(beam, trgEosId); // add beams with start-hypotheses to traceback grid
+    
+    // create one beam per batch entry with sentence-start hypothesis
+    Beams beams(origDimBatch, Beam(beamSize_, Hypothesis::New())); // array [origDimBatch] of array [maxBeamSize] of Hypothesis, keeps full size through search.
+                                                                   // batch purging is determined from an empty sub-beam.
+    std::vector<IndexType> batchIdxMap(origDimBatch); // Record at which batch entry a beam is looking. 
+                                                      // By default that corresponds to position in array, 
+                                                      // but shifts in the course of removing batch entries when they are finished.
+    for(int origBatchIdx = 0; origBatchIdx < origDimBatch; ++origBatchIdx) {
+      batchIdxMap[origBatchIdx] = origBatchIdx; // map to same position on initialization
+      auto& beam = beams[origBatchIdx];
+      histories[origBatchIdx]->add(beam, trgEosId); // add beams with start-hypotheses to traceback grid
 
       // Handle batch entries that consist only of source <EOS> i.e. these are empty lines
-      if(batch->front()->data()[batchIdx] == srcEosId) {
+      if(batch->front()->data()[origBatchIdx] == srcEosId) {
         // create a target <EOS> hypothesis that extends the start-hypothesis
         auto eosHyp = Hypothesis::New(/*prevHyp=*/    beam[0], 
                                       /*currWord=*/   trgEosId, 
                                       /*prevHypIdx=*/ 0, 
                                       /*pathScore=*/  0.f);
         auto eosBeam = Beam(beamSize_, eosHyp);      // create a dummy beam filled with <EOS>-hyps
-        histories[batchIdx]->add(eosBeam, trgEosId); // push dummy <EOS>-beam to traceback grid
-        beam.clear(); // zero out current beam, so it does not get used for further symbols as empty beams get omitted/dummy-filled everywhere
+        histories[origBatchIdx]->add(eosBeam, trgEosId); // push dummy <EOS>-beam to traceback grid
+        beam.clear(); // Zero out current beam, so it does not get used for further symbols as empty beams get omitted everywhere.
+                      // The corresponding neural states will be purged further down.
       }
     }
 
@@ -275,29 +319,30 @@ class BeamSearch {
     }
 
     // the decoding process updates the following state information in each output time step:
-    //  - beams: array [dimBatch] of array [localBeamSize] of Hypothesis
+    //  - beams: array [origDimBatch] of array [maxBeamSize] of Hypothesis
     //     - current output time step's set of active hypotheses, aka active search space
     //  - states[.]: ScorerState
     //     - NN state; one per scorer, e.g. 2 for ensemble of 2
     // and it forms the following return value
-    //  - histories: array [dimBatch] of History
-    //    with History: vector [t] of array [localBeamSize] of Hypothesis
+    //  - histories: array [origDimBatch] of History
+    //    with History: vector [t] of array [maxBeamSize] of Hypothesis
     //    with Hypothesis: (last word, aggregate score, prev Hypothesis)
 
+    IndexType currentDimBatch = origDimBatch;
+    auto prevBatchIdxMap = batchIdxMap; // [origBatchIdx -> currentBatchIdx] but shifted by one time step
     // main loop over output time steps
     for (size_t t = 0; ; t++) {
-      ABORT_IF(dimBatch != beams.size(), "Lost a batch entry??");
-
+      ABORT_IF(origDimBatch != beams.size(), "Lost a batch entry??");
       // determine beam size for next output time step, as max over still-active sentences
       // E.g. if all batch entries are down from beam 5 to no more than 4 surviving hyps, then
       // switch to beam of 4 for all. If all are done, then beam ends up being 0, and we are done.
-      size_t localBeamSize = 0; // @TODO: is there some std::algorithm for this?
+      size_t maxBeamSize = 0; // @TODO: is there some std::algorithm for this?
       for(auto& beam : beams)
-        if(beam.size() > localBeamSize)
-          localBeamSize = beam.size();
+        if(beam.size() > maxBeamSize)
+          maxBeamSize = beam.size();
 
       // done if all batch entries have reached EOS on all beam entries
-      if (localBeamSize == 0)
+      if (maxBeamSize == 0)
         break;
 
       for (size_t factorGroup = 0; factorGroup < numFactorGroups; factorGroup++) {
@@ -306,42 +351,70 @@ class BeamSearch {
         //**********************************************************************
         // create constant containing previous path scores for current beam
         // Also create mapping of hyp indices, for reordering the decoder-state tensors.
-        std::vector<IndexType> hypIndices; // [localBeamsize, 1, dimBatch, 1] (flattened) tensor index ((beamHypIdx, batchIdx), flattened) of prev hyp that a hyp originated from
-        std::vector<Word> prevWords;       // [localBeamsize, 1, dimBatch, 1] (flattened) word that a hyp ended in, for advancing the decoder-model's history
-        Expr prevPathScores;               // [localBeamSize, 1, dimBatch, 1], path score that a hyp ended in (last axis will broadcast into vocab size when adding expandedPathScores)
+        std::vector<IndexType> batchIndices;    // [1,           1, currentDimBatch, 1] indices of currently used batch indices with regard to current, actual tensors
+        std::vector<IndexType> hypIndices;      // [maxBeamSize, 1, currentDimBatch, 1] (flattened) tensor index ((beamHypIdx, batchIdx), flattened) of prev hyp that a hyp originated from
+        std::vector<Word> prevWords;            // [maxBeamSize, 1, currentDimBatch, 1] (flattened) word that a hyp ended in, for advancing the decoder-model's history
+        Expr prevPathScores;                    // [maxBeamSize, 1, currentDimBatch, 1], path score that a hyp ended in (last axis will broadcast into vocab size when adding expandedPathScores)
+        
         bool anyCanExpand = false; // stays false if all hyps are invalid factor expansions
         if(t == 0 && factorGroup == 0) { // no scores yet
           prevPathScores = graph->constant({1, 1, 1, 1}, inits::fromValue(0));
           anyCanExpand = true;
+
+          // at the beginning all batch entries are used
+          batchIndices.resize(origDimBatch);
+          std::iota(batchIndices.begin(), batchIndices.end(), 0);
         } else {
+          if(factorGroup == 0)                                                              // only factorGroup==0 can subselect neural state
+            for(int currentBatchIdx = 0; currentBatchIdx < beams.size(); ++currentBatchIdx) // loop over batch entries (active sentences)
+              if(!beams[currentBatchIdx].empty() || !PURGE_BATCH)                           // for each beam check
+                batchIndices.push_back(prevBatchIdxMap[currentBatchIdx]);                   // which batch entries were active in previous step
+            
           std::vector<float> prevScores;
-          for(size_t beamHypIdx = 0; beamHypIdx < localBeamSize; ++beamHypIdx) {
-            for(int batchIdx = 0; batchIdx < dimBatch; ++batchIdx) { // loop over batch entries (active sentences)
-              auto& beam = beams[batchIdx];
+          for(size_t beamHypIdx = 0; beamHypIdx < maxBeamSize; ++beamHypIdx) { // loop over globally maximal beam-size (maxBeamSize)
+            for(int origBatchIdx = 0; origBatchIdx < origDimBatch; ++origBatchIdx) { // loop over all batch entries (active and inactive)
+              auto& beam = beams[origBatchIdx];
               if(beamHypIdx < beam.size()) {
                 auto hyp = beam[beamHypIdx];
                 auto word = hyp->getWord();
                 auto canExpand = (!factoredVocab || factoredVocab->canExpandFactoredWord(hyp->getWord(), factorGroup));
                 //LOG(info, "[{}, {}] Can expand {} with {} -> {}", batchIdx, beamHypIdx, (*batch->back()->vocab())[hyp->getWord()], factorGroup, canExpand);
                 anyCanExpand |= canExpand;
-                hypIndices.push_back((IndexType)(hyp->getPrevStateIndex() * dimBatch + batchIdx)); // (beamHypIdx, batchIdx), flattened, for index_select() operation
+
+                auto currentBatchIdx = origBatchIdx;
+                if(PURGE_BATCH) {
+                  if(factorGroup == 0)
+                    currentBatchIdx = prevBatchIdxMap[origBatchIdx]; // subselection may happen for factorGroup == 0
+                  else
+                    currentBatchIdx = batchIdxMap[origBatchIdx];     // no subselection happens for factorGroup > 0, 
+                                                                     // but we treat it like a next step, since a step 
+                                                                     // happened for factorGroup == 0
+                }
+                  
+                auto hypIndex = (IndexType)(hyp->getPrevStateIndex() * currentDimBatch + currentBatchIdx); // (beamHypIdx, batchIdx), flattened, for index_select() operation
+
+                hypIndices.push_back(hypIndex); // (beamHypIdx, batchIdx), flattened as said above.
                 prevWords .push_back(word);
                 prevScores.push_back(canExpand ? hyp->getPathScore() : INVALID_PATH_SCORE);
-              } else {  // pad to localBeamSize (dummy hypothesis)
-                hypIndices.push_back(0);
-                prevWords.push_back(trgEosId);  // (unused, but must be valid)
-                prevScores.push_back((float)INVALID_PATH_SCORE);
+              } else {  // pad to maxBeamSize (dummy hypothesis)
+                if(!PURGE_BATCH || !beam.empty()) { // but only if we are not pruning and the beam is not deactivated yet
+                  hypIndices.push_back(0);
+                  prevWords.push_back(trgEosId);  // (unused, but must be valid)
+                  prevScores.push_back((float)INVALID_PATH_SCORE);
+                }
               }
             }
           }
-          prevPathScores = graph->constant({(int)localBeamSize, 1, dimBatch, 1}, inits::fromVector(prevScores));
+          if(factorGroup == 0) 
+            currentDimBatch = batchIndices.size(); // keep batch size constant for all factor groups in a time step
+          prevPathScores = graph->constant({(int)maxBeamSize, 1, (int)currentDimBatch, 1}, inits::fromVector(prevScores));
         }
         if (!anyCanExpand) // all words cannot expand this factor: skip
           continue;
 
         //**********************************************************************
         // compute expanded path scores with word prediction probs from all scorers
-        auto expandedPathScores = prevPathScores; // will become [localBeamSize, 1, dimBatch, dimVocab]
+        auto expandedPathScores = prevPathScores; // will become [maxBeamSize, 1, currDimBatch, dimVocab]
         Expr logProbs;
         for(size_t i = 0; i < scorers_.size(); ++i) {
           if (factorGroup == 0) {
@@ -356,15 +429,14 @@ class BeamSearch {
             //  LOG(info, "prevWords[{},{}]={} -> {}", t/numFactorGroups, factorGroup,
             //      factoredVocab ? factoredVocab->word2string(prevWords[kk]) : (*batch->back()->vocab())[prevWords[kk]],
             //      prevScores[kk]);
-            states[i] = scorers_[i]->step(graph, states[i], hypIndices, prevWords, dimBatch, (int)localBeamSize);
+            states[i] = scorers_[i]->step(graph, states[i], hypIndices, prevWords, batchIndices, (int)maxBeamSize);
             if (numFactorGroups == 1) // @TODO: this branch can go away
-              logProbs = states[i]->getLogProbs().getLogits(); // [localBeamSize, 1, dimBatch, dimVocab]
+              logProbs = states[i]->getLogProbs().getLogits(); // [maxBeamSize, 1, currentDimBatch, dimVocab]
             else
             {
               auto shortlist = scorers_[i]->getShortlist();
-              logProbs = states[i]->getLogProbs().getFactoredLogits(factorGroup, shortlist); // [localBeamSize, 1, dimBatch, dimVocab]
+              logProbs = states[i]->getLogProbs().getFactoredLogits(factorGroup, shortlist); // [maxBeamSize, 1, currentDimBatch, dimVocab]
             }
-            //logProbs->debug("logProbs");
           }
           else {
             // add secondary factors
@@ -376,14 +448,14 @@ class BeamSearch {
             // and push out other hypotheses. Hence, we exclude those here by setting the path score to
             // INVALID_PATH_SCORE. Instead, toHyps() explicitly propagates those hyps by simply copying the
             // previous hypothesis.
-            logProbs = states[i]->getLogProbs().getFactoredLogits(factorGroup, /*shortlist=*/ nullptr, hypIndices, localBeamSize); // [localBeamSize, 1, dimBatch, dimVocab]
+            logProbs = states[i]->getLogProbs().getFactoredLogits(factorGroup, /*shortlist=*/ nullptr, hypIndices, maxBeamSize); // [maxBeamSize, 1, currentDimBatch, dimVocab]
           }
-          // expand all hypotheses, [localBeamSize, 1, dimBatch, 1] -> [localBeamSize, 1, dimBatch, dimVocab]
+          // expand all hypotheses, [maxBeamSize, 1, currentDimBatch, 1] -> [maxBeamSize, 1, currentDimBatch, dimVocab]
           expandedPathScores = expandedPathScores + scorers_[i]->getWeight() * logProbs;
         }
 
         // make beams continuous
-        expandedPathScores = swapAxes(expandedPathScores, 0, 2); // -> [dimBatch, 1, localBeamSize, dimVocab]
+        expandedPathScores = swapAxes(expandedPathScores, 0, 2); // -> [currentDimBatch, 1, maxBeamSize, dimVocab]
 
         // perform NN computation
         if(t == 0 && factorGroup == 0)
@@ -393,7 +465,6 @@ class BeamSearch {
 
         //**********************************************************************
         // suppress specific symbols if not at right positions
-
         if(unkColId != -1 && factorGroup == 0)
           suppressWord(expandedPathScores, unkColId);
         for(auto state : states)
@@ -402,11 +473,11 @@ class BeamSearch {
         //**********************************************************************
         // perform beam search
 
-        // find N best amongst the (localBeamSize * dimVocab) hypotheses
-        std::vector<unsigned int> nBestKeys; // [dimBatch, localBeamSize] flattened -> (batchIdx, beamHypIdx, word idx) flattened
-        std::vector<float> nBestPathScores;  // [dimBatch, localBeamSize] flattened
-        getNBestList(/*in*/ expandedPathScores->val(), // [dimBatch, 1, localBeamSize, dimVocab or dimShortlist]
-                    /*N=*/localBeamSize,              // desired beam size
+        // find N best amongst the (maxBeamSize * dimVocab) hypotheses
+        std::vector<unsigned int> nBestKeys; // [currentDimBatch, maxBeamSize] flattened -> (batchIdx, beamHypIdx, word idx) flattened
+        std::vector<float> nBestPathScores;  // [currentDimBatch, maxBeamSize] flattened
+        getNBestList(/*in*/ expandedPathScores->val(), // [currentDimBatch, 1, maxBeamSize, dimVocab or dimShortlist]
+                    /*N=*/ maxBeamSize,              // desired beam size
                     /*out*/ nBestPathScores, /*out*/ nBestKeys,
                     /*first=*/t == 0 && factorGroup == 0); // @TODO: this is only used for checking presently, and should be removed altogether
         // Now, nBestPathScores contain N-best expandedPathScores for each batch and beam,
@@ -414,26 +485,30 @@ class BeamSearch {
 
         // combine N-best sets with existing search space (beams) to updated search space
         beams = toHyps(nBestKeys, nBestPathScores,
-                      /*nBestBeamSize*/expandedPathScores->shape()[-2], // used for interpretation of keys
-                      /*vocabSize=*/expandedPathScores->shape()[-1],    // used for interpretation of keys
-                      beams,
-                      states,    // used for keeping track of per-ensemble-member path score
-                      batch,     // only used for propagating alignment info
-                      factoredVocab, factorGroup);
+                       /*nBestBeamSize*/expandedPathScores->shape()[-2], // used for interpretation of keys
+                       /*vocabSize=*/expandedPathScores->shape()[-1],    // used for interpretation of keys
+                       beams,
+                       states,    // used for keeping track of per-ensemble-member path score
+                       batch,     // only used for propagating alignment info
+                       factoredVocab, factorGroup, 
+                       batchIdxMap); // used to create a reverse batch index map to recover original batch indices for this step
       } // END FOR factorGroup = 0 .. numFactorGroups-1
 
+      prevBatchIdxMap = batchIdxMap; // save current batchIdx map to be used in next step; we are then going to look one step back
+
       // remove all hyps that end in EOS
       // The position of a hyp in the beam may change.
-      const auto purgedNewBeams = purgeBeams(beams);
+      // in/out = shifts the batch index map if a beam gets fully purged
+      const auto purgedNewBeams = purgeBeams(beams, /*in/out=*/batchIdxMap);
 
       // add updated search space (beams) to our return value
       bool maxLengthReached = false;
-      for(int i = 0; i < dimBatch; ++i) {
+      for(int batchIdx = 0; batchIdx < origDimBatch; ++batchIdx) {
         // if this batch entry has surviving hyps then add them to the traceback grid
-        if(!beams[i].empty()) {
-          if (histories[i]->size() >= options_->get<float>("max-length-factor") * batch->front()->batchWidth())
+        if(!beams[batchIdx].empty()) { // if the beam is not empty expand the history object associated with the beam
+          if (histories[batchIdx]->size() >= options_->get<float>("max-length-factor") * batch->front()->batchWidth())
             maxLengthReached = true;
-          histories[i]->add(beams[i], trgEosId, purgedNewBeams[i].empty() || maxLengthReached);
+          histories[batchIdx]->add(beams[batchIdx], trgEosId, purgedNewBeams[batchIdx].empty() || maxLengthReached);
         }
       }
       if (maxLengthReached) // early exit if max length limit was reached
@@ -443,7 +518,7 @@ class BeamSearch {
       beams = purgedNewBeams;
     } // end of main loop over output time steps
 
-    return histories; // [dimBatch][t][N best hyps]
+    return histories; // [origDimBatch][t][N best hyps]
   }
 };
 }  // namespace marian
diff --git a/src/translator/history.h b/src/translator/history.h
index 8265fdb28..04b9d6052 100644
--- a/src/translator/history.h
+++ b/src/translator/history.h
@@ -14,8 +14,8 @@ class History {
   struct SentenceHypothesisCoord {
     bool operator<(const SentenceHypothesisCoord& hc) const { return normalizedPathScore < hc.normalizedPathScore; }
 
-    size_t i; // last time step of this sentence hypothesis
-    size_t j; // which beam entry
+    size_t timeStepIdx;        // last time step of this sentence hypothesis
+    size_t beamIdx;            // which beam entry
     float normalizedPathScore; // length-normalized sentence score
   };
 
@@ -25,14 +25,11 @@ class History {
   History(size_t lineNo, float alpha = 1.f, float wp_ = 0.f);
 
   void add(const Beam& beam, Word trgEosId, bool last = false) {
-    if(beam.back()->getPrevHyp() != nullptr) {
-      for(size_t j = 0; j < beam.size(); ++j)
-        if(beam[j]->getWord() == trgEosId || last) {
-          float pathScore =
-              (beam[j]->getPathScore() - wordPenalty(history_.size())) / lengthPenalty(history_.size());
-          topHyps_.push({history_.size(), j, pathScore});
-          // std::cerr << "Add " << history_.size() << " " << j << " " << pathScore
-          // << std::endl;
+    if(beam.back()->getPrevHyp() != nullptr) { // if not start hyp do
+      for(size_t beamIdx = 0; beamIdx < beam.size(); ++beamIdx)
+        if(beam[beamIdx]->getWord() == trgEosId || last) { // if this is a final hyp do
+          float pathScore = (beam[beamIdx]->getPathScore() - wordPenalty(history_.size())) / lengthPenalty(history_.size()); // get and normalize path score
+          topHyps_.push({history_.size(), beamIdx, pathScore}); // push final hyp on queue of scored hyps
         }
     }
     history_.push_back(beam);
@@ -45,11 +42,9 @@ class History {
     for (auto topHypsCopy = topHyps_; nbest.size() < n && !topHypsCopy.empty(); topHypsCopy.pop()) {
       auto bestHypCoord = topHypsCopy.top();
 
-      const size_t start = bestHypCoord.i; // last time step of this hypothesis
-      const size_t j     = bestHypCoord.j; // which beam entry
-      Hypothesis::PtrType bestHyp = history_[start][j];
-      // float c = bestHypCoord.normalizedPathScore;
-      // std::cerr << "h: " << start << " " << j << " " << c << std::endl;
+      const size_t timeStepIdx = bestHypCoord.timeStepIdx; // last time step of this hypothesis
+      const size_t beamIdx     = bestHypCoord.beamIdx;     // which beam entry
+      Hypothesis::PtrType bestHyp = history_[timeStepIdx][beamIdx];
 
       // trace back best path
       Words targetWords = bestHyp->tracebackWords();
@@ -65,7 +60,7 @@ class History {
   size_t getLineNum() const { return lineNo_; }
 
 private:
-  std::vector<Beam> history_; // [time step][index into beam] search grid
+  std::vector<Beam> history_; // [time step][index into beam] search grid @TODO: simplify as this is currently an expensive length count
   std::priority_queue<SentenceHypothesisCoord> topHyps_; // all sentence hypotheses (those that reached eos), sorted by score
   size_t lineNo_;
   float alpha_;
diff --git a/src/translator/scorers.h b/src/translator/scorers.h
index d3fc4f3b3..aca521f45 100755
--- a/src/translator/scorers.h
+++ b/src/translator/scorers.h
@@ -35,7 +35,7 @@ class Scorer {
                                 Ptr<ScorerState>,
                                 const std::vector<IndexType>&,
                                 const Words&,
-                                int dimBatch,
+                                const std::vector<IndexType>& batchIndices,
                                 int beamSize)
       = 0;
 
@@ -111,11 +111,11 @@ class ScorerWrapper : public Scorer {
                                 Ptr<ScorerState> state,
                                 const std::vector<IndexType>& hypIndices,
                                 const Words& words,
-                                int dimBatch,
+                                const std::vector<IndexType>& batchIndices,
                                 int beamSize) override {
     graph->switchParams(getName());
     auto wrapperState = std::dynamic_pointer_cast<ScorerWrapperState>(state);
-    auto newState = encdec_->step(graph, wrapperState->getState(), hypIndices, words, dimBatch, beamSize);
+    auto newState = encdec_->step(graph, wrapperState->getState(), hypIndices, words, batchIndices, beamSize);
     return New<ScorerWrapperState>(newState);
   }
 

From 5fb31b28d29ec79640a7a16d3a619932103ab90e Mon Sep 17 00:00:00 2001
From: Young Jin Kim <youki@microsoft.com>
Date: Wed, 13 Nov 2019 07:37:46 +0000
Subject: [PATCH 775/838] Merged PR 10415: Fix windows build errors

1. Added template definition for 'uint64_t', Windows has different definition of 'long' type.
2. Fixed warnings on windows.
---
 src/common/fastopt.cpp         |  7 +++++--
 src/data/corpus_base.cpp       | 12 ++++++------
 src/graph/expression_graph.h   |  6 +++---
 src/graph/parameters.h         |  4 ++--
 src/models/encoder_decoder.cpp |  2 +-
 src/translator/beam_search.h   |  4 ++--
 vs/Marian.vcxproj              |  7 +++++++
 vs/Marian.vcxproj.filters      | 15 +++++++++++++++
 8 files changed, 41 insertions(+), 16 deletions(-)

diff --git a/src/common/fastopt.cpp b/src/common/fastopt.cpp
index 779a1b532..ccdd7b543 100644
--- a/src/common/fastopt.cpp
+++ b/src/common/fastopt.cpp
@@ -23,7 +23,7 @@ struct Convert {
 // specialization for translating from string, @TODO check if this is required at all, mostly for compilation now.
 template <typename To>
 struct Convert<To, std::string> { 
-  static inline To apply(const std::string& from) { 
+  static inline To apply(const std::string& /* from */) { 
     ABORT("Not implemented");
   }
 };
@@ -84,7 +84,10 @@ std::vector<T> As<std::vector<T>>::apply(const FastOpt& node) {
 // specializations for simple vector types
 template struct As<std::vector<bool>>;
 template struct As<std::vector<int>>;
-template struct As<std::vector<unsigned long>>;
+// Windows and Unix based OS have different type definitions for 'unsigned long'.
+// So, we need an explicit definition for uint64_t. Otherwise, there's a linking error on windows.
+// https://software.intel.com/en-us/articles/size-of-long-integer-type-on-different-architecture-and-os/
+template struct As<std::vector<uint64_t>>;
 template struct As<std::vector<float>>;
 template struct As<std::vector<double>>;
 template struct As<std::vector<std::string>>;
diff --git a/src/data/corpus_base.cpp b/src/data/corpus_base.cpp
index d398c81a7..69d500ebf 100755
--- a/src/data/corpus_base.cpp
+++ b/src/data/corpus_base.cpp
@@ -84,19 +84,19 @@ CorpusBase::CorpusBase(Ptr<Options> options, bool translate)
                 "Vocabularies will be built separately for each file.");
 
       std::vector<int> vocabDims(paths_.size(), 0);
-      std::vector<std::string> vocabPaths(paths_.size());
+      std::vector<std::string> vocabPaths1(paths_.size());
       // Create vocabs if not provided
       for(size_t i = 0; i < paths_.size(); ++i) {
         Ptr<Vocab> vocab = New<Vocab>(options_, i);
         std::vector<std::string> trainPaths = { paths_[i] };
-        vocabDims[i] = vocab->loadOrCreate("", trainPaths, maxVocabs[i]);
-        vocabPaths[i] = paths_[i] + ".yml";
+        vocabDims[i] = (int) vocab->loadOrCreate("", trainPaths, maxVocabs[i]);
+        vocabPaths1[i] = paths_[i] + ".yml";
         vocabs_.emplace_back(vocab);
       }
       // TODO: this is not nice as it modifies the option object and needs to expose the changes
       // outside the corpus as models need to know about the vocabulary size; extract the vocab
       // creation functionality from the class.
-      options_->set("dim-vocabs", vocabDims, "vocabs", vocabPaths);
+      options_->set("dim-vocabs", vocabDims, "vocabs", vocabPaths1);
     } else {
       // Load all vocabs
       size_t numVocs = vocabPaths.size();
@@ -128,7 +128,7 @@ CorpusBase::CorpusBase(Ptr<Options> options, bool translate)
         // it wild not be created again, but just correctly loaded.
         auto pathsAndSize = groupVocab[vocabPaths[i]];
         std::vector<std::string> groupedPaths(pathsAndSize.paths.begin(), pathsAndSize.paths.end());
-        vocabDims[i] = vocab->loadOrCreate(vocabPaths[i], groupedPaths, pathsAndSize.size);
+        vocabDims[i] = (int) vocab->loadOrCreate(vocabPaths[i], groupedPaths, pathsAndSize.size);
         vocabs_.emplace_back(vocab);
       }
       // TODO: this is not nice as it modifies the option object and needs to expose the changes
@@ -150,7 +150,7 @@ CorpusBase::CorpusBase(Ptr<Options> options, bool translate)
     vocabDims.resize(numVocs, 0);
     for(size_t i = 0; i + 1 < numVocs; ++i) {
       Ptr<Vocab> vocab = New<Vocab>(options_, i);
-      vocabDims[i] = vocab->load(vocabPaths[i], maxVocabs[i]);
+      vocabDims[i] = (int) vocab->load(vocabPaths[i], maxVocabs[i]);
       vocabs_.emplace_back(vocab);
     }
     // TODO: As above, this is not nice as it modifies the option object and needs to expose the changes
diff --git a/src/graph/expression_graph.h b/src/graph/expression_graph.h
index 4fe422517..9e1222378 100755
--- a/src/graph/expression_graph.h
+++ b/src/graph/expression_graph.h
@@ -531,7 +531,7 @@ class ExpressionGraph : public std::enable_shared_from_this<ExpressionGraph> {
       auto defaultParams = std::dynamic_pointer_cast<MappedParameters>(it->second);
       if(!defaultParams) {
         // but it's not mapped, so delete it and replace it with a mapped version
-        auto defaultParams = New<MappedParameters>(defaultElementType_);
+        defaultParams = New<MappedParameters>(defaultElementType_);
         defaultParams->init(backend_);
         paramsByElementType_[defaultElementType_] = defaultParams;
       }
@@ -540,8 +540,8 @@ class ExpressionGraph : public std::enable_shared_from_this<ExpressionGraph> {
 
     // pre-populate parameters by type
     for(auto& item : items) {
-      auto it = paramsByElementType_.find(item.type);
-      if(it == paramsByElementType_.end()) {
+      auto it1 = paramsByElementType_.find(item.type);
+      if(it1 == paramsByElementType_.end()) {
         auto params = New<MappedParameters>(item.type);
         params->init(backend_);
         paramsByElementType_.insert({item.type, params});
diff --git a/src/graph/parameters.h b/src/graph/parameters.h
index e07aff072..25eb5db41 100644
--- a/src/graph/parameters.h
+++ b/src/graph/parameters.h
@@ -130,8 +130,8 @@ class MappedParameters : public Parameters {
   Ptr<Backend> backend_;
 
 public:
-  MappedParameters(Type acceptedElementType_) : Parameters(acceptedElementType_) {
-    LOG(debug, "Created mapped parameter object of type {}", acceptedElementType_);
+  MappedParameters(Type acceptedElementType) : Parameters(acceptedElementType) {
+    LOG(debug, "Created mapped parameter object of type {}", acceptedElementType);
   }
 
   virtual void init(Ptr<Backend> backend) override { backend_ = backend; }
diff --git a/src/models/encoder_decoder.cpp b/src/models/encoder_decoder.cpp
index 6a82f0ea9..09856a9cf 100755
--- a/src/models/encoder_decoder.cpp
+++ b/src/models/encoder_decoder.cpp
@@ -196,7 +196,7 @@ Ptr<DecoderState> EncoderDecoder::step(Ptr<ExpressionGraph> graph,
   state = hypIndices.empty() ? state : state->select(hypIndices, batchIndices, beamSize);
 
   // Fill state with embeddings based on last prediction
-  decoders_[0]->embeddingsFromPrediction(graph, state, words, batchIndices.size(), beamSize);
+  decoders_[0]->embeddingsFromPrediction(graph, state, words, (int) batchIndices.size(), beamSize);
   auto nextState = decoders_[0]->step(graph, state);
   
   return nextState;
diff --git a/src/translator/beam_search.h b/src/translator/beam_search.h
index d273de534..3e66a5dc4 100755
--- a/src/translator/beam_search.h
+++ b/src/translator/beam_search.h
@@ -235,7 +235,7 @@ class BeamSearch {
 
       if(PURGE_BATCH)
         if(newBeam.empty() && !beam.empty()) {      // previous beam had hyps, but all were finished in this step, newBeam will now stay empty
-          for(int i = beamIdx + 1; i < beams.size(); ++i) // for all entries above this beam
+          for(size_t i = beamIdx + 1; i < beams.size(); ++i) // for all entries above this beam
             batchIdxMap[i] = batchIdxMap[i] - 1;  // make them look at one batch index below, as the current entry will be removed from the batch.
       }
 
@@ -406,7 +406,7 @@ class BeamSearch {
             }
           }
           if(factorGroup == 0) 
-            currentDimBatch = batchIndices.size(); // keep batch size constant for all factor groups in a time step
+            currentDimBatch = (IndexType) batchIndices.size(); // keep batch size constant for all factor groups in a time step
           prevPathScores = graph->constant({(int)maxBeamSize, 1, (int)currentDimBatch, 1}, inits::fromVector(prevScores));
         }
         if (!anyCanExpand) // all words cannot expand this factor: skip
diff --git a/vs/Marian.vcxproj b/vs/Marian.vcxproj
index 6cd8f3416..e49a3ef6f 100755
--- a/vs/Marian.vcxproj
+++ b/vs/Marian.vcxproj
@@ -586,6 +586,10 @@
     <ClCompile Include="..\src\3rd_party\pathie-cpp\src\pathie_ifstream.cpp" />
     <ClCompile Include="..\src\3rd_party\pathie-cpp\src\pathie_ofstream.cpp" />
     <ClCompile Include="..\src\3rd_party\pathie-cpp\src\temp.cpp" />
+    <ClCompile Include="..\src\3rd_party\phf\phf.cc">
+      <WarningLevel Condition="'$(Configuration)|$(Platform)'=='Debug|x64'">TurnOffAllWarnings</WarningLevel>
+      <WarningLevel Condition="'$(Configuration)|$(Platform)'=='Release|x64'">TurnOffAllWarnings</WarningLevel>
+    </ClCompile>
     <ClCompile Include="..\src\3rd_party\sentencepiece\src\bpe_model.cc">
       <ExcludedFromBuild Condition="'$(Configuration)|$(Platform)'=='Debug|x64'">true</ExcludedFromBuild>
       <ExcludedFromBuild Condition="'$(Configuration)|$(Platform)'=='Release|x64'">true</ExcludedFromBuild>
@@ -962,6 +966,7 @@
     <ClInclude Include="..\src\3rd_party\pathie-cpp\include\pathie_ifstream.hpp" />
     <ClInclude Include="..\src\3rd_party\pathie-cpp\include\pathie_ofstream.hpp" />
     <ClInclude Include="..\src\3rd_party\pathie-cpp\include\temp.hpp" />
+    <ClInclude Include="..\src\3rd_party\phf\phf.h" />
     <ClInclude Include="..\src\3rd_party\sentencepiece\src\bpe_model.h">
       <ExcludedFromBuild Condition="'$(Configuration)|$(Platform)'=='Debug|x64'">true</ExcludedFromBuild>
       <ExcludedFromBuild Condition="'$(Configuration)|$(Platform)'=='Release|x64'">true</ExcludedFromBuild>
@@ -1094,6 +1099,7 @@
     <ClCompile Include="..\src\common\cli_helper.cpp" />
     <ClCompile Include="..\src\common\cli_wrapper.cpp" />
     <ClCompile Include="..\src\common\config_validator.cpp" />
+    <ClCompile Include="..\src\common\fastopt.cpp" />
     <ClCompile Include="..\src\common\filesystem.cpp" />
     <ClCompile Include="..\src\common\file_stream.cpp" />
     <ClCompile Include="..\src\common\io.cpp" />
@@ -1251,6 +1257,7 @@
     <ClInclude Include="..\src\common\cli_helper.h" />
     <ClInclude Include="..\src\common\cli_wrapper.h" />
     <ClInclude Include="..\src\common\config_validator.h" />
+    <ClInclude Include="..\src\common\fastopt.h" />
     <ClInclude Include="..\src\common\filesystem.h" />
     <ClInclude Include="..\src\common\hash.h" />
     <ClInclude Include="..\src\common\io.h" />
diff --git a/vs/Marian.vcxproj.filters b/vs/Marian.vcxproj.filters
index 87ba99453..4b4c14af2 100755
--- a/vs/Marian.vcxproj.filters
+++ b/vs/Marian.vcxproj.filters
@@ -733,6 +733,12 @@
     <ClCompile Include="..\src\common\types.cpp">
       <Filter>common</Filter>
     </ClCompile>
+    <ClCompile Include="..\src\common\fastopt.cpp">
+      <Filter>common</Filter>
+    </ClCompile>
+    <ClCompile Include="..\src\3rd_party\phf\phf.cc">
+      <Filter>3rd_party\phf</Filter>
+    </ClCompile>
   </ItemGroup>
   <ItemGroup>
     <ClInclude Include="..\src\marian.h" />
@@ -2050,6 +2056,12 @@
     <ClInclude Include="..\src\graph\expression_graph_packable.h">
       <Filter>graph</Filter>
     </ClInclude>
+    <ClInclude Include="..\src\common\fastopt.h">
+      <Filter>common</Filter>
+    </ClInclude>
+    <ClInclude Include="..\src\3rd_party\phf\phf.h">
+      <Filter>3rd_party\phf</Filter>
+    </ClInclude>
   </ItemGroup>
   <ItemGroup>
     <Filter Include="3rd_party">
@@ -2283,6 +2295,9 @@
     <Filter Include="3rd_party\half_float">
       <UniqueIdentifier>{defd3aec-3c56-4d70-a4bb-90ba9003d98d}</UniqueIdentifier>
     </Filter>
+    <Filter Include="3rd_party\phf">
+      <UniqueIdentifier>{352ac0e9-daed-437a-bc36-fb85ecd037eb}</UniqueIdentifier>
+    </Filter>
   </ItemGroup>
   <ItemGroup>
     <None Include="..\src\3rd_party\nccl\src\bootstrap.cu">

From 4c0698fc622431101c7cc4a90ae01c59960610a5 Mon Sep 17 00:00:00 2001
From: Ulrich Germann <ulrich.germann@gmail.com>
Date: Fri, 22 Nov 2019 02:12:13 +0000
Subject: [PATCH 776/838] Const diligence and thread safety with respect to
 Vocab and ShortlistGenerator

- Use pointers to const Vocab instead of Vocab in corpus_base.h and shortlist.h
- Declare some member functions of ShortlistGenerator const
- Use pointers to const ShortlistGenerator instead of ShortlistGenerator in
  numerous places.
- Use a thread-local random generator in shortlist.h
---
 src/data/corpus_base.h          |  8 ++++----
 src/data/shortlist.h            | 29 +++++++++++++++--------------
 src/models/costs.h              |  2 +-
 src/models/encoder_decoder.h    |  6 +++---
 src/translator/output_printer.h |  6 +++---
 src/translator/scorers.h        |  4 ++--
 src/translator/translator.h     | 14 +++++++-------
 7 files changed, 35 insertions(+), 34 deletions(-)

diff --git a/src/data/corpus_base.h b/src/data/corpus_base.h
index 8a8d46b87..d34ba9959 100644
--- a/src/data/corpus_base.h
+++ b/src/data/corpus_base.h
@@ -117,7 +117,7 @@ class SubBatch {
   size_t width_;
   size_t words_;
 
-  Ptr<Vocab> vocab_;
+  Ptr<const Vocab> vocab_;
   // ... TODO: add the length information (remember it)
 
 public:
@@ -127,7 +127,7 @@ class SubBatch {
    * @param size Number of sentences
    * @param width Number of words in the longest sentence
    */
-  SubBatch(size_t size, size_t width, const Ptr<Vocab>& vocab)
+  SubBatch(size_t size, size_t width, const Ptr<const Vocab>& vocab)
       : indices_(size * width, vocab ? vocab->getEosId() : Word::ZERO), // note: for gaps, we must use a valid index
         mask_(size * width, 0),
         size_(size),
@@ -153,7 +153,7 @@ class SubBatch {
   /**
    * @brief Accessors to the vocab_ field.
    */
-  const Ptr<Vocab>& vocab() const { return vocab_; }
+  const Ptr<const Vocab>& vocab() const { return vocab_; }
 
   /**
    * @brief The number of sentences in the batch.
@@ -461,7 +461,7 @@ class CorpusBatch : public Batch {
   /**
    * @brief Prints the batch in a readable form on stderr for debugging.
    */
-  void debug(bool printIndices = false) override { // prints word string if subbatch has vocab and 
+  void debug(bool printIndices = false) override { // prints word string if subbatch has vocab and
                                                    // printIndices == false otherwise only numeric indices
     std::cerr << "batches: " << sets() << std::endl;
 
diff --git a/src/data/shortlist.h b/src/data/shortlist.h
index a44268a90..8e26e36f0 100644
--- a/src/data/shortlist.h
+++ b/src/data/shortlist.h
@@ -23,7 +23,7 @@ class Shortlist {
 
   const std::vector<WordIndex>& indices() const { return indices_; }
   WordIndex reverseMap(int idx) { return indices_[idx]; }
-  
+
   int tryForwardMap(WordIndex wIdx) {
     auto first = std::lower_bound(indices_.begin(), indices_.end(), wIdx);
     if(first != indices_.end() && *first == wIdx)         // check if element not less than wIdx has been found and if equal to wIdx
@@ -36,11 +36,11 @@ class Shortlist {
 
 class ShortlistGenerator {
 public:
-  virtual Ptr<Shortlist> generate(Ptr<data::CorpusBatch> batch) = 0;
+  virtual Ptr<Shortlist> generate(Ptr<data::CorpusBatch> batch) const = 0;
 
   // Writes text version of (possibly) pruned short list to file
   // with given prefix and implementation-specific suffixes.
-  virtual void dump(const std::string& /*prefix*/) {
+  virtual void dump(const std::string& /*prefix*/) const {
     ABORT("Not implemented");
   }
 };
@@ -60,8 +60,8 @@ class SampledShortlistGenerator : public ShortlistGenerator {
   size_t trgIdx_;
   bool shared_{false};
 
-  std::random_device rd_;
-  std::mt19937 gen_;
+  // static thread_local std::random_device rd_;
+  static thread_local std::unique_ptr<std::mt19937> gen_;
 
 public:
   SampledShortlistGenerator(Ptr<Options> options,
@@ -71,8 +71,8 @@ class SampledShortlistGenerator : public ShortlistGenerator {
       : options_(options),
         srcIdx_(srcIdx),
         trgIdx_(trgIdx),
-        shared_(shared),
-        gen_(rd_()) {}
+        shared_(shared)
+        { }
 
   virtual Ptr<Shortlist> generate(Ptr<data::CorpusBatch> batch) override {
     auto srcBatch = (*batch)[srcIdx_];
@@ -123,8 +123,8 @@ class SampledShortlistGenerator : public ShortlistGenerator {
 class LexicalShortlistGenerator : public ShortlistGenerator {
 private:
   Ptr<Options> options_;
-  Ptr<Vocab> srcVocab_;
-  Ptr<Vocab> trgVocab_;
+  Ptr<const Vocab> srcVocab_;
+  Ptr<const Vocab> trgVocab_;
 
   size_t srcIdx_;
   size_t trgIdx_;
@@ -178,8 +178,8 @@ class LexicalShortlistGenerator : public ShortlistGenerator {
 
 public:
   LexicalShortlistGenerator(Ptr<Options> options,
-                            Ptr<Vocab> srcVocab,
-                            Ptr<Vocab> trgVocab,
+                            Ptr<const Vocab> srcVocab,
+                            Ptr<const Vocab> trgVocab,
                             size_t srcIdx = 0,
                             size_t trgIdx = 1,
                             bool shared = false)
@@ -206,6 +206,7 @@ class LexicalShortlistGenerator : public ShortlistGenerator {
         bestNum_,
         threshold);
 
+    // @TODO: Load and prune in one go.
     load(fname);
     prune(threshold);
 
@@ -213,7 +214,7 @@ class LexicalShortlistGenerator : public ShortlistGenerator {
       dump(dumpPath);
   }
 
-  virtual void dump(const std::string& prefix) override {
+  virtual void dump(const std::string& prefix) const override {
     // Dump top most frequent words from target vocabulary
     LOG(info, "[data] Saving shortlist dump to {}", prefix + ".{top,dic}");
     io::OutputFileStream outTop(prefix + ".top");
@@ -230,7 +231,7 @@ class LexicalShortlistGenerator : public ShortlistGenerator {
     }
   }
 
-  virtual Ptr<Shortlist> generate(Ptr<data::CorpusBatch> batch) override {
+  virtual Ptr<Shortlist> generate(Ptr<data::CorpusBatch> batch) const override {
     auto srcBatch = (*batch)[srcIdx_];
     // auto trgBatch = (*batch)[trgIdx_];
 
@@ -274,7 +275,7 @@ class FakeShortlistGenerator : public ShortlistGenerator {
     std::sort(indices_.begin(), indices_.end());
   }
 
-  Ptr<Shortlist> generate(Ptr<data::CorpusBatch> /*batch*/) override {
+  Ptr<Shortlist> generate(Ptr<data::CorpusBatch> /*batch*/) const override {
     return New<Shortlist>(indices_);
   }
 };
diff --git a/src/models/costs.h b/src/models/costs.h
index 776ca0999..03b247f0b 100755
--- a/src/models/costs.h
+++ b/src/models/costs.h
@@ -298,7 +298,7 @@ class Stepwise : public IEncoderDecoder {
   virtual Ptr<Options> getOptions() override { return encdec_->getOptions(); };
 
   virtual void setShortlistGenerator(
-      Ptr<data::ShortlistGenerator> shortlistGenerator) override {
+      Ptr<const data::ShortlistGenerator> shortlistGenerator) override {
     encdec_->setShortlistGenerator(shortlistGenerator);
   };
 
diff --git a/src/models/encoder_decoder.h b/src/models/encoder_decoder.h
index 89ee68f6d..599dbfdc4 100755
--- a/src/models/encoder_decoder.h
+++ b/src/models/encoder_decoder.h
@@ -50,7 +50,7 @@ class IEncoderDecoder : public models::IModel {
   virtual Ptr<Options> getOptions() = 0;
 
   virtual void setShortlistGenerator(
-      Ptr<data::ShortlistGenerator> shortlistGenerator)
+      Ptr<const data::ShortlistGenerator> shortlistGenerator)
       = 0;
 
   virtual Ptr<data::Shortlist> getShortlist() = 0;
@@ -60,7 +60,7 @@ class IEncoderDecoder : public models::IModel {
 
 class EncoderDecoder : public IEncoderDecoder, public LayerBase {
 protected:
-  Ptr<data::ShortlistGenerator> shortlistGenerator_;
+  Ptr<const data::ShortlistGenerator> shortlistGenerator_;
 
   const std::string prefix_;
   const bool inference_{ false };
@@ -120,7 +120,7 @@ class EncoderDecoder : public IEncoderDecoder, public LayerBase {
   }
 
   virtual void setShortlistGenerator(
-      Ptr<data::ShortlistGenerator> shortlistGenerator) override {
+      Ptr<const data::ShortlistGenerator> shortlistGenerator) override {
     shortlistGenerator_ = shortlistGenerator;
   };
 
diff --git a/src/translator/output_printer.h b/src/translator/output_printer.h
index f37ff5b96..c5e61f07d 100755
--- a/src/translator/output_printer.h
+++ b/src/translator/output_printer.h
@@ -13,7 +13,7 @@ namespace marian {
 
 class OutputPrinter {
 public:
-  OutputPrinter(Ptr<Options> options, Ptr<Vocab> vocab)
+  OutputPrinter(Ptr<const Options> options, Ptr<const Vocab> vocab)
       : vocab_(vocab),
         reverse_(options->get<bool>("right-left")),
         nbest_(options->get<bool>("n-best", false)
@@ -23,7 +23,7 @@ class OutputPrinter {
         alignmentThreshold_(getAlignmentThreshold(alignment_)) {}
 
   template <class OStream>
-  void print(Ptr<History> history, OStream& best1, OStream& bestn) {
+  void print(Ptr<const History> history, OStream& best1, OStream& bestn) {
     const auto& nbl = history->nBest(nbest_);
 
     for(size_t i = 0; i < nbl.size(); ++i) {
@@ -76,7 +76,7 @@ class OutputPrinter {
   }
 
 private:
-  Ptr<Vocab> vocab_;
+  Ptr<Vocab const> vocab_;
   bool reverse_{false};
   size_t nbest_{0};
   std::string alignment_;
diff --git a/src/translator/scorers.h b/src/translator/scorers.h
index aca521f45..c936514c6 100755
--- a/src/translator/scorers.h
+++ b/src/translator/scorers.h
@@ -41,7 +41,7 @@ class Scorer {
 
   virtual void init(Ptr<ExpressionGraph>) {}
 
-  virtual void setShortlistGenerator(Ptr<data::ShortlistGenerator> /*shortlistGenerator*/){};
+  virtual void setShortlistGenerator(Ptr<const data::ShortlistGenerator> /*shortlistGenerator*/){};
   virtual Ptr<data::Shortlist> getShortlist() { return nullptr; };
 
   virtual std::vector<float> getAlignment() { return {}; };
@@ -120,7 +120,7 @@ class ScorerWrapper : public Scorer {
   }
 
   virtual void setShortlistGenerator(
-      Ptr<data::ShortlistGenerator> shortlistGenerator) override {
+      Ptr<const data::ShortlistGenerator> shortlistGenerator) override {
     encdec_->setShortlistGenerator(shortlistGenerator);
   };
 
diff --git a/src/translator/translator.h b/src/translator/translator.h
index 5dd87f949..841d9868c 100755
--- a/src/translator/translator.h
+++ b/src/translator/translator.h
@@ -33,7 +33,7 @@ class Translate : public ModelTask {
 
   Ptr<data::Corpus> corpus_;
   Ptr<Vocab> trgVocab_;
-  Ptr<data::ShortlistGenerator> shortlistGenerator_;
+  Ptr<const data::ShortlistGenerator> shortlistGenerator_;
 
   size_t numDevices_;
 
@@ -42,12 +42,12 @@ class Translate : public ModelTask {
 #endif
 
 public:
-  Translate(Ptr<Options> options) 
+  Translate(Ptr<Options> options)
     : options_(New<Options>(options->clone())) { // @TODO: clone should return Ptr<Options> same as "with"?
     // This is currently safe as the translator is either created stand-alone or
     // or config is created anew from Options in the validator
 
-    options_->set("inference", true, 
+    options_->set("inference", true,
                   "shuffle", "none");
 
     corpus_ = New<data::Corpus>(options_, true);
@@ -72,7 +72,7 @@ class Translate : public ModelTask {
     auto models = options->get<std::vector<std::string>>("models");
     for(auto model : models) {
       marian::filesystem::Path modelPath(model);
-      ABORT_IF(modelPath.extension() != marian::filesystem::Path(".bin"), 
+      ABORT_IF(modelPath.extension() != marian::filesystem::Path(".bin"),
               "Non-binarized models cannot be mmapped");
       mmaps_.push_back(std::move(mio::mmap_source(model)));
     }
@@ -94,7 +94,7 @@ class Translate : public ModelTask {
 
 #if MMAP
         auto scorers = createScorers(options_, mmaps_);
-#else 
+#else
         auto scorers = createScorers(options_);
 #endif
         for(auto scorer : scorers) {
@@ -181,7 +181,7 @@ class TranslateService : public ModelServiceTask {
 public:
   virtual ~TranslateService() {}
 
-  TranslateService(Ptr<Options> options) 
+  TranslateService(Ptr<Options> options)
     : options_(New<Options>(options->clone())) {
     // initialize vocabs
     options_->set("inference", true);
@@ -206,7 +206,7 @@ class TranslateService : public ModelServiceTask {
     // initialize scorers
     for(auto device : devices) {
       auto graph = New<ExpressionGraph>(true);
-      
+
       auto precison = options_->get<std::vector<std::string>>("precision", {"float32"});
       graph->setDefaultElementType(typeFromString(precison[0])); // only use first type, used for parameter type in graph
       graph->setDevice(device);

From 61c0195e16ea51b6b7b87e3f0976c074e943eca3 Mon Sep 17 00:00:00 2001
From: Ulrich Germann <ugermann@inf.ed.ac.uk>
Date: Fri, 22 Nov 2019 15:25:11 +0000
Subject: [PATCH 777/838] Bug fixes (missing const; instantiate *gen_) in
 SampledShortlistGenerator.

---
 src/data/shortlist.h | 6 ++++--
 1 file changed, 4 insertions(+), 2 deletions(-)

diff --git a/src/data/shortlist.h b/src/data/shortlist.h
index 8e26e36f0..e5d084942 100644
--- a/src/data/shortlist.h
+++ b/src/data/shortlist.h
@@ -74,7 +74,7 @@ class SampledShortlistGenerator : public ShortlistGenerator {
         shared_(shared)
         { }
 
-  virtual Ptr<Shortlist> generate(Ptr<data::CorpusBatch> batch) override {
+  virtual Ptr<Shortlist> generate(Ptr<data::CorpusBatch> batch) const override {
     auto srcBatch = (*batch)[srcIdx_];
     auto trgBatch = (*batch)[trgIdx_];
 
@@ -93,8 +93,10 @@ class SampledShortlistGenerator : public ShortlistGenerator {
         indexSet.insert(i.toWordIndex());
 
     std::uniform_int_distribution<> dis((int)firstNum_, (int)maxVocab_);
+    if (gen_ == NULL)
+      gen_.reset(new std::mt19937(std::random_device{}()));
     while(indexSet.size() < total_ && indexSet.size() < maxVocab_)
-      indexSet.insert(dis(gen_));
+      indexSet.insert(dis(*gen_));
 
     // turn into vector and sort (selected indices)
     std::vector<WordIndex> idx(indexSet.begin(), indexSet.end());

From a27fda70d19fe3791658ad2e72380143aae22b43 Mon Sep 17 00:00:00 2001
From: Ulrich Germann <ugermann@inf.ed.ac.uk>
Date: Fri, 22 Nov 2019 23:02:38 +0000
Subject: [PATCH 778/838] Return exit code 15 (SIGTERM) after SIGTERM. (#551)

* Return exit code 15 (SIGTERM) after SIGTERM.
When marian receives signal SIGTERM and exits gracefully (save model & exit),
it should then exit with a non-zero exit code, to signal to any parent process
that it did not exit "naturally".

* Added explanatory comment about exiting marian_train with non-zero status after SIGTERM.
---
 src/command/marian_train.cpp | 11 ++++++++++-
 1 file changed, 10 insertions(+), 1 deletion(-)

diff --git a/src/command/marian_train.cpp b/src/command/marian_train.cpp
index 5d953243b..9d312bbb8 100644
--- a/src/command/marian_train.cpp
+++ b/src/command/marian_train.cpp
@@ -1,3 +1,4 @@
+#include <signal.h>
 #include "marian.h"
 
 #include "training/graph_group_async.h"
@@ -68,5 +69,13 @@ int main(int argc, char** argv) {
     }
   }
 
-  return 0;
+  // If we exit due to SIGTERM, exit with 128 + the signal number, as suggested
+  // for bash in http://tldp.org/LDP/abs/html/exitcodes.html. This allows parent
+  // scripts to determine if training terminated naturally or via SIGTERM.
+  // Whith this approach we can accommodate additional signals in the future.
+  // An alternative would be to return 124, which is what the timeout command
+  // returns for timeout -s SIGTERM <seconds> ...., because exiting after SIGTERM
+  // is not technically a fatal error (which is what the 128+x convention usually
+  // stands for).
+  return getSigtermFlag() ? (128 + SIGTERM) : 0;
 }

From b19820c8ba911ae2e993bf570622af03209d7c15 Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Fri, 22 Nov 2019 23:18:52 +0000
Subject: [PATCH 779/838] Merged PR 10588: bug fix: guided-alignment loss
 should not normalize by source length

The guided-alignment loss has two bugs related to two different types of normalization:
* The returned `RationalLoss` should not have its denominator weighted by `guided-alignment-weight`, except in `sum` mode.
* The CE loss cannot directly use the alignment labels as ground truth in the CE loss function, since CE requires one-hot labels, while the actual labels those are multi-hot. They must be renormalized.
---
 src/layers/guided_alignment.h | 69 ++++++++++++++++++++++++-----------
 1 file changed, 47 insertions(+), 22 deletions(-)
 mode change 100644 => 100755 src/layers/guided_alignment.h

diff --git a/src/layers/guided_alignment.h b/src/layers/guided_alignment.h
old mode 100644
new mode 100755
index 55ade3658..acc70c1f4
--- a/src/layers/guided_alignment.h
+++ b/src/layers/guided_alignment.h
@@ -1,43 +1,68 @@
 #pragma once
 
 #include "layers/loss.h"
+#include "common/logging.h"
 
 namespace marian {
 
 static inline RationalLoss guidedAlignmentCost(Ptr<ExpressionGraph> graph,
                                                Ptr<data::CorpusBatch> batch,
                                                Ptr<Options> options,
-                                               Expr attention) {
+                                               Expr attention) { // [beam depth=1, max src length, batch size, tgt length]
 
-  // @TODO: there should be positional masking here ... on the other hand, positions that are not 
-  // in a sentence should always agree (both being 0). Lack of masking affects label count only which is 
-  // probably negligible?
-
-  // @TODO: change "cost" to "loss"
-  std::string guidedLossType = options->get<std::string>("guided-alignment-cost");
-  float guidedScalar = options->get<float>("guided-alignment-weight");
+  std::string guidedLossType = options->get<std::string>("guided-alignment-cost");  // @TODO: change "cost" to "loss"
+  float guidedLossWeight = options->get<float>("guided-alignment-weight");
   
+  const auto& shape = attention->shape(); // [beam depth=1, max src length, batch size, tgt length]
   float epsilon = 1e-6f;
-  Expr alignment = constant_like(attention, inits::fromVector(batch->getGuidedAlignment()));
   Expr alignmentLoss; // sum up loss over all attention/alignment positions
-  if(guidedLossType == "mse") {
-    alignmentLoss = sum(flatten(square(attention - alignment))) / 2.f;
-  } else if(guidedLossType == "mult") {
-    alignmentLoss = -log(sum(flatten(attention * alignment)) + epsilon);
-  } else if(guidedLossType == "ce") {
+  size_t numLabels;
+  if(guidedLossType == "ce") {
+    // ce requires normalized probabilities
+    auto dimBatch    = shape[-2];
+    auto dimTrgWords = shape[-1];
+    auto dimSrcWords = shape[-3];
+    ABORT_IF(shape[-4] != 1, "Guided alignments with beam??");
+    auto normalizedAlignment = batch->getGuidedAlignment();
+    auto locate = [=](size_t s, size_t b, size_t t) { return ((s * dimBatch) + b) * dimTrgWords + t; };
+    for (size_t b = 0; b < dimBatch; b++) {
+      for (size_t t = 0; t < dimTrgWords; t++) {
+        float sum = 0;
+        for (size_t s = 0; s < dimSrcWords; s++)
+          sum += normalizedAlignment[locate(s, b, t)];
+        if (sum != 0 && sum != 1)
+          for (size_t s = 0; s < dimSrcWords; s++)
+            normalizedAlignment[locate(s, b, t)] /= sum;
+      }
+    }
+    auto alignment = constant_like(attention, inits::fromVector(normalizedAlignment));
     alignmentLoss = -sum(flatten(alignment * log(attention + epsilon)));
+    numLabels = batch->back()->batchWords();
+    ABORT_IF(numLabels > shape.elements() / shape[-3], "Num labels of guided alignment cost is off??");
   } else {
-    ABORT("Unknown alignment cost type: {}", guidedLossType);
+    auto alignment = constant_like(attention, inits::fromVector(batch->getGuidedAlignment()));
+    if(guidedLossType == "mse")
+      alignmentLoss = sum(flatten(square(attention - alignment))) / 2.f;
+    else if(guidedLossType == "mult") // @TODO: I don't know what this criterion is for. Can we remove it?
+      alignmentLoss = -log(sum(flatten(attention * alignment)) + epsilon);
+    else
+       ABORT("Unknown alignment cost type: {}", guidedLossType);
+    // every position is a label as they should all agree
+    // @TODO: there should be positional masking here ... on the other hand, positions that are not 
+    // in a sentence should always agree (both being 0). Lack of masking affects label count only which is 
+    // probably negligible?
+    numLabels = shape.elements();
   }
-  
-  alignmentLoss = guidedScalar * alignmentLoss; // weigh by scalar
 
-  // every position is a label as they should all agree, see caveat at the top.
-  size_t numLabels = alignment->shape().elements();
-  
   // Create label node, also weigh by scalar so labels and cost are in the same domain.
-  // Fractional label counts are OK
-  return RationalLoss(alignmentLoss, guidedScalar * numLabels);
+  // Fractional label counts are OK. But only if combined as "sum".
+  // @TODO: It is ugly to check the multi-loss type here, but doing this right requires
+  // a substantial rewrite of the multi-loss architecture, which is planned anyways.
+  std::string multiLossType = options->get<std::string>("multi-loss-type", "sum");
+  if (multiLossType == "sum")         // sum of sums
+    return RationalLoss(guidedLossWeight * alignmentLoss, guidedLossWeight * numLabels);
+  else
+    return RationalLoss(guidedLossWeight * alignmentLoss, (float)numLabels);
 }
 
 }  // namespace marian

From a1763e2d9e61600f46d894f7e2045c2f8e4036e5 Mon Sep 17 00:00:00 2001
From: Ulrich Germann <ugermann@inf.ed.ac.uk>
Date: Sat, 23 Nov 2019 04:35:39 +0000
Subject: [PATCH 780/838] Const diligence and thread safety ... (#553)

* Const diligence and thread safety with respect to Vocab and ShortlistGenerator

- Use pointers to const Vocab instead of Vocab in corpus_base.h and shortlist.h
- Declare some member functions of ShortlistGenerator const
- Use pointers to const ShortlistGenerator instead of ShortlistGenerator in
  numerous places.
- Use a thread-local random generator in shortlist.h

* Bug fixes (missing const; instantiate *gen_) in SampledShortlistGenerator.
---
 src/data/corpus_base.h          |  8 ++++----
 src/data/shortlist.h            | 35 ++++++++++++++++++---------------
 src/models/costs.h              |  2 +-
 src/models/encoder_decoder.h    |  6 +++---
 src/translator/output_printer.h |  6 +++---
 src/translator/scorers.h        |  4 ++--
 src/translator/translator.h     | 14 ++++++-------
 7 files changed, 39 insertions(+), 36 deletions(-)

diff --git a/src/data/corpus_base.h b/src/data/corpus_base.h
index 8a8d46b87..d34ba9959 100644
--- a/src/data/corpus_base.h
+++ b/src/data/corpus_base.h
@@ -117,7 +117,7 @@ class SubBatch {
   size_t width_;
   size_t words_;
 
-  Ptr<Vocab> vocab_;
+  Ptr<const Vocab> vocab_;
   // ... TODO: add the length information (remember it)
 
 public:
@@ -127,7 +127,7 @@ class SubBatch {
    * @param size Number of sentences
    * @param width Number of words in the longest sentence
    */
-  SubBatch(size_t size, size_t width, const Ptr<Vocab>& vocab)
+  SubBatch(size_t size, size_t width, const Ptr<const Vocab>& vocab)
       : indices_(size * width, vocab ? vocab->getEosId() : Word::ZERO), // note: for gaps, we must use a valid index
         mask_(size * width, 0),
         size_(size),
@@ -153,7 +153,7 @@ class SubBatch {
   /**
    * @brief Accessors to the vocab_ field.
    */
-  const Ptr<Vocab>& vocab() const { return vocab_; }
+  const Ptr<const Vocab>& vocab() const { return vocab_; }
 
   /**
    * @brief The number of sentences in the batch.
@@ -461,7 +461,7 @@ class CorpusBatch : public Batch {
   /**
    * @brief Prints the batch in a readable form on stderr for debugging.
    */
-  void debug(bool printIndices = false) override { // prints word string if subbatch has vocab and 
+  void debug(bool printIndices = false) override { // prints word string if subbatch has vocab and
                                                    // printIndices == false otherwise only numeric indices
     std::cerr << "batches: " << sets() << std::endl;
 
diff --git a/src/data/shortlist.h b/src/data/shortlist.h
index a44268a90..e5d084942 100644
--- a/src/data/shortlist.h
+++ b/src/data/shortlist.h
@@ -23,7 +23,7 @@ class Shortlist {
 
   const std::vector<WordIndex>& indices() const { return indices_; }
   WordIndex reverseMap(int idx) { return indices_[idx]; }
-  
+
   int tryForwardMap(WordIndex wIdx) {
     auto first = std::lower_bound(indices_.begin(), indices_.end(), wIdx);
     if(first != indices_.end() && *first == wIdx)         // check if element not less than wIdx has been found and if equal to wIdx
@@ -36,11 +36,11 @@ class Shortlist {
 
 class ShortlistGenerator {
 public:
-  virtual Ptr<Shortlist> generate(Ptr<data::CorpusBatch> batch) = 0;
+  virtual Ptr<Shortlist> generate(Ptr<data::CorpusBatch> batch) const = 0;
 
   // Writes text version of (possibly) pruned short list to file
   // with given prefix and implementation-specific suffixes.
-  virtual void dump(const std::string& /*prefix*/) {
+  virtual void dump(const std::string& /*prefix*/) const {
     ABORT("Not implemented");
   }
 };
@@ -60,8 +60,8 @@ class SampledShortlistGenerator : public ShortlistGenerator {
   size_t trgIdx_;
   bool shared_{false};
 
-  std::random_device rd_;
-  std::mt19937 gen_;
+  // static thread_local std::random_device rd_;
+  static thread_local std::unique_ptr<std::mt19937> gen_;
 
 public:
   SampledShortlistGenerator(Ptr<Options> options,
@@ -71,10 +71,10 @@ class SampledShortlistGenerator : public ShortlistGenerator {
       : options_(options),
         srcIdx_(srcIdx),
         trgIdx_(trgIdx),
-        shared_(shared),
-        gen_(rd_()) {}
+        shared_(shared)
+        { }
 
-  virtual Ptr<Shortlist> generate(Ptr<data::CorpusBatch> batch) override {
+  virtual Ptr<Shortlist> generate(Ptr<data::CorpusBatch> batch) const override {
     auto srcBatch = (*batch)[srcIdx_];
     auto trgBatch = (*batch)[trgIdx_];
 
@@ -93,8 +93,10 @@ class SampledShortlistGenerator : public ShortlistGenerator {
         indexSet.insert(i.toWordIndex());
 
     std::uniform_int_distribution<> dis((int)firstNum_, (int)maxVocab_);
+    if (gen_ == NULL)
+      gen_.reset(new std::mt19937(std::random_device{}()));
     while(indexSet.size() < total_ && indexSet.size() < maxVocab_)
-      indexSet.insert(dis(gen_));
+      indexSet.insert(dis(*gen_));
 
     // turn into vector and sort (selected indices)
     std::vector<WordIndex> idx(indexSet.begin(), indexSet.end());
@@ -123,8 +125,8 @@ class SampledShortlistGenerator : public ShortlistGenerator {
 class LexicalShortlistGenerator : public ShortlistGenerator {
 private:
   Ptr<Options> options_;
-  Ptr<Vocab> srcVocab_;
-  Ptr<Vocab> trgVocab_;
+  Ptr<const Vocab> srcVocab_;
+  Ptr<const Vocab> trgVocab_;
 
   size_t srcIdx_;
   size_t trgIdx_;
@@ -178,8 +180,8 @@ class LexicalShortlistGenerator : public ShortlistGenerator {
 
 public:
   LexicalShortlistGenerator(Ptr<Options> options,
-                            Ptr<Vocab> srcVocab,
-                            Ptr<Vocab> trgVocab,
+                            Ptr<const Vocab> srcVocab,
+                            Ptr<const Vocab> trgVocab,
                             size_t srcIdx = 0,
                             size_t trgIdx = 1,
                             bool shared = false)
@@ -206,6 +208,7 @@ class LexicalShortlistGenerator : public ShortlistGenerator {
         bestNum_,
         threshold);
 
+    // @TODO: Load and prune in one go.
     load(fname);
     prune(threshold);
 
@@ -213,7 +216,7 @@ class LexicalShortlistGenerator : public ShortlistGenerator {
       dump(dumpPath);
   }
 
-  virtual void dump(const std::string& prefix) override {
+  virtual void dump(const std::string& prefix) const override {
     // Dump top most frequent words from target vocabulary
     LOG(info, "[data] Saving shortlist dump to {}", prefix + ".{top,dic}");
     io::OutputFileStream outTop(prefix + ".top");
@@ -230,7 +233,7 @@ class LexicalShortlistGenerator : public ShortlistGenerator {
     }
   }
 
-  virtual Ptr<Shortlist> generate(Ptr<data::CorpusBatch> batch) override {
+  virtual Ptr<Shortlist> generate(Ptr<data::CorpusBatch> batch) const override {
     auto srcBatch = (*batch)[srcIdx_];
     // auto trgBatch = (*batch)[trgIdx_];
 
@@ -274,7 +277,7 @@ class FakeShortlistGenerator : public ShortlistGenerator {
     std::sort(indices_.begin(), indices_.end());
   }
 
-  Ptr<Shortlist> generate(Ptr<data::CorpusBatch> /*batch*/) override {
+  Ptr<Shortlist> generate(Ptr<data::CorpusBatch> /*batch*/) const override {
     return New<Shortlist>(indices_);
   }
 };
diff --git a/src/models/costs.h b/src/models/costs.h
index 776ca0999..03b247f0b 100755
--- a/src/models/costs.h
+++ b/src/models/costs.h
@@ -298,7 +298,7 @@ class Stepwise : public IEncoderDecoder {
   virtual Ptr<Options> getOptions() override { return encdec_->getOptions(); };
 
   virtual void setShortlistGenerator(
-      Ptr<data::ShortlistGenerator> shortlistGenerator) override {
+      Ptr<const data::ShortlistGenerator> shortlistGenerator) override {
     encdec_->setShortlistGenerator(shortlistGenerator);
   };
 
diff --git a/src/models/encoder_decoder.h b/src/models/encoder_decoder.h
index 89ee68f6d..599dbfdc4 100755
--- a/src/models/encoder_decoder.h
+++ b/src/models/encoder_decoder.h
@@ -50,7 +50,7 @@ class IEncoderDecoder : public models::IModel {
   virtual Ptr<Options> getOptions() = 0;
 
   virtual void setShortlistGenerator(
-      Ptr<data::ShortlistGenerator> shortlistGenerator)
+      Ptr<const data::ShortlistGenerator> shortlistGenerator)
       = 0;
 
   virtual Ptr<data::Shortlist> getShortlist() = 0;
@@ -60,7 +60,7 @@ class IEncoderDecoder : public models::IModel {
 
 class EncoderDecoder : public IEncoderDecoder, public LayerBase {
 protected:
-  Ptr<data::ShortlistGenerator> shortlistGenerator_;
+  Ptr<const data::ShortlistGenerator> shortlistGenerator_;
 
   const std::string prefix_;
   const bool inference_{ false };
@@ -120,7 +120,7 @@ class EncoderDecoder : public IEncoderDecoder, public LayerBase {
   }
 
   virtual void setShortlistGenerator(
-      Ptr<data::ShortlistGenerator> shortlistGenerator) override {
+      Ptr<const data::ShortlistGenerator> shortlistGenerator) override {
     shortlistGenerator_ = shortlistGenerator;
   };
 
diff --git a/src/translator/output_printer.h b/src/translator/output_printer.h
index f37ff5b96..c5e61f07d 100755
--- a/src/translator/output_printer.h
+++ b/src/translator/output_printer.h
@@ -13,7 +13,7 @@ namespace marian {
 
 class OutputPrinter {
 public:
-  OutputPrinter(Ptr<Options> options, Ptr<Vocab> vocab)
+  OutputPrinter(Ptr<const Options> options, Ptr<const Vocab> vocab)
       : vocab_(vocab),
         reverse_(options->get<bool>("right-left")),
         nbest_(options->get<bool>("n-best", false)
@@ -23,7 +23,7 @@ class OutputPrinter {
         alignmentThreshold_(getAlignmentThreshold(alignment_)) {}
 
   template <class OStream>
-  void print(Ptr<History> history, OStream& best1, OStream& bestn) {
+  void print(Ptr<const History> history, OStream& best1, OStream& bestn) {
     const auto& nbl = history->nBest(nbest_);
 
     for(size_t i = 0; i < nbl.size(); ++i) {
@@ -76,7 +76,7 @@ class OutputPrinter {
   }
 
 private:
-  Ptr<Vocab> vocab_;
+  Ptr<Vocab const> vocab_;
   bool reverse_{false};
   size_t nbest_{0};
   std::string alignment_;
diff --git a/src/translator/scorers.h b/src/translator/scorers.h
index aca521f45..c936514c6 100755
--- a/src/translator/scorers.h
+++ b/src/translator/scorers.h
@@ -41,7 +41,7 @@ class Scorer {
 
   virtual void init(Ptr<ExpressionGraph>) {}
 
-  virtual void setShortlistGenerator(Ptr<data::ShortlistGenerator> /*shortlistGenerator*/){};
+  virtual void setShortlistGenerator(Ptr<const data::ShortlistGenerator> /*shortlistGenerator*/){};
   virtual Ptr<data::Shortlist> getShortlist() { return nullptr; };
 
   virtual std::vector<float> getAlignment() { return {}; };
@@ -120,7 +120,7 @@ class ScorerWrapper : public Scorer {
   }
 
   virtual void setShortlistGenerator(
-      Ptr<data::ShortlistGenerator> shortlistGenerator) override {
+      Ptr<const data::ShortlistGenerator> shortlistGenerator) override {
     encdec_->setShortlistGenerator(shortlistGenerator);
   };
 
diff --git a/src/translator/translator.h b/src/translator/translator.h
index 5dd87f949..841d9868c 100755
--- a/src/translator/translator.h
+++ b/src/translator/translator.h
@@ -33,7 +33,7 @@ class Translate : public ModelTask {
 
   Ptr<data::Corpus> corpus_;
   Ptr<Vocab> trgVocab_;
-  Ptr<data::ShortlistGenerator> shortlistGenerator_;
+  Ptr<const data::ShortlistGenerator> shortlistGenerator_;
 
   size_t numDevices_;
 
@@ -42,12 +42,12 @@ class Translate : public ModelTask {
 #endif
 
 public:
-  Translate(Ptr<Options> options) 
+  Translate(Ptr<Options> options)
     : options_(New<Options>(options->clone())) { // @TODO: clone should return Ptr<Options> same as "with"?
     // This is currently safe as the translator is either created stand-alone or
     // or config is created anew from Options in the validator
 
-    options_->set("inference", true, 
+    options_->set("inference", true,
                   "shuffle", "none");
 
     corpus_ = New<data::Corpus>(options_, true);
@@ -72,7 +72,7 @@ class Translate : public ModelTask {
     auto models = options->get<std::vector<std::string>>("models");
     for(auto model : models) {
       marian::filesystem::Path modelPath(model);
-      ABORT_IF(modelPath.extension() != marian::filesystem::Path(".bin"), 
+      ABORT_IF(modelPath.extension() != marian::filesystem::Path(".bin"),
               "Non-binarized models cannot be mmapped");
       mmaps_.push_back(std::move(mio::mmap_source(model)));
     }
@@ -94,7 +94,7 @@ class Translate : public ModelTask {
 
 #if MMAP
         auto scorers = createScorers(options_, mmaps_);
-#else 
+#else
         auto scorers = createScorers(options_);
 #endif
         for(auto scorer : scorers) {
@@ -181,7 +181,7 @@ class TranslateService : public ModelServiceTask {
 public:
   virtual ~TranslateService() {}
 
-  TranslateService(Ptr<Options> options) 
+  TranslateService(Ptr<Options> options)
     : options_(New<Options>(options->clone())) {
     // initialize vocabs
     options_->set("inference", true);
@@ -206,7 +206,7 @@ class TranslateService : public ModelServiceTask {
     // initialize scorers
     for(auto device : devices) {
       auto graph = New<ExpressionGraph>(true);
-      
+
       auto precison = options_->get<std::vector<std::string>>("precision", {"float32"});
       graph->setDefaultElementType(typeFromString(precison[0])); // only use first type, used for parameter type in graph
       graph->setDevice(device);

From 93b7ed80fe1b369c05e26771adcf321beae24807 Mon Sep 17 00:00:00 2001
From: Martin Junczys-Dowmunt <Marcin.JunczysDowmunt@microsoft.com>
Date: Tue, 26 Nov 2019 01:48:07 +0000
Subject: [PATCH 781/838] Merged PR 10553: Fix multiple problems in reduce
 kernels that occurred during back-prop

This fixes a number of bugs in our GPU reduce-kernels that would manifest mainly for larger matrices and during back-prop. We also drop support for CUDA 8.0 to be able to take advantage of new GPU primitives introduced by NVidia in CUDA 9.0.
---
 CHANGELOG.md                        |   2 +
 CMakeLists.txt                      |   2 +-
 VERSION                             |   2 +-
 src/3rd_party/reduce_all.h          | 551 ++++++++++++----------------
 src/functional/tmp.h                | 131 ++++---
 src/graph/expression_operators.cpp  |  20 +-
 src/tensors/gpu/add.cu              |  42 ++-
 src/tensors/gpu/add_all.h           | 128 +++++++
 src/tensors/gpu/tensor_operators.cu | 103 ++----
 src/tensors/tensor.cpp              |  18 +-
 src/tensors/tensor_operators.h      |   4 +-
 src/tests/units/operator_tests.cpp  |  56 +++
 12 files changed, 579 insertions(+), 480 deletions(-)
 create mode 100644 src/tensors/gpu/add_all.h

diff --git a/CHANGELOG.md b/CHANGELOG.md
index cb5a86a8f..4682c6c25 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -24,6 +24,7 @@ and this project adheres to [Semantic Versioning](http://semver.org/spec/v2.0.0.
 - Gradient-checkpointing
 
 ### Fixed
+- Fixed multiple reduction kernels on GPU
 - Replace IntrusivePtr with std::uniq_ptr in FastOpt, fixes random segfaults 
   due to thread-non-safty of reference counting.
 - Make sure that items are 256-byte aligned during saving
@@ -38,6 +39,7 @@ and this project adheres to [Semantic Versioning](http://semver.org/spec/v2.0.0.
 - Compilation with CUDA 10.1
 
 ### Changed
+- Dropped support for CUDA 8.0, CUDA 9.0 is now minimal requirement
 - Removed autotuner for now, will be switched back on later
 - Boost depdendency is now optional and only required for marian_server 
 - Dropped support for g++-4.9
diff --git a/CMakeLists.txt b/CMakeLists.txt
index 09246d469..7e8d07c16 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -191,7 +191,7 @@ if(USE_STATIC_LIBS)
   endif()
 endif()
 
-find_package(CUDA "8.0") # TODO: only enable FP16-related options for compute_70 and higher.
+find_package(CUDA "9.0") # TODO: only enable FP16-related options for compute_70 and higher.
 if(CUDA_FOUND)
   # CUDA >= 10.0 requires CMake >= 3.12.2
   if((CUDA_VERSION VERSION_EQUAL "10.0" OR CUDA_VERSION VERSION_GREATER "10.0") AND (CMAKE_VERSION VERSION_LESS "3.12.2"))
diff --git a/VERSION b/VERSION
index 4f69174ff..747ee4f6e 100644
--- a/VERSION
+++ b/VERSION
@@ -1 +1 @@
-v1.8.21
+v1.8.22
diff --git a/src/3rd_party/reduce_all.h b/src/3rd_party/reduce_all.h
index c5285bbfa..26397e37b 100644
--- a/src/3rd_party/reduce_all.h
+++ b/src/3rd_party/reduce_all.h
@@ -1,347 +1,248 @@
-// This software contains source code provided by NVIDIA Corporation.
-
-/*
- * Copyright 1993-2015 NVIDIA Corporation.  All rights reserved.
+/* Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+ *
+ * Redistribution and use in source and binary forms, with or without
+ * modification, are permitted provided that the following conditions
+ * are met:
+ *  * Redistributions of source code must retain the above copyright
+ *    notice, this list of conditions and the following disclaimer.
+ *  * Redistributions in binary form must reproduce the above copyright
+ *    notice, this list of conditions and the following disclaimer in the
+ *    documentation and/or other materials provided with the distribution.
+ *  * Neither the name of NVIDIA CORPORATION nor the names of its
+ *    contributors may be used to endorse or promote products derived
+ *    from this software without specific prior written permission.
  *
- * Please refer to the NVIDIA end user license agreement (EULA) associated
- * with this source code for terms and conditions that govern your use of
- * this software. Any use, reproduction, disclosure, or distribution of
- * this software and related documentation outside the terms of the EULA
- * is strictly prohibited.
+ * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS ``AS IS'' AND ANY
+ * EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+ * IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
+ * PURPOSE ARE DISCLAIMED.  IN NO EVENT SHALL THE COPYRIGHT OWNER OR
+ * CONTRIBUTORS BE LIABLE FOR ANY DIRECINDIRECFunctor, T, AccTyf, pe, INCIDENTAL, SPECIAL,
+ * EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
+ * PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR
+ * PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY
+ * OF LIABILITY, WHETHER IN CONTRACSf, TRICT LIABILITY, OR TORT
+ * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+ * OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
  */
 
-/*
-MJD: Relevant text from the NVIDIA EULA:
-
-2.1 Sample Source Code Modification, Ownership and Distribution
-
-Subject to the terms of the SLA and this Supplement, NVIDIA hereby grants you a non-
-exclusive, non-transferable license, without the right to sublicense, during the applicable
-license term unless earlier terminated pursuant to the SLA, to have Authorized Users
-modify and create derivative works of CUDA Licensed Software that constitutes sample
-source code, when provided to you by NVIDIA in source code form. You hold all rights,
-title and interest in and to your modifications and derivative works of the sample source
-code software that you create as permitted hereunder (collective, Derivatives”), subject
-to NVIDIA’s underlying Intellectual Property Rights in and to the CUDA Licensed
-Software; provided, however that you grant NVIDIA and its Affiliates an irrevocable,
-perpetual, nonexclusive, worldwide, royalty-free paid-up license to make, have made,
-use, have used, reproduce, license, distribute, sublicense, transfer and otherwise
-commercialize Derivatives including (without limitation) with the CUDA Licensed
-Software or other NVIDIA products, technologies or materials. You may distribute the
-CUDA Supplement to Software License Agreement End User License Agreements (EULA) 
-DR-06739-001_v01_v9.0 | 14 sample source code as delivered by NVIDIA and/or your Derivatives, 
-provided that all NVIDIA copyright notices and trademarks are maintained and used properly 
-and the sample source code includes the following notice: “This software contains source code
-provided by NVIDIA Corporation.”
-*/
-
-#pragma once
 
-#include "tensors/tensor.h"
-
-#include <cuda_runtime.h>
+#include "functional/tmp.h"
+#include <cooperative_groups.h>
 
 namespace marian {
 
-template <unsigned int blockSize, typename AccType>
-__device__ void
-reduceBlock(volatile AccType *sdata, AccType mySum, const unsigned int tid)
-{
-    sdata[tid] = mySum;
-    __syncthreads();
-
-    // do reduction in shared mem
-    if (blockSize >= 512)
-    {
-        if (tid < 256)
-        {
-            sdata[tid] = mySum = mySum + sdata[tid + 256];
-        }
-
-        __syncthreads();
-    }
-
-    if (blockSize >= 256)
-    {
-        if (tid < 128)
-        {
-            sdata[tid] = mySum = mySum + sdata[tid + 128];
-        }
+namespace cg = cooperative_groups;
+
+// Utility class used to avoid linker errors with extern
+// unsized shared memory arrays with templated type
+template <class T>
+struct SharedMemory {
+  __device__ inline operator T *() {
+    extern __shared__ int __smem[];
+    return (T *)__smem;
+  }
+
+  __device__ inline operator const T *() const {
+    extern __shared__ int __smem[];
+    return (T *)__smem;
+  }
+};
+
+// specialize for double to avoid unaligned memory
+// access compile errors
+template <>
+struct SharedMemory<double> {
+  __device__ inline operator double *() {
+    extern __shared__ double __smem_d[];
+    return (double *)__smem_d;
+  }
+
+  __device__ inline operator const double *() const {
+    extern __shared__ double __smem_d[];
+    return (double *)__smem_d;
+  }
+};
 
-        __syncthreads();
-    }
-
-    if (blockSize >= 128)
-    {
-        if (tid <  64)
-        {
-            sdata[tid] = mySum = mySum + sdata[tid +  64];
-        }
 
-        __syncthreads();
-    }
-
-    if (tid < 32)
-    {
-        if (blockSize >=  64)
-        {
-            sdata[tid] = mySum = mySum + sdata[tid + 32];
-        }
-
-        if (blockSize >=  32)
-        {
-            sdata[tid] = mySum = mySum + sdata[tid + 16];
-        }
-
-        if (blockSize >=  16)
-        {
-            sdata[tid] = mySum = mySum + sdata[tid +  8];
-        }
-
-        if (blockSize >=   8)
-        {
-            sdata[tid] = mySum = mySum + sdata[tid +  4];
-        }
-
-        if (blockSize >=   4)
-        {
-            sdata[tid] = mySum = mySum + sdata[tid +  2];
-        }
-
-        if (blockSize >=   2)
-        {
-            sdata[tid] = mySum = mySum + sdata[tid +  1];
-        }
-    }
-}
+/*
+    This version adds multiple elements per thread sequentially.  This reduces
+   the overall cost of the algorithm while keeping the work complexity O(n) and
+   the step complexity O(log n). (Brent's Theorem optimization)
 
-template <unsigned int blockSize, bool nIsPow2, typename T, typename AccType, class Functor>
-__device__ void
-reduceBlocks(Functor f, T *g_idata, AccType *g_odata, unsigned int n)
-{
-    extern __shared__ AccType sdata[];
-
-    // perform first level of reduction,
-    // reading from global memory, writing to shared memory
-    unsigned int tid = threadIdx.x;
-    unsigned int i = blockIdx.x*(blockSize*2) + threadIdx.x;
-    unsigned int gridSize = blockSize*2*gridDim.x;
-    AccType mySum = 0;
-
-    // we reduce multiple elements per thread.  The number is determined by the
-    // number of active thread blocks (via gridDim).  More blocks will result
-    // in a larger gridSize and therefore fewer elements per thread
-    while (i < n)
-    {
-        mySum += f((AccType)g_idata[i]);
-
-        // ensure we don't read out of bounds -- this is optimized away for powerOf2 sized arrays
-        if (nIsPow2 || i + blockSize < n)
-            mySum += f((AccType)g_idata[i+blockSize]);
-
-        i += gridSize;
+    Note, this kernel needs a minimum of 64*sizeof(T) bytes of shared memory.
+    In other words if blockSize <= 32, allocate 64*sizeof(T) bytes.
+    If blockSize > 32, allocate blockSize*sizeof(T) bytes.
+*/
+template <typename T, typename AccType, unsigned int blockSize, bool nIsPow2Greater1, size_t K, class Functor, class AggFunctor>
+__global__ void reduceSinglePass(Functor functor, AccType aggInit, AggFunctor aggFunctor, AccType scale,
+                                 const functional::Shape full,
+                                 functional::Tensor<AccType> out,
+                                 functional::Array<functional::Tensor<T>, K> ins) {
+  int n = full.elements();
+
+  // Handle to thread block group
+  cg::thread_block cta = cg::this_thread_block();
+  AccType *sdata = SharedMemory<AccType>();
+
+  // perform first level of reduction,
+  // reading from global memory, writing to shared memory
+  unsigned int tid = threadIdx.x;
+  unsigned int i = blockIdx.x * blockSize * 2 + threadIdx.x;
+  unsigned int gridSize = blockSize * 2 * gridDim.x;
+
+  AccType mySum = aggInit;
+
+  // we reduceSinglePass multiple elements per thread.  The number is determined by the
+  // number of active thread blocks (via gridDim).  More blocks will result
+  // in a larger gridSize and therefore fewer elements per thread
+  while (i < n) {
+    mySum = aggFunctor(mySum, functional::applyWithCast<AccType>(functor, ins, i));
+
+    // ensure we don't read out of bounds -- this is optimized away for powerOf2
+    // sized arrays
+    if (nIsPow2Greater1 || i + blockSize < n) 
+      mySum = aggFunctor(mySum, functional::applyWithCast<AccType>(functor, ins, i + blockSize));
+
+    i += gridSize;
+  }
+
+  // each thread puts its local sum into shared memory
+  sdata[tid] = mySum;
+  cg::sync(cta);
+
+  // do reduction in shared mem
+  if ((blockSize >= 512) && (tid < 256)) {
+    sdata[tid] = mySum = aggFunctor(mySum, sdata[tid + 256]);
+  }
+
+  cg::sync(cta);
+
+  if ((blockSize >= 256) && (tid < 128)) {
+    sdata[tid] = mySum = aggFunctor(mySum, sdata[tid + 128]);
+  }
+
+  cg::sync(cta);
+
+  if ((blockSize >= 128) && (tid < 64)) {
+    sdata[tid] = mySum = aggFunctor(mySum, sdata[tid + 64]);
+  }
+
+  cg::sync(cta);
+
+  cg::thread_block_tile<32> tile32 = cg::tiled_partition<32>(cta);
+
+  if (cta.thread_rank() < 32) {
+    // Fetch final intermediate sum from 2nd warp
+    if (blockSize >= 64) 
+      mySum = aggFunctor(mySum, sdata[tid + 32]);
+    // reduce final warp using shuffle
+    for (int offset = tile32.size() / 2; offset > 0; offset /= 2) {
+      mySum = aggFunctor(mySum, tile32.shfl_down(mySum, offset));
     }
+  }
 
-    // do reduction in shared mem
-    reduceBlock<blockSize>(sdata, mySum, tid);
-
-    // write result for this block to global mem
-    if (tid == 0) g_odata[blockIdx.x] = sdata[0];
+  // write result for this block to global mem
+  if (cta.thread_rank() == 0) 
+    out[blockIdx.x] = aggFunctor(out[blockIdx.x], mySum * scale); // aggFunctor?
 }
 
-// Global variable used by reduceSinglePass to count how many blocks have finished
-__device__ unsigned int retirementCount = 0;
-
-cudaError_t setRetirementCount(int retCnt)
-{
-    return cudaMemcpyToSymbol(retirementCount, &retCnt, sizeof(unsigned int), 0, cudaMemcpyHostToDevice);
+static inline bool isPow2Greater1(unsigned int x) { // is power of two but also larger than 1, otherwise an out-of-bounds read occurs
+  return x > 1 && ((x & (x - 1)) == 0);
 }
 
-// This reduction kernel reduces an arbitrary size array in a single kernel invocation
-// It does so by keeping track of how many blocks have finished.  After each thread
-// block completes the reduction of its own block of data, it "takes a ticket" by
-// atomically incrementing a global counter.  If the ticket value is equal to the number
-// of thread blocks, then the block holding the ticket knows that it is the last block
-// to finish.  This last block is responsible for summing the results of all the other
-// blocks.
-//
-// In order for this to work, we must be sure that before a block takes a ticket, all
-// of its memory transactions have completed.  This is what __threadfence() does -- it
-// blocks until the results of all outstanding memory transactions within the
-// calling thread are visible to all other threads.
-//
-// For more details on the reduction algorithm (notably the multi-pass approach), see
-// the "reduction" sample in the CUDA SDK.
-
-template <unsigned int blockSize, bool nIsPow2, typename T, typename AccType, class Functor>
-__global__ void reduceSinglePass(Functor f, T *g_idata, AccType *g_odata, unsigned int n)
-{
-
-    //
-    // PHASE 1: Process all inputs assigned to this block
-    //
-
-    reduceBlocks<blockSize, nIsPow2, T, AccType>(f, g_idata, g_odata, n);
-
-    //
-    // PHASE 2: Last block finished will process all partial sums
-    //
-
-    if (gridDim.x > 1)
-    {
-        const unsigned int tid = threadIdx.x;
-        __shared__ bool amLast;
-        extern AccType __shared__ smem[];
-
-        // wait until all outstanding memory instructions in this thread are finished
-        __threadfence();
-
-        // Thread 0 takes a ticket
-        if (tid==0)
-        {
-            unsigned int ticket = atomicInc(&retirementCount, gridDim.x);
-            // If the ticket ID is equal to the number of blocks, we are the last block!
-            amLast = (ticket == gridDim.x-1);
-        }
-
-        __syncthreads();
-
-        // The last block sums the results of all other blocks
-        if (amLast)
-        {
-            int i = tid;
-            AccType mySum = 0;
-
-            while (i < gridDim.x)
-            {
-                mySum += g_odata[i];
-                i += blockSize;
-            }
-
-            reduceBlock<blockSize>(smem, mySum, tid);
-
-            if (tid==0)
-            {
-                g_odata[0] = smem[0];
-
-                // reset retirement count so that next run succeeds
-                retirementCount = 0;
-            }
-        }
-    }
+static inline unsigned int nextPow2(unsigned int x) {
+  --x;
+  x |= x >> 1;
+  x |= x >> 2;
+  x |= x >> 4;
+  x |= x >> 8;
+  x |= x >> 16;
+  return ++x;
 }
 
-bool isPow2(unsigned int x)
-{
-    return ((x&(x-1))==0);
-}
-
-template <typename T, typename AccType, class Functor>
-void ReduceAll(Functor f, Tensor blockMem, Tensor in)
-{
-    cudaSetDevice(in->getDeviceId().no);
-    int size = in->shape().elements();
-    int threads = std::min(MAX_THREADS, size);
-    int blocks  = std::min(MAX_BLOCKS, size / threads  + (size % threads != 0));
-
-    dim3 dimBlock(threads, 1, 1);
-    dim3 dimGrid(blocks, 1, 1);
-    int smemSize = threads * sizeof(AccType);
-
-    T* d_idata = in->data<T>();
-    AccType* d_odata = blockMem->data<AccType>();
-
-    // choose which of the optimized versions of reduction to launch
-    if (isPow2(size))
-    {
-        switch (threads)
-        {
-            case 512:
-                reduceSinglePass<512, true, T, AccType><<< dimGrid, dimBlock, smemSize >>>(f, d_idata, d_odata, size);
-                break;
-
-            case 256:
-                reduceSinglePass<256, true, T, AccType><<< dimGrid, dimBlock, smemSize >>>(f, d_idata, d_odata, size);
-                break;
-
-            case 128:
-                reduceSinglePass<128, true, T, AccType><<< dimGrid, dimBlock, smemSize >>>(f, d_idata, d_odata, size);
-                break;
-
-            case 64:
-                reduceSinglePass< 64, true, T, AccType><<< dimGrid, dimBlock, smemSize >>>(f, d_idata, d_odata, size);
-                break;
-
-            case 32:
-                reduceSinglePass< 32, true, T, AccType><<< dimGrid, dimBlock, smemSize >>>(f, d_idata, d_odata, size);
-                break;
-
-            case 16:
-                reduceSinglePass< 16, true, T, AccType><<< dimGrid, dimBlock, smemSize >>>(f, d_idata, d_odata, size);
-                break;
-
-            case  8:
-                reduceSinglePass<  8, true, T, AccType><<< dimGrid, dimBlock, smemSize >>>(f, d_idata, d_odata, size);
-                break;
-
-            case  4:
-                reduceSinglePass<  4, true, T, AccType><<< dimGrid, dimBlock, smemSize >>>(f, d_idata, d_odata, size);
-                break;
-
-            case  2:
-                reduceSinglePass<  2, true, T, AccType><<< dimGrid, dimBlock, smemSize >>>(f, d_idata, d_odata, size);
-                break;
-
-            case  1:
-                reduceSinglePass<  1, true, T, AccType><<< dimGrid, dimBlock, smemSize >>>(f, d_idata, d_odata, size);
-                break;
-        }
+////////////////////////////////////////////////////////////////////////////////
+// Wrapper function for kernel launch
+////////////////////////////////////////////////////////////////////////////////
+template <typename T, typename AccType, size_t K, class Functor, class AggFunctor>
+void reduceSinglePass(Functor functor, AccType aggInit, AggFunctor aggFunctor, AccType scale,
+                      const functional::Shape full,
+                      functional::Tensor<AccType> out,
+                      functional::Array<functional::Tensor<T>, K> ins,
+                      int threads, int blocks) {
+  int size = full.elements();
+  // when there is only one warp per block, we need to allocate two warps
+  // worth of shared memory so that we don't index shared memory out of bounds
+  int smemSize = (threads <= 32) ? 2 * threads * sizeof(AccType) : threads * sizeof(AccType);
+  dim3 dimBlock(threads, 1, 1);
+  dim3 dimGrid(blocks, 1, 1);
+
+  if (isPow2Greater1(size)) {
+    switch (threads) {
+      case 512:
+        reduceSinglePass<T, AccType, 512, true><<<dimGrid, dimBlock, smemSize>>>(functor, aggInit, aggFunctor, scale, full, out, ins);
+        break;
+      case 256:
+        reduceSinglePass<T, AccType, 256, true><<<dimGrid, dimBlock, smemSize>>>(functor, aggInit, aggFunctor, scale, full, out, ins);
+        break;
+      case 128:
+        reduceSinglePass<T, AccType, 128, true><<<dimGrid, dimBlock, smemSize>>>(functor, aggInit, aggFunctor, scale, full, out, ins);
+        break;
+      case 64:
+        reduceSinglePass<T, AccType, 64, true><<<dimGrid, dimBlock, smemSize>>>(functor, aggInit, aggFunctor, scale, full, out, ins);
+        break;
+      case 32:
+        reduceSinglePass<T, AccType, 32, true><<<dimGrid, dimBlock, smemSize>>>(functor, aggInit, aggFunctor, scale, full, out, ins);
+        break;
+      case 16:
+        reduceSinglePass<T, AccType, 16, true><<<dimGrid, dimBlock, smemSize>>>(functor, aggInit, aggFunctor, scale, full, out, ins);
+        break;
+      case 8:
+        reduceSinglePass<T, AccType, 8, true><<<dimGrid, dimBlock, smemSize>>>(functor, aggInit, aggFunctor, scale, full, out, ins);
+        break;
+      case 4:
+        reduceSinglePass<T, AccType, 4, true><<<dimGrid, dimBlock, smemSize>>>(functor, aggInit, aggFunctor, scale, full, out, ins);
+        break;
+      case 2:
+        reduceSinglePass<T, AccType, 2, true><<<dimGrid, dimBlock, smemSize>>>(functor, aggInit, aggFunctor, scale, full, out, ins);
+        break;
+      case 1:
+        reduceSinglePass<T, AccType, 1, true><<<dimGrid, dimBlock, smemSize>>>(functor, aggInit, aggFunctor, scale, full, out, ins);
+        break;
     }
-    else
-    {
-        switch (threads)
-        {
-            case 512:
-                reduceSinglePass<512, false, T, AccType><<< dimGrid, dimBlock, smemSize >>>(f, d_idata, d_odata, size);
-                break;
-
-            case 256:
-                reduceSinglePass<256, false, T, AccType><<< dimGrid, dimBlock, smemSize >>>(f, d_idata, d_odata, size);
-                break;
-
-            case 128:
-                reduceSinglePass<128, false, T, AccType><<< dimGrid, dimBlock, smemSize >>>(f, d_idata, d_odata, size);
-                break;
-
-            case 64:
-                reduceSinglePass< 64, false, T, AccType><<< dimGrid, dimBlock, smemSize >>>(f, d_idata, d_odata, size);
-                break;
-
-            case 32:
-                reduceSinglePass< 32, false, T, AccType><<< dimGrid, dimBlock, smemSize >>>(f, d_idata, d_odata, size);
-                break;
-
-            case 16:
-                reduceSinglePass< 16, false, T, AccType><<< dimGrid, dimBlock, smemSize >>>(f, d_idata, d_odata, size);
-                break;
-
-            case  8:
-                reduceSinglePass<  8, false, T, AccType><<< dimGrid, dimBlock, smemSize >>>(f, d_idata, d_odata, size);
-                break;
-
-            case  4:
-                reduceSinglePass<  4, false, T, AccType><<< dimGrid, dimBlock, smemSize >>>(f, d_idata, d_odata, size);
-                break;
-
-            case  2:
-                reduceSinglePass<  2, false, T, AccType><<< dimGrid, dimBlock, smemSize >>>(f, d_idata, d_odata, size);
-                break;
-
-            case  1:
-                reduceSinglePass<  1, false, T, AccType><<< dimGrid, dimBlock, smemSize >>>(f, d_idata, d_odata, size);
-                break;
-        }
+  } else {
+    switch (threads) {
+      case 512:
+        reduceSinglePass<T, AccType, 512, false><<<dimGrid, dimBlock, smemSize>>>(functor, aggInit, aggFunctor, scale, full, out, ins);
+        break;
+      case 256:
+        reduceSinglePass<T, AccType, 256, false><<<dimGrid, dimBlock, smemSize>>>(functor, aggInit, aggFunctor, scale, full, out, ins);
+        break;
+      case 128:
+        reduceSinglePass<T, AccType, 128, false><<<dimGrid, dimBlock, smemSize>>>(functor, aggInit, aggFunctor, scale, full, out, ins);
+        break;
+      case 64:
+        reduceSinglePass<T, AccType, 64, false><<<dimGrid, dimBlock, smemSize>>>(functor, aggInit, aggFunctor, scale, full, out, ins);
+        break;
+      case 32:
+        reduceSinglePass<T, AccType, 32, false><<<dimGrid, dimBlock, smemSize>>>(functor, aggInit, aggFunctor, scale, full, out, ins);
+        break;
+      case 16:
+        reduceSinglePass<T, AccType, 16, false><<<dimGrid, dimBlock, smemSize>>>(functor, aggInit, aggFunctor, scale, full, out, ins);
+        break;
+      case 8:
+        reduceSinglePass<T, AccType, 8, false><<<dimGrid, dimBlock, smemSize>>>(functor, aggInit, aggFunctor, scale, full, out, ins);
+        break;
+      case 4:
+        reduceSinglePass<T, AccType, 4, false><<<dimGrid, dimBlock, smemSize>>>(functor, aggInit, aggFunctor, scale, full, out, ins);
+        break;
+      case 2:
+        reduceSinglePass<T, AccType, 2, false><<<dimGrid, dimBlock, smemSize>>>(functor, aggInit, aggFunctor, scale, full, out, ins);
+        break;
+      case 1:
+        reduceSinglePass<T, AccType, 1, false><<<dimGrid, dimBlock, smemSize>>>(functor, aggInit, aggFunctor, scale, full, out, ins);
+        break;
     }
+  }
 }
 
-}
+}
\ No newline at end of file
diff --git a/src/functional/tmp.h b/src/functional/tmp.h
index 42dc1385f..a83c0ff43 100755
--- a/src/functional/tmp.h
+++ b/src/functional/tmp.h
@@ -9,136 +9,155 @@
 namespace marian {
 namespace functional {
 
-template <size_t K, class Functor>
+// This struct and its specializations are never used directly, only through apply and applyWithCast below.
+template <size_t K, class Functor, typename AccType> // K-ary application of Functor, elements are cast to AccType before application of Functor
 struct FApply {};
 
-template <class Functor>
-struct FApply<1, Functor> {
+template <class Functor, typename AccType>
+struct FApply<1, Functor, AccType> {
   template <typename ElementType>
-  HOST_DEVICE_INLINE static ElementType apply(
+  HOST_DEVICE_INLINE static AccType apply(
       Functor functor,
       functional::Array<functional::Tensor<ElementType>, 1>& in,
       const functional::Array<int, 1>& indices) {
-    return functor(in[0].data()[indices[0]]);
+    return functor((AccType)in[0].data()[indices[0]]); // indices is an array of offsets into multiple tensors, index[i] corresponds in[i] based on up to arity K
   }
 
   template <typename ElementType>
-  HOST_DEVICE_INLINE static ElementType apply(
+  HOST_DEVICE_INLINE static AccType apply(
       Functor functor,
       functional::Array<functional::Tensor<ElementType>, 1>& in,
       int index) {
-    return functor(in[0].data()[index]);
+    return functor((AccType)in[0].data()[index]);
   }
 };
 
-template <class Functor>
-struct FApply<2, Functor> {
+template <class Functor, typename AccType>
+struct FApply<2, Functor, AccType> {
   template <typename ElementType>
-  HOST_DEVICE_INLINE static ElementType apply(
+  HOST_DEVICE_INLINE static AccType apply(
       Functor functor,
       functional::Array<functional::Tensor<ElementType>, 2>& in,
       const functional::Array<int, 2>& indices) {
-    return functor(in[0].data()[indices[0]],
-                   in[1].data()[indices[1]]);
+    return functor((AccType)in[0].data()[indices[0]],
+                   (AccType)in[1].data()[indices[1]]);
   }
 
   template <typename ElementType>
-  HOST_DEVICE_INLINE static ElementType apply(
+  HOST_DEVICE_INLINE static AccType apply(
       Functor functor,
       functional::Array<functional::Tensor<ElementType>, 2>& in,
       int index) {
-    return functor(in[0].data()[index],
-                   in[1].data()[index]);
+    return functor((AccType)in[0].data()[index],
+                   (AccType)in[1].data()[index]);
   }
 };
 
-template <class Functor>
-struct FApply<3, Functor> {
+template <class Functor, typename AccType>
+struct FApply<3, Functor, AccType> {
   template <typename ElementType>
-  HOST_DEVICE_INLINE static ElementType apply(
+  HOST_DEVICE_INLINE static AccType apply(
       Functor functor,
       functional::Array<functional::Tensor<ElementType>, 3>& in,
       const functional::Array<int, 3>& indices) {
-    return functor(in[0].data()[indices[0]],
-                   in[1].data()[indices[1]],
-                   in[2].data()[indices[2]]);
+    return functor((AccType)in[0].data()[indices[0]],
+                   (AccType)in[1].data()[indices[1]],
+                   (AccType)in[2].data()[indices[2]]);
   }
 
   template <typename ElementType>
-  HOST_DEVICE_INLINE static ElementType apply(
+  HOST_DEVICE_INLINE static AccType apply(
       Functor functor,
       functional::Array<functional::Tensor<ElementType>, 3>& in,
       int index) {
-    return functor(in[0].data()[index],
-                   in[1].data()[index],
-                   in[2].data()[index]);
+    return functor((AccType)in[0].data()[index],
+                   (AccType)in[1].data()[index],
+                   (AccType)in[2].data()[index]);
   }
 };
 
-template <class Functor>
-struct FApply<4, Functor> {
+template <class Functor, typename AccType>
+struct FApply<4, Functor, AccType> {
   template <typename ElementType>
-  HOST_DEVICE_INLINE static ElementType apply(
+  HOST_DEVICE_INLINE static AccType apply(
       Functor functor,
       functional::Array<functional::Tensor<ElementType>, 4>& in,
       const functional::Array<int, 4>& indices) {
-    return functor(in[0].data()[indices[0]],
-                   in[1].data()[indices[1]],
-                   in[2].data()[indices[2]],
-                   in[3].data()[indices[3]]);
+    return functor((AccType)in[0].data()[indices[0]],
+                   (AccType)in[1].data()[indices[1]],
+                   (AccType)in[2].data()[indices[2]],
+                   (AccType)in[3].data()[indices[3]]);
   }
 
   template <typename ElementType>
-  HOST_DEVICE_INLINE static ElementType apply(
+  HOST_DEVICE_INLINE static AccType apply(
       Functor functor,
       functional::Array<functional::Tensor<ElementType>, 4>& in,
       int index) {
-    return functor(in[0].data()[index],
-                   in[1].data()[index],
-                   in[2].data()[index],
-                   in[3].data()[index]);
+    return functor((AccType)in[0].data()[index],
+                   (AccType)in[1].data()[index],
+                   (AccType)in[2].data()[index],
+                   (AccType)in[3].data()[index]);
   }
 };
 
-template <class Functor>
-struct FApply<5, Functor> {
+template <class Functor, typename AccType>
+struct FApply<5, Functor, AccType> {
   template <typename ElementType>
-  HOST_DEVICE_INLINE static ElementType apply(
+  HOST_DEVICE_INLINE static AccType apply(
       Functor functor,
       functional::Array<functional::Tensor<ElementType>, 5>& in,
       const functional::Array<int, 5>& indices) {
-    return functor(in[0].data()[indices[0]],
-                   in[1].data()[indices[1]],
-                   in[2].data()[indices[2]],
-                   in[3].data()[indices[3]],
-                   in[4].data()[indices[4]]);
+    return functor((AccType)in[0].data()[indices[0]],
+                   (AccType)in[1].data()[indices[1]],
+                   (AccType)in[2].data()[indices[2]],
+                   (AccType)in[3].data()[indices[3]],
+                   (AccType)in[4].data()[indices[4]]);
   }
 
   template <typename ElementType>
-  HOST_DEVICE_INLINE static ElementType apply(
+  HOST_DEVICE_INLINE static AccType apply(
       Functor functor,
       functional::Array<functional::Tensor<ElementType>, 5>& in,
       int index) {
-    return functor(in[0].data()[index], 
-                   in[1].data()[index], 
-                   in[2].data()[index], 
-                   in[3].data()[index], 
-                   in[4].data()[index]);
+    return functor((AccType)in[0].data()[index], 
+                   (AccType)in[1].data()[index], 
+                   (AccType)in[2].data()[index], 
+                   (AccType)in[3].data()[index], 
+                   (AccType)in[4].data()[index]);
   }
 };
 
-template <size_t K, class Functor, typename ElementType>
+/******************************************************************************/
+// Applying functor to sets of K tensors
+template <typename ElementType, size_t K, class Functor>
 HOST_DEVICE_INLINE ElementType apply(Functor functor,
                     functional::Array<functional::Tensor<ElementType>, K>& in,
                     const functional::Array<int, K>& indices) {
-  return FApply<K, Functor>::apply(functor, in, indices);
+  return FApply<K, Functor, ElementType>::apply(functor, in, indices); // functor is applied to same type as input ElementType, no casting required
 }
 
-template <size_t K, class Functor, typename ElementType>
+template <typename ElementType, size_t K, class Functor>
 HOST_DEVICE_INLINE ElementType apply(Functor functor,
                     functional::Array<functional::Tensor<ElementType>, K>& in,
                     int index) {
-  return FApply<K, Functor>::apply(functor, in, index);
+  return FApply<K, Functor, ElementType>::apply(functor, in, index); // functor is applied to same type as input ElementType, no casting required
+}
+
+template <typename AccType, typename ElementType, size_t K, class Functor>
+HOST_DEVICE_INLINE AccType applyWithCast(Functor functor,
+                    functional::Array<functional::Tensor<ElementType>, K>& in,
+                    const functional::Array<int, K>& indices) {
+  return FApply<K, Functor, AccType>::apply(functor, in, indices); // ElementType and AccType are potentially different, cast to AccType before applying functor.
+                                                                   // This is useful when accumulating e.g. 16-bit into 32-bit and we want to case to 32-bit before
+                                                                   // the functor is applied. L2-Norm is a good use-case since the square can be large. 
+}
+
+template <typename AccType, typename ElementType, size_t K, class Functor>
+HOST_DEVICE_INLINE AccType applyWithCast(Functor functor,
+                    functional::Array<functional::Tensor<ElementType>, K>& in,
+                    int index) {
+  return FApply<K, Functor, AccType>::apply(functor, in, index); // ElementType and AccType are potentially different, cast to AccType before applying functor
 }
 
 /******************************************************************************/
@@ -180,7 +199,7 @@ struct Loop<1, N, K> {
       for(size_t j = 0; j < K; ++j) {
         acc[j] = pAcc[j] + (dim[N - 1] + i) * in[j].shape().bstride(N - 1);
       }
-      agg = aggFunctor(agg, (AccType)apply<K>(functor, in, acc));
+      agg = aggFunctor(agg, applyWithCast<AccType>(functor, in, acc));
     }
     return agg;
   }
diff --git a/src/graph/expression_operators.cpp b/src/graph/expression_operators.cpp
index f0c06b67b..9994bf311 100755
--- a/src/graph/expression_operators.cpp
+++ b/src/graph/expression_operators.cpp
@@ -355,35 +355,51 @@ Expr slice(Expr a, int axis, Slice slice) { // numpy __getslice__ semantics, but
 }
 
 Expr sum(Expr a, int ax) {
+  if(a->shape()[ax] == 1) // nothing to reduce, sum of itself is a
+    return a;
   return Expression<ReduceNodeOp>(a, ax, ReduceNodeOpCode::sum);
 }
 
 Expr mean(Expr a, int ax) {
+  if(a->shape()[ax] == 1) // nothing to reduce, mean of itself is a
+    return a;
   return Expression<ReduceNodeOp>(a, ax, ReduceNodeOpCode::mean);
 }
 
 Expr std(Expr a, int ax) {
-  return Expression<ReduceNodeOp>(a - mean(a,ax), ax, ReduceNodeOpCode::rms);
+  if(a->shape()[ax] == 1) // nothing to reduce, std(a) = 0
+    return a - a;
+  return Expression<ReduceNodeOp>(a - mean(a, ax), ax, ReduceNodeOpCode::rms);
 }
 
-Expr var(Expr a, int ax) {
+Expr var(Expr a, int ax) { 
+  if(a->shape()[ax] == 1) // nothing to reduce, var(a) = 0
+    return a - a;
   return Expression<ReduceNodeOp>(a - mean(a, ax), ax, ReduceNodeOpCode::meanSqr);
 }
 
 Expr max(Expr a, int ax) {
+  if(a->shape()[ax] == 1) // nothing to reduce, max of itself is a
+    return a;
   return Expression<ReduceNodeOp>(a, ax, ReduceNodeOpCode::max);
 }
 
 Expr min(Expr a, int ax) {
+  if(a->shape()[ax] == 1) // nothing to reduce, min of itself is a
+    return a;
   return Expression<ReduceNodeOp>(a, ax, ReduceNodeOpCode::min);
 }
 
 Expr prod(Expr a, int ax) {
+  if(a->shape()[ax] == 1) // nothing to reduce, prod of itself is a
+    return a;
   return Expression<ReduceNodeOp>(a, ax, ReduceNodeOpCode::prod);
 }
 
 // log(sum(exp(a)))
 Expr logsumexp(Expr a, int ax) {
+  if(a->shape()[ax] == 1) // nothing to reduce, log(sum(exp(a))) = log(exp(a)) = a
+    return a;
   return Expression<ReduceNodeOp>(a, ax, ReduceNodeOpCode::logSumExp);
 }
 
diff --git a/src/tensors/gpu/add.cu b/src/tensors/gpu/add.cu
index 0a3f959a7..30fc115d9 100755
--- a/src/tensors/gpu/add.cu
+++ b/src/tensors/gpu/add.cu
@@ -1,4 +1,5 @@
 #include "tensors/gpu/add.h"
+#include "tensors/gpu/add_all.h"
 
 #include "tensors/gpu/cuda_helpers.h"
 
@@ -12,11 +13,13 @@ namespace marian {
 namespace gpu {
 
 template <size_t K, class Functor, class AggFunctor, typename T, typename AccType>
-__global__ void gAggregateGeneric(Functor functor, AccType aggInit, AggFunctor aggFunctor,
-                                  const functional::Shape full,
-                                  functional::Tensor<T> out,
-                                  functional::Array<functional::Tensor<T>, K> ins,
-                                  AccType scale = 1.0) {
+__global__ void gAggregateGeneric(Functor functor,                                 // functor applied to single corresponding elements in tensors (via broadcasting),
+                                  AccType aggInit,                                 // aggInit is starting value of accumulation (e.g. 0 for sum),
+                                  AggFunctor aggFunctor,                           // aggFunctor is used to accumulate values (e.g. sum),
+                                  const functional::Shape full,                    // maximal combined shape of all tensors via broadcasting
+                                  functional::Tensor<T> out,                       // output tensor
+                                  functional::Array<functional::Tensor<T>, K> ins, // input tensors
+                                  AccType scale = 1.0) {                           // scale accumulation result by scale. e.g. used for computing mean from sum over N elements with scale 1/N
   int outLength = out.shape().elements();
   bool same = outLength == full.elements();
   for(int i = 0; i < K; ++i)
@@ -32,10 +35,10 @@ __global__ void gAggregateGeneric(Functor functor, AccType aggInit, AggFunctor a
     int index = bid + blockDim.x * blockIdx.x + threadIdx.x;
     if(index < outLength) {
       if(same) {
-        out[index] = aggFunctor(out[index], functional::apply(functor, ins, index) * (T)scale);
+        out[index] = (T)aggFunctor((AccType)out[index], functional::applyWithCast<AccType>(functor, ins, index) * scale); // apply functors to with arguments cast to AccType
       } else {
         out.shape().dims(index, dims);
-        out[index] = aggFunctor(out[index], (T)(functional::loops(functor, aggInit, aggFunctor, ins, len, dims) * scale));
+        out[index] = (T)aggFunctor((AccType)out[index], functional::loops(functor, aggInit, aggFunctor, ins, len, dims) * scale); // apply functors to with arguments cast to AccType
       }
     }
   }
@@ -62,7 +65,7 @@ __global__ void gAggregateEqual(Functor functor, AggFunctor aggFunctor,
           indices[i] = ins[i].shape().bindex(dims);
       }
 
-      out[index] = aggFunctor(out[index], functional::apply(functor, ins, indices) * (T)scale);
+      out[index] = (T)aggFunctor((AccType)out[index], functional::applyWithCast<AccType>(functor, ins, indices) * scale);
     }
   }
 }
@@ -76,7 +79,7 @@ __global__ void gAggregateReduce(Functor functor, AccType aggInit, AggFunctor ag
   int rows = full.elements() / full.back();
   int cols = full.back();
 
-  bool same = true;
+  bool same = true; // do all inputs have the same number of elements?
   for(int i = 0; i < K; ++i)
     same = same && ins[i].shape().elements() == full.elements();
 
@@ -93,7 +96,7 @@ __global__ void gAggregateReduce(Functor functor, AccType aggInit, AggFunctor ag
         for(int tid = 0; tid < cols; tid += blockDim.x) {
           int id = tid + threadIdx.x;
           if(id < cols)
-            _sum[threadIdx.x] = aggFunctor(_sum[threadIdx.x], (AccType)functional::apply(functor, ins, j * cols + id));
+            _sum[threadIdx.x] = aggFunctor(_sum[threadIdx.x], functional::applyWithCast<AccType>(functor, ins, j * cols + id)); // casts to AccType before applying functor which then performs operation in AccType
         }
       } else {
         functional::Array<int, functional::Shape::size()> dims;
@@ -106,7 +109,7 @@ __global__ void gAggregateReduce(Functor functor, AccType aggInit, AggFunctor ag
             functional::Array<int, K> indices;
             for(int i = 0; i < K; ++i)
               indices[i] = ins[i].shape().bindex(dims);
-            _sum[threadIdx.x] = aggFunctor(_sum[threadIdx.x], (AccType)functional::apply(functor, ins, indices));
+            _sum[threadIdx.x] = aggFunctor(_sum[threadIdx.x], functional::applyWithCast<AccType>(functor, ins, indices));// casts to AccType before applying functor which then performs operation in AccType
           }
         }
       }
@@ -121,7 +124,8 @@ __global__ void gAggregateReduce(Functor functor, AccType aggInit, AggFunctor ag
         len = (len + 1) >> 1;
       }
       __syncthreads();
-      out[j] = aggFunctor(out[j], (T)(_sum[0] * scale));
+      if(threadIdx.x == 0) // only set value when in thread 0 in block
+        out[j] = aggFunctor(out[j], (T)(_sum[0] * scale));
     }
     __syncthreads();
   }
@@ -140,16 +144,16 @@ void AggregateTyped(Functor functor, AccType aggInit, AggFunctor aggFunctor, Acc
   functional::Tensor<T> gOut = out;
   functional::Array<functional::Tensor<T>, K> gIns = {tensors...};
 
-  if(full.back() != 1 && out->shape().back() == 1) {
-    size_t m = full.elements() / length;
-    size_t k = full.back();
+  if(out->shape().elements() == 1) { // reduce everything into a single element
+    AggregateAll<T, AccType>(nullptr, functor, aggInit, aggFunctor, scale, out, tensors...); // @TODO: pass allocator in here, currently uses cudaMalloc
+  } else if(full.back() != 1 && out->shape().back() == 1 && full.elements() / full.back() == length) { // element number of out and full shape on axis that are not reduced must match
+    size_t m = full.elements() / full.back(); // how many rows are we iterating over?
+    size_t k = full.back();                   // how many columns are being reduced to 1 in each row?
 
-    int blocks = std::min(MAX_BLOCKS, (int)m);
+    int blocks  = std::min(MAX_BLOCKS,  (int)m);
     int threads = std::min(MAX_THREADS, (int)k);
-    int shared = sizeof(AccType) * threads; 
-
+    int shared  = sizeof(AccType) * threads;
     gAggregateReduce<K, Functor, AggFunctor, T, AccType><<<blocks, threads, shared>>>(functor, aggInit, aggFunctor, full, gOut, gIns, scale);
-
   } else if(out->shape() == full) {
     int threads = std::min(MAX_THREADS, length);
     int blocks  = std::min(MAX_BLOCKS, length / threads + (length % threads != 0));
diff --git a/src/tensors/gpu/add_all.h b/src/tensors/gpu/add_all.h
new file mode 100644
index 000000000..5ef079a15
--- /dev/null
+++ b/src/tensors/gpu/add_all.h
@@ -0,0 +1,128 @@
+#pragma once
+
+// This header file provides wrappers around NVidia's reduce_all kernel with our custom aggregation functionality
+// This kernel reduces a tensor into a single value. We have modified it to allow for different types of aggregations
+// like summing or max etc.
+ 
+#include "tensors/gpu/cuda_helpers.h"
+#include "tensors/tensor.h"
+#include "tensors/allocator.h"
+#include "functional/functional.h"
+#include "functional/tensor.h"
+#include "tensors/tensor_operators.h"
+#include "3rd_party/reduce_all.h" // only works with CUDA >9.0, we are dropping CUDA 8.0 support, also changed in CMakeLists.txt
+
+#include <cuda.h>
+
+
+namespace marian {
+
+#if COMPILE_FP16
+// local overload to determine tensor type
+template <> inline Type typeId<half>()  { return Type::float16; }
+#endif
+
+template <typename T, typename AccType, class Functor, class AggFunctor, class... Tensors>
+void AggregateAll(Ptr<Allocator> allocator,
+                  Functor functor, 
+                  AccType aggInit,
+                  AggFunctor aggFunctor,
+                  AccType scale,
+                  Tensor out, 
+                  const Tensors... tensors) {
+  cudaSetDevice(out->getDeviceId().no);
+
+  static_assert(CUDA_VERSION >= 9000, "Marian requires CUDA_VERSION >= 9000 (9.0)");
+
+  constexpr size_t K = sizeof...(Tensors);                         // obtain arity K of tensors...
+  functional::Array<functional::Tensor<T>, K> gIns = {tensors...}; // convert to array of K objects of type functional::Tensor<T>
+  functional::Shape full = marian::Shape::broadcast({tensors...}); // compute maximal broadcasted shape
+
+  int size = full.elements();
+  int threads = (size < MAX_THREADS * 2) ? nextPow2((size + 1) / 2) : MAX_THREADS; // suggested in NVidia example for the all_reduce kernel
+  int blocks  = std::min(MAX_BLOCKS, (size + (threads * 2 - 1)) / (threads * 2));  // suggested in NVidia example for the all_reduce kernel
+
+  // The all_reduce kernel by nivida needs to perform multiple passes if the number of blocks needed to perform the reduction is larger than 1.
+  // Here we allocate the memory for the intermediate reductions for each block.
+  Tensor blockMem;
+  if(blocks > 1 || out->type() != typeId<AccType>()) { // if the out tensor does not have elementType AccType we need to allocate and convert later
+    MemoryPiece::PtrType temporaryMemory;
+    if(allocator) {
+      temporaryMemory = allocator->alloc<AccType>(blocks);
+    } else { // @TODO: get rid of this branch
+      uint8_t* temporaryMemoryPtr = 0;
+      CUDA_CHECK(cudaMalloc(&temporaryMemoryPtr, sizeof(AccType) * blocks)); 
+      temporaryMemory = MemoryPiece::New(temporaryMemoryPtr, sizeof(AccType) * blocks); // @TODO: consider implementing MemoryPiece::cudaMalloc<T>(size) for managed memory
+    }
+    blockMem = TensorBase::New(temporaryMemory,
+                               Shape({blocks}), 
+                               typeId<AccType>(), 
+                               out->getBackend());
+    blockMem->set(aggInit); // set temporary memory to aggInit
+  }
+  else {            // we are reducing into a single element now and the type matches, just use out as memory
+    blockMem = out; // do not set final output memory as we might be summing gradients... needs to be handled outside this function
+  }
+
+  functional::Tensor<AccType> gBlockMem = blockMem;
+  reduceSinglePass<T, AccType>(functor, aggInit, aggFunctor, scale, full, /*out=*/gBlockMem, /*in=*/gIns, threads, blocks);  // First pass reduction into intermediate memory
+
+  // If we actually needed more than one block to perform the first pass reduction, recursively run a second pass reduction over block memory until block memory has size 1.
+  if(blocks > 1) {
+    using namespace functional;
+    auto identity = _1; // transformation was done in first pass, hence only identity
+    AggregateAll<AccType, AccType>(allocator, identity, aggInit, aggFunctor, scale, out, /*in=*/blockMem); // Reducing AccType in AccType now (meta-reduction)
+  } else if(out->type() != typeId<AccType>()) { // it's only a single block, but we need to convert to different type, as mentioned above
+    CopyCast(out, blockMem);
+  }
+
+  if(blockMem != out) {
+    // Free temporary memory whether allocated in allocator or via cudaMalloc
+    if(allocator)
+      allocator->free(blockMem->memory());
+    else if(blockMem->memory()->data())
+      CUDA_CHECK(cudaFree(blockMem->memory()->data()));
+  }
+} 
+
+// Aggregates all values into a single tensor and returns the value of that tensor as a float
+// This does a GPU to CPU memory copy via TensorBase::scalar().
+// Used currently only for L2Norm computation
+template <typename T, typename AccType, class Functor, class AggFunctor, class... Tensors>
+AccType AggregateAllAndReturn(Ptr<Allocator> allocator, 
+                              Functor functor, 
+                              AccType aggInit,
+                              AggFunctor aggFunctor,
+                              AccType scale,
+                              const Tensors... tensors) {
+  MemoryPiece::PtrType temporaryMemory;
+  if(allocator) {
+    temporaryMemory = allocator->alloc<AccType>(1);
+  } else { // @TODO: get rid of this branch
+    uint8_t* temporaryMemoryPtr = 0;
+    CUDA_CHECK(cudaMalloc(&temporaryMemoryPtr, sizeof(AccType))); 
+    temporaryMemory = MemoryPiece::New(temporaryMemoryPtr, sizeof(AccType));
+  }
+
+  std::tuple<Tensors...> in(tensors...);
+
+  // Create a temporary tensor of size 1 to reduce into
+  auto out = TensorBase::New(temporaryMemory, 
+                             Shape({1}), 
+                             typeId<AccType>(), 
+                             std::get<0>(in)->getBackend());
+  out->set(aggInit); // init to aggInit
+
+  AggregateAll<T, AccType>(allocator, functor, aggInit, aggFunctor, scale, out, tensors...);
+
+  AccType outScalar = out->template scalar<AccType>(); // convert to float also if other underlying type
+  
+  if(allocator)
+    allocator->free(out->memory());
+  else if(out->memory()->data()) // @TODO: get rid of this branch
+    CUDA_CHECK(cudaFree(out->memory()->data()));
+
+  return outScalar;
+} 
+
+}
\ No newline at end of file
diff --git a/src/tensors/gpu/tensor_operators.cu b/src/tensors/gpu/tensor_operators.cu
index a2940e32f..cc9d84330 100644
--- a/src/tensors/gpu/tensor_operators.cu
+++ b/src/tensors/gpu/tensor_operators.cu
@@ -1,5 +1,3 @@
-//#include <thrust/transform_reduce.h>
-
 #include "common/types.h"
 #include "tensors/tensor_operators.h"
 
@@ -9,7 +7,7 @@
 #include "tensors/gpu/backend.h"
 #include "tensors/gpu/cuda_helpers.h"
 
-#include "3rd_party/reduce_all.h"
+#include "tensors/gpu/add_all.h"
 
 namespace marian {
 
@@ -588,6 +586,8 @@ __global__ void gSoftmax(T* out,
 
       // determine max (used below to improve numeric stability)
       T* _max = _share;
+      
+      // @TODO: what's going on here with fp16?
       _max[threadIdx.x] = -CUDA_FLT_MAX;  // mask
       // find max over column indices that have the same relative column index (=threadIdx.x) across all blocks of columns
       for(int tid = 0; tid < cols; tid += blockDim.x) {
@@ -1661,53 +1661,27 @@ void CrossEntropyPickBackward(Tensor out, Tensor adj, Tensor a, Tensor indices)
   }
 }
 
-float L2Norm(Tensor in, Ptr<Allocator> allocator) {
+// computes the L2Norm of tensor and returns value as flaot on the CPU, 
+// this is mostly used for diagnostic purposes and gradient clipping
+float L2Norm(Tensor in, Ptr<Allocator> allocator) { // @TODO: reverse order of arguments
   cudaSetDevice(in->getDeviceId().no);
 
   int size = in->shape().elements();
   int threads = std::min(MAX_THREADS, size);
-  int blocks = std::min(MAX_BLOCKS, size / threads + (size % threads != 0));
-
-  if(allocator) {
-    auto memoryPiece = allocator->alloc<float>(blocks);
-    auto blockMem = TensorBase::New(memoryPiece, Shape({1, blocks}), Type::float32, in->getBackend());
+  int blocks  = std::min(MAX_BLOCKS, size / threads + (size % threads != 0));
 
-    using namespace functional;
-    if(in->type() == Type::float32) {
-      ReduceAll<float, float>(_1 * _1, blockMem, in);
-#if COMPILE_FP16
-    } else if(in->type() == Type::float16) {
-      ReduceAll<half, float>(_1 * _1, blockMem, in);
-#endif
-    } else {
-      ABORT("L2Norm not implemented for type {}", in->type());
-    }
-    float dataCpu = sqrtf(blockMem->get<float>(0));
-    allocator->free(memoryPiece);
-    return dataCpu;
-  } else { // @TODO: this branch is to be removed with next PR, old version
-    uint8_t* data;
-    cudaMalloc(&data, blocks * sizeof(float));
-    Tensor out(TensorBase::New(MemoryPiece::New(data, blocks * sizeof(float)),
-                               Shape({1, blocks}),
-                               Type::float32,
-                               in->getBackend()));
- 
-    using namespace functional;
-    if(in->type() == Type::float32) {
-      ReduceAll<float, float>(_1 * _1, out, in);
+  using namespace functional;
+  float l2Norm;
+  if(in->type() == Type::float32) {
+    l2Norm = std::sqrt(AggregateAllAndReturn</*ElementType=*/float, /*AccType=*/float>(allocator, /*functor=*/_1 * _1, /*aggInit=*/0.f, /*aggFunctor=*/_1 + _2, /*scale=*/1.f, in));
 #if COMPILE_FP16
-    } else if(in->type() == Type::float16) {
-      ReduceAll<half, float>(_1 * _1, out, in);
+  } else if(in->type() == Type::float16) {
+    l2Norm = std::sqrt(AggregateAllAndReturn</*ElementType=*/half, /*AccType=*/float>(allocator, /*functor=*/_1 * _1, /*aggInit=*/0.f, /*aggFunctor=*/_1 + _2, /*scale=*/1.f, in));
 #endif
-    } else {
-      ABORT("L2Norm not implemented for type {}", in->type());
-    }
-    float dataCpu = sqrtf(out->get<float>(0));
-    out.reset();
-    cudaFree(data);
-    return dataCpu;
+  } else {
+    ABORT("L2Norm not implemented for type {}", in->type());
   }
+  return l2Norm;
 }
 
 template <typename T, typename AccType = float>
@@ -1761,22 +1735,22 @@ __global__ void gAtt(T* out,
 void Att(Tensor out, Tensor va, Tensor context, Tensor state) {
   cudaSetDevice(out->getDeviceId().no);
 
-  size_t m = out->shape().elements() / out->shape().back();
-  size_t k = context->shape()[-1];
-  size_t b = context->shape()[-2];
-  size_t t = context->shape()[-3];
+  size_t totalRows       = out->shape().elements() / out->shape().back(); // number of rows
+  size_t modelDim        = context->shape()[-1];                          // number of cols
+  size_t batchDim        = context->shape()[-2];
+  size_t contextWordsDim = context->shape()[-3];
 
-  int blocks = std::min(MAX_BLOCKS, (int)m);
-  int threads = std::min(MAX_THREADS, (int)k);
+  int blocks = std::min(MAX_BLOCKS, (int)totalRows);   
+  int threads = std::min(MAX_THREADS, (int)modelDim);
   int shared = sizeof(float) * threads;
 
   if(out->type() == Type::float32) {
     gAtt<float, float><<<blocks, threads, shared>>>(
-      out->data<float>(), va->data<float>(), context->data<float>(), state->data<float>(), m, k, b, t);
+      out->data<float>(), va->data<float>(), context->data<float>(), state->data<float>(), totalRows, modelDim, batchDim, contextWordsDim);
 #if COMPILE_FP16
   } else if (out->type() == Type::float16) {
     gAtt<half, float><<<blocks, threads, shared>>>(
-      out->data<half>(), va->data<half>(), context->data<half>(), state->data<half>(), m, k, b, t);
+      out->data<half>(), va->data<half>(), context->data<half>(), state->data<half>(), totalRows, modelDim, batchDim, contextWordsDim);
 #endif
   } else {
     ABORT("gAtt not implemented for type {}", out->type());
@@ -2005,10 +1979,10 @@ __global__ void gLayerNormalizationGrad(T* gradX,
   for(int bid = 0; bid < rows; bid += gridDim.x) {
     int j = bid + blockIdx.x;
     if(j < rows) {
-      AccType* sum_adj   = shared;
-      AccType* sum_adj_x = shared +     blockDim.x;
-      AccType* sum_x     = shared + 2 * blockDim.x;
-      AccType* sum_sqr   = shared + 3 * blockDim.x;
+      AccType* sum_adj   = shared;                   // sum of gradient coming in
+      AccType* sum_adj_l = shared +     blockDim.x;  // sum of gradient coming in times layerNorm from value
+      AccType* sum_x     = shared + 2 * blockDim.x;  // sum of input value x
+      AccType* sum_sqr   = shared + 3 * blockDim.x;  // sum of (x - mean)^2
 
       const T* xRow   =   x + j * cols;
       const T* yRow   =   y + j * cols;
@@ -2016,7 +1990,7 @@ __global__ void gLayerNormalizationGrad(T* gradX,
 
       sum_x[threadIdx.x]     = (AccType)0.0f;
       sum_adj[threadIdx.x]   = (AccType)0.0f;
-      sum_adj_x[threadIdx.x] = (AccType)0.0f;
+      sum_adj_l[threadIdx.x] = (AccType)0.0f;
       sum_sqr[threadIdx.x]   = (AccType)0.0f;
 
       for(int tid = 0; tid < cols; tid += blockDim.x) {
@@ -2030,7 +2004,7 @@ __global__ void gLayerNormalizationGrad(T* gradX,
           AccType lv     = (yv - betav) / (gammav + eps); // go back to LN(x) from scaled and shifted version for accumulation
 
           sum_x[threadIdx.x]     += xv;
-          sum_adj_x[threadIdx.x] += adjv * lv;
+          sum_adj_l[threadIdx.x] += adjv * lv;
           sum_adj[threadIdx.x]   += adjv;
         }
       }
@@ -2042,7 +2016,7 @@ __global__ void gLayerNormalizationGrad(T* gradX,
         if(threadIdx.x < (len >> 1)) {
           sum_x[threadIdx.x]     += sum_x[threadIdx.x     + skip]; // Accumulates in AccType
           sum_adj[threadIdx.x]   += sum_adj[threadIdx.x   + skip]; // Accumulates in AccType
-          sum_adj_x[threadIdx.x] += sum_adj_x[threadIdx.x + skip]; // Accumulates in AccType
+          sum_adj_l[threadIdx.x] += sum_adj_l[threadIdx.x + skip]; // Accumulates in AccType
         }
         len = (len + 1) >> 1;
       }
@@ -2074,28 +2048,27 @@ __global__ void gLayerNormalizationGrad(T* gradX,
 
       // Jacobian of layer norm
       // J = [ \frac{1}{N\sigma} (N\delta_{ij} - l_i l_j - 1) ]_{ij}
-      // J * a = dC/dx_i = ( N v_i - l_i \sum_j l_j a_j - \sum_j a_j ) / (N \sigma)
+      // J * a = dC/dx_i = ( N a_i - l_i \sum_j l_j a_j - \sum_j a_j ) / (N \sigma)
 
       for(int tid = 0; tid < cols; tid += blockDim.x) {
         int id = tid + threadIdx.x;
         if(id < cols) {
 
           AccType xv     = xRow[id];
-          //AccType yv     = yRow[id];
-          //AccType betav  = beta ? (AccType)beta[id] : (AccType)0.f;
           AccType gammav = (AccType)gamma[id];
           AccType adjv   = adjRow[id];
           AccType lv     = (xv - mean) / (sigma + eps);
 
-          AccType gradLv = N * adjv - lv * sum_adj_x[0] - sum_adj[0];
+          AccType gradLv = N * adjv - lv * sum_adj_l[0] - sum_adj[0];
           gradLv        /= N * (sigma + eps); // eps has to be inside parentheses for correct gradient
 
           AccType gradXv = gammav * gradLv;
 
-          // Keep LN gradient between [-10, 10]
-          // AccType sign = functional::Ops<AccType>::sgn(gradXv);
-          // AccType cutoff = (AccType)10.f;
-          // gradXv = functional::Ops<AccType>::abs(gradXv) > cutoff ? sign * cutoff : gradXv;
+          // Keep LN gradient between [-1000, 1000] for TensorOps, this currently used for making values fit into fp16. @TODO: to be fixed and removed.
+          AccType sign = functional::Ops<AccType>::sgn(gradXv);
+          AccType cutoff = (AccType)1000.f; // @TODO: expose this somehow as an option?
+                                            // or better: make obsolete.
+          gradXv = functional::Ops<AccType>::abs(gradXv) > cutoff ? sign * cutoff : gradXv;
 
           T* gradXRow      = gradX     + j * cols;
           gradXRow[id]    += (T)(gradXv);
diff --git a/src/tensors/tensor.cpp b/src/tensors/tensor.cpp
index 4a0c90b2b..7fa441f5a 100755
--- a/src/tensors/tensor.cpp
+++ b/src/tensors/tensor.cpp
@@ -28,18 +28,20 @@ std::string TensorBase::debug(int precision, int dispCols) {
   else
     strm << std::fixed << std::setprecision(0) << std::setfill(' ');
 
-  // double maxv = std::numeric_limits<double>::lowest();
-  // double minv = std::numeric_limits<double>::max();
-  // double l2Norm = 0.0;
+  double maxv = std::numeric_limits<double>::lowest();
+  double minv = std::numeric_limits<double>::max();
+  double l2Sum = 0.0;
+  for(int i = 0; i < values.size(); ++i) {
+    if((double)values[i] > maxv) maxv = values[i];
+    if((double)values[i] < minv) minv = values[i];
+    l2Sum += (double)values[i] * (double)values[i];
+  }
+  strm << "min: " << minv << " max: " << maxv << " l2-norm: " << sqrt(l2Sum) << std::endl;
 
   for(int i = 0; i < values.size(); ++i) {
     std::vector<int> dims;
     shape().dims(i, dims);
 
-    // if((double)values[i] > maxv) maxv = values[i];
-    // if((double)values[i] < minv) minv = values[i];
-    // l2Norm += (double)values[i] * (double)values[i];
-
     bool disp = true;
     for(int j = 0; j < dims.size(); ++j)
       disp = disp && (dims[j] < dispCols || dims[j] >= shape()[j] - dispCols);
@@ -95,8 +97,6 @@ std::string TensorBase::debug(int precision, int dispCols) {
     }
   }
   strm << std::endl;
-  //strm << "min: " << minv << " max: " << maxv << " l2-norm: " << sqrt(l2Norm);
-
   return strm.str();
 }
 
diff --git a/src/tensors/tensor_operators.h b/src/tensors/tensor_operators.h
index 420131b88..d75652403 100644
--- a/src/tensors/tensor_operators.h
+++ b/src/tensors/tensor_operators.h
@@ -54,12 +54,12 @@ void Add(Functor functor, float scale, marian::Tensor out, Tensors... tensors) {
     gpu::Add(functor, scale, out, tensors...);
   else
 #endif
-    cpu::Aggregate(functor, 0.0f, functional::_1 + functional::_2, scale, out, tensors...);
+    cpu::Aggregate(functor, /*aggInit=*/0.0f, functional::_1 + functional::_2, scale, out, tensors...);
 }
 
 template <class Functor, class... Tensors>
 void Add(Functor functor, marian::Tensor out, Tensors... tensors) {
-  Add(functor, 1, out, tensors...);
+  Add(functor, /*scale=*/1.f, out, tensors...);
 }
 
 template <class Functor, class AggFunctor, class... Tensors>
diff --git a/src/tests/units/operator_tests.cpp b/src/tests/units/operator_tests.cpp
index f65c59445..db7699c61 100644
--- a/src/tests/units/operator_tests.cpp
+++ b/src/tests/units/operator_tests.cpp
@@ -789,3 +789,59 @@ TEST_CASE("Expression graph supports basic math operations (cpu)", "[operator]")
   tests<float>(DeviceType::cpu);
 }
 #endif
+
+#ifdef BLAS_FOUND
+#ifdef CUDA_FOUND
+
+TEST_CASE("Compare aggregate operator", "[graph]") {
+  auto floatApprox = [](float x, float y) -> bool { return x == Approx(y).epsilon(0.001); };
+  
+  Config::seed = 1234;
+
+  std::vector<float> initc;
+  std::vector<float> inita;
+
+  {
+    auto graph = New<ExpressionGraph>();
+    graph->setDevice({0, DeviceType::cpu});
+    graph->reserveWorkspaceMB(40);
+
+    auto chl = graph->param("1x10x512x2048", {1, 10, 512, 2048}, inits::normal());
+    auto adj = graph->param("1x1x512x2048",  {1,  1, 512, 2048}, inits::normal());
+    graph->forward();
+
+    chl->val()->get(initc);
+    adj->val()->get(inita);
+  }
+
+  SECTION("initializing with zero (cpu)") {
+    std::vector<float> values1;
+    std::vector<float> values2;
+    
+    auto graph1 = New<ExpressionGraph>();
+    graph1->setDevice({0, DeviceType::cpu});
+    graph1->reserveWorkspaceMB(40);
+
+    auto graph2 = New<ExpressionGraph>();
+    graph2->setDevice({0, DeviceType::gpu});
+    graph2->reserveWorkspaceMB(40);
+  
+    auto chl1 = graph1->param("1x10x512x2048", {1, 10, 512, 2048}, inits::fromVector(initc));
+    auto adj1 = graph1->param("1x1x512x2048",  {1,  1, 512, 2048}, inits::fromVector(inita));
+    auto prod1 = scalar_product(chl1, adj1, -1);
+    graph1->forward();
+
+    auto chl2 = graph2->param("1x10x512x2048", {1, 10, 512, 2048}, inits::fromVector(initc));
+    auto adj2 = graph2->param("1x1x512x2048",  {1,  1, 512, 2048}, inits::fromVector(inita));
+    auto prod2 = scalar_product(chl2, adj2, -1);
+    graph2->forward();
+
+    prod1->val()->get(values1);
+    prod2->val()->get(values2);
+
+    CHECK( std::equal(values1.begin(), values1.end(), values2.begin(), floatApprox) );
+  }
+}
+
+  #endif
+  #endif
\ No newline at end of file

From 9e090e34720f32b97db8c640515d68100dba420f Mon Sep 17 00:00:00 2001
From: Marcin Junczys-Dowmunt <Marcin.JunczysDowmunt@microsoft.com>
Date: Mon, 25 Nov 2019 17:49:34 -0800
Subject: [PATCH 782/838] bump version

---
 CHANGELOG.md | 1 +
 VERSION      | 2 +-
 2 files changed, 2 insertions(+), 1 deletion(-)

diff --git a/CHANGELOG.md b/CHANGELOG.md
index 4682c6c25..27cf17848 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -25,6 +25,7 @@ and this project adheres to [Semantic Versioning](http://semver.org/spec/v2.0.0.
 
 ### Fixed
 - Fixed multiple reduction kernels on GPU
+- Fixed guided-alignment training with cross-entropy
 - Replace IntrusivePtr with std::uniq_ptr in FastOpt, fixes random segfaults 
   due to thread-non-safty of reference counting.
 - Make sure that items are 256-byte aligned during saving
diff --git a/VERSION b/VERSION
index 747ee4f6e..fa9af5966 100644
--- a/VERSION
+++ b/VERSION
@@ -1 +1 @@
-v1.8.22
+v1.8.23

From f07042b9c3855eb8ceb6b43e6bf6e02d551e11f8 Mon Sep 17 00:00:00 2001
From: Marcin Junczys-Dowmunt <Marcin.JunczysDowmunt@microsoft.com>
Date: Mon, 25 Nov 2019 19:20:03 -0800
Subject: [PATCH 783/838] bump version based on PRs

---
 CHANGELOG.md | 2 ++
 VERSION      | 2 +-
 2 files changed, 3 insertions(+), 1 deletion(-)

diff --git a/CHANGELOG.md b/CHANGELOG.md
index 27cf17848..25a492c39 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -8,6 +8,7 @@ and this project adheres to [Semantic Versioning](http://semver.org/spec/v2.0.0.
 ## [Unreleased]
 
 ### Added
+- CMakeLists.txt now looks for SSE 4.2
 - Purging of finished hypotheses during beam-search. A lot faster for large batches.
 - Faster option look-up, up to 20-30% faster translation
 - Added --cite and --authors flag
@@ -40,6 +41,7 @@ and this project adheres to [Semantic Versioning](http://semver.org/spec/v2.0.0.
 - Compilation with CUDA 10.1
 
 ### Changed
+- Return error signal on SIGTERM
 - Dropped support for CUDA 8.0, CUDA 9.0 is now minimal requirement
 - Removed autotuner for now, will be switched back on later
 - Boost depdendency is now optional and only required for marian_server 
diff --git a/VERSION b/VERSION
index fa9af5966..8f1c6be5f 100644
--- a/VERSION
+++ b/VERSION
@@ -1 +1 @@
-v1.8.23
+v1.8.26

From e12a5dbe171fd7ff4f45b3a0496e1044cd854c6d Mon Sep 17 00:00:00 2001
From: Marcin Junczys-Dowmunt <Marcin.JunczysDowmunt@microsoft.com>
Date: Mon, 25 Nov 2019 19:20:34 -0800
Subject: [PATCH 784/838] regression-tests/

---
 regression-tests | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/regression-tests b/regression-tests
index 2a5833e41..85d1da086 160000
--- a/regression-tests
+++ b/regression-tests
@@ -1 +1 @@
-Subproject commit 2a5833e41110c19f0bbe9f3cf2aa92caad96cf42
+Subproject commit 85d1da0867f5f2c80efa23aa2a363f37768bb3fb

From 120ab8fa75b538b4599752fe2d9dc956302a3966 Mon Sep 17 00:00:00 2001
From: Marcin Junczys-Dowmunt <Marcin.JunczysDowmunt@microsoft.com>
Date: Mon, 25 Nov 2019 20:51:35 -0800
Subject: [PATCH 785/838] increase tolerance for unit test

---
 src/tests/units/operator_tests.cpp | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/tests/units/operator_tests.cpp b/src/tests/units/operator_tests.cpp
index db7699c61..774a93deb 100644
--- a/src/tests/units/operator_tests.cpp
+++ b/src/tests/units/operator_tests.cpp
@@ -794,7 +794,7 @@ TEST_CASE("Expression graph supports basic math operations (cpu)", "[operator]")
 #ifdef CUDA_FOUND
 
 TEST_CASE("Compare aggregate operator", "[graph]") {
-  auto floatApprox = [](float x, float y) -> bool { return x == Approx(y).epsilon(0.001); };
+  auto floatApprox = [](float x, float y) -> bool { return x == Approx(y).epsilon(0.01); };
   
   Config::seed = 1234;
 

From 49b54a6180019aa78169e06680531257106d50b0 Mon Sep 17 00:00:00 2001
From: Marcin Junczys-Dowmunt <Marcin.JunczysDowmunt@microsoft.com>
Date: Tue, 26 Nov 2019 21:23:38 -0800
Subject: [PATCH 786/838] catch n and y as strings in FastOpt instead of as
 boolean values

---
 src/common/fastopt.h              | 7 +++++++
 src/tests/units/fastopt_tests.cpp | 6 ++++++
 2 files changed, 13 insertions(+)

diff --git a/src/common/fastopt.h b/src/common/fastopt.h
index 36bc7814b..9ddc3c2c3 100644
--- a/src/common/fastopt.h
+++ b/src/common/fastopt.h
@@ -154,6 +154,13 @@ class FastOpt {
   void makeScalar(const YAML::Node& v) {
     elements_ = 0;
     try {
+      // Cast node to text first, that works for any scalar node and test that it does not contain single characters
+      // that according to YAML could be boolean values. Unfortunately, we do not have any type information at this point. 
+      // This means we are disabling support for boolean values in YAML that are expressed with these characters. 
+      auto asText = v.as<std::string>();
+      if(asText.size() == 1 && asText.find_first_of("nyNYtfTF") == 0) // @TODO: should we disallow other strings too?
+        throw YAML::BadConversion(YAML::Mark()); // get's picked up by next catch block
+
       value_ = v.as<bool>();
       type_ = NodeType::Bool;
     } catch(const YAML::BadConversion& /*e*/) {
diff --git a/src/tests/units/fastopt_tests.cpp b/src/tests/units/fastopt_tests.cpp
index e846ab87a..cec6ab902 100644
--- a/src/tests/units/fastopt_tests.cpp
+++ b/src/tests/units/fastopt_tests.cpp
@@ -40,6 +40,9 @@ TEST_CASE("Options can be accessed", "[fastopt]") {
       "subnode: {"
       "  baz: [ 111.5, False ],"
       "  qux: 222,"
+      "  preprocess1: n,"
+      "  preprocess2: d,"
+      "  preprocess3: y,"
       "  }"
       "}");
 
@@ -57,6 +60,9 @@ TEST_CASE("Options can be accessed", "[fastopt]") {
     CHECK( o["subnode"]["baz"][0].as<float>() == 111.5f );
     CHECK( o["subnode"]["baz"][1].as<bool>() == false );
     CHECK( o["subnode"]["baz"][0].as<int>() == 111 );
+    CHECK( o["subnode"]["preprocess1"].as<std::string>() == "n" ); // don't allow "n" to be cast to boolean false while converting from YAML
+    CHECK( o["subnode"]["preprocess2"].as<std::string>() == "d" );
+    CHECK( o["subnode"]["preprocess3"].as<std::string>() == "y" ); // don't allow "y" to be cast to boolean true while converting from YAML
   }
 
   node["foo"] = "baz";

From 0197b89b43de80ba6bd36ab64a8c9f2507881ab4 Mon Sep 17 00:00:00 2001
From: Marcin Junczys-Dowmunt <Marcin.JunczysDowmunt@microsoft.com>
Date: Tue, 26 Nov 2019 21:24:22 -0800
Subject: [PATCH 787/838] bump version

---
 CHANGELOG.md | 1 +
 VERSION      | 2 +-
 2 files changed, 2 insertions(+), 1 deletion(-)

diff --git a/CHANGELOG.md b/CHANGELOG.md
index 25a492c39..ee01d320e 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -25,6 +25,7 @@ and this project adheres to [Semantic Versioning](http://semver.org/spec/v2.0.0.
 - Gradient-checkpointing
 
 ### Fixed
+- FastOpt now reads "n" and "y" values as strings, not as boolean values
 - Fixed multiple reduction kernels on GPU
 - Fixed guided-alignment training with cross-entropy
 - Replace IntrusivePtr with std::uniq_ptr in FastOpt, fixes random segfaults 
diff --git a/VERSION b/VERSION
index 8f1c6be5f..9d42e2415 100644
--- a/VERSION
+++ b/VERSION
@@ -1 +1 @@
-v1.8.26
+v1.8.27

From f007772a496e7aca5b2a91407534314f606a7e40 Mon Sep 17 00:00:00 2001
From: Marcin Junczys-Dowmunt <marcinjd@microsoft.com>
Date: Wed, 27 Nov 2019 19:27:49 -0800
Subject: [PATCH 788/838] Update README.md

---
 README.md | 1 -
 1 file changed, 1 deletion(-)

diff --git a/README.md b/README.md
index 150f2030d..17a33728a 100644
--- a/README.md
+++ b/README.md
@@ -1,7 +1,6 @@
 Marian
 ======
 
-[![Build Status CUDA 8](https://img.shields.io/jenkins/s/http/vali.inf.ed.ac.uk/jenkins/view/marian/job/marian-dev-cuda-8.0.svg?label=CUDA%208)](http://vali.inf.ed.ac.uk/jenkins/job/marian-dev-cuda-8.0/)
 [![Build Status CUDA 9](https://img.shields.io/jenkins/s/http/vali.inf.ed.ac.uk/jenkins/view/marian/job/marian-dev-cuda-9.2.svg?label=CUDA%209)](http://vali.inf.ed.ac.uk/jenkins/job/marian-dev-cuda-9.2/)
 [![Build Status CUDA 10](https://img.shields.io/jenkins/s/http/vali.inf.ed.ac.uk/jenkins/view/marian/job/marian-dev-cuda-10.1.svg?label=CUDA%2010)](http://vali.inf.ed.ac.uk/jenkins/job/marian-dev-cuda-10.1/)
 [![Build Status CPU](https://img.shields.io/jenkins/s/http/vali.inf.ed.ac.uk/jenkins/view/marian/job/marian-dev-cpu.svg?label=CPU)](http://vali.inf.ed.ac.uk/jenkins/job/marian-dev-cpu/)

From 9c9a240354931c7d3b6b63b4fcf32b13507cc388 Mon Sep 17 00:00:00 2001
From: Young Jin Kim <youki@microsoft.com>
Date: Tue, 3 Dec 2019 19:14:18 +0000
Subject: [PATCH 789/838] Merged PR 10266: FBGEMM based Int8 model

FBGEMM based Int8 model - working with the master
1. Added int8 implementation into packed_gemm.h/cpp with FBGEMM
2. Update FBGEMM library to make it work on windows
3. Split 'ispacked' into packed8 and packed16
4. Change all names for PackFp32 to PackFp16 which is more accurate
---
 src/3rd_party/fbgemm                          |   2 +-
 src/CMakeLists.txt                            |   2 +-
 src/command/marian_conv.cpp                   |   4 +-
 src/common/types.cpp                          |  18 +-
 src/graph/expression_operators.cpp            |  54 +-
 src/microsoft/quicksand.cpp                   |   2 +-
 src/tensors/backend.h                         |   9 -
 src/tensors/cpu/backend.h                     |   1 -
 src/tensors/cpu/expanded_gemm.h               | 205 -------
 src/tensors/cpu/fbgemm/expanded_gemm.h        | 400 ++++++++++++++
 .../cpu/fbgemm}/expression_graph_packable.h   |  60 +-
 src/tensors/cpu/fbgemm/packed_gemm.cpp        | 496 +++++++++++++++++
 src/tensors/cpu/fbgemm/packed_gemm.h          | 137 +++++
 src/tensors/cpu/sharp/packed_gemm.cpp         | 313 -----------
 src/tensors/cpu/sharp/packed_gemm.h           |  70 ---
 vs/Marian.sln                                 |   4 +-
 vs/Marian.vcxproj                             | 456 ++++++++++++----
 vs/Marian.vcxproj.filters                     | 514 +++++++++++++-----
 18 files changed, 1871 insertions(+), 876 deletions(-)
 delete mode 100644 src/tensors/cpu/expanded_gemm.h
 create mode 100644 src/tensors/cpu/fbgemm/expanded_gemm.h
 rename src/{graph => tensors/cpu/fbgemm}/expression_graph_packable.h (63%)
 create mode 100644 src/tensors/cpu/fbgemm/packed_gemm.cpp
 create mode 100644 src/tensors/cpu/fbgemm/packed_gemm.h
 delete mode 100644 src/tensors/cpu/sharp/packed_gemm.cpp
 delete mode 100644 src/tensors/cpu/sharp/packed_gemm.h

diff --git a/src/3rd_party/fbgemm b/src/3rd_party/fbgemm
index f0b354327..501f92e53 160000
--- a/src/3rd_party/fbgemm
+++ b/src/3rd_party/fbgemm
@@ -1 +1 @@
-Subproject commit f0b354327aaf2330c65340725b1981040c8bec9e
+Subproject commit 501f92e531378154daad708cd9aeb649637b4696
diff --git a/src/CMakeLists.txt b/src/CMakeLists.txt
index c8529d178..b549ce2d4 100644
--- a/src/CMakeLists.txt
+++ b/src/CMakeLists.txt
@@ -51,7 +51,7 @@ add_library(marian STATIC
   tensors/cpu/sharp/int_gemm.cpp
   tensors/cpu/sharp/avx_gemm.cpp
   tensors/cpu/sharp/sse_gemm.cpp
-  tensors/cpu/sharp/packed_gemm.cpp
+  tensors/cpu/fbgemm/packed_gemm.cpp
 
   graph/expression_graph.cpp
   graph/expression_operators.cpp
diff --git a/src/command/marian_conv.cpp b/src/command/marian_conv.cpp
index 5d41fc288..e1458bfdd 100644
--- a/src/command/marian_conv.cpp
+++ b/src/command/marian_conv.cpp
@@ -4,7 +4,7 @@
 
 #include <sstream>
 
-#include "graph/expression_graph_packable.h"
+#include "tensors/cpu/fbgemm/expression_graph_packable.h"
 
 int main(int argc, char** argv) {
   using namespace marian;
@@ -22,7 +22,7 @@ int main(int argc, char** argv) {
         "  ./marian-conv -f model.npz -t model.bin --gemm-type fp16packed");
     cli->add<std::string>("--from,-f", "Input model", "model.npz");
     cli->add<std::string>("--to,-t", "Output model", "model.bin");
-    cli->add<std::string>("--gemm-type,-g", "GEMM Type to be used with this weights", "mklfp32");
+    cli->add<std::string>("--gemm-type,-g", "GEMM Type to be used with this weights - float16, float32, int8packed, fp16packed", "float32");
     cli->parse(argc, argv);
     options->merge(config);
   }
diff --git a/src/common/types.cpp b/src/common/types.cpp
index d17c58f6c..e5c4e06fd 100644
--- a/src/common/types.cpp
+++ b/src/common/types.cpp
@@ -1,5 +1,5 @@
 #include "common/types.h"
-#include "tensors/cpu/sharp/packed_gemm.h"
+#include "tensors/cpu/fbgemm/packed_gemm.h"
 
 namespace marian {
 
@@ -8,11 +8,21 @@ namespace marian {
 // But for instance, for intransparent types like packed tensors, it cannot easily be inferred by
 // multiplying. All cases are handed here and can later be passed to allocators etc. 
 size_t requiredBytes(const Shape& shape, Type type) {
-  if(isPacked(type)) {
+#if USE_FBGEMM
+  if (type == Type::packed8)
+  {
+    int nrow, ncol;
     uint64_t packsize;
-    cpu::variant::PackInfoFp32(shape, false, packsize);
+    cpu::variant::fbgemmPacked8PackInfo(shape, false, /*out=*/nrow, /*out=*/ncol, /*out=*/packsize);
     return (size_t)packsize;
-  } else {
+  } else if (type == Type::packed16)
+  {
+    uint64_t packsize;
+    cpu::variant::fbgemmPacked16PackInfo(shape, false, /*out=*/packsize);
+    return (size_t)packsize;
+  } else
+#endif  // USE_FBGEMM
+  {
     return shape.elements() * sizeOf(type);
   }
 }
diff --git a/src/graph/expression_operators.cpp b/src/graph/expression_operators.cpp
index 9994bf311..df0b25b33 100755
--- a/src/graph/expression_operators.cpp
+++ b/src/graph/expression_operators.cpp
@@ -7,7 +7,7 @@
 
 #include "graph/auto_tuner.h"
 #include "tensors/cpu/int16.h"
-#include "tensors/cpu/expanded_gemm.h"
+#include "tensors/cpu/fbgemm/expanded_gemm.h"
 
 #if USE_FBGEMM
 #include "fbgemm/Utils.h"
@@ -416,17 +416,50 @@ Expr weighted_average(Expr in, Expr weights, int ax) {
 Expr dot(Expr a, Expr b, bool transA, bool transB, float scale) {
   auto device = a->graph()->getDeviceId().type;
   float clipValue = a->graph()->getBackend()->getClip();
+  // added support for packed GEMM API (fp16, int8)
+  Type aElementType = a->value_type();
+  Type bElementType = b->value_type();
 
   // Currently only true when command line options
   // --optimize --cpu-thread=N with N > 0 are set.
-  if(device == DeviceType::cpu && a->graph()->getBackend()->isOptimized()) {
-    // dotInt16 computes A * B.T, hence the transpose for B to get A * B
-    // if transA = false and transB = false.
-
-    return cpu::int16::dot(
-        cpu::int16::quantize(transA ? transpose(a) : a, clipValue),
-        cpu::int16::quantize(transB ? b : transpose(b), clipValue),
-        scale);
+  if(device == DeviceType::cpu) {
+    if(isFloat(aElementType) && isFloat(bElementType)) {
+      if(a->graph()->getBackend()->isOptimized()) {
+        // dotInt16 computes A * B.T, hence the transpose for B to get A * B
+        // if transA = false and transB = false.
+
+        return cpu::int16::dot(
+          cpu::int16::quantize(transA ? transpose(a) : a, clipValue),
+          cpu::int16::quantize(transB ? b : transpose(b), clipValue),
+          scale);
+      } else {
+        return Expression<DotNodeOp>(
+          clip(a, clipValue), clip(b, clipValue), transA, transB, scale);
+      }
+    } else if(isFloat(aElementType) && isPacked(bElementType)) {
+#if USE_FBGEMM
+      // 07/10/2019 - Use packed GEMM only if the cpu architecture supports AVX2
+      // one of the fbgemm's sub modules, cpuinfo (https://github.com/pytorch/cpuinfo).
+      // It looks at the cpu register
+      // (https://github.com/pytorch/cpuinfo/blob/master/src/x86/isa.c#L391),
+      // and this cpu lookup is executed only once and the state is kept in FBGEMM.
+      if(fbgemm::fbgemmHasAvx2Support()) {
+        // This variant of dot product can handle matrix multiplications with packed8 and packed16 weight matrix (B).
+        return cpu::variant::dot(clip(a, clipValue),
+                                 b,
+                                 b->shape(),
+                                 transA,
+                                 transB,
+                                 scale);
+      } else {
+        ABORT("AVX2 is not available. At least, AVX2 is needed to use fbgemm-based packed GEMM");
+      }
+#else
+      ABORT("Packed GEMM is not available in this build");
+#endif  // USE_FBGEMM
+    } else {
+      ABORT("Combination of types A: {} B: {} not supported", aElementType, bElementType);
+    }
   } else {
     return Expression<DotNodeOp>(
         clip(a, clipValue), clip(b, clipValue), transA, transB, scale);
@@ -485,6 +518,7 @@ Expr affine(Expr a, Expr b, Expr bias, bool transA, bool transB, float scale) {
       // (https://github.com/pytorch/cpuinfo/blob/master/src/x86/isa.c#L391),
       // and this cpu lookup is executed only once and the state is kept in FBGEMM.
       if(fbgemm::fbgemmHasAvx2Support()) {
+        // This variant of affine product can handle matrix multiplications with packed8 and packed16 weight matrix (B).
         return cpu::variant::affine(clip(a, clipValue),
                                     b,
                                     b->shape(),
@@ -493,7 +527,7 @@ Expr affine(Expr a, Expr b, Expr bias, bool transA, bool transB, float scale) {
                                     transB,
                                     scale);
       } else {
-        ABORT("No on-the-fly packing at the moment");
+        ABORT("AVX2 is not available. At least, AVX2 is needed to use fbgemm-based packed GEMM");
       }
 #else
       ABORT("Packed GEMM is not available in this build");
diff --git a/src/microsoft/quicksand.cpp b/src/microsoft/quicksand.cpp
index 6b30089c1..a734a46ea 100755
--- a/src/microsoft/quicksand.cpp
+++ b/src/microsoft/quicksand.cpp
@@ -10,7 +10,7 @@
 #include "translator/scorers.h"
 #include "data/alignment.h"
 #include "data/vocab_base.h"
-#include "graph/expression_graph_packable.h"
+#include "tensors/cpu/fbgemm/expression_graph_packable.h"
 
 #if USE_FBGEMM
 #include "fbgemm/Utils.h"
diff --git a/src/tensors/backend.h b/src/tensors/backend.h
index 8a9b2f906..01d002675 100644
--- a/src/tensors/backend.h
+++ b/src/tensors/backend.h
@@ -5,15 +5,6 @@
 
 namespace marian {
 
-// GEMM type enum
-typedef enum { 
-  Auto = 0,            // auto tuning between available GEMMs
-  MklFp32 = 1,         // MKL based GEMM, fp32
-  IntrinInt16 = 2,     // Intrinsic implementation of Int 16 GEMM
-  FbFp16Packed = 10,   // FBGEMM based fp16 GEMM with packing
-  FbInt8Packed = 11    // FBGEMM based int8 GEMM with packing
-} GemmType;
-
 class Backend {
 protected:
   DeviceId deviceId_;
diff --git a/src/tensors/cpu/backend.h b/src/tensors/cpu/backend.h
index 9414fbd2a..74bbf8082 100644
--- a/src/tensors/cpu/backend.h
+++ b/src/tensors/cpu/backend.h
@@ -12,7 +12,6 @@ namespace cpu {
 class Backend : public marian::Backend {
 protected:
   bool optimized_{false};
-  GemmType gemmType_{GemmType::Auto};
 
 public:
   Backend(DeviceId deviceId, size_t seed) : marian::Backend(deviceId, seed) {}
diff --git a/src/tensors/cpu/expanded_gemm.h b/src/tensors/cpu/expanded_gemm.h
deleted file mode 100644
index c9a119c37..000000000
--- a/src/tensors/cpu/expanded_gemm.h
+++ /dev/null
@@ -1,205 +0,0 @@
-#pragma once
-
-#include "graph/node.h"
-#include "tensors/cpu/sharp/packed_gemm.h"
-
-#if USE_FBGEMM
-#ifdef __GNUC__
-#pragma GCC diagnostic push
-#pragma GCC diagnostic ignored "-Wunused-variable"
-#endif
-
-#include "3rd_party/fbgemm/include/fbgemm/FbgemmFP16.h"
-
-#ifdef __GNUC__
-#pragma GCC diagnostic pop
-#endif
-
-using namespace fbgemm;
-#endif  // USE_FBGEMM
-
-namespace marian {
-namespace cpu {
-namespace variant {
-
-// Enumeration for the Matrix used in pack functions
-// A matrix - 0, B matrix - 1
-enum class PackMatrix : uint8_t {
-  A = 0x00,
-  B = 0x01
-};
-
-// Pack a matrix into cache utilization efficient way (block format)
-// PackMatrix packMat_: the type of packed matrix - A or B matrix
-// bool transpose_: transpose
-// int nrow_: the number of rows
-// int ncol_: the number of columns
-// int kernel_ncol_blocks_: the number of column blocks
-// int brow_: the number of rows in a block
-// int bcol_: the number of columns in a block
-// int last_brow_: the number of rows in the last block
-// int nbrow_: row index in a block
-// int nbcol_: column index in a block
-// uint64_t packsize_: the size of the packed matrix
-//                    (the number of fp16 elements + padding (1024) + extra temporary memory (256))
-struct PackNodeOp : public UnaryNodeOp {
-  PackMatrix packMat_;
-  bool transpose_;
-  int nrow_;
-  int ncol_;
-  int kernel_ncol_blocks_;
-  int brow_;
-  int bcol_;
-  int last_brow_;
-  int nbrow_;
-  int nbcol_;
-  uint64_t packsize_;
-
-  PackNodeOp(Expr a, PackMatrix packMat, bool transpose, float clipValue)
-      : UnaryNodeOp(a, newShape(a, transpose), Type::uint8),
-        packMat_(packMat),
-        transpose_(transpose) {
-    if(packMat != PackMatrix::B)
-      ABORT("Only prepacking of B (weight matrix) is supported");
-    if(clipValue != 0)
-      ABORT("Clipping is not supported");
-    if(!memoize_)
-      ABORT("Only constant weight node can be packed");
-  }
-
-  NodeOps forwardOps() override {
-    return {NodeOp(PackFp32(val_,
-                            child(0)->val()->data(),
-                            transpose_,
-                            nrow_,
-                            ncol_,
-                            kernel_ncol_blocks_,
-                            brow_,
-                            bcol_,
-                            last_brow_,
-                            nbrow_,
-                            nbcol_,
-                            packsize_))
-    };
-  }
-
-  NodeOps backwardOps() override {
-    ABORT("PackNodeOp only available for inference");
-    return {NodeOp(0)};
-  }
-
-  const std::string type() override { return "packMat"; }
-
-  Shape newShape(Expr a, bool transpose) {
-#if USE_FBGEMM
-    auto shapeMat = a->shape();
-    // Should be 2D - weight matrix
-    ABORT_IF(shapeMat.size() != 2,
-             "Weight Matrix should be 2D");
-    PackInfoFp32(shapeMat,
-                 transpose,
-                 nrow_,
-                 ncol_,
-                 kernel_ncol_blocks_,
-                 brow_,
-                 bcol_,
-                 last_brow_,
-                 nbrow_,
-                 nbcol_,
-                 packsize_);
-
-    Shape outShape({(int)packsize_});
-
-    return outShape;
-#else // USE_FBGEMM
-    ABORT("Packed GEMM requires a build with USE_FBGEMM enabled");
-    return Shape();
-#endif  // USE_FBGEMM
-  }
-};
-
-// Affine transform (matrix multiplication) using packed B matrix
-// float scalar_: scalar multiplier
-// size_t m_: the number of rows in A and C
-// size_t n_: the number of columns in B and C
-// size_t k_: the number of columns in A and the number of rows in C
-// bool transA_: transpose A
-// bool transB_: transpose B
-class AffineNodeOp : public NaryNodeOp {
-private:
-  float scalar_;
-  size_t m_;
-  size_t n_;
-  size_t k_;
-  bool transA_;
-  bool transB_;
-
-public:
-  AffineNodeOp(const std::vector<Expr>& nodes, Shape bShape, bool transA, bool transB, float scalar)
-      : NaryNodeOp(nodes, newShape(nodes[0], bShape, transA, transB), Type::float32),
-        scalar_(scalar) {
-    transA_ = transA;
-    transB_ = transB;
-    m_ = nodes[0]->shape().elements() / nodes[0]->shape()[-1];
-    k_ = nodes[0]->shape().back();
-    if(transA)
-      std::swap(m_, k_);
-
-    size_t l = bShape.elements() / bShape[-1];
-    n_ = bShape[-1];
-    if(transB)
-      std::swap(l, n_);
-  }
-
-  Shape newShape(Expr a, Shape bShape, bool transA, bool transB) {
-    auto shapeA = a->shape();
-    if(transA) {
-      shapeA.set(shapeA.size() - 2, a->shape()[shapeA.size() - 1]);
-      shapeA.set(shapeA.size() - 1, a->shape()[shapeA.size() - 2]);
-    }
-
-    auto shapeB = bShape;
-    if(transB) {
-      shapeB.set(shapeB.size() - 2, bShape[shapeB.size() - 1]);
-      shapeB.set(shapeB.size() - 1, bShape[shapeB.size() - 2]);
-    }
-
-    Shape outShape = shapeA;
-    outShape.set(outShape.size() - 1, shapeB[shapeB.size() - 1]);
-    ABORT_IF(shapeA[shapeA.size() - 1] != shapeB[shapeB.size() - 2],
-             "Matrix product requires inner dimensions to match");
-    return outShape;
-  }
-
-  NodeOps forwardOps() override {
-    return {
-      NodeOp(GemmPackFp32(val_,
-                          child(0)->val(),
-                          child(1)->val(),
-                          child(2)->val(),
-                          m_,
-                          n_,
-                          transA_))
-    };
-  }
-
-  NodeOps backwardOps() override {
-    ABORT("Only used for inference");
-    return {NodeOp(0)};
-  }
-
-  const std::string type() override { return "fp16packed"; }
-};
-
-static inline Expr affine(Expr a, Expr b, Shape bShape, Expr c, bool transA, bool transB, float scalar) {
-  std::vector<Expr> nodes = {a, b, c};
-  return Expression<cpu::variant::AffineNodeOp>(nodes, bShape, transA, transB, scalar);
-}
-
-static inline Expr pack(Expr a, PackMatrix packMat, bool transpose, float clipValue) {
-  return Expression<cpu::variant::PackNodeOp>(a, packMat, transpose, clipValue);
-}
-
-}  // namespace variant
-}  // namespace cpu
-}  // namespace marian
diff --git a/src/tensors/cpu/fbgemm/expanded_gemm.h b/src/tensors/cpu/fbgemm/expanded_gemm.h
new file mode 100644
index 000000000..0dd2ae8b2
--- /dev/null
+++ b/src/tensors/cpu/fbgemm/expanded_gemm.h
@@ -0,0 +1,400 @@
+#pragma once
+
+#include "graph/node.h"
+#include "packed_gemm.h"
+#include "tensors/cpu/sharp/int_gemm.h"
+
+#if USE_FBGEMM
+#ifdef __GNUC__
+#pragma GCC diagnostic push
+#pragma GCC diagnostic ignored "-Wunused-variable"
+#endif
+
+#include "3rd_party/fbgemm/include/fbgemm/FbgemmFP16.h"
+
+#ifdef __GNUC__
+#pragma GCC diagnostic pop
+#endif
+
+using namespace fbgemm;
+#endif  // USE_FBGEMM
+
+namespace marian {
+namespace cpu {
+namespace variant {
+
+// Enumeration for the Matrix used in pack functions
+// A matrix - 0, B matrix - 1
+enum class PackMatrix : uint8_t {
+  A = 0x00,
+  B = 0x01
+};
+
+// Pack a matrix (fp16) into cache utilization efficient way (block format) together with quantization into fp16
+// PackMatrix packMat_: the type of packed matrix - A or B matrix
+// bool transpose_: transpose
+// int nrow_: the number of rows
+// int ncol_: the number of columns
+// int kernel_ncol_blocks_: the number of column blocks
+// int brow_: the number of rows in a block
+// int bcol_: the number of columns in a block
+// int last_brow_: the number of rows in the last block
+// int nbrow_: row index in a block
+// int nbcol_: column index in a block
+// uint64_t packsize_: the size of the packed matrix
+//                    (the number of fp16 elements + padding (1024) + extra temporary memory (256))
+struct FbgemmPacked16PackNodeOp : public UnaryNodeOp {
+  PackMatrix packMat_;
+  bool transpose_;
+  int nrow_;
+  int ncol_;
+  int kernel_ncol_blocks_;
+  int brow_;
+  int bcol_;
+  int last_brow_;
+  int nbrow_;
+  int nbcol_;
+  uint64_t packsize_;
+
+  FbgemmPacked16PackNodeOp(Expr a, PackMatrix packMat, bool transpose, float clipValue)
+      : UnaryNodeOp(a, newShape(a, transpose), Type::uint8),
+        packMat_(packMat),
+        transpose_(transpose) {
+    if(packMat != PackMatrix::B)
+      ABORT("Only prepacking of B (weight matrix) is supported");
+    if(clipValue != 0)
+      ABORT("Clipping is not supported");
+    if(!memoize_)
+      ABORT("Only constant weight node can be packed");
+  }
+
+  NodeOps forwardOps() override {
+#if USE_FBGEMM
+    return {NodeOp(fbgemmPacked16Pack(val_,
+                                      child(0)->val()->data(),
+                                      transpose_,
+                                      nrow_,
+                                      ncol_,
+                                      kernel_ncol_blocks_,
+                                      brow_,
+                                      bcol_,
+                                      last_brow_,
+                                      nbrow_,
+                                      nbcol_,
+                                      packsize_))
+    };
+#else // USE_FBGEMM
+    ABORT("FbgemmPacked16PackNodeOp can only be used with FBGEMM enabled.");
+    return { NodeOp(0) };
+#endif  // USE_FBGEMM
+  }
+
+  NodeOps backwardOps() override {
+    ABORT("FbgemmPacked16PackNodeOp only available for inference");
+    return {NodeOp(0)};
+  }
+
+  const std::string type() override { return "packMatFp16"; }
+
+  Shape newShape(Expr a, bool transpose) {
+#if USE_FBGEMM
+    auto shapeMat = a->shape();
+    // Should be 2D - weight matrix
+    ABORT_IF(shapeMat.size() != 2,
+             "Weight Matrix should be 2D");
+    fbgemmPacked16PackInfo(shapeMat,
+                           transpose,
+                           nrow_,
+                           ncol_,
+                           kernel_ncol_blocks_,
+                           brow_,
+                           bcol_,
+                           last_brow_,
+                           nbrow_,
+                           nbcol_,
+                           packsize_);
+
+    Shape outShape({(int)packsize_});
+
+    return outShape;
+#else // USE_FBGEMM
+    ABORT("Packed GEMM requires a build with USE_FBGEMM enabled");
+    return Shape();
+#endif  // USE_FBGEMM
+  }
+};
+
+// Pack a matrix (int8) into cache utilization efficient way (block format) together with quantization into int8
+// PackMatrix packMat_: the type of packed matrix - A or B matrix
+// bool transpose_: transpose
+// int nrow_: the number of rows
+// int ncol_: the number of columns
+// uint64_t packsize_: the size of the packed matrix
+//                    (the size of int8 packed B from fbgemm:PackAWithQuantRowOffset + quantization scale, offset and zero point)
+struct FbgemmPacked8PackNodeOp : public UnaryNodeOp {
+  PackMatrix packMat_;
+  bool transpose_;
+  int nrow_;
+  int ncol_;
+  uint64_t packsize_;
+
+  FbgemmPacked8PackNodeOp(Expr a, PackMatrix packMat, bool transpose, float clipValue)
+      : UnaryNodeOp(a, newShape(a, transpose), Type::uint8),
+        packMat_(packMat),
+        transpose_(transpose) {
+    if(packMat != PackMatrix::B)
+      ABORT("Only prepacking of B (weight matrix) is supported");
+    if(clipValue != 0)
+      ABORT("Clipping is not supported");
+    if(!memoize_)
+      ABORT("Only constant weight node can be packed");
+  }
+
+  NodeOps forwardOps() override {
+#if USE_FBGEMM
+    return {NodeOp(fbgemmPacked8Pack(val_,
+                                     child(0)->val()->data(),
+                                     transpose_,
+                                     nrow_,
+                                     ncol_,
+                                     packsize_))
+    };
+#else // USE_FBGEMM
+    ABORT("FbgemmPacked8PackNodeOp can only be used with FBGEMM enabled.");
+    return { NodeOp(0) };
+#endif  // USE_FBGEMM
+  }
+
+  NodeOps backwardOps() override {
+    ABORT("FbgemmPacked8PackNodeOp only available for inference");
+    return {NodeOp(0)};
+  }
+
+  const std::string type() override { return "packMatInt8"; }
+
+  Shape newShape(Expr a, bool transpose) {
+#if USE_FBGEMM
+    fbgemmPacked8PackInfo(a->shape(), transpose, nrow_, ncol_, packsize_);
+    Shape outShape({(int)packsize_});
+
+    return outShape;
+#else // USE_FBGEMM
+    ABORT("Packed GEMM requires a build with USE_FBGEMM enabled");
+    return Shape();
+#endif  // USE_FBGEMM
+  }
+};
+
+// Affine transform (matrix multiplication) using packed B matrix
+// float scalar_: scalar multiplier
+// size_t m_: the number of rows in A and C
+// size_t n_: the number of columns in B and C
+// size_t k_: the number of columns in A and the number of rows in C
+// bool transA_: transpose A
+// bool transB_: transpose B
+class FbgemmPacked16AffineNodeOp : public NaryNodeOp {
+private:
+  float scalar_;
+  size_t m_;
+  size_t n_;
+  size_t k_;
+  bool transA_;
+  bool transB_;
+
+public:
+  FbgemmPacked16AffineNodeOp(const std::vector<Expr>& nodes, Shape bShape, bool transA, bool transB, float scalar)
+      : NaryNodeOp(nodes, newShape(nodes[0], bShape, transA, transB), Type::float32),
+        scalar_(scalar) {
+    transA_ = transA;
+    transB_ = transB;
+    m_ = nodes[0]->shape().elements() / nodes[0]->shape()[-1];
+    k_ = nodes[0]->shape().back();
+    if(transA)
+      std::swap(m_, k_);
+
+    size_t l = bShape.elements() / bShape[-1];
+    n_ = bShape[-1];
+    if(transB)
+      std::swap(l, n_);
+  }
+
+  Shape newShape(Expr a, Shape bShape, bool transA, bool transB) {
+    auto shapeA = a->shape();
+    if(transA) {
+      shapeA.set(shapeA.size() - 2, a->shape()[shapeA.size() - 1]);
+      shapeA.set(shapeA.size() - 1, a->shape()[shapeA.size() - 2]);
+    }
+
+    auto shapeB = bShape;
+    if(transB) {
+      shapeB.set(shapeB.size() - 2, bShape[shapeB.size() - 1]);
+      shapeB.set(shapeB.size() - 1, bShape[shapeB.size() - 2]);
+    }
+
+    Shape outShape = shapeA;
+    outShape.set(outShape.size() - 1, shapeB[shapeB.size() - 1]);
+    ABORT_IF(shapeA[shapeA.size() - 1] != shapeB[shapeB.size() - 2],
+             "Matrix product requires inner dimensions to match");
+    return outShape;
+  }
+
+  NodeOps forwardOps() override {
+#if USE_FBGEMM
+    return {
+      NodeOp(fbgemmPacked16Gemm(val_,
+                                child(0)->val(),
+                                child(1)->val(),
+                                children().size() > 2 ? child(2)->val() : nullptr, // pass only if it has a bias
+                                m_,
+                                n_,
+                                transA_))
+    };
+#else // USE_FBGEMM
+    ABORT("FbgemmPacked16AffineNodeOp can only be used with FBGEMM enabled.");
+    return { NodeOp(0) };
+#endif  // USE_FBGEMM
+  }
+
+  NodeOps backwardOps() override {
+    ABORT("Only used for inference");
+    return {NodeOp(0)};
+  }
+
+  const std::string type() override { return "fp16packed"; }
+};
+
+// Affine transform (matrix multiplication) using packed B matrix
+// Especially, this gemm performs quantized gemms in 8-bit integers.
+// float scalar_: scalar multiplier
+// size_t m_: the number of rows in A and C
+// size_t n_: the number of columns in B and C
+// size_t k_: the number of columns in A and the number of rows in C
+// bool transA_: transpose A
+// bool transB_: transpose B
+class FbgemmPacked8AffineNodeOp : public NaryNodeOp {
+private:
+  float scalar_;
+  size_t m_;
+  size_t n_;
+  size_t k_;
+  bool transA_;
+  bool transB_;
+
+public:
+  FbgemmPacked8AffineNodeOp(const std::vector<Expr>& nodes, Shape bShape, bool transA, bool transB, float scalar)
+      : NaryNodeOp(nodes, newShape(nodes[0], bShape, transA, transB), Type::float32),
+        scalar_(scalar) {
+    transA_ = transA;
+    transB_ = transB;
+    m_ = nodes[0]->shape().elements() / nodes[0]->shape()[-1];
+    k_ = nodes[0]->shape().back();
+    if(transA)
+      std::swap(m_, k_);
+
+    size_t l = bShape.elements() / bShape[-1];
+    n_ = bShape[-1];
+    if(transB)
+     std::swap(l, n_);
+  }
+
+  Shape newShape(Expr a, Shape bShape, bool transA, bool transB) {
+    auto shapeA = a->shape();
+    if(transA) {
+      shapeA.set(shapeA.size() - 2, a->shape()[shapeA.size() - 1]);
+      shapeA.set(shapeA.size() - 1, a->shape()[shapeA.size() - 2]);
+    }
+
+    auto shapeB = bShape;
+    if(transB) {
+     shapeB.set(shapeB.size() - 2, bShape[shapeB.size() - 1]);
+     shapeB.set(shapeB.size() - 1, bShape[shapeB.size() - 2]);
+    }
+
+    Shape outShape = shapeA;
+    outShape.set(outShape.size() - 1, shapeB[shapeB.size() - 1]);
+    ABORT_IF(shapeA[shapeA.size() - 1] != shapeB[shapeB.size() - 2],
+            "Matrix product requires inner dimensions to match");
+    return outShape;
+  }
+
+  NodeOps forwardOps() override {
+    NodeOps nodeOps;
+#if USE_FBGEMM
+    // Do addBias only if it has a bias term
+    if (children().size() > 2) {
+      nodeOps = { NodeOp(fbgemmPacked8Gemm(val_,
+                                           child(0)->val(),
+                                           child(1)->val(),
+                                           m_,
+                                           n_,
+                                           k_,
+                                           transA_,
+                                           transB_);
+                       marian::cpu::int16::AddBias(val_, child(2)->val())) };
+    } else {
+      nodeOps = { NodeOp(fbgemmPacked8Gemm(val_,
+                                           child(0)->val(),
+                                           child(1)->val(),
+                                           m_,
+                                           n_,
+                                           k_,
+                                           transA_,
+                                           transB_)) };
+    }
+#else // USE_FBGEMM
+    ABORT("FbgemmPacked8AffineNodeOp can only be used with FBGEMM enabled.");
+#endif  // USE_FBGEMM
+
+    return nodeOps;
+  }
+
+  NodeOps backwardOps() override {
+    ABORT("Only used for inference");
+    return {NodeOp(0)};
+  }
+
+  const std::string type() override { return "int8packed"; }
+};
+
+static inline Expr affine(Expr a, Expr b, Shape bShape, Expr c, bool transA, bool transB, float scalar) {
+  std::vector<Expr> nodes = {a, b, c};
+  Type elementType = b->value_type();
+
+  if (elementType == Type::packed16)
+    return Expression<cpu::variant::FbgemmPacked16AffineNodeOp>(nodes, bShape, transA, transB, scalar);
+  else if (elementType == Type::packed8)
+    return Expression<cpu::variant::FbgemmPacked8AffineNodeOp>(nodes, bShape, transA, transB, scalar);
+  else {
+    ABORT("Only int8 and fp16 are available. {}", elementType);
+    return nullptr;
+  }
+}
+
+static inline Expr pack(Type elementType, Expr a, PackMatrix packMat, bool transpose, float clipValue) {
+  if (elementType == Type::packed16)
+    return Expression<cpu::variant::FbgemmPacked16PackNodeOp>(a, packMat, transpose, clipValue);
+  else if (elementType == Type::packed8)
+    return Expression<cpu::variant::FbgemmPacked8PackNodeOp>(a, packMat, transpose, clipValue);
+  else {
+    ABORT("Only int8 and fp16 are available. {}", elementType);
+    return nullptr;
+  }
+}
+
+static inline Expr dot(Expr a, Expr b, Shape bShape, bool transA, bool transB, float scalar) {
+  std::vector<Expr> nodes = {a, b};
+  Type elementType = b->value_type();
+
+  if (elementType == Type::packed16)
+    return Expression<cpu::variant::FbgemmPacked16AffineNodeOp>(nodes, bShape, transA, transB, scalar);
+  else if (elementType == Type::packed8)
+    return Expression<cpu::variant::FbgemmPacked8AffineNodeOp>(nodes, bShape, transA, transB, scalar);
+  else {
+    ABORT("Only int8 and fp16 are available. {}", elementType);
+    return nullptr;
+  }
+}
+
+}  // namespace variant
+}  // namespace cpu
+}  // namespace marian
diff --git a/src/graph/expression_graph_packable.h b/src/tensors/cpu/fbgemm/expression_graph_packable.h
similarity index 63%
rename from src/graph/expression_graph_packable.h
rename to src/tensors/cpu/fbgemm/expression_graph_packable.h
index 62569cfa6..2214f071a 100644
--- a/src/graph/expression_graph_packable.h
+++ b/src/tensors/cpu/fbgemm/expression_graph_packable.h
@@ -1,7 +1,7 @@
 #pragma once
 
 #include "graph/expression_graph.h"
-#include "tensors/cpu/sharp/packed_gemm.h"
+#include "packed_gemm.h"
 
 namespace marian {
 
@@ -34,16 +34,60 @@ class ExpressionGraphPackable : public ExpressionGraph {
 
       Tensor val = p.second->val();
 
+#if USE_FBGEMM
       // save as packed format
-      // @TODO Hardcoded to find packable weights - all the weights used for affine op
-      if (saveGemmType == "fp16packed" && pName.find("_W") == pName.length() - 3) {
+      // @TODO Hardcoded to find packable weights - all the weights used for affine op (fp16), all the weights used for affine op and dot op (int8)
+      if (saveGemmType == "int8packed" && (pName.find("_W") == pName.length() - 3 || pName.find("_W") == pName.length() - 2))
+      {
+        using namespace marian::cpu::variant;
+
+        // packing information - size
+        int nrow;
+        int ncol;
+        uint64_t packsize;
+
+        fbgemmPacked8PackInfo(val->shape(),
+          pName.find("Wemb") != std::string::npos,
+          nrow,
+          ncol,
+          packsize);
+
+        auto allocator = New<TensorAllocator>(getBackend());
+
+        // buffer tensor to save packed matrix
+        Tensor packedTensor;
+        allocator->allocate(packedTensor, { 1, (int32_t)packsize }, Type::uint8);
+
+        //Pack B matrix into int8
+        fbgemmPacked8Pack(packedTensor,
+          val->data(),
+          pName.find("Wemb") != std::string::npos,
+          nrow,
+          ncol,
+          packsize);
+        io::Item item;
+        item.name = pName;
+        item.shape = val->shape();
+        item.type = Type::packed8;
+
+        // Use the actual memory as this will be aligned and padded.
+        // When memory mapping this is required. Shape keeps track of
+        // tensor size. Saving to *.npz will cut to size.
+        auto mem = packedTensor->memory();
+        item.bytes.resize(mem->size());
+        copy(backend_, mem->data<char>(), mem->data<char>() + mem->size(), item.bytes.data());
+
+        ioItems.emplace_back(std::move(item));
+
+      } else if (saveGemmType == "fp16packed" && pName.find("_W") == pName.length() - 3)
+      {
         using namespace marian::cpu::variant;
 
         // packing information
         int nrow, ncol, kernel_ncol_blocks, brow, bcol, last_brow, nbrow, nbcol;
         uint64_t packsize;
 
-        PackInfoFp32(val->shape(),
+        fbgemmPacked16PackInfo(val->shape(),
           false,
           nrow,
           ncol,
@@ -60,8 +104,8 @@ class ExpressionGraphPackable : public ExpressionGraph {
         Tensor packedTensor;
         allocator->allocate(packedTensor, { 1, (int32_t)packsize }, Type::uint8);
 
-        // PackFp32
-        PackFp32(packedTensor,
+        // fbgemmPacked16Pack
+        fbgemmPacked16Pack(packedTensor,
           val->data(),
           false,
           nrow,
@@ -86,7 +130,9 @@ class ExpressionGraphPackable : public ExpressionGraph {
         copy(backend_, mem->data<char>(), mem->data<char>() + mem->size(), item.bytes.data());
 
         ioItems.emplace_back(std::move(item));
-      } else {
+      } else
+#endif  // USE_FBGEMM
+      {
         io::Item item;
         val->get(item, pName);
         item.convert(saveElementType);
diff --git a/src/tensors/cpu/fbgemm/packed_gemm.cpp b/src/tensors/cpu/fbgemm/packed_gemm.cpp
new file mode 100644
index 000000000..e638f5d97
--- /dev/null
+++ b/src/tensors/cpu/fbgemm/packed_gemm.cpp
@@ -0,0 +1,496 @@
+#include "packed_gemm.h"
+#include "tensors/tensor_allocator.h"
+#include "tensors/tensor_operators.h"
+
+#include <emmintrin.h>
+#include <immintrin.h>
+#include <tmmintrin.h>
+#include <xmmintrin.h>
+#include <cassert>
+#include <cstddef>
+#include <unordered_map>
+//#include <chrono>
+
+#if USE_FBGEMM
+#ifdef _MSC_VER
+#pragma warning(disable: 4505) // 'fbgemmAlignedAlloc' in fbgemm.h: unreferenced local function has been removed (missing 'static inline')
+#pragma warning(disable: 4251) // 'fbgemm::CompressedSparseColumn::colptr_': class 'std::vector<int,std::allocator<_Ty>>' needs to have dll-interface to be used by clients of class 'fbgemm::CompressedSparseColumn'
+#pragma warning(disable: 4661) // 'fbgemm::PackMatrix<fbgemm::PackBMatrix<int8_t,int32_t>,int8_t,int32_t>::PackMatrix(int32_t,int32_t,inpType *,int,const fbgemm::BlockingFactors *)': no suitable definition provided for explicit template instantiation request
+#pragma warning(disable: 4244) // fbgemm\quantutils.h(51): warning C4244: 'return': conversion from 'const _Ty' to 'T2', possible loss of data
+// the following does not work; need to manually disable them in Linker options
+//#pragma comment(linker, "/ignore:4049") // locally defined symbol ...asmjit... imported
+//#pragma comment(linker, "/ignore:4217") // locally defined symbol ...asmjit... imported
+#endif
+
+#ifdef __GNUC__
+#pragma GCC diagnostic push
+#pragma GCC diagnostic ignored "-Wunused-variable"
+#endif
+#include "3rd_party/fbgemm/include/fbgemm/FbgemmFP16.h"
+#include "3rd_party/fbgemm/include/fbgemm/QuantUtils.h"
+#include "3rd_party/fbgemm/include/fbgemm/Fbgemm.h"
+#ifdef __GNUC__
+#pragma GCC diagnostic pop
+#endif
+
+#ifdef _OPENMP
+#include <omp.h>
+#endif
+
+#if MKL_FOUND
+#include <mkl.h>
+#include <mkl_types.h>
+#endif
+
+using namespace fbgemm;
+#endif // USE_FBGEMM
+
+namespace marian {
+namespace cpu {
+namespace variant { // Variants of GEMM implementations
+
+#if USE_FBGEMM
+// initialize with a dummy
+// When this class is instantiated,
+// the actual packing operation is happening. If we create this instance every time we call GEMM,
+// we are doing packing every time and very slow.
+// In Caffe2, the operator is stateful and hold an instance of this.
+// But, we don't have any logic for this in marian. We can only cache a tensor (which means a memory chunk).
+// So, for now, we keep the packed memory on our own 1D tensor, then when we call GEMM,
+// we just reuse this instance again and again by replacing the class members (including memory pointer). Eventually,
+// I will add a new constructor to the class in FBGEMM which accepts
+// pre - allocated and pre - packed memory as a parameter.After it's done,
+// this temporary buffer will be removed.
+// When constructing this dummy buffer, ones are used for all the parameters to allocate minimum amount of memory.
+//
+// In a multi marian instance setting (as a dynamic library),
+// different marian instances should not share this variable.
+static thread_local PackedGemmMatrixFP16 packedPlaceholder(1, 1, 1, 1, 1, 1, 1, 1);
+
+// Copied code from fbgemm. It's padding required from some kernel in FBGEMM
+// Verbatim - 'required by sw pipelined kernels'
+// https://github.com/marian-nmt/FBGEMM/blob/master/include/fbgemm/FbgemmFP16.h#L109
+const int PACK16_PADDING = 1024;  
+
+// This is a memory space to store auxiliary variables for FBGEMM (e.g. block row, block column, kernel_ncol_blocks and etc.)
+const int PACK16_SPECIALMEM = 256;
+
+// This is copied from FBGEMM code
+// A better way?
+// will be removed, when FBGEMM api is changed
+// blocked row-major format address arithmetic
+/**
+ * Returns the memory address in the packed (block formatted) matrix array of a specific element 
+ * indexed by the original non-packed array.
+ *
+ * @param r_ row index in the original matrix
+ * @param c_ column index in the original matrix
+ * @param brow_ row wide block index
+ * @param bcol_ column wide block index
+ * @param nbrow_ number of blocks in row
+ * @param nbcol_ number of blocks in column
+ * @param last_brow_ row number of the last block
+ */
+inline uint64_t addr(const int r_,
+                     const int c_,
+                     const int brow_,
+                     const int bcol_,
+                     const int nbrow_,
+                     const int nbcol_,
+                     const int last_brow_) {
+  uint64_t r = (uint64_t)r_;
+  uint64_t c = (uint64_t)c_;
+
+  uint64_t block_row_id = r / brow_;
+  uint64_t brow_offset = (block_row_id * nbcol_) * (brow_ * bcol_);
+  uint64_t block_col_id = c / bcol_;
+  uint64_t bcol_offset
+      = block_col_id * ((block_row_id != nbrow_ - 1) ? (brow_ * bcol_) : (last_brow_ * bcol_));
+  uint64_t block_offset = brow_offset + bcol_offset;
+  uint64_t inblock_offset = r % brow_ * bcol_ + c % bcol_;
+
+  uint64_t index = block_offset + inblock_offset;
+  return index;
+}
+
+void fbgemmPacked16PackInfo(const marian::Shape& shape,
+                            const bool transpose,
+                            uint64_t& packsize) {
+  int nrow, ncol, kernel_ncol_blocks, brow = 512, bcol, last_brow, nbrow, nbcol;
+  fbgemmPacked16PackInfo(shape, transpose, nrow, ncol, kernel_ncol_blocks, brow, bcol, last_brow, nbrow, nbcol, packsize);
+}
+
+void fbgemmPacked16PackInfo(const marian::Shape& shape,
+                            const bool transpose,
+                            int& nrow,
+                            int& ncol,
+                            int& kernel_ncol_blocks,
+                            int& brow,
+                            int& bcol,
+                            int& last_brow,
+                            int& nbrow,
+                            int& nbcol,
+                            uint64_t& packsize) {
+  nrow = transpose ? shape[1] : shape[0];
+  ncol = transpose ? shape[0] : shape[1];
+  kernel_ncol_blocks = 2;
+  brow = 512;
+  bcol = 8 * kernel_ncol_blocks;
+  last_brow = nrow % brow == 0 ? brow : nrow % brow;
+  nbrow = nrow % brow == 0 ? nrow / brow : (nrow + brow) / brow;
+  nbcol = ncol % bcol == 0 ? ncol / bcol : (ncol + bcol) / bcol;
+  ABORT_IF(ncol % bcol != 0, "ncol (number of columns) should be multiple of 16. {}", ncol);
+  packsize = ((nbrow * brow) * (nbcol * bcol)) * sizeof(fbgemm::float16) + PACK16_PADDING
+             + PACK16_SPECIALMEM;
+}
+
+void fbgemmPacked8PackInfo(const marian::Shape& shape,
+                           const bool transpose,
+                           int& nrow,
+                           int& ncol,
+                           uint64_t& packsize) {
+    // Should be 2D - weight matrix
+    ABORT_IF(shape.size() != 2,
+            "Weight Matrix should be 2D");
+    nrow = transpose ? shape[1] : shape[0];
+    ncol = transpose ? shape[0] : shape[1];
+    packsize = fbgemm::PackMatrix<fbgemm::PackBMatrix<int8_t>, int8_t>::packedBufferSize(
+        transpose ? shape[1] : shape[0],
+        transpose ? shape[0] : shape[1]);
+    // add extra space for storing some other variables specific to B matrix
+    // quantization sacles: 1 per column and float
+    // quantization offset: 1 per column and int32
+    // column offsets: 1 per column and int32
+    packsize += ncol * (sizeof(float) + sizeof(int32_t) + sizeof(int32_t));
+}
+
+// This function computes the offset values for each column which are used for compensating the remainders of quantized values
+// More detailed math is avilable in the FBGEMM's blog - https://engineering.fb.com/ml-applications/fbgemm/
+inline void col_offsets_with_zero_pt_s8acc32(
+    bool transpose,
+    int K,
+    int N,
+    const int8_t* Bint8,
+    const int32_t* B_zero_point,
+    int32_t* col_offsets,
+    int ncols_per_quant_group) {
+  for (int n = 0; n < N; ++n) {
+    int32_t sum = 0;
+    for (int k = 0; k < K; ++k) {
+      sum += transpose ? Bint8[k + n * K] : Bint8[k * N + n];
+    }
+    col_offsets[n] = sum - B_zero_point[n / ncols_per_quant_group] * K;
+  }
+}
+
+void fbgemmPacked16Pack(marian::Tensor out,
+                        const float* inData, // Packing is only available for 2D weight matrix in Marian. Otherwise, it's aborted in expanded_gemm.h.
+                        const bool transpose,
+                        const int nrow,
+                        const int ncol,
+                        const int kernel_ncol_blocks,
+                        const int brow,
+                        const int bcol,
+                        const int last_brow,
+                        const int nbrow,
+                        const int nbcol,
+                        const uint64_t packsize) {
+  // initialize memory
+  uint8_t* outmemorg = out->data<uint8_t>();
+  for(auto i = 0; i < packsize; i++) {
+    outmemorg[i] = 0;
+  }
+  // save the other auxiliary variables
+  uint64_t* auxmemsize = (uint64_t*)outmemorg;
+  auxmemsize[0] = packsize;
+  // save FBGEMM related parameters into the header of the allocated memory by marian
+  int32_t header[8];
+  header[0] = nrow;
+  header[1] = ncol;
+  header[2] = kernel_ncol_blocks;
+  header[3] = brow;
+  header[4] = bcol;
+  header[5] = last_brow;
+  header[6] = nbrow;
+  header[7] = nbcol;
+  memcpy(auxmemsize + 1, header, sizeof(header));
+  // cast to float16
+  fbgemm::float16* outmem = (fbgemm::float16*)(outmemorg + 256);
+  fbgemm::float16* dummy = new fbgemm::float16;
+  // pack the matrix
+  for(int i = 0; i < nrow; i++) {
+    for(int j = 0; j < ncol; j++) {
+      outmem[addr(i, j, brow, bcol, nbrow, nbcol, last_brow)]
+          = tconv(!transpose ? inData[i * ncol + j] : inData[i + nrow * j], *dummy);
+    }
+  }
+  delete dummy;
+}
+
+void fbgemmPacked8Pack(marian::Tensor out,
+                       const float* inData,
+                       const bool transpose,
+                       const int nrow,
+                       const int ncol,
+                       const uint64_t packsize) {
+  int k = nrow;
+  int n = ncol;
+  int len = k * n;
+
+  // 1. collect stats for each column
+  float* bqScale = new float[n];
+  int32_t* bqZeropoint = new int32_t[n];
+
+  const float* data = inData;
+  float val = 0;
+
+  if (transpose) {
+    for (int jj = 0; jj < n; jj++) {
+      float min = std::numeric_limits<float>::max(), max = std::numeric_limits<float>::min();
+      double mean = 0, sqrsum = 0;
+      for (int ii = 0; ii < k; ii++) {
+        val = data[jj * k + ii];
+        mean += val;
+        sqrsum += val * val;
+      }
+      mean /= k;
+      sqrsum /= k;
+      sqrsum -= mean * mean;
+      sqrsum = sqrt(sqrsum);
+
+      min = (float)(mean - 7.0f*sqrsum);
+      max = (float)(mean + 7.0f*sqrsum);
+      bqScale[jj] = (max - min) / 255;
+      bqZeropoint[jj] = (int32_t)(127 - max / bqScale[jj]);
+    }
+  } else {
+    for (int jj = 0; jj < n; jj++) {
+      float min = std::numeric_limits<float>::max(), max = std::numeric_limits<float>::min();
+      double mean = 0, sqrsum = 0;
+      for (int ii = 0; ii < k; ii++) {
+        val = data[jj + ii * n];
+        mean += val;
+        sqrsum += val * val;
+      }
+      mean /= k;
+      sqrsum /= k;
+      sqrsum -= mean * mean;
+      sqrsum = sqrt(sqrsum);
+
+      min = (float)(mean - 7.0f*sqrsum);
+      max = (float)(mean + 7.0f*sqrsum);
+      bqScale[jj] = (max - min) / 255;
+      bqZeropoint[jj] = (int32_t)(127 - max / bqScale[jj]);
+    }
+  }
+
+  // 2. quantize
+  int8_t* quantized = 0;
+#ifdef _MSC_VER
+  quantized = (int8_t*)_aligned_malloc(len, 256);
+#else
+  int result = posix_memalign((void**)&quantized, 256, len); result;
+  assert(result == 0);
+#endif
+  for (int jj = 0; jj < n; jj++) {
+    TensorQuantizationParams bQuantParam;
+    bQuantParam.scale = bqScale[jj];
+    bQuantParam.zero_point = bqZeropoint[jj];
+    bQuantParam.precision = 8;
+
+    if (transpose)
+      fbgemm::Quantize<int8_t>(data + jj * k, quantized + jj * k, k, bQuantParam);
+    else {
+      for (int ii = 0; ii < k; ii++) {
+        quantized[ii*n + jj] = fbgemm::Quantize<int8_t>(data[ii*n + jj], bQuantParam);
+      }
+    }
+  }
+
+  // 3. compute column offsets
+  int32_t* col_offsets = new int32_t[n];
+  col_offsets_with_zero_pt_s8acc32(transpose, k, n, quantized, bqZeropoint, col_offsets, 1);
+
+
+  int8_t* packedbuf = out->data<int8_t>();
+  for(auto i = 0; i < packsize; i++) {
+    packedbuf[i] = 0;
+  }
+
+  // 4. packing
+  PackBMatrix<int8_t> packedBN(
+      transpose ? matrix_op_t::Transpose : matrix_op_t::NoTranspose,
+      nrow, ncol, quantized, transpose ? nrow : ncol, packedbuf, 1);
+
+  // copy quantization scale
+  memcpy(packedbuf + (packsize - n * (sizeof(float) + sizeof(int32_t) + sizeof(int32_t))), bqScale, n * sizeof(float));
+  // copy quantization offset
+  memcpy(packedbuf + (packsize - n * (sizeof(int32_t) + sizeof(int32_t))), bqZeropoint, n * sizeof(int32_t));
+  // copy column offsets to the memory
+  memcpy(packedbuf + (packsize - n * sizeof(int32_t)), col_offsets, n * sizeof(int32_t));
+
+#ifdef _MSC_VER
+  _aligned_free(quantized);
+#else
+  free(quantized);
+#endif
+  delete[] col_offsets;
+  delete[] bqScale;
+  delete[] bqZeropoint;
+}
+
+// GEMM operation on the packed B matrix
+// C: output matrix
+// A: A matrix
+// B: B matrix (packed)
+// m: the number of rows in A and C
+// n: the number of columns in B and C
+// transA: transpose of A matrix
+// B is already packed. So, we don't need transB
+void fbgemmPacked16Gemm(marian::Tensor C,
+                        const marian::Tensor A,
+                        const marian::Tensor B,
+                        const marian::Tensor bias,
+                        const size_t m,
+                        const size_t n,
+                        const int transA) {
+  // row major
+  // keep the original mem
+  fbgemm::float16* pmat = packedPlaceholder.pmat_;
+  // retreive aux fields from the memory
+  uint64_t* packedmemSize = (uint64_t*)B->data();
+  packedPlaceholder.size_ = packedmemSize[0];
+  int32_t header[8];
+  memcpy(header, packedmemSize + 1, sizeof(header));
+  packedPlaceholder.nrow_ = header[0];
+  packedPlaceholder.ncol_ = header[1];
+  packedPlaceholder.kernel_ncol_blocks_ = header[2];
+  packedPlaceholder.brow_ = header[3];
+  packedPlaceholder.bcol_ = header[4];
+  packedPlaceholder.last_brow_ = header[5];
+  packedPlaceholder.nbrow_ = header[6];
+  packedPlaceholder.nbcol_ = header[7];
+
+  // packed matrix
+  packedPlaceholder.pmat_ = (fbgemm::float16*)(B->data<uint8_t>() + 256);
+
+  if(bias != nullptr) {
+#if MKL_FOUND
+    for(int i = 0; i < m; ++i) {
+      mkl_somatcopy('R', 'N', 1, n, 1, bias->data(), n, C->data() + n * i, n);
+    }
+#else
+    for(int i = 0; i < m; ++i) {
+      std::copy(bias->data(), bias->data() + n, C->data() + n * i);
+    }
+#endif
+  }
+
+#ifdef _OPENMP
+#pragma omp parallel
+#endif
+  {
+#ifdef _OPENMP
+    int num_threads = omp_get_num_threads();
+    int tid = omp_get_thread_num();
+#else
+    int num_threads = 1;
+    int tid = 0;
+#endif
+    fbgemm::cblas_gemm_compute(transA ? matrix_op_t::Transpose : matrix_op_t::NoTranspose,
+                      (int)m,
+                      A->data(),
+                      packedPlaceholder,
+                      bias != nullptr ? 1.0f : 0.0f,
+                      C->data(),
+                      tid,
+                      num_threads);
+  }
+
+  // return back the original mem
+  packedPlaceholder.pmat_ = pmat;
+}
+
+// GEMM operation on the packed B matrix in 8 bit integers
+// C: output matrix
+// A: A matrix
+// B: B matrix (packed)
+// m: the number of rows in A and C
+// n: the number of columns in B and C
+// k: the number of columns in A and the number of rows in B
+// transA: whether A matrix is transposed or not
+// transB: whether B matrix is transposed or not
+void fbgemmPacked8Gemm(marian::Tensor C,
+                       const marian::Tensor A,
+                       const marian::Tensor B,
+                       const size_t m,
+                       const size_t n,
+                       const size_t k,
+                       const int transA,
+                       const int transB) {
+  // compute range to quantize A (activations) - (min/max quantization)
+  float min_est = std::numeric_limits<float>::max(), max_est = std::numeric_limits<float>::min();
+
+  int elem = A->shape().elements();
+  float* data = A->data();
+  // AVX based find min/max
+  FindMinMax(data, &min_est, &max_est, elem);
+
+  float ascale = (max_est - min_est) / 255;
+  int32_t azeropoint = (int32_t)(255 - max_est / ascale);
+
+  std::vector<int32_t> row_offset_buf(PackAWithQuantRowOffset<uint8_t>::rowOffsetBufferSize());
+  PackAWithQuantRowOffset<uint8_t> packAN(
+      transA ? matrix_op_t::Transpose : matrix_op_t::NoTranspose,
+      (int32_t) (transA ? k : m),
+      (int32_t) (transA ? m : k),
+      A->data(),
+      (int32_t) (transA ? m : k),
+      nullptr, /*buffer for packed matrix*/
+      ascale,
+      azeropoint,
+      1, /*groups*/
+      row_offset_buf.data());
+
+  // packed matrix size of B
+  int bPackSize = PackMatrix<PackBMatrix<int8_t>, int8_t>::packedBufferSize((int32_t)k, (int32_t)n);
+
+  // retrieve B matrix
+  int8_t* bdata = B->data<int8_t>();
+  float* bqScale = new float[n];
+  memcpy(bqScale, bdata + bPackSize, n * sizeof(float));
+
+  int32_t* bqZeropoint = new int32_t[n];
+  memcpy(bqZeropoint, bdata + bPackSize + n * sizeof(float), n * sizeof(int32_t));
+
+  int32_t* col_offsets = new int32_t[n];
+  memcpy(col_offsets, bdata + bPackSize + n * (sizeof(float) + sizeof(int32_t)), n * sizeof(int32_t));
+
+  DoNothing<float, float> doNothingObj{};
+  ReQuantizeForFloat<false, QuantizationGranularity::OUT_CHANNEL> outputProcObj(
+      doNothingObj,
+      ascale,
+      bqScale,
+      azeropoint,
+      bqZeropoint,
+      packAN.getRowOffsetBuffer(),
+      col_offsets,
+      nullptr,
+      (std::uint32_t) n);
+
+  PackBMatrix<int8_t> repackedBN(
+    transB ? matrix_op_t::Transpose : matrix_op_t::NoTranspose, (int32_t) k, (int32_t) n, bdata, (int32_t) (transB ? k : n, 1));
+
+  // gemm computation
+  fbgemmPacked(packAN, repackedBN, C->data(), (int32_t*)C->data(), (int32_t) n, outputProcObj, 0, 1);
+
+  delete[] col_offsets;
+  delete[] bqZeropoint;
+  delete[] bqScale;
+}
+
+#endif // USE_FBGEMM
+
+}  // namespace variant
+}  // namespace cpu
+}  // namespace marian
diff --git a/src/tensors/cpu/fbgemm/packed_gemm.h b/src/tensors/cpu/fbgemm/packed_gemm.h
new file mode 100644
index 000000000..b41ad26ea
--- /dev/null
+++ b/src/tensors/cpu/fbgemm/packed_gemm.h
@@ -0,0 +1,137 @@
+#pragma once
+
+#include "tensors/tensor.h"
+
+namespace marian {
+namespace cpu {
+namespace variant { // Variants of GEMM implementations
+
+// Returns the byte size of packed matrix in fp16. It's calculated by fbgemm's internal logic due to the paddings and different layouts.
+// Packing with fp16 only targets AVX2 instruction sets for now.
+// See '3rd_party/fbgemm/include/fbgemm/FbgemmFP16.h'.
+// shape: shape of the tensor to be packed
+// transpose: the matrix is transposed
+// packsize (out): the size of the packed matrix in byte
+void fbgemmPacked16PackInfo(const marian::Shape& shape,
+                            const bool transpose,
+                            /*out*/uint64_t& packsize);
+
+// Returns the byte size of packed matrix in fp16. It's calculated by fbgemm's internal logic due to the paddings and different layouts.
+// This function returns some other extra variables
+// Packing with fp16 only targets AVX2 instruction sets for now.
+// See '3rd_party/fbgemm/include/fbgemm/FbgemmFP16.h'.
+// shape: shape of the tensor to be packed
+// transpose: the matrix is transposed
+// nrow (out): the number of rows
+// ncol (out): the number of columns
+// kernel_ncol_blocks (out): the number of column blocks
+// brow (out): the number of rows in a block
+// bcol (out): the number of columns in a block
+// last_brow (out): the number of rows in the last block
+// nbrow (out): row index in a block
+// nbcol (out): column index in a block
+// packsize (out): the size of the packed matrix in byte
+void fbgemmPacked16PackInfo(const marian::Shape& shape,
+                          const bool transpose,
+                          /*out*/int& nrow,
+                          /*out*/int& ncol,
+                          /*out*/int& kernel_ncol_blocks,
+                          /*out*/int& brow,
+                          /*out*/int& bcol,
+                          /*out*/int& last_brow,
+                          /*out*/int& nbrow,
+                          /*out*/int& nbcol,
+                          /*out*/uint64_t& packsize); // @TODO: change to size_t where appropriate
+
+// Returns the byte size of packed matrix in int8. It's calculated by fbgemm's internal logic due to the paddings and different layouts.
+// See '3rd_party/fbgemm/src/PackBMatrix.cc'.
+// shape: shape of the tensor to be packed
+// transpose: the matrix is transposed
+// nrow (out): the number of rows
+// ncol (out): the number of columns
+// packsize (out): the size of the packed matrix in byte
+void fbgemmPacked8PackInfo(const marian::Shape& shape,
+                           const bool transpose,
+                           /*out*/int& nrow,
+                           /*out*/int& ncol,
+                           /*out*/uint64_t& packsize);
+
+// Pack a matrix (fp16) into cache utilization efficient way (block format) into fp16
+// out: output tensor - packed format
+// inData: input tensor data - pointer of float data
+// transpose: the matrix is transposed
+// nrow: the number of rows
+// ncol: the number of columns
+// kernel_ncol_blocks: the number of column blocks
+// brow: the number of rows in a block
+// bcol: the number of columns in a block
+// last_brow: the number of rows in the last block
+// nbrow: row index in a block
+// nbcol: column index in a block
+// packsize: the size of the packed matrix
+//          (the number of fp16 elements + padding (1024) + extra temporary memory (256))
+void fbgemmPacked16Pack(marian::Tensor out,
+                        const float* inData,
+                        const bool transpose,
+                        const int nrow,
+                        const int ncol,
+                        const int kernel_ncol_blocks,
+                        const int brow,
+                        const int bcol,
+                        const int last_brow,
+                        const int nbrow,
+                        const int nbcol,
+                        const uint64_t packsize); // @TODO: change to size_t where appropriate
+
+// Pack a matrix (int8) into cache utilization efficient way (block format) together with quantization into int8
+// out: output tensor - packed format and quantized into int8
+// inData: input tensor data - pointer of float data
+// transpose: the matrix is transposed
+// nrow: the number of rows
+// ncol: the number of columns
+// packsize: the size of the packed matrix
+//          (the size of int8 packed B from fbgemm:PackAWithQuantRowOffset + quantization scale, offset and zero point)
+void fbgemmPacked8Pack(marian::Tensor out,
+                       const float* inData,
+                       const bool transpose,
+                       const int nrow,
+                       const int ncol,
+                       const uint64_t packsize); // @TODO: change to size_t where appropriate
+
+// GEMM operation on the packed B matrix
+// C: output matrix
+// A: A matrix
+// B: B matrix (packed)
+// m: the number of rows in A and C
+// n: the number of columns in B and C
+// transA: transpose of A matrix
+// B is already packed. So, we don't need transB
+void fbgemmPacked16Gemm(marian::Tensor C,
+                        const marian::Tensor A,
+                        const marian::Tensor B,
+                        const marian::Tensor bias,
+                        const size_t m,
+                        const size_t n,
+                        const int transA = 0);
+
+// GEMM operation on the packed B matrix in 8 bit integers
+// C: output matrix
+// A: A matrix
+// B: B matrix (packed)
+// m: the number of rows in A and C
+// n: the number of columns in B and C
+// k: the number of columns in A and rows in B
+// transA: transpose of A matrix
+// transB: transpose of B matrix
+void fbgemmPacked8Gemm(marian::Tensor C,
+                       const marian::Tensor A,
+                       const marian::Tensor B,
+                       const size_t m,
+                       const size_t n,
+                       const size_t k,
+                       const int transA = 0,
+                       const int transB = 0);
+
+}  // namespace variant
+}  // namespace cpu
+}  // namespace marian
diff --git a/src/tensors/cpu/sharp/packed_gemm.cpp b/src/tensors/cpu/sharp/packed_gemm.cpp
deleted file mode 100644
index a94335eef..000000000
--- a/src/tensors/cpu/sharp/packed_gemm.cpp
+++ /dev/null
@@ -1,313 +0,0 @@
-#include "packed_gemm.h"
-#include "tensors/tensor_allocator.h"
-#include "tensors/tensor_operators.h"
-
-#include <emmintrin.h>
-#include <immintrin.h>
-#include <tmmintrin.h>
-#include <xmmintrin.h>
-#include <cassert>
-#include <cstddef>
-#include <unordered_map>
-//#include <chrono>
-
-#if USE_FBGEMM
-#ifdef _MSC_VER
-#pragma warning(disable: 4505) // 'fbgemmAlignedAlloc' in fbgemm.h: unreferenced local function has been removed (missing 'static inline')
-#pragma warning(disable: 4251) // 'fbgemm::CompressedSparseColumn::colptr_': class 'std::vector<int,std::allocator<_Ty>>' needs to have dll-interface to be used by clients of class 'fbgemm::CompressedSparseColumn'
-// the following does not work; need to manually disable them in Linker options
-//#pragma comment(linker, "/ignore:4049") // locally defined symbol ...asmjit... imported
-//#pragma comment(linker, "/ignore:4217") // locally defined symbol ...asmjit... imported
-#endif
-
-#ifdef __GNUC__
-#pragma GCC diagnostic push
-#pragma GCC diagnostic ignored "-Wunused-variable"
-#endif
-#include "3rd_party/fbgemm/include/fbgemm/FbgemmFP16.h"
-#include "3rd_party/fbgemm/include/fbgemm/QuantUtils.h"
-#include "3rd_party/fbgemm/include/fbgemm/Fbgemm.h"
-#ifdef __GNUC__
-#pragma GCC diagnostic pop
-#endif
-
-#ifdef _OPENMP
-#include <omp.h>
-#endif
-
-#if MKL_FOUND
-#include <mkl.h>
-#include <mkl_types.h>
-#endif
-
-using namespace fbgemm;
-#endif // USE_FBGEMM
-
-namespace marian {
-namespace cpu {
-namespace variant { // Variants of GEMM implementations
-
-#if USE_FBGEMM
-// initialize with a dummy
-// When this class is instantiated,
-// the actual packing operation is happening. If we create this instance every time we call GEMM,
-// we are doing packing every time and very slow.
-// In Caffe2, the operator is stateful and hold an instance of this.
-// But, we don't have any logic for this in marian. We can only cache a tensor (which means a memory chunk).
-// So, for now, we keep the packed memory on our own 1D tensor, then when we call GEMM,
-// we just reuse this instance again and again by replacing the class members (including memory pointer). Eventually,
-// I will add a new constructor to the class in FBGEMM which accepts
-// pre - allocated and pre - packed memory as a parameter.After it's done,
-// this temporary buffer will be removed.
-// When constructing this dummy buffer, ones are used for all the parameters to allocate minimum amount of memory.
-//
-// In a multi marian instance setting (as a dynamic library),
-// different marian instances should not share this variable.
-static thread_local PackedGemmMatrixFP16 packedPlaceholder(1, 1, 1, 1, 1, 1, 1, 1);
-
-// Copied code from fbgemm. It's padding required from some kernel in FBGEMM
-// Verbatim - 'required by sw pipelined kernels'
-// https://github.com/marian-nmt/FBGEMM/blob/master/include/fbgemm/FbgemmFP16.h#L109
-const int PACK16_PADDING = 1024;  
-
-// This is a memory space to store auxiliary variables for FBGEMM (e.g. block row, block column, kernel_ncol_blocks and etc.)
-const int PACK16_SPECIALMEM = 256;
-
-// This is copied from FBGEMM code
-// A better way?
-// will be removed, when FBGEMM api is changed
-// blocked row-major format address arithmetic
-/**
- * Returns the memory address in the packed (block formatted) matrix array of a specific element 
- * indexed by the original non-packed array.
- *
- * @param r_ row index in the original matrix
- * @param c_ column index in the original matrix
- * @param brow_ row wide block index
- * @param bcol_ column wide block index
- * @param nbrow_ number of blocks in row
- * @param nbcol_ number of blocks in column
- * @param last_brow_ row number of the last block
- */
-inline uint64_t addr(const int r_,
-                     const int c_,
-                     const int brow_,
-                     const int bcol_,
-                     const int nbrow_,
-                     const int nbcol_,
-                     const int last_brow_) {
-  uint64_t r = (uint64_t)r_;
-  uint64_t c = (uint64_t)c_;
-
-  uint64_t block_row_id = r / brow_;
-  uint64_t brow_offset = (block_row_id * nbcol_) * (brow_ * bcol_);
-  uint64_t block_col_id = c / bcol_;
-  uint64_t bcol_offset
-      = block_col_id * ((block_row_id != nbrow_ - 1) ? (brow_ * bcol_) : (last_brow_ * bcol_));
-  uint64_t block_offset = brow_offset + bcol_offset;
-  uint64_t inblock_offset = r % brow_ * bcol_ + c % bcol_;
-
-  uint64_t index = block_offset + inblock_offset;
-  return index;
-}
-
-void PackInfoFp32(const marian::Shape& shape,
-                  const bool transpose,
-                  uint64_t& packsize) {
-  int nrow, ncol, kernel_ncol_blocks, brow = 512, bcol, last_brow, nbrow, nbcol;
-  PackInfoFp32(shape, transpose, nrow, ncol, kernel_ncol_blocks, brow, bcol, last_brow, nbrow, nbcol, packsize);
-}
-
-void PackInfoFp32(const marian::Shape& shape,
-                  const bool transpose,
-                  int& nrow,
-                  int& ncol,
-                  int& kernel_ncol_blocks,
-                  int& brow,
-                  int& bcol,
-                  int& last_brow,
-                  int& nbrow,
-                  int& nbcol,
-                  uint64_t& packsize) {
-  nrow = transpose ? shape[1] : shape[0];
-  ncol = transpose ? shape[0] : shape[1];
-  kernel_ncol_blocks = 2;
-  brow = 512;
-  bcol = 8 * kernel_ncol_blocks;
-  last_brow = nrow % brow == 0 ? brow : nrow % brow;
-  nbrow = nrow % brow == 0 ? nrow / brow : (nrow + brow) / brow;
-  nbcol = ncol % bcol == 0 ? ncol / bcol : (ncol + bcol) / bcol;
-  ABORT_IF(ncol % bcol != 0, "ncol (number of columns) should be multiple of 16. {}", ncol);
-  packsize = ((nbrow * brow) * (nbcol * bcol)) * sizeof(fbgemm::float16) + PACK16_PADDING
-             + PACK16_SPECIALMEM;
-}
-
-void PackFp32(marian::Tensor out,
-              const float* inData, // Packing is only available for 2D weight matrix in Marian. Otherwise, it's aborted in expanded_gemm.h.
-              const bool transpose,
-              const int nrow,
-              const int ncol,
-              const int kernel_ncol_blocks,
-              const int brow,
-              const int bcol,
-              const int last_brow,
-              const int nbrow,
-              const int nbcol,
-              const uint64_t packsize) {
-  // initialize memory
-  uint8_t* outmemorg = out->data<uint8_t>();
-  for(auto i = 0; i < packsize; i++) {
-    outmemorg[i] = 0;
-  }
-  // save the other auxiliary variables
-  uint64_t* auxmemsize = (uint64_t*)outmemorg;
-  auxmemsize[0] = packsize;
-  // save FBGEMM related parameters into the header of the allocated memory by marian
-  int32_t header[8];
-  header[0] = nrow;
-  header[1] = ncol;
-  header[2] = kernel_ncol_blocks;
-  header[3] = brow;
-  header[4] = bcol;
-  header[5] = last_brow;
-  header[6] = nbrow;
-  header[7] = nbcol;
-  memcpy(auxmemsize + 1, header, sizeof(header));
-  // cast to float16
-  fbgemm::float16* outmem = (fbgemm::float16*)(outmemorg + 256);
-  fbgemm::float16* dummy = new fbgemm::float16;
-  // pack the matrix
-  for(int i = 0; i < nrow; i++) {
-    for(int j = 0; j < ncol; j++) {
-      outmem[addr(i, j, brow, bcol, nbrow, nbcol, last_brow)]
-          = tconv(!transpose ? inData[i * ncol + j] : inData[i + nrow * j], *dummy);
-    }
-  }
-  delete dummy;
-}
-
-// GEMM operation on the packed B matrix
-// C: output matrix
-// A: A matrix
-// B: B matrix (packed)
-// m: the number of rows in A and C
-// n: the number of columns in B and C
-// transA: transpose of A matrix
-// B is already packed. So, we don't need transB
-void GemmPackFp32(marian::Tensor C,
-                  const marian::Tensor A,
-                  const marian::Tensor B,
-                  const marian::Tensor bias,
-                  const size_t m,
-                  const size_t n,
-                  const int transA) {
-  // row major
-  // keep the original mem
-  fbgemm::float16* pmat = packedPlaceholder.pmat_;
-  // retreive aux fields from the memory
-  uint64_t* packedmemSize = (uint64_t*)B->data();
-  packedPlaceholder.size_ = packedmemSize[0];
-  int32_t header[8];
-  memcpy(header, packedmemSize + 1, sizeof(header));
-  packedPlaceholder.nrow_ = header[0];
-  packedPlaceholder.ncol_ = header[1];
-  packedPlaceholder.kernel_ncol_blocks_ = header[2];
-  packedPlaceholder.brow_ = header[3];
-  packedPlaceholder.bcol_ = header[4];
-  packedPlaceholder.last_brow_ = header[5];
-  packedPlaceholder.nbrow_ = header[6];
-  packedPlaceholder.nbcol_ = header[7];
-
-  // packed matrix
-  packedPlaceholder.pmat_ = (fbgemm::float16*)(B->data<uint8_t>() + 256);
-
-  if(bias != nullptr) {
-#if MKL_FOUND
-    for(int i = 0; i < m; ++i) {
-      mkl_somatcopy('R', 'N', 1, n, 1, bias->data(), n, C->data() + n * i, n);
-    }
-#else
-    for(int i = 0; i < m; ++i) {
-      std::copy(bias->data(), bias->data() + n, C->data() + n * i);
-    }
-#endif
-  }
-
-#ifdef _OPENMP
-#pragma omp parallel
-#endif
-  {
-#ifdef _OPENMP
-    int num_threads = omp_get_num_threads();
-    int tid = omp_get_thread_num();
-#else
-    int num_threads = 1;
-    int tid = 0;
-#endif
-    fbgemm::cblas_gemm_compute(transA ? matrix_op_t::Transpose : matrix_op_t::NoTranspose,
-                      (int)m,
-                      A->data(),
-                      packedPlaceholder,
-                      bias != nullptr ? 1.0f : 0.0f,
-                      C->data(),
-                      tid,
-                      num_threads);
-  }
-
-  // return back the original mem
-  packedPlaceholder.pmat_ = pmat;
-}
-#else // USE_FBGEMM
-
-void PackInfoFp32(const marian::Shape& shape,
-                  const bool transpose,
-                  uint64_t& packsize) {
-  // does nothing. supports only FBGEMM based packed gemm at this moment.
-  ABORT("FBGEMM is needed to use packed GEMM.");
-}
-
-void PackInfoFp32(const marian::Shape& shape,
-                  const bool transpose,
-                  int& nrow,
-                  int& ncol,
-                  int& kernel_ncol_blocks,
-                  int& brow,
-                  int& bcol,
-                  int& last_brow,
-                  int& nbrow,
-                  int& nbcol,
-                  uint64_t& packsize) {
-  // does nothing. supports only FBGEMM based packed gemm at this moment.
-  ABORT("FBGEMM is needed to use packed GEMM.");
-}
-
-void PackFp32(marian::Tensor out,
-              const float* inData,
-              const bool transpose,
-              const int nrow,
-              const int ncol,
-              const int kernel_ncol_blocks,
-              const int brow,
-              const int bcol,
-              const int last_brow,
-              const int nbrow,
-              const int nbcol,
-              const uint64_t packsize) {
-                // does nothing. supports only FBGEMM based packed gemm at this moment.
-                ABORT("FBGEMM is needed to use packed GEMM.");
-}
-void GemmPackFp32(marian::Tensor C,
-                  const marian::Tensor A,
-                  const marian::Tensor B,
-                  const marian::Tensor bias,
-                  const size_t m,
-                  const size_t n,
-                  const int transA) {
-                // does nothing. supports only FBGEMM based packed gemm at this moment.
-                ABORT("FBGEMM is needed to use packed GEMM.");
-}
-#endif // USE_FBGEMM
-
-}  // namespace variant
-}  // namespace cpu
-}  // namespace marian
diff --git a/src/tensors/cpu/sharp/packed_gemm.h b/src/tensors/cpu/sharp/packed_gemm.h
deleted file mode 100644
index 70b093939..000000000
--- a/src/tensors/cpu/sharp/packed_gemm.h
+++ /dev/null
@@ -1,70 +0,0 @@
-#pragma once
-
-#include "tensors/tensor.h"
-
-namespace marian {
-namespace cpu {
-namespace variant { // Variants of GEMM implementations
-
-void PackInfoFp32(const marian::Shape& shape,
-                  const bool transpose,
-                  /*out*/uint64_t& packsize);
-
-void PackInfoFp32(const marian::Shape& shape,
-                  const bool transpose,
-                  int& nrow,
-                  int& ncol,
-                  int& kernel_ncol_blocks,
-                  int& brow,
-                  int& bcol,
-                  int& last_brow,
-                  int& nbrow,
-                  int& nbcol,
-                  /*out*/uint64_t& packsize); // @TODO: change to size_t where appropriate
-
-// Pack a matrix into cache utilization efficient way (block format)
-// out: output tensor - packed format
-// inData: input tensor data - pointer of float data
-// transpose: the matrix is transposed
-// nrow: the number of rows
-// ncol: the number of columns
-// kernel_ncol_blocks: the number of column blocks
-// brow: the number of rows in a block
-// bcol: the number of columns in a block
-// last_brow: the number of rows in the last block
-// nbrow: row index in a block
-// nbcol: column index in a block
-// packsize: the size of the packed matrix
-//          (the number of fp16 elements + padding (1024) + extra temporary memory (256))
-void PackFp32(marian::Tensor out,
-              const float* inData,
-              const bool transpose,
-              const int nrow,
-              const int ncol,
-              const int kernel_ncol_blocks,
-              const int brow,
-              const int bcol,
-              const int last_brow,
-              const int nbrow,
-              const int nbcol,
-              const uint64_t packsize); // @TODO: change to size_t where appropriate
-
-// GEMM operation on the packed B matrix
-// C: output matrix
-// A: A matrix
-// B: B matrix (packed)
-// m: the number of rows in A and C
-// n: the number of columns in B and C
-// transA: transpose of A matrix
-// B is already packed. So, we don't need transB
-void GemmPackFp32(marian::Tensor C,
-                  const marian::Tensor A,
-                  const marian::Tensor B,
-                  const marian::Tensor bias,
-                  const size_t m,
-                  const size_t n,
-                  const int transA = 0);
-
-}  // namespace variant
-}  // namespace cpu
-}  // namespace marian
diff --git a/vs/Marian.sln b/vs/Marian.sln
index 9cbbcefb7..b98a12462 100755
--- a/vs/Marian.sln
+++ b/vs/Marian.sln
@@ -1,7 +1,7 @@
 
 Microsoft Visual Studio Solution File, Format Version 12.00
 # Visual Studio 15
-VisualStudioVersion = 15.0.27703.2047
+VisualStudioVersion = 15.0.28307.902
 MinimumVisualStudioVersion = 10.0.40219.1
 Project("{8BC9CEB8-8B4A-11D0-8D11-00A0C91BC942}") = "Marian", "Marian.vcxproj", "{E2F320FE-0C01-4C80-810C-3A92205A29DC}"
 EndProject
@@ -20,6 +20,6 @@ Global
 		HideSolutionNode = FALSE
 	EndGlobalSection
 	GlobalSection(ExtensibilityGlobals) = postSolution
-		SolutionGuid = {8CA1BE8F-87A9-4094-B549-E8C790F79D8C}
+		SolutionGuid = {3B922907-3384-4D39-9CEB-816BF7BB390D}
 	EndGlobalSection
 EndGlobal
diff --git a/vs/Marian.vcxproj b/vs/Marian.vcxproj
index e49a3ef6f..241aa307d 100755
--- a/vs/Marian.vcxproj
+++ b/vs/Marian.vcxproj
@@ -43,14 +43,14 @@
   <PropertyGroup Condition="'$(Configuration)|$(Platform)'=='Debug|x64'">
     <LinkIncremental>true</LinkIncremental>
     <IntDir>$(SolutionDir)$(Platform)\$(Configuration)\Marian\</IntDir>
-    <IncludePath>$(CudaToolkitIncludeDir);..\src\3rd_party\fbgemm\third_party\cpuinfo\deps\clog\include;..\src\3rd_party\fbgemm\third_party\cpuinfo\src;..\src\3rd_party\fbgemm\third_party\cpuinfo\include;..\src\3rd_party\fbgemm\third_party\asmjit\src;%MKL_PATH%\include;..\src\3rd_party\fbgemm\include;..\src;..\src\3rd_party;%BOOST_INCLUDE_PATH%;%ZLIB_PATH%\include;$(VC_IncludePath);$(WindowsSDK_IncludePath)</IncludePath>
+    <IncludePath>$(CudaToolkitIncludeDir);..\src\3rd_party\fbgemm\third_party\googletest\googletest;..\src\3rd_party\fbgemm\third_party\googletest\googletest\include;..\src\3rd_party\fbgemm\third_party\cpuinfo\deps\clog\include;..\src\3rd_party\fbgemm\third_party\cpuinfo\src;..\src\3rd_party\fbgemm\third_party\cpuinfo\include;..\src\3rd_party\fbgemm;..\src\3rd_party\fbgemm\third_party\asmjit\src;%MKL_PATH%\include;..\src\3rd_party\fbgemm\include;..\src;..\src\3rd_party;%BOOST_INCLUDE_PATH%;%ZLIB_PATH%\include;$(VC_IncludePath);$(WindowsSDK_IncludePath)</IncludePath>
     <LibraryPath>$(CudaToolkitLibDir);%BOOST_LIB_PATH%;%ZLIB_PATH%\lib;%MKL_PATH%\lib\intel64;$(VC_LibraryPath_x64);$(WindowsSDK_LibraryPath_x64);$(NETFXKitsDir)Lib\um\x64</LibraryPath>
   </PropertyGroup>
   <PropertyGroup Condition="'$(Configuration)|$(Platform)'=='Release|x64'">
     <LinkIncremental>false</LinkIncremental>
     <ExecutablePath>$(ExecutablePath)</ExecutablePath>
     <IntDir>$(SolutionDir)$(Platform)\$(Configuration)\Marian\</IntDir>
-    <IncludePath>$(CudaToolkitIncludeDir);..\src\3rd_party\fbgemm\third_party\cpuinfo\deps\clog\include;..\src\3rd_party\fbgemm\third_party\cpuinfo\src;..\src\3rd_party\fbgemm\third_party\cpuinfo\include;..\src\3rd_party\fbgemm\third_party\asmjit\src;%MKL_PATH%\include;..\src\3rd_party\fbgemm\include;..\src;..\src\3rd_party;%BOOST_INCLUDE_PATH%;%ZLIB_PATH%\include;$(VC_IncludePath);$(WindowsSDK_IncludePath)</IncludePath>
+    <IncludePath>$(CudaToolkitIncludeDir);..\src\3rd_party\fbgemm\third_party\googletest\googletest;..\src\3rd_party\fbgemm\third_party\googletest\googletest\include;..\src\3rd_party\fbgemm\third_party\cpuinfo\deps\clog\include;..\src\3rd_party\fbgemm\third_party\cpuinfo\src;..\src\3rd_party\fbgemm\third_party\cpuinfo\include;..\src\3rd_party\fbgemm;..\src\3rd_party\fbgemm\third_party\asmjit\src;%MKL_PATH%\include;..\src\3rd_party\fbgemm\include;..\src;..\src\3rd_party;%BOOST_INCLUDE_PATH%;%ZLIB_PATH%\include;$(VC_IncludePath);$(WindowsSDK_IncludePath)</IncludePath>
     <LibraryPath>$(CudaToolkitLibDir);%BOOST_LIB_PATH%;%ZLIB_PATH%\lib;%MKL_PATH%\lib\intel64;$(VC_LibraryPath_x64);$(WindowsSDK_LibraryPath_x64);$(NETFXKitsDir)Lib\um\x64</LibraryPath>
   </PropertyGroup>
   <ItemDefinitionGroup>
@@ -70,7 +70,7 @@
       </PrecompiledHeader>
       <WarningLevel>Level4</WarningLevel>
       <Optimization>Disabled</Optimization>
-      <PreprocessorDefinitions>BOOST_CONFIG_SUPPRESS_OUTDATED_MESSAGE; FBGEMM_EXPORTS; USE_FBGEMM=1; USE_SSE2=1; CUDA_FOUND=1; MKL_FOUND=1; MPI_FOUND=1; BLAS_FOUND=1; MKL_ILP64; WIN32;_DEBUG;_CONSOLE;_LIB;%(PreprocessorDefinitions)</PreprocessorDefinitions>
+      <PreprocessorDefinitions>USE_MKL;ASMJIT_EXPORTS;BOOST_CONFIG_SUPPRESS_OUTDATED_MESSAGE; FBGEMM_EXPORTS; USE_FBGEMM=1; USE_SSE2=1; CUDA_FOUND=1; MKL_FOUND=1; MPI_FOUND=1; BLAS_FOUND=1; MKL_ILP64; WIN32;_DEBUG;_CONSOLE;_LIB;%(PreprocessorDefinitions)</PreprocessorDefinitions>
       <SDLCheck>false</SDLCheck>
       <TreatWarningAsError>true</TreatWarningAsError>
       <AdditionalOptions>/bigobj /arch:AVX %(AdditionalOptions)</AdditionalOptions>
@@ -107,7 +107,7 @@
       <Optimization>MaxSpeed</Optimization>
       <FunctionLevelLinking>true</FunctionLevelLinking>
       <IntrinsicFunctions>true</IntrinsicFunctions>
-      <PreprocessorDefinitions>BOOST_CONFIG_SUPPRESS_OUTDATED_MESSAGE; FBGEMM_EXPORTS; USE_FBGEMM=1; USE_SSE2=1; CUDA_FOUND=1; MKL_FOUND=1; MPI_FOUND=1; BLAS_FOUND=1; MKL_ILP64; WIN32;NDEBUG;_CONSOLE;_LIB;%(PreprocessorDefinitions)</PreprocessorDefinitions>
+      <PreprocessorDefinitions>USE_MKL;ASMJIT_EXPORTS;BOOST_CONFIG_SUPPRESS_OUTDATED_MESSAGE; FBGEMM_EXPORTS; USE_FBGEMM=1; USE_SSE2=1; CUDA_FOUND=1; MKL_FOUND=1; MPI_FOUND=1; BLAS_FOUND=1; MKL_ILP64; WIN32;NDEBUG;_CONSOLE;_LIB;%(PreprocessorDefinitions)</PreprocessorDefinitions>
       <SDLCheck>false</SDLCheck>
       <FavorSizeOrSpeed>Speed</FavorSizeOrSpeed>
       <AdditionalOptions>/d2Zi+ /bigobj /arch:AVX %(AdditionalOptions)</AdditionalOptions>
@@ -141,6 +141,102 @@
   </ItemDefinitionGroup>
   <ItemGroup>
     <ClCompile Include="..\src\3rd_party\ExceptionWithCallStack.cpp" />
+    <ClCompile Include="..\src\3rd_party\fbgemm\bench\BenchUtils.cc">
+      <ExcludedFromBuild Condition="'$(Configuration)|$(Platform)'=='Debug|x64'">true</ExcludedFromBuild>
+      <ExcludedFromBuild Condition="'$(Configuration)|$(Platform)'=='Release|x64'">true</ExcludedFromBuild>
+      <WarningLevel Condition="'$(Configuration)|$(Platform)'=='Debug|x64'">TurnOffAllWarnings</WarningLevel>
+      <WarningLevel Condition="'$(Configuration)|$(Platform)'=='Release|x64'">TurnOffAllWarnings</WarningLevel>
+    </ClCompile>
+    <ClCompile Include="..\src\3rd_party\fbgemm\bench\ConvUnifiedBenchmark.cc">
+      <ExcludedFromBuild Condition="'$(Configuration)|$(Platform)'=='Debug|x64'">true</ExcludedFromBuild>
+      <ExcludedFromBuild Condition="'$(Configuration)|$(Platform)'=='Release|x64'">true</ExcludedFromBuild>
+      <WarningLevel Condition="'$(Configuration)|$(Platform)'=='Debug|x64'">TurnOffAllWarnings</WarningLevel>
+      <WarningLevel Condition="'$(Configuration)|$(Platform)'=='Release|x64'">TurnOffAllWarnings</WarningLevel>
+    </ClCompile>
+    <ClCompile Include="..\src\3rd_party\fbgemm\bench\Depthwise3DBenchmark.cc">
+      <ExcludedFromBuild Condition="'$(Configuration)|$(Platform)'=='Debug|x64'">true</ExcludedFromBuild>
+      <ExcludedFromBuild Condition="'$(Configuration)|$(Platform)'=='Release|x64'">true</ExcludedFromBuild>
+      <WarningLevel Condition="'$(Configuration)|$(Platform)'=='Debug|x64'">TurnOffAllWarnings</WarningLevel>
+      <WarningLevel Condition="'$(Configuration)|$(Platform)'=='Release|x64'">TurnOffAllWarnings</WarningLevel>
+    </ClCompile>
+    <ClCompile Include="..\src\3rd_party\fbgemm\bench\DepthwiseBenchmark.cc">
+      <ExcludedFromBuild Condition="'$(Configuration)|$(Platform)'=='Debug|x64'">true</ExcludedFromBuild>
+      <ExcludedFromBuild Condition="'$(Configuration)|$(Platform)'=='Release|x64'">true</ExcludedFromBuild>
+      <WarningLevel Condition="'$(Configuration)|$(Platform)'=='Debug|x64'">TurnOffAllWarnings</WarningLevel>
+      <WarningLevel Condition="'$(Configuration)|$(Platform)'=='Release|x64'">TurnOffAllWarnings</WarningLevel>
+    </ClCompile>
+    <ClCompile Include="..\src\3rd_party\fbgemm\bench\FP16Benchmark.cc">
+      <ExcludedFromBuild Condition="'$(Configuration)|$(Platform)'=='Debug|x64'">true</ExcludedFromBuild>
+      <ExcludedFromBuild Condition="'$(Configuration)|$(Platform)'=='Release|x64'">true</ExcludedFromBuild>
+      <WarningLevel Condition="'$(Configuration)|$(Platform)'=='Debug|x64'">TurnOffAllWarnings</WarningLevel>
+      <WarningLevel Condition="'$(Configuration)|$(Platform)'=='Release|x64'">TurnOffAllWarnings</WarningLevel>
+    </ClCompile>
+    <ClCompile Include="..\src\3rd_party\fbgemm\bench\GEMMsBenchmark.cc">
+      <ExcludedFromBuild Condition="'$(Configuration)|$(Platform)'=='Debug|x64'">true</ExcludedFromBuild>
+      <ExcludedFromBuild Condition="'$(Configuration)|$(Platform)'=='Release|x64'">true</ExcludedFromBuild>
+      <WarningLevel Condition="'$(Configuration)|$(Platform)'=='Debug|x64'">TurnOffAllWarnings</WarningLevel>
+      <WarningLevel Condition="'$(Configuration)|$(Platform)'=='Release|x64'">TurnOffAllWarnings</WarningLevel>
+    </ClCompile>
+    <ClCompile Include="..\src\3rd_party\fbgemm\bench\GEMMsTunableBenchmark.cc">
+      <ExcludedFromBuild Condition="'$(Configuration)|$(Platform)'=='Debug|x64'">true</ExcludedFromBuild>
+      <ExcludedFromBuild Condition="'$(Configuration)|$(Platform)'=='Release|x64'">true</ExcludedFromBuild>
+      <WarningLevel Condition="'$(Configuration)|$(Platform)'=='Debug|x64'">TurnOffAllWarnings</WarningLevel>
+      <WarningLevel Condition="'$(Configuration)|$(Platform)'=='Release|x64'">TurnOffAllWarnings</WarningLevel>
+    </ClCompile>
+    <ClCompile Include="..\src\3rd_party\fbgemm\bench\GroupwiseConvRequantizeBenchmark.cc">
+      <ExcludedFromBuild Condition="'$(Configuration)|$(Platform)'=='Debug|x64'">true</ExcludedFromBuild>
+      <ExcludedFromBuild Condition="'$(Configuration)|$(Platform)'=='Release|x64'">true</ExcludedFromBuild>
+      <WarningLevel Condition="'$(Configuration)|$(Platform)'=='Debug|x64'">TurnOffAllWarnings</WarningLevel>
+      <WarningLevel Condition="'$(Configuration)|$(Platform)'=='Release|x64'">TurnOffAllWarnings</WarningLevel>
+    </ClCompile>
+    <ClCompile Include="..\src\3rd_party\fbgemm\bench\I8SpmdmBenchmark.cc">
+      <ExcludedFromBuild Condition="'$(Configuration)|$(Platform)'=='Debug|x64'">true</ExcludedFromBuild>
+      <ExcludedFromBuild Condition="'$(Configuration)|$(Platform)'=='Release|x64'">true</ExcludedFromBuild>
+      <WarningLevel Condition="'$(Configuration)|$(Platform)'=='Debug|x64'">TurnOffAllWarnings</WarningLevel>
+      <WarningLevel Condition="'$(Configuration)|$(Platform)'=='Release|x64'">TurnOffAllWarnings</WarningLevel>
+    </ClCompile>
+    <ClCompile Include="..\src\3rd_party\fbgemm\bench\Im2ColFusedRequantizeBenchmark.cc">
+      <ExcludedFromBuild Condition="'$(Configuration)|$(Platform)'=='Debug|x64'">true</ExcludedFromBuild>
+      <ExcludedFromBuild Condition="'$(Configuration)|$(Platform)'=='Release|x64'">true</ExcludedFromBuild>
+      <WarningLevel Condition="'$(Configuration)|$(Platform)'=='Debug|x64'">TurnOffAllWarnings</WarningLevel>
+      <WarningLevel Condition="'$(Configuration)|$(Platform)'=='Release|x64'">TurnOffAllWarnings</WarningLevel>
+    </ClCompile>
+    <ClCompile Include="..\src\3rd_party\fbgemm\bench\PackedFloatInOutBenchmark.cc">
+      <ExcludedFromBuild Condition="'$(Configuration)|$(Platform)'=='Debug|x64'">true</ExcludedFromBuild>
+      <ExcludedFromBuild Condition="'$(Configuration)|$(Platform)'=='Release|x64'">true</ExcludedFromBuild>
+      <WarningLevel Condition="'$(Configuration)|$(Platform)'=='Debug|x64'">TurnOffAllWarnings</WarningLevel>
+      <WarningLevel Condition="'$(Configuration)|$(Platform)'=='Release|x64'">TurnOffAllWarnings</WarningLevel>
+    </ClCompile>
+    <ClCompile Include="..\src\3rd_party\fbgemm\bench\PackedRequantizeAcc16Benchmark.cc">
+      <ExcludedFromBuild Condition="'$(Configuration)|$(Platform)'=='Debug|x64'">true</ExcludedFromBuild>
+      <ExcludedFromBuild Condition="'$(Configuration)|$(Platform)'=='Release|x64'">true</ExcludedFromBuild>
+      <WarningLevel Condition="'$(Configuration)|$(Platform)'=='Debug|x64'">TurnOffAllWarnings</WarningLevel>
+      <WarningLevel Condition="'$(Configuration)|$(Platform)'=='Release|x64'">TurnOffAllWarnings</WarningLevel>
+    </ClCompile>
+    <ClCompile Include="..\src\3rd_party\fbgemm\bench\PackedRequantizeAcc32Benchmark.cc">
+      <ExcludedFromBuild Condition="'$(Configuration)|$(Platform)'=='Debug|x64'">true</ExcludedFromBuild>
+      <ExcludedFromBuild Condition="'$(Configuration)|$(Platform)'=='Release|x64'">true</ExcludedFromBuild>
+      <WarningLevel Condition="'$(Configuration)|$(Platform)'=='Debug|x64'">TurnOffAllWarnings</WarningLevel>
+      <WarningLevel Condition="'$(Configuration)|$(Platform)'=='Release|x64'">TurnOffAllWarnings</WarningLevel>
+    </ClCompile>
+    <ClCompile Include="..\src\3rd_party\fbgemm\bench\RequantizeBenchmark.cc">
+      <ExcludedFromBuild Condition="'$(Configuration)|$(Platform)'=='Debug|x64'">true</ExcludedFromBuild>
+      <ExcludedFromBuild Condition="'$(Configuration)|$(Platform)'=='Release|x64'">true</ExcludedFromBuild>
+      <WarningLevel Condition="'$(Configuration)|$(Platform)'=='Debug|x64'">TurnOffAllWarnings</WarningLevel>
+      <WarningLevel Condition="'$(Configuration)|$(Platform)'=='Release|x64'">TurnOffAllWarnings</WarningLevel>
+    </ClCompile>
+    <ClCompile Include="..\src\3rd_party\fbgemm\bench\RowOffsetBenchmark.cc">
+      <ExcludedFromBuild Condition="'$(Configuration)|$(Platform)'=='Debug|x64'">true</ExcludedFromBuild>
+      <ExcludedFromBuild Condition="'$(Configuration)|$(Platform)'=='Release|x64'">true</ExcludedFromBuild>
+      <WarningLevel Condition="'$(Configuration)|$(Platform)'=='Debug|x64'">TurnOffAllWarnings</WarningLevel>
+      <WarningLevel Condition="'$(Configuration)|$(Platform)'=='Release|x64'">TurnOffAllWarnings</WarningLevel>
+    </ClCompile>
+    <ClCompile Include="..\src\3rd_party\fbgemm\src\codegen_fp16fp32.cc">
+      <ExcludedFromBuild Condition="'$(Configuration)|$(Platform)'=='Release|x64'">true</ExcludedFromBuild>
+      <ExcludedFromBuild Condition="'$(Configuration)|$(Platform)'=='Debug|x64'">true</ExcludedFromBuild>
+      <WarningLevel Condition="'$(Configuration)|$(Platform)'=='Debug|x64'">TurnOffAllWarnings</WarningLevel>
+      <WarningLevel Condition="'$(Configuration)|$(Platform)'=='Release|x64'">TurnOffAllWarnings</WarningLevel>
+    </ClCompile>
     <ClCompile Include="..\src\3rd_party\fbgemm\src\ExecuteKernel.cc">
       <TreatWarningAsError Condition="'$(Configuration)|$(Platform)'=='Release|x64'">true</TreatWarningAsError>
       <TreatWarningAsError Condition="'$(Configuration)|$(Platform)'=='Debug|x64'">true</TreatWarningAsError>
@@ -177,6 +273,12 @@
       <WarningLevel Condition="'$(Configuration)|$(Platform)'=='Debug|x64'">TurnOffAllWarnings</WarningLevel>
       <WarningLevel Condition="'$(Configuration)|$(Platform)'=='Release|x64'">TurnOffAllWarnings</WarningLevel>
     </ClCompile>
+    <ClCompile Include="..\src\3rd_party\fbgemm\src\FbgemmI8Depthwise3DAvx2.cc">
+      <TreatWarningAsError Condition="'$(Configuration)|$(Platform)'=='Debug|x64'">false</TreatWarningAsError>
+      <TreatWarningAsError Condition="'$(Configuration)|$(Platform)'=='Release|x64'">false</TreatWarningAsError>
+      <WarningLevel Condition="'$(Configuration)|$(Platform)'=='Debug|x64'">TurnOffAllWarnings</WarningLevel>
+      <WarningLevel Condition="'$(Configuration)|$(Platform)'=='Release|x64'">TurnOffAllWarnings</WarningLevel>
+    </ClCompile>
     <ClCompile Include="..\src\3rd_party\fbgemm\src\FbgemmI8DepthwiseAvx2.cc">
       <TreatWarningAsError Condition="'$(Configuration)|$(Platform)'=='Release|x64'">true</TreatWarningAsError>
       <TreatWarningAsError Condition="'$(Configuration)|$(Platform)'=='Debug|x64'">true</TreatWarningAsError>
@@ -201,6 +303,12 @@
       <WarningLevel Condition="'$(Configuration)|$(Platform)'=='Debug|x64'">TurnOffAllWarnings</WarningLevel>
       <WarningLevel Condition="'$(Configuration)|$(Platform)'=='Release|x64'">TurnOffAllWarnings</WarningLevel>
     </ClCompile>
+    <ClCompile Include="..\src\3rd_party\fbgemm\src\GenerateKernelU8S8S32ACC16Avx512VNNI.cc">
+      <TreatWarningAsError Condition="'$(Configuration)|$(Platform)'=='Debug|x64'">false</TreatWarningAsError>
+      <TreatWarningAsError Condition="'$(Configuration)|$(Platform)'=='Release|x64'">false</TreatWarningAsError>
+      <WarningLevel Condition="'$(Configuration)|$(Platform)'=='Debug|x64'">TurnOffAllWarnings</WarningLevel>
+      <WarningLevel Condition="'$(Configuration)|$(Platform)'=='Release|x64'">TurnOffAllWarnings</WarningLevel>
+    </ClCompile>
     <ClCompile Include="..\src\3rd_party\fbgemm\src\GenerateKernelU8S8S32ACC32.cc">
       <TreatWarningAsError Condition="'$(Configuration)|$(Platform)'=='Release|x64'">true</TreatWarningAsError>
       <TreatWarningAsError Condition="'$(Configuration)|$(Platform)'=='Debug|x64'">true</TreatWarningAsError>
@@ -213,6 +321,12 @@
       <WarningLevel Condition="'$(Configuration)|$(Platform)'=='Debug|x64'">TurnOffAllWarnings</WarningLevel>
       <WarningLevel Condition="'$(Configuration)|$(Platform)'=='Release|x64'">TurnOffAllWarnings</WarningLevel>
     </ClCompile>
+    <ClCompile Include="..\src\3rd_party\fbgemm\src\GenerateKernelU8S8S32ACC32Avx512VNNI.cc">
+      <TreatWarningAsError Condition="'$(Configuration)|$(Platform)'=='Debug|x64'">false</TreatWarningAsError>
+      <TreatWarningAsError Condition="'$(Configuration)|$(Platform)'=='Release|x64'">false</TreatWarningAsError>
+      <WarningLevel Condition="'$(Configuration)|$(Platform)'=='Debug|x64'">TurnOffAllWarnings</WarningLevel>
+      <WarningLevel Condition="'$(Configuration)|$(Platform)'=='Release|x64'">TurnOffAllWarnings</WarningLevel>
+    </ClCompile>
     <ClCompile Include="..\src\3rd_party\fbgemm\src\GroupwiseConvAcc32Avx2.cc">
       <TreatWarningAsError Condition="'$(Configuration)|$(Platform)'=='Release|x64'">true</TreatWarningAsError>
       <TreatWarningAsError Condition="'$(Configuration)|$(Platform)'=='Debug|x64'">true</TreatWarningAsError>
@@ -255,6 +369,12 @@
       <WarningLevel Condition="'$(Configuration)|$(Platform)'=='Debug|x64'">TurnOffAllWarnings</WarningLevel>
       <WarningLevel Condition="'$(Configuration)|$(Platform)'=='Release|x64'">TurnOffAllWarnings</WarningLevel>
     </ClCompile>
+    <ClCompile Include="..\src\3rd_party\fbgemm\src\PackDepthwiseConvMatrixAvx2.cc">
+      <TreatWarningAsError Condition="'$(Configuration)|$(Platform)'=='Debug|x64'">false</TreatWarningAsError>
+      <TreatWarningAsError Condition="'$(Configuration)|$(Platform)'=='Release|x64'">false</TreatWarningAsError>
+      <WarningLevel Condition="'$(Configuration)|$(Platform)'=='Debug|x64'">TurnOffAllWarnings</WarningLevel>
+      <WarningLevel Condition="'$(Configuration)|$(Platform)'=='Release|x64'">TurnOffAllWarnings</WarningLevel>
+    </ClCompile>
     <ClCompile Include="..\src\3rd_party\fbgemm\src\PackMatrix.cc">
       <TreatWarningAsError Condition="'$(Configuration)|$(Platform)'=='Release|x64'">true</TreatWarningAsError>
       <TreatWarningAsError Condition="'$(Configuration)|$(Platform)'=='Debug|x64'">true</TreatWarningAsError>
@@ -309,183 +429,273 @@
       <WarningLevel Condition="'$(Configuration)|$(Platform)'=='Debug|x64'">TurnOffAllWarnings</WarningLevel>
       <WarningLevel Condition="'$(Configuration)|$(Platform)'=='Release|x64'">TurnOffAllWarnings</WarningLevel>
     </ClCompile>
-    <ClCompile Include="..\src\3rd_party\fbgemm\third_party\asmjit\src\asmjit\base\arch.cpp">
-      <TreatWarningAsError Condition="'$(Configuration)|$(Platform)'=='Release|x64'">false</TreatWarningAsError>
+    <ClCompile Include="..\src\3rd_party\fbgemm\test\FP16Test.cc">
       <TreatWarningAsError Condition="'$(Configuration)|$(Platform)'=='Debug|x64'">false</TreatWarningAsError>
-      <WarningLevel Condition="'$(Configuration)|$(Platform)'=='Release|x64'">TurnOffAllWarnings</WarningLevel>
+      <TreatWarningAsError Condition="'$(Configuration)|$(Platform)'=='Release|x64'">false</TreatWarningAsError>
+      <ExcludedFromBuild Condition="'$(Configuration)|$(Platform)'=='Debug|x64'">true</ExcludedFromBuild>
+      <ExcludedFromBuild Condition="'$(Configuration)|$(Platform)'=='Release|x64'">true</ExcludedFromBuild>
       <WarningLevel Condition="'$(Configuration)|$(Platform)'=='Debug|x64'">TurnOffAllWarnings</WarningLevel>
+      <WarningLevel Condition="'$(Configuration)|$(Platform)'=='Release|x64'">TurnOffAllWarnings</WarningLevel>
     </ClCompile>
-    <ClCompile Include="..\src\3rd_party\fbgemm\third_party\asmjit\src\asmjit\base\assembler.cpp">
-      <TreatWarningAsError Condition="'$(Configuration)|$(Platform)'=='Release|x64'">false</TreatWarningAsError>
+    <ClCompile Include="..\src\3rd_party\fbgemm\test\GConvTest.cc">
       <TreatWarningAsError Condition="'$(Configuration)|$(Platform)'=='Debug|x64'">false</TreatWarningAsError>
+      <TreatWarningAsError Condition="'$(Configuration)|$(Platform)'=='Release|x64'">false</TreatWarningAsError>
+      <ExcludedFromBuild Condition="'$(Configuration)|$(Platform)'=='Debug|x64'">true</ExcludedFromBuild>
+      <ExcludedFromBuild Condition="'$(Configuration)|$(Platform)'=='Release|x64'">true</ExcludedFromBuild>
       <WarningLevel Condition="'$(Configuration)|$(Platform)'=='Debug|x64'">TurnOffAllWarnings</WarningLevel>
       <WarningLevel Condition="'$(Configuration)|$(Platform)'=='Release|x64'">TurnOffAllWarnings</WarningLevel>
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-    <ClCompile Include="..\src\3rd_party\fbgemm\third_party\asmjit\src\asmjit\base\codebuilder.cpp">
-      <TreatWarningAsError Condition="'$(Configuration)|$(Platform)'=='Release|x64'">false</TreatWarningAsError>
+    <ClCompile Include="..\src\3rd_party\fbgemm\test\I8DepthwiseTest.cc">
       <TreatWarningAsError Condition="'$(Configuration)|$(Platform)'=='Debug|x64'">false</TreatWarningAsError>
+      <TreatWarningAsError Condition="'$(Configuration)|$(Platform)'=='Release|x64'">false</TreatWarningAsError>
+      <ExcludedFromBuild Condition="'$(Configuration)|$(Platform)'=='Debug|x64'">true</ExcludedFromBuild>
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-      <TreatWarningAsError Condition="'$(Configuration)|$(Platform)'=='Debug|x64'">false</TreatWarningAsError>
+    <ClCompile Include="..\src\3rd_party\fbgemm\third_party\asmjit\src\asmjit\x86\x86rapass.cpp">
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       <WarningLevel Condition="'$(Configuration)|$(Platform)'=='Release|x64'">TurnOffAllWarnings</WarningLevel>
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+    <ClCompile Include="..\src\3rd_party\fbgemm\third_party\googletest\googletest\src\gtest-all.cc" />
+    <ClCompile Include="..\src\3rd_party\fbgemm\third_party\googletest\googletest\src\gtest_main.cc">
+      <ExcludedFromBuild Condition="'$(Configuration)|$(Platform)'=='Debug|x64'">true</ExcludedFromBuild>
+      <ExcludedFromBuild Condition="'$(Configuration)|$(Platform)'=='Release|x64'">true</ExcludedFromBuild>
+    </ClCompile>
     <ClCompile Include="..\src\3rd_party\pathie-cpp\src\entry_iterator.cpp" />
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     <ClCompile Include="..\src\3rd_party\pathie-cpp\src\path.cpp" />
@@ -795,6 +1010,8 @@
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     <ClInclude Include="..\src\3rd_party\avx_mathfun.h" />
     <ClInclude Include="..\src\3rd_party\ExceptionWithCallStack.h" />
+    <ClInclude Include="..\src\3rd_party\fbgemm\bench\AlignedVec.h" />
+    <ClInclude Include="..\src\3rd_party\fbgemm\bench\BenchUtils.h" />
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     <ClInclude Include="..\src\3rd_party\fbgemm\include\fbgemm\UtilsAvx2.h" />
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     <ClInclude Include="..\src\3rd_party\fbgemm\src\FbgemmFP16UKernelsAvx2.h" />
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-    <ClInclude Include="..\src\3rd_party\fbgemm\third_party\asmjit\src\asmjit\base\zone.h" />
+    <ClInclude Include="..\src\3rd_party\fbgemm\third_party\asmjit\src\asmjit\core.h" />
+    <ClInclude Include="..\src\3rd_party\fbgemm\third_party\asmjit\src\asmjit\core\arch.h" />
+    <ClInclude Include="..\src\3rd_party\fbgemm\third_party\asmjit\src\asmjit\core\assembler.h" />
+    <ClInclude Include="..\src\3rd_party\fbgemm\third_party\asmjit\src\asmjit\core\build.h" />
+    <ClInclude Include="..\src\3rd_party\fbgemm\third_party\asmjit\src\asmjit\core\builder.h" />
+    <ClInclude Include="..\src\3rd_party\fbgemm\third_party\asmjit\src\asmjit\core\callconv.h" />
+    <ClInclude Include="..\src\3rd_party\fbgemm\third_party\asmjit\src\asmjit\core\codebufferwriter_p.h" />
+    <ClInclude Include="..\src\3rd_party\fbgemm\third_party\asmjit\src\asmjit\core\codeholder.h" />
+    <ClInclude Include="..\src\3rd_party\fbgemm\third_party\asmjit\src\asmjit\core\compiler.h" />
+    <ClInclude Include="..\src\3rd_party\fbgemm\third_party\asmjit\src\asmjit\core\constpool.h" />
+    <ClInclude Include="..\src\3rd_party\fbgemm\third_party\asmjit\src\asmjit\core\cpuinfo.h" />
+    <ClInclude Include="..\src\3rd_party\fbgemm\third_party\asmjit\src\asmjit\core\datatypes.h" />
+    <ClInclude Include="..\src\3rd_party\fbgemm\third_party\asmjit\src\asmjit\core\emitter.h" />
+    <ClInclude Include="..\src\3rd_party\fbgemm\third_party\asmjit\src\asmjit\core\features.h" />
+    <ClInclude Include="..\src\3rd_party\fbgemm\third_party\asmjit\src\asmjit\core\func.h" />
+    <ClInclude Include="..\src\3rd_party\fbgemm\third_party\asmjit\src\asmjit\core\globals.h" />
+    <ClInclude Include="..\src\3rd_party\fbgemm\third_party\asmjit\src\asmjit\core\inst.h" />
+    <ClInclude Include="..\src\3rd_party\fbgemm\third_party\asmjit\src\asmjit\core\jitallocator.h" />
+    <ClInclude Include="..\src\3rd_party\fbgemm\third_party\asmjit\src\asmjit\core\jitruntime.h" />
+    <ClInclude Include="..\src\3rd_party\fbgemm\third_party\asmjit\src\asmjit\core\logging.h" />
+    <ClInclude Include="..\src\3rd_party\fbgemm\third_party\asmjit\src\asmjit\core\misc_p.h" />
+    <ClInclude Include="..\src\3rd_party\fbgemm\third_party\asmjit\src\asmjit\core\operand.h" />
+    <ClInclude Include="..\src\3rd_party\fbgemm\third_party\asmjit\src\asmjit\core\osutils.h" />
+    <ClInclude Include="..\src\3rd_party\fbgemm\third_party\asmjit\src\asmjit\core\raassignment_p.h" />
+    <ClInclude Include="..\src\3rd_party\fbgemm\third_party\asmjit\src\asmjit\core\rabuilders_p.h" />
+    <ClInclude Include="..\src\3rd_party\fbgemm\third_party\asmjit\src\asmjit\core\radefs_p.h" />
+    <ClInclude Include="..\src\3rd_party\fbgemm\third_party\asmjit\src\asmjit\core\ralocal_p.h" />
+    <ClInclude Include="..\src\3rd_party\fbgemm\third_party\asmjit\src\asmjit\core\rapass_p.h" />
+    <ClInclude Include="..\src\3rd_party\fbgemm\third_party\asmjit\src\asmjit\core\rastack_p.h" />
+    <ClInclude Include="..\src\3rd_party\fbgemm\third_party\asmjit\src\asmjit\core\string.h" />
+    <ClInclude Include="..\src\3rd_party\fbgemm\third_party\asmjit\src\asmjit\core\support.h" />
+    <ClInclude Include="..\src\3rd_party\fbgemm\third_party\asmjit\src\asmjit\core\target.h" />
+    <ClInclude Include="..\src\3rd_party\fbgemm\third_party\asmjit\src\asmjit\core\type.h" />
+    <ClInclude Include="..\src\3rd_party\fbgemm\third_party\asmjit\src\asmjit\core\virtmem.h" />
+    <ClInclude Include="..\src\3rd_party\fbgemm\third_party\asmjit\src\asmjit\core\zone.h" />
+    <ClInclude Include="..\src\3rd_party\fbgemm\third_party\asmjit\src\asmjit\core\zonehash.h" />
+    <ClInclude Include="..\src\3rd_party\fbgemm\third_party\asmjit\src\asmjit\core\zonelist.h" />
+    <ClInclude Include="..\src\3rd_party\fbgemm\third_party\asmjit\src\asmjit\core\zonestack.h" />
+    <ClInclude Include="..\src\3rd_party\fbgemm\third_party\asmjit\src\asmjit\core\zonestring.h" />
+    <ClInclude Include="..\src\3rd_party\fbgemm\third_party\asmjit\src\asmjit\core\zonetree.h" />
+    <ClInclude Include="..\src\3rd_party\fbgemm\third_party\asmjit\src\asmjit\core\zonevector.h" />
     <ClInclude Include="..\src\3rd_party\fbgemm\third_party\asmjit\src\asmjit\x86.h" />
     <ClInclude Include="..\src\3rd_party\fbgemm\third_party\asmjit\src\asmjit\x86\x86assembler.h" />
     <ClInclude Include="..\src\3rd_party\fbgemm\third_party\asmjit\src\asmjit\x86\x86builder.h" />
+    <ClInclude Include="..\src\3rd_party\fbgemm\third_party\asmjit\src\asmjit\x86\x86callconv_p.h" />
     <ClInclude Include="..\src\3rd_party\fbgemm\third_party\asmjit\src\asmjit\x86\x86compiler.h" />
     <ClInclude Include="..\src\3rd_party\fbgemm\third_party\asmjit\src\asmjit\x86\x86emitter.h" />
+    <ClInclude Include="..\src\3rd_party\fbgemm\third_party\asmjit\src\asmjit\x86\x86features.h" />
     <ClInclude Include="..\src\3rd_party\fbgemm\third_party\asmjit\src\asmjit\x86\x86globals.h" />
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-    <ClInclude Include="..\src\3rd_party\fbgemm\third_party\asmjit\src\asmjit\x86\x86instimpl_p.h" />
+    <ClInclude Include="..\src\3rd_party\fbgemm\third_party\asmjit\src\asmjit\x86\x86instapi_p.h" />
+    <ClInclude Include="..\src\3rd_party\fbgemm\third_party\asmjit\src\asmjit\x86\x86instdb.h" />
+    <ClInclude Include="..\src\3rd_party\fbgemm\third_party\asmjit\src\asmjit\x86\x86instdb_p.h" />
     <ClInclude Include="..\src\3rd_party\fbgemm\third_party\asmjit\src\asmjit\x86\x86internal_p.h" />
     <ClInclude Include="..\src\3rd_party\fbgemm\third_party\asmjit\src\asmjit\x86\x86logging_p.h" />
-    <ClInclude Include="..\src\3rd_party\fbgemm\third_party\asmjit\src\asmjit\x86\x86misc.h" />
+    <ClInclude Include="..\src\3rd_party\fbgemm\third_party\asmjit\src\asmjit\x86\x86opcode_p.h" />
     <ClInclude Include="..\src\3rd_party\fbgemm\third_party\asmjit\src\asmjit\x86\x86operand.h" />
-    <ClInclude Include="..\src\3rd_party\fbgemm\third_party\asmjit\src\asmjit\x86\x86regalloc_p.h" />
+    <ClInclude Include="..\src\3rd_party\fbgemm\third_party\asmjit\src\asmjit\x86\x86rapass_p.h" />
     <ClInclude Include="..\src\3rd_party\fbgemm\third_party\cpuinfo\deps\clog\include\clog.h" />
     <ClInclude Include="..\src\3rd_party\fbgemm\third_party\cpuinfo\include\cpuinfo-mock.h" />
     <ClInclude Include="..\src\3rd_party\fbgemm\third_party\cpuinfo\include\cpuinfo.h" />
@@ -868,6 +1106,7 @@
     <ClInclude Include="..\src\3rd_party\fbgemm\third_party\cpuinfo\src\x86\api.h" />
     <ClInclude Include="..\src\3rd_party\fbgemm\third_party\cpuinfo\src\x86\cpuid.h" />
     <ClInclude Include="..\src\3rd_party\fbgemm\third_party\cpuinfo\src\x86\windows\api.h" />
+    <ClInclude Include="..\src\3rd_party\fbgemm\third_party\googletest\googletest\src\gtest-internal-inl.h" />
     <ClInclude Include="..\src\3rd_party\half_float\umHalf.h" />
     <ClInclude Include="..\src\3rd_party\nccl\src\collectives\collectives.h">
       <ExcludedFromBuild Condition="'$(Configuration)|$(Platform)'=='Release|x64'">true</ExcludedFromBuild>
@@ -1142,13 +1381,10 @@
     <ClCompile Include="..\src\rescorer\score_collector.cpp" />
     <ClCompile Include="..\src\tensors\backend.cpp" />
     <ClCompile Include="..\src\tensors\cpu\device.cpp" />
+    <ClCompile Include="..\src\tensors\cpu\fbgemm\packed_gemm.cpp" />
     <ClCompile Include="..\src\tensors\cpu\prod.cpp" />
     <ClCompile Include="..\src\tensors\cpu\sharp\avx_gemm.cpp" />
     <ClCompile Include="..\src\tensors\cpu\sharp\int_gemm.cpp" />
-    <ClCompile Include="..\src\tensors\cpu\sharp\packed_gemm.cpp">
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-      <TreatWarningAsError Condition="'$(Configuration)|$(Platform)'=='Debug|x64'">false</TreatWarningAsError>
-    </ClCompile>
     <ClCompile Include="..\src\tensors\cpu\sharp\sse_gemm.cpp" />
     <ClCompile Include="..\src\tensors\cpu\tensor_operators.cpp" />
     <ClCompile Include="..\src\graph\expression_graph.cpp" />
@@ -1274,7 +1510,6 @@
     <ClInclude Include="..\src\examples\mnist\validator.h" />
     <ClInclude Include="..\src\functional\approx.h" />
     <ClInclude Include="..\src\functional\operators.h" />
-    <ClInclude Include="..\src\graph\expression_graph_packable.h" />
     <ClInclude Include="..\src\layers\loss.h" />
     <ClInclude Include="..\src\layers\weight.h" />
     <ClInclude Include="..\src\marian.h" />
@@ -1494,9 +1729,10 @@
     <ClInclude Include="..\src\rnn\types.h" />
     <ClInclude Include="..\src\tensors\allocator.h" />
     <ClInclude Include="..\src\tensors\backend.h" />
-    <ClInclude Include="..\src\tensors\cpu\expanded_gemm.h" />
+    <ClInclude Include="..\src\tensors\cpu\fbgemm\expanded_gemm.h" />
+    <ClInclude Include="..\src\tensors\cpu\fbgemm\expression_graph_packable.h" />
+    <ClInclude Include="..\src\tensors\cpu\fbgemm\packed_gemm.h" />
     <ClInclude Include="..\src\tensors\cpu\sharp\int_gemm.h" />
-    <ClInclude Include="..\src\tensors\cpu\sharp\packed_gemm.h" />
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     <ClInclude Include="..\src\tensors\dispatch.h" />
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diff --git a/vs/Marian.vcxproj.filters b/vs/Marian.vcxproj.filters
index 4b4c14af2..a4cbc827a 100755
--- a/vs/Marian.vcxproj.filters
+++ b/vs/Marian.vcxproj.filters
@@ -490,9 +490,6 @@
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       <Filter>3rd_party\fbgemm\src</Filter>
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@@ -616,19 +613,127 @@
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       <Filter>3rd_party\fbgemm\third_party\cpuinfo\src\x86\cacehe</Filter>
     </ClCompile>
+    <ClCompile Include="..\src\3rd_party\fbgemm\third_party\cpuinfo\deps\clog\src\clog.c">
+      <Filter>3rd_party\fbgemm\third_party\cpuinfo\deps\clog\src</Filter>
+    </ClCompile>
+    <ClCompile Include="..\src\common\aliases.cpp">
+      <Filter>common</Filter>
+    </ClCompile>
+    <ClCompile Include="..\src\common\filesystem.cpp">
+      <Filter>common</Filter>
+    </ClCompile>
+    <ClCompile Include="..\src\3rd_party\fbgemm\third_party\asmjit\src\asmjit\core\arch.cpp">
+      <Filter>3rd_party\fbgemm\third_party\asmjit\src\asmjit\core</Filter>
+    </ClCompile>
+    <ClCompile Include="..\src\3rd_party\fbgemm\third_party\asmjit\src\asmjit\core\assembler.cpp">
+      <Filter>3rd_party\fbgemm\third_party\asmjit\src\asmjit\core</Filter>
+    </ClCompile>
+    <ClCompile Include="..\src\3rd_party\fbgemm\third_party\asmjit\src\asmjit\core\builder.cpp">
+      <Filter>3rd_party\fbgemm\third_party\asmjit\src\asmjit\core</Filter>
+    </ClCompile>
+    <ClCompile Include="..\src\3rd_party\fbgemm\third_party\asmjit\src\asmjit\core\callconv.cpp">
+      <Filter>3rd_party\fbgemm\third_party\asmjit\src\asmjit\core</Filter>
+    </ClCompile>
+    <ClCompile Include="..\src\3rd_party\fbgemm\third_party\asmjit\src\asmjit\core\codeholder.cpp">
+      <Filter>3rd_party\fbgemm\third_party\asmjit\src\asmjit\core</Filter>
+    </ClCompile>
+    <ClCompile Include="..\src\3rd_party\fbgemm\third_party\asmjit\src\asmjit\core\compiler.cpp">
+      <Filter>3rd_party\fbgemm\third_party\asmjit\src\asmjit\core</Filter>
+    </ClCompile>
+    <ClCompile Include="..\src\3rd_party\fbgemm\third_party\asmjit\src\asmjit\core\constpool.cpp">
+      <Filter>3rd_party\fbgemm\third_party\asmjit\src\asmjit\core</Filter>
+    </ClCompile>
+    <ClCompile Include="..\src\3rd_party\fbgemm\third_party\asmjit\src\asmjit\core\cpuinfo.cpp">
+      <Filter>3rd_party\fbgemm\third_party\asmjit\src\asmjit\core</Filter>
+    </ClCompile>
+    <ClCompile Include="..\src\3rd_party\fbgemm\third_party\asmjit\src\asmjit\core\emitter.cpp">
+      <Filter>3rd_party\fbgemm\third_party\asmjit\src\asmjit\core</Filter>
+    </ClCompile>
+    <ClCompile Include="..\src\3rd_party\fbgemm\third_party\asmjit\src\asmjit\core\func.cpp">
+      <Filter>3rd_party\fbgemm\third_party\asmjit\src\asmjit\core</Filter>
+    </ClCompile>
+    <ClCompile Include="..\src\3rd_party\fbgemm\third_party\asmjit\src\asmjit\core\globals.cpp">
+      <Filter>3rd_party\fbgemm\third_party\asmjit\src\asmjit\core</Filter>
+    </ClCompile>
+    <ClCompile Include="..\src\3rd_party\fbgemm\third_party\asmjit\src\asmjit\core\inst.cpp">
+      <Filter>3rd_party\fbgemm\third_party\asmjit\src\asmjit\core</Filter>
+    </ClCompile>
+    <ClCompile Include="..\src\3rd_party\fbgemm\third_party\asmjit\src\asmjit\core\jitallocator.cpp">
+      <Filter>3rd_party\fbgemm\third_party\asmjit\src\asmjit\core</Filter>
+    </ClCompile>
+    <ClCompile Include="..\src\3rd_party\fbgemm\third_party\asmjit\src\asmjit\core\jitruntime.cpp">
+      <Filter>3rd_party\fbgemm\third_party\asmjit\src\asmjit\core</Filter>
+    </ClCompile>
+    <ClCompile Include="..\src\3rd_party\fbgemm\third_party\asmjit\src\asmjit\core\logging.cpp">
+      <Filter>3rd_party\fbgemm\third_party\asmjit\src\asmjit\core</Filter>
+    </ClCompile>
+    <ClCompile Include="..\src\3rd_party\fbgemm\third_party\asmjit\src\asmjit\core\operand.cpp">
+      <Filter>3rd_party\fbgemm\third_party\asmjit\src\asmjit\core</Filter>
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+    <ClInclude Include="..\src\3rd_party\fbgemm\third_party\asmjit\src\asmjit\core\globals.h">
+      <Filter>3rd_party\fbgemm\third_party\asmjit\src\asmjit\core</Filter>
+    </ClInclude>
+    <ClInclude Include="..\src\3rd_party\fbgemm\third_party\asmjit\src\asmjit\core\inst.h">
+      <Filter>3rd_party\fbgemm\third_party\asmjit\src\asmjit\core</Filter>
+    </ClInclude>
+    <ClInclude Include="..\src\3rd_party\fbgemm\third_party\asmjit\src\asmjit\core\jitallocator.h">
+      <Filter>3rd_party\fbgemm\third_party\asmjit\src\asmjit\core</Filter>
+    </ClInclude>
+    <ClInclude Include="..\src\3rd_party\fbgemm\third_party\asmjit\src\asmjit\core\jitruntime.h">
+      <Filter>3rd_party\fbgemm\third_party\asmjit\src\asmjit\core</Filter>
+    </ClInclude>
+    <ClInclude Include="..\src\3rd_party\fbgemm\third_party\asmjit\src\asmjit\core\logging.h">
+      <Filter>3rd_party\fbgemm\third_party\asmjit\src\asmjit\core</Filter>
+    </ClInclude>
+    <ClInclude Include="..\src\3rd_party\fbgemm\third_party\asmjit\src\asmjit\core\misc_p.h">
+      <Filter>3rd_party\fbgemm\third_party\asmjit\src\asmjit\core</Filter>
+    </ClInclude>
+    <ClInclude Include="..\src\3rd_party\fbgemm\third_party\asmjit\src\asmjit\core\operand.h">
+      <Filter>3rd_party\fbgemm\third_party\asmjit\src\asmjit\core</Filter>
+    </ClInclude>
+    <ClInclude Include="..\src\3rd_party\fbgemm\third_party\asmjit\src\asmjit\core\osutils.h">
+      <Filter>3rd_party\fbgemm\third_party\asmjit\src\asmjit\core</Filter>
+    </ClInclude>
+    <ClInclude Include="..\src\3rd_party\fbgemm\third_party\asmjit\src\asmjit\core\raassignment_p.h">
+      <Filter>3rd_party\fbgemm\third_party\asmjit\src\asmjit\core</Filter>
+    </ClInclude>
+    <ClInclude Include="..\src\3rd_party\fbgemm\third_party\asmjit\src\asmjit\core\rabuilders_p.h">
+      <Filter>3rd_party\fbgemm\third_party\asmjit\src\asmjit\core</Filter>
+    </ClInclude>
+    <ClInclude Include="..\src\3rd_party\fbgemm\third_party\asmjit\src\asmjit\core\radefs_p.h">
+      <Filter>3rd_party\fbgemm\third_party\asmjit\src\asmjit\core</Filter>
+    </ClInclude>
+    <ClInclude Include="..\src\3rd_party\fbgemm\third_party\asmjit\src\asmjit\core\ralocal_p.h">
+      <Filter>3rd_party\fbgemm\third_party\asmjit\src\asmjit\core</Filter>
+    </ClInclude>
+    <ClInclude Include="..\src\3rd_party\fbgemm\third_party\asmjit\src\asmjit\core\rapass_p.h">
+      <Filter>3rd_party\fbgemm\third_party\asmjit\src\asmjit\core</Filter>
+    </ClInclude>
+    <ClInclude Include="..\src\3rd_party\fbgemm\third_party\asmjit\src\asmjit\core\rastack_p.h">
+      <Filter>3rd_party\fbgemm\third_party\asmjit\src\asmjit\core</Filter>
+    </ClInclude>
+    <ClInclude Include="..\src\3rd_party\fbgemm\third_party\asmjit\src\asmjit\core\string.h">
+      <Filter>3rd_party\fbgemm\third_party\asmjit\src\asmjit\core</Filter>
+    </ClInclude>
+    <ClInclude Include="..\src\3rd_party\fbgemm\third_party\asmjit\src\asmjit\core\support.h">
+      <Filter>3rd_party\fbgemm\third_party\asmjit\src\asmjit\core</Filter>
+    </ClInclude>
+    <ClInclude Include="..\src\3rd_party\fbgemm\third_party\asmjit\src\asmjit\core\target.h">
+      <Filter>3rd_party\fbgemm\third_party\asmjit\src\asmjit\core</Filter>
+    </ClInclude>
+    <ClInclude Include="..\src\3rd_party\fbgemm\third_party\asmjit\src\asmjit\core\type.h">
+      <Filter>3rd_party\fbgemm\third_party\asmjit\src\asmjit\core</Filter>
+    </ClInclude>
+    <ClInclude Include="..\src\3rd_party\fbgemm\third_party\asmjit\src\asmjit\core\virtmem.h">
+      <Filter>3rd_party\fbgemm\third_party\asmjit\src\asmjit\core</Filter>
+    </ClInclude>
+    <ClInclude Include="..\src\3rd_party\fbgemm\third_party\asmjit\src\asmjit\core\zone.h">
+      <Filter>3rd_party\fbgemm\third_party\asmjit\src\asmjit\core</Filter>
+    </ClInclude>
+    <ClInclude Include="..\src\3rd_party\fbgemm\third_party\asmjit\src\asmjit\core\zonehash.h">
+      <Filter>3rd_party\fbgemm\third_party\asmjit\src\asmjit\core</Filter>
+    </ClInclude>
+    <ClInclude Include="..\src\3rd_party\fbgemm\third_party\asmjit\src\asmjit\core\zonelist.h">
+      <Filter>3rd_party\fbgemm\third_party\asmjit\src\asmjit\core</Filter>
+    </ClInclude>
+    <ClInclude Include="..\src\3rd_party\fbgemm\third_party\asmjit\src\asmjit\core\zonestack.h">
+      <Filter>3rd_party\fbgemm\third_party\asmjit\src\asmjit\core</Filter>
+    </ClInclude>
+    <ClInclude Include="..\src\3rd_party\fbgemm\third_party\asmjit\src\asmjit\core\zonestring.h">
+      <Filter>3rd_party\fbgemm\third_party\asmjit\src\asmjit\core</Filter>
+    </ClInclude>
+    <ClInclude Include="..\src\3rd_party\fbgemm\third_party\asmjit\src\asmjit\core\zonetree.h">
+      <Filter>3rd_party\fbgemm\third_party\asmjit\src\asmjit\core</Filter>
+    </ClInclude>
+    <ClInclude Include="..\src\3rd_party\fbgemm\third_party\asmjit\src\asmjit\core\zonevector.h">
+      <Filter>3rd_party\fbgemm\third_party\asmjit\src\asmjit\core</Filter>
     </ClInclude>
     <ClInclude Include="..\src\3rd_party\fbgemm\third_party\asmjit\src\asmjit\x86\x86assembler.h">
       <Filter>3rd_party\fbgemm\third_party\asmjit\src\asmjit\x86</Filter>
@@ -1930,19 +2176,28 @@
     <ClInclude Include="..\src\3rd_party\fbgemm\third_party\asmjit\src\asmjit\x86\x86builder.h">
       <Filter>3rd_party\fbgemm\third_party\asmjit\src\asmjit\x86</Filter>
     </ClInclude>
+    <ClInclude Include="..\src\3rd_party\fbgemm\third_party\asmjit\src\asmjit\x86\x86callconv_p.h">
+      <Filter>3rd_party\fbgemm\third_party\asmjit\src\asmjit\x86</Filter>
+    </ClInclude>
     <ClInclude Include="..\src\3rd_party\fbgemm\third_party\asmjit\src\asmjit\x86\x86compiler.h">
       <Filter>3rd_party\fbgemm\third_party\asmjit\src\asmjit\x86</Filter>
     </ClInclude>
     <ClInclude Include="..\src\3rd_party\fbgemm\third_party\asmjit\src\asmjit\x86\x86emitter.h">
       <Filter>3rd_party\fbgemm\third_party\asmjit\src\asmjit\x86</Filter>
     </ClInclude>
+    <ClInclude Include="..\src\3rd_party\fbgemm\third_party\asmjit\src\asmjit\x86\x86features.h">
+      <Filter>3rd_party\fbgemm\third_party\asmjit\src\asmjit\x86</Filter>
+    </ClInclude>
     <ClInclude Include="..\src\3rd_party\fbgemm\third_party\asmjit\src\asmjit\x86\x86globals.h">
       <Filter>3rd_party\fbgemm\third_party\asmjit\src\asmjit\x86</Filter>
     </ClInclude>
-    <ClInclude Include="..\src\3rd_party\fbgemm\third_party\asmjit\src\asmjit\x86\x86inst.h">
+    <ClInclude Include="..\src\3rd_party\fbgemm\third_party\asmjit\src\asmjit\x86\x86instapi_p.h">
       <Filter>3rd_party\fbgemm\third_party\asmjit\src\asmjit\x86</Filter>
     </ClInclude>
-    <ClInclude Include="..\src\3rd_party\fbgemm\third_party\asmjit\src\asmjit\x86\x86instimpl_p.h">
+    <ClInclude Include="..\src\3rd_party\fbgemm\third_party\asmjit\src\asmjit\x86\x86instdb.h">
+      <Filter>3rd_party\fbgemm\third_party\asmjit\src\asmjit\x86</Filter>
+    </ClInclude>
+    <ClInclude Include="..\src\3rd_party\fbgemm\third_party\asmjit\src\asmjit\x86\x86instdb_p.h">
       <Filter>3rd_party\fbgemm\third_party\asmjit\src\asmjit\x86</Filter>
     </ClInclude>
     <ClInclude Include="..\src\3rd_party\fbgemm\third_party\asmjit\src\asmjit\x86\x86internal_p.h">
@@ -1951,83 +2206,35 @@
     <ClInclude Include="..\src\3rd_party\fbgemm\third_party\asmjit\src\asmjit\x86\x86logging_p.h">
       <Filter>3rd_party\fbgemm\third_party\asmjit\src\asmjit\x86</Filter>
     </ClInclude>
-    <ClInclude Include="..\src\3rd_party\fbgemm\third_party\asmjit\src\asmjit\x86\x86misc.h">
+    <ClInclude Include="..\src\3rd_party\fbgemm\third_party\asmjit\src\asmjit\x86\x86opcode_p.h">
       <Filter>3rd_party\fbgemm\third_party\asmjit\src\asmjit\x86</Filter>
     </ClInclude>
     <ClInclude Include="..\src\3rd_party\fbgemm\third_party\asmjit\src\asmjit\x86\x86operand.h">
       <Filter>3rd_party\fbgemm\third_party\asmjit\src\asmjit\x86</Filter>
     </ClInclude>
-    <ClInclude Include="..\src\3rd_party\fbgemm\third_party\asmjit\src\asmjit\x86\x86regalloc_p.h">
+    <ClInclude Include="..\src\3rd_party\fbgemm\third_party\asmjit\src\asmjit\x86\x86rapass_p.h">
       <Filter>3rd_party\fbgemm\third_party\asmjit\src\asmjit\x86</Filter>
     </ClInclude>
-    <ClInclude Include="..\src\3rd_party\fbgemm\third_party\asmjit\src\asmjit\base\arch.h">
-      <Filter>3rd_party\fbgemm\third_party\asmjit\src\asmjit\base</Filter>
-    </ClInclude>
-    <ClInclude Include="..\src\3rd_party\fbgemm\third_party\asmjit\src\asmjit\base\assembler.h">
-      <Filter>3rd_party\fbgemm\third_party\asmjit\src\asmjit\base</Filter>
-    </ClInclude>
-    <ClInclude Include="..\src\3rd_party\fbgemm\third_party\asmjit\src\asmjit\base\codebuilder.h">
-      <Filter>3rd_party\fbgemm\third_party\asmjit\src\asmjit\base</Filter>
-    </ClInclude>
-    <ClInclude Include="..\src\3rd_party\fbgemm\third_party\asmjit\src\asmjit\base\codecompiler.h">
-      <Filter>3rd_party\fbgemm\third_party\asmjit\src\asmjit\base</Filter>
-    </ClInclude>
-    <ClInclude Include="..\src\3rd_party\fbgemm\third_party\asmjit\src\asmjit\base\codeemitter.h">
-      <Filter>3rd_party\fbgemm\third_party\asmjit\src\asmjit\base</Filter>
-    </ClInclude>
-    <ClInclude Include="..\src\3rd_party\fbgemm\third_party\asmjit\src\asmjit\base\codeholder.h">
-      <Filter>3rd_party\fbgemm\third_party\asmjit\src\asmjit\base</Filter>
-    </ClInclude>
-    <ClInclude Include="..\src\3rd_party\fbgemm\third_party\asmjit\src\asmjit\base\constpool.h">
-      <Filter>3rd_party\fbgemm\third_party\asmjit\src\asmjit\base</Filter>
-    </ClInclude>
-    <ClInclude Include="..\src\3rd_party\fbgemm\third_party\asmjit\src\asmjit\base\cpuinfo.h">
-      <Filter>3rd_party\fbgemm\third_party\asmjit\src\asmjit\base</Filter>
-    </ClInclude>
-    <ClInclude Include="..\src\3rd_party\fbgemm\third_party\asmjit\src\asmjit\base\func.h">
-      <Filter>3rd_party\fbgemm\third_party\asmjit\src\asmjit\base</Filter>
-    </ClInclude>
-    <ClInclude Include="..\src\3rd_party\fbgemm\third_party\asmjit\src\asmjit\base\globals.h">
-      <Filter>3rd_party\fbgemm\third_party\asmjit\src\asmjit\base</Filter>
-    </ClInclude>
-    <ClInclude Include="..\src\3rd_party\fbgemm\third_party\asmjit\src\asmjit\base\inst.h">
-      <Filter>3rd_party\fbgemm\third_party\asmjit\src\asmjit\base</Filter>
-    </ClInclude>
-    <ClInclude Include="..\src\3rd_party\fbgemm\third_party\asmjit\src\asmjit\base\logging.h">
-      <Filter>3rd_party\fbgemm\third_party\asmjit\src\asmjit\base</Filter>
-    </ClInclude>
-    <ClInclude Include="..\src\3rd_party\fbgemm\third_party\asmjit\src\asmjit\base\misc_p.h">
-      <Filter>3rd_party\fbgemm\third_party\asmjit\src\asmjit\base</Filter>
-    </ClInclude>
-    <ClInclude Include="..\src\3rd_party\fbgemm\third_party\asmjit\src\asmjit\base\operand.h">
-      <Filter>3rd_party\fbgemm\third_party\asmjit\src\asmjit\base</Filter>
-    </ClInclude>
-    <ClInclude Include="..\src\3rd_party\fbgemm\third_party\asmjit\src\asmjit\base\osutils.h">
-      <Filter>3rd_party\fbgemm\third_party\asmjit\src\asmjit\base</Filter>
-    </ClInclude>
-    <ClInclude Include="..\src\3rd_party\fbgemm\third_party\asmjit\src\asmjit\base\regalloc_p.h">
-      <Filter>3rd_party\fbgemm\third_party\asmjit\src\asmjit\base</Filter>
-    </ClInclude>
-    <ClInclude Include="..\src\3rd_party\fbgemm\third_party\asmjit\src\asmjit\base\runtime.h">
-      <Filter>3rd_party\fbgemm\third_party\asmjit\src\asmjit\base</Filter>
+    <ClInclude Include="..\src\3rd_party\fbgemm\src\CodeCache.h">
+      <Filter>3rd_party\fbgemm\src</Filter>
     </ClInclude>
-    <ClInclude Include="..\src\3rd_party\fbgemm\third_party\asmjit\src\asmjit\base\simdtypes.h">
-      <Filter>3rd_party\fbgemm\third_party\asmjit\src\asmjit\base</Filter>
+    <ClInclude Include="..\src\3rd_party\fbgemm\src\FbgemmI8DepthwiseAvx2-inl.h">
+      <Filter>3rd_party\fbgemm\src</Filter>
     </ClInclude>
-    <ClInclude Include="..\src\3rd_party\fbgemm\third_party\asmjit\src\asmjit\base\string.h">
-      <Filter>3rd_party\fbgemm\third_party\asmjit\src\asmjit\base</Filter>
+    <ClInclude Include="..\src\3rd_party\fbgemm\test\QuantizationHelpers.h">
+      <Filter>3rd_party\fbgemm\test</Filter>
     </ClInclude>
-    <ClInclude Include="..\src\3rd_party\fbgemm\third_party\asmjit\src\asmjit\base\utils.h">
-      <Filter>3rd_party\fbgemm\third_party\asmjit\src\asmjit\base</Filter>
+    <ClInclude Include="..\src\3rd_party\fbgemm\test\TestUtils.h">
+      <Filter>3rd_party\fbgemm\test</Filter>
     </ClInclude>
-    <ClInclude Include="..\src\3rd_party\fbgemm\third_party\asmjit\src\asmjit\base\vmem.h">
-      <Filter>3rd_party\fbgemm\third_party\asmjit\src\asmjit\base</Filter>
+    <ClInclude Include="..\src\3rd_party\fbgemm\bench\AlignedVec.h">
+      <Filter>3rd_party\fbgemm\bench</Filter>
     </ClInclude>
-    <ClInclude Include="..\src\3rd_party\fbgemm\third_party\asmjit\src\asmjit\base\zone.h">
-      <Filter>3rd_party\fbgemm\third_party\asmjit\src\asmjit\base</Filter>
+    <ClInclude Include="..\src\3rd_party\fbgemm\bench\BenchUtils.h">
+      <Filter>3rd_party\fbgemm\bench</Filter>
     </ClInclude>
-    <ClInclude Include="..\src\3rd_party\fbgemm\third_party\cpuinfo\deps\clog\include\clog.h">
-      <Filter>3rd_party\fbgemm\third_party\cpuinfo\deps\clog\include</Filter>
+    <ClInclude Include="..\src\3rd_party\fbgemm\third_party\googletest\googletest\src\gtest-internal-inl.h">
+      <Filter>3rd_party\fbgemm\third_party\googletest\googletest\src</Filter>
     </ClInclude>
     <ClInclude Include="..\src\3rd_party\half_float\umHalf.h">
       <Filter>3rd_party\half_float</Filter>
@@ -2053,15 +2260,24 @@
     <ClInclude Include="..\src\3rd_party\zstr\zstr.hpp">
       <Filter>3rd_party</Filter>
     </ClInclude>
-    <ClInclude Include="..\src\graph\expression_graph_packable.h">
-      <Filter>graph</Filter>
-    </ClInclude>
     <ClInclude Include="..\src\common\fastopt.h">
       <Filter>common</Filter>
     </ClInclude>
     <ClInclude Include="..\src\3rd_party\phf\phf.h">
       <Filter>3rd_party\phf</Filter>
     </ClInclude>
+    <ClInclude Include="..\src\3rd_party\fbgemm\third_party\asmjit\src\asmjit\core.h">
+      <Filter>3rd_party\fbgemm\third_party\asmjit\src\asmjit</Filter>
+    </ClInclude>
+    <ClInclude Include="..\src\tensors\cpu\fbgemm\expanded_gemm.h">
+      <Filter>tensors\cpu\fbgemm</Filter>
+    </ClInclude>
+    <ClInclude Include="..\src\tensors\cpu\fbgemm\expression_graph_packable.h">
+      <Filter>tensors\cpu\fbgemm</Filter>
+    </ClInclude>
+    <ClInclude Include="..\src\tensors\cpu\fbgemm\packed_gemm.h">
+      <Filter>tensors\cpu\fbgemm</Filter>
+    </ClInclude>
   </ItemGroup>
   <ItemGroup>
     <Filter Include="3rd_party">
@@ -2277,9 +2493,6 @@
     <Filter Include="3rd_party\fbgemm\third_party\asmjit\src\asmjit\x86">
       <UniqueIdentifier>{5818c959-7963-4d8e-9e87-b61f340476c2}</UniqueIdentifier>
     </Filter>
-    <Filter Include="3rd_party\fbgemm\third_party\asmjit\src\asmjit\base">
-      <UniqueIdentifier>{15414ec0-8761-4068-afef-822b7bed88df}</UniqueIdentifier>
-    </Filter>
     <Filter Include="3rd_party\fbgemm\third_party\cpuinfo\deps">
       <UniqueIdentifier>{d4505c8d-5e6e-4baf-8525-dc59ae8b6415}</UniqueIdentifier>
     </Filter>
@@ -2292,12 +2505,33 @@
     <Filter Include="3rd_party\fbgemm\third_party\cpuinfo\deps\clog\src">
       <UniqueIdentifier>{8fd74b1e-d3c1-4158-ad46-4a447222934e}</UniqueIdentifier>
     </Filter>
+    <Filter Include="3rd_party\fbgemm\third_party\asmjit\src\asmjit\core">
+      <UniqueIdentifier>{b3b34c5f-5b98-436a-b34c-11e2dccb7ea2}</UniqueIdentifier>
+    </Filter>
+    <Filter Include="3rd_party\fbgemm\test">
+      <UniqueIdentifier>{40576dca-07d5-4904-8119-ffbc982451a3}</UniqueIdentifier>
+    </Filter>
+    <Filter Include="3rd_party\fbgemm\bench">
+      <UniqueIdentifier>{9f11c8f1-78f7-47c6-9eac-34cd2c6cd909}</UniqueIdentifier>
+    </Filter>
+    <Filter Include="3rd_party\fbgemm\third_party\googletest">
+      <UniqueIdentifier>{75f9df88-0eb1-4d9a-858e-4e0b8fc3aa8a}</UniqueIdentifier>
+    </Filter>
+    <Filter Include="3rd_party\fbgemm\third_party\googletest\googletest">
+      <UniqueIdentifier>{9f77e916-1d2f-4c15-9eba-46bcbddd2658}</UniqueIdentifier>
+    </Filter>
+    <Filter Include="3rd_party\fbgemm\third_party\googletest\googletest\src">
+      <UniqueIdentifier>{050ba410-c56a-4607-8401-935f58f598b5}</UniqueIdentifier>
+    </Filter>
     <Filter Include="3rd_party\half_float">
       <UniqueIdentifier>{defd3aec-3c56-4d70-a4bb-90ba9003d98d}</UniqueIdentifier>
     </Filter>
     <Filter Include="3rd_party\phf">
       <UniqueIdentifier>{352ac0e9-daed-437a-bc36-fb85ecd037eb}</UniqueIdentifier>
     </Filter>
+    <Filter Include="tensors\cpu\fbgemm">
+      <UniqueIdentifier>{bf361868-f451-45b8-9695-570d67924972}</UniqueIdentifier>
+    </Filter>
   </ItemGroup>
   <ItemGroup>
     <None Include="..\src\3rd_party\nccl\src\bootstrap.cu">

From 183d0b8f65d1c2b6805f29e5404a7a50050d445b Mon Sep 17 00:00:00 2001
From: Young Jin Kim <youki@microsoft.com>
Date: Tue, 3 Dec 2019 11:56:58 -0800
Subject: [PATCH 790/838] Update fbgemm submodule to the master branch

---
 src/3rd_party/fbgemm | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/3rd_party/fbgemm b/src/3rd_party/fbgemm
index 501f92e53..84e66a976 160000
--- a/src/3rd_party/fbgemm
+++ b/src/3rd_party/fbgemm
@@ -1 +1 @@
-Subproject commit 501f92e531378154daad708cd9aeb649637b4696
+Subproject commit 84e66a976046180187724aff60a236c5378fde7c

From 67e9bc4957f257bd9fdd0bd1ea3158223823c442 Mon Sep 17 00:00:00 2001
From: Young Jin Kim <youki@microsoft.com>
Date: Tue, 3 Dec 2019 12:14:32 -0800
Subject: [PATCH 791/838] Fix windows build warning

---
 src/tensors/tensor.cpp | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/src/tensors/tensor.cpp b/src/tensors/tensor.cpp
index 7fa441f5a..6133732de 100755
--- a/src/tensors/tensor.cpp
+++ b/src/tensors/tensor.cpp
@@ -32,8 +32,8 @@ std::string TensorBase::debug(int precision, int dispCols) {
   double minv = std::numeric_limits<double>::max();
   double l2Sum = 0.0;
   for(int i = 0; i < values.size(); ++i) {
-    if((double)values[i] > maxv) maxv = values[i];
-    if((double)values[i] < minv) minv = values[i];
+    if((double)values[i] > maxv) maxv = (double)values[i];
+    if((double)values[i] < minv) minv = (double)values[i];
     l2Sum += (double)values[i] * (double)values[i];
   }
   strm << "min: " << minv << " max: " << maxv << " l2-norm: " << sqrt(l2Sum) << std::endl;

From 82da7d5219c768128b880f298e13e01b7e5fa399 Mon Sep 17 00:00:00 2001
From: Marcin Junczys-Dowmunt <Marcin.JunczysDowmunt@microsoft.com>
Date: Tue, 3 Dec 2019 20:32:50 -0800
Subject: [PATCH 792/838] do not warn for a number of warnings for cpp files
 from src/3rd_party/..

---
 CHANGELOG.md                 | 1 +
 VERSION                      | 2 +-
 src/3rd_party/CMakeLists.txt | 5 +++++
 3 files changed, 7 insertions(+), 1 deletion(-)

diff --git a/CHANGELOG.md b/CHANGELOG.md
index ee01d320e..b919a520c 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -8,6 +8,7 @@ and this project adheres to [Semantic Versioning](http://semver.org/spec/v2.0.0.
 ## [Unreleased]
 
 ### Added
+- Support for 8-bit matrix multiplication with FBGEMM
 - CMakeLists.txt now looks for SSE 4.2
 - Purging of finished hypotheses during beam-search. A lot faster for large batches.
 - Faster option look-up, up to 20-30% faster translation
diff --git a/VERSION b/VERSION
index 9d42e2415..dd78d2c09 100644
--- a/VERSION
+++ b/VERSION
@@ -1 +1 @@
-v1.8.27
+v1.8.28
diff --git a/src/3rd_party/CMakeLists.txt b/src/3rd_party/CMakeLists.txt
index c53239154..cc9da3fb8 100644
--- a/src/3rd_party/CMakeLists.txt
+++ b/src/3rd_party/CMakeLists.txt
@@ -13,6 +13,11 @@ if(USE_FBGEMM)
     set(CMAKE_SUPPRESS_DEVELOPER_WARNINGS 1 CACHE INTERNAL "No dev warnings")
   endif()
 
+  if(NOT MSVC)
+    # only locally disabled for the 3rd_party folder
+    set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -Wno-unused-value -Wno-unused-parameter -Wno-unused-variable -Wno-unused-function")
+  endif()
+
   set(FBGEMM_BUILD_TESTS OFF CACHE BOOL "Disable fbgemm tests")
   set(FBGEMM_BUILD_BENCHMARKS OFF CACHE BOOL "Disable fbgemm benchmark")
   add_subdirectory(./fbgemm)

From 4b4e6b5412344e0431a506ab2c089fe6898eb7d3 Mon Sep 17 00:00:00 2001
From: Martin Junczys-Dowmunt <Marcin.JunczysDowmunt@microsoft.com>
Date: Thu, 5 Dec 2019 00:33:15 +0000
Subject: [PATCH 793/838] Merged PR 10736: Unify options and type names

Make sure command line options and parameter names match, also use parameters instead of strings for passing gemmType for packing.
---
 README.md                                      |  1 -
 src/command/marian_conv.cpp                    | 18 +++++++++++++++---
 src/microsoft/quicksand.cpp                    |  8 ++++----
 src/tensors/cpu/fbgemm/expanded_gemm.h         |  4 ++--
 .../cpu/fbgemm/expression_graph_packable.h     | 12 +++++-------
 5 files changed, 26 insertions(+), 17 deletions(-)

diff --git a/README.md b/README.md
index 150f2030d..17a33728a 100644
--- a/README.md
+++ b/README.md
@@ -1,7 +1,6 @@
 Marian
 ======
 
-[![Build Status CUDA 8](https://img.shields.io/jenkins/s/http/vali.inf.ed.ac.uk/jenkins/view/marian/job/marian-dev-cuda-8.0.svg?label=CUDA%208)](http://vali.inf.ed.ac.uk/jenkins/job/marian-dev-cuda-8.0/)
 [![Build Status CUDA 9](https://img.shields.io/jenkins/s/http/vali.inf.ed.ac.uk/jenkins/view/marian/job/marian-dev-cuda-9.2.svg?label=CUDA%209)](http://vali.inf.ed.ac.uk/jenkins/job/marian-dev-cuda-9.2/)
 [![Build Status CUDA 10](https://img.shields.io/jenkins/s/http/vali.inf.ed.ac.uk/jenkins/view/marian/job/marian-dev-cuda-10.1.svg?label=CUDA%2010)](http://vali.inf.ed.ac.uk/jenkins/job/marian-dev-cuda-10.1/)
 [![Build Status CPU](https://img.shields.io/jenkins/s/http/vali.inf.ed.ac.uk/jenkins/view/marian/job/marian-dev-cpu.svg?label=CPU)](http://vali.inf.ed.ac.uk/jenkins/job/marian-dev-cpu/)
diff --git a/src/command/marian_conv.cpp b/src/command/marian_conv.cpp
index e1458bfdd..07681b144 100644
--- a/src/command/marian_conv.cpp
+++ b/src/command/marian_conv.cpp
@@ -19,16 +19,28 @@ int main(int argc, char** argv) {
         "Convert a model in the .npz format and normal memory layout to a mmap-able binary model which could be in normal memory layout or packed memory layout",
         "Allowed options",
         "Examples:\n"
-        "  ./marian-conv -f model.npz -t model.bin --gemm-type fp16packed");
+        "  ./marian-conv -f model.npz -t model.bin --gemm-type packed16");
     cli->add<std::string>("--from,-f", "Input model", "model.npz");
     cli->add<std::string>("--to,-t", "Output model", "model.bin");
-    cli->add<std::string>("--gemm-type,-g", "GEMM Type to be used with this weights - float16, float32, int8packed, fp16packed", "float32");
+    cli->add<std::string>("--gemm-type,-g", "GEMM Type to be used with this weights - float32, packed16, packed8", "float32");
     cli->parse(argc, argv);
     options->merge(config);
   }
   auto modelFrom = options->get<std::string>("from");
   auto modelTo = options->get<std::string>("to");
-  auto saveGemmType = options->get<std::string>("gemm-type");
+  
+  auto saveGemmTypeStr = options->get<std::string>("gemm-type");
+  Type saveGemmType;
+  if(saveGemmTypeStr == "float32") {
+    saveGemmType = Type::float32;
+  } else if(saveGemmTypeStr == "packed16") {
+    saveGemmType = Type::packed16;
+  } else if(saveGemmTypeStr == "packed8") {
+    saveGemmType = Type::packed8;
+  } else {
+    ABORT("Unknown gemm-type: {}", saveGemmTypeStr);
+  }
+
 
   LOG(info, "Outputting {}", modelTo);
 
diff --git a/src/microsoft/quicksand.cpp b/src/microsoft/quicksand.cpp
index a734a46ea..d174e6cb7 100755
--- a/src/microsoft/quicksand.cpp
+++ b/src/microsoft/quicksand.cpp
@@ -258,14 +258,14 @@ bool convertModel(std::string inputFile, std::string outputFile, int32_t targetP
 
   graph->load(inputFile);
   graph->forward();
-  std::string saveGemmType = "fp32default";
+  auto saveGemmType = Type::float32;
   if (targetPrec == 16)
-    saveGemmType = "fp16packed";
+    saveGemmType = Type::packed16;
   else if (targetPrec == 8)
-    saveGemmType = "int8packed";
+    saveGemmType = Type::packed8;
 
   // added a flag if the weights needs to be packed or not
-  graph->packAndSave(outputFile, configStr.str(), saveGemmType); // @TODO: this should just be type-based
+  graph->packAndSave(outputFile, configStr.str(), saveGemmType);
 
   std::cout << "Conversion Finished." << std::endl;
 
diff --git a/src/tensors/cpu/fbgemm/expanded_gemm.h b/src/tensors/cpu/fbgemm/expanded_gemm.h
index 0dd2ae8b2..a5c9ed06f 100644
--- a/src/tensors/cpu/fbgemm/expanded_gemm.h
+++ b/src/tensors/cpu/fbgemm/expanded_gemm.h
@@ -260,7 +260,7 @@ class FbgemmPacked16AffineNodeOp : public NaryNodeOp {
     return {NodeOp(0)};
   }
 
-  const std::string type() override { return "fp16packed"; }
+  const std::string type() override { return "gemmPacked16"; }
 };
 
 // Affine transform (matrix multiplication) using packed B matrix
@@ -353,7 +353,7 @@ class FbgemmPacked8AffineNodeOp : public NaryNodeOp {
     return {NodeOp(0)};
   }
 
-  const std::string type() override { return "int8packed"; }
+  const std::string type() override { return "gemmPacked8"; }
 };
 
 static inline Expr affine(Expr a, Expr b, Shape bShape, Expr c, bool transA, bool transB, float scalar) {
diff --git a/src/tensors/cpu/fbgemm/expression_graph_packable.h b/src/tensors/cpu/fbgemm/expression_graph_packable.h
index 2214f071a..b5a1c3899 100644
--- a/src/tensors/cpu/fbgemm/expression_graph_packable.h
+++ b/src/tensors/cpu/fbgemm/expression_graph_packable.h
@@ -20,7 +20,7 @@ class ExpressionGraphPackable : public ExpressionGraph {
 
   // Convert model weights into packed format and save to IO items.
   // @TODO: review this
-  void packAndSave(const std::string& name, const std::string& meta, std::string& saveGemmType, Type saveElementType = Type::float32) {
+  void packAndSave(const std::string& name, const std::string& meta, Type gemmElementType = Type::float32, Type saveElementType = Type::float32) {
     std::vector<io::Item> ioItems;
 
     // sorted by name in std::map
@@ -37,8 +37,7 @@ class ExpressionGraphPackable : public ExpressionGraph {
 #if USE_FBGEMM
       // save as packed format
       // @TODO Hardcoded to find packable weights - all the weights used for affine op (fp16), all the weights used for affine op and dot op (int8)
-      if (saveGemmType == "int8packed" && (pName.find("_W") == pName.length() - 3 || pName.find("_W") == pName.length() - 2))
-      {
+      if (gemmElementType == Type::packed8 && (pName.find("_W") == pName.length() - 3 || pName.find("_W") == pName.length() - 2)) {
         using namespace marian::cpu::variant;
 
         // packing information - size
@@ -68,7 +67,7 @@ class ExpressionGraphPackable : public ExpressionGraph {
         io::Item item;
         item.name = pName;
         item.shape = val->shape();
-        item.type = Type::packed8;
+        item.type = gemmElementType;
 
         // Use the actual memory as this will be aligned and padded.
         // When memory mapping this is required. Shape keeps track of
@@ -79,8 +78,7 @@ class ExpressionGraphPackable : public ExpressionGraph {
 
         ioItems.emplace_back(std::move(item));
 
-      } else if (saveGemmType == "fp16packed" && pName.find("_W") == pName.length() - 3)
-      {
+      } else if (gemmElementType == Type::packed16 && pName.find("_W") == pName.length() - 3) {
         using namespace marian::cpu::variant;
 
         // packing information
@@ -120,7 +118,7 @@ class ExpressionGraphPackable : public ExpressionGraph {
         io::Item item;
         item.name = pName;
         item.shape = val->shape();
-        item.type = Type::packed16;
+        item.type = gemmElementType;
 
         // Use the actual memory as this will be aligned and padded.
         // When memory mapping this is required. Shape keeps track of

From a6d0af024c5a6a25d7ea9ad246e2990f58970c4c Mon Sep 17 00:00:00 2001
From: Marcin Junczys-Dowmunt <Marcin.JunczysDowmunt@microsoft.com>
Date: Wed, 4 Dec 2019 16:34:51 -0800
Subject: [PATCH 794/838] bump version

---
 VERSION | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/VERSION b/VERSION
index dd78d2c09..f27bcfb3e 100644
--- a/VERSION
+++ b/VERSION
@@ -1 +1 @@
-v1.8.28
+v1.8.29

From 34e99da49b9bfe72cc1e56f9d57b42615a5f6621 Mon Sep 17 00:00:00 2001
From: Nikolay Bogoychev <nheart@gmail.com>
Date: Thu, 5 Dec 2019 16:12:17 +0000
Subject: [PATCH 795/838] =?UTF-8?q?Fix=20compilation=20on=20CPUs=20that=20?=
 =?UTF-8?q?don't=20support=20AVX=20and=20some=20white=20space=20i=E2=80=A6?=
 =?UTF-8?q?=20(#561)?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

* Fix compilation on CPUs that don't support AVX and some white space issues
* Change from ifdef to ifndef
---
 CMakeLists.txt                       |  2 ++
 src/common/types.h                   | 10 ++++++++--
 src/functional/operators.h           |  6 +++---
 src/functional/tensor.h              | 10 +++++-----
 src/tensors/cpu/element.h            |  2 ++
 src/tensors/cpu/tensor_operators.cpp | 12 ++++++++++--
 6 files changed, 30 insertions(+), 12 deletions(-)

diff --git a/CMakeLists.txt b/CMakeLists.txt
index ff5def0ec..8df006e12 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -117,6 +117,8 @@ else(MSVC)
       message(STATUS "AVX support found")
       set(INTRINSICS "${INTRINSICS} -mavx")
       list(APPEND INTRINSICS_NVCC -Xcompiler\ -mavx)
+    else()
+      add_definitions(-DNO_AVX)
     endif(AVX_FOUND)
     if(AVX2_FOUND)
       message(STATUS "AVX2 support found")
diff --git a/src/common/types.h b/src/common/types.h
index 28f0378dd..aaac97aca 100644
--- a/src/common/types.h
+++ b/src/common/types.h
@@ -31,7 +31,7 @@
 #include <cuda.h> // required to see CUDA_VERSION
 #if (CUDA_VERSION > 9000)
 #define COMPILE_FP16 1
-#else 
+#else
 #define COMPILE_FP16 0
 #endif
 #else
@@ -174,6 +174,7 @@ struct float32x4 {
 };
 
 // @TODO: consider how code can be shared via templating
+#ifndef NO_AVX
 struct float32x8 {
 private:
   __m256 f_;
@@ -199,6 +200,11 @@ struct float32x8 {
     return out;
   }
 };
+#else
+//Dummy version to get things to compile on older CPUs
+struct float32x8 {
+};
+#endif
 #endif
 
 // Internal to types.h, don't use. Use test functions below.
@@ -207,7 +213,7 @@ enum class TypeClass : size_t {
   unsigned_type = 0x200,
   float_type    = 0x400,
   packed_type   = 0x800, // special packed (CPU cache friendly) type class, used in FBGEMM, not meant to be used anywhere else
-            
+
   size_mask     = 0x0FF
 };
 
diff --git a/src/functional/operators.h b/src/functional/operators.h
index 08d8af38c..289f7121c 100755
--- a/src/functional/operators.h
+++ b/src/functional/operators.h
@@ -319,7 +319,7 @@ struct Ops<float32x4> {
 
 } // end namespace functional
 } // end namespace marian
-
+#ifndef NO_AVX
 #include "3rd_party/avx_mathfun.h"
 
 namespace marian {
@@ -438,7 +438,7 @@ struct Ops<float32x8> {
 
 } // end namespace functional
 } // end namespace marian
-
+#endif
 #endif // of "#ifndef __CUDACC__"
 
 #ifdef __CUDACC__
@@ -600,4 +600,4 @@ BINARY(sPReLU,     PReLU,     Ops<ElementType>::prelu(x, y));
 BINARY(sPReLUBack, PReLUback, Ops<ElementType>::preluBack(x, y));
 
 } // end namespace functional
-} // end namespace marian
\ No newline at end of file
+} // end namespace marian
diff --git a/src/functional/tensor.h b/src/functional/tensor.h
index 3948e8e9e..41bc064a8 100755
--- a/src/functional/tensor.h
+++ b/src/functional/tensor.h
@@ -7,8 +7,8 @@
 namespace marian {
 namespace functional {
 
-// By default for single valued types like float do nothing. Usually the number of elements in a tensor 
-// is correctly mirrored in the shape object. Only special multi-element types like float32x4 (4 floats), 
+// By default for single valued types like float do nothing. Usually the number of elements in a tensor
+// is correctly mirrored in the shape object. Only special multi-element types like float32x4 (4 floats),
 // float32x8 (8 floats) and half2 (2 half) require special handling done by specializations below.
 // Similar for multi-element integer types to be added later.
 template <typename T>
@@ -31,7 +31,7 @@ inline marian::Shape adapt<float32x4>(const marian::Shape& shape) {
   x4Shape.set(-1, shape[-1] / 4);
   return x4Shape;
 }
-
+#ifndef NO_AVX
 template <>
 inline marian::Shape adapt<float32x8>(const marian::Shape& shape) {
   ABORT_IF(shape[-1] % 8 != 0,
@@ -42,7 +42,7 @@ inline marian::Shape adapt<float32x8>(const marian::Shape& shape) {
   x8Shape.set(-1, shape[-1] / 8);
   return x8Shape;
 }
-
+#endif
 #endif
 
 template <typename T, const int D>
@@ -211,4 +211,4 @@ template <typename T>
 using Tensor = View<T, CONST_SHAPE_DIMS>;
 
 }  // namespace functional
-}  // namespace marian
\ No newline at end of file
+}  // namespace marian
diff --git a/src/tensors/cpu/element.h b/src/tensors/cpu/element.h
index c145ae825..81f2779a2 100644
--- a/src/tensors/cpu/element.h
+++ b/src/tensors/cpu/element.h
@@ -99,8 +99,10 @@ void elementFloat(const Functor& functor, marian::Tensor out, Tensors... tensors
 
   if(div8) {
     // std::cerr << "8: " << functor.to_string() << std::endl;
+#ifndef NO_AVX
     element<float32x8>(functor, out, tensors...);
     return;
+#endif
   }
 
   if(div4) {
diff --git a/src/tensors/cpu/tensor_operators.cpp b/src/tensors/cpu/tensor_operators.cpp
index f56d843b4..40f504a44 100755
--- a/src/tensors/cpu/tensor_operators.cpp
+++ b/src/tensors/cpu/tensor_operators.cpp
@@ -425,9 +425,13 @@ void Softmax(Tensor out, Tensor in) {
   matchOrAbort<float>(out->type());
   matchOrAbort<float>(in->type());
 
+#ifndef NO_AVX
   if(out->shape()[-1] % 8 == 0) {
     Softmax<float32x8>(out, in);
-  } else if(out->shape()[-1] % 4 == 0) {
+    return;
+  }
+#endif
+  if(out->shape()[-1] % 4 == 0) {
     Softmax<float32x4>(out, in);
   } else {
     Softmax<float>(out, in);
@@ -477,9 +481,13 @@ void LogSoftmax(Tensor out, Tensor in) {
   matchOrAbort<float>(out->type());
   matchOrAbort<float>(in->type());
 
+#ifndef NO_AVX
   if(out->shape()[-1] % 8 == 0) {
     LogSoftmax<float32x8>(out, in);
-  } else if(out->shape()[-1] % 4 == 0) {
+    return;
+  }
+#endif
+  if(out->shape()[-1] % 4 == 0) {
     LogSoftmax<float32x4>(out, in);
   } else {
     LogSoftmax<float>(out, in);

From ac20f77287de5857cbaf5a8d5c0b525035c42c54 Mon Sep 17 00:00:00 2001
From: Marcin Junczys-Dowmunt <Marcin.JunczysDowmunt@microsoft.com>
Date: Thu, 5 Dec 2019 10:30:27 -0800
Subject: [PATCH 796/838] abort when trying to use packed8 or packed16 without
 FBGEMM compiled into Marian

---
 src/command/marian_conv.cpp                       |  5 ++---
 .../cpu/fbgemm/expression_graph_packable.h        | 15 +++++++++------
 2 files changed, 11 insertions(+), 9 deletions(-)

diff --git a/src/command/marian_conv.cpp b/src/command/marian_conv.cpp
index 07681b144..42eff924d 100644
--- a/src/command/marian_conv.cpp
+++ b/src/command/marian_conv.cpp
@@ -22,14 +22,14 @@ int main(int argc, char** argv) {
         "  ./marian-conv -f model.npz -t model.bin --gemm-type packed16");
     cli->add<std::string>("--from,-f", "Input model", "model.npz");
     cli->add<std::string>("--to,-t", "Output model", "model.bin");
-    cli->add<std::string>("--gemm-type,-g", "GEMM Type to be used with this weights - float32, packed16, packed8", "float32");
+    cli->add<std::string>("--gemm-type,-g", "GEMM Type to be used: float32, packed16, packed8", "float32");
     cli->parse(argc, argv);
     options->merge(config);
   }
   auto modelFrom = options->get<std::string>("from");
   auto modelTo = options->get<std::string>("to");
   
-  auto saveGemmTypeStr = options->get<std::string>("gemm-type");
+  auto saveGemmTypeStr = options->get<std::string>("gemm-type", "float32");
   Type saveGemmType;
   if(saveGemmTypeStr == "float32") {
     saveGemmType = Type::float32;
@@ -41,7 +41,6 @@ int main(int argc, char** argv) {
     ABORT("Unknown gemm-type: {}", saveGemmTypeStr);
   }
 
-
   LOG(info, "Outputting {}", modelTo);
 
   YAML::Node config;
diff --git a/src/tensors/cpu/fbgemm/expression_graph_packable.h b/src/tensors/cpu/fbgemm/expression_graph_packable.h
index b5a1c3899..62bad1072 100644
--- a/src/tensors/cpu/fbgemm/expression_graph_packable.h
+++ b/src/tensors/cpu/fbgemm/expression_graph_packable.h
@@ -34,12 +34,11 @@ class ExpressionGraphPackable : public ExpressionGraph {
 
       Tensor val = p.second->val();
 
-#if USE_FBGEMM
       // save as packed format
       // @TODO Hardcoded to find packable weights - all the weights used for affine op (fp16), all the weights used for affine op and dot op (int8)
       if (gemmElementType == Type::packed8 && (pName.find("_W") == pName.length() - 3 || pName.find("_W") == pName.length() - 2)) {
+  #if USE_FBGEMM
         using namespace marian::cpu::variant;
-
         // packing information - size
         int nrow;
         int ncol;
@@ -77,8 +76,11 @@ class ExpressionGraphPackable : public ExpressionGraph {
         copy(backend_, mem->data<char>(), mem->data<char>() + mem->size(), item.bytes.data());
 
         ioItems.emplace_back(std::move(item));
-
+#else
+        ABORT("Packed type {} only supported when compiled with -DUSE_FBGEMM=on", gemmElementType);
+#endif
       } else if (gemmElementType == Type::packed16 && pName.find("_W") == pName.length() - 3) {
+#if USE_FBGEMM
         using namespace marian::cpu::variant;
 
         // packing information
@@ -128,9 +130,10 @@ class ExpressionGraphPackable : public ExpressionGraph {
         copy(backend_, mem->data<char>(), mem->data<char>() + mem->size(), item.bytes.data());
 
         ioItems.emplace_back(std::move(item));
-      } else
-#endif  // USE_FBGEMM
-      {
+#else
+        ABORT("Packed type {} only supported when compiled with -DUSE_FBGEMM=on", gemmElementType);
+#endif
+      } else {
         io::Item item;
         val->get(item, pName);
         item.convert(saveElementType);

From 6224cb16e285931c88e1421f09d43c6e24d0c3a0 Mon Sep 17 00:00:00 2001
From: Marcin Junczys-Dowmunt <Marcin.JunczysDowmunt@microsoft.com>
Date: Thu, 5 Dec 2019 10:44:57 -0800
Subject: [PATCH 797/838] bump patch

---
 CHANGELOG.md | 1 +
 VERSION      | 2 +-
 2 files changed, 2 insertions(+), 1 deletion(-)

diff --git a/CHANGELOG.md b/CHANGELOG.md
index b919a520c..fb82c8305 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -26,6 +26,7 @@ and this project adheres to [Semantic Versioning](http://semver.org/spec/v2.0.0.
 - Gradient-checkpointing
 
 ### Fixed
+- Fixed compilation on CPUs without support for AVX
 - FastOpt now reads "n" and "y" values as strings, not as boolean values
 - Fixed multiple reduction kernels on GPU
 - Fixed guided-alignment training with cross-entropy
diff --git a/VERSION b/VERSION
index f27bcfb3e..899e8c16d 100644
--- a/VERSION
+++ b/VERSION
@@ -1 +1 @@
-v1.8.29
+v1.8.30

From 734a8791dd10b24ed0924f9efde8fb0ac503f336 Mon Sep 17 00:00:00 2001
From: Kenneth Heafield <kpu@users.noreply.github.com>
Date: Thu, 5 Dec 2019 22:17:54 +0000
Subject: [PATCH 798/838] Use __AVX__ compiler define instead of custom and
 broken NO_AVX flag.  Fixes #559 (#564)

* Change to __AVX__ macro defined by compiler
* Source files should not be executable
---
 CMakeLists.txt                       | 2 --
 src/3rd_party/avx_mathfun.h          | 0
 src/3rd_party/sse_mathfun.h          | 0
 src/common/types.h                   | 2 +-
 src/functional/operators.h           | 2 +-
 src/functional/tensor.h              | 2 +-
 src/tensors/cpu/element.h            | 2 +-
 src/tensors/cpu/tensor_operators.cpp | 4 ++--
 8 files changed, 6 insertions(+), 8 deletions(-)
 mode change 100755 => 100644 src/3rd_party/avx_mathfun.h
 mode change 100755 => 100644 src/3rd_party/sse_mathfun.h

diff --git a/CMakeLists.txt b/CMakeLists.txt
index 8df006e12..ff5def0ec 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -117,8 +117,6 @@ else(MSVC)
       message(STATUS "AVX support found")
       set(INTRINSICS "${INTRINSICS} -mavx")
       list(APPEND INTRINSICS_NVCC -Xcompiler\ -mavx)
-    else()
-      add_definitions(-DNO_AVX)
     endif(AVX_FOUND)
     if(AVX2_FOUND)
       message(STATUS "AVX2 support found")
diff --git a/src/3rd_party/avx_mathfun.h b/src/3rd_party/avx_mathfun.h
old mode 100755
new mode 100644
diff --git a/src/3rd_party/sse_mathfun.h b/src/3rd_party/sse_mathfun.h
old mode 100755
new mode 100644
diff --git a/src/common/types.h b/src/common/types.h
index aaac97aca..8b9026a95 100644
--- a/src/common/types.h
+++ b/src/common/types.h
@@ -174,7 +174,7 @@ struct float32x4 {
 };
 
 // @TODO: consider how code can be shared via templating
-#ifndef NO_AVX
+#ifdef __AVX__
 struct float32x8 {
 private:
   __m256 f_;
diff --git a/src/functional/operators.h b/src/functional/operators.h
index 289f7121c..41fc74b06 100755
--- a/src/functional/operators.h
+++ b/src/functional/operators.h
@@ -319,7 +319,7 @@ struct Ops<float32x4> {
 
 } // end namespace functional
 } // end namespace marian
-#ifndef NO_AVX
+#ifdef __AVX__
 #include "3rd_party/avx_mathfun.h"
 
 namespace marian {
diff --git a/src/functional/tensor.h b/src/functional/tensor.h
index 41bc064a8..c928ff080 100755
--- a/src/functional/tensor.h
+++ b/src/functional/tensor.h
@@ -31,7 +31,7 @@ inline marian::Shape adapt<float32x4>(const marian::Shape& shape) {
   x4Shape.set(-1, shape[-1] / 4);
   return x4Shape;
 }
-#ifndef NO_AVX
+#ifdef __AVX__
 template <>
 inline marian::Shape adapt<float32x8>(const marian::Shape& shape) {
   ABORT_IF(shape[-1] % 8 != 0,
diff --git a/src/tensors/cpu/element.h b/src/tensors/cpu/element.h
index 81f2779a2..d98a0f9b1 100644
--- a/src/tensors/cpu/element.h
+++ b/src/tensors/cpu/element.h
@@ -99,7 +99,7 @@ void elementFloat(const Functor& functor, marian::Tensor out, Tensors... tensors
 
   if(div8) {
     // std::cerr << "8: " << functor.to_string() << std::endl;
-#ifndef NO_AVX
+#ifdef __AVX__
     element<float32x8>(functor, out, tensors...);
     return;
 #endif
diff --git a/src/tensors/cpu/tensor_operators.cpp b/src/tensors/cpu/tensor_operators.cpp
index 40f504a44..2a7d5f1c1 100755
--- a/src/tensors/cpu/tensor_operators.cpp
+++ b/src/tensors/cpu/tensor_operators.cpp
@@ -425,7 +425,7 @@ void Softmax(Tensor out, Tensor in) {
   matchOrAbort<float>(out->type());
   matchOrAbort<float>(in->type());
 
-#ifndef NO_AVX
+#ifdef __AVX__
   if(out->shape()[-1] % 8 == 0) {
     Softmax<float32x8>(out, in);
     return;
@@ -481,7 +481,7 @@ void LogSoftmax(Tensor out, Tensor in) {
   matchOrAbort<float>(out->type());
   matchOrAbort<float>(in->type());
 
-#ifndef NO_AVX
+#ifdef __AVX__
   if(out->shape()[-1] % 8 == 0) {
     LogSoftmax<float32x8>(out, in);
     return;

From c343ced9e1f6a2264946212eb4f8b3d9b777599f Mon Sep 17 00:00:00 2001
From: Roman Grundkiewicz <rgrundki@exseed.ed.ac.uk>
Date: Fri, 6 Dec 2019 03:41:36 +0000
Subject: [PATCH 799/838] Add lexical shortlists to marian-server (#560)

* Add lexical shortlists to marian-server

* Update CHANGELOG
---
 CHANGELOG.md                |  1 +
 src/translator/translator.h | 11 ++++++++++-
 2 files changed, 11 insertions(+), 1 deletion(-)

diff --git a/CHANGELOG.md b/CHANGELOG.md
index fb82c8305..02e427325 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -8,6 +8,7 @@ and this project adheres to [Semantic Versioning](http://semver.org/spec/v2.0.0.
 ## [Unreleased]
 
 ### Added
+- Support for lexical shortlists in marian-server
 - Support for 8-bit matrix multiplication with FBGEMM
 - CMakeLists.txt now looks for SSE 4.2
 - Purging of finished hypotheses during beam-search. A lot faster for large batches.
diff --git a/src/translator/translator.h b/src/translator/translator.h
index 841d9868c..88adf0e3d 100755
--- a/src/translator/translator.h
+++ b/src/translator/translator.h
@@ -175,6 +175,7 @@ class TranslateService : public ModelServiceTask {
 
   std::vector<Ptr<Vocab>> srcVocabs_;
   Ptr<Vocab> trgVocab_;
+  Ptr<const data::ShortlistGenerator> shortlistGenerator_;
 
   size_t numDevices_;
 
@@ -199,6 +200,11 @@ class TranslateService : public ModelServiceTask {
     trgVocab_ = New<Vocab>(options_, vocabPaths.size() - 1);
     trgVocab_->load(vocabPaths.back());
 
+    // load lexical shortlist
+    if(options_->hasAndNotEmpty("shortlist"))
+      shortlistGenerator_ = New<data::LexicalShortlistGenerator>(
+          options_, srcVocabs_.front(), trgVocab_, 0, 1, vocabPaths.front() == vocabPaths.back());
+
     // get device IDs
     auto devices = Config::getDevices(options_);
     numDevices_ = devices.size();
@@ -218,8 +224,11 @@ class TranslateService : public ModelServiceTask {
       graphs_.push_back(graph);
 
       auto scorers = createScorers(options_);
-      for(auto scorer : scorers)
+      for(auto scorer : scorers) {
         scorer->init(graph);
+        if(shortlistGenerator_)
+          scorer->setShortlistGenerator(shortlistGenerator_);
+      }
       scorers_.push_back(scorers);
     }
   }

From eb5f97240e8dc8184d561220265cab8027752b89 Mon Sep 17 00:00:00 2001
From: Roman Grundkiewicz <rgrundki@exseed.ed.ac.uk>
Date: Fri, 6 Dec 2019 13:27:48 +0000
Subject: [PATCH 800/838] Fix word weighting with max length cropping (#562)

Fix word weighting with max length cropping
---
 src/data/corpus_base.cpp | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/src/data/corpus_base.cpp b/src/data/corpus_base.cpp
index 69d500ebf..3065e5382 100755
--- a/src/data/corpus_base.cpp
+++ b/src/data/corpus_base.cpp
@@ -240,10 +240,10 @@ void CorpusBase::addWeightsToSentenceTuple(const std::string& line, SentenceTupl
 
   if(!elements.empty()) {
     std::vector<float> weights;
-    for(auto& e : elements) {
-      if(maxLengthCrop_ && weights.size() > maxLength_)
+    for(auto& e : elements) {                             // Iterate weights as strings
+      if(maxLengthCrop_ && weights.size() >= maxLength_)  // Cut if the input is going to be cut
         break;
-      weights.emplace_back(std::stof(e));
+      weights.emplace_back(std::stof(e));                 // Add a weight converted into float
     }
 
     if(rightLeft_)

From 2b14d490d171f9cf1980d407e265af9e44f97c0e Mon Sep 17 00:00:00 2001
From: Ulrich Germann <ugermann@inf.ed.ac.uk>
Date: Mon, 9 Dec 2019 17:05:44 +0200
Subject: [PATCH 801/838] Allow file name templated valid-translation-output
 files (#549)

This allows us to preserve validation output at each validation point.
Template parameters are explained in the help message.
---
 src/common/config_parser.cpp  |  8 +++++---
 src/training/scheduler.h      |  4 ++--
 src/training/training_state.h | 12 +++++++++++-
 src/training/validator.cpp    | 13 ++++++++++---
 src/training/validator.h      | 15 ++++++++++-----
 5 files changed, 38 insertions(+), 14 deletions(-)

diff --git a/src/common/config_parser.cpp b/src/common/config_parser.cpp
index 739df28d1..da78831af 100755
--- a/src/common/config_parser.cpp
+++ b/src/common/config_parser.cpp
@@ -562,8 +562,10 @@ void ConfigParser::addOptionsValidation(cli::CLIWrapper& cli) {
       "Additional args passed to --valid-script-path. These are inserted"
       " between the script path and the output translation-file path");
   cli.add<std::string>("--valid-translation-output",
-     "Path to store the translation");
-
+     "(Template for) path to store the translation. "
+     "E.g., validation-output-after-{U}-updates-{T}-tokens.txt. Template "
+     "parameters: {E} for epoch; {B} for No. of batches within epoch; "
+     "{U} for total No. of updates; {T} for total No. of tokens seen.");
   cli.add<bool>("--keep-best",
       "Keep best model for each validation metric");
   cli.add<std::string>("--valid-log",
@@ -612,7 +614,7 @@ void ConfigParser::addOptionsTranslation(cli::CLIWrapper& cli) {
       "Optimize speed aggressively sacrificing memory or precision");
   cli.add<bool>("--skip-cost",
       "Ignore model cost during translation, not recommended for beam-size > 1");
-  cli.add<bool>("--fp16", 
+  cli.add<bool>("--fp16",
       "Shortcut for mixed precision inference with float16, corresponds to: --precision float16");
   cli.add<std::vector<std::string>>("--precision",
       "Mixed precision for inference, set parameter type in expression graph",
diff --git a/src/training/scheduler.h b/src/training/scheduler.h
index c8e90c95e..19d389ad4 100644
--- a/src/training/scheduler.h
+++ b/src/training/scheduler.h
@@ -9,7 +9,7 @@
 namespace marian {
 
 bool getSigtermFlag();
-void installSignalHandlers(); 
+void installSignalHandlers();
 
 class Scheduler : public TrainingObserver {
 private:
@@ -229,7 +229,7 @@ class Scheduler : public TrainingObserver {
         continue;
 
       size_t stalledPrev = validator->stalled();
-      float value = validator->validate(graphs);
+      float value = validator->validate(graphs, state_);
       if(validator->stalled() > 0) {
         LOG_VALID(info,
                   "Ep. {} : Up. {} : {} : {} : stalled {} times (last best: {})",
diff --git a/src/training/training_state.h b/src/training/training_state.h
index 2ac2cd583..d39173be9 100644
--- a/src/training/training_state.h
+++ b/src/training/training_state.h
@@ -254,7 +254,7 @@ class TrainingState {
     seedCorpus = config["seed-corpus"].as<std::string>();
   }
 
-  void save(const std::string& name) {
+  void save(const std::string& name) const {
     std::ofstream fout(name);
     YAML::Node config;
 
@@ -291,6 +291,16 @@ class TrainingState {
     fout << config;
   }
 
+  std::string fillTemplate(const std::string& templ) const {
+    // The formatting below uses fmtlib, which is included with spdlog
+    // and is included via the logger.
+    return fmt::format(templ.c_str(),
+                       fmt::arg("E", epochs),
+                       fmt::arg("U", batches),
+                       fmt::arg("B", batchesEpoch),
+                       fmt::arg("T", labelsTotal));
+  }
+
 private:
   std::vector<Ptr<TrainingObserver>> observers_;
 };
diff --git a/src/training/validator.cpp b/src/training/validator.cpp
index 3a38e7a93..79829e3a1 100644
--- a/src/training/validator.cpp
+++ b/src/training/validator.cpp
@@ -303,7 +303,8 @@ ScriptValidator::ScriptValidator(std::vector<Ptr<Vocab>> vocabs, Ptr<Options> op
            "valid-script metric but no script given");
 }
 
-float ScriptValidator::validate(const std::vector<Ptr<ExpressionGraph>>& graphs) {
+float ScriptValidator::validate(const std::vector<Ptr<ExpressionGraph>>& graphs,
+                                Ptr<const TrainingState> /*ignored*/) {
   using namespace data;
   auto model = options_->get<std::string>("model");
   std::string suffix = model.substr(model.size() - 4);
@@ -331,7 +332,8 @@ TranslationValidator::TranslationValidator(std::vector<Ptr<Vocab>> vocabs, Ptr<O
   createBatchGenerator(/*isTranslating=*/true);
 }
 
-float TranslationValidator::validate(const std::vector<Ptr<ExpressionGraph>>& graphs) {
+float TranslationValidator::validate(const std::vector<Ptr<ExpressionGraph>>& graphs,
+                                     Ptr<const TrainingState> state) {
   using namespace data;
 
   // Generate batches
@@ -353,6 +355,8 @@ float TranslationValidator::validate(const std::vector<Ptr<ExpressionGraph>>& gr
 
   if(options_->hasAndNotEmpty("valid-translation-output")) {
     fileName = options_->get<std::string>("valid-translation-output");
+    // fileName can be a template with fields for training state parameters:
+    fileName = state->fillTemplate(fileName);
   } else {
     tempFile.reset(new io::TemporaryFile(options_->get<std::string>("tempdir"), false));
     fileName = tempFile->getFileName();
@@ -455,7 +459,8 @@ BleuValidator::BleuValidator(std::vector<Ptr<Vocab>> vocabs, Ptr<Options> option
   createBatchGenerator(/*isTranslating=*/true);
 }
 
-float BleuValidator::validate(const std::vector<Ptr<ExpressionGraph>>& graphs) {
+float BleuValidator::validate(const std::vector<Ptr<ExpressionGraph>>& graphs,
+                              Ptr<const TrainingState> state) {
   using namespace data;
 
   // Generate batches
@@ -495,6 +500,8 @@ float BleuValidator::validate(const std::vector<Ptr<ExpressionGraph>>& graphs) {
     Ptr<OutputCollector> collector;
     if(options_->hasAndNotEmpty("valid-translation-output")) {
       auto fileName = options_->get<std::string>("valid-translation-output");
+      // fileName can be a template with fields for training state parameters:
+      fileName = state->fillTemplate(fileName);
       collector = New<OutputCollector>(fileName);  // for debugging
     } else {
       collector = New<OutputCollector>(/* null */);  // don't print, but log
diff --git a/src/training/validator.h b/src/training/validator.h
index 2033a939c..e97f0da6f 100755
--- a/src/training/validator.h
+++ b/src/training/validator.h
@@ -37,7 +37,8 @@ class ValidatorBase : public TrainingObserver {
 public:
   ValidatorBase(bool lowerIsBetter) : lowerIsBetter_(lowerIsBetter), lastBest_{initScore()} {}
 
-  virtual float validate(const std::vector<Ptr<ExpressionGraph>>& graphs) = 0;
+  virtual float validate(const std::vector<Ptr<ExpressionGraph>>& graphs,
+                         Ptr<const TrainingState> state) = 0;
   virtual std::string type() = 0;
 
   float lastBest() { return lastBest_; }
@@ -83,7 +84,8 @@ class Validator : public ValidatorBase {
   }
 public:
 
-  virtual float validate(const std::vector<Ptr<ExpressionGraph>>& graphs) override {
+  virtual float validate(const std::vector<Ptr<ExpressionGraph>>& graphs,
+                         Ptr<const TrainingState> /*ignored*/) override {
     for(auto graph : graphs)
       graph->setInference(true);
 
@@ -176,7 +178,8 @@ class ScriptValidator : public Validator<data::Corpus, models::IModel> {
 public:
   ScriptValidator(std::vector<Ptr<Vocab>> vocabs, Ptr<Options> options);
 
-  virtual float validate(const std::vector<Ptr<ExpressionGraph>>& graphs) override;
+  virtual float validate(const std::vector<Ptr<ExpressionGraph>>& graphs,
+                         Ptr<const TrainingState> /*ignored*/) override;
 
   std::string type() override { return "valid-script"; }
 
@@ -191,7 +194,8 @@ class TranslationValidator : public Validator<data::Corpus, models::IModel> {
 public:
   TranslationValidator(std::vector<Ptr<Vocab>> vocabs, Ptr<Options> options);
 
-  virtual float validate(const std::vector<Ptr<ExpressionGraph>>& graphs) override;
+  virtual float validate(const std::vector<Ptr<ExpressionGraph>>& graphs,
+                         Ptr<const TrainingState> state) override;
 
   std::string type() override { return "translation"; }
 
@@ -209,7 +213,8 @@ class BleuValidator : public Validator<data::Corpus, models::IModel> {
 public:
   BleuValidator(std::vector<Ptr<Vocab>> vocabs, Ptr<Options> options, bool detok = false);
 
-  virtual float validate(const std::vector<Ptr<ExpressionGraph>>& graphs) override;
+  virtual float validate(const std::vector<Ptr<ExpressionGraph>>& graphs,
+                         Ptr<const TrainingState> state) override;
 
   // @TODO: why do we return this string, but not pass it to the constructor?
   std::string type() override { return detok_ ? "bleu-detok" : "bleu"; }

From 5be8558c35e4cfbe6bc6bd35edcfea0cb17048aa Mon Sep 17 00:00:00 2001
From: Marcin Junczys-Dowmunt <Marcin.JunczysDowmunt@microsoft.com>
Date: Thu, 12 Dec 2019 19:17:16 -0800
Subject: [PATCH 802/838] ammend changelog and bump version

---
 CHANGELOG.md     | 3 +++
 VERSION          | 2 +-
 regression-tests | 2 +-
 3 files changed, 5 insertions(+), 2 deletions(-)

diff --git a/CHANGELOG.md b/CHANGELOG.md
index 02e427325..048b6d053 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -5,9 +5,11 @@ All notable changes to this project will be documented in this file.
 The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/)
 and this project adheres to [Semantic Versioning](http://semver.org/spec/v2.0.0.html).
 
+
 ## [Unreleased]
 
 ### Added
+- Allow file name templated valid-translation-output files
 - Support for lexical shortlists in marian-server
 - Support for 8-bit matrix multiplication with FBGEMM
 - CMakeLists.txt now looks for SSE 4.2
@@ -27,6 +29,7 @@ and this project adheres to [Semantic Versioning](http://semver.org/spec/v2.0.0.
 - Gradient-checkpointing
 
 ### Fixed
+- Fix word weighting with max length cropping
 - Fixed compilation on CPUs without support for AVX
 - FastOpt now reads "n" and "y" values as strings, not as boolean values
 - Fixed multiple reduction kernels on GPU
diff --git a/VERSION b/VERSION
index 899e8c16d..f614df125 100644
--- a/VERSION
+++ b/VERSION
@@ -1 +1 @@
-v1.8.30
+v1.8.32
diff --git a/regression-tests b/regression-tests
index 85d1da086..9019bf3be 160000
--- a/regression-tests
+++ b/regression-tests
@@ -1 +1 @@
-Subproject commit 85d1da0867f5f2c80efa23aa2a363f37768bb3fb
+Subproject commit 9019bf3beeff355b719914cc52c65609c312960d

From e0500b20b896f587061cd9bce8a0a0f51c16c507 Mon Sep 17 00:00:00 2001
From: Martin Junczys-Dowmunt <Marcin.JunczysDowmunt@microsoft.com>
Date: Fri, 13 Dec 2019 18:55:36 +0000
Subject: [PATCH 803/838] Merged PR 10827: Sequential unlikelihood training and
 fixed gather operation

This implements Sequential Unlikelihood Training from https://arxiv.org/abs/1908.04319
* implementation as expensive multi-op, special node in-progress.
* fixed gather operator to work in batched cases
---
 CHANGELOG.md                         |  2 +
 src/common/config_parser.cpp         |  2 +
 src/data/corpus_base.h               | 13 +++----
 src/functional/shape.h               | 18 ++++-----
 src/graph/expression_operators.cpp   | 17 ++++++++-
 src/graph/expression_operators.h     |  2 +
 src/graph/node_operators_binary.h    |  6 ---
 src/layers/loss.cpp                  |  6 +++
 src/layers/loss.h                    | 57 +++++++++++++++++++++++++++-
 src/layers/weight.cpp                | 10 ++++-
 src/tensors/cpu/tensor_operators.cpp | 25 +++++++-----
 src/tensors/gpu/tensor_operators.cu  | 33 ++++++++++------
 src/tests/units/operator_tests.cpp   | 30 +++++++--------
 13 files changed, 157 insertions(+), 64 deletions(-)

diff --git a/CHANGELOG.md b/CHANGELOG.md
index 048b6d053..8921d47ed 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -9,6 +9,7 @@ and this project adheres to [Semantic Versioning](http://semver.org/spec/v2.0.0.
 ## [Unreleased]
 
 ### Added
+- Sequence-level unliklihood training
 - Allow file name templated valid-translation-output files
 - Support for lexical shortlists in marian-server
 - Support for 8-bit matrix multiplication with FBGEMM
@@ -29,6 +30,7 @@ and this project adheres to [Semantic Versioning](http://semver.org/spec/v2.0.0.
 - Gradient-checkpointing
 
 ### Fixed
+- Gather-operation for all index sizes
 - Fix word weighting with max length cropping
 - Fixed compilation on CPUs without support for AVX
 - FastOpt now reads "n" and "y" values as strings, not as boolean values
diff --git a/src/common/config_parser.cpp b/src/common/config_parser.cpp
index da78831af..93ae36441 100755
--- a/src/common/config_parser.cpp
+++ b/src/common/config_parser.cpp
@@ -328,6 +328,8 @@ void ConfigParser::addOptionsTraining(cli::CLIWrapper& cli) {
       "Optimization criterion: ce-mean, ce-mean-words, ce-sum, perplexity", "ce-mean");
   cli.add<std::string>("--multi-loss-type",
       "How to accumulate multi-objective losses: sum, scaled, mean", "sum");
+  cli.add<bool>("--unlikelihood-loss",
+      "Use word-level weights as indicators for sequence-level unlikelihood training");
   cli.add<bool>("--overwrite",
       "Do not create model checkpoints, only overwrite main model file with last checkpoint. "
       "Reduces disk usage");
diff --git a/src/data/corpus_base.h b/src/data/corpus_base.h
index d34ba9959..6abed37ca 100644
--- a/src/data/corpus_base.h
+++ b/src/data/corpus_base.h
@@ -198,8 +198,6 @@ class SubBatch {
               subWidth = j + 1;
         }
       }
-      //if (subWidth < width_)
-      //  LOG(info, "[data] sub-batch {} of {} wide batch has effective width of {}", pos / targetSize, width_, subWidth);
 
       // create sub-batch
       auto sb = New<SubBatch>(subSize, subWidth, vocab_);
@@ -369,7 +367,7 @@ class CorpusBatch : public Batch {
     // split each stream separately
     for(auto batchStream : subBatches_) {
       size_t i = 0; // index into split batch
-      for(auto splitSubBatch : batchStream->split(n, sizeLimit)) {
+      for(auto splitSubBatch : batchStream->split(n, sizeLimit)) { // splits a batch into pieces, can also change width
         if(subs.size() <= i)
           subs.resize(i + 1);
         subs[i++].push_back(splitSubBatch); // this forms tuples across streams
@@ -424,12 +422,11 @@ class CorpusBatch : public Batch {
     if(!dataWeights_.empty()) {
       size_t oldSize = size();
 
-      size_t width = 1;
-      // There are more weights than sentences, i.e. these are word weights.
-      if(dataWeights_.size() != oldSize)
-        width = subBatches_.back()->batchWidth();
-
       for(auto split : splits) {
+        auto cb = std::static_pointer_cast<CorpusBatch>(split);
+        size_t width = 1;                   // One weight per sentence in case of sentence-level weights
+        if(dataWeights_.size() != oldSize)  // if number of weights does not correspond to number of sentences we have word-level weights
+          width = cb->back()->batchWidth(); // splitting also affects width, hence we need to accomodate this here
         std::vector<float> ws(width * split->size(), 1.0f);
 
         // this needs to be split along the batch dimension
diff --git a/src/functional/shape.h b/src/functional/shape.h
index 8072615e9..fd354e1e0 100755
--- a/src/functional/shape.h
+++ b/src/functional/shape.h
@@ -145,20 +145,20 @@ struct ConstantShape {
 
   HOST_DEVICE_INLINE int elements() const { return (int)elements_; }
 
-  // The following functions iterate over shape dimensions and use resursive
+  // The following functions iterate over shape dimensions and use recursive
   // templates. They unroll over a compile-time defined number of dimensions.
 
   // Struct for recurrent template calls over shape dimensions,
   // version for K > 0
   template <const int K, const int D> struct I {
     HOST_DEVICE_INLINE static int index(const Array<int, D>& dims,
-                             const Array<int, D>& stride) {
+                                        const Array<int, D>& stride) {
       return dims[K] * stride[K] + I<K-1, D>::index(dims, stride);
     }
 
     HOST_DEVICE_INLINE static int index(int si,
-                             const Array<int, D>& shape,
-                             const Array<int, D>& stride) {
+                                        const Array<int, D>& shape,
+                                        const Array<int, D>& stride) {
       return (si % shape[K]) * stride[K] + I<K-1, D>::index(si / shape[K], shape, stride);
     }
 
@@ -175,19 +175,19 @@ struct ConstantShape {
   // specialization for K == 0
   template <const int D> struct I<0, D> {
     HOST_DEVICE_INLINE static int index(const Array<int, D>& dims,
-                             const Array<int, D>& stride) {
+                                        const Array<int, D>& stride) {
       return dims[0] * stride[0];
     }
 
     HOST_DEVICE_INLINE static int index(int si,
-                             const Array<int, D>& shape,
-                             const Array<int, D>& stride) {
+                                        const Array<int, D>& shape,
+                                        const Array<int, D>& stride) {
       return (si % shape[0]) * stride[0];
     }
 
    HOST_DEVICE_INLINE static void dims(int si,
-                            Array<int, D>& dims,
-                            const Array<int, D>& shape) {
+                                       Array<int, D>& dims,
+                                       const Array<int, D>& shape) {
       dims[0] = si % shape[0];
     }
   };
diff --git a/src/graph/expression_operators.cpp b/src/graph/expression_operators.cpp
index df0b25b33..e676d6a20 100755
--- a/src/graph/expression_operators.cpp
+++ b/src/graph/expression_operators.cpp
@@ -317,6 +317,7 @@ Expr index_select(Expr a, int axis, Expr indices) {
   indices = reshape(indices, shape); // move index to axis
   return gather(a, axis, indices);
 }
+
 Expr index_select(Expr a, int axis, const std::vector<IndexType>& indices) {
   auto indexExpr = a->graph()->indices(indices);
   return index_select(a, axis, indexExpr);
@@ -612,8 +613,20 @@ Expr cast(Expr a, Type type) {
   }
 }
 
-Expr cross_entropy(Expr a, Expr indices) {
-  return Expression<CrossEntropyNodeOp>(a, indices);
+Expr cross_entropy(Expr logits, Expr indices) {
+  return Expression<CrossEntropyNodeOp>(logits, indices);
+}
+
+// Unlikelihood loss based on https://arxiv.org/abs/1908.04319
+Expr unlikelihood(Expr logits, Expr indices) {
+  int dimBatch = logits->shape()[-2];
+  int dimTime  = logits->shape()[-3];
+
+  // @TODO: fix the outside of this function in decoder.h etc. 
+  auto indicesWithLayout = reshape(indices, {1, dimTime, dimBatch, 1});
+
+  // This is currently implemented with mutliple ops, might be worth doing a special operation like for cross_entropy
+  return -log(gather(1.f - softmax(logits), /*axis=*/-1, indicesWithLayout));
 }
 
 Expr plus(const std::vector<Expr>& nodes) {
diff --git a/src/graph/expression_operators.h b/src/graph/expression_operators.h
index 7ed231cd1..24edca8ac 100755
--- a/src/graph/expression_operators.h
+++ b/src/graph/expression_operators.h
@@ -200,6 +200,8 @@ Expr logsoftmax(Expr a);
 
 Expr cross_entropy(Expr a, Expr b);
 
+Expr unlikelihood(Expr a, Expr b);
+
 Expr scalar_product(Expr a, Expr b, int ax = 0);
 
 Expr weighted_average(Expr in, Expr weights, int ax = 0);
diff --git a/src/graph/node_operators_binary.h b/src/graph/node_operators_binary.h
index 0f05dfd08..530f98f6d 100644
--- a/src/graph/node_operators_binary.h
+++ b/src/graph/node_operators_binary.h
@@ -569,8 +569,6 @@ struct RowsNodeOp : public NaryNodeOp {
 //  out[i][j][k] = input[i][index[i][j][k]][k]  # if dim == 1
 //  out[i][j][k] = input[i][j][index[i][j][k]]  # if dim == 2
 // 'a' and 'indices' must have the same rank.
-// @TODO: The current implementation does not support batched indices (third scenario above).
-//        I.e. all axes of 'indices' except 'axis' must have dimension 1.
 struct GatherNodeOp : public NaryNodeOp {
   GatherNodeOp(Expr a, int axis, Expr indices)
       : NaryNodeOp({a, indices}, newShape(a, axis, indices), a->value_type()),
@@ -599,10 +597,6 @@ struct GatherNodeOp : public NaryNodeOp {
       if (i != axis) {
         ABORT_IF(indices->shape()[i] != shape[i] && indices->shape()[i] != 1,
             "Dimensions must match or broadcast for input ({}) and indices ({})", std::string(shape), std::string(indices->shape()));
-#if 1 // presently, this implementation does not support batched indices
-        ABORT_IF(indices->shape()[i] != 1,
-            "Presently, gather() does not implement batched indices");
-#endif
       }
     }
     return shape;
diff --git a/src/layers/loss.cpp b/src/layers/loss.cpp
index 5c4c99a9a..408f6fc73 100755
--- a/src/layers/loss.cpp
+++ b/src/layers/loss.cpp
@@ -7,9 +7,15 @@ Ptr<LabelwiseLoss> newLoss(Ptr<Options> options, bool inference) {
   float smoothing = inference ? 0.f : options->get<float>("label-smoothing");
   float factorWeight = options->get<float>("factor-weight", 1.0f);
   std::string costType = options->get<std::string>("cost-type", "ce-mean");
+  bool unlikelihood = options->get<bool>("unlikelihood-loss", false);
   
   if(costType == "ce-rescore") { // returns per-batch-item scores (while ce-mean reduces over batch)
     return New<RescorerLoss>();
+  } else if(unlikelihood) {  
+    ABORT_IF(!options->hasAndNotEmpty("data-weighting") 
+             && options->get<std::string>("data-weighting-type") != "word",
+             "Unlikelihood loss training requires error annotation in form of per-target-label scores");
+    return New<SequenceUnlikelihoodLoss>(smoothing, factorWeight); // this is a mix of CE-loss and unlikelihood less depending on values given for data-weighting
   } else {  // same as ce-mean  --@TODO: better check all allowed values, and fail for invalid ones. E.g. what about ce-sum?
     return New<CrossEntropyLoss>(smoothing, factorWeight);
   }
diff --git a/src/layers/loss.h b/src/layers/loss.h
index a64f0fcdb..f456a974e 100755
--- a/src/layers/loss.h
+++ b/src/layers/loss.h
@@ -366,13 +366,68 @@ class CrossEntropyLoss : public LabelwiseLoss {
     if(mask)
       ce = ce * cast(mask, Type::float32);
 
-    if(labelWeights)
+    if(labelWeights) {
+      // We currently do not know how to use target factors and word-level label weights together
+      bool wordlevel = labelWeights->shape()[-3] > 1; // Time-dimension is not trivially 1, hence we have word-level weights. 
+      ABORT_IF(wordlevel && logits.getNumFactorGroups() > 1, "CE loss with word-level label weights is not implemented for factors");
       ce = ce * cast(labelWeights, Type::float32);
+    }
 
     return ce;
   }
 };
 
+
+/**
+ * @brief Unlikelihood loss across last axis, summed up over batch and time dimensions. This is an 
+ * implementation of sequence-level unlikelihood loss from https://arxiv.org/abs/1908.04319. 
+ * We rely on word-level label weights where 1 is correct and 0 is marking an error. If there are not
+ * zeros for a sentence it going to be trained with normal CE loss if there is at least one 0 it is going
+ * to flip over to use SUL for that sentence to penalize the selected word.
+ * 
+ * SUL is implemented as:
+ * -log(gather(1 - softmax(logits), -1, indices))
+ * 
+ * Factors are currently not supported.
+ */
+class SequenceUnlikelihoodLoss : public CrossEntropyLoss {
+public:
+  SequenceUnlikelihoodLoss(float labelSmoothing, float factorWeight)
+  : CrossEntropyLoss(labelSmoothing, factorWeight) {} // cross-entropy already reduces over axis -1
+
+  SequenceUnlikelihoodLoss(const std::vector<int>& axes, float labelSmoothing, float factorWeight)
+  : CrossEntropyLoss(axes, labelSmoothing, factorWeight) {}
+
+protected:
+  virtual Expr compute(Logits logits, const Words& labels,
+                       Expr mask = nullptr, Expr labelWeights = nullptr) override {
+    auto ce = CrossEntropyLoss::compute(logits, labels, mask, /*labelWeights=*/nullptr); // don't pass label-weights to CE
+    if(!labelWeights)
+      return ce; // for validation, @TODO: maybe put rather abort or LOG_ONCE(warn, ...)?
+
+    // We currently do not know how to use target factors and word-level label weights together
+    ABORT_IF(logits.getNumFactorGroups() > 1, "Unlikelihood loss is not implemented for factors");
+
+    ABORT_IF(!mask, "mask is required"); // @TODO: check this, it seems weights for padding are by default 1, which would make this obsolete.
+    // use label weights, where 1 is GOOD and 0 is BAD. After inversion here, now 1 marks, mask again to eliminate padding (might be obsolete)
+    auto errorMask = (1.f - cast(labelWeights, Type::float32)) * cast(mask, Type::float32);
+  
+    auto ceUl = logits.applyLossFunction(labels, [&](Expr logits, Expr indices) {
+      return cast(unlikelihood(logits, indices), Type::float32);
+    });
+  
+    // compute if want to use CE or UL. If there are no errors train with CE, otherwise train _only on_ the errors with UL. This is the "mixed" training
+    // schedule from https://arxiv.org/abs/1908.04319. Providing labels with or without error scores we can easily switch between CE and UL. 
+    auto onlyCe  = eq(sum(errorMask, /*axis=*/-3), 0.f); // [1, 1, dimBatch, 1] - equal 1 if no errors are present
+    ceUl         = errorMask * ceUl;                     // don't use for correct label or padding
+
+    auto cost    = onlyCe * ce + (1.f - onlyCe) * ceUl;  // ce or unlikelihood part are never simultanously used as cost per batch entry 
+
+    return cost;
+  }
+};
+
+
 /**
  * @brief Cross entropy in rescorer used for computing sentences-level log probabilities
  */
diff --git a/src/layers/weight.cpp b/src/layers/weight.cpp
index 3370f3985..86d55c2e2 100644
--- a/src/layers/weight.cpp
+++ b/src/layers/weight.cpp
@@ -15,8 +15,16 @@ Expr DataWeighting::getWeights(Ptr<ExpressionGraph> graph,
   bool sentenceWeighting = weightingType_ == "sentence";
   int dimBatch = (int)batch->size();
   int dimWords = sentenceWeighting ? 1 : (int)batch->back()->batchWidth();
+
+  // This would abort anyway in fromVector(...), but has clearer error message
+  // here for this particular case
+  ABORT_IF(batch->getDataWeights().size() != dimWords * dimBatch, 
+           "Number of sentence/word-level weights ({}) does not match tensor size ({})",
+           batch->getDataWeights().size(), dimWords * dimBatch);
+
   auto weights = graph->constant({1, dimWords, dimBatch, 1},
                                  inits::fromVector(batch->getDataWeights()));
-  return weights;
+  return weights; // [1, dimWords, dimBatch, 1] in case of word-level weights or
+                  // [1,        1, dimBatch, 1] in case of sentence-level weights
 }
 }  // namespace marian
diff --git a/src/tensors/cpu/tensor_operators.cpp b/src/tensors/cpu/tensor_operators.cpp
index 2a7d5f1c1..caad6337b 100755
--- a/src/tensors/cpu/tensor_operators.cpp
+++ b/src/tensors/cpu/tensor_operators.cpp
@@ -686,20 +686,22 @@ void Select(Tensor out,
 
   // @TODO: make this efficient
   functional::Shape outShape = out->shape();
-  functional::Shape inShape = in->shape();
+  functional::Shape inShape  = in->shape();
+  functional::Shape idxShape = indices->shape();
   int length = outShape.elements();
 
   functional::Array<int, functional::Shape::size()> dims;
   int axisCPU = (int)(axis + functional::Shape::size() - out->shape().size());
 
-  if(axisCPU == 2) // specialization for axis==2, assuming N=4
+  if(axisCPU == 2 && outShape == idxShape) // specialization for axis==2 when there is no broadcasting, @TODO to be removed once we have a faster implementation below
     return SelectAxis2(out, in, indices);
 
   for(int index = 0; index < length; ++index) {
-    outShape.dims(index, dims);
-    dims[axisCPU] = (int)indices->data<IndexType>()[dims[axisCPU]];
-    int inIndex = inShape.index(dims);
-    out->data()[index] = in->data()[inIndex];
+    outShape.dims(index, dims);                                // compute dimension-based indices from global index;
+    int idxIndex = idxShape.bindex(dims);                      // return global index for indices based on dimension-specific indices from out, take broadcasting into account;
+    dims[axisCPU] = (int)indices->data<IndexType>()[idxIndex]; // substitute index of out-tensor with corresponding axis-local position from in-tensor;
+    int inIndex = inShape.index(dims);                         // compute global index from dimension-specific indices, no broadcasting as out and in match in all dimensions apart from axis
+    out->data()[index] = in->data()[inIndex];                  // assign corresponding values.
   }
 }
 
@@ -712,7 +714,8 @@ void Insert(Tensor out,
 
   // @TODO: make this efficient
   functional::Shape outShape = out->shape();
-  functional::Shape inShape = in->shape();
+  functional::Shape inShape  = in->shape();
+  functional::Shape idxShape = indices->shape();
 
   int length = inShape.elements();
   functional::Array<int, functional::Shape::size()> dims;
@@ -720,7 +723,8 @@ void Insert(Tensor out,
 
   for(int index = 0; index < length; ++index) {
     inShape.dims(index, dims);
-    dims[axisCPU] = (int)indices->data<IndexType>()[dims[axisCPU]];
+    int idxIndex = idxShape.bindex(dims); // broadcast index into indices tensor
+    dims[axisCPU] = (int)indices->data<IndexType>()[idxIndex];
     int outIndex = outShape.index(dims);
     out->data()[outIndex] += in->data()[index];
   }
@@ -887,7 +891,7 @@ void CrossEntropyPick(Tensor out, Tensor in, Tensor labelIndices) {
     // Groundtruth label index
     IndexType i = labelIndices->data<IndexType>()[j];
     // This appears to be safe i.e. that i >= 0 && i < cols is known
-    out->data()[j] = std::log(sum) - sp[i] + max;
+    out->data()[j] = std::log(sum) - sp[i] + max;    // -log(p_i) = - logsoftmax(x_i - max) = - (x_i - max) - log(sum_j exp(x_j - max))
   }
 }
 
@@ -920,7 +924,8 @@ void CrossEntropyPickBackward(Tensor out,
     // cross-entropy
     for(int i = 0; i < cols; ++i) {
       float sub = (float)(i == (int)labelIndices->data<IndexType>()[j]); // delta, true if label index and column index match
-      so[i] += adj->data()[j] * (std::exp(sp[i] - max) / sum - sub);
+      auto softmax = std::exp(sp[i] - max) / sum;
+      so[i] += adj->data()[j] * (softmax - sub);
     }
   }
 }
diff --git a/src/tensors/gpu/tensor_operators.cu b/src/tensors/gpu/tensor_operators.cu
index cc9d84330..2b5f51ce7 100644
--- a/src/tensors/gpu/tensor_operators.cu
+++ b/src/tensors/gpu/tensor_operators.cu
@@ -1132,7 +1132,8 @@ __global__ void gSelect(T* out,
                         const T* in,
                         const functional::Shape inShape,
                         int axis,
-                        IndexType* d_indices) {
+                        const IndexType* d_indices,
+                        const functional::Shape idxShape) {
   int length = outShape.elements();
   functional::Array<int, functional::Shape::size()> dims;
 
@@ -1140,7 +1141,8 @@ __global__ void gSelect(T* out,
     int index = bid + blockDim.x * blockIdx.x + threadIdx.x;
     if(index < length) {
       outShape.dims(index, dims);
-      dims[axis] = d_indices[dims[axis]];
+      int idxIndex = idxShape.bindex(dims); // broadcast index into indices tensor
+      dims[axis] = (int)d_indices[idxIndex];    
       int inIndex = inShape.index(dims);
       out[index] = in[inIndex];
     }
@@ -1153,7 +1155,8 @@ __global__ void gInsert(T* out,
                         const T* in,
                         const functional::Shape inShape,
                         int axis,
-                        IndexType* d_indices) {
+                        const IndexType* d_indices,
+                        const functional::Shape idxShape) {
   int length = inShape.elements();
   functional::Array<int, functional::Shape::size()> dims;
 
@@ -1161,7 +1164,8 @@ __global__ void gInsert(T* out,
     int index = bid + blockDim.x * blockIdx.x + threadIdx.x;
     if(index < length) {
       inShape.dims(index, dims);
-      dims[axis] = d_indices[dims[axis]];
+      int idxIndex = idxShape.bindex(dims); // broadcast index into indices tensor
+      dims[axis] = (int)d_indices[idxIndex];    
       int outIndex = outShape.index(dims);
       out[outIndex] += in[index]; // this is probably wrong, atomicAdd?
     }
@@ -1189,7 +1193,8 @@ void Select(Tensor out,
                                 in->data<float>(),
                                 in->shape(),
                                 axisGPU,
-                                indices->data<IndexType>());
+                                indices->data<IndexType>(), 
+                                indices->shape());
 #if COMPILE_FP16
   } else if (out->type() == Type::float16) {
     gSelect<<<blocks, threads>>>(out->data<half>(),
@@ -1197,7 +1202,8 @@ void Select(Tensor out,
                                 in->data<half>(),
                                 in->shape(),
                                 axisGPU,
-                                indices->data<IndexType>());
+                                indices->data<IndexType>(),
+                                indices->shape());
 #endif
   } else {
     ABORT("Select not implemented for type {}", out->type());
@@ -1224,7 +1230,8 @@ void Insert(Tensor out,
                                 in->data<float>(),
                                 in->shape(),
                                 axisGPU,
-                                indices->data<IndexType>());
+                                indices->data<IndexType>(),
+                                indices->shape());
 #if COMPILE_FP16
   } else if (out->type() == Type::float16) {
     gInsert<<<blocks, threads>>>(out->data<half>(),
@@ -1232,7 +1239,8 @@ void Insert(Tensor out,
                                 in->data<half>(),
                                 in->shape(),
                                 axisGPU,
-                                indices->data<IndexType>());
+                                indices->data<IndexType>(),
+                                indices->shape());
 #endif
   } else {
     ABORT("Insert not implemented for type {}", out->type());
@@ -1522,11 +1530,11 @@ __global__ void gCrossEntropyPick(T* out,
       __syncthreads();
 
       // cross-entropy
+      auto sum = _sum[0];
       for(int tid = 0; tid < cols; tid += blockDim.x) {
         int id = tid + threadIdx.x;
-        if(id == (int)pick[j]) {
-          out[j] = (T)functional::Ops<AccType>::log(_sum[0]) - sp[id] + max;
-        }
+        if(id == (int)pick[j])
+          out[j] = (T)functional::Ops<AccType>::log(sum) - sp[id] + max;
       }
     }
     __syncthreads();
@@ -1628,7 +1636,8 @@ __global__ void gCrossEntropyPickBackward(T* out,
         int id = tid + threadIdx.x;
         if(id < cols) {
           AccType sub = (AccType)(id == (int)pick[j]);
-          so[id] += (AccType)adj[j] * (functional::Ops<AccType>::exp(sp[id] - max) / _sum[0] - sub);
+          auto softmax = functional::Ops<AccType>::exp(sp[id] - max) / _sum[0];
+          so[id] += (AccType)adj[j] * (softmax - sub);
         }
       }
     }
diff --git a/src/tests/units/operator_tests.cpp b/src/tests/units/operator_tests.cpp
index 774a93deb..682ef4805 100644
--- a/src/tests/units/operator_tests.cpp
+++ b/src/tests/units/operator_tests.cpp
@@ -670,16 +670,16 @@ void tests(DeviceType device, Type floatType = Type::float32) {
     values.clear();
 
     std::vector<T> vA({  1, -2,   3,
-                            -4,  5,  -6,
-                             7, -8,   9,
-                           -10, 11, -12});
+                        -4,  5,  -6,
+                         7, -8,   9,
+                       -10, 11, -12});
     std::vector<T> vC({ 1,  -2, // C = np.array([1, -2, 3, -4, 5, -6, 7, -8, 9, -10, 11, -12]).reshape((2, 3, 2))
-                            3,  -4,
-                            5,  -6,
+                        3,  -4,
+                        5,  -6,
 
-                            7,  -8,
-                            9, -10,
-                           11, -12 });
+                        7,  -8,
+                        9, -10,
+                        11, -12 });
     std::vector<T> vB1({1, -2, 3});
     std::vector<T> vB2({1, -4, 7, -10});
     std::vector<T> vB3({-2, 5, -8, 11});
@@ -687,7 +687,7 @@ void tests(DeviceType device, Type floatType = Type::float32) {
     std::vector<T> vD1(vB4);
     std::vector<T> vD2({5, -6, 11, -12});
     std::vector<T> vD3({1, -2, 5, -6, 7, -8, 11, -12}); // C[:,(0,2),:]
-    //std::vector<float> vD4({5, -6, 3, -4, 7, -8, 11, -12}); // [C[0,(2,1),:],C[1,(0,2),:]]
+    std::vector<T> vD4({5, -6, 3, -4, 7, -8, 11, -12}); // [C[0,(2,1),:],C[1,(0,2),:]]
     std::vector<T> vS1({7, -8, 9});
     std::vector<T> vS2({-4, 5, -6, 7, -8, 9});
     std::vector<T> vS3({7, -8, 9, -10, 11, -12});
@@ -714,11 +714,11 @@ void tests(DeviceType device, Type floatType = Type::float32) {
     CHECK(D1->type() == "sliceView");
     CHECK(D2->type() == "gather");
     // enable this once gather() supports batched indices:
-    //auto D4 = gather(C, 1, graph->constant({2, 2, 1}, // [C[0,(2,1),:],C[1,(0,2),:]]
-    //                                       inits::fromVector(std::vector<IndexType>{
-    //                                         2, 1,
-    //                                         0, 2 }),
-    //                                       Type::uint32));
+    auto D4 = gather(C, 1, graph->constant({2, 2, 1}, // [C[0,(2,1),:],C[1,(0,2),:]]
+                                          inits::fromVector(std::vector<IndexType>{
+                                            2, 1,
+                                            0, 2 }),
+                                          Type::uint32));
 
     auto S1 = slice(A, 0, 2);
     auto S2 = narrow(A, 0, 1, 2);
@@ -736,7 +736,7 @@ void tests(DeviceType device, Type floatType = Type::float32) {
     CHECK(D1->shape() == Shape({1, 3, 2})); D1->val()->get(values); CHECK( values == vD1 );
     CHECK(D2->shape() == Shape({2, 1, 2})); D2->val()->get(values); CHECK( values == vD2 );
     CHECK(D3->shape() == Shape({2, 2, 2})); D3->val()->get(values); CHECK( values == vD3 );
-    //CHECK(D4->shape() == Shape({2, 2, 2})); D4->val()->get(values); CHECK( values == vD4 );
+    CHECK(D4->shape() == Shape({2, 2, 2})); D4->val()->get(values); CHECK( values == vD4 );
 
     CHECK(S1->shape() == Shape({1,3})); S1->val()->get(values); CHECK(values == vS1);
     CHECK(S2->shape() == Shape({2,3})); S2->val()->get(values); CHECK(values == vS2);

From eba7aed344c3f903b539da0fb6e71b248b3809b9 Mon Sep 17 00:00:00 2001
From: Marcin Junczys-Dowmunt <Marcin.JunczysDowmunt@microsoft.com>
Date: Fri, 13 Dec 2019 11:09:02 -0800
Subject: [PATCH 804/838] increase version

---
 VERSION | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/VERSION b/VERSION
index f614df125..a1419ab46 100644
--- a/VERSION
+++ b/VERSION
@@ -1 +1 @@
-v1.8.32
+v1.8.33

From f882f27c09d5c01b6a48a278ffeca6513e7c9e91 Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Fri, 20 Dec 2019 01:46:37 +0000
Subject: [PATCH 805/838] Merged PR 10692: new factor conditioning, inline
 fixing suppression, nan suppression

This adds two new features related to factored vocabs:
* a new conditioning mechanism that mimics a mini transformer layer between the emitted lemma and the factors. This affects only factored vocabs, and requires to be enabled explicitly. Change is in `generic.cpp`.
* in case of inline phrase-fixing, cross-attention is now no longer allows to look into the source sequence. This only affects inputs with `|is` factors or `<IOPEN>` tags. Change is in `states.h`.
* Adam optimizer now skips update if the gradient contains a NaN. Does not affect existing configs unless they produce NaNs. Change is in `optimizers.cpp`.
* reverts to old `LayerNorm` routine. *TODO*: Is this change still needed?

Additional changes:
* new method `locate()` for accessing batch data with array coordinates
* new overloads for `constant_like()` from vector directly (most used case)
* rvalue-ref version of `fromVector()`
---
 src/common/file_stream.cpp          |  0
 src/data/corpus.cpp                 | 11 ++--
 src/data/corpus_base.cpp            | 50 +++++++++++++++
 src/data/corpus_base.h              | 71 ++++++++++++---------
 src/data/factored_vocab.cpp         | 33 ++++++++--
 src/data/factored_vocab.h           | 14 +++++
 src/data/vocab.cpp                  |  0
 src/data/vocab_base.h               |  0
 src/graph/expression_operators.cpp  |  5 --
 src/graph/expression_operators.h    | 12 +++-
 src/graph/node_initializers.cpp     | 10 +++
 src/graph/node_initializers.h       |  2 +
 src/graph/node_operators_binary.h   |  4 +-
 src/layers/generic.cpp              | 96 ++++++++++++++++++++++++-----
 src/layers/generic.h                | 28 +++++++++
 src/layers/guided_alignment.h       | 17 +++--
 src/layers/loss.h                   |  6 +-
 src/models/states.h                 |  8 +--
 src/models/transformer.h            | 73 ++++++++--------------
 src/optimizers/optimizers.cpp       |  0
 src/tensors/gpu/tensor_operators.cu | 20 ++++--
 src/training/graph_group_sync.cpp   | 13 ++--
 src/training/scheduler.h            |  2 +-
 src/translator/beam_search.h        | 13 ++--
 24 files changed, 356 insertions(+), 132 deletions(-)
 mode change 100644 => 100755 src/common/file_stream.cpp
 mode change 100644 => 100755 src/data/corpus.cpp
 mode change 100644 => 100755 src/data/corpus_base.h
 mode change 100644 => 100755 src/data/factored_vocab.h
 mode change 100644 => 100755 src/data/vocab.cpp
 mode change 100644 => 100755 src/data/vocab_base.h
 mode change 100644 => 100755 src/graph/node_initializers.h
 mode change 100644 => 100755 src/graph/node_operators_binary.h
 mode change 100644 => 100755 src/models/states.h
 mode change 100644 => 100755 src/optimizers/optimizers.cpp
 mode change 100644 => 100755 src/tensors/gpu/tensor_operators.cu
 mode change 100644 => 100755 src/training/scheduler.h

diff --git a/src/common/file_stream.cpp b/src/common/file_stream.cpp
old mode 100644
new mode 100755
diff --git a/src/data/corpus.cpp b/src/data/corpus.cpp
old mode 100644
new mode 100755
index 84d5f9f42..234bc7635
--- a/src/data/corpus.cpp
+++ b/src/data/corpus.cpp
@@ -235,11 +235,12 @@ CorpusBase::batch_ptr Corpus::toBatch(const std::vector<Sample>& batchVector) {
   }
 
   std::vector<size_t> words(maxDims.size(), 0);
-  for(size_t i = 0; i < batchSize; ++i) {
-    for(size_t j = 0; j < maxDims.size(); ++j) {
-      for(size_t k = 0; k < batchVector[i][j].size(); ++k) {
-        subBatches[j]->data()[k * batchSize + i] = batchVector[i][j][k];
-        subBatches[j]->mask()[k * batchSize + i] = 1.f;
+  for(size_t b = 0; b < batchSize; ++b) {                    // loop over batch entries
+    for(size_t j = 0; j < maxDims.size(); ++j) {             // loop over streams
+      auto subBatch = subBatches[j];
+      for(size_t s = 0; s < batchVector[b][j].size(); ++s) { // loop over word positions
+        subBatch->data()[subBatch->locate(/*batchIdx=*/b, /*wordPos=*/s)/*s * batchSize + b*/] = batchVector[b][j][s];
+        subBatch->mask()[subBatch->locate(/*batchIdx=*/b, /*wordPos=*/s)/*s * batchSize + b*/] = 1.f;
         words[j]++;
       }
     }
diff --git a/src/data/corpus_base.cpp b/src/data/corpus_base.cpp
index 3065e5382..bb5fe735c 100755
--- a/src/data/corpus_base.cpp
+++ b/src/data/corpus_base.cpp
@@ -1,6 +1,7 @@
 #include <random>
 
 #include "data/corpus.h"
+#include "data/factored_vocab.h"
 
 namespace marian {
 namespace data {
@@ -330,5 +331,54 @@ void CorpusBase::initEOS(bool training = true) {
     }
 }
 
+// experimental: hide inline-fix source tokens from cross attention
+std::vector<float> SubBatch::crossMaskWithInlineFixSourceSuppressed() const
+{
+  const auto& srcVocab = *vocab();
+
+  auto factoredVocab = vocab()->tryAs<FactoredVocab>();
+  size_t inlineFixGroupIndex = 0, inlineFixSrc = 0;
+  auto hasInlineFixFactors = factoredVocab && factoredVocab->tryGetFactor(FactoredVocab_INLINE_FIX_WHAT_serialized, /*out*/ inlineFixGroupIndex, /*out*/ inlineFixSrc);
+
+  auto fixSrcId = srcVocab[FactoredVocab_FIX_SRC_ID_TAG];
+  auto fixTgtId = srcVocab[FactoredVocab_FIX_TGT_ID_TAG];
+  auto fixEndId = srcVocab[FactoredVocab_FIX_END_ID_TAG];
+  auto unkId = srcVocab.getUnkId();
+  auto hasInlineFixTags = fixSrcId != unkId && fixTgtId != unkId && fixEndId != unkId;
+
+  auto m = mask(); // default return value, which we will modify in-place below in case we need to
+  if (hasInlineFixFactors || hasInlineFixTags) {
+    LOG_ONCE(info, "[data] Suppressing cross-attention into inline-fix source tokens");
+
+    // example: force French translation of name "frank" to always be "franck"
+    //  - hasInlineFixFactors: "frank|is franck|it", "frank|is" cannot be cross-attended to
+    //  - hasInlineFixTags:    "<IOPEN> frank <IDELIM> franck <ICLOSE>", "frank" and all tags cannot be cross-attended to
+    auto dimBatch = batchSize();  // number of sentences in the batch
+    auto dimWidth = batchWidth(); // number of words in the longest sentence in the batch
+    const auto& d = data();
+    size_t numWords = 0;
+    for (size_t b = 0; b < dimBatch; b++) {     // loop over batch entries
+      bool inside = false;
+      for (size_t s = 0; s < dimWidth; s++) {  // loop over source positions
+        auto i = locate(/*batchIdx=*/b, /*wordPos=*/s);
+        if (!m[i])
+          break;
+        numWords++;
+        // keep track of entering/exiting the inline-fix source tags
+        auto w = d[i];
+        if (w == fixSrcId)
+          inside = true;
+        else if (w == fixTgtId)
+          inside = false;
+        bool wHasSrcIdFactor = hasInlineFixFactors && factoredVocab->getFactor(w, inlineFixGroupIndex) == inlineFixSrc;
+        if (inside || w == fixSrcId || w == fixTgtId || w == fixEndId || wHasSrcIdFactor)
+          m[i] = 0.0f; // decoder must not look at embedded source, nor the markup tokens
+      }
+    }
+    ABORT_IF(batchWords() != 0/*n/a*/ && numWords != batchWords(), "batchWords() inconsistency??");
+  }
+  return m;
+}
+
 }  // namespace data
 }  // namespace marian
diff --git a/src/data/corpus_base.h b/src/data/corpus_base.h
old mode 100644
new mode 100755
index 6abed37ca..f0d9b706a
--- a/src/data/corpus_base.h
+++ b/src/data/corpus_base.h
@@ -143,12 +143,19 @@ class SubBatch {
    * words (width) and \f$s\f$ is the number of sentences (size).
    */
   Words& data() { return indices_; }
+  const Words& data() const { return indices_; }
+  /**
+   * @brief compute flat index into data() and mask() vectors for given batch index and word index in sentence
+   */
+  size_t locate(size_t batchIdx, size_t wordPos) const { return locate(batchIdx, wordPos, size_); }
+  static size_t locate(size_t batchIdx, size_t wordPos, size_t batchSize) { return wordPos * batchSize + batchIdx; }
   /**
    * @brief Flat masking vector; 0 is used for masked words.
    *
    * @see data()
    */
   std::vector<float>& mask() { return mask_; }
+  const std::vector<float>& mask() const { return mask_; }
 
   /**
    * @brief Accessors to the vocab_ field.
@@ -158,15 +165,15 @@ class SubBatch {
   /**
    * @brief The number of sentences in the batch.
    */
-  size_t batchSize() { return size_; }
+  size_t batchSize() const { return size_; }
   /**
    * @brief The number of words in the longest sentence in the batch.
    */
-  size_t batchWidth() { return width_; };
+  size_t batchWidth() const { return width_; };
   /**
    * @brief The total number of words in the batch (not counting masked-out words).
    */
-  size_t batchWords() { return words_; }
+  size_t batchWords() const { return words_; }
 
   /**
    * @brief Splits the stream into sub-batches of equal size (except for last).
@@ -179,7 +186,7 @@ class SubBatch {
    *
    * @see marian::data::Batch::split(size_t n)
    */
-  std::vector<Ptr<SubBatch>> split(size_t n, size_t sizeLimit /*or SIZE_MAX*/) {
+  std::vector<Ptr<SubBatch>> split(size_t n, size_t sizeLimit /*or SIZE_MAX*/) const {
     ABORT_IF(size_ == 0, "Encountered sub-batch size of 0");
 
     auto size = std::min(size_, sizeLimit); // if limit is given then pretend the batch only has that many sentences
@@ -191,11 +198,11 @@ class SubBatch {
 
       // determine actual width (=max length) of this sub-batch, which may be smaller than the overall max length
       size_t subWidth = 0;
-      for(size_t j = 0; j < width_; ++j) {
-        for(size_t i = 0; i < subSize; ++i) {
-          if(mask_[j * size_ + (pos + i)] != 0)
-            if (subWidth < j + 1)
-              subWidth = j + 1;
+      for(size_t s = 0; s < width_; ++s) {
+        for(size_t b = 0; b < subSize; ++b) {
+          if(mask_[locate(/*batchIdx=*/pos + b, /*wordPos=*/s)] != 0)   // s * size_ + (pos + b)
+            if (subWidth < s + 1)
+              subWidth = s + 1;
         }
       }
 
@@ -203,12 +210,12 @@ class SubBatch {
       auto sb = New<SubBatch>(subSize, subWidth, vocab_);
 
       size_t words = 0;
-      for(size_t j = 0; j < subWidth; ++j) {
-        for(size_t i = 0; i < subSize; ++i) {
-          sb->data()[j * subSize + i] = indices_[j * size_ + (pos + i)];
-          sb->mask()[j * subSize + i] =    mask_[j * size_ + (pos + i)];
+      for(size_t s = 0; s < subWidth; ++s) {
+        for(size_t b = 0; b < subSize; ++b) {
+          sb->data()[locate(/*batchIdx=*/b, /*wordPos=*/s, /*batchSize=*/subSize)/*s * subSize + b*/] = indices_[locate(/*batchIdx=*/pos + b, /*wordPos=*/s)]; // s * size_ + (pos + b)
+          sb->mask()[locate(/*batchIdx=*/b, /*wordPos=*/s, /*batchSize=*/subSize)/*s * subSize + b*/] =    mask_[locate(/*batchIdx=*/pos + b, /*wordPos=*/s)]; // s * size_ + (pos + b)
 
-          if(mask_[j * size_ + (pos + i)] != 0)
+          if(mask_[locate(/*batchIdx=*/pos + b, /*wordPos=*/s)/*s * size_ + (pos + b)*/] != 0)
             words++;
         }
       }
@@ -220,6 +227,9 @@ class SubBatch {
   }
 
   void setWords(size_t words) { words_ = words; }
+
+  // experimental: hide inline-fix source tokens from cross attention
+  std::vector<float> crossMaskWithInlineFixSourceSuppressed() const;
 };
 
 /**
@@ -229,7 +239,7 @@ class SubBatch {
 class CorpusBatch : public Batch {
 protected:
   std::vector<Ptr<SubBatch>> subBatches_;
-  std::vector<float> guidedAlignment_;
+  std::vector<float> guidedAlignment_; // [max source len, batch size, max target len] flattened
   std::vector<float> dataWeights_;
 
 public:
@@ -300,7 +310,8 @@ class CorpusBatch : public Batch {
 
   /**
    * @brief Creates a batch filled with fake data. Used to determine the size of
-   * the batch object.
+   * the batch object. With guided-alignments and multiple encoders, those
+   * multiple source streams are expected to have the same lengths.
    *
    * @param lengths List of subbatch sizes.
    * @param batchSize Number of sentences in the batch.
@@ -333,6 +344,7 @@ class CorpusBatch : public Batch {
       return batch;
 
     if(options->get("guided-alignment", std::string("none")) != "none") {
+      // @TODO: if > 1 encoder, verify that all encoders have the same sentence lengths
       std::vector<float> alignment(batchSize * lengths.front() * lengths.back(),
                                    0.f);
       batch->setGuidedAlignment(std::move(alignment));
@@ -406,7 +418,7 @@ class CorpusBatch : public Batch {
           size_t bi = i + pos;
           for(size_t sid = 0; sid < srcWords; ++sid) {
             for(size_t tid = 0; tid < trgWords; ++tid) {
-              size_t bidx = sid * oldSize  * oldTrgWords + bi * oldTrgWords + tid;
+              size_t bidx = sid * oldSize  * oldTrgWords + bi * oldTrgWords + tid; // [sid, bi, tid]
               size_t idx  = sid * dimBatch *    trgWords +  i *    trgWords + tid;
               aligns[idx] = guidedAlignment_[bidx];
             }
@@ -432,9 +444,9 @@ class CorpusBatch : public Batch {
         // this needs to be split along the batch dimension
         // which is here the innermost dimension.
         // Should work for sentence-based weights, too.
-        for(size_t j = 0; j < width; ++j) {
-          for(size_t i = 0; i < split->size(); ++i) {
-            ws[j * split->size() + i] = dataWeights_[j * oldSize + i + pos];
+        for(size_t s = 0; s < width; ++s) {
+          for(size_t b = 0; b < split->size(); ++b) {
+            ws[s * split->size() + b] = dataWeights_[s * oldSize + b + pos]; // @TODO: use locate() as well
           }
         }
         split->setDataWeights(ws);
@@ -445,9 +457,13 @@ class CorpusBatch : public Batch {
     return splits;
   }
 
-  std::vector<float>& getGuidedAlignment() { return guidedAlignment_; }
+  const std::vector<float>& getGuidedAlignment() const { return guidedAlignment_; }  // [dimSrcWords, dimBatch, dimTrgWords] flattened
   void setGuidedAlignment(std::vector<float>&& aln) override {
-      guidedAlignment_ = std::move(aln);
+    guidedAlignment_ = std::move(aln);
+  }
+
+  size_t locateInGuidedAlignments(size_t b, size_t s, size_t t) {
+    return ((s * size()) + b) * widthTrg() + t;
   }
 
   std::vector<float>& getDataWeights() { return dataWeights_; }
@@ -469,15 +485,14 @@ class CorpusBatch : public Batch {
       std::cerr << std::endl;
     }
 
-    size_t b = 0;
+    size_t subBatchIndex = 0;
     for(auto sb : subBatches_) {
-      std::cerr << "batch " << b++ << ": " << std::endl;
+      std::cerr << "stream " << subBatchIndex++ << ": " << std::endl;
       const auto& vocab = sb->vocab();
-      for(size_t i = 0; i < sb->batchWidth(); i++) {
+      for(size_t s = 0; s < sb->batchWidth(); s++) {
         std::cerr << "\t w: ";
-        for(size_t j = 0; j < sb->batchSize(); j++) {
-          size_t idx = i * sb->batchSize() + j;
-          Word w = sb->data()[idx];
+        for(size_t b = 0; b < sb->batchSize(); b++) {
+          Word w = sb->data()[sb->locate(/*batchIdx=*/b, /*wordPos=*/s)]; // s * sb->batchSize() + b;
           if (vocab && !printIndices)
             std::cerr << (*vocab)[w] << " ";
           else
diff --git a/src/data/factored_vocab.cpp b/src/data/factored_vocab.cpp
index 144e87128..971bf626f 100755
--- a/src/data/factored_vocab.cpp
+++ b/src/data/factored_vocab.cpp
@@ -400,7 +400,7 @@ std::string FactoredVocab::word2string(Word word) const {
         res.append("?");
     }
     else
-      res.append(factorVocab_[(WordIndex)(index + groupRanges_[g].first)]);
+      res.append(getFactorName(g, index));
   }
   return res;
 }
@@ -431,6 +431,21 @@ Word FactoredVocab::string2word(const std::string& w) const {
   return word;
 }
 
+// does a specific factor exist in the vocabulary
+// Factor name must be given without separator. This function cannot be used for lemmas.
+bool FactoredVocab::tryGetFactor(const std::string& factorName, size_t& groupIndex, size_t& factorIndex) const {
+  WordIndex u;
+  if (factorVocab_.tryFind(factorSeparator_ + factorName, u))
+  {
+      groupIndex = factorGroups_[u];
+      ABORT_IF(u < groupRanges_[groupIndex].first || u >= groupRanges_[groupIndex].second, "Invalid factorGroups_ entry??");
+      factorIndex = u - groupRanges_[groupIndex].first;
+      return true;
+  }
+  else
+      return false;
+}
+
 // extract the factor index of a given factor type from the 'Word' representation
 size_t FactoredVocab::getFactor(Word word, size_t groupIndex) const {
   size_t index = word.toWordIndex();
@@ -565,12 +580,18 @@ void FactoredVocab::constructNormalizationInfoForVocab() {
 
 // decode a 'Word' array into the external string representation of that token sequence, as written to output files
 /*virtual*/ std::string FactoredVocab::decode(const Words& sentence, bool ignoreEOS /*= true*/) const /*override final*/ {
-  std::vector<std::string> decoded;
-  decoded.reserve(sentence.size());
-  for(auto w : sentence) {
+  std::vector<std::string> decoded; decoded.reserve(sentence.size());
+  for(auto w : sentence)
     if((w != getEosId() || !ignoreEOS))
       decoded.push_back((*this)[w]);
-  }
+  return utils::join(decoded, " ");
+}
+
+// diagnostics version of decode() that will not fail on partial words, will print EOS, and is a little slower
+std::string FactoredVocab::decodeForDiagnostics(const Words& sentence) const {
+  std::vector<std::string> decoded; decoded.reserve(sentence.size());
+  for (auto w : sentence)
+    decoded.push_back(word2string(w));
   return utils::join(decoded, " ");
 }
 
@@ -740,7 +761,7 @@ Ptr<IVocab> createFactoredVocab(const std::string& vocabPath) {
     static std::map<std::string, Ptr<IVocab>> s_cache;
     auto iter = s_cache.find(vocabPath);
     if (iter != s_cache.end()) {
-      LOG(info, "[vocab] Reusing existing vocabulary object in memory (vocab size {})", iter->second->size());
+      LOG_ONCE(info, "[vocab] Reusing existing vocabulary object in memory (vocab size {})", iter->second->size());
       return iter->second;
     }
     auto vocab = New<FactoredVocab>();
diff --git a/src/data/factored_vocab.h b/src/data/factored_vocab.h
old mode 100644
new mode 100755
index 9e7734c43..215e92f09
--- a/src/data/factored_vocab.h
+++ b/src/data/factored_vocab.h
@@ -66,6 +66,9 @@ class FactoredVocab : public IVocab {
   bool canExpandFactoredWord(Word word, size_t groupIndex) const { return lemmaHasFactorGroup(getFactor(word, 0), groupIndex); }
   size_t getFactor(Word word, size_t groupIndex) const;
   bool lemmaHasFactorGroup(size_t factor0Index, size_t g) const { return lemmaHasFactorGroup_[factor0Index][g]; }
+  const std::string& getFactorGroupPrefix(size_t groupIndex) const { return groupPrefixes_[groupIndex]; } // for diagnostics only
+  const std::string& getFactorName(size_t groupIndex, size_t factorIndex) const { return factorVocab_[(WordIndex)(factorIndex + groupRanges_[groupIndex].first)]; }
+  std::string decodeForDiagnostics(const Words& sentence) const;
 
   static constexpr size_t FACTOR_NOT_APPLICABLE = (SIZE_MAX - 1);
   static constexpr size_t FACTOR_NOT_SPECIFIED  = (SIZE_MAX - 2);
@@ -74,6 +77,17 @@ class FactoredVocab : public IVocab {
   static Ptr<FactoredVocab> tryCreateAndLoad(const std::string& path); // load from "vocab" option if it specifies a factored vocab
   std::string word2string(Word word) const;
   Word string2word(const std::string& w) const;
+  bool tryGetFactor(const std::string& factorGroupName, size_t& groupIndex, size_t& factorIndex) const; // note: factorGroupName given without separator
+
+  // some hard-coded constants from FactoredSegmenter
+  // The naming mimics the names in FactoredSegmenter.cs, and therefore intentionally does not follow Marian conventions.
+  // @TODO: We have more hard-coded constants throughout the code. Move them all here.
+  // @TODO: figure out how to do this with static const*/constexpr
+#define FactoredVocab_INLINE_FIX_WHAT_serialized "is"
+#define FactoredVocab_FIX_SRC_ID_TAG             "<IOPEN>"
+#define FactoredVocab_FIX_TGT_ID_TAG             "<IDELIM>"
+#define FactoredVocab_FIX_END_ID_TAG             "<ICLOSE>"
+
 private:
   void constructGroupInfoFromFactorVocab();
   void constructFactorIndexConversion();
diff --git a/src/data/vocab.cpp b/src/data/vocab.cpp
old mode 100644
new mode 100755
diff --git a/src/data/vocab_base.h b/src/data/vocab_base.h
old mode 100644
new mode 100755
diff --git a/src/graph/expression_operators.cpp b/src/graph/expression_operators.cpp
index e676d6a20..e3b0a2f18 100755
--- a/src/graph/expression_operators.cpp
+++ b/src/graph/expression_operators.cpp
@@ -284,11 +284,6 @@ Expr stopGradient(Expr a) {
   return res;
 }
 
-Expr constant_like(Expr a, const Ptr<inits::NodeInitializer>& init) {
-  auto graph = a->graph();
-  return graph->constant(a->shape(), init, a->value_type());
-}
-
 // gather() -- gather arbitrary elements along an axis; batched or non-batched
 Expr gather(Expr a, int axis, Expr indices) {
   return Expression<GatherNodeOp>(a, axis, indices);
diff --git a/src/graph/expression_operators.h b/src/graph/expression_operators.h
index 24edca8ac..7600d2be1 100755
--- a/src/graph/expression_operators.h
+++ b/src/graph/expression_operators.h
@@ -141,7 +141,17 @@ Expr atleast_4d(Expr a);
 Expr atleast_nd(Expr a, size_t dims);
 
 // create a constant of shape a->shape() and initialize with init
-Expr constant_like(Expr a, const Ptr<inits::NodeInitializer>& init);
+// @TODO: add a && version, to avoid a ref count. NodeInitializers are typically temps.
+// @TODO: and/or make this a template on init
+static inline Expr constant_like(Expr a, const Ptr<inits::NodeInitializer>& init) {
+  return a->graph()->constant(a->shape(), init, a->value_type());
+}
+
+// short-cut to init from std::vector, since we do this so often
+template<typename ElementType>
+Expr constant_like(Expr a, const std::vector<ElementType>& v) { return constant_like(a, inits::fromVector(std::move(v))); }
+template<typename ElementType>
+Expr constant_like(Expr a, std::vector<ElementType>&& v) { return constant_like(a, inits::fromVector(v)); }
 
 Expr flatten(Expr a);
 Expr flatten_2d(Expr a);
diff --git a/src/graph/node_initializers.cpp b/src/graph/node_initializers.cpp
index c2332906f..27796d337 100755
--- a/src/graph/node_initializers.cpp
+++ b/src/graph/node_initializers.cpp
@@ -145,10 +145,20 @@ Ptr<NodeInitializer> fromVector(const std::vector<T>& v) {
   return fromLambda([v](Tensor t) { t->set(v.data(), v.data() + v.size()); }, typeId<T>());
 }
 
+template <typename T>
+Ptr<NodeInitializer> fromVector(std::vector<T>&& v) {
+  return fromLambda([v](Tensor t) { t->set(v.data(), v.data() + v.size()); }, typeId<T>());
+}
+
 template Ptr<NodeInitializer> fromVector<float16>(const std::vector<float16>& v);
 template Ptr<NodeInitializer> fromVector<float>(const std::vector<float>& v);
 template Ptr<NodeInitializer> fromVector<IndexType>(const std::vector<IndexType>& v);
 
+// @TODO: can we remove the const& ones above? They always make a copy anyways, and often from a temp
+template Ptr<NodeInitializer> fromVector<float16>  (std::vector<float16>  && v);
+template Ptr<NodeInitializer> fromVector<float>    (std::vector<float>    && v);
+template Ptr<NodeInitializer> fromVector<IndexType>(std::vector<IndexType>&& v);
+
 Ptr<NodeInitializer> fromSparseVector(std::pair<std::vector<size_t>, std::vector<float>>& v) {
   return fromLambda([v](Tensor t) { t->set(1e-6); t->setSparse(v.first, v.second); });
 }
diff --git a/src/graph/node_initializers.h b/src/graph/node_initializers.h
old mode 100644
new mode 100755
index b8aa2f6ed..624816b9c
--- a/src/graph/node_initializers.h
+++ b/src/graph/node_initializers.h
@@ -143,6 +143,8 @@ Ptr<NodeInitializer> gumbel(float eps = 1e-5f);
 // @TODO: add documentation
 template <typename T>
 Ptr<NodeInitializer> fromVector(const std::vector<T>& v);
+template <typename T>
+Ptr<NodeInitializer> fromVector(std::vector<T>&& v);
 
 // @TODO: add documentation
 Ptr<NodeInitializer> fromSparseVector(std::pair<std::vector<size_t>, std::vector<float>>& v);
diff --git a/src/graph/node_operators_binary.h b/src/graph/node_operators_binary.h
old mode 100644
new mode 100755
index 530f98f6d..3158ee7ae
--- a/src/graph/node_operators_binary.h
+++ b/src/graph/node_operators_binary.h
@@ -859,7 +859,9 @@ struct MinimumNodeOp : public ElementBinaryNodeOp {
 
 struct CmpNodeOp : public ElementBinaryNodeOp {
   CmpNodeOp(Expr a, Expr b, int cmp_, bool not_) : ElementBinaryNodeOp(a, b), cmp_(cmp_), not_(not_) {
-    setTrainable(false); // has no gradient
+    //setTrainable(false); // has no gradient
+    // Note: ^^ Disabled because it currently causing Marian to choke, for unknown reasons.
+    //       Not setting this will not change the result since the vector of gradient functions is empty.
   }
 
   NodeOps forwardOps() override {
diff --git a/src/layers/generic.cpp b/src/layers/generic.cpp
index 7dc828d9e..e3984774a 100755
--- a/src/layers/generic.cpp
+++ b/src/layers/generic.cpp
@@ -4,7 +4,8 @@
 #include "layers/constructors.h"
 #include "layers/loss.h"
 #include "data/factored_vocab.h"
-#include "rnn/types.h" // for State::select()
+#include "rnn/types.h"     // for State::select()
+#include "models/states.h" // for EncoderState
 
 //using std::size_t; // not sure why this is needed
 
@@ -219,7 +220,7 @@ namespace marian {
       factoredVocab_ = FactoredVocab::tryCreateAndLoad(options_->get<std::string>("vocab", ""));
       if (factoredVocab_) {
         numOutputClasses = (int)factoredVocab_->factorVocabSize();
-        LOG(info, "[embedding] Factored outputs enabled");
+        LOG_ONCE(info, "[embedding] Factored outputs enabled");
       }
 
       if(tiedParam_) {
@@ -237,10 +238,10 @@ namespace marian {
 
       /*const*/ int lemmaDimEmb = options_->get<int>("lemma-dim-emb", 0);
       ABORT_IF(lemmaDimEmb && !factoredVocab_, "--lemma-dim-emb requires a factored vocabulary");
-      if (lemmaDimEmb > 0) {
+      if (lemmaDimEmb > 0) { // > 0 means to embed the (expected) word with a different embedding matrix
 #define HARDMAX_HACK
 #ifdef HARDMAX_HACK
-        lemmaDimEmb = lemmaDimEmb & 0xfffffffe;
+        lemmaDimEmb = lemmaDimEmb & 0xfffffffe; // hack to select hard-max: use an odd number
 #endif
         auto range = factoredVocab_->getGroupRange(0);
         auto lemmaVocabDim = (int)(range.second - range.first);
@@ -263,8 +264,9 @@ namespace marian {
         // project each factor separately
         auto numGroups = factoredVocab_->getNumGroups();
         std::vector<Ptr<RationalLoss>> allLogits(numGroups, nullptr); // (note: null entries for absent factors)
-        Expr input1 = input;
-        Expr Plemma = nullptr;
+        Expr input1 = input; // [B... x D]
+        Expr Plemma = nullptr;     // used for lemmaDimEmb=-1
+        Expr inputLemma = nullptr; // used for lemmaDimEmb=-2, -3
         for (size_t g = 0; g < numGroups; g++) {
           auto range = factoredVocab_->getGroupRange(g);
           if (g > 0 && range.first == range.second) // empty entry
@@ -280,6 +282,52 @@ namespace marian {
             factorWt = slice(Wt_, isLegacyUntransposedW ? -1 : 0, Slice((int)range.first, (int)range.second));
             factorB  = slice(b_,                              -1, Slice((int)range.first, (int)range.second));
           }
+          /*const*/ int lemmaDimEmb = options_->get<int>("lemma-dim-emb", 0);
+          if ((lemmaDimEmb == -2 || lemmaDimEmb == -3) && g > 0) { // -2/-3 means a gated transformer-like structure (-3 = hard-max)
+            LOG_ONCE(info, "[embedding] using lemma conditioning with gate");
+            // this mimics one transformer layer
+            //  - attention over two inputs:
+            //     - e = current lemma. We use the original embedding vector; specifically, expectation over all lemmas.
+            //     - input = hidden state FF(h_enc+h_dec)
+            //  - dot-prod attention to allow both sides to influence (unlike our recurrent self-attention)
+            //  - multi-head to allow for multiple conditions to be modeled
+            //  - add & norm, for gradient flow and scaling
+            //  - FF layer   --this is expensive; it is per-factor
+            // multi-head attention
+            int inputDim = input->shape()[-1];
+            int heads = 8;
+            auto name = options_->get<std::string>("prefix") + "_factor" + std::to_string(g);
+            auto Wq = graph_->param(name + "_Wq", { inputDim,  inputDim }, inits::glorotUniform());
+            auto Wk = graph_->param(name + "_Wk", { inputDim,  inputDim }, inits::glorotUniform());
+            auto Wv = graph_->param(name + "_Wv", { inputDim,  inputDim }, inits::glorotUniform());
+            auto toMultiHead = [&](Expr x, int heads) {
+              const auto& shape = x->shape();
+              int inputDim = shape[-1];
+              int otherDim = shape.elements() / inputDim;
+              ABORT_IF(inputDim / heads * heads != inputDim, "inputDim ({}) must be multiple of number of heads ({})", inputDim, heads);
+              return reshape(x, { otherDim, heads, 1, inputDim / heads });
+            };
+            input1 = inputLemma;
+            auto qm  = toMultiHead(dot(input1,         Wq), heads); // [B... x H x D/H] projected query
+            auto kdm = toMultiHead(dot(input1 - input, Wk), heads); // [B... x H x D/H] the two data vectors projected as keys. Use diff and sigmoid, instead of softmax.
+            auto vem = toMultiHead(dot(input1,         Wv), heads); // [B... x H x D/H] one of the two data vectors projected as values
+            auto vim = toMultiHead(dot(         input, Wv), heads); // [B... x H x D/H] the other
+            auto zm = bdot(qm, kdm, false, true);              // [B... x H x 1]
+            auto sm = sigmoid(zm);                // [B... x H x 1]
+            auto rm = sm * (vem - vim) + vim;     // [B... x H x D/H]
+            auto r = reshape(rm, input->shape()); // [B... x D]
+            // add & norm
+            input1 = r + input1;
+            input1 = layerNorm(input1, name + "_att");
+            // FF layer
+            auto ffnDropProb = 0.1f;    // @TODO: get as a parameter
+            auto ffnDim = inputDim * 2; // @TODO: get as a parameter
+            auto f = denseInline(input1, name + "_ffn", /*suffix=*/"1", ffnDim, (ActivationFunction*)relu, ffnDropProb);
+            f      = denseInline(f,      name + "_ffn", /*suffix=*/"2", inputDim);
+            // add & norm
+            input1 = f + input1;
+            input1 = layerNorm(input1, name + "_ffn");
+          }
           // @TODO: b_ should be a vector, not a matrix; but shotlists use cols() in, which requires a matrix
           auto factorLogits = affine(input1, factorWt, factorB, false, /*transB=*/isLegacyUntransposedW ? false : true, /*scale=*/1.0f); // [B... x U] factor logits
           // optionally add lemma-dependent bias
@@ -294,15 +342,28 @@ namespace marian {
           allLogits[g] = New<RationalLoss>(factorLogits, nullptr);
           // optionally add a soft embedding of lemma back to create some lemma dependency
           // @TODO: if this works, move it into lazyConstruct
-          /*const*/ int lemmaDimEmb = options_->get<int>("lemma-dim-emb", 0);
-          if (lemmaDimEmb < 0 && g == 0) {
-            ABORT_IF(shortlist_ && lemmaDimEmb != 0, "Lemma-dependent bias with short list is not yet implemented");
+          if (lemmaDimEmb == -2 && g == 0) { // -2 means a gated transformer-like structure
+            LOG_ONCE(info, "[embedding] using lemma conditioning with gate, soft-max version");
+            // get expected lemma embedding vector
+            auto factorLogSoftmax = logsoftmax(factorLogits); // [B... x U] note: with shortlist, this is not the full lemma set
+            auto factorSoftmax = exp(factorLogSoftmax);
+            inputLemma = dot(factorSoftmax, factorWt, false, /*transB=*/isLegacyUntransposedW ? true : false); // [B... x D]
+          }
+          else if (lemmaDimEmb == -3 && g == 0) { // same as -2 except with hard max
+            LOG_ONCE(info, "[embedding] using lemma conditioning with gate, hard-max version");
+            // get max-lemma embedding vector
+            auto maxVal = max(factorLogits, -1); // [B... x U] note: with shortlist, this is not the full lemma set
+            auto factorHardmax = eq(factorLogits, maxVal);
+            inputLemma = dot(factorHardmax, factorWt, false, /*transB=*/isLegacyUntransposedW ? true : false); // [B... x D]
+          }
+          else if (lemmaDimEmb == -1 && g == 0) { // -1 means learn a lemma-dependent bias
+            ABORT_IF(shortlist_, "Lemma-dependent bias with short list is not yet implemented");
             LOG_ONCE(info, "[embedding] using lemma-dependent bias");
             auto factorLogSoftmax = logsoftmax(factorLogits); // (we do that again later, CSE will kick in)
             auto z = /*stopGradient*/(factorLogSoftmax);
             Plemma = exp(z); // [B... x U]
           }
-          if (lemmaDimEmb > 0 && g == 0) {
+          else if (lemmaDimEmb > 0 && g == 0) { // > 0 means learn a re-embedding matrix
             LOG_ONCE(info, "[embedding] enabled re-embedding of lemma, at dim {}", lemmaDimEmb);
             // compute softmax. We compute logsoftmax() separately because this way, computation will be reused later via CSE
             auto factorLogSoftmax = logsoftmax(factorLogits);
@@ -349,7 +410,7 @@ namespace marian {
     factoredVocab_ = FactoredVocab::tryCreateAndLoad(options_->get<std::string>("vocab", ""));
     if (factoredVocab_) {
       dimVoc = (int)factoredVocab_->factorVocabSize();
-      LOG(info, "[embedding] Factored embeddings enabled");
+      LOG_ONCE(info, "[embedding] Factored embeddings enabled");
     }
 
     // Embedding layer initialization should depend only on embedding size, hence fanIn=false
@@ -389,7 +450,7 @@ namespace marian {
     auto graph = E_->graph();
     int dimBatch = (int)subBatch->batchSize();
     int dimEmb = E_->shape()[-1];
-    int dimWords = (int)subBatch->batchWidth();
+    int dimWidth = (int)subBatch->batchWidth();
 
     // factored embeddings:
     //  - regular:
@@ -419,9 +480,16 @@ namespace marian {
     //        - but forward pass weighs them down, so that all factors are in a similar numeric range
     //        - if it is required to be in a different range, the embeddings can still learn that, but more slowly
 
-    auto batchEmbeddings = apply(subBatch->data(), {dimWords, dimBatch, dimEmb});
-    auto batchMask = graph->constant({dimWords, dimBatch, 1},
+    auto batchEmbeddings = apply(subBatch->data(), {dimWidth, dimBatch, dimEmb});
+#if 0
+    auto batchMask = graph->constant({dimWidth, dimBatch, 1},
                                      inits::fromVector(subBatch->mask()));
+#else
+    // experimental: hide inline-fix source tokens from cross attention
+    auto batchMask = graph->constant({dimWidth, dimBatch, 1},
+                                     inits::fromVector(subBatch->crossMaskWithInlineFixSourceSuppressed()));
+#endif
+
     return std::make_tuple(batchEmbeddings, batchMask);
   }
 
diff --git a/src/layers/generic.h b/src/layers/generic.h
index 88a458acd..e899d8a07 100755
--- a/src/layers/generic.h
+++ b/src/layers/generic.h
@@ -412,4 +412,32 @@ class ULREmbedding : public LayerBase, public IEmbeddingLayer {
     ABORT("not implemented"); // @TODO: implement me
   }
 };
+
+// --- a few layers with built-in parameters created on the fly, without proper object
+// @TODO: change to a proper layer object
+
+// like affine() but with built-in parameters, activation, and dropout
+static inline
+Expr denseInline(Expr x, std::string prefix, std::string suffix, int outDim, const std::function<Expr(Expr)>& actFn = nullptr, float dropProb = 0.0f)
+{
+  auto graph = x->graph();
+
+  auto W = graph->param(prefix + "_W" + suffix, { x->shape()[-1], outDim }, inits::glorotUniform());
+  auto b = graph->param(prefix + "_b" + suffix, { 1,              outDim }, inits::zeros());
+
+  x = affine(x, W, b);
+  if (actFn)
+    x = actFn(x);
+  x = dropout(x, dropProb);
+  return x;
+}
+
+static inline
+Expr layerNorm(Expr x, std::string prefix, std::string suffix = std::string()) {
+  int dimModel = x->shape()[-1];
+  auto scale = x->graph()->param(prefix + "_ln_scale" + suffix, { 1, dimModel }, inits::ones());
+  auto bias  = x->graph()->param(prefix + "_ln_bias"  + suffix, { 1, dimModel }, inits::zeros());
+  return marian::layerNorm(x, scale, bias, 1e-6f);
+}
+
 }  // namespace marian
diff --git a/src/layers/guided_alignment.h b/src/layers/guided_alignment.h
index acc70c1f4..48ad65993 100755
--- a/src/layers/guided_alignment.h
+++ b/src/layers/guided_alignment.h
@@ -18,29 +18,36 @@ static inline RationalLoss guidedAlignmentCost(Ptr<ExpressionGraph> graph,
   Expr alignmentLoss; // sum up loss over all attention/alignment positions
   size_t numLabels;
   if(guidedLossType == "ce") {
-    // ce requires normalized probabilities
+    // normalizedAlignment is multi-hot, but ce requires normalized probabilities, so need to normalize to P(s|t)
     auto dimBatch    = shape[-2];
     auto dimTrgWords = shape[-1];
     auto dimSrcWords = shape[-3];
     ABORT_IF(shape[-4] != 1, "Guided alignments with beam??");
-    auto normalizedAlignment = batch->getGuidedAlignment();
+    auto normalizedAlignment = batch->getGuidedAlignment(); // [dimSrcWords, dimBatch, dimTrgWords] flattened, matches shape of 'attention'
+    auto srcBatch = batch->front();
+    const auto& srcMask = srcBatch->mask();
+    ABORT_IF(shape.elements() != normalizedAlignment.size(), "Attention-matrix and alignment shapes differ??");
+    ABORT_IF(dimBatch != batch->size() || dimTrgWords != batch->widthTrg() || dimSrcWords != batch->width(), "Attention-matrix and batch shapes differ??");
     auto locate = [=](size_t s, size_t b, size_t t) { return ((s * dimBatch) + b) * dimTrgWords + t; };
     for (size_t b = 0; b < dimBatch; b++) {
       for (size_t t = 0; t < dimTrgWords; t++) {
+        for (size_t s = 0; s < dimSrcWords; s++)
+          ABORT_IF(locate(s, b, t) != batch->locateInGuidedAlignments(b, s, t), "locate() and locateInGuidedAlignments() differ??");
+        // renormalize the alignment such that it sums up to 1
         float sum = 0;
         for (size_t s = 0; s < dimSrcWords; s++)
-          sum += normalizedAlignment[locate(s, b, t)];
+          sum += srcMask[srcBatch->locate(b, s)] * normalizedAlignment[locate(s, b, t)]; // these values are 0 or 1
         if (sum != 0 && sum != 1)
           for (size_t s = 0; s < dimSrcWords; s++)
             normalizedAlignment[locate(s, b, t)] /= sum;
       }
     }
-    auto alignment = constant_like(attention, inits::fromVector(normalizedAlignment));
+    auto alignment = constant_like(attention, std::move(normalizedAlignment));
     alignmentLoss = -sum(flatten(alignment * log(attention + epsilon)));
     numLabels = batch->back()->batchWords();
     ABORT_IF(numLabels > shape.elements() / shape[-3], "Num labels of guided alignment cost is off??");
   } else {
-    auto alignment = constant_like(attention, inits::fromVector(batch->getGuidedAlignment()));
+    auto alignment = constant_like(attention, batch->getGuidedAlignment());
     if(guidedLossType == "mse")
       alignmentLoss = sum(flatten(square(attention - alignment))) / 2.f;
     else if(guidedLossType == "mult") // @TODO: I don't know what this criterion is for. Can we remove it?
diff --git a/src/layers/loss.h b/src/layers/loss.h
index f456a974e..d9deaeb8f 100755
--- a/src/layers/loss.h
+++ b/src/layers/loss.h
@@ -206,7 +206,7 @@ class ScaledMultiRationalLoss : public MultiRationalLoss {
   virtual Expr accumulateLoss(const RationalLoss& current) override {
     if(loss_) {
       const auto& first = partialLosses_.front();
-      return loss_ + first.count() * (current.loss() / current.count()); // scale up/down to match scale of first loss
+      return loss_ + current.loss() * first.count() / current.count(); // scale up/down to match scale of first loss
     } else {
       return current.loss(); // first reference loss, keeps to scale with this one
     }
@@ -344,8 +344,8 @@ class CrossEntropyLoss : public LabelwiseLoss {
       // for bert training or classification the time dimension is lot.
       // Here safeguard against 2d classifier output, adds 1 on the left, non-op.
       Expr ce = cast(cross_entropy(logits, indices), Type::float32);
-      if (inFactor) {
-        LOG_ONCE("scaling factor losses with weight {}", factorWeight_);
+      if (inFactor && factorWeight_ != 1.0f) {
+        LOG_ONCE(info, "scaling factor losses with weight {}", factorWeight_);
         ce = ce * factorWeight_;
       }
       if (labelSmoothing_ > 0) {
diff --git a/src/models/states.h b/src/models/states.h
old mode 100644
new mode 100755
index e0a070847..21cf42a87
--- a/src/models/states.h
+++ b/src/models/states.h
@@ -9,7 +9,7 @@ namespace marian {
 class EncoderState {
 private:
   Expr context_;
-  Expr mask_;
+  Expr mask_;       // [beam depth=1, max length, batch size, vector dim=1] source mask
   Ptr<data::CorpusBatch> batch_;
 
 public:
@@ -18,9 +18,9 @@ class EncoderState {
 
   EncoderState() {}
 
-  virtual Expr getContext() { return context_; }
-  virtual Expr getAttended() { return context_; }
-  virtual Expr getMask() { return mask_; }
+  virtual Expr getContext()   const { return context_;   }
+  virtual Expr getAttended()  const { return context_;   }
+  virtual Expr getMask()      const { return mask_;      } // source batch mask; may have additional positions suppressed
 
   virtual const Words& getSourceWords() {
     return batch_->front()->data();
diff --git a/src/models/transformer.h b/src/models/transformer.h
index 991693541..8971c65fc 100755
--- a/src/models/transformer.h
+++ b/src/models/transformer.h
@@ -142,29 +142,6 @@ class Transformer : public EncoderOrDecoderBase {
     return reshape(output, {dimBeam, dimBatch, dimSteps, dimModel});
   }
 
-  // like affine() but with built-in parameters, activation, and dropout
-  static inline
-  Expr dense(Expr x, std::string prefix, std::string suffix, int outDim, const std::function<Expr(Expr)>& actFn = nullptr, float dropProb = 0.0f)
-  {
-    auto graph = x->graph();
-
-    auto W = graph->param(prefix + "_W" + suffix, { x->shape()[-1], outDim }, inits::glorotUniform());
-    auto b = graph->param(prefix + "_b" + suffix, { 1,              outDim }, inits::zeros());
-
-    x = affine(x, W, b);
-    if (actFn)
-      x = actFn(x);
-    x = dropout(x, dropProb);
-    return x;
-  }
-
-  Expr layerNorm(Expr x, std::string prefix, std::string suffix = std::string()) const {
-    int dimModel = x->shape()[-1];
-    auto scale = graph_->param(prefix + "_ln_scale" + suffix, { 1, dimModel }, inits::ones());
-    auto bias  = graph_->param(prefix + "_ln_bias"  + suffix, { 1, dimModel }, inits::zeros());
-    return marian::layerNorm(x, scale, bias, 1e-6f);
-  }
-
   Expr preProcess(std::string prefix, std::string ops, Expr input, float dropProb = 0.0f) const {
     auto output = input;
     for(auto op : ops) {
@@ -192,7 +169,7 @@ class Transformer : public EncoderOrDecoderBase {
       // highway connection
       else if(op == 'h') {
         int dimModel = input->shape()[-1];
-        auto t = dense(prevInput, prefix, /*suffix=*/"h", dimModel);
+        auto t = denseInline(prevInput, prefix, /*suffix=*/"h", dimModel);
         output = highway(output, prevInput, t);
       }
       // layer normalization
@@ -402,8 +379,8 @@ class Transformer : public EncoderOrDecoderBase {
 
     // the stack of FF layers
     for(int i = 1; i < depthFfn; ++i)
-      output = dense(output, prefix, /*suffix=*/std::to_string(i), dimFfn, actFn, ffnDropProb);
-    output = dense(output, prefix, /*suffix=*/std::to_string(depthFfn), dimModel);
+      output = denseInline(output, prefix, /*suffix=*/std::to_string(i), dimFfn, actFn, ffnDropProb);
+    output = denseInline(output, prefix, /*suffix=*/std::to_string(depthFfn), dimModel);
 
     auto opsPost = opt<std::string>("transformer-postprocess");
     output
@@ -430,14 +407,14 @@ class Transformer : public EncoderOrDecoderBase {
 
     // the stack of AAN layers
     for(int i = 1; i < depthAan; ++i)
-      y = dense(y, prefix, /*suffix=*/std::to_string(i), dimAan, actFn, aanDropProb);
+      y = denseInline(y, prefix, /*suffix=*/std::to_string(i), dimAan, actFn, aanDropProb);
     if(y->shape()[-1] != dimModel) // bring it back to the desired dimension if needed
-      y = dense(y, prefix, std::to_string(depthAan), dimModel);
+      y = denseInline(y, prefix, std::to_string(depthAan), dimModel);
 
     bool noGate = opt<bool>("transformer-aan-nogate");
     if(!noGate) {
-      auto gi = dense(x, prefix, /*suffix=*/"i", dimModel, (ActivationFunction*)sigmoid);
-      auto gf = dense(y, prefix, /*suffix=*/"f", dimModel, (ActivationFunction*)sigmoid);
+      auto gi = denseInline(x, prefix, /*suffix=*/"i", dimModel, (ActivationFunction*)sigmoid);
+      auto gf = denseInline(y, prefix, /*suffix=*/"f", dimModel, (ActivationFunction*)sigmoid);
       y = gi * x + gf * y;
     }
 
@@ -533,28 +510,29 @@ class EncoderTransformer : public Transformer<EncoderBase> {
     batchEmbeddings = addSpecialEmbeddings(batchEmbeddings, /*start=*/0, batch);
     
     // reorganize batch and timestep
-    batchEmbeddings = atleast_nd(batchEmbeddings, 4);
-    batchMask = atleast_nd(batchMask, 4);
-    auto layer = transposeTimeBatch(batchEmbeddings); // [-4: beam depth=1, -3: batch size, -2: max length, -1: vector dim]
-    auto layerMask
-      = reshape(transposeTimeBatch(batchMask), {1, dimBatch, 1, dimSrcWords}); // [-4: beam depth=1, -3: batch size, -2: vector dim=1, -1: max length]
+    batchEmbeddings = atleast_nd(batchEmbeddings, 4); // [beam depth=1, max length, batch size, vector dim]
+    batchMask       = atleast_nd(batchMask, 4);       // [beam depth=1, max length, batch size, vector dim=1]
 
-    auto opsEmb = opt<std::string>("transformer-postprocess-emb");
+    auto layer     = transposeTimeBatch(batchEmbeddings); // [beam depth=1, batch size, max length, vector dim]
+    auto layerMask = transposeTimeBatch(batchMask);       // [beam depth=1, batch size, max length, vector dim=1]
 
+    auto opsEmb = opt<std::string>("transformer-postprocess-emb");
     float dropProb = inference_ ? 0 : opt<float>("transformer-dropout");
     layer = preProcess(prefix_ + "_emb", opsEmb, layer, dropProb);
 
-    layerMask = transposedLogMask(layerMask); // [-4: batch size, -3: 1, -2: vector dim=1, -1: max length]
+    // LayerAttention expects mask in a different layout
+    layerMask = reshape(layerMask, {1, dimBatch, 1, dimSrcWords}); // [1,          batch size,            1,                      max length]
+    layerMask = transposedLogMask(layerMask);                      // [batch size, num heads broadcast=1, max length broadcast=1, max length]
 
     // apply encoder layers
+    // This is the Transformer Encoder stack.
     auto encDepth = opt<int>("enc-depth");
     for(int i = 1; i <= encDepth; ++i) {
       layer = LayerAttention(prefix_ + "_l" + std::to_string(i) + "_self",
                              layer, // query
                              layer, // keys
                              layer, // values
-                             layerMask);
-
+                             layerMask); // [batch size, num heads broadcast=1, max length broadcast=1, max length]
       layer = LayerFFN(prefix_ + "_l" + std::to_string(i) + "_ffn", layer);
     }
 
@@ -698,12 +676,14 @@ class DecoderTransformer : public Transformer<DecoderBase> {
 
     std::vector<Expr> encoderContexts;
     std::vector<Expr> encoderMasks;
-
     for(auto encoderState : state->getEncoderStates()) {
-      auto encoderContext = encoderState->getContext();
-      auto encoderMask = encoderState->getMask();
+      auto encoderContext = encoderState->getContext(); // encoder output
+      auto encoderMask = encoderState->getMask(); // note: may differ from Encoder self-attention mask in that additional positions are banned for cross-attention
+      encoderMask = atleast_nd(encoderMask, 4);
+
+      encoderContext = transposeTimeBatch(encoderContext); // [beam depth=1, batch size, max length, vector dim]
+      encoderMask    = transposeTimeBatch(encoderMask);    // [beam depth=1, max length, batch size, vector dim=1]
 
-      encoderContext = transposeTimeBatch(encoderContext); // [-4: beam depth=1, -3: batch size, -2: max length, -1: vector dim]
       int dimSrcWords = encoderContext->shape()[-2];
 
       // This would happen if something goes wrong during batch pruning.
@@ -712,10 +692,9 @@ class DecoderTransformer : public Transformer<DecoderBase> {
                encoderContext->shape()[-3], 
                dimBatch);
 
-      encoderMask = atleast_nd(encoderMask, 4);
-      encoderMask = reshape(transposeTimeBatch(encoderMask),
-                            {1, dimBatch, 1, dimSrcWords});
-      encoderMask = transposedLogMask(encoderMask);
+      // LayerAttention expects mask in a different layout
+      encoderMask = reshape(encoderMask, { 1, dimBatch, 1, dimSrcWords }); // [1,          batch size,            1,                      max length]
+      encoderMask = transposedLogMask(encoderMask);                        // [batch size, num heads broadcast=1, max length broadcast=1, max length]
       if(dimBeam > 1)
         encoderMask = repeat(encoderMask, dimBeam, /*axis=*/ -4);
 
diff --git a/src/optimizers/optimizers.cpp b/src/optimizers/optimizers.cpp
old mode 100644
new mode 100755
diff --git a/src/tensors/gpu/tensor_operators.cu b/src/tensors/gpu/tensor_operators.cu
old mode 100644
new mode 100755
index 2b5f51ce7..325643502
--- a/src/tensors/gpu/tensor_operators.cu
+++ b/src/tensors/gpu/tensor_operators.cu
@@ -980,7 +980,7 @@ __global__ void gPasteRows(T* out,
                            const IndexType* targetRowIdx,
                            size_t rows) {
   for(int bid = 0; bid < rows; bid += gridDim.x) {
-    int j = bid + blockIdx.x;
+    int j = bid + blockIdx.x; // index into 'indices' vector
     if(j < rows) {
       size_t dstId = targetRowIdx[j];
       size_t srcId = j;
@@ -988,11 +988,15 @@ __global__ void gPasteRows(T* out,
       T* rowOut = out + dstId * cols;
       const T* rowIn = in + srcId * cols;
 
+      // aggregate the entire row
       for(int tid = 0; tid < cols; tid += blockDim.x) {
-        int i = tid + threadIdx.x;
+        int i = tid + threadIdx.x; // column index   --@TODO: column index should be called 'j'
         if(i < cols) {
-          // @TODO: Do we need to get rid of this atomic add? It seems slow for fp16
-          atomics::atomicAdd(rowOut + i, rowIn[i]);
+          // Note: atomicAdd() not needed if number of blocks is 1. Avoid it because it is slow for fp16.
+          if (gridDim.x == 1)
+            rowOut[i] += rowIn[i];
+          else
+            atomics::atomicAdd(rowOut + i, rowIn[i]);
         }
       }
     }
@@ -1011,7 +1015,15 @@ void PasteRows(Tensor out,
   size_t rowsToCopy = indices->size();
 
   int threads = std::min(MAX_THREADS, (int)cols);
+#if 1   // @TODO: make this configurable with a 'deterministic' flag
+  // If we only use one block, then each core operates on a different column,
+  // hence the summation becomes deterministic.
+  // However, we only use e.g. 512 cores out of possibly 3000+, so this will be
+  // 6 x slower in this example.
+  int blocks = 1;
+#else
   int blocks = std::min(MAX_BLOCKS, (int)rowsToCopy);
+#endif
 
   if(out->type() == Type::float32) {
     gPasteRows<<<blocks, threads>>>(
diff --git a/src/training/graph_group_sync.cpp b/src/training/graph_group_sync.cpp
index 57a54769d..ce4fa179a 100755
--- a/src/training/graph_group_sync.cpp
+++ b/src/training/graph_group_sync.cpp
@@ -401,15 +401,20 @@ void SyncGraphGroup::update(std::vector<Ptr<data::Batch>> subBatches, size_t num
           paramsAvg_[idx], curParam, scheduler_->numberOfBatches(), updateTrgWords);
   };
 
-  comm_->scatterReduceAndResetGrads(); // reduce gradients across all devices (globally) into shards
-  comm_->foreach(update);              // per-shard model-update
-  comm_->allGatherParams();            // distribute param value shards back
-
   // cost across all local devices (scheduler will aggregate cross-process)
   StaticLoss localLoss;
   for(auto& l : localDeviceLosses) // localDeviceLosses is already summed up over delay steps
     localLoss += l;
 
+  // model update
+  if (std::isfinite(localLoss.loss) || mpi_->numMPIProcesses() > 1) { // guard against NaN (except with MPI, as this simple way could hang it)
+    comm_->scatterReduceAndResetGrads(); // reduce gradients across all devices and MPI nodes into shards
+    comm_->foreach(update);              // per-shard model-update
+    comm_->allGatherParams();            // distribute param value shards back
+  }
+  else
+    LOG(info, "[training] skipping {}-th update due to loss being {}", scheduler_->numberOfBatches(), localLoss.loss);
+
   if(scheduler_) {
     // track and log localLoss
     scheduler_->update(localLoss, numReadBatches, effectiveBatchSize, effectiveBatchTrgWords, mpi_);
diff --git a/src/training/scheduler.h b/src/training/scheduler.h
old mode 100644
new mode 100755
index 19d389ad4..5fa81d0f6
--- a/src/training/scheduler.h
+++ b/src/training/scheduler.h
@@ -358,7 +358,7 @@ class Scheduler : public TrainingObserver {
        && heartBeatTimer_.elapsed<std::chrono::minutes>() >= 10) {
       printf("PROGRESS: %.2f%%\nEVALERR: %.7f%%\n",
           (double)state_->epochs,
-          state_->costSum / state_->costCount / (mpi ? mpi->numMPIProcesses() : 1));
+          state_->costSum / (state_->costCount ? state_->costCount : 1) / (mpi ? mpi->numMPIProcesses() : 1));
       fflush(stdout);
       std::cout << "MBSIZE: " << batchLabels << " after " << state_->batches << " updates = " << state_->labelsTotal << " labels" << std::endl << std::flush;
       heartBeatTimer_.start();
diff --git a/src/translator/beam_search.h b/src/translator/beam_search.h
index 3e66a5dc4..584facac0 100755
--- a/src/translator/beam_search.h
+++ b/src/translator/beam_search.h
@@ -99,12 +99,17 @@ class BeamSearch {
         // starting with the lemma, then adding factors one by one.
         if (factorGroup == 0) {
           word = factoredVocab->lemma2Word(shortlist ? shortlist->reverseMap(wordIdx) : wordIdx); // @BUGBUG: reverseMap is only correct if factoredVocab_->getGroupRange(0).first == 0
-          //std::vector<size_t> factorIndices; factoredVocab->word2factors(word, factorIndices);
-          //LOG(info, "new lemma {},{}={} -> {}->{}", word.toWordIndex(), factorIndices[0], factoredVocab->word2string(word), prevHyp->getPathScore(), pathScore);
+          std::vector<size_t> factorIndices; factoredVocab->word2factors(word, factorIndices);
+          //LOG(info, "{} + {} ({}) -> {} -> {}",
+          //    factoredVocab->decode(prevHyp->tracebackWords()),
+          //    factoredVocab->word2string(word), factorIndices[0], prevHyp->getPathScore(), pathScore);
         }
         else {
-          //LOG(info, "expand word {}={} with factor[{}] {} -> {}->{}", beam[beamHypIdx]->getWord().toWordIndex(),
-          //    factoredVocab->word2string(beam[beamHypIdx]->getWord()), factorGroup, wordIdx, prevHyp->getPathScore(), pathScore);
+          //LOG(info, "{} |{} ({}) = {} ({}) -> {} -> {}",
+          //    factoredVocab->decodeForDiagnostics(beam[beamHypIdx]->tracebackWords()),
+          //    factoredVocab->getFactorGroupPrefix(factorGroup), factorGroup,
+          //    factoredVocab->getFactorName(factorGroup, wordIdx), wordIdx,
+          //    prevHyp->getPathScore(), pathScore);
           word = beam[beamHypIdx]->getWord();
           ABORT_IF(!factoredVocab->canExpandFactoredWord(word, factorGroup),
                    "A word without this factor snuck through to here??");

From 0dc1ef11d30806a112bad891a1bed513d1b769d9 Mon Sep 17 00:00:00 2001
From: Martin Junczys-Dowmunt <Marcin.JunczysDowmunt@microsoft.com>
Date: Mon, 23 Dec 2019 20:04:13 +0000
Subject: [PATCH 806/838] Merged PR 10797: Differentiate packed8 type by layout

For FBGEMM based int8 implementation, packed matrix (model) could be different based on available AVX instruction sets. This PR split packed8 format into two separate data formats (packed8avx2, packed8avx512). And, this enables any packed model can be generated on any machine.
* Added packed8avx2, packed8avx512 types, removed packe8 type
* Added blocking factors to the fbgemm interface based on the pack type for pack function and gem functions.
---
 src/command/marian_conv.cpp                   |  8 ++-
 src/common/types.cpp                          | 36 ++++++----
 src/common/types.h                            | 69 ++++++++++++++-----
 src/microsoft/quicksand.cpp                   |  2 +-
 src/tensors/cpu/fbgemm/expanded_gemm.h        | 20 ++++--
 .../cpu/fbgemm/expression_graph_packable.h    | 23 ++++---
 src/tensors/cpu/fbgemm/packed_gemm.cpp        | 69 ++++++++++++++++---
 src/tensors/cpu/fbgemm/packed_gemm.h          |  4 ++
 8 files changed, 173 insertions(+), 58 deletions(-)

diff --git a/src/command/marian_conv.cpp b/src/command/marian_conv.cpp
index 42eff924d..f77ea24d0 100644
--- a/src/command/marian_conv.cpp
+++ b/src/command/marian_conv.cpp
@@ -33,10 +33,12 @@ int main(int argc, char** argv) {
   Type saveGemmType;
   if(saveGemmTypeStr == "float32") {
     saveGemmType = Type::float32;
-  } else if(saveGemmTypeStr == "packed16") {
+  } else if(saveGemmTypeStr == "packed16") {  // packed16 only supports AVX2. AVX512 might be added later
     saveGemmType = Type::packed16;
-  } else if(saveGemmTypeStr == "packed8") {
-    saveGemmType = Type::packed8;
+  } else if(saveGemmTypeStr == "packed8avx2") { // packed8 for AVX2
+    saveGemmType = Type::packed8avx2;
+  } else if(saveGemmTypeStr == "packed8avx512") { // packed8 for AVX512
+    saveGemmType = Type::packed8avx512;
   } else {
     ABORT("Unknown gemm-type: {}", saveGemmTypeStr);
   }
diff --git a/src/common/types.cpp b/src/common/types.cpp
index e5c4e06fd..f358cdb62 100644
--- a/src/common/types.cpp
+++ b/src/common/types.cpp
@@ -9,22 +9,30 @@ namespace marian {
 // multiplying. All cases are handed here and can later be passed to allocators etc. 
 size_t requiredBytes(const Shape& shape, Type type) {
 #if USE_FBGEMM
-  if (type == Type::packed8)
-  {
-    int nrow, ncol;
-    uint64_t packsize;
-    cpu::variant::fbgemmPacked8PackInfo(shape, false, /*out=*/nrow, /*out=*/ncol, /*out=*/packsize);
-    return (size_t)packsize;
-  } else if (type == Type::packed16)
-  {
-    uint64_t packsize;
-    cpu::variant::fbgemmPacked16PackInfo(shape, false, /*out=*/packsize);
-    return (size_t)packsize;
-  } else
-#endif  // USE_FBGEMM
-  {
+  if (isPacked(type)) {
+    if (sizeOf(type) == 1) {
+      // Type::packed8avx2 || type == Type::packed8avx512
+      // AVX2 and AVX512 CPUs have different cache and vector lanes,
+      // so the optimal memory layouts for them are different.
+      int nrow, ncol;
+      uint64_t packsize;
+      cpu::variant::fbgemmPacked8PackInfo(shape, type, false, /*out=*/nrow, /*out=*/ncol, /*out=*/packsize);
+      return (size_t)packsize;
+    } else if (type == Type::packed16) {
+      uint64_t packsize;
+      cpu::variant::fbgemmPacked16PackInfo(shape, false, /*out=*/packsize);
+      return (size_t)packsize;
+    } else {
+      ABORT("Not a supported data type: {}", type);
+      return 0;
+    }
+  } else {
     return shape.elements() * sizeOf(type);
   }
+#else
+  return shape.elements() * sizeOf(type);
+#endif  // USE_FBGEMM
+  
 }
 
 }
\ No newline at end of file
diff --git a/src/common/types.h b/src/common/types.h
index 8b9026a95..24a8b4ec8 100644
--- a/src/common/types.h
+++ b/src/common/types.h
@@ -135,13 +135,19 @@ do { \
 namespace marian {
 
 // small struct to enable templating based on types use for packing
-struct packed8 {
+struct packed16 {
+  uint16_t x;
+};
+
+// small struct to enable templating based on types use for packing. This is a memory holder.
+// There's no difference between packed8avx2 and packed8avx512. But, they are separately defined to be distinguished.
+struct packed8avx2 {
   uint8_t x;
 };
 
-// small struct to enable templating based on types use for packing
-struct packed16 {
-  uint16_t x;
+// small struct to enable templating based on types use for packing. This is a memory holder.
+struct packed8avx512 {
+  uint8_t x;
 };
 
 #ifndef __CUDACC__ // vectorized types not available from .cu files
@@ -209,18 +215,25 @@ struct float32x8 {
 
 // Internal to types.h, don't use. Use test functions below.
 enum class TypeClass : size_t {
-  signed_type   = 0x100,
-  unsigned_type = 0x200,
-  float_type    = 0x400,
-  packed_type   = 0x800, // special packed (CPU cache friendly) type class, used in FBGEMM, not meant to be used anywhere else
+  signed_type   = 0x0100,
+  unsigned_type = 0x0200,
+  float_type    = 0x0400,
+
+  packed_type   = 0x0800, // special packed (CPU cache friendly) type class, used in FBGEMM, not meant to be used anywhere else
+  avx2_type     = 0x1000, // processor-specific layout for avx2, currently used for FBGEMM only
+  avx512_type   = 0x2000, // processor-specific layout for avx512, currently used for FBGEMM only
 
-  size_mask     = 0x0FF
+  size_mask     = 0x00FF
 };
 
 constexpr inline size_t operator+(TypeClass typeClass, size_t val) {
   return (size_t)typeClass + val;
 }
 
+constexpr inline size_t operator+(size_t val, TypeClass typeClass) {
+  return val + (size_t)typeClass;
+}
+
 // @TODO: rename to ElementType when things become stable, so it's easier to review
 enum class Type : size_t {
   int8     = TypeClass::signed_type + 1u,
@@ -237,8 +250,10 @@ enum class Type : size_t {
   float32  = TypeClass::float_type + 4u,
   float64  = TypeClass::float_type + 8u,
 
-  packed8  = TypeClass::packed_type + 1u, // special type for FBGEMM, not meant to be used anywhere else, not meant to be accessed invidually. Internal actual type (uint8) is meaningless.
-  packed16 = TypeClass::packed_type + 2u  // special type for FBGEMM, not meant to be used anywhere else, not meant to be accessed invidually. Internal actual type (uint16) is meaningless.
+  packed16      = TypeClass::packed_type + 2u,                          // special type for FBGEMM, not meant to be used anywhere else, not meant to be accessed invidually. Internal actual type (uint16) is meaningless.
+  packed8avx2   = TypeClass::packed_type + 1u + TypeClass::avx2_type,   // special type for FBGEMM with AVX2, not meant to be used anywhere else, not meant to be accessed invidually. Internal actual type (uint8) is meaningless.
+  packed8avx512 = TypeClass::packed_type + 1u + TypeClass::avx512_type, // special type for FBGEMM with AVX512, not meant to be used anywhere else, not meant to be accessed invidually. Internal actual type (uint8) is meaningless.
+  
 };
 
 static inline size_t operator&(TypeClass typeClass, Type type) {
@@ -269,6 +284,14 @@ static inline bool isPacked(Type type) {
   return (TypeClass::packed_type & type) != 0;
 }
 
+static inline bool isAvx2(Type type) {
+  return (TypeClass::avx2_type & type) != 0;
+}
+
+static inline bool isAvx512(Type type) {
+  return (TypeClass::avx512_type & type) != 0;
+}
+
 size_t requiredBytes(const Shape& shape, Type type); // towards Frank's vision of joint Shape/Type
 
 template <typename T>
@@ -290,8 +313,9 @@ template <> inline bool matchType<float16>(Type type)  { return type == Type::fl
 template <> inline bool matchType<float>(Type type)    { return type == Type::float32;  }
 template <> inline bool matchType<double>(Type type)   { return type == Type::float64;  }
 
-template <> inline bool matchType<packed8>(Type type)  { return type == Type::packed8;  }
-template <> inline bool matchType<packed16>(Type type) { return type == Type::packed16; }
+template <> inline bool matchType<packed16>(Type type)       { return type == Type::packed16;       }
+template <> inline bool matchType<packed8avx2>(Type type)    { return type == Type::packed8avx2;    }
+template <> inline bool matchType<packed8avx512>(Type type)  { return type == Type::packed8avx512;  }
 // clang-format on
 
 static inline std::ostream& operator<<(std::ostream& out, Type type) {
@@ -310,8 +334,9 @@ static inline std::ostream& operator<<(std::ostream& out, Type type) {
     case Type::float32 : out << "float32"; break;
     case Type::float64 : out << "float64"; break;
 
-    case Type::packed8 : out << "packed8"; break;
-    case Type::packed16: out << "packed16"; break;
+    case Type::packed16      : out << "packed16"; break;
+    case Type::packed8avx2   : out << "packed8avx2"; break;
+    case Type::packed8avx512 : out << "packed8avx512"; break;
   }
   return out;
 }
@@ -334,8 +359,9 @@ template <> inline std::string request<float16>()  { return "float16"; }
 template <> inline std::string request<float>()    { return "float32"; }
 template <> inline std::string request<double>()   { return "float64"; }
 
-template <> inline std::string request<packed8>()  { return "packed8"; }
 template <> inline std::string request<packed16>() { return "packed16"; }
+template <> inline std::string request<packed8avx2>()  { return "packed8avx2"; }
+template <> inline std::string request<packed8avx512>()  { return "packed8avx512"; }
 // clang-format on
 
 static Type inline typeFromString(const std::string& str) {
@@ -363,6 +389,13 @@ static Type inline typeFromString(const std::string& str) {
     return Type::float32;
   if(str == "float64")
     return Type::float64;
+  
+  if(str == "packed16")
+    return Type::packed16;
+  if(str == "packed8avx2")
+    return Type::packed8avx2;
+  if(str == "packed8avx512")
+    return Type::packed8avx512;
 
   ABORT("Unknown type {}", str);
 }
@@ -384,6 +417,10 @@ template <> inline Type typeId<float16>()  { return Type::float16; }
 template <> inline Type typeId<float>()    { return Type::float32; }
 template <> inline Type typeId<double>()   { return Type::float64; }
 
+template <> inline Type typeId<packed16>()      { return Type::packed16; }
+template <> inline Type typeId<packed8avx2>()   { return Type::packed8avx2; }
+template <> inline Type typeId<packed8avx512>() { return Type::packed8avx512; }
+
 // Abort if given C++ does not correspond to runtime type
 template <typename T>
 void matchOrAbort(Type type) {
diff --git a/src/microsoft/quicksand.cpp b/src/microsoft/quicksand.cpp
index d174e6cb7..0f0ea2533 100755
--- a/src/microsoft/quicksand.cpp
+++ b/src/microsoft/quicksand.cpp
@@ -262,7 +262,7 @@ bool convertModel(std::string inputFile, std::string outputFile, int32_t targetP
   if (targetPrec == 16)
     saveGemmType = Type::packed16;
   else if (targetPrec == 8)
-    saveGemmType = Type::packed8;
+    saveGemmType = Type::packed8avx2; // We currently use avx2 by default.
 
   // added a flag if the weights needs to be packed or not
   graph->packAndSave(outputFile, configStr.str(), saveGemmType);
diff --git a/src/tensors/cpu/fbgemm/expanded_gemm.h b/src/tensors/cpu/fbgemm/expanded_gemm.h
index a5c9ed06f..cf53fe1a1 100644
--- a/src/tensors/cpu/fbgemm/expanded_gemm.h
+++ b/src/tensors/cpu/fbgemm/expanded_gemm.h
@@ -126,6 +126,7 @@ struct FbgemmPacked16PackNodeOp : public UnaryNodeOp {
 
 // Pack a matrix (int8) into cache utilization efficient way (block format) together with quantization into int8
 // PackMatrix packMat_: the type of packed matrix - A or B matrix
+// marian::Type packType_: the type the input matrix is packed - packed8avx2 or packed8avx512
 // bool transpose_: transpose
 // int nrow_: the number of rows
 // int ncol_: the number of columns
@@ -133,14 +134,20 @@ struct FbgemmPacked16PackNodeOp : public UnaryNodeOp {
 //                    (the size of int8 packed B from fbgemm:PackAWithQuantRowOffset + quantization scale, offset and zero point)
 struct FbgemmPacked8PackNodeOp : public UnaryNodeOp {
   PackMatrix packMat_;
+  marian::Type packType_;
   bool transpose_;
   int nrow_;
   int ncol_;
   uint64_t packsize_;
 
-  FbgemmPacked8PackNodeOp(Expr a, PackMatrix packMat, bool transpose, float clipValue)
+  FbgemmPacked8PackNodeOp(Expr a,
+                          PackMatrix packMat,
+                          marian::Type packType,
+                          bool transpose,
+                          float clipValue)
       : UnaryNodeOp(a, newShape(a, transpose), Type::uint8),
         packMat_(packMat),
+        packType_(packType),
         transpose_(transpose) {
     if(packMat != PackMatrix::B)
       ABORT("Only prepacking of B (weight matrix) is supported");
@@ -154,6 +161,7 @@ struct FbgemmPacked8PackNodeOp : public UnaryNodeOp {
 #if USE_FBGEMM
     return {NodeOp(fbgemmPacked8Pack(val_,
                                      child(0)->val()->data(),
+                                     packType_,
                                      transpose_,
                                      nrow_,
                                      ncol_,
@@ -174,7 +182,7 @@ struct FbgemmPacked8PackNodeOp : public UnaryNodeOp {
 
   Shape newShape(Expr a, bool transpose) {
 #if USE_FBGEMM
-    fbgemmPacked8PackInfo(a->shape(), transpose, nrow_, ncol_, packsize_);
+    fbgemmPacked8PackInfo(a->shape(), packType_, transpose, nrow_, ncol_, packsize_);
     Shape outShape({(int)packsize_});
 
     return outShape;
@@ -362,7 +370,7 @@ static inline Expr affine(Expr a, Expr b, Shape bShape, Expr c, bool transA, boo
 
   if (elementType == Type::packed16)
     return Expression<cpu::variant::FbgemmPacked16AffineNodeOp>(nodes, bShape, transA, transB, scalar);
-  else if (elementType == Type::packed8)
+  else if (isPacked(elementType) && sizeOf(elementType) == 1)
     return Expression<cpu::variant::FbgemmPacked8AffineNodeOp>(nodes, bShape, transA, transB, scalar);
   else {
     ABORT("Only int8 and fp16 are available. {}", elementType);
@@ -373,8 +381,8 @@ static inline Expr affine(Expr a, Expr b, Shape bShape, Expr c, bool transA, boo
 static inline Expr pack(Type elementType, Expr a, PackMatrix packMat, bool transpose, float clipValue) {
   if (elementType == Type::packed16)
     return Expression<cpu::variant::FbgemmPacked16PackNodeOp>(a, packMat, transpose, clipValue);
-  else if (elementType == Type::packed8)
-    return Expression<cpu::variant::FbgemmPacked8PackNodeOp>(a, packMat, transpose, clipValue);
+  else if (isPacked(elementType) && sizeOf(elementType) == 1)
+    return Expression<cpu::variant::FbgemmPacked8PackNodeOp>(a, packMat, elementType, transpose, clipValue);
   else {
     ABORT("Only int8 and fp16 are available. {}", elementType);
     return nullptr;
@@ -387,7 +395,7 @@ static inline Expr dot(Expr a, Expr b, Shape bShape, bool transA, bool transB, f
 
   if (elementType == Type::packed16)
     return Expression<cpu::variant::FbgemmPacked16AffineNodeOp>(nodes, bShape, transA, transB, scalar);
-  else if (elementType == Type::packed8)
+  else if (isPacked(elementType) && sizeOf(elementType) == 1)
     return Expression<cpu::variant::FbgemmPacked8AffineNodeOp>(nodes, bShape, transA, transB, scalar);
   else {
     ABORT("Only int8 and fp16 are available. {}", elementType);
diff --git a/src/tensors/cpu/fbgemm/expression_graph_packable.h b/src/tensors/cpu/fbgemm/expression_graph_packable.h
index 62bad1072..743b7c8cb 100644
--- a/src/tensors/cpu/fbgemm/expression_graph_packable.h
+++ b/src/tensors/cpu/fbgemm/expression_graph_packable.h
@@ -36,7 +36,8 @@ class ExpressionGraphPackable : public ExpressionGraph {
 
       // save as packed format
       // @TODO Hardcoded to find packable weights - all the weights used for affine op (fp16), all the weights used for affine op and dot op (int8)
-      if (gemmElementType == Type::packed8 && (pName.find("_W") == pName.length() - 3 || pName.find("_W") == pName.length() - 2)) {
+      if ((gemmElementType == Type::packed8avx2 || gemmElementType == Type::packed8avx512)
+        && (pName.find("_W") == pName.length() - 3 || pName.find("_W") == pName.length() - 2)) {
   #if USE_FBGEMM
         using namespace marian::cpu::variant;
         // packing information - size
@@ -45,10 +46,11 @@ class ExpressionGraphPackable : public ExpressionGraph {
         uint64_t packsize;
 
         fbgemmPacked8PackInfo(val->shape(),
-          pName.find("Wemb") != std::string::npos,
-          nrow,
-          ncol,
-          packsize);
+                              gemmElementType,
+                              pName.find("Wemb") != std::string::npos,
+                              nrow,
+                              ncol,
+                              packsize);
 
         auto allocator = New<TensorAllocator>(getBackend());
 
@@ -58,11 +60,12 @@ class ExpressionGraphPackable : public ExpressionGraph {
 
         //Pack B matrix into int8
         fbgemmPacked8Pack(packedTensor,
-          val->data(),
-          pName.find("Wemb") != std::string::npos,
-          nrow,
-          ncol,
-          packsize);
+                          val->data(),
+                          gemmElementType,
+                          pName.find("Wemb") != std::string::npos,
+                          nrow,
+                          ncol,
+                          packsize);
         io::Item item;
         item.name = pName;
         item.shape = val->shape();
diff --git a/src/tensors/cpu/fbgemm/packed_gemm.cpp b/src/tensors/cpu/fbgemm/packed_gemm.cpp
index e638f5d97..30b488177 100644
--- a/src/tensors/cpu/fbgemm/packed_gemm.cpp
+++ b/src/tensors/cpu/fbgemm/packed_gemm.cpp
@@ -113,6 +113,39 @@ inline uint64_t addr(const int r_,
   return index;
 }
 
+// Memory blocking factors (parameters) for packing into AVX2 int8
+static const fbgemm::BlockingFactors Packed8Avx2BlockingFactors = {
+    PackingTraits<int8_t, int32_t, inst_set_t::avx2>::MR,
+    PackingTraits<int8_t, int32_t, inst_set_t::avx2>::NR,
+    PackingTraits<int8_t, int32_t, inst_set_t::avx2>::NR_MIN,
+    PackingTraits<int8_t, int32_t, inst_set_t::avx2>::ROW_INTERLEAVE,
+    PackingTraits<int8_t, int32_t, inst_set_t::avx2>::MCB,
+    PackingTraits<int8_t, int32_t, inst_set_t::avx2>::KCB,
+    PackingTraits<int8_t, int32_t, inst_set_t::avx2>::NCB
+};
+
+// Memory blocking factors (parameters) for packing into AVX512 int8
+static const fbgemm::BlockingFactors Packed8Avx512BlockingFactors = {
+    PackingTraits<int8_t, int32_t, inst_set_t::avx512>::MR,
+    PackingTraits<int8_t, int32_t, inst_set_t::avx512>::NR,
+    PackingTraits<int8_t, int32_t, inst_set_t::avx512>::NR_MIN,
+    PackingTraits<int8_t, int32_t, inst_set_t::avx512>::ROW_INTERLEAVE,
+    PackingTraits<int8_t, int32_t, inst_set_t::avx512>::MCB,
+    PackingTraits<int8_t, int32_t, inst_set_t::avx512>::KCB,
+    PackingTraits<int8_t, int32_t, inst_set_t::avx512>::NCB
+};
+
+// This function returns the correct blocking factors structure for given packing type.
+inline const fbgemm::BlockingFactors* getBlockingFactors(marian::Type packType) {
+  if(packType == Type::packed8avx2) {
+    return &Packed8Avx2BlockingFactors;
+  } else if(packType == Type::packed8avx512) {
+    return &Packed8Avx512BlockingFactors;
+  } else {
+    ABORT("Only avx2 and avx512 instruction sets are supported for int8. {}", packType);
+  }
+}
+
 void fbgemmPacked16PackInfo(const marian::Shape& shape,
                             const bool transpose,
                             uint64_t& packsize) {
@@ -145,6 +178,7 @@ void fbgemmPacked16PackInfo(const marian::Shape& shape,
 }
 
 void fbgemmPacked8PackInfo(const marian::Shape& shape,
+                           const marian::Type packType,
                            const bool transpose,
                            int& nrow,
                            int& ncol,
@@ -154,9 +188,12 @@ void fbgemmPacked8PackInfo(const marian::Shape& shape,
             "Weight Matrix should be 2D");
     nrow = transpose ? shape[1] : shape[0];
     ncol = transpose ? shape[0] : shape[1];
+
+    const fbgemm::BlockingFactors* params = getBlockingFactors(packType);
+
     packsize = fbgemm::PackMatrix<fbgemm::PackBMatrix<int8_t>, int8_t>::packedBufferSize(
         transpose ? shape[1] : shape[0],
-        transpose ? shape[0] : shape[1]);
+        transpose ? shape[0] : shape[1], params);
     // add extra space for storing some other variables specific to B matrix
     // quantization sacles: 1 per column and float
     // quantization offset: 1 per column and int32
@@ -229,6 +266,7 @@ void fbgemmPacked16Pack(marian::Tensor out,
 
 void fbgemmPacked8Pack(marian::Tensor out,
                        const float* inData,
+                       const marian::Type packType,
                        const bool transpose,
                        const int nrow,
                        const int ncol,
@@ -318,9 +356,11 @@ void fbgemmPacked8Pack(marian::Tensor out,
   }
 
   // 4. packing
+  const fbgemm::BlockingFactors* params = getBlockingFactors(packType);
+  
   PackBMatrix<int8_t> packedBN(
       transpose ? matrix_op_t::Transpose : matrix_op_t::NoTranspose,
-      nrow, ncol, quantized, transpose ? nrow : ncol, packedbuf, 1);
+      nrow, ncol, quantized, transpose ? nrow : ncol, packedbuf, 1, params);
 
   // copy quantization scale
   memcpy(packedbuf + (packsize - n * (sizeof(float) + sizeof(int32_t) + sizeof(int32_t))), bqScale, n * sizeof(float));
@@ -428,6 +468,18 @@ void fbgemmPacked8Gemm(marian::Tensor C,
                        const size_t k,
                        const int transA,
                        const int transB) {
+  // pack type
+  marian::Type packType = B->type();
+
+  const fbgemm::BlockingFactors* params = getBlockingFactors(packType);
+
+  if((packType == Type::packed8avx2 && fbgemmHasAvx512Support())
+     || (packType == Type::packed8avx512 && !fbgemmHasAvx512Support())) {
+    ABORT("FBGEMM doesn't allow to use {} packing order on {} CPUs",
+          packType == Type::packed8avx2 ? "AVX2" : "AVX512",
+          fbgemmHasAvx512Support() ? "AVX512" : "AVX2");
+  }
+
   // compute range to quantize A (activations) - (min/max quantization)
   float min_est = std::numeric_limits<float>::max(), max_est = std::numeric_limits<float>::min();
 
@@ -442,15 +494,16 @@ void fbgemmPacked8Gemm(marian::Tensor C,
   std::vector<int32_t> row_offset_buf(PackAWithQuantRowOffset<uint8_t>::rowOffsetBufferSize());
   PackAWithQuantRowOffset<uint8_t> packAN(
       transA ? matrix_op_t::Transpose : matrix_op_t::NoTranspose,
-      (int32_t) (transA ? k : m),
-      (int32_t) (transA ? m : k),
+      (int32_t)(transA ? k : m),
+      (int32_t)(transA ? m : k),
       A->data(),
-      (int32_t) (transA ? m : k),
+      (int32_t)(transA ? m : k),
       nullptr, /*buffer for packed matrix*/
       ascale,
       azeropoint,
       1, /*groups*/
-      row_offset_buf.data());
+      row_offset_buf.data(),
+      params);
 
   // packed matrix size of B
   int bPackSize = PackMatrix<PackBMatrix<int8_t>, int8_t>::packedBufferSize((int32_t)k, (int32_t)n);
@@ -479,10 +532,10 @@ void fbgemmPacked8Gemm(marian::Tensor C,
       (std::uint32_t) n);
 
   PackBMatrix<int8_t> repackedBN(
-    transB ? matrix_op_t::Transpose : matrix_op_t::NoTranspose, (int32_t) k, (int32_t) n, bdata, (int32_t) (transB ? k : n, 1));
+    transB ? matrix_op_t::Transpose : matrix_op_t::NoTranspose, (int32_t) k, (int32_t) n, bdata, (int32_t) (transB ? k : n), 1, params);
 
   // gemm computation
-  fbgemmPacked(packAN, repackedBN, C->data(), (int32_t*)C->data(), (int32_t) n, outputProcObj, 0, 1);
+  fbgemmPacked(packAN, repackedBN, C->data(), (int32_t*)C->data(), (int32_t) n, outputProcObj, 0, 1, params);
 
   delete[] col_offsets;
   delete[] bqZeropoint;
diff --git a/src/tensors/cpu/fbgemm/packed_gemm.h b/src/tensors/cpu/fbgemm/packed_gemm.h
index b41ad26ea..d0a63ea99 100644
--- a/src/tensors/cpu/fbgemm/packed_gemm.h
+++ b/src/tensors/cpu/fbgemm/packed_gemm.h
@@ -46,11 +46,13 @@ void fbgemmPacked16PackInfo(const marian::Shape& shape,
 // Returns the byte size of packed matrix in int8. It's calculated by fbgemm's internal logic due to the paddings and different layouts.
 // See '3rd_party/fbgemm/src/PackBMatrix.cc'.
 // shape: shape of the tensor to be packed
+// packType: Type to be packed - packed8avx2 or packed8avx512
 // transpose: the matrix is transposed
 // nrow (out): the number of rows
 // ncol (out): the number of columns
 // packsize (out): the size of the packed matrix in byte
 void fbgemmPacked8PackInfo(const marian::Shape& shape,
+                           const marian::Type packType,
                            const bool transpose,
                            /*out*/int& nrow,
                            /*out*/int& ncol,
@@ -86,6 +88,7 @@ void fbgemmPacked16Pack(marian::Tensor out,
 // Pack a matrix (int8) into cache utilization efficient way (block format) together with quantization into int8
 // out: output tensor - packed format and quantized into int8
 // inData: input tensor data - pointer of float data
+// packType: Type to be packed - packed8avx2 or packed8avx512
 // transpose: the matrix is transposed
 // nrow: the number of rows
 // ncol: the number of columns
@@ -93,6 +96,7 @@ void fbgemmPacked16Pack(marian::Tensor out,
 //          (the size of int8 packed B from fbgemm:PackAWithQuantRowOffset + quantization scale, offset and zero point)
 void fbgemmPacked8Pack(marian::Tensor out,
                        const float* inData,
+                       const marian::Type packType,
                        const bool transpose,
                        const int nrow,
                        const int ncol,

From 2bd986d8a775bd1fad07373c5d13d4ba24ec5b54 Mon Sep 17 00:00:00 2001
From: Marcin Junczys-Dowmunt <Marcin.JunczysDowmunt@microsoft.com>
Date: Mon, 23 Dec 2019 12:10:30 -0800
Subject: [PATCH 807/838] update version and changelog

---
 CHANGELOG.md | 1 +
 VERSION      | 2 +-
 2 files changed, 2 insertions(+), 1 deletion(-)

diff --git a/CHANGELOG.md b/CHANGELOG.md
index 8921d47ed..806a1757d 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -9,6 +9,7 @@ and this project adheres to [Semantic Versioning](http://semver.org/spec/v2.0.0.
 ## [Unreleased]
 
 ### Added
+- Separate quantization types for 8-bit FBGEMM for AVX2 and AVX512
 - Sequence-level unliklihood training
 - Allow file name templated valid-translation-output files
 - Support for lexical shortlists in marian-server
diff --git a/VERSION b/VERSION
index a1419ab46..e9bee49f7 100644
--- a/VERSION
+++ b/VERSION
@@ -1 +1 @@
-v1.8.33
+v1.8.35

From 24f062cd2719d874d0d5b01b658ab4d379403833 Mon Sep 17 00:00:00 2001
From: Roman Grundkiewicz <rgrundki@exseed.ed.ac.uk>
Date: Sat, 4 Jan 2020 03:10:21 +0000
Subject: [PATCH 808/838] Add option to print word-level scores (#501)

* Add printing word level scores

* Add option --no-spm-decode

* Fix precision for word-level scores

* Fix getting the no-spm-decode option

* Update CHANGELOG

* Add comments and refactor

* Print word-level scores next to other scores in an n-best list

* Remove --word-scores from marian-scorer

* Add --no-spm-decode only if compiled with SentencePiece

* Add comments

* Printing word scores before model scores in n-best lists

* Update VERSION

Co-authored-by: Marcin Junczys-Dowmunt <Marcin.JunczysDowmunt@microsoft.com>
---
 CHANGELOG.md                      |  2 ++
 VERSION                           |  2 +-
 src/common/config_parser.cpp      |  8 +++++
 src/data/alignment.cpp            |  4 +--
 src/data/sentencepiece_vocab.cpp  | 34 +++++++++++++--------
 src/translator/hypothesis.h       | 49 +++++++++++++++++++------------
 src/translator/output_printer.cpp | 13 ++++++--
 src/translator/output_printer.h   | 24 +++++++++++----
 8 files changed, 94 insertions(+), 42 deletions(-)

diff --git a/CHANGELOG.md b/CHANGELOG.md
index 806a1757d..734ea8916 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -9,6 +9,8 @@ and this project adheres to [Semantic Versioning](http://semver.org/spec/v2.0.0.
 ## [Unreleased]
 
 ### Added
+- An option to print word-level translation scores
+- An option to turn off automatic detokenization from SentencePiece
 - Separate quantization types for 8-bit FBGEMM for AVX2 and AVX512
 - Sequence-level unliklihood training
 - Allow file name templated valid-translation-output files
diff --git a/VERSION b/VERSION
index e9bee49f7..8726d325c 100644
--- a/VERSION
+++ b/VERSION
@@ -1 +1 @@
-v1.8.35
+v1.8.36
diff --git a/src/common/config_parser.cpp b/src/common/config_parser.cpp
index 93ae36441..894e7acc0 100755
--- a/src/common/config_parser.cpp
+++ b/src/common/config_parser.cpp
@@ -545,6 +545,8 @@ void ConfigParser::addOptionsValidation(cli::CLIWrapper& cli) {
       "Allow unknown words to appear in output");
   cli.add<bool>("--n-best",
       "Generate n-best list");
+  cli.add<bool>("--word-scores",
+      "Print word-level scores");
 
   // efficiency options
   cli.add<int>("--valid-mini-batch",
@@ -607,6 +609,12 @@ void ConfigParser::addOptionsTranslation(cli::CLIWrapper& cli) {
   cli.add<std::string>("--alignment",
      "Return word alignment. Possible values: 0.0-1.0, hard, soft")
     ->implicit_val("1");
+  cli.add<bool>("--word-scores",
+      "Print word-level scores");
+#ifdef USE_SENTENCEPIECE
+  cli.add<bool>("--no-spm-decode",
+      "Keep the output segmented into SentencePiece subwords");
+#endif
 
   addSuboptionsDevices(cli);
   addSuboptionsInputLength(cli);
diff --git a/src/data/alignment.cpp b/src/data/alignment.cpp
index 7a2a00030..928beb21f 100644
--- a/src/data/alignment.cpp
+++ b/src/data/alignment.cpp
@@ -8,9 +8,7 @@ namespace data {
 
 WordAlignment::WordAlignment() {}
 
-WordAlignment::WordAlignment(
-    const std::vector<Point>& align)
-    : data_(align) {}
+WordAlignment::WordAlignment(const std::vector<Point>& align) : data_(align) {}
 
 WordAlignment::WordAlignment(const std::string& line) {
   std::vector<std::string> atok = utils::splitAny(line, " -");
diff --git a/src/data/sentencepiece_vocab.cpp b/src/data/sentencepiece_vocab.cpp
index 2e9462b60..c168f6e3d 100644
--- a/src/data/sentencepiece_vocab.cpp
+++ b/src/data/sentencepiece_vocab.cpp
@@ -36,17 +36,18 @@ class SentencePieceVocab : public IVocab {
   std::mt19937 generator_;
   std::uniform_int_distribution<int> randInt_; // from 0 to INT_MAX
 
+  // Keeps sentences segmented into subword units
+  bool keepEncoded_{false};
+
   // Sample from one file, based on first algorithm from:
   // https://en.wikipedia.org/wiki/Reservoir_sampling
   void reservoirSampling(std::vector<std::string>& sample, size_t& seenLines,
                         const std::string& trainPath, size_t maxLines, size_t maxBytes) {
-
     ABORT_IF(maxLines == 0, "Sample needs to be larger 0");
 
-    std::unique_ptr<std::istream> trainStrm(
-			    trainPath == "stdin" ? new std::istream(std::cin.rdbuf())
-                           : new io::InputFileStream(trainPath)
-    );
+    std::unique_ptr<std::istream> trainStrm(trainPath == "stdin"
+                                                ? new std::istream(std::cin.rdbuf())
+                                                : new io::InputFileStream(trainPath));
 
     std::string line;
     while(getline(*trainStrm, line)) {
@@ -109,8 +110,10 @@ class SentencePieceVocab : public IVocab {
 
 public:
   SentencePieceVocab(Ptr<Options> options, size_t batchIndex)
-    : options_(options), batchIndex_(batchIndex), generator_((uint32_t)Config::seed) {
-
+      : options_(options),
+        batchIndex_(batchIndex),
+        generator_((uint32_t)Config::seed),
+        keepEncoded_(options->get<bool>("no-spm-decode", false)) {
     if(options_->has("sentencepiece-alphas")) {
       auto alphas = options_->get<std::vector<float>>("sentencepiece-alphas");
       if(alphas.size() <= batchIndex)
@@ -221,11 +224,18 @@ class SentencePieceVocab : public IVocab {
 
   std::string decode(const Words& sentence, bool /*ignoreEOS*/) const override {
     std::string line;
-    // convert vector of Word to vector of int
-    std::vector<int> spmSentence; spmSentence.reserve(sentence.size());
-    for (auto&& word : sentence)
-      spmSentence.push_back(word.toWordIndex());
-    spm_->Decode(spmSentence, &line);
+    if(keepEncoded_) {  // i.e. keep the sentence segmented into subword units
+      for(const Word& id : sentence)
+        line += (*this)[id] + " ";
+      line.pop_back();  // trim the trailing whitespace
+    } else {
+      // convert vector of Word to vector of int
+      std::vector<int> spmSentence;
+      spmSentence.reserve(sentence.size());
+      for(auto&& word : sentence)
+        spmSentence.push_back(word.toWordIndex());
+      spm_->Decode(spmSentence, &line);
+    }
     return line;
   }
 
diff --git a/src/translator/hypothesis.h b/src/translator/hypothesis.h
index a36296bd8..9bd21dcdc 100644
--- a/src/translator/hypothesis.h
+++ b/src/translator/hypothesis.h
@@ -42,33 +42,44 @@ class Hypothesis {
   float getPathScore() const { return pathScore_; }
 
   const std::vector<float>& getScoreBreakdown() { return scoreBreakdown_; }
-  void setScoreBreakdown(const std::vector<float>& scoreBreaddown) { scoreBreakdown_ = scoreBreaddown; }
+  void setScoreBreakdown(const std::vector<float>& scoreBreakdown) { scoreBreakdown_ = scoreBreakdown; }
 
   const std::vector<float>& getAlignment() { return alignment_; }
   void setAlignment(const std::vector<float>& align) { alignment_ = align; };
 
-  // helpers to trace back paths referenced from this hypothesis
-  Words tracebackWords()
-  {
-      Words targetWords;
-      for (auto hyp = this; hyp->getPrevHyp(); hyp = hyp->getPrevHyp().get()) {
-        targetWords.push_back(hyp->getWord());
-        // std::cerr << hyp->getWord() << " " << hyp << std::endl;
-      }
-      std::reverse(targetWords.begin(), targetWords.end());
-      return targetWords;
+  // trace back paths referenced from this hypothesis
+  Words tracebackWords() {
+    Words targetWords;
+    for(auto hyp = this; hyp->getPrevHyp(); hyp = hyp->getPrevHyp().get()) {
+      targetWords.push_back(hyp->getWord());
+    }
+    std::reverse(targetWords.begin(), targetWords.end());
+    return targetWords;
+  }
+
+  // calculate word-level scores for each target word by de-aggregating the path score
+  std::vector<float> tracebackWordScores() {
+    std::vector<float> scores;
+    // traverse hypotheses backward
+    for(auto hyp = this; hyp->getPrevHyp(); hyp = hyp->getPrevHyp().get()) {
+      // a path score is a cumulative score including scores from all preceding hypotheses (words),
+      // so calculate a word-level score by subtracting the previous path score from the current path score
+      auto prevPathScore = hyp->getPrevHyp() ? hyp->getPrevHyp().get()->pathScore_ : 0.f;
+      scores.push_back(hyp->pathScore_ - prevPathScore);
+    }
+    std::reverse(scores.begin(), scores.end());
+    return scores;
   }
 
   // get soft alignments [t][s] -> P(s|t) for each target word starting from the hyp one
   typedef data::SoftAlignment SoftAlignment;
-  SoftAlignment tracebackAlignment()
-  {
-      SoftAlignment align;
-      for (auto hyp = this; hyp->getPrevHyp(); hyp = hyp->getPrevHyp().get()) {
-          align.push_back(hyp->getAlignment());
-      }
-      std::reverse(align.begin(), align.end());
-      return align; // [t][s] -> P(s|t)
+  SoftAlignment tracebackAlignment() {
+    SoftAlignment align;
+    for(auto hyp = this; hyp->getPrevHyp(); hyp = hyp->getPrevHyp().get()) {
+      align.push_back(hyp->getAlignment());
+    }
+    std::reverse(align.begin(), align.end());
+    return align;  // [t][s] -> P(s|t)
   }
 
 private:
diff --git a/src/translator/output_printer.cpp b/src/translator/output_printer.cpp
index 7e2165ef8..f57ec9daa 100644
--- a/src/translator/output_printer.cpp
+++ b/src/translator/output_printer.cpp
@@ -1,5 +1,7 @@
 #include "output_printer.h"
 
+#include <sstream>
+
 namespace marian {
 
 std::string OutputPrinter::getAlignment(const Hypothesis::PtrType& hyp) {
@@ -19,11 +21,18 @@ std::string OutputPrinter::getAlignment(const Hypothesis::PtrType& hyp) {
   } else if(alignment_ == "hard") {
     return data::ConvertSoftAlignToHardAlign(align, 1.f).toString();
   } else if(alignmentThreshold_ > 0.f) {
-    return data::ConvertSoftAlignToHardAlign(align, alignmentThreshold_)
-        .toString();
+    return data::ConvertSoftAlignToHardAlign(align, alignmentThreshold_).toString();
   } else {
     ABORT("Unrecognized word alignment type");
   }
 }
 
+std::string OutputPrinter::getWordScores(const Hypothesis::PtrType& hyp) {
+  std::ostringstream scores;
+  scores.precision(5);
+  for(const auto& score : hyp->tracebackWordScores())
+    scores << " " << std::fixed << score;
+  return scores.str();
+}
+
 }  // namespace marian
diff --git a/src/translator/output_printer.h b/src/translator/output_printer.h
index c5e61f07d..603eedba5 100755
--- a/src/translator/output_printer.h
+++ b/src/translator/output_printer.h
@@ -20,12 +20,14 @@ class OutputPrinter {
                    ? options->get<size_t>("beam-size")
                    : 0),
         alignment_(options->get<std::string>("alignment", "")),
-        alignmentThreshold_(getAlignmentThreshold(alignment_)) {}
+        alignmentThreshold_(getAlignmentThreshold(alignment_)),
+        wordScores_(options->get<bool>("word-scores")) {}
 
   template <class OStream>
   void print(Ptr<const History> history, OStream& best1, OStream& bestn) {
     const auto& nbl = history->nBest(nbest_);
 
+    // prepare n-best list output
     for(size_t i = 0; i < nbl.size(); ++i) {
       const auto& result = nbl[i];
       const auto& hypo = std::get<1>(result);
@@ -40,6 +42,9 @@ class OutputPrinter {
       if(!alignment_.empty())
         bestn << " ||| " << getAlignment(hypo);
 
+      if(wordScores_)
+        bestn << " ||| WordScores=" << getWordScores(hypo);
+
       bestn << " |||";
       if(hypo->getScoreBreakdown().empty()) {
         bestn << " F0=" << hypo->getPathScore();
@@ -72,17 +77,26 @@ class OutputPrinter {
       best1 << " ||| " << getAlignment(hypo);
     }
 
+    if(wordScores_) {
+      const auto& hypo = std::get<1>(result);
+      best1 << " ||| WordScores=" << getWordScores(hypo);
+    }
+
     best1 << std::flush;
   }
 
 private:
   Ptr<Vocab const> vocab_;
-  bool reverse_{false};
-  size_t nbest_{0};
-  std::string alignment_;
-  float alignmentThreshold_{0.f};
+  bool reverse_{false};            // If it is a right-to-left model that needs reversed word order
+  size_t nbest_{0};                // Size of the n-best list to print
+  std::string alignment_;          // A non-empty string indicates the type of word alignment
+  float alignmentThreshold_{0.f};  // Threshold for converting attention into hard word alignment
+  bool wordScores_{false};         // Whether to print word-level scores or not
 
+  // Get word alignment pairs or soft alignment
   std::string getAlignment(const Hypothesis::PtrType& hyp);
+  // Get word-level scores
+  std::string getWordScores(const Hypothesis::PtrType& hyp);
 
   float getAlignmentThreshold(const std::string& str) {
     try {

From 88d998058917648510ebe66bf764a418ed1d3a8f Mon Sep 17 00:00:00 2001
From: Martin Junczys-Dowmunt <Marcin.JunczysDowmunt@microsoft.com>
Date: Sun, 5 Jan 2020 23:16:13 +0000
Subject: [PATCH 809/838] Merged PR 10996: A number of smaller changes and
 clean-up

* Downgrade NCCL to 2.3.7 as 2.4.2 is buggy (hangs with larger models)
* Actually enable gradient-checkpointing, previous option was inactive
* Clean-up training-only options that should not be displayed for decoder and scorer
* Re-enable conversion to FP16 if element types are compatible (belong to the same type class)
* A few typos and more verbose log messages.
---
 CHANGELOG.md                         |  1 +
 VERSION                              |  2 +-
 src/3rd_party/nccl                   |  2 +-
 src/command/marian_conv.cpp          |  2 +-
 src/common/config_parser.cpp         | 52 +++++++++++++++-------------
 src/common/types.h                   |  7 +++-
 src/data/corpus.cpp                  | 12 +++----
 src/graph/expression_graph.h         | 11 ++++--
 src/graph/expression_operators.cpp   |  4 +--
 src/layers/generic.cpp               |  6 +++-
 src/models/transformer.h             | 10 ++++++
 src/rescorer/rescorer.h              |  3 +-
 src/training/graph_group_async.cpp   |  1 +
 src/training/graph_group_singleton.h |  1 +
 src/training/graph_group_sync.cpp    | 14 +-------
 15 files changed, 73 insertions(+), 55 deletions(-)

diff --git a/CHANGELOG.md b/CHANGELOG.md
index 734ea8916..a3f9dcbb9 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -53,6 +53,7 @@ and this project adheres to [Semantic Versioning](http://semver.org/spec/v2.0.0.
 - Compilation with CUDA 10.1
 
 ### Changed
+- Downgrade NCCL to 2.3.7 as 2.4.2 is buggy (hangs with larger models)
 - Return error signal on SIGTERM
 - Dropped support for CUDA 8.0, CUDA 9.0 is now minimal requirement
 - Removed autotuner for now, will be switched back on later
diff --git a/VERSION b/VERSION
index 8726d325c..771725aee 100644
--- a/VERSION
+++ b/VERSION
@@ -1 +1 @@
-v1.8.36
+v1.8.37
diff --git a/src/3rd_party/nccl b/src/3rd_party/nccl
index 8e3a3f7c5..b56650c7f 160000
--- a/src/3rd_party/nccl
+++ b/src/3rd_party/nccl
@@ -1 +1 @@
-Subproject commit 8e3a3f7c5b520babff49cec54a866fa3eda3a3b6
+Subproject commit b56650c7f59b8cd40d18809784a6d6be38ef8acb
diff --git a/src/command/marian_conv.cpp b/src/command/marian_conv.cpp
index f77ea24d0..97f83acf1 100644
--- a/src/command/marian_conv.cpp
+++ b/src/command/marian_conv.cpp
@@ -22,7 +22,7 @@ int main(int argc, char** argv) {
         "  ./marian-conv -f model.npz -t model.bin --gemm-type packed16");
     cli->add<std::string>("--from,-f", "Input model", "model.npz");
     cli->add<std::string>("--to,-t", "Output model", "model.bin");
-    cli->add<std::string>("--gemm-type,-g", "GEMM Type to be used: float32, packed16, packed8", "float32");
+    cli->add<std::string>("--gemm-type,-g", "GEMM Type to be used: float32, packed16, packed8avx2, packed8avx512", "float32");
     cli->parse(argc, argv);
     options->merge(config);
   }
diff --git a/src/common/config_parser.cpp b/src/common/config_parser.cpp
index 894e7acc0..9bd01a20c 100755
--- a/src/common/config_parser.cpp
+++ b/src/common/config_parser.cpp
@@ -504,7 +504,7 @@ void ConfigParser::addOptionsTraining(cli::CLIWrapper& cli) {
      true);
 
   // add ULR settings
-  addSuboptionsULR(cli);
+  addSuboptionsULR(cli); 
 
   cli.add<std::vector<std::string>>("--task",
      "Use predefined set of options. Possible values: transformer, transformer-big");
@@ -638,8 +638,10 @@ void ConfigParser::addOptionsTranslation(cli::CLIWrapper& cli) {
      "Noise output layer with gumbel noise",
       false);
 
+#if 0 // @TODO: Ask Hany if there are any decoding-time options
   // add ULR settings
   addSuboptionsULR(cli);
+#endif
 
   cli.switchGroup(previous_group);
   // clang-format on
@@ -749,29 +751,31 @@ void ConfigParser::addSuboptionsBatching(cli::CLIWrapper& cli) {
       "Sorting strategy for maxi-batch: none, src, trg (not available for decoder)",
       defaultMaxiBatchSort);
 
-  cli.add<bool>("--shuffle-in-ram",
-      "Keep shuffled corpus in RAM, do not write to temp file");
-  // @TODO: Consider making the next two options options of the vocab instead, to make it more local in scope.
-  cli.add<size_t>("--all-caps-every",
-      "When forming minibatches, preprocess every Nth line on the fly to all-caps. Assumes UTF-8");
-  cli.add<size_t>("--english-title-case-every",
-      "When forming minibatches, preprocess every Nth line on the fly to title-case. Assumes English (ASCII only)");
-
-  cli.add<int>("--mini-batch-words-ref",
-      "If given, the following hyper parameters are adjusted as-if we had this mini-batch size: "
-      "--learn-rate, --optimizer-params, --exponential-smoothing, --mini-batch-warmup");
-  cli.add<std::string/*SchedulerPeriod*/>("--mini-batch-warmup",
-      "Linear ramp-up of MB size, up to this #updates (append 't' for up to this #target labels). "
-      "Auto-adjusted to --mini-batch-words-ref if given",
-      {"0"});
-  cli.add<bool>("--mini-batch-track-lr",
-      "Dynamically track mini-batch size inverse to actual learning rate (not considering lr-warmup)");
-  cli.add<size_t>("--mini-batch-overstuff",
-      "[experimental] Stuff this much more data into a minibatch, but scale down the LR and progress counter",
-      1);
-  cli.add<size_t>("--mini-batch-understuff",
-      "[experimental] Break each batch into this many updates",
-      1);
+  if(mode_ == cli::mode::training) {
+    cli.add<bool>("--shuffle-in-ram",
+        "Keep shuffled corpus in RAM, do not write to temp file");
+    // @TODO: Consider making the next two options options of the vocab instead, to make it more local in scope.
+    cli.add<size_t>("--all-caps-every",
+        "When forming minibatches, preprocess every Nth line on the fly to all-caps. Assumes UTF-8");
+    cli.add<size_t>("--english-title-case-every",
+        "When forming minibatches, preprocess every Nth line on the fly to title-case. Assumes English (ASCII only)");
+
+    cli.add<int>("--mini-batch-words-ref",
+        "If given, the following hyper parameters are adjusted as-if we had this mini-batch size: "
+        "--learn-rate, --optimizer-params, --exponential-smoothing, --mini-batch-warmup");
+    cli.add<std::string/*SchedulerPeriod*/>("--mini-batch-warmup",
+        "Linear ramp-up of MB size, up to this #updates (append 't' for up to this #target labels). "
+        "Auto-adjusted to --mini-batch-words-ref if given",
+        {"0"});
+    cli.add<bool>("--mini-batch-track-lr",
+        "Dynamically track mini-batch size inverse to actual learning rate (not considering lr-warmup)");
+    cli.add<size_t>("--mini-batch-overstuff",
+        "[experimental] Stuff this much more data into a minibatch, but scale down the LR and progress counter",
+        1);
+    cli.add<size_t>("--mini-batch-understuff",
+        "[experimental] Break each batch into this many updates",
+        1);
+  }
   // clang-format on
 }
 
diff --git a/src/common/types.h b/src/common/types.h
index 24a8b4ec8..552ae2254 100644
--- a/src/common/types.h
+++ b/src/common/types.h
@@ -223,7 +223,8 @@ enum class TypeClass : size_t {
   avx2_type     = 0x1000, // processor-specific layout for avx2, currently used for FBGEMM only
   avx512_type   = 0x2000, // processor-specific layout for avx512, currently used for FBGEMM only
 
-  size_mask     = 0x00FF
+  size_mask     = 0x00FF,
+  class_mask    = 0xFF00
 };
 
 constexpr inline size_t operator+(TypeClass typeClass, size_t val) {
@@ -260,6 +261,10 @@ static inline size_t operator&(TypeClass typeClass, Type type) {
   return (size_t)typeClass & (size_t)type;
 }
 
+static inline bool isSameTypeClass(Type type1, Type type2) {
+  return (TypeClass::class_mask & type1) == (TypeClass::class_mask & type2);
+}
+
 static inline size_t sizeOf(Type type) {
   return TypeClass::size_mask & type;
 }
diff --git a/src/data/corpus.cpp b/src/data/corpus.cpp
index 234bc7635..a861e6632 100755
--- a/src/data/corpus.cpp
+++ b/src/data/corpus.cpp
@@ -13,17 +13,17 @@ namespace data {
 
 Corpus::Corpus(Ptr<Options> options, bool translate /*= false*/)
     : CorpusBase(options, translate),
-        shuffleInRAM_(options_->get<bool>("shuffle-in-ram")),
-        allCapsEvery_(options_->get<size_t>("all-caps-every")),
-        titleCaseEvery_(options_->get<size_t>("english-title-case-every")) {}
+        shuffleInRAM_(options_->get<bool>("shuffle-in-ram", false)),
+        allCapsEvery_(options_->get<size_t>("all-caps-every", 0)),
+        titleCaseEvery_(options_->get<size_t>("english-title-case-every", 0)) {}
 
 Corpus::Corpus(std::vector<std::string> paths,
                std::vector<Ptr<Vocab>> vocabs,
                Ptr<Options> options)
     : CorpusBase(paths, vocabs, options),
-        shuffleInRAM_(options_->get<bool>("shuffle-in-ram")),
-        allCapsEvery_(options_->get<size_t>("all-caps-every")),
-        titleCaseEvery_(options_->get<size_t>("english-title-case-every")) {}
+        shuffleInRAM_(options_->get<bool>("shuffle-in-ram", false)),
+        allCapsEvery_(options_->get<size_t>("all-caps-every", 0)),
+        titleCaseEvery_(options_->get<size_t>("english-title-case-every", 0)) {}
 
 void Corpus::preprocessLine(std::string& line, size_t streamId) {
   if (allCapsEvery_ != 0 && pos_ % allCapsEvery_ == 0 && !inference_) {
diff --git a/src/graph/expression_graph.h b/src/graph/expression_graph.h
index 9e1222378..8a08c7491 100755
--- a/src/graph/expression_graph.h
+++ b/src/graph/expression_graph.h
@@ -354,7 +354,7 @@ class ExpressionGraph : public std::enable_shared_from_this<ExpressionGraph> {
              const Ptr<inits::NodeInitializer>& init,
              const Type elementType,
              bool fixed = false) {
-    // since this param is called with out a specified type, we assume defaultElementType but allow to check for a different type
+    // this param is called with a specified type
     return param(pname, shape, init, elementType, fixed, /*typeSpecified=*/true);
   }
 
@@ -362,7 +362,7 @@ class ExpressionGraph : public std::enable_shared_from_this<ExpressionGraph> {
              const Shape& shape,
              const Ptr<inits::NodeInitializer>& init,
              bool fixed = false) {
-    // since this param is called with out a specified type, we assume defaultElementType but allow to check for a different type
+    // since this param is called without a specified type, we assume defaultElementType but allow to check for a different type
     return param(pname, shape, init, defaultElementType_, fixed, /*typeSpecified=*/false);
   }
 
@@ -497,7 +497,12 @@ class ExpressionGraph : public std::enable_shared_from_this<ExpressionGraph> {
       // skip over special parameters starting with "special:"
       if(pName.substr(0, 8) == "special:")
         continue;
-      param(pName, item.shape, inits::fromItem(item), item.type, /*fixed=*/false);
+      
+      // if during loading the loaded type is of the same type class as the default element type, allow conversion;
+      // otherwise keep the loaded type. This is used when e.g. loading a float32 model as a float16 model as both
+      // have type class TypeClass::float_type.
+      auto loadElementType = isSameTypeClass(item.type, defaultElementType_) ? defaultElementType_ : item.type;
+      param(pName, item.shape, inits::fromItem(item), loadElementType, /*fixed=*/false);
     }
     if(markReloaded)
       setReloaded(true);
diff --git a/src/graph/expression_operators.cpp b/src/graph/expression_operators.cpp
index e3b0a2f18..f858d7309 100755
--- a/src/graph/expression_operators.cpp
+++ b/src/graph/expression_operators.cpp
@@ -617,10 +617,10 @@ Expr unlikelihood(Expr logits, Expr indices) {
   int dimBatch = logits->shape()[-2];
   int dimTime  = logits->shape()[-3];
 
-  // @TODO: fix the outside of this function in decoder.h etc. 
+  // @TODO: fix this outside of this function in decoder.h etc. 
   auto indicesWithLayout = reshape(indices, {1, dimTime, dimBatch, 1});
 
-  // This is currently implemented with mutliple ops, might be worth doing a special operation like for cross_entropy
+  // This is currently implemented with multiple ops, might be worth doing a special operation like for cross_entropy
   return -log(gather(1.f - softmax(logits), /*axis=*/-1, indicesWithLayout));
 }
 
diff --git a/src/layers/generic.cpp b/src/layers/generic.cpp
index e3984774a..45d660418 100755
--- a/src/layers/generic.cpp
+++ b/src/layers/generic.cpp
@@ -24,7 +24,11 @@ namespace marian {
     ABORT_IF(empty(), "Attempted to read out logits on empty Logits object");
 
     auto firstLogits = logits_.front()->loss();
-    ABORT_IF(labels.size() * firstLogits->shape()[-1] != firstLogits->shape().elements(), "Labels not matching logits shape??");
+    ABORT_IF(labels.size() * firstLogits->shape()[-1] != firstLogits->shape().elements(), 
+             "Labels not matching logits shape ({} != {}, {})??",
+             labels.size() * firstLogits->shape()[-1],
+             firstLogits->shape().elements(),
+             firstLogits->shape());
 
     // base case (no factors)
     if (!factoredVocab_) {
diff --git a/src/models/transformer.h b/src/models/transformer.h
index 8971c65fc..2211892a8 100755
--- a/src/models/transformer.h
+++ b/src/models/transformer.h
@@ -534,6 +534,7 @@ class EncoderTransformer : public Transformer<EncoderBase> {
                              layer, // values
                              layerMask); // [batch size, num heads broadcast=1, max length broadcast=1, max length]
       layer = LayerFFN(prefix_ + "_l" + std::to_string(i) + "_ffn", layer);
+      checkpoint(layer); // sets a manually specified checkpoint if gradient checkpointing is enabled, does nothing otherwise.
     }
 
     // restore organization of batch and time steps. This is currently required
@@ -700,6 +701,9 @@ class DecoderTransformer : public Transformer<DecoderBase> {
 
       encoderContexts.push_back(encoderContext);
       encoderMasks.push_back(encoderMask);
+
+      checkpoint(encoderContext);
+      checkpoint(encoderMask);
     }
 
     rnn::States prevDecoderStates = state->getStates();
@@ -735,6 +739,8 @@ class DecoderTransformer : public Transformer<DecoderBase> {
         ABORT("Unknown auto-regressive layer type in transformer decoder {}",
               layerType);
 
+      checkpoint(query);
+
       // source-target attention
       // Iterate over multiple encoders and simply stack the attention blocks
       if(encoderContexts.size() > 0) {
@@ -771,10 +777,14 @@ class DecoderTransformer : public Transformer<DecoderBase> {
         }
       }
 
+      checkpoint(query);
+
       // remember decoder state
       decoderStates.push_back(decoderState);
 
       query = LayerFFN(prefix_ + "_l" + layerNo + "_ffn", query); // [-4: beam depth=1, -3: batch size, -2: max length, -1: vector dim]
+
+      checkpoint(query);
     }
 
     auto decoderContext = transposeTimeBatch(query); // [-4: beam depth=1, -3: max length, -2: batch size, -1: vector dim]
diff --git a/src/rescorer/rescorer.h b/src/rescorer/rescorer.h
index 77e63bfc7..a201f3fb6 100755
--- a/src/rescorer/rescorer.h
+++ b/src/rescorer/rescorer.h
@@ -69,11 +69,10 @@ class Rescore : public ModelTask {
 
     for(auto device : devices) {
       auto graph = New<ExpressionGraph>(true);
-      graph->setDevice(device);
 
       auto precison = options_->get<std::vector<std::string>>("precision", {"float32"});
       graph->setDefaultElementType(typeFromString(precison[0])); // only use first type, used for parameter type in graph
-
+      graph->setDevice(device);
       graph->getBackend()->setClip(options_->get<float>("clip-gemm"));
       if (device.type == DeviceType::cpu) {
         graph->getBackend()->setOptimized(options_->get<bool>("optimize"));
diff --git a/src/training/graph_group_async.cpp b/src/training/graph_group_async.cpp
index 6ecaa1d9c..4636f4b0c 100644
--- a/src/training/graph_group_async.cpp
+++ b/src/training/graph_group_async.cpp
@@ -18,6 +18,7 @@ AsyncGraphGroup::AsyncGraphGroup(Ptr<Options> config, Ptr<IMPIWrapper> mpi)
   for(auto device : devices_) {
     auto graph = New<ExpressionGraph>();
     graph->setDevice(device);
+    graph->setCheckpointing(options_->get<bool>("gradient-checkpointing"));
     graph->getBackend()->setClip(options_->get<float>("clip-gemm"));
     graph->reserveWorkspaceMB(options_->get<size_t>("workspace"));
     graphs_.push_back(graph);
diff --git a/src/training/graph_group_singleton.h b/src/training/graph_group_singleton.h
index e2b1818cc..8a93af71a 100644
--- a/src/training/graph_group_singleton.h
+++ b/src/training/graph_group_singleton.h
@@ -34,6 +34,7 @@ class SingletonGraph : public GraphGroup, public ExponentialSmoothing {
     // Initialize graph
     graph_ = New<ExpressionGraph>();
     graph_->setDevice(deviceId);
+    graph_->setCheckpointing(options_->get<bool>("gradient-checkpointing"));
     graph_->getBackend()->setClip(options_->get<float>("clip-gemm"));
     graph_->reserveWorkspaceMB(options_->get<size_t>("workspace"));
     opt_ = Optimizer(options_);
diff --git a/src/training/graph_group_sync.cpp b/src/training/graph_group_sync.cpp
index ce4fa179a..719ee80e9 100755
--- a/src/training/graph_group_sync.cpp
+++ b/src/training/graph_group_sync.cpp
@@ -10,6 +10,7 @@ SyncGraphGroup::SyncGraphGroup(Ptr<Options> config, Ptr<IMPIWrapper> mpi)
   for(auto device : devices_) {
     auto graph = New<ExpressionGraph>();
     graph->setDevice(device);
+    graph->setCheckpointing(options_->get<bool>("gradient-checkpointing"));
     graph->reserveWorkspaceMB(options_->get<size_t>("workspace"));
     graph->getBackend()->setClip(options_->get<float>("clip-gemm"));
 
@@ -57,19 +58,6 @@ void SyncGraphGroup::initialize(const Ptr<data::Batch>& exampleBatch) {
     if (i > 0)
       graphs_[i]->params()->vals()->copyFrom(graphs_[0]->params()->vals());
   });
-  //ThreadPool pool(graphs_.size() - 1, graphs_.size() - 1);
-  //for(size_t i = 1; i < graphs_.size(); ++i) {
-  //  auto init = [&](size_t i) {
-  //    // initialize i-th graph and weights
-  //    builders_[i]->build(graphs_[i], exampleBatch);
-  //    graphs_[i]->forward();
-  //    // overwrite weights of i-th graph with weights from 0-th graph
-  //    graphs_[i]->params()->vals()->copyFrom(graphs_[0]->params()->vals());
-  //  };
-  //  pool.enqueue(init, i);
-  //}
-  //// ThreadPool destructor waits until completion of all tasks.
-  //// @TODO: can we use comm_->foreach()?
 }
 
 void SyncGraphGroup::initializeAvg() {

From 164d26cc36204316324a2fc76f7538a044dd192a Mon Sep 17 00:00:00 2001
From: Martin Junczys-Dowmunt <Marcin.JunczysDowmunt@microsoft.com>
Date: Mon, 6 Jan 2020 19:14:00 +0000
Subject: [PATCH 810/838] Merged PR 10999: Splitting up add_all.h into *.h,
 *.cu and *.inc

Splitting up header file into header and *.cu, comes with the price of having to include specializations for combinations of types as for element.inc and add.inc. No code changes otherwise.

Add CMake options to disable specific compute capabilities.

When run with `make -j16` this compiles in about 6 minutes instead of 7 minutes. Selecting only SM70 during compilation brings down the time to 3 minutes.
---
 CHANGELOG.md                 |   1 +
 CMakeLists.txt               |  23 +++++--
 VERSION                      |   2 +-
 src/3rd_party/CMakeLists.txt |  19 +++++-
 src/CMakeLists.txt           |   3 +-
 src/tensors/gpu/add.inc      |  24 ++++----
 src/tensors/gpu/add_all.cu   | 116 +++++++++++++++++++++++++++++++++++
 src/tensors/gpu/add_all.h    |  89 ++++++++-------------------
 src/tensors/gpu/add_all.inc  |  71 +++++++++++++++++++++
 9 files changed, 261 insertions(+), 87 deletions(-)
 create mode 100644 src/tensors/gpu/add_all.cu
 create mode 100644 src/tensors/gpu/add_all.inc

diff --git a/CHANGELOG.md b/CHANGELOG.md
index a3f9dcbb9..fea7c0409 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -9,6 +9,7 @@ and this project adheres to [Semantic Versioning](http://semver.org/spec/v2.0.0.
 ## [Unreleased]
 
 ### Added
+- Add CMAKE options to disable compilation for specific GPU SM types
 - An option to print word-level translation scores
 - An option to turn off automatic detokenization from SentencePiece
 - Separate quantization types for 8-bit FBGEMM for AVX2 and AVX512
diff --git a/CMakeLists.txt b/CMakeLists.txt
index ab5460e64..c442931f9 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -13,6 +13,10 @@ set(BUILD_ARCH native CACHE STRING "Compile for this CPU architecture.")
 # Custom CMake options
 option(COMPILE_CPU "Compile CPU version" ON)
 option(COMPILE_CUDA "Compile GPU version" ON)
+option(COMPILE_CUDA_SM35 "Compile GPU version with SM35 support" ON)
+option(COMPILE_CUDA_SM50 "Compile GPU version with SM50 support" ON)
+option(COMPILE_CUDA_SM60 "Compile GPU version with SM60 support" ON)
+option(COMPILE_CUDA_SM70 "Compile GPU version with SM70 support" ON)
 option(COMPILE_EXAMPLES "Compile examples" OFF)
 option(COMPILE_SERVER "Compile marian-server" OFF)
 option(COMPILE_TESTS "Compile tests" OFF)
@@ -181,8 +185,6 @@ set(EXT_LIBS ${EXT_LIBS} ${CMAKE_DL_LIBS})
 
 if(COMPILE_CUDA)
 
-LIST(APPEND COMPUTE -arch=sm_35; -gencode=arch=compute_35,code=sm_35; -gencode=arch=compute_50,code=sm_50; -gencode=arch=compute_52,code=sm_52; -gencode=arch=compute_60,code=sm_60; -gencode=arch=compute_61,code=sm_61;)
-
 if(USE_STATIC_LIBS)
   # link statically to stdlib libraries
   set(CMAKE_EXE_LINKER_FLAGS "-static-libgcc -static-libstdc++")
@@ -202,10 +204,19 @@ if(CUDA_FOUND)
   if((CUDA_VERSION VERSION_EQUAL "10.0" OR CUDA_VERSION VERSION_GREATER "10.0") AND (CMAKE_VERSION VERSION_LESS "3.12.2"))
       message(WARNING "On some Unix systems CUDA 10.0+ requires CMake 3.12.2+; you use CMake ${CMAKE_VERSION}")
   endif()
-
-  if(CUDA_VERSION VERSION_GREATER "8.0")
-    LIST(APPEND COMPUTE -gencode=arch=compute_70,code=sm_70; -gencode=arch=compute_70,code=compute_70)
-  endif()
+  
+  if(COMPILE_CUDA_SM35)
+    LIST(APPEND COMPUTE -arch=sm_35; -gencode=arch=compute_35,code=sm_35;)                             # Tesla K40 and above
+  endif(COMPILE_CUDA_SM35)
+  if(COMPILE_CUDA_SM50)
+    LIST(APPEND COMPUTE -gencode=arch=compute_50,code=sm_50; -gencode=arch=compute_52,code=sm_52;)     # Maxwell GPUs
+  endif(COMPILE_CUDA_SM50)
+  if(COMPILE_CUDA_SM60)
+    LIST(APPEND COMPUTE -gencode=arch=compute_60,code=sm_60; -gencode=arch=compute_61,code=sm_61;)     # Pascal GPUs
+  endif(COMPILE_CUDA_SM60)
+  if(COMPILE_CUDA_SM70)
+    LIST(APPEND COMPUTE -gencode=arch=compute_70,code=sm_70; -gencode=arch=compute_70,code=compute_70) # Volta GPUs
+  endif(COMPILE_CUDA_SM70)
 
   if(USE_STATIC_LIBS)
     find_library(CUDA_culibos_LIBRARY NAMES culibos PATHS ${CUDA_TOOLKIT_ROOT_DIR}/lib64)
diff --git a/VERSION b/VERSION
index 771725aee..b08f51ec8 100644
--- a/VERSION
+++ b/VERSION
@@ -1 +1 @@
-v1.8.37
+v1.8.38
diff --git a/src/3rd_party/CMakeLists.txt b/src/3rd_party/CMakeLists.txt
index cc9da3fb8..b40df585b 100644
--- a/src/3rd_party/CMakeLists.txt
+++ b/src/3rd_party/CMakeLists.txt
@@ -71,8 +71,23 @@ set(INSTALLS "") # this will contain a list of 3rd part dependencies that we ins
 if(CUDA_FOUND)
   if(USE_NCCL)
 
-    # disables compilation for sm_30 to avoid ptxas warning... that's general Kepler support. But K80s are supported for instance by sm_35
-    set(GENCODE "-gencode=arch=compute_35,code=sm_35 -gencode=arch=compute_50,code=sm_50 -gencode=arch=compute_60,code=sm_60 -gencode=arch=compute_61,code=sm_61")
+    # disables compilation for sm_30 to avoid ptxas warning... that is general Kepler support. But K80s are supported for instance by sm_35
+
+    set(GENCODE "")
+    if(COMPILE_CUDA_SM35)
+      set(GENCODE "${GENCODE} -gencode=arch=compute_35,code=sm_35")
+    endif(COMPILE_CUDA_SM35)
+    if(COMPILE_CUDA_SM50)
+      set(GENCODE "${GENCODE} -gencode=arch=compute_50,code=sm_50")
+    endif(COMPILE_CUDA_SM50)
+    if(COMPILE_CUDA_SM60)
+      set(GENCODE "${GENCODE} -gencode=arch=compute_60,code=sm_60 -gencode=arch=compute_61,code=sm_61")
+    endif(COMPILE_CUDA_SM60)
+    if(COMPILE_CUDA_SM70)
+      set(GENCODE "${GENCODE} -gencode=arch=compute_70,code=sm_70")
+    endif(COMPILE_CUDA_SM70)
+
+    message(${GENCODE})
 
     # install nccl in ${CMAKE_BINARY_DIR}/local similar to /usr/local linux installation
     ExternalProject_Add(nccl_install
diff --git a/src/CMakeLists.txt b/src/CMakeLists.txt
index b549ce2d4..c1a513908 100644
--- a/src/CMakeLists.txt
+++ b/src/CMakeLists.txt
@@ -138,7 +138,8 @@ cuda_add_library(marian_cuda
   tensors/gpu/algorithm.cu
   tensors/gpu/prod.cpp
   tensors/gpu/element.cu
-  tensors/gpu/add.cu
+  tensors/gpu/add.cu  
+  tensors/gpu/add_all.cu
   tensors/gpu/tensor_operators.cu
   tensors/gpu/cudnn_wrappers.cu
   translator/nth_element.cu
diff --git a/src/tensors/gpu/add.inc b/src/tensors/gpu/add.inc
index 1e11d011b..98723b9d0 100755
--- a/src/tensors/gpu/add.inc
+++ b/src/tensors/gpu/add.inc
@@ -15,21 +15,21 @@ template void Add<BinaryFunctor<elem::Mult, Assignee<1>, BinaryFunctor<elem::Div
 template void Add<BinaryFunctor<elem::Mult, Assignee<1>, BinaryFunctor<elem::sPReLUBack, Assignee<2>, Capture>>, marian::Tensor, marian::Tensor>(BinaryFunctor<elem::Mult, Assignee<1>, BinaryFunctor<elem::sPReLUBack, Assignee<2>, Capture>>, float, marian::Tensor, marian::Tensor, marian::Tensor);
 template void Add<BinaryFunctor<elem::Mult, Assignee<1>, UnaryFunctor<elem::sReLUBack, Assignee<2>>>, marian::Tensor, marian::Tensor>(BinaryFunctor<elem::Mult, Assignee<1>, UnaryFunctor<elem::sReLUBack, Assignee<2>>>, float, marian::Tensor, marian::Tensor, marian::Tensor);
 template void Add<BinaryFunctor<elem::Mult, BinaryFunctor<elem::Mult, Assignee<1>, Assignee<2>>, BinaryFunctor<elem::Minus, Capture, Assignee<2>>>, marian::Tensor, marian::Tensor>(BinaryFunctor<elem::Mult, BinaryFunctor<elem::Mult, Assignee<1>, Assignee<2>>, BinaryFunctor<elem::Minus, Capture, Assignee<2>>>, float, marian::Tensor, marian::Tensor, marian::Tensor);
-template void Add<BinaryFunctor<elem::Mult, BinaryFunctor<elem::Bump, Assignee<1>, Capture>, Assignee<2>>, marian::Tensor, marian::Tensor >(BinaryFunctor<elem::Mult, BinaryFunctor<elem::Bump, Assignee<1>, Capture>, Assignee<2> >, float, marian::Tensor, marian::Tensor, marian::Tensor);
+template void Add<BinaryFunctor<elem::Mult, BinaryFunctor<elem::Bump, Assignee<1>, Capture>, Assignee<2>>, marian::Tensor, marian::Tensor >(BinaryFunctor<elem::Mult, BinaryFunctor<elem::Bump, Assignee<1>, Capture>, Assignee<2>>, float, marian::Tensor, marian::Tensor, marian::Tensor);
 template void Add<BinaryFunctor<elem::Mult, BinaryFunctor<elem::Lt, Assignee<2>, Assignee<3>>, Assignee<1>>, marian::Tensor, marian::Tensor, marian::Tensor>(BinaryFunctor<elem::Mult, BinaryFunctor<elem::Lt, Assignee<2>, Assignee<3>>, Assignee<1>>, float, marian::Tensor, marian::Tensor, marian::Tensor, marian::Tensor);
 template void Add<BinaryFunctor<elem::Mult, BinaryFunctor<elem::Geq, Assignee<2>, Assignee<3>>, Assignee<1>>, marian::Tensor, marian::Tensor, marian::Tensor>(BinaryFunctor<elem::Mult, BinaryFunctor<elem::Geq, Assignee<2>, Assignee<3>>, Assignee<1>>, float, marian::Tensor, marian::Tensor, marian::Tensor, marian::Tensor);
 template void Add<BinaryFunctor<elem::Mult, Assignee<1>, UnaryFunctor<elem::Sigmoid, BinaryFunctor<elem::Minus, Assignee<3>, Assignee<2>>>>, marian::Tensor, marian::Tensor, marian::Tensor>(BinaryFunctor<elem::Mult, Assignee<1>, UnaryFunctor<elem::Sigmoid, BinaryFunctor<elem::Minus, Assignee<3>, Assignee<2>>>>, float, marian::Tensor, marian::Tensor, marian::Tensor, marian::Tensor);
 template void Add<BinaryFunctor<elem::Mult, BinaryFunctor<elem::Gt, Assignee<2>, Assignee<3>>, Assignee<1>>, marian::Tensor, marian::Tensor, marian::Tensor>(BinaryFunctor<elem::Mult, BinaryFunctor<elem::Gt, Assignee<2>, Assignee<3>>, Assignee<1>>, float, marian::Tensor, marian::Tensor, marian::Tensor, marian::Tensor);
 template void Add<BinaryFunctor<elem::Mult, BinaryFunctor<elem::Leq, Assignee<2>, Assignee<3>>, Assignee<1>>, marian::Tensor, marian::Tensor, marian::Tensor>(BinaryFunctor<elem::Mult, BinaryFunctor<elem::Leq, Assignee<2>, Assignee<3>>, Assignee<1>>, float, marian::Tensor, marian::Tensor, marian::Tensor, marian::Tensor);
 template void Add<BinaryFunctor<elem::Mult, Assignee<1>, UnaryFunctor<elem::Sigmoid, BinaryFunctor<elem::Minus, Assignee<2>, Assignee<3>>>>, marian::Tensor, marian::Tensor, marian::Tensor>(BinaryFunctor<elem::Mult, Assignee<1>, UnaryFunctor<elem::Sigmoid, BinaryFunctor<elem::Minus, Assignee<2>, Assignee<3>>>>, float, marian::Tensor, marian::Tensor, marian::Tensor, marian::Tensor);
-template void Add<BinaryFunctor<elem::Div, Assignee<1>, Assignee<2> >, marian::Tensor, marian::Tensor >(BinaryFunctor<elem::Div, Assignee<1>, Assignee<2> >, float, marian::Tensor, marian::Tensor, marian::Tensor);
-template void marian::gpu::Add<marian::functional::BinaryFunctor<marian::functional::elem::Mult, marian::functional::Assignee<1>, marian::functional::Assignee<1> >, marian::Tensor >(marian::functional::BinaryFunctor<marian::functional::elem::Mult, marian::functional::Assignee<1>, marian::functional::Assignee<1> >, float, marian::Tensor, marian::Tensor);
-template void marian::gpu::Aggregate<marian::functional::Assignee<1>, marian::functional::BinaryFunctor<marian::functional::elem::Min, marian::functional::Assignee<1>, marian::functional::Assignee<2> >, marian::Tensor >(marian::functional::Assignee<1>, float, marian::functional::BinaryFunctor<marian::functional::elem::Min, marian::functional::Assignee<1>, marian::functional::Assignee<2> >, float, marian::Tensor, marian::Tensor);
-template void marian::gpu::Aggregate<marian::functional::Assignee<1>, marian::functional::BinaryFunctor<marian::functional::elem::Max, marian::functional::Assignee<1>, marian::functional::Assignee<2> >, marian::Tensor >(marian::functional::Assignee<1>, float, marian::functional::BinaryFunctor<marian::functional::elem::Max, marian::functional::Assignee<1>, marian::functional::Assignee<2> >, float, marian::Tensor, marian::Tensor);
-template void marian::gpu::Aggregate<marian::functional::Assignee<1>, marian::functional::BinaryFunctor<marian::functional::elem::Mult, marian::functional::Assignee<1>, marian::functional::Assignee<2> >, marian::Tensor >(marian::functional::Assignee<1>, float, marian::functional::BinaryFunctor<marian::functional::elem::Mult, marian::functional::Assignee<1>, marian::functional::Assignee<2> >, float, marian::Tensor, marian::Tensor);
-template void marian::gpu::Aggregate<marian::functional::Assignee<1>, marian::functional::BinaryFunctor<marian::functional::elem::LogAddExp, marian::functional::Assignee<1>, marian::functional::Assignee<2> >, marian::Tensor >(marian::functional::Assignee<1>, float, marian::functional::BinaryFunctor<marian::functional::elem::LogAddExp, marian::functional::Assignee<1>, marian::functional::Assignee<2> >, float, marian::Tensor, marian::Tensor);
-template void marian::gpu::Add<marian::functional::BinaryFunctor<marian::functional::elem::Mult, marian::functional::BinaryFunctor<marian::functional::elem::Eq, marian::functional::Assignee<1>, marian::functional::Assignee<2> >, marian::functional::Assignee<3> >, marian::Tensor, marian::Tensor, marian::Tensor >(marian::functional::BinaryFunctor<marian::functional::elem::Mult, marian::functional::BinaryFunctor<marian::functional::elem::Eq, marian::functional::Assignee<1>, marian::functional::Assignee<2> >, marian::functional::Assignee<3> >, float, marian::Tensor, marian::Tensor, marian::Tensor, marian::Tensor);
-template void marian::gpu::Add<marian::functional::BinaryFunctor<marian::functional::elem::Mult, marian::functional::Assignee<1>, marian::functional::UnaryFunctor<marian::functional::elem::Exp, marian::functional::BinaryFunctor<marian::functional::elem::Minus, marian::functional::Assignee<2>, marian::functional::Assignee<3> > > >, marian::Tensor, marian::Tensor, marian::Tensor >(marian::functional::BinaryFunctor<marian::functional::elem::Mult, marian::functional::Assignee<1>, marian::functional::UnaryFunctor<marian::functional::elem::Exp, marian::functional::BinaryFunctor<marian::functional::elem::Minus, marian::functional::Assignee<2>, marian::functional::Assignee<3> > > >, float, marian::Tensor, marian::Tensor, marian::Tensor, marian::Tensor);
-template void marian::gpu::Add<marian::functional::BinaryFunctor<marian::functional::elem::Div, marian::functional::BinaryFunctor<marian::functional::elem::Mult, marian::functional::Assignee<1>, marian::functional::Assignee<2> >, marian::functional::Assignee<3> >, marian::Tensor, marian::Tensor, marian::Tensor >(marian::functional::BinaryFunctor<marian::functional::elem::Div, marian::functional::BinaryFunctor<marian::functional::elem::Mult, marian::functional::Assignee<1>, marian::functional::Assignee<2> >, marian::functional::Assignee<3> >, float, marian::Tensor, marian::Tensor, marian::Tensor, marian::Tensor);
-template void marian::gpu::Add<marian::functional::BinaryFunctor<marian::functional::elem::Mult, marian::functional::BinaryFunctor<marian::functional::elem::Mult, marian::functional::Assignee<1>, marian::functional::Capture>, marian::functional::Assignee<2> >, marian::Tensor, marian::Tensor >(marian::functional::BinaryFunctor<marian::functional::elem::Mult, marian::functional::BinaryFunctor<marian::functional::elem::Mult, marian::functional::Assignee<1>, marian::functional::Capture>, marian::functional::Assignee<2> >, float, marian::Tensor, marian::Tensor, marian::Tensor);
-template void marian::gpu::Add<marian::functional::BinaryFunctor<marian::functional::elem::Mult, marian::functional::Assignee<1>, marian::functional::BinaryFunctor<marian::functional::elem::Plus, marian::functional::BinaryFunctor<marian::functional::elem::Mult, marian::functional::Capture, marian::functional::Assignee<3> >, marian::functional::BinaryFunctor<marian::functional::elem::Mult, marian::functional::UnaryFunctor<marian::functional::elem::Sigmoid, marian::functional::BinaryFunctor<marian::functional::elem::Mult, marian::functional::Capture, marian::functional::Assignee<2> > >, marian::functional::BinaryFunctor<marian::functional::elem::Minus, marian::functional::Capture, marian::functional::BinaryFunctor<marian::functional::elem::Mult, marian::functional::Capture, marian::functional::Assignee<3> > > > > >, marian::Tensor, marian::Tensor, marian::Tensor >(marian::functional::BinaryFunctor<marian::functional::elem::Mult, marian::functional::Assignee<1>, marian::functional::BinaryFunctor<marian::functional::elem::Plus, marian::functional::BinaryFunctor<marian::functional::elem::Mult, marian::functional::Capture, marian::functional::Assignee<3> >, marian::functional::BinaryFunctor<marian::functional::elem::Mult, marian::functional::UnaryFunctor<marian::functional::elem::Sigmoid, marian::functional::BinaryFunctor<marian::functional::elem::Mult, marian::functional::Capture, marian::functional::Assignee<2> > >, marian::functional::BinaryFunctor<marian::functional::elem::Minus, marian::functional::Capture, marian::functional::BinaryFunctor<marian::functional::elem::Mult, marian::functional::Capture, marian::functional::Assignee<3> > > > > >, float, marian::Tensor, marian::Tensor, marian::Tensor, marian::Tensor);
\ No newline at end of file
+template void Add<BinaryFunctor<elem::Div, Assignee<1>, Assignee<2>>, marian::Tensor, marian::Tensor >(BinaryFunctor<elem::Div, Assignee<1>, Assignee<2>>, float, marian::Tensor, marian::Tensor, marian::Tensor);
+template void Add<BinaryFunctor<elem::Mult, Assignee<1>, Assignee<1>>, marian::Tensor >(BinaryFunctor<elem::Mult, Assignee<1>, Assignee<1>>, float, marian::Tensor, marian::Tensor);
+template void Aggregate<Assignee<1>, BinaryFunctor<elem::Min, Assignee<1>, Assignee<2>>, marian::Tensor >(Assignee<1>, float, BinaryFunctor<elem::Min, Assignee<1>, Assignee<2>>, float, marian::Tensor, marian::Tensor);
+template void Aggregate<Assignee<1>, BinaryFunctor<elem::Max, Assignee<1>, Assignee<2>>, marian::Tensor >(Assignee<1>, float, BinaryFunctor<elem::Max, Assignee<1>, Assignee<2>>, float, marian::Tensor, marian::Tensor);
+template void Aggregate<Assignee<1>, BinaryFunctor<elem::Mult, Assignee<1>, Assignee<2>>, marian::Tensor >(Assignee<1>, float, BinaryFunctor<elem::Mult, Assignee<1>, Assignee<2>>, float, marian::Tensor, marian::Tensor);
+template void Aggregate<Assignee<1>, BinaryFunctor<elem::LogAddExp, Assignee<1>, Assignee<2>>, marian::Tensor >(Assignee<1>, float, BinaryFunctor<elem::LogAddExp, Assignee<1>, Assignee<2>>, float, marian::Tensor, marian::Tensor);
+template void Add<BinaryFunctor<elem::Mult, BinaryFunctor<elem::Eq, Assignee<1>, Assignee<2>>, Assignee<3>>, marian::Tensor, marian::Tensor, marian::Tensor >(BinaryFunctor<elem::Mult, BinaryFunctor<elem::Eq, Assignee<1>, Assignee<2>>, Assignee<3>>, float, marian::Tensor, marian::Tensor, marian::Tensor, marian::Tensor);
+template void Add<BinaryFunctor<elem::Mult, Assignee<1>, UnaryFunctor<elem::Exp, BinaryFunctor<elem::Minus, Assignee<2>, Assignee<3>>>>, marian::Tensor, marian::Tensor, marian::Tensor >(BinaryFunctor<elem::Mult, Assignee<1>, UnaryFunctor<elem::Exp, BinaryFunctor<elem::Minus, Assignee<2>, Assignee<3>>>>, float, marian::Tensor, marian::Tensor, marian::Tensor, marian::Tensor);
+template void Add<BinaryFunctor<elem::Div, BinaryFunctor<elem::Mult, Assignee<1>, Assignee<2>>, Assignee<3>>, marian::Tensor, marian::Tensor, marian::Tensor >(BinaryFunctor<elem::Div, BinaryFunctor<elem::Mult, Assignee<1>, Assignee<2>>, Assignee<3>>, float, marian::Tensor, marian::Tensor, marian::Tensor, marian::Tensor);
+template void Add<BinaryFunctor<elem::Mult, BinaryFunctor<elem::Mult, Assignee<1>, Capture>, Assignee<2>>, marian::Tensor, marian::Tensor >(BinaryFunctor<elem::Mult, BinaryFunctor<elem::Mult, Assignee<1>, Capture>, Assignee<2>>, float, marian::Tensor, marian::Tensor, marian::Tensor);
+template void Add<BinaryFunctor<elem::Mult, Assignee<1>, BinaryFunctor<elem::Plus, BinaryFunctor<elem::Mult, Capture, Assignee<3>>, BinaryFunctor<elem::Mult, UnaryFunctor<elem::Sigmoid, BinaryFunctor<elem::Mult, Capture, Assignee<2>>>, BinaryFunctor<elem::Minus, Capture, BinaryFunctor<elem::Mult, Capture, Assignee<3>>>>>>, marian::Tensor, marian::Tensor, marian::Tensor >(BinaryFunctor<elem::Mult, Assignee<1>, BinaryFunctor<elem::Plus, BinaryFunctor<elem::Mult, Capture, Assignee<3>>, BinaryFunctor<elem::Mult, UnaryFunctor<elem::Sigmoid, BinaryFunctor<elem::Mult, Capture, Assignee<2>>>, BinaryFunctor<elem::Minus, Capture, BinaryFunctor<elem::Mult, Capture, Assignee<3>>>>>>, float, marian::Tensor, marian::Tensor, marian::Tensor, marian::Tensor);
\ No newline at end of file
diff --git a/src/tensors/gpu/add_all.cu b/src/tensors/gpu/add_all.cu
new file mode 100644
index 000000000..ad3ac2526
--- /dev/null
+++ b/src/tensors/gpu/add_all.cu
@@ -0,0 +1,116 @@
+#include "tensors/gpu/add_all.h"
+#include "tensors/gpu/cuda_helpers.h"
+#include "functional/functional.h"
+#include "tensors/tensor_operators.h"
+#include "3rd_party/reduce_all.h" // only works with CUDA >9.0, we are dropping CUDA 8.0 support, also changed in CMakeLists.txt
+
+namespace marian {
+
+#if COMPILE_FP16
+// local overload to determine tensor type
+template <> inline Type typeId<half>()  { return Type::float16; }
+#endif
+
+// Version with variadic template arguments, called by version with explicit arguments below
+template <typename T, typename AccType, class Functor, class AggFunctor, class... Tensors>
+void AggregateAllVar(Ptr<Allocator> allocator,
+                     Functor functor, 
+                     AccType aggInit,
+                     AggFunctor aggFunctor,
+                     AccType scale,
+                     Tensor out, 
+                     const Tensors... tensors) {
+  cudaSetDevice(out->getDeviceId().no);
+
+  static_assert(CUDA_VERSION >= 9000, "Marian requires CUDA_VERSION >= 9000 (9.0)");
+
+  constexpr size_t K = sizeof...(Tensors);                         // obtain arity K of tensors...
+  functional::Array<functional::Tensor<T>, K> gIns = {tensors...}; // convert to array of K objects of type functional::Tensor<T>
+  functional::Shape full = marian::Shape::broadcast({tensors...}); // compute maximal broadcasted shape
+
+  int size = full.elements();
+  int threads = (size < MAX_THREADS * 2) ? nextPow2((size + 1) / 2) : MAX_THREADS; // suggested in NVidia example for the all_reduce kernel
+  int blocks  = std::min(MAX_BLOCKS, (size + (threads * 2 - 1)) / (threads * 2));  // suggested in NVidia example for the all_reduce kernel
+
+  // The all_reduce kernel by nivida needs to perform multiple passes if the number of blocks needed to perform the reduction is larger than 1.
+  // Here we allocate the memory for the intermediate reductions for each block.
+  Tensor blockMem;
+  if(blocks > 1 || out->type() != typeId<AccType>()) { // if the out tensor does not have elementType AccType we need to allocate and convert later
+    MemoryPiece::PtrType temporaryMemory;
+    if(allocator) {
+      temporaryMemory = allocator->alloc<AccType>(blocks);
+    } else { // @TODO: get rid of this branch
+      uint8_t* temporaryMemoryPtr = 0;
+      CUDA_CHECK(cudaMalloc(&temporaryMemoryPtr, sizeof(AccType) * blocks)); 
+      temporaryMemory = MemoryPiece::New(temporaryMemoryPtr, sizeof(AccType) * blocks); // @TODO: consider implementing MemoryPiece::cudaMalloc<T>(size) for managed memory
+    }
+    blockMem = TensorBase::New(temporaryMemory,
+                               Shape({blocks}), 
+                               typeId<AccType>(), 
+                               out->getBackend());
+    blockMem->set(aggInit); // set temporary memory to aggInit
+  }
+  else {            // we are reducing into a single element now and the type matches, just use out as memory
+    blockMem = out; // do not set final output memory as we might be summing gradients... needs to be handled outside this function
+  }
+
+  functional::Tensor<AccType> gBlockMem = blockMem;
+  reduceSinglePass<T, AccType>(functor, aggInit, aggFunctor, scale, full, /*out=*/gBlockMem, /*in=*/gIns, threads, blocks);  // First pass reduction into intermediate memory
+
+  // If we actually needed more than one block to perform the first pass reduction, recursively run a second pass reduction over block memory until block memory has size 1.
+  if(blocks > 1) {
+    using namespace functional;
+    auto identity = _1; // transformation was done in first pass, hence only identity
+    AggregateAll<AccType, AccType>(allocator, identity, aggInit, aggFunctor, scale, out, /*in=*/blockMem); // Reducing AccType in AccType now (meta-reduction)
+  } else if(out->type() != typeId<AccType>()) { // it's only a single block, but we need to convert to different type, as mentioned above
+    CopyCast(out, blockMem);
+  }
+
+  if(blockMem != out) {
+    // Free temporary memory whether allocated in allocator or via cudaMalloc
+    if(allocator)
+      allocator->free(blockMem->memory());
+    else if(blockMem->memory()->data())
+      CUDA_CHECK(cudaFree(blockMem->memory()->data()));
+  }
+}
+
+template <typename T, typename AccType, class Functor, class AggFunctor>
+void AggregateAll(Ptr<Allocator> allocator,
+                  Functor functor, 
+                  AccType aggInit,
+                  AggFunctor aggFunctor,
+                  AccType scale,
+                  Tensor out, 
+                  const Tensor in1) {
+  AggregateAllVar<T, AccType>(allocator, functor, aggInit, aggFunctor, scale, out, in1);
+}
+
+template <typename T, typename AccType, class Functor, class AggFunctor>
+void AggregateAll(Ptr<Allocator> allocator,
+                  Functor functor, 
+                  AccType aggInit,
+                  AggFunctor aggFunctor,
+                  AccType scale,
+                  Tensor out, 
+                  const Tensor in1,
+                  const Tensor in2) {
+  AggregateAllVar<T, AccType>(allocator, functor, aggInit, aggFunctor, scale, out, in1, in2);
+}
+
+template <typename T, typename AccType, class Functor, class AggFunctor>
+void AggregateAll(Ptr<Allocator> allocator,
+                  Functor functor, 
+                  AccType aggInit,
+                  AggFunctor aggFunctor,
+                  AccType scale,
+                  Tensor out, 
+                  const Tensor in1,
+                  const Tensor in2,
+                  const Tensor in3) {
+  AggregateAllVar<T, AccType>(allocator, functor, aggInit, aggFunctor, scale, out, in1, in2, in3);
+}
+
+#include "tensors/gpu/add_all.inc"
+
+}
\ No newline at end of file
diff --git a/src/tensors/gpu/add_all.h b/src/tensors/gpu/add_all.h
index 5ef079a15..2e37fd497 100644
--- a/src/tensors/gpu/add_all.h
+++ b/src/tensors/gpu/add_all.h
@@ -3,87 +3,46 @@
 // This header file provides wrappers around NVidia's reduce_all kernel with our custom aggregation functionality
 // This kernel reduces a tensor into a single value. We have modified it to allow for different types of aggregations
 // like summing or max etc.
- 
+
 #include "tensors/gpu/cuda_helpers.h"
 #include "tensors/tensor.h"
 #include "tensors/allocator.h"
-#include "functional/functional.h"
 #include "functional/tensor.h"
 #include "tensors/tensor_operators.h"
-#include "3rd_party/reduce_all.h" // only works with CUDA >9.0, we are dropping CUDA 8.0 support, also changed in CMakeLists.txt
-
-#include <cuda.h>
-
 
 namespace marian {
 
-#if COMPILE_FP16
-// local overload to determine tensor type
-template <> inline Type typeId<half>()  { return Type::float16; }
-#endif
-
-template <typename T, typename AccType, class Functor, class AggFunctor, class... Tensors>
+// These function declarations are repeated as template specialization with variadic template arguments does not seem to work.
+// Here I am just creating version for 1, 2, and 3 arguments. To be extended if required.
+template <typename T, typename AccType, class Functor, class AggFunctor>
 void AggregateAll(Ptr<Allocator> allocator,
                   Functor functor, 
                   AccType aggInit,
                   AggFunctor aggFunctor,
                   AccType scale,
                   Tensor out, 
-                  const Tensors... tensors) {
-  cudaSetDevice(out->getDeviceId().no);
-
-  static_assert(CUDA_VERSION >= 9000, "Marian requires CUDA_VERSION >= 9000 (9.0)");
-
-  constexpr size_t K = sizeof...(Tensors);                         // obtain arity K of tensors...
-  functional::Array<functional::Tensor<T>, K> gIns = {tensors...}; // convert to array of K objects of type functional::Tensor<T>
-  functional::Shape full = marian::Shape::broadcast({tensors...}); // compute maximal broadcasted shape
+                  const Tensor in1);
 
-  int size = full.elements();
-  int threads = (size < MAX_THREADS * 2) ? nextPow2((size + 1) / 2) : MAX_THREADS; // suggested in NVidia example for the all_reduce kernel
-  int blocks  = std::min(MAX_BLOCKS, (size + (threads * 2 - 1)) / (threads * 2));  // suggested in NVidia example for the all_reduce kernel
-
-  // The all_reduce kernel by nivida needs to perform multiple passes if the number of blocks needed to perform the reduction is larger than 1.
-  // Here we allocate the memory for the intermediate reductions for each block.
-  Tensor blockMem;
-  if(blocks > 1 || out->type() != typeId<AccType>()) { // if the out tensor does not have elementType AccType we need to allocate and convert later
-    MemoryPiece::PtrType temporaryMemory;
-    if(allocator) {
-      temporaryMemory = allocator->alloc<AccType>(blocks);
-    } else { // @TODO: get rid of this branch
-      uint8_t* temporaryMemoryPtr = 0;
-      CUDA_CHECK(cudaMalloc(&temporaryMemoryPtr, sizeof(AccType) * blocks)); 
-      temporaryMemory = MemoryPiece::New(temporaryMemoryPtr, sizeof(AccType) * blocks); // @TODO: consider implementing MemoryPiece::cudaMalloc<T>(size) for managed memory
-    }
-    blockMem = TensorBase::New(temporaryMemory,
-                               Shape({blocks}), 
-                               typeId<AccType>(), 
-                               out->getBackend());
-    blockMem->set(aggInit); // set temporary memory to aggInit
-  }
-  else {            // we are reducing into a single element now and the type matches, just use out as memory
-    blockMem = out; // do not set final output memory as we might be summing gradients... needs to be handled outside this function
-  }
-
-  functional::Tensor<AccType> gBlockMem = blockMem;
-  reduceSinglePass<T, AccType>(functor, aggInit, aggFunctor, scale, full, /*out=*/gBlockMem, /*in=*/gIns, threads, blocks);  // First pass reduction into intermediate memory
-
-  // If we actually needed more than one block to perform the first pass reduction, recursively run a second pass reduction over block memory until block memory has size 1.
-  if(blocks > 1) {
-    using namespace functional;
-    auto identity = _1; // transformation was done in first pass, hence only identity
-    AggregateAll<AccType, AccType>(allocator, identity, aggInit, aggFunctor, scale, out, /*in=*/blockMem); // Reducing AccType in AccType now (meta-reduction)
-  } else if(out->type() != typeId<AccType>()) { // it's only a single block, but we need to convert to different type, as mentioned above
-    CopyCast(out, blockMem);
-  }
+template <typename T, typename AccType, class Functor, class AggFunctor>
+void AggregateAll(Ptr<Allocator> allocator,
+                  Functor functor, 
+                  AccType aggInit,
+                  AggFunctor aggFunctor,
+                  AccType scale,
+                  Tensor out, 
+                  const Tensor in1, 
+                  const Tensor in2);
 
-  if(blockMem != out) {
-    // Free temporary memory whether allocated in allocator or via cudaMalloc
-    if(allocator)
-      allocator->free(blockMem->memory());
-    else if(blockMem->memory()->data())
-      CUDA_CHECK(cudaFree(blockMem->memory()->data()));
-  }
-} 
+template <typename T, typename AccType, class Functor, class AggFunctor>
+void AggregateAll(Ptr<Allocator> allocator,
+                  Functor functor, 
+                  AccType aggInit,
+                  AggFunctor aggFunctor,
+                  AccType scale,
+                  Tensor out, 
+                  const Tensor in1, 
+                  const Tensor in2, 
+                  const Tensor in3);
 
 // Aggregates all values into a single tensor and returns the value of that tensor as a float
 // This does a GPU to CPU memory copy via TensorBase::scalar().
diff --git a/src/tensors/gpu/add_all.inc b/src/tensors/gpu/add_all.inc
new file mode 100644
index 000000000..73b0bda97
--- /dev/null
+++ b/src/tensors/gpu/add_all.inc
@@ -0,0 +1,71 @@
+// see element.inc for instructions on how to maintain this
+using namespace functional;
+
+template void AggregateAll<float, float, BinaryFunctor<elem::Mult, BinaryFunctor<elem::Mult, Capture, Assignee<1>>, Assignee<2>>, BinaryFunctor<elem::Plus, Assignee<1>, Assignee<2>>>(std::shared_ptr<Allocator>, BinaryFunctor<elem::Mult, BinaryFunctor<elem::Mult, Capture, Assignee<1>>, Assignee<2>>, float, BinaryFunctor<elem::Plus, Assignee<1>, Assignee<2>>, float, marian::Tensor, marian::Tensor, marian::Tensor);
+template void AggregateAll<float, float, BinaryFunctor<elem::Mult, BinaryFunctor<elem::Mult, Capture, BinaryFunctor<elem::Div, Capture, Assignee<1>>>, Assignee<2>>, BinaryFunctor<elem::Plus, Assignee<1>, Assignee<2>>>(std::shared_ptr<Allocator>, BinaryFunctor<elem::Mult, BinaryFunctor<elem::Mult, Capture, BinaryFunctor<elem::Div, Capture, Assignee<1>>>, Assignee<2>>, float, BinaryFunctor<elem::Plus, Assignee<1>, Assignee<2>>, float, marian::Tensor, marian::Tensor, marian::Tensor);
+template void AggregateAll<float, float, BinaryFunctor<elem::Mult, Assignee<1>, BinaryFunctor<elem::Minus, Capture, BinaryFunctor<elem::Mult, Assignee<2>, Assignee<2>>>>, BinaryFunctor<elem::Plus, Assignee<1>, Assignee<2>>>(std::shared_ptr<Allocator>, BinaryFunctor<elem::Mult, Assignee<1>, BinaryFunctor<elem::Minus, Capture, BinaryFunctor<elem::Mult, Assignee<2>, Assignee<2>>>>, float, BinaryFunctor<elem::Plus, Assignee<1>, Assignee<2>>, float, marian::Tensor, marian::Tensor, marian::Tensor);
+template void AggregateAll<float, float, BinaryFunctor<elem::Mult, Assignee<1>, Assignee<2>>, BinaryFunctor<elem::Plus, Assignee<1>, Assignee<2>>>(std::shared_ptr<Allocator>, BinaryFunctor<elem::Mult, Assignee<1>, Assignee<2>>, float, BinaryFunctor<elem::Plus, Assignee<1>, Assignee<2>>, float, marian::Tensor, marian::Tensor, marian::Tensor);
+template void AggregateAll<float, float, BinaryFunctor<elem::Mult, Capture, Assignee<1>>, BinaryFunctor<elem::Plus, Assignee<1>, Assignee<2>>>(std::shared_ptr<Allocator>, BinaryFunctor<elem::Mult, Capture, Assignee<1>>, float, BinaryFunctor<elem::Plus, Assignee<1>, Assignee<2>>, float, marian::Tensor, marian::Tensor);
+template void AggregateAll<float, float, BinaryFunctor<elem::Div, BinaryFunctor<elem::Mult, Assignee<1>, Capture>, Assignee<2>>, BinaryFunctor<elem::Plus, Assignee<1>, Assignee<2>>>(std::shared_ptr<Allocator>, BinaryFunctor<elem::Div, BinaryFunctor<elem::Mult, Assignee<1>, Capture>, Assignee<2>>, float, BinaryFunctor<elem::Plus, Assignee<1>, Assignee<2>>, float, marian::Tensor, marian::Tensor, marian::Tensor);
+template void AggregateAll<float, float, BinaryFunctor<elem::Div, BinaryFunctor<elem::Mult, UnaryFunctor<elem::Neg, Assignee<1>>, Assignee<2>>, BinaryFunctor<elem::Mult, Assignee<3>, Assignee<3>>>, BinaryFunctor<elem::Plus, Assignee<1>, Assignee<2>>>(std::shared_ptr<Allocator>, BinaryFunctor<elem::Div, BinaryFunctor<elem::Mult, UnaryFunctor<elem::Neg, Assignee<1>>, Assignee<2>>, BinaryFunctor<elem::Mult, Assignee<3>, Assignee<3>>>, float, BinaryFunctor<elem::Plus, Assignee<1>, Assignee<2>>, float, marian::Tensor, marian::Tensor, marian::Tensor, marian::Tensor);
+template void AggregateAll<float, float, UnaryFunctor<elem::Neg, Assignee<1>>, BinaryFunctor<elem::Plus, Assignee<1>, Assignee<2>>>(std::shared_ptr<Allocator>, UnaryFunctor<elem::Neg, Assignee<1>>, float, BinaryFunctor<elem::Plus, Assignee<1>, Assignee<2>>, float, marian::Tensor, marian::Tensor);
+template void AggregateAll<float, float, BinaryFunctor<elem::Mult, Assignee<1>, BinaryFunctor<elem::Plus, Assignee<3>, BinaryFunctor<elem::Mult, UnaryFunctor<elem::Sigmoid, Assignee<2>>, BinaryFunctor<elem::Minus, Capture, Assignee<3>>>>>, BinaryFunctor<elem::Plus, Assignee<1>, Assignee<2>>>(std::shared_ptr<Allocator>, BinaryFunctor<elem::Mult, Assignee<1>, BinaryFunctor<elem::Plus, Assignee<3>, BinaryFunctor<elem::Mult, UnaryFunctor<elem::Sigmoid, Assignee<2>>, BinaryFunctor<elem::Minus, Capture, Assignee<3>>>>>, float, BinaryFunctor<elem::Plus, Assignee<1>, Assignee<2>>, float, marian::Tensor, marian::Tensor, marian::Tensor, marian::Tensor);
+template void AggregateAll<float, float, BinaryFunctor<elem::Mult, Assignee<1>, UnaryFunctor<elem::Exp, Assignee<2>>>, BinaryFunctor<elem::Plus, Assignee<1>, Assignee<2>>>(std::shared_ptr<Allocator>, BinaryFunctor<elem::Mult, Assignee<1>, UnaryFunctor<elem::Exp, Assignee<2>>>, float, BinaryFunctor<elem::Plus, Assignee<1>, Assignee<2>>, float, marian::Tensor, marian::Tensor, marian::Tensor);
+template void AggregateAll<float, float, BinaryFunctor<elem::Mult, Assignee<1>, BinaryFunctor<elem::Div, Capture, Assignee<2>>>, BinaryFunctor<elem::Plus, Assignee<1>, Assignee<2>>>(std::shared_ptr<Allocator>, BinaryFunctor<elem::Mult, Assignee<1>, BinaryFunctor<elem::Div, Capture, Assignee<2>>>, float, BinaryFunctor<elem::Plus, Assignee<1>, Assignee<2>>, float, marian::Tensor, marian::Tensor, marian::Tensor);
+template void AggregateAll<float, float, BinaryFunctor<elem::Mult, Assignee<1>, BinaryFunctor<elem::sPReLUBack, Assignee<2>, Capture>>, BinaryFunctor<elem::Plus, Assignee<1>, Assignee<2>>>(std::shared_ptr<Allocator>, BinaryFunctor<elem::Mult, Assignee<1>, BinaryFunctor<elem::sPReLUBack, Assignee<2>, Capture>>, float, BinaryFunctor<elem::Plus, Assignee<1>, Assignee<2>>, float, marian::Tensor, marian::Tensor, marian::Tensor);
+template void AggregateAll<float, float, BinaryFunctor<elem::Mult, Assignee<1>, UnaryFunctor<elem::sReLUBack, Assignee<2>>>, BinaryFunctor<elem::Plus, Assignee<1>, Assignee<2>>>(std::shared_ptr<Allocator>, BinaryFunctor<elem::Mult, Assignee<1>, UnaryFunctor<elem::sReLUBack, Assignee<2>>>, float, BinaryFunctor<elem::Plus, Assignee<1>, Assignee<2>>, float, marian::Tensor, marian::Tensor, marian::Tensor);
+template void AggregateAll<float, float, BinaryFunctor<elem::Mult, BinaryFunctor<elem::Mult, Assignee<1>, Assignee<2>>, BinaryFunctor<elem::Minus, Capture, Assignee<2>>>, BinaryFunctor<elem::Plus, Assignee<1>, Assignee<2>>>(std::shared_ptr<Allocator>, BinaryFunctor<elem::Mult, BinaryFunctor<elem::Mult, Assignee<1>, Assignee<2>>, BinaryFunctor<elem::Minus, Capture, Assignee<2>>>, float, BinaryFunctor<elem::Plus, Assignee<1>, Assignee<2>>, float, marian::Tensor, marian::Tensor, marian::Tensor);
+template void AggregateAll<float, float, BinaryFunctor<elem::Mult, BinaryFunctor<elem::Bump, Assignee<1>, Capture>, Assignee<2>>, BinaryFunctor<elem::Plus, Assignee<1>, Assignee<2>>>(std::shared_ptr<Allocator>, BinaryFunctor<elem::Mult, BinaryFunctor<elem::Bump, Assignee<1>, Capture>, Assignee<2>>, float, BinaryFunctor<elem::Plus, Assignee<1>, Assignee<2>>, float, marian::Tensor, marian::Tensor, marian::Tensor);
+template void AggregateAll<float, float, BinaryFunctor<elem::Mult, BinaryFunctor<elem::Lt, Assignee<2>, Assignee<3>>, Assignee<1>>, BinaryFunctor<elem::Plus, Assignee<1>, Assignee<2>>>(std::shared_ptr<Allocator>, BinaryFunctor<elem::Mult, BinaryFunctor<elem::Lt, Assignee<2>, Assignee<3>>, Assignee<1>>, float, BinaryFunctor<elem::Plus, Assignee<1>, Assignee<2>>, float, marian::Tensor, marian::Tensor, marian::Tensor, marian::Tensor);
+template void AggregateAll<float, float, BinaryFunctor<elem::Mult, BinaryFunctor<elem::Geq, Assignee<2>, Assignee<3>>, Assignee<1>>, BinaryFunctor<elem::Plus, Assignee<1>, Assignee<2>>>(std::shared_ptr<Allocator>, BinaryFunctor<elem::Mult, BinaryFunctor<elem::Geq, Assignee<2>, Assignee<3>>, Assignee<1>>, float, BinaryFunctor<elem::Plus, Assignee<1>, Assignee<2>>, float, marian::Tensor, marian::Tensor, marian::Tensor, marian::Tensor);
+template void AggregateAll<float, float, BinaryFunctor<elem::Mult, Assignee<1>, UnaryFunctor<elem::Sigmoid, BinaryFunctor<elem::Minus, Assignee<3>, Assignee<2>>>>, BinaryFunctor<elem::Plus, Assignee<1>, Assignee<2>>>(std::shared_ptr<Allocator>, BinaryFunctor<elem::Mult, Assignee<1>, UnaryFunctor<elem::Sigmoid, BinaryFunctor<elem::Minus, Assignee<3>, Assignee<2>>>>, float, BinaryFunctor<elem::Plus, Assignee<1>, Assignee<2>>, float, marian::Tensor, marian::Tensor, marian::Tensor, marian::Tensor);
+template void AggregateAll<float, float, BinaryFunctor<elem::Mult, BinaryFunctor<elem::Gt, Assignee<2>, Assignee<3>>, Assignee<1>>, BinaryFunctor<elem::Plus, Assignee<1>, Assignee<2>>>(std::shared_ptr<Allocator>, BinaryFunctor<elem::Mult, BinaryFunctor<elem::Gt, Assignee<2>, Assignee<3>>, Assignee<1>>, float, BinaryFunctor<elem::Plus, Assignee<1>, Assignee<2>>, float, marian::Tensor, marian::Tensor, marian::Tensor, marian::Tensor);
+template void AggregateAll<float, float, BinaryFunctor<elem::Mult, BinaryFunctor<elem::Leq, Assignee<2>, Assignee<3>>, Assignee<1>>, BinaryFunctor<elem::Plus, Assignee<1>, Assignee<2>>>(std::shared_ptr<Allocator>, BinaryFunctor<elem::Mult, BinaryFunctor<elem::Leq, Assignee<2>, Assignee<3>>, Assignee<1>>, float, BinaryFunctor<elem::Plus, Assignee<1>, Assignee<2>>, float, marian::Tensor, marian::Tensor, marian::Tensor, marian::Tensor);
+template void AggregateAll<float, float, BinaryFunctor<elem::Mult, Assignee<1>, UnaryFunctor<elem::Sigmoid, BinaryFunctor<elem::Minus, Assignee<2>, Assignee<3>>>>, BinaryFunctor<elem::Plus, Assignee<1>, Assignee<2>>>(std::shared_ptr<Allocator>, BinaryFunctor<elem::Mult, Assignee<1>, UnaryFunctor<elem::Sigmoid, BinaryFunctor<elem::Minus, Assignee<2>, Assignee<3>>>>, float, BinaryFunctor<elem::Plus, Assignee<1>, Assignee<2>>, float, marian::Tensor, marian::Tensor, marian::Tensor, marian::Tensor);
+template void AggregateAll<float, float, BinaryFunctor<elem::Div, Assignee<1>, Assignee<2>>, BinaryFunctor<elem::Plus, Assignee<1>, Assignee<2>>>(std::shared_ptr<Allocator>, BinaryFunctor<elem::Div, Assignee<1>, Assignee<2>>, float, BinaryFunctor<elem::Plus, Assignee<1>, Assignee<2>>, float, marian::Tensor, marian::Tensor, marian::Tensor);
+template void AggregateAll<float, float, Assignee<1>, BinaryFunctor<elem::Min, Assignee<1>, Assignee<2>>>(std::shared_ptr<Allocator>, Assignee<1>, float, BinaryFunctor<elem::Min, Assignee<1>, Assignee<2>>, float, marian::Tensor, marian::Tensor);
+template void AggregateAll<float, float, Assignee<1>, BinaryFunctor<elem::Max, Assignee<1>, Assignee<2>>>(std::shared_ptr<Allocator>, Assignee<1>, float, BinaryFunctor<elem::Max, Assignee<1>, Assignee<2>>, float, marian::Tensor, marian::Tensor);
+template void AggregateAll<float, float, Assignee<1>, BinaryFunctor<elem::Mult, Assignee<1>, Assignee<2>>>(std::shared_ptr<Allocator>, Assignee<1>, float, BinaryFunctor<elem::Mult, Assignee<1>, Assignee<2>>, float, marian::Tensor, marian::Tensor);
+template void AggregateAll<float, float, Assignee<1>, BinaryFunctor<elem::LogAddExp, Assignee<1>, Assignee<2>>>(std::shared_ptr<Allocator>, Assignee<1>, float, BinaryFunctor<elem::LogAddExp, Assignee<1>, Assignee<2>>, float, marian::Tensor, marian::Tensor);
+template void AggregateAll<float, float, BinaryFunctor<elem::Mult, BinaryFunctor<elem::Eq, Assignee<1>, Assignee<2>>, Assignee<3>>, BinaryFunctor<elem::Plus, Assignee<1>, Assignee<2>>>(std::shared_ptr<Allocator>, BinaryFunctor<elem::Mult, BinaryFunctor<elem::Eq, Assignee<1>, Assignee<2>>, Assignee<3>>, float, BinaryFunctor<elem::Plus, Assignee<1>, Assignee<2>>, float, marian::Tensor, marian::Tensor, marian::Tensor, marian::Tensor);
+template void AggregateAll<float, float, BinaryFunctor<elem::Mult, Assignee<1>, UnaryFunctor<elem::Exp, BinaryFunctor<elem::Minus, Assignee<2>, Assignee<3>>>>, BinaryFunctor<elem::Plus, Assignee<1>, Assignee<2>>>(std::shared_ptr<Allocator>, BinaryFunctor<elem::Mult, Assignee<1>, UnaryFunctor<elem::Exp, BinaryFunctor<elem::Minus, Assignee<2>, Assignee<3>>>>, float, BinaryFunctor<elem::Plus, Assignee<1>, Assignee<2>>, float, marian::Tensor, marian::Tensor, marian::Tensor, marian::Tensor);
+template void AggregateAll<float, float, BinaryFunctor<elem::Div, BinaryFunctor<elem::Mult, Assignee<1>, Assignee<2>>, Assignee<3>>, BinaryFunctor<elem::Plus, Assignee<1>, Assignee<2>>>(std::shared_ptr<Allocator>, BinaryFunctor<elem::Div, BinaryFunctor<elem::Mult, Assignee<1>, Assignee<2>>, Assignee<3>>, float, BinaryFunctor<elem::Plus, Assignee<1>, Assignee<2>>, float, marian::Tensor, marian::Tensor, marian::Tensor, marian::Tensor);
+template void AggregateAll<float, float, BinaryFunctor<elem::Mult, BinaryFunctor<elem::Mult, Assignee<1>, Capture>, Assignee<2>>, BinaryFunctor<elem::Plus, Assignee<1>, Assignee<2>>>(std::shared_ptr<Allocator>, BinaryFunctor<elem::Mult, BinaryFunctor<elem::Mult, Assignee<1>, Capture>, Assignee<2>>, float, BinaryFunctor<elem::Plus, Assignee<1>, Assignee<2>>, float, marian::Tensor, marian::Tensor, marian::Tensor);
+template void AggregateAll<float, float, BinaryFunctor<elem::Mult, Assignee<1>, BinaryFunctor<elem::Plus, BinaryFunctor<elem::Mult, Capture, Assignee<3>>, BinaryFunctor<elem::Mult, UnaryFunctor<elem::Sigmoid, BinaryFunctor<elem::Mult, Capture, Assignee<2>>>, BinaryFunctor<elem::Minus, Capture, BinaryFunctor<elem::Mult, Capture, Assignee<3>>>>>>, BinaryFunctor<elem::Plus, Assignee<1>, Assignee<2>>>(std::shared_ptr<Allocator>, BinaryFunctor<elem::Mult, Assignee<1>, BinaryFunctor<elem::Plus, BinaryFunctor<elem::Mult, Capture, Assignee<3>>, BinaryFunctor<elem::Mult, UnaryFunctor<elem::Sigmoid, BinaryFunctor<elem::Mult, Capture, Assignee<2>>>, BinaryFunctor<elem::Minus, Capture, BinaryFunctor<elem::Mult, Capture, Assignee<3>>>>>>, float, BinaryFunctor<elem::Plus, Assignee<1>, Assignee<2>>, float, marian::Tensor, marian::Tensor, marian::Tensor, marian::Tensor);
+template void AggregateAll<float, float, BinaryFunctor<elem::Mult, Assignee<1>, Assignee<1>>, BinaryFunctor<elem::Plus, Assignee<1>, Assignee<2>>>(std::shared_ptr<Allocator>, BinaryFunctor<elem::Mult, Assignee<1>, Assignee<1>>, float, BinaryFunctor<elem::Plus, Assignee<1>, Assignee<2>>, float, marian::Tensor, marian::Tensor);
+
+#if COMPILE_FP16
+template void AggregateAll<__half, float, BinaryFunctor<elem::Mult, BinaryFunctor<elem::Mult, Capture, Assignee<1>>, Assignee<2>>, BinaryFunctor<elem::Plus, Assignee<1>, Assignee<2>>>(std::shared_ptr<Allocator>, BinaryFunctor<elem::Mult, BinaryFunctor<elem::Mult, Capture, Assignee<1>>, Assignee<2>>, float, BinaryFunctor<elem::Plus, Assignee<1>, Assignee<2>>, float, marian::Tensor, marian::Tensor, marian::Tensor);
+template void AggregateAll<__half, float, BinaryFunctor<elem::Mult, BinaryFunctor<elem::Mult, Capture, BinaryFunctor<elem::Div, Capture, Assignee<1>>>, Assignee<2>>, BinaryFunctor<elem::Plus, Assignee<1>, Assignee<2>>>(std::shared_ptr<Allocator>, BinaryFunctor<elem::Mult, BinaryFunctor<elem::Mult, Capture, BinaryFunctor<elem::Div, Capture, Assignee<1>>>, Assignee<2>>, float, BinaryFunctor<elem::Plus, Assignee<1>, Assignee<2>>, float, marian::Tensor, marian::Tensor, marian::Tensor);
+template void AggregateAll<__half, float, BinaryFunctor<elem::Mult, Assignee<1>, BinaryFunctor<elem::Minus, Capture, BinaryFunctor<elem::Mult, Assignee<2>, Assignee<2>>>>, BinaryFunctor<elem::Plus, Assignee<1>, Assignee<2>>>(std::shared_ptr<Allocator>, BinaryFunctor<elem::Mult, Assignee<1>, BinaryFunctor<elem::Minus, Capture, BinaryFunctor<elem::Mult, Assignee<2>, Assignee<2>>>>, float, BinaryFunctor<elem::Plus, Assignee<1>, Assignee<2>>, float, marian::Tensor, marian::Tensor, marian::Tensor);
+template void AggregateAll<__half, float, BinaryFunctor<elem::Mult, Assignee<1>, Assignee<2>>, BinaryFunctor<elem::Plus, Assignee<1>, Assignee<2>>>(std::shared_ptr<Allocator>, BinaryFunctor<elem::Mult, Assignee<1>, Assignee<2>>, float, BinaryFunctor<elem::Plus, Assignee<1>, Assignee<2>>, float, marian::Tensor, marian::Tensor, marian::Tensor);
+template void AggregateAll<__half, float, BinaryFunctor<elem::Mult, Capture, Assignee<1>>, BinaryFunctor<elem::Plus, Assignee<1>, Assignee<2>>>(std::shared_ptr<Allocator>, BinaryFunctor<elem::Mult, Capture, Assignee<1>>, float, BinaryFunctor<elem::Plus, Assignee<1>, Assignee<2>>, float, marian::Tensor, marian::Tensor);
+template void AggregateAll<__half, float, BinaryFunctor<elem::Div, BinaryFunctor<elem::Mult, Assignee<1>, Capture>, Assignee<2>>, BinaryFunctor<elem::Plus, Assignee<1>, Assignee<2>>>(std::shared_ptr<Allocator>, BinaryFunctor<elem::Div, BinaryFunctor<elem::Mult, Assignee<1>, Capture>, Assignee<2>>, float, BinaryFunctor<elem::Plus, Assignee<1>, Assignee<2>>, float, marian::Tensor, marian::Tensor, marian::Tensor);
+template void AggregateAll<__half, float, BinaryFunctor<elem::Div, BinaryFunctor<elem::Mult, UnaryFunctor<elem::Neg, Assignee<1>>, Assignee<2>>, BinaryFunctor<elem::Mult, Assignee<3>, Assignee<3>>>, BinaryFunctor<elem::Plus, Assignee<1>, Assignee<2>>>(std::shared_ptr<Allocator>, BinaryFunctor<elem::Div, BinaryFunctor<elem::Mult, UnaryFunctor<elem::Neg, Assignee<1>>, Assignee<2>>, BinaryFunctor<elem::Mult, Assignee<3>, Assignee<3>>>, float, BinaryFunctor<elem::Plus, Assignee<1>, Assignee<2>>, float, marian::Tensor, marian::Tensor, marian::Tensor, marian::Tensor);
+template void AggregateAll<__half, float, UnaryFunctor<elem::Neg, Assignee<1>>, BinaryFunctor<elem::Plus, Assignee<1>, Assignee<2>>>(std::shared_ptr<Allocator>, UnaryFunctor<elem::Neg, Assignee<1>>, float, BinaryFunctor<elem::Plus, Assignee<1>, Assignee<2>>, float, marian::Tensor, marian::Tensor);
+template void AggregateAll<__half, float, BinaryFunctor<elem::Mult, Assignee<1>, BinaryFunctor<elem::Plus, Assignee<3>, BinaryFunctor<elem::Mult, UnaryFunctor<elem::Sigmoid, Assignee<2>>, BinaryFunctor<elem::Minus, Capture, Assignee<3>>>>>, BinaryFunctor<elem::Plus, Assignee<1>, Assignee<2>>>(std::shared_ptr<Allocator>, BinaryFunctor<elem::Mult, Assignee<1>, BinaryFunctor<elem::Plus, Assignee<3>, BinaryFunctor<elem::Mult, UnaryFunctor<elem::Sigmoid, Assignee<2>>, BinaryFunctor<elem::Minus, Capture, Assignee<3>>>>>, float, BinaryFunctor<elem::Plus, Assignee<1>, Assignee<2>>, float, marian::Tensor, marian::Tensor, marian::Tensor, marian::Tensor);
+template void AggregateAll<__half, float, BinaryFunctor<elem::Mult, Assignee<1>, UnaryFunctor<elem::Exp, Assignee<2>>>, BinaryFunctor<elem::Plus, Assignee<1>, Assignee<2>>>(std::shared_ptr<Allocator>, BinaryFunctor<elem::Mult, Assignee<1>, UnaryFunctor<elem::Exp, Assignee<2>>>, float, BinaryFunctor<elem::Plus, Assignee<1>, Assignee<2>>, float, marian::Tensor, marian::Tensor, marian::Tensor);
+template void AggregateAll<__half, float, BinaryFunctor<elem::Mult, Assignee<1>, BinaryFunctor<elem::Div, Capture, Assignee<2>>>, BinaryFunctor<elem::Plus, Assignee<1>, Assignee<2>>>(std::shared_ptr<Allocator>, BinaryFunctor<elem::Mult, Assignee<1>, BinaryFunctor<elem::Div, Capture, Assignee<2>>>, float, BinaryFunctor<elem::Plus, Assignee<1>, Assignee<2>>, float, marian::Tensor, marian::Tensor, marian::Tensor);
+template void AggregateAll<__half, float, BinaryFunctor<elem::Mult, Assignee<1>, BinaryFunctor<elem::sPReLUBack, Assignee<2>, Capture>>, BinaryFunctor<elem::Plus, Assignee<1>, Assignee<2>>>(std::shared_ptr<Allocator>, BinaryFunctor<elem::Mult, Assignee<1>, BinaryFunctor<elem::sPReLUBack, Assignee<2>, Capture>>, float, BinaryFunctor<elem::Plus, Assignee<1>, Assignee<2>>, float, marian::Tensor, marian::Tensor, marian::Tensor);
+template void AggregateAll<__half, float, BinaryFunctor<elem::Mult, Assignee<1>, UnaryFunctor<elem::sReLUBack, Assignee<2>>>, BinaryFunctor<elem::Plus, Assignee<1>, Assignee<2>>>(std::shared_ptr<Allocator>, BinaryFunctor<elem::Mult, Assignee<1>, UnaryFunctor<elem::sReLUBack, Assignee<2>>>, float, BinaryFunctor<elem::Plus, Assignee<1>, Assignee<2>>, float, marian::Tensor, marian::Tensor, marian::Tensor);
+template void AggregateAll<__half, float, BinaryFunctor<elem::Mult, BinaryFunctor<elem::Mult, Assignee<1>, Assignee<2>>, BinaryFunctor<elem::Minus, Capture, Assignee<2>>>, BinaryFunctor<elem::Plus, Assignee<1>, Assignee<2>>>(std::shared_ptr<Allocator>, BinaryFunctor<elem::Mult, BinaryFunctor<elem::Mult, Assignee<1>, Assignee<2>>, BinaryFunctor<elem::Minus, Capture, Assignee<2>>>, float, BinaryFunctor<elem::Plus, Assignee<1>, Assignee<2>>, float, marian::Tensor, marian::Tensor, marian::Tensor);
+template void AggregateAll<__half, float, BinaryFunctor<elem::Mult, BinaryFunctor<elem::Bump, Assignee<1>, Capture>, Assignee<2>>, BinaryFunctor<elem::Plus, Assignee<1>, Assignee<2>>>(std::shared_ptr<Allocator>, BinaryFunctor<elem::Mult, BinaryFunctor<elem::Bump, Assignee<1>, Capture>, Assignee<2>>, float, BinaryFunctor<elem::Plus, Assignee<1>, Assignee<2>>, float, marian::Tensor, marian::Tensor, marian::Tensor);
+template void AggregateAll<__half, float, BinaryFunctor<elem::Mult, BinaryFunctor<elem::Lt, Assignee<2>, Assignee<3>>, Assignee<1>>, BinaryFunctor<elem::Plus, Assignee<1>, Assignee<2>>>(std::shared_ptr<Allocator>, BinaryFunctor<elem::Mult, BinaryFunctor<elem::Lt, Assignee<2>, Assignee<3>>, Assignee<1>>, float, BinaryFunctor<elem::Plus, Assignee<1>, Assignee<2>>, float, marian::Tensor, marian::Tensor, marian::Tensor, marian::Tensor);
+template void AggregateAll<__half, float, BinaryFunctor<elem::Mult, BinaryFunctor<elem::Geq, Assignee<2>, Assignee<3>>, Assignee<1>>, BinaryFunctor<elem::Plus, Assignee<1>, Assignee<2>>>(std::shared_ptr<Allocator>, BinaryFunctor<elem::Mult, BinaryFunctor<elem::Geq, Assignee<2>, Assignee<3>>, Assignee<1>>, float, BinaryFunctor<elem::Plus, Assignee<1>, Assignee<2>>, float, marian::Tensor, marian::Tensor, marian::Tensor, marian::Tensor);
+template void AggregateAll<__half, float, BinaryFunctor<elem::Mult, Assignee<1>, UnaryFunctor<elem::Sigmoid, BinaryFunctor<elem::Minus, Assignee<3>, Assignee<2>>>>, BinaryFunctor<elem::Plus, Assignee<1>, Assignee<2>>>(std::shared_ptr<Allocator>, BinaryFunctor<elem::Mult, Assignee<1>, UnaryFunctor<elem::Sigmoid, BinaryFunctor<elem::Minus, Assignee<3>, Assignee<2>>>>, float, BinaryFunctor<elem::Plus, Assignee<1>, Assignee<2>>, float, marian::Tensor, marian::Tensor, marian::Tensor, marian::Tensor);
+template void AggregateAll<__half, float, BinaryFunctor<elem::Mult, BinaryFunctor<elem::Gt, Assignee<2>, Assignee<3>>, Assignee<1>>, BinaryFunctor<elem::Plus, Assignee<1>, Assignee<2>>>(std::shared_ptr<Allocator>, BinaryFunctor<elem::Mult, BinaryFunctor<elem::Gt, Assignee<2>, Assignee<3>>, Assignee<1>>, float, BinaryFunctor<elem::Plus, Assignee<1>, Assignee<2>>, float, marian::Tensor, marian::Tensor, marian::Tensor, marian::Tensor);
+template void AggregateAll<__half, float, BinaryFunctor<elem::Mult, BinaryFunctor<elem::Leq, Assignee<2>, Assignee<3>>, Assignee<1>>, BinaryFunctor<elem::Plus, Assignee<1>, Assignee<2>>>(std::shared_ptr<Allocator>, BinaryFunctor<elem::Mult, BinaryFunctor<elem::Leq, Assignee<2>, Assignee<3>>, Assignee<1>>, float, BinaryFunctor<elem::Plus, Assignee<1>, Assignee<2>>, float, marian::Tensor, marian::Tensor, marian::Tensor, marian::Tensor);
+template void AggregateAll<__half, float, BinaryFunctor<elem::Mult, Assignee<1>, UnaryFunctor<elem::Sigmoid, BinaryFunctor<elem::Minus, Assignee<2>, Assignee<3>>>>, BinaryFunctor<elem::Plus, Assignee<1>, Assignee<2>>>(std::shared_ptr<Allocator>, BinaryFunctor<elem::Mult, Assignee<1>, UnaryFunctor<elem::Sigmoid, BinaryFunctor<elem::Minus, Assignee<2>, Assignee<3>>>>, float, BinaryFunctor<elem::Plus, Assignee<1>, Assignee<2>>, float, marian::Tensor, marian::Tensor, marian::Tensor, marian::Tensor);
+template void AggregateAll<__half, float, BinaryFunctor<elem::Div, Assignee<1>, Assignee<2>>, BinaryFunctor<elem::Plus, Assignee<1>, Assignee<2>>>(std::shared_ptr<Allocator>, BinaryFunctor<elem::Div, Assignee<1>, Assignee<2>>, float, BinaryFunctor<elem::Plus, Assignee<1>, Assignee<2>>, float, marian::Tensor, marian::Tensor, marian::Tensor);
+template void AggregateAll<__half, float, Assignee<1>, BinaryFunctor<elem::Min, Assignee<1>, Assignee<2>>>(std::shared_ptr<Allocator>, Assignee<1>, float, BinaryFunctor<elem::Min, Assignee<1>, Assignee<2>>, float, marian::Tensor, marian::Tensor);
+template void AggregateAll<__half, float, Assignee<1>, BinaryFunctor<elem::Max, Assignee<1>, Assignee<2>>>(std::shared_ptr<Allocator>, Assignee<1>, float, BinaryFunctor<elem::Max, Assignee<1>, Assignee<2>>, float, marian::Tensor, marian::Tensor);
+template void AggregateAll<__half, float, Assignee<1>, BinaryFunctor<elem::Mult, Assignee<1>, Assignee<2>>>(std::shared_ptr<Allocator>, Assignee<1>, float, BinaryFunctor<elem::Mult, Assignee<1>, Assignee<2>>, float, marian::Tensor, marian::Tensor);
+template void AggregateAll<__half, float, Assignee<1>, BinaryFunctor<elem::LogAddExp, Assignee<1>, Assignee<2>>>(std::shared_ptr<Allocator>, Assignee<1>, float, BinaryFunctor<elem::LogAddExp, Assignee<1>, Assignee<2>>, float, marian::Tensor, marian::Tensor);
+template void AggregateAll<__half, float, BinaryFunctor<elem::Mult, BinaryFunctor<elem::Eq, Assignee<1>, Assignee<2>>, Assignee<3>>, BinaryFunctor<elem::Plus, Assignee<1>, Assignee<2>>>(std::shared_ptr<Allocator>, BinaryFunctor<elem::Mult, BinaryFunctor<elem::Eq, Assignee<1>, Assignee<2>>, Assignee<3>>, float, BinaryFunctor<elem::Plus, Assignee<1>, Assignee<2>>, float, marian::Tensor, marian::Tensor, marian::Tensor, marian::Tensor);
+template void AggregateAll<__half, float, BinaryFunctor<elem::Mult, Assignee<1>, UnaryFunctor<elem::Exp, BinaryFunctor<elem::Minus, Assignee<2>, Assignee<3>>>>, BinaryFunctor<elem::Plus, Assignee<1>, Assignee<2>>>(std::shared_ptr<Allocator>, BinaryFunctor<elem::Mult, Assignee<1>, UnaryFunctor<elem::Exp, BinaryFunctor<elem::Minus, Assignee<2>, Assignee<3>>>>, float, BinaryFunctor<elem::Plus, Assignee<1>, Assignee<2>>, float, marian::Tensor, marian::Tensor, marian::Tensor, marian::Tensor);
+template void AggregateAll<__half, float, BinaryFunctor<elem::Div, BinaryFunctor<elem::Mult, Assignee<1>, Assignee<2>>, Assignee<3>>, BinaryFunctor<elem::Plus, Assignee<1>, Assignee<2>>>(std::shared_ptr<Allocator>, BinaryFunctor<elem::Div, BinaryFunctor<elem::Mult, Assignee<1>, Assignee<2>>, Assignee<3>>, float, BinaryFunctor<elem::Plus, Assignee<1>, Assignee<2>>, float, marian::Tensor, marian::Tensor, marian::Tensor, marian::Tensor);
+template void AggregateAll<__half, float, BinaryFunctor<elem::Mult, BinaryFunctor<elem::Mult, Assignee<1>, Capture>, Assignee<2>>, BinaryFunctor<elem::Plus, Assignee<1>, Assignee<2>>>(std::shared_ptr<Allocator>, BinaryFunctor<elem::Mult, BinaryFunctor<elem::Mult, Assignee<1>, Capture>, Assignee<2>>, float, BinaryFunctor<elem::Plus, Assignee<1>, Assignee<2>>, float, marian::Tensor, marian::Tensor, marian::Tensor);
+template void AggregateAll<__half, float, BinaryFunctor<elem::Mult, Assignee<1>, BinaryFunctor<elem::Plus, BinaryFunctor<elem::Mult, Capture, Assignee<3>>, BinaryFunctor<elem::Mult, UnaryFunctor<elem::Sigmoid, BinaryFunctor<elem::Mult, Capture, Assignee<2>>>, BinaryFunctor<elem::Minus, Capture, BinaryFunctor<elem::Mult, Capture, Assignee<3>>>>>>, BinaryFunctor<elem::Plus, Assignee<1>, Assignee<2>>>(std::shared_ptr<Allocator>, BinaryFunctor<elem::Mult, Assignee<1>, BinaryFunctor<elem::Plus, BinaryFunctor<elem::Mult, Capture, Assignee<3>>, BinaryFunctor<elem::Mult, UnaryFunctor<elem::Sigmoid, BinaryFunctor<elem::Mult, Capture, Assignee<2>>>, BinaryFunctor<elem::Minus, Capture, BinaryFunctor<elem::Mult, Capture, Assignee<3>>>>>>, float, BinaryFunctor<elem::Plus, Assignee<1>, Assignee<2>>, float, marian::Tensor, marian::Tensor, marian::Tensor, marian::Tensor);
+template void AggregateAll<__half, float, Assignee<1>, BinaryFunctor<elem::Plus, Assignee<1>, Assignee<2>>>(std::shared_ptr<Allocator>, Assignee<1>, float, BinaryFunctor<elem::Plus, Assignee<1>, Assignee<2>>, float, marian::Tensor, marian::Tensor);
+template void AggregateAll<__half, float, BinaryFunctor<elem::Mult, Assignee<1>, Assignee<1>>, BinaryFunctor<elem::Plus, Assignee<1>, Assignee<2>>>(std::shared_ptr<Allocator>, BinaryFunctor<elem::Mult, Assignee<1>, Assignee<1>>, float, BinaryFunctor<elem::Plus, Assignee<1>, Assignee<2>>, float, marian::Tensor, marian::Tensor);
+#endif
\ No newline at end of file

From 0fab6ea8500f6f0b000586107f9bd5c7d4c9ada8 Mon Sep 17 00:00:00 2001
From: Young Jin Kim <youki@microsoft.com>
Date: Sat, 11 Jan 2020 01:29:02 +0000
Subject: [PATCH 811/838] Merged PR 10709: Disable a warning in FBGEMM code.
 This issue only appears in debug build.

Disable a warning in FBGEMM code. This issue only appears in debug build.
---
 src/tensors/cpu/fbgemm/packed_gemm.cpp | 1 +
 1 file changed, 1 insertion(+)

diff --git a/src/tensors/cpu/fbgemm/packed_gemm.cpp b/src/tensors/cpu/fbgemm/packed_gemm.cpp
index 30b488177..a98d5e4ac 100644
--- a/src/tensors/cpu/fbgemm/packed_gemm.cpp
+++ b/src/tensors/cpu/fbgemm/packed_gemm.cpp
@@ -17,6 +17,7 @@
 #pragma warning(disable: 4251) // 'fbgemm::CompressedSparseColumn::colptr_': class 'std::vector<int,std::allocator<_Ty>>' needs to have dll-interface to be used by clients of class 'fbgemm::CompressedSparseColumn'
 #pragma warning(disable: 4661) // 'fbgemm::PackMatrix<fbgemm::PackBMatrix<int8_t,int32_t>,int8_t,int32_t>::PackMatrix(int32_t,int32_t,inpType *,int,const fbgemm::BlockingFactors *)': no suitable definition provided for explicit template instantiation request
 #pragma warning(disable: 4244) // fbgemm\quantutils.h(51): warning C4244: 'return': conversion from 'const _Ty' to 'T2', possible loss of data
+#pragma warning(disable: 4717) // 'fbgemm::PackMatrix<fbgemm::PackAWithQuantRowOffset<unsigned char,int>,unsigned char,int>::isThisLastKBlock': recursive on all control paths, function will cause runtime stack overflow
 // the following does not work; need to manually disable them in Linker options
 //#pragma comment(linker, "/ignore:4049") // locally defined symbol ...asmjit... imported
 //#pragma comment(linker, "/ignore:4217") // locally defined symbol ...asmjit... imported

From af02867fb1cfab784d02519aad3b630be1bf5bf6 Mon Sep 17 00:00:00 2001
From: Martin Junczys-Dowmunt <Marcin.JunczysDowmunt@microsoft.com>
Date: Sat, 11 Jan 2020 20:29:43 +0000
Subject: [PATCH 812/838] Merged PR 11103: Clear cache for RNN object between
 batches

* Clears cache for RNN object in transformer, otherwise stale tensor might be kept around.
* Add missing `hash()` and `equal` functions everywhere.
* Fixes bug from deployment test.
---
 examples                             |   2 +-
 src/3rd_party/CMakeLists.txt         |   2 -
 src/graph/node_operators_binary.h    | 151 +++++++++++++++++++++++++--
 src/graph/node_operators_unary.h     |   2 +
 src/models/decoder.h                 |   5 +-
 src/models/transformer.h             |   6 +-
 src/rnn/cells.cpp                    |  17 +++
 src/tensors/cpu/tensor_operators.cpp |   6 +-
 src/tensors/gpu/tensor_operators.cu  |  12 +--
 src/training/graph_group.h           |   2 +
 10 files changed, 180 insertions(+), 25 deletions(-)

diff --git a/examples b/examples
index 336740065..c19b7814d 160000
--- a/examples
+++ b/examples
@@ -1 +1 @@
-Subproject commit 336740065d9c23e53e912a1befff18981d9d27ab
+Subproject commit c19b7814d71febf1053bd93af6ac314b46204092
diff --git a/src/3rd_party/CMakeLists.txt b/src/3rd_party/CMakeLists.txt
index b40df585b..e100a6198 100644
--- a/src/3rd_party/CMakeLists.txt
+++ b/src/3rd_party/CMakeLists.txt
@@ -87,8 +87,6 @@ if(CUDA_FOUND)
       set(GENCODE "${GENCODE} -gencode=arch=compute_70,code=sm_70")
     endif(COMPILE_CUDA_SM70)
 
-    message(${GENCODE})
-
     # install nccl in ${CMAKE_BINARY_DIR}/local similar to /usr/local linux installation
     ExternalProject_Add(nccl_install
       SOURCE_DIR ${CMAKE_CURRENT_SOURCE_DIR}/nccl
diff --git a/src/graph/node_operators_binary.h b/src/graph/node_operators_binary.h
index 3158ee7ae..d12c2481e 100755
--- a/src/graph/node_operators_binary.h
+++ b/src/graph/node_operators_binary.h
@@ -63,7 +63,6 @@ class DotNodeOp : public NaryNodeOp {
     // df/dB += alpha * dot(op(A).T, D)
     // beta set to 1.0 in gemm, C = alpha * dot(op(A), op(B)) + beta * C
     // to sum gradients from different graph parts
-
     if(!transA_ && transB_)
       return {NodeOp(Prod(child(0)->grad(),
                           adj_,
@@ -130,6 +129,29 @@ class DotNodeOp : public NaryNodeOp {
 
   const std::string type() override { return "dot"; }
 
+  virtual size_t hash() override {
+    size_t seed = NaryNodeOp::hash();
+    util::hash_combine(seed, transA_);
+    util::hash_combine(seed, transB_);
+    util::hash_combine(seed, scalar_);
+    return seed;
+  }
+
+  virtual bool equal(Expr node) override {
+    if(!NaryNodeOp::equal(node))
+      return false;
+    auto cnode = std::dynamic_pointer_cast<DotNodeOp>(node);
+    if(!cnode)
+      return false;
+    if(transA_ != cnode->transA_)
+      return false;
+    if(transB_ != cnode->transB_)
+      return false;
+    if(scalar_ != cnode->scalar_)
+      return false;
+    return true;
+  }
+
   const std::string color() override { return "orange"; }
 };
 
@@ -274,6 +296,30 @@ class AffineNodeOp : public NaryNodeOp {
   }
 
   const std::string type() override { return "affine"; }
+
+  virtual size_t hash() override {
+    size_t seed = NaryNodeOp::hash();
+    util::hash_combine(seed, transA_);
+    util::hash_combine(seed, transB_);
+    util::hash_combine(seed, scalar_);
+    return seed;
+  }
+
+  virtual bool equal(Expr node) override {
+    if(!NaryNodeOp::equal(node))
+      return false;
+    auto cnode = std::dynamic_pointer_cast<AffineNodeOp>(node);
+    if(!cnode)
+      return false;
+    if(transA_ != cnode->transA_)
+      return false;
+    if(transB_ != cnode->transB_)
+      return false;
+    if(scalar_ != cnode->scalar_)
+      return false;
+    return true;
+  }
+
 };
 
 class DotBatchedNodeOp : public NaryNodeOp {
@@ -402,6 +448,29 @@ class DotBatchedNodeOp : public NaryNodeOp {
 
   const std::string type() override { return "bdot"; }
 
+  virtual size_t hash() override {
+    size_t seed = NaryNodeOp::hash();
+    util::hash_combine(seed, transA_);
+    util::hash_combine(seed, transB_);
+    util::hash_combine(seed, scalar_);
+    return seed;
+  }
+
+  virtual bool equal(Expr node) override {
+    if(!NaryNodeOp::equal(node))
+      return false;
+    auto cnode = std::dynamic_pointer_cast<DotBatchedNodeOp>(node);
+    if(!cnode)
+      return false;
+    if(transA_ != cnode->transA_)
+      return false;
+    if(transB_ != cnode->transB_)
+      return false;
+    if(scalar_ != cnode->scalar_)
+      return false;
+    return true;
+  }
+
   const std::string color() override { return "orange"; }
 };
 
@@ -443,18 +512,42 @@ class CSRDotNodeOp : public NaryNodeOp {
   }
 
   NodeOps backwardOps() override {
-    return {nullptr, // can't backprop into the sparse matrix (the gradient is dense)
-            nullptr,
-            nullptr,
-            NodeOp(CSRProd(child(3)->grad(), // child(3) = D
-                           graph()->allocator(),
-                           child(0)->val(), child(1)->val(), child(2)->val(), // children(0..2) = A
-                           adj_,
-                           /*transS=*/!transS_, /*swapOperands=*/swapOperands_, /*beta=*/1))};
+    return { nullptr, // can't backprop into the sparse matrix (the gradient is dense)
+             nullptr, 
+             nullptr,
+             NodeOp(CSRProd(child(3)->grad(), // child(3) = D
+                            graph()->allocator(),
+                            child(0)->val(), child(1)->val(), child(2)->val(), // children(0..2) = A
+                            adj_,
+                            /*transS=*/!transS_, /*swapOperands=*/swapOperands_, /*beta=*/1))};
   }
 
   const std::string type() override { return "csr_dot"; }
 
+  virtual size_t hash() override {
+    size_t seed = NaryNodeOp::hash();
+    for(auto s : shape())
+      util::hash_combine(seed, s);  
+    util::hash_combine(seed, transS_);
+    util::hash_combine(seed, swapOperands_);
+    return seed;
+  }
+
+  virtual bool equal(Expr node) override {
+    if(!NaryNodeOp::equal(node))
+      return false;
+    auto cnode = std::dynamic_pointer_cast<CSRDotNodeOp>(node);
+    if(!cnode)
+      return false;
+    if(transS_ != cnode->transS_)
+      return false;
+    if(shape() != cnode->shape())
+      return false;
+    if(swapOperands_ != cnode->swapOperands_)
+      return false;
+    return true;
+  }
+
   const std::string color() override { return "orange"; }
 };
 
@@ -883,6 +976,29 @@ struct CmpNodeOp : public ElementBinaryNodeOp {
     ABORT("Should not get here??");
   }
 
+  virtual size_t hash() override {
+    if(!hash_) {
+      size_t seed = NaryNodeOp::hash();
+      util::hash_combine(seed, cmp_);
+      util::hash_combine(seed, not_);
+      hash_ = seed;
+    }
+    return hash_;
+  }
+
+  virtual bool equal(Expr node) override {
+    if(!NaryNodeOp::equal(node))
+      return false;
+    auto cnode = std::dynamic_pointer_cast<CmpNodeOp>(node);
+    if(!cnode)
+      return false;
+    if(cmp_ != cnode->cmp_)
+      return false;
+    if(not_ != cnode->not_)
+      return false;
+    return true;
+  }
+
 private:
   int cmp_;  // -1: less; 0: equal; 1: greater
   bool not_; // invert result if true
@@ -1015,6 +1131,23 @@ struct LayerNormalizationOp : public NaryNodeOp {
 
   const std::string type() override { return "layer_normalization"; }
 
+  virtual size_t hash() override {
+    size_t seed = NaryNodeOp::hash();
+    util::hash_combine(seed, eps_);
+    return seed;
+  }
+
+  virtual bool equal(Expr node) override {
+    if(!NaryNodeOp::equal(node))
+      return false;
+    auto cnode = std::dynamic_pointer_cast<LayerNormalizationOp>(node);
+    if(!cnode)
+      return false;
+    if(eps_ != cnode->eps_)
+      return false;
+    return true;
+  }
+
 private:
   float eps_;
 };
diff --git a/src/graph/node_operators_unary.h b/src/graph/node_operators_unary.h
index b3f67bd69..a52ecf0e1 100755
--- a/src/graph/node_operators_unary.h
+++ b/src/graph/node_operators_unary.h
@@ -993,6 +993,8 @@ struct ShiftNodeOp : public UnaryNodeOp {
     if(!cnode)
       return false;
     if(shift_ != cnode->shift_)
+      return false;    
+    if(padValue_ != cnode->padValue_)
       return false;
     return true;
   }
diff --git a/src/models/decoder.h b/src/models/decoder.h
index bbad32c30..725573c19 100755
--- a/src/models/decoder.h
+++ b/src/models/decoder.h
@@ -47,13 +47,12 @@ class DecoderBase : public EncoderDecoderLayerBase {
 
     ABORT_IF(shortlist_, "How did a shortlist make it into training?");
 
-    const Words& data = subBatch->data();
-    Expr yData = graph_->indices(toWordIndexVector(data));
-
     auto yShifted = shift(y, {1, 0, 0});
 
     state->setTargetHistoryEmbeddings(yShifted);
     state->setTargetMask(yMask);
+    
+    const Words& data = subBatch->data();
     state->setTargetWords(data);
   }
 
diff --git a/src/models/transformer.h b/src/models/transformer.h
index 2211892a8..84f1cd651 100755
--- a/src/models/transformer.h
+++ b/src/models/transformer.h
@@ -459,7 +459,7 @@ class Transformer : public EncoderOrDecoderBase {
                        int /*startPos*/) const {
     float dropoutRnn = inference_ ? 0.f : opt<float>("dropout-rnn");
 
-    if(!perLayerRnn[prefix]) // lazily created and cache RNNs in the docoder to avoid costly recreation @TODO: turn this into class members
+    if(!perLayerRnn[prefix]) // lazily create and cache RNNs in the decoder to avoid costly recreation @TODO: turn this into class members
       perLayerRnn[prefix] = rnn::rnn(
           "type", opt<std::string>("dec-cell"),
           "prefix", prefix,
@@ -820,6 +820,10 @@ class DecoderTransformer : public Transformer<DecoderBase> {
       output_->clear();
     cache_.clear();
     alignments_.clear();
+    perLayerRnn_.clear(); // this needs to be cleared between batches. 
+    // @TODO: figure out how to detect stale nodes i.e. nodes that are referenced, 
+    // but where underlying memory has been deallocated by dropping all tensors 
+    // from a TensorAllocator object. This can happen during ExpressionGraph::clear()
   }
 };
 
diff --git a/src/rnn/cells.cpp b/src/rnn/cells.cpp
index e94675441..3d7eee210 100644
--- a/src/rnn/cells.cpp
+++ b/src/rnn/cells.cpp
@@ -43,6 +43,23 @@ struct GRUFastNodeOp : public NaryNodeOp {
   const std::string type() override { return "GRU-ops"; }
 
   const std::string color() override { return "yellow"; }
+
+  virtual size_t hash() override {
+    size_t seed = NaryNodeOp::hash();
+    util::hash_combine(seed, final_);
+    return seed;
+  }
+
+  virtual bool equal(Expr node) override {
+    if(!NaryNodeOp::equal(node))
+      return false;
+    auto cnode = std::dynamic_pointer_cast<GRUFastNodeOp>(node);
+    if(!cnode)
+      return false;
+    if(final_ != cnode->final_)
+      return false;
+    return true;
+  }
 };
 
 Expr gruOps(const std::vector<Expr>& nodes, bool final) {
diff --git a/src/tensors/cpu/tensor_operators.cpp b/src/tensors/cpu/tensor_operators.cpp
index caad6337b..91e058b84 100755
--- a/src/tensors/cpu/tensor_operators.cpp
+++ b/src/tensors/cpu/tensor_operators.cpp
@@ -1050,7 +1050,7 @@ void LayerNormalization(Tensor out_,
       sqSum += ex * ex;
     }
 
-    float sigma = std::sqrt(eps + sqSum / cols);
+    float sigma = std::sqrt(sqSum / cols + eps);
 
 #pragma omp simd
     for(int i = 0; i < cols; ++i) {
@@ -1112,7 +1112,7 @@ void LayerNormalizationGrad(Tensor gradX_,
         sum_sqr += ex * ex;
       }
 
-      float sigma = std::sqrt(eps + sum_sqr / cols);
+      float sigma = std::sqrt(sum_sqr / cols + eps);
 #pragma omp simd
       for(size_t i = 0; i < cols; ++i) {
         float grad_x = 0.f;
@@ -1154,7 +1154,7 @@ void LayerNormalizationGrad(Tensor gradX_,
         sum_sqr += ex * ex;
       }
 
-      float sigma = std::sqrt(eps + sum_sqr / cols);
+      float sigma = std::sqrt(sum_sqr / cols + eps);
 #pragma omp simd
       for(size_t i = 0; i < cols; ++i) {
         float grad_x = 0.f;
diff --git a/src/tensors/gpu/tensor_operators.cu b/src/tensors/gpu/tensor_operators.cu
index 325643502..eefcc405f 100755
--- a/src/tensors/gpu/tensor_operators.cu
+++ b/src/tensors/gpu/tensor_operators.cu
@@ -1925,7 +1925,7 @@ __global__ void gLNormalization(T* out,
         len = (len + 1) >> 1;
       }
       __syncthreads();
-      AccType sigma = functional::Ops<AccType>::sqrt(_sqSum[0] / N); // all AccType
+      AccType sigma = functional::Ops<AccType>::sqrt(_sqSum[0] / N + eps); // all AccType
       __syncthreads();
 
       for(int tid = 0; tid < cols; tid += blockDim.x) {
@@ -1934,7 +1934,7 @@ __global__ void gLNormalization(T* out,
           AccType gammav = (AccType)gamma[id];
           AccType xv     = (AccType)xRow[id];
           AccType betav  = beta ? (AccType)beta[id] : (AccType)0.f;
-          AccType lv     = (xv - mean) / (sigma + eps);
+          AccType lv     = (xv - mean) / sigma;
           AccType y      = gammav * lv + betav;
           yRow[id]       = (T)y;
         }
@@ -2022,7 +2022,7 @@ __global__ void gLayerNormalizationGrad(T* gradX,
           AccType betav  = beta ? (AccType)beta[id] : (AccType)0.f;
           AccType gammav = (AccType)gamma[id];
           AccType adjv   = adjRow[id];
-          AccType lv     = (yv - betav) / (gammav + eps); // go back to LN(x) from scaled and shifted version for accumulation
+          AccType lv     = (yv - betav) / gammav; // go back to LN(x) from scaled and shifted version for accumulation
 
           sum_x[threadIdx.x]     += xv;
           sum_adj_l[threadIdx.x] += adjv * lv;
@@ -2064,7 +2064,7 @@ __global__ void gLayerNormalizationGrad(T* gradX,
         len = (len + 1) >> 1;
       }
       __syncthreads();
-      AccType sigma = functional::Ops<AccType>::sqrt(sum_sqr[0] / N);
+      AccType sigma = functional::Ops<AccType>::sqrt(sum_sqr[0] / N + eps);
       __syncthreads();
 
       // Jacobian of layer norm
@@ -2078,10 +2078,10 @@ __global__ void gLayerNormalizationGrad(T* gradX,
           AccType xv     = xRow[id];
           AccType gammav = (AccType)gamma[id];
           AccType adjv   = adjRow[id];
-          AccType lv     = (xv - mean) / (sigma + eps);
+          AccType lv     = (xv - mean) / sigma;
 
           AccType gradLv = N * adjv - lv * sum_adj_l[0] - sum_adj[0];
-          gradLv        /= N * (sigma + eps); // eps has to be inside parentheses for correct gradient
+          gradLv        /= N * sigma; 
 
           AccType gradXv = gammav * gradLv;
 
diff --git a/src/training/graph_group.h b/src/training/graph_group.h
index 37c848b62..265f9785c 100644
--- a/src/training/graph_group.h
+++ b/src/training/graph_group.h
@@ -109,6 +109,8 @@ class GraphGroup {
         auto cost = model->build(graph, batch);
         fits = graph->fits();
 
+        LOG(debug, "[batching] length: {} - size: {} - fits: {}", lengths[0], current, fits);
+
         if(fits) {
           stats->add(batch, multiplier);
           start = current + 1;

From 1f7a63d3ff381ee16589b45a3d07fa4d38df35af Mon Sep 17 00:00:00 2001
From: Marcin Junczys-Dowmunt <Marcin.JunczysDowmunt@microsoft.com>
Date: Sat, 11 Jan 2020 12:31:34 -0800
Subject: [PATCH 813/838] bump version

---
 CHANGELOG.md | 2 ++
 VERSION      | 2 +-
 2 files changed, 3 insertions(+), 1 deletion(-)

diff --git a/CHANGELOG.md b/CHANGELOG.md
index fea7c0409..ed8efdba9 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -34,6 +34,7 @@ and this project adheres to [Semantic Versioning](http://semver.org/spec/v2.0.0.
 - Gradient-checkpointing
 
 ### Fixed
+- Clear RNN chache in transformer model, add correct hash functions to nodes
 - Gather-operation for all index sizes
 - Fix word weighting with max length cropping
 - Fixed compilation on CPUs without support for AVX
@@ -54,6 +55,7 @@ and this project adheres to [Semantic Versioning](http://semver.org/spec/v2.0.0.
 - Compilation with CUDA 10.1
 
 ### Changed
+- Revert LayerNorm eps to old position, i.e. sigma' = sqrt(sigma^2 + eps)
 - Downgrade NCCL to 2.3.7 as 2.4.2 is buggy (hangs with larger models)
 - Return error signal on SIGTERM
 - Dropped support for CUDA 8.0, CUDA 9.0 is now minimal requirement
diff --git a/VERSION b/VERSION
index b08f51ec8..dacf7d7fc 100644
--- a/VERSION
+++ b/VERSION
@@ -1 +1 @@
-v1.8.38
+v1.8.39

From 703fcf43471654f7dd8a579aa7d74271c99da2ec Mon Sep 17 00:00:00 2001
From: Marcin Junczys-Dowmunt <Marcin.JunczysDowmunt@microsoft.com>
Date: Sat, 11 Jan 2020 13:46:55 -0800
Subject: [PATCH 814/838] update regression test pointer

---
 regression-tests | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/regression-tests b/regression-tests
index 9019bf3be..171bf2b38 160000
--- a/regression-tests
+++ b/regression-tests
@@ -1 +1 @@
-Subproject commit 9019bf3beeff355b719914cc52c65609c312960d
+Subproject commit 171bf2b3826c1d4bb5a94a7c432f156b85ab6678

From b822cd4d121fa9ceba2ba1e9748099d3f1febff0 Mon Sep 17 00:00:00 2001
From: Marcin Junczys-Dowmunt <Marcin.JunczysDowmunt@microsoft.com>
Date: Sat, 11 Jan 2020 14:00:32 -0800
Subject: [PATCH 815/838] move regression test pointer

---
 regression-tests | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/regression-tests b/regression-tests
index 171bf2b38..c09a206a2 160000
--- a/regression-tests
+++ b/regression-tests
@@ -1 +1 @@
-Subproject commit 171bf2b3826c1d4bb5a94a7c432f156b85ab6678
+Subproject commit c09a206a28ab3e3196b3cc123b6ec6a181e50c2c

From b3a23108b432fd83ed9cf1fb4609efbca8e9b756 Mon Sep 17 00:00:00 2001
From: Martin Junczys-Dowmunt <Marcin.JunczysDowmunt@microsoft.com>
Date: Fri, 17 Jan 2020 21:52:33 +0000
Subject: [PATCH 816/838] Merged PR 11188: Handle empty inputs with batch
 purging

The previous mechanism to remove empty inputs does not play well with batch purging (removal of finished sentences). Now we reuse the batch purging mechanism to get rid of empty inputs by forcing EOS for all beam entries of a batch entry for the corresponding source batch entry. The purging then takes care of the rest. We set the probability to log(1) = 0.
---
 CHANGELOG.md                 |  1 +
 VERSION                      |  2 +-
 regression-tests             |  2 +-
 src/translator/beam_search.h | 48 ++++++++++++++++++------------------
 4 files changed, 27 insertions(+), 26 deletions(-)

diff --git a/CHANGELOG.md b/CHANGELOG.md
index ed8efdba9..29b281725 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -34,6 +34,7 @@ and this project adheres to [Semantic Versioning](http://semver.org/spec/v2.0.0.
 - Gradient-checkpointing
 
 ### Fixed
+- Fix empty source batch entries with batch purging
 - Clear RNN chache in transformer model, add correct hash functions to nodes
 - Gather-operation for all index sizes
 - Fix word weighting with max length cropping
diff --git a/VERSION b/VERSION
index dacf7d7fc..e8eb33e54 100644
--- a/VERSION
+++ b/VERSION
@@ -1 +1 @@
-v1.8.39
+v1.8.40
diff --git a/regression-tests b/regression-tests
index c09a206a2..6a08849b2 160000
--- a/regression-tests
+++ b/regression-tests
@@ -1 +1 @@
-Subproject commit c09a206a28ab3e3196b3cc123b6ec6a181e50c2c
+Subproject commit 6a08849b23f6c14eefbe12f4eb73dc638b962587
diff --git a/src/translator/beam_search.h b/src/translator/beam_search.h
index 584facac0..5ddc4ee02 100755
--- a/src/translator/beam_search.h
+++ b/src/translator/beam_search.h
@@ -39,6 +39,7 @@ class BeamSearch {
                const std::vector<Ptr<ScorerState /*const*/>>& states,
                Ptr<data::CorpusBatch /*const*/> batch, // for alignments only
                Ptr<FactoredVocab/*const*/> factoredVocab, size_t factorGroup,
+               const std::vector<bool>& dropBatchEntries, // [origDimBatch] - empty source batch entries are marked with true, should be cleared after first use.
                const std::vector<IndexType>& batchIdxMap) const { // [origBatchIdx -> currentBatchIdx]
     std::vector<float> align; // collects alignment information from the last executed time step
     if(options_->hasAndNotEmpty("alignment") && factorGroup == 0) 
@@ -49,9 +50,10 @@ class BeamSearch {
 
     // create a reverse batchMap to obtain original batchIdx in the starting batch size
     // and calculate the current batch size based on non-empty beams
-    std::vector<IndexType> reverseBatchIdxMap(batchIdxMap.size());
+    std::vector<IndexType> reverseBatchIdxMap; // empty if not purging batch entries
     size_t currentDimBatch = beams.size();
     if(PURGE_BATCH) {
+      reverseBatchIdxMap.resize(batchIdxMap.size()); // adjust size if doing batch purging.
       currentDimBatch = 0;
       for(int i = 0; i < batchIdxMap.size(); ++i) {
         reverseBatchIdxMap[batchIdxMap[i]] = i; // reverse batch index mapping, multiple occurences get overwritten with the last one, 
@@ -66,16 +68,22 @@ class BeamSearch {
       // They can be between 0 and (vocabSize * nBestBeamSize * batchSize)-1.
       // (beamHypIdx refers to the GPU tensors, *not* the beams[] array; they are not the same in case of purging)
       const auto  key       = nBestKeys[i];
-      const float pathScore = nBestPathScores[i]; // expanded path score for (batchIdx, beamHypIdx, word)
-
+      
       // decompose key into individual indices (batchIdx, beamHypIdx, wordIdx)
-      const auto wordIdx         = (WordIndex)(key % vocabSize);
-      const auto beamHypIdx      =            (key / vocabSize) % nBestBeamSize;
-      const auto currentBatchIdx =            (key / vocabSize) / nBestBeamSize;
+      const auto beamHypIdx      = (key / vocabSize) % nBestBeamSize;
+      const auto currentBatchIdx = (key / vocabSize) / nBestBeamSize;
+      const auto origBatchIdx    = reverseBatchIdxMap.empty() ? currentBatchIdx : reverseBatchIdxMap[currentBatchIdx]; // map currentBatchIdx back into original position within starting maximal batch size, required to find correct beam
 
-      auto origBatchIdx          = currentBatchIdx;
-      if(PURGE_BATCH)
-        origBatchIdx = reverseBatchIdxMap[currentBatchIdx]; // map currentBatchIdx back into original position within starting maximal batch size, required to find correct beam
+      bool dropHyp = !dropBatchEntries.empty() && dropBatchEntries[origBatchIdx];
+
+      // if we force=drop the hypothesis, assign EOS, otherwise the expected word id. 
+      const auto wordIdx    = dropHyp ? trgVocab_->getEosId().toWordIndex() : (WordIndex)(key % vocabSize);
+
+      // @TODO: We currently assign a log probability of 0 to all beam entries of the dropped batch entry, instead it might be a good idea to use
+      // the per Hyp pathScore without the current expansion (a bit hard to obtain). 
+      // For the case where we drop empty inputs, 0 is fine. For other use cases like a forced stop, the penultimate pathScore might be better. 
+      // For the empty hyp this would naturally result in 0, too. 
+      const float pathScore = dropHyp ? 0.f : nBestPathScores[i]; // 0 (Prob = 1, maximum score) if dropped or expanded path score for (batchIdx, beamHypIdx, word)
 
       const auto& beam = beams[origBatchIdx];
       auto& newBeam = newBeams[origBatchIdx]; // extended hypotheses are going to be placed in this new beam
@@ -85,7 +93,7 @@ class BeamSearch {
       if (pathScore <= INVALID_PATH_SCORE) // (dummy slot or word that cannot be expanded by current factor)
         continue;
 
-      ABORT_IF(beamHypIdx >= beam.size(), "Out of bounds beamHypIdx??");
+      ABORT_IF(beamHypIdx >= beam.size(), "Out of bounds beamHypIdx??"); // effectively this is equivalent to ABORT_IF(beams[origBatchIdx].empty(), ...)
 
       // map wordIdx to word
       auto prevBeamHypIdx = beamHypIdx; // back pointer
@@ -287,31 +295,22 @@ class BeamSearch {
       states.push_back(scorer->startState(graph, batch));
     }
 
-    const auto srcEosId = batch->front()->vocab()->getEosId();
-    
     // create one beam per batch entry with sentence-start hypothesis
     Beams beams(origDimBatch, Beam(beamSize_, Hypothesis::New())); // array [origDimBatch] of array [maxBeamSize] of Hypothesis, keeps full size through search.
                                                                    // batch purging is determined from an empty sub-beam.
     std::vector<IndexType> batchIdxMap(origDimBatch); // Record at which batch entry a beam is looking. 
                                                       // By default that corresponds to position in array, 
                                                       // but shifts in the course of removing batch entries when they are finished.
+
+    const std::vector<bool> emptyBatchEntries; // used for recording if there are empty input batch entries
     for(int origBatchIdx = 0; origBatchIdx < origDimBatch; ++origBatchIdx) {
       batchIdxMap[origBatchIdx] = origBatchIdx; // map to same position on initialization
       auto& beam = beams[origBatchIdx];
       histories[origBatchIdx]->add(beam, trgEosId); // add beams with start-hypotheses to traceback grid
 
-      // Handle batch entries that consist only of source <EOS> i.e. these are empty lines
-      if(batch->front()->data()[origBatchIdx] == srcEosId) {
-        // create a target <EOS> hypothesis that extends the start-hypothesis
-        auto eosHyp = Hypothesis::New(/*prevHyp=*/    beam[0], 
-                                      /*currWord=*/   trgEosId, 
-                                      /*prevHypIdx=*/ 0, 
-                                      /*pathScore=*/  0.f);
-        auto eosBeam = Beam(beamSize_, eosHyp);      // create a dummy beam filled with <EOS>-hyps
-        histories[origBatchIdx]->add(eosBeam, trgEosId); // push dummy <EOS>-beam to traceback grid
-        beam.clear(); // Zero out current beam, so it does not get used for further symbols as empty beams get omitted everywhere.
-                      // The corresponding neural states will be purged further down.
-      }
+      // Mark batch entries that consist only of source <EOS> i.e. these are empty lines. They will be forced to EOS and purged from batch
+      const auto& srcEosId = batch->front()->vocab()->getEosId();
+      const_cast<std::vector<bool>&>(emptyBatchEntries).push_back(batch->front()->data()[origBatchIdx] == srcEosId); // const_cast during construction
     }
 
     // determine index of UNK in the log prob vectors if we want to suppress it in the decoding process
@@ -496,6 +495,7 @@ class BeamSearch {
                        states,    // used for keeping track of per-ensemble-member path score
                        batch,     // only used for propagating alignment info
                        factoredVocab, factorGroup, 
+                       emptyBatchEntries, // [origDimBatch] - empty source batch entries are marked with true
                        batchIdxMap); // used to create a reverse batch index map to recover original batch indices for this step
       } // END FOR factorGroup = 0 .. numFactorGroups-1
 

From 7228698b061a0da90442d3350179020663d19f41 Mon Sep 17 00:00:00 2001
From: Ulrich Germann <ugermann@inf.ed.ac.uk>
Date: Wed, 29 Jan 2020 16:25:56 +0000
Subject: [PATCH 817/838] Make Vocab const in beam search. Remove some trailing
 whitespace.

---
 src/translator/beam_search.h | 38 ++++++++++++++++++------------------
 1 file changed, 19 insertions(+), 19 deletions(-)

diff --git a/src/translator/beam_search.h b/src/translator/beam_search.h
index 5ddc4ee02..c16af53f0 100755
--- a/src/translator/beam_search.h
+++ b/src/translator/beam_search.h
@@ -16,7 +16,7 @@ class BeamSearch {
   Ptr<Options> options_;
   std::vector<Ptr<Scorer>> scorers_;
   size_t beamSize_;
-  Ptr<Vocab> trgVocab_;
+  Ptr<const Vocab> trgVocab_;
 
   static constexpr auto INVALID_PATH_SCORE = -9999; // (@TODO: change to -9999.0 once C++ allows that)
   static constexpr auto PURGE_BATCH = true; // @TODO: diagnostic, to-be-removed once confirmed there are no issues.
@@ -24,7 +24,7 @@ class BeamSearch {
 public:
   BeamSearch(Ptr<Options> options,
              const std::vector<Ptr<Scorer>>& scorers,
-             Ptr<Vocab> trgVocab)
+             const Ptr<const Vocab> trgVocab)
       : options_(options),
         scorers_(scorers),
         beamSize_(options_->get<size_t>("beam-size")),
@@ -42,8 +42,8 @@ class BeamSearch {
                const std::vector<bool>& dropBatchEntries, // [origDimBatch] - empty source batch entries are marked with true, should be cleared after first use.
                const std::vector<IndexType>& batchIdxMap) const { // [origBatchIdx -> currentBatchIdx]
     std::vector<float> align; // collects alignment information from the last executed time step
-    if(options_->hasAndNotEmpty("alignment") && factorGroup == 0) 
-      align = scorers_[0]->getAlignment(); // [beam depth * max src length * current batch size] -> P(s|t); use alignments from the first scorer, even if ensemble, 
+    if(options_->hasAndNotEmpty("alignment") && factorGroup == 0)
+      align = scorers_[0]->getAlignment(); // [beam depth * max src length * current batch size] -> P(s|t); use alignments from the first scorer, even if ensemble,
 
     const auto origDimBatch = beams.size(); // see function search for definition of origDimBatch and currentDimBatch etc.
     Beams newBeams(origDimBatch);           // return value of this function goes here. There are always origDimBatch beams.
@@ -56,7 +56,7 @@ class BeamSearch {
       reverseBatchIdxMap.resize(batchIdxMap.size()); // adjust size if doing batch purging.
       currentDimBatch = 0;
       for(int i = 0; i < batchIdxMap.size(); ++i) {
-        reverseBatchIdxMap[batchIdxMap[i]] = i; // reverse batch index mapping, multiple occurences get overwritten with the last one, 
+        reverseBatchIdxMap[batchIdxMap[i]] = i; // reverse batch index mapping, multiple occurences get overwritten with the last one,
                                                 // which is expected due to down-shifting
         if(!beams[i].empty())
           currentDimBatch++;
@@ -143,12 +143,12 @@ class BeamSearch {
           auto lval = states[j]->getLogProbs().getFactoredLogitsTensor(factorGroup); // [maxBeamSize, 1, currentDimBatch, dimFactorVocab]
           // The flatting happens based on actual (current) batch size and batch index computed with batch-pruning as we are looking into the pruned tensor
           size_t flattenedLogitIndex = (beamHypIdx * currentDimBatch + currentBatchIdx) * vocabSize + wordIdx;  // (beam idx, batch idx, word idx); note: beam and batch are transposed, compared to 'key'
-          
+
           // @TODO: use a function on shape() to index, or new method val->at({i1, i2, i3, i4}) with broadcasting
           ABORT_IF(lval->shape() != Shape({(int)nBestBeamSize, 1, (int)currentDimBatch, (int)vocabSize}) &&
                    (beamHypIdx == 0 && lval->shape() != Shape({1, 1, (int)currentDimBatch, (int)vocabSize})),
                    "Unexpected shape of logits?? {} != {}", lval->shape(), Shape({(int)nBestBeamSize, 1, (int)currentDimBatch, (int)vocabSize}));
-          
+
           breakDown[j] += lval->get(flattenedLogitIndex);
         }
         hyp->setScoreBreakdown(breakDown);
@@ -162,7 +162,7 @@ class BeamSearch {
 
       newBeam.push_back(hyp);
     }
-    
+
     // if factored vocab and this is not the first factor, we need to
     // also propagate factored hypotheses that do not get expanded in this step because they don't have this factor
     if (factorGroup > 0) {
@@ -214,7 +214,7 @@ class BeamSearch {
     // in a single beam, i.e.:
     //   * [word1-batch1, word1-batch2, ..., word2-batch1, ...]
     //
-    
+
     size_t origDimBatch = batch->size();  // number of sentences in batch
     size_t batchWidth   = batch->width(); // max src length
 
@@ -243,7 +243,7 @@ class BeamSearch {
     for(auto beam : beams) {
       Beam newBeam; // a beam of surviving hyps
       for(auto hyp : beam)
-        if(hyp->getWord() != trgEosId) // if this hyp is not finished, 
+        if(hyp->getWord() != trgEosId) // if this hyp is not finished,
           newBeam.push_back(hyp);      // move over to beam of surviving hyps
 
       if(PURGE_BATCH)
@@ -298,8 +298,8 @@ class BeamSearch {
     // create one beam per batch entry with sentence-start hypothesis
     Beams beams(origDimBatch, Beam(beamSize_, Hypothesis::New())); // array [origDimBatch] of array [maxBeamSize] of Hypothesis, keeps full size through search.
                                                                    // batch purging is determined from an empty sub-beam.
-    std::vector<IndexType> batchIdxMap(origDimBatch); // Record at which batch entry a beam is looking. 
-                                                      // By default that corresponds to position in array, 
+    std::vector<IndexType> batchIdxMap(origDimBatch); // Record at which batch entry a beam is looking.
+                                                      // By default that corresponds to position in array,
                                                       // but shifts in the course of removing batch entries when they are finished.
 
     const std::vector<bool> emptyBatchEntries; // used for recording if there are empty input batch entries
@@ -359,7 +359,7 @@ class BeamSearch {
         std::vector<IndexType> hypIndices;      // [maxBeamSize, 1, currentDimBatch, 1] (flattened) tensor index ((beamHypIdx, batchIdx), flattened) of prev hyp that a hyp originated from
         std::vector<Word> prevWords;            // [maxBeamSize, 1, currentDimBatch, 1] (flattened) word that a hyp ended in, for advancing the decoder-model's history
         Expr prevPathScores;                    // [maxBeamSize, 1, currentDimBatch, 1], path score that a hyp ended in (last axis will broadcast into vocab size when adding expandedPathScores)
-        
+
         bool anyCanExpand = false; // stays false if all hyps are invalid factor expansions
         if(t == 0 && factorGroup == 0) { // no scores yet
           prevPathScores = graph->constant({1, 1, 1, 1}, inits::fromValue(0));
@@ -373,7 +373,7 @@ class BeamSearch {
             for(int currentBatchIdx = 0; currentBatchIdx < beams.size(); ++currentBatchIdx) // loop over batch entries (active sentences)
               if(!beams[currentBatchIdx].empty() || !PURGE_BATCH)                           // for each beam check
                 batchIndices.push_back(prevBatchIdxMap[currentBatchIdx]);                   // which batch entries were active in previous step
-            
+
           std::vector<float> prevScores;
           for(size_t beamHypIdx = 0; beamHypIdx < maxBeamSize; ++beamHypIdx) { // loop over globally maximal beam-size (maxBeamSize)
             for(int origBatchIdx = 0; origBatchIdx < origDimBatch; ++origBatchIdx) { // loop over all batch entries (active and inactive)
@@ -390,11 +390,11 @@ class BeamSearch {
                   if(factorGroup == 0)
                     currentBatchIdx = prevBatchIdxMap[origBatchIdx]; // subselection may happen for factorGroup == 0
                   else
-                    currentBatchIdx = batchIdxMap[origBatchIdx];     // no subselection happens for factorGroup > 0, 
-                                                                     // but we treat it like a next step, since a step 
+                    currentBatchIdx = batchIdxMap[origBatchIdx];     // no subselection happens for factorGroup > 0,
+                                                                     // but we treat it like a next step, since a step
                                                                      // happened for factorGroup == 0
                 }
-                  
+
                 auto hypIndex = (IndexType)(hyp->getPrevStateIndex() * currentDimBatch + currentBatchIdx); // (beamHypIdx, batchIdx), flattened, for index_select() operation
 
                 hypIndices.push_back(hypIndex); // (beamHypIdx, batchIdx), flattened as said above.
@@ -409,7 +409,7 @@ class BeamSearch {
               }
             }
           }
-          if(factorGroup == 0) 
+          if(factorGroup == 0)
             currentDimBatch = (IndexType) batchIndices.size(); // keep batch size constant for all factor groups in a time step
           prevPathScores = graph->constant({(int)maxBeamSize, 1, (int)currentDimBatch, 1}, inits::fromVector(prevScores));
         }
@@ -494,7 +494,7 @@ class BeamSearch {
                        beams,
                        states,    // used for keeping track of per-ensemble-member path score
                        batch,     // only used for propagating alignment info
-                       factoredVocab, factorGroup, 
+                       factoredVocab, factorGroup,
                        emptyBatchEntries, // [origDimBatch] - empty source batch entries are marked with true
                        batchIdxMap); // used to create a reverse batch index map to recover original batch indices for this step
       } // END FOR factorGroup = 0 .. numFactorGroups-1

From a43ccd6c28db8524ad1ca46be987a73238ba1950 Mon Sep 17 00:00:00 2001
From: Roman Grundkiewicz <rgrundki@exseed.ed.ac.uk>
Date: Wed, 29 Jan 2020 21:06:58 +0000
Subject: [PATCH 818/838] Mention sentence cropping for --valid-max-length
 (#588)

---
 src/common/config_parser.cpp | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/src/common/config_parser.cpp b/src/common/config_parser.cpp
index 9bd01a20c..ec57fbada 100755
--- a/src/common/config_parser.cpp
+++ b/src/common/config_parser.cpp
@@ -553,7 +553,8 @@ void ConfigParser::addOptionsValidation(cli::CLIWrapper& cli) {
       "Size of mini-batch used during validation",
       32);
   cli.add<size_t>("--valid-max-length",
-      "Maximum length of a sentence in a validating sentence pair",
+      "Maximum length of a sentence in a validating sentence pair. "
+      "Sentences longer than valid-max-length are cropped to valid-max-length",
       1000);
 
   // options for validation script

From ad28f99ac0eeba2b0985fb71822ed002a62e537d Mon Sep 17 00:00:00 2001
From: Roman Grundkiewicz <rgrundki@exseed.ed.ac.uk>
Date: Wed, 29 Jan 2020 21:08:45 +0000
Subject: [PATCH 819/838] Add --valid-reset-stalled (#587)

---
 CHANGELOG.md                 | 1 +
 src/common/config_parser.cpp | 2 ++
 src/training/scheduler.h     | 8 ++++++++
 3 files changed, 11 insertions(+)

diff --git a/CHANGELOG.md b/CHANGELOG.md
index 29b281725..625dcd9df 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -9,6 +9,7 @@ and this project adheres to [Semantic Versioning](http://semver.org/spec/v2.0.0.
 ## [Unreleased]
 
 ### Added
+- An option for resetting stalled validation metrics
 - Add CMAKE options to disable compilation for specific GPU SM types
 - An option to print word-level translation scores
 - An option to turn off automatic detokenization from SentencePiece
diff --git a/src/common/config_parser.cpp b/src/common/config_parser.cpp
index ec57fbada..2921cd2b4 100755
--- a/src/common/config_parser.cpp
+++ b/src/common/config_parser.cpp
@@ -525,6 +525,8 @@ void ConfigParser::addOptionsValidation(cli::CLIWrapper& cli) {
       "Metric to use during validation: cross-entropy, ce-mean-words, perplexity, valid-script, "
       "translation, bleu, bleu-detok. Multiple metrics can be specified",
       {"cross-entropy"});
+  cli.add<bool>("--valid-reset-stalled",
+     "Reset all stalled validation metrics when the training is restarted");
   cli.add<size_t>("--early-stopping",
      "Stop if the first validation metric does not improve for  arg  consecutive validation steps",
      10);
diff --git a/src/training/scheduler.h b/src/training/scheduler.h
index 5fa81d0f6..5dd69a9e6 100755
--- a/src/training/scheduler.h
+++ b/src/training/scheduler.h
@@ -380,6 +380,14 @@ class Scheduler : public TrainingObserver {
       state_->wordsDisp    = 0;
     }
 
+    if(options_->get<bool>("valid-reset-stalled")) {
+      state_->stalled      = 0;
+      state_->maxStalled   = 0;
+      for(const auto& validator : validators_) {
+        state_->validators[validator->type()]["stalled"] = 0;
+      }
+    }
+
     state_->newLoad();
   }
 

From 22ad592a1ddb684340aef6f27ef6b9d0e0302f2b Mon Sep 17 00:00:00 2001
From: Roman Grundkiewicz <rgrundki@exseed.ed.ac.uk>
Date: Wed, 29 Jan 2020 21:11:36 +0000
Subject: [PATCH 820/838] Add error message about missing --no-restore-corpus
 (#584)

---
 src/data/batch_generator.h | 14 ++++++++------
 1 file changed, 8 insertions(+), 6 deletions(-)

diff --git a/src/data/batch_generator.h b/src/data/batch_generator.h
index b44660865..f16a7a81c 100644
--- a/src/data/batch_generator.h
+++ b/src/data/batch_generator.h
@@ -66,10 +66,10 @@ class BatchGenerator : public RNGEngine {
   Ptr<Options> options_;
   bool restored_{false};
 
-  // replacing old shuffle_ with two variants that determine more fine-grained shuffling behavior. 
-  // Both set to false is equivalent to old shuffle_ == false. 
+  // replacing old shuffle_ with two variants that determine more fine-grained shuffling behavior.
+  // Both set to false is equivalent to old shuffle_ == false.
   // Now we can not shuffle the data, but shuffle batches. Useful for linear reading of very large data sets with pre-reading.
-  // Parameters like maxi-batch determine how much data is pre-read and sorted by length or other criteria. 
+  // Parameters like maxi-batch determine how much data is pre-read and sorted by length or other criteria.
   bool shuffleData_{false};    // determine if full data should be shuffled before reading and batching.
   bool shuffleBatches_{false}; // determine if batches should be shuffled after batching.
 
@@ -103,7 +103,7 @@ class BatchGenerator : public RNGEngine {
     };
 
     auto cmpNone = [](const Sample& a, const Sample& b) { return a.getId() < b.getId(); }; // sort in order of original ids = original data order unless shuffling
-    
+
     typedef std::function<bool(const Sample&, const Sample&)> cmp_type;
     typedef std::priority_queue<Sample, Samples, cmp_type> sample_queue;
 
@@ -229,7 +229,7 @@ class BatchGenerator : public RNGEngine {
 
   // this starts fillBatches() as a background operation
   void fetchBatchesAsync() {
-    ABORT_IF(futureBufferedBatches_.valid(), "attempted to restart futureBufferedBatches_ while still running");
+    ABORT_IF(futureBufferedBatches_.valid(), "Attempted to restart futureBufferedBatches_ while still running");
     futureBufferedBatches_ = threadPool_.enqueue([this]() {
       return fetchBatches();
     });
@@ -239,7 +239,9 @@ class BatchGenerator : public RNGEngine {
     if(bufferedBatches_.empty()) {
       // out of data: need to get next batch from background thread
       // We only get here if the future has been scheduled to run; it must be valid.
-      ABORT_IF(!futureBufferedBatches_.valid(), "attempted to wait for futureBufferedBatches_ when none pending");
+      ABORT_IF(!futureBufferedBatches_.valid(), "Attempted to wait for futureBufferedBatches_ when none pending.\n"
+          "This error often occurs when Marian tries to restore the training data iterator, but the corpus has been changed or replaced.\n"
+          "If you have changed the training corpus, add --no-restore-corpus to the training command and run it again.");
       bufferedBatches_ = std::move(futureBufferedBatches_.get());
       // if bg thread returns an empty swath, we hit the end of the epoch
       if (bufferedBatches_.empty()) {

From 533604024bcd36c3bda5dbc5b7558e0b443516ad Mon Sep 17 00:00:00 2001
From: Roman Grundkiewicz <rgrundki@exseed.ed.ac.uk>
Date: Wed, 29 Jan 2020 21:19:21 +0000
Subject: [PATCH 821/838] Update script exporting embeddings to support tied
 embeddings (#569)

---
 scripts/embeddings/export_embeddings.py | 29 ++++++++++++++-----------
 1 file changed, 16 insertions(+), 13 deletions(-)

diff --git a/scripts/embeddings/export_embeddings.py b/scripts/embeddings/export_embeddings.py
index 7aef07d5d..3b4f3314b 100755
--- a/scripts/embeddings/export_embeddings.py
+++ b/scripts/embeddings/export_embeddings.py
@@ -9,18 +9,22 @@
 
 
 def main():
-    desc = """Export word embedding from model"""
+    desc = """Export word embeddings from model"""
     parser = argparse.ArgumentParser(
         formatter_class=argparse.RawDescriptionHelpFormatter, description=desc)
-    parser.add_argument("-m", "--model", help="Model file", required=True)
-    parser.add_argument(
-        "-o", "--output-prefix", help="Output files prefix", required=True)
+    parser.add_argument("-m", "--model", help="path to model.npz file", required=True)
+    parser.add_argument("-o", "--output-prefix", help="prefix for output files", required=True)
     args = parser.parse_args()
 
     print("Loading model")
     model = np.load(args.model)
     special = yaml.load(model["special:model.yml"][:-1].tobytes())
 
+    if special["tied-embeddings-all"] or special["tied-embeddings-src"]:
+        all_emb = model["Wemb"]
+        export_emb(args.output_prefix + ".all", all_emb)
+        exit()
+
     if special["type"] == "amun":
         enc_emb = model["Wemb"]
         dec_emb = model["Wemb_dec"]
@@ -28,16 +32,15 @@ def main():
         enc_emb = model["encoder_Wemb"]
         dec_emb = model["decoder_Wemb"]
 
-    with open(args.output_prefix + ".src", "w") as out:
-        out.write("{0} {1}\n".format(*enc_emb.shape))
-        for i in range(enc_emb.shape[0]):
-            vec = " ".join("{0:.8f}".format(v) for v in enc_emb[i])
-            out.write("{0} {1}\n".format(i, vec))
+    export_emb(args.output_prefix + ".src", enc_emb)
+    export_emb(args.output_prefix + ".trg", dec_emb)
+
 
-    with open(args.output_prefix + ".trg", "w") as out:
-        out.write("{0} {1}\n".format(*dec_emb.shape))
-        for i in range(dec_emb.shape[0]):
-            vec = " ".join("{0:.8f}".format(v) for v in dec_emb[i])
+def export_emb(filename, emb):
+    with open(filename, "w") as out:
+        out.write("{0} {1}\n".format(*emb.shape))
+        for i in range(emb.shape[0]):
+            vec = " ".join("{0:.8f}".format(v) for v in emb[i])
             out.write("{0} {1}\n".format(i, vec))
 
 

From 990aeb5daf3fa8ed2417af180f0c23a29cf4214a Mon Sep 17 00:00:00 2001
From: Roman Grundkiewicz <rgrundki@exseed.ed.ac.uk>
Date: Fri, 31 Jan 2020 14:22:21 +0000
Subject: [PATCH 822/838] Add a warning message for an incorrect use of output
 sampling (#585)

Add a warning message if sampling with beam-size > 1 or ensembles
---
 src/translator/translator.h | 11 +++++++++++
 1 file changed, 11 insertions(+)

diff --git a/src/translator/translator.h b/src/translator/translator.h
index 88adf0e3d..cc68a4f01 100755
--- a/src/translator/translator.h
+++ b/src/translator/translator.h
@@ -109,6 +109,17 @@ class Translate : public ModelTask {
 
       threadPool.enqueue(task, device, id++);
     }
+
+    if(options_->get<bool>("output-sampling", false)) {
+      if(options_->get<size_t>("beam-size") > 1)
+        LOG(warn,
+            "[warning] Output sampling and beam search (beam-size > 1) are contradictory methods "
+            "and using them together is not recommended. Set beam-size to 1");
+      if(options_->get<std::vector<std::string>>("models").size() > 1)
+        LOG(warn,
+            "[warning] Output sampling and model ensembling are contradictory methods and using "
+            "them together is not recommended. Use a single model");
+    }
   }
 
   void run() override {

From 1044f7f58743b9f52f2bc61bf6ff1d1574690bf9 Mon Sep 17 00:00:00 2001
From: Martin Junczys-Dowmunt <Marcin.JunczysDowmunt@microsoft.com>
Date: Fri, 7 Feb 2020 01:22:21 +0000
Subject: [PATCH 823/838] Merged PR 11434: Fixes empty line handling with
 factored segmenter

Fixes empty line handling with factored segmenter. In my previous PR where I fixed general empty line handling I misunderstood the relation between WordIndex and factors and did an incorrect inverse look-up of the word index of EOS. Should be fixed now for FS, Should be no change when not using FS.
---
 src/translator/beam_search.h | 18 ++++++++++++++----
 1 file changed, 14 insertions(+), 4 deletions(-)

diff --git a/src/translator/beam_search.h b/src/translator/beam_search.h
index 5ddc4ee02..fca190276 100755
--- a/src/translator/beam_search.h
+++ b/src/translator/beam_search.h
@@ -74,10 +74,20 @@ class BeamSearch {
       const auto currentBatchIdx = (key / vocabSize) / nBestBeamSize;
       const auto origBatchIdx    = reverseBatchIdxMap.empty() ? currentBatchIdx : reverseBatchIdxMap[currentBatchIdx]; // map currentBatchIdx back into original position within starting maximal batch size, required to find correct beam
 
-      bool dropHyp = !dropBatchEntries.empty() && dropBatchEntries[origBatchIdx];
-
-      // if we force=drop the hypothesis, assign EOS, otherwise the expected word id. 
-      const auto wordIdx    = dropHyp ? trgVocab_->getEosId().toWordIndex() : (WordIndex)(key % vocabSize);
+      bool dropHyp = !dropBatchEntries.empty() && dropBatchEntries[origBatchIdx] && factorGroup == 0;
+      
+      WordIndex wordIdx;
+      if(dropHyp) { // if we force=drop the hypothesis, assign EOS, otherwise the expected word id.
+        if(factoredVocab) { // when using factoredVocab, extract the EOS lemma index from the word id, we predicting factors one by one here, hence lemma only
+          std::vector<size_t> eosFactors;
+          factoredVocab->word2factors(factoredVocab->getEosId(), eosFactors);
+          wordIdx = eosFactors[0]; 
+        } else { // without factoredVocab lemma index and word index are the same. Safe cruising. 
+          wordIdx = trgVocab_->getEosId().toWordIndex();
+        }
+      } else { // we are not dropping anything, just assign the normal index
+        wordIdx = (WordIndex)(key % vocabSize);
+      }
 
       // @TODO: We currently assign a log probability of 0 to all beam entries of the dropped batch entry, instead it might be a good idea to use
       // the per Hyp pathScore without the current expansion (a bit hard to obtain). 

From bb44c2a8b9807c26c4cd706b7560d48a8a4bd8dc Mon Sep 17 00:00:00 2001
From: Ulrich Germann <ugermann@inf.ed.ac.uk>
Date: Tue, 11 Feb 2020 10:29:20 +0000
Subject: [PATCH 824/838] Remove printout of variable to std::cerr (#596)

---
 src/common/cli_helper.h | 1 -
 1 file changed, 1 deletion(-)

diff --git a/src/common/cli_helper.h b/src/common/cli_helper.h
index 27761372a..4477f0c02 100644
--- a/src/common/cli_helper.h
+++ b/src/common/cli_helper.h
@@ -15,7 +15,6 @@ static inline std::string InterpolateEnvVars(std::string str) {
   // presently has the form /hdfs/VC instead of /{gfs,hdfs}/CLUSTER/VC
 
   // Catch stdin/stdout and do not process
-  std::cerr << str << std::endl;
   if(str == "stdin" || str == "stdout") {
     return str;
   }

From 64a67d5fc8f4e760f05a68b1db23df30dafd08fd Mon Sep 17 00:00:00 2001
From: Nikolay Bogoychev <nheart@gmail.com>
Date: Tue, 11 Feb 2020 19:36:13 +0000
Subject: [PATCH 825/838] Remove unused variables (#571)

---
 src/graph/expression_graph.h | 6 ------
 1 file changed, 6 deletions(-)

diff --git a/src/graph/expression_graph.h b/src/graph/expression_graph.h
index 8a08c7491..f7a781265 100755
--- a/src/graph/expression_graph.h
+++ b/src/graph/expression_graph.h
@@ -130,9 +130,6 @@ class ExpressionGraph : public std::enable_shared_from_this<ExpressionGraph> {
 
   bool inferenceOnly_{false};
 
-  // during inference, use optimizations that might lead to precision loss, e.g. 8-bit MatMul.
-  // At this moment, this is used for int16 qunatized Matmul - 11/1/2019
-  bool optimized_{false};
   bool checkpointing_{false}; // use gradient checkpointing if true
 
   bool reloaded_{false};
@@ -178,9 +175,6 @@ class ExpressionGraph : public std::enable_shared_from_this<ExpressionGraph> {
   void setInference(bool inference) { inferenceOnly_ = inference; }
   bool isInference() { return inferenceOnly_; }
 
-  void setOptimized(bool optimized) { optimized_ = optimized; }
-  bool isOptimized() { return (optimized_ && inferenceOnly_); }
-
   void setCheckpointing(bool checkpointing) { checkpointing_ = checkpointing; }
   bool isCheckpointing() { return checkpointing_; }
 

From a2a567c538e0ce1eee3ffd8c0d24fdd62a2a21d6 Mon Sep 17 00:00:00 2001
From: Kenneth Heafield <github@kheafield.com>
Date: Tue, 11 Feb 2020 20:10:50 +0000
Subject: [PATCH 826/838] clang: Disable unused-function warnings for 3rd-party
 NCCL library

---
 src/3rd_party/CMakeLists.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/3rd_party/CMakeLists.txt b/src/3rd_party/CMakeLists.txt
index e100a6198..c8d7119b5 100644
--- a/src/3rd_party/CMakeLists.txt
+++ b/src/3rd_party/CMakeLists.txt
@@ -95,7 +95,7 @@ if(CUDA_FOUND)
       BUILD_COMMAND
         $(MAKE) -f ${CMAKE_CURRENT_SOURCE_DIR}/nccl/Makefile src.build
         BUILDDIR=${CMAKE_BINARY_DIR}/local CUDA_HOME=${CUDA_TOOLKIT_ROOT_DIR}
-        CUDA8_GENCODE=${GENCODE} CXX=${CMAKE_CXX_COMPILER}
+        CUDA8_GENCODE=${GENCODE} CXX=${CMAKE_CXX_COMPILER} CXXFLAGS=-Wno-unused-function
       INSTALL_COMMAND "")
 
     set_target_properties(nccl PROPERTIES IMPORTED_LOCATION ${CMAKE_BINARY_DIR}/local/lib/libnccl_static.a)

From 0873311d4866532d5a6520d11b76be6fcee183fc Mon Sep 17 00:00:00 2001
From: Kenneth Heafield <github@kheafield.com>
Date: Tue, 11 Feb 2020 20:19:50 +0000
Subject: [PATCH 827/838] Gate warning on clang

---
 src/3rd_party/CMakeLists.txt | 8 +++++++-
 1 file changed, 7 insertions(+), 1 deletion(-)

diff --git a/src/3rd_party/CMakeLists.txt b/src/3rd_party/CMakeLists.txt
index c8d7119b5..1952dd755 100644
--- a/src/3rd_party/CMakeLists.txt
+++ b/src/3rd_party/CMakeLists.txt
@@ -86,6 +86,12 @@ if(CUDA_FOUND)
     if(COMPILE_CUDA_SM70)
       set(GENCODE "${GENCODE} -gencode=arch=compute_70,code=sm_70")
     endif(COMPILE_CUDA_SM70)
+    # clang generates a lot of unused function warnings for NCCL
+    if (CMAKE_CXX_COMPILER_ID STREQUAL "Clang")
+      set(NCCL_CXXFLAGS "-Wno-unused-function")
+    else()
+      set(NCCL_CXXFLAGS "")
+    endif()
 
     # install nccl in ${CMAKE_BINARY_DIR}/local similar to /usr/local linux installation
     ExternalProject_Add(nccl_install
@@ -95,7 +101,7 @@ if(CUDA_FOUND)
       BUILD_COMMAND
         $(MAKE) -f ${CMAKE_CURRENT_SOURCE_DIR}/nccl/Makefile src.build
         BUILDDIR=${CMAKE_BINARY_DIR}/local CUDA_HOME=${CUDA_TOOLKIT_ROOT_DIR}
-        CUDA8_GENCODE=${GENCODE} CXX=${CMAKE_CXX_COMPILER} CXXFLAGS=-Wno-unused-function
+        CUDA8_GENCODE=${GENCODE} CXX=${CMAKE_CXX_COMPILER} CXXFLAGS=${NCCL_CXXFLAGS}
       INSTALL_COMMAND "")
 
     set_target_properties(nccl PROPERTIES IMPORTED_LOCATION ${CMAKE_BINARY_DIR}/local/lib/libnccl_static.a)

From e09f7136ce638ac4a188c3333ad4cf5336b7fe55 Mon Sep 17 00:00:00 2001
From: Frank Seide <fseide@microsoft.com>
Date: Sat, 15 Feb 2020 07:38:16 +0000
Subject: [PATCH 828/838] Merged PR 11566: removed overstuff and understuff
 features

removed overstuff and understuff features
---
 src/common/config_parser.cpp      |  6 ----
 src/training/graph_group_sync.cpp | 52 ++++---------------------------
 src/training/graph_group_sync.h   |  2 +-
 src/translator/beam_search.h      |  2 +-
 4 files changed, 8 insertions(+), 54 deletions(-)
 mode change 100644 => 100755 src/training/graph_group_sync.h

diff --git a/src/common/config_parser.cpp b/src/common/config_parser.cpp
index 9bd01a20c..e7fd0a714 100755
--- a/src/common/config_parser.cpp
+++ b/src/common/config_parser.cpp
@@ -769,12 +769,6 @@ void ConfigParser::addSuboptionsBatching(cli::CLIWrapper& cli) {
         {"0"});
     cli.add<bool>("--mini-batch-track-lr",
         "Dynamically track mini-batch size inverse to actual learning rate (not considering lr-warmup)");
-    cli.add<size_t>("--mini-batch-overstuff",
-        "[experimental] Stuff this much more data into a minibatch, but scale down the LR and progress counter",
-        1);
-    cli.add<size_t>("--mini-batch-understuff",
-        "[experimental] Break each batch into this many updates",
-        1);
   }
   // clang-format on
 }
diff --git a/src/training/graph_group_sync.cpp b/src/training/graph_group_sync.cpp
index 719ee80e9..1b3c16ded 100755
--- a/src/training/graph_group_sync.cpp
+++ b/src/training/graph_group_sync.cpp
@@ -146,7 +146,7 @@ static double roundUpRatio(double ratio) {
 // helper routine that handles accumulation and load-balancing of sub-batches to fill all devices
 // It adds 'newBatch' to 'pendingBatches_', and if sufficient batches have been queued, then
 // returns 'pendingBatches_' in 'subBatches' and resets it. If not, it returns false.
-bool SyncGraphGroup::tryGetSubBatches(Ptr<data::Batch> newBatch, size_t overstuff,
+bool SyncGraphGroup::tryGetSubBatches(Ptr<data::Batch> newBatch,
     std::vector<Ptr<data::Batch>>& subBatches, size_t& numReadBatches) {
   // The reader delivers in chunks of these sizes, according to case:
   //  - no dynamic MB-size scaling:
@@ -199,9 +199,6 @@ bool SyncGraphGroup::tryGetSubBatches(Ptr<data::Batch> newBatch, size_t overstuf
     ratio *= (double)refBatchLabels / (double)(typicalTrgBatchWords_ * updateMultiplier_);
   }
 
-  // overstuff: blow up ratio by a factor, which we later factor into the learning rate
-  ratio *= (double)overstuff;
-
   // round up to full batches if within a certain error margin  --@BUGBUG: Not invariant w.r.t. GPU size, as ratio is relative to what fits into 1 GPU
   ratio = roundUpRatio(ratio);
 
@@ -267,41 +264,18 @@ bool SyncGraphGroup::tryGetSubBatches(Ptr<data::Batch> newBatch, size_t overstuf
 void SyncGraphGroup::update(Ptr<data::Batch> newBatch) /*override*/ {
   validate();
 
-  size_t overstuff = options_->get<size_t>("mini-batch-overstuff");
-  if (overstuff != 1)
-    LOG_ONCE(info, "Overstuffing minibatches by a factor of {}", overstuff);
   std::vector<Ptr<data::Batch>> subBatches;
   size_t numReadBatches; // actual #batches delivered by reader, for restoring from checkpoint   --@TODO: reader should checkpoint itself; should not go via the scheduler
-  bool gotSubBatches = tryGetSubBatches(newBatch, overstuff, subBatches, numReadBatches);
+  bool gotSubBatches = tryGetSubBatches(newBatch, subBatches, numReadBatches);
 
   // not enough data yet: return right away
   if (!gotSubBatches)
     return;
 
-  // for testing the hypothesis that one can always go smaller. This is independent of overstuff.
-  size_t understuff = options_->get<size_t>("mini-batch-understuff");
-  if (understuff != 1)
-    LOG_ONCE(info, "Understuffing minibatches by a factor of {}", understuff);
-  if (understuff == 1)
-    update(subBatches, numReadBatches);
-  else {
-    std::vector<Ptr<data::Batch>> subBatches1;
-    for (auto& b : subBatches) {
-      auto bbs = b->split(understuff);
-      for (auto& bb : bbs)
-        subBatches1.push_back(bb);
-    }
-    for (size_t i = 0; i < understuff; i++) {
-      std::vector<Ptr<data::Batch>> subBatchRange(subBatches1.begin() + i * subBatches1.size() / understuff, subBatches1.begin() + (i+1) * subBatches1.size() / understuff);
-      if (!subBatchRange.empty())
-        update(subBatchRange, numReadBatches * (i+1) / understuff - numReadBatches * i / understuff);
-    }
-  }
+  update(subBatches, numReadBatches);
 }
 
 void SyncGraphGroup::update(std::vector<Ptr<data::Batch>> subBatches, size_t numReadBatches) {
-  size_t overstuff  = options_->get<size_t>("mini-batch-overstuff");
-  //size_t understuff = options_->get<size_t>("mini-batch-understuff");
   // determine num words for dynamic hyper-parameter adjustment
   // @TODO: We can return these directly from tryGetSubBatches()
   size_t batchSize = 0;
@@ -310,9 +284,6 @@ void SyncGraphGroup::update(std::vector<Ptr<data::Batch>> subBatches, size_t num
     batchSize     += batch->size();
     batchTrgWords += batch->wordsTrg();
   }
-  // effective batch size: batch should be weighted like this. This will weight down the learning rate.
-  size_t effectiveBatchTrgWords = (size_t)ceil(batchTrgWords / (double)overstuff);
-  size_t effectiveBatchSize     = (size_t)ceil(batchSize     / (double)overstuff);
 
   // Helper to access the subBatches array
   auto getSubBatch = [&](size_t warp, size_t localDeviceIndex, size_t rank) -> Ptr<data::Batch> {
@@ -353,32 +324,21 @@ void SyncGraphGroup::update(std::vector<Ptr<data::Batch>> subBatches, size_t num
       auto rationalLoss = builders_[localDeviceIndex]->build(graph, subBatch);
       graph->forward();
 
-      StaticLoss tempLoss = *rationalLoss; // needed for overstuff
-      tempLoss.loss /= (float)overstuff; // @TODO: @fseide: scale only loss? should this scale labels too?
-
-      localDeviceLosses[localDeviceIndex] += tempLoss;
+      localDeviceLosses[localDeviceIndex] += *rationalLoss;
       graph->backward(/*zero=*/false); // (gradients are reset before we get here)
     }
   });
   // At this point, each device on each MPI process has a gradient aggregated over a subset of the sub-batches.
 
-  // only needed for overstuff now
-  float div = (float)overstuff; // (note: with Adam, a constant here makes no difference)
-
   // Update parameter shard with gradient shard
   auto update = [&](size_t idx, size_t begin, size_t end) {
     auto curGrad = graphs_[idx]->params()->grads()->subtensor(begin, end-begin);
     auto curParam = graphs_[idx]->params()->vals()->subtensor(begin, end-begin);
 
-    if(div != 1.f) {
-      using namespace functional;
-      Element(_1 = _1 / div, curGrad);   // average if overstuffed
-    }
-
     // actual model update
     auto updateTrgWords =
         /*if*/(options_->get<std::string>("cost-type") == "ce-sum") ?
-          effectiveBatchTrgWords // if overstuffing then bring the count back to the original value
+          batchTrgWords
         /*else*/:
           OptimizerBase::mbSizeNotProvided;
     shardOpt_[idx]->update(curParam, curGrad, updateTrgWords);
@@ -405,7 +365,7 @@ void SyncGraphGroup::update(std::vector<Ptr<data::Batch>> subBatches, size_t num
 
   if(scheduler_) {
     // track and log localLoss
-    scheduler_->update(localLoss, numReadBatches, effectiveBatchSize, effectiveBatchTrgWords, mpi_);
+    scheduler_->update(localLoss, numReadBatches, batchSize, batchTrgWords, mpi_);
 
     // save intermediate model (and optimizer state) to file
     if(scheduler_->saving())
diff --git a/src/training/graph_group_sync.h b/src/training/graph_group_sync.h
old mode 100644
new mode 100755
index b127296da..5cae53a97
--- a/src/training/graph_group_sync.h
+++ b/src/training/graph_group_sync.h
@@ -36,7 +36,7 @@ class SyncGraphGroup : public GraphGroup, public ExponentialSmoothing {
   void barrier() const { mpi_->barrier(); } // (we need this several times)
   void swapParamsAvg() { if (mvAvg_ && paramsAvg_.size() > 0) comm_->swapParams(paramsAvg_); } // note: must call this on all MPI ranks in parallel
 
-  bool tryGetSubBatches(Ptr<data::Batch> newBatch, size_t overstuff, std::vector<Ptr<data::Batch>>& subBatches, size_t& numReadBatches);
+  bool tryGetSubBatches(Ptr<data::Batch> newBatch, std::vector<Ptr<data::Batch>>& subBatches, size_t& numReadBatches);
   void update(std::vector<Ptr<data::Batch>> subBatches, size_t numReadBatches);
 
 public:
diff --git a/src/translator/beam_search.h b/src/translator/beam_search.h
index fca190276..4d02c1086 100755
--- a/src/translator/beam_search.h
+++ b/src/translator/beam_search.h
@@ -81,7 +81,7 @@ class BeamSearch {
         if(factoredVocab) { // when using factoredVocab, extract the EOS lemma index from the word id, we predicting factors one by one here, hence lemma only
           std::vector<size_t> eosFactors;
           factoredVocab->word2factors(factoredVocab->getEosId(), eosFactors);
-          wordIdx = eosFactors[0]; 
+          wordIdx = (WordIndex)eosFactors[0];
         } else { // without factoredVocab lemma index and word index are the same. Safe cruising. 
           wordIdx = trgVocab_->getEosId().toWordIndex();
         }

From 24df8f1cb8c366aa9d846ceba69e27a456c0002d Mon Sep 17 00:00:00 2001
From: Marcin Junczys-Dowmunt <marcinjd@microsoft.com>
Date: Sat, 15 Feb 2020 13:47:18 -0800
Subject: [PATCH 829/838] Revert "Merge pull request #599 from
 marian-nmt/nccl-unused-function"

This reverts commit 63272d1bc4275c3ea7282ac0d02caa995dd63b9e, reversing
changes made to 64a67d5fc8f4e760f05a68b1db23df30dafd08fd.
---
 src/3rd_party/CMakeLists.txt | 8 +-------
 1 file changed, 1 insertion(+), 7 deletions(-)

diff --git a/src/3rd_party/CMakeLists.txt b/src/3rd_party/CMakeLists.txt
index 1952dd755..e100a6198 100644
--- a/src/3rd_party/CMakeLists.txt
+++ b/src/3rd_party/CMakeLists.txt
@@ -86,12 +86,6 @@ if(CUDA_FOUND)
     if(COMPILE_CUDA_SM70)
       set(GENCODE "${GENCODE} -gencode=arch=compute_70,code=sm_70")
     endif(COMPILE_CUDA_SM70)
-    # clang generates a lot of unused function warnings for NCCL
-    if (CMAKE_CXX_COMPILER_ID STREQUAL "Clang")
-      set(NCCL_CXXFLAGS "-Wno-unused-function")
-    else()
-      set(NCCL_CXXFLAGS "")
-    endif()
 
     # install nccl in ${CMAKE_BINARY_DIR}/local similar to /usr/local linux installation
     ExternalProject_Add(nccl_install
@@ -101,7 +95,7 @@ if(CUDA_FOUND)
       BUILD_COMMAND
         $(MAKE) -f ${CMAKE_CURRENT_SOURCE_DIR}/nccl/Makefile src.build
         BUILDDIR=${CMAKE_BINARY_DIR}/local CUDA_HOME=${CUDA_TOOLKIT_ROOT_DIR}
-        CUDA8_GENCODE=${GENCODE} CXX=${CMAKE_CXX_COMPILER} CXXFLAGS=${NCCL_CXXFLAGS}
+        CUDA8_GENCODE=${GENCODE} CXX=${CMAKE_CXX_COMPILER}
       INSTALL_COMMAND "")
 
     set_target_properties(nccl PROPERTIES IMPORTED_LOCATION ${CMAKE_BINARY_DIR}/local/lib/libnccl_static.a)

From 03fbf31592dab9833e7d60908587ab70f048e591 Mon Sep 17 00:00:00 2001
From: "Pinzhen Chen (Patrick)" <PinzhenChen@users.noreply.github.com>
Date: Wed, 26 Feb 2020 13:10:56 +0000
Subject: [PATCH 830/838] Combine two for-loops in nth_element.cpp on CPU
 (#601)

---
 src/translator/nth_element.cpp | 68 ++++++++++++++--------------------
 1 file changed, 28 insertions(+), 40 deletions(-)

diff --git a/src/translator/nth_element.cpp b/src/translator/nth_element.cpp
index 4692a5057..8b2f89476 100644
--- a/src/translator/nth_element.cpp
+++ b/src/translator/nth_element.cpp
@@ -20,34 +20,6 @@ class NthElementCPU {
   NthElementCPU() {}
   NthElementCPU(const NthElementCPU& copy) = delete;
 
-private:
-  // for each batch, select the max N elements, where N is the beam size for this batch.
-  void selectNBest(const float* scores,
-                   const std::vector<int>& batchFirstElementIdxs,
-                   const std::vector<int>& cumulativeBeamSizes) {
-    int numProbs = batchFirstElementIdxs.back();
-    std::vector<int> idxs(numProbs);
-    std::iota(idxs.begin(), idxs.end(), 0);
-
-    size_t numBatches = batchFirstElementIdxs.size() - 1;
-    for(size_t batchIdx = 0; batchIdx < numBatches; ++batchIdx) {
-      int pos = cumulativeBeamSizes[batchIdx];
-      int beamSize = cumulativeBeamSizes[batchIdx + 1] - pos;
-
-      std::vector<int>::iterator begin = idxs.begin() + batchFirstElementIdxs[batchIdx];
-      std::vector<int>::iterator middle = begin + beamSize;
-      std::vector<int>::iterator end = idxs.begin() + batchFirstElementIdxs[batchIdx + 1];
-      std::partial_sort(
-          begin, middle, end, [&](int a, int b) { return scores[a] > scores[b]; });
-
-      while(begin != middle) {
-        int idx = *begin++;
-        h_res_idx[pos] = idx;
-        h_res[pos] = scores[idx];
-        ++pos;
-      }
-    }
-  }
 
 public:
   void getNBestList(Tensor scores, // [dimBatch, 1, beamSize, dimVocab or dimShortlist]
@@ -59,23 +31,39 @@ class NthElementCPU {
     const auto inputN    = scores->shape()[-2];
     const auto dimBatch  = scores->shape()[-4];
     ABORT_IF(inputN != (isFirst ? 1 : N), "Input tensor has wrong beam dim??"); // @TODO: Remove isFirst argument altogether
-
-    std::vector<int> cumulativeBeamSizes(dimBatch + 1, 0);
-    std::vector<int> batchFirstElementIdxs(dimBatch + 1, 0);
-
-    for(int batchIdx = 0; batchIdx < dimBatch; ++batchIdx) {
-      cumulativeBeamSizes[batchIdx + 1] = (batchIdx + 1) * (int)N;
-      batchFirstElementIdxs[batchIdx + 1] += (batchIdx + 1) * inputN * vocabSize;
-      ABORT_IF(cumulativeBeamSizes[batchIdx + 1] != cumulativeBeamSizes[batchIdx] + (int)N, "cumulativeBeamSizes wrong??");
-      ABORT_IF((isFirst ? batchIdx + 1 : cumulativeBeamSizes[batchIdx + 1]) != (batchIdx + 1) * inputN, "inputN wrong??");
-    }
-    ABORT_IF(cumulativeBeamSizes.back() != dimBatch * N, "cumulativeBeamSizes.back() wrong??");
+    const float* scoresData = scores->data();
 
     size_t maxSize = N * dimBatch;
     h_res.resize(maxSize);
     h_res_idx.resize(maxSize);
+    size_t pos = 0; // iterates through h_res and h_res_idx
+
+    size_t batchOffset = inputN * vocabSize;
+    std::vector<int> idxs(batchOffset); // re-used for each batch
+    std::iota(idxs.begin(), idxs.end(), 0);
 
-    selectNBest(scores->data(), batchFirstElementIdxs, cumulativeBeamSizes);
+    for(size_t batchIdx = 0; batchIdx < dimBatch; ++batchIdx) {
+
+      std::partial_sort( 
+        // sorts the top N (beam size) idxs by score to the front
+        idxs.begin(),
+        idxs.begin() + N,
+        idxs.end(),
+        [&](int a, int b) { return scoresData[a] > scoresData[b]; }
+      );
+
+      // copy top N idxs and scores to return vectors
+      for(size_t i = 0; i < N; ++i) {
+        int idx = idxs[i];
+        // since idxs is re-used for each batch, add batch offset to each idx to get absolute position
+        h_res_idx[pos] = idx + batchIdx * batchOffset;
+        h_res[pos] = scoresData[idx];
+        ++pos;
+      }
+
+      // advance pointer to next batch's beginning
+      scoresData += batchOffset;
+    }
     getPairs(/*cumulativeBeamSizes.back(),*/ outKeys, outPathScores);
   }
 

From 67b055fe4aad2fbf4a557a6655c4147c6de2f6d3 Mon Sep 17 00:00:00 2001
From: Roman Grundkiewicz <rgrundki@exseed.ed.ac.uk>
Date: Wed, 26 Feb 2020 14:13:47 +0000
Subject: [PATCH 831/838] Update the submodule regression-tests

---
 regression-tests | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/regression-tests b/regression-tests
index 6a08849b2..448cc1a8f 160000
--- a/regression-tests
+++ b/regression-tests
@@ -1 +1 @@
-Subproject commit 6a08849b23f6c14eefbe12f4eb73dc638b962587
+Subproject commit 448cc1a8ff2f37b102695dc5cea89cf285202ee7

From 00d2e999e3c24c9e8c836e800b3720bbe4a326a1 Mon Sep 17 00:00:00 2001
From: Roman Grundkiewicz <rgrundki@exseed.ed.ac.uk>
Date: Thu, 5 Mar 2020 21:08:23 +0000
Subject: [PATCH 832/838] Add support for compiling on Mac (and clang) (#598)

* Compile marian on mac and clang. Two linker errors left

* MacOS defines has a different definition for unsigned long

* Find OpenBLAS on mac

* Fix a typo in the BLAS detection

* Simplify and add comments

* Refactor cpu allocation code. Do not fallback to malloc

* Fix compilation warning on gcc

* Refactor memory allocation

* Make things compile with clang-8 with fewer warnings.

* Eliminate clang warnings when compiling examples and when compiling without MKL

* added USE_MKL option to compile without MKL for debugging even when MKL is installed

* fixed issues with compiling examples with clang

* Fix compile errors with clang in src/tests.

* Fix missing whitespace in error message in src/tests/sqlite.cpp.

* Responding to Frank Seide's code review.

* Eliminate clang warnings when compiling with -DUSE_FBGEMM=on.

* Fix compilation on gcc 8

* Get Marian to compile with Clang-10.

* Fix Clang-8 warnings when compiling with marian-server

* Add more comments and explicit unsigned long long for windows

* Pull in fbgemm that supports mac

* Fix warning flags order in CMakeLists.txt

Co-authored-by: Kenneth Heafield <kpu@users.noreply.github.com>
Co-authored-by: Ulrich Germann <ulrich.germann@gmail.com>
Co-authored-by: Roman Grundkiewicz <romang@amu.edu.pl>
---
 CMakeLists.txt                                |  43 ++++---
 cmake/FindCBLAS.cmake                         |   2 +-
 src/3rd_party/CMakeLists.txt                  |  45 +++++++-
 src/3rd_party/fbgemm                          |   2 +-
 src/3rd_party/half_float/umHalf.inl           |   4 +-
 .../pathie-cpp/src/entry_iterator.cpp         |   2 +-
 src/3rd_party/pathie-cpp/src/path.cpp         |   2 +-
 src/3rd_party/pathie-cpp/src/pathie.cpp       |   8 +-
 src/3rd_party/zstr/strict_fstream.hpp         |   2 +-
 src/CMakeLists.txt                            |   4 +
 src/command/marian_server.cpp                 |   2 +-
 src/common/definitions.h                      |  15 +++
 src/common/fastopt.cpp                        |  14 ++-
 src/common/fastopt.h                          |   5 +-
 src/common/file_stream.h                      |  17 ++-
 src/common/filesystem.h                       |  14 ++-
 src/common/intrusive_ptr.h                    |   6 +-
 src/common/logging.cpp                        |   2 +-
 src/common/types.h                            |  38 +++++--
 src/common/utils.cpp                          |  12 +-
 src/data/batch.h                              |   2 +-
 src/data/corpus_base.h                        |   1 +
 src/data/default_vocab.cpp                    |   3 +-
 src/data/shortlist.h                          |   7 +-
 src/data/text_input.h                         |   1 +
 src/data/vocab_base.h                         |   1 +
 src/examples/mnist/dataset.h                  |   3 +
 src/examples/mnist/model.h                    |   8 +-
 src/examples/mnist/validator.h                |   4 +-
 src/functional/operators.h                    | 107 +++++++++---------
 src/graph/node.h                              |  10 +-
 src/graph/node_initializers.h                 |  11 +-
 src/graph/node_operators_binary.h             |  17 +--
 src/graph/parameters.h                        |   8 +-
 src/layers/generic.h                          |   2 +
 src/layers/guided_alignment.h                 |   8 +-
 src/layers/loss.h                             |  19 ++--
 src/layers/weight.h                           |   1 +
 src/microsoft/quicksand.cpp                   |   3 +-
 src/microsoft/quicksand.h                     |   2 +
 src/models/costs.h                            |   9 ++
 src/models/encoder_decoder.h                  |   1 +
 src/models/model_base.h                       |   2 +
 src/models/model_task.h                       |   2 +
 src/models/s2s.h                              |   5 +-
 src/models/states.h                           |   7 +-
 src/optimizers/clippers.h                     |   1 +
 src/optimizers/optimizers.h                   |   4 +-
 src/rescorer/score_collector.h                |   1 +
 src/rnn/rnn.h                                 |   3 +-
 src/tensors/backend.h                         |   2 +-
 src/tensors/cpu/device.cpp                    |  49 ++++----
 src/tensors/cpu/fbgemm/expanded_gemm.h        |  45 ++++----
 src/tensors/cpu/tensor_operators.cpp          |  16 ++-
 src/tensors/rand.h                            |   4 +-
 src/tensors/tensor_operators.h                |   6 +-
 src/tests/prod.cpp                            |   2 +-
 src/tests/sqlite.cpp                          |   2 +
 src/training/communicator.cpp                 |   4 +-
 src/training/communicator.h                   |  11 +-
 .../gradient_dropping/sparse_tensor.h         |   2 +-
 src/training/graph_group.h                    |  12 +-
 src/training/graph_group_async.h              |   2 +-
 src/training/graph_group_multinode_sync.h     |   1 -
 src/training/graph_group_sync.h               |   1 -
 src/training/training_state.h                 |   1 +
 src/training/validator.h                      |   8 ++
 src/translator/output_collector.h             |   1 +
 src/translator/scorers.h                      |   7 ++
 69 files changed, 430 insertions(+), 238 deletions(-)

diff --git a/CMakeLists.txt b/CMakeLists.txt
index c442931f9..c27761cf0 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -24,6 +24,7 @@ option(USE_CCACHE "Use ccache compiler cache (https://ccache.dev)" OFF)
 option(USE_CUDNN "Use CUDNN library" OFF)
 option(USE_DOXYGEN "Build documentation with Doxygen" ON)
 option(USE_FBGEMM "Use FBGEMM" OFF)
+option(USE_MKL "Compile with MKL support" ON)
 option(USE_MPI "Use MPI library" OFF)
 option(USE_NCCL "Use NCCL library" ON)
 option(USE_SENTENCEPIECE "Download and compile SentencePiece" OFF)
@@ -33,7 +34,7 @@ option(USE_STATIC_LIBS "Link statically against non-system libs" OFF)
 if(USE_CCACHE)
 find_program(CCACHE_PROGRAM ccache)
 if(CCACHE_PROGRAM)
-  message(STATUS "Found and will be using ccache for faster repeat compilation (use cmake -DUSE_CCACHE=off to disable).")
+  message(STATUS "Will be using ccache for faster repeat compilation (use cmake -DUSE_CCACHE=off to disable).")
   set_property(GLOBAL PROPERTY RULE_LAUNCH_COMPILE "${CCACHE_PROGRAM}")
 else(CCACHE_PROGRAM)
   message(WARNING "Compilation with ccache requested but no ccache found.")
@@ -141,20 +142,32 @@ else(MSVC)
     add_definitions(-DUSE_FBGEMM=1)
   endif(USE_FBGEMM)
 
-  set(DISABLE_GLOBALLY "-Wno-unused-result")
+  if (CMAKE_CXX_COMPILER_ID MATCHES "Clang" AND CMAKE_CXX_COMPILER_VERSION VERSION_GREATER 9.0)
+    # Clang-10.0.0 complains when CUDA is newer than 10.1
+    set(CLANG_IGNORE_UNKNOWN_CUDA "-Wno-unknown-cuda-version")
+  endif()
+  set(DISABLE_GLOBALLY "-Wno-unused-result -Wno-unknown-warning-option ${CLANG_IGNORE_UNKNOWN_CUDA}")
 
   # These are used in src/CMakeLists.txt on a per-target basis
-  list(APPEND ALL_WARNINGS -Wall; -Werror; -Wno-unused-result; -Wno-deprecated; -Wno-pragmas; -Wno-unused-parameter; -Wextra; -Wno-unused-function;
-                          -Wno-unused-value; -Wno-unknown-pragmas; -Wno-sign-compare; -Wno-missing-field-initializers;)
+  list(APPEND ALL_WARNINGS -Wall; -Werror; -Wextra; -Wno-unused-result; -Wno-deprecated;
+    -Wno-pragmas; -Wno-unused-parameter; -Wno-unused-function;
+    -Wno-unused-value; -Wno-unknown-pragmas; -Wno-sign-compare;
+    -Wno-missing-field-initializers;)
 
   # This warning does not exist prior to gcc 5.0
   if(CMAKE_COMPILER_IS_GNUCC AND CMAKE_CXX_COMPILER_VERSION VERSION_GREATER 5.0)
-    list(APPEND ALL_WARNINGS -Wsuggest-override)
+    list(APPEND ALL_WARNINGS -Wsuggest-override -Wno-int-in-bool-context)
   endif()
 
-  set(CMAKE_CXX_FLAGS                 "-std=c++11 -pthread -Wl,--no-as-needed -fPIC ${DISABLE_GLOBALLY} -march=${BUILD_ARCH} ${INTRINSICS}")
-  set(CMAKE_CXX_FLAGS_RELEASE         "-Ofast -m64 -funroll-loops -ffinite-math-only -g -rdynamic")
-  set(CMAKE_CXX_FLAGS_DEBUG           "-O0 -g -rdynamic")
+  if(CMAKE_COMPILER_IS_GNUCC)
+    # these flags are not known to clang
+    set(CMAKE_GCC_FLAGS "-Wl,--no-as-needed")
+    set(CMAKE_RDYNAMIC_FLAG "-rdynamic")
+  endif(CMAKE_COMPILER_IS_GNUCC)
+
+  set(CMAKE_CXX_FLAGS                 "-std=c++11 -pthread ${CMAKE_GCC_FLAGS} -fPIC ${DISABLE_GLOBALLY} -march=${BUILD_ARCH} ${INTRINSICS}")
+  set(CMAKE_CXX_FLAGS_RELEASE         "-Ofast -m64 -funroll-loops -ffinite-math-only -g ${CMAKE_RDYNAMIC_FLAG}")
+  set(CMAKE_CXX_FLAGS_DEBUG           "-O0 -g ${CMAKE_RDYNAMIC_FLAG}")
   set(CMAKE_CXX_FLAGS_SLIM            "-Ofast -m64 -funroll-loops -ffinite-math-only -DNDEBUG")
   set(CMAKE_CXX_FLAGS_RELWITHDEBINFO  "${CMAKE_CXX_FLAGS_RELEASE}")
   set(CMAKE_CXX_FLAGS_PROFILE         "${CMAKE_CXX_FLAGS_RELEASE} -pg")
@@ -162,9 +175,9 @@ else(MSVC)
   set(CMAKE_CXX_FLAGS_PROFUSE         "${CMAKE_CXX_FLAGS_RELEASE} -fprofile-use -fprofile-correction")
 
   # these need to be set separately
-  set(CMAKE_C_FLAGS                 "-pthread -Wl,--no-as-needed -fPIC ${DISABLE_GLOBALLY} -march=${BUILD_ARCH} ${INTRINSICS}")
-  set(CMAKE_C_FLAGS_RELEASE         "-O3 -m64 -funroll-loops -ffinite-math-only -g -rdynamic")
-  set(CMAKE_C_FLAGS_DEBUG           "-O0 -g -rdynamic")
+  set(CMAKE_C_FLAGS                 "-pthread ${CMAKE_GCC_FLAGS} -fPIC ${DISABLE_GLOBALLY} -march=${BUILD_ARCH} ${INTRINSICS}")
+  set(CMAKE_C_FLAGS_RELEASE         "-O3 -m64 -funroll-loops -ffinite-math-only -g ${CMAKE_RDYNAMIC_FLAG}")
+  set(CMAKE_C_FLAGS_DEBUG           "-O0 -g ${CMAKE_RDYNAMIC_FLAG}")
   set(CMAKE_C_FLAGS_SLIM            "-O3 -m64 -funroll-loops -ffinite-math-only -DNDEBUG")
   set(CMAKE_C_FLAGS_RELWITHDEBINFO  "${CMAKE_C_FLAGS_RELEASE}")
   set(CMAKE_C_FLAGS_PROFILE         "${CMAKE_C_FLAGS_RELEASE} -pg")
@@ -204,7 +217,7 @@ if(CUDA_FOUND)
   if((CUDA_VERSION VERSION_EQUAL "10.0" OR CUDA_VERSION VERSION_GREATER "10.0") AND (CMAKE_VERSION VERSION_LESS "3.12.2"))
       message(WARNING "On some Unix systems CUDA 10.0+ requires CMake 3.12.2+; you use CMake ${CMAKE_VERSION}")
   endif()
-  
+
   if(COMPILE_CUDA_SM35)
     LIST(APPEND COMPUTE -arch=sm_35; -gencode=arch=compute_35,code=sm_35;)                             # Tesla K40 and above
   endif(COMPILE_CUDA_SM35)
@@ -323,13 +336,15 @@ if(USE_MPI)
 endif(USE_MPI)
 
 if(COMPILE_CPU)
-  find_package(MKL)
+  if(USE_MKL)
+    find_package(MKL)
+  endif(USE_MKL)
   if(MKL_FOUND)
     include_directories(${MKL_INCLUDE_DIR})
     set(EXT_LIBS ${EXT_LIBS} ${MKL_LIBRARIES})
     add_definitions(-DBLAS_FOUND=1 -DMKL_FOUND=1)
   else(MKL_FOUND)
-    set(BLA_VENDOR "OpenBLAS")
+    set(BLAS_VENDOR "OpenBLAS")
     find_package(BLAS)
     if(BLAS_FOUND)
       include(FindCBLAS)
diff --git a/cmake/FindCBLAS.cmake b/cmake/FindCBLAS.cmake
index 631da99b4..97b0d3f8a 100644
--- a/cmake/FindCBLAS.cmake
+++ b/cmake/FindCBLAS.cmake
@@ -54,7 +54,7 @@ MACRO(CHECK_ALL_LIBRARIES LIBRARIES INCLUDE _prefix _name _flags _list _include
       IF(APPLE) 
         FIND_LIBRARY(${_prefix}_${_library}_LIBRARY
           NAMES ${_library}
-          PATHS /usr/local/lib /usr/lib /usr/local/lib64 /usr/lib64 ENV 
+          PATHS /usr/local/lib /usr/lib /usr/local/lib64 /usr/lib64 /usr/local/opt/openblas/lib ENV
           DYLD_LIBRARY_PATH 
           )
       ELSE(APPLE)
diff --git a/src/3rd_party/CMakeLists.txt b/src/3rd_party/CMakeLists.txt
index e100a6198..9f3981af2 100644
--- a/src/3rd_party/CMakeLists.txt
+++ b/src/3rd_party/CMakeLists.txt
@@ -15,12 +15,22 @@ if(USE_FBGEMM)
 
   if(NOT MSVC)
     # only locally disabled for the 3rd_party folder
-    set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -Wno-unused-value -Wno-unused-parameter -Wno-unused-variable -Wno-unused-function")
+    # set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -Wno-unused-value -Wno-unused-parameter -Wno-unused-variable -Wno-unused-function")
+    set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -Wno-unused")
   endif()
 
   set(FBGEMM_BUILD_TESTS OFF CACHE BOOL "Disable fbgemm tests")
   set(FBGEMM_BUILD_BENCHMARKS OFF CACHE BOOL "Disable fbgemm benchmark")
   add_subdirectory(./fbgemm)
+
+  # asmjit (3rd-party submodule of fbgemm) sets -Wall -Wextra near the end of
+  # the compile options, invalidating any -Wno-... flags that we may have set
+  # earlier. Let's remove them.
+  get_property(ASMJIT_COMPILE_OPTIONS TARGET asmjit PROPERTY COMPILE_OPTIONS)
+  list(REMOVE_ITEM ASMJIT_COMPILE_OPTIONS -Wall -Wextra)
+  set_property(TARGET asmjit PROPERTY COMPILE_OPTIONS ${ASMJIT_COMPILE_OPTIONS})
+  message("   ASMJIT COMPILE FLAGS: ${ASMJIT_COMPILE_OPTIONS}")
+
 endif(USE_FBGEMM)
 
 if(USE_SENTENCEPIECE)
@@ -39,7 +49,7 @@ if(USE_SENTENCEPIECE)
     message(WARNING "You are compiling SentencePiece binaries with -DUSE_STATIC_LIBS=on. \
     This will cause spm_train to segfault. No need to worry if you do not intend to use that binary. \
     Marian support for SentencePiece will work fine.")
-    
+
     set(SPM_ENABLE_SHARED OFF CACHE BOOL "Builds shared libaries in addition to static libraries." FORCE)
     set(SPM_TCMALLOC_STATIC ON CACHE BOOL "Link static library of TCMALLOC." FORCE)
   else(USE_STATIC_LIBS)
@@ -51,8 +61,19 @@ if(USE_SENTENCEPIECE)
   include_directories(./sentencepiece)
 
   set_target_properties(spm_encode spm_decode spm_train spm_normalize spm_export_vocab
-                        PROPERTIES
-                        RUNTIME_OUTPUT_DIRECTORY "${CMAKE_BINARY_DIR}")
+    PROPERTIES RUNTIME_OUTPUT_DIRECTORY "${CMAKE_BINARY_DIR}")
+
+  if (CMAKE_CXX_COMPILER_ID MATCHES "Clang")
+    foreach(t sentencepiece sentencepiece_train sentencepiece_train-static
+        spm_decode spm_encode spm_export_vocab spm_normalize spm_train)
+      set_property(TARGET ${t} APPEND_STRING PROPERTY COMPILE_FLAGS " -Wno-tautological-compare -Wno-unused")
+      if (CMAKE_CXX_COMPILER_VERSION VERSION_GREATER 9.0)
+        set_property(TARGET ${t} APPEND_STRING PROPERTY COMPILE_FLAGS " -Wno-range-loop-construct")
+      endif()
+      # get_property(SENTENCEPIECE_COMPILE_FLAGS TARGET ${t} PROPERTY COMPILE_FLAGS)
+      # message("-- SENTENCPIECE: compile flags for target ${t}: ${SENTENCEPIECE_COMPILE_FLAGS}")
+    endforeach(t)
+  endif()
 
   if(USE_STATIC_LIBS)
     set(CMAKE_FIND_LIBRARY_SUFFIXES ${_ORIG_CMAKE_FIND_LIBRARY_SUFFIXES})
@@ -63,6 +84,22 @@ include_directories(./SQLiteCpp/include)
 include_directories(./CLI)
 include_directories(./pathie-cpp/include)
 
+if (CMAKE_CXX_COMPILER_ID MATCHES "Clang")
+  #set_target_properties(SQLiteCpp PROPERTIES COMPILE_FLAGS
+  set_property(TARGET SQLiteCpp APPEND_STRING PROPERTY COMPILE_FLAGS
+    " -Wno-parentheses-equality -Wno-unused-value")
+  if (CMAKE_CXX_COMPILER_VERSION VERSION_GREATER 9.0)
+    set_property(TARGET SQLiteCpp APPEND_STRING PROPERTY COMPILE_FLAGS
+      " -Wno-implicit-int-float-conversion")
+  endif()
+  set_property(TARGET libyaml-cpp APPEND_STRING PROPERTY COMPILE_FLAGS
+    " -fPIC -Wno-unused-value")
+  set_property(TARGET pathie-cpp APPEND_STRING PROPERTY COMPILE_FLAGS
+    " -fPIC -Wno-unused-value")
+endif()
+
+
+
 include_directories(./zlib)
 
 include(ExternalProject)
diff --git a/src/3rd_party/fbgemm b/src/3rd_party/fbgemm
index 84e66a976..f78e60988 160000
--- a/src/3rd_party/fbgemm
+++ b/src/3rd_party/fbgemm
@@ -1 +1 @@
-Subproject commit 84e66a976046180187724aff60a236c5378fde7c
+Subproject commit f78e60988329b9207d086c743cafce1ac1bea3ab
diff --git a/src/3rd_party/half_float/umHalf.inl b/src/3rd_party/half_float/umHalf.inl
index 4f065dad3..3f5285a2e 100644
--- a/src/3rd_party/half_float/umHalf.inl
+++ b/src/3rd_party/half_float/umHalf.inl
@@ -186,7 +186,7 @@ inline HalfFloat& HalfFloat::operator= (float other)
 inline bool HalfFloat::operator== (HalfFloat other) const
 {
 	// +0 and -0 are considered to be equal
-	if (!(bits << 1u) && !(other.bits << 1u))return true;
+	if ((bits << 1u) == 0 && (other.bits << 1u) == 0) return true;
 
 	return bits == other.bits && !this->IsNaN();
 }
@@ -194,7 +194,7 @@ inline bool HalfFloat::operator== (HalfFloat other) const
 inline bool HalfFloat::operator!= (HalfFloat other) const
 {
 	// +0 and -0 are considered to be equal
-	if (!(bits << 1u) && !(other.bits << 1u))return false;
+	if ((bits << 1u) == 0 && (other.bits << 1u) == 0) return false;
 
 	return bits != other.bits || this->IsNaN();
 }
diff --git a/src/3rd_party/pathie-cpp/src/entry_iterator.cpp b/src/3rd_party/pathie-cpp/src/entry_iterator.cpp
index 24e5b1101..baaf53949 100644
--- a/src/3rd_party/pathie-cpp/src/entry_iterator.cpp
+++ b/src/3rd_party/pathie-cpp/src/entry_iterator.cpp
@@ -31,7 +31,7 @@
 #include "../include/path.hpp"
 #include "../include/errors.hpp"
 
-#if defined(__unix__)
+#if defined(__unix__) || defined(__APPLE__)
 #include <sys/types.h>
 #include <dirent.h>
 #include <errno.h>
diff --git a/src/3rd_party/pathie-cpp/src/path.cpp b/src/3rd_party/pathie-cpp/src/path.cpp
index 07fba2711..e732e09c5 100644
--- a/src/3rd_party/pathie-cpp/src/path.cpp
+++ b/src/3rd_party/pathie-cpp/src/path.cpp
@@ -902,7 +902,7 @@ Path Path::pwd()
  */
 Path Path::exe()
 {
-#if defined(__linux__)
+#if defined(__linux__) || defined(__APPLE__)
   char buf[PATH_MAX];
   ssize_t size = ::readlink("/proc/self/exe", buf, PATH_MAX);
 
diff --git a/src/3rd_party/pathie-cpp/src/pathie.cpp b/src/3rd_party/pathie-cpp/src/pathie.cpp
index c0abc689a..d8e567e10 100644
--- a/src/3rd_party/pathie-cpp/src/pathie.cpp
+++ b/src/3rd_party/pathie-cpp/src/pathie.cpp
@@ -143,7 +143,7 @@ std::string Pathie::convert_encodings(const char* from_encoding, const char* to_
     errno  = 0;
     errsav = 0;
 
-#ifdef BSD
+#if defined(BSD) && ! defined(__APPLE__) //Since MacOS evolved from BSD, it is captured here but the iconv on macos behaves differently
     // What the heck. FreeBSD violates POSIX.1-2008: it declares iconv()
     // differently than mandated by POSIX: http://pubs.opengroup.org/onlinepubs/9699919799/functions/iconv.html
     // (it declares a `const' where it must not be).
@@ -181,11 +181,10 @@ std::string Pathie::convert_encodings(const char* from_encoding, const char* to_
 std::string Pathie::utf8_to_filename(const std::string& utf8)
 {
   bool fs_encoding_is_utf8 = false;
-
+  char* fsencoding = NULL;
 #if defined(__APPLE__) || defined(PATHIE_ASSUME_UTF8_ON_UNIX)
   fs_encoding_is_utf8 = true;
 #else
-  char* fsencoding = NULL;
   fsencoding = nl_langinfo(CODESET);
   fs_encoding_is_utf8 = (strcmp(fsencoding, "UTF-8") == 0);
 #endif
@@ -206,11 +205,10 @@ std::string Pathie::utf8_to_filename(const std::string& utf8)
 std::string Pathie::filename_to_utf8(const std::string& native_filename)
 {
   bool fs_encoding_is_utf8 = false;
-
+  char* fsencoding = NULL;
 #if defined(__APPLE__) || defined(PATHIE_ASSUME_UTF8_ON_UNIX)
   fs_encoding_is_utf8 = true;
 #else
-  char* fsencoding = NULL;
   fsencoding = nl_langinfo(CODESET);
   fs_encoding_is_utf8 = (strcmp(fsencoding, "UTF-8") == 0);
 #endif
diff --git a/src/3rd_party/zstr/strict_fstream.hpp b/src/3rd_party/zstr/strict_fstream.hpp
index fbc641640..7b1173931 100644
--- a/src/3rd_party/zstr/strict_fstream.hpp
+++ b/src/3rd_party/zstr/strict_fstream.hpp
@@ -27,7 +27,7 @@ static std::string strerror()
     {
         buff = "Unknown error";
     }
-#elif (_POSIX_C_SOURCE >= 200112L || _XOPEN_SOURCE >= 600) && ! _GNU_SOURCE
+#elif (_POSIX_C_SOURCE >= 200112L || _XOPEN_SOURCE >= 600 || __APPLE__) && ! _GNU_SOURCE
 // XSI-compliant strerror_r()
     if (strerror_r(errno, &buff[0], buff.size()) != 0)
     {
diff --git a/src/CMakeLists.txt b/src/CMakeLists.txt
index c1a513908..839a64f07 100644
--- a/src/CMakeLists.txt
+++ b/src/CMakeLists.txt
@@ -215,6 +215,10 @@ if(COMPILE_SERVER)
   set(EXECUTABLES ${EXECUTABLES} marian_server)
 endif(COMPILE_SERVER)
 
+if(APPLE) # This is a dependency of pathie but I can't seem to link it into that CMakeLists because we're not compiling it as a library.
+   set(EXT_LIBS ${EXT_LIBS} iconv)
+endif()
+
 foreach(exec ${EXECUTABLES})
   target_link_libraries(${exec} marian ${EXT_LIBS} ${EXT_LIBS} ${CMAKE_THREAD_LIBS_INIT})
   if(CUDA_FOUND)
diff --git a/src/command/marian_server.cpp b/src/command/marian_server.cpp
index 5df944d71..e4074bd1b 100644
--- a/src/command/marian_server.cpp
+++ b/src/command/marian_server.cpp
@@ -44,7 +44,7 @@ int main(int argc, char **argv) {
 
   // Error Codes for error code meanings
   // http://www.boost.org/doc/libs/1_55_0/doc/html/boost_asio/reference.html
-  translate.on_error = [](Ptr<WSServer::Connection> connection,
+  translate.on_error = [](Ptr<WSServer::Connection> /*connection*/,
                           const SimpleWeb::error_code &ec) {
     LOG(error, "Connection error: ({}) {}", ec.value(), ec.message());
   };
diff --git a/src/common/definitions.h b/src/common/definitions.h
index 45f5d5638..2df0f8f33 100755
--- a/src/common/definitions.h
+++ b/src/common/definitions.h
@@ -10,6 +10,21 @@
 #include <string>
 #include <vector>
 
+// The macro MAYBE_UNUSED is used to selectively disable
+// unused-variable warnings. C++17 defines the attribute
+// [[maybe_unused]], but I don't think we're at C++17 yet. We can add it when we reach C++17.
+// The compilers gcc and clang (and maybe others) define
+// __has_attribute and support __attribute__(unused) in C++11,
+#if defined __has_attribute
+#  if __has_attribute(unused)
+#    define MAYBE_UNUSED __attribute__((unused))
+#  else
+#    define MAYBE_UNUSED
+#  endif
+#endif
+
+
+
 #define THREAD_GUARD(body) [&]() { body; }() // test if THREAD_GUARD is neccessary, remove if no problems occur.
 #define NodeOp(op) [=]() { op; }
 
diff --git a/src/common/fastopt.cpp b/src/common/fastopt.cpp
index ccdd7b543..9af8e8445 100644
--- a/src/common/fastopt.cpp
+++ b/src/common/fastopt.cpp
@@ -84,10 +84,16 @@ std::vector<T> As<std::vector<T>>::apply(const FastOpt& node) {
 // specializations for simple vector types
 template struct As<std::vector<bool>>;
 template struct As<std::vector<int>>;
-// Windows and Unix based OS have different type definitions for 'unsigned long'.
-// So, we need an explicit definition for uint64_t. Otherwise, there's a linking error on windows.
+// Windows, Linux based OS and Mac have different type definitions for 'unsigned long'.
+// So, we need an explicit definitions for uint64_t, that cover different platforms.
+// Otherwise, there's a linking error on windows or Linux or Mac.
 // https://software.intel.com/en-us/articles/size-of-long-integer-type-on-different-architecture-and-os/
-template struct As<std::vector<uint64_t>>;
+// https://stackoverflow.com/questions/32021860/c-should-you-size-t-with-a-regular-array
+// MacOS: size_t = unsigned long (8 bytes), uint64_t = unsigned long long (8 bytes)
+// Linux: size_t = unsigned long (8 bytes), uint64_t = unsigned long (8 bytes)
+// Windows: size_t = unsigned long long (8 bytes), uint64_t = unsigned long long (8 bytes)
+template struct As<std::vector<unsigned long long>>;
+template struct As<std::vector<unsigned long>>;
 template struct As<std::vector<float>>;
 template struct As<std::vector<double>>;
 template struct As<std::vector<std::string>>;
@@ -103,4 +109,4 @@ std::pair<T1, T2> As<std::pair<T1, T2>>::apply(const FastOpt& node) {
 template struct As<std::pair<int, int>>;
 
 }
-}
\ No newline at end of file
+}
diff --git a/src/common/fastopt.h b/src/common/fastopt.h
index 9ddc3c2c3..3f735660b 100644
--- a/src/common/fastopt.h
+++ b/src/common/fastopt.h
@@ -367,7 +367,8 @@ class FastOpt {
   }
 
   const FastOpt& operator[](const char* const key) const {
-    return operator[](crc::crc(key));
+    // MacOS requires explicit cast to size_t before we can use it.
+    return operator[]((size_t)crc::crc(key));
   }
 
   const FastOpt& operator[](const std::string& key) const {
@@ -375,4 +376,4 @@ class FastOpt {
   }
 };
 
-}
\ No newline at end of file
+}
diff --git a/src/common/file_stream.h b/src/common/file_stream.h
index c81512079..9bbf33599 100644
--- a/src/common/file_stream.h
+++ b/src/common/file_stream.h
@@ -7,10 +7,21 @@
 #include "common/filesystem.h"
 #include "common/logging.h"
 
+// Even when compiling with clang, __GNUC__ may be defined, so
+// we need to add some extra checks to avoid compile errors with
+// respect to -Wsuggest-override.
 #ifdef __GNUC__
-#pragma GCC diagnostic push
-#pragma GCC diagnostic ignored "-Wsuggest-override"
+#  pragma GCC diagnostic push
+#  pragma GCC diagnostic ignored "-Wunused-value"
+#  if defined(__has_warning)
+#    if __has_warning("-Wsuggest-override")
+#      pragma GCC diagnostic ignored "-Wsuggest-override"
+#    endif
+#  else
+#    pragma GCC diagnostic ignored "-Wsuggest-override"
+#  endif
 #endif
+
 #ifdef _MSC_VER
 #pragma warning(push) // 4101: 'identifier' : unreferenced local variable. One parameter variable in zstr.hpp is not used.
 #pragma warning(disable : 4101)
@@ -82,7 +93,7 @@ class TemporaryFile : public OutputFileStream {
 
   void NormalizeTempPrefix(std::string& base) const;
   void MakeTemp(const std::string& base);
- 
+
 };
 
 }  // namespace io
diff --git a/src/common/filesystem.h b/src/common/filesystem.h
index d95125c5b..d7cb3da68 100644
--- a/src/common/filesystem.h
+++ b/src/common/filesystem.h
@@ -7,9 +7,19 @@
 // @TODO: go back to canonical names for functions and objects
 // as specified in C++17 so it becomes easy to move in the future
 
+// Even when compiling with clang, __GNUC__ may be defined, so
+// we need to add some extra checks to avoid compile errors with
+// respect to -Wsuggest-override.
 #ifdef __GNUC__
-#pragma GCC diagnostic push
-#pragma GCC diagnostic ignored "-Wsuggest-override"
+#  pragma GCC diagnostic push
+#  pragma GCC diagnostic ignored "-Wunused-value"
+#  if defined(__has_warning)
+#    if __has_warning("-Wsuggest-override")
+#      pragma GCC diagnostic ignored "-Wsuggest-override"
+#    endif
+#  else
+#    pragma GCC diagnostic ignored "-Wsuggest-override"
+#  endif
 #endif
 
 #include "3rd_party/pathie-cpp/include/path.hpp"  // @TODO: update to latest Pathie
diff --git a/src/common/intrusive_ptr.h b/src/common/intrusive_ptr.h
index fa412a8b7..d476dbc10 100644
--- a/src/common/intrusive_ptr.h
+++ b/src/common/intrusive_ptr.h
@@ -169,12 +169,12 @@ inline bool operator!=(const IntrusivePtr<T>& a, std::nullptr_t) {
 
 template<class T>
 inline bool operator==(T* a, const IntrusivePtr<T>& b) {
-  return b.get();
+  return a == b.get(); // used to say: return b.get(); That cannot be right. [UG]
 }
 
 template<class T>
 inline bool operator!=(T* a, const IntrusivePtr<T>& b) {
-  return b.get();
+  return a != b.get(); // used to say: return b.get(); That cannot be right. [UG]
 }
 
 template<class T, class U>
@@ -223,5 +223,3 @@ namespace std {
     }
   };
 }
-
-
diff --git a/src/common/logging.cpp b/src/common/logging.cpp
index 93bfcbd89..62d76feea 100644
--- a/src/common/logging.cpp
+++ b/src/common/logging.cpp
@@ -124,7 +124,7 @@ static void setErrorHandlers() {
   std::set_terminate(unhandledException);
 #ifdef __unix__
   // catch segfaults
-  struct sigaction sa = { 0 };
+  struct sigaction sa = { {0} };
   sigemptyset(&sa.sa_mask);
   sa.sa_flags = SA_SIGINFO;
   sa.sa_sigaction = [](int /*signal*/, siginfo_t*, void*) { ABORT("Segmentation fault"); };
diff --git a/src/common/types.h b/src/common/types.h
index 552ae2254..4bc4f9ad0 100644
--- a/src/common/types.h
+++ b/src/common/types.h
@@ -254,7 +254,7 @@ enum class Type : size_t {
   packed16      = TypeClass::packed_type + 2u,                          // special type for FBGEMM, not meant to be used anywhere else, not meant to be accessed invidually. Internal actual type (uint16) is meaningless.
   packed8avx2   = TypeClass::packed_type + 1u + TypeClass::avx2_type,   // special type for FBGEMM with AVX2, not meant to be used anywhere else, not meant to be accessed invidually. Internal actual type (uint8) is meaningless.
   packed8avx512 = TypeClass::packed_type + 1u + TypeClass::avx512_type, // special type for FBGEMM with AVX512, not meant to be used anywhere else, not meant to be accessed invidually. Internal actual type (uint8) is meaningless.
-  
+
 };
 
 static inline size_t operator&(TypeClass typeClass, Type type) {
@@ -394,7 +394,7 @@ static Type inline typeFromString(const std::string& str) {
     return Type::float32;
   if(str == "float64")
     return Type::float64;
-  
+
   if(str == "packed16")
     return Type::packed16;
   if(str == "packed8avx2")
@@ -437,19 +437,35 @@ void matchOrAbort(Type type) {
 
 namespace typeFitting { // own namespace instead of in class, otherwise we get error "explicit specialization in non-namespace scope"
 
-  // compares max for different types as constexpr, so can be used at compile-time to determine if RequestType type max fits into ReturnType max, see std::conditional below.
+  // Helper function for fitsIntoMax() below
+  // Returns the 'capacity' of a type: number of digits for integers,
+  // max_exponent for floats. We ignore the mantissa for floats.
+  template<typename X> constexpr int capacity() {
+    static_assert(std::is_arithmetic<X>::value || std::is_same<X,HalfFloat>::value,
+                  "Wrong type for this template");
+    return (std::is_integral<X>::value
+            ? std::numeric_limits<X>::digits
+            : std::numeric_limits<X>::max_exponent);
+ }
+
+
+  // Compare max for different types as constexpr, so can be used at compile-time to determine if RequestType type max fits into ReturnType max, see std::conditional below.
   template <typename RequestType, typename ReturnType>
-  constexpr bool fitsIntoMax() { return std::numeric_limits<RequestType>::max() <= std::numeric_limits<ReturnType>::max(); } // for built-in types everything is constexpr
+  constexpr bool fitsIntoMax() {
+    // We can't just compare std::numeric_limits<>::max(), because Clang-10
+    // complains about rounding errors when implicitly converting int to float
+    return ((!std::is_integral<RequestType>::value // RequestType is a float
+             && std::is_integral<ReturnType>::value) // ReturnType an integer
+            ? capacity<RequestType>() < capacity<ReturnType>() // special case
+            : capacity<RequestType>() <= capacity<ReturnType>()); // normal case
+  } // for built-in types everything is constexpr
 
-  // add specializations here when needed
-  template <> constexpr bool fitsIntoMax<float16, float>() { return true; };  // for float16 conversion to float is not constexpr, hence specializations
-  template <> constexpr bool fitsIntoMax<float, float16>() { return false; }; // for float16 conversion to float is not constexpr, hence specializations
 }
 
 template <typename ReturnType>
 class NumericLimits {
 private:
-  
+
   template <typename MaxType> void setLimitsMax() {
     max    = (ReturnType)std::numeric_limits<MaxType>::max();
     lowest = (ReturnType)std::numeric_limits<MaxType>::lowest();
@@ -459,10 +475,14 @@ class NumericLimits {
   void setLimits() {
     // check if the maximum of type RequestType fits into ReturnType
     constexpr bool fits = typeFitting::fitsIntoMax<RequestType, ReturnType>();
+    // sanity check:
+    static_assert(fits || typeFitting::fitsIntoMax<ReturnType, RequestType>(),
+                  "RequestType doesn't fit into ReturnType, and ReturnType doesn't "
+                  "fit into RequestType. fitsIntoMax is broken!");
     // and then use the smaller of each types to determine max, min, lowest.
     using MaxType = typename std::conditional<fits, RequestType, ReturnType>::type;
     setLimitsMax<MaxType>();
-    // @TODO: should we rather abort if the RequestType does not fit into ReturnType instead of clipping to smaller type? 
+    // @TODO: should we rather abort if the RequestType does not fit into ReturnType instead of clipping to smaller type?
     // ABORT_IF(!fits, "Type {} is too small to contain max of type {}", typeId<ReturnType>(), typeId<RequestType>());
   }
 
diff --git a/src/common/utils.cpp b/src/common/utils.cpp
index 80aee431e..3acb756d1 100755
--- a/src/common/utils.cpp
+++ b/src/common/utils.cpp
@@ -8,12 +8,22 @@
 #include <sstream>
 #include <string>
 #include <set>
-#ifdef __unix__
+#if defined(__unix__) || defined(__APPLE__)
 #include <unistd.h>
 #endif
 #include <codecvt>
 #include <cwctype>
 
+// MACOS lacks HOST_NAME_MAX
+#ifndef HOST_NAME_MAX
+# if defined(_POSIX_HOST_NAME_MAX)
+#  define HOST_NAME_MAX _POSIX_HOST_NAME_MAX
+# elif defined(MAXHOSTNAMELEN)
+#  define HOST_NAME_MAX MAXHOSTNAMELEN
+# endif
+#endif
+
+
 namespace marian {
 namespace utils {
 
diff --git a/src/data/batch.h b/src/data/batch.h
index b1b109817..3c592b315 100755
--- a/src/data/batch.h
+++ b/src/data/batch.h
@@ -26,7 +26,7 @@ class Batch {
 
   virtual void setGuidedAlignment(std::vector<float>&&) = 0;
   virtual void setDataWeights(const std::vector<float>&) = 0;
-
+  virtual ~Batch() {};
 protected:
   std::vector<size_t> sentenceIds_;
 };
diff --git a/src/data/corpus_base.h b/src/data/corpus_base.h
index f0d9b706a..e85e378ab 100755
--- a/src/data/corpus_base.h
+++ b/src/data/corpus_base.h
@@ -525,6 +525,7 @@ class CorpusBase
              const std::vector<Ptr<Vocab>>& vocabs,
              Ptr<Options> options);
 
+  virtual ~CorpusBase() {}
   virtual std::vector<Ptr<Vocab>>& getVocabs() = 0;
 
 protected:
diff --git a/src/data/default_vocab.cpp b/src/data/default_vocab.cpp
index 53f90a5d7..590e9931b 100644
--- a/src/data/default_vocab.cpp
+++ b/src/data/default_vocab.cpp
@@ -45,6 +45,7 @@ class DefaultVocab : public IVocab {
 
 public:
   // @TODO: choose between 'virtual' and 'final'. Can we derive from this class?
+  virtual ~DefaultVocab() {};
   virtual const std::string& canonicalExtension() const override { return suffixes_[0]; }
   virtual const std::vector<std::string>& suffixes() const override { return suffixes_; }
 
@@ -295,7 +296,7 @@ class DefaultVocab : public IVocab {
 class ClassVocab : public DefaultVocab {
 private:
   // Do nothing.
-  virtual void addRequiredVocabulary(const std::string& vocabPath, bool isJson) override { vocabPath; isJson; }
+  virtual void addRequiredVocabulary(const std::string& /*vocabPath*/, bool /*isJson*/) override {}
 
   // Not adding special class labels, only seen classes.
   virtual void create(const std::string& vocabPath,
diff --git a/src/data/shortlist.h b/src/data/shortlist.h
index e5d084942..177272df8 100644
--- a/src/data/shortlist.h
+++ b/src/data/shortlist.h
@@ -36,6 +36,8 @@ class Shortlist {
 
 class ShortlistGenerator {
 public:
+  virtual ~ShortlistGenerator() {}
+
   virtual Ptr<Shortlist> generate(Ptr<data::CorpusBatch> batch) const = 0;
 
   // Writes text version of (possibly) pruned short list to file
@@ -129,7 +131,6 @@ class LexicalShortlistGenerator : public ShortlistGenerator {
   Ptr<const Vocab> trgVocab_;
 
   size_t srcIdx_;
-  size_t trgIdx_;
   bool shared_{false};
 
   size_t firstNum_{100};
@@ -183,13 +184,12 @@ class LexicalShortlistGenerator : public ShortlistGenerator {
                             Ptr<const Vocab> srcVocab,
                             Ptr<const Vocab> trgVocab,
                             size_t srcIdx = 0,
-                            size_t trgIdx = 1,
+                            size_t /*trgIdx*/ = 1,
                             bool shared = false)
       : options_(options),
         srcVocab_(srcVocab),
         trgVocab_(trgVocab),
         srcIdx_(srcIdx),
-        trgIdx_(trgIdx),
         shared_(shared) {
     std::vector<std::string> vals = options_->get<std::vector<std::string>>("shortlist");
 
@@ -235,7 +235,6 @@ class LexicalShortlistGenerator : public ShortlistGenerator {
 
   virtual Ptr<Shortlist> generate(Ptr<data::CorpusBatch> batch) const override {
     auto srcBatch = (*batch)[srcIdx_];
-    // auto trgBatch = (*batch)[trgIdx_];
 
     // add firstNum most frequent words
     std::unordered_set<WordIndex> indexSet;
diff --git a/src/data/text_input.h b/src/data/text_input.h
index bdbcf64e9..db99ef6ae 100644
--- a/src/data/text_input.h
+++ b/src/data/text_input.h
@@ -37,6 +37,7 @@ class TextInput : public DatasetBase<SentenceTuple, TextIterator, CorpusBatch> {
   typedef SentenceTuple Sample;
 
   TextInput(std::vector<std::string> inputs, std::vector<Ptr<Vocab>> vocabs, Ptr<Options> options);
+  virtual ~TextInput() {}
 
   Sample next() override;
 
diff --git a/src/data/vocab_base.h b/src/data/vocab_base.h
index 6b6869d88..8c214c97e 100755
--- a/src/data/vocab_base.h
+++ b/src/data/vocab_base.h
@@ -57,6 +57,7 @@ class IVocab {
   virtual Word randWord() const {
     return Word::fromWordIndex(rand() % size());
   }
+  virtual ~IVocab() {};
 };
 
 class Options;
diff --git a/src/examples/mnist/dataset.h b/src/examples/mnist/dataset.h
index 0e73840bf..b0492b852 100644
--- a/src/examples/mnist/dataset.h
+++ b/src/examples/mnist/dataset.h
@@ -62,6 +62,7 @@ class DataBatch : public Batch {
   std::vector<Input> inputs_;
 
 public:
+
   std::vector<Input>& inputs() { return inputs_; }
 
   const std::vector<Input>& inputs() const { return inputs_; }
@@ -144,6 +145,8 @@ class MNISTData : public Dataset {
     loadData();
   }
 
+  virtual ~MNISTData(){}
+
   void loadData() override {
     ABORT_IF(paths_.size() != 2, "Paths to MNIST data files are not specified");
 
diff --git a/src/examples/mnist/model.h b/src/examples/mnist/model.h
index 24543ad1d..f7af16811 100755
--- a/src/examples/mnist/model.h
+++ b/src/examples/mnist/model.h
@@ -47,6 +47,8 @@ class MNISTLogsoftmax : public ILogProb {
 public:
   MNISTLogsoftmax() {}
 
+  virtual ~MNISTLogsoftmax(){}
+
   Logits apply(Ptr<IModel> model,
              Ptr<ExpressionGraph> graph,
              Ptr<data::Batch> batch,
@@ -61,13 +63,15 @@ class MnistFeedForwardNet : public IModel {
   typedef data::MNISTData dataset_type;
 
   template <class... Args>
-  MnistFeedForwardNet(Ptr<Options> options, Args... args)
+  MnistFeedForwardNet(Ptr<Options> options, Args... /*args*/)
       : options_(options), inference_(options->get<bool>("inference", false)) {}
 
+  virtual ~MnistFeedForwardNet(){}
+
   virtual Logits build(Ptr<ExpressionGraph> graph,
                      Ptr<data::Batch> batch,
                      bool /*clean*/ = false) override {
-    
+
     return Logits(apply(graph, batch, inference_));
   }
 
diff --git a/src/examples/mnist/validator.h b/src/examples/mnist/validator.h
index 7adc73f0b..232bb124e 100644
--- a/src/examples/mnist/validator.h
+++ b/src/examples/mnist/validator.h
@@ -19,7 +19,9 @@ class MNISTAccuracyValidator : public Validator<data::MNISTData, models::IModel>
     builder_ = models::createModelFromOptions(options, models::usage::translation);
   }
 
-  virtual void keepBest(const std::vector<Ptr<ExpressionGraph>>& graphs) override {
+  virtual ~MNISTAccuracyValidator(){}
+
+  virtual void keepBest(const std::vector<Ptr<ExpressionGraph>>& /*graphs*/) override {
     LOG(warn, "Keeping best model for MNIST examples is not supported");
   }
 
diff --git a/src/functional/operators.h b/src/functional/operators.h
index 41fc74b06..be6bb2e5c 100755
--- a/src/functional/operators.h
+++ b/src/functional/operators.h
@@ -7,55 +7,58 @@ namespace marian {
 namespace functional {
 
 // General template, will be used for any type without specializations
-// and will fail with an abort message.
+// and will fail at runtime with an abort message. Note that the
+// general template functions don't have named parameters on purpose,
+// because clang will warn about unused parameters during compilation.
+
 template <typename T>
 struct Ops {
-  static HOST_DEVICE_INLINE T tanh(const T& x) { ABORT("Unknown type"); }
-  static HOST_DEVICE_INLINE T sin(const T& x)  { ABORT("Unknown type"); }
-  static HOST_DEVICE_INLINE T cos(const T& x)  { ABORT("Unknown type"); }
-  static HOST_DEVICE_INLINE T tan(const T& x)  { ABORT("Unknown type"); }
-  static HOST_DEVICE_INLINE T log(const T& x)  { ABORT("Unknown type"); }
-  static HOST_DEVICE_INLINE T exp(const T& x)  { ABORT("Unknown type"); }
-  static HOST_DEVICE_INLINE T abs(const T& x)  { ABORT("Unknown type"); }
-  static HOST_DEVICE_INLINE T sqrt(const T& x) { ABORT("Unknown type"); }
-  static HOST_DEVICE_INLINE T neg(const T& x)  { ABORT("Unknown type"); }
-  static HOST_DEVICE_INLINE T sgn(const T& x)  { ABORT("Unknown type"); }
-
-  static HOST_DEVICE_INLINE T add(const T& x, const T& y)  { ABORT("Unknown type"); }
-  static HOST_DEVICE_INLINE T sub(const T& x, const T& y)  { ABORT("Unknown type"); }
-  static HOST_DEVICE_INLINE T mul(const T& x, const T& y)  { ABORT("Unknown type"); }
-  static HOST_DEVICE_INLINE T div(const T& x, const T& y)  { ABORT("Unknown type"); }
-
-  static HOST_DEVICE_INLINE T max(const T& x, const T& y)  { ABORT("Unknown type"); }
-  static HOST_DEVICE_INLINE T min(const T& x, const T& y)  { ABORT("Unknown type"); }
-  static HOST_DEVICE_INLINE T pow(const T& x, const T& y)  { ABORT("Unknown type"); }
-
-  static HOST_DEVICE_INLINE T negate(const T& x)  { ABORT("Unknown type"); }
-  static HOST_DEVICE_INLINE T eq(const T& x, const T& y)   { ABORT("Unknown type"); }
-  static HOST_DEVICE_INLINE T neq(const T& x, const T& y)  { ABORT("Unknown type"); }
-  static HOST_DEVICE_INLINE T gt(const T& x, const T& y)   { ABORT("Unknown type"); }
-  static HOST_DEVICE_INLINE T lt(const T& x, const T& y)   { ABORT("Unknown type"); }
-  static HOST_DEVICE_INLINE T geq(const T& x, const T& y)  { ABORT("Unknown type"); }
-  static HOST_DEVICE_INLINE T leq(const T& x, const T& y)  { ABORT("Unknown type"); }
-  static HOST_DEVICE_INLINE T _and(const T& x, const T& y) { ABORT("Unknown type"); } // 'and' is used by gcc
-  static HOST_DEVICE_INLINE T _or(const T& x, const T& y)  { ABORT("Unknown type"); } // 'or' is used by gcc
+  static HOST_DEVICE_INLINE T tanh(const T&) { ABORT("Unknown type"); }
+  static HOST_DEVICE_INLINE T sin(const T&)  { ABORT("Unknown type"); }
+  static HOST_DEVICE_INLINE T cos(const T&)  { ABORT("Unknown type"); }
+  static HOST_DEVICE_INLINE T tan(const T&)  { ABORT("Unknown type"); }
+  static HOST_DEVICE_INLINE T log(const T&)  { ABORT("Unknown type"); }
+  static HOST_DEVICE_INLINE T exp(const T&)  { ABORT("Unknown type"); }
+  static HOST_DEVICE_INLINE T abs(const T&)  { ABORT("Unknown type"); }
+  static HOST_DEVICE_INLINE T sqrt(const T&) { ABORT("Unknown type"); }
+  static HOST_DEVICE_INLINE T neg(const T&)  { ABORT("Unknown type"); }
+  static HOST_DEVICE_INLINE T sgn(const T&)  { ABORT("Unknown type"); }
+
+  static HOST_DEVICE_INLINE T add(const T&, const T&)  { ABORT("Unknown type"); }
+  static HOST_DEVICE_INLINE T sub(const T&, const T&)  { ABORT("Unknown type"); }
+  static HOST_DEVICE_INLINE T mul(const T&, const T&)  { ABORT("Unknown type"); }
+  static HOST_DEVICE_INLINE T div(const T&, const T&)  { ABORT("Unknown type"); }
+
+  static HOST_DEVICE_INLINE T max(const T&, const T&)  { ABORT("Unknown type"); }
+  static HOST_DEVICE_INLINE T min(const T&, const T&)  { ABORT("Unknown type"); }
+  static HOST_DEVICE_INLINE T pow(const T&, const T&)  { ABORT("Unknown type"); }
+
+  static HOST_DEVICE_INLINE T negate(const T&)  { ABORT("Unknown type"); }
+  static HOST_DEVICE_INLINE T eq(const T&, const T&)   { ABORT("Unknown type"); }
+  static HOST_DEVICE_INLINE T neq(const T&, const T&)  { ABORT("Unknown type"); }
+  static HOST_DEVICE_INLINE T gt(const T&, const T&)   { ABORT("Unknown type"); }
+  static HOST_DEVICE_INLINE T lt(const T&, const T&)   { ABORT("Unknown type"); }
+  static HOST_DEVICE_INLINE T geq(const T&, const T&)  { ABORT("Unknown type"); }
+  static HOST_DEVICE_INLINE T leq(const T&, const T&)  { ABORT("Unknown type"); }
+  static HOST_DEVICE_INLINE T _and(const T&, const T&) { ABORT("Unknown type"); } // 'and' is used by gcc
+  static HOST_DEVICE_INLINE T _or(const T&, const T&)  { ABORT("Unknown type"); } // 'or' is used by gcc
 
   // Neural Networks specific functions
-  static HOST_DEVICE_INLINE T sigmoid(const T& x)               { ABORT("Unknown type"); }
-  static HOST_DEVICE_INLINE T logaddexp(const T& x, const T& y) { ABORT("Unknown type"); }
-  static HOST_DEVICE_INLINE T clip(const T& x, const T& y)      { ABORT("Unknown type"); }
+  static HOST_DEVICE_INLINE T sigmoid(const T&)               { ABORT("Unknown type"); }
+  static HOST_DEVICE_INLINE T logaddexp(const T&, const T&) { ABORT("Unknown type"); }
+  static HOST_DEVICE_INLINE T clip(const T&, const T&)      { ABORT("Unknown type"); }
   // derivative of Clip, cut-off function
-  static HOST_DEVICE_INLINE T bump(const T& x, const T& y)      { ABORT("Unknown type"); }
-  static HOST_DEVICE_INLINE T relu(const T& x)                  { ABORT("Unknown type"); }
-  static HOST_DEVICE_INLINE T reluBack(const T& x)              { ABORT("Unknown type"); }
-  static HOST_DEVICE_INLINE T prelu(const T& x, const T& y)     { ABORT("Unknown type"); }
-  static HOST_DEVICE_INLINE T preluBack(const T& x, const T& y) { ABORT("Unknown type"); }
+  static HOST_DEVICE_INLINE T bump(const T&, const T&)      { ABORT("Unknown type"); }
+  static HOST_DEVICE_INLINE T relu(const T&)                  { ABORT("Unknown type"); }
+  static HOST_DEVICE_INLINE T reluBack(const T&)              { ABORT("Unknown type"); }
+  static HOST_DEVICE_INLINE T prelu(const T&, const T&)     { ABORT("Unknown type"); }
+  static HOST_DEVICE_INLINE T preluBack(const T&, const T&) { ABORT("Unknown type"); }
 
-  static HOST_DEVICE_INLINE T if_then_else(const T& x, const T& y, const T& z) { ABORT("Unknown type"); }
+  static HOST_DEVICE_INLINE T if_then_else(const T&, const T&, const T&) { ABORT("Unknown type"); }
 
-  static HOST_DEVICE_INLINE T sumReduce(const T& x) { ABORT("Unknown type"); }
-  static HOST_DEVICE_INLINE T maxReduce(const T& x) { ABORT("Unknown type"); }
-  static HOST_DEVICE_INLINE T minReduce(const T& x) { ABORT("Unknown type"); }
+  static HOST_DEVICE_INLINE T sumReduce(const T&) { ABORT("Unknown type"); }
+  static HOST_DEVICE_INLINE T maxReduce(const T&) { ABORT("Unknown type"); }
+  static HOST_DEVICE_INLINE T minReduce(const T&) { ABORT("Unknown type"); }
 };
 
 // Specialization for float
@@ -127,14 +130,14 @@ template <>
 struct Ops<double> {
   typedef double Single;
 
-  static HOST_DEVICE_INLINE double tanh(const double& x) { return tanh(x); }
-  static HOST_DEVICE_INLINE double sin(const double& x)  { return sin(x); }
-  static HOST_DEVICE_INLINE double cos(const double& x)  { return cos(x); }
-  static HOST_DEVICE_INLINE double tan(const double& x)  { return tan(x); }
-  static HOST_DEVICE_INLINE double log(const double& x)  { return log(x); }
-  static HOST_DEVICE_INLINE double exp(const double& x)  { return exp(x); }
-  static HOST_DEVICE_INLINE double abs(const double& x)  { return abs(x); }
-  static HOST_DEVICE_INLINE double sqrt(const double& x) { return sqrt(x); }
+  static HOST_DEVICE_INLINE double tanh(const double& x) { return std::tanh(x); }
+  static HOST_DEVICE_INLINE double sin(const double& x)  { return std::sin(x); }
+  static HOST_DEVICE_INLINE double cos(const double& x)  { return std::cos(x); }
+  static HOST_DEVICE_INLINE double tan(const double& x)  { return std::tan(x); }
+  static HOST_DEVICE_INLINE double log(const double& x)  { return std::log(x); }
+  static HOST_DEVICE_INLINE double exp(const double& x)  { return std::exp(x); }
+  static HOST_DEVICE_INLINE double abs(const double& x)  { return std::abs(x); }
+  static HOST_DEVICE_INLINE double sqrt(const double& x) { return std::sqrt(x); }
   static HOST_DEVICE_INLINE double neg(const double& x)  { return -x; }
   static HOST_DEVICE_INLINE double sgn(const double& x)  { return (0 < x) - (x < 0); }
 
@@ -145,7 +148,7 @@ struct Ops<double> {
 
   static HOST_DEVICE_INLINE double max(const double& x, const double& y)  { return x < y ? y : x; }
   static HOST_DEVICE_INLINE double min(const double& x, const double& y)  { return x < y ? x : y; }
-  static HOST_DEVICE_INLINE double pow(const double& x, const double& y)  { return pow(x, y); }
+  static HOST_DEVICE_INLINE double pow(const double& x, const double& y)  { return std::pow(x, y); }
 
 
   static HOST_DEVICE_INLINE double negate(const double& x)  { return !(bool)x; }
@@ -460,7 +463,7 @@ struct Ops<half> {
   static DEVICE_INLINE half exp(const half& x)  { return hexp(x); }
   static DEVICE_INLINE half sqrt(const half& x) { return hsqrt(x); }
   static DEVICE_INLINE half neg(const half& x)  { return -x; }
-  
+
   static DEVICE_INLINE half abs(const half& x)  { return fabs((float)x); }// @TODO half has this information somewhere in the struct, right?
   static DEVICE_INLINE half sgn(const half& x)  { half zero = 0.f; return (zero < x) - (x < zero); } // @TODO half has this information somewhere in the struct, right?
 
diff --git a/src/graph/node.h b/src/graph/node.h
index 4d591674f..c017eeb2c 100644
--- a/src/graph/node.h
+++ b/src/graph/node.h
@@ -40,7 +40,7 @@ class Node : public Chainable<Tensor> {
   std::string debugMessage_;
 
   Ptr<std::list<Expr>> subtape_; // a subtape is used to keep track of nodes that need to be freed and recomputed with gradient-checkpointing.
-  bool isCheckpoint_{false};     // true if this node has been selected to be a checkpoint, currently only done manually. 
+  bool isCheckpoint_{false};     // true if this node has been selected to be a checkpoint, currently only done manually.
 
   Ptr<AutoTunerRecorder> recorder_;
   size_t recorderHash_;
@@ -138,7 +138,7 @@ class Node : public Chainable<Tensor> {
 
   virtual std::string graphviz() override {
     std::stringstream ss;
-    ss << "\"" << this << "\" [" 
+    ss << "\"" << this << "\" ["
       << "shape=\"" << form() << "\", "
       << "label="   << label() << ", "
       << "style=\"filled\", "
@@ -147,7 +147,7 @@ class Node : public Chainable<Tensor> {
 
     for(auto&& child : children())
       ss << "\"" << child << "\" -> \"" << this << "\";" << std::endl;
-    
+
     if(subtape_) {
       for(auto&& dep : *subtape_)
         ss << "\"" << dep << "\" -> \"" << this << "\" [style=dotted];" << std::endl;
@@ -188,9 +188,9 @@ struct NaryNodeOp : public Node {
 
   // Deduce type automatically, but then all types must be the same
   // this is called automatically when no output type is specified.
-  // If the input types are mixed, the output type needs to be specified 
+  // If the input types are mixed, the output type needs to be specified
   // in the constructor.
-  Type commonType(const std::vector<Expr>& nodes) {
+  static Type commonType(const std::vector<Expr>& nodes) {
     ABORT_IF(nodes.size() == 0, "NaryNodeOp has no children");
     Type type = nodes[0]->value_type();
     for(int i = 1; i < nodes.size(); ++i)
diff --git a/src/graph/node_initializers.h b/src/graph/node_initializers.h
index 624816b9c..21dcc95d3 100755
--- a/src/graph/node_initializers.h
+++ b/src/graph/node_initializers.h
@@ -17,9 +17,9 @@ namespace inits {
 /**
  * Base class for specialized NodeInitializers.
  *
- * A NodeInitializer is a functor that is associated with parameters 
- * and constants, and is invoked on a tensor during node intialization. 
- * You need to override NodeIntializer::apply(Tensor) with your own 
+ * A NodeInitializer is a functor that is associated with parameters
+ * and constants, and is invoked on a tensor during node intialization.
+ * You need to override NodeIntializer::apply(Tensor) with your own
  * functionality or use a fromLambda intializer.
  *
  * See node_initializers.cpp for examples.
@@ -31,6 +31,7 @@ class NodeInitializer {
 public:
   virtual void apply(Tensor t) = 0;
   void setAllocator(Ptr<Allocator> allocator) { allocator_ = allocator; }
+  virtual ~NodeInitializer() {}
 };
 
 /**
@@ -135,7 +136,7 @@ Ptr<NodeInitializer> dropout(float dropoutProbabilty);
 
 /**
  * Intialize with gumbel noise, i.e. -log(-log(u)) where u ~ Uniform(0 + eps, 1 - eps)
- * 
+ *
  * @return A NodeInitializer
  */
 Ptr<NodeInitializer> gumbel(float eps = 1e-5f);
@@ -163,7 +164,7 @@ Ptr<NodeInitializer> fromWord2vec(const std::string& file,
 
 /**
  * Computes Google's Transformer-style sinusoidal position embeddings
- * starting from position 'start' taking into account batch and time 
+ * starting from position 'start' taking into account batch and time
  * dimensions of the tensor.
  *
  * Expected tensor layout {-2: time, -1: model}
diff --git a/src/graph/node_operators_binary.h b/src/graph/node_operators_binary.h
index d12c2481e..63158ffae 100755
--- a/src/graph/node_operators_binary.h
+++ b/src/graph/node_operators_binary.h
@@ -480,9 +480,12 @@ class CSRDotNodeOp : public NaryNodeOp {
   bool transS_;
   bool swapOperands_;
 public:
-  CSRDotNodeOp(const Shape& S_shape, Expr S_values, Expr S_indices, Expr S_offsets, Expr D, bool transS, bool swapOperands)
-      : NaryNodeOp({ S_values, S_indices, S_offsets, D }, newShape(S_shape, S_values, S_indices, S_offsets, D, transS, swapOperands), commonType({S_values, D})),
-                   transS_(transS), swapOperands_(swapOperands) {
+  CSRDotNodeOp(const Shape& S_shape, Expr S_values, Expr S_indices,
+               Expr S_offsets, Expr D, bool transS, bool swapOperands)
+    : NaryNodeOp({ S_values, S_indices, S_offsets, D },
+                 newShape(S_shape, S_values, S_indices, S_offsets, D, transS, swapOperands),
+                 NaryNodeOp::commonType({S_values, D})),
+      transS_(transS), swapOperands_(swapOperands) {
     matchOrAbort<IndexType>(S_indices->value_type());
     matchOrAbort<IndexType>(S_offsets->value_type());
   }
@@ -513,7 +516,7 @@ class CSRDotNodeOp : public NaryNodeOp {
 
   NodeOps backwardOps() override {
     return { nullptr, // can't backprop into the sparse matrix (the gradient is dense)
-             nullptr, 
+             nullptr,
              nullptr,
              NodeOp(CSRProd(child(3)->grad(), // child(3) = D
                             graph()->allocator(),
@@ -527,7 +530,7 @@ class CSRDotNodeOp : public NaryNodeOp {
   virtual size_t hash() override {
     size_t seed = NaryNodeOp::hash();
     for(auto s : shape())
-      util::hash_combine(seed, s);  
+      util::hash_combine(seed, s);
     util::hash_combine(seed, transS_);
     util::hash_combine(seed, swapOperands_);
     return seed;
@@ -1050,8 +1053,8 @@ struct ConcatenateNodeOp : public NaryNodeOp {
     auto checkShape = shape;
     for(auto child : nodes) {
       checkShape.set(ax_, child->shape()[ax_]); // don't abort on different sizes on axis dim.
-      ABORT_IF(checkShape != child->shape(), 
-               "Child shapes {} and {} cannot be concatenated along axis {}", 
+      ABORT_IF(checkShape != child->shape(),
+               "Child shapes {} and {} cannot be concatenated along axis {}",
                shape, child->shape(), ax);
 
       sum += child->shape()[ax_];
diff --git a/src/graph/parameters.h b/src/graph/parameters.h
index 25eb5db41..8b4af9dd5 100644
--- a/src/graph/parameters.h
+++ b/src/graph/parameters.h
@@ -10,10 +10,10 @@
 
 namespace marian {
 
-// @TODO: Currently an ExpressionGraph only supports one Parameters object and 
+// @TODO: Currently an ExpressionGraph only supports one Parameters object and
 // the type of parameters has to be the inside on Parameters object. This limits
 // parameter types to a single chosen type, e.g. only fp32 or only fp16. This should
-// be extended to allow multiple sets of parameters. 
+// be extended to allow multiple sets of parameters.
 // The reason here is to be able to efficiently compute updates of whole parameter
 // sets of one type.
 class Parameters {
@@ -40,7 +40,7 @@ class Parameters {
     LOG(debug, "Created parameter object of type {}", acceptedElementType_);
   }
 
-  ~Parameters() {
+  virtual ~Parameters() {
     LOG(debug, "Destroyed parameter object of type {}", acceptedElementType_);
   }
 
@@ -88,7 +88,7 @@ class Parameters {
 
       // sort parameters by name before allocation to make sure the memory layout after allocation is always the same
       std::sort(params_.begin(), params_.end(), [](Expr n1, Expr n2){ return n1->name() < n2->name(); });
-      
+
       for(auto p : params_) {
         if(!p->val()) {
           vals_->allocate(p->val(), p->shape(), p->value_type());
diff --git a/src/layers/generic.h b/src/layers/generic.h
index e899d8a07..a3b9bac45 100755
--- a/src/layers/generic.h
+++ b/src/layers/generic.h
@@ -39,6 +39,7 @@ class LayerBase {
 
 // Simplest layer interface: Unary function
 struct IUnaryLayer {
+  virtual ~IUnaryLayer() {}
   virtual Expr apply(Expr) = 0;
   virtual Expr apply(const std::vector<Expr>& es) {
     ABORT_IF(es.size() > 1, "Not implemented"); // simple stub
@@ -59,6 +60,7 @@ struct IEmbeddingLayer {
 
   // alternative from indices directly
   virtual Expr applyIndices(const std::vector<WordIndex>& embIdx, const Shape& shape) const = 0;
+  virtual ~IEmbeddingLayer() {}
 };
 
 // base class for Encoder and Decoder classes, which have embeddings and a batch index (=stream index)
diff --git a/src/layers/guided_alignment.h b/src/layers/guided_alignment.h
index 48ad65993..f08d3f092 100755
--- a/src/layers/guided_alignment.h
+++ b/src/layers/guided_alignment.h
@@ -5,14 +5,14 @@
 
 namespace marian {
 
-static inline RationalLoss guidedAlignmentCost(Ptr<ExpressionGraph> graph,
+static inline RationalLoss guidedAlignmentCost(Ptr<ExpressionGraph> /*graph*/,
                                                Ptr<data::CorpusBatch> batch,
                                                Ptr<Options> options,
                                                Expr attention) { // [beam depth=1, max src length, batch size, tgt length]
 
   std::string guidedLossType = options->get<std::string>("guided-alignment-cost");  // @TODO: change "cost" to "loss"
   float guidedLossWeight = options->get<float>("guided-alignment-weight");
-  
+
   const auto& shape = attention->shape(); // [beam depth=1, max src length, batch size, tgt length]
   float epsilon = 1e-6f;
   Expr alignmentLoss; // sum up loss over all attention/alignment positions
@@ -55,8 +55,8 @@ static inline RationalLoss guidedAlignmentCost(Ptr<ExpressionGraph> graph,
     else
        ABORT("Unknown alignment cost type: {}", guidedLossType);
     // every position is a label as they should all agree
-    // @TODO: there should be positional masking here ... on the other hand, positions that are not 
-    // in a sentence should always agree (both being 0). Lack of masking affects label count only which is 
+    // @TODO: there should be positional masking here ... on the other hand, positions that are not
+    // in a sentence should always agree (both being 0). Lack of masking affects label count only which is
     // probably negligible?
     numLabels = shape.elements();
   }
diff --git a/src/layers/loss.h b/src/layers/loss.h
index d9deaeb8f..43e89c1d3 100755
--- a/src/layers/loss.h
+++ b/src/layers/loss.h
@@ -331,6 +331,7 @@ class CrossEntropyLoss : public LabelwiseLoss {
   : LabelwiseLoss(axes), // cross-entropy already reduces over axis -1
     labelSmoothing_(labelSmoothing), factorWeight_(factorWeight) {}
 
+  virtual ~CrossEntropyLoss() {}
 protected:
   float labelSmoothing_; // interpolation factor for label smoothing, see below
   float factorWeight_;   // give extra weight to factors
@@ -368,7 +369,7 @@ class CrossEntropyLoss : public LabelwiseLoss {
 
     if(labelWeights) {
       // We currently do not know how to use target factors and word-level label weights together
-      bool wordlevel = labelWeights->shape()[-3] > 1; // Time-dimension is not trivially 1, hence we have word-level weights. 
+      bool wordlevel = labelWeights->shape()[-3] > 1; // Time-dimension is not trivially 1, hence we have word-level weights.
       ABORT_IF(wordlevel && logits.getNumFactorGroups() > 1, "CE loss with word-level label weights is not implemented for factors");
       ce = ce * cast(labelWeights, Type::float32);
     }
@@ -379,15 +380,15 @@ class CrossEntropyLoss : public LabelwiseLoss {
 
 
 /**
- * @brief Unlikelihood loss across last axis, summed up over batch and time dimensions. This is an 
- * implementation of sequence-level unlikelihood loss from https://arxiv.org/abs/1908.04319. 
+ * @brief Unlikelihood loss across last axis, summed up over batch and time dimensions. This is an
+ * implementation of sequence-level unlikelihood loss from https://arxiv.org/abs/1908.04319.
  * We rely on word-level label weights where 1 is correct and 0 is marking an error. If there are not
  * zeros for a sentence it going to be trained with normal CE loss if there is at least one 0 it is going
  * to flip over to use SUL for that sentence to penalize the selected word.
- * 
+ *
  * SUL is implemented as:
  * -log(gather(1 - softmax(logits), -1, indices))
- * 
+ *
  * Factors are currently not supported.
  */
 class SequenceUnlikelihoodLoss : public CrossEntropyLoss {
@@ -411,17 +412,17 @@ class SequenceUnlikelihoodLoss : public CrossEntropyLoss {
     ABORT_IF(!mask, "mask is required"); // @TODO: check this, it seems weights for padding are by default 1, which would make this obsolete.
     // use label weights, where 1 is GOOD and 0 is BAD. After inversion here, now 1 marks, mask again to eliminate padding (might be obsolete)
     auto errorMask = (1.f - cast(labelWeights, Type::float32)) * cast(mask, Type::float32);
-  
+
     auto ceUl = logits.applyLossFunction(labels, [&](Expr logits, Expr indices) {
       return cast(unlikelihood(logits, indices), Type::float32);
     });
-  
+
     // compute if want to use CE or UL. If there are no errors train with CE, otherwise train _only on_ the errors with UL. This is the "mixed" training
-    // schedule from https://arxiv.org/abs/1908.04319. Providing labels with or without error scores we can easily switch between CE and UL. 
+    // schedule from https://arxiv.org/abs/1908.04319. Providing labels with or without error scores we can easily switch between CE and UL.
     auto onlyCe  = eq(sum(errorMask, /*axis=*/-3), 0.f); // [1, 1, dimBatch, 1] - equal 1 if no errors are present
     ceUl         = errorMask * ceUl;                     // don't use for correct label or padding
 
-    auto cost    = onlyCe * ce + (1.f - onlyCe) * ceUl;  // ce or unlikelihood part are never simultanously used as cost per batch entry 
+    auto cost    = onlyCe * ce + (1.f - onlyCe) * ceUl;  // ce or unlikelihood part are never simultanously used as cost per batch entry
 
     return cost;
   }
diff --git a/src/layers/weight.h b/src/layers/weight.h
index 787c23769..74cf47d68 100644
--- a/src/layers/weight.h
+++ b/src/layers/weight.h
@@ -17,6 +17,7 @@ class WeightingBase {
   virtual void debugWeighting(std::vector<float> /*weightedMask*/,
                               std::vector<float> /*freqMask*/,
                               Ptr<data::CorpusBatch> /*batch*/){};
+  virtual ~WeightingBase() {}
 };
 
 class DataWeighting : public WeightingBase {
diff --git a/src/microsoft/quicksand.cpp b/src/microsoft/quicksand.cpp
index 0f0ea2533..2f7687aa4 100755
--- a/src/microsoft/quicksand.cpp
+++ b/src/microsoft/quicksand.cpp
@@ -41,6 +41,7 @@ class VocabWrapper : public IVocabWrapper {
   Ptr<Vocab> pImpl_;
 public:
   VocabWrapper(Ptr<Vocab> vocab) : pImpl_(vocab) {}
+  virtual ~VocabWrapper() {}
   WordIndex encode(const std::string& word) const override { return (*pImpl_)[word].toWordIndex(); }
   std::string decode(WordIndex id) const override { return (*pImpl_)[Word::fromWordIndex(id)]; }
   size_t size() const override { return pImpl_->size(); }
@@ -243,7 +244,7 @@ DecoderCpuAvxVersion parseCpuAvxVersion(std::string name) {
   }
 }
 
-// @TODO: clean-up this code and unify with marian-conv. The targetPrec parameter is not clear enought etc. 
+// @TODO: clean-up this code and unify with marian-conv. The targetPrec parameter is not clear enought etc.
 bool convertModel(std::string inputFile, std::string outputFile, int32_t targetPrec) {
   std::cout << "Converting from: " << inputFile << ", to: " << outputFile << std::endl;
 
diff --git a/src/microsoft/quicksand.h b/src/microsoft/quicksand.h
index 1089be148..87de19482 100755
--- a/src/microsoft/quicksand.h
+++ b/src/microsoft/quicksand.h
@@ -54,6 +54,8 @@ class IBeamSearchDecoder {
                      const std::vector<const void*>& ptrs)
       : options_(options), ptrs_(ptrs) {}
 
+  virtual ~IBeamSearchDecoder() {}
+
   virtual QSNBestBatch decode(const QSBatch& qsBatch,
                               size_t maxLength,
                               const std::unordered_set<WordIndex>& shortlist)
diff --git a/src/models/costs.h b/src/models/costs.h
index 03b247f0b..2a66e5499 100755
--- a/src/models/costs.h
+++ b/src/models/costs.h
@@ -25,6 +25,7 @@ class ICost {
                                        Ptr<ExpressionGraph> graph, // @TODO: why needed? Can it be gotten from model?
                                        Ptr<data::Batch> batch,
                                        bool clearGraph = true) = 0;
+  virtual ~ICost() {}
 };
 
 class EncoderDecoderCECost : public ICost {
@@ -51,6 +52,8 @@ class EncoderDecoderCECost : public ICost {
       weighter_ = WeightingFactory(options_);
   }
 
+  virtual ~EncoderDecoderCECost() {}
+
   Ptr<MultiRationalLoss> apply(Ptr<IModel> model,
              Ptr<ExpressionGraph> graph,
              Ptr<data::Batch> batch,
@@ -136,6 +139,8 @@ class Trainer : public ICriterionFunction {
   Trainer(Ptr<IModel> model, Ptr<ICost> cost)
       : model_(model), cost_(cost) {}
 
+  virtual ~Trainer() {}
+
   Ptr<IModel> getModel() { return model_; }
 
   virtual void load(Ptr<ExpressionGraph> graph,
@@ -179,6 +184,8 @@ class Scorer : public IModel {
   Scorer(Ptr<IModel> model, Ptr<ILogProb> cost)
       : model_(model), logProb_(cost) {}
 
+  virtual ~Scorer(){}
+
   Ptr<IModel> getModel() { return model_; }
 
   virtual void load(Ptr<ExpressionGraph> graph,
@@ -211,6 +218,7 @@ class ILogProbStep {
 
 class LogSoftmaxStep : public ILogProbStep {
 public:
+  virtual ~LogSoftmaxStep() {}
   virtual Ptr<DecoderState> apply(Ptr<DecoderState> state) override {
     // decoder needs normalized probabilities (note: skipped if beam 1 and --skip-cost)
     state->setLogProbs(state->getLogProbs().applyUnaryFunction(logsoftmax));
@@ -224,6 +232,7 @@ class LogSoftmaxStep : public ILogProbStep {
 // with --output-sampling during translation with marian-decoder
 class GumbelSoftmaxStep : public ILogProbStep {
 public:
+  virtual ~GumbelSoftmaxStep() {}
   virtual Ptr<DecoderState> apply(Ptr<DecoderState> state) override {
     state->setLogProbs(state->getLogProbs().applyUnaryFunctions(
       [](Expr logits){ // lemma gets gumbelled
diff --git a/src/models/encoder_decoder.h b/src/models/encoder_decoder.h
index 599dbfdc4..92c1647fa 100755
--- a/src/models/encoder_decoder.h
+++ b/src/models/encoder_decoder.h
@@ -11,6 +11,7 @@ namespace marian {
 
 class IEncoderDecoder : public models::IModel {
 public:
+  virtual ~IEncoderDecoder() {}
   virtual void load(Ptr<ExpressionGraph> graph,
                     const std::string& name,
                     bool markedReloaded = true) override
diff --git a/src/models/model_base.h b/src/models/model_base.h
index 016f3b8e3..5f76b380d 100644
--- a/src/models/model_base.h
+++ b/src/models/model_base.h
@@ -41,6 +41,8 @@ class IModel {
 // @TODO: Is there a better name?
 class ICriterionFunction {
 public:
+  virtual ~ICriterionFunction() {}
+
   virtual void load(Ptr<ExpressionGraph>,
                     const std::string&,
                     bool markReloaded = true)
diff --git a/src/models/model_task.h b/src/models/model_task.h
index 1fcf6eca5..96dfadd0c 100644
--- a/src/models/model_task.h
+++ b/src/models/model_task.h
@@ -5,10 +5,12 @@
 namespace marian {
 
 struct ModelTask {
+  virtual ~ModelTask() {}
   virtual void run() = 0;
 };
 
 struct ModelServiceTask {
+  virtual ~ModelServiceTask() {}
   virtual std::string run(const std::string&) = 0;
 };
 }  // namespace marian
diff --git a/src/models/s2s.h b/src/models/s2s.h
index 4c0fd0194..8d12f96e3 100755
--- a/src/models/s2s.h
+++ b/src/models/s2s.h
@@ -11,6 +11,7 @@ namespace marian {
 class EncoderS2S : public EncoderBase {
   using EncoderBase::EncoderBase;
 public:
+  virtual ~EncoderS2S() {}
   Expr applyEncoderRNN(Ptr<ExpressionGraph> graph,
                        Expr embeddings,
                        Expr mask,
@@ -254,7 +255,7 @@ class DecoderS2S : public DecoderBase {
     auto embeddings = state->getTargetHistoryEmbeddings();
 
     // The batch dimension of the inputs can change due to batch-pruning, in that case
-    // cached elements need to be rebuilt, in this case the mapped encoder context in the 
+    // cached elements need to be rebuilt, in this case the mapped encoder context in the
     // attention mechanism of the decoder RNN.
     int currDimBatch = embeddings->shape()[-2];
     if(!rnn_ || lastDimBatch_ != currDimBatch)  // if currDimBatch is different, rebuild the cached RNN
@@ -263,7 +264,7 @@ class DecoderS2S : public DecoderBase {
     // Also @TODO: maybe implement a Cached(build, updateIf) that runs a check and rebuild if required
     // at dereferecing :
     // rnn_ = Cached<decltype(constructDecoderRNN(graph, state))>(
-    //          /*build=*/[]{ return constructDecoderRNN(graph, state); }, 
+    //          /*build=*/[]{ return constructDecoderRNN(graph, state); },
     //          /*updateIf=*/[]{ return state->batchDimChanged() });
     // rnn_->transduce(...);
 
diff --git a/src/models/states.h b/src/models/states.h
index 21cf42a87..c2f9ee05a 100755
--- a/src/models/states.h
+++ b/src/models/states.h
@@ -17,6 +17,7 @@ class EncoderState {
       : context_(context), mask_(mask), batch_(batch) {}
 
   EncoderState() {}
+  virtual ~EncoderState() {}
 
   virtual Expr getContext()   const { return context_;   }
   virtual Expr getAttended()  const { return context_;   }
@@ -53,6 +54,7 @@ class DecoderState {
                const std::vector<Ptr<EncoderState>>& encStates,
                Ptr<data::CorpusBatch> batch)
       : states_(states), logProbs_(logProbs), encStates_(encStates), batch_(batch) {}
+  virtual ~DecoderState() {}
 
   // @TODO: Do we need all these to be virtual?
   virtual const std::vector<Ptr<EncoderState>>& getEncoderStates() const {
@@ -68,10 +70,10 @@ class DecoderState {
                                    int beamSize) const {
 
     std::vector<Ptr<EncoderState>> newEncStates;
-    for(auto& es : encStates_) 
+    for(auto& es : encStates_)
       // If the size of the batch dimension of the encoder state context changed, subselect the correct batch entries
       newEncStates.push_back(es->getContext()->shape()[-2] == batchIndices.size() ? es : es->select(batchIndices));
-      
+
     // hypindices matches batchIndices in terms of batch dimension, so we only need hypIndices
     auto selectedState = New<DecoderState>(
         states_.select(hypIndices, beamSize, /*isBatchMajor=*/false), logProbs_, newEncStates, batch_);
@@ -121,6 +123,7 @@ class ClassifierState {
   Words targetWords_;
 
 public:
+  virtual ~ClassifierState() {}
   virtual Expr getLogProbs() const { return logProbs_; }
   virtual void setLogProbs(Expr logProbs) { logProbs_ = logProbs; }
 
diff --git a/src/optimizers/clippers.h b/src/optimizers/clippers.h
index f953c2eca..67a50004f 100644
--- a/src/optimizers/clippers.h
+++ b/src/optimizers/clippers.h
@@ -16,6 +16,7 @@ namespace marian {
 class ClipperBase {
 public:
   virtual void clip(Tensor) = 0;
+  virtual ~ClipperBase() {}
 };
 
 typedef std::shared_ptr<ClipperBase> ClipperPtr;
diff --git a/src/optimizers/optimizers.h b/src/optimizers/optimizers.h
index 2b70ba848..d10af9196 100644
--- a/src/optimizers/optimizers.h
+++ b/src/optimizers/optimizers.h
@@ -29,6 +29,8 @@ class OptimizerBase : public TrainingObserver {
       LOG(info, "[optimizers] Learning rate gets automatically adjusted as if minibatch size was {}", refMBWordsParam_);
   }
 
+  virtual ~OptimizerBase() {}
+
   static constexpr size_t mbSizeNotProvided = SIZE_MAX;
 
   void update(Ptr<ExpressionGraph> graph, size_t mbSize = mbSizeNotProvided) {
@@ -114,7 +116,7 @@ class Sgd : public OptimizerBase {
 public:
   Sgd(float eta, size_t refMBWordsParam = 0, Ptr<ClipperBase> clipper = nullptr)
       : OptimizerBase(eta, refMBWordsParam, clipper) {}
-
+  virtual ~Sgd() {}
   virtual void setParams(const std::vector<float>& /*params*/) override {}
 private:
   void updateImpl(Tensor params, Tensor grads, size_t actualMBSize, size_t refMBWords) override;
diff --git a/src/rescorer/score_collector.h b/src/rescorer/score_collector.h
index ba4884366..38ba7a089 100644
--- a/src/rescorer/score_collector.h
+++ b/src/rescorer/score_collector.h
@@ -13,6 +13,7 @@ namespace marian {
 class ScoreCollector {
 public:
   ScoreCollector(const Ptr<Options>& options);
+  virtual ~ScoreCollector() {}
 
   virtual void Write(long id, const std::string& message);
   virtual void Write(long id,
diff --git a/src/rnn/rnn.h b/src/rnn/rnn.h
index c9131d87a..4efc569cc 100644
--- a/src/rnn/rnn.h
+++ b/src/rnn/rnn.h
@@ -35,7 +35,7 @@ class BaseRNN {
 public:
   BaseRNN(Ptr<ExpressionGraph> graph, Ptr<Options> options)
       : graph_(graph), options_(options) {}
-
+  virtual ~BaseRNN() {}
   virtual Expr transduce(Expr, Expr = nullptr) = 0;
   virtual Expr transduce(Expr, State, Expr = nullptr) = 0;
   virtual Expr transduce(Expr, States, Expr = nullptr) = 0;
@@ -113,6 +113,7 @@ class SingleLayerRNN : public BaseRNN {
 
 public:
   friend RNN;
+  virtual ~SingleLayerRNN() {}
 
   // @TODO: benchmark whether this concatenation is a good idea
   virtual Expr transduce(Expr input, Expr mask = nullptr) override {
diff --git a/src/tensors/backend.h b/src/tensors/backend.h
index 01d002675..ce8a5e603 100644
--- a/src/tensors/backend.h
+++ b/src/tensors/backend.h
@@ -17,7 +17,7 @@ class Backend {
 public:
   Backend(DeviceId deviceId, size_t seed)
       : deviceId_(deviceId), seed_(seed), randomGenerator_(createRandomGenerator(seed, deviceId)) {}
-
+  virtual ~Backend() {};
   virtual DeviceId getDeviceId() { return deviceId_; };
   virtual Ptr<RandomGenerator> getRandomGenerator() { return randomGenerator_; }
 
diff --git a/src/tensors/cpu/device.cpp b/src/tensors/cpu/device.cpp
index 941902a8f..40bb558b1 100644
--- a/src/tensors/cpu/device.cpp
+++ b/src/tensors/cpu/device.cpp
@@ -8,29 +8,40 @@
 
 namespace marian {
 namespace cpu {
+namespace {
 
 // allocate function for tensor reserve() below. 
-// Needed for AVX512, while not available on all compilers. It seems clang
-// does not have aligned_alloc for all cstlib versions. If AVX512 is not used
-// a simple malloc is probably fine. 
-// Should generate a runtime error otherwise as we have a check in the AVX512 
-// functions which tests for alignment. 
-#ifdef _WIN32
-#define MALLOC(size) _aligned_malloc(size, alignment_)
-#elif __GNUC__
-#define MALLOC(size) aligned_alloc(alignment_, size)
-#else
-#define MALLOC(size) malloc(size)
-#endif
+// Alignment is needed because we use AVX512 and AVX2 vectors. We should fail if we can't allocate aligned memory.
 
 #ifdef _WIN32
-#define FREE(ptr) _aligned_free(ptr)
+void *genericMalloc(size_t alignment, size_t size) {
+  void *ret = _aligned_malloc(size, alignment);
+  ABORT_IF(!ret, "Failed to allocate memory on CPU");
+  return ret;
+}
+void genericFree(void *ptr) {
+  _aligned_free(ptr);
+}
 #else
-#define FREE(ptr) free(ptr)
+// Linux and OS X.  There is no fallback to malloc because we need it to be aligned.
+void *genericMalloc(size_t alignment, size_t size) {
+  // On macos, aligned_alloc is available only on c++17
+  // Furthermore, it requires that the memory requested is an exact multiple of the alignment, otherwise it fails.
+  // posix_memalign is available both Mac (Since 2016) and Linux and in both gcc and clang
+  void *result;
+  // Error could be detected by return value or just remaining nullptr.
+  ABORT_IF(posix_memalign(&result, alignment, size), "Failed to allocate memory on CPU");
+  return result;
+}
+void genericFree(void *ptr) {
+  free(ptr);
+}
 #endif
 
+} // namespace
+
 Device::~Device() {
-  FREE(data_);
+  genericFree(data_);
 }
 
 void Device::reserve(size_t size) {
@@ -38,14 +49,12 @@ void Device::reserve(size_t size) {
   ABORT_IF(size < size_ || size == 0,
            "New size must be larger than old size and larger than 0");
 
+  uint8_t *temp = static_cast<uint8_t*>(genericMalloc(alignment_, size));
   if(data_) {
-    uint8_t *temp = static_cast<uint8_t*>(MALLOC(size));
     std::copy(data_, data_ + size_, temp);
-    FREE(data_);
-    data_ = temp;
-  } else {
-    data_ = static_cast<uint8_t*>(MALLOC(size));
+    genericFree(data_);
   }
+  data_ = temp;
   size_ = size;
 }
 }  // namespace cpu
diff --git a/src/tensors/cpu/fbgemm/expanded_gemm.h b/src/tensors/cpu/fbgemm/expanded_gemm.h
index cf53fe1a1..32cc6b122 100644
--- a/src/tensors/cpu/fbgemm/expanded_gemm.h
+++ b/src/tensors/cpu/fbgemm/expanded_gemm.h
@@ -17,6 +17,7 @@
 #endif
 
 using namespace fbgemm;
+// @TODO: don't use using namespace ...; in header files. Just don't. [UG]
 #endif  // USE_FBGEMM
 
 namespace marian {
@@ -96,7 +97,7 @@ struct FbgemmPacked16PackNodeOp : public UnaryNodeOp {
 
   const std::string type() override { return "packMatFp16"; }
 
-  Shape newShape(Expr a, bool transpose) {
+  Shape newShape(Expr MAYBE_UNUSED a, bool MAYBE_UNUSED transpose) {
 #if USE_FBGEMM
     auto shapeMat = a->shape();
     // Should be 2D - weight matrix
@@ -115,15 +116,14 @@ struct FbgemmPacked16PackNodeOp : public UnaryNodeOp {
                            packsize_);
 
     Shape outShape({(int)packsize_});
-
     return outShape;
-#else // USE_FBGEMM
+#else
     ABORT("Packed GEMM requires a build with USE_FBGEMM enabled");
     return Shape();
 #endif  // USE_FBGEMM
   }
 };
-
+  ;
 // Pack a matrix (int8) into cache utilization efficient way (block format) together with quantization into int8
 // PackMatrix packMat_: the type of packed matrix - A or B matrix
 // marian::Type packType_: the type the input matrix is packed - packed8avx2 or packed8avx512
@@ -132,6 +132,7 @@ struct FbgemmPacked16PackNodeOp : public UnaryNodeOp {
 // int ncol_: the number of columns
 // uint64_t packsize_: the size of the packed matrix
 //                    (the size of int8 packed B from fbgemm:PackAWithQuantRowOffset + quantization scale, offset and zero point)
+
 struct FbgemmPacked8PackNodeOp : public UnaryNodeOp {
   PackMatrix packMat_;
   marian::Type packType_;
@@ -180,19 +181,21 @@ struct FbgemmPacked8PackNodeOp : public UnaryNodeOp {
 
   const std::string type() override { return "packMatInt8"; }
 
-  Shape newShape(Expr a, bool transpose) {
 #if USE_FBGEMM
+  Shape newShape(Expr a, bool transpose) {
     fbgemmPacked8PackInfo(a->shape(), packType_, transpose, nrow_, ncol_, packsize_);
     Shape outShape({(int)packsize_});
-
     return outShape;
-#else // USE_FBGEMM
+  }
+#else
+  Shape newShape(Expr /*a*/, bool /*transpose*/) {
     ABORT("Packed GEMM requires a build with USE_FBGEMM enabled");
     return Shape();
-#endif  // USE_FBGEMM
   }
+#endif  // USE_FBGEMM
 };
 
+
 // Affine transform (matrix multiplication) using packed B matrix
 // float scalar_: scalar multiplier
 // size_t m_: the number of rows in A and C
@@ -202,7 +205,6 @@ struct FbgemmPacked8PackNodeOp : public UnaryNodeOp {
 // bool transB_: transpose B
 class FbgemmPacked16AffineNodeOp : public NaryNodeOp {
 private:
-  float scalar_;
   size_t m_;
   size_t n_;
   size_t k_;
@@ -210,9 +212,8 @@ class FbgemmPacked16AffineNodeOp : public NaryNodeOp {
   bool transB_;
 
 public:
-  FbgemmPacked16AffineNodeOp(const std::vector<Expr>& nodes, Shape bShape, bool transA, bool transB, float scalar)
-      : NaryNodeOp(nodes, newShape(nodes[0], bShape, transA, transB), Type::float32),
-        scalar_(scalar) {
+  FbgemmPacked16AffineNodeOp(const std::vector<Expr>& nodes, Shape bShape, bool transA, bool transB, float /*scalar*/)
+    : NaryNodeOp(nodes, newShape(nodes[0], bShape, transA, transB), Type::float32)/*, scalar_(scalar)*/ {
     transA_ = transA;
     transB_ = transB;
     m_ = nodes[0]->shape().elements() / nodes[0]->shape()[-1];
@@ -281,7 +282,6 @@ class FbgemmPacked16AffineNodeOp : public NaryNodeOp {
 // bool transB_: transpose B
 class FbgemmPacked8AffineNodeOp : public NaryNodeOp {
 private:
-  float scalar_;
   size_t m_;
   size_t n_;
   size_t k_;
@@ -289,9 +289,8 @@ class FbgemmPacked8AffineNodeOp : public NaryNodeOp {
   bool transB_;
 
 public:
-  FbgemmPacked8AffineNodeOp(const std::vector<Expr>& nodes, Shape bShape, bool transA, bool transB, float scalar)
-      : NaryNodeOp(nodes, newShape(nodes[0], bShape, transA, transB), Type::float32),
-        scalar_(scalar) {
+ FbgemmPacked8AffineNodeOp(const std::vector<Expr>& nodes, Shape bShape, bool transA, bool transB, float /*scalar*/)
+   : NaryNodeOp(nodes, newShape(nodes[0], bShape, transA, transB), Type::float32)/*, scalar_(scalar) */ {
     transA_ = transA;
     transB_ = transB;
     m_ = nodes[0]->shape().elements() / nodes[0]->shape()[-1];
@@ -302,7 +301,7 @@ class FbgemmPacked8AffineNodeOp : public NaryNodeOp {
     size_t l = bShape.elements() / bShape[-1];
     n_ = bShape[-1];
     if(transB)
-     std::swap(l, n_);
+      std::swap(l, n_);
   }
 
   Shape newShape(Expr a, Shape bShape, bool transA, bool transB) {
@@ -369,9 +368,9 @@ static inline Expr affine(Expr a, Expr b, Shape bShape, Expr c, bool transA, boo
   Type elementType = b->value_type();
 
   if (elementType == Type::packed16)
-    return Expression<cpu::variant::FbgemmPacked16AffineNodeOp>(nodes, bShape, transA, transB, scalar);
+    return Expression<FbgemmPacked16AffineNodeOp>(nodes, bShape, transA, transB, scalar);
   else if (isPacked(elementType) && sizeOf(elementType) == 1)
-    return Expression<cpu::variant::FbgemmPacked8AffineNodeOp>(nodes, bShape, transA, transB, scalar);
+    return Expression<FbgemmPacked8AffineNodeOp>(nodes, bShape, transA, transB, scalar);
   else {
     ABORT("Only int8 and fp16 are available. {}", elementType);
     return nullptr;
@@ -380,9 +379,9 @@ static inline Expr affine(Expr a, Expr b, Shape bShape, Expr c, bool transA, boo
 
 static inline Expr pack(Type elementType, Expr a, PackMatrix packMat, bool transpose, float clipValue) {
   if (elementType == Type::packed16)
-    return Expression<cpu::variant::FbgemmPacked16PackNodeOp>(a, packMat, transpose, clipValue);
+    return Expression<FbgemmPacked16PackNodeOp>(a, packMat, transpose, clipValue);
   else if (isPacked(elementType) && sizeOf(elementType) == 1)
-    return Expression<cpu::variant::FbgemmPacked8PackNodeOp>(a, packMat, elementType, transpose, clipValue);
+    return Expression<FbgemmPacked8PackNodeOp>(a, packMat, elementType, transpose, clipValue);
   else {
     ABORT("Only int8 and fp16 are available. {}", elementType);
     return nullptr;
@@ -394,9 +393,9 @@ static inline Expr dot(Expr a, Expr b, Shape bShape, bool transA, bool transB, f
   Type elementType = b->value_type();
 
   if (elementType == Type::packed16)
-    return Expression<cpu::variant::FbgemmPacked16AffineNodeOp>(nodes, bShape, transA, transB, scalar);
+    return Expression<FbgemmPacked16AffineNodeOp>(nodes, bShape, transA, transB, scalar);
   else if (isPacked(elementType) && sizeOf(elementType) == 1)
-    return Expression<cpu::variant::FbgemmPacked8AffineNodeOp>(nodes, bShape, transA, transB, scalar);
+    return Expression<FbgemmPacked8AffineNodeOp>(nodes, bShape, transA, transB, scalar);
   else {
     ABORT("Only int8 and fp16 are available. {}", elementType);
     return nullptr;
diff --git a/src/tensors/cpu/tensor_operators.cpp b/src/tensors/cpu/tensor_operators.cpp
index 91e058b84..ae5eeed57 100755
--- a/src/tensors/cpu/tensor_operators.cpp
+++ b/src/tensors/cpu/tensor_operators.cpp
@@ -20,7 +20,7 @@ namespace marian {
 
 namespace cpu {
 
-void IsNaN(const Tensor in, Ptr<Allocator> allocator, bool& /*isNaN*/, bool& /*isInf*/) {
+  void IsNaN(const Tensor /*in*/, Ptr<Allocator> /*allocator*/, bool& /*isNaN*/, bool& /*isInf*/) {
   ABORT("Not implemented");
 }
 
@@ -214,9 +214,11 @@ void Transpose0213(Tensor out, Tensor in) {
   }
 }
 
+// This function is called only when MKL is available.
+#if MKL_FOUND
 // Given a 4D array, transpose (swap) the initial 3 dimensions while keeping the last dimension.
 // e.g. 1234 --> 2134, 1234 --> 3214 (4 is always kept).
-// This is an optimized version for swapping first 3 dimensions 
+// This is an optimized version for swapping first 3 dimensions
 // assuming the last dimension is large enough to get benefits from vectorized copy.
 //
 // @param out output tensor
@@ -225,14 +227,13 @@ void Transpose0213(Tensor out, Tensor in) {
 template <bool add>
 void TransposeFirst3In4(Tensor out, Tensor in, const std::vector<int>& vAxis) {
   ABORT_IF(vAxis.size() != 4, "This function handles only 4D arrays.");
-#if MKL_FOUND
   int innermost = in->shape()[-1];
 
   int l1 = in->shape()[vAxis[0]];
   int l2 = in->shape()[vAxis[1]];
   int l3 = in->shape()[vAxis[2]];
 
-  // find the mapping between the transposed output dimensional indices (oi, oj, ok) 
+  // find the mapping between the transposed output dimensional indices (oi, oj, ok)
   // and original input dimensional indices (i, j, k)
   int oi, oj, ok;
 #pragma omp parallel for
@@ -275,11 +276,8 @@ void TransposeFirst3In4(Tensor out, Tensor in, const std::vector<int>& vAxis) {
       }
     }
   }
-#else
-  // it shouldn't come into here. This function is called only when MKL is available.
-  ABORT("Should not get here");
-#endif  // MKL_FOUND
 }
+#endif  // MKL_FOUND
 
 inline void transpose4x4_SSE(const float* A,
                              float* B,
@@ -656,7 +654,7 @@ void SelectAxis2(Tensor out,
 
   functional::Shape outShape = out->shape();
   functional::Shape inShape = in->shape();
-  
+
   auto idxData = indices->data<IndexType>();
   auto odata = out->data();
   const auto idata = in->data();
diff --git a/src/tensors/rand.h b/src/tensors/rand.h
index f3e356fb6..94b44a97a 100644
--- a/src/tensors/rand.h
+++ b/src/tensors/rand.h
@@ -15,11 +15,11 @@ class RandomGenerator {
 
 public:
   RandomGenerator(size_t seed) : seed_(seed) { }
-
+  virtual ~RandomGenerator() {}
   virtual void uniform(Tensor, float a, float b) = 0;
   virtual void normal(Tensor, float mean, float stddev) = 0;
 };
 
 Ptr<RandomGenerator> createRandomGenerator(size_t /*seed*/, DeviceId);
 
-}
\ No newline at end of file
+}
diff --git a/src/tensors/tensor_operators.h b/src/tensors/tensor_operators.h
index d75652403..227bc9533 100644
--- a/src/tensors/tensor_operators.h
+++ b/src/tensors/tensor_operators.h
@@ -25,7 +25,7 @@
 namespace marian {
 
 template <typename InIt, typename OutIt>
-void copy(Ptr<Backend> backend, const InIt beg, const InIt end, OutIt it) {
+void copy(Ptr<Backend>& MAYBE_UNUSED backend, const InIt beg, const InIt end, OutIt it) {
 #ifdef CUDA_FOUND
   if(backend->getDeviceId().type == DeviceType::gpu)
     gpu::copy(backend, beg, end, it);
@@ -119,7 +119,7 @@ DISPATCH3(Concatenate, marian::Tensor, const std::vector<marian::Tensor>&, int)
 
 // clang-format on
 
-// Bernoulli(tensor, 0.5f, 2.f, -1.f) generates a tensor composed of 50% of 1 and 50% of -1. 
+// Bernoulli(tensor, 0.5f, 2.f, -1.f) generates a tensor composed of 50% of 1 and 50% of -1.
 static inline void Bernoulli(Tensor resultTensor, float keepProb, float scale = 1.f, float shift = 0.f) {
   // in-place uniform distribution
   auto rnd = resultTensor->getBackend()->getRandomGenerator();
@@ -190,7 +190,7 @@ void LayerNormalizationGrad(Tensor gradX,
 }
 
 static inline void LayerNormalizationGrad(
-                            Ptr<Allocator> allocator,
+                            Ptr<Allocator> MAYBE_UNUSED allocator,
                             Tensor gradX,
                             Tensor gradGamma,
                             Tensor gradBeta,
diff --git a/src/tests/prod.cpp b/src/tests/prod.cpp
index 37809bacd..698712b9c 100644
--- a/src/tests/prod.cpp
+++ b/src/tests/prod.cpp
@@ -1,7 +1,7 @@
 #include "marian.h"
 #include "common/timer.h"
 
-int main(int argc, char** argv) {
+int main(int /*argc*/, char** /*argv*/) {
     using namespace marian;
 
     {
diff --git a/src/tests/sqlite.cpp b/src/tests/sqlite.cpp
index 217485149..f822dbc5c 100644
--- a/src/tests/sqlite.cpp
+++ b/src/tests/sqlite.cpp
@@ -8,6 +8,8 @@
 #include <fstream>
 
 int main(int argc, char** argv) {
+    ABORT_IF(argc != 3, "FATAL ERROR: Incorrect number of command line arguments "
+             "(expected: 2) for command {}.",argv[0]);
 
     SQLite::Database db("corpus.db", SQLite::OPEN_READWRITE|SQLite::OPEN_CREATE);
     db.exec("PRAGMA temp_store_directory = '/data1/marcinjd';");
diff --git a/src/training/communicator.cpp b/src/training/communicator.cpp
index be9b23be5..98b7be7f2 100644
--- a/src/training/communicator.cpp
+++ b/src/training/communicator.cpp
@@ -38,7 +38,7 @@ Ptr<ICommunicator> createCommunicator(
   }
 
   // the actual implementation is inside communicator.cu
-  return New<NCCLCommunicator>(graphs, mpi); 
+  return New<NCCLCommunicator>(graphs, mpi);
 #else // no CUDA or no NCCL
   noNccl; // (unused)
   return New<DefaultCommunicator>(graphs, mpi);
@@ -141,7 +141,7 @@ class FakeMPIWrapper : public IMPIWrapper
   FakeMPIWrapper(bool) {
     LOG(warn, "Compiled without MPI support. Falling back to FakeMPIWrapper");
   }
-
+  virtual ~FakeMPIWrapper() {}
   virtual size_t myMPIRank() const override { return 0; };
   virtual size_t numMPIProcesses() const override { return 1; };
 
diff --git a/src/training/communicator.h b/src/training/communicator.h
index 6ccce204f..472744910 100644
--- a/src/training/communicator.h
+++ b/src/training/communicator.h
@@ -156,11 +156,8 @@ class DefaultCommunicator : public ICommunicator {
   void scatterReduceAndResetGrads() const override {
     const_cast<DefaultCommunicator*>(this)->lazyInit();
 
-    int totalSize = (int)graphs_[0]->params()->vals()->size();
-    int shardSize = (int)ceil(totalSize / (float)graphs_.size());
-
     // Gather gradients from different devices into current gradient shards
-    auto scatter = [this, shardSize](size_t idx, size_t begin, size_t end) {
+    auto scatter = [this](size_t idx, size_t begin, size_t end) {
       auto curGrad = graphs_[idx]->params()->grads()->subtensor(begin, end-begin);
 
       // collect and sum gradients
@@ -176,7 +173,7 @@ class DefaultCommunicator : public ICommunicator {
     };
 
     // reset gradients outside current shard
-    auto reset = [this, shardSize](size_t idx, size_t begin, size_t end) {
+    auto reset = [this](size_t idx, size_t begin, size_t end) {
       auto grad = graphs_[idx]->params()->grads();
       if (begin > 0)
         grad->subtensor(0, begin)->set(0);
@@ -189,11 +186,9 @@ class DefaultCommunicator : public ICommunicator {
   }
 
   void allGatherParams() const override {
-    int totalSize = (int)graphs_[0]->params()->vals()->size();
-    int shardSize = (int)ceil(totalSize / (float)graphs_.size());
 
     // Update all graphs with parameter shard
-    auto gather = [this, shardSize](size_t idx, size_t begin, size_t end) {
+    auto gather = [this](size_t idx, size_t begin, size_t end) {
       auto getShard = [&](Ptr<ExpressionGraph> graph) {
         return graph->params()->vals()->subtensor(begin, end-begin);
       };
diff --git a/src/training/gradient_dropping/sparse_tensor.h b/src/training/gradient_dropping/sparse_tensor.h
index 5effa3d73..652a77ece 100644
--- a/src/training/gradient_dropping/sparse_tensor.h
+++ b/src/training/gradient_dropping/sparse_tensor.h
@@ -118,7 +118,7 @@ class SparseTensorBase : public std::enable_shared_from_this<SparseTensorBase> {
   }
 
   // Convert a tensor into a sparse tensor format
-  void fromDense(Tensor t) {
+  void fromDense(Tensor MAYBE_UNUSED t) {
     if(backend_->getDeviceId().type == DeviceType::cpu) {
       ABORT("Gradient Dropping for CPU is not yet supported");
     }
diff --git a/src/training/graph_group.h b/src/training/graph_group.h
index 265f9785c..56b8afe3d 100644
--- a/src/training/graph_group.h
+++ b/src/training/graph_group.h
@@ -54,10 +54,10 @@ class GraphGroup {
    * number of devices, which is passed in as the 'multiplier'.
    */
   // @TODO: Can this be made const? It seems wrong to have a stateful method that still returns a result.
-  virtual Ptr<data::BatchStats> collectStats(Ptr<ExpressionGraph> graph,
-                                             Ptr<models::ICriterionFunction> model,
-                                             const std::vector<Ptr<Vocab>>& vocabs,
-                                             double multiplier = 1.) {
+  Ptr<data::BatchStats> collectStats(Ptr<ExpressionGraph> graph,
+                                     Ptr<models::ICriterionFunction> model,
+                                     const std::vector<Ptr<Vocab>>& vocabs,
+                                     double multiplier = 1.) {
     auto stats = New<data::BatchStats>();
 
     size_t numFiles = options_->get<std::vector<std::string>>("train-sets").size();
@@ -92,8 +92,8 @@ class GraphGroup {
         maxBatch *= 2;
     }
 
-    // Do a binary search for maxmimum batch size that fits into given workspace memory 
-    // for a tested sentence length. 
+    // Do a binary search for maxmimum batch size that fits into given workspace memory
+    // for a tested sentence length.
     for(size_t i = step; i <= maxLength; i += step) {
       size_t start = 1;
       size_t end = maxBatch;
diff --git a/src/training/graph_group_async.h b/src/training/graph_group_async.h
index 0e27a6657..dd5254bb0 100644
--- a/src/training/graph_group_async.h
+++ b/src/training/graph_group_async.h
@@ -64,7 +64,7 @@ class AsyncGraphGroup : public GraphGroup, public ExponentialSmoothing {
   void save(Ptr<ExpressionGraph>, bool final = false);
 
   // @TODO: give it a fake batch generator which own vocabs instead of passing vocabs
-  Ptr<data::BatchStats> collectStats(const std::vector<Ptr<Vocab>>& vocabs) {
+  virtual Ptr<data::BatchStats> collectStats(const std::vector<Ptr<Vocab>>& vocabs) {
     return GraphGroup::collectStats(graphs_[0], builders_[0], vocabs);
   }
 
diff --git a/src/training/graph_group_multinode_sync.h b/src/training/graph_group_multinode_sync.h
index ff1fe4e97..0a089d7bb 100644
--- a/src/training/graph_group_multinode_sync.h
+++ b/src/training/graph_group_multinode_sync.h
@@ -63,7 +63,6 @@ class MultiNodeGraphGroupSync : public MultiNodeGraphGroupBase {
   Tensor paramsAvg_;
   std::vector<float> accGradientsSync_cpu;
   std::vector<float> receiveBuffer_cpu;
-  bool synchronization_happened{false};
 
   Ptr<OptimizerBase> syncOptimizer_;
 
diff --git a/src/training/graph_group_sync.h b/src/training/graph_group_sync.h
index b127296da..067d245c4 100644
--- a/src/training/graph_group_sync.h
+++ b/src/training/graph_group_sync.h
@@ -26,7 +26,6 @@ class SyncGraphGroup : public GraphGroup, public ExponentialSmoothing {
   // state for update()
   bool first_{ true };                           // gets interpreted and cleared by update()
   std::vector<Ptr<data::Batch>> pendingBatches_; // in case of dynamic MB-size scaling, we temporarly buffer up batches across update() calls until enough
-  size_t typicalTrgWords_{};                     // typical batch size in words (labels), 0 if unknown (e.g. specified in sentences)
   double updateMultiplier_{1};                  // multiplier not applied in collectStats() (no multiplier if not mini-batch-fit)
 
   void initialize(const Ptr<data::Batch>& exampleBatch);
diff --git a/src/training/training_state.h b/src/training/training_state.h
index d39173be9..1eb187b74 100644
--- a/src/training/training_state.h
+++ b/src/training/training_state.h
@@ -13,6 +13,7 @@ class TrainingState;
 
 class TrainingObserver {
 public:
+  virtual ~TrainingObserver() {}
   virtual void init(TrainingState&) {}
   virtual void actAfterEpoch(TrainingState&) {}
   virtual void actAfterBatches(TrainingState&) {}
diff --git a/src/training/validator.h b/src/training/validator.h
index e97f0da6f..1658dff3f 100755
--- a/src/training/validator.h
+++ b/src/training/validator.h
@@ -36,6 +36,7 @@ class ValidatorBase : public TrainingObserver {
 
 public:
   ValidatorBase(bool lowerIsBetter) : lowerIsBetter_(lowerIsBetter), lastBest_{initScore()} {}
+  virtual ~ValidatorBase() {}
 
   virtual float validate(const std::vector<Ptr<ExpressionGraph>>& graphs,
                          Ptr<const TrainingState> state) = 0;
@@ -51,6 +52,7 @@ class ValidatorBase : public TrainingObserver {
 template <class DataSet, class BuilderType> // @TODO: BuilderType doesn't really serve a purpose here? Review and remove.
 class Validator : public ValidatorBase {
 public:
+  virtual ~Validator() {}
   Validator(std::vector<Ptr<Vocab>> vocabs, Ptr<Options> options, bool lowerIsBetter = true)
       : ValidatorBase(lowerIsBetter),
         vocabs_(vocabs),
@@ -137,6 +139,7 @@ class CrossEntropyValidator : public Validator<data::Corpus, models::ICriterionF
 
 public:
   CrossEntropyValidator(std::vector<Ptr<Vocab>> vocabs, Ptr<Options> options);
+  virtual ~CrossEntropyValidator() {}
 
   std::string type() override { return options_->get<std::string>("cost-type"); }
 
@@ -148,6 +151,7 @@ class CrossEntropyValidator : public Validator<data::Corpus, models::ICriterionF
 class AccuracyValidator : public Validator<data::Corpus, models::IModel> {
 public:
   AccuracyValidator(std::vector<Ptr<Vocab>> vocabs, Ptr<Options> options);
+  virtual ~AccuracyValidator() {}
 
   std::string type() override { return "accuracy"; }
 
@@ -161,6 +165,7 @@ class BertAccuracyValidator : public Validator<data::Corpus, models::IModel> {
 
 public:
   BertAccuracyValidator(std::vector<Ptr<Vocab>> vocabs, Ptr<Options> options, bool evalMaskedLM);
+  virtual ~BertAccuracyValidator() {}
 
   std::string type() override {
     if(evalMaskedLM_)
@@ -177,6 +182,7 @@ class BertAccuracyValidator : public Validator<data::Corpus, models::IModel> {
 class ScriptValidator : public Validator<data::Corpus, models::IModel> {
 public:
   ScriptValidator(std::vector<Ptr<Vocab>> vocabs, Ptr<Options> options);
+  virtual ~ScriptValidator() {}
 
   virtual float validate(const std::vector<Ptr<ExpressionGraph>>& graphs,
                          Ptr<const TrainingState> /*ignored*/) override;
@@ -193,6 +199,7 @@ class ScriptValidator : public Validator<data::Corpus, models::IModel> {
 class TranslationValidator : public Validator<data::Corpus, models::IModel> {
 public:
   TranslationValidator(std::vector<Ptr<Vocab>> vocabs, Ptr<Options> options);
+  virtual ~TranslationValidator() {}
 
   virtual float validate(const std::vector<Ptr<ExpressionGraph>>& graphs,
                          Ptr<const TrainingState> state) override;
@@ -212,6 +219,7 @@ class TranslationValidator : public Validator<data::Corpus, models::IModel> {
 class BleuValidator : public Validator<data::Corpus, models::IModel> {
 public:
   BleuValidator(std::vector<Ptr<Vocab>> vocabs, Ptr<Options> options, bool detok = false);
+  virtual ~BleuValidator() {}
 
   virtual float validate(const std::vector<Ptr<ExpressionGraph>>& graphs,
                          Ptr<const TrainingState> state) override;
diff --git a/src/translator/output_collector.h b/src/translator/output_collector.h
index 9e0e12b53..ffcbd2d50 100644
--- a/src/translator/output_collector.h
+++ b/src/translator/output_collector.h
@@ -11,6 +11,7 @@ namespace marian {
 
 class PrintingStrategy {
 public:
+  virtual ~PrintingStrategy() {}
   virtual bool shouldBePrinted(long) = 0;
 };
 
diff --git a/src/translator/scorers.h b/src/translator/scorers.h
index c936514c6..a5a0be2cb 100755
--- a/src/translator/scorers.h
+++ b/src/translator/scorers.h
@@ -10,6 +10,8 @@ namespace marian {
 
 class ScorerState {
 public:
+  virtual ~ScorerState(){}
+
   virtual Logits getLogProbs() const = 0;
 
   virtual void blacklist(Expr /*totalCosts*/, Ptr<data::CorpusBatch> /*batch*/){};
@@ -24,6 +26,8 @@ class Scorer {
   Scorer(const std::string& name, float weight)
       : name_(name), weight_(weight) {}
 
+  virtual ~Scorer(){}
+
   std::string getName() { return name_; }
   float getWeight() { return weight_; }
 
@@ -53,6 +57,7 @@ class ScorerWrapperState : public ScorerState {
 
 public:
   ScorerWrapperState(Ptr<DecoderState> state) : state_(state) {}
+  virtual ~ScorerWrapperState() {}
 
   virtual Ptr<DecoderState> getState() { return state_; }
 
@@ -88,6 +93,8 @@ class ScorerWrapper : public Scorer {
         encdec_(std::static_pointer_cast<IEncoderDecoder>(encdec)),
         ptr_{ptr} {}
 
+  virtual ~ScorerWrapper() {}
+
   virtual void init(Ptr<ExpressionGraph> graph) override {
     graph->switchParams(getName());
     if(ptr_)

From 45b83b20f2bff5be4905a6619f130435e0e99e20 Mon Sep 17 00:00:00 2001
From: Martin Junczys-Dowmunt <Marcin.JunczysDowmunt@microsoft.com>
Date: Sat, 7 Mar 2020 03:59:10 +0000
Subject: [PATCH 833/838] Merged PR 11895: Use lowest() for INVALID_PATH_SCORE

* use lowest() as INVALID_PATH_SCORE insteaf of -9999 which caused problems with very long sequences
* add a number of aborts in relation of invalid path scores during beam search
---
 src/translator/beam_search.h | 11 ++++++-----
 src/translator/history.h     |  6 +++++-
 2 files changed, 11 insertions(+), 6 deletions(-)

diff --git a/src/translator/beam_search.h b/src/translator/beam_search.h
index 4d02c1086..05a490fea 100755
--- a/src/translator/beam_search.h
+++ b/src/translator/beam_search.h
@@ -18,8 +18,8 @@ class BeamSearch {
   size_t beamSize_;
   Ptr<Vocab> trgVocab_;
 
-  static constexpr auto INVALID_PATH_SCORE = -9999; // (@TODO: change to -9999.0 once C++ allows that)
-  static constexpr auto PURGE_BATCH = true; // @TODO: diagnostic, to-be-removed once confirmed there are no issues.
+  const float INVALID_PATH_SCORE = std::numeric_limits<float>::lowest(); // @TODO: observe this closely
+  const bool PURGE_BATCH = true; // @TODO: diagnostic, to-be-removed once confirmed there are no issues.
 
 public:
   BeamSearch(Ptr<Options> options,
@@ -98,11 +98,12 @@ class BeamSearch {
       const auto& beam = beams[origBatchIdx];
       auto& newBeam = newBeams[origBatchIdx]; // extended hypotheses are going to be placed in this new beam
 
-      if (newBeam.size() >= beam.size()) // getNBestList() generates N for all batch entries incl. those that already have a narrower beam
+      if(newBeam.size() >= beam.size()) // getNBestList() generates N for all batch entries incl. those that already have a narrower beam
         continue;
-      if (pathScore <= INVALID_PATH_SCORE) // (dummy slot or word that cannot be expanded by current factor)
+      if(pathScore == INVALID_PATH_SCORE) // (dummy slot or word that cannot be expanded by current factor)
         continue;
-
+      
+      ABORT_IF(pathScore < INVALID_PATH_SCORE, "Actual pathScore ({}) is lower than INVALID_PATH_SCORE ({})??", pathScore, INVALID_PATH_SCORE); // This should not happen in valid situations. Currently the only smaller value would be -inf (effect of overflow in summation?)
       ABORT_IF(beamHypIdx >= beam.size(), "Out of bounds beamHypIdx??"); // effectively this is equivalent to ABORT_IF(beams[origBatchIdx].empty(), ...)
 
       // map wordIdx to word
diff --git a/src/translator/history.h b/src/translator/history.h
index 04b9d6052..463c75a1c 100644
--- a/src/translator/history.h
+++ b/src/translator/history.h
@@ -55,7 +55,11 @@ class History {
     return nbest;
   }
 
-  Result top() const { return nBest(1)[0]; }
+  Result top() const { 
+    const NBestList& nbest = nBest(1);
+    ABORT_IF(nbest.empty(), "No hypotheses in n-best list??");
+    return nbest[0]; 
+  }
 
   size_t getLineNum() const { return lineNo_; }
 

From 015a218c993cd24958aa8fd9ceb1c2bd491809ca Mon Sep 17 00:00:00 2001
From: Martin Junczys-Dowmunt <Marcin.JunczysDowmunt@microsoft.com>
Date: Sat, 7 Mar 2020 04:29:50 +0000
Subject: [PATCH 834/838] Merged PR 11312: Guard scheduler against circular
 references

This is the first PR in a series to get my graph-group* clean-up into master. End-goal is to have clean label-based updates and fp16 in master.

This small change guards against circular references in the scheduler. For large models the freeing order is wrong which can prevent GPU memory to be freed at the correct time leaving no memory for the actual model. The weak references solve that.
---
 src/training/scheduler.h      |  7 +++----
 src/training/training.h       | 19 ++++++++++++-------
 src/training/training_state.h | 31 ++++++++++++++++++++++++-------
 3 files changed, 39 insertions(+), 18 deletions(-)

diff --git a/src/training/scheduler.h b/src/training/scheduler.h
index 5fa81d0f6..913b05278 100755
--- a/src/training/scheduler.h
+++ b/src/training/scheduler.h
@@ -196,8 +196,7 @@ class Scheduler : public TrainingObserver {
 
     registerTrainingObserver(validators_.back());
     if(!state_->loaded) {
-      state_->validators[validator->type()]["last-best"]
-          = validator->initScore();
+      state_->validators[validator->type()]["last-best"] = validator->initScore();
       state_->validators[validator->type()]["stalled"] = 0;
     }
     if(validators_.size() == 1)
@@ -215,12 +214,12 @@ class Scheduler : public TrainingObserver {
   }
 
   void validate(const std::vector<Ptr<ExpressionGraph>>& graphs,
-                bool final = false) {
+                bool isFinal = false) {
     // Do not validate if already validated (for instance, after the model is
     // loaded) or if validation is scheduled for another update, or when signal SIGTERM was received
     if(getSigtermFlag() // SIGTERM was received
        || state_->validated // already validated (in resumed training, for example)
-       || (!state_->enteredNewPeriodOf(options_->get<std::string>("valid-freq")) && !final)) // not now
+       || (!state_->enteredNewPeriodOf(options_->get<std::string>("valid-freq")) && !isFinal)) // not now
       return;
 
     bool firstValidator = true;
diff --git a/src/training/training.h b/src/training/training.h
index f7f2b404e..d47339635 100644
--- a/src/training/training.h
+++ b/src/training/training.h
@@ -34,8 +34,6 @@ class Train : public ModelTask {
 
     dataset->prepare();
 
-    auto trainState = New<TrainingState>(options_->get<float>("learn-rate"));
-    auto scheduler = New<Scheduler>(options_, trainState);
     auto mpi = initMPI(/*multiThreaded=*/!options_->get<bool>("sync-sgd")); // @TODO: do we need the multiThreaded distinction at all?
 
     Ptr<BatchStats> stats;
@@ -46,11 +44,20 @@ class Train : public ModelTask {
       // @TODO this should receive a function object that can generate a fake batch;
       // that way vocabs would not be exposed.
       auto model = New<ModelWrapper>(options_, mpi);
-      model->setScheduler(scheduler); // collectStats() needs to know about dynamic MB scaling
+
+      // use temporary scheduler to make sure everything gets destroyed properly
+      // otherwise the scheduler believes that registered objects still exist
+      auto tempTrainState = New<TrainingState>(options_->get<float>("learn-rate"));
+      auto tempScheduler = New<Scheduler>(options_, tempTrainState);
+
+      model->setScheduler(tempScheduler); // collectStats() needs to know about dynamic MB scaling
       stats = model->collectStats(dataset->getVocabs());
       LOG(info, "[batching] Done. Typical MB size is {} target words", stats->estimateTypicalTrgWords());
     }
 
+    auto trainState = New<TrainingState>(options_->get<float>("learn-rate"));
+    auto scheduler = New<Scheduler>(options_, trainState);
+
     if((options_->hasAndNotEmpty("valid-sets") || options_->hasAndNotEmpty("valid-script-path"))
        && SchedulingParameter::parse(options_->get<std::string>("valid-freq"))) {
       for(auto validator : Validators(dataset->getVocabs(), options_))
@@ -77,12 +84,10 @@ class Train : public ModelTask {
       restored = false;
 
       // main training loop for one epoch
-      for(auto batchIt = std::begin(*batchGenerator); // @TODO: try to use for(auto ...)
-          batchIt != std::end(*batchGenerator);
-          batchIt++) {
+      for(auto batch : *batchGenerator) {
         if (!scheduler->keepGoing())
           break;
-        model->update(*batchIt);
+        model->update(batch);
       }
 
       if(scheduler->keepGoing())
diff --git a/src/training/training_state.h b/src/training/training_state.h
index d39173be9..f8b9e5632 100644
--- a/src/training/training_state.h
+++ b/src/training/training_state.h
@@ -13,6 +13,8 @@ class TrainingState;
 
 class TrainingObserver {
 public:
+  virtual ~TrainingObserver() {}
+
   virtual void init(TrainingState&) {}
   virtual void actAfterEpoch(TrainingState&) {}
   virtual void actAfterBatches(TrainingState&) {}
@@ -129,8 +131,8 @@ class TrainingState {
   }
 
   void registerObserver(Ptr<TrainingObserver> observer) {
-    observers_.push_back(observer);
-    observers_.back()->init(*this);
+    observer->init(*this);
+    wObservers_.push_back(observer);
   }
 
   // return the totals count that corresponds to the given unit (batches, labels, or epochs)
@@ -183,8 +185,11 @@ class TrainingState {
 
   void newEpoch() {
     ++epochs;
-    for(auto observer : observers_)
+    for(auto wObserver : wObservers_) {
+      auto observer = wObserver.lock();
+      ABORT_IF(!observer, "Training observer object expired. Make sure all registered observers exist during scheduler life time");
       observer->actAfterEpoch(*this);
+    }
     samplesEpoch = 0;
     batchesEpoch = 0;
   }
@@ -194,22 +199,31 @@ class TrainingState {
     batchesEpoch += batchesInUpdate;
     loaded = false;
     validated = false;
-    for(auto observer : observers_)
+    for(auto wObserver : wObservers_) {
+      auto observer = wObserver.lock();
+      ABORT_IF(!observer, "Training observer object expired. Make sure all registered observers exist during scheduler life time");
       observer->actAfterBatches(*this);
+    }
   }
 
   void newStalled(size_t num) {
     stalled = num;
     if(num > maxStalled)
       ++maxStalled;
-    for(auto observer : observers_)
+    for(auto wObserver : wObservers_) {
+      auto observer = wObserver.lock();
+      ABORT_IF(!observer, "Training observer object expired. Make sure all registered observers exist during scheduler life time");
       observer->actAfterStalled(*this);
+    }
   }
 
   void newLoad() {
     loaded = true;
-    for(auto observer : observers_)
+    for(auto wObserver : wObservers_) {
+      auto observer = wObserver.lock();
+      ABORT_IF(!observer, "Training observer object expired. Make sure all registered observers exist during scheduler life time");
       observer->actAfterLoaded(*this);
+    }
   }
 
   void load(const std::string& name) {
@@ -302,6 +316,9 @@ class TrainingState {
   }
 
 private:
-  std::vector<Ptr<TrainingObserver>> observers_;
+  // this needs to be a vector of weak pointers, otherwise
+  // it is likely to cause circular dependencies.
+  std::vector<Weak<TrainingObserver>> wObservers_;
+
 };
 }  // namespace marian

From 9f294036270946f408fa9c0e4b45e0fa6ce48df6 Mon Sep 17 00:00:00 2001
From: Marcin Junczys-Dowmunt <marcinjd@microsoft.com>
Date: Fri, 6 Mar 2020 20:32:30 -0800
Subject: [PATCH 835/838] version and changelog

---
 CHANGELOG.md | 4 ++++
 VERSION      | 2 +-
 2 files changed, 5 insertions(+), 1 deletion(-)

diff --git a/CHANGELOG.md b/CHANGELOG.md
index 29b281725..439f1eb39 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -34,6 +34,9 @@ and this project adheres to [Semantic Versioning](http://semver.org/spec/v2.0.0.
 - Gradient-checkpointing
 
 ### Fixed
+- Replace value for INVALID_PATH_SCORE with std::numer_limits<float>::lowest() 
+  to avoid overflow with long sequences
+- Break up potential circular references for GraphGroup*
 - Fix empty source batch entries with batch purging
 - Clear RNN chache in transformer model, add correct hash functions to nodes
 - Gather-operation for all index sizes
@@ -65,6 +68,7 @@ and this project adheres to [Semantic Versioning](http://semver.org/spec/v2.0.0.
 - Dropped support for g++-4.9
 - Simplified file stream and temporary file handling
 - Unified node intializers, same function API.
+- Remove overstuff/understuff code
 
 ## [1.8.0] - 2019-09-04
 
diff --git a/VERSION b/VERSION
index e8eb33e54..08b7a0e2a 100644
--- a/VERSION
+++ b/VERSION
@@ -1 +1 @@
-v1.8.40
+v1.8.43

From bec7e029b10921686f6aff9966f374baa2b14134 Mon Sep 17 00:00:00 2001
From: Marcin Junczys-Dowmunt <marcinjd@microsoft.com>
Date: Fri, 6 Mar 2020 20:46:11 -0800
Subject: [PATCH 836/838] bump version

---
 CHANGELOG.md         | 2 ++
 VERSION              | 2 +-
 regression-tests     | 2 +-
 src/3rd_party/fbgemm | 2 +-
 4 files changed, 5 insertions(+), 3 deletions(-)

diff --git a/CHANGELOG.md b/CHANGELOG.md
index 625dcd9df..9972e7078 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -9,6 +9,7 @@ and this project adheres to [Semantic Versioning](http://semver.org/spec/v2.0.0.
 ## [Unreleased]
 
 ### Added
+- Add support for compiling on Mac (and clang)
 - An option for resetting stalled validation metrics
 - Add CMAKE options to disable compilation for specific GPU SM types
 - An option to print word-level translation scores
@@ -57,6 +58,7 @@ and this project adheres to [Semantic Versioning](http://semver.org/spec/v2.0.0.
 - Compilation with CUDA 10.1
 
 ### Changed
+- Combine two for-loops in nth_element.cpp on CPU
 - Revert LayerNorm eps to old position, i.e. sigma' = sqrt(sigma^2 + eps)
 - Downgrade NCCL to 2.3.7 as 2.4.2 is buggy (hangs with larger models)
 - Return error signal on SIGTERM
diff --git a/VERSION b/VERSION
index e8eb33e54..a5969e9f7 100644
--- a/VERSION
+++ b/VERSION
@@ -1 +1 @@
-v1.8.40
+v1.8.51
diff --git a/regression-tests b/regression-tests
index 448cc1a8f..6a08849b2 160000
--- a/regression-tests
+++ b/regression-tests
@@ -1 +1 @@
-Subproject commit 448cc1a8ff2f37b102695dc5cea89cf285202ee7
+Subproject commit 6a08849b23f6c14eefbe12f4eb73dc638b962587
diff --git a/src/3rd_party/fbgemm b/src/3rd_party/fbgemm
index f78e60988..84e66a976 160000
--- a/src/3rd_party/fbgemm
+++ b/src/3rd_party/fbgemm
@@ -1 +1 @@
-Subproject commit f78e60988329b9207d086c743cafce1ac1bea3ab
+Subproject commit 84e66a976046180187724aff60a236c5378fde7c

From cf7f0321f8515fd44830a7008037c7f8ae557546 Mon Sep 17 00:00:00 2001
From: Martin Junczys-Dowmunt <Marcin.JunczysDowmunt@microsoft.com>
Date: Tue, 10 Mar 2020 00:29:55 +0000
Subject: [PATCH 837/838] Merged PR 11920: Compare external master against
 internal master

Compare external master against internal master. Just double checking.
---
 CHANGELOG.md                                  |   3 +
 CMakeLists.txt                                |  43 ++++---
 VERSION                                       |   2 +-
 cmake/FindCBLAS.cmake                         |   2 +-
 scripts/embeddings/export_embeddings.py       |  29 ++---
 src/3rd_party/CMakeLists.txt                  |  45 +++++++-
 src/3rd_party/half_float/umHalf.inl           |   4 +-
 .../pathie-cpp/src/entry_iterator.cpp         |   2 +-
 src/3rd_party/pathie-cpp/src/path.cpp         |   2 +-
 src/3rd_party/pathie-cpp/src/pathie.cpp       |   8 +-
 src/3rd_party/zstr/strict_fstream.hpp         |   2 +-
 src/CMakeLists.txt                            |   4 +
 src/command/marian_server.cpp                 |   2 +-
 src/common/cli_helper.h                       |   1 -
 src/common/config_parser.cpp                  |   5 +-
 src/common/definitions.h                      |  15 +++
 src/common/fastopt.cpp                        |  14 ++-
 src/common/fastopt.h                          |   5 +-
 src/common/file_stream.h                      |  17 ++-
 src/common/filesystem.h                       |  14 ++-
 src/common/intrusive_ptr.h                    |  22 ++--
 src/common/logging.cpp                        |   2 +-
 src/common/types.h                            |  38 +++++--
 src/common/utils.cpp                          |  12 +-
 src/data/batch.h                              |   2 +-
 src/data/batch_generator.h                    |  14 ++-
 src/data/corpus_base.h                        |   1 +
 src/data/default_vocab.cpp                    |   3 +-
 src/data/shortlist.h                          |   7 +-
 src/data/text_input.h                         |   1 +
 src/data/vocab_base.h                         |   1 +
 src/examples/mnist/dataset.h                  |   3 +
 src/examples/mnist/model.h                    |   8 +-
 src/examples/mnist/validator.h                |   4 +-
 src/functional/operators.h                    | 107 +++++++++---------
 src/graph/expression_graph.h                  |   6 -
 src/graph/node.h                              |  10 +-
 src/graph/node_initializers.h                 |  11 +-
 src/graph/node_operators_binary.h             |  17 +--
 src/graph/parameters.h                        |   8 +-
 src/layers/generic.h                          |   2 +
 src/layers/guided_alignment.h                 |   8 +-
 src/layers/loss.h                             |  19 ++--
 src/layers/weight.h                           |   1 +
 src/microsoft/quicksand.cpp                   |   3 +-
 src/microsoft/quicksand.h                     |   2 +
 src/models/costs.h                            |   9 ++
 src/models/encoder_decoder.h                  |   1 +
 src/models/model_base.h                       |   2 +
 src/models/model_task.h                       |   2 +
 src/models/s2s.h                              |   5 +-
 src/models/states.h                           |   7 +-
 src/optimizers/clippers.h                     |   1 +
 src/optimizers/optimizers.h                   |   4 +-
 src/rescorer/score_collector.h                |   1 +
 src/rnn/rnn.h                                 |   3 +-
 src/tensors/backend.h                         |   2 +-
 src/tensors/cpu/device.cpp                    |  49 ++++----
 src/tensors/cpu/fbgemm/expanded_gemm.h        |  42 ++++---
 src/tensors/cpu/tensor_operators.cpp          |  16 ++-
 src/tensors/rand.h                            |   4 +-
 src/tensors/tensor_operators.h                |   6 +-
 src/tests/prod.cpp                            |   2 +-
 src/tests/sqlite.cpp                          |   2 +
 src/training/communicator.cpp                 |   4 +-
 src/training/communicator.h                   |  11 +-
 .../gradient_dropping/sparse_tensor.h         |   2 +-
 src/training/graph_group.h                    |  12 +-
 src/training/graph_group_async.h              |   2 +-
 src/training/graph_group_multinode_sync.h     |   1 -
 src/training/graph_group_sync.h               |   1 -
 src/training/scheduler.h                      |   8 ++
 src/training/training_state.h                 |   2 +-
 src/training/validator.h                      |   8 ++
 src/translator/beam_search.h                  |  38 +++----
 src/translator/nth_element.cpp                |  68 +++++------
 src/translator/output_collector.h             |   1 +
 src/translator/scorers.h                      |   7 ++
 src/translator/translator.h                   |  11 ++
 79 files changed, 533 insertions(+), 332 deletions(-)

diff --git a/CHANGELOG.md b/CHANGELOG.md
index 439f1eb39..317a3cad8 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -9,6 +9,8 @@ and this project adheres to [Semantic Versioning](http://semver.org/spec/v2.0.0.
 ## [Unreleased]
 
 ### Added
+- Add support for compiling on Mac (and clang)
+- An option for resetting stalled validation metrics
 - Add CMAKE options to disable compilation for specific GPU SM types
 - An option to print word-level translation scores
 - An option to turn off automatic detokenization from SentencePiece
@@ -59,6 +61,7 @@ and this project adheres to [Semantic Versioning](http://semver.org/spec/v2.0.0.
 - Compilation with CUDA 10.1
 
 ### Changed
+- Combine two for-loops in nth_element.cpp on CPU
 - Revert LayerNorm eps to old position, i.e. sigma' = sqrt(sigma^2 + eps)
 - Downgrade NCCL to 2.3.7 as 2.4.2 is buggy (hangs with larger models)
 - Return error signal on SIGTERM
diff --git a/CMakeLists.txt b/CMakeLists.txt
index c442931f9..c27761cf0 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -24,6 +24,7 @@ option(USE_CCACHE "Use ccache compiler cache (https://ccache.dev)" OFF)
 option(USE_CUDNN "Use CUDNN library" OFF)
 option(USE_DOXYGEN "Build documentation with Doxygen" ON)
 option(USE_FBGEMM "Use FBGEMM" OFF)
+option(USE_MKL "Compile with MKL support" ON)
 option(USE_MPI "Use MPI library" OFF)
 option(USE_NCCL "Use NCCL library" ON)
 option(USE_SENTENCEPIECE "Download and compile SentencePiece" OFF)
@@ -33,7 +34,7 @@ option(USE_STATIC_LIBS "Link statically against non-system libs" OFF)
 if(USE_CCACHE)
 find_program(CCACHE_PROGRAM ccache)
 if(CCACHE_PROGRAM)
-  message(STATUS "Found and will be using ccache for faster repeat compilation (use cmake -DUSE_CCACHE=off to disable).")
+  message(STATUS "Will be using ccache for faster repeat compilation (use cmake -DUSE_CCACHE=off to disable).")
   set_property(GLOBAL PROPERTY RULE_LAUNCH_COMPILE "${CCACHE_PROGRAM}")
 else(CCACHE_PROGRAM)
   message(WARNING "Compilation with ccache requested but no ccache found.")
@@ -141,20 +142,32 @@ else(MSVC)
     add_definitions(-DUSE_FBGEMM=1)
   endif(USE_FBGEMM)
 
-  set(DISABLE_GLOBALLY "-Wno-unused-result")
+  if (CMAKE_CXX_COMPILER_ID MATCHES "Clang" AND CMAKE_CXX_COMPILER_VERSION VERSION_GREATER 9.0)
+    # Clang-10.0.0 complains when CUDA is newer than 10.1
+    set(CLANG_IGNORE_UNKNOWN_CUDA "-Wno-unknown-cuda-version")
+  endif()
+  set(DISABLE_GLOBALLY "-Wno-unused-result -Wno-unknown-warning-option ${CLANG_IGNORE_UNKNOWN_CUDA}")
 
   # These are used in src/CMakeLists.txt on a per-target basis
-  list(APPEND ALL_WARNINGS -Wall; -Werror; -Wno-unused-result; -Wno-deprecated; -Wno-pragmas; -Wno-unused-parameter; -Wextra; -Wno-unused-function;
-                          -Wno-unused-value; -Wno-unknown-pragmas; -Wno-sign-compare; -Wno-missing-field-initializers;)
+  list(APPEND ALL_WARNINGS -Wall; -Werror; -Wextra; -Wno-unused-result; -Wno-deprecated;
+    -Wno-pragmas; -Wno-unused-parameter; -Wno-unused-function;
+    -Wno-unused-value; -Wno-unknown-pragmas; -Wno-sign-compare;
+    -Wno-missing-field-initializers;)
 
   # This warning does not exist prior to gcc 5.0
   if(CMAKE_COMPILER_IS_GNUCC AND CMAKE_CXX_COMPILER_VERSION VERSION_GREATER 5.0)
-    list(APPEND ALL_WARNINGS -Wsuggest-override)
+    list(APPEND ALL_WARNINGS -Wsuggest-override -Wno-int-in-bool-context)
   endif()
 
-  set(CMAKE_CXX_FLAGS                 "-std=c++11 -pthread -Wl,--no-as-needed -fPIC ${DISABLE_GLOBALLY} -march=${BUILD_ARCH} ${INTRINSICS}")
-  set(CMAKE_CXX_FLAGS_RELEASE         "-Ofast -m64 -funroll-loops -ffinite-math-only -g -rdynamic")
-  set(CMAKE_CXX_FLAGS_DEBUG           "-O0 -g -rdynamic")
+  if(CMAKE_COMPILER_IS_GNUCC)
+    # these flags are not known to clang
+    set(CMAKE_GCC_FLAGS "-Wl,--no-as-needed")
+    set(CMAKE_RDYNAMIC_FLAG "-rdynamic")
+  endif(CMAKE_COMPILER_IS_GNUCC)
+
+  set(CMAKE_CXX_FLAGS                 "-std=c++11 -pthread ${CMAKE_GCC_FLAGS} -fPIC ${DISABLE_GLOBALLY} -march=${BUILD_ARCH} ${INTRINSICS}")
+  set(CMAKE_CXX_FLAGS_RELEASE         "-Ofast -m64 -funroll-loops -ffinite-math-only -g ${CMAKE_RDYNAMIC_FLAG}")
+  set(CMAKE_CXX_FLAGS_DEBUG           "-O0 -g ${CMAKE_RDYNAMIC_FLAG}")
   set(CMAKE_CXX_FLAGS_SLIM            "-Ofast -m64 -funroll-loops -ffinite-math-only -DNDEBUG")
   set(CMAKE_CXX_FLAGS_RELWITHDEBINFO  "${CMAKE_CXX_FLAGS_RELEASE}")
   set(CMAKE_CXX_FLAGS_PROFILE         "${CMAKE_CXX_FLAGS_RELEASE} -pg")
@@ -162,9 +175,9 @@ else(MSVC)
   set(CMAKE_CXX_FLAGS_PROFUSE         "${CMAKE_CXX_FLAGS_RELEASE} -fprofile-use -fprofile-correction")
 
   # these need to be set separately
-  set(CMAKE_C_FLAGS                 "-pthread -Wl,--no-as-needed -fPIC ${DISABLE_GLOBALLY} -march=${BUILD_ARCH} ${INTRINSICS}")
-  set(CMAKE_C_FLAGS_RELEASE         "-O3 -m64 -funroll-loops -ffinite-math-only -g -rdynamic")
-  set(CMAKE_C_FLAGS_DEBUG           "-O0 -g -rdynamic")
+  set(CMAKE_C_FLAGS                 "-pthread ${CMAKE_GCC_FLAGS} -fPIC ${DISABLE_GLOBALLY} -march=${BUILD_ARCH} ${INTRINSICS}")
+  set(CMAKE_C_FLAGS_RELEASE         "-O3 -m64 -funroll-loops -ffinite-math-only -g ${CMAKE_RDYNAMIC_FLAG}")
+  set(CMAKE_C_FLAGS_DEBUG           "-O0 -g ${CMAKE_RDYNAMIC_FLAG}")
   set(CMAKE_C_FLAGS_SLIM            "-O3 -m64 -funroll-loops -ffinite-math-only -DNDEBUG")
   set(CMAKE_C_FLAGS_RELWITHDEBINFO  "${CMAKE_C_FLAGS_RELEASE}")
   set(CMAKE_C_FLAGS_PROFILE         "${CMAKE_C_FLAGS_RELEASE} -pg")
@@ -204,7 +217,7 @@ if(CUDA_FOUND)
   if((CUDA_VERSION VERSION_EQUAL "10.0" OR CUDA_VERSION VERSION_GREATER "10.0") AND (CMAKE_VERSION VERSION_LESS "3.12.2"))
       message(WARNING "On some Unix systems CUDA 10.0+ requires CMake 3.12.2+; you use CMake ${CMAKE_VERSION}")
   endif()
-  
+
   if(COMPILE_CUDA_SM35)
     LIST(APPEND COMPUTE -arch=sm_35; -gencode=arch=compute_35,code=sm_35;)                             # Tesla K40 and above
   endif(COMPILE_CUDA_SM35)
@@ -323,13 +336,15 @@ if(USE_MPI)
 endif(USE_MPI)
 
 if(COMPILE_CPU)
-  find_package(MKL)
+  if(USE_MKL)
+    find_package(MKL)
+  endif(USE_MKL)
   if(MKL_FOUND)
     include_directories(${MKL_INCLUDE_DIR})
     set(EXT_LIBS ${EXT_LIBS} ${MKL_LIBRARIES})
     add_definitions(-DBLAS_FOUND=1 -DMKL_FOUND=1)
   else(MKL_FOUND)
-    set(BLA_VENDOR "OpenBLAS")
+    set(BLAS_VENDOR "OpenBLAS")
     find_package(BLAS)
     if(BLAS_FOUND)
       include(FindCBLAS)
diff --git a/VERSION b/VERSION
index 08b7a0e2a..a5969e9f7 100644
--- a/VERSION
+++ b/VERSION
@@ -1 +1 @@
-v1.8.43
+v1.8.51
diff --git a/cmake/FindCBLAS.cmake b/cmake/FindCBLAS.cmake
index 631da99b4..97b0d3f8a 100644
--- a/cmake/FindCBLAS.cmake
+++ b/cmake/FindCBLAS.cmake
@@ -54,7 +54,7 @@ MACRO(CHECK_ALL_LIBRARIES LIBRARIES INCLUDE _prefix _name _flags _list _include
       IF(APPLE) 
         FIND_LIBRARY(${_prefix}_${_library}_LIBRARY
           NAMES ${_library}
-          PATHS /usr/local/lib /usr/lib /usr/local/lib64 /usr/lib64 ENV 
+          PATHS /usr/local/lib /usr/lib /usr/local/lib64 /usr/lib64 /usr/local/opt/openblas/lib ENV
           DYLD_LIBRARY_PATH 
           )
       ELSE(APPLE)
diff --git a/scripts/embeddings/export_embeddings.py b/scripts/embeddings/export_embeddings.py
index 7aef07d5d..3b4f3314b 100755
--- a/scripts/embeddings/export_embeddings.py
+++ b/scripts/embeddings/export_embeddings.py
@@ -9,18 +9,22 @@
 
 
 def main():
-    desc = """Export word embedding from model"""
+    desc = """Export word embeddings from model"""
     parser = argparse.ArgumentParser(
         formatter_class=argparse.RawDescriptionHelpFormatter, description=desc)
-    parser.add_argument("-m", "--model", help="Model file", required=True)
-    parser.add_argument(
-        "-o", "--output-prefix", help="Output files prefix", required=True)
+    parser.add_argument("-m", "--model", help="path to model.npz file", required=True)
+    parser.add_argument("-o", "--output-prefix", help="prefix for output files", required=True)
     args = parser.parse_args()
 
     print("Loading model")
     model = np.load(args.model)
     special = yaml.load(model["special:model.yml"][:-1].tobytes())
 
+    if special["tied-embeddings-all"] or special["tied-embeddings-src"]:
+        all_emb = model["Wemb"]
+        export_emb(args.output_prefix + ".all", all_emb)
+        exit()
+
     if special["type"] == "amun":
         enc_emb = model["Wemb"]
         dec_emb = model["Wemb_dec"]
@@ -28,16 +32,15 @@ def main():
         enc_emb = model["encoder_Wemb"]
         dec_emb = model["decoder_Wemb"]
 
-    with open(args.output_prefix + ".src", "w") as out:
-        out.write("{0} {1}\n".format(*enc_emb.shape))
-        for i in range(enc_emb.shape[0]):
-            vec = " ".join("{0:.8f}".format(v) for v in enc_emb[i])
-            out.write("{0} {1}\n".format(i, vec))
+    export_emb(args.output_prefix + ".src", enc_emb)
+    export_emb(args.output_prefix + ".trg", dec_emb)
+
 
-    with open(args.output_prefix + ".trg", "w") as out:
-        out.write("{0} {1}\n".format(*dec_emb.shape))
-        for i in range(dec_emb.shape[0]):
-            vec = " ".join("{0:.8f}".format(v) for v in dec_emb[i])
+def export_emb(filename, emb):
+    with open(filename, "w") as out:
+        out.write("{0} {1}\n".format(*emb.shape))
+        for i in range(emb.shape[0]):
+            vec = " ".join("{0:.8f}".format(v) for v in emb[i])
             out.write("{0} {1}\n".format(i, vec))
 
 
diff --git a/src/3rd_party/CMakeLists.txt b/src/3rd_party/CMakeLists.txt
index e100a6198..9f3981af2 100644
--- a/src/3rd_party/CMakeLists.txt
+++ b/src/3rd_party/CMakeLists.txt
@@ -15,12 +15,22 @@ if(USE_FBGEMM)
 
   if(NOT MSVC)
     # only locally disabled for the 3rd_party folder
-    set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -Wno-unused-value -Wno-unused-parameter -Wno-unused-variable -Wno-unused-function")
+    # set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -Wno-unused-value -Wno-unused-parameter -Wno-unused-variable -Wno-unused-function")
+    set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -Wno-unused")
   endif()
 
   set(FBGEMM_BUILD_TESTS OFF CACHE BOOL "Disable fbgemm tests")
   set(FBGEMM_BUILD_BENCHMARKS OFF CACHE BOOL "Disable fbgemm benchmark")
   add_subdirectory(./fbgemm)
+
+  # asmjit (3rd-party submodule of fbgemm) sets -Wall -Wextra near the end of
+  # the compile options, invalidating any -Wno-... flags that we may have set
+  # earlier. Let's remove them.
+  get_property(ASMJIT_COMPILE_OPTIONS TARGET asmjit PROPERTY COMPILE_OPTIONS)
+  list(REMOVE_ITEM ASMJIT_COMPILE_OPTIONS -Wall -Wextra)
+  set_property(TARGET asmjit PROPERTY COMPILE_OPTIONS ${ASMJIT_COMPILE_OPTIONS})
+  message("   ASMJIT COMPILE FLAGS: ${ASMJIT_COMPILE_OPTIONS}")
+
 endif(USE_FBGEMM)
 
 if(USE_SENTENCEPIECE)
@@ -39,7 +49,7 @@ if(USE_SENTENCEPIECE)
     message(WARNING "You are compiling SentencePiece binaries with -DUSE_STATIC_LIBS=on. \
     This will cause spm_train to segfault. No need to worry if you do not intend to use that binary. \
     Marian support for SentencePiece will work fine.")
-    
+
     set(SPM_ENABLE_SHARED OFF CACHE BOOL "Builds shared libaries in addition to static libraries." FORCE)
     set(SPM_TCMALLOC_STATIC ON CACHE BOOL "Link static library of TCMALLOC." FORCE)
   else(USE_STATIC_LIBS)
@@ -51,8 +61,19 @@ if(USE_SENTENCEPIECE)
   include_directories(./sentencepiece)
 
   set_target_properties(spm_encode spm_decode spm_train spm_normalize spm_export_vocab
-                        PROPERTIES
-                        RUNTIME_OUTPUT_DIRECTORY "${CMAKE_BINARY_DIR}")
+    PROPERTIES RUNTIME_OUTPUT_DIRECTORY "${CMAKE_BINARY_DIR}")
+
+  if (CMAKE_CXX_COMPILER_ID MATCHES "Clang")
+    foreach(t sentencepiece sentencepiece_train sentencepiece_train-static
+        spm_decode spm_encode spm_export_vocab spm_normalize spm_train)
+      set_property(TARGET ${t} APPEND_STRING PROPERTY COMPILE_FLAGS " -Wno-tautological-compare -Wno-unused")
+      if (CMAKE_CXX_COMPILER_VERSION VERSION_GREATER 9.0)
+        set_property(TARGET ${t} APPEND_STRING PROPERTY COMPILE_FLAGS " -Wno-range-loop-construct")
+      endif()
+      # get_property(SENTENCEPIECE_COMPILE_FLAGS TARGET ${t} PROPERTY COMPILE_FLAGS)
+      # message("-- SENTENCPIECE: compile flags for target ${t}: ${SENTENCEPIECE_COMPILE_FLAGS}")
+    endforeach(t)
+  endif()
 
   if(USE_STATIC_LIBS)
     set(CMAKE_FIND_LIBRARY_SUFFIXES ${_ORIG_CMAKE_FIND_LIBRARY_SUFFIXES})
@@ -63,6 +84,22 @@ include_directories(./SQLiteCpp/include)
 include_directories(./CLI)
 include_directories(./pathie-cpp/include)
 
+if (CMAKE_CXX_COMPILER_ID MATCHES "Clang")
+  #set_target_properties(SQLiteCpp PROPERTIES COMPILE_FLAGS
+  set_property(TARGET SQLiteCpp APPEND_STRING PROPERTY COMPILE_FLAGS
+    " -Wno-parentheses-equality -Wno-unused-value")
+  if (CMAKE_CXX_COMPILER_VERSION VERSION_GREATER 9.0)
+    set_property(TARGET SQLiteCpp APPEND_STRING PROPERTY COMPILE_FLAGS
+      " -Wno-implicit-int-float-conversion")
+  endif()
+  set_property(TARGET libyaml-cpp APPEND_STRING PROPERTY COMPILE_FLAGS
+    " -fPIC -Wno-unused-value")
+  set_property(TARGET pathie-cpp APPEND_STRING PROPERTY COMPILE_FLAGS
+    " -fPIC -Wno-unused-value")
+endif()
+
+
+
 include_directories(./zlib)
 
 include(ExternalProject)
diff --git a/src/3rd_party/half_float/umHalf.inl b/src/3rd_party/half_float/umHalf.inl
index 4f065dad3..3f5285a2e 100644
--- a/src/3rd_party/half_float/umHalf.inl
+++ b/src/3rd_party/half_float/umHalf.inl
@@ -186,7 +186,7 @@ inline HalfFloat& HalfFloat::operator= (float other)
 inline bool HalfFloat::operator== (HalfFloat other) const
 {
 	// +0 and -0 are considered to be equal
-	if (!(bits << 1u) && !(other.bits << 1u))return true;
+	if ((bits << 1u) == 0 && (other.bits << 1u) == 0) return true;
 
 	return bits == other.bits && !this->IsNaN();
 }
@@ -194,7 +194,7 @@ inline bool HalfFloat::operator== (HalfFloat other) const
 inline bool HalfFloat::operator!= (HalfFloat other) const
 {
 	// +0 and -0 are considered to be equal
-	if (!(bits << 1u) && !(other.bits << 1u))return false;
+	if ((bits << 1u) == 0 && (other.bits << 1u) == 0) return false;
 
 	return bits != other.bits || this->IsNaN();
 }
diff --git a/src/3rd_party/pathie-cpp/src/entry_iterator.cpp b/src/3rd_party/pathie-cpp/src/entry_iterator.cpp
index 24e5b1101..baaf53949 100644
--- a/src/3rd_party/pathie-cpp/src/entry_iterator.cpp
+++ b/src/3rd_party/pathie-cpp/src/entry_iterator.cpp
@@ -31,7 +31,7 @@
 #include "../include/path.hpp"
 #include "../include/errors.hpp"
 
-#if defined(__unix__)
+#if defined(__unix__) || defined(__APPLE__)
 #include <sys/types.h>
 #include <dirent.h>
 #include <errno.h>
diff --git a/src/3rd_party/pathie-cpp/src/path.cpp b/src/3rd_party/pathie-cpp/src/path.cpp
index 07fba2711..e732e09c5 100644
--- a/src/3rd_party/pathie-cpp/src/path.cpp
+++ b/src/3rd_party/pathie-cpp/src/path.cpp
@@ -902,7 +902,7 @@ Path Path::pwd()
  */
 Path Path::exe()
 {
-#if defined(__linux__)
+#if defined(__linux__) || defined(__APPLE__)
   char buf[PATH_MAX];
   ssize_t size = ::readlink("/proc/self/exe", buf, PATH_MAX);
 
diff --git a/src/3rd_party/pathie-cpp/src/pathie.cpp b/src/3rd_party/pathie-cpp/src/pathie.cpp
index c0abc689a..d8e567e10 100644
--- a/src/3rd_party/pathie-cpp/src/pathie.cpp
+++ b/src/3rd_party/pathie-cpp/src/pathie.cpp
@@ -143,7 +143,7 @@ std::string Pathie::convert_encodings(const char* from_encoding, const char* to_
     errno  = 0;
     errsav = 0;
 
-#ifdef BSD
+#if defined(BSD) && ! defined(__APPLE__) //Since MacOS evolved from BSD, it is captured here but the iconv on macos behaves differently
     // What the heck. FreeBSD violates POSIX.1-2008: it declares iconv()
     // differently than mandated by POSIX: http://pubs.opengroup.org/onlinepubs/9699919799/functions/iconv.html
     // (it declares a `const' where it must not be).
@@ -181,11 +181,10 @@ std::string Pathie::convert_encodings(const char* from_encoding, const char* to_
 std::string Pathie::utf8_to_filename(const std::string& utf8)
 {
   bool fs_encoding_is_utf8 = false;
-
+  char* fsencoding = NULL;
 #if defined(__APPLE__) || defined(PATHIE_ASSUME_UTF8_ON_UNIX)
   fs_encoding_is_utf8 = true;
 #else
-  char* fsencoding = NULL;
   fsencoding = nl_langinfo(CODESET);
   fs_encoding_is_utf8 = (strcmp(fsencoding, "UTF-8") == 0);
 #endif
@@ -206,11 +205,10 @@ std::string Pathie::utf8_to_filename(const std::string& utf8)
 std::string Pathie::filename_to_utf8(const std::string& native_filename)
 {
   bool fs_encoding_is_utf8 = false;
-
+  char* fsencoding = NULL;
 #if defined(__APPLE__) || defined(PATHIE_ASSUME_UTF8_ON_UNIX)
   fs_encoding_is_utf8 = true;
 #else
-  char* fsencoding = NULL;
   fsencoding = nl_langinfo(CODESET);
   fs_encoding_is_utf8 = (strcmp(fsencoding, "UTF-8") == 0);
 #endif
diff --git a/src/3rd_party/zstr/strict_fstream.hpp b/src/3rd_party/zstr/strict_fstream.hpp
index fbc641640..7b1173931 100644
--- a/src/3rd_party/zstr/strict_fstream.hpp
+++ b/src/3rd_party/zstr/strict_fstream.hpp
@@ -27,7 +27,7 @@ static std::string strerror()
     {
         buff = "Unknown error";
     }
-#elif (_POSIX_C_SOURCE >= 200112L || _XOPEN_SOURCE >= 600) && ! _GNU_SOURCE
+#elif (_POSIX_C_SOURCE >= 200112L || _XOPEN_SOURCE >= 600 || __APPLE__) && ! _GNU_SOURCE
 // XSI-compliant strerror_r()
     if (strerror_r(errno, &buff[0], buff.size()) != 0)
     {
diff --git a/src/CMakeLists.txt b/src/CMakeLists.txt
index c1a513908..839a64f07 100644
--- a/src/CMakeLists.txt
+++ b/src/CMakeLists.txt
@@ -215,6 +215,10 @@ if(COMPILE_SERVER)
   set(EXECUTABLES ${EXECUTABLES} marian_server)
 endif(COMPILE_SERVER)
 
+if(APPLE) # This is a dependency of pathie but I can't seem to link it into that CMakeLists because we're not compiling it as a library.
+   set(EXT_LIBS ${EXT_LIBS} iconv)
+endif()
+
 foreach(exec ${EXECUTABLES})
   target_link_libraries(${exec} marian ${EXT_LIBS} ${EXT_LIBS} ${CMAKE_THREAD_LIBS_INIT})
   if(CUDA_FOUND)
diff --git a/src/command/marian_server.cpp b/src/command/marian_server.cpp
index 5df944d71..e4074bd1b 100644
--- a/src/command/marian_server.cpp
+++ b/src/command/marian_server.cpp
@@ -44,7 +44,7 @@ int main(int argc, char **argv) {
 
   // Error Codes for error code meanings
   // http://www.boost.org/doc/libs/1_55_0/doc/html/boost_asio/reference.html
-  translate.on_error = [](Ptr<WSServer::Connection> connection,
+  translate.on_error = [](Ptr<WSServer::Connection> /*connection*/,
                           const SimpleWeb::error_code &ec) {
     LOG(error, "Connection error: ({}) {}", ec.value(), ec.message());
   };
diff --git a/src/common/cli_helper.h b/src/common/cli_helper.h
index 27761372a..4477f0c02 100644
--- a/src/common/cli_helper.h
+++ b/src/common/cli_helper.h
@@ -15,7 +15,6 @@ static inline std::string InterpolateEnvVars(std::string str) {
   // presently has the form /hdfs/VC instead of /{gfs,hdfs}/CLUSTER/VC
 
   // Catch stdin/stdout and do not process
-  std::cerr << str << std::endl;
   if(str == "stdin" || str == "stdout") {
     return str;
   }
diff --git a/src/common/config_parser.cpp b/src/common/config_parser.cpp
index e7fd0a714..8dfe28cb8 100755
--- a/src/common/config_parser.cpp
+++ b/src/common/config_parser.cpp
@@ -525,6 +525,8 @@ void ConfigParser::addOptionsValidation(cli::CLIWrapper& cli) {
       "Metric to use during validation: cross-entropy, ce-mean-words, perplexity, valid-script, "
       "translation, bleu, bleu-detok. Multiple metrics can be specified",
       {"cross-entropy"});
+  cli.add<bool>("--valid-reset-stalled",
+     "Reset all stalled validation metrics when the training is restarted");
   cli.add<size_t>("--early-stopping",
      "Stop if the first validation metric does not improve for  arg  consecutive validation steps",
      10);
@@ -553,7 +555,8 @@ void ConfigParser::addOptionsValidation(cli::CLIWrapper& cli) {
       "Size of mini-batch used during validation",
       32);
   cli.add<size_t>("--valid-max-length",
-      "Maximum length of a sentence in a validating sentence pair",
+      "Maximum length of a sentence in a validating sentence pair. "
+      "Sentences longer than valid-max-length are cropped to valid-max-length",
       1000);
 
   // options for validation script
diff --git a/src/common/definitions.h b/src/common/definitions.h
index 45f5d5638..96eb6ed19 100755
--- a/src/common/definitions.h
+++ b/src/common/definitions.h
@@ -10,6 +10,21 @@
 #include <string>
 #include <vector>
 
+// The macro MAYBE_UNUSED is used to selectively disable
+// unused-variable warnings. C++17 defines the attribute
+// [[maybe_unused]], but I don't think we're at C++17 yet. We can add it when we reach C++17.
+// The compilers gcc and clang (and maybe others) define
+// __has_attribute and support __attribute__(unused) in C++11,
+#if defined __has_attribute
+#  if __has_attribute(unused)
+#    define MAYBE_UNUSED __attribute__((unused))
+#  else
+#    define MAYBE_UNUSED
+#  endif
+#else
+#  define MAYBE_UNUSED
+#endif
+
 #define THREAD_GUARD(body) [&]() { body; }() // test if THREAD_GUARD is neccessary, remove if no problems occur.
 #define NodeOp(op) [=]() { op; }
 
diff --git a/src/common/fastopt.cpp b/src/common/fastopt.cpp
index ccdd7b543..9af8e8445 100644
--- a/src/common/fastopt.cpp
+++ b/src/common/fastopt.cpp
@@ -84,10 +84,16 @@ std::vector<T> As<std::vector<T>>::apply(const FastOpt& node) {
 // specializations for simple vector types
 template struct As<std::vector<bool>>;
 template struct As<std::vector<int>>;
-// Windows and Unix based OS have different type definitions for 'unsigned long'.
-// So, we need an explicit definition for uint64_t. Otherwise, there's a linking error on windows.
+// Windows, Linux based OS and Mac have different type definitions for 'unsigned long'.
+// So, we need an explicit definitions for uint64_t, that cover different platforms.
+// Otherwise, there's a linking error on windows or Linux or Mac.
 // https://software.intel.com/en-us/articles/size-of-long-integer-type-on-different-architecture-and-os/
-template struct As<std::vector<uint64_t>>;
+// https://stackoverflow.com/questions/32021860/c-should-you-size-t-with-a-regular-array
+// MacOS: size_t = unsigned long (8 bytes), uint64_t = unsigned long long (8 bytes)
+// Linux: size_t = unsigned long (8 bytes), uint64_t = unsigned long (8 bytes)
+// Windows: size_t = unsigned long long (8 bytes), uint64_t = unsigned long long (8 bytes)
+template struct As<std::vector<unsigned long long>>;
+template struct As<std::vector<unsigned long>>;
 template struct As<std::vector<float>>;
 template struct As<std::vector<double>>;
 template struct As<std::vector<std::string>>;
@@ -103,4 +109,4 @@ std::pair<T1, T2> As<std::pair<T1, T2>>::apply(const FastOpt& node) {
 template struct As<std::pair<int, int>>;
 
 }
-}
\ No newline at end of file
+}
diff --git a/src/common/fastopt.h b/src/common/fastopt.h
index 9ddc3c2c3..3f735660b 100644
--- a/src/common/fastopt.h
+++ b/src/common/fastopt.h
@@ -367,7 +367,8 @@ class FastOpt {
   }
 
   const FastOpt& operator[](const char* const key) const {
-    return operator[](crc::crc(key));
+    // MacOS requires explicit cast to size_t before we can use it.
+    return operator[]((size_t)crc::crc(key));
   }
 
   const FastOpt& operator[](const std::string& key) const {
@@ -375,4 +376,4 @@ class FastOpt {
   }
 };
 
-}
\ No newline at end of file
+}
diff --git a/src/common/file_stream.h b/src/common/file_stream.h
index c81512079..9bbf33599 100644
--- a/src/common/file_stream.h
+++ b/src/common/file_stream.h
@@ -7,10 +7,21 @@
 #include "common/filesystem.h"
 #include "common/logging.h"
 
+// Even when compiling with clang, __GNUC__ may be defined, so
+// we need to add some extra checks to avoid compile errors with
+// respect to -Wsuggest-override.
 #ifdef __GNUC__
-#pragma GCC diagnostic push
-#pragma GCC diagnostic ignored "-Wsuggest-override"
+#  pragma GCC diagnostic push
+#  pragma GCC diagnostic ignored "-Wunused-value"
+#  if defined(__has_warning)
+#    if __has_warning("-Wsuggest-override")
+#      pragma GCC diagnostic ignored "-Wsuggest-override"
+#    endif
+#  else
+#    pragma GCC diagnostic ignored "-Wsuggest-override"
+#  endif
 #endif
+
 #ifdef _MSC_VER
 #pragma warning(push) // 4101: 'identifier' : unreferenced local variable. One parameter variable in zstr.hpp is not used.
 #pragma warning(disable : 4101)
@@ -82,7 +93,7 @@ class TemporaryFile : public OutputFileStream {
 
   void NormalizeTempPrefix(std::string& base) const;
   void MakeTemp(const std::string& base);
- 
+
 };
 
 }  // namespace io
diff --git a/src/common/filesystem.h b/src/common/filesystem.h
index d95125c5b..d7cb3da68 100644
--- a/src/common/filesystem.h
+++ b/src/common/filesystem.h
@@ -7,9 +7,19 @@
 // @TODO: go back to canonical names for functions and objects
 // as specified in C++17 so it becomes easy to move in the future
 
+// Even when compiling with clang, __GNUC__ may be defined, so
+// we need to add some extra checks to avoid compile errors with
+// respect to -Wsuggest-override.
 #ifdef __GNUC__
-#pragma GCC diagnostic push
-#pragma GCC diagnostic ignored "-Wsuggest-override"
+#  pragma GCC diagnostic push
+#  pragma GCC diagnostic ignored "-Wunused-value"
+#  if defined(__has_warning)
+#    if __has_warning("-Wsuggest-override")
+#      pragma GCC diagnostic ignored "-Wsuggest-override"
+#    endif
+#  else
+#    pragma GCC diagnostic ignored "-Wsuggest-override"
+#  endif
 #endif
 
 #include "3rd_party/pathie-cpp/include/path.hpp"  // @TODO: update to latest Pathie
diff --git a/src/common/intrusive_ptr.h b/src/common/intrusive_ptr.h
index fa412a8b7..4fe501ba6 100644
--- a/src/common/intrusive_ptr.h
+++ b/src/common/intrusive_ptr.h
@@ -148,33 +148,33 @@ inline bool operator!=(const IntrusivePtr<T>& a, const IntrusivePtr<U>& b) {
 }
 
 template<class T>
-inline bool operator==(const IntrusivePtr<T>& a, T* b) {
-  return a.get() == b;
+inline bool operator==(const IntrusivePtr<T>& a, std::nullptr_t) {
+  return a.get() == 0;
 }
 
 template<class T>
-inline bool operator!=(const IntrusivePtr<T>& a, T* b) {
-  return a.get() != b;
+inline bool operator!=(const IntrusivePtr<T>& a, std::nullptr_t) {
+  return a.get() != 0;
 }
 
 template<class T>
-inline bool operator==(const IntrusivePtr<T>& a, std::nullptr_t) {
-  return a.get() == 0;
+inline bool operator==(const IntrusivePtr<T>& a, T* b) {
+  return a.get() == b;
 }
 
 template<class T>
-inline bool operator!=(const IntrusivePtr<T>& a, std::nullptr_t) {
-  return a.get() != 0;
+inline bool operator!=(const IntrusivePtr<T>& a, T* b) {
+  return a.get() != b;
 }
 
 template<class T>
 inline bool operator==(T* a, const IntrusivePtr<T>& b) {
-  return b.get();
+  return a == b.get();
 }
 
 template<class T>
 inline bool operator!=(T* a, const IntrusivePtr<T>& b) {
-  return b.get();
+  return a != b.get();
 }
 
 template<class T, class U>
@@ -223,5 +223,3 @@ namespace std {
     }
   };
 }
-
-
diff --git a/src/common/logging.cpp b/src/common/logging.cpp
index 93bfcbd89..62d76feea 100644
--- a/src/common/logging.cpp
+++ b/src/common/logging.cpp
@@ -124,7 +124,7 @@ static void setErrorHandlers() {
   std::set_terminate(unhandledException);
 #ifdef __unix__
   // catch segfaults
-  struct sigaction sa = { 0 };
+  struct sigaction sa = { {0} };
   sigemptyset(&sa.sa_mask);
   sa.sa_flags = SA_SIGINFO;
   sa.sa_sigaction = [](int /*signal*/, siginfo_t*, void*) { ABORT("Segmentation fault"); };
diff --git a/src/common/types.h b/src/common/types.h
index 552ae2254..4bc4f9ad0 100644
--- a/src/common/types.h
+++ b/src/common/types.h
@@ -254,7 +254,7 @@ enum class Type : size_t {
   packed16      = TypeClass::packed_type + 2u,                          // special type for FBGEMM, not meant to be used anywhere else, not meant to be accessed invidually. Internal actual type (uint16) is meaningless.
   packed8avx2   = TypeClass::packed_type + 1u + TypeClass::avx2_type,   // special type for FBGEMM with AVX2, not meant to be used anywhere else, not meant to be accessed invidually. Internal actual type (uint8) is meaningless.
   packed8avx512 = TypeClass::packed_type + 1u + TypeClass::avx512_type, // special type for FBGEMM with AVX512, not meant to be used anywhere else, not meant to be accessed invidually. Internal actual type (uint8) is meaningless.
-  
+
 };
 
 static inline size_t operator&(TypeClass typeClass, Type type) {
@@ -394,7 +394,7 @@ static Type inline typeFromString(const std::string& str) {
     return Type::float32;
   if(str == "float64")
     return Type::float64;
-  
+
   if(str == "packed16")
     return Type::packed16;
   if(str == "packed8avx2")
@@ -437,19 +437,35 @@ void matchOrAbort(Type type) {
 
 namespace typeFitting { // own namespace instead of in class, otherwise we get error "explicit specialization in non-namespace scope"
 
-  // compares max for different types as constexpr, so can be used at compile-time to determine if RequestType type max fits into ReturnType max, see std::conditional below.
+  // Helper function for fitsIntoMax() below
+  // Returns the 'capacity' of a type: number of digits for integers,
+  // max_exponent for floats. We ignore the mantissa for floats.
+  template<typename X> constexpr int capacity() {
+    static_assert(std::is_arithmetic<X>::value || std::is_same<X,HalfFloat>::value,
+                  "Wrong type for this template");
+    return (std::is_integral<X>::value
+            ? std::numeric_limits<X>::digits
+            : std::numeric_limits<X>::max_exponent);
+ }
+
+
+  // Compare max for different types as constexpr, so can be used at compile-time to determine if RequestType type max fits into ReturnType max, see std::conditional below.
   template <typename RequestType, typename ReturnType>
-  constexpr bool fitsIntoMax() { return std::numeric_limits<RequestType>::max() <= std::numeric_limits<ReturnType>::max(); } // for built-in types everything is constexpr
+  constexpr bool fitsIntoMax() {
+    // We can't just compare std::numeric_limits<>::max(), because Clang-10
+    // complains about rounding errors when implicitly converting int to float
+    return ((!std::is_integral<RequestType>::value // RequestType is a float
+             && std::is_integral<ReturnType>::value) // ReturnType an integer
+            ? capacity<RequestType>() < capacity<ReturnType>() // special case
+            : capacity<RequestType>() <= capacity<ReturnType>()); // normal case
+  } // for built-in types everything is constexpr
 
-  // add specializations here when needed
-  template <> constexpr bool fitsIntoMax<float16, float>() { return true; };  // for float16 conversion to float is not constexpr, hence specializations
-  template <> constexpr bool fitsIntoMax<float, float16>() { return false; }; // for float16 conversion to float is not constexpr, hence specializations
 }
 
 template <typename ReturnType>
 class NumericLimits {
 private:
-  
+
   template <typename MaxType> void setLimitsMax() {
     max    = (ReturnType)std::numeric_limits<MaxType>::max();
     lowest = (ReturnType)std::numeric_limits<MaxType>::lowest();
@@ -459,10 +475,14 @@ class NumericLimits {
   void setLimits() {
     // check if the maximum of type RequestType fits into ReturnType
     constexpr bool fits = typeFitting::fitsIntoMax<RequestType, ReturnType>();
+    // sanity check:
+    static_assert(fits || typeFitting::fitsIntoMax<ReturnType, RequestType>(),
+                  "RequestType doesn't fit into ReturnType, and ReturnType doesn't "
+                  "fit into RequestType. fitsIntoMax is broken!");
     // and then use the smaller of each types to determine max, min, lowest.
     using MaxType = typename std::conditional<fits, RequestType, ReturnType>::type;
     setLimitsMax<MaxType>();
-    // @TODO: should we rather abort if the RequestType does not fit into ReturnType instead of clipping to smaller type? 
+    // @TODO: should we rather abort if the RequestType does not fit into ReturnType instead of clipping to smaller type?
     // ABORT_IF(!fits, "Type {} is too small to contain max of type {}", typeId<ReturnType>(), typeId<RequestType>());
   }
 
diff --git a/src/common/utils.cpp b/src/common/utils.cpp
index 80aee431e..3acb756d1 100755
--- a/src/common/utils.cpp
+++ b/src/common/utils.cpp
@@ -8,12 +8,22 @@
 #include <sstream>
 #include <string>
 #include <set>
-#ifdef __unix__
+#if defined(__unix__) || defined(__APPLE__)
 #include <unistd.h>
 #endif
 #include <codecvt>
 #include <cwctype>
 
+// MACOS lacks HOST_NAME_MAX
+#ifndef HOST_NAME_MAX
+# if defined(_POSIX_HOST_NAME_MAX)
+#  define HOST_NAME_MAX _POSIX_HOST_NAME_MAX
+# elif defined(MAXHOSTNAMELEN)
+#  define HOST_NAME_MAX MAXHOSTNAMELEN
+# endif
+#endif
+
+
 namespace marian {
 namespace utils {
 
diff --git a/src/data/batch.h b/src/data/batch.h
index b1b109817..3c592b315 100755
--- a/src/data/batch.h
+++ b/src/data/batch.h
@@ -26,7 +26,7 @@ class Batch {
 
   virtual void setGuidedAlignment(std::vector<float>&&) = 0;
   virtual void setDataWeights(const std::vector<float>&) = 0;
-
+  virtual ~Batch() {};
 protected:
   std::vector<size_t> sentenceIds_;
 };
diff --git a/src/data/batch_generator.h b/src/data/batch_generator.h
index b44660865..f16a7a81c 100644
--- a/src/data/batch_generator.h
+++ b/src/data/batch_generator.h
@@ -66,10 +66,10 @@ class BatchGenerator : public RNGEngine {
   Ptr<Options> options_;
   bool restored_{false};
 
-  // replacing old shuffle_ with two variants that determine more fine-grained shuffling behavior. 
-  // Both set to false is equivalent to old shuffle_ == false. 
+  // replacing old shuffle_ with two variants that determine more fine-grained shuffling behavior.
+  // Both set to false is equivalent to old shuffle_ == false.
   // Now we can not shuffle the data, but shuffle batches. Useful for linear reading of very large data sets with pre-reading.
-  // Parameters like maxi-batch determine how much data is pre-read and sorted by length or other criteria. 
+  // Parameters like maxi-batch determine how much data is pre-read and sorted by length or other criteria.
   bool shuffleData_{false};    // determine if full data should be shuffled before reading and batching.
   bool shuffleBatches_{false}; // determine if batches should be shuffled after batching.
 
@@ -103,7 +103,7 @@ class BatchGenerator : public RNGEngine {
     };
 
     auto cmpNone = [](const Sample& a, const Sample& b) { return a.getId() < b.getId(); }; // sort in order of original ids = original data order unless shuffling
-    
+
     typedef std::function<bool(const Sample&, const Sample&)> cmp_type;
     typedef std::priority_queue<Sample, Samples, cmp_type> sample_queue;
 
@@ -229,7 +229,7 @@ class BatchGenerator : public RNGEngine {
 
   // this starts fillBatches() as a background operation
   void fetchBatchesAsync() {
-    ABORT_IF(futureBufferedBatches_.valid(), "attempted to restart futureBufferedBatches_ while still running");
+    ABORT_IF(futureBufferedBatches_.valid(), "Attempted to restart futureBufferedBatches_ while still running");
     futureBufferedBatches_ = threadPool_.enqueue([this]() {
       return fetchBatches();
     });
@@ -239,7 +239,9 @@ class BatchGenerator : public RNGEngine {
     if(bufferedBatches_.empty()) {
       // out of data: need to get next batch from background thread
       // We only get here if the future has been scheduled to run; it must be valid.
-      ABORT_IF(!futureBufferedBatches_.valid(), "attempted to wait for futureBufferedBatches_ when none pending");
+      ABORT_IF(!futureBufferedBatches_.valid(), "Attempted to wait for futureBufferedBatches_ when none pending.\n"
+          "This error often occurs when Marian tries to restore the training data iterator, but the corpus has been changed or replaced.\n"
+          "If you have changed the training corpus, add --no-restore-corpus to the training command and run it again.");
       bufferedBatches_ = std::move(futureBufferedBatches_.get());
       // if bg thread returns an empty swath, we hit the end of the epoch
       if (bufferedBatches_.empty()) {
diff --git a/src/data/corpus_base.h b/src/data/corpus_base.h
index f0d9b706a..e85e378ab 100755
--- a/src/data/corpus_base.h
+++ b/src/data/corpus_base.h
@@ -525,6 +525,7 @@ class CorpusBase
              const std::vector<Ptr<Vocab>>& vocabs,
              Ptr<Options> options);
 
+  virtual ~CorpusBase() {}
   virtual std::vector<Ptr<Vocab>>& getVocabs() = 0;
 
 protected:
diff --git a/src/data/default_vocab.cpp b/src/data/default_vocab.cpp
index 53f90a5d7..590e9931b 100644
--- a/src/data/default_vocab.cpp
+++ b/src/data/default_vocab.cpp
@@ -45,6 +45,7 @@ class DefaultVocab : public IVocab {
 
 public:
   // @TODO: choose between 'virtual' and 'final'. Can we derive from this class?
+  virtual ~DefaultVocab() {};
   virtual const std::string& canonicalExtension() const override { return suffixes_[0]; }
   virtual const std::vector<std::string>& suffixes() const override { return suffixes_; }
 
@@ -295,7 +296,7 @@ class DefaultVocab : public IVocab {
 class ClassVocab : public DefaultVocab {
 private:
   // Do nothing.
-  virtual void addRequiredVocabulary(const std::string& vocabPath, bool isJson) override { vocabPath; isJson; }
+  virtual void addRequiredVocabulary(const std::string& /*vocabPath*/, bool /*isJson*/) override {}
 
   // Not adding special class labels, only seen classes.
   virtual void create(const std::string& vocabPath,
diff --git a/src/data/shortlist.h b/src/data/shortlist.h
index e5d084942..177272df8 100644
--- a/src/data/shortlist.h
+++ b/src/data/shortlist.h
@@ -36,6 +36,8 @@ class Shortlist {
 
 class ShortlistGenerator {
 public:
+  virtual ~ShortlistGenerator() {}
+
   virtual Ptr<Shortlist> generate(Ptr<data::CorpusBatch> batch) const = 0;
 
   // Writes text version of (possibly) pruned short list to file
@@ -129,7 +131,6 @@ class LexicalShortlistGenerator : public ShortlistGenerator {
   Ptr<const Vocab> trgVocab_;
 
   size_t srcIdx_;
-  size_t trgIdx_;
   bool shared_{false};
 
   size_t firstNum_{100};
@@ -183,13 +184,12 @@ class LexicalShortlistGenerator : public ShortlistGenerator {
                             Ptr<const Vocab> srcVocab,
                             Ptr<const Vocab> trgVocab,
                             size_t srcIdx = 0,
-                            size_t trgIdx = 1,
+                            size_t /*trgIdx*/ = 1,
                             bool shared = false)
       : options_(options),
         srcVocab_(srcVocab),
         trgVocab_(trgVocab),
         srcIdx_(srcIdx),
-        trgIdx_(trgIdx),
         shared_(shared) {
     std::vector<std::string> vals = options_->get<std::vector<std::string>>("shortlist");
 
@@ -235,7 +235,6 @@ class LexicalShortlistGenerator : public ShortlistGenerator {
 
   virtual Ptr<Shortlist> generate(Ptr<data::CorpusBatch> batch) const override {
     auto srcBatch = (*batch)[srcIdx_];
-    // auto trgBatch = (*batch)[trgIdx_];
 
     // add firstNum most frequent words
     std::unordered_set<WordIndex> indexSet;
diff --git a/src/data/text_input.h b/src/data/text_input.h
index bdbcf64e9..db99ef6ae 100644
--- a/src/data/text_input.h
+++ b/src/data/text_input.h
@@ -37,6 +37,7 @@ class TextInput : public DatasetBase<SentenceTuple, TextIterator, CorpusBatch> {
   typedef SentenceTuple Sample;
 
   TextInput(std::vector<std::string> inputs, std::vector<Ptr<Vocab>> vocabs, Ptr<Options> options);
+  virtual ~TextInput() {}
 
   Sample next() override;
 
diff --git a/src/data/vocab_base.h b/src/data/vocab_base.h
index 6b6869d88..8c214c97e 100755
--- a/src/data/vocab_base.h
+++ b/src/data/vocab_base.h
@@ -57,6 +57,7 @@ class IVocab {
   virtual Word randWord() const {
     return Word::fromWordIndex(rand() % size());
   }
+  virtual ~IVocab() {};
 };
 
 class Options;
diff --git a/src/examples/mnist/dataset.h b/src/examples/mnist/dataset.h
index 0e73840bf..b0492b852 100644
--- a/src/examples/mnist/dataset.h
+++ b/src/examples/mnist/dataset.h
@@ -62,6 +62,7 @@ class DataBatch : public Batch {
   std::vector<Input> inputs_;
 
 public:
+
   std::vector<Input>& inputs() { return inputs_; }
 
   const std::vector<Input>& inputs() const { return inputs_; }
@@ -144,6 +145,8 @@ class MNISTData : public Dataset {
     loadData();
   }
 
+  virtual ~MNISTData(){}
+
   void loadData() override {
     ABORT_IF(paths_.size() != 2, "Paths to MNIST data files are not specified");
 
diff --git a/src/examples/mnist/model.h b/src/examples/mnist/model.h
index 24543ad1d..f7af16811 100755
--- a/src/examples/mnist/model.h
+++ b/src/examples/mnist/model.h
@@ -47,6 +47,8 @@ class MNISTLogsoftmax : public ILogProb {
 public:
   MNISTLogsoftmax() {}
 
+  virtual ~MNISTLogsoftmax(){}
+
   Logits apply(Ptr<IModel> model,
              Ptr<ExpressionGraph> graph,
              Ptr<data::Batch> batch,
@@ -61,13 +63,15 @@ class MnistFeedForwardNet : public IModel {
   typedef data::MNISTData dataset_type;
 
   template <class... Args>
-  MnistFeedForwardNet(Ptr<Options> options, Args... args)
+  MnistFeedForwardNet(Ptr<Options> options, Args... /*args*/)
       : options_(options), inference_(options->get<bool>("inference", false)) {}
 
+  virtual ~MnistFeedForwardNet(){}
+
   virtual Logits build(Ptr<ExpressionGraph> graph,
                      Ptr<data::Batch> batch,
                      bool /*clean*/ = false) override {
-    
+
     return Logits(apply(graph, batch, inference_));
   }
 
diff --git a/src/examples/mnist/validator.h b/src/examples/mnist/validator.h
index 7adc73f0b..232bb124e 100644
--- a/src/examples/mnist/validator.h
+++ b/src/examples/mnist/validator.h
@@ -19,7 +19,9 @@ class MNISTAccuracyValidator : public Validator<data::MNISTData, models::IModel>
     builder_ = models::createModelFromOptions(options, models::usage::translation);
   }
 
-  virtual void keepBest(const std::vector<Ptr<ExpressionGraph>>& graphs) override {
+  virtual ~MNISTAccuracyValidator(){}
+
+  virtual void keepBest(const std::vector<Ptr<ExpressionGraph>>& /*graphs*/) override {
     LOG(warn, "Keeping best model for MNIST examples is not supported");
   }
 
diff --git a/src/functional/operators.h b/src/functional/operators.h
index 41fc74b06..be6bb2e5c 100755
--- a/src/functional/operators.h
+++ b/src/functional/operators.h
@@ -7,55 +7,58 @@ namespace marian {
 namespace functional {
 
 // General template, will be used for any type without specializations
-// and will fail with an abort message.
+// and will fail at runtime with an abort message. Note that the
+// general template functions don't have named parameters on purpose,
+// because clang will warn about unused parameters during compilation.
+
 template <typename T>
 struct Ops {
-  static HOST_DEVICE_INLINE T tanh(const T& x) { ABORT("Unknown type"); }
-  static HOST_DEVICE_INLINE T sin(const T& x)  { ABORT("Unknown type"); }
-  static HOST_DEVICE_INLINE T cos(const T& x)  { ABORT("Unknown type"); }
-  static HOST_DEVICE_INLINE T tan(const T& x)  { ABORT("Unknown type"); }
-  static HOST_DEVICE_INLINE T log(const T& x)  { ABORT("Unknown type"); }
-  static HOST_DEVICE_INLINE T exp(const T& x)  { ABORT("Unknown type"); }
-  static HOST_DEVICE_INLINE T abs(const T& x)  { ABORT("Unknown type"); }
-  static HOST_DEVICE_INLINE T sqrt(const T& x) { ABORT("Unknown type"); }
-  static HOST_DEVICE_INLINE T neg(const T& x)  { ABORT("Unknown type"); }
-  static HOST_DEVICE_INLINE T sgn(const T& x)  { ABORT("Unknown type"); }
-
-  static HOST_DEVICE_INLINE T add(const T& x, const T& y)  { ABORT("Unknown type"); }
-  static HOST_DEVICE_INLINE T sub(const T& x, const T& y)  { ABORT("Unknown type"); }
-  static HOST_DEVICE_INLINE T mul(const T& x, const T& y)  { ABORT("Unknown type"); }
-  static HOST_DEVICE_INLINE T div(const T& x, const T& y)  { ABORT("Unknown type"); }
-
-  static HOST_DEVICE_INLINE T max(const T& x, const T& y)  { ABORT("Unknown type"); }
-  static HOST_DEVICE_INLINE T min(const T& x, const T& y)  { ABORT("Unknown type"); }
-  static HOST_DEVICE_INLINE T pow(const T& x, const T& y)  { ABORT("Unknown type"); }
-
-  static HOST_DEVICE_INLINE T negate(const T& x)  { ABORT("Unknown type"); }
-  static HOST_DEVICE_INLINE T eq(const T& x, const T& y)   { ABORT("Unknown type"); }
-  static HOST_DEVICE_INLINE T neq(const T& x, const T& y)  { ABORT("Unknown type"); }
-  static HOST_DEVICE_INLINE T gt(const T& x, const T& y)   { ABORT("Unknown type"); }
-  static HOST_DEVICE_INLINE T lt(const T& x, const T& y)   { ABORT("Unknown type"); }
-  static HOST_DEVICE_INLINE T geq(const T& x, const T& y)  { ABORT("Unknown type"); }
-  static HOST_DEVICE_INLINE T leq(const T& x, const T& y)  { ABORT("Unknown type"); }
-  static HOST_DEVICE_INLINE T _and(const T& x, const T& y) { ABORT("Unknown type"); } // 'and' is used by gcc
-  static HOST_DEVICE_INLINE T _or(const T& x, const T& y)  { ABORT("Unknown type"); } // 'or' is used by gcc
+  static HOST_DEVICE_INLINE T tanh(const T&) { ABORT("Unknown type"); }
+  static HOST_DEVICE_INLINE T sin(const T&)  { ABORT("Unknown type"); }
+  static HOST_DEVICE_INLINE T cos(const T&)  { ABORT("Unknown type"); }
+  static HOST_DEVICE_INLINE T tan(const T&)  { ABORT("Unknown type"); }
+  static HOST_DEVICE_INLINE T log(const T&)  { ABORT("Unknown type"); }
+  static HOST_DEVICE_INLINE T exp(const T&)  { ABORT("Unknown type"); }
+  static HOST_DEVICE_INLINE T abs(const T&)  { ABORT("Unknown type"); }
+  static HOST_DEVICE_INLINE T sqrt(const T&) { ABORT("Unknown type"); }
+  static HOST_DEVICE_INLINE T neg(const T&)  { ABORT("Unknown type"); }
+  static HOST_DEVICE_INLINE T sgn(const T&)  { ABORT("Unknown type"); }
+
+  static HOST_DEVICE_INLINE T add(const T&, const T&)  { ABORT("Unknown type"); }
+  static HOST_DEVICE_INLINE T sub(const T&, const T&)  { ABORT("Unknown type"); }
+  static HOST_DEVICE_INLINE T mul(const T&, const T&)  { ABORT("Unknown type"); }
+  static HOST_DEVICE_INLINE T div(const T&, const T&)  { ABORT("Unknown type"); }
+
+  static HOST_DEVICE_INLINE T max(const T&, const T&)  { ABORT("Unknown type"); }
+  static HOST_DEVICE_INLINE T min(const T&, const T&)  { ABORT("Unknown type"); }
+  static HOST_DEVICE_INLINE T pow(const T&, const T&)  { ABORT("Unknown type"); }
+
+  static HOST_DEVICE_INLINE T negate(const T&)  { ABORT("Unknown type"); }
+  static HOST_DEVICE_INLINE T eq(const T&, const T&)   { ABORT("Unknown type"); }
+  static HOST_DEVICE_INLINE T neq(const T&, const T&)  { ABORT("Unknown type"); }
+  static HOST_DEVICE_INLINE T gt(const T&, const T&)   { ABORT("Unknown type"); }
+  static HOST_DEVICE_INLINE T lt(const T&, const T&)   { ABORT("Unknown type"); }
+  static HOST_DEVICE_INLINE T geq(const T&, const T&)  { ABORT("Unknown type"); }
+  static HOST_DEVICE_INLINE T leq(const T&, const T&)  { ABORT("Unknown type"); }
+  static HOST_DEVICE_INLINE T _and(const T&, const T&) { ABORT("Unknown type"); } // 'and' is used by gcc
+  static HOST_DEVICE_INLINE T _or(const T&, const T&)  { ABORT("Unknown type"); } // 'or' is used by gcc
 
   // Neural Networks specific functions
-  static HOST_DEVICE_INLINE T sigmoid(const T& x)               { ABORT("Unknown type"); }
-  static HOST_DEVICE_INLINE T logaddexp(const T& x, const T& y) { ABORT("Unknown type"); }
-  static HOST_DEVICE_INLINE T clip(const T& x, const T& y)      { ABORT("Unknown type"); }
+  static HOST_DEVICE_INLINE T sigmoid(const T&)               { ABORT("Unknown type"); }
+  static HOST_DEVICE_INLINE T logaddexp(const T&, const T&) { ABORT("Unknown type"); }
+  static HOST_DEVICE_INLINE T clip(const T&, const T&)      { ABORT("Unknown type"); }
   // derivative of Clip, cut-off function
-  static HOST_DEVICE_INLINE T bump(const T& x, const T& y)      { ABORT("Unknown type"); }
-  static HOST_DEVICE_INLINE T relu(const T& x)                  { ABORT("Unknown type"); }
-  static HOST_DEVICE_INLINE T reluBack(const T& x)              { ABORT("Unknown type"); }
-  static HOST_DEVICE_INLINE T prelu(const T& x, const T& y)     { ABORT("Unknown type"); }
-  static HOST_DEVICE_INLINE T preluBack(const T& x, const T& y) { ABORT("Unknown type"); }
+  static HOST_DEVICE_INLINE T bump(const T&, const T&)      { ABORT("Unknown type"); }
+  static HOST_DEVICE_INLINE T relu(const T&)                  { ABORT("Unknown type"); }
+  static HOST_DEVICE_INLINE T reluBack(const T&)              { ABORT("Unknown type"); }
+  static HOST_DEVICE_INLINE T prelu(const T&, const T&)     { ABORT("Unknown type"); }
+  static HOST_DEVICE_INLINE T preluBack(const T&, const T&) { ABORT("Unknown type"); }
 
-  static HOST_DEVICE_INLINE T if_then_else(const T& x, const T& y, const T& z) { ABORT("Unknown type"); }
+  static HOST_DEVICE_INLINE T if_then_else(const T&, const T&, const T&) { ABORT("Unknown type"); }
 
-  static HOST_DEVICE_INLINE T sumReduce(const T& x) { ABORT("Unknown type"); }
-  static HOST_DEVICE_INLINE T maxReduce(const T& x) { ABORT("Unknown type"); }
-  static HOST_DEVICE_INLINE T minReduce(const T& x) { ABORT("Unknown type"); }
+  static HOST_DEVICE_INLINE T sumReduce(const T&) { ABORT("Unknown type"); }
+  static HOST_DEVICE_INLINE T maxReduce(const T&) { ABORT("Unknown type"); }
+  static HOST_DEVICE_INLINE T minReduce(const T&) { ABORT("Unknown type"); }
 };
 
 // Specialization for float
@@ -127,14 +130,14 @@ template <>
 struct Ops<double> {
   typedef double Single;
 
-  static HOST_DEVICE_INLINE double tanh(const double& x) { return tanh(x); }
-  static HOST_DEVICE_INLINE double sin(const double& x)  { return sin(x); }
-  static HOST_DEVICE_INLINE double cos(const double& x)  { return cos(x); }
-  static HOST_DEVICE_INLINE double tan(const double& x)  { return tan(x); }
-  static HOST_DEVICE_INLINE double log(const double& x)  { return log(x); }
-  static HOST_DEVICE_INLINE double exp(const double& x)  { return exp(x); }
-  static HOST_DEVICE_INLINE double abs(const double& x)  { return abs(x); }
-  static HOST_DEVICE_INLINE double sqrt(const double& x) { return sqrt(x); }
+  static HOST_DEVICE_INLINE double tanh(const double& x) { return std::tanh(x); }
+  static HOST_DEVICE_INLINE double sin(const double& x)  { return std::sin(x); }
+  static HOST_DEVICE_INLINE double cos(const double& x)  { return std::cos(x); }
+  static HOST_DEVICE_INLINE double tan(const double& x)  { return std::tan(x); }
+  static HOST_DEVICE_INLINE double log(const double& x)  { return std::log(x); }
+  static HOST_DEVICE_INLINE double exp(const double& x)  { return std::exp(x); }
+  static HOST_DEVICE_INLINE double abs(const double& x)  { return std::abs(x); }
+  static HOST_DEVICE_INLINE double sqrt(const double& x) { return std::sqrt(x); }
   static HOST_DEVICE_INLINE double neg(const double& x)  { return -x; }
   static HOST_DEVICE_INLINE double sgn(const double& x)  { return (0 < x) - (x < 0); }
 
@@ -145,7 +148,7 @@ struct Ops<double> {
 
   static HOST_DEVICE_INLINE double max(const double& x, const double& y)  { return x < y ? y : x; }
   static HOST_DEVICE_INLINE double min(const double& x, const double& y)  { return x < y ? x : y; }
-  static HOST_DEVICE_INLINE double pow(const double& x, const double& y)  { return pow(x, y); }
+  static HOST_DEVICE_INLINE double pow(const double& x, const double& y)  { return std::pow(x, y); }
 
 
   static HOST_DEVICE_INLINE double negate(const double& x)  { return !(bool)x; }
@@ -460,7 +463,7 @@ struct Ops<half> {
   static DEVICE_INLINE half exp(const half& x)  { return hexp(x); }
   static DEVICE_INLINE half sqrt(const half& x) { return hsqrt(x); }
   static DEVICE_INLINE half neg(const half& x)  { return -x; }
-  
+
   static DEVICE_INLINE half abs(const half& x)  { return fabs((float)x); }// @TODO half has this information somewhere in the struct, right?
   static DEVICE_INLINE half sgn(const half& x)  { half zero = 0.f; return (zero < x) - (x < zero); } // @TODO half has this information somewhere in the struct, right?
 
diff --git a/src/graph/expression_graph.h b/src/graph/expression_graph.h
index 8a08c7491..f7a781265 100755
--- a/src/graph/expression_graph.h
+++ b/src/graph/expression_graph.h
@@ -130,9 +130,6 @@ class ExpressionGraph : public std::enable_shared_from_this<ExpressionGraph> {
 
   bool inferenceOnly_{false};
 
-  // during inference, use optimizations that might lead to precision loss, e.g. 8-bit MatMul.
-  // At this moment, this is used for int16 qunatized Matmul - 11/1/2019
-  bool optimized_{false};
   bool checkpointing_{false}; // use gradient checkpointing if true
 
   bool reloaded_{false};
@@ -178,9 +175,6 @@ class ExpressionGraph : public std::enable_shared_from_this<ExpressionGraph> {
   void setInference(bool inference) { inferenceOnly_ = inference; }
   bool isInference() { return inferenceOnly_; }
 
-  void setOptimized(bool optimized) { optimized_ = optimized; }
-  bool isOptimized() { return (optimized_ && inferenceOnly_); }
-
   void setCheckpointing(bool checkpointing) { checkpointing_ = checkpointing; }
   bool isCheckpointing() { return checkpointing_; }
 
diff --git a/src/graph/node.h b/src/graph/node.h
index 4d591674f..c017eeb2c 100644
--- a/src/graph/node.h
+++ b/src/graph/node.h
@@ -40,7 +40,7 @@ class Node : public Chainable<Tensor> {
   std::string debugMessage_;
 
   Ptr<std::list<Expr>> subtape_; // a subtape is used to keep track of nodes that need to be freed and recomputed with gradient-checkpointing.
-  bool isCheckpoint_{false};     // true if this node has been selected to be a checkpoint, currently only done manually. 
+  bool isCheckpoint_{false};     // true if this node has been selected to be a checkpoint, currently only done manually.
 
   Ptr<AutoTunerRecorder> recorder_;
   size_t recorderHash_;
@@ -138,7 +138,7 @@ class Node : public Chainable<Tensor> {
 
   virtual std::string graphviz() override {
     std::stringstream ss;
-    ss << "\"" << this << "\" [" 
+    ss << "\"" << this << "\" ["
       << "shape=\"" << form() << "\", "
       << "label="   << label() << ", "
       << "style=\"filled\", "
@@ -147,7 +147,7 @@ class Node : public Chainable<Tensor> {
 
     for(auto&& child : children())
       ss << "\"" << child << "\" -> \"" << this << "\";" << std::endl;
-    
+
     if(subtape_) {
       for(auto&& dep : *subtape_)
         ss << "\"" << dep << "\" -> \"" << this << "\" [style=dotted];" << std::endl;
@@ -188,9 +188,9 @@ struct NaryNodeOp : public Node {
 
   // Deduce type automatically, but then all types must be the same
   // this is called automatically when no output type is specified.
-  // If the input types are mixed, the output type needs to be specified 
+  // If the input types are mixed, the output type needs to be specified
   // in the constructor.
-  Type commonType(const std::vector<Expr>& nodes) {
+  static Type commonType(const std::vector<Expr>& nodes) {
     ABORT_IF(nodes.size() == 0, "NaryNodeOp has no children");
     Type type = nodes[0]->value_type();
     for(int i = 1; i < nodes.size(); ++i)
diff --git a/src/graph/node_initializers.h b/src/graph/node_initializers.h
index 624816b9c..21dcc95d3 100755
--- a/src/graph/node_initializers.h
+++ b/src/graph/node_initializers.h
@@ -17,9 +17,9 @@ namespace inits {
 /**
  * Base class for specialized NodeInitializers.
  *
- * A NodeInitializer is a functor that is associated with parameters 
- * and constants, and is invoked on a tensor during node intialization. 
- * You need to override NodeIntializer::apply(Tensor) with your own 
+ * A NodeInitializer is a functor that is associated with parameters
+ * and constants, and is invoked on a tensor during node intialization.
+ * You need to override NodeIntializer::apply(Tensor) with your own
  * functionality or use a fromLambda intializer.
  *
  * See node_initializers.cpp for examples.
@@ -31,6 +31,7 @@ class NodeInitializer {
 public:
   virtual void apply(Tensor t) = 0;
   void setAllocator(Ptr<Allocator> allocator) { allocator_ = allocator; }
+  virtual ~NodeInitializer() {}
 };
 
 /**
@@ -135,7 +136,7 @@ Ptr<NodeInitializer> dropout(float dropoutProbabilty);
 
 /**
  * Intialize with gumbel noise, i.e. -log(-log(u)) where u ~ Uniform(0 + eps, 1 - eps)
- * 
+ *
  * @return A NodeInitializer
  */
 Ptr<NodeInitializer> gumbel(float eps = 1e-5f);
@@ -163,7 +164,7 @@ Ptr<NodeInitializer> fromWord2vec(const std::string& file,
 
 /**
  * Computes Google's Transformer-style sinusoidal position embeddings
- * starting from position 'start' taking into account batch and time 
+ * starting from position 'start' taking into account batch and time
  * dimensions of the tensor.
  *
  * Expected tensor layout {-2: time, -1: model}
diff --git a/src/graph/node_operators_binary.h b/src/graph/node_operators_binary.h
index d12c2481e..63158ffae 100755
--- a/src/graph/node_operators_binary.h
+++ b/src/graph/node_operators_binary.h
@@ -480,9 +480,12 @@ class CSRDotNodeOp : public NaryNodeOp {
   bool transS_;
   bool swapOperands_;
 public:
-  CSRDotNodeOp(const Shape& S_shape, Expr S_values, Expr S_indices, Expr S_offsets, Expr D, bool transS, bool swapOperands)
-      : NaryNodeOp({ S_values, S_indices, S_offsets, D }, newShape(S_shape, S_values, S_indices, S_offsets, D, transS, swapOperands), commonType({S_values, D})),
-                   transS_(transS), swapOperands_(swapOperands) {
+  CSRDotNodeOp(const Shape& S_shape, Expr S_values, Expr S_indices,
+               Expr S_offsets, Expr D, bool transS, bool swapOperands)
+    : NaryNodeOp({ S_values, S_indices, S_offsets, D },
+                 newShape(S_shape, S_values, S_indices, S_offsets, D, transS, swapOperands),
+                 NaryNodeOp::commonType({S_values, D})),
+      transS_(transS), swapOperands_(swapOperands) {
     matchOrAbort<IndexType>(S_indices->value_type());
     matchOrAbort<IndexType>(S_offsets->value_type());
   }
@@ -513,7 +516,7 @@ class CSRDotNodeOp : public NaryNodeOp {
 
   NodeOps backwardOps() override {
     return { nullptr, // can't backprop into the sparse matrix (the gradient is dense)
-             nullptr, 
+             nullptr,
              nullptr,
              NodeOp(CSRProd(child(3)->grad(), // child(3) = D
                             graph()->allocator(),
@@ -527,7 +530,7 @@ class CSRDotNodeOp : public NaryNodeOp {
   virtual size_t hash() override {
     size_t seed = NaryNodeOp::hash();
     for(auto s : shape())
-      util::hash_combine(seed, s);  
+      util::hash_combine(seed, s);
     util::hash_combine(seed, transS_);
     util::hash_combine(seed, swapOperands_);
     return seed;
@@ -1050,8 +1053,8 @@ struct ConcatenateNodeOp : public NaryNodeOp {
     auto checkShape = shape;
     for(auto child : nodes) {
       checkShape.set(ax_, child->shape()[ax_]); // don't abort on different sizes on axis dim.
-      ABORT_IF(checkShape != child->shape(), 
-               "Child shapes {} and {} cannot be concatenated along axis {}", 
+      ABORT_IF(checkShape != child->shape(),
+               "Child shapes {} and {} cannot be concatenated along axis {}",
                shape, child->shape(), ax);
 
       sum += child->shape()[ax_];
diff --git a/src/graph/parameters.h b/src/graph/parameters.h
index 25eb5db41..8b4af9dd5 100644
--- a/src/graph/parameters.h
+++ b/src/graph/parameters.h
@@ -10,10 +10,10 @@
 
 namespace marian {
 
-// @TODO: Currently an ExpressionGraph only supports one Parameters object and 
+// @TODO: Currently an ExpressionGraph only supports one Parameters object and
 // the type of parameters has to be the inside on Parameters object. This limits
 // parameter types to a single chosen type, e.g. only fp32 or only fp16. This should
-// be extended to allow multiple sets of parameters. 
+// be extended to allow multiple sets of parameters.
 // The reason here is to be able to efficiently compute updates of whole parameter
 // sets of one type.
 class Parameters {
@@ -40,7 +40,7 @@ class Parameters {
     LOG(debug, "Created parameter object of type {}", acceptedElementType_);
   }
 
-  ~Parameters() {
+  virtual ~Parameters() {
     LOG(debug, "Destroyed parameter object of type {}", acceptedElementType_);
   }
 
@@ -88,7 +88,7 @@ class Parameters {
 
       // sort parameters by name before allocation to make sure the memory layout after allocation is always the same
       std::sort(params_.begin(), params_.end(), [](Expr n1, Expr n2){ return n1->name() < n2->name(); });
-      
+
       for(auto p : params_) {
         if(!p->val()) {
           vals_->allocate(p->val(), p->shape(), p->value_type());
diff --git a/src/layers/generic.h b/src/layers/generic.h
index e899d8a07..a3b9bac45 100755
--- a/src/layers/generic.h
+++ b/src/layers/generic.h
@@ -39,6 +39,7 @@ class LayerBase {
 
 // Simplest layer interface: Unary function
 struct IUnaryLayer {
+  virtual ~IUnaryLayer() {}
   virtual Expr apply(Expr) = 0;
   virtual Expr apply(const std::vector<Expr>& es) {
     ABORT_IF(es.size() > 1, "Not implemented"); // simple stub
@@ -59,6 +60,7 @@ struct IEmbeddingLayer {
 
   // alternative from indices directly
   virtual Expr applyIndices(const std::vector<WordIndex>& embIdx, const Shape& shape) const = 0;
+  virtual ~IEmbeddingLayer() {}
 };
 
 // base class for Encoder and Decoder classes, which have embeddings and a batch index (=stream index)
diff --git a/src/layers/guided_alignment.h b/src/layers/guided_alignment.h
index 48ad65993..f08d3f092 100755
--- a/src/layers/guided_alignment.h
+++ b/src/layers/guided_alignment.h
@@ -5,14 +5,14 @@
 
 namespace marian {
 
-static inline RationalLoss guidedAlignmentCost(Ptr<ExpressionGraph> graph,
+static inline RationalLoss guidedAlignmentCost(Ptr<ExpressionGraph> /*graph*/,
                                                Ptr<data::CorpusBatch> batch,
                                                Ptr<Options> options,
                                                Expr attention) { // [beam depth=1, max src length, batch size, tgt length]
 
   std::string guidedLossType = options->get<std::string>("guided-alignment-cost");  // @TODO: change "cost" to "loss"
   float guidedLossWeight = options->get<float>("guided-alignment-weight");
-  
+
   const auto& shape = attention->shape(); // [beam depth=1, max src length, batch size, tgt length]
   float epsilon = 1e-6f;
   Expr alignmentLoss; // sum up loss over all attention/alignment positions
@@ -55,8 +55,8 @@ static inline RationalLoss guidedAlignmentCost(Ptr<ExpressionGraph> graph,
     else
        ABORT("Unknown alignment cost type: {}", guidedLossType);
     // every position is a label as they should all agree
-    // @TODO: there should be positional masking here ... on the other hand, positions that are not 
-    // in a sentence should always agree (both being 0). Lack of masking affects label count only which is 
+    // @TODO: there should be positional masking here ... on the other hand, positions that are not
+    // in a sentence should always agree (both being 0). Lack of masking affects label count only which is
     // probably negligible?
     numLabels = shape.elements();
   }
diff --git a/src/layers/loss.h b/src/layers/loss.h
index d9deaeb8f..43e89c1d3 100755
--- a/src/layers/loss.h
+++ b/src/layers/loss.h
@@ -331,6 +331,7 @@ class CrossEntropyLoss : public LabelwiseLoss {
   : LabelwiseLoss(axes), // cross-entropy already reduces over axis -1
     labelSmoothing_(labelSmoothing), factorWeight_(factorWeight) {}
 
+  virtual ~CrossEntropyLoss() {}
 protected:
   float labelSmoothing_; // interpolation factor for label smoothing, see below
   float factorWeight_;   // give extra weight to factors
@@ -368,7 +369,7 @@ class CrossEntropyLoss : public LabelwiseLoss {
 
     if(labelWeights) {
       // We currently do not know how to use target factors and word-level label weights together
-      bool wordlevel = labelWeights->shape()[-3] > 1; // Time-dimension is not trivially 1, hence we have word-level weights. 
+      bool wordlevel = labelWeights->shape()[-3] > 1; // Time-dimension is not trivially 1, hence we have word-level weights.
       ABORT_IF(wordlevel && logits.getNumFactorGroups() > 1, "CE loss with word-level label weights is not implemented for factors");
       ce = ce * cast(labelWeights, Type::float32);
     }
@@ -379,15 +380,15 @@ class CrossEntropyLoss : public LabelwiseLoss {
 
 
 /**
- * @brief Unlikelihood loss across last axis, summed up over batch and time dimensions. This is an 
- * implementation of sequence-level unlikelihood loss from https://arxiv.org/abs/1908.04319. 
+ * @brief Unlikelihood loss across last axis, summed up over batch and time dimensions. This is an
+ * implementation of sequence-level unlikelihood loss from https://arxiv.org/abs/1908.04319.
  * We rely on word-level label weights where 1 is correct and 0 is marking an error. If there are not
  * zeros for a sentence it going to be trained with normal CE loss if there is at least one 0 it is going
  * to flip over to use SUL for that sentence to penalize the selected word.
- * 
+ *
  * SUL is implemented as:
  * -log(gather(1 - softmax(logits), -1, indices))
- * 
+ *
  * Factors are currently not supported.
  */
 class SequenceUnlikelihoodLoss : public CrossEntropyLoss {
@@ -411,17 +412,17 @@ class SequenceUnlikelihoodLoss : public CrossEntropyLoss {
     ABORT_IF(!mask, "mask is required"); // @TODO: check this, it seems weights for padding are by default 1, which would make this obsolete.
     // use label weights, where 1 is GOOD and 0 is BAD. After inversion here, now 1 marks, mask again to eliminate padding (might be obsolete)
     auto errorMask = (1.f - cast(labelWeights, Type::float32)) * cast(mask, Type::float32);
-  
+
     auto ceUl = logits.applyLossFunction(labels, [&](Expr logits, Expr indices) {
       return cast(unlikelihood(logits, indices), Type::float32);
     });
-  
+
     // compute if want to use CE or UL. If there are no errors train with CE, otherwise train _only on_ the errors with UL. This is the "mixed" training
-    // schedule from https://arxiv.org/abs/1908.04319. Providing labels with or without error scores we can easily switch between CE and UL. 
+    // schedule from https://arxiv.org/abs/1908.04319. Providing labels with or without error scores we can easily switch between CE and UL.
     auto onlyCe  = eq(sum(errorMask, /*axis=*/-3), 0.f); // [1, 1, dimBatch, 1] - equal 1 if no errors are present
     ceUl         = errorMask * ceUl;                     // don't use for correct label or padding
 
-    auto cost    = onlyCe * ce + (1.f - onlyCe) * ceUl;  // ce or unlikelihood part are never simultanously used as cost per batch entry 
+    auto cost    = onlyCe * ce + (1.f - onlyCe) * ceUl;  // ce or unlikelihood part are never simultanously used as cost per batch entry
 
     return cost;
   }
diff --git a/src/layers/weight.h b/src/layers/weight.h
index 787c23769..74cf47d68 100644
--- a/src/layers/weight.h
+++ b/src/layers/weight.h
@@ -17,6 +17,7 @@ class WeightingBase {
   virtual void debugWeighting(std::vector<float> /*weightedMask*/,
                               std::vector<float> /*freqMask*/,
                               Ptr<data::CorpusBatch> /*batch*/){};
+  virtual ~WeightingBase() {}
 };
 
 class DataWeighting : public WeightingBase {
diff --git a/src/microsoft/quicksand.cpp b/src/microsoft/quicksand.cpp
index 0f0ea2533..2f7687aa4 100755
--- a/src/microsoft/quicksand.cpp
+++ b/src/microsoft/quicksand.cpp
@@ -41,6 +41,7 @@ class VocabWrapper : public IVocabWrapper {
   Ptr<Vocab> pImpl_;
 public:
   VocabWrapper(Ptr<Vocab> vocab) : pImpl_(vocab) {}
+  virtual ~VocabWrapper() {}
   WordIndex encode(const std::string& word) const override { return (*pImpl_)[word].toWordIndex(); }
   std::string decode(WordIndex id) const override { return (*pImpl_)[Word::fromWordIndex(id)]; }
   size_t size() const override { return pImpl_->size(); }
@@ -243,7 +244,7 @@ DecoderCpuAvxVersion parseCpuAvxVersion(std::string name) {
   }
 }
 
-// @TODO: clean-up this code and unify with marian-conv. The targetPrec parameter is not clear enought etc. 
+// @TODO: clean-up this code and unify with marian-conv. The targetPrec parameter is not clear enought etc.
 bool convertModel(std::string inputFile, std::string outputFile, int32_t targetPrec) {
   std::cout << "Converting from: " << inputFile << ", to: " << outputFile << std::endl;
 
diff --git a/src/microsoft/quicksand.h b/src/microsoft/quicksand.h
index 1089be148..87de19482 100755
--- a/src/microsoft/quicksand.h
+++ b/src/microsoft/quicksand.h
@@ -54,6 +54,8 @@ class IBeamSearchDecoder {
                      const std::vector<const void*>& ptrs)
       : options_(options), ptrs_(ptrs) {}
 
+  virtual ~IBeamSearchDecoder() {}
+
   virtual QSNBestBatch decode(const QSBatch& qsBatch,
                               size_t maxLength,
                               const std::unordered_set<WordIndex>& shortlist)
diff --git a/src/models/costs.h b/src/models/costs.h
index 03b247f0b..2a66e5499 100755
--- a/src/models/costs.h
+++ b/src/models/costs.h
@@ -25,6 +25,7 @@ class ICost {
                                        Ptr<ExpressionGraph> graph, // @TODO: why needed? Can it be gotten from model?
                                        Ptr<data::Batch> batch,
                                        bool clearGraph = true) = 0;
+  virtual ~ICost() {}
 };
 
 class EncoderDecoderCECost : public ICost {
@@ -51,6 +52,8 @@ class EncoderDecoderCECost : public ICost {
       weighter_ = WeightingFactory(options_);
   }
 
+  virtual ~EncoderDecoderCECost() {}
+
   Ptr<MultiRationalLoss> apply(Ptr<IModel> model,
              Ptr<ExpressionGraph> graph,
              Ptr<data::Batch> batch,
@@ -136,6 +139,8 @@ class Trainer : public ICriterionFunction {
   Trainer(Ptr<IModel> model, Ptr<ICost> cost)
       : model_(model), cost_(cost) {}
 
+  virtual ~Trainer() {}
+
   Ptr<IModel> getModel() { return model_; }
 
   virtual void load(Ptr<ExpressionGraph> graph,
@@ -179,6 +184,8 @@ class Scorer : public IModel {
   Scorer(Ptr<IModel> model, Ptr<ILogProb> cost)
       : model_(model), logProb_(cost) {}
 
+  virtual ~Scorer(){}
+
   Ptr<IModel> getModel() { return model_; }
 
   virtual void load(Ptr<ExpressionGraph> graph,
@@ -211,6 +218,7 @@ class ILogProbStep {
 
 class LogSoftmaxStep : public ILogProbStep {
 public:
+  virtual ~LogSoftmaxStep() {}
   virtual Ptr<DecoderState> apply(Ptr<DecoderState> state) override {
     // decoder needs normalized probabilities (note: skipped if beam 1 and --skip-cost)
     state->setLogProbs(state->getLogProbs().applyUnaryFunction(logsoftmax));
@@ -224,6 +232,7 @@ class LogSoftmaxStep : public ILogProbStep {
 // with --output-sampling during translation with marian-decoder
 class GumbelSoftmaxStep : public ILogProbStep {
 public:
+  virtual ~GumbelSoftmaxStep() {}
   virtual Ptr<DecoderState> apply(Ptr<DecoderState> state) override {
     state->setLogProbs(state->getLogProbs().applyUnaryFunctions(
       [](Expr logits){ // lemma gets gumbelled
diff --git a/src/models/encoder_decoder.h b/src/models/encoder_decoder.h
index 599dbfdc4..92c1647fa 100755
--- a/src/models/encoder_decoder.h
+++ b/src/models/encoder_decoder.h
@@ -11,6 +11,7 @@ namespace marian {
 
 class IEncoderDecoder : public models::IModel {
 public:
+  virtual ~IEncoderDecoder() {}
   virtual void load(Ptr<ExpressionGraph> graph,
                     const std::string& name,
                     bool markedReloaded = true) override
diff --git a/src/models/model_base.h b/src/models/model_base.h
index 016f3b8e3..5f76b380d 100644
--- a/src/models/model_base.h
+++ b/src/models/model_base.h
@@ -41,6 +41,8 @@ class IModel {
 // @TODO: Is there a better name?
 class ICriterionFunction {
 public:
+  virtual ~ICriterionFunction() {}
+
   virtual void load(Ptr<ExpressionGraph>,
                     const std::string&,
                     bool markReloaded = true)
diff --git a/src/models/model_task.h b/src/models/model_task.h
index 1fcf6eca5..96dfadd0c 100644
--- a/src/models/model_task.h
+++ b/src/models/model_task.h
@@ -5,10 +5,12 @@
 namespace marian {
 
 struct ModelTask {
+  virtual ~ModelTask() {}
   virtual void run() = 0;
 };
 
 struct ModelServiceTask {
+  virtual ~ModelServiceTask() {}
   virtual std::string run(const std::string&) = 0;
 };
 }  // namespace marian
diff --git a/src/models/s2s.h b/src/models/s2s.h
index 4c0fd0194..8d12f96e3 100755
--- a/src/models/s2s.h
+++ b/src/models/s2s.h
@@ -11,6 +11,7 @@ namespace marian {
 class EncoderS2S : public EncoderBase {
   using EncoderBase::EncoderBase;
 public:
+  virtual ~EncoderS2S() {}
   Expr applyEncoderRNN(Ptr<ExpressionGraph> graph,
                        Expr embeddings,
                        Expr mask,
@@ -254,7 +255,7 @@ class DecoderS2S : public DecoderBase {
     auto embeddings = state->getTargetHistoryEmbeddings();
 
     // The batch dimension of the inputs can change due to batch-pruning, in that case
-    // cached elements need to be rebuilt, in this case the mapped encoder context in the 
+    // cached elements need to be rebuilt, in this case the mapped encoder context in the
     // attention mechanism of the decoder RNN.
     int currDimBatch = embeddings->shape()[-2];
     if(!rnn_ || lastDimBatch_ != currDimBatch)  // if currDimBatch is different, rebuild the cached RNN
@@ -263,7 +264,7 @@ class DecoderS2S : public DecoderBase {
     // Also @TODO: maybe implement a Cached(build, updateIf) that runs a check and rebuild if required
     // at dereferecing :
     // rnn_ = Cached<decltype(constructDecoderRNN(graph, state))>(
-    //          /*build=*/[]{ return constructDecoderRNN(graph, state); }, 
+    //          /*build=*/[]{ return constructDecoderRNN(graph, state); },
     //          /*updateIf=*/[]{ return state->batchDimChanged() });
     // rnn_->transduce(...);
 
diff --git a/src/models/states.h b/src/models/states.h
index 21cf42a87..c2f9ee05a 100755
--- a/src/models/states.h
+++ b/src/models/states.h
@@ -17,6 +17,7 @@ class EncoderState {
       : context_(context), mask_(mask), batch_(batch) {}
 
   EncoderState() {}
+  virtual ~EncoderState() {}
 
   virtual Expr getContext()   const { return context_;   }
   virtual Expr getAttended()  const { return context_;   }
@@ -53,6 +54,7 @@ class DecoderState {
                const std::vector<Ptr<EncoderState>>& encStates,
                Ptr<data::CorpusBatch> batch)
       : states_(states), logProbs_(logProbs), encStates_(encStates), batch_(batch) {}
+  virtual ~DecoderState() {}
 
   // @TODO: Do we need all these to be virtual?
   virtual const std::vector<Ptr<EncoderState>>& getEncoderStates() const {
@@ -68,10 +70,10 @@ class DecoderState {
                                    int beamSize) const {
 
     std::vector<Ptr<EncoderState>> newEncStates;
-    for(auto& es : encStates_) 
+    for(auto& es : encStates_)
       // If the size of the batch dimension of the encoder state context changed, subselect the correct batch entries
       newEncStates.push_back(es->getContext()->shape()[-2] == batchIndices.size() ? es : es->select(batchIndices));
-      
+
     // hypindices matches batchIndices in terms of batch dimension, so we only need hypIndices
     auto selectedState = New<DecoderState>(
         states_.select(hypIndices, beamSize, /*isBatchMajor=*/false), logProbs_, newEncStates, batch_);
@@ -121,6 +123,7 @@ class ClassifierState {
   Words targetWords_;
 
 public:
+  virtual ~ClassifierState() {}
   virtual Expr getLogProbs() const { return logProbs_; }
   virtual void setLogProbs(Expr logProbs) { logProbs_ = logProbs; }
 
diff --git a/src/optimizers/clippers.h b/src/optimizers/clippers.h
index f953c2eca..67a50004f 100644
--- a/src/optimizers/clippers.h
+++ b/src/optimizers/clippers.h
@@ -16,6 +16,7 @@ namespace marian {
 class ClipperBase {
 public:
   virtual void clip(Tensor) = 0;
+  virtual ~ClipperBase() {}
 };
 
 typedef std::shared_ptr<ClipperBase> ClipperPtr;
diff --git a/src/optimizers/optimizers.h b/src/optimizers/optimizers.h
index 2b70ba848..d10af9196 100644
--- a/src/optimizers/optimizers.h
+++ b/src/optimizers/optimizers.h
@@ -29,6 +29,8 @@ class OptimizerBase : public TrainingObserver {
       LOG(info, "[optimizers] Learning rate gets automatically adjusted as if minibatch size was {}", refMBWordsParam_);
   }
 
+  virtual ~OptimizerBase() {}
+
   static constexpr size_t mbSizeNotProvided = SIZE_MAX;
 
   void update(Ptr<ExpressionGraph> graph, size_t mbSize = mbSizeNotProvided) {
@@ -114,7 +116,7 @@ class Sgd : public OptimizerBase {
 public:
   Sgd(float eta, size_t refMBWordsParam = 0, Ptr<ClipperBase> clipper = nullptr)
       : OptimizerBase(eta, refMBWordsParam, clipper) {}
-
+  virtual ~Sgd() {}
   virtual void setParams(const std::vector<float>& /*params*/) override {}
 private:
   void updateImpl(Tensor params, Tensor grads, size_t actualMBSize, size_t refMBWords) override;
diff --git a/src/rescorer/score_collector.h b/src/rescorer/score_collector.h
index ba4884366..38ba7a089 100644
--- a/src/rescorer/score_collector.h
+++ b/src/rescorer/score_collector.h
@@ -13,6 +13,7 @@ namespace marian {
 class ScoreCollector {
 public:
   ScoreCollector(const Ptr<Options>& options);
+  virtual ~ScoreCollector() {}
 
   virtual void Write(long id, const std::string& message);
   virtual void Write(long id,
diff --git a/src/rnn/rnn.h b/src/rnn/rnn.h
index c9131d87a..4efc569cc 100644
--- a/src/rnn/rnn.h
+++ b/src/rnn/rnn.h
@@ -35,7 +35,7 @@ class BaseRNN {
 public:
   BaseRNN(Ptr<ExpressionGraph> graph, Ptr<Options> options)
       : graph_(graph), options_(options) {}
-
+  virtual ~BaseRNN() {}
   virtual Expr transduce(Expr, Expr = nullptr) = 0;
   virtual Expr transduce(Expr, State, Expr = nullptr) = 0;
   virtual Expr transduce(Expr, States, Expr = nullptr) = 0;
@@ -113,6 +113,7 @@ class SingleLayerRNN : public BaseRNN {
 
 public:
   friend RNN;
+  virtual ~SingleLayerRNN() {}
 
   // @TODO: benchmark whether this concatenation is a good idea
   virtual Expr transduce(Expr input, Expr mask = nullptr) override {
diff --git a/src/tensors/backend.h b/src/tensors/backend.h
index 01d002675..ce8a5e603 100644
--- a/src/tensors/backend.h
+++ b/src/tensors/backend.h
@@ -17,7 +17,7 @@ class Backend {
 public:
   Backend(DeviceId deviceId, size_t seed)
       : deviceId_(deviceId), seed_(seed), randomGenerator_(createRandomGenerator(seed, deviceId)) {}
-
+  virtual ~Backend() {};
   virtual DeviceId getDeviceId() { return deviceId_; };
   virtual Ptr<RandomGenerator> getRandomGenerator() { return randomGenerator_; }
 
diff --git a/src/tensors/cpu/device.cpp b/src/tensors/cpu/device.cpp
index 941902a8f..40bb558b1 100644
--- a/src/tensors/cpu/device.cpp
+++ b/src/tensors/cpu/device.cpp
@@ -8,29 +8,40 @@
 
 namespace marian {
 namespace cpu {
+namespace {
 
 // allocate function for tensor reserve() below. 
-// Needed for AVX512, while not available on all compilers. It seems clang
-// does not have aligned_alloc for all cstlib versions. If AVX512 is not used
-// a simple malloc is probably fine. 
-// Should generate a runtime error otherwise as we have a check in the AVX512 
-// functions which tests for alignment. 
-#ifdef _WIN32
-#define MALLOC(size) _aligned_malloc(size, alignment_)
-#elif __GNUC__
-#define MALLOC(size) aligned_alloc(alignment_, size)
-#else
-#define MALLOC(size) malloc(size)
-#endif
+// Alignment is needed because we use AVX512 and AVX2 vectors. We should fail if we can't allocate aligned memory.
 
 #ifdef _WIN32
-#define FREE(ptr) _aligned_free(ptr)
+void *genericMalloc(size_t alignment, size_t size) {
+  void *ret = _aligned_malloc(size, alignment);
+  ABORT_IF(!ret, "Failed to allocate memory on CPU");
+  return ret;
+}
+void genericFree(void *ptr) {
+  _aligned_free(ptr);
+}
 #else
-#define FREE(ptr) free(ptr)
+// Linux and OS X.  There is no fallback to malloc because we need it to be aligned.
+void *genericMalloc(size_t alignment, size_t size) {
+  // On macos, aligned_alloc is available only on c++17
+  // Furthermore, it requires that the memory requested is an exact multiple of the alignment, otherwise it fails.
+  // posix_memalign is available both Mac (Since 2016) and Linux and in both gcc and clang
+  void *result;
+  // Error could be detected by return value or just remaining nullptr.
+  ABORT_IF(posix_memalign(&result, alignment, size), "Failed to allocate memory on CPU");
+  return result;
+}
+void genericFree(void *ptr) {
+  free(ptr);
+}
 #endif
 
+} // namespace
+
 Device::~Device() {
-  FREE(data_);
+  genericFree(data_);
 }
 
 void Device::reserve(size_t size) {
@@ -38,14 +49,12 @@ void Device::reserve(size_t size) {
   ABORT_IF(size < size_ || size == 0,
            "New size must be larger than old size and larger than 0");
 
+  uint8_t *temp = static_cast<uint8_t*>(genericMalloc(alignment_, size));
   if(data_) {
-    uint8_t *temp = static_cast<uint8_t*>(MALLOC(size));
     std::copy(data_, data_ + size_, temp);
-    FREE(data_);
-    data_ = temp;
-  } else {
-    data_ = static_cast<uint8_t*>(MALLOC(size));
+    genericFree(data_);
   }
+  data_ = temp;
   size_ = size;
 }
 }  // namespace cpu
diff --git a/src/tensors/cpu/fbgemm/expanded_gemm.h b/src/tensors/cpu/fbgemm/expanded_gemm.h
index cf53fe1a1..38c543c75 100644
--- a/src/tensors/cpu/fbgemm/expanded_gemm.h
+++ b/src/tensors/cpu/fbgemm/expanded_gemm.h
@@ -17,6 +17,7 @@
 #endif
 
 using namespace fbgemm;
+// @TODO: don't use using namespace ...; in header files. Just don't. [UG]
 #endif  // USE_FBGEMM
 
 namespace marian {
@@ -96,7 +97,7 @@ struct FbgemmPacked16PackNodeOp : public UnaryNodeOp {
 
   const std::string type() override { return "packMatFp16"; }
 
-  Shape newShape(Expr a, bool transpose) {
+  Shape newShape(Expr MAYBE_UNUSED a, bool MAYBE_UNUSED transpose) {
 #if USE_FBGEMM
     auto shapeMat = a->shape();
     // Should be 2D - weight matrix
@@ -115,9 +116,8 @@ struct FbgemmPacked16PackNodeOp : public UnaryNodeOp {
                            packsize_);
 
     Shape outShape({(int)packsize_});
-
     return outShape;
-#else // USE_FBGEMM
+#else
     ABORT("Packed GEMM requires a build with USE_FBGEMM enabled");
     return Shape();
 #endif  // USE_FBGEMM
@@ -180,19 +180,21 @@ struct FbgemmPacked8PackNodeOp : public UnaryNodeOp {
 
   const std::string type() override { return "packMatInt8"; }
 
-  Shape newShape(Expr a, bool transpose) {
 #if USE_FBGEMM
+  Shape newShape(Expr a, bool transpose) {
     fbgemmPacked8PackInfo(a->shape(), packType_, transpose, nrow_, ncol_, packsize_);
     Shape outShape({(int)packsize_});
-
     return outShape;
-#else // USE_FBGEMM
+  }
+#else
+  Shape newShape(Expr /*a*/, bool /*transpose*/) {
     ABORT("Packed GEMM requires a build with USE_FBGEMM enabled");
     return Shape();
-#endif  // USE_FBGEMM
   }
+#endif  // USE_FBGEMM
 };
 
+
 // Affine transform (matrix multiplication) using packed B matrix
 // float scalar_: scalar multiplier
 // size_t m_: the number of rows in A and C
@@ -202,7 +204,6 @@ struct FbgemmPacked8PackNodeOp : public UnaryNodeOp {
 // bool transB_: transpose B
 class FbgemmPacked16AffineNodeOp : public NaryNodeOp {
 private:
-  float scalar_;
   size_t m_;
   size_t n_;
   size_t k_;
@@ -210,9 +211,8 @@ class FbgemmPacked16AffineNodeOp : public NaryNodeOp {
   bool transB_;
 
 public:
-  FbgemmPacked16AffineNodeOp(const std::vector<Expr>& nodes, Shape bShape, bool transA, bool transB, float scalar)
-      : NaryNodeOp(nodes, newShape(nodes[0], bShape, transA, transB), Type::float32),
-        scalar_(scalar) {
+  FbgemmPacked16AffineNodeOp(const std::vector<Expr>& nodes, Shape bShape, bool transA, bool transB, float /*scalar*/)
+    : NaryNodeOp(nodes, newShape(nodes[0], bShape, transA, transB), Type::float32)/*, scalar_(scalar)*/ {
     transA_ = transA;
     transB_ = transB;
     m_ = nodes[0]->shape().elements() / nodes[0]->shape()[-1];
@@ -281,7 +281,6 @@ class FbgemmPacked16AffineNodeOp : public NaryNodeOp {
 // bool transB_: transpose B
 class FbgemmPacked8AffineNodeOp : public NaryNodeOp {
 private:
-  float scalar_;
   size_t m_;
   size_t n_;
   size_t k_;
@@ -289,9 +288,8 @@ class FbgemmPacked8AffineNodeOp : public NaryNodeOp {
   bool transB_;
 
 public:
-  FbgemmPacked8AffineNodeOp(const std::vector<Expr>& nodes, Shape bShape, bool transA, bool transB, float scalar)
-      : NaryNodeOp(nodes, newShape(nodes[0], bShape, transA, transB), Type::float32),
-        scalar_(scalar) {
+ FbgemmPacked8AffineNodeOp(const std::vector<Expr>& nodes, Shape bShape, bool transA, bool transB, float /*scalar*/)
+   : NaryNodeOp(nodes, newShape(nodes[0], bShape, transA, transB), Type::float32)/*, scalar_(scalar) */ {
     transA_ = transA;
     transB_ = transB;
     m_ = nodes[0]->shape().elements() / nodes[0]->shape()[-1];
@@ -302,7 +300,7 @@ class FbgemmPacked8AffineNodeOp : public NaryNodeOp {
     size_t l = bShape.elements() / bShape[-1];
     n_ = bShape[-1];
     if(transB)
-     std::swap(l, n_);
+      std::swap(l, n_);
   }
 
   Shape newShape(Expr a, Shape bShape, bool transA, bool transB) {
@@ -369,9 +367,9 @@ static inline Expr affine(Expr a, Expr b, Shape bShape, Expr c, bool transA, boo
   Type elementType = b->value_type();
 
   if (elementType == Type::packed16)
-    return Expression<cpu::variant::FbgemmPacked16AffineNodeOp>(nodes, bShape, transA, transB, scalar);
+    return Expression<FbgemmPacked16AffineNodeOp>(nodes, bShape, transA, transB, scalar);
   else if (isPacked(elementType) && sizeOf(elementType) == 1)
-    return Expression<cpu::variant::FbgemmPacked8AffineNodeOp>(nodes, bShape, transA, transB, scalar);
+    return Expression<FbgemmPacked8AffineNodeOp>(nodes, bShape, transA, transB, scalar);
   else {
     ABORT("Only int8 and fp16 are available. {}", elementType);
     return nullptr;
@@ -380,9 +378,9 @@ static inline Expr affine(Expr a, Expr b, Shape bShape, Expr c, bool transA, boo
 
 static inline Expr pack(Type elementType, Expr a, PackMatrix packMat, bool transpose, float clipValue) {
   if (elementType == Type::packed16)
-    return Expression<cpu::variant::FbgemmPacked16PackNodeOp>(a, packMat, transpose, clipValue);
+    return Expression<FbgemmPacked16PackNodeOp>(a, packMat, transpose, clipValue);
   else if (isPacked(elementType) && sizeOf(elementType) == 1)
-    return Expression<cpu::variant::FbgemmPacked8PackNodeOp>(a, packMat, elementType, transpose, clipValue);
+    return Expression<FbgemmPacked8PackNodeOp>(a, packMat, elementType, transpose, clipValue);
   else {
     ABORT("Only int8 and fp16 are available. {}", elementType);
     return nullptr;
@@ -394,9 +392,9 @@ static inline Expr dot(Expr a, Expr b, Shape bShape, bool transA, bool transB, f
   Type elementType = b->value_type();
 
   if (elementType == Type::packed16)
-    return Expression<cpu::variant::FbgemmPacked16AffineNodeOp>(nodes, bShape, transA, transB, scalar);
+    return Expression<FbgemmPacked16AffineNodeOp>(nodes, bShape, transA, transB, scalar);
   else if (isPacked(elementType) && sizeOf(elementType) == 1)
-    return Expression<cpu::variant::FbgemmPacked8AffineNodeOp>(nodes, bShape, transA, transB, scalar);
+    return Expression<FbgemmPacked8AffineNodeOp>(nodes, bShape, transA, transB, scalar);
   else {
     ABORT("Only int8 and fp16 are available. {}", elementType);
     return nullptr;
diff --git a/src/tensors/cpu/tensor_operators.cpp b/src/tensors/cpu/tensor_operators.cpp
index 91e058b84..ae5eeed57 100755
--- a/src/tensors/cpu/tensor_operators.cpp
+++ b/src/tensors/cpu/tensor_operators.cpp
@@ -20,7 +20,7 @@ namespace marian {
 
 namespace cpu {
 
-void IsNaN(const Tensor in, Ptr<Allocator> allocator, bool& /*isNaN*/, bool& /*isInf*/) {
+  void IsNaN(const Tensor /*in*/, Ptr<Allocator> /*allocator*/, bool& /*isNaN*/, bool& /*isInf*/) {
   ABORT("Not implemented");
 }
 
@@ -214,9 +214,11 @@ void Transpose0213(Tensor out, Tensor in) {
   }
 }
 
+// This function is called only when MKL is available.
+#if MKL_FOUND
 // Given a 4D array, transpose (swap) the initial 3 dimensions while keeping the last dimension.
 // e.g. 1234 --> 2134, 1234 --> 3214 (4 is always kept).
-// This is an optimized version for swapping first 3 dimensions 
+// This is an optimized version for swapping first 3 dimensions
 // assuming the last dimension is large enough to get benefits from vectorized copy.
 //
 // @param out output tensor
@@ -225,14 +227,13 @@ void Transpose0213(Tensor out, Tensor in) {
 template <bool add>
 void TransposeFirst3In4(Tensor out, Tensor in, const std::vector<int>& vAxis) {
   ABORT_IF(vAxis.size() != 4, "This function handles only 4D arrays.");
-#if MKL_FOUND
   int innermost = in->shape()[-1];
 
   int l1 = in->shape()[vAxis[0]];
   int l2 = in->shape()[vAxis[1]];
   int l3 = in->shape()[vAxis[2]];
 
-  // find the mapping between the transposed output dimensional indices (oi, oj, ok) 
+  // find the mapping between the transposed output dimensional indices (oi, oj, ok)
   // and original input dimensional indices (i, j, k)
   int oi, oj, ok;
 #pragma omp parallel for
@@ -275,11 +276,8 @@ void TransposeFirst3In4(Tensor out, Tensor in, const std::vector<int>& vAxis) {
       }
     }
   }
-#else
-  // it shouldn't come into here. This function is called only when MKL is available.
-  ABORT("Should not get here");
-#endif  // MKL_FOUND
 }
+#endif  // MKL_FOUND
 
 inline void transpose4x4_SSE(const float* A,
                              float* B,
@@ -656,7 +654,7 @@ void SelectAxis2(Tensor out,
 
   functional::Shape outShape = out->shape();
   functional::Shape inShape = in->shape();
-  
+
   auto idxData = indices->data<IndexType>();
   auto odata = out->data();
   const auto idata = in->data();
diff --git a/src/tensors/rand.h b/src/tensors/rand.h
index f3e356fb6..94b44a97a 100644
--- a/src/tensors/rand.h
+++ b/src/tensors/rand.h
@@ -15,11 +15,11 @@ class RandomGenerator {
 
 public:
   RandomGenerator(size_t seed) : seed_(seed) { }
-
+  virtual ~RandomGenerator() {}
   virtual void uniform(Tensor, float a, float b) = 0;
   virtual void normal(Tensor, float mean, float stddev) = 0;
 };
 
 Ptr<RandomGenerator> createRandomGenerator(size_t /*seed*/, DeviceId);
 
-}
\ No newline at end of file
+}
diff --git a/src/tensors/tensor_operators.h b/src/tensors/tensor_operators.h
index d75652403..227bc9533 100644
--- a/src/tensors/tensor_operators.h
+++ b/src/tensors/tensor_operators.h
@@ -25,7 +25,7 @@
 namespace marian {
 
 template <typename InIt, typename OutIt>
-void copy(Ptr<Backend> backend, const InIt beg, const InIt end, OutIt it) {
+void copy(Ptr<Backend>& MAYBE_UNUSED backend, const InIt beg, const InIt end, OutIt it) {
 #ifdef CUDA_FOUND
   if(backend->getDeviceId().type == DeviceType::gpu)
     gpu::copy(backend, beg, end, it);
@@ -119,7 +119,7 @@ DISPATCH3(Concatenate, marian::Tensor, const std::vector<marian::Tensor>&, int)
 
 // clang-format on
 
-// Bernoulli(tensor, 0.5f, 2.f, -1.f) generates a tensor composed of 50% of 1 and 50% of -1. 
+// Bernoulli(tensor, 0.5f, 2.f, -1.f) generates a tensor composed of 50% of 1 and 50% of -1.
 static inline void Bernoulli(Tensor resultTensor, float keepProb, float scale = 1.f, float shift = 0.f) {
   // in-place uniform distribution
   auto rnd = resultTensor->getBackend()->getRandomGenerator();
@@ -190,7 +190,7 @@ void LayerNormalizationGrad(Tensor gradX,
 }
 
 static inline void LayerNormalizationGrad(
-                            Ptr<Allocator> allocator,
+                            Ptr<Allocator> MAYBE_UNUSED allocator,
                             Tensor gradX,
                             Tensor gradGamma,
                             Tensor gradBeta,
diff --git a/src/tests/prod.cpp b/src/tests/prod.cpp
index 37809bacd..698712b9c 100644
--- a/src/tests/prod.cpp
+++ b/src/tests/prod.cpp
@@ -1,7 +1,7 @@
 #include "marian.h"
 #include "common/timer.h"
 
-int main(int argc, char** argv) {
+int main(int /*argc*/, char** /*argv*/) {
     using namespace marian;
 
     {
diff --git a/src/tests/sqlite.cpp b/src/tests/sqlite.cpp
index 217485149..f822dbc5c 100644
--- a/src/tests/sqlite.cpp
+++ b/src/tests/sqlite.cpp
@@ -8,6 +8,8 @@
 #include <fstream>
 
 int main(int argc, char** argv) {
+    ABORT_IF(argc != 3, "FATAL ERROR: Incorrect number of command line arguments "
+             "(expected: 2) for command {}.",argv[0]);
 
     SQLite::Database db("corpus.db", SQLite::OPEN_READWRITE|SQLite::OPEN_CREATE);
     db.exec("PRAGMA temp_store_directory = '/data1/marcinjd';");
diff --git a/src/training/communicator.cpp b/src/training/communicator.cpp
index be9b23be5..98b7be7f2 100644
--- a/src/training/communicator.cpp
+++ b/src/training/communicator.cpp
@@ -38,7 +38,7 @@ Ptr<ICommunicator> createCommunicator(
   }
 
   // the actual implementation is inside communicator.cu
-  return New<NCCLCommunicator>(graphs, mpi); 
+  return New<NCCLCommunicator>(graphs, mpi);
 #else // no CUDA or no NCCL
   noNccl; // (unused)
   return New<DefaultCommunicator>(graphs, mpi);
@@ -141,7 +141,7 @@ class FakeMPIWrapper : public IMPIWrapper
   FakeMPIWrapper(bool) {
     LOG(warn, "Compiled without MPI support. Falling back to FakeMPIWrapper");
   }
-
+  virtual ~FakeMPIWrapper() {}
   virtual size_t myMPIRank() const override { return 0; };
   virtual size_t numMPIProcesses() const override { return 1; };
 
diff --git a/src/training/communicator.h b/src/training/communicator.h
index 6ccce204f..472744910 100644
--- a/src/training/communicator.h
+++ b/src/training/communicator.h
@@ -156,11 +156,8 @@ class DefaultCommunicator : public ICommunicator {
   void scatterReduceAndResetGrads() const override {
     const_cast<DefaultCommunicator*>(this)->lazyInit();
 
-    int totalSize = (int)graphs_[0]->params()->vals()->size();
-    int shardSize = (int)ceil(totalSize / (float)graphs_.size());
-
     // Gather gradients from different devices into current gradient shards
-    auto scatter = [this, shardSize](size_t idx, size_t begin, size_t end) {
+    auto scatter = [this](size_t idx, size_t begin, size_t end) {
       auto curGrad = graphs_[idx]->params()->grads()->subtensor(begin, end-begin);
 
       // collect and sum gradients
@@ -176,7 +173,7 @@ class DefaultCommunicator : public ICommunicator {
     };
 
     // reset gradients outside current shard
-    auto reset = [this, shardSize](size_t idx, size_t begin, size_t end) {
+    auto reset = [this](size_t idx, size_t begin, size_t end) {
       auto grad = graphs_[idx]->params()->grads();
       if (begin > 0)
         grad->subtensor(0, begin)->set(0);
@@ -189,11 +186,9 @@ class DefaultCommunicator : public ICommunicator {
   }
 
   void allGatherParams() const override {
-    int totalSize = (int)graphs_[0]->params()->vals()->size();
-    int shardSize = (int)ceil(totalSize / (float)graphs_.size());
 
     // Update all graphs with parameter shard
-    auto gather = [this, shardSize](size_t idx, size_t begin, size_t end) {
+    auto gather = [this](size_t idx, size_t begin, size_t end) {
       auto getShard = [&](Ptr<ExpressionGraph> graph) {
         return graph->params()->vals()->subtensor(begin, end-begin);
       };
diff --git a/src/training/gradient_dropping/sparse_tensor.h b/src/training/gradient_dropping/sparse_tensor.h
index 5effa3d73..652a77ece 100644
--- a/src/training/gradient_dropping/sparse_tensor.h
+++ b/src/training/gradient_dropping/sparse_tensor.h
@@ -118,7 +118,7 @@ class SparseTensorBase : public std::enable_shared_from_this<SparseTensorBase> {
   }
 
   // Convert a tensor into a sparse tensor format
-  void fromDense(Tensor t) {
+  void fromDense(Tensor MAYBE_UNUSED t) {
     if(backend_->getDeviceId().type == DeviceType::cpu) {
       ABORT("Gradient Dropping for CPU is not yet supported");
     }
diff --git a/src/training/graph_group.h b/src/training/graph_group.h
index 265f9785c..56b8afe3d 100644
--- a/src/training/graph_group.h
+++ b/src/training/graph_group.h
@@ -54,10 +54,10 @@ class GraphGroup {
    * number of devices, which is passed in as the 'multiplier'.
    */
   // @TODO: Can this be made const? It seems wrong to have a stateful method that still returns a result.
-  virtual Ptr<data::BatchStats> collectStats(Ptr<ExpressionGraph> graph,
-                                             Ptr<models::ICriterionFunction> model,
-                                             const std::vector<Ptr<Vocab>>& vocabs,
-                                             double multiplier = 1.) {
+  Ptr<data::BatchStats> collectStats(Ptr<ExpressionGraph> graph,
+                                     Ptr<models::ICriterionFunction> model,
+                                     const std::vector<Ptr<Vocab>>& vocabs,
+                                     double multiplier = 1.) {
     auto stats = New<data::BatchStats>();
 
     size_t numFiles = options_->get<std::vector<std::string>>("train-sets").size();
@@ -92,8 +92,8 @@ class GraphGroup {
         maxBatch *= 2;
     }
 
-    // Do a binary search for maxmimum batch size that fits into given workspace memory 
-    // for a tested sentence length. 
+    // Do a binary search for maxmimum batch size that fits into given workspace memory
+    // for a tested sentence length.
     for(size_t i = step; i <= maxLength; i += step) {
       size_t start = 1;
       size_t end = maxBatch;
diff --git a/src/training/graph_group_async.h b/src/training/graph_group_async.h
index 0e27a6657..dd5254bb0 100644
--- a/src/training/graph_group_async.h
+++ b/src/training/graph_group_async.h
@@ -64,7 +64,7 @@ class AsyncGraphGroup : public GraphGroup, public ExponentialSmoothing {
   void save(Ptr<ExpressionGraph>, bool final = false);
 
   // @TODO: give it a fake batch generator which own vocabs instead of passing vocabs
-  Ptr<data::BatchStats> collectStats(const std::vector<Ptr<Vocab>>& vocabs) {
+  virtual Ptr<data::BatchStats> collectStats(const std::vector<Ptr<Vocab>>& vocabs) {
     return GraphGroup::collectStats(graphs_[0], builders_[0], vocabs);
   }
 
diff --git a/src/training/graph_group_multinode_sync.h b/src/training/graph_group_multinode_sync.h
index ff1fe4e97..0a089d7bb 100644
--- a/src/training/graph_group_multinode_sync.h
+++ b/src/training/graph_group_multinode_sync.h
@@ -63,7 +63,6 @@ class MultiNodeGraphGroupSync : public MultiNodeGraphGroupBase {
   Tensor paramsAvg_;
   std::vector<float> accGradientsSync_cpu;
   std::vector<float> receiveBuffer_cpu;
-  bool synchronization_happened{false};
 
   Ptr<OptimizerBase> syncOptimizer_;
 
diff --git a/src/training/graph_group_sync.h b/src/training/graph_group_sync.h
index 5cae53a97..147b172c8 100755
--- a/src/training/graph_group_sync.h
+++ b/src/training/graph_group_sync.h
@@ -26,7 +26,6 @@ class SyncGraphGroup : public GraphGroup, public ExponentialSmoothing {
   // state for update()
   bool first_{ true };                           // gets interpreted and cleared by update()
   std::vector<Ptr<data::Batch>> pendingBatches_; // in case of dynamic MB-size scaling, we temporarly buffer up batches across update() calls until enough
-  size_t typicalTrgWords_{};                     // typical batch size in words (labels), 0 if unknown (e.g. specified in sentences)
   double updateMultiplier_{1};                  // multiplier not applied in collectStats() (no multiplier if not mini-batch-fit)
 
   void initialize(const Ptr<data::Batch>& exampleBatch);
diff --git a/src/training/scheduler.h b/src/training/scheduler.h
index 913b05278..7e601632c 100755
--- a/src/training/scheduler.h
+++ b/src/training/scheduler.h
@@ -379,6 +379,14 @@ class Scheduler : public TrainingObserver {
       state_->wordsDisp    = 0;
     }
 
+    if(options_->get<bool>("valid-reset-stalled")) {
+      state_->stalled      = 0;
+      state_->maxStalled   = 0;
+      for(const auto& validator : validators_) {
+        state_->validators[validator->type()]["stalled"] = 0;
+      }
+    }
+
     state_->newLoad();
   }
 
diff --git a/src/training/training_state.h b/src/training/training_state.h
index f8b9e5632..c1ddfc837 100644
--- a/src/training/training_state.h
+++ b/src/training/training_state.h
@@ -14,7 +14,7 @@ class TrainingState;
 class TrainingObserver {
 public:
   virtual ~TrainingObserver() {}
-
+  
   virtual void init(TrainingState&) {}
   virtual void actAfterEpoch(TrainingState&) {}
   virtual void actAfterBatches(TrainingState&) {}
diff --git a/src/training/validator.h b/src/training/validator.h
index e97f0da6f..1658dff3f 100755
--- a/src/training/validator.h
+++ b/src/training/validator.h
@@ -36,6 +36,7 @@ class ValidatorBase : public TrainingObserver {
 
 public:
   ValidatorBase(bool lowerIsBetter) : lowerIsBetter_(lowerIsBetter), lastBest_{initScore()} {}
+  virtual ~ValidatorBase() {}
 
   virtual float validate(const std::vector<Ptr<ExpressionGraph>>& graphs,
                          Ptr<const TrainingState> state) = 0;
@@ -51,6 +52,7 @@ class ValidatorBase : public TrainingObserver {
 template <class DataSet, class BuilderType> // @TODO: BuilderType doesn't really serve a purpose here? Review and remove.
 class Validator : public ValidatorBase {
 public:
+  virtual ~Validator() {}
   Validator(std::vector<Ptr<Vocab>> vocabs, Ptr<Options> options, bool lowerIsBetter = true)
       : ValidatorBase(lowerIsBetter),
         vocabs_(vocabs),
@@ -137,6 +139,7 @@ class CrossEntropyValidator : public Validator<data::Corpus, models::ICriterionF
 
 public:
   CrossEntropyValidator(std::vector<Ptr<Vocab>> vocabs, Ptr<Options> options);
+  virtual ~CrossEntropyValidator() {}
 
   std::string type() override { return options_->get<std::string>("cost-type"); }
 
@@ -148,6 +151,7 @@ class CrossEntropyValidator : public Validator<data::Corpus, models::ICriterionF
 class AccuracyValidator : public Validator<data::Corpus, models::IModel> {
 public:
   AccuracyValidator(std::vector<Ptr<Vocab>> vocabs, Ptr<Options> options);
+  virtual ~AccuracyValidator() {}
 
   std::string type() override { return "accuracy"; }
 
@@ -161,6 +165,7 @@ class BertAccuracyValidator : public Validator<data::Corpus, models::IModel> {
 
 public:
   BertAccuracyValidator(std::vector<Ptr<Vocab>> vocabs, Ptr<Options> options, bool evalMaskedLM);
+  virtual ~BertAccuracyValidator() {}
 
   std::string type() override {
     if(evalMaskedLM_)
@@ -177,6 +182,7 @@ class BertAccuracyValidator : public Validator<data::Corpus, models::IModel> {
 class ScriptValidator : public Validator<data::Corpus, models::IModel> {
 public:
   ScriptValidator(std::vector<Ptr<Vocab>> vocabs, Ptr<Options> options);
+  virtual ~ScriptValidator() {}
 
   virtual float validate(const std::vector<Ptr<ExpressionGraph>>& graphs,
                          Ptr<const TrainingState> /*ignored*/) override;
@@ -193,6 +199,7 @@ class ScriptValidator : public Validator<data::Corpus, models::IModel> {
 class TranslationValidator : public Validator<data::Corpus, models::IModel> {
 public:
   TranslationValidator(std::vector<Ptr<Vocab>> vocabs, Ptr<Options> options);
+  virtual ~TranslationValidator() {}
 
   virtual float validate(const std::vector<Ptr<ExpressionGraph>>& graphs,
                          Ptr<const TrainingState> state) override;
@@ -212,6 +219,7 @@ class TranslationValidator : public Validator<data::Corpus, models::IModel> {
 class BleuValidator : public Validator<data::Corpus, models::IModel> {
 public:
   BleuValidator(std::vector<Ptr<Vocab>> vocabs, Ptr<Options> options, bool detok = false);
+  virtual ~BleuValidator() {}
 
   virtual float validate(const std::vector<Ptr<ExpressionGraph>>& graphs,
                          Ptr<const TrainingState> state) override;
diff --git a/src/translator/beam_search.h b/src/translator/beam_search.h
index 05a490fea..4ea17d8b1 100755
--- a/src/translator/beam_search.h
+++ b/src/translator/beam_search.h
@@ -16,7 +16,7 @@ class BeamSearch {
   Ptr<Options> options_;
   std::vector<Ptr<Scorer>> scorers_;
   size_t beamSize_;
-  Ptr<Vocab> trgVocab_;
+  Ptr<const Vocab> trgVocab_;
 
   const float INVALID_PATH_SCORE = std::numeric_limits<float>::lowest(); // @TODO: observe this closely
   const bool PURGE_BATCH = true; // @TODO: diagnostic, to-be-removed once confirmed there are no issues.
@@ -24,7 +24,7 @@ class BeamSearch {
 public:
   BeamSearch(Ptr<Options> options,
              const std::vector<Ptr<Scorer>>& scorers,
-             Ptr<Vocab> trgVocab)
+             const Ptr<const Vocab> trgVocab)
       : options_(options),
         scorers_(scorers),
         beamSize_(options_->get<size_t>("beam-size")),
@@ -42,8 +42,8 @@ class BeamSearch {
                const std::vector<bool>& dropBatchEntries, // [origDimBatch] - empty source batch entries are marked with true, should be cleared after first use.
                const std::vector<IndexType>& batchIdxMap) const { // [origBatchIdx -> currentBatchIdx]
     std::vector<float> align; // collects alignment information from the last executed time step
-    if(options_->hasAndNotEmpty("alignment") && factorGroup == 0) 
-      align = scorers_[0]->getAlignment(); // [beam depth * max src length * current batch size] -> P(s|t); use alignments from the first scorer, even if ensemble, 
+    if(options_->hasAndNotEmpty("alignment") && factorGroup == 0)
+      align = scorers_[0]->getAlignment(); // [beam depth * max src length * current batch size] -> P(s|t); use alignments from the first scorer, even if ensemble,
 
     const auto origDimBatch = beams.size(); // see function search for definition of origDimBatch and currentDimBatch etc.
     Beams newBeams(origDimBatch);           // return value of this function goes here. There are always origDimBatch beams.
@@ -56,7 +56,7 @@ class BeamSearch {
       reverseBatchIdxMap.resize(batchIdxMap.size()); // adjust size if doing batch purging.
       currentDimBatch = 0;
       for(int i = 0; i < batchIdxMap.size(); ++i) {
-        reverseBatchIdxMap[batchIdxMap[i]] = i; // reverse batch index mapping, multiple occurences get overwritten with the last one, 
+        reverseBatchIdxMap[batchIdxMap[i]] = i; // reverse batch index mapping, multiple occurences get overwritten with the last one,
                                                 // which is expected due to down-shifting
         if(!beams[i].empty())
           currentDimBatch++;
@@ -154,12 +154,12 @@ class BeamSearch {
           auto lval = states[j]->getLogProbs().getFactoredLogitsTensor(factorGroup); // [maxBeamSize, 1, currentDimBatch, dimFactorVocab]
           // The flatting happens based on actual (current) batch size and batch index computed with batch-pruning as we are looking into the pruned tensor
           size_t flattenedLogitIndex = (beamHypIdx * currentDimBatch + currentBatchIdx) * vocabSize + wordIdx;  // (beam idx, batch idx, word idx); note: beam and batch are transposed, compared to 'key'
-          
+
           // @TODO: use a function on shape() to index, or new method val->at({i1, i2, i3, i4}) with broadcasting
           ABORT_IF(lval->shape() != Shape({(int)nBestBeamSize, 1, (int)currentDimBatch, (int)vocabSize}) &&
                    (beamHypIdx == 0 && lval->shape() != Shape({1, 1, (int)currentDimBatch, (int)vocabSize})),
                    "Unexpected shape of logits?? {} != {}", lval->shape(), Shape({(int)nBestBeamSize, 1, (int)currentDimBatch, (int)vocabSize}));
-          
+
           breakDown[j] += lval->get(flattenedLogitIndex);
         }
         hyp->setScoreBreakdown(breakDown);
@@ -173,7 +173,7 @@ class BeamSearch {
 
       newBeam.push_back(hyp);
     }
-    
+
     // if factored vocab and this is not the first factor, we need to
     // also propagate factored hypotheses that do not get expanded in this step because they don't have this factor
     if (factorGroup > 0) {
@@ -225,7 +225,7 @@ class BeamSearch {
     // in a single beam, i.e.:
     //   * [word1-batch1, word1-batch2, ..., word2-batch1, ...]
     //
-    
+
     size_t origDimBatch = batch->size();  // number of sentences in batch
     size_t batchWidth   = batch->width(); // max src length
 
@@ -254,7 +254,7 @@ class BeamSearch {
     for(auto beam : beams) {
       Beam newBeam; // a beam of surviving hyps
       for(auto hyp : beam)
-        if(hyp->getWord() != trgEosId) // if this hyp is not finished, 
+        if(hyp->getWord() != trgEosId) // if this hyp is not finished,
           newBeam.push_back(hyp);      // move over to beam of surviving hyps
 
       if(PURGE_BATCH)
@@ -309,8 +309,8 @@ class BeamSearch {
     // create one beam per batch entry with sentence-start hypothesis
     Beams beams(origDimBatch, Beam(beamSize_, Hypothesis::New())); // array [origDimBatch] of array [maxBeamSize] of Hypothesis, keeps full size through search.
                                                                    // batch purging is determined from an empty sub-beam.
-    std::vector<IndexType> batchIdxMap(origDimBatch); // Record at which batch entry a beam is looking. 
-                                                      // By default that corresponds to position in array, 
+    std::vector<IndexType> batchIdxMap(origDimBatch); // Record at which batch entry a beam is looking.
+                                                      // By default that corresponds to position in array,
                                                       // but shifts in the course of removing batch entries when they are finished.
 
     const std::vector<bool> emptyBatchEntries; // used for recording if there are empty input batch entries
@@ -370,7 +370,7 @@ class BeamSearch {
         std::vector<IndexType> hypIndices;      // [maxBeamSize, 1, currentDimBatch, 1] (flattened) tensor index ((beamHypIdx, batchIdx), flattened) of prev hyp that a hyp originated from
         std::vector<Word> prevWords;            // [maxBeamSize, 1, currentDimBatch, 1] (flattened) word that a hyp ended in, for advancing the decoder-model's history
         Expr prevPathScores;                    // [maxBeamSize, 1, currentDimBatch, 1], path score that a hyp ended in (last axis will broadcast into vocab size when adding expandedPathScores)
-        
+
         bool anyCanExpand = false; // stays false if all hyps are invalid factor expansions
         if(t == 0 && factorGroup == 0) { // no scores yet
           prevPathScores = graph->constant({1, 1, 1, 1}, inits::fromValue(0));
@@ -384,7 +384,7 @@ class BeamSearch {
             for(int currentBatchIdx = 0; currentBatchIdx < beams.size(); ++currentBatchIdx) // loop over batch entries (active sentences)
               if(!beams[currentBatchIdx].empty() || !PURGE_BATCH)                           // for each beam check
                 batchIndices.push_back(prevBatchIdxMap[currentBatchIdx]);                   // which batch entries were active in previous step
-            
+
           std::vector<float> prevScores;
           for(size_t beamHypIdx = 0; beamHypIdx < maxBeamSize; ++beamHypIdx) { // loop over globally maximal beam-size (maxBeamSize)
             for(int origBatchIdx = 0; origBatchIdx < origDimBatch; ++origBatchIdx) { // loop over all batch entries (active and inactive)
@@ -401,11 +401,11 @@ class BeamSearch {
                   if(factorGroup == 0)
                     currentBatchIdx = prevBatchIdxMap[origBatchIdx]; // subselection may happen for factorGroup == 0
                   else
-                    currentBatchIdx = batchIdxMap[origBatchIdx];     // no subselection happens for factorGroup > 0, 
-                                                                     // but we treat it like a next step, since a step 
+                    currentBatchIdx = batchIdxMap[origBatchIdx];     // no subselection happens for factorGroup > 0,
+                                                                     // but we treat it like a next step, since a step
                                                                      // happened for factorGroup == 0
                 }
-                  
+
                 auto hypIndex = (IndexType)(hyp->getPrevStateIndex() * currentDimBatch + currentBatchIdx); // (beamHypIdx, batchIdx), flattened, for index_select() operation
 
                 hypIndices.push_back(hypIndex); // (beamHypIdx, batchIdx), flattened as said above.
@@ -420,7 +420,7 @@ class BeamSearch {
               }
             }
           }
-          if(factorGroup == 0) 
+          if(factorGroup == 0)
             currentDimBatch = (IndexType) batchIndices.size(); // keep batch size constant for all factor groups in a time step
           prevPathScores = graph->constant({(int)maxBeamSize, 1, (int)currentDimBatch, 1}, inits::fromVector(prevScores));
         }
@@ -505,7 +505,7 @@ class BeamSearch {
                        beams,
                        states,    // used for keeping track of per-ensemble-member path score
                        batch,     // only used for propagating alignment info
-                       factoredVocab, factorGroup, 
+                       factoredVocab, factorGroup,
                        emptyBatchEntries, // [origDimBatch] - empty source batch entries are marked with true
                        batchIdxMap); // used to create a reverse batch index map to recover original batch indices for this step
       } // END FOR factorGroup = 0 .. numFactorGroups-1
diff --git a/src/translator/nth_element.cpp b/src/translator/nth_element.cpp
index 4692a5057..8b2f89476 100644
--- a/src/translator/nth_element.cpp
+++ b/src/translator/nth_element.cpp
@@ -20,34 +20,6 @@ class NthElementCPU {
   NthElementCPU() {}
   NthElementCPU(const NthElementCPU& copy) = delete;
 
-private:
-  // for each batch, select the max N elements, where N is the beam size for this batch.
-  void selectNBest(const float* scores,
-                   const std::vector<int>& batchFirstElementIdxs,
-                   const std::vector<int>& cumulativeBeamSizes) {
-    int numProbs = batchFirstElementIdxs.back();
-    std::vector<int> idxs(numProbs);
-    std::iota(idxs.begin(), idxs.end(), 0);
-
-    size_t numBatches = batchFirstElementIdxs.size() - 1;
-    for(size_t batchIdx = 0; batchIdx < numBatches; ++batchIdx) {
-      int pos = cumulativeBeamSizes[batchIdx];
-      int beamSize = cumulativeBeamSizes[batchIdx + 1] - pos;
-
-      std::vector<int>::iterator begin = idxs.begin() + batchFirstElementIdxs[batchIdx];
-      std::vector<int>::iterator middle = begin + beamSize;
-      std::vector<int>::iterator end = idxs.begin() + batchFirstElementIdxs[batchIdx + 1];
-      std::partial_sort(
-          begin, middle, end, [&](int a, int b) { return scores[a] > scores[b]; });
-
-      while(begin != middle) {
-        int idx = *begin++;
-        h_res_idx[pos] = idx;
-        h_res[pos] = scores[idx];
-        ++pos;
-      }
-    }
-  }
 
 public:
   void getNBestList(Tensor scores, // [dimBatch, 1, beamSize, dimVocab or dimShortlist]
@@ -59,23 +31,39 @@ class NthElementCPU {
     const auto inputN    = scores->shape()[-2];
     const auto dimBatch  = scores->shape()[-4];
     ABORT_IF(inputN != (isFirst ? 1 : N), "Input tensor has wrong beam dim??"); // @TODO: Remove isFirst argument altogether
-
-    std::vector<int> cumulativeBeamSizes(dimBatch + 1, 0);
-    std::vector<int> batchFirstElementIdxs(dimBatch + 1, 0);
-
-    for(int batchIdx = 0; batchIdx < dimBatch; ++batchIdx) {
-      cumulativeBeamSizes[batchIdx + 1] = (batchIdx + 1) * (int)N;
-      batchFirstElementIdxs[batchIdx + 1] += (batchIdx + 1) * inputN * vocabSize;
-      ABORT_IF(cumulativeBeamSizes[batchIdx + 1] != cumulativeBeamSizes[batchIdx] + (int)N, "cumulativeBeamSizes wrong??");
-      ABORT_IF((isFirst ? batchIdx + 1 : cumulativeBeamSizes[batchIdx + 1]) != (batchIdx + 1) * inputN, "inputN wrong??");
-    }
-    ABORT_IF(cumulativeBeamSizes.back() != dimBatch * N, "cumulativeBeamSizes.back() wrong??");
+    const float* scoresData = scores->data();
 
     size_t maxSize = N * dimBatch;
     h_res.resize(maxSize);
     h_res_idx.resize(maxSize);
+    size_t pos = 0; // iterates through h_res and h_res_idx
+
+    size_t batchOffset = inputN * vocabSize;
+    std::vector<int> idxs(batchOffset); // re-used for each batch
+    std::iota(idxs.begin(), idxs.end(), 0);
 
-    selectNBest(scores->data(), batchFirstElementIdxs, cumulativeBeamSizes);
+    for(size_t batchIdx = 0; batchIdx < dimBatch; ++batchIdx) {
+
+      std::partial_sort( 
+        // sorts the top N (beam size) idxs by score to the front
+        idxs.begin(),
+        idxs.begin() + N,
+        idxs.end(),
+        [&](int a, int b) { return scoresData[a] > scoresData[b]; }
+      );
+
+      // copy top N idxs and scores to return vectors
+      for(size_t i = 0; i < N; ++i) {
+        int idx = idxs[i];
+        // since idxs is re-used for each batch, add batch offset to each idx to get absolute position
+        h_res_idx[pos] = idx + batchIdx * batchOffset;
+        h_res[pos] = scoresData[idx];
+        ++pos;
+      }
+
+      // advance pointer to next batch's beginning
+      scoresData += batchOffset;
+    }
     getPairs(/*cumulativeBeamSizes.back(),*/ outKeys, outPathScores);
   }
 
diff --git a/src/translator/output_collector.h b/src/translator/output_collector.h
index 9e0e12b53..ffcbd2d50 100644
--- a/src/translator/output_collector.h
+++ b/src/translator/output_collector.h
@@ -11,6 +11,7 @@ namespace marian {
 
 class PrintingStrategy {
 public:
+  virtual ~PrintingStrategy() {}
   virtual bool shouldBePrinted(long) = 0;
 };
 
diff --git a/src/translator/scorers.h b/src/translator/scorers.h
index c936514c6..a5a0be2cb 100755
--- a/src/translator/scorers.h
+++ b/src/translator/scorers.h
@@ -10,6 +10,8 @@ namespace marian {
 
 class ScorerState {
 public:
+  virtual ~ScorerState(){}
+
   virtual Logits getLogProbs() const = 0;
 
   virtual void blacklist(Expr /*totalCosts*/, Ptr<data::CorpusBatch> /*batch*/){};
@@ -24,6 +26,8 @@ class Scorer {
   Scorer(const std::string& name, float weight)
       : name_(name), weight_(weight) {}
 
+  virtual ~Scorer(){}
+
   std::string getName() { return name_; }
   float getWeight() { return weight_; }
 
@@ -53,6 +57,7 @@ class ScorerWrapperState : public ScorerState {
 
 public:
   ScorerWrapperState(Ptr<DecoderState> state) : state_(state) {}
+  virtual ~ScorerWrapperState() {}
 
   virtual Ptr<DecoderState> getState() { return state_; }
 
@@ -88,6 +93,8 @@ class ScorerWrapper : public Scorer {
         encdec_(std::static_pointer_cast<IEncoderDecoder>(encdec)),
         ptr_{ptr} {}
 
+  virtual ~ScorerWrapper() {}
+
   virtual void init(Ptr<ExpressionGraph> graph) override {
     graph->switchParams(getName());
     if(ptr_)
diff --git a/src/translator/translator.h b/src/translator/translator.h
index 88adf0e3d..cc68a4f01 100755
--- a/src/translator/translator.h
+++ b/src/translator/translator.h
@@ -109,6 +109,17 @@ class Translate : public ModelTask {
 
       threadPool.enqueue(task, device, id++);
     }
+
+    if(options_->get<bool>("output-sampling", false)) {
+      if(options_->get<size_t>("beam-size") > 1)
+        LOG(warn,
+            "[warning] Output sampling and beam search (beam-size > 1) are contradictory methods "
+            "and using them together is not recommended. Set beam-size to 1");
+      if(options_->get<std::vector<std::string>>("models").size() > 1)
+        LOG(warn,
+            "[warning] Output sampling and model ensembling are contradictory methods and using "
+            "them together is not recommended. Use a single model");
+    }
   }
 
   void run() override {

From aad22c9d09e594c5319fb846351a532dac85e089 Mon Sep 17 00:00:00 2001
From: Roman Grundkiewicz <rgrundki@exseed.ed.ac.uk>
Date: Tue, 10 Mar 2020 19:29:50 +0200
Subject: [PATCH 838/838] Add option for printing CMake cached variables (#583)

* Add option --build-info
---
 .gitignore                      |  3 +-
 CHANGELOG.md                    |  1 +
 CMakeLists.txt                  | 22 ++++++++++++++
 VERSION                         |  2 +-
 cmake/GetCacheVariables.cmake   | 52 +++++++++++++++++++++++++++++++++
 src/CMakeLists.txt              |  1 +
 src/common/build_info.cpp.in    | 18 ++++++++++++
 src/common/build_info.h         | 13 +++++++++
 src/common/config_parser.cpp    | 15 +++++++++-
 src/common/project_version.h.in |  4 +--
 10 files changed, 126 insertions(+), 5 deletions(-)
 create mode 100644 cmake/GetCacheVariables.cmake
 create mode 100644 src/common/build_info.cpp.in
 create mode 100644 src/common/build_info.h

diff --git a/.gitignore b/.gitignore
index 800804419..956ce6847 100644
--- a/.gitignore
+++ b/.gitignore
@@ -1,6 +1,7 @@
 # Config files from CMake
 src/common/project_version.h
 src/common/git_revision.h
+src/common/build_info.cpp
 
 *.vcxproj.user
 /vs/x64
@@ -61,4 +62,4 @@ examples/mnist/*ubyte
 
 .vs
 .vscode
-
+
diff --git a/CHANGELOG.md b/CHANGELOG.md
index 317a3cad8..487c07a1d 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -9,6 +9,7 @@ and this project adheres to [Semantic Versioning](http://semver.org/spec/v2.0.0.
 ## [Unreleased]
 
 ### Added
+- An option to print cached variables from CMake
 - Add support for compiling on Mac (and clang)
 - An option for resetting stalled validation metrics
 - Add CMAKE options to disable compilation for specific GPU SM types
diff --git a/CMakeLists.txt b/CMakeLists.txt
index c27761cf0..46d9c6c91 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -56,6 +56,7 @@ if(NOT CMAKE_BUILD_TYPE)
   set(CMAKE_BUILD_TYPE "Release")
 endif()
 
+###############################################################################
 # Set compilation flags
 if(MSVC)
 # These are used in src/CMakeLists.txt on a per-target basis
@@ -185,6 +186,7 @@ else(MSVC)
   set(CMAKE_C_FLAGS_PROFUSE         "${CMAKE_C_FLAGS_RELEASE} -fprofile-use -fprofile-correction")
 endif(MSVC)
 
+###############################################################################
 # Downloading SentencePiece if requested and set to compile with it.
 # Requires all the dependencies imposed by SentencePiece
 if(USE_SENTENCEPIECE)
@@ -196,6 +198,7 @@ endif()
 # Find packages
 set(EXT_LIBS ${EXT_LIBS} ${CMAKE_DL_LIBS})
 
+###############################################################################
 if(COMPILE_CUDA)
 
 if(USE_STATIC_LIBS)
@@ -316,6 +319,8 @@ if(USE_STATIC_LIBS)
   endif()
 endif()
 
+###############################################################################
+# Find Tcmalloc
 if(NOT WIN32)
   find_package(Tcmalloc)
   if(Tcmalloc_FOUND)
@@ -326,6 +331,8 @@ if(NOT WIN32)
   endif(Tcmalloc_FOUND)
 endif()
 
+###############################################################################
+# Find MPI
 if(USE_MPI)
   find_package(MPI 2.0)
   if(MPI_FOUND)
@@ -335,6 +342,8 @@ if(USE_MPI)
   endif(MPI_FOUND)
 endif(USE_MPI)
 
+###############################################################################
+# Find MKL
 if(COMPILE_CPU)
   if(USE_MKL)
     find_package(MKL)
@@ -357,6 +366,8 @@ if(COMPILE_CPU)
   endif(MKL_FOUND)
 endif(COMPILE_CPU)
 
+###############################################################################
+# Find OpenSSL
 set(BOOST_COMPONENTS "")
 if(COMPILE_SERVER)
   find_package(OpenSSL)
@@ -375,10 +386,13 @@ if(USE_STATIC_LIBS)
   set(CMAKE_FIND_LIBRARY_SUFFIXES ${_ORIG_CMAKE_FIND_LIBRARY_SUFFIXES})
 endif()
 
+# TODO: move inside if(BOOST_COMPONENTS) ?
 if(USE_STATIC_LIBS)
   set(Boost_USE_STATIC_LIBS ON)
 endif()
 
+###############################################################################
+# Find Boost if required
 if(BOOST_COMPONENTS)
   find_package(Boost COMPONENTS ${BOOST_COMPONENTS})
   if(Boost_FOUND)
@@ -390,6 +404,7 @@ if(BOOST_COMPONENTS)
   endif(Boost_FOUND)
 endif(BOOST_COMPONENTS)
 
+###############################################################################
 if(COMPILE_TESTS)
   enable_testing()
 endif(COMPILE_TESTS)
@@ -402,10 +417,17 @@ endif(COMPILE_EXAMPLES)
 configure_file(${CMAKE_CURRENT_SOURCE_DIR}/src/common/project_version.h.in
                ${CMAKE_CURRENT_SOURCE_DIR}/src/common/project_version.h @ONLY)
 
+# Generate build_info.cpp with CMake cache variables
+include(GetCacheVariables)
+
+configure_file(${CMAKE_CURRENT_SOURCE_DIR}/src/common/build_info.cpp.in
+               ${CMAKE_CURRENT_SOURCE_DIR}/src/common/build_info.cpp @ONLY)
+
 # Compile source files
 include_directories(${marian_SOURCE_DIR}/src)
 add_subdirectory(src)
 
+###############################################################################
 if(USE_DOXYGEN)
 # Add a target to generate API documentation with Doxygen
 find_package(Doxygen)
diff --git a/VERSION b/VERSION
index a5969e9f7..6959dfcad 100644
--- a/VERSION
+++ b/VERSION
@@ -1 +1 @@
-v1.8.51
+v1.8.52
diff --git a/cmake/GetCacheVariables.cmake b/cmake/GetCacheVariables.cmake
new file mode 100644
index 000000000..563ade79e
--- /dev/null
+++ b/cmake/GetCacheVariables.cmake
@@ -0,0 +1,52 @@
+##
+# This module extracts CMake cached variables into a variable.
+#
+# Author: snukky
+#
+# This module sets the following variables:
+# * PROJECT_CMAKE_CACHE - to the output of "cmake -L" - an uncached list of
+#     non-advanced cached variables
+# * PROJECT_CMAKE_CACHE_ADVANCED - to the output of "cmake -LA" - an uncached
+#     list of advanced cached variables
+#
+
+set(PROJECT_CMAKE_CACHE "")
+set(PROJECT_CMAKE_CACHE_ADVANCED "")
+
+# Get all CMake variables
+get_cmake_property(_variableNames VARIABLES)
+list(SORT _variableNames)
+list(REMOVE_DUPLICATES _variableNames)
+
+foreach(_variableName ${_variableNames})
+  # If it is a cache variable
+  get_property(_cachePropIsSet CACHE "${_variableName}" PROPERTY VALUE SET)
+  if(_cachePropIsSet)
+    # Get the variable's type
+    get_property(_variableType CACHE ${_variableName} PROPERTY TYPE)
+
+    # Get the variable's value
+    set(_variableValue "${${_variableName}}")
+
+    # Skip static or internal cached variables, cmake -L[A] does not print them, see
+    # https://github.com/Kitware/CMake/blob/master/Source/cmakemain.cxx#L282
+    if( (NOT "${_variableType}" STREQUAL "STATIC") AND
+        (NOT "${_variableType}" STREQUAL "INTERNAL") AND
+        (NOT "${_variableValue}" STREQUAL "") )
+
+
+        set(PROJECT_CMAKE_CACHE_ADVANCED "${PROJECT_CMAKE_CACHE_ADVANCED}    \"${_variableName}=${_variableValue}\\n\"\n")
+
+        # Get the variable's advanced flag
+        get_property(_isAdvanced CACHE ${_variableName} PROPERTY ADVANCED SET)
+        if(NOT _isAdvanced)
+          set(PROJECT_CMAKE_CACHE "${PROJECT_CMAKE_CACHE}    \"${_variableName}=${_variableValue}\\n\"\n")
+        endif()
+
+        # Print variables for debugging
+        #message(STATUS "${_variableName}=${${_variableName}}")
+        #message(STATUS "  Type=${_variableType}")
+        #message(STATUS "  Advanced=${_isAdvanced}")
+    endif()
+  endif(_cachePropIsSet)
+endforeach()
diff --git a/src/CMakeLists.txt b/src/CMakeLists.txt
index 839a64f07..6d5a0b1f2 100644
--- a/src/CMakeLists.txt
+++ b/src/CMakeLists.txt
@@ -20,6 +20,7 @@ add_library(marian STATIC
   common/config_validator.cpp
   common/options.cpp
   common/binary.cpp
+  common/build_info.cpp
   common/io.cpp
   common/filesystem.cpp
   common/file_stream.cpp
diff --git a/src/common/build_info.cpp.in b/src/common/build_info.cpp.in
new file mode 100644
index 000000000..b8d12e8a5
--- /dev/null
+++ b/src/common/build_info.cpp.in
@@ -0,0 +1,18 @@
+#include "common/build_info.h"
+
+/*
+ * File build_info.cpp is generated using CMake. Do NOT modify it manually! Edit
+ * build_info.cpp.in file instead.
+ */
+
+std::string marian::cmakeBuildOptions() {
+  return ""
+@PROJECT_CMAKE_CACHE@
+  ;
+}
+
+std::string marian::cmakeBuildOptionsAdvanced() {
+  return ""
+@PROJECT_CMAKE_CACHE_ADVANCED@
+  ;
+}
diff --git a/src/common/build_info.h b/src/common/build_info.h
new file mode 100644
index 000000000..f9e4a57c1
--- /dev/null
+++ b/src/common/build_info.h
@@ -0,0 +1,13 @@
+#pragma once
+
+#include <string>
+
+namespace marian {
+
+// Returns list of non-advanced cache variables used by CMake
+std::string cmakeBuildOptions();
+
+// Returns list of advanced cache variables used by CMake
+std::string cmakeBuildOptionsAdvanced();
+
+} // namespace marian
diff --git a/src/common/config_parser.cpp b/src/common/config_parser.cpp
index 8dfe28cb8..9c711eaad 100755
--- a/src/common/config_parser.cpp
+++ b/src/common/config_parser.cpp
@@ -1,4 +1,5 @@
 #include "common/authors.h"
+#include "common/build_info.h"
 #include "common/cli_helper.h"
 #include "common/config.h"
 #include "common/config_parser.h"
@@ -111,6 +112,9 @@ void ConfigParser::addOptionsGeneral(cli::CLIWrapper& cli) {
     "Print list of authors and exit");
   cli.add<bool>("--cite",
     "Print citation and exit");
+  cli.add<std::string>("--build-info",
+    "Print CMake build options and exit. Set to 'all' to print advanced options")
+    ->implicit_val("basic");
   cli.add<std::vector<std::string>>("--config,-c",
     "Configuration file(s). If multiple, later overrides earlier");
   cli.add<size_t>("--workspace,-w",
@@ -504,7 +508,7 @@ void ConfigParser::addOptionsTraining(cli::CLIWrapper& cli) {
      true);
 
   // add ULR settings
-  addSuboptionsULR(cli); 
+  addSuboptionsULR(cli);
 
   cli.add<std::vector<std::string>>("--task",
      "Use predefined set of options. Possible values: transformer, transformer-big");
@@ -835,6 +839,15 @@ Ptr<Options> ConfigParser::parseOptions(int argc, char** argv, bool doValidate){
     exit(0);
   }
 
+  auto buildInfo = get<std::string>("build-info");
+  if(!buildInfo.empty() && buildInfo != "false") {
+    if(buildInfo == "all")
+      std::cerr << cmakeBuildOptionsAdvanced() << std::endl;
+    else
+      std::cerr << cmakeBuildOptions() << std::endl;
+    exit(0);
+  }
+
   // get paths to extra config files
   auto configPaths = findConfigPaths();
   if(!configPaths.empty()) {
diff --git a/src/common/project_version.h.in b/src/common/project_version.h.in
index 756f2e56c..815aff133 100644
--- a/src/common/project_version.h.in
+++ b/src/common/project_version.h.in
@@ -1,8 +1,8 @@
 #pragma once
 
 /*
- * File project-version.h is generated using CMake. Do NOT modify it manually! Edit
- * project-version.h.in file instead.
+ * File project_version.h is generated using CMake. Do NOT modify it manually! Edit
+ * project_version.h.in file instead.
  */
 
 // e.g. v1.2.3-beta+1.abc123d